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A super-spreading ewe infects hundreds with Q fever at a farmers' market in Germany
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1618839/
SHA: ee1b5a9618dcc4080ed100486cedd0969e80fa4d
Authors: Porten, Klaudia; Rissland, Jürgen; Tigges, Almira; Broll, Susanne; Hopp, Wilfried; Lunemann, Mechthild; van Treeck, Ulrich; Kimmig, Peter; Brockmann, Stefan O; Wagner-Wiening, Christiane; Hellenbrand, Wiebke; Buchholz, Udo
Date: 2006-10-06
DOI: 10.1186/1471-2334-6-147
License: cc-by
Abstract: BACKGROUND: In May 2003 the Soest County Health Department was informed of an unusually large number of patients hospitalized with atypical pneumonia. METHODS: In exploratory interviews patients mentioned having visited a farmers' market where a sheep had lambed. Serologic testing confirmed the diagnosis of Q fever. We asked local health departments in Germany to identiy notified Q fever patients who had visited the farmers market. To investigate risk factors for infection we conducted a case control study (cases were Q fever patients, controls were randomly selected Soest citizens) and a cohort study among vendors at the market. The sheep exhibited at the market, the herd from which it originated as well as sheep from herds held in the vicinity of Soest were tested for Coxiella burnetii (C. burnetii). RESULTS: A total of 299 reported Q fever cases was linked to this outbreak. The mean incubation period was 21 days, with an interquartile range of 16–24 days. The case control study identified close proximity to and stopping for at least a few seconds at the sheep's pen as significant risk factors. Vendors within approximately 6 meters of the sheep's pen were at increased risk for disease compared to those located farther away. Wind played no significant role. The clinical attack rate of adults and children was estimated as 20% and 3%, respectively, 25% of cases were hospitalized. The ewe that had lambed as well as 25% of its herd tested positive for C. burnetii antibodies. CONCLUSION: Due to its size and point source nature this outbreak permitted assessment of fundamental, but seldom studied epidemiological parameters. As a consequence of this outbreak, it was recommended that pregnant sheep not be displayed in public during the 3(rd )trimester and to test animals in petting zoos regularly for C. burnetii.
Text: Q fever is a worldwide zoonosis caused by Coxiella burnetii (C. burnetii), a small, gram-negative obligate intracellular bacterium. C. burnetii displays antigenic variation with an infectious phase I and less infectious phase II. The primary reservoir from which human infection occurs consists of sheep, goat and cattle. Although C. burnetii infections in animals are usually asymptomatic, they may cause abortions in sheep and goats [1] . High concentrations of C. burnetii can be found in birth products of infected mammals [2] . Humans frequently acquire infection through inhalation of contaminated aerosols from parturient fluids, placenta or wool [1] . Because the infectious dose is very low [3] and C. burnetii is able to survive in a spore-like state for months to years, outbreaks among humans have also occurred through contaminated dust carried by wind over large distances [4] [5] [6] .
C. burnetii infection in humans is asymptomatic in approximately 50% of cases. Approximately 5% of cases are hospitalized, and fatal cases are rare [1] . The clinical presentation of acute Q fever is variable and can resemble many other infectious diseases [2] . However, the most frequent clinical manifestation of acute Q fever is a self-limited febrile illness associated with severe headache. Atypical pneumonia and hepatitis are the major clinical manifestations of more severe disease. Acute Q fever may be complicated by meningoencephalitis or myocarditis. Rarely a chronic form of Q fever develops months after the acute illness, most commonly in the form of endocarditis [1] . Children develop clinical disease less frequently [7, 8] . Because of its non-specific presentation Q fever can only be suspected on clinical grounds and requires serologic confirmation. While the indirect immunofluorescence assay (IFA) is considered to be the reference method, complement fixation (CF), ELISA and microagglutination (MA) can also be used [9] . Acute infections are diagnosed by elevated IgG and/or IgM anti-phase II antibodies, while raised anti-phase I IgG antibodies are characteristic for chronic infections [1] .
In Germany, acute Q fever is a notifiable disease. Between 1991 and 2000 the annual number of cases varied from 46 to 273 cases per year [10] . In 2001 and 2002, 293 and 191 cases were notified, respectively [11, 12] .
On May 26, 2003 the health department of Soest was informed by a local hospital of an unusually large number of patients with atypical pneumonia. Some patients reported having visited a farmers' market that took place on May 3 and 4, 2003 in a spa town near Soest. Since the etiology was unclear, pathogens such as SARS coronavirus were considered and strict infection control measures implemented until the diagnosis of Q fever was confirmed.
An outbreak investigation team was formed and included public health professionals from the local health department, the local veterinary health department, the state health department, the National Consulting Laboratory (NCL) for Coxiellae and the Robert Koch-Institute (RKI), the federal public health institute. Because of the size and point source appearance of the outbreak the objective of the investigation was to identify etiologic factors relevant to the prevention and control of Q fever as well as to assess epidemiological parameters that can be rarely studied otherwise.
On May 26 and 27, 2003 we conducted exploratory interviews with patients in Soest hospitalized due to atypical pneumonia.
Attending physicians were requested to test serum of patients with atypical pneumonia for Mycoplasma pneumoniae, Chlamydia pneumoniae, Legionella pneumophila, Coxiella burnetii, Influenza A and B, Parainfluenza 1-3, Adenovirus and Enterovirus. Throat swabs were tested for Influenza virus, Adenovirus and SARS-Coronavirus. Laboratory confirmation of an acute Q fever infection was defined as the presence of IgM antibodies against phase II C. burnetii antigens (ELISA or IFA), a 4-fold increase in anti-phase II IgG antibody titer (ELISA or IFA) or in anti phase II antibody titer by CF between acute and convalescent sera. A chronic infection was confirmed when both anti-phase I IgG and anti-phase II IgG antibody titers were raised.
Because patients with valvular heart defects and pregnant women are at high risk of developing chronic infection [13, 14] we alerted internists and gynaecologists through the journal of the German Medical Association and asked them to send serum samples to the NCL if they identified patients from these risk groups who had been at the farmers' market during the outbreak.
The objective of the first case control study was to establish whether there was a link between the farmers' market and the outbreak and to identify other potential risk factors. We conducted telephone interviews using a standardised questionnaire that asked about attendance at the farmers' market, having been within 1 km distance of one of 6 sheep flocks in the area, tick bites and consumption of unpasteurized milk, sheep or goat cheese. For the purpose of CCS1 we defined a case (CCS1 case) as an adult resident of the town of Soest notified to the statutory sur-veillance system with Q fever, having symptom onset between May 4 and June 3, 2003. Exclusion criterion was a negative IgM-titer against phase II antigens. Two controls per case were recruited from Soest inhabitants by random digit dialing.
We calculated the attributable fraction of cases exposed to the farmers' market on May 4 (AFE) as (OR-1)/OR and the attributable fraction for all cases due to this exposure as:
The farmers' market was held in a spa town near Soest with many visitors from other areas of the state and even the entire country. To determine the outbreak size we therefore asked local public health departments in Germany to ascertain a possible link to the farmers' market in Soest for all patients notified with Q-fever. A case in this context ("notified case") was defined as any person with a clinical diagnosis compatible with Q fever with or without laboratory confirmation and history of exposure to the farmers' market.
Local health departments also reported whether a notified case was hospitalized. To obtain an independent, second estimate of the proportion of hospitalizations among symptomatic patients beyond that reported through the statutory surveillance system we calculated the proportion of hospitalized patients among those persons fulfilling the clinical case definition (as used in the vendors' study (s.b.)) identified through random sampling of the Soest population (within CCS2 (s.b.)) as well as in two cohorts (vendors' study and the 9 sailor friends (see below)).
The objective of CCS2 was to identify risk factors associated with attendance of the farmers' market on the second day. We used the same case definition as in CCS1, but included only persons that had visited the farmers' market on May 4, the second day of the market. We selected controls again randomly from the telephone registry of Soest and included only those persons who had visited the farmers' market on May 4 and had not been ill with fever afterwards. Potential controls who became ill were excluded for analysis in CCS2, but were still fully interviewed. This permitted calculation of the attack rate among visitors to the market (see below "Estimation of the overall attack rate") and gave an estimate of the proportion of clinically ill cases that were hospitalized (s.a.).
In the vendors' study we investigated whether the distance of the vendor stands from the sheep pen or dispersion of C. burnetii by wind were relevant risk factors for acquiring Q fever. We obtained a list of all vendors including the approximate location of the stands from the organizer. In addition we asked the local weather station for the predominant wind direction on May 4, 2003. Telephone interviews were performed using standardized questionnaires. A case was defined as a person with onset of fever between May 4 and June 3, 2003 and at least three of the following symptoms: headache, cough, dyspnea, joint pain, muscle pain, weight loss of more than 2 kg, fatigue, nausea or vomiting.
The relative distance of the stands to the sheep pen was estimated by counting the stands between the sheep pen and the stand in question. Each stand was considered to be one stand unit (approximately 3 meters). Larger stands were counted as 2 units. The direction of the wind in relation to the sheep pen was defined by dividing the wind rose (360°) in 4 equal parts of 90°. The predominant wind direction during the market was south-south-east ( Figure 1 ). For the purpose of the analysis we divided the market area into 4 sections with the sheep pen at its center. In section 1 the wind was blowing towards the sheep pen (plus minus 45°). Section 4 was on the opposite side, i.e. where the wind blew from the sheep pen towards the stands, and sections 2 and 3 were east and west with respect to the wind direction, respectively. Location of the stands in reference to the sheep pen was thus defined in two ways: as the absolute distance to the sheep pen (in stand units or meters) and in reference to the wind direction.
We identified a small cohort of 9 sailor friends who visited the farmers' market on May 4, 2003. All of these were serologically tested independently of symptoms. We could therefore calculate the proportion of laboratory confirmed persons who met the clinical case definition (as defined in the cohort study on vendors).
The overall attack rate among adults was estimated based on the following sources:
(1) Interviews undertaken for recruitment of controls for CCS2 allowed the proportion of adults that acquired symptomatic Q fever among those who visited the farmers' market on the second day;
Attributable fraction AFE Number of cases exposed All cases = *
(2) Interviews of cases and controls in CCS2 yielded information about accompanying adults and how many of these became later "ill with fever";
(3) Results of the small cohort of 9 sailor friends (s.a.);
(4) Results from the cohort study on vendors.
Local health departments that identified outbreak cases of Q fever (s.a. "determination of outbreak size and descriptive epidemiology") interviewed patients about the number of persons that had accompanied them to the farmers' market and whether any of these had become ill with fever afterwards. However, as there was no differentiation between adults and children, calculations to estimate the attack rate among adults were performed both with and without this source.
To count cases in (1), (3) and (4) we used the clinical case definition as defined in the cohort study on vendors.
For the calculation of the attack rate among children elicited in CCS2 was the same for all visitors. The number of children that visited the market could then be estimated from the total number of visitors as estimated by the organizers. We then estimated the number of symptomatic children (numerator). For this we assumed that the proportion of children with Q fever that were seen by physicians and were consequently notified was the same as that of adults. It was calculated as:
Thus the true number of children with Q fever was estimated by the number of reported children divided by the estimated proportion reported. Then the attack rate among children could be estimated as follows:
Because this calculation was based on several assumptions (number of visitors, proportion of adult visitors and clinical attack rate among adults) we performed a sensitivity analysis where the values of these variables varied.
Serum was collected from all sheep and cows displayed in the farmers' market as well as from all sheep of the respective home flocks (70 animals). Samples of 25 sheep from five other flocks in the Soest area were also tested for C. burnetii. Tests were performed by ELISA with a phase I and phase II antigen mixture.
We conducted statistical analysis with Epi Info, version 6.04 (CDC, Atlanta, USA). Dichotomous variables in the case control and cohort studies were compared using the Chi-Square test and numerical variables using the Kruskal-Wallis test. P-values smaller than 0.05 were considered statistically significant.
The outbreak investigation was conducted within the framework of the Communicable Diseases Law Reform Act of Germany. Mandatory regulations were observed.
Patients at the local hospital in Soest reported that a farmers' market had taken place on May 3 and 4, 2003 in a spa town close to the town of Soest. It was located in a park along the main promenade, spanning a distance of approximately 500 meters. The market attracted mainly three groups of people: locals, inhabitants of the greater Soest region, patients from the spa sanatoria and their visiting family or friends. Initial interviewees mentioned also that they had spent time at the sheep pen watching new-born lambs that had been born in the early morning hours of May 4, 2003 . The ewe had eaten the placenta but the parturient fluid on the ground had merely been covered with fresh straw.
Overall 171 (65%) of 263 serum samples submitted to the NCL were positive for IgM anti-phase II antibodies by ELISA. Results of throat swabs and serum were negative for other infectious agents. (Figure 2 ). If we assume that symptom onset in cases was normally distributed with a mean of 21 days, 95% of cases (mean +/-2 standard deviations) had their onset between day 10 and 31. The two notified cases with early onset on May 6 and 8, respectively, were laboratory confirmed and additional interviews did not reveal any additional risk factors. Of the 298 cases with known gender, 158 (53%) were male and 140 (47%) were female. Of the notified cases, 189 (63%) were from the county of Soest, 104 (35%) were Porportion reported number of notified adults number of vis = i iting adults attack rate among adults * Attack rate among children estimated true number of childr = e en with Q fever estimated number of children at the market from other counties in the same federal state (Northrhine Westphalia) and 6 (2%) were from five other federal states in Germany (Figure 3 ). Only eight (3%) cases were less than 18 years of age, the mean and median age was 54 and 56 years, respectively ( Figure 4 ). 75 (25%) of 297 notified cases were hospitalized, none died. Calculation of the proportion of cases hospitalized through other information sources revealed that 4 of 19 (21%; 95% CI = 6-46%;
(1/5 (CCS2), 2/11 (vendors study) and 1/3 (sailor friends)) clinically ill cases were hospitalized.
Laboratory confirmation was reported in 167 (56%) outbreak cases; 66 (22%) were confirmed by an increase in anti-phase II antibody titer (CF), 89 (30%) had IgM antibodies against phase II antigens, 11 (4%) were positive in both tests and one was confirmed by culture. No information was available as to whether the 132 (44%) cases without laboratory confirmation were laboratory tested.
18 patients with valvular heart defects and eleven pregnant women were examined. None of them had clinical signs of Q fever. Two (11%) of 18 cardiological patients and four (36%) of 11 pregnant women had an acute Q fever infection. During childbirth strict hygienic measures were implemented. Lochia and colostrum of all infected women were tested by polymerase chain reaction and were positive in only one woman (case 3; Table 1 ). Serological follow-up of the mothers detected chronic infection in the same woman (case 3) 12 weeks after delivery. One year follow-up of two newborn children (of cases 1 and 3) identified neither acute nor chronic Q fever infections.
We recruited 20 cases and 36 controls who visited the farmers' market on May 4 for the second case control study. They did not differ significantly in age and gender (OR for male sex = 1.7; 95%CI = 0.5-5.3; p = 0.26; p-value for age = 0.23). Seventeen (85%) of 20 cases indicated that they had seen the cow (that also was on display at the market next to the sheep) compared to 7 (32%) of Geographical location of Q fever outbreak cases notified to the statutory surveillance system Figure 3 Geographical location of Q fever outbreak cases notified to the statutory surveillance system. or directly at the gate of the sheep pen compared to 8 (32%) of 25 controls (OR = 5.0; 95%CI = 1.2-22.3; p = 0.03). Touching the sheep was also significantly more common among cases (5/20 (25%) CCS2 cases vs. 0/22 (0%) controls; OR undefined; lower 95% CI = 1.1; p = 0.02). 17 (85%) of 20 CCS2 cases, but only 6 (25%) of 24 controls stopped for at least a few seconds at or in the sheep pen, the reference for this variable was "having passed by the pen without stopping" (OR = 17.0; 95%CI = 3.0-112.5; p < 0.01). Among CCS2 cases, self-reported proximity to or time spent with/close to the sheep was not associated with a shorter incubation period.
We were able to contact and interview 75 (86%) of 87 vendors, and received second hand information about 7 more (overall response rate: 94%). Fourty-five (56%) were male and 35 (44%) were female. 13 (16%) met the clinical case definition. Of the 11 vendors who worked within two stand units of the sheep pen, 6 (55%) became cases compared to only 7 (10%) of 70 persons who worked in a stand at a greater distance (relative risk (RR) = 5.5 (95%CI = 2.3-13.2; p = 0.002); Figure 1 ). Of these 7 vendors, 4 had spent time within 5 meters of the pen on May 4, one had been near the pen, but at a distance of more than 5 meters, and no information on this variable was available for the remaining 2. In the section of the market facing the wind coming from the pen (section 4, Figure 1 ), 4 (9%) of 44 vendors became cases, compared to 2 (13%) of 15 persons who worked in section 1 (p = 0.6). Among 22 persons who worked in stands that were perpendicular to the wind direction, 7 (32%) became cases. (Table 3 ). In all scenarios the AR among adults was significantly higher than that among children ( Figure 5 ).
In total, 5 lambs and 5 ewes were displayed on the market, one of them was pregnant and gave birth to twin lambs at 6:30 a.m. on May 4, 2003 . Of these, 3 ewes including the one that had lambed tested positive for C. burnetii. The animals came from a flock of 67 ewes, of which 66 had given birth between February and June. The majority of the births (57 (86%)) had occurred in February and March, usually inside a stable or on a meadow located away from the town. Six ewes aborted, had stillbirths or abnormally weak lambs. Among all ewes, 17/67 (25%) tested positive for C. burnetii.
The percentage of sheep that tested positive in the other 5 sheep flocks in the region ranged from 8% to 24% (8%; 12%; 12%; 16%; 24%).
We have described one of the largest Q fever outbreaks in Germany which, due to its point-source nature, provided the opportunity to assess many epidemiological features of the disease that can be rarely studied otherwise.
In 1954, more than 500 cases of Q fever were, similar to this outbreak, linked to the abortion of an infected cow at a farmers' market [15] . More recently a large outbreak occurred in Jena (Thuringia) in 2005 with 322 reported cases [16] associated with exposure to a herd of sheep kept on a meadow close to the housing area in which the cases occurred.
The first case control study served to confirm the hypothesis of an association between the outbreak and the farmers' market. The fact that only attendance on the second, but not the first day was strongly associated with illness pointed towards the role of the ewe that had given birth Persons accompanying notified cases (source 5) were a mixture of adults and children and are therefore listed separately.
in the early morning hours of May 4, 2005 . This strong association and the very high attributable fraction among all cases suggested a point source and justified defining cases notified through the reporting system as outbreak cases if they were clinically compatible with Q fever and gave a history of having visited the farmers' market. The point-source nature of the outbreak permitted calculation of the incubation period of cases which averaged 21 days and ranged from 2 to 48 days with an interquartile range of 16 to 24 days. This is compatible with the literature [1] . An additional interview with the two cases with early onset (2 and 4 days after attending the market on May 4, Attack rates among adults and children in a most likely scenario and 8 other scenarios Figure 5 Attack rates among adults and children in a most likely scenario and 8 other scenarios. Most likely scenario: 3000 visitors, 83% adult visitors and 20% clinical attack rate among adults. Scenarios 1-8 varied in the assumptions made for "number of visitors", "proportion of adult visitors" and "attack rate among adults" (see Table 3 ). Displayed are attack rates and 95% confidence intervals. respectively) could not identify any other source of infection. A short incubation period was recently observed in another Q fever outbreak in which the infectious dose was likely very high [17] .
The second case control study among persons who visited the market on May 4 demonstrated that both close proximity to the ewe and duration of exposure were important risk factors. This finding was confirmed by the cohort study on vendors which showed that those who worked in a stand close to (within 6 meters) the sheep pen were at significantly higher risk of acquiring Q fever. The study failed to show a significant role of the location of the stand in reference to the wind direction, although we must take into account that the wind was likely not always and exactly as reported by the weather station. However, if the wind had been important at all more cases might have been expected to have occurred among vendors situated at a greater distance to the sheep.
According to statutory surveillance system data, the proportion of clinical cases hospitalized was 25%, similar to the proportion of 21% found in persons pooled from the other studies conducted. Several publications report lower proportions than that found in this investigation: 4% (8/ 191) [7] , 5% [1] and 10% (4/39) [5] ), and there was at least one study with a much higher proportion (63% (10/ 16)) [18] . It is unlikely that hospitals reported cases with Q fever more frequently than private physicians because the proportion hospitalized among Q fever patients identified through random telephone calls in the Soest population or those in the two cohorts was similar to that of notified cases. Thus reporting bias is an unlikely explanation for the relatively high proportion of cases hospitalized. Alternative explanations include overly cautious referral practices on the part of attending physicians or the presumably high infectious dose of the organism in this outbreak, e.g. in those cases that spent time in the sheep pen.
The estimated attack rate among adults in the four studies varied between 16% and 33%. The estimate of 23% based on the random sample of persons visiting the market on the second day would seem most immune to recall bias, even if this cannot be entirely ruled out. The estimation based on information about persons accompanying the cases may be subject to an overestimation because these individuals presumably had a higher probability of being close to the sheep pen, similar to the cases. On the other hand the estimate from the cohort study on vendors might be an underestimate, since the vendors obviously had a different purpose for being at the market and may have been less interested in having a look at the sheep. Nevertheless, all estimates were independent from each other and considering the various possible biases, they were remarkably similar. In comparison, in a different outbreak in Germany, in which inhabitants of a village were exposed to a large herd of sheep (n = 1000-2000) [5, 7] the attack rate was estimated as 16%. In a similar outbreak in Switzerland several villages were exposed to approximately 900 sheep [19] . In the most severely affected village, the clinical attack rate was 16% (estimated from the data provided) [19] . It is remarkable that in the outbreak described here, the infectious potential of one pregnant ewe -upon lambing -was comparable to that of entire herds, albeit in different settings.
Our estimate of the proportion of serologically confirmed cases that became symptomatic (50% (3/6)) is based on a very small sample, but consistent with the international literature. In the above mentioned Swiss outbreak, 46% of serologically positive patients developed clinical disease [7] .
Only approximately half of all symptomatic cases were reported to the statutory surveillance system. Patients who did not seek health care due to mild disease as well as underdiagnosis or underreporting may have contributed to the missing other half. Our estimated 3% attack rate among children is based on a number of successive assumptions and must therefore be interpreted with caution. Nevertheless, sensitivity analysis confirmed that adults had a significantly elevated attack rate compared to children. While it has been suggested that children are at lower risk than adults for developing symptomatic illness [7, 8] few data have been published regarding attack rates of children in comparison to adults.
The estimated C. burnetii seroprevalence in the sheep flocks in the area varied from 8% to 24%. The 25% seroprevalence in the flock of the exhibited animals together with a positive polymerase chain reaction in an afterbirth in June 2003 suggested a recent infection of the flock [20] . Seroprevalence among sheep flocks related to human outbreaks tend to be substantially higher than those in flocks not related to human outbreaks. The median seroprevalence in a number of relevant studies performed in the context of human outbreaks [7, 20, 21] , was 40% compared to 1% in sheep flocks not linked to human outbreaks [20] .
This outbreak shows the dramatic consequences of putting a large number of susceptible individuals in close contact to a single infected ewe that (in such a setting) can turn into a super-spreader upon lambing. There is always a cultural component in the interaction between people and animals, and these may contribute to outbreaks or changing patterns of incidence. During the past decades urbanization of rural areas and changes in animal husbandry have occurred [20] , with more recent attempts to put a "deprived" urban population "in touch" with farm animals. Petting zoos, family farm vacations or the display of (farm) animals at a market such as this may lead to new avenues for the transmission of zoonotic infectious agents [20, [22] [23] [24] . While not all eventualities can be foreseen, it is important to raise awareness in pet and livestock owners as well as to strengthen recommendations where necessary. This outbreak led to the amendment and extension of existing recommendations [25] which now forbid the display of sheep in the latter third of their pregnancy and require regular testing of animals for C. burnetii in petting zoos, where there is close contact between humans and animals.
Due to the size and point source nature this outbreak permitted reassessment of fundamental, but seldom studied epidemiological parameters of Q fever. It also served to revise public health recommendations to account for the changing type and frequency of contact of susceptible humans with potentially infectious animals.
Abbreviations AFE = attributable fraction of cases exposed
The author(s) declare that they have no competing interests.
|
What was the median seropresence of C. burnetti in sheep flocks not linked to human outbreaks?
| 5,202 |
1%
| 28,273 |
1,583 |
A super-spreading ewe infects hundreds with Q fever at a farmers' market in Germany
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1618839/
SHA: ee1b5a9618dcc4080ed100486cedd0969e80fa4d
Authors: Porten, Klaudia; Rissland, Jürgen; Tigges, Almira; Broll, Susanne; Hopp, Wilfried; Lunemann, Mechthild; van Treeck, Ulrich; Kimmig, Peter; Brockmann, Stefan O; Wagner-Wiening, Christiane; Hellenbrand, Wiebke; Buchholz, Udo
Date: 2006-10-06
DOI: 10.1186/1471-2334-6-147
License: cc-by
Abstract: BACKGROUND: In May 2003 the Soest County Health Department was informed of an unusually large number of patients hospitalized with atypical pneumonia. METHODS: In exploratory interviews patients mentioned having visited a farmers' market where a sheep had lambed. Serologic testing confirmed the diagnosis of Q fever. We asked local health departments in Germany to identiy notified Q fever patients who had visited the farmers market. To investigate risk factors for infection we conducted a case control study (cases were Q fever patients, controls were randomly selected Soest citizens) and a cohort study among vendors at the market. The sheep exhibited at the market, the herd from which it originated as well as sheep from herds held in the vicinity of Soest were tested for Coxiella burnetii (C. burnetii). RESULTS: A total of 299 reported Q fever cases was linked to this outbreak. The mean incubation period was 21 days, with an interquartile range of 16–24 days. The case control study identified close proximity to and stopping for at least a few seconds at the sheep's pen as significant risk factors. Vendors within approximately 6 meters of the sheep's pen were at increased risk for disease compared to those located farther away. Wind played no significant role. The clinical attack rate of adults and children was estimated as 20% and 3%, respectively, 25% of cases were hospitalized. The ewe that had lambed as well as 25% of its herd tested positive for C. burnetii antibodies. CONCLUSION: Due to its size and point source nature this outbreak permitted assessment of fundamental, but seldom studied epidemiological parameters. As a consequence of this outbreak, it was recommended that pregnant sheep not be displayed in public during the 3(rd )trimester and to test animals in petting zoos regularly for C. burnetii.
Text: Q fever is a worldwide zoonosis caused by Coxiella burnetii (C. burnetii), a small, gram-negative obligate intracellular bacterium. C. burnetii displays antigenic variation with an infectious phase I and less infectious phase II. The primary reservoir from which human infection occurs consists of sheep, goat and cattle. Although C. burnetii infections in animals are usually asymptomatic, they may cause abortions in sheep and goats [1] . High concentrations of C. burnetii can be found in birth products of infected mammals [2] . Humans frequently acquire infection through inhalation of contaminated aerosols from parturient fluids, placenta or wool [1] . Because the infectious dose is very low [3] and C. burnetii is able to survive in a spore-like state for months to years, outbreaks among humans have also occurred through contaminated dust carried by wind over large distances [4] [5] [6] .
C. burnetii infection in humans is asymptomatic in approximately 50% of cases. Approximately 5% of cases are hospitalized, and fatal cases are rare [1] . The clinical presentation of acute Q fever is variable and can resemble many other infectious diseases [2] . However, the most frequent clinical manifestation of acute Q fever is a self-limited febrile illness associated with severe headache. Atypical pneumonia and hepatitis are the major clinical manifestations of more severe disease. Acute Q fever may be complicated by meningoencephalitis or myocarditis. Rarely a chronic form of Q fever develops months after the acute illness, most commonly in the form of endocarditis [1] . Children develop clinical disease less frequently [7, 8] . Because of its non-specific presentation Q fever can only be suspected on clinical grounds and requires serologic confirmation. While the indirect immunofluorescence assay (IFA) is considered to be the reference method, complement fixation (CF), ELISA and microagglutination (MA) can also be used [9] . Acute infections are diagnosed by elevated IgG and/or IgM anti-phase II antibodies, while raised anti-phase I IgG antibodies are characteristic for chronic infections [1] .
In Germany, acute Q fever is a notifiable disease. Between 1991 and 2000 the annual number of cases varied from 46 to 273 cases per year [10] . In 2001 and 2002, 293 and 191 cases were notified, respectively [11, 12] .
On May 26, 2003 the health department of Soest was informed by a local hospital of an unusually large number of patients with atypical pneumonia. Some patients reported having visited a farmers' market that took place on May 3 and 4, 2003 in a spa town near Soest. Since the etiology was unclear, pathogens such as SARS coronavirus were considered and strict infection control measures implemented until the diagnosis of Q fever was confirmed.
An outbreak investigation team was formed and included public health professionals from the local health department, the local veterinary health department, the state health department, the National Consulting Laboratory (NCL) for Coxiellae and the Robert Koch-Institute (RKI), the federal public health institute. Because of the size and point source appearance of the outbreak the objective of the investigation was to identify etiologic factors relevant to the prevention and control of Q fever as well as to assess epidemiological parameters that can be rarely studied otherwise.
On May 26 and 27, 2003 we conducted exploratory interviews with patients in Soest hospitalized due to atypical pneumonia.
Attending physicians were requested to test serum of patients with atypical pneumonia for Mycoplasma pneumoniae, Chlamydia pneumoniae, Legionella pneumophila, Coxiella burnetii, Influenza A and B, Parainfluenza 1-3, Adenovirus and Enterovirus. Throat swabs were tested for Influenza virus, Adenovirus and SARS-Coronavirus. Laboratory confirmation of an acute Q fever infection was defined as the presence of IgM antibodies against phase II C. burnetii antigens (ELISA or IFA), a 4-fold increase in anti-phase II IgG antibody titer (ELISA or IFA) or in anti phase II antibody titer by CF between acute and convalescent sera. A chronic infection was confirmed when both anti-phase I IgG and anti-phase II IgG antibody titers were raised.
Because patients with valvular heart defects and pregnant women are at high risk of developing chronic infection [13, 14] we alerted internists and gynaecologists through the journal of the German Medical Association and asked them to send serum samples to the NCL if they identified patients from these risk groups who had been at the farmers' market during the outbreak.
The objective of the first case control study was to establish whether there was a link between the farmers' market and the outbreak and to identify other potential risk factors. We conducted telephone interviews using a standardised questionnaire that asked about attendance at the farmers' market, having been within 1 km distance of one of 6 sheep flocks in the area, tick bites and consumption of unpasteurized milk, sheep or goat cheese. For the purpose of CCS1 we defined a case (CCS1 case) as an adult resident of the town of Soest notified to the statutory sur-veillance system with Q fever, having symptom onset between May 4 and June 3, 2003. Exclusion criterion was a negative IgM-titer against phase II antigens. Two controls per case were recruited from Soest inhabitants by random digit dialing.
We calculated the attributable fraction of cases exposed to the farmers' market on May 4 (AFE) as (OR-1)/OR and the attributable fraction for all cases due to this exposure as:
The farmers' market was held in a spa town near Soest with many visitors from other areas of the state and even the entire country. To determine the outbreak size we therefore asked local public health departments in Germany to ascertain a possible link to the farmers' market in Soest for all patients notified with Q-fever. A case in this context ("notified case") was defined as any person with a clinical diagnosis compatible with Q fever with or without laboratory confirmation and history of exposure to the farmers' market.
Local health departments also reported whether a notified case was hospitalized. To obtain an independent, second estimate of the proportion of hospitalizations among symptomatic patients beyond that reported through the statutory surveillance system we calculated the proportion of hospitalized patients among those persons fulfilling the clinical case definition (as used in the vendors' study (s.b.)) identified through random sampling of the Soest population (within CCS2 (s.b.)) as well as in two cohorts (vendors' study and the 9 sailor friends (see below)).
The objective of CCS2 was to identify risk factors associated with attendance of the farmers' market on the second day. We used the same case definition as in CCS1, but included only persons that had visited the farmers' market on May 4, the second day of the market. We selected controls again randomly from the telephone registry of Soest and included only those persons who had visited the farmers' market on May 4 and had not been ill with fever afterwards. Potential controls who became ill were excluded for analysis in CCS2, but were still fully interviewed. This permitted calculation of the attack rate among visitors to the market (see below "Estimation of the overall attack rate") and gave an estimate of the proportion of clinically ill cases that were hospitalized (s.a.).
In the vendors' study we investigated whether the distance of the vendor stands from the sheep pen or dispersion of C. burnetii by wind were relevant risk factors for acquiring Q fever. We obtained a list of all vendors including the approximate location of the stands from the organizer. In addition we asked the local weather station for the predominant wind direction on May 4, 2003. Telephone interviews were performed using standardized questionnaires. A case was defined as a person with onset of fever between May 4 and June 3, 2003 and at least three of the following symptoms: headache, cough, dyspnea, joint pain, muscle pain, weight loss of more than 2 kg, fatigue, nausea or vomiting.
The relative distance of the stands to the sheep pen was estimated by counting the stands between the sheep pen and the stand in question. Each stand was considered to be one stand unit (approximately 3 meters). Larger stands were counted as 2 units. The direction of the wind in relation to the sheep pen was defined by dividing the wind rose (360°) in 4 equal parts of 90°. The predominant wind direction during the market was south-south-east ( Figure 1 ). For the purpose of the analysis we divided the market area into 4 sections with the sheep pen at its center. In section 1 the wind was blowing towards the sheep pen (plus minus 45°). Section 4 was on the opposite side, i.e. where the wind blew from the sheep pen towards the stands, and sections 2 and 3 were east and west with respect to the wind direction, respectively. Location of the stands in reference to the sheep pen was thus defined in two ways: as the absolute distance to the sheep pen (in stand units or meters) and in reference to the wind direction.
We identified a small cohort of 9 sailor friends who visited the farmers' market on May 4, 2003. All of these were serologically tested independently of symptoms. We could therefore calculate the proportion of laboratory confirmed persons who met the clinical case definition (as defined in the cohort study on vendors).
The overall attack rate among adults was estimated based on the following sources:
(1) Interviews undertaken for recruitment of controls for CCS2 allowed the proportion of adults that acquired symptomatic Q fever among those who visited the farmers' market on the second day;
Attributable fraction AFE Number of cases exposed All cases = *
(2) Interviews of cases and controls in CCS2 yielded information about accompanying adults and how many of these became later "ill with fever";
(3) Results of the small cohort of 9 sailor friends (s.a.);
(4) Results from the cohort study on vendors.
Local health departments that identified outbreak cases of Q fever (s.a. "determination of outbreak size and descriptive epidemiology") interviewed patients about the number of persons that had accompanied them to the farmers' market and whether any of these had become ill with fever afterwards. However, as there was no differentiation between adults and children, calculations to estimate the attack rate among adults were performed both with and without this source.
To count cases in (1), (3) and (4) we used the clinical case definition as defined in the cohort study on vendors.
For the calculation of the attack rate among children elicited in CCS2 was the same for all visitors. The number of children that visited the market could then be estimated from the total number of visitors as estimated by the organizers. We then estimated the number of symptomatic children (numerator). For this we assumed that the proportion of children with Q fever that were seen by physicians and were consequently notified was the same as that of adults. It was calculated as:
Thus the true number of children with Q fever was estimated by the number of reported children divided by the estimated proportion reported. Then the attack rate among children could be estimated as follows:
Because this calculation was based on several assumptions (number of visitors, proportion of adult visitors and clinical attack rate among adults) we performed a sensitivity analysis where the values of these variables varied.
Serum was collected from all sheep and cows displayed in the farmers' market as well as from all sheep of the respective home flocks (70 animals). Samples of 25 sheep from five other flocks in the Soest area were also tested for C. burnetii. Tests were performed by ELISA with a phase I and phase II antigen mixture.
We conducted statistical analysis with Epi Info, version 6.04 (CDC, Atlanta, USA). Dichotomous variables in the case control and cohort studies were compared using the Chi-Square test and numerical variables using the Kruskal-Wallis test. P-values smaller than 0.05 were considered statistically significant.
The outbreak investigation was conducted within the framework of the Communicable Diseases Law Reform Act of Germany. Mandatory regulations were observed.
Patients at the local hospital in Soest reported that a farmers' market had taken place on May 3 and 4, 2003 in a spa town close to the town of Soest. It was located in a park along the main promenade, spanning a distance of approximately 500 meters. The market attracted mainly three groups of people: locals, inhabitants of the greater Soest region, patients from the spa sanatoria and their visiting family or friends. Initial interviewees mentioned also that they had spent time at the sheep pen watching new-born lambs that had been born in the early morning hours of May 4, 2003 . The ewe had eaten the placenta but the parturient fluid on the ground had merely been covered with fresh straw.
Overall 171 (65%) of 263 serum samples submitted to the NCL were positive for IgM anti-phase II antibodies by ELISA. Results of throat swabs and serum were negative for other infectious agents. (Figure 2 ). If we assume that symptom onset in cases was normally distributed with a mean of 21 days, 95% of cases (mean +/-2 standard deviations) had their onset between day 10 and 31. The two notified cases with early onset on May 6 and 8, respectively, were laboratory confirmed and additional interviews did not reveal any additional risk factors. Of the 298 cases with known gender, 158 (53%) were male and 140 (47%) were female. Of the notified cases, 189 (63%) were from the county of Soest, 104 (35%) were Porportion reported number of notified adults number of vis = i iting adults attack rate among adults * Attack rate among children estimated true number of childr = e en with Q fever estimated number of children at the market from other counties in the same federal state (Northrhine Westphalia) and 6 (2%) were from five other federal states in Germany (Figure 3 ). Only eight (3%) cases were less than 18 years of age, the mean and median age was 54 and 56 years, respectively ( Figure 4 ). 75 (25%) of 297 notified cases were hospitalized, none died. Calculation of the proportion of cases hospitalized through other information sources revealed that 4 of 19 (21%; 95% CI = 6-46%;
(1/5 (CCS2), 2/11 (vendors study) and 1/3 (sailor friends)) clinically ill cases were hospitalized.
Laboratory confirmation was reported in 167 (56%) outbreak cases; 66 (22%) were confirmed by an increase in anti-phase II antibody titer (CF), 89 (30%) had IgM antibodies against phase II antigens, 11 (4%) were positive in both tests and one was confirmed by culture. No information was available as to whether the 132 (44%) cases without laboratory confirmation were laboratory tested.
18 patients with valvular heart defects and eleven pregnant women were examined. None of them had clinical signs of Q fever. Two (11%) of 18 cardiological patients and four (36%) of 11 pregnant women had an acute Q fever infection. During childbirth strict hygienic measures were implemented. Lochia and colostrum of all infected women were tested by polymerase chain reaction and were positive in only one woman (case 3; Table 1 ). Serological follow-up of the mothers detected chronic infection in the same woman (case 3) 12 weeks after delivery. One year follow-up of two newborn children (of cases 1 and 3) identified neither acute nor chronic Q fever infections.
We recruited 20 cases and 36 controls who visited the farmers' market on May 4 for the second case control study. They did not differ significantly in age and gender (OR for male sex = 1.7; 95%CI = 0.5-5.3; p = 0.26; p-value for age = 0.23). Seventeen (85%) of 20 cases indicated that they had seen the cow (that also was on display at the market next to the sheep) compared to 7 (32%) of Geographical location of Q fever outbreak cases notified to the statutory surveillance system Figure 3 Geographical location of Q fever outbreak cases notified to the statutory surveillance system. or directly at the gate of the sheep pen compared to 8 (32%) of 25 controls (OR = 5.0; 95%CI = 1.2-22.3; p = 0.03). Touching the sheep was also significantly more common among cases (5/20 (25%) CCS2 cases vs. 0/22 (0%) controls; OR undefined; lower 95% CI = 1.1; p = 0.02). 17 (85%) of 20 CCS2 cases, but only 6 (25%) of 24 controls stopped for at least a few seconds at or in the sheep pen, the reference for this variable was "having passed by the pen without stopping" (OR = 17.0; 95%CI = 3.0-112.5; p < 0.01). Among CCS2 cases, self-reported proximity to or time spent with/close to the sheep was not associated with a shorter incubation period.
We were able to contact and interview 75 (86%) of 87 vendors, and received second hand information about 7 more (overall response rate: 94%). Fourty-five (56%) were male and 35 (44%) were female. 13 (16%) met the clinical case definition. Of the 11 vendors who worked within two stand units of the sheep pen, 6 (55%) became cases compared to only 7 (10%) of 70 persons who worked in a stand at a greater distance (relative risk (RR) = 5.5 (95%CI = 2.3-13.2; p = 0.002); Figure 1 ). Of these 7 vendors, 4 had spent time within 5 meters of the pen on May 4, one had been near the pen, but at a distance of more than 5 meters, and no information on this variable was available for the remaining 2. In the section of the market facing the wind coming from the pen (section 4, Figure 1 ), 4 (9%) of 44 vendors became cases, compared to 2 (13%) of 15 persons who worked in section 1 (p = 0.6). Among 22 persons who worked in stands that were perpendicular to the wind direction, 7 (32%) became cases. (Table 3 ). In all scenarios the AR among adults was significantly higher than that among children ( Figure 5 ).
In total, 5 lambs and 5 ewes were displayed on the market, one of them was pregnant and gave birth to twin lambs at 6:30 a.m. on May 4, 2003 . Of these, 3 ewes including the one that had lambed tested positive for C. burnetii. The animals came from a flock of 67 ewes, of which 66 had given birth between February and June. The majority of the births (57 (86%)) had occurred in February and March, usually inside a stable or on a meadow located away from the town. Six ewes aborted, had stillbirths or abnormally weak lambs. Among all ewes, 17/67 (25%) tested positive for C. burnetii.
The percentage of sheep that tested positive in the other 5 sheep flocks in the region ranged from 8% to 24% (8%; 12%; 12%; 16%; 24%).
We have described one of the largest Q fever outbreaks in Germany which, due to its point-source nature, provided the opportunity to assess many epidemiological features of the disease that can be rarely studied otherwise.
In 1954, more than 500 cases of Q fever were, similar to this outbreak, linked to the abortion of an infected cow at a farmers' market [15] . More recently a large outbreak occurred in Jena (Thuringia) in 2005 with 322 reported cases [16] associated with exposure to a herd of sheep kept on a meadow close to the housing area in which the cases occurred.
The first case control study served to confirm the hypothesis of an association between the outbreak and the farmers' market. The fact that only attendance on the second, but not the first day was strongly associated with illness pointed towards the role of the ewe that had given birth Persons accompanying notified cases (source 5) were a mixture of adults and children and are therefore listed separately.
in the early morning hours of May 4, 2005 . This strong association and the very high attributable fraction among all cases suggested a point source and justified defining cases notified through the reporting system as outbreak cases if they were clinically compatible with Q fever and gave a history of having visited the farmers' market. The point-source nature of the outbreak permitted calculation of the incubation period of cases which averaged 21 days and ranged from 2 to 48 days with an interquartile range of 16 to 24 days. This is compatible with the literature [1] . An additional interview with the two cases with early onset (2 and 4 days after attending the market on May 4, Attack rates among adults and children in a most likely scenario and 8 other scenarios Figure 5 Attack rates among adults and children in a most likely scenario and 8 other scenarios. Most likely scenario: 3000 visitors, 83% adult visitors and 20% clinical attack rate among adults. Scenarios 1-8 varied in the assumptions made for "number of visitors", "proportion of adult visitors" and "attack rate among adults" (see Table 3 ). Displayed are attack rates and 95% confidence intervals. respectively) could not identify any other source of infection. A short incubation period was recently observed in another Q fever outbreak in which the infectious dose was likely very high [17] .
The second case control study among persons who visited the market on May 4 demonstrated that both close proximity to the ewe and duration of exposure were important risk factors. This finding was confirmed by the cohort study on vendors which showed that those who worked in a stand close to (within 6 meters) the sheep pen were at significantly higher risk of acquiring Q fever. The study failed to show a significant role of the location of the stand in reference to the wind direction, although we must take into account that the wind was likely not always and exactly as reported by the weather station. However, if the wind had been important at all more cases might have been expected to have occurred among vendors situated at a greater distance to the sheep.
According to statutory surveillance system data, the proportion of clinical cases hospitalized was 25%, similar to the proportion of 21% found in persons pooled from the other studies conducted. Several publications report lower proportions than that found in this investigation: 4% (8/ 191) [7] , 5% [1] and 10% (4/39) [5] ), and there was at least one study with a much higher proportion (63% (10/ 16)) [18] . It is unlikely that hospitals reported cases with Q fever more frequently than private physicians because the proportion hospitalized among Q fever patients identified through random telephone calls in the Soest population or those in the two cohorts was similar to that of notified cases. Thus reporting bias is an unlikely explanation for the relatively high proportion of cases hospitalized. Alternative explanations include overly cautious referral practices on the part of attending physicians or the presumably high infectious dose of the organism in this outbreak, e.g. in those cases that spent time in the sheep pen.
The estimated attack rate among adults in the four studies varied between 16% and 33%. The estimate of 23% based on the random sample of persons visiting the market on the second day would seem most immune to recall bias, even if this cannot be entirely ruled out. The estimation based on information about persons accompanying the cases may be subject to an overestimation because these individuals presumably had a higher probability of being close to the sheep pen, similar to the cases. On the other hand the estimate from the cohort study on vendors might be an underestimate, since the vendors obviously had a different purpose for being at the market and may have been less interested in having a look at the sheep. Nevertheless, all estimates were independent from each other and considering the various possible biases, they were remarkably similar. In comparison, in a different outbreak in Germany, in which inhabitants of a village were exposed to a large herd of sheep (n = 1000-2000) [5, 7] the attack rate was estimated as 16%. In a similar outbreak in Switzerland several villages were exposed to approximately 900 sheep [19] . In the most severely affected village, the clinical attack rate was 16% (estimated from the data provided) [19] . It is remarkable that in the outbreak described here, the infectious potential of one pregnant ewe -upon lambing -was comparable to that of entire herds, albeit in different settings.
Our estimate of the proportion of serologically confirmed cases that became symptomatic (50% (3/6)) is based on a very small sample, but consistent with the international literature. In the above mentioned Swiss outbreak, 46% of serologically positive patients developed clinical disease [7] .
Only approximately half of all symptomatic cases were reported to the statutory surveillance system. Patients who did not seek health care due to mild disease as well as underdiagnosis or underreporting may have contributed to the missing other half. Our estimated 3% attack rate among children is based on a number of successive assumptions and must therefore be interpreted with caution. Nevertheless, sensitivity analysis confirmed that adults had a significantly elevated attack rate compared to children. While it has been suggested that children are at lower risk than adults for developing symptomatic illness [7, 8] few data have been published regarding attack rates of children in comparison to adults.
The estimated C. burnetii seroprevalence in the sheep flocks in the area varied from 8% to 24%. The 25% seroprevalence in the flock of the exhibited animals together with a positive polymerase chain reaction in an afterbirth in June 2003 suggested a recent infection of the flock [20] . Seroprevalence among sheep flocks related to human outbreaks tend to be substantially higher than those in flocks not related to human outbreaks. The median seroprevalence in a number of relevant studies performed in the context of human outbreaks [7, 20, 21] , was 40% compared to 1% in sheep flocks not linked to human outbreaks [20] .
This outbreak shows the dramatic consequences of putting a large number of susceptible individuals in close contact to a single infected ewe that (in such a setting) can turn into a super-spreader upon lambing. There is always a cultural component in the interaction between people and animals, and these may contribute to outbreaks or changing patterns of incidence. During the past decades urbanization of rural areas and changes in animal husbandry have occurred [20] , with more recent attempts to put a "deprived" urban population "in touch" with farm animals. Petting zoos, family farm vacations or the display of (farm) animals at a market such as this may lead to new avenues for the transmission of zoonotic infectious agents [20, [22] [23] [24] . While not all eventualities can be foreseen, it is important to raise awareness in pet and livestock owners as well as to strengthen recommendations where necessary. This outbreak led to the amendment and extension of existing recommendations [25] which now forbid the display of sheep in the latter third of their pregnancy and require regular testing of animals for C. burnetii in petting zoos, where there is close contact between humans and animals.
Due to the size and point source nature this outbreak permitted reassessment of fundamental, but seldom studied epidemiological parameters of Q fever. It also served to revise public health recommendations to account for the changing type and frequency of contact of susceptible humans with potentially infectious animals.
Abbreviations AFE = attributable fraction of cases exposed
The author(s) declare that they have no competing interests.
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What important risk factors to infection were found during the second case-controlled study?
| 5,203 |
close proximity to the ewe and duration of exposure
| 23,543 |
1,583 |
A super-spreading ewe infects hundreds with Q fever at a farmers' market in Germany
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1618839/
SHA: ee1b5a9618dcc4080ed100486cedd0969e80fa4d
Authors: Porten, Klaudia; Rissland, Jürgen; Tigges, Almira; Broll, Susanne; Hopp, Wilfried; Lunemann, Mechthild; van Treeck, Ulrich; Kimmig, Peter; Brockmann, Stefan O; Wagner-Wiening, Christiane; Hellenbrand, Wiebke; Buchholz, Udo
Date: 2006-10-06
DOI: 10.1186/1471-2334-6-147
License: cc-by
Abstract: BACKGROUND: In May 2003 the Soest County Health Department was informed of an unusually large number of patients hospitalized with atypical pneumonia. METHODS: In exploratory interviews patients mentioned having visited a farmers' market where a sheep had lambed. Serologic testing confirmed the diagnosis of Q fever. We asked local health departments in Germany to identiy notified Q fever patients who had visited the farmers market. To investigate risk factors for infection we conducted a case control study (cases were Q fever patients, controls were randomly selected Soest citizens) and a cohort study among vendors at the market. The sheep exhibited at the market, the herd from which it originated as well as sheep from herds held in the vicinity of Soest were tested for Coxiella burnetii (C. burnetii). RESULTS: A total of 299 reported Q fever cases was linked to this outbreak. The mean incubation period was 21 days, with an interquartile range of 16–24 days. The case control study identified close proximity to and stopping for at least a few seconds at the sheep's pen as significant risk factors. Vendors within approximately 6 meters of the sheep's pen were at increased risk for disease compared to those located farther away. Wind played no significant role. The clinical attack rate of adults and children was estimated as 20% and 3%, respectively, 25% of cases were hospitalized. The ewe that had lambed as well as 25% of its herd tested positive for C. burnetii antibodies. CONCLUSION: Due to its size and point source nature this outbreak permitted assessment of fundamental, but seldom studied epidemiological parameters. As a consequence of this outbreak, it was recommended that pregnant sheep not be displayed in public during the 3(rd )trimester and to test animals in petting zoos regularly for C. burnetii.
Text: Q fever is a worldwide zoonosis caused by Coxiella burnetii (C. burnetii), a small, gram-negative obligate intracellular bacterium. C. burnetii displays antigenic variation with an infectious phase I and less infectious phase II. The primary reservoir from which human infection occurs consists of sheep, goat and cattle. Although C. burnetii infections in animals are usually asymptomatic, they may cause abortions in sheep and goats [1] . High concentrations of C. burnetii can be found in birth products of infected mammals [2] . Humans frequently acquire infection through inhalation of contaminated aerosols from parturient fluids, placenta or wool [1] . Because the infectious dose is very low [3] and C. burnetii is able to survive in a spore-like state for months to years, outbreaks among humans have also occurred through contaminated dust carried by wind over large distances [4] [5] [6] .
C. burnetii infection in humans is asymptomatic in approximately 50% of cases. Approximately 5% of cases are hospitalized, and fatal cases are rare [1] . The clinical presentation of acute Q fever is variable and can resemble many other infectious diseases [2] . However, the most frequent clinical manifestation of acute Q fever is a self-limited febrile illness associated with severe headache. Atypical pneumonia and hepatitis are the major clinical manifestations of more severe disease. Acute Q fever may be complicated by meningoencephalitis or myocarditis. Rarely a chronic form of Q fever develops months after the acute illness, most commonly in the form of endocarditis [1] . Children develop clinical disease less frequently [7, 8] . Because of its non-specific presentation Q fever can only be suspected on clinical grounds and requires serologic confirmation. While the indirect immunofluorescence assay (IFA) is considered to be the reference method, complement fixation (CF), ELISA and microagglutination (MA) can also be used [9] . Acute infections are diagnosed by elevated IgG and/or IgM anti-phase II antibodies, while raised anti-phase I IgG antibodies are characteristic for chronic infections [1] .
In Germany, acute Q fever is a notifiable disease. Between 1991 and 2000 the annual number of cases varied from 46 to 273 cases per year [10] . In 2001 and 2002, 293 and 191 cases were notified, respectively [11, 12] .
On May 26, 2003 the health department of Soest was informed by a local hospital of an unusually large number of patients with atypical pneumonia. Some patients reported having visited a farmers' market that took place on May 3 and 4, 2003 in a spa town near Soest. Since the etiology was unclear, pathogens such as SARS coronavirus were considered and strict infection control measures implemented until the diagnosis of Q fever was confirmed.
An outbreak investigation team was formed and included public health professionals from the local health department, the local veterinary health department, the state health department, the National Consulting Laboratory (NCL) for Coxiellae and the Robert Koch-Institute (RKI), the federal public health institute. Because of the size and point source appearance of the outbreak the objective of the investigation was to identify etiologic factors relevant to the prevention and control of Q fever as well as to assess epidemiological parameters that can be rarely studied otherwise.
On May 26 and 27, 2003 we conducted exploratory interviews with patients in Soest hospitalized due to atypical pneumonia.
Attending physicians were requested to test serum of patients with atypical pneumonia for Mycoplasma pneumoniae, Chlamydia pneumoniae, Legionella pneumophila, Coxiella burnetii, Influenza A and B, Parainfluenza 1-3, Adenovirus and Enterovirus. Throat swabs were tested for Influenza virus, Adenovirus and SARS-Coronavirus. Laboratory confirmation of an acute Q fever infection was defined as the presence of IgM antibodies against phase II C. burnetii antigens (ELISA or IFA), a 4-fold increase in anti-phase II IgG antibody titer (ELISA or IFA) or in anti phase II antibody titer by CF between acute and convalescent sera. A chronic infection was confirmed when both anti-phase I IgG and anti-phase II IgG antibody titers were raised.
Because patients with valvular heart defects and pregnant women are at high risk of developing chronic infection [13, 14] we alerted internists and gynaecologists through the journal of the German Medical Association and asked them to send serum samples to the NCL if they identified patients from these risk groups who had been at the farmers' market during the outbreak.
The objective of the first case control study was to establish whether there was a link between the farmers' market and the outbreak and to identify other potential risk factors. We conducted telephone interviews using a standardised questionnaire that asked about attendance at the farmers' market, having been within 1 km distance of one of 6 sheep flocks in the area, tick bites and consumption of unpasteurized milk, sheep or goat cheese. For the purpose of CCS1 we defined a case (CCS1 case) as an adult resident of the town of Soest notified to the statutory sur-veillance system with Q fever, having symptom onset between May 4 and June 3, 2003. Exclusion criterion was a negative IgM-titer against phase II antigens. Two controls per case were recruited from Soest inhabitants by random digit dialing.
We calculated the attributable fraction of cases exposed to the farmers' market on May 4 (AFE) as (OR-1)/OR and the attributable fraction for all cases due to this exposure as:
The farmers' market was held in a spa town near Soest with many visitors from other areas of the state and even the entire country. To determine the outbreak size we therefore asked local public health departments in Germany to ascertain a possible link to the farmers' market in Soest for all patients notified with Q-fever. A case in this context ("notified case") was defined as any person with a clinical diagnosis compatible with Q fever with or without laboratory confirmation and history of exposure to the farmers' market.
Local health departments also reported whether a notified case was hospitalized. To obtain an independent, second estimate of the proportion of hospitalizations among symptomatic patients beyond that reported through the statutory surveillance system we calculated the proportion of hospitalized patients among those persons fulfilling the clinical case definition (as used in the vendors' study (s.b.)) identified through random sampling of the Soest population (within CCS2 (s.b.)) as well as in two cohorts (vendors' study and the 9 sailor friends (see below)).
The objective of CCS2 was to identify risk factors associated with attendance of the farmers' market on the second day. We used the same case definition as in CCS1, but included only persons that had visited the farmers' market on May 4, the second day of the market. We selected controls again randomly from the telephone registry of Soest and included only those persons who had visited the farmers' market on May 4 and had not been ill with fever afterwards. Potential controls who became ill were excluded for analysis in CCS2, but were still fully interviewed. This permitted calculation of the attack rate among visitors to the market (see below "Estimation of the overall attack rate") and gave an estimate of the proportion of clinically ill cases that were hospitalized (s.a.).
In the vendors' study we investigated whether the distance of the vendor stands from the sheep pen or dispersion of C. burnetii by wind were relevant risk factors for acquiring Q fever. We obtained a list of all vendors including the approximate location of the stands from the organizer. In addition we asked the local weather station for the predominant wind direction on May 4, 2003. Telephone interviews were performed using standardized questionnaires. A case was defined as a person with onset of fever between May 4 and June 3, 2003 and at least three of the following symptoms: headache, cough, dyspnea, joint pain, muscle pain, weight loss of more than 2 kg, fatigue, nausea or vomiting.
The relative distance of the stands to the sheep pen was estimated by counting the stands between the sheep pen and the stand in question. Each stand was considered to be one stand unit (approximately 3 meters). Larger stands were counted as 2 units. The direction of the wind in relation to the sheep pen was defined by dividing the wind rose (360°) in 4 equal parts of 90°. The predominant wind direction during the market was south-south-east ( Figure 1 ). For the purpose of the analysis we divided the market area into 4 sections with the sheep pen at its center. In section 1 the wind was blowing towards the sheep pen (plus minus 45°). Section 4 was on the opposite side, i.e. where the wind blew from the sheep pen towards the stands, and sections 2 and 3 were east and west with respect to the wind direction, respectively. Location of the stands in reference to the sheep pen was thus defined in two ways: as the absolute distance to the sheep pen (in stand units or meters) and in reference to the wind direction.
We identified a small cohort of 9 sailor friends who visited the farmers' market on May 4, 2003. All of these were serologically tested independently of symptoms. We could therefore calculate the proportion of laboratory confirmed persons who met the clinical case definition (as defined in the cohort study on vendors).
The overall attack rate among adults was estimated based on the following sources:
(1) Interviews undertaken for recruitment of controls for CCS2 allowed the proportion of adults that acquired symptomatic Q fever among those who visited the farmers' market on the second day;
Attributable fraction AFE Number of cases exposed All cases = *
(2) Interviews of cases and controls in CCS2 yielded information about accompanying adults and how many of these became later "ill with fever";
(3) Results of the small cohort of 9 sailor friends (s.a.);
(4) Results from the cohort study on vendors.
Local health departments that identified outbreak cases of Q fever (s.a. "determination of outbreak size and descriptive epidemiology") interviewed patients about the number of persons that had accompanied them to the farmers' market and whether any of these had become ill with fever afterwards. However, as there was no differentiation between adults and children, calculations to estimate the attack rate among adults were performed both with and without this source.
To count cases in (1), (3) and (4) we used the clinical case definition as defined in the cohort study on vendors.
For the calculation of the attack rate among children elicited in CCS2 was the same for all visitors. The number of children that visited the market could then be estimated from the total number of visitors as estimated by the organizers. We then estimated the number of symptomatic children (numerator). For this we assumed that the proportion of children with Q fever that were seen by physicians and were consequently notified was the same as that of adults. It was calculated as:
Thus the true number of children with Q fever was estimated by the number of reported children divided by the estimated proportion reported. Then the attack rate among children could be estimated as follows:
Because this calculation was based on several assumptions (number of visitors, proportion of adult visitors and clinical attack rate among adults) we performed a sensitivity analysis where the values of these variables varied.
Serum was collected from all sheep and cows displayed in the farmers' market as well as from all sheep of the respective home flocks (70 animals). Samples of 25 sheep from five other flocks in the Soest area were also tested for C. burnetii. Tests were performed by ELISA with a phase I and phase II antigen mixture.
We conducted statistical analysis with Epi Info, version 6.04 (CDC, Atlanta, USA). Dichotomous variables in the case control and cohort studies were compared using the Chi-Square test and numerical variables using the Kruskal-Wallis test. P-values smaller than 0.05 were considered statistically significant.
The outbreak investigation was conducted within the framework of the Communicable Diseases Law Reform Act of Germany. Mandatory regulations were observed.
Patients at the local hospital in Soest reported that a farmers' market had taken place on May 3 and 4, 2003 in a spa town close to the town of Soest. It was located in a park along the main promenade, spanning a distance of approximately 500 meters. The market attracted mainly three groups of people: locals, inhabitants of the greater Soest region, patients from the spa sanatoria and their visiting family or friends. Initial interviewees mentioned also that they had spent time at the sheep pen watching new-born lambs that had been born in the early morning hours of May 4, 2003 . The ewe had eaten the placenta but the parturient fluid on the ground had merely been covered with fresh straw.
Overall 171 (65%) of 263 serum samples submitted to the NCL were positive for IgM anti-phase II antibodies by ELISA. Results of throat swabs and serum were negative for other infectious agents. (Figure 2 ). If we assume that symptom onset in cases was normally distributed with a mean of 21 days, 95% of cases (mean +/-2 standard deviations) had their onset between day 10 and 31. The two notified cases with early onset on May 6 and 8, respectively, were laboratory confirmed and additional interviews did not reveal any additional risk factors. Of the 298 cases with known gender, 158 (53%) were male and 140 (47%) were female. Of the notified cases, 189 (63%) were from the county of Soest, 104 (35%) were Porportion reported number of notified adults number of vis = i iting adults attack rate among adults * Attack rate among children estimated true number of childr = e en with Q fever estimated number of children at the market from other counties in the same federal state (Northrhine Westphalia) and 6 (2%) were from five other federal states in Germany (Figure 3 ). Only eight (3%) cases were less than 18 years of age, the mean and median age was 54 and 56 years, respectively ( Figure 4 ). 75 (25%) of 297 notified cases were hospitalized, none died. Calculation of the proportion of cases hospitalized through other information sources revealed that 4 of 19 (21%; 95% CI = 6-46%;
(1/5 (CCS2), 2/11 (vendors study) and 1/3 (sailor friends)) clinically ill cases were hospitalized.
Laboratory confirmation was reported in 167 (56%) outbreak cases; 66 (22%) were confirmed by an increase in anti-phase II antibody titer (CF), 89 (30%) had IgM antibodies against phase II antigens, 11 (4%) were positive in both tests and one was confirmed by culture. No information was available as to whether the 132 (44%) cases without laboratory confirmation were laboratory tested.
18 patients with valvular heart defects and eleven pregnant women were examined. None of them had clinical signs of Q fever. Two (11%) of 18 cardiological patients and four (36%) of 11 pregnant women had an acute Q fever infection. During childbirth strict hygienic measures were implemented. Lochia and colostrum of all infected women were tested by polymerase chain reaction and were positive in only one woman (case 3; Table 1 ). Serological follow-up of the mothers detected chronic infection in the same woman (case 3) 12 weeks after delivery. One year follow-up of two newborn children (of cases 1 and 3) identified neither acute nor chronic Q fever infections.
We recruited 20 cases and 36 controls who visited the farmers' market on May 4 for the second case control study. They did not differ significantly in age and gender (OR for male sex = 1.7; 95%CI = 0.5-5.3; p = 0.26; p-value for age = 0.23). Seventeen (85%) of 20 cases indicated that they had seen the cow (that also was on display at the market next to the sheep) compared to 7 (32%) of Geographical location of Q fever outbreak cases notified to the statutory surveillance system Figure 3 Geographical location of Q fever outbreak cases notified to the statutory surveillance system. or directly at the gate of the sheep pen compared to 8 (32%) of 25 controls (OR = 5.0; 95%CI = 1.2-22.3; p = 0.03). Touching the sheep was also significantly more common among cases (5/20 (25%) CCS2 cases vs. 0/22 (0%) controls; OR undefined; lower 95% CI = 1.1; p = 0.02). 17 (85%) of 20 CCS2 cases, but only 6 (25%) of 24 controls stopped for at least a few seconds at or in the sheep pen, the reference for this variable was "having passed by the pen without stopping" (OR = 17.0; 95%CI = 3.0-112.5; p < 0.01). Among CCS2 cases, self-reported proximity to or time spent with/close to the sheep was not associated with a shorter incubation period.
We were able to contact and interview 75 (86%) of 87 vendors, and received second hand information about 7 more (overall response rate: 94%). Fourty-five (56%) were male and 35 (44%) were female. 13 (16%) met the clinical case definition. Of the 11 vendors who worked within two stand units of the sheep pen, 6 (55%) became cases compared to only 7 (10%) of 70 persons who worked in a stand at a greater distance (relative risk (RR) = 5.5 (95%CI = 2.3-13.2; p = 0.002); Figure 1 ). Of these 7 vendors, 4 had spent time within 5 meters of the pen on May 4, one had been near the pen, but at a distance of more than 5 meters, and no information on this variable was available for the remaining 2. In the section of the market facing the wind coming from the pen (section 4, Figure 1 ), 4 (9%) of 44 vendors became cases, compared to 2 (13%) of 15 persons who worked in section 1 (p = 0.6). Among 22 persons who worked in stands that were perpendicular to the wind direction, 7 (32%) became cases. (Table 3 ). In all scenarios the AR among adults was significantly higher than that among children ( Figure 5 ).
In total, 5 lambs and 5 ewes were displayed on the market, one of them was pregnant and gave birth to twin lambs at 6:30 a.m. on May 4, 2003 . Of these, 3 ewes including the one that had lambed tested positive for C. burnetii. The animals came from a flock of 67 ewes, of which 66 had given birth between February and June. The majority of the births (57 (86%)) had occurred in February and March, usually inside a stable or on a meadow located away from the town. Six ewes aborted, had stillbirths or abnormally weak lambs. Among all ewes, 17/67 (25%) tested positive for C. burnetii.
The percentage of sheep that tested positive in the other 5 sheep flocks in the region ranged from 8% to 24% (8%; 12%; 12%; 16%; 24%).
We have described one of the largest Q fever outbreaks in Germany which, due to its point-source nature, provided the opportunity to assess many epidemiological features of the disease that can be rarely studied otherwise.
In 1954, more than 500 cases of Q fever were, similar to this outbreak, linked to the abortion of an infected cow at a farmers' market [15] . More recently a large outbreak occurred in Jena (Thuringia) in 2005 with 322 reported cases [16] associated with exposure to a herd of sheep kept on a meadow close to the housing area in which the cases occurred.
The first case control study served to confirm the hypothesis of an association between the outbreak and the farmers' market. The fact that only attendance on the second, but not the first day was strongly associated with illness pointed towards the role of the ewe that had given birth Persons accompanying notified cases (source 5) were a mixture of adults and children and are therefore listed separately.
in the early morning hours of May 4, 2005 . This strong association and the very high attributable fraction among all cases suggested a point source and justified defining cases notified through the reporting system as outbreak cases if they were clinically compatible with Q fever and gave a history of having visited the farmers' market. The point-source nature of the outbreak permitted calculation of the incubation period of cases which averaged 21 days and ranged from 2 to 48 days with an interquartile range of 16 to 24 days. This is compatible with the literature [1] . An additional interview with the two cases with early onset (2 and 4 days after attending the market on May 4, Attack rates among adults and children in a most likely scenario and 8 other scenarios Figure 5 Attack rates among adults and children in a most likely scenario and 8 other scenarios. Most likely scenario: 3000 visitors, 83% adult visitors and 20% clinical attack rate among adults. Scenarios 1-8 varied in the assumptions made for "number of visitors", "proportion of adult visitors" and "attack rate among adults" (see Table 3 ). Displayed are attack rates and 95% confidence intervals. respectively) could not identify any other source of infection. A short incubation period was recently observed in another Q fever outbreak in which the infectious dose was likely very high [17] .
The second case control study among persons who visited the market on May 4 demonstrated that both close proximity to the ewe and duration of exposure were important risk factors. This finding was confirmed by the cohort study on vendors which showed that those who worked in a stand close to (within 6 meters) the sheep pen were at significantly higher risk of acquiring Q fever. The study failed to show a significant role of the location of the stand in reference to the wind direction, although we must take into account that the wind was likely not always and exactly as reported by the weather station. However, if the wind had been important at all more cases might have been expected to have occurred among vendors situated at a greater distance to the sheep.
According to statutory surveillance system data, the proportion of clinical cases hospitalized was 25%, similar to the proportion of 21% found in persons pooled from the other studies conducted. Several publications report lower proportions than that found in this investigation: 4% (8/ 191) [7] , 5% [1] and 10% (4/39) [5] ), and there was at least one study with a much higher proportion (63% (10/ 16)) [18] . It is unlikely that hospitals reported cases with Q fever more frequently than private physicians because the proportion hospitalized among Q fever patients identified through random telephone calls in the Soest population or those in the two cohorts was similar to that of notified cases. Thus reporting bias is an unlikely explanation for the relatively high proportion of cases hospitalized. Alternative explanations include overly cautious referral practices on the part of attending physicians or the presumably high infectious dose of the organism in this outbreak, e.g. in those cases that spent time in the sheep pen.
The estimated attack rate among adults in the four studies varied between 16% and 33%. The estimate of 23% based on the random sample of persons visiting the market on the second day would seem most immune to recall bias, even if this cannot be entirely ruled out. The estimation based on information about persons accompanying the cases may be subject to an overestimation because these individuals presumably had a higher probability of being close to the sheep pen, similar to the cases. On the other hand the estimate from the cohort study on vendors might be an underestimate, since the vendors obviously had a different purpose for being at the market and may have been less interested in having a look at the sheep. Nevertheless, all estimates were independent from each other and considering the various possible biases, they were remarkably similar. In comparison, in a different outbreak in Germany, in which inhabitants of a village were exposed to a large herd of sheep (n = 1000-2000) [5, 7] the attack rate was estimated as 16%. In a similar outbreak in Switzerland several villages were exposed to approximately 900 sheep [19] . In the most severely affected village, the clinical attack rate was 16% (estimated from the data provided) [19] . It is remarkable that in the outbreak described here, the infectious potential of one pregnant ewe -upon lambing -was comparable to that of entire herds, albeit in different settings.
Our estimate of the proportion of serologically confirmed cases that became symptomatic (50% (3/6)) is based on a very small sample, but consistent with the international literature. In the above mentioned Swiss outbreak, 46% of serologically positive patients developed clinical disease [7] .
Only approximately half of all symptomatic cases were reported to the statutory surveillance system. Patients who did not seek health care due to mild disease as well as underdiagnosis or underreporting may have contributed to the missing other half. Our estimated 3% attack rate among children is based on a number of successive assumptions and must therefore be interpreted with caution. Nevertheless, sensitivity analysis confirmed that adults had a significantly elevated attack rate compared to children. While it has been suggested that children are at lower risk than adults for developing symptomatic illness [7, 8] few data have been published regarding attack rates of children in comparison to adults.
The estimated C. burnetii seroprevalence in the sheep flocks in the area varied from 8% to 24%. The 25% seroprevalence in the flock of the exhibited animals together with a positive polymerase chain reaction in an afterbirth in June 2003 suggested a recent infection of the flock [20] . Seroprevalence among sheep flocks related to human outbreaks tend to be substantially higher than those in flocks not related to human outbreaks. The median seroprevalence in a number of relevant studies performed in the context of human outbreaks [7, 20, 21] , was 40% compared to 1% in sheep flocks not linked to human outbreaks [20] .
This outbreak shows the dramatic consequences of putting a large number of susceptible individuals in close contact to a single infected ewe that (in such a setting) can turn into a super-spreader upon lambing. There is always a cultural component in the interaction between people and animals, and these may contribute to outbreaks or changing patterns of incidence. During the past decades urbanization of rural areas and changes in animal husbandry have occurred [20] , with more recent attempts to put a "deprived" urban population "in touch" with farm animals. Petting zoos, family farm vacations or the display of (farm) animals at a market such as this may lead to new avenues for the transmission of zoonotic infectious agents [20, [22] [23] [24] . While not all eventualities can be foreseen, it is important to raise awareness in pet and livestock owners as well as to strengthen recommendations where necessary. This outbreak led to the amendment and extension of existing recommendations [25] which now forbid the display of sheep in the latter third of their pregnancy and require regular testing of animals for C. burnetii in petting zoos, where there is close contact between humans and animals.
Due to the size and point source nature this outbreak permitted reassessment of fundamental, but seldom studied epidemiological parameters of Q fever. It also served to revise public health recommendations to account for the changing type and frequency of contact of susceptible humans with potentially infectious animals.
Abbreviations AFE = attributable fraction of cases exposed
The author(s) declare that they have no competing interests.
|
What was the interquartile range of the incubation period?
| 5,204 |
16 to 24 days
| 22,586 |
1,583 |
A super-spreading ewe infects hundreds with Q fever at a farmers' market in Germany
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1618839/
SHA: ee1b5a9618dcc4080ed100486cedd0969e80fa4d
Authors: Porten, Klaudia; Rissland, Jürgen; Tigges, Almira; Broll, Susanne; Hopp, Wilfried; Lunemann, Mechthild; van Treeck, Ulrich; Kimmig, Peter; Brockmann, Stefan O; Wagner-Wiening, Christiane; Hellenbrand, Wiebke; Buchholz, Udo
Date: 2006-10-06
DOI: 10.1186/1471-2334-6-147
License: cc-by
Abstract: BACKGROUND: In May 2003 the Soest County Health Department was informed of an unusually large number of patients hospitalized with atypical pneumonia. METHODS: In exploratory interviews patients mentioned having visited a farmers' market where a sheep had lambed. Serologic testing confirmed the diagnosis of Q fever. We asked local health departments in Germany to identiy notified Q fever patients who had visited the farmers market. To investigate risk factors for infection we conducted a case control study (cases were Q fever patients, controls were randomly selected Soest citizens) and a cohort study among vendors at the market. The sheep exhibited at the market, the herd from which it originated as well as sheep from herds held in the vicinity of Soest were tested for Coxiella burnetii (C. burnetii). RESULTS: A total of 299 reported Q fever cases was linked to this outbreak. The mean incubation period was 21 days, with an interquartile range of 16–24 days. The case control study identified close proximity to and stopping for at least a few seconds at the sheep's pen as significant risk factors. Vendors within approximately 6 meters of the sheep's pen were at increased risk for disease compared to those located farther away. Wind played no significant role. The clinical attack rate of adults and children was estimated as 20% and 3%, respectively, 25% of cases were hospitalized. The ewe that had lambed as well as 25% of its herd tested positive for C. burnetii antibodies. CONCLUSION: Due to its size and point source nature this outbreak permitted assessment of fundamental, but seldom studied epidemiological parameters. As a consequence of this outbreak, it was recommended that pregnant sheep not be displayed in public during the 3(rd )trimester and to test animals in petting zoos regularly for C. burnetii.
Text: Q fever is a worldwide zoonosis caused by Coxiella burnetii (C. burnetii), a small, gram-negative obligate intracellular bacterium. C. burnetii displays antigenic variation with an infectious phase I and less infectious phase II. The primary reservoir from which human infection occurs consists of sheep, goat and cattle. Although C. burnetii infections in animals are usually asymptomatic, they may cause abortions in sheep and goats [1] . High concentrations of C. burnetii can be found in birth products of infected mammals [2] . Humans frequently acquire infection through inhalation of contaminated aerosols from parturient fluids, placenta or wool [1] . Because the infectious dose is very low [3] and C. burnetii is able to survive in a spore-like state for months to years, outbreaks among humans have also occurred through contaminated dust carried by wind over large distances [4] [5] [6] .
C. burnetii infection in humans is asymptomatic in approximately 50% of cases. Approximately 5% of cases are hospitalized, and fatal cases are rare [1] . The clinical presentation of acute Q fever is variable and can resemble many other infectious diseases [2] . However, the most frequent clinical manifestation of acute Q fever is a self-limited febrile illness associated with severe headache. Atypical pneumonia and hepatitis are the major clinical manifestations of more severe disease. Acute Q fever may be complicated by meningoencephalitis or myocarditis. Rarely a chronic form of Q fever develops months after the acute illness, most commonly in the form of endocarditis [1] . Children develop clinical disease less frequently [7, 8] . Because of its non-specific presentation Q fever can only be suspected on clinical grounds and requires serologic confirmation. While the indirect immunofluorescence assay (IFA) is considered to be the reference method, complement fixation (CF), ELISA and microagglutination (MA) can also be used [9] . Acute infections are diagnosed by elevated IgG and/or IgM anti-phase II antibodies, while raised anti-phase I IgG antibodies are characteristic for chronic infections [1] .
In Germany, acute Q fever is a notifiable disease. Between 1991 and 2000 the annual number of cases varied from 46 to 273 cases per year [10] . In 2001 and 2002, 293 and 191 cases were notified, respectively [11, 12] .
On May 26, 2003 the health department of Soest was informed by a local hospital of an unusually large number of patients with atypical pneumonia. Some patients reported having visited a farmers' market that took place on May 3 and 4, 2003 in a spa town near Soest. Since the etiology was unclear, pathogens such as SARS coronavirus were considered and strict infection control measures implemented until the diagnosis of Q fever was confirmed.
An outbreak investigation team was formed and included public health professionals from the local health department, the local veterinary health department, the state health department, the National Consulting Laboratory (NCL) for Coxiellae and the Robert Koch-Institute (RKI), the federal public health institute. Because of the size and point source appearance of the outbreak the objective of the investigation was to identify etiologic factors relevant to the prevention and control of Q fever as well as to assess epidemiological parameters that can be rarely studied otherwise.
On May 26 and 27, 2003 we conducted exploratory interviews with patients in Soest hospitalized due to atypical pneumonia.
Attending physicians were requested to test serum of patients with atypical pneumonia for Mycoplasma pneumoniae, Chlamydia pneumoniae, Legionella pneumophila, Coxiella burnetii, Influenza A and B, Parainfluenza 1-3, Adenovirus and Enterovirus. Throat swabs were tested for Influenza virus, Adenovirus and SARS-Coronavirus. Laboratory confirmation of an acute Q fever infection was defined as the presence of IgM antibodies against phase II C. burnetii antigens (ELISA or IFA), a 4-fold increase in anti-phase II IgG antibody titer (ELISA or IFA) or in anti phase II antibody titer by CF between acute and convalescent sera. A chronic infection was confirmed when both anti-phase I IgG and anti-phase II IgG antibody titers were raised.
Because patients with valvular heart defects and pregnant women are at high risk of developing chronic infection [13, 14] we alerted internists and gynaecologists through the journal of the German Medical Association and asked them to send serum samples to the NCL if they identified patients from these risk groups who had been at the farmers' market during the outbreak.
The objective of the first case control study was to establish whether there was a link between the farmers' market and the outbreak and to identify other potential risk factors. We conducted telephone interviews using a standardised questionnaire that asked about attendance at the farmers' market, having been within 1 km distance of one of 6 sheep flocks in the area, tick bites and consumption of unpasteurized milk, sheep or goat cheese. For the purpose of CCS1 we defined a case (CCS1 case) as an adult resident of the town of Soest notified to the statutory sur-veillance system with Q fever, having symptom onset between May 4 and June 3, 2003. Exclusion criterion was a negative IgM-titer against phase II antigens. Two controls per case were recruited from Soest inhabitants by random digit dialing.
We calculated the attributable fraction of cases exposed to the farmers' market on May 4 (AFE) as (OR-1)/OR and the attributable fraction for all cases due to this exposure as:
The farmers' market was held in a spa town near Soest with many visitors from other areas of the state and even the entire country. To determine the outbreak size we therefore asked local public health departments in Germany to ascertain a possible link to the farmers' market in Soest for all patients notified with Q-fever. A case in this context ("notified case") was defined as any person with a clinical diagnosis compatible with Q fever with or without laboratory confirmation and history of exposure to the farmers' market.
Local health departments also reported whether a notified case was hospitalized. To obtain an independent, second estimate of the proportion of hospitalizations among symptomatic patients beyond that reported through the statutory surveillance system we calculated the proportion of hospitalized patients among those persons fulfilling the clinical case definition (as used in the vendors' study (s.b.)) identified through random sampling of the Soest population (within CCS2 (s.b.)) as well as in two cohorts (vendors' study and the 9 sailor friends (see below)).
The objective of CCS2 was to identify risk factors associated with attendance of the farmers' market on the second day. We used the same case definition as in CCS1, but included only persons that had visited the farmers' market on May 4, the second day of the market. We selected controls again randomly from the telephone registry of Soest and included only those persons who had visited the farmers' market on May 4 and had not been ill with fever afterwards. Potential controls who became ill were excluded for analysis in CCS2, but were still fully interviewed. This permitted calculation of the attack rate among visitors to the market (see below "Estimation of the overall attack rate") and gave an estimate of the proportion of clinically ill cases that were hospitalized (s.a.).
In the vendors' study we investigated whether the distance of the vendor stands from the sheep pen or dispersion of C. burnetii by wind were relevant risk factors for acquiring Q fever. We obtained a list of all vendors including the approximate location of the stands from the organizer. In addition we asked the local weather station for the predominant wind direction on May 4, 2003. Telephone interviews were performed using standardized questionnaires. A case was defined as a person with onset of fever between May 4 and June 3, 2003 and at least three of the following symptoms: headache, cough, dyspnea, joint pain, muscle pain, weight loss of more than 2 kg, fatigue, nausea or vomiting.
The relative distance of the stands to the sheep pen was estimated by counting the stands between the sheep pen and the stand in question. Each stand was considered to be one stand unit (approximately 3 meters). Larger stands were counted as 2 units. The direction of the wind in relation to the sheep pen was defined by dividing the wind rose (360°) in 4 equal parts of 90°. The predominant wind direction during the market was south-south-east ( Figure 1 ). For the purpose of the analysis we divided the market area into 4 sections with the sheep pen at its center. In section 1 the wind was blowing towards the sheep pen (plus minus 45°). Section 4 was on the opposite side, i.e. where the wind blew from the sheep pen towards the stands, and sections 2 and 3 were east and west with respect to the wind direction, respectively. Location of the stands in reference to the sheep pen was thus defined in two ways: as the absolute distance to the sheep pen (in stand units or meters) and in reference to the wind direction.
We identified a small cohort of 9 sailor friends who visited the farmers' market on May 4, 2003. All of these were serologically tested independently of symptoms. We could therefore calculate the proportion of laboratory confirmed persons who met the clinical case definition (as defined in the cohort study on vendors).
The overall attack rate among adults was estimated based on the following sources:
(1) Interviews undertaken for recruitment of controls for CCS2 allowed the proportion of adults that acquired symptomatic Q fever among those who visited the farmers' market on the second day;
Attributable fraction AFE Number of cases exposed All cases = *
(2) Interviews of cases and controls in CCS2 yielded information about accompanying adults and how many of these became later "ill with fever";
(3) Results of the small cohort of 9 sailor friends (s.a.);
(4) Results from the cohort study on vendors.
Local health departments that identified outbreak cases of Q fever (s.a. "determination of outbreak size and descriptive epidemiology") interviewed patients about the number of persons that had accompanied them to the farmers' market and whether any of these had become ill with fever afterwards. However, as there was no differentiation between adults and children, calculations to estimate the attack rate among adults were performed both with and without this source.
To count cases in (1), (3) and (4) we used the clinical case definition as defined in the cohort study on vendors.
For the calculation of the attack rate among children elicited in CCS2 was the same for all visitors. The number of children that visited the market could then be estimated from the total number of visitors as estimated by the organizers. We then estimated the number of symptomatic children (numerator). For this we assumed that the proportion of children with Q fever that were seen by physicians and were consequently notified was the same as that of adults. It was calculated as:
Thus the true number of children with Q fever was estimated by the number of reported children divided by the estimated proportion reported. Then the attack rate among children could be estimated as follows:
Because this calculation was based on several assumptions (number of visitors, proportion of adult visitors and clinical attack rate among adults) we performed a sensitivity analysis where the values of these variables varied.
Serum was collected from all sheep and cows displayed in the farmers' market as well as from all sheep of the respective home flocks (70 animals). Samples of 25 sheep from five other flocks in the Soest area were also tested for C. burnetii. Tests were performed by ELISA with a phase I and phase II antigen mixture.
We conducted statistical analysis with Epi Info, version 6.04 (CDC, Atlanta, USA). Dichotomous variables in the case control and cohort studies were compared using the Chi-Square test and numerical variables using the Kruskal-Wallis test. P-values smaller than 0.05 were considered statistically significant.
The outbreak investigation was conducted within the framework of the Communicable Diseases Law Reform Act of Germany. Mandatory regulations were observed.
Patients at the local hospital in Soest reported that a farmers' market had taken place on May 3 and 4, 2003 in a spa town close to the town of Soest. It was located in a park along the main promenade, spanning a distance of approximately 500 meters. The market attracted mainly three groups of people: locals, inhabitants of the greater Soest region, patients from the spa sanatoria and their visiting family or friends. Initial interviewees mentioned also that they had spent time at the sheep pen watching new-born lambs that had been born in the early morning hours of May 4, 2003 . The ewe had eaten the placenta but the parturient fluid on the ground had merely been covered with fresh straw.
Overall 171 (65%) of 263 serum samples submitted to the NCL were positive for IgM anti-phase II antibodies by ELISA. Results of throat swabs and serum were negative for other infectious agents. (Figure 2 ). If we assume that symptom onset in cases was normally distributed with a mean of 21 days, 95% of cases (mean +/-2 standard deviations) had their onset between day 10 and 31. The two notified cases with early onset on May 6 and 8, respectively, were laboratory confirmed and additional interviews did not reveal any additional risk factors. Of the 298 cases with known gender, 158 (53%) were male and 140 (47%) were female. Of the notified cases, 189 (63%) were from the county of Soest, 104 (35%) were Porportion reported number of notified adults number of vis = i iting adults attack rate among adults * Attack rate among children estimated true number of childr = e en with Q fever estimated number of children at the market from other counties in the same federal state (Northrhine Westphalia) and 6 (2%) were from five other federal states in Germany (Figure 3 ). Only eight (3%) cases were less than 18 years of age, the mean and median age was 54 and 56 years, respectively ( Figure 4 ). 75 (25%) of 297 notified cases were hospitalized, none died. Calculation of the proportion of cases hospitalized through other information sources revealed that 4 of 19 (21%; 95% CI = 6-46%;
(1/5 (CCS2), 2/11 (vendors study) and 1/3 (sailor friends)) clinically ill cases were hospitalized.
Laboratory confirmation was reported in 167 (56%) outbreak cases; 66 (22%) were confirmed by an increase in anti-phase II antibody titer (CF), 89 (30%) had IgM antibodies against phase II antigens, 11 (4%) were positive in both tests and one was confirmed by culture. No information was available as to whether the 132 (44%) cases without laboratory confirmation were laboratory tested.
18 patients with valvular heart defects and eleven pregnant women were examined. None of them had clinical signs of Q fever. Two (11%) of 18 cardiological patients and four (36%) of 11 pregnant women had an acute Q fever infection. During childbirth strict hygienic measures were implemented. Lochia and colostrum of all infected women were tested by polymerase chain reaction and were positive in only one woman (case 3; Table 1 ). Serological follow-up of the mothers detected chronic infection in the same woman (case 3) 12 weeks after delivery. One year follow-up of two newborn children (of cases 1 and 3) identified neither acute nor chronic Q fever infections.
We recruited 20 cases and 36 controls who visited the farmers' market on May 4 for the second case control study. They did not differ significantly in age and gender (OR for male sex = 1.7; 95%CI = 0.5-5.3; p = 0.26; p-value for age = 0.23). Seventeen (85%) of 20 cases indicated that they had seen the cow (that also was on display at the market next to the sheep) compared to 7 (32%) of Geographical location of Q fever outbreak cases notified to the statutory surveillance system Figure 3 Geographical location of Q fever outbreak cases notified to the statutory surveillance system. or directly at the gate of the sheep pen compared to 8 (32%) of 25 controls (OR = 5.0; 95%CI = 1.2-22.3; p = 0.03). Touching the sheep was also significantly more common among cases (5/20 (25%) CCS2 cases vs. 0/22 (0%) controls; OR undefined; lower 95% CI = 1.1; p = 0.02). 17 (85%) of 20 CCS2 cases, but only 6 (25%) of 24 controls stopped for at least a few seconds at or in the sheep pen, the reference for this variable was "having passed by the pen without stopping" (OR = 17.0; 95%CI = 3.0-112.5; p < 0.01). Among CCS2 cases, self-reported proximity to or time spent with/close to the sheep was not associated with a shorter incubation period.
We were able to contact and interview 75 (86%) of 87 vendors, and received second hand information about 7 more (overall response rate: 94%). Fourty-five (56%) were male and 35 (44%) were female. 13 (16%) met the clinical case definition. Of the 11 vendors who worked within two stand units of the sheep pen, 6 (55%) became cases compared to only 7 (10%) of 70 persons who worked in a stand at a greater distance (relative risk (RR) = 5.5 (95%CI = 2.3-13.2; p = 0.002); Figure 1 ). Of these 7 vendors, 4 had spent time within 5 meters of the pen on May 4, one had been near the pen, but at a distance of more than 5 meters, and no information on this variable was available for the remaining 2. In the section of the market facing the wind coming from the pen (section 4, Figure 1 ), 4 (9%) of 44 vendors became cases, compared to 2 (13%) of 15 persons who worked in section 1 (p = 0.6). Among 22 persons who worked in stands that were perpendicular to the wind direction, 7 (32%) became cases. (Table 3 ). In all scenarios the AR among adults was significantly higher than that among children ( Figure 5 ).
In total, 5 lambs and 5 ewes were displayed on the market, one of them was pregnant and gave birth to twin lambs at 6:30 a.m. on May 4, 2003 . Of these, 3 ewes including the one that had lambed tested positive for C. burnetii. The animals came from a flock of 67 ewes, of which 66 had given birth between February and June. The majority of the births (57 (86%)) had occurred in February and March, usually inside a stable or on a meadow located away from the town. Six ewes aborted, had stillbirths or abnormally weak lambs. Among all ewes, 17/67 (25%) tested positive for C. burnetii.
The percentage of sheep that tested positive in the other 5 sheep flocks in the region ranged from 8% to 24% (8%; 12%; 12%; 16%; 24%).
We have described one of the largest Q fever outbreaks in Germany which, due to its point-source nature, provided the opportunity to assess many epidemiological features of the disease that can be rarely studied otherwise.
In 1954, more than 500 cases of Q fever were, similar to this outbreak, linked to the abortion of an infected cow at a farmers' market [15] . More recently a large outbreak occurred in Jena (Thuringia) in 2005 with 322 reported cases [16] associated with exposure to a herd of sheep kept on a meadow close to the housing area in which the cases occurred.
The first case control study served to confirm the hypothesis of an association between the outbreak and the farmers' market. The fact that only attendance on the second, but not the first day was strongly associated with illness pointed towards the role of the ewe that had given birth Persons accompanying notified cases (source 5) were a mixture of adults and children and are therefore listed separately.
in the early morning hours of May 4, 2005 . This strong association and the very high attributable fraction among all cases suggested a point source and justified defining cases notified through the reporting system as outbreak cases if they were clinically compatible with Q fever and gave a history of having visited the farmers' market. The point-source nature of the outbreak permitted calculation of the incubation period of cases which averaged 21 days and ranged from 2 to 48 days with an interquartile range of 16 to 24 days. This is compatible with the literature [1] . An additional interview with the two cases with early onset (2 and 4 days after attending the market on May 4, Attack rates among adults and children in a most likely scenario and 8 other scenarios Figure 5 Attack rates among adults and children in a most likely scenario and 8 other scenarios. Most likely scenario: 3000 visitors, 83% adult visitors and 20% clinical attack rate among adults. Scenarios 1-8 varied in the assumptions made for "number of visitors", "proportion of adult visitors" and "attack rate among adults" (see Table 3 ). Displayed are attack rates and 95% confidence intervals. respectively) could not identify any other source of infection. A short incubation period was recently observed in another Q fever outbreak in which the infectious dose was likely very high [17] .
The second case control study among persons who visited the market on May 4 demonstrated that both close proximity to the ewe and duration of exposure were important risk factors. This finding was confirmed by the cohort study on vendors which showed that those who worked in a stand close to (within 6 meters) the sheep pen were at significantly higher risk of acquiring Q fever. The study failed to show a significant role of the location of the stand in reference to the wind direction, although we must take into account that the wind was likely not always and exactly as reported by the weather station. However, if the wind had been important at all more cases might have been expected to have occurred among vendors situated at a greater distance to the sheep.
According to statutory surveillance system data, the proportion of clinical cases hospitalized was 25%, similar to the proportion of 21% found in persons pooled from the other studies conducted. Several publications report lower proportions than that found in this investigation: 4% (8/ 191) [7] , 5% [1] and 10% (4/39) [5] ), and there was at least one study with a much higher proportion (63% (10/ 16)) [18] . It is unlikely that hospitals reported cases with Q fever more frequently than private physicians because the proportion hospitalized among Q fever patients identified through random telephone calls in the Soest population or those in the two cohorts was similar to that of notified cases. Thus reporting bias is an unlikely explanation for the relatively high proportion of cases hospitalized. Alternative explanations include overly cautious referral practices on the part of attending physicians or the presumably high infectious dose of the organism in this outbreak, e.g. in those cases that spent time in the sheep pen.
The estimated attack rate among adults in the four studies varied between 16% and 33%. The estimate of 23% based on the random sample of persons visiting the market on the second day would seem most immune to recall bias, even if this cannot be entirely ruled out. The estimation based on information about persons accompanying the cases may be subject to an overestimation because these individuals presumably had a higher probability of being close to the sheep pen, similar to the cases. On the other hand the estimate from the cohort study on vendors might be an underestimate, since the vendors obviously had a different purpose for being at the market and may have been less interested in having a look at the sheep. Nevertheless, all estimates were independent from each other and considering the various possible biases, they were remarkably similar. In comparison, in a different outbreak in Germany, in which inhabitants of a village were exposed to a large herd of sheep (n = 1000-2000) [5, 7] the attack rate was estimated as 16%. In a similar outbreak in Switzerland several villages were exposed to approximately 900 sheep [19] . In the most severely affected village, the clinical attack rate was 16% (estimated from the data provided) [19] . It is remarkable that in the outbreak described here, the infectious potential of one pregnant ewe -upon lambing -was comparable to that of entire herds, albeit in different settings.
Our estimate of the proportion of serologically confirmed cases that became symptomatic (50% (3/6)) is based on a very small sample, but consistent with the international literature. In the above mentioned Swiss outbreak, 46% of serologically positive patients developed clinical disease [7] .
Only approximately half of all symptomatic cases were reported to the statutory surveillance system. Patients who did not seek health care due to mild disease as well as underdiagnosis or underreporting may have contributed to the missing other half. Our estimated 3% attack rate among children is based on a number of successive assumptions and must therefore be interpreted with caution. Nevertheless, sensitivity analysis confirmed that adults had a significantly elevated attack rate compared to children. While it has been suggested that children are at lower risk than adults for developing symptomatic illness [7, 8] few data have been published regarding attack rates of children in comparison to adults.
The estimated C. burnetii seroprevalence in the sheep flocks in the area varied from 8% to 24%. The 25% seroprevalence in the flock of the exhibited animals together with a positive polymerase chain reaction in an afterbirth in June 2003 suggested a recent infection of the flock [20] . Seroprevalence among sheep flocks related to human outbreaks tend to be substantially higher than those in flocks not related to human outbreaks. The median seroprevalence in a number of relevant studies performed in the context of human outbreaks [7, 20, 21] , was 40% compared to 1% in sheep flocks not linked to human outbreaks [20] .
This outbreak shows the dramatic consequences of putting a large number of susceptible individuals in close contact to a single infected ewe that (in such a setting) can turn into a super-spreader upon lambing. There is always a cultural component in the interaction between people and animals, and these may contribute to outbreaks or changing patterns of incidence. During the past decades urbanization of rural areas and changes in animal husbandry have occurred [20] , with more recent attempts to put a "deprived" urban population "in touch" with farm animals. Petting zoos, family farm vacations or the display of (farm) animals at a market such as this may lead to new avenues for the transmission of zoonotic infectious agents [20, [22] [23] [24] . While not all eventualities can be foreseen, it is important to raise awareness in pet and livestock owners as well as to strengthen recommendations where necessary. This outbreak led to the amendment and extension of existing recommendations [25] which now forbid the display of sheep in the latter third of their pregnancy and require regular testing of animals for C. burnetii in petting zoos, where there is close contact between humans and animals.
Due to the size and point source nature this outbreak permitted reassessment of fundamental, but seldom studied epidemiological parameters of Q fever. It also served to revise public health recommendations to account for the changing type and frequency of contact of susceptible humans with potentially infectious animals.
Abbreviations AFE = attributable fraction of cases exposed
The author(s) declare that they have no competing interests.
|
How many controls were used in the second case study?
| 5,205 |
36
| 18,033 |
1,583 |
A super-spreading ewe infects hundreds with Q fever at a farmers' market in Germany
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1618839/
SHA: ee1b5a9618dcc4080ed100486cedd0969e80fa4d
Authors: Porten, Klaudia; Rissland, Jürgen; Tigges, Almira; Broll, Susanne; Hopp, Wilfried; Lunemann, Mechthild; van Treeck, Ulrich; Kimmig, Peter; Brockmann, Stefan O; Wagner-Wiening, Christiane; Hellenbrand, Wiebke; Buchholz, Udo
Date: 2006-10-06
DOI: 10.1186/1471-2334-6-147
License: cc-by
Abstract: BACKGROUND: In May 2003 the Soest County Health Department was informed of an unusually large number of patients hospitalized with atypical pneumonia. METHODS: In exploratory interviews patients mentioned having visited a farmers' market where a sheep had lambed. Serologic testing confirmed the diagnosis of Q fever. We asked local health departments in Germany to identiy notified Q fever patients who had visited the farmers market. To investigate risk factors for infection we conducted a case control study (cases were Q fever patients, controls were randomly selected Soest citizens) and a cohort study among vendors at the market. The sheep exhibited at the market, the herd from which it originated as well as sheep from herds held in the vicinity of Soest were tested for Coxiella burnetii (C. burnetii). RESULTS: A total of 299 reported Q fever cases was linked to this outbreak. The mean incubation period was 21 days, with an interquartile range of 16–24 days. The case control study identified close proximity to and stopping for at least a few seconds at the sheep's pen as significant risk factors. Vendors within approximately 6 meters of the sheep's pen were at increased risk for disease compared to those located farther away. Wind played no significant role. The clinical attack rate of adults and children was estimated as 20% and 3%, respectively, 25% of cases were hospitalized. The ewe that had lambed as well as 25% of its herd tested positive for C. burnetii antibodies. CONCLUSION: Due to its size and point source nature this outbreak permitted assessment of fundamental, but seldom studied epidemiological parameters. As a consequence of this outbreak, it was recommended that pregnant sheep not be displayed in public during the 3(rd )trimester and to test animals in petting zoos regularly for C. burnetii.
Text: Q fever is a worldwide zoonosis caused by Coxiella burnetii (C. burnetii), a small, gram-negative obligate intracellular bacterium. C. burnetii displays antigenic variation with an infectious phase I and less infectious phase II. The primary reservoir from which human infection occurs consists of sheep, goat and cattle. Although C. burnetii infections in animals are usually asymptomatic, they may cause abortions in sheep and goats [1] . High concentrations of C. burnetii can be found in birth products of infected mammals [2] . Humans frequently acquire infection through inhalation of contaminated aerosols from parturient fluids, placenta or wool [1] . Because the infectious dose is very low [3] and C. burnetii is able to survive in a spore-like state for months to years, outbreaks among humans have also occurred through contaminated dust carried by wind over large distances [4] [5] [6] .
C. burnetii infection in humans is asymptomatic in approximately 50% of cases. Approximately 5% of cases are hospitalized, and fatal cases are rare [1] . The clinical presentation of acute Q fever is variable and can resemble many other infectious diseases [2] . However, the most frequent clinical manifestation of acute Q fever is a self-limited febrile illness associated with severe headache. Atypical pneumonia and hepatitis are the major clinical manifestations of more severe disease. Acute Q fever may be complicated by meningoencephalitis or myocarditis. Rarely a chronic form of Q fever develops months after the acute illness, most commonly in the form of endocarditis [1] . Children develop clinical disease less frequently [7, 8] . Because of its non-specific presentation Q fever can only be suspected on clinical grounds and requires serologic confirmation. While the indirect immunofluorescence assay (IFA) is considered to be the reference method, complement fixation (CF), ELISA and microagglutination (MA) can also be used [9] . Acute infections are diagnosed by elevated IgG and/or IgM anti-phase II antibodies, while raised anti-phase I IgG antibodies are characteristic for chronic infections [1] .
In Germany, acute Q fever is a notifiable disease. Between 1991 and 2000 the annual number of cases varied from 46 to 273 cases per year [10] . In 2001 and 2002, 293 and 191 cases were notified, respectively [11, 12] .
On May 26, 2003 the health department of Soest was informed by a local hospital of an unusually large number of patients with atypical pneumonia. Some patients reported having visited a farmers' market that took place on May 3 and 4, 2003 in a spa town near Soest. Since the etiology was unclear, pathogens such as SARS coronavirus were considered and strict infection control measures implemented until the diagnosis of Q fever was confirmed.
An outbreak investigation team was formed and included public health professionals from the local health department, the local veterinary health department, the state health department, the National Consulting Laboratory (NCL) for Coxiellae and the Robert Koch-Institute (RKI), the federal public health institute. Because of the size and point source appearance of the outbreak the objective of the investigation was to identify etiologic factors relevant to the prevention and control of Q fever as well as to assess epidemiological parameters that can be rarely studied otherwise.
On May 26 and 27, 2003 we conducted exploratory interviews with patients in Soest hospitalized due to atypical pneumonia.
Attending physicians were requested to test serum of patients with atypical pneumonia for Mycoplasma pneumoniae, Chlamydia pneumoniae, Legionella pneumophila, Coxiella burnetii, Influenza A and B, Parainfluenza 1-3, Adenovirus and Enterovirus. Throat swabs were tested for Influenza virus, Adenovirus and SARS-Coronavirus. Laboratory confirmation of an acute Q fever infection was defined as the presence of IgM antibodies against phase II C. burnetii antigens (ELISA or IFA), a 4-fold increase in anti-phase II IgG antibody titer (ELISA or IFA) or in anti phase II antibody titer by CF between acute and convalescent sera. A chronic infection was confirmed when both anti-phase I IgG and anti-phase II IgG antibody titers were raised.
Because patients with valvular heart defects and pregnant women are at high risk of developing chronic infection [13, 14] we alerted internists and gynaecologists through the journal of the German Medical Association and asked them to send serum samples to the NCL if they identified patients from these risk groups who had been at the farmers' market during the outbreak.
The objective of the first case control study was to establish whether there was a link between the farmers' market and the outbreak and to identify other potential risk factors. We conducted telephone interviews using a standardised questionnaire that asked about attendance at the farmers' market, having been within 1 km distance of one of 6 sheep flocks in the area, tick bites and consumption of unpasteurized milk, sheep or goat cheese. For the purpose of CCS1 we defined a case (CCS1 case) as an adult resident of the town of Soest notified to the statutory sur-veillance system with Q fever, having symptom onset between May 4 and June 3, 2003. Exclusion criterion was a negative IgM-titer against phase II antigens. Two controls per case were recruited from Soest inhabitants by random digit dialing.
We calculated the attributable fraction of cases exposed to the farmers' market on May 4 (AFE) as (OR-1)/OR and the attributable fraction for all cases due to this exposure as:
The farmers' market was held in a spa town near Soest with many visitors from other areas of the state and even the entire country. To determine the outbreak size we therefore asked local public health departments in Germany to ascertain a possible link to the farmers' market in Soest for all patients notified with Q-fever. A case in this context ("notified case") was defined as any person with a clinical diagnosis compatible with Q fever with or without laboratory confirmation and history of exposure to the farmers' market.
Local health departments also reported whether a notified case was hospitalized. To obtain an independent, second estimate of the proportion of hospitalizations among symptomatic patients beyond that reported through the statutory surveillance system we calculated the proportion of hospitalized patients among those persons fulfilling the clinical case definition (as used in the vendors' study (s.b.)) identified through random sampling of the Soest population (within CCS2 (s.b.)) as well as in two cohorts (vendors' study and the 9 sailor friends (see below)).
The objective of CCS2 was to identify risk factors associated with attendance of the farmers' market on the second day. We used the same case definition as in CCS1, but included only persons that had visited the farmers' market on May 4, the second day of the market. We selected controls again randomly from the telephone registry of Soest and included only those persons who had visited the farmers' market on May 4 and had not been ill with fever afterwards. Potential controls who became ill were excluded for analysis in CCS2, but were still fully interviewed. This permitted calculation of the attack rate among visitors to the market (see below "Estimation of the overall attack rate") and gave an estimate of the proportion of clinically ill cases that were hospitalized (s.a.).
In the vendors' study we investigated whether the distance of the vendor stands from the sheep pen or dispersion of C. burnetii by wind were relevant risk factors for acquiring Q fever. We obtained a list of all vendors including the approximate location of the stands from the organizer. In addition we asked the local weather station for the predominant wind direction on May 4, 2003. Telephone interviews were performed using standardized questionnaires. A case was defined as a person with onset of fever between May 4 and June 3, 2003 and at least three of the following symptoms: headache, cough, dyspnea, joint pain, muscle pain, weight loss of more than 2 kg, fatigue, nausea or vomiting.
The relative distance of the stands to the sheep pen was estimated by counting the stands between the sheep pen and the stand in question. Each stand was considered to be one stand unit (approximately 3 meters). Larger stands were counted as 2 units. The direction of the wind in relation to the sheep pen was defined by dividing the wind rose (360°) in 4 equal parts of 90°. The predominant wind direction during the market was south-south-east ( Figure 1 ). For the purpose of the analysis we divided the market area into 4 sections with the sheep pen at its center. In section 1 the wind was blowing towards the sheep pen (plus minus 45°). Section 4 was on the opposite side, i.e. where the wind blew from the sheep pen towards the stands, and sections 2 and 3 were east and west with respect to the wind direction, respectively. Location of the stands in reference to the sheep pen was thus defined in two ways: as the absolute distance to the sheep pen (in stand units or meters) and in reference to the wind direction.
We identified a small cohort of 9 sailor friends who visited the farmers' market on May 4, 2003. All of these were serologically tested independently of symptoms. We could therefore calculate the proportion of laboratory confirmed persons who met the clinical case definition (as defined in the cohort study on vendors).
The overall attack rate among adults was estimated based on the following sources:
(1) Interviews undertaken for recruitment of controls for CCS2 allowed the proportion of adults that acquired symptomatic Q fever among those who visited the farmers' market on the second day;
Attributable fraction AFE Number of cases exposed All cases = *
(2) Interviews of cases and controls in CCS2 yielded information about accompanying adults and how many of these became later "ill with fever";
(3) Results of the small cohort of 9 sailor friends (s.a.);
(4) Results from the cohort study on vendors.
Local health departments that identified outbreak cases of Q fever (s.a. "determination of outbreak size and descriptive epidemiology") interviewed patients about the number of persons that had accompanied them to the farmers' market and whether any of these had become ill with fever afterwards. However, as there was no differentiation between adults and children, calculations to estimate the attack rate among adults were performed both with and without this source.
To count cases in (1), (3) and (4) we used the clinical case definition as defined in the cohort study on vendors.
For the calculation of the attack rate among children elicited in CCS2 was the same for all visitors. The number of children that visited the market could then be estimated from the total number of visitors as estimated by the organizers. We then estimated the number of symptomatic children (numerator). For this we assumed that the proportion of children with Q fever that were seen by physicians and were consequently notified was the same as that of adults. It was calculated as:
Thus the true number of children with Q fever was estimated by the number of reported children divided by the estimated proportion reported. Then the attack rate among children could be estimated as follows:
Because this calculation was based on several assumptions (number of visitors, proportion of adult visitors and clinical attack rate among adults) we performed a sensitivity analysis where the values of these variables varied.
Serum was collected from all sheep and cows displayed in the farmers' market as well as from all sheep of the respective home flocks (70 animals). Samples of 25 sheep from five other flocks in the Soest area were also tested for C. burnetii. Tests were performed by ELISA with a phase I and phase II antigen mixture.
We conducted statistical analysis with Epi Info, version 6.04 (CDC, Atlanta, USA). Dichotomous variables in the case control and cohort studies were compared using the Chi-Square test and numerical variables using the Kruskal-Wallis test. P-values smaller than 0.05 were considered statistically significant.
The outbreak investigation was conducted within the framework of the Communicable Diseases Law Reform Act of Germany. Mandatory regulations were observed.
Patients at the local hospital in Soest reported that a farmers' market had taken place on May 3 and 4, 2003 in a spa town close to the town of Soest. It was located in a park along the main promenade, spanning a distance of approximately 500 meters. The market attracted mainly three groups of people: locals, inhabitants of the greater Soest region, patients from the spa sanatoria and their visiting family or friends. Initial interviewees mentioned also that they had spent time at the sheep pen watching new-born lambs that had been born in the early morning hours of May 4, 2003 . The ewe had eaten the placenta but the parturient fluid on the ground had merely been covered with fresh straw.
Overall 171 (65%) of 263 serum samples submitted to the NCL were positive for IgM anti-phase II antibodies by ELISA. Results of throat swabs and serum were negative for other infectious agents. (Figure 2 ). If we assume that symptom onset in cases was normally distributed with a mean of 21 days, 95% of cases (mean +/-2 standard deviations) had their onset between day 10 and 31. The two notified cases with early onset on May 6 and 8, respectively, were laboratory confirmed and additional interviews did not reveal any additional risk factors. Of the 298 cases with known gender, 158 (53%) were male and 140 (47%) were female. Of the notified cases, 189 (63%) were from the county of Soest, 104 (35%) were Porportion reported number of notified adults number of vis = i iting adults attack rate among adults * Attack rate among children estimated true number of childr = e en with Q fever estimated number of children at the market from other counties in the same federal state (Northrhine Westphalia) and 6 (2%) were from five other federal states in Germany (Figure 3 ). Only eight (3%) cases were less than 18 years of age, the mean and median age was 54 and 56 years, respectively ( Figure 4 ). 75 (25%) of 297 notified cases were hospitalized, none died. Calculation of the proportion of cases hospitalized through other information sources revealed that 4 of 19 (21%; 95% CI = 6-46%;
(1/5 (CCS2), 2/11 (vendors study) and 1/3 (sailor friends)) clinically ill cases were hospitalized.
Laboratory confirmation was reported in 167 (56%) outbreak cases; 66 (22%) were confirmed by an increase in anti-phase II antibody titer (CF), 89 (30%) had IgM antibodies against phase II antigens, 11 (4%) were positive in both tests and one was confirmed by culture. No information was available as to whether the 132 (44%) cases without laboratory confirmation were laboratory tested.
18 patients with valvular heart defects and eleven pregnant women were examined. None of them had clinical signs of Q fever. Two (11%) of 18 cardiological patients and four (36%) of 11 pregnant women had an acute Q fever infection. During childbirth strict hygienic measures were implemented. Lochia and colostrum of all infected women were tested by polymerase chain reaction and were positive in only one woman (case 3; Table 1 ). Serological follow-up of the mothers detected chronic infection in the same woman (case 3) 12 weeks after delivery. One year follow-up of two newborn children (of cases 1 and 3) identified neither acute nor chronic Q fever infections.
We recruited 20 cases and 36 controls who visited the farmers' market on May 4 for the second case control study. They did not differ significantly in age and gender (OR for male sex = 1.7; 95%CI = 0.5-5.3; p = 0.26; p-value for age = 0.23). Seventeen (85%) of 20 cases indicated that they had seen the cow (that also was on display at the market next to the sheep) compared to 7 (32%) of Geographical location of Q fever outbreak cases notified to the statutory surveillance system Figure 3 Geographical location of Q fever outbreak cases notified to the statutory surveillance system. or directly at the gate of the sheep pen compared to 8 (32%) of 25 controls (OR = 5.0; 95%CI = 1.2-22.3; p = 0.03). Touching the sheep was also significantly more common among cases (5/20 (25%) CCS2 cases vs. 0/22 (0%) controls; OR undefined; lower 95% CI = 1.1; p = 0.02). 17 (85%) of 20 CCS2 cases, but only 6 (25%) of 24 controls stopped for at least a few seconds at or in the sheep pen, the reference for this variable was "having passed by the pen without stopping" (OR = 17.0; 95%CI = 3.0-112.5; p < 0.01). Among CCS2 cases, self-reported proximity to or time spent with/close to the sheep was not associated with a shorter incubation period.
We were able to contact and interview 75 (86%) of 87 vendors, and received second hand information about 7 more (overall response rate: 94%). Fourty-five (56%) were male and 35 (44%) were female. 13 (16%) met the clinical case definition. Of the 11 vendors who worked within two stand units of the sheep pen, 6 (55%) became cases compared to only 7 (10%) of 70 persons who worked in a stand at a greater distance (relative risk (RR) = 5.5 (95%CI = 2.3-13.2; p = 0.002); Figure 1 ). Of these 7 vendors, 4 had spent time within 5 meters of the pen on May 4, one had been near the pen, but at a distance of more than 5 meters, and no information on this variable was available for the remaining 2. In the section of the market facing the wind coming from the pen (section 4, Figure 1 ), 4 (9%) of 44 vendors became cases, compared to 2 (13%) of 15 persons who worked in section 1 (p = 0.6). Among 22 persons who worked in stands that were perpendicular to the wind direction, 7 (32%) became cases. (Table 3 ). In all scenarios the AR among adults was significantly higher than that among children ( Figure 5 ).
In total, 5 lambs and 5 ewes were displayed on the market, one of them was pregnant and gave birth to twin lambs at 6:30 a.m. on May 4, 2003 . Of these, 3 ewes including the one that had lambed tested positive for C. burnetii. The animals came from a flock of 67 ewes, of which 66 had given birth between February and June. The majority of the births (57 (86%)) had occurred in February and March, usually inside a stable or on a meadow located away from the town. Six ewes aborted, had stillbirths or abnormally weak lambs. Among all ewes, 17/67 (25%) tested positive for C. burnetii.
The percentage of sheep that tested positive in the other 5 sheep flocks in the region ranged from 8% to 24% (8%; 12%; 12%; 16%; 24%).
We have described one of the largest Q fever outbreaks in Germany which, due to its point-source nature, provided the opportunity to assess many epidemiological features of the disease that can be rarely studied otherwise.
In 1954, more than 500 cases of Q fever were, similar to this outbreak, linked to the abortion of an infected cow at a farmers' market [15] . More recently a large outbreak occurred in Jena (Thuringia) in 2005 with 322 reported cases [16] associated with exposure to a herd of sheep kept on a meadow close to the housing area in which the cases occurred.
The first case control study served to confirm the hypothesis of an association between the outbreak and the farmers' market. The fact that only attendance on the second, but not the first day was strongly associated with illness pointed towards the role of the ewe that had given birth Persons accompanying notified cases (source 5) were a mixture of adults and children and are therefore listed separately.
in the early morning hours of May 4, 2005 . This strong association and the very high attributable fraction among all cases suggested a point source and justified defining cases notified through the reporting system as outbreak cases if they were clinically compatible with Q fever and gave a history of having visited the farmers' market. The point-source nature of the outbreak permitted calculation of the incubation period of cases which averaged 21 days and ranged from 2 to 48 days with an interquartile range of 16 to 24 days. This is compatible with the literature [1] . An additional interview with the two cases with early onset (2 and 4 days after attending the market on May 4, Attack rates among adults and children in a most likely scenario and 8 other scenarios Figure 5 Attack rates among adults and children in a most likely scenario and 8 other scenarios. Most likely scenario: 3000 visitors, 83% adult visitors and 20% clinical attack rate among adults. Scenarios 1-8 varied in the assumptions made for "number of visitors", "proportion of adult visitors" and "attack rate among adults" (see Table 3 ). Displayed are attack rates and 95% confidence intervals. respectively) could not identify any other source of infection. A short incubation period was recently observed in another Q fever outbreak in which the infectious dose was likely very high [17] .
The second case control study among persons who visited the market on May 4 demonstrated that both close proximity to the ewe and duration of exposure were important risk factors. This finding was confirmed by the cohort study on vendors which showed that those who worked in a stand close to (within 6 meters) the sheep pen were at significantly higher risk of acquiring Q fever. The study failed to show a significant role of the location of the stand in reference to the wind direction, although we must take into account that the wind was likely not always and exactly as reported by the weather station. However, if the wind had been important at all more cases might have been expected to have occurred among vendors situated at a greater distance to the sheep.
According to statutory surveillance system data, the proportion of clinical cases hospitalized was 25%, similar to the proportion of 21% found in persons pooled from the other studies conducted. Several publications report lower proportions than that found in this investigation: 4% (8/ 191) [7] , 5% [1] and 10% (4/39) [5] ), and there was at least one study with a much higher proportion (63% (10/ 16)) [18] . It is unlikely that hospitals reported cases with Q fever more frequently than private physicians because the proportion hospitalized among Q fever patients identified through random telephone calls in the Soest population or those in the two cohorts was similar to that of notified cases. Thus reporting bias is an unlikely explanation for the relatively high proportion of cases hospitalized. Alternative explanations include overly cautious referral practices on the part of attending physicians or the presumably high infectious dose of the organism in this outbreak, e.g. in those cases that spent time in the sheep pen.
The estimated attack rate among adults in the four studies varied between 16% and 33%. The estimate of 23% based on the random sample of persons visiting the market on the second day would seem most immune to recall bias, even if this cannot be entirely ruled out. The estimation based on information about persons accompanying the cases may be subject to an overestimation because these individuals presumably had a higher probability of being close to the sheep pen, similar to the cases. On the other hand the estimate from the cohort study on vendors might be an underestimate, since the vendors obviously had a different purpose for being at the market and may have been less interested in having a look at the sheep. Nevertheless, all estimates were independent from each other and considering the various possible biases, they were remarkably similar. In comparison, in a different outbreak in Germany, in which inhabitants of a village were exposed to a large herd of sheep (n = 1000-2000) [5, 7] the attack rate was estimated as 16%. In a similar outbreak in Switzerland several villages were exposed to approximately 900 sheep [19] . In the most severely affected village, the clinical attack rate was 16% (estimated from the data provided) [19] . It is remarkable that in the outbreak described here, the infectious potential of one pregnant ewe -upon lambing -was comparable to that of entire herds, albeit in different settings.
Our estimate of the proportion of serologically confirmed cases that became symptomatic (50% (3/6)) is based on a very small sample, but consistent with the international literature. In the above mentioned Swiss outbreak, 46% of serologically positive patients developed clinical disease [7] .
Only approximately half of all symptomatic cases were reported to the statutory surveillance system. Patients who did not seek health care due to mild disease as well as underdiagnosis or underreporting may have contributed to the missing other half. Our estimated 3% attack rate among children is based on a number of successive assumptions and must therefore be interpreted with caution. Nevertheless, sensitivity analysis confirmed that adults had a significantly elevated attack rate compared to children. While it has been suggested that children are at lower risk than adults for developing symptomatic illness [7, 8] few data have been published regarding attack rates of children in comparison to adults.
The estimated C. burnetii seroprevalence in the sheep flocks in the area varied from 8% to 24%. The 25% seroprevalence in the flock of the exhibited animals together with a positive polymerase chain reaction in an afterbirth in June 2003 suggested a recent infection of the flock [20] . Seroprevalence among sheep flocks related to human outbreaks tend to be substantially higher than those in flocks not related to human outbreaks. The median seroprevalence in a number of relevant studies performed in the context of human outbreaks [7, 20, 21] , was 40% compared to 1% in sheep flocks not linked to human outbreaks [20] .
This outbreak shows the dramatic consequences of putting a large number of susceptible individuals in close contact to a single infected ewe that (in such a setting) can turn into a super-spreader upon lambing. There is always a cultural component in the interaction between people and animals, and these may contribute to outbreaks or changing patterns of incidence. During the past decades urbanization of rural areas and changes in animal husbandry have occurred [20] , with more recent attempts to put a "deprived" urban population "in touch" with farm animals. Petting zoos, family farm vacations or the display of (farm) animals at a market such as this may lead to new avenues for the transmission of zoonotic infectious agents [20, [22] [23] [24] . While not all eventualities can be foreseen, it is important to raise awareness in pet and livestock owners as well as to strengthen recommendations where necessary. This outbreak led to the amendment and extension of existing recommendations [25] which now forbid the display of sheep in the latter third of their pregnancy and require regular testing of animals for C. burnetii in petting zoos, where there is close contact between humans and animals.
Due to the size and point source nature this outbreak permitted reassessment of fundamental, but seldom studied epidemiological parameters of Q fever. It also served to revise public health recommendations to account for the changing type and frequency of contact of susceptible humans with potentially infectious animals.
Abbreviations AFE = attributable fraction of cases exposed
The author(s) declare that they have no competing interests.
|
What public event was linked with the outbreak?
| 5,206 |
farmers' market
| 14,811 |
1,583 |
A super-spreading ewe infects hundreds with Q fever at a farmers' market in Germany
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1618839/
SHA: ee1b5a9618dcc4080ed100486cedd0969e80fa4d
Authors: Porten, Klaudia; Rissland, Jürgen; Tigges, Almira; Broll, Susanne; Hopp, Wilfried; Lunemann, Mechthild; van Treeck, Ulrich; Kimmig, Peter; Brockmann, Stefan O; Wagner-Wiening, Christiane; Hellenbrand, Wiebke; Buchholz, Udo
Date: 2006-10-06
DOI: 10.1186/1471-2334-6-147
License: cc-by
Abstract: BACKGROUND: In May 2003 the Soest County Health Department was informed of an unusually large number of patients hospitalized with atypical pneumonia. METHODS: In exploratory interviews patients mentioned having visited a farmers' market where a sheep had lambed. Serologic testing confirmed the diagnosis of Q fever. We asked local health departments in Germany to identiy notified Q fever patients who had visited the farmers market. To investigate risk factors for infection we conducted a case control study (cases were Q fever patients, controls were randomly selected Soest citizens) and a cohort study among vendors at the market. The sheep exhibited at the market, the herd from which it originated as well as sheep from herds held in the vicinity of Soest were tested for Coxiella burnetii (C. burnetii). RESULTS: A total of 299 reported Q fever cases was linked to this outbreak. The mean incubation period was 21 days, with an interquartile range of 16–24 days. The case control study identified close proximity to and stopping for at least a few seconds at the sheep's pen as significant risk factors. Vendors within approximately 6 meters of the sheep's pen were at increased risk for disease compared to those located farther away. Wind played no significant role. The clinical attack rate of adults and children was estimated as 20% and 3%, respectively, 25% of cases were hospitalized. The ewe that had lambed as well as 25% of its herd tested positive for C. burnetii antibodies. CONCLUSION: Due to its size and point source nature this outbreak permitted assessment of fundamental, but seldom studied epidemiological parameters. As a consequence of this outbreak, it was recommended that pregnant sheep not be displayed in public during the 3(rd )trimester and to test animals in petting zoos regularly for C. burnetii.
Text: Q fever is a worldwide zoonosis caused by Coxiella burnetii (C. burnetii), a small, gram-negative obligate intracellular bacterium. C. burnetii displays antigenic variation with an infectious phase I and less infectious phase II. The primary reservoir from which human infection occurs consists of sheep, goat and cattle. Although C. burnetii infections in animals are usually asymptomatic, they may cause abortions in sheep and goats [1] . High concentrations of C. burnetii can be found in birth products of infected mammals [2] . Humans frequently acquire infection through inhalation of contaminated aerosols from parturient fluids, placenta or wool [1] . Because the infectious dose is very low [3] and C. burnetii is able to survive in a spore-like state for months to years, outbreaks among humans have also occurred through contaminated dust carried by wind over large distances [4] [5] [6] .
C. burnetii infection in humans is asymptomatic in approximately 50% of cases. Approximately 5% of cases are hospitalized, and fatal cases are rare [1] . The clinical presentation of acute Q fever is variable and can resemble many other infectious diseases [2] . However, the most frequent clinical manifestation of acute Q fever is a self-limited febrile illness associated with severe headache. Atypical pneumonia and hepatitis are the major clinical manifestations of more severe disease. Acute Q fever may be complicated by meningoencephalitis or myocarditis. Rarely a chronic form of Q fever develops months after the acute illness, most commonly in the form of endocarditis [1] . Children develop clinical disease less frequently [7, 8] . Because of its non-specific presentation Q fever can only be suspected on clinical grounds and requires serologic confirmation. While the indirect immunofluorescence assay (IFA) is considered to be the reference method, complement fixation (CF), ELISA and microagglutination (MA) can also be used [9] . Acute infections are diagnosed by elevated IgG and/or IgM anti-phase II antibodies, while raised anti-phase I IgG antibodies are characteristic for chronic infections [1] .
In Germany, acute Q fever is a notifiable disease. Between 1991 and 2000 the annual number of cases varied from 46 to 273 cases per year [10] . In 2001 and 2002, 293 and 191 cases were notified, respectively [11, 12] .
On May 26, 2003 the health department of Soest was informed by a local hospital of an unusually large number of patients with atypical pneumonia. Some patients reported having visited a farmers' market that took place on May 3 and 4, 2003 in a spa town near Soest. Since the etiology was unclear, pathogens such as SARS coronavirus were considered and strict infection control measures implemented until the diagnosis of Q fever was confirmed.
An outbreak investigation team was formed and included public health professionals from the local health department, the local veterinary health department, the state health department, the National Consulting Laboratory (NCL) for Coxiellae and the Robert Koch-Institute (RKI), the federal public health institute. Because of the size and point source appearance of the outbreak the objective of the investigation was to identify etiologic factors relevant to the prevention and control of Q fever as well as to assess epidemiological parameters that can be rarely studied otherwise.
On May 26 and 27, 2003 we conducted exploratory interviews with patients in Soest hospitalized due to atypical pneumonia.
Attending physicians were requested to test serum of patients with atypical pneumonia for Mycoplasma pneumoniae, Chlamydia pneumoniae, Legionella pneumophila, Coxiella burnetii, Influenza A and B, Parainfluenza 1-3, Adenovirus and Enterovirus. Throat swabs were tested for Influenza virus, Adenovirus and SARS-Coronavirus. Laboratory confirmation of an acute Q fever infection was defined as the presence of IgM antibodies against phase II C. burnetii antigens (ELISA or IFA), a 4-fold increase in anti-phase II IgG antibody titer (ELISA or IFA) or in anti phase II antibody titer by CF between acute and convalescent sera. A chronic infection was confirmed when both anti-phase I IgG and anti-phase II IgG antibody titers were raised.
Because patients with valvular heart defects and pregnant women are at high risk of developing chronic infection [13, 14] we alerted internists and gynaecologists through the journal of the German Medical Association and asked them to send serum samples to the NCL if they identified patients from these risk groups who had been at the farmers' market during the outbreak.
The objective of the first case control study was to establish whether there was a link between the farmers' market and the outbreak and to identify other potential risk factors. We conducted telephone interviews using a standardised questionnaire that asked about attendance at the farmers' market, having been within 1 km distance of one of 6 sheep flocks in the area, tick bites and consumption of unpasteurized milk, sheep or goat cheese. For the purpose of CCS1 we defined a case (CCS1 case) as an adult resident of the town of Soest notified to the statutory sur-veillance system with Q fever, having symptom onset between May 4 and June 3, 2003. Exclusion criterion was a negative IgM-titer against phase II antigens. Two controls per case were recruited from Soest inhabitants by random digit dialing.
We calculated the attributable fraction of cases exposed to the farmers' market on May 4 (AFE) as (OR-1)/OR and the attributable fraction for all cases due to this exposure as:
The farmers' market was held in a spa town near Soest with many visitors from other areas of the state and even the entire country. To determine the outbreak size we therefore asked local public health departments in Germany to ascertain a possible link to the farmers' market in Soest for all patients notified with Q-fever. A case in this context ("notified case") was defined as any person with a clinical diagnosis compatible with Q fever with or without laboratory confirmation and history of exposure to the farmers' market.
Local health departments also reported whether a notified case was hospitalized. To obtain an independent, second estimate of the proportion of hospitalizations among symptomatic patients beyond that reported through the statutory surveillance system we calculated the proportion of hospitalized patients among those persons fulfilling the clinical case definition (as used in the vendors' study (s.b.)) identified through random sampling of the Soest population (within CCS2 (s.b.)) as well as in two cohorts (vendors' study and the 9 sailor friends (see below)).
The objective of CCS2 was to identify risk factors associated with attendance of the farmers' market on the second day. We used the same case definition as in CCS1, but included only persons that had visited the farmers' market on May 4, the second day of the market. We selected controls again randomly from the telephone registry of Soest and included only those persons who had visited the farmers' market on May 4 and had not been ill with fever afterwards. Potential controls who became ill were excluded for analysis in CCS2, but were still fully interviewed. This permitted calculation of the attack rate among visitors to the market (see below "Estimation of the overall attack rate") and gave an estimate of the proportion of clinically ill cases that were hospitalized (s.a.).
In the vendors' study we investigated whether the distance of the vendor stands from the sheep pen or dispersion of C. burnetii by wind were relevant risk factors for acquiring Q fever. We obtained a list of all vendors including the approximate location of the stands from the organizer. In addition we asked the local weather station for the predominant wind direction on May 4, 2003. Telephone interviews were performed using standardized questionnaires. A case was defined as a person with onset of fever between May 4 and June 3, 2003 and at least three of the following symptoms: headache, cough, dyspnea, joint pain, muscle pain, weight loss of more than 2 kg, fatigue, nausea or vomiting.
The relative distance of the stands to the sheep pen was estimated by counting the stands between the sheep pen and the stand in question. Each stand was considered to be one stand unit (approximately 3 meters). Larger stands were counted as 2 units. The direction of the wind in relation to the sheep pen was defined by dividing the wind rose (360°) in 4 equal parts of 90°. The predominant wind direction during the market was south-south-east ( Figure 1 ). For the purpose of the analysis we divided the market area into 4 sections with the sheep pen at its center. In section 1 the wind was blowing towards the sheep pen (plus minus 45°). Section 4 was on the opposite side, i.e. where the wind blew from the sheep pen towards the stands, and sections 2 and 3 were east and west with respect to the wind direction, respectively. Location of the stands in reference to the sheep pen was thus defined in two ways: as the absolute distance to the sheep pen (in stand units or meters) and in reference to the wind direction.
We identified a small cohort of 9 sailor friends who visited the farmers' market on May 4, 2003. All of these were serologically tested independently of symptoms. We could therefore calculate the proportion of laboratory confirmed persons who met the clinical case definition (as defined in the cohort study on vendors).
The overall attack rate among adults was estimated based on the following sources:
(1) Interviews undertaken for recruitment of controls for CCS2 allowed the proportion of adults that acquired symptomatic Q fever among those who visited the farmers' market on the second day;
Attributable fraction AFE Number of cases exposed All cases = *
(2) Interviews of cases and controls in CCS2 yielded information about accompanying adults and how many of these became later "ill with fever";
(3) Results of the small cohort of 9 sailor friends (s.a.);
(4) Results from the cohort study on vendors.
Local health departments that identified outbreak cases of Q fever (s.a. "determination of outbreak size and descriptive epidemiology") interviewed patients about the number of persons that had accompanied them to the farmers' market and whether any of these had become ill with fever afterwards. However, as there was no differentiation between adults and children, calculations to estimate the attack rate among adults were performed both with and without this source.
To count cases in (1), (3) and (4) we used the clinical case definition as defined in the cohort study on vendors.
For the calculation of the attack rate among children elicited in CCS2 was the same for all visitors. The number of children that visited the market could then be estimated from the total number of visitors as estimated by the organizers. We then estimated the number of symptomatic children (numerator). For this we assumed that the proportion of children with Q fever that were seen by physicians and were consequently notified was the same as that of adults. It was calculated as:
Thus the true number of children with Q fever was estimated by the number of reported children divided by the estimated proportion reported. Then the attack rate among children could be estimated as follows:
Because this calculation was based on several assumptions (number of visitors, proportion of adult visitors and clinical attack rate among adults) we performed a sensitivity analysis where the values of these variables varied.
Serum was collected from all sheep and cows displayed in the farmers' market as well as from all sheep of the respective home flocks (70 animals). Samples of 25 sheep from five other flocks in the Soest area were also tested for C. burnetii. Tests were performed by ELISA with a phase I and phase II antigen mixture.
We conducted statistical analysis with Epi Info, version 6.04 (CDC, Atlanta, USA). Dichotomous variables in the case control and cohort studies were compared using the Chi-Square test and numerical variables using the Kruskal-Wallis test. P-values smaller than 0.05 were considered statistically significant.
The outbreak investigation was conducted within the framework of the Communicable Diseases Law Reform Act of Germany. Mandatory regulations were observed.
Patients at the local hospital in Soest reported that a farmers' market had taken place on May 3 and 4, 2003 in a spa town close to the town of Soest. It was located in a park along the main promenade, spanning a distance of approximately 500 meters. The market attracted mainly three groups of people: locals, inhabitants of the greater Soest region, patients from the spa sanatoria and their visiting family or friends. Initial interviewees mentioned also that they had spent time at the sheep pen watching new-born lambs that had been born in the early morning hours of May 4, 2003 . The ewe had eaten the placenta but the parturient fluid on the ground had merely been covered with fresh straw.
Overall 171 (65%) of 263 serum samples submitted to the NCL were positive for IgM anti-phase II antibodies by ELISA. Results of throat swabs and serum were negative for other infectious agents. (Figure 2 ). If we assume that symptom onset in cases was normally distributed with a mean of 21 days, 95% of cases (mean +/-2 standard deviations) had their onset between day 10 and 31. The two notified cases with early onset on May 6 and 8, respectively, were laboratory confirmed and additional interviews did not reveal any additional risk factors. Of the 298 cases with known gender, 158 (53%) were male and 140 (47%) were female. Of the notified cases, 189 (63%) were from the county of Soest, 104 (35%) were Porportion reported number of notified adults number of vis = i iting adults attack rate among adults * Attack rate among children estimated true number of childr = e en with Q fever estimated number of children at the market from other counties in the same federal state (Northrhine Westphalia) and 6 (2%) were from five other federal states in Germany (Figure 3 ). Only eight (3%) cases were less than 18 years of age, the mean and median age was 54 and 56 years, respectively ( Figure 4 ). 75 (25%) of 297 notified cases were hospitalized, none died. Calculation of the proportion of cases hospitalized through other information sources revealed that 4 of 19 (21%; 95% CI = 6-46%;
(1/5 (CCS2), 2/11 (vendors study) and 1/3 (sailor friends)) clinically ill cases were hospitalized.
Laboratory confirmation was reported in 167 (56%) outbreak cases; 66 (22%) were confirmed by an increase in anti-phase II antibody titer (CF), 89 (30%) had IgM antibodies against phase II antigens, 11 (4%) were positive in both tests and one was confirmed by culture. No information was available as to whether the 132 (44%) cases without laboratory confirmation were laboratory tested.
18 patients with valvular heart defects and eleven pregnant women were examined. None of them had clinical signs of Q fever. Two (11%) of 18 cardiological patients and four (36%) of 11 pregnant women had an acute Q fever infection. During childbirth strict hygienic measures were implemented. Lochia and colostrum of all infected women were tested by polymerase chain reaction and were positive in only one woman (case 3; Table 1 ). Serological follow-up of the mothers detected chronic infection in the same woman (case 3) 12 weeks after delivery. One year follow-up of two newborn children (of cases 1 and 3) identified neither acute nor chronic Q fever infections.
We recruited 20 cases and 36 controls who visited the farmers' market on May 4 for the second case control study. They did not differ significantly in age and gender (OR for male sex = 1.7; 95%CI = 0.5-5.3; p = 0.26; p-value for age = 0.23). Seventeen (85%) of 20 cases indicated that they had seen the cow (that also was on display at the market next to the sheep) compared to 7 (32%) of Geographical location of Q fever outbreak cases notified to the statutory surveillance system Figure 3 Geographical location of Q fever outbreak cases notified to the statutory surveillance system. or directly at the gate of the sheep pen compared to 8 (32%) of 25 controls (OR = 5.0; 95%CI = 1.2-22.3; p = 0.03). Touching the sheep was also significantly more common among cases (5/20 (25%) CCS2 cases vs. 0/22 (0%) controls; OR undefined; lower 95% CI = 1.1; p = 0.02). 17 (85%) of 20 CCS2 cases, but only 6 (25%) of 24 controls stopped for at least a few seconds at or in the sheep pen, the reference for this variable was "having passed by the pen without stopping" (OR = 17.0; 95%CI = 3.0-112.5; p < 0.01). Among CCS2 cases, self-reported proximity to or time spent with/close to the sheep was not associated with a shorter incubation period.
We were able to contact and interview 75 (86%) of 87 vendors, and received second hand information about 7 more (overall response rate: 94%). Fourty-five (56%) were male and 35 (44%) were female. 13 (16%) met the clinical case definition. Of the 11 vendors who worked within two stand units of the sheep pen, 6 (55%) became cases compared to only 7 (10%) of 70 persons who worked in a stand at a greater distance (relative risk (RR) = 5.5 (95%CI = 2.3-13.2; p = 0.002); Figure 1 ). Of these 7 vendors, 4 had spent time within 5 meters of the pen on May 4, one had been near the pen, but at a distance of more than 5 meters, and no information on this variable was available for the remaining 2. In the section of the market facing the wind coming from the pen (section 4, Figure 1 ), 4 (9%) of 44 vendors became cases, compared to 2 (13%) of 15 persons who worked in section 1 (p = 0.6). Among 22 persons who worked in stands that were perpendicular to the wind direction, 7 (32%) became cases. (Table 3 ). In all scenarios the AR among adults was significantly higher than that among children ( Figure 5 ).
In total, 5 lambs and 5 ewes were displayed on the market, one of them was pregnant and gave birth to twin lambs at 6:30 a.m. on May 4, 2003 . Of these, 3 ewes including the one that had lambed tested positive for C. burnetii. The animals came from a flock of 67 ewes, of which 66 had given birth between February and June. The majority of the births (57 (86%)) had occurred in February and March, usually inside a stable or on a meadow located away from the town. Six ewes aborted, had stillbirths or abnormally weak lambs. Among all ewes, 17/67 (25%) tested positive for C. burnetii.
The percentage of sheep that tested positive in the other 5 sheep flocks in the region ranged from 8% to 24% (8%; 12%; 12%; 16%; 24%).
We have described one of the largest Q fever outbreaks in Germany which, due to its point-source nature, provided the opportunity to assess many epidemiological features of the disease that can be rarely studied otherwise.
In 1954, more than 500 cases of Q fever were, similar to this outbreak, linked to the abortion of an infected cow at a farmers' market [15] . More recently a large outbreak occurred in Jena (Thuringia) in 2005 with 322 reported cases [16] associated with exposure to a herd of sheep kept on a meadow close to the housing area in which the cases occurred.
The first case control study served to confirm the hypothesis of an association between the outbreak and the farmers' market. The fact that only attendance on the second, but not the first day was strongly associated with illness pointed towards the role of the ewe that had given birth Persons accompanying notified cases (source 5) were a mixture of adults and children and are therefore listed separately.
in the early morning hours of May 4, 2005 . This strong association and the very high attributable fraction among all cases suggested a point source and justified defining cases notified through the reporting system as outbreak cases if they were clinically compatible with Q fever and gave a history of having visited the farmers' market. The point-source nature of the outbreak permitted calculation of the incubation period of cases which averaged 21 days and ranged from 2 to 48 days with an interquartile range of 16 to 24 days. This is compatible with the literature [1] . An additional interview with the two cases with early onset (2 and 4 days after attending the market on May 4, Attack rates among adults and children in a most likely scenario and 8 other scenarios Figure 5 Attack rates among adults and children in a most likely scenario and 8 other scenarios. Most likely scenario: 3000 visitors, 83% adult visitors and 20% clinical attack rate among adults. Scenarios 1-8 varied in the assumptions made for "number of visitors", "proportion of adult visitors" and "attack rate among adults" (see Table 3 ). Displayed are attack rates and 95% confidence intervals. respectively) could not identify any other source of infection. A short incubation period was recently observed in another Q fever outbreak in which the infectious dose was likely very high [17] .
The second case control study among persons who visited the market on May 4 demonstrated that both close proximity to the ewe and duration of exposure were important risk factors. This finding was confirmed by the cohort study on vendors which showed that those who worked in a stand close to (within 6 meters) the sheep pen were at significantly higher risk of acquiring Q fever. The study failed to show a significant role of the location of the stand in reference to the wind direction, although we must take into account that the wind was likely not always and exactly as reported by the weather station. However, if the wind had been important at all more cases might have been expected to have occurred among vendors situated at a greater distance to the sheep.
According to statutory surveillance system data, the proportion of clinical cases hospitalized was 25%, similar to the proportion of 21% found in persons pooled from the other studies conducted. Several publications report lower proportions than that found in this investigation: 4% (8/ 191) [7] , 5% [1] and 10% (4/39) [5] ), and there was at least one study with a much higher proportion (63% (10/ 16)) [18] . It is unlikely that hospitals reported cases with Q fever more frequently than private physicians because the proportion hospitalized among Q fever patients identified through random telephone calls in the Soest population or those in the two cohorts was similar to that of notified cases. Thus reporting bias is an unlikely explanation for the relatively high proportion of cases hospitalized. Alternative explanations include overly cautious referral practices on the part of attending physicians or the presumably high infectious dose of the organism in this outbreak, e.g. in those cases that spent time in the sheep pen.
The estimated attack rate among adults in the four studies varied between 16% and 33%. The estimate of 23% based on the random sample of persons visiting the market on the second day would seem most immune to recall bias, even if this cannot be entirely ruled out. The estimation based on information about persons accompanying the cases may be subject to an overestimation because these individuals presumably had a higher probability of being close to the sheep pen, similar to the cases. On the other hand the estimate from the cohort study on vendors might be an underestimate, since the vendors obviously had a different purpose for being at the market and may have been less interested in having a look at the sheep. Nevertheless, all estimates were independent from each other and considering the various possible biases, they were remarkably similar. In comparison, in a different outbreak in Germany, in which inhabitants of a village were exposed to a large herd of sheep (n = 1000-2000) [5, 7] the attack rate was estimated as 16%. In a similar outbreak in Switzerland several villages were exposed to approximately 900 sheep [19] . In the most severely affected village, the clinical attack rate was 16% (estimated from the data provided) [19] . It is remarkable that in the outbreak described here, the infectious potential of one pregnant ewe -upon lambing -was comparable to that of entire herds, albeit in different settings.
Our estimate of the proportion of serologically confirmed cases that became symptomatic (50% (3/6)) is based on a very small sample, but consistent with the international literature. In the above mentioned Swiss outbreak, 46% of serologically positive patients developed clinical disease [7] .
Only approximately half of all symptomatic cases were reported to the statutory surveillance system. Patients who did not seek health care due to mild disease as well as underdiagnosis or underreporting may have contributed to the missing other half. Our estimated 3% attack rate among children is based on a number of successive assumptions and must therefore be interpreted with caution. Nevertheless, sensitivity analysis confirmed that adults had a significantly elevated attack rate compared to children. While it has been suggested that children are at lower risk than adults for developing symptomatic illness [7, 8] few data have been published regarding attack rates of children in comparison to adults.
The estimated C. burnetii seroprevalence in the sheep flocks in the area varied from 8% to 24%. The 25% seroprevalence in the flock of the exhibited animals together with a positive polymerase chain reaction in an afterbirth in June 2003 suggested a recent infection of the flock [20] . Seroprevalence among sheep flocks related to human outbreaks tend to be substantially higher than those in flocks not related to human outbreaks. The median seroprevalence in a number of relevant studies performed in the context of human outbreaks [7, 20, 21] , was 40% compared to 1% in sheep flocks not linked to human outbreaks [20] .
This outbreak shows the dramatic consequences of putting a large number of susceptible individuals in close contact to a single infected ewe that (in such a setting) can turn into a super-spreader upon lambing. There is always a cultural component in the interaction between people and animals, and these may contribute to outbreaks or changing patterns of incidence. During the past decades urbanization of rural areas and changes in animal husbandry have occurred [20] , with more recent attempts to put a "deprived" urban population "in touch" with farm animals. Petting zoos, family farm vacations or the display of (farm) animals at a market such as this may lead to new avenues for the transmission of zoonotic infectious agents [20, [22] [23] [24] . While not all eventualities can be foreseen, it is important to raise awareness in pet and livestock owners as well as to strengthen recommendations where necessary. This outbreak led to the amendment and extension of existing recommendations [25] which now forbid the display of sheep in the latter third of their pregnancy and require regular testing of animals for C. burnetii in petting zoos, where there is close contact between humans and animals.
Due to the size and point source nature this outbreak permitted reassessment of fundamental, but seldom studied epidemiological parameters of Q fever. It also served to revise public health recommendations to account for the changing type and frequency of contact of susceptible humans with potentially infectious animals.
Abbreviations AFE = attributable fraction of cases exposed
The author(s) declare that they have no competing interests.
|
What causes Q fever?
| 5,207 |
Coxiella burnetii (C. burnetii)
| 2,378 |
1,583 |
A super-spreading ewe infects hundreds with Q fever at a farmers' market in Germany
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1618839/
SHA: ee1b5a9618dcc4080ed100486cedd0969e80fa4d
Authors: Porten, Klaudia; Rissland, Jürgen; Tigges, Almira; Broll, Susanne; Hopp, Wilfried; Lunemann, Mechthild; van Treeck, Ulrich; Kimmig, Peter; Brockmann, Stefan O; Wagner-Wiening, Christiane; Hellenbrand, Wiebke; Buchholz, Udo
Date: 2006-10-06
DOI: 10.1186/1471-2334-6-147
License: cc-by
Abstract: BACKGROUND: In May 2003 the Soest County Health Department was informed of an unusually large number of patients hospitalized with atypical pneumonia. METHODS: In exploratory interviews patients mentioned having visited a farmers' market where a sheep had lambed. Serologic testing confirmed the diagnosis of Q fever. We asked local health departments in Germany to identiy notified Q fever patients who had visited the farmers market. To investigate risk factors for infection we conducted a case control study (cases were Q fever patients, controls were randomly selected Soest citizens) and a cohort study among vendors at the market. The sheep exhibited at the market, the herd from which it originated as well as sheep from herds held in the vicinity of Soest were tested for Coxiella burnetii (C. burnetii). RESULTS: A total of 299 reported Q fever cases was linked to this outbreak. The mean incubation period was 21 days, with an interquartile range of 16–24 days. The case control study identified close proximity to and stopping for at least a few seconds at the sheep's pen as significant risk factors. Vendors within approximately 6 meters of the sheep's pen were at increased risk for disease compared to those located farther away. Wind played no significant role. The clinical attack rate of adults and children was estimated as 20% and 3%, respectively, 25% of cases were hospitalized. The ewe that had lambed as well as 25% of its herd tested positive for C. burnetii antibodies. CONCLUSION: Due to its size and point source nature this outbreak permitted assessment of fundamental, but seldom studied epidemiological parameters. As a consequence of this outbreak, it was recommended that pregnant sheep not be displayed in public during the 3(rd )trimester and to test animals in petting zoos regularly for C. burnetii.
Text: Q fever is a worldwide zoonosis caused by Coxiella burnetii (C. burnetii), a small, gram-negative obligate intracellular bacterium. C. burnetii displays antigenic variation with an infectious phase I and less infectious phase II. The primary reservoir from which human infection occurs consists of sheep, goat and cattle. Although C. burnetii infections in animals are usually asymptomatic, they may cause abortions in sheep and goats [1] . High concentrations of C. burnetii can be found in birth products of infected mammals [2] . Humans frequently acquire infection through inhalation of contaminated aerosols from parturient fluids, placenta or wool [1] . Because the infectious dose is very low [3] and C. burnetii is able to survive in a spore-like state for months to years, outbreaks among humans have also occurred through contaminated dust carried by wind over large distances [4] [5] [6] .
C. burnetii infection in humans is asymptomatic in approximately 50% of cases. Approximately 5% of cases are hospitalized, and fatal cases are rare [1] . The clinical presentation of acute Q fever is variable and can resemble many other infectious diseases [2] . However, the most frequent clinical manifestation of acute Q fever is a self-limited febrile illness associated with severe headache. Atypical pneumonia and hepatitis are the major clinical manifestations of more severe disease. Acute Q fever may be complicated by meningoencephalitis or myocarditis. Rarely a chronic form of Q fever develops months after the acute illness, most commonly in the form of endocarditis [1] . Children develop clinical disease less frequently [7, 8] . Because of its non-specific presentation Q fever can only be suspected on clinical grounds and requires serologic confirmation. While the indirect immunofluorescence assay (IFA) is considered to be the reference method, complement fixation (CF), ELISA and microagglutination (MA) can also be used [9] . Acute infections are diagnosed by elevated IgG and/or IgM anti-phase II antibodies, while raised anti-phase I IgG antibodies are characteristic for chronic infections [1] .
In Germany, acute Q fever is a notifiable disease. Between 1991 and 2000 the annual number of cases varied from 46 to 273 cases per year [10] . In 2001 and 2002, 293 and 191 cases were notified, respectively [11, 12] .
On May 26, 2003 the health department of Soest was informed by a local hospital of an unusually large number of patients with atypical pneumonia. Some patients reported having visited a farmers' market that took place on May 3 and 4, 2003 in a spa town near Soest. Since the etiology was unclear, pathogens such as SARS coronavirus were considered and strict infection control measures implemented until the diagnosis of Q fever was confirmed.
An outbreak investigation team was formed and included public health professionals from the local health department, the local veterinary health department, the state health department, the National Consulting Laboratory (NCL) for Coxiellae and the Robert Koch-Institute (RKI), the federal public health institute. Because of the size and point source appearance of the outbreak the objective of the investigation was to identify etiologic factors relevant to the prevention and control of Q fever as well as to assess epidemiological parameters that can be rarely studied otherwise.
On May 26 and 27, 2003 we conducted exploratory interviews with patients in Soest hospitalized due to atypical pneumonia.
Attending physicians were requested to test serum of patients with atypical pneumonia for Mycoplasma pneumoniae, Chlamydia pneumoniae, Legionella pneumophila, Coxiella burnetii, Influenza A and B, Parainfluenza 1-3, Adenovirus and Enterovirus. Throat swabs were tested for Influenza virus, Adenovirus and SARS-Coronavirus. Laboratory confirmation of an acute Q fever infection was defined as the presence of IgM antibodies against phase II C. burnetii antigens (ELISA or IFA), a 4-fold increase in anti-phase II IgG antibody titer (ELISA or IFA) or in anti phase II antibody titer by CF between acute and convalescent sera. A chronic infection was confirmed when both anti-phase I IgG and anti-phase II IgG antibody titers were raised.
Because patients with valvular heart defects and pregnant women are at high risk of developing chronic infection [13, 14] we alerted internists and gynaecologists through the journal of the German Medical Association and asked them to send serum samples to the NCL if they identified patients from these risk groups who had been at the farmers' market during the outbreak.
The objective of the first case control study was to establish whether there was a link between the farmers' market and the outbreak and to identify other potential risk factors. We conducted telephone interviews using a standardised questionnaire that asked about attendance at the farmers' market, having been within 1 km distance of one of 6 sheep flocks in the area, tick bites and consumption of unpasteurized milk, sheep or goat cheese. For the purpose of CCS1 we defined a case (CCS1 case) as an adult resident of the town of Soest notified to the statutory sur-veillance system with Q fever, having symptom onset between May 4 and June 3, 2003. Exclusion criterion was a negative IgM-titer against phase II antigens. Two controls per case were recruited from Soest inhabitants by random digit dialing.
We calculated the attributable fraction of cases exposed to the farmers' market on May 4 (AFE) as (OR-1)/OR and the attributable fraction for all cases due to this exposure as:
The farmers' market was held in a spa town near Soest with many visitors from other areas of the state and even the entire country. To determine the outbreak size we therefore asked local public health departments in Germany to ascertain a possible link to the farmers' market in Soest for all patients notified with Q-fever. A case in this context ("notified case") was defined as any person with a clinical diagnosis compatible with Q fever with or without laboratory confirmation and history of exposure to the farmers' market.
Local health departments also reported whether a notified case was hospitalized. To obtain an independent, second estimate of the proportion of hospitalizations among symptomatic patients beyond that reported through the statutory surveillance system we calculated the proportion of hospitalized patients among those persons fulfilling the clinical case definition (as used in the vendors' study (s.b.)) identified through random sampling of the Soest population (within CCS2 (s.b.)) as well as in two cohorts (vendors' study and the 9 sailor friends (see below)).
The objective of CCS2 was to identify risk factors associated with attendance of the farmers' market on the second day. We used the same case definition as in CCS1, but included only persons that had visited the farmers' market on May 4, the second day of the market. We selected controls again randomly from the telephone registry of Soest and included only those persons who had visited the farmers' market on May 4 and had not been ill with fever afterwards. Potential controls who became ill were excluded for analysis in CCS2, but were still fully interviewed. This permitted calculation of the attack rate among visitors to the market (see below "Estimation of the overall attack rate") and gave an estimate of the proportion of clinically ill cases that were hospitalized (s.a.).
In the vendors' study we investigated whether the distance of the vendor stands from the sheep pen or dispersion of C. burnetii by wind were relevant risk factors for acquiring Q fever. We obtained a list of all vendors including the approximate location of the stands from the organizer. In addition we asked the local weather station for the predominant wind direction on May 4, 2003. Telephone interviews were performed using standardized questionnaires. A case was defined as a person with onset of fever between May 4 and June 3, 2003 and at least three of the following symptoms: headache, cough, dyspnea, joint pain, muscle pain, weight loss of more than 2 kg, fatigue, nausea or vomiting.
The relative distance of the stands to the sheep pen was estimated by counting the stands between the sheep pen and the stand in question. Each stand was considered to be one stand unit (approximately 3 meters). Larger stands were counted as 2 units. The direction of the wind in relation to the sheep pen was defined by dividing the wind rose (360°) in 4 equal parts of 90°. The predominant wind direction during the market was south-south-east ( Figure 1 ). For the purpose of the analysis we divided the market area into 4 sections with the sheep pen at its center. In section 1 the wind was blowing towards the sheep pen (plus minus 45°). Section 4 was on the opposite side, i.e. where the wind blew from the sheep pen towards the stands, and sections 2 and 3 were east and west with respect to the wind direction, respectively. Location of the stands in reference to the sheep pen was thus defined in two ways: as the absolute distance to the sheep pen (in stand units or meters) and in reference to the wind direction.
We identified a small cohort of 9 sailor friends who visited the farmers' market on May 4, 2003. All of these were serologically tested independently of symptoms. We could therefore calculate the proportion of laboratory confirmed persons who met the clinical case definition (as defined in the cohort study on vendors).
The overall attack rate among adults was estimated based on the following sources:
(1) Interviews undertaken for recruitment of controls for CCS2 allowed the proportion of adults that acquired symptomatic Q fever among those who visited the farmers' market on the second day;
Attributable fraction AFE Number of cases exposed All cases = *
(2) Interviews of cases and controls in CCS2 yielded information about accompanying adults and how many of these became later "ill with fever";
(3) Results of the small cohort of 9 sailor friends (s.a.);
(4) Results from the cohort study on vendors.
Local health departments that identified outbreak cases of Q fever (s.a. "determination of outbreak size and descriptive epidemiology") interviewed patients about the number of persons that had accompanied them to the farmers' market and whether any of these had become ill with fever afterwards. However, as there was no differentiation between adults and children, calculations to estimate the attack rate among adults were performed both with and without this source.
To count cases in (1), (3) and (4) we used the clinical case definition as defined in the cohort study on vendors.
For the calculation of the attack rate among children elicited in CCS2 was the same for all visitors. The number of children that visited the market could then be estimated from the total number of visitors as estimated by the organizers. We then estimated the number of symptomatic children (numerator). For this we assumed that the proportion of children with Q fever that were seen by physicians and were consequently notified was the same as that of adults. It was calculated as:
Thus the true number of children with Q fever was estimated by the number of reported children divided by the estimated proportion reported. Then the attack rate among children could be estimated as follows:
Because this calculation was based on several assumptions (number of visitors, proportion of adult visitors and clinical attack rate among adults) we performed a sensitivity analysis where the values of these variables varied.
Serum was collected from all sheep and cows displayed in the farmers' market as well as from all sheep of the respective home flocks (70 animals). Samples of 25 sheep from five other flocks in the Soest area were also tested for C. burnetii. Tests were performed by ELISA with a phase I and phase II antigen mixture.
We conducted statistical analysis with Epi Info, version 6.04 (CDC, Atlanta, USA). Dichotomous variables in the case control and cohort studies were compared using the Chi-Square test and numerical variables using the Kruskal-Wallis test. P-values smaller than 0.05 were considered statistically significant.
The outbreak investigation was conducted within the framework of the Communicable Diseases Law Reform Act of Germany. Mandatory regulations were observed.
Patients at the local hospital in Soest reported that a farmers' market had taken place on May 3 and 4, 2003 in a spa town close to the town of Soest. It was located in a park along the main promenade, spanning a distance of approximately 500 meters. The market attracted mainly three groups of people: locals, inhabitants of the greater Soest region, patients from the spa sanatoria and their visiting family or friends. Initial interviewees mentioned also that they had spent time at the sheep pen watching new-born lambs that had been born in the early morning hours of May 4, 2003 . The ewe had eaten the placenta but the parturient fluid on the ground had merely been covered with fresh straw.
Overall 171 (65%) of 263 serum samples submitted to the NCL were positive for IgM anti-phase II antibodies by ELISA. Results of throat swabs and serum were negative for other infectious agents. (Figure 2 ). If we assume that symptom onset in cases was normally distributed with a mean of 21 days, 95% of cases (mean +/-2 standard deviations) had their onset between day 10 and 31. The two notified cases with early onset on May 6 and 8, respectively, were laboratory confirmed and additional interviews did not reveal any additional risk factors. Of the 298 cases with known gender, 158 (53%) were male and 140 (47%) were female. Of the notified cases, 189 (63%) were from the county of Soest, 104 (35%) were Porportion reported number of notified adults number of vis = i iting adults attack rate among adults * Attack rate among children estimated true number of childr = e en with Q fever estimated number of children at the market from other counties in the same federal state (Northrhine Westphalia) and 6 (2%) were from five other federal states in Germany (Figure 3 ). Only eight (3%) cases were less than 18 years of age, the mean and median age was 54 and 56 years, respectively ( Figure 4 ). 75 (25%) of 297 notified cases were hospitalized, none died. Calculation of the proportion of cases hospitalized through other information sources revealed that 4 of 19 (21%; 95% CI = 6-46%;
(1/5 (CCS2), 2/11 (vendors study) and 1/3 (sailor friends)) clinically ill cases were hospitalized.
Laboratory confirmation was reported in 167 (56%) outbreak cases; 66 (22%) were confirmed by an increase in anti-phase II antibody titer (CF), 89 (30%) had IgM antibodies against phase II antigens, 11 (4%) were positive in both tests and one was confirmed by culture. No information was available as to whether the 132 (44%) cases without laboratory confirmation were laboratory tested.
18 patients with valvular heart defects and eleven pregnant women were examined. None of them had clinical signs of Q fever. Two (11%) of 18 cardiological patients and four (36%) of 11 pregnant women had an acute Q fever infection. During childbirth strict hygienic measures were implemented. Lochia and colostrum of all infected women were tested by polymerase chain reaction and were positive in only one woman (case 3; Table 1 ). Serological follow-up of the mothers detected chronic infection in the same woman (case 3) 12 weeks after delivery. One year follow-up of two newborn children (of cases 1 and 3) identified neither acute nor chronic Q fever infections.
We recruited 20 cases and 36 controls who visited the farmers' market on May 4 for the second case control study. They did not differ significantly in age and gender (OR for male sex = 1.7; 95%CI = 0.5-5.3; p = 0.26; p-value for age = 0.23). Seventeen (85%) of 20 cases indicated that they had seen the cow (that also was on display at the market next to the sheep) compared to 7 (32%) of Geographical location of Q fever outbreak cases notified to the statutory surveillance system Figure 3 Geographical location of Q fever outbreak cases notified to the statutory surveillance system. or directly at the gate of the sheep pen compared to 8 (32%) of 25 controls (OR = 5.0; 95%CI = 1.2-22.3; p = 0.03). Touching the sheep was also significantly more common among cases (5/20 (25%) CCS2 cases vs. 0/22 (0%) controls; OR undefined; lower 95% CI = 1.1; p = 0.02). 17 (85%) of 20 CCS2 cases, but only 6 (25%) of 24 controls stopped for at least a few seconds at or in the sheep pen, the reference for this variable was "having passed by the pen without stopping" (OR = 17.0; 95%CI = 3.0-112.5; p < 0.01). Among CCS2 cases, self-reported proximity to or time spent with/close to the sheep was not associated with a shorter incubation period.
We were able to contact and interview 75 (86%) of 87 vendors, and received second hand information about 7 more (overall response rate: 94%). Fourty-five (56%) were male and 35 (44%) were female. 13 (16%) met the clinical case definition. Of the 11 vendors who worked within two stand units of the sheep pen, 6 (55%) became cases compared to only 7 (10%) of 70 persons who worked in a stand at a greater distance (relative risk (RR) = 5.5 (95%CI = 2.3-13.2; p = 0.002); Figure 1 ). Of these 7 vendors, 4 had spent time within 5 meters of the pen on May 4, one had been near the pen, but at a distance of more than 5 meters, and no information on this variable was available for the remaining 2. In the section of the market facing the wind coming from the pen (section 4, Figure 1 ), 4 (9%) of 44 vendors became cases, compared to 2 (13%) of 15 persons who worked in section 1 (p = 0.6). Among 22 persons who worked in stands that were perpendicular to the wind direction, 7 (32%) became cases. (Table 3 ). In all scenarios the AR among adults was significantly higher than that among children ( Figure 5 ).
In total, 5 lambs and 5 ewes were displayed on the market, one of them was pregnant and gave birth to twin lambs at 6:30 a.m. on May 4, 2003 . Of these, 3 ewes including the one that had lambed tested positive for C. burnetii. The animals came from a flock of 67 ewes, of which 66 had given birth between February and June. The majority of the births (57 (86%)) had occurred in February and March, usually inside a stable or on a meadow located away from the town. Six ewes aborted, had stillbirths or abnormally weak lambs. Among all ewes, 17/67 (25%) tested positive for C. burnetii.
The percentage of sheep that tested positive in the other 5 sheep flocks in the region ranged from 8% to 24% (8%; 12%; 12%; 16%; 24%).
We have described one of the largest Q fever outbreaks in Germany which, due to its point-source nature, provided the opportunity to assess many epidemiological features of the disease that can be rarely studied otherwise.
In 1954, more than 500 cases of Q fever were, similar to this outbreak, linked to the abortion of an infected cow at a farmers' market [15] . More recently a large outbreak occurred in Jena (Thuringia) in 2005 with 322 reported cases [16] associated with exposure to a herd of sheep kept on a meadow close to the housing area in which the cases occurred.
The first case control study served to confirm the hypothesis of an association between the outbreak and the farmers' market. The fact that only attendance on the second, but not the first day was strongly associated with illness pointed towards the role of the ewe that had given birth Persons accompanying notified cases (source 5) were a mixture of adults and children and are therefore listed separately.
in the early morning hours of May 4, 2005 . This strong association and the very high attributable fraction among all cases suggested a point source and justified defining cases notified through the reporting system as outbreak cases if they were clinically compatible with Q fever and gave a history of having visited the farmers' market. The point-source nature of the outbreak permitted calculation of the incubation period of cases which averaged 21 days and ranged from 2 to 48 days with an interquartile range of 16 to 24 days. This is compatible with the literature [1] . An additional interview with the two cases with early onset (2 and 4 days after attending the market on May 4, Attack rates among adults and children in a most likely scenario and 8 other scenarios Figure 5 Attack rates among adults and children in a most likely scenario and 8 other scenarios. Most likely scenario: 3000 visitors, 83% adult visitors and 20% clinical attack rate among adults. Scenarios 1-8 varied in the assumptions made for "number of visitors", "proportion of adult visitors" and "attack rate among adults" (see Table 3 ). Displayed are attack rates and 95% confidence intervals. respectively) could not identify any other source of infection. A short incubation period was recently observed in another Q fever outbreak in which the infectious dose was likely very high [17] .
The second case control study among persons who visited the market on May 4 demonstrated that both close proximity to the ewe and duration of exposure were important risk factors. This finding was confirmed by the cohort study on vendors which showed that those who worked in a stand close to (within 6 meters) the sheep pen were at significantly higher risk of acquiring Q fever. The study failed to show a significant role of the location of the stand in reference to the wind direction, although we must take into account that the wind was likely not always and exactly as reported by the weather station. However, if the wind had been important at all more cases might have been expected to have occurred among vendors situated at a greater distance to the sheep.
According to statutory surveillance system data, the proportion of clinical cases hospitalized was 25%, similar to the proportion of 21% found in persons pooled from the other studies conducted. Several publications report lower proportions than that found in this investigation: 4% (8/ 191) [7] , 5% [1] and 10% (4/39) [5] ), and there was at least one study with a much higher proportion (63% (10/ 16)) [18] . It is unlikely that hospitals reported cases with Q fever more frequently than private physicians because the proportion hospitalized among Q fever patients identified through random telephone calls in the Soest population or those in the two cohorts was similar to that of notified cases. Thus reporting bias is an unlikely explanation for the relatively high proportion of cases hospitalized. Alternative explanations include overly cautious referral practices on the part of attending physicians or the presumably high infectious dose of the organism in this outbreak, e.g. in those cases that spent time in the sheep pen.
The estimated attack rate among adults in the four studies varied between 16% and 33%. The estimate of 23% based on the random sample of persons visiting the market on the second day would seem most immune to recall bias, even if this cannot be entirely ruled out. The estimation based on information about persons accompanying the cases may be subject to an overestimation because these individuals presumably had a higher probability of being close to the sheep pen, similar to the cases. On the other hand the estimate from the cohort study on vendors might be an underestimate, since the vendors obviously had a different purpose for being at the market and may have been less interested in having a look at the sheep. Nevertheless, all estimates were independent from each other and considering the various possible biases, they were remarkably similar. In comparison, in a different outbreak in Germany, in which inhabitants of a village were exposed to a large herd of sheep (n = 1000-2000) [5, 7] the attack rate was estimated as 16%. In a similar outbreak in Switzerland several villages were exposed to approximately 900 sheep [19] . In the most severely affected village, the clinical attack rate was 16% (estimated from the data provided) [19] . It is remarkable that in the outbreak described here, the infectious potential of one pregnant ewe -upon lambing -was comparable to that of entire herds, albeit in different settings.
Our estimate of the proportion of serologically confirmed cases that became symptomatic (50% (3/6)) is based on a very small sample, but consistent with the international literature. In the above mentioned Swiss outbreak, 46% of serologically positive patients developed clinical disease [7] .
Only approximately half of all symptomatic cases were reported to the statutory surveillance system. Patients who did not seek health care due to mild disease as well as underdiagnosis or underreporting may have contributed to the missing other half. Our estimated 3% attack rate among children is based on a number of successive assumptions and must therefore be interpreted with caution. Nevertheless, sensitivity analysis confirmed that adults had a significantly elevated attack rate compared to children. While it has been suggested that children are at lower risk than adults for developing symptomatic illness [7, 8] few data have been published regarding attack rates of children in comparison to adults.
The estimated C. burnetii seroprevalence in the sheep flocks in the area varied from 8% to 24%. The 25% seroprevalence in the flock of the exhibited animals together with a positive polymerase chain reaction in an afterbirth in June 2003 suggested a recent infection of the flock [20] . Seroprevalence among sheep flocks related to human outbreaks tend to be substantially higher than those in flocks not related to human outbreaks. The median seroprevalence in a number of relevant studies performed in the context of human outbreaks [7, 20, 21] , was 40% compared to 1% in sheep flocks not linked to human outbreaks [20] .
This outbreak shows the dramatic consequences of putting a large number of susceptible individuals in close contact to a single infected ewe that (in such a setting) can turn into a super-spreader upon lambing. There is always a cultural component in the interaction between people and animals, and these may contribute to outbreaks or changing patterns of incidence. During the past decades urbanization of rural areas and changes in animal husbandry have occurred [20] , with more recent attempts to put a "deprived" urban population "in touch" with farm animals. Petting zoos, family farm vacations or the display of (farm) animals at a market such as this may lead to new avenues for the transmission of zoonotic infectious agents [20, [22] [23] [24] . While not all eventualities can be foreseen, it is important to raise awareness in pet and livestock owners as well as to strengthen recommendations where necessary. This outbreak led to the amendment and extension of existing recommendations [25] which now forbid the display of sheep in the latter third of their pregnancy and require regular testing of animals for C. burnetii in petting zoos, where there is close contact between humans and animals.
Due to the size and point source nature this outbreak permitted reassessment of fundamental, but seldom studied epidemiological parameters of Q fever. It also served to revise public health recommendations to account for the changing type and frequency of contact of susceptible humans with potentially infectious animals.
Abbreviations AFE = attributable fraction of cases exposed
The author(s) declare that they have no competing interests.
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What is Coxiella burnetii?
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small, gram-negative obligate intracellular bacterium
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1,583 |
A super-spreading ewe infects hundreds with Q fever at a farmers' market in Germany
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1618839/
SHA: ee1b5a9618dcc4080ed100486cedd0969e80fa4d
Authors: Porten, Klaudia; Rissland, Jürgen; Tigges, Almira; Broll, Susanne; Hopp, Wilfried; Lunemann, Mechthild; van Treeck, Ulrich; Kimmig, Peter; Brockmann, Stefan O; Wagner-Wiening, Christiane; Hellenbrand, Wiebke; Buchholz, Udo
Date: 2006-10-06
DOI: 10.1186/1471-2334-6-147
License: cc-by
Abstract: BACKGROUND: In May 2003 the Soest County Health Department was informed of an unusually large number of patients hospitalized with atypical pneumonia. METHODS: In exploratory interviews patients mentioned having visited a farmers' market where a sheep had lambed. Serologic testing confirmed the diagnosis of Q fever. We asked local health departments in Germany to identiy notified Q fever patients who had visited the farmers market. To investigate risk factors for infection we conducted a case control study (cases were Q fever patients, controls were randomly selected Soest citizens) and a cohort study among vendors at the market. The sheep exhibited at the market, the herd from which it originated as well as sheep from herds held in the vicinity of Soest were tested for Coxiella burnetii (C. burnetii). RESULTS: A total of 299 reported Q fever cases was linked to this outbreak. The mean incubation period was 21 days, with an interquartile range of 16–24 days. The case control study identified close proximity to and stopping for at least a few seconds at the sheep's pen as significant risk factors. Vendors within approximately 6 meters of the sheep's pen were at increased risk for disease compared to those located farther away. Wind played no significant role. The clinical attack rate of adults and children was estimated as 20% and 3%, respectively, 25% of cases were hospitalized. The ewe that had lambed as well as 25% of its herd tested positive for C. burnetii antibodies. CONCLUSION: Due to its size and point source nature this outbreak permitted assessment of fundamental, but seldom studied epidemiological parameters. As a consequence of this outbreak, it was recommended that pregnant sheep not be displayed in public during the 3(rd )trimester and to test animals in petting zoos regularly for C. burnetii.
Text: Q fever is a worldwide zoonosis caused by Coxiella burnetii (C. burnetii), a small, gram-negative obligate intracellular bacterium. C. burnetii displays antigenic variation with an infectious phase I and less infectious phase II. The primary reservoir from which human infection occurs consists of sheep, goat and cattle. Although C. burnetii infections in animals are usually asymptomatic, they may cause abortions in sheep and goats [1] . High concentrations of C. burnetii can be found in birth products of infected mammals [2] . Humans frequently acquire infection through inhalation of contaminated aerosols from parturient fluids, placenta or wool [1] . Because the infectious dose is very low [3] and C. burnetii is able to survive in a spore-like state for months to years, outbreaks among humans have also occurred through contaminated dust carried by wind over large distances [4] [5] [6] .
C. burnetii infection in humans is asymptomatic in approximately 50% of cases. Approximately 5% of cases are hospitalized, and fatal cases are rare [1] . The clinical presentation of acute Q fever is variable and can resemble many other infectious diseases [2] . However, the most frequent clinical manifestation of acute Q fever is a self-limited febrile illness associated with severe headache. Atypical pneumonia and hepatitis are the major clinical manifestations of more severe disease. Acute Q fever may be complicated by meningoencephalitis or myocarditis. Rarely a chronic form of Q fever develops months after the acute illness, most commonly in the form of endocarditis [1] . Children develop clinical disease less frequently [7, 8] . Because of its non-specific presentation Q fever can only be suspected on clinical grounds and requires serologic confirmation. While the indirect immunofluorescence assay (IFA) is considered to be the reference method, complement fixation (CF), ELISA and microagglutination (MA) can also be used [9] . Acute infections are diagnosed by elevated IgG and/or IgM anti-phase II antibodies, while raised anti-phase I IgG antibodies are characteristic for chronic infections [1] .
In Germany, acute Q fever is a notifiable disease. Between 1991 and 2000 the annual number of cases varied from 46 to 273 cases per year [10] . In 2001 and 2002, 293 and 191 cases were notified, respectively [11, 12] .
On May 26, 2003 the health department of Soest was informed by a local hospital of an unusually large number of patients with atypical pneumonia. Some patients reported having visited a farmers' market that took place on May 3 and 4, 2003 in a spa town near Soest. Since the etiology was unclear, pathogens such as SARS coronavirus were considered and strict infection control measures implemented until the diagnosis of Q fever was confirmed.
An outbreak investigation team was formed and included public health professionals from the local health department, the local veterinary health department, the state health department, the National Consulting Laboratory (NCL) for Coxiellae and the Robert Koch-Institute (RKI), the federal public health institute. Because of the size and point source appearance of the outbreak the objective of the investigation was to identify etiologic factors relevant to the prevention and control of Q fever as well as to assess epidemiological parameters that can be rarely studied otherwise.
On May 26 and 27, 2003 we conducted exploratory interviews with patients in Soest hospitalized due to atypical pneumonia.
Attending physicians were requested to test serum of patients with atypical pneumonia for Mycoplasma pneumoniae, Chlamydia pneumoniae, Legionella pneumophila, Coxiella burnetii, Influenza A and B, Parainfluenza 1-3, Adenovirus and Enterovirus. Throat swabs were tested for Influenza virus, Adenovirus and SARS-Coronavirus. Laboratory confirmation of an acute Q fever infection was defined as the presence of IgM antibodies against phase II C. burnetii antigens (ELISA or IFA), a 4-fold increase in anti-phase II IgG antibody titer (ELISA or IFA) or in anti phase II antibody titer by CF between acute and convalescent sera. A chronic infection was confirmed when both anti-phase I IgG and anti-phase II IgG antibody titers were raised.
Because patients with valvular heart defects and pregnant women are at high risk of developing chronic infection [13, 14] we alerted internists and gynaecologists through the journal of the German Medical Association and asked them to send serum samples to the NCL if they identified patients from these risk groups who had been at the farmers' market during the outbreak.
The objective of the first case control study was to establish whether there was a link between the farmers' market and the outbreak and to identify other potential risk factors. We conducted telephone interviews using a standardised questionnaire that asked about attendance at the farmers' market, having been within 1 km distance of one of 6 sheep flocks in the area, tick bites and consumption of unpasteurized milk, sheep or goat cheese. For the purpose of CCS1 we defined a case (CCS1 case) as an adult resident of the town of Soest notified to the statutory sur-veillance system with Q fever, having symptom onset between May 4 and June 3, 2003. Exclusion criterion was a negative IgM-titer against phase II antigens. Two controls per case were recruited from Soest inhabitants by random digit dialing.
We calculated the attributable fraction of cases exposed to the farmers' market on May 4 (AFE) as (OR-1)/OR and the attributable fraction for all cases due to this exposure as:
The farmers' market was held in a spa town near Soest with many visitors from other areas of the state and even the entire country. To determine the outbreak size we therefore asked local public health departments in Germany to ascertain a possible link to the farmers' market in Soest for all patients notified with Q-fever. A case in this context ("notified case") was defined as any person with a clinical diagnosis compatible with Q fever with or without laboratory confirmation and history of exposure to the farmers' market.
Local health departments also reported whether a notified case was hospitalized. To obtain an independent, second estimate of the proportion of hospitalizations among symptomatic patients beyond that reported through the statutory surveillance system we calculated the proportion of hospitalized patients among those persons fulfilling the clinical case definition (as used in the vendors' study (s.b.)) identified through random sampling of the Soest population (within CCS2 (s.b.)) as well as in two cohorts (vendors' study and the 9 sailor friends (see below)).
The objective of CCS2 was to identify risk factors associated with attendance of the farmers' market on the second day. We used the same case definition as in CCS1, but included only persons that had visited the farmers' market on May 4, the second day of the market. We selected controls again randomly from the telephone registry of Soest and included only those persons who had visited the farmers' market on May 4 and had not been ill with fever afterwards. Potential controls who became ill were excluded for analysis in CCS2, but were still fully interviewed. This permitted calculation of the attack rate among visitors to the market (see below "Estimation of the overall attack rate") and gave an estimate of the proportion of clinically ill cases that were hospitalized (s.a.).
In the vendors' study we investigated whether the distance of the vendor stands from the sheep pen or dispersion of C. burnetii by wind were relevant risk factors for acquiring Q fever. We obtained a list of all vendors including the approximate location of the stands from the organizer. In addition we asked the local weather station for the predominant wind direction on May 4, 2003. Telephone interviews were performed using standardized questionnaires. A case was defined as a person with onset of fever between May 4 and June 3, 2003 and at least three of the following symptoms: headache, cough, dyspnea, joint pain, muscle pain, weight loss of more than 2 kg, fatigue, nausea or vomiting.
The relative distance of the stands to the sheep pen was estimated by counting the stands between the sheep pen and the stand in question. Each stand was considered to be one stand unit (approximately 3 meters). Larger stands were counted as 2 units. The direction of the wind in relation to the sheep pen was defined by dividing the wind rose (360°) in 4 equal parts of 90°. The predominant wind direction during the market was south-south-east ( Figure 1 ). For the purpose of the analysis we divided the market area into 4 sections with the sheep pen at its center. In section 1 the wind was blowing towards the sheep pen (plus minus 45°). Section 4 was on the opposite side, i.e. where the wind blew from the sheep pen towards the stands, and sections 2 and 3 were east and west with respect to the wind direction, respectively. Location of the stands in reference to the sheep pen was thus defined in two ways: as the absolute distance to the sheep pen (in stand units or meters) and in reference to the wind direction.
We identified a small cohort of 9 sailor friends who visited the farmers' market on May 4, 2003. All of these were serologically tested independently of symptoms. We could therefore calculate the proportion of laboratory confirmed persons who met the clinical case definition (as defined in the cohort study on vendors).
The overall attack rate among adults was estimated based on the following sources:
(1) Interviews undertaken for recruitment of controls for CCS2 allowed the proportion of adults that acquired symptomatic Q fever among those who visited the farmers' market on the second day;
Attributable fraction AFE Number of cases exposed All cases = *
(2) Interviews of cases and controls in CCS2 yielded information about accompanying adults and how many of these became later "ill with fever";
(3) Results of the small cohort of 9 sailor friends (s.a.);
(4) Results from the cohort study on vendors.
Local health departments that identified outbreak cases of Q fever (s.a. "determination of outbreak size and descriptive epidemiology") interviewed patients about the number of persons that had accompanied them to the farmers' market and whether any of these had become ill with fever afterwards. However, as there was no differentiation between adults and children, calculations to estimate the attack rate among adults were performed both with and without this source.
To count cases in (1), (3) and (4) we used the clinical case definition as defined in the cohort study on vendors.
For the calculation of the attack rate among children elicited in CCS2 was the same for all visitors. The number of children that visited the market could then be estimated from the total number of visitors as estimated by the organizers. We then estimated the number of symptomatic children (numerator). For this we assumed that the proportion of children with Q fever that were seen by physicians and were consequently notified was the same as that of adults. It was calculated as:
Thus the true number of children with Q fever was estimated by the number of reported children divided by the estimated proportion reported. Then the attack rate among children could be estimated as follows:
Because this calculation was based on several assumptions (number of visitors, proportion of adult visitors and clinical attack rate among adults) we performed a sensitivity analysis where the values of these variables varied.
Serum was collected from all sheep and cows displayed in the farmers' market as well as from all sheep of the respective home flocks (70 animals). Samples of 25 sheep from five other flocks in the Soest area were also tested for C. burnetii. Tests were performed by ELISA with a phase I and phase II antigen mixture.
We conducted statistical analysis with Epi Info, version 6.04 (CDC, Atlanta, USA). Dichotomous variables in the case control and cohort studies were compared using the Chi-Square test and numerical variables using the Kruskal-Wallis test. P-values smaller than 0.05 were considered statistically significant.
The outbreak investigation was conducted within the framework of the Communicable Diseases Law Reform Act of Germany. Mandatory regulations were observed.
Patients at the local hospital in Soest reported that a farmers' market had taken place on May 3 and 4, 2003 in a spa town close to the town of Soest. It was located in a park along the main promenade, spanning a distance of approximately 500 meters. The market attracted mainly three groups of people: locals, inhabitants of the greater Soest region, patients from the spa sanatoria and their visiting family or friends. Initial interviewees mentioned also that they had spent time at the sheep pen watching new-born lambs that had been born in the early morning hours of May 4, 2003 . The ewe had eaten the placenta but the parturient fluid on the ground had merely been covered with fresh straw.
Overall 171 (65%) of 263 serum samples submitted to the NCL were positive for IgM anti-phase II antibodies by ELISA. Results of throat swabs and serum were negative for other infectious agents. (Figure 2 ). If we assume that symptom onset in cases was normally distributed with a mean of 21 days, 95% of cases (mean +/-2 standard deviations) had their onset between day 10 and 31. The two notified cases with early onset on May 6 and 8, respectively, were laboratory confirmed and additional interviews did not reveal any additional risk factors. Of the 298 cases with known gender, 158 (53%) were male and 140 (47%) were female. Of the notified cases, 189 (63%) were from the county of Soest, 104 (35%) were Porportion reported number of notified adults number of vis = i iting adults attack rate among adults * Attack rate among children estimated true number of childr = e en with Q fever estimated number of children at the market from other counties in the same federal state (Northrhine Westphalia) and 6 (2%) were from five other federal states in Germany (Figure 3 ). Only eight (3%) cases were less than 18 years of age, the mean and median age was 54 and 56 years, respectively ( Figure 4 ). 75 (25%) of 297 notified cases were hospitalized, none died. Calculation of the proportion of cases hospitalized through other information sources revealed that 4 of 19 (21%; 95% CI = 6-46%;
(1/5 (CCS2), 2/11 (vendors study) and 1/3 (sailor friends)) clinically ill cases were hospitalized.
Laboratory confirmation was reported in 167 (56%) outbreak cases; 66 (22%) were confirmed by an increase in anti-phase II antibody titer (CF), 89 (30%) had IgM antibodies against phase II antigens, 11 (4%) were positive in both tests and one was confirmed by culture. No information was available as to whether the 132 (44%) cases without laboratory confirmation were laboratory tested.
18 patients with valvular heart defects and eleven pregnant women were examined. None of them had clinical signs of Q fever. Two (11%) of 18 cardiological patients and four (36%) of 11 pregnant women had an acute Q fever infection. During childbirth strict hygienic measures were implemented. Lochia and colostrum of all infected women were tested by polymerase chain reaction and were positive in only one woman (case 3; Table 1 ). Serological follow-up of the mothers detected chronic infection in the same woman (case 3) 12 weeks after delivery. One year follow-up of two newborn children (of cases 1 and 3) identified neither acute nor chronic Q fever infections.
We recruited 20 cases and 36 controls who visited the farmers' market on May 4 for the second case control study. They did not differ significantly in age and gender (OR for male sex = 1.7; 95%CI = 0.5-5.3; p = 0.26; p-value for age = 0.23). Seventeen (85%) of 20 cases indicated that they had seen the cow (that also was on display at the market next to the sheep) compared to 7 (32%) of Geographical location of Q fever outbreak cases notified to the statutory surveillance system Figure 3 Geographical location of Q fever outbreak cases notified to the statutory surveillance system. or directly at the gate of the sheep pen compared to 8 (32%) of 25 controls (OR = 5.0; 95%CI = 1.2-22.3; p = 0.03). Touching the sheep was also significantly more common among cases (5/20 (25%) CCS2 cases vs. 0/22 (0%) controls; OR undefined; lower 95% CI = 1.1; p = 0.02). 17 (85%) of 20 CCS2 cases, but only 6 (25%) of 24 controls stopped for at least a few seconds at or in the sheep pen, the reference for this variable was "having passed by the pen without stopping" (OR = 17.0; 95%CI = 3.0-112.5; p < 0.01). Among CCS2 cases, self-reported proximity to or time spent with/close to the sheep was not associated with a shorter incubation period.
We were able to contact and interview 75 (86%) of 87 vendors, and received second hand information about 7 more (overall response rate: 94%). Fourty-five (56%) were male and 35 (44%) were female. 13 (16%) met the clinical case definition. Of the 11 vendors who worked within two stand units of the sheep pen, 6 (55%) became cases compared to only 7 (10%) of 70 persons who worked in a stand at a greater distance (relative risk (RR) = 5.5 (95%CI = 2.3-13.2; p = 0.002); Figure 1 ). Of these 7 vendors, 4 had spent time within 5 meters of the pen on May 4, one had been near the pen, but at a distance of more than 5 meters, and no information on this variable was available for the remaining 2. In the section of the market facing the wind coming from the pen (section 4, Figure 1 ), 4 (9%) of 44 vendors became cases, compared to 2 (13%) of 15 persons who worked in section 1 (p = 0.6). Among 22 persons who worked in stands that were perpendicular to the wind direction, 7 (32%) became cases. (Table 3 ). In all scenarios the AR among adults was significantly higher than that among children ( Figure 5 ).
In total, 5 lambs and 5 ewes were displayed on the market, one of them was pregnant and gave birth to twin lambs at 6:30 a.m. on May 4, 2003 . Of these, 3 ewes including the one that had lambed tested positive for C. burnetii. The animals came from a flock of 67 ewes, of which 66 had given birth between February and June. The majority of the births (57 (86%)) had occurred in February and March, usually inside a stable or on a meadow located away from the town. Six ewes aborted, had stillbirths or abnormally weak lambs. Among all ewes, 17/67 (25%) tested positive for C. burnetii.
The percentage of sheep that tested positive in the other 5 sheep flocks in the region ranged from 8% to 24% (8%; 12%; 12%; 16%; 24%).
We have described one of the largest Q fever outbreaks in Germany which, due to its point-source nature, provided the opportunity to assess many epidemiological features of the disease that can be rarely studied otherwise.
In 1954, more than 500 cases of Q fever were, similar to this outbreak, linked to the abortion of an infected cow at a farmers' market [15] . More recently a large outbreak occurred in Jena (Thuringia) in 2005 with 322 reported cases [16] associated with exposure to a herd of sheep kept on a meadow close to the housing area in which the cases occurred.
The first case control study served to confirm the hypothesis of an association between the outbreak and the farmers' market. The fact that only attendance on the second, but not the first day was strongly associated with illness pointed towards the role of the ewe that had given birth Persons accompanying notified cases (source 5) were a mixture of adults and children and are therefore listed separately.
in the early morning hours of May 4, 2005 . This strong association and the very high attributable fraction among all cases suggested a point source and justified defining cases notified through the reporting system as outbreak cases if they were clinically compatible with Q fever and gave a history of having visited the farmers' market. The point-source nature of the outbreak permitted calculation of the incubation period of cases which averaged 21 days and ranged from 2 to 48 days with an interquartile range of 16 to 24 days. This is compatible with the literature [1] . An additional interview with the two cases with early onset (2 and 4 days after attending the market on May 4, Attack rates among adults and children in a most likely scenario and 8 other scenarios Figure 5 Attack rates among adults and children in a most likely scenario and 8 other scenarios. Most likely scenario: 3000 visitors, 83% adult visitors and 20% clinical attack rate among adults. Scenarios 1-8 varied in the assumptions made for "number of visitors", "proportion of adult visitors" and "attack rate among adults" (see Table 3 ). Displayed are attack rates and 95% confidence intervals. respectively) could not identify any other source of infection. A short incubation period was recently observed in another Q fever outbreak in which the infectious dose was likely very high [17] .
The second case control study among persons who visited the market on May 4 demonstrated that both close proximity to the ewe and duration of exposure were important risk factors. This finding was confirmed by the cohort study on vendors which showed that those who worked in a stand close to (within 6 meters) the sheep pen were at significantly higher risk of acquiring Q fever. The study failed to show a significant role of the location of the stand in reference to the wind direction, although we must take into account that the wind was likely not always and exactly as reported by the weather station. However, if the wind had been important at all more cases might have been expected to have occurred among vendors situated at a greater distance to the sheep.
According to statutory surveillance system data, the proportion of clinical cases hospitalized was 25%, similar to the proportion of 21% found in persons pooled from the other studies conducted. Several publications report lower proportions than that found in this investigation: 4% (8/ 191) [7] , 5% [1] and 10% (4/39) [5] ), and there was at least one study with a much higher proportion (63% (10/ 16)) [18] . It is unlikely that hospitals reported cases with Q fever more frequently than private physicians because the proportion hospitalized among Q fever patients identified through random telephone calls in the Soest population or those in the two cohorts was similar to that of notified cases. Thus reporting bias is an unlikely explanation for the relatively high proportion of cases hospitalized. Alternative explanations include overly cautious referral practices on the part of attending physicians or the presumably high infectious dose of the organism in this outbreak, e.g. in those cases that spent time in the sheep pen.
The estimated attack rate among adults in the four studies varied between 16% and 33%. The estimate of 23% based on the random sample of persons visiting the market on the second day would seem most immune to recall bias, even if this cannot be entirely ruled out. The estimation based on information about persons accompanying the cases may be subject to an overestimation because these individuals presumably had a higher probability of being close to the sheep pen, similar to the cases. On the other hand the estimate from the cohort study on vendors might be an underestimate, since the vendors obviously had a different purpose for being at the market and may have been less interested in having a look at the sheep. Nevertheless, all estimates were independent from each other and considering the various possible biases, they were remarkably similar. In comparison, in a different outbreak in Germany, in which inhabitants of a village were exposed to a large herd of sheep (n = 1000-2000) [5, 7] the attack rate was estimated as 16%. In a similar outbreak in Switzerland several villages were exposed to approximately 900 sheep [19] . In the most severely affected village, the clinical attack rate was 16% (estimated from the data provided) [19] . It is remarkable that in the outbreak described here, the infectious potential of one pregnant ewe -upon lambing -was comparable to that of entire herds, albeit in different settings.
Our estimate of the proportion of serologically confirmed cases that became symptomatic (50% (3/6)) is based on a very small sample, but consistent with the international literature. In the above mentioned Swiss outbreak, 46% of serologically positive patients developed clinical disease [7] .
Only approximately half of all symptomatic cases were reported to the statutory surveillance system. Patients who did not seek health care due to mild disease as well as underdiagnosis or underreporting may have contributed to the missing other half. Our estimated 3% attack rate among children is based on a number of successive assumptions and must therefore be interpreted with caution. Nevertheless, sensitivity analysis confirmed that adults had a significantly elevated attack rate compared to children. While it has been suggested that children are at lower risk than adults for developing symptomatic illness [7, 8] few data have been published regarding attack rates of children in comparison to adults.
The estimated C. burnetii seroprevalence in the sheep flocks in the area varied from 8% to 24%. The 25% seroprevalence in the flock of the exhibited animals together with a positive polymerase chain reaction in an afterbirth in June 2003 suggested a recent infection of the flock [20] . Seroprevalence among sheep flocks related to human outbreaks tend to be substantially higher than those in flocks not related to human outbreaks. The median seroprevalence in a number of relevant studies performed in the context of human outbreaks [7, 20, 21] , was 40% compared to 1% in sheep flocks not linked to human outbreaks [20] .
This outbreak shows the dramatic consequences of putting a large number of susceptible individuals in close contact to a single infected ewe that (in such a setting) can turn into a super-spreader upon lambing. There is always a cultural component in the interaction between people and animals, and these may contribute to outbreaks or changing patterns of incidence. During the past decades urbanization of rural areas and changes in animal husbandry have occurred [20] , with more recent attempts to put a "deprived" urban population "in touch" with farm animals. Petting zoos, family farm vacations or the display of (farm) animals at a market such as this may lead to new avenues for the transmission of zoonotic infectious agents [20, [22] [23] [24] . While not all eventualities can be foreseen, it is important to raise awareness in pet and livestock owners as well as to strengthen recommendations where necessary. This outbreak led to the amendment and extension of existing recommendations [25] which now forbid the display of sheep in the latter third of their pregnancy and require regular testing of animals for C. burnetii in petting zoos, where there is close contact between humans and animals.
Due to the size and point source nature this outbreak permitted reassessment of fundamental, but seldom studied epidemiological parameters of Q fever. It also served to revise public health recommendations to account for the changing type and frequency of contact of susceptible humans with potentially infectious animals.
Abbreviations AFE = attributable fraction of cases exposed
The author(s) declare that they have no competing interests.
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What is the primary reservoir for Coxiella burnetii?
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sheep, goat and cattle
| 2,634 |
1,583 |
A super-spreading ewe infects hundreds with Q fever at a farmers' market in Germany
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1618839/
SHA: ee1b5a9618dcc4080ed100486cedd0969e80fa4d
Authors: Porten, Klaudia; Rissland, Jürgen; Tigges, Almira; Broll, Susanne; Hopp, Wilfried; Lunemann, Mechthild; van Treeck, Ulrich; Kimmig, Peter; Brockmann, Stefan O; Wagner-Wiening, Christiane; Hellenbrand, Wiebke; Buchholz, Udo
Date: 2006-10-06
DOI: 10.1186/1471-2334-6-147
License: cc-by
Abstract: BACKGROUND: In May 2003 the Soest County Health Department was informed of an unusually large number of patients hospitalized with atypical pneumonia. METHODS: In exploratory interviews patients mentioned having visited a farmers' market where a sheep had lambed. Serologic testing confirmed the diagnosis of Q fever. We asked local health departments in Germany to identiy notified Q fever patients who had visited the farmers market. To investigate risk factors for infection we conducted a case control study (cases were Q fever patients, controls were randomly selected Soest citizens) and a cohort study among vendors at the market. The sheep exhibited at the market, the herd from which it originated as well as sheep from herds held in the vicinity of Soest were tested for Coxiella burnetii (C. burnetii). RESULTS: A total of 299 reported Q fever cases was linked to this outbreak. The mean incubation period was 21 days, with an interquartile range of 16–24 days. The case control study identified close proximity to and stopping for at least a few seconds at the sheep's pen as significant risk factors. Vendors within approximately 6 meters of the sheep's pen were at increased risk for disease compared to those located farther away. Wind played no significant role. The clinical attack rate of adults and children was estimated as 20% and 3%, respectively, 25% of cases were hospitalized. The ewe that had lambed as well as 25% of its herd tested positive for C. burnetii antibodies. CONCLUSION: Due to its size and point source nature this outbreak permitted assessment of fundamental, but seldom studied epidemiological parameters. As a consequence of this outbreak, it was recommended that pregnant sheep not be displayed in public during the 3(rd )trimester and to test animals in petting zoos regularly for C. burnetii.
Text: Q fever is a worldwide zoonosis caused by Coxiella burnetii (C. burnetii), a small, gram-negative obligate intracellular bacterium. C. burnetii displays antigenic variation with an infectious phase I and less infectious phase II. The primary reservoir from which human infection occurs consists of sheep, goat and cattle. Although C. burnetii infections in animals are usually asymptomatic, they may cause abortions in sheep and goats [1] . High concentrations of C. burnetii can be found in birth products of infected mammals [2] . Humans frequently acquire infection through inhalation of contaminated aerosols from parturient fluids, placenta or wool [1] . Because the infectious dose is very low [3] and C. burnetii is able to survive in a spore-like state for months to years, outbreaks among humans have also occurred through contaminated dust carried by wind over large distances [4] [5] [6] .
C. burnetii infection in humans is asymptomatic in approximately 50% of cases. Approximately 5% of cases are hospitalized, and fatal cases are rare [1] . The clinical presentation of acute Q fever is variable and can resemble many other infectious diseases [2] . However, the most frequent clinical manifestation of acute Q fever is a self-limited febrile illness associated with severe headache. Atypical pneumonia and hepatitis are the major clinical manifestations of more severe disease. Acute Q fever may be complicated by meningoencephalitis or myocarditis. Rarely a chronic form of Q fever develops months after the acute illness, most commonly in the form of endocarditis [1] . Children develop clinical disease less frequently [7, 8] . Because of its non-specific presentation Q fever can only be suspected on clinical grounds and requires serologic confirmation. While the indirect immunofluorescence assay (IFA) is considered to be the reference method, complement fixation (CF), ELISA and microagglutination (MA) can also be used [9] . Acute infections are diagnosed by elevated IgG and/or IgM anti-phase II antibodies, while raised anti-phase I IgG antibodies are characteristic for chronic infections [1] .
In Germany, acute Q fever is a notifiable disease. Between 1991 and 2000 the annual number of cases varied from 46 to 273 cases per year [10] . In 2001 and 2002, 293 and 191 cases were notified, respectively [11, 12] .
On May 26, 2003 the health department of Soest was informed by a local hospital of an unusually large number of patients with atypical pneumonia. Some patients reported having visited a farmers' market that took place on May 3 and 4, 2003 in a spa town near Soest. Since the etiology was unclear, pathogens such as SARS coronavirus were considered and strict infection control measures implemented until the diagnosis of Q fever was confirmed.
An outbreak investigation team was formed and included public health professionals from the local health department, the local veterinary health department, the state health department, the National Consulting Laboratory (NCL) for Coxiellae and the Robert Koch-Institute (RKI), the federal public health institute. Because of the size and point source appearance of the outbreak the objective of the investigation was to identify etiologic factors relevant to the prevention and control of Q fever as well as to assess epidemiological parameters that can be rarely studied otherwise.
On May 26 and 27, 2003 we conducted exploratory interviews with patients in Soest hospitalized due to atypical pneumonia.
Attending physicians were requested to test serum of patients with atypical pneumonia for Mycoplasma pneumoniae, Chlamydia pneumoniae, Legionella pneumophila, Coxiella burnetii, Influenza A and B, Parainfluenza 1-3, Adenovirus and Enterovirus. Throat swabs were tested for Influenza virus, Adenovirus and SARS-Coronavirus. Laboratory confirmation of an acute Q fever infection was defined as the presence of IgM antibodies against phase II C. burnetii antigens (ELISA or IFA), a 4-fold increase in anti-phase II IgG antibody titer (ELISA or IFA) or in anti phase II antibody titer by CF between acute and convalescent sera. A chronic infection was confirmed when both anti-phase I IgG and anti-phase II IgG antibody titers were raised.
Because patients with valvular heart defects and pregnant women are at high risk of developing chronic infection [13, 14] we alerted internists and gynaecologists through the journal of the German Medical Association and asked them to send serum samples to the NCL if they identified patients from these risk groups who had been at the farmers' market during the outbreak.
The objective of the first case control study was to establish whether there was a link between the farmers' market and the outbreak and to identify other potential risk factors. We conducted telephone interviews using a standardised questionnaire that asked about attendance at the farmers' market, having been within 1 km distance of one of 6 sheep flocks in the area, tick bites and consumption of unpasteurized milk, sheep or goat cheese. For the purpose of CCS1 we defined a case (CCS1 case) as an adult resident of the town of Soest notified to the statutory sur-veillance system with Q fever, having symptom onset between May 4 and June 3, 2003. Exclusion criterion was a negative IgM-titer against phase II antigens. Two controls per case were recruited from Soest inhabitants by random digit dialing.
We calculated the attributable fraction of cases exposed to the farmers' market on May 4 (AFE) as (OR-1)/OR and the attributable fraction for all cases due to this exposure as:
The farmers' market was held in a spa town near Soest with many visitors from other areas of the state and even the entire country. To determine the outbreak size we therefore asked local public health departments in Germany to ascertain a possible link to the farmers' market in Soest for all patients notified with Q-fever. A case in this context ("notified case") was defined as any person with a clinical diagnosis compatible with Q fever with or without laboratory confirmation and history of exposure to the farmers' market.
Local health departments also reported whether a notified case was hospitalized. To obtain an independent, second estimate of the proportion of hospitalizations among symptomatic patients beyond that reported through the statutory surveillance system we calculated the proportion of hospitalized patients among those persons fulfilling the clinical case definition (as used in the vendors' study (s.b.)) identified through random sampling of the Soest population (within CCS2 (s.b.)) as well as in two cohorts (vendors' study and the 9 sailor friends (see below)).
The objective of CCS2 was to identify risk factors associated with attendance of the farmers' market on the second day. We used the same case definition as in CCS1, but included only persons that had visited the farmers' market on May 4, the second day of the market. We selected controls again randomly from the telephone registry of Soest and included only those persons who had visited the farmers' market on May 4 and had not been ill with fever afterwards. Potential controls who became ill were excluded for analysis in CCS2, but were still fully interviewed. This permitted calculation of the attack rate among visitors to the market (see below "Estimation of the overall attack rate") and gave an estimate of the proportion of clinically ill cases that were hospitalized (s.a.).
In the vendors' study we investigated whether the distance of the vendor stands from the sheep pen or dispersion of C. burnetii by wind were relevant risk factors for acquiring Q fever. We obtained a list of all vendors including the approximate location of the stands from the organizer. In addition we asked the local weather station for the predominant wind direction on May 4, 2003. Telephone interviews were performed using standardized questionnaires. A case was defined as a person with onset of fever between May 4 and June 3, 2003 and at least three of the following symptoms: headache, cough, dyspnea, joint pain, muscle pain, weight loss of more than 2 kg, fatigue, nausea or vomiting.
The relative distance of the stands to the sheep pen was estimated by counting the stands between the sheep pen and the stand in question. Each stand was considered to be one stand unit (approximately 3 meters). Larger stands were counted as 2 units. The direction of the wind in relation to the sheep pen was defined by dividing the wind rose (360°) in 4 equal parts of 90°. The predominant wind direction during the market was south-south-east ( Figure 1 ). For the purpose of the analysis we divided the market area into 4 sections with the sheep pen at its center. In section 1 the wind was blowing towards the sheep pen (plus minus 45°). Section 4 was on the opposite side, i.e. where the wind blew from the sheep pen towards the stands, and sections 2 and 3 were east and west with respect to the wind direction, respectively. Location of the stands in reference to the sheep pen was thus defined in two ways: as the absolute distance to the sheep pen (in stand units or meters) and in reference to the wind direction.
We identified a small cohort of 9 sailor friends who visited the farmers' market on May 4, 2003. All of these were serologically tested independently of symptoms. We could therefore calculate the proportion of laboratory confirmed persons who met the clinical case definition (as defined in the cohort study on vendors).
The overall attack rate among adults was estimated based on the following sources:
(1) Interviews undertaken for recruitment of controls for CCS2 allowed the proportion of adults that acquired symptomatic Q fever among those who visited the farmers' market on the second day;
Attributable fraction AFE Number of cases exposed All cases = *
(2) Interviews of cases and controls in CCS2 yielded information about accompanying adults and how many of these became later "ill with fever";
(3) Results of the small cohort of 9 sailor friends (s.a.);
(4) Results from the cohort study on vendors.
Local health departments that identified outbreak cases of Q fever (s.a. "determination of outbreak size and descriptive epidemiology") interviewed patients about the number of persons that had accompanied them to the farmers' market and whether any of these had become ill with fever afterwards. However, as there was no differentiation between adults and children, calculations to estimate the attack rate among adults were performed both with and without this source.
To count cases in (1), (3) and (4) we used the clinical case definition as defined in the cohort study on vendors.
For the calculation of the attack rate among children elicited in CCS2 was the same for all visitors. The number of children that visited the market could then be estimated from the total number of visitors as estimated by the organizers. We then estimated the number of symptomatic children (numerator). For this we assumed that the proportion of children with Q fever that were seen by physicians and were consequently notified was the same as that of adults. It was calculated as:
Thus the true number of children with Q fever was estimated by the number of reported children divided by the estimated proportion reported. Then the attack rate among children could be estimated as follows:
Because this calculation was based on several assumptions (number of visitors, proportion of adult visitors and clinical attack rate among adults) we performed a sensitivity analysis where the values of these variables varied.
Serum was collected from all sheep and cows displayed in the farmers' market as well as from all sheep of the respective home flocks (70 animals). Samples of 25 sheep from five other flocks in the Soest area were also tested for C. burnetii. Tests were performed by ELISA with a phase I and phase II antigen mixture.
We conducted statistical analysis with Epi Info, version 6.04 (CDC, Atlanta, USA). Dichotomous variables in the case control and cohort studies were compared using the Chi-Square test and numerical variables using the Kruskal-Wallis test. P-values smaller than 0.05 were considered statistically significant.
The outbreak investigation was conducted within the framework of the Communicable Diseases Law Reform Act of Germany. Mandatory regulations were observed.
Patients at the local hospital in Soest reported that a farmers' market had taken place on May 3 and 4, 2003 in a spa town close to the town of Soest. It was located in a park along the main promenade, spanning a distance of approximately 500 meters. The market attracted mainly three groups of people: locals, inhabitants of the greater Soest region, patients from the spa sanatoria and their visiting family or friends. Initial interviewees mentioned also that they had spent time at the sheep pen watching new-born lambs that had been born in the early morning hours of May 4, 2003 . The ewe had eaten the placenta but the parturient fluid on the ground had merely been covered with fresh straw.
Overall 171 (65%) of 263 serum samples submitted to the NCL were positive for IgM anti-phase II antibodies by ELISA. Results of throat swabs and serum were negative for other infectious agents. (Figure 2 ). If we assume that symptom onset in cases was normally distributed with a mean of 21 days, 95% of cases (mean +/-2 standard deviations) had their onset between day 10 and 31. The two notified cases with early onset on May 6 and 8, respectively, were laboratory confirmed and additional interviews did not reveal any additional risk factors. Of the 298 cases with known gender, 158 (53%) were male and 140 (47%) were female. Of the notified cases, 189 (63%) were from the county of Soest, 104 (35%) were Porportion reported number of notified adults number of vis = i iting adults attack rate among adults * Attack rate among children estimated true number of childr = e en with Q fever estimated number of children at the market from other counties in the same federal state (Northrhine Westphalia) and 6 (2%) were from five other federal states in Germany (Figure 3 ). Only eight (3%) cases were less than 18 years of age, the mean and median age was 54 and 56 years, respectively ( Figure 4 ). 75 (25%) of 297 notified cases were hospitalized, none died. Calculation of the proportion of cases hospitalized through other information sources revealed that 4 of 19 (21%; 95% CI = 6-46%;
(1/5 (CCS2), 2/11 (vendors study) and 1/3 (sailor friends)) clinically ill cases were hospitalized.
Laboratory confirmation was reported in 167 (56%) outbreak cases; 66 (22%) were confirmed by an increase in anti-phase II antibody titer (CF), 89 (30%) had IgM antibodies against phase II antigens, 11 (4%) were positive in both tests and one was confirmed by culture. No information was available as to whether the 132 (44%) cases without laboratory confirmation were laboratory tested.
18 patients with valvular heart defects and eleven pregnant women were examined. None of them had clinical signs of Q fever. Two (11%) of 18 cardiological patients and four (36%) of 11 pregnant women had an acute Q fever infection. During childbirth strict hygienic measures were implemented. Lochia and colostrum of all infected women were tested by polymerase chain reaction and were positive in only one woman (case 3; Table 1 ). Serological follow-up of the mothers detected chronic infection in the same woman (case 3) 12 weeks after delivery. One year follow-up of two newborn children (of cases 1 and 3) identified neither acute nor chronic Q fever infections.
We recruited 20 cases and 36 controls who visited the farmers' market on May 4 for the second case control study. They did not differ significantly in age and gender (OR for male sex = 1.7; 95%CI = 0.5-5.3; p = 0.26; p-value for age = 0.23). Seventeen (85%) of 20 cases indicated that they had seen the cow (that also was on display at the market next to the sheep) compared to 7 (32%) of Geographical location of Q fever outbreak cases notified to the statutory surveillance system Figure 3 Geographical location of Q fever outbreak cases notified to the statutory surveillance system. or directly at the gate of the sheep pen compared to 8 (32%) of 25 controls (OR = 5.0; 95%CI = 1.2-22.3; p = 0.03). Touching the sheep was also significantly more common among cases (5/20 (25%) CCS2 cases vs. 0/22 (0%) controls; OR undefined; lower 95% CI = 1.1; p = 0.02). 17 (85%) of 20 CCS2 cases, but only 6 (25%) of 24 controls stopped for at least a few seconds at or in the sheep pen, the reference for this variable was "having passed by the pen without stopping" (OR = 17.0; 95%CI = 3.0-112.5; p < 0.01). Among CCS2 cases, self-reported proximity to or time spent with/close to the sheep was not associated with a shorter incubation period.
We were able to contact and interview 75 (86%) of 87 vendors, and received second hand information about 7 more (overall response rate: 94%). Fourty-five (56%) were male and 35 (44%) were female. 13 (16%) met the clinical case definition. Of the 11 vendors who worked within two stand units of the sheep pen, 6 (55%) became cases compared to only 7 (10%) of 70 persons who worked in a stand at a greater distance (relative risk (RR) = 5.5 (95%CI = 2.3-13.2; p = 0.002); Figure 1 ). Of these 7 vendors, 4 had spent time within 5 meters of the pen on May 4, one had been near the pen, but at a distance of more than 5 meters, and no information on this variable was available for the remaining 2. In the section of the market facing the wind coming from the pen (section 4, Figure 1 ), 4 (9%) of 44 vendors became cases, compared to 2 (13%) of 15 persons who worked in section 1 (p = 0.6). Among 22 persons who worked in stands that were perpendicular to the wind direction, 7 (32%) became cases. (Table 3 ). In all scenarios the AR among adults was significantly higher than that among children ( Figure 5 ).
In total, 5 lambs and 5 ewes were displayed on the market, one of them was pregnant and gave birth to twin lambs at 6:30 a.m. on May 4, 2003 . Of these, 3 ewes including the one that had lambed tested positive for C. burnetii. The animals came from a flock of 67 ewes, of which 66 had given birth between February and June. The majority of the births (57 (86%)) had occurred in February and March, usually inside a stable or on a meadow located away from the town. Six ewes aborted, had stillbirths or abnormally weak lambs. Among all ewes, 17/67 (25%) tested positive for C. burnetii.
The percentage of sheep that tested positive in the other 5 sheep flocks in the region ranged from 8% to 24% (8%; 12%; 12%; 16%; 24%).
We have described one of the largest Q fever outbreaks in Germany which, due to its point-source nature, provided the opportunity to assess many epidemiological features of the disease that can be rarely studied otherwise.
In 1954, more than 500 cases of Q fever were, similar to this outbreak, linked to the abortion of an infected cow at a farmers' market [15] . More recently a large outbreak occurred in Jena (Thuringia) in 2005 with 322 reported cases [16] associated with exposure to a herd of sheep kept on a meadow close to the housing area in which the cases occurred.
The first case control study served to confirm the hypothesis of an association between the outbreak and the farmers' market. The fact that only attendance on the second, but not the first day was strongly associated with illness pointed towards the role of the ewe that had given birth Persons accompanying notified cases (source 5) were a mixture of adults and children and are therefore listed separately.
in the early morning hours of May 4, 2005 . This strong association and the very high attributable fraction among all cases suggested a point source and justified defining cases notified through the reporting system as outbreak cases if they were clinically compatible with Q fever and gave a history of having visited the farmers' market. The point-source nature of the outbreak permitted calculation of the incubation period of cases which averaged 21 days and ranged from 2 to 48 days with an interquartile range of 16 to 24 days. This is compatible with the literature [1] . An additional interview with the two cases with early onset (2 and 4 days after attending the market on May 4, Attack rates among adults and children in a most likely scenario and 8 other scenarios Figure 5 Attack rates among adults and children in a most likely scenario and 8 other scenarios. Most likely scenario: 3000 visitors, 83% adult visitors and 20% clinical attack rate among adults. Scenarios 1-8 varied in the assumptions made for "number of visitors", "proportion of adult visitors" and "attack rate among adults" (see Table 3 ). Displayed are attack rates and 95% confidence intervals. respectively) could not identify any other source of infection. A short incubation period was recently observed in another Q fever outbreak in which the infectious dose was likely very high [17] .
The second case control study among persons who visited the market on May 4 demonstrated that both close proximity to the ewe and duration of exposure were important risk factors. This finding was confirmed by the cohort study on vendors which showed that those who worked in a stand close to (within 6 meters) the sheep pen were at significantly higher risk of acquiring Q fever. The study failed to show a significant role of the location of the stand in reference to the wind direction, although we must take into account that the wind was likely not always and exactly as reported by the weather station. However, if the wind had been important at all more cases might have been expected to have occurred among vendors situated at a greater distance to the sheep.
According to statutory surveillance system data, the proportion of clinical cases hospitalized was 25%, similar to the proportion of 21% found in persons pooled from the other studies conducted. Several publications report lower proportions than that found in this investigation: 4% (8/ 191) [7] , 5% [1] and 10% (4/39) [5] ), and there was at least one study with a much higher proportion (63% (10/ 16)) [18] . It is unlikely that hospitals reported cases with Q fever more frequently than private physicians because the proportion hospitalized among Q fever patients identified through random telephone calls in the Soest population or those in the two cohorts was similar to that of notified cases. Thus reporting bias is an unlikely explanation for the relatively high proportion of cases hospitalized. Alternative explanations include overly cautious referral practices on the part of attending physicians or the presumably high infectious dose of the organism in this outbreak, e.g. in those cases that spent time in the sheep pen.
The estimated attack rate among adults in the four studies varied between 16% and 33%. The estimate of 23% based on the random sample of persons visiting the market on the second day would seem most immune to recall bias, even if this cannot be entirely ruled out. The estimation based on information about persons accompanying the cases may be subject to an overestimation because these individuals presumably had a higher probability of being close to the sheep pen, similar to the cases. On the other hand the estimate from the cohort study on vendors might be an underestimate, since the vendors obviously had a different purpose for being at the market and may have been less interested in having a look at the sheep. Nevertheless, all estimates were independent from each other and considering the various possible biases, they were remarkably similar. In comparison, in a different outbreak in Germany, in which inhabitants of a village were exposed to a large herd of sheep (n = 1000-2000) [5, 7] the attack rate was estimated as 16%. In a similar outbreak in Switzerland several villages were exposed to approximately 900 sheep [19] . In the most severely affected village, the clinical attack rate was 16% (estimated from the data provided) [19] . It is remarkable that in the outbreak described here, the infectious potential of one pregnant ewe -upon lambing -was comparable to that of entire herds, albeit in different settings.
Our estimate of the proportion of serologically confirmed cases that became symptomatic (50% (3/6)) is based on a very small sample, but consistent with the international literature. In the above mentioned Swiss outbreak, 46% of serologically positive patients developed clinical disease [7] .
Only approximately half of all symptomatic cases were reported to the statutory surveillance system. Patients who did not seek health care due to mild disease as well as underdiagnosis or underreporting may have contributed to the missing other half. Our estimated 3% attack rate among children is based on a number of successive assumptions and must therefore be interpreted with caution. Nevertheless, sensitivity analysis confirmed that adults had a significantly elevated attack rate compared to children. While it has been suggested that children are at lower risk than adults for developing symptomatic illness [7, 8] few data have been published regarding attack rates of children in comparison to adults.
The estimated C. burnetii seroprevalence in the sheep flocks in the area varied from 8% to 24%. The 25% seroprevalence in the flock of the exhibited animals together with a positive polymerase chain reaction in an afterbirth in June 2003 suggested a recent infection of the flock [20] . Seroprevalence among sheep flocks related to human outbreaks tend to be substantially higher than those in flocks not related to human outbreaks. The median seroprevalence in a number of relevant studies performed in the context of human outbreaks [7, 20, 21] , was 40% compared to 1% in sheep flocks not linked to human outbreaks [20] .
This outbreak shows the dramatic consequences of putting a large number of susceptible individuals in close contact to a single infected ewe that (in such a setting) can turn into a super-spreader upon lambing. There is always a cultural component in the interaction between people and animals, and these may contribute to outbreaks or changing patterns of incidence. During the past decades urbanization of rural areas and changes in animal husbandry have occurred [20] , with more recent attempts to put a "deprived" urban population "in touch" with farm animals. Petting zoos, family farm vacations or the display of (farm) animals at a market such as this may lead to new avenues for the transmission of zoonotic infectious agents [20, [22] [23] [24] . While not all eventualities can be foreseen, it is important to raise awareness in pet and livestock owners as well as to strengthen recommendations where necessary. This outbreak led to the amendment and extension of existing recommendations [25] which now forbid the display of sheep in the latter third of their pregnancy and require regular testing of animals for C. burnetii in petting zoos, where there is close contact between humans and animals.
Due to the size and point source nature this outbreak permitted reassessment of fundamental, but seldom studied epidemiological parameters of Q fever. It also served to revise public health recommendations to account for the changing type and frequency of contact of susceptible humans with potentially infectious animals.
Abbreviations AFE = attributable fraction of cases exposed
The author(s) declare that they have no competing interests.
|
How are humans typically infected with Coxiella burnetii?
| 5,210 |
inhalation of contaminated aerosols from parturient fluids, placenta or wool
| 2,913 |
1,588 |
Prediction of lncRNA-protein interactions using HeteSim scores based on heterogeneous networks
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5473862/
SHA: f4f9ea9e0aeb74d3601ee316b84292638c59cc53
Authors: Xiao, Yun; Zhang, Jingpu; Deng, Lei
Date: 2017-06-16
DOI: 10.1038/s41598-017-03986-1
License: cc-by
Abstract: Massive studies have indicated that long non-coding RNAs (lncRNAs) are critical for the regulation of cellular biological processes by binding with RNA-related proteins. However, only a few experimentally supported lncRNA-protein associations have been reported. Existing network-based methods are typically focused on intrinsic features of lncRNA and protein but ignore the information implicit in the topologies of biological networks associated with lncRNAs. Considering the limitations in previous methods, we propose PLPIHS, an effective computational method for Predicting lncRNA-Protein Interactions using HeteSim Scores. PLPIHS uses the HeteSim measure to calculate the relatedness score for each lncRNA-protein pair in the heterogeneous network, which consists of lncRNA-lncRNA similarity network, lncRNA-protein association network and protein-protein interaction network. An SVM classifier to predict lncRNA-protein interactions is built with the HeteSim scores. The results show that PLPIHS performs significantly better than the existing state-of-the-art approaches and achieves an AUC score of 0.97 in the leave-one-out validation test. We also compare the performances of networks with different connectivity density and find that PLPIHS performs well across all the networks. Furthermore, we use the proposed method to identify the related proteins for lncRNA MALAT1. Highly-ranked proteins are verified by the biological studies and demonstrate the effectiveness of our method.
Text: most commonly used approach is guilt-by-association (GBA) 19 , which provides the central top-down principle for analyzing gene networks in functional terms or assessing their quality in encoding functional information. New emerged methods, including the Katz method 20 , Combining dATa Across species using Positive-Unlabeled Learning Techniques(CATAPULT) 19 , Random Walk with Restart (RWR) 21 , and LncRNA-protein Interaction prediction based on Heterogeneous Network model (LPIHN) 22 , have extended the association from just direct protein interactions to more distant connections in various ways. The KATZ measure 20 is a weighted sum of the number of paths in the network that measures the similarity of two nodes. CATAPULT 19 is a supervised machine learning method that uses a biased support vector machine where the features are derived from walks in a heterogeneous gene-trait network. RWR 21 is a method for prioritization of candidate genes by use of a global network distance measure, random walk analysis, for definition of similarities in protein-protein interaction networks and it add weight to the assumption that phenotypically similar diseases are associated with disturbances of subnetworks within the larger protein interactome that extend beyond the disease proteins themselves. LPIHN 22 is a network-based method by implement a random walk on a heterogeneous network. PRINCE is a global method based on formulating constraints on the prioritization function that relate to its smoothness over the network and usage of prior information. Compared with LPIHN and RWR, PRINCE propagates information in a smaller network but contains more connotative meaning when build the initial probability values and has made great performance in gene prioritization 23 and disease identification 24 .
However, many existing network-based methods simply view objects in heterogeneous networks as the same type and do not consider the subtle semantic meanings of different paths. In this paper, we adopt a method named HeteSim, which is a path-based measure to calculate the relevance between objects in heterogeneous network 25 . The basic idea is that similar objects are more likely to be related to some other objects. Considering the relatedness of heterogeneous objects is path-constrained, HeteSim gives a uniform and symmetric measure for arbitrary paths to evaluate the relatedness of heterogeneous object pair (same or different types) with one single score. Due to the relevance path not only captures the semantics information but also constrains the walk path, the score is also a path-based similarity measure.
An example of HeteSim score is illustrated in (Fig. 1 ). The number of paths from A to C and B to C is 3 and 2, respectively. The walk count between A and C is larger than B and C, which might indicate that A is more closer to C than B. But the connectivity between B and C is more intense than A and C in the sight of HeteSim score, since most edges starting from B are connected with C, when A only has a small part of edges connected with C.
Here, we propose a method named PLPIHS (Fig. 2) to predict lncRNA-Protein interactions using HeteSim scores. We first construct a heterogeneous network consisting of a lncRNA-lncRNA similarity network, a lncRNA-protein association network and a protein-protein interaction network. Then, we use the HeteSim measure to calculate the score for each lncRNA-protein pair in the network. A SVM classifier is built based on the scores of different paths. We compare our PLPIHS with PRINCE, RWR and LPIHN and find that PLPIHS outperforms the other methods in many performance measures.
Validation measures. LOOCV(Leave-One-Out Cross Validation) 26 is implemented on the verified lncR-NA-protein associations to evaluate the performance of LPIHN 22 . We leave a known lncRNA-protein pair in turn as the test sample and all the other known lncRNA-protein pairs are regarded as training samples. In order to improve the accuracy of PLPIHS, we remove all connected lncRNAs and proteins while in each validation round. Receiver Operating Characteristic(ROC) curve 27 is used to evaluate the prediction performance, which plots true-positive rate (TPR, sensitivity or recall) versus false-positive rate (FPR, 1-specificity) at different rank cutoffs. When varying the rank cutoffs of successful prediction, we can obtain the corresponding TPR and FPR. In this way, ROC curve is drawn and the area under the curve(AUC) is calculated as well. For a rank threshold, sensitivity(SEN) 28 and specificity(SPE) 29 These measurements are also used to assess the capability of PLPIHS during the preprocessing procedure.
Affection of network preprocessing characteristics. In this paper, we only have two kinds of objects, lncRNA and protein. Thus, the paths from a lncRNA to a protein in our heterogeneous network with length less than six is listed in Table 1 . In order to pick out the most efficient paths, we compared the performances of these 14 paths under different combinations (Fig. 3) . We can see that all paths achieve a favorable status except path 1′~2′. Path 1′~14′ obtains the best performance across all measures, which means that the path with length greater than three contains more significant meanings. The constant factor β is used to control the influence of longer paths. The longer the path length is, the smaller the inhibiting factor is. Path length equals 3 matches with constant β, path length equals 4 matches with constant β*β and path length equals 5 matches with constant β*β*β. Table 2 shows that β has tiny impact on the final results and β = 0.2, 0.4 and 0.7 achieved the best AUC score and the others are not far behind yet.
To further verify the dependability of our method, we compare the three networks of different connectivity density under different cutoff value 0.3, 0.5 and 0.9 (see lncRNA-Protein associations). The results are shown in Fig. 4 . There are tiny performance differences between different sparse networks. The AUC score of the 0.5 network is higher than that of others while the 0.9 network outperforms others in ACC, SEN, MCC and F1-Score. This suggests that PLPIHS performs well across networks with different densities. Table 1 . The paths from a lncRNA to a protein in our heterogeneous network with length less than six.
the RWR method, there is only one restart probability r and it's effects is very slight, which is proved by experiments. The parameter r is set as 0.5 in this comparison. In order to calculate the performance of the different methods, we use a leave-one-out cross validation procedure. We extract 2000 lncRNA-protein associations from the 0.9 network as positive samples, the same number of negative samples are chosen randomly from the 0.3 network as well, avoiding the error caused by imbalance dataset. The gold set which containing 185 lncRNA-protein interactions downloaded from NPinter database has been included in positive pairs as well. In the lncRNA protein prioritization, each lncRNA-protein interaction is utilized as the test set in turn and the remaining associations are used as training data. The whole experiment will be repeated 4000 times to testing each lncRNA-protein pairs in the dataset. ROC curve is drawn based on true positive rate (TPR) and false positive rate (FPR) at different thresholds. The AUC score is utilized to measure the performance. AUC = 1 demonstrates the perfect performance and AUC = 0.5 demonstrates the random performance.The ROC curve of PLPIHS, LPIHS, PRINCE and RWR are plotted in Fig. 5 . The results show that the AUC score of PLPIHS in 0.3 network is 96.8%, which is higher than that of PRINCE, LPIHN and RWR, achieving an AUC value of 81.3%, 88.4% and 79.2%, respectively. Similarly, PLPIHS outperforms other methods in 0.5 network and 0.9 network as well.
Performance evaluation by independent test. For further validation, we also randomly selected 2000 lncRNA-protein associations from the rest of positive samples in 0.9 network and the same number of negative interactions are picked out from the remaining negative samples of 0.3 network to generate the independent test data set. Since the existing network based methods is not suitable for independent test, we only evaluate the performance for the proposed PLPIHS. The independent test results are shown in Fig. 6 , an AUC score of 0.879 is achieved by PLPIHS, illustrating the effectiveness and advantage of the proposed approach. Case Studies. By applying the proposed PLPIHS method, novel candidate lncRNA-related proteins are predicted using LOOCV. We applied PLPIHS onto the 2000 known lncRNA-protein associations, which includes 1511 lncRNAs and 344 proteins to infer novel lncRNA-protein interactions. As a result, an area under the ROC curve of 0.9669, 0.9705 and 0.9703 (Fig. 5) is achieved using the three networks of different connectivity density, which demonstrate that our proposed method is effective in recovering known lncRNA-related proteins.
To further illustrate the application of our approach, a case study of lncRNA MALAT1(ensemble ID: ENSG00000251562) is examined. MALAT1 is a long non-coding RNA which is over-expressed in many human oncogenic tissues and regulates cell cycle and survival 31 . MALAT1 have been identified in multiple types of physiological processes, such as alternative splicing, nuclear organization, epigenetic modulating of gene expression. A large amount of evidence indicates that MALAT1 also closely relates to various pathological processes, including diabetes complications, cancers and so on 32, 33 .
MALAT1 is associated with 68 proteins in NPInter 3.0 34 . We construct the interaction networks of lncRNA MALAT1 by using the prediction results of these four methods (Fig. 7) . Among the 68 known lncRNA-protein interactions, PLPIHS wrongly predicts 6 interactions, while 13 associations are predicted mistakenly by PRINCE and RWR method and 15 lncRNA-protein pairs are falsely predicted by the LPIHN method.
We manually check the top 10 proteins in the ranked list under 0.5 network ( Table 3) .Three of the top 10 predicted proteins have interactions with MALAT1, and most of them had high ranks in the predicted protein lists. For example, In the investigation of colorectal cancer (CRC), MALAT1 could bind to SFPQ, thus releasing PTBP2 from the SFPQ/PTBP2 complex and the interaction between MALAT1 and SFPQ could be a novel therapeutic target for CRC 35 . MALAT1 interacts with SR proteins (SRSF1, SRSF2, SRSF3 and SRSF5) and regulates cellular levels of phosphorylated forms of SR proteins 36 . And it is also as target of TARDBP to play the biological performance and found that TDP-43 bound to long ncRNAs in highly sequence-specific manner in tissue from subjects with or without FTLD-TDP, the MALAT1 ncRNA recruits splicing factors to nuclear speckles and affects phosphorylation of serine/arginine-rich splicing factor proteins 37, 38 . All these results indicate that our proposed method is effective and reliable in identifying novel lncRNA-related proteins.
LncRNAs are involved in a wide range of biological functions through diverse molecular mechanisms often including the interaction with one or more protein partners 12, 13 . Only a small number of lncRNA-protein interactions have been well-characterized. Computational methods can be helpful in suggesting potential interactions for possible experimentation 25 . In this study, we use HeteSim measure to calculate the relevance between lncRNA and protein in a heterogeneous network. The importance of inferring novel lncRNA-protein interactions by considering the subtle semantic meanings of different paths in the heterogeneous network have been verified 39 . We first construct a heterogeneous network consisting of a lncRNA-lncRNA similarity network, a lncRNA-protein association network and a protein-protein interaction network. Then, we use the HeteSim measure to calculate a score for each lncRNA-protein pairs in each path. Finally, a SVM classifier is used to combine the scores of different paths and making predictions. We compare the proposed PLPIHS with PRINCE, RWR and LPIHN and find that PLPIHS obtain an AUC score of 0.9679 in 0.3 network, which is significantly higher than PRINCE, RWR and LPIHN (0.813, 0.884 and 0.7918, respectively). We also compare the performance of these four methods in networks of different connectivity density. As a result, PLPIHS outperforms the other method across all the networks. Moreover, when analysing the predicted proteins interacted with lncRNA MALAT1, PLPIHS successfully predicts 63 out of 68 associations, while PRINCE, RWR and LPIHN retrieve much lower interactions of 57, 57 and 53, respectively. And the top-ranked lncRNA-protein interactions predicted by our method are supported by existing literatures. The results highlight the advantages of our proposed method in predicting possible lncRNA-protein interactions.
Methods lncRNA-Protein associations. All human lncRNA genes and protein-coding genes are downloaded from the GENCODE Release 24 9 . A total of 15941 lncRNA genes and 20284 protein-coding genes are extracted. To obtain genome-wide lncRNA and protein-coding gene associations, we combine three sources of data:
• Co-expression data from COXPRESdb 40 . Three preprocessed co-expression datasets (Hsa.c4-1, Hsa2.c2-0 and Hsa3.c1-0) including pre-calculated pairwise Pearson's correlation coefficients for human were collected from COXPRESdb. The correlations are calculated as follows:
where C(l, p) is the overall correlation between gene l (lncRNA) and protein-coding gene p, C d (l, p) is the correlation score between l and p in dataset d, D is the number of gene pairs (l and p) with positive correlation scores. Gene pairs with negative correlation scores are removed.
• Co-expression data from ArrayExpress 41 and GEO 42 . We obtained the co-expresionn data from the work of Jiang et al. 43 . RNA-Seq raw data of 19 human normal tissues are obtained from ArrayExpress (E-MTAB-513) and GEO (GSE30554). TopHat and Cufflinks with the default parameters are used to calculate the expression values. Pearson's correlation coefficients are used to evaluate the co-expression of lncRNA-protein pairs. • lncRNA-protein interaction data. We download known lncRNA-protein interaction dataset from Protein-protein interactions. We obtain the protein-protein interaction (PPI) data from STRING database V10.0 45 , which contains weighted protein interactions derived from computational prediction methods, high-throughput experiments, and text mining. The confidence scores are computed by combining the probabilities from the different evidence channels, correcting for the probability of randomly observing an interaction.
The HeteSim measure. The HeteSim measure is a uniform and symmetric relevance measure. It can be used to calculate the relatedness of objects with the same or different types in a uniform framework, and it is also a path-constrained measure to estimate the relatedness of object pairs based on the search path that connects two objects through a sequence of node types 39 . Further, the HeteSim score has some good properties (i.e., selfmaximum and symmetric), which have achieved positive performance in many studies 25 . In this study, we use HeteSim scores to measure the similarities between lncRNAs and proteins.
Definition 1 Transition probability matrix 39 L and P are two kinds of object in the heterogeneous network, (I LP ) n*m is an adjacent matrix between L and P, then the normalized matrix of I LP along the row vector is defined as
LP LP k m LP 1 Definition 2 Reachable probability matrix 39 In a heterogeneous network, the reachable probability matrix R for path = + PP P ( ) n 1 2 1 of length n, where P i belongs to any objects in the heterogeneous network, can be expressed as
P P P P P P n n 1 2 2 3 1 Based on the definitions above, the steps of calculating HeteSim scores between two kinds of objects (lncRNA and protein) can be presented as follows:
• Split the path into two parts. When the length n of path is even, we can split it into = P P ( )
Otherwise, if n is odd, the path cannot be divided into two equallength paths. In order to deal with such problem, we need to split the path twice by setting , respectively. Then, we can obtain a HeteSim score for each mid value, the final score will be the average of the two scores.
• Achieve the transition probability matrix and reachable probability matrix under the path L and R . • Calculate the HeteSim score: where − R 1 is the reverse path of R . An example of calculating HeteSim score is indicated in Fig. 8 . We can see that there are three kinds of objects L, T and P in the network. The simplified steps of computing HeteSim score between l3 and p2 under the path = (LTP) is as follows:
• Split the path into two components = LT ( )
• Given the adjacent matrix I LT and I TP below, which means the interactions between lncRNAs and proteins, we can obtain the transition probability matrix T LT and T TP by normalizing the two matrix along the row vector. The PLPIHS method. Among a heterogeneous network, different paths can express different semantic meanings. For instance, a lncRNA and a protein is connected via 'lncRNA-lncRNA-protein' path or 'lncRNA-protein-protein' path representing totally different meanings. The former means that if a lncRNA is associated with a protein, then another lncRNA similar to the lncRNA will be potential associated with the protein. The latter shows that if a protein associated with a lncRNA, then another protein interacted with the protein will be likely associated with the lncRNA. Therefore, the potential information hidden in each path is extraordinary essential to be taken into account during prediction.
The PLPIHS framework is illustrated in Fig. 2 . Firstly, we construct a heterogeneous network consisting of a lncRNA-lncRNA similarity network, a lncRNA-protein association network and a protein-protein interaction network. Three kinds of sparse networks are obtained from the heterogeneous network under different cutoff value 0.3, 0.5 and 0.9 (see lncRNA-Protein associations). The larger cutoff is, the network is more sparse. A total of 15941 lncRNAs genes and 20284 protein-coding genes are extracted as presented in Section 2.3. We randomly take out 1511 lncRNAs and 344 proteins to construct a smaller network for the following experiments in consideration of computing costs. The construction of the smaller heterogeneous networks under different cutoff values are shown in Table 4 , where 'lnc2lnc' denotes the lncRNA-lncRNA network, 'lnc2code' denotes the lncRNA-protein network and 'code2code' denotes the protein-lncRNA network. Table 1 . We use id to indicate the path combination, i.e., 1′~2′ represents path 'LLP' and path 'LPP' . Next, we calculate the heteSim score for each lncRNA-protein pair under each path. The results of different paths are used as different features. And we combine a constant factor β to inhibit the influence of longer paths.The longer the path length is, the smaller the inhibiting factor is. Finally, a SVM classifier is built with these scores to predict potential lncRNA-protein associations. On the account of the HeteSim measure is based on the path-based relevance framework 39 , it can effectively dig out the subtle semantics of each paths.
|
What are critical to regulate cellular biological processes?
| 3,687 |
long non-coding RNAs
| 354 |
1,588 |
Prediction of lncRNA-protein interactions using HeteSim scores based on heterogeneous networks
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5473862/
SHA: f4f9ea9e0aeb74d3601ee316b84292638c59cc53
Authors: Xiao, Yun; Zhang, Jingpu; Deng, Lei
Date: 2017-06-16
DOI: 10.1038/s41598-017-03986-1
License: cc-by
Abstract: Massive studies have indicated that long non-coding RNAs (lncRNAs) are critical for the regulation of cellular biological processes by binding with RNA-related proteins. However, only a few experimentally supported lncRNA-protein associations have been reported. Existing network-based methods are typically focused on intrinsic features of lncRNA and protein but ignore the information implicit in the topologies of biological networks associated with lncRNAs. Considering the limitations in previous methods, we propose PLPIHS, an effective computational method for Predicting lncRNA-Protein Interactions using HeteSim Scores. PLPIHS uses the HeteSim measure to calculate the relatedness score for each lncRNA-protein pair in the heterogeneous network, which consists of lncRNA-lncRNA similarity network, lncRNA-protein association network and protein-protein interaction network. An SVM classifier to predict lncRNA-protein interactions is built with the HeteSim scores. The results show that PLPIHS performs significantly better than the existing state-of-the-art approaches and achieves an AUC score of 0.97 in the leave-one-out validation test. We also compare the performances of networks with different connectivity density and find that PLPIHS performs well across all the networks. Furthermore, we use the proposed method to identify the related proteins for lncRNA MALAT1. Highly-ranked proteins are verified by the biological studies and demonstrate the effectiveness of our method.
Text: most commonly used approach is guilt-by-association (GBA) 19 , which provides the central top-down principle for analyzing gene networks in functional terms or assessing their quality in encoding functional information. New emerged methods, including the Katz method 20 , Combining dATa Across species using Positive-Unlabeled Learning Techniques(CATAPULT) 19 , Random Walk with Restart (RWR) 21 , and LncRNA-protein Interaction prediction based on Heterogeneous Network model (LPIHN) 22 , have extended the association from just direct protein interactions to more distant connections in various ways. The KATZ measure 20 is a weighted sum of the number of paths in the network that measures the similarity of two nodes. CATAPULT 19 is a supervised machine learning method that uses a biased support vector machine where the features are derived from walks in a heterogeneous gene-trait network. RWR 21 is a method for prioritization of candidate genes by use of a global network distance measure, random walk analysis, for definition of similarities in protein-protein interaction networks and it add weight to the assumption that phenotypically similar diseases are associated with disturbances of subnetworks within the larger protein interactome that extend beyond the disease proteins themselves. LPIHN 22 is a network-based method by implement a random walk on a heterogeneous network. PRINCE is a global method based on formulating constraints on the prioritization function that relate to its smoothness over the network and usage of prior information. Compared with LPIHN and RWR, PRINCE propagates information in a smaller network but contains more connotative meaning when build the initial probability values and has made great performance in gene prioritization 23 and disease identification 24 .
However, many existing network-based methods simply view objects in heterogeneous networks as the same type and do not consider the subtle semantic meanings of different paths. In this paper, we adopt a method named HeteSim, which is a path-based measure to calculate the relevance between objects in heterogeneous network 25 . The basic idea is that similar objects are more likely to be related to some other objects. Considering the relatedness of heterogeneous objects is path-constrained, HeteSim gives a uniform and symmetric measure for arbitrary paths to evaluate the relatedness of heterogeneous object pair (same or different types) with one single score. Due to the relevance path not only captures the semantics information but also constrains the walk path, the score is also a path-based similarity measure.
An example of HeteSim score is illustrated in (Fig. 1 ). The number of paths from A to C and B to C is 3 and 2, respectively. The walk count between A and C is larger than B and C, which might indicate that A is more closer to C than B. But the connectivity between B and C is more intense than A and C in the sight of HeteSim score, since most edges starting from B are connected with C, when A only has a small part of edges connected with C.
Here, we propose a method named PLPIHS (Fig. 2) to predict lncRNA-Protein interactions using HeteSim scores. We first construct a heterogeneous network consisting of a lncRNA-lncRNA similarity network, a lncRNA-protein association network and a protein-protein interaction network. Then, we use the HeteSim measure to calculate the score for each lncRNA-protein pair in the network. A SVM classifier is built based on the scores of different paths. We compare our PLPIHS with PRINCE, RWR and LPIHN and find that PLPIHS outperforms the other methods in many performance measures.
Validation measures. LOOCV(Leave-One-Out Cross Validation) 26 is implemented on the verified lncR-NA-protein associations to evaluate the performance of LPIHN 22 . We leave a known lncRNA-protein pair in turn as the test sample and all the other known lncRNA-protein pairs are regarded as training samples. In order to improve the accuracy of PLPIHS, we remove all connected lncRNAs and proteins while in each validation round. Receiver Operating Characteristic(ROC) curve 27 is used to evaluate the prediction performance, which plots true-positive rate (TPR, sensitivity or recall) versus false-positive rate (FPR, 1-specificity) at different rank cutoffs. When varying the rank cutoffs of successful prediction, we can obtain the corresponding TPR and FPR. In this way, ROC curve is drawn and the area under the curve(AUC) is calculated as well. For a rank threshold, sensitivity(SEN) 28 and specificity(SPE) 29 These measurements are also used to assess the capability of PLPIHS during the preprocessing procedure.
Affection of network preprocessing characteristics. In this paper, we only have two kinds of objects, lncRNA and protein. Thus, the paths from a lncRNA to a protein in our heterogeneous network with length less than six is listed in Table 1 . In order to pick out the most efficient paths, we compared the performances of these 14 paths under different combinations (Fig. 3) . We can see that all paths achieve a favorable status except path 1′~2′. Path 1′~14′ obtains the best performance across all measures, which means that the path with length greater than three contains more significant meanings. The constant factor β is used to control the influence of longer paths. The longer the path length is, the smaller the inhibiting factor is. Path length equals 3 matches with constant β, path length equals 4 matches with constant β*β and path length equals 5 matches with constant β*β*β. Table 2 shows that β has tiny impact on the final results and β = 0.2, 0.4 and 0.7 achieved the best AUC score and the others are not far behind yet.
To further verify the dependability of our method, we compare the three networks of different connectivity density under different cutoff value 0.3, 0.5 and 0.9 (see lncRNA-Protein associations). The results are shown in Fig. 4 . There are tiny performance differences between different sparse networks. The AUC score of the 0.5 network is higher than that of others while the 0.9 network outperforms others in ACC, SEN, MCC and F1-Score. This suggests that PLPIHS performs well across networks with different densities. Table 1 . The paths from a lncRNA to a protein in our heterogeneous network with length less than six.
the RWR method, there is only one restart probability r and it's effects is very slight, which is proved by experiments. The parameter r is set as 0.5 in this comparison. In order to calculate the performance of the different methods, we use a leave-one-out cross validation procedure. We extract 2000 lncRNA-protein associations from the 0.9 network as positive samples, the same number of negative samples are chosen randomly from the 0.3 network as well, avoiding the error caused by imbalance dataset. The gold set which containing 185 lncRNA-protein interactions downloaded from NPinter database has been included in positive pairs as well. In the lncRNA protein prioritization, each lncRNA-protein interaction is utilized as the test set in turn and the remaining associations are used as training data. The whole experiment will be repeated 4000 times to testing each lncRNA-protein pairs in the dataset. ROC curve is drawn based on true positive rate (TPR) and false positive rate (FPR) at different thresholds. The AUC score is utilized to measure the performance. AUC = 1 demonstrates the perfect performance and AUC = 0.5 demonstrates the random performance.The ROC curve of PLPIHS, LPIHS, PRINCE and RWR are plotted in Fig. 5 . The results show that the AUC score of PLPIHS in 0.3 network is 96.8%, which is higher than that of PRINCE, LPIHN and RWR, achieving an AUC value of 81.3%, 88.4% and 79.2%, respectively. Similarly, PLPIHS outperforms other methods in 0.5 network and 0.9 network as well.
Performance evaluation by independent test. For further validation, we also randomly selected 2000 lncRNA-protein associations from the rest of positive samples in 0.9 network and the same number of negative interactions are picked out from the remaining negative samples of 0.3 network to generate the independent test data set. Since the existing network based methods is not suitable for independent test, we only evaluate the performance for the proposed PLPIHS. The independent test results are shown in Fig. 6 , an AUC score of 0.879 is achieved by PLPIHS, illustrating the effectiveness and advantage of the proposed approach. Case Studies. By applying the proposed PLPIHS method, novel candidate lncRNA-related proteins are predicted using LOOCV. We applied PLPIHS onto the 2000 known lncRNA-protein associations, which includes 1511 lncRNAs and 344 proteins to infer novel lncRNA-protein interactions. As a result, an area under the ROC curve of 0.9669, 0.9705 and 0.9703 (Fig. 5) is achieved using the three networks of different connectivity density, which demonstrate that our proposed method is effective in recovering known lncRNA-related proteins.
To further illustrate the application of our approach, a case study of lncRNA MALAT1(ensemble ID: ENSG00000251562) is examined. MALAT1 is a long non-coding RNA which is over-expressed in many human oncogenic tissues and regulates cell cycle and survival 31 . MALAT1 have been identified in multiple types of physiological processes, such as alternative splicing, nuclear organization, epigenetic modulating of gene expression. A large amount of evidence indicates that MALAT1 also closely relates to various pathological processes, including diabetes complications, cancers and so on 32, 33 .
MALAT1 is associated with 68 proteins in NPInter 3.0 34 . We construct the interaction networks of lncRNA MALAT1 by using the prediction results of these four methods (Fig. 7) . Among the 68 known lncRNA-protein interactions, PLPIHS wrongly predicts 6 interactions, while 13 associations are predicted mistakenly by PRINCE and RWR method and 15 lncRNA-protein pairs are falsely predicted by the LPIHN method.
We manually check the top 10 proteins in the ranked list under 0.5 network ( Table 3) .Three of the top 10 predicted proteins have interactions with MALAT1, and most of them had high ranks in the predicted protein lists. For example, In the investigation of colorectal cancer (CRC), MALAT1 could bind to SFPQ, thus releasing PTBP2 from the SFPQ/PTBP2 complex and the interaction between MALAT1 and SFPQ could be a novel therapeutic target for CRC 35 . MALAT1 interacts with SR proteins (SRSF1, SRSF2, SRSF3 and SRSF5) and regulates cellular levels of phosphorylated forms of SR proteins 36 . And it is also as target of TARDBP to play the biological performance and found that TDP-43 bound to long ncRNAs in highly sequence-specific manner in tissue from subjects with or without FTLD-TDP, the MALAT1 ncRNA recruits splicing factors to nuclear speckles and affects phosphorylation of serine/arginine-rich splicing factor proteins 37, 38 . All these results indicate that our proposed method is effective and reliable in identifying novel lncRNA-related proteins.
LncRNAs are involved in a wide range of biological functions through diverse molecular mechanisms often including the interaction with one or more protein partners 12, 13 . Only a small number of lncRNA-protein interactions have been well-characterized. Computational methods can be helpful in suggesting potential interactions for possible experimentation 25 . In this study, we use HeteSim measure to calculate the relevance between lncRNA and protein in a heterogeneous network. The importance of inferring novel lncRNA-protein interactions by considering the subtle semantic meanings of different paths in the heterogeneous network have been verified 39 . We first construct a heterogeneous network consisting of a lncRNA-lncRNA similarity network, a lncRNA-protein association network and a protein-protein interaction network. Then, we use the HeteSim measure to calculate a score for each lncRNA-protein pairs in each path. Finally, a SVM classifier is used to combine the scores of different paths and making predictions. We compare the proposed PLPIHS with PRINCE, RWR and LPIHN and find that PLPIHS obtain an AUC score of 0.9679 in 0.3 network, which is significantly higher than PRINCE, RWR and LPIHN (0.813, 0.884 and 0.7918, respectively). We also compare the performance of these four methods in networks of different connectivity density. As a result, PLPIHS outperforms the other method across all the networks. Moreover, when analysing the predicted proteins interacted with lncRNA MALAT1, PLPIHS successfully predicts 63 out of 68 associations, while PRINCE, RWR and LPIHN retrieve much lower interactions of 57, 57 and 53, respectively. And the top-ranked lncRNA-protein interactions predicted by our method are supported by existing literatures. The results highlight the advantages of our proposed method in predicting possible lncRNA-protein interactions.
Methods lncRNA-Protein associations. All human lncRNA genes and protein-coding genes are downloaded from the GENCODE Release 24 9 . A total of 15941 lncRNA genes and 20284 protein-coding genes are extracted. To obtain genome-wide lncRNA and protein-coding gene associations, we combine three sources of data:
• Co-expression data from COXPRESdb 40 . Three preprocessed co-expression datasets (Hsa.c4-1, Hsa2.c2-0 and Hsa3.c1-0) including pre-calculated pairwise Pearson's correlation coefficients for human were collected from COXPRESdb. The correlations are calculated as follows:
where C(l, p) is the overall correlation between gene l (lncRNA) and protein-coding gene p, C d (l, p) is the correlation score between l and p in dataset d, D is the number of gene pairs (l and p) with positive correlation scores. Gene pairs with negative correlation scores are removed.
• Co-expression data from ArrayExpress 41 and GEO 42 . We obtained the co-expresionn data from the work of Jiang et al. 43 . RNA-Seq raw data of 19 human normal tissues are obtained from ArrayExpress (E-MTAB-513) and GEO (GSE30554). TopHat and Cufflinks with the default parameters are used to calculate the expression values. Pearson's correlation coefficients are used to evaluate the co-expression of lncRNA-protein pairs. • lncRNA-protein interaction data. We download known lncRNA-protein interaction dataset from Protein-protein interactions. We obtain the protein-protein interaction (PPI) data from STRING database V10.0 45 , which contains weighted protein interactions derived from computational prediction methods, high-throughput experiments, and text mining. The confidence scores are computed by combining the probabilities from the different evidence channels, correcting for the probability of randomly observing an interaction.
The HeteSim measure. The HeteSim measure is a uniform and symmetric relevance measure. It can be used to calculate the relatedness of objects with the same or different types in a uniform framework, and it is also a path-constrained measure to estimate the relatedness of object pairs based on the search path that connects two objects through a sequence of node types 39 . Further, the HeteSim score has some good properties (i.e., selfmaximum and symmetric), which have achieved positive performance in many studies 25 . In this study, we use HeteSim scores to measure the similarities between lncRNAs and proteins.
Definition 1 Transition probability matrix 39 L and P are two kinds of object in the heterogeneous network, (I LP ) n*m is an adjacent matrix between L and P, then the normalized matrix of I LP along the row vector is defined as
LP LP k m LP 1 Definition 2 Reachable probability matrix 39 In a heterogeneous network, the reachable probability matrix R for path = + PP P ( ) n 1 2 1 of length n, where P i belongs to any objects in the heterogeneous network, can be expressed as
P P P P P P n n 1 2 2 3 1 Based on the definitions above, the steps of calculating HeteSim scores between two kinds of objects (lncRNA and protein) can be presented as follows:
• Split the path into two parts. When the length n of path is even, we can split it into = P P ( )
Otherwise, if n is odd, the path cannot be divided into two equallength paths. In order to deal with such problem, we need to split the path twice by setting , respectively. Then, we can obtain a HeteSim score for each mid value, the final score will be the average of the two scores.
• Achieve the transition probability matrix and reachable probability matrix under the path L and R . • Calculate the HeteSim score: where − R 1 is the reverse path of R . An example of calculating HeteSim score is indicated in Fig. 8 . We can see that there are three kinds of objects L, T and P in the network. The simplified steps of computing HeteSim score between l3 and p2 under the path = (LTP) is as follows:
• Split the path into two components = LT ( )
• Given the adjacent matrix I LT and I TP below, which means the interactions between lncRNAs and proteins, we can obtain the transition probability matrix T LT and T TP by normalizing the two matrix along the row vector. The PLPIHS method. Among a heterogeneous network, different paths can express different semantic meanings. For instance, a lncRNA and a protein is connected via 'lncRNA-lncRNA-protein' path or 'lncRNA-protein-protein' path representing totally different meanings. The former means that if a lncRNA is associated with a protein, then another lncRNA similar to the lncRNA will be potential associated with the protein. The latter shows that if a protein associated with a lncRNA, then another protein interacted with the protein will be likely associated with the lncRNA. Therefore, the potential information hidden in each path is extraordinary essential to be taken into account during prediction.
The PLPIHS framework is illustrated in Fig. 2 . Firstly, we construct a heterogeneous network consisting of a lncRNA-lncRNA similarity network, a lncRNA-protein association network and a protein-protein interaction network. Three kinds of sparse networks are obtained from the heterogeneous network under different cutoff value 0.3, 0.5 and 0.9 (see lncRNA-Protein associations). The larger cutoff is, the network is more sparse. A total of 15941 lncRNAs genes and 20284 protein-coding genes are extracted as presented in Section 2.3. We randomly take out 1511 lncRNAs and 344 proteins to construct a smaller network for the following experiments in consideration of computing costs. The construction of the smaller heterogeneous networks under different cutoff values are shown in Table 4 , where 'lnc2lnc' denotes the lncRNA-lncRNA network, 'lnc2code' denotes the lncRNA-protein network and 'code2code' denotes the protein-lncRNA network. Table 1 . We use id to indicate the path combination, i.e., 1′~2′ represents path 'LLP' and path 'LPP' . Next, we calculate the heteSim score for each lncRNA-protein pair under each path. The results of different paths are used as different features. And we combine a constant factor β to inhibit the influence of longer paths.The longer the path length is, the smaller the inhibiting factor is. Finally, a SVM classifier is built with these scores to predict potential lncRNA-protein associations. On the account of the HeteSim measure is based on the path-based relevance framework 39 , it can effectively dig out the subtle semantics of each paths.
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How is the HeteSim measured used?
| 3,688 |
calculate the relatedness of objects with the same or different types
| 16,718 |
1,588 |
Prediction of lncRNA-protein interactions using HeteSim scores based on heterogeneous networks
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5473862/
SHA: f4f9ea9e0aeb74d3601ee316b84292638c59cc53
Authors: Xiao, Yun; Zhang, Jingpu; Deng, Lei
Date: 2017-06-16
DOI: 10.1038/s41598-017-03986-1
License: cc-by
Abstract: Massive studies have indicated that long non-coding RNAs (lncRNAs) are critical for the regulation of cellular biological processes by binding with RNA-related proteins. However, only a few experimentally supported lncRNA-protein associations have been reported. Existing network-based methods are typically focused on intrinsic features of lncRNA and protein but ignore the information implicit in the topologies of biological networks associated with lncRNAs. Considering the limitations in previous methods, we propose PLPIHS, an effective computational method for Predicting lncRNA-Protein Interactions using HeteSim Scores. PLPIHS uses the HeteSim measure to calculate the relatedness score for each lncRNA-protein pair in the heterogeneous network, which consists of lncRNA-lncRNA similarity network, lncRNA-protein association network and protein-protein interaction network. An SVM classifier to predict lncRNA-protein interactions is built with the HeteSim scores. The results show that PLPIHS performs significantly better than the existing state-of-the-art approaches and achieves an AUC score of 0.97 in the leave-one-out validation test. We also compare the performances of networks with different connectivity density and find that PLPIHS performs well across all the networks. Furthermore, we use the proposed method to identify the related proteins for lncRNA MALAT1. Highly-ranked proteins are verified by the biological studies and demonstrate the effectiveness of our method.
Text: most commonly used approach is guilt-by-association (GBA) 19 , which provides the central top-down principle for analyzing gene networks in functional terms or assessing their quality in encoding functional information. New emerged methods, including the Katz method 20 , Combining dATa Across species using Positive-Unlabeled Learning Techniques(CATAPULT) 19 , Random Walk with Restart (RWR) 21 , and LncRNA-protein Interaction prediction based on Heterogeneous Network model (LPIHN) 22 , have extended the association from just direct protein interactions to more distant connections in various ways. The KATZ measure 20 is a weighted sum of the number of paths in the network that measures the similarity of two nodes. CATAPULT 19 is a supervised machine learning method that uses a biased support vector machine where the features are derived from walks in a heterogeneous gene-trait network. RWR 21 is a method for prioritization of candidate genes by use of a global network distance measure, random walk analysis, for definition of similarities in protein-protein interaction networks and it add weight to the assumption that phenotypically similar diseases are associated with disturbances of subnetworks within the larger protein interactome that extend beyond the disease proteins themselves. LPIHN 22 is a network-based method by implement a random walk on a heterogeneous network. PRINCE is a global method based on formulating constraints on the prioritization function that relate to its smoothness over the network and usage of prior information. Compared with LPIHN and RWR, PRINCE propagates information in a smaller network but contains more connotative meaning when build the initial probability values and has made great performance in gene prioritization 23 and disease identification 24 .
However, many existing network-based methods simply view objects in heterogeneous networks as the same type and do not consider the subtle semantic meanings of different paths. In this paper, we adopt a method named HeteSim, which is a path-based measure to calculate the relevance between objects in heterogeneous network 25 . The basic idea is that similar objects are more likely to be related to some other objects. Considering the relatedness of heterogeneous objects is path-constrained, HeteSim gives a uniform and symmetric measure for arbitrary paths to evaluate the relatedness of heterogeneous object pair (same or different types) with one single score. Due to the relevance path not only captures the semantics information but also constrains the walk path, the score is also a path-based similarity measure.
An example of HeteSim score is illustrated in (Fig. 1 ). The number of paths from A to C and B to C is 3 and 2, respectively. The walk count between A and C is larger than B and C, which might indicate that A is more closer to C than B. But the connectivity between B and C is more intense than A and C in the sight of HeteSim score, since most edges starting from B are connected with C, when A only has a small part of edges connected with C.
Here, we propose a method named PLPIHS (Fig. 2) to predict lncRNA-Protein interactions using HeteSim scores. We first construct a heterogeneous network consisting of a lncRNA-lncRNA similarity network, a lncRNA-protein association network and a protein-protein interaction network. Then, we use the HeteSim measure to calculate the score for each lncRNA-protein pair in the network. A SVM classifier is built based on the scores of different paths. We compare our PLPIHS with PRINCE, RWR and LPIHN and find that PLPIHS outperforms the other methods in many performance measures.
Validation measures. LOOCV(Leave-One-Out Cross Validation) 26 is implemented on the verified lncR-NA-protein associations to evaluate the performance of LPIHN 22 . We leave a known lncRNA-protein pair in turn as the test sample and all the other known lncRNA-protein pairs are regarded as training samples. In order to improve the accuracy of PLPIHS, we remove all connected lncRNAs and proteins while in each validation round. Receiver Operating Characteristic(ROC) curve 27 is used to evaluate the prediction performance, which plots true-positive rate (TPR, sensitivity or recall) versus false-positive rate (FPR, 1-specificity) at different rank cutoffs. When varying the rank cutoffs of successful prediction, we can obtain the corresponding TPR and FPR. In this way, ROC curve is drawn and the area under the curve(AUC) is calculated as well. For a rank threshold, sensitivity(SEN) 28 and specificity(SPE) 29 These measurements are also used to assess the capability of PLPIHS during the preprocessing procedure.
Affection of network preprocessing characteristics. In this paper, we only have two kinds of objects, lncRNA and protein. Thus, the paths from a lncRNA to a protein in our heterogeneous network with length less than six is listed in Table 1 . In order to pick out the most efficient paths, we compared the performances of these 14 paths under different combinations (Fig. 3) . We can see that all paths achieve a favorable status except path 1′~2′. Path 1′~14′ obtains the best performance across all measures, which means that the path with length greater than three contains more significant meanings. The constant factor β is used to control the influence of longer paths. The longer the path length is, the smaller the inhibiting factor is. Path length equals 3 matches with constant β, path length equals 4 matches with constant β*β and path length equals 5 matches with constant β*β*β. Table 2 shows that β has tiny impact on the final results and β = 0.2, 0.4 and 0.7 achieved the best AUC score and the others are not far behind yet.
To further verify the dependability of our method, we compare the three networks of different connectivity density under different cutoff value 0.3, 0.5 and 0.9 (see lncRNA-Protein associations). The results are shown in Fig. 4 . There are tiny performance differences between different sparse networks. The AUC score of the 0.5 network is higher than that of others while the 0.9 network outperforms others in ACC, SEN, MCC and F1-Score. This suggests that PLPIHS performs well across networks with different densities. Table 1 . The paths from a lncRNA to a protein in our heterogeneous network with length less than six.
the RWR method, there is only one restart probability r and it's effects is very slight, which is proved by experiments. The parameter r is set as 0.5 in this comparison. In order to calculate the performance of the different methods, we use a leave-one-out cross validation procedure. We extract 2000 lncRNA-protein associations from the 0.9 network as positive samples, the same number of negative samples are chosen randomly from the 0.3 network as well, avoiding the error caused by imbalance dataset. The gold set which containing 185 lncRNA-protein interactions downloaded from NPinter database has been included in positive pairs as well. In the lncRNA protein prioritization, each lncRNA-protein interaction is utilized as the test set in turn and the remaining associations are used as training data. The whole experiment will be repeated 4000 times to testing each lncRNA-protein pairs in the dataset. ROC curve is drawn based on true positive rate (TPR) and false positive rate (FPR) at different thresholds. The AUC score is utilized to measure the performance. AUC = 1 demonstrates the perfect performance and AUC = 0.5 demonstrates the random performance.The ROC curve of PLPIHS, LPIHS, PRINCE and RWR are plotted in Fig. 5 . The results show that the AUC score of PLPIHS in 0.3 network is 96.8%, which is higher than that of PRINCE, LPIHN and RWR, achieving an AUC value of 81.3%, 88.4% and 79.2%, respectively. Similarly, PLPIHS outperforms other methods in 0.5 network and 0.9 network as well.
Performance evaluation by independent test. For further validation, we also randomly selected 2000 lncRNA-protein associations from the rest of positive samples in 0.9 network and the same number of negative interactions are picked out from the remaining negative samples of 0.3 network to generate the independent test data set. Since the existing network based methods is not suitable for independent test, we only evaluate the performance for the proposed PLPIHS. The independent test results are shown in Fig. 6 , an AUC score of 0.879 is achieved by PLPIHS, illustrating the effectiveness and advantage of the proposed approach. Case Studies. By applying the proposed PLPIHS method, novel candidate lncRNA-related proteins are predicted using LOOCV. We applied PLPIHS onto the 2000 known lncRNA-protein associations, which includes 1511 lncRNAs and 344 proteins to infer novel lncRNA-protein interactions. As a result, an area under the ROC curve of 0.9669, 0.9705 and 0.9703 (Fig. 5) is achieved using the three networks of different connectivity density, which demonstrate that our proposed method is effective in recovering known lncRNA-related proteins.
To further illustrate the application of our approach, a case study of lncRNA MALAT1(ensemble ID: ENSG00000251562) is examined. MALAT1 is a long non-coding RNA which is over-expressed in many human oncogenic tissues and regulates cell cycle and survival 31 . MALAT1 have been identified in multiple types of physiological processes, such as alternative splicing, nuclear organization, epigenetic modulating of gene expression. A large amount of evidence indicates that MALAT1 also closely relates to various pathological processes, including diabetes complications, cancers and so on 32, 33 .
MALAT1 is associated with 68 proteins in NPInter 3.0 34 . We construct the interaction networks of lncRNA MALAT1 by using the prediction results of these four methods (Fig. 7) . Among the 68 known lncRNA-protein interactions, PLPIHS wrongly predicts 6 interactions, while 13 associations are predicted mistakenly by PRINCE and RWR method and 15 lncRNA-protein pairs are falsely predicted by the LPIHN method.
We manually check the top 10 proteins in the ranked list under 0.5 network ( Table 3) .Three of the top 10 predicted proteins have interactions with MALAT1, and most of them had high ranks in the predicted protein lists. For example, In the investigation of colorectal cancer (CRC), MALAT1 could bind to SFPQ, thus releasing PTBP2 from the SFPQ/PTBP2 complex and the interaction between MALAT1 and SFPQ could be a novel therapeutic target for CRC 35 . MALAT1 interacts with SR proteins (SRSF1, SRSF2, SRSF3 and SRSF5) and regulates cellular levels of phosphorylated forms of SR proteins 36 . And it is also as target of TARDBP to play the biological performance and found that TDP-43 bound to long ncRNAs in highly sequence-specific manner in tissue from subjects with or without FTLD-TDP, the MALAT1 ncRNA recruits splicing factors to nuclear speckles and affects phosphorylation of serine/arginine-rich splicing factor proteins 37, 38 . All these results indicate that our proposed method is effective and reliable in identifying novel lncRNA-related proteins.
LncRNAs are involved in a wide range of biological functions through diverse molecular mechanisms often including the interaction with one or more protein partners 12, 13 . Only a small number of lncRNA-protein interactions have been well-characterized. Computational methods can be helpful in suggesting potential interactions for possible experimentation 25 . In this study, we use HeteSim measure to calculate the relevance between lncRNA and protein in a heterogeneous network. The importance of inferring novel lncRNA-protein interactions by considering the subtle semantic meanings of different paths in the heterogeneous network have been verified 39 . We first construct a heterogeneous network consisting of a lncRNA-lncRNA similarity network, a lncRNA-protein association network and a protein-protein interaction network. Then, we use the HeteSim measure to calculate a score for each lncRNA-protein pairs in each path. Finally, a SVM classifier is used to combine the scores of different paths and making predictions. We compare the proposed PLPIHS with PRINCE, RWR and LPIHN and find that PLPIHS obtain an AUC score of 0.9679 in 0.3 network, which is significantly higher than PRINCE, RWR and LPIHN (0.813, 0.884 and 0.7918, respectively). We also compare the performance of these four methods in networks of different connectivity density. As a result, PLPIHS outperforms the other method across all the networks. Moreover, when analysing the predicted proteins interacted with lncRNA MALAT1, PLPIHS successfully predicts 63 out of 68 associations, while PRINCE, RWR and LPIHN retrieve much lower interactions of 57, 57 and 53, respectively. And the top-ranked lncRNA-protein interactions predicted by our method are supported by existing literatures. The results highlight the advantages of our proposed method in predicting possible lncRNA-protein interactions.
Methods lncRNA-Protein associations. All human lncRNA genes and protein-coding genes are downloaded from the GENCODE Release 24 9 . A total of 15941 lncRNA genes and 20284 protein-coding genes are extracted. To obtain genome-wide lncRNA and protein-coding gene associations, we combine three sources of data:
• Co-expression data from COXPRESdb 40 . Three preprocessed co-expression datasets (Hsa.c4-1, Hsa2.c2-0 and Hsa3.c1-0) including pre-calculated pairwise Pearson's correlation coefficients for human were collected from COXPRESdb. The correlations are calculated as follows:
where C(l, p) is the overall correlation between gene l (lncRNA) and protein-coding gene p, C d (l, p) is the correlation score between l and p in dataset d, D is the number of gene pairs (l and p) with positive correlation scores. Gene pairs with negative correlation scores are removed.
• Co-expression data from ArrayExpress 41 and GEO 42 . We obtained the co-expresionn data from the work of Jiang et al. 43 . RNA-Seq raw data of 19 human normal tissues are obtained from ArrayExpress (E-MTAB-513) and GEO (GSE30554). TopHat and Cufflinks with the default parameters are used to calculate the expression values. Pearson's correlation coefficients are used to evaluate the co-expression of lncRNA-protein pairs. • lncRNA-protein interaction data. We download known lncRNA-protein interaction dataset from Protein-protein interactions. We obtain the protein-protein interaction (PPI) data from STRING database V10.0 45 , which contains weighted protein interactions derived from computational prediction methods, high-throughput experiments, and text mining. The confidence scores are computed by combining the probabilities from the different evidence channels, correcting for the probability of randomly observing an interaction.
The HeteSim measure. The HeteSim measure is a uniform and symmetric relevance measure. It can be used to calculate the relatedness of objects with the same or different types in a uniform framework, and it is also a path-constrained measure to estimate the relatedness of object pairs based on the search path that connects two objects through a sequence of node types 39 . Further, the HeteSim score has some good properties (i.e., selfmaximum and symmetric), which have achieved positive performance in many studies 25 . In this study, we use HeteSim scores to measure the similarities between lncRNAs and proteins.
Definition 1 Transition probability matrix 39 L and P are two kinds of object in the heterogeneous network, (I LP ) n*m is an adjacent matrix between L and P, then the normalized matrix of I LP along the row vector is defined as
LP LP k m LP 1 Definition 2 Reachable probability matrix 39 In a heterogeneous network, the reachable probability matrix R for path = + PP P ( ) n 1 2 1 of length n, where P i belongs to any objects in the heterogeneous network, can be expressed as
P P P P P P n n 1 2 2 3 1 Based on the definitions above, the steps of calculating HeteSim scores between two kinds of objects (lncRNA and protein) can be presented as follows:
• Split the path into two parts. When the length n of path is even, we can split it into = P P ( )
Otherwise, if n is odd, the path cannot be divided into two equallength paths. In order to deal with such problem, we need to split the path twice by setting , respectively. Then, we can obtain a HeteSim score for each mid value, the final score will be the average of the two scores.
• Achieve the transition probability matrix and reachable probability matrix under the path L and R . • Calculate the HeteSim score: where − R 1 is the reverse path of R . An example of calculating HeteSim score is indicated in Fig. 8 . We can see that there are three kinds of objects L, T and P in the network. The simplified steps of computing HeteSim score between l3 and p2 under the path = (LTP) is as follows:
• Split the path into two components = LT ( )
• Given the adjacent matrix I LT and I TP below, which means the interactions between lncRNAs and proteins, we can obtain the transition probability matrix T LT and T TP by normalizing the two matrix along the row vector. The PLPIHS method. Among a heterogeneous network, different paths can express different semantic meanings. For instance, a lncRNA and a protein is connected via 'lncRNA-lncRNA-protein' path or 'lncRNA-protein-protein' path representing totally different meanings. The former means that if a lncRNA is associated with a protein, then another lncRNA similar to the lncRNA will be potential associated with the protein. The latter shows that if a protein associated with a lncRNA, then another protein interacted with the protein will be likely associated with the lncRNA. Therefore, the potential information hidden in each path is extraordinary essential to be taken into account during prediction.
The PLPIHS framework is illustrated in Fig. 2 . Firstly, we construct a heterogeneous network consisting of a lncRNA-lncRNA similarity network, a lncRNA-protein association network and a protein-protein interaction network. Three kinds of sparse networks are obtained from the heterogeneous network under different cutoff value 0.3, 0.5 and 0.9 (see lncRNA-Protein associations). The larger cutoff is, the network is more sparse. A total of 15941 lncRNAs genes and 20284 protein-coding genes are extracted as presented in Section 2.3. We randomly take out 1511 lncRNAs and 344 proteins to construct a smaller network for the following experiments in consideration of computing costs. The construction of the smaller heterogeneous networks under different cutoff values are shown in Table 4 , where 'lnc2lnc' denotes the lncRNA-lncRNA network, 'lnc2code' denotes the lncRNA-protein network and 'code2code' denotes the protein-lncRNA network. Table 1 . We use id to indicate the path combination, i.e., 1′~2′ represents path 'LLP' and path 'LPP' . Next, we calculate the heteSim score for each lncRNA-protein pair under each path. The results of different paths are used as different features. And we combine a constant factor β to inhibit the influence of longer paths.The longer the path length is, the smaller the inhibiting factor is. Finally, a SVM classifier is built with these scores to predict potential lncRNA-protein associations. On the account of the HeteSim measure is based on the path-based relevance framework 39 , it can effectively dig out the subtle semantics of each paths.
|
What kind of data is included in the STRING database?
| 3,689 |
weighted protein interactions
| 16,316 |
1,588 |
Prediction of lncRNA-protein interactions using HeteSim scores based on heterogeneous networks
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5473862/
SHA: f4f9ea9e0aeb74d3601ee316b84292638c59cc53
Authors: Xiao, Yun; Zhang, Jingpu; Deng, Lei
Date: 2017-06-16
DOI: 10.1038/s41598-017-03986-1
License: cc-by
Abstract: Massive studies have indicated that long non-coding RNAs (lncRNAs) are critical for the regulation of cellular biological processes by binding with RNA-related proteins. However, only a few experimentally supported lncRNA-protein associations have been reported. Existing network-based methods are typically focused on intrinsic features of lncRNA and protein but ignore the information implicit in the topologies of biological networks associated with lncRNAs. Considering the limitations in previous methods, we propose PLPIHS, an effective computational method for Predicting lncRNA-Protein Interactions using HeteSim Scores. PLPIHS uses the HeteSim measure to calculate the relatedness score for each lncRNA-protein pair in the heterogeneous network, which consists of lncRNA-lncRNA similarity network, lncRNA-protein association network and protein-protein interaction network. An SVM classifier to predict lncRNA-protein interactions is built with the HeteSim scores. The results show that PLPIHS performs significantly better than the existing state-of-the-art approaches and achieves an AUC score of 0.97 in the leave-one-out validation test. We also compare the performances of networks with different connectivity density and find that PLPIHS performs well across all the networks. Furthermore, we use the proposed method to identify the related proteins for lncRNA MALAT1. Highly-ranked proteins are verified by the biological studies and demonstrate the effectiveness of our method.
Text: most commonly used approach is guilt-by-association (GBA) 19 , which provides the central top-down principle for analyzing gene networks in functional terms or assessing their quality in encoding functional information. New emerged methods, including the Katz method 20 , Combining dATa Across species using Positive-Unlabeled Learning Techniques(CATAPULT) 19 , Random Walk with Restart (RWR) 21 , and LncRNA-protein Interaction prediction based on Heterogeneous Network model (LPIHN) 22 , have extended the association from just direct protein interactions to more distant connections in various ways. The KATZ measure 20 is a weighted sum of the number of paths in the network that measures the similarity of two nodes. CATAPULT 19 is a supervised machine learning method that uses a biased support vector machine where the features are derived from walks in a heterogeneous gene-trait network. RWR 21 is a method for prioritization of candidate genes by use of a global network distance measure, random walk analysis, for definition of similarities in protein-protein interaction networks and it add weight to the assumption that phenotypically similar diseases are associated with disturbances of subnetworks within the larger protein interactome that extend beyond the disease proteins themselves. LPIHN 22 is a network-based method by implement a random walk on a heterogeneous network. PRINCE is a global method based on formulating constraints on the prioritization function that relate to its smoothness over the network and usage of prior information. Compared with LPIHN and RWR, PRINCE propagates information in a smaller network but contains more connotative meaning when build the initial probability values and has made great performance in gene prioritization 23 and disease identification 24 .
However, many existing network-based methods simply view objects in heterogeneous networks as the same type and do not consider the subtle semantic meanings of different paths. In this paper, we adopt a method named HeteSim, which is a path-based measure to calculate the relevance between objects in heterogeneous network 25 . The basic idea is that similar objects are more likely to be related to some other objects. Considering the relatedness of heterogeneous objects is path-constrained, HeteSim gives a uniform and symmetric measure for arbitrary paths to evaluate the relatedness of heterogeneous object pair (same or different types) with one single score. Due to the relevance path not only captures the semantics information but also constrains the walk path, the score is also a path-based similarity measure.
An example of HeteSim score is illustrated in (Fig. 1 ). The number of paths from A to C and B to C is 3 and 2, respectively. The walk count between A and C is larger than B and C, which might indicate that A is more closer to C than B. But the connectivity between B and C is more intense than A and C in the sight of HeteSim score, since most edges starting from B are connected with C, when A only has a small part of edges connected with C.
Here, we propose a method named PLPIHS (Fig. 2) to predict lncRNA-Protein interactions using HeteSim scores. We first construct a heterogeneous network consisting of a lncRNA-lncRNA similarity network, a lncRNA-protein association network and a protein-protein interaction network. Then, we use the HeteSim measure to calculate the score for each lncRNA-protein pair in the network. A SVM classifier is built based on the scores of different paths. We compare our PLPIHS with PRINCE, RWR and LPIHN and find that PLPIHS outperforms the other methods in many performance measures.
Validation measures. LOOCV(Leave-One-Out Cross Validation) 26 is implemented on the verified lncR-NA-protein associations to evaluate the performance of LPIHN 22 . We leave a known lncRNA-protein pair in turn as the test sample and all the other known lncRNA-protein pairs are regarded as training samples. In order to improve the accuracy of PLPIHS, we remove all connected lncRNAs and proteins while in each validation round. Receiver Operating Characteristic(ROC) curve 27 is used to evaluate the prediction performance, which plots true-positive rate (TPR, sensitivity or recall) versus false-positive rate (FPR, 1-specificity) at different rank cutoffs. When varying the rank cutoffs of successful prediction, we can obtain the corresponding TPR and FPR. In this way, ROC curve is drawn and the area under the curve(AUC) is calculated as well. For a rank threshold, sensitivity(SEN) 28 and specificity(SPE) 29 These measurements are also used to assess the capability of PLPIHS during the preprocessing procedure.
Affection of network preprocessing characteristics. In this paper, we only have two kinds of objects, lncRNA and protein. Thus, the paths from a lncRNA to a protein in our heterogeneous network with length less than six is listed in Table 1 . In order to pick out the most efficient paths, we compared the performances of these 14 paths under different combinations (Fig. 3) . We can see that all paths achieve a favorable status except path 1′~2′. Path 1′~14′ obtains the best performance across all measures, which means that the path with length greater than three contains more significant meanings. The constant factor β is used to control the influence of longer paths. The longer the path length is, the smaller the inhibiting factor is. Path length equals 3 matches with constant β, path length equals 4 matches with constant β*β and path length equals 5 matches with constant β*β*β. Table 2 shows that β has tiny impact on the final results and β = 0.2, 0.4 and 0.7 achieved the best AUC score and the others are not far behind yet.
To further verify the dependability of our method, we compare the three networks of different connectivity density under different cutoff value 0.3, 0.5 and 0.9 (see lncRNA-Protein associations). The results are shown in Fig. 4 . There are tiny performance differences between different sparse networks. The AUC score of the 0.5 network is higher than that of others while the 0.9 network outperforms others in ACC, SEN, MCC and F1-Score. This suggests that PLPIHS performs well across networks with different densities. Table 1 . The paths from a lncRNA to a protein in our heterogeneous network with length less than six.
the RWR method, there is only one restart probability r and it's effects is very slight, which is proved by experiments. The parameter r is set as 0.5 in this comparison. In order to calculate the performance of the different methods, we use a leave-one-out cross validation procedure. We extract 2000 lncRNA-protein associations from the 0.9 network as positive samples, the same number of negative samples are chosen randomly from the 0.3 network as well, avoiding the error caused by imbalance dataset. The gold set which containing 185 lncRNA-protein interactions downloaded from NPinter database has been included in positive pairs as well. In the lncRNA protein prioritization, each lncRNA-protein interaction is utilized as the test set in turn and the remaining associations are used as training data. The whole experiment will be repeated 4000 times to testing each lncRNA-protein pairs in the dataset. ROC curve is drawn based on true positive rate (TPR) and false positive rate (FPR) at different thresholds. The AUC score is utilized to measure the performance. AUC = 1 demonstrates the perfect performance and AUC = 0.5 demonstrates the random performance.The ROC curve of PLPIHS, LPIHS, PRINCE and RWR are plotted in Fig. 5 . The results show that the AUC score of PLPIHS in 0.3 network is 96.8%, which is higher than that of PRINCE, LPIHN and RWR, achieving an AUC value of 81.3%, 88.4% and 79.2%, respectively. Similarly, PLPIHS outperforms other methods in 0.5 network and 0.9 network as well.
Performance evaluation by independent test. For further validation, we also randomly selected 2000 lncRNA-protein associations from the rest of positive samples in 0.9 network and the same number of negative interactions are picked out from the remaining negative samples of 0.3 network to generate the independent test data set. Since the existing network based methods is not suitable for independent test, we only evaluate the performance for the proposed PLPIHS. The independent test results are shown in Fig. 6 , an AUC score of 0.879 is achieved by PLPIHS, illustrating the effectiveness and advantage of the proposed approach. Case Studies. By applying the proposed PLPIHS method, novel candidate lncRNA-related proteins are predicted using LOOCV. We applied PLPIHS onto the 2000 known lncRNA-protein associations, which includes 1511 lncRNAs and 344 proteins to infer novel lncRNA-protein interactions. As a result, an area under the ROC curve of 0.9669, 0.9705 and 0.9703 (Fig. 5) is achieved using the three networks of different connectivity density, which demonstrate that our proposed method is effective in recovering known lncRNA-related proteins.
To further illustrate the application of our approach, a case study of lncRNA MALAT1(ensemble ID: ENSG00000251562) is examined. MALAT1 is a long non-coding RNA which is over-expressed in many human oncogenic tissues and regulates cell cycle and survival 31 . MALAT1 have been identified in multiple types of physiological processes, such as alternative splicing, nuclear organization, epigenetic modulating of gene expression. A large amount of evidence indicates that MALAT1 also closely relates to various pathological processes, including diabetes complications, cancers and so on 32, 33 .
MALAT1 is associated with 68 proteins in NPInter 3.0 34 . We construct the interaction networks of lncRNA MALAT1 by using the prediction results of these four methods (Fig. 7) . Among the 68 known lncRNA-protein interactions, PLPIHS wrongly predicts 6 interactions, while 13 associations are predicted mistakenly by PRINCE and RWR method and 15 lncRNA-protein pairs are falsely predicted by the LPIHN method.
We manually check the top 10 proteins in the ranked list under 0.5 network ( Table 3) .Three of the top 10 predicted proteins have interactions with MALAT1, and most of them had high ranks in the predicted protein lists. For example, In the investigation of colorectal cancer (CRC), MALAT1 could bind to SFPQ, thus releasing PTBP2 from the SFPQ/PTBP2 complex and the interaction between MALAT1 and SFPQ could be a novel therapeutic target for CRC 35 . MALAT1 interacts with SR proteins (SRSF1, SRSF2, SRSF3 and SRSF5) and regulates cellular levels of phosphorylated forms of SR proteins 36 . And it is also as target of TARDBP to play the biological performance and found that TDP-43 bound to long ncRNAs in highly sequence-specific manner in tissue from subjects with or without FTLD-TDP, the MALAT1 ncRNA recruits splicing factors to nuclear speckles and affects phosphorylation of serine/arginine-rich splicing factor proteins 37, 38 . All these results indicate that our proposed method is effective and reliable in identifying novel lncRNA-related proteins.
LncRNAs are involved in a wide range of biological functions through diverse molecular mechanisms often including the interaction with one or more protein partners 12, 13 . Only a small number of lncRNA-protein interactions have been well-characterized. Computational methods can be helpful in suggesting potential interactions for possible experimentation 25 . In this study, we use HeteSim measure to calculate the relevance between lncRNA and protein in a heterogeneous network. The importance of inferring novel lncRNA-protein interactions by considering the subtle semantic meanings of different paths in the heterogeneous network have been verified 39 . We first construct a heterogeneous network consisting of a lncRNA-lncRNA similarity network, a lncRNA-protein association network and a protein-protein interaction network. Then, we use the HeteSim measure to calculate a score for each lncRNA-protein pairs in each path. Finally, a SVM classifier is used to combine the scores of different paths and making predictions. We compare the proposed PLPIHS with PRINCE, RWR and LPIHN and find that PLPIHS obtain an AUC score of 0.9679 in 0.3 network, which is significantly higher than PRINCE, RWR and LPIHN (0.813, 0.884 and 0.7918, respectively). We also compare the performance of these four methods in networks of different connectivity density. As a result, PLPIHS outperforms the other method across all the networks. Moreover, when analysing the predicted proteins interacted with lncRNA MALAT1, PLPIHS successfully predicts 63 out of 68 associations, while PRINCE, RWR and LPIHN retrieve much lower interactions of 57, 57 and 53, respectively. And the top-ranked lncRNA-protein interactions predicted by our method are supported by existing literatures. The results highlight the advantages of our proposed method in predicting possible lncRNA-protein interactions.
Methods lncRNA-Protein associations. All human lncRNA genes and protein-coding genes are downloaded from the GENCODE Release 24 9 . A total of 15941 lncRNA genes and 20284 protein-coding genes are extracted. To obtain genome-wide lncRNA and protein-coding gene associations, we combine three sources of data:
• Co-expression data from COXPRESdb 40 . Three preprocessed co-expression datasets (Hsa.c4-1, Hsa2.c2-0 and Hsa3.c1-0) including pre-calculated pairwise Pearson's correlation coefficients for human were collected from COXPRESdb. The correlations are calculated as follows:
where C(l, p) is the overall correlation between gene l (lncRNA) and protein-coding gene p, C d (l, p) is the correlation score between l and p in dataset d, D is the number of gene pairs (l and p) with positive correlation scores. Gene pairs with negative correlation scores are removed.
• Co-expression data from ArrayExpress 41 and GEO 42 . We obtained the co-expresionn data from the work of Jiang et al. 43 . RNA-Seq raw data of 19 human normal tissues are obtained from ArrayExpress (E-MTAB-513) and GEO (GSE30554). TopHat and Cufflinks with the default parameters are used to calculate the expression values. Pearson's correlation coefficients are used to evaluate the co-expression of lncRNA-protein pairs. • lncRNA-protein interaction data. We download known lncRNA-protein interaction dataset from Protein-protein interactions. We obtain the protein-protein interaction (PPI) data from STRING database V10.0 45 , which contains weighted protein interactions derived from computational prediction methods, high-throughput experiments, and text mining. The confidence scores are computed by combining the probabilities from the different evidence channels, correcting for the probability of randomly observing an interaction.
The HeteSim measure. The HeteSim measure is a uniform and symmetric relevance measure. It can be used to calculate the relatedness of objects with the same or different types in a uniform framework, and it is also a path-constrained measure to estimate the relatedness of object pairs based on the search path that connects two objects through a sequence of node types 39 . Further, the HeteSim score has some good properties (i.e., selfmaximum and symmetric), which have achieved positive performance in many studies 25 . In this study, we use HeteSim scores to measure the similarities between lncRNAs and proteins.
Definition 1 Transition probability matrix 39 L and P are two kinds of object in the heterogeneous network, (I LP ) n*m is an adjacent matrix between L and P, then the normalized matrix of I LP along the row vector is defined as
LP LP k m LP 1 Definition 2 Reachable probability matrix 39 In a heterogeneous network, the reachable probability matrix R for path = + PP P ( ) n 1 2 1 of length n, where P i belongs to any objects in the heterogeneous network, can be expressed as
P P P P P P n n 1 2 2 3 1 Based on the definitions above, the steps of calculating HeteSim scores between two kinds of objects (lncRNA and protein) can be presented as follows:
• Split the path into two parts. When the length n of path is even, we can split it into = P P ( )
Otherwise, if n is odd, the path cannot be divided into two equallength paths. In order to deal with such problem, we need to split the path twice by setting , respectively. Then, we can obtain a HeteSim score for each mid value, the final score will be the average of the two scores.
• Achieve the transition probability matrix and reachable probability matrix under the path L and R . • Calculate the HeteSim score: where − R 1 is the reverse path of R . An example of calculating HeteSim score is indicated in Fig. 8 . We can see that there are three kinds of objects L, T and P in the network. The simplified steps of computing HeteSim score between l3 and p2 under the path = (LTP) is as follows:
• Split the path into two components = LT ( )
• Given the adjacent matrix I LT and I TP below, which means the interactions between lncRNAs and proteins, we can obtain the transition probability matrix T LT and T TP by normalizing the two matrix along the row vector. The PLPIHS method. Among a heterogeneous network, different paths can express different semantic meanings. For instance, a lncRNA and a protein is connected via 'lncRNA-lncRNA-protein' path or 'lncRNA-protein-protein' path representing totally different meanings. The former means that if a lncRNA is associated with a protein, then another lncRNA similar to the lncRNA will be potential associated with the protein. The latter shows that if a protein associated with a lncRNA, then another protein interacted with the protein will be likely associated with the lncRNA. Therefore, the potential information hidden in each path is extraordinary essential to be taken into account during prediction.
The PLPIHS framework is illustrated in Fig. 2 . Firstly, we construct a heterogeneous network consisting of a lncRNA-lncRNA similarity network, a lncRNA-protein association network and a protein-protein interaction network. Three kinds of sparse networks are obtained from the heterogeneous network under different cutoff value 0.3, 0.5 and 0.9 (see lncRNA-Protein associations). The larger cutoff is, the network is more sparse. A total of 15941 lncRNAs genes and 20284 protein-coding genes are extracted as presented in Section 2.3. We randomly take out 1511 lncRNAs and 344 proteins to construct a smaller network for the following experiments in consideration of computing costs. The construction of the smaller heterogeneous networks under different cutoff values are shown in Table 4 , where 'lnc2lnc' denotes the lncRNA-lncRNA network, 'lnc2code' denotes the lncRNA-protein network and 'code2code' denotes the protein-lncRNA network. Table 1 . We use id to indicate the path combination, i.e., 1′~2′ represents path 'LLP' and path 'LPP' . Next, we calculate the heteSim score for each lncRNA-protein pair under each path. The results of different paths are used as different features. And we combine a constant factor β to inhibit the influence of longer paths.The longer the path length is, the smaller the inhibiting factor is. Finally, a SVM classifier is built with these scores to predict potential lncRNA-protein associations. On the account of the HeteSim measure is based on the path-based relevance framework 39 , it can effectively dig out the subtle semantics of each paths.
|
What is the function of MALAT1?
| 3,690 |
regulates cell cycle and survival
| 11,065 |
1,554 |
Multimodal Imaging in an Unusual Cluster of Multiple Evanescent White Dot Syndrome
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5444036/
SHA: ee3cc22161595e877450737882a52950fd179672
Authors: Gal-Or, Orly; Priel, Ethan; Rosenblatt, Irit; Shulman, Shiri; Kramer, Michal
Date: 2017-05-11
DOI: 10.1155/2017/7535320
License: cc-by
Abstract: OBJECTIVE: To describe an unusual cluster of multiple evanescent white dot syndrome (MEWDS) encountered within a 3-month period. METHODS: This retrospective observation study is comprised of seven patients who presented with MEWDS in a 3-month period in central Israel. Data were collected from patients' medical records on clinical, multimodal imaging, and viral serology findings. RESULTS: Six women and one man of mean age 31.5 ± 7.2 years. Three reported a precedent viral infection. All had unilateral decreased vision. Funduscopy revealed foveal granularity. MAIN IMAGING FINDINGS: Hyperfluorescent spots on blue autofluorescence (BAF), hypofluorescent spots on indocyanine green angiography, dark lesions on infrared photos, and ellipsoid zone irregularities on spectral domain optical coherence tomography (SD-OCT). Resolution of the spots on BAF correlated with anatomic (SD-OCT) and visual recovery. OCT angiography performed following the convalescence stage demonstrated intact retinal and choroidal flow. Serologic findings were inconclusive. CONCLUSION: We report a unique cluster of MEWDS patients presented in a short period of time. SD-OCT findings of ellipsoid zone disruption in combination with other multimodal imaging modalities are outlined meticulously. Recognizing these imaging features along with high index of clinical suspicion is important for the diagnosis of MEWDS. Serologic testing might be considered in future patients.
Text: Multiple evanescent white dot syndrome (MEWDS) was first described in 1984 as a rare, sudden onset of unilateral chorioretinopathy, with the predominant sign being multifocal yellow-white spots throughout the retina [1, 2] . The clinical spectrum of MEWDS has expanded over the years to include bilaterality and recurrences [3] or an atypical presentation involving the fovea without the white spots [4] . Symptoms include acute onset of decreased visual acuity unilaterally accompanied in most cases by photopsia and scotomata. A prodromal flu-like illness has been reported in up to 50% of cases [1] . One report described a patient with elevated levels of total serum IgG during the disease course and negative findings for IgM to herpes zoster, herpes simplex, mumps, and measles [5] .
Although MEWDS is suspected to occur as a consequence of a viral-like infection in genetically susceptible individuals, its precise pathogenesis remains unknown. Recovery is gradual, over weeks to months, and the visual prognosis is very favorable [2] . Treatment is usually not required.
The incidence of MEWDS is unknown. Only small case series are reported in the literature [4] [5] [6] [7] [8] [9] [10] [11] [12] . One of the largest described 34 affected patients reviewed over several years' period [1, 13, 14] .
The aim of the present report was to describe an unusual cluster of seven cases of MEWDS encountered within a 3month period, with an emphasis on the clinical presentation and multimodal imaging findings. The cluster prompted us to seek a common infectious association.
A retrospective observational study was conducted in seven patients who presented with MEWDS between July and September 2013 at two tertiary medical centers in central Israel. Data on background, clinical, and laboratory parameters were collected from the medical files. The study was approved by the institutional ethics review board.
All patients underwent a comprehensive ophthalmic examination and multimodal imaging tests, including blue autofluorescence (BAF), fluorescein angiography (FA) and/ or indocyanine green angiography (ICGA), infrared (IR) photography, and spectral domain optical coherence tomography (SD-OCT). Images were acquired with the HRA-2 and the Spectralis HRA + OCT devices (Heidelberg Engineering, Heidelberg, Germany) at the following wavelengths: BAFexcitation 488 nm, barrier cut-off 496 nm; IR-820 nm; ICGA-excitation 790 nm, emission 800 nm; and SD-OCTsuperluminescent diode light source 870 nm. The volume scan option was used to acquire the multiple SD-OCT scans (25-49 horizontal scans over a 6 mm region covering the area of pathology). Precise registration between findings seen on IR or BAF and SD-OCT was enabled by the dual-beam laser eye-tracking system, where one laser is used to image the retina and the other laser to perform the OCT scans. Accurate rescanning in areas of interest was ensured by the Spectralis follow-up function which automatically places subsequent scans on the same location as the previous ones.
OCT angiography images were acquired using the RTVue XR Avanti with AngioVue (Optovue Inc., Fremont, California, USA), with an A-scan-rate of 70 000 scans per second, a light source of 840 nm, and a bandwidth of 45 nm. Macular cubes (3 × 3 mm) were acquired, each cube consisting of 304 clusters of 2 repeated B-scans containing 304 A-scans each. Split-spectrum amplitude decorrelation technology was employed to improve the signal-to-noise ratio by splitting the spectrum to generate multiple repeat OCT frames from 2 original repeat OCT frames [15] .
Motion correction was performed using registration of 2 orthogonally captured imaging volumes. Automatic segmentation of the retinal layers was performed by the viewing software and was used to generate en face projection images after adjusting the level of the segmented layer on the B-scans.
Serology testing was performed for viruses commonly present at the time of the patients' presentation, namely, immunoglobulin IgG and IgM for herpes simplex virus (HSV) I-II, varicella zoster virus (VZV), West Nile virus, coxsackievirus, echovirus (subgroup of enterovirus), and corona virus.
Findings. There were one male and six female patients of mean age 31.5 ± 7.2 years (range 22-41 years). Table 1 summarizes the demographic data. Three patients reported a prodromal virus infection.
All patients presented with acute onset of unilateral decreased vision. The best corrected visual acuity at presentation ranged from 6/9 to 6/30 in the affected eye. None of the patients had signs of anterior or vitreous inflammation in the affected eye. Funduscopic findings at presentation included foveal granularity in six patients; in four patients (patients 1, 4, 5, and 6), it was the sole pathologic retinal finding ( Figure 1 ); and in three patients (patients 2, 3, and 7), foveal granularity was associated with faint white retinal lesions (Figure 2 ), located mainly in the midperipheral retina extending to the periphery. Patient 6 had a swollen disc and mild signs of optic neuropathy (mild red desaturation, enlarged blind spot on visual field). Patient 6 underwent neurological evaluation due to initial presentation mimicking optic neuritis. Neurological evaluation including full neurological exam and neuroimaging excluded additional neurological deficit, before the diagnosis of MEWDS was established. The clinical findings are summarized in Table 2. 3.2. Multimodal Imaging Findings. Patients who underwent imaging less than 2 weeks from onset of symptoms had the most typical findings.
BAF revealed hyperautofluorescent lesions in the macula between and along the arcades in four patients (patients 1, 3, 6, and 7). IR photos showed dark lesions in similar, though not identical, locations ( Figure 3 ). Patients 1 and 6, who underwent ICGA, had hypofluorescent lesions in numbers typically exceeding those detected by both clinical and other imaging modalities. B-scan SD-OCT through the fovea showed a disrupted inner segment ellipsoid zone band of varied severity in all 7 affected eyes. The ellipsoid zone hyper reflective band on SD-OCT anatomically correlates to photoreceptors' inner segment, ellipsoid section densely packed with mitochondria [16] . The transient disruption of the foveal ellipsoid zone on SD-OCT corresponded to the clinically apparent foveal granularity. In patient 5, who presented with sole retinal finding of foveal granularity and mild optic disc leakage on FA, the SD-OCT finding of ellipsoid zone disruption was the main sign for diagnosis MEWDS (Figure 1 ). Foveal hyperreflectivity found in 3 patients (patients 1, 4, and 7) was noted extending into the inner retinal layers (Figure 4 ). The lesions identified on the BAF, IR, and ICGA images corresponded to the areas of disruption of the ellipsoid zone, on the SD-OCT scans ( Figure 3 ). FA demonstrated nonspecific early punctate hyperfluorescent lesions, with slight staining during the early phase, in four patients (patients 2, 3, 6, and 7). These lesions did not correspond to the findings by either the clinical or other imaging modalities. No pathology was noted in the foveal area despite the presence of typical foveal granularity. Mild optic disc leakage was evident in four patients (patients 1, 4, 5, and 6).
During the course of the disease, the hyperautofluorescent areas decreased in number and faded without leaving hypoautofluorescent abnormalities. The resolution of the BAF lesions corresponded to the anatomic recovery observed on SD-OCT. The foveal hyperreflectivity disappeared as well ( Figure 5 ). Figure 6 .
Four patients (patients 1, 4, 6, and 7) underwent serological testing with negative results except for a common result of elevated titer of IgG to VZV.
After 6 months of follow-up, the best corrected visual acuity ranged from 6/6 to 6/6.6 ( Table 2 ).
Although MEDWS is traditionally considered as a rare syndrome [2] , we report an unusual cluster of seven patients who presented within a three-month period. All patients were otherwise healthy, and all presented with decreased vision in one eye. This cluster of cases could break to some measure the statistical improbability of the rarity of the disease. The atypical presentation in most of our patients could suggest that MEWDS is underdiagnosed. However, it may be in line with the speculation that sometimes atypical findings may simply reflect the moment in time in which the patients were examined and are not a true atypical presentation [4] . In its original description by Jampol et al. [2] , MEWDS cases were unilateral with fundus presentation including numerous white dots scattered in the posterior pole and beyond the arcades. During the disease course, granularity appearance of the macula develops in most cases and, when seen, determines the diagnosis. The number of white spots is very variable, and in fact, they may be absent. Given that characteristic white dots were not present in four patients (patients 1, 4, 5, and 6), we were guided by other fundus features, in particular foveal granularity, symptoms, multimodal imaging, and clinical course. While the presumed pathogenesis of MEWDS involves a viral infection, only few reports to date have described a search for the pathogen [5, [17] [18] [19] . The present cluster of cases provided us with a unique opportunity to seek a common viral denominator. Serological testing yielded only an elevated titer of IgG to VZV, most often an indicative of past VZV infection or vaccination; thus, we could not make any generalization regarding these findings.
Multimodal imaging (BAF, SD-OCT, IR, FA, and ICGA) has proven to have high value in the challenging diagnosis of MEWDS. Most of the findings noted here have been described separately in earlier reports [7-9, 11, 12] . However, the present study offered two important advantages. We were able to examine all patients with simultaneously acquired imaging, and multiple correlations between the imaging findings and the clinical evaluation were possible. Moreover, the relatively large size of the cohort and the repeated scans allowed us to verify the imaging findings in this rare disease.
We observed corresponding locations of the dark spots on IR images, the hyperautofluorescent spots on the BAF images, and the foci of outer retinal pathology on SD-OCT images. Small hyperreflective points, located in the ganglion cell layer, the ellipsoid zone, and the choriocapillaris, have been noted and described on "en face" EDI SD-OCT [20] . However, we noted a unique finding of foveal hyperreflectivity extending into the inner retinal layers. Our finding reinforces a recently described finding in the literature [14] which is believed to be pathognomonic to MEWDS. During the disease course, both the IR and the BAF findings faded in concurrence with the anatomical resolution of the disruption in the ellipsoid zone and the foveal hyperreflective lesion on SD-OCT. Thus, IR images may provide an easy, widely available imaging modality for follow-up of patients with MEWDS. Although IR autofluorescent changes were recently described in patients with MEWDS [21, 22] , this modality is not widely available, whereas IR imaging is routinely performed. Furthermore, on the basis of our findings with multimodal imaging, we suggest that the diagnosis of MEWDS can be established with the simultaneous use of such noninvasive techniques as BAF, IR, and SD-OCT. ICGA and FA may be reserved for secondary use, when findings are equivocal. OCTA is relatively new noninvasive imaging modality that demonstrates flow characteristics of the vascular network within the regional circulation to construct noninvasive images of the vascular network. En face images generated by OCTA also allow us to study the spatial relationships between vasculature and adjacent retinal/choroidal layers with greater precision than dye angiography, and OCTA findings demonstrated no flow impairment in the retinal and choroidal vasculature of the patients scanned after convalescence stage.
We cannot overestimate the role of multimodal imaging in these patients, since not too often, the diagnosis is mistaken for optic neuritis, and clinical findings are very subtle.
Limitations of the study were the variability in time from disease onset to serologic testing, making the IgM results hard to interpret. Therefore, we consider these tests inconclusive. Secondly, not all the patients had imaging with all modalities. In addition, future research is required using OCT angiography to study the nature of the dots in MEWDS patients and its correlation to other multimodal imaging modalities in the acute and convalescent stage.
In conclusion, we present a large unique cluster of patients who presented with MEWDS over a short period Figure 6 : OCTA images following convalescence stage of patients 7's right eye (a-b) and 6's left eye (c-d). The green and red lines represent the x and y axes. Patient 7 after recurrent episodes. 3 × 3 mm OCT angiogram of the choriocapillaris (a1), superficial layer (a2), and deep layer (a3) centered at the macula without any flow compromise. Corresponding x-axis OCT structural B-scan (b1) simultaneously obtained during the same scan as the OCT angiogram with flow overlay at the cross-section demonstrated by the green line in (a1). SD-OCT (b2) demonstrating normal anatomy of the outer retina 6 months after the first acute episode. Patient 6, 3× 3 mm OCT angiogram of the choriocapillaris (c1), superficial layer (c2), and deep layer (c3) centered at the macula without any flow compromise. 3 × 3 mm en face structural OCT (d1) of the choriocapillaris centered at the macula as in c1. This image was simultaneously obtained during the same scan as the OCT angiogram in (c). En face structural OCT of the deep (d2) and outer retina (d3). of time. To the best of our knowledge, such a cluster was not previously reported in the literature nor encountered by us at different seasons. The diagnosis was supported by the presence of key features of foveal granularity and disruption of the ellipsoid zone on OCT and their correlation with the hyperautofluorescent lesions identified on BAF. Attention should also be addressed to the dark spots demonstrated on IR images, which may serve as an additional diagnostic clue provided by a noninvasive imaging modality. The disease course in our patients was typical for MEWDS, with almost complete recovery of visual acuity. The specific pathogenesis of MEWDS is unknown but is believed to be an inflammatory condition following a viral infection. We suggest continued serological testing in patients who meet the clinical criteria. The clinical signs of MEWDS are subtle, such that the diagnosis relies on a high index of suspicion.
The authors have no conflict of interest to declare.
|
What is multiple evanescent white dot syndrome?
| 581 |
a rare, sudden onset of unilateral chorioretinopathy
| 1,882 |
1,554 |
Multimodal Imaging in an Unusual Cluster of Multiple Evanescent White Dot Syndrome
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5444036/
SHA: ee3cc22161595e877450737882a52950fd179672
Authors: Gal-Or, Orly; Priel, Ethan; Rosenblatt, Irit; Shulman, Shiri; Kramer, Michal
Date: 2017-05-11
DOI: 10.1155/2017/7535320
License: cc-by
Abstract: OBJECTIVE: To describe an unusual cluster of multiple evanescent white dot syndrome (MEWDS) encountered within a 3-month period. METHODS: This retrospective observation study is comprised of seven patients who presented with MEWDS in a 3-month period in central Israel. Data were collected from patients' medical records on clinical, multimodal imaging, and viral serology findings. RESULTS: Six women and one man of mean age 31.5 ± 7.2 years. Three reported a precedent viral infection. All had unilateral decreased vision. Funduscopy revealed foveal granularity. MAIN IMAGING FINDINGS: Hyperfluorescent spots on blue autofluorescence (BAF), hypofluorescent spots on indocyanine green angiography, dark lesions on infrared photos, and ellipsoid zone irregularities on spectral domain optical coherence tomography (SD-OCT). Resolution of the spots on BAF correlated with anatomic (SD-OCT) and visual recovery. OCT angiography performed following the convalescence stage demonstrated intact retinal and choroidal flow. Serologic findings were inconclusive. CONCLUSION: We report a unique cluster of MEWDS patients presented in a short period of time. SD-OCT findings of ellipsoid zone disruption in combination with other multimodal imaging modalities are outlined meticulously. Recognizing these imaging features along with high index of clinical suspicion is important for the diagnosis of MEWDS. Serologic testing might be considered in future patients.
Text: Multiple evanescent white dot syndrome (MEWDS) was first described in 1984 as a rare, sudden onset of unilateral chorioretinopathy, with the predominant sign being multifocal yellow-white spots throughout the retina [1, 2] . The clinical spectrum of MEWDS has expanded over the years to include bilaterality and recurrences [3] or an atypical presentation involving the fovea without the white spots [4] . Symptoms include acute onset of decreased visual acuity unilaterally accompanied in most cases by photopsia and scotomata. A prodromal flu-like illness has been reported in up to 50% of cases [1] . One report described a patient with elevated levels of total serum IgG during the disease course and negative findings for IgM to herpes zoster, herpes simplex, mumps, and measles [5] .
Although MEWDS is suspected to occur as a consequence of a viral-like infection in genetically susceptible individuals, its precise pathogenesis remains unknown. Recovery is gradual, over weeks to months, and the visual prognosis is very favorable [2] . Treatment is usually not required.
The incidence of MEWDS is unknown. Only small case series are reported in the literature [4] [5] [6] [7] [8] [9] [10] [11] [12] . One of the largest described 34 affected patients reviewed over several years' period [1, 13, 14] .
The aim of the present report was to describe an unusual cluster of seven cases of MEWDS encountered within a 3month period, with an emphasis on the clinical presentation and multimodal imaging findings. The cluster prompted us to seek a common infectious association.
A retrospective observational study was conducted in seven patients who presented with MEWDS between July and September 2013 at two tertiary medical centers in central Israel. Data on background, clinical, and laboratory parameters were collected from the medical files. The study was approved by the institutional ethics review board.
All patients underwent a comprehensive ophthalmic examination and multimodal imaging tests, including blue autofluorescence (BAF), fluorescein angiography (FA) and/ or indocyanine green angiography (ICGA), infrared (IR) photography, and spectral domain optical coherence tomography (SD-OCT). Images were acquired with the HRA-2 and the Spectralis HRA + OCT devices (Heidelberg Engineering, Heidelberg, Germany) at the following wavelengths: BAFexcitation 488 nm, barrier cut-off 496 nm; IR-820 nm; ICGA-excitation 790 nm, emission 800 nm; and SD-OCTsuperluminescent diode light source 870 nm. The volume scan option was used to acquire the multiple SD-OCT scans (25-49 horizontal scans over a 6 mm region covering the area of pathology). Precise registration between findings seen on IR or BAF and SD-OCT was enabled by the dual-beam laser eye-tracking system, where one laser is used to image the retina and the other laser to perform the OCT scans. Accurate rescanning in areas of interest was ensured by the Spectralis follow-up function which automatically places subsequent scans on the same location as the previous ones.
OCT angiography images were acquired using the RTVue XR Avanti with AngioVue (Optovue Inc., Fremont, California, USA), with an A-scan-rate of 70 000 scans per second, a light source of 840 nm, and a bandwidth of 45 nm. Macular cubes (3 × 3 mm) were acquired, each cube consisting of 304 clusters of 2 repeated B-scans containing 304 A-scans each. Split-spectrum amplitude decorrelation technology was employed to improve the signal-to-noise ratio by splitting the spectrum to generate multiple repeat OCT frames from 2 original repeat OCT frames [15] .
Motion correction was performed using registration of 2 orthogonally captured imaging volumes. Automatic segmentation of the retinal layers was performed by the viewing software and was used to generate en face projection images after adjusting the level of the segmented layer on the B-scans.
Serology testing was performed for viruses commonly present at the time of the patients' presentation, namely, immunoglobulin IgG and IgM for herpes simplex virus (HSV) I-II, varicella zoster virus (VZV), West Nile virus, coxsackievirus, echovirus (subgroup of enterovirus), and corona virus.
Findings. There were one male and six female patients of mean age 31.5 ± 7.2 years (range 22-41 years). Table 1 summarizes the demographic data. Three patients reported a prodromal virus infection.
All patients presented with acute onset of unilateral decreased vision. The best corrected visual acuity at presentation ranged from 6/9 to 6/30 in the affected eye. None of the patients had signs of anterior or vitreous inflammation in the affected eye. Funduscopic findings at presentation included foveal granularity in six patients; in four patients (patients 1, 4, 5, and 6), it was the sole pathologic retinal finding ( Figure 1 ); and in three patients (patients 2, 3, and 7), foveal granularity was associated with faint white retinal lesions (Figure 2 ), located mainly in the midperipheral retina extending to the periphery. Patient 6 had a swollen disc and mild signs of optic neuropathy (mild red desaturation, enlarged blind spot on visual field). Patient 6 underwent neurological evaluation due to initial presentation mimicking optic neuritis. Neurological evaluation including full neurological exam and neuroimaging excluded additional neurological deficit, before the diagnosis of MEWDS was established. The clinical findings are summarized in Table 2. 3.2. Multimodal Imaging Findings. Patients who underwent imaging less than 2 weeks from onset of symptoms had the most typical findings.
BAF revealed hyperautofluorescent lesions in the macula between and along the arcades in four patients (patients 1, 3, 6, and 7). IR photos showed dark lesions in similar, though not identical, locations ( Figure 3 ). Patients 1 and 6, who underwent ICGA, had hypofluorescent lesions in numbers typically exceeding those detected by both clinical and other imaging modalities. B-scan SD-OCT through the fovea showed a disrupted inner segment ellipsoid zone band of varied severity in all 7 affected eyes. The ellipsoid zone hyper reflective band on SD-OCT anatomically correlates to photoreceptors' inner segment, ellipsoid section densely packed with mitochondria [16] . The transient disruption of the foveal ellipsoid zone on SD-OCT corresponded to the clinically apparent foveal granularity. In patient 5, who presented with sole retinal finding of foveal granularity and mild optic disc leakage on FA, the SD-OCT finding of ellipsoid zone disruption was the main sign for diagnosis MEWDS (Figure 1 ). Foveal hyperreflectivity found in 3 patients (patients 1, 4, and 7) was noted extending into the inner retinal layers (Figure 4 ). The lesions identified on the BAF, IR, and ICGA images corresponded to the areas of disruption of the ellipsoid zone, on the SD-OCT scans ( Figure 3 ). FA demonstrated nonspecific early punctate hyperfluorescent lesions, with slight staining during the early phase, in four patients (patients 2, 3, 6, and 7). These lesions did not correspond to the findings by either the clinical or other imaging modalities. No pathology was noted in the foveal area despite the presence of typical foveal granularity. Mild optic disc leakage was evident in four patients (patients 1, 4, 5, and 6).
During the course of the disease, the hyperautofluorescent areas decreased in number and faded without leaving hypoautofluorescent abnormalities. The resolution of the BAF lesions corresponded to the anatomic recovery observed on SD-OCT. The foveal hyperreflectivity disappeared as well ( Figure 5 ). Figure 6 .
Four patients (patients 1, 4, 6, and 7) underwent serological testing with negative results except for a common result of elevated titer of IgG to VZV.
After 6 months of follow-up, the best corrected visual acuity ranged from 6/6 to 6/6.6 ( Table 2 ).
Although MEDWS is traditionally considered as a rare syndrome [2] , we report an unusual cluster of seven patients who presented within a three-month period. All patients were otherwise healthy, and all presented with decreased vision in one eye. This cluster of cases could break to some measure the statistical improbability of the rarity of the disease. The atypical presentation in most of our patients could suggest that MEWDS is underdiagnosed. However, it may be in line with the speculation that sometimes atypical findings may simply reflect the moment in time in which the patients were examined and are not a true atypical presentation [4] . In its original description by Jampol et al. [2] , MEWDS cases were unilateral with fundus presentation including numerous white dots scattered in the posterior pole and beyond the arcades. During the disease course, granularity appearance of the macula develops in most cases and, when seen, determines the diagnosis. The number of white spots is very variable, and in fact, they may be absent. Given that characteristic white dots were not present in four patients (patients 1, 4, 5, and 6), we were guided by other fundus features, in particular foveal granularity, symptoms, multimodal imaging, and clinical course. While the presumed pathogenesis of MEWDS involves a viral infection, only few reports to date have described a search for the pathogen [5, [17] [18] [19] . The present cluster of cases provided us with a unique opportunity to seek a common viral denominator. Serological testing yielded only an elevated titer of IgG to VZV, most often an indicative of past VZV infection or vaccination; thus, we could not make any generalization regarding these findings.
Multimodal imaging (BAF, SD-OCT, IR, FA, and ICGA) has proven to have high value in the challenging diagnosis of MEWDS. Most of the findings noted here have been described separately in earlier reports [7-9, 11, 12] . However, the present study offered two important advantages. We were able to examine all patients with simultaneously acquired imaging, and multiple correlations between the imaging findings and the clinical evaluation were possible. Moreover, the relatively large size of the cohort and the repeated scans allowed us to verify the imaging findings in this rare disease.
We observed corresponding locations of the dark spots on IR images, the hyperautofluorescent spots on the BAF images, and the foci of outer retinal pathology on SD-OCT images. Small hyperreflective points, located in the ganglion cell layer, the ellipsoid zone, and the choriocapillaris, have been noted and described on "en face" EDI SD-OCT [20] . However, we noted a unique finding of foveal hyperreflectivity extending into the inner retinal layers. Our finding reinforces a recently described finding in the literature [14] which is believed to be pathognomonic to MEWDS. During the disease course, both the IR and the BAF findings faded in concurrence with the anatomical resolution of the disruption in the ellipsoid zone and the foveal hyperreflective lesion on SD-OCT. Thus, IR images may provide an easy, widely available imaging modality for follow-up of patients with MEWDS. Although IR autofluorescent changes were recently described in patients with MEWDS [21, 22] , this modality is not widely available, whereas IR imaging is routinely performed. Furthermore, on the basis of our findings with multimodal imaging, we suggest that the diagnosis of MEWDS can be established with the simultaneous use of such noninvasive techniques as BAF, IR, and SD-OCT. ICGA and FA may be reserved for secondary use, when findings are equivocal. OCTA is relatively new noninvasive imaging modality that demonstrates flow characteristics of the vascular network within the regional circulation to construct noninvasive images of the vascular network. En face images generated by OCTA also allow us to study the spatial relationships between vasculature and adjacent retinal/choroidal layers with greater precision than dye angiography, and OCTA findings demonstrated no flow impairment in the retinal and choroidal vasculature of the patients scanned after convalescence stage.
We cannot overestimate the role of multimodal imaging in these patients, since not too often, the diagnosis is mistaken for optic neuritis, and clinical findings are very subtle.
Limitations of the study were the variability in time from disease onset to serologic testing, making the IgM results hard to interpret. Therefore, we consider these tests inconclusive. Secondly, not all the patients had imaging with all modalities. In addition, future research is required using OCT angiography to study the nature of the dots in MEWDS patients and its correlation to other multimodal imaging modalities in the acute and convalescent stage.
In conclusion, we present a large unique cluster of patients who presented with MEWDS over a short period Figure 6 : OCTA images following convalescence stage of patients 7's right eye (a-b) and 6's left eye (c-d). The green and red lines represent the x and y axes. Patient 7 after recurrent episodes. 3 × 3 mm OCT angiogram of the choriocapillaris (a1), superficial layer (a2), and deep layer (a3) centered at the macula without any flow compromise. Corresponding x-axis OCT structural B-scan (b1) simultaneously obtained during the same scan as the OCT angiogram with flow overlay at the cross-section demonstrated by the green line in (a1). SD-OCT (b2) demonstrating normal anatomy of the outer retina 6 months after the first acute episode. Patient 6, 3× 3 mm OCT angiogram of the choriocapillaris (c1), superficial layer (c2), and deep layer (c3) centered at the macula without any flow compromise. 3 × 3 mm en face structural OCT (d1) of the choriocapillaris centered at the macula as in c1. This image was simultaneously obtained during the same scan as the OCT angiogram in (c). En face structural OCT of the deep (d2) and outer retina (d3). of time. To the best of our knowledge, such a cluster was not previously reported in the literature nor encountered by us at different seasons. The diagnosis was supported by the presence of key features of foveal granularity and disruption of the ellipsoid zone on OCT and their correlation with the hyperautofluorescent lesions identified on BAF. Attention should also be addressed to the dark spots demonstrated on IR images, which may serve as an additional diagnostic clue provided by a noninvasive imaging modality. The disease course in our patients was typical for MEWDS, with almost complete recovery of visual acuity. The specific pathogenesis of MEWDS is unknown but is believed to be an inflammatory condition following a viral infection. We suggest continued serological testing in patients who meet the clinical criteria. The clinical signs of MEWDS are subtle, such that the diagnosis relies on a high index of suspicion.
The authors have no conflict of interest to declare.
|
What precedes about half of the reported cases of MEWDS?
| 582 |
flu-like illness
| 2,345 |
1,554 |
Multimodal Imaging in an Unusual Cluster of Multiple Evanescent White Dot Syndrome
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5444036/
SHA: ee3cc22161595e877450737882a52950fd179672
Authors: Gal-Or, Orly; Priel, Ethan; Rosenblatt, Irit; Shulman, Shiri; Kramer, Michal
Date: 2017-05-11
DOI: 10.1155/2017/7535320
License: cc-by
Abstract: OBJECTIVE: To describe an unusual cluster of multiple evanescent white dot syndrome (MEWDS) encountered within a 3-month period. METHODS: This retrospective observation study is comprised of seven patients who presented with MEWDS in a 3-month period in central Israel. Data were collected from patients' medical records on clinical, multimodal imaging, and viral serology findings. RESULTS: Six women and one man of mean age 31.5 ± 7.2 years. Three reported a precedent viral infection. All had unilateral decreased vision. Funduscopy revealed foveal granularity. MAIN IMAGING FINDINGS: Hyperfluorescent spots on blue autofluorescence (BAF), hypofluorescent spots on indocyanine green angiography, dark lesions on infrared photos, and ellipsoid zone irregularities on spectral domain optical coherence tomography (SD-OCT). Resolution of the spots on BAF correlated with anatomic (SD-OCT) and visual recovery. OCT angiography performed following the convalescence stage demonstrated intact retinal and choroidal flow. Serologic findings were inconclusive. CONCLUSION: We report a unique cluster of MEWDS patients presented in a short period of time. SD-OCT findings of ellipsoid zone disruption in combination with other multimodal imaging modalities are outlined meticulously. Recognizing these imaging features along with high index of clinical suspicion is important for the diagnosis of MEWDS. Serologic testing might be considered in future patients.
Text: Multiple evanescent white dot syndrome (MEWDS) was first described in 1984 as a rare, sudden onset of unilateral chorioretinopathy, with the predominant sign being multifocal yellow-white spots throughout the retina [1, 2] . The clinical spectrum of MEWDS has expanded over the years to include bilaterality and recurrences [3] or an atypical presentation involving the fovea without the white spots [4] . Symptoms include acute onset of decreased visual acuity unilaterally accompanied in most cases by photopsia and scotomata. A prodromal flu-like illness has been reported in up to 50% of cases [1] . One report described a patient with elevated levels of total serum IgG during the disease course and negative findings for IgM to herpes zoster, herpes simplex, mumps, and measles [5] .
Although MEWDS is suspected to occur as a consequence of a viral-like infection in genetically susceptible individuals, its precise pathogenesis remains unknown. Recovery is gradual, over weeks to months, and the visual prognosis is very favorable [2] . Treatment is usually not required.
The incidence of MEWDS is unknown. Only small case series are reported in the literature [4] [5] [6] [7] [8] [9] [10] [11] [12] . One of the largest described 34 affected patients reviewed over several years' period [1, 13, 14] .
The aim of the present report was to describe an unusual cluster of seven cases of MEWDS encountered within a 3month period, with an emphasis on the clinical presentation and multimodal imaging findings. The cluster prompted us to seek a common infectious association.
A retrospective observational study was conducted in seven patients who presented with MEWDS between July and September 2013 at two tertiary medical centers in central Israel. Data on background, clinical, and laboratory parameters were collected from the medical files. The study was approved by the institutional ethics review board.
All patients underwent a comprehensive ophthalmic examination and multimodal imaging tests, including blue autofluorescence (BAF), fluorescein angiography (FA) and/ or indocyanine green angiography (ICGA), infrared (IR) photography, and spectral domain optical coherence tomography (SD-OCT). Images were acquired with the HRA-2 and the Spectralis HRA + OCT devices (Heidelberg Engineering, Heidelberg, Germany) at the following wavelengths: BAFexcitation 488 nm, barrier cut-off 496 nm; IR-820 nm; ICGA-excitation 790 nm, emission 800 nm; and SD-OCTsuperluminescent diode light source 870 nm. The volume scan option was used to acquire the multiple SD-OCT scans (25-49 horizontal scans over a 6 mm region covering the area of pathology). Precise registration between findings seen on IR or BAF and SD-OCT was enabled by the dual-beam laser eye-tracking system, where one laser is used to image the retina and the other laser to perform the OCT scans. Accurate rescanning in areas of interest was ensured by the Spectralis follow-up function which automatically places subsequent scans on the same location as the previous ones.
OCT angiography images were acquired using the RTVue XR Avanti with AngioVue (Optovue Inc., Fremont, California, USA), with an A-scan-rate of 70 000 scans per second, a light source of 840 nm, and a bandwidth of 45 nm. Macular cubes (3 × 3 mm) were acquired, each cube consisting of 304 clusters of 2 repeated B-scans containing 304 A-scans each. Split-spectrum amplitude decorrelation technology was employed to improve the signal-to-noise ratio by splitting the spectrum to generate multiple repeat OCT frames from 2 original repeat OCT frames [15] .
Motion correction was performed using registration of 2 orthogonally captured imaging volumes. Automatic segmentation of the retinal layers was performed by the viewing software and was used to generate en face projection images after adjusting the level of the segmented layer on the B-scans.
Serology testing was performed for viruses commonly present at the time of the patients' presentation, namely, immunoglobulin IgG and IgM for herpes simplex virus (HSV) I-II, varicella zoster virus (VZV), West Nile virus, coxsackievirus, echovirus (subgroup of enterovirus), and corona virus.
Findings. There were one male and six female patients of mean age 31.5 ± 7.2 years (range 22-41 years). Table 1 summarizes the demographic data. Three patients reported a prodromal virus infection.
All patients presented with acute onset of unilateral decreased vision. The best corrected visual acuity at presentation ranged from 6/9 to 6/30 in the affected eye. None of the patients had signs of anterior or vitreous inflammation in the affected eye. Funduscopic findings at presentation included foveal granularity in six patients; in four patients (patients 1, 4, 5, and 6), it was the sole pathologic retinal finding ( Figure 1 ); and in three patients (patients 2, 3, and 7), foveal granularity was associated with faint white retinal lesions (Figure 2 ), located mainly in the midperipheral retina extending to the periphery. Patient 6 had a swollen disc and mild signs of optic neuropathy (mild red desaturation, enlarged blind spot on visual field). Patient 6 underwent neurological evaluation due to initial presentation mimicking optic neuritis. Neurological evaluation including full neurological exam and neuroimaging excluded additional neurological deficit, before the diagnosis of MEWDS was established. The clinical findings are summarized in Table 2. 3.2. Multimodal Imaging Findings. Patients who underwent imaging less than 2 weeks from onset of symptoms had the most typical findings.
BAF revealed hyperautofluorescent lesions in the macula between and along the arcades in four patients (patients 1, 3, 6, and 7). IR photos showed dark lesions in similar, though not identical, locations ( Figure 3 ). Patients 1 and 6, who underwent ICGA, had hypofluorescent lesions in numbers typically exceeding those detected by both clinical and other imaging modalities. B-scan SD-OCT through the fovea showed a disrupted inner segment ellipsoid zone band of varied severity in all 7 affected eyes. The ellipsoid zone hyper reflective band on SD-OCT anatomically correlates to photoreceptors' inner segment, ellipsoid section densely packed with mitochondria [16] . The transient disruption of the foveal ellipsoid zone on SD-OCT corresponded to the clinically apparent foveal granularity. In patient 5, who presented with sole retinal finding of foveal granularity and mild optic disc leakage on FA, the SD-OCT finding of ellipsoid zone disruption was the main sign for diagnosis MEWDS (Figure 1 ). Foveal hyperreflectivity found in 3 patients (patients 1, 4, and 7) was noted extending into the inner retinal layers (Figure 4 ). The lesions identified on the BAF, IR, and ICGA images corresponded to the areas of disruption of the ellipsoid zone, on the SD-OCT scans ( Figure 3 ). FA demonstrated nonspecific early punctate hyperfluorescent lesions, with slight staining during the early phase, in four patients (patients 2, 3, 6, and 7). These lesions did not correspond to the findings by either the clinical or other imaging modalities. No pathology was noted in the foveal area despite the presence of typical foveal granularity. Mild optic disc leakage was evident in four patients (patients 1, 4, 5, and 6).
During the course of the disease, the hyperautofluorescent areas decreased in number and faded without leaving hypoautofluorescent abnormalities. The resolution of the BAF lesions corresponded to the anatomic recovery observed on SD-OCT. The foveal hyperreflectivity disappeared as well ( Figure 5 ). Figure 6 .
Four patients (patients 1, 4, 6, and 7) underwent serological testing with negative results except for a common result of elevated titer of IgG to VZV.
After 6 months of follow-up, the best corrected visual acuity ranged from 6/6 to 6/6.6 ( Table 2 ).
Although MEDWS is traditionally considered as a rare syndrome [2] , we report an unusual cluster of seven patients who presented within a three-month period. All patients were otherwise healthy, and all presented with decreased vision in one eye. This cluster of cases could break to some measure the statistical improbability of the rarity of the disease. The atypical presentation in most of our patients could suggest that MEWDS is underdiagnosed. However, it may be in line with the speculation that sometimes atypical findings may simply reflect the moment in time in which the patients were examined and are not a true atypical presentation [4] . In its original description by Jampol et al. [2] , MEWDS cases were unilateral with fundus presentation including numerous white dots scattered in the posterior pole and beyond the arcades. During the disease course, granularity appearance of the macula develops in most cases and, when seen, determines the diagnosis. The number of white spots is very variable, and in fact, they may be absent. Given that characteristic white dots were not present in four patients (patients 1, 4, 5, and 6), we were guided by other fundus features, in particular foveal granularity, symptoms, multimodal imaging, and clinical course. While the presumed pathogenesis of MEWDS involves a viral infection, only few reports to date have described a search for the pathogen [5, [17] [18] [19] . The present cluster of cases provided us with a unique opportunity to seek a common viral denominator. Serological testing yielded only an elevated titer of IgG to VZV, most often an indicative of past VZV infection or vaccination; thus, we could not make any generalization regarding these findings.
Multimodal imaging (BAF, SD-OCT, IR, FA, and ICGA) has proven to have high value in the challenging diagnosis of MEWDS. Most of the findings noted here have been described separately in earlier reports [7-9, 11, 12] . However, the present study offered two important advantages. We were able to examine all patients with simultaneously acquired imaging, and multiple correlations between the imaging findings and the clinical evaluation were possible. Moreover, the relatively large size of the cohort and the repeated scans allowed us to verify the imaging findings in this rare disease.
We observed corresponding locations of the dark spots on IR images, the hyperautofluorescent spots on the BAF images, and the foci of outer retinal pathology on SD-OCT images. Small hyperreflective points, located in the ganglion cell layer, the ellipsoid zone, and the choriocapillaris, have been noted and described on "en face" EDI SD-OCT [20] . However, we noted a unique finding of foveal hyperreflectivity extending into the inner retinal layers. Our finding reinforces a recently described finding in the literature [14] which is believed to be pathognomonic to MEWDS. During the disease course, both the IR and the BAF findings faded in concurrence with the anatomical resolution of the disruption in the ellipsoid zone and the foveal hyperreflective lesion on SD-OCT. Thus, IR images may provide an easy, widely available imaging modality for follow-up of patients with MEWDS. Although IR autofluorescent changes were recently described in patients with MEWDS [21, 22] , this modality is not widely available, whereas IR imaging is routinely performed. Furthermore, on the basis of our findings with multimodal imaging, we suggest that the diagnosis of MEWDS can be established with the simultaneous use of such noninvasive techniques as BAF, IR, and SD-OCT. ICGA and FA may be reserved for secondary use, when findings are equivocal. OCTA is relatively new noninvasive imaging modality that demonstrates flow characteristics of the vascular network within the regional circulation to construct noninvasive images of the vascular network. En face images generated by OCTA also allow us to study the spatial relationships between vasculature and adjacent retinal/choroidal layers with greater precision than dye angiography, and OCTA findings demonstrated no flow impairment in the retinal and choroidal vasculature of the patients scanned after convalescence stage.
We cannot overestimate the role of multimodal imaging in these patients, since not too often, the diagnosis is mistaken for optic neuritis, and clinical findings are very subtle.
Limitations of the study were the variability in time from disease onset to serologic testing, making the IgM results hard to interpret. Therefore, we consider these tests inconclusive. Secondly, not all the patients had imaging with all modalities. In addition, future research is required using OCT angiography to study the nature of the dots in MEWDS patients and its correlation to other multimodal imaging modalities in the acute and convalescent stage.
In conclusion, we present a large unique cluster of patients who presented with MEWDS over a short period Figure 6 : OCTA images following convalescence stage of patients 7's right eye (a-b) and 6's left eye (c-d). The green and red lines represent the x and y axes. Patient 7 after recurrent episodes. 3 × 3 mm OCT angiogram of the choriocapillaris (a1), superficial layer (a2), and deep layer (a3) centered at the macula without any flow compromise. Corresponding x-axis OCT structural B-scan (b1) simultaneously obtained during the same scan as the OCT angiogram with flow overlay at the cross-section demonstrated by the green line in (a1). SD-OCT (b2) demonstrating normal anatomy of the outer retina 6 months after the first acute episode. Patient 6, 3× 3 mm OCT angiogram of the choriocapillaris (c1), superficial layer (c2), and deep layer (c3) centered at the macula without any flow compromise. 3 × 3 mm en face structural OCT (d1) of the choriocapillaris centered at the macula as in c1. This image was simultaneously obtained during the same scan as the OCT angiogram in (c). En face structural OCT of the deep (d2) and outer retina (d3). of time. To the best of our knowledge, such a cluster was not previously reported in the literature nor encountered by us at different seasons. The diagnosis was supported by the presence of key features of foveal granularity and disruption of the ellipsoid zone on OCT and their correlation with the hyperautofluorescent lesions identified on BAF. Attention should also be addressed to the dark spots demonstrated on IR images, which may serve as an additional diagnostic clue provided by a noninvasive imaging modality. The disease course in our patients was typical for MEWDS, with almost complete recovery of visual acuity. The specific pathogenesis of MEWDS is unknown but is believed to be an inflammatory condition following a viral infection. We suggest continued serological testing in patients who meet the clinical criteria. The clinical signs of MEWDS are subtle, such that the diagnosis relies on a high index of suspicion.
The authors have no conflict of interest to declare.
|
What types of viruses can be diagnosed through serological testing?
| 583 |
herpes simplex virus (HSV) I-II, varicella zoster virus (VZV), West Nile virus, coxsackievirus, echovirus (subgroup of enterovirus), and corona virus
| 5,843 |
1,554 |
Multimodal Imaging in an Unusual Cluster of Multiple Evanescent White Dot Syndrome
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5444036/
SHA: ee3cc22161595e877450737882a52950fd179672
Authors: Gal-Or, Orly; Priel, Ethan; Rosenblatt, Irit; Shulman, Shiri; Kramer, Michal
Date: 2017-05-11
DOI: 10.1155/2017/7535320
License: cc-by
Abstract: OBJECTIVE: To describe an unusual cluster of multiple evanescent white dot syndrome (MEWDS) encountered within a 3-month period. METHODS: This retrospective observation study is comprised of seven patients who presented with MEWDS in a 3-month period in central Israel. Data were collected from patients' medical records on clinical, multimodal imaging, and viral serology findings. RESULTS: Six women and one man of mean age 31.5 ± 7.2 years. Three reported a precedent viral infection. All had unilateral decreased vision. Funduscopy revealed foveal granularity. MAIN IMAGING FINDINGS: Hyperfluorescent spots on blue autofluorescence (BAF), hypofluorescent spots on indocyanine green angiography, dark lesions on infrared photos, and ellipsoid zone irregularities on spectral domain optical coherence tomography (SD-OCT). Resolution of the spots on BAF correlated with anatomic (SD-OCT) and visual recovery. OCT angiography performed following the convalescence stage demonstrated intact retinal and choroidal flow. Serologic findings were inconclusive. CONCLUSION: We report a unique cluster of MEWDS patients presented in a short period of time. SD-OCT findings of ellipsoid zone disruption in combination with other multimodal imaging modalities are outlined meticulously. Recognizing these imaging features along with high index of clinical suspicion is important for the diagnosis of MEWDS. Serologic testing might be considered in future patients.
Text: Multiple evanescent white dot syndrome (MEWDS) was first described in 1984 as a rare, sudden onset of unilateral chorioretinopathy, with the predominant sign being multifocal yellow-white spots throughout the retina [1, 2] . The clinical spectrum of MEWDS has expanded over the years to include bilaterality and recurrences [3] or an atypical presentation involving the fovea without the white spots [4] . Symptoms include acute onset of decreased visual acuity unilaterally accompanied in most cases by photopsia and scotomata. A prodromal flu-like illness has been reported in up to 50% of cases [1] . One report described a patient with elevated levels of total serum IgG during the disease course and negative findings for IgM to herpes zoster, herpes simplex, mumps, and measles [5] .
Although MEWDS is suspected to occur as a consequence of a viral-like infection in genetically susceptible individuals, its precise pathogenesis remains unknown. Recovery is gradual, over weeks to months, and the visual prognosis is very favorable [2] . Treatment is usually not required.
The incidence of MEWDS is unknown. Only small case series are reported in the literature [4] [5] [6] [7] [8] [9] [10] [11] [12] . One of the largest described 34 affected patients reviewed over several years' period [1, 13, 14] .
The aim of the present report was to describe an unusual cluster of seven cases of MEWDS encountered within a 3month period, with an emphasis on the clinical presentation and multimodal imaging findings. The cluster prompted us to seek a common infectious association.
A retrospective observational study was conducted in seven patients who presented with MEWDS between July and September 2013 at two tertiary medical centers in central Israel. Data on background, clinical, and laboratory parameters were collected from the medical files. The study was approved by the institutional ethics review board.
All patients underwent a comprehensive ophthalmic examination and multimodal imaging tests, including blue autofluorescence (BAF), fluorescein angiography (FA) and/ or indocyanine green angiography (ICGA), infrared (IR) photography, and spectral domain optical coherence tomography (SD-OCT). Images were acquired with the HRA-2 and the Spectralis HRA + OCT devices (Heidelberg Engineering, Heidelberg, Germany) at the following wavelengths: BAFexcitation 488 nm, barrier cut-off 496 nm; IR-820 nm; ICGA-excitation 790 nm, emission 800 nm; and SD-OCTsuperluminescent diode light source 870 nm. The volume scan option was used to acquire the multiple SD-OCT scans (25-49 horizontal scans over a 6 mm region covering the area of pathology). Precise registration between findings seen on IR or BAF and SD-OCT was enabled by the dual-beam laser eye-tracking system, where one laser is used to image the retina and the other laser to perform the OCT scans. Accurate rescanning in areas of interest was ensured by the Spectralis follow-up function which automatically places subsequent scans on the same location as the previous ones.
OCT angiography images were acquired using the RTVue XR Avanti with AngioVue (Optovue Inc., Fremont, California, USA), with an A-scan-rate of 70 000 scans per second, a light source of 840 nm, and a bandwidth of 45 nm. Macular cubes (3 × 3 mm) were acquired, each cube consisting of 304 clusters of 2 repeated B-scans containing 304 A-scans each. Split-spectrum amplitude decorrelation technology was employed to improve the signal-to-noise ratio by splitting the spectrum to generate multiple repeat OCT frames from 2 original repeat OCT frames [15] .
Motion correction was performed using registration of 2 orthogonally captured imaging volumes. Automatic segmentation of the retinal layers was performed by the viewing software and was used to generate en face projection images after adjusting the level of the segmented layer on the B-scans.
Serology testing was performed for viruses commonly present at the time of the patients' presentation, namely, immunoglobulin IgG and IgM for herpes simplex virus (HSV) I-II, varicella zoster virus (VZV), West Nile virus, coxsackievirus, echovirus (subgroup of enterovirus), and corona virus.
Findings. There were one male and six female patients of mean age 31.5 ± 7.2 years (range 22-41 years). Table 1 summarizes the demographic data. Three patients reported a prodromal virus infection.
All patients presented with acute onset of unilateral decreased vision. The best corrected visual acuity at presentation ranged from 6/9 to 6/30 in the affected eye. None of the patients had signs of anterior or vitreous inflammation in the affected eye. Funduscopic findings at presentation included foveal granularity in six patients; in four patients (patients 1, 4, 5, and 6), it was the sole pathologic retinal finding ( Figure 1 ); and in three patients (patients 2, 3, and 7), foveal granularity was associated with faint white retinal lesions (Figure 2 ), located mainly in the midperipheral retina extending to the periphery. Patient 6 had a swollen disc and mild signs of optic neuropathy (mild red desaturation, enlarged blind spot on visual field). Patient 6 underwent neurological evaluation due to initial presentation mimicking optic neuritis. Neurological evaluation including full neurological exam and neuroimaging excluded additional neurological deficit, before the diagnosis of MEWDS was established. The clinical findings are summarized in Table 2. 3.2. Multimodal Imaging Findings. Patients who underwent imaging less than 2 weeks from onset of symptoms had the most typical findings.
BAF revealed hyperautofluorescent lesions in the macula between and along the arcades in four patients (patients 1, 3, 6, and 7). IR photos showed dark lesions in similar, though not identical, locations ( Figure 3 ). Patients 1 and 6, who underwent ICGA, had hypofluorescent lesions in numbers typically exceeding those detected by both clinical and other imaging modalities. B-scan SD-OCT through the fovea showed a disrupted inner segment ellipsoid zone band of varied severity in all 7 affected eyes. The ellipsoid zone hyper reflective band on SD-OCT anatomically correlates to photoreceptors' inner segment, ellipsoid section densely packed with mitochondria [16] . The transient disruption of the foveal ellipsoid zone on SD-OCT corresponded to the clinically apparent foveal granularity. In patient 5, who presented with sole retinal finding of foveal granularity and mild optic disc leakage on FA, the SD-OCT finding of ellipsoid zone disruption was the main sign for diagnosis MEWDS (Figure 1 ). Foveal hyperreflectivity found in 3 patients (patients 1, 4, and 7) was noted extending into the inner retinal layers (Figure 4 ). The lesions identified on the BAF, IR, and ICGA images corresponded to the areas of disruption of the ellipsoid zone, on the SD-OCT scans ( Figure 3 ). FA demonstrated nonspecific early punctate hyperfluorescent lesions, with slight staining during the early phase, in four patients (patients 2, 3, 6, and 7). These lesions did not correspond to the findings by either the clinical or other imaging modalities. No pathology was noted in the foveal area despite the presence of typical foveal granularity. Mild optic disc leakage was evident in four patients (patients 1, 4, 5, and 6).
During the course of the disease, the hyperautofluorescent areas decreased in number and faded without leaving hypoautofluorescent abnormalities. The resolution of the BAF lesions corresponded to the anatomic recovery observed on SD-OCT. The foveal hyperreflectivity disappeared as well ( Figure 5 ). Figure 6 .
Four patients (patients 1, 4, 6, and 7) underwent serological testing with negative results except for a common result of elevated titer of IgG to VZV.
After 6 months of follow-up, the best corrected visual acuity ranged from 6/6 to 6/6.6 ( Table 2 ).
Although MEDWS is traditionally considered as a rare syndrome [2] , we report an unusual cluster of seven patients who presented within a three-month period. All patients were otherwise healthy, and all presented with decreased vision in one eye. This cluster of cases could break to some measure the statistical improbability of the rarity of the disease. The atypical presentation in most of our patients could suggest that MEWDS is underdiagnosed. However, it may be in line with the speculation that sometimes atypical findings may simply reflect the moment in time in which the patients were examined and are not a true atypical presentation [4] . In its original description by Jampol et al. [2] , MEWDS cases were unilateral with fundus presentation including numerous white dots scattered in the posterior pole and beyond the arcades. During the disease course, granularity appearance of the macula develops in most cases and, when seen, determines the diagnosis. The number of white spots is very variable, and in fact, they may be absent. Given that characteristic white dots were not present in four patients (patients 1, 4, 5, and 6), we were guided by other fundus features, in particular foveal granularity, symptoms, multimodal imaging, and clinical course. While the presumed pathogenesis of MEWDS involves a viral infection, only few reports to date have described a search for the pathogen [5, [17] [18] [19] . The present cluster of cases provided us with a unique opportunity to seek a common viral denominator. Serological testing yielded only an elevated titer of IgG to VZV, most often an indicative of past VZV infection or vaccination; thus, we could not make any generalization regarding these findings.
Multimodal imaging (BAF, SD-OCT, IR, FA, and ICGA) has proven to have high value in the challenging diagnosis of MEWDS. Most of the findings noted here have been described separately in earlier reports [7-9, 11, 12] . However, the present study offered two important advantages. We were able to examine all patients with simultaneously acquired imaging, and multiple correlations between the imaging findings and the clinical evaluation were possible. Moreover, the relatively large size of the cohort and the repeated scans allowed us to verify the imaging findings in this rare disease.
We observed corresponding locations of the dark spots on IR images, the hyperautofluorescent spots on the BAF images, and the foci of outer retinal pathology on SD-OCT images. Small hyperreflective points, located in the ganglion cell layer, the ellipsoid zone, and the choriocapillaris, have been noted and described on "en face" EDI SD-OCT [20] . However, we noted a unique finding of foveal hyperreflectivity extending into the inner retinal layers. Our finding reinforces a recently described finding in the literature [14] which is believed to be pathognomonic to MEWDS. During the disease course, both the IR and the BAF findings faded in concurrence with the anatomical resolution of the disruption in the ellipsoid zone and the foveal hyperreflective lesion on SD-OCT. Thus, IR images may provide an easy, widely available imaging modality for follow-up of patients with MEWDS. Although IR autofluorescent changes were recently described in patients with MEWDS [21, 22] , this modality is not widely available, whereas IR imaging is routinely performed. Furthermore, on the basis of our findings with multimodal imaging, we suggest that the diagnosis of MEWDS can be established with the simultaneous use of such noninvasive techniques as BAF, IR, and SD-OCT. ICGA and FA may be reserved for secondary use, when findings are equivocal. OCTA is relatively new noninvasive imaging modality that demonstrates flow characteristics of the vascular network within the regional circulation to construct noninvasive images of the vascular network. En face images generated by OCTA also allow us to study the spatial relationships between vasculature and adjacent retinal/choroidal layers with greater precision than dye angiography, and OCTA findings demonstrated no flow impairment in the retinal and choroidal vasculature of the patients scanned after convalescence stage.
We cannot overestimate the role of multimodal imaging in these patients, since not too often, the diagnosis is mistaken for optic neuritis, and clinical findings are very subtle.
Limitations of the study were the variability in time from disease onset to serologic testing, making the IgM results hard to interpret. Therefore, we consider these tests inconclusive. Secondly, not all the patients had imaging with all modalities. In addition, future research is required using OCT angiography to study the nature of the dots in MEWDS patients and its correlation to other multimodal imaging modalities in the acute and convalescent stage.
In conclusion, we present a large unique cluster of patients who presented with MEWDS over a short period Figure 6 : OCTA images following convalescence stage of patients 7's right eye (a-b) and 6's left eye (c-d). The green and red lines represent the x and y axes. Patient 7 after recurrent episodes. 3 × 3 mm OCT angiogram of the choriocapillaris (a1), superficial layer (a2), and deep layer (a3) centered at the macula without any flow compromise. Corresponding x-axis OCT structural B-scan (b1) simultaneously obtained during the same scan as the OCT angiogram with flow overlay at the cross-section demonstrated by the green line in (a1). SD-OCT (b2) demonstrating normal anatomy of the outer retina 6 months after the first acute episode. Patient 6, 3× 3 mm OCT angiogram of the choriocapillaris (c1), superficial layer (c2), and deep layer (c3) centered at the macula without any flow compromise. 3 × 3 mm en face structural OCT (d1) of the choriocapillaris centered at the macula as in c1. This image was simultaneously obtained during the same scan as the OCT angiogram in (c). En face structural OCT of the deep (d2) and outer retina (d3). of time. To the best of our knowledge, such a cluster was not previously reported in the literature nor encountered by us at different seasons. The diagnosis was supported by the presence of key features of foveal granularity and disruption of the ellipsoid zone on OCT and their correlation with the hyperautofluorescent lesions identified on BAF. Attention should also be addressed to the dark spots demonstrated on IR images, which may serve as an additional diagnostic clue provided by a noninvasive imaging modality. The disease course in our patients was typical for MEWDS, with almost complete recovery of visual acuity. The specific pathogenesis of MEWDS is unknown but is believed to be an inflammatory condition following a viral infection. We suggest continued serological testing in patients who meet the clinical criteria. The clinical signs of MEWDS are subtle, such that the diagnosis relies on a high index of suspicion.
The authors have no conflict of interest to declare.
|
What type of clinical test can differentiate multiple evanescent white dot syndrome (MEWDS) from optic neuritis?
| 584 |
multimodal imaging
| 13,925 |
1,568 |
Etiology of respiratory tract infections in the community and clinic in Ilorin, Nigeria
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5719735/
SHA: f2e835d2cde5f42054dbd0c20d4060721135c518
Authors: Kolawole, Olatunji; Oguntoye, Michael; Dam, Tina; Chunara, Rumi
Date: 2017-12-07
DOI: 10.1186/s13104-017-3063-1
License: cc-by
Abstract: OBJECTIVE: Recognizing increasing interest in community disease surveillance globally, the goal of this study was to investigate whether respiratory viruses circulating in the community may be represented through clinical (hospital) surveillance in Nigeria. RESULTS: Children were selected via convenience sampling from communities and a tertiary care center (n = 91) during spring 2017 in Ilorin, Nigeria. Nasal swabs were collected and tested using polymerase chain reaction. The majority (79.1%) of subjects were under 6 years old, of whom 46 were infected (63.9%). A total of 33 of the 91 subjects had one or more respiratory tract virus; there were 10 cases of triple infection and 5 of quadruple. Parainfluenza virus 4, respiratory syncytial virus B and enterovirus were the most common viruses in the clinical sample; present in 93.8% (15/16) of clinical subjects, and 6.7% (5/75) of community subjects (significant difference, p < 0.001). Coronavirus OC43 was the most common virus detected in community members (13.3%, 10/75). A different strain, Coronavirus OC 229 E/NL63 was detected among subjects from the clinic (2/16) and not detected in the community. This pilot study provides evidence that data from the community can potentially represent different information than that sourced clinically, suggesting the need for community surveillance to enhance public health efforts and scientific understanding of respiratory infections.
Text: Acute Respiratory Infections (ARIs) (the cause of both upper respiratory tract infections (URIs) and lower respiratory tract infections (LRIs)) are a major cause of death among children under 5 years old particularly in developing countries where the burden of disease is 2-5 times higher than in developed countries [1] . While these viruses usually cause mild cold-like symptoms and can be self-limiting, in recent years novel coronaviruses such as severe acute respiratory syndrome (SARS) and Middle East respiratory syndrome (MERS) have evolved and infected humans, causing severe illness, epidemics and pandemics [2] . Currently, the majority of all infectious disease outbreaks as recorded by the World Health Organization (WHO) occur in the continent of Africa where there is high transmission risk [3, 4] . Further, in developing areas (both rural and urban), there are increasing risk factors such as human-animal interfaces (due to residential-proximity to livestock). These changing epidemiological patterns have resulted in calls for improved ARI surveillance, especially in places of high transmission risk [5] .
Nigeria is one such place with high prevalence of many of the risk factors implicated in ARI among children including; age, sex, overcrowding, nutritional status, socio-economic status, and where study of ARIs is currently limited [6] . These broad risk factors alongside limited resources have indicated the need for community-based initiatives for surveillance and interventions [6, 7] . For ARI surveillance in particular, infections in the community are those that do not get reported clinically. Clinical data generally represents the most severe cases, and those from locations with access to healthcare institutions. In Nigeria, hospitals are visited only when symptoms are very severe. Thus, it is hypothesized that viral information from clinical sampling is insufficient to either capture disease incidence in general populations or its predictability from symptoms [8] . Efforts worldwide including in East and Southern Africa have been focused on developing community-based participatory disease surveillance methods [9] [10] [11] [12] [13] . Community-based approaches have been shown useful for learning more about emerging respiratory infections such as assessing under-reporting [14] , types of viruses prevalent in communities [10] , and prediction of epidemics [15] .
Concurrently, advancements in molecular identification methods have enabled studies regarding the emergence and epidemiology of ARI viruses in many locations (e.g. novel polyomaviruses in Australia [16, 17] , human coronavirus Erasmus Medical Center (HCoV-EMC) in the Middle East and United Kingdom [18, 19] , SARS in Canada and China [20] [21] [22] ), yet research regarding the molecular epidemiology of ARI viruses in Nigeria is limited. Diagnostic methods available and other constraints have limited studies there to serological surveys of only a few of these viruses and only in clinical populations [23, 24] . Thus, the utility of community-based surveillance may be appropriate in contexts such as in Nigeria, and the purpose of this pilot study was to investigate if clinical cases may describe the entire picture of ARI among children in Nigeria.
We performed a cross-sectional study in three community centers and one clinical in Ilorin, Nigeria. Ilorin is in Kwara state and is the 6th largest city in Nigeria by population [25] . Three Local Government Areas (Ilorin East, Ilorin South and Ilorin West LGAs) were the community sites and Children's Specialist Hospital, Ilorin the clinical site. Convenience sampling was used for the purposes of this pilot study, and samples were obtained from March 28 to April 5 2017. Inclusion criteria were: children less than 14 years old who had visible symptoms of ARI within the communities or those confirmed at the hospital with ARI. Exclusion criteria were: children who were 14 and above, not showing signs of ARI and subjects whose parents did not give consent. Twenty-five children with symptoms were selected each from the three community locations while 16 symptomatic children were sampled from the hospital. The total sample size (n = 91) was arrived at based on materials and processing cost constraints, as well as to provide enough samples to enable descriptive understanding of viral circulation patterns estimated from other community-based studies [10] .
Disease Surveillance and Notification Officers, who are employed by the State Ministry of Health and familiar with the communities in this study, performed specimen and data collection. Symptoms considered were derived in accordance with other ARI surveillance efforts: sore throat, fever, couch, running nose, vomiting, body ache, leg pain, nausea, chills, shortness of breath [10, 26] . Gender and age, type of residential area (rural/urban), education level, proximity of residence to livestock, proximity to an untarred road and number of people who sleep in same room, were all recorded. The general difference between the two settings was that those from the hospital had severe illnesses, while those from the community were generally "healthy" but exhibiting ARI symptoms (i.e. mild illness).
Nasal swabs were collected from the subjects and stored in DNA/RNA shield (Zymo Research, Irvine, California). Collected samples were spinned and the swab removed. Residues containing the nasal samples were stored at -20 °C prior to molecular analysis.
Viral RNA was isolated using ZR Viral RNA ™ Kit (Zymo Research, Irvine, California) per manufacturer instructions (http://www.zymoresearch.com/downloads/dl/file/ id/147/r1034i.pdf ). Real-time PCR (polymerase chain reaction), commonly used in ARI studies [10, 19, 27] , was then carried out using RV15 One Step ACE Detection Kit, catalogue numbers RV0716K01008007 and RV0717B01008001 (Seegene, Seoul, South Korea) for detection of 15 human viruses: parainfluenza virus 1, 2, 3 and 4 (PIV1-4), respiratory syncytial virus (RSV) A and B, influenza A and B (FLUA, FLUB), rhinovirus type A-C, adenovirus (ADV), coronavirus (OC 229 E/NL63, OC43), enterovirus (HEV), metapneumovirus (hMPV) and bocavirus (BoV).
Reagents were validated in the experimental location using an inbuilt validation protocol to confirm issues of false negative and false positive results were not of concern. Amplification reaction was carried out as described by the manufacturer: reverse transcription 50 °C-30′, initial activation 94°-15′, 45 cycles: denaturation 94°-30″, annealing 60°-1′ 30″, extension 72°-1, final extension 72°-10′, hold 4°. Visualization was performed using electrophoresis on a 2% agarose gel in TBE 1X with EtBr, in presence of RV15 OneStep A/B/C Markers; molecular weight marker. Specimen processing was not blinded as there was no risk of experimental bias. Standardized procedures were used for community and clinic sampling.
All statistical analyses were performed using R version 3.2.4. Univariate statistics [mean and 95% confidence interval (CI)] are described. Bivariate statistics (difference in proportions) were assessed using a two-proportion z-test. A p value < 0.001 was considered significant. No observations used in this study had any missing data for analyses in this study.
Basic participant demographics are summarized in PCR results showed that ten different viruses (influenza A, coronavirus OC 229 E/NL63, RSVA, RSV B, parainfluenza 1-4) were detected. Figure 1 shows how these infections were distributed across virus types as well as in the community versus clinic samples. In sum, a total of 33 of the 91 subjects surveyed had one or more respiratory tract virus (36.3%, 95% CI 26.6-47.0%, Fig. 1 ). Furthermore, 10 of those cases were triple infections and 5 were quadruple infections (illustrated by color of bars in Fig. 1 ). Figure 2 indicates how frequently each pair of viruses were found in the same participant; co-infections were most common among enterovirus and parainfluenza virus 4 (Fig. 2) .
We also compared and contrasted the clinical and community results. Parainfluenza virus 4, respiratory syncytial virus B and enterovirus were the most common viruses found in the clinical sample. These three infections resulted in 41 viruses detected in 15 subjects clinically, and eight infections detected in five people in the community. Together they infected 94% (15/16, 95% CI 67.7-99.7%) of clinical subjects, and 7% (5/75, 95% CI 2.5-15.5%) in the community (significant difference, p < 0.001). The most common virus detected in community samples was Coronavirus OC43; this virus was detected in 13.3% (95% CI 6.9-23.6%) people in the community and not in any of the clinical samples. However a different strain, coronavirus OC 229 E/NL63 was detected in 12.5% of the clinical subjects (2/16, 95% CI 2.2-39.6%) and not detected in the community. Double, triple and quadruple infections were another common feature of note.
We identified ten different respiratory tract viruses among the subjects as shown in Fig. 1 . Samples collected from the Children's specialist hospital showed 100% prevalence rate of infection with one or more viruses. This might not be surprising, as the basic difference between the community and clinic samples was an increased severity of illness in the clinical sample. This may also explain the high level of co-infection found among the clinical subjects. The most prevalent virus in the clinical sample (coronavirus OC43) was not detected in the community sample. Further, there was a significant difference between prevalence of the most common viruses in the clinical sample (parainfluenza virus 4, respiratory syncytial virus B and enterovirus) and their prevalence in the community. Finally, some of the viruses detected in this study have not been detected and implicated with ARIs in Nigeria. There is no report, to the best of our knowledge, implicating coronavirus in ARIs in Nigeria, and it was detected in 12 subjects in this study. Although cases of double and triple infections were observed in a study in Nigeria in 2011 [28] , as far as we are aware, reports of quadruple infections are rare and have not been reported in Nigeria previously.
Due to the unique nature of the data generated in this study and novelty of work in the setting, it is not possible to exactly compare results to other studies. For example, though we found a similar study regarding ARIs in clinical subjects in Burkina Faso [27] , due to the small sample size from this study it would not be feasible to infer or compare prevalence rates. Studies of ARI etiology have mostly been generally focused in areas of the world that are more developed [29] , and it is important to note that the availability of molecular diagnostic methods as employed in this study substantially improve the ability to detect viruses which hitherto have not been detected in Nigeria. Further, findings from this work also add to the growing body of research that shows value of community-data in infectious disease surveillance [8] . As most of the work to-date has been in higher resource areas of the world this study adds perspective from an area where healthcare resources are lower.
In conclusion, results of this study provide evidence for active community surveillance to enhance public health surveillance and scientific understanding of ARIs. This is not only because a minority of children with severe infection are admitted to the hospital in areas such this in Nigeria, but also findings from this pilot study which indicate that viral circulation in the community may not get detected clinically [29] . This pilot study indicates that in areas of Nigeria, etiology of ARIs ascertained from clinical samples may not represent all of the ARIs circulating in the community.
The main limitation of the study is the sample size. In particular, the sample is not equally representative across all ages. However, the sample size was big enough to ascertain significant differences in community and clinic sourced viruses, and provides a qualitative understanding of viral etiology in samples from the community and clinic. Moreover, the sample was largely concentrated on subjects under 6 years, who are amongst the groups at highest risk of ARIs. Despite the small sample size, samples here indicate that circulation patterns in the community may differ from those in the clinic. In addition, this study resulted in unique findings Given that resources are limited for research and practice, we hope these pilot results may motivate further systematic investigations into how community-generated data can best be used in ARI surveillance. Results of this study can inform a larger study, representative across demographic and locations to systematically assess the etiology of infection and differences in clinical and community cohorts. A larger study will also enable accounting for potential confounders such as environmental risk factors. Finally, while it may be intuitive, findings from this pilot study shed light on the scope of differences in ARI patterns including different types and strains of circulating viruses. Also, because PCR was used for viral detection, the study was limited to detection of viruses in the primer sets. Given that these are the most up-to-date and common viruses, this approach was deemed sufficient for this initial investigation.
The study was conceived by RC and OK. RC and OK, MO and TD were involved in the design of the study, which was conducted by MO and TD. RC and OK analyzed the data. RC and OK wrote and revised the manuscript. All authors read and approved the final manuscript.
|
What were the most common viruses sampled from nasal swabs in Ilorin, Nigeria
| 1,597 |
Parainfluenza virus 4, respiratory syncytial virus B and enterovirus
| 1,041 |
1,568 |
Etiology of respiratory tract infections in the community and clinic in Ilorin, Nigeria
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5719735/
SHA: f2e835d2cde5f42054dbd0c20d4060721135c518
Authors: Kolawole, Olatunji; Oguntoye, Michael; Dam, Tina; Chunara, Rumi
Date: 2017-12-07
DOI: 10.1186/s13104-017-3063-1
License: cc-by
Abstract: OBJECTIVE: Recognizing increasing interest in community disease surveillance globally, the goal of this study was to investigate whether respiratory viruses circulating in the community may be represented through clinical (hospital) surveillance in Nigeria. RESULTS: Children were selected via convenience sampling from communities and a tertiary care center (n = 91) during spring 2017 in Ilorin, Nigeria. Nasal swabs were collected and tested using polymerase chain reaction. The majority (79.1%) of subjects were under 6 years old, of whom 46 were infected (63.9%). A total of 33 of the 91 subjects had one or more respiratory tract virus; there were 10 cases of triple infection and 5 of quadruple. Parainfluenza virus 4, respiratory syncytial virus B and enterovirus were the most common viruses in the clinical sample; present in 93.8% (15/16) of clinical subjects, and 6.7% (5/75) of community subjects (significant difference, p < 0.001). Coronavirus OC43 was the most common virus detected in community members (13.3%, 10/75). A different strain, Coronavirus OC 229 E/NL63 was detected among subjects from the clinic (2/16) and not detected in the community. This pilot study provides evidence that data from the community can potentially represent different information than that sourced clinically, suggesting the need for community surveillance to enhance public health efforts and scientific understanding of respiratory infections.
Text: Acute Respiratory Infections (ARIs) (the cause of both upper respiratory tract infections (URIs) and lower respiratory tract infections (LRIs)) are a major cause of death among children under 5 years old particularly in developing countries where the burden of disease is 2-5 times higher than in developed countries [1] . While these viruses usually cause mild cold-like symptoms and can be self-limiting, in recent years novel coronaviruses such as severe acute respiratory syndrome (SARS) and Middle East respiratory syndrome (MERS) have evolved and infected humans, causing severe illness, epidemics and pandemics [2] . Currently, the majority of all infectious disease outbreaks as recorded by the World Health Organization (WHO) occur in the continent of Africa where there is high transmission risk [3, 4] . Further, in developing areas (both rural and urban), there are increasing risk factors such as human-animal interfaces (due to residential-proximity to livestock). These changing epidemiological patterns have resulted in calls for improved ARI surveillance, especially in places of high transmission risk [5] .
Nigeria is one such place with high prevalence of many of the risk factors implicated in ARI among children including; age, sex, overcrowding, nutritional status, socio-economic status, and where study of ARIs is currently limited [6] . These broad risk factors alongside limited resources have indicated the need for community-based initiatives for surveillance and interventions [6, 7] . For ARI surveillance in particular, infections in the community are those that do not get reported clinically. Clinical data generally represents the most severe cases, and those from locations with access to healthcare institutions. In Nigeria, hospitals are visited only when symptoms are very severe. Thus, it is hypothesized that viral information from clinical sampling is insufficient to either capture disease incidence in general populations or its predictability from symptoms [8] . Efforts worldwide including in East and Southern Africa have been focused on developing community-based participatory disease surveillance methods [9] [10] [11] [12] [13] . Community-based approaches have been shown useful for learning more about emerging respiratory infections such as assessing under-reporting [14] , types of viruses prevalent in communities [10] , and prediction of epidemics [15] .
Concurrently, advancements in molecular identification methods have enabled studies regarding the emergence and epidemiology of ARI viruses in many locations (e.g. novel polyomaviruses in Australia [16, 17] , human coronavirus Erasmus Medical Center (HCoV-EMC) in the Middle East and United Kingdom [18, 19] , SARS in Canada and China [20] [21] [22] ), yet research regarding the molecular epidemiology of ARI viruses in Nigeria is limited. Diagnostic methods available and other constraints have limited studies there to serological surveys of only a few of these viruses and only in clinical populations [23, 24] . Thus, the utility of community-based surveillance may be appropriate in contexts such as in Nigeria, and the purpose of this pilot study was to investigate if clinical cases may describe the entire picture of ARI among children in Nigeria.
We performed a cross-sectional study in three community centers and one clinical in Ilorin, Nigeria. Ilorin is in Kwara state and is the 6th largest city in Nigeria by population [25] . Three Local Government Areas (Ilorin East, Ilorin South and Ilorin West LGAs) were the community sites and Children's Specialist Hospital, Ilorin the clinical site. Convenience sampling was used for the purposes of this pilot study, and samples were obtained from March 28 to April 5 2017. Inclusion criteria were: children less than 14 years old who had visible symptoms of ARI within the communities or those confirmed at the hospital with ARI. Exclusion criteria were: children who were 14 and above, not showing signs of ARI and subjects whose parents did not give consent. Twenty-five children with symptoms were selected each from the three community locations while 16 symptomatic children were sampled from the hospital. The total sample size (n = 91) was arrived at based on materials and processing cost constraints, as well as to provide enough samples to enable descriptive understanding of viral circulation patterns estimated from other community-based studies [10] .
Disease Surveillance and Notification Officers, who are employed by the State Ministry of Health and familiar with the communities in this study, performed specimen and data collection. Symptoms considered were derived in accordance with other ARI surveillance efforts: sore throat, fever, couch, running nose, vomiting, body ache, leg pain, nausea, chills, shortness of breath [10, 26] . Gender and age, type of residential area (rural/urban), education level, proximity of residence to livestock, proximity to an untarred road and number of people who sleep in same room, were all recorded. The general difference between the two settings was that those from the hospital had severe illnesses, while those from the community were generally "healthy" but exhibiting ARI symptoms (i.e. mild illness).
Nasal swabs were collected from the subjects and stored in DNA/RNA shield (Zymo Research, Irvine, California). Collected samples were spinned and the swab removed. Residues containing the nasal samples were stored at -20 °C prior to molecular analysis.
Viral RNA was isolated using ZR Viral RNA ™ Kit (Zymo Research, Irvine, California) per manufacturer instructions (http://www.zymoresearch.com/downloads/dl/file/ id/147/r1034i.pdf ). Real-time PCR (polymerase chain reaction), commonly used in ARI studies [10, 19, 27] , was then carried out using RV15 One Step ACE Detection Kit, catalogue numbers RV0716K01008007 and RV0717B01008001 (Seegene, Seoul, South Korea) for detection of 15 human viruses: parainfluenza virus 1, 2, 3 and 4 (PIV1-4), respiratory syncytial virus (RSV) A and B, influenza A and B (FLUA, FLUB), rhinovirus type A-C, adenovirus (ADV), coronavirus (OC 229 E/NL63, OC43), enterovirus (HEV), metapneumovirus (hMPV) and bocavirus (BoV).
Reagents were validated in the experimental location using an inbuilt validation protocol to confirm issues of false negative and false positive results were not of concern. Amplification reaction was carried out as described by the manufacturer: reverse transcription 50 °C-30′, initial activation 94°-15′, 45 cycles: denaturation 94°-30″, annealing 60°-1′ 30″, extension 72°-1, final extension 72°-10′, hold 4°. Visualization was performed using electrophoresis on a 2% agarose gel in TBE 1X with EtBr, in presence of RV15 OneStep A/B/C Markers; molecular weight marker. Specimen processing was not blinded as there was no risk of experimental bias. Standardized procedures were used for community and clinic sampling.
All statistical analyses were performed using R version 3.2.4. Univariate statistics [mean and 95% confidence interval (CI)] are described. Bivariate statistics (difference in proportions) were assessed using a two-proportion z-test. A p value < 0.001 was considered significant. No observations used in this study had any missing data for analyses in this study.
Basic participant demographics are summarized in PCR results showed that ten different viruses (influenza A, coronavirus OC 229 E/NL63, RSVA, RSV B, parainfluenza 1-4) were detected. Figure 1 shows how these infections were distributed across virus types as well as in the community versus clinic samples. In sum, a total of 33 of the 91 subjects surveyed had one or more respiratory tract virus (36.3%, 95% CI 26.6-47.0%, Fig. 1 ). Furthermore, 10 of those cases were triple infections and 5 were quadruple infections (illustrated by color of bars in Fig. 1 ). Figure 2 indicates how frequently each pair of viruses were found in the same participant; co-infections were most common among enterovirus and parainfluenza virus 4 (Fig. 2) .
We also compared and contrasted the clinical and community results. Parainfluenza virus 4, respiratory syncytial virus B and enterovirus were the most common viruses found in the clinical sample. These three infections resulted in 41 viruses detected in 15 subjects clinically, and eight infections detected in five people in the community. Together they infected 94% (15/16, 95% CI 67.7-99.7%) of clinical subjects, and 7% (5/75, 95% CI 2.5-15.5%) in the community (significant difference, p < 0.001). The most common virus detected in community samples was Coronavirus OC43; this virus was detected in 13.3% (95% CI 6.9-23.6%) people in the community and not in any of the clinical samples. However a different strain, coronavirus OC 229 E/NL63 was detected in 12.5% of the clinical subjects (2/16, 95% CI 2.2-39.6%) and not detected in the community. Double, triple and quadruple infections were another common feature of note.
We identified ten different respiratory tract viruses among the subjects as shown in Fig. 1 . Samples collected from the Children's specialist hospital showed 100% prevalence rate of infection with one or more viruses. This might not be surprising, as the basic difference between the community and clinic samples was an increased severity of illness in the clinical sample. This may also explain the high level of co-infection found among the clinical subjects. The most prevalent virus in the clinical sample (coronavirus OC43) was not detected in the community sample. Further, there was a significant difference between prevalence of the most common viruses in the clinical sample (parainfluenza virus 4, respiratory syncytial virus B and enterovirus) and their prevalence in the community. Finally, some of the viruses detected in this study have not been detected and implicated with ARIs in Nigeria. There is no report, to the best of our knowledge, implicating coronavirus in ARIs in Nigeria, and it was detected in 12 subjects in this study. Although cases of double and triple infections were observed in a study in Nigeria in 2011 [28] , as far as we are aware, reports of quadruple infections are rare and have not been reported in Nigeria previously.
Due to the unique nature of the data generated in this study and novelty of work in the setting, it is not possible to exactly compare results to other studies. For example, though we found a similar study regarding ARIs in clinical subjects in Burkina Faso [27] , due to the small sample size from this study it would not be feasible to infer or compare prevalence rates. Studies of ARI etiology have mostly been generally focused in areas of the world that are more developed [29] , and it is important to note that the availability of molecular diagnostic methods as employed in this study substantially improve the ability to detect viruses which hitherto have not been detected in Nigeria. Further, findings from this work also add to the growing body of research that shows value of community-data in infectious disease surveillance [8] . As most of the work to-date has been in higher resource areas of the world this study adds perspective from an area where healthcare resources are lower.
In conclusion, results of this study provide evidence for active community surveillance to enhance public health surveillance and scientific understanding of ARIs. This is not only because a minority of children with severe infection are admitted to the hospital in areas such this in Nigeria, but also findings from this pilot study which indicate that viral circulation in the community may not get detected clinically [29] . This pilot study indicates that in areas of Nigeria, etiology of ARIs ascertained from clinical samples may not represent all of the ARIs circulating in the community.
The main limitation of the study is the sample size. In particular, the sample is not equally representative across all ages. However, the sample size was big enough to ascertain significant differences in community and clinic sourced viruses, and provides a qualitative understanding of viral etiology in samples from the community and clinic. Moreover, the sample was largely concentrated on subjects under 6 years, who are amongst the groups at highest risk of ARIs. Despite the small sample size, samples here indicate that circulation patterns in the community may differ from those in the clinic. In addition, this study resulted in unique findings Given that resources are limited for research and practice, we hope these pilot results may motivate further systematic investigations into how community-generated data can best be used in ARI surveillance. Results of this study can inform a larger study, representative across demographic and locations to systematically assess the etiology of infection and differences in clinical and community cohorts. A larger study will also enable accounting for potential confounders such as environmental risk factors. Finally, while it may be intuitive, findings from this pilot study shed light on the scope of differences in ARI patterns including different types and strains of circulating viruses. Also, because PCR was used for viral detection, the study was limited to detection of viruses in the primer sets. Given that these are the most up-to-date and common viruses, this approach was deemed sufficient for this initial investigation.
The study was conceived by RC and OK. RC and OK, MO and TD were involved in the design of the study, which was conducted by MO and TD. RC and OK analyzed the data. RC and OK wrote and revised the manuscript. All authors read and approved the final manuscript.
|
What was the most common virus detected in community members in this sample?
| 1,598 |
Coronavirus OC43
| 1,285 |
1,568 |
Etiology of respiratory tract infections in the community and clinic in Ilorin, Nigeria
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5719735/
SHA: f2e835d2cde5f42054dbd0c20d4060721135c518
Authors: Kolawole, Olatunji; Oguntoye, Michael; Dam, Tina; Chunara, Rumi
Date: 2017-12-07
DOI: 10.1186/s13104-017-3063-1
License: cc-by
Abstract: OBJECTIVE: Recognizing increasing interest in community disease surveillance globally, the goal of this study was to investigate whether respiratory viruses circulating in the community may be represented through clinical (hospital) surveillance in Nigeria. RESULTS: Children were selected via convenience sampling from communities and a tertiary care center (n = 91) during spring 2017 in Ilorin, Nigeria. Nasal swabs were collected and tested using polymerase chain reaction. The majority (79.1%) of subjects were under 6 years old, of whom 46 were infected (63.9%). A total of 33 of the 91 subjects had one or more respiratory tract virus; there were 10 cases of triple infection and 5 of quadruple. Parainfluenza virus 4, respiratory syncytial virus B and enterovirus were the most common viruses in the clinical sample; present in 93.8% (15/16) of clinical subjects, and 6.7% (5/75) of community subjects (significant difference, p < 0.001). Coronavirus OC43 was the most common virus detected in community members (13.3%, 10/75). A different strain, Coronavirus OC 229 E/NL63 was detected among subjects from the clinic (2/16) and not detected in the community. This pilot study provides evidence that data from the community can potentially represent different information than that sourced clinically, suggesting the need for community surveillance to enhance public health efforts and scientific understanding of respiratory infections.
Text: Acute Respiratory Infections (ARIs) (the cause of both upper respiratory tract infections (URIs) and lower respiratory tract infections (LRIs)) are a major cause of death among children under 5 years old particularly in developing countries where the burden of disease is 2-5 times higher than in developed countries [1] . While these viruses usually cause mild cold-like symptoms and can be self-limiting, in recent years novel coronaviruses such as severe acute respiratory syndrome (SARS) and Middle East respiratory syndrome (MERS) have evolved and infected humans, causing severe illness, epidemics and pandemics [2] . Currently, the majority of all infectious disease outbreaks as recorded by the World Health Organization (WHO) occur in the continent of Africa where there is high transmission risk [3, 4] . Further, in developing areas (both rural and urban), there are increasing risk factors such as human-animal interfaces (due to residential-proximity to livestock). These changing epidemiological patterns have resulted in calls for improved ARI surveillance, especially in places of high transmission risk [5] .
Nigeria is one such place with high prevalence of many of the risk factors implicated in ARI among children including; age, sex, overcrowding, nutritional status, socio-economic status, and where study of ARIs is currently limited [6] . These broad risk factors alongside limited resources have indicated the need for community-based initiatives for surveillance and interventions [6, 7] . For ARI surveillance in particular, infections in the community are those that do not get reported clinically. Clinical data generally represents the most severe cases, and those from locations with access to healthcare institutions. In Nigeria, hospitals are visited only when symptoms are very severe. Thus, it is hypothesized that viral information from clinical sampling is insufficient to either capture disease incidence in general populations or its predictability from symptoms [8] . Efforts worldwide including in East and Southern Africa have been focused on developing community-based participatory disease surveillance methods [9] [10] [11] [12] [13] . Community-based approaches have been shown useful for learning more about emerging respiratory infections such as assessing under-reporting [14] , types of viruses prevalent in communities [10] , and prediction of epidemics [15] .
Concurrently, advancements in molecular identification methods have enabled studies regarding the emergence and epidemiology of ARI viruses in many locations (e.g. novel polyomaviruses in Australia [16, 17] , human coronavirus Erasmus Medical Center (HCoV-EMC) in the Middle East and United Kingdom [18, 19] , SARS in Canada and China [20] [21] [22] ), yet research regarding the molecular epidemiology of ARI viruses in Nigeria is limited. Diagnostic methods available and other constraints have limited studies there to serological surveys of only a few of these viruses and only in clinical populations [23, 24] . Thus, the utility of community-based surveillance may be appropriate in contexts such as in Nigeria, and the purpose of this pilot study was to investigate if clinical cases may describe the entire picture of ARI among children in Nigeria.
We performed a cross-sectional study in three community centers and one clinical in Ilorin, Nigeria. Ilorin is in Kwara state and is the 6th largest city in Nigeria by population [25] . Three Local Government Areas (Ilorin East, Ilorin South and Ilorin West LGAs) were the community sites and Children's Specialist Hospital, Ilorin the clinical site. Convenience sampling was used for the purposes of this pilot study, and samples were obtained from March 28 to April 5 2017. Inclusion criteria were: children less than 14 years old who had visible symptoms of ARI within the communities or those confirmed at the hospital with ARI. Exclusion criteria were: children who were 14 and above, not showing signs of ARI and subjects whose parents did not give consent. Twenty-five children with symptoms were selected each from the three community locations while 16 symptomatic children were sampled from the hospital. The total sample size (n = 91) was arrived at based on materials and processing cost constraints, as well as to provide enough samples to enable descriptive understanding of viral circulation patterns estimated from other community-based studies [10] .
Disease Surveillance and Notification Officers, who are employed by the State Ministry of Health and familiar with the communities in this study, performed specimen and data collection. Symptoms considered were derived in accordance with other ARI surveillance efforts: sore throat, fever, couch, running nose, vomiting, body ache, leg pain, nausea, chills, shortness of breath [10, 26] . Gender and age, type of residential area (rural/urban), education level, proximity of residence to livestock, proximity to an untarred road and number of people who sleep in same room, were all recorded. The general difference between the two settings was that those from the hospital had severe illnesses, while those from the community were generally "healthy" but exhibiting ARI symptoms (i.e. mild illness).
Nasal swabs were collected from the subjects and stored in DNA/RNA shield (Zymo Research, Irvine, California). Collected samples were spinned and the swab removed. Residues containing the nasal samples were stored at -20 °C prior to molecular analysis.
Viral RNA was isolated using ZR Viral RNA ™ Kit (Zymo Research, Irvine, California) per manufacturer instructions (http://www.zymoresearch.com/downloads/dl/file/ id/147/r1034i.pdf ). Real-time PCR (polymerase chain reaction), commonly used in ARI studies [10, 19, 27] , was then carried out using RV15 One Step ACE Detection Kit, catalogue numbers RV0716K01008007 and RV0717B01008001 (Seegene, Seoul, South Korea) for detection of 15 human viruses: parainfluenza virus 1, 2, 3 and 4 (PIV1-4), respiratory syncytial virus (RSV) A and B, influenza A and B (FLUA, FLUB), rhinovirus type A-C, adenovirus (ADV), coronavirus (OC 229 E/NL63, OC43), enterovirus (HEV), metapneumovirus (hMPV) and bocavirus (BoV).
Reagents were validated in the experimental location using an inbuilt validation protocol to confirm issues of false negative and false positive results were not of concern. Amplification reaction was carried out as described by the manufacturer: reverse transcription 50 °C-30′, initial activation 94°-15′, 45 cycles: denaturation 94°-30″, annealing 60°-1′ 30″, extension 72°-1, final extension 72°-10′, hold 4°. Visualization was performed using electrophoresis on a 2% agarose gel in TBE 1X with EtBr, in presence of RV15 OneStep A/B/C Markers; molecular weight marker. Specimen processing was not blinded as there was no risk of experimental bias. Standardized procedures were used for community and clinic sampling.
All statistical analyses were performed using R version 3.2.4. Univariate statistics [mean and 95% confidence interval (CI)] are described. Bivariate statistics (difference in proportions) were assessed using a two-proportion z-test. A p value < 0.001 was considered significant. No observations used in this study had any missing data for analyses in this study.
Basic participant demographics are summarized in PCR results showed that ten different viruses (influenza A, coronavirus OC 229 E/NL63, RSVA, RSV B, parainfluenza 1-4) were detected. Figure 1 shows how these infections were distributed across virus types as well as in the community versus clinic samples. In sum, a total of 33 of the 91 subjects surveyed had one or more respiratory tract virus (36.3%, 95% CI 26.6-47.0%, Fig. 1 ). Furthermore, 10 of those cases were triple infections and 5 were quadruple infections (illustrated by color of bars in Fig. 1 ). Figure 2 indicates how frequently each pair of viruses were found in the same participant; co-infections were most common among enterovirus and parainfluenza virus 4 (Fig. 2) .
We also compared and contrasted the clinical and community results. Parainfluenza virus 4, respiratory syncytial virus B and enterovirus were the most common viruses found in the clinical sample. These three infections resulted in 41 viruses detected in 15 subjects clinically, and eight infections detected in five people in the community. Together they infected 94% (15/16, 95% CI 67.7-99.7%) of clinical subjects, and 7% (5/75, 95% CI 2.5-15.5%) in the community (significant difference, p < 0.001). The most common virus detected in community samples was Coronavirus OC43; this virus was detected in 13.3% (95% CI 6.9-23.6%) people in the community and not in any of the clinical samples. However a different strain, coronavirus OC 229 E/NL63 was detected in 12.5% of the clinical subjects (2/16, 95% CI 2.2-39.6%) and not detected in the community. Double, triple and quadruple infections were another common feature of note.
We identified ten different respiratory tract viruses among the subjects as shown in Fig. 1 . Samples collected from the Children's specialist hospital showed 100% prevalence rate of infection with one or more viruses. This might not be surprising, as the basic difference between the community and clinic samples was an increased severity of illness in the clinical sample. This may also explain the high level of co-infection found among the clinical subjects. The most prevalent virus in the clinical sample (coronavirus OC43) was not detected in the community sample. Further, there was a significant difference between prevalence of the most common viruses in the clinical sample (parainfluenza virus 4, respiratory syncytial virus B and enterovirus) and their prevalence in the community. Finally, some of the viruses detected in this study have not been detected and implicated with ARIs in Nigeria. There is no report, to the best of our knowledge, implicating coronavirus in ARIs in Nigeria, and it was detected in 12 subjects in this study. Although cases of double and triple infections were observed in a study in Nigeria in 2011 [28] , as far as we are aware, reports of quadruple infections are rare and have not been reported in Nigeria previously.
Due to the unique nature of the data generated in this study and novelty of work in the setting, it is not possible to exactly compare results to other studies. For example, though we found a similar study regarding ARIs in clinical subjects in Burkina Faso [27] , due to the small sample size from this study it would not be feasible to infer or compare prevalence rates. Studies of ARI etiology have mostly been generally focused in areas of the world that are more developed [29] , and it is important to note that the availability of molecular diagnostic methods as employed in this study substantially improve the ability to detect viruses which hitherto have not been detected in Nigeria. Further, findings from this work also add to the growing body of research that shows value of community-data in infectious disease surveillance [8] . As most of the work to-date has been in higher resource areas of the world this study adds perspective from an area where healthcare resources are lower.
In conclusion, results of this study provide evidence for active community surveillance to enhance public health surveillance and scientific understanding of ARIs. This is not only because a minority of children with severe infection are admitted to the hospital in areas such this in Nigeria, but also findings from this pilot study which indicate that viral circulation in the community may not get detected clinically [29] . This pilot study indicates that in areas of Nigeria, etiology of ARIs ascertained from clinical samples may not represent all of the ARIs circulating in the community.
The main limitation of the study is the sample size. In particular, the sample is not equally representative across all ages. However, the sample size was big enough to ascertain significant differences in community and clinic sourced viruses, and provides a qualitative understanding of viral etiology in samples from the community and clinic. Moreover, the sample was largely concentrated on subjects under 6 years, who are amongst the groups at highest risk of ARIs. Despite the small sample size, samples here indicate that circulation patterns in the community may differ from those in the clinic. In addition, this study resulted in unique findings Given that resources are limited for research and practice, we hope these pilot results may motivate further systematic investigations into how community-generated data can best be used in ARI surveillance. Results of this study can inform a larger study, representative across demographic and locations to systematically assess the etiology of infection and differences in clinical and community cohorts. A larger study will also enable accounting for potential confounders such as environmental risk factors. Finally, while it may be intuitive, findings from this pilot study shed light on the scope of differences in ARI patterns including different types and strains of circulating viruses. Also, because PCR was used for viral detection, the study was limited to detection of viruses in the primer sets. Given that these are the most up-to-date and common viruses, this approach was deemed sufficient for this initial investigation.
The study was conceived by RC and OK. RC and OK, MO and TD were involved in the design of the study, which was conducted by MO and TD. RC and OK analyzed the data. RC and OK wrote and revised the manuscript. All authors read and approved the final manuscript.
|
How bad is the burden of disease in developing countries?
| 1,599 |
2-5 times higher than in developed countries
| 2,063 |
1,568 |
Etiology of respiratory tract infections in the community and clinic in Ilorin, Nigeria
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5719735/
SHA: f2e835d2cde5f42054dbd0c20d4060721135c518
Authors: Kolawole, Olatunji; Oguntoye, Michael; Dam, Tina; Chunara, Rumi
Date: 2017-12-07
DOI: 10.1186/s13104-017-3063-1
License: cc-by
Abstract: OBJECTIVE: Recognizing increasing interest in community disease surveillance globally, the goal of this study was to investigate whether respiratory viruses circulating in the community may be represented through clinical (hospital) surveillance in Nigeria. RESULTS: Children were selected via convenience sampling from communities and a tertiary care center (n = 91) during spring 2017 in Ilorin, Nigeria. Nasal swabs were collected and tested using polymerase chain reaction. The majority (79.1%) of subjects were under 6 years old, of whom 46 were infected (63.9%). A total of 33 of the 91 subjects had one or more respiratory tract virus; there were 10 cases of triple infection and 5 of quadruple. Parainfluenza virus 4, respiratory syncytial virus B and enterovirus were the most common viruses in the clinical sample; present in 93.8% (15/16) of clinical subjects, and 6.7% (5/75) of community subjects (significant difference, p < 0.001). Coronavirus OC43 was the most common virus detected in community members (13.3%, 10/75). A different strain, Coronavirus OC 229 E/NL63 was detected among subjects from the clinic (2/16) and not detected in the community. This pilot study provides evidence that data from the community can potentially represent different information than that sourced clinically, suggesting the need for community surveillance to enhance public health efforts and scientific understanding of respiratory infections.
Text: Acute Respiratory Infections (ARIs) (the cause of both upper respiratory tract infections (URIs) and lower respiratory tract infections (LRIs)) are a major cause of death among children under 5 years old particularly in developing countries where the burden of disease is 2-5 times higher than in developed countries [1] . While these viruses usually cause mild cold-like symptoms and can be self-limiting, in recent years novel coronaviruses such as severe acute respiratory syndrome (SARS) and Middle East respiratory syndrome (MERS) have evolved and infected humans, causing severe illness, epidemics and pandemics [2] . Currently, the majority of all infectious disease outbreaks as recorded by the World Health Organization (WHO) occur in the continent of Africa where there is high transmission risk [3, 4] . Further, in developing areas (both rural and urban), there are increasing risk factors such as human-animal interfaces (due to residential-proximity to livestock). These changing epidemiological patterns have resulted in calls for improved ARI surveillance, especially in places of high transmission risk [5] .
Nigeria is one such place with high prevalence of many of the risk factors implicated in ARI among children including; age, sex, overcrowding, nutritional status, socio-economic status, and where study of ARIs is currently limited [6] . These broad risk factors alongside limited resources have indicated the need for community-based initiatives for surveillance and interventions [6, 7] . For ARI surveillance in particular, infections in the community are those that do not get reported clinically. Clinical data generally represents the most severe cases, and those from locations with access to healthcare institutions. In Nigeria, hospitals are visited only when symptoms are very severe. Thus, it is hypothesized that viral information from clinical sampling is insufficient to either capture disease incidence in general populations or its predictability from symptoms [8] . Efforts worldwide including in East and Southern Africa have been focused on developing community-based participatory disease surveillance methods [9] [10] [11] [12] [13] . Community-based approaches have been shown useful for learning more about emerging respiratory infections such as assessing under-reporting [14] , types of viruses prevalent in communities [10] , and prediction of epidemics [15] .
Concurrently, advancements in molecular identification methods have enabled studies regarding the emergence and epidemiology of ARI viruses in many locations (e.g. novel polyomaviruses in Australia [16, 17] , human coronavirus Erasmus Medical Center (HCoV-EMC) in the Middle East and United Kingdom [18, 19] , SARS in Canada and China [20] [21] [22] ), yet research regarding the molecular epidemiology of ARI viruses in Nigeria is limited. Diagnostic methods available and other constraints have limited studies there to serological surveys of only a few of these viruses and only in clinical populations [23, 24] . Thus, the utility of community-based surveillance may be appropriate in contexts such as in Nigeria, and the purpose of this pilot study was to investigate if clinical cases may describe the entire picture of ARI among children in Nigeria.
We performed a cross-sectional study in three community centers and one clinical in Ilorin, Nigeria. Ilorin is in Kwara state and is the 6th largest city in Nigeria by population [25] . Three Local Government Areas (Ilorin East, Ilorin South and Ilorin West LGAs) were the community sites and Children's Specialist Hospital, Ilorin the clinical site. Convenience sampling was used for the purposes of this pilot study, and samples were obtained from March 28 to April 5 2017. Inclusion criteria were: children less than 14 years old who had visible symptoms of ARI within the communities or those confirmed at the hospital with ARI. Exclusion criteria were: children who were 14 and above, not showing signs of ARI and subjects whose parents did not give consent. Twenty-five children with symptoms were selected each from the three community locations while 16 symptomatic children were sampled from the hospital. The total sample size (n = 91) was arrived at based on materials and processing cost constraints, as well as to provide enough samples to enable descriptive understanding of viral circulation patterns estimated from other community-based studies [10] .
Disease Surveillance and Notification Officers, who are employed by the State Ministry of Health and familiar with the communities in this study, performed specimen and data collection. Symptoms considered were derived in accordance with other ARI surveillance efforts: sore throat, fever, couch, running nose, vomiting, body ache, leg pain, nausea, chills, shortness of breath [10, 26] . Gender and age, type of residential area (rural/urban), education level, proximity of residence to livestock, proximity to an untarred road and number of people who sleep in same room, were all recorded. The general difference between the two settings was that those from the hospital had severe illnesses, while those from the community were generally "healthy" but exhibiting ARI symptoms (i.e. mild illness).
Nasal swabs were collected from the subjects and stored in DNA/RNA shield (Zymo Research, Irvine, California). Collected samples were spinned and the swab removed. Residues containing the nasal samples were stored at -20 °C prior to molecular analysis.
Viral RNA was isolated using ZR Viral RNA ™ Kit (Zymo Research, Irvine, California) per manufacturer instructions (http://www.zymoresearch.com/downloads/dl/file/ id/147/r1034i.pdf ). Real-time PCR (polymerase chain reaction), commonly used in ARI studies [10, 19, 27] , was then carried out using RV15 One Step ACE Detection Kit, catalogue numbers RV0716K01008007 and RV0717B01008001 (Seegene, Seoul, South Korea) for detection of 15 human viruses: parainfluenza virus 1, 2, 3 and 4 (PIV1-4), respiratory syncytial virus (RSV) A and B, influenza A and B (FLUA, FLUB), rhinovirus type A-C, adenovirus (ADV), coronavirus (OC 229 E/NL63, OC43), enterovirus (HEV), metapneumovirus (hMPV) and bocavirus (BoV).
Reagents were validated in the experimental location using an inbuilt validation protocol to confirm issues of false negative and false positive results were not of concern. Amplification reaction was carried out as described by the manufacturer: reverse transcription 50 °C-30′, initial activation 94°-15′, 45 cycles: denaturation 94°-30″, annealing 60°-1′ 30″, extension 72°-1, final extension 72°-10′, hold 4°. Visualization was performed using electrophoresis on a 2% agarose gel in TBE 1X with EtBr, in presence of RV15 OneStep A/B/C Markers; molecular weight marker. Specimen processing was not blinded as there was no risk of experimental bias. Standardized procedures were used for community and clinic sampling.
All statistical analyses were performed using R version 3.2.4. Univariate statistics [mean and 95% confidence interval (CI)] are described. Bivariate statistics (difference in proportions) were assessed using a two-proportion z-test. A p value < 0.001 was considered significant. No observations used in this study had any missing data for analyses in this study.
Basic participant demographics are summarized in PCR results showed that ten different viruses (influenza A, coronavirus OC 229 E/NL63, RSVA, RSV B, parainfluenza 1-4) were detected. Figure 1 shows how these infections were distributed across virus types as well as in the community versus clinic samples. In sum, a total of 33 of the 91 subjects surveyed had one or more respiratory tract virus (36.3%, 95% CI 26.6-47.0%, Fig. 1 ). Furthermore, 10 of those cases were triple infections and 5 were quadruple infections (illustrated by color of bars in Fig. 1 ). Figure 2 indicates how frequently each pair of viruses were found in the same participant; co-infections were most common among enterovirus and parainfluenza virus 4 (Fig. 2) .
We also compared and contrasted the clinical and community results. Parainfluenza virus 4, respiratory syncytial virus B and enterovirus were the most common viruses found in the clinical sample. These three infections resulted in 41 viruses detected in 15 subjects clinically, and eight infections detected in five people in the community. Together they infected 94% (15/16, 95% CI 67.7-99.7%) of clinical subjects, and 7% (5/75, 95% CI 2.5-15.5%) in the community (significant difference, p < 0.001). The most common virus detected in community samples was Coronavirus OC43; this virus was detected in 13.3% (95% CI 6.9-23.6%) people in the community and not in any of the clinical samples. However a different strain, coronavirus OC 229 E/NL63 was detected in 12.5% of the clinical subjects (2/16, 95% CI 2.2-39.6%) and not detected in the community. Double, triple and quadruple infections were another common feature of note.
We identified ten different respiratory tract viruses among the subjects as shown in Fig. 1 . Samples collected from the Children's specialist hospital showed 100% prevalence rate of infection with one or more viruses. This might not be surprising, as the basic difference between the community and clinic samples was an increased severity of illness in the clinical sample. This may also explain the high level of co-infection found among the clinical subjects. The most prevalent virus in the clinical sample (coronavirus OC43) was not detected in the community sample. Further, there was a significant difference between prevalence of the most common viruses in the clinical sample (parainfluenza virus 4, respiratory syncytial virus B and enterovirus) and their prevalence in the community. Finally, some of the viruses detected in this study have not been detected and implicated with ARIs in Nigeria. There is no report, to the best of our knowledge, implicating coronavirus in ARIs in Nigeria, and it was detected in 12 subjects in this study. Although cases of double and triple infections were observed in a study in Nigeria in 2011 [28] , as far as we are aware, reports of quadruple infections are rare and have not been reported in Nigeria previously.
Due to the unique nature of the data generated in this study and novelty of work in the setting, it is not possible to exactly compare results to other studies. For example, though we found a similar study regarding ARIs in clinical subjects in Burkina Faso [27] , due to the small sample size from this study it would not be feasible to infer or compare prevalence rates. Studies of ARI etiology have mostly been generally focused in areas of the world that are more developed [29] , and it is important to note that the availability of molecular diagnostic methods as employed in this study substantially improve the ability to detect viruses which hitherto have not been detected in Nigeria. Further, findings from this work also add to the growing body of research that shows value of community-data in infectious disease surveillance [8] . As most of the work to-date has been in higher resource areas of the world this study adds perspective from an area where healthcare resources are lower.
In conclusion, results of this study provide evidence for active community surveillance to enhance public health surveillance and scientific understanding of ARIs. This is not only because a minority of children with severe infection are admitted to the hospital in areas such this in Nigeria, but also findings from this pilot study which indicate that viral circulation in the community may not get detected clinically [29] . This pilot study indicates that in areas of Nigeria, etiology of ARIs ascertained from clinical samples may not represent all of the ARIs circulating in the community.
The main limitation of the study is the sample size. In particular, the sample is not equally representative across all ages. However, the sample size was big enough to ascertain significant differences in community and clinic sourced viruses, and provides a qualitative understanding of viral etiology in samples from the community and clinic. Moreover, the sample was largely concentrated on subjects under 6 years, who are amongst the groups at highest risk of ARIs. Despite the small sample size, samples here indicate that circulation patterns in the community may differ from those in the clinic. In addition, this study resulted in unique findings Given that resources are limited for research and practice, we hope these pilot results may motivate further systematic investigations into how community-generated data can best be used in ARI surveillance. Results of this study can inform a larger study, representative across demographic and locations to systematically assess the etiology of infection and differences in clinical and community cohorts. A larger study will also enable accounting for potential confounders such as environmental risk factors. Finally, while it may be intuitive, findings from this pilot study shed light on the scope of differences in ARI patterns including different types and strains of circulating viruses. Also, because PCR was used for viral detection, the study was limited to detection of viruses in the primer sets. Given that these are the most up-to-date and common viruses, this approach was deemed sufficient for this initial investigation.
The study was conceived by RC and OK. RC and OK, MO and TD were involved in the design of the study, which was conducted by MO and TD. RC and OK analyzed the data. RC and OK wrote and revised the manuscript. All authors read and approved the final manuscript.
|
Where do the majority of all infectious disease outbreaks happen?
| 1,600 |
Africa
| 2,552 |
1,568 |
Etiology of respiratory tract infections in the community and clinic in Ilorin, Nigeria
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5719735/
SHA: f2e835d2cde5f42054dbd0c20d4060721135c518
Authors: Kolawole, Olatunji; Oguntoye, Michael; Dam, Tina; Chunara, Rumi
Date: 2017-12-07
DOI: 10.1186/s13104-017-3063-1
License: cc-by
Abstract: OBJECTIVE: Recognizing increasing interest in community disease surveillance globally, the goal of this study was to investigate whether respiratory viruses circulating in the community may be represented through clinical (hospital) surveillance in Nigeria. RESULTS: Children were selected via convenience sampling from communities and a tertiary care center (n = 91) during spring 2017 in Ilorin, Nigeria. Nasal swabs were collected and tested using polymerase chain reaction. The majority (79.1%) of subjects were under 6 years old, of whom 46 were infected (63.9%). A total of 33 of the 91 subjects had one or more respiratory tract virus; there were 10 cases of triple infection and 5 of quadruple. Parainfluenza virus 4, respiratory syncytial virus B and enterovirus were the most common viruses in the clinical sample; present in 93.8% (15/16) of clinical subjects, and 6.7% (5/75) of community subjects (significant difference, p < 0.001). Coronavirus OC43 was the most common virus detected in community members (13.3%, 10/75). A different strain, Coronavirus OC 229 E/NL63 was detected among subjects from the clinic (2/16) and not detected in the community. This pilot study provides evidence that data from the community can potentially represent different information than that sourced clinically, suggesting the need for community surveillance to enhance public health efforts and scientific understanding of respiratory infections.
Text: Acute Respiratory Infections (ARIs) (the cause of both upper respiratory tract infections (URIs) and lower respiratory tract infections (LRIs)) are a major cause of death among children under 5 years old particularly in developing countries where the burden of disease is 2-5 times higher than in developed countries [1] . While these viruses usually cause mild cold-like symptoms and can be self-limiting, in recent years novel coronaviruses such as severe acute respiratory syndrome (SARS) and Middle East respiratory syndrome (MERS) have evolved and infected humans, causing severe illness, epidemics and pandemics [2] . Currently, the majority of all infectious disease outbreaks as recorded by the World Health Organization (WHO) occur in the continent of Africa where there is high transmission risk [3, 4] . Further, in developing areas (both rural and urban), there are increasing risk factors such as human-animal interfaces (due to residential-proximity to livestock). These changing epidemiological patterns have resulted in calls for improved ARI surveillance, especially in places of high transmission risk [5] .
Nigeria is one such place with high prevalence of many of the risk factors implicated in ARI among children including; age, sex, overcrowding, nutritional status, socio-economic status, and where study of ARIs is currently limited [6] . These broad risk factors alongside limited resources have indicated the need for community-based initiatives for surveillance and interventions [6, 7] . For ARI surveillance in particular, infections in the community are those that do not get reported clinically. Clinical data generally represents the most severe cases, and those from locations with access to healthcare institutions. In Nigeria, hospitals are visited only when symptoms are very severe. Thus, it is hypothesized that viral information from clinical sampling is insufficient to either capture disease incidence in general populations or its predictability from symptoms [8] . Efforts worldwide including in East and Southern Africa have been focused on developing community-based participatory disease surveillance methods [9] [10] [11] [12] [13] . Community-based approaches have been shown useful for learning more about emerging respiratory infections such as assessing under-reporting [14] , types of viruses prevalent in communities [10] , and prediction of epidemics [15] .
Concurrently, advancements in molecular identification methods have enabled studies regarding the emergence and epidemiology of ARI viruses in many locations (e.g. novel polyomaviruses in Australia [16, 17] , human coronavirus Erasmus Medical Center (HCoV-EMC) in the Middle East and United Kingdom [18, 19] , SARS in Canada and China [20] [21] [22] ), yet research regarding the molecular epidemiology of ARI viruses in Nigeria is limited. Diagnostic methods available and other constraints have limited studies there to serological surveys of only a few of these viruses and only in clinical populations [23, 24] . Thus, the utility of community-based surveillance may be appropriate in contexts such as in Nigeria, and the purpose of this pilot study was to investigate if clinical cases may describe the entire picture of ARI among children in Nigeria.
We performed a cross-sectional study in three community centers and one clinical in Ilorin, Nigeria. Ilorin is in Kwara state and is the 6th largest city in Nigeria by population [25] . Three Local Government Areas (Ilorin East, Ilorin South and Ilorin West LGAs) were the community sites and Children's Specialist Hospital, Ilorin the clinical site. Convenience sampling was used for the purposes of this pilot study, and samples were obtained from March 28 to April 5 2017. Inclusion criteria were: children less than 14 years old who had visible symptoms of ARI within the communities or those confirmed at the hospital with ARI. Exclusion criteria were: children who were 14 and above, not showing signs of ARI and subjects whose parents did not give consent. Twenty-five children with symptoms were selected each from the three community locations while 16 symptomatic children were sampled from the hospital. The total sample size (n = 91) was arrived at based on materials and processing cost constraints, as well as to provide enough samples to enable descriptive understanding of viral circulation patterns estimated from other community-based studies [10] .
Disease Surveillance and Notification Officers, who are employed by the State Ministry of Health and familiar with the communities in this study, performed specimen and data collection. Symptoms considered were derived in accordance with other ARI surveillance efforts: sore throat, fever, couch, running nose, vomiting, body ache, leg pain, nausea, chills, shortness of breath [10, 26] . Gender and age, type of residential area (rural/urban), education level, proximity of residence to livestock, proximity to an untarred road and number of people who sleep in same room, were all recorded. The general difference between the two settings was that those from the hospital had severe illnesses, while those from the community were generally "healthy" but exhibiting ARI symptoms (i.e. mild illness).
Nasal swabs were collected from the subjects and stored in DNA/RNA shield (Zymo Research, Irvine, California). Collected samples were spinned and the swab removed. Residues containing the nasal samples were stored at -20 °C prior to molecular analysis.
Viral RNA was isolated using ZR Viral RNA ™ Kit (Zymo Research, Irvine, California) per manufacturer instructions (http://www.zymoresearch.com/downloads/dl/file/ id/147/r1034i.pdf ). Real-time PCR (polymerase chain reaction), commonly used in ARI studies [10, 19, 27] , was then carried out using RV15 One Step ACE Detection Kit, catalogue numbers RV0716K01008007 and RV0717B01008001 (Seegene, Seoul, South Korea) for detection of 15 human viruses: parainfluenza virus 1, 2, 3 and 4 (PIV1-4), respiratory syncytial virus (RSV) A and B, influenza A and B (FLUA, FLUB), rhinovirus type A-C, adenovirus (ADV), coronavirus (OC 229 E/NL63, OC43), enterovirus (HEV), metapneumovirus (hMPV) and bocavirus (BoV).
Reagents were validated in the experimental location using an inbuilt validation protocol to confirm issues of false negative and false positive results were not of concern. Amplification reaction was carried out as described by the manufacturer: reverse transcription 50 °C-30′, initial activation 94°-15′, 45 cycles: denaturation 94°-30″, annealing 60°-1′ 30″, extension 72°-1, final extension 72°-10′, hold 4°. Visualization was performed using electrophoresis on a 2% agarose gel in TBE 1X with EtBr, in presence of RV15 OneStep A/B/C Markers; molecular weight marker. Specimen processing was not blinded as there was no risk of experimental bias. Standardized procedures were used for community and clinic sampling.
All statistical analyses were performed using R version 3.2.4. Univariate statistics [mean and 95% confidence interval (CI)] are described. Bivariate statistics (difference in proportions) were assessed using a two-proportion z-test. A p value < 0.001 was considered significant. No observations used in this study had any missing data for analyses in this study.
Basic participant demographics are summarized in PCR results showed that ten different viruses (influenza A, coronavirus OC 229 E/NL63, RSVA, RSV B, parainfluenza 1-4) were detected. Figure 1 shows how these infections were distributed across virus types as well as in the community versus clinic samples. In sum, a total of 33 of the 91 subjects surveyed had one or more respiratory tract virus (36.3%, 95% CI 26.6-47.0%, Fig. 1 ). Furthermore, 10 of those cases were triple infections and 5 were quadruple infections (illustrated by color of bars in Fig. 1 ). Figure 2 indicates how frequently each pair of viruses were found in the same participant; co-infections were most common among enterovirus and parainfluenza virus 4 (Fig. 2) .
We also compared and contrasted the clinical and community results. Parainfluenza virus 4, respiratory syncytial virus B and enterovirus were the most common viruses found in the clinical sample. These three infections resulted in 41 viruses detected in 15 subjects clinically, and eight infections detected in five people in the community. Together they infected 94% (15/16, 95% CI 67.7-99.7%) of clinical subjects, and 7% (5/75, 95% CI 2.5-15.5%) in the community (significant difference, p < 0.001). The most common virus detected in community samples was Coronavirus OC43; this virus was detected in 13.3% (95% CI 6.9-23.6%) people in the community and not in any of the clinical samples. However a different strain, coronavirus OC 229 E/NL63 was detected in 12.5% of the clinical subjects (2/16, 95% CI 2.2-39.6%) and not detected in the community. Double, triple and quadruple infections were another common feature of note.
We identified ten different respiratory tract viruses among the subjects as shown in Fig. 1 . Samples collected from the Children's specialist hospital showed 100% prevalence rate of infection with one or more viruses. This might not be surprising, as the basic difference between the community and clinic samples was an increased severity of illness in the clinical sample. This may also explain the high level of co-infection found among the clinical subjects. The most prevalent virus in the clinical sample (coronavirus OC43) was not detected in the community sample. Further, there was a significant difference between prevalence of the most common viruses in the clinical sample (parainfluenza virus 4, respiratory syncytial virus B and enterovirus) and their prevalence in the community. Finally, some of the viruses detected in this study have not been detected and implicated with ARIs in Nigeria. There is no report, to the best of our knowledge, implicating coronavirus in ARIs in Nigeria, and it was detected in 12 subjects in this study. Although cases of double and triple infections were observed in a study in Nigeria in 2011 [28] , as far as we are aware, reports of quadruple infections are rare and have not been reported in Nigeria previously.
Due to the unique nature of the data generated in this study and novelty of work in the setting, it is not possible to exactly compare results to other studies. For example, though we found a similar study regarding ARIs in clinical subjects in Burkina Faso [27] , due to the small sample size from this study it would not be feasible to infer or compare prevalence rates. Studies of ARI etiology have mostly been generally focused in areas of the world that are more developed [29] , and it is important to note that the availability of molecular diagnostic methods as employed in this study substantially improve the ability to detect viruses which hitherto have not been detected in Nigeria. Further, findings from this work also add to the growing body of research that shows value of community-data in infectious disease surveillance [8] . As most of the work to-date has been in higher resource areas of the world this study adds perspective from an area where healthcare resources are lower.
In conclusion, results of this study provide evidence for active community surveillance to enhance public health surveillance and scientific understanding of ARIs. This is not only because a minority of children with severe infection are admitted to the hospital in areas such this in Nigeria, but also findings from this pilot study which indicate that viral circulation in the community may not get detected clinically [29] . This pilot study indicates that in areas of Nigeria, etiology of ARIs ascertained from clinical samples may not represent all of the ARIs circulating in the community.
The main limitation of the study is the sample size. In particular, the sample is not equally representative across all ages. However, the sample size was big enough to ascertain significant differences in community and clinic sourced viruses, and provides a qualitative understanding of viral etiology in samples from the community and clinic. Moreover, the sample was largely concentrated on subjects under 6 years, who are amongst the groups at highest risk of ARIs. Despite the small sample size, samples here indicate that circulation patterns in the community may differ from those in the clinic. In addition, this study resulted in unique findings Given that resources are limited for research and practice, we hope these pilot results may motivate further systematic investigations into how community-generated data can best be used in ARI surveillance. Results of this study can inform a larger study, representative across demographic and locations to systematically assess the etiology of infection and differences in clinical and community cohorts. A larger study will also enable accounting for potential confounders such as environmental risk factors. Finally, while it may be intuitive, findings from this pilot study shed light on the scope of differences in ARI patterns including different types and strains of circulating viruses. Also, because PCR was used for viral detection, the study was limited to detection of viruses in the primer sets. Given that these are the most up-to-date and common viruses, this approach was deemed sufficient for this initial investigation.
The study was conceived by RC and OK. RC and OK, MO and TD were involved in the design of the study, which was conducted by MO and TD. RC and OK analyzed the data. RC and OK wrote and revised the manuscript. All authors read and approved the final manuscript.
|
What are some risk factors for countries to experience a high prevalence of Acute Respiratory Infections?
| 1,601 |
age, sex, overcrowding, nutritional status, socio-economic status, and where study of ARIs is currently limited
| 3,037 |
1,568 |
Etiology of respiratory tract infections in the community and clinic in Ilorin, Nigeria
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5719735/
SHA: f2e835d2cde5f42054dbd0c20d4060721135c518
Authors: Kolawole, Olatunji; Oguntoye, Michael; Dam, Tina; Chunara, Rumi
Date: 2017-12-07
DOI: 10.1186/s13104-017-3063-1
License: cc-by
Abstract: OBJECTIVE: Recognizing increasing interest in community disease surveillance globally, the goal of this study was to investigate whether respiratory viruses circulating in the community may be represented through clinical (hospital) surveillance in Nigeria. RESULTS: Children were selected via convenience sampling from communities and a tertiary care center (n = 91) during spring 2017 in Ilorin, Nigeria. Nasal swabs were collected and tested using polymerase chain reaction. The majority (79.1%) of subjects were under 6 years old, of whom 46 were infected (63.9%). A total of 33 of the 91 subjects had one or more respiratory tract virus; there were 10 cases of triple infection and 5 of quadruple. Parainfluenza virus 4, respiratory syncytial virus B and enterovirus were the most common viruses in the clinical sample; present in 93.8% (15/16) of clinical subjects, and 6.7% (5/75) of community subjects (significant difference, p < 0.001). Coronavirus OC43 was the most common virus detected in community members (13.3%, 10/75). A different strain, Coronavirus OC 229 E/NL63 was detected among subjects from the clinic (2/16) and not detected in the community. This pilot study provides evidence that data from the community can potentially represent different information than that sourced clinically, suggesting the need for community surveillance to enhance public health efforts and scientific understanding of respiratory infections.
Text: Acute Respiratory Infections (ARIs) (the cause of both upper respiratory tract infections (URIs) and lower respiratory tract infections (LRIs)) are a major cause of death among children under 5 years old particularly in developing countries where the burden of disease is 2-5 times higher than in developed countries [1] . While these viruses usually cause mild cold-like symptoms and can be self-limiting, in recent years novel coronaviruses such as severe acute respiratory syndrome (SARS) and Middle East respiratory syndrome (MERS) have evolved and infected humans, causing severe illness, epidemics and pandemics [2] . Currently, the majority of all infectious disease outbreaks as recorded by the World Health Organization (WHO) occur in the continent of Africa where there is high transmission risk [3, 4] . Further, in developing areas (both rural and urban), there are increasing risk factors such as human-animal interfaces (due to residential-proximity to livestock). These changing epidemiological patterns have resulted in calls for improved ARI surveillance, especially in places of high transmission risk [5] .
Nigeria is one such place with high prevalence of many of the risk factors implicated in ARI among children including; age, sex, overcrowding, nutritional status, socio-economic status, and where study of ARIs is currently limited [6] . These broad risk factors alongside limited resources have indicated the need for community-based initiatives for surveillance and interventions [6, 7] . For ARI surveillance in particular, infections in the community are those that do not get reported clinically. Clinical data generally represents the most severe cases, and those from locations with access to healthcare institutions. In Nigeria, hospitals are visited only when symptoms are very severe. Thus, it is hypothesized that viral information from clinical sampling is insufficient to either capture disease incidence in general populations or its predictability from symptoms [8] . Efforts worldwide including in East and Southern Africa have been focused on developing community-based participatory disease surveillance methods [9] [10] [11] [12] [13] . Community-based approaches have been shown useful for learning more about emerging respiratory infections such as assessing under-reporting [14] , types of viruses prevalent in communities [10] , and prediction of epidemics [15] .
Concurrently, advancements in molecular identification methods have enabled studies regarding the emergence and epidemiology of ARI viruses in many locations (e.g. novel polyomaviruses in Australia [16, 17] , human coronavirus Erasmus Medical Center (HCoV-EMC) in the Middle East and United Kingdom [18, 19] , SARS in Canada and China [20] [21] [22] ), yet research regarding the molecular epidemiology of ARI viruses in Nigeria is limited. Diagnostic methods available and other constraints have limited studies there to serological surveys of only a few of these viruses and only in clinical populations [23, 24] . Thus, the utility of community-based surveillance may be appropriate in contexts such as in Nigeria, and the purpose of this pilot study was to investigate if clinical cases may describe the entire picture of ARI among children in Nigeria.
We performed a cross-sectional study in three community centers and one clinical in Ilorin, Nigeria. Ilorin is in Kwara state and is the 6th largest city in Nigeria by population [25] . Three Local Government Areas (Ilorin East, Ilorin South and Ilorin West LGAs) were the community sites and Children's Specialist Hospital, Ilorin the clinical site. Convenience sampling was used for the purposes of this pilot study, and samples were obtained from March 28 to April 5 2017. Inclusion criteria were: children less than 14 years old who had visible symptoms of ARI within the communities or those confirmed at the hospital with ARI. Exclusion criteria were: children who were 14 and above, not showing signs of ARI and subjects whose parents did not give consent. Twenty-five children with symptoms were selected each from the three community locations while 16 symptomatic children were sampled from the hospital. The total sample size (n = 91) was arrived at based on materials and processing cost constraints, as well as to provide enough samples to enable descriptive understanding of viral circulation patterns estimated from other community-based studies [10] .
Disease Surveillance and Notification Officers, who are employed by the State Ministry of Health and familiar with the communities in this study, performed specimen and data collection. Symptoms considered were derived in accordance with other ARI surveillance efforts: sore throat, fever, couch, running nose, vomiting, body ache, leg pain, nausea, chills, shortness of breath [10, 26] . Gender and age, type of residential area (rural/urban), education level, proximity of residence to livestock, proximity to an untarred road and number of people who sleep in same room, were all recorded. The general difference between the two settings was that those from the hospital had severe illnesses, while those from the community were generally "healthy" but exhibiting ARI symptoms (i.e. mild illness).
Nasal swabs were collected from the subjects and stored in DNA/RNA shield (Zymo Research, Irvine, California). Collected samples were spinned and the swab removed. Residues containing the nasal samples were stored at -20 °C prior to molecular analysis.
Viral RNA was isolated using ZR Viral RNA ™ Kit (Zymo Research, Irvine, California) per manufacturer instructions (http://www.zymoresearch.com/downloads/dl/file/ id/147/r1034i.pdf ). Real-time PCR (polymerase chain reaction), commonly used in ARI studies [10, 19, 27] , was then carried out using RV15 One Step ACE Detection Kit, catalogue numbers RV0716K01008007 and RV0717B01008001 (Seegene, Seoul, South Korea) for detection of 15 human viruses: parainfluenza virus 1, 2, 3 and 4 (PIV1-4), respiratory syncytial virus (RSV) A and B, influenza A and B (FLUA, FLUB), rhinovirus type A-C, adenovirus (ADV), coronavirus (OC 229 E/NL63, OC43), enterovirus (HEV), metapneumovirus (hMPV) and bocavirus (BoV).
Reagents were validated in the experimental location using an inbuilt validation protocol to confirm issues of false negative and false positive results were not of concern. Amplification reaction was carried out as described by the manufacturer: reverse transcription 50 °C-30′, initial activation 94°-15′, 45 cycles: denaturation 94°-30″, annealing 60°-1′ 30″, extension 72°-1, final extension 72°-10′, hold 4°. Visualization was performed using electrophoresis on a 2% agarose gel in TBE 1X with EtBr, in presence of RV15 OneStep A/B/C Markers; molecular weight marker. Specimen processing was not blinded as there was no risk of experimental bias. Standardized procedures were used for community and clinic sampling.
All statistical analyses were performed using R version 3.2.4. Univariate statistics [mean and 95% confidence interval (CI)] are described. Bivariate statistics (difference in proportions) were assessed using a two-proportion z-test. A p value < 0.001 was considered significant. No observations used in this study had any missing data for analyses in this study.
Basic participant demographics are summarized in PCR results showed that ten different viruses (influenza A, coronavirus OC 229 E/NL63, RSVA, RSV B, parainfluenza 1-4) were detected. Figure 1 shows how these infections were distributed across virus types as well as in the community versus clinic samples. In sum, a total of 33 of the 91 subjects surveyed had one or more respiratory tract virus (36.3%, 95% CI 26.6-47.0%, Fig. 1 ). Furthermore, 10 of those cases were triple infections and 5 were quadruple infections (illustrated by color of bars in Fig. 1 ). Figure 2 indicates how frequently each pair of viruses were found in the same participant; co-infections were most common among enterovirus and parainfluenza virus 4 (Fig. 2) .
We also compared and contrasted the clinical and community results. Parainfluenza virus 4, respiratory syncytial virus B and enterovirus were the most common viruses found in the clinical sample. These three infections resulted in 41 viruses detected in 15 subjects clinically, and eight infections detected in five people in the community. Together they infected 94% (15/16, 95% CI 67.7-99.7%) of clinical subjects, and 7% (5/75, 95% CI 2.5-15.5%) in the community (significant difference, p < 0.001). The most common virus detected in community samples was Coronavirus OC43; this virus was detected in 13.3% (95% CI 6.9-23.6%) people in the community and not in any of the clinical samples. However a different strain, coronavirus OC 229 E/NL63 was detected in 12.5% of the clinical subjects (2/16, 95% CI 2.2-39.6%) and not detected in the community. Double, triple and quadruple infections were another common feature of note.
We identified ten different respiratory tract viruses among the subjects as shown in Fig. 1 . Samples collected from the Children's specialist hospital showed 100% prevalence rate of infection with one or more viruses. This might not be surprising, as the basic difference between the community and clinic samples was an increased severity of illness in the clinical sample. This may also explain the high level of co-infection found among the clinical subjects. The most prevalent virus in the clinical sample (coronavirus OC43) was not detected in the community sample. Further, there was a significant difference between prevalence of the most common viruses in the clinical sample (parainfluenza virus 4, respiratory syncytial virus B and enterovirus) and their prevalence in the community. Finally, some of the viruses detected in this study have not been detected and implicated with ARIs in Nigeria. There is no report, to the best of our knowledge, implicating coronavirus in ARIs in Nigeria, and it was detected in 12 subjects in this study. Although cases of double and triple infections were observed in a study in Nigeria in 2011 [28] , as far as we are aware, reports of quadruple infections are rare and have not been reported in Nigeria previously.
Due to the unique nature of the data generated in this study and novelty of work in the setting, it is not possible to exactly compare results to other studies. For example, though we found a similar study regarding ARIs in clinical subjects in Burkina Faso [27] , due to the small sample size from this study it would not be feasible to infer or compare prevalence rates. Studies of ARI etiology have mostly been generally focused in areas of the world that are more developed [29] , and it is important to note that the availability of molecular diagnostic methods as employed in this study substantially improve the ability to detect viruses which hitherto have not been detected in Nigeria. Further, findings from this work also add to the growing body of research that shows value of community-data in infectious disease surveillance [8] . As most of the work to-date has been in higher resource areas of the world this study adds perspective from an area where healthcare resources are lower.
In conclusion, results of this study provide evidence for active community surveillance to enhance public health surveillance and scientific understanding of ARIs. This is not only because a minority of children with severe infection are admitted to the hospital in areas such this in Nigeria, but also findings from this pilot study which indicate that viral circulation in the community may not get detected clinically [29] . This pilot study indicates that in areas of Nigeria, etiology of ARIs ascertained from clinical samples may not represent all of the ARIs circulating in the community.
The main limitation of the study is the sample size. In particular, the sample is not equally representative across all ages. However, the sample size was big enough to ascertain significant differences in community and clinic sourced viruses, and provides a qualitative understanding of viral etiology in samples from the community and clinic. Moreover, the sample was largely concentrated on subjects under 6 years, who are amongst the groups at highest risk of ARIs. Despite the small sample size, samples here indicate that circulation patterns in the community may differ from those in the clinic. In addition, this study resulted in unique findings Given that resources are limited for research and practice, we hope these pilot results may motivate further systematic investigations into how community-generated data can best be used in ARI surveillance. Results of this study can inform a larger study, representative across demographic and locations to systematically assess the etiology of infection and differences in clinical and community cohorts. A larger study will also enable accounting for potential confounders such as environmental risk factors. Finally, while it may be intuitive, findings from this pilot study shed light on the scope of differences in ARI patterns including different types and strains of circulating viruses. Also, because PCR was used for viral detection, the study was limited to detection of viruses in the primer sets. Given that these are the most up-to-date and common viruses, this approach was deemed sufficient for this initial investigation.
The study was conceived by RC and OK. RC and OK, MO and TD were involved in the design of the study, which was conducted by MO and TD. RC and OK analyzed the data. RC and OK wrote and revised the manuscript. All authors read and approved the final manuscript.
|
What symptoms are associated with acute respiratory infections?
| 1,602 |
sore throat, fever, couch, running nose, vomiting, body ache, leg pain, nausea, chills, shortness of breath
| 6,502 |
1,568 |
Etiology of respiratory tract infections in the community and clinic in Ilorin, Nigeria
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5719735/
SHA: f2e835d2cde5f42054dbd0c20d4060721135c518
Authors: Kolawole, Olatunji; Oguntoye, Michael; Dam, Tina; Chunara, Rumi
Date: 2017-12-07
DOI: 10.1186/s13104-017-3063-1
License: cc-by
Abstract: OBJECTIVE: Recognizing increasing interest in community disease surveillance globally, the goal of this study was to investigate whether respiratory viruses circulating in the community may be represented through clinical (hospital) surveillance in Nigeria. RESULTS: Children were selected via convenience sampling from communities and a tertiary care center (n = 91) during spring 2017 in Ilorin, Nigeria. Nasal swabs were collected and tested using polymerase chain reaction. The majority (79.1%) of subjects were under 6 years old, of whom 46 were infected (63.9%). A total of 33 of the 91 subjects had one or more respiratory tract virus; there were 10 cases of triple infection and 5 of quadruple. Parainfluenza virus 4, respiratory syncytial virus B and enterovirus were the most common viruses in the clinical sample; present in 93.8% (15/16) of clinical subjects, and 6.7% (5/75) of community subjects (significant difference, p < 0.001). Coronavirus OC43 was the most common virus detected in community members (13.3%, 10/75). A different strain, Coronavirus OC 229 E/NL63 was detected among subjects from the clinic (2/16) and not detected in the community. This pilot study provides evidence that data from the community can potentially represent different information than that sourced clinically, suggesting the need for community surveillance to enhance public health efforts and scientific understanding of respiratory infections.
Text: Acute Respiratory Infections (ARIs) (the cause of both upper respiratory tract infections (URIs) and lower respiratory tract infections (LRIs)) are a major cause of death among children under 5 years old particularly in developing countries where the burden of disease is 2-5 times higher than in developed countries [1] . While these viruses usually cause mild cold-like symptoms and can be self-limiting, in recent years novel coronaviruses such as severe acute respiratory syndrome (SARS) and Middle East respiratory syndrome (MERS) have evolved and infected humans, causing severe illness, epidemics and pandemics [2] . Currently, the majority of all infectious disease outbreaks as recorded by the World Health Organization (WHO) occur in the continent of Africa where there is high transmission risk [3, 4] . Further, in developing areas (both rural and urban), there are increasing risk factors such as human-animal interfaces (due to residential-proximity to livestock). These changing epidemiological patterns have resulted in calls for improved ARI surveillance, especially in places of high transmission risk [5] .
Nigeria is one such place with high prevalence of many of the risk factors implicated in ARI among children including; age, sex, overcrowding, nutritional status, socio-economic status, and where study of ARIs is currently limited [6] . These broad risk factors alongside limited resources have indicated the need for community-based initiatives for surveillance and interventions [6, 7] . For ARI surveillance in particular, infections in the community are those that do not get reported clinically. Clinical data generally represents the most severe cases, and those from locations with access to healthcare institutions. In Nigeria, hospitals are visited only when symptoms are very severe. Thus, it is hypothesized that viral information from clinical sampling is insufficient to either capture disease incidence in general populations or its predictability from symptoms [8] . Efforts worldwide including in East and Southern Africa have been focused on developing community-based participatory disease surveillance methods [9] [10] [11] [12] [13] . Community-based approaches have been shown useful for learning more about emerging respiratory infections such as assessing under-reporting [14] , types of viruses prevalent in communities [10] , and prediction of epidemics [15] .
Concurrently, advancements in molecular identification methods have enabled studies regarding the emergence and epidemiology of ARI viruses in many locations (e.g. novel polyomaviruses in Australia [16, 17] , human coronavirus Erasmus Medical Center (HCoV-EMC) in the Middle East and United Kingdom [18, 19] , SARS in Canada and China [20] [21] [22] ), yet research regarding the molecular epidemiology of ARI viruses in Nigeria is limited. Diagnostic methods available and other constraints have limited studies there to serological surveys of only a few of these viruses and only in clinical populations [23, 24] . Thus, the utility of community-based surveillance may be appropriate in contexts such as in Nigeria, and the purpose of this pilot study was to investigate if clinical cases may describe the entire picture of ARI among children in Nigeria.
We performed a cross-sectional study in three community centers and one clinical in Ilorin, Nigeria. Ilorin is in Kwara state and is the 6th largest city in Nigeria by population [25] . Three Local Government Areas (Ilorin East, Ilorin South and Ilorin West LGAs) were the community sites and Children's Specialist Hospital, Ilorin the clinical site. Convenience sampling was used for the purposes of this pilot study, and samples were obtained from March 28 to April 5 2017. Inclusion criteria were: children less than 14 years old who had visible symptoms of ARI within the communities or those confirmed at the hospital with ARI. Exclusion criteria were: children who were 14 and above, not showing signs of ARI and subjects whose parents did not give consent. Twenty-five children with symptoms were selected each from the three community locations while 16 symptomatic children were sampled from the hospital. The total sample size (n = 91) was arrived at based on materials and processing cost constraints, as well as to provide enough samples to enable descriptive understanding of viral circulation patterns estimated from other community-based studies [10] .
Disease Surveillance and Notification Officers, who are employed by the State Ministry of Health and familiar with the communities in this study, performed specimen and data collection. Symptoms considered were derived in accordance with other ARI surveillance efforts: sore throat, fever, couch, running nose, vomiting, body ache, leg pain, nausea, chills, shortness of breath [10, 26] . Gender and age, type of residential area (rural/urban), education level, proximity of residence to livestock, proximity to an untarred road and number of people who sleep in same room, were all recorded. The general difference between the two settings was that those from the hospital had severe illnesses, while those from the community were generally "healthy" but exhibiting ARI symptoms (i.e. mild illness).
Nasal swabs were collected from the subjects and stored in DNA/RNA shield (Zymo Research, Irvine, California). Collected samples were spinned and the swab removed. Residues containing the nasal samples were stored at -20 °C prior to molecular analysis.
Viral RNA was isolated using ZR Viral RNA ™ Kit (Zymo Research, Irvine, California) per manufacturer instructions (http://www.zymoresearch.com/downloads/dl/file/ id/147/r1034i.pdf ). Real-time PCR (polymerase chain reaction), commonly used in ARI studies [10, 19, 27] , was then carried out using RV15 One Step ACE Detection Kit, catalogue numbers RV0716K01008007 and RV0717B01008001 (Seegene, Seoul, South Korea) for detection of 15 human viruses: parainfluenza virus 1, 2, 3 and 4 (PIV1-4), respiratory syncytial virus (RSV) A and B, influenza A and B (FLUA, FLUB), rhinovirus type A-C, adenovirus (ADV), coronavirus (OC 229 E/NL63, OC43), enterovirus (HEV), metapneumovirus (hMPV) and bocavirus (BoV).
Reagents were validated in the experimental location using an inbuilt validation protocol to confirm issues of false negative and false positive results were not of concern. Amplification reaction was carried out as described by the manufacturer: reverse transcription 50 °C-30′, initial activation 94°-15′, 45 cycles: denaturation 94°-30″, annealing 60°-1′ 30″, extension 72°-1, final extension 72°-10′, hold 4°. Visualization was performed using electrophoresis on a 2% agarose gel in TBE 1X with EtBr, in presence of RV15 OneStep A/B/C Markers; molecular weight marker. Specimen processing was not blinded as there was no risk of experimental bias. Standardized procedures were used for community and clinic sampling.
All statistical analyses were performed using R version 3.2.4. Univariate statistics [mean and 95% confidence interval (CI)] are described. Bivariate statistics (difference in proportions) were assessed using a two-proportion z-test. A p value < 0.001 was considered significant. No observations used in this study had any missing data for analyses in this study.
Basic participant demographics are summarized in PCR results showed that ten different viruses (influenza A, coronavirus OC 229 E/NL63, RSVA, RSV B, parainfluenza 1-4) were detected. Figure 1 shows how these infections were distributed across virus types as well as in the community versus clinic samples. In sum, a total of 33 of the 91 subjects surveyed had one or more respiratory tract virus (36.3%, 95% CI 26.6-47.0%, Fig. 1 ). Furthermore, 10 of those cases were triple infections and 5 were quadruple infections (illustrated by color of bars in Fig. 1 ). Figure 2 indicates how frequently each pair of viruses were found in the same participant; co-infections were most common among enterovirus and parainfluenza virus 4 (Fig. 2) .
We also compared and contrasted the clinical and community results. Parainfluenza virus 4, respiratory syncytial virus B and enterovirus were the most common viruses found in the clinical sample. These three infections resulted in 41 viruses detected in 15 subjects clinically, and eight infections detected in five people in the community. Together they infected 94% (15/16, 95% CI 67.7-99.7%) of clinical subjects, and 7% (5/75, 95% CI 2.5-15.5%) in the community (significant difference, p < 0.001). The most common virus detected in community samples was Coronavirus OC43; this virus was detected in 13.3% (95% CI 6.9-23.6%) people in the community and not in any of the clinical samples. However a different strain, coronavirus OC 229 E/NL63 was detected in 12.5% of the clinical subjects (2/16, 95% CI 2.2-39.6%) and not detected in the community. Double, triple and quadruple infections were another common feature of note.
We identified ten different respiratory tract viruses among the subjects as shown in Fig. 1 . Samples collected from the Children's specialist hospital showed 100% prevalence rate of infection with one or more viruses. This might not be surprising, as the basic difference between the community and clinic samples was an increased severity of illness in the clinical sample. This may also explain the high level of co-infection found among the clinical subjects. The most prevalent virus in the clinical sample (coronavirus OC43) was not detected in the community sample. Further, there was a significant difference between prevalence of the most common viruses in the clinical sample (parainfluenza virus 4, respiratory syncytial virus B and enterovirus) and their prevalence in the community. Finally, some of the viruses detected in this study have not been detected and implicated with ARIs in Nigeria. There is no report, to the best of our knowledge, implicating coronavirus in ARIs in Nigeria, and it was detected in 12 subjects in this study. Although cases of double and triple infections were observed in a study in Nigeria in 2011 [28] , as far as we are aware, reports of quadruple infections are rare and have not been reported in Nigeria previously.
Due to the unique nature of the data generated in this study and novelty of work in the setting, it is not possible to exactly compare results to other studies. For example, though we found a similar study regarding ARIs in clinical subjects in Burkina Faso [27] , due to the small sample size from this study it would not be feasible to infer or compare prevalence rates. Studies of ARI etiology have mostly been generally focused in areas of the world that are more developed [29] , and it is important to note that the availability of molecular diagnostic methods as employed in this study substantially improve the ability to detect viruses which hitherto have not been detected in Nigeria. Further, findings from this work also add to the growing body of research that shows value of community-data in infectious disease surveillance [8] . As most of the work to-date has been in higher resource areas of the world this study adds perspective from an area where healthcare resources are lower.
In conclusion, results of this study provide evidence for active community surveillance to enhance public health surveillance and scientific understanding of ARIs. This is not only because a minority of children with severe infection are admitted to the hospital in areas such this in Nigeria, but also findings from this pilot study which indicate that viral circulation in the community may not get detected clinically [29] . This pilot study indicates that in areas of Nigeria, etiology of ARIs ascertained from clinical samples may not represent all of the ARIs circulating in the community.
The main limitation of the study is the sample size. In particular, the sample is not equally representative across all ages. However, the sample size was big enough to ascertain significant differences in community and clinic sourced viruses, and provides a qualitative understanding of viral etiology in samples from the community and clinic. Moreover, the sample was largely concentrated on subjects under 6 years, who are amongst the groups at highest risk of ARIs. Despite the small sample size, samples here indicate that circulation patterns in the community may differ from those in the clinic. In addition, this study resulted in unique findings Given that resources are limited for research and practice, we hope these pilot results may motivate further systematic investigations into how community-generated data can best be used in ARI surveillance. Results of this study can inform a larger study, representative across demographic and locations to systematically assess the etiology of infection and differences in clinical and community cohorts. A larger study will also enable accounting for potential confounders such as environmental risk factors. Finally, while it may be intuitive, findings from this pilot study shed light on the scope of differences in ARI patterns including different types and strains of circulating viruses. Also, because PCR was used for viral detection, the study was limited to detection of viruses in the primer sets. Given that these are the most up-to-date and common viruses, this approach was deemed sufficient for this initial investigation.
The study was conceived by RC and OK. RC and OK, MO and TD were involved in the design of the study, which was conducted by MO and TD. RC and OK analyzed the data. RC and OK wrote and revised the manuscript. All authors read and approved the final manuscript.
|
What was the most common virus detected in community samples in Ilorin, Nigeria?
| 1,603 |
Coronavirus OC43
| 10,380 |
1,568 |
Etiology of respiratory tract infections in the community and clinic in Ilorin, Nigeria
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5719735/
SHA: f2e835d2cde5f42054dbd0c20d4060721135c518
Authors: Kolawole, Olatunji; Oguntoye, Michael; Dam, Tina; Chunara, Rumi
Date: 2017-12-07
DOI: 10.1186/s13104-017-3063-1
License: cc-by
Abstract: OBJECTIVE: Recognizing increasing interest in community disease surveillance globally, the goal of this study was to investigate whether respiratory viruses circulating in the community may be represented through clinical (hospital) surveillance in Nigeria. RESULTS: Children were selected via convenience sampling from communities and a tertiary care center (n = 91) during spring 2017 in Ilorin, Nigeria. Nasal swabs were collected and tested using polymerase chain reaction. The majority (79.1%) of subjects were under 6 years old, of whom 46 were infected (63.9%). A total of 33 of the 91 subjects had one or more respiratory tract virus; there were 10 cases of triple infection and 5 of quadruple. Parainfluenza virus 4, respiratory syncytial virus B and enterovirus were the most common viruses in the clinical sample; present in 93.8% (15/16) of clinical subjects, and 6.7% (5/75) of community subjects (significant difference, p < 0.001). Coronavirus OC43 was the most common virus detected in community members (13.3%, 10/75). A different strain, Coronavirus OC 229 E/NL63 was detected among subjects from the clinic (2/16) and not detected in the community. This pilot study provides evidence that data from the community can potentially represent different information than that sourced clinically, suggesting the need for community surveillance to enhance public health efforts and scientific understanding of respiratory infections.
Text: Acute Respiratory Infections (ARIs) (the cause of both upper respiratory tract infections (URIs) and lower respiratory tract infections (LRIs)) are a major cause of death among children under 5 years old particularly in developing countries where the burden of disease is 2-5 times higher than in developed countries [1] . While these viruses usually cause mild cold-like symptoms and can be self-limiting, in recent years novel coronaviruses such as severe acute respiratory syndrome (SARS) and Middle East respiratory syndrome (MERS) have evolved and infected humans, causing severe illness, epidemics and pandemics [2] . Currently, the majority of all infectious disease outbreaks as recorded by the World Health Organization (WHO) occur in the continent of Africa where there is high transmission risk [3, 4] . Further, in developing areas (both rural and urban), there are increasing risk factors such as human-animal interfaces (due to residential-proximity to livestock). These changing epidemiological patterns have resulted in calls for improved ARI surveillance, especially in places of high transmission risk [5] .
Nigeria is one such place with high prevalence of many of the risk factors implicated in ARI among children including; age, sex, overcrowding, nutritional status, socio-economic status, and where study of ARIs is currently limited [6] . These broad risk factors alongside limited resources have indicated the need for community-based initiatives for surveillance and interventions [6, 7] . For ARI surveillance in particular, infections in the community are those that do not get reported clinically. Clinical data generally represents the most severe cases, and those from locations with access to healthcare institutions. In Nigeria, hospitals are visited only when symptoms are very severe. Thus, it is hypothesized that viral information from clinical sampling is insufficient to either capture disease incidence in general populations or its predictability from symptoms [8] . Efforts worldwide including in East and Southern Africa have been focused on developing community-based participatory disease surveillance methods [9] [10] [11] [12] [13] . Community-based approaches have been shown useful for learning more about emerging respiratory infections such as assessing under-reporting [14] , types of viruses prevalent in communities [10] , and prediction of epidemics [15] .
Concurrently, advancements in molecular identification methods have enabled studies regarding the emergence and epidemiology of ARI viruses in many locations (e.g. novel polyomaviruses in Australia [16, 17] , human coronavirus Erasmus Medical Center (HCoV-EMC) in the Middle East and United Kingdom [18, 19] , SARS in Canada and China [20] [21] [22] ), yet research regarding the molecular epidemiology of ARI viruses in Nigeria is limited. Diagnostic methods available and other constraints have limited studies there to serological surveys of only a few of these viruses and only in clinical populations [23, 24] . Thus, the utility of community-based surveillance may be appropriate in contexts such as in Nigeria, and the purpose of this pilot study was to investigate if clinical cases may describe the entire picture of ARI among children in Nigeria.
We performed a cross-sectional study in three community centers and one clinical in Ilorin, Nigeria. Ilorin is in Kwara state and is the 6th largest city in Nigeria by population [25] . Three Local Government Areas (Ilorin East, Ilorin South and Ilorin West LGAs) were the community sites and Children's Specialist Hospital, Ilorin the clinical site. Convenience sampling was used for the purposes of this pilot study, and samples were obtained from March 28 to April 5 2017. Inclusion criteria were: children less than 14 years old who had visible symptoms of ARI within the communities or those confirmed at the hospital with ARI. Exclusion criteria were: children who were 14 and above, not showing signs of ARI and subjects whose parents did not give consent. Twenty-five children with symptoms were selected each from the three community locations while 16 symptomatic children were sampled from the hospital. The total sample size (n = 91) was arrived at based on materials and processing cost constraints, as well as to provide enough samples to enable descriptive understanding of viral circulation patterns estimated from other community-based studies [10] .
Disease Surveillance and Notification Officers, who are employed by the State Ministry of Health and familiar with the communities in this study, performed specimen and data collection. Symptoms considered were derived in accordance with other ARI surveillance efforts: sore throat, fever, couch, running nose, vomiting, body ache, leg pain, nausea, chills, shortness of breath [10, 26] . Gender and age, type of residential area (rural/urban), education level, proximity of residence to livestock, proximity to an untarred road and number of people who sleep in same room, were all recorded. The general difference between the two settings was that those from the hospital had severe illnesses, while those from the community were generally "healthy" but exhibiting ARI symptoms (i.e. mild illness).
Nasal swabs were collected from the subjects and stored in DNA/RNA shield (Zymo Research, Irvine, California). Collected samples were spinned and the swab removed. Residues containing the nasal samples were stored at -20 °C prior to molecular analysis.
Viral RNA was isolated using ZR Viral RNA ™ Kit (Zymo Research, Irvine, California) per manufacturer instructions (http://www.zymoresearch.com/downloads/dl/file/ id/147/r1034i.pdf ). Real-time PCR (polymerase chain reaction), commonly used in ARI studies [10, 19, 27] , was then carried out using RV15 One Step ACE Detection Kit, catalogue numbers RV0716K01008007 and RV0717B01008001 (Seegene, Seoul, South Korea) for detection of 15 human viruses: parainfluenza virus 1, 2, 3 and 4 (PIV1-4), respiratory syncytial virus (RSV) A and B, influenza A and B (FLUA, FLUB), rhinovirus type A-C, adenovirus (ADV), coronavirus (OC 229 E/NL63, OC43), enterovirus (HEV), metapneumovirus (hMPV) and bocavirus (BoV).
Reagents were validated in the experimental location using an inbuilt validation protocol to confirm issues of false negative and false positive results were not of concern. Amplification reaction was carried out as described by the manufacturer: reverse transcription 50 °C-30′, initial activation 94°-15′, 45 cycles: denaturation 94°-30″, annealing 60°-1′ 30″, extension 72°-1, final extension 72°-10′, hold 4°. Visualization was performed using electrophoresis on a 2% agarose gel in TBE 1X with EtBr, in presence of RV15 OneStep A/B/C Markers; molecular weight marker. Specimen processing was not blinded as there was no risk of experimental bias. Standardized procedures were used for community and clinic sampling.
All statistical analyses were performed using R version 3.2.4. Univariate statistics [mean and 95% confidence interval (CI)] are described. Bivariate statistics (difference in proportions) were assessed using a two-proportion z-test. A p value < 0.001 was considered significant. No observations used in this study had any missing data for analyses in this study.
Basic participant demographics are summarized in PCR results showed that ten different viruses (influenza A, coronavirus OC 229 E/NL63, RSVA, RSV B, parainfluenza 1-4) were detected. Figure 1 shows how these infections were distributed across virus types as well as in the community versus clinic samples. In sum, a total of 33 of the 91 subjects surveyed had one or more respiratory tract virus (36.3%, 95% CI 26.6-47.0%, Fig. 1 ). Furthermore, 10 of those cases were triple infections and 5 were quadruple infections (illustrated by color of bars in Fig. 1 ). Figure 2 indicates how frequently each pair of viruses were found in the same participant; co-infections were most common among enterovirus and parainfluenza virus 4 (Fig. 2) .
We also compared and contrasted the clinical and community results. Parainfluenza virus 4, respiratory syncytial virus B and enterovirus were the most common viruses found in the clinical sample. These three infections resulted in 41 viruses detected in 15 subjects clinically, and eight infections detected in five people in the community. Together they infected 94% (15/16, 95% CI 67.7-99.7%) of clinical subjects, and 7% (5/75, 95% CI 2.5-15.5%) in the community (significant difference, p < 0.001). The most common virus detected in community samples was Coronavirus OC43; this virus was detected in 13.3% (95% CI 6.9-23.6%) people in the community and not in any of the clinical samples. However a different strain, coronavirus OC 229 E/NL63 was detected in 12.5% of the clinical subjects (2/16, 95% CI 2.2-39.6%) and not detected in the community. Double, triple and quadruple infections were another common feature of note.
We identified ten different respiratory tract viruses among the subjects as shown in Fig. 1 . Samples collected from the Children's specialist hospital showed 100% prevalence rate of infection with one or more viruses. This might not be surprising, as the basic difference between the community and clinic samples was an increased severity of illness in the clinical sample. This may also explain the high level of co-infection found among the clinical subjects. The most prevalent virus in the clinical sample (coronavirus OC43) was not detected in the community sample. Further, there was a significant difference between prevalence of the most common viruses in the clinical sample (parainfluenza virus 4, respiratory syncytial virus B and enterovirus) and their prevalence in the community. Finally, some of the viruses detected in this study have not been detected and implicated with ARIs in Nigeria. There is no report, to the best of our knowledge, implicating coronavirus in ARIs in Nigeria, and it was detected in 12 subjects in this study. Although cases of double and triple infections were observed in a study in Nigeria in 2011 [28] , as far as we are aware, reports of quadruple infections are rare and have not been reported in Nigeria previously.
Due to the unique nature of the data generated in this study and novelty of work in the setting, it is not possible to exactly compare results to other studies. For example, though we found a similar study regarding ARIs in clinical subjects in Burkina Faso [27] , due to the small sample size from this study it would not be feasible to infer or compare prevalence rates. Studies of ARI etiology have mostly been generally focused in areas of the world that are more developed [29] , and it is important to note that the availability of molecular diagnostic methods as employed in this study substantially improve the ability to detect viruses which hitherto have not been detected in Nigeria. Further, findings from this work also add to the growing body of research that shows value of community-data in infectious disease surveillance [8] . As most of the work to-date has been in higher resource areas of the world this study adds perspective from an area where healthcare resources are lower.
In conclusion, results of this study provide evidence for active community surveillance to enhance public health surveillance and scientific understanding of ARIs. This is not only because a minority of children with severe infection are admitted to the hospital in areas such this in Nigeria, but also findings from this pilot study which indicate that viral circulation in the community may not get detected clinically [29] . This pilot study indicates that in areas of Nigeria, etiology of ARIs ascertained from clinical samples may not represent all of the ARIs circulating in the community.
The main limitation of the study is the sample size. In particular, the sample is not equally representative across all ages. However, the sample size was big enough to ascertain significant differences in community and clinic sourced viruses, and provides a qualitative understanding of viral etiology in samples from the community and clinic. Moreover, the sample was largely concentrated on subjects under 6 years, who are amongst the groups at highest risk of ARIs. Despite the small sample size, samples here indicate that circulation patterns in the community may differ from those in the clinic. In addition, this study resulted in unique findings Given that resources are limited for research and practice, we hope these pilot results may motivate further systematic investigations into how community-generated data can best be used in ARI surveillance. Results of this study can inform a larger study, representative across demographic and locations to systematically assess the etiology of infection and differences in clinical and community cohorts. A larger study will also enable accounting for potential confounders such as environmental risk factors. Finally, while it may be intuitive, findings from this pilot study shed light on the scope of differences in ARI patterns including different types and strains of circulating viruses. Also, because PCR was used for viral detection, the study was limited to detection of viruses in the primer sets. Given that these are the most up-to-date and common viruses, this approach was deemed sufficient for this initial investigation.
The study was conceived by RC and OK. RC and OK, MO and TD were involved in the design of the study, which was conducted by MO and TD. RC and OK analyzed the data. RC and OK wrote and revised the manuscript. All authors read and approved the final manuscript.
|
What was the prevalence of Coronavirus OC43 in community samples in Ilorin, Nigeria?
| 1,604 |
13.3% (95% CI 6.9-23.6%)
| 10,425 |
1,568 |
Etiology of respiratory tract infections in the community and clinic in Ilorin, Nigeria
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5719735/
SHA: f2e835d2cde5f42054dbd0c20d4060721135c518
Authors: Kolawole, Olatunji; Oguntoye, Michael; Dam, Tina; Chunara, Rumi
Date: 2017-12-07
DOI: 10.1186/s13104-017-3063-1
License: cc-by
Abstract: OBJECTIVE: Recognizing increasing interest in community disease surveillance globally, the goal of this study was to investigate whether respiratory viruses circulating in the community may be represented through clinical (hospital) surveillance in Nigeria. RESULTS: Children were selected via convenience sampling from communities and a tertiary care center (n = 91) during spring 2017 in Ilorin, Nigeria. Nasal swabs were collected and tested using polymerase chain reaction. The majority (79.1%) of subjects were under 6 years old, of whom 46 were infected (63.9%). A total of 33 of the 91 subjects had one or more respiratory tract virus; there were 10 cases of triple infection and 5 of quadruple. Parainfluenza virus 4, respiratory syncytial virus B and enterovirus were the most common viruses in the clinical sample; present in 93.8% (15/16) of clinical subjects, and 6.7% (5/75) of community subjects (significant difference, p < 0.001). Coronavirus OC43 was the most common virus detected in community members (13.3%, 10/75). A different strain, Coronavirus OC 229 E/NL63 was detected among subjects from the clinic (2/16) and not detected in the community. This pilot study provides evidence that data from the community can potentially represent different information than that sourced clinically, suggesting the need for community surveillance to enhance public health efforts and scientific understanding of respiratory infections.
Text: Acute Respiratory Infections (ARIs) (the cause of both upper respiratory tract infections (URIs) and lower respiratory tract infections (LRIs)) are a major cause of death among children under 5 years old particularly in developing countries where the burden of disease is 2-5 times higher than in developed countries [1] . While these viruses usually cause mild cold-like symptoms and can be self-limiting, in recent years novel coronaviruses such as severe acute respiratory syndrome (SARS) and Middle East respiratory syndrome (MERS) have evolved and infected humans, causing severe illness, epidemics and pandemics [2] . Currently, the majority of all infectious disease outbreaks as recorded by the World Health Organization (WHO) occur in the continent of Africa where there is high transmission risk [3, 4] . Further, in developing areas (both rural and urban), there are increasing risk factors such as human-animal interfaces (due to residential-proximity to livestock). These changing epidemiological patterns have resulted in calls for improved ARI surveillance, especially in places of high transmission risk [5] .
Nigeria is one such place with high prevalence of many of the risk factors implicated in ARI among children including; age, sex, overcrowding, nutritional status, socio-economic status, and where study of ARIs is currently limited [6] . These broad risk factors alongside limited resources have indicated the need for community-based initiatives for surveillance and interventions [6, 7] . For ARI surveillance in particular, infections in the community are those that do not get reported clinically. Clinical data generally represents the most severe cases, and those from locations with access to healthcare institutions. In Nigeria, hospitals are visited only when symptoms are very severe. Thus, it is hypothesized that viral information from clinical sampling is insufficient to either capture disease incidence in general populations or its predictability from symptoms [8] . Efforts worldwide including in East and Southern Africa have been focused on developing community-based participatory disease surveillance methods [9] [10] [11] [12] [13] . Community-based approaches have been shown useful for learning more about emerging respiratory infections such as assessing under-reporting [14] , types of viruses prevalent in communities [10] , and prediction of epidemics [15] .
Concurrently, advancements in molecular identification methods have enabled studies regarding the emergence and epidemiology of ARI viruses in many locations (e.g. novel polyomaviruses in Australia [16, 17] , human coronavirus Erasmus Medical Center (HCoV-EMC) in the Middle East and United Kingdom [18, 19] , SARS in Canada and China [20] [21] [22] ), yet research regarding the molecular epidemiology of ARI viruses in Nigeria is limited. Diagnostic methods available and other constraints have limited studies there to serological surveys of only a few of these viruses and only in clinical populations [23, 24] . Thus, the utility of community-based surveillance may be appropriate in contexts such as in Nigeria, and the purpose of this pilot study was to investigate if clinical cases may describe the entire picture of ARI among children in Nigeria.
We performed a cross-sectional study in three community centers and one clinical in Ilorin, Nigeria. Ilorin is in Kwara state and is the 6th largest city in Nigeria by population [25] . Three Local Government Areas (Ilorin East, Ilorin South and Ilorin West LGAs) were the community sites and Children's Specialist Hospital, Ilorin the clinical site. Convenience sampling was used for the purposes of this pilot study, and samples were obtained from March 28 to April 5 2017. Inclusion criteria were: children less than 14 years old who had visible symptoms of ARI within the communities or those confirmed at the hospital with ARI. Exclusion criteria were: children who were 14 and above, not showing signs of ARI and subjects whose parents did not give consent. Twenty-five children with symptoms were selected each from the three community locations while 16 symptomatic children were sampled from the hospital. The total sample size (n = 91) was arrived at based on materials and processing cost constraints, as well as to provide enough samples to enable descriptive understanding of viral circulation patterns estimated from other community-based studies [10] .
Disease Surveillance and Notification Officers, who are employed by the State Ministry of Health and familiar with the communities in this study, performed specimen and data collection. Symptoms considered were derived in accordance with other ARI surveillance efforts: sore throat, fever, couch, running nose, vomiting, body ache, leg pain, nausea, chills, shortness of breath [10, 26] . Gender and age, type of residential area (rural/urban), education level, proximity of residence to livestock, proximity to an untarred road and number of people who sleep in same room, were all recorded. The general difference between the two settings was that those from the hospital had severe illnesses, while those from the community were generally "healthy" but exhibiting ARI symptoms (i.e. mild illness).
Nasal swabs were collected from the subjects and stored in DNA/RNA shield (Zymo Research, Irvine, California). Collected samples were spinned and the swab removed. Residues containing the nasal samples were stored at -20 °C prior to molecular analysis.
Viral RNA was isolated using ZR Viral RNA ™ Kit (Zymo Research, Irvine, California) per manufacturer instructions (http://www.zymoresearch.com/downloads/dl/file/ id/147/r1034i.pdf ). Real-time PCR (polymerase chain reaction), commonly used in ARI studies [10, 19, 27] , was then carried out using RV15 One Step ACE Detection Kit, catalogue numbers RV0716K01008007 and RV0717B01008001 (Seegene, Seoul, South Korea) for detection of 15 human viruses: parainfluenza virus 1, 2, 3 and 4 (PIV1-4), respiratory syncytial virus (RSV) A and B, influenza A and B (FLUA, FLUB), rhinovirus type A-C, adenovirus (ADV), coronavirus (OC 229 E/NL63, OC43), enterovirus (HEV), metapneumovirus (hMPV) and bocavirus (BoV).
Reagents were validated in the experimental location using an inbuilt validation protocol to confirm issues of false negative and false positive results were not of concern. Amplification reaction was carried out as described by the manufacturer: reverse transcription 50 °C-30′, initial activation 94°-15′, 45 cycles: denaturation 94°-30″, annealing 60°-1′ 30″, extension 72°-1, final extension 72°-10′, hold 4°. Visualization was performed using electrophoresis on a 2% agarose gel in TBE 1X with EtBr, in presence of RV15 OneStep A/B/C Markers; molecular weight marker. Specimen processing was not blinded as there was no risk of experimental bias. Standardized procedures were used for community and clinic sampling.
All statistical analyses were performed using R version 3.2.4. Univariate statistics [mean and 95% confidence interval (CI)] are described. Bivariate statistics (difference in proportions) were assessed using a two-proportion z-test. A p value < 0.001 was considered significant. No observations used in this study had any missing data for analyses in this study.
Basic participant demographics are summarized in PCR results showed that ten different viruses (influenza A, coronavirus OC 229 E/NL63, RSVA, RSV B, parainfluenza 1-4) were detected. Figure 1 shows how these infections were distributed across virus types as well as in the community versus clinic samples. In sum, a total of 33 of the 91 subjects surveyed had one or more respiratory tract virus (36.3%, 95% CI 26.6-47.0%, Fig. 1 ). Furthermore, 10 of those cases were triple infections and 5 were quadruple infections (illustrated by color of bars in Fig. 1 ). Figure 2 indicates how frequently each pair of viruses were found in the same participant; co-infections were most common among enterovirus and parainfluenza virus 4 (Fig. 2) .
We also compared and contrasted the clinical and community results. Parainfluenza virus 4, respiratory syncytial virus B and enterovirus were the most common viruses found in the clinical sample. These three infections resulted in 41 viruses detected in 15 subjects clinically, and eight infections detected in five people in the community. Together they infected 94% (15/16, 95% CI 67.7-99.7%) of clinical subjects, and 7% (5/75, 95% CI 2.5-15.5%) in the community (significant difference, p < 0.001). The most common virus detected in community samples was Coronavirus OC43; this virus was detected in 13.3% (95% CI 6.9-23.6%) people in the community and not in any of the clinical samples. However a different strain, coronavirus OC 229 E/NL63 was detected in 12.5% of the clinical subjects (2/16, 95% CI 2.2-39.6%) and not detected in the community. Double, triple and quadruple infections were another common feature of note.
We identified ten different respiratory tract viruses among the subjects as shown in Fig. 1 . Samples collected from the Children's specialist hospital showed 100% prevalence rate of infection with one or more viruses. This might not be surprising, as the basic difference between the community and clinic samples was an increased severity of illness in the clinical sample. This may also explain the high level of co-infection found among the clinical subjects. The most prevalent virus in the clinical sample (coronavirus OC43) was not detected in the community sample. Further, there was a significant difference between prevalence of the most common viruses in the clinical sample (parainfluenza virus 4, respiratory syncytial virus B and enterovirus) and their prevalence in the community. Finally, some of the viruses detected in this study have not been detected and implicated with ARIs in Nigeria. There is no report, to the best of our knowledge, implicating coronavirus in ARIs in Nigeria, and it was detected in 12 subjects in this study. Although cases of double and triple infections were observed in a study in Nigeria in 2011 [28] , as far as we are aware, reports of quadruple infections are rare and have not been reported in Nigeria previously.
Due to the unique nature of the data generated in this study and novelty of work in the setting, it is not possible to exactly compare results to other studies. For example, though we found a similar study regarding ARIs in clinical subjects in Burkina Faso [27] , due to the small sample size from this study it would not be feasible to infer or compare prevalence rates. Studies of ARI etiology have mostly been generally focused in areas of the world that are more developed [29] , and it is important to note that the availability of molecular diagnostic methods as employed in this study substantially improve the ability to detect viruses which hitherto have not been detected in Nigeria. Further, findings from this work also add to the growing body of research that shows value of community-data in infectious disease surveillance [8] . As most of the work to-date has been in higher resource areas of the world this study adds perspective from an area where healthcare resources are lower.
In conclusion, results of this study provide evidence for active community surveillance to enhance public health surveillance and scientific understanding of ARIs. This is not only because a minority of children with severe infection are admitted to the hospital in areas such this in Nigeria, but also findings from this pilot study which indicate that viral circulation in the community may not get detected clinically [29] . This pilot study indicates that in areas of Nigeria, etiology of ARIs ascertained from clinical samples may not represent all of the ARIs circulating in the community.
The main limitation of the study is the sample size. In particular, the sample is not equally representative across all ages. However, the sample size was big enough to ascertain significant differences in community and clinic sourced viruses, and provides a qualitative understanding of viral etiology in samples from the community and clinic. Moreover, the sample was largely concentrated on subjects under 6 years, who are amongst the groups at highest risk of ARIs. Despite the small sample size, samples here indicate that circulation patterns in the community may differ from those in the clinic. In addition, this study resulted in unique findings Given that resources are limited for research and practice, we hope these pilot results may motivate further systematic investigations into how community-generated data can best be used in ARI surveillance. Results of this study can inform a larger study, representative across demographic and locations to systematically assess the etiology of infection and differences in clinical and community cohorts. A larger study will also enable accounting for potential confounders such as environmental risk factors. Finally, while it may be intuitive, findings from this pilot study shed light on the scope of differences in ARI patterns including different types and strains of circulating viruses. Also, because PCR was used for viral detection, the study was limited to detection of viruses in the primer sets. Given that these are the most up-to-date and common viruses, this approach was deemed sufficient for this initial investigation.
The study was conceived by RC and OK. RC and OK, MO and TD were involved in the design of the study, which was conducted by MO and TD. RC and OK analyzed the data. RC and OK wrote and revised the manuscript. All authors read and approved the final manuscript.
|
What was the prevalence of Coronavirus OC 229 E/NL63 in clinical subjects in Ilorin, Nigeria?
| 1,605 |
12.5%
| 10,584 |
1,568 |
Etiology of respiratory tract infections in the community and clinic in Ilorin, Nigeria
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5719735/
SHA: f2e835d2cde5f42054dbd0c20d4060721135c518
Authors: Kolawole, Olatunji; Oguntoye, Michael; Dam, Tina; Chunara, Rumi
Date: 2017-12-07
DOI: 10.1186/s13104-017-3063-1
License: cc-by
Abstract: OBJECTIVE: Recognizing increasing interest in community disease surveillance globally, the goal of this study was to investigate whether respiratory viruses circulating in the community may be represented through clinical (hospital) surveillance in Nigeria. RESULTS: Children were selected via convenience sampling from communities and a tertiary care center (n = 91) during spring 2017 in Ilorin, Nigeria. Nasal swabs were collected and tested using polymerase chain reaction. The majority (79.1%) of subjects were under 6 years old, of whom 46 were infected (63.9%). A total of 33 of the 91 subjects had one or more respiratory tract virus; there were 10 cases of triple infection and 5 of quadruple. Parainfluenza virus 4, respiratory syncytial virus B and enterovirus were the most common viruses in the clinical sample; present in 93.8% (15/16) of clinical subjects, and 6.7% (5/75) of community subjects (significant difference, p < 0.001). Coronavirus OC43 was the most common virus detected in community members (13.3%, 10/75). A different strain, Coronavirus OC 229 E/NL63 was detected among subjects from the clinic (2/16) and not detected in the community. This pilot study provides evidence that data from the community can potentially represent different information than that sourced clinically, suggesting the need for community surveillance to enhance public health efforts and scientific understanding of respiratory infections.
Text: Acute Respiratory Infections (ARIs) (the cause of both upper respiratory tract infections (URIs) and lower respiratory tract infections (LRIs)) are a major cause of death among children under 5 years old particularly in developing countries where the burden of disease is 2-5 times higher than in developed countries [1] . While these viruses usually cause mild cold-like symptoms and can be self-limiting, in recent years novel coronaviruses such as severe acute respiratory syndrome (SARS) and Middle East respiratory syndrome (MERS) have evolved and infected humans, causing severe illness, epidemics and pandemics [2] . Currently, the majority of all infectious disease outbreaks as recorded by the World Health Organization (WHO) occur in the continent of Africa where there is high transmission risk [3, 4] . Further, in developing areas (both rural and urban), there are increasing risk factors such as human-animal interfaces (due to residential-proximity to livestock). These changing epidemiological patterns have resulted in calls for improved ARI surveillance, especially in places of high transmission risk [5] .
Nigeria is one such place with high prevalence of many of the risk factors implicated in ARI among children including; age, sex, overcrowding, nutritional status, socio-economic status, and where study of ARIs is currently limited [6] . These broad risk factors alongside limited resources have indicated the need for community-based initiatives for surveillance and interventions [6, 7] . For ARI surveillance in particular, infections in the community are those that do not get reported clinically. Clinical data generally represents the most severe cases, and those from locations with access to healthcare institutions. In Nigeria, hospitals are visited only when symptoms are very severe. Thus, it is hypothesized that viral information from clinical sampling is insufficient to either capture disease incidence in general populations or its predictability from symptoms [8] . Efforts worldwide including in East and Southern Africa have been focused on developing community-based participatory disease surveillance methods [9] [10] [11] [12] [13] . Community-based approaches have been shown useful for learning more about emerging respiratory infections such as assessing under-reporting [14] , types of viruses prevalent in communities [10] , and prediction of epidemics [15] .
Concurrently, advancements in molecular identification methods have enabled studies regarding the emergence and epidemiology of ARI viruses in many locations (e.g. novel polyomaviruses in Australia [16, 17] , human coronavirus Erasmus Medical Center (HCoV-EMC) in the Middle East and United Kingdom [18, 19] , SARS in Canada and China [20] [21] [22] ), yet research regarding the molecular epidemiology of ARI viruses in Nigeria is limited. Diagnostic methods available and other constraints have limited studies there to serological surveys of only a few of these viruses and only in clinical populations [23, 24] . Thus, the utility of community-based surveillance may be appropriate in contexts such as in Nigeria, and the purpose of this pilot study was to investigate if clinical cases may describe the entire picture of ARI among children in Nigeria.
We performed a cross-sectional study in three community centers and one clinical in Ilorin, Nigeria. Ilorin is in Kwara state and is the 6th largest city in Nigeria by population [25] . Three Local Government Areas (Ilorin East, Ilorin South and Ilorin West LGAs) were the community sites and Children's Specialist Hospital, Ilorin the clinical site. Convenience sampling was used for the purposes of this pilot study, and samples were obtained from March 28 to April 5 2017. Inclusion criteria were: children less than 14 years old who had visible symptoms of ARI within the communities or those confirmed at the hospital with ARI. Exclusion criteria were: children who were 14 and above, not showing signs of ARI and subjects whose parents did not give consent. Twenty-five children with symptoms were selected each from the three community locations while 16 symptomatic children were sampled from the hospital. The total sample size (n = 91) was arrived at based on materials and processing cost constraints, as well as to provide enough samples to enable descriptive understanding of viral circulation patterns estimated from other community-based studies [10] .
Disease Surveillance and Notification Officers, who are employed by the State Ministry of Health and familiar with the communities in this study, performed specimen and data collection. Symptoms considered were derived in accordance with other ARI surveillance efforts: sore throat, fever, couch, running nose, vomiting, body ache, leg pain, nausea, chills, shortness of breath [10, 26] . Gender and age, type of residential area (rural/urban), education level, proximity of residence to livestock, proximity to an untarred road and number of people who sleep in same room, were all recorded. The general difference between the two settings was that those from the hospital had severe illnesses, while those from the community were generally "healthy" but exhibiting ARI symptoms (i.e. mild illness).
Nasal swabs were collected from the subjects and stored in DNA/RNA shield (Zymo Research, Irvine, California). Collected samples were spinned and the swab removed. Residues containing the nasal samples were stored at -20 °C prior to molecular analysis.
Viral RNA was isolated using ZR Viral RNA ™ Kit (Zymo Research, Irvine, California) per manufacturer instructions (http://www.zymoresearch.com/downloads/dl/file/ id/147/r1034i.pdf ). Real-time PCR (polymerase chain reaction), commonly used in ARI studies [10, 19, 27] , was then carried out using RV15 One Step ACE Detection Kit, catalogue numbers RV0716K01008007 and RV0717B01008001 (Seegene, Seoul, South Korea) for detection of 15 human viruses: parainfluenza virus 1, 2, 3 and 4 (PIV1-4), respiratory syncytial virus (RSV) A and B, influenza A and B (FLUA, FLUB), rhinovirus type A-C, adenovirus (ADV), coronavirus (OC 229 E/NL63, OC43), enterovirus (HEV), metapneumovirus (hMPV) and bocavirus (BoV).
Reagents were validated in the experimental location using an inbuilt validation protocol to confirm issues of false negative and false positive results were not of concern. Amplification reaction was carried out as described by the manufacturer: reverse transcription 50 °C-30′, initial activation 94°-15′, 45 cycles: denaturation 94°-30″, annealing 60°-1′ 30″, extension 72°-1, final extension 72°-10′, hold 4°. Visualization was performed using electrophoresis on a 2% agarose gel in TBE 1X with EtBr, in presence of RV15 OneStep A/B/C Markers; molecular weight marker. Specimen processing was not blinded as there was no risk of experimental bias. Standardized procedures were used for community and clinic sampling.
All statistical analyses were performed using R version 3.2.4. Univariate statistics [mean and 95% confidence interval (CI)] are described. Bivariate statistics (difference in proportions) were assessed using a two-proportion z-test. A p value < 0.001 was considered significant. No observations used in this study had any missing data for analyses in this study.
Basic participant demographics are summarized in PCR results showed that ten different viruses (influenza A, coronavirus OC 229 E/NL63, RSVA, RSV B, parainfluenza 1-4) were detected. Figure 1 shows how these infections were distributed across virus types as well as in the community versus clinic samples. In sum, a total of 33 of the 91 subjects surveyed had one or more respiratory tract virus (36.3%, 95% CI 26.6-47.0%, Fig. 1 ). Furthermore, 10 of those cases were triple infections and 5 were quadruple infections (illustrated by color of bars in Fig. 1 ). Figure 2 indicates how frequently each pair of viruses were found in the same participant; co-infections were most common among enterovirus and parainfluenza virus 4 (Fig. 2) .
We also compared and contrasted the clinical and community results. Parainfluenza virus 4, respiratory syncytial virus B and enterovirus were the most common viruses found in the clinical sample. These three infections resulted in 41 viruses detected in 15 subjects clinically, and eight infections detected in five people in the community. Together they infected 94% (15/16, 95% CI 67.7-99.7%) of clinical subjects, and 7% (5/75, 95% CI 2.5-15.5%) in the community (significant difference, p < 0.001). The most common virus detected in community samples was Coronavirus OC43; this virus was detected in 13.3% (95% CI 6.9-23.6%) people in the community and not in any of the clinical samples. However a different strain, coronavirus OC 229 E/NL63 was detected in 12.5% of the clinical subjects (2/16, 95% CI 2.2-39.6%) and not detected in the community. Double, triple and quadruple infections were another common feature of note.
We identified ten different respiratory tract viruses among the subjects as shown in Fig. 1 . Samples collected from the Children's specialist hospital showed 100% prevalence rate of infection with one or more viruses. This might not be surprising, as the basic difference between the community and clinic samples was an increased severity of illness in the clinical sample. This may also explain the high level of co-infection found among the clinical subjects. The most prevalent virus in the clinical sample (coronavirus OC43) was not detected in the community sample. Further, there was a significant difference between prevalence of the most common viruses in the clinical sample (parainfluenza virus 4, respiratory syncytial virus B and enterovirus) and their prevalence in the community. Finally, some of the viruses detected in this study have not been detected and implicated with ARIs in Nigeria. There is no report, to the best of our knowledge, implicating coronavirus in ARIs in Nigeria, and it was detected in 12 subjects in this study. Although cases of double and triple infections were observed in a study in Nigeria in 2011 [28] , as far as we are aware, reports of quadruple infections are rare and have not been reported in Nigeria previously.
Due to the unique nature of the data generated in this study and novelty of work in the setting, it is not possible to exactly compare results to other studies. For example, though we found a similar study regarding ARIs in clinical subjects in Burkina Faso [27] , due to the small sample size from this study it would not be feasible to infer or compare prevalence rates. Studies of ARI etiology have mostly been generally focused in areas of the world that are more developed [29] , and it is important to note that the availability of molecular diagnostic methods as employed in this study substantially improve the ability to detect viruses which hitherto have not been detected in Nigeria. Further, findings from this work also add to the growing body of research that shows value of community-data in infectious disease surveillance [8] . As most of the work to-date has been in higher resource areas of the world this study adds perspective from an area where healthcare resources are lower.
In conclusion, results of this study provide evidence for active community surveillance to enhance public health surveillance and scientific understanding of ARIs. This is not only because a minority of children with severe infection are admitted to the hospital in areas such this in Nigeria, but also findings from this pilot study which indicate that viral circulation in the community may not get detected clinically [29] . This pilot study indicates that in areas of Nigeria, etiology of ARIs ascertained from clinical samples may not represent all of the ARIs circulating in the community.
The main limitation of the study is the sample size. In particular, the sample is not equally representative across all ages. However, the sample size was big enough to ascertain significant differences in community and clinic sourced viruses, and provides a qualitative understanding of viral etiology in samples from the community and clinic. Moreover, the sample was largely concentrated on subjects under 6 years, who are amongst the groups at highest risk of ARIs. Despite the small sample size, samples here indicate that circulation patterns in the community may differ from those in the clinic. In addition, this study resulted in unique findings Given that resources are limited for research and practice, we hope these pilot results may motivate further systematic investigations into how community-generated data can best be used in ARI surveillance. Results of this study can inform a larger study, representative across demographic and locations to systematically assess the etiology of infection and differences in clinical and community cohorts. A larger study will also enable accounting for potential confounders such as environmental risk factors. Finally, while it may be intuitive, findings from this pilot study shed light on the scope of differences in ARI patterns including different types and strains of circulating viruses. Also, because PCR was used for viral detection, the study was limited to detection of viruses in the primer sets. Given that these are the most up-to-date and common viruses, this approach was deemed sufficient for this initial investigation.
The study was conceived by RC and OK. RC and OK, MO and TD were involved in the design of the study, which was conducted by MO and TD. RC and OK analyzed the data. RC and OK wrote and revised the manuscript. All authors read and approved the final manuscript.
|
What was the difference between community and clinic cases of acute respiratory infections?
| 1,606 |
increased severity of illness in the clinical sample
| 11,074 |
1,568 |
Etiology of respiratory tract infections in the community and clinic in Ilorin, Nigeria
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5719735/
SHA: f2e835d2cde5f42054dbd0c20d4060721135c518
Authors: Kolawole, Olatunji; Oguntoye, Michael; Dam, Tina; Chunara, Rumi
Date: 2017-12-07
DOI: 10.1186/s13104-017-3063-1
License: cc-by
Abstract: OBJECTIVE: Recognizing increasing interest in community disease surveillance globally, the goal of this study was to investigate whether respiratory viruses circulating in the community may be represented through clinical (hospital) surveillance in Nigeria. RESULTS: Children were selected via convenience sampling from communities and a tertiary care center (n = 91) during spring 2017 in Ilorin, Nigeria. Nasal swabs were collected and tested using polymerase chain reaction. The majority (79.1%) of subjects were under 6 years old, of whom 46 were infected (63.9%). A total of 33 of the 91 subjects had one or more respiratory tract virus; there were 10 cases of triple infection and 5 of quadruple. Parainfluenza virus 4, respiratory syncytial virus B and enterovirus were the most common viruses in the clinical sample; present in 93.8% (15/16) of clinical subjects, and 6.7% (5/75) of community subjects (significant difference, p < 0.001). Coronavirus OC43 was the most common virus detected in community members (13.3%, 10/75). A different strain, Coronavirus OC 229 E/NL63 was detected among subjects from the clinic (2/16) and not detected in the community. This pilot study provides evidence that data from the community can potentially represent different information than that sourced clinically, suggesting the need for community surveillance to enhance public health efforts and scientific understanding of respiratory infections.
Text: Acute Respiratory Infections (ARIs) (the cause of both upper respiratory tract infections (URIs) and lower respiratory tract infections (LRIs)) are a major cause of death among children under 5 years old particularly in developing countries where the burden of disease is 2-5 times higher than in developed countries [1] . While these viruses usually cause mild cold-like symptoms and can be self-limiting, in recent years novel coronaviruses such as severe acute respiratory syndrome (SARS) and Middle East respiratory syndrome (MERS) have evolved and infected humans, causing severe illness, epidemics and pandemics [2] . Currently, the majority of all infectious disease outbreaks as recorded by the World Health Organization (WHO) occur in the continent of Africa where there is high transmission risk [3, 4] . Further, in developing areas (both rural and urban), there are increasing risk factors such as human-animal interfaces (due to residential-proximity to livestock). These changing epidemiological patterns have resulted in calls for improved ARI surveillance, especially in places of high transmission risk [5] .
Nigeria is one such place with high prevalence of many of the risk factors implicated in ARI among children including; age, sex, overcrowding, nutritional status, socio-economic status, and where study of ARIs is currently limited [6] . These broad risk factors alongside limited resources have indicated the need for community-based initiatives for surveillance and interventions [6, 7] . For ARI surveillance in particular, infections in the community are those that do not get reported clinically. Clinical data generally represents the most severe cases, and those from locations with access to healthcare institutions. In Nigeria, hospitals are visited only when symptoms are very severe. Thus, it is hypothesized that viral information from clinical sampling is insufficient to either capture disease incidence in general populations or its predictability from symptoms [8] . Efforts worldwide including in East and Southern Africa have been focused on developing community-based participatory disease surveillance methods [9] [10] [11] [12] [13] . Community-based approaches have been shown useful for learning more about emerging respiratory infections such as assessing under-reporting [14] , types of viruses prevalent in communities [10] , and prediction of epidemics [15] .
Concurrently, advancements in molecular identification methods have enabled studies regarding the emergence and epidemiology of ARI viruses in many locations (e.g. novel polyomaviruses in Australia [16, 17] , human coronavirus Erasmus Medical Center (HCoV-EMC) in the Middle East and United Kingdom [18, 19] , SARS in Canada and China [20] [21] [22] ), yet research regarding the molecular epidemiology of ARI viruses in Nigeria is limited. Diagnostic methods available and other constraints have limited studies there to serological surveys of only a few of these viruses and only in clinical populations [23, 24] . Thus, the utility of community-based surveillance may be appropriate in contexts such as in Nigeria, and the purpose of this pilot study was to investigate if clinical cases may describe the entire picture of ARI among children in Nigeria.
We performed a cross-sectional study in three community centers and one clinical in Ilorin, Nigeria. Ilorin is in Kwara state and is the 6th largest city in Nigeria by population [25] . Three Local Government Areas (Ilorin East, Ilorin South and Ilorin West LGAs) were the community sites and Children's Specialist Hospital, Ilorin the clinical site. Convenience sampling was used for the purposes of this pilot study, and samples were obtained from March 28 to April 5 2017. Inclusion criteria were: children less than 14 years old who had visible symptoms of ARI within the communities or those confirmed at the hospital with ARI. Exclusion criteria were: children who were 14 and above, not showing signs of ARI and subjects whose parents did not give consent. Twenty-five children with symptoms were selected each from the three community locations while 16 symptomatic children were sampled from the hospital. The total sample size (n = 91) was arrived at based on materials and processing cost constraints, as well as to provide enough samples to enable descriptive understanding of viral circulation patterns estimated from other community-based studies [10] .
Disease Surveillance and Notification Officers, who are employed by the State Ministry of Health and familiar with the communities in this study, performed specimen and data collection. Symptoms considered were derived in accordance with other ARI surveillance efforts: sore throat, fever, couch, running nose, vomiting, body ache, leg pain, nausea, chills, shortness of breath [10, 26] . Gender and age, type of residential area (rural/urban), education level, proximity of residence to livestock, proximity to an untarred road and number of people who sleep in same room, were all recorded. The general difference between the two settings was that those from the hospital had severe illnesses, while those from the community were generally "healthy" but exhibiting ARI symptoms (i.e. mild illness).
Nasal swabs were collected from the subjects and stored in DNA/RNA shield (Zymo Research, Irvine, California). Collected samples were spinned and the swab removed. Residues containing the nasal samples were stored at -20 °C prior to molecular analysis.
Viral RNA was isolated using ZR Viral RNA ™ Kit (Zymo Research, Irvine, California) per manufacturer instructions (http://www.zymoresearch.com/downloads/dl/file/ id/147/r1034i.pdf ). Real-time PCR (polymerase chain reaction), commonly used in ARI studies [10, 19, 27] , was then carried out using RV15 One Step ACE Detection Kit, catalogue numbers RV0716K01008007 and RV0717B01008001 (Seegene, Seoul, South Korea) for detection of 15 human viruses: parainfluenza virus 1, 2, 3 and 4 (PIV1-4), respiratory syncytial virus (RSV) A and B, influenza A and B (FLUA, FLUB), rhinovirus type A-C, adenovirus (ADV), coronavirus (OC 229 E/NL63, OC43), enterovirus (HEV), metapneumovirus (hMPV) and bocavirus (BoV).
Reagents were validated in the experimental location using an inbuilt validation protocol to confirm issues of false negative and false positive results were not of concern. Amplification reaction was carried out as described by the manufacturer: reverse transcription 50 °C-30′, initial activation 94°-15′, 45 cycles: denaturation 94°-30″, annealing 60°-1′ 30″, extension 72°-1, final extension 72°-10′, hold 4°. Visualization was performed using electrophoresis on a 2% agarose gel in TBE 1X with EtBr, in presence of RV15 OneStep A/B/C Markers; molecular weight marker. Specimen processing was not blinded as there was no risk of experimental bias. Standardized procedures were used for community and clinic sampling.
All statistical analyses were performed using R version 3.2.4. Univariate statistics [mean and 95% confidence interval (CI)] are described. Bivariate statistics (difference in proportions) were assessed using a two-proportion z-test. A p value < 0.001 was considered significant. No observations used in this study had any missing data for analyses in this study.
Basic participant demographics are summarized in PCR results showed that ten different viruses (influenza A, coronavirus OC 229 E/NL63, RSVA, RSV B, parainfluenza 1-4) were detected. Figure 1 shows how these infections were distributed across virus types as well as in the community versus clinic samples. In sum, a total of 33 of the 91 subjects surveyed had one or more respiratory tract virus (36.3%, 95% CI 26.6-47.0%, Fig. 1 ). Furthermore, 10 of those cases were triple infections and 5 were quadruple infections (illustrated by color of bars in Fig. 1 ). Figure 2 indicates how frequently each pair of viruses were found in the same participant; co-infections were most common among enterovirus and parainfluenza virus 4 (Fig. 2) .
We also compared and contrasted the clinical and community results. Parainfluenza virus 4, respiratory syncytial virus B and enterovirus were the most common viruses found in the clinical sample. These three infections resulted in 41 viruses detected in 15 subjects clinically, and eight infections detected in five people in the community. Together they infected 94% (15/16, 95% CI 67.7-99.7%) of clinical subjects, and 7% (5/75, 95% CI 2.5-15.5%) in the community (significant difference, p < 0.001). The most common virus detected in community samples was Coronavirus OC43; this virus was detected in 13.3% (95% CI 6.9-23.6%) people in the community and not in any of the clinical samples. However a different strain, coronavirus OC 229 E/NL63 was detected in 12.5% of the clinical subjects (2/16, 95% CI 2.2-39.6%) and not detected in the community. Double, triple and quadruple infections were another common feature of note.
We identified ten different respiratory tract viruses among the subjects as shown in Fig. 1 . Samples collected from the Children's specialist hospital showed 100% prevalence rate of infection with one or more viruses. This might not be surprising, as the basic difference between the community and clinic samples was an increased severity of illness in the clinical sample. This may also explain the high level of co-infection found among the clinical subjects. The most prevalent virus in the clinical sample (coronavirus OC43) was not detected in the community sample. Further, there was a significant difference between prevalence of the most common viruses in the clinical sample (parainfluenza virus 4, respiratory syncytial virus B and enterovirus) and their prevalence in the community. Finally, some of the viruses detected in this study have not been detected and implicated with ARIs in Nigeria. There is no report, to the best of our knowledge, implicating coronavirus in ARIs in Nigeria, and it was detected in 12 subjects in this study. Although cases of double and triple infections were observed in a study in Nigeria in 2011 [28] , as far as we are aware, reports of quadruple infections are rare and have not been reported in Nigeria previously.
Due to the unique nature of the data generated in this study and novelty of work in the setting, it is not possible to exactly compare results to other studies. For example, though we found a similar study regarding ARIs in clinical subjects in Burkina Faso [27] , due to the small sample size from this study it would not be feasible to infer or compare prevalence rates. Studies of ARI etiology have mostly been generally focused in areas of the world that are more developed [29] , and it is important to note that the availability of molecular diagnostic methods as employed in this study substantially improve the ability to detect viruses which hitherto have not been detected in Nigeria. Further, findings from this work also add to the growing body of research that shows value of community-data in infectious disease surveillance [8] . As most of the work to-date has been in higher resource areas of the world this study adds perspective from an area where healthcare resources are lower.
In conclusion, results of this study provide evidence for active community surveillance to enhance public health surveillance and scientific understanding of ARIs. This is not only because a minority of children with severe infection are admitted to the hospital in areas such this in Nigeria, but also findings from this pilot study which indicate that viral circulation in the community may not get detected clinically [29] . This pilot study indicates that in areas of Nigeria, etiology of ARIs ascertained from clinical samples may not represent all of the ARIs circulating in the community.
The main limitation of the study is the sample size. In particular, the sample is not equally representative across all ages. However, the sample size was big enough to ascertain significant differences in community and clinic sourced viruses, and provides a qualitative understanding of viral etiology in samples from the community and clinic. Moreover, the sample was largely concentrated on subjects under 6 years, who are amongst the groups at highest risk of ARIs. Despite the small sample size, samples here indicate that circulation patterns in the community may differ from those in the clinic. In addition, this study resulted in unique findings Given that resources are limited for research and practice, we hope these pilot results may motivate further systematic investigations into how community-generated data can best be used in ARI surveillance. Results of this study can inform a larger study, representative across demographic and locations to systematically assess the etiology of infection and differences in clinical and community cohorts. A larger study will also enable accounting for potential confounders such as environmental risk factors. Finally, while it may be intuitive, findings from this pilot study shed light on the scope of differences in ARI patterns including different types and strains of circulating viruses. Also, because PCR was used for viral detection, the study was limited to detection of viruses in the primer sets. Given that these are the most up-to-date and common viruses, this approach was deemed sufficient for this initial investigation.
The study was conceived by RC and OK. RC and OK, MO and TD were involved in the design of the study, which was conducted by MO and TD. RC and OK analyzed the data. RC and OK wrote and revised the manuscript. All authors read and approved the final manuscript.
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How can countries enhance public health surveillance?
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active community surveillance
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Aetiology of Acute Respiratory Tract Infections in Hospitalised Children in Cyprus
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4720120/
SHA: efd27ff0ac04dd60838266386aaebb5df80f4fa9
Authors: Richter, Jan; Panayiotou, Christakis; Tryfonos, Christina; Koptides, Dana; Koliou, Maria; Kalogirou, Nikolas; Georgiou, Eleni; Christodoulou, Christina
Date: 2016-01-13
DOI: 10.1371/journal.pone.0147041
License: cc-by
Abstract: In order to improve clinical management and prevention of viral infections in hospitalised children improved etiological insight is needed. The aim of the present study was to assess the spectrum of respiratory viral pathogens in children admitted to hospital with acute respiratory tract infections in Cyprus. For this purpose nasopharyngeal swab samples from 424 children less than 12 years of age with acute respiratory tract infections were collected over three epidemic seasons and were analysed for the presence of the most common 15 respiratory viruses. A viral pathogen was identified in 86% of the samples, with multiple infections being observed in almost 20% of the samples. The most frequently detected viruses were RSV (30.4%) and Rhinovirus (27.4%). RSV exhibited a clear seasonality with marked peaks in January/February, while rhinovirus infections did not exhibit a pronounced seasonality being detected almost throughout the year. While RSV and PIV3 incidence decreased significantly with age, the opposite was observed for influenza A and B as well as adenovirus infections. The data presented expand our understanding of the epidemiology of viral respiratory tract infections in Cypriot children and will be helpful to the clinicians and researchers interested in the treatment and control of viral respiratory tract infections.
Text: Viral Respiratory tract infections (RTI) represent a major public health problem because of their world-wide occurrence, ease of transmission and considerable morbidity and mortality effecting people of all ages. Children are on average infected two to three times more frequently than adults, with acute RTIs being the most common infection in childhood [1, 2] . Illnesses caused by respiratory viruses include, among others, common colds, pharyngitis, croup, bronchiolitis, viral pneumonia and otitis media. Rapid diagnosis is important not only for timely therapeutic intervention but also for the identification of a beginning influenza epidemic and the avoidance of unnecessary antibiotic treatment [3, 4] .
RTIs are a major cause of morbidity and mortality worldwide. Acute RTI is most common in children under five years of age, and represents 30-50% of the paediatric medical admissions, as well as 20-40% of hospitalizations in children. Respiratory infections cluster during winter and early spring months. The leading viral agents include respiratory syncytial virus (RSV), influenza A and B (INF-A, INF-B) viruses, parainfluenza viruses (PIVs), and human adenoviruses (HAdVs). In addition, there is a continuously increasing list of new respiratory viruses that contribute significantly to the burden of acute respiratory infections, such as the recently identified human metapneumovirus (HMPV) and human Bocavirus (HBoV) [5] .
Acute RTIs are classified as upper (UTRIs) and lower RTI (LRTIs), according to the involved anatomic localization. URTIs cause non-severe but widespread epidemics that are responsible for continuous circulation of pathogens in the community. LRTIs have been classified as frank pneumonia and bronchiolitis with clinical, radiological and etiological features that usually overlap [6, 7] . Viruses are again the foremost agents of LRTIs often misdiagnosed as bacterial in origin and hence treated with antibiotics unnecessarily [8] .
The main aim of this study was to determine the aetiology of acute respiratory tract infections in Cypriot children and assess the epidemiology of the identified viral pathogens over three epidemic seasons.
The study was approved by the Cyprus National Bioethics Committee. Accordingly, written informed consent was obtained from parents prior to sample taking. Between November 2010 and October 2013, 485 nasopharyngeal swab samples were collected from children up to 12 years of age, who had been hospitalized with acute respiratory tract infection at the Archbishop Makarios III hospital, Nicosia. Clinical and demographic information including symptoms, duration of hospitalisation, diagnosis and treatment were recorded. Nasal swab samples were collected using the BD Universal Viral Transport Collection Kit. Viral RNA/DNA was extracted from 400 μl sample using the iPrep PureLink Virus Kit on an iPrep purification instrument (Invitrogen).
A set of four multiplex Real-Time RT-PCR assays was established and validated for the detection of the 15 most common respiratory viruses as follows: assay 1: influenzaviruses A and B, RSV, assay 2: parainfluenzaviruses 1-4, assay 3: HAdV, enteroviruses, HMPV and HBoV and assay 4: rhinoviruses and the human coronaviruses OC43, NL63 and 229E (Table 1) .
Published primer and probe sets were used as a basis for designing the assays, however, all primer/probe sequences were checked against newly build sequence alignments of all viruses tested and were modified, if necessary, to account for possible sequence variations. For this purpose, all available complete genome sequences were obtained for each virus from GenBank, imported into the BioEdit Sequence Alignment Editor v7.1.7 and aligned using ClustalX. In case of mismatches between published primers/probe and target sequences, modifications were applied, as indicated in Table 1 . The alignments for the viruses, which necessitated changes to the primers/probe are available in Fasta-Format as supplement S1-S4 Files.
Primer concentrations and reaction conditions for the four assays were subsequently optimised for multiplexing. In order to assess the sensitivity and specificity of the assays, the laboratory enrolled for two consecutive years in Quality Control for Molecular Diagnostics (QCMD) external quality assessment schemes for all viruses, except Bocavirus, which was unavailable. In summary, the established assays were able to correctly identify all viruses tested, proving their suitability for diagnostic application.
A possible correlation of virus prevalence and age of infection was assessed using univariate analyses. The Fisher's exact test was used where cell counts below 5 were encountered; otherwise, the chi-squared test was performed. The same statistical tests were used to compare the frequency of subjects with single or multiple infections between age groups. In addition, Pearson correlation was used to examine co-infections of different viruses. All statistical analyses were performed using StataSE 12 (StatCorp. 2007. College Station, TX, USA).
The present study was a prospective investigation of children hospitalized with acute respiratory tract infections between November 2010 and October 2013 in Cyprus. The median age of the children was 15 months (range: 0-140 months) with 243 being male and 181 female (male/ female ratio 1.34). The age distribution is shown in Fig 1.
Out of the 424 samples analysed, 364 (85.8%) were positive for one or more viruses. Results are summarized in Table 2 .The most commonly detected viruses were RSV, which was found in 129 (30.4%) patients and rhinoviruses in 116 (27.4%) accounting together for almost 60% of all detections. With moderate frequency have been detected HAdV in 31(7.3%) patients, influenza A in 28 (6.6%), HBoV in 24 (5.7%), enteroviruses and PIV 3 in 23 (5.4%) of patients respectively, and Influenza B in 21 (5.0%). A low frequency was exhibited by HMPV with 16 (3.8%) positive samples, human coronavirus OC43 with 13 (3.1%), PIV 1 with 12 (2.8%), PIV 4 with 9 (2.1%), PIV 2 with 7 (1.7%) and HCoV NL63 with 6 (1.4%). Coronavirus 229E could be detected only in a single sample.
Co-infections with two or more viruses were observed in 84 out of the 364 positive samples (see Table 2 ). Dual infections accounted for 17% of all positive samples and three viruses were detected in 2.7% of samples). A single patient sample displayed a quadruple infection being simultaneously positive for RSV, rhinovirus, HBoV and influenza B. Table 3 summarizes the frequency of each virus in single vs. multiple infections as well as the number of co-occurrences of viruses for each possible virus combination. In absolute terms the most common combination observed was RSV/rhinovirus. As a percentage, however, the virus appearing most often in co- infections was HBoV, which was found in more than 70% of cases together with another virus, followed by coronaviruses HCoV OC43 and HCoV NL63 with 61% and 67%, respectively. On the other hand, the viruses most rarely seen in co-infections were influenza viruses A and B as well as RSV. Pearson correlation coefficients were calculated to examine the likelihood of co-infections of different viruses. The results of the analysis are summarized in Table 1 in S1 Table. Significant correlation (P-value < 0.05) was seen mostly for co-infections with RSV, however correlations were very weak (r<0.3) and negative. This finding can probably be explained by the fact that RSV infections occurred predominantly in the very young, where co-infections were less frequently observed. On the other hand, a significant positive correlation was observed for enterovirus and rhinovirus co-infection hinting maybe at similarities in circulation patterns and/or transmission modes.
Regarding seasonality, different patterns of circulations could be observed for RSV, rhinoviruses and influenzaviruses (A and B combined) (Fig 2) , with RSV and influenza exhibiting a clear seasonality with marked peaks in January/February, while rhinovirus infections did not exhibit a pronounced seasonality being detected almost throughout the year. However, as more than 100 different rhinovirus strains have been identified to be circulating worldwide in parallel and successively, a potential seasonality of individual rhinovirus serotypes may be masked by overlapping patterns [18, 19] .
The data was further analysed with regard to the age distribution of virus infection (see Table 2 ). In infants up to 3 months old, RSV was by far the most common pathogen (58.1%), followed by rhinovirus (20.3%) and PIV3 with 8.1% each. The incidence of RSV, however, decreases significantly with increasing age (p-value < 0.0001) dropping to 13% in children older than 3 years old, while the reverse relationship is observed for Influenza A and B and HAdV. Rhinoviruses, HBoV and enteroviruses are most frequently observed in children from 4 months to 3 years of age. The age dependency of the virus incidence is visualized in Fig 3 for the seven most frequently observed viruses. The positivity rate also showed a trend according to the age group dropping from 90.5% in the under 3-month old to 78.3% in the 4-12 years old (p-value = 0.020). This may point to an increasing role of pathogens not included in the assays, such as bacterial infections in older children.
Regarding multiple infections, children less than 3 month of age and those older than 4 years had a significantly smaller risk to present with multiple infections as compared to the other two age groups (p-value = 0.014).
A reason for this could be that very young children have limited contact to others reducing thereby the chance for a co-infection, whereas children older than 3 years already established immunity to an increasing number of viruses encountered previously.
This study for the first time examined the aetiology of acute respiratory tract infections in hospitalised children in Cyprus. Four multiplex Real-Time RT-PCR assays were developed in order to detect the most common respiratory viral pathogens in a fast and cost-effective way. The high rate of positive samples (85.8%) is evidence of the high sensitivity of the Multiplex-assays used and that the range of viruses included in the analysis is comprehensive. Many previous studies have shown detection rates ranging from below 50% to 75% [20] [21] [22] [23] [24] .
The most common viruses detected were RSV and rhinovirus accounting for almost 60% of all cases. Both viruses were reported previously by others as the major aetiology for respiratory viral infections in young children with rhinoviruses being recognized increasingly for their role in lower respiratory tract infections [20, [25] [26] [27] [28] [29] [30] .
Our data support the results of similar studies performed in the Middle East region. A recently published study found that RSV was the most commonly detected virus in nasopharyngeal swabs from children presenting symptoms of RTIs and in addition to that it also showed that RSV infections follow a similar circulation pattern peaking from December to March [31] . Another study has revealed that RSV and PIV3 incidence decreases significantly with age, whereas the opposite is observed for influenza and adenovirus infections, a trend that was also observed in our study [26] .
Mixed infections were observed in approximately 20% of all samples, which is in the middle of previously reported rates ranging from 10 to almost 40%. HBoV, HCoV and EV were found most frequently in co-infections. All three subtypes of HCoV were co-detected with several other viruses, while HBoV was co-detected mainly with HRV and RSV. In the case of EV infections, EV were almost predominantly associated with HRV. The rare presence of InfA and InfB viruses in multiple infections witnessed in our study was also observed elsewhere [32, 33] . Even though this study did not allow for investigating a possible association between multiple infections and disease severity, a review of the literature shows that such a potential association is still subject to controversy, since there are reports showing no relationship of multiple virus infection with respiratoty illness severity on one hand or a significant association on the other. Studies have shown that viral co-infection was significantly associated with longer duration of illness symptoms, but with a decreased severity in hospitalized children regarding oxygen requirement and intensive care unit admission, whereas the findings of other studies have indicated that severe clinical phenotypes were more prevalent in co-infection patients, especially in RSV co-infections that may increase the severity of RSV associated disease in children [25, [34] [35] [36] [37] [38] [39] [40] .
Viral respiratory infections continue to be a worldwide health concern. As the clinical symptoms of patients with acute respiratory tract infections do usually not allow a discrimination of viral or bacterial aetiology, rapid and reliable diagnostic tools are required for better antibiotic stewardship and the implementation of appropriate infection control measures [4, 41] . The data presented expand our understanding of the epidemiology of viral respiratory tract infections in Cypriot children and will be helpful to the clinicians and researchers interested in the treatment and control of viral respiratory tract infections.
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Why do respiratory tract infections pose major public health problems?
| 1,608 |
world-wide occurrence, ease of transmission and considerable morbidity and mortality effecting people of all ages
| 1,878 |
1,566 |
Aetiology of Acute Respiratory Tract Infections in Hospitalised Children in Cyprus
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4720120/
SHA: efd27ff0ac04dd60838266386aaebb5df80f4fa9
Authors: Richter, Jan; Panayiotou, Christakis; Tryfonos, Christina; Koptides, Dana; Koliou, Maria; Kalogirou, Nikolas; Georgiou, Eleni; Christodoulou, Christina
Date: 2016-01-13
DOI: 10.1371/journal.pone.0147041
License: cc-by
Abstract: In order to improve clinical management and prevention of viral infections in hospitalised children improved etiological insight is needed. The aim of the present study was to assess the spectrum of respiratory viral pathogens in children admitted to hospital with acute respiratory tract infections in Cyprus. For this purpose nasopharyngeal swab samples from 424 children less than 12 years of age with acute respiratory tract infections were collected over three epidemic seasons and were analysed for the presence of the most common 15 respiratory viruses. A viral pathogen was identified in 86% of the samples, with multiple infections being observed in almost 20% of the samples. The most frequently detected viruses were RSV (30.4%) and Rhinovirus (27.4%). RSV exhibited a clear seasonality with marked peaks in January/February, while rhinovirus infections did not exhibit a pronounced seasonality being detected almost throughout the year. While RSV and PIV3 incidence decreased significantly with age, the opposite was observed for influenza A and B as well as adenovirus infections. The data presented expand our understanding of the epidemiology of viral respiratory tract infections in Cypriot children and will be helpful to the clinicians and researchers interested in the treatment and control of viral respiratory tract infections.
Text: Viral Respiratory tract infections (RTI) represent a major public health problem because of their world-wide occurrence, ease of transmission and considerable morbidity and mortality effecting people of all ages. Children are on average infected two to three times more frequently than adults, with acute RTIs being the most common infection in childhood [1, 2] . Illnesses caused by respiratory viruses include, among others, common colds, pharyngitis, croup, bronchiolitis, viral pneumonia and otitis media. Rapid diagnosis is important not only for timely therapeutic intervention but also for the identification of a beginning influenza epidemic and the avoidance of unnecessary antibiotic treatment [3, 4] .
RTIs are a major cause of morbidity and mortality worldwide. Acute RTI is most common in children under five years of age, and represents 30-50% of the paediatric medical admissions, as well as 20-40% of hospitalizations in children. Respiratory infections cluster during winter and early spring months. The leading viral agents include respiratory syncytial virus (RSV), influenza A and B (INF-A, INF-B) viruses, parainfluenza viruses (PIVs), and human adenoviruses (HAdVs). In addition, there is a continuously increasing list of new respiratory viruses that contribute significantly to the burden of acute respiratory infections, such as the recently identified human metapneumovirus (HMPV) and human Bocavirus (HBoV) [5] .
Acute RTIs are classified as upper (UTRIs) and lower RTI (LRTIs), according to the involved anatomic localization. URTIs cause non-severe but widespread epidemics that are responsible for continuous circulation of pathogens in the community. LRTIs have been classified as frank pneumonia and bronchiolitis with clinical, radiological and etiological features that usually overlap [6, 7] . Viruses are again the foremost agents of LRTIs often misdiagnosed as bacterial in origin and hence treated with antibiotics unnecessarily [8] .
The main aim of this study was to determine the aetiology of acute respiratory tract infections in Cypriot children and assess the epidemiology of the identified viral pathogens over three epidemic seasons.
The study was approved by the Cyprus National Bioethics Committee. Accordingly, written informed consent was obtained from parents prior to sample taking. Between November 2010 and October 2013, 485 nasopharyngeal swab samples were collected from children up to 12 years of age, who had been hospitalized with acute respiratory tract infection at the Archbishop Makarios III hospital, Nicosia. Clinical and demographic information including symptoms, duration of hospitalisation, diagnosis and treatment were recorded. Nasal swab samples were collected using the BD Universal Viral Transport Collection Kit. Viral RNA/DNA was extracted from 400 μl sample using the iPrep PureLink Virus Kit on an iPrep purification instrument (Invitrogen).
A set of four multiplex Real-Time RT-PCR assays was established and validated for the detection of the 15 most common respiratory viruses as follows: assay 1: influenzaviruses A and B, RSV, assay 2: parainfluenzaviruses 1-4, assay 3: HAdV, enteroviruses, HMPV and HBoV and assay 4: rhinoviruses and the human coronaviruses OC43, NL63 and 229E (Table 1) .
Published primer and probe sets were used as a basis for designing the assays, however, all primer/probe sequences were checked against newly build sequence alignments of all viruses tested and were modified, if necessary, to account for possible sequence variations. For this purpose, all available complete genome sequences were obtained for each virus from GenBank, imported into the BioEdit Sequence Alignment Editor v7.1.7 and aligned using ClustalX. In case of mismatches between published primers/probe and target sequences, modifications were applied, as indicated in Table 1 . The alignments for the viruses, which necessitated changes to the primers/probe are available in Fasta-Format as supplement S1-S4 Files.
Primer concentrations and reaction conditions for the four assays were subsequently optimised for multiplexing. In order to assess the sensitivity and specificity of the assays, the laboratory enrolled for two consecutive years in Quality Control for Molecular Diagnostics (QCMD) external quality assessment schemes for all viruses, except Bocavirus, which was unavailable. In summary, the established assays were able to correctly identify all viruses tested, proving their suitability for diagnostic application.
A possible correlation of virus prevalence and age of infection was assessed using univariate analyses. The Fisher's exact test was used where cell counts below 5 were encountered; otherwise, the chi-squared test was performed. The same statistical tests were used to compare the frequency of subjects with single or multiple infections between age groups. In addition, Pearson correlation was used to examine co-infections of different viruses. All statistical analyses were performed using StataSE 12 (StatCorp. 2007. College Station, TX, USA).
The present study was a prospective investigation of children hospitalized with acute respiratory tract infections between November 2010 and October 2013 in Cyprus. The median age of the children was 15 months (range: 0-140 months) with 243 being male and 181 female (male/ female ratio 1.34). The age distribution is shown in Fig 1.
Out of the 424 samples analysed, 364 (85.8%) were positive for one or more viruses. Results are summarized in Table 2 .The most commonly detected viruses were RSV, which was found in 129 (30.4%) patients and rhinoviruses in 116 (27.4%) accounting together for almost 60% of all detections. With moderate frequency have been detected HAdV in 31(7.3%) patients, influenza A in 28 (6.6%), HBoV in 24 (5.7%), enteroviruses and PIV 3 in 23 (5.4%) of patients respectively, and Influenza B in 21 (5.0%). A low frequency was exhibited by HMPV with 16 (3.8%) positive samples, human coronavirus OC43 with 13 (3.1%), PIV 1 with 12 (2.8%), PIV 4 with 9 (2.1%), PIV 2 with 7 (1.7%) and HCoV NL63 with 6 (1.4%). Coronavirus 229E could be detected only in a single sample.
Co-infections with two or more viruses were observed in 84 out of the 364 positive samples (see Table 2 ). Dual infections accounted for 17% of all positive samples and three viruses were detected in 2.7% of samples). A single patient sample displayed a quadruple infection being simultaneously positive for RSV, rhinovirus, HBoV and influenza B. Table 3 summarizes the frequency of each virus in single vs. multiple infections as well as the number of co-occurrences of viruses for each possible virus combination. In absolute terms the most common combination observed was RSV/rhinovirus. As a percentage, however, the virus appearing most often in co- infections was HBoV, which was found in more than 70% of cases together with another virus, followed by coronaviruses HCoV OC43 and HCoV NL63 with 61% and 67%, respectively. On the other hand, the viruses most rarely seen in co-infections were influenza viruses A and B as well as RSV. Pearson correlation coefficients were calculated to examine the likelihood of co-infections of different viruses. The results of the analysis are summarized in Table 1 in S1 Table. Significant correlation (P-value < 0.05) was seen mostly for co-infections with RSV, however correlations were very weak (r<0.3) and negative. This finding can probably be explained by the fact that RSV infections occurred predominantly in the very young, where co-infections were less frequently observed. On the other hand, a significant positive correlation was observed for enterovirus and rhinovirus co-infection hinting maybe at similarities in circulation patterns and/or transmission modes.
Regarding seasonality, different patterns of circulations could be observed for RSV, rhinoviruses and influenzaviruses (A and B combined) (Fig 2) , with RSV and influenza exhibiting a clear seasonality with marked peaks in January/February, while rhinovirus infections did not exhibit a pronounced seasonality being detected almost throughout the year. However, as more than 100 different rhinovirus strains have been identified to be circulating worldwide in parallel and successively, a potential seasonality of individual rhinovirus serotypes may be masked by overlapping patterns [18, 19] .
The data was further analysed with regard to the age distribution of virus infection (see Table 2 ). In infants up to 3 months old, RSV was by far the most common pathogen (58.1%), followed by rhinovirus (20.3%) and PIV3 with 8.1% each. The incidence of RSV, however, decreases significantly with increasing age (p-value < 0.0001) dropping to 13% in children older than 3 years old, while the reverse relationship is observed for Influenza A and B and HAdV. Rhinoviruses, HBoV and enteroviruses are most frequently observed in children from 4 months to 3 years of age. The age dependency of the virus incidence is visualized in Fig 3 for the seven most frequently observed viruses. The positivity rate also showed a trend according to the age group dropping from 90.5% in the under 3-month old to 78.3% in the 4-12 years old (p-value = 0.020). This may point to an increasing role of pathogens not included in the assays, such as bacterial infections in older children.
Regarding multiple infections, children less than 3 month of age and those older than 4 years had a significantly smaller risk to present with multiple infections as compared to the other two age groups (p-value = 0.014).
A reason for this could be that very young children have limited contact to others reducing thereby the chance for a co-infection, whereas children older than 3 years already established immunity to an increasing number of viruses encountered previously.
This study for the first time examined the aetiology of acute respiratory tract infections in hospitalised children in Cyprus. Four multiplex Real-Time RT-PCR assays were developed in order to detect the most common respiratory viral pathogens in a fast and cost-effective way. The high rate of positive samples (85.8%) is evidence of the high sensitivity of the Multiplex-assays used and that the range of viruses included in the analysis is comprehensive. Many previous studies have shown detection rates ranging from below 50% to 75% [20] [21] [22] [23] [24] .
The most common viruses detected were RSV and rhinovirus accounting for almost 60% of all cases. Both viruses were reported previously by others as the major aetiology for respiratory viral infections in young children with rhinoviruses being recognized increasingly for their role in lower respiratory tract infections [20, [25] [26] [27] [28] [29] [30] .
Our data support the results of similar studies performed in the Middle East region. A recently published study found that RSV was the most commonly detected virus in nasopharyngeal swabs from children presenting symptoms of RTIs and in addition to that it also showed that RSV infections follow a similar circulation pattern peaking from December to March [31] . Another study has revealed that RSV and PIV3 incidence decreases significantly with age, whereas the opposite is observed for influenza and adenovirus infections, a trend that was also observed in our study [26] .
Mixed infections were observed in approximately 20% of all samples, which is in the middle of previously reported rates ranging from 10 to almost 40%. HBoV, HCoV and EV were found most frequently in co-infections. All three subtypes of HCoV were co-detected with several other viruses, while HBoV was co-detected mainly with HRV and RSV. In the case of EV infections, EV were almost predominantly associated with HRV. The rare presence of InfA and InfB viruses in multiple infections witnessed in our study was also observed elsewhere [32, 33] . Even though this study did not allow for investigating a possible association between multiple infections and disease severity, a review of the literature shows that such a potential association is still subject to controversy, since there are reports showing no relationship of multiple virus infection with respiratoty illness severity on one hand or a significant association on the other. Studies have shown that viral co-infection was significantly associated with longer duration of illness symptoms, but with a decreased severity in hospitalized children regarding oxygen requirement and intensive care unit admission, whereas the findings of other studies have indicated that severe clinical phenotypes were more prevalent in co-infection patients, especially in RSV co-infections that may increase the severity of RSV associated disease in children [25, [34] [35] [36] [37] [38] [39] [40] .
Viral respiratory infections continue to be a worldwide health concern. As the clinical symptoms of patients with acute respiratory tract infections do usually not allow a discrimination of viral or bacterial aetiology, rapid and reliable diagnostic tools are required for better antibiotic stewardship and the implementation of appropriate infection control measures [4, 41] . The data presented expand our understanding of the epidemiology of viral respiratory tract infections in Cypriot children and will be helpful to the clinicians and researchers interested in the treatment and control of viral respiratory tract infections.
|
How much of a greater risk are children than adults to viral infections?
| 1,609 |
two to three times more frequently
| 2,026 |
1,566 |
Aetiology of Acute Respiratory Tract Infections in Hospitalised Children in Cyprus
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4720120/
SHA: efd27ff0ac04dd60838266386aaebb5df80f4fa9
Authors: Richter, Jan; Panayiotou, Christakis; Tryfonos, Christina; Koptides, Dana; Koliou, Maria; Kalogirou, Nikolas; Georgiou, Eleni; Christodoulou, Christina
Date: 2016-01-13
DOI: 10.1371/journal.pone.0147041
License: cc-by
Abstract: In order to improve clinical management and prevention of viral infections in hospitalised children improved etiological insight is needed. The aim of the present study was to assess the spectrum of respiratory viral pathogens in children admitted to hospital with acute respiratory tract infections in Cyprus. For this purpose nasopharyngeal swab samples from 424 children less than 12 years of age with acute respiratory tract infections were collected over three epidemic seasons and were analysed for the presence of the most common 15 respiratory viruses. A viral pathogen was identified in 86% of the samples, with multiple infections being observed in almost 20% of the samples. The most frequently detected viruses were RSV (30.4%) and Rhinovirus (27.4%). RSV exhibited a clear seasonality with marked peaks in January/February, while rhinovirus infections did not exhibit a pronounced seasonality being detected almost throughout the year. While RSV and PIV3 incidence decreased significantly with age, the opposite was observed for influenza A and B as well as adenovirus infections. The data presented expand our understanding of the epidemiology of viral respiratory tract infections in Cypriot children and will be helpful to the clinicians and researchers interested in the treatment and control of viral respiratory tract infections.
Text: Viral Respiratory tract infections (RTI) represent a major public health problem because of their world-wide occurrence, ease of transmission and considerable morbidity and mortality effecting people of all ages. Children are on average infected two to three times more frequently than adults, with acute RTIs being the most common infection in childhood [1, 2] . Illnesses caused by respiratory viruses include, among others, common colds, pharyngitis, croup, bronchiolitis, viral pneumonia and otitis media. Rapid diagnosis is important not only for timely therapeutic intervention but also for the identification of a beginning influenza epidemic and the avoidance of unnecessary antibiotic treatment [3, 4] .
RTIs are a major cause of morbidity and mortality worldwide. Acute RTI is most common in children under five years of age, and represents 30-50% of the paediatric medical admissions, as well as 20-40% of hospitalizations in children. Respiratory infections cluster during winter and early spring months. The leading viral agents include respiratory syncytial virus (RSV), influenza A and B (INF-A, INF-B) viruses, parainfluenza viruses (PIVs), and human adenoviruses (HAdVs). In addition, there is a continuously increasing list of new respiratory viruses that contribute significantly to the burden of acute respiratory infections, such as the recently identified human metapneumovirus (HMPV) and human Bocavirus (HBoV) [5] .
Acute RTIs are classified as upper (UTRIs) and lower RTI (LRTIs), according to the involved anatomic localization. URTIs cause non-severe but widespread epidemics that are responsible for continuous circulation of pathogens in the community. LRTIs have been classified as frank pneumonia and bronchiolitis with clinical, radiological and etiological features that usually overlap [6, 7] . Viruses are again the foremost agents of LRTIs often misdiagnosed as bacterial in origin and hence treated with antibiotics unnecessarily [8] .
The main aim of this study was to determine the aetiology of acute respiratory tract infections in Cypriot children and assess the epidemiology of the identified viral pathogens over three epidemic seasons.
The study was approved by the Cyprus National Bioethics Committee. Accordingly, written informed consent was obtained from parents prior to sample taking. Between November 2010 and October 2013, 485 nasopharyngeal swab samples were collected from children up to 12 years of age, who had been hospitalized with acute respiratory tract infection at the Archbishop Makarios III hospital, Nicosia. Clinical and demographic information including symptoms, duration of hospitalisation, diagnosis and treatment were recorded. Nasal swab samples were collected using the BD Universal Viral Transport Collection Kit. Viral RNA/DNA was extracted from 400 μl sample using the iPrep PureLink Virus Kit on an iPrep purification instrument (Invitrogen).
A set of four multiplex Real-Time RT-PCR assays was established and validated for the detection of the 15 most common respiratory viruses as follows: assay 1: influenzaviruses A and B, RSV, assay 2: parainfluenzaviruses 1-4, assay 3: HAdV, enteroviruses, HMPV and HBoV and assay 4: rhinoviruses and the human coronaviruses OC43, NL63 and 229E (Table 1) .
Published primer and probe sets were used as a basis for designing the assays, however, all primer/probe sequences were checked against newly build sequence alignments of all viruses tested and were modified, if necessary, to account for possible sequence variations. For this purpose, all available complete genome sequences were obtained for each virus from GenBank, imported into the BioEdit Sequence Alignment Editor v7.1.7 and aligned using ClustalX. In case of mismatches between published primers/probe and target sequences, modifications were applied, as indicated in Table 1 . The alignments for the viruses, which necessitated changes to the primers/probe are available in Fasta-Format as supplement S1-S4 Files.
Primer concentrations and reaction conditions for the four assays were subsequently optimised for multiplexing. In order to assess the sensitivity and specificity of the assays, the laboratory enrolled for two consecutive years in Quality Control for Molecular Diagnostics (QCMD) external quality assessment schemes for all viruses, except Bocavirus, which was unavailable. In summary, the established assays were able to correctly identify all viruses tested, proving their suitability for diagnostic application.
A possible correlation of virus prevalence and age of infection was assessed using univariate analyses. The Fisher's exact test was used where cell counts below 5 were encountered; otherwise, the chi-squared test was performed. The same statistical tests were used to compare the frequency of subjects with single or multiple infections between age groups. In addition, Pearson correlation was used to examine co-infections of different viruses. All statistical analyses were performed using StataSE 12 (StatCorp. 2007. College Station, TX, USA).
The present study was a prospective investigation of children hospitalized with acute respiratory tract infections between November 2010 and October 2013 in Cyprus. The median age of the children was 15 months (range: 0-140 months) with 243 being male and 181 female (male/ female ratio 1.34). The age distribution is shown in Fig 1.
Out of the 424 samples analysed, 364 (85.8%) were positive for one or more viruses. Results are summarized in Table 2 .The most commonly detected viruses were RSV, which was found in 129 (30.4%) patients and rhinoviruses in 116 (27.4%) accounting together for almost 60% of all detections. With moderate frequency have been detected HAdV in 31(7.3%) patients, influenza A in 28 (6.6%), HBoV in 24 (5.7%), enteroviruses and PIV 3 in 23 (5.4%) of patients respectively, and Influenza B in 21 (5.0%). A low frequency was exhibited by HMPV with 16 (3.8%) positive samples, human coronavirus OC43 with 13 (3.1%), PIV 1 with 12 (2.8%), PIV 4 with 9 (2.1%), PIV 2 with 7 (1.7%) and HCoV NL63 with 6 (1.4%). Coronavirus 229E could be detected only in a single sample.
Co-infections with two or more viruses were observed in 84 out of the 364 positive samples (see Table 2 ). Dual infections accounted for 17% of all positive samples and three viruses were detected in 2.7% of samples). A single patient sample displayed a quadruple infection being simultaneously positive for RSV, rhinovirus, HBoV and influenza B. Table 3 summarizes the frequency of each virus in single vs. multiple infections as well as the number of co-occurrences of viruses for each possible virus combination. In absolute terms the most common combination observed was RSV/rhinovirus. As a percentage, however, the virus appearing most often in co- infections was HBoV, which was found in more than 70% of cases together with another virus, followed by coronaviruses HCoV OC43 and HCoV NL63 with 61% and 67%, respectively. On the other hand, the viruses most rarely seen in co-infections were influenza viruses A and B as well as RSV. Pearson correlation coefficients were calculated to examine the likelihood of co-infections of different viruses. The results of the analysis are summarized in Table 1 in S1 Table. Significant correlation (P-value < 0.05) was seen mostly for co-infections with RSV, however correlations were very weak (r<0.3) and negative. This finding can probably be explained by the fact that RSV infections occurred predominantly in the very young, where co-infections were less frequently observed. On the other hand, a significant positive correlation was observed for enterovirus and rhinovirus co-infection hinting maybe at similarities in circulation patterns and/or transmission modes.
Regarding seasonality, different patterns of circulations could be observed for RSV, rhinoviruses and influenzaviruses (A and B combined) (Fig 2) , with RSV and influenza exhibiting a clear seasonality with marked peaks in January/February, while rhinovirus infections did not exhibit a pronounced seasonality being detected almost throughout the year. However, as more than 100 different rhinovirus strains have been identified to be circulating worldwide in parallel and successively, a potential seasonality of individual rhinovirus serotypes may be masked by overlapping patterns [18, 19] .
The data was further analysed with regard to the age distribution of virus infection (see Table 2 ). In infants up to 3 months old, RSV was by far the most common pathogen (58.1%), followed by rhinovirus (20.3%) and PIV3 with 8.1% each. The incidence of RSV, however, decreases significantly with increasing age (p-value < 0.0001) dropping to 13% in children older than 3 years old, while the reverse relationship is observed for Influenza A and B and HAdV. Rhinoviruses, HBoV and enteroviruses are most frequently observed in children from 4 months to 3 years of age. The age dependency of the virus incidence is visualized in Fig 3 for the seven most frequently observed viruses. The positivity rate also showed a trend according to the age group dropping from 90.5% in the under 3-month old to 78.3% in the 4-12 years old (p-value = 0.020). This may point to an increasing role of pathogens not included in the assays, such as bacterial infections in older children.
Regarding multiple infections, children less than 3 month of age and those older than 4 years had a significantly smaller risk to present with multiple infections as compared to the other two age groups (p-value = 0.014).
A reason for this could be that very young children have limited contact to others reducing thereby the chance for a co-infection, whereas children older than 3 years already established immunity to an increasing number of viruses encountered previously.
This study for the first time examined the aetiology of acute respiratory tract infections in hospitalised children in Cyprus. Four multiplex Real-Time RT-PCR assays were developed in order to detect the most common respiratory viral pathogens in a fast and cost-effective way. The high rate of positive samples (85.8%) is evidence of the high sensitivity of the Multiplex-assays used and that the range of viruses included in the analysis is comprehensive. Many previous studies have shown detection rates ranging from below 50% to 75% [20] [21] [22] [23] [24] .
The most common viruses detected were RSV and rhinovirus accounting for almost 60% of all cases. Both viruses were reported previously by others as the major aetiology for respiratory viral infections in young children with rhinoviruses being recognized increasingly for their role in lower respiratory tract infections [20, [25] [26] [27] [28] [29] [30] .
Our data support the results of similar studies performed in the Middle East region. A recently published study found that RSV was the most commonly detected virus in nasopharyngeal swabs from children presenting symptoms of RTIs and in addition to that it also showed that RSV infections follow a similar circulation pattern peaking from December to March [31] . Another study has revealed that RSV and PIV3 incidence decreases significantly with age, whereas the opposite is observed for influenza and adenovirus infections, a trend that was also observed in our study [26] .
Mixed infections were observed in approximately 20% of all samples, which is in the middle of previously reported rates ranging from 10 to almost 40%. HBoV, HCoV and EV were found most frequently in co-infections. All three subtypes of HCoV were co-detected with several other viruses, while HBoV was co-detected mainly with HRV and RSV. In the case of EV infections, EV were almost predominantly associated with HRV. The rare presence of InfA and InfB viruses in multiple infections witnessed in our study was also observed elsewhere [32, 33] . Even though this study did not allow for investigating a possible association between multiple infections and disease severity, a review of the literature shows that such a potential association is still subject to controversy, since there are reports showing no relationship of multiple virus infection with respiratoty illness severity on one hand or a significant association on the other. Studies have shown that viral co-infection was significantly associated with longer duration of illness symptoms, but with a decreased severity in hospitalized children regarding oxygen requirement and intensive care unit admission, whereas the findings of other studies have indicated that severe clinical phenotypes were more prevalent in co-infection patients, especially in RSV co-infections that may increase the severity of RSV associated disease in children [25, [34] [35] [36] [37] [38] [39] [40] .
Viral respiratory infections continue to be a worldwide health concern. As the clinical symptoms of patients with acute respiratory tract infections do usually not allow a discrimination of viral or bacterial aetiology, rapid and reliable diagnostic tools are required for better antibiotic stewardship and the implementation of appropriate infection control measures [4, 41] . The data presented expand our understanding of the epidemiology of viral respiratory tract infections in Cypriot children and will be helpful to the clinicians and researchers interested in the treatment and control of viral respiratory tract infections.
|
What is the most common infection in childhood?
| 1,610 |
acute RTI
| 2,079 |
1,566 |
Aetiology of Acute Respiratory Tract Infections in Hospitalised Children in Cyprus
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4720120/
SHA: efd27ff0ac04dd60838266386aaebb5df80f4fa9
Authors: Richter, Jan; Panayiotou, Christakis; Tryfonos, Christina; Koptides, Dana; Koliou, Maria; Kalogirou, Nikolas; Georgiou, Eleni; Christodoulou, Christina
Date: 2016-01-13
DOI: 10.1371/journal.pone.0147041
License: cc-by
Abstract: In order to improve clinical management and prevention of viral infections in hospitalised children improved etiological insight is needed. The aim of the present study was to assess the spectrum of respiratory viral pathogens in children admitted to hospital with acute respiratory tract infections in Cyprus. For this purpose nasopharyngeal swab samples from 424 children less than 12 years of age with acute respiratory tract infections were collected over three epidemic seasons and were analysed for the presence of the most common 15 respiratory viruses. A viral pathogen was identified in 86% of the samples, with multiple infections being observed in almost 20% of the samples. The most frequently detected viruses were RSV (30.4%) and Rhinovirus (27.4%). RSV exhibited a clear seasonality with marked peaks in January/February, while rhinovirus infections did not exhibit a pronounced seasonality being detected almost throughout the year. While RSV and PIV3 incidence decreased significantly with age, the opposite was observed for influenza A and B as well as adenovirus infections. The data presented expand our understanding of the epidemiology of viral respiratory tract infections in Cypriot children and will be helpful to the clinicians and researchers interested in the treatment and control of viral respiratory tract infections.
Text: Viral Respiratory tract infections (RTI) represent a major public health problem because of their world-wide occurrence, ease of transmission and considerable morbidity and mortality effecting people of all ages. Children are on average infected two to three times more frequently than adults, with acute RTIs being the most common infection in childhood [1, 2] . Illnesses caused by respiratory viruses include, among others, common colds, pharyngitis, croup, bronchiolitis, viral pneumonia and otitis media. Rapid diagnosis is important not only for timely therapeutic intervention but also for the identification of a beginning influenza epidemic and the avoidance of unnecessary antibiotic treatment [3, 4] .
RTIs are a major cause of morbidity and mortality worldwide. Acute RTI is most common in children under five years of age, and represents 30-50% of the paediatric medical admissions, as well as 20-40% of hospitalizations in children. Respiratory infections cluster during winter and early spring months. The leading viral agents include respiratory syncytial virus (RSV), influenza A and B (INF-A, INF-B) viruses, parainfluenza viruses (PIVs), and human adenoviruses (HAdVs). In addition, there is a continuously increasing list of new respiratory viruses that contribute significantly to the burden of acute respiratory infections, such as the recently identified human metapneumovirus (HMPV) and human Bocavirus (HBoV) [5] .
Acute RTIs are classified as upper (UTRIs) and lower RTI (LRTIs), according to the involved anatomic localization. URTIs cause non-severe but widespread epidemics that are responsible for continuous circulation of pathogens in the community. LRTIs have been classified as frank pneumonia and bronchiolitis with clinical, radiological and etiological features that usually overlap [6, 7] . Viruses are again the foremost agents of LRTIs often misdiagnosed as bacterial in origin and hence treated with antibiotics unnecessarily [8] .
The main aim of this study was to determine the aetiology of acute respiratory tract infections in Cypriot children and assess the epidemiology of the identified viral pathogens over three epidemic seasons.
The study was approved by the Cyprus National Bioethics Committee. Accordingly, written informed consent was obtained from parents prior to sample taking. Between November 2010 and October 2013, 485 nasopharyngeal swab samples were collected from children up to 12 years of age, who had been hospitalized with acute respiratory tract infection at the Archbishop Makarios III hospital, Nicosia. Clinical and demographic information including symptoms, duration of hospitalisation, diagnosis and treatment were recorded. Nasal swab samples were collected using the BD Universal Viral Transport Collection Kit. Viral RNA/DNA was extracted from 400 μl sample using the iPrep PureLink Virus Kit on an iPrep purification instrument (Invitrogen).
A set of four multiplex Real-Time RT-PCR assays was established and validated for the detection of the 15 most common respiratory viruses as follows: assay 1: influenzaviruses A and B, RSV, assay 2: parainfluenzaviruses 1-4, assay 3: HAdV, enteroviruses, HMPV and HBoV and assay 4: rhinoviruses and the human coronaviruses OC43, NL63 and 229E (Table 1) .
Published primer and probe sets were used as a basis for designing the assays, however, all primer/probe sequences were checked against newly build sequence alignments of all viruses tested and were modified, if necessary, to account for possible sequence variations. For this purpose, all available complete genome sequences were obtained for each virus from GenBank, imported into the BioEdit Sequence Alignment Editor v7.1.7 and aligned using ClustalX. In case of mismatches between published primers/probe and target sequences, modifications were applied, as indicated in Table 1 . The alignments for the viruses, which necessitated changes to the primers/probe are available in Fasta-Format as supplement S1-S4 Files.
Primer concentrations and reaction conditions for the four assays were subsequently optimised for multiplexing. In order to assess the sensitivity and specificity of the assays, the laboratory enrolled for two consecutive years in Quality Control for Molecular Diagnostics (QCMD) external quality assessment schemes for all viruses, except Bocavirus, which was unavailable. In summary, the established assays were able to correctly identify all viruses tested, proving their suitability for diagnostic application.
A possible correlation of virus prevalence and age of infection was assessed using univariate analyses. The Fisher's exact test was used where cell counts below 5 were encountered; otherwise, the chi-squared test was performed. The same statistical tests were used to compare the frequency of subjects with single or multiple infections between age groups. In addition, Pearson correlation was used to examine co-infections of different viruses. All statistical analyses were performed using StataSE 12 (StatCorp. 2007. College Station, TX, USA).
The present study was a prospective investigation of children hospitalized with acute respiratory tract infections between November 2010 and October 2013 in Cyprus. The median age of the children was 15 months (range: 0-140 months) with 243 being male and 181 female (male/ female ratio 1.34). The age distribution is shown in Fig 1.
Out of the 424 samples analysed, 364 (85.8%) were positive for one or more viruses. Results are summarized in Table 2 .The most commonly detected viruses were RSV, which was found in 129 (30.4%) patients and rhinoviruses in 116 (27.4%) accounting together for almost 60% of all detections. With moderate frequency have been detected HAdV in 31(7.3%) patients, influenza A in 28 (6.6%), HBoV in 24 (5.7%), enteroviruses and PIV 3 in 23 (5.4%) of patients respectively, and Influenza B in 21 (5.0%). A low frequency was exhibited by HMPV with 16 (3.8%) positive samples, human coronavirus OC43 with 13 (3.1%), PIV 1 with 12 (2.8%), PIV 4 with 9 (2.1%), PIV 2 with 7 (1.7%) and HCoV NL63 with 6 (1.4%). Coronavirus 229E could be detected only in a single sample.
Co-infections with two or more viruses were observed in 84 out of the 364 positive samples (see Table 2 ). Dual infections accounted for 17% of all positive samples and three viruses were detected in 2.7% of samples). A single patient sample displayed a quadruple infection being simultaneously positive for RSV, rhinovirus, HBoV and influenza B. Table 3 summarizes the frequency of each virus in single vs. multiple infections as well as the number of co-occurrences of viruses for each possible virus combination. In absolute terms the most common combination observed was RSV/rhinovirus. As a percentage, however, the virus appearing most often in co- infections was HBoV, which was found in more than 70% of cases together with another virus, followed by coronaviruses HCoV OC43 and HCoV NL63 with 61% and 67%, respectively. On the other hand, the viruses most rarely seen in co-infections were influenza viruses A and B as well as RSV. Pearson correlation coefficients were calculated to examine the likelihood of co-infections of different viruses. The results of the analysis are summarized in Table 1 in S1 Table. Significant correlation (P-value < 0.05) was seen mostly for co-infections with RSV, however correlations were very weak (r<0.3) and negative. This finding can probably be explained by the fact that RSV infections occurred predominantly in the very young, where co-infections were less frequently observed. On the other hand, a significant positive correlation was observed for enterovirus and rhinovirus co-infection hinting maybe at similarities in circulation patterns and/or transmission modes.
Regarding seasonality, different patterns of circulations could be observed for RSV, rhinoviruses and influenzaviruses (A and B combined) (Fig 2) , with RSV and influenza exhibiting a clear seasonality with marked peaks in January/February, while rhinovirus infections did not exhibit a pronounced seasonality being detected almost throughout the year. However, as more than 100 different rhinovirus strains have been identified to be circulating worldwide in parallel and successively, a potential seasonality of individual rhinovirus serotypes may be masked by overlapping patterns [18, 19] .
The data was further analysed with regard to the age distribution of virus infection (see Table 2 ). In infants up to 3 months old, RSV was by far the most common pathogen (58.1%), followed by rhinovirus (20.3%) and PIV3 with 8.1% each. The incidence of RSV, however, decreases significantly with increasing age (p-value < 0.0001) dropping to 13% in children older than 3 years old, while the reverse relationship is observed for Influenza A and B and HAdV. Rhinoviruses, HBoV and enteroviruses are most frequently observed in children from 4 months to 3 years of age. The age dependency of the virus incidence is visualized in Fig 3 for the seven most frequently observed viruses. The positivity rate also showed a trend according to the age group dropping from 90.5% in the under 3-month old to 78.3% in the 4-12 years old (p-value = 0.020). This may point to an increasing role of pathogens not included in the assays, such as bacterial infections in older children.
Regarding multiple infections, children less than 3 month of age and those older than 4 years had a significantly smaller risk to present with multiple infections as compared to the other two age groups (p-value = 0.014).
A reason for this could be that very young children have limited contact to others reducing thereby the chance for a co-infection, whereas children older than 3 years already established immunity to an increasing number of viruses encountered previously.
This study for the first time examined the aetiology of acute respiratory tract infections in hospitalised children in Cyprus. Four multiplex Real-Time RT-PCR assays were developed in order to detect the most common respiratory viral pathogens in a fast and cost-effective way. The high rate of positive samples (85.8%) is evidence of the high sensitivity of the Multiplex-assays used and that the range of viruses included in the analysis is comprehensive. Many previous studies have shown detection rates ranging from below 50% to 75% [20] [21] [22] [23] [24] .
The most common viruses detected were RSV and rhinovirus accounting for almost 60% of all cases. Both viruses were reported previously by others as the major aetiology for respiratory viral infections in young children with rhinoviruses being recognized increasingly for their role in lower respiratory tract infections [20, [25] [26] [27] [28] [29] [30] .
Our data support the results of similar studies performed in the Middle East region. A recently published study found that RSV was the most commonly detected virus in nasopharyngeal swabs from children presenting symptoms of RTIs and in addition to that it also showed that RSV infections follow a similar circulation pattern peaking from December to March [31] . Another study has revealed that RSV and PIV3 incidence decreases significantly with age, whereas the opposite is observed for influenza and adenovirus infections, a trend that was also observed in our study [26] .
Mixed infections were observed in approximately 20% of all samples, which is in the middle of previously reported rates ranging from 10 to almost 40%. HBoV, HCoV and EV were found most frequently in co-infections. All three subtypes of HCoV were co-detected with several other viruses, while HBoV was co-detected mainly with HRV and RSV. In the case of EV infections, EV were almost predominantly associated with HRV. The rare presence of InfA and InfB viruses in multiple infections witnessed in our study was also observed elsewhere [32, 33] . Even though this study did not allow for investigating a possible association between multiple infections and disease severity, a review of the literature shows that such a potential association is still subject to controversy, since there are reports showing no relationship of multiple virus infection with respiratoty illness severity on one hand or a significant association on the other. Studies have shown that viral co-infection was significantly associated with longer duration of illness symptoms, but with a decreased severity in hospitalized children regarding oxygen requirement and intensive care unit admission, whereas the findings of other studies have indicated that severe clinical phenotypes were more prevalent in co-infection patients, especially in RSV co-infections that may increase the severity of RSV associated disease in children [25, [34] [35] [36] [37] [38] [39] [40] .
Viral respiratory infections continue to be a worldwide health concern. As the clinical symptoms of patients with acute respiratory tract infections do usually not allow a discrimination of viral or bacterial aetiology, rapid and reliable diagnostic tools are required for better antibiotic stewardship and the implementation of appropriate infection control measures [4, 41] . The data presented expand our understanding of the epidemiology of viral respiratory tract infections in Cypriot children and will be helpful to the clinicians and researchers interested in the treatment and control of viral respiratory tract infections.
|
What can respiratory viruses cause?
| 1,611 |
common colds, pharyngitis, croup, bronchiolitis, viral pneumonia and otitis media
| 2,207 |
1,566 |
Aetiology of Acute Respiratory Tract Infections in Hospitalised Children in Cyprus
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4720120/
SHA: efd27ff0ac04dd60838266386aaebb5df80f4fa9
Authors: Richter, Jan; Panayiotou, Christakis; Tryfonos, Christina; Koptides, Dana; Koliou, Maria; Kalogirou, Nikolas; Georgiou, Eleni; Christodoulou, Christina
Date: 2016-01-13
DOI: 10.1371/journal.pone.0147041
License: cc-by
Abstract: In order to improve clinical management and prevention of viral infections in hospitalised children improved etiological insight is needed. The aim of the present study was to assess the spectrum of respiratory viral pathogens in children admitted to hospital with acute respiratory tract infections in Cyprus. For this purpose nasopharyngeal swab samples from 424 children less than 12 years of age with acute respiratory tract infections were collected over three epidemic seasons and were analysed for the presence of the most common 15 respiratory viruses. A viral pathogen was identified in 86% of the samples, with multiple infections being observed in almost 20% of the samples. The most frequently detected viruses were RSV (30.4%) and Rhinovirus (27.4%). RSV exhibited a clear seasonality with marked peaks in January/February, while rhinovirus infections did not exhibit a pronounced seasonality being detected almost throughout the year. While RSV and PIV3 incidence decreased significantly with age, the opposite was observed for influenza A and B as well as adenovirus infections. The data presented expand our understanding of the epidemiology of viral respiratory tract infections in Cypriot children and will be helpful to the clinicians and researchers interested in the treatment and control of viral respiratory tract infections.
Text: Viral Respiratory tract infections (RTI) represent a major public health problem because of their world-wide occurrence, ease of transmission and considerable morbidity and mortality effecting people of all ages. Children are on average infected two to three times more frequently than adults, with acute RTIs being the most common infection in childhood [1, 2] . Illnesses caused by respiratory viruses include, among others, common colds, pharyngitis, croup, bronchiolitis, viral pneumonia and otitis media. Rapid diagnosis is important not only for timely therapeutic intervention but also for the identification of a beginning influenza epidemic and the avoidance of unnecessary antibiotic treatment [3, 4] .
RTIs are a major cause of morbidity and mortality worldwide. Acute RTI is most common in children under five years of age, and represents 30-50% of the paediatric medical admissions, as well as 20-40% of hospitalizations in children. Respiratory infections cluster during winter and early spring months. The leading viral agents include respiratory syncytial virus (RSV), influenza A and B (INF-A, INF-B) viruses, parainfluenza viruses (PIVs), and human adenoviruses (HAdVs). In addition, there is a continuously increasing list of new respiratory viruses that contribute significantly to the burden of acute respiratory infections, such as the recently identified human metapneumovirus (HMPV) and human Bocavirus (HBoV) [5] .
Acute RTIs are classified as upper (UTRIs) and lower RTI (LRTIs), according to the involved anatomic localization. URTIs cause non-severe but widespread epidemics that are responsible for continuous circulation of pathogens in the community. LRTIs have been classified as frank pneumonia and bronchiolitis with clinical, radiological and etiological features that usually overlap [6, 7] . Viruses are again the foremost agents of LRTIs often misdiagnosed as bacterial in origin and hence treated with antibiotics unnecessarily [8] .
The main aim of this study was to determine the aetiology of acute respiratory tract infections in Cypriot children and assess the epidemiology of the identified viral pathogens over three epidemic seasons.
The study was approved by the Cyprus National Bioethics Committee. Accordingly, written informed consent was obtained from parents prior to sample taking. Between November 2010 and October 2013, 485 nasopharyngeal swab samples were collected from children up to 12 years of age, who had been hospitalized with acute respiratory tract infection at the Archbishop Makarios III hospital, Nicosia. Clinical and demographic information including symptoms, duration of hospitalisation, diagnosis and treatment were recorded. Nasal swab samples were collected using the BD Universal Viral Transport Collection Kit. Viral RNA/DNA was extracted from 400 μl sample using the iPrep PureLink Virus Kit on an iPrep purification instrument (Invitrogen).
A set of four multiplex Real-Time RT-PCR assays was established and validated for the detection of the 15 most common respiratory viruses as follows: assay 1: influenzaviruses A and B, RSV, assay 2: parainfluenzaviruses 1-4, assay 3: HAdV, enteroviruses, HMPV and HBoV and assay 4: rhinoviruses and the human coronaviruses OC43, NL63 and 229E (Table 1) .
Published primer and probe sets were used as a basis for designing the assays, however, all primer/probe sequences were checked against newly build sequence alignments of all viruses tested and were modified, if necessary, to account for possible sequence variations. For this purpose, all available complete genome sequences were obtained for each virus from GenBank, imported into the BioEdit Sequence Alignment Editor v7.1.7 and aligned using ClustalX. In case of mismatches between published primers/probe and target sequences, modifications were applied, as indicated in Table 1 . The alignments for the viruses, which necessitated changes to the primers/probe are available in Fasta-Format as supplement S1-S4 Files.
Primer concentrations and reaction conditions for the four assays were subsequently optimised for multiplexing. In order to assess the sensitivity and specificity of the assays, the laboratory enrolled for two consecutive years in Quality Control for Molecular Diagnostics (QCMD) external quality assessment schemes for all viruses, except Bocavirus, which was unavailable. In summary, the established assays were able to correctly identify all viruses tested, proving their suitability for diagnostic application.
A possible correlation of virus prevalence and age of infection was assessed using univariate analyses. The Fisher's exact test was used where cell counts below 5 were encountered; otherwise, the chi-squared test was performed. The same statistical tests were used to compare the frequency of subjects with single or multiple infections between age groups. In addition, Pearson correlation was used to examine co-infections of different viruses. All statistical analyses were performed using StataSE 12 (StatCorp. 2007. College Station, TX, USA).
The present study was a prospective investigation of children hospitalized with acute respiratory tract infections between November 2010 and October 2013 in Cyprus. The median age of the children was 15 months (range: 0-140 months) with 243 being male and 181 female (male/ female ratio 1.34). The age distribution is shown in Fig 1.
Out of the 424 samples analysed, 364 (85.8%) were positive for one or more viruses. Results are summarized in Table 2 .The most commonly detected viruses were RSV, which was found in 129 (30.4%) patients and rhinoviruses in 116 (27.4%) accounting together for almost 60% of all detections. With moderate frequency have been detected HAdV in 31(7.3%) patients, influenza A in 28 (6.6%), HBoV in 24 (5.7%), enteroviruses and PIV 3 in 23 (5.4%) of patients respectively, and Influenza B in 21 (5.0%). A low frequency was exhibited by HMPV with 16 (3.8%) positive samples, human coronavirus OC43 with 13 (3.1%), PIV 1 with 12 (2.8%), PIV 4 with 9 (2.1%), PIV 2 with 7 (1.7%) and HCoV NL63 with 6 (1.4%). Coronavirus 229E could be detected only in a single sample.
Co-infections with two or more viruses were observed in 84 out of the 364 positive samples (see Table 2 ). Dual infections accounted for 17% of all positive samples and three viruses were detected in 2.7% of samples). A single patient sample displayed a quadruple infection being simultaneously positive for RSV, rhinovirus, HBoV and influenza B. Table 3 summarizes the frequency of each virus in single vs. multiple infections as well as the number of co-occurrences of viruses for each possible virus combination. In absolute terms the most common combination observed was RSV/rhinovirus. As a percentage, however, the virus appearing most often in co- infections was HBoV, which was found in more than 70% of cases together with another virus, followed by coronaviruses HCoV OC43 and HCoV NL63 with 61% and 67%, respectively. On the other hand, the viruses most rarely seen in co-infections were influenza viruses A and B as well as RSV. Pearson correlation coefficients were calculated to examine the likelihood of co-infections of different viruses. The results of the analysis are summarized in Table 1 in S1 Table. Significant correlation (P-value < 0.05) was seen mostly for co-infections with RSV, however correlations were very weak (r<0.3) and negative. This finding can probably be explained by the fact that RSV infections occurred predominantly in the very young, where co-infections were less frequently observed. On the other hand, a significant positive correlation was observed for enterovirus and rhinovirus co-infection hinting maybe at similarities in circulation patterns and/or transmission modes.
Regarding seasonality, different patterns of circulations could be observed for RSV, rhinoviruses and influenzaviruses (A and B combined) (Fig 2) , with RSV and influenza exhibiting a clear seasonality with marked peaks in January/February, while rhinovirus infections did not exhibit a pronounced seasonality being detected almost throughout the year. However, as more than 100 different rhinovirus strains have been identified to be circulating worldwide in parallel and successively, a potential seasonality of individual rhinovirus serotypes may be masked by overlapping patterns [18, 19] .
The data was further analysed with regard to the age distribution of virus infection (see Table 2 ). In infants up to 3 months old, RSV was by far the most common pathogen (58.1%), followed by rhinovirus (20.3%) and PIV3 with 8.1% each. The incidence of RSV, however, decreases significantly with increasing age (p-value < 0.0001) dropping to 13% in children older than 3 years old, while the reverse relationship is observed for Influenza A and B and HAdV. Rhinoviruses, HBoV and enteroviruses are most frequently observed in children from 4 months to 3 years of age. The age dependency of the virus incidence is visualized in Fig 3 for the seven most frequently observed viruses. The positivity rate also showed a trend according to the age group dropping from 90.5% in the under 3-month old to 78.3% in the 4-12 years old (p-value = 0.020). This may point to an increasing role of pathogens not included in the assays, such as bacterial infections in older children.
Regarding multiple infections, children less than 3 month of age and those older than 4 years had a significantly smaller risk to present with multiple infections as compared to the other two age groups (p-value = 0.014).
A reason for this could be that very young children have limited contact to others reducing thereby the chance for a co-infection, whereas children older than 3 years already established immunity to an increasing number of viruses encountered previously.
This study for the first time examined the aetiology of acute respiratory tract infections in hospitalised children in Cyprus. Four multiplex Real-Time RT-PCR assays were developed in order to detect the most common respiratory viral pathogens in a fast and cost-effective way. The high rate of positive samples (85.8%) is evidence of the high sensitivity of the Multiplex-assays used and that the range of viruses included in the analysis is comprehensive. Many previous studies have shown detection rates ranging from below 50% to 75% [20] [21] [22] [23] [24] .
The most common viruses detected were RSV and rhinovirus accounting for almost 60% of all cases. Both viruses were reported previously by others as the major aetiology for respiratory viral infections in young children with rhinoviruses being recognized increasingly for their role in lower respiratory tract infections [20, [25] [26] [27] [28] [29] [30] .
Our data support the results of similar studies performed in the Middle East region. A recently published study found that RSV was the most commonly detected virus in nasopharyngeal swabs from children presenting symptoms of RTIs and in addition to that it also showed that RSV infections follow a similar circulation pattern peaking from December to March [31] . Another study has revealed that RSV and PIV3 incidence decreases significantly with age, whereas the opposite is observed for influenza and adenovirus infections, a trend that was also observed in our study [26] .
Mixed infections were observed in approximately 20% of all samples, which is in the middle of previously reported rates ranging from 10 to almost 40%. HBoV, HCoV and EV were found most frequently in co-infections. All three subtypes of HCoV were co-detected with several other viruses, while HBoV was co-detected mainly with HRV and RSV. In the case of EV infections, EV were almost predominantly associated with HRV. The rare presence of InfA and InfB viruses in multiple infections witnessed in our study was also observed elsewhere [32, 33] . Even though this study did not allow for investigating a possible association between multiple infections and disease severity, a review of the literature shows that such a potential association is still subject to controversy, since there are reports showing no relationship of multiple virus infection with respiratoty illness severity on one hand or a significant association on the other. Studies have shown that viral co-infection was significantly associated with longer duration of illness symptoms, but with a decreased severity in hospitalized children regarding oxygen requirement and intensive care unit admission, whereas the findings of other studies have indicated that severe clinical phenotypes were more prevalent in co-infection patients, especially in RSV co-infections that may increase the severity of RSV associated disease in children [25, [34] [35] [36] [37] [38] [39] [40] .
Viral respiratory infections continue to be a worldwide health concern. As the clinical symptoms of patients with acute respiratory tract infections do usually not allow a discrimination of viral or bacterial aetiology, rapid and reliable diagnostic tools are required for better antibiotic stewardship and the implementation of appropriate infection control measures [4, 41] . The data presented expand our understanding of the epidemiology of viral respiratory tract infections in Cypriot children and will be helpful to the clinicians and researchers interested in the treatment and control of viral respiratory tract infections.
|
When do respiratory infections usually happen?
| 1,613 |
during winter and early spring months
| 2,759 |
1,566 |
Aetiology of Acute Respiratory Tract Infections in Hospitalised Children in Cyprus
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4720120/
SHA: efd27ff0ac04dd60838266386aaebb5df80f4fa9
Authors: Richter, Jan; Panayiotou, Christakis; Tryfonos, Christina; Koptides, Dana; Koliou, Maria; Kalogirou, Nikolas; Georgiou, Eleni; Christodoulou, Christina
Date: 2016-01-13
DOI: 10.1371/journal.pone.0147041
License: cc-by
Abstract: In order to improve clinical management and prevention of viral infections in hospitalised children improved etiological insight is needed. The aim of the present study was to assess the spectrum of respiratory viral pathogens in children admitted to hospital with acute respiratory tract infections in Cyprus. For this purpose nasopharyngeal swab samples from 424 children less than 12 years of age with acute respiratory tract infections were collected over three epidemic seasons and were analysed for the presence of the most common 15 respiratory viruses. A viral pathogen was identified in 86% of the samples, with multiple infections being observed in almost 20% of the samples. The most frequently detected viruses were RSV (30.4%) and Rhinovirus (27.4%). RSV exhibited a clear seasonality with marked peaks in January/February, while rhinovirus infections did not exhibit a pronounced seasonality being detected almost throughout the year. While RSV and PIV3 incidence decreased significantly with age, the opposite was observed for influenza A and B as well as adenovirus infections. The data presented expand our understanding of the epidemiology of viral respiratory tract infections in Cypriot children and will be helpful to the clinicians and researchers interested in the treatment and control of viral respiratory tract infections.
Text: Viral Respiratory tract infections (RTI) represent a major public health problem because of their world-wide occurrence, ease of transmission and considerable morbidity and mortality effecting people of all ages. Children are on average infected two to three times more frequently than adults, with acute RTIs being the most common infection in childhood [1, 2] . Illnesses caused by respiratory viruses include, among others, common colds, pharyngitis, croup, bronchiolitis, viral pneumonia and otitis media. Rapid diagnosis is important not only for timely therapeutic intervention but also for the identification of a beginning influenza epidemic and the avoidance of unnecessary antibiotic treatment [3, 4] .
RTIs are a major cause of morbidity and mortality worldwide. Acute RTI is most common in children under five years of age, and represents 30-50% of the paediatric medical admissions, as well as 20-40% of hospitalizations in children. Respiratory infections cluster during winter and early spring months. The leading viral agents include respiratory syncytial virus (RSV), influenza A and B (INF-A, INF-B) viruses, parainfluenza viruses (PIVs), and human adenoviruses (HAdVs). In addition, there is a continuously increasing list of new respiratory viruses that contribute significantly to the burden of acute respiratory infections, such as the recently identified human metapneumovirus (HMPV) and human Bocavirus (HBoV) [5] .
Acute RTIs are classified as upper (UTRIs) and lower RTI (LRTIs), according to the involved anatomic localization. URTIs cause non-severe but widespread epidemics that are responsible for continuous circulation of pathogens in the community. LRTIs have been classified as frank pneumonia and bronchiolitis with clinical, radiological and etiological features that usually overlap [6, 7] . Viruses are again the foremost agents of LRTIs often misdiagnosed as bacterial in origin and hence treated with antibiotics unnecessarily [8] .
The main aim of this study was to determine the aetiology of acute respiratory tract infections in Cypriot children and assess the epidemiology of the identified viral pathogens over three epidemic seasons.
The study was approved by the Cyprus National Bioethics Committee. Accordingly, written informed consent was obtained from parents prior to sample taking. Between November 2010 and October 2013, 485 nasopharyngeal swab samples were collected from children up to 12 years of age, who had been hospitalized with acute respiratory tract infection at the Archbishop Makarios III hospital, Nicosia. Clinical and demographic information including symptoms, duration of hospitalisation, diagnosis and treatment were recorded. Nasal swab samples were collected using the BD Universal Viral Transport Collection Kit. Viral RNA/DNA was extracted from 400 μl sample using the iPrep PureLink Virus Kit on an iPrep purification instrument (Invitrogen).
A set of four multiplex Real-Time RT-PCR assays was established and validated for the detection of the 15 most common respiratory viruses as follows: assay 1: influenzaviruses A and B, RSV, assay 2: parainfluenzaviruses 1-4, assay 3: HAdV, enteroviruses, HMPV and HBoV and assay 4: rhinoviruses and the human coronaviruses OC43, NL63 and 229E (Table 1) .
Published primer and probe sets were used as a basis for designing the assays, however, all primer/probe sequences were checked against newly build sequence alignments of all viruses tested and were modified, if necessary, to account for possible sequence variations. For this purpose, all available complete genome sequences were obtained for each virus from GenBank, imported into the BioEdit Sequence Alignment Editor v7.1.7 and aligned using ClustalX. In case of mismatches between published primers/probe and target sequences, modifications were applied, as indicated in Table 1 . The alignments for the viruses, which necessitated changes to the primers/probe are available in Fasta-Format as supplement S1-S4 Files.
Primer concentrations and reaction conditions for the four assays were subsequently optimised for multiplexing. In order to assess the sensitivity and specificity of the assays, the laboratory enrolled for two consecutive years in Quality Control for Molecular Diagnostics (QCMD) external quality assessment schemes for all viruses, except Bocavirus, which was unavailable. In summary, the established assays were able to correctly identify all viruses tested, proving their suitability for diagnostic application.
A possible correlation of virus prevalence and age of infection was assessed using univariate analyses. The Fisher's exact test was used where cell counts below 5 were encountered; otherwise, the chi-squared test was performed. The same statistical tests were used to compare the frequency of subjects with single or multiple infections between age groups. In addition, Pearson correlation was used to examine co-infections of different viruses. All statistical analyses were performed using StataSE 12 (StatCorp. 2007. College Station, TX, USA).
The present study was a prospective investigation of children hospitalized with acute respiratory tract infections between November 2010 and October 2013 in Cyprus. The median age of the children was 15 months (range: 0-140 months) with 243 being male and 181 female (male/ female ratio 1.34). The age distribution is shown in Fig 1.
Out of the 424 samples analysed, 364 (85.8%) were positive for one or more viruses. Results are summarized in Table 2 .The most commonly detected viruses were RSV, which was found in 129 (30.4%) patients and rhinoviruses in 116 (27.4%) accounting together for almost 60% of all detections. With moderate frequency have been detected HAdV in 31(7.3%) patients, influenza A in 28 (6.6%), HBoV in 24 (5.7%), enteroviruses and PIV 3 in 23 (5.4%) of patients respectively, and Influenza B in 21 (5.0%). A low frequency was exhibited by HMPV with 16 (3.8%) positive samples, human coronavirus OC43 with 13 (3.1%), PIV 1 with 12 (2.8%), PIV 4 with 9 (2.1%), PIV 2 with 7 (1.7%) and HCoV NL63 with 6 (1.4%). Coronavirus 229E could be detected only in a single sample.
Co-infections with two or more viruses were observed in 84 out of the 364 positive samples (see Table 2 ). Dual infections accounted for 17% of all positive samples and three viruses were detected in 2.7% of samples). A single patient sample displayed a quadruple infection being simultaneously positive for RSV, rhinovirus, HBoV and influenza B. Table 3 summarizes the frequency of each virus in single vs. multiple infections as well as the number of co-occurrences of viruses for each possible virus combination. In absolute terms the most common combination observed was RSV/rhinovirus. As a percentage, however, the virus appearing most often in co- infections was HBoV, which was found in more than 70% of cases together with another virus, followed by coronaviruses HCoV OC43 and HCoV NL63 with 61% and 67%, respectively. On the other hand, the viruses most rarely seen in co-infections were influenza viruses A and B as well as RSV. Pearson correlation coefficients were calculated to examine the likelihood of co-infections of different viruses. The results of the analysis are summarized in Table 1 in S1 Table. Significant correlation (P-value < 0.05) was seen mostly for co-infections with RSV, however correlations were very weak (r<0.3) and negative. This finding can probably be explained by the fact that RSV infections occurred predominantly in the very young, where co-infections were less frequently observed. On the other hand, a significant positive correlation was observed for enterovirus and rhinovirus co-infection hinting maybe at similarities in circulation patterns and/or transmission modes.
Regarding seasonality, different patterns of circulations could be observed for RSV, rhinoviruses and influenzaviruses (A and B combined) (Fig 2) , with RSV and influenza exhibiting a clear seasonality with marked peaks in January/February, while rhinovirus infections did not exhibit a pronounced seasonality being detected almost throughout the year. However, as more than 100 different rhinovirus strains have been identified to be circulating worldwide in parallel and successively, a potential seasonality of individual rhinovirus serotypes may be masked by overlapping patterns [18, 19] .
The data was further analysed with regard to the age distribution of virus infection (see Table 2 ). In infants up to 3 months old, RSV was by far the most common pathogen (58.1%), followed by rhinovirus (20.3%) and PIV3 with 8.1% each. The incidence of RSV, however, decreases significantly with increasing age (p-value < 0.0001) dropping to 13% in children older than 3 years old, while the reverse relationship is observed for Influenza A and B and HAdV. Rhinoviruses, HBoV and enteroviruses are most frequently observed in children from 4 months to 3 years of age. The age dependency of the virus incidence is visualized in Fig 3 for the seven most frequently observed viruses. The positivity rate also showed a trend according to the age group dropping from 90.5% in the under 3-month old to 78.3% in the 4-12 years old (p-value = 0.020). This may point to an increasing role of pathogens not included in the assays, such as bacterial infections in older children.
Regarding multiple infections, children less than 3 month of age and those older than 4 years had a significantly smaller risk to present with multiple infections as compared to the other two age groups (p-value = 0.014).
A reason for this could be that very young children have limited contact to others reducing thereby the chance for a co-infection, whereas children older than 3 years already established immunity to an increasing number of viruses encountered previously.
This study for the first time examined the aetiology of acute respiratory tract infections in hospitalised children in Cyprus. Four multiplex Real-Time RT-PCR assays were developed in order to detect the most common respiratory viral pathogens in a fast and cost-effective way. The high rate of positive samples (85.8%) is evidence of the high sensitivity of the Multiplex-assays used and that the range of viruses included in the analysis is comprehensive. Many previous studies have shown detection rates ranging from below 50% to 75% [20] [21] [22] [23] [24] .
The most common viruses detected were RSV and rhinovirus accounting for almost 60% of all cases. Both viruses were reported previously by others as the major aetiology for respiratory viral infections in young children with rhinoviruses being recognized increasingly for their role in lower respiratory tract infections [20, [25] [26] [27] [28] [29] [30] .
Our data support the results of similar studies performed in the Middle East region. A recently published study found that RSV was the most commonly detected virus in nasopharyngeal swabs from children presenting symptoms of RTIs and in addition to that it also showed that RSV infections follow a similar circulation pattern peaking from December to March [31] . Another study has revealed that RSV and PIV3 incidence decreases significantly with age, whereas the opposite is observed for influenza and adenovirus infections, a trend that was also observed in our study [26] .
Mixed infections were observed in approximately 20% of all samples, which is in the middle of previously reported rates ranging from 10 to almost 40%. HBoV, HCoV and EV were found most frequently in co-infections. All three subtypes of HCoV were co-detected with several other viruses, while HBoV was co-detected mainly with HRV and RSV. In the case of EV infections, EV were almost predominantly associated with HRV. The rare presence of InfA and InfB viruses in multiple infections witnessed in our study was also observed elsewhere [32, 33] . Even though this study did not allow for investigating a possible association between multiple infections and disease severity, a review of the literature shows that such a potential association is still subject to controversy, since there are reports showing no relationship of multiple virus infection with respiratoty illness severity on one hand or a significant association on the other. Studies have shown that viral co-infection was significantly associated with longer duration of illness symptoms, but with a decreased severity in hospitalized children regarding oxygen requirement and intensive care unit admission, whereas the findings of other studies have indicated that severe clinical phenotypes were more prevalent in co-infection patients, especially in RSV co-infections that may increase the severity of RSV associated disease in children [25, [34] [35] [36] [37] [38] [39] [40] .
Viral respiratory infections continue to be a worldwide health concern. As the clinical symptoms of patients with acute respiratory tract infections do usually not allow a discrimination of viral or bacterial aetiology, rapid and reliable diagnostic tools are required for better antibiotic stewardship and the implementation of appropriate infection control measures [4, 41] . The data presented expand our understanding of the epidemiology of viral respiratory tract infections in Cypriot children and will be helpful to the clinicians and researchers interested in the treatment and control of viral respiratory tract infections.
|
What are the most common viruses?
| 1,614 |
respiratory syncytial virus (RSV), influenza A and B (INF-A, INF-B) viruses, parainfluenza viruses (PIVs), and human adenoviruses (HAdVs)
| 2,831 |
1,566 |
Aetiology of Acute Respiratory Tract Infections in Hospitalised Children in Cyprus
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4720120/
SHA: efd27ff0ac04dd60838266386aaebb5df80f4fa9
Authors: Richter, Jan; Panayiotou, Christakis; Tryfonos, Christina; Koptides, Dana; Koliou, Maria; Kalogirou, Nikolas; Georgiou, Eleni; Christodoulou, Christina
Date: 2016-01-13
DOI: 10.1371/journal.pone.0147041
License: cc-by
Abstract: In order to improve clinical management and prevention of viral infections in hospitalised children improved etiological insight is needed. The aim of the present study was to assess the spectrum of respiratory viral pathogens in children admitted to hospital with acute respiratory tract infections in Cyprus. For this purpose nasopharyngeal swab samples from 424 children less than 12 years of age with acute respiratory tract infections were collected over three epidemic seasons and were analysed for the presence of the most common 15 respiratory viruses. A viral pathogen was identified in 86% of the samples, with multiple infections being observed in almost 20% of the samples. The most frequently detected viruses were RSV (30.4%) and Rhinovirus (27.4%). RSV exhibited a clear seasonality with marked peaks in January/February, while rhinovirus infections did not exhibit a pronounced seasonality being detected almost throughout the year. While RSV and PIV3 incidence decreased significantly with age, the opposite was observed for influenza A and B as well as adenovirus infections. The data presented expand our understanding of the epidemiology of viral respiratory tract infections in Cypriot children and will be helpful to the clinicians and researchers interested in the treatment and control of viral respiratory tract infections.
Text: Viral Respiratory tract infections (RTI) represent a major public health problem because of their world-wide occurrence, ease of transmission and considerable morbidity and mortality effecting people of all ages. Children are on average infected two to three times more frequently than adults, with acute RTIs being the most common infection in childhood [1, 2] . Illnesses caused by respiratory viruses include, among others, common colds, pharyngitis, croup, bronchiolitis, viral pneumonia and otitis media. Rapid diagnosis is important not only for timely therapeutic intervention but also for the identification of a beginning influenza epidemic and the avoidance of unnecessary antibiotic treatment [3, 4] .
RTIs are a major cause of morbidity and mortality worldwide. Acute RTI is most common in children under five years of age, and represents 30-50% of the paediatric medical admissions, as well as 20-40% of hospitalizations in children. Respiratory infections cluster during winter and early spring months. The leading viral agents include respiratory syncytial virus (RSV), influenza A and B (INF-A, INF-B) viruses, parainfluenza viruses (PIVs), and human adenoviruses (HAdVs). In addition, there is a continuously increasing list of new respiratory viruses that contribute significantly to the burden of acute respiratory infections, such as the recently identified human metapneumovirus (HMPV) and human Bocavirus (HBoV) [5] .
Acute RTIs are classified as upper (UTRIs) and lower RTI (LRTIs), according to the involved anatomic localization. URTIs cause non-severe but widespread epidemics that are responsible for continuous circulation of pathogens in the community. LRTIs have been classified as frank pneumonia and bronchiolitis with clinical, radiological and etiological features that usually overlap [6, 7] . Viruses are again the foremost agents of LRTIs often misdiagnosed as bacterial in origin and hence treated with antibiotics unnecessarily [8] .
The main aim of this study was to determine the aetiology of acute respiratory tract infections in Cypriot children and assess the epidemiology of the identified viral pathogens over three epidemic seasons.
The study was approved by the Cyprus National Bioethics Committee. Accordingly, written informed consent was obtained from parents prior to sample taking. Between November 2010 and October 2013, 485 nasopharyngeal swab samples were collected from children up to 12 years of age, who had been hospitalized with acute respiratory tract infection at the Archbishop Makarios III hospital, Nicosia. Clinical and demographic information including symptoms, duration of hospitalisation, diagnosis and treatment were recorded. Nasal swab samples were collected using the BD Universal Viral Transport Collection Kit. Viral RNA/DNA was extracted from 400 μl sample using the iPrep PureLink Virus Kit on an iPrep purification instrument (Invitrogen).
A set of four multiplex Real-Time RT-PCR assays was established and validated for the detection of the 15 most common respiratory viruses as follows: assay 1: influenzaviruses A and B, RSV, assay 2: parainfluenzaviruses 1-4, assay 3: HAdV, enteroviruses, HMPV and HBoV and assay 4: rhinoviruses and the human coronaviruses OC43, NL63 and 229E (Table 1) .
Published primer and probe sets were used as a basis for designing the assays, however, all primer/probe sequences were checked against newly build sequence alignments of all viruses tested and were modified, if necessary, to account for possible sequence variations. For this purpose, all available complete genome sequences were obtained for each virus from GenBank, imported into the BioEdit Sequence Alignment Editor v7.1.7 and aligned using ClustalX. In case of mismatches between published primers/probe and target sequences, modifications were applied, as indicated in Table 1 . The alignments for the viruses, which necessitated changes to the primers/probe are available in Fasta-Format as supplement S1-S4 Files.
Primer concentrations and reaction conditions for the four assays were subsequently optimised for multiplexing. In order to assess the sensitivity and specificity of the assays, the laboratory enrolled for two consecutive years in Quality Control for Molecular Diagnostics (QCMD) external quality assessment schemes for all viruses, except Bocavirus, which was unavailable. In summary, the established assays were able to correctly identify all viruses tested, proving their suitability for diagnostic application.
A possible correlation of virus prevalence and age of infection was assessed using univariate analyses. The Fisher's exact test was used where cell counts below 5 were encountered; otherwise, the chi-squared test was performed. The same statistical tests were used to compare the frequency of subjects with single or multiple infections between age groups. In addition, Pearson correlation was used to examine co-infections of different viruses. All statistical analyses were performed using StataSE 12 (StatCorp. 2007. College Station, TX, USA).
The present study was a prospective investigation of children hospitalized with acute respiratory tract infections between November 2010 and October 2013 in Cyprus. The median age of the children was 15 months (range: 0-140 months) with 243 being male and 181 female (male/ female ratio 1.34). The age distribution is shown in Fig 1.
Out of the 424 samples analysed, 364 (85.8%) were positive for one or more viruses. Results are summarized in Table 2 .The most commonly detected viruses were RSV, which was found in 129 (30.4%) patients and rhinoviruses in 116 (27.4%) accounting together for almost 60% of all detections. With moderate frequency have been detected HAdV in 31(7.3%) patients, influenza A in 28 (6.6%), HBoV in 24 (5.7%), enteroviruses and PIV 3 in 23 (5.4%) of patients respectively, and Influenza B in 21 (5.0%). A low frequency was exhibited by HMPV with 16 (3.8%) positive samples, human coronavirus OC43 with 13 (3.1%), PIV 1 with 12 (2.8%), PIV 4 with 9 (2.1%), PIV 2 with 7 (1.7%) and HCoV NL63 with 6 (1.4%). Coronavirus 229E could be detected only in a single sample.
Co-infections with two or more viruses were observed in 84 out of the 364 positive samples (see Table 2 ). Dual infections accounted for 17% of all positive samples and three viruses were detected in 2.7% of samples). A single patient sample displayed a quadruple infection being simultaneously positive for RSV, rhinovirus, HBoV and influenza B. Table 3 summarizes the frequency of each virus in single vs. multiple infections as well as the number of co-occurrences of viruses for each possible virus combination. In absolute terms the most common combination observed was RSV/rhinovirus. As a percentage, however, the virus appearing most often in co- infections was HBoV, which was found in more than 70% of cases together with another virus, followed by coronaviruses HCoV OC43 and HCoV NL63 with 61% and 67%, respectively. On the other hand, the viruses most rarely seen in co-infections were influenza viruses A and B as well as RSV. Pearson correlation coefficients were calculated to examine the likelihood of co-infections of different viruses. The results of the analysis are summarized in Table 1 in S1 Table. Significant correlation (P-value < 0.05) was seen mostly for co-infections with RSV, however correlations were very weak (r<0.3) and negative. This finding can probably be explained by the fact that RSV infections occurred predominantly in the very young, where co-infections were less frequently observed. On the other hand, a significant positive correlation was observed for enterovirus and rhinovirus co-infection hinting maybe at similarities in circulation patterns and/or transmission modes.
Regarding seasonality, different patterns of circulations could be observed for RSV, rhinoviruses and influenzaviruses (A and B combined) (Fig 2) , with RSV and influenza exhibiting a clear seasonality with marked peaks in January/February, while rhinovirus infections did not exhibit a pronounced seasonality being detected almost throughout the year. However, as more than 100 different rhinovirus strains have been identified to be circulating worldwide in parallel and successively, a potential seasonality of individual rhinovirus serotypes may be masked by overlapping patterns [18, 19] .
The data was further analysed with regard to the age distribution of virus infection (see Table 2 ). In infants up to 3 months old, RSV was by far the most common pathogen (58.1%), followed by rhinovirus (20.3%) and PIV3 with 8.1% each. The incidence of RSV, however, decreases significantly with increasing age (p-value < 0.0001) dropping to 13% in children older than 3 years old, while the reverse relationship is observed for Influenza A and B and HAdV. Rhinoviruses, HBoV and enteroviruses are most frequently observed in children from 4 months to 3 years of age. The age dependency of the virus incidence is visualized in Fig 3 for the seven most frequently observed viruses. The positivity rate also showed a trend according to the age group dropping from 90.5% in the under 3-month old to 78.3% in the 4-12 years old (p-value = 0.020). This may point to an increasing role of pathogens not included in the assays, such as bacterial infections in older children.
Regarding multiple infections, children less than 3 month of age and those older than 4 years had a significantly smaller risk to present with multiple infections as compared to the other two age groups (p-value = 0.014).
A reason for this could be that very young children have limited contact to others reducing thereby the chance for a co-infection, whereas children older than 3 years already established immunity to an increasing number of viruses encountered previously.
This study for the first time examined the aetiology of acute respiratory tract infections in hospitalised children in Cyprus. Four multiplex Real-Time RT-PCR assays were developed in order to detect the most common respiratory viral pathogens in a fast and cost-effective way. The high rate of positive samples (85.8%) is evidence of the high sensitivity of the Multiplex-assays used and that the range of viruses included in the analysis is comprehensive. Many previous studies have shown detection rates ranging from below 50% to 75% [20] [21] [22] [23] [24] .
The most common viruses detected were RSV and rhinovirus accounting for almost 60% of all cases. Both viruses were reported previously by others as the major aetiology for respiratory viral infections in young children with rhinoviruses being recognized increasingly for their role in lower respiratory tract infections [20, [25] [26] [27] [28] [29] [30] .
Our data support the results of similar studies performed in the Middle East region. A recently published study found that RSV was the most commonly detected virus in nasopharyngeal swabs from children presenting symptoms of RTIs and in addition to that it also showed that RSV infections follow a similar circulation pattern peaking from December to March [31] . Another study has revealed that RSV and PIV3 incidence decreases significantly with age, whereas the opposite is observed for influenza and adenovirus infections, a trend that was also observed in our study [26] .
Mixed infections were observed in approximately 20% of all samples, which is in the middle of previously reported rates ranging from 10 to almost 40%. HBoV, HCoV and EV were found most frequently in co-infections. All three subtypes of HCoV were co-detected with several other viruses, while HBoV was co-detected mainly with HRV and RSV. In the case of EV infections, EV were almost predominantly associated with HRV. The rare presence of InfA and InfB viruses in multiple infections witnessed in our study was also observed elsewhere [32, 33] . Even though this study did not allow for investigating a possible association between multiple infections and disease severity, a review of the literature shows that such a potential association is still subject to controversy, since there are reports showing no relationship of multiple virus infection with respiratoty illness severity on one hand or a significant association on the other. Studies have shown that viral co-infection was significantly associated with longer duration of illness symptoms, but with a decreased severity in hospitalized children regarding oxygen requirement and intensive care unit admission, whereas the findings of other studies have indicated that severe clinical phenotypes were more prevalent in co-infection patients, especially in RSV co-infections that may increase the severity of RSV associated disease in children [25, [34] [35] [36] [37] [38] [39] [40] .
Viral respiratory infections continue to be a worldwide health concern. As the clinical symptoms of patients with acute respiratory tract infections do usually not allow a discrimination of viral or bacterial aetiology, rapid and reliable diagnostic tools are required for better antibiotic stewardship and the implementation of appropriate infection control measures [4, 41] . The data presented expand our understanding of the epidemiology of viral respiratory tract infections in Cypriot children and will be helpful to the clinicians and researchers interested in the treatment and control of viral respiratory tract infections.
|
What is the most common viral infection for infants up to 3 months old?
| 1,629 |
RSV
| 10,296 |
1,566 |
Aetiology of Acute Respiratory Tract Infections in Hospitalised Children in Cyprus
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4720120/
SHA: efd27ff0ac04dd60838266386aaebb5df80f4fa9
Authors: Richter, Jan; Panayiotou, Christakis; Tryfonos, Christina; Koptides, Dana; Koliou, Maria; Kalogirou, Nikolas; Georgiou, Eleni; Christodoulou, Christina
Date: 2016-01-13
DOI: 10.1371/journal.pone.0147041
License: cc-by
Abstract: In order to improve clinical management and prevention of viral infections in hospitalised children improved etiological insight is needed. The aim of the present study was to assess the spectrum of respiratory viral pathogens in children admitted to hospital with acute respiratory tract infections in Cyprus. For this purpose nasopharyngeal swab samples from 424 children less than 12 years of age with acute respiratory tract infections were collected over three epidemic seasons and were analysed for the presence of the most common 15 respiratory viruses. A viral pathogen was identified in 86% of the samples, with multiple infections being observed in almost 20% of the samples. The most frequently detected viruses were RSV (30.4%) and Rhinovirus (27.4%). RSV exhibited a clear seasonality with marked peaks in January/February, while rhinovirus infections did not exhibit a pronounced seasonality being detected almost throughout the year. While RSV and PIV3 incidence decreased significantly with age, the opposite was observed for influenza A and B as well as adenovirus infections. The data presented expand our understanding of the epidemiology of viral respiratory tract infections in Cypriot children and will be helpful to the clinicians and researchers interested in the treatment and control of viral respiratory tract infections.
Text: Viral Respiratory tract infections (RTI) represent a major public health problem because of their world-wide occurrence, ease of transmission and considerable morbidity and mortality effecting people of all ages. Children are on average infected two to three times more frequently than adults, with acute RTIs being the most common infection in childhood [1, 2] . Illnesses caused by respiratory viruses include, among others, common colds, pharyngitis, croup, bronchiolitis, viral pneumonia and otitis media. Rapid diagnosis is important not only for timely therapeutic intervention but also for the identification of a beginning influenza epidemic and the avoidance of unnecessary antibiotic treatment [3, 4] .
RTIs are a major cause of morbidity and mortality worldwide. Acute RTI is most common in children under five years of age, and represents 30-50% of the paediatric medical admissions, as well as 20-40% of hospitalizations in children. Respiratory infections cluster during winter and early spring months. The leading viral agents include respiratory syncytial virus (RSV), influenza A and B (INF-A, INF-B) viruses, parainfluenza viruses (PIVs), and human adenoviruses (HAdVs). In addition, there is a continuously increasing list of new respiratory viruses that contribute significantly to the burden of acute respiratory infections, such as the recently identified human metapneumovirus (HMPV) and human Bocavirus (HBoV) [5] .
Acute RTIs are classified as upper (UTRIs) and lower RTI (LRTIs), according to the involved anatomic localization. URTIs cause non-severe but widespread epidemics that are responsible for continuous circulation of pathogens in the community. LRTIs have been classified as frank pneumonia and bronchiolitis with clinical, radiological and etiological features that usually overlap [6, 7] . Viruses are again the foremost agents of LRTIs often misdiagnosed as bacterial in origin and hence treated with antibiotics unnecessarily [8] .
The main aim of this study was to determine the aetiology of acute respiratory tract infections in Cypriot children and assess the epidemiology of the identified viral pathogens over three epidemic seasons.
The study was approved by the Cyprus National Bioethics Committee. Accordingly, written informed consent was obtained from parents prior to sample taking. Between November 2010 and October 2013, 485 nasopharyngeal swab samples were collected from children up to 12 years of age, who had been hospitalized with acute respiratory tract infection at the Archbishop Makarios III hospital, Nicosia. Clinical and demographic information including symptoms, duration of hospitalisation, diagnosis and treatment were recorded. Nasal swab samples were collected using the BD Universal Viral Transport Collection Kit. Viral RNA/DNA was extracted from 400 μl sample using the iPrep PureLink Virus Kit on an iPrep purification instrument (Invitrogen).
A set of four multiplex Real-Time RT-PCR assays was established and validated for the detection of the 15 most common respiratory viruses as follows: assay 1: influenzaviruses A and B, RSV, assay 2: parainfluenzaviruses 1-4, assay 3: HAdV, enteroviruses, HMPV and HBoV and assay 4: rhinoviruses and the human coronaviruses OC43, NL63 and 229E (Table 1) .
Published primer and probe sets were used as a basis for designing the assays, however, all primer/probe sequences were checked against newly build sequence alignments of all viruses tested and were modified, if necessary, to account for possible sequence variations. For this purpose, all available complete genome sequences were obtained for each virus from GenBank, imported into the BioEdit Sequence Alignment Editor v7.1.7 and aligned using ClustalX. In case of mismatches between published primers/probe and target sequences, modifications were applied, as indicated in Table 1 . The alignments for the viruses, which necessitated changes to the primers/probe are available in Fasta-Format as supplement S1-S4 Files.
Primer concentrations and reaction conditions for the four assays were subsequently optimised for multiplexing. In order to assess the sensitivity and specificity of the assays, the laboratory enrolled for two consecutive years in Quality Control for Molecular Diagnostics (QCMD) external quality assessment schemes for all viruses, except Bocavirus, which was unavailable. In summary, the established assays were able to correctly identify all viruses tested, proving their suitability for diagnostic application.
A possible correlation of virus prevalence and age of infection was assessed using univariate analyses. The Fisher's exact test was used where cell counts below 5 were encountered; otherwise, the chi-squared test was performed. The same statistical tests were used to compare the frequency of subjects with single or multiple infections between age groups. In addition, Pearson correlation was used to examine co-infections of different viruses. All statistical analyses were performed using StataSE 12 (StatCorp. 2007. College Station, TX, USA).
The present study was a prospective investigation of children hospitalized with acute respiratory tract infections between November 2010 and October 2013 in Cyprus. The median age of the children was 15 months (range: 0-140 months) with 243 being male and 181 female (male/ female ratio 1.34). The age distribution is shown in Fig 1.
Out of the 424 samples analysed, 364 (85.8%) were positive for one or more viruses. Results are summarized in Table 2 .The most commonly detected viruses were RSV, which was found in 129 (30.4%) patients and rhinoviruses in 116 (27.4%) accounting together for almost 60% of all detections. With moderate frequency have been detected HAdV in 31(7.3%) patients, influenza A in 28 (6.6%), HBoV in 24 (5.7%), enteroviruses and PIV 3 in 23 (5.4%) of patients respectively, and Influenza B in 21 (5.0%). A low frequency was exhibited by HMPV with 16 (3.8%) positive samples, human coronavirus OC43 with 13 (3.1%), PIV 1 with 12 (2.8%), PIV 4 with 9 (2.1%), PIV 2 with 7 (1.7%) and HCoV NL63 with 6 (1.4%). Coronavirus 229E could be detected only in a single sample.
Co-infections with two or more viruses were observed in 84 out of the 364 positive samples (see Table 2 ). Dual infections accounted for 17% of all positive samples and three viruses were detected in 2.7% of samples). A single patient sample displayed a quadruple infection being simultaneously positive for RSV, rhinovirus, HBoV and influenza B. Table 3 summarizes the frequency of each virus in single vs. multiple infections as well as the number of co-occurrences of viruses for each possible virus combination. In absolute terms the most common combination observed was RSV/rhinovirus. As a percentage, however, the virus appearing most often in co- infections was HBoV, which was found in more than 70% of cases together with another virus, followed by coronaviruses HCoV OC43 and HCoV NL63 with 61% and 67%, respectively. On the other hand, the viruses most rarely seen in co-infections were influenza viruses A and B as well as RSV. Pearson correlation coefficients were calculated to examine the likelihood of co-infections of different viruses. The results of the analysis are summarized in Table 1 in S1 Table. Significant correlation (P-value < 0.05) was seen mostly for co-infections with RSV, however correlations were very weak (r<0.3) and negative. This finding can probably be explained by the fact that RSV infections occurred predominantly in the very young, where co-infections were less frequently observed. On the other hand, a significant positive correlation was observed for enterovirus and rhinovirus co-infection hinting maybe at similarities in circulation patterns and/or transmission modes.
Regarding seasonality, different patterns of circulations could be observed for RSV, rhinoviruses and influenzaviruses (A and B combined) (Fig 2) , with RSV and influenza exhibiting a clear seasonality with marked peaks in January/February, while rhinovirus infections did not exhibit a pronounced seasonality being detected almost throughout the year. However, as more than 100 different rhinovirus strains have been identified to be circulating worldwide in parallel and successively, a potential seasonality of individual rhinovirus serotypes may be masked by overlapping patterns [18, 19] .
The data was further analysed with regard to the age distribution of virus infection (see Table 2 ). In infants up to 3 months old, RSV was by far the most common pathogen (58.1%), followed by rhinovirus (20.3%) and PIV3 with 8.1% each. The incidence of RSV, however, decreases significantly with increasing age (p-value < 0.0001) dropping to 13% in children older than 3 years old, while the reverse relationship is observed for Influenza A and B and HAdV. Rhinoviruses, HBoV and enteroviruses are most frequently observed in children from 4 months to 3 years of age. The age dependency of the virus incidence is visualized in Fig 3 for the seven most frequently observed viruses. The positivity rate also showed a trend according to the age group dropping from 90.5% in the under 3-month old to 78.3% in the 4-12 years old (p-value = 0.020). This may point to an increasing role of pathogens not included in the assays, such as bacterial infections in older children.
Regarding multiple infections, children less than 3 month of age and those older than 4 years had a significantly smaller risk to present with multiple infections as compared to the other two age groups (p-value = 0.014).
A reason for this could be that very young children have limited contact to others reducing thereby the chance for a co-infection, whereas children older than 3 years already established immunity to an increasing number of viruses encountered previously.
This study for the first time examined the aetiology of acute respiratory tract infections in hospitalised children in Cyprus. Four multiplex Real-Time RT-PCR assays were developed in order to detect the most common respiratory viral pathogens in a fast and cost-effective way. The high rate of positive samples (85.8%) is evidence of the high sensitivity of the Multiplex-assays used and that the range of viruses included in the analysis is comprehensive. Many previous studies have shown detection rates ranging from below 50% to 75% [20] [21] [22] [23] [24] .
The most common viruses detected were RSV and rhinovirus accounting for almost 60% of all cases. Both viruses were reported previously by others as the major aetiology for respiratory viral infections in young children with rhinoviruses being recognized increasingly for their role in lower respiratory tract infections [20, [25] [26] [27] [28] [29] [30] .
Our data support the results of similar studies performed in the Middle East region. A recently published study found that RSV was the most commonly detected virus in nasopharyngeal swabs from children presenting symptoms of RTIs and in addition to that it also showed that RSV infections follow a similar circulation pattern peaking from December to March [31] . Another study has revealed that RSV and PIV3 incidence decreases significantly with age, whereas the opposite is observed for influenza and adenovirus infections, a trend that was also observed in our study [26] .
Mixed infections were observed in approximately 20% of all samples, which is in the middle of previously reported rates ranging from 10 to almost 40%. HBoV, HCoV and EV were found most frequently in co-infections. All three subtypes of HCoV were co-detected with several other viruses, while HBoV was co-detected mainly with HRV and RSV. In the case of EV infections, EV were almost predominantly associated with HRV. The rare presence of InfA and InfB viruses in multiple infections witnessed in our study was also observed elsewhere [32, 33] . Even though this study did not allow for investigating a possible association between multiple infections and disease severity, a review of the literature shows that such a potential association is still subject to controversy, since there are reports showing no relationship of multiple virus infection with respiratoty illness severity on one hand or a significant association on the other. Studies have shown that viral co-infection was significantly associated with longer duration of illness symptoms, but with a decreased severity in hospitalized children regarding oxygen requirement and intensive care unit admission, whereas the findings of other studies have indicated that severe clinical phenotypes were more prevalent in co-infection patients, especially in RSV co-infections that may increase the severity of RSV associated disease in children [25, [34] [35] [36] [37] [38] [39] [40] .
Viral respiratory infections continue to be a worldwide health concern. As the clinical symptoms of patients with acute respiratory tract infections do usually not allow a discrimination of viral or bacterial aetiology, rapid and reliable diagnostic tools are required for better antibiotic stewardship and the implementation of appropriate infection control measures [4, 41] . The data presented expand our understanding of the epidemiology of viral respiratory tract infections in Cypriot children and will be helpful to the clinicians and researchers interested in the treatment and control of viral respiratory tract infections.
|
What is the incidence of RSV in children older than 3 years of age?
| 1,630 |
13%
| 10,507 |
1,548 |
The First Detection of Equine Coronavirus in Adult Horses and Foals in Ireland
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6832964/
SHA: eee5a9068ade4c6776f189045115a90a5785e983
Authors: Nemoto, Manabu; Schofield, Warren; Cullinane, Ann
Date: 2019-10-14
DOI: 10.3390/v11100946
License: cc-by
Abstract: The objective of this study was to investigate the presence of equine coronavirus (ECoV) in clinical samples submitted to a diagnostic laboratory in Ireland. A total of 424 clinical samples were examined from equids with enteric disease in 24 Irish counties between 2011 and 2015. A real-time reverse transcription polymerase chain reaction was used to detect ECoV RNA. Nucleocapsid, spike and the region from the p4.7 to p12.7 genes of positive samples were sequenced, and sequence and phylogenetic analyses were conducted. Five samples (1.2%) collected in 2011 and 2013 tested positive for ECoV. Positive samples were collected from adult horses, Thoroughbred foals and a donkey foal. Sequence and/or phylogenetic analysis showed that nucleocapsid, spike and p12.7 genes were highly conserved and were closely related to ECoVs identified in other countries. In contrast, the region from p4.7 and the non-coding region following the p4.7 gene had deletions or insertions. The differences in the p4.7 region between the Irish ECoVs and other ECoVs indicated that the Irish viruses were distinguishable from those circulating in other countries. This is the first report of ECoV detected in both foals and adult horses in Ireland.
Text: Equine coronavirus (ECoV) is a positive-stranded RNA virus and belongs to the species Betacoronavirus 1 in the genus Betacoronavirus [1, 2] . The clinical signs associated with ECoV infection during outbreaks in the USA [3] and Japan [4] [5] [6] were fever, anorexia, lethargy and diarrhoea. The same clinical signs were also recorded in an experimental challenge study using Japanese draft horses [7] . The main transmission route is considered to be faecal-oral [7] and ECoV is usually detected in faecal samples. However, the molecular detection of ECoV in faeces from horses with diarrhoea, does not prove causation. Coronaviruses can cause both enteric and respiratory disease in many avian and mammalian species but ECoV is less likely to be found in respiratory secretions than in faeces [8, 9] .
Both molecular and seroepidemiology studies suggest that ECoV may be more prevalent in the USA than in other countries [10] . ECoV was detected in samples collected from equids in 48 states of the USA [11] . In central Kentucky, approximately 30% of both healthy and diarrheic Thoroughbred foals were infected with ECoV [12] . All of the qPCR positive foals with diarrhoea were co-infected with other pathogens such as rotavirus or Clostridium perfringens, suggesting that there was potential for ECoV to be over-diagnosed as a causative agent in complex diseases. In contrast in Japan, although an outbreak of diarrhoea occurred among ECoV-infected draft horses at one racecourse [4] [5] [6] , there have been no similar outbreaks subsequently, and all rectal swabs collected from diarrheic Thoroughbred foals were negative. Furthermore, only 2.5% of the rectal swabs collected from healthy foals in the largest Thoroughbred horse breeding region in Japan were positive for ECoV [13] . In France, 2.8% of 395 faecal samples and 0.5% of 200 respiratory samples collected in 58 counties tested positive for ECoV [9] . Similar to the reports from Japan and France, a low prevalence of ECoV was also observed in the UK [14] , Saudi Arabia and Oman [15] . The objective of this study was to investigate the presence of ECoV in clinical samples submitted to a diagnostic laboratory in Ireland. The samples were tested by real-time reverse transcription polymerase chain reaction (rRT-PCR) as it has been shown to be the most sensitive diagnostic method for ECoV [16] and is routinely employed as an alternative to virus isolation in diagnostic laboratories worldwide, both for timely diagnosis and in epidemiological studies [9, 10] . Virus isolation and biological characterisation were beyond the capacity of this study, which was similar in scope to that of the studies in horse populations in the USA, Europe and Asia [8, 9, 13, 14] . The rRT-PCR assay was performed as previously described using a primer set targeting the nucleocapsid (N) gene (ECoV-380f, ECoV-522r and ECoV-436p) [3] (Table 1) and AgPath-ID One-Step RT-PCR Kit (Thermo Fisher Scientific, MA, USA) according to the manufacturer's instructions. To prove that the extraction was successful and that there was no inhibition during rRT-PCR amplification, an internal positive control primer/probe (PrimerDesign, Southampton, UK) was added to the master mix. Thermal cycling conditions were; 48 • C for 10 min and 95 • C for 10 min, followed by 40 cycles at 94 • C for 15 s and 60 • C for 45 s. The SuperScript III One-Step RT-PCR System with Platinum Taq High Fidelity (Thermo Fisher Scientific, MA, USA) was used for sequencing analysis of two of the five ECoV samples identified. There was inadequate viral nucleic acid in the other three samples for sequencing. The primer sets used to amplify the nucleocapsid (N) gene [4] , the partial spike (S) gene [9] , and the region from the p4.7 to p12.7 genes of non-structural proteins (Oue, personal communication) are shown in Table 1 . The RT-PCR products were sequenced commercially by GATC Biotech (Cologne, Germany). Sequence analysis was performed using the BLAST and CLUSTALW programs, and Vector NTI Advance 11.5 software (Thermo Fisher Scientific, MA, USA). Phylogenetic analysis of nucleotide sequences was conducted with MEGA software Version 5.2 [17] . A phylogenetic tree was constructed based on nucleotide sequences of the K2+G (N gene) and TN93 (S gene) using the maximum likelihood method. MEGA software was used to select the optimal substitution models. Statistical analysis of the tree was performed with the bootstrap test (1000 replicates) for multiple alignments. The complete genome sequences of NC99 (EF446615) [2] , Tokachi09 (LC061272), Obihiro12-1 (LC061273) and Obihiro12-2 (LC061274) [1] , the N (AB671298) and S (AB671299) genes of Obihiro2004, the N gene of Hidaka-No.61/2012 (LC054263) and Hidaka-No.119/2012 (LC054264) [13] , the S gene of ECoV_FRA_2011/1 (KC178705), ECoV_FRA_2011/2 (KC178704), ECoV_FRA_2012/1 (KC178703), ECoV_FRA_2012/2 (KC178702) and ECoV_FRA_2012/3 (KC178701) [9] were used in sequence and/or phylogenetic analysis.
The accession numbers registered in GenBank/EMBL/DDBJ are as follows: the complete sequences of the N gene; 11V11708/IRL (LC149485) and 13V08313/IRL (LC149486), the partial sequences of the S gene; 11V11708/IRL (LC149487) and13V08313/IRL (LC149488) and the complete sequences from the p4.7 to p12.7 genes; 11V11708/IRL (LC149489) and13V08313/IRL (LC149490). One six-week-old foal was the only clinical case on a public Thoroughbred stud farm with approximately 30 mares when it presented with diarrhoea. Recovery took over three weeks during which it received fluid therapy, probiotics, antiulcer medication and antibiotics. The second foal was a 14-day-old filly, which had been hospitalised with diarrhoea two days prior to sample collection. The foal responded well to supportive treatment and at the time of sample collection, the diarrhoea had resolved. The five ECoV positive samples tested negative for equine rotavirus.
The nucleotide sequences of the complete N gene, the partial S gene and the region from the p4.7 to p12.7 genes of two positive samples (11V11708/IRL/2011 and 13V08313/IRL/2013) were determined. The nucleotide identities of the N and S genes of the two Irish ECoVs were 99.8% (1338/1341 nucleotides) and 99.5% (650/653 nucleotides), respectively. The nucleotide identities of the N gene of the two Irish ECoVs and the ECoVs from other continents are summarised in Table 2 .
Phylogenetic analysis was performed for the nucleotide sequences of the complete N and partial S genes (Figure 1 ). The analysis for the N gene showed that Irish ECoVs were independently clustered although they were closely related to Japanese viruses identified after 2009. In the phylogenetic tree of the S gene, Irish ECoVs were closely related to all other ECoVs analysed.
The length of the region from the p4.7 to p12.7 genes in the two viruses was 544 base pairs. Compared with NC99, Irish ECoVs, had a total of 37 nucleotide deletions within p4.7 and the non-coding region following the p4.7 gene. Compared with Obihiro 12-1 and 12-2, Irish ECoVs had a three-nucleotide insertion. When compared with Tokachi09, the Irish ECoVs had a 148-nucleotide insertion (see Figure S1 ). The p12.7 gene of the two Irish ECoVs did not have deletions or insertions, and the nucleotide identities were 98.8-99.7% between these viruses and the other ECoVs (NC99, Tokachi09, Obihiro12-1 and Obihiro12-2).
This study provides the first report of ECoV circulating in Ireland, the third European country with a significant horse industry where the virus has been detected in horses with enteric disease. However, detection of ECoV in faeces samples from horses with enteric disease does not prove
This study provides the first report of ECoV circulating in Ireland, the third European country with a significant horse industry where the virus has been detected in horses with enteric disease. However, detection of ECoV in faeces samples from horses with enteric disease does not prove causation. In this study, 424 samples collected between 2011 and 2015 from equids with enteric disease were tested, and only five samples (1.2%) were positive for ECoV. The inclusion of an internal positive control in the rRT-PCR eliminated the possibility of false negative results due to the presence of PCR inhibitors but the high content of nucleases associated with faeces samples may have caused some RNA degradation. However, this low prevalence of ECoV is similar to that identified in France [9] and among Thoroughbred foals in Japan [13] .
Although ECoV has been identified on three continents, little is known about the genetic and pathogenic diversity in field viruses. In this study, sequence and phylogenetic analysis (Figure 1 ) demonstrated a high level of homology between viruses detected in a donkey and a horse in two provinces in Ireland in different years. This suggests that Irish ECoVs may have low genetic diversity. Compared with the ECoVs of other countries, the N, S and p12.7 genes of the two Irish viruses were highly conserved. In contrast, the region from p4.7 and the non-coding region following the p4.7 gene had deletions or insertions ( Figure S1 ). Because of polymorphism in this region, this region could be useful for epidemiological investigation [5] . The differences in the p4.7 region between the Irish ECoVs and other ECoVs indicated that the viruses in Ireland may be distinguishable from those circulating in other countries. The positive samples were collected in November (1), March (1) and April (3) in this study. Higher case numbers are identified in the USA during the colder months (October to April) [11] , and our results were consistent with the circulation period in USA. It has been reported that outbreaks mainly occurred among adult riding, racing and show horses in USA [11] . The choice of cases to include in the current study may not have been optimal for detection of ECoV as the majority of samples were from foals. However, two positive samples were collected from adult horses in a combined riding school/show jumping yard in the West of Ireland. At the time of sample collection in April 2013, the monthly mean temperatures were below long-term average and in parts of the West, were the coldest in 24 years [18] . Cold weather may have been a predisposing factor to the ECoV infection on the farm.
Two positive samples were collected from Thoroughbred foals. A faeces sample collected from one foal with severe watery diarrhoea and inappetance was positive for ECoV but a sample collected three days later tested negative. A potential difficulty in detecting ECoV from naturally infected horses has been noted previously as serial samples from seven sick horses in the USA suggested that ECoV only persisted for three to nine days in faeces [3] . In both cases, the diarrhoea may have been caused by other unidentified coinfecting pathogens as has been suggested by investigators in the USA [12] . This is the first report of ECoV detection in faeces samples from both foals and adult horses in Ireland. The viruses identified in Ireland are genetically closely related to the Japanese viruses and the results of this study give no indication of significant genetic or phenotypic diversity. In recent years, there has been an increase in awareness and testing for ECoV in the USA and elsewhere [10] . Horse breeding and racing activities in Ireland are the most prominent and important of any country on a per capita basis. There are over 50 Thoroughbred horses per 10,000 of population in Ireland, compared to between three and five for Great Britain, France and the USA [19] . Thus, an investigation of ECoV in Ireland is pertinent not only to increase awareness nationally of the epidemiology of the virus and promote discussion on its clinical importance, but also to inform the industry globally of the health status of Irish horses. Ireland exports horses all over the world. By illustration, in 2016 the country was the second biggest seller of bloodstock at public auctions second only to the USA [19] .
Many questions remain with regard to the clinical significance of ECoV. The outbreak at a draft-horse racetrack in Japan in 2009 affected 132 of approximately 600 horses and resulted in non-starters and the implementation of movement restrictions [4] . However, draft horses appear to have a higher infection rate than other breeds and an outbreak of similar severity has not been reported in Thoroughbred racehorses [10, 20] . The much higher incidence of ECoV positive Thoroughbred foals identified in Kentucky compared to similar populations internationally suggests an increased susceptibility to ECoV infection in that population. In the past, specific environmental factors were associated with extensive reproductive loss in the Kentucky area and to a lesser extent in other states [21] , but predisposing regional factors such as differences in management, environment or husbandry have not been identified for ECoV. It has been suggested that ECoV is a coinfecting agent in foals with diarrhoea and clinical infections have predominantly been reported in adult horses with a mono-infection with EcoV [10] . There was no indication from the results of this study that coronavirus is a major cause of diarrhoea in Irish horses but the introduction of rRT-PCR as a routine diagnostic test will assist in elucidating the significance of this virus to the Irish breeding, racing and sports industries. The primary focus in future will be on testing adult horses that present with anorexia, lethargy, fever and changes in faecal character as a significant association has been demonstrated between this clinical status and molecular detection of ECoV in faeces [11] .
|
What is the distance between the p4.7 and p12.7 genes in the Irish versus Japanese equine coronavirus variants?
| 2,125 |
544 base pairs
| 8,393 |
1,548 |
The First Detection of Equine Coronavirus in Adult Horses and Foals in Ireland
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6832964/
SHA: eee5a9068ade4c6776f189045115a90a5785e983
Authors: Nemoto, Manabu; Schofield, Warren; Cullinane, Ann
Date: 2019-10-14
DOI: 10.3390/v11100946
License: cc-by
Abstract: The objective of this study was to investigate the presence of equine coronavirus (ECoV) in clinical samples submitted to a diagnostic laboratory in Ireland. A total of 424 clinical samples were examined from equids with enteric disease in 24 Irish counties between 2011 and 2015. A real-time reverse transcription polymerase chain reaction was used to detect ECoV RNA. Nucleocapsid, spike and the region from the p4.7 to p12.7 genes of positive samples were sequenced, and sequence and phylogenetic analyses were conducted. Five samples (1.2%) collected in 2011 and 2013 tested positive for ECoV. Positive samples were collected from adult horses, Thoroughbred foals and a donkey foal. Sequence and/or phylogenetic analysis showed that nucleocapsid, spike and p12.7 genes were highly conserved and were closely related to ECoVs identified in other countries. In contrast, the region from p4.7 and the non-coding region following the p4.7 gene had deletions or insertions. The differences in the p4.7 region between the Irish ECoVs and other ECoVs indicated that the Irish viruses were distinguishable from those circulating in other countries. This is the first report of ECoV detected in both foals and adult horses in Ireland.
Text: Equine coronavirus (ECoV) is a positive-stranded RNA virus and belongs to the species Betacoronavirus 1 in the genus Betacoronavirus [1, 2] . The clinical signs associated with ECoV infection during outbreaks in the USA [3] and Japan [4] [5] [6] were fever, anorexia, lethargy and diarrhoea. The same clinical signs were also recorded in an experimental challenge study using Japanese draft horses [7] . The main transmission route is considered to be faecal-oral [7] and ECoV is usually detected in faecal samples. However, the molecular detection of ECoV in faeces from horses with diarrhoea, does not prove causation. Coronaviruses can cause both enteric and respiratory disease in many avian and mammalian species but ECoV is less likely to be found in respiratory secretions than in faeces [8, 9] .
Both molecular and seroepidemiology studies suggest that ECoV may be more prevalent in the USA than in other countries [10] . ECoV was detected in samples collected from equids in 48 states of the USA [11] . In central Kentucky, approximately 30% of both healthy and diarrheic Thoroughbred foals were infected with ECoV [12] . All of the qPCR positive foals with diarrhoea were co-infected with other pathogens such as rotavirus or Clostridium perfringens, suggesting that there was potential for ECoV to be over-diagnosed as a causative agent in complex diseases. In contrast in Japan, although an outbreak of diarrhoea occurred among ECoV-infected draft horses at one racecourse [4] [5] [6] , there have been no similar outbreaks subsequently, and all rectal swabs collected from diarrheic Thoroughbred foals were negative. Furthermore, only 2.5% of the rectal swabs collected from healthy foals in the largest Thoroughbred horse breeding region in Japan were positive for ECoV [13] . In France, 2.8% of 395 faecal samples and 0.5% of 200 respiratory samples collected in 58 counties tested positive for ECoV [9] . Similar to the reports from Japan and France, a low prevalence of ECoV was also observed in the UK [14] , Saudi Arabia and Oman [15] . The objective of this study was to investigate the presence of ECoV in clinical samples submitted to a diagnostic laboratory in Ireland. The samples were tested by real-time reverse transcription polymerase chain reaction (rRT-PCR) as it has been shown to be the most sensitive diagnostic method for ECoV [16] and is routinely employed as an alternative to virus isolation in diagnostic laboratories worldwide, both for timely diagnosis and in epidemiological studies [9, 10] . Virus isolation and biological characterisation were beyond the capacity of this study, which was similar in scope to that of the studies in horse populations in the USA, Europe and Asia [8, 9, 13, 14] . The rRT-PCR assay was performed as previously described using a primer set targeting the nucleocapsid (N) gene (ECoV-380f, ECoV-522r and ECoV-436p) [3] (Table 1) and AgPath-ID One-Step RT-PCR Kit (Thermo Fisher Scientific, MA, USA) according to the manufacturer's instructions. To prove that the extraction was successful and that there was no inhibition during rRT-PCR amplification, an internal positive control primer/probe (PrimerDesign, Southampton, UK) was added to the master mix. Thermal cycling conditions were; 48 • C for 10 min and 95 • C for 10 min, followed by 40 cycles at 94 • C for 15 s and 60 • C for 45 s. The SuperScript III One-Step RT-PCR System with Platinum Taq High Fidelity (Thermo Fisher Scientific, MA, USA) was used for sequencing analysis of two of the five ECoV samples identified. There was inadequate viral nucleic acid in the other three samples for sequencing. The primer sets used to amplify the nucleocapsid (N) gene [4] , the partial spike (S) gene [9] , and the region from the p4.7 to p12.7 genes of non-structural proteins (Oue, personal communication) are shown in Table 1 . The RT-PCR products were sequenced commercially by GATC Biotech (Cologne, Germany). Sequence analysis was performed using the BLAST and CLUSTALW programs, and Vector NTI Advance 11.5 software (Thermo Fisher Scientific, MA, USA). Phylogenetic analysis of nucleotide sequences was conducted with MEGA software Version 5.2 [17] . A phylogenetic tree was constructed based on nucleotide sequences of the K2+G (N gene) and TN93 (S gene) using the maximum likelihood method. MEGA software was used to select the optimal substitution models. Statistical analysis of the tree was performed with the bootstrap test (1000 replicates) for multiple alignments. The complete genome sequences of NC99 (EF446615) [2] , Tokachi09 (LC061272), Obihiro12-1 (LC061273) and Obihiro12-2 (LC061274) [1] , the N (AB671298) and S (AB671299) genes of Obihiro2004, the N gene of Hidaka-No.61/2012 (LC054263) and Hidaka-No.119/2012 (LC054264) [13] , the S gene of ECoV_FRA_2011/1 (KC178705), ECoV_FRA_2011/2 (KC178704), ECoV_FRA_2012/1 (KC178703), ECoV_FRA_2012/2 (KC178702) and ECoV_FRA_2012/3 (KC178701) [9] were used in sequence and/or phylogenetic analysis.
The accession numbers registered in GenBank/EMBL/DDBJ are as follows: the complete sequences of the N gene; 11V11708/IRL (LC149485) and 13V08313/IRL (LC149486), the partial sequences of the S gene; 11V11708/IRL (LC149487) and13V08313/IRL (LC149488) and the complete sequences from the p4.7 to p12.7 genes; 11V11708/IRL (LC149489) and13V08313/IRL (LC149490). One six-week-old foal was the only clinical case on a public Thoroughbred stud farm with approximately 30 mares when it presented with diarrhoea. Recovery took over three weeks during which it received fluid therapy, probiotics, antiulcer medication and antibiotics. The second foal was a 14-day-old filly, which had been hospitalised with diarrhoea two days prior to sample collection. The foal responded well to supportive treatment and at the time of sample collection, the diarrhoea had resolved. The five ECoV positive samples tested negative for equine rotavirus.
The nucleotide sequences of the complete N gene, the partial S gene and the region from the p4.7 to p12.7 genes of two positive samples (11V11708/IRL/2011 and 13V08313/IRL/2013) were determined. The nucleotide identities of the N and S genes of the two Irish ECoVs were 99.8% (1338/1341 nucleotides) and 99.5% (650/653 nucleotides), respectively. The nucleotide identities of the N gene of the two Irish ECoVs and the ECoVs from other continents are summarised in Table 2 .
Phylogenetic analysis was performed for the nucleotide sequences of the complete N and partial S genes (Figure 1 ). The analysis for the N gene showed that Irish ECoVs were independently clustered although they were closely related to Japanese viruses identified after 2009. In the phylogenetic tree of the S gene, Irish ECoVs were closely related to all other ECoVs analysed.
The length of the region from the p4.7 to p12.7 genes in the two viruses was 544 base pairs. Compared with NC99, Irish ECoVs, had a total of 37 nucleotide deletions within p4.7 and the non-coding region following the p4.7 gene. Compared with Obihiro 12-1 and 12-2, Irish ECoVs had a three-nucleotide insertion. When compared with Tokachi09, the Irish ECoVs had a 148-nucleotide insertion (see Figure S1 ). The p12.7 gene of the two Irish ECoVs did not have deletions or insertions, and the nucleotide identities were 98.8-99.7% between these viruses and the other ECoVs (NC99, Tokachi09, Obihiro12-1 and Obihiro12-2).
This study provides the first report of ECoV circulating in Ireland, the third European country with a significant horse industry where the virus has been detected in horses with enteric disease. However, detection of ECoV in faeces samples from horses with enteric disease does not prove
This study provides the first report of ECoV circulating in Ireland, the third European country with a significant horse industry where the virus has been detected in horses with enteric disease. However, detection of ECoV in faeces samples from horses with enteric disease does not prove causation. In this study, 424 samples collected between 2011 and 2015 from equids with enteric disease were tested, and only five samples (1.2%) were positive for ECoV. The inclusion of an internal positive control in the rRT-PCR eliminated the possibility of false negative results due to the presence of PCR inhibitors but the high content of nucleases associated with faeces samples may have caused some RNA degradation. However, this low prevalence of ECoV is similar to that identified in France [9] and among Thoroughbred foals in Japan [13] .
Although ECoV has been identified on three continents, little is known about the genetic and pathogenic diversity in field viruses. In this study, sequence and phylogenetic analysis (Figure 1 ) demonstrated a high level of homology between viruses detected in a donkey and a horse in two provinces in Ireland in different years. This suggests that Irish ECoVs may have low genetic diversity. Compared with the ECoVs of other countries, the N, S and p12.7 genes of the two Irish viruses were highly conserved. In contrast, the region from p4.7 and the non-coding region following the p4.7 gene had deletions or insertions ( Figure S1 ). Because of polymorphism in this region, this region could be useful for epidemiological investigation [5] . The differences in the p4.7 region between the Irish ECoVs and other ECoVs indicated that the viruses in Ireland may be distinguishable from those circulating in other countries. The positive samples were collected in November (1), March (1) and April (3) in this study. Higher case numbers are identified in the USA during the colder months (October to April) [11] , and our results were consistent with the circulation period in USA. It has been reported that outbreaks mainly occurred among adult riding, racing and show horses in USA [11] . The choice of cases to include in the current study may not have been optimal for detection of ECoV as the majority of samples were from foals. However, two positive samples were collected from adult horses in a combined riding school/show jumping yard in the West of Ireland. At the time of sample collection in April 2013, the monthly mean temperatures were below long-term average and in parts of the West, were the coldest in 24 years [18] . Cold weather may have been a predisposing factor to the ECoV infection on the farm.
Two positive samples were collected from Thoroughbred foals. A faeces sample collected from one foal with severe watery diarrhoea and inappetance was positive for ECoV but a sample collected three days later tested negative. A potential difficulty in detecting ECoV from naturally infected horses has been noted previously as serial samples from seven sick horses in the USA suggested that ECoV only persisted for three to nine days in faeces [3] . In both cases, the diarrhoea may have been caused by other unidentified coinfecting pathogens as has been suggested by investigators in the USA [12] . This is the first report of ECoV detection in faeces samples from both foals and adult horses in Ireland. The viruses identified in Ireland are genetically closely related to the Japanese viruses and the results of this study give no indication of significant genetic or phenotypic diversity. In recent years, there has been an increase in awareness and testing for ECoV in the USA and elsewhere [10] . Horse breeding and racing activities in Ireland are the most prominent and important of any country on a per capita basis. There are over 50 Thoroughbred horses per 10,000 of population in Ireland, compared to between three and five for Great Britain, France and the USA [19] . Thus, an investigation of ECoV in Ireland is pertinent not only to increase awareness nationally of the epidemiology of the virus and promote discussion on its clinical importance, but also to inform the industry globally of the health status of Irish horses. Ireland exports horses all over the world. By illustration, in 2016 the country was the second biggest seller of bloodstock at public auctions second only to the USA [19] .
Many questions remain with regard to the clinical significance of ECoV. The outbreak at a draft-horse racetrack in Japan in 2009 affected 132 of approximately 600 horses and resulted in non-starters and the implementation of movement restrictions [4] . However, draft horses appear to have a higher infection rate than other breeds and an outbreak of similar severity has not been reported in Thoroughbred racehorses [10, 20] . The much higher incidence of ECoV positive Thoroughbred foals identified in Kentucky compared to similar populations internationally suggests an increased susceptibility to ECoV infection in that population. In the past, specific environmental factors were associated with extensive reproductive loss in the Kentucky area and to a lesser extent in other states [21] , but predisposing regional factors such as differences in management, environment or husbandry have not been identified for ECoV. It has been suggested that ECoV is a coinfecting agent in foals with diarrhoea and clinical infections have predominantly been reported in adult horses with a mono-infection with EcoV [10] . There was no indication from the results of this study that coronavirus is a major cause of diarrhoea in Irish horses but the introduction of rRT-PCR as a routine diagnostic test will assist in elucidating the significance of this virus to the Irish breeding, racing and sports industries. The primary focus in future will be on testing adult horses that present with anorexia, lethargy, fever and changes in faecal character as a significant association has been demonstrated between this clinical status and molecular detection of ECoV in faeces [11] .
|
What is the difference between the Tokachi09 and Irish coronavirus genomic sequences?
| 2,126 |
148-nucleotide insertion
| 8,679 |
1,548 |
The First Detection of Equine Coronavirus in Adult Horses and Foals in Ireland
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6832964/
SHA: eee5a9068ade4c6776f189045115a90a5785e983
Authors: Nemoto, Manabu; Schofield, Warren; Cullinane, Ann
Date: 2019-10-14
DOI: 10.3390/v11100946
License: cc-by
Abstract: The objective of this study was to investigate the presence of equine coronavirus (ECoV) in clinical samples submitted to a diagnostic laboratory in Ireland. A total of 424 clinical samples were examined from equids with enteric disease in 24 Irish counties between 2011 and 2015. A real-time reverse transcription polymerase chain reaction was used to detect ECoV RNA. Nucleocapsid, spike and the region from the p4.7 to p12.7 genes of positive samples were sequenced, and sequence and phylogenetic analyses were conducted. Five samples (1.2%) collected in 2011 and 2013 tested positive for ECoV. Positive samples were collected from adult horses, Thoroughbred foals and a donkey foal. Sequence and/or phylogenetic analysis showed that nucleocapsid, spike and p12.7 genes were highly conserved and were closely related to ECoVs identified in other countries. In contrast, the region from p4.7 and the non-coding region following the p4.7 gene had deletions or insertions. The differences in the p4.7 region between the Irish ECoVs and other ECoVs indicated that the Irish viruses were distinguishable from those circulating in other countries. This is the first report of ECoV detected in both foals and adult horses in Ireland.
Text: Equine coronavirus (ECoV) is a positive-stranded RNA virus and belongs to the species Betacoronavirus 1 in the genus Betacoronavirus [1, 2] . The clinical signs associated with ECoV infection during outbreaks in the USA [3] and Japan [4] [5] [6] were fever, anorexia, lethargy and diarrhoea. The same clinical signs were also recorded in an experimental challenge study using Japanese draft horses [7] . The main transmission route is considered to be faecal-oral [7] and ECoV is usually detected in faecal samples. However, the molecular detection of ECoV in faeces from horses with diarrhoea, does not prove causation. Coronaviruses can cause both enteric and respiratory disease in many avian and mammalian species but ECoV is less likely to be found in respiratory secretions than in faeces [8, 9] .
Both molecular and seroepidemiology studies suggest that ECoV may be more prevalent in the USA than in other countries [10] . ECoV was detected in samples collected from equids in 48 states of the USA [11] . In central Kentucky, approximately 30% of both healthy and diarrheic Thoroughbred foals were infected with ECoV [12] . All of the qPCR positive foals with diarrhoea were co-infected with other pathogens such as rotavirus or Clostridium perfringens, suggesting that there was potential for ECoV to be over-diagnosed as a causative agent in complex diseases. In contrast in Japan, although an outbreak of diarrhoea occurred among ECoV-infected draft horses at one racecourse [4] [5] [6] , there have been no similar outbreaks subsequently, and all rectal swabs collected from diarrheic Thoroughbred foals were negative. Furthermore, only 2.5% of the rectal swabs collected from healthy foals in the largest Thoroughbred horse breeding region in Japan were positive for ECoV [13] . In France, 2.8% of 395 faecal samples and 0.5% of 200 respiratory samples collected in 58 counties tested positive for ECoV [9] . Similar to the reports from Japan and France, a low prevalence of ECoV was also observed in the UK [14] , Saudi Arabia and Oman [15] . The objective of this study was to investigate the presence of ECoV in clinical samples submitted to a diagnostic laboratory in Ireland. The samples were tested by real-time reverse transcription polymerase chain reaction (rRT-PCR) as it has been shown to be the most sensitive diagnostic method for ECoV [16] and is routinely employed as an alternative to virus isolation in diagnostic laboratories worldwide, both for timely diagnosis and in epidemiological studies [9, 10] . Virus isolation and biological characterisation were beyond the capacity of this study, which was similar in scope to that of the studies in horse populations in the USA, Europe and Asia [8, 9, 13, 14] . The rRT-PCR assay was performed as previously described using a primer set targeting the nucleocapsid (N) gene (ECoV-380f, ECoV-522r and ECoV-436p) [3] (Table 1) and AgPath-ID One-Step RT-PCR Kit (Thermo Fisher Scientific, MA, USA) according to the manufacturer's instructions. To prove that the extraction was successful and that there was no inhibition during rRT-PCR amplification, an internal positive control primer/probe (PrimerDesign, Southampton, UK) was added to the master mix. Thermal cycling conditions were; 48 • C for 10 min and 95 • C for 10 min, followed by 40 cycles at 94 • C for 15 s and 60 • C for 45 s. The SuperScript III One-Step RT-PCR System with Platinum Taq High Fidelity (Thermo Fisher Scientific, MA, USA) was used for sequencing analysis of two of the five ECoV samples identified. There was inadequate viral nucleic acid in the other three samples for sequencing. The primer sets used to amplify the nucleocapsid (N) gene [4] , the partial spike (S) gene [9] , and the region from the p4.7 to p12.7 genes of non-structural proteins (Oue, personal communication) are shown in Table 1 . The RT-PCR products were sequenced commercially by GATC Biotech (Cologne, Germany). Sequence analysis was performed using the BLAST and CLUSTALW programs, and Vector NTI Advance 11.5 software (Thermo Fisher Scientific, MA, USA). Phylogenetic analysis of nucleotide sequences was conducted with MEGA software Version 5.2 [17] . A phylogenetic tree was constructed based on nucleotide sequences of the K2+G (N gene) and TN93 (S gene) using the maximum likelihood method. MEGA software was used to select the optimal substitution models. Statistical analysis of the tree was performed with the bootstrap test (1000 replicates) for multiple alignments. The complete genome sequences of NC99 (EF446615) [2] , Tokachi09 (LC061272), Obihiro12-1 (LC061273) and Obihiro12-2 (LC061274) [1] , the N (AB671298) and S (AB671299) genes of Obihiro2004, the N gene of Hidaka-No.61/2012 (LC054263) and Hidaka-No.119/2012 (LC054264) [13] , the S gene of ECoV_FRA_2011/1 (KC178705), ECoV_FRA_2011/2 (KC178704), ECoV_FRA_2012/1 (KC178703), ECoV_FRA_2012/2 (KC178702) and ECoV_FRA_2012/3 (KC178701) [9] were used in sequence and/or phylogenetic analysis.
The accession numbers registered in GenBank/EMBL/DDBJ are as follows: the complete sequences of the N gene; 11V11708/IRL (LC149485) and 13V08313/IRL (LC149486), the partial sequences of the S gene; 11V11708/IRL (LC149487) and13V08313/IRL (LC149488) and the complete sequences from the p4.7 to p12.7 genes; 11V11708/IRL (LC149489) and13V08313/IRL (LC149490). One six-week-old foal was the only clinical case on a public Thoroughbred stud farm with approximately 30 mares when it presented with diarrhoea. Recovery took over three weeks during which it received fluid therapy, probiotics, antiulcer medication and antibiotics. The second foal was a 14-day-old filly, which had been hospitalised with diarrhoea two days prior to sample collection. The foal responded well to supportive treatment and at the time of sample collection, the diarrhoea had resolved. The five ECoV positive samples tested negative for equine rotavirus.
The nucleotide sequences of the complete N gene, the partial S gene and the region from the p4.7 to p12.7 genes of two positive samples (11V11708/IRL/2011 and 13V08313/IRL/2013) were determined. The nucleotide identities of the N and S genes of the two Irish ECoVs were 99.8% (1338/1341 nucleotides) and 99.5% (650/653 nucleotides), respectively. The nucleotide identities of the N gene of the two Irish ECoVs and the ECoVs from other continents are summarised in Table 2 .
Phylogenetic analysis was performed for the nucleotide sequences of the complete N and partial S genes (Figure 1 ). The analysis for the N gene showed that Irish ECoVs were independently clustered although they were closely related to Japanese viruses identified after 2009. In the phylogenetic tree of the S gene, Irish ECoVs were closely related to all other ECoVs analysed.
The length of the region from the p4.7 to p12.7 genes in the two viruses was 544 base pairs. Compared with NC99, Irish ECoVs, had a total of 37 nucleotide deletions within p4.7 and the non-coding region following the p4.7 gene. Compared with Obihiro 12-1 and 12-2, Irish ECoVs had a three-nucleotide insertion. When compared with Tokachi09, the Irish ECoVs had a 148-nucleotide insertion (see Figure S1 ). The p12.7 gene of the two Irish ECoVs did not have deletions or insertions, and the nucleotide identities were 98.8-99.7% between these viruses and the other ECoVs (NC99, Tokachi09, Obihiro12-1 and Obihiro12-2).
This study provides the first report of ECoV circulating in Ireland, the third European country with a significant horse industry where the virus has been detected in horses with enteric disease. However, detection of ECoV in faeces samples from horses with enteric disease does not prove
This study provides the first report of ECoV circulating in Ireland, the third European country with a significant horse industry where the virus has been detected in horses with enteric disease. However, detection of ECoV in faeces samples from horses with enteric disease does not prove causation. In this study, 424 samples collected between 2011 and 2015 from equids with enteric disease were tested, and only five samples (1.2%) were positive for ECoV. The inclusion of an internal positive control in the rRT-PCR eliminated the possibility of false negative results due to the presence of PCR inhibitors but the high content of nucleases associated with faeces samples may have caused some RNA degradation. However, this low prevalence of ECoV is similar to that identified in France [9] and among Thoroughbred foals in Japan [13] .
Although ECoV has been identified on three continents, little is known about the genetic and pathogenic diversity in field viruses. In this study, sequence and phylogenetic analysis (Figure 1 ) demonstrated a high level of homology between viruses detected in a donkey and a horse in two provinces in Ireland in different years. This suggests that Irish ECoVs may have low genetic diversity. Compared with the ECoVs of other countries, the N, S and p12.7 genes of the two Irish viruses were highly conserved. In contrast, the region from p4.7 and the non-coding region following the p4.7 gene had deletions or insertions ( Figure S1 ). Because of polymorphism in this region, this region could be useful for epidemiological investigation [5] . The differences in the p4.7 region between the Irish ECoVs and other ECoVs indicated that the viruses in Ireland may be distinguishable from those circulating in other countries. The positive samples were collected in November (1), March (1) and April (3) in this study. Higher case numbers are identified in the USA during the colder months (October to April) [11] , and our results were consistent with the circulation period in USA. It has been reported that outbreaks mainly occurred among adult riding, racing and show horses in USA [11] . The choice of cases to include in the current study may not have been optimal for detection of ECoV as the majority of samples were from foals. However, two positive samples were collected from adult horses in a combined riding school/show jumping yard in the West of Ireland. At the time of sample collection in April 2013, the monthly mean temperatures were below long-term average and in parts of the West, were the coldest in 24 years [18] . Cold weather may have been a predisposing factor to the ECoV infection on the farm.
Two positive samples were collected from Thoroughbred foals. A faeces sample collected from one foal with severe watery diarrhoea and inappetance was positive for ECoV but a sample collected three days later tested negative. A potential difficulty in detecting ECoV from naturally infected horses has been noted previously as serial samples from seven sick horses in the USA suggested that ECoV only persisted for three to nine days in faeces [3] . In both cases, the diarrhoea may have been caused by other unidentified coinfecting pathogens as has been suggested by investigators in the USA [12] . This is the first report of ECoV detection in faeces samples from both foals and adult horses in Ireland. The viruses identified in Ireland are genetically closely related to the Japanese viruses and the results of this study give no indication of significant genetic or phenotypic diversity. In recent years, there has been an increase in awareness and testing for ECoV in the USA and elsewhere [10] . Horse breeding and racing activities in Ireland are the most prominent and important of any country on a per capita basis. There are over 50 Thoroughbred horses per 10,000 of population in Ireland, compared to between three and five for Great Britain, France and the USA [19] . Thus, an investigation of ECoV in Ireland is pertinent not only to increase awareness nationally of the epidemiology of the virus and promote discussion on its clinical importance, but also to inform the industry globally of the health status of Irish horses. Ireland exports horses all over the world. By illustration, in 2016 the country was the second biggest seller of bloodstock at public auctions second only to the USA [19] .
Many questions remain with regard to the clinical significance of ECoV. The outbreak at a draft-horse racetrack in Japan in 2009 affected 132 of approximately 600 horses and resulted in non-starters and the implementation of movement restrictions [4] . However, draft horses appear to have a higher infection rate than other breeds and an outbreak of similar severity has not been reported in Thoroughbred racehorses [10, 20] . The much higher incidence of ECoV positive Thoroughbred foals identified in Kentucky compared to similar populations internationally suggests an increased susceptibility to ECoV infection in that population. In the past, specific environmental factors were associated with extensive reproductive loss in the Kentucky area and to a lesser extent in other states [21] , but predisposing regional factors such as differences in management, environment or husbandry have not been identified for ECoV. It has been suggested that ECoV is a coinfecting agent in foals with diarrhoea and clinical infections have predominantly been reported in adult horses with a mono-infection with EcoV [10] . There was no indication from the results of this study that coronavirus is a major cause of diarrhoea in Irish horses but the introduction of rRT-PCR as a routine diagnostic test will assist in elucidating the significance of this virus to the Irish breeding, racing and sports industries. The primary focus in future will be on testing adult horses that present with anorexia, lethargy, fever and changes in faecal character as a significant association has been demonstrated between this clinical status and molecular detection of ECoV in faeces [11] .
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What suggests that Irish equine coronaviruses may have a low genetic diversity?
| 2,127 |
high level of homology between viruses
| 10,276 |
1,548 |
The First Detection of Equine Coronavirus in Adult Horses and Foals in Ireland
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6832964/
SHA: eee5a9068ade4c6776f189045115a90a5785e983
Authors: Nemoto, Manabu; Schofield, Warren; Cullinane, Ann
Date: 2019-10-14
DOI: 10.3390/v11100946
License: cc-by
Abstract: The objective of this study was to investigate the presence of equine coronavirus (ECoV) in clinical samples submitted to a diagnostic laboratory in Ireland. A total of 424 clinical samples were examined from equids with enteric disease in 24 Irish counties between 2011 and 2015. A real-time reverse transcription polymerase chain reaction was used to detect ECoV RNA. Nucleocapsid, spike and the region from the p4.7 to p12.7 genes of positive samples were sequenced, and sequence and phylogenetic analyses were conducted. Five samples (1.2%) collected in 2011 and 2013 tested positive for ECoV. Positive samples were collected from adult horses, Thoroughbred foals and a donkey foal. Sequence and/or phylogenetic analysis showed that nucleocapsid, spike and p12.7 genes were highly conserved and were closely related to ECoVs identified in other countries. In contrast, the region from p4.7 and the non-coding region following the p4.7 gene had deletions or insertions. The differences in the p4.7 region between the Irish ECoVs and other ECoVs indicated that the Irish viruses were distinguishable from those circulating in other countries. This is the first report of ECoV detected in both foals and adult horses in Ireland.
Text: Equine coronavirus (ECoV) is a positive-stranded RNA virus and belongs to the species Betacoronavirus 1 in the genus Betacoronavirus [1, 2] . The clinical signs associated with ECoV infection during outbreaks in the USA [3] and Japan [4] [5] [6] were fever, anorexia, lethargy and diarrhoea. The same clinical signs were also recorded in an experimental challenge study using Japanese draft horses [7] . The main transmission route is considered to be faecal-oral [7] and ECoV is usually detected in faecal samples. However, the molecular detection of ECoV in faeces from horses with diarrhoea, does not prove causation. Coronaviruses can cause both enteric and respiratory disease in many avian and mammalian species but ECoV is less likely to be found in respiratory secretions than in faeces [8, 9] .
Both molecular and seroepidemiology studies suggest that ECoV may be more prevalent in the USA than in other countries [10] . ECoV was detected in samples collected from equids in 48 states of the USA [11] . In central Kentucky, approximately 30% of both healthy and diarrheic Thoroughbred foals were infected with ECoV [12] . All of the qPCR positive foals with diarrhoea were co-infected with other pathogens such as rotavirus or Clostridium perfringens, suggesting that there was potential for ECoV to be over-diagnosed as a causative agent in complex diseases. In contrast in Japan, although an outbreak of diarrhoea occurred among ECoV-infected draft horses at one racecourse [4] [5] [6] , there have been no similar outbreaks subsequently, and all rectal swabs collected from diarrheic Thoroughbred foals were negative. Furthermore, only 2.5% of the rectal swabs collected from healthy foals in the largest Thoroughbred horse breeding region in Japan were positive for ECoV [13] . In France, 2.8% of 395 faecal samples and 0.5% of 200 respiratory samples collected in 58 counties tested positive for ECoV [9] . Similar to the reports from Japan and France, a low prevalence of ECoV was also observed in the UK [14] , Saudi Arabia and Oman [15] . The objective of this study was to investigate the presence of ECoV in clinical samples submitted to a diagnostic laboratory in Ireland. The samples were tested by real-time reverse transcription polymerase chain reaction (rRT-PCR) as it has been shown to be the most sensitive diagnostic method for ECoV [16] and is routinely employed as an alternative to virus isolation in diagnostic laboratories worldwide, both for timely diagnosis and in epidemiological studies [9, 10] . Virus isolation and biological characterisation were beyond the capacity of this study, which was similar in scope to that of the studies in horse populations in the USA, Europe and Asia [8, 9, 13, 14] . The rRT-PCR assay was performed as previously described using a primer set targeting the nucleocapsid (N) gene (ECoV-380f, ECoV-522r and ECoV-436p) [3] (Table 1) and AgPath-ID One-Step RT-PCR Kit (Thermo Fisher Scientific, MA, USA) according to the manufacturer's instructions. To prove that the extraction was successful and that there was no inhibition during rRT-PCR amplification, an internal positive control primer/probe (PrimerDesign, Southampton, UK) was added to the master mix. Thermal cycling conditions were; 48 • C for 10 min and 95 • C for 10 min, followed by 40 cycles at 94 • C for 15 s and 60 • C for 45 s. The SuperScript III One-Step RT-PCR System with Platinum Taq High Fidelity (Thermo Fisher Scientific, MA, USA) was used for sequencing analysis of two of the five ECoV samples identified. There was inadequate viral nucleic acid in the other three samples for sequencing. The primer sets used to amplify the nucleocapsid (N) gene [4] , the partial spike (S) gene [9] , and the region from the p4.7 to p12.7 genes of non-structural proteins (Oue, personal communication) are shown in Table 1 . The RT-PCR products were sequenced commercially by GATC Biotech (Cologne, Germany). Sequence analysis was performed using the BLAST and CLUSTALW programs, and Vector NTI Advance 11.5 software (Thermo Fisher Scientific, MA, USA). Phylogenetic analysis of nucleotide sequences was conducted with MEGA software Version 5.2 [17] . A phylogenetic tree was constructed based on nucleotide sequences of the K2+G (N gene) and TN93 (S gene) using the maximum likelihood method. MEGA software was used to select the optimal substitution models. Statistical analysis of the tree was performed with the bootstrap test (1000 replicates) for multiple alignments. The complete genome sequences of NC99 (EF446615) [2] , Tokachi09 (LC061272), Obihiro12-1 (LC061273) and Obihiro12-2 (LC061274) [1] , the N (AB671298) and S (AB671299) genes of Obihiro2004, the N gene of Hidaka-No.61/2012 (LC054263) and Hidaka-No.119/2012 (LC054264) [13] , the S gene of ECoV_FRA_2011/1 (KC178705), ECoV_FRA_2011/2 (KC178704), ECoV_FRA_2012/1 (KC178703), ECoV_FRA_2012/2 (KC178702) and ECoV_FRA_2012/3 (KC178701) [9] were used in sequence and/or phylogenetic analysis.
The accession numbers registered in GenBank/EMBL/DDBJ are as follows: the complete sequences of the N gene; 11V11708/IRL (LC149485) and 13V08313/IRL (LC149486), the partial sequences of the S gene; 11V11708/IRL (LC149487) and13V08313/IRL (LC149488) and the complete sequences from the p4.7 to p12.7 genes; 11V11708/IRL (LC149489) and13V08313/IRL (LC149490). One six-week-old foal was the only clinical case on a public Thoroughbred stud farm with approximately 30 mares when it presented with diarrhoea. Recovery took over three weeks during which it received fluid therapy, probiotics, antiulcer medication and antibiotics. The second foal was a 14-day-old filly, which had been hospitalised with diarrhoea two days prior to sample collection. The foal responded well to supportive treatment and at the time of sample collection, the diarrhoea had resolved. The five ECoV positive samples tested negative for equine rotavirus.
The nucleotide sequences of the complete N gene, the partial S gene and the region from the p4.7 to p12.7 genes of two positive samples (11V11708/IRL/2011 and 13V08313/IRL/2013) were determined. The nucleotide identities of the N and S genes of the two Irish ECoVs were 99.8% (1338/1341 nucleotides) and 99.5% (650/653 nucleotides), respectively. The nucleotide identities of the N gene of the two Irish ECoVs and the ECoVs from other continents are summarised in Table 2 .
Phylogenetic analysis was performed for the nucleotide sequences of the complete N and partial S genes (Figure 1 ). The analysis for the N gene showed that Irish ECoVs were independently clustered although they were closely related to Japanese viruses identified after 2009. In the phylogenetic tree of the S gene, Irish ECoVs were closely related to all other ECoVs analysed.
The length of the region from the p4.7 to p12.7 genes in the two viruses was 544 base pairs. Compared with NC99, Irish ECoVs, had a total of 37 nucleotide deletions within p4.7 and the non-coding region following the p4.7 gene. Compared with Obihiro 12-1 and 12-2, Irish ECoVs had a three-nucleotide insertion. When compared with Tokachi09, the Irish ECoVs had a 148-nucleotide insertion (see Figure S1 ). The p12.7 gene of the two Irish ECoVs did not have deletions or insertions, and the nucleotide identities were 98.8-99.7% between these viruses and the other ECoVs (NC99, Tokachi09, Obihiro12-1 and Obihiro12-2).
This study provides the first report of ECoV circulating in Ireland, the third European country with a significant horse industry where the virus has been detected in horses with enteric disease. However, detection of ECoV in faeces samples from horses with enteric disease does not prove
This study provides the first report of ECoV circulating in Ireland, the third European country with a significant horse industry where the virus has been detected in horses with enteric disease. However, detection of ECoV in faeces samples from horses with enteric disease does not prove causation. In this study, 424 samples collected between 2011 and 2015 from equids with enteric disease were tested, and only five samples (1.2%) were positive for ECoV. The inclusion of an internal positive control in the rRT-PCR eliminated the possibility of false negative results due to the presence of PCR inhibitors but the high content of nucleases associated with faeces samples may have caused some RNA degradation. However, this low prevalence of ECoV is similar to that identified in France [9] and among Thoroughbred foals in Japan [13] .
Although ECoV has been identified on three continents, little is known about the genetic and pathogenic diversity in field viruses. In this study, sequence and phylogenetic analysis (Figure 1 ) demonstrated a high level of homology between viruses detected in a donkey and a horse in two provinces in Ireland in different years. This suggests that Irish ECoVs may have low genetic diversity. Compared with the ECoVs of other countries, the N, S and p12.7 genes of the two Irish viruses were highly conserved. In contrast, the region from p4.7 and the non-coding region following the p4.7 gene had deletions or insertions ( Figure S1 ). Because of polymorphism in this region, this region could be useful for epidemiological investigation [5] . The differences in the p4.7 region between the Irish ECoVs and other ECoVs indicated that the viruses in Ireland may be distinguishable from those circulating in other countries. The positive samples were collected in November (1), March (1) and April (3) in this study. Higher case numbers are identified in the USA during the colder months (October to April) [11] , and our results were consistent with the circulation period in USA. It has been reported that outbreaks mainly occurred among adult riding, racing and show horses in USA [11] . The choice of cases to include in the current study may not have been optimal for detection of ECoV as the majority of samples were from foals. However, two positive samples were collected from adult horses in a combined riding school/show jumping yard in the West of Ireland. At the time of sample collection in April 2013, the monthly mean temperatures were below long-term average and in parts of the West, were the coldest in 24 years [18] . Cold weather may have been a predisposing factor to the ECoV infection on the farm.
Two positive samples were collected from Thoroughbred foals. A faeces sample collected from one foal with severe watery diarrhoea and inappetance was positive for ECoV but a sample collected three days later tested negative. A potential difficulty in detecting ECoV from naturally infected horses has been noted previously as serial samples from seven sick horses in the USA suggested that ECoV only persisted for three to nine days in faeces [3] . In both cases, the diarrhoea may have been caused by other unidentified coinfecting pathogens as has been suggested by investigators in the USA [12] . This is the first report of ECoV detection in faeces samples from both foals and adult horses in Ireland. The viruses identified in Ireland are genetically closely related to the Japanese viruses and the results of this study give no indication of significant genetic or phenotypic diversity. In recent years, there has been an increase in awareness and testing for ECoV in the USA and elsewhere [10] . Horse breeding and racing activities in Ireland are the most prominent and important of any country on a per capita basis. There are over 50 Thoroughbred horses per 10,000 of population in Ireland, compared to between three and five for Great Britain, France and the USA [19] . Thus, an investigation of ECoV in Ireland is pertinent not only to increase awareness nationally of the epidemiology of the virus and promote discussion on its clinical importance, but also to inform the industry globally of the health status of Irish horses. Ireland exports horses all over the world. By illustration, in 2016 the country was the second biggest seller of bloodstock at public auctions second only to the USA [19] .
Many questions remain with regard to the clinical significance of ECoV. The outbreak at a draft-horse racetrack in Japan in 2009 affected 132 of approximately 600 horses and resulted in non-starters and the implementation of movement restrictions [4] . However, draft horses appear to have a higher infection rate than other breeds and an outbreak of similar severity has not been reported in Thoroughbred racehorses [10, 20] . The much higher incidence of ECoV positive Thoroughbred foals identified in Kentucky compared to similar populations internationally suggests an increased susceptibility to ECoV infection in that population. In the past, specific environmental factors were associated with extensive reproductive loss in the Kentucky area and to a lesser extent in other states [21] , but predisposing regional factors such as differences in management, environment or husbandry have not been identified for ECoV. It has been suggested that ECoV is a coinfecting agent in foals with diarrhoea and clinical infections have predominantly been reported in adult horses with a mono-infection with EcoV [10] . There was no indication from the results of this study that coronavirus is a major cause of diarrhoea in Irish horses but the introduction of rRT-PCR as a routine diagnostic test will assist in elucidating the significance of this virus to the Irish breeding, racing and sports industries. The primary focus in future will be on testing adult horses that present with anorexia, lethargy, fever and changes in faecal character as a significant association has been demonstrated between this clinical status and molecular detection of ECoV in faeces [11] .
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Where have most outbreaks of equine coronavirus occurred in the United States?
| 2,128 |
adult riding, racing and show horses
| 11,305 |
1,574 |
Population-Based Pertussis Incidence and Risk Factors in Infants Less Than 6 Months in Nepal
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5907881/
SHA: ef821e34873d4752ecae41cd9dfc08a5e6db45e2
Authors: Hughes, Michelle M; Englund, Janet A; Kuypers, Jane; Tielsch, James M; Khatry, Subarna K; Shrestha, Laxman; LeClerq, Steven C; Steinhoff, Mark; Katz, Joanne
Date: 2017-03-01
DOI: 10.1093/jpids/piw079
License: cc-by
Abstract: BACKGROUND: Pertussis is estimated to cause 2 percent of childhood deaths globally and is a growing public health problem in developed countries despite high vaccination coverage. Infants are at greatest risk of morbidity and mortality. Maternal vaccination during pregnancy may be effective to prevent pertussis in young infants, but population-based estimates of disease burden in infants are lacking, particularly in low-income countries. The objective of this study was to estimate the incidence of pertussis in infants less than 6 months of age in Sarlahi District, Nepal. METHODS: Nested within a population-based randomized controlled trial of influenza vaccination during pregnancy, infants were visited weekly from birth through 6 months to assess respiratory illness in the prior week. If any respiratory symptoms had occurred, a nasal swab was collected and tested with a multitarget pertussis polymerase chain reaction (PCR) assay. The prospective cohort study includes infants observed between May 2011 and August 2014. RESULTS: The incidence of PCR-confirmed Bordetella pertussis was 13.3 cases per 1000 infant-years (95% confidence interval, 7.7–21.3) in a cohort of 3483 infants with at least 1 day of follow-up. CONCLUSIONS: In a population-based active home surveillance for respiratory illness, a low risk for pertussis was estimated among infants in rural Nepal. Nepal’s immunization program, which includes a childhood whole cell pertussis vaccine, may be effective in controlling pertussis in infants.
Text: A resurgence of pertussis across age groups has occurred in several countries in recent years [1] . Middle-and high-income countries that use an acellular pertussis vaccine for the primary vaccination series have been particularly affected [2, 3] , and infants and adolescents have experienced the greatest increase [4] . Factors that may contribute to the increased risk of pertussis include rapidly waning immunity from those vaccinated with acellular vaccines [1, 5, 6] , asymptomatic transmission from individuals vaccinated with acellular vaccines [7] , genetic adaption of Bordetella pertussis [8] , vaccination delay or refusal [9] , improved surveillance and laboratory capabilities [2] , and overall increased awareness of the continuing circulation of B pertussis [1] . Some countries experiencing epidemic pertussis, including the United States, United Kingdom, and Argentina, now recommend pertussis immunization in pregnancy and vaccination of close contacts [10, 11] to protect the youngest infants from pertussis before they can be vaccinated themselves [12] . Recent data from maternal vaccination trials demonstrate the ability of antibodies to be transferred from mothers to their infants in pregnancy and their persistence in infants [13] .
Global estimates of pertussis show the highest childhood burden in Southeast Asia [14] . In this region, maternal pertussis vaccination during pregnancy may be a way to protect infants, similar to the approach using tetanus toxoid vaccine. However, globally only 1 population-based estimate of pertussis in infants from birth has been conducted (Senegal) [15] , and surveillance and laboratory capabilities in Asia are lacking [16, 17] . The World Health Organization (WHO) recently recommended that countries using whole cell pertussis vaccines continue to do so in light of recent data indicating that acellular pertussis vaccines are less effective than whole cell pertussis vaccines [18] . Population-based data are needed, especially in low-income settings, to provide a more accurate estimate of the burden of pertussis in infants to inform childhood and maternal immunization policies [19, 20] .
We report on a prospective cohort study following infants weekly in their homes to monitor for pertussis disease from birth to age 6 months. The objective was to provide a population-based estimate of laboratory-confirmed pertussis incidence in infants less than 6 months of age in the Sarlahi District, Nepal.
The study was nested within 2 consecutive randomized controlled trials of maternal influenza vaccination during pregnancy set in the Sarlahi District, located in the central Terai (low-lying plains) region of Nepal [21] . At the start of the trial, prevalent pregnancies were identified through a census of all households in the catchment area. For the duration of the trial, field workers visited all households in the communities, every 5 weeks, where married women (15-40 years) resided, for surveillance of incident pregnancies. Once a pregnancy was identified, women provided consent and were enrolled. From April 25, 2011 through September 9, 2013, women between 17 and 34 weeks gestation were randomized and vaccinated with either an influenza vaccine or placebo. The study was a population-based prospective cohort of infants followed from birth through 6 months postpartum. Approval for the study was obtained from the Institutional Review Boards at the Johns Hopkins Bloomberg School of Public Health, Cincinnati Children's Medical Center, the Institute of Medicine at Tribhuvan University, Kathmandu, and the Nepal Health Research Council. The trials are registered at Clinicaltrials.gov (NCT01034254).
At baseline, information was collected on household structure, socioeconomic status, and demographics. At enrollment, date of last menstrual period and pregnancy history data were collected. As soon as possible after delivery, the mother and infant were visited to collect detailed birth information including infant weight and breastfeeding status. From birth through 6 months, postpartum infants were visited weekly by a field worker, who recorded any infant respiratory symptoms in the past 7 days. If an infant had any of the following symptoms, a mid-nasal nylon flocked swab was collected: fever, cough, wheeze, difficulty breathing, or ear infection. Starting on August 17, 2012, new symptoms, more specific for pertussis, were added to the weekly morbidity visit: apnea, cyanosis, cough with vomit, or whoop/whooping cough. The swabs were stored for up to 1 week at room temperature in PrimeStore Molecular Transport Medium (Longhorn Diagnostics LLC, Bethesda, MD). In addition to these signs, mothers were asked which, if any, infant vaccinations were received in the past 7 days, including pertussis vaccination [22] . Mid-nasal swabs were also collected on a weekly basis from mothers from enrollment through 6 months postpartum who reported fever plus one additional morbidity (cough, sore throat, nasal congestion, or myalgia). All nasal swabs collected from infants were tested for B pertussis, Bordetella parapertussis, and Bordetella bronchispetica. Only the nasal swabs of mothers whose infants tested positive for any of these pathogens were tested for the same pathogens.
Real-time polymerase chain reaction (PCR) testing was conducted at the University of Washington's Molecular Virology Laboratory according to previously published methods [23] . Two-target PCR was used to assess the presence of 3 Bordetella species: B pertussis, B parapertussis, and B bronchiseptica. The amplified targets were chromosomal repeated insertion sequence IS481 (IS) and the polymorphic pertussis toxin ptxA promoter region (PT).
After amplification, the melting points of the amplicons were measured in an iCycler (Bio-Rad). A sample was interpreted as positive when the target(s) had a melting temperature within the species-specific acceptable range and a computed tomography ≤42. A sample was negative if none of the targets tested positive or a single positive target was not reproducible. Maternal nasal swabs were tested for those mothers whose infants tested positive for any Bordetella species
Polymerase chain reaction was also performed for several viral infections (influenza, rhinovirus [RV], respiratory syncytial virus [RSV], bocavirus [BoV], human metapneumovirus, coronavirus, adenovirus, and parainfluenza [1] [2] [3] [4] ) as previously described [21] .
Of 3693 women enrolled, 3646 infants were live born to 3621 women (Supplementary Figure 1 ). Infants were included in this analysis if they were followed for any length of the follow-up period (0 to 180 days); median total follow-up was 146 days per infant (Supplementary Figure 2) . The final dataset consists of 3483 infants, contributing 1280 infant-years of observation, with at least 1 follow-up visit during the first 6 months. This includes infants from the entire trial period, both before and after more pertussis-specific additions to the weekly symptom questionnaire.
At baseline, data on household structure were gathered. At enrollment, women reported their literacy status (binary) and pregnancy history. The field workers identified their ethnicity into 2 broad groups (Pahadi, a group originating from the hills; or Madeshi, a group originating from north India) from names and observation. Women were categorized as nulliparous or multiparous. Responses to 25 questions about household construction, water and sanitation, and household assets were used to develop an index to measure the socioeconomic status of households. Binary variables for each of the 25 questions and a mean SES score were calculated for each household.
Gestational age was measured using a woman's report of date of last menstrual period during pregnancy surveillance. Birth weight was collected as soon as possible after birth using a digital scale (Tanita model BD-585, precision to nearest 10 grams). Birth weights collected >72 hours after birth were excluded from the analysis. Small for gestational age (SGA) was calculated using the sex-specific 10th percentile cutoff described by Alexander et al [24] and the INTERGROWTH-21 standards [25] . Women were asked within how many hours of birth breastfeeding was initiated and binary breastfeeding categories were created (≤1 hour versus >1 hour postdelivery).
Incidence was calculated as the number of pertussis cases per 1000 infant-years at risk. Poisson exact 95% confidence intervals (CIs) were constructed. Characteristics of infant pertussis cases were compared with nonpertussis cases using bivariate Poisson regression. Characteristics of all pertussis respiratory episodes were compared with nonpertussis respiratory episodes; t tests were used for continuous predictors and Fisher's exact tests were used for categorical associations due to the low number of pertussis episodes. All statistical analyses were conducted in Stata/SE 14.1.
A total of 3483 infants had 4283 episodes of respiratory illness between May 18, 2011 and April 30, 2014. Thirty-nine percent (n = 1350) of infants experienced no respiratory episodes. The incidence of respiratory illness was 3.6 episodes per infant-year (95% CI, 3.5-3.7). Mean episode duration was 4.7 days (95% CI, 4.6-4.9). A total of 3930 (92%) episodes were matched to 1 or more pertussis-tested nasal swabs from 2026 infants (Supplementary Figure 1) .
Seventeen cases of B pertussis were identified from 19 nasal swabs (nasal swabs were positive on 2 consecutive weeks for 2 infants). The incidence of PCR-confirmed B pertussis was 13.3 cases per 1000-infant years (95% CI, 7.7-21.3). Five cases of B parapertussis were detected with an incidence of 3.9 cases per 1000 infant-years (95% CI, 1.3-9.1). No cases of B bronchiseptica were identified.
The average pertussis episode duration was 8 days (range, 2-33) ( Table 1 ). Mean age of onset of symptoms was 83 days (range, 19-137) (median, 80; interquartile range, 63-109). The most common symptoms were cough, difficulty breathing, and cough with vomit. None of the additional symptoms related to pertussis that were added in year 2 (cyanosis, apnea, cough with vomit, and whoop) resulted in collection of nasal swabs based solely on these additional symptoms. Pertussis episodes were statistically significantly more likely to include difficulty breathing, cough with vomit, and whoop compared with other respiratory illness. Six infants had at least 1 pertussis vaccination before pertussis disease onset (three <2 weeks and three >2 weeks before pertussis illness) with a mean of 18 days from vaccination to illness compared with 49 days for nonpertussis episodes (P = .03). Five infants received their first pertussis vaccination postpertussis disease onset, whereas 6 infants received no pertussis vaccination in the first 180 days. Three fourths of pertussis episodes were coinfected with at least 1 virus, with RV and BoV the most common. Cases of pertussis were more likely to be infected with BoV than respiratory cases due to causes other than pertussis. The majority of cases occurred between February 2013 and January 2014 (Figure 1) .
No statistically significant differences between risk factors for pertussis and nonpertussis cases ( Table 2) were documented. Given the low number of pertussis cases, the lack of a statistical association is not evidence of nonassociation. No deaths occurred in infants who had pertussis. Of the 8 mothers of B pertussis-positive infants who had a nasal swab collected (14 nasal swabs total) during their own follow-up, none were positive for any pertussis species.
The 5 B parapertussis cases were primarily male whose mothers were primiparous, literate, and Pahadi ethnicity (Supplementary Table 1 ). No mothers of infants who had B parapertussis had a nasal swab collected during follow-up.
The average B parapertussis episode duration was 4 days (Supplementary Table 2 ). Mean age of onset of symptoms was 58 days with a range of 7-95 days. The most common symptoms were cough and wheeze. Rhinovirus and RSV were the only coinfections observed. All B parapertussis cases occurred between September 2011 and February 2012 ( Figure 1 ).
A low incidence of pertussis and generally mild clinical presentation were found in infants <6 months in Nepal. To our knowledge, this represents one of the first population-based active surveillance of PCR-confirmed pertussis among young infants in Asia. Acellular pertussis vaccine trials conducted in the 1990s found the average pertussis incidence in the whole cell vaccine groups ranged from 1 to 37 cases per 1000 infantyears [26] . Our finding of 13 B pertussis cases per 1000 infantyears was on the lower end of this range. In the United States in 2014, the estimated pertussis incidence in infants less than 6 months was 2 cases per 1000 infant-years [27] , much lower than observed in our study; however, this passive surveillance system likely vastly underestimates pertussis incidence. Thus, there is a need for active surveillance data such as ours. Furthermore, given our highly sensitive case detection method, many of our pertussis cases would likely not have been detected in the previous acellular pertussis vaccine trials. More stringent respiratory symptom criteria would have lowered our incidence estimate even further. The low incidence was found in a population where pentavalent vaccine (Pentavac: Diphtheria, Tetanus, Pertussis [Whole Cell], Hepatitis-B and Haemophilus Type b Conjugate Vaccine; Serum Institute of India Pvt. Ltd), scheduled for administration at 6, 10, and 14 weeks, is received with significant delays (7% of infants received all 3 recommended pertussis vaccines by 6 months) [22] . These data support the WHO's recommendation that countries using whole cell pertussis vaccine continue to do so given that the majority of outbreaks have been concentrated in countries using the acellular pertussis vaccine [2] . Recent studies suggest that protection from acellular pertussis vaccine is not as strong or long lasting as that conferred by the whole cell pertussis vaccine [6, 28] .
Another contributing factor to the low pertussis incidence observed could be that surveillance was conducted during a period of low pertussis transmission. Pertussis is a cyclical disease, thought to peak every 2 to 4 years, and we may have captured the burden at a low circulation period [6] . We observed over 70% of our B pertussis cases over a 1-year period. This increase from earlier observation periods could indicate a temporary rise in pertussis consistent with its cyclical pattern or a true increase in the baseline burden. Previous research on pertussis seasonality has in different places and time periods demonstrated various periods of peak transmission or no discernable patterns [29, 30] . Although our data do not support a seasonal pattern, the numbers observed are too low to be conclusive.
Pertussis symptom duration and severity were mild compared with the classic pertussis case presentation. Only 3 of the 17 cases fulfilled the WHO criteria, which requires a minimum of 2 weeks of cough, whoop, or posttussive vomiting [31] . Studies on pertussis in infants have generally been clinic-based, hospital-based, or in an outbreak, which therefore required a certain severity of illness for parents to recognize a need for medical attention [29, 30, 32] . These study designs and passive surveillance efforts therefore may have missed milder pertussis cases [33] . Our study, which required only 1 respiratory symptom for a nasal swab to be collected, had increased sensitivity to detect a range of pertussis case presentations. An alternative explanation for the mild cases seen could be an increase in the proportion of mild compared with severe pertussis cases in Nepal.
Although cough, difficulty breathing, and cough with vomit were the most common symptoms, no symptom was present in all B pertussis cases. During an epidemic period in Washington state, among infants <1 year, who had a minimum of 14 days cough plus an additional symptom, 82% had posttussive emesis, 29% had apnea, 26% had whoop, and 42% had cyanosis [32] . A study of US neonates with pertussis showed the symptom prevalence to be 97% for cough, 91% for cyanosis, 58% for apnea, and 3% for fever [34] . Our study found lower or equal symptom prevalence with the exception of fever. Fever prevalence was higher in our study, similar to that found in Peru [29] .
Although not statistically significant, infants with pertussis were more likely to have been born preterm, low birth weight, and SGA, and their mothers were more likely to be primiparous. These findings are similar to previous studies showing no difference in pertussis cases by sex [29, 35, 36] or crowding [35] but showing differences by birth weight [36] . Coinfections were common, consistent with findings from other hospital-based studies [33] . Codetection of B pertussis and B parapertussis with respiratory viruses may be due to asymptomatic pertussis carriage. The incidence of B parapertussis of 4 cases per 1000 person-years was comparable to that of 2 per 1000 person-years found in the Italian acellular pertussis vaccine trial in 1992-1993 [37] . The duration of illness was shorter for B parapertussis with a maximum duration of 6 days compared with a maximum of 33 days for B pertussis. A milder presentation is consistent with clinical knowledge of B parapertussis infection [37, 38] . Bordetella parapertussis cases occurred only during a 5-month period.
There were several study design limitations. We cannot be certain whether the reported symptoms were caused by pertussis, another organism, or whether symptoms were related to 2 or more etiologic agents. We were unable to perform multivariate regression modeling for characteristics associated with pertussis disease and pertussis cases due to the small number of cases we detected.
Infant respiratory symptoms were reported by parents, who may have missed signs that might have been observed by a healthcare worker. However, the criteria for collection of the nasal swab were broad and did not require sophisticated clinical skills. However, apnea and cyanosis may have been difficult for parents to identify. Although the criteria for specimen collection changed in year 2, no infant experienced a pertussis-specific symptom in isolation without also having one of the originally specified respiratory symptoms. These data support our assumption that we were unlikely to have missed pertussis cases in year 1 with our less sensitive respiratory symptom criteria.
Nasal swabs were collected in the mid-nasal region for influenza virus detection, which may have lowered the sensitivity of pertussis detection. In a field site, the acceptability of an additional nasopharyngeal swab would likely have increased the participant refusal rate. This would have decreased the generalizability of our results to the entire population. Although nasopharyngeal swabs or nasopharyngeal aspirates are the recommended specimen collection method [39] , the nasopharyngeal region was established as the collection area of choice when the diagnostic measure was culture, which has low sensitivity. Recent data demonstrated the comparability of using mid-nasal versus nasopharyngeal swabs in PCR pertussis detection [40] .
Strengths of the study included being a population-based, prospective study, with very low refusal rates. Risk factors, clinical symptoms, and coinfections were prospectively identified without the potential bias that may occur when these data are collected retrospectively or in clinical settings. The community-based design allows generalizability of these results to the entire population and not just those seeking care at a health facility or in an outbreak situation. The Sarlahi District is located in the Terai region where the majority of Nepalese reside, and it has similar demographics to the entire population of Nepal [41] . Sarlahi's location near sea level and on the border with India supports the generalizability of these results to many populations living on the Indian subcontinent. The weekly active surveillance with sensitive criteria for pertussis testing was able to detect mild and atypical pertussis cases, which may have been missed by previous traditional surveillance. The multitarget PCR method allowed highly sensitive and specific detection of 2 additional Bordetella species beyond the primary B pertussis target.
We observed a low incidence of pertussis in infants in a whole cell vaccine environment. Pertussis cases were generally milder than expected compared with traditional pertussis clinical definitions. These data support clinicians considering pertussis in their differential diagnosis of infants with mild respiratory symptoms. Policymakers in Nepal will need to weigh the benefit of an additional prenatal pertussis vaccine or a switch to acellular primary pertussis vaccine with the low burden of pertussis in infants less than 6 months. Our study demonstrated that mid-nasal swabs were able to detect pertussis using a sensitive multitarget PCR. The less invasive mid-nasal nasal swab is an attractive alternative for pertussis nasal swab collection, and further research is needed to compare this collection site with nasopharyngeal swabs. In the future, this method may enhance population-based surveillance efforts.
|
What kind of pertussis vaccine is used in middle and high income countries?
| 2,167 |
acellular
| 2,107 |
1,574 |
Population-Based Pertussis Incidence and Risk Factors in Infants Less Than 6 Months in Nepal
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5907881/
SHA: ef821e34873d4752ecae41cd9dfc08a5e6db45e2
Authors: Hughes, Michelle M; Englund, Janet A; Kuypers, Jane; Tielsch, James M; Khatry, Subarna K; Shrestha, Laxman; LeClerq, Steven C; Steinhoff, Mark; Katz, Joanne
Date: 2017-03-01
DOI: 10.1093/jpids/piw079
License: cc-by
Abstract: BACKGROUND: Pertussis is estimated to cause 2 percent of childhood deaths globally and is a growing public health problem in developed countries despite high vaccination coverage. Infants are at greatest risk of morbidity and mortality. Maternal vaccination during pregnancy may be effective to prevent pertussis in young infants, but population-based estimates of disease burden in infants are lacking, particularly in low-income countries. The objective of this study was to estimate the incidence of pertussis in infants less than 6 months of age in Sarlahi District, Nepal. METHODS: Nested within a population-based randomized controlled trial of influenza vaccination during pregnancy, infants were visited weekly from birth through 6 months to assess respiratory illness in the prior week. If any respiratory symptoms had occurred, a nasal swab was collected and tested with a multitarget pertussis polymerase chain reaction (PCR) assay. The prospective cohort study includes infants observed between May 2011 and August 2014. RESULTS: The incidence of PCR-confirmed Bordetella pertussis was 13.3 cases per 1000 infant-years (95% confidence interval, 7.7–21.3) in a cohort of 3483 infants with at least 1 day of follow-up. CONCLUSIONS: In a population-based active home surveillance for respiratory illness, a low risk for pertussis was estimated among infants in rural Nepal. Nepal’s immunization program, which includes a childhood whole cell pertussis vaccine, may be effective in controlling pertussis in infants.
Text: A resurgence of pertussis across age groups has occurred in several countries in recent years [1] . Middle-and high-income countries that use an acellular pertussis vaccine for the primary vaccination series have been particularly affected [2, 3] , and infants and adolescents have experienced the greatest increase [4] . Factors that may contribute to the increased risk of pertussis include rapidly waning immunity from those vaccinated with acellular vaccines [1, 5, 6] , asymptomatic transmission from individuals vaccinated with acellular vaccines [7] , genetic adaption of Bordetella pertussis [8] , vaccination delay or refusal [9] , improved surveillance and laboratory capabilities [2] , and overall increased awareness of the continuing circulation of B pertussis [1] . Some countries experiencing epidemic pertussis, including the United States, United Kingdom, and Argentina, now recommend pertussis immunization in pregnancy and vaccination of close contacts [10, 11] to protect the youngest infants from pertussis before they can be vaccinated themselves [12] . Recent data from maternal vaccination trials demonstrate the ability of antibodies to be transferred from mothers to their infants in pregnancy and their persistence in infants [13] .
Global estimates of pertussis show the highest childhood burden in Southeast Asia [14] . In this region, maternal pertussis vaccination during pregnancy may be a way to protect infants, similar to the approach using tetanus toxoid vaccine. However, globally only 1 population-based estimate of pertussis in infants from birth has been conducted (Senegal) [15] , and surveillance and laboratory capabilities in Asia are lacking [16, 17] . The World Health Organization (WHO) recently recommended that countries using whole cell pertussis vaccines continue to do so in light of recent data indicating that acellular pertussis vaccines are less effective than whole cell pertussis vaccines [18] . Population-based data are needed, especially in low-income settings, to provide a more accurate estimate of the burden of pertussis in infants to inform childhood and maternal immunization policies [19, 20] .
We report on a prospective cohort study following infants weekly in their homes to monitor for pertussis disease from birth to age 6 months. The objective was to provide a population-based estimate of laboratory-confirmed pertussis incidence in infants less than 6 months of age in the Sarlahi District, Nepal.
The study was nested within 2 consecutive randomized controlled trials of maternal influenza vaccination during pregnancy set in the Sarlahi District, located in the central Terai (low-lying plains) region of Nepal [21] . At the start of the trial, prevalent pregnancies were identified through a census of all households in the catchment area. For the duration of the trial, field workers visited all households in the communities, every 5 weeks, where married women (15-40 years) resided, for surveillance of incident pregnancies. Once a pregnancy was identified, women provided consent and were enrolled. From April 25, 2011 through September 9, 2013, women between 17 and 34 weeks gestation were randomized and vaccinated with either an influenza vaccine or placebo. The study was a population-based prospective cohort of infants followed from birth through 6 months postpartum. Approval for the study was obtained from the Institutional Review Boards at the Johns Hopkins Bloomberg School of Public Health, Cincinnati Children's Medical Center, the Institute of Medicine at Tribhuvan University, Kathmandu, and the Nepal Health Research Council. The trials are registered at Clinicaltrials.gov (NCT01034254).
At baseline, information was collected on household structure, socioeconomic status, and demographics. At enrollment, date of last menstrual period and pregnancy history data were collected. As soon as possible after delivery, the mother and infant were visited to collect detailed birth information including infant weight and breastfeeding status. From birth through 6 months, postpartum infants were visited weekly by a field worker, who recorded any infant respiratory symptoms in the past 7 days. If an infant had any of the following symptoms, a mid-nasal nylon flocked swab was collected: fever, cough, wheeze, difficulty breathing, or ear infection. Starting on August 17, 2012, new symptoms, more specific for pertussis, were added to the weekly morbidity visit: apnea, cyanosis, cough with vomit, or whoop/whooping cough. The swabs were stored for up to 1 week at room temperature in PrimeStore Molecular Transport Medium (Longhorn Diagnostics LLC, Bethesda, MD). In addition to these signs, mothers were asked which, if any, infant vaccinations were received in the past 7 days, including pertussis vaccination [22] . Mid-nasal swabs were also collected on a weekly basis from mothers from enrollment through 6 months postpartum who reported fever plus one additional morbidity (cough, sore throat, nasal congestion, or myalgia). All nasal swabs collected from infants were tested for B pertussis, Bordetella parapertussis, and Bordetella bronchispetica. Only the nasal swabs of mothers whose infants tested positive for any of these pathogens were tested for the same pathogens.
Real-time polymerase chain reaction (PCR) testing was conducted at the University of Washington's Molecular Virology Laboratory according to previously published methods [23] . Two-target PCR was used to assess the presence of 3 Bordetella species: B pertussis, B parapertussis, and B bronchiseptica. The amplified targets were chromosomal repeated insertion sequence IS481 (IS) and the polymorphic pertussis toxin ptxA promoter region (PT).
After amplification, the melting points of the amplicons were measured in an iCycler (Bio-Rad). A sample was interpreted as positive when the target(s) had a melting temperature within the species-specific acceptable range and a computed tomography ≤42. A sample was negative if none of the targets tested positive or a single positive target was not reproducible. Maternal nasal swabs were tested for those mothers whose infants tested positive for any Bordetella species
Polymerase chain reaction was also performed for several viral infections (influenza, rhinovirus [RV], respiratory syncytial virus [RSV], bocavirus [BoV], human metapneumovirus, coronavirus, adenovirus, and parainfluenza [1] [2] [3] [4] ) as previously described [21] .
Of 3693 women enrolled, 3646 infants were live born to 3621 women (Supplementary Figure 1 ). Infants were included in this analysis if they were followed for any length of the follow-up period (0 to 180 days); median total follow-up was 146 days per infant (Supplementary Figure 2) . The final dataset consists of 3483 infants, contributing 1280 infant-years of observation, with at least 1 follow-up visit during the first 6 months. This includes infants from the entire trial period, both before and after more pertussis-specific additions to the weekly symptom questionnaire.
At baseline, data on household structure were gathered. At enrollment, women reported their literacy status (binary) and pregnancy history. The field workers identified their ethnicity into 2 broad groups (Pahadi, a group originating from the hills; or Madeshi, a group originating from north India) from names and observation. Women were categorized as nulliparous or multiparous. Responses to 25 questions about household construction, water and sanitation, and household assets were used to develop an index to measure the socioeconomic status of households. Binary variables for each of the 25 questions and a mean SES score were calculated for each household.
Gestational age was measured using a woman's report of date of last menstrual period during pregnancy surveillance. Birth weight was collected as soon as possible after birth using a digital scale (Tanita model BD-585, precision to nearest 10 grams). Birth weights collected >72 hours after birth were excluded from the analysis. Small for gestational age (SGA) was calculated using the sex-specific 10th percentile cutoff described by Alexander et al [24] and the INTERGROWTH-21 standards [25] . Women were asked within how many hours of birth breastfeeding was initiated and binary breastfeeding categories were created (≤1 hour versus >1 hour postdelivery).
Incidence was calculated as the number of pertussis cases per 1000 infant-years at risk. Poisson exact 95% confidence intervals (CIs) were constructed. Characteristics of infant pertussis cases were compared with nonpertussis cases using bivariate Poisson regression. Characteristics of all pertussis respiratory episodes were compared with nonpertussis respiratory episodes; t tests were used for continuous predictors and Fisher's exact tests were used for categorical associations due to the low number of pertussis episodes. All statistical analyses were conducted in Stata/SE 14.1.
A total of 3483 infants had 4283 episodes of respiratory illness between May 18, 2011 and April 30, 2014. Thirty-nine percent (n = 1350) of infants experienced no respiratory episodes. The incidence of respiratory illness was 3.6 episodes per infant-year (95% CI, 3.5-3.7). Mean episode duration was 4.7 days (95% CI, 4.6-4.9). A total of 3930 (92%) episodes were matched to 1 or more pertussis-tested nasal swabs from 2026 infants (Supplementary Figure 1) .
Seventeen cases of B pertussis were identified from 19 nasal swabs (nasal swabs were positive on 2 consecutive weeks for 2 infants). The incidence of PCR-confirmed B pertussis was 13.3 cases per 1000-infant years (95% CI, 7.7-21.3). Five cases of B parapertussis were detected with an incidence of 3.9 cases per 1000 infant-years (95% CI, 1.3-9.1). No cases of B bronchiseptica were identified.
The average pertussis episode duration was 8 days (range, 2-33) ( Table 1 ). Mean age of onset of symptoms was 83 days (range, 19-137) (median, 80; interquartile range, 63-109). The most common symptoms were cough, difficulty breathing, and cough with vomit. None of the additional symptoms related to pertussis that were added in year 2 (cyanosis, apnea, cough with vomit, and whoop) resulted in collection of nasal swabs based solely on these additional symptoms. Pertussis episodes were statistically significantly more likely to include difficulty breathing, cough with vomit, and whoop compared with other respiratory illness. Six infants had at least 1 pertussis vaccination before pertussis disease onset (three <2 weeks and three >2 weeks before pertussis illness) with a mean of 18 days from vaccination to illness compared with 49 days for nonpertussis episodes (P = .03). Five infants received their first pertussis vaccination postpertussis disease onset, whereas 6 infants received no pertussis vaccination in the first 180 days. Three fourths of pertussis episodes were coinfected with at least 1 virus, with RV and BoV the most common. Cases of pertussis were more likely to be infected with BoV than respiratory cases due to causes other than pertussis. The majority of cases occurred between February 2013 and January 2014 (Figure 1) .
No statistically significant differences between risk factors for pertussis and nonpertussis cases ( Table 2) were documented. Given the low number of pertussis cases, the lack of a statistical association is not evidence of nonassociation. No deaths occurred in infants who had pertussis. Of the 8 mothers of B pertussis-positive infants who had a nasal swab collected (14 nasal swabs total) during their own follow-up, none were positive for any pertussis species.
The 5 B parapertussis cases were primarily male whose mothers were primiparous, literate, and Pahadi ethnicity (Supplementary Table 1 ). No mothers of infants who had B parapertussis had a nasal swab collected during follow-up.
The average B parapertussis episode duration was 4 days (Supplementary Table 2 ). Mean age of onset of symptoms was 58 days with a range of 7-95 days. The most common symptoms were cough and wheeze. Rhinovirus and RSV were the only coinfections observed. All B parapertussis cases occurred between September 2011 and February 2012 ( Figure 1 ).
A low incidence of pertussis and generally mild clinical presentation were found in infants <6 months in Nepal. To our knowledge, this represents one of the first population-based active surveillance of PCR-confirmed pertussis among young infants in Asia. Acellular pertussis vaccine trials conducted in the 1990s found the average pertussis incidence in the whole cell vaccine groups ranged from 1 to 37 cases per 1000 infantyears [26] . Our finding of 13 B pertussis cases per 1000 infantyears was on the lower end of this range. In the United States in 2014, the estimated pertussis incidence in infants less than 6 months was 2 cases per 1000 infant-years [27] , much lower than observed in our study; however, this passive surveillance system likely vastly underestimates pertussis incidence. Thus, there is a need for active surveillance data such as ours. Furthermore, given our highly sensitive case detection method, many of our pertussis cases would likely not have been detected in the previous acellular pertussis vaccine trials. More stringent respiratory symptom criteria would have lowered our incidence estimate even further. The low incidence was found in a population where pentavalent vaccine (Pentavac: Diphtheria, Tetanus, Pertussis [Whole Cell], Hepatitis-B and Haemophilus Type b Conjugate Vaccine; Serum Institute of India Pvt. Ltd), scheduled for administration at 6, 10, and 14 weeks, is received with significant delays (7% of infants received all 3 recommended pertussis vaccines by 6 months) [22] . These data support the WHO's recommendation that countries using whole cell pertussis vaccine continue to do so given that the majority of outbreaks have been concentrated in countries using the acellular pertussis vaccine [2] . Recent studies suggest that protection from acellular pertussis vaccine is not as strong or long lasting as that conferred by the whole cell pertussis vaccine [6, 28] .
Another contributing factor to the low pertussis incidence observed could be that surveillance was conducted during a period of low pertussis transmission. Pertussis is a cyclical disease, thought to peak every 2 to 4 years, and we may have captured the burden at a low circulation period [6] . We observed over 70% of our B pertussis cases over a 1-year period. This increase from earlier observation periods could indicate a temporary rise in pertussis consistent with its cyclical pattern or a true increase in the baseline burden. Previous research on pertussis seasonality has in different places and time periods demonstrated various periods of peak transmission or no discernable patterns [29, 30] . Although our data do not support a seasonal pattern, the numbers observed are too low to be conclusive.
Pertussis symptom duration and severity were mild compared with the classic pertussis case presentation. Only 3 of the 17 cases fulfilled the WHO criteria, which requires a minimum of 2 weeks of cough, whoop, or posttussive vomiting [31] . Studies on pertussis in infants have generally been clinic-based, hospital-based, or in an outbreak, which therefore required a certain severity of illness for parents to recognize a need for medical attention [29, 30, 32] . These study designs and passive surveillance efforts therefore may have missed milder pertussis cases [33] . Our study, which required only 1 respiratory symptom for a nasal swab to be collected, had increased sensitivity to detect a range of pertussis case presentations. An alternative explanation for the mild cases seen could be an increase in the proportion of mild compared with severe pertussis cases in Nepal.
Although cough, difficulty breathing, and cough with vomit were the most common symptoms, no symptom was present in all B pertussis cases. During an epidemic period in Washington state, among infants <1 year, who had a minimum of 14 days cough plus an additional symptom, 82% had posttussive emesis, 29% had apnea, 26% had whoop, and 42% had cyanosis [32] . A study of US neonates with pertussis showed the symptom prevalence to be 97% for cough, 91% for cyanosis, 58% for apnea, and 3% for fever [34] . Our study found lower or equal symptom prevalence with the exception of fever. Fever prevalence was higher in our study, similar to that found in Peru [29] .
Although not statistically significant, infants with pertussis were more likely to have been born preterm, low birth weight, and SGA, and their mothers were more likely to be primiparous. These findings are similar to previous studies showing no difference in pertussis cases by sex [29, 35, 36] or crowding [35] but showing differences by birth weight [36] . Coinfections were common, consistent with findings from other hospital-based studies [33] . Codetection of B pertussis and B parapertussis with respiratory viruses may be due to asymptomatic pertussis carriage. The incidence of B parapertussis of 4 cases per 1000 person-years was comparable to that of 2 per 1000 person-years found in the Italian acellular pertussis vaccine trial in 1992-1993 [37] . The duration of illness was shorter for B parapertussis with a maximum duration of 6 days compared with a maximum of 33 days for B pertussis. A milder presentation is consistent with clinical knowledge of B parapertussis infection [37, 38] . Bordetella parapertussis cases occurred only during a 5-month period.
There were several study design limitations. We cannot be certain whether the reported symptoms were caused by pertussis, another organism, or whether symptoms were related to 2 or more etiologic agents. We were unable to perform multivariate regression modeling for characteristics associated with pertussis disease and pertussis cases due to the small number of cases we detected.
Infant respiratory symptoms were reported by parents, who may have missed signs that might have been observed by a healthcare worker. However, the criteria for collection of the nasal swab were broad and did not require sophisticated clinical skills. However, apnea and cyanosis may have been difficult for parents to identify. Although the criteria for specimen collection changed in year 2, no infant experienced a pertussis-specific symptom in isolation without also having one of the originally specified respiratory symptoms. These data support our assumption that we were unlikely to have missed pertussis cases in year 1 with our less sensitive respiratory symptom criteria.
Nasal swabs were collected in the mid-nasal region for influenza virus detection, which may have lowered the sensitivity of pertussis detection. In a field site, the acceptability of an additional nasopharyngeal swab would likely have increased the participant refusal rate. This would have decreased the generalizability of our results to the entire population. Although nasopharyngeal swabs or nasopharyngeal aspirates are the recommended specimen collection method [39] , the nasopharyngeal region was established as the collection area of choice when the diagnostic measure was culture, which has low sensitivity. Recent data demonstrated the comparability of using mid-nasal versus nasopharyngeal swabs in PCR pertussis detection [40] .
Strengths of the study included being a population-based, prospective study, with very low refusal rates. Risk factors, clinical symptoms, and coinfections were prospectively identified without the potential bias that may occur when these data are collected retrospectively or in clinical settings. The community-based design allows generalizability of these results to the entire population and not just those seeking care at a health facility or in an outbreak situation. The Sarlahi District is located in the Terai region where the majority of Nepalese reside, and it has similar demographics to the entire population of Nepal [41] . Sarlahi's location near sea level and on the border with India supports the generalizability of these results to many populations living on the Indian subcontinent. The weekly active surveillance with sensitive criteria for pertussis testing was able to detect mild and atypical pertussis cases, which may have been missed by previous traditional surveillance. The multitarget PCR method allowed highly sensitive and specific detection of 2 additional Bordetella species beyond the primary B pertussis target.
We observed a low incidence of pertussis in infants in a whole cell vaccine environment. Pertussis cases were generally milder than expected compared with traditional pertussis clinical definitions. These data support clinicians considering pertussis in their differential diagnosis of infants with mild respiratory symptoms. Policymakers in Nepal will need to weigh the benefit of an additional prenatal pertussis vaccine or a switch to acellular primary pertussis vaccine with the low burden of pertussis in infants less than 6 months. Our study demonstrated that mid-nasal swabs were able to detect pertussis using a sensitive multitarget PCR. The less invasive mid-nasal nasal swab is an attractive alternative for pertussis nasal swab collection, and further research is needed to compare this collection site with nasopharyngeal swabs. In the future, this method may enhance population-based surveillance efforts.
|
Where is the highest rate of childhood pertussis globally?
| 2,168 |
Southeast Asia
| 3,290 |
1,574 |
Population-Based Pertussis Incidence and Risk Factors in Infants Less Than 6 Months in Nepal
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5907881/
SHA: ef821e34873d4752ecae41cd9dfc08a5e6db45e2
Authors: Hughes, Michelle M; Englund, Janet A; Kuypers, Jane; Tielsch, James M; Khatry, Subarna K; Shrestha, Laxman; LeClerq, Steven C; Steinhoff, Mark; Katz, Joanne
Date: 2017-03-01
DOI: 10.1093/jpids/piw079
License: cc-by
Abstract: BACKGROUND: Pertussis is estimated to cause 2 percent of childhood deaths globally and is a growing public health problem in developed countries despite high vaccination coverage. Infants are at greatest risk of morbidity and mortality. Maternal vaccination during pregnancy may be effective to prevent pertussis in young infants, but population-based estimates of disease burden in infants are lacking, particularly in low-income countries. The objective of this study was to estimate the incidence of pertussis in infants less than 6 months of age in Sarlahi District, Nepal. METHODS: Nested within a population-based randomized controlled trial of influenza vaccination during pregnancy, infants were visited weekly from birth through 6 months to assess respiratory illness in the prior week. If any respiratory symptoms had occurred, a nasal swab was collected and tested with a multitarget pertussis polymerase chain reaction (PCR) assay. The prospective cohort study includes infants observed between May 2011 and August 2014. RESULTS: The incidence of PCR-confirmed Bordetella pertussis was 13.3 cases per 1000 infant-years (95% confidence interval, 7.7–21.3) in a cohort of 3483 infants with at least 1 day of follow-up. CONCLUSIONS: In a population-based active home surveillance for respiratory illness, a low risk for pertussis was estimated among infants in rural Nepal. Nepal’s immunization program, which includes a childhood whole cell pertussis vaccine, may be effective in controlling pertussis in infants.
Text: A resurgence of pertussis across age groups has occurred in several countries in recent years [1] . Middle-and high-income countries that use an acellular pertussis vaccine for the primary vaccination series have been particularly affected [2, 3] , and infants and adolescents have experienced the greatest increase [4] . Factors that may contribute to the increased risk of pertussis include rapidly waning immunity from those vaccinated with acellular vaccines [1, 5, 6] , asymptomatic transmission from individuals vaccinated with acellular vaccines [7] , genetic adaption of Bordetella pertussis [8] , vaccination delay or refusal [9] , improved surveillance and laboratory capabilities [2] , and overall increased awareness of the continuing circulation of B pertussis [1] . Some countries experiencing epidemic pertussis, including the United States, United Kingdom, and Argentina, now recommend pertussis immunization in pregnancy and vaccination of close contacts [10, 11] to protect the youngest infants from pertussis before they can be vaccinated themselves [12] . Recent data from maternal vaccination trials demonstrate the ability of antibodies to be transferred from mothers to their infants in pregnancy and their persistence in infants [13] .
Global estimates of pertussis show the highest childhood burden in Southeast Asia [14] . In this region, maternal pertussis vaccination during pregnancy may be a way to protect infants, similar to the approach using tetanus toxoid vaccine. However, globally only 1 population-based estimate of pertussis in infants from birth has been conducted (Senegal) [15] , and surveillance and laboratory capabilities in Asia are lacking [16, 17] . The World Health Organization (WHO) recently recommended that countries using whole cell pertussis vaccines continue to do so in light of recent data indicating that acellular pertussis vaccines are less effective than whole cell pertussis vaccines [18] . Population-based data are needed, especially in low-income settings, to provide a more accurate estimate of the burden of pertussis in infants to inform childhood and maternal immunization policies [19, 20] .
We report on a prospective cohort study following infants weekly in their homes to monitor for pertussis disease from birth to age 6 months. The objective was to provide a population-based estimate of laboratory-confirmed pertussis incidence in infants less than 6 months of age in the Sarlahi District, Nepal.
The study was nested within 2 consecutive randomized controlled trials of maternal influenza vaccination during pregnancy set in the Sarlahi District, located in the central Terai (low-lying plains) region of Nepal [21] . At the start of the trial, prevalent pregnancies were identified through a census of all households in the catchment area. For the duration of the trial, field workers visited all households in the communities, every 5 weeks, where married women (15-40 years) resided, for surveillance of incident pregnancies. Once a pregnancy was identified, women provided consent and were enrolled. From April 25, 2011 through September 9, 2013, women between 17 and 34 weeks gestation were randomized and vaccinated with either an influenza vaccine or placebo. The study was a population-based prospective cohort of infants followed from birth through 6 months postpartum. Approval for the study was obtained from the Institutional Review Boards at the Johns Hopkins Bloomberg School of Public Health, Cincinnati Children's Medical Center, the Institute of Medicine at Tribhuvan University, Kathmandu, and the Nepal Health Research Council. The trials are registered at Clinicaltrials.gov (NCT01034254).
At baseline, information was collected on household structure, socioeconomic status, and demographics. At enrollment, date of last menstrual period and pregnancy history data were collected. As soon as possible after delivery, the mother and infant were visited to collect detailed birth information including infant weight and breastfeeding status. From birth through 6 months, postpartum infants were visited weekly by a field worker, who recorded any infant respiratory symptoms in the past 7 days. If an infant had any of the following symptoms, a mid-nasal nylon flocked swab was collected: fever, cough, wheeze, difficulty breathing, or ear infection. Starting on August 17, 2012, new symptoms, more specific for pertussis, were added to the weekly morbidity visit: apnea, cyanosis, cough with vomit, or whoop/whooping cough. The swabs were stored for up to 1 week at room temperature in PrimeStore Molecular Transport Medium (Longhorn Diagnostics LLC, Bethesda, MD). In addition to these signs, mothers were asked which, if any, infant vaccinations were received in the past 7 days, including pertussis vaccination [22] . Mid-nasal swabs were also collected on a weekly basis from mothers from enrollment through 6 months postpartum who reported fever plus one additional morbidity (cough, sore throat, nasal congestion, or myalgia). All nasal swabs collected from infants were tested for B pertussis, Bordetella parapertussis, and Bordetella bronchispetica. Only the nasal swabs of mothers whose infants tested positive for any of these pathogens were tested for the same pathogens.
Real-time polymerase chain reaction (PCR) testing was conducted at the University of Washington's Molecular Virology Laboratory according to previously published methods [23] . Two-target PCR was used to assess the presence of 3 Bordetella species: B pertussis, B parapertussis, and B bronchiseptica. The amplified targets were chromosomal repeated insertion sequence IS481 (IS) and the polymorphic pertussis toxin ptxA promoter region (PT).
After amplification, the melting points of the amplicons were measured in an iCycler (Bio-Rad). A sample was interpreted as positive when the target(s) had a melting temperature within the species-specific acceptable range and a computed tomography ≤42. A sample was negative if none of the targets tested positive or a single positive target was not reproducible. Maternal nasal swabs were tested for those mothers whose infants tested positive for any Bordetella species
Polymerase chain reaction was also performed for several viral infections (influenza, rhinovirus [RV], respiratory syncytial virus [RSV], bocavirus [BoV], human metapneumovirus, coronavirus, adenovirus, and parainfluenza [1] [2] [3] [4] ) as previously described [21] .
Of 3693 women enrolled, 3646 infants were live born to 3621 women (Supplementary Figure 1 ). Infants were included in this analysis if they were followed for any length of the follow-up period (0 to 180 days); median total follow-up was 146 days per infant (Supplementary Figure 2) . The final dataset consists of 3483 infants, contributing 1280 infant-years of observation, with at least 1 follow-up visit during the first 6 months. This includes infants from the entire trial period, both before and after more pertussis-specific additions to the weekly symptom questionnaire.
At baseline, data on household structure were gathered. At enrollment, women reported their literacy status (binary) and pregnancy history. The field workers identified their ethnicity into 2 broad groups (Pahadi, a group originating from the hills; or Madeshi, a group originating from north India) from names and observation. Women were categorized as nulliparous or multiparous. Responses to 25 questions about household construction, water and sanitation, and household assets were used to develop an index to measure the socioeconomic status of households. Binary variables for each of the 25 questions and a mean SES score were calculated for each household.
Gestational age was measured using a woman's report of date of last menstrual period during pregnancy surveillance. Birth weight was collected as soon as possible after birth using a digital scale (Tanita model BD-585, precision to nearest 10 grams). Birth weights collected >72 hours after birth were excluded from the analysis. Small for gestational age (SGA) was calculated using the sex-specific 10th percentile cutoff described by Alexander et al [24] and the INTERGROWTH-21 standards [25] . Women were asked within how many hours of birth breastfeeding was initiated and binary breastfeeding categories were created (≤1 hour versus >1 hour postdelivery).
Incidence was calculated as the number of pertussis cases per 1000 infant-years at risk. Poisson exact 95% confidence intervals (CIs) were constructed. Characteristics of infant pertussis cases were compared with nonpertussis cases using bivariate Poisson regression. Characteristics of all pertussis respiratory episodes were compared with nonpertussis respiratory episodes; t tests were used for continuous predictors and Fisher's exact tests were used for categorical associations due to the low number of pertussis episodes. All statistical analyses were conducted in Stata/SE 14.1.
A total of 3483 infants had 4283 episodes of respiratory illness between May 18, 2011 and April 30, 2014. Thirty-nine percent (n = 1350) of infants experienced no respiratory episodes. The incidence of respiratory illness was 3.6 episodes per infant-year (95% CI, 3.5-3.7). Mean episode duration was 4.7 days (95% CI, 4.6-4.9). A total of 3930 (92%) episodes were matched to 1 or more pertussis-tested nasal swabs from 2026 infants (Supplementary Figure 1) .
Seventeen cases of B pertussis were identified from 19 nasal swabs (nasal swabs were positive on 2 consecutive weeks for 2 infants). The incidence of PCR-confirmed B pertussis was 13.3 cases per 1000-infant years (95% CI, 7.7-21.3). Five cases of B parapertussis were detected with an incidence of 3.9 cases per 1000 infant-years (95% CI, 1.3-9.1). No cases of B bronchiseptica were identified.
The average pertussis episode duration was 8 days (range, 2-33) ( Table 1 ). Mean age of onset of symptoms was 83 days (range, 19-137) (median, 80; interquartile range, 63-109). The most common symptoms were cough, difficulty breathing, and cough with vomit. None of the additional symptoms related to pertussis that were added in year 2 (cyanosis, apnea, cough with vomit, and whoop) resulted in collection of nasal swabs based solely on these additional symptoms. Pertussis episodes were statistically significantly more likely to include difficulty breathing, cough with vomit, and whoop compared with other respiratory illness. Six infants had at least 1 pertussis vaccination before pertussis disease onset (three <2 weeks and three >2 weeks before pertussis illness) with a mean of 18 days from vaccination to illness compared with 49 days for nonpertussis episodes (P = .03). Five infants received their first pertussis vaccination postpertussis disease onset, whereas 6 infants received no pertussis vaccination in the first 180 days. Three fourths of pertussis episodes were coinfected with at least 1 virus, with RV and BoV the most common. Cases of pertussis were more likely to be infected with BoV than respiratory cases due to causes other than pertussis. The majority of cases occurred between February 2013 and January 2014 (Figure 1) .
No statistically significant differences between risk factors for pertussis and nonpertussis cases ( Table 2) were documented. Given the low number of pertussis cases, the lack of a statistical association is not evidence of nonassociation. No deaths occurred in infants who had pertussis. Of the 8 mothers of B pertussis-positive infants who had a nasal swab collected (14 nasal swabs total) during their own follow-up, none were positive for any pertussis species.
The 5 B parapertussis cases were primarily male whose mothers were primiparous, literate, and Pahadi ethnicity (Supplementary Table 1 ). No mothers of infants who had B parapertussis had a nasal swab collected during follow-up.
The average B parapertussis episode duration was 4 days (Supplementary Table 2 ). Mean age of onset of symptoms was 58 days with a range of 7-95 days. The most common symptoms were cough and wheeze. Rhinovirus and RSV were the only coinfections observed. All B parapertussis cases occurred between September 2011 and February 2012 ( Figure 1 ).
A low incidence of pertussis and generally mild clinical presentation were found in infants <6 months in Nepal. To our knowledge, this represents one of the first population-based active surveillance of PCR-confirmed pertussis among young infants in Asia. Acellular pertussis vaccine trials conducted in the 1990s found the average pertussis incidence in the whole cell vaccine groups ranged from 1 to 37 cases per 1000 infantyears [26] . Our finding of 13 B pertussis cases per 1000 infantyears was on the lower end of this range. In the United States in 2014, the estimated pertussis incidence in infants less than 6 months was 2 cases per 1000 infant-years [27] , much lower than observed in our study; however, this passive surveillance system likely vastly underestimates pertussis incidence. Thus, there is a need for active surveillance data such as ours. Furthermore, given our highly sensitive case detection method, many of our pertussis cases would likely not have been detected in the previous acellular pertussis vaccine trials. More stringent respiratory symptom criteria would have lowered our incidence estimate even further. The low incidence was found in a population where pentavalent vaccine (Pentavac: Diphtheria, Tetanus, Pertussis [Whole Cell], Hepatitis-B and Haemophilus Type b Conjugate Vaccine; Serum Institute of India Pvt. Ltd), scheduled for administration at 6, 10, and 14 weeks, is received with significant delays (7% of infants received all 3 recommended pertussis vaccines by 6 months) [22] . These data support the WHO's recommendation that countries using whole cell pertussis vaccine continue to do so given that the majority of outbreaks have been concentrated in countries using the acellular pertussis vaccine [2] . Recent studies suggest that protection from acellular pertussis vaccine is not as strong or long lasting as that conferred by the whole cell pertussis vaccine [6, 28] .
Another contributing factor to the low pertussis incidence observed could be that surveillance was conducted during a period of low pertussis transmission. Pertussis is a cyclical disease, thought to peak every 2 to 4 years, and we may have captured the burden at a low circulation period [6] . We observed over 70% of our B pertussis cases over a 1-year period. This increase from earlier observation periods could indicate a temporary rise in pertussis consistent with its cyclical pattern or a true increase in the baseline burden. Previous research on pertussis seasonality has in different places and time periods demonstrated various periods of peak transmission or no discernable patterns [29, 30] . Although our data do not support a seasonal pattern, the numbers observed are too low to be conclusive.
Pertussis symptom duration and severity were mild compared with the classic pertussis case presentation. Only 3 of the 17 cases fulfilled the WHO criteria, which requires a minimum of 2 weeks of cough, whoop, or posttussive vomiting [31] . Studies on pertussis in infants have generally been clinic-based, hospital-based, or in an outbreak, which therefore required a certain severity of illness for parents to recognize a need for medical attention [29, 30, 32] . These study designs and passive surveillance efforts therefore may have missed milder pertussis cases [33] . Our study, which required only 1 respiratory symptom for a nasal swab to be collected, had increased sensitivity to detect a range of pertussis case presentations. An alternative explanation for the mild cases seen could be an increase in the proportion of mild compared with severe pertussis cases in Nepal.
Although cough, difficulty breathing, and cough with vomit were the most common symptoms, no symptom was present in all B pertussis cases. During an epidemic period in Washington state, among infants <1 year, who had a minimum of 14 days cough plus an additional symptom, 82% had posttussive emesis, 29% had apnea, 26% had whoop, and 42% had cyanosis [32] . A study of US neonates with pertussis showed the symptom prevalence to be 97% for cough, 91% for cyanosis, 58% for apnea, and 3% for fever [34] . Our study found lower or equal symptom prevalence with the exception of fever. Fever prevalence was higher in our study, similar to that found in Peru [29] .
Although not statistically significant, infants with pertussis were more likely to have been born preterm, low birth weight, and SGA, and their mothers were more likely to be primiparous. These findings are similar to previous studies showing no difference in pertussis cases by sex [29, 35, 36] or crowding [35] but showing differences by birth weight [36] . Coinfections were common, consistent with findings from other hospital-based studies [33] . Codetection of B pertussis and B parapertussis with respiratory viruses may be due to asymptomatic pertussis carriage. The incidence of B parapertussis of 4 cases per 1000 person-years was comparable to that of 2 per 1000 person-years found in the Italian acellular pertussis vaccine trial in 1992-1993 [37] . The duration of illness was shorter for B parapertussis with a maximum duration of 6 days compared with a maximum of 33 days for B pertussis. A milder presentation is consistent with clinical knowledge of B parapertussis infection [37, 38] . Bordetella parapertussis cases occurred only during a 5-month period.
There were several study design limitations. We cannot be certain whether the reported symptoms were caused by pertussis, another organism, or whether symptoms were related to 2 or more etiologic agents. We were unable to perform multivariate regression modeling for characteristics associated with pertussis disease and pertussis cases due to the small number of cases we detected.
Infant respiratory symptoms were reported by parents, who may have missed signs that might have been observed by a healthcare worker. However, the criteria for collection of the nasal swab were broad and did not require sophisticated clinical skills. However, apnea and cyanosis may have been difficult for parents to identify. Although the criteria for specimen collection changed in year 2, no infant experienced a pertussis-specific symptom in isolation without also having one of the originally specified respiratory symptoms. These data support our assumption that we were unlikely to have missed pertussis cases in year 1 with our less sensitive respiratory symptom criteria.
Nasal swabs were collected in the mid-nasal region for influenza virus detection, which may have lowered the sensitivity of pertussis detection. In a field site, the acceptability of an additional nasopharyngeal swab would likely have increased the participant refusal rate. This would have decreased the generalizability of our results to the entire population. Although nasopharyngeal swabs or nasopharyngeal aspirates are the recommended specimen collection method [39] , the nasopharyngeal region was established as the collection area of choice when the diagnostic measure was culture, which has low sensitivity. Recent data demonstrated the comparability of using mid-nasal versus nasopharyngeal swabs in PCR pertussis detection [40] .
Strengths of the study included being a population-based, prospective study, with very low refusal rates. Risk factors, clinical symptoms, and coinfections were prospectively identified without the potential bias that may occur when these data are collected retrospectively or in clinical settings. The community-based design allows generalizability of these results to the entire population and not just those seeking care at a health facility or in an outbreak situation. The Sarlahi District is located in the Terai region where the majority of Nepalese reside, and it has similar demographics to the entire population of Nepal [41] . Sarlahi's location near sea level and on the border with India supports the generalizability of these results to many populations living on the Indian subcontinent. The weekly active surveillance with sensitive criteria for pertussis testing was able to detect mild and atypical pertussis cases, which may have been missed by previous traditional surveillance. The multitarget PCR method allowed highly sensitive and specific detection of 2 additional Bordetella species beyond the primary B pertussis target.
We observed a low incidence of pertussis in infants in a whole cell vaccine environment. Pertussis cases were generally milder than expected compared with traditional pertussis clinical definitions. These data support clinicians considering pertussis in their differential diagnosis of infants with mild respiratory symptoms. Policymakers in Nepal will need to weigh the benefit of an additional prenatal pertussis vaccine or a switch to acellular primary pertussis vaccine with the low burden of pertussis in infants less than 6 months. Our study demonstrated that mid-nasal swabs were able to detect pertussis using a sensitive multitarget PCR. The less invasive mid-nasal nasal swab is an attractive alternative for pertussis nasal swab collection, and further research is needed to compare this collection site with nasopharyngeal swabs. In the future, this method may enhance population-based surveillance efforts.
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What type of pertussis vaccine has been recently recommended by the WHO?
| 2,169 |
whole cell pertussis vaccines
| 3,739 |
1,574 |
Population-Based Pertussis Incidence and Risk Factors in Infants Less Than 6 Months in Nepal
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5907881/
SHA: ef821e34873d4752ecae41cd9dfc08a5e6db45e2
Authors: Hughes, Michelle M; Englund, Janet A; Kuypers, Jane; Tielsch, James M; Khatry, Subarna K; Shrestha, Laxman; LeClerq, Steven C; Steinhoff, Mark; Katz, Joanne
Date: 2017-03-01
DOI: 10.1093/jpids/piw079
License: cc-by
Abstract: BACKGROUND: Pertussis is estimated to cause 2 percent of childhood deaths globally and is a growing public health problem in developed countries despite high vaccination coverage. Infants are at greatest risk of morbidity and mortality. Maternal vaccination during pregnancy may be effective to prevent pertussis in young infants, but population-based estimates of disease burden in infants are lacking, particularly in low-income countries. The objective of this study was to estimate the incidence of pertussis in infants less than 6 months of age in Sarlahi District, Nepal. METHODS: Nested within a population-based randomized controlled trial of influenza vaccination during pregnancy, infants were visited weekly from birth through 6 months to assess respiratory illness in the prior week. If any respiratory symptoms had occurred, a nasal swab was collected and tested with a multitarget pertussis polymerase chain reaction (PCR) assay. The prospective cohort study includes infants observed between May 2011 and August 2014. RESULTS: The incidence of PCR-confirmed Bordetella pertussis was 13.3 cases per 1000 infant-years (95% confidence interval, 7.7–21.3) in a cohort of 3483 infants with at least 1 day of follow-up. CONCLUSIONS: In a population-based active home surveillance for respiratory illness, a low risk for pertussis was estimated among infants in rural Nepal. Nepal’s immunization program, which includes a childhood whole cell pertussis vaccine, may be effective in controlling pertussis in infants.
Text: A resurgence of pertussis across age groups has occurred in several countries in recent years [1] . Middle-and high-income countries that use an acellular pertussis vaccine for the primary vaccination series have been particularly affected [2, 3] , and infants and adolescents have experienced the greatest increase [4] . Factors that may contribute to the increased risk of pertussis include rapidly waning immunity from those vaccinated with acellular vaccines [1, 5, 6] , asymptomatic transmission from individuals vaccinated with acellular vaccines [7] , genetic adaption of Bordetella pertussis [8] , vaccination delay or refusal [9] , improved surveillance and laboratory capabilities [2] , and overall increased awareness of the continuing circulation of B pertussis [1] . Some countries experiencing epidemic pertussis, including the United States, United Kingdom, and Argentina, now recommend pertussis immunization in pregnancy and vaccination of close contacts [10, 11] to protect the youngest infants from pertussis before they can be vaccinated themselves [12] . Recent data from maternal vaccination trials demonstrate the ability of antibodies to be transferred from mothers to their infants in pregnancy and their persistence in infants [13] .
Global estimates of pertussis show the highest childhood burden in Southeast Asia [14] . In this region, maternal pertussis vaccination during pregnancy may be a way to protect infants, similar to the approach using tetanus toxoid vaccine. However, globally only 1 population-based estimate of pertussis in infants from birth has been conducted (Senegal) [15] , and surveillance and laboratory capabilities in Asia are lacking [16, 17] . The World Health Organization (WHO) recently recommended that countries using whole cell pertussis vaccines continue to do so in light of recent data indicating that acellular pertussis vaccines are less effective than whole cell pertussis vaccines [18] . Population-based data are needed, especially in low-income settings, to provide a more accurate estimate of the burden of pertussis in infants to inform childhood and maternal immunization policies [19, 20] .
We report on a prospective cohort study following infants weekly in their homes to monitor for pertussis disease from birth to age 6 months. The objective was to provide a population-based estimate of laboratory-confirmed pertussis incidence in infants less than 6 months of age in the Sarlahi District, Nepal.
The study was nested within 2 consecutive randomized controlled trials of maternal influenza vaccination during pregnancy set in the Sarlahi District, located in the central Terai (low-lying plains) region of Nepal [21] . At the start of the trial, prevalent pregnancies were identified through a census of all households in the catchment area. For the duration of the trial, field workers visited all households in the communities, every 5 weeks, where married women (15-40 years) resided, for surveillance of incident pregnancies. Once a pregnancy was identified, women provided consent and were enrolled. From April 25, 2011 through September 9, 2013, women between 17 and 34 weeks gestation were randomized and vaccinated with either an influenza vaccine or placebo. The study was a population-based prospective cohort of infants followed from birth through 6 months postpartum. Approval for the study was obtained from the Institutional Review Boards at the Johns Hopkins Bloomberg School of Public Health, Cincinnati Children's Medical Center, the Institute of Medicine at Tribhuvan University, Kathmandu, and the Nepal Health Research Council. The trials are registered at Clinicaltrials.gov (NCT01034254).
At baseline, information was collected on household structure, socioeconomic status, and demographics. At enrollment, date of last menstrual period and pregnancy history data were collected. As soon as possible after delivery, the mother and infant were visited to collect detailed birth information including infant weight and breastfeeding status. From birth through 6 months, postpartum infants were visited weekly by a field worker, who recorded any infant respiratory symptoms in the past 7 days. If an infant had any of the following symptoms, a mid-nasal nylon flocked swab was collected: fever, cough, wheeze, difficulty breathing, or ear infection. Starting on August 17, 2012, new symptoms, more specific for pertussis, were added to the weekly morbidity visit: apnea, cyanosis, cough with vomit, or whoop/whooping cough. The swabs were stored for up to 1 week at room temperature in PrimeStore Molecular Transport Medium (Longhorn Diagnostics LLC, Bethesda, MD). In addition to these signs, mothers were asked which, if any, infant vaccinations were received in the past 7 days, including pertussis vaccination [22] . Mid-nasal swabs were also collected on a weekly basis from mothers from enrollment through 6 months postpartum who reported fever plus one additional morbidity (cough, sore throat, nasal congestion, or myalgia). All nasal swabs collected from infants were tested for B pertussis, Bordetella parapertussis, and Bordetella bronchispetica. Only the nasal swabs of mothers whose infants tested positive for any of these pathogens were tested for the same pathogens.
Real-time polymerase chain reaction (PCR) testing was conducted at the University of Washington's Molecular Virology Laboratory according to previously published methods [23] . Two-target PCR was used to assess the presence of 3 Bordetella species: B pertussis, B parapertussis, and B bronchiseptica. The amplified targets were chromosomal repeated insertion sequence IS481 (IS) and the polymorphic pertussis toxin ptxA promoter region (PT).
After amplification, the melting points of the amplicons were measured in an iCycler (Bio-Rad). A sample was interpreted as positive when the target(s) had a melting temperature within the species-specific acceptable range and a computed tomography ≤42. A sample was negative if none of the targets tested positive or a single positive target was not reproducible. Maternal nasal swabs were tested for those mothers whose infants tested positive for any Bordetella species
Polymerase chain reaction was also performed for several viral infections (influenza, rhinovirus [RV], respiratory syncytial virus [RSV], bocavirus [BoV], human metapneumovirus, coronavirus, adenovirus, and parainfluenza [1] [2] [3] [4] ) as previously described [21] .
Of 3693 women enrolled, 3646 infants were live born to 3621 women (Supplementary Figure 1 ). Infants were included in this analysis if they were followed for any length of the follow-up period (0 to 180 days); median total follow-up was 146 days per infant (Supplementary Figure 2) . The final dataset consists of 3483 infants, contributing 1280 infant-years of observation, with at least 1 follow-up visit during the first 6 months. This includes infants from the entire trial period, both before and after more pertussis-specific additions to the weekly symptom questionnaire.
At baseline, data on household structure were gathered. At enrollment, women reported their literacy status (binary) and pregnancy history. The field workers identified their ethnicity into 2 broad groups (Pahadi, a group originating from the hills; or Madeshi, a group originating from north India) from names and observation. Women were categorized as nulliparous or multiparous. Responses to 25 questions about household construction, water and sanitation, and household assets were used to develop an index to measure the socioeconomic status of households. Binary variables for each of the 25 questions and a mean SES score were calculated for each household.
Gestational age was measured using a woman's report of date of last menstrual period during pregnancy surveillance. Birth weight was collected as soon as possible after birth using a digital scale (Tanita model BD-585, precision to nearest 10 grams). Birth weights collected >72 hours after birth were excluded from the analysis. Small for gestational age (SGA) was calculated using the sex-specific 10th percentile cutoff described by Alexander et al [24] and the INTERGROWTH-21 standards [25] . Women were asked within how many hours of birth breastfeeding was initiated and binary breastfeeding categories were created (≤1 hour versus >1 hour postdelivery).
Incidence was calculated as the number of pertussis cases per 1000 infant-years at risk. Poisson exact 95% confidence intervals (CIs) were constructed. Characteristics of infant pertussis cases were compared with nonpertussis cases using bivariate Poisson regression. Characteristics of all pertussis respiratory episodes were compared with nonpertussis respiratory episodes; t tests were used for continuous predictors and Fisher's exact tests were used for categorical associations due to the low number of pertussis episodes. All statistical analyses were conducted in Stata/SE 14.1.
A total of 3483 infants had 4283 episodes of respiratory illness between May 18, 2011 and April 30, 2014. Thirty-nine percent (n = 1350) of infants experienced no respiratory episodes. The incidence of respiratory illness was 3.6 episodes per infant-year (95% CI, 3.5-3.7). Mean episode duration was 4.7 days (95% CI, 4.6-4.9). A total of 3930 (92%) episodes were matched to 1 or more pertussis-tested nasal swabs from 2026 infants (Supplementary Figure 1) .
Seventeen cases of B pertussis were identified from 19 nasal swabs (nasal swabs were positive on 2 consecutive weeks for 2 infants). The incidence of PCR-confirmed B pertussis was 13.3 cases per 1000-infant years (95% CI, 7.7-21.3). Five cases of B parapertussis were detected with an incidence of 3.9 cases per 1000 infant-years (95% CI, 1.3-9.1). No cases of B bronchiseptica were identified.
The average pertussis episode duration was 8 days (range, 2-33) ( Table 1 ). Mean age of onset of symptoms was 83 days (range, 19-137) (median, 80; interquartile range, 63-109). The most common symptoms were cough, difficulty breathing, and cough with vomit. None of the additional symptoms related to pertussis that were added in year 2 (cyanosis, apnea, cough with vomit, and whoop) resulted in collection of nasal swabs based solely on these additional symptoms. Pertussis episodes were statistically significantly more likely to include difficulty breathing, cough with vomit, and whoop compared with other respiratory illness. Six infants had at least 1 pertussis vaccination before pertussis disease onset (three <2 weeks and three >2 weeks before pertussis illness) with a mean of 18 days from vaccination to illness compared with 49 days for nonpertussis episodes (P = .03). Five infants received their first pertussis vaccination postpertussis disease onset, whereas 6 infants received no pertussis vaccination in the first 180 days. Three fourths of pertussis episodes were coinfected with at least 1 virus, with RV and BoV the most common. Cases of pertussis were more likely to be infected with BoV than respiratory cases due to causes other than pertussis. The majority of cases occurred between February 2013 and January 2014 (Figure 1) .
No statistically significant differences between risk factors for pertussis and nonpertussis cases ( Table 2) were documented. Given the low number of pertussis cases, the lack of a statistical association is not evidence of nonassociation. No deaths occurred in infants who had pertussis. Of the 8 mothers of B pertussis-positive infants who had a nasal swab collected (14 nasal swabs total) during their own follow-up, none were positive for any pertussis species.
The 5 B parapertussis cases were primarily male whose mothers were primiparous, literate, and Pahadi ethnicity (Supplementary Table 1 ). No mothers of infants who had B parapertussis had a nasal swab collected during follow-up.
The average B parapertussis episode duration was 4 days (Supplementary Table 2 ). Mean age of onset of symptoms was 58 days with a range of 7-95 days. The most common symptoms were cough and wheeze. Rhinovirus and RSV were the only coinfections observed. All B parapertussis cases occurred between September 2011 and February 2012 ( Figure 1 ).
A low incidence of pertussis and generally mild clinical presentation were found in infants <6 months in Nepal. To our knowledge, this represents one of the first population-based active surveillance of PCR-confirmed pertussis among young infants in Asia. Acellular pertussis vaccine trials conducted in the 1990s found the average pertussis incidence in the whole cell vaccine groups ranged from 1 to 37 cases per 1000 infantyears [26] . Our finding of 13 B pertussis cases per 1000 infantyears was on the lower end of this range. In the United States in 2014, the estimated pertussis incidence in infants less than 6 months was 2 cases per 1000 infant-years [27] , much lower than observed in our study; however, this passive surveillance system likely vastly underestimates pertussis incidence. Thus, there is a need for active surveillance data such as ours. Furthermore, given our highly sensitive case detection method, many of our pertussis cases would likely not have been detected in the previous acellular pertussis vaccine trials. More stringent respiratory symptom criteria would have lowered our incidence estimate even further. The low incidence was found in a population where pentavalent vaccine (Pentavac: Diphtheria, Tetanus, Pertussis [Whole Cell], Hepatitis-B and Haemophilus Type b Conjugate Vaccine; Serum Institute of India Pvt. Ltd), scheduled for administration at 6, 10, and 14 weeks, is received with significant delays (7% of infants received all 3 recommended pertussis vaccines by 6 months) [22] . These data support the WHO's recommendation that countries using whole cell pertussis vaccine continue to do so given that the majority of outbreaks have been concentrated in countries using the acellular pertussis vaccine [2] . Recent studies suggest that protection from acellular pertussis vaccine is not as strong or long lasting as that conferred by the whole cell pertussis vaccine [6, 28] .
Another contributing factor to the low pertussis incidence observed could be that surveillance was conducted during a period of low pertussis transmission. Pertussis is a cyclical disease, thought to peak every 2 to 4 years, and we may have captured the burden at a low circulation period [6] . We observed over 70% of our B pertussis cases over a 1-year period. This increase from earlier observation periods could indicate a temporary rise in pertussis consistent with its cyclical pattern or a true increase in the baseline burden. Previous research on pertussis seasonality has in different places and time periods demonstrated various periods of peak transmission or no discernable patterns [29, 30] . Although our data do not support a seasonal pattern, the numbers observed are too low to be conclusive.
Pertussis symptom duration and severity were mild compared with the classic pertussis case presentation. Only 3 of the 17 cases fulfilled the WHO criteria, which requires a minimum of 2 weeks of cough, whoop, or posttussive vomiting [31] . Studies on pertussis in infants have generally been clinic-based, hospital-based, or in an outbreak, which therefore required a certain severity of illness for parents to recognize a need for medical attention [29, 30, 32] . These study designs and passive surveillance efforts therefore may have missed milder pertussis cases [33] . Our study, which required only 1 respiratory symptom for a nasal swab to be collected, had increased sensitivity to detect a range of pertussis case presentations. An alternative explanation for the mild cases seen could be an increase in the proportion of mild compared with severe pertussis cases in Nepal.
Although cough, difficulty breathing, and cough with vomit were the most common symptoms, no symptom was present in all B pertussis cases. During an epidemic period in Washington state, among infants <1 year, who had a minimum of 14 days cough plus an additional symptom, 82% had posttussive emesis, 29% had apnea, 26% had whoop, and 42% had cyanosis [32] . A study of US neonates with pertussis showed the symptom prevalence to be 97% for cough, 91% for cyanosis, 58% for apnea, and 3% for fever [34] . Our study found lower or equal symptom prevalence with the exception of fever. Fever prevalence was higher in our study, similar to that found in Peru [29] .
Although not statistically significant, infants with pertussis were more likely to have been born preterm, low birth weight, and SGA, and their mothers were more likely to be primiparous. These findings are similar to previous studies showing no difference in pertussis cases by sex [29, 35, 36] or crowding [35] but showing differences by birth weight [36] . Coinfections were common, consistent with findings from other hospital-based studies [33] . Codetection of B pertussis and B parapertussis with respiratory viruses may be due to asymptomatic pertussis carriage. The incidence of B parapertussis of 4 cases per 1000 person-years was comparable to that of 2 per 1000 person-years found in the Italian acellular pertussis vaccine trial in 1992-1993 [37] . The duration of illness was shorter for B parapertussis with a maximum duration of 6 days compared with a maximum of 33 days for B pertussis. A milder presentation is consistent with clinical knowledge of B parapertussis infection [37, 38] . Bordetella parapertussis cases occurred only during a 5-month period.
There were several study design limitations. We cannot be certain whether the reported symptoms were caused by pertussis, another organism, or whether symptoms were related to 2 or more etiologic agents. We were unable to perform multivariate regression modeling for characteristics associated with pertussis disease and pertussis cases due to the small number of cases we detected.
Infant respiratory symptoms were reported by parents, who may have missed signs that might have been observed by a healthcare worker. However, the criteria for collection of the nasal swab were broad and did not require sophisticated clinical skills. However, apnea and cyanosis may have been difficult for parents to identify. Although the criteria for specimen collection changed in year 2, no infant experienced a pertussis-specific symptom in isolation without also having one of the originally specified respiratory symptoms. These data support our assumption that we were unlikely to have missed pertussis cases in year 1 with our less sensitive respiratory symptom criteria.
Nasal swabs were collected in the mid-nasal region for influenza virus detection, which may have lowered the sensitivity of pertussis detection. In a field site, the acceptability of an additional nasopharyngeal swab would likely have increased the participant refusal rate. This would have decreased the generalizability of our results to the entire population. Although nasopharyngeal swabs or nasopharyngeal aspirates are the recommended specimen collection method [39] , the nasopharyngeal region was established as the collection area of choice when the diagnostic measure was culture, which has low sensitivity. Recent data demonstrated the comparability of using mid-nasal versus nasopharyngeal swabs in PCR pertussis detection [40] .
Strengths of the study included being a population-based, prospective study, with very low refusal rates. Risk factors, clinical symptoms, and coinfections were prospectively identified without the potential bias that may occur when these data are collected retrospectively or in clinical settings. The community-based design allows generalizability of these results to the entire population and not just those seeking care at a health facility or in an outbreak situation. The Sarlahi District is located in the Terai region where the majority of Nepalese reside, and it has similar demographics to the entire population of Nepal [41] . Sarlahi's location near sea level and on the border with India supports the generalizability of these results to many populations living on the Indian subcontinent. The weekly active surveillance with sensitive criteria for pertussis testing was able to detect mild and atypical pertussis cases, which may have been missed by previous traditional surveillance. The multitarget PCR method allowed highly sensitive and specific detection of 2 additional Bordetella species beyond the primary B pertussis target.
We observed a low incidence of pertussis in infants in a whole cell vaccine environment. Pertussis cases were generally milder than expected compared with traditional pertussis clinical definitions. These data support clinicians considering pertussis in their differential diagnosis of infants with mild respiratory symptoms. Policymakers in Nepal will need to weigh the benefit of an additional prenatal pertussis vaccine or a switch to acellular primary pertussis vaccine with the low burden of pertussis in infants less than 6 months. Our study demonstrated that mid-nasal swabs were able to detect pertussis using a sensitive multitarget PCR. The less invasive mid-nasal nasal swab is an attractive alternative for pertussis nasal swab collection, and further research is needed to compare this collection site with nasopharyngeal swabs. In the future, this method may enhance population-based surveillance efforts.
|
What are the clinical symptoms of pertussis?
| 2,170 |
apnea, cyanosis, cough with vomit, or whoop/whooping cough
| 6,426 |
1,574 |
Population-Based Pertussis Incidence and Risk Factors in Infants Less Than 6 Months in Nepal
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5907881/
SHA: ef821e34873d4752ecae41cd9dfc08a5e6db45e2
Authors: Hughes, Michelle M; Englund, Janet A; Kuypers, Jane; Tielsch, James M; Khatry, Subarna K; Shrestha, Laxman; LeClerq, Steven C; Steinhoff, Mark; Katz, Joanne
Date: 2017-03-01
DOI: 10.1093/jpids/piw079
License: cc-by
Abstract: BACKGROUND: Pertussis is estimated to cause 2 percent of childhood deaths globally and is a growing public health problem in developed countries despite high vaccination coverage. Infants are at greatest risk of morbidity and mortality. Maternal vaccination during pregnancy may be effective to prevent pertussis in young infants, but population-based estimates of disease burden in infants are lacking, particularly in low-income countries. The objective of this study was to estimate the incidence of pertussis in infants less than 6 months of age in Sarlahi District, Nepal. METHODS: Nested within a population-based randomized controlled trial of influenza vaccination during pregnancy, infants were visited weekly from birth through 6 months to assess respiratory illness in the prior week. If any respiratory symptoms had occurred, a nasal swab was collected and tested with a multitarget pertussis polymerase chain reaction (PCR) assay. The prospective cohort study includes infants observed between May 2011 and August 2014. RESULTS: The incidence of PCR-confirmed Bordetella pertussis was 13.3 cases per 1000 infant-years (95% confidence interval, 7.7–21.3) in a cohort of 3483 infants with at least 1 day of follow-up. CONCLUSIONS: In a population-based active home surveillance for respiratory illness, a low risk for pertussis was estimated among infants in rural Nepal. Nepal’s immunization program, which includes a childhood whole cell pertussis vaccine, may be effective in controlling pertussis in infants.
Text: A resurgence of pertussis across age groups has occurred in several countries in recent years [1] . Middle-and high-income countries that use an acellular pertussis vaccine for the primary vaccination series have been particularly affected [2, 3] , and infants and adolescents have experienced the greatest increase [4] . Factors that may contribute to the increased risk of pertussis include rapidly waning immunity from those vaccinated with acellular vaccines [1, 5, 6] , asymptomatic transmission from individuals vaccinated with acellular vaccines [7] , genetic adaption of Bordetella pertussis [8] , vaccination delay or refusal [9] , improved surveillance and laboratory capabilities [2] , and overall increased awareness of the continuing circulation of B pertussis [1] . Some countries experiencing epidemic pertussis, including the United States, United Kingdom, and Argentina, now recommend pertussis immunization in pregnancy and vaccination of close contacts [10, 11] to protect the youngest infants from pertussis before they can be vaccinated themselves [12] . Recent data from maternal vaccination trials demonstrate the ability of antibodies to be transferred from mothers to their infants in pregnancy and their persistence in infants [13] .
Global estimates of pertussis show the highest childhood burden in Southeast Asia [14] . In this region, maternal pertussis vaccination during pregnancy may be a way to protect infants, similar to the approach using tetanus toxoid vaccine. However, globally only 1 population-based estimate of pertussis in infants from birth has been conducted (Senegal) [15] , and surveillance and laboratory capabilities in Asia are lacking [16, 17] . The World Health Organization (WHO) recently recommended that countries using whole cell pertussis vaccines continue to do so in light of recent data indicating that acellular pertussis vaccines are less effective than whole cell pertussis vaccines [18] . Population-based data are needed, especially in low-income settings, to provide a more accurate estimate of the burden of pertussis in infants to inform childhood and maternal immunization policies [19, 20] .
We report on a prospective cohort study following infants weekly in their homes to monitor for pertussis disease from birth to age 6 months. The objective was to provide a population-based estimate of laboratory-confirmed pertussis incidence in infants less than 6 months of age in the Sarlahi District, Nepal.
The study was nested within 2 consecutive randomized controlled trials of maternal influenza vaccination during pregnancy set in the Sarlahi District, located in the central Terai (low-lying plains) region of Nepal [21] . At the start of the trial, prevalent pregnancies were identified through a census of all households in the catchment area. For the duration of the trial, field workers visited all households in the communities, every 5 weeks, where married women (15-40 years) resided, for surveillance of incident pregnancies. Once a pregnancy was identified, women provided consent and were enrolled. From April 25, 2011 through September 9, 2013, women between 17 and 34 weeks gestation were randomized and vaccinated with either an influenza vaccine or placebo. The study was a population-based prospective cohort of infants followed from birth through 6 months postpartum. Approval for the study was obtained from the Institutional Review Boards at the Johns Hopkins Bloomberg School of Public Health, Cincinnati Children's Medical Center, the Institute of Medicine at Tribhuvan University, Kathmandu, and the Nepal Health Research Council. The trials are registered at Clinicaltrials.gov (NCT01034254).
At baseline, information was collected on household structure, socioeconomic status, and demographics. At enrollment, date of last menstrual period and pregnancy history data were collected. As soon as possible after delivery, the mother and infant were visited to collect detailed birth information including infant weight and breastfeeding status. From birth through 6 months, postpartum infants were visited weekly by a field worker, who recorded any infant respiratory symptoms in the past 7 days. If an infant had any of the following symptoms, a mid-nasal nylon flocked swab was collected: fever, cough, wheeze, difficulty breathing, or ear infection. Starting on August 17, 2012, new symptoms, more specific for pertussis, were added to the weekly morbidity visit: apnea, cyanosis, cough with vomit, or whoop/whooping cough. The swabs were stored for up to 1 week at room temperature in PrimeStore Molecular Transport Medium (Longhorn Diagnostics LLC, Bethesda, MD). In addition to these signs, mothers were asked which, if any, infant vaccinations were received in the past 7 days, including pertussis vaccination [22] . Mid-nasal swabs were also collected on a weekly basis from mothers from enrollment through 6 months postpartum who reported fever plus one additional morbidity (cough, sore throat, nasal congestion, or myalgia). All nasal swabs collected from infants were tested for B pertussis, Bordetella parapertussis, and Bordetella bronchispetica. Only the nasal swabs of mothers whose infants tested positive for any of these pathogens were tested for the same pathogens.
Real-time polymerase chain reaction (PCR) testing was conducted at the University of Washington's Molecular Virology Laboratory according to previously published methods [23] . Two-target PCR was used to assess the presence of 3 Bordetella species: B pertussis, B parapertussis, and B bronchiseptica. The amplified targets were chromosomal repeated insertion sequence IS481 (IS) and the polymorphic pertussis toxin ptxA promoter region (PT).
After amplification, the melting points of the amplicons were measured in an iCycler (Bio-Rad). A sample was interpreted as positive when the target(s) had a melting temperature within the species-specific acceptable range and a computed tomography ≤42. A sample was negative if none of the targets tested positive or a single positive target was not reproducible. Maternal nasal swabs were tested for those mothers whose infants tested positive for any Bordetella species
Polymerase chain reaction was also performed for several viral infections (influenza, rhinovirus [RV], respiratory syncytial virus [RSV], bocavirus [BoV], human metapneumovirus, coronavirus, adenovirus, and parainfluenza [1] [2] [3] [4] ) as previously described [21] .
Of 3693 women enrolled, 3646 infants were live born to 3621 women (Supplementary Figure 1 ). Infants were included in this analysis if they were followed for any length of the follow-up period (0 to 180 days); median total follow-up was 146 days per infant (Supplementary Figure 2) . The final dataset consists of 3483 infants, contributing 1280 infant-years of observation, with at least 1 follow-up visit during the first 6 months. This includes infants from the entire trial period, both before and after more pertussis-specific additions to the weekly symptom questionnaire.
At baseline, data on household structure were gathered. At enrollment, women reported their literacy status (binary) and pregnancy history. The field workers identified their ethnicity into 2 broad groups (Pahadi, a group originating from the hills; or Madeshi, a group originating from north India) from names and observation. Women were categorized as nulliparous or multiparous. Responses to 25 questions about household construction, water and sanitation, and household assets were used to develop an index to measure the socioeconomic status of households. Binary variables for each of the 25 questions and a mean SES score were calculated for each household.
Gestational age was measured using a woman's report of date of last menstrual period during pregnancy surveillance. Birth weight was collected as soon as possible after birth using a digital scale (Tanita model BD-585, precision to nearest 10 grams). Birth weights collected >72 hours after birth were excluded from the analysis. Small for gestational age (SGA) was calculated using the sex-specific 10th percentile cutoff described by Alexander et al [24] and the INTERGROWTH-21 standards [25] . Women were asked within how many hours of birth breastfeeding was initiated and binary breastfeeding categories were created (≤1 hour versus >1 hour postdelivery).
Incidence was calculated as the number of pertussis cases per 1000 infant-years at risk. Poisson exact 95% confidence intervals (CIs) were constructed. Characteristics of infant pertussis cases were compared with nonpertussis cases using bivariate Poisson regression. Characteristics of all pertussis respiratory episodes were compared with nonpertussis respiratory episodes; t tests were used for continuous predictors and Fisher's exact tests were used for categorical associations due to the low number of pertussis episodes. All statistical analyses were conducted in Stata/SE 14.1.
A total of 3483 infants had 4283 episodes of respiratory illness between May 18, 2011 and April 30, 2014. Thirty-nine percent (n = 1350) of infants experienced no respiratory episodes. The incidence of respiratory illness was 3.6 episodes per infant-year (95% CI, 3.5-3.7). Mean episode duration was 4.7 days (95% CI, 4.6-4.9). A total of 3930 (92%) episodes were matched to 1 or more pertussis-tested nasal swabs from 2026 infants (Supplementary Figure 1) .
Seventeen cases of B pertussis were identified from 19 nasal swabs (nasal swabs were positive on 2 consecutive weeks for 2 infants). The incidence of PCR-confirmed B pertussis was 13.3 cases per 1000-infant years (95% CI, 7.7-21.3). Five cases of B parapertussis were detected with an incidence of 3.9 cases per 1000 infant-years (95% CI, 1.3-9.1). No cases of B bronchiseptica were identified.
The average pertussis episode duration was 8 days (range, 2-33) ( Table 1 ). Mean age of onset of symptoms was 83 days (range, 19-137) (median, 80; interquartile range, 63-109). The most common symptoms were cough, difficulty breathing, and cough with vomit. None of the additional symptoms related to pertussis that were added in year 2 (cyanosis, apnea, cough with vomit, and whoop) resulted in collection of nasal swabs based solely on these additional symptoms. Pertussis episodes were statistically significantly more likely to include difficulty breathing, cough with vomit, and whoop compared with other respiratory illness. Six infants had at least 1 pertussis vaccination before pertussis disease onset (three <2 weeks and three >2 weeks before pertussis illness) with a mean of 18 days from vaccination to illness compared with 49 days for nonpertussis episodes (P = .03). Five infants received their first pertussis vaccination postpertussis disease onset, whereas 6 infants received no pertussis vaccination in the first 180 days. Three fourths of pertussis episodes were coinfected with at least 1 virus, with RV and BoV the most common. Cases of pertussis were more likely to be infected with BoV than respiratory cases due to causes other than pertussis. The majority of cases occurred between February 2013 and January 2014 (Figure 1) .
No statistically significant differences between risk factors for pertussis and nonpertussis cases ( Table 2) were documented. Given the low number of pertussis cases, the lack of a statistical association is not evidence of nonassociation. No deaths occurred in infants who had pertussis. Of the 8 mothers of B pertussis-positive infants who had a nasal swab collected (14 nasal swabs total) during their own follow-up, none were positive for any pertussis species.
The 5 B parapertussis cases were primarily male whose mothers were primiparous, literate, and Pahadi ethnicity (Supplementary Table 1 ). No mothers of infants who had B parapertussis had a nasal swab collected during follow-up.
The average B parapertussis episode duration was 4 days (Supplementary Table 2 ). Mean age of onset of symptoms was 58 days with a range of 7-95 days. The most common symptoms were cough and wheeze. Rhinovirus and RSV were the only coinfections observed. All B parapertussis cases occurred between September 2011 and February 2012 ( Figure 1 ).
A low incidence of pertussis and generally mild clinical presentation were found in infants <6 months in Nepal. To our knowledge, this represents one of the first population-based active surveillance of PCR-confirmed pertussis among young infants in Asia. Acellular pertussis vaccine trials conducted in the 1990s found the average pertussis incidence in the whole cell vaccine groups ranged from 1 to 37 cases per 1000 infantyears [26] . Our finding of 13 B pertussis cases per 1000 infantyears was on the lower end of this range. In the United States in 2014, the estimated pertussis incidence in infants less than 6 months was 2 cases per 1000 infant-years [27] , much lower than observed in our study; however, this passive surveillance system likely vastly underestimates pertussis incidence. Thus, there is a need for active surveillance data such as ours. Furthermore, given our highly sensitive case detection method, many of our pertussis cases would likely not have been detected in the previous acellular pertussis vaccine trials. More stringent respiratory symptom criteria would have lowered our incidence estimate even further. The low incidence was found in a population where pentavalent vaccine (Pentavac: Diphtheria, Tetanus, Pertussis [Whole Cell], Hepatitis-B and Haemophilus Type b Conjugate Vaccine; Serum Institute of India Pvt. Ltd), scheduled for administration at 6, 10, and 14 weeks, is received with significant delays (7% of infants received all 3 recommended pertussis vaccines by 6 months) [22] . These data support the WHO's recommendation that countries using whole cell pertussis vaccine continue to do so given that the majority of outbreaks have been concentrated in countries using the acellular pertussis vaccine [2] . Recent studies suggest that protection from acellular pertussis vaccine is not as strong or long lasting as that conferred by the whole cell pertussis vaccine [6, 28] .
Another contributing factor to the low pertussis incidence observed could be that surveillance was conducted during a period of low pertussis transmission. Pertussis is a cyclical disease, thought to peak every 2 to 4 years, and we may have captured the burden at a low circulation period [6] . We observed over 70% of our B pertussis cases over a 1-year period. This increase from earlier observation periods could indicate a temporary rise in pertussis consistent with its cyclical pattern or a true increase in the baseline burden. Previous research on pertussis seasonality has in different places and time periods demonstrated various periods of peak transmission or no discernable patterns [29, 30] . Although our data do not support a seasonal pattern, the numbers observed are too low to be conclusive.
Pertussis symptom duration and severity were mild compared with the classic pertussis case presentation. Only 3 of the 17 cases fulfilled the WHO criteria, which requires a minimum of 2 weeks of cough, whoop, or posttussive vomiting [31] . Studies on pertussis in infants have generally been clinic-based, hospital-based, or in an outbreak, which therefore required a certain severity of illness for parents to recognize a need for medical attention [29, 30, 32] . These study designs and passive surveillance efforts therefore may have missed milder pertussis cases [33] . Our study, which required only 1 respiratory symptom for a nasal swab to be collected, had increased sensitivity to detect a range of pertussis case presentations. An alternative explanation for the mild cases seen could be an increase in the proportion of mild compared with severe pertussis cases in Nepal.
Although cough, difficulty breathing, and cough with vomit were the most common symptoms, no symptom was present in all B pertussis cases. During an epidemic period in Washington state, among infants <1 year, who had a minimum of 14 days cough plus an additional symptom, 82% had posttussive emesis, 29% had apnea, 26% had whoop, and 42% had cyanosis [32] . A study of US neonates with pertussis showed the symptom prevalence to be 97% for cough, 91% for cyanosis, 58% for apnea, and 3% for fever [34] . Our study found lower or equal symptom prevalence with the exception of fever. Fever prevalence was higher in our study, similar to that found in Peru [29] .
Although not statistically significant, infants with pertussis were more likely to have been born preterm, low birth weight, and SGA, and their mothers were more likely to be primiparous. These findings are similar to previous studies showing no difference in pertussis cases by sex [29, 35, 36] or crowding [35] but showing differences by birth weight [36] . Coinfections were common, consistent with findings from other hospital-based studies [33] . Codetection of B pertussis and B parapertussis with respiratory viruses may be due to asymptomatic pertussis carriage. The incidence of B parapertussis of 4 cases per 1000 person-years was comparable to that of 2 per 1000 person-years found in the Italian acellular pertussis vaccine trial in 1992-1993 [37] . The duration of illness was shorter for B parapertussis with a maximum duration of 6 days compared with a maximum of 33 days for B pertussis. A milder presentation is consistent with clinical knowledge of B parapertussis infection [37, 38] . Bordetella parapertussis cases occurred only during a 5-month period.
There were several study design limitations. We cannot be certain whether the reported symptoms were caused by pertussis, another organism, or whether symptoms were related to 2 or more etiologic agents. We were unable to perform multivariate regression modeling for characteristics associated with pertussis disease and pertussis cases due to the small number of cases we detected.
Infant respiratory symptoms were reported by parents, who may have missed signs that might have been observed by a healthcare worker. However, the criteria for collection of the nasal swab were broad and did not require sophisticated clinical skills. However, apnea and cyanosis may have been difficult for parents to identify. Although the criteria for specimen collection changed in year 2, no infant experienced a pertussis-specific symptom in isolation without also having one of the originally specified respiratory symptoms. These data support our assumption that we were unlikely to have missed pertussis cases in year 1 with our less sensitive respiratory symptom criteria.
Nasal swabs were collected in the mid-nasal region for influenza virus detection, which may have lowered the sensitivity of pertussis detection. In a field site, the acceptability of an additional nasopharyngeal swab would likely have increased the participant refusal rate. This would have decreased the generalizability of our results to the entire population. Although nasopharyngeal swabs or nasopharyngeal aspirates are the recommended specimen collection method [39] , the nasopharyngeal region was established as the collection area of choice when the diagnostic measure was culture, which has low sensitivity. Recent data demonstrated the comparability of using mid-nasal versus nasopharyngeal swabs in PCR pertussis detection [40] .
Strengths of the study included being a population-based, prospective study, with very low refusal rates. Risk factors, clinical symptoms, and coinfections were prospectively identified without the potential bias that may occur when these data are collected retrospectively or in clinical settings. The community-based design allows generalizability of these results to the entire population and not just those seeking care at a health facility or in an outbreak situation. The Sarlahi District is located in the Terai region where the majority of Nepalese reside, and it has similar demographics to the entire population of Nepal [41] . Sarlahi's location near sea level and on the border with India supports the generalizability of these results to many populations living on the Indian subcontinent. The weekly active surveillance with sensitive criteria for pertussis testing was able to detect mild and atypical pertussis cases, which may have been missed by previous traditional surveillance. The multitarget PCR method allowed highly sensitive and specific detection of 2 additional Bordetella species beyond the primary B pertussis target.
We observed a low incidence of pertussis in infants in a whole cell vaccine environment. Pertussis cases were generally milder than expected compared with traditional pertussis clinical definitions. These data support clinicians considering pertussis in their differential diagnosis of infants with mild respiratory symptoms. Policymakers in Nepal will need to weigh the benefit of an additional prenatal pertussis vaccine or a switch to acellular primary pertussis vaccine with the low burden of pertussis in infants less than 6 months. Our study demonstrated that mid-nasal swabs were able to detect pertussis using a sensitive multitarget PCR. The less invasive mid-nasal nasal swab is an attractive alternative for pertussis nasal swab collection, and further research is needed to compare this collection site with nasopharyngeal swabs. In the future, this method may enhance population-based surveillance efforts.
|
What type of swabs are used to sample patients with pertussis?
| 2,171 |
mid-nasal nylon flocked
| 6,206 |
1,574 |
Population-Based Pertussis Incidence and Risk Factors in Infants Less Than 6 Months in Nepal
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5907881/
SHA: ef821e34873d4752ecae41cd9dfc08a5e6db45e2
Authors: Hughes, Michelle M; Englund, Janet A; Kuypers, Jane; Tielsch, James M; Khatry, Subarna K; Shrestha, Laxman; LeClerq, Steven C; Steinhoff, Mark; Katz, Joanne
Date: 2017-03-01
DOI: 10.1093/jpids/piw079
License: cc-by
Abstract: BACKGROUND: Pertussis is estimated to cause 2 percent of childhood deaths globally and is a growing public health problem in developed countries despite high vaccination coverage. Infants are at greatest risk of morbidity and mortality. Maternal vaccination during pregnancy may be effective to prevent pertussis in young infants, but population-based estimates of disease burden in infants are lacking, particularly in low-income countries. The objective of this study was to estimate the incidence of pertussis in infants less than 6 months of age in Sarlahi District, Nepal. METHODS: Nested within a population-based randomized controlled trial of influenza vaccination during pregnancy, infants were visited weekly from birth through 6 months to assess respiratory illness in the prior week. If any respiratory symptoms had occurred, a nasal swab was collected and tested with a multitarget pertussis polymerase chain reaction (PCR) assay. The prospective cohort study includes infants observed between May 2011 and August 2014. RESULTS: The incidence of PCR-confirmed Bordetella pertussis was 13.3 cases per 1000 infant-years (95% confidence interval, 7.7–21.3) in a cohort of 3483 infants with at least 1 day of follow-up. CONCLUSIONS: In a population-based active home surveillance for respiratory illness, a low risk for pertussis was estimated among infants in rural Nepal. Nepal’s immunization program, which includes a childhood whole cell pertussis vaccine, may be effective in controlling pertussis in infants.
Text: A resurgence of pertussis across age groups has occurred in several countries in recent years [1] . Middle-and high-income countries that use an acellular pertussis vaccine for the primary vaccination series have been particularly affected [2, 3] , and infants and adolescents have experienced the greatest increase [4] . Factors that may contribute to the increased risk of pertussis include rapidly waning immunity from those vaccinated with acellular vaccines [1, 5, 6] , asymptomatic transmission from individuals vaccinated with acellular vaccines [7] , genetic adaption of Bordetella pertussis [8] , vaccination delay or refusal [9] , improved surveillance and laboratory capabilities [2] , and overall increased awareness of the continuing circulation of B pertussis [1] . Some countries experiencing epidemic pertussis, including the United States, United Kingdom, and Argentina, now recommend pertussis immunization in pregnancy and vaccination of close contacts [10, 11] to protect the youngest infants from pertussis before they can be vaccinated themselves [12] . Recent data from maternal vaccination trials demonstrate the ability of antibodies to be transferred from mothers to their infants in pregnancy and their persistence in infants [13] .
Global estimates of pertussis show the highest childhood burden in Southeast Asia [14] . In this region, maternal pertussis vaccination during pregnancy may be a way to protect infants, similar to the approach using tetanus toxoid vaccine. However, globally only 1 population-based estimate of pertussis in infants from birth has been conducted (Senegal) [15] , and surveillance and laboratory capabilities in Asia are lacking [16, 17] . The World Health Organization (WHO) recently recommended that countries using whole cell pertussis vaccines continue to do so in light of recent data indicating that acellular pertussis vaccines are less effective than whole cell pertussis vaccines [18] . Population-based data are needed, especially in low-income settings, to provide a more accurate estimate of the burden of pertussis in infants to inform childhood and maternal immunization policies [19, 20] .
We report on a prospective cohort study following infants weekly in their homes to monitor for pertussis disease from birth to age 6 months. The objective was to provide a population-based estimate of laboratory-confirmed pertussis incidence in infants less than 6 months of age in the Sarlahi District, Nepal.
The study was nested within 2 consecutive randomized controlled trials of maternal influenza vaccination during pregnancy set in the Sarlahi District, located in the central Terai (low-lying plains) region of Nepal [21] . At the start of the trial, prevalent pregnancies were identified through a census of all households in the catchment area. For the duration of the trial, field workers visited all households in the communities, every 5 weeks, where married women (15-40 years) resided, for surveillance of incident pregnancies. Once a pregnancy was identified, women provided consent and were enrolled. From April 25, 2011 through September 9, 2013, women between 17 and 34 weeks gestation were randomized and vaccinated with either an influenza vaccine or placebo. The study was a population-based prospective cohort of infants followed from birth through 6 months postpartum. Approval for the study was obtained from the Institutional Review Boards at the Johns Hopkins Bloomberg School of Public Health, Cincinnati Children's Medical Center, the Institute of Medicine at Tribhuvan University, Kathmandu, and the Nepal Health Research Council. The trials are registered at Clinicaltrials.gov (NCT01034254).
At baseline, information was collected on household structure, socioeconomic status, and demographics. At enrollment, date of last menstrual period and pregnancy history data were collected. As soon as possible after delivery, the mother and infant were visited to collect detailed birth information including infant weight and breastfeeding status. From birth through 6 months, postpartum infants were visited weekly by a field worker, who recorded any infant respiratory symptoms in the past 7 days. If an infant had any of the following symptoms, a mid-nasal nylon flocked swab was collected: fever, cough, wheeze, difficulty breathing, or ear infection. Starting on August 17, 2012, new symptoms, more specific for pertussis, were added to the weekly morbidity visit: apnea, cyanosis, cough with vomit, or whoop/whooping cough. The swabs were stored for up to 1 week at room temperature in PrimeStore Molecular Transport Medium (Longhorn Diagnostics LLC, Bethesda, MD). In addition to these signs, mothers were asked which, if any, infant vaccinations were received in the past 7 days, including pertussis vaccination [22] . Mid-nasal swabs were also collected on a weekly basis from mothers from enrollment through 6 months postpartum who reported fever plus one additional morbidity (cough, sore throat, nasal congestion, or myalgia). All nasal swabs collected from infants were tested for B pertussis, Bordetella parapertussis, and Bordetella bronchispetica. Only the nasal swabs of mothers whose infants tested positive for any of these pathogens were tested for the same pathogens.
Real-time polymerase chain reaction (PCR) testing was conducted at the University of Washington's Molecular Virology Laboratory according to previously published methods [23] . Two-target PCR was used to assess the presence of 3 Bordetella species: B pertussis, B parapertussis, and B bronchiseptica. The amplified targets were chromosomal repeated insertion sequence IS481 (IS) and the polymorphic pertussis toxin ptxA promoter region (PT).
After amplification, the melting points of the amplicons were measured in an iCycler (Bio-Rad). A sample was interpreted as positive when the target(s) had a melting temperature within the species-specific acceptable range and a computed tomography ≤42. A sample was negative if none of the targets tested positive or a single positive target was not reproducible. Maternal nasal swabs were tested for those mothers whose infants tested positive for any Bordetella species
Polymerase chain reaction was also performed for several viral infections (influenza, rhinovirus [RV], respiratory syncytial virus [RSV], bocavirus [BoV], human metapneumovirus, coronavirus, adenovirus, and parainfluenza [1] [2] [3] [4] ) as previously described [21] .
Of 3693 women enrolled, 3646 infants were live born to 3621 women (Supplementary Figure 1 ). Infants were included in this analysis if they were followed for any length of the follow-up period (0 to 180 days); median total follow-up was 146 days per infant (Supplementary Figure 2) . The final dataset consists of 3483 infants, contributing 1280 infant-years of observation, with at least 1 follow-up visit during the first 6 months. This includes infants from the entire trial period, both before and after more pertussis-specific additions to the weekly symptom questionnaire.
At baseline, data on household structure were gathered. At enrollment, women reported their literacy status (binary) and pregnancy history. The field workers identified their ethnicity into 2 broad groups (Pahadi, a group originating from the hills; or Madeshi, a group originating from north India) from names and observation. Women were categorized as nulliparous or multiparous. Responses to 25 questions about household construction, water and sanitation, and household assets were used to develop an index to measure the socioeconomic status of households. Binary variables for each of the 25 questions and a mean SES score were calculated for each household.
Gestational age was measured using a woman's report of date of last menstrual period during pregnancy surveillance. Birth weight was collected as soon as possible after birth using a digital scale (Tanita model BD-585, precision to nearest 10 grams). Birth weights collected >72 hours after birth were excluded from the analysis. Small for gestational age (SGA) was calculated using the sex-specific 10th percentile cutoff described by Alexander et al [24] and the INTERGROWTH-21 standards [25] . Women were asked within how many hours of birth breastfeeding was initiated and binary breastfeeding categories were created (≤1 hour versus >1 hour postdelivery).
Incidence was calculated as the number of pertussis cases per 1000 infant-years at risk. Poisson exact 95% confidence intervals (CIs) were constructed. Characteristics of infant pertussis cases were compared with nonpertussis cases using bivariate Poisson regression. Characteristics of all pertussis respiratory episodes were compared with nonpertussis respiratory episodes; t tests were used for continuous predictors and Fisher's exact tests were used for categorical associations due to the low number of pertussis episodes. All statistical analyses were conducted in Stata/SE 14.1.
A total of 3483 infants had 4283 episodes of respiratory illness between May 18, 2011 and April 30, 2014. Thirty-nine percent (n = 1350) of infants experienced no respiratory episodes. The incidence of respiratory illness was 3.6 episodes per infant-year (95% CI, 3.5-3.7). Mean episode duration was 4.7 days (95% CI, 4.6-4.9). A total of 3930 (92%) episodes were matched to 1 or more pertussis-tested nasal swabs from 2026 infants (Supplementary Figure 1) .
Seventeen cases of B pertussis were identified from 19 nasal swabs (nasal swabs were positive on 2 consecutive weeks for 2 infants). The incidence of PCR-confirmed B pertussis was 13.3 cases per 1000-infant years (95% CI, 7.7-21.3). Five cases of B parapertussis were detected with an incidence of 3.9 cases per 1000 infant-years (95% CI, 1.3-9.1). No cases of B bronchiseptica were identified.
The average pertussis episode duration was 8 days (range, 2-33) ( Table 1 ). Mean age of onset of symptoms was 83 days (range, 19-137) (median, 80; interquartile range, 63-109). The most common symptoms were cough, difficulty breathing, and cough with vomit. None of the additional symptoms related to pertussis that were added in year 2 (cyanosis, apnea, cough with vomit, and whoop) resulted in collection of nasal swabs based solely on these additional symptoms. Pertussis episodes were statistically significantly more likely to include difficulty breathing, cough with vomit, and whoop compared with other respiratory illness. Six infants had at least 1 pertussis vaccination before pertussis disease onset (three <2 weeks and three >2 weeks before pertussis illness) with a mean of 18 days from vaccination to illness compared with 49 days for nonpertussis episodes (P = .03). Five infants received their first pertussis vaccination postpertussis disease onset, whereas 6 infants received no pertussis vaccination in the first 180 days. Three fourths of pertussis episodes were coinfected with at least 1 virus, with RV and BoV the most common. Cases of pertussis were more likely to be infected with BoV than respiratory cases due to causes other than pertussis. The majority of cases occurred between February 2013 and January 2014 (Figure 1) .
No statistically significant differences between risk factors for pertussis and nonpertussis cases ( Table 2) were documented. Given the low number of pertussis cases, the lack of a statistical association is not evidence of nonassociation. No deaths occurred in infants who had pertussis. Of the 8 mothers of B pertussis-positive infants who had a nasal swab collected (14 nasal swabs total) during their own follow-up, none were positive for any pertussis species.
The 5 B parapertussis cases were primarily male whose mothers were primiparous, literate, and Pahadi ethnicity (Supplementary Table 1 ). No mothers of infants who had B parapertussis had a nasal swab collected during follow-up.
The average B parapertussis episode duration was 4 days (Supplementary Table 2 ). Mean age of onset of symptoms was 58 days with a range of 7-95 days. The most common symptoms were cough and wheeze. Rhinovirus and RSV were the only coinfections observed. All B parapertussis cases occurred between September 2011 and February 2012 ( Figure 1 ).
A low incidence of pertussis and generally mild clinical presentation were found in infants <6 months in Nepal. To our knowledge, this represents one of the first population-based active surveillance of PCR-confirmed pertussis among young infants in Asia. Acellular pertussis vaccine trials conducted in the 1990s found the average pertussis incidence in the whole cell vaccine groups ranged from 1 to 37 cases per 1000 infantyears [26] . Our finding of 13 B pertussis cases per 1000 infantyears was on the lower end of this range. In the United States in 2014, the estimated pertussis incidence in infants less than 6 months was 2 cases per 1000 infant-years [27] , much lower than observed in our study; however, this passive surveillance system likely vastly underestimates pertussis incidence. Thus, there is a need for active surveillance data such as ours. Furthermore, given our highly sensitive case detection method, many of our pertussis cases would likely not have been detected in the previous acellular pertussis vaccine trials. More stringent respiratory symptom criteria would have lowered our incidence estimate even further. The low incidence was found in a population where pentavalent vaccine (Pentavac: Diphtheria, Tetanus, Pertussis [Whole Cell], Hepatitis-B and Haemophilus Type b Conjugate Vaccine; Serum Institute of India Pvt. Ltd), scheduled for administration at 6, 10, and 14 weeks, is received with significant delays (7% of infants received all 3 recommended pertussis vaccines by 6 months) [22] . These data support the WHO's recommendation that countries using whole cell pertussis vaccine continue to do so given that the majority of outbreaks have been concentrated in countries using the acellular pertussis vaccine [2] . Recent studies suggest that protection from acellular pertussis vaccine is not as strong or long lasting as that conferred by the whole cell pertussis vaccine [6, 28] .
Another contributing factor to the low pertussis incidence observed could be that surveillance was conducted during a period of low pertussis transmission. Pertussis is a cyclical disease, thought to peak every 2 to 4 years, and we may have captured the burden at a low circulation period [6] . We observed over 70% of our B pertussis cases over a 1-year period. This increase from earlier observation periods could indicate a temporary rise in pertussis consistent with its cyclical pattern or a true increase in the baseline burden. Previous research on pertussis seasonality has in different places and time periods demonstrated various periods of peak transmission or no discernable patterns [29, 30] . Although our data do not support a seasonal pattern, the numbers observed are too low to be conclusive.
Pertussis symptom duration and severity were mild compared with the classic pertussis case presentation. Only 3 of the 17 cases fulfilled the WHO criteria, which requires a minimum of 2 weeks of cough, whoop, or posttussive vomiting [31] . Studies on pertussis in infants have generally been clinic-based, hospital-based, or in an outbreak, which therefore required a certain severity of illness for parents to recognize a need for medical attention [29, 30, 32] . These study designs and passive surveillance efforts therefore may have missed milder pertussis cases [33] . Our study, which required only 1 respiratory symptom for a nasal swab to be collected, had increased sensitivity to detect a range of pertussis case presentations. An alternative explanation for the mild cases seen could be an increase in the proportion of mild compared with severe pertussis cases in Nepal.
Although cough, difficulty breathing, and cough with vomit were the most common symptoms, no symptom was present in all B pertussis cases. During an epidemic period in Washington state, among infants <1 year, who had a minimum of 14 days cough plus an additional symptom, 82% had posttussive emesis, 29% had apnea, 26% had whoop, and 42% had cyanosis [32] . A study of US neonates with pertussis showed the symptom prevalence to be 97% for cough, 91% for cyanosis, 58% for apnea, and 3% for fever [34] . Our study found lower or equal symptom prevalence with the exception of fever. Fever prevalence was higher in our study, similar to that found in Peru [29] .
Although not statistically significant, infants with pertussis were more likely to have been born preterm, low birth weight, and SGA, and their mothers were more likely to be primiparous. These findings are similar to previous studies showing no difference in pertussis cases by sex [29, 35, 36] or crowding [35] but showing differences by birth weight [36] . Coinfections were common, consistent with findings from other hospital-based studies [33] . Codetection of B pertussis and B parapertussis with respiratory viruses may be due to asymptomatic pertussis carriage. The incidence of B parapertussis of 4 cases per 1000 person-years was comparable to that of 2 per 1000 person-years found in the Italian acellular pertussis vaccine trial in 1992-1993 [37] . The duration of illness was shorter for B parapertussis with a maximum duration of 6 days compared with a maximum of 33 days for B pertussis. A milder presentation is consistent with clinical knowledge of B parapertussis infection [37, 38] . Bordetella parapertussis cases occurred only during a 5-month period.
There were several study design limitations. We cannot be certain whether the reported symptoms were caused by pertussis, another organism, or whether symptoms were related to 2 or more etiologic agents. We were unable to perform multivariate regression modeling for characteristics associated with pertussis disease and pertussis cases due to the small number of cases we detected.
Infant respiratory symptoms were reported by parents, who may have missed signs that might have been observed by a healthcare worker. However, the criteria for collection of the nasal swab were broad and did not require sophisticated clinical skills. However, apnea and cyanosis may have been difficult for parents to identify. Although the criteria for specimen collection changed in year 2, no infant experienced a pertussis-specific symptom in isolation without also having one of the originally specified respiratory symptoms. These data support our assumption that we were unlikely to have missed pertussis cases in year 1 with our less sensitive respiratory symptom criteria.
Nasal swabs were collected in the mid-nasal region for influenza virus detection, which may have lowered the sensitivity of pertussis detection. In a field site, the acceptability of an additional nasopharyngeal swab would likely have increased the participant refusal rate. This would have decreased the generalizability of our results to the entire population. Although nasopharyngeal swabs or nasopharyngeal aspirates are the recommended specimen collection method [39] , the nasopharyngeal region was established as the collection area of choice when the diagnostic measure was culture, which has low sensitivity. Recent data demonstrated the comparability of using mid-nasal versus nasopharyngeal swabs in PCR pertussis detection [40] .
Strengths of the study included being a population-based, prospective study, with very low refusal rates. Risk factors, clinical symptoms, and coinfections were prospectively identified without the potential bias that may occur when these data are collected retrospectively or in clinical settings. The community-based design allows generalizability of these results to the entire population and not just those seeking care at a health facility or in an outbreak situation. The Sarlahi District is located in the Terai region where the majority of Nepalese reside, and it has similar demographics to the entire population of Nepal [41] . Sarlahi's location near sea level and on the border with India supports the generalizability of these results to many populations living on the Indian subcontinent. The weekly active surveillance with sensitive criteria for pertussis testing was able to detect mild and atypical pertussis cases, which may have been missed by previous traditional surveillance. The multitarget PCR method allowed highly sensitive and specific detection of 2 additional Bordetella species beyond the primary B pertussis target.
We observed a low incidence of pertussis in infants in a whole cell vaccine environment. Pertussis cases were generally milder than expected compared with traditional pertussis clinical definitions. These data support clinicians considering pertussis in their differential diagnosis of infants with mild respiratory symptoms. Policymakers in Nepal will need to weigh the benefit of an additional prenatal pertussis vaccine or a switch to acellular primary pertussis vaccine with the low burden of pertussis in infants less than 6 months. Our study demonstrated that mid-nasal swabs were able to detect pertussis using a sensitive multitarget PCR. The less invasive mid-nasal nasal swab is an attractive alternative for pertussis nasal swab collection, and further research is needed to compare this collection site with nasopharyngeal swabs. In the future, this method may enhance population-based surveillance efforts.
|
How frequently do pertussis outbreaks peak?
| 2,173 |
every 2 to 4 years
| 16,315 |
1,574 |
Population-Based Pertussis Incidence and Risk Factors in Infants Less Than 6 Months in Nepal
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5907881/
SHA: ef821e34873d4752ecae41cd9dfc08a5e6db45e2
Authors: Hughes, Michelle M; Englund, Janet A; Kuypers, Jane; Tielsch, James M; Khatry, Subarna K; Shrestha, Laxman; LeClerq, Steven C; Steinhoff, Mark; Katz, Joanne
Date: 2017-03-01
DOI: 10.1093/jpids/piw079
License: cc-by
Abstract: BACKGROUND: Pertussis is estimated to cause 2 percent of childhood deaths globally and is a growing public health problem in developed countries despite high vaccination coverage. Infants are at greatest risk of morbidity and mortality. Maternal vaccination during pregnancy may be effective to prevent pertussis in young infants, but population-based estimates of disease burden in infants are lacking, particularly in low-income countries. The objective of this study was to estimate the incidence of pertussis in infants less than 6 months of age in Sarlahi District, Nepal. METHODS: Nested within a population-based randomized controlled trial of influenza vaccination during pregnancy, infants were visited weekly from birth through 6 months to assess respiratory illness in the prior week. If any respiratory symptoms had occurred, a nasal swab was collected and tested with a multitarget pertussis polymerase chain reaction (PCR) assay. The prospective cohort study includes infants observed between May 2011 and August 2014. RESULTS: The incidence of PCR-confirmed Bordetella pertussis was 13.3 cases per 1000 infant-years (95% confidence interval, 7.7–21.3) in a cohort of 3483 infants with at least 1 day of follow-up. CONCLUSIONS: In a population-based active home surveillance for respiratory illness, a low risk for pertussis was estimated among infants in rural Nepal. Nepal’s immunization program, which includes a childhood whole cell pertussis vaccine, may be effective in controlling pertussis in infants.
Text: A resurgence of pertussis across age groups has occurred in several countries in recent years [1] . Middle-and high-income countries that use an acellular pertussis vaccine for the primary vaccination series have been particularly affected [2, 3] , and infants and adolescents have experienced the greatest increase [4] . Factors that may contribute to the increased risk of pertussis include rapidly waning immunity from those vaccinated with acellular vaccines [1, 5, 6] , asymptomatic transmission from individuals vaccinated with acellular vaccines [7] , genetic adaption of Bordetella pertussis [8] , vaccination delay or refusal [9] , improved surveillance and laboratory capabilities [2] , and overall increased awareness of the continuing circulation of B pertussis [1] . Some countries experiencing epidemic pertussis, including the United States, United Kingdom, and Argentina, now recommend pertussis immunization in pregnancy and vaccination of close contacts [10, 11] to protect the youngest infants from pertussis before they can be vaccinated themselves [12] . Recent data from maternal vaccination trials demonstrate the ability of antibodies to be transferred from mothers to their infants in pregnancy and their persistence in infants [13] .
Global estimates of pertussis show the highest childhood burden in Southeast Asia [14] . In this region, maternal pertussis vaccination during pregnancy may be a way to protect infants, similar to the approach using tetanus toxoid vaccine. However, globally only 1 population-based estimate of pertussis in infants from birth has been conducted (Senegal) [15] , and surveillance and laboratory capabilities in Asia are lacking [16, 17] . The World Health Organization (WHO) recently recommended that countries using whole cell pertussis vaccines continue to do so in light of recent data indicating that acellular pertussis vaccines are less effective than whole cell pertussis vaccines [18] . Population-based data are needed, especially in low-income settings, to provide a more accurate estimate of the burden of pertussis in infants to inform childhood and maternal immunization policies [19, 20] .
We report on a prospective cohort study following infants weekly in their homes to monitor for pertussis disease from birth to age 6 months. The objective was to provide a population-based estimate of laboratory-confirmed pertussis incidence in infants less than 6 months of age in the Sarlahi District, Nepal.
The study was nested within 2 consecutive randomized controlled trials of maternal influenza vaccination during pregnancy set in the Sarlahi District, located in the central Terai (low-lying plains) region of Nepal [21] . At the start of the trial, prevalent pregnancies were identified through a census of all households in the catchment area. For the duration of the trial, field workers visited all households in the communities, every 5 weeks, where married women (15-40 years) resided, for surveillance of incident pregnancies. Once a pregnancy was identified, women provided consent and were enrolled. From April 25, 2011 through September 9, 2013, women between 17 and 34 weeks gestation were randomized and vaccinated with either an influenza vaccine or placebo. The study was a population-based prospective cohort of infants followed from birth through 6 months postpartum. Approval for the study was obtained from the Institutional Review Boards at the Johns Hopkins Bloomberg School of Public Health, Cincinnati Children's Medical Center, the Institute of Medicine at Tribhuvan University, Kathmandu, and the Nepal Health Research Council. The trials are registered at Clinicaltrials.gov (NCT01034254).
At baseline, information was collected on household structure, socioeconomic status, and demographics. At enrollment, date of last menstrual period and pregnancy history data were collected. As soon as possible after delivery, the mother and infant were visited to collect detailed birth information including infant weight and breastfeeding status. From birth through 6 months, postpartum infants were visited weekly by a field worker, who recorded any infant respiratory symptoms in the past 7 days. If an infant had any of the following symptoms, a mid-nasal nylon flocked swab was collected: fever, cough, wheeze, difficulty breathing, or ear infection. Starting on August 17, 2012, new symptoms, more specific for pertussis, were added to the weekly morbidity visit: apnea, cyanosis, cough with vomit, or whoop/whooping cough. The swabs were stored for up to 1 week at room temperature in PrimeStore Molecular Transport Medium (Longhorn Diagnostics LLC, Bethesda, MD). In addition to these signs, mothers were asked which, if any, infant vaccinations were received in the past 7 days, including pertussis vaccination [22] . Mid-nasal swabs were also collected on a weekly basis from mothers from enrollment through 6 months postpartum who reported fever plus one additional morbidity (cough, sore throat, nasal congestion, or myalgia). All nasal swabs collected from infants were tested for B pertussis, Bordetella parapertussis, and Bordetella bronchispetica. Only the nasal swabs of mothers whose infants tested positive for any of these pathogens were tested for the same pathogens.
Real-time polymerase chain reaction (PCR) testing was conducted at the University of Washington's Molecular Virology Laboratory according to previously published methods [23] . Two-target PCR was used to assess the presence of 3 Bordetella species: B pertussis, B parapertussis, and B bronchiseptica. The amplified targets were chromosomal repeated insertion sequence IS481 (IS) and the polymorphic pertussis toxin ptxA promoter region (PT).
After amplification, the melting points of the amplicons were measured in an iCycler (Bio-Rad). A sample was interpreted as positive when the target(s) had a melting temperature within the species-specific acceptable range and a computed tomography ≤42. A sample was negative if none of the targets tested positive or a single positive target was not reproducible. Maternal nasal swabs were tested for those mothers whose infants tested positive for any Bordetella species
Polymerase chain reaction was also performed for several viral infections (influenza, rhinovirus [RV], respiratory syncytial virus [RSV], bocavirus [BoV], human metapneumovirus, coronavirus, adenovirus, and parainfluenza [1] [2] [3] [4] ) as previously described [21] .
Of 3693 women enrolled, 3646 infants were live born to 3621 women (Supplementary Figure 1 ). Infants were included in this analysis if they were followed for any length of the follow-up period (0 to 180 days); median total follow-up was 146 days per infant (Supplementary Figure 2) . The final dataset consists of 3483 infants, contributing 1280 infant-years of observation, with at least 1 follow-up visit during the first 6 months. This includes infants from the entire trial period, both before and after more pertussis-specific additions to the weekly symptom questionnaire.
At baseline, data on household structure were gathered. At enrollment, women reported their literacy status (binary) and pregnancy history. The field workers identified their ethnicity into 2 broad groups (Pahadi, a group originating from the hills; or Madeshi, a group originating from north India) from names and observation. Women were categorized as nulliparous or multiparous. Responses to 25 questions about household construction, water and sanitation, and household assets were used to develop an index to measure the socioeconomic status of households. Binary variables for each of the 25 questions and a mean SES score were calculated for each household.
Gestational age was measured using a woman's report of date of last menstrual period during pregnancy surveillance. Birth weight was collected as soon as possible after birth using a digital scale (Tanita model BD-585, precision to nearest 10 grams). Birth weights collected >72 hours after birth were excluded from the analysis. Small for gestational age (SGA) was calculated using the sex-specific 10th percentile cutoff described by Alexander et al [24] and the INTERGROWTH-21 standards [25] . Women were asked within how many hours of birth breastfeeding was initiated and binary breastfeeding categories were created (≤1 hour versus >1 hour postdelivery).
Incidence was calculated as the number of pertussis cases per 1000 infant-years at risk. Poisson exact 95% confidence intervals (CIs) were constructed. Characteristics of infant pertussis cases were compared with nonpertussis cases using bivariate Poisson regression. Characteristics of all pertussis respiratory episodes were compared with nonpertussis respiratory episodes; t tests were used for continuous predictors and Fisher's exact tests were used for categorical associations due to the low number of pertussis episodes. All statistical analyses were conducted in Stata/SE 14.1.
A total of 3483 infants had 4283 episodes of respiratory illness between May 18, 2011 and April 30, 2014. Thirty-nine percent (n = 1350) of infants experienced no respiratory episodes. The incidence of respiratory illness was 3.6 episodes per infant-year (95% CI, 3.5-3.7). Mean episode duration was 4.7 days (95% CI, 4.6-4.9). A total of 3930 (92%) episodes were matched to 1 or more pertussis-tested nasal swabs from 2026 infants (Supplementary Figure 1) .
Seventeen cases of B pertussis were identified from 19 nasal swabs (nasal swabs were positive on 2 consecutive weeks for 2 infants). The incidence of PCR-confirmed B pertussis was 13.3 cases per 1000-infant years (95% CI, 7.7-21.3). Five cases of B parapertussis were detected with an incidence of 3.9 cases per 1000 infant-years (95% CI, 1.3-9.1). No cases of B bronchiseptica were identified.
The average pertussis episode duration was 8 days (range, 2-33) ( Table 1 ). Mean age of onset of symptoms was 83 days (range, 19-137) (median, 80; interquartile range, 63-109). The most common symptoms were cough, difficulty breathing, and cough with vomit. None of the additional symptoms related to pertussis that were added in year 2 (cyanosis, apnea, cough with vomit, and whoop) resulted in collection of nasal swabs based solely on these additional symptoms. Pertussis episodes were statistically significantly more likely to include difficulty breathing, cough with vomit, and whoop compared with other respiratory illness. Six infants had at least 1 pertussis vaccination before pertussis disease onset (three <2 weeks and three >2 weeks before pertussis illness) with a mean of 18 days from vaccination to illness compared with 49 days for nonpertussis episodes (P = .03). Five infants received their first pertussis vaccination postpertussis disease onset, whereas 6 infants received no pertussis vaccination in the first 180 days. Three fourths of pertussis episodes were coinfected with at least 1 virus, with RV and BoV the most common. Cases of pertussis were more likely to be infected with BoV than respiratory cases due to causes other than pertussis. The majority of cases occurred between February 2013 and January 2014 (Figure 1) .
No statistically significant differences between risk factors for pertussis and nonpertussis cases ( Table 2) were documented. Given the low number of pertussis cases, the lack of a statistical association is not evidence of nonassociation. No deaths occurred in infants who had pertussis. Of the 8 mothers of B pertussis-positive infants who had a nasal swab collected (14 nasal swabs total) during their own follow-up, none were positive for any pertussis species.
The 5 B parapertussis cases were primarily male whose mothers were primiparous, literate, and Pahadi ethnicity (Supplementary Table 1 ). No mothers of infants who had B parapertussis had a nasal swab collected during follow-up.
The average B parapertussis episode duration was 4 days (Supplementary Table 2 ). Mean age of onset of symptoms was 58 days with a range of 7-95 days. The most common symptoms were cough and wheeze. Rhinovirus and RSV were the only coinfections observed. All B parapertussis cases occurred between September 2011 and February 2012 ( Figure 1 ).
A low incidence of pertussis and generally mild clinical presentation were found in infants <6 months in Nepal. To our knowledge, this represents one of the first population-based active surveillance of PCR-confirmed pertussis among young infants in Asia. Acellular pertussis vaccine trials conducted in the 1990s found the average pertussis incidence in the whole cell vaccine groups ranged from 1 to 37 cases per 1000 infantyears [26] . Our finding of 13 B pertussis cases per 1000 infantyears was on the lower end of this range. In the United States in 2014, the estimated pertussis incidence in infants less than 6 months was 2 cases per 1000 infant-years [27] , much lower than observed in our study; however, this passive surveillance system likely vastly underestimates pertussis incidence. Thus, there is a need for active surveillance data such as ours. Furthermore, given our highly sensitive case detection method, many of our pertussis cases would likely not have been detected in the previous acellular pertussis vaccine trials. More stringent respiratory symptom criteria would have lowered our incidence estimate even further. The low incidence was found in a population where pentavalent vaccine (Pentavac: Diphtheria, Tetanus, Pertussis [Whole Cell], Hepatitis-B and Haemophilus Type b Conjugate Vaccine; Serum Institute of India Pvt. Ltd), scheduled for administration at 6, 10, and 14 weeks, is received with significant delays (7% of infants received all 3 recommended pertussis vaccines by 6 months) [22] . These data support the WHO's recommendation that countries using whole cell pertussis vaccine continue to do so given that the majority of outbreaks have been concentrated in countries using the acellular pertussis vaccine [2] . Recent studies suggest that protection from acellular pertussis vaccine is not as strong or long lasting as that conferred by the whole cell pertussis vaccine [6, 28] .
Another contributing factor to the low pertussis incidence observed could be that surveillance was conducted during a period of low pertussis transmission. Pertussis is a cyclical disease, thought to peak every 2 to 4 years, and we may have captured the burden at a low circulation period [6] . We observed over 70% of our B pertussis cases over a 1-year period. This increase from earlier observation periods could indicate a temporary rise in pertussis consistent with its cyclical pattern or a true increase in the baseline burden. Previous research on pertussis seasonality has in different places and time periods demonstrated various periods of peak transmission or no discernable patterns [29, 30] . Although our data do not support a seasonal pattern, the numbers observed are too low to be conclusive.
Pertussis symptom duration and severity were mild compared with the classic pertussis case presentation. Only 3 of the 17 cases fulfilled the WHO criteria, which requires a minimum of 2 weeks of cough, whoop, or posttussive vomiting [31] . Studies on pertussis in infants have generally been clinic-based, hospital-based, or in an outbreak, which therefore required a certain severity of illness for parents to recognize a need for medical attention [29, 30, 32] . These study designs and passive surveillance efforts therefore may have missed milder pertussis cases [33] . Our study, which required only 1 respiratory symptom for a nasal swab to be collected, had increased sensitivity to detect a range of pertussis case presentations. An alternative explanation for the mild cases seen could be an increase in the proportion of mild compared with severe pertussis cases in Nepal.
Although cough, difficulty breathing, and cough with vomit were the most common symptoms, no symptom was present in all B pertussis cases. During an epidemic period in Washington state, among infants <1 year, who had a minimum of 14 days cough plus an additional symptom, 82% had posttussive emesis, 29% had apnea, 26% had whoop, and 42% had cyanosis [32] . A study of US neonates with pertussis showed the symptom prevalence to be 97% for cough, 91% for cyanosis, 58% for apnea, and 3% for fever [34] . Our study found lower or equal symptom prevalence with the exception of fever. Fever prevalence was higher in our study, similar to that found in Peru [29] .
Although not statistically significant, infants with pertussis were more likely to have been born preterm, low birth weight, and SGA, and their mothers were more likely to be primiparous. These findings are similar to previous studies showing no difference in pertussis cases by sex [29, 35, 36] or crowding [35] but showing differences by birth weight [36] . Coinfections were common, consistent with findings from other hospital-based studies [33] . Codetection of B pertussis and B parapertussis with respiratory viruses may be due to asymptomatic pertussis carriage. The incidence of B parapertussis of 4 cases per 1000 person-years was comparable to that of 2 per 1000 person-years found in the Italian acellular pertussis vaccine trial in 1992-1993 [37] . The duration of illness was shorter for B parapertussis with a maximum duration of 6 days compared with a maximum of 33 days for B pertussis. A milder presentation is consistent with clinical knowledge of B parapertussis infection [37, 38] . Bordetella parapertussis cases occurred only during a 5-month period.
There were several study design limitations. We cannot be certain whether the reported symptoms were caused by pertussis, another organism, or whether symptoms were related to 2 or more etiologic agents. We were unable to perform multivariate regression modeling for characteristics associated with pertussis disease and pertussis cases due to the small number of cases we detected.
Infant respiratory symptoms were reported by parents, who may have missed signs that might have been observed by a healthcare worker. However, the criteria for collection of the nasal swab were broad and did not require sophisticated clinical skills. However, apnea and cyanosis may have been difficult for parents to identify. Although the criteria for specimen collection changed in year 2, no infant experienced a pertussis-specific symptom in isolation without also having one of the originally specified respiratory symptoms. These data support our assumption that we were unlikely to have missed pertussis cases in year 1 with our less sensitive respiratory symptom criteria.
Nasal swabs were collected in the mid-nasal region for influenza virus detection, which may have lowered the sensitivity of pertussis detection. In a field site, the acceptability of an additional nasopharyngeal swab would likely have increased the participant refusal rate. This would have decreased the generalizability of our results to the entire population. Although nasopharyngeal swabs or nasopharyngeal aspirates are the recommended specimen collection method [39] , the nasopharyngeal region was established as the collection area of choice when the diagnostic measure was culture, which has low sensitivity. Recent data demonstrated the comparability of using mid-nasal versus nasopharyngeal swabs in PCR pertussis detection [40] .
Strengths of the study included being a population-based, prospective study, with very low refusal rates. Risk factors, clinical symptoms, and coinfections were prospectively identified without the potential bias that may occur when these data are collected retrospectively or in clinical settings. The community-based design allows generalizability of these results to the entire population and not just those seeking care at a health facility or in an outbreak situation. The Sarlahi District is located in the Terai region where the majority of Nepalese reside, and it has similar demographics to the entire population of Nepal [41] . Sarlahi's location near sea level and on the border with India supports the generalizability of these results to many populations living on the Indian subcontinent. The weekly active surveillance with sensitive criteria for pertussis testing was able to detect mild and atypical pertussis cases, which may have been missed by previous traditional surveillance. The multitarget PCR method allowed highly sensitive and specific detection of 2 additional Bordetella species beyond the primary B pertussis target.
We observed a low incidence of pertussis in infants in a whole cell vaccine environment. Pertussis cases were generally milder than expected compared with traditional pertussis clinical definitions. These data support clinicians considering pertussis in their differential diagnosis of infants with mild respiratory symptoms. Policymakers in Nepal will need to weigh the benefit of an additional prenatal pertussis vaccine or a switch to acellular primary pertussis vaccine with the low burden of pertussis in infants less than 6 months. Our study demonstrated that mid-nasal swabs were able to detect pertussis using a sensitive multitarget PCR. The less invasive mid-nasal nasal swab is an attractive alternative for pertussis nasal swab collection, and further research is needed to compare this collection site with nasopharyngeal swabs. In the future, this method may enhance population-based surveillance efforts.
|
What is the WHO criteria for a pertussis infection?
| 2,174 |
a minimum of 2 weeks of cough, whoop, or posttussive vomiting
| 17,093 |
1,581 |
Estimating Sensitivity of Laboratory Testing for Influenza in Canada through Modelling
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2722738/
SHA: f3a5b128f4800dbbb0f49ee409acb2c0216e24dc
Authors: Schanzer, Dena L.; Garner, Michael J.; Hatchette, Todd F.; Langley, Joanne M.; Aziz, Samina; Tam, Theresa W. S.
Date: 2009-08-18
DOI: 10.1371/journal.pone.0006681
License: cc-by
Abstract: BACKGROUND: The weekly proportion of laboratory tests that are positive for influenza is used in public health surveillance systems to identify periods of influenza activity. We aimed to estimate the sensitivity of influenza testing in Canada based on results of a national respiratory virus surveillance system. METHODS AND FINDINGS: The weekly number of influenza-negative tests from 1999 to 2006 was modelled as a function of laboratory-confirmed positive tests for influenza, respiratory syncytial virus (RSV), adenovirus and parainfluenza viruses, seasonality, and trend using Poisson regression. Sensitivity was calculated as the number of influenza positive tests divided by the number of influenza positive tests plus the model-estimated number of false negative tests. The sensitivity of influenza testing was estimated to be 33% (95%CI 32–34%), varying from 30–40% depending on the season and region. CONCLUSIONS: The estimated sensitivity of influenza tests reported to this national laboratory surveillance system is considerably less than reported test characteristics for most laboratory tests. A number of factors may explain this difference, including sample quality and specimen procurement issues as well as test characteristics. Improved diagnosis would permit better estimation of the burden of influenza.
Text: Although influenza virus infection is associated with considerable morbidity and mortality [1] [2] [3] , laboratory confirmation of clinical illness is the exception rather than the rule. Clinicians do not routinely seek laboratory confirmation for several reasons: diagnosis will often not alter patient management, a paucity of real-time, accurate, inexpensive testing methods [4] and because influenza is not recognized as the etiology of the clinical presentation [5] . Accurate diagnosis of influenza-like illness, however, could improve clinical care through reduced use of antibiotics and ancillary testing, and more appropriate use of antiviral therapy [6] . Although rapid influenza tests such as pointof-care tests are purported to generate results in a timely fashion to influence clinical care, the performance characteristics of the currently available tests are sub-optimal [7] . New technologies with improved sensitivity such as reverse-transcriptase polymerase chain reaction (RT-PCR) [8] as well as the use of more effective collection systems such as the flocked nasopharyngeal swab compared to traditional rayon wound swabs, and the recommendation to collect more ideal specimens, such as nasopharyngeal swabs rather than throat swabs are likely to improve diagnostic sensitivity [9] [10] [11] [12] . The performance characteristics of currently available tests for influenza vary considerably and the overall sensitivities of these tests when used in routine practice are also dependent on the type of specimen collected, the age of the patient and point in their illness in which they are sampled [4, 9, [13] [14] [15] .
We sought to estimate the sensitivity of influenza testing based on results of a national respiratory virus surveillance system using a model-based method [1, 2, [16] [17] [18] .
Weekly respiratory virus identifications from September 1999 to August 2006 were obtained from the Respiratory Virus Detection Surveillance System (RVDSS), Public Health Agency of Canada [19, 20] . The RVDSS collects, collates, and reports weekly data from participating laboratories on the number of tests performed and the number of specimens confirmed positive for influenza, respiratory syncytial virus (RSV), para-influenza virus (PIV), and adenovirus. Specimens are generally submitted to laboratories by clinicians in the course of clinical care, and by clinicians participating in one of our national influenza surveillance programs, (FluWatch [20] ). Indicators of influenza activity are reported year round on a weekly basis to the FluWatch program. The RVDSS is supplemented by case reports of influenza positive cases [19, 21] . From the case reports, influenza A was confirmed in all age groups and sporadic cases were confirmed in the off-season months of June through September. Infants and children under the age of 5 years accounted for 25% of the influenza A positive tests, and persons over the age 65 years another 35%. Unfortunately, FluWatch surveillance data does not provide the total number of tests by age. Testing practices are known to be varied [22, 23] . The predominant testing methods used for influenza detection varied considerably by province or laboratory and over time. For the 2005/06 season a survey of laboratory techniques in current use indicated that culture accounted for 44% of the diagnostic tests with RT-PCR, rapid antigen tests and direct fluorescent-antibody assay (DFA) accounting for 21%, 19%, and 16% respectively [23] .
The weekly number of tests negative for influenza was modelled, using Poisson regression, as a function of viral identifications for influenza, RSV, adenovirus and PIV as well as a baseline consisting of seasonality, trend and holiday variables. The estimated baseline implicitly accounts for influenza tests on specimens taken from patients with respiratory infections due to respiratory pathogens other than the four viruses captured in the RVDSS, as long as both the testing behaviour of clinicians and respiratory illnesses caused by other respiratory pathogens follow a consistent seasonal pattern as prescribed by the model (see below,
The Poisson regression model with a linear link function was estimated using SAS [24] PROC GENMOD: Coefficients b 5 to b 9 are multipliers. The weekly number of influenza negative tests estimated to be falsely negative is given by b 5 InflA w +b 6 InflB w . The weekly number of influenza negative tests attributed to RSV is given by b 7 RSVp w. , and similarly for adenovirus and PIV. For each positive influenza A test, an additional b 5 tests above baseline were performed and found to be negative. By specifying a linear link, a value of 0.33, say, for coefficient b 5 , means that for every test for which influenza A was confirmed, 0.33 additional tests, on average, were performed on truly influenza A positive specimens and found to be negativewhich corresponds to a sensitivity of 75%.
Sensitivity was calculated as the number of influenza positive tests divided by the number of influenza positive tests plus the model-estimated number of false negative tests, or equivalently, the estimates of sensitivity for influenza A and B are given by 1/ (1+b 5 ) and 1/(1+b 6 ) respectively. The false negative rate is 1 minus sensitivity. While the null value for b 5 is zero, which indicates no statistical association between the number of influenza positive tests and the number of influenza negative tests, the corresponding null value for sensitivity is 1.
For each test confirmed positive for RSV, on average b 7 tests were performed for influenza and found to be negative for influenza. These b 7 tests are attributed to an RSV infection, however the number of influenza-negative tests that actually tested positive for RSV is unknown. If all specimens had been tested for the same viruses (panel tests), 1/b 7 would correspond to the sensitivity for RSV testing, and the sensitivity for adenovirus and PIV given by 1/b 8 and 1/b 9 respectively. Some laboratories are known to test for viruses sequentially [22] , and so 1/b 7 -1/b 9 were not interpreted as estimates of the sensitivity for other viruses. Sequential testing may occur if a rapid test for influenza is negative and the laboratory then performs PCR or culture testing. Similarly in young children with a respiratory illness in the winter, rapid tests for RSV infection may be performed first, and only specimens with negative results submitted for subsequent testing for influenza or other respiratory viruses [25] . By contrast, many laboratories conduct panel tests for multiple viruses for ease of handling, decreased patient sampling, and recognition that coinfection can occur. Either form of sequential testing would not bias the estimate of sensitivity applicable to test results reported to RVDSS, though significant use of rapid antigen tests in the laboratories reporting to RVDSS would reduce the overall sensitivity. As a single specimen may undergo multiple tests, the false-negative rate applicable to a specimen that has undergone multiple tests would be expected to be much lower than the system average for individual tests. Parameters b 1. to b 4 account for trends and the seasonality of truly negative specimens (patients presenting with other acute respiratory infections).
Over 50,000 tests for influenza were reported to the RVDSS each year, peaking in 2004/05 at 101,000. Overall 10% of the influenza tests were positive for influenza, ranging from 4% to 13% depending on the season. The proportion positive for RSV, parainfluenza and adenovirus averaged 9%, 3% and 2% respectively. As seen in Figure 1 , no virus was identified in 75% of specimens submitted for testing (white area under the curve). Even for the winter months of December through April, one of these 4 viruses was identified on average in no more than 30% of the specimens. The strong and consistent synchronization of negative tests with influenza positive tests, as seen in Figure 1 , is suggestive that false negative results contributed to the large number of negative tests during periods of influenza activity.
The sensitivity for influenza A testing averaged 33.7% (with model-estimated 95% confidence intervals of 33.3-34.1) for the 1999/2000-2005/06 period. Influenza B testing had a similar estimated sensitivity at 34.7 (95% CI 33.4-36.1). Estimated sensitivities varied somewhat from season to season, generally ranging from 30%-40% (Table 1) , and provincial level estimates, as well, were within a similar range. Stratifying by province or season produced similar estimates for the sensitivity of influenza A testing: 32% (95% CI 30-34) and 36% (95% CI 33-41) respectively. Estimates of sensitivity based on test results reported to the RVDSS for individual laboratories with sufficient data to fit the model showed significant variation, with estimates of sensitivity ranging from 25-65%. As expected, laboratories using primarily rapid antigen tests had lower estimated sensitivities, and laboratories that used PCR methods had higher sensitivity estimates. However, information on testing procedures is limited primarily to the 2005/06 survey. As well, additional irregularities were noticed in the laboratory data and not all laboratories provided sufficient data to fit the model. Figure 2 illustrates a good model fit where the weekly number of influenza negative tests is well explained by the model covariates, with a few exceptions. Firstly, it is evident that additional specimens were tested during the SARS period, as indicated by the period where the number of weekly influenza negative tests exceeded the expected number, or equivalently, a period of successive positive residuals. Residuals typically capture random variation; hence represent tests that can not be allocated based on the specified model. In addition to the SARS period, testing appears to have been elevated for a number of weeks in January 2000 during the peak of the 1999/2000 A/ Sydney/05/97 (H3N2) season in which respiratory admissions were unusually elevated [26, 27] , and in December 2003, when an elevated risk of paediatric deaths associated with the A/Fujian/411/02 (H3N2) strain [28] was identified in the US. As these periods corresponded to a period of heightened public awareness due to severe influenza outbreaks, parameter estimation was repeated without these data points. Exclusion of these data points did not alter the sensitivity estimate for influenza.
The attribution of influenza negative test results to influenza and other viruses is illustrated in Figure 3 . The baseline curve is the model estimate of the number of tests that were likely truly negative for all four viruses tested. A reduction in specimen collection and testing, primarily for viruses other than influenza, is also evident over the Christmas period ( Figure 3) .
The weekly proportion of tests confirmed positive for influenza peaked each season at 15 to 30%. Accounting for the model estimated false negative rate suggests that during periods of peak influenza activity, 40-90% of tests were performed on specimens taken from persons recently infected with influenza. Influenza was confirmed in only 14% of specimens sent for testing over the winter period, whereas the sensitivity estimate would imply that up to 40% of influenza tests could be attributed to an influenza infection. The corresponding figures for the whole year indicate that 10% of specimens were confirmed positive for influenza and 30% of influenza tests could be model-attributed to an influenza infection annually.
Despite a relatively large number of tests in the off-season, the number of influenza positive tests was almost negligible; suggesting that the false positive rate applicable to RVDSS influenza testing is minimal.
The model estimated sensitivity based on influenza test results reported to the RVDSS of 30-40% is much lower than the standard assay sensitivities documented in the literature. Standard sensitivities for diagnostic procedures used by participating laboratories ranged from 64% for rapid antigen tests to 95% for RT-PCR tests, averaging 75% for the study period [23] . As performance characteristics of specific tests are generally based on high quality specimens, the difference of approximately 40% is likely linked to any one of many operational procedures that affects the quality of the specimen and its procurement. Unlike validation studies, our samples are taken from a variety of clinical settings and processed with a variety of procedures across the country. As well, variation in the indications for diagnostic testing may vary across the country.
As there are many other respiratory pathogens that are not routinely tested for, or reported to the RVDSS, including human metapneumovirus (hMPV), coronaviruses, and rhinoviruses for which patients may seek medical care and present with influenza like illness [29] [30] [31] [32] , a large proportion of negative test results was expected. The overall model fit, and the general consistency of the sensitivity estimates, suggests that these many respiratory viruses were reasonably accounted for by the seasonal baseline and that the strong association between the number of influenza positive and influenza negative tests on a weekly basis is indicative of a significant number of false negative results, rather than the activity of another virus or viruses exactly synchronous with influenza. The latter would bias the estimated sensitivity of the system downwards. However, to significantly and consistently bias the estimate, the degree of synchronization would have to be fairly strong, persist over the whole study period, and occur in all provinces. Synchronization was not observed among the RVDSS viruses (influenza A, influenza B, RSV, adenovirus and PIV), and elsewhere other viruses such as rhinovirus, coronavirus and hMPV accounted for only a small proportion of the viral identifications and were not found to be synchronized with influenza [33] . As well, patients may present for care due to a secondary bacterial infection. While any specimen would likely test negative as the virus, at this point, is likely not detectable, the model would statistically attribute a negative test in this case to the primary infection; one of the four RVDSS viruses or to the seasonal baseline that represents other respiratory infections, depending on the level of viral activity at the time of the test. This is not considered a source of bias.
The large variation in false negative rates estimated for individual laboratories reporting to the RVDSS suggests that standardization of sample procurement, testing and reporting procedures would likely reduce the overall false negative rate. The accuracy of diagnostic tests is known to be affected by the quality of the specimen [10, 11] , its handling, the timing of collection after symptom onset, and the age of the patient [14, 15] . Even with the most sensitive molecular methodologies, yield was shown to be strongly related to the time since onset of symptoms [9, 14] , with a 3-fold decline in proportion positive within 3 to 5 days after onset of symptoms for both RT-PCR and culture procedures. For most laboratory tests, specimen procurement within 72 hours of from the onset of symptoms is recommended [6] , yet patients often present much later in the course of illness. Estimates of the median time since onset of symptoms suggest a delay of 3 and 5 days for outpatient and inpatients respectively [15] , however these estimates are limited to patients with laboratory confirmed influenza. In addition, there are inherent differences in the performance characteristics of the currently used diagnostic tests [4, 6, 8, [34] [35] [36] [37] [38] . Lack of standardization between diagnostic tests and algorithms used in different laboratories reporting to the RVDSS adds to this complexity. The routine use of RT-PCR testing has only recently become available in Canada (only 20% of tests used RT-PCR methods as of 2005/06 [23] ), but increased use of this modality is expected to improve accuracy.
Population or system level sensitivity estimates that include the effects of sample quality are limited. Grijalva and colleagues [39] estimated the diagnostic sensitivity in a capture recapture study of children hospitalized for respiratory complications at 69% for a RT-PCR based system and 39% for a clinical-laboratory based system (passive surveillance of tests performed during clinical practice, and using a variety of commercially available tests).
Though the expected proportion of influenza tests that were due to influenza infections is unknown and variable, our model estimate of 30% appears plausible. Cooper and colleagues [33] attributed 22% of telephone health calls for cold/flu to influenza over two relatively mild years, and elsewhere 20% of admissions for acute respiratory infections (including influenza) in adults aged 20-64 years were attributed to influenza, and 42% for seniors [1] .
While there are limitations with this approach, there are no other simple alternatives to assist in the interpretation of the RVDSS data. It would have been helpful to analyze data based on each specimen sent for testing. With only the number of weekly tests and number of positive results, we were unable to calculate the number of specimens that were actually found to be negative for all four viruses, or to estimate the extent of co-infection. Coinfection, which was not accounted for in our model, could result in an under-estimation of the number of falsely negative tests, as the attribution of an influenza negative test that was actually coinfected with influenza and another respiratory virus would have to be split between the viruses. With auxiliary information associated with each specimen, model estimates of false negative rates based on, for example, test type, time since onset of symptoms, age of the patient, or clinical presentation would have allowed us to explore the reasons for the high false negative rates. As the false negative rate appears to be laboratory dependant (data not shown), this estimated range is applicable only to the RVDSS for the study period. A significant reduction in the false negative rate is anticipated as methods become standardized and with the uptake of the new RT-PCR methods. As positive results, particularly for culture, are often obtained a week or more after the specimen was received, some positive results may have been reported in a different week than the test. Multiple test results for a single specimen may have also contributed to reporting irregularities. These irregularities would tend to bias the estimated parameter towards zero, and hence the estimated sensitivity towards 1. Considering the overall model fit and the relative severity of influenza [1] , we conclude that our estimate of sensitivity may be slightly over-estimated (number of false negatives under-estimated).
Poor test sensitivity contributes to the chronic underestimation of the burden of influenza in the general population. Since estimates of the burden of illness drive planning for preventive and therapeutic interventions, it is important to improve all aspects leading to improved diagnostic accuracy. We have illustrated a simple method that uses the surveillance data itself to estimate the system wide sensitivity associated with the weekly proportion of tests confirmed positive. Although our estimate of sensitivity is only applicable to the interpretation of the RVDSS data over the study period, similar estimates for specific cohorts or laboratory procedures may help guide further investigation into the reasons for the large number of false negative test results. The capacity for improved diagnostic accuracy will ultimately improve our understanding of the epidemiology of influenza.
|
What is used by the Canadian Public Health System to identify periods of influenza activity?
| 2,175 |
weekly proportion of laboratory tests that are positive for influenza
| 404 |
1,581 |
Estimating Sensitivity of Laboratory Testing for Influenza in Canada through Modelling
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2722738/
SHA: f3a5b128f4800dbbb0f49ee409acb2c0216e24dc
Authors: Schanzer, Dena L.; Garner, Michael J.; Hatchette, Todd F.; Langley, Joanne M.; Aziz, Samina; Tam, Theresa W. S.
Date: 2009-08-18
DOI: 10.1371/journal.pone.0006681
License: cc-by
Abstract: BACKGROUND: The weekly proportion of laboratory tests that are positive for influenza is used in public health surveillance systems to identify periods of influenza activity. We aimed to estimate the sensitivity of influenza testing in Canada based on results of a national respiratory virus surveillance system. METHODS AND FINDINGS: The weekly number of influenza-negative tests from 1999 to 2006 was modelled as a function of laboratory-confirmed positive tests for influenza, respiratory syncytial virus (RSV), adenovirus and parainfluenza viruses, seasonality, and trend using Poisson regression. Sensitivity was calculated as the number of influenza positive tests divided by the number of influenza positive tests plus the model-estimated number of false negative tests. The sensitivity of influenza testing was estimated to be 33% (95%CI 32–34%), varying from 30–40% depending on the season and region. CONCLUSIONS: The estimated sensitivity of influenza tests reported to this national laboratory surveillance system is considerably less than reported test characteristics for most laboratory tests. A number of factors may explain this difference, including sample quality and specimen procurement issues as well as test characteristics. Improved diagnosis would permit better estimation of the burden of influenza.
Text: Although influenza virus infection is associated with considerable morbidity and mortality [1] [2] [3] , laboratory confirmation of clinical illness is the exception rather than the rule. Clinicians do not routinely seek laboratory confirmation for several reasons: diagnosis will often not alter patient management, a paucity of real-time, accurate, inexpensive testing methods [4] and because influenza is not recognized as the etiology of the clinical presentation [5] . Accurate diagnosis of influenza-like illness, however, could improve clinical care through reduced use of antibiotics and ancillary testing, and more appropriate use of antiviral therapy [6] . Although rapid influenza tests such as pointof-care tests are purported to generate results in a timely fashion to influence clinical care, the performance characteristics of the currently available tests are sub-optimal [7] . New technologies with improved sensitivity such as reverse-transcriptase polymerase chain reaction (RT-PCR) [8] as well as the use of more effective collection systems such as the flocked nasopharyngeal swab compared to traditional rayon wound swabs, and the recommendation to collect more ideal specimens, such as nasopharyngeal swabs rather than throat swabs are likely to improve diagnostic sensitivity [9] [10] [11] [12] . The performance characteristics of currently available tests for influenza vary considerably and the overall sensitivities of these tests when used in routine practice are also dependent on the type of specimen collected, the age of the patient and point in their illness in which they are sampled [4, 9, [13] [14] [15] .
We sought to estimate the sensitivity of influenza testing based on results of a national respiratory virus surveillance system using a model-based method [1, 2, [16] [17] [18] .
Weekly respiratory virus identifications from September 1999 to August 2006 were obtained from the Respiratory Virus Detection Surveillance System (RVDSS), Public Health Agency of Canada [19, 20] . The RVDSS collects, collates, and reports weekly data from participating laboratories on the number of tests performed and the number of specimens confirmed positive for influenza, respiratory syncytial virus (RSV), para-influenza virus (PIV), and adenovirus. Specimens are generally submitted to laboratories by clinicians in the course of clinical care, and by clinicians participating in one of our national influenza surveillance programs, (FluWatch [20] ). Indicators of influenza activity are reported year round on a weekly basis to the FluWatch program. The RVDSS is supplemented by case reports of influenza positive cases [19, 21] . From the case reports, influenza A was confirmed in all age groups and sporadic cases were confirmed in the off-season months of June through September. Infants and children under the age of 5 years accounted for 25% of the influenza A positive tests, and persons over the age 65 years another 35%. Unfortunately, FluWatch surveillance data does not provide the total number of tests by age. Testing practices are known to be varied [22, 23] . The predominant testing methods used for influenza detection varied considerably by province or laboratory and over time. For the 2005/06 season a survey of laboratory techniques in current use indicated that culture accounted for 44% of the diagnostic tests with RT-PCR, rapid antigen tests and direct fluorescent-antibody assay (DFA) accounting for 21%, 19%, and 16% respectively [23] .
The weekly number of tests negative for influenza was modelled, using Poisson regression, as a function of viral identifications for influenza, RSV, adenovirus and PIV as well as a baseline consisting of seasonality, trend and holiday variables. The estimated baseline implicitly accounts for influenza tests on specimens taken from patients with respiratory infections due to respiratory pathogens other than the four viruses captured in the RVDSS, as long as both the testing behaviour of clinicians and respiratory illnesses caused by other respiratory pathogens follow a consistent seasonal pattern as prescribed by the model (see below,
The Poisson regression model with a linear link function was estimated using SAS [24] PROC GENMOD: Coefficients b 5 to b 9 are multipliers. The weekly number of influenza negative tests estimated to be falsely negative is given by b 5 InflA w +b 6 InflB w . The weekly number of influenza negative tests attributed to RSV is given by b 7 RSVp w. , and similarly for adenovirus and PIV. For each positive influenza A test, an additional b 5 tests above baseline were performed and found to be negative. By specifying a linear link, a value of 0.33, say, for coefficient b 5 , means that for every test for which influenza A was confirmed, 0.33 additional tests, on average, were performed on truly influenza A positive specimens and found to be negativewhich corresponds to a sensitivity of 75%.
Sensitivity was calculated as the number of influenza positive tests divided by the number of influenza positive tests plus the model-estimated number of false negative tests, or equivalently, the estimates of sensitivity for influenza A and B are given by 1/ (1+b 5 ) and 1/(1+b 6 ) respectively. The false negative rate is 1 minus sensitivity. While the null value for b 5 is zero, which indicates no statistical association between the number of influenza positive tests and the number of influenza negative tests, the corresponding null value for sensitivity is 1.
For each test confirmed positive for RSV, on average b 7 tests were performed for influenza and found to be negative for influenza. These b 7 tests are attributed to an RSV infection, however the number of influenza-negative tests that actually tested positive for RSV is unknown. If all specimens had been tested for the same viruses (panel tests), 1/b 7 would correspond to the sensitivity for RSV testing, and the sensitivity for adenovirus and PIV given by 1/b 8 and 1/b 9 respectively. Some laboratories are known to test for viruses sequentially [22] , and so 1/b 7 -1/b 9 were not interpreted as estimates of the sensitivity for other viruses. Sequential testing may occur if a rapid test for influenza is negative and the laboratory then performs PCR or culture testing. Similarly in young children with a respiratory illness in the winter, rapid tests for RSV infection may be performed first, and only specimens with negative results submitted for subsequent testing for influenza or other respiratory viruses [25] . By contrast, many laboratories conduct panel tests for multiple viruses for ease of handling, decreased patient sampling, and recognition that coinfection can occur. Either form of sequential testing would not bias the estimate of sensitivity applicable to test results reported to RVDSS, though significant use of rapid antigen tests in the laboratories reporting to RVDSS would reduce the overall sensitivity. As a single specimen may undergo multiple tests, the false-negative rate applicable to a specimen that has undergone multiple tests would be expected to be much lower than the system average for individual tests. Parameters b 1. to b 4 account for trends and the seasonality of truly negative specimens (patients presenting with other acute respiratory infections).
Over 50,000 tests for influenza were reported to the RVDSS each year, peaking in 2004/05 at 101,000. Overall 10% of the influenza tests were positive for influenza, ranging from 4% to 13% depending on the season. The proportion positive for RSV, parainfluenza and adenovirus averaged 9%, 3% and 2% respectively. As seen in Figure 1 , no virus was identified in 75% of specimens submitted for testing (white area under the curve). Even for the winter months of December through April, one of these 4 viruses was identified on average in no more than 30% of the specimens. The strong and consistent synchronization of negative tests with influenza positive tests, as seen in Figure 1 , is suggestive that false negative results contributed to the large number of negative tests during periods of influenza activity.
The sensitivity for influenza A testing averaged 33.7% (with model-estimated 95% confidence intervals of 33.3-34.1) for the 1999/2000-2005/06 period. Influenza B testing had a similar estimated sensitivity at 34.7 (95% CI 33.4-36.1). Estimated sensitivities varied somewhat from season to season, generally ranging from 30%-40% (Table 1) , and provincial level estimates, as well, were within a similar range. Stratifying by province or season produced similar estimates for the sensitivity of influenza A testing: 32% (95% CI 30-34) and 36% (95% CI 33-41) respectively. Estimates of sensitivity based on test results reported to the RVDSS for individual laboratories with sufficient data to fit the model showed significant variation, with estimates of sensitivity ranging from 25-65%. As expected, laboratories using primarily rapid antigen tests had lower estimated sensitivities, and laboratories that used PCR methods had higher sensitivity estimates. However, information on testing procedures is limited primarily to the 2005/06 survey. As well, additional irregularities were noticed in the laboratory data and not all laboratories provided sufficient data to fit the model. Figure 2 illustrates a good model fit where the weekly number of influenza negative tests is well explained by the model covariates, with a few exceptions. Firstly, it is evident that additional specimens were tested during the SARS period, as indicated by the period where the number of weekly influenza negative tests exceeded the expected number, or equivalently, a period of successive positive residuals. Residuals typically capture random variation; hence represent tests that can not be allocated based on the specified model. In addition to the SARS period, testing appears to have been elevated for a number of weeks in January 2000 during the peak of the 1999/2000 A/ Sydney/05/97 (H3N2) season in which respiratory admissions were unusually elevated [26, 27] , and in December 2003, when an elevated risk of paediatric deaths associated with the A/Fujian/411/02 (H3N2) strain [28] was identified in the US. As these periods corresponded to a period of heightened public awareness due to severe influenza outbreaks, parameter estimation was repeated without these data points. Exclusion of these data points did not alter the sensitivity estimate for influenza.
The attribution of influenza negative test results to influenza and other viruses is illustrated in Figure 3 . The baseline curve is the model estimate of the number of tests that were likely truly negative for all four viruses tested. A reduction in specimen collection and testing, primarily for viruses other than influenza, is also evident over the Christmas period ( Figure 3) .
The weekly proportion of tests confirmed positive for influenza peaked each season at 15 to 30%. Accounting for the model estimated false negative rate suggests that during periods of peak influenza activity, 40-90% of tests were performed on specimens taken from persons recently infected with influenza. Influenza was confirmed in only 14% of specimens sent for testing over the winter period, whereas the sensitivity estimate would imply that up to 40% of influenza tests could be attributed to an influenza infection. The corresponding figures for the whole year indicate that 10% of specimens were confirmed positive for influenza and 30% of influenza tests could be model-attributed to an influenza infection annually.
Despite a relatively large number of tests in the off-season, the number of influenza positive tests was almost negligible; suggesting that the false positive rate applicable to RVDSS influenza testing is minimal.
The model estimated sensitivity based on influenza test results reported to the RVDSS of 30-40% is much lower than the standard assay sensitivities documented in the literature. Standard sensitivities for diagnostic procedures used by participating laboratories ranged from 64% for rapid antigen tests to 95% for RT-PCR tests, averaging 75% for the study period [23] . As performance characteristics of specific tests are generally based on high quality specimens, the difference of approximately 40% is likely linked to any one of many operational procedures that affects the quality of the specimen and its procurement. Unlike validation studies, our samples are taken from a variety of clinical settings and processed with a variety of procedures across the country. As well, variation in the indications for diagnostic testing may vary across the country.
As there are many other respiratory pathogens that are not routinely tested for, or reported to the RVDSS, including human metapneumovirus (hMPV), coronaviruses, and rhinoviruses for which patients may seek medical care and present with influenza like illness [29] [30] [31] [32] , a large proportion of negative test results was expected. The overall model fit, and the general consistency of the sensitivity estimates, suggests that these many respiratory viruses were reasonably accounted for by the seasonal baseline and that the strong association between the number of influenza positive and influenza negative tests on a weekly basis is indicative of a significant number of false negative results, rather than the activity of another virus or viruses exactly synchronous with influenza. The latter would bias the estimated sensitivity of the system downwards. However, to significantly and consistently bias the estimate, the degree of synchronization would have to be fairly strong, persist over the whole study period, and occur in all provinces. Synchronization was not observed among the RVDSS viruses (influenza A, influenza B, RSV, adenovirus and PIV), and elsewhere other viruses such as rhinovirus, coronavirus and hMPV accounted for only a small proportion of the viral identifications and were not found to be synchronized with influenza [33] . As well, patients may present for care due to a secondary bacterial infection. While any specimen would likely test negative as the virus, at this point, is likely not detectable, the model would statistically attribute a negative test in this case to the primary infection; one of the four RVDSS viruses or to the seasonal baseline that represents other respiratory infections, depending on the level of viral activity at the time of the test. This is not considered a source of bias.
The large variation in false negative rates estimated for individual laboratories reporting to the RVDSS suggests that standardization of sample procurement, testing and reporting procedures would likely reduce the overall false negative rate. The accuracy of diagnostic tests is known to be affected by the quality of the specimen [10, 11] , its handling, the timing of collection after symptom onset, and the age of the patient [14, 15] . Even with the most sensitive molecular methodologies, yield was shown to be strongly related to the time since onset of symptoms [9, 14] , with a 3-fold decline in proportion positive within 3 to 5 days after onset of symptoms for both RT-PCR and culture procedures. For most laboratory tests, specimen procurement within 72 hours of from the onset of symptoms is recommended [6] , yet patients often present much later in the course of illness. Estimates of the median time since onset of symptoms suggest a delay of 3 and 5 days for outpatient and inpatients respectively [15] , however these estimates are limited to patients with laboratory confirmed influenza. In addition, there are inherent differences in the performance characteristics of the currently used diagnostic tests [4, 6, 8, [34] [35] [36] [37] [38] . Lack of standardization between diagnostic tests and algorithms used in different laboratories reporting to the RVDSS adds to this complexity. The routine use of RT-PCR testing has only recently become available in Canada (only 20% of tests used RT-PCR methods as of 2005/06 [23] ), but increased use of this modality is expected to improve accuracy.
Population or system level sensitivity estimates that include the effects of sample quality are limited. Grijalva and colleagues [39] estimated the diagnostic sensitivity in a capture recapture study of children hospitalized for respiratory complications at 69% for a RT-PCR based system and 39% for a clinical-laboratory based system (passive surveillance of tests performed during clinical practice, and using a variety of commercially available tests).
Though the expected proportion of influenza tests that were due to influenza infections is unknown and variable, our model estimate of 30% appears plausible. Cooper and colleagues [33] attributed 22% of telephone health calls for cold/flu to influenza over two relatively mild years, and elsewhere 20% of admissions for acute respiratory infections (including influenza) in adults aged 20-64 years were attributed to influenza, and 42% for seniors [1] .
While there are limitations with this approach, there are no other simple alternatives to assist in the interpretation of the RVDSS data. It would have been helpful to analyze data based on each specimen sent for testing. With only the number of weekly tests and number of positive results, we were unable to calculate the number of specimens that were actually found to be negative for all four viruses, or to estimate the extent of co-infection. Coinfection, which was not accounted for in our model, could result in an under-estimation of the number of falsely negative tests, as the attribution of an influenza negative test that was actually coinfected with influenza and another respiratory virus would have to be split between the viruses. With auxiliary information associated with each specimen, model estimates of false negative rates based on, for example, test type, time since onset of symptoms, age of the patient, or clinical presentation would have allowed us to explore the reasons for the high false negative rates. As the false negative rate appears to be laboratory dependant (data not shown), this estimated range is applicable only to the RVDSS for the study period. A significant reduction in the false negative rate is anticipated as methods become standardized and with the uptake of the new RT-PCR methods. As positive results, particularly for culture, are often obtained a week or more after the specimen was received, some positive results may have been reported in a different week than the test. Multiple test results for a single specimen may have also contributed to reporting irregularities. These irregularities would tend to bias the estimated parameter towards zero, and hence the estimated sensitivity towards 1. Considering the overall model fit and the relative severity of influenza [1] , we conclude that our estimate of sensitivity may be slightly over-estimated (number of false negatives under-estimated).
Poor test sensitivity contributes to the chronic underestimation of the burden of influenza in the general population. Since estimates of the burden of illness drive planning for preventive and therapeutic interventions, it is important to improve all aspects leading to improved diagnostic accuracy. We have illustrated a simple method that uses the surveillance data itself to estimate the system wide sensitivity associated with the weekly proportion of tests confirmed positive. Although our estimate of sensitivity is only applicable to the interpretation of the RVDSS data over the study period, similar estimates for specific cohorts or laboratory procedures may help guide further investigation into the reasons for the large number of false negative test results. The capacity for improved diagnostic accuracy will ultimately improve our understanding of the epidemiology of influenza.
|
Why is laboratory confirmation of influenza infection not commonly performed?
| 2,176 |
diagnosis will often not alter patient management, a paucity of real-time, accurate, inexpensive testing methods [4] and because influenza is not recognized as the etiology of the clinical presentation
| 1,987 |
1,581 |
Estimating Sensitivity of Laboratory Testing for Influenza in Canada through Modelling
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2722738/
SHA: f3a5b128f4800dbbb0f49ee409acb2c0216e24dc
Authors: Schanzer, Dena L.; Garner, Michael J.; Hatchette, Todd F.; Langley, Joanne M.; Aziz, Samina; Tam, Theresa W. S.
Date: 2009-08-18
DOI: 10.1371/journal.pone.0006681
License: cc-by
Abstract: BACKGROUND: The weekly proportion of laboratory tests that are positive for influenza is used in public health surveillance systems to identify periods of influenza activity. We aimed to estimate the sensitivity of influenza testing in Canada based on results of a national respiratory virus surveillance system. METHODS AND FINDINGS: The weekly number of influenza-negative tests from 1999 to 2006 was modelled as a function of laboratory-confirmed positive tests for influenza, respiratory syncytial virus (RSV), adenovirus and parainfluenza viruses, seasonality, and trend using Poisson regression. Sensitivity was calculated as the number of influenza positive tests divided by the number of influenza positive tests plus the model-estimated number of false negative tests. The sensitivity of influenza testing was estimated to be 33% (95%CI 32–34%), varying from 30–40% depending on the season and region. CONCLUSIONS: The estimated sensitivity of influenza tests reported to this national laboratory surveillance system is considerably less than reported test characteristics for most laboratory tests. A number of factors may explain this difference, including sample quality and specimen procurement issues as well as test characteristics. Improved diagnosis would permit better estimation of the burden of influenza.
Text: Although influenza virus infection is associated with considerable morbidity and mortality [1] [2] [3] , laboratory confirmation of clinical illness is the exception rather than the rule. Clinicians do not routinely seek laboratory confirmation for several reasons: diagnosis will often not alter patient management, a paucity of real-time, accurate, inexpensive testing methods [4] and because influenza is not recognized as the etiology of the clinical presentation [5] . Accurate diagnosis of influenza-like illness, however, could improve clinical care through reduced use of antibiotics and ancillary testing, and more appropriate use of antiviral therapy [6] . Although rapid influenza tests such as pointof-care tests are purported to generate results in a timely fashion to influence clinical care, the performance characteristics of the currently available tests are sub-optimal [7] . New technologies with improved sensitivity such as reverse-transcriptase polymerase chain reaction (RT-PCR) [8] as well as the use of more effective collection systems such as the flocked nasopharyngeal swab compared to traditional rayon wound swabs, and the recommendation to collect more ideal specimens, such as nasopharyngeal swabs rather than throat swabs are likely to improve diagnostic sensitivity [9] [10] [11] [12] . The performance characteristics of currently available tests for influenza vary considerably and the overall sensitivities of these tests when used in routine practice are also dependent on the type of specimen collected, the age of the patient and point in their illness in which they are sampled [4, 9, [13] [14] [15] .
We sought to estimate the sensitivity of influenza testing based on results of a national respiratory virus surveillance system using a model-based method [1, 2, [16] [17] [18] .
Weekly respiratory virus identifications from September 1999 to August 2006 were obtained from the Respiratory Virus Detection Surveillance System (RVDSS), Public Health Agency of Canada [19, 20] . The RVDSS collects, collates, and reports weekly data from participating laboratories on the number of tests performed and the number of specimens confirmed positive for influenza, respiratory syncytial virus (RSV), para-influenza virus (PIV), and adenovirus. Specimens are generally submitted to laboratories by clinicians in the course of clinical care, and by clinicians participating in one of our national influenza surveillance programs, (FluWatch [20] ). Indicators of influenza activity are reported year round on a weekly basis to the FluWatch program. The RVDSS is supplemented by case reports of influenza positive cases [19, 21] . From the case reports, influenza A was confirmed in all age groups and sporadic cases were confirmed in the off-season months of June through September. Infants and children under the age of 5 years accounted for 25% of the influenza A positive tests, and persons over the age 65 years another 35%. Unfortunately, FluWatch surveillance data does not provide the total number of tests by age. Testing practices are known to be varied [22, 23] . The predominant testing methods used for influenza detection varied considerably by province or laboratory and over time. For the 2005/06 season a survey of laboratory techniques in current use indicated that culture accounted for 44% of the diagnostic tests with RT-PCR, rapid antigen tests and direct fluorescent-antibody assay (DFA) accounting for 21%, 19%, and 16% respectively [23] .
The weekly number of tests negative for influenza was modelled, using Poisson regression, as a function of viral identifications for influenza, RSV, adenovirus and PIV as well as a baseline consisting of seasonality, trend and holiday variables. The estimated baseline implicitly accounts for influenza tests on specimens taken from patients with respiratory infections due to respiratory pathogens other than the four viruses captured in the RVDSS, as long as both the testing behaviour of clinicians and respiratory illnesses caused by other respiratory pathogens follow a consistent seasonal pattern as prescribed by the model (see below,
The Poisson regression model with a linear link function was estimated using SAS [24] PROC GENMOD: Coefficients b 5 to b 9 are multipliers. The weekly number of influenza negative tests estimated to be falsely negative is given by b 5 InflA w +b 6 InflB w . The weekly number of influenza negative tests attributed to RSV is given by b 7 RSVp w. , and similarly for adenovirus and PIV. For each positive influenza A test, an additional b 5 tests above baseline were performed and found to be negative. By specifying a linear link, a value of 0.33, say, for coefficient b 5 , means that for every test for which influenza A was confirmed, 0.33 additional tests, on average, were performed on truly influenza A positive specimens and found to be negativewhich corresponds to a sensitivity of 75%.
Sensitivity was calculated as the number of influenza positive tests divided by the number of influenza positive tests plus the model-estimated number of false negative tests, or equivalently, the estimates of sensitivity for influenza A and B are given by 1/ (1+b 5 ) and 1/(1+b 6 ) respectively. The false negative rate is 1 minus sensitivity. While the null value for b 5 is zero, which indicates no statistical association between the number of influenza positive tests and the number of influenza negative tests, the corresponding null value for sensitivity is 1.
For each test confirmed positive for RSV, on average b 7 tests were performed for influenza and found to be negative for influenza. These b 7 tests are attributed to an RSV infection, however the number of influenza-negative tests that actually tested positive for RSV is unknown. If all specimens had been tested for the same viruses (panel tests), 1/b 7 would correspond to the sensitivity for RSV testing, and the sensitivity for adenovirus and PIV given by 1/b 8 and 1/b 9 respectively. Some laboratories are known to test for viruses sequentially [22] , and so 1/b 7 -1/b 9 were not interpreted as estimates of the sensitivity for other viruses. Sequential testing may occur if a rapid test for influenza is negative and the laboratory then performs PCR or culture testing. Similarly in young children with a respiratory illness in the winter, rapid tests for RSV infection may be performed first, and only specimens with negative results submitted for subsequent testing for influenza or other respiratory viruses [25] . By contrast, many laboratories conduct panel tests for multiple viruses for ease of handling, decreased patient sampling, and recognition that coinfection can occur. Either form of sequential testing would not bias the estimate of sensitivity applicable to test results reported to RVDSS, though significant use of rapid antigen tests in the laboratories reporting to RVDSS would reduce the overall sensitivity. As a single specimen may undergo multiple tests, the false-negative rate applicable to a specimen that has undergone multiple tests would be expected to be much lower than the system average for individual tests. Parameters b 1. to b 4 account for trends and the seasonality of truly negative specimens (patients presenting with other acute respiratory infections).
Over 50,000 tests for influenza were reported to the RVDSS each year, peaking in 2004/05 at 101,000. Overall 10% of the influenza tests were positive for influenza, ranging from 4% to 13% depending on the season. The proportion positive for RSV, parainfluenza and adenovirus averaged 9%, 3% and 2% respectively. As seen in Figure 1 , no virus was identified in 75% of specimens submitted for testing (white area under the curve). Even for the winter months of December through April, one of these 4 viruses was identified on average in no more than 30% of the specimens. The strong and consistent synchronization of negative tests with influenza positive tests, as seen in Figure 1 , is suggestive that false negative results contributed to the large number of negative tests during periods of influenza activity.
The sensitivity for influenza A testing averaged 33.7% (with model-estimated 95% confidence intervals of 33.3-34.1) for the 1999/2000-2005/06 period. Influenza B testing had a similar estimated sensitivity at 34.7 (95% CI 33.4-36.1). Estimated sensitivities varied somewhat from season to season, generally ranging from 30%-40% (Table 1) , and provincial level estimates, as well, were within a similar range. Stratifying by province or season produced similar estimates for the sensitivity of influenza A testing: 32% (95% CI 30-34) and 36% (95% CI 33-41) respectively. Estimates of sensitivity based on test results reported to the RVDSS for individual laboratories with sufficient data to fit the model showed significant variation, with estimates of sensitivity ranging from 25-65%. As expected, laboratories using primarily rapid antigen tests had lower estimated sensitivities, and laboratories that used PCR methods had higher sensitivity estimates. However, information on testing procedures is limited primarily to the 2005/06 survey. As well, additional irregularities were noticed in the laboratory data and not all laboratories provided sufficient data to fit the model. Figure 2 illustrates a good model fit where the weekly number of influenza negative tests is well explained by the model covariates, with a few exceptions. Firstly, it is evident that additional specimens were tested during the SARS period, as indicated by the period where the number of weekly influenza negative tests exceeded the expected number, or equivalently, a period of successive positive residuals. Residuals typically capture random variation; hence represent tests that can not be allocated based on the specified model. In addition to the SARS period, testing appears to have been elevated for a number of weeks in January 2000 during the peak of the 1999/2000 A/ Sydney/05/97 (H3N2) season in which respiratory admissions were unusually elevated [26, 27] , and in December 2003, when an elevated risk of paediatric deaths associated with the A/Fujian/411/02 (H3N2) strain [28] was identified in the US. As these periods corresponded to a period of heightened public awareness due to severe influenza outbreaks, parameter estimation was repeated without these data points. Exclusion of these data points did not alter the sensitivity estimate for influenza.
The attribution of influenza negative test results to influenza and other viruses is illustrated in Figure 3 . The baseline curve is the model estimate of the number of tests that were likely truly negative for all four viruses tested. A reduction in specimen collection and testing, primarily for viruses other than influenza, is also evident over the Christmas period ( Figure 3) .
The weekly proportion of tests confirmed positive for influenza peaked each season at 15 to 30%. Accounting for the model estimated false negative rate suggests that during periods of peak influenza activity, 40-90% of tests were performed on specimens taken from persons recently infected with influenza. Influenza was confirmed in only 14% of specimens sent for testing over the winter period, whereas the sensitivity estimate would imply that up to 40% of influenza tests could be attributed to an influenza infection. The corresponding figures for the whole year indicate that 10% of specimens were confirmed positive for influenza and 30% of influenza tests could be model-attributed to an influenza infection annually.
Despite a relatively large number of tests in the off-season, the number of influenza positive tests was almost negligible; suggesting that the false positive rate applicable to RVDSS influenza testing is minimal.
The model estimated sensitivity based on influenza test results reported to the RVDSS of 30-40% is much lower than the standard assay sensitivities documented in the literature. Standard sensitivities for diagnostic procedures used by participating laboratories ranged from 64% for rapid antigen tests to 95% for RT-PCR tests, averaging 75% for the study period [23] . As performance characteristics of specific tests are generally based on high quality specimens, the difference of approximately 40% is likely linked to any one of many operational procedures that affects the quality of the specimen and its procurement. Unlike validation studies, our samples are taken from a variety of clinical settings and processed with a variety of procedures across the country. As well, variation in the indications for diagnostic testing may vary across the country.
As there are many other respiratory pathogens that are not routinely tested for, or reported to the RVDSS, including human metapneumovirus (hMPV), coronaviruses, and rhinoviruses for which patients may seek medical care and present with influenza like illness [29] [30] [31] [32] , a large proportion of negative test results was expected. The overall model fit, and the general consistency of the sensitivity estimates, suggests that these many respiratory viruses were reasonably accounted for by the seasonal baseline and that the strong association between the number of influenza positive and influenza negative tests on a weekly basis is indicative of a significant number of false negative results, rather than the activity of another virus or viruses exactly synchronous with influenza. The latter would bias the estimated sensitivity of the system downwards. However, to significantly and consistently bias the estimate, the degree of synchronization would have to be fairly strong, persist over the whole study period, and occur in all provinces. Synchronization was not observed among the RVDSS viruses (influenza A, influenza B, RSV, adenovirus and PIV), and elsewhere other viruses such as rhinovirus, coronavirus and hMPV accounted for only a small proportion of the viral identifications and were not found to be synchronized with influenza [33] . As well, patients may present for care due to a secondary bacterial infection. While any specimen would likely test negative as the virus, at this point, is likely not detectable, the model would statistically attribute a negative test in this case to the primary infection; one of the four RVDSS viruses or to the seasonal baseline that represents other respiratory infections, depending on the level of viral activity at the time of the test. This is not considered a source of bias.
The large variation in false negative rates estimated for individual laboratories reporting to the RVDSS suggests that standardization of sample procurement, testing and reporting procedures would likely reduce the overall false negative rate. The accuracy of diagnostic tests is known to be affected by the quality of the specimen [10, 11] , its handling, the timing of collection after symptom onset, and the age of the patient [14, 15] . Even with the most sensitive molecular methodologies, yield was shown to be strongly related to the time since onset of symptoms [9, 14] , with a 3-fold decline in proportion positive within 3 to 5 days after onset of symptoms for both RT-PCR and culture procedures. For most laboratory tests, specimen procurement within 72 hours of from the onset of symptoms is recommended [6] , yet patients often present much later in the course of illness. Estimates of the median time since onset of symptoms suggest a delay of 3 and 5 days for outpatient and inpatients respectively [15] , however these estimates are limited to patients with laboratory confirmed influenza. In addition, there are inherent differences in the performance characteristics of the currently used diagnostic tests [4, 6, 8, [34] [35] [36] [37] [38] . Lack of standardization between diagnostic tests and algorithms used in different laboratories reporting to the RVDSS adds to this complexity. The routine use of RT-PCR testing has only recently become available in Canada (only 20% of tests used RT-PCR methods as of 2005/06 [23] ), but increased use of this modality is expected to improve accuracy.
Population or system level sensitivity estimates that include the effects of sample quality are limited. Grijalva and colleagues [39] estimated the diagnostic sensitivity in a capture recapture study of children hospitalized for respiratory complications at 69% for a RT-PCR based system and 39% for a clinical-laboratory based system (passive surveillance of tests performed during clinical practice, and using a variety of commercially available tests).
Though the expected proportion of influenza tests that were due to influenza infections is unknown and variable, our model estimate of 30% appears plausible. Cooper and colleagues [33] attributed 22% of telephone health calls for cold/flu to influenza over two relatively mild years, and elsewhere 20% of admissions for acute respiratory infections (including influenza) in adults aged 20-64 years were attributed to influenza, and 42% for seniors [1] .
While there are limitations with this approach, there are no other simple alternatives to assist in the interpretation of the RVDSS data. It would have been helpful to analyze data based on each specimen sent for testing. With only the number of weekly tests and number of positive results, we were unable to calculate the number of specimens that were actually found to be negative for all four viruses, or to estimate the extent of co-infection. Coinfection, which was not accounted for in our model, could result in an under-estimation of the number of falsely negative tests, as the attribution of an influenza negative test that was actually coinfected with influenza and another respiratory virus would have to be split between the viruses. With auxiliary information associated with each specimen, model estimates of false negative rates based on, for example, test type, time since onset of symptoms, age of the patient, or clinical presentation would have allowed us to explore the reasons for the high false negative rates. As the false negative rate appears to be laboratory dependant (data not shown), this estimated range is applicable only to the RVDSS for the study period. A significant reduction in the false negative rate is anticipated as methods become standardized and with the uptake of the new RT-PCR methods. As positive results, particularly for culture, are often obtained a week or more after the specimen was received, some positive results may have been reported in a different week than the test. Multiple test results for a single specimen may have also contributed to reporting irregularities. These irregularities would tend to bias the estimated parameter towards zero, and hence the estimated sensitivity towards 1. Considering the overall model fit and the relative severity of influenza [1] , we conclude that our estimate of sensitivity may be slightly over-estimated (number of false negatives under-estimated).
Poor test sensitivity contributes to the chronic underestimation of the burden of influenza in the general population. Since estimates of the burden of illness drive planning for preventive and therapeutic interventions, it is important to improve all aspects leading to improved diagnostic accuracy. We have illustrated a simple method that uses the surveillance data itself to estimate the system wide sensitivity associated with the weekly proportion of tests confirmed positive. Although our estimate of sensitivity is only applicable to the interpretation of the RVDSS data over the study period, similar estimates for specific cohorts or laboratory procedures may help guide further investigation into the reasons for the large number of false negative test results. The capacity for improved diagnostic accuracy will ultimately improve our understanding of the epidemiology of influenza.
|
What types of viral infections are monitored through Canada's Respiratory Virus Detection Surveillance System (RVDSS)?
| 2,177 |
nfluenza, respiratory syncytial virus (RSV), para-influenza virus (PIV), and adenovirus
| 3,914 |
1,593 |
Can’t RIDD off viruses
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4061530/
SHA: ef58c6e2790539f30df14acc260ae2af4b5f3d1f
Authors: Bhattacharyya, Sankar
Date: 2014-06-18
DOI: 10.3389/fmicb.2014.00292
License: cc-by
Abstract: The mammalian genome has evolved to encode a battery of mechanisms, to mitigate a progression in the life cycle of an invasive viral pathogen. Although apparently disadvantaged by their dependence on the host biosynthetic processes, an immensely faster rate of evolution provides viruses with an edge in this conflict. In this review, I have discussed the potential anti-virus activity of inositol-requiring enzyme 1 (IRE1), a well characterized effector of the cellular homeostatic response to an overloading of the endoplasmic reticulum (ER) protein-folding capacity. IRE1, an ER-membrane-resident ribonuclease (RNase), upon activation catalyses regulated cleavage of select protein-coding and non-coding host RNAs, using an RNase domain which is homologous to that of the known anti-viral effector RNaseL. The latter operates as part of the Oligoadenylate synthetase OAS/RNaseL system of anti-viral defense mechanism. Protein-coding RNA substrates are differentially treated by the IRE1 RNase to either augment, through cytoplasmic splicing of an intron in the Xbp1 transcript, or suppress gene expression. This referred suppression of gene expression is mediated through degradative cleavage of a select cohort of cellular RNA transcripts, initiating the regulated IRE1-dependent decay (RIDD) pathway. The review first discusses the anti-viral mechanism of the OAS/RNaseL system and evasion tactics employed by different viruses. This is followed by a review of the RIDD pathway and its potential effect on the stability of viral RNAs. I conclude with a comparison of the enzymatic activity of the two RNases followed by deliberations on the physiological consequences of their activation.
Text: Establishment of infection by a virus, even in permissive host cells, is beset with a plethora of challenges from innate-antiviral and cell-death pathways. Therefore, the host response to a virus infection might prove to be inhibitory for the viral life cycle in a direct or an indirect manner. The direct mechanism involves expression of multiple anti-viral genes that have evolved to recognize, react, and thereby rid the infected host of the viral nucleic acid (Zhou et al., 1997; Thompson et al., 2011) . On the other hand the pathways, e.g., those that culminate in initiating an apoptotic death for the host cell, indirectly serve to limit the spread of virus (Roulston et al., 1999) . A major difference between these two mechanisms is that while the former response is transmissible to neighboring uninfected cells through interferon (IFN) signaling, the latter is observed mostly in cis. Recent reports, however, have demonstrated transmission of an apoptotic signal between cells that are in contact through gap junctions, although such a signaling from an virus infected host cell to an uninfected one is not known yet (Cusato et al., 2003; Udawatte and Ripps, 2005; Kameritsch et al., 2013) . Successful viral pathogens, through a process of active selection, have evolved to replicate and simultaneously evade or block either of these host responses. The viral nucleic acids which could be the genome (positive-sense singlestranded RNA virus) or RNA derived from transcription of the genome [negative-stranded single-sense RNA or double-stranded RNA (dsRNA) or DNA virus], offer critical targets for both detection and eradication. The viral nucleic acid targeting armaments in the host arsenal include those that recognize the associated molecular patterns like toll-like receptors (TLRs), DDX58 (or RIG-1), IFIH1 (or MDA5), IFIT proteins [IFN-stimulated genes (ISG)56 and ISF54], etc. (Aoshi et al., 2011; Bowzard et al., 2011; Jensen and Thomsen, 2012) . This is followed by IFN signaling and expression or activation of factors that target the inducer for degradation or modification like OAS/ribonuclease L (RNaseL) system, APOBEC3, MCPIP1, the ZC3HAV1/exosome system and RNAi pathways (Gao et al., 2002; Sheehy et al., 2002; Guo et al., 2007; Daffis et al., 2010; Sidahmed and Wilkie, 2010; Schmidt et al., 2012; Cho et al., 2013a; Lin et al., 2013) . In this review we focus on two proteins containing homologous RNase domains, RNaseL with a known direct antiviral function and Inositolrequiring enzyme 1 (IRE1 or ERN1) which has an RNaseL-like RNase domain with a known role in homeostatic response to unfolded proteins in the endoplasmic reticulum (ER) and a potential to function as an antiviral (Figure 1 ; Tirasophon et al., 2000) .
In mammalian cells the tell-tale signs of RNA virus infection, like the presence of cytosolic RNA having 5 -ppp or extensive (>30 bp) dsRNA segments are detected by dedicated pathogen associated molecular pattern receptors (PAMPs) or pattern recognition receptors (PRRs) in the host cell, like RIG-1, MDA5, and the IFIT family of proteins (Aoshi et al., 2011; Bowzard et al., 2011; Vabret and Blander, 2013) . The transduction of a signal of this recognition results in the expression of IFN genes the products www.frontiersin.org FIGURE 1 | Schematic representation of the ribonuclease activity of IRE1 and RNaseL showing cross-talk between the paths catalysed by the enzymes. The figure shows activation of RNase activity following dimerization triggered by either accumulation of unfolded proteins in the ER-lumen or synthesis of 2-5A by the enzyme OAS, respectively, for IRE1 and RNaseL. The cleavage of Xbp1u by IRE1 releases an intron thus generating Xbp1s. The IRE1 targets in RIDD pathway or all RNaseL substrates are shown to undergo degradative cleavage. The cleavage products generated through degradation of the respective substrate is shown to potentially interact with RIG-I thereby leading to Interferon secretion and trans-activation of Oas genes through Interferon signaling. Abbreviations: RIG-I = retinoic acid inducible gene-I, Ifnb = interferon beta gene loci, IFN = interferons, ISG = interferon-sensitive genes, 2-5A = 2 -5 oligoadenylates.
of which upon secretion outside the cell bind to cognate receptors, initiating further downstream signaling (Figure 1 ; Randall and Goodbourn, 2008) . The genes that are regulated as a result of IFN signaling are termed as IFN-stimulated or IFN-regulated genes (ISGs or IRGs; Sen and Sarkar, 2007; Schoggins and Rice, 2011) . Oligoadenylate synthetase or OAS genes are canonical ISGs that convert ATP into 2 -5 linked oligoadenylates (2-5A) by an unique enzymatic mechanism (Figure 1 ; Hartmann et al., 2003) . Further, they are RNA-binding proteins that function like PRRs, in a way that the 2-5A synthesizing activity needs to be induced through an interaction with dsRNA (Minks et al., 1979; Hartmann et al., 2003) . In a host cell infected by an RNA virus, such dsRNA is present in the form of replication-intermediates (RI), which are synthesized by the virus-encoded RNA-dependent RNA polymerases (RdRp) and subsequently used by the same enzyme to synthesize more genomic RNA, through asymmetric transcription (Weber et al., 2006) . However, the replications complexes (RCs) harboring these RI molecules are found secluded inside host-membrane derived vesicles, at least in positive-strand RNA viruses, a group which contains many human pathogens (Uchil and Satchidanandam, 2003; Denison, 2008) . Reports from different groups suggest OAS proteins to be distributed both in the cytoplasm as well as in membrane-associated fractions, perhaps indicating an evolution of the host anti-viral methodologies towards detection of the membrane-associated viral dsRNAs (Marie et al., 1990; Lin et al., 2009) . DNA viruses on the other hand, produce dsRNA by annealing of RNA derived from transcription of both strands in the same viral genomic loci, which are probably detected by the cytoplasmic pool of OAS proteins (Jacobs and Langland, 1996; Weber et al., 2006) . Post-activation the OAS enzymes synthesize 2-5A molecules in a non-processive reaction producing oligomers which, although potentially ranging in size from dimeric to multimeric, are functionally active only in a trimeric or tetrameric form (Dong et al., 1994; Sarkar et al., 1999; Silverman, 2007) . These small ligands, which bear phosphate groups (1-3) at the 5 end and hydroxyl groups at the 2 and 3 positions, serve as co-factor which can specifically interact with and thereby allosterically activate, existing RNaseL molecules (Knight et al., 1980; Zhou et al., 1997 Zhou et al., , 2005 Sarkar et al., 1999) . As part of a physiological control system these 2-5A oligomers are quite unstable in that they are highly susceptible to degradation by cellular 5 -phosphatases and PDE12 (2 -phosphodiesterase; Silverman et al., 1981; Johnston and Hearl, 1987; Kubota et al., 2004; Schmidt et al., 2012) . Viral strategies to evade or overcome this host defense mechanism ranges from preventing IFN signaling which would hinder the induction of OAS expression or thwarting activation of expressed OAS proteins by either shielding the viral dsRNA from interacting with it or modulating the host pathway to synthesize inactive 2-5A derivatives (Cayley et al., 1984; Hersh et al., 1984; Rice et al., 1985; Maitra et al., 1994; Beattie et al., 1995; Rivas et al., 1998; Child et al., 2004; Min and Krug, 2006; Sanchez and Mohr, 2007; Sorgeloos et al., 2013) . Shielding of viral RNA from interacting with OAS is possible through enclosure of dsRNA replication intermediates in membrane enclosed compartments as observed in many flaviviruses (Ahlquist, 2006; Miller and Krijnse-Locker, 2008; Miorin et al., 2013) .
RNaseL is a 741 amino acid protein containing three predominantly structured region, an N-terminal ankyrin repeat domain (ARD), a middle catalytically inactive pseudo-kinase (PK) and a C-terminal RNase domain (Figure 2A ; Hassel et al., 1993; Zhou et al., 1993) . The activity of the RNase domain is negatively regulated by the ARD, which is relieved upon binding of 2-5A molecules to ankyrin repeats 2 and 4 followed by a conformational alteration (Figure 1 ; Hassel et al., 1993; Tanaka et al., 2004; Nakanishi et al., 2005) . In support of this contention, deletion of the ARD has been demonstrated to produce constitutively active RNaseL, although with dramatically lower RNase activity (Dong and Silverman, 1997) . However, recent reports suggest that while 2-5A links the ankyrin repeats from adjacent molecules leading to formation of dimer and higher order structures, at sufficiently high in vitro concentrations, RNaseL could oligomerize even in the absence of 2-5A . Nonetheless, in vivo the RNaseL nuclease activity still seems to be under the sole regulation of 2-5A (Al-Saif and Khabar, 2012) . In order to exploit this dependence, multiple viruses like mouse hepatitis virus (MHV) and rotavirus group A (RVA) have evolved to encode phosphodiesterases capable of hydrolysing the 2 -5 linkages in 2-5A and thereby attenuate the RNaseL cleavage activity (Zhao et al., 2012; Zhang et al., 2013) . In addition to 5 -phosphatases and 2 -phosphodiesterases to reduce ClustalW alignment of primary sequence from a segment of the PK domain indicating amino acid residues which are important for interacting with nucleotide cofactors. The conserved lysine residues, critical for this interaction (K599 for IRE1 and K392 in RNaseL) are underlined. (C) Alignment of the KEN domains in RNaseL and IRE1. The amino acids highlighted and numbered in IRE1 are critical for the IRE1 RNase activity (Tirasophon et al., 2000) .
the endogenous 2-5A levels, mammalian genomes encode posttranscriptional and post-translation inhibitors of RNaseL activity in the form of microRNA-29 and the protein ABCE1 (RNaseL inhibitor or RLI), respectively (Bisbal et al., 1995; Lee et al., 2013) . Direct inhibition of RNaseL function is also observed upon infection by Picornaviruses through, either inducing the expression of ABCE1 or exercising a unique inhibitory property of a segment of the viral RNA (Martinand et al., 1998 (Martinand et al., , 1999 Townsend et al., 2008; Sorgeloos et al., 2013) .
Once activated by 2-5A, RNaseL can degrade single-stranded RNA irrespective of its origin (virus or host) although there seems to exist a bias towards cleavage of viral RNA (Wreschner et al., 1981a; Silverman et al., 1983; Li et al., 1998) . RNA sequences that are predominantly cleaved by RNaseL are U-rich with the cleavage points being typically at the 3 end of UA or UG or UU di-nucleotides, leaving a 5 -OH and a 3 -monophosphate in the cleavage product (Floyd-Smith et al., 1981; Wreschner et al., 1981b) . A recent report shows a more general consensus of 5 -UNN-3 with the cleavage point between the second and the third nucleotide (Han et al., 2014) . Cellular targets of RNaseL include both ribosomal RNA (rRNA) and mRNAs, the latter predominantly representing genes involved in protein biosynthesis (Wreschner et al., 1981a; Al-Ahmadi et al., 2009; Andersen et al., 2009) . Additionally, RNaseL activity can also degrade specific ISG mRNA transcripts and thereby attenuate the effect of IFN signaling (Li et al., 2000) . Probably an evolution towards insulating gene expression from RNaseL activity is observed in the coding region of mammalian genes where the UU/UA dinucleotide frequency is rarer (Bisbal et al., 2000; Khabar et al., 2003; Al-Saif and Khabar, 2012) . Perhaps not surprisingly, with a much faster rate of evolution, similar observations have been made with respect to evasion of RNaseL mediated degradation by viral RNAs too (Han and Barton, 2002; Washenberger et al., 2007) . Moreover, nucleoside modifications in host mRNAs, rarely observed in viral RNAs, have also been shown to confer protection from RNaseL (Anderson et al., 2011) . In addition to directly targeting viral RNA, the reduction in functional ribosomes and ribosomal protein mRNA affects viral protein synthesis and replication in an indirect manner. Probably, as a reflection of these effects on cellular RNAs, RNaseL is implicated as one of the factors determining the anti-proliferative effect of IFN activity . The anti-viral activity of RNaseL extends beyond direct cleavage of viral RNA, through stimulation of RIG-I by the cleavage product (Malathi et al., , 2007 (Malathi et al., , 2010 . A global effect of RNaseL is observed in the form of autophagy induced through c-jun N-terminal kinase (JNK) signaling and apoptosis, probably as a consequence of rRNA cleavage (Li et al., 2004; Chakrabarti et al., 2012; Siddiqui and Malathi, 2012) . RNaseL has also been demonstrated to play a role in apoptotic cell death initiated by pharmacological agents extending the physiological role of this pathway beyond the boundary of being only an anti-viral mechanism (Castelli et al., 1997 (Castelli et al., , 1998 .
The ER serves as a conduit for maturation of cellular proteins which are either secreted or destined to be associated with a membrane for its function. An exclusive microenvironment (high Calcium ion and unique ratio of reduced to oxidized glutathione) along with a battery of ER-lumen resident enzymes (foldases, chaperones, and lectins) catalyse/mediate the necessary folding, disulfide-bond formation, and glycosylation reactions (Schroder and Kaufman, 2005) . A perturbation of the folding capacity, due to either physiological disturbances or virus infection, can lead to an accumulation of unfolded proteins in the ER lumen, which signals an unfolded protein response (UPR). UPR encompasses a networked transcriptional and translational gene-expression program, initiated by three ER-membrane resident sensors namely IRE1 or ERN1, PKR-like ER Kinase (PERK or EIF2AK3) and activating transcription factor 6 (ATF6; Hetz, 2012) . IRE1 is a type I single-pass trans-membrane protein in which, similar to what is observed with RNaseL, the N-terminal resident in the ER lumen serves as sensor and the cytosolic C-terminal as the effector (Figure 1 ; Chen and Brandizzi, 2013) . The IRE1 coding gene is present in genomes ranging from yeast to mammals and in the latter is ubiquitously expressed in all tissues (Tirasophon et al., 1998) . Signal transduction by stimulated IRE1 initiates multiple gene regulatory pathways with either pro-survival or pro-apoptotic consequences (Kaufman, 1999) . During homeostasis or unstressed conditions the sensor molecules are monomeric, a state maintained co-operatively by the " absence" of unfolded proteins and the "presence" of HSPA5 (GRP78 or Bip, an ERresident chaperone) molecules bound to a membrane-proximal disordered segment of the protein in the ER-lumen-resident Nterminus (Credle et al., 2005) . Accumulated unfolded proteins in the lumen triggers coupling of this domain from adjacent sensor molecules through a combination of (a) titration of the bound HSPA5 chaperone molecules and (b) direct tethering by malfolded protein molecules (Shamu and Walter, 1996; Credle et al., 2005; Aragon et al., 2009; Korennykh et al., 2009) . Abutting of the luminal domains juxtapose the cytosolic C-terminal segments, leading to an aggregation of the IRE1 molecules into distinct ER-membrane foci (Kimata et al., 2007; Li et al., 2010) . The C-terminal segment has a serine/threonine kinase domain and a RNase domain homologous to that of RNaseL (Figure 1 ; Tirasophon et al., 1998 Tirasophon et al., , 2000 . A trans-autophosphorylation by the kinase domain allosterically activates the RNase domain (Tirasophon et al., 2000; Lee et al., 2008; Korennykh et al., 2009) . In fact, exogenous over-expression of IRE1 in mammalian cells lead to activation suggesting that, under homeostatic conditions, the non-juxtaposition of cytosolic domains maintains an inactive IRE1 (Tirasophon et al., 1998) . Once activated, IRE1 performs cleavage of a variety of RNA substrates mediated by its RNase domain, in addition to phosphorylating and thereby activating JNK (Cox and Walter, 1996; Urano et al., 2000) . Depending on the RNA substrate, the cleavage catalyzed by IRE1 RNase produces differential consequence. Although scission of the Xbp1 mRNA transcript at two internal positions is followed by splicing of the internal segment through ligation of the terminal cleavage products, that in all other known IRE1 target RNA is followed by degradation (Figure 1 ; Sidrauski and Walter, 1997; Calfon et al., 2002) . The latter mode of negative regulation of gene expression is termed as the regulated IRE1-dependent decay (RIDD) pathway (Hollien and Weissman, 2006; Oikawa et al., 2007; Iqbal et al., 2008; Lipson et al., 2008) . Gene transcripts regulated by RIDD pathway includes that from IRE1 (i.e., selftranscripts), probably in a negative feedback loop mechanism (Tirasophon et al., 2000) . In addition to protein coding RNA, RIDD pathway down-regulates the level of a host of microRNA precursors (pre-miRNAs) and can potentially cleave in the anticodon loop of tRNA Phe (Korennykh et al., 2011; Upton et al., 2012) .
The IRE1 RNase domain cleaves the Xbp1u (u for unspliced) mRNA transcript at two precise internal positions within the open reading frame (ORF) generating three segments, the terminal two of which are ligated by a tRNA ligase in yeast and by an unknown ligase in mammalian cells, to produce the Xbp1s (s for spliced) mRNA transcript (Figure 1 ; Yoshida et al., 2001) . The Xbp1s thus generated has a longer ORF, which is created by a frame-shift in the coding sequence downstream of the splice site (Cox and Walter, 1996; Calfon et al., 2002) . A similar dual endonucleolytic cleavage is also observed to initiate the XRN1 and Ski2-3-8 dependent degradation of transcripts in the RIDD degradation pathway (Hollien and Weissman, 2006) . The RIDD target transcript genes are predominantly those that encode membrane-associated or secretory proteins and which are not necessary for ER proteinfolding reactions (Hollien and Weissman, 2006) . The cleavage of Xbp1 and the RIDD-target transcripts constitute homeostatic or pro-survival response by IRE1 since XBP1S trans-activates genes encoding multiple chaperones (to fold unfolded proteins) and the ERAD pathway genes (to degrade terminally misfolded proteins) whereas RIDD reduces flux of polypeptides entering the ER lumen (Lee et al., 2003; Hollien and Weissman, 2006) . On the other hand, cleavage of pre-miRNA transcripts which are processed in the cell to generate CASPASE-2 mRNA (Casp2) controlling miRNAs, constitutes the pro-apoptotic function of IRE1 (Upton et al., 2012) . Another pro-apoptotic signal from IRE1 emanates from signaling through phosphorylation of JNK1 (Urano et al., 2000) . Although in the initial phase RIDD activity does not cleave mRNAs encoding essential ER proteins, at later stages of chronic UPR such transcripts are rendered susceptible to degradation promoting apoptosis induction (Han et al., 2009; Bhattacharyya et al., 2014) .
Infection of mammalian cells by a multitude of viruses induce an UPR which is sometimes characterized by suppression of signaling by one or more of the three sensor(s; Su et al., 2002; Tardif et al., 2002; He, 2006; Yu et al., 2006 Yu et al., , 2013 Medigeshi et al., 2007; Zhang et al., 2010; Merquiol et al., 2011) . Among these at least two viruses from diverse families, HCMV (a DNA virus) and hepatitis C virus (a hepacivirus), interfere with IRE1 signaling by different mechanism (Tardif et al., 2004; Stahl et al., 2013 ). An observed inhibition of any cellular function by a virus infection could suggest a potential anti-virus function for it, which the virus has evolved to evade through blocking some critical step(s). In both the cases mentioned above, stability of the viral proteins seems to be affected by ERAD-mediated degradation, although other potential anti-viral effect of IRE1 activation are not clear yet (Isler et al., 2005; Saeed et al., 2011) . Interestingly, host mRNA fragments produced following IRE1 activation during bacterial infection, has been shown to activate RIG-I signaling (Figure 1 ; Cho et al., 2013b) . Theoretically, other functions of IRE1 can also have anti-viral effect necessitating its inhibition for uninhibited viral replication. It is, however, still not clear whether IRE1 is able to cleave any viral RNA (or mRNA) in a manner similar to that of other RIDD targets (Figure 1) . The possibilities of such a direct anti-viral function are encouraged by the fact that all these viruses encode at least one protein which, as part of its maturation process, requires glycosylation and disulfide-bond formation. Such a necessity would entail translation of the mRNA encoding such a protein, which in case of positive-sense single-stranded RNA viruses would mean the genome, in association with the ER-membrane (Figure 1 ; Lerner et al., 2003) . Additionally for many RNA viruses, replication complexes are housed in ER-derived vesicular structures (Denison, 2008; den Boon et al., 2010) . Considering the proximity of IRE1 and these virus-derived RNAs it is tempting to speculate that probably at some point of time in the viral life cycle one or more virus-associated RNA would be susceptible to cleavage by IRE1. However, studies with at least two viruses have shown that instead of increasing viral titre, inhibiting the RNase activity of activated IRE1 has an opposite effect (Hassan et al., 2012; Bhattacharyya et al., 2014) . This implies potential benefits of IRE1 activation through one or more of the following, (a) expression of chaperones or other pro-viral molecules downstream of XBP1Supregulation or JNK-activation, (b) cleavage of potential anti-viral gene mRNA transcripts by RIDD activity. However, the mode of protection for the viral RNA from RIDD activity is still not clear. It is possible that the viral proteins create a subdomain within the ER membrane, which through some mechanism excludes IRE1 from diffusing near the genomic RNA, thereby protecting the replication complexes (Denison, 2008) . It is therefore probably not surprising that single-stranded plus-sense RNA viruses encode a polyprotein, which produces replication complexes in cis, promoting formation of such subdomains (Egger et al., 2000) . The fact that IRE1 forms bulky oligomers of higher order probably aggravates such an exclusion of the activated sensor molecules from vicinity of the viral replication complexes. The UPR signaling eventually attenuate during chronic ER-stress and since that is what a virus-induced UPR mimics, probably the viral RNA needs protection only during the initial phase of UPR activation (Lin et al., 2007) . Since the choice of RIDD target seems to be grossly driven towards mRNAs that encode ER-transitory but are not ER-essential proteins, it is also possible that one or more viral protein have evolved to mimic a host protein the transcript of which is RIDD-resistant (Hollien and Weissman, 2006) . Most of the RIDD target mRNA are observed to be ER-membrane associated, the proximity to IRE1 facilitating association and cleavage (Figure 1 ; Hollien and Weissman, 2006) . Although ER-association for an mRNA is possible without the mediation of ribosomes, Gaddam and co-workers reported that continued association with polysomes for a membrane-bound mRNA can confer protection from IRE1 cleavage (Cui et al., 2012; Gaddam et al., 2013) . This would suggest important implications for the observed refractory nature of Japanese encephalitis virus (JEV) and influenza virus RNA to RIDD cleavage (Hassan et al., 2012; Bhattacharyya et al., 2014) . In contrast to Influenza virus, flaviviruses (which include JEV) do not suppress host protein synthesis implying the absence of a global inhibition on translation as would be expected during UPR (Clyde et al., 2006; Edgil et al., 2006) . Therefore, a continued translation of viral RNA in spite of UPR activation can in principle confer protection from the pattern of RNA cleavage observed in the RIDD pathway.
IRE1 and RNaseL, in addition to biochemical similarities in protein kinase domain and structural similarities in their RNase domain, share the functional consequences of their activation in initiating cellular apoptosis through JNK signaling (Table 1 and Figure 2 ; Liu and Lin, 2005; Dhanasekaran and Reddy, 2008) . Though initial discoveries were made in the context of homeostatic and anti-viral role for the former and latter, differences between the pathways are narrowed by further advances in research. In the same vein, while inhibition of IRE1 signaling in virus infected cells indicates a potential anti-viral role, www.frontiersin.org (Tirasophon et al., 1998; Dong and Silverman, 1999; Papa et al., 2003; Lin et al., 2007) Nature of RNase substrates Both 28S rRNA and mRNAs IRE1β can cleave both 28S rRNA and mRNA while IRE1α substrates include only mRNAs (Iwawaki et al., 2001) Dissimilarities
Cleavage substrates Beside 28S rRNA, predominantly cleaves mRNAs encoding ribosomal proteins (Andersen et al., 2009) Xbp1u and other mRNAs in addition to microRNA precursors which are targeted as part of the RIDD pathway Selection of cleavage site Cleaved between 2nd and 3rd nucleotide positions of UN/N sites (Han et al., 2014) RNA sequence with the consensus of 5 -CUGCAG-3 in association with a stem-loop (SL) structure essential for recognition of Xbp1u and other mRNAs (Oikawa et al., 2010) association of RNaseL mutations with generation of prostate cancer extends the ambit of influence of this anti-viral effector to more non-infectious physiological disorders (Silverman, 2003) . Biochemically, the similarity in their RNase domains does not extend to the choice of either substrates or cleavage point, which are downstream of UU or UA in RNaseL and downstream of G (predominantly) for IRE1 ( Figure 2C ; Yoshida et al., 2001; Hollien and Weissman, 2006; Upton et al., 2012) . Further, while RNaseL cleaves pre-dominantly in single-stranded region, IRE1 seems to cleave equally well in single-and double-stranded region (Upton et al., 2012) . However, a recent report suggested a consensus cleavage site with the sequence UN/N, in RNaseL targets and in those mRNAs that are cleaved by IRE1 as part of the RIDD pathway (Han et al., 2014) . Access to potential cleavage substrate for RNaseL is conjectured to be facilitated through its association with polyribosomes, while no such association is known for IRE1 (Salehzada et al., 1991) . Possibilities exist that IRE1 would have preferential distribution in the rough ER which, upon activation, would give it ready access to mRNAs for initiating the RIDD pathway.
In the context of a virus infection, the pathway leading from both these proteins have the potential to lead to cell death. Notwithstanding the fact that this might be an efficient way of virus clearance, it also portends pathological outcomes for the infected organism. Future research would probably lead to design of drugs targeting these proteins based on the structural homology of their effector domains, regulating the pathological denouement of their activation without compromising their anti-viral or potential anti-viral functions.
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the potential anti-virus activity of inositol-requiring enzyme 1 (IRE1), a well characterized effector of the cellular homeostatic response to an overloading of the endoplasmic reticulum (ER) protein-folding capacity. IRE1, an ER-membrane-resident ribonuclease (RNase), upon activation catalyses regulated cleavage of select protein-coding and non-coding host RNAs, using an RNase domain which is homologous to that of the known anti-viral effector RNaseL.
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Can’t RIDD off viruses
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4061530/
SHA: ef58c6e2790539f30df14acc260ae2af4b5f3d1f
Authors: Bhattacharyya, Sankar
Date: 2014-06-18
DOI: 10.3389/fmicb.2014.00292
License: cc-by
Abstract: The mammalian genome has evolved to encode a battery of mechanisms, to mitigate a progression in the life cycle of an invasive viral pathogen. Although apparently disadvantaged by their dependence on the host biosynthetic processes, an immensely faster rate of evolution provides viruses with an edge in this conflict. In this review, I have discussed the potential anti-virus activity of inositol-requiring enzyme 1 (IRE1), a well characterized effector of the cellular homeostatic response to an overloading of the endoplasmic reticulum (ER) protein-folding capacity. IRE1, an ER-membrane-resident ribonuclease (RNase), upon activation catalyses regulated cleavage of select protein-coding and non-coding host RNAs, using an RNase domain which is homologous to that of the known anti-viral effector RNaseL. The latter operates as part of the Oligoadenylate synthetase OAS/RNaseL system of anti-viral defense mechanism. Protein-coding RNA substrates are differentially treated by the IRE1 RNase to either augment, through cytoplasmic splicing of an intron in the Xbp1 transcript, or suppress gene expression. This referred suppression of gene expression is mediated through degradative cleavage of a select cohort of cellular RNA transcripts, initiating the regulated IRE1-dependent decay (RIDD) pathway. The review first discusses the anti-viral mechanism of the OAS/RNaseL system and evasion tactics employed by different viruses. This is followed by a review of the RIDD pathway and its potential effect on the stability of viral RNAs. I conclude with a comparison of the enzymatic activity of the two RNases followed by deliberations on the physiological consequences of their activation.
Text: Establishment of infection by a virus, even in permissive host cells, is beset with a plethora of challenges from innate-antiviral and cell-death pathways. Therefore, the host response to a virus infection might prove to be inhibitory for the viral life cycle in a direct or an indirect manner. The direct mechanism involves expression of multiple anti-viral genes that have evolved to recognize, react, and thereby rid the infected host of the viral nucleic acid (Zhou et al., 1997; Thompson et al., 2011) . On the other hand the pathways, e.g., those that culminate in initiating an apoptotic death for the host cell, indirectly serve to limit the spread of virus (Roulston et al., 1999) . A major difference between these two mechanisms is that while the former response is transmissible to neighboring uninfected cells through interferon (IFN) signaling, the latter is observed mostly in cis. Recent reports, however, have demonstrated transmission of an apoptotic signal between cells that are in contact through gap junctions, although such a signaling from an virus infected host cell to an uninfected one is not known yet (Cusato et al., 2003; Udawatte and Ripps, 2005; Kameritsch et al., 2013) . Successful viral pathogens, through a process of active selection, have evolved to replicate and simultaneously evade or block either of these host responses. The viral nucleic acids which could be the genome (positive-sense singlestranded RNA virus) or RNA derived from transcription of the genome [negative-stranded single-sense RNA or double-stranded RNA (dsRNA) or DNA virus], offer critical targets for both detection and eradication. The viral nucleic acid targeting armaments in the host arsenal include those that recognize the associated molecular patterns like toll-like receptors (TLRs), DDX58 (or RIG-1), IFIH1 (or MDA5), IFIT proteins [IFN-stimulated genes (ISG)56 and ISF54], etc. (Aoshi et al., 2011; Bowzard et al., 2011; Jensen and Thomsen, 2012) . This is followed by IFN signaling and expression or activation of factors that target the inducer for degradation or modification like OAS/ribonuclease L (RNaseL) system, APOBEC3, MCPIP1, the ZC3HAV1/exosome system and RNAi pathways (Gao et al., 2002; Sheehy et al., 2002; Guo et al., 2007; Daffis et al., 2010; Sidahmed and Wilkie, 2010; Schmidt et al., 2012; Cho et al., 2013a; Lin et al., 2013) . In this review we focus on two proteins containing homologous RNase domains, RNaseL with a known direct antiviral function and Inositolrequiring enzyme 1 (IRE1 or ERN1) which has an RNaseL-like RNase domain with a known role in homeostatic response to unfolded proteins in the endoplasmic reticulum (ER) and a potential to function as an antiviral (Figure 1 ; Tirasophon et al., 2000) .
In mammalian cells the tell-tale signs of RNA virus infection, like the presence of cytosolic RNA having 5 -ppp or extensive (>30 bp) dsRNA segments are detected by dedicated pathogen associated molecular pattern receptors (PAMPs) or pattern recognition receptors (PRRs) in the host cell, like RIG-1, MDA5, and the IFIT family of proteins (Aoshi et al., 2011; Bowzard et al., 2011; Vabret and Blander, 2013) . The transduction of a signal of this recognition results in the expression of IFN genes the products www.frontiersin.org FIGURE 1 | Schematic representation of the ribonuclease activity of IRE1 and RNaseL showing cross-talk between the paths catalysed by the enzymes. The figure shows activation of RNase activity following dimerization triggered by either accumulation of unfolded proteins in the ER-lumen or synthesis of 2-5A by the enzyme OAS, respectively, for IRE1 and RNaseL. The cleavage of Xbp1u by IRE1 releases an intron thus generating Xbp1s. The IRE1 targets in RIDD pathway or all RNaseL substrates are shown to undergo degradative cleavage. The cleavage products generated through degradation of the respective substrate is shown to potentially interact with RIG-I thereby leading to Interferon secretion and trans-activation of Oas genes through Interferon signaling. Abbreviations: RIG-I = retinoic acid inducible gene-I, Ifnb = interferon beta gene loci, IFN = interferons, ISG = interferon-sensitive genes, 2-5A = 2 -5 oligoadenylates.
of which upon secretion outside the cell bind to cognate receptors, initiating further downstream signaling (Figure 1 ; Randall and Goodbourn, 2008) . The genes that are regulated as a result of IFN signaling are termed as IFN-stimulated or IFN-regulated genes (ISGs or IRGs; Sen and Sarkar, 2007; Schoggins and Rice, 2011) . Oligoadenylate synthetase or OAS genes are canonical ISGs that convert ATP into 2 -5 linked oligoadenylates (2-5A) by an unique enzymatic mechanism (Figure 1 ; Hartmann et al., 2003) . Further, they are RNA-binding proteins that function like PRRs, in a way that the 2-5A synthesizing activity needs to be induced through an interaction with dsRNA (Minks et al., 1979; Hartmann et al., 2003) . In a host cell infected by an RNA virus, such dsRNA is present in the form of replication-intermediates (RI), which are synthesized by the virus-encoded RNA-dependent RNA polymerases (RdRp) and subsequently used by the same enzyme to synthesize more genomic RNA, through asymmetric transcription (Weber et al., 2006) . However, the replications complexes (RCs) harboring these RI molecules are found secluded inside host-membrane derived vesicles, at least in positive-strand RNA viruses, a group which contains many human pathogens (Uchil and Satchidanandam, 2003; Denison, 2008) . Reports from different groups suggest OAS proteins to be distributed both in the cytoplasm as well as in membrane-associated fractions, perhaps indicating an evolution of the host anti-viral methodologies towards detection of the membrane-associated viral dsRNAs (Marie et al., 1990; Lin et al., 2009) . DNA viruses on the other hand, produce dsRNA by annealing of RNA derived from transcription of both strands in the same viral genomic loci, which are probably detected by the cytoplasmic pool of OAS proteins (Jacobs and Langland, 1996; Weber et al., 2006) . Post-activation the OAS enzymes synthesize 2-5A molecules in a non-processive reaction producing oligomers which, although potentially ranging in size from dimeric to multimeric, are functionally active only in a trimeric or tetrameric form (Dong et al., 1994; Sarkar et al., 1999; Silverman, 2007) . These small ligands, which bear phosphate groups (1-3) at the 5 end and hydroxyl groups at the 2 and 3 positions, serve as co-factor which can specifically interact with and thereby allosterically activate, existing RNaseL molecules (Knight et al., 1980; Zhou et al., 1997 Zhou et al., , 2005 Sarkar et al., 1999) . As part of a physiological control system these 2-5A oligomers are quite unstable in that they are highly susceptible to degradation by cellular 5 -phosphatases and PDE12 (2 -phosphodiesterase; Silverman et al., 1981; Johnston and Hearl, 1987; Kubota et al., 2004; Schmidt et al., 2012) . Viral strategies to evade or overcome this host defense mechanism ranges from preventing IFN signaling which would hinder the induction of OAS expression or thwarting activation of expressed OAS proteins by either shielding the viral dsRNA from interacting with it or modulating the host pathway to synthesize inactive 2-5A derivatives (Cayley et al., 1984; Hersh et al., 1984; Rice et al., 1985; Maitra et al., 1994; Beattie et al., 1995; Rivas et al., 1998; Child et al., 2004; Min and Krug, 2006; Sanchez and Mohr, 2007; Sorgeloos et al., 2013) . Shielding of viral RNA from interacting with OAS is possible through enclosure of dsRNA replication intermediates in membrane enclosed compartments as observed in many flaviviruses (Ahlquist, 2006; Miller and Krijnse-Locker, 2008; Miorin et al., 2013) .
RNaseL is a 741 amino acid protein containing three predominantly structured region, an N-terminal ankyrin repeat domain (ARD), a middle catalytically inactive pseudo-kinase (PK) and a C-terminal RNase domain (Figure 2A ; Hassel et al., 1993; Zhou et al., 1993) . The activity of the RNase domain is negatively regulated by the ARD, which is relieved upon binding of 2-5A molecules to ankyrin repeats 2 and 4 followed by a conformational alteration (Figure 1 ; Hassel et al., 1993; Tanaka et al., 2004; Nakanishi et al., 2005) . In support of this contention, deletion of the ARD has been demonstrated to produce constitutively active RNaseL, although with dramatically lower RNase activity (Dong and Silverman, 1997) . However, recent reports suggest that while 2-5A links the ankyrin repeats from adjacent molecules leading to formation of dimer and higher order structures, at sufficiently high in vitro concentrations, RNaseL could oligomerize even in the absence of 2-5A . Nonetheless, in vivo the RNaseL nuclease activity still seems to be under the sole regulation of 2-5A (Al-Saif and Khabar, 2012) . In order to exploit this dependence, multiple viruses like mouse hepatitis virus (MHV) and rotavirus group A (RVA) have evolved to encode phosphodiesterases capable of hydrolysing the 2 -5 linkages in 2-5A and thereby attenuate the RNaseL cleavage activity (Zhao et al., 2012; Zhang et al., 2013) . In addition to 5 -phosphatases and 2 -phosphodiesterases to reduce ClustalW alignment of primary sequence from a segment of the PK domain indicating amino acid residues which are important for interacting with nucleotide cofactors. The conserved lysine residues, critical for this interaction (K599 for IRE1 and K392 in RNaseL) are underlined. (C) Alignment of the KEN domains in RNaseL and IRE1. The amino acids highlighted and numbered in IRE1 are critical for the IRE1 RNase activity (Tirasophon et al., 2000) .
the endogenous 2-5A levels, mammalian genomes encode posttranscriptional and post-translation inhibitors of RNaseL activity in the form of microRNA-29 and the protein ABCE1 (RNaseL inhibitor or RLI), respectively (Bisbal et al., 1995; Lee et al., 2013) . Direct inhibition of RNaseL function is also observed upon infection by Picornaviruses through, either inducing the expression of ABCE1 or exercising a unique inhibitory property of a segment of the viral RNA (Martinand et al., 1998 (Martinand et al., , 1999 Townsend et al., 2008; Sorgeloos et al., 2013) .
Once activated by 2-5A, RNaseL can degrade single-stranded RNA irrespective of its origin (virus or host) although there seems to exist a bias towards cleavage of viral RNA (Wreschner et al., 1981a; Silverman et al., 1983; Li et al., 1998) . RNA sequences that are predominantly cleaved by RNaseL are U-rich with the cleavage points being typically at the 3 end of UA or UG or UU di-nucleotides, leaving a 5 -OH and a 3 -monophosphate in the cleavage product (Floyd-Smith et al., 1981; Wreschner et al., 1981b) . A recent report shows a more general consensus of 5 -UNN-3 with the cleavage point between the second and the third nucleotide (Han et al., 2014) . Cellular targets of RNaseL include both ribosomal RNA (rRNA) and mRNAs, the latter predominantly representing genes involved in protein biosynthesis (Wreschner et al., 1981a; Al-Ahmadi et al., 2009; Andersen et al., 2009) . Additionally, RNaseL activity can also degrade specific ISG mRNA transcripts and thereby attenuate the effect of IFN signaling (Li et al., 2000) . Probably an evolution towards insulating gene expression from RNaseL activity is observed in the coding region of mammalian genes where the UU/UA dinucleotide frequency is rarer (Bisbal et al., 2000; Khabar et al., 2003; Al-Saif and Khabar, 2012) . Perhaps not surprisingly, with a much faster rate of evolution, similar observations have been made with respect to evasion of RNaseL mediated degradation by viral RNAs too (Han and Barton, 2002; Washenberger et al., 2007) . Moreover, nucleoside modifications in host mRNAs, rarely observed in viral RNAs, have also been shown to confer protection from RNaseL (Anderson et al., 2011) . In addition to directly targeting viral RNA, the reduction in functional ribosomes and ribosomal protein mRNA affects viral protein synthesis and replication in an indirect manner. Probably, as a reflection of these effects on cellular RNAs, RNaseL is implicated as one of the factors determining the anti-proliferative effect of IFN activity . The anti-viral activity of RNaseL extends beyond direct cleavage of viral RNA, through stimulation of RIG-I by the cleavage product (Malathi et al., , 2007 (Malathi et al., , 2010 . A global effect of RNaseL is observed in the form of autophagy induced through c-jun N-terminal kinase (JNK) signaling and apoptosis, probably as a consequence of rRNA cleavage (Li et al., 2004; Chakrabarti et al., 2012; Siddiqui and Malathi, 2012) . RNaseL has also been demonstrated to play a role in apoptotic cell death initiated by pharmacological agents extending the physiological role of this pathway beyond the boundary of being only an anti-viral mechanism (Castelli et al., 1997 (Castelli et al., , 1998 .
The ER serves as a conduit for maturation of cellular proteins which are either secreted or destined to be associated with a membrane for its function. An exclusive microenvironment (high Calcium ion and unique ratio of reduced to oxidized glutathione) along with a battery of ER-lumen resident enzymes (foldases, chaperones, and lectins) catalyse/mediate the necessary folding, disulfide-bond formation, and glycosylation reactions (Schroder and Kaufman, 2005) . A perturbation of the folding capacity, due to either physiological disturbances or virus infection, can lead to an accumulation of unfolded proteins in the ER lumen, which signals an unfolded protein response (UPR). UPR encompasses a networked transcriptional and translational gene-expression program, initiated by three ER-membrane resident sensors namely IRE1 or ERN1, PKR-like ER Kinase (PERK or EIF2AK3) and activating transcription factor 6 (ATF6; Hetz, 2012) . IRE1 is a type I single-pass trans-membrane protein in which, similar to what is observed with RNaseL, the N-terminal resident in the ER lumen serves as sensor and the cytosolic C-terminal as the effector (Figure 1 ; Chen and Brandizzi, 2013) . The IRE1 coding gene is present in genomes ranging from yeast to mammals and in the latter is ubiquitously expressed in all tissues (Tirasophon et al., 1998) . Signal transduction by stimulated IRE1 initiates multiple gene regulatory pathways with either pro-survival or pro-apoptotic consequences (Kaufman, 1999) . During homeostasis or unstressed conditions the sensor molecules are monomeric, a state maintained co-operatively by the " absence" of unfolded proteins and the "presence" of HSPA5 (GRP78 or Bip, an ERresident chaperone) molecules bound to a membrane-proximal disordered segment of the protein in the ER-lumen-resident Nterminus (Credle et al., 2005) . Accumulated unfolded proteins in the lumen triggers coupling of this domain from adjacent sensor molecules through a combination of (a) titration of the bound HSPA5 chaperone molecules and (b) direct tethering by malfolded protein molecules (Shamu and Walter, 1996; Credle et al., 2005; Aragon et al., 2009; Korennykh et al., 2009) . Abutting of the luminal domains juxtapose the cytosolic C-terminal segments, leading to an aggregation of the IRE1 molecules into distinct ER-membrane foci (Kimata et al., 2007; Li et al., 2010) . The C-terminal segment has a serine/threonine kinase domain and a RNase domain homologous to that of RNaseL (Figure 1 ; Tirasophon et al., 1998 Tirasophon et al., , 2000 . A trans-autophosphorylation by the kinase domain allosterically activates the RNase domain (Tirasophon et al., 2000; Lee et al., 2008; Korennykh et al., 2009) . In fact, exogenous over-expression of IRE1 in mammalian cells lead to activation suggesting that, under homeostatic conditions, the non-juxtaposition of cytosolic domains maintains an inactive IRE1 (Tirasophon et al., 1998) . Once activated, IRE1 performs cleavage of a variety of RNA substrates mediated by its RNase domain, in addition to phosphorylating and thereby activating JNK (Cox and Walter, 1996; Urano et al., 2000) . Depending on the RNA substrate, the cleavage catalyzed by IRE1 RNase produces differential consequence. Although scission of the Xbp1 mRNA transcript at two internal positions is followed by splicing of the internal segment through ligation of the terminal cleavage products, that in all other known IRE1 target RNA is followed by degradation (Figure 1 ; Sidrauski and Walter, 1997; Calfon et al., 2002) . The latter mode of negative regulation of gene expression is termed as the regulated IRE1-dependent decay (RIDD) pathway (Hollien and Weissman, 2006; Oikawa et al., 2007; Iqbal et al., 2008; Lipson et al., 2008) . Gene transcripts regulated by RIDD pathway includes that from IRE1 (i.e., selftranscripts), probably in a negative feedback loop mechanism (Tirasophon et al., 2000) . In addition to protein coding RNA, RIDD pathway down-regulates the level of a host of microRNA precursors (pre-miRNAs) and can potentially cleave in the anticodon loop of tRNA Phe (Korennykh et al., 2011; Upton et al., 2012) .
The IRE1 RNase domain cleaves the Xbp1u (u for unspliced) mRNA transcript at two precise internal positions within the open reading frame (ORF) generating three segments, the terminal two of which are ligated by a tRNA ligase in yeast and by an unknown ligase in mammalian cells, to produce the Xbp1s (s for spliced) mRNA transcript (Figure 1 ; Yoshida et al., 2001) . The Xbp1s thus generated has a longer ORF, which is created by a frame-shift in the coding sequence downstream of the splice site (Cox and Walter, 1996; Calfon et al., 2002) . A similar dual endonucleolytic cleavage is also observed to initiate the XRN1 and Ski2-3-8 dependent degradation of transcripts in the RIDD degradation pathway (Hollien and Weissman, 2006) . The RIDD target transcript genes are predominantly those that encode membrane-associated or secretory proteins and which are not necessary for ER proteinfolding reactions (Hollien and Weissman, 2006) . The cleavage of Xbp1 and the RIDD-target transcripts constitute homeostatic or pro-survival response by IRE1 since XBP1S trans-activates genes encoding multiple chaperones (to fold unfolded proteins) and the ERAD pathway genes (to degrade terminally misfolded proteins) whereas RIDD reduces flux of polypeptides entering the ER lumen (Lee et al., 2003; Hollien and Weissman, 2006) . On the other hand, cleavage of pre-miRNA transcripts which are processed in the cell to generate CASPASE-2 mRNA (Casp2) controlling miRNAs, constitutes the pro-apoptotic function of IRE1 (Upton et al., 2012) . Another pro-apoptotic signal from IRE1 emanates from signaling through phosphorylation of JNK1 (Urano et al., 2000) . Although in the initial phase RIDD activity does not cleave mRNAs encoding essential ER proteins, at later stages of chronic UPR such transcripts are rendered susceptible to degradation promoting apoptosis induction (Han et al., 2009; Bhattacharyya et al., 2014) .
Infection of mammalian cells by a multitude of viruses induce an UPR which is sometimes characterized by suppression of signaling by one or more of the three sensor(s; Su et al., 2002; Tardif et al., 2002; He, 2006; Yu et al., 2006 Yu et al., , 2013 Medigeshi et al., 2007; Zhang et al., 2010; Merquiol et al., 2011) . Among these at least two viruses from diverse families, HCMV (a DNA virus) and hepatitis C virus (a hepacivirus), interfere with IRE1 signaling by different mechanism (Tardif et al., 2004; Stahl et al., 2013 ). An observed inhibition of any cellular function by a virus infection could suggest a potential anti-virus function for it, which the virus has evolved to evade through blocking some critical step(s). In both the cases mentioned above, stability of the viral proteins seems to be affected by ERAD-mediated degradation, although other potential anti-viral effect of IRE1 activation are not clear yet (Isler et al., 2005; Saeed et al., 2011) . Interestingly, host mRNA fragments produced following IRE1 activation during bacterial infection, has been shown to activate RIG-I signaling (Figure 1 ; Cho et al., 2013b) . Theoretically, other functions of IRE1 can also have anti-viral effect necessitating its inhibition for uninhibited viral replication. It is, however, still not clear whether IRE1 is able to cleave any viral RNA (or mRNA) in a manner similar to that of other RIDD targets (Figure 1) . The possibilities of such a direct anti-viral function are encouraged by the fact that all these viruses encode at least one protein which, as part of its maturation process, requires glycosylation and disulfide-bond formation. Such a necessity would entail translation of the mRNA encoding such a protein, which in case of positive-sense single-stranded RNA viruses would mean the genome, in association with the ER-membrane (Figure 1 ; Lerner et al., 2003) . Additionally for many RNA viruses, replication complexes are housed in ER-derived vesicular structures (Denison, 2008; den Boon et al., 2010) . Considering the proximity of IRE1 and these virus-derived RNAs it is tempting to speculate that probably at some point of time in the viral life cycle one or more virus-associated RNA would be susceptible to cleavage by IRE1. However, studies with at least two viruses have shown that instead of increasing viral titre, inhibiting the RNase activity of activated IRE1 has an opposite effect (Hassan et al., 2012; Bhattacharyya et al., 2014) . This implies potential benefits of IRE1 activation through one or more of the following, (a) expression of chaperones or other pro-viral molecules downstream of XBP1Supregulation or JNK-activation, (b) cleavage of potential anti-viral gene mRNA transcripts by RIDD activity. However, the mode of protection for the viral RNA from RIDD activity is still not clear. It is possible that the viral proteins create a subdomain within the ER membrane, which through some mechanism excludes IRE1 from diffusing near the genomic RNA, thereby protecting the replication complexes (Denison, 2008) . It is therefore probably not surprising that single-stranded plus-sense RNA viruses encode a polyprotein, which produces replication complexes in cis, promoting formation of such subdomains (Egger et al., 2000) . The fact that IRE1 forms bulky oligomers of higher order probably aggravates such an exclusion of the activated sensor molecules from vicinity of the viral replication complexes. The UPR signaling eventually attenuate during chronic ER-stress and since that is what a virus-induced UPR mimics, probably the viral RNA needs protection only during the initial phase of UPR activation (Lin et al., 2007) . Since the choice of RIDD target seems to be grossly driven towards mRNAs that encode ER-transitory but are not ER-essential proteins, it is also possible that one or more viral protein have evolved to mimic a host protein the transcript of which is RIDD-resistant (Hollien and Weissman, 2006) . Most of the RIDD target mRNA are observed to be ER-membrane associated, the proximity to IRE1 facilitating association and cleavage (Figure 1 ; Hollien and Weissman, 2006) . Although ER-association for an mRNA is possible without the mediation of ribosomes, Gaddam and co-workers reported that continued association with polysomes for a membrane-bound mRNA can confer protection from IRE1 cleavage (Cui et al., 2012; Gaddam et al., 2013) . This would suggest important implications for the observed refractory nature of Japanese encephalitis virus (JEV) and influenza virus RNA to RIDD cleavage (Hassan et al., 2012; Bhattacharyya et al., 2014) . In contrast to Influenza virus, flaviviruses (which include JEV) do not suppress host protein synthesis implying the absence of a global inhibition on translation as would be expected during UPR (Clyde et al., 2006; Edgil et al., 2006) . Therefore, a continued translation of viral RNA in spite of UPR activation can in principle confer protection from the pattern of RNA cleavage observed in the RIDD pathway.
IRE1 and RNaseL, in addition to biochemical similarities in protein kinase domain and structural similarities in their RNase domain, share the functional consequences of their activation in initiating cellular apoptosis through JNK signaling (Table 1 and Figure 2 ; Liu and Lin, 2005; Dhanasekaran and Reddy, 2008) . Though initial discoveries were made in the context of homeostatic and anti-viral role for the former and latter, differences between the pathways are narrowed by further advances in research. In the same vein, while inhibition of IRE1 signaling in virus infected cells indicates a potential anti-viral role, www.frontiersin.org (Tirasophon et al., 1998; Dong and Silverman, 1999; Papa et al., 2003; Lin et al., 2007) Nature of RNase substrates Both 28S rRNA and mRNAs IRE1β can cleave both 28S rRNA and mRNA while IRE1α substrates include only mRNAs (Iwawaki et al., 2001) Dissimilarities
Cleavage substrates Beside 28S rRNA, predominantly cleaves mRNAs encoding ribosomal proteins (Andersen et al., 2009) Xbp1u and other mRNAs in addition to microRNA precursors which are targeted as part of the RIDD pathway Selection of cleavage site Cleaved between 2nd and 3rd nucleotide positions of UN/N sites (Han et al., 2014) RNA sequence with the consensus of 5 -CUGCAG-3 in association with a stem-loop (SL) structure essential for recognition of Xbp1u and other mRNAs (Oikawa et al., 2010) association of RNaseL mutations with generation of prostate cancer extends the ambit of influence of this anti-viral effector to more non-infectious physiological disorders (Silverman, 2003) . Biochemically, the similarity in their RNase domains does not extend to the choice of either substrates or cleavage point, which are downstream of UU or UA in RNaseL and downstream of G (predominantly) for IRE1 ( Figure 2C ; Yoshida et al., 2001; Hollien and Weissman, 2006; Upton et al., 2012) . Further, while RNaseL cleaves pre-dominantly in single-stranded region, IRE1 seems to cleave equally well in single-and double-stranded region (Upton et al., 2012) . However, a recent report suggested a consensus cleavage site with the sequence UN/N, in RNaseL targets and in those mRNAs that are cleaved by IRE1 as part of the RIDD pathway (Han et al., 2014) . Access to potential cleavage substrate for RNaseL is conjectured to be facilitated through its association with polyribosomes, while no such association is known for IRE1 (Salehzada et al., 1991) . Possibilities exist that IRE1 would have preferential distribution in the rough ER which, upon activation, would give it ready access to mRNAs for initiating the RIDD pathway.
In the context of a virus infection, the pathway leading from both these proteins have the potential to lead to cell death. Notwithstanding the fact that this might be an efficient way of virus clearance, it also portends pathological outcomes for the infected organism. Future research would probably lead to design of drugs targeting these proteins based on the structural homology of their effector domains, regulating the pathological denouement of their activation without compromising their anti-viral or potential anti-viral functions.
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The review first discusses the anti-viral mechanism of the OAS/RNaseL system and evasion tactics employed by different viruses. This is followed by a review of the RIDD pathway and its potential effect on the stability of viral RNAs.
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Can’t RIDD off viruses
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4061530/
SHA: ef58c6e2790539f30df14acc260ae2af4b5f3d1f
Authors: Bhattacharyya, Sankar
Date: 2014-06-18
DOI: 10.3389/fmicb.2014.00292
License: cc-by
Abstract: The mammalian genome has evolved to encode a battery of mechanisms, to mitigate a progression in the life cycle of an invasive viral pathogen. Although apparently disadvantaged by their dependence on the host biosynthetic processes, an immensely faster rate of evolution provides viruses with an edge in this conflict. In this review, I have discussed the potential anti-virus activity of inositol-requiring enzyme 1 (IRE1), a well characterized effector of the cellular homeostatic response to an overloading of the endoplasmic reticulum (ER) protein-folding capacity. IRE1, an ER-membrane-resident ribonuclease (RNase), upon activation catalyses regulated cleavage of select protein-coding and non-coding host RNAs, using an RNase domain which is homologous to that of the known anti-viral effector RNaseL. The latter operates as part of the Oligoadenylate synthetase OAS/RNaseL system of anti-viral defense mechanism. Protein-coding RNA substrates are differentially treated by the IRE1 RNase to either augment, through cytoplasmic splicing of an intron in the Xbp1 transcript, or suppress gene expression. This referred suppression of gene expression is mediated through degradative cleavage of a select cohort of cellular RNA transcripts, initiating the regulated IRE1-dependent decay (RIDD) pathway. The review first discusses the anti-viral mechanism of the OAS/RNaseL system and evasion tactics employed by different viruses. This is followed by a review of the RIDD pathway and its potential effect on the stability of viral RNAs. I conclude with a comparison of the enzymatic activity of the two RNases followed by deliberations on the physiological consequences of their activation.
Text: Establishment of infection by a virus, even in permissive host cells, is beset with a plethora of challenges from innate-antiviral and cell-death pathways. Therefore, the host response to a virus infection might prove to be inhibitory for the viral life cycle in a direct or an indirect manner. The direct mechanism involves expression of multiple anti-viral genes that have evolved to recognize, react, and thereby rid the infected host of the viral nucleic acid (Zhou et al., 1997; Thompson et al., 2011) . On the other hand the pathways, e.g., those that culminate in initiating an apoptotic death for the host cell, indirectly serve to limit the spread of virus (Roulston et al., 1999) . A major difference between these two mechanisms is that while the former response is transmissible to neighboring uninfected cells through interferon (IFN) signaling, the latter is observed mostly in cis. Recent reports, however, have demonstrated transmission of an apoptotic signal between cells that are in contact through gap junctions, although such a signaling from an virus infected host cell to an uninfected one is not known yet (Cusato et al., 2003; Udawatte and Ripps, 2005; Kameritsch et al., 2013) . Successful viral pathogens, through a process of active selection, have evolved to replicate and simultaneously evade or block either of these host responses. The viral nucleic acids which could be the genome (positive-sense singlestranded RNA virus) or RNA derived from transcription of the genome [negative-stranded single-sense RNA or double-stranded RNA (dsRNA) or DNA virus], offer critical targets for both detection and eradication. The viral nucleic acid targeting armaments in the host arsenal include those that recognize the associated molecular patterns like toll-like receptors (TLRs), DDX58 (or RIG-1), IFIH1 (or MDA5), IFIT proteins [IFN-stimulated genes (ISG)56 and ISF54], etc. (Aoshi et al., 2011; Bowzard et al., 2011; Jensen and Thomsen, 2012) . This is followed by IFN signaling and expression or activation of factors that target the inducer for degradation or modification like OAS/ribonuclease L (RNaseL) system, APOBEC3, MCPIP1, the ZC3HAV1/exosome system and RNAi pathways (Gao et al., 2002; Sheehy et al., 2002; Guo et al., 2007; Daffis et al., 2010; Sidahmed and Wilkie, 2010; Schmidt et al., 2012; Cho et al., 2013a; Lin et al., 2013) . In this review we focus on two proteins containing homologous RNase domains, RNaseL with a known direct antiviral function and Inositolrequiring enzyme 1 (IRE1 or ERN1) which has an RNaseL-like RNase domain with a known role in homeostatic response to unfolded proteins in the endoplasmic reticulum (ER) and a potential to function as an antiviral (Figure 1 ; Tirasophon et al., 2000) .
In mammalian cells the tell-tale signs of RNA virus infection, like the presence of cytosolic RNA having 5 -ppp or extensive (>30 bp) dsRNA segments are detected by dedicated pathogen associated molecular pattern receptors (PAMPs) or pattern recognition receptors (PRRs) in the host cell, like RIG-1, MDA5, and the IFIT family of proteins (Aoshi et al., 2011; Bowzard et al., 2011; Vabret and Blander, 2013) . The transduction of a signal of this recognition results in the expression of IFN genes the products www.frontiersin.org FIGURE 1 | Schematic representation of the ribonuclease activity of IRE1 and RNaseL showing cross-talk between the paths catalysed by the enzymes. The figure shows activation of RNase activity following dimerization triggered by either accumulation of unfolded proteins in the ER-lumen or synthesis of 2-5A by the enzyme OAS, respectively, for IRE1 and RNaseL. The cleavage of Xbp1u by IRE1 releases an intron thus generating Xbp1s. The IRE1 targets in RIDD pathway or all RNaseL substrates are shown to undergo degradative cleavage. The cleavage products generated through degradation of the respective substrate is shown to potentially interact with RIG-I thereby leading to Interferon secretion and trans-activation of Oas genes through Interferon signaling. Abbreviations: RIG-I = retinoic acid inducible gene-I, Ifnb = interferon beta gene loci, IFN = interferons, ISG = interferon-sensitive genes, 2-5A = 2 -5 oligoadenylates.
of which upon secretion outside the cell bind to cognate receptors, initiating further downstream signaling (Figure 1 ; Randall and Goodbourn, 2008) . The genes that are regulated as a result of IFN signaling are termed as IFN-stimulated or IFN-regulated genes (ISGs or IRGs; Sen and Sarkar, 2007; Schoggins and Rice, 2011) . Oligoadenylate synthetase or OAS genes are canonical ISGs that convert ATP into 2 -5 linked oligoadenylates (2-5A) by an unique enzymatic mechanism (Figure 1 ; Hartmann et al., 2003) . Further, they are RNA-binding proteins that function like PRRs, in a way that the 2-5A synthesizing activity needs to be induced through an interaction with dsRNA (Minks et al., 1979; Hartmann et al., 2003) . In a host cell infected by an RNA virus, such dsRNA is present in the form of replication-intermediates (RI), which are synthesized by the virus-encoded RNA-dependent RNA polymerases (RdRp) and subsequently used by the same enzyme to synthesize more genomic RNA, through asymmetric transcription (Weber et al., 2006) . However, the replications complexes (RCs) harboring these RI molecules are found secluded inside host-membrane derived vesicles, at least in positive-strand RNA viruses, a group which contains many human pathogens (Uchil and Satchidanandam, 2003; Denison, 2008) . Reports from different groups suggest OAS proteins to be distributed both in the cytoplasm as well as in membrane-associated fractions, perhaps indicating an evolution of the host anti-viral methodologies towards detection of the membrane-associated viral dsRNAs (Marie et al., 1990; Lin et al., 2009) . DNA viruses on the other hand, produce dsRNA by annealing of RNA derived from transcription of both strands in the same viral genomic loci, which are probably detected by the cytoplasmic pool of OAS proteins (Jacobs and Langland, 1996; Weber et al., 2006) . Post-activation the OAS enzymes synthesize 2-5A molecules in a non-processive reaction producing oligomers which, although potentially ranging in size from dimeric to multimeric, are functionally active only in a trimeric or tetrameric form (Dong et al., 1994; Sarkar et al., 1999; Silverman, 2007) . These small ligands, which bear phosphate groups (1-3) at the 5 end and hydroxyl groups at the 2 and 3 positions, serve as co-factor which can specifically interact with and thereby allosterically activate, existing RNaseL molecules (Knight et al., 1980; Zhou et al., 1997 Zhou et al., , 2005 Sarkar et al., 1999) . As part of a physiological control system these 2-5A oligomers are quite unstable in that they are highly susceptible to degradation by cellular 5 -phosphatases and PDE12 (2 -phosphodiesterase; Silverman et al., 1981; Johnston and Hearl, 1987; Kubota et al., 2004; Schmidt et al., 2012) . Viral strategies to evade or overcome this host defense mechanism ranges from preventing IFN signaling which would hinder the induction of OAS expression or thwarting activation of expressed OAS proteins by either shielding the viral dsRNA from interacting with it or modulating the host pathway to synthesize inactive 2-5A derivatives (Cayley et al., 1984; Hersh et al., 1984; Rice et al., 1985; Maitra et al., 1994; Beattie et al., 1995; Rivas et al., 1998; Child et al., 2004; Min and Krug, 2006; Sanchez and Mohr, 2007; Sorgeloos et al., 2013) . Shielding of viral RNA from interacting with OAS is possible through enclosure of dsRNA replication intermediates in membrane enclosed compartments as observed in many flaviviruses (Ahlquist, 2006; Miller and Krijnse-Locker, 2008; Miorin et al., 2013) .
RNaseL is a 741 amino acid protein containing three predominantly structured region, an N-terminal ankyrin repeat domain (ARD), a middle catalytically inactive pseudo-kinase (PK) and a C-terminal RNase domain (Figure 2A ; Hassel et al., 1993; Zhou et al., 1993) . The activity of the RNase domain is negatively regulated by the ARD, which is relieved upon binding of 2-5A molecules to ankyrin repeats 2 and 4 followed by a conformational alteration (Figure 1 ; Hassel et al., 1993; Tanaka et al., 2004; Nakanishi et al., 2005) . In support of this contention, deletion of the ARD has been demonstrated to produce constitutively active RNaseL, although with dramatically lower RNase activity (Dong and Silverman, 1997) . However, recent reports suggest that while 2-5A links the ankyrin repeats from adjacent molecules leading to formation of dimer and higher order structures, at sufficiently high in vitro concentrations, RNaseL could oligomerize even in the absence of 2-5A . Nonetheless, in vivo the RNaseL nuclease activity still seems to be under the sole regulation of 2-5A (Al-Saif and Khabar, 2012) . In order to exploit this dependence, multiple viruses like mouse hepatitis virus (MHV) and rotavirus group A (RVA) have evolved to encode phosphodiesterases capable of hydrolysing the 2 -5 linkages in 2-5A and thereby attenuate the RNaseL cleavage activity (Zhao et al., 2012; Zhang et al., 2013) . In addition to 5 -phosphatases and 2 -phosphodiesterases to reduce ClustalW alignment of primary sequence from a segment of the PK domain indicating amino acid residues which are important for interacting with nucleotide cofactors. The conserved lysine residues, critical for this interaction (K599 for IRE1 and K392 in RNaseL) are underlined. (C) Alignment of the KEN domains in RNaseL and IRE1. The amino acids highlighted and numbered in IRE1 are critical for the IRE1 RNase activity (Tirasophon et al., 2000) .
the endogenous 2-5A levels, mammalian genomes encode posttranscriptional and post-translation inhibitors of RNaseL activity in the form of microRNA-29 and the protein ABCE1 (RNaseL inhibitor or RLI), respectively (Bisbal et al., 1995; Lee et al., 2013) . Direct inhibition of RNaseL function is also observed upon infection by Picornaviruses through, either inducing the expression of ABCE1 or exercising a unique inhibitory property of a segment of the viral RNA (Martinand et al., 1998 (Martinand et al., , 1999 Townsend et al., 2008; Sorgeloos et al., 2013) .
Once activated by 2-5A, RNaseL can degrade single-stranded RNA irrespective of its origin (virus or host) although there seems to exist a bias towards cleavage of viral RNA (Wreschner et al., 1981a; Silverman et al., 1983; Li et al., 1998) . RNA sequences that are predominantly cleaved by RNaseL are U-rich with the cleavage points being typically at the 3 end of UA or UG or UU di-nucleotides, leaving a 5 -OH and a 3 -monophosphate in the cleavage product (Floyd-Smith et al., 1981; Wreschner et al., 1981b) . A recent report shows a more general consensus of 5 -UNN-3 with the cleavage point between the second and the third nucleotide (Han et al., 2014) . Cellular targets of RNaseL include both ribosomal RNA (rRNA) and mRNAs, the latter predominantly representing genes involved in protein biosynthesis (Wreschner et al., 1981a; Al-Ahmadi et al., 2009; Andersen et al., 2009) . Additionally, RNaseL activity can also degrade specific ISG mRNA transcripts and thereby attenuate the effect of IFN signaling (Li et al., 2000) . Probably an evolution towards insulating gene expression from RNaseL activity is observed in the coding region of mammalian genes where the UU/UA dinucleotide frequency is rarer (Bisbal et al., 2000; Khabar et al., 2003; Al-Saif and Khabar, 2012) . Perhaps not surprisingly, with a much faster rate of evolution, similar observations have been made with respect to evasion of RNaseL mediated degradation by viral RNAs too (Han and Barton, 2002; Washenberger et al., 2007) . Moreover, nucleoside modifications in host mRNAs, rarely observed in viral RNAs, have also been shown to confer protection from RNaseL (Anderson et al., 2011) . In addition to directly targeting viral RNA, the reduction in functional ribosomes and ribosomal protein mRNA affects viral protein synthesis and replication in an indirect manner. Probably, as a reflection of these effects on cellular RNAs, RNaseL is implicated as one of the factors determining the anti-proliferative effect of IFN activity . The anti-viral activity of RNaseL extends beyond direct cleavage of viral RNA, through stimulation of RIG-I by the cleavage product (Malathi et al., , 2007 (Malathi et al., , 2010 . A global effect of RNaseL is observed in the form of autophagy induced through c-jun N-terminal kinase (JNK) signaling and apoptosis, probably as a consequence of rRNA cleavage (Li et al., 2004; Chakrabarti et al., 2012; Siddiqui and Malathi, 2012) . RNaseL has also been demonstrated to play a role in apoptotic cell death initiated by pharmacological agents extending the physiological role of this pathway beyond the boundary of being only an anti-viral mechanism (Castelli et al., 1997 (Castelli et al., , 1998 .
The ER serves as a conduit for maturation of cellular proteins which are either secreted or destined to be associated with a membrane for its function. An exclusive microenvironment (high Calcium ion and unique ratio of reduced to oxidized glutathione) along with a battery of ER-lumen resident enzymes (foldases, chaperones, and lectins) catalyse/mediate the necessary folding, disulfide-bond formation, and glycosylation reactions (Schroder and Kaufman, 2005) . A perturbation of the folding capacity, due to either physiological disturbances or virus infection, can lead to an accumulation of unfolded proteins in the ER lumen, which signals an unfolded protein response (UPR). UPR encompasses a networked transcriptional and translational gene-expression program, initiated by three ER-membrane resident sensors namely IRE1 or ERN1, PKR-like ER Kinase (PERK or EIF2AK3) and activating transcription factor 6 (ATF6; Hetz, 2012) . IRE1 is a type I single-pass trans-membrane protein in which, similar to what is observed with RNaseL, the N-terminal resident in the ER lumen serves as sensor and the cytosolic C-terminal as the effector (Figure 1 ; Chen and Brandizzi, 2013) . The IRE1 coding gene is present in genomes ranging from yeast to mammals and in the latter is ubiquitously expressed in all tissues (Tirasophon et al., 1998) . Signal transduction by stimulated IRE1 initiates multiple gene regulatory pathways with either pro-survival or pro-apoptotic consequences (Kaufman, 1999) . During homeostasis or unstressed conditions the sensor molecules are monomeric, a state maintained co-operatively by the " absence" of unfolded proteins and the "presence" of HSPA5 (GRP78 or Bip, an ERresident chaperone) molecules bound to a membrane-proximal disordered segment of the protein in the ER-lumen-resident Nterminus (Credle et al., 2005) . Accumulated unfolded proteins in the lumen triggers coupling of this domain from adjacent sensor molecules through a combination of (a) titration of the bound HSPA5 chaperone molecules and (b) direct tethering by malfolded protein molecules (Shamu and Walter, 1996; Credle et al., 2005; Aragon et al., 2009; Korennykh et al., 2009) . Abutting of the luminal domains juxtapose the cytosolic C-terminal segments, leading to an aggregation of the IRE1 molecules into distinct ER-membrane foci (Kimata et al., 2007; Li et al., 2010) . The C-terminal segment has a serine/threonine kinase domain and a RNase domain homologous to that of RNaseL (Figure 1 ; Tirasophon et al., 1998 Tirasophon et al., , 2000 . A trans-autophosphorylation by the kinase domain allosterically activates the RNase domain (Tirasophon et al., 2000; Lee et al., 2008; Korennykh et al., 2009) . In fact, exogenous over-expression of IRE1 in mammalian cells lead to activation suggesting that, under homeostatic conditions, the non-juxtaposition of cytosolic domains maintains an inactive IRE1 (Tirasophon et al., 1998) . Once activated, IRE1 performs cleavage of a variety of RNA substrates mediated by its RNase domain, in addition to phosphorylating and thereby activating JNK (Cox and Walter, 1996; Urano et al., 2000) . Depending on the RNA substrate, the cleavage catalyzed by IRE1 RNase produces differential consequence. Although scission of the Xbp1 mRNA transcript at two internal positions is followed by splicing of the internal segment through ligation of the terminal cleavage products, that in all other known IRE1 target RNA is followed by degradation (Figure 1 ; Sidrauski and Walter, 1997; Calfon et al., 2002) . The latter mode of negative regulation of gene expression is termed as the regulated IRE1-dependent decay (RIDD) pathway (Hollien and Weissman, 2006; Oikawa et al., 2007; Iqbal et al., 2008; Lipson et al., 2008) . Gene transcripts regulated by RIDD pathway includes that from IRE1 (i.e., selftranscripts), probably in a negative feedback loop mechanism (Tirasophon et al., 2000) . In addition to protein coding RNA, RIDD pathway down-regulates the level of a host of microRNA precursors (pre-miRNAs) and can potentially cleave in the anticodon loop of tRNA Phe (Korennykh et al., 2011; Upton et al., 2012) .
The IRE1 RNase domain cleaves the Xbp1u (u for unspliced) mRNA transcript at two precise internal positions within the open reading frame (ORF) generating three segments, the terminal two of which are ligated by a tRNA ligase in yeast and by an unknown ligase in mammalian cells, to produce the Xbp1s (s for spliced) mRNA transcript (Figure 1 ; Yoshida et al., 2001) . The Xbp1s thus generated has a longer ORF, which is created by a frame-shift in the coding sequence downstream of the splice site (Cox and Walter, 1996; Calfon et al., 2002) . A similar dual endonucleolytic cleavage is also observed to initiate the XRN1 and Ski2-3-8 dependent degradation of transcripts in the RIDD degradation pathway (Hollien and Weissman, 2006) . The RIDD target transcript genes are predominantly those that encode membrane-associated or secretory proteins and which are not necessary for ER proteinfolding reactions (Hollien and Weissman, 2006) . The cleavage of Xbp1 and the RIDD-target transcripts constitute homeostatic or pro-survival response by IRE1 since XBP1S trans-activates genes encoding multiple chaperones (to fold unfolded proteins) and the ERAD pathway genes (to degrade terminally misfolded proteins) whereas RIDD reduces flux of polypeptides entering the ER lumen (Lee et al., 2003; Hollien and Weissman, 2006) . On the other hand, cleavage of pre-miRNA transcripts which are processed in the cell to generate CASPASE-2 mRNA (Casp2) controlling miRNAs, constitutes the pro-apoptotic function of IRE1 (Upton et al., 2012) . Another pro-apoptotic signal from IRE1 emanates from signaling through phosphorylation of JNK1 (Urano et al., 2000) . Although in the initial phase RIDD activity does not cleave mRNAs encoding essential ER proteins, at later stages of chronic UPR such transcripts are rendered susceptible to degradation promoting apoptosis induction (Han et al., 2009; Bhattacharyya et al., 2014) .
Infection of mammalian cells by a multitude of viruses induce an UPR which is sometimes characterized by suppression of signaling by one or more of the three sensor(s; Su et al., 2002; Tardif et al., 2002; He, 2006; Yu et al., 2006 Yu et al., , 2013 Medigeshi et al., 2007; Zhang et al., 2010; Merquiol et al., 2011) . Among these at least two viruses from diverse families, HCMV (a DNA virus) and hepatitis C virus (a hepacivirus), interfere with IRE1 signaling by different mechanism (Tardif et al., 2004; Stahl et al., 2013 ). An observed inhibition of any cellular function by a virus infection could suggest a potential anti-virus function for it, which the virus has evolved to evade through blocking some critical step(s). In both the cases mentioned above, stability of the viral proteins seems to be affected by ERAD-mediated degradation, although other potential anti-viral effect of IRE1 activation are not clear yet (Isler et al., 2005; Saeed et al., 2011) . Interestingly, host mRNA fragments produced following IRE1 activation during bacterial infection, has been shown to activate RIG-I signaling (Figure 1 ; Cho et al., 2013b) . Theoretically, other functions of IRE1 can also have anti-viral effect necessitating its inhibition for uninhibited viral replication. It is, however, still not clear whether IRE1 is able to cleave any viral RNA (or mRNA) in a manner similar to that of other RIDD targets (Figure 1) . The possibilities of such a direct anti-viral function are encouraged by the fact that all these viruses encode at least one protein which, as part of its maturation process, requires glycosylation and disulfide-bond formation. Such a necessity would entail translation of the mRNA encoding such a protein, which in case of positive-sense single-stranded RNA viruses would mean the genome, in association with the ER-membrane (Figure 1 ; Lerner et al., 2003) . Additionally for many RNA viruses, replication complexes are housed in ER-derived vesicular structures (Denison, 2008; den Boon et al., 2010) . Considering the proximity of IRE1 and these virus-derived RNAs it is tempting to speculate that probably at some point of time in the viral life cycle one or more virus-associated RNA would be susceptible to cleavage by IRE1. However, studies with at least two viruses have shown that instead of increasing viral titre, inhibiting the RNase activity of activated IRE1 has an opposite effect (Hassan et al., 2012; Bhattacharyya et al., 2014) . This implies potential benefits of IRE1 activation through one or more of the following, (a) expression of chaperones or other pro-viral molecules downstream of XBP1Supregulation or JNK-activation, (b) cleavage of potential anti-viral gene mRNA transcripts by RIDD activity. However, the mode of protection for the viral RNA from RIDD activity is still not clear. It is possible that the viral proteins create a subdomain within the ER membrane, which through some mechanism excludes IRE1 from diffusing near the genomic RNA, thereby protecting the replication complexes (Denison, 2008) . It is therefore probably not surprising that single-stranded plus-sense RNA viruses encode a polyprotein, which produces replication complexes in cis, promoting formation of such subdomains (Egger et al., 2000) . The fact that IRE1 forms bulky oligomers of higher order probably aggravates such an exclusion of the activated sensor molecules from vicinity of the viral replication complexes. The UPR signaling eventually attenuate during chronic ER-stress and since that is what a virus-induced UPR mimics, probably the viral RNA needs protection only during the initial phase of UPR activation (Lin et al., 2007) . Since the choice of RIDD target seems to be grossly driven towards mRNAs that encode ER-transitory but are not ER-essential proteins, it is also possible that one or more viral protein have evolved to mimic a host protein the transcript of which is RIDD-resistant (Hollien and Weissman, 2006) . Most of the RIDD target mRNA are observed to be ER-membrane associated, the proximity to IRE1 facilitating association and cleavage (Figure 1 ; Hollien and Weissman, 2006) . Although ER-association for an mRNA is possible without the mediation of ribosomes, Gaddam and co-workers reported that continued association with polysomes for a membrane-bound mRNA can confer protection from IRE1 cleavage (Cui et al., 2012; Gaddam et al., 2013) . This would suggest important implications for the observed refractory nature of Japanese encephalitis virus (JEV) and influenza virus RNA to RIDD cleavage (Hassan et al., 2012; Bhattacharyya et al., 2014) . In contrast to Influenza virus, flaviviruses (which include JEV) do not suppress host protein synthesis implying the absence of a global inhibition on translation as would be expected during UPR (Clyde et al., 2006; Edgil et al., 2006) . Therefore, a continued translation of viral RNA in spite of UPR activation can in principle confer protection from the pattern of RNA cleavage observed in the RIDD pathway.
IRE1 and RNaseL, in addition to biochemical similarities in protein kinase domain and structural similarities in their RNase domain, share the functional consequences of their activation in initiating cellular apoptosis through JNK signaling (Table 1 and Figure 2 ; Liu and Lin, 2005; Dhanasekaran and Reddy, 2008) . Though initial discoveries were made in the context of homeostatic and anti-viral role for the former and latter, differences between the pathways are narrowed by further advances in research. In the same vein, while inhibition of IRE1 signaling in virus infected cells indicates a potential anti-viral role, www.frontiersin.org (Tirasophon et al., 1998; Dong and Silverman, 1999; Papa et al., 2003; Lin et al., 2007) Nature of RNase substrates Both 28S rRNA and mRNAs IRE1β can cleave both 28S rRNA and mRNA while IRE1α substrates include only mRNAs (Iwawaki et al., 2001) Dissimilarities
Cleavage substrates Beside 28S rRNA, predominantly cleaves mRNAs encoding ribosomal proteins (Andersen et al., 2009) Xbp1u and other mRNAs in addition to microRNA precursors which are targeted as part of the RIDD pathway Selection of cleavage site Cleaved between 2nd and 3rd nucleotide positions of UN/N sites (Han et al., 2014) RNA sequence with the consensus of 5 -CUGCAG-3 in association with a stem-loop (SL) structure essential for recognition of Xbp1u and other mRNAs (Oikawa et al., 2010) association of RNaseL mutations with generation of prostate cancer extends the ambit of influence of this anti-viral effector to more non-infectious physiological disorders (Silverman, 2003) . Biochemically, the similarity in their RNase domains does not extend to the choice of either substrates or cleavage point, which are downstream of UU or UA in RNaseL and downstream of G (predominantly) for IRE1 ( Figure 2C ; Yoshida et al., 2001; Hollien and Weissman, 2006; Upton et al., 2012) . Further, while RNaseL cleaves pre-dominantly in single-stranded region, IRE1 seems to cleave equally well in single-and double-stranded region (Upton et al., 2012) . However, a recent report suggested a consensus cleavage site with the sequence UN/N, in RNaseL targets and in those mRNAs that are cleaved by IRE1 as part of the RIDD pathway (Han et al., 2014) . Access to potential cleavage substrate for RNaseL is conjectured to be facilitated through its association with polyribosomes, while no such association is known for IRE1 (Salehzada et al., 1991) . Possibilities exist that IRE1 would have preferential distribution in the rough ER which, upon activation, would give it ready access to mRNAs for initiating the RIDD pathway.
In the context of a virus infection, the pathway leading from both these proteins have the potential to lead to cell death. Notwithstanding the fact that this might be an efficient way of virus clearance, it also portends pathological outcomes for the infected organism. Future research would probably lead to design of drugs targeting these proteins based on the structural homology of their effector domains, regulating the pathological denouement of their activation without compromising their anti-viral or potential anti-viral functions.
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What is reported in this publication?
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comparison of the enzymatic activity of the two RNases followed by deliberations on the physiological consequences of their activation.
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Can’t RIDD off viruses
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4061530/
SHA: ef58c6e2790539f30df14acc260ae2af4b5f3d1f
Authors: Bhattacharyya, Sankar
Date: 2014-06-18
DOI: 10.3389/fmicb.2014.00292
License: cc-by
Abstract: The mammalian genome has evolved to encode a battery of mechanisms, to mitigate a progression in the life cycle of an invasive viral pathogen. Although apparently disadvantaged by their dependence on the host biosynthetic processes, an immensely faster rate of evolution provides viruses with an edge in this conflict. In this review, I have discussed the potential anti-virus activity of inositol-requiring enzyme 1 (IRE1), a well characterized effector of the cellular homeostatic response to an overloading of the endoplasmic reticulum (ER) protein-folding capacity. IRE1, an ER-membrane-resident ribonuclease (RNase), upon activation catalyses regulated cleavage of select protein-coding and non-coding host RNAs, using an RNase domain which is homologous to that of the known anti-viral effector RNaseL. The latter operates as part of the Oligoadenylate synthetase OAS/RNaseL system of anti-viral defense mechanism. Protein-coding RNA substrates are differentially treated by the IRE1 RNase to either augment, through cytoplasmic splicing of an intron in the Xbp1 transcript, or suppress gene expression. This referred suppression of gene expression is mediated through degradative cleavage of a select cohort of cellular RNA transcripts, initiating the regulated IRE1-dependent decay (RIDD) pathway. The review first discusses the anti-viral mechanism of the OAS/RNaseL system and evasion tactics employed by different viruses. This is followed by a review of the RIDD pathway and its potential effect on the stability of viral RNAs. I conclude with a comparison of the enzymatic activity of the two RNases followed by deliberations on the physiological consequences of their activation.
Text: Establishment of infection by a virus, even in permissive host cells, is beset with a plethora of challenges from innate-antiviral and cell-death pathways. Therefore, the host response to a virus infection might prove to be inhibitory for the viral life cycle in a direct or an indirect manner. The direct mechanism involves expression of multiple anti-viral genes that have evolved to recognize, react, and thereby rid the infected host of the viral nucleic acid (Zhou et al., 1997; Thompson et al., 2011) . On the other hand the pathways, e.g., those that culminate in initiating an apoptotic death for the host cell, indirectly serve to limit the spread of virus (Roulston et al., 1999) . A major difference between these two mechanisms is that while the former response is transmissible to neighboring uninfected cells through interferon (IFN) signaling, the latter is observed mostly in cis. Recent reports, however, have demonstrated transmission of an apoptotic signal between cells that are in contact through gap junctions, although such a signaling from an virus infected host cell to an uninfected one is not known yet (Cusato et al., 2003; Udawatte and Ripps, 2005; Kameritsch et al., 2013) . Successful viral pathogens, through a process of active selection, have evolved to replicate and simultaneously evade or block either of these host responses. The viral nucleic acids which could be the genome (positive-sense singlestranded RNA virus) or RNA derived from transcription of the genome [negative-stranded single-sense RNA or double-stranded RNA (dsRNA) or DNA virus], offer critical targets for both detection and eradication. The viral nucleic acid targeting armaments in the host arsenal include those that recognize the associated molecular patterns like toll-like receptors (TLRs), DDX58 (or RIG-1), IFIH1 (or MDA5), IFIT proteins [IFN-stimulated genes (ISG)56 and ISF54], etc. (Aoshi et al., 2011; Bowzard et al., 2011; Jensen and Thomsen, 2012) . This is followed by IFN signaling and expression or activation of factors that target the inducer for degradation or modification like OAS/ribonuclease L (RNaseL) system, APOBEC3, MCPIP1, the ZC3HAV1/exosome system and RNAi pathways (Gao et al., 2002; Sheehy et al., 2002; Guo et al., 2007; Daffis et al., 2010; Sidahmed and Wilkie, 2010; Schmidt et al., 2012; Cho et al., 2013a; Lin et al., 2013) . In this review we focus on two proteins containing homologous RNase domains, RNaseL with a known direct antiviral function and Inositolrequiring enzyme 1 (IRE1 or ERN1) which has an RNaseL-like RNase domain with a known role in homeostatic response to unfolded proteins in the endoplasmic reticulum (ER) and a potential to function as an antiviral (Figure 1 ; Tirasophon et al., 2000) .
In mammalian cells the tell-tale signs of RNA virus infection, like the presence of cytosolic RNA having 5 -ppp or extensive (>30 bp) dsRNA segments are detected by dedicated pathogen associated molecular pattern receptors (PAMPs) or pattern recognition receptors (PRRs) in the host cell, like RIG-1, MDA5, and the IFIT family of proteins (Aoshi et al., 2011; Bowzard et al., 2011; Vabret and Blander, 2013) . The transduction of a signal of this recognition results in the expression of IFN genes the products www.frontiersin.org FIGURE 1 | Schematic representation of the ribonuclease activity of IRE1 and RNaseL showing cross-talk between the paths catalysed by the enzymes. The figure shows activation of RNase activity following dimerization triggered by either accumulation of unfolded proteins in the ER-lumen or synthesis of 2-5A by the enzyme OAS, respectively, for IRE1 and RNaseL. The cleavage of Xbp1u by IRE1 releases an intron thus generating Xbp1s. The IRE1 targets in RIDD pathway or all RNaseL substrates are shown to undergo degradative cleavage. The cleavage products generated through degradation of the respective substrate is shown to potentially interact with RIG-I thereby leading to Interferon secretion and trans-activation of Oas genes through Interferon signaling. Abbreviations: RIG-I = retinoic acid inducible gene-I, Ifnb = interferon beta gene loci, IFN = interferons, ISG = interferon-sensitive genes, 2-5A = 2 -5 oligoadenylates.
of which upon secretion outside the cell bind to cognate receptors, initiating further downstream signaling (Figure 1 ; Randall and Goodbourn, 2008) . The genes that are regulated as a result of IFN signaling are termed as IFN-stimulated or IFN-regulated genes (ISGs or IRGs; Sen and Sarkar, 2007; Schoggins and Rice, 2011) . Oligoadenylate synthetase or OAS genes are canonical ISGs that convert ATP into 2 -5 linked oligoadenylates (2-5A) by an unique enzymatic mechanism (Figure 1 ; Hartmann et al., 2003) . Further, they are RNA-binding proteins that function like PRRs, in a way that the 2-5A synthesizing activity needs to be induced through an interaction with dsRNA (Minks et al., 1979; Hartmann et al., 2003) . In a host cell infected by an RNA virus, such dsRNA is present in the form of replication-intermediates (RI), which are synthesized by the virus-encoded RNA-dependent RNA polymerases (RdRp) and subsequently used by the same enzyme to synthesize more genomic RNA, through asymmetric transcription (Weber et al., 2006) . However, the replications complexes (RCs) harboring these RI molecules are found secluded inside host-membrane derived vesicles, at least in positive-strand RNA viruses, a group which contains many human pathogens (Uchil and Satchidanandam, 2003; Denison, 2008) . Reports from different groups suggest OAS proteins to be distributed both in the cytoplasm as well as in membrane-associated fractions, perhaps indicating an evolution of the host anti-viral methodologies towards detection of the membrane-associated viral dsRNAs (Marie et al., 1990; Lin et al., 2009) . DNA viruses on the other hand, produce dsRNA by annealing of RNA derived from transcription of both strands in the same viral genomic loci, which are probably detected by the cytoplasmic pool of OAS proteins (Jacobs and Langland, 1996; Weber et al., 2006) . Post-activation the OAS enzymes synthesize 2-5A molecules in a non-processive reaction producing oligomers which, although potentially ranging in size from dimeric to multimeric, are functionally active only in a trimeric or tetrameric form (Dong et al., 1994; Sarkar et al., 1999; Silverman, 2007) . These small ligands, which bear phosphate groups (1-3) at the 5 end and hydroxyl groups at the 2 and 3 positions, serve as co-factor which can specifically interact with and thereby allosterically activate, existing RNaseL molecules (Knight et al., 1980; Zhou et al., 1997 Zhou et al., , 2005 Sarkar et al., 1999) . As part of a physiological control system these 2-5A oligomers are quite unstable in that they are highly susceptible to degradation by cellular 5 -phosphatases and PDE12 (2 -phosphodiesterase; Silverman et al., 1981; Johnston and Hearl, 1987; Kubota et al., 2004; Schmidt et al., 2012) . Viral strategies to evade or overcome this host defense mechanism ranges from preventing IFN signaling which would hinder the induction of OAS expression or thwarting activation of expressed OAS proteins by either shielding the viral dsRNA from interacting with it or modulating the host pathway to synthesize inactive 2-5A derivatives (Cayley et al., 1984; Hersh et al., 1984; Rice et al., 1985; Maitra et al., 1994; Beattie et al., 1995; Rivas et al., 1998; Child et al., 2004; Min and Krug, 2006; Sanchez and Mohr, 2007; Sorgeloos et al., 2013) . Shielding of viral RNA from interacting with OAS is possible through enclosure of dsRNA replication intermediates in membrane enclosed compartments as observed in many flaviviruses (Ahlquist, 2006; Miller and Krijnse-Locker, 2008; Miorin et al., 2013) .
RNaseL is a 741 amino acid protein containing three predominantly structured region, an N-terminal ankyrin repeat domain (ARD), a middle catalytically inactive pseudo-kinase (PK) and a C-terminal RNase domain (Figure 2A ; Hassel et al., 1993; Zhou et al., 1993) . The activity of the RNase domain is negatively regulated by the ARD, which is relieved upon binding of 2-5A molecules to ankyrin repeats 2 and 4 followed by a conformational alteration (Figure 1 ; Hassel et al., 1993; Tanaka et al., 2004; Nakanishi et al., 2005) . In support of this contention, deletion of the ARD has been demonstrated to produce constitutively active RNaseL, although with dramatically lower RNase activity (Dong and Silverman, 1997) . However, recent reports suggest that while 2-5A links the ankyrin repeats from adjacent molecules leading to formation of dimer and higher order structures, at sufficiently high in vitro concentrations, RNaseL could oligomerize even in the absence of 2-5A . Nonetheless, in vivo the RNaseL nuclease activity still seems to be under the sole regulation of 2-5A (Al-Saif and Khabar, 2012) . In order to exploit this dependence, multiple viruses like mouse hepatitis virus (MHV) and rotavirus group A (RVA) have evolved to encode phosphodiesterases capable of hydrolysing the 2 -5 linkages in 2-5A and thereby attenuate the RNaseL cleavage activity (Zhao et al., 2012; Zhang et al., 2013) . In addition to 5 -phosphatases and 2 -phosphodiesterases to reduce ClustalW alignment of primary sequence from a segment of the PK domain indicating amino acid residues which are important for interacting with nucleotide cofactors. The conserved lysine residues, critical for this interaction (K599 for IRE1 and K392 in RNaseL) are underlined. (C) Alignment of the KEN domains in RNaseL and IRE1. The amino acids highlighted and numbered in IRE1 are critical for the IRE1 RNase activity (Tirasophon et al., 2000) .
the endogenous 2-5A levels, mammalian genomes encode posttranscriptional and post-translation inhibitors of RNaseL activity in the form of microRNA-29 and the protein ABCE1 (RNaseL inhibitor or RLI), respectively (Bisbal et al., 1995; Lee et al., 2013) . Direct inhibition of RNaseL function is also observed upon infection by Picornaviruses through, either inducing the expression of ABCE1 or exercising a unique inhibitory property of a segment of the viral RNA (Martinand et al., 1998 (Martinand et al., , 1999 Townsend et al., 2008; Sorgeloos et al., 2013) .
Once activated by 2-5A, RNaseL can degrade single-stranded RNA irrespective of its origin (virus or host) although there seems to exist a bias towards cleavage of viral RNA (Wreschner et al., 1981a; Silverman et al., 1983; Li et al., 1998) . RNA sequences that are predominantly cleaved by RNaseL are U-rich with the cleavage points being typically at the 3 end of UA or UG or UU di-nucleotides, leaving a 5 -OH and a 3 -monophosphate in the cleavage product (Floyd-Smith et al., 1981; Wreschner et al., 1981b) . A recent report shows a more general consensus of 5 -UNN-3 with the cleavage point between the second and the third nucleotide (Han et al., 2014) . Cellular targets of RNaseL include both ribosomal RNA (rRNA) and mRNAs, the latter predominantly representing genes involved in protein biosynthesis (Wreschner et al., 1981a; Al-Ahmadi et al., 2009; Andersen et al., 2009) . Additionally, RNaseL activity can also degrade specific ISG mRNA transcripts and thereby attenuate the effect of IFN signaling (Li et al., 2000) . Probably an evolution towards insulating gene expression from RNaseL activity is observed in the coding region of mammalian genes where the UU/UA dinucleotide frequency is rarer (Bisbal et al., 2000; Khabar et al., 2003; Al-Saif and Khabar, 2012) . Perhaps not surprisingly, with a much faster rate of evolution, similar observations have been made with respect to evasion of RNaseL mediated degradation by viral RNAs too (Han and Barton, 2002; Washenberger et al., 2007) . Moreover, nucleoside modifications in host mRNAs, rarely observed in viral RNAs, have also been shown to confer protection from RNaseL (Anderson et al., 2011) . In addition to directly targeting viral RNA, the reduction in functional ribosomes and ribosomal protein mRNA affects viral protein synthesis and replication in an indirect manner. Probably, as a reflection of these effects on cellular RNAs, RNaseL is implicated as one of the factors determining the anti-proliferative effect of IFN activity . The anti-viral activity of RNaseL extends beyond direct cleavage of viral RNA, through stimulation of RIG-I by the cleavage product (Malathi et al., , 2007 (Malathi et al., , 2010 . A global effect of RNaseL is observed in the form of autophagy induced through c-jun N-terminal kinase (JNK) signaling and apoptosis, probably as a consequence of rRNA cleavage (Li et al., 2004; Chakrabarti et al., 2012; Siddiqui and Malathi, 2012) . RNaseL has also been demonstrated to play a role in apoptotic cell death initiated by pharmacological agents extending the physiological role of this pathway beyond the boundary of being only an anti-viral mechanism (Castelli et al., 1997 (Castelli et al., , 1998 .
The ER serves as a conduit for maturation of cellular proteins which are either secreted or destined to be associated with a membrane for its function. An exclusive microenvironment (high Calcium ion and unique ratio of reduced to oxidized glutathione) along with a battery of ER-lumen resident enzymes (foldases, chaperones, and lectins) catalyse/mediate the necessary folding, disulfide-bond formation, and glycosylation reactions (Schroder and Kaufman, 2005) . A perturbation of the folding capacity, due to either physiological disturbances or virus infection, can lead to an accumulation of unfolded proteins in the ER lumen, which signals an unfolded protein response (UPR). UPR encompasses a networked transcriptional and translational gene-expression program, initiated by three ER-membrane resident sensors namely IRE1 or ERN1, PKR-like ER Kinase (PERK or EIF2AK3) and activating transcription factor 6 (ATF6; Hetz, 2012) . IRE1 is a type I single-pass trans-membrane protein in which, similar to what is observed with RNaseL, the N-terminal resident in the ER lumen serves as sensor and the cytosolic C-terminal as the effector (Figure 1 ; Chen and Brandizzi, 2013) . The IRE1 coding gene is present in genomes ranging from yeast to mammals and in the latter is ubiquitously expressed in all tissues (Tirasophon et al., 1998) . Signal transduction by stimulated IRE1 initiates multiple gene regulatory pathways with either pro-survival or pro-apoptotic consequences (Kaufman, 1999) . During homeostasis or unstressed conditions the sensor molecules are monomeric, a state maintained co-operatively by the " absence" of unfolded proteins and the "presence" of HSPA5 (GRP78 or Bip, an ERresident chaperone) molecules bound to a membrane-proximal disordered segment of the protein in the ER-lumen-resident Nterminus (Credle et al., 2005) . Accumulated unfolded proteins in the lumen triggers coupling of this domain from adjacent sensor molecules through a combination of (a) titration of the bound HSPA5 chaperone molecules and (b) direct tethering by malfolded protein molecules (Shamu and Walter, 1996; Credle et al., 2005; Aragon et al., 2009; Korennykh et al., 2009) . Abutting of the luminal domains juxtapose the cytosolic C-terminal segments, leading to an aggregation of the IRE1 molecules into distinct ER-membrane foci (Kimata et al., 2007; Li et al., 2010) . The C-terminal segment has a serine/threonine kinase domain and a RNase domain homologous to that of RNaseL (Figure 1 ; Tirasophon et al., 1998 Tirasophon et al., , 2000 . A trans-autophosphorylation by the kinase domain allosterically activates the RNase domain (Tirasophon et al., 2000; Lee et al., 2008; Korennykh et al., 2009) . In fact, exogenous over-expression of IRE1 in mammalian cells lead to activation suggesting that, under homeostatic conditions, the non-juxtaposition of cytosolic domains maintains an inactive IRE1 (Tirasophon et al., 1998) . Once activated, IRE1 performs cleavage of a variety of RNA substrates mediated by its RNase domain, in addition to phosphorylating and thereby activating JNK (Cox and Walter, 1996; Urano et al., 2000) . Depending on the RNA substrate, the cleavage catalyzed by IRE1 RNase produces differential consequence. Although scission of the Xbp1 mRNA transcript at two internal positions is followed by splicing of the internal segment through ligation of the terminal cleavage products, that in all other known IRE1 target RNA is followed by degradation (Figure 1 ; Sidrauski and Walter, 1997; Calfon et al., 2002) . The latter mode of negative regulation of gene expression is termed as the regulated IRE1-dependent decay (RIDD) pathway (Hollien and Weissman, 2006; Oikawa et al., 2007; Iqbal et al., 2008; Lipson et al., 2008) . Gene transcripts regulated by RIDD pathway includes that from IRE1 (i.e., selftranscripts), probably in a negative feedback loop mechanism (Tirasophon et al., 2000) . In addition to protein coding RNA, RIDD pathway down-regulates the level of a host of microRNA precursors (pre-miRNAs) and can potentially cleave in the anticodon loop of tRNA Phe (Korennykh et al., 2011; Upton et al., 2012) .
The IRE1 RNase domain cleaves the Xbp1u (u for unspliced) mRNA transcript at two precise internal positions within the open reading frame (ORF) generating three segments, the terminal two of which are ligated by a tRNA ligase in yeast and by an unknown ligase in mammalian cells, to produce the Xbp1s (s for spliced) mRNA transcript (Figure 1 ; Yoshida et al., 2001) . The Xbp1s thus generated has a longer ORF, which is created by a frame-shift in the coding sequence downstream of the splice site (Cox and Walter, 1996; Calfon et al., 2002) . A similar dual endonucleolytic cleavage is also observed to initiate the XRN1 and Ski2-3-8 dependent degradation of transcripts in the RIDD degradation pathway (Hollien and Weissman, 2006) . The RIDD target transcript genes are predominantly those that encode membrane-associated or secretory proteins and which are not necessary for ER proteinfolding reactions (Hollien and Weissman, 2006) . The cleavage of Xbp1 and the RIDD-target transcripts constitute homeostatic or pro-survival response by IRE1 since XBP1S trans-activates genes encoding multiple chaperones (to fold unfolded proteins) and the ERAD pathway genes (to degrade terminally misfolded proteins) whereas RIDD reduces flux of polypeptides entering the ER lumen (Lee et al., 2003; Hollien and Weissman, 2006) . On the other hand, cleavage of pre-miRNA transcripts which are processed in the cell to generate CASPASE-2 mRNA (Casp2) controlling miRNAs, constitutes the pro-apoptotic function of IRE1 (Upton et al., 2012) . Another pro-apoptotic signal from IRE1 emanates from signaling through phosphorylation of JNK1 (Urano et al., 2000) . Although in the initial phase RIDD activity does not cleave mRNAs encoding essential ER proteins, at later stages of chronic UPR such transcripts are rendered susceptible to degradation promoting apoptosis induction (Han et al., 2009; Bhattacharyya et al., 2014) .
Infection of mammalian cells by a multitude of viruses induce an UPR which is sometimes characterized by suppression of signaling by one or more of the three sensor(s; Su et al., 2002; Tardif et al., 2002; He, 2006; Yu et al., 2006 Yu et al., , 2013 Medigeshi et al., 2007; Zhang et al., 2010; Merquiol et al., 2011) . Among these at least two viruses from diverse families, HCMV (a DNA virus) and hepatitis C virus (a hepacivirus), interfere with IRE1 signaling by different mechanism (Tardif et al., 2004; Stahl et al., 2013 ). An observed inhibition of any cellular function by a virus infection could suggest a potential anti-virus function for it, which the virus has evolved to evade through blocking some critical step(s). In both the cases mentioned above, stability of the viral proteins seems to be affected by ERAD-mediated degradation, although other potential anti-viral effect of IRE1 activation are not clear yet (Isler et al., 2005; Saeed et al., 2011) . Interestingly, host mRNA fragments produced following IRE1 activation during bacterial infection, has been shown to activate RIG-I signaling (Figure 1 ; Cho et al., 2013b) . Theoretically, other functions of IRE1 can also have anti-viral effect necessitating its inhibition for uninhibited viral replication. It is, however, still not clear whether IRE1 is able to cleave any viral RNA (or mRNA) in a manner similar to that of other RIDD targets (Figure 1) . The possibilities of such a direct anti-viral function are encouraged by the fact that all these viruses encode at least one protein which, as part of its maturation process, requires glycosylation and disulfide-bond formation. Such a necessity would entail translation of the mRNA encoding such a protein, which in case of positive-sense single-stranded RNA viruses would mean the genome, in association with the ER-membrane (Figure 1 ; Lerner et al., 2003) . Additionally for many RNA viruses, replication complexes are housed in ER-derived vesicular structures (Denison, 2008; den Boon et al., 2010) . Considering the proximity of IRE1 and these virus-derived RNAs it is tempting to speculate that probably at some point of time in the viral life cycle one or more virus-associated RNA would be susceptible to cleavage by IRE1. However, studies with at least two viruses have shown that instead of increasing viral titre, inhibiting the RNase activity of activated IRE1 has an opposite effect (Hassan et al., 2012; Bhattacharyya et al., 2014) . This implies potential benefits of IRE1 activation through one or more of the following, (a) expression of chaperones or other pro-viral molecules downstream of XBP1Supregulation or JNK-activation, (b) cleavage of potential anti-viral gene mRNA transcripts by RIDD activity. However, the mode of protection for the viral RNA from RIDD activity is still not clear. It is possible that the viral proteins create a subdomain within the ER membrane, which through some mechanism excludes IRE1 from diffusing near the genomic RNA, thereby protecting the replication complexes (Denison, 2008) . It is therefore probably not surprising that single-stranded plus-sense RNA viruses encode a polyprotein, which produces replication complexes in cis, promoting formation of such subdomains (Egger et al., 2000) . The fact that IRE1 forms bulky oligomers of higher order probably aggravates such an exclusion of the activated sensor molecules from vicinity of the viral replication complexes. The UPR signaling eventually attenuate during chronic ER-stress and since that is what a virus-induced UPR mimics, probably the viral RNA needs protection only during the initial phase of UPR activation (Lin et al., 2007) . Since the choice of RIDD target seems to be grossly driven towards mRNAs that encode ER-transitory but are not ER-essential proteins, it is also possible that one or more viral protein have evolved to mimic a host protein the transcript of which is RIDD-resistant (Hollien and Weissman, 2006) . Most of the RIDD target mRNA are observed to be ER-membrane associated, the proximity to IRE1 facilitating association and cleavage (Figure 1 ; Hollien and Weissman, 2006) . Although ER-association for an mRNA is possible without the mediation of ribosomes, Gaddam and co-workers reported that continued association with polysomes for a membrane-bound mRNA can confer protection from IRE1 cleavage (Cui et al., 2012; Gaddam et al., 2013) . This would suggest important implications for the observed refractory nature of Japanese encephalitis virus (JEV) and influenza virus RNA to RIDD cleavage (Hassan et al., 2012; Bhattacharyya et al., 2014) . In contrast to Influenza virus, flaviviruses (which include JEV) do not suppress host protein synthesis implying the absence of a global inhibition on translation as would be expected during UPR (Clyde et al., 2006; Edgil et al., 2006) . Therefore, a continued translation of viral RNA in spite of UPR activation can in principle confer protection from the pattern of RNA cleavage observed in the RIDD pathway.
IRE1 and RNaseL, in addition to biochemical similarities in protein kinase domain and structural similarities in their RNase domain, share the functional consequences of their activation in initiating cellular apoptosis through JNK signaling (Table 1 and Figure 2 ; Liu and Lin, 2005; Dhanasekaran and Reddy, 2008) . Though initial discoveries were made in the context of homeostatic and anti-viral role for the former and latter, differences between the pathways are narrowed by further advances in research. In the same vein, while inhibition of IRE1 signaling in virus infected cells indicates a potential anti-viral role, www.frontiersin.org (Tirasophon et al., 1998; Dong and Silverman, 1999; Papa et al., 2003; Lin et al., 2007) Nature of RNase substrates Both 28S rRNA and mRNAs IRE1β can cleave both 28S rRNA and mRNA while IRE1α substrates include only mRNAs (Iwawaki et al., 2001) Dissimilarities
Cleavage substrates Beside 28S rRNA, predominantly cleaves mRNAs encoding ribosomal proteins (Andersen et al., 2009) Xbp1u and other mRNAs in addition to microRNA precursors which are targeted as part of the RIDD pathway Selection of cleavage site Cleaved between 2nd and 3rd nucleotide positions of UN/N sites (Han et al., 2014) RNA sequence with the consensus of 5 -CUGCAG-3 in association with a stem-loop (SL) structure essential for recognition of Xbp1u and other mRNAs (Oikawa et al., 2010) association of RNaseL mutations with generation of prostate cancer extends the ambit of influence of this anti-viral effector to more non-infectious physiological disorders (Silverman, 2003) . Biochemically, the similarity in their RNase domains does not extend to the choice of either substrates or cleavage point, which are downstream of UU or UA in RNaseL and downstream of G (predominantly) for IRE1 ( Figure 2C ; Yoshida et al., 2001; Hollien and Weissman, 2006; Upton et al., 2012) . Further, while RNaseL cleaves pre-dominantly in single-stranded region, IRE1 seems to cleave equally well in single-and double-stranded region (Upton et al., 2012) . However, a recent report suggested a consensus cleavage site with the sequence UN/N, in RNaseL targets and in those mRNAs that are cleaved by IRE1 as part of the RIDD pathway (Han et al., 2014) . Access to potential cleavage substrate for RNaseL is conjectured to be facilitated through its association with polyribosomes, while no such association is known for IRE1 (Salehzada et al., 1991) . Possibilities exist that IRE1 would have preferential distribution in the rough ER which, upon activation, would give it ready access to mRNAs for initiating the RIDD pathway.
In the context of a virus infection, the pathway leading from both these proteins have the potential to lead to cell death. Notwithstanding the fact that this might be an efficient way of virus clearance, it also portends pathological outcomes for the infected organism. Future research would probably lead to design of drugs targeting these proteins based on the structural homology of their effector domains, regulating the pathological denouement of their activation without compromising their anti-viral or potential anti-viral functions.
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What is the conclusion regarding IRE1 and RNaseL proteins?
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In the context of a virus infection, the pathway leading from both these proteins have the potential to lead to cell death. Notwithstanding the fact that this might be an efficient way of virus clearance, it also portends pathological outcomes for the infected organism. Future research would probably lead to design of drugs targeting these proteins based on the structural homology of their effector domains, regulating the pathological denouement of their activation without compromising their anti-viral or potential anti-viral functions.
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Nearly Complete Genome Sequence of an Echovirus 30 Strain from a Cluster of Aseptic Meningitis Cases in California, September 2017
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6953510/
SHA: f0c4d40e1879dd1a049298f151940ac168b5f5a7
Authors: Pan, Chao-Yang; Huynh, Thalia; Padilla, Tasha; Chen, Alice; Ng, Terry Fei Fan; Marine, Rachel L.; Castro, Christina J.; Nix, W. Allan; Wadford, Debra A.
Date: 2019-10-31
DOI: 10.1128/mra.01085-19
License: cc-by
Abstract: We report the nearly complete genome sequence of a human enterovirus, a strain of echovirus 30, obtained from a cerebrospinal fluid specimen from a teenaged patient with aseptic meningitis in September 2017.
Text: E choviruses are members of the Enterovirus B species of the Enterovirus (EV) genus in the Picornaviridae family of nonenveloped, single-stranded, positive-sense RNA viruses. Echoviruses were named from the acronym enteric cytopathic human orphan virus at the time of their discovery in the 1950s but were later found to be associated with respiratory illness, hand-foot-and-mouth disease, and aseptic meningitis, similar to other enteroviruses (1) .
According to the California Code of Regulations, meningitis cases are reportable to the California Department of Public Health (CDPH) within 1 day of identification of etiology (2) . In the fall of 2017, a cluster of aseptic meningitis cases from a northern California high school were reported to the CDPH. The Viral and Rickettsial Disease Laboratory (VRDL) at the CDPH detected EV from 19 of 30 patients (63%) by real-time reverse transcription-PCR (RT-PCR), as previously described (3) . We generated and analyzed partial capsid (viral protein 1 [VP1]) sequences using methods developed by Minnaar et al. (4) . Fifteen of 19 (79%) EV-positive patients were confirmed to have echovirus 30 (E-30), using cerebrospinal fluid (CSF) samples. This cluster of E-30 meningitis cases is similar to previously reported E-30 aseptic meningitis cases (5, 6) in symptoms and epidemiology.
Here, we report a nearly complete genome sequence from one of the E-30-positive CSF specimens. The CSF was processed by centrifugation, 0.45-m filtration, and nuclease treatment prior to extraction using the NucliSENS easyMAG system (bioMérieux, Durham, NC) (7). The extracted nucleic acids were then treated with DNase to yield RNA, which was subjected to random reverse transcription and PCR (7) . The next-generation sequencing (NGS) library was prepared using a Nextera XT kit and sequenced on a MiSeq platform 300-cycle paired-end run (Illumina, San Diego, CA). The NGS data were analyzed using an in-house Centers for Disease Control and Prevention (CDC) pipeline which involves the removal of host sequences using bowtie2/2.3.3.1, primer removal, low-quality (below Q20) and read length (Ͻ50 nucleotides) filtering using cutadapt 1.18, read duplication removal using a Dedup.py script, de novo assembly using SPAdes 3.7 default parameters, and BLAST search of the resultant contigs (8) . There were a total of 141,329 postprocessing FASTQ reads. The final consensus genome was inspected and annotated using Geneious v10.0.9 (9) . The contig was built from 15,712 reads, assembled to an E-30 reference genome (GenBank accession number JX976773), and deemed nearly complete by comparison to the reference, and the termini were determined as part of the protocol (7). The total GC content is 48.3% for 7,155 bases. The average read coverage was 260-fold for the E-30 genome.
The genome sequence was designated E-30 USA/2017/CA-RGDS-1005. Its VP1 sequence was confirmed by the CDC Picornavirus Laboratory to be nearly identical to those of E-30s identified in an aseptic meningitis outbreak that occurred in the fall of 2017 in Nevada; it also has greater than 99% nucleotide identity to the VP1 sequences of E-30 strains from the southern United States identified by the CDC in May 2017 (GenBank accession numbers MG584831 and MG584832), as measured using the online version of blastn (https://blast.ncbi.nlm.nih.gov/Blast.cgi). The genome sequence of E-30 USA/2017/CA-RGDS-1005 shares less than 89% nucleotide identity (NI) and less than 98% amino acid identity (AI) with other publicly available E-30 sequences. The sequence contains the complete protein-coding region, with short sections in the untranslated regions (UTRs) missing due to a lack of read coverage (approximately 182 and 90 nucleotides of the 5= and 3= UTRs, respectively). The enterovirus polyprotein can be divided into one structural (P1-capsid) and two nonstructural (P2 and P3) regions. The polyprotein regions of the E-30 genome reported here share 96%, 88%, and 84% NI (P1, P2, and P3, respectively) with other E-30 sequences in GenBank.
Data availability. The E-30 sequence of USA/2017/CA-RGDS-1005 has been deposited in GenBank under the accession number MK238483. The quality-filtered FASTQ reads have been deposited in the Sequence Read Archive with the run accession number SRR10082176.
The contributions of the California Department of Public Health Viral and Rickettsial Disease Laboratory were supported in part by the Epidemiology and Laboratory Capacity for Infectious Diseases Cooperative Agreement number 6 NU50CK000410 from the U.S. Centers for Disease Control and Prevention. This work was partly funded by federal appropriations to the Centers for Disease Control and Prevention, through the Advanced Molecular Detection Initiative line item.
The findings and conclusions in this report are those of the author(s) and do not necessarily represent the official position of the Centers for Disease Control and Prevention.
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What is the structure of an Echovirus?
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nonenveloped, single-stranded, positive-sense RNA
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Nearly Complete Genome Sequence of an Echovirus 30 Strain from a Cluster of Aseptic Meningitis Cases in California, September 2017
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6953510/
SHA: f0c4d40e1879dd1a049298f151940ac168b5f5a7
Authors: Pan, Chao-Yang; Huynh, Thalia; Padilla, Tasha; Chen, Alice; Ng, Terry Fei Fan; Marine, Rachel L.; Castro, Christina J.; Nix, W. Allan; Wadford, Debra A.
Date: 2019-10-31
DOI: 10.1128/mra.01085-19
License: cc-by
Abstract: We report the nearly complete genome sequence of a human enterovirus, a strain of echovirus 30, obtained from a cerebrospinal fluid specimen from a teenaged patient with aseptic meningitis in September 2017.
Text: E choviruses are members of the Enterovirus B species of the Enterovirus (EV) genus in the Picornaviridae family of nonenveloped, single-stranded, positive-sense RNA viruses. Echoviruses were named from the acronym enteric cytopathic human orphan virus at the time of their discovery in the 1950s but were later found to be associated with respiratory illness, hand-foot-and-mouth disease, and aseptic meningitis, similar to other enteroviruses (1) .
According to the California Code of Regulations, meningitis cases are reportable to the California Department of Public Health (CDPH) within 1 day of identification of etiology (2) . In the fall of 2017, a cluster of aseptic meningitis cases from a northern California high school were reported to the CDPH. The Viral and Rickettsial Disease Laboratory (VRDL) at the CDPH detected EV from 19 of 30 patients (63%) by real-time reverse transcription-PCR (RT-PCR), as previously described (3) . We generated and analyzed partial capsid (viral protein 1 [VP1]) sequences using methods developed by Minnaar et al. (4) . Fifteen of 19 (79%) EV-positive patients were confirmed to have echovirus 30 (E-30), using cerebrospinal fluid (CSF) samples. This cluster of E-30 meningitis cases is similar to previously reported E-30 aseptic meningitis cases (5, 6) in symptoms and epidemiology.
Here, we report a nearly complete genome sequence from one of the E-30-positive CSF specimens. The CSF was processed by centrifugation, 0.45-m filtration, and nuclease treatment prior to extraction using the NucliSENS easyMAG system (bioMérieux, Durham, NC) (7). The extracted nucleic acids were then treated with DNase to yield RNA, which was subjected to random reverse transcription and PCR (7) . The next-generation sequencing (NGS) library was prepared using a Nextera XT kit and sequenced on a MiSeq platform 300-cycle paired-end run (Illumina, San Diego, CA). The NGS data were analyzed using an in-house Centers for Disease Control and Prevention (CDC) pipeline which involves the removal of host sequences using bowtie2/2.3.3.1, primer removal, low-quality (below Q20) and read length (Ͻ50 nucleotides) filtering using cutadapt 1.18, read duplication removal using a Dedup.py script, de novo assembly using SPAdes 3.7 default parameters, and BLAST search of the resultant contigs (8) . There were a total of 141,329 postprocessing FASTQ reads. The final consensus genome was inspected and annotated using Geneious v10.0.9 (9) . The contig was built from 15,712 reads, assembled to an E-30 reference genome (GenBank accession number JX976773), and deemed nearly complete by comparison to the reference, and the termini were determined as part of the protocol (7). The total GC content is 48.3% for 7,155 bases. The average read coverage was 260-fold for the E-30 genome.
The genome sequence was designated E-30 USA/2017/CA-RGDS-1005. Its VP1 sequence was confirmed by the CDC Picornavirus Laboratory to be nearly identical to those of E-30s identified in an aseptic meningitis outbreak that occurred in the fall of 2017 in Nevada; it also has greater than 99% nucleotide identity to the VP1 sequences of E-30 strains from the southern United States identified by the CDC in May 2017 (GenBank accession numbers MG584831 and MG584832), as measured using the online version of blastn (https://blast.ncbi.nlm.nih.gov/Blast.cgi). The genome sequence of E-30 USA/2017/CA-RGDS-1005 shares less than 89% nucleotide identity (NI) and less than 98% amino acid identity (AI) with other publicly available E-30 sequences. The sequence contains the complete protein-coding region, with short sections in the untranslated regions (UTRs) missing due to a lack of read coverage (approximately 182 and 90 nucleotides of the 5= and 3= UTRs, respectively). The enterovirus polyprotein can be divided into one structural (P1-capsid) and two nonstructural (P2 and P3) regions. The polyprotein regions of the E-30 genome reported here share 96%, 88%, and 84% NI (P1, P2, and P3, respectively) with other E-30 sequences in GenBank.
Data availability. The E-30 sequence of USA/2017/CA-RGDS-1005 has been deposited in GenBank under the accession number MK238483. The quality-filtered FASTQ reads have been deposited in the Sequence Read Archive with the run accession number SRR10082176.
The contributions of the California Department of Public Health Viral and Rickettsial Disease Laboratory were supported in part by the Epidemiology and Laboratory Capacity for Infectious Diseases Cooperative Agreement number 6 NU50CK000410 from the U.S. Centers for Disease Control and Prevention. This work was partly funded by federal appropriations to the Centers for Disease Control and Prevention, through the Advanced Molecular Detection Initiative line item.
The findings and conclusions in this report are those of the author(s) and do not necessarily represent the official position of the Centers for Disease Control and Prevention.
|
What diseases are associated with echoviruses?
| 2,989 |
respiratory illness, hand-foot-and-mouth disease, and aseptic meningitis,
| 1,020 |
1,550 |
Nearly Complete Genome Sequence of an Echovirus 30 Strain from a Cluster of Aseptic Meningitis Cases in California, September 2017
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6953510/
SHA: f0c4d40e1879dd1a049298f151940ac168b5f5a7
Authors: Pan, Chao-Yang; Huynh, Thalia; Padilla, Tasha; Chen, Alice; Ng, Terry Fei Fan; Marine, Rachel L.; Castro, Christina J.; Nix, W. Allan; Wadford, Debra A.
Date: 2019-10-31
DOI: 10.1128/mra.01085-19
License: cc-by
Abstract: We report the nearly complete genome sequence of a human enterovirus, a strain of echovirus 30, obtained from a cerebrospinal fluid specimen from a teenaged patient with aseptic meningitis in September 2017.
Text: E choviruses are members of the Enterovirus B species of the Enterovirus (EV) genus in the Picornaviridae family of nonenveloped, single-stranded, positive-sense RNA viruses. Echoviruses were named from the acronym enteric cytopathic human orphan virus at the time of their discovery in the 1950s but were later found to be associated with respiratory illness, hand-foot-and-mouth disease, and aseptic meningitis, similar to other enteroviruses (1) .
According to the California Code of Regulations, meningitis cases are reportable to the California Department of Public Health (CDPH) within 1 day of identification of etiology (2) . In the fall of 2017, a cluster of aseptic meningitis cases from a northern California high school were reported to the CDPH. The Viral and Rickettsial Disease Laboratory (VRDL) at the CDPH detected EV from 19 of 30 patients (63%) by real-time reverse transcription-PCR (RT-PCR), as previously described (3) . We generated and analyzed partial capsid (viral protein 1 [VP1]) sequences using methods developed by Minnaar et al. (4) . Fifteen of 19 (79%) EV-positive patients were confirmed to have echovirus 30 (E-30), using cerebrospinal fluid (CSF) samples. This cluster of E-30 meningitis cases is similar to previously reported E-30 aseptic meningitis cases (5, 6) in symptoms and epidemiology.
Here, we report a nearly complete genome sequence from one of the E-30-positive CSF specimens. The CSF was processed by centrifugation, 0.45-m filtration, and nuclease treatment prior to extraction using the NucliSENS easyMAG system (bioMérieux, Durham, NC) (7). The extracted nucleic acids were then treated with DNase to yield RNA, which was subjected to random reverse transcription and PCR (7) . The next-generation sequencing (NGS) library was prepared using a Nextera XT kit and sequenced on a MiSeq platform 300-cycle paired-end run (Illumina, San Diego, CA). The NGS data were analyzed using an in-house Centers for Disease Control and Prevention (CDC) pipeline which involves the removal of host sequences using bowtie2/2.3.3.1, primer removal, low-quality (below Q20) and read length (Ͻ50 nucleotides) filtering using cutadapt 1.18, read duplication removal using a Dedup.py script, de novo assembly using SPAdes 3.7 default parameters, and BLAST search of the resultant contigs (8) . There were a total of 141,329 postprocessing FASTQ reads. The final consensus genome was inspected and annotated using Geneious v10.0.9 (9) . The contig was built from 15,712 reads, assembled to an E-30 reference genome (GenBank accession number JX976773), and deemed nearly complete by comparison to the reference, and the termini were determined as part of the protocol (7). The total GC content is 48.3% for 7,155 bases. The average read coverage was 260-fold for the E-30 genome.
The genome sequence was designated E-30 USA/2017/CA-RGDS-1005. Its VP1 sequence was confirmed by the CDC Picornavirus Laboratory to be nearly identical to those of E-30s identified in an aseptic meningitis outbreak that occurred in the fall of 2017 in Nevada; it also has greater than 99% nucleotide identity to the VP1 sequences of E-30 strains from the southern United States identified by the CDC in May 2017 (GenBank accession numbers MG584831 and MG584832), as measured using the online version of blastn (https://blast.ncbi.nlm.nih.gov/Blast.cgi). The genome sequence of E-30 USA/2017/CA-RGDS-1005 shares less than 89% nucleotide identity (NI) and less than 98% amino acid identity (AI) with other publicly available E-30 sequences. The sequence contains the complete protein-coding region, with short sections in the untranslated regions (UTRs) missing due to a lack of read coverage (approximately 182 and 90 nucleotides of the 5= and 3= UTRs, respectively). The enterovirus polyprotein can be divided into one structural (P1-capsid) and two nonstructural (P2 and P3) regions. The polyprotein regions of the E-30 genome reported here share 96%, 88%, and 84% NI (P1, P2, and P3, respectively) with other E-30 sequences in GenBank.
Data availability. The E-30 sequence of USA/2017/CA-RGDS-1005 has been deposited in GenBank under the accession number MK238483. The quality-filtered FASTQ reads have been deposited in the Sequence Read Archive with the run accession number SRR10082176.
The contributions of the California Department of Public Health Viral and Rickettsial Disease Laboratory were supported in part by the Epidemiology and Laboratory Capacity for Infectious Diseases Cooperative Agreement number 6 NU50CK000410 from the U.S. Centers for Disease Control and Prevention. This work was partly funded by federal appropriations to the Centers for Disease Control and Prevention, through the Advanced Molecular Detection Initiative line item.
The findings and conclusions in this report are those of the author(s) and do not necessarily represent the official position of the Centers for Disease Control and Prevention.
|
In California, to where are meningitis cases reported according to the California Code of Regulations?
| 2,990 |
California Department of Public Health (CDPH)
| 1,220 |
1,550 |
Nearly Complete Genome Sequence of an Echovirus 30 Strain from a Cluster of Aseptic Meningitis Cases in California, September 2017
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6953510/
SHA: f0c4d40e1879dd1a049298f151940ac168b5f5a7
Authors: Pan, Chao-Yang; Huynh, Thalia; Padilla, Tasha; Chen, Alice; Ng, Terry Fei Fan; Marine, Rachel L.; Castro, Christina J.; Nix, W. Allan; Wadford, Debra A.
Date: 2019-10-31
DOI: 10.1128/mra.01085-19
License: cc-by
Abstract: We report the nearly complete genome sequence of a human enterovirus, a strain of echovirus 30, obtained from a cerebrospinal fluid specimen from a teenaged patient with aseptic meningitis in September 2017.
Text: E choviruses are members of the Enterovirus B species of the Enterovirus (EV) genus in the Picornaviridae family of nonenveloped, single-stranded, positive-sense RNA viruses. Echoviruses were named from the acronym enteric cytopathic human orphan virus at the time of their discovery in the 1950s but were later found to be associated with respiratory illness, hand-foot-and-mouth disease, and aseptic meningitis, similar to other enteroviruses (1) .
According to the California Code of Regulations, meningitis cases are reportable to the California Department of Public Health (CDPH) within 1 day of identification of etiology (2) . In the fall of 2017, a cluster of aseptic meningitis cases from a northern California high school were reported to the CDPH. The Viral and Rickettsial Disease Laboratory (VRDL) at the CDPH detected EV from 19 of 30 patients (63%) by real-time reverse transcription-PCR (RT-PCR), as previously described (3) . We generated and analyzed partial capsid (viral protein 1 [VP1]) sequences using methods developed by Minnaar et al. (4) . Fifteen of 19 (79%) EV-positive patients were confirmed to have echovirus 30 (E-30), using cerebrospinal fluid (CSF) samples. This cluster of E-30 meningitis cases is similar to previously reported E-30 aseptic meningitis cases (5, 6) in symptoms and epidemiology.
Here, we report a nearly complete genome sequence from one of the E-30-positive CSF specimens. The CSF was processed by centrifugation, 0.45-m filtration, and nuclease treatment prior to extraction using the NucliSENS easyMAG system (bioMérieux, Durham, NC) (7). The extracted nucleic acids were then treated with DNase to yield RNA, which was subjected to random reverse transcription and PCR (7) . The next-generation sequencing (NGS) library was prepared using a Nextera XT kit and sequenced on a MiSeq platform 300-cycle paired-end run (Illumina, San Diego, CA). The NGS data were analyzed using an in-house Centers for Disease Control and Prevention (CDC) pipeline which involves the removal of host sequences using bowtie2/2.3.3.1, primer removal, low-quality (below Q20) and read length (Ͻ50 nucleotides) filtering using cutadapt 1.18, read duplication removal using a Dedup.py script, de novo assembly using SPAdes 3.7 default parameters, and BLAST search of the resultant contigs (8) . There were a total of 141,329 postprocessing FASTQ reads. The final consensus genome was inspected and annotated using Geneious v10.0.9 (9) . The contig was built from 15,712 reads, assembled to an E-30 reference genome (GenBank accession number JX976773), and deemed nearly complete by comparison to the reference, and the termini were determined as part of the protocol (7). The total GC content is 48.3% for 7,155 bases. The average read coverage was 260-fold for the E-30 genome.
The genome sequence was designated E-30 USA/2017/CA-RGDS-1005. Its VP1 sequence was confirmed by the CDC Picornavirus Laboratory to be nearly identical to those of E-30s identified in an aseptic meningitis outbreak that occurred in the fall of 2017 in Nevada; it also has greater than 99% nucleotide identity to the VP1 sequences of E-30 strains from the southern United States identified by the CDC in May 2017 (GenBank accession numbers MG584831 and MG584832), as measured using the online version of blastn (https://blast.ncbi.nlm.nih.gov/Blast.cgi). The genome sequence of E-30 USA/2017/CA-RGDS-1005 shares less than 89% nucleotide identity (NI) and less than 98% amino acid identity (AI) with other publicly available E-30 sequences. The sequence contains the complete protein-coding region, with short sections in the untranslated regions (UTRs) missing due to a lack of read coverage (approximately 182 and 90 nucleotides of the 5= and 3= UTRs, respectively). The enterovirus polyprotein can be divided into one structural (P1-capsid) and two nonstructural (P2 and P3) regions. The polyprotein regions of the E-30 genome reported here share 96%, 88%, and 84% NI (P1, P2, and P3, respectively) with other E-30 sequences in GenBank.
Data availability. The E-30 sequence of USA/2017/CA-RGDS-1005 has been deposited in GenBank under the accession number MK238483. The quality-filtered FASTQ reads have been deposited in the Sequence Read Archive with the run accession number SRR10082176.
The contributions of the California Department of Public Health Viral and Rickettsial Disease Laboratory were supported in part by the Epidemiology and Laboratory Capacity for Infectious Diseases Cooperative Agreement number 6 NU50CK000410 from the U.S. Centers for Disease Control and Prevention. This work was partly funded by federal appropriations to the Centers for Disease Control and Prevention, through the Advanced Molecular Detection Initiative line item.
The findings and conclusions in this report are those of the author(s) and do not necessarily represent the official position of the Centers for Disease Control and Prevention.
|
According to the California Code of Regulations, when should a meningitis case be reported?
| 2,991 |
within 1 day of identification
| 1,266 |
1,550 |
Nearly Complete Genome Sequence of an Echovirus 30 Strain from a Cluster of Aseptic Meningitis Cases in California, September 2017
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6953510/
SHA: f0c4d40e1879dd1a049298f151940ac168b5f5a7
Authors: Pan, Chao-Yang; Huynh, Thalia; Padilla, Tasha; Chen, Alice; Ng, Terry Fei Fan; Marine, Rachel L.; Castro, Christina J.; Nix, W. Allan; Wadford, Debra A.
Date: 2019-10-31
DOI: 10.1128/mra.01085-19
License: cc-by
Abstract: We report the nearly complete genome sequence of a human enterovirus, a strain of echovirus 30, obtained from a cerebrospinal fluid specimen from a teenaged patient with aseptic meningitis in September 2017.
Text: E choviruses are members of the Enterovirus B species of the Enterovirus (EV) genus in the Picornaviridae family of nonenveloped, single-stranded, positive-sense RNA viruses. Echoviruses were named from the acronym enteric cytopathic human orphan virus at the time of their discovery in the 1950s but were later found to be associated with respiratory illness, hand-foot-and-mouth disease, and aseptic meningitis, similar to other enteroviruses (1) .
According to the California Code of Regulations, meningitis cases are reportable to the California Department of Public Health (CDPH) within 1 day of identification of etiology (2) . In the fall of 2017, a cluster of aseptic meningitis cases from a northern California high school were reported to the CDPH. The Viral and Rickettsial Disease Laboratory (VRDL) at the CDPH detected EV from 19 of 30 patients (63%) by real-time reverse transcription-PCR (RT-PCR), as previously described (3) . We generated and analyzed partial capsid (viral protein 1 [VP1]) sequences using methods developed by Minnaar et al. (4) . Fifteen of 19 (79%) EV-positive patients were confirmed to have echovirus 30 (E-30), using cerebrospinal fluid (CSF) samples. This cluster of E-30 meningitis cases is similar to previously reported E-30 aseptic meningitis cases (5, 6) in symptoms and epidemiology.
Here, we report a nearly complete genome sequence from one of the E-30-positive CSF specimens. The CSF was processed by centrifugation, 0.45-m filtration, and nuclease treatment prior to extraction using the NucliSENS easyMAG system (bioMérieux, Durham, NC) (7). The extracted nucleic acids were then treated with DNase to yield RNA, which was subjected to random reverse transcription and PCR (7) . The next-generation sequencing (NGS) library was prepared using a Nextera XT kit and sequenced on a MiSeq platform 300-cycle paired-end run (Illumina, San Diego, CA). The NGS data were analyzed using an in-house Centers for Disease Control and Prevention (CDC) pipeline which involves the removal of host sequences using bowtie2/2.3.3.1, primer removal, low-quality (below Q20) and read length (Ͻ50 nucleotides) filtering using cutadapt 1.18, read duplication removal using a Dedup.py script, de novo assembly using SPAdes 3.7 default parameters, and BLAST search of the resultant contigs (8) . There were a total of 141,329 postprocessing FASTQ reads. The final consensus genome was inspected and annotated using Geneious v10.0.9 (9) . The contig was built from 15,712 reads, assembled to an E-30 reference genome (GenBank accession number JX976773), and deemed nearly complete by comparison to the reference, and the termini were determined as part of the protocol (7). The total GC content is 48.3% for 7,155 bases. The average read coverage was 260-fold for the E-30 genome.
The genome sequence was designated E-30 USA/2017/CA-RGDS-1005. Its VP1 sequence was confirmed by the CDC Picornavirus Laboratory to be nearly identical to those of E-30s identified in an aseptic meningitis outbreak that occurred in the fall of 2017 in Nevada; it also has greater than 99% nucleotide identity to the VP1 sequences of E-30 strains from the southern United States identified by the CDC in May 2017 (GenBank accession numbers MG584831 and MG584832), as measured using the online version of blastn (https://blast.ncbi.nlm.nih.gov/Blast.cgi). The genome sequence of E-30 USA/2017/CA-RGDS-1005 shares less than 89% nucleotide identity (NI) and less than 98% amino acid identity (AI) with other publicly available E-30 sequences. The sequence contains the complete protein-coding region, with short sections in the untranslated regions (UTRs) missing due to a lack of read coverage (approximately 182 and 90 nucleotides of the 5= and 3= UTRs, respectively). The enterovirus polyprotein can be divided into one structural (P1-capsid) and two nonstructural (P2 and P3) regions. The polyprotein regions of the E-30 genome reported here share 96%, 88%, and 84% NI (P1, P2, and P3, respectively) with other E-30 sequences in GenBank.
Data availability. The E-30 sequence of USA/2017/CA-RGDS-1005 has been deposited in GenBank under the accession number MK238483. The quality-filtered FASTQ reads have been deposited in the Sequence Read Archive with the run accession number SRR10082176.
The contributions of the California Department of Public Health Viral and Rickettsial Disease Laboratory were supported in part by the Epidemiology and Laboratory Capacity for Infectious Diseases Cooperative Agreement number 6 NU50CK000410 from the U.S. Centers for Disease Control and Prevention. This work was partly funded by federal appropriations to the Centers for Disease Control and Prevention, through the Advanced Molecular Detection Initiative line item.
The findings and conclusions in this report are those of the author(s) and do not necessarily represent the official position of the Centers for Disease Control and Prevention.
|
Where is the Viral and Rickettsial Disease Laboratory located?
| 2,992 |
CDPH
| 1,499 |
1,550 |
Nearly Complete Genome Sequence of an Echovirus 30 Strain from a Cluster of Aseptic Meningitis Cases in California, September 2017
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6953510/
SHA: f0c4d40e1879dd1a049298f151940ac168b5f5a7
Authors: Pan, Chao-Yang; Huynh, Thalia; Padilla, Tasha; Chen, Alice; Ng, Terry Fei Fan; Marine, Rachel L.; Castro, Christina J.; Nix, W. Allan; Wadford, Debra A.
Date: 2019-10-31
DOI: 10.1128/mra.01085-19
License: cc-by
Abstract: We report the nearly complete genome sequence of a human enterovirus, a strain of echovirus 30, obtained from a cerebrospinal fluid specimen from a teenaged patient with aseptic meningitis in September 2017.
Text: E choviruses are members of the Enterovirus B species of the Enterovirus (EV) genus in the Picornaviridae family of nonenveloped, single-stranded, positive-sense RNA viruses. Echoviruses were named from the acronym enteric cytopathic human orphan virus at the time of their discovery in the 1950s but were later found to be associated with respiratory illness, hand-foot-and-mouth disease, and aseptic meningitis, similar to other enteroviruses (1) .
According to the California Code of Regulations, meningitis cases are reportable to the California Department of Public Health (CDPH) within 1 day of identification of etiology (2) . In the fall of 2017, a cluster of aseptic meningitis cases from a northern California high school were reported to the CDPH. The Viral and Rickettsial Disease Laboratory (VRDL) at the CDPH detected EV from 19 of 30 patients (63%) by real-time reverse transcription-PCR (RT-PCR), as previously described (3) . We generated and analyzed partial capsid (viral protein 1 [VP1]) sequences using methods developed by Minnaar et al. (4) . Fifteen of 19 (79%) EV-positive patients were confirmed to have echovirus 30 (E-30), using cerebrospinal fluid (CSF) samples. This cluster of E-30 meningitis cases is similar to previously reported E-30 aseptic meningitis cases (5, 6) in symptoms and epidemiology.
Here, we report a nearly complete genome sequence from one of the E-30-positive CSF specimens. The CSF was processed by centrifugation, 0.45-m filtration, and nuclease treatment prior to extraction using the NucliSENS easyMAG system (bioMérieux, Durham, NC) (7). The extracted nucleic acids were then treated with DNase to yield RNA, which was subjected to random reverse transcription and PCR (7) . The next-generation sequencing (NGS) library was prepared using a Nextera XT kit and sequenced on a MiSeq platform 300-cycle paired-end run (Illumina, San Diego, CA). The NGS data were analyzed using an in-house Centers for Disease Control and Prevention (CDC) pipeline which involves the removal of host sequences using bowtie2/2.3.3.1, primer removal, low-quality (below Q20) and read length (Ͻ50 nucleotides) filtering using cutadapt 1.18, read duplication removal using a Dedup.py script, de novo assembly using SPAdes 3.7 default parameters, and BLAST search of the resultant contigs (8) . There were a total of 141,329 postprocessing FASTQ reads. The final consensus genome was inspected and annotated using Geneious v10.0.9 (9) . The contig was built from 15,712 reads, assembled to an E-30 reference genome (GenBank accession number JX976773), and deemed nearly complete by comparison to the reference, and the termini were determined as part of the protocol (7). The total GC content is 48.3% for 7,155 bases. The average read coverage was 260-fold for the E-30 genome.
The genome sequence was designated E-30 USA/2017/CA-RGDS-1005. Its VP1 sequence was confirmed by the CDC Picornavirus Laboratory to be nearly identical to those of E-30s identified in an aseptic meningitis outbreak that occurred in the fall of 2017 in Nevada; it also has greater than 99% nucleotide identity to the VP1 sequences of E-30 strains from the southern United States identified by the CDC in May 2017 (GenBank accession numbers MG584831 and MG584832), as measured using the online version of blastn (https://blast.ncbi.nlm.nih.gov/Blast.cgi). The genome sequence of E-30 USA/2017/CA-RGDS-1005 shares less than 89% nucleotide identity (NI) and less than 98% amino acid identity (AI) with other publicly available E-30 sequences. The sequence contains the complete protein-coding region, with short sections in the untranslated regions (UTRs) missing due to a lack of read coverage (approximately 182 and 90 nucleotides of the 5= and 3= UTRs, respectively). The enterovirus polyprotein can be divided into one structural (P1-capsid) and two nonstructural (P2 and P3) regions. The polyprotein regions of the E-30 genome reported here share 96%, 88%, and 84% NI (P1, P2, and P3, respectively) with other E-30 sequences in GenBank.
Data availability. The E-30 sequence of USA/2017/CA-RGDS-1005 has been deposited in GenBank under the accession number MK238483. The quality-filtered FASTQ reads have been deposited in the Sequence Read Archive with the run accession number SRR10082176.
The contributions of the California Department of Public Health Viral and Rickettsial Disease Laboratory were supported in part by the Epidemiology and Laboratory Capacity for Infectious Diseases Cooperative Agreement number 6 NU50CK000410 from the U.S. Centers for Disease Control and Prevention. This work was partly funded by federal appropriations to the Centers for Disease Control and Prevention, through the Advanced Molecular Detection Initiative line item.
The findings and conclusions in this report are those of the author(s) and do not necessarily represent the official position of the Centers for Disease Control and Prevention.
|
What type of reference genome was used in the study?
| 2,993 |
E-30
| 3,206 |
1,550 |
Nearly Complete Genome Sequence of an Echovirus 30 Strain from a Cluster of Aseptic Meningitis Cases in California, September 2017
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6953510/
SHA: f0c4d40e1879dd1a049298f151940ac168b5f5a7
Authors: Pan, Chao-Yang; Huynh, Thalia; Padilla, Tasha; Chen, Alice; Ng, Terry Fei Fan; Marine, Rachel L.; Castro, Christina J.; Nix, W. Allan; Wadford, Debra A.
Date: 2019-10-31
DOI: 10.1128/mra.01085-19
License: cc-by
Abstract: We report the nearly complete genome sequence of a human enterovirus, a strain of echovirus 30, obtained from a cerebrospinal fluid specimen from a teenaged patient with aseptic meningitis in September 2017.
Text: E choviruses are members of the Enterovirus B species of the Enterovirus (EV) genus in the Picornaviridae family of nonenveloped, single-stranded, positive-sense RNA viruses. Echoviruses were named from the acronym enteric cytopathic human orphan virus at the time of their discovery in the 1950s but were later found to be associated with respiratory illness, hand-foot-and-mouth disease, and aseptic meningitis, similar to other enteroviruses (1) .
According to the California Code of Regulations, meningitis cases are reportable to the California Department of Public Health (CDPH) within 1 day of identification of etiology (2) . In the fall of 2017, a cluster of aseptic meningitis cases from a northern California high school were reported to the CDPH. The Viral and Rickettsial Disease Laboratory (VRDL) at the CDPH detected EV from 19 of 30 patients (63%) by real-time reverse transcription-PCR (RT-PCR), as previously described (3) . We generated and analyzed partial capsid (viral protein 1 [VP1]) sequences using methods developed by Minnaar et al. (4) . Fifteen of 19 (79%) EV-positive patients were confirmed to have echovirus 30 (E-30), using cerebrospinal fluid (CSF) samples. This cluster of E-30 meningitis cases is similar to previously reported E-30 aseptic meningitis cases (5, 6) in symptoms and epidemiology.
Here, we report a nearly complete genome sequence from one of the E-30-positive CSF specimens. The CSF was processed by centrifugation, 0.45-m filtration, and nuclease treatment prior to extraction using the NucliSENS easyMAG system (bioMérieux, Durham, NC) (7). The extracted nucleic acids were then treated with DNase to yield RNA, which was subjected to random reverse transcription and PCR (7) . The next-generation sequencing (NGS) library was prepared using a Nextera XT kit and sequenced on a MiSeq platform 300-cycle paired-end run (Illumina, San Diego, CA). The NGS data were analyzed using an in-house Centers for Disease Control and Prevention (CDC) pipeline which involves the removal of host sequences using bowtie2/2.3.3.1, primer removal, low-quality (below Q20) and read length (Ͻ50 nucleotides) filtering using cutadapt 1.18, read duplication removal using a Dedup.py script, de novo assembly using SPAdes 3.7 default parameters, and BLAST search of the resultant contigs (8) . There were a total of 141,329 postprocessing FASTQ reads. The final consensus genome was inspected and annotated using Geneious v10.0.9 (9) . The contig was built from 15,712 reads, assembled to an E-30 reference genome (GenBank accession number JX976773), and deemed nearly complete by comparison to the reference, and the termini were determined as part of the protocol (7). The total GC content is 48.3% for 7,155 bases. The average read coverage was 260-fold for the E-30 genome.
The genome sequence was designated E-30 USA/2017/CA-RGDS-1005. Its VP1 sequence was confirmed by the CDC Picornavirus Laboratory to be nearly identical to those of E-30s identified in an aseptic meningitis outbreak that occurred in the fall of 2017 in Nevada; it also has greater than 99% nucleotide identity to the VP1 sequences of E-30 strains from the southern United States identified by the CDC in May 2017 (GenBank accession numbers MG584831 and MG584832), as measured using the online version of blastn (https://blast.ncbi.nlm.nih.gov/Blast.cgi). The genome sequence of E-30 USA/2017/CA-RGDS-1005 shares less than 89% nucleotide identity (NI) and less than 98% amino acid identity (AI) with other publicly available E-30 sequences. The sequence contains the complete protein-coding region, with short sections in the untranslated regions (UTRs) missing due to a lack of read coverage (approximately 182 and 90 nucleotides of the 5= and 3= UTRs, respectively). The enterovirus polyprotein can be divided into one structural (P1-capsid) and two nonstructural (P2 and P3) regions. The polyprotein regions of the E-30 genome reported here share 96%, 88%, and 84% NI (P1, P2, and P3, respectively) with other E-30 sequences in GenBank.
Data availability. The E-30 sequence of USA/2017/CA-RGDS-1005 has been deposited in GenBank under the accession number MK238483. The quality-filtered FASTQ reads have been deposited in the Sequence Read Archive with the run accession number SRR10082176.
The contributions of the California Department of Public Health Viral and Rickettsial Disease Laboratory were supported in part by the Epidemiology and Laboratory Capacity for Infectious Diseases Cooperative Agreement number 6 NU50CK000410 from the U.S. Centers for Disease Control and Prevention. This work was partly funded by federal appropriations to the Centers for Disease Control and Prevention, through the Advanced Molecular Detection Initiative line item.
The findings and conclusions in this report are those of the author(s) and do not necessarily represent the official position of the Centers for Disease Control and Prevention.
|
What was the read coverage for the E-30 genome in this study?
| 2,994 |
260-fold
| 3,462 |
1,550 |
Nearly Complete Genome Sequence of an Echovirus 30 Strain from a Cluster of Aseptic Meningitis Cases in California, September 2017
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6953510/
SHA: f0c4d40e1879dd1a049298f151940ac168b5f5a7
Authors: Pan, Chao-Yang; Huynh, Thalia; Padilla, Tasha; Chen, Alice; Ng, Terry Fei Fan; Marine, Rachel L.; Castro, Christina J.; Nix, W. Allan; Wadford, Debra A.
Date: 2019-10-31
DOI: 10.1128/mra.01085-19
License: cc-by
Abstract: We report the nearly complete genome sequence of a human enterovirus, a strain of echovirus 30, obtained from a cerebrospinal fluid specimen from a teenaged patient with aseptic meningitis in September 2017.
Text: E choviruses are members of the Enterovirus B species of the Enterovirus (EV) genus in the Picornaviridae family of nonenveloped, single-stranded, positive-sense RNA viruses. Echoviruses were named from the acronym enteric cytopathic human orphan virus at the time of their discovery in the 1950s but were later found to be associated with respiratory illness, hand-foot-and-mouth disease, and aseptic meningitis, similar to other enteroviruses (1) .
According to the California Code of Regulations, meningitis cases are reportable to the California Department of Public Health (CDPH) within 1 day of identification of etiology (2) . In the fall of 2017, a cluster of aseptic meningitis cases from a northern California high school were reported to the CDPH. The Viral and Rickettsial Disease Laboratory (VRDL) at the CDPH detected EV from 19 of 30 patients (63%) by real-time reverse transcription-PCR (RT-PCR), as previously described (3) . We generated and analyzed partial capsid (viral protein 1 [VP1]) sequences using methods developed by Minnaar et al. (4) . Fifteen of 19 (79%) EV-positive patients were confirmed to have echovirus 30 (E-30), using cerebrospinal fluid (CSF) samples. This cluster of E-30 meningitis cases is similar to previously reported E-30 aseptic meningitis cases (5, 6) in symptoms and epidemiology.
Here, we report a nearly complete genome sequence from one of the E-30-positive CSF specimens. The CSF was processed by centrifugation, 0.45-m filtration, and nuclease treatment prior to extraction using the NucliSENS easyMAG system (bioMérieux, Durham, NC) (7). The extracted nucleic acids were then treated with DNase to yield RNA, which was subjected to random reverse transcription and PCR (7) . The next-generation sequencing (NGS) library was prepared using a Nextera XT kit and sequenced on a MiSeq platform 300-cycle paired-end run (Illumina, San Diego, CA). The NGS data were analyzed using an in-house Centers for Disease Control and Prevention (CDC) pipeline which involves the removal of host sequences using bowtie2/2.3.3.1, primer removal, low-quality (below Q20) and read length (Ͻ50 nucleotides) filtering using cutadapt 1.18, read duplication removal using a Dedup.py script, de novo assembly using SPAdes 3.7 default parameters, and BLAST search of the resultant contigs (8) . There were a total of 141,329 postprocessing FASTQ reads. The final consensus genome was inspected and annotated using Geneious v10.0.9 (9) . The contig was built from 15,712 reads, assembled to an E-30 reference genome (GenBank accession number JX976773), and deemed nearly complete by comparison to the reference, and the termini were determined as part of the protocol (7). The total GC content is 48.3% for 7,155 bases. The average read coverage was 260-fold for the E-30 genome.
The genome sequence was designated E-30 USA/2017/CA-RGDS-1005. Its VP1 sequence was confirmed by the CDC Picornavirus Laboratory to be nearly identical to those of E-30s identified in an aseptic meningitis outbreak that occurred in the fall of 2017 in Nevada; it also has greater than 99% nucleotide identity to the VP1 sequences of E-30 strains from the southern United States identified by the CDC in May 2017 (GenBank accession numbers MG584831 and MG584832), as measured using the online version of blastn (https://blast.ncbi.nlm.nih.gov/Blast.cgi). The genome sequence of E-30 USA/2017/CA-RGDS-1005 shares less than 89% nucleotide identity (NI) and less than 98% amino acid identity (AI) with other publicly available E-30 sequences. The sequence contains the complete protein-coding region, with short sections in the untranslated regions (UTRs) missing due to a lack of read coverage (approximately 182 and 90 nucleotides of the 5= and 3= UTRs, respectively). The enterovirus polyprotein can be divided into one structural (P1-capsid) and two nonstructural (P2 and P3) regions. The polyprotein regions of the E-30 genome reported here share 96%, 88%, and 84% NI (P1, P2, and P3, respectively) with other E-30 sequences in GenBank.
Data availability. The E-30 sequence of USA/2017/CA-RGDS-1005 has been deposited in GenBank under the accession number MK238483. The quality-filtered FASTQ reads have been deposited in the Sequence Read Archive with the run accession number SRR10082176.
The contributions of the California Department of Public Health Viral and Rickettsial Disease Laboratory were supported in part by the Epidemiology and Laboratory Capacity for Infectious Diseases Cooperative Agreement number 6 NU50CK000410 from the U.S. Centers for Disease Control and Prevention. This work was partly funded by federal appropriations to the Centers for Disease Control and Prevention, through the Advanced Molecular Detection Initiative line item.
The findings and conclusions in this report are those of the author(s) and do not necessarily represent the official position of the Centers for Disease Control and Prevention.
|
What are the structural regions of the enterovirus polyprotein in this study?
| 2,995 |
one structural (P1-capsid) and two nonstructural (P2 and P3) regions
| 4,508 |
1,572 |
iNR-Drug: Predicting the Interaction of Drugs with Nuclear Receptors in Cellular Networking
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3975431/
SHA: ee55aea26f816403476a7cb71816b8ecb1110329
Authors: Fan, Yue-Nong; Xiao, Xuan; Min, Jian-Liang; Chou, Kuo-Chen
Date: 2014-03-19
DOI: 10.3390/ijms15034915
License: cc-by
Abstract: Nuclear receptors (NRs) are closely associated with various major diseases such as cancer, diabetes, inflammatory disease, and osteoporosis. Therefore, NRs have become a frequent target for drug development. During the process of developing drugs against these diseases by targeting NRs, we are often facing a problem: Given a NR and chemical compound, can we identify whether they are really in interaction with each other in a cell? To address this problem, a predictor called “iNR-Drug” was developed. In the predictor, the drug compound concerned was formulated by a 256-D (dimensional) vector derived from its molecular fingerprint, and the NR by a 500-D vector formed by incorporating its sequential evolution information and physicochemical features into the general form of pseudo amino acid composition, and the prediction engine was operated by the SVM (support vector machine) algorithm. Compared with the existing prediction methods in this area, iNR-Drug not only can yield a higher success rate, but is also featured by a user-friendly web-server established at http://www.jci-bioinfo.cn/iNR-Drug/, which is particularly useful for most experimental scientists to obtain their desired data in a timely manner. It is anticipated that the iNR-Drug server may become a useful high throughput tool for both basic research and drug development, and that the current approach may be easily extended to study the interactions of drug with other targets as well.
Text: With the ability to directly bind to DNA ( Figure 1 ) and regulate the expression of adjacent genes, nuclear receptors (NRs) are a class of ligand-inducible transcription factors. They regulate various biological processes, such as homeostasis, differentiation, embryonic development, and organ physiology [1] [2] [3] . The NR superfamily has been classified into seven families: NR0 (knirps or DAX like) [4, 5] ; NR1 (thyroid hormone like), NR2 (HNF4-like), NR3 (estrogen like), NR4 (nerve growth factor IB-like), NR5 (fushi tarazu-F1 like), and NR6 (germ cell nuclear factor like). Since they are involved in almost all aspects of human physiology and are implicated in many major diseases such as cancer, diabetes and osteoporosis, nuclear receptors have become major drug targets [6, 7] , along with G protein-coupled receptors (GPCRs) [8] [9] [10] [11] [12] [13] [14] [15] [16] [17] , ion channels [18] [19] [20] , and kinase proteins [21] [22] [23] [24] . Identification of drug-target interactions is one of the most important steps for the new medicine development [25, 26] . The method usually adopted in this step is molecular docking simulation [27] [28] [29] [30] [31] [32] [33] [34] [35] [36] [37] [38] [39] [40] [41] [42] [43] . However, to make molecular docking study feasible, a reliable 3D (three dimensional) structure of the target protein is the prerequisite condition. Although X-ray crystallography is a powerful tool in determining protein 3D structures, it is time-consuming and expensive. Particularly, not all proteins can be successfully crystallized. For example, membrane proteins are very difficult to crystallize and most of them will not dissolve in normal solvents. Therefore, so far very few membrane protein 3D structures have been determined. Although NMR (Nuclear Magnetic Resonance) is indeed a very powerful tool in determining the 3D structures of membrane proteins as indicated by a series of recent publications (see, e.g., [44] [45] [46] [47] [48] [49] [50] [51] and a review article [20] ), it is also time-consuming and costly. To acquire the 3D structural information in a timely manner, one has to resort to various structural bioinformatics tools (see, e.g., [37] ), particularly the homologous modeling approach as utilized for a series of protein receptors urgently needed during the process of drug development [19, [52] [53] [54] [55] [56] [57] . Unfortunately, the number of dependable templates for developing high quality 3D structures by means of homology modeling is very limited [37] .
To overcome the aforementioned problems, it would be of help to develop a computational method for predicting the interactions of drugs with nuclear receptors in cellular networking based on the sequences information of the latter. The results thus obtained can be used to pre-exclude the compounds identified not in interaction with the nuclear receptors, so as to timely stop wasting time and money on those unpromising compounds [58] .
Actually, based on the functional groups and biological features, a powerful method was developed recently [59] for this purpose. However, further development in this regard is definitely needed due to the following reasons. (a) He et al. [59] did not provide a publicly accessible web-server for their method, and hence its practical application value is quite limited, particularly for the broad experimental scientists; (b) The prediction quality can be further enhanced by incorporating some key features into the formulation of NR-drug (nuclear receptor and drug) samples via the general form of pseudo amino acid composition [60] .
The present study was initiated with an attempt to develop a new method for predicting the interaction of drugs with nuclear receptors by addressing the two points.
As demonstrated by a series of recent publications [10, 18, [61] [62] [63] [64] [65] [66] [67] [68] [69] [70] and summarized in a comprehensive review [60] , to establish a really effective statistical predictor for a biomedical system, we need to consider the following steps: (a) select or construct a valid benchmark dataset to train and test the predictor; (b) represent the statistical samples with an effective formulation that can truly reflect their intrinsic correlation with the object to be predicted; (c) introduce or develop a powerful algorithm or engine to operate the prediction; (d) properly perform cross-validation tests to objectively evaluate the anticipated accuracy of the predictor; (e) establish a user-friendly web-server for the predictor that is accessible to the public. Below, let us elaborate how to deal with these steps.
The data used in the current study were collected from KEGG (Kyoto Encyclopedia of Genes and Genomes) [71] at http://www.kegg.jp/kegg/. KEGG is a database resource for understanding high-level functions and utilities of the biological system, such as the cell, the organism and the ecosystem, from molecular-level information, especially large-scale molecular datasets generated by genome sequencing and other high-throughput experimental technologies. Here, the benchmark dataset can be formulated as
where is the positive subset that consists of the interactive drug-NR pairs only, while the negative subset that contains of the non-interactive drug-NR pairs only, and the symbol represents the union in the set theory. The so-called "interactive" pair here means the pair whose two counterparts are interacting with each other in the drug-target networks as defined in the KEGG database [71] ; while the "non-interactive" pair means that its two counterparts are not interacting with each other in the drug-target networks. The positive dataset contains 86 drug-NR pairs, which were taken from He et al. [59] . The negative dataset contains 172 non-interactive drug-NR pairs, which were derived according to the following procedures: (a) separating each of the pairs in into single drug and NR; (b) re-coupling each of the single drugs with each of the single NRs into pairs in a way that none of them occurred in ; (c) randomly picking the pairs thus formed until reaching the number two times as many as the pairs in . The 86 interactive drug-NR pairs and 172 non-interactive drug-NR pairs are given in Supplementary Information S1, from which we can see that the 86 + 172 = 258 pairs in the current benchmark dataset are actually formed by 25 different NRs and 53 different compounds.
Since each of the samples in the current network system contains a drug (compound) and a NR (protein), the following procedures were taken to represent the drug-NR pair sample.
First, for the drug part in the current benchmark dataset, we can use a 256-D vector to formulate it as given by
where D represents the vector for a drug compound, and d i its i-th (i = 1,2, ,256) component that can be derived by following the "2D molecular fingerprint procedure" as elaborated in [10] . The 53 molecular fingerprint vectors thus obtained for the 53 drugs in are, respectively, given in Supplementary Information S2.
The protein sequences of the 25 different NRs in are listed in Supplementary Information S3. Suppose the sequence of a nuclear receptor protein P with L residues is generally expressed by
where 1 R represents the 1st residue of the protein sequence P , 2 R the 2nd residue, and so forth. Now the problem is how to effectively represent the sequence of Equation (3) with a non-sequential or discrete model [72] . This is because all the existing operation engines, such as covariance discriminant (CD) [17, 65, [73] [74] [75] [76] [77] [78] [79] , neural network [80] [81] [82] , support vector machine (SVM) [62] [63] [64] 83] , random forest [84, 85] , conditional random field [66] , nearest neighbor (NN) [86, 87] ; K-nearest neighbor (KNN) [88] [89] [90] , OET-KNN [91] [92] [93] [94] , and Fuzzy K-nearest neighbor [10, 12, 18, 69, 95] , can only handle vector but not sequence samples. However, a vector defined in a discrete model may completely lose all the sequence-order information and hence limit the quality of prediction. Facing such a dilemma, can we find an approach to partially incorporate the sequence-order effects? Actually, one of the most challenging problems in computational biology is how to formulate a biological sequence with a discrete model or a vector, yet still keep considerable sequence order information. To avoid completely losing the sequence-order information for proteins, the pseudo amino acid composition [96, 97] or Chou's PseAAC [98] was proposed. Ever since the concept of PseAAC was proposed in 2001 [96] , it has penetrated into almost all the areas of computational proteomics, such as predicting anticancer peptides [99] , predicting protein subcellular location [100] [101] [102] [103] [104] [105] [106] , predicting membrane protein types [107, 108] , predicting protein submitochondria locations [109] [110] [111] [112] , predicting GABA(A) receptor proteins [113] , predicting enzyme subfamily classes [114] , predicting antibacterial peptides [115] , predicting supersecondary structure [116] , predicting bacterial virulent proteins [117] , predicting protein structural class [118] , predicting the cofactors of oxidoreductases [119] , predicting metalloproteinase family [120] , identifying cysteine S-nitrosylation sites in proteins [66] , identifying bacterial secreted proteins [121] , identifying antibacterial peptides [115] , identifying allergenic proteins [122] , identifying protein quaternary structural attributes [123, 124] , identifying risk type of human papillomaviruses [125] , identifying cyclin proteins [126] , identifying GPCRs and their types [15, 16] , discriminating outer membrane proteins [127] , classifying amino acids [128] , detecting remote homologous proteins [129] , among many others (see a long list of papers cited in the References section of [60] ). Moreover, the concept of PseAAC was further extended to represent the feature vectors of nucleotides [65] , as well as other biological samples (see, e.g., [130] [131] [132] ). Because it has been widely and increasingly used, recently two powerful soft-wares, called "PseAAC-Builder" [133] and "propy" [134] , were established for generating various special Chou's pseudo-amino acid compositions, in addition to the web-server "PseAAC" [135] built in 2008.
According to a comprehensive review [60] , the general form of PseAAC for a protein sequence P is formulated by
where the subscript is an integer, and its value as well as the components ( 1, 2, , ) u u will depend on how to extract the desired information from the amino acid sequence of P (cf. Equation (3)). Below, let us describe how to extract useful information to define the components of PseAAC for the NR samples concerned. First, many earlier studies (see, e.g., [136] [137] [138] [139] [140] [141] ) have indicated that the amino acid composition (AAC) of a protein plays an important role in determining its attributes. The AAC contains 20 components with each representing the occurrence frequency of one of the 20 native amino acids in the protein concerned. Thus, such 20 AAC components were used here to define the first 20 elements in Equation (4); i.e., (1) ( 1, 2, , 20) ii fi (5) where f i (1) is the normalized occurrence frequency of the i-th type native amino acid in the nuclear receptor concerned. Since AAC did not contain any sequence order information, the following steps were taken to make up this shortcoming.
To avoid completely losing the local or short-range sequence order information, we considered the approach of dipeptide composition. It contained 20 × 20 = 400 components [142] . Such 400 components were used to define the next 400 elements in Equation (4); i.e., (2) 20 ( 1, 2, , 400) jj fj
where (2) j f is the normalized occurrence frequency of the j-th dipeptides in the nuclear receptor concerned. To incorporate the global or long-range sequence order information, let us consider the following approach. According to molecular evolution, all biological sequences have developed starting out from a very limited number of ancestral samples. Driven by various evolutionary forces such as mutation, recombination, gene conversion, genetic drift, and selection, they have undergone many changes including changes of single residues, insertions and deletions of several residues [143] , gene doubling, and gene fusion. With the accumulation of these changes over a long period of time, many original similarities between initial and resultant amino acid sequences are gradually faded out, but the corresponding proteins may still share many common attributes [37] , such as having basically the same biological function and residing at a same subcellular location [144, 145] . To extract the sequential evolution information and use it to define the components of Equation (4), the PSSM (Position Specific Scoring Matrix) was used as described below.
According to Schaffer [146] , the sequence evolution information of a nuclear receptor protein P with L amino acid residues can be expressed by a 20 L matrix, as given by
where (7) were generated by using PSI-BLAST [147] to search the UniProtKB/Swiss-Prot database (The Universal Protein Resource (UniProt); http://www.uniprot.org/) through three iterations with 0.001 as the E-value cutoff for multiple sequence alignment against the sequence of the nuclear receptor concerned. In order to make every element in Equation (7) be scaled from their original score ranges into the region of [0, 1], we performed a conversion through the standard sigmoid function to make it become
Now we extract the useful information from Equation (8)
Moreover, we used the grey system model approach as elaborated in [68] to further define the next 60 components of Equation (4) ( 1, 2, , 20)
In the above equation, w 1 , w 2 , and w 3 are weight factors, which were all set to 1 in the current study; f j (1) has the same meaning as in Equation (5)
where
and
Combining Equations (5), (6), (10) and (12), we found that the total number of the components obtained via the current approach for the PseAAC of Equation (4)
and each of the 500 components is given by (1) (
Since the elements in Equations (2) and (4) are well defined, we can now formulate the drug-NR pair by combining the two equations as given by (19) where G represents the drug-NR pair, Å the orthogonal sum, and the 256 + 500 = 756 components are defined by Equations (2) and (18) . For the sake of convenience, let us use x i (i = 1, 2, , 756) to represent the 756 components in Equation (19); i.e., (20) To optimize the prediction quality with a time-saving approach, similar to the treatment [148] [149] [150] , let us convert Equation (20) to
where the symbol means taking the average of the quantity therein, and SD means the corresponding standard derivation.
In this study, the SVM (support vector machine) was used as the operation engine. SVM has been widely used in the realm of bioinformatics (see, e.g., [62] [63] [64] [151] [152] [153] [154] ). The basic idea of SVM is to transform the data into a high dimensional feature space, and then determine the optimal separating hyperplane using a kernel function. For a brief formulation of SVM and how it works, see the papers [155, 156] ; for more details about SVM, see a monograph [157] .
In this study, the LIBSVM package [158] was used as an implementation of SVM, which can be downloaded from http://www.csie.ntu.edu.tw/~cjlin/libsvm/, the popular radial basis function (RBF) was taken as the kernel function. For the current SVM classifier, there were two uncertain parameters: penalty parameter C and kernel parameter . The method of how to determine the two parameters will be given later.
The predictor obtained via the aforementioned procedure is called iNR-Drug, where "i" means identify, and "NR-Drug" means the interaction between nuclear receptor and drug compound. To provide an intuitive overall picture, a flowchart is provided in Figure 2 to show the process of how the predictor works in identifying the interactions between nuclear receptors and drug compounds.
To provide a more intuitive and easier-to-understand method to measure the prediction quality, the following set of metrics based on the formulation used by Chou [159] [160] [161] in predicting signal peptides was adopted. According to Chou's formulation, the sensitivity, specificity, overall accuracy, and Matthew's correlation coefficient can be respectively expressed as [62, [65] [66] [67] Sn 1
where N is the total number of the interactive NR-drug pairs investigated while N the number of the interactive NR-drug pairs incorrectly predicted as the non-interactive NR-drug pairs; N the total number of the non-interactive NR-drug pairs investigated while N the number of the non-interactive NR-drug pairs incorrectly predicted as the interactive NR-drug pairs.
According to Equation (23) we can easily see the following. When 0 N meaning none of the interactive NR-drug pairs was mispredicted to be a non-interactive NR-drug pair, we have the sensitivity Sn = 1; while NN meaning that all the interactive NR-drug pairs were mispredicted to be the non-interactive NR-drug pairs, we have the sensitivity Sn = 0 . Likewise, when 0 N meaning none of the non-interactive NR-drug pairs was mispredicted, we have the specificity Sp we have MCC = 0 meaning total disagreement between prediction and observation. As we can see from the above discussion, it is much more intuitive and easier to understand when using Equation (23) to examine a predictor for its four metrics, particularly for its Mathew's correlation coefficient. It is instructive to point out that the metrics as defined in Equation (23) are valid for single label systems; for multi-label systems, a set of more complicated metrics should be used as given in [162] .
How to properly test a predictor for its anticipated success rates is very important for its development as well as its potential application value. Generally speaking, the following three cross-validation methods are often used to examine the quality of a predictor and its effectiveness in practical application: independent dataset test, subsampling or K-fold (such as five-fold, seven-fold, or 10-fold) crossover test and jackknife test [163] . However, as elaborated by a penetrating analysis in [164] , considerable arbitrariness exists in the independent dataset test. Also, as demonstrated in [165] , the subsampling (or K-fold crossover validation) test cannot avoid arbitrariness either. Only the jackknife test is the least arbitrary that can always yield a unique result for a given benchmark dataset [73, 74, 156, [166] [167] [168] . Therefore, the jackknife test has been widely recognized and increasingly utilized by investigators to examine the quality of various predictors (see, e.g., [14, 15, 68, 99, 106, 107, 124, 169, 170] ). Accordingly, in this study the jackknife test was also adopted to evaluate the accuracy of the current predictor.
As mentioned above, the SVM operation engine contains two uncertain parameters C and . To find their optimal values, a 2-D grid search was conducted by the jackknife test on the benchmark dataset . The results thus obtained are shown in Figure 3 , from which it can be seen that the iNR-Drug predictor reaches its optimal status when C = 2 3 and 9 2 . The corresponding rates for the four metrics (cf. Equation (23)) are given in Table 1 , where for facilitating comparison, the overall accuracy Acc reported by He et al. [59] on the same benchmark dataset is also given although no results were reported by them for Sn, Sp and MCC. It can be observed from the table that the overall accuracy obtained by iNR-Drug is remarkably higher that of He et al. [59] , and that the rates achieved by iNR-Drug for the other three metrics are also quite higher. These facts indicate that the current predictor not only can yield higher overall prediction accuracy but also is quite stable with low false prediction rates.
As mentioned above (Section 3.2), the jackknife test is the most objective method for examining the quality of a predictor. However, as a demonstration to show how to practically use the current predictor, we took 41 NR-drug pairs from the study by Yamanishi et al. [171] that had been confirmed by experiments as interactive pairs. For such an independent dataset, 34 were correctly identified by iNR-Drug as interactive pairs, i.e., Sn = 34 / 41 = 82.92%, which is quite consistent with the rate of 79.07% achieved by the predictor on the benchmark dataset via the jackknife test as reported in Table 1 .
It is anticipated that the iNR-Drug predictor developed in this paper may become a useful high throughput tool for both basic research and drug development, and that the current approach may be easily extended to study the interactions of drug with other targets as well. Since user-friendly and publicly accessible web-servers represent the future direction for developing practically more useful predictors [98, 172] , a publicly accessible web-server for iNR-Drug was established.
For the convenience of the vast majority of biologists and pharmaceutical scientists, here let us provide a step-by-step guide to show how the users can easily get the desired result by using iNR-Drug web-server without the need to follow the complicated mathematical equations presented in this paper for the process of developing the predictor and its integrity.
Step 1. Open the web server at the site http://www.jci-bioinfo.cn/iNR-Drug/ and you will see the top page of the predictor on your computer screen, as shown in Figure 4 . Click on the Read Me button to see a brief introduction about iNR-Drug predictor and the caveat when using it.
Step 2. Either type or copy/paste the query NR-drug pairs into the input box at the center of Figure 4 . Each query pair consists of two parts: one is for the nuclear receptor sequence, and the other for the drug. The NR sequence should be in FASTA format, while the drug in the KEGG code beginning with the symbol #. Examples for the query pairs input and the corresponding output can be seen by clicking on the Example button right above the input box. Step 3. Click on the Submit button to see the predicted result. For example, if you use the three query pairs in the Example window as the input, after clicking the Submit button, you will see on your screen that the "hsa:2099" NR and the "D00066" drug are an interactive pair, and that the "hsa:2908" NR and the "D00088" drug are also an interactive pair, but that the "hsa:5468" NR and the "D00279" drug are not an interactive pair. All these results are fully consistent with the experimental observations. It takes about 3 minutes before each of these results is shown on the screen; of course, the more query pairs there is, the more time that is usually needed.
Step 4. Click on the Citation button to find the relevant paper that documents the detailed development and algorithm of iNR-Durg.
Step 5. Click on the Data button to download the benchmark dataset used to train and test the iNR-Durg predictor.
Step 6. The program code is also available by clicking the button download on the lower panel of Figure 4 .
|
What can nuclear receptors regulate?
| 1,635 |
homeostasis, differentiation, embryonic development, and organ physiology
| 2,040 |
1,572 |
iNR-Drug: Predicting the Interaction of Drugs with Nuclear Receptors in Cellular Networking
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3975431/
SHA: ee55aea26f816403476a7cb71816b8ecb1110329
Authors: Fan, Yue-Nong; Xiao, Xuan; Min, Jian-Liang; Chou, Kuo-Chen
Date: 2014-03-19
DOI: 10.3390/ijms15034915
License: cc-by
Abstract: Nuclear receptors (NRs) are closely associated with various major diseases such as cancer, diabetes, inflammatory disease, and osteoporosis. Therefore, NRs have become a frequent target for drug development. During the process of developing drugs against these diseases by targeting NRs, we are often facing a problem: Given a NR and chemical compound, can we identify whether they are really in interaction with each other in a cell? To address this problem, a predictor called “iNR-Drug” was developed. In the predictor, the drug compound concerned was formulated by a 256-D (dimensional) vector derived from its molecular fingerprint, and the NR by a 500-D vector formed by incorporating its sequential evolution information and physicochemical features into the general form of pseudo amino acid composition, and the prediction engine was operated by the SVM (support vector machine) algorithm. Compared with the existing prediction methods in this area, iNR-Drug not only can yield a higher success rate, but is also featured by a user-friendly web-server established at http://www.jci-bioinfo.cn/iNR-Drug/, which is particularly useful for most experimental scientists to obtain their desired data in a timely manner. It is anticipated that the iNR-Drug server may become a useful high throughput tool for both basic research and drug development, and that the current approach may be easily extended to study the interactions of drug with other targets as well.
Text: With the ability to directly bind to DNA ( Figure 1 ) and regulate the expression of adjacent genes, nuclear receptors (NRs) are a class of ligand-inducible transcription factors. They regulate various biological processes, such as homeostasis, differentiation, embryonic development, and organ physiology [1] [2] [3] . The NR superfamily has been classified into seven families: NR0 (knirps or DAX like) [4, 5] ; NR1 (thyroid hormone like), NR2 (HNF4-like), NR3 (estrogen like), NR4 (nerve growth factor IB-like), NR5 (fushi tarazu-F1 like), and NR6 (germ cell nuclear factor like). Since they are involved in almost all aspects of human physiology and are implicated in many major diseases such as cancer, diabetes and osteoporosis, nuclear receptors have become major drug targets [6, 7] , along with G protein-coupled receptors (GPCRs) [8] [9] [10] [11] [12] [13] [14] [15] [16] [17] , ion channels [18] [19] [20] , and kinase proteins [21] [22] [23] [24] . Identification of drug-target interactions is one of the most important steps for the new medicine development [25, 26] . The method usually adopted in this step is molecular docking simulation [27] [28] [29] [30] [31] [32] [33] [34] [35] [36] [37] [38] [39] [40] [41] [42] [43] . However, to make molecular docking study feasible, a reliable 3D (three dimensional) structure of the target protein is the prerequisite condition. Although X-ray crystallography is a powerful tool in determining protein 3D structures, it is time-consuming and expensive. Particularly, not all proteins can be successfully crystallized. For example, membrane proteins are very difficult to crystallize and most of them will not dissolve in normal solvents. Therefore, so far very few membrane protein 3D structures have been determined. Although NMR (Nuclear Magnetic Resonance) is indeed a very powerful tool in determining the 3D structures of membrane proteins as indicated by a series of recent publications (see, e.g., [44] [45] [46] [47] [48] [49] [50] [51] and a review article [20] ), it is also time-consuming and costly. To acquire the 3D structural information in a timely manner, one has to resort to various structural bioinformatics tools (see, e.g., [37] ), particularly the homologous modeling approach as utilized for a series of protein receptors urgently needed during the process of drug development [19, [52] [53] [54] [55] [56] [57] . Unfortunately, the number of dependable templates for developing high quality 3D structures by means of homology modeling is very limited [37] .
To overcome the aforementioned problems, it would be of help to develop a computational method for predicting the interactions of drugs with nuclear receptors in cellular networking based on the sequences information of the latter. The results thus obtained can be used to pre-exclude the compounds identified not in interaction with the nuclear receptors, so as to timely stop wasting time and money on those unpromising compounds [58] .
Actually, based on the functional groups and biological features, a powerful method was developed recently [59] for this purpose. However, further development in this regard is definitely needed due to the following reasons. (a) He et al. [59] did not provide a publicly accessible web-server for their method, and hence its practical application value is quite limited, particularly for the broad experimental scientists; (b) The prediction quality can be further enhanced by incorporating some key features into the formulation of NR-drug (nuclear receptor and drug) samples via the general form of pseudo amino acid composition [60] .
The present study was initiated with an attempt to develop a new method for predicting the interaction of drugs with nuclear receptors by addressing the two points.
As demonstrated by a series of recent publications [10, 18, [61] [62] [63] [64] [65] [66] [67] [68] [69] [70] and summarized in a comprehensive review [60] , to establish a really effective statistical predictor for a biomedical system, we need to consider the following steps: (a) select or construct a valid benchmark dataset to train and test the predictor; (b) represent the statistical samples with an effective formulation that can truly reflect their intrinsic correlation with the object to be predicted; (c) introduce or develop a powerful algorithm or engine to operate the prediction; (d) properly perform cross-validation tests to objectively evaluate the anticipated accuracy of the predictor; (e) establish a user-friendly web-server for the predictor that is accessible to the public. Below, let us elaborate how to deal with these steps.
The data used in the current study were collected from KEGG (Kyoto Encyclopedia of Genes and Genomes) [71] at http://www.kegg.jp/kegg/. KEGG is a database resource for understanding high-level functions and utilities of the biological system, such as the cell, the organism and the ecosystem, from molecular-level information, especially large-scale molecular datasets generated by genome sequencing and other high-throughput experimental technologies. Here, the benchmark dataset can be formulated as
where is the positive subset that consists of the interactive drug-NR pairs only, while the negative subset that contains of the non-interactive drug-NR pairs only, and the symbol represents the union in the set theory. The so-called "interactive" pair here means the pair whose two counterparts are interacting with each other in the drug-target networks as defined in the KEGG database [71] ; while the "non-interactive" pair means that its two counterparts are not interacting with each other in the drug-target networks. The positive dataset contains 86 drug-NR pairs, which were taken from He et al. [59] . The negative dataset contains 172 non-interactive drug-NR pairs, which were derived according to the following procedures: (a) separating each of the pairs in into single drug and NR; (b) re-coupling each of the single drugs with each of the single NRs into pairs in a way that none of them occurred in ; (c) randomly picking the pairs thus formed until reaching the number two times as many as the pairs in . The 86 interactive drug-NR pairs and 172 non-interactive drug-NR pairs are given in Supplementary Information S1, from which we can see that the 86 + 172 = 258 pairs in the current benchmark dataset are actually formed by 25 different NRs and 53 different compounds.
Since each of the samples in the current network system contains a drug (compound) and a NR (protein), the following procedures were taken to represent the drug-NR pair sample.
First, for the drug part in the current benchmark dataset, we can use a 256-D vector to formulate it as given by
where D represents the vector for a drug compound, and d i its i-th (i = 1,2, ,256) component that can be derived by following the "2D molecular fingerprint procedure" as elaborated in [10] . The 53 molecular fingerprint vectors thus obtained for the 53 drugs in are, respectively, given in Supplementary Information S2.
The protein sequences of the 25 different NRs in are listed in Supplementary Information S3. Suppose the sequence of a nuclear receptor protein P with L residues is generally expressed by
where 1 R represents the 1st residue of the protein sequence P , 2 R the 2nd residue, and so forth. Now the problem is how to effectively represent the sequence of Equation (3) with a non-sequential or discrete model [72] . This is because all the existing operation engines, such as covariance discriminant (CD) [17, 65, [73] [74] [75] [76] [77] [78] [79] , neural network [80] [81] [82] , support vector machine (SVM) [62] [63] [64] 83] , random forest [84, 85] , conditional random field [66] , nearest neighbor (NN) [86, 87] ; K-nearest neighbor (KNN) [88] [89] [90] , OET-KNN [91] [92] [93] [94] , and Fuzzy K-nearest neighbor [10, 12, 18, 69, 95] , can only handle vector but not sequence samples. However, a vector defined in a discrete model may completely lose all the sequence-order information and hence limit the quality of prediction. Facing such a dilemma, can we find an approach to partially incorporate the sequence-order effects? Actually, one of the most challenging problems in computational biology is how to formulate a biological sequence with a discrete model or a vector, yet still keep considerable sequence order information. To avoid completely losing the sequence-order information for proteins, the pseudo amino acid composition [96, 97] or Chou's PseAAC [98] was proposed. Ever since the concept of PseAAC was proposed in 2001 [96] , it has penetrated into almost all the areas of computational proteomics, such as predicting anticancer peptides [99] , predicting protein subcellular location [100] [101] [102] [103] [104] [105] [106] , predicting membrane protein types [107, 108] , predicting protein submitochondria locations [109] [110] [111] [112] , predicting GABA(A) receptor proteins [113] , predicting enzyme subfamily classes [114] , predicting antibacterial peptides [115] , predicting supersecondary structure [116] , predicting bacterial virulent proteins [117] , predicting protein structural class [118] , predicting the cofactors of oxidoreductases [119] , predicting metalloproteinase family [120] , identifying cysteine S-nitrosylation sites in proteins [66] , identifying bacterial secreted proteins [121] , identifying antibacterial peptides [115] , identifying allergenic proteins [122] , identifying protein quaternary structural attributes [123, 124] , identifying risk type of human papillomaviruses [125] , identifying cyclin proteins [126] , identifying GPCRs and their types [15, 16] , discriminating outer membrane proteins [127] , classifying amino acids [128] , detecting remote homologous proteins [129] , among many others (see a long list of papers cited in the References section of [60] ). Moreover, the concept of PseAAC was further extended to represent the feature vectors of nucleotides [65] , as well as other biological samples (see, e.g., [130] [131] [132] ). Because it has been widely and increasingly used, recently two powerful soft-wares, called "PseAAC-Builder" [133] and "propy" [134] , were established for generating various special Chou's pseudo-amino acid compositions, in addition to the web-server "PseAAC" [135] built in 2008.
According to a comprehensive review [60] , the general form of PseAAC for a protein sequence P is formulated by
where the subscript is an integer, and its value as well as the components ( 1, 2, , ) u u will depend on how to extract the desired information from the amino acid sequence of P (cf. Equation (3)). Below, let us describe how to extract useful information to define the components of PseAAC for the NR samples concerned. First, many earlier studies (see, e.g., [136] [137] [138] [139] [140] [141] ) have indicated that the amino acid composition (AAC) of a protein plays an important role in determining its attributes. The AAC contains 20 components with each representing the occurrence frequency of one of the 20 native amino acids in the protein concerned. Thus, such 20 AAC components were used here to define the first 20 elements in Equation (4); i.e., (1) ( 1, 2, , 20) ii fi (5) where f i (1) is the normalized occurrence frequency of the i-th type native amino acid in the nuclear receptor concerned. Since AAC did not contain any sequence order information, the following steps were taken to make up this shortcoming.
To avoid completely losing the local or short-range sequence order information, we considered the approach of dipeptide composition. It contained 20 × 20 = 400 components [142] . Such 400 components were used to define the next 400 elements in Equation (4); i.e., (2) 20 ( 1, 2, , 400) jj fj
where (2) j f is the normalized occurrence frequency of the j-th dipeptides in the nuclear receptor concerned. To incorporate the global or long-range sequence order information, let us consider the following approach. According to molecular evolution, all biological sequences have developed starting out from a very limited number of ancestral samples. Driven by various evolutionary forces such as mutation, recombination, gene conversion, genetic drift, and selection, they have undergone many changes including changes of single residues, insertions and deletions of several residues [143] , gene doubling, and gene fusion. With the accumulation of these changes over a long period of time, many original similarities between initial and resultant amino acid sequences are gradually faded out, but the corresponding proteins may still share many common attributes [37] , such as having basically the same biological function and residing at a same subcellular location [144, 145] . To extract the sequential evolution information and use it to define the components of Equation (4), the PSSM (Position Specific Scoring Matrix) was used as described below.
According to Schaffer [146] , the sequence evolution information of a nuclear receptor protein P with L amino acid residues can be expressed by a 20 L matrix, as given by
where (7) were generated by using PSI-BLAST [147] to search the UniProtKB/Swiss-Prot database (The Universal Protein Resource (UniProt); http://www.uniprot.org/) through three iterations with 0.001 as the E-value cutoff for multiple sequence alignment against the sequence of the nuclear receptor concerned. In order to make every element in Equation (7) be scaled from their original score ranges into the region of [0, 1], we performed a conversion through the standard sigmoid function to make it become
Now we extract the useful information from Equation (8)
Moreover, we used the grey system model approach as elaborated in [68] to further define the next 60 components of Equation (4) ( 1, 2, , 20)
In the above equation, w 1 , w 2 , and w 3 are weight factors, which were all set to 1 in the current study; f j (1) has the same meaning as in Equation (5)
where
and
Combining Equations (5), (6), (10) and (12), we found that the total number of the components obtained via the current approach for the PseAAC of Equation (4)
and each of the 500 components is given by (1) (
Since the elements in Equations (2) and (4) are well defined, we can now formulate the drug-NR pair by combining the two equations as given by (19) where G represents the drug-NR pair, Å the orthogonal sum, and the 256 + 500 = 756 components are defined by Equations (2) and (18) . For the sake of convenience, let us use x i (i = 1, 2, , 756) to represent the 756 components in Equation (19); i.e., (20) To optimize the prediction quality with a time-saving approach, similar to the treatment [148] [149] [150] , let us convert Equation (20) to
where the symbol means taking the average of the quantity therein, and SD means the corresponding standard derivation.
In this study, the SVM (support vector machine) was used as the operation engine. SVM has been widely used in the realm of bioinformatics (see, e.g., [62] [63] [64] [151] [152] [153] [154] ). The basic idea of SVM is to transform the data into a high dimensional feature space, and then determine the optimal separating hyperplane using a kernel function. For a brief formulation of SVM and how it works, see the papers [155, 156] ; for more details about SVM, see a monograph [157] .
In this study, the LIBSVM package [158] was used as an implementation of SVM, which can be downloaded from http://www.csie.ntu.edu.tw/~cjlin/libsvm/, the popular radial basis function (RBF) was taken as the kernel function. For the current SVM classifier, there were two uncertain parameters: penalty parameter C and kernel parameter . The method of how to determine the two parameters will be given later.
The predictor obtained via the aforementioned procedure is called iNR-Drug, where "i" means identify, and "NR-Drug" means the interaction between nuclear receptor and drug compound. To provide an intuitive overall picture, a flowchart is provided in Figure 2 to show the process of how the predictor works in identifying the interactions between nuclear receptors and drug compounds.
To provide a more intuitive and easier-to-understand method to measure the prediction quality, the following set of metrics based on the formulation used by Chou [159] [160] [161] in predicting signal peptides was adopted. According to Chou's formulation, the sensitivity, specificity, overall accuracy, and Matthew's correlation coefficient can be respectively expressed as [62, [65] [66] [67] Sn 1
where N is the total number of the interactive NR-drug pairs investigated while N the number of the interactive NR-drug pairs incorrectly predicted as the non-interactive NR-drug pairs; N the total number of the non-interactive NR-drug pairs investigated while N the number of the non-interactive NR-drug pairs incorrectly predicted as the interactive NR-drug pairs.
According to Equation (23) we can easily see the following. When 0 N meaning none of the interactive NR-drug pairs was mispredicted to be a non-interactive NR-drug pair, we have the sensitivity Sn = 1; while NN meaning that all the interactive NR-drug pairs were mispredicted to be the non-interactive NR-drug pairs, we have the sensitivity Sn = 0 . Likewise, when 0 N meaning none of the non-interactive NR-drug pairs was mispredicted, we have the specificity Sp we have MCC = 0 meaning total disagreement between prediction and observation. As we can see from the above discussion, it is much more intuitive and easier to understand when using Equation (23) to examine a predictor for its four metrics, particularly for its Mathew's correlation coefficient. It is instructive to point out that the metrics as defined in Equation (23) are valid for single label systems; for multi-label systems, a set of more complicated metrics should be used as given in [162] .
How to properly test a predictor for its anticipated success rates is very important for its development as well as its potential application value. Generally speaking, the following three cross-validation methods are often used to examine the quality of a predictor and its effectiveness in practical application: independent dataset test, subsampling or K-fold (such as five-fold, seven-fold, or 10-fold) crossover test and jackknife test [163] . However, as elaborated by a penetrating analysis in [164] , considerable arbitrariness exists in the independent dataset test. Also, as demonstrated in [165] , the subsampling (or K-fold crossover validation) test cannot avoid arbitrariness either. Only the jackknife test is the least arbitrary that can always yield a unique result for a given benchmark dataset [73, 74, 156, [166] [167] [168] . Therefore, the jackknife test has been widely recognized and increasingly utilized by investigators to examine the quality of various predictors (see, e.g., [14, 15, 68, 99, 106, 107, 124, 169, 170] ). Accordingly, in this study the jackknife test was also adopted to evaluate the accuracy of the current predictor.
As mentioned above, the SVM operation engine contains two uncertain parameters C and . To find their optimal values, a 2-D grid search was conducted by the jackknife test on the benchmark dataset . The results thus obtained are shown in Figure 3 , from which it can be seen that the iNR-Drug predictor reaches its optimal status when C = 2 3 and 9 2 . The corresponding rates for the four metrics (cf. Equation (23)) are given in Table 1 , where for facilitating comparison, the overall accuracy Acc reported by He et al. [59] on the same benchmark dataset is also given although no results were reported by them for Sn, Sp and MCC. It can be observed from the table that the overall accuracy obtained by iNR-Drug is remarkably higher that of He et al. [59] , and that the rates achieved by iNR-Drug for the other three metrics are also quite higher. These facts indicate that the current predictor not only can yield higher overall prediction accuracy but also is quite stable with low false prediction rates.
As mentioned above (Section 3.2), the jackknife test is the most objective method for examining the quality of a predictor. However, as a demonstration to show how to practically use the current predictor, we took 41 NR-drug pairs from the study by Yamanishi et al. [171] that had been confirmed by experiments as interactive pairs. For such an independent dataset, 34 were correctly identified by iNR-Drug as interactive pairs, i.e., Sn = 34 / 41 = 82.92%, which is quite consistent with the rate of 79.07% achieved by the predictor on the benchmark dataset via the jackknife test as reported in Table 1 .
It is anticipated that the iNR-Drug predictor developed in this paper may become a useful high throughput tool for both basic research and drug development, and that the current approach may be easily extended to study the interactions of drug with other targets as well. Since user-friendly and publicly accessible web-servers represent the future direction for developing practically more useful predictors [98, 172] , a publicly accessible web-server for iNR-Drug was established.
For the convenience of the vast majority of biologists and pharmaceutical scientists, here let us provide a step-by-step guide to show how the users can easily get the desired result by using iNR-Drug web-server without the need to follow the complicated mathematical equations presented in this paper for the process of developing the predictor and its integrity.
Step 1. Open the web server at the site http://www.jci-bioinfo.cn/iNR-Drug/ and you will see the top page of the predictor on your computer screen, as shown in Figure 4 . Click on the Read Me button to see a brief introduction about iNR-Drug predictor and the caveat when using it.
Step 2. Either type or copy/paste the query NR-drug pairs into the input box at the center of Figure 4 . Each query pair consists of two parts: one is for the nuclear receptor sequence, and the other for the drug. The NR sequence should be in FASTA format, while the drug in the KEGG code beginning with the symbol #. Examples for the query pairs input and the corresponding output can be seen by clicking on the Example button right above the input box. Step 3. Click on the Submit button to see the predicted result. For example, if you use the three query pairs in the Example window as the input, after clicking the Submit button, you will see on your screen that the "hsa:2099" NR and the "D00066" drug are an interactive pair, and that the "hsa:2908" NR and the "D00088" drug are also an interactive pair, but that the "hsa:5468" NR and the "D00279" drug are not an interactive pair. All these results are fully consistent with the experimental observations. It takes about 3 minutes before each of these results is shown on the screen; of course, the more query pairs there is, the more time that is usually needed.
Step 4. Click on the Citation button to find the relevant paper that documents the detailed development and algorithm of iNR-Durg.
Step 5. Click on the Data button to download the benchmark dataset used to train and test the iNR-Durg predictor.
Step 6. The program code is also available by clicking the button download on the lower panel of Figure 4 .
|
What are associated with cancer, diabetes, inflammatory disease, and osteoporosis?
| 3,258 |
Nuclear receptors (NRs)
| 332 |
1,572 |
iNR-Drug: Predicting the Interaction of Drugs with Nuclear Receptors in Cellular Networking
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3975431/
SHA: ee55aea26f816403476a7cb71816b8ecb1110329
Authors: Fan, Yue-Nong; Xiao, Xuan; Min, Jian-Liang; Chou, Kuo-Chen
Date: 2014-03-19
DOI: 10.3390/ijms15034915
License: cc-by
Abstract: Nuclear receptors (NRs) are closely associated with various major diseases such as cancer, diabetes, inflammatory disease, and osteoporosis. Therefore, NRs have become a frequent target for drug development. During the process of developing drugs against these diseases by targeting NRs, we are often facing a problem: Given a NR and chemical compound, can we identify whether they are really in interaction with each other in a cell? To address this problem, a predictor called “iNR-Drug” was developed. In the predictor, the drug compound concerned was formulated by a 256-D (dimensional) vector derived from its molecular fingerprint, and the NR by a 500-D vector formed by incorporating its sequential evolution information and physicochemical features into the general form of pseudo amino acid composition, and the prediction engine was operated by the SVM (support vector machine) algorithm. Compared with the existing prediction methods in this area, iNR-Drug not only can yield a higher success rate, but is also featured by a user-friendly web-server established at http://www.jci-bioinfo.cn/iNR-Drug/, which is particularly useful for most experimental scientists to obtain their desired data in a timely manner. It is anticipated that the iNR-Drug server may become a useful high throughput tool for both basic research and drug development, and that the current approach may be easily extended to study the interactions of drug with other targets as well.
Text: With the ability to directly bind to DNA ( Figure 1 ) and regulate the expression of adjacent genes, nuclear receptors (NRs) are a class of ligand-inducible transcription factors. They regulate various biological processes, such as homeostasis, differentiation, embryonic development, and organ physiology [1] [2] [3] . The NR superfamily has been classified into seven families: NR0 (knirps or DAX like) [4, 5] ; NR1 (thyroid hormone like), NR2 (HNF4-like), NR3 (estrogen like), NR4 (nerve growth factor IB-like), NR5 (fushi tarazu-F1 like), and NR6 (germ cell nuclear factor like). Since they are involved in almost all aspects of human physiology and are implicated in many major diseases such as cancer, diabetes and osteoporosis, nuclear receptors have become major drug targets [6, 7] , along with G protein-coupled receptors (GPCRs) [8] [9] [10] [11] [12] [13] [14] [15] [16] [17] , ion channels [18] [19] [20] , and kinase proteins [21] [22] [23] [24] . Identification of drug-target interactions is one of the most important steps for the new medicine development [25, 26] . The method usually adopted in this step is molecular docking simulation [27] [28] [29] [30] [31] [32] [33] [34] [35] [36] [37] [38] [39] [40] [41] [42] [43] . However, to make molecular docking study feasible, a reliable 3D (three dimensional) structure of the target protein is the prerequisite condition. Although X-ray crystallography is a powerful tool in determining protein 3D structures, it is time-consuming and expensive. Particularly, not all proteins can be successfully crystallized. For example, membrane proteins are very difficult to crystallize and most of them will not dissolve in normal solvents. Therefore, so far very few membrane protein 3D structures have been determined. Although NMR (Nuclear Magnetic Resonance) is indeed a very powerful tool in determining the 3D structures of membrane proteins as indicated by a series of recent publications (see, e.g., [44] [45] [46] [47] [48] [49] [50] [51] and a review article [20] ), it is also time-consuming and costly. To acquire the 3D structural information in a timely manner, one has to resort to various structural bioinformatics tools (see, e.g., [37] ), particularly the homologous modeling approach as utilized for a series of protein receptors urgently needed during the process of drug development [19, [52] [53] [54] [55] [56] [57] . Unfortunately, the number of dependable templates for developing high quality 3D structures by means of homology modeling is very limited [37] .
To overcome the aforementioned problems, it would be of help to develop a computational method for predicting the interactions of drugs with nuclear receptors in cellular networking based on the sequences information of the latter. The results thus obtained can be used to pre-exclude the compounds identified not in interaction with the nuclear receptors, so as to timely stop wasting time and money on those unpromising compounds [58] .
Actually, based on the functional groups and biological features, a powerful method was developed recently [59] for this purpose. However, further development in this regard is definitely needed due to the following reasons. (a) He et al. [59] did not provide a publicly accessible web-server for their method, and hence its practical application value is quite limited, particularly for the broad experimental scientists; (b) The prediction quality can be further enhanced by incorporating some key features into the formulation of NR-drug (nuclear receptor and drug) samples via the general form of pseudo amino acid composition [60] .
The present study was initiated with an attempt to develop a new method for predicting the interaction of drugs with nuclear receptors by addressing the two points.
As demonstrated by a series of recent publications [10, 18, [61] [62] [63] [64] [65] [66] [67] [68] [69] [70] and summarized in a comprehensive review [60] , to establish a really effective statistical predictor for a biomedical system, we need to consider the following steps: (a) select or construct a valid benchmark dataset to train and test the predictor; (b) represent the statistical samples with an effective formulation that can truly reflect their intrinsic correlation with the object to be predicted; (c) introduce or develop a powerful algorithm or engine to operate the prediction; (d) properly perform cross-validation tests to objectively evaluate the anticipated accuracy of the predictor; (e) establish a user-friendly web-server for the predictor that is accessible to the public. Below, let us elaborate how to deal with these steps.
The data used in the current study were collected from KEGG (Kyoto Encyclopedia of Genes and Genomes) [71] at http://www.kegg.jp/kegg/. KEGG is a database resource for understanding high-level functions and utilities of the biological system, such as the cell, the organism and the ecosystem, from molecular-level information, especially large-scale molecular datasets generated by genome sequencing and other high-throughput experimental technologies. Here, the benchmark dataset can be formulated as
where is the positive subset that consists of the interactive drug-NR pairs only, while the negative subset that contains of the non-interactive drug-NR pairs only, and the symbol represents the union in the set theory. The so-called "interactive" pair here means the pair whose two counterparts are interacting with each other in the drug-target networks as defined in the KEGG database [71] ; while the "non-interactive" pair means that its two counterparts are not interacting with each other in the drug-target networks. The positive dataset contains 86 drug-NR pairs, which were taken from He et al. [59] . The negative dataset contains 172 non-interactive drug-NR pairs, which were derived according to the following procedures: (a) separating each of the pairs in into single drug and NR; (b) re-coupling each of the single drugs with each of the single NRs into pairs in a way that none of them occurred in ; (c) randomly picking the pairs thus formed until reaching the number two times as many as the pairs in . The 86 interactive drug-NR pairs and 172 non-interactive drug-NR pairs are given in Supplementary Information S1, from which we can see that the 86 + 172 = 258 pairs in the current benchmark dataset are actually formed by 25 different NRs and 53 different compounds.
Since each of the samples in the current network system contains a drug (compound) and a NR (protein), the following procedures were taken to represent the drug-NR pair sample.
First, for the drug part in the current benchmark dataset, we can use a 256-D vector to formulate it as given by
where D represents the vector for a drug compound, and d i its i-th (i = 1,2, ,256) component that can be derived by following the "2D molecular fingerprint procedure" as elaborated in [10] . The 53 molecular fingerprint vectors thus obtained for the 53 drugs in are, respectively, given in Supplementary Information S2.
The protein sequences of the 25 different NRs in are listed in Supplementary Information S3. Suppose the sequence of a nuclear receptor protein P with L residues is generally expressed by
where 1 R represents the 1st residue of the protein sequence P , 2 R the 2nd residue, and so forth. Now the problem is how to effectively represent the sequence of Equation (3) with a non-sequential or discrete model [72] . This is because all the existing operation engines, such as covariance discriminant (CD) [17, 65, [73] [74] [75] [76] [77] [78] [79] , neural network [80] [81] [82] , support vector machine (SVM) [62] [63] [64] 83] , random forest [84, 85] , conditional random field [66] , nearest neighbor (NN) [86, 87] ; K-nearest neighbor (KNN) [88] [89] [90] , OET-KNN [91] [92] [93] [94] , and Fuzzy K-nearest neighbor [10, 12, 18, 69, 95] , can only handle vector but not sequence samples. However, a vector defined in a discrete model may completely lose all the sequence-order information and hence limit the quality of prediction. Facing such a dilemma, can we find an approach to partially incorporate the sequence-order effects? Actually, one of the most challenging problems in computational biology is how to formulate a biological sequence with a discrete model or a vector, yet still keep considerable sequence order information. To avoid completely losing the sequence-order information for proteins, the pseudo amino acid composition [96, 97] or Chou's PseAAC [98] was proposed. Ever since the concept of PseAAC was proposed in 2001 [96] , it has penetrated into almost all the areas of computational proteomics, such as predicting anticancer peptides [99] , predicting protein subcellular location [100] [101] [102] [103] [104] [105] [106] , predicting membrane protein types [107, 108] , predicting protein submitochondria locations [109] [110] [111] [112] , predicting GABA(A) receptor proteins [113] , predicting enzyme subfamily classes [114] , predicting antibacterial peptides [115] , predicting supersecondary structure [116] , predicting bacterial virulent proteins [117] , predicting protein structural class [118] , predicting the cofactors of oxidoreductases [119] , predicting metalloproteinase family [120] , identifying cysteine S-nitrosylation sites in proteins [66] , identifying bacterial secreted proteins [121] , identifying antibacterial peptides [115] , identifying allergenic proteins [122] , identifying protein quaternary structural attributes [123, 124] , identifying risk type of human papillomaviruses [125] , identifying cyclin proteins [126] , identifying GPCRs and their types [15, 16] , discriminating outer membrane proteins [127] , classifying amino acids [128] , detecting remote homologous proteins [129] , among many others (see a long list of papers cited in the References section of [60] ). Moreover, the concept of PseAAC was further extended to represent the feature vectors of nucleotides [65] , as well as other biological samples (see, e.g., [130] [131] [132] ). Because it has been widely and increasingly used, recently two powerful soft-wares, called "PseAAC-Builder" [133] and "propy" [134] , were established for generating various special Chou's pseudo-amino acid compositions, in addition to the web-server "PseAAC" [135] built in 2008.
According to a comprehensive review [60] , the general form of PseAAC for a protein sequence P is formulated by
where the subscript is an integer, and its value as well as the components ( 1, 2, , ) u u will depend on how to extract the desired information from the amino acid sequence of P (cf. Equation (3)). Below, let us describe how to extract useful information to define the components of PseAAC for the NR samples concerned. First, many earlier studies (see, e.g., [136] [137] [138] [139] [140] [141] ) have indicated that the amino acid composition (AAC) of a protein plays an important role in determining its attributes. The AAC contains 20 components with each representing the occurrence frequency of one of the 20 native amino acids in the protein concerned. Thus, such 20 AAC components were used here to define the first 20 elements in Equation (4); i.e., (1) ( 1, 2, , 20) ii fi (5) where f i (1) is the normalized occurrence frequency of the i-th type native amino acid in the nuclear receptor concerned. Since AAC did not contain any sequence order information, the following steps were taken to make up this shortcoming.
To avoid completely losing the local or short-range sequence order information, we considered the approach of dipeptide composition. It contained 20 × 20 = 400 components [142] . Such 400 components were used to define the next 400 elements in Equation (4); i.e., (2) 20 ( 1, 2, , 400) jj fj
where (2) j f is the normalized occurrence frequency of the j-th dipeptides in the nuclear receptor concerned. To incorporate the global or long-range sequence order information, let us consider the following approach. According to molecular evolution, all biological sequences have developed starting out from a very limited number of ancestral samples. Driven by various evolutionary forces such as mutation, recombination, gene conversion, genetic drift, and selection, they have undergone many changes including changes of single residues, insertions and deletions of several residues [143] , gene doubling, and gene fusion. With the accumulation of these changes over a long period of time, many original similarities between initial and resultant amino acid sequences are gradually faded out, but the corresponding proteins may still share many common attributes [37] , such as having basically the same biological function and residing at a same subcellular location [144, 145] . To extract the sequential evolution information and use it to define the components of Equation (4), the PSSM (Position Specific Scoring Matrix) was used as described below.
According to Schaffer [146] , the sequence evolution information of a nuclear receptor protein P with L amino acid residues can be expressed by a 20 L matrix, as given by
where (7) were generated by using PSI-BLAST [147] to search the UniProtKB/Swiss-Prot database (The Universal Protein Resource (UniProt); http://www.uniprot.org/) through three iterations with 0.001 as the E-value cutoff for multiple sequence alignment against the sequence of the nuclear receptor concerned. In order to make every element in Equation (7) be scaled from their original score ranges into the region of [0, 1], we performed a conversion through the standard sigmoid function to make it become
Now we extract the useful information from Equation (8)
Moreover, we used the grey system model approach as elaborated in [68] to further define the next 60 components of Equation (4) ( 1, 2, , 20)
In the above equation, w 1 , w 2 , and w 3 are weight factors, which were all set to 1 in the current study; f j (1) has the same meaning as in Equation (5)
where
and
Combining Equations (5), (6), (10) and (12), we found that the total number of the components obtained via the current approach for the PseAAC of Equation (4)
and each of the 500 components is given by (1) (
Since the elements in Equations (2) and (4) are well defined, we can now formulate the drug-NR pair by combining the two equations as given by (19) where G represents the drug-NR pair, Å the orthogonal sum, and the 256 + 500 = 756 components are defined by Equations (2) and (18) . For the sake of convenience, let us use x i (i = 1, 2, , 756) to represent the 756 components in Equation (19); i.e., (20) To optimize the prediction quality with a time-saving approach, similar to the treatment [148] [149] [150] , let us convert Equation (20) to
where the symbol means taking the average of the quantity therein, and SD means the corresponding standard derivation.
In this study, the SVM (support vector machine) was used as the operation engine. SVM has been widely used in the realm of bioinformatics (see, e.g., [62] [63] [64] [151] [152] [153] [154] ). The basic idea of SVM is to transform the data into a high dimensional feature space, and then determine the optimal separating hyperplane using a kernel function. For a brief formulation of SVM and how it works, see the papers [155, 156] ; for more details about SVM, see a monograph [157] .
In this study, the LIBSVM package [158] was used as an implementation of SVM, which can be downloaded from http://www.csie.ntu.edu.tw/~cjlin/libsvm/, the popular radial basis function (RBF) was taken as the kernel function. For the current SVM classifier, there were two uncertain parameters: penalty parameter C and kernel parameter . The method of how to determine the two parameters will be given later.
The predictor obtained via the aforementioned procedure is called iNR-Drug, where "i" means identify, and "NR-Drug" means the interaction between nuclear receptor and drug compound. To provide an intuitive overall picture, a flowchart is provided in Figure 2 to show the process of how the predictor works in identifying the interactions between nuclear receptors and drug compounds.
To provide a more intuitive and easier-to-understand method to measure the prediction quality, the following set of metrics based on the formulation used by Chou [159] [160] [161] in predicting signal peptides was adopted. According to Chou's formulation, the sensitivity, specificity, overall accuracy, and Matthew's correlation coefficient can be respectively expressed as [62, [65] [66] [67] Sn 1
where N is the total number of the interactive NR-drug pairs investigated while N the number of the interactive NR-drug pairs incorrectly predicted as the non-interactive NR-drug pairs; N the total number of the non-interactive NR-drug pairs investigated while N the number of the non-interactive NR-drug pairs incorrectly predicted as the interactive NR-drug pairs.
According to Equation (23) we can easily see the following. When 0 N meaning none of the interactive NR-drug pairs was mispredicted to be a non-interactive NR-drug pair, we have the sensitivity Sn = 1; while NN meaning that all the interactive NR-drug pairs were mispredicted to be the non-interactive NR-drug pairs, we have the sensitivity Sn = 0 . Likewise, when 0 N meaning none of the non-interactive NR-drug pairs was mispredicted, we have the specificity Sp we have MCC = 0 meaning total disagreement between prediction and observation. As we can see from the above discussion, it is much more intuitive and easier to understand when using Equation (23) to examine a predictor for its four metrics, particularly for its Mathew's correlation coefficient. It is instructive to point out that the metrics as defined in Equation (23) are valid for single label systems; for multi-label systems, a set of more complicated metrics should be used as given in [162] .
How to properly test a predictor for its anticipated success rates is very important for its development as well as its potential application value. Generally speaking, the following three cross-validation methods are often used to examine the quality of a predictor and its effectiveness in practical application: independent dataset test, subsampling or K-fold (such as five-fold, seven-fold, or 10-fold) crossover test and jackknife test [163] . However, as elaborated by a penetrating analysis in [164] , considerable arbitrariness exists in the independent dataset test. Also, as demonstrated in [165] , the subsampling (or K-fold crossover validation) test cannot avoid arbitrariness either. Only the jackknife test is the least arbitrary that can always yield a unique result for a given benchmark dataset [73, 74, 156, [166] [167] [168] . Therefore, the jackknife test has been widely recognized and increasingly utilized by investigators to examine the quality of various predictors (see, e.g., [14, 15, 68, 99, 106, 107, 124, 169, 170] ). Accordingly, in this study the jackknife test was also adopted to evaluate the accuracy of the current predictor.
As mentioned above, the SVM operation engine contains two uncertain parameters C and . To find their optimal values, a 2-D grid search was conducted by the jackknife test on the benchmark dataset . The results thus obtained are shown in Figure 3 , from which it can be seen that the iNR-Drug predictor reaches its optimal status when C = 2 3 and 9 2 . The corresponding rates for the four metrics (cf. Equation (23)) are given in Table 1 , where for facilitating comparison, the overall accuracy Acc reported by He et al. [59] on the same benchmark dataset is also given although no results were reported by them for Sn, Sp and MCC. It can be observed from the table that the overall accuracy obtained by iNR-Drug is remarkably higher that of He et al. [59] , and that the rates achieved by iNR-Drug for the other three metrics are also quite higher. These facts indicate that the current predictor not only can yield higher overall prediction accuracy but also is quite stable with low false prediction rates.
As mentioned above (Section 3.2), the jackknife test is the most objective method for examining the quality of a predictor. However, as a demonstration to show how to practically use the current predictor, we took 41 NR-drug pairs from the study by Yamanishi et al. [171] that had been confirmed by experiments as interactive pairs. For such an independent dataset, 34 were correctly identified by iNR-Drug as interactive pairs, i.e., Sn = 34 / 41 = 82.92%, which is quite consistent with the rate of 79.07% achieved by the predictor on the benchmark dataset via the jackknife test as reported in Table 1 .
It is anticipated that the iNR-Drug predictor developed in this paper may become a useful high throughput tool for both basic research and drug development, and that the current approach may be easily extended to study the interactions of drug with other targets as well. Since user-friendly and publicly accessible web-servers represent the future direction for developing practically more useful predictors [98, 172] , a publicly accessible web-server for iNR-Drug was established.
For the convenience of the vast majority of biologists and pharmaceutical scientists, here let us provide a step-by-step guide to show how the users can easily get the desired result by using iNR-Drug web-server without the need to follow the complicated mathematical equations presented in this paper for the process of developing the predictor and its integrity.
Step 1. Open the web server at the site http://www.jci-bioinfo.cn/iNR-Drug/ and you will see the top page of the predictor on your computer screen, as shown in Figure 4 . Click on the Read Me button to see a brief introduction about iNR-Drug predictor and the caveat when using it.
Step 2. Either type or copy/paste the query NR-drug pairs into the input box at the center of Figure 4 . Each query pair consists of two parts: one is for the nuclear receptor sequence, and the other for the drug. The NR sequence should be in FASTA format, while the drug in the KEGG code beginning with the symbol #. Examples for the query pairs input and the corresponding output can be seen by clicking on the Example button right above the input box. Step 3. Click on the Submit button to see the predicted result. For example, if you use the three query pairs in the Example window as the input, after clicking the Submit button, you will see on your screen that the "hsa:2099" NR and the "D00066" drug are an interactive pair, and that the "hsa:2908" NR and the "D00088" drug are also an interactive pair, but that the "hsa:5468" NR and the "D00279" drug are not an interactive pair. All these results are fully consistent with the experimental observations. It takes about 3 minutes before each of these results is shown on the screen; of course, the more query pairs there is, the more time that is usually needed.
Step 4. Click on the Citation button to find the relevant paper that documents the detailed development and algorithm of iNR-Durg.
Step 5. Click on the Data button to download the benchmark dataset used to train and test the iNR-Durg predictor.
Step 6. The program code is also available by clicking the button download on the lower panel of Figure 4 .
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What are nuclear receptors (NRs)?
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class of ligand-inducible transcription factors
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1,572 |
iNR-Drug: Predicting the Interaction of Drugs with Nuclear Receptors in Cellular Networking
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3975431/
SHA: ee55aea26f816403476a7cb71816b8ecb1110329
Authors: Fan, Yue-Nong; Xiao, Xuan; Min, Jian-Liang; Chou, Kuo-Chen
Date: 2014-03-19
DOI: 10.3390/ijms15034915
License: cc-by
Abstract: Nuclear receptors (NRs) are closely associated with various major diseases such as cancer, diabetes, inflammatory disease, and osteoporosis. Therefore, NRs have become a frequent target for drug development. During the process of developing drugs against these diseases by targeting NRs, we are often facing a problem: Given a NR and chemical compound, can we identify whether they are really in interaction with each other in a cell? To address this problem, a predictor called “iNR-Drug” was developed. In the predictor, the drug compound concerned was formulated by a 256-D (dimensional) vector derived from its molecular fingerprint, and the NR by a 500-D vector formed by incorporating its sequential evolution information and physicochemical features into the general form of pseudo amino acid composition, and the prediction engine was operated by the SVM (support vector machine) algorithm. Compared with the existing prediction methods in this area, iNR-Drug not only can yield a higher success rate, but is also featured by a user-friendly web-server established at http://www.jci-bioinfo.cn/iNR-Drug/, which is particularly useful for most experimental scientists to obtain their desired data in a timely manner. It is anticipated that the iNR-Drug server may become a useful high throughput tool for both basic research and drug development, and that the current approach may be easily extended to study the interactions of drug with other targets as well.
Text: With the ability to directly bind to DNA ( Figure 1 ) and regulate the expression of adjacent genes, nuclear receptors (NRs) are a class of ligand-inducible transcription factors. They regulate various biological processes, such as homeostasis, differentiation, embryonic development, and organ physiology [1] [2] [3] . The NR superfamily has been classified into seven families: NR0 (knirps or DAX like) [4, 5] ; NR1 (thyroid hormone like), NR2 (HNF4-like), NR3 (estrogen like), NR4 (nerve growth factor IB-like), NR5 (fushi tarazu-F1 like), and NR6 (germ cell nuclear factor like). Since they are involved in almost all aspects of human physiology and are implicated in many major diseases such as cancer, diabetes and osteoporosis, nuclear receptors have become major drug targets [6, 7] , along with G protein-coupled receptors (GPCRs) [8] [9] [10] [11] [12] [13] [14] [15] [16] [17] , ion channels [18] [19] [20] , and kinase proteins [21] [22] [23] [24] . Identification of drug-target interactions is one of the most important steps for the new medicine development [25, 26] . The method usually adopted in this step is molecular docking simulation [27] [28] [29] [30] [31] [32] [33] [34] [35] [36] [37] [38] [39] [40] [41] [42] [43] . However, to make molecular docking study feasible, a reliable 3D (three dimensional) structure of the target protein is the prerequisite condition. Although X-ray crystallography is a powerful tool in determining protein 3D structures, it is time-consuming and expensive. Particularly, not all proteins can be successfully crystallized. For example, membrane proteins are very difficult to crystallize and most of them will not dissolve in normal solvents. Therefore, so far very few membrane protein 3D structures have been determined. Although NMR (Nuclear Magnetic Resonance) is indeed a very powerful tool in determining the 3D structures of membrane proteins as indicated by a series of recent publications (see, e.g., [44] [45] [46] [47] [48] [49] [50] [51] and a review article [20] ), it is also time-consuming and costly. To acquire the 3D structural information in a timely manner, one has to resort to various structural bioinformatics tools (see, e.g., [37] ), particularly the homologous modeling approach as utilized for a series of protein receptors urgently needed during the process of drug development [19, [52] [53] [54] [55] [56] [57] . Unfortunately, the number of dependable templates for developing high quality 3D structures by means of homology modeling is very limited [37] .
To overcome the aforementioned problems, it would be of help to develop a computational method for predicting the interactions of drugs with nuclear receptors in cellular networking based on the sequences information of the latter. The results thus obtained can be used to pre-exclude the compounds identified not in interaction with the nuclear receptors, so as to timely stop wasting time and money on those unpromising compounds [58] .
Actually, based on the functional groups and biological features, a powerful method was developed recently [59] for this purpose. However, further development in this regard is definitely needed due to the following reasons. (a) He et al. [59] did not provide a publicly accessible web-server for their method, and hence its practical application value is quite limited, particularly for the broad experimental scientists; (b) The prediction quality can be further enhanced by incorporating some key features into the formulation of NR-drug (nuclear receptor and drug) samples via the general form of pseudo amino acid composition [60] .
The present study was initiated with an attempt to develop a new method for predicting the interaction of drugs with nuclear receptors by addressing the two points.
As demonstrated by a series of recent publications [10, 18, [61] [62] [63] [64] [65] [66] [67] [68] [69] [70] and summarized in a comprehensive review [60] , to establish a really effective statistical predictor for a biomedical system, we need to consider the following steps: (a) select or construct a valid benchmark dataset to train and test the predictor; (b) represent the statistical samples with an effective formulation that can truly reflect their intrinsic correlation with the object to be predicted; (c) introduce or develop a powerful algorithm or engine to operate the prediction; (d) properly perform cross-validation tests to objectively evaluate the anticipated accuracy of the predictor; (e) establish a user-friendly web-server for the predictor that is accessible to the public. Below, let us elaborate how to deal with these steps.
The data used in the current study were collected from KEGG (Kyoto Encyclopedia of Genes and Genomes) [71] at http://www.kegg.jp/kegg/. KEGG is a database resource for understanding high-level functions and utilities of the biological system, such as the cell, the organism and the ecosystem, from molecular-level information, especially large-scale molecular datasets generated by genome sequencing and other high-throughput experimental technologies. Here, the benchmark dataset can be formulated as
where is the positive subset that consists of the interactive drug-NR pairs only, while the negative subset that contains of the non-interactive drug-NR pairs only, and the symbol represents the union in the set theory. The so-called "interactive" pair here means the pair whose two counterparts are interacting with each other in the drug-target networks as defined in the KEGG database [71] ; while the "non-interactive" pair means that its two counterparts are not interacting with each other in the drug-target networks. The positive dataset contains 86 drug-NR pairs, which were taken from He et al. [59] . The negative dataset contains 172 non-interactive drug-NR pairs, which were derived according to the following procedures: (a) separating each of the pairs in into single drug and NR; (b) re-coupling each of the single drugs with each of the single NRs into pairs in a way that none of them occurred in ; (c) randomly picking the pairs thus formed until reaching the number two times as many as the pairs in . The 86 interactive drug-NR pairs and 172 non-interactive drug-NR pairs are given in Supplementary Information S1, from which we can see that the 86 + 172 = 258 pairs in the current benchmark dataset are actually formed by 25 different NRs and 53 different compounds.
Since each of the samples in the current network system contains a drug (compound) and a NR (protein), the following procedures were taken to represent the drug-NR pair sample.
First, for the drug part in the current benchmark dataset, we can use a 256-D vector to formulate it as given by
where D represents the vector for a drug compound, and d i its i-th (i = 1,2, ,256) component that can be derived by following the "2D molecular fingerprint procedure" as elaborated in [10] . The 53 molecular fingerprint vectors thus obtained for the 53 drugs in are, respectively, given in Supplementary Information S2.
The protein sequences of the 25 different NRs in are listed in Supplementary Information S3. Suppose the sequence of a nuclear receptor protein P with L residues is generally expressed by
where 1 R represents the 1st residue of the protein sequence P , 2 R the 2nd residue, and so forth. Now the problem is how to effectively represent the sequence of Equation (3) with a non-sequential or discrete model [72] . This is because all the existing operation engines, such as covariance discriminant (CD) [17, 65, [73] [74] [75] [76] [77] [78] [79] , neural network [80] [81] [82] , support vector machine (SVM) [62] [63] [64] 83] , random forest [84, 85] , conditional random field [66] , nearest neighbor (NN) [86, 87] ; K-nearest neighbor (KNN) [88] [89] [90] , OET-KNN [91] [92] [93] [94] , and Fuzzy K-nearest neighbor [10, 12, 18, 69, 95] , can only handle vector but not sequence samples. However, a vector defined in a discrete model may completely lose all the sequence-order information and hence limit the quality of prediction. Facing such a dilemma, can we find an approach to partially incorporate the sequence-order effects? Actually, one of the most challenging problems in computational biology is how to formulate a biological sequence with a discrete model or a vector, yet still keep considerable sequence order information. To avoid completely losing the sequence-order information for proteins, the pseudo amino acid composition [96, 97] or Chou's PseAAC [98] was proposed. Ever since the concept of PseAAC was proposed in 2001 [96] , it has penetrated into almost all the areas of computational proteomics, such as predicting anticancer peptides [99] , predicting protein subcellular location [100] [101] [102] [103] [104] [105] [106] , predicting membrane protein types [107, 108] , predicting protein submitochondria locations [109] [110] [111] [112] , predicting GABA(A) receptor proteins [113] , predicting enzyme subfamily classes [114] , predicting antibacterial peptides [115] , predicting supersecondary structure [116] , predicting bacterial virulent proteins [117] , predicting protein structural class [118] , predicting the cofactors of oxidoreductases [119] , predicting metalloproteinase family [120] , identifying cysteine S-nitrosylation sites in proteins [66] , identifying bacterial secreted proteins [121] , identifying antibacterial peptides [115] , identifying allergenic proteins [122] , identifying protein quaternary structural attributes [123, 124] , identifying risk type of human papillomaviruses [125] , identifying cyclin proteins [126] , identifying GPCRs and their types [15, 16] , discriminating outer membrane proteins [127] , classifying amino acids [128] , detecting remote homologous proteins [129] , among many others (see a long list of papers cited in the References section of [60] ). Moreover, the concept of PseAAC was further extended to represent the feature vectors of nucleotides [65] , as well as other biological samples (see, e.g., [130] [131] [132] ). Because it has been widely and increasingly used, recently two powerful soft-wares, called "PseAAC-Builder" [133] and "propy" [134] , were established for generating various special Chou's pseudo-amino acid compositions, in addition to the web-server "PseAAC" [135] built in 2008.
According to a comprehensive review [60] , the general form of PseAAC for a protein sequence P is formulated by
where the subscript is an integer, and its value as well as the components ( 1, 2, , ) u u will depend on how to extract the desired information from the amino acid sequence of P (cf. Equation (3)). Below, let us describe how to extract useful information to define the components of PseAAC for the NR samples concerned. First, many earlier studies (see, e.g., [136] [137] [138] [139] [140] [141] ) have indicated that the amino acid composition (AAC) of a protein plays an important role in determining its attributes. The AAC contains 20 components with each representing the occurrence frequency of one of the 20 native amino acids in the protein concerned. Thus, such 20 AAC components were used here to define the first 20 elements in Equation (4); i.e., (1) ( 1, 2, , 20) ii fi (5) where f i (1) is the normalized occurrence frequency of the i-th type native amino acid in the nuclear receptor concerned. Since AAC did not contain any sequence order information, the following steps were taken to make up this shortcoming.
To avoid completely losing the local or short-range sequence order information, we considered the approach of dipeptide composition. It contained 20 × 20 = 400 components [142] . Such 400 components were used to define the next 400 elements in Equation (4); i.e., (2) 20 ( 1, 2, , 400) jj fj
where (2) j f is the normalized occurrence frequency of the j-th dipeptides in the nuclear receptor concerned. To incorporate the global or long-range sequence order information, let us consider the following approach. According to molecular evolution, all biological sequences have developed starting out from a very limited number of ancestral samples. Driven by various evolutionary forces such as mutation, recombination, gene conversion, genetic drift, and selection, they have undergone many changes including changes of single residues, insertions and deletions of several residues [143] , gene doubling, and gene fusion. With the accumulation of these changes over a long period of time, many original similarities between initial and resultant amino acid sequences are gradually faded out, but the corresponding proteins may still share many common attributes [37] , such as having basically the same biological function and residing at a same subcellular location [144, 145] . To extract the sequential evolution information and use it to define the components of Equation (4), the PSSM (Position Specific Scoring Matrix) was used as described below.
According to Schaffer [146] , the sequence evolution information of a nuclear receptor protein P with L amino acid residues can be expressed by a 20 L matrix, as given by
where (7) were generated by using PSI-BLAST [147] to search the UniProtKB/Swiss-Prot database (The Universal Protein Resource (UniProt); http://www.uniprot.org/) through three iterations with 0.001 as the E-value cutoff for multiple sequence alignment against the sequence of the nuclear receptor concerned. In order to make every element in Equation (7) be scaled from their original score ranges into the region of [0, 1], we performed a conversion through the standard sigmoid function to make it become
Now we extract the useful information from Equation (8)
Moreover, we used the grey system model approach as elaborated in [68] to further define the next 60 components of Equation (4) ( 1, 2, , 20)
In the above equation, w 1 , w 2 , and w 3 are weight factors, which were all set to 1 in the current study; f j (1) has the same meaning as in Equation (5)
where
and
Combining Equations (5), (6), (10) and (12), we found that the total number of the components obtained via the current approach for the PseAAC of Equation (4)
and each of the 500 components is given by (1) (
Since the elements in Equations (2) and (4) are well defined, we can now formulate the drug-NR pair by combining the two equations as given by (19) where G represents the drug-NR pair, Å the orthogonal sum, and the 256 + 500 = 756 components are defined by Equations (2) and (18) . For the sake of convenience, let us use x i (i = 1, 2, , 756) to represent the 756 components in Equation (19); i.e., (20) To optimize the prediction quality with a time-saving approach, similar to the treatment [148] [149] [150] , let us convert Equation (20) to
where the symbol means taking the average of the quantity therein, and SD means the corresponding standard derivation.
In this study, the SVM (support vector machine) was used as the operation engine. SVM has been widely used in the realm of bioinformatics (see, e.g., [62] [63] [64] [151] [152] [153] [154] ). The basic idea of SVM is to transform the data into a high dimensional feature space, and then determine the optimal separating hyperplane using a kernel function. For a brief formulation of SVM and how it works, see the papers [155, 156] ; for more details about SVM, see a monograph [157] .
In this study, the LIBSVM package [158] was used as an implementation of SVM, which can be downloaded from http://www.csie.ntu.edu.tw/~cjlin/libsvm/, the popular radial basis function (RBF) was taken as the kernel function. For the current SVM classifier, there were two uncertain parameters: penalty parameter C and kernel parameter . The method of how to determine the two parameters will be given later.
The predictor obtained via the aforementioned procedure is called iNR-Drug, where "i" means identify, and "NR-Drug" means the interaction between nuclear receptor and drug compound. To provide an intuitive overall picture, a flowchart is provided in Figure 2 to show the process of how the predictor works in identifying the interactions between nuclear receptors and drug compounds.
To provide a more intuitive and easier-to-understand method to measure the prediction quality, the following set of metrics based on the formulation used by Chou [159] [160] [161] in predicting signal peptides was adopted. According to Chou's formulation, the sensitivity, specificity, overall accuracy, and Matthew's correlation coefficient can be respectively expressed as [62, [65] [66] [67] Sn 1
where N is the total number of the interactive NR-drug pairs investigated while N the number of the interactive NR-drug pairs incorrectly predicted as the non-interactive NR-drug pairs; N the total number of the non-interactive NR-drug pairs investigated while N the number of the non-interactive NR-drug pairs incorrectly predicted as the interactive NR-drug pairs.
According to Equation (23) we can easily see the following. When 0 N meaning none of the interactive NR-drug pairs was mispredicted to be a non-interactive NR-drug pair, we have the sensitivity Sn = 1; while NN meaning that all the interactive NR-drug pairs were mispredicted to be the non-interactive NR-drug pairs, we have the sensitivity Sn = 0 . Likewise, when 0 N meaning none of the non-interactive NR-drug pairs was mispredicted, we have the specificity Sp we have MCC = 0 meaning total disagreement between prediction and observation. As we can see from the above discussion, it is much more intuitive and easier to understand when using Equation (23) to examine a predictor for its four metrics, particularly for its Mathew's correlation coefficient. It is instructive to point out that the metrics as defined in Equation (23) are valid for single label systems; for multi-label systems, a set of more complicated metrics should be used as given in [162] .
How to properly test a predictor for its anticipated success rates is very important for its development as well as its potential application value. Generally speaking, the following three cross-validation methods are often used to examine the quality of a predictor and its effectiveness in practical application: independent dataset test, subsampling or K-fold (such as five-fold, seven-fold, or 10-fold) crossover test and jackknife test [163] . However, as elaborated by a penetrating analysis in [164] , considerable arbitrariness exists in the independent dataset test. Also, as demonstrated in [165] , the subsampling (or K-fold crossover validation) test cannot avoid arbitrariness either. Only the jackknife test is the least arbitrary that can always yield a unique result for a given benchmark dataset [73, 74, 156, [166] [167] [168] . Therefore, the jackknife test has been widely recognized and increasingly utilized by investigators to examine the quality of various predictors (see, e.g., [14, 15, 68, 99, 106, 107, 124, 169, 170] ). Accordingly, in this study the jackknife test was also adopted to evaluate the accuracy of the current predictor.
As mentioned above, the SVM operation engine contains two uncertain parameters C and . To find their optimal values, a 2-D grid search was conducted by the jackknife test on the benchmark dataset . The results thus obtained are shown in Figure 3 , from which it can be seen that the iNR-Drug predictor reaches its optimal status when C = 2 3 and 9 2 . The corresponding rates for the four metrics (cf. Equation (23)) are given in Table 1 , where for facilitating comparison, the overall accuracy Acc reported by He et al. [59] on the same benchmark dataset is also given although no results were reported by them for Sn, Sp and MCC. It can be observed from the table that the overall accuracy obtained by iNR-Drug is remarkably higher that of He et al. [59] , and that the rates achieved by iNR-Drug for the other three metrics are also quite higher. These facts indicate that the current predictor not only can yield higher overall prediction accuracy but also is quite stable with low false prediction rates.
As mentioned above (Section 3.2), the jackknife test is the most objective method for examining the quality of a predictor. However, as a demonstration to show how to practically use the current predictor, we took 41 NR-drug pairs from the study by Yamanishi et al. [171] that had been confirmed by experiments as interactive pairs. For such an independent dataset, 34 were correctly identified by iNR-Drug as interactive pairs, i.e., Sn = 34 / 41 = 82.92%, which is quite consistent with the rate of 79.07% achieved by the predictor on the benchmark dataset via the jackknife test as reported in Table 1 .
It is anticipated that the iNR-Drug predictor developed in this paper may become a useful high throughput tool for both basic research and drug development, and that the current approach may be easily extended to study the interactions of drug with other targets as well. Since user-friendly and publicly accessible web-servers represent the future direction for developing practically more useful predictors [98, 172] , a publicly accessible web-server for iNR-Drug was established.
For the convenience of the vast majority of biologists and pharmaceutical scientists, here let us provide a step-by-step guide to show how the users can easily get the desired result by using iNR-Drug web-server without the need to follow the complicated mathematical equations presented in this paper for the process of developing the predictor and its integrity.
Step 1. Open the web server at the site http://www.jci-bioinfo.cn/iNR-Drug/ and you will see the top page of the predictor on your computer screen, as shown in Figure 4 . Click on the Read Me button to see a brief introduction about iNR-Drug predictor and the caveat when using it.
Step 2. Either type or copy/paste the query NR-drug pairs into the input box at the center of Figure 4 . Each query pair consists of two parts: one is for the nuclear receptor sequence, and the other for the drug. The NR sequence should be in FASTA format, while the drug in the KEGG code beginning with the symbol #. Examples for the query pairs input and the corresponding output can be seen by clicking on the Example button right above the input box. Step 3. Click on the Submit button to see the predicted result. For example, if you use the three query pairs in the Example window as the input, after clicking the Submit button, you will see on your screen that the "hsa:2099" NR and the "D00066" drug are an interactive pair, and that the "hsa:2908" NR and the "D00088" drug are also an interactive pair, but that the "hsa:5468" NR and the "D00279" drug are not an interactive pair. All these results are fully consistent with the experimental observations. It takes about 3 minutes before each of these results is shown on the screen; of course, the more query pairs there is, the more time that is usually needed.
Step 4. Click on the Citation button to find the relevant paper that documents the detailed development and algorithm of iNR-Durg.
Step 5. Click on the Data button to download the benchmark dataset used to train and test the iNR-Durg predictor.
Step 6. The program code is also available by clicking the button download on the lower panel of Figure 4 .
|
What biological factors for nuclear receptors regulate?
| 3,260 |
homeostasis, differentiation, embryonic development, and organ physiology
| 2,040 |
1,572 |
iNR-Drug: Predicting the Interaction of Drugs with Nuclear Receptors in Cellular Networking
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3975431/
SHA: ee55aea26f816403476a7cb71816b8ecb1110329
Authors: Fan, Yue-Nong; Xiao, Xuan; Min, Jian-Liang; Chou, Kuo-Chen
Date: 2014-03-19
DOI: 10.3390/ijms15034915
License: cc-by
Abstract: Nuclear receptors (NRs) are closely associated with various major diseases such as cancer, diabetes, inflammatory disease, and osteoporosis. Therefore, NRs have become a frequent target for drug development. During the process of developing drugs against these diseases by targeting NRs, we are often facing a problem: Given a NR and chemical compound, can we identify whether they are really in interaction with each other in a cell? To address this problem, a predictor called “iNR-Drug” was developed. In the predictor, the drug compound concerned was formulated by a 256-D (dimensional) vector derived from its molecular fingerprint, and the NR by a 500-D vector formed by incorporating its sequential evolution information and physicochemical features into the general form of pseudo amino acid composition, and the prediction engine was operated by the SVM (support vector machine) algorithm. Compared with the existing prediction methods in this area, iNR-Drug not only can yield a higher success rate, but is also featured by a user-friendly web-server established at http://www.jci-bioinfo.cn/iNR-Drug/, which is particularly useful for most experimental scientists to obtain their desired data in a timely manner. It is anticipated that the iNR-Drug server may become a useful high throughput tool for both basic research and drug development, and that the current approach may be easily extended to study the interactions of drug with other targets as well.
Text: With the ability to directly bind to DNA ( Figure 1 ) and regulate the expression of adjacent genes, nuclear receptors (NRs) are a class of ligand-inducible transcription factors. They regulate various biological processes, such as homeostasis, differentiation, embryonic development, and organ physiology [1] [2] [3] . The NR superfamily has been classified into seven families: NR0 (knirps or DAX like) [4, 5] ; NR1 (thyroid hormone like), NR2 (HNF4-like), NR3 (estrogen like), NR4 (nerve growth factor IB-like), NR5 (fushi tarazu-F1 like), and NR6 (germ cell nuclear factor like). Since they are involved in almost all aspects of human physiology and are implicated in many major diseases such as cancer, diabetes and osteoporosis, nuclear receptors have become major drug targets [6, 7] , along with G protein-coupled receptors (GPCRs) [8] [9] [10] [11] [12] [13] [14] [15] [16] [17] , ion channels [18] [19] [20] , and kinase proteins [21] [22] [23] [24] . Identification of drug-target interactions is one of the most important steps for the new medicine development [25, 26] . The method usually adopted in this step is molecular docking simulation [27] [28] [29] [30] [31] [32] [33] [34] [35] [36] [37] [38] [39] [40] [41] [42] [43] . However, to make molecular docking study feasible, a reliable 3D (three dimensional) structure of the target protein is the prerequisite condition. Although X-ray crystallography is a powerful tool in determining protein 3D structures, it is time-consuming and expensive. Particularly, not all proteins can be successfully crystallized. For example, membrane proteins are very difficult to crystallize and most of them will not dissolve in normal solvents. Therefore, so far very few membrane protein 3D structures have been determined. Although NMR (Nuclear Magnetic Resonance) is indeed a very powerful tool in determining the 3D structures of membrane proteins as indicated by a series of recent publications (see, e.g., [44] [45] [46] [47] [48] [49] [50] [51] and a review article [20] ), it is also time-consuming and costly. To acquire the 3D structural information in a timely manner, one has to resort to various structural bioinformatics tools (see, e.g., [37] ), particularly the homologous modeling approach as utilized for a series of protein receptors urgently needed during the process of drug development [19, [52] [53] [54] [55] [56] [57] . Unfortunately, the number of dependable templates for developing high quality 3D structures by means of homology modeling is very limited [37] .
To overcome the aforementioned problems, it would be of help to develop a computational method for predicting the interactions of drugs with nuclear receptors in cellular networking based on the sequences information of the latter. The results thus obtained can be used to pre-exclude the compounds identified not in interaction with the nuclear receptors, so as to timely stop wasting time and money on those unpromising compounds [58] .
Actually, based on the functional groups and biological features, a powerful method was developed recently [59] for this purpose. However, further development in this regard is definitely needed due to the following reasons. (a) He et al. [59] did not provide a publicly accessible web-server for their method, and hence its practical application value is quite limited, particularly for the broad experimental scientists; (b) The prediction quality can be further enhanced by incorporating some key features into the formulation of NR-drug (nuclear receptor and drug) samples via the general form of pseudo amino acid composition [60] .
The present study was initiated with an attempt to develop a new method for predicting the interaction of drugs with nuclear receptors by addressing the two points.
As demonstrated by a series of recent publications [10, 18, [61] [62] [63] [64] [65] [66] [67] [68] [69] [70] and summarized in a comprehensive review [60] , to establish a really effective statistical predictor for a biomedical system, we need to consider the following steps: (a) select or construct a valid benchmark dataset to train and test the predictor; (b) represent the statistical samples with an effective formulation that can truly reflect their intrinsic correlation with the object to be predicted; (c) introduce or develop a powerful algorithm or engine to operate the prediction; (d) properly perform cross-validation tests to objectively evaluate the anticipated accuracy of the predictor; (e) establish a user-friendly web-server for the predictor that is accessible to the public. Below, let us elaborate how to deal with these steps.
The data used in the current study were collected from KEGG (Kyoto Encyclopedia of Genes and Genomes) [71] at http://www.kegg.jp/kegg/. KEGG is a database resource for understanding high-level functions and utilities of the biological system, such as the cell, the organism and the ecosystem, from molecular-level information, especially large-scale molecular datasets generated by genome sequencing and other high-throughput experimental technologies. Here, the benchmark dataset can be formulated as
where is the positive subset that consists of the interactive drug-NR pairs only, while the negative subset that contains of the non-interactive drug-NR pairs only, and the symbol represents the union in the set theory. The so-called "interactive" pair here means the pair whose two counterparts are interacting with each other in the drug-target networks as defined in the KEGG database [71] ; while the "non-interactive" pair means that its two counterparts are not interacting with each other in the drug-target networks. The positive dataset contains 86 drug-NR pairs, which were taken from He et al. [59] . The negative dataset contains 172 non-interactive drug-NR pairs, which were derived according to the following procedures: (a) separating each of the pairs in into single drug and NR; (b) re-coupling each of the single drugs with each of the single NRs into pairs in a way that none of them occurred in ; (c) randomly picking the pairs thus formed until reaching the number two times as many as the pairs in . The 86 interactive drug-NR pairs and 172 non-interactive drug-NR pairs are given in Supplementary Information S1, from which we can see that the 86 + 172 = 258 pairs in the current benchmark dataset are actually formed by 25 different NRs and 53 different compounds.
Since each of the samples in the current network system contains a drug (compound) and a NR (protein), the following procedures were taken to represent the drug-NR pair sample.
First, for the drug part in the current benchmark dataset, we can use a 256-D vector to formulate it as given by
where D represents the vector for a drug compound, and d i its i-th (i = 1,2, ,256) component that can be derived by following the "2D molecular fingerprint procedure" as elaborated in [10] . The 53 molecular fingerprint vectors thus obtained for the 53 drugs in are, respectively, given in Supplementary Information S2.
The protein sequences of the 25 different NRs in are listed in Supplementary Information S3. Suppose the sequence of a nuclear receptor protein P with L residues is generally expressed by
where 1 R represents the 1st residue of the protein sequence P , 2 R the 2nd residue, and so forth. Now the problem is how to effectively represent the sequence of Equation (3) with a non-sequential or discrete model [72] . This is because all the existing operation engines, such as covariance discriminant (CD) [17, 65, [73] [74] [75] [76] [77] [78] [79] , neural network [80] [81] [82] , support vector machine (SVM) [62] [63] [64] 83] , random forest [84, 85] , conditional random field [66] , nearest neighbor (NN) [86, 87] ; K-nearest neighbor (KNN) [88] [89] [90] , OET-KNN [91] [92] [93] [94] , and Fuzzy K-nearest neighbor [10, 12, 18, 69, 95] , can only handle vector but not sequence samples. However, a vector defined in a discrete model may completely lose all the sequence-order information and hence limit the quality of prediction. Facing such a dilemma, can we find an approach to partially incorporate the sequence-order effects? Actually, one of the most challenging problems in computational biology is how to formulate a biological sequence with a discrete model or a vector, yet still keep considerable sequence order information. To avoid completely losing the sequence-order information for proteins, the pseudo amino acid composition [96, 97] or Chou's PseAAC [98] was proposed. Ever since the concept of PseAAC was proposed in 2001 [96] , it has penetrated into almost all the areas of computational proteomics, such as predicting anticancer peptides [99] , predicting protein subcellular location [100] [101] [102] [103] [104] [105] [106] , predicting membrane protein types [107, 108] , predicting protein submitochondria locations [109] [110] [111] [112] , predicting GABA(A) receptor proteins [113] , predicting enzyme subfamily classes [114] , predicting antibacterial peptides [115] , predicting supersecondary structure [116] , predicting bacterial virulent proteins [117] , predicting protein structural class [118] , predicting the cofactors of oxidoreductases [119] , predicting metalloproteinase family [120] , identifying cysteine S-nitrosylation sites in proteins [66] , identifying bacterial secreted proteins [121] , identifying antibacterial peptides [115] , identifying allergenic proteins [122] , identifying protein quaternary structural attributes [123, 124] , identifying risk type of human papillomaviruses [125] , identifying cyclin proteins [126] , identifying GPCRs and their types [15, 16] , discriminating outer membrane proteins [127] , classifying amino acids [128] , detecting remote homologous proteins [129] , among many others (see a long list of papers cited in the References section of [60] ). Moreover, the concept of PseAAC was further extended to represent the feature vectors of nucleotides [65] , as well as other biological samples (see, e.g., [130] [131] [132] ). Because it has been widely and increasingly used, recently two powerful soft-wares, called "PseAAC-Builder" [133] and "propy" [134] , were established for generating various special Chou's pseudo-amino acid compositions, in addition to the web-server "PseAAC" [135] built in 2008.
According to a comprehensive review [60] , the general form of PseAAC for a protein sequence P is formulated by
where the subscript is an integer, and its value as well as the components ( 1, 2, , ) u u will depend on how to extract the desired information from the amino acid sequence of P (cf. Equation (3)). Below, let us describe how to extract useful information to define the components of PseAAC for the NR samples concerned. First, many earlier studies (see, e.g., [136] [137] [138] [139] [140] [141] ) have indicated that the amino acid composition (AAC) of a protein plays an important role in determining its attributes. The AAC contains 20 components with each representing the occurrence frequency of one of the 20 native amino acids in the protein concerned. Thus, such 20 AAC components were used here to define the first 20 elements in Equation (4); i.e., (1) ( 1, 2, , 20) ii fi (5) where f i (1) is the normalized occurrence frequency of the i-th type native amino acid in the nuclear receptor concerned. Since AAC did not contain any sequence order information, the following steps were taken to make up this shortcoming.
To avoid completely losing the local or short-range sequence order information, we considered the approach of dipeptide composition. It contained 20 × 20 = 400 components [142] . Such 400 components were used to define the next 400 elements in Equation (4); i.e., (2) 20 ( 1, 2, , 400) jj fj
where (2) j f is the normalized occurrence frequency of the j-th dipeptides in the nuclear receptor concerned. To incorporate the global or long-range sequence order information, let us consider the following approach. According to molecular evolution, all biological sequences have developed starting out from a very limited number of ancestral samples. Driven by various evolutionary forces such as mutation, recombination, gene conversion, genetic drift, and selection, they have undergone many changes including changes of single residues, insertions and deletions of several residues [143] , gene doubling, and gene fusion. With the accumulation of these changes over a long period of time, many original similarities between initial and resultant amino acid sequences are gradually faded out, but the corresponding proteins may still share many common attributes [37] , such as having basically the same biological function and residing at a same subcellular location [144, 145] . To extract the sequential evolution information and use it to define the components of Equation (4), the PSSM (Position Specific Scoring Matrix) was used as described below.
According to Schaffer [146] , the sequence evolution information of a nuclear receptor protein P with L amino acid residues can be expressed by a 20 L matrix, as given by
where (7) were generated by using PSI-BLAST [147] to search the UniProtKB/Swiss-Prot database (The Universal Protein Resource (UniProt); http://www.uniprot.org/) through three iterations with 0.001 as the E-value cutoff for multiple sequence alignment against the sequence of the nuclear receptor concerned. In order to make every element in Equation (7) be scaled from their original score ranges into the region of [0, 1], we performed a conversion through the standard sigmoid function to make it become
Now we extract the useful information from Equation (8)
Moreover, we used the grey system model approach as elaborated in [68] to further define the next 60 components of Equation (4) ( 1, 2, , 20)
In the above equation, w 1 , w 2 , and w 3 are weight factors, which were all set to 1 in the current study; f j (1) has the same meaning as in Equation (5)
where
and
Combining Equations (5), (6), (10) and (12), we found that the total number of the components obtained via the current approach for the PseAAC of Equation (4)
and each of the 500 components is given by (1) (
Since the elements in Equations (2) and (4) are well defined, we can now formulate the drug-NR pair by combining the two equations as given by (19) where G represents the drug-NR pair, Å the orthogonal sum, and the 256 + 500 = 756 components are defined by Equations (2) and (18) . For the sake of convenience, let us use x i (i = 1, 2, , 756) to represent the 756 components in Equation (19); i.e., (20) To optimize the prediction quality with a time-saving approach, similar to the treatment [148] [149] [150] , let us convert Equation (20) to
where the symbol means taking the average of the quantity therein, and SD means the corresponding standard derivation.
In this study, the SVM (support vector machine) was used as the operation engine. SVM has been widely used in the realm of bioinformatics (see, e.g., [62] [63] [64] [151] [152] [153] [154] ). The basic idea of SVM is to transform the data into a high dimensional feature space, and then determine the optimal separating hyperplane using a kernel function. For a brief formulation of SVM and how it works, see the papers [155, 156] ; for more details about SVM, see a monograph [157] .
In this study, the LIBSVM package [158] was used as an implementation of SVM, which can be downloaded from http://www.csie.ntu.edu.tw/~cjlin/libsvm/, the popular radial basis function (RBF) was taken as the kernel function. For the current SVM classifier, there were two uncertain parameters: penalty parameter C and kernel parameter . The method of how to determine the two parameters will be given later.
The predictor obtained via the aforementioned procedure is called iNR-Drug, where "i" means identify, and "NR-Drug" means the interaction between nuclear receptor and drug compound. To provide an intuitive overall picture, a flowchart is provided in Figure 2 to show the process of how the predictor works in identifying the interactions between nuclear receptors and drug compounds.
To provide a more intuitive and easier-to-understand method to measure the prediction quality, the following set of metrics based on the formulation used by Chou [159] [160] [161] in predicting signal peptides was adopted. According to Chou's formulation, the sensitivity, specificity, overall accuracy, and Matthew's correlation coefficient can be respectively expressed as [62, [65] [66] [67] Sn 1
where N is the total number of the interactive NR-drug pairs investigated while N the number of the interactive NR-drug pairs incorrectly predicted as the non-interactive NR-drug pairs; N the total number of the non-interactive NR-drug pairs investigated while N the number of the non-interactive NR-drug pairs incorrectly predicted as the interactive NR-drug pairs.
According to Equation (23) we can easily see the following. When 0 N meaning none of the interactive NR-drug pairs was mispredicted to be a non-interactive NR-drug pair, we have the sensitivity Sn = 1; while NN meaning that all the interactive NR-drug pairs were mispredicted to be the non-interactive NR-drug pairs, we have the sensitivity Sn = 0 . Likewise, when 0 N meaning none of the non-interactive NR-drug pairs was mispredicted, we have the specificity Sp we have MCC = 0 meaning total disagreement between prediction and observation. As we can see from the above discussion, it is much more intuitive and easier to understand when using Equation (23) to examine a predictor for its four metrics, particularly for its Mathew's correlation coefficient. It is instructive to point out that the metrics as defined in Equation (23) are valid for single label systems; for multi-label systems, a set of more complicated metrics should be used as given in [162] .
How to properly test a predictor for its anticipated success rates is very important for its development as well as its potential application value. Generally speaking, the following three cross-validation methods are often used to examine the quality of a predictor and its effectiveness in practical application: independent dataset test, subsampling or K-fold (such as five-fold, seven-fold, or 10-fold) crossover test and jackknife test [163] . However, as elaborated by a penetrating analysis in [164] , considerable arbitrariness exists in the independent dataset test. Also, as demonstrated in [165] , the subsampling (or K-fold crossover validation) test cannot avoid arbitrariness either. Only the jackknife test is the least arbitrary that can always yield a unique result for a given benchmark dataset [73, 74, 156, [166] [167] [168] . Therefore, the jackknife test has been widely recognized and increasingly utilized by investigators to examine the quality of various predictors (see, e.g., [14, 15, 68, 99, 106, 107, 124, 169, 170] ). Accordingly, in this study the jackknife test was also adopted to evaluate the accuracy of the current predictor.
As mentioned above, the SVM operation engine contains two uncertain parameters C and . To find their optimal values, a 2-D grid search was conducted by the jackknife test on the benchmark dataset . The results thus obtained are shown in Figure 3 , from which it can be seen that the iNR-Drug predictor reaches its optimal status when C = 2 3 and 9 2 . The corresponding rates for the four metrics (cf. Equation (23)) are given in Table 1 , where for facilitating comparison, the overall accuracy Acc reported by He et al. [59] on the same benchmark dataset is also given although no results were reported by them for Sn, Sp and MCC. It can be observed from the table that the overall accuracy obtained by iNR-Drug is remarkably higher that of He et al. [59] , and that the rates achieved by iNR-Drug for the other three metrics are also quite higher. These facts indicate that the current predictor not only can yield higher overall prediction accuracy but also is quite stable with low false prediction rates.
As mentioned above (Section 3.2), the jackknife test is the most objective method for examining the quality of a predictor. However, as a demonstration to show how to practically use the current predictor, we took 41 NR-drug pairs from the study by Yamanishi et al. [171] that had been confirmed by experiments as interactive pairs. For such an independent dataset, 34 were correctly identified by iNR-Drug as interactive pairs, i.e., Sn = 34 / 41 = 82.92%, which is quite consistent with the rate of 79.07% achieved by the predictor on the benchmark dataset via the jackknife test as reported in Table 1 .
It is anticipated that the iNR-Drug predictor developed in this paper may become a useful high throughput tool for both basic research and drug development, and that the current approach may be easily extended to study the interactions of drug with other targets as well. Since user-friendly and publicly accessible web-servers represent the future direction for developing practically more useful predictors [98, 172] , a publicly accessible web-server for iNR-Drug was established.
For the convenience of the vast majority of biologists and pharmaceutical scientists, here let us provide a step-by-step guide to show how the users can easily get the desired result by using iNR-Drug web-server without the need to follow the complicated mathematical equations presented in this paper for the process of developing the predictor and its integrity.
Step 1. Open the web server at the site http://www.jci-bioinfo.cn/iNR-Drug/ and you will see the top page of the predictor on your computer screen, as shown in Figure 4 . Click on the Read Me button to see a brief introduction about iNR-Drug predictor and the caveat when using it.
Step 2. Either type or copy/paste the query NR-drug pairs into the input box at the center of Figure 4 . Each query pair consists of two parts: one is for the nuclear receptor sequence, and the other for the drug. The NR sequence should be in FASTA format, while the drug in the KEGG code beginning with the symbol #. Examples for the query pairs input and the corresponding output can be seen by clicking on the Example button right above the input box. Step 3. Click on the Submit button to see the predicted result. For example, if you use the three query pairs in the Example window as the input, after clicking the Submit button, you will see on your screen that the "hsa:2099" NR and the "D00066" drug are an interactive pair, and that the "hsa:2908" NR and the "D00088" drug are also an interactive pair, but that the "hsa:5468" NR and the "D00279" drug are not an interactive pair. All these results are fully consistent with the experimental observations. It takes about 3 minutes before each of these results is shown on the screen; of course, the more query pairs there is, the more time that is usually needed.
Step 4. Click on the Citation button to find the relevant paper that documents the detailed development and algorithm of iNR-Durg.
Step 5. Click on the Data button to download the benchmark dataset used to train and test the iNR-Durg predictor.
Step 6. The program code is also available by clicking the button download on the lower panel of Figure 4 .
|
How many families are in the NR superfamily?
| 3,261 |
seven
| 2,172 |
1,572 |
iNR-Drug: Predicting the Interaction of Drugs with Nuclear Receptors in Cellular Networking
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3975431/
SHA: ee55aea26f816403476a7cb71816b8ecb1110329
Authors: Fan, Yue-Nong; Xiao, Xuan; Min, Jian-Liang; Chou, Kuo-Chen
Date: 2014-03-19
DOI: 10.3390/ijms15034915
License: cc-by
Abstract: Nuclear receptors (NRs) are closely associated with various major diseases such as cancer, diabetes, inflammatory disease, and osteoporosis. Therefore, NRs have become a frequent target for drug development. During the process of developing drugs against these diseases by targeting NRs, we are often facing a problem: Given a NR and chemical compound, can we identify whether they are really in interaction with each other in a cell? To address this problem, a predictor called “iNR-Drug” was developed. In the predictor, the drug compound concerned was formulated by a 256-D (dimensional) vector derived from its molecular fingerprint, and the NR by a 500-D vector formed by incorporating its sequential evolution information and physicochemical features into the general form of pseudo amino acid composition, and the prediction engine was operated by the SVM (support vector machine) algorithm. Compared with the existing prediction methods in this area, iNR-Drug not only can yield a higher success rate, but is also featured by a user-friendly web-server established at http://www.jci-bioinfo.cn/iNR-Drug/, which is particularly useful for most experimental scientists to obtain their desired data in a timely manner. It is anticipated that the iNR-Drug server may become a useful high throughput tool for both basic research and drug development, and that the current approach may be easily extended to study the interactions of drug with other targets as well.
Text: With the ability to directly bind to DNA ( Figure 1 ) and regulate the expression of adjacent genes, nuclear receptors (NRs) are a class of ligand-inducible transcription factors. They regulate various biological processes, such as homeostasis, differentiation, embryonic development, and organ physiology [1] [2] [3] . The NR superfamily has been classified into seven families: NR0 (knirps or DAX like) [4, 5] ; NR1 (thyroid hormone like), NR2 (HNF4-like), NR3 (estrogen like), NR4 (nerve growth factor IB-like), NR5 (fushi tarazu-F1 like), and NR6 (germ cell nuclear factor like). Since they are involved in almost all aspects of human physiology and are implicated in many major diseases such as cancer, diabetes and osteoporosis, nuclear receptors have become major drug targets [6, 7] , along with G protein-coupled receptors (GPCRs) [8] [9] [10] [11] [12] [13] [14] [15] [16] [17] , ion channels [18] [19] [20] , and kinase proteins [21] [22] [23] [24] . Identification of drug-target interactions is one of the most important steps for the new medicine development [25, 26] . The method usually adopted in this step is molecular docking simulation [27] [28] [29] [30] [31] [32] [33] [34] [35] [36] [37] [38] [39] [40] [41] [42] [43] . However, to make molecular docking study feasible, a reliable 3D (three dimensional) structure of the target protein is the prerequisite condition. Although X-ray crystallography is a powerful tool in determining protein 3D structures, it is time-consuming and expensive. Particularly, not all proteins can be successfully crystallized. For example, membrane proteins are very difficult to crystallize and most of them will not dissolve in normal solvents. Therefore, so far very few membrane protein 3D structures have been determined. Although NMR (Nuclear Magnetic Resonance) is indeed a very powerful tool in determining the 3D structures of membrane proteins as indicated by a series of recent publications (see, e.g., [44] [45] [46] [47] [48] [49] [50] [51] and a review article [20] ), it is also time-consuming and costly. To acquire the 3D structural information in a timely manner, one has to resort to various structural bioinformatics tools (see, e.g., [37] ), particularly the homologous modeling approach as utilized for a series of protein receptors urgently needed during the process of drug development [19, [52] [53] [54] [55] [56] [57] . Unfortunately, the number of dependable templates for developing high quality 3D structures by means of homology modeling is very limited [37] .
To overcome the aforementioned problems, it would be of help to develop a computational method for predicting the interactions of drugs with nuclear receptors in cellular networking based on the sequences information of the latter. The results thus obtained can be used to pre-exclude the compounds identified not in interaction with the nuclear receptors, so as to timely stop wasting time and money on those unpromising compounds [58] .
Actually, based on the functional groups and biological features, a powerful method was developed recently [59] for this purpose. However, further development in this regard is definitely needed due to the following reasons. (a) He et al. [59] did not provide a publicly accessible web-server for their method, and hence its practical application value is quite limited, particularly for the broad experimental scientists; (b) The prediction quality can be further enhanced by incorporating some key features into the formulation of NR-drug (nuclear receptor and drug) samples via the general form of pseudo amino acid composition [60] .
The present study was initiated with an attempt to develop a new method for predicting the interaction of drugs with nuclear receptors by addressing the two points.
As demonstrated by a series of recent publications [10, 18, [61] [62] [63] [64] [65] [66] [67] [68] [69] [70] and summarized in a comprehensive review [60] , to establish a really effective statistical predictor for a biomedical system, we need to consider the following steps: (a) select or construct a valid benchmark dataset to train and test the predictor; (b) represent the statistical samples with an effective formulation that can truly reflect their intrinsic correlation with the object to be predicted; (c) introduce or develop a powerful algorithm or engine to operate the prediction; (d) properly perform cross-validation tests to objectively evaluate the anticipated accuracy of the predictor; (e) establish a user-friendly web-server for the predictor that is accessible to the public. Below, let us elaborate how to deal with these steps.
The data used in the current study were collected from KEGG (Kyoto Encyclopedia of Genes and Genomes) [71] at http://www.kegg.jp/kegg/. KEGG is a database resource for understanding high-level functions and utilities of the biological system, such as the cell, the organism and the ecosystem, from molecular-level information, especially large-scale molecular datasets generated by genome sequencing and other high-throughput experimental technologies. Here, the benchmark dataset can be formulated as
where is the positive subset that consists of the interactive drug-NR pairs only, while the negative subset that contains of the non-interactive drug-NR pairs only, and the symbol represents the union in the set theory. The so-called "interactive" pair here means the pair whose two counterparts are interacting with each other in the drug-target networks as defined in the KEGG database [71] ; while the "non-interactive" pair means that its two counterparts are not interacting with each other in the drug-target networks. The positive dataset contains 86 drug-NR pairs, which were taken from He et al. [59] . The negative dataset contains 172 non-interactive drug-NR pairs, which were derived according to the following procedures: (a) separating each of the pairs in into single drug and NR; (b) re-coupling each of the single drugs with each of the single NRs into pairs in a way that none of them occurred in ; (c) randomly picking the pairs thus formed until reaching the number two times as many as the pairs in . The 86 interactive drug-NR pairs and 172 non-interactive drug-NR pairs are given in Supplementary Information S1, from which we can see that the 86 + 172 = 258 pairs in the current benchmark dataset are actually formed by 25 different NRs and 53 different compounds.
Since each of the samples in the current network system contains a drug (compound) and a NR (protein), the following procedures were taken to represent the drug-NR pair sample.
First, for the drug part in the current benchmark dataset, we can use a 256-D vector to formulate it as given by
where D represents the vector for a drug compound, and d i its i-th (i = 1,2, ,256) component that can be derived by following the "2D molecular fingerprint procedure" as elaborated in [10] . The 53 molecular fingerprint vectors thus obtained for the 53 drugs in are, respectively, given in Supplementary Information S2.
The protein sequences of the 25 different NRs in are listed in Supplementary Information S3. Suppose the sequence of a nuclear receptor protein P with L residues is generally expressed by
where 1 R represents the 1st residue of the protein sequence P , 2 R the 2nd residue, and so forth. Now the problem is how to effectively represent the sequence of Equation (3) with a non-sequential or discrete model [72] . This is because all the existing operation engines, such as covariance discriminant (CD) [17, 65, [73] [74] [75] [76] [77] [78] [79] , neural network [80] [81] [82] , support vector machine (SVM) [62] [63] [64] 83] , random forest [84, 85] , conditional random field [66] , nearest neighbor (NN) [86, 87] ; K-nearest neighbor (KNN) [88] [89] [90] , OET-KNN [91] [92] [93] [94] , and Fuzzy K-nearest neighbor [10, 12, 18, 69, 95] , can only handle vector but not sequence samples. However, a vector defined in a discrete model may completely lose all the sequence-order information and hence limit the quality of prediction. Facing such a dilemma, can we find an approach to partially incorporate the sequence-order effects? Actually, one of the most challenging problems in computational biology is how to formulate a biological sequence with a discrete model or a vector, yet still keep considerable sequence order information. To avoid completely losing the sequence-order information for proteins, the pseudo amino acid composition [96, 97] or Chou's PseAAC [98] was proposed. Ever since the concept of PseAAC was proposed in 2001 [96] , it has penetrated into almost all the areas of computational proteomics, such as predicting anticancer peptides [99] , predicting protein subcellular location [100] [101] [102] [103] [104] [105] [106] , predicting membrane protein types [107, 108] , predicting protein submitochondria locations [109] [110] [111] [112] , predicting GABA(A) receptor proteins [113] , predicting enzyme subfamily classes [114] , predicting antibacterial peptides [115] , predicting supersecondary structure [116] , predicting bacterial virulent proteins [117] , predicting protein structural class [118] , predicting the cofactors of oxidoreductases [119] , predicting metalloproteinase family [120] , identifying cysteine S-nitrosylation sites in proteins [66] , identifying bacterial secreted proteins [121] , identifying antibacterial peptides [115] , identifying allergenic proteins [122] , identifying protein quaternary structural attributes [123, 124] , identifying risk type of human papillomaviruses [125] , identifying cyclin proteins [126] , identifying GPCRs and their types [15, 16] , discriminating outer membrane proteins [127] , classifying amino acids [128] , detecting remote homologous proteins [129] , among many others (see a long list of papers cited in the References section of [60] ). Moreover, the concept of PseAAC was further extended to represent the feature vectors of nucleotides [65] , as well as other biological samples (see, e.g., [130] [131] [132] ). Because it has been widely and increasingly used, recently two powerful soft-wares, called "PseAAC-Builder" [133] and "propy" [134] , were established for generating various special Chou's pseudo-amino acid compositions, in addition to the web-server "PseAAC" [135] built in 2008.
According to a comprehensive review [60] , the general form of PseAAC for a protein sequence P is formulated by
where the subscript is an integer, and its value as well as the components ( 1, 2, , ) u u will depend on how to extract the desired information from the amino acid sequence of P (cf. Equation (3)). Below, let us describe how to extract useful information to define the components of PseAAC for the NR samples concerned. First, many earlier studies (see, e.g., [136] [137] [138] [139] [140] [141] ) have indicated that the amino acid composition (AAC) of a protein plays an important role in determining its attributes. The AAC contains 20 components with each representing the occurrence frequency of one of the 20 native amino acids in the protein concerned. Thus, such 20 AAC components were used here to define the first 20 elements in Equation (4); i.e., (1) ( 1, 2, , 20) ii fi (5) where f i (1) is the normalized occurrence frequency of the i-th type native amino acid in the nuclear receptor concerned. Since AAC did not contain any sequence order information, the following steps were taken to make up this shortcoming.
To avoid completely losing the local or short-range sequence order information, we considered the approach of dipeptide composition. It contained 20 × 20 = 400 components [142] . Such 400 components were used to define the next 400 elements in Equation (4); i.e., (2) 20 ( 1, 2, , 400) jj fj
where (2) j f is the normalized occurrence frequency of the j-th dipeptides in the nuclear receptor concerned. To incorporate the global or long-range sequence order information, let us consider the following approach. According to molecular evolution, all biological sequences have developed starting out from a very limited number of ancestral samples. Driven by various evolutionary forces such as mutation, recombination, gene conversion, genetic drift, and selection, they have undergone many changes including changes of single residues, insertions and deletions of several residues [143] , gene doubling, and gene fusion. With the accumulation of these changes over a long period of time, many original similarities between initial and resultant amino acid sequences are gradually faded out, but the corresponding proteins may still share many common attributes [37] , such as having basically the same biological function and residing at a same subcellular location [144, 145] . To extract the sequential evolution information and use it to define the components of Equation (4), the PSSM (Position Specific Scoring Matrix) was used as described below.
According to Schaffer [146] , the sequence evolution information of a nuclear receptor protein P with L amino acid residues can be expressed by a 20 L matrix, as given by
where (7) were generated by using PSI-BLAST [147] to search the UniProtKB/Swiss-Prot database (The Universal Protein Resource (UniProt); http://www.uniprot.org/) through three iterations with 0.001 as the E-value cutoff for multiple sequence alignment against the sequence of the nuclear receptor concerned. In order to make every element in Equation (7) be scaled from their original score ranges into the region of [0, 1], we performed a conversion through the standard sigmoid function to make it become
Now we extract the useful information from Equation (8)
Moreover, we used the grey system model approach as elaborated in [68] to further define the next 60 components of Equation (4) ( 1, 2, , 20)
In the above equation, w 1 , w 2 , and w 3 are weight factors, which were all set to 1 in the current study; f j (1) has the same meaning as in Equation (5)
where
and
Combining Equations (5), (6), (10) and (12), we found that the total number of the components obtained via the current approach for the PseAAC of Equation (4)
and each of the 500 components is given by (1) (
Since the elements in Equations (2) and (4) are well defined, we can now formulate the drug-NR pair by combining the two equations as given by (19) where G represents the drug-NR pair, Å the orthogonal sum, and the 256 + 500 = 756 components are defined by Equations (2) and (18) . For the sake of convenience, let us use x i (i = 1, 2, , 756) to represent the 756 components in Equation (19); i.e., (20) To optimize the prediction quality with a time-saving approach, similar to the treatment [148] [149] [150] , let us convert Equation (20) to
where the symbol means taking the average of the quantity therein, and SD means the corresponding standard derivation.
In this study, the SVM (support vector machine) was used as the operation engine. SVM has been widely used in the realm of bioinformatics (see, e.g., [62] [63] [64] [151] [152] [153] [154] ). The basic idea of SVM is to transform the data into a high dimensional feature space, and then determine the optimal separating hyperplane using a kernel function. For a brief formulation of SVM and how it works, see the papers [155, 156] ; for more details about SVM, see a monograph [157] .
In this study, the LIBSVM package [158] was used as an implementation of SVM, which can be downloaded from http://www.csie.ntu.edu.tw/~cjlin/libsvm/, the popular radial basis function (RBF) was taken as the kernel function. For the current SVM classifier, there were two uncertain parameters: penalty parameter C and kernel parameter . The method of how to determine the two parameters will be given later.
The predictor obtained via the aforementioned procedure is called iNR-Drug, where "i" means identify, and "NR-Drug" means the interaction between nuclear receptor and drug compound. To provide an intuitive overall picture, a flowchart is provided in Figure 2 to show the process of how the predictor works in identifying the interactions between nuclear receptors and drug compounds.
To provide a more intuitive and easier-to-understand method to measure the prediction quality, the following set of metrics based on the formulation used by Chou [159] [160] [161] in predicting signal peptides was adopted. According to Chou's formulation, the sensitivity, specificity, overall accuracy, and Matthew's correlation coefficient can be respectively expressed as [62, [65] [66] [67] Sn 1
where N is the total number of the interactive NR-drug pairs investigated while N the number of the interactive NR-drug pairs incorrectly predicted as the non-interactive NR-drug pairs; N the total number of the non-interactive NR-drug pairs investigated while N the number of the non-interactive NR-drug pairs incorrectly predicted as the interactive NR-drug pairs.
According to Equation (23) we can easily see the following. When 0 N meaning none of the interactive NR-drug pairs was mispredicted to be a non-interactive NR-drug pair, we have the sensitivity Sn = 1; while NN meaning that all the interactive NR-drug pairs were mispredicted to be the non-interactive NR-drug pairs, we have the sensitivity Sn = 0 . Likewise, when 0 N meaning none of the non-interactive NR-drug pairs was mispredicted, we have the specificity Sp we have MCC = 0 meaning total disagreement between prediction and observation. As we can see from the above discussion, it is much more intuitive and easier to understand when using Equation (23) to examine a predictor for its four metrics, particularly for its Mathew's correlation coefficient. It is instructive to point out that the metrics as defined in Equation (23) are valid for single label systems; for multi-label systems, a set of more complicated metrics should be used as given in [162] .
How to properly test a predictor for its anticipated success rates is very important for its development as well as its potential application value. Generally speaking, the following three cross-validation methods are often used to examine the quality of a predictor and its effectiveness in practical application: independent dataset test, subsampling or K-fold (such as five-fold, seven-fold, or 10-fold) crossover test and jackknife test [163] . However, as elaborated by a penetrating analysis in [164] , considerable arbitrariness exists in the independent dataset test. Also, as demonstrated in [165] , the subsampling (or K-fold crossover validation) test cannot avoid arbitrariness either. Only the jackknife test is the least arbitrary that can always yield a unique result for a given benchmark dataset [73, 74, 156, [166] [167] [168] . Therefore, the jackknife test has been widely recognized and increasingly utilized by investigators to examine the quality of various predictors (see, e.g., [14, 15, 68, 99, 106, 107, 124, 169, 170] ). Accordingly, in this study the jackknife test was also adopted to evaluate the accuracy of the current predictor.
As mentioned above, the SVM operation engine contains two uncertain parameters C and . To find their optimal values, a 2-D grid search was conducted by the jackknife test on the benchmark dataset . The results thus obtained are shown in Figure 3 , from which it can be seen that the iNR-Drug predictor reaches its optimal status when C = 2 3 and 9 2 . The corresponding rates for the four metrics (cf. Equation (23)) are given in Table 1 , where for facilitating comparison, the overall accuracy Acc reported by He et al. [59] on the same benchmark dataset is also given although no results were reported by them for Sn, Sp and MCC. It can be observed from the table that the overall accuracy obtained by iNR-Drug is remarkably higher that of He et al. [59] , and that the rates achieved by iNR-Drug for the other three metrics are also quite higher. These facts indicate that the current predictor not only can yield higher overall prediction accuracy but also is quite stable with low false prediction rates.
As mentioned above (Section 3.2), the jackknife test is the most objective method for examining the quality of a predictor. However, as a demonstration to show how to practically use the current predictor, we took 41 NR-drug pairs from the study by Yamanishi et al. [171] that had been confirmed by experiments as interactive pairs. For such an independent dataset, 34 were correctly identified by iNR-Drug as interactive pairs, i.e., Sn = 34 / 41 = 82.92%, which is quite consistent with the rate of 79.07% achieved by the predictor on the benchmark dataset via the jackknife test as reported in Table 1 .
It is anticipated that the iNR-Drug predictor developed in this paper may become a useful high throughput tool for both basic research and drug development, and that the current approach may be easily extended to study the interactions of drug with other targets as well. Since user-friendly and publicly accessible web-servers represent the future direction for developing practically more useful predictors [98, 172] , a publicly accessible web-server for iNR-Drug was established.
For the convenience of the vast majority of biologists and pharmaceutical scientists, here let us provide a step-by-step guide to show how the users can easily get the desired result by using iNR-Drug web-server without the need to follow the complicated mathematical equations presented in this paper for the process of developing the predictor and its integrity.
Step 1. Open the web server at the site http://www.jci-bioinfo.cn/iNR-Drug/ and you will see the top page of the predictor on your computer screen, as shown in Figure 4 . Click on the Read Me button to see a brief introduction about iNR-Drug predictor and the caveat when using it.
Step 2. Either type or copy/paste the query NR-drug pairs into the input box at the center of Figure 4 . Each query pair consists of two parts: one is for the nuclear receptor sequence, and the other for the drug. The NR sequence should be in FASTA format, while the drug in the KEGG code beginning with the symbol #. Examples for the query pairs input and the corresponding output can be seen by clicking on the Example button right above the input box. Step 3. Click on the Submit button to see the predicted result. For example, if you use the three query pairs in the Example window as the input, after clicking the Submit button, you will see on your screen that the "hsa:2099" NR and the "D00066" drug are an interactive pair, and that the "hsa:2908" NR and the "D00088" drug are also an interactive pair, but that the "hsa:5468" NR and the "D00279" drug are not an interactive pair. All these results are fully consistent with the experimental observations. It takes about 3 minutes before each of these results is shown on the screen; of course, the more query pairs there is, the more time that is usually needed.
Step 4. Click on the Citation button to find the relevant paper that documents the detailed development and algorithm of iNR-Durg.
Step 5. Click on the Data button to download the benchmark dataset used to train and test the iNR-Durg predictor.
Step 6. The program code is also available by clicking the button download on the lower panel of Figure 4 .
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What is a prerequisite to make a molecular docking study feasible?
| 3,262 |
a reliable 3D (three dimensional) structure of the target protein
| 3,102 |
1,572 |
iNR-Drug: Predicting the Interaction of Drugs with Nuclear Receptors in Cellular Networking
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3975431/
SHA: ee55aea26f816403476a7cb71816b8ecb1110329
Authors: Fan, Yue-Nong; Xiao, Xuan; Min, Jian-Liang; Chou, Kuo-Chen
Date: 2014-03-19
DOI: 10.3390/ijms15034915
License: cc-by
Abstract: Nuclear receptors (NRs) are closely associated with various major diseases such as cancer, diabetes, inflammatory disease, and osteoporosis. Therefore, NRs have become a frequent target for drug development. During the process of developing drugs against these diseases by targeting NRs, we are often facing a problem: Given a NR and chemical compound, can we identify whether they are really in interaction with each other in a cell? To address this problem, a predictor called “iNR-Drug” was developed. In the predictor, the drug compound concerned was formulated by a 256-D (dimensional) vector derived from its molecular fingerprint, and the NR by a 500-D vector formed by incorporating its sequential evolution information and physicochemical features into the general form of pseudo amino acid composition, and the prediction engine was operated by the SVM (support vector machine) algorithm. Compared with the existing prediction methods in this area, iNR-Drug not only can yield a higher success rate, but is also featured by a user-friendly web-server established at http://www.jci-bioinfo.cn/iNR-Drug/, which is particularly useful for most experimental scientists to obtain their desired data in a timely manner. It is anticipated that the iNR-Drug server may become a useful high throughput tool for both basic research and drug development, and that the current approach may be easily extended to study the interactions of drug with other targets as well.
Text: With the ability to directly bind to DNA ( Figure 1 ) and regulate the expression of adjacent genes, nuclear receptors (NRs) are a class of ligand-inducible transcription factors. They regulate various biological processes, such as homeostasis, differentiation, embryonic development, and organ physiology [1] [2] [3] . The NR superfamily has been classified into seven families: NR0 (knirps or DAX like) [4, 5] ; NR1 (thyroid hormone like), NR2 (HNF4-like), NR3 (estrogen like), NR4 (nerve growth factor IB-like), NR5 (fushi tarazu-F1 like), and NR6 (germ cell nuclear factor like). Since they are involved in almost all aspects of human physiology and are implicated in many major diseases such as cancer, diabetes and osteoporosis, nuclear receptors have become major drug targets [6, 7] , along with G protein-coupled receptors (GPCRs) [8] [9] [10] [11] [12] [13] [14] [15] [16] [17] , ion channels [18] [19] [20] , and kinase proteins [21] [22] [23] [24] . Identification of drug-target interactions is one of the most important steps for the new medicine development [25, 26] . The method usually adopted in this step is molecular docking simulation [27] [28] [29] [30] [31] [32] [33] [34] [35] [36] [37] [38] [39] [40] [41] [42] [43] . However, to make molecular docking study feasible, a reliable 3D (three dimensional) structure of the target protein is the prerequisite condition. Although X-ray crystallography is a powerful tool in determining protein 3D structures, it is time-consuming and expensive. Particularly, not all proteins can be successfully crystallized. For example, membrane proteins are very difficult to crystallize and most of them will not dissolve in normal solvents. Therefore, so far very few membrane protein 3D structures have been determined. Although NMR (Nuclear Magnetic Resonance) is indeed a very powerful tool in determining the 3D structures of membrane proteins as indicated by a series of recent publications (see, e.g., [44] [45] [46] [47] [48] [49] [50] [51] and a review article [20] ), it is also time-consuming and costly. To acquire the 3D structural information in a timely manner, one has to resort to various structural bioinformatics tools (see, e.g., [37] ), particularly the homologous modeling approach as utilized for a series of protein receptors urgently needed during the process of drug development [19, [52] [53] [54] [55] [56] [57] . Unfortunately, the number of dependable templates for developing high quality 3D structures by means of homology modeling is very limited [37] .
To overcome the aforementioned problems, it would be of help to develop a computational method for predicting the interactions of drugs with nuclear receptors in cellular networking based on the sequences information of the latter. The results thus obtained can be used to pre-exclude the compounds identified not in interaction with the nuclear receptors, so as to timely stop wasting time and money on those unpromising compounds [58] .
Actually, based on the functional groups and biological features, a powerful method was developed recently [59] for this purpose. However, further development in this regard is definitely needed due to the following reasons. (a) He et al. [59] did not provide a publicly accessible web-server for their method, and hence its practical application value is quite limited, particularly for the broad experimental scientists; (b) The prediction quality can be further enhanced by incorporating some key features into the formulation of NR-drug (nuclear receptor and drug) samples via the general form of pseudo amino acid composition [60] .
The present study was initiated with an attempt to develop a new method for predicting the interaction of drugs with nuclear receptors by addressing the two points.
As demonstrated by a series of recent publications [10, 18, [61] [62] [63] [64] [65] [66] [67] [68] [69] [70] and summarized in a comprehensive review [60] , to establish a really effective statistical predictor for a biomedical system, we need to consider the following steps: (a) select or construct a valid benchmark dataset to train and test the predictor; (b) represent the statistical samples with an effective formulation that can truly reflect their intrinsic correlation with the object to be predicted; (c) introduce or develop a powerful algorithm or engine to operate the prediction; (d) properly perform cross-validation tests to objectively evaluate the anticipated accuracy of the predictor; (e) establish a user-friendly web-server for the predictor that is accessible to the public. Below, let us elaborate how to deal with these steps.
The data used in the current study were collected from KEGG (Kyoto Encyclopedia of Genes and Genomes) [71] at http://www.kegg.jp/kegg/. KEGG is a database resource for understanding high-level functions and utilities of the biological system, such as the cell, the organism and the ecosystem, from molecular-level information, especially large-scale molecular datasets generated by genome sequencing and other high-throughput experimental technologies. Here, the benchmark dataset can be formulated as
where is the positive subset that consists of the interactive drug-NR pairs only, while the negative subset that contains of the non-interactive drug-NR pairs only, and the symbol represents the union in the set theory. The so-called "interactive" pair here means the pair whose two counterparts are interacting with each other in the drug-target networks as defined in the KEGG database [71] ; while the "non-interactive" pair means that its two counterparts are not interacting with each other in the drug-target networks. The positive dataset contains 86 drug-NR pairs, which were taken from He et al. [59] . The negative dataset contains 172 non-interactive drug-NR pairs, which were derived according to the following procedures: (a) separating each of the pairs in into single drug and NR; (b) re-coupling each of the single drugs with each of the single NRs into pairs in a way that none of them occurred in ; (c) randomly picking the pairs thus formed until reaching the number two times as many as the pairs in . The 86 interactive drug-NR pairs and 172 non-interactive drug-NR pairs are given in Supplementary Information S1, from which we can see that the 86 + 172 = 258 pairs in the current benchmark dataset are actually formed by 25 different NRs and 53 different compounds.
Since each of the samples in the current network system contains a drug (compound) and a NR (protein), the following procedures were taken to represent the drug-NR pair sample.
First, for the drug part in the current benchmark dataset, we can use a 256-D vector to formulate it as given by
where D represents the vector for a drug compound, and d i its i-th (i = 1,2, ,256) component that can be derived by following the "2D molecular fingerprint procedure" as elaborated in [10] . The 53 molecular fingerprint vectors thus obtained for the 53 drugs in are, respectively, given in Supplementary Information S2.
The protein sequences of the 25 different NRs in are listed in Supplementary Information S3. Suppose the sequence of a nuclear receptor protein P with L residues is generally expressed by
where 1 R represents the 1st residue of the protein sequence P , 2 R the 2nd residue, and so forth. Now the problem is how to effectively represent the sequence of Equation (3) with a non-sequential or discrete model [72] . This is because all the existing operation engines, such as covariance discriminant (CD) [17, 65, [73] [74] [75] [76] [77] [78] [79] , neural network [80] [81] [82] , support vector machine (SVM) [62] [63] [64] 83] , random forest [84, 85] , conditional random field [66] , nearest neighbor (NN) [86, 87] ; K-nearest neighbor (KNN) [88] [89] [90] , OET-KNN [91] [92] [93] [94] , and Fuzzy K-nearest neighbor [10, 12, 18, 69, 95] , can only handle vector but not sequence samples. However, a vector defined in a discrete model may completely lose all the sequence-order information and hence limit the quality of prediction. Facing such a dilemma, can we find an approach to partially incorporate the sequence-order effects? Actually, one of the most challenging problems in computational biology is how to formulate a biological sequence with a discrete model or a vector, yet still keep considerable sequence order information. To avoid completely losing the sequence-order information for proteins, the pseudo amino acid composition [96, 97] or Chou's PseAAC [98] was proposed. Ever since the concept of PseAAC was proposed in 2001 [96] , it has penetrated into almost all the areas of computational proteomics, such as predicting anticancer peptides [99] , predicting protein subcellular location [100] [101] [102] [103] [104] [105] [106] , predicting membrane protein types [107, 108] , predicting protein submitochondria locations [109] [110] [111] [112] , predicting GABA(A) receptor proteins [113] , predicting enzyme subfamily classes [114] , predicting antibacterial peptides [115] , predicting supersecondary structure [116] , predicting bacterial virulent proteins [117] , predicting protein structural class [118] , predicting the cofactors of oxidoreductases [119] , predicting metalloproteinase family [120] , identifying cysteine S-nitrosylation sites in proteins [66] , identifying bacterial secreted proteins [121] , identifying antibacterial peptides [115] , identifying allergenic proteins [122] , identifying protein quaternary structural attributes [123, 124] , identifying risk type of human papillomaviruses [125] , identifying cyclin proteins [126] , identifying GPCRs and their types [15, 16] , discriminating outer membrane proteins [127] , classifying amino acids [128] , detecting remote homologous proteins [129] , among many others (see a long list of papers cited in the References section of [60] ). Moreover, the concept of PseAAC was further extended to represent the feature vectors of nucleotides [65] , as well as other biological samples (see, e.g., [130] [131] [132] ). Because it has been widely and increasingly used, recently two powerful soft-wares, called "PseAAC-Builder" [133] and "propy" [134] , were established for generating various special Chou's pseudo-amino acid compositions, in addition to the web-server "PseAAC" [135] built in 2008.
According to a comprehensive review [60] , the general form of PseAAC for a protein sequence P is formulated by
where the subscript is an integer, and its value as well as the components ( 1, 2, , ) u u will depend on how to extract the desired information from the amino acid sequence of P (cf. Equation (3)). Below, let us describe how to extract useful information to define the components of PseAAC for the NR samples concerned. First, many earlier studies (see, e.g., [136] [137] [138] [139] [140] [141] ) have indicated that the amino acid composition (AAC) of a protein plays an important role in determining its attributes. The AAC contains 20 components with each representing the occurrence frequency of one of the 20 native amino acids in the protein concerned. Thus, such 20 AAC components were used here to define the first 20 elements in Equation (4); i.e., (1) ( 1, 2, , 20) ii fi (5) where f i (1) is the normalized occurrence frequency of the i-th type native amino acid in the nuclear receptor concerned. Since AAC did not contain any sequence order information, the following steps were taken to make up this shortcoming.
To avoid completely losing the local or short-range sequence order information, we considered the approach of dipeptide composition. It contained 20 × 20 = 400 components [142] . Such 400 components were used to define the next 400 elements in Equation (4); i.e., (2) 20 ( 1, 2, , 400) jj fj
where (2) j f is the normalized occurrence frequency of the j-th dipeptides in the nuclear receptor concerned. To incorporate the global or long-range sequence order information, let us consider the following approach. According to molecular evolution, all biological sequences have developed starting out from a very limited number of ancestral samples. Driven by various evolutionary forces such as mutation, recombination, gene conversion, genetic drift, and selection, they have undergone many changes including changes of single residues, insertions and deletions of several residues [143] , gene doubling, and gene fusion. With the accumulation of these changes over a long period of time, many original similarities between initial and resultant amino acid sequences are gradually faded out, but the corresponding proteins may still share many common attributes [37] , such as having basically the same biological function and residing at a same subcellular location [144, 145] . To extract the sequential evolution information and use it to define the components of Equation (4), the PSSM (Position Specific Scoring Matrix) was used as described below.
According to Schaffer [146] , the sequence evolution information of a nuclear receptor protein P with L amino acid residues can be expressed by a 20 L matrix, as given by
where (7) were generated by using PSI-BLAST [147] to search the UniProtKB/Swiss-Prot database (The Universal Protein Resource (UniProt); http://www.uniprot.org/) through three iterations with 0.001 as the E-value cutoff for multiple sequence alignment against the sequence of the nuclear receptor concerned. In order to make every element in Equation (7) be scaled from their original score ranges into the region of [0, 1], we performed a conversion through the standard sigmoid function to make it become
Now we extract the useful information from Equation (8)
Moreover, we used the grey system model approach as elaborated in [68] to further define the next 60 components of Equation (4) ( 1, 2, , 20)
In the above equation, w 1 , w 2 , and w 3 are weight factors, which were all set to 1 in the current study; f j (1) has the same meaning as in Equation (5)
where
and
Combining Equations (5), (6), (10) and (12), we found that the total number of the components obtained via the current approach for the PseAAC of Equation (4)
and each of the 500 components is given by (1) (
Since the elements in Equations (2) and (4) are well defined, we can now formulate the drug-NR pair by combining the two equations as given by (19) where G represents the drug-NR pair, Å the orthogonal sum, and the 256 + 500 = 756 components are defined by Equations (2) and (18) . For the sake of convenience, let us use x i (i = 1, 2, , 756) to represent the 756 components in Equation (19); i.e., (20) To optimize the prediction quality with a time-saving approach, similar to the treatment [148] [149] [150] , let us convert Equation (20) to
where the symbol means taking the average of the quantity therein, and SD means the corresponding standard derivation.
In this study, the SVM (support vector machine) was used as the operation engine. SVM has been widely used in the realm of bioinformatics (see, e.g., [62] [63] [64] [151] [152] [153] [154] ). The basic idea of SVM is to transform the data into a high dimensional feature space, and then determine the optimal separating hyperplane using a kernel function. For a brief formulation of SVM and how it works, see the papers [155, 156] ; for more details about SVM, see a monograph [157] .
In this study, the LIBSVM package [158] was used as an implementation of SVM, which can be downloaded from http://www.csie.ntu.edu.tw/~cjlin/libsvm/, the popular radial basis function (RBF) was taken as the kernel function. For the current SVM classifier, there were two uncertain parameters: penalty parameter C and kernel parameter . The method of how to determine the two parameters will be given later.
The predictor obtained via the aforementioned procedure is called iNR-Drug, where "i" means identify, and "NR-Drug" means the interaction between nuclear receptor and drug compound. To provide an intuitive overall picture, a flowchart is provided in Figure 2 to show the process of how the predictor works in identifying the interactions between nuclear receptors and drug compounds.
To provide a more intuitive and easier-to-understand method to measure the prediction quality, the following set of metrics based on the formulation used by Chou [159] [160] [161] in predicting signal peptides was adopted. According to Chou's formulation, the sensitivity, specificity, overall accuracy, and Matthew's correlation coefficient can be respectively expressed as [62, [65] [66] [67] Sn 1
where N is the total number of the interactive NR-drug pairs investigated while N the number of the interactive NR-drug pairs incorrectly predicted as the non-interactive NR-drug pairs; N the total number of the non-interactive NR-drug pairs investigated while N the number of the non-interactive NR-drug pairs incorrectly predicted as the interactive NR-drug pairs.
According to Equation (23) we can easily see the following. When 0 N meaning none of the interactive NR-drug pairs was mispredicted to be a non-interactive NR-drug pair, we have the sensitivity Sn = 1; while NN meaning that all the interactive NR-drug pairs were mispredicted to be the non-interactive NR-drug pairs, we have the sensitivity Sn = 0 . Likewise, when 0 N meaning none of the non-interactive NR-drug pairs was mispredicted, we have the specificity Sp we have MCC = 0 meaning total disagreement between prediction and observation. As we can see from the above discussion, it is much more intuitive and easier to understand when using Equation (23) to examine a predictor for its four metrics, particularly for its Mathew's correlation coefficient. It is instructive to point out that the metrics as defined in Equation (23) are valid for single label systems; for multi-label systems, a set of more complicated metrics should be used as given in [162] .
How to properly test a predictor for its anticipated success rates is very important for its development as well as its potential application value. Generally speaking, the following three cross-validation methods are often used to examine the quality of a predictor and its effectiveness in practical application: independent dataset test, subsampling or K-fold (such as five-fold, seven-fold, or 10-fold) crossover test and jackknife test [163] . However, as elaborated by a penetrating analysis in [164] , considerable arbitrariness exists in the independent dataset test. Also, as demonstrated in [165] , the subsampling (or K-fold crossover validation) test cannot avoid arbitrariness either. Only the jackknife test is the least arbitrary that can always yield a unique result for a given benchmark dataset [73, 74, 156, [166] [167] [168] . Therefore, the jackknife test has been widely recognized and increasingly utilized by investigators to examine the quality of various predictors (see, e.g., [14, 15, 68, 99, 106, 107, 124, 169, 170] ). Accordingly, in this study the jackknife test was also adopted to evaluate the accuracy of the current predictor.
As mentioned above, the SVM operation engine contains two uncertain parameters C and . To find their optimal values, a 2-D grid search was conducted by the jackknife test on the benchmark dataset . The results thus obtained are shown in Figure 3 , from which it can be seen that the iNR-Drug predictor reaches its optimal status when C = 2 3 and 9 2 . The corresponding rates for the four metrics (cf. Equation (23)) are given in Table 1 , where for facilitating comparison, the overall accuracy Acc reported by He et al. [59] on the same benchmark dataset is also given although no results were reported by them for Sn, Sp and MCC. It can be observed from the table that the overall accuracy obtained by iNR-Drug is remarkably higher that of He et al. [59] , and that the rates achieved by iNR-Drug for the other three metrics are also quite higher. These facts indicate that the current predictor not only can yield higher overall prediction accuracy but also is quite stable with low false prediction rates.
As mentioned above (Section 3.2), the jackknife test is the most objective method for examining the quality of a predictor. However, as a demonstration to show how to practically use the current predictor, we took 41 NR-drug pairs from the study by Yamanishi et al. [171] that had been confirmed by experiments as interactive pairs. For such an independent dataset, 34 were correctly identified by iNR-Drug as interactive pairs, i.e., Sn = 34 / 41 = 82.92%, which is quite consistent with the rate of 79.07% achieved by the predictor on the benchmark dataset via the jackknife test as reported in Table 1 .
It is anticipated that the iNR-Drug predictor developed in this paper may become a useful high throughput tool for both basic research and drug development, and that the current approach may be easily extended to study the interactions of drug with other targets as well. Since user-friendly and publicly accessible web-servers represent the future direction for developing practically more useful predictors [98, 172] , a publicly accessible web-server for iNR-Drug was established.
For the convenience of the vast majority of biologists and pharmaceutical scientists, here let us provide a step-by-step guide to show how the users can easily get the desired result by using iNR-Drug web-server without the need to follow the complicated mathematical equations presented in this paper for the process of developing the predictor and its integrity.
Step 1. Open the web server at the site http://www.jci-bioinfo.cn/iNR-Drug/ and you will see the top page of the predictor on your computer screen, as shown in Figure 4 . Click on the Read Me button to see a brief introduction about iNR-Drug predictor and the caveat when using it.
Step 2. Either type or copy/paste the query NR-drug pairs into the input box at the center of Figure 4 . Each query pair consists of two parts: one is for the nuclear receptor sequence, and the other for the drug. The NR sequence should be in FASTA format, while the drug in the KEGG code beginning with the symbol #. Examples for the query pairs input and the corresponding output can be seen by clicking on the Example button right above the input box. Step 3. Click on the Submit button to see the predicted result. For example, if you use the three query pairs in the Example window as the input, after clicking the Submit button, you will see on your screen that the "hsa:2099" NR and the "D00066" drug are an interactive pair, and that the "hsa:2908" NR and the "D00088" drug are also an interactive pair, but that the "hsa:5468" NR and the "D00279" drug are not an interactive pair. All these results are fully consistent with the experimental observations. It takes about 3 minutes before each of these results is shown on the screen; of course, the more query pairs there is, the more time that is usually needed.
Step 4. Click on the Citation button to find the relevant paper that documents the detailed development and algorithm of iNR-Durg.
Step 5. Click on the Data button to download the benchmark dataset used to train and test the iNR-Durg predictor.
Step 6. The program code is also available by clicking the button download on the lower panel of Figure 4 .
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What tool can be used to determine the 3D structure of proteins?
| 3,263 |
X-ray crystallography
| 3,208 |
1,572 |
iNR-Drug: Predicting the Interaction of Drugs with Nuclear Receptors in Cellular Networking
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3975431/
SHA: ee55aea26f816403476a7cb71816b8ecb1110329
Authors: Fan, Yue-Nong; Xiao, Xuan; Min, Jian-Liang; Chou, Kuo-Chen
Date: 2014-03-19
DOI: 10.3390/ijms15034915
License: cc-by
Abstract: Nuclear receptors (NRs) are closely associated with various major diseases such as cancer, diabetes, inflammatory disease, and osteoporosis. Therefore, NRs have become a frequent target for drug development. During the process of developing drugs against these diseases by targeting NRs, we are often facing a problem: Given a NR and chemical compound, can we identify whether they are really in interaction with each other in a cell? To address this problem, a predictor called “iNR-Drug” was developed. In the predictor, the drug compound concerned was formulated by a 256-D (dimensional) vector derived from its molecular fingerprint, and the NR by a 500-D vector formed by incorporating its sequential evolution information and physicochemical features into the general form of pseudo amino acid composition, and the prediction engine was operated by the SVM (support vector machine) algorithm. Compared with the existing prediction methods in this area, iNR-Drug not only can yield a higher success rate, but is also featured by a user-friendly web-server established at http://www.jci-bioinfo.cn/iNR-Drug/, which is particularly useful for most experimental scientists to obtain their desired data in a timely manner. It is anticipated that the iNR-Drug server may become a useful high throughput tool for both basic research and drug development, and that the current approach may be easily extended to study the interactions of drug with other targets as well.
Text: With the ability to directly bind to DNA ( Figure 1 ) and regulate the expression of adjacent genes, nuclear receptors (NRs) are a class of ligand-inducible transcription factors. They regulate various biological processes, such as homeostasis, differentiation, embryonic development, and organ physiology [1] [2] [3] . The NR superfamily has been classified into seven families: NR0 (knirps or DAX like) [4, 5] ; NR1 (thyroid hormone like), NR2 (HNF4-like), NR3 (estrogen like), NR4 (nerve growth factor IB-like), NR5 (fushi tarazu-F1 like), and NR6 (germ cell nuclear factor like). Since they are involved in almost all aspects of human physiology and are implicated in many major diseases such as cancer, diabetes and osteoporosis, nuclear receptors have become major drug targets [6, 7] , along with G protein-coupled receptors (GPCRs) [8] [9] [10] [11] [12] [13] [14] [15] [16] [17] , ion channels [18] [19] [20] , and kinase proteins [21] [22] [23] [24] . Identification of drug-target interactions is one of the most important steps for the new medicine development [25, 26] . The method usually adopted in this step is molecular docking simulation [27] [28] [29] [30] [31] [32] [33] [34] [35] [36] [37] [38] [39] [40] [41] [42] [43] . However, to make molecular docking study feasible, a reliable 3D (three dimensional) structure of the target protein is the prerequisite condition. Although X-ray crystallography is a powerful tool in determining protein 3D structures, it is time-consuming and expensive. Particularly, not all proteins can be successfully crystallized. For example, membrane proteins are very difficult to crystallize and most of them will not dissolve in normal solvents. Therefore, so far very few membrane protein 3D structures have been determined. Although NMR (Nuclear Magnetic Resonance) is indeed a very powerful tool in determining the 3D structures of membrane proteins as indicated by a series of recent publications (see, e.g., [44] [45] [46] [47] [48] [49] [50] [51] and a review article [20] ), it is also time-consuming and costly. To acquire the 3D structural information in a timely manner, one has to resort to various structural bioinformatics tools (see, e.g., [37] ), particularly the homologous modeling approach as utilized for a series of protein receptors urgently needed during the process of drug development [19, [52] [53] [54] [55] [56] [57] . Unfortunately, the number of dependable templates for developing high quality 3D structures by means of homology modeling is very limited [37] .
To overcome the aforementioned problems, it would be of help to develop a computational method for predicting the interactions of drugs with nuclear receptors in cellular networking based on the sequences information of the latter. The results thus obtained can be used to pre-exclude the compounds identified not in interaction with the nuclear receptors, so as to timely stop wasting time and money on those unpromising compounds [58] .
Actually, based on the functional groups and biological features, a powerful method was developed recently [59] for this purpose. However, further development in this regard is definitely needed due to the following reasons. (a) He et al. [59] did not provide a publicly accessible web-server for their method, and hence its practical application value is quite limited, particularly for the broad experimental scientists; (b) The prediction quality can be further enhanced by incorporating some key features into the formulation of NR-drug (nuclear receptor and drug) samples via the general form of pseudo amino acid composition [60] .
The present study was initiated with an attempt to develop a new method for predicting the interaction of drugs with nuclear receptors by addressing the two points.
As demonstrated by a series of recent publications [10, 18, [61] [62] [63] [64] [65] [66] [67] [68] [69] [70] and summarized in a comprehensive review [60] , to establish a really effective statistical predictor for a biomedical system, we need to consider the following steps: (a) select or construct a valid benchmark dataset to train and test the predictor; (b) represent the statistical samples with an effective formulation that can truly reflect their intrinsic correlation with the object to be predicted; (c) introduce or develop a powerful algorithm or engine to operate the prediction; (d) properly perform cross-validation tests to objectively evaluate the anticipated accuracy of the predictor; (e) establish a user-friendly web-server for the predictor that is accessible to the public. Below, let us elaborate how to deal with these steps.
The data used in the current study were collected from KEGG (Kyoto Encyclopedia of Genes and Genomes) [71] at http://www.kegg.jp/kegg/. KEGG is a database resource for understanding high-level functions and utilities of the biological system, such as the cell, the organism and the ecosystem, from molecular-level information, especially large-scale molecular datasets generated by genome sequencing and other high-throughput experimental technologies. Here, the benchmark dataset can be formulated as
where is the positive subset that consists of the interactive drug-NR pairs only, while the negative subset that contains of the non-interactive drug-NR pairs only, and the symbol represents the union in the set theory. The so-called "interactive" pair here means the pair whose two counterparts are interacting with each other in the drug-target networks as defined in the KEGG database [71] ; while the "non-interactive" pair means that its two counterparts are not interacting with each other in the drug-target networks. The positive dataset contains 86 drug-NR pairs, which were taken from He et al. [59] . The negative dataset contains 172 non-interactive drug-NR pairs, which were derived according to the following procedures: (a) separating each of the pairs in into single drug and NR; (b) re-coupling each of the single drugs with each of the single NRs into pairs in a way that none of them occurred in ; (c) randomly picking the pairs thus formed until reaching the number two times as many as the pairs in . The 86 interactive drug-NR pairs and 172 non-interactive drug-NR pairs are given in Supplementary Information S1, from which we can see that the 86 + 172 = 258 pairs in the current benchmark dataset are actually formed by 25 different NRs and 53 different compounds.
Since each of the samples in the current network system contains a drug (compound) and a NR (protein), the following procedures were taken to represent the drug-NR pair sample.
First, for the drug part in the current benchmark dataset, we can use a 256-D vector to formulate it as given by
where D represents the vector for a drug compound, and d i its i-th (i = 1,2, ,256) component that can be derived by following the "2D molecular fingerprint procedure" as elaborated in [10] . The 53 molecular fingerprint vectors thus obtained for the 53 drugs in are, respectively, given in Supplementary Information S2.
The protein sequences of the 25 different NRs in are listed in Supplementary Information S3. Suppose the sequence of a nuclear receptor protein P with L residues is generally expressed by
where 1 R represents the 1st residue of the protein sequence P , 2 R the 2nd residue, and so forth. Now the problem is how to effectively represent the sequence of Equation (3) with a non-sequential or discrete model [72] . This is because all the existing operation engines, such as covariance discriminant (CD) [17, 65, [73] [74] [75] [76] [77] [78] [79] , neural network [80] [81] [82] , support vector machine (SVM) [62] [63] [64] 83] , random forest [84, 85] , conditional random field [66] , nearest neighbor (NN) [86, 87] ; K-nearest neighbor (KNN) [88] [89] [90] , OET-KNN [91] [92] [93] [94] , and Fuzzy K-nearest neighbor [10, 12, 18, 69, 95] , can only handle vector but not sequence samples. However, a vector defined in a discrete model may completely lose all the sequence-order information and hence limit the quality of prediction. Facing such a dilemma, can we find an approach to partially incorporate the sequence-order effects? Actually, one of the most challenging problems in computational biology is how to formulate a biological sequence with a discrete model or a vector, yet still keep considerable sequence order information. To avoid completely losing the sequence-order information for proteins, the pseudo amino acid composition [96, 97] or Chou's PseAAC [98] was proposed. Ever since the concept of PseAAC was proposed in 2001 [96] , it has penetrated into almost all the areas of computational proteomics, such as predicting anticancer peptides [99] , predicting protein subcellular location [100] [101] [102] [103] [104] [105] [106] , predicting membrane protein types [107, 108] , predicting protein submitochondria locations [109] [110] [111] [112] , predicting GABA(A) receptor proteins [113] , predicting enzyme subfamily classes [114] , predicting antibacterial peptides [115] , predicting supersecondary structure [116] , predicting bacterial virulent proteins [117] , predicting protein structural class [118] , predicting the cofactors of oxidoreductases [119] , predicting metalloproteinase family [120] , identifying cysteine S-nitrosylation sites in proteins [66] , identifying bacterial secreted proteins [121] , identifying antibacterial peptides [115] , identifying allergenic proteins [122] , identifying protein quaternary structural attributes [123, 124] , identifying risk type of human papillomaviruses [125] , identifying cyclin proteins [126] , identifying GPCRs and their types [15, 16] , discriminating outer membrane proteins [127] , classifying amino acids [128] , detecting remote homologous proteins [129] , among many others (see a long list of papers cited in the References section of [60] ). Moreover, the concept of PseAAC was further extended to represent the feature vectors of nucleotides [65] , as well as other biological samples (see, e.g., [130] [131] [132] ). Because it has been widely and increasingly used, recently two powerful soft-wares, called "PseAAC-Builder" [133] and "propy" [134] , were established for generating various special Chou's pseudo-amino acid compositions, in addition to the web-server "PseAAC" [135] built in 2008.
According to a comprehensive review [60] , the general form of PseAAC for a protein sequence P is formulated by
where the subscript is an integer, and its value as well as the components ( 1, 2, , ) u u will depend on how to extract the desired information from the amino acid sequence of P (cf. Equation (3)). Below, let us describe how to extract useful information to define the components of PseAAC for the NR samples concerned. First, many earlier studies (see, e.g., [136] [137] [138] [139] [140] [141] ) have indicated that the amino acid composition (AAC) of a protein plays an important role in determining its attributes. The AAC contains 20 components with each representing the occurrence frequency of one of the 20 native amino acids in the protein concerned. Thus, such 20 AAC components were used here to define the first 20 elements in Equation (4); i.e., (1) ( 1, 2, , 20) ii fi (5) where f i (1) is the normalized occurrence frequency of the i-th type native amino acid in the nuclear receptor concerned. Since AAC did not contain any sequence order information, the following steps were taken to make up this shortcoming.
To avoid completely losing the local or short-range sequence order information, we considered the approach of dipeptide composition. It contained 20 × 20 = 400 components [142] . Such 400 components were used to define the next 400 elements in Equation (4); i.e., (2) 20 ( 1, 2, , 400) jj fj
where (2) j f is the normalized occurrence frequency of the j-th dipeptides in the nuclear receptor concerned. To incorporate the global or long-range sequence order information, let us consider the following approach. According to molecular evolution, all biological sequences have developed starting out from a very limited number of ancestral samples. Driven by various evolutionary forces such as mutation, recombination, gene conversion, genetic drift, and selection, they have undergone many changes including changes of single residues, insertions and deletions of several residues [143] , gene doubling, and gene fusion. With the accumulation of these changes over a long period of time, many original similarities between initial and resultant amino acid sequences are gradually faded out, but the corresponding proteins may still share many common attributes [37] , such as having basically the same biological function and residing at a same subcellular location [144, 145] . To extract the sequential evolution information and use it to define the components of Equation (4), the PSSM (Position Specific Scoring Matrix) was used as described below.
According to Schaffer [146] , the sequence evolution information of a nuclear receptor protein P with L amino acid residues can be expressed by a 20 L matrix, as given by
where (7) were generated by using PSI-BLAST [147] to search the UniProtKB/Swiss-Prot database (The Universal Protein Resource (UniProt); http://www.uniprot.org/) through three iterations with 0.001 as the E-value cutoff for multiple sequence alignment against the sequence of the nuclear receptor concerned. In order to make every element in Equation (7) be scaled from their original score ranges into the region of [0, 1], we performed a conversion through the standard sigmoid function to make it become
Now we extract the useful information from Equation (8)
Moreover, we used the grey system model approach as elaborated in [68] to further define the next 60 components of Equation (4) ( 1, 2, , 20)
In the above equation, w 1 , w 2 , and w 3 are weight factors, which were all set to 1 in the current study; f j (1) has the same meaning as in Equation (5)
where
and
Combining Equations (5), (6), (10) and (12), we found that the total number of the components obtained via the current approach for the PseAAC of Equation (4)
and each of the 500 components is given by (1) (
Since the elements in Equations (2) and (4) are well defined, we can now formulate the drug-NR pair by combining the two equations as given by (19) where G represents the drug-NR pair, Å the orthogonal sum, and the 256 + 500 = 756 components are defined by Equations (2) and (18) . For the sake of convenience, let us use x i (i = 1, 2, , 756) to represent the 756 components in Equation (19); i.e., (20) To optimize the prediction quality with a time-saving approach, similar to the treatment [148] [149] [150] , let us convert Equation (20) to
where the symbol means taking the average of the quantity therein, and SD means the corresponding standard derivation.
In this study, the SVM (support vector machine) was used as the operation engine. SVM has been widely used in the realm of bioinformatics (see, e.g., [62] [63] [64] [151] [152] [153] [154] ). The basic idea of SVM is to transform the data into a high dimensional feature space, and then determine the optimal separating hyperplane using a kernel function. For a brief formulation of SVM and how it works, see the papers [155, 156] ; for more details about SVM, see a monograph [157] .
In this study, the LIBSVM package [158] was used as an implementation of SVM, which can be downloaded from http://www.csie.ntu.edu.tw/~cjlin/libsvm/, the popular radial basis function (RBF) was taken as the kernel function. For the current SVM classifier, there were two uncertain parameters: penalty parameter C and kernel parameter . The method of how to determine the two parameters will be given later.
The predictor obtained via the aforementioned procedure is called iNR-Drug, where "i" means identify, and "NR-Drug" means the interaction between nuclear receptor and drug compound. To provide an intuitive overall picture, a flowchart is provided in Figure 2 to show the process of how the predictor works in identifying the interactions between nuclear receptors and drug compounds.
To provide a more intuitive and easier-to-understand method to measure the prediction quality, the following set of metrics based on the formulation used by Chou [159] [160] [161] in predicting signal peptides was adopted. According to Chou's formulation, the sensitivity, specificity, overall accuracy, and Matthew's correlation coefficient can be respectively expressed as [62, [65] [66] [67] Sn 1
where N is the total number of the interactive NR-drug pairs investigated while N the number of the interactive NR-drug pairs incorrectly predicted as the non-interactive NR-drug pairs; N the total number of the non-interactive NR-drug pairs investigated while N the number of the non-interactive NR-drug pairs incorrectly predicted as the interactive NR-drug pairs.
According to Equation (23) we can easily see the following. When 0 N meaning none of the interactive NR-drug pairs was mispredicted to be a non-interactive NR-drug pair, we have the sensitivity Sn = 1; while NN meaning that all the interactive NR-drug pairs were mispredicted to be the non-interactive NR-drug pairs, we have the sensitivity Sn = 0 . Likewise, when 0 N meaning none of the non-interactive NR-drug pairs was mispredicted, we have the specificity Sp we have MCC = 0 meaning total disagreement between prediction and observation. As we can see from the above discussion, it is much more intuitive and easier to understand when using Equation (23) to examine a predictor for its four metrics, particularly for its Mathew's correlation coefficient. It is instructive to point out that the metrics as defined in Equation (23) are valid for single label systems; for multi-label systems, a set of more complicated metrics should be used as given in [162] .
How to properly test a predictor for its anticipated success rates is very important for its development as well as its potential application value. Generally speaking, the following three cross-validation methods are often used to examine the quality of a predictor and its effectiveness in practical application: independent dataset test, subsampling or K-fold (such as five-fold, seven-fold, or 10-fold) crossover test and jackknife test [163] . However, as elaborated by a penetrating analysis in [164] , considerable arbitrariness exists in the independent dataset test. Also, as demonstrated in [165] , the subsampling (or K-fold crossover validation) test cannot avoid arbitrariness either. Only the jackknife test is the least arbitrary that can always yield a unique result for a given benchmark dataset [73, 74, 156, [166] [167] [168] . Therefore, the jackknife test has been widely recognized and increasingly utilized by investigators to examine the quality of various predictors (see, e.g., [14, 15, 68, 99, 106, 107, 124, 169, 170] ). Accordingly, in this study the jackknife test was also adopted to evaluate the accuracy of the current predictor.
As mentioned above, the SVM operation engine contains two uncertain parameters C and . To find their optimal values, a 2-D grid search was conducted by the jackknife test on the benchmark dataset . The results thus obtained are shown in Figure 3 , from which it can be seen that the iNR-Drug predictor reaches its optimal status when C = 2 3 and 9 2 . The corresponding rates for the four metrics (cf. Equation (23)) are given in Table 1 , where for facilitating comparison, the overall accuracy Acc reported by He et al. [59] on the same benchmark dataset is also given although no results were reported by them for Sn, Sp and MCC. It can be observed from the table that the overall accuracy obtained by iNR-Drug is remarkably higher that of He et al. [59] , and that the rates achieved by iNR-Drug for the other three metrics are also quite higher. These facts indicate that the current predictor not only can yield higher overall prediction accuracy but also is quite stable with low false prediction rates.
As mentioned above (Section 3.2), the jackknife test is the most objective method for examining the quality of a predictor. However, as a demonstration to show how to practically use the current predictor, we took 41 NR-drug pairs from the study by Yamanishi et al. [171] that had been confirmed by experiments as interactive pairs. For such an independent dataset, 34 were correctly identified by iNR-Drug as interactive pairs, i.e., Sn = 34 / 41 = 82.92%, which is quite consistent with the rate of 79.07% achieved by the predictor on the benchmark dataset via the jackknife test as reported in Table 1 .
It is anticipated that the iNR-Drug predictor developed in this paper may become a useful high throughput tool for both basic research and drug development, and that the current approach may be easily extended to study the interactions of drug with other targets as well. Since user-friendly and publicly accessible web-servers represent the future direction for developing practically more useful predictors [98, 172] , a publicly accessible web-server for iNR-Drug was established.
For the convenience of the vast majority of biologists and pharmaceutical scientists, here let us provide a step-by-step guide to show how the users can easily get the desired result by using iNR-Drug web-server without the need to follow the complicated mathematical equations presented in this paper for the process of developing the predictor and its integrity.
Step 1. Open the web server at the site http://www.jci-bioinfo.cn/iNR-Drug/ and you will see the top page of the predictor on your computer screen, as shown in Figure 4 . Click on the Read Me button to see a brief introduction about iNR-Drug predictor and the caveat when using it.
Step 2. Either type or copy/paste the query NR-drug pairs into the input box at the center of Figure 4 . Each query pair consists of two parts: one is for the nuclear receptor sequence, and the other for the drug. The NR sequence should be in FASTA format, while the drug in the KEGG code beginning with the symbol #. Examples for the query pairs input and the corresponding output can be seen by clicking on the Example button right above the input box. Step 3. Click on the Submit button to see the predicted result. For example, if you use the three query pairs in the Example window as the input, after clicking the Submit button, you will see on your screen that the "hsa:2099" NR and the "D00066" drug are an interactive pair, and that the "hsa:2908" NR and the "D00088" drug are also an interactive pair, but that the "hsa:5468" NR and the "D00279" drug are not an interactive pair. All these results are fully consistent with the experimental observations. It takes about 3 minutes before each of these results is shown on the screen; of course, the more query pairs there is, the more time that is usually needed.
Step 4. Click on the Citation button to find the relevant paper that documents the detailed development and algorithm of iNR-Durg.
Step 5. Click on the Data button to download the benchmark dataset used to train and test the iNR-Durg predictor.
Step 6. The program code is also available by clicking the button download on the lower panel of Figure 4 .
|
What are the shortcomings of X-ray crystallography?
| 3,264 |
time-consuming and expensive
| 3,293 |
1,572 |
iNR-Drug: Predicting the Interaction of Drugs with Nuclear Receptors in Cellular Networking
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3975431/
SHA: ee55aea26f816403476a7cb71816b8ecb1110329
Authors: Fan, Yue-Nong; Xiao, Xuan; Min, Jian-Liang; Chou, Kuo-Chen
Date: 2014-03-19
DOI: 10.3390/ijms15034915
License: cc-by
Abstract: Nuclear receptors (NRs) are closely associated with various major diseases such as cancer, diabetes, inflammatory disease, and osteoporosis. Therefore, NRs have become a frequent target for drug development. During the process of developing drugs against these diseases by targeting NRs, we are often facing a problem: Given a NR and chemical compound, can we identify whether they are really in interaction with each other in a cell? To address this problem, a predictor called “iNR-Drug” was developed. In the predictor, the drug compound concerned was formulated by a 256-D (dimensional) vector derived from its molecular fingerprint, and the NR by a 500-D vector formed by incorporating its sequential evolution information and physicochemical features into the general form of pseudo amino acid composition, and the prediction engine was operated by the SVM (support vector machine) algorithm. Compared with the existing prediction methods in this area, iNR-Drug not only can yield a higher success rate, but is also featured by a user-friendly web-server established at http://www.jci-bioinfo.cn/iNR-Drug/, which is particularly useful for most experimental scientists to obtain their desired data in a timely manner. It is anticipated that the iNR-Drug server may become a useful high throughput tool for both basic research and drug development, and that the current approach may be easily extended to study the interactions of drug with other targets as well.
Text: With the ability to directly bind to DNA ( Figure 1 ) and regulate the expression of adjacent genes, nuclear receptors (NRs) are a class of ligand-inducible transcription factors. They regulate various biological processes, such as homeostasis, differentiation, embryonic development, and organ physiology [1] [2] [3] . The NR superfamily has been classified into seven families: NR0 (knirps or DAX like) [4, 5] ; NR1 (thyroid hormone like), NR2 (HNF4-like), NR3 (estrogen like), NR4 (nerve growth factor IB-like), NR5 (fushi tarazu-F1 like), and NR6 (germ cell nuclear factor like). Since they are involved in almost all aspects of human physiology and are implicated in many major diseases such as cancer, diabetes and osteoporosis, nuclear receptors have become major drug targets [6, 7] , along with G protein-coupled receptors (GPCRs) [8] [9] [10] [11] [12] [13] [14] [15] [16] [17] , ion channels [18] [19] [20] , and kinase proteins [21] [22] [23] [24] . Identification of drug-target interactions is one of the most important steps for the new medicine development [25, 26] . The method usually adopted in this step is molecular docking simulation [27] [28] [29] [30] [31] [32] [33] [34] [35] [36] [37] [38] [39] [40] [41] [42] [43] . However, to make molecular docking study feasible, a reliable 3D (three dimensional) structure of the target protein is the prerequisite condition. Although X-ray crystallography is a powerful tool in determining protein 3D structures, it is time-consuming and expensive. Particularly, not all proteins can be successfully crystallized. For example, membrane proteins are very difficult to crystallize and most of them will not dissolve in normal solvents. Therefore, so far very few membrane protein 3D structures have been determined. Although NMR (Nuclear Magnetic Resonance) is indeed a very powerful tool in determining the 3D structures of membrane proteins as indicated by a series of recent publications (see, e.g., [44] [45] [46] [47] [48] [49] [50] [51] and a review article [20] ), it is also time-consuming and costly. To acquire the 3D structural information in a timely manner, one has to resort to various structural bioinformatics tools (see, e.g., [37] ), particularly the homologous modeling approach as utilized for a series of protein receptors urgently needed during the process of drug development [19, [52] [53] [54] [55] [56] [57] . Unfortunately, the number of dependable templates for developing high quality 3D structures by means of homology modeling is very limited [37] .
To overcome the aforementioned problems, it would be of help to develop a computational method for predicting the interactions of drugs with nuclear receptors in cellular networking based on the sequences information of the latter. The results thus obtained can be used to pre-exclude the compounds identified not in interaction with the nuclear receptors, so as to timely stop wasting time and money on those unpromising compounds [58] .
Actually, based on the functional groups and biological features, a powerful method was developed recently [59] for this purpose. However, further development in this regard is definitely needed due to the following reasons. (a) He et al. [59] did not provide a publicly accessible web-server for their method, and hence its practical application value is quite limited, particularly for the broad experimental scientists; (b) The prediction quality can be further enhanced by incorporating some key features into the formulation of NR-drug (nuclear receptor and drug) samples via the general form of pseudo amino acid composition [60] .
The present study was initiated with an attempt to develop a new method for predicting the interaction of drugs with nuclear receptors by addressing the two points.
As demonstrated by a series of recent publications [10, 18, [61] [62] [63] [64] [65] [66] [67] [68] [69] [70] and summarized in a comprehensive review [60] , to establish a really effective statistical predictor for a biomedical system, we need to consider the following steps: (a) select or construct a valid benchmark dataset to train and test the predictor; (b) represent the statistical samples with an effective formulation that can truly reflect their intrinsic correlation with the object to be predicted; (c) introduce or develop a powerful algorithm or engine to operate the prediction; (d) properly perform cross-validation tests to objectively evaluate the anticipated accuracy of the predictor; (e) establish a user-friendly web-server for the predictor that is accessible to the public. Below, let us elaborate how to deal with these steps.
The data used in the current study were collected from KEGG (Kyoto Encyclopedia of Genes and Genomes) [71] at http://www.kegg.jp/kegg/. KEGG is a database resource for understanding high-level functions and utilities of the biological system, such as the cell, the organism and the ecosystem, from molecular-level information, especially large-scale molecular datasets generated by genome sequencing and other high-throughput experimental technologies. Here, the benchmark dataset can be formulated as
where is the positive subset that consists of the interactive drug-NR pairs only, while the negative subset that contains of the non-interactive drug-NR pairs only, and the symbol represents the union in the set theory. The so-called "interactive" pair here means the pair whose two counterparts are interacting with each other in the drug-target networks as defined in the KEGG database [71] ; while the "non-interactive" pair means that its two counterparts are not interacting with each other in the drug-target networks. The positive dataset contains 86 drug-NR pairs, which were taken from He et al. [59] . The negative dataset contains 172 non-interactive drug-NR pairs, which were derived according to the following procedures: (a) separating each of the pairs in into single drug and NR; (b) re-coupling each of the single drugs with each of the single NRs into pairs in a way that none of them occurred in ; (c) randomly picking the pairs thus formed until reaching the number two times as many as the pairs in . The 86 interactive drug-NR pairs and 172 non-interactive drug-NR pairs are given in Supplementary Information S1, from which we can see that the 86 + 172 = 258 pairs in the current benchmark dataset are actually formed by 25 different NRs and 53 different compounds.
Since each of the samples in the current network system contains a drug (compound) and a NR (protein), the following procedures were taken to represent the drug-NR pair sample.
First, for the drug part in the current benchmark dataset, we can use a 256-D vector to formulate it as given by
where D represents the vector for a drug compound, and d i its i-th (i = 1,2, ,256) component that can be derived by following the "2D molecular fingerprint procedure" as elaborated in [10] . The 53 molecular fingerprint vectors thus obtained for the 53 drugs in are, respectively, given in Supplementary Information S2.
The protein sequences of the 25 different NRs in are listed in Supplementary Information S3. Suppose the sequence of a nuclear receptor protein P with L residues is generally expressed by
where 1 R represents the 1st residue of the protein sequence P , 2 R the 2nd residue, and so forth. Now the problem is how to effectively represent the sequence of Equation (3) with a non-sequential or discrete model [72] . This is because all the existing operation engines, such as covariance discriminant (CD) [17, 65, [73] [74] [75] [76] [77] [78] [79] , neural network [80] [81] [82] , support vector machine (SVM) [62] [63] [64] 83] , random forest [84, 85] , conditional random field [66] , nearest neighbor (NN) [86, 87] ; K-nearest neighbor (KNN) [88] [89] [90] , OET-KNN [91] [92] [93] [94] , and Fuzzy K-nearest neighbor [10, 12, 18, 69, 95] , can only handle vector but not sequence samples. However, a vector defined in a discrete model may completely lose all the sequence-order information and hence limit the quality of prediction. Facing such a dilemma, can we find an approach to partially incorporate the sequence-order effects? Actually, one of the most challenging problems in computational biology is how to formulate a biological sequence with a discrete model or a vector, yet still keep considerable sequence order information. To avoid completely losing the sequence-order information for proteins, the pseudo amino acid composition [96, 97] or Chou's PseAAC [98] was proposed. Ever since the concept of PseAAC was proposed in 2001 [96] , it has penetrated into almost all the areas of computational proteomics, such as predicting anticancer peptides [99] , predicting protein subcellular location [100] [101] [102] [103] [104] [105] [106] , predicting membrane protein types [107, 108] , predicting protein submitochondria locations [109] [110] [111] [112] , predicting GABA(A) receptor proteins [113] , predicting enzyme subfamily classes [114] , predicting antibacterial peptides [115] , predicting supersecondary structure [116] , predicting bacterial virulent proteins [117] , predicting protein structural class [118] , predicting the cofactors of oxidoreductases [119] , predicting metalloproteinase family [120] , identifying cysteine S-nitrosylation sites in proteins [66] , identifying bacterial secreted proteins [121] , identifying antibacterial peptides [115] , identifying allergenic proteins [122] , identifying protein quaternary structural attributes [123, 124] , identifying risk type of human papillomaviruses [125] , identifying cyclin proteins [126] , identifying GPCRs and their types [15, 16] , discriminating outer membrane proteins [127] , classifying amino acids [128] , detecting remote homologous proteins [129] , among many others (see a long list of papers cited in the References section of [60] ). Moreover, the concept of PseAAC was further extended to represent the feature vectors of nucleotides [65] , as well as other biological samples (see, e.g., [130] [131] [132] ). Because it has been widely and increasingly used, recently two powerful soft-wares, called "PseAAC-Builder" [133] and "propy" [134] , were established for generating various special Chou's pseudo-amino acid compositions, in addition to the web-server "PseAAC" [135] built in 2008.
According to a comprehensive review [60] , the general form of PseAAC for a protein sequence P is formulated by
where the subscript is an integer, and its value as well as the components ( 1, 2, , ) u u will depend on how to extract the desired information from the amino acid sequence of P (cf. Equation (3)). Below, let us describe how to extract useful information to define the components of PseAAC for the NR samples concerned. First, many earlier studies (see, e.g., [136] [137] [138] [139] [140] [141] ) have indicated that the amino acid composition (AAC) of a protein plays an important role in determining its attributes. The AAC contains 20 components with each representing the occurrence frequency of one of the 20 native amino acids in the protein concerned. Thus, such 20 AAC components were used here to define the first 20 elements in Equation (4); i.e., (1) ( 1, 2, , 20) ii fi (5) where f i (1) is the normalized occurrence frequency of the i-th type native amino acid in the nuclear receptor concerned. Since AAC did not contain any sequence order information, the following steps were taken to make up this shortcoming.
To avoid completely losing the local or short-range sequence order information, we considered the approach of dipeptide composition. It contained 20 × 20 = 400 components [142] . Such 400 components were used to define the next 400 elements in Equation (4); i.e., (2) 20 ( 1, 2, , 400) jj fj
where (2) j f is the normalized occurrence frequency of the j-th dipeptides in the nuclear receptor concerned. To incorporate the global or long-range sequence order information, let us consider the following approach. According to molecular evolution, all biological sequences have developed starting out from a very limited number of ancestral samples. Driven by various evolutionary forces such as mutation, recombination, gene conversion, genetic drift, and selection, they have undergone many changes including changes of single residues, insertions and deletions of several residues [143] , gene doubling, and gene fusion. With the accumulation of these changes over a long period of time, many original similarities between initial and resultant amino acid sequences are gradually faded out, but the corresponding proteins may still share many common attributes [37] , such as having basically the same biological function and residing at a same subcellular location [144, 145] . To extract the sequential evolution information and use it to define the components of Equation (4), the PSSM (Position Specific Scoring Matrix) was used as described below.
According to Schaffer [146] , the sequence evolution information of a nuclear receptor protein P with L amino acid residues can be expressed by a 20 L matrix, as given by
where (7) were generated by using PSI-BLAST [147] to search the UniProtKB/Swiss-Prot database (The Universal Protein Resource (UniProt); http://www.uniprot.org/) through three iterations with 0.001 as the E-value cutoff for multiple sequence alignment against the sequence of the nuclear receptor concerned. In order to make every element in Equation (7) be scaled from their original score ranges into the region of [0, 1], we performed a conversion through the standard sigmoid function to make it become
Now we extract the useful information from Equation (8)
Moreover, we used the grey system model approach as elaborated in [68] to further define the next 60 components of Equation (4) ( 1, 2, , 20)
In the above equation, w 1 , w 2 , and w 3 are weight factors, which were all set to 1 in the current study; f j (1) has the same meaning as in Equation (5)
where
and
Combining Equations (5), (6), (10) and (12), we found that the total number of the components obtained via the current approach for the PseAAC of Equation (4)
and each of the 500 components is given by (1) (
Since the elements in Equations (2) and (4) are well defined, we can now formulate the drug-NR pair by combining the two equations as given by (19) where G represents the drug-NR pair, Å the orthogonal sum, and the 256 + 500 = 756 components are defined by Equations (2) and (18) . For the sake of convenience, let us use x i (i = 1, 2, , 756) to represent the 756 components in Equation (19); i.e., (20) To optimize the prediction quality with a time-saving approach, similar to the treatment [148] [149] [150] , let us convert Equation (20) to
where the symbol means taking the average of the quantity therein, and SD means the corresponding standard derivation.
In this study, the SVM (support vector machine) was used as the operation engine. SVM has been widely used in the realm of bioinformatics (see, e.g., [62] [63] [64] [151] [152] [153] [154] ). The basic idea of SVM is to transform the data into a high dimensional feature space, and then determine the optimal separating hyperplane using a kernel function. For a brief formulation of SVM and how it works, see the papers [155, 156] ; for more details about SVM, see a monograph [157] .
In this study, the LIBSVM package [158] was used as an implementation of SVM, which can be downloaded from http://www.csie.ntu.edu.tw/~cjlin/libsvm/, the popular radial basis function (RBF) was taken as the kernel function. For the current SVM classifier, there were two uncertain parameters: penalty parameter C and kernel parameter . The method of how to determine the two parameters will be given later.
The predictor obtained via the aforementioned procedure is called iNR-Drug, where "i" means identify, and "NR-Drug" means the interaction between nuclear receptor and drug compound. To provide an intuitive overall picture, a flowchart is provided in Figure 2 to show the process of how the predictor works in identifying the interactions between nuclear receptors and drug compounds.
To provide a more intuitive and easier-to-understand method to measure the prediction quality, the following set of metrics based on the formulation used by Chou [159] [160] [161] in predicting signal peptides was adopted. According to Chou's formulation, the sensitivity, specificity, overall accuracy, and Matthew's correlation coefficient can be respectively expressed as [62, [65] [66] [67] Sn 1
where N is the total number of the interactive NR-drug pairs investigated while N the number of the interactive NR-drug pairs incorrectly predicted as the non-interactive NR-drug pairs; N the total number of the non-interactive NR-drug pairs investigated while N the number of the non-interactive NR-drug pairs incorrectly predicted as the interactive NR-drug pairs.
According to Equation (23) we can easily see the following. When 0 N meaning none of the interactive NR-drug pairs was mispredicted to be a non-interactive NR-drug pair, we have the sensitivity Sn = 1; while NN meaning that all the interactive NR-drug pairs were mispredicted to be the non-interactive NR-drug pairs, we have the sensitivity Sn = 0 . Likewise, when 0 N meaning none of the non-interactive NR-drug pairs was mispredicted, we have the specificity Sp we have MCC = 0 meaning total disagreement between prediction and observation. As we can see from the above discussion, it is much more intuitive and easier to understand when using Equation (23) to examine a predictor for its four metrics, particularly for its Mathew's correlation coefficient. It is instructive to point out that the metrics as defined in Equation (23) are valid for single label systems; for multi-label systems, a set of more complicated metrics should be used as given in [162] .
How to properly test a predictor for its anticipated success rates is very important for its development as well as its potential application value. Generally speaking, the following three cross-validation methods are often used to examine the quality of a predictor and its effectiveness in practical application: independent dataset test, subsampling or K-fold (such as five-fold, seven-fold, or 10-fold) crossover test and jackknife test [163] . However, as elaborated by a penetrating analysis in [164] , considerable arbitrariness exists in the independent dataset test. Also, as demonstrated in [165] , the subsampling (or K-fold crossover validation) test cannot avoid arbitrariness either. Only the jackknife test is the least arbitrary that can always yield a unique result for a given benchmark dataset [73, 74, 156, [166] [167] [168] . Therefore, the jackknife test has been widely recognized and increasingly utilized by investigators to examine the quality of various predictors (see, e.g., [14, 15, 68, 99, 106, 107, 124, 169, 170] ). Accordingly, in this study the jackknife test was also adopted to evaluate the accuracy of the current predictor.
As mentioned above, the SVM operation engine contains two uncertain parameters C and . To find their optimal values, a 2-D grid search was conducted by the jackknife test on the benchmark dataset . The results thus obtained are shown in Figure 3 , from which it can be seen that the iNR-Drug predictor reaches its optimal status when C = 2 3 and 9 2 . The corresponding rates for the four metrics (cf. Equation (23)) are given in Table 1 , where for facilitating comparison, the overall accuracy Acc reported by He et al. [59] on the same benchmark dataset is also given although no results were reported by them for Sn, Sp and MCC. It can be observed from the table that the overall accuracy obtained by iNR-Drug is remarkably higher that of He et al. [59] , and that the rates achieved by iNR-Drug for the other three metrics are also quite higher. These facts indicate that the current predictor not only can yield higher overall prediction accuracy but also is quite stable with low false prediction rates.
As mentioned above (Section 3.2), the jackknife test is the most objective method for examining the quality of a predictor. However, as a demonstration to show how to practically use the current predictor, we took 41 NR-drug pairs from the study by Yamanishi et al. [171] that had been confirmed by experiments as interactive pairs. For such an independent dataset, 34 were correctly identified by iNR-Drug as interactive pairs, i.e., Sn = 34 / 41 = 82.92%, which is quite consistent with the rate of 79.07% achieved by the predictor on the benchmark dataset via the jackknife test as reported in Table 1 .
It is anticipated that the iNR-Drug predictor developed in this paper may become a useful high throughput tool for both basic research and drug development, and that the current approach may be easily extended to study the interactions of drug with other targets as well. Since user-friendly and publicly accessible web-servers represent the future direction for developing practically more useful predictors [98, 172] , a publicly accessible web-server for iNR-Drug was established.
For the convenience of the vast majority of biologists and pharmaceutical scientists, here let us provide a step-by-step guide to show how the users can easily get the desired result by using iNR-Drug web-server without the need to follow the complicated mathematical equations presented in this paper for the process of developing the predictor and its integrity.
Step 1. Open the web server at the site http://www.jci-bioinfo.cn/iNR-Drug/ and you will see the top page of the predictor on your computer screen, as shown in Figure 4 . Click on the Read Me button to see a brief introduction about iNR-Drug predictor and the caveat when using it.
Step 2. Either type or copy/paste the query NR-drug pairs into the input box at the center of Figure 4 . Each query pair consists of two parts: one is for the nuclear receptor sequence, and the other for the drug. The NR sequence should be in FASTA format, while the drug in the KEGG code beginning with the symbol #. Examples for the query pairs input and the corresponding output can be seen by clicking on the Example button right above the input box. Step 3. Click on the Submit button to see the predicted result. For example, if you use the three query pairs in the Example window as the input, after clicking the Submit button, you will see on your screen that the "hsa:2099" NR and the "D00066" drug are an interactive pair, and that the "hsa:2908" NR and the "D00088" drug are also an interactive pair, but that the "hsa:5468" NR and the "D00279" drug are not an interactive pair. All these results are fully consistent with the experimental observations. It takes about 3 minutes before each of these results is shown on the screen; of course, the more query pairs there is, the more time that is usually needed.
Step 4. Click on the Citation button to find the relevant paper that documents the detailed development and algorithm of iNR-Durg.
Step 5. Click on the Data button to download the benchmark dataset used to train and test the iNR-Durg predictor.
Step 6. The program code is also available by clicking the button download on the lower panel of Figure 4 .
|
What types of proteins are difficult to crystallize?
| 3,265 |
membrane proteins
| 3,401 |
1,572 |
iNR-Drug: Predicting the Interaction of Drugs with Nuclear Receptors in Cellular Networking
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3975431/
SHA: ee55aea26f816403476a7cb71816b8ecb1110329
Authors: Fan, Yue-Nong; Xiao, Xuan; Min, Jian-Liang; Chou, Kuo-Chen
Date: 2014-03-19
DOI: 10.3390/ijms15034915
License: cc-by
Abstract: Nuclear receptors (NRs) are closely associated with various major diseases such as cancer, diabetes, inflammatory disease, and osteoporosis. Therefore, NRs have become a frequent target for drug development. During the process of developing drugs against these diseases by targeting NRs, we are often facing a problem: Given a NR and chemical compound, can we identify whether they are really in interaction with each other in a cell? To address this problem, a predictor called “iNR-Drug” was developed. In the predictor, the drug compound concerned was formulated by a 256-D (dimensional) vector derived from its molecular fingerprint, and the NR by a 500-D vector formed by incorporating its sequential evolution information and physicochemical features into the general form of pseudo amino acid composition, and the prediction engine was operated by the SVM (support vector machine) algorithm. Compared with the existing prediction methods in this area, iNR-Drug not only can yield a higher success rate, but is also featured by a user-friendly web-server established at http://www.jci-bioinfo.cn/iNR-Drug/, which is particularly useful for most experimental scientists to obtain their desired data in a timely manner. It is anticipated that the iNR-Drug server may become a useful high throughput tool for both basic research and drug development, and that the current approach may be easily extended to study the interactions of drug with other targets as well.
Text: With the ability to directly bind to DNA ( Figure 1 ) and regulate the expression of adjacent genes, nuclear receptors (NRs) are a class of ligand-inducible transcription factors. They regulate various biological processes, such as homeostasis, differentiation, embryonic development, and organ physiology [1] [2] [3] . The NR superfamily has been classified into seven families: NR0 (knirps or DAX like) [4, 5] ; NR1 (thyroid hormone like), NR2 (HNF4-like), NR3 (estrogen like), NR4 (nerve growth factor IB-like), NR5 (fushi tarazu-F1 like), and NR6 (germ cell nuclear factor like). Since they are involved in almost all aspects of human physiology and are implicated in many major diseases such as cancer, diabetes and osteoporosis, nuclear receptors have become major drug targets [6, 7] , along with G protein-coupled receptors (GPCRs) [8] [9] [10] [11] [12] [13] [14] [15] [16] [17] , ion channels [18] [19] [20] , and kinase proteins [21] [22] [23] [24] . Identification of drug-target interactions is one of the most important steps for the new medicine development [25, 26] . The method usually adopted in this step is molecular docking simulation [27] [28] [29] [30] [31] [32] [33] [34] [35] [36] [37] [38] [39] [40] [41] [42] [43] . However, to make molecular docking study feasible, a reliable 3D (three dimensional) structure of the target protein is the prerequisite condition. Although X-ray crystallography is a powerful tool in determining protein 3D structures, it is time-consuming and expensive. Particularly, not all proteins can be successfully crystallized. For example, membrane proteins are very difficult to crystallize and most of them will not dissolve in normal solvents. Therefore, so far very few membrane protein 3D structures have been determined. Although NMR (Nuclear Magnetic Resonance) is indeed a very powerful tool in determining the 3D structures of membrane proteins as indicated by a series of recent publications (see, e.g., [44] [45] [46] [47] [48] [49] [50] [51] and a review article [20] ), it is also time-consuming and costly. To acquire the 3D structural information in a timely manner, one has to resort to various structural bioinformatics tools (see, e.g., [37] ), particularly the homologous modeling approach as utilized for a series of protein receptors urgently needed during the process of drug development [19, [52] [53] [54] [55] [56] [57] . Unfortunately, the number of dependable templates for developing high quality 3D structures by means of homology modeling is very limited [37] .
To overcome the aforementioned problems, it would be of help to develop a computational method for predicting the interactions of drugs with nuclear receptors in cellular networking based on the sequences information of the latter. The results thus obtained can be used to pre-exclude the compounds identified not in interaction with the nuclear receptors, so as to timely stop wasting time and money on those unpromising compounds [58] .
Actually, based on the functional groups and biological features, a powerful method was developed recently [59] for this purpose. However, further development in this regard is definitely needed due to the following reasons. (a) He et al. [59] did not provide a publicly accessible web-server for their method, and hence its practical application value is quite limited, particularly for the broad experimental scientists; (b) The prediction quality can be further enhanced by incorporating some key features into the formulation of NR-drug (nuclear receptor and drug) samples via the general form of pseudo amino acid composition [60] .
The present study was initiated with an attempt to develop a new method for predicting the interaction of drugs with nuclear receptors by addressing the two points.
As demonstrated by a series of recent publications [10, 18, [61] [62] [63] [64] [65] [66] [67] [68] [69] [70] and summarized in a comprehensive review [60] , to establish a really effective statistical predictor for a biomedical system, we need to consider the following steps: (a) select or construct a valid benchmark dataset to train and test the predictor; (b) represent the statistical samples with an effective formulation that can truly reflect their intrinsic correlation with the object to be predicted; (c) introduce or develop a powerful algorithm or engine to operate the prediction; (d) properly perform cross-validation tests to objectively evaluate the anticipated accuracy of the predictor; (e) establish a user-friendly web-server for the predictor that is accessible to the public. Below, let us elaborate how to deal with these steps.
The data used in the current study were collected from KEGG (Kyoto Encyclopedia of Genes and Genomes) [71] at http://www.kegg.jp/kegg/. KEGG is a database resource for understanding high-level functions and utilities of the biological system, such as the cell, the organism and the ecosystem, from molecular-level information, especially large-scale molecular datasets generated by genome sequencing and other high-throughput experimental technologies. Here, the benchmark dataset can be formulated as
where is the positive subset that consists of the interactive drug-NR pairs only, while the negative subset that contains of the non-interactive drug-NR pairs only, and the symbol represents the union in the set theory. The so-called "interactive" pair here means the pair whose two counterparts are interacting with each other in the drug-target networks as defined in the KEGG database [71] ; while the "non-interactive" pair means that its two counterparts are not interacting with each other in the drug-target networks. The positive dataset contains 86 drug-NR pairs, which were taken from He et al. [59] . The negative dataset contains 172 non-interactive drug-NR pairs, which were derived according to the following procedures: (a) separating each of the pairs in into single drug and NR; (b) re-coupling each of the single drugs with each of the single NRs into pairs in a way that none of them occurred in ; (c) randomly picking the pairs thus formed until reaching the number two times as many as the pairs in . The 86 interactive drug-NR pairs and 172 non-interactive drug-NR pairs are given in Supplementary Information S1, from which we can see that the 86 + 172 = 258 pairs in the current benchmark dataset are actually formed by 25 different NRs and 53 different compounds.
Since each of the samples in the current network system contains a drug (compound) and a NR (protein), the following procedures were taken to represent the drug-NR pair sample.
First, for the drug part in the current benchmark dataset, we can use a 256-D vector to formulate it as given by
where D represents the vector for a drug compound, and d i its i-th (i = 1,2, ,256) component that can be derived by following the "2D molecular fingerprint procedure" as elaborated in [10] . The 53 molecular fingerprint vectors thus obtained for the 53 drugs in are, respectively, given in Supplementary Information S2.
The protein sequences of the 25 different NRs in are listed in Supplementary Information S3. Suppose the sequence of a nuclear receptor protein P with L residues is generally expressed by
where 1 R represents the 1st residue of the protein sequence P , 2 R the 2nd residue, and so forth. Now the problem is how to effectively represent the sequence of Equation (3) with a non-sequential or discrete model [72] . This is because all the existing operation engines, such as covariance discriminant (CD) [17, 65, [73] [74] [75] [76] [77] [78] [79] , neural network [80] [81] [82] , support vector machine (SVM) [62] [63] [64] 83] , random forest [84, 85] , conditional random field [66] , nearest neighbor (NN) [86, 87] ; K-nearest neighbor (KNN) [88] [89] [90] , OET-KNN [91] [92] [93] [94] , and Fuzzy K-nearest neighbor [10, 12, 18, 69, 95] , can only handle vector but not sequence samples. However, a vector defined in a discrete model may completely lose all the sequence-order information and hence limit the quality of prediction. Facing such a dilemma, can we find an approach to partially incorporate the sequence-order effects? Actually, one of the most challenging problems in computational biology is how to formulate a biological sequence with a discrete model or a vector, yet still keep considerable sequence order information. To avoid completely losing the sequence-order information for proteins, the pseudo amino acid composition [96, 97] or Chou's PseAAC [98] was proposed. Ever since the concept of PseAAC was proposed in 2001 [96] , it has penetrated into almost all the areas of computational proteomics, such as predicting anticancer peptides [99] , predicting protein subcellular location [100] [101] [102] [103] [104] [105] [106] , predicting membrane protein types [107, 108] , predicting protein submitochondria locations [109] [110] [111] [112] , predicting GABA(A) receptor proteins [113] , predicting enzyme subfamily classes [114] , predicting antibacterial peptides [115] , predicting supersecondary structure [116] , predicting bacterial virulent proteins [117] , predicting protein structural class [118] , predicting the cofactors of oxidoreductases [119] , predicting metalloproteinase family [120] , identifying cysteine S-nitrosylation sites in proteins [66] , identifying bacterial secreted proteins [121] , identifying antibacterial peptides [115] , identifying allergenic proteins [122] , identifying protein quaternary structural attributes [123, 124] , identifying risk type of human papillomaviruses [125] , identifying cyclin proteins [126] , identifying GPCRs and their types [15, 16] , discriminating outer membrane proteins [127] , classifying amino acids [128] , detecting remote homologous proteins [129] , among many others (see a long list of papers cited in the References section of [60] ). Moreover, the concept of PseAAC was further extended to represent the feature vectors of nucleotides [65] , as well as other biological samples (see, e.g., [130] [131] [132] ). Because it has been widely and increasingly used, recently two powerful soft-wares, called "PseAAC-Builder" [133] and "propy" [134] , were established for generating various special Chou's pseudo-amino acid compositions, in addition to the web-server "PseAAC" [135] built in 2008.
According to a comprehensive review [60] , the general form of PseAAC for a protein sequence P is formulated by
where the subscript is an integer, and its value as well as the components ( 1, 2, , ) u u will depend on how to extract the desired information from the amino acid sequence of P (cf. Equation (3)). Below, let us describe how to extract useful information to define the components of PseAAC for the NR samples concerned. First, many earlier studies (see, e.g., [136] [137] [138] [139] [140] [141] ) have indicated that the amino acid composition (AAC) of a protein plays an important role in determining its attributes. The AAC contains 20 components with each representing the occurrence frequency of one of the 20 native amino acids in the protein concerned. Thus, such 20 AAC components were used here to define the first 20 elements in Equation (4); i.e., (1) ( 1, 2, , 20) ii fi (5) where f i (1) is the normalized occurrence frequency of the i-th type native amino acid in the nuclear receptor concerned. Since AAC did not contain any sequence order information, the following steps were taken to make up this shortcoming.
To avoid completely losing the local or short-range sequence order information, we considered the approach of dipeptide composition. It contained 20 × 20 = 400 components [142] . Such 400 components were used to define the next 400 elements in Equation (4); i.e., (2) 20 ( 1, 2, , 400) jj fj
where (2) j f is the normalized occurrence frequency of the j-th dipeptides in the nuclear receptor concerned. To incorporate the global or long-range sequence order information, let us consider the following approach. According to molecular evolution, all biological sequences have developed starting out from a very limited number of ancestral samples. Driven by various evolutionary forces such as mutation, recombination, gene conversion, genetic drift, and selection, they have undergone many changes including changes of single residues, insertions and deletions of several residues [143] , gene doubling, and gene fusion. With the accumulation of these changes over a long period of time, many original similarities between initial and resultant amino acid sequences are gradually faded out, but the corresponding proteins may still share many common attributes [37] , such as having basically the same biological function and residing at a same subcellular location [144, 145] . To extract the sequential evolution information and use it to define the components of Equation (4), the PSSM (Position Specific Scoring Matrix) was used as described below.
According to Schaffer [146] , the sequence evolution information of a nuclear receptor protein P with L amino acid residues can be expressed by a 20 L matrix, as given by
where (7) were generated by using PSI-BLAST [147] to search the UniProtKB/Swiss-Prot database (The Universal Protein Resource (UniProt); http://www.uniprot.org/) through three iterations with 0.001 as the E-value cutoff for multiple sequence alignment against the sequence of the nuclear receptor concerned. In order to make every element in Equation (7) be scaled from their original score ranges into the region of [0, 1], we performed a conversion through the standard sigmoid function to make it become
Now we extract the useful information from Equation (8)
Moreover, we used the grey system model approach as elaborated in [68] to further define the next 60 components of Equation (4) ( 1, 2, , 20)
In the above equation, w 1 , w 2 , and w 3 are weight factors, which were all set to 1 in the current study; f j (1) has the same meaning as in Equation (5)
where
and
Combining Equations (5), (6), (10) and (12), we found that the total number of the components obtained via the current approach for the PseAAC of Equation (4)
and each of the 500 components is given by (1) (
Since the elements in Equations (2) and (4) are well defined, we can now formulate the drug-NR pair by combining the two equations as given by (19) where G represents the drug-NR pair, Å the orthogonal sum, and the 256 + 500 = 756 components are defined by Equations (2) and (18) . For the sake of convenience, let us use x i (i = 1, 2, , 756) to represent the 756 components in Equation (19); i.e., (20) To optimize the prediction quality with a time-saving approach, similar to the treatment [148] [149] [150] , let us convert Equation (20) to
where the symbol means taking the average of the quantity therein, and SD means the corresponding standard derivation.
In this study, the SVM (support vector machine) was used as the operation engine. SVM has been widely used in the realm of bioinformatics (see, e.g., [62] [63] [64] [151] [152] [153] [154] ). The basic idea of SVM is to transform the data into a high dimensional feature space, and then determine the optimal separating hyperplane using a kernel function. For a brief formulation of SVM and how it works, see the papers [155, 156] ; for more details about SVM, see a monograph [157] .
In this study, the LIBSVM package [158] was used as an implementation of SVM, which can be downloaded from http://www.csie.ntu.edu.tw/~cjlin/libsvm/, the popular radial basis function (RBF) was taken as the kernel function. For the current SVM classifier, there were two uncertain parameters: penalty parameter C and kernel parameter . The method of how to determine the two parameters will be given later.
The predictor obtained via the aforementioned procedure is called iNR-Drug, where "i" means identify, and "NR-Drug" means the interaction between nuclear receptor and drug compound. To provide an intuitive overall picture, a flowchart is provided in Figure 2 to show the process of how the predictor works in identifying the interactions between nuclear receptors and drug compounds.
To provide a more intuitive and easier-to-understand method to measure the prediction quality, the following set of metrics based on the formulation used by Chou [159] [160] [161] in predicting signal peptides was adopted. According to Chou's formulation, the sensitivity, specificity, overall accuracy, and Matthew's correlation coefficient can be respectively expressed as [62, [65] [66] [67] Sn 1
where N is the total number of the interactive NR-drug pairs investigated while N the number of the interactive NR-drug pairs incorrectly predicted as the non-interactive NR-drug pairs; N the total number of the non-interactive NR-drug pairs investigated while N the number of the non-interactive NR-drug pairs incorrectly predicted as the interactive NR-drug pairs.
According to Equation (23) we can easily see the following. When 0 N meaning none of the interactive NR-drug pairs was mispredicted to be a non-interactive NR-drug pair, we have the sensitivity Sn = 1; while NN meaning that all the interactive NR-drug pairs were mispredicted to be the non-interactive NR-drug pairs, we have the sensitivity Sn = 0 . Likewise, when 0 N meaning none of the non-interactive NR-drug pairs was mispredicted, we have the specificity Sp we have MCC = 0 meaning total disagreement between prediction and observation. As we can see from the above discussion, it is much more intuitive and easier to understand when using Equation (23) to examine a predictor for its four metrics, particularly for its Mathew's correlation coefficient. It is instructive to point out that the metrics as defined in Equation (23) are valid for single label systems; for multi-label systems, a set of more complicated metrics should be used as given in [162] .
How to properly test a predictor for its anticipated success rates is very important for its development as well as its potential application value. Generally speaking, the following three cross-validation methods are often used to examine the quality of a predictor and its effectiveness in practical application: independent dataset test, subsampling or K-fold (such as five-fold, seven-fold, or 10-fold) crossover test and jackknife test [163] . However, as elaborated by a penetrating analysis in [164] , considerable arbitrariness exists in the independent dataset test. Also, as demonstrated in [165] , the subsampling (or K-fold crossover validation) test cannot avoid arbitrariness either. Only the jackknife test is the least arbitrary that can always yield a unique result for a given benchmark dataset [73, 74, 156, [166] [167] [168] . Therefore, the jackknife test has been widely recognized and increasingly utilized by investigators to examine the quality of various predictors (see, e.g., [14, 15, 68, 99, 106, 107, 124, 169, 170] ). Accordingly, in this study the jackknife test was also adopted to evaluate the accuracy of the current predictor.
As mentioned above, the SVM operation engine contains two uncertain parameters C and . To find their optimal values, a 2-D grid search was conducted by the jackknife test on the benchmark dataset . The results thus obtained are shown in Figure 3 , from which it can be seen that the iNR-Drug predictor reaches its optimal status when C = 2 3 and 9 2 . The corresponding rates for the four metrics (cf. Equation (23)) are given in Table 1 , where for facilitating comparison, the overall accuracy Acc reported by He et al. [59] on the same benchmark dataset is also given although no results were reported by them for Sn, Sp and MCC. It can be observed from the table that the overall accuracy obtained by iNR-Drug is remarkably higher that of He et al. [59] , and that the rates achieved by iNR-Drug for the other three metrics are also quite higher. These facts indicate that the current predictor not only can yield higher overall prediction accuracy but also is quite stable with low false prediction rates.
As mentioned above (Section 3.2), the jackknife test is the most objective method for examining the quality of a predictor. However, as a demonstration to show how to practically use the current predictor, we took 41 NR-drug pairs from the study by Yamanishi et al. [171] that had been confirmed by experiments as interactive pairs. For such an independent dataset, 34 were correctly identified by iNR-Drug as interactive pairs, i.e., Sn = 34 / 41 = 82.92%, which is quite consistent with the rate of 79.07% achieved by the predictor on the benchmark dataset via the jackknife test as reported in Table 1 .
It is anticipated that the iNR-Drug predictor developed in this paper may become a useful high throughput tool for both basic research and drug development, and that the current approach may be easily extended to study the interactions of drug with other targets as well. Since user-friendly and publicly accessible web-servers represent the future direction for developing practically more useful predictors [98, 172] , a publicly accessible web-server for iNR-Drug was established.
For the convenience of the vast majority of biologists and pharmaceutical scientists, here let us provide a step-by-step guide to show how the users can easily get the desired result by using iNR-Drug web-server without the need to follow the complicated mathematical equations presented in this paper for the process of developing the predictor and its integrity.
Step 1. Open the web server at the site http://www.jci-bioinfo.cn/iNR-Drug/ and you will see the top page of the predictor on your computer screen, as shown in Figure 4 . Click on the Read Me button to see a brief introduction about iNR-Drug predictor and the caveat when using it.
Step 2. Either type or copy/paste the query NR-drug pairs into the input box at the center of Figure 4 . Each query pair consists of two parts: one is for the nuclear receptor sequence, and the other for the drug. The NR sequence should be in FASTA format, while the drug in the KEGG code beginning with the symbol #. Examples for the query pairs input and the corresponding output can be seen by clicking on the Example button right above the input box. Step 3. Click on the Submit button to see the predicted result. For example, if you use the three query pairs in the Example window as the input, after clicking the Submit button, you will see on your screen that the "hsa:2099" NR and the "D00066" drug are an interactive pair, and that the "hsa:2908" NR and the "D00088" drug are also an interactive pair, but that the "hsa:5468" NR and the "D00279" drug are not an interactive pair. All these results are fully consistent with the experimental observations. It takes about 3 minutes before each of these results is shown on the screen; of course, the more query pairs there is, the more time that is usually needed.
Step 4. Click on the Citation button to find the relevant paper that documents the detailed development and algorithm of iNR-Durg.
Step 5. Click on the Data button to download the benchmark dataset used to train and test the iNR-Durg predictor.
Step 6. The program code is also available by clicking the button download on the lower panel of Figure 4 .
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Where were the data collected for this study?
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KEGG (Kyoto Encyclopedia of Genes and Genomes)
| 6,509 |
1,576 |
Characterization of a New Member of Alphacoronavirus with Unique Genomic Features in Rhinolophus Bats
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6521148/
SHA: ee14de143337eec0e9708f8139bfac2b7b8fdd27
Authors: Wang, Ning; Luo, Chuming; Liu, Haizhou; Yang, Xinglou; Hu, Ben; Zhang, Wei; Li, Bei; Zhu, Yan; Zhu, Guangjian; Shen, Xurui; Peng, Cheng; Shi, Zhengli
Date: 2019-04-24
DOI: 10.3390/v11040379
License: cc-by
Abstract: Bats have been identified as a natural reservoir of a variety of coronaviruses (CoVs). Several of them have caused diseases in humans and domestic animals by interspecies transmission. Considering the diversity of bat coronaviruses, bat species and populations, we expect to discover more bat CoVs through virus surveillance. In this study, we described a new member of alphaCoV (BtCoV/Rh/YN2012) in bats with unique genome features. Unique accessory genes, ORF4a and ORF4b were found between the spike gene and the envelope gene, while ORF8 gene was found downstream of the nucleocapsid gene. All the putative genes were further confirmed by reverse-transcription analyses. One unique gene at the 3’ end of the BtCoV/Rh/YN2012 genome, ORF9, exhibits ~30% amino acid identity to ORF7a of the SARS-related coronavirus. Functional analysis showed ORF4a protein can activate IFN-β production, whereas ORF3a can regulate NF-κB production. We also screened the spike-mediated virus entry using the spike-pseudotyped retroviruses system, although failed to find any fully permissive cells. Our results expand the knowledge on the genetic diversity of bat coronaviruses. Continuous screening of bat viruses will help us further understand the important role played by bats in coronavirus evolution and transmission.
Text: Members of the Coronaviridae family are enveloped, non-segmented, positive-strand RNA viruses with genome sizes ranging from 26-32 kb [1] . These viruses are classified into two subfamilies: Letovirinae, which contains the only genus: Alphaletovirus; and Orthocoronavirinae (CoV), which consists of alpha, beta, gamma, and deltacoronaviruses (CoVs) [2, 3] . Alpha and betacoronaviruses mainly infect mammals and cause human and animal diseases. Gamma-and delta-CoVs mainly infect birds, but some can also infect mammals [4, 5] . Six human CoVs (HCoVs) are known to cause human diseases. HCoV-HKU1, HCoV-OC43, HCoV-229E, and HCoV-NL63 commonly cause mild respiratory illness or asymptomatic infection; however, severe acute respiratory syndrome coronavirus (SARS-CoV) and
All sampling procedures were performed by veterinarians, with approval from Animal Ethics Committee of the Wuhan Institute of Virology (WIVH5210201). The study was conducted in accordance with the Guide for the Care and Use of Wild Mammals in Research of the People's Republic of China.
Bat fecal swab and pellet samples were collected from November 2004 to November 2014 in different seasons in Southern China, as described previously [16] .
Viral RNA was extracted from 200 µL of fecal swab or pellet samples using the High Pure Viral RNA Kit (Roche Diagnostics GmbH, Mannheim, Germany) as per the manufacturer's instructions. RNA was eluted in 50 µL of elution buffer, aliquoted, and stored at -80 • C. One-step hemi-nested reverse-transcription (RT-) PCR (Invitrogen, San Diego, CA, USA) was employed to detect coronavirus, as previously described [17, 18] .
To confirm the bat species of an individual sample, we PCR amplified the cytochrome b (Cytob) and/or NADH dehydrogenase subunit 1 (ND1) gene using DNA extracted from the feces or swabs [19, 20] . The gene sequences were assembled excluding the primer sequences. BLASTN was used to identify host species based on the most closely related sequences with the highest query coverage and a minimum identity of 95%.
Full genomic sequences were determined by one-step PCR (Invitrogen, San Diego, CA, USA) amplification with degenerate primers (Table S1 ) designed on the basis of multiple alignments of available alpha-CoV sequences deposited in GenBank or amplified with SuperScript IV Reverse Transcriptase (Invitrogen) and Expand Long Template PCR System (Roche Diagnostics GmbH, Mannheim, Germany) with specific primers (primer sequences are available upon request). Sequences of the 5' and 3' genomic ends were obtained by 5' and 3' rapid amplification of cDNA ends (SMARTer Viruses 2019, 11, 379 3 of 19 RACE 5'/3' Kit; Clontech, Mountain View, CA, USA), respectively. PCR products were gel-purified and subjected directly to sequencing. PCR products over 5kb were subjected to deep sequencing using Hiseq2500 system. For some fragments, the PCR products were cloned into the pGEM-T Easy Vector (Promega, Madison, WI, USA) for sequencing. At least five independent clones were sequenced to obtain a consensus sequence.
The Next Generation Sequencing (NGS) data were filtered and mapped to the reference sequence of BatCoV HKU10 (GenBank accession number NC_018871) using Geneious 7.1.8 [21] . Genomes were preliminarily assembled using DNAStar lasergene V7 (DNAStar, Madison, WI, USA). Putative open reading frames (ORFs) were predicted using NCBI's ORF finder (https://www.ncbi.nlm.nih.gov/ orffinder/) with a minimal ORF length of 150 nt, followed by manual inspection. The sequences of the 5' untranslated region (5'-UTR) and 3'-UTR were defined, and the leader sequence, the leader and body transcriptional regulatory sequence (TRS) were identified as previously described [22] . The cleavage of the 16 nonstructural proteins coded by ORF1ab was determined by alignment of aa sequences of other CoVs and the recognition pattern of the 3C-like proteinase and papain-like proteinase. Phylogenetic trees based on nt or aa sequences were constructed using the maximum likelihood algorithm with bootstrap values determined by 1000 replicates in the MEGA 6 software package [23] . Full-length genome sequences obtained in this study were aligned with those of previously reported alpha-CoVs using MUSCLE [24] . The aligned sequences were scanned for recombination events by using Recombination Detection Program [25] . Potential recombination events as suggested by strong p-values (<10 -20 ) were confirmed using similarity plot and bootscan analyses implemented in Simplot 3.5.1 [26] . The number of synonymous substitutions per synonymous site, Ks, and the number of nonsynonymous substitutions per nonsynonymous site, Ka, for each coding region were calculated using the Ka/Ks calculation tool of the Norwegian Bioinformatics Platform (http://services.cbu.uib.no/tools/kaks) with default parameters [27] . The protein homology detection was analyzed using HHpred (https://toolkit.tuebingen.mpg.de/#/tools/hhpred) with default parameters [28] .
A set of nested RT-PCRs was employed to determine the presence of viral subgenomic mRNAs in the CoV-positive samples [29] . Forward primers were designed targeting the leader sequence at the 5'-end of the complete genome, while reverse primers were designed within the ORFs. Specific and suspected amplicons of expected sizes were purified and then cloned into the pGEM-T Easy vector for sequencing.
Bat primary or immortalized cells (Rhinolophus sinicus kidney immortalized cells, RsKT; Rhinolophus sinicus Lung primary cells, RsLu4323; Rhinolophus sinicus brain immortalized cells, RsBrT; Rhinolophus affinis kidney primary cells, RaK4324; Rousettus leschenaultii Kidney immortalized cells, RlKT; Hipposideros pratti lung immortalized cells, HpLuT) generated in our laboratory were all cultured in DMEM/F12 with 15% FBS. Pteropus alecto kidney cells (Paki) was maintained in DMEM/F12 supplemented with 10% FBS. Other cells were maintained according to the recommendations of American Type Culture Collection (ATCC, www.atcc.org).
The putative accessory genes of the newly detected virus were generated by RT-PCR from viral RNA extracted from fecal samples, as described previously [30] . The influenza virus NS1 plasmid was generated in our lab [31] . The human bocavirus (HBoV) VP2 plasmid was kindly provided by prof. Hanzhong Wang of the Wuhan Institute of Virology, Chinese Academy of Sciences. SARS-CoV ORF7a was synthesized by Sangon Biotech. The transfections were performed with Lipofectamine 3000 Reagent (Life Technologies). Expression of these accessory genes were analyzed by Western blotting using an mAb (Roche Diagnostics GmbH, Mannheim, Germany) against the HA tag.
The virus isolation was performed as previously described [12] . Briefly, fecal supernatant was acquired via gradient centrifugation and then added to Vero E6 cells, 1:10 diluted in DMEM. After incubation at 37°C for 1 h the inoculum was replaced by fresh DMEM containing 2% FBS and the antibiotic-antimycotic (Gibco, Grand Island, NY, USA). Three blind passages were carried out. Cells were checked daily for cytopathic effect. Both culture supernatant and cell pellet were examined for CoV by RT-PCR [17] .
Apoptosis was analyzed as previously described [18] . Briefly, 293T cells in 12-well plates were transfected with 3 µg of expression plasmid or empty vector, and the cells were collected 24 h post transfection. Apoptosis was detected by flow cytometry using by the Annexin V-FITC/PI Apoptosis Detection Kit (YEASEN, Shanghai, China) following the manufacturer's instructions. Annexin-V-positive and PI-negative cells were considered to be in the early apoptotic phase and those stained for both Annexin V and PI were deemed to undergo late apoptosis or necrosis. All experiments were repeated three times. Student's t-test was used to evaluate the data, with p < 0.05 considered significant.
HEK 293T cells were seeded in 24-well plates and then co-transfected with reporter plasmids (pRL-TK and pIFN-βIFN-or pNF-κB-Luc) [30] , as well as plasmids expressing accessory genes, empty vector plasmid pcAGGS, influenza virus NS1 [32] , SARS-CoV ORF7a [33] , or HBoV VP2 [34] . At 24 h post transfection, cells were treated with Sendai virus (SeV) (100 hemagglutinin units [HAU]/mL) or human tumor necrosis factor alpha (TNF-α; R&D system) for 6 h to activate IFNβ or NF-κB, respectively. Cell lysates were prepared, and luciferase activity was measured using the dual-luciferase assay kit (Promega, Madison, WI, USA) according to the manufacturer's instructions.
Retroviruses pseudotyped with BtCoV/Rh/YN2012 RsYN1, RsYN3, RaGD, or MERS-CoV spike, or no spike (mock) were used to infect human, bat or other mammalian cells in 96-well plates. The pseudovirus particles were confirmed with Western blotting and negative-staining electromicroscopy. The production process, measurements of infection and luciferase activity were conducted, as described previously [35, 36] .
The complete genome nucleotide sequences of BtCoV/Rh/YN2012 strains RsYN1, RsYN2, RsYN3, and RaGD obtained in this study have been submitted to the GenBank under MG916901 to MG916904.
The surveillance was performed between November 2004 to November 2014 in 19 provinces of China. In total, 2061 fecal samples were collected from at least 12 Rhinolophus bat species ( Figure 1A ). CoVs were detected in 209 of these samples ( Figure 1B and Table 1 ). Partial RdRp sequences suggested the presence of at least 8 different CoVs. Five of these viruses are related to known species: Mi-BatCoV 1 (>94% nt identity), Mi-BatCoV HKU8 [37] (>93% nt identity), BtRf-AlphaCoV/HuB2013 [11] (>99% nt identity), SARSr-CoV [38] (>89% nt identity), and HKU2-related CoV [39] (>85% nt identity). While the other three CoV sequences showed less than 83% nt identity to known CoV species. These three viruses should represent novel CoV species. Virus isolation was performed as previously described [12] , but was not successful. identity). While the other three CoV sequences showed less than 83% nt identity to known CoV species. These three viruses should represent novel CoV species. Virus isolation was performed as previously described [12] , but was not successful.
We next characterized a novel alpha-CoV, BtCoV/Rh/YN2012. It was detected in 3 R.affinis and 6 R.sinicus, respectively. Based on the sequences, we defined three genotypes, which represented by RsYN1, RsYN3, and RaGD, respectively. Strain RsYN2 was classified into the RsYN3 genotype. Four full-length genomes were obtained. Three of them were from R.sinicus (Strain RsYN1, RsYN2, and RsYN3), while the other one was from R.affinis (Strain RaGD). The sizes of these 4 genomes are between 28,715 to 29,102, with G+C contents between 39.0% to 41.3%. The genomes exhibit similar structures and transcription regulatory sequences (TRS) that are identical to those of other alpha-CoVs ( Figure 2 and Table 2 ). Exceptions including three additional ORFs (ORF3b, ORF4a and ORF4b) were observed. All the 4 strains have ORF4a & ORF4b, while only strain RsYN1 has ORF3b.
The replicase gene, ORF1ab, occupies~20.4 kb of the genome. The replicase gene, ORF1ab, occupies~20.4 kb of the genome. It encodes polyproteins 1a and 1ab, which could be cleaved into 16 non-structural proteins (Nsp1-Nsp16). The 3'-end of the cleavage sites recognized by 3C-like proteinase (Nsp4-Nsp10, Nsp12-Nsp16) and papain-like proteinase (Nsp1-Nsp3) were confirmed. The proteins including Nsp3 (papain-like 2 proteas, PL2pro), Nsp5 (chymotrypsin-like protease, 3CLpro), Nsp12 (RdRp), Nsp13 (helicase), and other proteins of unknown function ( Table 3 ). The 7 concatenated domains of polyprotein 1 shared <90% aa sequence identity with those of other known alpha-CoVs ( Table 2 ), suggesting that these viruses represent a novel CoV species within the alpha-CoV. The closest assigned CoV species to BtCoV/Rh/YN2012 are BtCoV-HKU10 and BtRf-AlphaCoV/Hub2013. The three strains from Yunnan Province were clustered into two genotypes (83% genome identity) correlated to their sampling location. The third genotype represented by strain RaGD was isolated to strains found in Yunnan (<75.4% genome identity). We then examined the individual genes ( Table 2) . All of the genes showed low aa sequence identity to known CoVs. The four strains of BtCoV/Rh/YN2012 showed genetic diversity among all different genes except ORF1ab (>83.7% aa identity). Notably, the spike proteins are highly divergent among these strains. Other structure proteins (E, M, and N) are more conserved than the spike and other accessory proteins. Comparing the accessory genes among these four strains revealed that the strains of the same genotype shared a 100% identical ORF3a. However, the proteins encoded by ORF3as were highly divergent among different genotypes (<65% aa identity). The putative accessory genes were also BLASTed against GenBank records. Most accessory genes have no homologues in GenBank-database, except for ORF3a (52.0-55.5% aa identity with BatCoV HKU10 ORF3) and ORF9 (28.1-32.0% aa identity with SARSr-CoV ORF7a). We analyzed the protein homology with HHpred software. The results showed that ORF9s and SARS-CoV OR7a are homologues (possibility: 100%, E value <10 −48 ). We further screened the genomes for potential recombination evidence. No significant recombination breakpoint was detected by bootscan analysis.
To confirm the presence of subgenomic RNA, we designed a set of primers targeting all the predicted ORFs as described. The amplicons were firstly confirmed via agarose-gel electrophoresis and then sequencing ( Figure 3 and Table 2 ). The sequences showed that all the ORFs, except ORF4b, had preceding TRS. Hence, the ORF4b may be translated from bicistronic mRNAs. In RsYN1, an additional subgenomic RNA starting inside the ORF3a was found through sequencing, which led to a unique ORF3b.
To confirm the presence of subgenomic RNA, we designed a set of primers targeting all the predicted ORFs as described. The amplicons were firstly confirmed via agarose-gel electrophoresis and then sequencing ( Figure 3 and Table 2 ). The sequences showed that all the ORFs, except ORF4b, had preceding TRS. Hence, the ORF4b may be translated from bicistronic mRNAs. In RsYN1, an additional subgenomic RNA starting inside the ORF3a was found through sequencing, which led to a unique ORF3b.
Phylogenetic trees were constructed using the aa sequences of RdRp and S of BtCoV/Rh/YN2012 and other representative CoVs (Figure 4) . In both trees, all BtCoV/Rh/YN2012 were clustered together and formed a distinct lineage to other known coronavirus species. Two distinct sublineages were observed within BtCoV/Rh/YN2012. One was from Ra sampled in Guangdong, while the other was from Rs sampled in Yunnan Among the strains from Yunnan, RsYN2 and RsYN3 were clustered together, while RsYN1 was isolated. The topology of these four strains was correlated to the sampling location. The relatively long branches reflect a high diversity among these strains, indicating a long independent evolution history.
Phylogenetic trees were constructed using the aa sequences of RdRp and S of BtCoV/Rh/YN2012 and other representative CoVs (Figure 4) . In both trees, all BtCoV/Rh/YN2012 were clustered together and formed a distinct lineage to other known coronavirus species. Two distinct sublineages were observed within BtCoV/Rh/YN2012. One was from Ra sampled in Guangdong, while the other was from Rs sampled in Yunnan Among the strains from Yunnan, RsYN2 and RsYN3 were clustered together, while RsYN1 was isolated. The topology of these four strains was correlated to the sampling location. The relatively long branches reflect a high diversity among these strains, indicating a long independent evolution history.
Phylogenetic trees were constructed using the aa sequences of RdRp and S of BtCoV/Rh/YN2012 and other representative CoVs (Figure 4) . In both trees, all BtCoV/Rh/YN2012 were clustered together and formed a distinct lineage to other known coronavirus species. Two distinct sublineages were observed within BtCoV/Rh/YN2012. One was from Ra sampled in Guangdong, while the other was from Rs sampled in Yunnan Among the strains from Yunnan, RsYN2 and RsYN3 were clustered together, while RsYN1 was isolated. The topology of these four strains was correlated to the sampling location. The relatively long branches reflect a high diversity among these strains, indicating a long independent evolution history.
The Ka/Ks ratios (Ks is the number of synonymous substitutions per synonymous sites and Ka is the number of nonsynonymous substitutions per nonsynonymous site) were calculated for all genes. The Ka/Ks ratios for most of the genes were generally low, which indicates these genes were under purified selection. However, the Ka/Ks ratios of ORF4a, ORF4b, and ORF9 (0.727, 0.623, and 0.843, respectively) were significantly higher than those of other ORFs (Table 4 ). For further selection pressure evaluation of the ORF4a and ORF4b gene, we sequenced another four ORF4a and ORF4b genes (strain Rs4223, Rs4236, Rs4240, and Ra13576 was shown in Figure 1B
As SARS-CoV ORF7a was reported to induce apoptosis, we conducted apoptosis analysis on BtCoV/Rh/YN2012 ORF9, a~30% aa identity homologue of SARSr-CoV ORF7a. We transiently transfected ORF9 of BtCoV/Rh/YN2012 into HEK293T cells to examine whether this ORF9 triggers apoptosis. Western blot was performed to confirm the expression of ORF9s and SARS-CoV ORF7a ( Figure S1 ). ORF9 couldn't induce apoptosis as the ORF7a of SARS-CoV Tor2 ( Figure S2 ). The results indicated that BtCoV/Rh/YN2012 ORF9 was not involved in apoptosis induction.
To determine whether these accessory proteins modulate IFN induction, we transfected reporter plasmids (pIFNβ-Luc and pRL-TK) and expression plasmids to 293T cells. All the cells over-expressing the accessory genes, as well as influenza virus NS1 (strain PR8), HBoV VP2, or empty vector were tested for luciferase activity after SeV infection. Luciferase activity stimulated by SeV was remarkably higher than that without SeV treatment as expected. Influenza virus NS1 inhibits the expression from IFN promoter, while HBoV VP2 activate the expression. Compared to those controls, the ORF4a proteins exhibit an active effect as HBoV VP2 ( Figure 5A ). Other accessory proteins showed no effect on IFN production ( Figure S3 ). Expression of these accessory genes were confirmed by Western blot ( Figure S1 ). was remarkably higher than that without SeV treatment as expected. Influenza virus NS1 inhibits the expression from IFN promoter, while HBoV VP2 activate the expression. Compared to those controls, the ORF4a proteins exhibit an active effect as HBoV VP2 ( Figure 5A ). Other accessory proteins showed no effect on IFN production ( Figure S3 ). Expression of these accessory genes were confirmed by Western blot (Figure S1 ). Samples were collected at 6 h postinfection, followed by dual-luciferase assay. The results were expressed as the firefly luciferase value normalized to that of Renilla luciferase. (B) ORF3a protein activate NF-κB. 293T cells were transfected with 100 ng pNF-κB-Luc, 10 ng pRL-TK, empty vector (500 ng), an NS1-expressing plasmid (500 ng), a SARS-CoV ORF7a-expressing plasmid (500 ng), or ORF3a-expressing plasmids (500 ng). After 24 h, the cells were treated with TNF-α. Dual-luciferase activity was determined after 6 h. The results were expressed as the firefly luciferase activity normalized to that of Renilla luciferase. The experiments were performed three times independently. Data are representative of at least three independent experiments, with each determination performed in triplicate (mean ± SD of fold change). Asterisks indicate significant differences between groups (compared with Empty vector-NC, p < 0.05, as determined by student t test).
NF-κB plays an important role in regulating the immune response to viral infection and is also a key factor frequently targeted by viruses for taking over the host cell. In this study, we tested if these accessory proteins could modulate NF-κB. 293T cells were co-transfected with reporter Samples were collected at 6 h postinfection, followed by dual-luciferase assay. The results were expressed as the firefly luciferase value normalized to that of Renilla luciferase. (B) ORF3a protein activate NF-κB. 293T cells were transfected with 100 ng pNF-κB-Luc, 10 ng pRL-TK, empty vector (500 ng), an NS1-expressing plasmid (500 ng), a SARS-CoV ORF7a-expressing plasmid (500 ng), or ORF3a-expressing plasmids (500 ng). After 24 h, the cells were treated with TNF-α. Dual-luciferase activity was determined after 6 h. The results were expressed as the firefly luciferase activity normalized to that of Renilla luciferase. The experiments were performed three times independently. Data are representative of at least three independent experiments, with each determination performed in triplicate (mean ± SD of fold change). Asterisks indicate significant differences between groups (compared with Empty vector-NC, p < 0.05, as determined by student t test).
NF-κB plays an important role in regulating the immune response to viral infection and is also a key factor frequently targeted by viruses for taking over the host cell. In this study, we tested if these accessory proteins could modulate NF-κB. 293T cells were co-transfected with reporter plasmids (pNF-κB-Luc and pRL-TK), as well as accessory protein-expressing plasmids, or controls (empty vector, NS1, SARS-CoV Tor2-ORF7a). The cells were mock treated or treated with TNF-α for 6 h at 24 h post-transfection. The luciferase activity was determined. RsYN1-ORF3a and RaGD-ORF3a activated NF-κB as SARS-CoV ORF7a, whereas RsYN2-ORF3a inhibited NF-κB as NS1 ( Figure 5B ). Expressions of ORF3as were confirmed with Western blot ( Figure S1 ). Other accessory proteins did not modulate NF-κB production ( Figure S4 ).
To understand the infectivity of these newly detected BtCoV/Rh/YN2012, we selected the RsYN1, RsYN3 and RaGD spike proteins for spike-mediated pseudovirus entry studies. Both Western blot analysis and negative-staining electron microscopy observation confirmed the preparation of BtCoV/Rh/YN2012 successfully ( Figure S5 ). A total of 11 human cell lines, 8 bat cells, and 9 other mammal cell lines were tested, and no strong positive was found (Table S2) .
In this study, a novel alpha-CoV species, BtCoV/Rh/YN2012, was identified in two Rhinolophus species. The 4 strains with full-length genome were sequences. The 7 conserved replicase domains of these viruses possessed <90% aa sequence identity to those of other known alpha-CoVs, which defines a new species in accordance with the ICTV taxonomy standard [42] . These novel alpha-CoVs showed high genetic diversity in their structural and non-structural genes. Strain RaGD from R. affinis, collected in Guangdong province, formed a divergent independent branch from the other 3 strains from R. sinicus, sampled in Yunnan Province, indicating an independent evolution process associated with geographic isolation and host restrain. Though collected from same province, these three virus strains formed two genotypes correlated to sampling locations. These two genotypes had low genome sequence identity, especially in the S gene and accessory genes. Considering the remote geographic location of the host bat habitat, the host tropism, and the virus diversity, we suppose BtCoV/Rh/YN2012 may have spread in these two provinces with a long history of circulation in their natural reservoir, Rhinolophus bats. With the sequence evidence, we suppose that these viruses are still rapidly evolving.
Our study revealed that BtCoV/Rh/YN2012 has a unique genome structure compared to other alpha-CoVs. First, novel accessory genes, which had no homologues, were identified in the genomes. Second, multiple TRSs were found between S and E genes while other alphacoronavirus only had one TRS there. These TRSs precede ORF3a, ORF3b (only in RsYN1), and ORF4a/b respectively. Third, accessory gene ORF9 showed homology with those of other known CoV species in another coronavirus genus, especially with accessory genes from SARSr-CoV.
Accessory genes are usually involved in virus-host interactions during CoV infection [43] . In most CoVs, accessory genes are dispensable for virus replication. However, an intact 3c gene of feline CoV was required for viral replication in the gut [44] [45] [46] . Deletion of the genus-specific genes in mouse hepatitis virus led to a reduction in virulence [47] . SARS-CoV ORF7a, which was identified to be involved in the suppression of RNA silencing [48] , inhibition of cellular protein synthesis [49] , cell-cycle blockage [50] , and apoptosis induction [51, 52] . In this study, we found that BtCoV/Rh/YN2012 ORF9 shares~30% aa sequence identity with SARS-CoV ORF7a. Interestingly, BtCoV/Rh/YN2012 and SARSr-CoV were both detected in R. sinicus from the same cave. We suppose that SARS-CoV and BtCoV/Rh/YN2012 may have acquired ORF7a or ORF9 from a common ancestor through genome recombination or horizontal gene transfer. Whereas, ORF9 of BtCoV/Rh/YN2012 failed to induce apoptosis or activate NF-κB production, these differences may be induced by the divergent evolution of these proteins in different pressure.
Though different BtCoV/Rh/YN2012 ORF4a share <64.4% amino acid identity, all of them could activate IFN-β. ORF3a from RsYN1 and RaGD upregulated NF-κB, but the homologue from RsYN2 downregulated NF-κB expression. These differences may be caused by amino acid sequence variations and may contribute to a viruses' pathogenicity with a different pathway.
Though lacking of intestinal cell lines from the natural host of BtCoV/Rh/YN2012, we screened the cell tropism of their spike protein through pseudotyped retrovirus entry with human, bat and other mammalian cell lines. Most of cell lines screened were unsusceptible to BtCoV/Rh/YN2012, indicating a low risk of interspecies transmission to human and other animals. Multiple reasons may lead to failed infection of coronavirus spike-pseudotyped retrovirus system, including receptor absence in target cells, failed recognition to the receptor homologue from non-host species, maladaptation in non-host cells during the spike maturation or virus entry, or the limitation of retrovirus system in stimulating coronavirus entry. The weak infectivity of RsYN1 pseudotyped retrovirus in Huh-7 cells could be explained by the binding of spike protein to polysaccharide secreted to the surface. The assumption needs to be further confirmed by experiments.
Our long-term surveillances suggest that Rhinolophus bats seem to harbor a wide diversity of CoVs. Coincidently, the two highly pathogenic agents, SARS-CoV and Rh-BatCoV HKU2 both originated from Rhinolophus bats. Considering the diversity of CoVs carried by this bat genus and their wide geographical distribution, there may be a low risk of spillover of these viruses to other animals and humans. Long-term surveillances and pathogenesis studies will help to prevent future human and animal diseases caused by these bat CoVs.
Supplementary Materials: The following are available online at http://www.mdpi.com/1999-4915/11/4/379/s1, Figure S1 : western blot analysis of the expression of accessory proteins. Figure S2 : Apoptosis analysis of ORF9 proteins of BtCoV/Rh/YN2012. Figure S3 : Functional analysis of ORF3a, ORF3b, ORF4b, ORF8 and ORF9 proteins on the production of Type I interferon. Figure S4 : Functional analysis of ORF3b, ORF4a, ORF4b, ORF8 and ORF9 proteins on the production of NF-κB. Figure S5 : Characteristic of BtCoV/Rh/YN2012 spike mediated pseudovirus. Table S1 : General primers for AlphaCoVs genome sequencing. Table S2 : Primers for the detection of viral sugbenomic mRNAs. Table S3
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Who performed the sampling procedures?
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veterinarians
| 2,560 |
1,576 |
Characterization of a New Member of Alphacoronavirus with Unique Genomic Features in Rhinolophus Bats
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6521148/
SHA: ee14de143337eec0e9708f8139bfac2b7b8fdd27
Authors: Wang, Ning; Luo, Chuming; Liu, Haizhou; Yang, Xinglou; Hu, Ben; Zhang, Wei; Li, Bei; Zhu, Yan; Zhu, Guangjian; Shen, Xurui; Peng, Cheng; Shi, Zhengli
Date: 2019-04-24
DOI: 10.3390/v11040379
License: cc-by
Abstract: Bats have been identified as a natural reservoir of a variety of coronaviruses (CoVs). Several of them have caused diseases in humans and domestic animals by interspecies transmission. Considering the diversity of bat coronaviruses, bat species and populations, we expect to discover more bat CoVs through virus surveillance. In this study, we described a new member of alphaCoV (BtCoV/Rh/YN2012) in bats with unique genome features. Unique accessory genes, ORF4a and ORF4b were found between the spike gene and the envelope gene, while ORF8 gene was found downstream of the nucleocapsid gene. All the putative genes were further confirmed by reverse-transcription analyses. One unique gene at the 3’ end of the BtCoV/Rh/YN2012 genome, ORF9, exhibits ~30% amino acid identity to ORF7a of the SARS-related coronavirus. Functional analysis showed ORF4a protein can activate IFN-β production, whereas ORF3a can regulate NF-κB production. We also screened the spike-mediated virus entry using the spike-pseudotyped retroviruses system, although failed to find any fully permissive cells. Our results expand the knowledge on the genetic diversity of bat coronaviruses. Continuous screening of bat viruses will help us further understand the important role played by bats in coronavirus evolution and transmission.
Text: Members of the Coronaviridae family are enveloped, non-segmented, positive-strand RNA viruses with genome sizes ranging from 26-32 kb [1] . These viruses are classified into two subfamilies: Letovirinae, which contains the only genus: Alphaletovirus; and Orthocoronavirinae (CoV), which consists of alpha, beta, gamma, and deltacoronaviruses (CoVs) [2, 3] . Alpha and betacoronaviruses mainly infect mammals and cause human and animal diseases. Gamma-and delta-CoVs mainly infect birds, but some can also infect mammals [4, 5] . Six human CoVs (HCoVs) are known to cause human diseases. HCoV-HKU1, HCoV-OC43, HCoV-229E, and HCoV-NL63 commonly cause mild respiratory illness or asymptomatic infection; however, severe acute respiratory syndrome coronavirus (SARS-CoV) and
All sampling procedures were performed by veterinarians, with approval from Animal Ethics Committee of the Wuhan Institute of Virology (WIVH5210201). The study was conducted in accordance with the Guide for the Care and Use of Wild Mammals in Research of the People's Republic of China.
Bat fecal swab and pellet samples were collected from November 2004 to November 2014 in different seasons in Southern China, as described previously [16] .
Viral RNA was extracted from 200 µL of fecal swab or pellet samples using the High Pure Viral RNA Kit (Roche Diagnostics GmbH, Mannheim, Germany) as per the manufacturer's instructions. RNA was eluted in 50 µL of elution buffer, aliquoted, and stored at -80 • C. One-step hemi-nested reverse-transcription (RT-) PCR (Invitrogen, San Diego, CA, USA) was employed to detect coronavirus, as previously described [17, 18] .
To confirm the bat species of an individual sample, we PCR amplified the cytochrome b (Cytob) and/or NADH dehydrogenase subunit 1 (ND1) gene using DNA extracted from the feces or swabs [19, 20] . The gene sequences were assembled excluding the primer sequences. BLASTN was used to identify host species based on the most closely related sequences with the highest query coverage and a minimum identity of 95%.
Full genomic sequences were determined by one-step PCR (Invitrogen, San Diego, CA, USA) amplification with degenerate primers (Table S1 ) designed on the basis of multiple alignments of available alpha-CoV sequences deposited in GenBank or amplified with SuperScript IV Reverse Transcriptase (Invitrogen) and Expand Long Template PCR System (Roche Diagnostics GmbH, Mannheim, Germany) with specific primers (primer sequences are available upon request). Sequences of the 5' and 3' genomic ends were obtained by 5' and 3' rapid amplification of cDNA ends (SMARTer Viruses 2019, 11, 379 3 of 19 RACE 5'/3' Kit; Clontech, Mountain View, CA, USA), respectively. PCR products were gel-purified and subjected directly to sequencing. PCR products over 5kb were subjected to deep sequencing using Hiseq2500 system. For some fragments, the PCR products were cloned into the pGEM-T Easy Vector (Promega, Madison, WI, USA) for sequencing. At least five independent clones were sequenced to obtain a consensus sequence.
The Next Generation Sequencing (NGS) data were filtered and mapped to the reference sequence of BatCoV HKU10 (GenBank accession number NC_018871) using Geneious 7.1.8 [21] . Genomes were preliminarily assembled using DNAStar lasergene V7 (DNAStar, Madison, WI, USA). Putative open reading frames (ORFs) were predicted using NCBI's ORF finder (https://www.ncbi.nlm.nih.gov/ orffinder/) with a minimal ORF length of 150 nt, followed by manual inspection. The sequences of the 5' untranslated region (5'-UTR) and 3'-UTR were defined, and the leader sequence, the leader and body transcriptional regulatory sequence (TRS) were identified as previously described [22] . The cleavage of the 16 nonstructural proteins coded by ORF1ab was determined by alignment of aa sequences of other CoVs and the recognition pattern of the 3C-like proteinase and papain-like proteinase. Phylogenetic trees based on nt or aa sequences were constructed using the maximum likelihood algorithm with bootstrap values determined by 1000 replicates in the MEGA 6 software package [23] . Full-length genome sequences obtained in this study were aligned with those of previously reported alpha-CoVs using MUSCLE [24] . The aligned sequences were scanned for recombination events by using Recombination Detection Program [25] . Potential recombination events as suggested by strong p-values (<10 -20 ) were confirmed using similarity plot and bootscan analyses implemented in Simplot 3.5.1 [26] . The number of synonymous substitutions per synonymous site, Ks, and the number of nonsynonymous substitutions per nonsynonymous site, Ka, for each coding region were calculated using the Ka/Ks calculation tool of the Norwegian Bioinformatics Platform (http://services.cbu.uib.no/tools/kaks) with default parameters [27] . The protein homology detection was analyzed using HHpred (https://toolkit.tuebingen.mpg.de/#/tools/hhpred) with default parameters [28] .
A set of nested RT-PCRs was employed to determine the presence of viral subgenomic mRNAs in the CoV-positive samples [29] . Forward primers were designed targeting the leader sequence at the 5'-end of the complete genome, while reverse primers were designed within the ORFs. Specific and suspected amplicons of expected sizes were purified and then cloned into the pGEM-T Easy vector for sequencing.
Bat primary or immortalized cells (Rhinolophus sinicus kidney immortalized cells, RsKT; Rhinolophus sinicus Lung primary cells, RsLu4323; Rhinolophus sinicus brain immortalized cells, RsBrT; Rhinolophus affinis kidney primary cells, RaK4324; Rousettus leschenaultii Kidney immortalized cells, RlKT; Hipposideros pratti lung immortalized cells, HpLuT) generated in our laboratory were all cultured in DMEM/F12 with 15% FBS. Pteropus alecto kidney cells (Paki) was maintained in DMEM/F12 supplemented with 10% FBS. Other cells were maintained according to the recommendations of American Type Culture Collection (ATCC, www.atcc.org).
The putative accessory genes of the newly detected virus were generated by RT-PCR from viral RNA extracted from fecal samples, as described previously [30] . The influenza virus NS1 plasmid was generated in our lab [31] . The human bocavirus (HBoV) VP2 plasmid was kindly provided by prof. Hanzhong Wang of the Wuhan Institute of Virology, Chinese Academy of Sciences. SARS-CoV ORF7a was synthesized by Sangon Biotech. The transfections were performed with Lipofectamine 3000 Reagent (Life Technologies). Expression of these accessory genes were analyzed by Western blotting using an mAb (Roche Diagnostics GmbH, Mannheim, Germany) against the HA tag.
The virus isolation was performed as previously described [12] . Briefly, fecal supernatant was acquired via gradient centrifugation and then added to Vero E6 cells, 1:10 diluted in DMEM. After incubation at 37°C for 1 h the inoculum was replaced by fresh DMEM containing 2% FBS and the antibiotic-antimycotic (Gibco, Grand Island, NY, USA). Three blind passages were carried out. Cells were checked daily for cytopathic effect. Both culture supernatant and cell pellet were examined for CoV by RT-PCR [17] .
Apoptosis was analyzed as previously described [18] . Briefly, 293T cells in 12-well plates were transfected with 3 µg of expression plasmid or empty vector, and the cells were collected 24 h post transfection. Apoptosis was detected by flow cytometry using by the Annexin V-FITC/PI Apoptosis Detection Kit (YEASEN, Shanghai, China) following the manufacturer's instructions. Annexin-V-positive and PI-negative cells were considered to be in the early apoptotic phase and those stained for both Annexin V and PI were deemed to undergo late apoptosis or necrosis. All experiments were repeated three times. Student's t-test was used to evaluate the data, with p < 0.05 considered significant.
HEK 293T cells were seeded in 24-well plates and then co-transfected with reporter plasmids (pRL-TK and pIFN-βIFN-or pNF-κB-Luc) [30] , as well as plasmids expressing accessory genes, empty vector plasmid pcAGGS, influenza virus NS1 [32] , SARS-CoV ORF7a [33] , or HBoV VP2 [34] . At 24 h post transfection, cells were treated with Sendai virus (SeV) (100 hemagglutinin units [HAU]/mL) or human tumor necrosis factor alpha (TNF-α; R&D system) for 6 h to activate IFNβ or NF-κB, respectively. Cell lysates were prepared, and luciferase activity was measured using the dual-luciferase assay kit (Promega, Madison, WI, USA) according to the manufacturer's instructions.
Retroviruses pseudotyped with BtCoV/Rh/YN2012 RsYN1, RsYN3, RaGD, or MERS-CoV spike, or no spike (mock) were used to infect human, bat or other mammalian cells in 96-well plates. The pseudovirus particles were confirmed with Western blotting and negative-staining electromicroscopy. The production process, measurements of infection and luciferase activity were conducted, as described previously [35, 36] .
The complete genome nucleotide sequences of BtCoV/Rh/YN2012 strains RsYN1, RsYN2, RsYN3, and RaGD obtained in this study have been submitted to the GenBank under MG916901 to MG916904.
The surveillance was performed between November 2004 to November 2014 in 19 provinces of China. In total, 2061 fecal samples were collected from at least 12 Rhinolophus bat species ( Figure 1A ). CoVs were detected in 209 of these samples ( Figure 1B and Table 1 ). Partial RdRp sequences suggested the presence of at least 8 different CoVs. Five of these viruses are related to known species: Mi-BatCoV 1 (>94% nt identity), Mi-BatCoV HKU8 [37] (>93% nt identity), BtRf-AlphaCoV/HuB2013 [11] (>99% nt identity), SARSr-CoV [38] (>89% nt identity), and HKU2-related CoV [39] (>85% nt identity). While the other three CoV sequences showed less than 83% nt identity to known CoV species. These three viruses should represent novel CoV species. Virus isolation was performed as previously described [12] , but was not successful. identity). While the other three CoV sequences showed less than 83% nt identity to known CoV species. These three viruses should represent novel CoV species. Virus isolation was performed as previously described [12] , but was not successful.
We next characterized a novel alpha-CoV, BtCoV/Rh/YN2012. It was detected in 3 R.affinis and 6 R.sinicus, respectively. Based on the sequences, we defined three genotypes, which represented by RsYN1, RsYN3, and RaGD, respectively. Strain RsYN2 was classified into the RsYN3 genotype. Four full-length genomes were obtained. Three of them were from R.sinicus (Strain RsYN1, RsYN2, and RsYN3), while the other one was from R.affinis (Strain RaGD). The sizes of these 4 genomes are between 28,715 to 29,102, with G+C contents between 39.0% to 41.3%. The genomes exhibit similar structures and transcription regulatory sequences (TRS) that are identical to those of other alpha-CoVs ( Figure 2 and Table 2 ). Exceptions including three additional ORFs (ORF3b, ORF4a and ORF4b) were observed. All the 4 strains have ORF4a & ORF4b, while only strain RsYN1 has ORF3b.
The replicase gene, ORF1ab, occupies~20.4 kb of the genome. The replicase gene, ORF1ab, occupies~20.4 kb of the genome. It encodes polyproteins 1a and 1ab, which could be cleaved into 16 non-structural proteins (Nsp1-Nsp16). The 3'-end of the cleavage sites recognized by 3C-like proteinase (Nsp4-Nsp10, Nsp12-Nsp16) and papain-like proteinase (Nsp1-Nsp3) were confirmed. The proteins including Nsp3 (papain-like 2 proteas, PL2pro), Nsp5 (chymotrypsin-like protease, 3CLpro), Nsp12 (RdRp), Nsp13 (helicase), and other proteins of unknown function ( Table 3 ). The 7 concatenated domains of polyprotein 1 shared <90% aa sequence identity with those of other known alpha-CoVs ( Table 2 ), suggesting that these viruses represent a novel CoV species within the alpha-CoV. The closest assigned CoV species to BtCoV/Rh/YN2012 are BtCoV-HKU10 and BtRf-AlphaCoV/Hub2013. The three strains from Yunnan Province were clustered into two genotypes (83% genome identity) correlated to their sampling location. The third genotype represented by strain RaGD was isolated to strains found in Yunnan (<75.4% genome identity). We then examined the individual genes ( Table 2) . All of the genes showed low aa sequence identity to known CoVs. The four strains of BtCoV/Rh/YN2012 showed genetic diversity among all different genes except ORF1ab (>83.7% aa identity). Notably, the spike proteins are highly divergent among these strains. Other structure proteins (E, M, and N) are more conserved than the spike and other accessory proteins. Comparing the accessory genes among these four strains revealed that the strains of the same genotype shared a 100% identical ORF3a. However, the proteins encoded by ORF3as were highly divergent among different genotypes (<65% aa identity). The putative accessory genes were also BLASTed against GenBank records. Most accessory genes have no homologues in GenBank-database, except for ORF3a (52.0-55.5% aa identity with BatCoV HKU10 ORF3) and ORF9 (28.1-32.0% aa identity with SARSr-CoV ORF7a). We analyzed the protein homology with HHpred software. The results showed that ORF9s and SARS-CoV OR7a are homologues (possibility: 100%, E value <10 −48 ). We further screened the genomes for potential recombination evidence. No significant recombination breakpoint was detected by bootscan analysis.
To confirm the presence of subgenomic RNA, we designed a set of primers targeting all the predicted ORFs as described. The amplicons were firstly confirmed via agarose-gel electrophoresis and then sequencing ( Figure 3 and Table 2 ). The sequences showed that all the ORFs, except ORF4b, had preceding TRS. Hence, the ORF4b may be translated from bicistronic mRNAs. In RsYN1, an additional subgenomic RNA starting inside the ORF3a was found through sequencing, which led to a unique ORF3b.
To confirm the presence of subgenomic RNA, we designed a set of primers targeting all the predicted ORFs as described. The amplicons were firstly confirmed via agarose-gel electrophoresis and then sequencing ( Figure 3 and Table 2 ). The sequences showed that all the ORFs, except ORF4b, had preceding TRS. Hence, the ORF4b may be translated from bicistronic mRNAs. In RsYN1, an additional subgenomic RNA starting inside the ORF3a was found through sequencing, which led to a unique ORF3b.
Phylogenetic trees were constructed using the aa sequences of RdRp and S of BtCoV/Rh/YN2012 and other representative CoVs (Figure 4) . In both trees, all BtCoV/Rh/YN2012 were clustered together and formed a distinct lineage to other known coronavirus species. Two distinct sublineages were observed within BtCoV/Rh/YN2012. One was from Ra sampled in Guangdong, while the other was from Rs sampled in Yunnan Among the strains from Yunnan, RsYN2 and RsYN3 were clustered together, while RsYN1 was isolated. The topology of these four strains was correlated to the sampling location. The relatively long branches reflect a high diversity among these strains, indicating a long independent evolution history.
Phylogenetic trees were constructed using the aa sequences of RdRp and S of BtCoV/Rh/YN2012 and other representative CoVs (Figure 4) . In both trees, all BtCoV/Rh/YN2012 were clustered together and formed a distinct lineage to other known coronavirus species. Two distinct sublineages were observed within BtCoV/Rh/YN2012. One was from Ra sampled in Guangdong, while the other was from Rs sampled in Yunnan Among the strains from Yunnan, RsYN2 and RsYN3 were clustered together, while RsYN1 was isolated. The topology of these four strains was correlated to the sampling location. The relatively long branches reflect a high diversity among these strains, indicating a long independent evolution history.
Phylogenetic trees were constructed using the aa sequences of RdRp and S of BtCoV/Rh/YN2012 and other representative CoVs (Figure 4) . In both trees, all BtCoV/Rh/YN2012 were clustered together and formed a distinct lineage to other known coronavirus species. Two distinct sublineages were observed within BtCoV/Rh/YN2012. One was from Ra sampled in Guangdong, while the other was from Rs sampled in Yunnan Among the strains from Yunnan, RsYN2 and RsYN3 were clustered together, while RsYN1 was isolated. The topology of these four strains was correlated to the sampling location. The relatively long branches reflect a high diversity among these strains, indicating a long independent evolution history.
The Ka/Ks ratios (Ks is the number of synonymous substitutions per synonymous sites and Ka is the number of nonsynonymous substitutions per nonsynonymous site) were calculated for all genes. The Ka/Ks ratios for most of the genes were generally low, which indicates these genes were under purified selection. However, the Ka/Ks ratios of ORF4a, ORF4b, and ORF9 (0.727, 0.623, and 0.843, respectively) were significantly higher than those of other ORFs (Table 4 ). For further selection pressure evaluation of the ORF4a and ORF4b gene, we sequenced another four ORF4a and ORF4b genes (strain Rs4223, Rs4236, Rs4240, and Ra13576 was shown in Figure 1B
As SARS-CoV ORF7a was reported to induce apoptosis, we conducted apoptosis analysis on BtCoV/Rh/YN2012 ORF9, a~30% aa identity homologue of SARSr-CoV ORF7a. We transiently transfected ORF9 of BtCoV/Rh/YN2012 into HEK293T cells to examine whether this ORF9 triggers apoptosis. Western blot was performed to confirm the expression of ORF9s and SARS-CoV ORF7a ( Figure S1 ). ORF9 couldn't induce apoptosis as the ORF7a of SARS-CoV Tor2 ( Figure S2 ). The results indicated that BtCoV/Rh/YN2012 ORF9 was not involved in apoptosis induction.
To determine whether these accessory proteins modulate IFN induction, we transfected reporter plasmids (pIFNβ-Luc and pRL-TK) and expression plasmids to 293T cells. All the cells over-expressing the accessory genes, as well as influenza virus NS1 (strain PR8), HBoV VP2, or empty vector were tested for luciferase activity after SeV infection. Luciferase activity stimulated by SeV was remarkably higher than that without SeV treatment as expected. Influenza virus NS1 inhibits the expression from IFN promoter, while HBoV VP2 activate the expression. Compared to those controls, the ORF4a proteins exhibit an active effect as HBoV VP2 ( Figure 5A ). Other accessory proteins showed no effect on IFN production ( Figure S3 ). Expression of these accessory genes were confirmed by Western blot ( Figure S1 ). was remarkably higher than that without SeV treatment as expected. Influenza virus NS1 inhibits the expression from IFN promoter, while HBoV VP2 activate the expression. Compared to those controls, the ORF4a proteins exhibit an active effect as HBoV VP2 ( Figure 5A ). Other accessory proteins showed no effect on IFN production ( Figure S3 ). Expression of these accessory genes were confirmed by Western blot (Figure S1 ). Samples were collected at 6 h postinfection, followed by dual-luciferase assay. The results were expressed as the firefly luciferase value normalized to that of Renilla luciferase. (B) ORF3a protein activate NF-κB. 293T cells were transfected with 100 ng pNF-κB-Luc, 10 ng pRL-TK, empty vector (500 ng), an NS1-expressing plasmid (500 ng), a SARS-CoV ORF7a-expressing plasmid (500 ng), or ORF3a-expressing plasmids (500 ng). After 24 h, the cells were treated with TNF-α. Dual-luciferase activity was determined after 6 h. The results were expressed as the firefly luciferase activity normalized to that of Renilla luciferase. The experiments were performed three times independently. Data are representative of at least three independent experiments, with each determination performed in triplicate (mean ± SD of fold change). Asterisks indicate significant differences between groups (compared with Empty vector-NC, p < 0.05, as determined by student t test).
NF-κB plays an important role in regulating the immune response to viral infection and is also a key factor frequently targeted by viruses for taking over the host cell. In this study, we tested if these accessory proteins could modulate NF-κB. 293T cells were co-transfected with reporter Samples were collected at 6 h postinfection, followed by dual-luciferase assay. The results were expressed as the firefly luciferase value normalized to that of Renilla luciferase. (B) ORF3a protein activate NF-κB. 293T cells were transfected with 100 ng pNF-κB-Luc, 10 ng pRL-TK, empty vector (500 ng), an NS1-expressing plasmid (500 ng), a SARS-CoV ORF7a-expressing plasmid (500 ng), or ORF3a-expressing plasmids (500 ng). After 24 h, the cells were treated with TNF-α. Dual-luciferase activity was determined after 6 h. The results were expressed as the firefly luciferase activity normalized to that of Renilla luciferase. The experiments were performed three times independently. Data are representative of at least three independent experiments, with each determination performed in triplicate (mean ± SD of fold change). Asterisks indicate significant differences between groups (compared with Empty vector-NC, p < 0.05, as determined by student t test).
NF-κB plays an important role in regulating the immune response to viral infection and is also a key factor frequently targeted by viruses for taking over the host cell. In this study, we tested if these accessory proteins could modulate NF-κB. 293T cells were co-transfected with reporter plasmids (pNF-κB-Luc and pRL-TK), as well as accessory protein-expressing plasmids, or controls (empty vector, NS1, SARS-CoV Tor2-ORF7a). The cells were mock treated or treated with TNF-α for 6 h at 24 h post-transfection. The luciferase activity was determined. RsYN1-ORF3a and RaGD-ORF3a activated NF-κB as SARS-CoV ORF7a, whereas RsYN2-ORF3a inhibited NF-κB as NS1 ( Figure 5B ). Expressions of ORF3as were confirmed with Western blot ( Figure S1 ). Other accessory proteins did not modulate NF-κB production ( Figure S4 ).
To understand the infectivity of these newly detected BtCoV/Rh/YN2012, we selected the RsYN1, RsYN3 and RaGD spike proteins for spike-mediated pseudovirus entry studies. Both Western blot analysis and negative-staining electron microscopy observation confirmed the preparation of BtCoV/Rh/YN2012 successfully ( Figure S5 ). A total of 11 human cell lines, 8 bat cells, and 9 other mammal cell lines were tested, and no strong positive was found (Table S2) .
In this study, a novel alpha-CoV species, BtCoV/Rh/YN2012, was identified in two Rhinolophus species. The 4 strains with full-length genome were sequences. The 7 conserved replicase domains of these viruses possessed <90% aa sequence identity to those of other known alpha-CoVs, which defines a new species in accordance with the ICTV taxonomy standard [42] . These novel alpha-CoVs showed high genetic diversity in their structural and non-structural genes. Strain RaGD from R. affinis, collected in Guangdong province, formed a divergent independent branch from the other 3 strains from R. sinicus, sampled in Yunnan Province, indicating an independent evolution process associated with geographic isolation and host restrain. Though collected from same province, these three virus strains formed two genotypes correlated to sampling locations. These two genotypes had low genome sequence identity, especially in the S gene and accessory genes. Considering the remote geographic location of the host bat habitat, the host tropism, and the virus diversity, we suppose BtCoV/Rh/YN2012 may have spread in these two provinces with a long history of circulation in their natural reservoir, Rhinolophus bats. With the sequence evidence, we suppose that these viruses are still rapidly evolving.
Our study revealed that BtCoV/Rh/YN2012 has a unique genome structure compared to other alpha-CoVs. First, novel accessory genes, which had no homologues, were identified in the genomes. Second, multiple TRSs were found between S and E genes while other alphacoronavirus only had one TRS there. These TRSs precede ORF3a, ORF3b (only in RsYN1), and ORF4a/b respectively. Third, accessory gene ORF9 showed homology with those of other known CoV species in another coronavirus genus, especially with accessory genes from SARSr-CoV.
Accessory genes are usually involved in virus-host interactions during CoV infection [43] . In most CoVs, accessory genes are dispensable for virus replication. However, an intact 3c gene of feline CoV was required for viral replication in the gut [44] [45] [46] . Deletion of the genus-specific genes in mouse hepatitis virus led to a reduction in virulence [47] . SARS-CoV ORF7a, which was identified to be involved in the suppression of RNA silencing [48] , inhibition of cellular protein synthesis [49] , cell-cycle blockage [50] , and apoptosis induction [51, 52] . In this study, we found that BtCoV/Rh/YN2012 ORF9 shares~30% aa sequence identity with SARS-CoV ORF7a. Interestingly, BtCoV/Rh/YN2012 and SARSr-CoV were both detected in R. sinicus from the same cave. We suppose that SARS-CoV and BtCoV/Rh/YN2012 may have acquired ORF7a or ORF9 from a common ancestor through genome recombination or horizontal gene transfer. Whereas, ORF9 of BtCoV/Rh/YN2012 failed to induce apoptosis or activate NF-κB production, these differences may be induced by the divergent evolution of these proteins in different pressure.
Though different BtCoV/Rh/YN2012 ORF4a share <64.4% amino acid identity, all of them could activate IFN-β. ORF3a from RsYN1 and RaGD upregulated NF-κB, but the homologue from RsYN2 downregulated NF-κB expression. These differences may be caused by amino acid sequence variations and may contribute to a viruses' pathogenicity with a different pathway.
Though lacking of intestinal cell lines from the natural host of BtCoV/Rh/YN2012, we screened the cell tropism of their spike protein through pseudotyped retrovirus entry with human, bat and other mammalian cell lines. Most of cell lines screened were unsusceptible to BtCoV/Rh/YN2012, indicating a low risk of interspecies transmission to human and other animals. Multiple reasons may lead to failed infection of coronavirus spike-pseudotyped retrovirus system, including receptor absence in target cells, failed recognition to the receptor homologue from non-host species, maladaptation in non-host cells during the spike maturation or virus entry, or the limitation of retrovirus system in stimulating coronavirus entry. The weak infectivity of RsYN1 pseudotyped retrovirus in Huh-7 cells could be explained by the binding of spike protein to polysaccharide secreted to the surface. The assumption needs to be further confirmed by experiments.
Our long-term surveillances suggest that Rhinolophus bats seem to harbor a wide diversity of CoVs. Coincidently, the two highly pathogenic agents, SARS-CoV and Rh-BatCoV HKU2 both originated from Rhinolophus bats. Considering the diversity of CoVs carried by this bat genus and their wide geographical distribution, there may be a low risk of spillover of these viruses to other animals and humans. Long-term surveillances and pathogenesis studies will help to prevent future human and animal diseases caused by these bat CoVs.
Supplementary Materials: The following are available online at http://www.mdpi.com/1999-4915/11/4/379/s1, Figure S1 : western blot analysis of the expression of accessory proteins. Figure S2 : Apoptosis analysis of ORF9 proteins of BtCoV/Rh/YN2012. Figure S3 : Functional analysis of ORF3a, ORF3b, ORF4b, ORF8 and ORF9 proteins on the production of Type I interferon. Figure S4 : Functional analysis of ORF3b, ORF4a, ORF4b, ORF8 and ORF9 proteins on the production of NF-κB. Figure S5 : Characteristic of BtCoV/Rh/YN2012 spike mediated pseudovirus. Table S1 : General primers for AlphaCoVs genome sequencing. Table S2 : Primers for the detection of viral sugbenomic mRNAs. Table S3
|
When were the fecal samples collected?
| 3,681 |
from November 2004 to November 2014
| 2,855 |
1,576 |
Characterization of a New Member of Alphacoronavirus with Unique Genomic Features in Rhinolophus Bats
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6521148/
SHA: ee14de143337eec0e9708f8139bfac2b7b8fdd27
Authors: Wang, Ning; Luo, Chuming; Liu, Haizhou; Yang, Xinglou; Hu, Ben; Zhang, Wei; Li, Bei; Zhu, Yan; Zhu, Guangjian; Shen, Xurui; Peng, Cheng; Shi, Zhengli
Date: 2019-04-24
DOI: 10.3390/v11040379
License: cc-by
Abstract: Bats have been identified as a natural reservoir of a variety of coronaviruses (CoVs). Several of them have caused diseases in humans and domestic animals by interspecies transmission. Considering the diversity of bat coronaviruses, bat species and populations, we expect to discover more bat CoVs through virus surveillance. In this study, we described a new member of alphaCoV (BtCoV/Rh/YN2012) in bats with unique genome features. Unique accessory genes, ORF4a and ORF4b were found between the spike gene and the envelope gene, while ORF8 gene was found downstream of the nucleocapsid gene. All the putative genes were further confirmed by reverse-transcription analyses. One unique gene at the 3’ end of the BtCoV/Rh/YN2012 genome, ORF9, exhibits ~30% amino acid identity to ORF7a of the SARS-related coronavirus. Functional analysis showed ORF4a protein can activate IFN-β production, whereas ORF3a can regulate NF-κB production. We also screened the spike-mediated virus entry using the spike-pseudotyped retroviruses system, although failed to find any fully permissive cells. Our results expand the knowledge on the genetic diversity of bat coronaviruses. Continuous screening of bat viruses will help us further understand the important role played by bats in coronavirus evolution and transmission.
Text: Members of the Coronaviridae family are enveloped, non-segmented, positive-strand RNA viruses with genome sizes ranging from 26-32 kb [1] . These viruses are classified into two subfamilies: Letovirinae, which contains the only genus: Alphaletovirus; and Orthocoronavirinae (CoV), which consists of alpha, beta, gamma, and deltacoronaviruses (CoVs) [2, 3] . Alpha and betacoronaviruses mainly infect mammals and cause human and animal diseases. Gamma-and delta-CoVs mainly infect birds, but some can also infect mammals [4, 5] . Six human CoVs (HCoVs) are known to cause human diseases. HCoV-HKU1, HCoV-OC43, HCoV-229E, and HCoV-NL63 commonly cause mild respiratory illness or asymptomatic infection; however, severe acute respiratory syndrome coronavirus (SARS-CoV) and
All sampling procedures were performed by veterinarians, with approval from Animal Ethics Committee of the Wuhan Institute of Virology (WIVH5210201). The study was conducted in accordance with the Guide for the Care and Use of Wild Mammals in Research of the People's Republic of China.
Bat fecal swab and pellet samples were collected from November 2004 to November 2014 in different seasons in Southern China, as described previously [16] .
Viral RNA was extracted from 200 µL of fecal swab or pellet samples using the High Pure Viral RNA Kit (Roche Diagnostics GmbH, Mannheim, Germany) as per the manufacturer's instructions. RNA was eluted in 50 µL of elution buffer, aliquoted, and stored at -80 • C. One-step hemi-nested reverse-transcription (RT-) PCR (Invitrogen, San Diego, CA, USA) was employed to detect coronavirus, as previously described [17, 18] .
To confirm the bat species of an individual sample, we PCR amplified the cytochrome b (Cytob) and/or NADH dehydrogenase subunit 1 (ND1) gene using DNA extracted from the feces or swabs [19, 20] . The gene sequences were assembled excluding the primer sequences. BLASTN was used to identify host species based on the most closely related sequences with the highest query coverage and a minimum identity of 95%.
Full genomic sequences were determined by one-step PCR (Invitrogen, San Diego, CA, USA) amplification with degenerate primers (Table S1 ) designed on the basis of multiple alignments of available alpha-CoV sequences deposited in GenBank or amplified with SuperScript IV Reverse Transcriptase (Invitrogen) and Expand Long Template PCR System (Roche Diagnostics GmbH, Mannheim, Germany) with specific primers (primer sequences are available upon request). Sequences of the 5' and 3' genomic ends were obtained by 5' and 3' rapid amplification of cDNA ends (SMARTer Viruses 2019, 11, 379 3 of 19 RACE 5'/3' Kit; Clontech, Mountain View, CA, USA), respectively. PCR products were gel-purified and subjected directly to sequencing. PCR products over 5kb were subjected to deep sequencing using Hiseq2500 system. For some fragments, the PCR products were cloned into the pGEM-T Easy Vector (Promega, Madison, WI, USA) for sequencing. At least five independent clones were sequenced to obtain a consensus sequence.
The Next Generation Sequencing (NGS) data were filtered and mapped to the reference sequence of BatCoV HKU10 (GenBank accession number NC_018871) using Geneious 7.1.8 [21] . Genomes were preliminarily assembled using DNAStar lasergene V7 (DNAStar, Madison, WI, USA). Putative open reading frames (ORFs) were predicted using NCBI's ORF finder (https://www.ncbi.nlm.nih.gov/ orffinder/) with a minimal ORF length of 150 nt, followed by manual inspection. The sequences of the 5' untranslated region (5'-UTR) and 3'-UTR were defined, and the leader sequence, the leader and body transcriptional regulatory sequence (TRS) were identified as previously described [22] . The cleavage of the 16 nonstructural proteins coded by ORF1ab was determined by alignment of aa sequences of other CoVs and the recognition pattern of the 3C-like proteinase and papain-like proteinase. Phylogenetic trees based on nt or aa sequences were constructed using the maximum likelihood algorithm with bootstrap values determined by 1000 replicates in the MEGA 6 software package [23] . Full-length genome sequences obtained in this study were aligned with those of previously reported alpha-CoVs using MUSCLE [24] . The aligned sequences were scanned for recombination events by using Recombination Detection Program [25] . Potential recombination events as suggested by strong p-values (<10 -20 ) were confirmed using similarity plot and bootscan analyses implemented in Simplot 3.5.1 [26] . The number of synonymous substitutions per synonymous site, Ks, and the number of nonsynonymous substitutions per nonsynonymous site, Ka, for each coding region were calculated using the Ka/Ks calculation tool of the Norwegian Bioinformatics Platform (http://services.cbu.uib.no/tools/kaks) with default parameters [27] . The protein homology detection was analyzed using HHpred (https://toolkit.tuebingen.mpg.de/#/tools/hhpred) with default parameters [28] .
A set of nested RT-PCRs was employed to determine the presence of viral subgenomic mRNAs in the CoV-positive samples [29] . Forward primers were designed targeting the leader sequence at the 5'-end of the complete genome, while reverse primers were designed within the ORFs. Specific and suspected amplicons of expected sizes were purified and then cloned into the pGEM-T Easy vector for sequencing.
Bat primary or immortalized cells (Rhinolophus sinicus kidney immortalized cells, RsKT; Rhinolophus sinicus Lung primary cells, RsLu4323; Rhinolophus sinicus brain immortalized cells, RsBrT; Rhinolophus affinis kidney primary cells, RaK4324; Rousettus leschenaultii Kidney immortalized cells, RlKT; Hipposideros pratti lung immortalized cells, HpLuT) generated in our laboratory were all cultured in DMEM/F12 with 15% FBS. Pteropus alecto kidney cells (Paki) was maintained in DMEM/F12 supplemented with 10% FBS. Other cells were maintained according to the recommendations of American Type Culture Collection (ATCC, www.atcc.org).
The putative accessory genes of the newly detected virus were generated by RT-PCR from viral RNA extracted from fecal samples, as described previously [30] . The influenza virus NS1 plasmid was generated in our lab [31] . The human bocavirus (HBoV) VP2 plasmid was kindly provided by prof. Hanzhong Wang of the Wuhan Institute of Virology, Chinese Academy of Sciences. SARS-CoV ORF7a was synthesized by Sangon Biotech. The transfections were performed with Lipofectamine 3000 Reagent (Life Technologies). Expression of these accessory genes were analyzed by Western blotting using an mAb (Roche Diagnostics GmbH, Mannheim, Germany) against the HA tag.
The virus isolation was performed as previously described [12] . Briefly, fecal supernatant was acquired via gradient centrifugation and then added to Vero E6 cells, 1:10 diluted in DMEM. After incubation at 37°C for 1 h the inoculum was replaced by fresh DMEM containing 2% FBS and the antibiotic-antimycotic (Gibco, Grand Island, NY, USA). Three blind passages were carried out. Cells were checked daily for cytopathic effect. Both culture supernatant and cell pellet were examined for CoV by RT-PCR [17] .
Apoptosis was analyzed as previously described [18] . Briefly, 293T cells in 12-well plates were transfected with 3 µg of expression plasmid or empty vector, and the cells were collected 24 h post transfection. Apoptosis was detected by flow cytometry using by the Annexin V-FITC/PI Apoptosis Detection Kit (YEASEN, Shanghai, China) following the manufacturer's instructions. Annexin-V-positive and PI-negative cells were considered to be in the early apoptotic phase and those stained for both Annexin V and PI were deemed to undergo late apoptosis or necrosis. All experiments were repeated three times. Student's t-test was used to evaluate the data, with p < 0.05 considered significant.
HEK 293T cells were seeded in 24-well plates and then co-transfected with reporter plasmids (pRL-TK and pIFN-βIFN-or pNF-κB-Luc) [30] , as well as plasmids expressing accessory genes, empty vector plasmid pcAGGS, influenza virus NS1 [32] , SARS-CoV ORF7a [33] , or HBoV VP2 [34] . At 24 h post transfection, cells were treated with Sendai virus (SeV) (100 hemagglutinin units [HAU]/mL) or human tumor necrosis factor alpha (TNF-α; R&D system) for 6 h to activate IFNβ or NF-κB, respectively. Cell lysates were prepared, and luciferase activity was measured using the dual-luciferase assay kit (Promega, Madison, WI, USA) according to the manufacturer's instructions.
Retroviruses pseudotyped with BtCoV/Rh/YN2012 RsYN1, RsYN3, RaGD, or MERS-CoV spike, or no spike (mock) were used to infect human, bat or other mammalian cells in 96-well plates. The pseudovirus particles were confirmed with Western blotting and negative-staining electromicroscopy. The production process, measurements of infection and luciferase activity were conducted, as described previously [35, 36] .
The complete genome nucleotide sequences of BtCoV/Rh/YN2012 strains RsYN1, RsYN2, RsYN3, and RaGD obtained in this study have been submitted to the GenBank under MG916901 to MG916904.
The surveillance was performed between November 2004 to November 2014 in 19 provinces of China. In total, 2061 fecal samples were collected from at least 12 Rhinolophus bat species ( Figure 1A ). CoVs were detected in 209 of these samples ( Figure 1B and Table 1 ). Partial RdRp sequences suggested the presence of at least 8 different CoVs. Five of these viruses are related to known species: Mi-BatCoV 1 (>94% nt identity), Mi-BatCoV HKU8 [37] (>93% nt identity), BtRf-AlphaCoV/HuB2013 [11] (>99% nt identity), SARSr-CoV [38] (>89% nt identity), and HKU2-related CoV [39] (>85% nt identity). While the other three CoV sequences showed less than 83% nt identity to known CoV species. These three viruses should represent novel CoV species. Virus isolation was performed as previously described [12] , but was not successful. identity). While the other three CoV sequences showed less than 83% nt identity to known CoV species. These three viruses should represent novel CoV species. Virus isolation was performed as previously described [12] , but was not successful.
We next characterized a novel alpha-CoV, BtCoV/Rh/YN2012. It was detected in 3 R.affinis and 6 R.sinicus, respectively. Based on the sequences, we defined three genotypes, which represented by RsYN1, RsYN3, and RaGD, respectively. Strain RsYN2 was classified into the RsYN3 genotype. Four full-length genomes were obtained. Three of them were from R.sinicus (Strain RsYN1, RsYN2, and RsYN3), while the other one was from R.affinis (Strain RaGD). The sizes of these 4 genomes are between 28,715 to 29,102, with G+C contents between 39.0% to 41.3%. The genomes exhibit similar structures and transcription regulatory sequences (TRS) that are identical to those of other alpha-CoVs ( Figure 2 and Table 2 ). Exceptions including three additional ORFs (ORF3b, ORF4a and ORF4b) were observed. All the 4 strains have ORF4a & ORF4b, while only strain RsYN1 has ORF3b.
The replicase gene, ORF1ab, occupies~20.4 kb of the genome. The replicase gene, ORF1ab, occupies~20.4 kb of the genome. It encodes polyproteins 1a and 1ab, which could be cleaved into 16 non-structural proteins (Nsp1-Nsp16). The 3'-end of the cleavage sites recognized by 3C-like proteinase (Nsp4-Nsp10, Nsp12-Nsp16) and papain-like proteinase (Nsp1-Nsp3) were confirmed. The proteins including Nsp3 (papain-like 2 proteas, PL2pro), Nsp5 (chymotrypsin-like protease, 3CLpro), Nsp12 (RdRp), Nsp13 (helicase), and other proteins of unknown function ( Table 3 ). The 7 concatenated domains of polyprotein 1 shared <90% aa sequence identity with those of other known alpha-CoVs ( Table 2 ), suggesting that these viruses represent a novel CoV species within the alpha-CoV. The closest assigned CoV species to BtCoV/Rh/YN2012 are BtCoV-HKU10 and BtRf-AlphaCoV/Hub2013. The three strains from Yunnan Province were clustered into two genotypes (83% genome identity) correlated to their sampling location. The third genotype represented by strain RaGD was isolated to strains found in Yunnan (<75.4% genome identity). We then examined the individual genes ( Table 2) . All of the genes showed low aa sequence identity to known CoVs. The four strains of BtCoV/Rh/YN2012 showed genetic diversity among all different genes except ORF1ab (>83.7% aa identity). Notably, the spike proteins are highly divergent among these strains. Other structure proteins (E, M, and N) are more conserved than the spike and other accessory proteins. Comparing the accessory genes among these four strains revealed that the strains of the same genotype shared a 100% identical ORF3a. However, the proteins encoded by ORF3as were highly divergent among different genotypes (<65% aa identity). The putative accessory genes were also BLASTed against GenBank records. Most accessory genes have no homologues in GenBank-database, except for ORF3a (52.0-55.5% aa identity with BatCoV HKU10 ORF3) and ORF9 (28.1-32.0% aa identity with SARSr-CoV ORF7a). We analyzed the protein homology with HHpred software. The results showed that ORF9s and SARS-CoV OR7a are homologues (possibility: 100%, E value <10 −48 ). We further screened the genomes for potential recombination evidence. No significant recombination breakpoint was detected by bootscan analysis.
To confirm the presence of subgenomic RNA, we designed a set of primers targeting all the predicted ORFs as described. The amplicons were firstly confirmed via agarose-gel electrophoresis and then sequencing ( Figure 3 and Table 2 ). The sequences showed that all the ORFs, except ORF4b, had preceding TRS. Hence, the ORF4b may be translated from bicistronic mRNAs. In RsYN1, an additional subgenomic RNA starting inside the ORF3a was found through sequencing, which led to a unique ORF3b.
To confirm the presence of subgenomic RNA, we designed a set of primers targeting all the predicted ORFs as described. The amplicons were firstly confirmed via agarose-gel electrophoresis and then sequencing ( Figure 3 and Table 2 ). The sequences showed that all the ORFs, except ORF4b, had preceding TRS. Hence, the ORF4b may be translated from bicistronic mRNAs. In RsYN1, an additional subgenomic RNA starting inside the ORF3a was found through sequencing, which led to a unique ORF3b.
Phylogenetic trees were constructed using the aa sequences of RdRp and S of BtCoV/Rh/YN2012 and other representative CoVs (Figure 4) . In both trees, all BtCoV/Rh/YN2012 were clustered together and formed a distinct lineage to other known coronavirus species. Two distinct sublineages were observed within BtCoV/Rh/YN2012. One was from Ra sampled in Guangdong, while the other was from Rs sampled in Yunnan Among the strains from Yunnan, RsYN2 and RsYN3 were clustered together, while RsYN1 was isolated. The topology of these four strains was correlated to the sampling location. The relatively long branches reflect a high diversity among these strains, indicating a long independent evolution history.
Phylogenetic trees were constructed using the aa sequences of RdRp and S of BtCoV/Rh/YN2012 and other representative CoVs (Figure 4) . In both trees, all BtCoV/Rh/YN2012 were clustered together and formed a distinct lineage to other known coronavirus species. Two distinct sublineages were observed within BtCoV/Rh/YN2012. One was from Ra sampled in Guangdong, while the other was from Rs sampled in Yunnan Among the strains from Yunnan, RsYN2 and RsYN3 were clustered together, while RsYN1 was isolated. The topology of these four strains was correlated to the sampling location. The relatively long branches reflect a high diversity among these strains, indicating a long independent evolution history.
Phylogenetic trees were constructed using the aa sequences of RdRp and S of BtCoV/Rh/YN2012 and other representative CoVs (Figure 4) . In both trees, all BtCoV/Rh/YN2012 were clustered together and formed a distinct lineage to other known coronavirus species. Two distinct sublineages were observed within BtCoV/Rh/YN2012. One was from Ra sampled in Guangdong, while the other was from Rs sampled in Yunnan Among the strains from Yunnan, RsYN2 and RsYN3 were clustered together, while RsYN1 was isolated. The topology of these four strains was correlated to the sampling location. The relatively long branches reflect a high diversity among these strains, indicating a long independent evolution history.
The Ka/Ks ratios (Ks is the number of synonymous substitutions per synonymous sites and Ka is the number of nonsynonymous substitutions per nonsynonymous site) were calculated for all genes. The Ka/Ks ratios for most of the genes were generally low, which indicates these genes were under purified selection. However, the Ka/Ks ratios of ORF4a, ORF4b, and ORF9 (0.727, 0.623, and 0.843, respectively) were significantly higher than those of other ORFs (Table 4 ). For further selection pressure evaluation of the ORF4a and ORF4b gene, we sequenced another four ORF4a and ORF4b genes (strain Rs4223, Rs4236, Rs4240, and Ra13576 was shown in Figure 1B
As SARS-CoV ORF7a was reported to induce apoptosis, we conducted apoptosis analysis on BtCoV/Rh/YN2012 ORF9, a~30% aa identity homologue of SARSr-CoV ORF7a. We transiently transfected ORF9 of BtCoV/Rh/YN2012 into HEK293T cells to examine whether this ORF9 triggers apoptosis. Western blot was performed to confirm the expression of ORF9s and SARS-CoV ORF7a ( Figure S1 ). ORF9 couldn't induce apoptosis as the ORF7a of SARS-CoV Tor2 ( Figure S2 ). The results indicated that BtCoV/Rh/YN2012 ORF9 was not involved in apoptosis induction.
To determine whether these accessory proteins modulate IFN induction, we transfected reporter plasmids (pIFNβ-Luc and pRL-TK) and expression plasmids to 293T cells. All the cells over-expressing the accessory genes, as well as influenza virus NS1 (strain PR8), HBoV VP2, or empty vector were tested for luciferase activity after SeV infection. Luciferase activity stimulated by SeV was remarkably higher than that without SeV treatment as expected. Influenza virus NS1 inhibits the expression from IFN promoter, while HBoV VP2 activate the expression. Compared to those controls, the ORF4a proteins exhibit an active effect as HBoV VP2 ( Figure 5A ). Other accessory proteins showed no effect on IFN production ( Figure S3 ). Expression of these accessory genes were confirmed by Western blot ( Figure S1 ). was remarkably higher than that without SeV treatment as expected. Influenza virus NS1 inhibits the expression from IFN promoter, while HBoV VP2 activate the expression. Compared to those controls, the ORF4a proteins exhibit an active effect as HBoV VP2 ( Figure 5A ). Other accessory proteins showed no effect on IFN production ( Figure S3 ). Expression of these accessory genes were confirmed by Western blot (Figure S1 ). Samples were collected at 6 h postinfection, followed by dual-luciferase assay. The results were expressed as the firefly luciferase value normalized to that of Renilla luciferase. (B) ORF3a protein activate NF-κB. 293T cells were transfected with 100 ng pNF-κB-Luc, 10 ng pRL-TK, empty vector (500 ng), an NS1-expressing plasmid (500 ng), a SARS-CoV ORF7a-expressing plasmid (500 ng), or ORF3a-expressing plasmids (500 ng). After 24 h, the cells were treated with TNF-α. Dual-luciferase activity was determined after 6 h. The results were expressed as the firefly luciferase activity normalized to that of Renilla luciferase. The experiments were performed three times independently. Data are representative of at least three independent experiments, with each determination performed in triplicate (mean ± SD of fold change). Asterisks indicate significant differences between groups (compared with Empty vector-NC, p < 0.05, as determined by student t test).
NF-κB plays an important role in regulating the immune response to viral infection and is also a key factor frequently targeted by viruses for taking over the host cell. In this study, we tested if these accessory proteins could modulate NF-κB. 293T cells were co-transfected with reporter Samples were collected at 6 h postinfection, followed by dual-luciferase assay. The results were expressed as the firefly luciferase value normalized to that of Renilla luciferase. (B) ORF3a protein activate NF-κB. 293T cells were transfected with 100 ng pNF-κB-Luc, 10 ng pRL-TK, empty vector (500 ng), an NS1-expressing plasmid (500 ng), a SARS-CoV ORF7a-expressing plasmid (500 ng), or ORF3a-expressing plasmids (500 ng). After 24 h, the cells were treated with TNF-α. Dual-luciferase activity was determined after 6 h. The results were expressed as the firefly luciferase activity normalized to that of Renilla luciferase. The experiments were performed three times independently. Data are representative of at least three independent experiments, with each determination performed in triplicate (mean ± SD of fold change). Asterisks indicate significant differences between groups (compared with Empty vector-NC, p < 0.05, as determined by student t test).
NF-κB plays an important role in regulating the immune response to viral infection and is also a key factor frequently targeted by viruses for taking over the host cell. In this study, we tested if these accessory proteins could modulate NF-κB. 293T cells were co-transfected with reporter plasmids (pNF-κB-Luc and pRL-TK), as well as accessory protein-expressing plasmids, or controls (empty vector, NS1, SARS-CoV Tor2-ORF7a). The cells were mock treated or treated with TNF-α for 6 h at 24 h post-transfection. The luciferase activity was determined. RsYN1-ORF3a and RaGD-ORF3a activated NF-κB as SARS-CoV ORF7a, whereas RsYN2-ORF3a inhibited NF-κB as NS1 ( Figure 5B ). Expressions of ORF3as were confirmed with Western blot ( Figure S1 ). Other accessory proteins did not modulate NF-κB production ( Figure S4 ).
To understand the infectivity of these newly detected BtCoV/Rh/YN2012, we selected the RsYN1, RsYN3 and RaGD spike proteins for spike-mediated pseudovirus entry studies. Both Western blot analysis and negative-staining electron microscopy observation confirmed the preparation of BtCoV/Rh/YN2012 successfully ( Figure S5 ). A total of 11 human cell lines, 8 bat cells, and 9 other mammal cell lines were tested, and no strong positive was found (Table S2) .
In this study, a novel alpha-CoV species, BtCoV/Rh/YN2012, was identified in two Rhinolophus species. The 4 strains with full-length genome were sequences. The 7 conserved replicase domains of these viruses possessed <90% aa sequence identity to those of other known alpha-CoVs, which defines a new species in accordance with the ICTV taxonomy standard [42] . These novel alpha-CoVs showed high genetic diversity in their structural and non-structural genes. Strain RaGD from R. affinis, collected in Guangdong province, formed a divergent independent branch from the other 3 strains from R. sinicus, sampled in Yunnan Province, indicating an independent evolution process associated with geographic isolation and host restrain. Though collected from same province, these three virus strains formed two genotypes correlated to sampling locations. These two genotypes had low genome sequence identity, especially in the S gene and accessory genes. Considering the remote geographic location of the host bat habitat, the host tropism, and the virus diversity, we suppose BtCoV/Rh/YN2012 may have spread in these two provinces with a long history of circulation in their natural reservoir, Rhinolophus bats. With the sequence evidence, we suppose that these viruses are still rapidly evolving.
Our study revealed that BtCoV/Rh/YN2012 has a unique genome structure compared to other alpha-CoVs. First, novel accessory genes, which had no homologues, were identified in the genomes. Second, multiple TRSs were found between S and E genes while other alphacoronavirus only had one TRS there. These TRSs precede ORF3a, ORF3b (only in RsYN1), and ORF4a/b respectively. Third, accessory gene ORF9 showed homology with those of other known CoV species in another coronavirus genus, especially with accessory genes from SARSr-CoV.
Accessory genes are usually involved in virus-host interactions during CoV infection [43] . In most CoVs, accessory genes are dispensable for virus replication. However, an intact 3c gene of feline CoV was required for viral replication in the gut [44] [45] [46] . Deletion of the genus-specific genes in mouse hepatitis virus led to a reduction in virulence [47] . SARS-CoV ORF7a, which was identified to be involved in the suppression of RNA silencing [48] , inhibition of cellular protein synthesis [49] , cell-cycle blockage [50] , and apoptosis induction [51, 52] . In this study, we found that BtCoV/Rh/YN2012 ORF9 shares~30% aa sequence identity with SARS-CoV ORF7a. Interestingly, BtCoV/Rh/YN2012 and SARSr-CoV were both detected in R. sinicus from the same cave. We suppose that SARS-CoV and BtCoV/Rh/YN2012 may have acquired ORF7a or ORF9 from a common ancestor through genome recombination or horizontal gene transfer. Whereas, ORF9 of BtCoV/Rh/YN2012 failed to induce apoptosis or activate NF-κB production, these differences may be induced by the divergent evolution of these proteins in different pressure.
Though different BtCoV/Rh/YN2012 ORF4a share <64.4% amino acid identity, all of them could activate IFN-β. ORF3a from RsYN1 and RaGD upregulated NF-κB, but the homologue from RsYN2 downregulated NF-κB expression. These differences may be caused by amino acid sequence variations and may contribute to a viruses' pathogenicity with a different pathway.
Though lacking of intestinal cell lines from the natural host of BtCoV/Rh/YN2012, we screened the cell tropism of their spike protein through pseudotyped retrovirus entry with human, bat and other mammalian cell lines. Most of cell lines screened were unsusceptible to BtCoV/Rh/YN2012, indicating a low risk of interspecies transmission to human and other animals. Multiple reasons may lead to failed infection of coronavirus spike-pseudotyped retrovirus system, including receptor absence in target cells, failed recognition to the receptor homologue from non-host species, maladaptation in non-host cells during the spike maturation or virus entry, or the limitation of retrovirus system in stimulating coronavirus entry. The weak infectivity of RsYN1 pseudotyped retrovirus in Huh-7 cells could be explained by the binding of spike protein to polysaccharide secreted to the surface. The assumption needs to be further confirmed by experiments.
Our long-term surveillances suggest that Rhinolophus bats seem to harbor a wide diversity of CoVs. Coincidently, the two highly pathogenic agents, SARS-CoV and Rh-BatCoV HKU2 both originated from Rhinolophus bats. Considering the diversity of CoVs carried by this bat genus and their wide geographical distribution, there may be a low risk of spillover of these viruses to other animals and humans. Long-term surveillances and pathogenesis studies will help to prevent future human and animal diseases caused by these bat CoVs.
Supplementary Materials: The following are available online at http://www.mdpi.com/1999-4915/11/4/379/s1, Figure S1 : western blot analysis of the expression of accessory proteins. Figure S2 : Apoptosis analysis of ORF9 proteins of BtCoV/Rh/YN2012. Figure S3 : Functional analysis of ORF3a, ORF3b, ORF4b, ORF8 and ORF9 proteins on the production of Type I interferon. Figure S4 : Functional analysis of ORF3b, ORF4a, ORF4b, ORF8 and ORF9 proteins on the production of NF-κB. Figure S5 : Characteristic of BtCoV/Rh/YN2012 spike mediated pseudovirus. Table S1 : General primers for AlphaCoVs genome sequencing. Table S2 : Primers for the detection of viral sugbenomic mRNAs. Table S3
|
What reference genome was used in the study?
| 3,682 |
BatCoV HKU10
| 4,900 |
1,576 |
Characterization of a New Member of Alphacoronavirus with Unique Genomic Features in Rhinolophus Bats
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6521148/
SHA: ee14de143337eec0e9708f8139bfac2b7b8fdd27
Authors: Wang, Ning; Luo, Chuming; Liu, Haizhou; Yang, Xinglou; Hu, Ben; Zhang, Wei; Li, Bei; Zhu, Yan; Zhu, Guangjian; Shen, Xurui; Peng, Cheng; Shi, Zhengli
Date: 2019-04-24
DOI: 10.3390/v11040379
License: cc-by
Abstract: Bats have been identified as a natural reservoir of a variety of coronaviruses (CoVs). Several of them have caused diseases in humans and domestic animals by interspecies transmission. Considering the diversity of bat coronaviruses, bat species and populations, we expect to discover more bat CoVs through virus surveillance. In this study, we described a new member of alphaCoV (BtCoV/Rh/YN2012) in bats with unique genome features. Unique accessory genes, ORF4a and ORF4b were found between the spike gene and the envelope gene, while ORF8 gene was found downstream of the nucleocapsid gene. All the putative genes were further confirmed by reverse-transcription analyses. One unique gene at the 3’ end of the BtCoV/Rh/YN2012 genome, ORF9, exhibits ~30% amino acid identity to ORF7a of the SARS-related coronavirus. Functional analysis showed ORF4a protein can activate IFN-β production, whereas ORF3a can regulate NF-κB production. We also screened the spike-mediated virus entry using the spike-pseudotyped retroviruses system, although failed to find any fully permissive cells. Our results expand the knowledge on the genetic diversity of bat coronaviruses. Continuous screening of bat viruses will help us further understand the important role played by bats in coronavirus evolution and transmission.
Text: Members of the Coronaviridae family are enveloped, non-segmented, positive-strand RNA viruses with genome sizes ranging from 26-32 kb [1] . These viruses are classified into two subfamilies: Letovirinae, which contains the only genus: Alphaletovirus; and Orthocoronavirinae (CoV), which consists of alpha, beta, gamma, and deltacoronaviruses (CoVs) [2, 3] . Alpha and betacoronaviruses mainly infect mammals and cause human and animal diseases. Gamma-and delta-CoVs mainly infect birds, but some can also infect mammals [4, 5] . Six human CoVs (HCoVs) are known to cause human diseases. HCoV-HKU1, HCoV-OC43, HCoV-229E, and HCoV-NL63 commonly cause mild respiratory illness or asymptomatic infection; however, severe acute respiratory syndrome coronavirus (SARS-CoV) and
All sampling procedures were performed by veterinarians, with approval from Animal Ethics Committee of the Wuhan Institute of Virology (WIVH5210201). The study was conducted in accordance with the Guide for the Care and Use of Wild Mammals in Research of the People's Republic of China.
Bat fecal swab and pellet samples were collected from November 2004 to November 2014 in different seasons in Southern China, as described previously [16] .
Viral RNA was extracted from 200 µL of fecal swab or pellet samples using the High Pure Viral RNA Kit (Roche Diagnostics GmbH, Mannheim, Germany) as per the manufacturer's instructions. RNA was eluted in 50 µL of elution buffer, aliquoted, and stored at -80 • C. One-step hemi-nested reverse-transcription (RT-) PCR (Invitrogen, San Diego, CA, USA) was employed to detect coronavirus, as previously described [17, 18] .
To confirm the bat species of an individual sample, we PCR amplified the cytochrome b (Cytob) and/or NADH dehydrogenase subunit 1 (ND1) gene using DNA extracted from the feces or swabs [19, 20] . The gene sequences were assembled excluding the primer sequences. BLASTN was used to identify host species based on the most closely related sequences with the highest query coverage and a minimum identity of 95%.
Full genomic sequences were determined by one-step PCR (Invitrogen, San Diego, CA, USA) amplification with degenerate primers (Table S1 ) designed on the basis of multiple alignments of available alpha-CoV sequences deposited in GenBank or amplified with SuperScript IV Reverse Transcriptase (Invitrogen) and Expand Long Template PCR System (Roche Diagnostics GmbH, Mannheim, Germany) with specific primers (primer sequences are available upon request). Sequences of the 5' and 3' genomic ends were obtained by 5' and 3' rapid amplification of cDNA ends (SMARTer Viruses 2019, 11, 379 3 of 19 RACE 5'/3' Kit; Clontech, Mountain View, CA, USA), respectively. PCR products were gel-purified and subjected directly to sequencing. PCR products over 5kb were subjected to deep sequencing using Hiseq2500 system. For some fragments, the PCR products were cloned into the pGEM-T Easy Vector (Promega, Madison, WI, USA) for sequencing. At least five independent clones were sequenced to obtain a consensus sequence.
The Next Generation Sequencing (NGS) data were filtered and mapped to the reference sequence of BatCoV HKU10 (GenBank accession number NC_018871) using Geneious 7.1.8 [21] . Genomes were preliminarily assembled using DNAStar lasergene V7 (DNAStar, Madison, WI, USA). Putative open reading frames (ORFs) were predicted using NCBI's ORF finder (https://www.ncbi.nlm.nih.gov/ orffinder/) with a minimal ORF length of 150 nt, followed by manual inspection. The sequences of the 5' untranslated region (5'-UTR) and 3'-UTR were defined, and the leader sequence, the leader and body transcriptional regulatory sequence (TRS) were identified as previously described [22] . The cleavage of the 16 nonstructural proteins coded by ORF1ab was determined by alignment of aa sequences of other CoVs and the recognition pattern of the 3C-like proteinase and papain-like proteinase. Phylogenetic trees based on nt or aa sequences were constructed using the maximum likelihood algorithm with bootstrap values determined by 1000 replicates in the MEGA 6 software package [23] . Full-length genome sequences obtained in this study were aligned with those of previously reported alpha-CoVs using MUSCLE [24] . The aligned sequences were scanned for recombination events by using Recombination Detection Program [25] . Potential recombination events as suggested by strong p-values (<10 -20 ) were confirmed using similarity plot and bootscan analyses implemented in Simplot 3.5.1 [26] . The number of synonymous substitutions per synonymous site, Ks, and the number of nonsynonymous substitutions per nonsynonymous site, Ka, for each coding region were calculated using the Ka/Ks calculation tool of the Norwegian Bioinformatics Platform (http://services.cbu.uib.no/tools/kaks) with default parameters [27] . The protein homology detection was analyzed using HHpred (https://toolkit.tuebingen.mpg.de/#/tools/hhpred) with default parameters [28] .
A set of nested RT-PCRs was employed to determine the presence of viral subgenomic mRNAs in the CoV-positive samples [29] . Forward primers were designed targeting the leader sequence at the 5'-end of the complete genome, while reverse primers were designed within the ORFs. Specific and suspected amplicons of expected sizes were purified and then cloned into the pGEM-T Easy vector for sequencing.
Bat primary or immortalized cells (Rhinolophus sinicus kidney immortalized cells, RsKT; Rhinolophus sinicus Lung primary cells, RsLu4323; Rhinolophus sinicus brain immortalized cells, RsBrT; Rhinolophus affinis kidney primary cells, RaK4324; Rousettus leschenaultii Kidney immortalized cells, RlKT; Hipposideros pratti lung immortalized cells, HpLuT) generated in our laboratory were all cultured in DMEM/F12 with 15% FBS. Pteropus alecto kidney cells (Paki) was maintained in DMEM/F12 supplemented with 10% FBS. Other cells were maintained according to the recommendations of American Type Culture Collection (ATCC, www.atcc.org).
The putative accessory genes of the newly detected virus were generated by RT-PCR from viral RNA extracted from fecal samples, as described previously [30] . The influenza virus NS1 plasmid was generated in our lab [31] . The human bocavirus (HBoV) VP2 plasmid was kindly provided by prof. Hanzhong Wang of the Wuhan Institute of Virology, Chinese Academy of Sciences. SARS-CoV ORF7a was synthesized by Sangon Biotech. The transfections were performed with Lipofectamine 3000 Reagent (Life Technologies). Expression of these accessory genes were analyzed by Western blotting using an mAb (Roche Diagnostics GmbH, Mannheim, Germany) against the HA tag.
The virus isolation was performed as previously described [12] . Briefly, fecal supernatant was acquired via gradient centrifugation and then added to Vero E6 cells, 1:10 diluted in DMEM. After incubation at 37°C for 1 h the inoculum was replaced by fresh DMEM containing 2% FBS and the antibiotic-antimycotic (Gibco, Grand Island, NY, USA). Three blind passages were carried out. Cells were checked daily for cytopathic effect. Both culture supernatant and cell pellet were examined for CoV by RT-PCR [17] .
Apoptosis was analyzed as previously described [18] . Briefly, 293T cells in 12-well plates were transfected with 3 µg of expression plasmid or empty vector, and the cells were collected 24 h post transfection. Apoptosis was detected by flow cytometry using by the Annexin V-FITC/PI Apoptosis Detection Kit (YEASEN, Shanghai, China) following the manufacturer's instructions. Annexin-V-positive and PI-negative cells were considered to be in the early apoptotic phase and those stained for both Annexin V and PI were deemed to undergo late apoptosis or necrosis. All experiments were repeated three times. Student's t-test was used to evaluate the data, with p < 0.05 considered significant.
HEK 293T cells were seeded in 24-well plates and then co-transfected with reporter plasmids (pRL-TK and pIFN-βIFN-or pNF-κB-Luc) [30] , as well as plasmids expressing accessory genes, empty vector plasmid pcAGGS, influenza virus NS1 [32] , SARS-CoV ORF7a [33] , or HBoV VP2 [34] . At 24 h post transfection, cells were treated with Sendai virus (SeV) (100 hemagglutinin units [HAU]/mL) or human tumor necrosis factor alpha (TNF-α; R&D system) for 6 h to activate IFNβ or NF-κB, respectively. Cell lysates were prepared, and luciferase activity was measured using the dual-luciferase assay kit (Promega, Madison, WI, USA) according to the manufacturer's instructions.
Retroviruses pseudotyped with BtCoV/Rh/YN2012 RsYN1, RsYN3, RaGD, or MERS-CoV spike, or no spike (mock) were used to infect human, bat or other mammalian cells in 96-well plates. The pseudovirus particles were confirmed with Western blotting and negative-staining electromicroscopy. The production process, measurements of infection and luciferase activity were conducted, as described previously [35, 36] .
The complete genome nucleotide sequences of BtCoV/Rh/YN2012 strains RsYN1, RsYN2, RsYN3, and RaGD obtained in this study have been submitted to the GenBank under MG916901 to MG916904.
The surveillance was performed between November 2004 to November 2014 in 19 provinces of China. In total, 2061 fecal samples were collected from at least 12 Rhinolophus bat species ( Figure 1A ). CoVs were detected in 209 of these samples ( Figure 1B and Table 1 ). Partial RdRp sequences suggested the presence of at least 8 different CoVs. Five of these viruses are related to known species: Mi-BatCoV 1 (>94% nt identity), Mi-BatCoV HKU8 [37] (>93% nt identity), BtRf-AlphaCoV/HuB2013 [11] (>99% nt identity), SARSr-CoV [38] (>89% nt identity), and HKU2-related CoV [39] (>85% nt identity). While the other three CoV sequences showed less than 83% nt identity to known CoV species. These three viruses should represent novel CoV species. Virus isolation was performed as previously described [12] , but was not successful. identity). While the other three CoV sequences showed less than 83% nt identity to known CoV species. These three viruses should represent novel CoV species. Virus isolation was performed as previously described [12] , but was not successful.
We next characterized a novel alpha-CoV, BtCoV/Rh/YN2012. It was detected in 3 R.affinis and 6 R.sinicus, respectively. Based on the sequences, we defined three genotypes, which represented by RsYN1, RsYN3, and RaGD, respectively. Strain RsYN2 was classified into the RsYN3 genotype. Four full-length genomes were obtained. Three of them were from R.sinicus (Strain RsYN1, RsYN2, and RsYN3), while the other one was from R.affinis (Strain RaGD). The sizes of these 4 genomes are between 28,715 to 29,102, with G+C contents between 39.0% to 41.3%. The genomes exhibit similar structures and transcription regulatory sequences (TRS) that are identical to those of other alpha-CoVs ( Figure 2 and Table 2 ). Exceptions including three additional ORFs (ORF3b, ORF4a and ORF4b) were observed. All the 4 strains have ORF4a & ORF4b, while only strain RsYN1 has ORF3b.
The replicase gene, ORF1ab, occupies~20.4 kb of the genome. The replicase gene, ORF1ab, occupies~20.4 kb of the genome. It encodes polyproteins 1a and 1ab, which could be cleaved into 16 non-structural proteins (Nsp1-Nsp16). The 3'-end of the cleavage sites recognized by 3C-like proteinase (Nsp4-Nsp10, Nsp12-Nsp16) and papain-like proteinase (Nsp1-Nsp3) were confirmed. The proteins including Nsp3 (papain-like 2 proteas, PL2pro), Nsp5 (chymotrypsin-like protease, 3CLpro), Nsp12 (RdRp), Nsp13 (helicase), and other proteins of unknown function ( Table 3 ). The 7 concatenated domains of polyprotein 1 shared <90% aa sequence identity with those of other known alpha-CoVs ( Table 2 ), suggesting that these viruses represent a novel CoV species within the alpha-CoV. The closest assigned CoV species to BtCoV/Rh/YN2012 are BtCoV-HKU10 and BtRf-AlphaCoV/Hub2013. The three strains from Yunnan Province were clustered into two genotypes (83% genome identity) correlated to their sampling location. The third genotype represented by strain RaGD was isolated to strains found in Yunnan (<75.4% genome identity). We then examined the individual genes ( Table 2) . All of the genes showed low aa sequence identity to known CoVs. The four strains of BtCoV/Rh/YN2012 showed genetic diversity among all different genes except ORF1ab (>83.7% aa identity). Notably, the spike proteins are highly divergent among these strains. Other structure proteins (E, M, and N) are more conserved than the spike and other accessory proteins. Comparing the accessory genes among these four strains revealed that the strains of the same genotype shared a 100% identical ORF3a. However, the proteins encoded by ORF3as were highly divergent among different genotypes (<65% aa identity). The putative accessory genes were also BLASTed against GenBank records. Most accessory genes have no homologues in GenBank-database, except for ORF3a (52.0-55.5% aa identity with BatCoV HKU10 ORF3) and ORF9 (28.1-32.0% aa identity with SARSr-CoV ORF7a). We analyzed the protein homology with HHpred software. The results showed that ORF9s and SARS-CoV OR7a are homologues (possibility: 100%, E value <10 −48 ). We further screened the genomes for potential recombination evidence. No significant recombination breakpoint was detected by bootscan analysis.
To confirm the presence of subgenomic RNA, we designed a set of primers targeting all the predicted ORFs as described. The amplicons were firstly confirmed via agarose-gel electrophoresis and then sequencing ( Figure 3 and Table 2 ). The sequences showed that all the ORFs, except ORF4b, had preceding TRS. Hence, the ORF4b may be translated from bicistronic mRNAs. In RsYN1, an additional subgenomic RNA starting inside the ORF3a was found through sequencing, which led to a unique ORF3b.
To confirm the presence of subgenomic RNA, we designed a set of primers targeting all the predicted ORFs as described. The amplicons were firstly confirmed via agarose-gel electrophoresis and then sequencing ( Figure 3 and Table 2 ). The sequences showed that all the ORFs, except ORF4b, had preceding TRS. Hence, the ORF4b may be translated from bicistronic mRNAs. In RsYN1, an additional subgenomic RNA starting inside the ORF3a was found through sequencing, which led to a unique ORF3b.
Phylogenetic trees were constructed using the aa sequences of RdRp and S of BtCoV/Rh/YN2012 and other representative CoVs (Figure 4) . In both trees, all BtCoV/Rh/YN2012 were clustered together and formed a distinct lineage to other known coronavirus species. Two distinct sublineages were observed within BtCoV/Rh/YN2012. One was from Ra sampled in Guangdong, while the other was from Rs sampled in Yunnan Among the strains from Yunnan, RsYN2 and RsYN3 were clustered together, while RsYN1 was isolated. The topology of these four strains was correlated to the sampling location. The relatively long branches reflect a high diversity among these strains, indicating a long independent evolution history.
Phylogenetic trees were constructed using the aa sequences of RdRp and S of BtCoV/Rh/YN2012 and other representative CoVs (Figure 4) . In both trees, all BtCoV/Rh/YN2012 were clustered together and formed a distinct lineage to other known coronavirus species. Two distinct sublineages were observed within BtCoV/Rh/YN2012. One was from Ra sampled in Guangdong, while the other was from Rs sampled in Yunnan Among the strains from Yunnan, RsYN2 and RsYN3 were clustered together, while RsYN1 was isolated. The topology of these four strains was correlated to the sampling location. The relatively long branches reflect a high diversity among these strains, indicating a long independent evolution history.
Phylogenetic trees were constructed using the aa sequences of RdRp and S of BtCoV/Rh/YN2012 and other representative CoVs (Figure 4) . In both trees, all BtCoV/Rh/YN2012 were clustered together and formed a distinct lineage to other known coronavirus species. Two distinct sublineages were observed within BtCoV/Rh/YN2012. One was from Ra sampled in Guangdong, while the other was from Rs sampled in Yunnan Among the strains from Yunnan, RsYN2 and RsYN3 were clustered together, while RsYN1 was isolated. The topology of these four strains was correlated to the sampling location. The relatively long branches reflect a high diversity among these strains, indicating a long independent evolution history.
The Ka/Ks ratios (Ks is the number of synonymous substitutions per synonymous sites and Ka is the number of nonsynonymous substitutions per nonsynonymous site) were calculated for all genes. The Ka/Ks ratios for most of the genes were generally low, which indicates these genes were under purified selection. However, the Ka/Ks ratios of ORF4a, ORF4b, and ORF9 (0.727, 0.623, and 0.843, respectively) were significantly higher than those of other ORFs (Table 4 ). For further selection pressure evaluation of the ORF4a and ORF4b gene, we sequenced another four ORF4a and ORF4b genes (strain Rs4223, Rs4236, Rs4240, and Ra13576 was shown in Figure 1B
As SARS-CoV ORF7a was reported to induce apoptosis, we conducted apoptosis analysis on BtCoV/Rh/YN2012 ORF9, a~30% aa identity homologue of SARSr-CoV ORF7a. We transiently transfected ORF9 of BtCoV/Rh/YN2012 into HEK293T cells to examine whether this ORF9 triggers apoptosis. Western blot was performed to confirm the expression of ORF9s and SARS-CoV ORF7a ( Figure S1 ). ORF9 couldn't induce apoptosis as the ORF7a of SARS-CoV Tor2 ( Figure S2 ). The results indicated that BtCoV/Rh/YN2012 ORF9 was not involved in apoptosis induction.
To determine whether these accessory proteins modulate IFN induction, we transfected reporter plasmids (pIFNβ-Luc and pRL-TK) and expression plasmids to 293T cells. All the cells over-expressing the accessory genes, as well as influenza virus NS1 (strain PR8), HBoV VP2, or empty vector were tested for luciferase activity after SeV infection. Luciferase activity stimulated by SeV was remarkably higher than that without SeV treatment as expected. Influenza virus NS1 inhibits the expression from IFN promoter, while HBoV VP2 activate the expression. Compared to those controls, the ORF4a proteins exhibit an active effect as HBoV VP2 ( Figure 5A ). Other accessory proteins showed no effect on IFN production ( Figure S3 ). Expression of these accessory genes were confirmed by Western blot ( Figure S1 ). was remarkably higher than that without SeV treatment as expected. Influenza virus NS1 inhibits the expression from IFN promoter, while HBoV VP2 activate the expression. Compared to those controls, the ORF4a proteins exhibit an active effect as HBoV VP2 ( Figure 5A ). Other accessory proteins showed no effect on IFN production ( Figure S3 ). Expression of these accessory genes were confirmed by Western blot (Figure S1 ). Samples were collected at 6 h postinfection, followed by dual-luciferase assay. The results were expressed as the firefly luciferase value normalized to that of Renilla luciferase. (B) ORF3a protein activate NF-κB. 293T cells were transfected with 100 ng pNF-κB-Luc, 10 ng pRL-TK, empty vector (500 ng), an NS1-expressing plasmid (500 ng), a SARS-CoV ORF7a-expressing plasmid (500 ng), or ORF3a-expressing plasmids (500 ng). After 24 h, the cells were treated with TNF-α. Dual-luciferase activity was determined after 6 h. The results were expressed as the firefly luciferase activity normalized to that of Renilla luciferase. The experiments were performed three times independently. Data are representative of at least three independent experiments, with each determination performed in triplicate (mean ± SD of fold change). Asterisks indicate significant differences between groups (compared with Empty vector-NC, p < 0.05, as determined by student t test).
NF-κB plays an important role in regulating the immune response to viral infection and is also a key factor frequently targeted by viruses for taking over the host cell. In this study, we tested if these accessory proteins could modulate NF-κB. 293T cells were co-transfected with reporter Samples were collected at 6 h postinfection, followed by dual-luciferase assay. The results were expressed as the firefly luciferase value normalized to that of Renilla luciferase. (B) ORF3a protein activate NF-κB. 293T cells were transfected with 100 ng pNF-κB-Luc, 10 ng pRL-TK, empty vector (500 ng), an NS1-expressing plasmid (500 ng), a SARS-CoV ORF7a-expressing plasmid (500 ng), or ORF3a-expressing plasmids (500 ng). After 24 h, the cells were treated with TNF-α. Dual-luciferase activity was determined after 6 h. The results were expressed as the firefly luciferase activity normalized to that of Renilla luciferase. The experiments were performed three times independently. Data are representative of at least three independent experiments, with each determination performed in triplicate (mean ± SD of fold change). Asterisks indicate significant differences between groups (compared with Empty vector-NC, p < 0.05, as determined by student t test).
NF-κB plays an important role in regulating the immune response to viral infection and is also a key factor frequently targeted by viruses for taking over the host cell. In this study, we tested if these accessory proteins could modulate NF-κB. 293T cells were co-transfected with reporter plasmids (pNF-κB-Luc and pRL-TK), as well as accessory protein-expressing plasmids, or controls (empty vector, NS1, SARS-CoV Tor2-ORF7a). The cells were mock treated or treated with TNF-α for 6 h at 24 h post-transfection. The luciferase activity was determined. RsYN1-ORF3a and RaGD-ORF3a activated NF-κB as SARS-CoV ORF7a, whereas RsYN2-ORF3a inhibited NF-κB as NS1 ( Figure 5B ). Expressions of ORF3as were confirmed with Western blot ( Figure S1 ). Other accessory proteins did not modulate NF-κB production ( Figure S4 ).
To understand the infectivity of these newly detected BtCoV/Rh/YN2012, we selected the RsYN1, RsYN3 and RaGD spike proteins for spike-mediated pseudovirus entry studies. Both Western blot analysis and negative-staining electron microscopy observation confirmed the preparation of BtCoV/Rh/YN2012 successfully ( Figure S5 ). A total of 11 human cell lines, 8 bat cells, and 9 other mammal cell lines were tested, and no strong positive was found (Table S2) .
In this study, a novel alpha-CoV species, BtCoV/Rh/YN2012, was identified in two Rhinolophus species. The 4 strains with full-length genome were sequences. The 7 conserved replicase domains of these viruses possessed <90% aa sequence identity to those of other known alpha-CoVs, which defines a new species in accordance with the ICTV taxonomy standard [42] . These novel alpha-CoVs showed high genetic diversity in their structural and non-structural genes. Strain RaGD from R. affinis, collected in Guangdong province, formed a divergent independent branch from the other 3 strains from R. sinicus, sampled in Yunnan Province, indicating an independent evolution process associated with geographic isolation and host restrain. Though collected from same province, these three virus strains formed two genotypes correlated to sampling locations. These two genotypes had low genome sequence identity, especially in the S gene and accessory genes. Considering the remote geographic location of the host bat habitat, the host tropism, and the virus diversity, we suppose BtCoV/Rh/YN2012 may have spread in these two provinces with a long history of circulation in their natural reservoir, Rhinolophus bats. With the sequence evidence, we suppose that these viruses are still rapidly evolving.
Our study revealed that BtCoV/Rh/YN2012 has a unique genome structure compared to other alpha-CoVs. First, novel accessory genes, which had no homologues, were identified in the genomes. Second, multiple TRSs were found between S and E genes while other alphacoronavirus only had one TRS there. These TRSs precede ORF3a, ORF3b (only in RsYN1), and ORF4a/b respectively. Third, accessory gene ORF9 showed homology with those of other known CoV species in another coronavirus genus, especially with accessory genes from SARSr-CoV.
Accessory genes are usually involved in virus-host interactions during CoV infection [43] . In most CoVs, accessory genes are dispensable for virus replication. However, an intact 3c gene of feline CoV was required for viral replication in the gut [44] [45] [46] . Deletion of the genus-specific genes in mouse hepatitis virus led to a reduction in virulence [47] . SARS-CoV ORF7a, which was identified to be involved in the suppression of RNA silencing [48] , inhibition of cellular protein synthesis [49] , cell-cycle blockage [50] , and apoptosis induction [51, 52] . In this study, we found that BtCoV/Rh/YN2012 ORF9 shares~30% aa sequence identity with SARS-CoV ORF7a. Interestingly, BtCoV/Rh/YN2012 and SARSr-CoV were both detected in R. sinicus from the same cave. We suppose that SARS-CoV and BtCoV/Rh/YN2012 may have acquired ORF7a or ORF9 from a common ancestor through genome recombination or horizontal gene transfer. Whereas, ORF9 of BtCoV/Rh/YN2012 failed to induce apoptosis or activate NF-κB production, these differences may be induced by the divergent evolution of these proteins in different pressure.
Though different BtCoV/Rh/YN2012 ORF4a share <64.4% amino acid identity, all of them could activate IFN-β. ORF3a from RsYN1 and RaGD upregulated NF-κB, but the homologue from RsYN2 downregulated NF-κB expression. These differences may be caused by amino acid sequence variations and may contribute to a viruses' pathogenicity with a different pathway.
Though lacking of intestinal cell lines from the natural host of BtCoV/Rh/YN2012, we screened the cell tropism of their spike protein through pseudotyped retrovirus entry with human, bat and other mammalian cell lines. Most of cell lines screened were unsusceptible to BtCoV/Rh/YN2012, indicating a low risk of interspecies transmission to human and other animals. Multiple reasons may lead to failed infection of coronavirus spike-pseudotyped retrovirus system, including receptor absence in target cells, failed recognition to the receptor homologue from non-host species, maladaptation in non-host cells during the spike maturation or virus entry, or the limitation of retrovirus system in stimulating coronavirus entry. The weak infectivity of RsYN1 pseudotyped retrovirus in Huh-7 cells could be explained by the binding of spike protein to polysaccharide secreted to the surface. The assumption needs to be further confirmed by experiments.
Our long-term surveillances suggest that Rhinolophus bats seem to harbor a wide diversity of CoVs. Coincidently, the two highly pathogenic agents, SARS-CoV and Rh-BatCoV HKU2 both originated from Rhinolophus bats. Considering the diversity of CoVs carried by this bat genus and their wide geographical distribution, there may be a low risk of spillover of these viruses to other animals and humans. Long-term surveillances and pathogenesis studies will help to prevent future human and animal diseases caused by these bat CoVs.
Supplementary Materials: The following are available online at http://www.mdpi.com/1999-4915/11/4/379/s1, Figure S1 : western blot analysis of the expression of accessory proteins. Figure S2 : Apoptosis analysis of ORF9 proteins of BtCoV/Rh/YN2012. Figure S3 : Functional analysis of ORF3a, ORF3b, ORF4b, ORF8 and ORF9 proteins on the production of Type I interferon. Figure S4 : Functional analysis of ORF3b, ORF4a, ORF4b, ORF8 and ORF9 proteins on the production of NF-κB. Figure S5 : Characteristic of BtCoV/Rh/YN2012 spike mediated pseudovirus. Table S1 : General primers for AlphaCoVs genome sequencing. Table S2 : Primers for the detection of viral sugbenomic mRNAs. Table S3
|
What is the length of the replicase gene ORF1ab?
| 3,684 |
20.4 kb
| 12,855 |
1,576 |
Characterization of a New Member of Alphacoronavirus with Unique Genomic Features in Rhinolophus Bats
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6521148/
SHA: ee14de143337eec0e9708f8139bfac2b7b8fdd27
Authors: Wang, Ning; Luo, Chuming; Liu, Haizhou; Yang, Xinglou; Hu, Ben; Zhang, Wei; Li, Bei; Zhu, Yan; Zhu, Guangjian; Shen, Xurui; Peng, Cheng; Shi, Zhengli
Date: 2019-04-24
DOI: 10.3390/v11040379
License: cc-by
Abstract: Bats have been identified as a natural reservoir of a variety of coronaviruses (CoVs). Several of them have caused diseases in humans and domestic animals by interspecies transmission. Considering the diversity of bat coronaviruses, bat species and populations, we expect to discover more bat CoVs through virus surveillance. In this study, we described a new member of alphaCoV (BtCoV/Rh/YN2012) in bats with unique genome features. Unique accessory genes, ORF4a and ORF4b were found between the spike gene and the envelope gene, while ORF8 gene was found downstream of the nucleocapsid gene. All the putative genes were further confirmed by reverse-transcription analyses. One unique gene at the 3’ end of the BtCoV/Rh/YN2012 genome, ORF9, exhibits ~30% amino acid identity to ORF7a of the SARS-related coronavirus. Functional analysis showed ORF4a protein can activate IFN-β production, whereas ORF3a can regulate NF-κB production. We also screened the spike-mediated virus entry using the spike-pseudotyped retroviruses system, although failed to find any fully permissive cells. Our results expand the knowledge on the genetic diversity of bat coronaviruses. Continuous screening of bat viruses will help us further understand the important role played by bats in coronavirus evolution and transmission.
Text: Members of the Coronaviridae family are enveloped, non-segmented, positive-strand RNA viruses with genome sizes ranging from 26-32 kb [1] . These viruses are classified into two subfamilies: Letovirinae, which contains the only genus: Alphaletovirus; and Orthocoronavirinae (CoV), which consists of alpha, beta, gamma, and deltacoronaviruses (CoVs) [2, 3] . Alpha and betacoronaviruses mainly infect mammals and cause human and animal diseases. Gamma-and delta-CoVs mainly infect birds, but some can also infect mammals [4, 5] . Six human CoVs (HCoVs) are known to cause human diseases. HCoV-HKU1, HCoV-OC43, HCoV-229E, and HCoV-NL63 commonly cause mild respiratory illness or asymptomatic infection; however, severe acute respiratory syndrome coronavirus (SARS-CoV) and
All sampling procedures were performed by veterinarians, with approval from Animal Ethics Committee of the Wuhan Institute of Virology (WIVH5210201). The study was conducted in accordance with the Guide for the Care and Use of Wild Mammals in Research of the People's Republic of China.
Bat fecal swab and pellet samples were collected from November 2004 to November 2014 in different seasons in Southern China, as described previously [16] .
Viral RNA was extracted from 200 µL of fecal swab or pellet samples using the High Pure Viral RNA Kit (Roche Diagnostics GmbH, Mannheim, Germany) as per the manufacturer's instructions. RNA was eluted in 50 µL of elution buffer, aliquoted, and stored at -80 • C. One-step hemi-nested reverse-transcription (RT-) PCR (Invitrogen, San Diego, CA, USA) was employed to detect coronavirus, as previously described [17, 18] .
To confirm the bat species of an individual sample, we PCR amplified the cytochrome b (Cytob) and/or NADH dehydrogenase subunit 1 (ND1) gene using DNA extracted from the feces or swabs [19, 20] . The gene sequences were assembled excluding the primer sequences. BLASTN was used to identify host species based on the most closely related sequences with the highest query coverage and a minimum identity of 95%.
Full genomic sequences were determined by one-step PCR (Invitrogen, San Diego, CA, USA) amplification with degenerate primers (Table S1 ) designed on the basis of multiple alignments of available alpha-CoV sequences deposited in GenBank or amplified with SuperScript IV Reverse Transcriptase (Invitrogen) and Expand Long Template PCR System (Roche Diagnostics GmbH, Mannheim, Germany) with specific primers (primer sequences are available upon request). Sequences of the 5' and 3' genomic ends were obtained by 5' and 3' rapid amplification of cDNA ends (SMARTer Viruses 2019, 11, 379 3 of 19 RACE 5'/3' Kit; Clontech, Mountain View, CA, USA), respectively. PCR products were gel-purified and subjected directly to sequencing. PCR products over 5kb were subjected to deep sequencing using Hiseq2500 system. For some fragments, the PCR products were cloned into the pGEM-T Easy Vector (Promega, Madison, WI, USA) for sequencing. At least five independent clones were sequenced to obtain a consensus sequence.
The Next Generation Sequencing (NGS) data were filtered and mapped to the reference sequence of BatCoV HKU10 (GenBank accession number NC_018871) using Geneious 7.1.8 [21] . Genomes were preliminarily assembled using DNAStar lasergene V7 (DNAStar, Madison, WI, USA). Putative open reading frames (ORFs) were predicted using NCBI's ORF finder (https://www.ncbi.nlm.nih.gov/ orffinder/) with a minimal ORF length of 150 nt, followed by manual inspection. The sequences of the 5' untranslated region (5'-UTR) and 3'-UTR were defined, and the leader sequence, the leader and body transcriptional regulatory sequence (TRS) were identified as previously described [22] . The cleavage of the 16 nonstructural proteins coded by ORF1ab was determined by alignment of aa sequences of other CoVs and the recognition pattern of the 3C-like proteinase and papain-like proteinase. Phylogenetic trees based on nt or aa sequences were constructed using the maximum likelihood algorithm with bootstrap values determined by 1000 replicates in the MEGA 6 software package [23] . Full-length genome sequences obtained in this study were aligned with those of previously reported alpha-CoVs using MUSCLE [24] . The aligned sequences were scanned for recombination events by using Recombination Detection Program [25] . Potential recombination events as suggested by strong p-values (<10 -20 ) were confirmed using similarity plot and bootscan analyses implemented in Simplot 3.5.1 [26] . The number of synonymous substitutions per synonymous site, Ks, and the number of nonsynonymous substitutions per nonsynonymous site, Ka, for each coding region were calculated using the Ka/Ks calculation tool of the Norwegian Bioinformatics Platform (http://services.cbu.uib.no/tools/kaks) with default parameters [27] . The protein homology detection was analyzed using HHpred (https://toolkit.tuebingen.mpg.de/#/tools/hhpred) with default parameters [28] .
A set of nested RT-PCRs was employed to determine the presence of viral subgenomic mRNAs in the CoV-positive samples [29] . Forward primers were designed targeting the leader sequence at the 5'-end of the complete genome, while reverse primers were designed within the ORFs. Specific and suspected amplicons of expected sizes were purified and then cloned into the pGEM-T Easy vector for sequencing.
Bat primary or immortalized cells (Rhinolophus sinicus kidney immortalized cells, RsKT; Rhinolophus sinicus Lung primary cells, RsLu4323; Rhinolophus sinicus brain immortalized cells, RsBrT; Rhinolophus affinis kidney primary cells, RaK4324; Rousettus leschenaultii Kidney immortalized cells, RlKT; Hipposideros pratti lung immortalized cells, HpLuT) generated in our laboratory were all cultured in DMEM/F12 with 15% FBS. Pteropus alecto kidney cells (Paki) was maintained in DMEM/F12 supplemented with 10% FBS. Other cells were maintained according to the recommendations of American Type Culture Collection (ATCC, www.atcc.org).
The putative accessory genes of the newly detected virus were generated by RT-PCR from viral RNA extracted from fecal samples, as described previously [30] . The influenza virus NS1 plasmid was generated in our lab [31] . The human bocavirus (HBoV) VP2 plasmid was kindly provided by prof. Hanzhong Wang of the Wuhan Institute of Virology, Chinese Academy of Sciences. SARS-CoV ORF7a was synthesized by Sangon Biotech. The transfections were performed with Lipofectamine 3000 Reagent (Life Technologies). Expression of these accessory genes were analyzed by Western blotting using an mAb (Roche Diagnostics GmbH, Mannheim, Germany) against the HA tag.
The virus isolation was performed as previously described [12] . Briefly, fecal supernatant was acquired via gradient centrifugation and then added to Vero E6 cells, 1:10 diluted in DMEM. After incubation at 37°C for 1 h the inoculum was replaced by fresh DMEM containing 2% FBS and the antibiotic-antimycotic (Gibco, Grand Island, NY, USA). Three blind passages were carried out. Cells were checked daily for cytopathic effect. Both culture supernatant and cell pellet were examined for CoV by RT-PCR [17] .
Apoptosis was analyzed as previously described [18] . Briefly, 293T cells in 12-well plates were transfected with 3 µg of expression plasmid or empty vector, and the cells were collected 24 h post transfection. Apoptosis was detected by flow cytometry using by the Annexin V-FITC/PI Apoptosis Detection Kit (YEASEN, Shanghai, China) following the manufacturer's instructions. Annexin-V-positive and PI-negative cells were considered to be in the early apoptotic phase and those stained for both Annexin V and PI were deemed to undergo late apoptosis or necrosis. All experiments were repeated three times. Student's t-test was used to evaluate the data, with p < 0.05 considered significant.
HEK 293T cells were seeded in 24-well plates and then co-transfected with reporter plasmids (pRL-TK and pIFN-βIFN-or pNF-κB-Luc) [30] , as well as plasmids expressing accessory genes, empty vector plasmid pcAGGS, influenza virus NS1 [32] , SARS-CoV ORF7a [33] , or HBoV VP2 [34] . At 24 h post transfection, cells were treated with Sendai virus (SeV) (100 hemagglutinin units [HAU]/mL) or human tumor necrosis factor alpha (TNF-α; R&D system) for 6 h to activate IFNβ or NF-κB, respectively. Cell lysates were prepared, and luciferase activity was measured using the dual-luciferase assay kit (Promega, Madison, WI, USA) according to the manufacturer's instructions.
Retroviruses pseudotyped with BtCoV/Rh/YN2012 RsYN1, RsYN3, RaGD, or MERS-CoV spike, or no spike (mock) were used to infect human, bat or other mammalian cells in 96-well plates. The pseudovirus particles were confirmed with Western blotting and negative-staining electromicroscopy. The production process, measurements of infection and luciferase activity were conducted, as described previously [35, 36] .
The complete genome nucleotide sequences of BtCoV/Rh/YN2012 strains RsYN1, RsYN2, RsYN3, and RaGD obtained in this study have been submitted to the GenBank under MG916901 to MG916904.
The surveillance was performed between November 2004 to November 2014 in 19 provinces of China. In total, 2061 fecal samples were collected from at least 12 Rhinolophus bat species ( Figure 1A ). CoVs were detected in 209 of these samples ( Figure 1B and Table 1 ). Partial RdRp sequences suggested the presence of at least 8 different CoVs. Five of these viruses are related to known species: Mi-BatCoV 1 (>94% nt identity), Mi-BatCoV HKU8 [37] (>93% nt identity), BtRf-AlphaCoV/HuB2013 [11] (>99% nt identity), SARSr-CoV [38] (>89% nt identity), and HKU2-related CoV [39] (>85% nt identity). While the other three CoV sequences showed less than 83% nt identity to known CoV species. These three viruses should represent novel CoV species. Virus isolation was performed as previously described [12] , but was not successful. identity). While the other three CoV sequences showed less than 83% nt identity to known CoV species. These three viruses should represent novel CoV species. Virus isolation was performed as previously described [12] , but was not successful.
We next characterized a novel alpha-CoV, BtCoV/Rh/YN2012. It was detected in 3 R.affinis and 6 R.sinicus, respectively. Based on the sequences, we defined three genotypes, which represented by RsYN1, RsYN3, and RaGD, respectively. Strain RsYN2 was classified into the RsYN3 genotype. Four full-length genomes were obtained. Three of them were from R.sinicus (Strain RsYN1, RsYN2, and RsYN3), while the other one was from R.affinis (Strain RaGD). The sizes of these 4 genomes are between 28,715 to 29,102, with G+C contents between 39.0% to 41.3%. The genomes exhibit similar structures and transcription regulatory sequences (TRS) that are identical to those of other alpha-CoVs ( Figure 2 and Table 2 ). Exceptions including three additional ORFs (ORF3b, ORF4a and ORF4b) were observed. All the 4 strains have ORF4a & ORF4b, while only strain RsYN1 has ORF3b.
The replicase gene, ORF1ab, occupies~20.4 kb of the genome. The replicase gene, ORF1ab, occupies~20.4 kb of the genome. It encodes polyproteins 1a and 1ab, which could be cleaved into 16 non-structural proteins (Nsp1-Nsp16). The 3'-end of the cleavage sites recognized by 3C-like proteinase (Nsp4-Nsp10, Nsp12-Nsp16) and papain-like proteinase (Nsp1-Nsp3) were confirmed. The proteins including Nsp3 (papain-like 2 proteas, PL2pro), Nsp5 (chymotrypsin-like protease, 3CLpro), Nsp12 (RdRp), Nsp13 (helicase), and other proteins of unknown function ( Table 3 ). The 7 concatenated domains of polyprotein 1 shared <90% aa sequence identity with those of other known alpha-CoVs ( Table 2 ), suggesting that these viruses represent a novel CoV species within the alpha-CoV. The closest assigned CoV species to BtCoV/Rh/YN2012 are BtCoV-HKU10 and BtRf-AlphaCoV/Hub2013. The three strains from Yunnan Province were clustered into two genotypes (83% genome identity) correlated to their sampling location. The third genotype represented by strain RaGD was isolated to strains found in Yunnan (<75.4% genome identity). We then examined the individual genes ( Table 2) . All of the genes showed low aa sequence identity to known CoVs. The four strains of BtCoV/Rh/YN2012 showed genetic diversity among all different genes except ORF1ab (>83.7% aa identity). Notably, the spike proteins are highly divergent among these strains. Other structure proteins (E, M, and N) are more conserved than the spike and other accessory proteins. Comparing the accessory genes among these four strains revealed that the strains of the same genotype shared a 100% identical ORF3a. However, the proteins encoded by ORF3as were highly divergent among different genotypes (<65% aa identity). The putative accessory genes were also BLASTed against GenBank records. Most accessory genes have no homologues in GenBank-database, except for ORF3a (52.0-55.5% aa identity with BatCoV HKU10 ORF3) and ORF9 (28.1-32.0% aa identity with SARSr-CoV ORF7a). We analyzed the protein homology with HHpred software. The results showed that ORF9s and SARS-CoV OR7a are homologues (possibility: 100%, E value <10 −48 ). We further screened the genomes for potential recombination evidence. No significant recombination breakpoint was detected by bootscan analysis.
To confirm the presence of subgenomic RNA, we designed a set of primers targeting all the predicted ORFs as described. The amplicons were firstly confirmed via agarose-gel electrophoresis and then sequencing ( Figure 3 and Table 2 ). The sequences showed that all the ORFs, except ORF4b, had preceding TRS. Hence, the ORF4b may be translated from bicistronic mRNAs. In RsYN1, an additional subgenomic RNA starting inside the ORF3a was found through sequencing, which led to a unique ORF3b.
To confirm the presence of subgenomic RNA, we designed a set of primers targeting all the predicted ORFs as described. The amplicons were firstly confirmed via agarose-gel electrophoresis and then sequencing ( Figure 3 and Table 2 ). The sequences showed that all the ORFs, except ORF4b, had preceding TRS. Hence, the ORF4b may be translated from bicistronic mRNAs. In RsYN1, an additional subgenomic RNA starting inside the ORF3a was found through sequencing, which led to a unique ORF3b.
Phylogenetic trees were constructed using the aa sequences of RdRp and S of BtCoV/Rh/YN2012 and other representative CoVs (Figure 4) . In both trees, all BtCoV/Rh/YN2012 were clustered together and formed a distinct lineage to other known coronavirus species. Two distinct sublineages were observed within BtCoV/Rh/YN2012. One was from Ra sampled in Guangdong, while the other was from Rs sampled in Yunnan Among the strains from Yunnan, RsYN2 and RsYN3 were clustered together, while RsYN1 was isolated. The topology of these four strains was correlated to the sampling location. The relatively long branches reflect a high diversity among these strains, indicating a long independent evolution history.
Phylogenetic trees were constructed using the aa sequences of RdRp and S of BtCoV/Rh/YN2012 and other representative CoVs (Figure 4) . In both trees, all BtCoV/Rh/YN2012 were clustered together and formed a distinct lineage to other known coronavirus species. Two distinct sublineages were observed within BtCoV/Rh/YN2012. One was from Ra sampled in Guangdong, while the other was from Rs sampled in Yunnan Among the strains from Yunnan, RsYN2 and RsYN3 were clustered together, while RsYN1 was isolated. The topology of these four strains was correlated to the sampling location. The relatively long branches reflect a high diversity among these strains, indicating a long independent evolution history.
Phylogenetic trees were constructed using the aa sequences of RdRp and S of BtCoV/Rh/YN2012 and other representative CoVs (Figure 4) . In both trees, all BtCoV/Rh/YN2012 were clustered together and formed a distinct lineage to other known coronavirus species. Two distinct sublineages were observed within BtCoV/Rh/YN2012. One was from Ra sampled in Guangdong, while the other was from Rs sampled in Yunnan Among the strains from Yunnan, RsYN2 and RsYN3 were clustered together, while RsYN1 was isolated. The topology of these four strains was correlated to the sampling location. The relatively long branches reflect a high diversity among these strains, indicating a long independent evolution history.
The Ka/Ks ratios (Ks is the number of synonymous substitutions per synonymous sites and Ka is the number of nonsynonymous substitutions per nonsynonymous site) were calculated for all genes. The Ka/Ks ratios for most of the genes were generally low, which indicates these genes were under purified selection. However, the Ka/Ks ratios of ORF4a, ORF4b, and ORF9 (0.727, 0.623, and 0.843, respectively) were significantly higher than those of other ORFs (Table 4 ). For further selection pressure evaluation of the ORF4a and ORF4b gene, we sequenced another four ORF4a and ORF4b genes (strain Rs4223, Rs4236, Rs4240, and Ra13576 was shown in Figure 1B
As SARS-CoV ORF7a was reported to induce apoptosis, we conducted apoptosis analysis on BtCoV/Rh/YN2012 ORF9, a~30% aa identity homologue of SARSr-CoV ORF7a. We transiently transfected ORF9 of BtCoV/Rh/YN2012 into HEK293T cells to examine whether this ORF9 triggers apoptosis. Western blot was performed to confirm the expression of ORF9s and SARS-CoV ORF7a ( Figure S1 ). ORF9 couldn't induce apoptosis as the ORF7a of SARS-CoV Tor2 ( Figure S2 ). The results indicated that BtCoV/Rh/YN2012 ORF9 was not involved in apoptosis induction.
To determine whether these accessory proteins modulate IFN induction, we transfected reporter plasmids (pIFNβ-Luc and pRL-TK) and expression plasmids to 293T cells. All the cells over-expressing the accessory genes, as well as influenza virus NS1 (strain PR8), HBoV VP2, or empty vector were tested for luciferase activity after SeV infection. Luciferase activity stimulated by SeV was remarkably higher than that without SeV treatment as expected. Influenza virus NS1 inhibits the expression from IFN promoter, while HBoV VP2 activate the expression. Compared to those controls, the ORF4a proteins exhibit an active effect as HBoV VP2 ( Figure 5A ). Other accessory proteins showed no effect on IFN production ( Figure S3 ). Expression of these accessory genes were confirmed by Western blot ( Figure S1 ). was remarkably higher than that without SeV treatment as expected. Influenza virus NS1 inhibits the expression from IFN promoter, while HBoV VP2 activate the expression. Compared to those controls, the ORF4a proteins exhibit an active effect as HBoV VP2 ( Figure 5A ). Other accessory proteins showed no effect on IFN production ( Figure S3 ). Expression of these accessory genes were confirmed by Western blot (Figure S1 ). Samples were collected at 6 h postinfection, followed by dual-luciferase assay. The results were expressed as the firefly luciferase value normalized to that of Renilla luciferase. (B) ORF3a protein activate NF-κB. 293T cells were transfected with 100 ng pNF-κB-Luc, 10 ng pRL-TK, empty vector (500 ng), an NS1-expressing plasmid (500 ng), a SARS-CoV ORF7a-expressing plasmid (500 ng), or ORF3a-expressing plasmids (500 ng). After 24 h, the cells were treated with TNF-α. Dual-luciferase activity was determined after 6 h. The results were expressed as the firefly luciferase activity normalized to that of Renilla luciferase. The experiments were performed three times independently. Data are representative of at least three independent experiments, with each determination performed in triplicate (mean ± SD of fold change). Asterisks indicate significant differences between groups (compared with Empty vector-NC, p < 0.05, as determined by student t test).
NF-κB plays an important role in regulating the immune response to viral infection and is also a key factor frequently targeted by viruses for taking over the host cell. In this study, we tested if these accessory proteins could modulate NF-κB. 293T cells were co-transfected with reporter Samples were collected at 6 h postinfection, followed by dual-luciferase assay. The results were expressed as the firefly luciferase value normalized to that of Renilla luciferase. (B) ORF3a protein activate NF-κB. 293T cells were transfected with 100 ng pNF-κB-Luc, 10 ng pRL-TK, empty vector (500 ng), an NS1-expressing plasmid (500 ng), a SARS-CoV ORF7a-expressing plasmid (500 ng), or ORF3a-expressing plasmids (500 ng). After 24 h, the cells were treated with TNF-α. Dual-luciferase activity was determined after 6 h. The results were expressed as the firefly luciferase activity normalized to that of Renilla luciferase. The experiments were performed three times independently. Data are representative of at least three independent experiments, with each determination performed in triplicate (mean ± SD of fold change). Asterisks indicate significant differences between groups (compared with Empty vector-NC, p < 0.05, as determined by student t test).
NF-κB plays an important role in regulating the immune response to viral infection and is also a key factor frequently targeted by viruses for taking over the host cell. In this study, we tested if these accessory proteins could modulate NF-κB. 293T cells were co-transfected with reporter plasmids (pNF-κB-Luc and pRL-TK), as well as accessory protein-expressing plasmids, or controls (empty vector, NS1, SARS-CoV Tor2-ORF7a). The cells were mock treated or treated with TNF-α for 6 h at 24 h post-transfection. The luciferase activity was determined. RsYN1-ORF3a and RaGD-ORF3a activated NF-κB as SARS-CoV ORF7a, whereas RsYN2-ORF3a inhibited NF-κB as NS1 ( Figure 5B ). Expressions of ORF3as were confirmed with Western blot ( Figure S1 ). Other accessory proteins did not modulate NF-κB production ( Figure S4 ).
To understand the infectivity of these newly detected BtCoV/Rh/YN2012, we selected the RsYN1, RsYN3 and RaGD spike proteins for spike-mediated pseudovirus entry studies. Both Western blot analysis and negative-staining electron microscopy observation confirmed the preparation of BtCoV/Rh/YN2012 successfully ( Figure S5 ). A total of 11 human cell lines, 8 bat cells, and 9 other mammal cell lines were tested, and no strong positive was found (Table S2) .
In this study, a novel alpha-CoV species, BtCoV/Rh/YN2012, was identified in two Rhinolophus species. The 4 strains with full-length genome were sequences. The 7 conserved replicase domains of these viruses possessed <90% aa sequence identity to those of other known alpha-CoVs, which defines a new species in accordance with the ICTV taxonomy standard [42] . These novel alpha-CoVs showed high genetic diversity in their structural and non-structural genes. Strain RaGD from R. affinis, collected in Guangdong province, formed a divergent independent branch from the other 3 strains from R. sinicus, sampled in Yunnan Province, indicating an independent evolution process associated with geographic isolation and host restrain. Though collected from same province, these three virus strains formed two genotypes correlated to sampling locations. These two genotypes had low genome sequence identity, especially in the S gene and accessory genes. Considering the remote geographic location of the host bat habitat, the host tropism, and the virus diversity, we suppose BtCoV/Rh/YN2012 may have spread in these two provinces with a long history of circulation in their natural reservoir, Rhinolophus bats. With the sequence evidence, we suppose that these viruses are still rapidly evolving.
Our study revealed that BtCoV/Rh/YN2012 has a unique genome structure compared to other alpha-CoVs. First, novel accessory genes, which had no homologues, were identified in the genomes. Second, multiple TRSs were found between S and E genes while other alphacoronavirus only had one TRS there. These TRSs precede ORF3a, ORF3b (only in RsYN1), and ORF4a/b respectively. Third, accessory gene ORF9 showed homology with those of other known CoV species in another coronavirus genus, especially with accessory genes from SARSr-CoV.
Accessory genes are usually involved in virus-host interactions during CoV infection [43] . In most CoVs, accessory genes are dispensable for virus replication. However, an intact 3c gene of feline CoV was required for viral replication in the gut [44] [45] [46] . Deletion of the genus-specific genes in mouse hepatitis virus led to a reduction in virulence [47] . SARS-CoV ORF7a, which was identified to be involved in the suppression of RNA silencing [48] , inhibition of cellular protein synthesis [49] , cell-cycle blockage [50] , and apoptosis induction [51, 52] . In this study, we found that BtCoV/Rh/YN2012 ORF9 shares~30% aa sequence identity with SARS-CoV ORF7a. Interestingly, BtCoV/Rh/YN2012 and SARSr-CoV were both detected in R. sinicus from the same cave. We suppose that SARS-CoV and BtCoV/Rh/YN2012 may have acquired ORF7a or ORF9 from a common ancestor through genome recombination or horizontal gene transfer. Whereas, ORF9 of BtCoV/Rh/YN2012 failed to induce apoptosis or activate NF-κB production, these differences may be induced by the divergent evolution of these proteins in different pressure.
Though different BtCoV/Rh/YN2012 ORF4a share <64.4% amino acid identity, all of them could activate IFN-β. ORF3a from RsYN1 and RaGD upregulated NF-κB, but the homologue from RsYN2 downregulated NF-κB expression. These differences may be caused by amino acid sequence variations and may contribute to a viruses' pathogenicity with a different pathway.
Though lacking of intestinal cell lines from the natural host of BtCoV/Rh/YN2012, we screened the cell tropism of their spike protein through pseudotyped retrovirus entry with human, bat and other mammalian cell lines. Most of cell lines screened were unsusceptible to BtCoV/Rh/YN2012, indicating a low risk of interspecies transmission to human and other animals. Multiple reasons may lead to failed infection of coronavirus spike-pseudotyped retrovirus system, including receptor absence in target cells, failed recognition to the receptor homologue from non-host species, maladaptation in non-host cells during the spike maturation or virus entry, or the limitation of retrovirus system in stimulating coronavirus entry. The weak infectivity of RsYN1 pseudotyped retrovirus in Huh-7 cells could be explained by the binding of spike protein to polysaccharide secreted to the surface. The assumption needs to be further confirmed by experiments.
Our long-term surveillances suggest that Rhinolophus bats seem to harbor a wide diversity of CoVs. Coincidently, the two highly pathogenic agents, SARS-CoV and Rh-BatCoV HKU2 both originated from Rhinolophus bats. Considering the diversity of CoVs carried by this bat genus and their wide geographical distribution, there may be a low risk of spillover of these viruses to other animals and humans. Long-term surveillances and pathogenesis studies will help to prevent future human and animal diseases caused by these bat CoVs.
Supplementary Materials: The following are available online at http://www.mdpi.com/1999-4915/11/4/379/s1, Figure S1 : western blot analysis of the expression of accessory proteins. Figure S2 : Apoptosis analysis of ORF9 proteins of BtCoV/Rh/YN2012. Figure S3 : Functional analysis of ORF3a, ORF3b, ORF4b, ORF8 and ORF9 proteins on the production of Type I interferon. Figure S4 : Functional analysis of ORF3b, ORF4a, ORF4b, ORF8 and ORF9 proteins on the production of NF-κB. Figure S5 : Characteristic of BtCoV/Rh/YN2012 spike mediated pseudovirus. Table S1 : General primers for AlphaCoVs genome sequencing. Table S2 : Primers for the detection of viral sugbenomic mRNAs. Table S3
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What type of coronavirus was detected in R. affinis and R. sinicus species?
| 3,683 |
BtCoV/Rh/YN2012
| 11,997 |
1,576 |
Characterization of a New Member of Alphacoronavirus with Unique Genomic Features in Rhinolophus Bats
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6521148/
SHA: ee14de143337eec0e9708f8139bfac2b7b8fdd27
Authors: Wang, Ning; Luo, Chuming; Liu, Haizhou; Yang, Xinglou; Hu, Ben; Zhang, Wei; Li, Bei; Zhu, Yan; Zhu, Guangjian; Shen, Xurui; Peng, Cheng; Shi, Zhengli
Date: 2019-04-24
DOI: 10.3390/v11040379
License: cc-by
Abstract: Bats have been identified as a natural reservoir of a variety of coronaviruses (CoVs). Several of them have caused diseases in humans and domestic animals by interspecies transmission. Considering the diversity of bat coronaviruses, bat species and populations, we expect to discover more bat CoVs through virus surveillance. In this study, we described a new member of alphaCoV (BtCoV/Rh/YN2012) in bats with unique genome features. Unique accessory genes, ORF4a and ORF4b were found between the spike gene and the envelope gene, while ORF8 gene was found downstream of the nucleocapsid gene. All the putative genes were further confirmed by reverse-transcription analyses. One unique gene at the 3’ end of the BtCoV/Rh/YN2012 genome, ORF9, exhibits ~30% amino acid identity to ORF7a of the SARS-related coronavirus. Functional analysis showed ORF4a protein can activate IFN-β production, whereas ORF3a can regulate NF-κB production. We also screened the spike-mediated virus entry using the spike-pseudotyped retroviruses system, although failed to find any fully permissive cells. Our results expand the knowledge on the genetic diversity of bat coronaviruses. Continuous screening of bat viruses will help us further understand the important role played by bats in coronavirus evolution and transmission.
Text: Members of the Coronaviridae family are enveloped, non-segmented, positive-strand RNA viruses with genome sizes ranging from 26-32 kb [1] . These viruses are classified into two subfamilies: Letovirinae, which contains the only genus: Alphaletovirus; and Orthocoronavirinae (CoV), which consists of alpha, beta, gamma, and deltacoronaviruses (CoVs) [2, 3] . Alpha and betacoronaviruses mainly infect mammals and cause human and animal diseases. Gamma-and delta-CoVs mainly infect birds, but some can also infect mammals [4, 5] . Six human CoVs (HCoVs) are known to cause human diseases. HCoV-HKU1, HCoV-OC43, HCoV-229E, and HCoV-NL63 commonly cause mild respiratory illness or asymptomatic infection; however, severe acute respiratory syndrome coronavirus (SARS-CoV) and
All sampling procedures were performed by veterinarians, with approval from Animal Ethics Committee of the Wuhan Institute of Virology (WIVH5210201). The study was conducted in accordance with the Guide for the Care and Use of Wild Mammals in Research of the People's Republic of China.
Bat fecal swab and pellet samples were collected from November 2004 to November 2014 in different seasons in Southern China, as described previously [16] .
Viral RNA was extracted from 200 µL of fecal swab or pellet samples using the High Pure Viral RNA Kit (Roche Diagnostics GmbH, Mannheim, Germany) as per the manufacturer's instructions. RNA was eluted in 50 µL of elution buffer, aliquoted, and stored at -80 • C. One-step hemi-nested reverse-transcription (RT-) PCR (Invitrogen, San Diego, CA, USA) was employed to detect coronavirus, as previously described [17, 18] .
To confirm the bat species of an individual sample, we PCR amplified the cytochrome b (Cytob) and/or NADH dehydrogenase subunit 1 (ND1) gene using DNA extracted from the feces or swabs [19, 20] . The gene sequences were assembled excluding the primer sequences. BLASTN was used to identify host species based on the most closely related sequences with the highest query coverage and a minimum identity of 95%.
Full genomic sequences were determined by one-step PCR (Invitrogen, San Diego, CA, USA) amplification with degenerate primers (Table S1 ) designed on the basis of multiple alignments of available alpha-CoV sequences deposited in GenBank or amplified with SuperScript IV Reverse Transcriptase (Invitrogen) and Expand Long Template PCR System (Roche Diagnostics GmbH, Mannheim, Germany) with specific primers (primer sequences are available upon request). Sequences of the 5' and 3' genomic ends were obtained by 5' and 3' rapid amplification of cDNA ends (SMARTer Viruses 2019, 11, 379 3 of 19 RACE 5'/3' Kit; Clontech, Mountain View, CA, USA), respectively. PCR products were gel-purified and subjected directly to sequencing. PCR products over 5kb were subjected to deep sequencing using Hiseq2500 system. For some fragments, the PCR products were cloned into the pGEM-T Easy Vector (Promega, Madison, WI, USA) for sequencing. At least five independent clones were sequenced to obtain a consensus sequence.
The Next Generation Sequencing (NGS) data were filtered and mapped to the reference sequence of BatCoV HKU10 (GenBank accession number NC_018871) using Geneious 7.1.8 [21] . Genomes were preliminarily assembled using DNAStar lasergene V7 (DNAStar, Madison, WI, USA). Putative open reading frames (ORFs) were predicted using NCBI's ORF finder (https://www.ncbi.nlm.nih.gov/ orffinder/) with a minimal ORF length of 150 nt, followed by manual inspection. The sequences of the 5' untranslated region (5'-UTR) and 3'-UTR were defined, and the leader sequence, the leader and body transcriptional regulatory sequence (TRS) were identified as previously described [22] . The cleavage of the 16 nonstructural proteins coded by ORF1ab was determined by alignment of aa sequences of other CoVs and the recognition pattern of the 3C-like proteinase and papain-like proteinase. Phylogenetic trees based on nt or aa sequences were constructed using the maximum likelihood algorithm with bootstrap values determined by 1000 replicates in the MEGA 6 software package [23] . Full-length genome sequences obtained in this study were aligned with those of previously reported alpha-CoVs using MUSCLE [24] . The aligned sequences were scanned for recombination events by using Recombination Detection Program [25] . Potential recombination events as suggested by strong p-values (<10 -20 ) were confirmed using similarity plot and bootscan analyses implemented in Simplot 3.5.1 [26] . The number of synonymous substitutions per synonymous site, Ks, and the number of nonsynonymous substitutions per nonsynonymous site, Ka, for each coding region were calculated using the Ka/Ks calculation tool of the Norwegian Bioinformatics Platform (http://services.cbu.uib.no/tools/kaks) with default parameters [27] . The protein homology detection was analyzed using HHpred (https://toolkit.tuebingen.mpg.de/#/tools/hhpred) with default parameters [28] .
A set of nested RT-PCRs was employed to determine the presence of viral subgenomic mRNAs in the CoV-positive samples [29] . Forward primers were designed targeting the leader sequence at the 5'-end of the complete genome, while reverse primers were designed within the ORFs. Specific and suspected amplicons of expected sizes were purified and then cloned into the pGEM-T Easy vector for sequencing.
Bat primary or immortalized cells (Rhinolophus sinicus kidney immortalized cells, RsKT; Rhinolophus sinicus Lung primary cells, RsLu4323; Rhinolophus sinicus brain immortalized cells, RsBrT; Rhinolophus affinis kidney primary cells, RaK4324; Rousettus leschenaultii Kidney immortalized cells, RlKT; Hipposideros pratti lung immortalized cells, HpLuT) generated in our laboratory were all cultured in DMEM/F12 with 15% FBS. Pteropus alecto kidney cells (Paki) was maintained in DMEM/F12 supplemented with 10% FBS. Other cells were maintained according to the recommendations of American Type Culture Collection (ATCC, www.atcc.org).
The putative accessory genes of the newly detected virus were generated by RT-PCR from viral RNA extracted from fecal samples, as described previously [30] . The influenza virus NS1 plasmid was generated in our lab [31] . The human bocavirus (HBoV) VP2 plasmid was kindly provided by prof. Hanzhong Wang of the Wuhan Institute of Virology, Chinese Academy of Sciences. SARS-CoV ORF7a was synthesized by Sangon Biotech. The transfections were performed with Lipofectamine 3000 Reagent (Life Technologies). Expression of these accessory genes were analyzed by Western blotting using an mAb (Roche Diagnostics GmbH, Mannheim, Germany) against the HA tag.
The virus isolation was performed as previously described [12] . Briefly, fecal supernatant was acquired via gradient centrifugation and then added to Vero E6 cells, 1:10 diluted in DMEM. After incubation at 37°C for 1 h the inoculum was replaced by fresh DMEM containing 2% FBS and the antibiotic-antimycotic (Gibco, Grand Island, NY, USA). Three blind passages were carried out. Cells were checked daily for cytopathic effect. Both culture supernatant and cell pellet were examined for CoV by RT-PCR [17] .
Apoptosis was analyzed as previously described [18] . Briefly, 293T cells in 12-well plates were transfected with 3 µg of expression plasmid or empty vector, and the cells were collected 24 h post transfection. Apoptosis was detected by flow cytometry using by the Annexin V-FITC/PI Apoptosis Detection Kit (YEASEN, Shanghai, China) following the manufacturer's instructions. Annexin-V-positive and PI-negative cells were considered to be in the early apoptotic phase and those stained for both Annexin V and PI were deemed to undergo late apoptosis or necrosis. All experiments were repeated three times. Student's t-test was used to evaluate the data, with p < 0.05 considered significant.
HEK 293T cells were seeded in 24-well plates and then co-transfected with reporter plasmids (pRL-TK and pIFN-βIFN-or pNF-κB-Luc) [30] , as well as plasmids expressing accessory genes, empty vector plasmid pcAGGS, influenza virus NS1 [32] , SARS-CoV ORF7a [33] , or HBoV VP2 [34] . At 24 h post transfection, cells were treated with Sendai virus (SeV) (100 hemagglutinin units [HAU]/mL) or human tumor necrosis factor alpha (TNF-α; R&D system) for 6 h to activate IFNβ or NF-κB, respectively. Cell lysates were prepared, and luciferase activity was measured using the dual-luciferase assay kit (Promega, Madison, WI, USA) according to the manufacturer's instructions.
Retroviruses pseudotyped with BtCoV/Rh/YN2012 RsYN1, RsYN3, RaGD, or MERS-CoV spike, or no spike (mock) were used to infect human, bat or other mammalian cells in 96-well plates. The pseudovirus particles were confirmed with Western blotting and negative-staining electromicroscopy. The production process, measurements of infection and luciferase activity were conducted, as described previously [35, 36] .
The complete genome nucleotide sequences of BtCoV/Rh/YN2012 strains RsYN1, RsYN2, RsYN3, and RaGD obtained in this study have been submitted to the GenBank under MG916901 to MG916904.
The surveillance was performed between November 2004 to November 2014 in 19 provinces of China. In total, 2061 fecal samples were collected from at least 12 Rhinolophus bat species ( Figure 1A ). CoVs were detected in 209 of these samples ( Figure 1B and Table 1 ). Partial RdRp sequences suggested the presence of at least 8 different CoVs. Five of these viruses are related to known species: Mi-BatCoV 1 (>94% nt identity), Mi-BatCoV HKU8 [37] (>93% nt identity), BtRf-AlphaCoV/HuB2013 [11] (>99% nt identity), SARSr-CoV [38] (>89% nt identity), and HKU2-related CoV [39] (>85% nt identity). While the other three CoV sequences showed less than 83% nt identity to known CoV species. These three viruses should represent novel CoV species. Virus isolation was performed as previously described [12] , but was not successful. identity). While the other three CoV sequences showed less than 83% nt identity to known CoV species. These three viruses should represent novel CoV species. Virus isolation was performed as previously described [12] , but was not successful.
We next characterized a novel alpha-CoV, BtCoV/Rh/YN2012. It was detected in 3 R.affinis and 6 R.sinicus, respectively. Based on the sequences, we defined three genotypes, which represented by RsYN1, RsYN3, and RaGD, respectively. Strain RsYN2 was classified into the RsYN3 genotype. Four full-length genomes were obtained. Three of them were from R.sinicus (Strain RsYN1, RsYN2, and RsYN3), while the other one was from R.affinis (Strain RaGD). The sizes of these 4 genomes are between 28,715 to 29,102, with G+C contents between 39.0% to 41.3%. The genomes exhibit similar structures and transcription regulatory sequences (TRS) that are identical to those of other alpha-CoVs ( Figure 2 and Table 2 ). Exceptions including three additional ORFs (ORF3b, ORF4a and ORF4b) were observed. All the 4 strains have ORF4a & ORF4b, while only strain RsYN1 has ORF3b.
The replicase gene, ORF1ab, occupies~20.4 kb of the genome. The replicase gene, ORF1ab, occupies~20.4 kb of the genome. It encodes polyproteins 1a and 1ab, which could be cleaved into 16 non-structural proteins (Nsp1-Nsp16). The 3'-end of the cleavage sites recognized by 3C-like proteinase (Nsp4-Nsp10, Nsp12-Nsp16) and papain-like proteinase (Nsp1-Nsp3) were confirmed. The proteins including Nsp3 (papain-like 2 proteas, PL2pro), Nsp5 (chymotrypsin-like protease, 3CLpro), Nsp12 (RdRp), Nsp13 (helicase), and other proteins of unknown function ( Table 3 ). The 7 concatenated domains of polyprotein 1 shared <90% aa sequence identity with those of other known alpha-CoVs ( Table 2 ), suggesting that these viruses represent a novel CoV species within the alpha-CoV. The closest assigned CoV species to BtCoV/Rh/YN2012 are BtCoV-HKU10 and BtRf-AlphaCoV/Hub2013. The three strains from Yunnan Province were clustered into two genotypes (83% genome identity) correlated to their sampling location. The third genotype represented by strain RaGD was isolated to strains found in Yunnan (<75.4% genome identity). We then examined the individual genes ( Table 2) . All of the genes showed low aa sequence identity to known CoVs. The four strains of BtCoV/Rh/YN2012 showed genetic diversity among all different genes except ORF1ab (>83.7% aa identity). Notably, the spike proteins are highly divergent among these strains. Other structure proteins (E, M, and N) are more conserved than the spike and other accessory proteins. Comparing the accessory genes among these four strains revealed that the strains of the same genotype shared a 100% identical ORF3a. However, the proteins encoded by ORF3as were highly divergent among different genotypes (<65% aa identity). The putative accessory genes were also BLASTed against GenBank records. Most accessory genes have no homologues in GenBank-database, except for ORF3a (52.0-55.5% aa identity with BatCoV HKU10 ORF3) and ORF9 (28.1-32.0% aa identity with SARSr-CoV ORF7a). We analyzed the protein homology with HHpred software. The results showed that ORF9s and SARS-CoV OR7a are homologues (possibility: 100%, E value <10 −48 ). We further screened the genomes for potential recombination evidence. No significant recombination breakpoint was detected by bootscan analysis.
To confirm the presence of subgenomic RNA, we designed a set of primers targeting all the predicted ORFs as described. The amplicons were firstly confirmed via agarose-gel electrophoresis and then sequencing ( Figure 3 and Table 2 ). The sequences showed that all the ORFs, except ORF4b, had preceding TRS. Hence, the ORF4b may be translated from bicistronic mRNAs. In RsYN1, an additional subgenomic RNA starting inside the ORF3a was found through sequencing, which led to a unique ORF3b.
To confirm the presence of subgenomic RNA, we designed a set of primers targeting all the predicted ORFs as described. The amplicons were firstly confirmed via agarose-gel electrophoresis and then sequencing ( Figure 3 and Table 2 ). The sequences showed that all the ORFs, except ORF4b, had preceding TRS. Hence, the ORF4b may be translated from bicistronic mRNAs. In RsYN1, an additional subgenomic RNA starting inside the ORF3a was found through sequencing, which led to a unique ORF3b.
Phylogenetic trees were constructed using the aa sequences of RdRp and S of BtCoV/Rh/YN2012 and other representative CoVs (Figure 4) . In both trees, all BtCoV/Rh/YN2012 were clustered together and formed a distinct lineage to other known coronavirus species. Two distinct sublineages were observed within BtCoV/Rh/YN2012. One was from Ra sampled in Guangdong, while the other was from Rs sampled in Yunnan Among the strains from Yunnan, RsYN2 and RsYN3 were clustered together, while RsYN1 was isolated. The topology of these four strains was correlated to the sampling location. The relatively long branches reflect a high diversity among these strains, indicating a long independent evolution history.
Phylogenetic trees were constructed using the aa sequences of RdRp and S of BtCoV/Rh/YN2012 and other representative CoVs (Figure 4) . In both trees, all BtCoV/Rh/YN2012 were clustered together and formed a distinct lineage to other known coronavirus species. Two distinct sublineages were observed within BtCoV/Rh/YN2012. One was from Ra sampled in Guangdong, while the other was from Rs sampled in Yunnan Among the strains from Yunnan, RsYN2 and RsYN3 were clustered together, while RsYN1 was isolated. The topology of these four strains was correlated to the sampling location. The relatively long branches reflect a high diversity among these strains, indicating a long independent evolution history.
Phylogenetic trees were constructed using the aa sequences of RdRp and S of BtCoV/Rh/YN2012 and other representative CoVs (Figure 4) . In both trees, all BtCoV/Rh/YN2012 were clustered together and formed a distinct lineage to other known coronavirus species. Two distinct sublineages were observed within BtCoV/Rh/YN2012. One was from Ra sampled in Guangdong, while the other was from Rs sampled in Yunnan Among the strains from Yunnan, RsYN2 and RsYN3 were clustered together, while RsYN1 was isolated. The topology of these four strains was correlated to the sampling location. The relatively long branches reflect a high diversity among these strains, indicating a long independent evolution history.
The Ka/Ks ratios (Ks is the number of synonymous substitutions per synonymous sites and Ka is the number of nonsynonymous substitutions per nonsynonymous site) were calculated for all genes. The Ka/Ks ratios for most of the genes were generally low, which indicates these genes were under purified selection. However, the Ka/Ks ratios of ORF4a, ORF4b, and ORF9 (0.727, 0.623, and 0.843, respectively) were significantly higher than those of other ORFs (Table 4 ). For further selection pressure evaluation of the ORF4a and ORF4b gene, we sequenced another four ORF4a and ORF4b genes (strain Rs4223, Rs4236, Rs4240, and Ra13576 was shown in Figure 1B
As SARS-CoV ORF7a was reported to induce apoptosis, we conducted apoptosis analysis on BtCoV/Rh/YN2012 ORF9, a~30% aa identity homologue of SARSr-CoV ORF7a. We transiently transfected ORF9 of BtCoV/Rh/YN2012 into HEK293T cells to examine whether this ORF9 triggers apoptosis. Western blot was performed to confirm the expression of ORF9s and SARS-CoV ORF7a ( Figure S1 ). ORF9 couldn't induce apoptosis as the ORF7a of SARS-CoV Tor2 ( Figure S2 ). The results indicated that BtCoV/Rh/YN2012 ORF9 was not involved in apoptosis induction.
To determine whether these accessory proteins modulate IFN induction, we transfected reporter plasmids (pIFNβ-Luc and pRL-TK) and expression plasmids to 293T cells. All the cells over-expressing the accessory genes, as well as influenza virus NS1 (strain PR8), HBoV VP2, or empty vector were tested for luciferase activity after SeV infection. Luciferase activity stimulated by SeV was remarkably higher than that without SeV treatment as expected. Influenza virus NS1 inhibits the expression from IFN promoter, while HBoV VP2 activate the expression. Compared to those controls, the ORF4a proteins exhibit an active effect as HBoV VP2 ( Figure 5A ). Other accessory proteins showed no effect on IFN production ( Figure S3 ). Expression of these accessory genes were confirmed by Western blot ( Figure S1 ). was remarkably higher than that without SeV treatment as expected. Influenza virus NS1 inhibits the expression from IFN promoter, while HBoV VP2 activate the expression. Compared to those controls, the ORF4a proteins exhibit an active effect as HBoV VP2 ( Figure 5A ). Other accessory proteins showed no effect on IFN production ( Figure S3 ). Expression of these accessory genes were confirmed by Western blot (Figure S1 ). Samples were collected at 6 h postinfection, followed by dual-luciferase assay. The results were expressed as the firefly luciferase value normalized to that of Renilla luciferase. (B) ORF3a protein activate NF-κB. 293T cells were transfected with 100 ng pNF-κB-Luc, 10 ng pRL-TK, empty vector (500 ng), an NS1-expressing plasmid (500 ng), a SARS-CoV ORF7a-expressing plasmid (500 ng), or ORF3a-expressing plasmids (500 ng). After 24 h, the cells were treated with TNF-α. Dual-luciferase activity was determined after 6 h. The results were expressed as the firefly luciferase activity normalized to that of Renilla luciferase. The experiments were performed three times independently. Data are representative of at least three independent experiments, with each determination performed in triplicate (mean ± SD of fold change). Asterisks indicate significant differences between groups (compared with Empty vector-NC, p < 0.05, as determined by student t test).
NF-κB plays an important role in regulating the immune response to viral infection and is also a key factor frequently targeted by viruses for taking over the host cell. In this study, we tested if these accessory proteins could modulate NF-κB. 293T cells were co-transfected with reporter Samples were collected at 6 h postinfection, followed by dual-luciferase assay. The results were expressed as the firefly luciferase value normalized to that of Renilla luciferase. (B) ORF3a protein activate NF-κB. 293T cells were transfected with 100 ng pNF-κB-Luc, 10 ng pRL-TK, empty vector (500 ng), an NS1-expressing plasmid (500 ng), a SARS-CoV ORF7a-expressing plasmid (500 ng), or ORF3a-expressing plasmids (500 ng). After 24 h, the cells were treated with TNF-α. Dual-luciferase activity was determined after 6 h. The results were expressed as the firefly luciferase activity normalized to that of Renilla luciferase. The experiments were performed three times independently. Data are representative of at least three independent experiments, with each determination performed in triplicate (mean ± SD of fold change). Asterisks indicate significant differences between groups (compared with Empty vector-NC, p < 0.05, as determined by student t test).
NF-κB plays an important role in regulating the immune response to viral infection and is also a key factor frequently targeted by viruses for taking over the host cell. In this study, we tested if these accessory proteins could modulate NF-κB. 293T cells were co-transfected with reporter plasmids (pNF-κB-Luc and pRL-TK), as well as accessory protein-expressing plasmids, or controls (empty vector, NS1, SARS-CoV Tor2-ORF7a). The cells were mock treated or treated with TNF-α for 6 h at 24 h post-transfection. The luciferase activity was determined. RsYN1-ORF3a and RaGD-ORF3a activated NF-κB as SARS-CoV ORF7a, whereas RsYN2-ORF3a inhibited NF-κB as NS1 ( Figure 5B ). Expressions of ORF3as were confirmed with Western blot ( Figure S1 ). Other accessory proteins did not modulate NF-κB production ( Figure S4 ).
To understand the infectivity of these newly detected BtCoV/Rh/YN2012, we selected the RsYN1, RsYN3 and RaGD spike proteins for spike-mediated pseudovirus entry studies. Both Western blot analysis and negative-staining electron microscopy observation confirmed the preparation of BtCoV/Rh/YN2012 successfully ( Figure S5 ). A total of 11 human cell lines, 8 bat cells, and 9 other mammal cell lines were tested, and no strong positive was found (Table S2) .
In this study, a novel alpha-CoV species, BtCoV/Rh/YN2012, was identified in two Rhinolophus species. The 4 strains with full-length genome were sequences. The 7 conserved replicase domains of these viruses possessed <90% aa sequence identity to those of other known alpha-CoVs, which defines a new species in accordance with the ICTV taxonomy standard [42] . These novel alpha-CoVs showed high genetic diversity in their structural and non-structural genes. Strain RaGD from R. affinis, collected in Guangdong province, formed a divergent independent branch from the other 3 strains from R. sinicus, sampled in Yunnan Province, indicating an independent evolution process associated with geographic isolation and host restrain. Though collected from same province, these three virus strains formed two genotypes correlated to sampling locations. These two genotypes had low genome sequence identity, especially in the S gene and accessory genes. Considering the remote geographic location of the host bat habitat, the host tropism, and the virus diversity, we suppose BtCoV/Rh/YN2012 may have spread in these two provinces with a long history of circulation in their natural reservoir, Rhinolophus bats. With the sequence evidence, we suppose that these viruses are still rapidly evolving.
Our study revealed that BtCoV/Rh/YN2012 has a unique genome structure compared to other alpha-CoVs. First, novel accessory genes, which had no homologues, were identified in the genomes. Second, multiple TRSs were found between S and E genes while other alphacoronavirus only had one TRS there. These TRSs precede ORF3a, ORF3b (only in RsYN1), and ORF4a/b respectively. Third, accessory gene ORF9 showed homology with those of other known CoV species in another coronavirus genus, especially with accessory genes from SARSr-CoV.
Accessory genes are usually involved in virus-host interactions during CoV infection [43] . In most CoVs, accessory genes are dispensable for virus replication. However, an intact 3c gene of feline CoV was required for viral replication in the gut [44] [45] [46] . Deletion of the genus-specific genes in mouse hepatitis virus led to a reduction in virulence [47] . SARS-CoV ORF7a, which was identified to be involved in the suppression of RNA silencing [48] , inhibition of cellular protein synthesis [49] , cell-cycle blockage [50] , and apoptosis induction [51, 52] . In this study, we found that BtCoV/Rh/YN2012 ORF9 shares~30% aa sequence identity with SARS-CoV ORF7a. Interestingly, BtCoV/Rh/YN2012 and SARSr-CoV were both detected in R. sinicus from the same cave. We suppose that SARS-CoV and BtCoV/Rh/YN2012 may have acquired ORF7a or ORF9 from a common ancestor through genome recombination or horizontal gene transfer. Whereas, ORF9 of BtCoV/Rh/YN2012 failed to induce apoptosis or activate NF-κB production, these differences may be induced by the divergent evolution of these proteins in different pressure.
Though different BtCoV/Rh/YN2012 ORF4a share <64.4% amino acid identity, all of them could activate IFN-β. ORF3a from RsYN1 and RaGD upregulated NF-κB, but the homologue from RsYN2 downregulated NF-κB expression. These differences may be caused by amino acid sequence variations and may contribute to a viruses' pathogenicity with a different pathway.
Though lacking of intestinal cell lines from the natural host of BtCoV/Rh/YN2012, we screened the cell tropism of their spike protein through pseudotyped retrovirus entry with human, bat and other mammalian cell lines. Most of cell lines screened were unsusceptible to BtCoV/Rh/YN2012, indicating a low risk of interspecies transmission to human and other animals. Multiple reasons may lead to failed infection of coronavirus spike-pseudotyped retrovirus system, including receptor absence in target cells, failed recognition to the receptor homologue from non-host species, maladaptation in non-host cells during the spike maturation or virus entry, or the limitation of retrovirus system in stimulating coronavirus entry. The weak infectivity of RsYN1 pseudotyped retrovirus in Huh-7 cells could be explained by the binding of spike protein to polysaccharide secreted to the surface. The assumption needs to be further confirmed by experiments.
Our long-term surveillances suggest that Rhinolophus bats seem to harbor a wide diversity of CoVs. Coincidently, the two highly pathogenic agents, SARS-CoV and Rh-BatCoV HKU2 both originated from Rhinolophus bats. Considering the diversity of CoVs carried by this bat genus and their wide geographical distribution, there may be a low risk of spillover of these viruses to other animals and humans. Long-term surveillances and pathogenesis studies will help to prevent future human and animal diseases caused by these bat CoVs.
Supplementary Materials: The following are available online at http://www.mdpi.com/1999-4915/11/4/379/s1, Figure S1 : western blot analysis of the expression of accessory proteins. Figure S2 : Apoptosis analysis of ORF9 proteins of BtCoV/Rh/YN2012. Figure S3 : Functional analysis of ORF3a, ORF3b, ORF4b, ORF8 and ORF9 proteins on the production of Type I interferon. Figure S4 : Functional analysis of ORF3b, ORF4a, ORF4b, ORF8 and ORF9 proteins on the production of NF-κB. Figure S5 : Characteristic of BtCoV/Rh/YN2012 spike mediated pseudovirus. Table S1 : General primers for AlphaCoVs genome sequencing. Table S2 : Primers for the detection of viral sugbenomic mRNAs. Table S3
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What is a natural reservoir of coronavirus?
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Bats
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Characterization of a New Member of Alphacoronavirus with Unique Genomic Features in Rhinolophus Bats
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6521148/
SHA: ee14de143337eec0e9708f8139bfac2b7b8fdd27
Authors: Wang, Ning; Luo, Chuming; Liu, Haizhou; Yang, Xinglou; Hu, Ben; Zhang, Wei; Li, Bei; Zhu, Yan; Zhu, Guangjian; Shen, Xurui; Peng, Cheng; Shi, Zhengli
Date: 2019-04-24
DOI: 10.3390/v11040379
License: cc-by
Abstract: Bats have been identified as a natural reservoir of a variety of coronaviruses (CoVs). Several of them have caused diseases in humans and domestic animals by interspecies transmission. Considering the diversity of bat coronaviruses, bat species and populations, we expect to discover more bat CoVs through virus surveillance. In this study, we described a new member of alphaCoV (BtCoV/Rh/YN2012) in bats with unique genome features. Unique accessory genes, ORF4a and ORF4b were found between the spike gene and the envelope gene, while ORF8 gene was found downstream of the nucleocapsid gene. All the putative genes were further confirmed by reverse-transcription analyses. One unique gene at the 3’ end of the BtCoV/Rh/YN2012 genome, ORF9, exhibits ~30% amino acid identity to ORF7a of the SARS-related coronavirus. Functional analysis showed ORF4a protein can activate IFN-β production, whereas ORF3a can regulate NF-κB production. We also screened the spike-mediated virus entry using the spike-pseudotyped retroviruses system, although failed to find any fully permissive cells. Our results expand the knowledge on the genetic diversity of bat coronaviruses. Continuous screening of bat viruses will help us further understand the important role played by bats in coronavirus evolution and transmission.
Text: Members of the Coronaviridae family are enveloped, non-segmented, positive-strand RNA viruses with genome sizes ranging from 26-32 kb [1] . These viruses are classified into two subfamilies: Letovirinae, which contains the only genus: Alphaletovirus; and Orthocoronavirinae (CoV), which consists of alpha, beta, gamma, and deltacoronaviruses (CoVs) [2, 3] . Alpha and betacoronaviruses mainly infect mammals and cause human and animal diseases. Gamma-and delta-CoVs mainly infect birds, but some can also infect mammals [4, 5] . Six human CoVs (HCoVs) are known to cause human diseases. HCoV-HKU1, HCoV-OC43, HCoV-229E, and HCoV-NL63 commonly cause mild respiratory illness or asymptomatic infection; however, severe acute respiratory syndrome coronavirus (SARS-CoV) and
All sampling procedures were performed by veterinarians, with approval from Animal Ethics Committee of the Wuhan Institute of Virology (WIVH5210201). The study was conducted in accordance with the Guide for the Care and Use of Wild Mammals in Research of the People's Republic of China.
Bat fecal swab and pellet samples were collected from November 2004 to November 2014 in different seasons in Southern China, as described previously [16] .
Viral RNA was extracted from 200 µL of fecal swab or pellet samples using the High Pure Viral RNA Kit (Roche Diagnostics GmbH, Mannheim, Germany) as per the manufacturer's instructions. RNA was eluted in 50 µL of elution buffer, aliquoted, and stored at -80 • C. One-step hemi-nested reverse-transcription (RT-) PCR (Invitrogen, San Diego, CA, USA) was employed to detect coronavirus, as previously described [17, 18] .
To confirm the bat species of an individual sample, we PCR amplified the cytochrome b (Cytob) and/or NADH dehydrogenase subunit 1 (ND1) gene using DNA extracted from the feces or swabs [19, 20] . The gene sequences were assembled excluding the primer sequences. BLASTN was used to identify host species based on the most closely related sequences with the highest query coverage and a minimum identity of 95%.
Full genomic sequences were determined by one-step PCR (Invitrogen, San Diego, CA, USA) amplification with degenerate primers (Table S1 ) designed on the basis of multiple alignments of available alpha-CoV sequences deposited in GenBank or amplified with SuperScript IV Reverse Transcriptase (Invitrogen) and Expand Long Template PCR System (Roche Diagnostics GmbH, Mannheim, Germany) with specific primers (primer sequences are available upon request). Sequences of the 5' and 3' genomic ends were obtained by 5' and 3' rapid amplification of cDNA ends (SMARTer Viruses 2019, 11, 379 3 of 19 RACE 5'/3' Kit; Clontech, Mountain View, CA, USA), respectively. PCR products were gel-purified and subjected directly to sequencing. PCR products over 5kb were subjected to deep sequencing using Hiseq2500 system. For some fragments, the PCR products were cloned into the pGEM-T Easy Vector (Promega, Madison, WI, USA) for sequencing. At least five independent clones were sequenced to obtain a consensus sequence.
The Next Generation Sequencing (NGS) data were filtered and mapped to the reference sequence of BatCoV HKU10 (GenBank accession number NC_018871) using Geneious 7.1.8 [21] . Genomes were preliminarily assembled using DNAStar lasergene V7 (DNAStar, Madison, WI, USA). Putative open reading frames (ORFs) were predicted using NCBI's ORF finder (https://www.ncbi.nlm.nih.gov/ orffinder/) with a minimal ORF length of 150 nt, followed by manual inspection. The sequences of the 5' untranslated region (5'-UTR) and 3'-UTR were defined, and the leader sequence, the leader and body transcriptional regulatory sequence (TRS) were identified as previously described [22] . The cleavage of the 16 nonstructural proteins coded by ORF1ab was determined by alignment of aa sequences of other CoVs and the recognition pattern of the 3C-like proteinase and papain-like proteinase. Phylogenetic trees based on nt or aa sequences were constructed using the maximum likelihood algorithm with bootstrap values determined by 1000 replicates in the MEGA 6 software package [23] . Full-length genome sequences obtained in this study were aligned with those of previously reported alpha-CoVs using MUSCLE [24] . The aligned sequences were scanned for recombination events by using Recombination Detection Program [25] . Potential recombination events as suggested by strong p-values (<10 -20 ) were confirmed using similarity plot and bootscan analyses implemented in Simplot 3.5.1 [26] . The number of synonymous substitutions per synonymous site, Ks, and the number of nonsynonymous substitutions per nonsynonymous site, Ka, for each coding region were calculated using the Ka/Ks calculation tool of the Norwegian Bioinformatics Platform (http://services.cbu.uib.no/tools/kaks) with default parameters [27] . The protein homology detection was analyzed using HHpred (https://toolkit.tuebingen.mpg.de/#/tools/hhpred) with default parameters [28] .
A set of nested RT-PCRs was employed to determine the presence of viral subgenomic mRNAs in the CoV-positive samples [29] . Forward primers were designed targeting the leader sequence at the 5'-end of the complete genome, while reverse primers were designed within the ORFs. Specific and suspected amplicons of expected sizes were purified and then cloned into the pGEM-T Easy vector for sequencing.
Bat primary or immortalized cells (Rhinolophus sinicus kidney immortalized cells, RsKT; Rhinolophus sinicus Lung primary cells, RsLu4323; Rhinolophus sinicus brain immortalized cells, RsBrT; Rhinolophus affinis kidney primary cells, RaK4324; Rousettus leschenaultii Kidney immortalized cells, RlKT; Hipposideros pratti lung immortalized cells, HpLuT) generated in our laboratory were all cultured in DMEM/F12 with 15% FBS. Pteropus alecto kidney cells (Paki) was maintained in DMEM/F12 supplemented with 10% FBS. Other cells were maintained according to the recommendations of American Type Culture Collection (ATCC, www.atcc.org).
The putative accessory genes of the newly detected virus were generated by RT-PCR from viral RNA extracted from fecal samples, as described previously [30] . The influenza virus NS1 plasmid was generated in our lab [31] . The human bocavirus (HBoV) VP2 plasmid was kindly provided by prof. Hanzhong Wang of the Wuhan Institute of Virology, Chinese Academy of Sciences. SARS-CoV ORF7a was synthesized by Sangon Biotech. The transfections were performed with Lipofectamine 3000 Reagent (Life Technologies). Expression of these accessory genes were analyzed by Western blotting using an mAb (Roche Diagnostics GmbH, Mannheim, Germany) against the HA tag.
The virus isolation was performed as previously described [12] . Briefly, fecal supernatant was acquired via gradient centrifugation and then added to Vero E6 cells, 1:10 diluted in DMEM. After incubation at 37°C for 1 h the inoculum was replaced by fresh DMEM containing 2% FBS and the antibiotic-antimycotic (Gibco, Grand Island, NY, USA). Three blind passages were carried out. Cells were checked daily for cytopathic effect. Both culture supernatant and cell pellet were examined for CoV by RT-PCR [17] .
Apoptosis was analyzed as previously described [18] . Briefly, 293T cells in 12-well plates were transfected with 3 µg of expression plasmid or empty vector, and the cells were collected 24 h post transfection. Apoptosis was detected by flow cytometry using by the Annexin V-FITC/PI Apoptosis Detection Kit (YEASEN, Shanghai, China) following the manufacturer's instructions. Annexin-V-positive and PI-negative cells were considered to be in the early apoptotic phase and those stained for both Annexin V and PI were deemed to undergo late apoptosis or necrosis. All experiments were repeated three times. Student's t-test was used to evaluate the data, with p < 0.05 considered significant.
HEK 293T cells were seeded in 24-well plates and then co-transfected with reporter plasmids (pRL-TK and pIFN-βIFN-or pNF-κB-Luc) [30] , as well as plasmids expressing accessory genes, empty vector plasmid pcAGGS, influenza virus NS1 [32] , SARS-CoV ORF7a [33] , or HBoV VP2 [34] . At 24 h post transfection, cells were treated with Sendai virus (SeV) (100 hemagglutinin units [HAU]/mL) or human tumor necrosis factor alpha (TNF-α; R&D system) for 6 h to activate IFNβ or NF-κB, respectively. Cell lysates were prepared, and luciferase activity was measured using the dual-luciferase assay kit (Promega, Madison, WI, USA) according to the manufacturer's instructions.
Retroviruses pseudotyped with BtCoV/Rh/YN2012 RsYN1, RsYN3, RaGD, or MERS-CoV spike, or no spike (mock) were used to infect human, bat or other mammalian cells in 96-well plates. The pseudovirus particles were confirmed with Western blotting and negative-staining electromicroscopy. The production process, measurements of infection and luciferase activity were conducted, as described previously [35, 36] .
The complete genome nucleotide sequences of BtCoV/Rh/YN2012 strains RsYN1, RsYN2, RsYN3, and RaGD obtained in this study have been submitted to the GenBank under MG916901 to MG916904.
The surveillance was performed between November 2004 to November 2014 in 19 provinces of China. In total, 2061 fecal samples were collected from at least 12 Rhinolophus bat species ( Figure 1A ). CoVs were detected in 209 of these samples ( Figure 1B and Table 1 ). Partial RdRp sequences suggested the presence of at least 8 different CoVs. Five of these viruses are related to known species: Mi-BatCoV 1 (>94% nt identity), Mi-BatCoV HKU8 [37] (>93% nt identity), BtRf-AlphaCoV/HuB2013 [11] (>99% nt identity), SARSr-CoV [38] (>89% nt identity), and HKU2-related CoV [39] (>85% nt identity). While the other three CoV sequences showed less than 83% nt identity to known CoV species. These three viruses should represent novel CoV species. Virus isolation was performed as previously described [12] , but was not successful. identity). While the other three CoV sequences showed less than 83% nt identity to known CoV species. These three viruses should represent novel CoV species. Virus isolation was performed as previously described [12] , but was not successful.
We next characterized a novel alpha-CoV, BtCoV/Rh/YN2012. It was detected in 3 R.affinis and 6 R.sinicus, respectively. Based on the sequences, we defined three genotypes, which represented by RsYN1, RsYN3, and RaGD, respectively. Strain RsYN2 was classified into the RsYN3 genotype. Four full-length genomes were obtained. Three of them were from R.sinicus (Strain RsYN1, RsYN2, and RsYN3), while the other one was from R.affinis (Strain RaGD). The sizes of these 4 genomes are between 28,715 to 29,102, with G+C contents between 39.0% to 41.3%. The genomes exhibit similar structures and transcription regulatory sequences (TRS) that are identical to those of other alpha-CoVs ( Figure 2 and Table 2 ). Exceptions including three additional ORFs (ORF3b, ORF4a and ORF4b) were observed. All the 4 strains have ORF4a & ORF4b, while only strain RsYN1 has ORF3b.
The replicase gene, ORF1ab, occupies~20.4 kb of the genome. The replicase gene, ORF1ab, occupies~20.4 kb of the genome. It encodes polyproteins 1a and 1ab, which could be cleaved into 16 non-structural proteins (Nsp1-Nsp16). The 3'-end of the cleavage sites recognized by 3C-like proteinase (Nsp4-Nsp10, Nsp12-Nsp16) and papain-like proteinase (Nsp1-Nsp3) were confirmed. The proteins including Nsp3 (papain-like 2 proteas, PL2pro), Nsp5 (chymotrypsin-like protease, 3CLpro), Nsp12 (RdRp), Nsp13 (helicase), and other proteins of unknown function ( Table 3 ). The 7 concatenated domains of polyprotein 1 shared <90% aa sequence identity with those of other known alpha-CoVs ( Table 2 ), suggesting that these viruses represent a novel CoV species within the alpha-CoV. The closest assigned CoV species to BtCoV/Rh/YN2012 are BtCoV-HKU10 and BtRf-AlphaCoV/Hub2013. The three strains from Yunnan Province were clustered into two genotypes (83% genome identity) correlated to their sampling location. The third genotype represented by strain RaGD was isolated to strains found in Yunnan (<75.4% genome identity). We then examined the individual genes ( Table 2) . All of the genes showed low aa sequence identity to known CoVs. The four strains of BtCoV/Rh/YN2012 showed genetic diversity among all different genes except ORF1ab (>83.7% aa identity). Notably, the spike proteins are highly divergent among these strains. Other structure proteins (E, M, and N) are more conserved than the spike and other accessory proteins. Comparing the accessory genes among these four strains revealed that the strains of the same genotype shared a 100% identical ORF3a. However, the proteins encoded by ORF3as were highly divergent among different genotypes (<65% aa identity). The putative accessory genes were also BLASTed against GenBank records. Most accessory genes have no homologues in GenBank-database, except for ORF3a (52.0-55.5% aa identity with BatCoV HKU10 ORF3) and ORF9 (28.1-32.0% aa identity with SARSr-CoV ORF7a). We analyzed the protein homology with HHpred software. The results showed that ORF9s and SARS-CoV OR7a are homologues (possibility: 100%, E value <10 −48 ). We further screened the genomes for potential recombination evidence. No significant recombination breakpoint was detected by bootscan analysis.
To confirm the presence of subgenomic RNA, we designed a set of primers targeting all the predicted ORFs as described. The amplicons were firstly confirmed via agarose-gel electrophoresis and then sequencing ( Figure 3 and Table 2 ). The sequences showed that all the ORFs, except ORF4b, had preceding TRS. Hence, the ORF4b may be translated from bicistronic mRNAs. In RsYN1, an additional subgenomic RNA starting inside the ORF3a was found through sequencing, which led to a unique ORF3b.
To confirm the presence of subgenomic RNA, we designed a set of primers targeting all the predicted ORFs as described. The amplicons were firstly confirmed via agarose-gel electrophoresis and then sequencing ( Figure 3 and Table 2 ). The sequences showed that all the ORFs, except ORF4b, had preceding TRS. Hence, the ORF4b may be translated from bicistronic mRNAs. In RsYN1, an additional subgenomic RNA starting inside the ORF3a was found through sequencing, which led to a unique ORF3b.
Phylogenetic trees were constructed using the aa sequences of RdRp and S of BtCoV/Rh/YN2012 and other representative CoVs (Figure 4) . In both trees, all BtCoV/Rh/YN2012 were clustered together and formed a distinct lineage to other known coronavirus species. Two distinct sublineages were observed within BtCoV/Rh/YN2012. One was from Ra sampled in Guangdong, while the other was from Rs sampled in Yunnan Among the strains from Yunnan, RsYN2 and RsYN3 were clustered together, while RsYN1 was isolated. The topology of these four strains was correlated to the sampling location. The relatively long branches reflect a high diversity among these strains, indicating a long independent evolution history.
Phylogenetic trees were constructed using the aa sequences of RdRp and S of BtCoV/Rh/YN2012 and other representative CoVs (Figure 4) . In both trees, all BtCoV/Rh/YN2012 were clustered together and formed a distinct lineage to other known coronavirus species. Two distinct sublineages were observed within BtCoV/Rh/YN2012. One was from Ra sampled in Guangdong, while the other was from Rs sampled in Yunnan Among the strains from Yunnan, RsYN2 and RsYN3 were clustered together, while RsYN1 was isolated. The topology of these four strains was correlated to the sampling location. The relatively long branches reflect a high diversity among these strains, indicating a long independent evolution history.
Phylogenetic trees were constructed using the aa sequences of RdRp and S of BtCoV/Rh/YN2012 and other representative CoVs (Figure 4) . In both trees, all BtCoV/Rh/YN2012 were clustered together and formed a distinct lineage to other known coronavirus species. Two distinct sublineages were observed within BtCoV/Rh/YN2012. One was from Ra sampled in Guangdong, while the other was from Rs sampled in Yunnan Among the strains from Yunnan, RsYN2 and RsYN3 were clustered together, while RsYN1 was isolated. The topology of these four strains was correlated to the sampling location. The relatively long branches reflect a high diversity among these strains, indicating a long independent evolution history.
The Ka/Ks ratios (Ks is the number of synonymous substitutions per synonymous sites and Ka is the number of nonsynonymous substitutions per nonsynonymous site) were calculated for all genes. The Ka/Ks ratios for most of the genes were generally low, which indicates these genes were under purified selection. However, the Ka/Ks ratios of ORF4a, ORF4b, and ORF9 (0.727, 0.623, and 0.843, respectively) were significantly higher than those of other ORFs (Table 4 ). For further selection pressure evaluation of the ORF4a and ORF4b gene, we sequenced another four ORF4a and ORF4b genes (strain Rs4223, Rs4236, Rs4240, and Ra13576 was shown in Figure 1B
As SARS-CoV ORF7a was reported to induce apoptosis, we conducted apoptosis analysis on BtCoV/Rh/YN2012 ORF9, a~30% aa identity homologue of SARSr-CoV ORF7a. We transiently transfected ORF9 of BtCoV/Rh/YN2012 into HEK293T cells to examine whether this ORF9 triggers apoptosis. Western blot was performed to confirm the expression of ORF9s and SARS-CoV ORF7a ( Figure S1 ). ORF9 couldn't induce apoptosis as the ORF7a of SARS-CoV Tor2 ( Figure S2 ). The results indicated that BtCoV/Rh/YN2012 ORF9 was not involved in apoptosis induction.
To determine whether these accessory proteins modulate IFN induction, we transfected reporter plasmids (pIFNβ-Luc and pRL-TK) and expression plasmids to 293T cells. All the cells over-expressing the accessory genes, as well as influenza virus NS1 (strain PR8), HBoV VP2, or empty vector were tested for luciferase activity after SeV infection. Luciferase activity stimulated by SeV was remarkably higher than that without SeV treatment as expected. Influenza virus NS1 inhibits the expression from IFN promoter, while HBoV VP2 activate the expression. Compared to those controls, the ORF4a proteins exhibit an active effect as HBoV VP2 ( Figure 5A ). Other accessory proteins showed no effect on IFN production ( Figure S3 ). Expression of these accessory genes were confirmed by Western blot ( Figure S1 ). was remarkably higher than that without SeV treatment as expected. Influenza virus NS1 inhibits the expression from IFN promoter, while HBoV VP2 activate the expression. Compared to those controls, the ORF4a proteins exhibit an active effect as HBoV VP2 ( Figure 5A ). Other accessory proteins showed no effect on IFN production ( Figure S3 ). Expression of these accessory genes were confirmed by Western blot (Figure S1 ). Samples were collected at 6 h postinfection, followed by dual-luciferase assay. The results were expressed as the firefly luciferase value normalized to that of Renilla luciferase. (B) ORF3a protein activate NF-κB. 293T cells were transfected with 100 ng pNF-κB-Luc, 10 ng pRL-TK, empty vector (500 ng), an NS1-expressing plasmid (500 ng), a SARS-CoV ORF7a-expressing plasmid (500 ng), or ORF3a-expressing plasmids (500 ng). After 24 h, the cells were treated with TNF-α. Dual-luciferase activity was determined after 6 h. The results were expressed as the firefly luciferase activity normalized to that of Renilla luciferase. The experiments were performed three times independently. Data are representative of at least three independent experiments, with each determination performed in triplicate (mean ± SD of fold change). Asterisks indicate significant differences between groups (compared with Empty vector-NC, p < 0.05, as determined by student t test).
NF-κB plays an important role in regulating the immune response to viral infection and is also a key factor frequently targeted by viruses for taking over the host cell. In this study, we tested if these accessory proteins could modulate NF-κB. 293T cells were co-transfected with reporter Samples were collected at 6 h postinfection, followed by dual-luciferase assay. The results were expressed as the firefly luciferase value normalized to that of Renilla luciferase. (B) ORF3a protein activate NF-κB. 293T cells were transfected with 100 ng pNF-κB-Luc, 10 ng pRL-TK, empty vector (500 ng), an NS1-expressing plasmid (500 ng), a SARS-CoV ORF7a-expressing plasmid (500 ng), or ORF3a-expressing plasmids (500 ng). After 24 h, the cells were treated with TNF-α. Dual-luciferase activity was determined after 6 h. The results were expressed as the firefly luciferase activity normalized to that of Renilla luciferase. The experiments were performed three times independently. Data are representative of at least three independent experiments, with each determination performed in triplicate (mean ± SD of fold change). Asterisks indicate significant differences between groups (compared with Empty vector-NC, p < 0.05, as determined by student t test).
NF-κB plays an important role in regulating the immune response to viral infection and is also a key factor frequently targeted by viruses for taking over the host cell. In this study, we tested if these accessory proteins could modulate NF-κB. 293T cells were co-transfected with reporter plasmids (pNF-κB-Luc and pRL-TK), as well as accessory protein-expressing plasmids, or controls (empty vector, NS1, SARS-CoV Tor2-ORF7a). The cells were mock treated or treated with TNF-α for 6 h at 24 h post-transfection. The luciferase activity was determined. RsYN1-ORF3a and RaGD-ORF3a activated NF-κB as SARS-CoV ORF7a, whereas RsYN2-ORF3a inhibited NF-κB as NS1 ( Figure 5B ). Expressions of ORF3as were confirmed with Western blot ( Figure S1 ). Other accessory proteins did not modulate NF-κB production ( Figure S4 ).
To understand the infectivity of these newly detected BtCoV/Rh/YN2012, we selected the RsYN1, RsYN3 and RaGD spike proteins for spike-mediated pseudovirus entry studies. Both Western blot analysis and negative-staining electron microscopy observation confirmed the preparation of BtCoV/Rh/YN2012 successfully ( Figure S5 ). A total of 11 human cell lines, 8 bat cells, and 9 other mammal cell lines were tested, and no strong positive was found (Table S2) .
In this study, a novel alpha-CoV species, BtCoV/Rh/YN2012, was identified in two Rhinolophus species. The 4 strains with full-length genome were sequences. The 7 conserved replicase domains of these viruses possessed <90% aa sequence identity to those of other known alpha-CoVs, which defines a new species in accordance with the ICTV taxonomy standard [42] . These novel alpha-CoVs showed high genetic diversity in their structural and non-structural genes. Strain RaGD from R. affinis, collected in Guangdong province, formed a divergent independent branch from the other 3 strains from R. sinicus, sampled in Yunnan Province, indicating an independent evolution process associated with geographic isolation and host restrain. Though collected from same province, these three virus strains formed two genotypes correlated to sampling locations. These two genotypes had low genome sequence identity, especially in the S gene and accessory genes. Considering the remote geographic location of the host bat habitat, the host tropism, and the virus diversity, we suppose BtCoV/Rh/YN2012 may have spread in these two provinces with a long history of circulation in their natural reservoir, Rhinolophus bats. With the sequence evidence, we suppose that these viruses are still rapidly evolving.
Our study revealed that BtCoV/Rh/YN2012 has a unique genome structure compared to other alpha-CoVs. First, novel accessory genes, which had no homologues, were identified in the genomes. Second, multiple TRSs were found between S and E genes while other alphacoronavirus only had one TRS there. These TRSs precede ORF3a, ORF3b (only in RsYN1), and ORF4a/b respectively. Third, accessory gene ORF9 showed homology with those of other known CoV species in another coronavirus genus, especially with accessory genes from SARSr-CoV.
Accessory genes are usually involved in virus-host interactions during CoV infection [43] . In most CoVs, accessory genes are dispensable for virus replication. However, an intact 3c gene of feline CoV was required for viral replication in the gut [44] [45] [46] . Deletion of the genus-specific genes in mouse hepatitis virus led to a reduction in virulence [47] . SARS-CoV ORF7a, which was identified to be involved in the suppression of RNA silencing [48] , inhibition of cellular protein synthesis [49] , cell-cycle blockage [50] , and apoptosis induction [51, 52] . In this study, we found that BtCoV/Rh/YN2012 ORF9 shares~30% aa sequence identity with SARS-CoV ORF7a. Interestingly, BtCoV/Rh/YN2012 and SARSr-CoV were both detected in R. sinicus from the same cave. We suppose that SARS-CoV and BtCoV/Rh/YN2012 may have acquired ORF7a or ORF9 from a common ancestor through genome recombination or horizontal gene transfer. Whereas, ORF9 of BtCoV/Rh/YN2012 failed to induce apoptosis or activate NF-κB production, these differences may be induced by the divergent evolution of these proteins in different pressure.
Though different BtCoV/Rh/YN2012 ORF4a share <64.4% amino acid identity, all of them could activate IFN-β. ORF3a from RsYN1 and RaGD upregulated NF-κB, but the homologue from RsYN2 downregulated NF-κB expression. These differences may be caused by amino acid sequence variations and may contribute to a viruses' pathogenicity with a different pathway.
Though lacking of intestinal cell lines from the natural host of BtCoV/Rh/YN2012, we screened the cell tropism of their spike protein through pseudotyped retrovirus entry with human, bat and other mammalian cell lines. Most of cell lines screened were unsusceptible to BtCoV/Rh/YN2012, indicating a low risk of interspecies transmission to human and other animals. Multiple reasons may lead to failed infection of coronavirus spike-pseudotyped retrovirus system, including receptor absence in target cells, failed recognition to the receptor homologue from non-host species, maladaptation in non-host cells during the spike maturation or virus entry, or the limitation of retrovirus system in stimulating coronavirus entry. The weak infectivity of RsYN1 pseudotyped retrovirus in Huh-7 cells could be explained by the binding of spike protein to polysaccharide secreted to the surface. The assumption needs to be further confirmed by experiments.
Our long-term surveillances suggest that Rhinolophus bats seem to harbor a wide diversity of CoVs. Coincidently, the two highly pathogenic agents, SARS-CoV and Rh-BatCoV HKU2 both originated from Rhinolophus bats. Considering the diversity of CoVs carried by this bat genus and their wide geographical distribution, there may be a low risk of spillover of these viruses to other animals and humans. Long-term surveillances and pathogenesis studies will help to prevent future human and animal diseases caused by these bat CoVs.
Supplementary Materials: The following are available online at http://www.mdpi.com/1999-4915/11/4/379/s1, Figure S1 : western blot analysis of the expression of accessory proteins. Figure S2 : Apoptosis analysis of ORF9 proteins of BtCoV/Rh/YN2012. Figure S3 : Functional analysis of ORF3a, ORF3b, ORF4b, ORF8 and ORF9 proteins on the production of Type I interferon. Figure S4 : Functional analysis of ORF3b, ORF4a, ORF4b, ORF8 and ORF9 proteins on the production of NF-κB. Figure S5 : Characteristic of BtCoV/Rh/YN2012 spike mediated pseudovirus. Table S1 : General primers for AlphaCoVs genome sequencing. Table S2 : Primers for the detection of viral sugbenomic mRNAs. Table S3
|
What is the genome size of the coronavirus?
| 3,676 |
26-32 kb
| 1,871 |
1,576 |
Characterization of a New Member of Alphacoronavirus with Unique Genomic Features in Rhinolophus Bats
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6521148/
SHA: ee14de143337eec0e9708f8139bfac2b7b8fdd27
Authors: Wang, Ning; Luo, Chuming; Liu, Haizhou; Yang, Xinglou; Hu, Ben; Zhang, Wei; Li, Bei; Zhu, Yan; Zhu, Guangjian; Shen, Xurui; Peng, Cheng; Shi, Zhengli
Date: 2019-04-24
DOI: 10.3390/v11040379
License: cc-by
Abstract: Bats have been identified as a natural reservoir of a variety of coronaviruses (CoVs). Several of them have caused diseases in humans and domestic animals by interspecies transmission. Considering the diversity of bat coronaviruses, bat species and populations, we expect to discover more bat CoVs through virus surveillance. In this study, we described a new member of alphaCoV (BtCoV/Rh/YN2012) in bats with unique genome features. Unique accessory genes, ORF4a and ORF4b were found between the spike gene and the envelope gene, while ORF8 gene was found downstream of the nucleocapsid gene. All the putative genes were further confirmed by reverse-transcription analyses. One unique gene at the 3’ end of the BtCoV/Rh/YN2012 genome, ORF9, exhibits ~30% amino acid identity to ORF7a of the SARS-related coronavirus. Functional analysis showed ORF4a protein can activate IFN-β production, whereas ORF3a can regulate NF-κB production. We also screened the spike-mediated virus entry using the spike-pseudotyped retroviruses system, although failed to find any fully permissive cells. Our results expand the knowledge on the genetic diversity of bat coronaviruses. Continuous screening of bat viruses will help us further understand the important role played by bats in coronavirus evolution and transmission.
Text: Members of the Coronaviridae family are enveloped, non-segmented, positive-strand RNA viruses with genome sizes ranging from 26-32 kb [1] . These viruses are classified into two subfamilies: Letovirinae, which contains the only genus: Alphaletovirus; and Orthocoronavirinae (CoV), which consists of alpha, beta, gamma, and deltacoronaviruses (CoVs) [2, 3] . Alpha and betacoronaviruses mainly infect mammals and cause human and animal diseases. Gamma-and delta-CoVs mainly infect birds, but some can also infect mammals [4, 5] . Six human CoVs (HCoVs) are known to cause human diseases. HCoV-HKU1, HCoV-OC43, HCoV-229E, and HCoV-NL63 commonly cause mild respiratory illness or asymptomatic infection; however, severe acute respiratory syndrome coronavirus (SARS-CoV) and
All sampling procedures were performed by veterinarians, with approval from Animal Ethics Committee of the Wuhan Institute of Virology (WIVH5210201). The study was conducted in accordance with the Guide for the Care and Use of Wild Mammals in Research of the People's Republic of China.
Bat fecal swab and pellet samples were collected from November 2004 to November 2014 in different seasons in Southern China, as described previously [16] .
Viral RNA was extracted from 200 µL of fecal swab or pellet samples using the High Pure Viral RNA Kit (Roche Diagnostics GmbH, Mannheim, Germany) as per the manufacturer's instructions. RNA was eluted in 50 µL of elution buffer, aliquoted, and stored at -80 • C. One-step hemi-nested reverse-transcription (RT-) PCR (Invitrogen, San Diego, CA, USA) was employed to detect coronavirus, as previously described [17, 18] .
To confirm the bat species of an individual sample, we PCR amplified the cytochrome b (Cytob) and/or NADH dehydrogenase subunit 1 (ND1) gene using DNA extracted from the feces or swabs [19, 20] . The gene sequences were assembled excluding the primer sequences. BLASTN was used to identify host species based on the most closely related sequences with the highest query coverage and a minimum identity of 95%.
Full genomic sequences were determined by one-step PCR (Invitrogen, San Diego, CA, USA) amplification with degenerate primers (Table S1 ) designed on the basis of multiple alignments of available alpha-CoV sequences deposited in GenBank or amplified with SuperScript IV Reverse Transcriptase (Invitrogen) and Expand Long Template PCR System (Roche Diagnostics GmbH, Mannheim, Germany) with specific primers (primer sequences are available upon request). Sequences of the 5' and 3' genomic ends were obtained by 5' and 3' rapid amplification of cDNA ends (SMARTer Viruses 2019, 11, 379 3 of 19 RACE 5'/3' Kit; Clontech, Mountain View, CA, USA), respectively. PCR products were gel-purified and subjected directly to sequencing. PCR products over 5kb were subjected to deep sequencing using Hiseq2500 system. For some fragments, the PCR products were cloned into the pGEM-T Easy Vector (Promega, Madison, WI, USA) for sequencing. At least five independent clones were sequenced to obtain a consensus sequence.
The Next Generation Sequencing (NGS) data were filtered and mapped to the reference sequence of BatCoV HKU10 (GenBank accession number NC_018871) using Geneious 7.1.8 [21] . Genomes were preliminarily assembled using DNAStar lasergene V7 (DNAStar, Madison, WI, USA). Putative open reading frames (ORFs) were predicted using NCBI's ORF finder (https://www.ncbi.nlm.nih.gov/ orffinder/) with a minimal ORF length of 150 nt, followed by manual inspection. The sequences of the 5' untranslated region (5'-UTR) and 3'-UTR were defined, and the leader sequence, the leader and body transcriptional regulatory sequence (TRS) were identified as previously described [22] . The cleavage of the 16 nonstructural proteins coded by ORF1ab was determined by alignment of aa sequences of other CoVs and the recognition pattern of the 3C-like proteinase and papain-like proteinase. Phylogenetic trees based on nt or aa sequences were constructed using the maximum likelihood algorithm with bootstrap values determined by 1000 replicates in the MEGA 6 software package [23] . Full-length genome sequences obtained in this study were aligned with those of previously reported alpha-CoVs using MUSCLE [24] . The aligned sequences were scanned for recombination events by using Recombination Detection Program [25] . Potential recombination events as suggested by strong p-values (<10 -20 ) were confirmed using similarity plot and bootscan analyses implemented in Simplot 3.5.1 [26] . The number of synonymous substitutions per synonymous site, Ks, and the number of nonsynonymous substitutions per nonsynonymous site, Ka, for each coding region were calculated using the Ka/Ks calculation tool of the Norwegian Bioinformatics Platform (http://services.cbu.uib.no/tools/kaks) with default parameters [27] . The protein homology detection was analyzed using HHpred (https://toolkit.tuebingen.mpg.de/#/tools/hhpred) with default parameters [28] .
A set of nested RT-PCRs was employed to determine the presence of viral subgenomic mRNAs in the CoV-positive samples [29] . Forward primers were designed targeting the leader sequence at the 5'-end of the complete genome, while reverse primers were designed within the ORFs. Specific and suspected amplicons of expected sizes were purified and then cloned into the pGEM-T Easy vector for sequencing.
Bat primary or immortalized cells (Rhinolophus sinicus kidney immortalized cells, RsKT; Rhinolophus sinicus Lung primary cells, RsLu4323; Rhinolophus sinicus brain immortalized cells, RsBrT; Rhinolophus affinis kidney primary cells, RaK4324; Rousettus leschenaultii Kidney immortalized cells, RlKT; Hipposideros pratti lung immortalized cells, HpLuT) generated in our laboratory were all cultured in DMEM/F12 with 15% FBS. Pteropus alecto kidney cells (Paki) was maintained in DMEM/F12 supplemented with 10% FBS. Other cells were maintained according to the recommendations of American Type Culture Collection (ATCC, www.atcc.org).
The putative accessory genes of the newly detected virus were generated by RT-PCR from viral RNA extracted from fecal samples, as described previously [30] . The influenza virus NS1 plasmid was generated in our lab [31] . The human bocavirus (HBoV) VP2 plasmid was kindly provided by prof. Hanzhong Wang of the Wuhan Institute of Virology, Chinese Academy of Sciences. SARS-CoV ORF7a was synthesized by Sangon Biotech. The transfections were performed with Lipofectamine 3000 Reagent (Life Technologies). Expression of these accessory genes were analyzed by Western blotting using an mAb (Roche Diagnostics GmbH, Mannheim, Germany) against the HA tag.
The virus isolation was performed as previously described [12] . Briefly, fecal supernatant was acquired via gradient centrifugation and then added to Vero E6 cells, 1:10 diluted in DMEM. After incubation at 37°C for 1 h the inoculum was replaced by fresh DMEM containing 2% FBS and the antibiotic-antimycotic (Gibco, Grand Island, NY, USA). Three blind passages were carried out. Cells were checked daily for cytopathic effect. Both culture supernatant and cell pellet were examined for CoV by RT-PCR [17] .
Apoptosis was analyzed as previously described [18] . Briefly, 293T cells in 12-well plates were transfected with 3 µg of expression plasmid or empty vector, and the cells were collected 24 h post transfection. Apoptosis was detected by flow cytometry using by the Annexin V-FITC/PI Apoptosis Detection Kit (YEASEN, Shanghai, China) following the manufacturer's instructions. Annexin-V-positive and PI-negative cells were considered to be in the early apoptotic phase and those stained for both Annexin V and PI were deemed to undergo late apoptosis or necrosis. All experiments were repeated three times. Student's t-test was used to evaluate the data, with p < 0.05 considered significant.
HEK 293T cells were seeded in 24-well plates and then co-transfected with reporter plasmids (pRL-TK and pIFN-βIFN-or pNF-κB-Luc) [30] , as well as plasmids expressing accessory genes, empty vector plasmid pcAGGS, influenza virus NS1 [32] , SARS-CoV ORF7a [33] , or HBoV VP2 [34] . At 24 h post transfection, cells were treated with Sendai virus (SeV) (100 hemagglutinin units [HAU]/mL) or human tumor necrosis factor alpha (TNF-α; R&D system) for 6 h to activate IFNβ or NF-κB, respectively. Cell lysates were prepared, and luciferase activity was measured using the dual-luciferase assay kit (Promega, Madison, WI, USA) according to the manufacturer's instructions.
Retroviruses pseudotyped with BtCoV/Rh/YN2012 RsYN1, RsYN3, RaGD, or MERS-CoV spike, or no spike (mock) were used to infect human, bat or other mammalian cells in 96-well plates. The pseudovirus particles were confirmed with Western blotting and negative-staining electromicroscopy. The production process, measurements of infection and luciferase activity were conducted, as described previously [35, 36] .
The complete genome nucleotide sequences of BtCoV/Rh/YN2012 strains RsYN1, RsYN2, RsYN3, and RaGD obtained in this study have been submitted to the GenBank under MG916901 to MG916904.
The surveillance was performed between November 2004 to November 2014 in 19 provinces of China. In total, 2061 fecal samples were collected from at least 12 Rhinolophus bat species ( Figure 1A ). CoVs were detected in 209 of these samples ( Figure 1B and Table 1 ). Partial RdRp sequences suggested the presence of at least 8 different CoVs. Five of these viruses are related to known species: Mi-BatCoV 1 (>94% nt identity), Mi-BatCoV HKU8 [37] (>93% nt identity), BtRf-AlphaCoV/HuB2013 [11] (>99% nt identity), SARSr-CoV [38] (>89% nt identity), and HKU2-related CoV [39] (>85% nt identity). While the other three CoV sequences showed less than 83% nt identity to known CoV species. These three viruses should represent novel CoV species. Virus isolation was performed as previously described [12] , but was not successful. identity). While the other three CoV sequences showed less than 83% nt identity to known CoV species. These three viruses should represent novel CoV species. Virus isolation was performed as previously described [12] , but was not successful.
We next characterized a novel alpha-CoV, BtCoV/Rh/YN2012. It was detected in 3 R.affinis and 6 R.sinicus, respectively. Based on the sequences, we defined three genotypes, which represented by RsYN1, RsYN3, and RaGD, respectively. Strain RsYN2 was classified into the RsYN3 genotype. Four full-length genomes were obtained. Three of them were from R.sinicus (Strain RsYN1, RsYN2, and RsYN3), while the other one was from R.affinis (Strain RaGD). The sizes of these 4 genomes are between 28,715 to 29,102, with G+C contents between 39.0% to 41.3%. The genomes exhibit similar structures and transcription regulatory sequences (TRS) that are identical to those of other alpha-CoVs ( Figure 2 and Table 2 ). Exceptions including three additional ORFs (ORF3b, ORF4a and ORF4b) were observed. All the 4 strains have ORF4a & ORF4b, while only strain RsYN1 has ORF3b.
The replicase gene, ORF1ab, occupies~20.4 kb of the genome. The replicase gene, ORF1ab, occupies~20.4 kb of the genome. It encodes polyproteins 1a and 1ab, which could be cleaved into 16 non-structural proteins (Nsp1-Nsp16). The 3'-end of the cleavage sites recognized by 3C-like proteinase (Nsp4-Nsp10, Nsp12-Nsp16) and papain-like proteinase (Nsp1-Nsp3) were confirmed. The proteins including Nsp3 (papain-like 2 proteas, PL2pro), Nsp5 (chymotrypsin-like protease, 3CLpro), Nsp12 (RdRp), Nsp13 (helicase), and other proteins of unknown function ( Table 3 ). The 7 concatenated domains of polyprotein 1 shared <90% aa sequence identity with those of other known alpha-CoVs ( Table 2 ), suggesting that these viruses represent a novel CoV species within the alpha-CoV. The closest assigned CoV species to BtCoV/Rh/YN2012 are BtCoV-HKU10 and BtRf-AlphaCoV/Hub2013. The three strains from Yunnan Province were clustered into two genotypes (83% genome identity) correlated to their sampling location. The third genotype represented by strain RaGD was isolated to strains found in Yunnan (<75.4% genome identity). We then examined the individual genes ( Table 2) . All of the genes showed low aa sequence identity to known CoVs. The four strains of BtCoV/Rh/YN2012 showed genetic diversity among all different genes except ORF1ab (>83.7% aa identity). Notably, the spike proteins are highly divergent among these strains. Other structure proteins (E, M, and N) are more conserved than the spike and other accessory proteins. Comparing the accessory genes among these four strains revealed that the strains of the same genotype shared a 100% identical ORF3a. However, the proteins encoded by ORF3as were highly divergent among different genotypes (<65% aa identity). The putative accessory genes were also BLASTed against GenBank records. Most accessory genes have no homologues in GenBank-database, except for ORF3a (52.0-55.5% aa identity with BatCoV HKU10 ORF3) and ORF9 (28.1-32.0% aa identity with SARSr-CoV ORF7a). We analyzed the protein homology with HHpred software. The results showed that ORF9s and SARS-CoV OR7a are homologues (possibility: 100%, E value <10 −48 ). We further screened the genomes for potential recombination evidence. No significant recombination breakpoint was detected by bootscan analysis.
To confirm the presence of subgenomic RNA, we designed a set of primers targeting all the predicted ORFs as described. The amplicons were firstly confirmed via agarose-gel electrophoresis and then sequencing ( Figure 3 and Table 2 ). The sequences showed that all the ORFs, except ORF4b, had preceding TRS. Hence, the ORF4b may be translated from bicistronic mRNAs. In RsYN1, an additional subgenomic RNA starting inside the ORF3a was found through sequencing, which led to a unique ORF3b.
To confirm the presence of subgenomic RNA, we designed a set of primers targeting all the predicted ORFs as described. The amplicons were firstly confirmed via agarose-gel electrophoresis and then sequencing ( Figure 3 and Table 2 ). The sequences showed that all the ORFs, except ORF4b, had preceding TRS. Hence, the ORF4b may be translated from bicistronic mRNAs. In RsYN1, an additional subgenomic RNA starting inside the ORF3a was found through sequencing, which led to a unique ORF3b.
Phylogenetic trees were constructed using the aa sequences of RdRp and S of BtCoV/Rh/YN2012 and other representative CoVs (Figure 4) . In both trees, all BtCoV/Rh/YN2012 were clustered together and formed a distinct lineage to other known coronavirus species. Two distinct sublineages were observed within BtCoV/Rh/YN2012. One was from Ra sampled in Guangdong, while the other was from Rs sampled in Yunnan Among the strains from Yunnan, RsYN2 and RsYN3 were clustered together, while RsYN1 was isolated. The topology of these four strains was correlated to the sampling location. The relatively long branches reflect a high diversity among these strains, indicating a long independent evolution history.
Phylogenetic trees were constructed using the aa sequences of RdRp and S of BtCoV/Rh/YN2012 and other representative CoVs (Figure 4) . In both trees, all BtCoV/Rh/YN2012 were clustered together and formed a distinct lineage to other known coronavirus species. Two distinct sublineages were observed within BtCoV/Rh/YN2012. One was from Ra sampled in Guangdong, while the other was from Rs sampled in Yunnan Among the strains from Yunnan, RsYN2 and RsYN3 were clustered together, while RsYN1 was isolated. The topology of these four strains was correlated to the sampling location. The relatively long branches reflect a high diversity among these strains, indicating a long independent evolution history.
Phylogenetic trees were constructed using the aa sequences of RdRp and S of BtCoV/Rh/YN2012 and other representative CoVs (Figure 4) . In both trees, all BtCoV/Rh/YN2012 were clustered together and formed a distinct lineage to other known coronavirus species. Two distinct sublineages were observed within BtCoV/Rh/YN2012. One was from Ra sampled in Guangdong, while the other was from Rs sampled in Yunnan Among the strains from Yunnan, RsYN2 and RsYN3 were clustered together, while RsYN1 was isolated. The topology of these four strains was correlated to the sampling location. The relatively long branches reflect a high diversity among these strains, indicating a long independent evolution history.
The Ka/Ks ratios (Ks is the number of synonymous substitutions per synonymous sites and Ka is the number of nonsynonymous substitutions per nonsynonymous site) were calculated for all genes. The Ka/Ks ratios for most of the genes were generally low, which indicates these genes were under purified selection. However, the Ka/Ks ratios of ORF4a, ORF4b, and ORF9 (0.727, 0.623, and 0.843, respectively) were significantly higher than those of other ORFs (Table 4 ). For further selection pressure evaluation of the ORF4a and ORF4b gene, we sequenced another four ORF4a and ORF4b genes (strain Rs4223, Rs4236, Rs4240, and Ra13576 was shown in Figure 1B
As SARS-CoV ORF7a was reported to induce apoptosis, we conducted apoptosis analysis on BtCoV/Rh/YN2012 ORF9, a~30% aa identity homologue of SARSr-CoV ORF7a. We transiently transfected ORF9 of BtCoV/Rh/YN2012 into HEK293T cells to examine whether this ORF9 triggers apoptosis. Western blot was performed to confirm the expression of ORF9s and SARS-CoV ORF7a ( Figure S1 ). ORF9 couldn't induce apoptosis as the ORF7a of SARS-CoV Tor2 ( Figure S2 ). The results indicated that BtCoV/Rh/YN2012 ORF9 was not involved in apoptosis induction.
To determine whether these accessory proteins modulate IFN induction, we transfected reporter plasmids (pIFNβ-Luc and pRL-TK) and expression plasmids to 293T cells. All the cells over-expressing the accessory genes, as well as influenza virus NS1 (strain PR8), HBoV VP2, or empty vector were tested for luciferase activity after SeV infection. Luciferase activity stimulated by SeV was remarkably higher than that without SeV treatment as expected. Influenza virus NS1 inhibits the expression from IFN promoter, while HBoV VP2 activate the expression. Compared to those controls, the ORF4a proteins exhibit an active effect as HBoV VP2 ( Figure 5A ). Other accessory proteins showed no effect on IFN production ( Figure S3 ). Expression of these accessory genes were confirmed by Western blot ( Figure S1 ). was remarkably higher than that without SeV treatment as expected. Influenza virus NS1 inhibits the expression from IFN promoter, while HBoV VP2 activate the expression. Compared to those controls, the ORF4a proteins exhibit an active effect as HBoV VP2 ( Figure 5A ). Other accessory proteins showed no effect on IFN production ( Figure S3 ). Expression of these accessory genes were confirmed by Western blot (Figure S1 ). Samples were collected at 6 h postinfection, followed by dual-luciferase assay. The results were expressed as the firefly luciferase value normalized to that of Renilla luciferase. (B) ORF3a protein activate NF-κB. 293T cells were transfected with 100 ng pNF-κB-Luc, 10 ng pRL-TK, empty vector (500 ng), an NS1-expressing plasmid (500 ng), a SARS-CoV ORF7a-expressing plasmid (500 ng), or ORF3a-expressing plasmids (500 ng). After 24 h, the cells were treated with TNF-α. Dual-luciferase activity was determined after 6 h. The results were expressed as the firefly luciferase activity normalized to that of Renilla luciferase. The experiments were performed three times independently. Data are representative of at least three independent experiments, with each determination performed in triplicate (mean ± SD of fold change). Asterisks indicate significant differences between groups (compared with Empty vector-NC, p < 0.05, as determined by student t test).
NF-κB plays an important role in regulating the immune response to viral infection and is also a key factor frequently targeted by viruses for taking over the host cell. In this study, we tested if these accessory proteins could modulate NF-κB. 293T cells were co-transfected with reporter Samples were collected at 6 h postinfection, followed by dual-luciferase assay. The results were expressed as the firefly luciferase value normalized to that of Renilla luciferase. (B) ORF3a protein activate NF-κB. 293T cells were transfected with 100 ng pNF-κB-Luc, 10 ng pRL-TK, empty vector (500 ng), an NS1-expressing plasmid (500 ng), a SARS-CoV ORF7a-expressing plasmid (500 ng), or ORF3a-expressing plasmids (500 ng). After 24 h, the cells were treated with TNF-α. Dual-luciferase activity was determined after 6 h. The results were expressed as the firefly luciferase activity normalized to that of Renilla luciferase. The experiments were performed three times independently. Data are representative of at least three independent experiments, with each determination performed in triplicate (mean ± SD of fold change). Asterisks indicate significant differences between groups (compared with Empty vector-NC, p < 0.05, as determined by student t test).
NF-κB plays an important role in regulating the immune response to viral infection and is also a key factor frequently targeted by viruses for taking over the host cell. In this study, we tested if these accessory proteins could modulate NF-κB. 293T cells were co-transfected with reporter plasmids (pNF-κB-Luc and pRL-TK), as well as accessory protein-expressing plasmids, or controls (empty vector, NS1, SARS-CoV Tor2-ORF7a). The cells were mock treated or treated with TNF-α for 6 h at 24 h post-transfection. The luciferase activity was determined. RsYN1-ORF3a and RaGD-ORF3a activated NF-κB as SARS-CoV ORF7a, whereas RsYN2-ORF3a inhibited NF-κB as NS1 ( Figure 5B ). Expressions of ORF3as were confirmed with Western blot ( Figure S1 ). Other accessory proteins did not modulate NF-κB production ( Figure S4 ).
To understand the infectivity of these newly detected BtCoV/Rh/YN2012, we selected the RsYN1, RsYN3 and RaGD spike proteins for spike-mediated pseudovirus entry studies. Both Western blot analysis and negative-staining electron microscopy observation confirmed the preparation of BtCoV/Rh/YN2012 successfully ( Figure S5 ). A total of 11 human cell lines, 8 bat cells, and 9 other mammal cell lines were tested, and no strong positive was found (Table S2) .
In this study, a novel alpha-CoV species, BtCoV/Rh/YN2012, was identified in two Rhinolophus species. The 4 strains with full-length genome were sequences. The 7 conserved replicase domains of these viruses possessed <90% aa sequence identity to those of other known alpha-CoVs, which defines a new species in accordance with the ICTV taxonomy standard [42] . These novel alpha-CoVs showed high genetic diversity in their structural and non-structural genes. Strain RaGD from R. affinis, collected in Guangdong province, formed a divergent independent branch from the other 3 strains from R. sinicus, sampled in Yunnan Province, indicating an independent evolution process associated with geographic isolation and host restrain. Though collected from same province, these three virus strains formed two genotypes correlated to sampling locations. These two genotypes had low genome sequence identity, especially in the S gene and accessory genes. Considering the remote geographic location of the host bat habitat, the host tropism, and the virus diversity, we suppose BtCoV/Rh/YN2012 may have spread in these two provinces with a long history of circulation in their natural reservoir, Rhinolophus bats. With the sequence evidence, we suppose that these viruses are still rapidly evolving.
Our study revealed that BtCoV/Rh/YN2012 has a unique genome structure compared to other alpha-CoVs. First, novel accessory genes, which had no homologues, were identified in the genomes. Second, multiple TRSs were found between S and E genes while other alphacoronavirus only had one TRS there. These TRSs precede ORF3a, ORF3b (only in RsYN1), and ORF4a/b respectively. Third, accessory gene ORF9 showed homology with those of other known CoV species in another coronavirus genus, especially with accessory genes from SARSr-CoV.
Accessory genes are usually involved in virus-host interactions during CoV infection [43] . In most CoVs, accessory genes are dispensable for virus replication. However, an intact 3c gene of feline CoV was required for viral replication in the gut [44] [45] [46] . Deletion of the genus-specific genes in mouse hepatitis virus led to a reduction in virulence [47] . SARS-CoV ORF7a, which was identified to be involved in the suppression of RNA silencing [48] , inhibition of cellular protein synthesis [49] , cell-cycle blockage [50] , and apoptosis induction [51, 52] . In this study, we found that BtCoV/Rh/YN2012 ORF9 shares~30% aa sequence identity with SARS-CoV ORF7a. Interestingly, BtCoV/Rh/YN2012 and SARSr-CoV were both detected in R. sinicus from the same cave. We suppose that SARS-CoV and BtCoV/Rh/YN2012 may have acquired ORF7a or ORF9 from a common ancestor through genome recombination or horizontal gene transfer. Whereas, ORF9 of BtCoV/Rh/YN2012 failed to induce apoptosis or activate NF-κB production, these differences may be induced by the divergent evolution of these proteins in different pressure.
Though different BtCoV/Rh/YN2012 ORF4a share <64.4% amino acid identity, all of them could activate IFN-β. ORF3a from RsYN1 and RaGD upregulated NF-κB, but the homologue from RsYN2 downregulated NF-κB expression. These differences may be caused by amino acid sequence variations and may contribute to a viruses' pathogenicity with a different pathway.
Though lacking of intestinal cell lines from the natural host of BtCoV/Rh/YN2012, we screened the cell tropism of their spike protein through pseudotyped retrovirus entry with human, bat and other mammalian cell lines. Most of cell lines screened were unsusceptible to BtCoV/Rh/YN2012, indicating a low risk of interspecies transmission to human and other animals. Multiple reasons may lead to failed infection of coronavirus spike-pseudotyped retrovirus system, including receptor absence in target cells, failed recognition to the receptor homologue from non-host species, maladaptation in non-host cells during the spike maturation or virus entry, or the limitation of retrovirus system in stimulating coronavirus entry. The weak infectivity of RsYN1 pseudotyped retrovirus in Huh-7 cells could be explained by the binding of spike protein to polysaccharide secreted to the surface. The assumption needs to be further confirmed by experiments.
Our long-term surveillances suggest that Rhinolophus bats seem to harbor a wide diversity of CoVs. Coincidently, the two highly pathogenic agents, SARS-CoV and Rh-BatCoV HKU2 both originated from Rhinolophus bats. Considering the diversity of CoVs carried by this bat genus and their wide geographical distribution, there may be a low risk of spillover of these viruses to other animals and humans. Long-term surveillances and pathogenesis studies will help to prevent future human and animal diseases caused by these bat CoVs.
Supplementary Materials: The following are available online at http://www.mdpi.com/1999-4915/11/4/379/s1, Figure S1 : western blot analysis of the expression of accessory proteins. Figure S2 : Apoptosis analysis of ORF9 proteins of BtCoV/Rh/YN2012. Figure S3 : Functional analysis of ORF3a, ORF3b, ORF4b, ORF8 and ORF9 proteins on the production of Type I interferon. Figure S4 : Functional analysis of ORF3b, ORF4a, ORF4b, ORF8 and ORF9 proteins on the production of NF-κB. Figure S5 : Characteristic of BtCoV/Rh/YN2012 spike mediated pseudovirus. Table S1 : General primers for AlphaCoVs genome sequencing. Table S2 : Primers for the detection of viral sugbenomic mRNAs. Table S3
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What is the structure of the coronavirus?
| 3,677 |
enveloped, non-segmented, positive-strand RNA viruses
| 1,786 |
1,576 |
Characterization of a New Member of Alphacoronavirus with Unique Genomic Features in Rhinolophus Bats
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6521148/
SHA: ee14de143337eec0e9708f8139bfac2b7b8fdd27
Authors: Wang, Ning; Luo, Chuming; Liu, Haizhou; Yang, Xinglou; Hu, Ben; Zhang, Wei; Li, Bei; Zhu, Yan; Zhu, Guangjian; Shen, Xurui; Peng, Cheng; Shi, Zhengli
Date: 2019-04-24
DOI: 10.3390/v11040379
License: cc-by
Abstract: Bats have been identified as a natural reservoir of a variety of coronaviruses (CoVs). Several of them have caused diseases in humans and domestic animals by interspecies transmission. Considering the diversity of bat coronaviruses, bat species and populations, we expect to discover more bat CoVs through virus surveillance. In this study, we described a new member of alphaCoV (BtCoV/Rh/YN2012) in bats with unique genome features. Unique accessory genes, ORF4a and ORF4b were found between the spike gene and the envelope gene, while ORF8 gene was found downstream of the nucleocapsid gene. All the putative genes were further confirmed by reverse-transcription analyses. One unique gene at the 3’ end of the BtCoV/Rh/YN2012 genome, ORF9, exhibits ~30% amino acid identity to ORF7a of the SARS-related coronavirus. Functional analysis showed ORF4a protein can activate IFN-β production, whereas ORF3a can regulate NF-κB production. We also screened the spike-mediated virus entry using the spike-pseudotyped retroviruses system, although failed to find any fully permissive cells. Our results expand the knowledge on the genetic diversity of bat coronaviruses. Continuous screening of bat viruses will help us further understand the important role played by bats in coronavirus evolution and transmission.
Text: Members of the Coronaviridae family are enveloped, non-segmented, positive-strand RNA viruses with genome sizes ranging from 26-32 kb [1] . These viruses are classified into two subfamilies: Letovirinae, which contains the only genus: Alphaletovirus; and Orthocoronavirinae (CoV), which consists of alpha, beta, gamma, and deltacoronaviruses (CoVs) [2, 3] . Alpha and betacoronaviruses mainly infect mammals and cause human and animal diseases. Gamma-and delta-CoVs mainly infect birds, but some can also infect mammals [4, 5] . Six human CoVs (HCoVs) are known to cause human diseases. HCoV-HKU1, HCoV-OC43, HCoV-229E, and HCoV-NL63 commonly cause mild respiratory illness or asymptomatic infection; however, severe acute respiratory syndrome coronavirus (SARS-CoV) and
All sampling procedures were performed by veterinarians, with approval from Animal Ethics Committee of the Wuhan Institute of Virology (WIVH5210201). The study was conducted in accordance with the Guide for the Care and Use of Wild Mammals in Research of the People's Republic of China.
Bat fecal swab and pellet samples were collected from November 2004 to November 2014 in different seasons in Southern China, as described previously [16] .
Viral RNA was extracted from 200 µL of fecal swab or pellet samples using the High Pure Viral RNA Kit (Roche Diagnostics GmbH, Mannheim, Germany) as per the manufacturer's instructions. RNA was eluted in 50 µL of elution buffer, aliquoted, and stored at -80 • C. One-step hemi-nested reverse-transcription (RT-) PCR (Invitrogen, San Diego, CA, USA) was employed to detect coronavirus, as previously described [17, 18] .
To confirm the bat species of an individual sample, we PCR amplified the cytochrome b (Cytob) and/or NADH dehydrogenase subunit 1 (ND1) gene using DNA extracted from the feces or swabs [19, 20] . The gene sequences were assembled excluding the primer sequences. BLASTN was used to identify host species based on the most closely related sequences with the highest query coverage and a minimum identity of 95%.
Full genomic sequences were determined by one-step PCR (Invitrogen, San Diego, CA, USA) amplification with degenerate primers (Table S1 ) designed on the basis of multiple alignments of available alpha-CoV sequences deposited in GenBank or amplified with SuperScript IV Reverse Transcriptase (Invitrogen) and Expand Long Template PCR System (Roche Diagnostics GmbH, Mannheim, Germany) with specific primers (primer sequences are available upon request). Sequences of the 5' and 3' genomic ends were obtained by 5' and 3' rapid amplification of cDNA ends (SMARTer Viruses 2019, 11, 379 3 of 19 RACE 5'/3' Kit; Clontech, Mountain View, CA, USA), respectively. PCR products were gel-purified and subjected directly to sequencing. PCR products over 5kb were subjected to deep sequencing using Hiseq2500 system. For some fragments, the PCR products were cloned into the pGEM-T Easy Vector (Promega, Madison, WI, USA) for sequencing. At least five independent clones were sequenced to obtain a consensus sequence.
The Next Generation Sequencing (NGS) data were filtered and mapped to the reference sequence of BatCoV HKU10 (GenBank accession number NC_018871) using Geneious 7.1.8 [21] . Genomes were preliminarily assembled using DNAStar lasergene V7 (DNAStar, Madison, WI, USA). Putative open reading frames (ORFs) were predicted using NCBI's ORF finder (https://www.ncbi.nlm.nih.gov/ orffinder/) with a minimal ORF length of 150 nt, followed by manual inspection. The sequences of the 5' untranslated region (5'-UTR) and 3'-UTR were defined, and the leader sequence, the leader and body transcriptional regulatory sequence (TRS) were identified as previously described [22] . The cleavage of the 16 nonstructural proteins coded by ORF1ab was determined by alignment of aa sequences of other CoVs and the recognition pattern of the 3C-like proteinase and papain-like proteinase. Phylogenetic trees based on nt or aa sequences were constructed using the maximum likelihood algorithm with bootstrap values determined by 1000 replicates in the MEGA 6 software package [23] . Full-length genome sequences obtained in this study were aligned with those of previously reported alpha-CoVs using MUSCLE [24] . The aligned sequences were scanned for recombination events by using Recombination Detection Program [25] . Potential recombination events as suggested by strong p-values (<10 -20 ) were confirmed using similarity plot and bootscan analyses implemented in Simplot 3.5.1 [26] . The number of synonymous substitutions per synonymous site, Ks, and the number of nonsynonymous substitutions per nonsynonymous site, Ka, for each coding region were calculated using the Ka/Ks calculation tool of the Norwegian Bioinformatics Platform (http://services.cbu.uib.no/tools/kaks) with default parameters [27] . The protein homology detection was analyzed using HHpred (https://toolkit.tuebingen.mpg.de/#/tools/hhpred) with default parameters [28] .
A set of nested RT-PCRs was employed to determine the presence of viral subgenomic mRNAs in the CoV-positive samples [29] . Forward primers were designed targeting the leader sequence at the 5'-end of the complete genome, while reverse primers were designed within the ORFs. Specific and suspected amplicons of expected sizes were purified and then cloned into the pGEM-T Easy vector for sequencing.
Bat primary or immortalized cells (Rhinolophus sinicus kidney immortalized cells, RsKT; Rhinolophus sinicus Lung primary cells, RsLu4323; Rhinolophus sinicus brain immortalized cells, RsBrT; Rhinolophus affinis kidney primary cells, RaK4324; Rousettus leschenaultii Kidney immortalized cells, RlKT; Hipposideros pratti lung immortalized cells, HpLuT) generated in our laboratory were all cultured in DMEM/F12 with 15% FBS. Pteropus alecto kidney cells (Paki) was maintained in DMEM/F12 supplemented with 10% FBS. Other cells were maintained according to the recommendations of American Type Culture Collection (ATCC, www.atcc.org).
The putative accessory genes of the newly detected virus were generated by RT-PCR from viral RNA extracted from fecal samples, as described previously [30] . The influenza virus NS1 plasmid was generated in our lab [31] . The human bocavirus (HBoV) VP2 plasmid was kindly provided by prof. Hanzhong Wang of the Wuhan Institute of Virology, Chinese Academy of Sciences. SARS-CoV ORF7a was synthesized by Sangon Biotech. The transfections were performed with Lipofectamine 3000 Reagent (Life Technologies). Expression of these accessory genes were analyzed by Western blotting using an mAb (Roche Diagnostics GmbH, Mannheim, Germany) against the HA tag.
The virus isolation was performed as previously described [12] . Briefly, fecal supernatant was acquired via gradient centrifugation and then added to Vero E6 cells, 1:10 diluted in DMEM. After incubation at 37°C for 1 h the inoculum was replaced by fresh DMEM containing 2% FBS and the antibiotic-antimycotic (Gibco, Grand Island, NY, USA). Three blind passages were carried out. Cells were checked daily for cytopathic effect. Both culture supernatant and cell pellet were examined for CoV by RT-PCR [17] .
Apoptosis was analyzed as previously described [18] . Briefly, 293T cells in 12-well plates were transfected with 3 µg of expression plasmid or empty vector, and the cells were collected 24 h post transfection. Apoptosis was detected by flow cytometry using by the Annexin V-FITC/PI Apoptosis Detection Kit (YEASEN, Shanghai, China) following the manufacturer's instructions. Annexin-V-positive and PI-negative cells were considered to be in the early apoptotic phase and those stained for both Annexin V and PI were deemed to undergo late apoptosis or necrosis. All experiments were repeated three times. Student's t-test was used to evaluate the data, with p < 0.05 considered significant.
HEK 293T cells were seeded in 24-well plates and then co-transfected with reporter plasmids (pRL-TK and pIFN-βIFN-or pNF-κB-Luc) [30] , as well as plasmids expressing accessory genes, empty vector plasmid pcAGGS, influenza virus NS1 [32] , SARS-CoV ORF7a [33] , or HBoV VP2 [34] . At 24 h post transfection, cells were treated with Sendai virus (SeV) (100 hemagglutinin units [HAU]/mL) or human tumor necrosis factor alpha (TNF-α; R&D system) for 6 h to activate IFNβ or NF-κB, respectively. Cell lysates were prepared, and luciferase activity was measured using the dual-luciferase assay kit (Promega, Madison, WI, USA) according to the manufacturer's instructions.
Retroviruses pseudotyped with BtCoV/Rh/YN2012 RsYN1, RsYN3, RaGD, or MERS-CoV spike, or no spike (mock) were used to infect human, bat or other mammalian cells in 96-well plates. The pseudovirus particles were confirmed with Western blotting and negative-staining electromicroscopy. The production process, measurements of infection and luciferase activity were conducted, as described previously [35, 36] .
The complete genome nucleotide sequences of BtCoV/Rh/YN2012 strains RsYN1, RsYN2, RsYN3, and RaGD obtained in this study have been submitted to the GenBank under MG916901 to MG916904.
The surveillance was performed between November 2004 to November 2014 in 19 provinces of China. In total, 2061 fecal samples were collected from at least 12 Rhinolophus bat species ( Figure 1A ). CoVs were detected in 209 of these samples ( Figure 1B and Table 1 ). Partial RdRp sequences suggested the presence of at least 8 different CoVs. Five of these viruses are related to known species: Mi-BatCoV 1 (>94% nt identity), Mi-BatCoV HKU8 [37] (>93% nt identity), BtRf-AlphaCoV/HuB2013 [11] (>99% nt identity), SARSr-CoV [38] (>89% nt identity), and HKU2-related CoV [39] (>85% nt identity). While the other three CoV sequences showed less than 83% nt identity to known CoV species. These three viruses should represent novel CoV species. Virus isolation was performed as previously described [12] , but was not successful. identity). While the other three CoV sequences showed less than 83% nt identity to known CoV species. These three viruses should represent novel CoV species. Virus isolation was performed as previously described [12] , but was not successful.
We next characterized a novel alpha-CoV, BtCoV/Rh/YN2012. It was detected in 3 R.affinis and 6 R.sinicus, respectively. Based on the sequences, we defined three genotypes, which represented by RsYN1, RsYN3, and RaGD, respectively. Strain RsYN2 was classified into the RsYN3 genotype. Four full-length genomes were obtained. Three of them were from R.sinicus (Strain RsYN1, RsYN2, and RsYN3), while the other one was from R.affinis (Strain RaGD). The sizes of these 4 genomes are between 28,715 to 29,102, with G+C contents between 39.0% to 41.3%. The genomes exhibit similar structures and transcription regulatory sequences (TRS) that are identical to those of other alpha-CoVs ( Figure 2 and Table 2 ). Exceptions including three additional ORFs (ORF3b, ORF4a and ORF4b) were observed. All the 4 strains have ORF4a & ORF4b, while only strain RsYN1 has ORF3b.
The replicase gene, ORF1ab, occupies~20.4 kb of the genome. The replicase gene, ORF1ab, occupies~20.4 kb of the genome. It encodes polyproteins 1a and 1ab, which could be cleaved into 16 non-structural proteins (Nsp1-Nsp16). The 3'-end of the cleavage sites recognized by 3C-like proteinase (Nsp4-Nsp10, Nsp12-Nsp16) and papain-like proteinase (Nsp1-Nsp3) were confirmed. The proteins including Nsp3 (papain-like 2 proteas, PL2pro), Nsp5 (chymotrypsin-like protease, 3CLpro), Nsp12 (RdRp), Nsp13 (helicase), and other proteins of unknown function ( Table 3 ). The 7 concatenated domains of polyprotein 1 shared <90% aa sequence identity with those of other known alpha-CoVs ( Table 2 ), suggesting that these viruses represent a novel CoV species within the alpha-CoV. The closest assigned CoV species to BtCoV/Rh/YN2012 are BtCoV-HKU10 and BtRf-AlphaCoV/Hub2013. The three strains from Yunnan Province were clustered into two genotypes (83% genome identity) correlated to their sampling location. The third genotype represented by strain RaGD was isolated to strains found in Yunnan (<75.4% genome identity). We then examined the individual genes ( Table 2) . All of the genes showed low aa sequence identity to known CoVs. The four strains of BtCoV/Rh/YN2012 showed genetic diversity among all different genes except ORF1ab (>83.7% aa identity). Notably, the spike proteins are highly divergent among these strains. Other structure proteins (E, M, and N) are more conserved than the spike and other accessory proteins. Comparing the accessory genes among these four strains revealed that the strains of the same genotype shared a 100% identical ORF3a. However, the proteins encoded by ORF3as were highly divergent among different genotypes (<65% aa identity). The putative accessory genes were also BLASTed against GenBank records. Most accessory genes have no homologues in GenBank-database, except for ORF3a (52.0-55.5% aa identity with BatCoV HKU10 ORF3) and ORF9 (28.1-32.0% aa identity with SARSr-CoV ORF7a). We analyzed the protein homology with HHpred software. The results showed that ORF9s and SARS-CoV OR7a are homologues (possibility: 100%, E value <10 −48 ). We further screened the genomes for potential recombination evidence. No significant recombination breakpoint was detected by bootscan analysis.
To confirm the presence of subgenomic RNA, we designed a set of primers targeting all the predicted ORFs as described. The amplicons were firstly confirmed via agarose-gel electrophoresis and then sequencing ( Figure 3 and Table 2 ). The sequences showed that all the ORFs, except ORF4b, had preceding TRS. Hence, the ORF4b may be translated from bicistronic mRNAs. In RsYN1, an additional subgenomic RNA starting inside the ORF3a was found through sequencing, which led to a unique ORF3b.
To confirm the presence of subgenomic RNA, we designed a set of primers targeting all the predicted ORFs as described. The amplicons were firstly confirmed via agarose-gel electrophoresis and then sequencing ( Figure 3 and Table 2 ). The sequences showed that all the ORFs, except ORF4b, had preceding TRS. Hence, the ORF4b may be translated from bicistronic mRNAs. In RsYN1, an additional subgenomic RNA starting inside the ORF3a was found through sequencing, which led to a unique ORF3b.
Phylogenetic trees were constructed using the aa sequences of RdRp and S of BtCoV/Rh/YN2012 and other representative CoVs (Figure 4) . In both trees, all BtCoV/Rh/YN2012 were clustered together and formed a distinct lineage to other known coronavirus species. Two distinct sublineages were observed within BtCoV/Rh/YN2012. One was from Ra sampled in Guangdong, while the other was from Rs sampled in Yunnan Among the strains from Yunnan, RsYN2 and RsYN3 were clustered together, while RsYN1 was isolated. The topology of these four strains was correlated to the sampling location. The relatively long branches reflect a high diversity among these strains, indicating a long independent evolution history.
Phylogenetic trees were constructed using the aa sequences of RdRp and S of BtCoV/Rh/YN2012 and other representative CoVs (Figure 4) . In both trees, all BtCoV/Rh/YN2012 were clustered together and formed a distinct lineage to other known coronavirus species. Two distinct sublineages were observed within BtCoV/Rh/YN2012. One was from Ra sampled in Guangdong, while the other was from Rs sampled in Yunnan Among the strains from Yunnan, RsYN2 and RsYN3 were clustered together, while RsYN1 was isolated. The topology of these four strains was correlated to the sampling location. The relatively long branches reflect a high diversity among these strains, indicating a long independent evolution history.
Phylogenetic trees were constructed using the aa sequences of RdRp and S of BtCoV/Rh/YN2012 and other representative CoVs (Figure 4) . In both trees, all BtCoV/Rh/YN2012 were clustered together and formed a distinct lineage to other known coronavirus species. Two distinct sublineages were observed within BtCoV/Rh/YN2012. One was from Ra sampled in Guangdong, while the other was from Rs sampled in Yunnan Among the strains from Yunnan, RsYN2 and RsYN3 were clustered together, while RsYN1 was isolated. The topology of these four strains was correlated to the sampling location. The relatively long branches reflect a high diversity among these strains, indicating a long independent evolution history.
The Ka/Ks ratios (Ks is the number of synonymous substitutions per synonymous sites and Ka is the number of nonsynonymous substitutions per nonsynonymous site) were calculated for all genes. The Ka/Ks ratios for most of the genes were generally low, which indicates these genes were under purified selection. However, the Ka/Ks ratios of ORF4a, ORF4b, and ORF9 (0.727, 0.623, and 0.843, respectively) were significantly higher than those of other ORFs (Table 4 ). For further selection pressure evaluation of the ORF4a and ORF4b gene, we sequenced another four ORF4a and ORF4b genes (strain Rs4223, Rs4236, Rs4240, and Ra13576 was shown in Figure 1B
As SARS-CoV ORF7a was reported to induce apoptosis, we conducted apoptosis analysis on BtCoV/Rh/YN2012 ORF9, a~30% aa identity homologue of SARSr-CoV ORF7a. We transiently transfected ORF9 of BtCoV/Rh/YN2012 into HEK293T cells to examine whether this ORF9 triggers apoptosis. Western blot was performed to confirm the expression of ORF9s and SARS-CoV ORF7a ( Figure S1 ). ORF9 couldn't induce apoptosis as the ORF7a of SARS-CoV Tor2 ( Figure S2 ). The results indicated that BtCoV/Rh/YN2012 ORF9 was not involved in apoptosis induction.
To determine whether these accessory proteins modulate IFN induction, we transfected reporter plasmids (pIFNβ-Luc and pRL-TK) and expression plasmids to 293T cells. All the cells over-expressing the accessory genes, as well as influenza virus NS1 (strain PR8), HBoV VP2, or empty vector were tested for luciferase activity after SeV infection. Luciferase activity stimulated by SeV was remarkably higher than that without SeV treatment as expected. Influenza virus NS1 inhibits the expression from IFN promoter, while HBoV VP2 activate the expression. Compared to those controls, the ORF4a proteins exhibit an active effect as HBoV VP2 ( Figure 5A ). Other accessory proteins showed no effect on IFN production ( Figure S3 ). Expression of these accessory genes were confirmed by Western blot ( Figure S1 ). was remarkably higher than that without SeV treatment as expected. Influenza virus NS1 inhibits the expression from IFN promoter, while HBoV VP2 activate the expression. Compared to those controls, the ORF4a proteins exhibit an active effect as HBoV VP2 ( Figure 5A ). Other accessory proteins showed no effect on IFN production ( Figure S3 ). Expression of these accessory genes were confirmed by Western blot (Figure S1 ). Samples were collected at 6 h postinfection, followed by dual-luciferase assay. The results were expressed as the firefly luciferase value normalized to that of Renilla luciferase. (B) ORF3a protein activate NF-κB. 293T cells were transfected with 100 ng pNF-κB-Luc, 10 ng pRL-TK, empty vector (500 ng), an NS1-expressing plasmid (500 ng), a SARS-CoV ORF7a-expressing plasmid (500 ng), or ORF3a-expressing plasmids (500 ng). After 24 h, the cells were treated with TNF-α. Dual-luciferase activity was determined after 6 h. The results were expressed as the firefly luciferase activity normalized to that of Renilla luciferase. The experiments were performed three times independently. Data are representative of at least three independent experiments, with each determination performed in triplicate (mean ± SD of fold change). Asterisks indicate significant differences between groups (compared with Empty vector-NC, p < 0.05, as determined by student t test).
NF-κB plays an important role in regulating the immune response to viral infection and is also a key factor frequently targeted by viruses for taking over the host cell. In this study, we tested if these accessory proteins could modulate NF-κB. 293T cells were co-transfected with reporter Samples were collected at 6 h postinfection, followed by dual-luciferase assay. The results were expressed as the firefly luciferase value normalized to that of Renilla luciferase. (B) ORF3a protein activate NF-κB. 293T cells were transfected with 100 ng pNF-κB-Luc, 10 ng pRL-TK, empty vector (500 ng), an NS1-expressing plasmid (500 ng), a SARS-CoV ORF7a-expressing plasmid (500 ng), or ORF3a-expressing plasmids (500 ng). After 24 h, the cells were treated with TNF-α. Dual-luciferase activity was determined after 6 h. The results were expressed as the firefly luciferase activity normalized to that of Renilla luciferase. The experiments were performed three times independently. Data are representative of at least three independent experiments, with each determination performed in triplicate (mean ± SD of fold change). Asterisks indicate significant differences between groups (compared with Empty vector-NC, p < 0.05, as determined by student t test).
NF-κB plays an important role in regulating the immune response to viral infection and is also a key factor frequently targeted by viruses for taking over the host cell. In this study, we tested if these accessory proteins could modulate NF-κB. 293T cells were co-transfected with reporter plasmids (pNF-κB-Luc and pRL-TK), as well as accessory protein-expressing plasmids, or controls (empty vector, NS1, SARS-CoV Tor2-ORF7a). The cells were mock treated or treated with TNF-α for 6 h at 24 h post-transfection. The luciferase activity was determined. RsYN1-ORF3a and RaGD-ORF3a activated NF-κB as SARS-CoV ORF7a, whereas RsYN2-ORF3a inhibited NF-κB as NS1 ( Figure 5B ). Expressions of ORF3as were confirmed with Western blot ( Figure S1 ). Other accessory proteins did not modulate NF-κB production ( Figure S4 ).
To understand the infectivity of these newly detected BtCoV/Rh/YN2012, we selected the RsYN1, RsYN3 and RaGD spike proteins for spike-mediated pseudovirus entry studies. Both Western blot analysis and negative-staining electron microscopy observation confirmed the preparation of BtCoV/Rh/YN2012 successfully ( Figure S5 ). A total of 11 human cell lines, 8 bat cells, and 9 other mammal cell lines were tested, and no strong positive was found (Table S2) .
In this study, a novel alpha-CoV species, BtCoV/Rh/YN2012, was identified in two Rhinolophus species. The 4 strains with full-length genome were sequences. The 7 conserved replicase domains of these viruses possessed <90% aa sequence identity to those of other known alpha-CoVs, which defines a new species in accordance with the ICTV taxonomy standard [42] . These novel alpha-CoVs showed high genetic diversity in their structural and non-structural genes. Strain RaGD from R. affinis, collected in Guangdong province, formed a divergent independent branch from the other 3 strains from R. sinicus, sampled in Yunnan Province, indicating an independent evolution process associated with geographic isolation and host restrain. Though collected from same province, these three virus strains formed two genotypes correlated to sampling locations. These two genotypes had low genome sequence identity, especially in the S gene and accessory genes. Considering the remote geographic location of the host bat habitat, the host tropism, and the virus diversity, we suppose BtCoV/Rh/YN2012 may have spread in these two provinces with a long history of circulation in their natural reservoir, Rhinolophus bats. With the sequence evidence, we suppose that these viruses are still rapidly evolving.
Our study revealed that BtCoV/Rh/YN2012 has a unique genome structure compared to other alpha-CoVs. First, novel accessory genes, which had no homologues, were identified in the genomes. Second, multiple TRSs were found between S and E genes while other alphacoronavirus only had one TRS there. These TRSs precede ORF3a, ORF3b (only in RsYN1), and ORF4a/b respectively. Third, accessory gene ORF9 showed homology with those of other known CoV species in another coronavirus genus, especially with accessory genes from SARSr-CoV.
Accessory genes are usually involved in virus-host interactions during CoV infection [43] . In most CoVs, accessory genes are dispensable for virus replication. However, an intact 3c gene of feline CoV was required for viral replication in the gut [44] [45] [46] . Deletion of the genus-specific genes in mouse hepatitis virus led to a reduction in virulence [47] . SARS-CoV ORF7a, which was identified to be involved in the suppression of RNA silencing [48] , inhibition of cellular protein synthesis [49] , cell-cycle blockage [50] , and apoptosis induction [51, 52] . In this study, we found that BtCoV/Rh/YN2012 ORF9 shares~30% aa sequence identity with SARS-CoV ORF7a. Interestingly, BtCoV/Rh/YN2012 and SARSr-CoV were both detected in R. sinicus from the same cave. We suppose that SARS-CoV and BtCoV/Rh/YN2012 may have acquired ORF7a or ORF9 from a common ancestor through genome recombination or horizontal gene transfer. Whereas, ORF9 of BtCoV/Rh/YN2012 failed to induce apoptosis or activate NF-κB production, these differences may be induced by the divergent evolution of these proteins in different pressure.
Though different BtCoV/Rh/YN2012 ORF4a share <64.4% amino acid identity, all of them could activate IFN-β. ORF3a from RsYN1 and RaGD upregulated NF-κB, but the homologue from RsYN2 downregulated NF-κB expression. These differences may be caused by amino acid sequence variations and may contribute to a viruses' pathogenicity with a different pathway.
Though lacking of intestinal cell lines from the natural host of BtCoV/Rh/YN2012, we screened the cell tropism of their spike protein through pseudotyped retrovirus entry with human, bat and other mammalian cell lines. Most of cell lines screened were unsusceptible to BtCoV/Rh/YN2012, indicating a low risk of interspecies transmission to human and other animals. Multiple reasons may lead to failed infection of coronavirus spike-pseudotyped retrovirus system, including receptor absence in target cells, failed recognition to the receptor homologue from non-host species, maladaptation in non-host cells during the spike maturation or virus entry, or the limitation of retrovirus system in stimulating coronavirus entry. The weak infectivity of RsYN1 pseudotyped retrovirus in Huh-7 cells could be explained by the binding of spike protein to polysaccharide secreted to the surface. The assumption needs to be further confirmed by experiments.
Our long-term surveillances suggest that Rhinolophus bats seem to harbor a wide diversity of CoVs. Coincidently, the two highly pathogenic agents, SARS-CoV and Rh-BatCoV HKU2 both originated from Rhinolophus bats. Considering the diversity of CoVs carried by this bat genus and their wide geographical distribution, there may be a low risk of spillover of these viruses to other animals and humans. Long-term surveillances and pathogenesis studies will help to prevent future human and animal diseases caused by these bat CoVs.
Supplementary Materials: The following are available online at http://www.mdpi.com/1999-4915/11/4/379/s1, Figure S1 : western blot analysis of the expression of accessory proteins. Figure S2 : Apoptosis analysis of ORF9 proteins of BtCoV/Rh/YN2012. Figure S3 : Functional analysis of ORF3a, ORF3b, ORF4b, ORF8 and ORF9 proteins on the production of Type I interferon. Figure S4 : Functional analysis of ORF3b, ORF4a, ORF4b, ORF8 and ORF9 proteins on the production of NF-κB. Figure S5 : Characteristic of BtCoV/Rh/YN2012 spike mediated pseudovirus. Table S1 : General primers for AlphaCoVs genome sequencing. Table S2 : Primers for the detection of viral sugbenomic mRNAs. Table S3
|
What animals do gamma and delta coronavirus mainly infect?
| 3,678 |
birds
| 2,226 |
1,576 |
Characterization of a New Member of Alphacoronavirus with Unique Genomic Features in Rhinolophus Bats
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6521148/
SHA: ee14de143337eec0e9708f8139bfac2b7b8fdd27
Authors: Wang, Ning; Luo, Chuming; Liu, Haizhou; Yang, Xinglou; Hu, Ben; Zhang, Wei; Li, Bei; Zhu, Yan; Zhu, Guangjian; Shen, Xurui; Peng, Cheng; Shi, Zhengli
Date: 2019-04-24
DOI: 10.3390/v11040379
License: cc-by
Abstract: Bats have been identified as a natural reservoir of a variety of coronaviruses (CoVs). Several of them have caused diseases in humans and domestic animals by interspecies transmission. Considering the diversity of bat coronaviruses, bat species and populations, we expect to discover more bat CoVs through virus surveillance. In this study, we described a new member of alphaCoV (BtCoV/Rh/YN2012) in bats with unique genome features. Unique accessory genes, ORF4a and ORF4b were found between the spike gene and the envelope gene, while ORF8 gene was found downstream of the nucleocapsid gene. All the putative genes were further confirmed by reverse-transcription analyses. One unique gene at the 3’ end of the BtCoV/Rh/YN2012 genome, ORF9, exhibits ~30% amino acid identity to ORF7a of the SARS-related coronavirus. Functional analysis showed ORF4a protein can activate IFN-β production, whereas ORF3a can regulate NF-κB production. We also screened the spike-mediated virus entry using the spike-pseudotyped retroviruses system, although failed to find any fully permissive cells. Our results expand the knowledge on the genetic diversity of bat coronaviruses. Continuous screening of bat viruses will help us further understand the important role played by bats in coronavirus evolution and transmission.
Text: Members of the Coronaviridae family are enveloped, non-segmented, positive-strand RNA viruses with genome sizes ranging from 26-32 kb [1] . These viruses are classified into two subfamilies: Letovirinae, which contains the only genus: Alphaletovirus; and Orthocoronavirinae (CoV), which consists of alpha, beta, gamma, and deltacoronaviruses (CoVs) [2, 3] . Alpha and betacoronaviruses mainly infect mammals and cause human and animal diseases. Gamma-and delta-CoVs mainly infect birds, but some can also infect mammals [4, 5] . Six human CoVs (HCoVs) are known to cause human diseases. HCoV-HKU1, HCoV-OC43, HCoV-229E, and HCoV-NL63 commonly cause mild respiratory illness or asymptomatic infection; however, severe acute respiratory syndrome coronavirus (SARS-CoV) and
All sampling procedures were performed by veterinarians, with approval from Animal Ethics Committee of the Wuhan Institute of Virology (WIVH5210201). The study was conducted in accordance with the Guide for the Care and Use of Wild Mammals in Research of the People's Republic of China.
Bat fecal swab and pellet samples were collected from November 2004 to November 2014 in different seasons in Southern China, as described previously [16] .
Viral RNA was extracted from 200 µL of fecal swab or pellet samples using the High Pure Viral RNA Kit (Roche Diagnostics GmbH, Mannheim, Germany) as per the manufacturer's instructions. RNA was eluted in 50 µL of elution buffer, aliquoted, and stored at -80 • C. One-step hemi-nested reverse-transcription (RT-) PCR (Invitrogen, San Diego, CA, USA) was employed to detect coronavirus, as previously described [17, 18] .
To confirm the bat species of an individual sample, we PCR amplified the cytochrome b (Cytob) and/or NADH dehydrogenase subunit 1 (ND1) gene using DNA extracted from the feces or swabs [19, 20] . The gene sequences were assembled excluding the primer sequences. BLASTN was used to identify host species based on the most closely related sequences with the highest query coverage and a minimum identity of 95%.
Full genomic sequences were determined by one-step PCR (Invitrogen, San Diego, CA, USA) amplification with degenerate primers (Table S1 ) designed on the basis of multiple alignments of available alpha-CoV sequences deposited in GenBank or amplified with SuperScript IV Reverse Transcriptase (Invitrogen) and Expand Long Template PCR System (Roche Diagnostics GmbH, Mannheim, Germany) with specific primers (primer sequences are available upon request). Sequences of the 5' and 3' genomic ends were obtained by 5' and 3' rapid amplification of cDNA ends (SMARTer Viruses 2019, 11, 379 3 of 19 RACE 5'/3' Kit; Clontech, Mountain View, CA, USA), respectively. PCR products were gel-purified and subjected directly to sequencing. PCR products over 5kb were subjected to deep sequencing using Hiseq2500 system. For some fragments, the PCR products were cloned into the pGEM-T Easy Vector (Promega, Madison, WI, USA) for sequencing. At least five independent clones were sequenced to obtain a consensus sequence.
The Next Generation Sequencing (NGS) data were filtered and mapped to the reference sequence of BatCoV HKU10 (GenBank accession number NC_018871) using Geneious 7.1.8 [21] . Genomes were preliminarily assembled using DNAStar lasergene V7 (DNAStar, Madison, WI, USA). Putative open reading frames (ORFs) were predicted using NCBI's ORF finder (https://www.ncbi.nlm.nih.gov/ orffinder/) with a minimal ORF length of 150 nt, followed by manual inspection. The sequences of the 5' untranslated region (5'-UTR) and 3'-UTR were defined, and the leader sequence, the leader and body transcriptional regulatory sequence (TRS) were identified as previously described [22] . The cleavage of the 16 nonstructural proteins coded by ORF1ab was determined by alignment of aa sequences of other CoVs and the recognition pattern of the 3C-like proteinase and papain-like proteinase. Phylogenetic trees based on nt or aa sequences were constructed using the maximum likelihood algorithm with bootstrap values determined by 1000 replicates in the MEGA 6 software package [23] . Full-length genome sequences obtained in this study were aligned with those of previously reported alpha-CoVs using MUSCLE [24] . The aligned sequences were scanned for recombination events by using Recombination Detection Program [25] . Potential recombination events as suggested by strong p-values (<10 -20 ) were confirmed using similarity plot and bootscan analyses implemented in Simplot 3.5.1 [26] . The number of synonymous substitutions per synonymous site, Ks, and the number of nonsynonymous substitutions per nonsynonymous site, Ka, for each coding region were calculated using the Ka/Ks calculation tool of the Norwegian Bioinformatics Platform (http://services.cbu.uib.no/tools/kaks) with default parameters [27] . The protein homology detection was analyzed using HHpred (https://toolkit.tuebingen.mpg.de/#/tools/hhpred) with default parameters [28] .
A set of nested RT-PCRs was employed to determine the presence of viral subgenomic mRNAs in the CoV-positive samples [29] . Forward primers were designed targeting the leader sequence at the 5'-end of the complete genome, while reverse primers were designed within the ORFs. Specific and suspected amplicons of expected sizes were purified and then cloned into the pGEM-T Easy vector for sequencing.
Bat primary or immortalized cells (Rhinolophus sinicus kidney immortalized cells, RsKT; Rhinolophus sinicus Lung primary cells, RsLu4323; Rhinolophus sinicus brain immortalized cells, RsBrT; Rhinolophus affinis kidney primary cells, RaK4324; Rousettus leschenaultii Kidney immortalized cells, RlKT; Hipposideros pratti lung immortalized cells, HpLuT) generated in our laboratory were all cultured in DMEM/F12 with 15% FBS. Pteropus alecto kidney cells (Paki) was maintained in DMEM/F12 supplemented with 10% FBS. Other cells were maintained according to the recommendations of American Type Culture Collection (ATCC, www.atcc.org).
The putative accessory genes of the newly detected virus were generated by RT-PCR from viral RNA extracted from fecal samples, as described previously [30] . The influenza virus NS1 plasmid was generated in our lab [31] . The human bocavirus (HBoV) VP2 plasmid was kindly provided by prof. Hanzhong Wang of the Wuhan Institute of Virology, Chinese Academy of Sciences. SARS-CoV ORF7a was synthesized by Sangon Biotech. The transfections were performed with Lipofectamine 3000 Reagent (Life Technologies). Expression of these accessory genes were analyzed by Western blotting using an mAb (Roche Diagnostics GmbH, Mannheim, Germany) against the HA tag.
The virus isolation was performed as previously described [12] . Briefly, fecal supernatant was acquired via gradient centrifugation and then added to Vero E6 cells, 1:10 diluted in DMEM. After incubation at 37°C for 1 h the inoculum was replaced by fresh DMEM containing 2% FBS and the antibiotic-antimycotic (Gibco, Grand Island, NY, USA). Three blind passages were carried out. Cells were checked daily for cytopathic effect. Both culture supernatant and cell pellet were examined for CoV by RT-PCR [17] .
Apoptosis was analyzed as previously described [18] . Briefly, 293T cells in 12-well plates were transfected with 3 µg of expression plasmid or empty vector, and the cells were collected 24 h post transfection. Apoptosis was detected by flow cytometry using by the Annexin V-FITC/PI Apoptosis Detection Kit (YEASEN, Shanghai, China) following the manufacturer's instructions. Annexin-V-positive and PI-negative cells were considered to be in the early apoptotic phase and those stained for both Annexin V and PI were deemed to undergo late apoptosis or necrosis. All experiments were repeated three times. Student's t-test was used to evaluate the data, with p < 0.05 considered significant.
HEK 293T cells were seeded in 24-well plates and then co-transfected with reporter plasmids (pRL-TK and pIFN-βIFN-or pNF-κB-Luc) [30] , as well as plasmids expressing accessory genes, empty vector plasmid pcAGGS, influenza virus NS1 [32] , SARS-CoV ORF7a [33] , or HBoV VP2 [34] . At 24 h post transfection, cells were treated with Sendai virus (SeV) (100 hemagglutinin units [HAU]/mL) or human tumor necrosis factor alpha (TNF-α; R&D system) for 6 h to activate IFNβ or NF-κB, respectively. Cell lysates were prepared, and luciferase activity was measured using the dual-luciferase assay kit (Promega, Madison, WI, USA) according to the manufacturer's instructions.
Retroviruses pseudotyped with BtCoV/Rh/YN2012 RsYN1, RsYN3, RaGD, or MERS-CoV spike, or no spike (mock) were used to infect human, bat or other mammalian cells in 96-well plates. The pseudovirus particles were confirmed with Western blotting and negative-staining electromicroscopy. The production process, measurements of infection and luciferase activity were conducted, as described previously [35, 36] .
The complete genome nucleotide sequences of BtCoV/Rh/YN2012 strains RsYN1, RsYN2, RsYN3, and RaGD obtained in this study have been submitted to the GenBank under MG916901 to MG916904.
The surveillance was performed between November 2004 to November 2014 in 19 provinces of China. In total, 2061 fecal samples were collected from at least 12 Rhinolophus bat species ( Figure 1A ). CoVs were detected in 209 of these samples ( Figure 1B and Table 1 ). Partial RdRp sequences suggested the presence of at least 8 different CoVs. Five of these viruses are related to known species: Mi-BatCoV 1 (>94% nt identity), Mi-BatCoV HKU8 [37] (>93% nt identity), BtRf-AlphaCoV/HuB2013 [11] (>99% nt identity), SARSr-CoV [38] (>89% nt identity), and HKU2-related CoV [39] (>85% nt identity). While the other three CoV sequences showed less than 83% nt identity to known CoV species. These three viruses should represent novel CoV species. Virus isolation was performed as previously described [12] , but was not successful. identity). While the other three CoV sequences showed less than 83% nt identity to known CoV species. These three viruses should represent novel CoV species. Virus isolation was performed as previously described [12] , but was not successful.
We next characterized a novel alpha-CoV, BtCoV/Rh/YN2012. It was detected in 3 R.affinis and 6 R.sinicus, respectively. Based on the sequences, we defined three genotypes, which represented by RsYN1, RsYN3, and RaGD, respectively. Strain RsYN2 was classified into the RsYN3 genotype. Four full-length genomes were obtained. Three of them were from R.sinicus (Strain RsYN1, RsYN2, and RsYN3), while the other one was from R.affinis (Strain RaGD). The sizes of these 4 genomes are between 28,715 to 29,102, with G+C contents between 39.0% to 41.3%. The genomes exhibit similar structures and transcription regulatory sequences (TRS) that are identical to those of other alpha-CoVs ( Figure 2 and Table 2 ). Exceptions including three additional ORFs (ORF3b, ORF4a and ORF4b) were observed. All the 4 strains have ORF4a & ORF4b, while only strain RsYN1 has ORF3b.
The replicase gene, ORF1ab, occupies~20.4 kb of the genome. The replicase gene, ORF1ab, occupies~20.4 kb of the genome. It encodes polyproteins 1a and 1ab, which could be cleaved into 16 non-structural proteins (Nsp1-Nsp16). The 3'-end of the cleavage sites recognized by 3C-like proteinase (Nsp4-Nsp10, Nsp12-Nsp16) and papain-like proteinase (Nsp1-Nsp3) were confirmed. The proteins including Nsp3 (papain-like 2 proteas, PL2pro), Nsp5 (chymotrypsin-like protease, 3CLpro), Nsp12 (RdRp), Nsp13 (helicase), and other proteins of unknown function ( Table 3 ). The 7 concatenated domains of polyprotein 1 shared <90% aa sequence identity with those of other known alpha-CoVs ( Table 2 ), suggesting that these viruses represent a novel CoV species within the alpha-CoV. The closest assigned CoV species to BtCoV/Rh/YN2012 are BtCoV-HKU10 and BtRf-AlphaCoV/Hub2013. The three strains from Yunnan Province were clustered into two genotypes (83% genome identity) correlated to their sampling location. The third genotype represented by strain RaGD was isolated to strains found in Yunnan (<75.4% genome identity). We then examined the individual genes ( Table 2) . All of the genes showed low aa sequence identity to known CoVs. The four strains of BtCoV/Rh/YN2012 showed genetic diversity among all different genes except ORF1ab (>83.7% aa identity). Notably, the spike proteins are highly divergent among these strains. Other structure proteins (E, M, and N) are more conserved than the spike and other accessory proteins. Comparing the accessory genes among these four strains revealed that the strains of the same genotype shared a 100% identical ORF3a. However, the proteins encoded by ORF3as were highly divergent among different genotypes (<65% aa identity). The putative accessory genes were also BLASTed against GenBank records. Most accessory genes have no homologues in GenBank-database, except for ORF3a (52.0-55.5% aa identity with BatCoV HKU10 ORF3) and ORF9 (28.1-32.0% aa identity with SARSr-CoV ORF7a). We analyzed the protein homology with HHpred software. The results showed that ORF9s and SARS-CoV OR7a are homologues (possibility: 100%, E value <10 −48 ). We further screened the genomes for potential recombination evidence. No significant recombination breakpoint was detected by bootscan analysis.
To confirm the presence of subgenomic RNA, we designed a set of primers targeting all the predicted ORFs as described. The amplicons were firstly confirmed via agarose-gel electrophoresis and then sequencing ( Figure 3 and Table 2 ). The sequences showed that all the ORFs, except ORF4b, had preceding TRS. Hence, the ORF4b may be translated from bicistronic mRNAs. In RsYN1, an additional subgenomic RNA starting inside the ORF3a was found through sequencing, which led to a unique ORF3b.
To confirm the presence of subgenomic RNA, we designed a set of primers targeting all the predicted ORFs as described. The amplicons were firstly confirmed via agarose-gel electrophoresis and then sequencing ( Figure 3 and Table 2 ). The sequences showed that all the ORFs, except ORF4b, had preceding TRS. Hence, the ORF4b may be translated from bicistronic mRNAs. In RsYN1, an additional subgenomic RNA starting inside the ORF3a was found through sequencing, which led to a unique ORF3b.
Phylogenetic trees were constructed using the aa sequences of RdRp and S of BtCoV/Rh/YN2012 and other representative CoVs (Figure 4) . In both trees, all BtCoV/Rh/YN2012 were clustered together and formed a distinct lineage to other known coronavirus species. Two distinct sublineages were observed within BtCoV/Rh/YN2012. One was from Ra sampled in Guangdong, while the other was from Rs sampled in Yunnan Among the strains from Yunnan, RsYN2 and RsYN3 were clustered together, while RsYN1 was isolated. The topology of these four strains was correlated to the sampling location. The relatively long branches reflect a high diversity among these strains, indicating a long independent evolution history.
Phylogenetic trees were constructed using the aa sequences of RdRp and S of BtCoV/Rh/YN2012 and other representative CoVs (Figure 4) . In both trees, all BtCoV/Rh/YN2012 were clustered together and formed a distinct lineage to other known coronavirus species. Two distinct sublineages were observed within BtCoV/Rh/YN2012. One was from Ra sampled in Guangdong, while the other was from Rs sampled in Yunnan Among the strains from Yunnan, RsYN2 and RsYN3 were clustered together, while RsYN1 was isolated. The topology of these four strains was correlated to the sampling location. The relatively long branches reflect a high diversity among these strains, indicating a long independent evolution history.
Phylogenetic trees were constructed using the aa sequences of RdRp and S of BtCoV/Rh/YN2012 and other representative CoVs (Figure 4) . In both trees, all BtCoV/Rh/YN2012 were clustered together and formed a distinct lineage to other known coronavirus species. Two distinct sublineages were observed within BtCoV/Rh/YN2012. One was from Ra sampled in Guangdong, while the other was from Rs sampled in Yunnan Among the strains from Yunnan, RsYN2 and RsYN3 were clustered together, while RsYN1 was isolated. The topology of these four strains was correlated to the sampling location. The relatively long branches reflect a high diversity among these strains, indicating a long independent evolution history.
The Ka/Ks ratios (Ks is the number of synonymous substitutions per synonymous sites and Ka is the number of nonsynonymous substitutions per nonsynonymous site) were calculated for all genes. The Ka/Ks ratios for most of the genes were generally low, which indicates these genes were under purified selection. However, the Ka/Ks ratios of ORF4a, ORF4b, and ORF9 (0.727, 0.623, and 0.843, respectively) were significantly higher than those of other ORFs (Table 4 ). For further selection pressure evaluation of the ORF4a and ORF4b gene, we sequenced another four ORF4a and ORF4b genes (strain Rs4223, Rs4236, Rs4240, and Ra13576 was shown in Figure 1B
As SARS-CoV ORF7a was reported to induce apoptosis, we conducted apoptosis analysis on BtCoV/Rh/YN2012 ORF9, a~30% aa identity homologue of SARSr-CoV ORF7a. We transiently transfected ORF9 of BtCoV/Rh/YN2012 into HEK293T cells to examine whether this ORF9 triggers apoptosis. Western blot was performed to confirm the expression of ORF9s and SARS-CoV ORF7a ( Figure S1 ). ORF9 couldn't induce apoptosis as the ORF7a of SARS-CoV Tor2 ( Figure S2 ). The results indicated that BtCoV/Rh/YN2012 ORF9 was not involved in apoptosis induction.
To determine whether these accessory proteins modulate IFN induction, we transfected reporter plasmids (pIFNβ-Luc and pRL-TK) and expression plasmids to 293T cells. All the cells over-expressing the accessory genes, as well as influenza virus NS1 (strain PR8), HBoV VP2, or empty vector were tested for luciferase activity after SeV infection. Luciferase activity stimulated by SeV was remarkably higher than that without SeV treatment as expected. Influenza virus NS1 inhibits the expression from IFN promoter, while HBoV VP2 activate the expression. Compared to those controls, the ORF4a proteins exhibit an active effect as HBoV VP2 ( Figure 5A ). Other accessory proteins showed no effect on IFN production ( Figure S3 ). Expression of these accessory genes were confirmed by Western blot ( Figure S1 ). was remarkably higher than that without SeV treatment as expected. Influenza virus NS1 inhibits the expression from IFN promoter, while HBoV VP2 activate the expression. Compared to those controls, the ORF4a proteins exhibit an active effect as HBoV VP2 ( Figure 5A ). Other accessory proteins showed no effect on IFN production ( Figure S3 ). Expression of these accessory genes were confirmed by Western blot (Figure S1 ). Samples were collected at 6 h postinfection, followed by dual-luciferase assay. The results were expressed as the firefly luciferase value normalized to that of Renilla luciferase. (B) ORF3a protein activate NF-κB. 293T cells were transfected with 100 ng pNF-κB-Luc, 10 ng pRL-TK, empty vector (500 ng), an NS1-expressing plasmid (500 ng), a SARS-CoV ORF7a-expressing plasmid (500 ng), or ORF3a-expressing plasmids (500 ng). After 24 h, the cells were treated with TNF-α. Dual-luciferase activity was determined after 6 h. The results were expressed as the firefly luciferase activity normalized to that of Renilla luciferase. The experiments were performed three times independently. Data are representative of at least three independent experiments, with each determination performed in triplicate (mean ± SD of fold change). Asterisks indicate significant differences between groups (compared with Empty vector-NC, p < 0.05, as determined by student t test).
NF-κB plays an important role in regulating the immune response to viral infection and is also a key factor frequently targeted by viruses for taking over the host cell. In this study, we tested if these accessory proteins could modulate NF-κB. 293T cells were co-transfected with reporter Samples were collected at 6 h postinfection, followed by dual-luciferase assay. The results were expressed as the firefly luciferase value normalized to that of Renilla luciferase. (B) ORF3a protein activate NF-κB. 293T cells were transfected with 100 ng pNF-κB-Luc, 10 ng pRL-TK, empty vector (500 ng), an NS1-expressing plasmid (500 ng), a SARS-CoV ORF7a-expressing plasmid (500 ng), or ORF3a-expressing plasmids (500 ng). After 24 h, the cells were treated with TNF-α. Dual-luciferase activity was determined after 6 h. The results were expressed as the firefly luciferase activity normalized to that of Renilla luciferase. The experiments were performed three times independently. Data are representative of at least three independent experiments, with each determination performed in triplicate (mean ± SD of fold change). Asterisks indicate significant differences between groups (compared with Empty vector-NC, p < 0.05, as determined by student t test).
NF-κB plays an important role in regulating the immune response to viral infection and is also a key factor frequently targeted by viruses for taking over the host cell. In this study, we tested if these accessory proteins could modulate NF-κB. 293T cells were co-transfected with reporter plasmids (pNF-κB-Luc and pRL-TK), as well as accessory protein-expressing plasmids, or controls (empty vector, NS1, SARS-CoV Tor2-ORF7a). The cells were mock treated or treated with TNF-α for 6 h at 24 h post-transfection. The luciferase activity was determined. RsYN1-ORF3a and RaGD-ORF3a activated NF-κB as SARS-CoV ORF7a, whereas RsYN2-ORF3a inhibited NF-κB as NS1 ( Figure 5B ). Expressions of ORF3as were confirmed with Western blot ( Figure S1 ). Other accessory proteins did not modulate NF-κB production ( Figure S4 ).
To understand the infectivity of these newly detected BtCoV/Rh/YN2012, we selected the RsYN1, RsYN3 and RaGD spike proteins for spike-mediated pseudovirus entry studies. Both Western blot analysis and negative-staining electron microscopy observation confirmed the preparation of BtCoV/Rh/YN2012 successfully ( Figure S5 ). A total of 11 human cell lines, 8 bat cells, and 9 other mammal cell lines were tested, and no strong positive was found (Table S2) .
In this study, a novel alpha-CoV species, BtCoV/Rh/YN2012, was identified in two Rhinolophus species. The 4 strains with full-length genome were sequences. The 7 conserved replicase domains of these viruses possessed <90% aa sequence identity to those of other known alpha-CoVs, which defines a new species in accordance with the ICTV taxonomy standard [42] . These novel alpha-CoVs showed high genetic diversity in their structural and non-structural genes. Strain RaGD from R. affinis, collected in Guangdong province, formed a divergent independent branch from the other 3 strains from R. sinicus, sampled in Yunnan Province, indicating an independent evolution process associated with geographic isolation and host restrain. Though collected from same province, these three virus strains formed two genotypes correlated to sampling locations. These two genotypes had low genome sequence identity, especially in the S gene and accessory genes. Considering the remote geographic location of the host bat habitat, the host tropism, and the virus diversity, we suppose BtCoV/Rh/YN2012 may have spread in these two provinces with a long history of circulation in their natural reservoir, Rhinolophus bats. With the sequence evidence, we suppose that these viruses are still rapidly evolving.
Our study revealed that BtCoV/Rh/YN2012 has a unique genome structure compared to other alpha-CoVs. First, novel accessory genes, which had no homologues, were identified in the genomes. Second, multiple TRSs were found between S and E genes while other alphacoronavirus only had one TRS there. These TRSs precede ORF3a, ORF3b (only in RsYN1), and ORF4a/b respectively. Third, accessory gene ORF9 showed homology with those of other known CoV species in another coronavirus genus, especially with accessory genes from SARSr-CoV.
Accessory genes are usually involved in virus-host interactions during CoV infection [43] . In most CoVs, accessory genes are dispensable for virus replication. However, an intact 3c gene of feline CoV was required for viral replication in the gut [44] [45] [46] . Deletion of the genus-specific genes in mouse hepatitis virus led to a reduction in virulence [47] . SARS-CoV ORF7a, which was identified to be involved in the suppression of RNA silencing [48] , inhibition of cellular protein synthesis [49] , cell-cycle blockage [50] , and apoptosis induction [51, 52] . In this study, we found that BtCoV/Rh/YN2012 ORF9 shares~30% aa sequence identity with SARS-CoV ORF7a. Interestingly, BtCoV/Rh/YN2012 and SARSr-CoV were both detected in R. sinicus from the same cave. We suppose that SARS-CoV and BtCoV/Rh/YN2012 may have acquired ORF7a or ORF9 from a common ancestor through genome recombination or horizontal gene transfer. Whereas, ORF9 of BtCoV/Rh/YN2012 failed to induce apoptosis or activate NF-κB production, these differences may be induced by the divergent evolution of these proteins in different pressure.
Though different BtCoV/Rh/YN2012 ORF4a share <64.4% amino acid identity, all of them could activate IFN-β. ORF3a from RsYN1 and RaGD upregulated NF-κB, but the homologue from RsYN2 downregulated NF-κB expression. These differences may be caused by amino acid sequence variations and may contribute to a viruses' pathogenicity with a different pathway.
Though lacking of intestinal cell lines from the natural host of BtCoV/Rh/YN2012, we screened the cell tropism of their spike protein through pseudotyped retrovirus entry with human, bat and other mammalian cell lines. Most of cell lines screened were unsusceptible to BtCoV/Rh/YN2012, indicating a low risk of interspecies transmission to human and other animals. Multiple reasons may lead to failed infection of coronavirus spike-pseudotyped retrovirus system, including receptor absence in target cells, failed recognition to the receptor homologue from non-host species, maladaptation in non-host cells during the spike maturation or virus entry, or the limitation of retrovirus system in stimulating coronavirus entry. The weak infectivity of RsYN1 pseudotyped retrovirus in Huh-7 cells could be explained by the binding of spike protein to polysaccharide secreted to the surface. The assumption needs to be further confirmed by experiments.
Our long-term surveillances suggest that Rhinolophus bats seem to harbor a wide diversity of CoVs. Coincidently, the two highly pathogenic agents, SARS-CoV and Rh-BatCoV HKU2 both originated from Rhinolophus bats. Considering the diversity of CoVs carried by this bat genus and their wide geographical distribution, there may be a low risk of spillover of these viruses to other animals and humans. Long-term surveillances and pathogenesis studies will help to prevent future human and animal diseases caused by these bat CoVs.
Supplementary Materials: The following are available online at http://www.mdpi.com/1999-4915/11/4/379/s1, Figure S1 : western blot analysis of the expression of accessory proteins. Figure S2 : Apoptosis analysis of ORF9 proteins of BtCoV/Rh/YN2012. Figure S3 : Functional analysis of ORF3a, ORF3b, ORF4b, ORF8 and ORF9 proteins on the production of Type I interferon. Figure S4 : Functional analysis of ORF3b, ORF4a, ORF4b, ORF8 and ORF9 proteins on the production of NF-κB. Figure S5 : Characteristic of BtCoV/Rh/YN2012 spike mediated pseudovirus. Table S1 : General primers for AlphaCoVs genome sequencing. Table S2 : Primers for the detection of viral sugbenomic mRNAs. Table S3
|
How many types of coronaviruses are known to cause human disease?
| 3,679 |
Six
| 2,275 |
1,576 |
Characterization of a New Member of Alphacoronavirus with Unique Genomic Features in Rhinolophus Bats
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6521148/
SHA: ee14de143337eec0e9708f8139bfac2b7b8fdd27
Authors: Wang, Ning; Luo, Chuming; Liu, Haizhou; Yang, Xinglou; Hu, Ben; Zhang, Wei; Li, Bei; Zhu, Yan; Zhu, Guangjian; Shen, Xurui; Peng, Cheng; Shi, Zhengli
Date: 2019-04-24
DOI: 10.3390/v11040379
License: cc-by
Abstract: Bats have been identified as a natural reservoir of a variety of coronaviruses (CoVs). Several of them have caused diseases in humans and domestic animals by interspecies transmission. Considering the diversity of bat coronaviruses, bat species and populations, we expect to discover more bat CoVs through virus surveillance. In this study, we described a new member of alphaCoV (BtCoV/Rh/YN2012) in bats with unique genome features. Unique accessory genes, ORF4a and ORF4b were found between the spike gene and the envelope gene, while ORF8 gene was found downstream of the nucleocapsid gene. All the putative genes were further confirmed by reverse-transcription analyses. One unique gene at the 3’ end of the BtCoV/Rh/YN2012 genome, ORF9, exhibits ~30% amino acid identity to ORF7a of the SARS-related coronavirus. Functional analysis showed ORF4a protein can activate IFN-β production, whereas ORF3a can regulate NF-κB production. We also screened the spike-mediated virus entry using the spike-pseudotyped retroviruses system, although failed to find any fully permissive cells. Our results expand the knowledge on the genetic diversity of bat coronaviruses. Continuous screening of bat viruses will help us further understand the important role played by bats in coronavirus evolution and transmission.
Text: Members of the Coronaviridae family are enveloped, non-segmented, positive-strand RNA viruses with genome sizes ranging from 26-32 kb [1] . These viruses are classified into two subfamilies: Letovirinae, which contains the only genus: Alphaletovirus; and Orthocoronavirinae (CoV), which consists of alpha, beta, gamma, and deltacoronaviruses (CoVs) [2, 3] . Alpha and betacoronaviruses mainly infect mammals and cause human and animal diseases. Gamma-and delta-CoVs mainly infect birds, but some can also infect mammals [4, 5] . Six human CoVs (HCoVs) are known to cause human diseases. HCoV-HKU1, HCoV-OC43, HCoV-229E, and HCoV-NL63 commonly cause mild respiratory illness or asymptomatic infection; however, severe acute respiratory syndrome coronavirus (SARS-CoV) and
All sampling procedures were performed by veterinarians, with approval from Animal Ethics Committee of the Wuhan Institute of Virology (WIVH5210201). The study was conducted in accordance with the Guide for the Care and Use of Wild Mammals in Research of the People's Republic of China.
Bat fecal swab and pellet samples were collected from November 2004 to November 2014 in different seasons in Southern China, as described previously [16] .
Viral RNA was extracted from 200 µL of fecal swab or pellet samples using the High Pure Viral RNA Kit (Roche Diagnostics GmbH, Mannheim, Germany) as per the manufacturer's instructions. RNA was eluted in 50 µL of elution buffer, aliquoted, and stored at -80 • C. One-step hemi-nested reverse-transcription (RT-) PCR (Invitrogen, San Diego, CA, USA) was employed to detect coronavirus, as previously described [17, 18] .
To confirm the bat species of an individual sample, we PCR amplified the cytochrome b (Cytob) and/or NADH dehydrogenase subunit 1 (ND1) gene using DNA extracted from the feces or swabs [19, 20] . The gene sequences were assembled excluding the primer sequences. BLASTN was used to identify host species based on the most closely related sequences with the highest query coverage and a minimum identity of 95%.
Full genomic sequences were determined by one-step PCR (Invitrogen, San Diego, CA, USA) amplification with degenerate primers (Table S1 ) designed on the basis of multiple alignments of available alpha-CoV sequences deposited in GenBank or amplified with SuperScript IV Reverse Transcriptase (Invitrogen) and Expand Long Template PCR System (Roche Diagnostics GmbH, Mannheim, Germany) with specific primers (primer sequences are available upon request). Sequences of the 5' and 3' genomic ends were obtained by 5' and 3' rapid amplification of cDNA ends (SMARTer Viruses 2019, 11, 379 3 of 19 RACE 5'/3' Kit; Clontech, Mountain View, CA, USA), respectively. PCR products were gel-purified and subjected directly to sequencing. PCR products over 5kb were subjected to deep sequencing using Hiseq2500 system. For some fragments, the PCR products were cloned into the pGEM-T Easy Vector (Promega, Madison, WI, USA) for sequencing. At least five independent clones were sequenced to obtain a consensus sequence.
The Next Generation Sequencing (NGS) data were filtered and mapped to the reference sequence of BatCoV HKU10 (GenBank accession number NC_018871) using Geneious 7.1.8 [21] . Genomes were preliminarily assembled using DNAStar lasergene V7 (DNAStar, Madison, WI, USA). Putative open reading frames (ORFs) were predicted using NCBI's ORF finder (https://www.ncbi.nlm.nih.gov/ orffinder/) with a minimal ORF length of 150 nt, followed by manual inspection. The sequences of the 5' untranslated region (5'-UTR) and 3'-UTR were defined, and the leader sequence, the leader and body transcriptional regulatory sequence (TRS) were identified as previously described [22] . The cleavage of the 16 nonstructural proteins coded by ORF1ab was determined by alignment of aa sequences of other CoVs and the recognition pattern of the 3C-like proteinase and papain-like proteinase. Phylogenetic trees based on nt or aa sequences were constructed using the maximum likelihood algorithm with bootstrap values determined by 1000 replicates in the MEGA 6 software package [23] . Full-length genome sequences obtained in this study were aligned with those of previously reported alpha-CoVs using MUSCLE [24] . The aligned sequences were scanned for recombination events by using Recombination Detection Program [25] . Potential recombination events as suggested by strong p-values (<10 -20 ) were confirmed using similarity plot and bootscan analyses implemented in Simplot 3.5.1 [26] . The number of synonymous substitutions per synonymous site, Ks, and the number of nonsynonymous substitutions per nonsynonymous site, Ka, for each coding region were calculated using the Ka/Ks calculation tool of the Norwegian Bioinformatics Platform (http://services.cbu.uib.no/tools/kaks) with default parameters [27] . The protein homology detection was analyzed using HHpred (https://toolkit.tuebingen.mpg.de/#/tools/hhpred) with default parameters [28] .
A set of nested RT-PCRs was employed to determine the presence of viral subgenomic mRNAs in the CoV-positive samples [29] . Forward primers were designed targeting the leader sequence at the 5'-end of the complete genome, while reverse primers were designed within the ORFs. Specific and suspected amplicons of expected sizes were purified and then cloned into the pGEM-T Easy vector for sequencing.
Bat primary or immortalized cells (Rhinolophus sinicus kidney immortalized cells, RsKT; Rhinolophus sinicus Lung primary cells, RsLu4323; Rhinolophus sinicus brain immortalized cells, RsBrT; Rhinolophus affinis kidney primary cells, RaK4324; Rousettus leschenaultii Kidney immortalized cells, RlKT; Hipposideros pratti lung immortalized cells, HpLuT) generated in our laboratory were all cultured in DMEM/F12 with 15% FBS. Pteropus alecto kidney cells (Paki) was maintained in DMEM/F12 supplemented with 10% FBS. Other cells were maintained according to the recommendations of American Type Culture Collection (ATCC, www.atcc.org).
The putative accessory genes of the newly detected virus were generated by RT-PCR from viral RNA extracted from fecal samples, as described previously [30] . The influenza virus NS1 plasmid was generated in our lab [31] . The human bocavirus (HBoV) VP2 plasmid was kindly provided by prof. Hanzhong Wang of the Wuhan Institute of Virology, Chinese Academy of Sciences. SARS-CoV ORF7a was synthesized by Sangon Biotech. The transfections were performed with Lipofectamine 3000 Reagent (Life Technologies). Expression of these accessory genes were analyzed by Western blotting using an mAb (Roche Diagnostics GmbH, Mannheim, Germany) against the HA tag.
The virus isolation was performed as previously described [12] . Briefly, fecal supernatant was acquired via gradient centrifugation and then added to Vero E6 cells, 1:10 diluted in DMEM. After incubation at 37°C for 1 h the inoculum was replaced by fresh DMEM containing 2% FBS and the antibiotic-antimycotic (Gibco, Grand Island, NY, USA). Three blind passages were carried out. Cells were checked daily for cytopathic effect. Both culture supernatant and cell pellet were examined for CoV by RT-PCR [17] .
Apoptosis was analyzed as previously described [18] . Briefly, 293T cells in 12-well plates were transfected with 3 µg of expression plasmid or empty vector, and the cells were collected 24 h post transfection. Apoptosis was detected by flow cytometry using by the Annexin V-FITC/PI Apoptosis Detection Kit (YEASEN, Shanghai, China) following the manufacturer's instructions. Annexin-V-positive and PI-negative cells were considered to be in the early apoptotic phase and those stained for both Annexin V and PI were deemed to undergo late apoptosis or necrosis. All experiments were repeated three times. Student's t-test was used to evaluate the data, with p < 0.05 considered significant.
HEK 293T cells were seeded in 24-well plates and then co-transfected with reporter plasmids (pRL-TK and pIFN-βIFN-or pNF-κB-Luc) [30] , as well as plasmids expressing accessory genes, empty vector plasmid pcAGGS, influenza virus NS1 [32] , SARS-CoV ORF7a [33] , or HBoV VP2 [34] . At 24 h post transfection, cells were treated with Sendai virus (SeV) (100 hemagglutinin units [HAU]/mL) or human tumor necrosis factor alpha (TNF-α; R&D system) for 6 h to activate IFNβ or NF-κB, respectively. Cell lysates were prepared, and luciferase activity was measured using the dual-luciferase assay kit (Promega, Madison, WI, USA) according to the manufacturer's instructions.
Retroviruses pseudotyped with BtCoV/Rh/YN2012 RsYN1, RsYN3, RaGD, or MERS-CoV spike, or no spike (mock) were used to infect human, bat or other mammalian cells in 96-well plates. The pseudovirus particles were confirmed with Western blotting and negative-staining electromicroscopy. The production process, measurements of infection and luciferase activity were conducted, as described previously [35, 36] .
The complete genome nucleotide sequences of BtCoV/Rh/YN2012 strains RsYN1, RsYN2, RsYN3, and RaGD obtained in this study have been submitted to the GenBank under MG916901 to MG916904.
The surveillance was performed between November 2004 to November 2014 in 19 provinces of China. In total, 2061 fecal samples were collected from at least 12 Rhinolophus bat species ( Figure 1A ). CoVs were detected in 209 of these samples ( Figure 1B and Table 1 ). Partial RdRp sequences suggested the presence of at least 8 different CoVs. Five of these viruses are related to known species: Mi-BatCoV 1 (>94% nt identity), Mi-BatCoV HKU8 [37] (>93% nt identity), BtRf-AlphaCoV/HuB2013 [11] (>99% nt identity), SARSr-CoV [38] (>89% nt identity), and HKU2-related CoV [39] (>85% nt identity). While the other three CoV sequences showed less than 83% nt identity to known CoV species. These three viruses should represent novel CoV species. Virus isolation was performed as previously described [12] , but was not successful. identity). While the other three CoV sequences showed less than 83% nt identity to known CoV species. These three viruses should represent novel CoV species. Virus isolation was performed as previously described [12] , but was not successful.
We next characterized a novel alpha-CoV, BtCoV/Rh/YN2012. It was detected in 3 R.affinis and 6 R.sinicus, respectively. Based on the sequences, we defined three genotypes, which represented by RsYN1, RsYN3, and RaGD, respectively. Strain RsYN2 was classified into the RsYN3 genotype. Four full-length genomes were obtained. Three of them were from R.sinicus (Strain RsYN1, RsYN2, and RsYN3), while the other one was from R.affinis (Strain RaGD). The sizes of these 4 genomes are between 28,715 to 29,102, with G+C contents between 39.0% to 41.3%. The genomes exhibit similar structures and transcription regulatory sequences (TRS) that are identical to those of other alpha-CoVs ( Figure 2 and Table 2 ). Exceptions including three additional ORFs (ORF3b, ORF4a and ORF4b) were observed. All the 4 strains have ORF4a & ORF4b, while only strain RsYN1 has ORF3b.
The replicase gene, ORF1ab, occupies~20.4 kb of the genome. The replicase gene, ORF1ab, occupies~20.4 kb of the genome. It encodes polyproteins 1a and 1ab, which could be cleaved into 16 non-structural proteins (Nsp1-Nsp16). The 3'-end of the cleavage sites recognized by 3C-like proteinase (Nsp4-Nsp10, Nsp12-Nsp16) and papain-like proteinase (Nsp1-Nsp3) were confirmed. The proteins including Nsp3 (papain-like 2 proteas, PL2pro), Nsp5 (chymotrypsin-like protease, 3CLpro), Nsp12 (RdRp), Nsp13 (helicase), and other proteins of unknown function ( Table 3 ). The 7 concatenated domains of polyprotein 1 shared <90% aa sequence identity with those of other known alpha-CoVs ( Table 2 ), suggesting that these viruses represent a novel CoV species within the alpha-CoV. The closest assigned CoV species to BtCoV/Rh/YN2012 are BtCoV-HKU10 and BtRf-AlphaCoV/Hub2013. The three strains from Yunnan Province were clustered into two genotypes (83% genome identity) correlated to their sampling location. The third genotype represented by strain RaGD was isolated to strains found in Yunnan (<75.4% genome identity). We then examined the individual genes ( Table 2) . All of the genes showed low aa sequence identity to known CoVs. The four strains of BtCoV/Rh/YN2012 showed genetic diversity among all different genes except ORF1ab (>83.7% aa identity). Notably, the spike proteins are highly divergent among these strains. Other structure proteins (E, M, and N) are more conserved than the spike and other accessory proteins. Comparing the accessory genes among these four strains revealed that the strains of the same genotype shared a 100% identical ORF3a. However, the proteins encoded by ORF3as were highly divergent among different genotypes (<65% aa identity). The putative accessory genes were also BLASTed against GenBank records. Most accessory genes have no homologues in GenBank-database, except for ORF3a (52.0-55.5% aa identity with BatCoV HKU10 ORF3) and ORF9 (28.1-32.0% aa identity with SARSr-CoV ORF7a). We analyzed the protein homology with HHpred software. The results showed that ORF9s and SARS-CoV OR7a are homologues (possibility: 100%, E value <10 −48 ). We further screened the genomes for potential recombination evidence. No significant recombination breakpoint was detected by bootscan analysis.
To confirm the presence of subgenomic RNA, we designed a set of primers targeting all the predicted ORFs as described. The amplicons were firstly confirmed via agarose-gel electrophoresis and then sequencing ( Figure 3 and Table 2 ). The sequences showed that all the ORFs, except ORF4b, had preceding TRS. Hence, the ORF4b may be translated from bicistronic mRNAs. In RsYN1, an additional subgenomic RNA starting inside the ORF3a was found through sequencing, which led to a unique ORF3b.
To confirm the presence of subgenomic RNA, we designed a set of primers targeting all the predicted ORFs as described. The amplicons were firstly confirmed via agarose-gel electrophoresis and then sequencing ( Figure 3 and Table 2 ). The sequences showed that all the ORFs, except ORF4b, had preceding TRS. Hence, the ORF4b may be translated from bicistronic mRNAs. In RsYN1, an additional subgenomic RNA starting inside the ORF3a was found through sequencing, which led to a unique ORF3b.
Phylogenetic trees were constructed using the aa sequences of RdRp and S of BtCoV/Rh/YN2012 and other representative CoVs (Figure 4) . In both trees, all BtCoV/Rh/YN2012 were clustered together and formed a distinct lineage to other known coronavirus species. Two distinct sublineages were observed within BtCoV/Rh/YN2012. One was from Ra sampled in Guangdong, while the other was from Rs sampled in Yunnan Among the strains from Yunnan, RsYN2 and RsYN3 were clustered together, while RsYN1 was isolated. The topology of these four strains was correlated to the sampling location. The relatively long branches reflect a high diversity among these strains, indicating a long independent evolution history.
Phylogenetic trees were constructed using the aa sequences of RdRp and S of BtCoV/Rh/YN2012 and other representative CoVs (Figure 4) . In both trees, all BtCoV/Rh/YN2012 were clustered together and formed a distinct lineage to other known coronavirus species. Two distinct sublineages were observed within BtCoV/Rh/YN2012. One was from Ra sampled in Guangdong, while the other was from Rs sampled in Yunnan Among the strains from Yunnan, RsYN2 and RsYN3 were clustered together, while RsYN1 was isolated. The topology of these four strains was correlated to the sampling location. The relatively long branches reflect a high diversity among these strains, indicating a long independent evolution history.
Phylogenetic trees were constructed using the aa sequences of RdRp and S of BtCoV/Rh/YN2012 and other representative CoVs (Figure 4) . In both trees, all BtCoV/Rh/YN2012 were clustered together and formed a distinct lineage to other known coronavirus species. Two distinct sublineages were observed within BtCoV/Rh/YN2012. One was from Ra sampled in Guangdong, while the other was from Rs sampled in Yunnan Among the strains from Yunnan, RsYN2 and RsYN3 were clustered together, while RsYN1 was isolated. The topology of these four strains was correlated to the sampling location. The relatively long branches reflect a high diversity among these strains, indicating a long independent evolution history.
The Ka/Ks ratios (Ks is the number of synonymous substitutions per synonymous sites and Ka is the number of nonsynonymous substitutions per nonsynonymous site) were calculated for all genes. The Ka/Ks ratios for most of the genes were generally low, which indicates these genes were under purified selection. However, the Ka/Ks ratios of ORF4a, ORF4b, and ORF9 (0.727, 0.623, and 0.843, respectively) were significantly higher than those of other ORFs (Table 4 ). For further selection pressure evaluation of the ORF4a and ORF4b gene, we sequenced another four ORF4a and ORF4b genes (strain Rs4223, Rs4236, Rs4240, and Ra13576 was shown in Figure 1B
As SARS-CoV ORF7a was reported to induce apoptosis, we conducted apoptosis analysis on BtCoV/Rh/YN2012 ORF9, a~30% aa identity homologue of SARSr-CoV ORF7a. We transiently transfected ORF9 of BtCoV/Rh/YN2012 into HEK293T cells to examine whether this ORF9 triggers apoptosis. Western blot was performed to confirm the expression of ORF9s and SARS-CoV ORF7a ( Figure S1 ). ORF9 couldn't induce apoptosis as the ORF7a of SARS-CoV Tor2 ( Figure S2 ). The results indicated that BtCoV/Rh/YN2012 ORF9 was not involved in apoptosis induction.
To determine whether these accessory proteins modulate IFN induction, we transfected reporter plasmids (pIFNβ-Luc and pRL-TK) and expression plasmids to 293T cells. All the cells over-expressing the accessory genes, as well as influenza virus NS1 (strain PR8), HBoV VP2, or empty vector were tested for luciferase activity after SeV infection. Luciferase activity stimulated by SeV was remarkably higher than that without SeV treatment as expected. Influenza virus NS1 inhibits the expression from IFN promoter, while HBoV VP2 activate the expression. Compared to those controls, the ORF4a proteins exhibit an active effect as HBoV VP2 ( Figure 5A ). Other accessory proteins showed no effect on IFN production ( Figure S3 ). Expression of these accessory genes were confirmed by Western blot ( Figure S1 ). was remarkably higher than that without SeV treatment as expected. Influenza virus NS1 inhibits the expression from IFN promoter, while HBoV VP2 activate the expression. Compared to those controls, the ORF4a proteins exhibit an active effect as HBoV VP2 ( Figure 5A ). Other accessory proteins showed no effect on IFN production ( Figure S3 ). Expression of these accessory genes were confirmed by Western blot (Figure S1 ). Samples were collected at 6 h postinfection, followed by dual-luciferase assay. The results were expressed as the firefly luciferase value normalized to that of Renilla luciferase. (B) ORF3a protein activate NF-κB. 293T cells were transfected with 100 ng pNF-κB-Luc, 10 ng pRL-TK, empty vector (500 ng), an NS1-expressing plasmid (500 ng), a SARS-CoV ORF7a-expressing plasmid (500 ng), or ORF3a-expressing plasmids (500 ng). After 24 h, the cells were treated with TNF-α. Dual-luciferase activity was determined after 6 h. The results were expressed as the firefly luciferase activity normalized to that of Renilla luciferase. The experiments were performed three times independently. Data are representative of at least three independent experiments, with each determination performed in triplicate (mean ± SD of fold change). Asterisks indicate significant differences between groups (compared with Empty vector-NC, p < 0.05, as determined by student t test).
NF-κB plays an important role in regulating the immune response to viral infection and is also a key factor frequently targeted by viruses for taking over the host cell. In this study, we tested if these accessory proteins could modulate NF-κB. 293T cells were co-transfected with reporter Samples were collected at 6 h postinfection, followed by dual-luciferase assay. The results were expressed as the firefly luciferase value normalized to that of Renilla luciferase. (B) ORF3a protein activate NF-κB. 293T cells were transfected with 100 ng pNF-κB-Luc, 10 ng pRL-TK, empty vector (500 ng), an NS1-expressing plasmid (500 ng), a SARS-CoV ORF7a-expressing plasmid (500 ng), or ORF3a-expressing plasmids (500 ng). After 24 h, the cells were treated with TNF-α. Dual-luciferase activity was determined after 6 h. The results were expressed as the firefly luciferase activity normalized to that of Renilla luciferase. The experiments were performed three times independently. Data are representative of at least three independent experiments, with each determination performed in triplicate (mean ± SD of fold change). Asterisks indicate significant differences between groups (compared with Empty vector-NC, p < 0.05, as determined by student t test).
NF-κB plays an important role in regulating the immune response to viral infection and is also a key factor frequently targeted by viruses for taking over the host cell. In this study, we tested if these accessory proteins could modulate NF-κB. 293T cells were co-transfected with reporter plasmids (pNF-κB-Luc and pRL-TK), as well as accessory protein-expressing plasmids, or controls (empty vector, NS1, SARS-CoV Tor2-ORF7a). The cells were mock treated or treated with TNF-α for 6 h at 24 h post-transfection. The luciferase activity was determined. RsYN1-ORF3a and RaGD-ORF3a activated NF-κB as SARS-CoV ORF7a, whereas RsYN2-ORF3a inhibited NF-κB as NS1 ( Figure 5B ). Expressions of ORF3as were confirmed with Western blot ( Figure S1 ). Other accessory proteins did not modulate NF-κB production ( Figure S4 ).
To understand the infectivity of these newly detected BtCoV/Rh/YN2012, we selected the RsYN1, RsYN3 and RaGD spike proteins for spike-mediated pseudovirus entry studies. Both Western blot analysis and negative-staining electron microscopy observation confirmed the preparation of BtCoV/Rh/YN2012 successfully ( Figure S5 ). A total of 11 human cell lines, 8 bat cells, and 9 other mammal cell lines were tested, and no strong positive was found (Table S2) .
In this study, a novel alpha-CoV species, BtCoV/Rh/YN2012, was identified in two Rhinolophus species. The 4 strains with full-length genome were sequences. The 7 conserved replicase domains of these viruses possessed <90% aa sequence identity to those of other known alpha-CoVs, which defines a new species in accordance with the ICTV taxonomy standard [42] . These novel alpha-CoVs showed high genetic diversity in their structural and non-structural genes. Strain RaGD from R. affinis, collected in Guangdong province, formed a divergent independent branch from the other 3 strains from R. sinicus, sampled in Yunnan Province, indicating an independent evolution process associated with geographic isolation and host restrain. Though collected from same province, these three virus strains formed two genotypes correlated to sampling locations. These two genotypes had low genome sequence identity, especially in the S gene and accessory genes. Considering the remote geographic location of the host bat habitat, the host tropism, and the virus diversity, we suppose BtCoV/Rh/YN2012 may have spread in these two provinces with a long history of circulation in their natural reservoir, Rhinolophus bats. With the sequence evidence, we suppose that these viruses are still rapidly evolving.
Our study revealed that BtCoV/Rh/YN2012 has a unique genome structure compared to other alpha-CoVs. First, novel accessory genes, which had no homologues, were identified in the genomes. Second, multiple TRSs were found between S and E genes while other alphacoronavirus only had one TRS there. These TRSs precede ORF3a, ORF3b (only in RsYN1), and ORF4a/b respectively. Third, accessory gene ORF9 showed homology with those of other known CoV species in another coronavirus genus, especially with accessory genes from SARSr-CoV.
Accessory genes are usually involved in virus-host interactions during CoV infection [43] . In most CoVs, accessory genes are dispensable for virus replication. However, an intact 3c gene of feline CoV was required for viral replication in the gut [44] [45] [46] . Deletion of the genus-specific genes in mouse hepatitis virus led to a reduction in virulence [47] . SARS-CoV ORF7a, which was identified to be involved in the suppression of RNA silencing [48] , inhibition of cellular protein synthesis [49] , cell-cycle blockage [50] , and apoptosis induction [51, 52] . In this study, we found that BtCoV/Rh/YN2012 ORF9 shares~30% aa sequence identity with SARS-CoV ORF7a. Interestingly, BtCoV/Rh/YN2012 and SARSr-CoV were both detected in R. sinicus from the same cave. We suppose that SARS-CoV and BtCoV/Rh/YN2012 may have acquired ORF7a or ORF9 from a common ancestor through genome recombination or horizontal gene transfer. Whereas, ORF9 of BtCoV/Rh/YN2012 failed to induce apoptosis or activate NF-κB production, these differences may be induced by the divergent evolution of these proteins in different pressure.
Though different BtCoV/Rh/YN2012 ORF4a share <64.4% amino acid identity, all of them could activate IFN-β. ORF3a from RsYN1 and RaGD upregulated NF-κB, but the homologue from RsYN2 downregulated NF-κB expression. These differences may be caused by amino acid sequence variations and may contribute to a viruses' pathogenicity with a different pathway.
Though lacking of intestinal cell lines from the natural host of BtCoV/Rh/YN2012, we screened the cell tropism of their spike protein through pseudotyped retrovirus entry with human, bat and other mammalian cell lines. Most of cell lines screened were unsusceptible to BtCoV/Rh/YN2012, indicating a low risk of interspecies transmission to human and other animals. Multiple reasons may lead to failed infection of coronavirus spike-pseudotyped retrovirus system, including receptor absence in target cells, failed recognition to the receptor homologue from non-host species, maladaptation in non-host cells during the spike maturation or virus entry, or the limitation of retrovirus system in stimulating coronavirus entry. The weak infectivity of RsYN1 pseudotyped retrovirus in Huh-7 cells could be explained by the binding of spike protein to polysaccharide secreted to the surface. The assumption needs to be further confirmed by experiments.
Our long-term surveillances suggest that Rhinolophus bats seem to harbor a wide diversity of CoVs. Coincidently, the two highly pathogenic agents, SARS-CoV and Rh-BatCoV HKU2 both originated from Rhinolophus bats. Considering the diversity of CoVs carried by this bat genus and their wide geographical distribution, there may be a low risk of spillover of these viruses to other animals and humans. Long-term surveillances and pathogenesis studies will help to prevent future human and animal diseases caused by these bat CoVs.
Supplementary Materials: The following are available online at http://www.mdpi.com/1999-4915/11/4/379/s1, Figure S1 : western blot analysis of the expression of accessory proteins. Figure S2 : Apoptosis analysis of ORF9 proteins of BtCoV/Rh/YN2012. Figure S3 : Functional analysis of ORF3a, ORF3b, ORF4b, ORF8 and ORF9 proteins on the production of Type I interferon. Figure S4 : Functional analysis of ORF3b, ORF4a, ORF4b, ORF8 and ORF9 proteins on the production of NF-κB. Figure S5 : Characteristic of BtCoV/Rh/YN2012 spike mediated pseudovirus. Table S1 : General primers for AlphaCoVs genome sequencing. Table S2 : Primers for the detection of viral sugbenomic mRNAs. Table S3
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What plays a role in regulating the immune response to a viral infection?
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NF-κB
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1,576 |
Characterization of a New Member of Alphacoronavirus with Unique Genomic Features in Rhinolophus Bats
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6521148/
SHA: ee14de143337eec0e9708f8139bfac2b7b8fdd27
Authors: Wang, Ning; Luo, Chuming; Liu, Haizhou; Yang, Xinglou; Hu, Ben; Zhang, Wei; Li, Bei; Zhu, Yan; Zhu, Guangjian; Shen, Xurui; Peng, Cheng; Shi, Zhengli
Date: 2019-04-24
DOI: 10.3390/v11040379
License: cc-by
Abstract: Bats have been identified as a natural reservoir of a variety of coronaviruses (CoVs). Several of them have caused diseases in humans and domestic animals by interspecies transmission. Considering the diversity of bat coronaviruses, bat species and populations, we expect to discover more bat CoVs through virus surveillance. In this study, we described a new member of alphaCoV (BtCoV/Rh/YN2012) in bats with unique genome features. Unique accessory genes, ORF4a and ORF4b were found between the spike gene and the envelope gene, while ORF8 gene was found downstream of the nucleocapsid gene. All the putative genes were further confirmed by reverse-transcription analyses. One unique gene at the 3’ end of the BtCoV/Rh/YN2012 genome, ORF9, exhibits ~30% amino acid identity to ORF7a of the SARS-related coronavirus. Functional analysis showed ORF4a protein can activate IFN-β production, whereas ORF3a can regulate NF-κB production. We also screened the spike-mediated virus entry using the spike-pseudotyped retroviruses system, although failed to find any fully permissive cells. Our results expand the knowledge on the genetic diversity of bat coronaviruses. Continuous screening of bat viruses will help us further understand the important role played by bats in coronavirus evolution and transmission.
Text: Members of the Coronaviridae family are enveloped, non-segmented, positive-strand RNA viruses with genome sizes ranging from 26-32 kb [1] . These viruses are classified into two subfamilies: Letovirinae, which contains the only genus: Alphaletovirus; and Orthocoronavirinae (CoV), which consists of alpha, beta, gamma, and deltacoronaviruses (CoVs) [2, 3] . Alpha and betacoronaviruses mainly infect mammals and cause human and animal diseases. Gamma-and delta-CoVs mainly infect birds, but some can also infect mammals [4, 5] . Six human CoVs (HCoVs) are known to cause human diseases. HCoV-HKU1, HCoV-OC43, HCoV-229E, and HCoV-NL63 commonly cause mild respiratory illness or asymptomatic infection; however, severe acute respiratory syndrome coronavirus (SARS-CoV) and
All sampling procedures were performed by veterinarians, with approval from Animal Ethics Committee of the Wuhan Institute of Virology (WIVH5210201). The study was conducted in accordance with the Guide for the Care and Use of Wild Mammals in Research of the People's Republic of China.
Bat fecal swab and pellet samples were collected from November 2004 to November 2014 in different seasons in Southern China, as described previously [16] .
Viral RNA was extracted from 200 µL of fecal swab or pellet samples using the High Pure Viral RNA Kit (Roche Diagnostics GmbH, Mannheim, Germany) as per the manufacturer's instructions. RNA was eluted in 50 µL of elution buffer, aliquoted, and stored at -80 • C. One-step hemi-nested reverse-transcription (RT-) PCR (Invitrogen, San Diego, CA, USA) was employed to detect coronavirus, as previously described [17, 18] .
To confirm the bat species of an individual sample, we PCR amplified the cytochrome b (Cytob) and/or NADH dehydrogenase subunit 1 (ND1) gene using DNA extracted from the feces or swabs [19, 20] . The gene sequences were assembled excluding the primer sequences. BLASTN was used to identify host species based on the most closely related sequences with the highest query coverage and a minimum identity of 95%.
Full genomic sequences were determined by one-step PCR (Invitrogen, San Diego, CA, USA) amplification with degenerate primers (Table S1 ) designed on the basis of multiple alignments of available alpha-CoV sequences deposited in GenBank or amplified with SuperScript IV Reverse Transcriptase (Invitrogen) and Expand Long Template PCR System (Roche Diagnostics GmbH, Mannheim, Germany) with specific primers (primer sequences are available upon request). Sequences of the 5' and 3' genomic ends were obtained by 5' and 3' rapid amplification of cDNA ends (SMARTer Viruses 2019, 11, 379 3 of 19 RACE 5'/3' Kit; Clontech, Mountain View, CA, USA), respectively. PCR products were gel-purified and subjected directly to sequencing. PCR products over 5kb were subjected to deep sequencing using Hiseq2500 system. For some fragments, the PCR products were cloned into the pGEM-T Easy Vector (Promega, Madison, WI, USA) for sequencing. At least five independent clones were sequenced to obtain a consensus sequence.
The Next Generation Sequencing (NGS) data were filtered and mapped to the reference sequence of BatCoV HKU10 (GenBank accession number NC_018871) using Geneious 7.1.8 [21] . Genomes were preliminarily assembled using DNAStar lasergene V7 (DNAStar, Madison, WI, USA). Putative open reading frames (ORFs) were predicted using NCBI's ORF finder (https://www.ncbi.nlm.nih.gov/ orffinder/) with a minimal ORF length of 150 nt, followed by manual inspection. The sequences of the 5' untranslated region (5'-UTR) and 3'-UTR were defined, and the leader sequence, the leader and body transcriptional regulatory sequence (TRS) were identified as previously described [22] . The cleavage of the 16 nonstructural proteins coded by ORF1ab was determined by alignment of aa sequences of other CoVs and the recognition pattern of the 3C-like proteinase and papain-like proteinase. Phylogenetic trees based on nt or aa sequences were constructed using the maximum likelihood algorithm with bootstrap values determined by 1000 replicates in the MEGA 6 software package [23] . Full-length genome sequences obtained in this study were aligned with those of previously reported alpha-CoVs using MUSCLE [24] . The aligned sequences were scanned for recombination events by using Recombination Detection Program [25] . Potential recombination events as suggested by strong p-values (<10 -20 ) were confirmed using similarity plot and bootscan analyses implemented in Simplot 3.5.1 [26] . The number of synonymous substitutions per synonymous site, Ks, and the number of nonsynonymous substitutions per nonsynonymous site, Ka, for each coding region were calculated using the Ka/Ks calculation tool of the Norwegian Bioinformatics Platform (http://services.cbu.uib.no/tools/kaks) with default parameters [27] . The protein homology detection was analyzed using HHpred (https://toolkit.tuebingen.mpg.de/#/tools/hhpred) with default parameters [28] .
A set of nested RT-PCRs was employed to determine the presence of viral subgenomic mRNAs in the CoV-positive samples [29] . Forward primers were designed targeting the leader sequence at the 5'-end of the complete genome, while reverse primers were designed within the ORFs. Specific and suspected amplicons of expected sizes were purified and then cloned into the pGEM-T Easy vector for sequencing.
Bat primary or immortalized cells (Rhinolophus sinicus kidney immortalized cells, RsKT; Rhinolophus sinicus Lung primary cells, RsLu4323; Rhinolophus sinicus brain immortalized cells, RsBrT; Rhinolophus affinis kidney primary cells, RaK4324; Rousettus leschenaultii Kidney immortalized cells, RlKT; Hipposideros pratti lung immortalized cells, HpLuT) generated in our laboratory were all cultured in DMEM/F12 with 15% FBS. Pteropus alecto kidney cells (Paki) was maintained in DMEM/F12 supplemented with 10% FBS. Other cells were maintained according to the recommendations of American Type Culture Collection (ATCC, www.atcc.org).
The putative accessory genes of the newly detected virus were generated by RT-PCR from viral RNA extracted from fecal samples, as described previously [30] . The influenza virus NS1 plasmid was generated in our lab [31] . The human bocavirus (HBoV) VP2 plasmid was kindly provided by prof. Hanzhong Wang of the Wuhan Institute of Virology, Chinese Academy of Sciences. SARS-CoV ORF7a was synthesized by Sangon Biotech. The transfections were performed with Lipofectamine 3000 Reagent (Life Technologies). Expression of these accessory genes were analyzed by Western blotting using an mAb (Roche Diagnostics GmbH, Mannheim, Germany) against the HA tag.
The virus isolation was performed as previously described [12] . Briefly, fecal supernatant was acquired via gradient centrifugation and then added to Vero E6 cells, 1:10 diluted in DMEM. After incubation at 37°C for 1 h the inoculum was replaced by fresh DMEM containing 2% FBS and the antibiotic-antimycotic (Gibco, Grand Island, NY, USA). Three blind passages were carried out. Cells were checked daily for cytopathic effect. Both culture supernatant and cell pellet were examined for CoV by RT-PCR [17] .
Apoptosis was analyzed as previously described [18] . Briefly, 293T cells in 12-well plates were transfected with 3 µg of expression plasmid or empty vector, and the cells were collected 24 h post transfection. Apoptosis was detected by flow cytometry using by the Annexin V-FITC/PI Apoptosis Detection Kit (YEASEN, Shanghai, China) following the manufacturer's instructions. Annexin-V-positive and PI-negative cells were considered to be in the early apoptotic phase and those stained for both Annexin V and PI were deemed to undergo late apoptosis or necrosis. All experiments were repeated three times. Student's t-test was used to evaluate the data, with p < 0.05 considered significant.
HEK 293T cells were seeded in 24-well plates and then co-transfected with reporter plasmids (pRL-TK and pIFN-βIFN-or pNF-κB-Luc) [30] , as well as plasmids expressing accessory genes, empty vector plasmid pcAGGS, influenza virus NS1 [32] , SARS-CoV ORF7a [33] , or HBoV VP2 [34] . At 24 h post transfection, cells were treated with Sendai virus (SeV) (100 hemagglutinin units [HAU]/mL) or human tumor necrosis factor alpha (TNF-α; R&D system) for 6 h to activate IFNβ or NF-κB, respectively. Cell lysates were prepared, and luciferase activity was measured using the dual-luciferase assay kit (Promega, Madison, WI, USA) according to the manufacturer's instructions.
Retroviruses pseudotyped with BtCoV/Rh/YN2012 RsYN1, RsYN3, RaGD, or MERS-CoV spike, or no spike (mock) were used to infect human, bat or other mammalian cells in 96-well plates. The pseudovirus particles were confirmed with Western blotting and negative-staining electromicroscopy. The production process, measurements of infection and luciferase activity were conducted, as described previously [35, 36] .
The complete genome nucleotide sequences of BtCoV/Rh/YN2012 strains RsYN1, RsYN2, RsYN3, and RaGD obtained in this study have been submitted to the GenBank under MG916901 to MG916904.
The surveillance was performed between November 2004 to November 2014 in 19 provinces of China. In total, 2061 fecal samples were collected from at least 12 Rhinolophus bat species ( Figure 1A ). CoVs were detected in 209 of these samples ( Figure 1B and Table 1 ). Partial RdRp sequences suggested the presence of at least 8 different CoVs. Five of these viruses are related to known species: Mi-BatCoV 1 (>94% nt identity), Mi-BatCoV HKU8 [37] (>93% nt identity), BtRf-AlphaCoV/HuB2013 [11] (>99% nt identity), SARSr-CoV [38] (>89% nt identity), and HKU2-related CoV [39] (>85% nt identity). While the other three CoV sequences showed less than 83% nt identity to known CoV species. These three viruses should represent novel CoV species. Virus isolation was performed as previously described [12] , but was not successful. identity). While the other three CoV sequences showed less than 83% nt identity to known CoV species. These three viruses should represent novel CoV species. Virus isolation was performed as previously described [12] , but was not successful.
We next characterized a novel alpha-CoV, BtCoV/Rh/YN2012. It was detected in 3 R.affinis and 6 R.sinicus, respectively. Based on the sequences, we defined three genotypes, which represented by RsYN1, RsYN3, and RaGD, respectively. Strain RsYN2 was classified into the RsYN3 genotype. Four full-length genomes were obtained. Three of them were from R.sinicus (Strain RsYN1, RsYN2, and RsYN3), while the other one was from R.affinis (Strain RaGD). The sizes of these 4 genomes are between 28,715 to 29,102, with G+C contents between 39.0% to 41.3%. The genomes exhibit similar structures and transcription regulatory sequences (TRS) that are identical to those of other alpha-CoVs ( Figure 2 and Table 2 ). Exceptions including three additional ORFs (ORF3b, ORF4a and ORF4b) were observed. All the 4 strains have ORF4a & ORF4b, while only strain RsYN1 has ORF3b.
The replicase gene, ORF1ab, occupies~20.4 kb of the genome. The replicase gene, ORF1ab, occupies~20.4 kb of the genome. It encodes polyproteins 1a and 1ab, which could be cleaved into 16 non-structural proteins (Nsp1-Nsp16). The 3'-end of the cleavage sites recognized by 3C-like proteinase (Nsp4-Nsp10, Nsp12-Nsp16) and papain-like proteinase (Nsp1-Nsp3) were confirmed. The proteins including Nsp3 (papain-like 2 proteas, PL2pro), Nsp5 (chymotrypsin-like protease, 3CLpro), Nsp12 (RdRp), Nsp13 (helicase), and other proteins of unknown function ( Table 3 ). The 7 concatenated domains of polyprotein 1 shared <90% aa sequence identity with those of other known alpha-CoVs ( Table 2 ), suggesting that these viruses represent a novel CoV species within the alpha-CoV. The closest assigned CoV species to BtCoV/Rh/YN2012 are BtCoV-HKU10 and BtRf-AlphaCoV/Hub2013. The three strains from Yunnan Province were clustered into two genotypes (83% genome identity) correlated to their sampling location. The third genotype represented by strain RaGD was isolated to strains found in Yunnan (<75.4% genome identity). We then examined the individual genes ( Table 2) . All of the genes showed low aa sequence identity to known CoVs. The four strains of BtCoV/Rh/YN2012 showed genetic diversity among all different genes except ORF1ab (>83.7% aa identity). Notably, the spike proteins are highly divergent among these strains. Other structure proteins (E, M, and N) are more conserved than the spike and other accessory proteins. Comparing the accessory genes among these four strains revealed that the strains of the same genotype shared a 100% identical ORF3a. However, the proteins encoded by ORF3as were highly divergent among different genotypes (<65% aa identity). The putative accessory genes were also BLASTed against GenBank records. Most accessory genes have no homologues in GenBank-database, except for ORF3a (52.0-55.5% aa identity with BatCoV HKU10 ORF3) and ORF9 (28.1-32.0% aa identity with SARSr-CoV ORF7a). We analyzed the protein homology with HHpred software. The results showed that ORF9s and SARS-CoV OR7a are homologues (possibility: 100%, E value <10 −48 ). We further screened the genomes for potential recombination evidence. No significant recombination breakpoint was detected by bootscan analysis.
To confirm the presence of subgenomic RNA, we designed a set of primers targeting all the predicted ORFs as described. The amplicons were firstly confirmed via agarose-gel electrophoresis and then sequencing ( Figure 3 and Table 2 ). The sequences showed that all the ORFs, except ORF4b, had preceding TRS. Hence, the ORF4b may be translated from bicistronic mRNAs. In RsYN1, an additional subgenomic RNA starting inside the ORF3a was found through sequencing, which led to a unique ORF3b.
To confirm the presence of subgenomic RNA, we designed a set of primers targeting all the predicted ORFs as described. The amplicons were firstly confirmed via agarose-gel electrophoresis and then sequencing ( Figure 3 and Table 2 ). The sequences showed that all the ORFs, except ORF4b, had preceding TRS. Hence, the ORF4b may be translated from bicistronic mRNAs. In RsYN1, an additional subgenomic RNA starting inside the ORF3a was found through sequencing, which led to a unique ORF3b.
Phylogenetic trees were constructed using the aa sequences of RdRp and S of BtCoV/Rh/YN2012 and other representative CoVs (Figure 4) . In both trees, all BtCoV/Rh/YN2012 were clustered together and formed a distinct lineage to other known coronavirus species. Two distinct sublineages were observed within BtCoV/Rh/YN2012. One was from Ra sampled in Guangdong, while the other was from Rs sampled in Yunnan Among the strains from Yunnan, RsYN2 and RsYN3 were clustered together, while RsYN1 was isolated. The topology of these four strains was correlated to the sampling location. The relatively long branches reflect a high diversity among these strains, indicating a long independent evolution history.
Phylogenetic trees were constructed using the aa sequences of RdRp and S of BtCoV/Rh/YN2012 and other representative CoVs (Figure 4) . In both trees, all BtCoV/Rh/YN2012 were clustered together and formed a distinct lineage to other known coronavirus species. Two distinct sublineages were observed within BtCoV/Rh/YN2012. One was from Ra sampled in Guangdong, while the other was from Rs sampled in Yunnan Among the strains from Yunnan, RsYN2 and RsYN3 were clustered together, while RsYN1 was isolated. The topology of these four strains was correlated to the sampling location. The relatively long branches reflect a high diversity among these strains, indicating a long independent evolution history.
Phylogenetic trees were constructed using the aa sequences of RdRp and S of BtCoV/Rh/YN2012 and other representative CoVs (Figure 4) . In both trees, all BtCoV/Rh/YN2012 were clustered together and formed a distinct lineage to other known coronavirus species. Two distinct sublineages were observed within BtCoV/Rh/YN2012. One was from Ra sampled in Guangdong, while the other was from Rs sampled in Yunnan Among the strains from Yunnan, RsYN2 and RsYN3 were clustered together, while RsYN1 was isolated. The topology of these four strains was correlated to the sampling location. The relatively long branches reflect a high diversity among these strains, indicating a long independent evolution history.
The Ka/Ks ratios (Ks is the number of synonymous substitutions per synonymous sites and Ka is the number of nonsynonymous substitutions per nonsynonymous site) were calculated for all genes. The Ka/Ks ratios for most of the genes were generally low, which indicates these genes were under purified selection. However, the Ka/Ks ratios of ORF4a, ORF4b, and ORF9 (0.727, 0.623, and 0.843, respectively) were significantly higher than those of other ORFs (Table 4 ). For further selection pressure evaluation of the ORF4a and ORF4b gene, we sequenced another four ORF4a and ORF4b genes (strain Rs4223, Rs4236, Rs4240, and Ra13576 was shown in Figure 1B
As SARS-CoV ORF7a was reported to induce apoptosis, we conducted apoptosis analysis on BtCoV/Rh/YN2012 ORF9, a~30% aa identity homologue of SARSr-CoV ORF7a. We transiently transfected ORF9 of BtCoV/Rh/YN2012 into HEK293T cells to examine whether this ORF9 triggers apoptosis. Western blot was performed to confirm the expression of ORF9s and SARS-CoV ORF7a ( Figure S1 ). ORF9 couldn't induce apoptosis as the ORF7a of SARS-CoV Tor2 ( Figure S2 ). The results indicated that BtCoV/Rh/YN2012 ORF9 was not involved in apoptosis induction.
To determine whether these accessory proteins modulate IFN induction, we transfected reporter plasmids (pIFNβ-Luc and pRL-TK) and expression plasmids to 293T cells. All the cells over-expressing the accessory genes, as well as influenza virus NS1 (strain PR8), HBoV VP2, or empty vector were tested for luciferase activity after SeV infection. Luciferase activity stimulated by SeV was remarkably higher than that without SeV treatment as expected. Influenza virus NS1 inhibits the expression from IFN promoter, while HBoV VP2 activate the expression. Compared to those controls, the ORF4a proteins exhibit an active effect as HBoV VP2 ( Figure 5A ). Other accessory proteins showed no effect on IFN production ( Figure S3 ). Expression of these accessory genes were confirmed by Western blot ( Figure S1 ). was remarkably higher than that without SeV treatment as expected. Influenza virus NS1 inhibits the expression from IFN promoter, while HBoV VP2 activate the expression. Compared to those controls, the ORF4a proteins exhibit an active effect as HBoV VP2 ( Figure 5A ). Other accessory proteins showed no effect on IFN production ( Figure S3 ). Expression of these accessory genes were confirmed by Western blot (Figure S1 ). Samples were collected at 6 h postinfection, followed by dual-luciferase assay. The results were expressed as the firefly luciferase value normalized to that of Renilla luciferase. (B) ORF3a protein activate NF-κB. 293T cells were transfected with 100 ng pNF-κB-Luc, 10 ng pRL-TK, empty vector (500 ng), an NS1-expressing plasmid (500 ng), a SARS-CoV ORF7a-expressing plasmid (500 ng), or ORF3a-expressing plasmids (500 ng). After 24 h, the cells were treated with TNF-α. Dual-luciferase activity was determined after 6 h. The results were expressed as the firefly luciferase activity normalized to that of Renilla luciferase. The experiments were performed three times independently. Data are representative of at least three independent experiments, with each determination performed in triplicate (mean ± SD of fold change). Asterisks indicate significant differences between groups (compared with Empty vector-NC, p < 0.05, as determined by student t test).
NF-κB plays an important role in regulating the immune response to viral infection and is also a key factor frequently targeted by viruses for taking over the host cell. In this study, we tested if these accessory proteins could modulate NF-κB. 293T cells were co-transfected with reporter Samples were collected at 6 h postinfection, followed by dual-luciferase assay. The results were expressed as the firefly luciferase value normalized to that of Renilla luciferase. (B) ORF3a protein activate NF-κB. 293T cells were transfected with 100 ng pNF-κB-Luc, 10 ng pRL-TK, empty vector (500 ng), an NS1-expressing plasmid (500 ng), a SARS-CoV ORF7a-expressing plasmid (500 ng), or ORF3a-expressing plasmids (500 ng). After 24 h, the cells were treated with TNF-α. Dual-luciferase activity was determined after 6 h. The results were expressed as the firefly luciferase activity normalized to that of Renilla luciferase. The experiments were performed three times independently. Data are representative of at least three independent experiments, with each determination performed in triplicate (mean ± SD of fold change). Asterisks indicate significant differences between groups (compared with Empty vector-NC, p < 0.05, as determined by student t test).
NF-κB plays an important role in regulating the immune response to viral infection and is also a key factor frequently targeted by viruses for taking over the host cell. In this study, we tested if these accessory proteins could modulate NF-κB. 293T cells were co-transfected with reporter plasmids (pNF-κB-Luc and pRL-TK), as well as accessory protein-expressing plasmids, or controls (empty vector, NS1, SARS-CoV Tor2-ORF7a). The cells were mock treated or treated with TNF-α for 6 h at 24 h post-transfection. The luciferase activity was determined. RsYN1-ORF3a and RaGD-ORF3a activated NF-κB as SARS-CoV ORF7a, whereas RsYN2-ORF3a inhibited NF-κB as NS1 ( Figure 5B ). Expressions of ORF3as were confirmed with Western blot ( Figure S1 ). Other accessory proteins did not modulate NF-κB production ( Figure S4 ).
To understand the infectivity of these newly detected BtCoV/Rh/YN2012, we selected the RsYN1, RsYN3 and RaGD spike proteins for spike-mediated pseudovirus entry studies. Both Western blot analysis and negative-staining electron microscopy observation confirmed the preparation of BtCoV/Rh/YN2012 successfully ( Figure S5 ). A total of 11 human cell lines, 8 bat cells, and 9 other mammal cell lines were tested, and no strong positive was found (Table S2) .
In this study, a novel alpha-CoV species, BtCoV/Rh/YN2012, was identified in two Rhinolophus species. The 4 strains with full-length genome were sequences. The 7 conserved replicase domains of these viruses possessed <90% aa sequence identity to those of other known alpha-CoVs, which defines a new species in accordance with the ICTV taxonomy standard [42] . These novel alpha-CoVs showed high genetic diversity in their structural and non-structural genes. Strain RaGD from R. affinis, collected in Guangdong province, formed a divergent independent branch from the other 3 strains from R. sinicus, sampled in Yunnan Province, indicating an independent evolution process associated with geographic isolation and host restrain. Though collected from same province, these three virus strains formed two genotypes correlated to sampling locations. These two genotypes had low genome sequence identity, especially in the S gene and accessory genes. Considering the remote geographic location of the host bat habitat, the host tropism, and the virus diversity, we suppose BtCoV/Rh/YN2012 may have spread in these two provinces with a long history of circulation in their natural reservoir, Rhinolophus bats. With the sequence evidence, we suppose that these viruses are still rapidly evolving.
Our study revealed that BtCoV/Rh/YN2012 has a unique genome structure compared to other alpha-CoVs. First, novel accessory genes, which had no homologues, were identified in the genomes. Second, multiple TRSs were found between S and E genes while other alphacoronavirus only had one TRS there. These TRSs precede ORF3a, ORF3b (only in RsYN1), and ORF4a/b respectively. Third, accessory gene ORF9 showed homology with those of other known CoV species in another coronavirus genus, especially with accessory genes from SARSr-CoV.
Accessory genes are usually involved in virus-host interactions during CoV infection [43] . In most CoVs, accessory genes are dispensable for virus replication. However, an intact 3c gene of feline CoV was required for viral replication in the gut [44] [45] [46] . Deletion of the genus-specific genes in mouse hepatitis virus led to a reduction in virulence [47] . SARS-CoV ORF7a, which was identified to be involved in the suppression of RNA silencing [48] , inhibition of cellular protein synthesis [49] , cell-cycle blockage [50] , and apoptosis induction [51, 52] . In this study, we found that BtCoV/Rh/YN2012 ORF9 shares~30% aa sequence identity with SARS-CoV ORF7a. Interestingly, BtCoV/Rh/YN2012 and SARSr-CoV were both detected in R. sinicus from the same cave. We suppose that SARS-CoV and BtCoV/Rh/YN2012 may have acquired ORF7a or ORF9 from a common ancestor through genome recombination or horizontal gene transfer. Whereas, ORF9 of BtCoV/Rh/YN2012 failed to induce apoptosis or activate NF-κB production, these differences may be induced by the divergent evolution of these proteins in different pressure.
Though different BtCoV/Rh/YN2012 ORF4a share <64.4% amino acid identity, all of them could activate IFN-β. ORF3a from RsYN1 and RaGD upregulated NF-κB, but the homologue from RsYN2 downregulated NF-κB expression. These differences may be caused by amino acid sequence variations and may contribute to a viruses' pathogenicity with a different pathway.
Though lacking of intestinal cell lines from the natural host of BtCoV/Rh/YN2012, we screened the cell tropism of their spike protein through pseudotyped retrovirus entry with human, bat and other mammalian cell lines. Most of cell lines screened were unsusceptible to BtCoV/Rh/YN2012, indicating a low risk of interspecies transmission to human and other animals. Multiple reasons may lead to failed infection of coronavirus spike-pseudotyped retrovirus system, including receptor absence in target cells, failed recognition to the receptor homologue from non-host species, maladaptation in non-host cells during the spike maturation or virus entry, or the limitation of retrovirus system in stimulating coronavirus entry. The weak infectivity of RsYN1 pseudotyped retrovirus in Huh-7 cells could be explained by the binding of spike protein to polysaccharide secreted to the surface. The assumption needs to be further confirmed by experiments.
Our long-term surveillances suggest that Rhinolophus bats seem to harbor a wide diversity of CoVs. Coincidently, the two highly pathogenic agents, SARS-CoV and Rh-BatCoV HKU2 both originated from Rhinolophus bats. Considering the diversity of CoVs carried by this bat genus and their wide geographical distribution, there may be a low risk of spillover of these viruses to other animals and humans. Long-term surveillances and pathogenesis studies will help to prevent future human and animal diseases caused by these bat CoVs.
Supplementary Materials: The following are available online at http://www.mdpi.com/1999-4915/11/4/379/s1, Figure S1 : western blot analysis of the expression of accessory proteins. Figure S2 : Apoptosis analysis of ORF9 proteins of BtCoV/Rh/YN2012. Figure S3 : Functional analysis of ORF3a, ORF3b, ORF4b, ORF8 and ORF9 proteins on the production of Type I interferon. Figure S4 : Functional analysis of ORF3b, ORF4a, ORF4b, ORF8 and ORF9 proteins on the production of NF-κB. Figure S5 : Characteristic of BtCoV/Rh/YN2012 spike mediated pseudovirus. Table S1 : General primers for AlphaCoVs genome sequencing. Table S2 : Primers for the detection of viral sugbenomic mRNAs. Table S3
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What is the conclusion of the coronavirus long-term surveillance studies?
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Rhinolophus bats seem to harbor a wide diversity of CoVs
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In Vitro Antiviral Activity of Circular Triple Helix Forming Oligonucleotide RNA towards Feline Infectious Peritonitis Virus Replication
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3950953/
SHA: f5ad2323eb387f6e271e2842bb2cc4a33504fde3
Authors: Choong, Oi Kuan; Mehrbod, Parvaneh; Tejo, Bimo Ario; Omar, Abdul Rahman
Date: 2014-02-20
DOI: 10.1155/2014/654712
License: cc-by
Abstract: Feline Infectious Peritonitis (FIP) is a severe fatal immune-augmented disease in cat population. It is caused by FIP virus (FIPV), a virulent mutant strain of Feline Enteric Coronavirus (FECV). Current treatments and prophylactics are not effective. The in vitro antiviral properties of five circular Triple-Helix Forming Oligonucleotide (TFO) RNAs (TFO1 to TFO5), which target the different regions of virulent feline coronavirus (FCoV) strain FIPV WSU 79-1146 genome, were tested in FIPV-infected Crandell-Rees Feline Kidney (CRFK) cells. RT-qPCR results showed that the circular TFO RNAs, except TFO2, inhibit FIPV replication, where the viral genome copy numbers decreased significantly by 5-fold log(10) from 10(14) in the virus-inoculated cells to 10(9) in the circular TFO RNAs-transfected cells. Furthermore, the binding of the circular TFO RNA with the targeted viral genome segment was also confirmed using electrophoretic mobility shift assay. The strength of binding kinetics between the TFO RNAs and their target regions was demonstrated by NanoITC assay. In conclusion, the circular TFOs have the potential to be further developed as antiviral agents against FIPV infection.
Text: Feline Infectious Peritonitis Virus (FIPV) is an enveloped virus with a nonsegmented, positive sense, single-stranded RNA genome. FIPV is grouped as feline coronavirus (FCoV), under the family Coronaviridae. FCoV is divided into two biotypes, namely, Feline Enteric Coronavirus (FECV), a ubiquitous enteric biotype of FCoV, and FIPV, a virulent biotype of FCoV [1] . The relationship between these two biotypes still remains unclear. Two hypotheses have been proposed, (i) internal mutation theory and (ii) circulating high virulent-low virulent theory. Internal mutation theory stated that the development of FIP is due to the exposure of cat to variants of FCoV which have been mutated by gaining the ability to replicate within the macrophages [2] , while the circulating high virulent-low virulent theory explains the existence of both distinctive pathogenic and benign lineages of viruses within the cat population [3] .
Study has shown that about 40-80% of cats are detected with FECV shedding in their faeces [4] . About 12% of these FECV-positive cats have developed immune-mediated fatal FIP disease [4] . The prevalence of FIP among felines is due to continual cycles of infection and reinfection of FECV and indiscernible clinical symptoms of infected cats with FECV at an early stage before the progressive development of FIPV.
Vaccination against FIPV with an attenuated, temperature-sensitive strain of type II FIPV induces low antibody titre in kittens that have not been exposed to FCoV. However, there is considerable controversy on the safety and efficacy of this vaccine, since the vaccine contains type 2 strain, whereas type 1 viruses are more prevalent in the field [4] . In addition, antibodies against FIPV do not protect infected cats but enhance the infection of monocytes and macrophages via a mechanism known as Antibody-Dependent Enhancement [1] . Besides vaccines, several antiviral drugs such as ribavirin, 2 BioMed Research International interferons, and immunosuppressive drugs have been used as treatments for FIPV-infected cats, mainly to suppress the inflammatory and detrimental immune response [5] [6] [7] [8] . However, those treatments were ineffective. Hence, there is still significant unmet medical need to develop effective treatments and prophylactics for FIPV infection.
Triple Helix Forming Oligonucleotide (TFO) is defined as homopyrimidine oligonucleotides, which can form a sequence-specific triple helix by Hoogsteen bonds to the major groove of a complementary homopyrimidinehomopurine stretch in duplex DNA [9] . Furthermore, double helical RNA or DNA-RNA hybrids can be targeted as a template for triple helix formation, once the strand composition on the stabilities of triple helical complexes is determined [10] . Hence, TFO has been used to impede gene expressions by transcription inhibition of viral genes or oncogenes [11] [12] [13] [14] [15] [16] . The main purpose of this study is to develop and evaluate the in vitro antiviral properties of circular TFO RNAs against FIPV replication.
serotype II strain WSU 79-1146 (ATCC no. VR-1777) was grown in CRFK cells. A serial 10-fold dilution of FIPV was prepared from the working stock. Confluent 96-well plate was inoculated with 100 L of each virus dilution/well. The plate was incubated in a humidified incubator at 37 ∘ C, 5% CO 2 . Cytopathic effects (CPE) development was observed. The results were recorded after 72 hours and the virus tissue culture infective dose 50 (TCID 50 ) was calculated using Reed and Muench's method [17] .
Oligonucleotide RNA. The Triple Helix Forming Oligonucleotides (TFOs) were designed based on the genome sequence of FIPV serotype II strain WSU 79-1146 (Accession no: AY994055) [18] . TFOs, which specifically target the different regions of the FIPV genome, and one unrelated TFO were constructed ( Table 1 ). The specificity of the TFOs was identified using BLAST search in the NCBI database. The designed linear TFOs were synthesized by Dharmacon Research (USA), whereby the 5 and 3 ends of the linear TFOs were modified with phosphate (PO 4 ) group and hydroxide (OH) group, respectively. These modifications were necessary for the circularization of linear TFO. The process of circularization, using the T4 RNA ligase 1 (ssRNA ligase) (New England Biolabs Inc., England), was carried out according to the manufacturer's protocol. After ligation, the circular TFO RNAs were recovered by ethanol precipitation and the purity of the circular TFO RNAs was measured using spectrophotometer.
Denaturing of urea polyacrylamide gel electrophoresis was performed as described before [19] with modification. Briefly, 20% of denatured urea polyacrylamide gel was prepared and polymerized for 30 minutes. Then, the gel was prerun at 20 to 40 V for 45 minutes. Five L of TFO RNA mixed with 5 L of urea loading buffer was heated at 92 ∘ C for 2 minutes and immediately chilled on ice. It was run on the gel at 200 V for 45 minutes. Finally, the gel was stained with ethidium bromide (Sigma, USA) and viewed with a Bio-Rad Gel Doc XR system (CA, USA). (EMSA) . The target regions of the FIPV genome were synthesized by Dharmacon Research (USA) ( Table 1) . Each TFO RNA was mixed with the target region in 1X binding buffer containing 25 mM Tris-HCl, 6 mM MgCl 2 , and 10 mMNaCl in a final volume of 10 L and subsequently incubated at 37 ∘ C for 2 hours. The sample was run on 15% native polyacrylamide gel at 80 V, in cool condition. The stained gel was viewed by a Bio-Rad Gel Doc XR system.
Regions. The binding strength was measured using a nano Isothermal Titration Calorimeter (ITC) (TA instruments, Newcastle, UK). The RNA sample mixtures, consisting of circular TFOs (0.0002 mM), were incubated with their respective synthetic target regions (0.015 mM) using 1X binding buffer as the diluent. The experiment was run at 37 ∘ C with 2 L/injection, for a total of 25 injections. Data was collected every 250 seconds and analyzed using the NanoAnalyze software v2.3.6 provided by the manufacturer.
This experiment was conducted in CRFK cells, where 3 × 10 4 cell/well was seeded in 96-well plate to reach 80% confluency 24 hours prior to transfection. One hundred nM of TFO RNAs was separately transfected into the CRFK cells using a HiPerFect Transfection Reagent (Qiagen, Germany), as per the manufacturer's protocol. The plate was incubated at 37 ∘ C with 5% CO 2 for 6 hours. Then, the cultures were infected with 100TCID 50 of FIPV serotype II strain WSU 79-1146 for 1 hour at 37 ∘ C (100 L/well). Finally, the viral inoculum was replaced by fresh maintenance media (MEM containing 1% FBS and 1% pen/strep). Virus-infected and uninfected cells were maintained as positive and negative controls, respectively. The morphology of the cultures was recorded 72 hours after infection and samples were harvested at this time point and stored at −80 ∘ C prior to RNA extraction.
Inhibition. Different concentrations of circular TFO1 RNA (25 nM, 50 nM, 100 nM, and 500 nM) were transfected into CRFK cells. The plate was incubated for 6 hours followed by virus inoculation for 1 hour at 37 ∘ C with 5% CO2. The cells were processed as described above.
Madin-Darby Canine Kidney (MDCK) cell (ATCC no. CCL-34), at a concentration of 4 × 10 4 cell/well, was seeded in 96-well plate to reach 80% confluency 24 hours prior to transfection. Transfection was performed the same as before. One hundred nM of circular TFO RNA was transfected into MDCK cells. Following 6 hours
ORF1a/1b and 530-541
ORF1a/1b and 7399-7411
ORF1a/1b and 14048-14061
- * Highlighted in bold indicated the binding region. * * Unrelated circular TFO. [20, 21] , respectively. The reverse transcriptase quantitative real-time PCR (RT-qPCR) was performed using a Bio-Rad CFX96 real-time system (BioRad, USA). The reaction was amplified in a final volume of 25 L using a SensiMix SYBR No-ROX One-Step Kit (Bioline, UK), which consisted of 12.5 L 2X SensiMix SYBR No-Rox One-
Step reaction buffer, 10 M forward and reverse primers, 10 units RiboSafe RNase inhibitor, and 5 L template RNA. Absolute quantification approach was used to quantify qPCR results where a standard curve of a serial dilution of virus was plotted before the quantification. Amount of the virus in the samples was quantified based on this standard curve.
Analysis. Data statistical analysis was performed using SPSS 18.0. Data were represented as mean ± SE of three independent tests. One-way ANOVA, Tukey post hoc test was used to analyze the significant level among the data. ≤ 0.05 was considered significant. genome, which play important roles in viral replication, were selected as the target binding sites for the triplex formation. The target regions were 5 untranslated region (5 UTR), Open Reading Frames (ORFs) 1a and 1b, and 3 untranslated region (3 UTR) ( Table 1 ). The TFOs were designed in duplex, as they can bind with the single stranded target region and reshape into triplex. Both ends of the duplex TFOs were ligated with a linker sequence or clamps (C-C) to construct circular TFO RNA.
Denaturing PAGE assay was carried out after the ligation process to determine the formation of the circular TFO. As shown in Figure 1 , the circular TFO RNAs migrated faster than the linear TFO RNAs, when subjected to 20% denaturing PAGE.
Target Region. The binding ability was determined using Electrophoretic Mobility Shift Assay (EMSA) [23] . The appearance of the slow mobility band indicates the successful hybridization of circular TFO RNA with its target region. The binding ability of different TFO RNAs (TFO1 to TFO5) against their target regions was determined by EMSA (Figure 2) . TFO1, TFO3, TFO4, and TFO5 showed slow mobility band, while TFO2 showed the lack of an upward shifted band. This indicates the possession of triplex binding ability for all circular TFO RNAs, except TFO2.
TFO RNA. Study on the interaction and hybridization of TFO towards its target region is crucial, since the stronger the binding is, the more stable the triplex structure forms. As shown in supplementary Figure 1 (Table 3) .
The antiviral effect of circular TFO RNAs was investigated by RT-qPCR assay at 72 hours after transfection. The results showed viral RNA genome copy numbers of 3.65 × 10 9 , 3.22 × 10 14 , 5.04 × 10 9 , 5.01 × 10 9 , 4.41 × 10 9 , and 3.96 × 10 14 in cells treated with TFO1, TFO2, TFO3, TFO4, TFO5, and TFO7, respectively. The data analyzed by one-way ANOVA, Tukey post hoc test showed significant high viral RNA genome copy number of 4.03 × 10 14 for virus inoculated cells as compared to circular TFO1, TFO3, TFO4, and TFO5 treatments ( ≤ 0.05). The viral RNA copies of circular TFO2, linear TFO3 and TFO4, and unrelated circular TFO7 RNAs transfected cells also showed high viral RNA copy numbers which did not show significant differences to the infected cells ( ≥ 0.05) ( Figure 3 ). The morphological changes of the cells were also captured 72 hours after transfection. The cells transfected with circular TFO1, TFO3, TFO4, and TFO5 appeared to be in good condition following virus inoculation, while the cells transfected with circular TFO2 and linear TFO3 and TFO4 showed visible cytopathic effect (CPE), the same as virus inoculated cells (supplementary Figure 2) . Furthermore, cells transfected with TFO only remain viable indicating that TFO treatment is generally not toxic to the cells. Hence, these results illustrated the capacity of circular TFO RNAs (except TFO2) to inhibit FIPV replication.
Concentrations on FIPV Replication. Circular TFO1 was used to examine the dose-response relationship as a representative to other TFOs. The experimental conditions were identical to that of the previous experiment, except for TFO1 concentrations of 25 nM, 50 nM, 100 nM, and 500 nM. There was no significant reduction in viral RNA genome copies using the concentration of 25 nM TFO1. The other concentrations caused significant reductions in copy numbers as compared to the virus-infected cells. However, no significant difference was detected in copy numbers from all of these concentrations ( Figure 4 ).
The specificity of the TFO towards FIPV was tested, using TFO1 and TFO5, as the proper representatives of TFOs, on influenza A virus H1N1 New Jersey 8/76. The analyzed data using one-way ANOVA, Tukey post hoc test did not show significant reductions in the copies of viral RNA for both TFOs compared to the influenza virus inoculated cells ( ≥ 0.05) (supplementary Figure 3 ). Complex structure G4/Cir4 Figure 2 : EMSA analysis. EMSA analysis illustrated the binding of circular TFO 1, 3, 4, and 5 to the target regions as evidenced by upward band shift. Binding of each circular TFO except circular TFO2 to its respective target forms a complex that migrates slower than unbound TFO. G1 to G5 represent the target region for circular TFO1 to TFO5 and Cir1 to Cir5 represent the circular TFO1 to TFO5, respectively. in the replication process [24] . Meanwhile, the ORF1a/1b of FIPV are translated into polyproteins that are cleaved into nonstructural proteins which assemble into replicationtranscription complexes together with other viral proteins [24] . Hence, the development of molecular therapy targeting these critical regions may provide the possibility to inhibit FIPV replication.
Development of antiviral therapies against FIPV using siRNA [25] and viral protease inhibitors [26] Figure 4 : TFO1 dose-response study for inhibiting FIPV replication. The concentrations of 50 nM and higher showed significant antiviral effects. 50 nM of circular TFO1 RNA was able to reduce viral copy number by 5-fold log 10 from 10 14 to 10 9 , while 100 and 500 nM showed 4-fold reduction. Data are averages of 3 independent tests (mean ± SE). * Significantly different from FIPV-infected group.
as potential new treatments against FIPV infection. In this study, circular Triple Helix Forming Oligonucleotide (TFO) RNAs, specifically targeting the short regions of viral genome for triplex formation, were designed and evaluated. TFO1 and TFO2 targeted the 5 and 3 UTRs of the viral genome, respectively. TFO3 to TFO5 targeted different regions of the ORF1a/1b on FIPV genome. Prior to in vitro antiviral study, the ligated circular TFOs were evaluated using PAGE analysis. All of the circularised TFO showed faster migration pattern compared to the linear TFO; however, only slight variation was detected for some of the TFO (Figure 1 ). The reason for this is not clear but probably due to the differences in length and the tertiary structures of the TFOs leading to differences in the migration rate. EMSA was used to show the binding capability of each circular TFO towards the target region in the FIPV genome except for TFO2 which showed lack of formation of complex structure upon hybridization ( Figure 2) . The EMSA result also concurred with the antiviral study, where all circular TFOs (except TFO2) were able to demonstrate a significant reduction in the viral RNA genome copy numbers by 5-fold log 10 from 10 14 in virus inoculated cells to 10 9 in TFO-transfected cells (Figure 3 ). However, no antiviral properties were detected from the linear TFOs and unrelated circular TFO7 RNA, confirming that the antiviral activity is associated with specific binding of circular TFOs towards targeted regions.
Furthermore, the binding of the circular TFO to the target region was confirmed by nanoITC analysis; where the low value and high stability allowed TFOs to compete effectively with the target regions for inhibiting transcription in cell-free systems. Since, TFO1 shows the lowest value (Table 3) , the antiviral properties of this TFO were evaluated in doseresponse study. As shown in Figure 4 , 50 and 100 nM of TFO1 showed similar antiviral effects indicating the potential therapeutic application of TFO1 on FIPV replication. However, increasing the concentration of TFO1 to 500 nm failed to reduce the viral load further probably due to inefficiency of the transfection reagent to transfect the TFO into the cells. In addition, the virus has fast replication rate upon in vitro infection, where previous study on the growth of FIPV in CRFK cells showed that by 2 hours approximately 67% of FIPV 79-1146 were internalized by CRFK cells by endocytosis increasing to more than 70% at 3 hours [27, 28] . The above finding probably also explained the reason why no antiviral effect was detected when the transfection of the TFO was performed on virus-infected cells (data not shown).
The antiviral properties, as demonstrated by the circular TFOs, were probably associated with the binding of the TFO to the target region, based on both the Watson-Crick and Hoogsteen hydrogen bonds, which enhance the stability in terms of enthalpy, which is brought about by joining together two out of three strands of the triple helix in the proper orientation [29] . Therefore, the triplex formation is tightly bonded and not easy to detach. Furthermore, the circular TFOs were designed in such way that the presence of hydrogen bonding donors and acceptors in the purines is able to form two hydrogen bonds, while the pyrimidine bases can only form one additional hydrogen bond with incoming third bases [30] . However, there are various factors that may limit the activity of TFOs in cells like intracellular degradation of the TFO and limited accessibility of the TFO to the target sites which can prevent triplex formation [31] . These findings may also explain the inability of the designed TFO1 to inhibit further virus replication in dose-response study (Figure 4) .
Various molecular-based therapies against infectious diseases and cancer have been developed and tested. However, only the siRNA-based therapy has been studied extensively as a novel antiviral and anticancer therapy [32, 33] . Recently, McDonagh et al. [25] developed siRNA with antiviral activity against the FIPV 79-1146, where the designed siRNA was able to reduce the copy number of viral genome compared with virus-infected cells. The potential therapeutic application of TFOs, such as linear TFO conjugated with psoralen to inhibit the transcription of human immunodeficiency provirus [13] and TFO to inhibit the transcription of 1(I) collagen in rat fibroblasts [14] , has also been reported. In addition, short TFO conjugated with daunomycin targeting the promoter region of oncogene has been designed and evaluated on human cancer cells [31] . These studies indicated the flexibility of using TFO-based oligonucleotides as a potential molecular-based therapy. In this study, we demonstrated short circular TFO RNAs between 28 and 34 mers (Table 1) , which are able to inhibit FIPV replication by binding to specific target regions of the FIPV genome. All designed circular TFOs (except TFO2) showed significant inhibitory effects against FIPV replication. The TFOs that formed triplex structures showed antiviral effects towards FIPV replication. The reason why TFO2 failed to show any interaction with the target region or antiviral activity is probably due to the length of TFO2 (i.e., 24 mers), which might be insufficient to a triplex formation upon hybridization (Figure 2 ), be effective enough to suppress viral RNA transcription, and eventually inhibit virus replication. Nevertheless, the inability of TFO2 to show antiviral effect due to failure in the formation of functional tertiary structure of the triplex formation cannot be ruled out. In vitro antiviral study which showed no antiviral property for unrelated TFO (TFO7) and also inability of circular TFO1 and TFO5 to inhibit influenza A virus H1N1 infected cells confirms the specificity of the TFOs' activity.
In conclusion, the circular TFO RNA has the potential to be developed as a therapy against FIPV in cats. However, further studies on TFO specificity, actual mechanism of circular TFO RNA in the transcription alteration consequence of inhibiting the viral transcription process, and in vivo animal studies are important for this approach to work as a therapy in the future.
|
What is the cause of Feline Infectious Peritonitis (FIP)?
| 4,051 |
FIP virus (FIPV)
| 503 |
1,590 |
In Vitro Antiviral Activity of Circular Triple Helix Forming Oligonucleotide RNA towards Feline Infectious Peritonitis Virus Replication
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3950953/
SHA: f5ad2323eb387f6e271e2842bb2cc4a33504fde3
Authors: Choong, Oi Kuan; Mehrbod, Parvaneh; Tejo, Bimo Ario; Omar, Abdul Rahman
Date: 2014-02-20
DOI: 10.1155/2014/654712
License: cc-by
Abstract: Feline Infectious Peritonitis (FIP) is a severe fatal immune-augmented disease in cat population. It is caused by FIP virus (FIPV), a virulent mutant strain of Feline Enteric Coronavirus (FECV). Current treatments and prophylactics are not effective. The in vitro antiviral properties of five circular Triple-Helix Forming Oligonucleotide (TFO) RNAs (TFO1 to TFO5), which target the different regions of virulent feline coronavirus (FCoV) strain FIPV WSU 79-1146 genome, were tested in FIPV-infected Crandell-Rees Feline Kidney (CRFK) cells. RT-qPCR results showed that the circular TFO RNAs, except TFO2, inhibit FIPV replication, where the viral genome copy numbers decreased significantly by 5-fold log(10) from 10(14) in the virus-inoculated cells to 10(9) in the circular TFO RNAs-transfected cells. Furthermore, the binding of the circular TFO RNA with the targeted viral genome segment was also confirmed using electrophoretic mobility shift assay. The strength of binding kinetics between the TFO RNAs and their target regions was demonstrated by NanoITC assay. In conclusion, the circular TFOs have the potential to be further developed as antiviral agents against FIPV infection.
Text: Feline Infectious Peritonitis Virus (FIPV) is an enveloped virus with a nonsegmented, positive sense, single-stranded RNA genome. FIPV is grouped as feline coronavirus (FCoV), under the family Coronaviridae. FCoV is divided into two biotypes, namely, Feline Enteric Coronavirus (FECV), a ubiquitous enteric biotype of FCoV, and FIPV, a virulent biotype of FCoV [1] . The relationship between these two biotypes still remains unclear. Two hypotheses have been proposed, (i) internal mutation theory and (ii) circulating high virulent-low virulent theory. Internal mutation theory stated that the development of FIP is due to the exposure of cat to variants of FCoV which have been mutated by gaining the ability to replicate within the macrophages [2] , while the circulating high virulent-low virulent theory explains the existence of both distinctive pathogenic and benign lineages of viruses within the cat population [3] .
Study has shown that about 40-80% of cats are detected with FECV shedding in their faeces [4] . About 12% of these FECV-positive cats have developed immune-mediated fatal FIP disease [4] . The prevalence of FIP among felines is due to continual cycles of infection and reinfection of FECV and indiscernible clinical symptoms of infected cats with FECV at an early stage before the progressive development of FIPV.
Vaccination against FIPV with an attenuated, temperature-sensitive strain of type II FIPV induces low antibody titre in kittens that have not been exposed to FCoV. However, there is considerable controversy on the safety and efficacy of this vaccine, since the vaccine contains type 2 strain, whereas type 1 viruses are more prevalent in the field [4] . In addition, antibodies against FIPV do not protect infected cats but enhance the infection of monocytes and macrophages via a mechanism known as Antibody-Dependent Enhancement [1] . Besides vaccines, several antiviral drugs such as ribavirin, 2 BioMed Research International interferons, and immunosuppressive drugs have been used as treatments for FIPV-infected cats, mainly to suppress the inflammatory and detrimental immune response [5] [6] [7] [8] . However, those treatments were ineffective. Hence, there is still significant unmet medical need to develop effective treatments and prophylactics for FIPV infection.
Triple Helix Forming Oligonucleotide (TFO) is defined as homopyrimidine oligonucleotides, which can form a sequence-specific triple helix by Hoogsteen bonds to the major groove of a complementary homopyrimidinehomopurine stretch in duplex DNA [9] . Furthermore, double helical RNA or DNA-RNA hybrids can be targeted as a template for triple helix formation, once the strand composition on the stabilities of triple helical complexes is determined [10] . Hence, TFO has been used to impede gene expressions by transcription inhibition of viral genes or oncogenes [11] [12] [13] [14] [15] [16] . The main purpose of this study is to develop and evaluate the in vitro antiviral properties of circular TFO RNAs against FIPV replication.
serotype II strain WSU 79-1146 (ATCC no. VR-1777) was grown in CRFK cells. A serial 10-fold dilution of FIPV was prepared from the working stock. Confluent 96-well plate was inoculated with 100 L of each virus dilution/well. The plate was incubated in a humidified incubator at 37 ∘ C, 5% CO 2 . Cytopathic effects (CPE) development was observed. The results were recorded after 72 hours and the virus tissue culture infective dose 50 (TCID 50 ) was calculated using Reed and Muench's method [17] .
Oligonucleotide RNA. The Triple Helix Forming Oligonucleotides (TFOs) were designed based on the genome sequence of FIPV serotype II strain WSU 79-1146 (Accession no: AY994055) [18] . TFOs, which specifically target the different regions of the FIPV genome, and one unrelated TFO were constructed ( Table 1 ). The specificity of the TFOs was identified using BLAST search in the NCBI database. The designed linear TFOs were synthesized by Dharmacon Research (USA), whereby the 5 and 3 ends of the linear TFOs were modified with phosphate (PO 4 ) group and hydroxide (OH) group, respectively. These modifications were necessary for the circularization of linear TFO. The process of circularization, using the T4 RNA ligase 1 (ssRNA ligase) (New England Biolabs Inc., England), was carried out according to the manufacturer's protocol. After ligation, the circular TFO RNAs were recovered by ethanol precipitation and the purity of the circular TFO RNAs was measured using spectrophotometer.
Denaturing of urea polyacrylamide gel electrophoresis was performed as described before [19] with modification. Briefly, 20% of denatured urea polyacrylamide gel was prepared and polymerized for 30 minutes. Then, the gel was prerun at 20 to 40 V for 45 minutes. Five L of TFO RNA mixed with 5 L of urea loading buffer was heated at 92 ∘ C for 2 minutes and immediately chilled on ice. It was run on the gel at 200 V for 45 minutes. Finally, the gel was stained with ethidium bromide (Sigma, USA) and viewed with a Bio-Rad Gel Doc XR system (CA, USA). (EMSA) . The target regions of the FIPV genome were synthesized by Dharmacon Research (USA) ( Table 1) . Each TFO RNA was mixed with the target region in 1X binding buffer containing 25 mM Tris-HCl, 6 mM MgCl 2 , and 10 mMNaCl in a final volume of 10 L and subsequently incubated at 37 ∘ C for 2 hours. The sample was run on 15% native polyacrylamide gel at 80 V, in cool condition. The stained gel was viewed by a Bio-Rad Gel Doc XR system.
Regions. The binding strength was measured using a nano Isothermal Titration Calorimeter (ITC) (TA instruments, Newcastle, UK). The RNA sample mixtures, consisting of circular TFOs (0.0002 mM), were incubated with their respective synthetic target regions (0.015 mM) using 1X binding buffer as the diluent. The experiment was run at 37 ∘ C with 2 L/injection, for a total of 25 injections. Data was collected every 250 seconds and analyzed using the NanoAnalyze software v2.3.6 provided by the manufacturer.
This experiment was conducted in CRFK cells, where 3 × 10 4 cell/well was seeded in 96-well plate to reach 80% confluency 24 hours prior to transfection. One hundred nM of TFO RNAs was separately transfected into the CRFK cells using a HiPerFect Transfection Reagent (Qiagen, Germany), as per the manufacturer's protocol. The plate was incubated at 37 ∘ C with 5% CO 2 for 6 hours. Then, the cultures were infected with 100TCID 50 of FIPV serotype II strain WSU 79-1146 for 1 hour at 37 ∘ C (100 L/well). Finally, the viral inoculum was replaced by fresh maintenance media (MEM containing 1% FBS and 1% pen/strep). Virus-infected and uninfected cells were maintained as positive and negative controls, respectively. The morphology of the cultures was recorded 72 hours after infection and samples were harvested at this time point and stored at −80 ∘ C prior to RNA extraction.
Inhibition. Different concentrations of circular TFO1 RNA (25 nM, 50 nM, 100 nM, and 500 nM) were transfected into CRFK cells. The plate was incubated for 6 hours followed by virus inoculation for 1 hour at 37 ∘ C with 5% CO2. The cells were processed as described above.
Madin-Darby Canine Kidney (MDCK) cell (ATCC no. CCL-34), at a concentration of 4 × 10 4 cell/well, was seeded in 96-well plate to reach 80% confluency 24 hours prior to transfection. Transfection was performed the same as before. One hundred nM of circular TFO RNA was transfected into MDCK cells. Following 6 hours
ORF1a/1b and 530-541
ORF1a/1b and 7399-7411
ORF1a/1b and 14048-14061
- * Highlighted in bold indicated the binding region. * * Unrelated circular TFO. [20, 21] , respectively. The reverse transcriptase quantitative real-time PCR (RT-qPCR) was performed using a Bio-Rad CFX96 real-time system (BioRad, USA). The reaction was amplified in a final volume of 25 L using a SensiMix SYBR No-ROX One-Step Kit (Bioline, UK), which consisted of 12.5 L 2X SensiMix SYBR No-Rox One-
Step reaction buffer, 10 M forward and reverse primers, 10 units RiboSafe RNase inhibitor, and 5 L template RNA. Absolute quantification approach was used to quantify qPCR results where a standard curve of a serial dilution of virus was plotted before the quantification. Amount of the virus in the samples was quantified based on this standard curve.
Analysis. Data statistical analysis was performed using SPSS 18.0. Data were represented as mean ± SE of three independent tests. One-way ANOVA, Tukey post hoc test was used to analyze the significant level among the data. ≤ 0.05 was considered significant. genome, which play important roles in viral replication, were selected as the target binding sites for the triplex formation. The target regions were 5 untranslated region (5 UTR), Open Reading Frames (ORFs) 1a and 1b, and 3 untranslated region (3 UTR) ( Table 1 ). The TFOs were designed in duplex, as they can bind with the single stranded target region and reshape into triplex. Both ends of the duplex TFOs were ligated with a linker sequence or clamps (C-C) to construct circular TFO RNA.
Denaturing PAGE assay was carried out after the ligation process to determine the formation of the circular TFO. As shown in Figure 1 , the circular TFO RNAs migrated faster than the linear TFO RNAs, when subjected to 20% denaturing PAGE.
Target Region. The binding ability was determined using Electrophoretic Mobility Shift Assay (EMSA) [23] . The appearance of the slow mobility band indicates the successful hybridization of circular TFO RNA with its target region. The binding ability of different TFO RNAs (TFO1 to TFO5) against their target regions was determined by EMSA (Figure 2) . TFO1, TFO3, TFO4, and TFO5 showed slow mobility band, while TFO2 showed the lack of an upward shifted band. This indicates the possession of triplex binding ability for all circular TFO RNAs, except TFO2.
TFO RNA. Study on the interaction and hybridization of TFO towards its target region is crucial, since the stronger the binding is, the more stable the triplex structure forms. As shown in supplementary Figure 1 (Table 3) .
The antiviral effect of circular TFO RNAs was investigated by RT-qPCR assay at 72 hours after transfection. The results showed viral RNA genome copy numbers of 3.65 × 10 9 , 3.22 × 10 14 , 5.04 × 10 9 , 5.01 × 10 9 , 4.41 × 10 9 , and 3.96 × 10 14 in cells treated with TFO1, TFO2, TFO3, TFO4, TFO5, and TFO7, respectively. The data analyzed by one-way ANOVA, Tukey post hoc test showed significant high viral RNA genome copy number of 4.03 × 10 14 for virus inoculated cells as compared to circular TFO1, TFO3, TFO4, and TFO5 treatments ( ≤ 0.05). The viral RNA copies of circular TFO2, linear TFO3 and TFO4, and unrelated circular TFO7 RNAs transfected cells also showed high viral RNA copy numbers which did not show significant differences to the infected cells ( ≥ 0.05) ( Figure 3 ). The morphological changes of the cells were also captured 72 hours after transfection. The cells transfected with circular TFO1, TFO3, TFO4, and TFO5 appeared to be in good condition following virus inoculation, while the cells transfected with circular TFO2 and linear TFO3 and TFO4 showed visible cytopathic effect (CPE), the same as virus inoculated cells (supplementary Figure 2) . Furthermore, cells transfected with TFO only remain viable indicating that TFO treatment is generally not toxic to the cells. Hence, these results illustrated the capacity of circular TFO RNAs (except TFO2) to inhibit FIPV replication.
Concentrations on FIPV Replication. Circular TFO1 was used to examine the dose-response relationship as a representative to other TFOs. The experimental conditions were identical to that of the previous experiment, except for TFO1 concentrations of 25 nM, 50 nM, 100 nM, and 500 nM. There was no significant reduction in viral RNA genome copies using the concentration of 25 nM TFO1. The other concentrations caused significant reductions in copy numbers as compared to the virus-infected cells. However, no significant difference was detected in copy numbers from all of these concentrations ( Figure 4 ).
The specificity of the TFO towards FIPV was tested, using TFO1 and TFO5, as the proper representatives of TFOs, on influenza A virus H1N1 New Jersey 8/76. The analyzed data using one-way ANOVA, Tukey post hoc test did not show significant reductions in the copies of viral RNA for both TFOs compared to the influenza virus inoculated cells ( ≥ 0.05) (supplementary Figure 3 ). Complex structure G4/Cir4 Figure 2 : EMSA analysis. EMSA analysis illustrated the binding of circular TFO 1, 3, 4, and 5 to the target regions as evidenced by upward band shift. Binding of each circular TFO except circular TFO2 to its respective target forms a complex that migrates slower than unbound TFO. G1 to G5 represent the target region for circular TFO1 to TFO5 and Cir1 to Cir5 represent the circular TFO1 to TFO5, respectively. in the replication process [24] . Meanwhile, the ORF1a/1b of FIPV are translated into polyproteins that are cleaved into nonstructural proteins which assemble into replicationtranscription complexes together with other viral proteins [24] . Hence, the development of molecular therapy targeting these critical regions may provide the possibility to inhibit FIPV replication.
Development of antiviral therapies against FIPV using siRNA [25] and viral protease inhibitors [26] Figure 4 : TFO1 dose-response study for inhibiting FIPV replication. The concentrations of 50 nM and higher showed significant antiviral effects. 50 nM of circular TFO1 RNA was able to reduce viral copy number by 5-fold log 10 from 10 14 to 10 9 , while 100 and 500 nM showed 4-fold reduction. Data are averages of 3 independent tests (mean ± SE). * Significantly different from FIPV-infected group.
as potential new treatments against FIPV infection. In this study, circular Triple Helix Forming Oligonucleotide (TFO) RNAs, specifically targeting the short regions of viral genome for triplex formation, were designed and evaluated. TFO1 and TFO2 targeted the 5 and 3 UTRs of the viral genome, respectively. TFO3 to TFO5 targeted different regions of the ORF1a/1b on FIPV genome. Prior to in vitro antiviral study, the ligated circular TFOs were evaluated using PAGE analysis. All of the circularised TFO showed faster migration pattern compared to the linear TFO; however, only slight variation was detected for some of the TFO (Figure 1 ). The reason for this is not clear but probably due to the differences in length and the tertiary structures of the TFOs leading to differences in the migration rate. EMSA was used to show the binding capability of each circular TFO towards the target region in the FIPV genome except for TFO2 which showed lack of formation of complex structure upon hybridization ( Figure 2) . The EMSA result also concurred with the antiviral study, where all circular TFOs (except TFO2) were able to demonstrate a significant reduction in the viral RNA genome copy numbers by 5-fold log 10 from 10 14 in virus inoculated cells to 10 9 in TFO-transfected cells (Figure 3 ). However, no antiviral properties were detected from the linear TFOs and unrelated circular TFO7 RNA, confirming that the antiviral activity is associated with specific binding of circular TFOs towards targeted regions.
Furthermore, the binding of the circular TFO to the target region was confirmed by nanoITC analysis; where the low value and high stability allowed TFOs to compete effectively with the target regions for inhibiting transcription in cell-free systems. Since, TFO1 shows the lowest value (Table 3) , the antiviral properties of this TFO were evaluated in doseresponse study. As shown in Figure 4 , 50 and 100 nM of TFO1 showed similar antiviral effects indicating the potential therapeutic application of TFO1 on FIPV replication. However, increasing the concentration of TFO1 to 500 nm failed to reduce the viral load further probably due to inefficiency of the transfection reagent to transfect the TFO into the cells. In addition, the virus has fast replication rate upon in vitro infection, where previous study on the growth of FIPV in CRFK cells showed that by 2 hours approximately 67% of FIPV 79-1146 were internalized by CRFK cells by endocytosis increasing to more than 70% at 3 hours [27, 28] . The above finding probably also explained the reason why no antiviral effect was detected when the transfection of the TFO was performed on virus-infected cells (data not shown).
The antiviral properties, as demonstrated by the circular TFOs, were probably associated with the binding of the TFO to the target region, based on both the Watson-Crick and Hoogsteen hydrogen bonds, which enhance the stability in terms of enthalpy, which is brought about by joining together two out of three strands of the triple helix in the proper orientation [29] . Therefore, the triplex formation is tightly bonded and not easy to detach. Furthermore, the circular TFOs were designed in such way that the presence of hydrogen bonding donors and acceptors in the purines is able to form two hydrogen bonds, while the pyrimidine bases can only form one additional hydrogen bond with incoming third bases [30] . However, there are various factors that may limit the activity of TFOs in cells like intracellular degradation of the TFO and limited accessibility of the TFO to the target sites which can prevent triplex formation [31] . These findings may also explain the inability of the designed TFO1 to inhibit further virus replication in dose-response study (Figure 4) .
Various molecular-based therapies against infectious diseases and cancer have been developed and tested. However, only the siRNA-based therapy has been studied extensively as a novel antiviral and anticancer therapy [32, 33] . Recently, McDonagh et al. [25] developed siRNA with antiviral activity against the FIPV 79-1146, where the designed siRNA was able to reduce the copy number of viral genome compared with virus-infected cells. The potential therapeutic application of TFOs, such as linear TFO conjugated with psoralen to inhibit the transcription of human immunodeficiency provirus [13] and TFO to inhibit the transcription of 1(I) collagen in rat fibroblasts [14] , has also been reported. In addition, short TFO conjugated with daunomycin targeting the promoter region of oncogene has been designed and evaluated on human cancer cells [31] . These studies indicated the flexibility of using TFO-based oligonucleotides as a potential molecular-based therapy. In this study, we demonstrated short circular TFO RNAs between 28 and 34 mers (Table 1) , which are able to inhibit FIPV replication by binding to specific target regions of the FIPV genome. All designed circular TFOs (except TFO2) showed significant inhibitory effects against FIPV replication. The TFOs that formed triplex structures showed antiviral effects towards FIPV replication. The reason why TFO2 failed to show any interaction with the target region or antiviral activity is probably due to the length of TFO2 (i.e., 24 mers), which might be insufficient to a triplex formation upon hybridization (Figure 2 ), be effective enough to suppress viral RNA transcription, and eventually inhibit virus replication. Nevertheless, the inability of TFO2 to show antiviral effect due to failure in the formation of functional tertiary structure of the triplex formation cannot be ruled out. In vitro antiviral study which showed no antiviral property for unrelated TFO (TFO7) and also inability of circular TFO1 and TFO5 to inhibit influenza A virus H1N1 infected cells confirms the specificity of the TFOs' activity.
In conclusion, the circular TFO RNA has the potential to be developed as a therapy against FIPV in cats. However, further studies on TFO specificity, actual mechanism of circular TFO RNA in the transcription alteration consequence of inhibiting the viral transcription process, and in vivo animal studies are important for this approach to work as a therapy in the future.
|
What is the molecular structure of Feline Infectious Peritonitis Virus?
| 4,052 |
enveloped virus with a nonsegmented, positive sense, single-stranded RNA genome
| 1,635 |
1,590 |
In Vitro Antiviral Activity of Circular Triple Helix Forming Oligonucleotide RNA towards Feline Infectious Peritonitis Virus Replication
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3950953/
SHA: f5ad2323eb387f6e271e2842bb2cc4a33504fde3
Authors: Choong, Oi Kuan; Mehrbod, Parvaneh; Tejo, Bimo Ario; Omar, Abdul Rahman
Date: 2014-02-20
DOI: 10.1155/2014/654712
License: cc-by
Abstract: Feline Infectious Peritonitis (FIP) is a severe fatal immune-augmented disease in cat population. It is caused by FIP virus (FIPV), a virulent mutant strain of Feline Enteric Coronavirus (FECV). Current treatments and prophylactics are not effective. The in vitro antiviral properties of five circular Triple-Helix Forming Oligonucleotide (TFO) RNAs (TFO1 to TFO5), which target the different regions of virulent feline coronavirus (FCoV) strain FIPV WSU 79-1146 genome, were tested in FIPV-infected Crandell-Rees Feline Kidney (CRFK) cells. RT-qPCR results showed that the circular TFO RNAs, except TFO2, inhibit FIPV replication, where the viral genome copy numbers decreased significantly by 5-fold log(10) from 10(14) in the virus-inoculated cells to 10(9) in the circular TFO RNAs-transfected cells. Furthermore, the binding of the circular TFO RNA with the targeted viral genome segment was also confirmed using electrophoretic mobility shift assay. The strength of binding kinetics between the TFO RNAs and their target regions was demonstrated by NanoITC assay. In conclusion, the circular TFOs have the potential to be further developed as antiviral agents against FIPV infection.
Text: Feline Infectious Peritonitis Virus (FIPV) is an enveloped virus with a nonsegmented, positive sense, single-stranded RNA genome. FIPV is grouped as feline coronavirus (FCoV), under the family Coronaviridae. FCoV is divided into two biotypes, namely, Feline Enteric Coronavirus (FECV), a ubiquitous enteric biotype of FCoV, and FIPV, a virulent biotype of FCoV [1] . The relationship between these two biotypes still remains unclear. Two hypotheses have been proposed, (i) internal mutation theory and (ii) circulating high virulent-low virulent theory. Internal mutation theory stated that the development of FIP is due to the exposure of cat to variants of FCoV which have been mutated by gaining the ability to replicate within the macrophages [2] , while the circulating high virulent-low virulent theory explains the existence of both distinctive pathogenic and benign lineages of viruses within the cat population [3] .
Study has shown that about 40-80% of cats are detected with FECV shedding in their faeces [4] . About 12% of these FECV-positive cats have developed immune-mediated fatal FIP disease [4] . The prevalence of FIP among felines is due to continual cycles of infection and reinfection of FECV and indiscernible clinical symptoms of infected cats with FECV at an early stage before the progressive development of FIPV.
Vaccination against FIPV with an attenuated, temperature-sensitive strain of type II FIPV induces low antibody titre in kittens that have not been exposed to FCoV. However, there is considerable controversy on the safety and efficacy of this vaccine, since the vaccine contains type 2 strain, whereas type 1 viruses are more prevalent in the field [4] . In addition, antibodies against FIPV do not protect infected cats but enhance the infection of monocytes and macrophages via a mechanism known as Antibody-Dependent Enhancement [1] . Besides vaccines, several antiviral drugs such as ribavirin, 2 BioMed Research International interferons, and immunosuppressive drugs have been used as treatments for FIPV-infected cats, mainly to suppress the inflammatory and detrimental immune response [5] [6] [7] [8] . However, those treatments were ineffective. Hence, there is still significant unmet medical need to develop effective treatments and prophylactics for FIPV infection.
Triple Helix Forming Oligonucleotide (TFO) is defined as homopyrimidine oligonucleotides, which can form a sequence-specific triple helix by Hoogsteen bonds to the major groove of a complementary homopyrimidinehomopurine stretch in duplex DNA [9] . Furthermore, double helical RNA or DNA-RNA hybrids can be targeted as a template for triple helix formation, once the strand composition on the stabilities of triple helical complexes is determined [10] . Hence, TFO has been used to impede gene expressions by transcription inhibition of viral genes or oncogenes [11] [12] [13] [14] [15] [16] . The main purpose of this study is to develop and evaluate the in vitro antiviral properties of circular TFO RNAs against FIPV replication.
serotype II strain WSU 79-1146 (ATCC no. VR-1777) was grown in CRFK cells. A serial 10-fold dilution of FIPV was prepared from the working stock. Confluent 96-well plate was inoculated with 100 L of each virus dilution/well. The plate was incubated in a humidified incubator at 37 ∘ C, 5% CO 2 . Cytopathic effects (CPE) development was observed. The results were recorded after 72 hours and the virus tissue culture infective dose 50 (TCID 50 ) was calculated using Reed and Muench's method [17] .
Oligonucleotide RNA. The Triple Helix Forming Oligonucleotides (TFOs) were designed based on the genome sequence of FIPV serotype II strain WSU 79-1146 (Accession no: AY994055) [18] . TFOs, which specifically target the different regions of the FIPV genome, and one unrelated TFO were constructed ( Table 1 ). The specificity of the TFOs was identified using BLAST search in the NCBI database. The designed linear TFOs were synthesized by Dharmacon Research (USA), whereby the 5 and 3 ends of the linear TFOs were modified with phosphate (PO 4 ) group and hydroxide (OH) group, respectively. These modifications were necessary for the circularization of linear TFO. The process of circularization, using the T4 RNA ligase 1 (ssRNA ligase) (New England Biolabs Inc., England), was carried out according to the manufacturer's protocol. After ligation, the circular TFO RNAs were recovered by ethanol precipitation and the purity of the circular TFO RNAs was measured using spectrophotometer.
Denaturing of urea polyacrylamide gel electrophoresis was performed as described before [19] with modification. Briefly, 20% of denatured urea polyacrylamide gel was prepared and polymerized for 30 minutes. Then, the gel was prerun at 20 to 40 V for 45 minutes. Five L of TFO RNA mixed with 5 L of urea loading buffer was heated at 92 ∘ C for 2 minutes and immediately chilled on ice. It was run on the gel at 200 V for 45 minutes. Finally, the gel was stained with ethidium bromide (Sigma, USA) and viewed with a Bio-Rad Gel Doc XR system (CA, USA). (EMSA) . The target regions of the FIPV genome were synthesized by Dharmacon Research (USA) ( Table 1) . Each TFO RNA was mixed with the target region in 1X binding buffer containing 25 mM Tris-HCl, 6 mM MgCl 2 , and 10 mMNaCl in a final volume of 10 L and subsequently incubated at 37 ∘ C for 2 hours. The sample was run on 15% native polyacrylamide gel at 80 V, in cool condition. The stained gel was viewed by a Bio-Rad Gel Doc XR system.
Regions. The binding strength was measured using a nano Isothermal Titration Calorimeter (ITC) (TA instruments, Newcastle, UK). The RNA sample mixtures, consisting of circular TFOs (0.0002 mM), were incubated with their respective synthetic target regions (0.015 mM) using 1X binding buffer as the diluent. The experiment was run at 37 ∘ C with 2 L/injection, for a total of 25 injections. Data was collected every 250 seconds and analyzed using the NanoAnalyze software v2.3.6 provided by the manufacturer.
This experiment was conducted in CRFK cells, where 3 × 10 4 cell/well was seeded in 96-well plate to reach 80% confluency 24 hours prior to transfection. One hundred nM of TFO RNAs was separately transfected into the CRFK cells using a HiPerFect Transfection Reagent (Qiagen, Germany), as per the manufacturer's protocol. The plate was incubated at 37 ∘ C with 5% CO 2 for 6 hours. Then, the cultures were infected with 100TCID 50 of FIPV serotype II strain WSU 79-1146 for 1 hour at 37 ∘ C (100 L/well). Finally, the viral inoculum was replaced by fresh maintenance media (MEM containing 1% FBS and 1% pen/strep). Virus-infected and uninfected cells were maintained as positive and negative controls, respectively. The morphology of the cultures was recorded 72 hours after infection and samples were harvested at this time point and stored at −80 ∘ C prior to RNA extraction.
Inhibition. Different concentrations of circular TFO1 RNA (25 nM, 50 nM, 100 nM, and 500 nM) were transfected into CRFK cells. The plate was incubated for 6 hours followed by virus inoculation for 1 hour at 37 ∘ C with 5% CO2. The cells were processed as described above.
Madin-Darby Canine Kidney (MDCK) cell (ATCC no. CCL-34), at a concentration of 4 × 10 4 cell/well, was seeded in 96-well plate to reach 80% confluency 24 hours prior to transfection. Transfection was performed the same as before. One hundred nM of circular TFO RNA was transfected into MDCK cells. Following 6 hours
ORF1a/1b and 530-541
ORF1a/1b and 7399-7411
ORF1a/1b and 14048-14061
- * Highlighted in bold indicated the binding region. * * Unrelated circular TFO. [20, 21] , respectively. The reverse transcriptase quantitative real-time PCR (RT-qPCR) was performed using a Bio-Rad CFX96 real-time system (BioRad, USA). The reaction was amplified in a final volume of 25 L using a SensiMix SYBR No-ROX One-Step Kit (Bioline, UK), which consisted of 12.5 L 2X SensiMix SYBR No-Rox One-
Step reaction buffer, 10 M forward and reverse primers, 10 units RiboSafe RNase inhibitor, and 5 L template RNA. Absolute quantification approach was used to quantify qPCR results where a standard curve of a serial dilution of virus was plotted before the quantification. Amount of the virus in the samples was quantified based on this standard curve.
Analysis. Data statistical analysis was performed using SPSS 18.0. Data were represented as mean ± SE of three independent tests. One-way ANOVA, Tukey post hoc test was used to analyze the significant level among the data. ≤ 0.05 was considered significant. genome, which play important roles in viral replication, were selected as the target binding sites for the triplex formation. The target regions were 5 untranslated region (5 UTR), Open Reading Frames (ORFs) 1a and 1b, and 3 untranslated region (3 UTR) ( Table 1 ). The TFOs were designed in duplex, as they can bind with the single stranded target region and reshape into triplex. Both ends of the duplex TFOs were ligated with a linker sequence or clamps (C-C) to construct circular TFO RNA.
Denaturing PAGE assay was carried out after the ligation process to determine the formation of the circular TFO. As shown in Figure 1 , the circular TFO RNAs migrated faster than the linear TFO RNAs, when subjected to 20% denaturing PAGE.
Target Region. The binding ability was determined using Electrophoretic Mobility Shift Assay (EMSA) [23] . The appearance of the slow mobility band indicates the successful hybridization of circular TFO RNA with its target region. The binding ability of different TFO RNAs (TFO1 to TFO5) against their target regions was determined by EMSA (Figure 2) . TFO1, TFO3, TFO4, and TFO5 showed slow mobility band, while TFO2 showed the lack of an upward shifted band. This indicates the possession of triplex binding ability for all circular TFO RNAs, except TFO2.
TFO RNA. Study on the interaction and hybridization of TFO towards its target region is crucial, since the stronger the binding is, the more stable the triplex structure forms. As shown in supplementary Figure 1 (Table 3) .
The antiviral effect of circular TFO RNAs was investigated by RT-qPCR assay at 72 hours after transfection. The results showed viral RNA genome copy numbers of 3.65 × 10 9 , 3.22 × 10 14 , 5.04 × 10 9 , 5.01 × 10 9 , 4.41 × 10 9 , and 3.96 × 10 14 in cells treated with TFO1, TFO2, TFO3, TFO4, TFO5, and TFO7, respectively. The data analyzed by one-way ANOVA, Tukey post hoc test showed significant high viral RNA genome copy number of 4.03 × 10 14 for virus inoculated cells as compared to circular TFO1, TFO3, TFO4, and TFO5 treatments ( ≤ 0.05). The viral RNA copies of circular TFO2, linear TFO3 and TFO4, and unrelated circular TFO7 RNAs transfected cells also showed high viral RNA copy numbers which did not show significant differences to the infected cells ( ≥ 0.05) ( Figure 3 ). The morphological changes of the cells were also captured 72 hours after transfection. The cells transfected with circular TFO1, TFO3, TFO4, and TFO5 appeared to be in good condition following virus inoculation, while the cells transfected with circular TFO2 and linear TFO3 and TFO4 showed visible cytopathic effect (CPE), the same as virus inoculated cells (supplementary Figure 2) . Furthermore, cells transfected with TFO only remain viable indicating that TFO treatment is generally not toxic to the cells. Hence, these results illustrated the capacity of circular TFO RNAs (except TFO2) to inhibit FIPV replication.
Concentrations on FIPV Replication. Circular TFO1 was used to examine the dose-response relationship as a representative to other TFOs. The experimental conditions were identical to that of the previous experiment, except for TFO1 concentrations of 25 nM, 50 nM, 100 nM, and 500 nM. There was no significant reduction in viral RNA genome copies using the concentration of 25 nM TFO1. The other concentrations caused significant reductions in copy numbers as compared to the virus-infected cells. However, no significant difference was detected in copy numbers from all of these concentrations ( Figure 4 ).
The specificity of the TFO towards FIPV was tested, using TFO1 and TFO5, as the proper representatives of TFOs, on influenza A virus H1N1 New Jersey 8/76. The analyzed data using one-way ANOVA, Tukey post hoc test did not show significant reductions in the copies of viral RNA for both TFOs compared to the influenza virus inoculated cells ( ≥ 0.05) (supplementary Figure 3 ). Complex structure G4/Cir4 Figure 2 : EMSA analysis. EMSA analysis illustrated the binding of circular TFO 1, 3, 4, and 5 to the target regions as evidenced by upward band shift. Binding of each circular TFO except circular TFO2 to its respective target forms a complex that migrates slower than unbound TFO. G1 to G5 represent the target region for circular TFO1 to TFO5 and Cir1 to Cir5 represent the circular TFO1 to TFO5, respectively. in the replication process [24] . Meanwhile, the ORF1a/1b of FIPV are translated into polyproteins that are cleaved into nonstructural proteins which assemble into replicationtranscription complexes together with other viral proteins [24] . Hence, the development of molecular therapy targeting these critical regions may provide the possibility to inhibit FIPV replication.
Development of antiviral therapies against FIPV using siRNA [25] and viral protease inhibitors [26] Figure 4 : TFO1 dose-response study for inhibiting FIPV replication. The concentrations of 50 nM and higher showed significant antiviral effects. 50 nM of circular TFO1 RNA was able to reduce viral copy number by 5-fold log 10 from 10 14 to 10 9 , while 100 and 500 nM showed 4-fold reduction. Data are averages of 3 independent tests (mean ± SE). * Significantly different from FIPV-infected group.
as potential new treatments against FIPV infection. In this study, circular Triple Helix Forming Oligonucleotide (TFO) RNAs, specifically targeting the short regions of viral genome for triplex formation, were designed and evaluated. TFO1 and TFO2 targeted the 5 and 3 UTRs of the viral genome, respectively. TFO3 to TFO5 targeted different regions of the ORF1a/1b on FIPV genome. Prior to in vitro antiviral study, the ligated circular TFOs were evaluated using PAGE analysis. All of the circularised TFO showed faster migration pattern compared to the linear TFO; however, only slight variation was detected for some of the TFO (Figure 1 ). The reason for this is not clear but probably due to the differences in length and the tertiary structures of the TFOs leading to differences in the migration rate. EMSA was used to show the binding capability of each circular TFO towards the target region in the FIPV genome except for TFO2 which showed lack of formation of complex structure upon hybridization ( Figure 2) . The EMSA result also concurred with the antiviral study, where all circular TFOs (except TFO2) were able to demonstrate a significant reduction in the viral RNA genome copy numbers by 5-fold log 10 from 10 14 in virus inoculated cells to 10 9 in TFO-transfected cells (Figure 3 ). However, no antiviral properties were detected from the linear TFOs and unrelated circular TFO7 RNA, confirming that the antiviral activity is associated with specific binding of circular TFOs towards targeted regions.
Furthermore, the binding of the circular TFO to the target region was confirmed by nanoITC analysis; where the low value and high stability allowed TFOs to compete effectively with the target regions for inhibiting transcription in cell-free systems. Since, TFO1 shows the lowest value (Table 3) , the antiviral properties of this TFO were evaluated in doseresponse study. As shown in Figure 4 , 50 and 100 nM of TFO1 showed similar antiviral effects indicating the potential therapeutic application of TFO1 on FIPV replication. However, increasing the concentration of TFO1 to 500 nm failed to reduce the viral load further probably due to inefficiency of the transfection reagent to transfect the TFO into the cells. In addition, the virus has fast replication rate upon in vitro infection, where previous study on the growth of FIPV in CRFK cells showed that by 2 hours approximately 67% of FIPV 79-1146 were internalized by CRFK cells by endocytosis increasing to more than 70% at 3 hours [27, 28] . The above finding probably also explained the reason why no antiviral effect was detected when the transfection of the TFO was performed on virus-infected cells (data not shown).
The antiviral properties, as demonstrated by the circular TFOs, were probably associated with the binding of the TFO to the target region, based on both the Watson-Crick and Hoogsteen hydrogen bonds, which enhance the stability in terms of enthalpy, which is brought about by joining together two out of three strands of the triple helix in the proper orientation [29] . Therefore, the triplex formation is tightly bonded and not easy to detach. Furthermore, the circular TFOs were designed in such way that the presence of hydrogen bonding donors and acceptors in the purines is able to form two hydrogen bonds, while the pyrimidine bases can only form one additional hydrogen bond with incoming third bases [30] . However, there are various factors that may limit the activity of TFOs in cells like intracellular degradation of the TFO and limited accessibility of the TFO to the target sites which can prevent triplex formation [31] . These findings may also explain the inability of the designed TFO1 to inhibit further virus replication in dose-response study (Figure 4) .
Various molecular-based therapies against infectious diseases and cancer have been developed and tested. However, only the siRNA-based therapy has been studied extensively as a novel antiviral and anticancer therapy [32, 33] . Recently, McDonagh et al. [25] developed siRNA with antiviral activity against the FIPV 79-1146, where the designed siRNA was able to reduce the copy number of viral genome compared with virus-infected cells. The potential therapeutic application of TFOs, such as linear TFO conjugated with psoralen to inhibit the transcription of human immunodeficiency provirus [13] and TFO to inhibit the transcription of 1(I) collagen in rat fibroblasts [14] , has also been reported. In addition, short TFO conjugated with daunomycin targeting the promoter region of oncogene has been designed and evaluated on human cancer cells [31] . These studies indicated the flexibility of using TFO-based oligonucleotides as a potential molecular-based therapy. In this study, we demonstrated short circular TFO RNAs between 28 and 34 mers (Table 1) , which are able to inhibit FIPV replication by binding to specific target regions of the FIPV genome. All designed circular TFOs (except TFO2) showed significant inhibitory effects against FIPV replication. The TFOs that formed triplex structures showed antiviral effects towards FIPV replication. The reason why TFO2 failed to show any interaction with the target region or antiviral activity is probably due to the length of TFO2 (i.e., 24 mers), which might be insufficient to a triplex formation upon hybridization (Figure 2 ), be effective enough to suppress viral RNA transcription, and eventually inhibit virus replication. Nevertheless, the inability of TFO2 to show antiviral effect due to failure in the formation of functional tertiary structure of the triplex formation cannot be ruled out. In vitro antiviral study which showed no antiviral property for unrelated TFO (TFO7) and also inability of circular TFO1 and TFO5 to inhibit influenza A virus H1N1 infected cells confirms the specificity of the TFOs' activity.
In conclusion, the circular TFO RNA has the potential to be developed as a therapy against FIPV in cats. However, further studies on TFO specificity, actual mechanism of circular TFO RNA in the transcription alteration consequence of inhibiting the viral transcription process, and in vivo animal studies are important for this approach to work as a therapy in the future.
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How is FECV detected in cats?
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shedding in their faeces
| 2,578 |
1,590 |
In Vitro Antiviral Activity of Circular Triple Helix Forming Oligonucleotide RNA towards Feline Infectious Peritonitis Virus Replication
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3950953/
SHA: f5ad2323eb387f6e271e2842bb2cc4a33504fde3
Authors: Choong, Oi Kuan; Mehrbod, Parvaneh; Tejo, Bimo Ario; Omar, Abdul Rahman
Date: 2014-02-20
DOI: 10.1155/2014/654712
License: cc-by
Abstract: Feline Infectious Peritonitis (FIP) is a severe fatal immune-augmented disease in cat population. It is caused by FIP virus (FIPV), a virulent mutant strain of Feline Enteric Coronavirus (FECV). Current treatments and prophylactics are not effective. The in vitro antiviral properties of five circular Triple-Helix Forming Oligonucleotide (TFO) RNAs (TFO1 to TFO5), which target the different regions of virulent feline coronavirus (FCoV) strain FIPV WSU 79-1146 genome, were tested in FIPV-infected Crandell-Rees Feline Kidney (CRFK) cells. RT-qPCR results showed that the circular TFO RNAs, except TFO2, inhibit FIPV replication, where the viral genome copy numbers decreased significantly by 5-fold log(10) from 10(14) in the virus-inoculated cells to 10(9) in the circular TFO RNAs-transfected cells. Furthermore, the binding of the circular TFO RNA with the targeted viral genome segment was also confirmed using electrophoretic mobility shift assay. The strength of binding kinetics between the TFO RNAs and their target regions was demonstrated by NanoITC assay. In conclusion, the circular TFOs have the potential to be further developed as antiviral agents against FIPV infection.
Text: Feline Infectious Peritonitis Virus (FIPV) is an enveloped virus with a nonsegmented, positive sense, single-stranded RNA genome. FIPV is grouped as feline coronavirus (FCoV), under the family Coronaviridae. FCoV is divided into two biotypes, namely, Feline Enteric Coronavirus (FECV), a ubiquitous enteric biotype of FCoV, and FIPV, a virulent biotype of FCoV [1] . The relationship between these two biotypes still remains unclear. Two hypotheses have been proposed, (i) internal mutation theory and (ii) circulating high virulent-low virulent theory. Internal mutation theory stated that the development of FIP is due to the exposure of cat to variants of FCoV which have been mutated by gaining the ability to replicate within the macrophages [2] , while the circulating high virulent-low virulent theory explains the existence of both distinctive pathogenic and benign lineages of viruses within the cat population [3] .
Study has shown that about 40-80% of cats are detected with FECV shedding in their faeces [4] . About 12% of these FECV-positive cats have developed immune-mediated fatal FIP disease [4] . The prevalence of FIP among felines is due to continual cycles of infection and reinfection of FECV and indiscernible clinical symptoms of infected cats with FECV at an early stage before the progressive development of FIPV.
Vaccination against FIPV with an attenuated, temperature-sensitive strain of type II FIPV induces low antibody titre in kittens that have not been exposed to FCoV. However, there is considerable controversy on the safety and efficacy of this vaccine, since the vaccine contains type 2 strain, whereas type 1 viruses are more prevalent in the field [4] . In addition, antibodies against FIPV do not protect infected cats but enhance the infection of monocytes and macrophages via a mechanism known as Antibody-Dependent Enhancement [1] . Besides vaccines, several antiviral drugs such as ribavirin, 2 BioMed Research International interferons, and immunosuppressive drugs have been used as treatments for FIPV-infected cats, mainly to suppress the inflammatory and detrimental immune response [5] [6] [7] [8] . However, those treatments were ineffective. Hence, there is still significant unmet medical need to develop effective treatments and prophylactics for FIPV infection.
Triple Helix Forming Oligonucleotide (TFO) is defined as homopyrimidine oligonucleotides, which can form a sequence-specific triple helix by Hoogsteen bonds to the major groove of a complementary homopyrimidinehomopurine stretch in duplex DNA [9] . Furthermore, double helical RNA or DNA-RNA hybrids can be targeted as a template for triple helix formation, once the strand composition on the stabilities of triple helical complexes is determined [10] . Hence, TFO has been used to impede gene expressions by transcription inhibition of viral genes or oncogenes [11] [12] [13] [14] [15] [16] . The main purpose of this study is to develop and evaluate the in vitro antiviral properties of circular TFO RNAs against FIPV replication.
serotype II strain WSU 79-1146 (ATCC no. VR-1777) was grown in CRFK cells. A serial 10-fold dilution of FIPV was prepared from the working stock. Confluent 96-well plate was inoculated with 100 L of each virus dilution/well. The plate was incubated in a humidified incubator at 37 ∘ C, 5% CO 2 . Cytopathic effects (CPE) development was observed. The results were recorded after 72 hours and the virus tissue culture infective dose 50 (TCID 50 ) was calculated using Reed and Muench's method [17] .
Oligonucleotide RNA. The Triple Helix Forming Oligonucleotides (TFOs) were designed based on the genome sequence of FIPV serotype II strain WSU 79-1146 (Accession no: AY994055) [18] . TFOs, which specifically target the different regions of the FIPV genome, and one unrelated TFO were constructed ( Table 1 ). The specificity of the TFOs was identified using BLAST search in the NCBI database. The designed linear TFOs were synthesized by Dharmacon Research (USA), whereby the 5 and 3 ends of the linear TFOs were modified with phosphate (PO 4 ) group and hydroxide (OH) group, respectively. These modifications were necessary for the circularization of linear TFO. The process of circularization, using the T4 RNA ligase 1 (ssRNA ligase) (New England Biolabs Inc., England), was carried out according to the manufacturer's protocol. After ligation, the circular TFO RNAs were recovered by ethanol precipitation and the purity of the circular TFO RNAs was measured using spectrophotometer.
Denaturing of urea polyacrylamide gel electrophoresis was performed as described before [19] with modification. Briefly, 20% of denatured urea polyacrylamide gel was prepared and polymerized for 30 minutes. Then, the gel was prerun at 20 to 40 V for 45 minutes. Five L of TFO RNA mixed with 5 L of urea loading buffer was heated at 92 ∘ C for 2 minutes and immediately chilled on ice. It was run on the gel at 200 V for 45 minutes. Finally, the gel was stained with ethidium bromide (Sigma, USA) and viewed with a Bio-Rad Gel Doc XR system (CA, USA). (EMSA) . The target regions of the FIPV genome were synthesized by Dharmacon Research (USA) ( Table 1) . Each TFO RNA was mixed with the target region in 1X binding buffer containing 25 mM Tris-HCl, 6 mM MgCl 2 , and 10 mMNaCl in a final volume of 10 L and subsequently incubated at 37 ∘ C for 2 hours. The sample was run on 15% native polyacrylamide gel at 80 V, in cool condition. The stained gel was viewed by a Bio-Rad Gel Doc XR system.
Regions. The binding strength was measured using a nano Isothermal Titration Calorimeter (ITC) (TA instruments, Newcastle, UK). The RNA sample mixtures, consisting of circular TFOs (0.0002 mM), were incubated with their respective synthetic target regions (0.015 mM) using 1X binding buffer as the diluent. The experiment was run at 37 ∘ C with 2 L/injection, for a total of 25 injections. Data was collected every 250 seconds and analyzed using the NanoAnalyze software v2.3.6 provided by the manufacturer.
This experiment was conducted in CRFK cells, where 3 × 10 4 cell/well was seeded in 96-well plate to reach 80% confluency 24 hours prior to transfection. One hundred nM of TFO RNAs was separately transfected into the CRFK cells using a HiPerFect Transfection Reagent (Qiagen, Germany), as per the manufacturer's protocol. The plate was incubated at 37 ∘ C with 5% CO 2 for 6 hours. Then, the cultures were infected with 100TCID 50 of FIPV serotype II strain WSU 79-1146 for 1 hour at 37 ∘ C (100 L/well). Finally, the viral inoculum was replaced by fresh maintenance media (MEM containing 1% FBS and 1% pen/strep). Virus-infected and uninfected cells were maintained as positive and negative controls, respectively. The morphology of the cultures was recorded 72 hours after infection and samples were harvested at this time point and stored at −80 ∘ C prior to RNA extraction.
Inhibition. Different concentrations of circular TFO1 RNA (25 nM, 50 nM, 100 nM, and 500 nM) were transfected into CRFK cells. The plate was incubated for 6 hours followed by virus inoculation for 1 hour at 37 ∘ C with 5% CO2. The cells were processed as described above.
Madin-Darby Canine Kidney (MDCK) cell (ATCC no. CCL-34), at a concentration of 4 × 10 4 cell/well, was seeded in 96-well plate to reach 80% confluency 24 hours prior to transfection. Transfection was performed the same as before. One hundred nM of circular TFO RNA was transfected into MDCK cells. Following 6 hours
ORF1a/1b and 530-541
ORF1a/1b and 7399-7411
ORF1a/1b and 14048-14061
- * Highlighted in bold indicated the binding region. * * Unrelated circular TFO. [20, 21] , respectively. The reverse transcriptase quantitative real-time PCR (RT-qPCR) was performed using a Bio-Rad CFX96 real-time system (BioRad, USA). The reaction was amplified in a final volume of 25 L using a SensiMix SYBR No-ROX One-Step Kit (Bioline, UK), which consisted of 12.5 L 2X SensiMix SYBR No-Rox One-
Step reaction buffer, 10 M forward and reverse primers, 10 units RiboSafe RNase inhibitor, and 5 L template RNA. Absolute quantification approach was used to quantify qPCR results where a standard curve of a serial dilution of virus was plotted before the quantification. Amount of the virus in the samples was quantified based on this standard curve.
Analysis. Data statistical analysis was performed using SPSS 18.0. Data were represented as mean ± SE of three independent tests. One-way ANOVA, Tukey post hoc test was used to analyze the significant level among the data. ≤ 0.05 was considered significant. genome, which play important roles in viral replication, were selected as the target binding sites for the triplex formation. The target regions were 5 untranslated region (5 UTR), Open Reading Frames (ORFs) 1a and 1b, and 3 untranslated region (3 UTR) ( Table 1 ). The TFOs were designed in duplex, as they can bind with the single stranded target region and reshape into triplex. Both ends of the duplex TFOs were ligated with a linker sequence or clamps (C-C) to construct circular TFO RNA.
Denaturing PAGE assay was carried out after the ligation process to determine the formation of the circular TFO. As shown in Figure 1 , the circular TFO RNAs migrated faster than the linear TFO RNAs, when subjected to 20% denaturing PAGE.
Target Region. The binding ability was determined using Electrophoretic Mobility Shift Assay (EMSA) [23] . The appearance of the slow mobility band indicates the successful hybridization of circular TFO RNA with its target region. The binding ability of different TFO RNAs (TFO1 to TFO5) against their target regions was determined by EMSA (Figure 2) . TFO1, TFO3, TFO4, and TFO5 showed slow mobility band, while TFO2 showed the lack of an upward shifted band. This indicates the possession of triplex binding ability for all circular TFO RNAs, except TFO2.
TFO RNA. Study on the interaction and hybridization of TFO towards its target region is crucial, since the stronger the binding is, the more stable the triplex structure forms. As shown in supplementary Figure 1 (Table 3) .
The antiviral effect of circular TFO RNAs was investigated by RT-qPCR assay at 72 hours after transfection. The results showed viral RNA genome copy numbers of 3.65 × 10 9 , 3.22 × 10 14 , 5.04 × 10 9 , 5.01 × 10 9 , 4.41 × 10 9 , and 3.96 × 10 14 in cells treated with TFO1, TFO2, TFO3, TFO4, TFO5, and TFO7, respectively. The data analyzed by one-way ANOVA, Tukey post hoc test showed significant high viral RNA genome copy number of 4.03 × 10 14 for virus inoculated cells as compared to circular TFO1, TFO3, TFO4, and TFO5 treatments ( ≤ 0.05). The viral RNA copies of circular TFO2, linear TFO3 and TFO4, and unrelated circular TFO7 RNAs transfected cells also showed high viral RNA copy numbers which did not show significant differences to the infected cells ( ≥ 0.05) ( Figure 3 ). The morphological changes of the cells were also captured 72 hours after transfection. The cells transfected with circular TFO1, TFO3, TFO4, and TFO5 appeared to be in good condition following virus inoculation, while the cells transfected with circular TFO2 and linear TFO3 and TFO4 showed visible cytopathic effect (CPE), the same as virus inoculated cells (supplementary Figure 2) . Furthermore, cells transfected with TFO only remain viable indicating that TFO treatment is generally not toxic to the cells. Hence, these results illustrated the capacity of circular TFO RNAs (except TFO2) to inhibit FIPV replication.
Concentrations on FIPV Replication. Circular TFO1 was used to examine the dose-response relationship as a representative to other TFOs. The experimental conditions were identical to that of the previous experiment, except for TFO1 concentrations of 25 nM, 50 nM, 100 nM, and 500 nM. There was no significant reduction in viral RNA genome copies using the concentration of 25 nM TFO1. The other concentrations caused significant reductions in copy numbers as compared to the virus-infected cells. However, no significant difference was detected in copy numbers from all of these concentrations ( Figure 4 ).
The specificity of the TFO towards FIPV was tested, using TFO1 and TFO5, as the proper representatives of TFOs, on influenza A virus H1N1 New Jersey 8/76. The analyzed data using one-way ANOVA, Tukey post hoc test did not show significant reductions in the copies of viral RNA for both TFOs compared to the influenza virus inoculated cells ( ≥ 0.05) (supplementary Figure 3 ). Complex structure G4/Cir4 Figure 2 : EMSA analysis. EMSA analysis illustrated the binding of circular TFO 1, 3, 4, and 5 to the target regions as evidenced by upward band shift. Binding of each circular TFO except circular TFO2 to its respective target forms a complex that migrates slower than unbound TFO. G1 to G5 represent the target region for circular TFO1 to TFO5 and Cir1 to Cir5 represent the circular TFO1 to TFO5, respectively. in the replication process [24] . Meanwhile, the ORF1a/1b of FIPV are translated into polyproteins that are cleaved into nonstructural proteins which assemble into replicationtranscription complexes together with other viral proteins [24] . Hence, the development of molecular therapy targeting these critical regions may provide the possibility to inhibit FIPV replication.
Development of antiviral therapies against FIPV using siRNA [25] and viral protease inhibitors [26] Figure 4 : TFO1 dose-response study for inhibiting FIPV replication. The concentrations of 50 nM and higher showed significant antiviral effects. 50 nM of circular TFO1 RNA was able to reduce viral copy number by 5-fold log 10 from 10 14 to 10 9 , while 100 and 500 nM showed 4-fold reduction. Data are averages of 3 independent tests (mean ± SE). * Significantly different from FIPV-infected group.
as potential new treatments against FIPV infection. In this study, circular Triple Helix Forming Oligonucleotide (TFO) RNAs, specifically targeting the short regions of viral genome for triplex formation, were designed and evaluated. TFO1 and TFO2 targeted the 5 and 3 UTRs of the viral genome, respectively. TFO3 to TFO5 targeted different regions of the ORF1a/1b on FIPV genome. Prior to in vitro antiviral study, the ligated circular TFOs were evaluated using PAGE analysis. All of the circularised TFO showed faster migration pattern compared to the linear TFO; however, only slight variation was detected for some of the TFO (Figure 1 ). The reason for this is not clear but probably due to the differences in length and the tertiary structures of the TFOs leading to differences in the migration rate. EMSA was used to show the binding capability of each circular TFO towards the target region in the FIPV genome except for TFO2 which showed lack of formation of complex structure upon hybridization ( Figure 2) . The EMSA result also concurred with the antiviral study, where all circular TFOs (except TFO2) were able to demonstrate a significant reduction in the viral RNA genome copy numbers by 5-fold log 10 from 10 14 in virus inoculated cells to 10 9 in TFO-transfected cells (Figure 3 ). However, no antiviral properties were detected from the linear TFOs and unrelated circular TFO7 RNA, confirming that the antiviral activity is associated with specific binding of circular TFOs towards targeted regions.
Furthermore, the binding of the circular TFO to the target region was confirmed by nanoITC analysis; where the low value and high stability allowed TFOs to compete effectively with the target regions for inhibiting transcription in cell-free systems. Since, TFO1 shows the lowest value (Table 3) , the antiviral properties of this TFO were evaluated in doseresponse study. As shown in Figure 4 , 50 and 100 nM of TFO1 showed similar antiviral effects indicating the potential therapeutic application of TFO1 on FIPV replication. However, increasing the concentration of TFO1 to 500 nm failed to reduce the viral load further probably due to inefficiency of the transfection reagent to transfect the TFO into the cells. In addition, the virus has fast replication rate upon in vitro infection, where previous study on the growth of FIPV in CRFK cells showed that by 2 hours approximately 67% of FIPV 79-1146 were internalized by CRFK cells by endocytosis increasing to more than 70% at 3 hours [27, 28] . The above finding probably also explained the reason why no antiviral effect was detected when the transfection of the TFO was performed on virus-infected cells (data not shown).
The antiviral properties, as demonstrated by the circular TFOs, were probably associated with the binding of the TFO to the target region, based on both the Watson-Crick and Hoogsteen hydrogen bonds, which enhance the stability in terms of enthalpy, which is brought about by joining together two out of three strands of the triple helix in the proper orientation [29] . Therefore, the triplex formation is tightly bonded and not easy to detach. Furthermore, the circular TFOs were designed in such way that the presence of hydrogen bonding donors and acceptors in the purines is able to form two hydrogen bonds, while the pyrimidine bases can only form one additional hydrogen bond with incoming third bases [30] . However, there are various factors that may limit the activity of TFOs in cells like intracellular degradation of the TFO and limited accessibility of the TFO to the target sites which can prevent triplex formation [31] . These findings may also explain the inability of the designed TFO1 to inhibit further virus replication in dose-response study (Figure 4) .
Various molecular-based therapies against infectious diseases and cancer have been developed and tested. However, only the siRNA-based therapy has been studied extensively as a novel antiviral and anticancer therapy [32, 33] . Recently, McDonagh et al. [25] developed siRNA with antiviral activity against the FIPV 79-1146, where the designed siRNA was able to reduce the copy number of viral genome compared with virus-infected cells. The potential therapeutic application of TFOs, such as linear TFO conjugated with psoralen to inhibit the transcription of human immunodeficiency provirus [13] and TFO to inhibit the transcription of 1(I) collagen in rat fibroblasts [14] , has also been reported. In addition, short TFO conjugated with daunomycin targeting the promoter region of oncogene has been designed and evaluated on human cancer cells [31] . These studies indicated the flexibility of using TFO-based oligonucleotides as a potential molecular-based therapy. In this study, we demonstrated short circular TFO RNAs between 28 and 34 mers (Table 1) , which are able to inhibit FIPV replication by binding to specific target regions of the FIPV genome. All designed circular TFOs (except TFO2) showed significant inhibitory effects against FIPV replication. The TFOs that formed triplex structures showed antiviral effects towards FIPV replication. The reason why TFO2 failed to show any interaction with the target region or antiviral activity is probably due to the length of TFO2 (i.e., 24 mers), which might be insufficient to a triplex formation upon hybridization (Figure 2 ), be effective enough to suppress viral RNA transcription, and eventually inhibit virus replication. Nevertheless, the inability of TFO2 to show antiviral effect due to failure in the formation of functional tertiary structure of the triplex formation cannot be ruled out. In vitro antiviral study which showed no antiviral property for unrelated TFO (TFO7) and also inability of circular TFO1 and TFO5 to inhibit influenza A virus H1N1 infected cells confirms the specificity of the TFOs' activity.
In conclusion, the circular TFO RNA has the potential to be developed as a therapy against FIPV in cats. However, further studies on TFO specificity, actual mechanism of circular TFO RNA in the transcription alteration consequence of inhibiting the viral transcription process, and in vivo animal studies are important for this approach to work as a therapy in the future.
|
What type of vaccine is used to protect against FIPV infection?
| 4,055 |
an attenuated, temperature-sensitive strain of type II FIPV
| 2,958 |
1,590 |
In Vitro Antiviral Activity of Circular Triple Helix Forming Oligonucleotide RNA towards Feline Infectious Peritonitis Virus Replication
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3950953/
SHA: f5ad2323eb387f6e271e2842bb2cc4a33504fde3
Authors: Choong, Oi Kuan; Mehrbod, Parvaneh; Tejo, Bimo Ario; Omar, Abdul Rahman
Date: 2014-02-20
DOI: 10.1155/2014/654712
License: cc-by
Abstract: Feline Infectious Peritonitis (FIP) is a severe fatal immune-augmented disease in cat population. It is caused by FIP virus (FIPV), a virulent mutant strain of Feline Enteric Coronavirus (FECV). Current treatments and prophylactics are not effective. The in vitro antiviral properties of five circular Triple-Helix Forming Oligonucleotide (TFO) RNAs (TFO1 to TFO5), which target the different regions of virulent feline coronavirus (FCoV) strain FIPV WSU 79-1146 genome, were tested in FIPV-infected Crandell-Rees Feline Kidney (CRFK) cells. RT-qPCR results showed that the circular TFO RNAs, except TFO2, inhibit FIPV replication, where the viral genome copy numbers decreased significantly by 5-fold log(10) from 10(14) in the virus-inoculated cells to 10(9) in the circular TFO RNAs-transfected cells. Furthermore, the binding of the circular TFO RNA with the targeted viral genome segment was also confirmed using electrophoretic mobility shift assay. The strength of binding kinetics between the TFO RNAs and their target regions was demonstrated by NanoITC assay. In conclusion, the circular TFOs have the potential to be further developed as antiviral agents against FIPV infection.
Text: Feline Infectious Peritonitis Virus (FIPV) is an enveloped virus with a nonsegmented, positive sense, single-stranded RNA genome. FIPV is grouped as feline coronavirus (FCoV), under the family Coronaviridae. FCoV is divided into two biotypes, namely, Feline Enteric Coronavirus (FECV), a ubiquitous enteric biotype of FCoV, and FIPV, a virulent biotype of FCoV [1] . The relationship between these two biotypes still remains unclear. Two hypotheses have been proposed, (i) internal mutation theory and (ii) circulating high virulent-low virulent theory. Internal mutation theory stated that the development of FIP is due to the exposure of cat to variants of FCoV which have been mutated by gaining the ability to replicate within the macrophages [2] , while the circulating high virulent-low virulent theory explains the existence of both distinctive pathogenic and benign lineages of viruses within the cat population [3] .
Study has shown that about 40-80% of cats are detected with FECV shedding in their faeces [4] . About 12% of these FECV-positive cats have developed immune-mediated fatal FIP disease [4] . The prevalence of FIP among felines is due to continual cycles of infection and reinfection of FECV and indiscernible clinical symptoms of infected cats with FECV at an early stage before the progressive development of FIPV.
Vaccination against FIPV with an attenuated, temperature-sensitive strain of type II FIPV induces low antibody titre in kittens that have not been exposed to FCoV. However, there is considerable controversy on the safety and efficacy of this vaccine, since the vaccine contains type 2 strain, whereas type 1 viruses are more prevalent in the field [4] . In addition, antibodies against FIPV do not protect infected cats but enhance the infection of monocytes and macrophages via a mechanism known as Antibody-Dependent Enhancement [1] . Besides vaccines, several antiviral drugs such as ribavirin, 2 BioMed Research International interferons, and immunosuppressive drugs have been used as treatments for FIPV-infected cats, mainly to suppress the inflammatory and detrimental immune response [5] [6] [7] [8] . However, those treatments were ineffective. Hence, there is still significant unmet medical need to develop effective treatments and prophylactics for FIPV infection.
Triple Helix Forming Oligonucleotide (TFO) is defined as homopyrimidine oligonucleotides, which can form a sequence-specific triple helix by Hoogsteen bonds to the major groove of a complementary homopyrimidinehomopurine stretch in duplex DNA [9] . Furthermore, double helical RNA or DNA-RNA hybrids can be targeted as a template for triple helix formation, once the strand composition on the stabilities of triple helical complexes is determined [10] . Hence, TFO has been used to impede gene expressions by transcription inhibition of viral genes or oncogenes [11] [12] [13] [14] [15] [16] . The main purpose of this study is to develop and evaluate the in vitro antiviral properties of circular TFO RNAs against FIPV replication.
serotype II strain WSU 79-1146 (ATCC no. VR-1777) was grown in CRFK cells. A serial 10-fold dilution of FIPV was prepared from the working stock. Confluent 96-well plate was inoculated with 100 L of each virus dilution/well. The plate was incubated in a humidified incubator at 37 ∘ C, 5% CO 2 . Cytopathic effects (CPE) development was observed. The results were recorded after 72 hours and the virus tissue culture infective dose 50 (TCID 50 ) was calculated using Reed and Muench's method [17] .
Oligonucleotide RNA. The Triple Helix Forming Oligonucleotides (TFOs) were designed based on the genome sequence of FIPV serotype II strain WSU 79-1146 (Accession no: AY994055) [18] . TFOs, which specifically target the different regions of the FIPV genome, and one unrelated TFO were constructed ( Table 1 ). The specificity of the TFOs was identified using BLAST search in the NCBI database. The designed linear TFOs were synthesized by Dharmacon Research (USA), whereby the 5 and 3 ends of the linear TFOs were modified with phosphate (PO 4 ) group and hydroxide (OH) group, respectively. These modifications were necessary for the circularization of linear TFO. The process of circularization, using the T4 RNA ligase 1 (ssRNA ligase) (New England Biolabs Inc., England), was carried out according to the manufacturer's protocol. After ligation, the circular TFO RNAs were recovered by ethanol precipitation and the purity of the circular TFO RNAs was measured using spectrophotometer.
Denaturing of urea polyacrylamide gel electrophoresis was performed as described before [19] with modification. Briefly, 20% of denatured urea polyacrylamide gel was prepared and polymerized for 30 minutes. Then, the gel was prerun at 20 to 40 V for 45 minutes. Five L of TFO RNA mixed with 5 L of urea loading buffer was heated at 92 ∘ C for 2 minutes and immediately chilled on ice. It was run on the gel at 200 V for 45 minutes. Finally, the gel was stained with ethidium bromide (Sigma, USA) and viewed with a Bio-Rad Gel Doc XR system (CA, USA). (EMSA) . The target regions of the FIPV genome were synthesized by Dharmacon Research (USA) ( Table 1) . Each TFO RNA was mixed with the target region in 1X binding buffer containing 25 mM Tris-HCl, 6 mM MgCl 2 , and 10 mMNaCl in a final volume of 10 L and subsequently incubated at 37 ∘ C for 2 hours. The sample was run on 15% native polyacrylamide gel at 80 V, in cool condition. The stained gel was viewed by a Bio-Rad Gel Doc XR system.
Regions. The binding strength was measured using a nano Isothermal Titration Calorimeter (ITC) (TA instruments, Newcastle, UK). The RNA sample mixtures, consisting of circular TFOs (0.0002 mM), were incubated with their respective synthetic target regions (0.015 mM) using 1X binding buffer as the diluent. The experiment was run at 37 ∘ C with 2 L/injection, for a total of 25 injections. Data was collected every 250 seconds and analyzed using the NanoAnalyze software v2.3.6 provided by the manufacturer.
This experiment was conducted in CRFK cells, where 3 × 10 4 cell/well was seeded in 96-well plate to reach 80% confluency 24 hours prior to transfection. One hundred nM of TFO RNAs was separately transfected into the CRFK cells using a HiPerFect Transfection Reagent (Qiagen, Germany), as per the manufacturer's protocol. The plate was incubated at 37 ∘ C with 5% CO 2 for 6 hours. Then, the cultures were infected with 100TCID 50 of FIPV serotype II strain WSU 79-1146 for 1 hour at 37 ∘ C (100 L/well). Finally, the viral inoculum was replaced by fresh maintenance media (MEM containing 1% FBS and 1% pen/strep). Virus-infected and uninfected cells were maintained as positive and negative controls, respectively. The morphology of the cultures was recorded 72 hours after infection and samples were harvested at this time point and stored at −80 ∘ C prior to RNA extraction.
Inhibition. Different concentrations of circular TFO1 RNA (25 nM, 50 nM, 100 nM, and 500 nM) were transfected into CRFK cells. The plate was incubated for 6 hours followed by virus inoculation for 1 hour at 37 ∘ C with 5% CO2. The cells were processed as described above.
Madin-Darby Canine Kidney (MDCK) cell (ATCC no. CCL-34), at a concentration of 4 × 10 4 cell/well, was seeded in 96-well plate to reach 80% confluency 24 hours prior to transfection. Transfection was performed the same as before. One hundred nM of circular TFO RNA was transfected into MDCK cells. Following 6 hours
ORF1a/1b and 530-541
ORF1a/1b and 7399-7411
ORF1a/1b and 14048-14061
- * Highlighted in bold indicated the binding region. * * Unrelated circular TFO. [20, 21] , respectively. The reverse transcriptase quantitative real-time PCR (RT-qPCR) was performed using a Bio-Rad CFX96 real-time system (BioRad, USA). The reaction was amplified in a final volume of 25 L using a SensiMix SYBR No-ROX One-Step Kit (Bioline, UK), which consisted of 12.5 L 2X SensiMix SYBR No-Rox One-
Step reaction buffer, 10 M forward and reverse primers, 10 units RiboSafe RNase inhibitor, and 5 L template RNA. Absolute quantification approach was used to quantify qPCR results where a standard curve of a serial dilution of virus was plotted before the quantification. Amount of the virus in the samples was quantified based on this standard curve.
Analysis. Data statistical analysis was performed using SPSS 18.0. Data were represented as mean ± SE of three independent tests. One-way ANOVA, Tukey post hoc test was used to analyze the significant level among the data. ≤ 0.05 was considered significant. genome, which play important roles in viral replication, were selected as the target binding sites for the triplex formation. The target regions were 5 untranslated region (5 UTR), Open Reading Frames (ORFs) 1a and 1b, and 3 untranslated region (3 UTR) ( Table 1 ). The TFOs were designed in duplex, as they can bind with the single stranded target region and reshape into triplex. Both ends of the duplex TFOs were ligated with a linker sequence or clamps (C-C) to construct circular TFO RNA.
Denaturing PAGE assay was carried out after the ligation process to determine the formation of the circular TFO. As shown in Figure 1 , the circular TFO RNAs migrated faster than the linear TFO RNAs, when subjected to 20% denaturing PAGE.
Target Region. The binding ability was determined using Electrophoretic Mobility Shift Assay (EMSA) [23] . The appearance of the slow mobility band indicates the successful hybridization of circular TFO RNA with its target region. The binding ability of different TFO RNAs (TFO1 to TFO5) against their target regions was determined by EMSA (Figure 2) . TFO1, TFO3, TFO4, and TFO5 showed slow mobility band, while TFO2 showed the lack of an upward shifted band. This indicates the possession of triplex binding ability for all circular TFO RNAs, except TFO2.
TFO RNA. Study on the interaction and hybridization of TFO towards its target region is crucial, since the stronger the binding is, the more stable the triplex structure forms. As shown in supplementary Figure 1 (Table 3) .
The antiviral effect of circular TFO RNAs was investigated by RT-qPCR assay at 72 hours after transfection. The results showed viral RNA genome copy numbers of 3.65 × 10 9 , 3.22 × 10 14 , 5.04 × 10 9 , 5.01 × 10 9 , 4.41 × 10 9 , and 3.96 × 10 14 in cells treated with TFO1, TFO2, TFO3, TFO4, TFO5, and TFO7, respectively. The data analyzed by one-way ANOVA, Tukey post hoc test showed significant high viral RNA genome copy number of 4.03 × 10 14 for virus inoculated cells as compared to circular TFO1, TFO3, TFO4, and TFO5 treatments ( ≤ 0.05). The viral RNA copies of circular TFO2, linear TFO3 and TFO4, and unrelated circular TFO7 RNAs transfected cells also showed high viral RNA copy numbers which did not show significant differences to the infected cells ( ≥ 0.05) ( Figure 3 ). The morphological changes of the cells were also captured 72 hours after transfection. The cells transfected with circular TFO1, TFO3, TFO4, and TFO5 appeared to be in good condition following virus inoculation, while the cells transfected with circular TFO2 and linear TFO3 and TFO4 showed visible cytopathic effect (CPE), the same as virus inoculated cells (supplementary Figure 2) . Furthermore, cells transfected with TFO only remain viable indicating that TFO treatment is generally not toxic to the cells. Hence, these results illustrated the capacity of circular TFO RNAs (except TFO2) to inhibit FIPV replication.
Concentrations on FIPV Replication. Circular TFO1 was used to examine the dose-response relationship as a representative to other TFOs. The experimental conditions were identical to that of the previous experiment, except for TFO1 concentrations of 25 nM, 50 nM, 100 nM, and 500 nM. There was no significant reduction in viral RNA genome copies using the concentration of 25 nM TFO1. The other concentrations caused significant reductions in copy numbers as compared to the virus-infected cells. However, no significant difference was detected in copy numbers from all of these concentrations ( Figure 4 ).
The specificity of the TFO towards FIPV was tested, using TFO1 and TFO5, as the proper representatives of TFOs, on influenza A virus H1N1 New Jersey 8/76. The analyzed data using one-way ANOVA, Tukey post hoc test did not show significant reductions in the copies of viral RNA for both TFOs compared to the influenza virus inoculated cells ( ≥ 0.05) (supplementary Figure 3 ). Complex structure G4/Cir4 Figure 2 : EMSA analysis. EMSA analysis illustrated the binding of circular TFO 1, 3, 4, and 5 to the target regions as evidenced by upward band shift. Binding of each circular TFO except circular TFO2 to its respective target forms a complex that migrates slower than unbound TFO. G1 to G5 represent the target region for circular TFO1 to TFO5 and Cir1 to Cir5 represent the circular TFO1 to TFO5, respectively. in the replication process [24] . Meanwhile, the ORF1a/1b of FIPV are translated into polyproteins that are cleaved into nonstructural proteins which assemble into replicationtranscription complexes together with other viral proteins [24] . Hence, the development of molecular therapy targeting these critical regions may provide the possibility to inhibit FIPV replication.
Development of antiviral therapies against FIPV using siRNA [25] and viral protease inhibitors [26] Figure 4 : TFO1 dose-response study for inhibiting FIPV replication. The concentrations of 50 nM and higher showed significant antiviral effects. 50 nM of circular TFO1 RNA was able to reduce viral copy number by 5-fold log 10 from 10 14 to 10 9 , while 100 and 500 nM showed 4-fold reduction. Data are averages of 3 independent tests (mean ± SE). * Significantly different from FIPV-infected group.
as potential new treatments against FIPV infection. In this study, circular Triple Helix Forming Oligonucleotide (TFO) RNAs, specifically targeting the short regions of viral genome for triplex formation, were designed and evaluated. TFO1 and TFO2 targeted the 5 and 3 UTRs of the viral genome, respectively. TFO3 to TFO5 targeted different regions of the ORF1a/1b on FIPV genome. Prior to in vitro antiviral study, the ligated circular TFOs were evaluated using PAGE analysis. All of the circularised TFO showed faster migration pattern compared to the linear TFO; however, only slight variation was detected for some of the TFO (Figure 1 ). The reason for this is not clear but probably due to the differences in length and the tertiary structures of the TFOs leading to differences in the migration rate. EMSA was used to show the binding capability of each circular TFO towards the target region in the FIPV genome except for TFO2 which showed lack of formation of complex structure upon hybridization ( Figure 2) . The EMSA result also concurred with the antiviral study, where all circular TFOs (except TFO2) were able to demonstrate a significant reduction in the viral RNA genome copy numbers by 5-fold log 10 from 10 14 in virus inoculated cells to 10 9 in TFO-transfected cells (Figure 3 ). However, no antiviral properties were detected from the linear TFOs and unrelated circular TFO7 RNA, confirming that the antiviral activity is associated with specific binding of circular TFOs towards targeted regions.
Furthermore, the binding of the circular TFO to the target region was confirmed by nanoITC analysis; where the low value and high stability allowed TFOs to compete effectively with the target regions for inhibiting transcription in cell-free systems. Since, TFO1 shows the lowest value (Table 3) , the antiviral properties of this TFO were evaluated in doseresponse study. As shown in Figure 4 , 50 and 100 nM of TFO1 showed similar antiviral effects indicating the potential therapeutic application of TFO1 on FIPV replication. However, increasing the concentration of TFO1 to 500 nm failed to reduce the viral load further probably due to inefficiency of the transfection reagent to transfect the TFO into the cells. In addition, the virus has fast replication rate upon in vitro infection, where previous study on the growth of FIPV in CRFK cells showed that by 2 hours approximately 67% of FIPV 79-1146 were internalized by CRFK cells by endocytosis increasing to more than 70% at 3 hours [27, 28] . The above finding probably also explained the reason why no antiviral effect was detected when the transfection of the TFO was performed on virus-infected cells (data not shown).
The antiviral properties, as demonstrated by the circular TFOs, were probably associated with the binding of the TFO to the target region, based on both the Watson-Crick and Hoogsteen hydrogen bonds, which enhance the stability in terms of enthalpy, which is brought about by joining together two out of three strands of the triple helix in the proper orientation [29] . Therefore, the triplex formation is tightly bonded and not easy to detach. Furthermore, the circular TFOs were designed in such way that the presence of hydrogen bonding donors and acceptors in the purines is able to form two hydrogen bonds, while the pyrimidine bases can only form one additional hydrogen bond with incoming third bases [30] . However, there are various factors that may limit the activity of TFOs in cells like intracellular degradation of the TFO and limited accessibility of the TFO to the target sites which can prevent triplex formation [31] . These findings may also explain the inability of the designed TFO1 to inhibit further virus replication in dose-response study (Figure 4) .
Various molecular-based therapies against infectious diseases and cancer have been developed and tested. However, only the siRNA-based therapy has been studied extensively as a novel antiviral and anticancer therapy [32, 33] . Recently, McDonagh et al. [25] developed siRNA with antiviral activity against the FIPV 79-1146, where the designed siRNA was able to reduce the copy number of viral genome compared with virus-infected cells. The potential therapeutic application of TFOs, such as linear TFO conjugated with psoralen to inhibit the transcription of human immunodeficiency provirus [13] and TFO to inhibit the transcription of 1(I) collagen in rat fibroblasts [14] , has also been reported. In addition, short TFO conjugated with daunomycin targeting the promoter region of oncogene has been designed and evaluated on human cancer cells [31] . These studies indicated the flexibility of using TFO-based oligonucleotides as a potential molecular-based therapy. In this study, we demonstrated short circular TFO RNAs between 28 and 34 mers (Table 1) , which are able to inhibit FIPV replication by binding to specific target regions of the FIPV genome. All designed circular TFOs (except TFO2) showed significant inhibitory effects against FIPV replication. The TFOs that formed triplex structures showed antiviral effects towards FIPV replication. The reason why TFO2 failed to show any interaction with the target region or antiviral activity is probably due to the length of TFO2 (i.e., 24 mers), which might be insufficient to a triplex formation upon hybridization (Figure 2 ), be effective enough to suppress viral RNA transcription, and eventually inhibit virus replication. Nevertheless, the inability of TFO2 to show antiviral effect due to failure in the formation of functional tertiary structure of the triplex formation cannot be ruled out. In vitro antiviral study which showed no antiviral property for unrelated TFO (TFO7) and also inability of circular TFO1 and TFO5 to inhibit influenza A virus H1N1 infected cells confirms the specificity of the TFOs' activity.
In conclusion, the circular TFO RNA has the potential to be developed as a therapy against FIPV in cats. However, further studies on TFO specificity, actual mechanism of circular TFO RNA in the transcription alteration consequence of inhibiting the viral transcription process, and in vivo animal studies are important for this approach to work as a therapy in the future.
|
Why is their controversy surrounding the FIPV vaccine?
| 4,056 |
the vaccine contains type 2 strain, whereas type 1 viruses are more prevalent in the field
| 3,185 |
1,590 |
In Vitro Antiviral Activity of Circular Triple Helix Forming Oligonucleotide RNA towards Feline Infectious Peritonitis Virus Replication
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3950953/
SHA: f5ad2323eb387f6e271e2842bb2cc4a33504fde3
Authors: Choong, Oi Kuan; Mehrbod, Parvaneh; Tejo, Bimo Ario; Omar, Abdul Rahman
Date: 2014-02-20
DOI: 10.1155/2014/654712
License: cc-by
Abstract: Feline Infectious Peritonitis (FIP) is a severe fatal immune-augmented disease in cat population. It is caused by FIP virus (FIPV), a virulent mutant strain of Feline Enteric Coronavirus (FECV). Current treatments and prophylactics are not effective. The in vitro antiviral properties of five circular Triple-Helix Forming Oligonucleotide (TFO) RNAs (TFO1 to TFO5), which target the different regions of virulent feline coronavirus (FCoV) strain FIPV WSU 79-1146 genome, were tested in FIPV-infected Crandell-Rees Feline Kidney (CRFK) cells. RT-qPCR results showed that the circular TFO RNAs, except TFO2, inhibit FIPV replication, where the viral genome copy numbers decreased significantly by 5-fold log(10) from 10(14) in the virus-inoculated cells to 10(9) in the circular TFO RNAs-transfected cells. Furthermore, the binding of the circular TFO RNA with the targeted viral genome segment was also confirmed using electrophoretic mobility shift assay. The strength of binding kinetics between the TFO RNAs and their target regions was demonstrated by NanoITC assay. In conclusion, the circular TFOs have the potential to be further developed as antiviral agents against FIPV infection.
Text: Feline Infectious Peritonitis Virus (FIPV) is an enveloped virus with a nonsegmented, positive sense, single-stranded RNA genome. FIPV is grouped as feline coronavirus (FCoV), under the family Coronaviridae. FCoV is divided into two biotypes, namely, Feline Enteric Coronavirus (FECV), a ubiquitous enteric biotype of FCoV, and FIPV, a virulent biotype of FCoV [1] . The relationship between these two biotypes still remains unclear. Two hypotheses have been proposed, (i) internal mutation theory and (ii) circulating high virulent-low virulent theory. Internal mutation theory stated that the development of FIP is due to the exposure of cat to variants of FCoV which have been mutated by gaining the ability to replicate within the macrophages [2] , while the circulating high virulent-low virulent theory explains the existence of both distinctive pathogenic and benign lineages of viruses within the cat population [3] .
Study has shown that about 40-80% of cats are detected with FECV shedding in their faeces [4] . About 12% of these FECV-positive cats have developed immune-mediated fatal FIP disease [4] . The prevalence of FIP among felines is due to continual cycles of infection and reinfection of FECV and indiscernible clinical symptoms of infected cats with FECV at an early stage before the progressive development of FIPV.
Vaccination against FIPV with an attenuated, temperature-sensitive strain of type II FIPV induces low antibody titre in kittens that have not been exposed to FCoV. However, there is considerable controversy on the safety and efficacy of this vaccine, since the vaccine contains type 2 strain, whereas type 1 viruses are more prevalent in the field [4] . In addition, antibodies against FIPV do not protect infected cats but enhance the infection of monocytes and macrophages via a mechanism known as Antibody-Dependent Enhancement [1] . Besides vaccines, several antiviral drugs such as ribavirin, 2 BioMed Research International interferons, and immunosuppressive drugs have been used as treatments for FIPV-infected cats, mainly to suppress the inflammatory and detrimental immune response [5] [6] [7] [8] . However, those treatments were ineffective. Hence, there is still significant unmet medical need to develop effective treatments and prophylactics for FIPV infection.
Triple Helix Forming Oligonucleotide (TFO) is defined as homopyrimidine oligonucleotides, which can form a sequence-specific triple helix by Hoogsteen bonds to the major groove of a complementary homopyrimidinehomopurine stretch in duplex DNA [9] . Furthermore, double helical RNA or DNA-RNA hybrids can be targeted as a template for triple helix formation, once the strand composition on the stabilities of triple helical complexes is determined [10] . Hence, TFO has been used to impede gene expressions by transcription inhibition of viral genes or oncogenes [11] [12] [13] [14] [15] [16] . The main purpose of this study is to develop and evaluate the in vitro antiviral properties of circular TFO RNAs against FIPV replication.
serotype II strain WSU 79-1146 (ATCC no. VR-1777) was grown in CRFK cells. A serial 10-fold dilution of FIPV was prepared from the working stock. Confluent 96-well plate was inoculated with 100 L of each virus dilution/well. The plate was incubated in a humidified incubator at 37 ∘ C, 5% CO 2 . Cytopathic effects (CPE) development was observed. The results were recorded after 72 hours and the virus tissue culture infective dose 50 (TCID 50 ) was calculated using Reed and Muench's method [17] .
Oligonucleotide RNA. The Triple Helix Forming Oligonucleotides (TFOs) were designed based on the genome sequence of FIPV serotype II strain WSU 79-1146 (Accession no: AY994055) [18] . TFOs, which specifically target the different regions of the FIPV genome, and one unrelated TFO were constructed ( Table 1 ). The specificity of the TFOs was identified using BLAST search in the NCBI database. The designed linear TFOs were synthesized by Dharmacon Research (USA), whereby the 5 and 3 ends of the linear TFOs were modified with phosphate (PO 4 ) group and hydroxide (OH) group, respectively. These modifications were necessary for the circularization of linear TFO. The process of circularization, using the T4 RNA ligase 1 (ssRNA ligase) (New England Biolabs Inc., England), was carried out according to the manufacturer's protocol. After ligation, the circular TFO RNAs were recovered by ethanol precipitation and the purity of the circular TFO RNAs was measured using spectrophotometer.
Denaturing of urea polyacrylamide gel electrophoresis was performed as described before [19] with modification. Briefly, 20% of denatured urea polyacrylamide gel was prepared and polymerized for 30 minutes. Then, the gel was prerun at 20 to 40 V for 45 minutes. Five L of TFO RNA mixed with 5 L of urea loading buffer was heated at 92 ∘ C for 2 minutes and immediately chilled on ice. It was run on the gel at 200 V for 45 minutes. Finally, the gel was stained with ethidium bromide (Sigma, USA) and viewed with a Bio-Rad Gel Doc XR system (CA, USA). (EMSA) . The target regions of the FIPV genome were synthesized by Dharmacon Research (USA) ( Table 1) . Each TFO RNA was mixed with the target region in 1X binding buffer containing 25 mM Tris-HCl, 6 mM MgCl 2 , and 10 mMNaCl in a final volume of 10 L and subsequently incubated at 37 ∘ C for 2 hours. The sample was run on 15% native polyacrylamide gel at 80 V, in cool condition. The stained gel was viewed by a Bio-Rad Gel Doc XR system.
Regions. The binding strength was measured using a nano Isothermal Titration Calorimeter (ITC) (TA instruments, Newcastle, UK). The RNA sample mixtures, consisting of circular TFOs (0.0002 mM), were incubated with their respective synthetic target regions (0.015 mM) using 1X binding buffer as the diluent. The experiment was run at 37 ∘ C with 2 L/injection, for a total of 25 injections. Data was collected every 250 seconds and analyzed using the NanoAnalyze software v2.3.6 provided by the manufacturer.
This experiment was conducted in CRFK cells, where 3 × 10 4 cell/well was seeded in 96-well plate to reach 80% confluency 24 hours prior to transfection. One hundred nM of TFO RNAs was separately transfected into the CRFK cells using a HiPerFect Transfection Reagent (Qiagen, Germany), as per the manufacturer's protocol. The plate was incubated at 37 ∘ C with 5% CO 2 for 6 hours. Then, the cultures were infected with 100TCID 50 of FIPV serotype II strain WSU 79-1146 for 1 hour at 37 ∘ C (100 L/well). Finally, the viral inoculum was replaced by fresh maintenance media (MEM containing 1% FBS and 1% pen/strep). Virus-infected and uninfected cells were maintained as positive and negative controls, respectively. The morphology of the cultures was recorded 72 hours after infection and samples were harvested at this time point and stored at −80 ∘ C prior to RNA extraction.
Inhibition. Different concentrations of circular TFO1 RNA (25 nM, 50 nM, 100 nM, and 500 nM) were transfected into CRFK cells. The plate was incubated for 6 hours followed by virus inoculation for 1 hour at 37 ∘ C with 5% CO2. The cells were processed as described above.
Madin-Darby Canine Kidney (MDCK) cell (ATCC no. CCL-34), at a concentration of 4 × 10 4 cell/well, was seeded in 96-well plate to reach 80% confluency 24 hours prior to transfection. Transfection was performed the same as before. One hundred nM of circular TFO RNA was transfected into MDCK cells. Following 6 hours
ORF1a/1b and 530-541
ORF1a/1b and 7399-7411
ORF1a/1b and 14048-14061
- * Highlighted in bold indicated the binding region. * * Unrelated circular TFO. [20, 21] , respectively. The reverse transcriptase quantitative real-time PCR (RT-qPCR) was performed using a Bio-Rad CFX96 real-time system (BioRad, USA). The reaction was amplified in a final volume of 25 L using a SensiMix SYBR No-ROX One-Step Kit (Bioline, UK), which consisted of 12.5 L 2X SensiMix SYBR No-Rox One-
Step reaction buffer, 10 M forward and reverse primers, 10 units RiboSafe RNase inhibitor, and 5 L template RNA. Absolute quantification approach was used to quantify qPCR results where a standard curve of a serial dilution of virus was plotted before the quantification. Amount of the virus in the samples was quantified based on this standard curve.
Analysis. Data statistical analysis was performed using SPSS 18.0. Data were represented as mean ± SE of three independent tests. One-way ANOVA, Tukey post hoc test was used to analyze the significant level among the data. ≤ 0.05 was considered significant. genome, which play important roles in viral replication, were selected as the target binding sites for the triplex formation. The target regions were 5 untranslated region (5 UTR), Open Reading Frames (ORFs) 1a and 1b, and 3 untranslated region (3 UTR) ( Table 1 ). The TFOs were designed in duplex, as they can bind with the single stranded target region and reshape into triplex. Both ends of the duplex TFOs were ligated with a linker sequence or clamps (C-C) to construct circular TFO RNA.
Denaturing PAGE assay was carried out after the ligation process to determine the formation of the circular TFO. As shown in Figure 1 , the circular TFO RNAs migrated faster than the linear TFO RNAs, when subjected to 20% denaturing PAGE.
Target Region. The binding ability was determined using Electrophoretic Mobility Shift Assay (EMSA) [23] . The appearance of the slow mobility band indicates the successful hybridization of circular TFO RNA with its target region. The binding ability of different TFO RNAs (TFO1 to TFO5) against their target regions was determined by EMSA (Figure 2) . TFO1, TFO3, TFO4, and TFO5 showed slow mobility band, while TFO2 showed the lack of an upward shifted band. This indicates the possession of triplex binding ability for all circular TFO RNAs, except TFO2.
TFO RNA. Study on the interaction and hybridization of TFO towards its target region is crucial, since the stronger the binding is, the more stable the triplex structure forms. As shown in supplementary Figure 1 (Table 3) .
The antiviral effect of circular TFO RNAs was investigated by RT-qPCR assay at 72 hours after transfection. The results showed viral RNA genome copy numbers of 3.65 × 10 9 , 3.22 × 10 14 , 5.04 × 10 9 , 5.01 × 10 9 , 4.41 × 10 9 , and 3.96 × 10 14 in cells treated with TFO1, TFO2, TFO3, TFO4, TFO5, and TFO7, respectively. The data analyzed by one-way ANOVA, Tukey post hoc test showed significant high viral RNA genome copy number of 4.03 × 10 14 for virus inoculated cells as compared to circular TFO1, TFO3, TFO4, and TFO5 treatments ( ≤ 0.05). The viral RNA copies of circular TFO2, linear TFO3 and TFO4, and unrelated circular TFO7 RNAs transfected cells also showed high viral RNA copy numbers which did not show significant differences to the infected cells ( ≥ 0.05) ( Figure 3 ). The morphological changes of the cells were also captured 72 hours after transfection. The cells transfected with circular TFO1, TFO3, TFO4, and TFO5 appeared to be in good condition following virus inoculation, while the cells transfected with circular TFO2 and linear TFO3 and TFO4 showed visible cytopathic effect (CPE), the same as virus inoculated cells (supplementary Figure 2) . Furthermore, cells transfected with TFO only remain viable indicating that TFO treatment is generally not toxic to the cells. Hence, these results illustrated the capacity of circular TFO RNAs (except TFO2) to inhibit FIPV replication.
Concentrations on FIPV Replication. Circular TFO1 was used to examine the dose-response relationship as a representative to other TFOs. The experimental conditions were identical to that of the previous experiment, except for TFO1 concentrations of 25 nM, 50 nM, 100 nM, and 500 nM. There was no significant reduction in viral RNA genome copies using the concentration of 25 nM TFO1. The other concentrations caused significant reductions in copy numbers as compared to the virus-infected cells. However, no significant difference was detected in copy numbers from all of these concentrations ( Figure 4 ).
The specificity of the TFO towards FIPV was tested, using TFO1 and TFO5, as the proper representatives of TFOs, on influenza A virus H1N1 New Jersey 8/76. The analyzed data using one-way ANOVA, Tukey post hoc test did not show significant reductions in the copies of viral RNA for both TFOs compared to the influenza virus inoculated cells ( ≥ 0.05) (supplementary Figure 3 ). Complex structure G4/Cir4 Figure 2 : EMSA analysis. EMSA analysis illustrated the binding of circular TFO 1, 3, 4, and 5 to the target regions as evidenced by upward band shift. Binding of each circular TFO except circular TFO2 to its respective target forms a complex that migrates slower than unbound TFO. G1 to G5 represent the target region for circular TFO1 to TFO5 and Cir1 to Cir5 represent the circular TFO1 to TFO5, respectively. in the replication process [24] . Meanwhile, the ORF1a/1b of FIPV are translated into polyproteins that are cleaved into nonstructural proteins which assemble into replicationtranscription complexes together with other viral proteins [24] . Hence, the development of molecular therapy targeting these critical regions may provide the possibility to inhibit FIPV replication.
Development of antiviral therapies against FIPV using siRNA [25] and viral protease inhibitors [26] Figure 4 : TFO1 dose-response study for inhibiting FIPV replication. The concentrations of 50 nM and higher showed significant antiviral effects. 50 nM of circular TFO1 RNA was able to reduce viral copy number by 5-fold log 10 from 10 14 to 10 9 , while 100 and 500 nM showed 4-fold reduction. Data are averages of 3 independent tests (mean ± SE). * Significantly different from FIPV-infected group.
as potential new treatments against FIPV infection. In this study, circular Triple Helix Forming Oligonucleotide (TFO) RNAs, specifically targeting the short regions of viral genome for triplex formation, were designed and evaluated. TFO1 and TFO2 targeted the 5 and 3 UTRs of the viral genome, respectively. TFO3 to TFO5 targeted different regions of the ORF1a/1b on FIPV genome. Prior to in vitro antiviral study, the ligated circular TFOs were evaluated using PAGE analysis. All of the circularised TFO showed faster migration pattern compared to the linear TFO; however, only slight variation was detected for some of the TFO (Figure 1 ). The reason for this is not clear but probably due to the differences in length and the tertiary structures of the TFOs leading to differences in the migration rate. EMSA was used to show the binding capability of each circular TFO towards the target region in the FIPV genome except for TFO2 which showed lack of formation of complex structure upon hybridization ( Figure 2) . The EMSA result also concurred with the antiviral study, where all circular TFOs (except TFO2) were able to demonstrate a significant reduction in the viral RNA genome copy numbers by 5-fold log 10 from 10 14 in virus inoculated cells to 10 9 in TFO-transfected cells (Figure 3 ). However, no antiviral properties were detected from the linear TFOs and unrelated circular TFO7 RNA, confirming that the antiviral activity is associated with specific binding of circular TFOs towards targeted regions.
Furthermore, the binding of the circular TFO to the target region was confirmed by nanoITC analysis; where the low value and high stability allowed TFOs to compete effectively with the target regions for inhibiting transcription in cell-free systems. Since, TFO1 shows the lowest value (Table 3) , the antiviral properties of this TFO were evaluated in doseresponse study. As shown in Figure 4 , 50 and 100 nM of TFO1 showed similar antiviral effects indicating the potential therapeutic application of TFO1 on FIPV replication. However, increasing the concentration of TFO1 to 500 nm failed to reduce the viral load further probably due to inefficiency of the transfection reagent to transfect the TFO into the cells. In addition, the virus has fast replication rate upon in vitro infection, where previous study on the growth of FIPV in CRFK cells showed that by 2 hours approximately 67% of FIPV 79-1146 were internalized by CRFK cells by endocytosis increasing to more than 70% at 3 hours [27, 28] . The above finding probably also explained the reason why no antiviral effect was detected when the transfection of the TFO was performed on virus-infected cells (data not shown).
The antiviral properties, as demonstrated by the circular TFOs, were probably associated with the binding of the TFO to the target region, based on both the Watson-Crick and Hoogsteen hydrogen bonds, which enhance the stability in terms of enthalpy, which is brought about by joining together two out of three strands of the triple helix in the proper orientation [29] . Therefore, the triplex formation is tightly bonded and not easy to detach. Furthermore, the circular TFOs were designed in such way that the presence of hydrogen bonding donors and acceptors in the purines is able to form two hydrogen bonds, while the pyrimidine bases can only form one additional hydrogen bond with incoming third bases [30] . However, there are various factors that may limit the activity of TFOs in cells like intracellular degradation of the TFO and limited accessibility of the TFO to the target sites which can prevent triplex formation [31] . These findings may also explain the inability of the designed TFO1 to inhibit further virus replication in dose-response study (Figure 4) .
Various molecular-based therapies against infectious diseases and cancer have been developed and tested. However, only the siRNA-based therapy has been studied extensively as a novel antiviral and anticancer therapy [32, 33] . Recently, McDonagh et al. [25] developed siRNA with antiviral activity against the FIPV 79-1146, where the designed siRNA was able to reduce the copy number of viral genome compared with virus-infected cells. The potential therapeutic application of TFOs, such as linear TFO conjugated with psoralen to inhibit the transcription of human immunodeficiency provirus [13] and TFO to inhibit the transcription of 1(I) collagen in rat fibroblasts [14] , has also been reported. In addition, short TFO conjugated with daunomycin targeting the promoter region of oncogene has been designed and evaluated on human cancer cells [31] . These studies indicated the flexibility of using TFO-based oligonucleotides as a potential molecular-based therapy. In this study, we demonstrated short circular TFO RNAs between 28 and 34 mers (Table 1) , which are able to inhibit FIPV replication by binding to specific target regions of the FIPV genome. All designed circular TFOs (except TFO2) showed significant inhibitory effects against FIPV replication. The TFOs that formed triplex structures showed antiviral effects towards FIPV replication. The reason why TFO2 failed to show any interaction with the target region or antiviral activity is probably due to the length of TFO2 (i.e., 24 mers), which might be insufficient to a triplex formation upon hybridization (Figure 2 ), be effective enough to suppress viral RNA transcription, and eventually inhibit virus replication. Nevertheless, the inability of TFO2 to show antiviral effect due to failure in the formation of functional tertiary structure of the triplex formation cannot be ruled out. In vitro antiviral study which showed no antiviral property for unrelated TFO (TFO7) and also inability of circular TFO1 and TFO5 to inhibit influenza A virus H1N1 infected cells confirms the specificity of the TFOs' activity.
In conclusion, the circular TFO RNA has the potential to be developed as a therapy against FIPV in cats. However, further studies on TFO specificity, actual mechanism of circular TFO RNA in the transcription alteration consequence of inhibiting the viral transcription process, and in vivo animal studies are important for this approach to work as a therapy in the future.
|
For how long was the denatured polyacrylamide gel polymerized?
| 4,057 |
30 minutes
| 6,326 |
1,590 |
In Vitro Antiviral Activity of Circular Triple Helix Forming Oligonucleotide RNA towards Feline Infectious Peritonitis Virus Replication
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3950953/
SHA: f5ad2323eb387f6e271e2842bb2cc4a33504fde3
Authors: Choong, Oi Kuan; Mehrbod, Parvaneh; Tejo, Bimo Ario; Omar, Abdul Rahman
Date: 2014-02-20
DOI: 10.1155/2014/654712
License: cc-by
Abstract: Feline Infectious Peritonitis (FIP) is a severe fatal immune-augmented disease in cat population. It is caused by FIP virus (FIPV), a virulent mutant strain of Feline Enteric Coronavirus (FECV). Current treatments and prophylactics are not effective. The in vitro antiviral properties of five circular Triple-Helix Forming Oligonucleotide (TFO) RNAs (TFO1 to TFO5), which target the different regions of virulent feline coronavirus (FCoV) strain FIPV WSU 79-1146 genome, were tested in FIPV-infected Crandell-Rees Feline Kidney (CRFK) cells. RT-qPCR results showed that the circular TFO RNAs, except TFO2, inhibit FIPV replication, where the viral genome copy numbers decreased significantly by 5-fold log(10) from 10(14) in the virus-inoculated cells to 10(9) in the circular TFO RNAs-transfected cells. Furthermore, the binding of the circular TFO RNA with the targeted viral genome segment was also confirmed using electrophoretic mobility shift assay. The strength of binding kinetics between the TFO RNAs and their target regions was demonstrated by NanoITC assay. In conclusion, the circular TFOs have the potential to be further developed as antiviral agents against FIPV infection.
Text: Feline Infectious Peritonitis Virus (FIPV) is an enveloped virus with a nonsegmented, positive sense, single-stranded RNA genome. FIPV is grouped as feline coronavirus (FCoV), under the family Coronaviridae. FCoV is divided into two biotypes, namely, Feline Enteric Coronavirus (FECV), a ubiquitous enteric biotype of FCoV, and FIPV, a virulent biotype of FCoV [1] . The relationship between these two biotypes still remains unclear. Two hypotheses have been proposed, (i) internal mutation theory and (ii) circulating high virulent-low virulent theory. Internal mutation theory stated that the development of FIP is due to the exposure of cat to variants of FCoV which have been mutated by gaining the ability to replicate within the macrophages [2] , while the circulating high virulent-low virulent theory explains the existence of both distinctive pathogenic and benign lineages of viruses within the cat population [3] .
Study has shown that about 40-80% of cats are detected with FECV shedding in their faeces [4] . About 12% of these FECV-positive cats have developed immune-mediated fatal FIP disease [4] . The prevalence of FIP among felines is due to continual cycles of infection and reinfection of FECV and indiscernible clinical symptoms of infected cats with FECV at an early stage before the progressive development of FIPV.
Vaccination against FIPV with an attenuated, temperature-sensitive strain of type II FIPV induces low antibody titre in kittens that have not been exposed to FCoV. However, there is considerable controversy on the safety and efficacy of this vaccine, since the vaccine contains type 2 strain, whereas type 1 viruses are more prevalent in the field [4] . In addition, antibodies against FIPV do not protect infected cats but enhance the infection of monocytes and macrophages via a mechanism known as Antibody-Dependent Enhancement [1] . Besides vaccines, several antiviral drugs such as ribavirin, 2 BioMed Research International interferons, and immunosuppressive drugs have been used as treatments for FIPV-infected cats, mainly to suppress the inflammatory and detrimental immune response [5] [6] [7] [8] . However, those treatments were ineffective. Hence, there is still significant unmet medical need to develop effective treatments and prophylactics for FIPV infection.
Triple Helix Forming Oligonucleotide (TFO) is defined as homopyrimidine oligonucleotides, which can form a sequence-specific triple helix by Hoogsteen bonds to the major groove of a complementary homopyrimidinehomopurine stretch in duplex DNA [9] . Furthermore, double helical RNA or DNA-RNA hybrids can be targeted as a template for triple helix formation, once the strand composition on the stabilities of triple helical complexes is determined [10] . Hence, TFO has been used to impede gene expressions by transcription inhibition of viral genes or oncogenes [11] [12] [13] [14] [15] [16] . The main purpose of this study is to develop and evaluate the in vitro antiviral properties of circular TFO RNAs against FIPV replication.
serotype II strain WSU 79-1146 (ATCC no. VR-1777) was grown in CRFK cells. A serial 10-fold dilution of FIPV was prepared from the working stock. Confluent 96-well plate was inoculated with 100 L of each virus dilution/well. The plate was incubated in a humidified incubator at 37 ∘ C, 5% CO 2 . Cytopathic effects (CPE) development was observed. The results were recorded after 72 hours and the virus tissue culture infective dose 50 (TCID 50 ) was calculated using Reed and Muench's method [17] .
Oligonucleotide RNA. The Triple Helix Forming Oligonucleotides (TFOs) were designed based on the genome sequence of FIPV serotype II strain WSU 79-1146 (Accession no: AY994055) [18] . TFOs, which specifically target the different regions of the FIPV genome, and one unrelated TFO were constructed ( Table 1 ). The specificity of the TFOs was identified using BLAST search in the NCBI database. The designed linear TFOs were synthesized by Dharmacon Research (USA), whereby the 5 and 3 ends of the linear TFOs were modified with phosphate (PO 4 ) group and hydroxide (OH) group, respectively. These modifications were necessary for the circularization of linear TFO. The process of circularization, using the T4 RNA ligase 1 (ssRNA ligase) (New England Biolabs Inc., England), was carried out according to the manufacturer's protocol. After ligation, the circular TFO RNAs were recovered by ethanol precipitation and the purity of the circular TFO RNAs was measured using spectrophotometer.
Denaturing of urea polyacrylamide gel electrophoresis was performed as described before [19] with modification. Briefly, 20% of denatured urea polyacrylamide gel was prepared and polymerized for 30 minutes. Then, the gel was prerun at 20 to 40 V for 45 minutes. Five L of TFO RNA mixed with 5 L of urea loading buffer was heated at 92 ∘ C for 2 minutes and immediately chilled on ice. It was run on the gel at 200 V for 45 minutes. Finally, the gel was stained with ethidium bromide (Sigma, USA) and viewed with a Bio-Rad Gel Doc XR system (CA, USA). (EMSA) . The target regions of the FIPV genome were synthesized by Dharmacon Research (USA) ( Table 1) . Each TFO RNA was mixed with the target region in 1X binding buffer containing 25 mM Tris-HCl, 6 mM MgCl 2 , and 10 mMNaCl in a final volume of 10 L and subsequently incubated at 37 ∘ C for 2 hours. The sample was run on 15% native polyacrylamide gel at 80 V, in cool condition. The stained gel was viewed by a Bio-Rad Gel Doc XR system.
Regions. The binding strength was measured using a nano Isothermal Titration Calorimeter (ITC) (TA instruments, Newcastle, UK). The RNA sample mixtures, consisting of circular TFOs (0.0002 mM), were incubated with their respective synthetic target regions (0.015 mM) using 1X binding buffer as the diluent. The experiment was run at 37 ∘ C with 2 L/injection, for a total of 25 injections. Data was collected every 250 seconds and analyzed using the NanoAnalyze software v2.3.6 provided by the manufacturer.
This experiment was conducted in CRFK cells, where 3 × 10 4 cell/well was seeded in 96-well plate to reach 80% confluency 24 hours prior to transfection. One hundred nM of TFO RNAs was separately transfected into the CRFK cells using a HiPerFect Transfection Reagent (Qiagen, Germany), as per the manufacturer's protocol. The plate was incubated at 37 ∘ C with 5% CO 2 for 6 hours. Then, the cultures were infected with 100TCID 50 of FIPV serotype II strain WSU 79-1146 for 1 hour at 37 ∘ C (100 L/well). Finally, the viral inoculum was replaced by fresh maintenance media (MEM containing 1% FBS and 1% pen/strep). Virus-infected and uninfected cells were maintained as positive and negative controls, respectively. The morphology of the cultures was recorded 72 hours after infection and samples were harvested at this time point and stored at −80 ∘ C prior to RNA extraction.
Inhibition. Different concentrations of circular TFO1 RNA (25 nM, 50 nM, 100 nM, and 500 nM) were transfected into CRFK cells. The plate was incubated for 6 hours followed by virus inoculation for 1 hour at 37 ∘ C with 5% CO2. The cells were processed as described above.
Madin-Darby Canine Kidney (MDCK) cell (ATCC no. CCL-34), at a concentration of 4 × 10 4 cell/well, was seeded in 96-well plate to reach 80% confluency 24 hours prior to transfection. Transfection was performed the same as before. One hundred nM of circular TFO RNA was transfected into MDCK cells. Following 6 hours
ORF1a/1b and 530-541
ORF1a/1b and 7399-7411
ORF1a/1b and 14048-14061
- * Highlighted in bold indicated the binding region. * * Unrelated circular TFO. [20, 21] , respectively. The reverse transcriptase quantitative real-time PCR (RT-qPCR) was performed using a Bio-Rad CFX96 real-time system (BioRad, USA). The reaction was amplified in a final volume of 25 L using a SensiMix SYBR No-ROX One-Step Kit (Bioline, UK), which consisted of 12.5 L 2X SensiMix SYBR No-Rox One-
Step reaction buffer, 10 M forward and reverse primers, 10 units RiboSafe RNase inhibitor, and 5 L template RNA. Absolute quantification approach was used to quantify qPCR results where a standard curve of a serial dilution of virus was plotted before the quantification. Amount of the virus in the samples was quantified based on this standard curve.
Analysis. Data statistical analysis was performed using SPSS 18.0. Data were represented as mean ± SE of three independent tests. One-way ANOVA, Tukey post hoc test was used to analyze the significant level among the data. ≤ 0.05 was considered significant. genome, which play important roles in viral replication, were selected as the target binding sites for the triplex formation. The target regions were 5 untranslated region (5 UTR), Open Reading Frames (ORFs) 1a and 1b, and 3 untranslated region (3 UTR) ( Table 1 ). The TFOs were designed in duplex, as they can bind with the single stranded target region and reshape into triplex. Both ends of the duplex TFOs were ligated with a linker sequence or clamps (C-C) to construct circular TFO RNA.
Denaturing PAGE assay was carried out after the ligation process to determine the formation of the circular TFO. As shown in Figure 1 , the circular TFO RNAs migrated faster than the linear TFO RNAs, when subjected to 20% denaturing PAGE.
Target Region. The binding ability was determined using Electrophoretic Mobility Shift Assay (EMSA) [23] . The appearance of the slow mobility band indicates the successful hybridization of circular TFO RNA with its target region. The binding ability of different TFO RNAs (TFO1 to TFO5) against their target regions was determined by EMSA (Figure 2) . TFO1, TFO3, TFO4, and TFO5 showed slow mobility band, while TFO2 showed the lack of an upward shifted band. This indicates the possession of triplex binding ability for all circular TFO RNAs, except TFO2.
TFO RNA. Study on the interaction and hybridization of TFO towards its target region is crucial, since the stronger the binding is, the more stable the triplex structure forms. As shown in supplementary Figure 1 (Table 3) .
The antiviral effect of circular TFO RNAs was investigated by RT-qPCR assay at 72 hours after transfection. The results showed viral RNA genome copy numbers of 3.65 × 10 9 , 3.22 × 10 14 , 5.04 × 10 9 , 5.01 × 10 9 , 4.41 × 10 9 , and 3.96 × 10 14 in cells treated with TFO1, TFO2, TFO3, TFO4, TFO5, and TFO7, respectively. The data analyzed by one-way ANOVA, Tukey post hoc test showed significant high viral RNA genome copy number of 4.03 × 10 14 for virus inoculated cells as compared to circular TFO1, TFO3, TFO4, and TFO5 treatments ( ≤ 0.05). The viral RNA copies of circular TFO2, linear TFO3 and TFO4, and unrelated circular TFO7 RNAs transfected cells also showed high viral RNA copy numbers which did not show significant differences to the infected cells ( ≥ 0.05) ( Figure 3 ). The morphological changes of the cells were also captured 72 hours after transfection. The cells transfected with circular TFO1, TFO3, TFO4, and TFO5 appeared to be in good condition following virus inoculation, while the cells transfected with circular TFO2 and linear TFO3 and TFO4 showed visible cytopathic effect (CPE), the same as virus inoculated cells (supplementary Figure 2) . Furthermore, cells transfected with TFO only remain viable indicating that TFO treatment is generally not toxic to the cells. Hence, these results illustrated the capacity of circular TFO RNAs (except TFO2) to inhibit FIPV replication.
Concentrations on FIPV Replication. Circular TFO1 was used to examine the dose-response relationship as a representative to other TFOs. The experimental conditions were identical to that of the previous experiment, except for TFO1 concentrations of 25 nM, 50 nM, 100 nM, and 500 nM. There was no significant reduction in viral RNA genome copies using the concentration of 25 nM TFO1. The other concentrations caused significant reductions in copy numbers as compared to the virus-infected cells. However, no significant difference was detected in copy numbers from all of these concentrations ( Figure 4 ).
The specificity of the TFO towards FIPV was tested, using TFO1 and TFO5, as the proper representatives of TFOs, on influenza A virus H1N1 New Jersey 8/76. The analyzed data using one-way ANOVA, Tukey post hoc test did not show significant reductions in the copies of viral RNA for both TFOs compared to the influenza virus inoculated cells ( ≥ 0.05) (supplementary Figure 3 ). Complex structure G4/Cir4 Figure 2 : EMSA analysis. EMSA analysis illustrated the binding of circular TFO 1, 3, 4, and 5 to the target regions as evidenced by upward band shift. Binding of each circular TFO except circular TFO2 to its respective target forms a complex that migrates slower than unbound TFO. G1 to G5 represent the target region for circular TFO1 to TFO5 and Cir1 to Cir5 represent the circular TFO1 to TFO5, respectively. in the replication process [24] . Meanwhile, the ORF1a/1b of FIPV are translated into polyproteins that are cleaved into nonstructural proteins which assemble into replicationtranscription complexes together with other viral proteins [24] . Hence, the development of molecular therapy targeting these critical regions may provide the possibility to inhibit FIPV replication.
Development of antiviral therapies against FIPV using siRNA [25] and viral protease inhibitors [26] Figure 4 : TFO1 dose-response study for inhibiting FIPV replication. The concentrations of 50 nM and higher showed significant antiviral effects. 50 nM of circular TFO1 RNA was able to reduce viral copy number by 5-fold log 10 from 10 14 to 10 9 , while 100 and 500 nM showed 4-fold reduction. Data are averages of 3 independent tests (mean ± SE). * Significantly different from FIPV-infected group.
as potential new treatments against FIPV infection. In this study, circular Triple Helix Forming Oligonucleotide (TFO) RNAs, specifically targeting the short regions of viral genome for triplex formation, were designed and evaluated. TFO1 and TFO2 targeted the 5 and 3 UTRs of the viral genome, respectively. TFO3 to TFO5 targeted different regions of the ORF1a/1b on FIPV genome. Prior to in vitro antiviral study, the ligated circular TFOs were evaluated using PAGE analysis. All of the circularised TFO showed faster migration pattern compared to the linear TFO; however, only slight variation was detected for some of the TFO (Figure 1 ). The reason for this is not clear but probably due to the differences in length and the tertiary structures of the TFOs leading to differences in the migration rate. EMSA was used to show the binding capability of each circular TFO towards the target region in the FIPV genome except for TFO2 which showed lack of formation of complex structure upon hybridization ( Figure 2) . The EMSA result also concurred with the antiviral study, where all circular TFOs (except TFO2) were able to demonstrate a significant reduction in the viral RNA genome copy numbers by 5-fold log 10 from 10 14 in virus inoculated cells to 10 9 in TFO-transfected cells (Figure 3 ). However, no antiviral properties were detected from the linear TFOs and unrelated circular TFO7 RNA, confirming that the antiviral activity is associated with specific binding of circular TFOs towards targeted regions.
Furthermore, the binding of the circular TFO to the target region was confirmed by nanoITC analysis; where the low value and high stability allowed TFOs to compete effectively with the target regions for inhibiting transcription in cell-free systems. Since, TFO1 shows the lowest value (Table 3) , the antiviral properties of this TFO were evaluated in doseresponse study. As shown in Figure 4 , 50 and 100 nM of TFO1 showed similar antiviral effects indicating the potential therapeutic application of TFO1 on FIPV replication. However, increasing the concentration of TFO1 to 500 nm failed to reduce the viral load further probably due to inefficiency of the transfection reagent to transfect the TFO into the cells. In addition, the virus has fast replication rate upon in vitro infection, where previous study on the growth of FIPV in CRFK cells showed that by 2 hours approximately 67% of FIPV 79-1146 were internalized by CRFK cells by endocytosis increasing to more than 70% at 3 hours [27, 28] . The above finding probably also explained the reason why no antiviral effect was detected when the transfection of the TFO was performed on virus-infected cells (data not shown).
The antiviral properties, as demonstrated by the circular TFOs, were probably associated with the binding of the TFO to the target region, based on both the Watson-Crick and Hoogsteen hydrogen bonds, which enhance the stability in terms of enthalpy, which is brought about by joining together two out of three strands of the triple helix in the proper orientation [29] . Therefore, the triplex formation is tightly bonded and not easy to detach. Furthermore, the circular TFOs were designed in such way that the presence of hydrogen bonding donors and acceptors in the purines is able to form two hydrogen bonds, while the pyrimidine bases can only form one additional hydrogen bond with incoming third bases [30] . However, there are various factors that may limit the activity of TFOs in cells like intracellular degradation of the TFO and limited accessibility of the TFO to the target sites which can prevent triplex formation [31] . These findings may also explain the inability of the designed TFO1 to inhibit further virus replication in dose-response study (Figure 4) .
Various molecular-based therapies against infectious diseases and cancer have been developed and tested. However, only the siRNA-based therapy has been studied extensively as a novel antiviral and anticancer therapy [32, 33] . Recently, McDonagh et al. [25] developed siRNA with antiviral activity against the FIPV 79-1146, where the designed siRNA was able to reduce the copy number of viral genome compared with virus-infected cells. The potential therapeutic application of TFOs, such as linear TFO conjugated with psoralen to inhibit the transcription of human immunodeficiency provirus [13] and TFO to inhibit the transcription of 1(I) collagen in rat fibroblasts [14] , has also been reported. In addition, short TFO conjugated with daunomycin targeting the promoter region of oncogene has been designed and evaluated on human cancer cells [31] . These studies indicated the flexibility of using TFO-based oligonucleotides as a potential molecular-based therapy. In this study, we demonstrated short circular TFO RNAs between 28 and 34 mers (Table 1) , which are able to inhibit FIPV replication by binding to specific target regions of the FIPV genome. All designed circular TFOs (except TFO2) showed significant inhibitory effects against FIPV replication. The TFOs that formed triplex structures showed antiviral effects towards FIPV replication. The reason why TFO2 failed to show any interaction with the target region or antiviral activity is probably due to the length of TFO2 (i.e., 24 mers), which might be insufficient to a triplex formation upon hybridization (Figure 2 ), be effective enough to suppress viral RNA transcription, and eventually inhibit virus replication. Nevertheless, the inability of TFO2 to show antiviral effect due to failure in the formation of functional tertiary structure of the triplex formation cannot be ruled out. In vitro antiviral study which showed no antiviral property for unrelated TFO (TFO7) and also inability of circular TFO1 and TFO5 to inhibit influenza A virus H1N1 infected cells confirms the specificity of the TFOs' activity.
In conclusion, the circular TFO RNA has the potential to be developed as a therapy against FIPV in cats. However, further studies on TFO specificity, actual mechanism of circular TFO RNA in the transcription alteration consequence of inhibiting the viral transcription process, and in vivo animal studies are important for this approach to work as a therapy in the future.
|
How was the binding strength measured?
| 4,058 |
nano Isothermal Titration Calorimeter (ITC)
| 7,176 |
1,578 |
Inhibitory Effect and Possible Mechanism of Action of Patchouli Alcohol against Influenza A (H2N2) Virus
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6264369/
SHA: f2d842780b9928cc70f38a4458553f2431877603
Authors: Wu, Huaxing; Li, Beili; Wang, Xue; Jin, Mingyuan; Wang, Guonian
Date: 2011-08-03
DOI: 10.3390/molecules16086489
License: cc-by
Abstract: In the present study, the anti-influenza A (H2N2) virus activity of patchouli alcohol was studied in vitro, in vivo and in silico. The CC(50) of patchouli alcohol was above 20 µM. Patchouli alcohol could inhibit influenza virus with an IC(50) of 4.03 ± 0.23 µM. MTT assay showed that the inhibition by patchouli alcohol appears strongly after penetration of the virus into the cell. In the influenza mouse model, patchouli alcohol showed obvious protection against the viral infection at a dose of 5 mg/kg/day. Flexible docking and molecular dynamic simulations indicated that patchouli alcohol was bound to the neuraminidase protein of influenza virus, with an interaction energy of –40.38 kcal mol(–1). The invariant key active-site residues Asp151, Arg152, Glu119, Glu276 and Tyr406 played important roles during the binding process. Based on spatial and energetic criteria, patchouli alcohol interfered with the NA functions. Results presented here suggest that patchouli alcohol possesses anti-influenza A (H2N2) virus properties, and therefore is a potential source of anti-influenza agents for the pharmaceutical industry.
Text: The influenza virus, which is one of the main causes of acute respiratory infections in humans, can lead to annual epidemics and infrequent pandemics. The two influenza pandemics of the 20 th century, "Asian Influenza (1957/H2N2)" and "Hong Kong Influenza (1968/H3N2)" resulted in the deaths of an estimated 2-3 million people globally [1, 2] . Today, their descendants continue to cause the majority of influenza infections in humans [3] . So far as it is learned that the most effective antiviral drug is the neuraminidase (NA) inhibitor, which target the NA glycoproteins of influenza A and B virus [4, 5] .
The release of new virions from the infected cell is a key step in the influenza life cycle and need neuraminidase (NA) to cleave the α-ketosidic linkage between terminal sialic acid and an adjacent sugar residue [6] . The NA inhibitors were designed to prevent the key step by blocking the active site of enzyme and thus allow sufficient time for the host immune systems to remove infected viruses [7] . Consistent efforts have been devoted to the development of NA inhibitors, using the crystal structure of the N2 sub-type NA protein [8] [9] [10] [11] [12] [13] [14] [15] . Indeed, oseltamivir (Tamiflu) is the representative NA inhibitor that has proven to be uniquely applicable oral drug in clinical practice for the treatment of influenza infection [4, 8, 9] . However, with an increase in medical use, the oseltamivir-resistant strains have been found and probably lead to a large scale outbreak of novel pandemic flu [16, 17] .
Patchouli alcohol ( Figure 1 ) has been well known for over a century. It is a major constituent of the pungent oil from the East Indian shrub Pogostemon cablin (Blanco) Benth, and widely used in fragrances. Patchouli oil is an important essential oil in the perfume industry, used to give a base and lasting character to a fragrance [16, 17] . The essential oil is very appreciated for its characteristic pleasant and long lasting woody, earthy, and camphoraceous odor, as well as for its fixative properties, being suitable for use in soaps and cosmetic products [16, 17] . The aerial part of Pogostemon cablin has wildly been used for the treatment of the common cold and as an antifungal agent in China [16, 17] . Moreover, the plant is widely used in Traditional Chinese Medicine as it presents various types of pharmacological activity according to the composition of the oil [16, 17] . Patchouli alcohol, as the major volatile constituent of patchouli oil, has been found to strongly inhibit H1N1 replication and weakly inhibit B/Ibaraki/2/85 replication [18] . To the best of our knowledge, the anti-influenza virus (H2N2) activities of patchouli alcohol have not been evaluated yet. Therefore, the aim of the present study was to evaluate the anti-influenza A virus (H2N2) activity of patchouli alcohol by MTT assay and mouse influenza model. On such basis, explicitly solvated docking and molecular dynamic (MD) methods were applied to investigative the binding mode involving patchouli alcohol with influenza virus NA protein. We anticipate that the insight into the understanding of inhibiting mechanism will be of value in the rational design of novel anti-influenza drugs.
First the efficacy of patchouli alcohol on influenza A (H2N2) virus replication and cell viability were examined. CC 50 was used to express the cytotoxicity of patchouli alcohol on MDCK. The CC 50 of patchouli alcohol was above 20 mM, which indicated that patchouli alcohol did not affect the growth of MDCK (Table 1) . Thus, it seems that the antiviral effects of patchouli alcohol were not due to the cytotoxicity. Moreover, patchouli alcohol was found to inhibit influenza A (H2N2) virus with an IC 50 of 4.03 ± 0.23 µM. Based on the IC 50 and CC 50 values, the selectivity index (SI) was calculated as >4.96. It is reported that a SI of 4 or more is appropriate for an antiviral agent [18] , suggesting that patchouli alcohol can be judged to have anti-influenza A (H2N2) virus activity.
Until now, it has been found that patchouli alcohol showed dose-dependent anti-influenza virus (A/PR/8/34, H1N1) activity, with an IC 50 value of 2.635 µM. Furthermore, it showed weak activity against B/Ibaraki/2/85 (IC 50 = 40.82 µM) [19] . With the addition of the above H2N2 inhibitory activity, we have a comprehensively view of the anti-influenza activity of patchouli alcohol.
Cells were pretreated with patchouli alcohol prior to virus infection (pretreatment cells), viruses were pretreated prior to infection (pretreatment virus), and patchouli alcohol was added during the adsorption period (adsorption) or after penetration of the viruses into cells (replication). Experiments were repeated independently three times and data presented are the average of three experiments. The symbols * indicated very significant difference p < 0.01 with respect to other mode (pretreatment virus, adsorption and pretreatment cell).
As shown in Figure 2 , patchouli alcohol showed anti-influenza A (H2N2) virus activity in a timedependent manner. It showed best antiviral activity when added at a concentration of 8 µM during the replication period with inhibition of the viral replication of 97.68% ± 2.09% for influenza A (H2N2) at 72 h. However, no significant effect was detected when patchouli alcohol was used for pretreatment of cells or viruses or when patchouli alcohol was only added during the adsorption phase. These results suggested that the inhibition of influenza A (H2N2) virus by patchouli alcohol appears to occur much more strongly after penetration of the virus into the cell. Besides, biochemical studies have indicated that the bioactivity of NA protein is essential determinant after the replication of influenza A (H2N2) virus [20] [21] [22] . Hence, we conclude that the function of NA protein may be suppressed by patchouli alcohol.
To evaluate the toxicity of patchouli alcohol, the mean value of body weight of mice in each group was statistically analyzed. The mean weights of mice administered at the 2 mg/kg/dose oseltamivir, 2 mg/kg/dose patchouli alcohol and 10 mg/kg/dose of patchouli alcohol one time daily for 7 days were not significantly different compared with the normal control mice, showing no toxicity of patchouli alcohol and oseltamivir within the testing concentration (P > 0.05). Physiological status was observed in virus infection mice. Three days after viral infection, some mice, especially mice in the H2N2 infected control group showed changes in behavior, such as a tendency to huddle, diminished vitality, and ruffled fur, etc. In the mouse influenza model, viral infection leads to loss of body weight and high mortality. Therefore, the efficacy of patchouli alcohol and oseltamivir were evaluated on the basis of survival rate measured for 15 days post-infection, for treated infected animals relative to untreated infected (control) animals. A comparison of efficacy of patchouli alcohol and oseltamivir in vivo mouse influenza model (oral treatment) showed that at a dose of 5 mg/kg/day, patchouli alcohol showed obvious protection against the influenza virus, as the mean day to death was detected as 11.8 ± 1.1 (Table 2) . When the dose was lowered to 1 mg/kg/day, patchouli alcohol showed weaker protection (measured by Survivors/total) than that of 5 mg/kg/day, the mean day to death was 7.5 ± 1.8. Whereas oseltamivir at this dose level (1 mg/kg/day) showed 50% protection (measured by survivors/total) against the influenza virus. In the H2N2 infected control group, there were no survivors. In view of both in vitro and in vivo data, we conclude that patchouli alcohol could be used in the treatment of human influenza virus infections.
Based on the above experiment data, patchouli alcohol is determined to be bound within NA protein. As the total energies and backbone root-mean-square-deviations (RMSD) in Figure 3 indicate, the energy-minimized patchouli alcohol-NA complex has been in equilibrium since about 0.5 ns, and then retains quite stable in the last 19.5 ns. It is consistent with the previous MD results of other NA inhibitors [23] [24] [25] [26] [27] [28] . Accordingly, the geometric and energetic analyses were made on the average structures of 0.5~20.0 ns MD trajectories, where the system has been already at equilibrium. The interaction energy (E inter ) of patchouli alcohol with NA was calculated at −40.38 kcal mol −1 , where the vdW rather than electrostatic interactions were found to play a dominant role, contribute to about 72% (−29.18 kcal mol −1 ). As shown in Figure 4 , the patchouli alcohol was bound at the active site which also bound to oseltamivir and zanamivir [28] . As Figure 5 shows, the oxygen atom of patchouli alcohol was oriented towards the sidechains of residues Glu119 and Tyr406, with one H-bond formed with each residue. The values of distances in Figure 6 further reveal that the docked complex remains rather stable throughout the simulation, with the average distances of Glu119:OE2patchouli alcohol:O and Tyr406:OH -patchouli alcohol:O less than 2.8 Å. The sum contributions (E sum ) of residues Glu119 and Tyr406 amounted to −8.46 and −7.37 kcal mol −1 , respectively (Table 3) . Besides, patchouli alcohol was stabilized by residues Arg118, Asp151, Arg152, Trp178, Ala246, Glu276, Arg292, Asn294 and Gln347, especially residues Asp151, Arg152 and Glu276 ( Figure 5 and Table 3 ). As a matter of fact, residues Asp151, Arg152, Glu119, Glu276 and Tyr406 of the NA protein have already received enough attention from rational drug designs [14, 30, 31] . The catalytic residues Asp151, Arg152 and Glu276 are crucial to the NA functions and the residues Glu119 and Tyr406 are important to stabilize the NA active sites [32, 33] . It suggests that the NA functions will be affected by the presence of patchouli alcohol, consistent with the above experiments. Patchouli alcohol matches with the NA active site and has an acceptable interaction energy. Considering the obvious structure discrepancies against current NA inhibitors, it represents an ideal lead compound for the designs of novel anti-influenza agents.
Patchouli alcohol and oseltamivir were obtained from Sigma Chemical Co. (St. Louis, MO, USA, purity > 99%) and was stored in glass vials with Teflon sealed caps at −20 ± 0.5 °C in the absence of light.
MDCK (Madin-Darby canine kidney) was purchased from Harbin Veterinary Research Institute (Harbin, Heilongjiang, China). The cells were grown in monolayer culture with Eagle's minimum essential medium (EMEM) supplemented with 10% fetal calf serum (FCS), 100 U/mL penicillin and 100 μg/mL streptomycin. The monolayers were removed from their plastic surfaces and serially passaged whenever they became confluent. Cells were plated out onto 96-well culture plates for cytotoxicity and anti-influenza assays, and propagated at 37 °C in an atmosphere of 5% CO 2 .
The influenza strain A/Leningrad/134/17/1957 H2N2) was purchased from National Control Institute of Veterinary Bioproducts and Pharmaceuticals (Beijing, China). Virus was routinely grown on MDCK cells. The stock cultures were prepared from supernatants of infected cells and stored at −80 °C.
The cellular toxicity of patchouli alcohol on MDCK cells was assessed by the MTT method. Briefly, cells were seeded on a microtiter plate in the absence or presence of various concentrations (20 µM -0.0098 µM) of patchouli alcohol (eight replicates) and incubated at 37 °C in a humidified atmosphere of 5% CO 2 for 72 h. The supernatants were discarded, washed with PBS twice and MTT reagent (5 mg/mL in PBS) was added to each well. After incubation at 37 °C for 4 h, the supernatants were removed, then 200 μL DMSO was added and incubated at 37 °C for another 30 min. After that the plates were read on an ELISA reader (Thermo Molecular Devices Co., Union City, USA) at 570/630 nm. The mean OD of the cell control wells was assigned a value of 100%. The maximal non-toxic concentration (TD 0 ) and 50% cytotoxic concentration (CC 50 ) were calculated by linear regression analysis of the dose-response curves generated from the data.
Inhibition of virus replication was measured by the MTT method. Serial dilution of the treated virus was adsorbed to the cells for 1 h at 37 °C. The residual inoculum was discared and infected cells were added with EMEM containing 2% FCS. Each assay was performed in eight replicates. After incubation for 72 h at 37 °C, the cultures were measured by MTT method as described above. The concentration of patchouli alcohol and oseltamivir which inhibited virus numbers by 50% (IC 50 ) was determined from dose-response curves.
Cells and viruses were incubated with patchouli alcohol at different stages during the viral infection cycle in order to determine the mode of antiviral action. Cells were pretreated with patchouli alcohol before viral infection, viruses were incubated with patchouli alcohol before infection and cells and viruses were incubated together with patchouli alcohol during adsorption or after penetration of the virus into the host cells. Patchouli alcohol was always used at the nontoxic concentration. Cell monolayers were pretreated with patchouli alcohol prior to inoculation with virus by adding patchouli alcohol to the culture medium and incubation for 1 h at 37 °C. The compound was aspirated and cells were washed immediately before the influenza A (H2N2) inoculum was added. For pretreatment virus, Influenza A (H2N2) was incubated in medium containing patchouli alcohol for 1h at room temperature prior to infection of MDCK cells. For analyzing the anti-influenza A (H2N2) inhibition during the adsorption period, the same amount of influenza A (H2N2) was mixed with the drug and added to the cells immediately. After 1 h of adsorption at 37 °C, the inoculum was removed and DMEM supplemented with 2 % FCS were added to the cells. The effect of patchouli alcohol against influenza A (H2N2) was also tested during the replication period by adding it after adsorption, as typical performed in anti-influenza A (H2N2) susceptibility studies. Each assay was run in eight replicates.
Kunming mice, weighing 18-22 g (6 weeks of age) were purchased from Harbin Veterinary Research Institute Animal Co., Ltd. (Harbin, Heilongjiang, China) . First, the toxicity of patchouli alcohol and oseltamivir was assessed in the healthy mice by the loss of body weight compared with the control group (2% DMSO in physiological saline). The mice were orally administered with 10 mg/kg/dose patchouli alcohol, 2 mg/kg/dose patchouli alcohol or 2 mg/kg/dose oseltamivir (dissolved in 2% DMSO in physiological saline) one time daily for 7 days. The weight of mice was determined daily. We conducted procedures according to Principle of Laboratory Animal Care (NIH Publication No. 85 -23, revised 1985) and the guidelines of the Peking University Animal Research Committee.
Kunming mice were anesthetized with isoflurane and exposed to virus (A/Leningrad/134/17/1957) by intranasal instillation. Drugs were prepared in 2% DMSO in physiological saline and administered 4 h prior to virus exposure and continued daily for 5 days. All mice were observed daily for changes in weight and for any deaths. Parameters for evaluation of antiviral activity included weight loss, reduction in mortality and/or increase in mean day to death (MDD) determined through 15 days.
The N2 sub-type neuraminidase crystal structure (PDB code 1IVD) was obtained from the RCSB Protein Data Bank [34] . For convenience, the structure is named as NA hereafter. Geometry and partial atomic charges of the patchouli alcohol ( Figure 1) were calculated with the Discover 3.0 module (Insight II 2005) [35] by applying the BFGS algorithm [36] and the consistent-valence force-field (CVFF), with a convergence criterion of 0.01 kcal mol −1 Å −1 . The docking and molecular dynamics (MD) simulations were performed by the general protocols in the Insight II 2005 software packages, consistent with the previous literatures [24, 26, 28, 35, [37] [38] [39] . During the MD simulations, the canonical ensemble (NVT) was employed at normal temperature (300 K). The MD temperature was controlled by the velocity scaling thermostat [40] . Integrations of the classical equations of motion were achieved using the Verlet algorithm. The systems were solvated in a large sphere of TIP3P water molecules [40] with the radius of 35.0 Å, which is enough to hold the ensembles [40] . The MD trajectories were generated using a 1.0-fs time step for a total of 20.0 ns, saved at 5.0-ps intervals. The interaction energies of patchouli alcohol with NA and the respective residues at the NA active site were calculated by the Docking module [35], over the 0.5~20.0 ns MD trajectories.
All results are expressed as mean values ± standard deviations (SDs) (n = 3). The significance of difference was calculated by one-way analysis of variance, and values p < 0.001 were considered to be significant.
In conclusion, patchouli alcohol possesses anti-influenza A (H2N2) virus activity via interference with the NA function that cleaves the α-glycosidic bond between sialic acid and glycoconjugate. Our results provide the promising information for the potential use of patchouli alcohol in the treatment of influenza A (H2N2) virus infectious disease. Further mechanistic studies on the anti-influenza A virus activity are needed to support this point of view.
|
What was the focus of this study?
| 4,070 |
the anti-influenza A (H2N2) virus activity of patchouli alcohol
| 376 |
1,578 |
Inhibitory Effect and Possible Mechanism of Action of Patchouli Alcohol against Influenza A (H2N2) Virus
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6264369/
SHA: f2d842780b9928cc70f38a4458553f2431877603
Authors: Wu, Huaxing; Li, Beili; Wang, Xue; Jin, Mingyuan; Wang, Guonian
Date: 2011-08-03
DOI: 10.3390/molecules16086489
License: cc-by
Abstract: In the present study, the anti-influenza A (H2N2) virus activity of patchouli alcohol was studied in vitro, in vivo and in silico. The CC(50) of patchouli alcohol was above 20 µM. Patchouli alcohol could inhibit influenza virus with an IC(50) of 4.03 ± 0.23 µM. MTT assay showed that the inhibition by patchouli alcohol appears strongly after penetration of the virus into the cell. In the influenza mouse model, patchouli alcohol showed obvious protection against the viral infection at a dose of 5 mg/kg/day. Flexible docking and molecular dynamic simulations indicated that patchouli alcohol was bound to the neuraminidase protein of influenza virus, with an interaction energy of –40.38 kcal mol(–1). The invariant key active-site residues Asp151, Arg152, Glu119, Glu276 and Tyr406 played important roles during the binding process. Based on spatial and energetic criteria, patchouli alcohol interfered with the NA functions. Results presented here suggest that patchouli alcohol possesses anti-influenza A (H2N2) virus properties, and therefore is a potential source of anti-influenza agents for the pharmaceutical industry.
Text: The influenza virus, which is one of the main causes of acute respiratory infections in humans, can lead to annual epidemics and infrequent pandemics. The two influenza pandemics of the 20 th century, "Asian Influenza (1957/H2N2)" and "Hong Kong Influenza (1968/H3N2)" resulted in the deaths of an estimated 2-3 million people globally [1, 2] . Today, their descendants continue to cause the majority of influenza infections in humans [3] . So far as it is learned that the most effective antiviral drug is the neuraminidase (NA) inhibitor, which target the NA glycoproteins of influenza A and B virus [4, 5] .
The release of new virions from the infected cell is a key step in the influenza life cycle and need neuraminidase (NA) to cleave the α-ketosidic linkage between terminal sialic acid and an adjacent sugar residue [6] . The NA inhibitors were designed to prevent the key step by blocking the active site of enzyme and thus allow sufficient time for the host immune systems to remove infected viruses [7] . Consistent efforts have been devoted to the development of NA inhibitors, using the crystal structure of the N2 sub-type NA protein [8] [9] [10] [11] [12] [13] [14] [15] . Indeed, oseltamivir (Tamiflu) is the representative NA inhibitor that has proven to be uniquely applicable oral drug in clinical practice for the treatment of influenza infection [4, 8, 9] . However, with an increase in medical use, the oseltamivir-resistant strains have been found and probably lead to a large scale outbreak of novel pandemic flu [16, 17] .
Patchouli alcohol ( Figure 1 ) has been well known for over a century. It is a major constituent of the pungent oil from the East Indian shrub Pogostemon cablin (Blanco) Benth, and widely used in fragrances. Patchouli oil is an important essential oil in the perfume industry, used to give a base and lasting character to a fragrance [16, 17] . The essential oil is very appreciated for its characteristic pleasant and long lasting woody, earthy, and camphoraceous odor, as well as for its fixative properties, being suitable for use in soaps and cosmetic products [16, 17] . The aerial part of Pogostemon cablin has wildly been used for the treatment of the common cold and as an antifungal agent in China [16, 17] . Moreover, the plant is widely used in Traditional Chinese Medicine as it presents various types of pharmacological activity according to the composition of the oil [16, 17] . Patchouli alcohol, as the major volatile constituent of patchouli oil, has been found to strongly inhibit H1N1 replication and weakly inhibit B/Ibaraki/2/85 replication [18] . To the best of our knowledge, the anti-influenza virus (H2N2) activities of patchouli alcohol have not been evaluated yet. Therefore, the aim of the present study was to evaluate the anti-influenza A virus (H2N2) activity of patchouli alcohol by MTT assay and mouse influenza model. On such basis, explicitly solvated docking and molecular dynamic (MD) methods were applied to investigative the binding mode involving patchouli alcohol with influenza virus NA protein. We anticipate that the insight into the understanding of inhibiting mechanism will be of value in the rational design of novel anti-influenza drugs.
First the efficacy of patchouli alcohol on influenza A (H2N2) virus replication and cell viability were examined. CC 50 was used to express the cytotoxicity of patchouli alcohol on MDCK. The CC 50 of patchouli alcohol was above 20 mM, which indicated that patchouli alcohol did not affect the growth of MDCK (Table 1) . Thus, it seems that the antiviral effects of patchouli alcohol were not due to the cytotoxicity. Moreover, patchouli alcohol was found to inhibit influenza A (H2N2) virus with an IC 50 of 4.03 ± 0.23 µM. Based on the IC 50 and CC 50 values, the selectivity index (SI) was calculated as >4.96. It is reported that a SI of 4 or more is appropriate for an antiviral agent [18] , suggesting that patchouli alcohol can be judged to have anti-influenza A (H2N2) virus activity.
Until now, it has been found that patchouli alcohol showed dose-dependent anti-influenza virus (A/PR/8/34, H1N1) activity, with an IC 50 value of 2.635 µM. Furthermore, it showed weak activity against B/Ibaraki/2/85 (IC 50 = 40.82 µM) [19] . With the addition of the above H2N2 inhibitory activity, we have a comprehensively view of the anti-influenza activity of patchouli alcohol.
Cells were pretreated with patchouli alcohol prior to virus infection (pretreatment cells), viruses were pretreated prior to infection (pretreatment virus), and patchouli alcohol was added during the adsorption period (adsorption) or after penetration of the viruses into cells (replication). Experiments were repeated independently three times and data presented are the average of three experiments. The symbols * indicated very significant difference p < 0.01 with respect to other mode (pretreatment virus, adsorption and pretreatment cell).
As shown in Figure 2 , patchouli alcohol showed anti-influenza A (H2N2) virus activity in a timedependent manner. It showed best antiviral activity when added at a concentration of 8 µM during the replication period with inhibition of the viral replication of 97.68% ± 2.09% for influenza A (H2N2) at 72 h. However, no significant effect was detected when patchouli alcohol was used for pretreatment of cells or viruses or when patchouli alcohol was only added during the adsorption phase. These results suggested that the inhibition of influenza A (H2N2) virus by patchouli alcohol appears to occur much more strongly after penetration of the virus into the cell. Besides, biochemical studies have indicated that the bioactivity of NA protein is essential determinant after the replication of influenza A (H2N2) virus [20] [21] [22] . Hence, we conclude that the function of NA protein may be suppressed by patchouli alcohol.
To evaluate the toxicity of patchouli alcohol, the mean value of body weight of mice in each group was statistically analyzed. The mean weights of mice administered at the 2 mg/kg/dose oseltamivir, 2 mg/kg/dose patchouli alcohol and 10 mg/kg/dose of patchouli alcohol one time daily for 7 days were not significantly different compared with the normal control mice, showing no toxicity of patchouli alcohol and oseltamivir within the testing concentration (P > 0.05). Physiological status was observed in virus infection mice. Three days after viral infection, some mice, especially mice in the H2N2 infected control group showed changes in behavior, such as a tendency to huddle, diminished vitality, and ruffled fur, etc. In the mouse influenza model, viral infection leads to loss of body weight and high mortality. Therefore, the efficacy of patchouli alcohol and oseltamivir were evaluated on the basis of survival rate measured for 15 days post-infection, for treated infected animals relative to untreated infected (control) animals. A comparison of efficacy of patchouli alcohol and oseltamivir in vivo mouse influenza model (oral treatment) showed that at a dose of 5 mg/kg/day, patchouli alcohol showed obvious protection against the influenza virus, as the mean day to death was detected as 11.8 ± 1.1 (Table 2) . When the dose was lowered to 1 mg/kg/day, patchouli alcohol showed weaker protection (measured by Survivors/total) than that of 5 mg/kg/day, the mean day to death was 7.5 ± 1.8. Whereas oseltamivir at this dose level (1 mg/kg/day) showed 50% protection (measured by survivors/total) against the influenza virus. In the H2N2 infected control group, there were no survivors. In view of both in vitro and in vivo data, we conclude that patchouli alcohol could be used in the treatment of human influenza virus infections.
Based on the above experiment data, patchouli alcohol is determined to be bound within NA protein. As the total energies and backbone root-mean-square-deviations (RMSD) in Figure 3 indicate, the energy-minimized patchouli alcohol-NA complex has been in equilibrium since about 0.5 ns, and then retains quite stable in the last 19.5 ns. It is consistent with the previous MD results of other NA inhibitors [23] [24] [25] [26] [27] [28] . Accordingly, the geometric and energetic analyses were made on the average structures of 0.5~20.0 ns MD trajectories, where the system has been already at equilibrium. The interaction energy (E inter ) of patchouli alcohol with NA was calculated at −40.38 kcal mol −1 , where the vdW rather than electrostatic interactions were found to play a dominant role, contribute to about 72% (−29.18 kcal mol −1 ). As shown in Figure 4 , the patchouli alcohol was bound at the active site which also bound to oseltamivir and zanamivir [28] . As Figure 5 shows, the oxygen atom of patchouli alcohol was oriented towards the sidechains of residues Glu119 and Tyr406, with one H-bond formed with each residue. The values of distances in Figure 6 further reveal that the docked complex remains rather stable throughout the simulation, with the average distances of Glu119:OE2patchouli alcohol:O and Tyr406:OH -patchouli alcohol:O less than 2.8 Å. The sum contributions (E sum ) of residues Glu119 and Tyr406 amounted to −8.46 and −7.37 kcal mol −1 , respectively (Table 3) . Besides, patchouli alcohol was stabilized by residues Arg118, Asp151, Arg152, Trp178, Ala246, Glu276, Arg292, Asn294 and Gln347, especially residues Asp151, Arg152 and Glu276 ( Figure 5 and Table 3 ). As a matter of fact, residues Asp151, Arg152, Glu119, Glu276 and Tyr406 of the NA protein have already received enough attention from rational drug designs [14, 30, 31] . The catalytic residues Asp151, Arg152 and Glu276 are crucial to the NA functions and the residues Glu119 and Tyr406 are important to stabilize the NA active sites [32, 33] . It suggests that the NA functions will be affected by the presence of patchouli alcohol, consistent with the above experiments. Patchouli alcohol matches with the NA active site and has an acceptable interaction energy. Considering the obvious structure discrepancies against current NA inhibitors, it represents an ideal lead compound for the designs of novel anti-influenza agents.
Patchouli alcohol and oseltamivir were obtained from Sigma Chemical Co. (St. Louis, MO, USA, purity > 99%) and was stored in glass vials with Teflon sealed caps at −20 ± 0.5 °C in the absence of light.
MDCK (Madin-Darby canine kidney) was purchased from Harbin Veterinary Research Institute (Harbin, Heilongjiang, China). The cells were grown in monolayer culture with Eagle's minimum essential medium (EMEM) supplemented with 10% fetal calf serum (FCS), 100 U/mL penicillin and 100 μg/mL streptomycin. The monolayers were removed from their plastic surfaces and serially passaged whenever they became confluent. Cells were plated out onto 96-well culture plates for cytotoxicity and anti-influenza assays, and propagated at 37 °C in an atmosphere of 5% CO 2 .
The influenza strain A/Leningrad/134/17/1957 H2N2) was purchased from National Control Institute of Veterinary Bioproducts and Pharmaceuticals (Beijing, China). Virus was routinely grown on MDCK cells. The stock cultures were prepared from supernatants of infected cells and stored at −80 °C.
The cellular toxicity of patchouli alcohol on MDCK cells was assessed by the MTT method. Briefly, cells were seeded on a microtiter plate in the absence or presence of various concentrations (20 µM -0.0098 µM) of patchouli alcohol (eight replicates) and incubated at 37 °C in a humidified atmosphere of 5% CO 2 for 72 h. The supernatants were discarded, washed with PBS twice and MTT reagent (5 mg/mL in PBS) was added to each well. After incubation at 37 °C for 4 h, the supernatants were removed, then 200 μL DMSO was added and incubated at 37 °C for another 30 min. After that the plates were read on an ELISA reader (Thermo Molecular Devices Co., Union City, USA) at 570/630 nm. The mean OD of the cell control wells was assigned a value of 100%. The maximal non-toxic concentration (TD 0 ) and 50% cytotoxic concentration (CC 50 ) were calculated by linear regression analysis of the dose-response curves generated from the data.
Inhibition of virus replication was measured by the MTT method. Serial dilution of the treated virus was adsorbed to the cells for 1 h at 37 °C. The residual inoculum was discared and infected cells were added with EMEM containing 2% FCS. Each assay was performed in eight replicates. After incubation for 72 h at 37 °C, the cultures were measured by MTT method as described above. The concentration of patchouli alcohol and oseltamivir which inhibited virus numbers by 50% (IC 50 ) was determined from dose-response curves.
Cells and viruses were incubated with patchouli alcohol at different stages during the viral infection cycle in order to determine the mode of antiviral action. Cells were pretreated with patchouli alcohol before viral infection, viruses were incubated with patchouli alcohol before infection and cells and viruses were incubated together with patchouli alcohol during adsorption or after penetration of the virus into the host cells. Patchouli alcohol was always used at the nontoxic concentration. Cell monolayers were pretreated with patchouli alcohol prior to inoculation with virus by adding patchouli alcohol to the culture medium and incubation for 1 h at 37 °C. The compound was aspirated and cells were washed immediately before the influenza A (H2N2) inoculum was added. For pretreatment virus, Influenza A (H2N2) was incubated in medium containing patchouli alcohol for 1h at room temperature prior to infection of MDCK cells. For analyzing the anti-influenza A (H2N2) inhibition during the adsorption period, the same amount of influenza A (H2N2) was mixed with the drug and added to the cells immediately. After 1 h of adsorption at 37 °C, the inoculum was removed and DMEM supplemented with 2 % FCS were added to the cells. The effect of patchouli alcohol against influenza A (H2N2) was also tested during the replication period by adding it after adsorption, as typical performed in anti-influenza A (H2N2) susceptibility studies. Each assay was run in eight replicates.
Kunming mice, weighing 18-22 g (6 weeks of age) were purchased from Harbin Veterinary Research Institute Animal Co., Ltd. (Harbin, Heilongjiang, China) . First, the toxicity of patchouli alcohol and oseltamivir was assessed in the healthy mice by the loss of body weight compared with the control group (2% DMSO in physiological saline). The mice were orally administered with 10 mg/kg/dose patchouli alcohol, 2 mg/kg/dose patchouli alcohol or 2 mg/kg/dose oseltamivir (dissolved in 2% DMSO in physiological saline) one time daily for 7 days. The weight of mice was determined daily. We conducted procedures according to Principle of Laboratory Animal Care (NIH Publication No. 85 -23, revised 1985) and the guidelines of the Peking University Animal Research Committee.
Kunming mice were anesthetized with isoflurane and exposed to virus (A/Leningrad/134/17/1957) by intranasal instillation. Drugs were prepared in 2% DMSO in physiological saline and administered 4 h prior to virus exposure and continued daily for 5 days. All mice were observed daily for changes in weight and for any deaths. Parameters for evaluation of antiviral activity included weight loss, reduction in mortality and/or increase in mean day to death (MDD) determined through 15 days.
The N2 sub-type neuraminidase crystal structure (PDB code 1IVD) was obtained from the RCSB Protein Data Bank [34] . For convenience, the structure is named as NA hereafter. Geometry and partial atomic charges of the patchouli alcohol ( Figure 1) were calculated with the Discover 3.0 module (Insight II 2005) [35] by applying the BFGS algorithm [36] and the consistent-valence force-field (CVFF), with a convergence criterion of 0.01 kcal mol −1 Å −1 . The docking and molecular dynamics (MD) simulations were performed by the general protocols in the Insight II 2005 software packages, consistent with the previous literatures [24, 26, 28, 35, [37] [38] [39] . During the MD simulations, the canonical ensemble (NVT) was employed at normal temperature (300 K). The MD temperature was controlled by the velocity scaling thermostat [40] . Integrations of the classical equations of motion were achieved using the Verlet algorithm. The systems were solvated in a large sphere of TIP3P water molecules [40] with the radius of 35.0 Å, which is enough to hold the ensembles [40] . The MD trajectories were generated using a 1.0-fs time step for a total of 20.0 ns, saved at 5.0-ps intervals. The interaction energies of patchouli alcohol with NA and the respective residues at the NA active site were calculated by the Docking module [35], over the 0.5~20.0 ns MD trajectories.
All results are expressed as mean values ± standard deviations (SDs) (n = 3). The significance of difference was calculated by one-way analysis of variance, and values p < 0.001 were considered to be significant.
In conclusion, patchouli alcohol possesses anti-influenza A (H2N2) virus activity via interference with the NA function that cleaves the α-glycosidic bond between sialic acid and glycoconjugate. Our results provide the promising information for the potential use of patchouli alcohol in the treatment of influenza A (H2N2) virus infectious disease. Further mechanistic studies on the anti-influenza A virus activity are needed to support this point of view.
|
What do neuroaminidase inhibitors target?
| 4,071 |
NA glycoproteins of influenza A and B virus
| 2,050 |
1,578 |
Inhibitory Effect and Possible Mechanism of Action of Patchouli Alcohol against Influenza A (H2N2) Virus
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6264369/
SHA: f2d842780b9928cc70f38a4458553f2431877603
Authors: Wu, Huaxing; Li, Beili; Wang, Xue; Jin, Mingyuan; Wang, Guonian
Date: 2011-08-03
DOI: 10.3390/molecules16086489
License: cc-by
Abstract: In the present study, the anti-influenza A (H2N2) virus activity of patchouli alcohol was studied in vitro, in vivo and in silico. The CC(50) of patchouli alcohol was above 20 µM. Patchouli alcohol could inhibit influenza virus with an IC(50) of 4.03 ± 0.23 µM. MTT assay showed that the inhibition by patchouli alcohol appears strongly after penetration of the virus into the cell. In the influenza mouse model, patchouli alcohol showed obvious protection against the viral infection at a dose of 5 mg/kg/day. Flexible docking and molecular dynamic simulations indicated that patchouli alcohol was bound to the neuraminidase protein of influenza virus, with an interaction energy of –40.38 kcal mol(–1). The invariant key active-site residues Asp151, Arg152, Glu119, Glu276 and Tyr406 played important roles during the binding process. Based on spatial and energetic criteria, patchouli alcohol interfered with the NA functions. Results presented here suggest that patchouli alcohol possesses anti-influenza A (H2N2) virus properties, and therefore is a potential source of anti-influenza agents for the pharmaceutical industry.
Text: The influenza virus, which is one of the main causes of acute respiratory infections in humans, can lead to annual epidemics and infrequent pandemics. The two influenza pandemics of the 20 th century, "Asian Influenza (1957/H2N2)" and "Hong Kong Influenza (1968/H3N2)" resulted in the deaths of an estimated 2-3 million people globally [1, 2] . Today, their descendants continue to cause the majority of influenza infections in humans [3] . So far as it is learned that the most effective antiviral drug is the neuraminidase (NA) inhibitor, which target the NA glycoproteins of influenza A and B virus [4, 5] .
The release of new virions from the infected cell is a key step in the influenza life cycle and need neuraminidase (NA) to cleave the α-ketosidic linkage between terminal sialic acid and an adjacent sugar residue [6] . The NA inhibitors were designed to prevent the key step by blocking the active site of enzyme and thus allow sufficient time for the host immune systems to remove infected viruses [7] . Consistent efforts have been devoted to the development of NA inhibitors, using the crystal structure of the N2 sub-type NA protein [8] [9] [10] [11] [12] [13] [14] [15] . Indeed, oseltamivir (Tamiflu) is the representative NA inhibitor that has proven to be uniquely applicable oral drug in clinical practice for the treatment of influenza infection [4, 8, 9] . However, with an increase in medical use, the oseltamivir-resistant strains have been found and probably lead to a large scale outbreak of novel pandemic flu [16, 17] .
Patchouli alcohol ( Figure 1 ) has been well known for over a century. It is a major constituent of the pungent oil from the East Indian shrub Pogostemon cablin (Blanco) Benth, and widely used in fragrances. Patchouli oil is an important essential oil in the perfume industry, used to give a base and lasting character to a fragrance [16, 17] . The essential oil is very appreciated for its characteristic pleasant and long lasting woody, earthy, and camphoraceous odor, as well as for its fixative properties, being suitable for use in soaps and cosmetic products [16, 17] . The aerial part of Pogostemon cablin has wildly been used for the treatment of the common cold and as an antifungal agent in China [16, 17] . Moreover, the plant is widely used in Traditional Chinese Medicine as it presents various types of pharmacological activity according to the composition of the oil [16, 17] . Patchouli alcohol, as the major volatile constituent of patchouli oil, has been found to strongly inhibit H1N1 replication and weakly inhibit B/Ibaraki/2/85 replication [18] . To the best of our knowledge, the anti-influenza virus (H2N2) activities of patchouli alcohol have not been evaluated yet. Therefore, the aim of the present study was to evaluate the anti-influenza A virus (H2N2) activity of patchouli alcohol by MTT assay and mouse influenza model. On such basis, explicitly solvated docking and molecular dynamic (MD) methods were applied to investigative the binding mode involving patchouli alcohol with influenza virus NA protein. We anticipate that the insight into the understanding of inhibiting mechanism will be of value in the rational design of novel anti-influenza drugs.
First the efficacy of patchouli alcohol on influenza A (H2N2) virus replication and cell viability were examined. CC 50 was used to express the cytotoxicity of patchouli alcohol on MDCK. The CC 50 of patchouli alcohol was above 20 mM, which indicated that patchouli alcohol did not affect the growth of MDCK (Table 1) . Thus, it seems that the antiviral effects of patchouli alcohol were not due to the cytotoxicity. Moreover, patchouli alcohol was found to inhibit influenza A (H2N2) virus with an IC 50 of 4.03 ± 0.23 µM. Based on the IC 50 and CC 50 values, the selectivity index (SI) was calculated as >4.96. It is reported that a SI of 4 or more is appropriate for an antiviral agent [18] , suggesting that patchouli alcohol can be judged to have anti-influenza A (H2N2) virus activity.
Until now, it has been found that patchouli alcohol showed dose-dependent anti-influenza virus (A/PR/8/34, H1N1) activity, with an IC 50 value of 2.635 µM. Furthermore, it showed weak activity against B/Ibaraki/2/85 (IC 50 = 40.82 µM) [19] . With the addition of the above H2N2 inhibitory activity, we have a comprehensively view of the anti-influenza activity of patchouli alcohol.
Cells were pretreated with patchouli alcohol prior to virus infection (pretreatment cells), viruses were pretreated prior to infection (pretreatment virus), and patchouli alcohol was added during the adsorption period (adsorption) or after penetration of the viruses into cells (replication). Experiments were repeated independently three times and data presented are the average of three experiments. The symbols * indicated very significant difference p < 0.01 with respect to other mode (pretreatment virus, adsorption and pretreatment cell).
As shown in Figure 2 , patchouli alcohol showed anti-influenza A (H2N2) virus activity in a timedependent manner. It showed best antiviral activity when added at a concentration of 8 µM during the replication period with inhibition of the viral replication of 97.68% ± 2.09% for influenza A (H2N2) at 72 h. However, no significant effect was detected when patchouli alcohol was used for pretreatment of cells or viruses or when patchouli alcohol was only added during the adsorption phase. These results suggested that the inhibition of influenza A (H2N2) virus by patchouli alcohol appears to occur much more strongly after penetration of the virus into the cell. Besides, biochemical studies have indicated that the bioactivity of NA protein is essential determinant after the replication of influenza A (H2N2) virus [20] [21] [22] . Hence, we conclude that the function of NA protein may be suppressed by patchouli alcohol.
To evaluate the toxicity of patchouli alcohol, the mean value of body weight of mice in each group was statistically analyzed. The mean weights of mice administered at the 2 mg/kg/dose oseltamivir, 2 mg/kg/dose patchouli alcohol and 10 mg/kg/dose of patchouli alcohol one time daily for 7 days were not significantly different compared with the normal control mice, showing no toxicity of patchouli alcohol and oseltamivir within the testing concentration (P > 0.05). Physiological status was observed in virus infection mice. Three days after viral infection, some mice, especially mice in the H2N2 infected control group showed changes in behavior, such as a tendency to huddle, diminished vitality, and ruffled fur, etc. In the mouse influenza model, viral infection leads to loss of body weight and high mortality. Therefore, the efficacy of patchouli alcohol and oseltamivir were evaluated on the basis of survival rate measured for 15 days post-infection, for treated infected animals relative to untreated infected (control) animals. A comparison of efficacy of patchouli alcohol and oseltamivir in vivo mouse influenza model (oral treatment) showed that at a dose of 5 mg/kg/day, patchouli alcohol showed obvious protection against the influenza virus, as the mean day to death was detected as 11.8 ± 1.1 (Table 2) . When the dose was lowered to 1 mg/kg/day, patchouli alcohol showed weaker protection (measured by Survivors/total) than that of 5 mg/kg/day, the mean day to death was 7.5 ± 1.8. Whereas oseltamivir at this dose level (1 mg/kg/day) showed 50% protection (measured by survivors/total) against the influenza virus. In the H2N2 infected control group, there were no survivors. In view of both in vitro and in vivo data, we conclude that patchouli alcohol could be used in the treatment of human influenza virus infections.
Based on the above experiment data, patchouli alcohol is determined to be bound within NA protein. As the total energies and backbone root-mean-square-deviations (RMSD) in Figure 3 indicate, the energy-minimized patchouli alcohol-NA complex has been in equilibrium since about 0.5 ns, and then retains quite stable in the last 19.5 ns. It is consistent with the previous MD results of other NA inhibitors [23] [24] [25] [26] [27] [28] . Accordingly, the geometric and energetic analyses were made on the average structures of 0.5~20.0 ns MD trajectories, where the system has been already at equilibrium. The interaction energy (E inter ) of patchouli alcohol with NA was calculated at −40.38 kcal mol −1 , where the vdW rather than electrostatic interactions were found to play a dominant role, contribute to about 72% (−29.18 kcal mol −1 ). As shown in Figure 4 , the patchouli alcohol was bound at the active site which also bound to oseltamivir and zanamivir [28] . As Figure 5 shows, the oxygen atom of patchouli alcohol was oriented towards the sidechains of residues Glu119 and Tyr406, with one H-bond formed with each residue. The values of distances in Figure 6 further reveal that the docked complex remains rather stable throughout the simulation, with the average distances of Glu119:OE2patchouli alcohol:O and Tyr406:OH -patchouli alcohol:O less than 2.8 Å. The sum contributions (E sum ) of residues Glu119 and Tyr406 amounted to −8.46 and −7.37 kcal mol −1 , respectively (Table 3) . Besides, patchouli alcohol was stabilized by residues Arg118, Asp151, Arg152, Trp178, Ala246, Glu276, Arg292, Asn294 and Gln347, especially residues Asp151, Arg152 and Glu276 ( Figure 5 and Table 3 ). As a matter of fact, residues Asp151, Arg152, Glu119, Glu276 and Tyr406 of the NA protein have already received enough attention from rational drug designs [14, 30, 31] . The catalytic residues Asp151, Arg152 and Glu276 are crucial to the NA functions and the residues Glu119 and Tyr406 are important to stabilize the NA active sites [32, 33] . It suggests that the NA functions will be affected by the presence of patchouli alcohol, consistent with the above experiments. Patchouli alcohol matches with the NA active site and has an acceptable interaction energy. Considering the obvious structure discrepancies against current NA inhibitors, it represents an ideal lead compound for the designs of novel anti-influenza agents.
Patchouli alcohol and oseltamivir were obtained from Sigma Chemical Co. (St. Louis, MO, USA, purity > 99%) and was stored in glass vials with Teflon sealed caps at −20 ± 0.5 °C in the absence of light.
MDCK (Madin-Darby canine kidney) was purchased from Harbin Veterinary Research Institute (Harbin, Heilongjiang, China). The cells were grown in monolayer culture with Eagle's minimum essential medium (EMEM) supplemented with 10% fetal calf serum (FCS), 100 U/mL penicillin and 100 μg/mL streptomycin. The monolayers were removed from their plastic surfaces and serially passaged whenever they became confluent. Cells were plated out onto 96-well culture plates for cytotoxicity and anti-influenza assays, and propagated at 37 °C in an atmosphere of 5% CO 2 .
The influenza strain A/Leningrad/134/17/1957 H2N2) was purchased from National Control Institute of Veterinary Bioproducts and Pharmaceuticals (Beijing, China). Virus was routinely grown on MDCK cells. The stock cultures were prepared from supernatants of infected cells and stored at −80 °C.
The cellular toxicity of patchouli alcohol on MDCK cells was assessed by the MTT method. Briefly, cells were seeded on a microtiter plate in the absence or presence of various concentrations (20 µM -0.0098 µM) of patchouli alcohol (eight replicates) and incubated at 37 °C in a humidified atmosphere of 5% CO 2 for 72 h. The supernatants were discarded, washed with PBS twice and MTT reagent (5 mg/mL in PBS) was added to each well. After incubation at 37 °C for 4 h, the supernatants were removed, then 200 μL DMSO was added and incubated at 37 °C for another 30 min. After that the plates were read on an ELISA reader (Thermo Molecular Devices Co., Union City, USA) at 570/630 nm. The mean OD of the cell control wells was assigned a value of 100%. The maximal non-toxic concentration (TD 0 ) and 50% cytotoxic concentration (CC 50 ) were calculated by linear regression analysis of the dose-response curves generated from the data.
Inhibition of virus replication was measured by the MTT method. Serial dilution of the treated virus was adsorbed to the cells for 1 h at 37 °C. The residual inoculum was discared and infected cells were added with EMEM containing 2% FCS. Each assay was performed in eight replicates. After incubation for 72 h at 37 °C, the cultures were measured by MTT method as described above. The concentration of patchouli alcohol and oseltamivir which inhibited virus numbers by 50% (IC 50 ) was determined from dose-response curves.
Cells and viruses were incubated with patchouli alcohol at different stages during the viral infection cycle in order to determine the mode of antiviral action. Cells were pretreated with patchouli alcohol before viral infection, viruses were incubated with patchouli alcohol before infection and cells and viruses were incubated together with patchouli alcohol during adsorption or after penetration of the virus into the host cells. Patchouli alcohol was always used at the nontoxic concentration. Cell monolayers were pretreated with patchouli alcohol prior to inoculation with virus by adding patchouli alcohol to the culture medium and incubation for 1 h at 37 °C. The compound was aspirated and cells were washed immediately before the influenza A (H2N2) inoculum was added. For pretreatment virus, Influenza A (H2N2) was incubated in medium containing patchouli alcohol for 1h at room temperature prior to infection of MDCK cells. For analyzing the anti-influenza A (H2N2) inhibition during the adsorption period, the same amount of influenza A (H2N2) was mixed with the drug and added to the cells immediately. After 1 h of adsorption at 37 °C, the inoculum was removed and DMEM supplemented with 2 % FCS were added to the cells. The effect of patchouli alcohol against influenza A (H2N2) was also tested during the replication period by adding it after adsorption, as typical performed in anti-influenza A (H2N2) susceptibility studies. Each assay was run in eight replicates.
Kunming mice, weighing 18-22 g (6 weeks of age) were purchased from Harbin Veterinary Research Institute Animal Co., Ltd. (Harbin, Heilongjiang, China) . First, the toxicity of patchouli alcohol and oseltamivir was assessed in the healthy mice by the loss of body weight compared with the control group (2% DMSO in physiological saline). The mice were orally administered with 10 mg/kg/dose patchouli alcohol, 2 mg/kg/dose patchouli alcohol or 2 mg/kg/dose oseltamivir (dissolved in 2% DMSO in physiological saline) one time daily for 7 days. The weight of mice was determined daily. We conducted procedures according to Principle of Laboratory Animal Care (NIH Publication No. 85 -23, revised 1985) and the guidelines of the Peking University Animal Research Committee.
Kunming mice were anesthetized with isoflurane and exposed to virus (A/Leningrad/134/17/1957) by intranasal instillation. Drugs were prepared in 2% DMSO in physiological saline and administered 4 h prior to virus exposure and continued daily for 5 days. All mice were observed daily for changes in weight and for any deaths. Parameters for evaluation of antiviral activity included weight loss, reduction in mortality and/or increase in mean day to death (MDD) determined through 15 days.
The N2 sub-type neuraminidase crystal structure (PDB code 1IVD) was obtained from the RCSB Protein Data Bank [34] . For convenience, the structure is named as NA hereafter. Geometry and partial atomic charges of the patchouli alcohol ( Figure 1) were calculated with the Discover 3.0 module (Insight II 2005) [35] by applying the BFGS algorithm [36] and the consistent-valence force-field (CVFF), with a convergence criterion of 0.01 kcal mol −1 Å −1 . The docking and molecular dynamics (MD) simulations were performed by the general protocols in the Insight II 2005 software packages, consistent with the previous literatures [24, 26, 28, 35, [37] [38] [39] . During the MD simulations, the canonical ensemble (NVT) was employed at normal temperature (300 K). The MD temperature was controlled by the velocity scaling thermostat [40] . Integrations of the classical equations of motion were achieved using the Verlet algorithm. The systems were solvated in a large sphere of TIP3P water molecules [40] with the radius of 35.0 Å, which is enough to hold the ensembles [40] . The MD trajectories were generated using a 1.0-fs time step for a total of 20.0 ns, saved at 5.0-ps intervals. The interaction energies of patchouli alcohol with NA and the respective residues at the NA active site were calculated by the Docking module [35], over the 0.5~20.0 ns MD trajectories.
All results are expressed as mean values ± standard deviations (SDs) (n = 3). The significance of difference was calculated by one-way analysis of variance, and values p < 0.001 were considered to be significant.
In conclusion, patchouli alcohol possesses anti-influenza A (H2N2) virus activity via interference with the NA function that cleaves the α-glycosidic bond between sialic acid and glycoconjugate. Our results provide the promising information for the potential use of patchouli alcohol in the treatment of influenza A (H2N2) virus infectious disease. Further mechanistic studies on the anti-influenza A virus activity are needed to support this point of view.
|
What is the function of neuroaminidase in the influenza virus?
| 4,072 |
cleave the α-ketosidic linkage between terminal sialic acid and an adjacent sugar residue
| 2,227 |
1,578 |
Inhibitory Effect and Possible Mechanism of Action of Patchouli Alcohol against Influenza A (H2N2) Virus
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6264369/
SHA: f2d842780b9928cc70f38a4458553f2431877603
Authors: Wu, Huaxing; Li, Beili; Wang, Xue; Jin, Mingyuan; Wang, Guonian
Date: 2011-08-03
DOI: 10.3390/molecules16086489
License: cc-by
Abstract: In the present study, the anti-influenza A (H2N2) virus activity of patchouli alcohol was studied in vitro, in vivo and in silico. The CC(50) of patchouli alcohol was above 20 µM. Patchouli alcohol could inhibit influenza virus with an IC(50) of 4.03 ± 0.23 µM. MTT assay showed that the inhibition by patchouli alcohol appears strongly after penetration of the virus into the cell. In the influenza mouse model, patchouli alcohol showed obvious protection against the viral infection at a dose of 5 mg/kg/day. Flexible docking and molecular dynamic simulations indicated that patchouli alcohol was bound to the neuraminidase protein of influenza virus, with an interaction energy of –40.38 kcal mol(–1). The invariant key active-site residues Asp151, Arg152, Glu119, Glu276 and Tyr406 played important roles during the binding process. Based on spatial and energetic criteria, patchouli alcohol interfered with the NA functions. Results presented here suggest that patchouli alcohol possesses anti-influenza A (H2N2) virus properties, and therefore is a potential source of anti-influenza agents for the pharmaceutical industry.
Text: The influenza virus, which is one of the main causes of acute respiratory infections in humans, can lead to annual epidemics and infrequent pandemics. The two influenza pandemics of the 20 th century, "Asian Influenza (1957/H2N2)" and "Hong Kong Influenza (1968/H3N2)" resulted in the deaths of an estimated 2-3 million people globally [1, 2] . Today, their descendants continue to cause the majority of influenza infections in humans [3] . So far as it is learned that the most effective antiviral drug is the neuraminidase (NA) inhibitor, which target the NA glycoproteins of influenza A and B virus [4, 5] .
The release of new virions from the infected cell is a key step in the influenza life cycle and need neuraminidase (NA) to cleave the α-ketosidic linkage between terminal sialic acid and an adjacent sugar residue [6] . The NA inhibitors were designed to prevent the key step by blocking the active site of enzyme and thus allow sufficient time for the host immune systems to remove infected viruses [7] . Consistent efforts have been devoted to the development of NA inhibitors, using the crystal structure of the N2 sub-type NA protein [8] [9] [10] [11] [12] [13] [14] [15] . Indeed, oseltamivir (Tamiflu) is the representative NA inhibitor that has proven to be uniquely applicable oral drug in clinical practice for the treatment of influenza infection [4, 8, 9] . However, with an increase in medical use, the oseltamivir-resistant strains have been found and probably lead to a large scale outbreak of novel pandemic flu [16, 17] .
Patchouli alcohol ( Figure 1 ) has been well known for over a century. It is a major constituent of the pungent oil from the East Indian shrub Pogostemon cablin (Blanco) Benth, and widely used in fragrances. Patchouli oil is an important essential oil in the perfume industry, used to give a base and lasting character to a fragrance [16, 17] . The essential oil is very appreciated for its characteristic pleasant and long lasting woody, earthy, and camphoraceous odor, as well as for its fixative properties, being suitable for use in soaps and cosmetic products [16, 17] . The aerial part of Pogostemon cablin has wildly been used for the treatment of the common cold and as an antifungal agent in China [16, 17] . Moreover, the plant is widely used in Traditional Chinese Medicine as it presents various types of pharmacological activity according to the composition of the oil [16, 17] . Patchouli alcohol, as the major volatile constituent of patchouli oil, has been found to strongly inhibit H1N1 replication and weakly inhibit B/Ibaraki/2/85 replication [18] . To the best of our knowledge, the anti-influenza virus (H2N2) activities of patchouli alcohol have not been evaluated yet. Therefore, the aim of the present study was to evaluate the anti-influenza A virus (H2N2) activity of patchouli alcohol by MTT assay and mouse influenza model. On such basis, explicitly solvated docking and molecular dynamic (MD) methods were applied to investigative the binding mode involving patchouli alcohol with influenza virus NA protein. We anticipate that the insight into the understanding of inhibiting mechanism will be of value in the rational design of novel anti-influenza drugs.
First the efficacy of patchouli alcohol on influenza A (H2N2) virus replication and cell viability were examined. CC 50 was used to express the cytotoxicity of patchouli alcohol on MDCK. The CC 50 of patchouli alcohol was above 20 mM, which indicated that patchouli alcohol did not affect the growth of MDCK (Table 1) . Thus, it seems that the antiviral effects of patchouli alcohol were not due to the cytotoxicity. Moreover, patchouli alcohol was found to inhibit influenza A (H2N2) virus with an IC 50 of 4.03 ± 0.23 µM. Based on the IC 50 and CC 50 values, the selectivity index (SI) was calculated as >4.96. It is reported that a SI of 4 or more is appropriate for an antiviral agent [18] , suggesting that patchouli alcohol can be judged to have anti-influenza A (H2N2) virus activity.
Until now, it has been found that patchouli alcohol showed dose-dependent anti-influenza virus (A/PR/8/34, H1N1) activity, with an IC 50 value of 2.635 µM. Furthermore, it showed weak activity against B/Ibaraki/2/85 (IC 50 = 40.82 µM) [19] . With the addition of the above H2N2 inhibitory activity, we have a comprehensively view of the anti-influenza activity of patchouli alcohol.
Cells were pretreated with patchouli alcohol prior to virus infection (pretreatment cells), viruses were pretreated prior to infection (pretreatment virus), and patchouli alcohol was added during the adsorption period (adsorption) or after penetration of the viruses into cells (replication). Experiments were repeated independently three times and data presented are the average of three experiments. The symbols * indicated very significant difference p < 0.01 with respect to other mode (pretreatment virus, adsorption and pretreatment cell).
As shown in Figure 2 , patchouli alcohol showed anti-influenza A (H2N2) virus activity in a timedependent manner. It showed best antiviral activity when added at a concentration of 8 µM during the replication period with inhibition of the viral replication of 97.68% ± 2.09% for influenza A (H2N2) at 72 h. However, no significant effect was detected when patchouli alcohol was used for pretreatment of cells or viruses or when patchouli alcohol was only added during the adsorption phase. These results suggested that the inhibition of influenza A (H2N2) virus by patchouli alcohol appears to occur much more strongly after penetration of the virus into the cell. Besides, biochemical studies have indicated that the bioactivity of NA protein is essential determinant after the replication of influenza A (H2N2) virus [20] [21] [22] . Hence, we conclude that the function of NA protein may be suppressed by patchouli alcohol.
To evaluate the toxicity of patchouli alcohol, the mean value of body weight of mice in each group was statistically analyzed. The mean weights of mice administered at the 2 mg/kg/dose oseltamivir, 2 mg/kg/dose patchouli alcohol and 10 mg/kg/dose of patchouli alcohol one time daily for 7 days were not significantly different compared with the normal control mice, showing no toxicity of patchouli alcohol and oseltamivir within the testing concentration (P > 0.05). Physiological status was observed in virus infection mice. Three days after viral infection, some mice, especially mice in the H2N2 infected control group showed changes in behavior, such as a tendency to huddle, diminished vitality, and ruffled fur, etc. In the mouse influenza model, viral infection leads to loss of body weight and high mortality. Therefore, the efficacy of patchouli alcohol and oseltamivir were evaluated on the basis of survival rate measured for 15 days post-infection, for treated infected animals relative to untreated infected (control) animals. A comparison of efficacy of patchouli alcohol and oseltamivir in vivo mouse influenza model (oral treatment) showed that at a dose of 5 mg/kg/day, patchouli alcohol showed obvious protection against the influenza virus, as the mean day to death was detected as 11.8 ± 1.1 (Table 2) . When the dose was lowered to 1 mg/kg/day, patchouli alcohol showed weaker protection (measured by Survivors/total) than that of 5 mg/kg/day, the mean day to death was 7.5 ± 1.8. Whereas oseltamivir at this dose level (1 mg/kg/day) showed 50% protection (measured by survivors/total) against the influenza virus. In the H2N2 infected control group, there were no survivors. In view of both in vitro and in vivo data, we conclude that patchouli alcohol could be used in the treatment of human influenza virus infections.
Based on the above experiment data, patchouli alcohol is determined to be bound within NA protein. As the total energies and backbone root-mean-square-deviations (RMSD) in Figure 3 indicate, the energy-minimized patchouli alcohol-NA complex has been in equilibrium since about 0.5 ns, and then retains quite stable in the last 19.5 ns. It is consistent with the previous MD results of other NA inhibitors [23] [24] [25] [26] [27] [28] . Accordingly, the geometric and energetic analyses were made on the average structures of 0.5~20.0 ns MD trajectories, where the system has been already at equilibrium. The interaction energy (E inter ) of patchouli alcohol with NA was calculated at −40.38 kcal mol −1 , where the vdW rather than electrostatic interactions were found to play a dominant role, contribute to about 72% (−29.18 kcal mol −1 ). As shown in Figure 4 , the patchouli alcohol was bound at the active site which also bound to oseltamivir and zanamivir [28] . As Figure 5 shows, the oxygen atom of patchouli alcohol was oriented towards the sidechains of residues Glu119 and Tyr406, with one H-bond formed with each residue. The values of distances in Figure 6 further reveal that the docked complex remains rather stable throughout the simulation, with the average distances of Glu119:OE2patchouli alcohol:O and Tyr406:OH -patchouli alcohol:O less than 2.8 Å. The sum contributions (E sum ) of residues Glu119 and Tyr406 amounted to −8.46 and −7.37 kcal mol −1 , respectively (Table 3) . Besides, patchouli alcohol was stabilized by residues Arg118, Asp151, Arg152, Trp178, Ala246, Glu276, Arg292, Asn294 and Gln347, especially residues Asp151, Arg152 and Glu276 ( Figure 5 and Table 3 ). As a matter of fact, residues Asp151, Arg152, Glu119, Glu276 and Tyr406 of the NA protein have already received enough attention from rational drug designs [14, 30, 31] . The catalytic residues Asp151, Arg152 and Glu276 are crucial to the NA functions and the residues Glu119 and Tyr406 are important to stabilize the NA active sites [32, 33] . It suggests that the NA functions will be affected by the presence of patchouli alcohol, consistent with the above experiments. Patchouli alcohol matches with the NA active site and has an acceptable interaction energy. Considering the obvious structure discrepancies against current NA inhibitors, it represents an ideal lead compound for the designs of novel anti-influenza agents.
Patchouli alcohol and oseltamivir were obtained from Sigma Chemical Co. (St. Louis, MO, USA, purity > 99%) and was stored in glass vials with Teflon sealed caps at −20 ± 0.5 °C in the absence of light.
MDCK (Madin-Darby canine kidney) was purchased from Harbin Veterinary Research Institute (Harbin, Heilongjiang, China). The cells were grown in monolayer culture with Eagle's minimum essential medium (EMEM) supplemented with 10% fetal calf serum (FCS), 100 U/mL penicillin and 100 μg/mL streptomycin. The monolayers were removed from their plastic surfaces and serially passaged whenever they became confluent. Cells were plated out onto 96-well culture plates for cytotoxicity and anti-influenza assays, and propagated at 37 °C in an atmosphere of 5% CO 2 .
The influenza strain A/Leningrad/134/17/1957 H2N2) was purchased from National Control Institute of Veterinary Bioproducts and Pharmaceuticals (Beijing, China). Virus was routinely grown on MDCK cells. The stock cultures were prepared from supernatants of infected cells and stored at −80 °C.
The cellular toxicity of patchouli alcohol on MDCK cells was assessed by the MTT method. Briefly, cells were seeded on a microtiter plate in the absence or presence of various concentrations (20 µM -0.0098 µM) of patchouli alcohol (eight replicates) and incubated at 37 °C in a humidified atmosphere of 5% CO 2 for 72 h. The supernatants were discarded, washed with PBS twice and MTT reagent (5 mg/mL in PBS) was added to each well. After incubation at 37 °C for 4 h, the supernatants were removed, then 200 μL DMSO was added and incubated at 37 °C for another 30 min. After that the plates were read on an ELISA reader (Thermo Molecular Devices Co., Union City, USA) at 570/630 nm. The mean OD of the cell control wells was assigned a value of 100%. The maximal non-toxic concentration (TD 0 ) and 50% cytotoxic concentration (CC 50 ) were calculated by linear regression analysis of the dose-response curves generated from the data.
Inhibition of virus replication was measured by the MTT method. Serial dilution of the treated virus was adsorbed to the cells for 1 h at 37 °C. The residual inoculum was discared and infected cells were added with EMEM containing 2% FCS. Each assay was performed in eight replicates. After incubation for 72 h at 37 °C, the cultures were measured by MTT method as described above. The concentration of patchouli alcohol and oseltamivir which inhibited virus numbers by 50% (IC 50 ) was determined from dose-response curves.
Cells and viruses were incubated with patchouli alcohol at different stages during the viral infection cycle in order to determine the mode of antiviral action. Cells were pretreated with patchouli alcohol before viral infection, viruses were incubated with patchouli alcohol before infection and cells and viruses were incubated together with patchouli alcohol during adsorption or after penetration of the virus into the host cells. Patchouli alcohol was always used at the nontoxic concentration. Cell monolayers were pretreated with patchouli alcohol prior to inoculation with virus by adding patchouli alcohol to the culture medium and incubation for 1 h at 37 °C. The compound was aspirated and cells were washed immediately before the influenza A (H2N2) inoculum was added. For pretreatment virus, Influenza A (H2N2) was incubated in medium containing patchouli alcohol for 1h at room temperature prior to infection of MDCK cells. For analyzing the anti-influenza A (H2N2) inhibition during the adsorption period, the same amount of influenza A (H2N2) was mixed with the drug and added to the cells immediately. After 1 h of adsorption at 37 °C, the inoculum was removed and DMEM supplemented with 2 % FCS were added to the cells. The effect of patchouli alcohol against influenza A (H2N2) was also tested during the replication period by adding it after adsorption, as typical performed in anti-influenza A (H2N2) susceptibility studies. Each assay was run in eight replicates.
Kunming mice, weighing 18-22 g (6 weeks of age) were purchased from Harbin Veterinary Research Institute Animal Co., Ltd. (Harbin, Heilongjiang, China) . First, the toxicity of patchouli alcohol and oseltamivir was assessed in the healthy mice by the loss of body weight compared with the control group (2% DMSO in physiological saline). The mice were orally administered with 10 mg/kg/dose patchouli alcohol, 2 mg/kg/dose patchouli alcohol or 2 mg/kg/dose oseltamivir (dissolved in 2% DMSO in physiological saline) one time daily for 7 days. The weight of mice was determined daily. We conducted procedures according to Principle of Laboratory Animal Care (NIH Publication No. 85 -23, revised 1985) and the guidelines of the Peking University Animal Research Committee.
Kunming mice were anesthetized with isoflurane and exposed to virus (A/Leningrad/134/17/1957) by intranasal instillation. Drugs were prepared in 2% DMSO in physiological saline and administered 4 h prior to virus exposure and continued daily for 5 days. All mice were observed daily for changes in weight and for any deaths. Parameters for evaluation of antiviral activity included weight loss, reduction in mortality and/or increase in mean day to death (MDD) determined through 15 days.
The N2 sub-type neuraminidase crystal structure (PDB code 1IVD) was obtained from the RCSB Protein Data Bank [34] . For convenience, the structure is named as NA hereafter. Geometry and partial atomic charges of the patchouli alcohol ( Figure 1) were calculated with the Discover 3.0 module (Insight II 2005) [35] by applying the BFGS algorithm [36] and the consistent-valence force-field (CVFF), with a convergence criterion of 0.01 kcal mol −1 Å −1 . The docking and molecular dynamics (MD) simulations were performed by the general protocols in the Insight II 2005 software packages, consistent with the previous literatures [24, 26, 28, 35, [37] [38] [39] . During the MD simulations, the canonical ensemble (NVT) was employed at normal temperature (300 K). The MD temperature was controlled by the velocity scaling thermostat [40] . Integrations of the classical equations of motion were achieved using the Verlet algorithm. The systems were solvated in a large sphere of TIP3P water molecules [40] with the radius of 35.0 Å, which is enough to hold the ensembles [40] . The MD trajectories were generated using a 1.0-fs time step for a total of 20.0 ns, saved at 5.0-ps intervals. The interaction energies of patchouli alcohol with NA and the respective residues at the NA active site were calculated by the Docking module [35], over the 0.5~20.0 ns MD trajectories.
All results are expressed as mean values ± standard deviations (SDs) (n = 3). The significance of difference was calculated by one-way analysis of variance, and values p < 0.001 were considered to be significant.
In conclusion, patchouli alcohol possesses anti-influenza A (H2N2) virus activity via interference with the NA function that cleaves the α-glycosidic bond between sialic acid and glycoconjugate. Our results provide the promising information for the potential use of patchouli alcohol in the treatment of influenza A (H2N2) virus infectious disease. Further mechanistic studies on the anti-influenza A virus activity are needed to support this point of view.
|
What is Tamiflu?
| 4,073 |
NA inhibitor
| 2,733 |
1,578 |
Inhibitory Effect and Possible Mechanism of Action of Patchouli Alcohol against Influenza A (H2N2) Virus
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6264369/
SHA: f2d842780b9928cc70f38a4458553f2431877603
Authors: Wu, Huaxing; Li, Beili; Wang, Xue; Jin, Mingyuan; Wang, Guonian
Date: 2011-08-03
DOI: 10.3390/molecules16086489
License: cc-by
Abstract: In the present study, the anti-influenza A (H2N2) virus activity of patchouli alcohol was studied in vitro, in vivo and in silico. The CC(50) of patchouli alcohol was above 20 µM. Patchouli alcohol could inhibit influenza virus with an IC(50) of 4.03 ± 0.23 µM. MTT assay showed that the inhibition by patchouli alcohol appears strongly after penetration of the virus into the cell. In the influenza mouse model, patchouli alcohol showed obvious protection against the viral infection at a dose of 5 mg/kg/day. Flexible docking and molecular dynamic simulations indicated that patchouli alcohol was bound to the neuraminidase protein of influenza virus, with an interaction energy of –40.38 kcal mol(–1). The invariant key active-site residues Asp151, Arg152, Glu119, Glu276 and Tyr406 played important roles during the binding process. Based on spatial and energetic criteria, patchouli alcohol interfered with the NA functions. Results presented here suggest that patchouli alcohol possesses anti-influenza A (H2N2) virus properties, and therefore is a potential source of anti-influenza agents for the pharmaceutical industry.
Text: The influenza virus, which is one of the main causes of acute respiratory infections in humans, can lead to annual epidemics and infrequent pandemics. The two influenza pandemics of the 20 th century, "Asian Influenza (1957/H2N2)" and "Hong Kong Influenza (1968/H3N2)" resulted in the deaths of an estimated 2-3 million people globally [1, 2] . Today, their descendants continue to cause the majority of influenza infections in humans [3] . So far as it is learned that the most effective antiviral drug is the neuraminidase (NA) inhibitor, which target the NA glycoproteins of influenza A and B virus [4, 5] .
The release of new virions from the infected cell is a key step in the influenza life cycle and need neuraminidase (NA) to cleave the α-ketosidic linkage between terminal sialic acid and an adjacent sugar residue [6] . The NA inhibitors were designed to prevent the key step by blocking the active site of enzyme and thus allow sufficient time for the host immune systems to remove infected viruses [7] . Consistent efforts have been devoted to the development of NA inhibitors, using the crystal structure of the N2 sub-type NA protein [8] [9] [10] [11] [12] [13] [14] [15] . Indeed, oseltamivir (Tamiflu) is the representative NA inhibitor that has proven to be uniquely applicable oral drug in clinical practice for the treatment of influenza infection [4, 8, 9] . However, with an increase in medical use, the oseltamivir-resistant strains have been found and probably lead to a large scale outbreak of novel pandemic flu [16, 17] .
Patchouli alcohol ( Figure 1 ) has been well known for over a century. It is a major constituent of the pungent oil from the East Indian shrub Pogostemon cablin (Blanco) Benth, and widely used in fragrances. Patchouli oil is an important essential oil in the perfume industry, used to give a base and lasting character to a fragrance [16, 17] . The essential oil is very appreciated for its characteristic pleasant and long lasting woody, earthy, and camphoraceous odor, as well as for its fixative properties, being suitable for use in soaps and cosmetic products [16, 17] . The aerial part of Pogostemon cablin has wildly been used for the treatment of the common cold and as an antifungal agent in China [16, 17] . Moreover, the plant is widely used in Traditional Chinese Medicine as it presents various types of pharmacological activity according to the composition of the oil [16, 17] . Patchouli alcohol, as the major volatile constituent of patchouli oil, has been found to strongly inhibit H1N1 replication and weakly inhibit B/Ibaraki/2/85 replication [18] . To the best of our knowledge, the anti-influenza virus (H2N2) activities of patchouli alcohol have not been evaluated yet. Therefore, the aim of the present study was to evaluate the anti-influenza A virus (H2N2) activity of patchouli alcohol by MTT assay and mouse influenza model. On such basis, explicitly solvated docking and molecular dynamic (MD) methods were applied to investigative the binding mode involving patchouli alcohol with influenza virus NA protein. We anticipate that the insight into the understanding of inhibiting mechanism will be of value in the rational design of novel anti-influenza drugs.
First the efficacy of patchouli alcohol on influenza A (H2N2) virus replication and cell viability were examined. CC 50 was used to express the cytotoxicity of patchouli alcohol on MDCK. The CC 50 of patchouli alcohol was above 20 mM, which indicated that patchouli alcohol did not affect the growth of MDCK (Table 1) . Thus, it seems that the antiviral effects of patchouli alcohol were not due to the cytotoxicity. Moreover, patchouli alcohol was found to inhibit influenza A (H2N2) virus with an IC 50 of 4.03 ± 0.23 µM. Based on the IC 50 and CC 50 values, the selectivity index (SI) was calculated as >4.96. It is reported that a SI of 4 or more is appropriate for an antiviral agent [18] , suggesting that patchouli alcohol can be judged to have anti-influenza A (H2N2) virus activity.
Until now, it has been found that patchouli alcohol showed dose-dependent anti-influenza virus (A/PR/8/34, H1N1) activity, with an IC 50 value of 2.635 µM. Furthermore, it showed weak activity against B/Ibaraki/2/85 (IC 50 = 40.82 µM) [19] . With the addition of the above H2N2 inhibitory activity, we have a comprehensively view of the anti-influenza activity of patchouli alcohol.
Cells were pretreated with patchouli alcohol prior to virus infection (pretreatment cells), viruses were pretreated prior to infection (pretreatment virus), and patchouli alcohol was added during the adsorption period (adsorption) or after penetration of the viruses into cells (replication). Experiments were repeated independently three times and data presented are the average of three experiments. The symbols * indicated very significant difference p < 0.01 with respect to other mode (pretreatment virus, adsorption and pretreatment cell).
As shown in Figure 2 , patchouli alcohol showed anti-influenza A (H2N2) virus activity in a timedependent manner. It showed best antiviral activity when added at a concentration of 8 µM during the replication period with inhibition of the viral replication of 97.68% ± 2.09% for influenza A (H2N2) at 72 h. However, no significant effect was detected when patchouli alcohol was used for pretreatment of cells or viruses or when patchouli alcohol was only added during the adsorption phase. These results suggested that the inhibition of influenza A (H2N2) virus by patchouli alcohol appears to occur much more strongly after penetration of the virus into the cell. Besides, biochemical studies have indicated that the bioactivity of NA protein is essential determinant after the replication of influenza A (H2N2) virus [20] [21] [22] . Hence, we conclude that the function of NA protein may be suppressed by patchouli alcohol.
To evaluate the toxicity of patchouli alcohol, the mean value of body weight of mice in each group was statistically analyzed. The mean weights of mice administered at the 2 mg/kg/dose oseltamivir, 2 mg/kg/dose patchouli alcohol and 10 mg/kg/dose of patchouli alcohol one time daily for 7 days were not significantly different compared with the normal control mice, showing no toxicity of patchouli alcohol and oseltamivir within the testing concentration (P > 0.05). Physiological status was observed in virus infection mice. Three days after viral infection, some mice, especially mice in the H2N2 infected control group showed changes in behavior, such as a tendency to huddle, diminished vitality, and ruffled fur, etc. In the mouse influenza model, viral infection leads to loss of body weight and high mortality. Therefore, the efficacy of patchouli alcohol and oseltamivir were evaluated on the basis of survival rate measured for 15 days post-infection, for treated infected animals relative to untreated infected (control) animals. A comparison of efficacy of patchouli alcohol and oseltamivir in vivo mouse influenza model (oral treatment) showed that at a dose of 5 mg/kg/day, patchouli alcohol showed obvious protection against the influenza virus, as the mean day to death was detected as 11.8 ± 1.1 (Table 2) . When the dose was lowered to 1 mg/kg/day, patchouli alcohol showed weaker protection (measured by Survivors/total) than that of 5 mg/kg/day, the mean day to death was 7.5 ± 1.8. Whereas oseltamivir at this dose level (1 mg/kg/day) showed 50% protection (measured by survivors/total) against the influenza virus. In the H2N2 infected control group, there were no survivors. In view of both in vitro and in vivo data, we conclude that patchouli alcohol could be used in the treatment of human influenza virus infections.
Based on the above experiment data, patchouli alcohol is determined to be bound within NA protein. As the total energies and backbone root-mean-square-deviations (RMSD) in Figure 3 indicate, the energy-minimized patchouli alcohol-NA complex has been in equilibrium since about 0.5 ns, and then retains quite stable in the last 19.5 ns. It is consistent with the previous MD results of other NA inhibitors [23] [24] [25] [26] [27] [28] . Accordingly, the geometric and energetic analyses were made on the average structures of 0.5~20.0 ns MD trajectories, where the system has been already at equilibrium. The interaction energy (E inter ) of patchouli alcohol with NA was calculated at −40.38 kcal mol −1 , where the vdW rather than electrostatic interactions were found to play a dominant role, contribute to about 72% (−29.18 kcal mol −1 ). As shown in Figure 4 , the patchouli alcohol was bound at the active site which also bound to oseltamivir and zanamivir [28] . As Figure 5 shows, the oxygen atom of patchouli alcohol was oriented towards the sidechains of residues Glu119 and Tyr406, with one H-bond formed with each residue. The values of distances in Figure 6 further reveal that the docked complex remains rather stable throughout the simulation, with the average distances of Glu119:OE2patchouli alcohol:O and Tyr406:OH -patchouli alcohol:O less than 2.8 Å. The sum contributions (E sum ) of residues Glu119 and Tyr406 amounted to −8.46 and −7.37 kcal mol −1 , respectively (Table 3) . Besides, patchouli alcohol was stabilized by residues Arg118, Asp151, Arg152, Trp178, Ala246, Glu276, Arg292, Asn294 and Gln347, especially residues Asp151, Arg152 and Glu276 ( Figure 5 and Table 3 ). As a matter of fact, residues Asp151, Arg152, Glu119, Glu276 and Tyr406 of the NA protein have already received enough attention from rational drug designs [14, 30, 31] . The catalytic residues Asp151, Arg152 and Glu276 are crucial to the NA functions and the residues Glu119 and Tyr406 are important to stabilize the NA active sites [32, 33] . It suggests that the NA functions will be affected by the presence of patchouli alcohol, consistent with the above experiments. Patchouli alcohol matches with the NA active site and has an acceptable interaction energy. Considering the obvious structure discrepancies against current NA inhibitors, it represents an ideal lead compound for the designs of novel anti-influenza agents.
Patchouli alcohol and oseltamivir were obtained from Sigma Chemical Co. (St. Louis, MO, USA, purity > 99%) and was stored in glass vials with Teflon sealed caps at −20 ± 0.5 °C in the absence of light.
MDCK (Madin-Darby canine kidney) was purchased from Harbin Veterinary Research Institute (Harbin, Heilongjiang, China). The cells were grown in monolayer culture with Eagle's minimum essential medium (EMEM) supplemented with 10% fetal calf serum (FCS), 100 U/mL penicillin and 100 μg/mL streptomycin. The monolayers were removed from their plastic surfaces and serially passaged whenever they became confluent. Cells were plated out onto 96-well culture plates for cytotoxicity and anti-influenza assays, and propagated at 37 °C in an atmosphere of 5% CO 2 .
The influenza strain A/Leningrad/134/17/1957 H2N2) was purchased from National Control Institute of Veterinary Bioproducts and Pharmaceuticals (Beijing, China). Virus was routinely grown on MDCK cells. The stock cultures were prepared from supernatants of infected cells and stored at −80 °C.
The cellular toxicity of patchouli alcohol on MDCK cells was assessed by the MTT method. Briefly, cells were seeded on a microtiter plate in the absence or presence of various concentrations (20 µM -0.0098 µM) of patchouli alcohol (eight replicates) and incubated at 37 °C in a humidified atmosphere of 5% CO 2 for 72 h. The supernatants were discarded, washed with PBS twice and MTT reagent (5 mg/mL in PBS) was added to each well. After incubation at 37 °C for 4 h, the supernatants were removed, then 200 μL DMSO was added and incubated at 37 °C for another 30 min. After that the plates were read on an ELISA reader (Thermo Molecular Devices Co., Union City, USA) at 570/630 nm. The mean OD of the cell control wells was assigned a value of 100%. The maximal non-toxic concentration (TD 0 ) and 50% cytotoxic concentration (CC 50 ) were calculated by linear regression analysis of the dose-response curves generated from the data.
Inhibition of virus replication was measured by the MTT method. Serial dilution of the treated virus was adsorbed to the cells for 1 h at 37 °C. The residual inoculum was discared and infected cells were added with EMEM containing 2% FCS. Each assay was performed in eight replicates. After incubation for 72 h at 37 °C, the cultures were measured by MTT method as described above. The concentration of patchouli alcohol and oseltamivir which inhibited virus numbers by 50% (IC 50 ) was determined from dose-response curves.
Cells and viruses were incubated with patchouli alcohol at different stages during the viral infection cycle in order to determine the mode of antiviral action. Cells were pretreated with patchouli alcohol before viral infection, viruses were incubated with patchouli alcohol before infection and cells and viruses were incubated together with patchouli alcohol during adsorption or after penetration of the virus into the host cells. Patchouli alcohol was always used at the nontoxic concentration. Cell monolayers were pretreated with patchouli alcohol prior to inoculation with virus by adding patchouli alcohol to the culture medium and incubation for 1 h at 37 °C. The compound was aspirated and cells were washed immediately before the influenza A (H2N2) inoculum was added. For pretreatment virus, Influenza A (H2N2) was incubated in medium containing patchouli alcohol for 1h at room temperature prior to infection of MDCK cells. For analyzing the anti-influenza A (H2N2) inhibition during the adsorption period, the same amount of influenza A (H2N2) was mixed with the drug and added to the cells immediately. After 1 h of adsorption at 37 °C, the inoculum was removed and DMEM supplemented with 2 % FCS were added to the cells. The effect of patchouli alcohol against influenza A (H2N2) was also tested during the replication period by adding it after adsorption, as typical performed in anti-influenza A (H2N2) susceptibility studies. Each assay was run in eight replicates.
Kunming mice, weighing 18-22 g (6 weeks of age) were purchased from Harbin Veterinary Research Institute Animal Co., Ltd. (Harbin, Heilongjiang, China) . First, the toxicity of patchouli alcohol and oseltamivir was assessed in the healthy mice by the loss of body weight compared with the control group (2% DMSO in physiological saline). The mice were orally administered with 10 mg/kg/dose patchouli alcohol, 2 mg/kg/dose patchouli alcohol or 2 mg/kg/dose oseltamivir (dissolved in 2% DMSO in physiological saline) one time daily for 7 days. The weight of mice was determined daily. We conducted procedures according to Principle of Laboratory Animal Care (NIH Publication No. 85 -23, revised 1985) and the guidelines of the Peking University Animal Research Committee.
Kunming mice were anesthetized with isoflurane and exposed to virus (A/Leningrad/134/17/1957) by intranasal instillation. Drugs were prepared in 2% DMSO in physiological saline and administered 4 h prior to virus exposure and continued daily for 5 days. All mice were observed daily for changes in weight and for any deaths. Parameters for evaluation of antiviral activity included weight loss, reduction in mortality and/or increase in mean day to death (MDD) determined through 15 days.
The N2 sub-type neuraminidase crystal structure (PDB code 1IVD) was obtained from the RCSB Protein Data Bank [34] . For convenience, the structure is named as NA hereafter. Geometry and partial atomic charges of the patchouli alcohol ( Figure 1) were calculated with the Discover 3.0 module (Insight II 2005) [35] by applying the BFGS algorithm [36] and the consistent-valence force-field (CVFF), with a convergence criterion of 0.01 kcal mol −1 Å −1 . The docking and molecular dynamics (MD) simulations were performed by the general protocols in the Insight II 2005 software packages, consistent with the previous literatures [24, 26, 28, 35, [37] [38] [39] . During the MD simulations, the canonical ensemble (NVT) was employed at normal temperature (300 K). The MD temperature was controlled by the velocity scaling thermostat [40] . Integrations of the classical equations of motion were achieved using the Verlet algorithm. The systems were solvated in a large sphere of TIP3P water molecules [40] with the radius of 35.0 Å, which is enough to hold the ensembles [40] . The MD trajectories were generated using a 1.0-fs time step for a total of 20.0 ns, saved at 5.0-ps intervals. The interaction energies of patchouli alcohol with NA and the respective residues at the NA active site were calculated by the Docking module [35], over the 0.5~20.0 ns MD trajectories.
All results are expressed as mean values ± standard deviations (SDs) (n = 3). The significance of difference was calculated by one-way analysis of variance, and values p < 0.001 were considered to be significant.
In conclusion, patchouli alcohol possesses anti-influenza A (H2N2) virus activity via interference with the NA function that cleaves the α-glycosidic bond between sialic acid and glycoconjugate. Our results provide the promising information for the potential use of patchouli alcohol in the treatment of influenza A (H2N2) virus infectious disease. Further mechanistic studies on the anti-influenza A virus activity are needed to support this point of view.
|
What was the test for the level of cytotoxicity used in this study?
| 4,074 |
CC 50
| 4,844 |
1,578 |
Inhibitory Effect and Possible Mechanism of Action of Patchouli Alcohol against Influenza A (H2N2) Virus
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6264369/
SHA: f2d842780b9928cc70f38a4458553f2431877603
Authors: Wu, Huaxing; Li, Beili; Wang, Xue; Jin, Mingyuan; Wang, Guonian
Date: 2011-08-03
DOI: 10.3390/molecules16086489
License: cc-by
Abstract: In the present study, the anti-influenza A (H2N2) virus activity of patchouli alcohol was studied in vitro, in vivo and in silico. The CC(50) of patchouli alcohol was above 20 µM. Patchouli alcohol could inhibit influenza virus with an IC(50) of 4.03 ± 0.23 µM. MTT assay showed that the inhibition by patchouli alcohol appears strongly after penetration of the virus into the cell. In the influenza mouse model, patchouli alcohol showed obvious protection against the viral infection at a dose of 5 mg/kg/day. Flexible docking and molecular dynamic simulations indicated that patchouli alcohol was bound to the neuraminidase protein of influenza virus, with an interaction energy of –40.38 kcal mol(–1). The invariant key active-site residues Asp151, Arg152, Glu119, Glu276 and Tyr406 played important roles during the binding process. Based on spatial and energetic criteria, patchouli alcohol interfered with the NA functions. Results presented here suggest that patchouli alcohol possesses anti-influenza A (H2N2) virus properties, and therefore is a potential source of anti-influenza agents for the pharmaceutical industry.
Text: The influenza virus, which is one of the main causes of acute respiratory infections in humans, can lead to annual epidemics and infrequent pandemics. The two influenza pandemics of the 20 th century, "Asian Influenza (1957/H2N2)" and "Hong Kong Influenza (1968/H3N2)" resulted in the deaths of an estimated 2-3 million people globally [1, 2] . Today, their descendants continue to cause the majority of influenza infections in humans [3] . So far as it is learned that the most effective antiviral drug is the neuraminidase (NA) inhibitor, which target the NA glycoproteins of influenza A and B virus [4, 5] .
The release of new virions from the infected cell is a key step in the influenza life cycle and need neuraminidase (NA) to cleave the α-ketosidic linkage between terminal sialic acid and an adjacent sugar residue [6] . The NA inhibitors were designed to prevent the key step by blocking the active site of enzyme and thus allow sufficient time for the host immune systems to remove infected viruses [7] . Consistent efforts have been devoted to the development of NA inhibitors, using the crystal structure of the N2 sub-type NA protein [8] [9] [10] [11] [12] [13] [14] [15] . Indeed, oseltamivir (Tamiflu) is the representative NA inhibitor that has proven to be uniquely applicable oral drug in clinical practice for the treatment of influenza infection [4, 8, 9] . However, with an increase in medical use, the oseltamivir-resistant strains have been found and probably lead to a large scale outbreak of novel pandemic flu [16, 17] .
Patchouli alcohol ( Figure 1 ) has been well known for over a century. It is a major constituent of the pungent oil from the East Indian shrub Pogostemon cablin (Blanco) Benth, and widely used in fragrances. Patchouli oil is an important essential oil in the perfume industry, used to give a base and lasting character to a fragrance [16, 17] . The essential oil is very appreciated for its characteristic pleasant and long lasting woody, earthy, and camphoraceous odor, as well as for its fixative properties, being suitable for use in soaps and cosmetic products [16, 17] . The aerial part of Pogostemon cablin has wildly been used for the treatment of the common cold and as an antifungal agent in China [16, 17] . Moreover, the plant is widely used in Traditional Chinese Medicine as it presents various types of pharmacological activity according to the composition of the oil [16, 17] . Patchouli alcohol, as the major volatile constituent of patchouli oil, has been found to strongly inhibit H1N1 replication and weakly inhibit B/Ibaraki/2/85 replication [18] . To the best of our knowledge, the anti-influenza virus (H2N2) activities of patchouli alcohol have not been evaluated yet. Therefore, the aim of the present study was to evaluate the anti-influenza A virus (H2N2) activity of patchouli alcohol by MTT assay and mouse influenza model. On such basis, explicitly solvated docking and molecular dynamic (MD) methods were applied to investigative the binding mode involving patchouli alcohol with influenza virus NA protein. We anticipate that the insight into the understanding of inhibiting mechanism will be of value in the rational design of novel anti-influenza drugs.
First the efficacy of patchouli alcohol on influenza A (H2N2) virus replication and cell viability were examined. CC 50 was used to express the cytotoxicity of patchouli alcohol on MDCK. The CC 50 of patchouli alcohol was above 20 mM, which indicated that patchouli alcohol did not affect the growth of MDCK (Table 1) . Thus, it seems that the antiviral effects of patchouli alcohol were not due to the cytotoxicity. Moreover, patchouli alcohol was found to inhibit influenza A (H2N2) virus with an IC 50 of 4.03 ± 0.23 µM. Based on the IC 50 and CC 50 values, the selectivity index (SI) was calculated as >4.96. It is reported that a SI of 4 or more is appropriate for an antiviral agent [18] , suggesting that patchouli alcohol can be judged to have anti-influenza A (H2N2) virus activity.
Until now, it has been found that patchouli alcohol showed dose-dependent anti-influenza virus (A/PR/8/34, H1N1) activity, with an IC 50 value of 2.635 µM. Furthermore, it showed weak activity against B/Ibaraki/2/85 (IC 50 = 40.82 µM) [19] . With the addition of the above H2N2 inhibitory activity, we have a comprehensively view of the anti-influenza activity of patchouli alcohol.
Cells were pretreated with patchouli alcohol prior to virus infection (pretreatment cells), viruses were pretreated prior to infection (pretreatment virus), and patchouli alcohol was added during the adsorption period (adsorption) or after penetration of the viruses into cells (replication). Experiments were repeated independently three times and data presented are the average of three experiments. The symbols * indicated very significant difference p < 0.01 with respect to other mode (pretreatment virus, adsorption and pretreatment cell).
As shown in Figure 2 , patchouli alcohol showed anti-influenza A (H2N2) virus activity in a timedependent manner. It showed best antiviral activity when added at a concentration of 8 µM during the replication period with inhibition of the viral replication of 97.68% ± 2.09% for influenza A (H2N2) at 72 h. However, no significant effect was detected when patchouli alcohol was used for pretreatment of cells or viruses or when patchouli alcohol was only added during the adsorption phase. These results suggested that the inhibition of influenza A (H2N2) virus by patchouli alcohol appears to occur much more strongly after penetration of the virus into the cell. Besides, biochemical studies have indicated that the bioactivity of NA protein is essential determinant after the replication of influenza A (H2N2) virus [20] [21] [22] . Hence, we conclude that the function of NA protein may be suppressed by patchouli alcohol.
To evaluate the toxicity of patchouli alcohol, the mean value of body weight of mice in each group was statistically analyzed. The mean weights of mice administered at the 2 mg/kg/dose oseltamivir, 2 mg/kg/dose patchouli alcohol and 10 mg/kg/dose of patchouli alcohol one time daily for 7 days were not significantly different compared with the normal control mice, showing no toxicity of patchouli alcohol and oseltamivir within the testing concentration (P > 0.05). Physiological status was observed in virus infection mice. Three days after viral infection, some mice, especially mice in the H2N2 infected control group showed changes in behavior, such as a tendency to huddle, diminished vitality, and ruffled fur, etc. In the mouse influenza model, viral infection leads to loss of body weight and high mortality. Therefore, the efficacy of patchouli alcohol and oseltamivir were evaluated on the basis of survival rate measured for 15 days post-infection, for treated infected animals relative to untreated infected (control) animals. A comparison of efficacy of patchouli alcohol and oseltamivir in vivo mouse influenza model (oral treatment) showed that at a dose of 5 mg/kg/day, patchouli alcohol showed obvious protection against the influenza virus, as the mean day to death was detected as 11.8 ± 1.1 (Table 2) . When the dose was lowered to 1 mg/kg/day, patchouli alcohol showed weaker protection (measured by Survivors/total) than that of 5 mg/kg/day, the mean day to death was 7.5 ± 1.8. Whereas oseltamivir at this dose level (1 mg/kg/day) showed 50% protection (measured by survivors/total) against the influenza virus. In the H2N2 infected control group, there were no survivors. In view of both in vitro and in vivo data, we conclude that patchouli alcohol could be used in the treatment of human influenza virus infections.
Based on the above experiment data, patchouli alcohol is determined to be bound within NA protein. As the total energies and backbone root-mean-square-deviations (RMSD) in Figure 3 indicate, the energy-minimized patchouli alcohol-NA complex has been in equilibrium since about 0.5 ns, and then retains quite stable in the last 19.5 ns. It is consistent with the previous MD results of other NA inhibitors [23] [24] [25] [26] [27] [28] . Accordingly, the geometric and energetic analyses were made on the average structures of 0.5~20.0 ns MD trajectories, where the system has been already at equilibrium. The interaction energy (E inter ) of patchouli alcohol with NA was calculated at −40.38 kcal mol −1 , where the vdW rather than electrostatic interactions were found to play a dominant role, contribute to about 72% (−29.18 kcal mol −1 ). As shown in Figure 4 , the patchouli alcohol was bound at the active site which also bound to oseltamivir and zanamivir [28] . As Figure 5 shows, the oxygen atom of patchouli alcohol was oriented towards the sidechains of residues Glu119 and Tyr406, with one H-bond formed with each residue. The values of distances in Figure 6 further reveal that the docked complex remains rather stable throughout the simulation, with the average distances of Glu119:OE2patchouli alcohol:O and Tyr406:OH -patchouli alcohol:O less than 2.8 Å. The sum contributions (E sum ) of residues Glu119 and Tyr406 amounted to −8.46 and −7.37 kcal mol −1 , respectively (Table 3) . Besides, patchouli alcohol was stabilized by residues Arg118, Asp151, Arg152, Trp178, Ala246, Glu276, Arg292, Asn294 and Gln347, especially residues Asp151, Arg152 and Glu276 ( Figure 5 and Table 3 ). As a matter of fact, residues Asp151, Arg152, Glu119, Glu276 and Tyr406 of the NA protein have already received enough attention from rational drug designs [14, 30, 31] . The catalytic residues Asp151, Arg152 and Glu276 are crucial to the NA functions and the residues Glu119 and Tyr406 are important to stabilize the NA active sites [32, 33] . It suggests that the NA functions will be affected by the presence of patchouli alcohol, consistent with the above experiments. Patchouli alcohol matches with the NA active site and has an acceptable interaction energy. Considering the obvious structure discrepancies against current NA inhibitors, it represents an ideal lead compound for the designs of novel anti-influenza agents.
Patchouli alcohol and oseltamivir were obtained from Sigma Chemical Co. (St. Louis, MO, USA, purity > 99%) and was stored in glass vials with Teflon sealed caps at −20 ± 0.5 °C in the absence of light.
MDCK (Madin-Darby canine kidney) was purchased from Harbin Veterinary Research Institute (Harbin, Heilongjiang, China). The cells were grown in monolayer culture with Eagle's minimum essential medium (EMEM) supplemented with 10% fetal calf serum (FCS), 100 U/mL penicillin and 100 μg/mL streptomycin. The monolayers were removed from their plastic surfaces and serially passaged whenever they became confluent. Cells were plated out onto 96-well culture plates for cytotoxicity and anti-influenza assays, and propagated at 37 °C in an atmosphere of 5% CO 2 .
The influenza strain A/Leningrad/134/17/1957 H2N2) was purchased from National Control Institute of Veterinary Bioproducts and Pharmaceuticals (Beijing, China). Virus was routinely grown on MDCK cells. The stock cultures were prepared from supernatants of infected cells and stored at −80 °C.
The cellular toxicity of patchouli alcohol on MDCK cells was assessed by the MTT method. Briefly, cells were seeded on a microtiter plate in the absence or presence of various concentrations (20 µM -0.0098 µM) of patchouli alcohol (eight replicates) and incubated at 37 °C in a humidified atmosphere of 5% CO 2 for 72 h. The supernatants were discarded, washed with PBS twice and MTT reagent (5 mg/mL in PBS) was added to each well. After incubation at 37 °C for 4 h, the supernatants were removed, then 200 μL DMSO was added and incubated at 37 °C for another 30 min. After that the plates were read on an ELISA reader (Thermo Molecular Devices Co., Union City, USA) at 570/630 nm. The mean OD of the cell control wells was assigned a value of 100%. The maximal non-toxic concentration (TD 0 ) and 50% cytotoxic concentration (CC 50 ) were calculated by linear regression analysis of the dose-response curves generated from the data.
Inhibition of virus replication was measured by the MTT method. Serial dilution of the treated virus was adsorbed to the cells for 1 h at 37 °C. The residual inoculum was discared and infected cells were added with EMEM containing 2% FCS. Each assay was performed in eight replicates. After incubation for 72 h at 37 °C, the cultures were measured by MTT method as described above. The concentration of patchouli alcohol and oseltamivir which inhibited virus numbers by 50% (IC 50 ) was determined from dose-response curves.
Cells and viruses were incubated with patchouli alcohol at different stages during the viral infection cycle in order to determine the mode of antiviral action. Cells were pretreated with patchouli alcohol before viral infection, viruses were incubated with patchouli alcohol before infection and cells and viruses were incubated together with patchouli alcohol during adsorption or after penetration of the virus into the host cells. Patchouli alcohol was always used at the nontoxic concentration. Cell monolayers were pretreated with patchouli alcohol prior to inoculation with virus by adding patchouli alcohol to the culture medium and incubation for 1 h at 37 °C. The compound was aspirated and cells were washed immediately before the influenza A (H2N2) inoculum was added. For pretreatment virus, Influenza A (H2N2) was incubated in medium containing patchouli alcohol for 1h at room temperature prior to infection of MDCK cells. For analyzing the anti-influenza A (H2N2) inhibition during the adsorption period, the same amount of influenza A (H2N2) was mixed with the drug and added to the cells immediately. After 1 h of adsorption at 37 °C, the inoculum was removed and DMEM supplemented with 2 % FCS were added to the cells. The effect of patchouli alcohol against influenza A (H2N2) was also tested during the replication period by adding it after adsorption, as typical performed in anti-influenza A (H2N2) susceptibility studies. Each assay was run in eight replicates.
Kunming mice, weighing 18-22 g (6 weeks of age) were purchased from Harbin Veterinary Research Institute Animal Co., Ltd. (Harbin, Heilongjiang, China) . First, the toxicity of patchouli alcohol and oseltamivir was assessed in the healthy mice by the loss of body weight compared with the control group (2% DMSO in physiological saline). The mice were orally administered with 10 mg/kg/dose patchouli alcohol, 2 mg/kg/dose patchouli alcohol or 2 mg/kg/dose oseltamivir (dissolved in 2% DMSO in physiological saline) one time daily for 7 days. The weight of mice was determined daily. We conducted procedures according to Principle of Laboratory Animal Care (NIH Publication No. 85 -23, revised 1985) and the guidelines of the Peking University Animal Research Committee.
Kunming mice were anesthetized with isoflurane and exposed to virus (A/Leningrad/134/17/1957) by intranasal instillation. Drugs were prepared in 2% DMSO in physiological saline and administered 4 h prior to virus exposure and continued daily for 5 days. All mice were observed daily for changes in weight and for any deaths. Parameters for evaluation of antiviral activity included weight loss, reduction in mortality and/or increase in mean day to death (MDD) determined through 15 days.
The N2 sub-type neuraminidase crystal structure (PDB code 1IVD) was obtained from the RCSB Protein Data Bank [34] . For convenience, the structure is named as NA hereafter. Geometry and partial atomic charges of the patchouli alcohol ( Figure 1) were calculated with the Discover 3.0 module (Insight II 2005) [35] by applying the BFGS algorithm [36] and the consistent-valence force-field (CVFF), with a convergence criterion of 0.01 kcal mol −1 Å −1 . The docking and molecular dynamics (MD) simulations were performed by the general protocols in the Insight II 2005 software packages, consistent with the previous literatures [24, 26, 28, 35, [37] [38] [39] . During the MD simulations, the canonical ensemble (NVT) was employed at normal temperature (300 K). The MD temperature was controlled by the velocity scaling thermostat [40] . Integrations of the classical equations of motion were achieved using the Verlet algorithm. The systems were solvated in a large sphere of TIP3P water molecules [40] with the radius of 35.0 Å, which is enough to hold the ensembles [40] . The MD trajectories were generated using a 1.0-fs time step for a total of 20.0 ns, saved at 5.0-ps intervals. The interaction energies of patchouli alcohol with NA and the respective residues at the NA active site were calculated by the Docking module [35], over the 0.5~20.0 ns MD trajectories.
All results are expressed as mean values ± standard deviations (SDs) (n = 3). The significance of difference was calculated by one-way analysis of variance, and values p < 0.001 were considered to be significant.
In conclusion, patchouli alcohol possesses anti-influenza A (H2N2) virus activity via interference with the NA function that cleaves the α-glycosidic bond between sialic acid and glycoconjugate. Our results provide the promising information for the potential use of patchouli alcohol in the treatment of influenza A (H2N2) virus infectious disease. Further mechanistic studies on the anti-influenza A virus activity are needed to support this point of view.
|
What method was used to measure the inhibition of viral replication?
| 4,075 |
MTT method
| 13,705 |
1,578 |
Inhibitory Effect and Possible Mechanism of Action of Patchouli Alcohol against Influenza A (H2N2) Virus
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6264369/
SHA: f2d842780b9928cc70f38a4458553f2431877603
Authors: Wu, Huaxing; Li, Beili; Wang, Xue; Jin, Mingyuan; Wang, Guonian
Date: 2011-08-03
DOI: 10.3390/molecules16086489
License: cc-by
Abstract: In the present study, the anti-influenza A (H2N2) virus activity of patchouli alcohol was studied in vitro, in vivo and in silico. The CC(50) of patchouli alcohol was above 20 µM. Patchouli alcohol could inhibit influenza virus with an IC(50) of 4.03 ± 0.23 µM. MTT assay showed that the inhibition by patchouli alcohol appears strongly after penetration of the virus into the cell. In the influenza mouse model, patchouli alcohol showed obvious protection against the viral infection at a dose of 5 mg/kg/day. Flexible docking and molecular dynamic simulations indicated that patchouli alcohol was bound to the neuraminidase protein of influenza virus, with an interaction energy of –40.38 kcal mol(–1). The invariant key active-site residues Asp151, Arg152, Glu119, Glu276 and Tyr406 played important roles during the binding process. Based on spatial and energetic criteria, patchouli alcohol interfered with the NA functions. Results presented here suggest that patchouli alcohol possesses anti-influenza A (H2N2) virus properties, and therefore is a potential source of anti-influenza agents for the pharmaceutical industry.
Text: The influenza virus, which is one of the main causes of acute respiratory infections in humans, can lead to annual epidemics and infrequent pandemics. The two influenza pandemics of the 20 th century, "Asian Influenza (1957/H2N2)" and "Hong Kong Influenza (1968/H3N2)" resulted in the deaths of an estimated 2-3 million people globally [1, 2] . Today, their descendants continue to cause the majority of influenza infections in humans [3] . So far as it is learned that the most effective antiviral drug is the neuraminidase (NA) inhibitor, which target the NA glycoproteins of influenza A and B virus [4, 5] .
The release of new virions from the infected cell is a key step in the influenza life cycle and need neuraminidase (NA) to cleave the α-ketosidic linkage between terminal sialic acid and an adjacent sugar residue [6] . The NA inhibitors were designed to prevent the key step by blocking the active site of enzyme and thus allow sufficient time for the host immune systems to remove infected viruses [7] . Consistent efforts have been devoted to the development of NA inhibitors, using the crystal structure of the N2 sub-type NA protein [8] [9] [10] [11] [12] [13] [14] [15] . Indeed, oseltamivir (Tamiflu) is the representative NA inhibitor that has proven to be uniquely applicable oral drug in clinical practice for the treatment of influenza infection [4, 8, 9] . However, with an increase in medical use, the oseltamivir-resistant strains have been found and probably lead to a large scale outbreak of novel pandemic flu [16, 17] .
Patchouli alcohol ( Figure 1 ) has been well known for over a century. It is a major constituent of the pungent oil from the East Indian shrub Pogostemon cablin (Blanco) Benth, and widely used in fragrances. Patchouli oil is an important essential oil in the perfume industry, used to give a base and lasting character to a fragrance [16, 17] . The essential oil is very appreciated for its characteristic pleasant and long lasting woody, earthy, and camphoraceous odor, as well as for its fixative properties, being suitable for use in soaps and cosmetic products [16, 17] . The aerial part of Pogostemon cablin has wildly been used for the treatment of the common cold and as an antifungal agent in China [16, 17] . Moreover, the plant is widely used in Traditional Chinese Medicine as it presents various types of pharmacological activity according to the composition of the oil [16, 17] . Patchouli alcohol, as the major volatile constituent of patchouli oil, has been found to strongly inhibit H1N1 replication and weakly inhibit B/Ibaraki/2/85 replication [18] . To the best of our knowledge, the anti-influenza virus (H2N2) activities of patchouli alcohol have not been evaluated yet. Therefore, the aim of the present study was to evaluate the anti-influenza A virus (H2N2) activity of patchouli alcohol by MTT assay and mouse influenza model. On such basis, explicitly solvated docking and molecular dynamic (MD) methods were applied to investigative the binding mode involving patchouli alcohol with influenza virus NA protein. We anticipate that the insight into the understanding of inhibiting mechanism will be of value in the rational design of novel anti-influenza drugs.
First the efficacy of patchouli alcohol on influenza A (H2N2) virus replication and cell viability were examined. CC 50 was used to express the cytotoxicity of patchouli alcohol on MDCK. The CC 50 of patchouli alcohol was above 20 mM, which indicated that patchouli alcohol did not affect the growth of MDCK (Table 1) . Thus, it seems that the antiviral effects of patchouli alcohol were not due to the cytotoxicity. Moreover, patchouli alcohol was found to inhibit influenza A (H2N2) virus with an IC 50 of 4.03 ± 0.23 µM. Based on the IC 50 and CC 50 values, the selectivity index (SI) was calculated as >4.96. It is reported that a SI of 4 or more is appropriate for an antiviral agent [18] , suggesting that patchouli alcohol can be judged to have anti-influenza A (H2N2) virus activity.
Until now, it has been found that patchouli alcohol showed dose-dependent anti-influenza virus (A/PR/8/34, H1N1) activity, with an IC 50 value of 2.635 µM. Furthermore, it showed weak activity against B/Ibaraki/2/85 (IC 50 = 40.82 µM) [19] . With the addition of the above H2N2 inhibitory activity, we have a comprehensively view of the anti-influenza activity of patchouli alcohol.
Cells were pretreated with patchouli alcohol prior to virus infection (pretreatment cells), viruses were pretreated prior to infection (pretreatment virus), and patchouli alcohol was added during the adsorption period (adsorption) or after penetration of the viruses into cells (replication). Experiments were repeated independently three times and data presented are the average of three experiments. The symbols * indicated very significant difference p < 0.01 with respect to other mode (pretreatment virus, adsorption and pretreatment cell).
As shown in Figure 2 , patchouli alcohol showed anti-influenza A (H2N2) virus activity in a timedependent manner. It showed best antiviral activity when added at a concentration of 8 µM during the replication period with inhibition of the viral replication of 97.68% ± 2.09% for influenza A (H2N2) at 72 h. However, no significant effect was detected when patchouli alcohol was used for pretreatment of cells or viruses or when patchouli alcohol was only added during the adsorption phase. These results suggested that the inhibition of influenza A (H2N2) virus by patchouli alcohol appears to occur much more strongly after penetration of the virus into the cell. Besides, biochemical studies have indicated that the bioactivity of NA protein is essential determinant after the replication of influenza A (H2N2) virus [20] [21] [22] . Hence, we conclude that the function of NA protein may be suppressed by patchouli alcohol.
To evaluate the toxicity of patchouli alcohol, the mean value of body weight of mice in each group was statistically analyzed. The mean weights of mice administered at the 2 mg/kg/dose oseltamivir, 2 mg/kg/dose patchouli alcohol and 10 mg/kg/dose of patchouli alcohol one time daily for 7 days were not significantly different compared with the normal control mice, showing no toxicity of patchouli alcohol and oseltamivir within the testing concentration (P > 0.05). Physiological status was observed in virus infection mice. Three days after viral infection, some mice, especially mice in the H2N2 infected control group showed changes in behavior, such as a tendency to huddle, diminished vitality, and ruffled fur, etc. In the mouse influenza model, viral infection leads to loss of body weight and high mortality. Therefore, the efficacy of patchouli alcohol and oseltamivir were evaluated on the basis of survival rate measured for 15 days post-infection, for treated infected animals relative to untreated infected (control) animals. A comparison of efficacy of patchouli alcohol and oseltamivir in vivo mouse influenza model (oral treatment) showed that at a dose of 5 mg/kg/day, patchouli alcohol showed obvious protection against the influenza virus, as the mean day to death was detected as 11.8 ± 1.1 (Table 2) . When the dose was lowered to 1 mg/kg/day, patchouli alcohol showed weaker protection (measured by Survivors/total) than that of 5 mg/kg/day, the mean day to death was 7.5 ± 1.8. Whereas oseltamivir at this dose level (1 mg/kg/day) showed 50% protection (measured by survivors/total) against the influenza virus. In the H2N2 infected control group, there were no survivors. In view of both in vitro and in vivo data, we conclude that patchouli alcohol could be used in the treatment of human influenza virus infections.
Based on the above experiment data, patchouli alcohol is determined to be bound within NA protein. As the total energies and backbone root-mean-square-deviations (RMSD) in Figure 3 indicate, the energy-minimized patchouli alcohol-NA complex has been in equilibrium since about 0.5 ns, and then retains quite stable in the last 19.5 ns. It is consistent with the previous MD results of other NA inhibitors [23] [24] [25] [26] [27] [28] . Accordingly, the geometric and energetic analyses were made on the average structures of 0.5~20.0 ns MD trajectories, where the system has been already at equilibrium. The interaction energy (E inter ) of patchouli alcohol with NA was calculated at −40.38 kcal mol −1 , where the vdW rather than electrostatic interactions were found to play a dominant role, contribute to about 72% (−29.18 kcal mol −1 ). As shown in Figure 4 , the patchouli alcohol was bound at the active site which also bound to oseltamivir and zanamivir [28] . As Figure 5 shows, the oxygen atom of patchouli alcohol was oriented towards the sidechains of residues Glu119 and Tyr406, with one H-bond formed with each residue. The values of distances in Figure 6 further reveal that the docked complex remains rather stable throughout the simulation, with the average distances of Glu119:OE2patchouli alcohol:O and Tyr406:OH -patchouli alcohol:O less than 2.8 Å. The sum contributions (E sum ) of residues Glu119 and Tyr406 amounted to −8.46 and −7.37 kcal mol −1 , respectively (Table 3) . Besides, patchouli alcohol was stabilized by residues Arg118, Asp151, Arg152, Trp178, Ala246, Glu276, Arg292, Asn294 and Gln347, especially residues Asp151, Arg152 and Glu276 ( Figure 5 and Table 3 ). As a matter of fact, residues Asp151, Arg152, Glu119, Glu276 and Tyr406 of the NA protein have already received enough attention from rational drug designs [14, 30, 31] . The catalytic residues Asp151, Arg152 and Glu276 are crucial to the NA functions and the residues Glu119 and Tyr406 are important to stabilize the NA active sites [32, 33] . It suggests that the NA functions will be affected by the presence of patchouli alcohol, consistent with the above experiments. Patchouli alcohol matches with the NA active site and has an acceptable interaction energy. Considering the obvious structure discrepancies against current NA inhibitors, it represents an ideal lead compound for the designs of novel anti-influenza agents.
Patchouli alcohol and oseltamivir were obtained from Sigma Chemical Co. (St. Louis, MO, USA, purity > 99%) and was stored in glass vials with Teflon sealed caps at −20 ± 0.5 °C in the absence of light.
MDCK (Madin-Darby canine kidney) was purchased from Harbin Veterinary Research Institute (Harbin, Heilongjiang, China). The cells were grown in monolayer culture with Eagle's minimum essential medium (EMEM) supplemented with 10% fetal calf serum (FCS), 100 U/mL penicillin and 100 μg/mL streptomycin. The monolayers were removed from their plastic surfaces and serially passaged whenever they became confluent. Cells were plated out onto 96-well culture plates for cytotoxicity and anti-influenza assays, and propagated at 37 °C in an atmosphere of 5% CO 2 .
The influenza strain A/Leningrad/134/17/1957 H2N2) was purchased from National Control Institute of Veterinary Bioproducts and Pharmaceuticals (Beijing, China). Virus was routinely grown on MDCK cells. The stock cultures were prepared from supernatants of infected cells and stored at −80 °C.
The cellular toxicity of patchouli alcohol on MDCK cells was assessed by the MTT method. Briefly, cells were seeded on a microtiter plate in the absence or presence of various concentrations (20 µM -0.0098 µM) of patchouli alcohol (eight replicates) and incubated at 37 °C in a humidified atmosphere of 5% CO 2 for 72 h. The supernatants were discarded, washed with PBS twice and MTT reagent (5 mg/mL in PBS) was added to each well. After incubation at 37 °C for 4 h, the supernatants were removed, then 200 μL DMSO was added and incubated at 37 °C for another 30 min. After that the plates were read on an ELISA reader (Thermo Molecular Devices Co., Union City, USA) at 570/630 nm. The mean OD of the cell control wells was assigned a value of 100%. The maximal non-toxic concentration (TD 0 ) and 50% cytotoxic concentration (CC 50 ) were calculated by linear regression analysis of the dose-response curves generated from the data.
Inhibition of virus replication was measured by the MTT method. Serial dilution of the treated virus was adsorbed to the cells for 1 h at 37 °C. The residual inoculum was discared and infected cells were added with EMEM containing 2% FCS. Each assay was performed in eight replicates. After incubation for 72 h at 37 °C, the cultures were measured by MTT method as described above. The concentration of patchouli alcohol and oseltamivir which inhibited virus numbers by 50% (IC 50 ) was determined from dose-response curves.
Cells and viruses were incubated with patchouli alcohol at different stages during the viral infection cycle in order to determine the mode of antiviral action. Cells were pretreated with patchouli alcohol before viral infection, viruses were incubated with patchouli alcohol before infection and cells and viruses were incubated together with patchouli alcohol during adsorption or after penetration of the virus into the host cells. Patchouli alcohol was always used at the nontoxic concentration. Cell monolayers were pretreated with patchouli alcohol prior to inoculation with virus by adding patchouli alcohol to the culture medium and incubation for 1 h at 37 °C. The compound was aspirated and cells were washed immediately before the influenza A (H2N2) inoculum was added. For pretreatment virus, Influenza A (H2N2) was incubated in medium containing patchouli alcohol for 1h at room temperature prior to infection of MDCK cells. For analyzing the anti-influenza A (H2N2) inhibition during the adsorption period, the same amount of influenza A (H2N2) was mixed with the drug and added to the cells immediately. After 1 h of adsorption at 37 °C, the inoculum was removed and DMEM supplemented with 2 % FCS were added to the cells. The effect of patchouli alcohol against influenza A (H2N2) was also tested during the replication period by adding it after adsorption, as typical performed in anti-influenza A (H2N2) susceptibility studies. Each assay was run in eight replicates.
Kunming mice, weighing 18-22 g (6 weeks of age) were purchased from Harbin Veterinary Research Institute Animal Co., Ltd. (Harbin, Heilongjiang, China) . First, the toxicity of patchouli alcohol and oseltamivir was assessed in the healthy mice by the loss of body weight compared with the control group (2% DMSO in physiological saline). The mice were orally administered with 10 mg/kg/dose patchouli alcohol, 2 mg/kg/dose patchouli alcohol or 2 mg/kg/dose oseltamivir (dissolved in 2% DMSO in physiological saline) one time daily for 7 days. The weight of mice was determined daily. We conducted procedures according to Principle of Laboratory Animal Care (NIH Publication No. 85 -23, revised 1985) and the guidelines of the Peking University Animal Research Committee.
Kunming mice were anesthetized with isoflurane and exposed to virus (A/Leningrad/134/17/1957) by intranasal instillation. Drugs were prepared in 2% DMSO in physiological saline and administered 4 h prior to virus exposure and continued daily for 5 days. All mice were observed daily for changes in weight and for any deaths. Parameters for evaluation of antiviral activity included weight loss, reduction in mortality and/or increase in mean day to death (MDD) determined through 15 days.
The N2 sub-type neuraminidase crystal structure (PDB code 1IVD) was obtained from the RCSB Protein Data Bank [34] . For convenience, the structure is named as NA hereafter. Geometry and partial atomic charges of the patchouli alcohol ( Figure 1) were calculated with the Discover 3.0 module (Insight II 2005) [35] by applying the BFGS algorithm [36] and the consistent-valence force-field (CVFF), with a convergence criterion of 0.01 kcal mol −1 Å −1 . The docking and molecular dynamics (MD) simulations were performed by the general protocols in the Insight II 2005 software packages, consistent with the previous literatures [24, 26, 28, 35, [37] [38] [39] . During the MD simulations, the canonical ensemble (NVT) was employed at normal temperature (300 K). The MD temperature was controlled by the velocity scaling thermostat [40] . Integrations of the classical equations of motion were achieved using the Verlet algorithm. The systems were solvated in a large sphere of TIP3P water molecules [40] with the radius of 35.0 Å, which is enough to hold the ensembles [40] . The MD trajectories were generated using a 1.0-fs time step for a total of 20.0 ns, saved at 5.0-ps intervals. The interaction energies of patchouli alcohol with NA and the respective residues at the NA active site were calculated by the Docking module [35], over the 0.5~20.0 ns MD trajectories.
All results are expressed as mean values ± standard deviations (SDs) (n = 3). The significance of difference was calculated by one-way analysis of variance, and values p < 0.001 were considered to be significant.
In conclusion, patchouli alcohol possesses anti-influenza A (H2N2) virus activity via interference with the NA function that cleaves the α-glycosidic bond between sialic acid and glycoconjugate. Our results provide the promising information for the potential use of patchouli alcohol in the treatment of influenza A (H2N2) virus infectious disease. Further mechanistic studies on the anti-influenza A virus activity are needed to support this point of view.
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What was the conclusion of this study?
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patchouli alcohol possesses anti-influenza A (H2N2) virus activity
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1,584 |
Viral and bacterial co-infection in severe pneumonia triggers innate immune responses and specifically enhances IP-10: a translational study
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5138590/
SHA: ef3d6cabc804e5eb587b34249b539c1b5efa4cc4
Authors: Hoffmann, Jonathan; Machado, Daniela; Terrier, Olivier; Pouzol, Stephane; Messaoudi, Mélina; Basualdo, Wilma; Espínola, Emilio E; Guillen, Rosa M.; Rosa-Calatrava, Manuel; Picot, Valentina; Bénet, Thomas; Endtz, Hubert; Russomando, Graciela; Paranhos-Baccalà, Gláucia
Date: 2016-12-06
DOI: 10.1038/srep38532
License: cc-by
Abstract: Mixed viral and bacterial infections are widely described in community-acquired pneumonia; however, the clinical implications of co-infection on the associated immunopathology remain poorly studied. In this study, microRNA, mRNA and cytokine/chemokine secretion profiling were investigated for human monocyte-derived macrophages infected in-vitro with Influenza virus A/H1N1 and/or Streptococcus pneumoniae. We observed that the in-vitro co-infection synergistically increased interferon-γ-induced protein-10 (CXCL10, IP-10) expression compared to the singly-infected cells conditions. We demonstrated that endogenous miRNA-200a-3p, whose expression was synergistically induced following co-infection, indirectly regulates CXCL10 expression by targeting suppressor of cytokine signaling-6 (SOCS-6), a well-known regulator of the JAK-STAT signaling pathway. Additionally, in a subsequent clinical pilot study, immunomodulators levels were evaluated in samples from 74 children (≤5 years-old) hospitalized with viral and/or bacterial community-acquired pneumonia. Clinically, among the 74 cases of pneumonia, patients with identified mixed-detection had significantly higher (3.6-fold) serum IP-10 levels than those with a single detection (P = 0.03), and were significantly associated with severe pneumonia (P < 0.01). This study demonstrates that viral and bacterial co-infection modulates the JAK-STAT signaling pathway and leads to exacerbated IP-10 expression, which could play a major role in the pathogenesis of pneumonia.
Text: Scientific RepoRts | 6:38532 | DOI: 10 .1038/srep38532 pathogenesis of several diseases and has been suggested as a potential biomarker of viral infection 10, 11 , late-onset bacterial infection in premature infants 12 , and a promising biomarker of sepsis and septic shock 13, 14 . Combined analysis of IP-10 and IFN-γ has also been reported as a useful biomarker for diagnosis and monitoring therapeutic efficacy in patients with active tuberculosis [15] [16] [17] , and both remain detectable in the urine of patients with pulmonary diseases in the absence of renal dysfunction 18 .
With airway epithelial cells 19 , resident alveolar macrophages (AMs) and blood monocytes-derived macrophages (recruited into tissues under inflammatory conditions 20, 21 ) represent a major line of defense against both pneumococcal (through their high phagocytic capacity [22] [23] [24] ) and influenza infection 25, 26 . So far, no studies have yet focused on the intracellular mechanisms that regulate IP-10 in human blood leukocytes during mixed IAV and SP infection. Several studies indicated that host non-coding small RNAs (including microRNAs) may function as immunomodulators by regulating several pivotal intracellular processes, such as the innate immune response 27 and antiviral activity 28, 29 ; both of these processes are closely related to toll-like receptor (TLR) signaling pathways.
In this study, we firstly investigated the in vitro intracellular mechanisms that mediate the innate immune response in IAV and/or SP infected human monocyte-derived macrophages (MDMs). Using this approach, we observed that mixed-infection of MDMs induces a synergistic production of IP-10 which can be related to a miRNA-200a/JAK-STAT/SOCS-6 regulatory pathway. Subsequently, in a retrospective analysis of clinical samples collected from children ≤ 5 years-old hospitalized with pneumonia, we confirmed that serum IP-10 level could be related to both viral and/or bacterial etiologies and disease severity.
Characteristics of MDMs infected by IAV and/or SP. Initially, we investigated in vitro the impact of single and mixed IAV and SP infection on MDMs. Firstly, active replication of IAV was assessed by qRT-PCR and quantification of new infectious viral particles in the cell supernatants ( Fig. 1a,b ). IAV titer increased over time after single infection with IAV and correlated with increased production of negative-strand IAV RNA. Maximum viral replication was observed at 18-24 hours post-infection, after which time both RNA replication and the quantity of infectious particles decreased. In this in vitro model, subsequent challenge of IAV-infected MDMs with SP had no significant impact on the production of new infectious viral particles (Fig. 1b) . Together, these results indicate permissive and productive infection of MDMs by IAV. Secondly, we evaluated whether MDMs are permissive for both IAV and SP infection. The presence of pneumococci within IAV-and SP-infected primary MDMs was confirmed at 8 h post-infection (Fig. 1c) , suggesting that MDMs are permissive for viral and bacterial co-infection in the early steps of infection. Importantly, confocal co-detection of mixed IAV and SP was only effective following 8 h post-infection due to the bactericidal impact of SP internalization within human macrophages (after 24 h, data not shown). Thirdly, we evaluated the impact of single and mixed infection with IAV and SP on MDM viability. Mixed infection significantly decreased cell viability (65.2 ± 4.5% total cell death at 48 hours post-infection; P < 0.0001) compared to single SP and IAV infection (39.6 ± 1.7% and 17.4 ± 1.1% total cell death, respectively; Fig. 1d ). Taken together, these results confirmed human MDMs are permissive to mixed viral and bacterial infection. mRNA, microRNA and protein expression profiling reveal an overall induction of the host innate immune response following IAV and/or SP infection of MDMs. To investigate the innate immune response orchestrated by IAV-and SP-infected human MDMs, we firstly evaluated the expression of 84 genes involved in the innate and adaptive immune responses (Table S1) ; the major differentially-expressed genes are summarized in Fig. 2a . Expression profiling indicated an overall induction of genes related to the JAK-STAT, NF-Κ β and TLR signaling pathways. Indeed, all interferon-stimulated genes (ISGs) screened, including CXCL10 (fold-change [FC] = 240.9), CCL-2 (FC = 34.2) and MX-1 (FC = 151.4) were upregulated following mixed infection compared to uninfected cells, most of which are closely related to STAT-1 (FC = 52.3), IRF-7 (FC = 6.8) and IFNB1 (FC = 5.2) also found upregulated in mixed infected cells. Secondly, we investigated the endogenous microRNA expression profiles of IAV-and SP-infected MDMs. A selection of microRNAs that were found to be differentially-expressed under different infection conditions are shown in Fig. 2b and Table S2 . MiRNA-200a-3p was overexpressed after both single IAV (FC = 6.9), single SP (FC = 3.7) and mixed IAV/SP infection (FC = 7.3), indicating this miRNA may play a role in the innate immune response to viral and bacterial co-infection. Similar miRNA-200a-3p dysregulation profiles were obtained following IAV and/or SP infections of human macrophages-like (THP-1 monocytes-derived macrophages) or primary MDMs (data not shown). Thirdly, the secreted levels of various antiviral, pro-inflammatory and immunomodulatory cytokines/chemokines were assayed in IAV-and SP-infected-THP-1 and primary MDM cell supernatants. We observed a remarkable correlation between the mRNA and protein expression profiles of single or mixed infected MDMs especially regarding CXCL-10 and IP-10 expression. Indeed, the level of IP-10 was synergistically increased in the supernatant of IAV-infected THP-1 MDMs exposed to SP (mean: 30,589 ± 16,484 pg ml −1 ) compared to single IAV infection (1,439 ± 566.5 pg ml −1 ) and single SP infection (4,472 ± 2,001 pg ml −1 ; P≤ 0.05; Fig. 2c ) at 24 hours after infection. In those cells, IP-10 expression reduced over time (48 to 72 hours), coinciding with a significant higher proportion of necrotic and apoptotic cells (Fig. 1d) . The synergistic expression of IP-10 was similarly observed at 24 hours post-infection using primary MDMs (Fig. 2d) . Significantly increased secretion of the other tested cytokines and chemokines was not observed post-infection, even in mixed infected MDMs (Fig. S1 ). Interestingly, a significant production of IP-10 was also observed in supernatants of primary human airway epithelial cells (HAEC) mixed-infected by IAV and SP compared to the single infections (Fig. 2e) . Taken together, the mRNA and protein profiling results suggested that mixed viral and bacterial infection of MDMs induces a synergistic pro-inflammatory response related to the type-1 interferon and JAK-STAT signaling pathways, with IP-10 as signature of IAV/SP co-infection. Among all microRNAs screened, miR-200a-3p was the most Scientific RepoRts | 6:38532 | DOI: 10.1038/srep38532 overexpressed in IAV/SP co-infection of human MDMs. In the remainder of this study, we decided to investigate the interconnection between miR-200a-3p expression and the innate immune response.
Endogenous miRNA-200a-3p expression correlates with CXCL10 (IP-10) induction following mixed IAV and SP infection of human MDMs. Using a specific Taqman probe assay targeting miR-200a-3p, we confirmed a significant upregulation of miR-200a-3p following mixed IAV and SP infection of human MDMs (Fig. 3a) . In this experiment, a more marked up-regulation of miR-200a-3p was observed following IAV+ SP compared to results obtained previously (Fig. 2b) . This discrepancy has been attributed to the use of two different approaches to quantify miR-200a-3p expression. The use of a target-specific stem-loop reverse transcription primer in Fig. 3a allows a better sensitivity of miR-200a-3p detection compared to the non-specific fluorescent dye used in Fig. 2b . As the general trend was suggestive of a synergistic induction of miR-200a-3p in response to mixed infection (Fig. 3a) , we hypothesized microRNA-200a-3p may play a role in the regulation of CXCL10 (IP-10), which was also synergistically upregulated in mixed-infected MDMs ( Fig. 2c and d) and primary HAEC ( Statistical analyses were performed using two-way ANOVA with Tukey's post-hoc test; *P < 0.05; **P < 0.01; ***P < 0.001; ****P < 0.0001.
Scientific RepoRts | 6:38532 | DOI: 10.1038/srep38532 CXCL10 (Fig. 3d) . These results suggested miR-200a-3p indirectly regulates CXCL10 and led us to hypothesize that miR-200a-3p controls a potential repressor of the JAK-STAT signaling pathway. . At 18 h after transfection, the MDMs were singly or mixed infected as described previously. At 8 h post-IAV and/or SP infection, total mRNA was extracted and amplified by PCR using specific primers for the indicated genes. Values represent median ± IQR (a, c) or mean ± SEM (d, e) of three biological replicates. Statistical analyses were performed using a Kruskal-Wallis test (non-parametric, one-way ANOVA with Dunn's post-hoc test) for data presented in (a, c). An ordinary two-way ANOVA (with Tukey's post-hoc multiple comparison test) was used for data presented in (d, e). *P < 0.05; **P < 0.01; ***P < 0.001; ****P < 0.0001. MiRNA-200a-3p indirectly regulates IP-10 expression by targeting SOCS6. As shown in Fig. 2a , several JAK-STAT signaling pathway genes were deregulated in mixed IAV-and SP-infected human MDMs; therefore, we hypothesized that miR-200a-3p directly regulates a regulator of the JAK-STAT signaling pathway. Predictive target analysis indicated that the 3' UTR of suppressor of cytokine signaling-6 (SOCS6) may be targeted by miR-200a-3p (Fig. 3b) . SOCS proteins constitute a class of negative regulators of JAK-STAT signaling pathways that are induced by both cytokines and TLR signaling. MiRNA-200a-3p was not predicted to target any of the other six members of the SOCS gene family. Transfection of human MDMs with MIM-200a downregulated SOCS6 (FC = 0.57) while inhibition of miR-200a-3p (INH-200a) upregulated SOCS6 (FC = 1.55), confirming that miR-200a-3p effectively regulates the expression of SOCS6 (Fig. 3e) . Moreover, SOCS6 was synergistically downregulated in IAV-or IAV/SP-infected MDMs overexpressing miRNA-200a (Fig. 3e) , suggesting that both infection and miR-200a-3p negatively regulate the expression of SOCS6. Finally, western blotting confirmed that expression of SOCS-6 sharply reduced following infection, especially after mixed IAV and SP infection (Fig. 3f) .
These results indicate miR-200a-3p is strongly induced in response to mixed viral and bacterial co-infection, which in turn leads to downregulation of the JAK-STAT regulator SOCS-6 at both the mRNA and protein levels and subsequent upregulation of IP-10.
analyses demonstrated mixed IAV and SP infection of human MDMs and HAEC induced significant production of IP-10. As blood leukocytes and respiratory tract epithelial cells actively contribute to inflammation during pneumonia, we hypothesized the level of IP-10 in serum of patient with pneumonia may be both indicative of mixed respiratory infection and disease severity. As part of a prospective, hospital-based, multicenter case-control study on the etiology of pneumonia among children under 5-years-old, a total of 74 patients (44 male, 30 female) were included in this pilot evaluation. According to WHO guidelines, retrospective analysis indicated 44 (59.5%) children had clinical signs of non-severe pneumonia and 30 (40.5%) children had signs of severe pneumonia. The main patient characteristics at inclusion are shown in Table 1 . Patients with severe pneumonia had significant more recorded episodes of dyspnea (P < 0.001), cyanosis (P = 0.03), lower chest indrawing (P < 0.001), dullness to percussion (P < 0.001) and lethargy (P < 0.001) during chest examination than patient with non-severe pneumonia. Moreover, pleural effusions were significantly more observed among critically ill patients and the duration of hospitalization was significantly longer for the children with severe pneumonia than for those with non-severe pneumonia (P = 0.0015). Two deaths occurred within the group of children retrospectively defined with severe pneumonia. Evaluation of the systemic inflammatory response of the 74 cases is shown in Table 2 . Serum level of CRP, IP-10, PCT, G-CSF, IL-6, IL-8 and MIP-1β were significantly more elevated in serum samples from critically ill patients. Patients with severe pneumonia had significantly higher (4.2-fold) serum IP-10 levels than those with a non-severe pneumonia (P < 0.001) suggesting IP-10 as a promising prognostic marker in pneumonia. Diagnostic accuracy measures for predicting pneumonia severity using blood-based biomarkers are summarized in Table S3 . Briefly, in this study, the optimal IP-10 cut-off value for identifying patient with severe pneumonia was 4,240 pg ml −1 , with an area under the receiver operating characteristic curve of 0.69 (95% CI, 0.57 to 0.82, P < 0.001). Defining as positive a serum IP-10 level above this cut-off resulted in a sensitivity of 63.3%, specificity of 63.6% and a positive likelihood ratio of 1.74. Prognostic values of IP-10 were closed to procalcitonin (PCT; AUC = 0.70; 95% IC, 0.58 to 0.82, P < 0.001) and IL-6 (AUC = 0.70; 95% IC, 0.58-0.83, P < 0.001).
Multiplex PCR-based screening of respiratory and blood samples reveal a high variety of pathogen associations (Table 3) . Respiratory viruses were detected in the nasal aspirates (NAs) of 63/74 patients (85.1%). Etiological bacteria of pneumonia (S. pneumoniae, n = 19; S. aureus, n = 1; or H. influenzae type B, n = 7) were identified via real-time PCR in the blood samples of 27/74 (36.5%) of the patients. Multiplex PCR assays allowed the identification of respiratory bacteria in the blood of 19 patients with negative blood culture results. Among the 74 cases PCR-positive for respiratory pathogens, a single virus or bacteria were detected in the NAs of 7 (9.4%) and 3 (4.0%) patients, respectively; these 10/74 (13.5%) cases were defined as the single infection group. The mixed infection group included the 62/74 (83.8%) cases in which (1) multiple viruses and/or bacteria were identified in NAs (38/74; 51.3%) without any bacteria identified in blood samples or (2) one or more viruses and/or bacteria were identified in NAs and associated with a blood bacteremia (24/74; 32.4%). We evaluated whether IP-10 serum level could correlate with the viral and bacterial etiologies of pneumonia. Patients with mixed infection had significant higher (3.6-fold) IP-10 serum level than patient with single detection (P = 0.03; Table 4 ). A stratified analysis reveals that the highest IP-10 serum level was observed among patients with both several respiratory pathogens identified (mixed-detection group) and severe pneumonia (14,427 pg ml −1 , IQR (3,981-82,994). In detail, a remarkable IP-10 serum level (142,531 pg ml −1 ), representing 33-fold higher above cut-off value predicting pneumonia severity was observed in patient with hRV in NA co-detected with S. pneumoniae (serotype 14) in pleural effusion and blood. In concordance with our in-vitro model of co-infection, a significant IP-10 level (90,338 pg ml −1 ) was quantified in blood sample of patient with severe bacteremic pneumococcal (serotype 14) pneumonia with a positive co-detection of Influenza B virus in NA. Taken together, these results suggest that high serum IP-10 levels are significantly associated with mixed viral and bacterial detection and also related to pneumonia pathogenesis.
This study provides additional in vitro and clinical data to improve our understanding of the immunopathology of mixed viral and bacterial pneumonia (Fig. 4) .
The in vitro model of influenza and pneumococcal superinfection of human MDMs demonstrated that mixed infection synergistically induced release of the pro-inflammatory chemokine IP-10, strongly suggesting human Scientific RepoRts | 6:38532 | DOI: 10.1038/srep38532 blood leukocytes contribute to the immunopathology of pneumonia. Additionally, transcriptomics and omics analyses provided new data on the inflammatory pathways that are activated during mixed infection and related to synergistic induction of the pro-inflammatory chemokine IP-10 in mixed infected cells. Our observations are consistent with a recent study describing IP-10 induction as host-proteome signature of both viral and bacterial infections 30 . Of the differentially-expressed genes observed in mixed infected MDMs, the transcription factors STAT-1 and IRF-7 appear to play crucial roles in the regulation of interferon-stimulated genes including CXCL10 (IP-10). By focusing on the intracellular mechanisms that regulate inflammatory pathways, we demonstrated a novel role for miRNA-200a-3p in the regulation of CXCL10 (IP-10). These observations are consistent with previous reports showing that RNA virus infection upregulates miR-155 in macrophages and dendritic cells and also regulates suppressor of cytokine signaling 1 (SOCS1), suggesting the existence of a miRNA/JAK-STAT/SOCS regulatory pathway during viral infection 29 . Our study suggests co-infection leads to overexpression of miR-200a-3p, which in turn targets and downregulates the JAK-STAT regulator SOCS-6 and consequently increases CXCL10 (IP-10) expression. Interestingly, a complementary in-silico approach reveals that several microRNAs that were found dysregulated in our experiments of IAV and SP co-infection of MDMs or HAEC, might target several genes of SOCS family and play similar role than miR-200a-3p. Indeed, miRNA-142-3p might target SOCS4, 5, 6 mRNA while miRNA-194-5p might target SOCS2, 3, 4, 5 and 7 mRNA. These observations underline that intra-cellular regulation of IP-10 is not limited to the contribution of a sole microRNA. A complex inter-relationship between numerous host microRNAs and inhibitors of the JAK-STAT signaling pathway occur to control host innate inflammatory response against viral and/or bacterial infections. Clinically, the majority of pediatric CAP cases in this study were associated with both positive viral and/or bacterial detection. Respiratory microorganisms were detected in 97% of cases; 51.3% of which were viral-viral, viral-bacterial or bacterial-bacterial co-detected only in nasal aspirates, 32.4% of which co-detected in both nasal aspirates and blood samples. These data are consistent with previous etiological studies of pediatric CAP 3,31-33 . S. pneumoniae was the major bacteria identified in blood (19/74; 25.7%) and mainly co-detected with respiratory viruses in NAs (16/19; 84.2%). We observed a very high diversity of viral and bacterial associations in biological samples from children with pneumonia. In comparison with IAV and SP14 combination evaluated in-vitro, no pneumonia cases were singly influenza and pneumococcus infected, and no similar co-detection with those two pathogens has been clinically observed. Nevertheless, Influenza B (IVB) virus was identified in 5 patients and two of them had a positive SP co-detection in blood (one non-typable strain and one serotype 14 using our molecular typing test). IVB and SP14 combination seems to be the nearest pathogen co-detection to that in-vitro investigated. Clinically, this co-detection was associated with both a very high IP-10 expression and a very severe pneumonia case definition. Interestingly, our translational pilot evaluation reveals IP-10 expression can be induced by several different viral and/or bacterial combinations. As immune response to each pathogen is different, further in-vitro investigations using different pathogens associations are needed to better characterize the mechanisms involved in the immunopathology of pneumonia.
In this cohort, highest serum IP-10 levels were identified among patients with both several pathogen detected and severe pneumonia, suggesting a significant role of IP-10 on pneumonia pathogenesis. Indeed, high plasma levels of IP-10 have previously been reported in patients with sepsis 12 , and were associated with high mortality rate, especially among patients with CAP 34 . Additionally, the IP-10-CXCR3 axis has been related to acute immune lung injury and lymphocyte apoptosis during the development of severe acute respiratory syndrome (SARS) 35, 36 . Moreover, an in vivo study that modeled influenza and pneumococcal superinfection in mice indicated that pro-inflammatory chemokines, including IP-10, play a crucial role in influenza-induced susceptibility to lung neutrophilia, severe immunopathology and mortality 37 . In this study, markedly elevated IP-10 (92,809 pg ml −1 ) combined with the highest PCT level (74.4 pg ml −1 ) were quantified in the serum sample of a child who died, in whom S. pneumoniae (serotype 9 V) was identified in the blood (PCR and blood culture) and co-detected with Haemophilus influenzae type B in nasal aspirate. These observations suggest an interrelationship between co-detection, elevated serum IP-10 and the pathogenesis of pneumonia.
Several limitations of this pilot translational study need to be acknowledged before concluding mixed infection is related to elevated IP-10 and disease severity. Indeed, although viral shedding (e.g., of HRV and HBoV) is common in asymptomatic children, we were unable to evaluate the levels of immunomodulators in the serum samples of a control group. Moreover, although the samples were collected within the first 24 hours after admission, only a single blood sample was processed for each patient. Therefore, a larger, longitudinal study on the etiology and severity of pneumonia will be necessary to confirm these results. In conclusion, the present findings suggest that mixed respiratory infections and IP-10 may play major, interconnected roles in the pathogenesis of pneumonia. Clinically, assessment and monitoring of induced IP-10 serum level may assist clinicians to improve diagnosis and patient management of severe community-acquired pneumonia.
Viral and bacterial strains. The 10 ng ml −1 M-CSF (Miltenyi Biotec). THP− 1 MDMs were obtained by culturing cells with 10 ng ml -1 phorbol myristate acetate (PMA; Invivogen, Toulouse, France) for 72 hours. Human airway epithelial cells (HAEC, bronchial cell type) originated from a 54-years old woman with no pathology reported (batch number MD056501) were provided by Mucilair (Epithelix, Geneva, Switzerland). Sterility, tissue integrity (TEER), mucus production and cilia beating frequency have been certified by the company. Gene expression profiling. Total cellular mRNA was purified using the RNeasy kit (Qiagen, Hilden, Germany). Reverse-transcription of total mRNA was performed using the RT 2 First Strand Kit (SABiosciences, Hilden, Germany). The expression of 84 genes involved in the human innate and adaptive immune responses was evaluated using the RT 2 profiler ™ PCR Array (SABiosciences) according to the manufacturer's recommendations. The Δ Δ Ct method was applied to calculate the fold changes in gene expression for each gene relative to uninfected control cells using the web-based RT 2 profiler PCR Array Data Analysis software (SABiosciences).
MicroRNA profiling array. Total cellular microRNAs were purified using the miRNeasy Mini kit (Qiagen) and reverse-transcribed using the miScript Reverse Transcription kit (Qiagen). The profiling of 84 miRNAs was performed using the Human Immunopathology miScript miRNA PCR Array kit (Qiagen) according to the manufacturer's instructions. Data were analyzed using the miScript miRNA PCR array data analysis web portal.
In silico miRNA target prediction. MiRNA target genes were retrieved and compiled using TargetScan 38 and microRNA.org resource 39 . The interactions between miRNAs and intracellular pathways were predicted using DIANA-miRPath v2.0 40 .
THP-1 MDMs were seeded in 24-well plates (0.5 × 10 6 per well) in triplicate, exposed to Influenza A H1N1 (A/Solomon islands/3/2006) virus (IAV) under serum-free conditions for 1 hour and then cultured for 3 hours in fresh RPMI-1640 containing 2% FBS. Streptococcus pneumoniae (SP) serotype 14 was added at 4 hours after IAV infection. Gentamicin (10 μ g ml −1 ) was added 2 hours after SP infection (i.e. 6 hours post-influenza infection) and maintained in the culture media throughout the experiment to kill extracellular bacteria and limit bacterial growth. Cell viability was determined by flow-cytometry using the FITC/Annexin V apoptosis detection kit (BD Biosciences), according to the manufacturer's instructions. #4427975) . In this assay, fold changes have been defined by the Δ Δ Ct method using control RNU-44 and -48 as reference microRNAs. Total mRNA was purified from transfected and infected MDMs using the RNeasy kit (Qiagen) and specific primers were used to amplify transforming growth factor beta-2 (TGFB2; F: 5′ -CCATCCCGCCCACTTTCTAC-3′ , R: 5′ -AGCTCAATCCGTTGTTCAGGC-3′ ), SOCS6 (F: 5′ -AAGAATTCATCCCTTGGATTAGGTAAC-3′ , R: 5′ -CAGACTGGAGGTCGTGGAA-3′ ) 41 43 , and 3) absence of wheezing at auscultation, and, 4) first symptoms appearing within the last 14 days, and 5) radiological confirmation of pneumonia as per WHO guidelines 44 . Based on these primary criteria defining pneumonia cases, all 74 cases were retrospectively re-evaluated according to the WHO "Pocket book of hospital care for children" 45 criteria to evaluate pneumonia severity. Cases that died during the study, or who had at least one additional clinical signs including central cyanosis, dullness to percussion during chest examination, prostration/lethargy, pleural effusion observed on chest radiography were retrospectively included in the severe pneumonia group. Patients without any of these additional clinical signs were included in the non-severe pneumonia group. Table 4 . a IP-10 values are expressed in pg ml -1 . IP-10 concentration differences between groups were compared using unpaired Mann-Whitney tests; significant changes (P < 0.05) are in bold. Clinical and molecular analysis. Nasopharyngeal aspirates (NAs) and whole blood samples were collected from children within 24 hours of admission. Whole blood samples were used for complete blood counts, blood culture and multiplex real-time PCR to identify Staphylococcus aureus, Streptococcus pneumoniae and Haemophilus influenzae type B 46 . S. pneumoniae serotypes were defined using a 11 multiplex real-time PCR assay targeting the 40 most frequently represented serotypes or serogroups according to protocol developed by Messaoudi et al. 47 . Serum C-reactive protein (CRP; AssayPro, St. Charles, Missouri, United States) and Procalcitonin (PCT; VIDAS B.R.A.H.M.S; bioMérieux) were quantified from whole-blood samples. Multiplex real-time non quantitative PCR (Fast-Track Diagnostic, Sliema, Malta) was used to detect 19 viruses and five bacteria in the respiratory specimens (NAs and pleural effusions). Mixed detection was defined as 1) PCR-positive for multiple viruses in NAs, 2) positive blood culture or PCR-positive for multiple bacteria in blood or 3) PCR-positive for one or multiple viruses in NAs and one or multiple bacteria in blood (identified by PCR and blood culture).
Ethical approval. The study protocol, informed consent statement, clinical research form, any amendments and all other study documents were submitted to and approved by the Ethical Committee of the Instituto de Investigaciones en Ciencias de la Salud, the Universidad Nacional de Asunción (IICS-UNA) and the Hospital Pediátrico Niños de Acosta Ñu. Informed consent was obtained from all subjects involved in this study. The clinical investigation was conducted according to the principles expressed in the Declaration of Helsinki.
Statistical analysis. The Chi-square test and Fisher's exact test were used to compare categorical variables; continuous variables and non-normally distributed data were compared using the Mann-Whitney U-test; normally distributed data were compared using unpaired t-tests. Comparative analyses between experimental conditions (i.e., MOCK, IAV, SP or IAV + SP) were performed using one-way ANOVA with Tukey's post-hoc test or Kruskal-Wallis analysis with Dunn's post-hoc tests. Receiver operating curve (ROC) analysis was used to determine the optimal cut-off value for IP-10 to differentiate between non-severe and severe pneumonia cases. P < 0.05 was considered statistically significant. All statistical analyses were performed using GraphPad Prism (La Jolla, California, United States).
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What statistical tests were used to compare categorical variables?
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Chi-square test and Fisher's exact test
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1,584 |
Viral and bacterial co-infection in severe pneumonia triggers innate immune responses and specifically enhances IP-10: a translational study
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5138590/
SHA: ef3d6cabc804e5eb587b34249b539c1b5efa4cc4
Authors: Hoffmann, Jonathan; Machado, Daniela; Terrier, Olivier; Pouzol, Stephane; Messaoudi, Mélina; Basualdo, Wilma; Espínola, Emilio E; Guillen, Rosa M.; Rosa-Calatrava, Manuel; Picot, Valentina; Bénet, Thomas; Endtz, Hubert; Russomando, Graciela; Paranhos-Baccalà, Gláucia
Date: 2016-12-06
DOI: 10.1038/srep38532
License: cc-by
Abstract: Mixed viral and bacterial infections are widely described in community-acquired pneumonia; however, the clinical implications of co-infection on the associated immunopathology remain poorly studied. In this study, microRNA, mRNA and cytokine/chemokine secretion profiling were investigated for human monocyte-derived macrophages infected in-vitro with Influenza virus A/H1N1 and/or Streptococcus pneumoniae. We observed that the in-vitro co-infection synergistically increased interferon-γ-induced protein-10 (CXCL10, IP-10) expression compared to the singly-infected cells conditions. We demonstrated that endogenous miRNA-200a-3p, whose expression was synergistically induced following co-infection, indirectly regulates CXCL10 expression by targeting suppressor of cytokine signaling-6 (SOCS-6), a well-known regulator of the JAK-STAT signaling pathway. Additionally, in a subsequent clinical pilot study, immunomodulators levels were evaluated in samples from 74 children (≤5 years-old) hospitalized with viral and/or bacterial community-acquired pneumonia. Clinically, among the 74 cases of pneumonia, patients with identified mixed-detection had significantly higher (3.6-fold) serum IP-10 levels than those with a single detection (P = 0.03), and were significantly associated with severe pneumonia (P < 0.01). This study demonstrates that viral and bacterial co-infection modulates the JAK-STAT signaling pathway and leads to exacerbated IP-10 expression, which could play a major role in the pathogenesis of pneumonia.
Text: Scientific RepoRts | 6:38532 | DOI: 10 .1038/srep38532 pathogenesis of several diseases and has been suggested as a potential biomarker of viral infection 10, 11 , late-onset bacterial infection in premature infants 12 , and a promising biomarker of sepsis and septic shock 13, 14 . Combined analysis of IP-10 and IFN-γ has also been reported as a useful biomarker for diagnosis and monitoring therapeutic efficacy in patients with active tuberculosis [15] [16] [17] , and both remain detectable in the urine of patients with pulmonary diseases in the absence of renal dysfunction 18 .
With airway epithelial cells 19 , resident alveolar macrophages (AMs) and blood monocytes-derived macrophages (recruited into tissues under inflammatory conditions 20, 21 ) represent a major line of defense against both pneumococcal (through their high phagocytic capacity [22] [23] [24] ) and influenza infection 25, 26 . So far, no studies have yet focused on the intracellular mechanisms that regulate IP-10 in human blood leukocytes during mixed IAV and SP infection. Several studies indicated that host non-coding small RNAs (including microRNAs) may function as immunomodulators by regulating several pivotal intracellular processes, such as the innate immune response 27 and antiviral activity 28, 29 ; both of these processes are closely related to toll-like receptor (TLR) signaling pathways.
In this study, we firstly investigated the in vitro intracellular mechanisms that mediate the innate immune response in IAV and/or SP infected human monocyte-derived macrophages (MDMs). Using this approach, we observed that mixed-infection of MDMs induces a synergistic production of IP-10 which can be related to a miRNA-200a/JAK-STAT/SOCS-6 regulatory pathway. Subsequently, in a retrospective analysis of clinical samples collected from children ≤ 5 years-old hospitalized with pneumonia, we confirmed that serum IP-10 level could be related to both viral and/or bacterial etiologies and disease severity.
Characteristics of MDMs infected by IAV and/or SP. Initially, we investigated in vitro the impact of single and mixed IAV and SP infection on MDMs. Firstly, active replication of IAV was assessed by qRT-PCR and quantification of new infectious viral particles in the cell supernatants ( Fig. 1a,b ). IAV titer increased over time after single infection with IAV and correlated with increased production of negative-strand IAV RNA. Maximum viral replication was observed at 18-24 hours post-infection, after which time both RNA replication and the quantity of infectious particles decreased. In this in vitro model, subsequent challenge of IAV-infected MDMs with SP had no significant impact on the production of new infectious viral particles (Fig. 1b) . Together, these results indicate permissive and productive infection of MDMs by IAV. Secondly, we evaluated whether MDMs are permissive for both IAV and SP infection. The presence of pneumococci within IAV-and SP-infected primary MDMs was confirmed at 8 h post-infection (Fig. 1c) , suggesting that MDMs are permissive for viral and bacterial co-infection in the early steps of infection. Importantly, confocal co-detection of mixed IAV and SP was only effective following 8 h post-infection due to the bactericidal impact of SP internalization within human macrophages (after 24 h, data not shown). Thirdly, we evaluated the impact of single and mixed infection with IAV and SP on MDM viability. Mixed infection significantly decreased cell viability (65.2 ± 4.5% total cell death at 48 hours post-infection; P < 0.0001) compared to single SP and IAV infection (39.6 ± 1.7% and 17.4 ± 1.1% total cell death, respectively; Fig. 1d ). Taken together, these results confirmed human MDMs are permissive to mixed viral and bacterial infection. mRNA, microRNA and protein expression profiling reveal an overall induction of the host innate immune response following IAV and/or SP infection of MDMs. To investigate the innate immune response orchestrated by IAV-and SP-infected human MDMs, we firstly evaluated the expression of 84 genes involved in the innate and adaptive immune responses (Table S1) ; the major differentially-expressed genes are summarized in Fig. 2a . Expression profiling indicated an overall induction of genes related to the JAK-STAT, NF-Κ β and TLR signaling pathways. Indeed, all interferon-stimulated genes (ISGs) screened, including CXCL10 (fold-change [FC] = 240.9), CCL-2 (FC = 34.2) and MX-1 (FC = 151.4) were upregulated following mixed infection compared to uninfected cells, most of which are closely related to STAT-1 (FC = 52.3), IRF-7 (FC = 6.8) and IFNB1 (FC = 5.2) also found upregulated in mixed infected cells. Secondly, we investigated the endogenous microRNA expression profiles of IAV-and SP-infected MDMs. A selection of microRNAs that were found to be differentially-expressed under different infection conditions are shown in Fig. 2b and Table S2 . MiRNA-200a-3p was overexpressed after both single IAV (FC = 6.9), single SP (FC = 3.7) and mixed IAV/SP infection (FC = 7.3), indicating this miRNA may play a role in the innate immune response to viral and bacterial co-infection. Similar miRNA-200a-3p dysregulation profiles were obtained following IAV and/or SP infections of human macrophages-like (THP-1 monocytes-derived macrophages) or primary MDMs (data not shown). Thirdly, the secreted levels of various antiviral, pro-inflammatory and immunomodulatory cytokines/chemokines were assayed in IAV-and SP-infected-THP-1 and primary MDM cell supernatants. We observed a remarkable correlation between the mRNA and protein expression profiles of single or mixed infected MDMs especially regarding CXCL-10 and IP-10 expression. Indeed, the level of IP-10 was synergistically increased in the supernatant of IAV-infected THP-1 MDMs exposed to SP (mean: 30,589 ± 16,484 pg ml −1 ) compared to single IAV infection (1,439 ± 566.5 pg ml −1 ) and single SP infection (4,472 ± 2,001 pg ml −1 ; P≤ 0.05; Fig. 2c ) at 24 hours after infection. In those cells, IP-10 expression reduced over time (48 to 72 hours), coinciding with a significant higher proportion of necrotic and apoptotic cells (Fig. 1d) . The synergistic expression of IP-10 was similarly observed at 24 hours post-infection using primary MDMs (Fig. 2d) . Significantly increased secretion of the other tested cytokines and chemokines was not observed post-infection, even in mixed infected MDMs (Fig. S1 ). Interestingly, a significant production of IP-10 was also observed in supernatants of primary human airway epithelial cells (HAEC) mixed-infected by IAV and SP compared to the single infections (Fig. 2e) . Taken together, the mRNA and protein profiling results suggested that mixed viral and bacterial infection of MDMs induces a synergistic pro-inflammatory response related to the type-1 interferon and JAK-STAT signaling pathways, with IP-10 as signature of IAV/SP co-infection. Among all microRNAs screened, miR-200a-3p was the most Scientific RepoRts | 6:38532 | DOI: 10.1038/srep38532 overexpressed in IAV/SP co-infection of human MDMs. In the remainder of this study, we decided to investigate the interconnection between miR-200a-3p expression and the innate immune response.
Endogenous miRNA-200a-3p expression correlates with CXCL10 (IP-10) induction following mixed IAV and SP infection of human MDMs. Using a specific Taqman probe assay targeting miR-200a-3p, we confirmed a significant upregulation of miR-200a-3p following mixed IAV and SP infection of human MDMs (Fig. 3a) . In this experiment, a more marked up-regulation of miR-200a-3p was observed following IAV+ SP compared to results obtained previously (Fig. 2b) . This discrepancy has been attributed to the use of two different approaches to quantify miR-200a-3p expression. The use of a target-specific stem-loop reverse transcription primer in Fig. 3a allows a better sensitivity of miR-200a-3p detection compared to the non-specific fluorescent dye used in Fig. 2b . As the general trend was suggestive of a synergistic induction of miR-200a-3p in response to mixed infection (Fig. 3a) , we hypothesized microRNA-200a-3p may play a role in the regulation of CXCL10 (IP-10), which was also synergistically upregulated in mixed-infected MDMs ( Fig. 2c and d) and primary HAEC ( Statistical analyses were performed using two-way ANOVA with Tukey's post-hoc test; *P < 0.05; **P < 0.01; ***P < 0.001; ****P < 0.0001.
Scientific RepoRts | 6:38532 | DOI: 10.1038/srep38532 CXCL10 (Fig. 3d) . These results suggested miR-200a-3p indirectly regulates CXCL10 and led us to hypothesize that miR-200a-3p controls a potential repressor of the JAK-STAT signaling pathway. . At 18 h after transfection, the MDMs were singly or mixed infected as described previously. At 8 h post-IAV and/or SP infection, total mRNA was extracted and amplified by PCR using specific primers for the indicated genes. Values represent median ± IQR (a, c) or mean ± SEM (d, e) of three biological replicates. Statistical analyses were performed using a Kruskal-Wallis test (non-parametric, one-way ANOVA with Dunn's post-hoc test) for data presented in (a, c). An ordinary two-way ANOVA (with Tukey's post-hoc multiple comparison test) was used for data presented in (d, e). *P < 0.05; **P < 0.01; ***P < 0.001; ****P < 0.0001. MiRNA-200a-3p indirectly regulates IP-10 expression by targeting SOCS6. As shown in Fig. 2a , several JAK-STAT signaling pathway genes were deregulated in mixed IAV-and SP-infected human MDMs; therefore, we hypothesized that miR-200a-3p directly regulates a regulator of the JAK-STAT signaling pathway. Predictive target analysis indicated that the 3' UTR of suppressor of cytokine signaling-6 (SOCS6) may be targeted by miR-200a-3p (Fig. 3b) . SOCS proteins constitute a class of negative regulators of JAK-STAT signaling pathways that are induced by both cytokines and TLR signaling. MiRNA-200a-3p was not predicted to target any of the other six members of the SOCS gene family. Transfection of human MDMs with MIM-200a downregulated SOCS6 (FC = 0.57) while inhibition of miR-200a-3p (INH-200a) upregulated SOCS6 (FC = 1.55), confirming that miR-200a-3p effectively regulates the expression of SOCS6 (Fig. 3e) . Moreover, SOCS6 was synergistically downregulated in IAV-or IAV/SP-infected MDMs overexpressing miRNA-200a (Fig. 3e) , suggesting that both infection and miR-200a-3p negatively regulate the expression of SOCS6. Finally, western blotting confirmed that expression of SOCS-6 sharply reduced following infection, especially after mixed IAV and SP infection (Fig. 3f) .
These results indicate miR-200a-3p is strongly induced in response to mixed viral and bacterial co-infection, which in turn leads to downregulation of the JAK-STAT regulator SOCS-6 at both the mRNA and protein levels and subsequent upregulation of IP-10.
analyses demonstrated mixed IAV and SP infection of human MDMs and HAEC induced significant production of IP-10. As blood leukocytes and respiratory tract epithelial cells actively contribute to inflammation during pneumonia, we hypothesized the level of IP-10 in serum of patient with pneumonia may be both indicative of mixed respiratory infection and disease severity. As part of a prospective, hospital-based, multicenter case-control study on the etiology of pneumonia among children under 5-years-old, a total of 74 patients (44 male, 30 female) were included in this pilot evaluation. According to WHO guidelines, retrospective analysis indicated 44 (59.5%) children had clinical signs of non-severe pneumonia and 30 (40.5%) children had signs of severe pneumonia. The main patient characteristics at inclusion are shown in Table 1 . Patients with severe pneumonia had significant more recorded episodes of dyspnea (P < 0.001), cyanosis (P = 0.03), lower chest indrawing (P < 0.001), dullness to percussion (P < 0.001) and lethargy (P < 0.001) during chest examination than patient with non-severe pneumonia. Moreover, pleural effusions were significantly more observed among critically ill patients and the duration of hospitalization was significantly longer for the children with severe pneumonia than for those with non-severe pneumonia (P = 0.0015). Two deaths occurred within the group of children retrospectively defined with severe pneumonia. Evaluation of the systemic inflammatory response of the 74 cases is shown in Table 2 . Serum level of CRP, IP-10, PCT, G-CSF, IL-6, IL-8 and MIP-1β were significantly more elevated in serum samples from critically ill patients. Patients with severe pneumonia had significantly higher (4.2-fold) serum IP-10 levels than those with a non-severe pneumonia (P < 0.001) suggesting IP-10 as a promising prognostic marker in pneumonia. Diagnostic accuracy measures for predicting pneumonia severity using blood-based biomarkers are summarized in Table S3 . Briefly, in this study, the optimal IP-10 cut-off value for identifying patient with severe pneumonia was 4,240 pg ml −1 , with an area under the receiver operating characteristic curve of 0.69 (95% CI, 0.57 to 0.82, P < 0.001). Defining as positive a serum IP-10 level above this cut-off resulted in a sensitivity of 63.3%, specificity of 63.6% and a positive likelihood ratio of 1.74. Prognostic values of IP-10 were closed to procalcitonin (PCT; AUC = 0.70; 95% IC, 0.58 to 0.82, P < 0.001) and IL-6 (AUC = 0.70; 95% IC, 0.58-0.83, P < 0.001).
Multiplex PCR-based screening of respiratory and blood samples reveal a high variety of pathogen associations (Table 3) . Respiratory viruses were detected in the nasal aspirates (NAs) of 63/74 patients (85.1%). Etiological bacteria of pneumonia (S. pneumoniae, n = 19; S. aureus, n = 1; or H. influenzae type B, n = 7) were identified via real-time PCR in the blood samples of 27/74 (36.5%) of the patients. Multiplex PCR assays allowed the identification of respiratory bacteria in the blood of 19 patients with negative blood culture results. Among the 74 cases PCR-positive for respiratory pathogens, a single virus or bacteria were detected in the NAs of 7 (9.4%) and 3 (4.0%) patients, respectively; these 10/74 (13.5%) cases were defined as the single infection group. The mixed infection group included the 62/74 (83.8%) cases in which (1) multiple viruses and/or bacteria were identified in NAs (38/74; 51.3%) without any bacteria identified in blood samples or (2) one or more viruses and/or bacteria were identified in NAs and associated with a blood bacteremia (24/74; 32.4%). We evaluated whether IP-10 serum level could correlate with the viral and bacterial etiologies of pneumonia. Patients with mixed infection had significant higher (3.6-fold) IP-10 serum level than patient with single detection (P = 0.03; Table 4 ). A stratified analysis reveals that the highest IP-10 serum level was observed among patients with both several respiratory pathogens identified (mixed-detection group) and severe pneumonia (14,427 pg ml −1 , IQR (3,981-82,994). In detail, a remarkable IP-10 serum level (142,531 pg ml −1 ), representing 33-fold higher above cut-off value predicting pneumonia severity was observed in patient with hRV in NA co-detected with S. pneumoniae (serotype 14) in pleural effusion and blood. In concordance with our in-vitro model of co-infection, a significant IP-10 level (90,338 pg ml −1 ) was quantified in blood sample of patient with severe bacteremic pneumococcal (serotype 14) pneumonia with a positive co-detection of Influenza B virus in NA. Taken together, these results suggest that high serum IP-10 levels are significantly associated with mixed viral and bacterial detection and also related to pneumonia pathogenesis.
This study provides additional in vitro and clinical data to improve our understanding of the immunopathology of mixed viral and bacterial pneumonia (Fig. 4) .
The in vitro model of influenza and pneumococcal superinfection of human MDMs demonstrated that mixed infection synergistically induced release of the pro-inflammatory chemokine IP-10, strongly suggesting human Scientific RepoRts | 6:38532 | DOI: 10.1038/srep38532 blood leukocytes contribute to the immunopathology of pneumonia. Additionally, transcriptomics and omics analyses provided new data on the inflammatory pathways that are activated during mixed infection and related to synergistic induction of the pro-inflammatory chemokine IP-10 in mixed infected cells. Our observations are consistent with a recent study describing IP-10 induction as host-proteome signature of both viral and bacterial infections 30 . Of the differentially-expressed genes observed in mixed infected MDMs, the transcription factors STAT-1 and IRF-7 appear to play crucial roles in the regulation of interferon-stimulated genes including CXCL10 (IP-10). By focusing on the intracellular mechanisms that regulate inflammatory pathways, we demonstrated a novel role for miRNA-200a-3p in the regulation of CXCL10 (IP-10). These observations are consistent with previous reports showing that RNA virus infection upregulates miR-155 in macrophages and dendritic cells and also regulates suppressor of cytokine signaling 1 (SOCS1), suggesting the existence of a miRNA/JAK-STAT/SOCS regulatory pathway during viral infection 29 . Our study suggests co-infection leads to overexpression of miR-200a-3p, which in turn targets and downregulates the JAK-STAT regulator SOCS-6 and consequently increases CXCL10 (IP-10) expression. Interestingly, a complementary in-silico approach reveals that several microRNAs that were found dysregulated in our experiments of IAV and SP co-infection of MDMs or HAEC, might target several genes of SOCS family and play similar role than miR-200a-3p. Indeed, miRNA-142-3p might target SOCS4, 5, 6 mRNA while miRNA-194-5p might target SOCS2, 3, 4, 5 and 7 mRNA. These observations underline that intra-cellular regulation of IP-10 is not limited to the contribution of a sole microRNA. A complex inter-relationship between numerous host microRNAs and inhibitors of the JAK-STAT signaling pathway occur to control host innate inflammatory response against viral and/or bacterial infections. Clinically, the majority of pediatric CAP cases in this study were associated with both positive viral and/or bacterial detection. Respiratory microorganisms were detected in 97% of cases; 51.3% of which were viral-viral, viral-bacterial or bacterial-bacterial co-detected only in nasal aspirates, 32.4% of which co-detected in both nasal aspirates and blood samples. These data are consistent with previous etiological studies of pediatric CAP 3,31-33 . S. pneumoniae was the major bacteria identified in blood (19/74; 25.7%) and mainly co-detected with respiratory viruses in NAs (16/19; 84.2%). We observed a very high diversity of viral and bacterial associations in biological samples from children with pneumonia. In comparison with IAV and SP14 combination evaluated in-vitro, no pneumonia cases were singly influenza and pneumococcus infected, and no similar co-detection with those two pathogens has been clinically observed. Nevertheless, Influenza B (IVB) virus was identified in 5 patients and two of them had a positive SP co-detection in blood (one non-typable strain and one serotype 14 using our molecular typing test). IVB and SP14 combination seems to be the nearest pathogen co-detection to that in-vitro investigated. Clinically, this co-detection was associated with both a very high IP-10 expression and a very severe pneumonia case definition. Interestingly, our translational pilot evaluation reveals IP-10 expression can be induced by several different viral and/or bacterial combinations. As immune response to each pathogen is different, further in-vitro investigations using different pathogens associations are needed to better characterize the mechanisms involved in the immunopathology of pneumonia.
In this cohort, highest serum IP-10 levels were identified among patients with both several pathogen detected and severe pneumonia, suggesting a significant role of IP-10 on pneumonia pathogenesis. Indeed, high plasma levels of IP-10 have previously been reported in patients with sepsis 12 , and were associated with high mortality rate, especially among patients with CAP 34 . Additionally, the IP-10-CXCR3 axis has been related to acute immune lung injury and lymphocyte apoptosis during the development of severe acute respiratory syndrome (SARS) 35, 36 . Moreover, an in vivo study that modeled influenza and pneumococcal superinfection in mice indicated that pro-inflammatory chemokines, including IP-10, play a crucial role in influenza-induced susceptibility to lung neutrophilia, severe immunopathology and mortality 37 . In this study, markedly elevated IP-10 (92,809 pg ml −1 ) combined with the highest PCT level (74.4 pg ml −1 ) were quantified in the serum sample of a child who died, in whom S. pneumoniae (serotype 9 V) was identified in the blood (PCR and blood culture) and co-detected with Haemophilus influenzae type B in nasal aspirate. These observations suggest an interrelationship between co-detection, elevated serum IP-10 and the pathogenesis of pneumonia.
Several limitations of this pilot translational study need to be acknowledged before concluding mixed infection is related to elevated IP-10 and disease severity. Indeed, although viral shedding (e.g., of HRV and HBoV) is common in asymptomatic children, we were unable to evaluate the levels of immunomodulators in the serum samples of a control group. Moreover, although the samples were collected within the first 24 hours after admission, only a single blood sample was processed for each patient. Therefore, a larger, longitudinal study on the etiology and severity of pneumonia will be necessary to confirm these results. In conclusion, the present findings suggest that mixed respiratory infections and IP-10 may play major, interconnected roles in the pathogenesis of pneumonia. Clinically, assessment and monitoring of induced IP-10 serum level may assist clinicians to improve diagnosis and patient management of severe community-acquired pneumonia.
Viral and bacterial strains. The 10 ng ml −1 M-CSF (Miltenyi Biotec). THP− 1 MDMs were obtained by culturing cells with 10 ng ml -1 phorbol myristate acetate (PMA; Invivogen, Toulouse, France) for 72 hours. Human airway epithelial cells (HAEC, bronchial cell type) originated from a 54-years old woman with no pathology reported (batch number MD056501) were provided by Mucilair (Epithelix, Geneva, Switzerland). Sterility, tissue integrity (TEER), mucus production and cilia beating frequency have been certified by the company. Gene expression profiling. Total cellular mRNA was purified using the RNeasy kit (Qiagen, Hilden, Germany). Reverse-transcription of total mRNA was performed using the RT 2 First Strand Kit (SABiosciences, Hilden, Germany). The expression of 84 genes involved in the human innate and adaptive immune responses was evaluated using the RT 2 profiler ™ PCR Array (SABiosciences) according to the manufacturer's recommendations. The Δ Δ Ct method was applied to calculate the fold changes in gene expression for each gene relative to uninfected control cells using the web-based RT 2 profiler PCR Array Data Analysis software (SABiosciences).
MicroRNA profiling array. Total cellular microRNAs were purified using the miRNeasy Mini kit (Qiagen) and reverse-transcribed using the miScript Reverse Transcription kit (Qiagen). The profiling of 84 miRNAs was performed using the Human Immunopathology miScript miRNA PCR Array kit (Qiagen) according to the manufacturer's instructions. Data were analyzed using the miScript miRNA PCR array data analysis web portal.
In silico miRNA target prediction. MiRNA target genes were retrieved and compiled using TargetScan 38 and microRNA.org resource 39 . The interactions between miRNAs and intracellular pathways were predicted using DIANA-miRPath v2.0 40 .
THP-1 MDMs were seeded in 24-well plates (0.5 × 10 6 per well) in triplicate, exposed to Influenza A H1N1 (A/Solomon islands/3/2006) virus (IAV) under serum-free conditions for 1 hour and then cultured for 3 hours in fresh RPMI-1640 containing 2% FBS. Streptococcus pneumoniae (SP) serotype 14 was added at 4 hours after IAV infection. Gentamicin (10 μ g ml −1 ) was added 2 hours after SP infection (i.e. 6 hours post-influenza infection) and maintained in the culture media throughout the experiment to kill extracellular bacteria and limit bacterial growth. Cell viability was determined by flow-cytometry using the FITC/Annexin V apoptosis detection kit (BD Biosciences), according to the manufacturer's instructions. #4427975) . In this assay, fold changes have been defined by the Δ Δ Ct method using control RNU-44 and -48 as reference microRNAs. Total mRNA was purified from transfected and infected MDMs using the RNeasy kit (Qiagen) and specific primers were used to amplify transforming growth factor beta-2 (TGFB2; F: 5′ -CCATCCCGCCCACTTTCTAC-3′ , R: 5′ -AGCTCAATCCGTTGTTCAGGC-3′ ), SOCS6 (F: 5′ -AAGAATTCATCCCTTGGATTAGGTAAC-3′ , R: 5′ -CAGACTGGAGGTCGTGGAA-3′ ) 41 43 , and 3) absence of wheezing at auscultation, and, 4) first symptoms appearing within the last 14 days, and 5) radiological confirmation of pneumonia as per WHO guidelines 44 . Based on these primary criteria defining pneumonia cases, all 74 cases were retrospectively re-evaluated according to the WHO "Pocket book of hospital care for children" 45 criteria to evaluate pneumonia severity. Cases that died during the study, or who had at least one additional clinical signs including central cyanosis, dullness to percussion during chest examination, prostration/lethargy, pleural effusion observed on chest radiography were retrospectively included in the severe pneumonia group. Patients without any of these additional clinical signs were included in the non-severe pneumonia group. Table 4 . a IP-10 values are expressed in pg ml -1 . IP-10 concentration differences between groups were compared using unpaired Mann-Whitney tests; significant changes (P < 0.05) are in bold. Clinical and molecular analysis. Nasopharyngeal aspirates (NAs) and whole blood samples were collected from children within 24 hours of admission. Whole blood samples were used for complete blood counts, blood culture and multiplex real-time PCR to identify Staphylococcus aureus, Streptococcus pneumoniae and Haemophilus influenzae type B 46 . S. pneumoniae serotypes were defined using a 11 multiplex real-time PCR assay targeting the 40 most frequently represented serotypes or serogroups according to protocol developed by Messaoudi et al. 47 . Serum C-reactive protein (CRP; AssayPro, St. Charles, Missouri, United States) and Procalcitonin (PCT; VIDAS B.R.A.H.M.S; bioMérieux) were quantified from whole-blood samples. Multiplex real-time non quantitative PCR (Fast-Track Diagnostic, Sliema, Malta) was used to detect 19 viruses and five bacteria in the respiratory specimens (NAs and pleural effusions). Mixed detection was defined as 1) PCR-positive for multiple viruses in NAs, 2) positive blood culture or PCR-positive for multiple bacteria in blood or 3) PCR-positive for one or multiple viruses in NAs and one or multiple bacteria in blood (identified by PCR and blood culture).
Ethical approval. The study protocol, informed consent statement, clinical research form, any amendments and all other study documents were submitted to and approved by the Ethical Committee of the Instituto de Investigaciones en Ciencias de la Salud, the Universidad Nacional de Asunción (IICS-UNA) and the Hospital Pediátrico Niños de Acosta Ñu. Informed consent was obtained from all subjects involved in this study. The clinical investigation was conducted according to the principles expressed in the Declaration of Helsinki.
Statistical analysis. The Chi-square test and Fisher's exact test were used to compare categorical variables; continuous variables and non-normally distributed data were compared using the Mann-Whitney U-test; normally distributed data were compared using unpaired t-tests. Comparative analyses between experimental conditions (i.e., MOCK, IAV, SP or IAV + SP) were performed using one-way ANOVA with Tukey's post-hoc test or Kruskal-Wallis analysis with Dunn's post-hoc tests. Receiver operating curve (ROC) analysis was used to determine the optimal cut-off value for IP-10 to differentiate between non-severe and severe pneumonia cases. P < 0.05 was considered statistically significant. All statistical analyses were performed using GraphPad Prism (La Jolla, California, United States).
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What was a severe limitation of this study?
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unable to evaluate the levels of immunomodulators in the serum samples of a control group
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