Escherichia coli responsible for intestinal infections in piglets. Phages vB_EcoM_F1, vB_EcoM_FB, vB_EcoS_FP, vB_EcoM_FT, vB_EcoM_SP1, vB_EcoP_SP5M, vB_EcoP_SP7, and vB_EcoS_SP8 were isolated between 2007 and 2018 in the Iberian Peninsula. These viruses span the three tailed phage families, Podoviridae, Siphoviridae, and Myoviridae.We report eight phages infecting enterotoxigenic  Escherichia coli responsible for intestinal infections in piglets. Phages vB_EcoM_F1, vB_EcoM_FB, vB_EcoS_FP, vB_EcoM_FT, vB_EcoM_SP1, vB_EcoP_SP5M, vB_EcoP_SP7, and vB_EcoS_SP8 were isolated between 2007 and 2018 in the Iberian Peninsula. These viruses span the three tailed phage families, Podoviridae, Siphoviridae, and Myoviridae.We report eight phages infecting enterotoxigenic  Escherichia coli (ETEC) infections cause diarrhea and death among weaning and postweaning piglets  in Geneious Prime v2020.1 software .Here, we present eight complete nd Spain . The excnd Spain , isolateisolates  were incisolates . DNA wasisolates . Reads wsoftware . Genomicsoftware  and compsoftware , tRNAscasoftware , ARAGORNsoftware , and HHpsoftware   revealed high homology with several viruses from the nonredundant database . Phages The analyses of these phages’ genomes together with additional studies focused on their fitness can provide new resources to combat ETEC infections.E. coli phage genome sequences are listed in PRJNA646048.The GenBank accession numbers of the


Our software introduces new visualization technology that enables independent layers of interactivity using Plotly in R, which aids in the exploration of large biological datasets. The bigPint package presents modernized versions of scatterplot matrices, volcano plots, and litre plots through the implementation of layered interactivity. These graphics have detected normalization issues, differential expression designation problems, and common analysis errors in public RNA-sequencing datasets. Researchers can apply bigPint graphics to their data by following recommended pipelines written in reproducible code in the user manual. In this paper, we explain how we achieved the independent layers of interactivity that are behind bigPint graphics. Pseudocode and source code are provided. Computational scientists can leverage our open-source code to expand upon our layered interactive technology and/or apply it in new ways toward other computational biology tasks.Interactive data visualization is imperative in the biological sciences. The development of independent layers of interactivity has been in pursuit in the visualization community. We developed bigPint, a data visualization package available on Bioconductor under the GPL-3 license ( Biological disciplines face the challenge of increasingly large and complex data. One necessary approach toward eliciting information is data visualization. Newer visualization tools incorporate interactive capabilities that allow scientists to extract information more efficiently than static counterparts. In this paper, we introduce technology that allows multiple independent layers of interactive visualization written in open-source code. This technology can be repurposed across various biological problems. Here, we apply this technology to RNA-sequencing data, a popular next-generation sequencing approach that provides snapshots of RNA quantity in biological samples at given moments in time. It can be used to investigate cellular differences between health and disease, cellular changes in response to external stimuli, and additional biological inquiries. RNA-sequencing data is large, noisy, and biased. It requires sophisticated normalization. The most popular open-source RNA-sequencing data analysis software focuses on models, with little emphasis on integrating effective visualization tools. This is despite sound evidence that RNA-sequencing data is most effectively explored using graphical and numerical approaches in a complementary fashion. The software we introduce can make it easier for researchers to use models and visuals in an integrated fashion during RNA-sequencing data analysis. PLOS Computational Biology Software paper.This is a Interactive data visualization is increasingly imperative in the biological sciences . When peInteractive visualization tools for genomic data can have restricted access when only available on certain operating systems and/or when requiring payment –5. TheseWe recently developed bigPint, an interactive RNA-sequencing data visualization software package available on Bioconductor. In the current paper, we will now explain the technical innovations and merits of the bigPint package, including new interactive visualization techniques that we believe can be helpful in the development and usage of future biological visualization software.https://lindsayrutter.github.io/bigPint/articles/pipeline). This pipeline uses reproducible code and sample data from the bigPint package, so you can smoothly follow along each line of example code. For additional details, we recommend users to view articles in the Get Started tab on the package website (https://lindsayrutter.github.io/bigPint).For users who would like to immediately try out the package hands-on and apply bigPint graphics to their data, we recommend consulting the example pipeline  format and contains the read counts for all genes of interest. The value in row i and column j should indicate how many reads have been assigned to gene i in sample j. This is the same input format required in popular RNA-seq count-based statistical packages, such as DESeq2, edgeR, limma, EBSeq, and BaySeq Editor's specific comments:thank you for your patience while this manuscript was under review, as there was difficulty sourcing appropriately qualified reviewers. Please thoroughly address the comments of both reviewers in your revisions, in particular those focused on software architecture, reliability, and ease of use.Reviewer's Responses to QuestionsComments to the Authors:Please note here if the review is uploaded as an attachment.Reviewer #1: Rutter and Cook discuss their new R package offering data visualization techniques geared towards RNA-seq data. The data visualization techniques make clever use of interactivity to highlight aspects of the data, which enable researchers to identify common analysis errors. While I like the concept of the presented visualization techniques, I am not convinced that this will be a widely applied tool. The tool felt clunky and still produced several errors. I will outline these as well as other issues/suggestions below:1. The authors claim that their software can be used for the exploration of any large biological datasets. However, their choice of plots, in particular the volcanoplot, suggests to me a focus on RNA-seq data. I would suggest the inclusion of another example for a non RNA-seq dataset or making it clear that this package targets RNA-seq data exclusively.https://github.com/dreamRs/shinylogs).2. It is great that all plots are easily downloadable, but for the purpose of replicability it would be great if logs of all user interactions could be provided. This can, for example, be handled with shinylogs ```{r cut_and_paste_from_bit_ly_spmCode, include=FALSE, eval=FALSE}# I have removed this from the review because it uses up too many words...# trust me please, I have cut and pasted it verbatim.```When I run this, a window opens and then closes again. Interesting my datais a Value - not Data in my Global Environment and it says "NULL (empty)"Why? Well let's run the data acquisition by itself.Annoyingly this is not a reproducible problem.Sometimes it seems to work but I don't know why.```{r test_data_download, include=TRUE}data <- bigPint:::PKGENVIR$DATAstr(data)```Still NULL, sadly.All the other code examples use the same code to get data in so I'm not going to try them here. I have tried them previously.#### Where else can I go for help?https://www.bioconductor.org/packages/release/bioc/html/bigPint.html].Let's check (Bioconductor)[The page is a good starting point.It has a green build which is a good sign.https://www.bioconductor.org/packages/release/bioc/vignettes/bigPint/inst/doc/bioconductor.R]Check out the (R-script)[Woops! It seems blank. A bit frustrating.https://www.bioconductor.org/packages/release/bioc/vignettes/bigPint/inst/doc/bioconductor.html)[HTML Vignette](https://lindsayrutter.github.%20io/bigPint/)! Woops again!points to website with a [rotten url](Let's go to the Github Repository and see what we can find...https://lindsayrutter.github.io/bigPint/) is in the manuscriptThe [link]data("soybean_cn_sub")soybean_cn_sub <- soybean_cn_subapp <- plotSMApp(data=soybean_cn_sub)if (interactive) {shiny::runApp(app)}```Good new is that the data function works and gives us some data!```{r str_data}str(soybean_cn_sub)```Nice data.frame produced.Bad news is that my interactive plot still won't work!Well actually it does work when I run this again.It is a bit slow and it gives lots of red text:'scatter' objects don't have these attributes: 't2'What happens if I try the```{r try_PKGENVIR$DATA_again}data <- bigPint:::PKGENVIR$DATA```It works! If I do it after I run your App. Wonder why? I'm not going to spend any time working through that at the moment.Now the script above works with lots of error messages.#### Back to square one, can I make the static hex plots```{r static_hex}data(soybean_ir_sub)soybean_ir_sub <- logdata(soybean_ir_sub_metrics)ret <- plotLitrelength(ret)names(ret)[1]ret[[1]]```Answer is Yes! Good and it looks interesting with points highlighted on it.I probably should have started here in the first place but hey...#### I need to try to make the apps work...https://rdrr.io/bioc/bigPint/man/plotVolcanoApp.html]I found some code here:  <- logapp <- plotLitreAppif (interactive) {shiny::runApp}```I can see the number of genes in each hexagram if I hover over it - nice.Plot gene works to give orange spots which can be hovered over to identify. However, I'm not sure how I am selecting those genes.I have worked out that it is going through the genes by rank. However, because that information is off the bottom of the screen, it took me a while to realise.I need to watch the video again!https://lindsayrutter.github.io/bigPint/articles/interactive.html]Back to (website)app <- plotSMApp(data=soybean_cn_sub)if (interactive) {shiny::runApp(app)}```This works! Excellent.Selecting hexagrams works.Downloading IDs works.Downloading plots doesn't :-(Need to open in Browser as advised!Could add that as error message?OK so it does work but it produces LOTS of warnings.I wonder why?Warning: 'scatter' objects don't have these attributes: 't2'Lots of repeat of this warning.#### Try plotLitreApp```{r plotSMAapp_again}data("soybean_ir_sub")data("soybean_ir_sub_metrics")soybean_ir_sub_log <- soybean_ir_subsoybean_ir_sub_log <- logapp <- plotLitreAppif (interactive) {shiny::runApp}```#### Try plotPCPApp```{r plotPCPApp}soybean_ir_sub_st = as.data.frame, 1,scale)))soybean_ir_sub_st$ID = as.character(soybean_ir_sub$ID)soybean_ir_sub_st = soybean_ir_sub_stcolnames(soybean_ir_sub_st) = colnames(soybean_ir_sub)nID = which)soybean_ir_sub_st = 0plotGenes = filter %>%select(ID)pcpDat = filterapp <- plotPCPApp(data = pcpDat)if (interactive) {shiny::runApp}```Works!In Browser, I can save images as advised.Nice job.#### Try Volcano app from websitehttps://lindsayrutter.github.io/bigPint/articles/interactive.html#volcano-plot-app) when accessed on 12 Oct 2019.Sadly no code on [website](https://rdrr.io/bioc/bigPint/man/plotVolcanoApp.html] instead as above. Made it work again!Example code from (here) en_GB.UTF-8/en_GB.UTF-8/en_GB.UTF-8/C/en_GB.UTF-8/en_GB.UTF-8attached base packages:[1] stats graphics grDevices utils datasets methods[7] baseother attached packages:[1] shinycssloaders_0.2.0 Hmisc_4.2-0 Formula_1.2-3[4] survival_2.44-1.1 lattice_0.20-38 RColorBrewer_1.1-2[7] GGally_1.4.0 data.table_1.12.2 dplyr_0.8.3[10] stringr_1.4.0 hexbin_1.27.3 tidyr_1.0.0[13] htmlwidgets_1.5.1 plotly_4.9.0 ggplot2_3.2.1[16] shinydashboard_0.7.1 shiny_1.4.0 bigPint_1.0.0loaded via a namespace (and not attached):[1] Rcpp_1.0.2 assertthat_0.2.1 zeallot_0.1.0[4] digest_0.6.21 mime_0.7 R6_2.4.0[7] plyr_1.8.4 backports_1.1.5 acepack_1.4.1[10] httr_1.4.1 pillar_1.4.2 rlang_0.4.0[13] lazyeval_0.2.2 rstudioapi_0.10 rpart_4.1-15[16] Matrix_1.2-17 checkmate_1.9.4 labeling_0.3[19] splines_3.6.1 foreign_0.8-72 munsell_0.5.0[22] compiler_3.6.1 httpuv_1.5.2 xfun_0.10[25] pkgconfig_2.0.3 base64enc_0.1-3 htmltools_0.4.0[28] nnet_7.3-12 tidyselect_0.2.5 tibble_2.1.3[31] gridExtra_2.3 htmlTable_1.13.2 reshape_0.8.8[34] viridisLite_0.3.0 withr_2.1.2 crayon_1.3.4[37] later_1.0.0 grid_3.6.1 jsonlite_1.6[40] xtable_1.8-4 gtable_0.3.0 lifecycle_0.1.0[43] magrittr_1.5 scales_1.0.0 stringi_1.4.3[46] promises_1.1.0 latticeExtra_0.6-28 ellipsis_0.3.0[49] vctrs_0.2.0 tools_3.6.1 glue_1.3.1[52] purrr_0.3.2 crosstalk_1.0.0 fastmap_1.0.1[55] yaml_2.2.0 colorspace_1.4-1 cluster_2.1.0[58] knitr_1.25**********Have all data underlying the figures and results presented in the manuscript been provided?PLOS Computational Biologydata availability policy, and numerical data that underlies graphs or summary statistics should be provided in spreadsheet form as supporting information.Large-scale datasets should be made available via a public repository as described in the Reviewer #1: YesReviewer #2: Yes**********what does this mean?). If published, this will include your full peer review and any attached files.PLOS authors have the option to publish the peer review history of their article  is very helpful.**********Have all data underlying the figures and results presented in the manuscript been provided?PLOS Computational Biologydata availability policy, and numerical data that underlies graphs or summary statistics should be provided in spreadsheet form as supporting information.Large-scale datasets should be made available via a public repository as described in the Reviewer #1: Yes**********what does this mean?). If published, this will include your full peer review and any attached files.PLOS authors have the option to publish the peer review history of their article  1223-442824 | ploscompbiol.org | @PLOSCompBiolPLOS Computational Biology | Carlyle House, Carlyle Road, Cambridge CB4 3DN | United Kingdom


R0) of 3.4  in Weifang, and a mean effective reproduction number (t)R that falls below 1 on 4 February. We further estimate the number of infections through time and compare these estimates to confirmed diagnoses by the Weifang Centers for Disease Control. We find that these estimates are consistent with reported cases and there is unlikely to be a large undiagnosed burden of infection over the period we studied.Analysis of genetic sequence data from the SARS-CoV-2 pandemic can provide insights into epidemic origins, worldwide dispersal, and epidemiological history. With few exceptions, genomic epidemiological analysis has focused on geographically distributed data sets with few isolates in any given location. Here, we report an analysis of 20 whole SARS- CoV-2 genomes from a single relatively small and geographically constrained outbreak in Weifang, People’s Republic of China. Using Bayesian model-based phylodynamic methods, we estimate a mean basic reproduction number ( These data comprise 20 whole-genome sequences from confirmed COVID-19 cases in Weifang, Shandong Province, People’s Republic of China. The data were collected over the course of several weeks up to 10 February 2020, and overlap with a period of intensifying public health and social distancing measures. These interventions included public health messaging, establishing phone hot-lines, encouraging home isolation for recent visitors from Wuhan (January 23–26), optimising triage of suspected cases in hospitals (January 24), travel restrictions (January 26), extending school closures, and establishing ‘fever clinics’ for consultation and diagnosis (January 27) . In contModel-based phylodynamic methods have been previously used to analyse sequence data from Wuhan and exported international cases . Using aAs of 10 February 2020, 136 suspected cases and 214 close contacts were diagnosed by Weifang Center for Disease Control and Prevention; of these, 38 cases were confirmed positive with SARS-CoV-2. The median age of patients was 36 (range: 6–75). Two of twenty patients suffered severe or critical illness.Viral RNA was extracted using the Maxwell 16 Viral Total Nucleic Acid Purification Kit (Promega AS1150) with the magnetic bead method, and the RNeasy Mini Kit (QIAGEN 74104) with the column method. Quantitative reverse transcription polymerase chain reaction (RT-qPCR) was carried out using the 2019 novel coronavirus nucleic acid detection kit  to confirm the presence of SARS-CoV-2 viral RNA with cycle threshold (Ct) values ranging from 17 to 34, targeting the highly conservative region (ORF1ab/N gene) in the SARS-CoV-2 genome.Concentration of RNA samples was measured by the Qubit RNA HS Assay Kit . The enzyme DNase was used to remove host DNA. The remaining RNA was used to construct the single-stranded circular DNA library with the MGIEasy RNA Library preparation reagent set . Purified RNA was then fragmented. Using these short fragments as templates, random hexamers were used to synthesise the first-strand cDNA and then the second strand. Using the short double-strand DNA, a DNA library was constructed through end repair, adaptor ligation and PCR amplification. PCR products were transformed into a single-strand circular DNA library through DNA-denaturation and circularisation. DNA nanoballs (DNBs) were generated with the single-strand circular DNA library by rolling circle replication. The DNBs were loaded into the flow cell and pair-end 100 bp sequencing was performed on DNBSEQ-T7 platform 8 . Twenty genomes were assembled with length from 26,840 to 29,882 nucleotides.>100× average sequencing depth across the SARS-CoV-2 genome were subsampled to achieve 100× sequencing depth before being assembled.Total reads were first processed using Kraken v0.10.5 (default parameters) with a self-built database of Coronaviridae genomes  to identify Coronaviridae-like reads. To remove low-quality reads, duplications and adaptor contaminations, fastp v0.19.5 (parameters: -q 20-u 20 -n 1 -l 50) and SOAPnuke v1.5.6 (parameters: -l 20 -q 0.2 -E 50 -n 0.02 -5 0 -Q 2 -G -d) were used. The Coronaviridae-like reads of samples with <100× average sequencing depth were directly assembled de novo with SPAdes v3.14.0 using default settings. The Coronaviridae-like reads of samples with The 20 Weifang sequences have mean 1.1 per cent N content and are deposited in GISAID (gisaid.org).The phylodynamic model is designed to account for 1, nonlinear epidemic dynamics in Weifang with a realistic course of infection (incubation and infectious periods), 2, variance in transmission rates that can influence epidemic size estimates, and 3, migration of lineages in and out of Weifang.The maximum number of daily confirmed COVID-19 cases occurred on February 5, but it is unknown when the maximum prevalence of infection occurred. To capture a nonlinear decrease in cases following epidemic peak, and to account for a realistic distribution of generation times, we use an extension of the susceptible- exposed-infectious-recovered (SEIR) model  for epidJ which has a higher transmission rate (τ -fold higher) than the I compartment.To estimate total numbers infected, the phylodynamic model must account for epidemiological variables which are known to significantly influence genetic diversity . ForemosJ compartment with probability h,p or otherwise to I. The model is implemented as a system of ordinary differential equations: The variance of the implied offspring distribution is calibrated to give a similar over-dispersion to that of the SARS epidemic. Upon leaving the incubation period, individuals progress to the Y (t)) serves as a source of new infections and is assumed to be growing exponentially (at rate ρ) over this time period.The outbreak in Weifang was seeded by multiple lineages imported at various times from the rest of China. We therefore account for location of sampling in our model. Migration is modelled as a bi-directional process with rates proportional to epidemic size in Weifang. The larger reservoir of COVID-19 cases outside of Weifang  , η is the per-lineage rate of migration out of Weifang, and the total rate of migration in and out of Weifang is ηX.Migration only depends on the size of variables in the Weifang compartment and thus does not influence epidemic dynamics; it will only influence the inferred probability that a lineage resides within Weifang. For compartment η, β and ρ are estimated. Additionally, we estimate initial sizes of Y, E, and S. Initial values of I, J, and R are fixed at 0. Other parameters are fixed based on prior information. We fix 1/γ0 = 4.1 days and 1/γ1 = 3.8 days  and the initial growth rate in cases was approximately 22 per cent per day, consistent with those estimated in other settings and during the early epidemic in Wuhan  when Weifang was implementing a variety of public health interventions and contact tracing to limit epidemic spread. Our central estimate of tR drops below 1 on the 4th of February.Effective reproduction number over time is shown in in Wuhan . SamplinAlthough previous studies have shown the significance of realistic modelling for fidelity of phylogenetic inference , our anaIn this analysis, there is a mean of three pairwise differences among sequences from Weifang; the corresponding number among the sequences outside of Weifang is eight.There is correspondingly low confidence in tree topology , and onlThe earliest Weifang sequence was sampled on 25 January from a patient who first showed symptoms on 16 January. These dates cover a similar range as the posterior TMRCA of all Weifang sequences .Our analysis of 20 SARS-CoV-2 genomes has confirmed independent observations regarding the rate of spread and burden of infection in Weifang, China. Surveillance of COVID-19 is rendered difficult by high proportions of illness with mild severity and an unknown proportion of asymptomatic infection . The exttR over time, falling below 1 on 4 February, suggests a slower rate of spread outside of Wuhan and effective control strategies implemented in late January. It is consistent with a previous modelling study of Shandong province  is a limitation of this study. However, this represents a significant proportion of the total number of cases reported; there were thirty-eight confirmed cases at the date of the last genetic sample (10 February), rising no further than forty-four from 16 February onwards . DespiteFurther, it is possible that the outbreak observed in Weifang could be due not to community transmission, but rather multiple importations. However, given that we sampled the reference set from a GISAID database downloaded in June, it is reasonable to assume close genetic matches would have been chosen. A maximum-likelihood tree of the entire alignment  shows thCommunity transmission is further supported by the fact that cases were identified via contact tracing. This forms another limitation, as it suggests non-random sampling of cases in Weifang. This could lead to an underestimate of the total number of cases in Weifang. However, as a large proportion of reported cases were included in this analysis, the bias is unlikely to be too significant.β has a constant value, tR can decrease only as a result of depleting susceptibles. The decrease in tR is therefore a constraint in the model and occurred even when sampling from the prior. Despite this, the genetic data was informative on the value of β (and therefore R0), which in turn affects the date at which tR falls below 1. Our analysis demonstrates a reliable mean estimate of R0, with a narrower uncertainty, compared to sampling from the prior. Although other methods which allow for time-varying transmission rate (including other PhyDyn model templates) or models with a piece-wise tR function . Accession numbers for sequences from Weifang: EPI_ISL_413691 EPI_ISL_413693 EPI_ISL_413694, EPI_ISL_413695 EPI_ISL_413696 EPI_ISL_413697, EPI_ISL_413711 EPI_ISL_413729 EPI_ISL_413746, EPI_ISL_413747 EPI_ISL_413748 EPI_ISL_413749, EPI_ISL_413750 EPI_ISL_413751 EPI_ISL_413752, EPI_ISL_413753 EPI_ISL_413761 EPI_ISL_413791, EPI_ISL_413809 EPI_ISL_413692. Accession numbers for sequences from outside of Weifang: EPI_ISL_414380 EPI_ISL_437621 EPI_ISL_429092, EPI_ISL_418327 EPI_ISL_416335 EPI_ISL_413854, EPI_ISL_402121 EPI_ISL_408480 EPI_ISL_418503, EPI_ISL_450196 EPI_ISL_417030 EPI_ISL_424356, EPI_ISL_451351 EPI_ISL_408010 EPI_ISL_430742, EPI_ISL_416366 EPI_ISL_451343 EPI_ISL_416381, EPI_ISL_407988 EPI_ISL_413882 EPI_ISL_413881, EPI_ISL_413879 EPI_ISL_411954 EPI_ISL_417184, EPI_ISL_418992 EPI_ISL_454935 EPI_ISL_414569, EPI_ISL_416570 EPI_ISL_416600 EPI_ISL_413608, EPI_ISL_451347 EPI_ISL_419242 EPI_ISL_414485, EPI_ISL_414005 EPI_ISL_430847 EPI_ISL_415580, EPI_ISL_413595 EPI_ISL_455376 EPI_ISL_417101, EPI_ISL_417168 EPI_ISL_455410 EPI_ISL_424081, EPI_ISL_440461 EPI_ISL_440433 EPI_ISL_455696, EPI_ISL_444577 EPI_ISL_456208 EPI_ISL_434463, EPI_ISL_437264 EPI_ISL_452673 EPI_ISL_437515, EPI_ISL_437185 EPI_ISL_427257 EPI_ISL_432722, EPI_ISL_437704 EPI_ISL_461275 EPI_ISL_403932.Virus Evolution online.veaa102_Supplementary_DataClick here for additional data file.


The data set compiled in this file refers to the Multizone EnergyPlus model, used in the investigations of the research article entitled "Natural ventilation potential from weather analyses and building simulation". The technical information regarding the model has been grouped into tables, which include: the general simulation settings, the properties of the building materials, the Airflow Network opening settings used in the annual investigation, in addition to the controls established in the Energy Management System (EMS) for hybrid ventilation system operation. The user behaviour, regarding the living and bedrooms occupancy schedule, is also presented in a graph. This data set is made available to the public to clarify details of the EnergyPlus model and how the hybrid operation was defined. In this way, other researchers can perform an extended analysis of the information. Different configurations/ simulation techniques could be employed based on this available data, so different studies might be compared.•The data presented in this article can assist designers and researchers who deal with the modelling of naturally ventilated buildings, especially with Airflow Network and multizone approach.•The use of Energy Management System (EMS) to model hybrid ventilation operation could be adopted as a reference for further research on naturally ventilated buildings.1The data in this article present the input data regarding the EnergyPlus model used at the investigations addressed in the research paper. General simulation settings are summarised in The model set up is based on consolidated practices used in studies involving INES' experimental houses, and therefore does not use the EnergyPlus database. Since they are originally unoccupied, a classic family occupancy schedule was established, which would represent an extreme/worse possible scenario.Besides, the EnergyPlus input data files (.idf) are available for download in the Mendeley repository 2The controls developed in the Energy Management System (EMS) object for the consolidation of the hybrid behaviour at the annual analyses are presented below. The operation mode was adopted in all occupied zones, exemplified here by the living room zone.The set up enables the following changes: triggering the thermal load calculation at a temperature different from the thermostat; deactivation of the thermal load calculation only after occupancy in a room is null; hybrid control, where the local thermal prognosis is not allowed to occur together with the window opening for natural ventilation in the same time step.All objects in class: energymanagementsystem:sensorEnergyManagementSystem:Sensor,OT_Living, !- NameLiving, !- Output:Variable or Output:Meter Index Key NameZone Operative Temperature; !- Output:Variable or Output:Meter NameEnergyManagementSystem:Sensor,Occ_Living, !- NameLiving_Occ, !- Output:Variable or Output:Meter Index Key NamePeople Occupant Count; !- Output:Variable or Output:Meter NameEnergyManagementSystem:Sensor,Ext_Temp, !- NameEnvironment, !- Output:Variable or Output:Meter Index Key NameSite Outdoor Air Drybulb Temperature; !- Output:Variable or Output:Meter NameEnergyManagementSystem:Sensor,T_Living, !- NameLiving, !- Output:Variable or Output:Meter Index Key NameZone Mean Air Temperature; !- Output:Variable or Output:Meter NameEnergyManagementSystem:Sensor,Heat_Living, !- NameHeat_Living, !- Output:Variable or Output:Meter Index Key NameSchedule Value; !- Output:Variable or Output:Meter NameAll objects in class: energymanagementsystem:ActuatorEnergyManagementSystem:Actuator,HeaterControl_Living, !- NameHeat_Living, !- Actuated Component Unique NameSchedule:Constant, !- Actuated Component TypeSchedule Value; !- Actuated Component Control TypeEnergyManagementSystem:Actuator,NVControl_Living, !- NameNV_Living, !- Actuated Component Unique NameSchedule:Constant, !- Actuated Component TypeSchedule Value; !- Actuated Component Control TypeAll objects in class: energymanagementsystem:programcallingmanagerEnergyManagementSystem:ProgramCallingManager,HybridControl, !- NameBeginTimestepBeforePredictor, !- EnergyPlus Model Calling PointHyb_Living, !- Program Name 1All objects in class: energymanagementsystem:programEnergyManagementSystem:Program,Hyb_Living, !- NameSET Temp_Heat = T_Living <= 19, !- Program Line 1IF ((Occ_Living > 0) && (Temp_Heat ==1)), !- Program Line 2SET HeaterControl_Living =1, !- A4SET NVControl_Living = 0,!- A5ELSEIF ((Occ_Living > 0) && (Heat_Living >0)), !- A6SET HeaterControl_Living = 1, !- A7SET NVControl_Living = 0,!- A8ELSEIF (Occ_Living > 0), !- A9IF ((Ext_Temp<T_Living) && (Ext_Temp>20)), !- A10SET HeaterControl_Living = 0, !- A11SET NVControl_Living = 1,!- A12ELSEIF ((Ext_Temp>T_Living) && (Ext_Temp>20)), !- A13SET HeaterControl_Living = 0, !- A14SET NVControl_Living = 0,!- A15ELSEIF (Ext_Temp<20), !- A16SET HeaterControl_Living = 0, !- A17SET NVControl_Living = 0,!- A18ENDIF, !- A19ELSEIF (Occ_Living == 0),!- A20SET HeaterControl_Living = 0, !- A21SET NVControl_Living = 0,!- A22ENDIF; !- A23Nayara R. M. Sakiyama: Conceptualization, Methodology, Software, Data-curation, Formal analysis, Investigation, Writing-Original draft preparation; Leonardo Mazzaferro: Software, Visualization, Validation, Writing-Reviewing and Editing; Joyce C. Carlo: Supervision; Timea Bejat: Resources; Harald Garrecht: Project administration.The authors declare that they have no known competing financial interests or personal relationships which have, or could be perceived to have, influenced the work reported in this article.


Aim: Whole genome and peptide mutation analysis can specify effective vaccine and therapeutics against severe acute respiratory coronavirus-2 (SARS-CoV-2). Materials & methods: Whole genome similarity for Bangladeshi SARS-CoV-2 was determined using ClustalW and BLASTn. Phylogenetic analysis was conducted using neighbor-joining method. Results: 100% of isolates in Bangladesh were in the G clade. We found 99.98–100% sequence similarity among Bangladeshi isolates and isolates of England, Greece, USA, Saudi Arabia and India. Deletion of bases at 5′ untranslated region and 3′ untranslated region was detected. Substitution 261 (E→D) at NSP13 and 1109 (F→L) at spike (S) protein were detected. Substitution 377 (D→G) at nucleocapsid with common substitution 614 (D→G) at S were also detected. Conclusion: This study will provide baseline data for development of an effective vaccine or therapeutics against SARS-CoV-2.  Coronaviridae), previously named as 2019-novel coronavirus (2019-nCoV), has emerged as a pandemic virus [Betacoronavirus – SARS-CoV (2003), HCoV NL63 (2004), HKU1 (2005), MERS-CoV (2012) – have been reported to cause human respiratory system infection [A novel species of coronavirus, severe acute respiratory coronavirus-2 (SARS-CoV-2)  databases. COVID-19 cases and fatalities data were collected from Worldometers (www.worldometers.info/coronavirus), Johns Hopkins University COVID-19 database (https://coronavirus.jhu.edu/), Epidemiology, Disease Control and Research (www.iedcr.gov.bd/website/) website and Directorate General of Health Services (www.dghs.gov.bd/index.php/bd/) in Bangladesh website. Various environmental data were collected from Bangladesh Meteorological Department (http://live4.bmd.gov.bd/satelite/v/sat_infrared/) and AccuWeather (www.accuweather.com). Each month was divided into four equal weeks (W1–W4) except the last week (W5) contained 3 days in January, 1 day in February, 3 days in March, 2 days in April and 3 days in May, respectively. Appropriate institutional review board approval was taken from Biosafety, Biosecurity and Ethical Committee of Jahangirnagar University for this study. Approval number was BBEC, JU/M 2020/COVID-19/(10)1.Data were collected from different databases. Whole genomes of SARS-CoV-2 were collected from GISAID . Sequence homology was determined using the BLASTn program. Multiple sequence alignment was conducted in BioEdit 7.2.6 using the ClustalW Multiple Alignment algorithm . MutatioPhylogenetic and molecular evolutionary relationship analyses of Bangladeshi SARS-CoV-2 were conducted using the whole genome sequences of the references by the MEGA-X software . PhylogeMexico/CDMX-InDRE|EPI_ISL_412972, Luxembourg/LNS2299410|EPI_ISL_421745, United Arab Emirates/L0881|EPI_ISL_435125, Latvia|EPI_ISL_437090, Saudi Arabia/Jeddah66|EPI_ISL_437755, Saudi Arabia/Jeddah60|EPI_ISL_443182, Singapore/126|EPI_ISL_443218, USA/NY-NYUMC623|EPI_ISL_444740, Chile/Santiago_40|EPI_ISL_445319, Wales/PHWC-2B55B|EPI_ISL_445726, Germany/FFM3|EPI_ISL_447609, Greece/259_31928|EPI_ISL_447651, France/10008DM|EPI_ISL_447697, France/10003SN|EPI_ISL_447719, France/10006HC|EPI_ISL_447727, Greece/54_37163|EPI_ISL_447835, USA/DC-CDC-0019|EPI_ISL_447840, Denmark/ALAB-SSI-899|EPI_ISL_444964, Guangdong/SYSU-IHV|EPI_ISL_444969, Luxembourg/LNS5711030|EPI_ISL_445072, Serbia/Novi Pazar-363|EPI_ISL_445087, Japan/Hu_Kng_19-865|EPI_ISL_445183, Sweden/20-07741|EPI_ISL_445241, Romania/279067|EPI_ISL_445243, Chile/Santiago_80|EPI_ISL_445377, Wales/PHWC-2F065|EPI_ISL_446193, Thailand/Bangkok-0079|EPI_ISL_447020, Romania/279068|EPI_ISL_447054, Georgia/Tb-6598|EPI_ISL_447055, DRC/3653|EPI_ISL_447231, Israel/130710062|EPI_ISL_447469, Spain/Valencia597|EPI_ISL_447519, Spain/Valencia598|EPI_ISL_447520, India/CCMB_J278|EPI_ISL_447565, Australia/QLDID941|EPI_ISL_447594, Germany/FFM7|EPI_ISL_447613, Taiwan/NTU27|EPI_ISL_447621, Greece/238_31927|EPI_ISL_447645, France/10007LJ|EPI_ISL_447725, Greece/55_36015|EPI_ISL_447834, Greece/145_34726|EPI_ISL_447836, Norway/2200|EPI_ISL_447837, India/CCMB_K499|EPI_ISL_447865, Iceland/604|EPI_ISL_424624, Mexico/CDMX-INER_04|EPI_ISL_424626, Czech Republic/IAB20-006-16|EPI_ISL_426581, Uruguay/UY-9|EPI_ISL_426584, Spain/Madrid_H12_2902|EPI_ISL_428701, Russia/Moscow-77620|EPI_ISL_428889, Jordan/SR-036|EPI_ISL_429996, Russia/StPetersburg-RII4917S|EPI_ISL_430070, Beijing/BJ589|EPI_ISL_430738, Argentina/PAIS_A005|EPI_ISL_430797, Greece/150|EPI_ISL_434485, Luxembourg/LNS8258882|EPI_ISL_434491, Myanmar/NIH-4385|EPI_ISL_434709, Vietnam/OUCRU022|EPI_ISL_435303, Spain/Valencia247|EPI_ISL_436342, Russia/Moscow-GCBL3|EPI_ISL_436717, Italy/TE5543|EPI_ISL_436720, Latvia/017|EPI_ISL_437096, Indonesia/JKT-EIJK03|EPI_ISL_437191, Austria/Graz-MUG4|EPI_ISL_437200, Germany/BAV-MVP0062|EPI_ISL_437261, Turkey/HSGM-10232|EPI_ISL_437334, Poland/Wro-02|EPI_ISL_437625, Denmark/ALAB-SSI-137|EPI_ISL_437649, Saudi Arabia/Madinah258|EPI_ISL_437742, USA/WA-UW-6546|EPI_ISL_437860, Greece/246_32206|EPI_ISL_437905, Austria/CeMM0023|EPI_ISL_437913, Japan/Donner9|EPI_ISL_438954, Scotland/EDB3941|EPI_ISL_439666, Northern Ireland/NIRE-FAAED|EPI_ISL_441737, Saudi Arabia/Makkah19|EPI_ISL_443166, Singapore/130|EPI_ISL_443222, France/B5688|EPI_ISL_443293, England/LOND-D5453|EPI_ISL_444222, England/LOND-D606D|EPI_ISL_444272, Australia/QLDID937|EPI_ISL_444611, USA/NY-NYUMC610|EPI_ISL_444727, Wuhan/IVDC-HB-01|EPI_ISL_402119, Wuhan/WH05|EPI_ISL_408978, Cambodia/0012|EPI_ISL_411902, South Korea/SNU01|EPI_ISL_411929, Switzerland/1000477806|EPI_ISL_413024, Italy/UniSR1|EPI_ISL_413489, New Zealand/01|EPI_ISL_413490, South Korea/KUMC03|EPI_ISL_413513, India/1-31|EPI_ISL_413523, USA/CruiseA-24|EPI_ISL_414483, Hong Kong/VM20002582|EPI_ISL_414569, USA/WA-UW75|EPI_ISL_415603, Peru/010|EPI_ISL_415787, Brazil/SPBR-09|EPI_ISL_416031, Brazil/SPBR-10|EPI_ISL_416032, Iceland/242|EPI_ISL_417570, Colombia/Antioquia79256|EPI_ISL_417924, Portugal/PT0021|EPI_ISL_418006, Canada/ON_PHL3802|EPI_ISL_418337, Canada/ON_PHL5710|EPI_ISL_418338, Finland/14M26|EPI_ISL_418406, Germany/NRW-24|EPI_ISL_419541, Georgia/Tb-1352|EPI_ISL_420140, Belgium/ULG-10018|EPI_ISL_421196, Taiwan/NTU11|EPI_ISL_422413 and Netherlands/NA_163|EPI_ISL_422698.Reference sequences of SARS-CoV-2 from 58 countries were used for the phylogenetic analysis. GISAID references are: 2. During 1 February 2020 to 9 June 2020, the minimum temperature average was 20°C, maximum temperature average was 32.5°C and mean temperature average was 26.5°C in Dhaka  had been reported from Dhaka, the capital of Bangladesh with an average increase rate of 5973 cases/week and 81 fatalities/week in the country. COVID-19 is increasing relatively slowly in Dhaka, which has a population density of 121,720/miin Dhaka . Along wThe first seven sequenced SARS-CoV-2 in Bangladesh were from the G clade. Compared with 40,000 whole genomes, Bangladeshi SARS-CoV-2 were found to have 100–99.98% sequence similarity with reference sequences. The first sequenced whole genome in Bangladesh, Bangladesh/CHRF had 99.99% sequence similarity with whole genome of SARS-CoV-2 from Germany/FFM3, Sweden/20-07237, USA/NY-NYUMC623, Saudi Arabia/KAUST-Jeddah60, Latvia/011, United Arab Emirates/L0881 and Mexico/CDMX-InDRE_01 . AnotherWhole genomes of the first seven SARS-CoV-2 in Bangladesh were analyzed. The first sequence, CHR|FEPI_ISL_437912, from Bangladesh was 29903 bases in length, sharing the highest similarity with the Wuhan reference sequences (NC_045512/Wuhan-Hu-1) by length. Another six whole genomes, DNAS_CPH_467|EPI_ISL_445213, DNAS_CPH_471|EPI_ISL_445214, DNAS_CPH_427|EPI_ISL_445215, DNAS_CPH_466|EPI_ISL_445216, DNAS_CPH_436|EPI_ISL_445217 and Akbiomed|EPI_ISL_445244, had sequence length of 29642, 29833, 29829, 29828, 29706 and 29823 bases, respectively. CHR|FEPI_ISL_437912 did not have any deletion mutation while the other six isolates had significant deletion at both ends. Furthermore, deletion of the first 25 bases at 5′ untranslated region (UTR) and 40–60 bases at 3′ UTR stem loop region were commonly detected in these six isolates. Besides, numbers of deletion mutations were detected in DNAS_CPH_467|EPI_ISL_445213 and DNAS_CPH_436|EPI_ISL_445217 after 27,000 bases. Insertion mutation was detected in only one whole genome, Akbiomed|EPI_ISL_445244, between position 202 and 203. Substitution point mutation was the most common in Bangladeshi SARS-CoV-2. At 5′ UTR, substitution 241 (C→T) was found in all the isolates from Bangladesh. Most frequently detected substitution point mutations at ORF1ab  region were 1163 (A→T), 3037 (C→T) and 14408 (C→T) in Bangladeshi SARS-CoV-2. In the spike protein-coding regions, substitution mutation 23403 (A→G) was frequent, while 24887 (T→C) was detected in only DNAS_CPH_427|EPI_ISL_445215. Large number of substitution and deletion were found at ORF7a, ORF7b and ORF8 (27394–28259) in DNAS_CPH_436|EPI_ISL_445217. Substitution point mutations 28881 (G→A), 28882 (G→A) and 28883 (G→C) in N protein region were common in most of the Bangladeshi isolates. Large number of substitutions and deletion mutations were also found at N protein (28274–29533), ORF10 (29558–29674) and 3′ UTR (29675–29903) regions in four genomes .In amino acid peptide sequence, significant and rare point mutations were found in Bangladeshi novel coronavirus genomes. At NSP2 protein, 120 (I→F) was detected for the first time in SARS-CoV-2 genome. While at NSP3, 1184 (Q→H) was very rare and detected in Bangladeshi isolates. At NSP6, 3 (V→M) was detected for the first time in DNAS_CPH_467/2020|EPI_ISL_445213 globally. Furthermore, at NSP12, 323 (P→L) was found in all of the Bangladeshi isolates. However, at NSP13, 261 (E→D) was detected in CHRF/EPI_ISL_437912. Spike proteins common mutation 614 (D→G) was present in every isolate from Bangladesh. Of note, 1109 (F→L) at S protein was detected for the first time in DNAS_CPH_471/2020|EPI_ISL_445214 worldwide. At NS3, 172 (G→C) was detected in some of the Bangladeshi isolates. Furthermore, at N protein, 203 (R→K), 204 (G→R) and rare 377 (D→G) were detected in some of isolates .The novel coronavirus has triggered the ongoing COVID-19 pandemic by infecting over 7.2 million people worldwide ,22. WholThe first seven isolates in Bangladesh were in the G clade. Among the first seven isolates, 57% (four of seven) were detected in male and 43% (three of seven) female. Furthermore, the highest percentage  of SARS-CoV-2 in Bangladesh was detected in patients of 21–30 years, followed by 28.6% in 31–40 years, 14.3% in 11–20 years and 14.3% in 41–50 years, respectively. The distribution of gender was similar with previous studies in Europe, China and Asia, but age distribution of COVID-19 patients in Bangladesh was unique ,24.The phylogenetic analysis revealed that the first sequenced SARS-CoV-2 in Bangladesh CHRF|EPI_ISL_437912 was closely related with beta coronavirus from the UAE, Latvia, Saudi Arabia, Mexico and the USA and clustered with them. In the BLAST analysis of CHRF|EPI_ISL_437912, this study detected 99.99% sequence similarity with Germany/EPI_ISL_447609, Sweden/EPI_ISL_445231, USA/NY/EPI_ISL_444740, Saudi Arabia/Jeddah60/EPI_ISL_443182, Latvia/EPI_ISL_437090, United Arab Emirates/EPI_ISL_435125 and Mexico/EPI_ISL_412972. This indicates the probable evolutionary linkage of this isolate with European, Middle East and American beta coronaviruses. Another four of these isolates, DNAS_CPH_467|EPI_ISL_445213, DNAS_CPH_471|EPI_ISL_445214, DNAS_CPH_427|EPI_ISL_445215 and DNAS_CPH_436|EPI_ISL_445217 clustered with each other and were closely related with coronavirus of Greece and Spain in the phylogenetic tree. Of note, DNAS_CPH_466|EPI_ISL_445216 clustered with coronavirus of England and Myanmar, and this cluster was closely related with isolates of France and Germany as well. Bangladeshi isolates DNAS_CPH_467|EPI_ISL_445213, DNAS_CPH_471|EPI_ISL_445214 and DNAS_CPH_427|EPI_ISL_445215 shared 99.98% sequence identity with USA/EPI_ISL_447840, Greece/EPI_ISL_447835, Wales/EPI_ISL_445726, Chile/EPI_ISL_445319 and Germany/EPI_ISL_447609 in BLAST analysis. Furthermore, isolate DNAS_CPH_466|EPI_ISL_445216 was found to have 100% sequence similarity with Luxembourg/EPI_ISL_421745 and France/EPI_ISL_447727, while isolate DNAS_CPH_436|EPI_ISL_445217 had 99.99% similarity with USA/EPI_ISL_447840 and Greece/EPI_ISL_447835. Isolate Akbiomed|EPI_ISL_445244 clustered with beta coronavirus of Georgia, Jordan and England in the phylogenetic tree, while shared 99.99% sequence similarity with USA/EPI_ISL_447840, India/EPI_ISL_447554 and India/EPI_ISL_447047 in BLAST. This study detected that Bangladeshi SARS-CoV-2 had significant evolutionary relationships with European, American and Asian SARS-CoV-2.cis-acting elements interaction during the virus replication and RNA synthesis. Besides 5′ UTR (1–25) deletion, Akbiomed|EPI_ISL_445244 isolate had a number of point synonymous mutations at SL1, SL5A and SL5B regions with one insertion of G between 202 and 203 base in the SL5A region. Furthermore, substitution point mutation 241 (C→T) was common in all Bangladeshi isolates. Deletion and substitution mutations at SL5A and SL5B of 5′ UTR are involved in altered efficiency of coronavirus replication and infection pattern by changing interaction of the genome with viral nucleocapsid protein (N) and nsp1 protein [cis-acting elements [Whole genome analysis of novel coronavirus is necessary to understand its infectivity, fatality associated with specific variants and to predict any alteration of efficacy of possible drug or vaccine due to target proteins modification of the virus ,26. In w protein . Of noteelements . AlteratIn the protein-coding regions, significant substitution point mutation was detected in Bangladeshi isolates. At ORF1ab (266–21555) regions, 1163 (A→T), 3037 (C→T) and 14408 (C→T) were frequent in Bangladeshi isolates. Substitution of isoleucine with phenylalanine at NSP2 120 (I→F) was detected in this study . AnotherSubstitution mutation at 25609 (G→T) of ORF3a was detected in three of the seven isolates. Mutation at ORF3a is most frequent in Europe followed by Asia, Oceania and North America, respectively ,24. AlteSpecific mutations, both deletion and substitution, in Bangladeshi coronavirus isolates were detected at various multiple sites in the genome and in the peptide chain. Of note, several new mutations at ORF1ab regions, specifically in the RdRp and its accessory proteins, will allow the virus to multiply without proof reading that will increase the possibility of accumulating more new mutations in the genome. Furthermore, along with a previous mutation associated with high case fatality, a new mutation at spike protein was also detected that increases the virus’ chance of escaping antibodies or drugs targeting the spike protein. To the best of our knowledge, this is one of the first studies to report phylogenetic and genomic analysis of the first seven sequenced novel coronavirus in Bangladesh. This study reported significant new mutations at important sites in the novel coronavirus genome and antigenic peptide regions. These mutations will affect virus replication strategy and antigenic properties that will ultimately change the virus capability to infect and help the virus to escape from antibodies and drugs. This study will be the baseline database of coronavirus genome analysis that will help to predict effective vaccine and drug targets of coronavirus.In a pandemic like COVID-19, whole genome analysis of the pathogen is important in order to understand the transmission and severity of the disease accurately. With limited resources, the number of whole genome analysis in Bangladesh is lower than in other developing countries. This study investigated the total mutation, phylogeny and evolution of the first seven whole genomes of SARS-CoV-2 in Bangladesh. They were closely related with each other and isolates from Germany, the USA, Saudi Arabia, France, Greece and India. Acquisition of unique mutations along with common mutations throughout the genome suggested rapid change of the circulating strains in Bangladesh. This study will provide a baseline to whole genome research of novel coronaviruses in Bangladesh.Approximately 57% (four of seven) severe acute respiratory coronavirus-2 (SARS-CoV-2) isolates were detected in male and 43% (three of seven) in female patients.Approximately 100% Bangladeshi SARS-CoV-2 shared 99.98–100% sequence similarity with isolates of Germany, Sweden, the USA, Saudi Arabia, England, Myanmar and India.Deletion of bases at 5′ untranslated region and 3′ untranslated region in Bangladeshi isolates was specified.New substitution 1109 (F→L) with deadly substitution 614 (D→G) at spike protein was detected.Mutations at RNA-dependent RNA polymerase regions may lead to accumulation of random mutations in the novel coronavirus genome in future.


Dear Editor,Cohesin is a multiprotein complex that not only is essential for cell division but also has key roles in genome organization that underpin its gene regulatory function. Recurrent mutations of genes encoding cohesin subunits occur in myeloid malignancies at 10%–12% , and theRUNX1 and ERG genes (RUNX1 and ERG are precociously transcribed in response to phorbol 12-myristate 13-acetate (PMA)-induced megakaryocytic differentiation.Cohesin depletion was previously shown to alter chromatin accessibility and transcription of the RG genes , which eRG genes  and founASTAG2-null and BSTAG2-null)  defined for K562, CD34+ primary cord blood cells, and CD14+ monocytes , which is only lowly expressed in K562 cells (STAG2-null clones while mRNA was reduced dramatically following 6 h of treatment (STAG2-null cells (RUNX1/ERG transcription and reduce leukaemic stem cell-associated KIT expression in STAG2 mutant cells.62 cells . Followireatment . Howeverll cells , implyinRUNX1 and ERG, which causes aberrant enhancer-amplified transcription in response to differentiation signals. We show that enhancer suppression using BET inhibitor JQ1 prevents aberrant RUNX1 and ERG signal-induced transcription in STAG2 mutant cells and reduces leukaemic stem cell characteristics of STAG2 mutants.Overall, our results suggest that cohesin-STAG2 depletion de-constrains the chromatin surrounding Antony_et_al_supplementary_material_mjz114Click here for additional data file.Supplementary_Data1_Differential_genes_STAG2nullversus_WT_mjz114Click here for additional data file.Supplementary_Data2_Array_CGH_mjz114Click here for additional data file.Supplementary_Data3_ATAC-seq_differential_STAG2nullA_versus_WT_mjz114Click here for additional data file.Supplementary_Data4_Superenhancers_mjz114Click here for additional data file.


The malignancy potential of the laryngeal lesions are one of the major concerns of the surgeons about choosing the treatment options, forming surgical margins, deciding the follow-up periods. Finding a biomarker to overcome these concerns are ongoing challenges and recently microRNAs (miRNAs) are attributed as possible candidates since they can regulate gene expressions in the human genome. The objective of our study was to investigate their capability as a transformation biomarker for malignant laryngeal lesions.n = 10 in each). miRNA profiling was carried out by quantitative Real-Time polymerase chain reaction (RT-qPCR) and data were analyzed according to fold regulation.We investigated mature miRNA expressions in paraffin-embedded surgical specimens of human laryngeal tissues grouped as benign, premalignant or malignant , 2.72 (p = 0.028) and 3.01 (p = 0.022) fold upregulated respectively in premalignant lesions compared to the benign lesions. Moreover, their expressions were approximately 2.76 fold higher in the malignant group than in the premalignant group compared to the benign group. Besides them, significant 7.57 (p = 0.036), 4.45 (p = 0.045) and 5.98 (p = 0.023) fold upregulations of Hs_miR-21_5p, Hs_miR-218_3p, and Hs_miR-210_3p were noticed in the malignant group but not in the premalignant group when compared to the benign group, respectively.Our results demonstrated that 9 miRNAs were upregulated as the lesions become more malignant. Among them Hs_miR-183_5p, Hs_miR-155_5p, and Hs_miR-106b_3p might be followed as transformation marker, whereas Hs_miR-21_5p, Hs_miR-218_3p, and Hs_miR-210_3p might be a biomarker prone to malignancy.MiRNAs might have important value to help the clinicians for their concerns about the malignancy potentials of the laryngeal lesions.  Laryngeal carcinoma is one of the most common malignancies in the head and neck region with a good prognosis when in the early stages . However, has reported significant upregulation and downregulation of 17 (including miR-21) and 9 miRNAs in laryngeal squamous cell carcinoma, particularly [MicroRNAs (miRNA) are endogenous, small, non-coding RNAs that are 21–24 nucleotides in length and known to regulate gene expression by silencing target transcripts via complement base-pairing in various pathways including embryogenesis, development, differentiation, and apoptosis . miRNAs icularly . The givHs_miR-21_5p, Hs_miR-106b_3p, Hs_miR-375_5p, Hs_miR-155_5p, Hs_let7a_5p, Hs_miR-210_3p, Hs_miR-425_3p, Hs_miR-183_5p, and Hs_miR-218_3p expressions were investigated within paraffin-embedded specimens that were obtained from patients who underwent surgery because of laryngeal lesions between the years 2012 and 2015. After local ethical committee approval (2014/0196), informed consent was obtained from the patients. The samples were analyzed within 3 groups as benign , premalignant  and malignant . Patients who had previous cancer history or had been treated with radiochemotherapy were all excluded. One expert pathologist reviewed 5-μm sections containing the lesions of interest from the FFPE samples. The sections were transferred to the genetics laboratory in suitable conditions.Here, miRNAs were isolated from the paraffin-embedded tissues of the patients using the miRNeasy FFPE Kit (Qiagen) according to the manufacturer’s protocol. Briefly, xylene and ethanol (%96–100) were used to remove paraffin before total RNA isolation. Next, the pellet was treated with 10 μl of proteinase K. The fully deparaffinized and lysed laryngeal tumor supernatant was transferred to a new microcentrifuge tube and DNase I was added to eliminate the DNA content. Then, RNeasy MinElute Spin Column (Qiagen) and relevant buffers of the kit were used with subsequent centrifugation and flow-through steps at 8000 g for 15 s. Finally, miRNA-enriched total RNA was eluted with 14 μl of RNase-free water.cDNAs were randomly primed from 5 μg of miRNA-enriched total RNA with miScript II Reverse Transcription (Rt) Kit (Qiagen). Briefly, reverse transcription PCR was performed with 4 μl of 5x miScript HiSpec Buffer, 2 μl of 10x Nucleic Acid Mix, 1 μl of miScript Reverse Transcriptase Mix, 8 μl of RNase-free water and 5 μl of template RNA, with a total of volume of 20 μl. The Rt reaction was incubated at 37 °C for 60 min and 95 °C for 5 min. The cDNA was then diluted with 200 μl of nuclease-free water for further use in real-time PCRs.Nine miRNAs covering a variety of miRNA sequences were selected, and mature miRNA expression was determined via quantitative real-time polymerase chain reaction (RT-qPCR) with a QuantiTech SYBR Green PCR Kit (Qiagen) on the Rotor-Gene® Q  instrument using the 2.1.0.9 software. RT-PCR was performed twice for each biological cDNA samples after optimization, including negative and non-template controls.R2 value of 0.9963. The slope of the standard curve was determined to be − 3.410, and R2 = 0.99630. CT values were exported from the RT-PCR instrument after normalization via the ‘Dynamic Tube’ and ‘Slope Correction’ options of the RT-PCR software used. For determining fold change, samples were normalized using housekeeping genes SNORD68, SNORD95 and MIRTC relevant for miRNA studies. Global mean normalization was used for Ct values and calculated concentrations were exported into the Excel spreadsheet, and the average value of duplicate Ct values was converted to quantities for analysis. The quality of expended mature miRNAs was checked via melt curve analyses using SYBR Green. Then, the Ct data were analyzed according to the fold-change (2(−ΔΔCT)) method and converted into fold regulation values using the online miScript miRNA PCR Array Data Analysis Tool (www.qiagen.com). p values under 0.05 were considered statistically significant.The threshold was manually determined as 0.025 in all reactions, and standards were calculated as follows: conc = 10ˆ (− 0.293*CT + 7.516); and CT values were calculated as follows: CT = − 3.410*log (conc) + 25.632, with an Patient demographic data were shown in Table Fold regulations of the miRNA expressions in premalignant, malignant laryngeal lesions compared to benign lesions were given in Table Hs_miR-375_5p, Hs_miR-183_5p, Hs_miR-155_5p, Hs_let-7a_5p, Hs_miR-21_5p and Hs_miR-106b_3p expressions were upregulated; however, in comparison to benign group, only the overexpression of Hs_miR-183_5p, Hs_miR-155_5pandHs_miR-106b_3p were statistically significant. (p < 0.05). The most upregulated miRNA was Hs_miR-183_5p, whereas Hs_miR-155_5p expression represented the lowest fold regulation (less than 2 folds).In the premalignant group Hs_miR-375_5p, Hs_miR-183_5p, Hs_miR-155_5p, Hs_let-7a_5p, Hs_miR-425_3p, Hs_miR-21_5p, Hs_miR-218_3p, Hs_miR-210_3pandHs_miR-106b_3p were all upregulated in the malignant group compared to benign group. However, in addition to significantly upregulated miRNAs in the premalignant group , overexpressions of Hs_miR-218_3p, Hs_miR-210_3p and Hs_miR-21_5p were also statistically significant. (p < 0.05). The most upregulated miRNA was Hs_miR-183_5p, whereas Hs_miR-218_3p was the lowest.Hs_miR-183_5p, Hs_miR-155_5p, and Hs_miR-106b_3p expressions were statistically significant in both premalignant and malignant groups compared to the benign group, whereas Hs_miR-21_5p, Hs_miR-218_3p, and Hs_miR-210_3p expressions were statistically significant only in the malignant group.In other words, Hs_miR-425_3p whereas Hs_miR-106b_3p was the lowest  was identified in Caenorhabditis elegans; to date, thousands of miRNAs have been discovered and many more remain unknown [The importance of miRNAs in gene regulation has emerged in recent years. After the first miRNA  unknown –12. The  unknown –16. HereHs_miR-106b_3p is another miRNA that targets the 3’UTR of retinoblastoma (RB) gene [RB largely inhibited by miR-106b which further results in the induction of laryngeal carcinoma cells proliferation by deceased control of RB on G1/S transition of the cell cycle [Hs_miR-106b_3p downregulation [Hs_miR-106b_3p and lymph node metastasis, cancer stage in supraglottic laryngeal carcinoma [Hs_miR-106b_3p expressions both in the premalignant and malignant groups, respectively  gene  and uprell cycle . Though gulation , Sun et arcinoma . In our ly Table . However, Hs_miR-155_5p was also another miRNA that significant 2.72 and 7.75 fold increased expressions were noticed in the premalignant and malignant group compared to the benign group, respectively . Downregulation of the latter has found to induce apoptosis and inhibit cellular proliferation and migration of laryngeal squamous carcinoma cells [EGR1 and PTEN [egulated . There anificant . In our ma cells . Moreoveand PTEN . Hence, , Hs_miR-21_5p upregulations were supposed to be oncogenic for different kinds of tumors [Hs_miR-21_5p would be a prognostic marker in head and neck tumors that is relevant to our results in which the Hs_miR-21_5p was one of the three miRNAs that significantly upregulated solely in the malignant laryngeal tumors group , survival and growth of tumor cells were reduced, and apoptosis was induced [Hs_miR-21_5p [Hs_miR-21_5p expression and 5-year survival rates in the head and neck carcinomas [Hs_miR-21_5p was an oncomir which is overexpressed in laryngeal carcinomas compared to adjacent normal laryngeal tissue [Hs_miR-21_5p expression was 3.45-fold and 7.57-fold increased in the premalignant and malignant group, respectively , a target gene of Hs_miR-218_3p, inhibited migration and invasion in tumor cells [Hs_miR-218_3p could clarify the mechanism of local recurrence and distant metastasis [miR-218 in the HPV-positive group [Hs_miR-218_3p by the E6 oncogene could cause overexpression of LAMB3, which is a target of Hs_miR-218_3p [miR-218 regulated TFF1. Likewise, overexpression of Hs_miR-218_3p in the malignant group might negatively regulate TFF1 in an Erk1/2-dependent manner and promote malignancy as suggested [Hs_miR-218_3p in laryngeal carcinomas. Thus, we believe that unknown factors such as viral infections can affect the results, which should be investigated in larger series.The next miRNA which was significantly overexpressed only in the metastatic group was the or cells . Additiotastasis . Besidesve group . In anotR-218_3p . In oppouggested . HoweverHs_miR-210_3p whether or not it is a tumor suppressor or an oncomir is still ongoing for many tumor types. Apart from that, Gee et al. indicated a correlation between the upregulation of Hs_miR-210_3p and poor prognosis in head and neck tumors by helping the vitality of tumor cells in hypoxic conditions [Hs_miR-210_3p expression was significantly 7.31 fold increased in the malignant group  [Hs_miR-375_5p overexpression inhibited cell proliferation, migration, invasion and resulted in increased apoptosis via IGF1R expression [miR-375, increasing levels of miR-375 expression could provide a significant reduction in IGF1R levels and its downstream signaling molecule AKT in laryngeal carcinoma cells [PDK-1 as the target of Hs_miR-375_5p that contributed to AKT activation [Hs_miR-375_5p expression and indicated that the ratio of miR-21/miR-375 had a 94% sensitivity and 94% specificity for distinguishing normal tissue from laryngeal carcinoma tissue [On the other hand, k tumors , 46. Ovell cycle . Also, ipression . Since tma cells . Quaamartivation . Despitea tissue .Hs_miR-106b_3p and Hs_miR-21_5p, while downregulation of Hs_miR-375_5p expression in laryngeal carcinoma tissue compared to adjacent normal tissue [Hs_miR-106b_3p and Hs_miR-21_5p expressions in poor and moderately differentiated laryngeal carcinomas were more upregulated than that in the benign and dysplastic laryngeal tissues [Hs_miR-106b_3p and Hs_miR-21_5p were consistent with our data the downregulation of Hs_miR-375_5p expression particularly in advanced stages than in earlier stages was inconsistent [Apart from that, Yu et al. discovered overexpression of l tissue . Further tissues . Despitensistent . Even thHs_miR-425_3p served as an oncomir and stimulated cell proliferation and inhibited apoptosis [Hs_miR-425_3p upregulation and lymph node metastasis in laryngeal carcinomas [Hs_miR-425_3p expressions were insignificantly increased (Table Hs_let-7a_5p expression was insignificantly 4.12-fold and 10.54-fold upregulated in premalignant and malignant laryngeal tissues when compared to benign laryngeal samples (Table let-7a expression was significantly downregulated in laryngeal squamous cell carcinomas compared to adjacent normal tissues and was significantly further decreased in non-differentiated carcinoma tissues compared with moderately and well-differentiated ones [let-7a expression was insignificantly further upregulated as the tissues became more malignant. (Table let-7a compared with adjacent normal tissues [let-7a could have different impacts on different individuals or that let-7a may not take part in the pathogenesis of all laryngeal carcinomas [Hs_miR-375_5p, Hs_miR-425_3p, and Hs_let-7a_5p.In addition to this, poptosis . Furtherrcinomas . Notwithted ones . In cont tissues . They surcinomas . In the Hs_miR-21_5p, Hs_miR-218_3p, and Hs_miR-210_3p can be a potential biomarker for malignant laryngeal carcinomas. Furthermore, Hs_miR-183_5p, Hs_miR-155_5p, and Hs_miR-106b_3p, each upregulated both in premalignant and malignant groups compared to benign hyperplasia, might have a great value to help physicians to determine the malignancy potential of the laryngeal lesions as the transformation biomarkers upon prognosis.Our study is one of the first to compare the expression levels of several different miRNAs between benign, premalignant and malignant laryngeal lesions with a relatively larger series upon the literature. They indicated that


HemaSphere. 2020;4:e407), the author made grammar- and style-based corrections to the text as well as to Figure 1. These adjustments have been made and do not affect the outcome of this publication.Since the publication of the article entitled “Cytokine Profiling as a Novel Complementary Tool to Predict Prognosis in MPNs?” (https://journals.lww.com/hemasphere/Fulltext/2020/06000/Cytokine_Profiling_as_a_Novel_Complementary_Tool.17.aspxThese additions have been made online:


Aedes (Tanakius) togoi  (Diptera: Culicidae), is found in coastal east Asia in climates ranging from subtropical to subarctic. However, a disjunct population in the Pacific Northwest of North America has an ambiguous heritage. Two potential models explain the presence of Ae. togoi in North America: ancient Beringian dispersal or modern anthropogenic introduction. Genetic studies have thus far proved inconclusive. Here we described the putative ancient distribution of Ae. togoi habitat in east Asia and examined the climatic feasibility of a Beringian introduction into North America using modern distribution records and ecological niche modeling of bioclimatic data from the last interglacial period , the last glacial maximum , and the mid-Holocene (~6000 BP). Our results suggest that suitable climatic conditions existed for Ae. togoi to arrive in North America through natural dispersal as well as to persist there until present times. Furthermore, we find that ancient distributions of suitable Ae. togoi habitat in east Asia may explain the genetic relationships between Ae. togoi populations identified in other studies. These findings indicate the utility of ecological niche modeling as a complementary tool for studying insect phylogeography.The coastal rock pool mosquito,  Aedes (Tanakius) togoi , breeds in pools of brackish or salt-water above the high tide level on rocky shorelines and, sporadically, in containers of freshwater further inland  and northern Washington, United States  dorsalis larvae from the supralittoral coastal rock pools at Caulfield Cove in North Vancouver, BC . Thus, it is possible that Ae. togoi could have gone unnoticed in North America until sometime in the early-to-mid 20th century. in 1907  and firs in 1907 . However in 1907 . Furtheruver, BC  has beene. togoi . Aedes d marshes  while thAe. togoi: a lineage from temperate and subarctic regions of Japan, China, Taiwan, and other parts of Southeast Asia, a lineage from the subtropical islands of Japan, a lineage from subarctic Japan, and a lineage from Canada . Some data points did not occur on pixels within bioclimatic data due to the irregular shape of coastline or the small size of islands. When this occurred, we relocated data points to the closest 1 km2 raster cell.In the present study, we compiled 86 Asian records and 49 North American records of udy area , personaAe. togoi in Asia, and one for the North Pacific from within Latitude 0°N to 80°N and Longitude 95°E across the Pacific to 100°W, encompassing all known Ae. togoi populations in Asia and North America and possible unknown populations in the North Pacific. We projected a model from the Asian study area (hereafter referred to as model 1), obtained using Asian Ae. togoi populations, onto the North Pacific study area, following methodology for projecting habitat of invasive species into new areas (Ae. togoi populations (hereafter referred to as model 2), as if it were indigenous. We used WGS84 Mercator projection for maps in the last glacial maximum, and WGS84/PDC Mercator projection for all other maps.We selected two study areas: one from Asia from within Latitude 0°N to 49°N and Longitude 94°E to 157°E . We also downloaded corresponding sets of variables based on climate conditions from: 1) the last interglacial period  at a scale of 1 km2 (2 (2 (Ae. togoi overwintering strategy (Ae. togoi observations (model 1) (Ae. togoi observations (model 2)  the 1 km2  m2   and all model 2) , each wiAe. togoi. We used default Maxent settings with and without linear features, and with and without quadratic features, with 1,000 replications, each with 2,000 training iterations, 20% of data points withheld for subsampling, clamping applied, and 10,000 background points. We analyzed results based on extensive presence of predicted suitable habitat on maps  calculated from the maximum sensitivity and specificity, a recommended approach for transforming the results of species distribution models into binary presence/absence predictions (PO) of the receiving operator characteristic (ROC). This integral gives a sum between 0 and 1 with a result >0.5 indicative of a model that is more accurate than random, >0.7 representing a useful model, >0.9 representing an excellent model, and a result of 1 indicating a perfect fit (PO of our three best candidate modes as measured by AICc and selected the top performer across both metrics (PO were transformed to a percentage and used as a relative metric of variable importance. We built maps with QGIS version 3.4.3 (https://crc806db.uni-koeln.de/) to add glacial coverage layers where applicable.Maximum entropy niche modeling (Maxent) is a common approach for species habitat modeling . Maxent dictions , as wellfect fit . We comp metrics  and 3. Won 3.4.3 , and dowon 3.4.3  from thePO of 0.912 (±0.031) and model 2 has a mean AUCPO of 0.972 (±0.009), both representing models with excellent fits . Consistent with a niche conservatism hypothesis .Mean diurnal range and minimum temperature of the coldest month were important environmental variables in each of our models. In addition, precipitation during the coldest quarter was also critical in model 1, while mean temperature during the warmest quarter was important in model 2. There is evidence that, under certain conditions, temperature fluctuations (rather than absolute temperature) can have negative effects on the development rate and survival of some mosquitoes . With thdes spp. , and ano America . Such pa America  and over America . Insulat America  and coul dry out , and it survival . This vades spp. . Despitedes spp. , the meades spp.  indicatepothesis , this ovAe. togoi into North America  can also produce slightly different projections, indicating the inherent uncertainty present in any individual approach , Cook Inlet, the Alexander Archipelago, and the North Coast of British Columbia. We also note a need for Ae. togoi surveillance across its novel predicted range in more heavily surveyed areas, including the Hawaiian Islands and the Mariana Archipelago. To further investigate the origins of Ae. togoi in North America, we propose a combination of survey efforts and population genetic analyses based on mitochondrial and nuclear genome sequencing (Ultimately, our modeling is based on climate data alone and is subject to the realities of limited sampling and, thus, cannot definitely prove or disprove either an anthropogenic introduction or Beringian dispersal for the presence of approach . In futuapproach . Howeverapproach . We propquencing .ieaa035_suppl_Supplementary_Figure_1Click here for additional data file.ieaa035_suppl_Supplementary_Figure_2Click here for additional data file.ieaa035_suppl_Supplementary_Figure_3Click here for additional data file.ieaa035_suppl_Supplementary_Figure_4Click here for additional data file.ieaa035_suppl_Supplementary_Figure_5Click here for additional data file.ieaa035_suppl_Supplementary_Figure_6Click here for additional data file.ieaa035_suppl_Supplementary_Figure_7Click here for additional data file.ieaa035_suppl_Supplementary_Figure_8Click here for additional data file.ieaa035_suppl_Supplementary_Figure_9Click here for additional data file.ieaa035_suppl_Supplementary_Figure_10Click here for additional data file.ieaa035_suppl_Supplementary_Figure_11Click here for additional data file.ieaa035_suppl_Supplementary_Figure_12Click here for additional data file.ieaa035_suppl_Supplementary_Figure_13Click here for additional data file.ieaa035_suppl_Supplementary_Figure_14Click here for additional data file.ieaa035_suppl_Supplementary_Figure_15Click here for additional data file.ieaa035_suppl_Supplementary_Table_1Click here for additional data file.ieaa035_suppl_Supplementary_Table_2Click here for additional data file.ieaa035_suppl_Supplementary_Table_3Click here for additional data file.ieaa035_suppl_Supplementary_Table_4Click here for additional data file.ieaa035_suppl_Supplementary_Table_5Click here for additional data file.ieaa035_suppl_Supplementary_FilesClick here for additional data file.


Capnocytophaga and OTU269_Treponema acted as gatekeepers for both of the two clustered microbiotas. Nine OTUs assigned to seven taxa, i.e., Alloprevotella, Atopobium, Megasphaera, Oribacterium, Prevotella, Stomatobaculum, and Veillonella, were associated with both H7N9 patients with and without secondary bacterial lung infection in Cluster_1. In addition, two groups of healthy cohorts may have potential different susceptibilities to H7N9 infection. These findings suggest that two OP microbial colonization states of H7N9 patients were at different dysbiosis states, which may help determine the health status of H7N9 patients, as well as the susceptibility of healthy subjects to H7N9 infection.The dysbiosis of oropharyngeal (OP) microbiota is associated with multiple diseases, including H7N9 infection. Different OP microbial colonization states may reflect different severities or stages of disease and affect the effectiveness of the treatments. Current study aims to determine the vital bacteria that could possibly drive the OP microbiota in the H7N9 patients to more severe microbial dysbiosis state. The OP microbiotas of 42 H7N9 patients and 30 healthy subjects were analyzed by a series of bioinformatics and statistical analyses. Two clusters of OP microbiotas in H7N9 patients, i.e., Cluster_1_Diseased and Cluster_2_Diseased, were determined at two microbial colonization states by Partition Around Medoids (PAM) clustering analysis, each characterized by distinct operational taxonomic units (OTUs) and functional metabolites. Cluster_1_Diseased was determined at more severe dysbiosis status compared with Cluster_2_Diseased, while OTU143_ Avian influenza has caused great mortalities to human beings and animals during the last two decades –4. The hAcinetobacter baumanii, Candida albicans, Flavobacterium indologenes, Klebsiella, Pseudomonas, and Staphylococcus have been isolated from blood, white blood cell, or sputum of H7N9 patients with SBLI (H7N9_SBLI), whereas no bacterium was isolated from those in the H7N9 patients without SBLI (H7N9_NSI)  is a usual condition in the H7N9 patients (7N9_NSI) .Atopobium, Eubacterium, Leptotrichia, Oribacterium, Rothia, Solobacterium, and Streptococcus in H7N9_SBLI than H7N9_NSI  clustering analysis in order to determine their microbial colonization states. Before PAM clustering, the average silhouette method was used to determine the optimal numbers of clusters for all the OP microbiotas .2 test was performed to compare the numbers of healthy and H7N9 cohorts in the two clusters.Two clusters of OP microbiotas were determined in healthy cohorts  and H7N9 cohorts . A Pearson χt test.Permutation analysis of variance (PERMANOVA) was performed in R software version 3.6.1 with the vegan package  to deterLinear discriminant analysis (LDA) effect size (LEfSe) was performed using Kruskal–Wallis test (α < 0.05), followed by a Wilcoxon rank-sum test (α < 0.05), and a one-against-all strategy for multiclass analysis . It was P values were computed by a permutation step, followed by a bootstrap procedure to merge the P values into one final P value using a method by Brown (Co-occurrence Network (CoNet) analysis was carried out to investigate the co-occurrence and coexclusion of OTUs in Cluster_1_Diseased and Cluster_2_Diseased and to determine the top 10 OTUs with most correlations in the OP bacterial networks of Cluster_1_Diseased and Cluster_2_Diseased. The detailed processes followed the procedures described by Wagner Mackenzie et al. . Brieflyby Brown .Gatekeepers were defined as the phylotypes interacting with different parts of the network to hold together the bacterial community , 27. In The OTUs differentiating the OP microbiotas of H7N9 patients from those of healthy subjects were determined by using the Galaxy implementation of LEfSe run by Huttenhower laboratory .t test. The same data transformation and statistical approach were applied for the comparison of the AIDRs of Cluster_1_Diseased and Cluster_2_Diseased.Dysbiosis ratios of bacterial taxa were associated with different diseases and conditions , 29. In U test was used to compare the abundances of the OTUs associated with H7N9 between Cluster_1_Diseased and Cluster_2_Diseased. The same approach was performed for the comparisons of OTUs associated with healthy cohort between Cluster_1_Diseased and Cluster_2_Diseased. A Pearson χ2 test was applied to compare the numbers of OTUs that were associated with H7N9 and more abundant in Cluster_1_Diseased or Cluster_2_Diseased. The same test was carried out for the comparisons of the numbers of OTUs, which were associated with healthy cohort and more abundant in Cluster_1_Diseased or Cluster_2_Diseased.A series of statistical analyses were also performed to help determine the dysbiosis status of the two clusters of OP microbiotas in H7N9 patients. Mann–Whitney The two clustered OP microbiotas in H7N9_NSI (or H7N9_SBLI) were compared to determine whether some OTUs could possibly drive the OP microbiotas to worse microbial dysbiosis state in both H7N9_NSI and H7N9_SBLI.t test, after being transformed in log10 to satisfy the assumptions of normal distribution and equal variance. The same approaches were applied for the comparisons of the AIDRs of H7N9_SBLI cohorts in the two clusters.Avian influenza dysbiosis ratios of H7N9_NSI cohorts in the two clusters were compared by a An LEfSe analysis was applied to determine the OTUs differentiating the two clustered OP microbiotas in H7N9_NSI . The same approach was carried out for determining the OTUs associated with each of the two clustered OP microbiotas in H7N9_SBLI .Similarly, an LEfSe analysis was used to determine the functional metabolites associated with Cluster_1_H7N9_NSI or Cluster_2_H7N9_NSI. The same analysis was used to determine the functional metabolites associated with Cluster_1_H7N9_SBLI or Cluster_2_H7N9_SBLI.The OTUs associated with both Cluster_1_H7N9_NSI and Cluster_1_H7N9_SBLI were determined by an online program Venny diagram version 2.1 . The samt test.Permutation analysis of variance was applied to determine the difference between Cluster_1_Healthy and Cluster_2_Healthy. Avian influenza dysbiosis ratios of Cluster_1_Healthy and Cluster_2_Healthy were transformed in log10 to satisfy the assumptions of normal distribution and equal variance, before being compared by a An LEfSe analysis was applied to determine the OTUs associated with each of the two clustered OP microbiotas in healthy cohorts. The same approach was used for identifying the functional metabolites associated with Cluster_1_Healthy or Cluster_2_Healthy.2 = 9.933, P = 0.002).Silhouette analysis identified two as the most optimal number for clustering all the 72 OP microbiotas . TherefoBacteroidetes, Fusobacteria, and Proteobacteria were more abundant in Cluster_2_Diseased compared with Cluster_1_Diseased, whereas Firmicutes and Saccharibacteria were more abundant in Cluster_1_Diseased than in Cluster_2_Diseased. Nine most abundant orders in the H7N9 patients constituted >90% abundance of all the orders in H7N9 microbiotas, among which Campylobacterales, Clostridiales, Flavobacteriales, Lactobacillales, and Selenomonadales had greater abundances in Cluster_1_Diseased compared with Cluster_2_Diseased, whereas Bacteroidales, Fusobacteriales, Neisseriales, and Pasteurellales were more abundant in Cluster_2_Diseased than in Cluster_1_Diseased.The five most abundant phyla in the OP microbiotas of H7N9 patients accounted for >90% abundance of the OP microbiotas. Among them, R2 = 0.090, P < 0.001). The dissimilarity between Cluster_1_Diseased and Cluster_2_Diseased was relatively high (SIMPER dissimilarity = 64.8%) according to SIMPER results. The similarity within Cluster_1_Diseased (SIMPER average similarity = 38.2%) was lower than that within Cluster_2_Diseased (SIMPER average similarity = 44.1%). Both richness and diversity were similar in Cluster_1_Diseased and Cluster_2_Diseased , but the evenness was significantly higher in Cluster_1_Diseased than in Cluster_2_Diseased   .Neisseria) with the largest LDA scores were closely associated with Cluster_1_Diseased and Cluster_2_Diseased, respectively , suggesting a lower AIDR was likely to indicate the dysbiosis of OP microbiotas in H7N9 patients compared with healthy subjects. The AIDR was significantly higher in Cluster_2_Diseased (5.28 ± 1.12 SE) than Cluster_1_Diseased (1.48 ± 0.21 SE) .The LEfSe results showed that 69 OTUs were associated with H7N9, and 22 OTUs were associated with healthy cohort, which were used for the calculations and comparisons of AIDRs in different groups . The heaP < 0.05) .Eleven (of 69) OTUs associated with H7N9 were determined with significantly different abundances between Cluster_1_Dieased and Cluster_2_Diseased  . Among tP < 0.02) .Likewise, seven (of 22) OTUs associated with healthy cohort were determined with significantly different abundances between the two clustered H7N9 microbiotas  . Among tThese above results consistently suggested that Cluster_2_Diseased were at better dysbiosis status compared with Cluster_1_Diseased.The two bacterial networks of Cluster_1_Diseased and Cluster_2_Diseased were determined by CoNet analysis . None ofCapnocytophaga and OTU269_Treponema were determined as gatekeepers for both Cluster_1_Diseased and Cluster_2_Diseased.Fragmentation results demonstrated a lower fragmentation score in Cluster_2_Diseased  than that of Cluster_1_Diseased , suggesting that Cluster_2_Diseased had stronger co-occurrence patterns and greater biotic interactions than Cluster_1_Diseased. A group of nine OTUs was determined to be gatekeepers of Cluster_1_Diseased, and another group of nine OTUs was identified as gatekeepers of Cluster_2_Diseased . OTU143_The two clustered OP microbiotas in H7N9_NSI or H7N9_SBLI were compared to determine the bacteria that could possibly drive the OP microbiotas to more severe microbial dysbiosis state in both H7N9_NSI and H7N9_SBLI.t test, P = 0.001). Likewise, Cluster_2_H7N9_SBLI (4.44 ± 1.76 SE) had a significantly higher AIDR compared with Cluster_1_H7N9_SBLI (1.53 ± 0.30 SE) .Avian influenza dysbiosis ratio was significantly higher in Cluster_2_H7N9_NSI (5.98 ± 1.51 SE) compared with Cluster_1_H7N9_NSI (1.43 ± 0.31 SE) (LEfSe results showed that 33 OTUs were associated with OP microbiotas of H7N9_NSI in Cluster_1 (Cluster_1_H7N9_NSI), and 20 OTUs were associated with OP microbiotas of H7N9_NSI in Cluster_2 (Cluster_2_H7N9_NSI) . A totalLikewise, a total of 20 OTUs were more associated with Cluster_1_H7N9_SBLI, and 41 OTUs were more associated with Cluster_2_H7N9_SBLI . A totalAlloprevotella, Atopobium, Megasphaera, Oribacterium, Prevotella, Stomatobaculum, and Veillonella. Likewise, four OTUs  assigned to Alloprevotella or Porphyromonas were associated with both Cluster_2_H7N9_NSI and Cluster_2_H7N9_SBLI  associated with both Cluster_1_H7N9_NSI and Cluster_1_H7N9_SBLI , which w7N9_SBLI .In addition, five of the 20 OTUs  associated with Cluster_2_H7N9_NSI were also identified being associated with healthy cohort. Likewise, five of 41 OTUs  associated with Cluster_2_H7N9_SBLI were also determined being associated with healthy cohort.Dialister was negatively correlated with four other OTUs associated with Cluster_2_H7N9_SBLI . Avian influenza dysbiosis ratio was significantly higher in Cluster_2_Healthy (19.74 ± 3.83 SE) than Cluster_1_Healthy (4.83 ± 1.91 SE) .The PERMANOVA results showed a significant difference between the Cluster_1_Healthy and Cluster_2_Healthy  were associated with Cluster_1_Diseased, whereas no Veillonella was associated with Cluster_2_Diseased, suggesting that the increased Veillonella in the OP microbiota could be a source of Veillonella in the gut of H7N9 patients with Cluster_1_Diseased.bacteria . In the controls . In the Capnocytophaga was part of resident OP microbiota in human and could be opportunistic pathogens of extraoral infections , suggesting these phylotypes were likely to cause more severe conditions in H7N9 patients. Some Alloprevotella and Porphyromonas species were determined as opportunistic oral pathogens (Alloprevotella, OTU20_Porphyromonas, OTU26_Porphyromonas, and OTU193_Alloprevotella were associated with both Cluster_2_H7N9_NSI and Cluster_2_H7N9_SBLI, suggesting the four phylotypes were more likely as opportunistic pathogens for inducing H7N9_NSI or H7N9_SBLI.H7N9_NSI . The curathogens –64. In tDialister species were associated with some oral diseases, such as periodontal disease, apical periodontitis, and dentinal caries (Dialister was negatively correlated with four OTUs in Cluster_2_H7N9_SBLI, suggesting this phylotype was more likely as pathogenic bacteria and had competitive interactions with some other phylotypes within this OP microbial colonization state. We acknowledge that further studies are needed to confirm it.A few l caries –67. AmonMethyl-accepting chemotaxis protein is vital to the cell survival, pathogenesis, and biodegradation . In the Veillonella was more abundant in the gut of H7N9 patients than healthy controls as described above (Veillonella and methyl-accepting chemotaxis protein were determined as the phylotype and functional metabolite most associated with Cluster_1_Healthy, suggesting they were more likely to enhance the greater susceptibility of the healthy subjects to H7N9 infection.Greater AIDR was determined in Cluster_2_Healthy than Cluster_1_Healthy, suggesting the healthy subjects in Cluster_2 could be more tolerant to H7N9 infection than those in Cluster_1. ed above , whereased above . In the Alloprevotella, Atopobium, Megasphaera, Oribacterium, Prevotella, Stomatobaculum, and Veillonella were likely to drive the OP microbiotas to worse microbial dysbiosis state in both H7N9_NSI and H7N9_SBLI. In addition, healthy subjects could have different susceptibilities to H7N9 infection.In conclusion, two OP microbial colonization states were determined in the H7N9 patients, each characterized by distinct phylotypes. Nine phylotypes assigned to PRJNA638222.The raw sequencing data were deposited in NCBI under BioProject accession no. The studies involving human participants were reviewed and approved by The Institutional Review Board and Ethics Committee of the First Affiliated Hospital of Zhejiang University. The patients/participants provided their written informed consent to participate in this study.HZha and LL designed the study. HL and HZhang collected samples and provided the raw sequencing data. HZha, JW, and KC contributed to the data analyses. HZha interpreted the results and drafted the manuscript. HZ, HL, and JW reviewed the manuscript. QW, JL, QL, and YL contributed to the literature search and participate in the study design. All authors approved the final manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Supplementary Table 1 and Supplementary Table 2. The expression values given for PDK4 in Supplementary Table 1, ALL_M, CLL_M, AHL_M, CHL_M contrasts were −3.90808, 2.10011, −4.12057, and −4.12057 the correct values are −4.1009, 2.07292, −4.63904, and 3.05659 same value should appear at the “Max_expression_level” column in HL_node_table in Supplementary Table 2.In the original article, there was a mistake in MT4 given in Supplementary Table 1 for ALL_H, CLL_H, AHL_H, and CHL_H are −1.20147, 1.485881, −1.19557, and 1.0025, the correct values are −3.1675, −1.82983, −1.35669, and −1.84142. To reflect this change, columns “Max_expression_level,” “Max_Tissue,” and “Up/Down” on Supplementary Table 2, “LL_node_table” tab is corrected.Similarly the expression values of The authors apologize for this error and state that this does not change the scientific conclusions of the article in any way. The original article has been updated.