Drosophila macrophages, also known as plasmatocytes or hemocytes, utilizes enhanced cortical F-actin levels stimulated by the Drosophila member of the fos proto oncogene transcription factor family . RNA sequencing analysis and live imaging show that Dfos enhances F-actin levels around the entire macrophage surface by increasing mRNA levels of the membrane spanning molecular scaffold tetraspanin TM4SF, and the actin cross-linking filamin Cheerio, which are themselves required for invasion. Both the filamin and the tetraspanin enhance the cortical activity of Rho1 and the formin Diaphanous and thus the assembly of cortical actin, which is a critical function since expressing a dominant active form of Diaphanous can rescue the Dfos macrophage invasion defect. In vivo imaging shows that Dfos enhances the efficiency of the initial phases of macrophage tissue entry. Genetic evidence argues that this Dfos-induced program in macrophages counteracts the constraint produced by the tension of surrounding tissues and buffers the properties of the macrophage nucleus from affecting tissue entry. We thus identify strengthening the cortical actin cytoskeleton through Dfos as a key process allowing efficient forward movement of an immune cell into surrounding tissues.The infiltration of immune cells into tissues underlies the establishment of tissue-resident macrophages and responses to infections and tumors. Yet the mechanisms immune cells utilize to negotiate tissue barriers in living organisms are not well understood, and a role for cortical actin has not been examined. Here, we find that the tissue invasion of  The infiltration of immune cells into tissue underlies the establishment of tissue-resident macrophages, and responses to infections and tumors, but how do they overcome tissue barriers? This study shows that macrophages upregulate the proto-oncogene Fos, increasing the density and crosslinking of cortical actin, thereby counteracting the tension of surrounding tissues and protecting the macrophage nucleus. The classical model of cell migration on a surface postulated in the 1980s by Abercrombie has been extended . Much ofMigration in 2D and 3D environments requires actin polymerization to power forward progress. The assembly of actin at the leading edge, when coupled to Integrin adhesion to anchor points in the surrounding extracellular matrix (ECM), can allow the front of the cell to progress . This anDrosophila macrophage migration into the embryonic germband (gb) to investigate mechanisms of immune cell tissue invasion. Macrophages, also called plasmatocytes or hemocytes, are the primary phagocytic cell in Drosophila and share striking similarities with vertebrate macrophages  (control), UAS-cher RNAi KK107451, UAS-TM4SF RNAi KK102206, UAS-Lam RNAi1 GD45636, UAS-Lam RNAi2 KK107419 lines were obtained from the Vienna Drosophila Resource Center (Austria).SF, USA) . Oregon Here, we list the lines used in each figure; we state first the name from FlyBase; in parentheses, the name used in the figure panels is provided.Oregon R. srpHemo-GAL4, UAS-GFP (control). srpHemo-Gal4, srpHemo-H2A::3xmCherry/P{CaryP}attP2 (control). srpHemo-GAL4, UAS-GFP; kay1(Dfos1). srpHemo-GAL4, UAS-GFP::nls/+ (control 1). srpHemo-GAL4, UAS-GFP/+; kay1(Dfos1). srpHemo-GAL4, UAS-GFP::nls/+; kay2(Dfos2). srpHemo-GAL4, UAS-GFP::nls/(UAS-Fra)2; kay2. 10XUAS-IVS-myr::GFP/+; srpHemo-Gal4, srpHemo-H2A::3xmCherry/+ (control 2 and control). UAS-DfosDN/+; srpHemo-Gal4, srpHemo-H2A::3xmCherry/+ (mac>DfosDN). srpHemo-Gal4, srpHemo-H2A::3xmCherry/ UAS GFP::nls (ctrl). srpHemo-Gal4, srpHemo-H2A::3xmCherry/UAS-fbz (mac>DfosDN). srpHemo-Gal4, srpHemo-H2A::3xmCherry /+ (ctrl). srpHemo-Gal4, srpHemo-H2A::3xmCherry/UAS-DfosDN (mac>DfosDN). UAS-GFP; srpHemo-Gal4, srpHemo-H2A::3xmCherry (ctrl). UAS-Dfos RNAi HMS00254/srpHemo-Gal4, srpHemo-H2A::3xmCherry (mac>DfosRNAi1). UAS-Dfos RNAi JF02804/srpHemo-Gal4, srpHemo-H2A::3xmCherry (mac>DfosRNAi2). srpHemo-GAL4, UAS-GFP::nls/+ or /(UAS-Fra)2 (mac>Dfos). UAS-GFP; UAS-Dfos RNAi HMS00254/ srpHemo-Gal4, srpHemo-H2A::3xmCherry (mac>DfosRNAi1+ GFP). UAS-GFP; UAS-Dfos RNAi JF02804/srpHemo-Gal4, srpHemo-H2A::3xmCherry (mac>DfosRNAi2+ GFP).srpHemo-H2A::3xmCherry/+ (control). srpHemo-Gal4, srpHemo-H2A::3xmCherry/+ (3 movies) and Resille::GFP/+; srpHemo-Gal4, srpHemo-H2A::3xmCherry/+  and Resille::GFP/+; srpHemo-Gal4, srpHemo-H2A::3xmCherry/+ (3 movies) and Resille::GFP/+; srpHemo-Gal4, srpHemo-H2A::3xmCherry/UAS-DfosDN . srpHemo-Gal4, srpHemo-H2A::3xmCherry/UAS-fbz (mac>DfosDN). srpHemo-GAL4, UAS-GFP.nls/+ (control). srpHemo-GAL4, UAS-GFP.nls/+; kay2(Dfos2).UAS-Dicer2;; srpHemo-Gal4, srpHemo-H2A::3xmCherry/w1118 (control). UAS-Dicer2; UAS-TM4SF RNAi KK10220/+; srpHemo-Gal4, srpHemo-H2A::3xmCherry/+ (mac>TM4SF RNAi). UAS-Dicer2; UAS-cher RNAi KK107451/+; srpHemo-Gal4, srpHemo-H2A::3xmCherry/+ (mac>cher RNAi). UAS-Dicer2; UAS-cher RNAi KK107451/UAS-TM4SF RNAi KK102206; srpHemo-Gal4, srpHemo-H2A::3xmCherry/+ . srpHemo-GAL4, UAS-mCherry::nls/UAS-mCD8::GFP (control). srpHemo-GAL4, UAS-mCherry::nls/UAS-mCD8::GFP; UAS-fbz/+ (mac>DfosDN). srpHemo-GAL4,UAS-mCherry::nls/UAS-cheerio::FLAG; UAS-fbz/+ . srpHemo-GAL4,UAS-mCherry.nls/UAS-TM4SF; UAS-fbz/+ . srpHemo-GAL4, UAS-mCherry::nls/ UAS-TM4SF (mac>TM4SF). srpHemo-GAL4, UAS-mCherry::nls/UAS-cher (mac>cher). srpHemo-Gal4, srpHemo-3xmCherry/+ (control). srpHemo-Gal4, srpHemo-3xmCherry/UAS-fbz (mac>DfosDN).srpHemo-3xmCherry; kay1 (Dfos1) and srpHemo-3xmCherry; +. srpHemo-Gal4, srpHemo-moe::3xmCherry/+;UAS-mCD8::GFP/+(Control). srpHemo-Gal4, srpHemo-moe::3xmCherry/UAS-fbz (mac>DfosDN). w118. srpHemo-Gal4, srpHemo-moe::3xmCherry/w118 (Control). srpHemo-Gal4, srpHemo-moe::3xmCherry/UAS-cher RNAi KK107451 (mac>cher RNAi). srpHemo-Gal4, srpHemo-moe::3xmCherry/UAS-TM4SF RNAi KK102206 (mac>TM4SF RNAi). srpHemo-GAL4, UAS-mCherry.nls/UAS-mCD8::GFP (control). srpHemo-GAL4, UAS-mCherry.nls/UAS-DiaΔDad::EGFP; UAS-fbz/+ . srpHemo-GAL4, UAS-mCherry.nls/UAS-mCD8::GFP; UAS-fbz/+ (mac>DfosDN). srpHemo-GAL4, UAS-mCherry.nls/ UAS-DiaΔDad::EGFP (mac>diaCA). UAS-GFPnls; srpHemo-Gal4, srpHemo-H2A::3xmCherry. #2: UAS-GFPnls/srpHemo-Gal4, srpHemo-H2A::3xmCherry; Dfos1. #3: UAS-GFPnls/ srpHemo-Gal4, srpHemo-H2A::3xmCherry; Dfos2. #4: UAS-DiaΔDad::EGFP/srpHemo-Gal4, srpHemo-H2A::3xmCherry; Dfos1. #5: UAS-DiaΔDad::EGFP/srpHemo-Gal4, srpHemo-H2A::3xmCherry; Dfos2. UAS-Dicer2;; srpHemo-Gal4, srpHemo-H2A::3xmCherry/P{CaryP}attP40 (control). UAS-Dicer2;+; srpHemo-Gal4, srpHemo-H2A::3xmCherry/ UAS-dia RNAi HM05027 (mac>dia RNAi1). UAS-Dicer2;+; srpHemo-Gal4, srpHemo-H2A::3xmCherry/UAS-dia RNAi HMS00308 (mac>dia RNAi2). UAS-dia::EGFP/+; UAS-nlacz/ srpHemo-Gal4, 10XUAS-IVS-myr::tdTomato. UAS-dia::EGFP/+; UAS-Rho1N.19)/srpHemo-Gal4, 10XUAS-IVS-myr::tdTomato (mac>Rho1DN). UAS-dia::EGFP/+; UAS-fbz/srpHemo-Gal4, 10XUAS-IVS-myr::tdTomato (mac>DfosDN). UAS-dia::EGFP/+; UAS-cher RNAi KK107451/srpHemo-Gal4, 10XUAS-IVS-myr::tdTomato (mac>cher RNAi). UAS-dia::EGFP/+; UAS-TM4SF RNAi KK102206/srpHemo-Gal4, 10XUAS-IVS-myr::tdTomato (mac>TM4SF RNAi). UAS-diaRBD::GFP/+; srpHemo-Gal4, 10XUAS-IVS-myr::tdTomato/UAS-nlacZ (control). UAS-diaRBD::GFP/+; srpHemo-Gal4, 10XUAS-IVS-myr::tdTomato/UAS-Rho1N.19 (mac>Rho1DN). UAS-diaRBD::GFP/UAS-fbz; srpHemo-Gal4, 10XUAS-IVS-myr::tdTomato/+ (mac>DfosDN). UAS-diaRBD::GFP/UAS-cher RNAi KK107451; srpHemo-Gal4, 10XUAS-IVS-myr::tdTomato/+ (mac>cher RNAi). UAS-diaRBD::GFP/UAS-TM4SF RNAi KK102206; srpHemo-Gal4, 10XUAS-IVS-myr::tdTomato/+ (mac>TM4SF RNAi).srpHemo-Gal4 UAS-LifeActGFP UAS-RedStinger (control); srpHemo-Gal4 UAS-LifeActGFP UAS-RedStinger; UAS-DfosDN (mac>DfosDN). srpHemo-Gal4, UAS-CLIP::GFP, UAS-RedStinger (control). srpHemo-Gal4, UAS-CLIP::GFP, UAS-RedStinger; UAS-fbz (mac>DfosDN). srpHemo-GAL4, UAS-mCherry.nls/UAS-mCD8::GFP (control). srpHemo-GAL4, UAS-mCherry.nls/UAS-Lamin RNAi GD45636, KK107419 . srpHemo-GAL4, UAS-mCherry.nls/UAS-LaminC RNAi TRIP JF01406 (mac>LamC RNAi). srpHemo-GAL4, UAS-mCherry.nls/UAS-mCD8::GFP; UAS-fbz/+ (mac>DfosDN). srpHemo-GAL4, UAS-mCherry.nls/UAS-Lam RNAi ; UAS-fbz/+ . srpHemo-GAL4, UAS-mCherry.nls/UAS-LaminC RNAi TRIP JF01406; UAS-fbz/+ . e22c-Gal4,srpHemo-H2A::3xmCherry/+ (control). srpHemo-QF/ srpHemo-H2A::3xmCherry; QUAS-fbz/UAS-Rho1.N12 (mac<>DfosDN). e22c-Gal4, srpHemo-H2A::3xmCherry/srpHemo-QF; +/ UAS-Rho1.N12 (ecto>Rho1DN). srpHemo-QF/ e22c-Gal4, srpHemo-H2A::3xmCherry; UAS-Rho1.N12/QUAS-fbz . +;UAS-GFP::nls, srpHemo-GAL4 (control). +;UAS-GFP::Lamin, srpHemo-GAL4 (mac>Lam).UAS-fbz (UAS-Dfos DN) line [The fragment was amplified from genomic DNA of the published DN) line  using pry1M{vas-int.Dm}ZH-2A w*; M{3xP3-RFP.attP}ZH-51D, BL 24483), to produce second chromosome inserts. All male survivors were crossed to w; Sp/CyO; PrDr/TM3Ser virgins. Transformants were recognized by eye color and crossed again to w; Sp/CyO; PrDr/TM3Ser virgins to get rid of the X chromosomal integrase.The TM4SF open reading frame was amplified from the DGRC GH07902 cDNA clone , using primers acagcgGAATTCATGGCATTGCCGAAGAAAAT and acagcgTCTAGATTAAAAGCTAATCGTCTGTCATT. The PCR product and the pUASt-aTTB vector (DGRC plasmid #1419) were digested with EcoRI and XbaI, and ligated. After sequencing, the construct was injected into the landing site line,  against the C-terminal end of g. Then 10 μg of the cleared lysate were separated by SDS-PAGE using 4% to 15% Mini-PROTEAN TGX Precast Protein gels  and blotted onto a Amersham Protran Premium western blotting nitrocellulose membrane . The nitrocellulose membrane was blocked with Pierce Clear Milk blocking buffer  and incubated in blocking buffer with anti-mCherry  at 1:1,000, and anti-Profilin  [Cages were prefed on fresh yeast plates for 2 days. Late stage 11/early stage 12 embryos were handpicked using a Leica M205 fluorescent microscope on ice-cold apple juice plates. They were transferred to RIPA buffer  with a Halt Protease/Phosphatase inhibitor cocktail  and lysed. After a 30-minute incubation on ice, they were centrifuged 15 minutes at 4°C at 15,000#chi 1J)  at 1:50 Dfos2 for which the setting was 25°C.Embryos were dechorionated in 50% bleach for 5 minutes, washed with water, and mounted in halocarbon oil 27 (Sigma) on a 24 × 50 mm high precision coverslip  between 2 bridges (approximately 0.5 cm high) of coverslips glued on top of each other or mounted in halocarbon oil 27 (Sigma) between an 18 × 18 mm coverslip  and an oxygen permeable membrane (YSI). The embryo was imaged on an upright multiphoton microscope  equipped with a W Plan-Apochromat 40X/1.4 oil immersion objective (Olympus). GFP and mCherry were imaged at 860 nm and 1,100 nm excitation wavelengths, respectively, using a Ti-Sapphire femtosecond laser system (Coherent Chameleon Ultra) combined with optical parametric oscillator technology (Coherent Chameleon Compact OPO). Excitation intensity profiles were adjusted to tissue penetration depth and Z-sectioning for imaging was set at 1 μm for tracking. For long-term imaging, movies were acquired for 60 to 150 minutes with a frame rate of 25 to 45 seconds. A temperature control unit set to 29°C was utilized for all genotypes except Dfos RNAi, embryos with 70% gb retraction (Stage 13) were used for vnc counts. The pre-gb zone was defined based on embryo and yolk autofluorescence as an area on the yolk sac underneath the amnioserosa with borders defined posteriorly by the gb ectoderm and anteriorly by the head. Macrophages were visualized using confocal microscopy with a Z-stack step size of 2 μm, and macrophage numbers within the gb or the segments of the vnc were calculated in individual slices (and then aggregated) using the Cell Counter plugin in FIJI. Total macrophage numbers were obtained using Imaris (Bitplane) by detecting all the macrophage nuclei as spots.Autofluorescence of the embryo revealed the position of the gb for staging of fixed samples. Embryos with 40% (±5%) gb retraction (Stage 12) were analyzed for macrophage numbers in the pre-gb, within the gb, along the vnc, and in the whole embryo. For the srpHemo-H2A::3xmCherry and the surrounding tissues with Resille::GFP, or with only macrophages labeled by srpHemo-H2A::3xmCherry, or srpHemo>GFP::nls were imaged, and 250 × 250 × 40 μm3 3D stacks were typically acquired with approximately 0.2 × 0.2 × 1 μm3 voxel size every 39 to 41 seconds for approximately 2 hours. For imaging macrophages on vnc, frames were acquired at every 40 to 43 seconds for 30 minutes after macrophages started spreading into abdominal segment 2  are its instantaneous velocities. Only trajectories with a minimal duration of 15 time frames were used. Calculated persistence values were averaged over all trajectories to obtain a persistence index (I) for the duration of measurement (with −1 being the lowest and 1 the maximum). From 3 to 6 embryos were recorded and analyzed for each genotype; numbers of control and perturbed embryos are equal in each pairwise comparison.Cell speed and persistence were calculated from nuclei positions using custom Python scripts as described elsewhere . Briefly2 in each embryo. Analyses were carried out using standard Fiji software. From 4 to 5 embryos were analyzed per genotype. Macrophages in the pre-gb or gb entry zones were analyzed.The junctional intensity of F-actin  was calculated using linescan analysis as previously described  with thesrpHemo-moe::3xmCherry reporter line [DfosDN, cher RNAi, or TM4SF RNAi. Embryos were collected for 5 hours 30 minutes at 29°C, dechorionated in 50% bleach for 5 minutes, rinsed thoroughly with water, and aligned laterally side by side under a stereomicroscope using a fluorescence lamp to check for the presence of mCherry. Aligned embryos were then mounted as described in the live imaging section above. To image Moe::3xmCherry, a Zeiss LSM800 inverted microscope was used with the following settings: Plan-APOCHROMAT 40x/1.4 Oil, DIC, WD = 0.13 objective, 1.5× zoom, 1,025 × 1,025 pixel, speed 8, heating chamber set to 29°C, z-interval 1 μm. Laser settings were kept constant in all experiments. Images were acquired during macrophage invasion into the gb (St 12). Pseudo-coloring was conducted for the mCherry red channel. Each pixel in the image has a color ascribed to it via the fire “Look Up Table” translating the level of intensity of the mCherry channel into a defined amount of each color. The highest intensity of the image is represented as very bright yellow, and all other gray values are depicted as colors on the scale accordingly.To quantify cortical F-actin intensity in living embryos, a ter line  was crost test with Welch’s correction for DfosDN and one way-ANOVA for cher RNAi and TM4SF RNAi.For quantification of Moe::3xmCherry intensity, an ROI was drawn in Fiji software around macrophages at the gb entry site in 20 z-stacks for each embryo. The area mean intensity was measured in all ROIs, and the average/embryo was calculated. To normalize fluorescence intensities per batch, the average intensity/embryo of all ROIs in each sample was divided by the arithmetic mean of the average intensity/embryo of all ROIs in the control per batch. The normalized average intensities/embryo were then compared to each other using a https://github.com/Axmasha/Image_analysis_scripts.Methanol-fixed St 11 embryos were mounted either after staining with GFP antibody (Dia::GFP) or without staining (DiaRBD::GFP) and imaged with a Zeiss Inverted LSM800, Plain-Apochromat 63X/1.4 Oil Objective at an XY-resolution of 0.1 μm and a Z-resolution of 1 μm . All macrophages within 40 μm of the gb were analyzed. For the quantification of the levels of DiaRBD or the complete Dia protein at the plasma membrane versus the cytoplasm, confocal images were processed using Fiji and MATLAB-R2017b (MathWorks). Individual focal planes were used to segment a profile corresponding to an 8-pixel wide line drawn across the single outer membrane of individual macrophages chosen such that the extracellular portion of the line extended into surrounding tissue or space and not another macrophage. The corresponding intensity profiles of the Myr::Tomato and Dia::GFP or DiaRBD::GFP channels were extracted in Fiji using a custom macro and analyzed further using a custom MATLAB script. The membrane region was defined by finding the maximal value in the Tomato intensity profile and centering a 0.8-μm interval around it. The background was calculated for each GFP profile as the mean intensity in the 2 μm outside the cell, flanking the membrane region, and substracted from the entire profile. The integrated Dia::GFP or DiaRBD::GFP intensity at the membrane was calculated within the 0.8-μm interval defined above. The integrated cytoplasmic Dia::GFP or DiaRBD::GFP level was calculated as the mean intensity of 2 μm of the GFP profile inside the cell flanking the membrane region. Image analysis scripts are publicly available at UAS-CLIP::GFP under the control of srpHemo-Gal4. Briefly, 3D-stacks with 1 μm Z resolution were acquired every 35 to 45 seconds for approximately 1 hour. As the strength of the GAL4 expression increased over time, laser power was adjusted during acquisition to reach the best possible quality of visualization. Images acquired from mutiphoton microscopy were initially processed with ImSpector software (LaVision Bio Tec) to compile channels from the imaging data.Laterally oriented embryos were used to measure the maximal length and width of macrophages expressing We started measuring from the time the cell body of the first macrophage fully appeared at the interface between the ectoderm and mesoderm and yolk sac until it had moved 30 μm along the ectoderm mesoderm interface. At each time frame, a line was drawn in Fiji along the longest dimension of the macrophage in the direction of its front-rear polarization axis, denoted the maximal cell length, and along the orthologonal longest dimension, which was considered maximal cell width. We did not observe long CLIP::GFP protrusions, but when a small protrusion was present, it was not included in the length measurement; within this gb region, the front of the first macrophage was clearly outlined with CLIP::GFP. The border between the first and second entering macrophages was drawn based on the uninterrupted intense line of CLIP::GFP at the base of the first macrophage; only cells with a clearly visible border were measured. The length-to-width ratio was quantified for each time frame, and a probability density function was plotted: 5 embryos were recorded for each genotype.srpHemo-Gal4 UAS-LifeAct::GFP were used to image macrophage actin live with a 3D-stack resolution of 1 μm. See above description of CLIP::GFP labeled macrophage imaging for laser power and image compilation. Laser power was also increased further in the DfosDN samples to enhance actin visualization. We measured the length of the filopodia-like protrusion of the first entering macrophage with Imaris software (Bitplane) from the time when the protrusion was inserted into the ectoderm, mesoderm, and yolk sac interface until the macrophage started to translocate its cell body into that location.Laterally oriented embryos expressing w; +; srpHemo-Gal4, srpHemo-3xmCherry/+ or w; +; srpHemo-Gal4, srpHemo-3xmCherry/ UAS-DfosDN genotypes were placed into plastic cages closed with apple juice plates with applied yeast to enhance egg laying. Collections were performed at 29°C for 1 hour, then kept at 29°C for additional 5 hours 15 minutes to reach stage 11 to early stage 12. Embryos were harvested for 2 days with 6 to 7 collections per day and stored meanwhile at +4°C to slow down development. Collected embryos were dissociated and the macrophages sorted as previously described [5 macrophages were sorted within 30 minutes.Adult flies of either escribed . About 15 macrophages. RNA sequencing was performed by the CSF facility of Vienna Biocenter according to standard procedures (https://www.vbcf.ac.at/facilities/next-generation-sequencing/) on 3 replicates. Briefly, the cDNA library was synthesized using QuantSeq 3′ mRNA-seq Library Prep kit and sequenced on the Illumina HiSeq 2500 platform. The reads were mapped to the Drosophila melanogaster Ensembl BDGP6 reference genome with STAR (version 2.5.1b). The read counts for each gene were detected using HTSeq (version 0.5.4p3). Flybase annotation (r6.19) was used in both mapping and read counting. Counts were normalized to arbitrary units using the TMM normalization from edgeR package in R. Prior to statistical testing, the data were voom transformed, and then the differential expression between the sample groups was calculated with limma package in R. The functional analyses were done using the topGO and gage packages in R [Total RNA was isolated from FACS-sorted macrophages using Qiagen RNeasy Mini kit (Cat No. 74104). The quality and concentration of RNA was determined using Agilent 6000 Pico kit (Cat No. 5067–1513) on an Agilent 2100 Bioanalyzer: on average about 100 ng of total RNA was extracted from 1.5 × 10ges in R ,109. RNARNA isolation and qPCR was performed from bones of wild-type C57BL/6 mice and from bones and OS of H2-c-fosLTR as previously described with the primers in p-value <0.05 was considered statistically significant .Mouse experiments: Data are shown as mean ± SEM. One-way ANOVA followed by Tukey multiple comparisons posttest was applied to compare experimental groups. Statistical analysis was performed using GraphPad Prism 6.0 software. A Drosophila experiments: Statistical tests as well as the number of embryos, cells, tracks, or contacts assessed are listed in the figure legends. All statistical analyses were performed using GraphPad PRISM or R Studio, and significance was determined using a 95% confidence interval. No statistical method was used to predetermine sample size.Dfos mutant analyses in Representative images of Dfos antibody staining were analyzed per replicate per genotype and in situ hybridization are from experiments that were repeated 2 times with many embryos with reproducible results. S1 TableComparison of TF mRNA expression in macrophages at stages 11–12 and 13–16, based on data in . TFs exp(TIF)Click here for additional data file.S2 TableGenes are ordered according to the adjusted p-value from the RNA sequencing. Function is based on Flybase assignments . The mur(TIF)Click here for additional data file.S1 Raw imagessrpHemo-moe::3xmCherry expressing either CD8::GFP (ctrl) or DfosDN in macrophages. Rightmost western blot also contains a w- lane. Top row shows blots probed with an mCherry antibody, bottom row the same blots probed with a profilin antibody as a loading control. Cropped versions of the blots are shown in Three original uncropped western blots of St 11 embryo extracts from (PDF)Click here for additional data file.S1 DataThis Excel file contains the raw data of the quantification of embryo macrophage counts and linescan analyses along with movie outputs. Each tab in the file names the figure panel whose graph is based on the data shown in that chart.(XLSX)Click here for additional data file.S2 DatasrpHemo-3xmCherry control embryos and those expressing DfosDN in macrophages. The mean from 3 samples is shown for each genotype, organized by Flybase IDs (Fbgn), along with statistical analyses.Compendium of the RNA sequencing data obtained from FACSed macrophages from Stages 11–12 (XLSB)Click here for additional data file.S1 Fig2Dfos mutant embryos compared to the control (p = 0.37) SD: 6, 7. (C) The total number of macrophages (see schematic at left) was not altered from that in the control embryos expressing DfosDN in macrophages (p = 0.12). SD: 60, 120.  The number of macrophages (green) along the vnc (outlined by black dotted line in the schematic on the left) shows no significant difference between the control and (D) macrophages that express DfosDN or (E) either of 2 RNAi lines against Dfos. (D) DfosDN p = 0.88, 0.99, >0.99. 1Dfos RNAi (TRiP HMS00254) p = 0.21, 0.06, 0.11, 0.072, 0.033, 0.30, 0.56. 2Dfos RNAi (TRiP JF02804) p = 0.34, 0.15, 0.83, 0.27, 0.47, 1.0, 0.45. (D) SD: Ctrl 3, 3, 3, 0.8; DfosDN 6, 3, 0.7. (E) SD: Ctrl 6, 3, 3, 3, 2, 0.3; 1Dfos RNAi 6, 3, 3, 3, 2, 2, 0.3; 2Dfos RNAi 6, 2, 3, 2, 3, 1, 0.4.  Macrophage numbers in the pre-gb (see schematic at left) are increased compared to the control for lines expressing (F) DfosDN or (G) one of 2 different UAS-Dfos RNAi constructs in macrophages under srpHemo-GAL4 control. (F) p = 0.04, SD: 19, 29. (G) 1Dfos RNAi p < 0.0009, 2Dfos RNAi p < 0.0001. SD: 12, 9, 14. (H) Macrophage numbers in the gb are not significantly altered compared to the control upon overexpression of Dfos in macrophages (p = 0.14). SD: 22, 14. (I) Macrophage numbers in the gb for lines expressing one of 2 different UAS-Dfos RNAi constructs in macrophages under srpHemo-GAL4 control and lines, which additionally express UAS-GFP. Control vs. 1mac>Dfos RNAi (TRiP HMS00254) or Control vs. 2mac>Dfos RNAi (TRiP JF02804), p < 0.0001. 1mac>Dfos RNAi vs. 1 + GFPmac>Dfos RNAi or 2mac>Dfos RNAi vs. 2 + GFPmac>Dfos RNAi, p > 0.99. SD: 33, 47, 34. The effect of each Dfos RNAi was eliminated upon simultaneous expression of another UAS construct. Macrophages are labeled using either srpHemo-Gal4 driving UAS-GFP or srpHemo-H2A::3xmCherry. “mac>” indicates srpHemo-GAL4 driver expressing UAS constructs specifically in macrophages. Histograms show mean ± SEM ***p < 0.005, **p < 0.01, *p < 0.05. Unpaired t test was used for statistics, except for G, I, which used one-way ANOVA. The number of embryos analyzed for that genotype is shown within each column in the graphs. In D, n = 6 embryos for the control and n = 9 for Dfos DN. In E, n = 9 embryos for control, 15 and 11 for Dfos RNAis. Scale bar in A: 10 μm. The data underlying the graphs can be found in (A) Dfos protein (green) is detected with an antibody in macrophages (magenta) in embryos from the stages as indicated. (B-I) Quantification in mid St 12 embryos. (B) The number of macrophages (green) in the pre-gb zone (outlined by a black dotted line in the schematic on the left) showed no significant change in (TIF)Click here for additional data file.S2 Fig2Dfos macrophages entering the gb (outlined by the dashed line). Time in minutes shown in the top right corner of each image. (C) Quantification of macrophage speed shows a significant reduction in the speed of 2Dfos macrophages in the pre-gb zone and at gb entry, but none in the head. Regions analyzed indicated in left schematic. Speed in head: control = 2.59 μm/min, 2Dfos = 2.68 μm/min, p = 0.40; speed in pre-gb = 3.38 μm/min, 2Dfos = 2.47 μm/min, p = 2.38e-06; speed in gb entry: control = 2.35 μm/min, 2Dfos = 1.62 μm/min, p = 0.0003. Macrophages are labeled using srpHemo-H2A::3xmCherry. Histograms show mean ± SEM. ****p < 0.0001, ***p < 0.005, **p < 0.01, *p < 0.05. Unpaired t test was used for statistics. The number of analyzed macrophages for each genotype shown within each graph column. Tracks were obtained from movies of 3 embryos each for control and mac>DfosDN for pre-gb entry in A, 4 each for gb entry in A, 3 each for the vnc in A, 4 each of control and 4 2Dfos embryos for head and pre-gb in C, and 3 embryos each for gb entry in C. Scale bars: 10 μm. The data underlying the graphs can be found in (A) Quantification reveals that the directional persistence of macrophages expressing DfosDN (0.58) is unchanged (0.56) in the pre-gb area (p = 0.66) but decreased during gb entry (0.65) (0.72), p = 0.038 and along the vnc (0.54) compared to the control (0.61), p = 0.00026. Left schematic shows pre-gb area in yellow, gb entry outlined in solid line. Boxed area in right schematic shows analyzed area of vnc. (B) Movie stills showing wild-type and (TIF)Click here for additional data file.S3 FigDhc36C 0.02, CG14204 0.03, CG42402 0.04, CR43767 0.046, TM4SF 0.03, CG42260 0.0011, cher 0.046, GstT4 0.018, Xrp1 0.0011, Tspo 0.046, CG31337 0.046. Frl, DAAM, dia, capu all >0.99.  Quantification of the macrophage numbers in (D) the pre-gb zone and (E) along the vnc from embryos expressing RNAi against cher (KK 107451), or TM4SF in macrophages (KK 102206) driven by srpHemo-Gal4 shows no significant alteration. The number in the column in (D) corresponds to the number of embryos analyzed. Control vs. cher RNAi p = 0.33. Control vs. TM4SF RNAi p = 0.05. Control vs. cher/TM4SF RNAi p = 0.67. (D) SD: 20, 20, 19, 13. For (E), n = 13 embryos for control and n = 15 for each cher RNAi and TM4SF RNAi. Control vs. cher RNAi p = 0.97 for T1, p = 0.33 for T2, p = 0.88 for T3. Control vs. TM4SF RNAi p = 0.52 for T1, p = 0.76 for T2, p = 0.35 for T3. SD: ctrl 6.5, 5.4, 0.6; cher RNAi 5.0, 3.3, 0.8; TM4SF RNAi 4.4, 4.9, 1.9. (F-I) q-PCR analysis of mRNA extracted from the bones of mice that are wild type, tg for MHC c-fos, viral 3′ UTR, and those in which c-fos transgenesis has led to an OS. Analysis of mRNA expression shows that (F) higher Fos levels in OS correlate with higher levels of (G) the glutathione S transferase Gstt3, and (H) the slit receptor Eva1c, but not (I) Tspo. Bone and OS RNA isolated from the same transgenic mouse, n = 4 mice per group, age 5 to 6 months. p-values = 0.86, 0.0028, 0.0013 in (F), 0.79, 0.0001, 0.0003 in (G), 1.0, 0.054, 0.049 in (H), 0.37, 0.33, 0.040 in (I). SD: 0.7, 0.6, 2.6 in (F); 0.2, 0.3, 1.1 in (G); 0.4, 0.2, 1.5 in (H); 0.1, 0.2, 0.2 in (I). Histograms show mean ± SEM ***p < 0.005, **p < 0.01, *p < 0.05. Unpaired t test or one-way ANOVA with Tukey post hoc were used for statistics of quantifications. Significance is based on adjusted p-values. The data underlying the graphs can be found in (A-C) Comparative mRNA expression levels as determined from RNA sequencing analysis of FACS-sorted wild-type macrophages and those expressing DfosDN, n = 3 biological replicates.  Genes down-regulated in macrophages expressing DfosDN are shown, separated into those with (A) strong and (B) moderate expression in wild-type macrophages. (C) Expression levels of Drosophila formin family genes are unchanged. Fold enrichment is normalized. p-values: (TIF)Click here for additional data file.S4 FigsrpHemo-moe::3xmCherry expressing either CD8::GFP (ctrl) or DfosDN in macrophages. Left western blot also contains w- lane. Original uncropped western blots can be found in 1Dfos or 2Dfos embryos completely rescued the macrophage gb invasion defect. p-values: Control vs. 1Dfos or vs. 2Dfos p = 0.0004 or p = 0.0055, respectively; Control vs. 1 mac>DiaCADfos or vs. 2 mac>DiaCADfos p > 0.999; 1Dfos vs. 1 mac>DiaCADfos p = 0.0005; 2Dfos vs. 2 mac>DiaCADfos p = 0.035. SD: 20, 23, 18, 19, 7.8.  There was no significant change in the number of macrophages in (C) the pre-gb zone or (D) along the vnc in embryos expressing either of 2 different RNAi lines against dia expressed in macrophages. Pre-gb: Control vs. 1dia RNAi p = 0.54, Control vs. 2dia RNAi p = 0.77. vnc: Control vs. 1dia RNAi p = 0.99, Control vs. 2dia RNAi p = 0.95. RNAi1 = TRiP HMS05027, RNAi2 = TRiP HMS00308. (C) SD: 9, 12, 13. (D) SD: Ctrl 5.2, 6.4, 2.5, 0.4; 1dia RNAi 5.6, 6.8, 1.7, 0.2; 2dia RNAi 5.1, 4.9, 2.1, 0.6.  Two further examples of line profiles used for the determination of the membrane-to-cytoplasmic ratios in cher RNAi, or TM4SF RNAi as shown in the schematic in E. Line length approximately 8 μm. Blue lines indicate mean GFP intensity on the membrane and in cytoplasm. Histograms show mean ± SEM ***p < 0.005, **p < 0.01, *p <0.05. One-way ANOVA with Tukey post hoc was used for statistics of quantification. The number in each column corresponds to the number of analyzed embryos. “mac>” indicates srpHemo-GAL4 driver expressing UAS constructs in macrophages. Macrophages are labeled using srpHemo-H2A::3xmCherry. The data underlying the graphs can be found in (A) Three western blots probed with an mCherry antibody of St 11 embryo extracts from (TIF)Click here for additional data file.S5 FigsrpHemo-Gal4 (“mac>”) driving UAS-LifeActGFP. White stars indicate the tip of each actin protrusion. Scale bar 5 μm. (B) Microtubules are labeled with srpHemo-Gal4 driving UAS-CLIP::GFP. Spatially matched stills of the first macrophage expressing DfosDN and control extending protrusions into the gb slightly before entering with the body of the cell. As DfosDN macrophages have a delay in entry, the stills from the DfosDN movie are from a later developmental time point than the control. (C) Quantification of macrophage maximum length and maximum width shows that DfosDN expressing macrophages are 23% longer and 12% thinner than wild-type macrophages inside the gb (indicated in schematic above by dashed box). Control vs. DfosDN maximum length p = 0.0005, SD: 3.4, 5.7; control vs. DfosDN maximum width p = 0.0025, SD: 1.3, 1.0. (D) Quantification of the maximum length and maximum width of macrophages in the pre-gb zone (indicated in schematic by dashed box) shows that macrophages expressing DfosDN are 9% shorter and 9% thinner than wild-type macrophages. Control vs. DfosDN maximum length p = 0.0095, SD: 2.2, 2.0; control vs. DfosDN maximum width p = 0.005, SD: 2.3, 1.9. (E) Overexpression of UAS-Lam in macrophages through srpHemo-Gal4 (mac>) causes no change in their number in the gb compared to the control. p = 0.65, SD: 15, 18. Histograms show mean ± SEM ***p < 0.005, **p < 0.01, *p < 0.05. Unpaired t test was used for statistics of quantification. The number of measurements per genotype is shown in each columns. The data underlying the graphs can be found in (A) Representative image showing actin protrusions of the first macrophage entering the gb in the control and in lines expressing DfosDN in macrophages. Actin was visualized by (TIF)Click here for additional data file.S6 FigProposed interactions of proteins at the cell cortex in wild-type macrophages during gb infiltration as shown in (TIF)Click here for additional data file.S1 MoviesrpHemo-H2A::3xmCherry are imaged while entering the gb in control embryos (left) and embryos in which macrophages express DfosDN (right). Time in minutes is indicated in the upper right corner. Scale bar: 10 μm. DfosDN, dominant negative version of Dfos; DN, dominant negative; gb, germband.Movies corresponding to stills shown in (AVI)Click here for additional data file.S2 MoviesrpHemo-Gal4 driving UAS-GFP::nls are imaged during their migration along the segments of the vnc in control embryos (left) and embryos in which DfosDN is expressed in macrophages (right). Time in minutes is indicated in the upper right corner. Scale bar: 10 μm. DfosDN, dominant negative version of Dfos; vnc, ventral nerve cord.Movies corresponding to stills shown in (AVI)Click here for additional data file.S3 MoviesrpHemo-Gal4 driving UAS-GFP::nls are imaged while entering the gb in control embryos (left) and 2Dfos mutant embryos (right). Time is indicated in minutes. Scale bar: 10 μm. gb, germband.Movies corresponding to stills shown in (AVI)Click here for additional data file.S4 MoviesrpHemo-Gal4 driving UAS-LifeAct::GFP is imaged during gb entry in control embryos (left) and embryos with macrophages expressing DfosDN (right). Note the extended protrusion of the DfosDN expressing macrophages. Time is indicated in minutes. Scale bar: 10 μm. DfosDN, dominant negative version of Dfos; gb, germband.Movies corresponding to stills shown in (AVI)Click here for additional data file.S5 MoviesrpHemo-Gal4 driving UAS-CLIP::GFP. They are imaged during gb entry in control embryos (left) and embryos with macrophages expressing DfosDN (right). Note the extended shape of the DfosDN expressing macrophages. Time is indicated in minutes. Scale bar: 10 μm. DfosDN, dominant negative version of Dfos; gb, germband.Movies corresponding to stills shown in (AVI)Click here for additional data file.


Amycolatopsis sp. CA-230715, a potentially interesting producer of natural products. The genome of CA-230715 was sequenced using PacBio, Illumina, and Nanopore technologies. It consists of a circular 10,363,158-nucleotide (nt) chromosome and a circular 12,080-nt plasmid.We report the sequencing, assembly, and annotation of the genome of  Amycolatopsis is a recognized source of secondary metabolites . The original colony was isolated from a serial dilution of a soil suspension plated onto HANOB medium  after incubation for 5 weeks at 28°C/70% relative humidity. For DNA isolation, the strain was grown in liquid yeast extract-malt extract (YEME) medium . Subread generation and adapter removal were performed using SMRT Analysis v2.3 software. A KAPA HyperPlus library was sequenced on an Illumina MiSeq instrument , yielding 4,477,879 read clusters (2 × 150 nt), totaling 1,273,625,221 nt. Nanopore data were generated on a MinION device using the SQK-RBK004 kit and a FLO-MIN106D R9.4 Rev-D flow cell  . Default software parameters were used except where otherwise noted. The Illumina reads were adapter and quality trimmed using AdapterRemoval2 v2.1.7  similarity to Amycolatopsis nigrescens CSC17Ta-90, and GTDB-tk v1.5.1, R202 , SRR12367305 (PacBio), and SRR12367307 (Nanopore). The GenBank accession numbers are CP059997.1 (chromosome) and CP059998.1 (plasmid).All data are available under BioProject accession number


Legionella pneumophila secretes toxins into the host cell that induce the non-canonical processing and activation of the ER stress sensor and transcription factor ATF6 via a mechanism that is distinct from the canonical pathway activated by unfolded protein buildup. Legionella pneumophila (L.p.) secretes ∼330 effector proteins into the host cell to sculpt an ER-derived replicative niche. We previously reported five L.p. effectors that inhibit IRE1, a key sensor of the homeostatic unfolded protein response (UPR) pathway. In this study, we discovered a subset of L.p. toxins that selectively activate the UPR sensor ATF6, resulting in its cleavage, nuclear translocation, and target gene transcription. In a deviation from the conventional model, this L.p.–dependent activation of ATF6 does not require its transport to the Golgi or its cleavage by the S1P/S2P proteases. We believe that our findings highlight the unique regulatory control that L.p. exerts upon the three UPR sensors and expand the repertoire of bacterial proteins that selectively perturb host homeostatic pathways.The intracellular bacterial pathogen  Legionella pneumophila (L.p.), expertly manipulate host cell function to create their replicative niche. L.p. uses the specialized Dot/Icm Type IVB secretion system (T4SS) to translocate roughly 300 bacterial effector proteins into the host cytosol  with the host endosomal machinery. Instead these effectors facilitate the remodeling of the LCV into a compartment that supports pathogen replication  to allow for the antibody-mediated opsonization of L.p. Surprisingly, and in contrast to DTT treatment, infecting these cells with wild type L.p. (WT L.p.) resulted in the near complete processing of endogenous ATF6-FL into two distinct fragments—a major fragment of ∼75 kD that we designate as ATF6-P  did not affect ATF6-FL protein levels  is processed upon cleavage into an ∼55 kD N-terminal fragment (ATF6-N) that translocates to the nucleus and activates transcription . Both threatment . ImportaL.p. infection has been shown to influence the IRE-1 branch of the UPR . HEK293-FcγR cells were pre-treated for 3 h with the proteasome inhibitor MG-132 or control media. ER stress induction using DTT led to rapid ATF6 processing after 1 h, whereas prolonged exposure to DTT for 3 h resulted in recovery of ATF6 signal due to autoregulatory feedback from UPR induction . In contnfection . When cet impact . Previout impact . It was epletion . To testwith CHX . Similared cells . Howevercontrols . As L.p.ynthesis , we consynthesis . Consist WT L.p. . Taken tL.p. induced ATF6-FL processing into the ATF6-P (∼75 kD) and ATF6-LMW (∼30 kD) fragments affected its distal function as a nuclear transcription factor. To address this question, we used the N-terminal tagged GFP fusion protein of ATF6-FL (GFP-ATF6-FL)  domain, whereas GFP-ATF6 1-331, GFP-ATF6 1-343, and GFP-ATF6 1-355 possessed partial bZIP domains (WT L.p. (WT L.p. but not ΔdotA L.p. (WT L.p. migrated on a reducing SDS–PAGE gel in a manner similar to the GFP-ATF6 1-331 truncation mutant (L.p. 5 h lane at high exposure). Significantly, an in vitro transcription and translation product of the DNA encoding the ATF6 1-331 protein migrated as a sharp band at a MW of ∼30 kD on a reducing SDS–PAGE and was detected by an antibody raised against ATF6   (Chen etATF6-FL) . During ATF6-FL)  Indeed, ATF6-FL) . After 1ATF6-FL) . We thene JF-644 . Analyseme point . Strikinreatment . These r domains . We coll and GalT-RFP (red) and stained with Hoechst solution (blue). DTT was added to cells at t = 0. Still frame images were taken every 5 min over 2 h 30 min. Video 2Halo-tag WT-L.p. Infected cells were imaged directly after spin-fection with no more than 30 min elapsed time between centrifugation and image acquisition at t = 0. Still frame images were taken every 10 min over 12 h. Download videoTime-lapse wide-field microscopy movie of HeLa-FcγRII cells expressing GFP-ATF6 (green) and stained with Hoechst solution (blue). Cells were infected with JF-646 stained L.p. infection, we monitored the mRNA levels of ATF6 target genes by quantitative real time PCR (qRT-PCR), including UPR regulator/ER chaperone BiP (HSPA5), in HEK293-FcγR cells. As expected, UPR induction with DTT increased expression of ER quality control genes BiP and HERPUD1 by greater than fivefold in comparison to control (DMSO-treated) cells  cells . Interespression . To gainreatment . When thL.p. infection was dependent on the ATF6-LMW. As the ATF6-LMW resembled the sequence architecture of the ATF6 1-331 mutant protein .We then undertook two orthogonal approaches to determine if the transcriptional program induced during tein see , we firstein see  Fig 3D)L.p. infe3T cells . To compficiency . Indeed,L.p. demonstrate the activation of the ATF6 pathway during L.p. infection.In sum, the gene expression changes in concomitance with ATF6 processing induced by L.p. infection, ATF6-FL is processed into a ATF6-P fragment and a ATF6-LMW fragment that retains transcriptional activity  During activity  and 3A–Epparatus . It is wpathways . Howeverhe Golgi . Treatmehe Golgi  and S3A,A levels  and S3B.A levels  and S3A pression  and S3B.93 cells , along wmRNA see . In macrragments . Yet sur and on both the ATF6 S1P and S2P cleavage sites   that have both overlapping and distinct specificities. Pre-treatment of cells with this protease inhibitor cocktail did not affect the loss of ATF6-FL activated by WT L.p. , Legionella wadsworthii (L. wad), and Legionella longbeachae (L. lon)—were tested in addition to L. pneumophila strains—Philadelphia str. (WT L.p. Phila or ΔdotA L.p. Phila), Paris str. (L.p. Paris), Lens str. (L.p. Lens), and Serogroup 6 str. (L.p. SG6). The Legionella species and strains were used to infect HEK293-FcγR and RAW264.7 macrophage cells and endogenous ATF6-FL levels were monitored by immunoblotting. Whereas most of the species and strains tested recapitulated the loss of ATF6-FL as seen with wild type L.p. (see above), infecting cells with either L. wadsworthii or the L. pneumophila Paris str. did not result in an efficient processing of ATF6-FL L.p. PhilL.p. effectors lpg0519 and lpg2131 for further characterization. We generated GFP tagged fusion constructs of lpg0159 and lpg2131 and transiently expressed them in U2OS and HEK293 FcγR cells. Unfortunately, the ectopic expression of tagged Lpg2131 in cells resulted in toxicity and confounded our interpretation of the results obtained (data not shown). We thus excluded this effector from further analyses. In contrast, GFP-Lpg0519, when expressed in U2OS cells, was relatively non-toxic and localized to the ER as observed by colocalization with the ER marker mCherry-KDEL  ATF6-FL is processed into two fragments, a higher MW ATF6-P fragment (∼75 kD) and a lower MW ATF6-LMW fragment (∼30 kD)  . Analysi(∼30 kD)  and 2. I(∼30 kD)  and S2B;(∼30 kD) ; and (3) fashion . These fL.p. infected cells  that activates the ERSE reporter in cells  that presumably evolved from the same species of L. pneumophila process ATF6 differently in their mammalian hosts due to a small but significant divergence in their effector repertoires  Lpg0519 folds and functions as an atypical protease; or (b) Lpg0519 promotes the activity of an ER localized host protease. In either scenario, the answers to these questions using molecular tools derived from L. pneumophila, will enhance our understanding of ATF6 regulation in physiology and pathology.The primary targets of the  amoebae . It is wan hosts . Examplean hosts . In this enzymes . Homologertoires . One of o the ER . Bioinfoo the ER , did not-FL loss . This suLegionella strains were gifts from Craig Roy’s laboratory at Yale University. Legionella strains used in this study were routinely cultivated on Charcoal Yeast Extract agar. The ΔdotA, ΔsidC-sdcA, and IPTG-inducible Halo-expressing strains were derived from the parental Lp01 strain. The Δ2,3,4,6,7, Δ2,3,6,7, and Δ2,3,6 L.p. strains (7-translation L. pneumophila strain (L. pneumophila serogroup 1 Lp02 (L. pneumophila Paris B1 strain was purchased from ATCC (ATCC 700833). Chloramphenicol (10 μg/ml), IPTG (0.1 mM), and thymidine (100 μg/ml) were added to Charcoal Yeast Extract agar plates as needed. L.p. were harvested from 2-d heavy patches and used to infect cells.All  strains , and thea strain  was a gip 1 Lp02 . L. pneulpg0519 was amplified from L.p. (Lp01) genomic DNA and cloned into the pEGFP-C2 vector.GFP-ATF6, HA-ATF6, and HA-ATF6 1–373 were kind gifts from Ron Prywes . The GFP-ATF6 truncation mutants were cloned from the GFP-ATF6 backbone and generated by Genscript Inc. For in vitro transcription and translation, the ATF6 1–331 fragment was sub-cloned between HindIII and SalI sites on the pGEM3Z vector carrying an SP6 promoter. GFP-ATF6 S1P mutant and GFP-ATF6 S1P/S2P mutant constructs were generated by site directed mutagenesis using the Q5 Site-Directed Mutagenesis Kit (NEB). For the effector screen, Myc-tagged effector proteins were amplified from a plasmid effector library that was a kind gift from Russell Vance, University of California, Berkeley. To generate N-terminal GFP tagged Lpg0519, 2. RAW264.7 macrophages were cultured in Roswell Park Memorial Institute media (RPMI) (Corning) supplemented with 10% FBS at 37°C and 5% CO2. Cells were placed in poly-L-lysine–treated plates and grown to 90% confluency. Drug treatments were performed at final concentration, 200 nM Tg (Enzo Life Sciences), 1 mM DTT , protease inhibitor cocktail (100 μM 4-(2-aminoethyl)benzenesulfonyl fluoride [AEBSF] [Sigma-Aldrich], 10 μM Tosyl phenylalanyl chloromethyl ketone [TPCK] [Sigma-Aldrich], 10 μM Calpain Inhibitor I [Sigma-Aldrich], 10 μM E 64 Protease inhibitor [EMD Millipore], 100 μM phenylmethylsulfonyl fluoride [PMSF] [Sigma-Aldrich]), 5 nM Bafilomycin A1 (Sigma-Aldrich), 1 μM PF-429242 (Sigma-Aldrich), 5 μM/20 μM MG-132 (Enzo Life Sciences), 25 μM Cycloheximide (Sigma-Aldrich), 10 μM Ceapin A7  supplemented with 10% FBS at 37°C and 5% COeapin A7 . Ceapin-For transient transfections, cells were grown to 70% confluency and transfected with 2 μg of plasmid for 60 and 35 mm dishes, or 1 μg per well for 24-well plates using JetPRIME (Polyplus-transfection) according to the manufacturer’s instructions. Cells were incubated with transfection reagent for 4 h, then media replaced with fresh DMEM supplemented with 10% FBS. For siRNA transfections, cells were grown to 30–50% confluency and transfected using Oligofectamine (Thermo Fisher Scientific) according to the manufactures protocol. Cells were grown for 72 h before application of treatment conditions. The following siRNA oligos used were purchased from Sigma-Aldrich: SEL1L-TTAACTTGAACTCCTCTCCCATAGA, Scramble-GCATACTCAACTACTTCGCATACTT; ATF6- GAACAGGGCTCAAATTCTC, Scramble-GCTAGTGCACAAGTACCTA.Legionella polyclonal antibody  was used at 1:2,000 and incubated for 20 min at room temperature. Immediately after infection, cells were centrifuged for 5 min at 100g. After centrifugation, cells were left at 37°C for an additional 60 min. After 1 hour, cells were washed with 1× PBS to remove extracellular bacteria. DMEM supplemented with 10% FBS or the same media supplemented with treatment reagent was replaced after the washes in PBS. Infected cells were harvested at the designated times.Cells were infected at a MOI of 100, 50, 25 or 5. If cells required opsonization, BiP (Human) forward- CATCACGCCGTCCTATGTCG, reverse- CGTCAAAGACCGTGTTCTCG; HERPUD1 (Human) forward- AACGGCATGTTTTGCATCTG, reverse- GGGGAAGAAAGGTTCCGAAG; SEL1L (Human) forward- AAACCAGCTTTGACCGCCAT, reverse- GTCATAGGTTGTAGCACACCAC; HYOU1 (Human) forward- GAGGAGGCGAGTCTGTTGG, reverse- GCACTCCAGGTTTGACAATGG; ATF6 (Human) forward- AGAGAAGCCTGTCACTGGTC, reverse- TAATCGACTGCTGCTTTGCC; DNAJB11 (human) forward- AACCTGAGCACCTTTTGCCT, reverse- GGTTCCGGTCGGGATGAAG, BiP (mouse) forward- ACTTGGGGACCACCTATTCCT, reverse- GTTGCCCTGATCGTTGGCTA; Dnajb11 (mouse) forward- TTGGAGGAACCCCTCGTCA, reverse- CTCTTGCCGACAGTTGCATTT; Hsp90B1 (mouse) forward- GTTCGTCAGAGCTGATGATGAA, reverse- GCGTTTAACCCATCCAACTGAAT; Atf6 (mouse) forward- TCGCCTTTTAGTCCGGTTCTT, reverse- GGCTCCATAGGTCTGACTCC; Actin (mouse) forward- GGCTGTATTCCCCTCCATCG, reverse- CCAGTTGGTAACAATGCCATGT.HEK-293 FCγ cell mRNA was harvested and isolated using Direct-zol RNA Miniprep Plus (Zymo Research) according to the manufacturer’s protocol. cDNA synthesis was performed using QuantiTect Reverse Transcription Kit (QIAGEN) and cDNA reactions were primed with poly dT. Relative quantitative PCR was performed using iTaq Universal SYBR Green Supermix (Bio-Rad). GAPDH or HPRT mRNA (for human cells) or Actin mRNA (for RAW264.7) was used for normalization. Uninfected and untreated HEK-293 FCγRII and RAW264.7 macrophage cells were used as the endogenous control for each qRT-PCR analysis. The following qRT-PCR primers were used: Mammalian cells were lysed in radioimmunoprecipitation assay buffer (RIPA) buffer with the addition of protease (Roche cOmplete), and phosphatase inhibitors (GB Sciences). Protein levels of lysates were determined using the Bio-Rad DC/RC assay. Equal amounts of protein lysate were boiled with SDS load buffer, and equal amounts of protein were loaded. Immunoblotting was performed with the following antibodies: GAPDH , ATF6 rabbit polyclonal , ATF6 mouse monoclonal , β-Actin , α-Tubulin , SEL1L , ABCD3 , GFP tag , BiP , ATF4 .L.p. as needed. For ubiquitin recruitment assays, HEK293 FcγR cells were infected with L.p. at MOI = 5. 1 h after infection, cells were washed twice with PBS to remove extracellular bacteria and incubated for 2 h more. For co-localization assays, Cos7 cells were co-transfected with pcDNA-FcγRII and GFP-ATF6α, then infected with L.p. at MOI = 10 and infected for 1, 4, or 8 h. For 4- and 8-h time points, cells were washed with PBS after 1 h to remove extracellular bacteria. Coverslips were mounted with ProLong Diamond Antifade Mountant (Thermo Fisher Scientific) incubated at 37°C for 10 min, then imaged directly. For immunofluorescence, coverslips were washed with cold 1× PBS, fixed with 4% paraformaldehyde in PBS for 10 min at room temperature, permeabilized in 0.1% saponin in PBS, and blocked in 3% BSA in PBS, and then incubated with the appropriate primary and secondary antibodies diluted in 3% BSA. Nuclei were stained with Hoechst 33342 dye for 10 min before mounting on microscope slides. Coverslips were imaged using an inverted Nikon Eclipse Ti-E spinning disk confocal microscope equipped with a Prime 95B 25mm CMOS camera (Photometrics) camera. Antibodies used were Ubiquitin , and Secondary Antibody, Alexa Fluor 546 or Alexa Fluor 488 (Thermo Fisher Scientific).Cells were plated on 12 mm glass coverslips in 24-well plates. After 24 h, cells were fixed, treated with drugs, or infected with WT- or ΔdotA L.p. that were previously stained with HaloTag-Janelia Fluor 646 conjugates  to facilitate HaloTag-JF646 conjugation. After 15 min incubation with ligand in the dark, L. pneumophila were washed 1× with water. Stained bacteria were resuspended in 2 ml of DMEM lacking phenol red (Gibco) and used for infection of cells. Imaging of cells took place in a controlled chamber maintaining 37°C with 5% CO2. A random selection of cells was imaged at 60× magnification at 5- or 10-min intervals for 12 h using a Nikon Eclipse Ti2 microscope with a Nikon DS-Qi2 camera. Cells that died or lost focus over the time course were omitted from analysis.HeLa FcγRII cells expressing GFP-ATF6 were plated on 35 mm poly-lysine–coated imaging dishes (Cellvis). Cells were infected at MOI = 5 with njugates . For staIn vitro transcription and translation of luciferase and the ATF6 1–331 fragment were performed using the TnT coupled Reticulocyte Lysate Systems kit from Promega. Briefly, 1 μg of DNA encoding for luciferase or the ATF6 1–331 fragment downstream of an SP6 promoter were incubated with TnT reaction buffer, RNA polymerase, amino acids, RNAsin ribonuclease inhibitor (Promega), Magnesium actetate (0.25 mM) and rabbit reticulocyte lysate as a source of ribosomes and translation machinery in a test tube at 30°C for 90 min. 20% of the total reaction mixture was then boiled with Lammeli buffer and run on a 4–15% reducing SDS–PAGE gel before immunoblotting with an ATF6 antibody .Legionella effectors as previously described (2 with 1 s exposure. Background signal was subtracted from untreated untransfected cells. Treatment conditions were normalized to control cells and represented as fold change in relative luminiscence.ONE-Glo Luciferase assay system was purchased from Promega. HEK-293T ERSE-Luciferase cells, provided as a gift from the Peter Walter laboratory at UC San Francisco and described previously , were seescribed  for 24 ht test was performed using three biological replicates. Statistical significance: *P-value < 0.05; **P-value<0.01, ***P-value <0.001. Image analysis was performed using ImageJ.GraphPad Prism 6 software was used for statistical analysis. Where statistical analysis was performed an unpaired


Rumex madaio Makino. The bacteriostatic rate of the extract was 75% against 23 species of common pathogenic bacteria. The extract was further purified using the preparative high-performance liquid chromatography (Prep-HPLC) technique, and five separated componential complexes (CC) were obtained. Among these, the CC 1 significantly increased cell surface hydrophobicity and membrane permeability and decreased membrane fluidity, which damaged cell structure integrity of Gram-positive and -negative pathogens tested. A total of 58 different compounds in the extract were identified using ultra-HPLC and mass spectrometry (UHPLC-MS) techniques. Comparative transcriptomic analyses revealed a number of differentially expressed genes and various changed metabolic pathways mediated by the CC1 action, such as down-regulated carbohydrate transport and/or utilization and energy metabolism in four pathogenic strains tested. Overall, the results in this study demonstrated that the CC1 from R. madaio Makino are promising candidates for antibacterial medicine and human health care products.Outbreaks and prevalence of infectious diseases worldwide are some of the major contributors to morbidity and morbidity in humans. Pharmacophageous plants are the best source for searching antibacterial compounds with low toxicity to humans. In this study, we identified, for the first time, antibacterial components and action modes of methanol-phase extract from such one edible herbaceous plant  R. madaio Makino is an edible, perennial and herbaceous plant that belongs to the Dicotyledoneae class, Polygonaceae family, and Rumex genus. According to the National Compilation of Chinese Herbal Medicine (1996 Edition), leaf and root tissues of R. madaio Makino can be used as medicine such as clearing heat and detoxification, removing blood stasis, and defecating and killing insects. Nevertheless, current studies on the antibacterial activity of R. madaio Makino are rare.China is one of the richest countries in biodiversity, with very high levels of plant endemism . PharmacR. madaio Makino were for the first time identified. The objectives of this study were: (1) to extract bioactive substances from R. madaio Makino using the methanol and chloroform extraction (MCE) method, and determine their inhibition activity against 23 species of pathogenic bacteria; (2) to purify the methanol-phase extract from R. madaio Makino by preparation high-performance liquid chromatography (Prep-HPLC) analysis, and identify bioactive compounds in componential complex 1 (CC 1) using an ultra-HPLC and mass spectrometry (UHPLC-MS) technique; (3) to determine cell surface hydrophobicity, cell membrane permeability, fluidity, and the damage of four representative pathogenic strains treated with the CC 1; (4) to decipher possible molecular mechanisms underlying antibacterial activity by comparative transcriptomic analysis. The results of this study meet the increasing need for novel antibacterial agent candidates against common pathogenic bacteria.In this study, antibacterial components and action modes of methanol-phase extract from R. madaio Makino were extracted using the MCE method. The results showed that the water loss rate of the plant material was 93.32%, and extraction rates of the methanol phase and chloroform phase were 32.10% and 29.60%, respectively. Antibacterial activity of the crude extracts against 23 species of pathogenic bacteria was determined, most of which are common foodborne pathogens, and the results are presented in R. madaio Makino showed a bacteriostatic rate of 39%, inhibiting 2 species of Gram-positive and 11 species of Gram-negative pathogens  showed weak or no antibacterial activity, indicating that bioactive compounds in the methanol-phase extract from V. alginolyticus ATCC17749 and V. parahaemolyticus ATCC17802; 128 μg/mL against B. cereus A1-1; and 256 μg/mL against V. parahaemolyticus B4-10.MIC values of the CC 1 were also determined, which was 64 μg/mL against B. cereus A1-1, the 2 h treatment by the CC 1 resulted in the bacterial cell surface shrinking seriously, the flagella breaking, and some contents leaking. After being treated for 4 h, cell surface shrinkage was intensified, and more cells were ruptured. After being treated for 6 h, the cell structure was seriously damaged, a large number of contents exuded, and only a few cells still maintained rod shape  analysis. As shown in od shape A. For th for 6 h C. These p < 0.05) when compared with the control groups (V. parahaemolyticus ATCC17802 (1.47-fold), V. parahaemolyticus B4-10 (1.62-fold) and B. cereus A1-1 (1.42-fold) after being treated with the CC1 for 2 h (p < 0.05), whereas a similar change was observed in the treatment group of V. alginolyticus ATCC17749 (1.48-fold) after being treated for 4 h. Moreover, the highest increase in cell surface hydrophobicity was observed in B. cereus A1-1 (3.75-fold) after being treated with the CC1 for 6 h . However, a significant decrease in membrane fluidity of these three strains was observed after the treatment for 4 h. Additionally, cell membrane fluidity significantly declined in B. cereus A1-1 (1.20-fold) treated with the CC 1 for 2 h, and sharply lost for 6 h (8.11-fold)  B. The chd-galactopyranoside (o-nitrophenyl)-β-d-galactopyranoside (ONPG) was used as a probe to monitor the inner cell membrane permeability of the four bacterial strains, and the results were illustrated in R. madaio Makino on inner cell membrane permeability was observed among the four treatment groups. For example, V. alginolyticus ATCC17749 did not change significantly in the inner cell membrane permeability after the treatment for 2 h (p > 0.05), whereas a significant increase was observed after being treated for 4 h (1.15-fold) and 6 h (1.18-fold), respectively (p < 0.05) (The o-nitrophenyl-β- < 0.05) .N-Phenyl-1-naphthylamine (NPN) was used as a probe to monitor the bacterial outer membrane permeability. As shown in p < 0.01). The highest increase was found in B. cereus A1-1 (6.06-fold) after being treated for 6 h, whereas an opposite pattern was observed in V. parahaemolyticus ATCC17802 (1.77-fold).p < 0.05), which raised with the increase in treatment time. Significant damage was observed in B. cereus A1-1 (2.95-fold) and V. parahaemolyticus B4-10 (2.21-fold) after being treated for 2 h, whereas a similar change was found in the other two strains treated for 4 h. Moreover, cell membrane damage of B. cereus A1-1 was the most severe among the four strains after being treated for 6 h (8.54-fold).As shown in R. madaio Makino significantly increased bacterial cell surface hydrophobicity and membrane permeability and decreased membrane fluidity of V. parahaemolyticus ATCC17802, V. parahaemolyticus B4-10, V. alginolyticus ATCC17749, and B. cereus A1-1, consistent with the observed bacterial surface structure by the TEM analysis. The damaged cell surface and membrane structure integrity were beneficial for the CC1 to penetrate bacterial cell envelope to target intracellular processes.Taken together, these results demonstrated that the CC 1 from 2O was subjected to UHPLC-MS analysis. As shown in R. madaio Makino.The obtained CC 1 resolved in HR. madaio Makino, we determined transcriptomes of the four bacterial strains treated for 6 h using Illumina RNA sequencing technology. A complete list of DEGs in the four strains was available in the NCBI SRA database  under the accession number PRJNA767551. To validate the transcriptome data, we examined 32 representative DEGs , and 78 genes were down-regulated (FC ≤ 0.5). Based on the comparative transcriptomic analyses, 11 significantly changed metabolic pathways were identified, including valine, leucine and isoleucine degradation; nitrogen, histidine, tryptophan, glyoxylate and dicarboxylate metabolisms; quorum sensing (QS); lysine degradation; fatty acid degradation; amino sugar and nucleotide sugar metabolism; ABC transporters; and mitogen-activated protein kinase (MAPK) signal pathway (Approximately 6.73% (316/4698) of  pathway .V. alginolyticus ATCC17749 (2.002- to 87.807-fold) (p < 0.05) (p < 0.05); six DEGs encoding key enzymes in the histidine metabolism were also significantly up-regulated (2.001- to 3.187-fold) (p < 0.05); similarly, in the tryptophan metabolism, expression of three DEGs were significantly enhanced (2.123- to 5.154-fold) (p < 0.05); additionally, in the lysine degradation, expression of a transcriptional regulator (N646_3623) and an arginine/lysine/ornithine decarboxylase (N646_1979) were significantly up-regulated (2.972- to 3.332-fold) (p < 0.05). These four pathways are related to amino acid degradation metabolisms.Remarkably, approximately 60 DEGs involved in 10 changed metabolic pathways were significantly up-regulated in  < 0.05) . For exap < 0.05), in which, specifically, one DEG encoding a hydroxylamine reductase (N646_0236) was greatly enhanced to express (87.807-fold).Meanwhile, eight DEGs in the nitrogen metabolism were also significantly up-regulated (2.193- to 87.807-fold)  (p < 0.05)  . ABC trap < 0.05)  ( < 0.05) , which lV. parahaemolyticus ATCC17802 genes were expressed differently in the experimental group compared with the control group. Among these, 128 genes showed higher transcription levels (FC ≥ 2.0), and 789 genes were down-regulated (FC ≤ 0.5). Comparative transcriptome analyses revealed 20 significantly changed metabolic pathways, including methane, nitrogen, glycerolipid, propanoate, sulfur, starch and sucrose, taurine and hypotaurine, phosphonate and phosphinate, and biotin metabolisms; glucagon, and hypoxia inducible factor-1 (HIF-1) signaling pathway; benzoate and ethylbenzene degradation; glycolysis/gluconeogenesis; flagellar assembly; apoptosis; bacterial chemotaxis; cationic antimicrobial peptide (CAMP) resistance; necroptosis, and RNA transport  of ransport .p < 0.05) (VP_RS18295), the other seven DEGs were significantly down-regulation (0.087- to 0.433-fold) (p < 0.05); in the propanoate metabolic pathway, express of four DEGs were significantly depressed (0.051- to 0.240-fold) (p < 0.05); in the starch and sucrose metabolisms, except for a 4-alpha-glucono transfer (VP_RS22910), the other five DEGs were significantly down-regulated (0.206- to 0.499-fold) (p < 0.05). These three metabolic pathways were related to carbohydrate metabolisms. Their overall down-regulation trend indicated inactive carbon source transportation and/or utilization, which likely resulted in insufficient energy supply.Notably, approximately 77 DEGs involved in 12 changed metabolic pathways were significantly down-regulated (0.05- to 0.491-fold) ( < 0.05) . For exaV. parahaemolyticus ATCC17802 were also significantly inhibited (p < 0.05). For example, the DEG encoding a pyruvate dehydrogenase complex dihydrolipoyllysine-residue acetyltransferase (VP_RS12210) was significantly down-regulated (0.331-fold), which connects glycolysis with tricarboxylic acid cycle (TCA) and plays a key role in glucose metabolism [Approximately 44 DEGs involved in six energy metabolism pathways in tabolism . The dowtabolism , which cV. parahaemolyticus ATCC17802 (0.055- to 0.49-fold) (p < 0.05), which indicated the depressed flagellum assembly that led to inactive motility of V. parahaemolyticus ATCC17802. The 17 down-regulated DEGs in the bacterial chemotaxis [p < 0.05) provided indirect evidence for this result.The bacterial flagellum is a complex mobility machine with a diversity of roles in pathogenesis, including attachment, colonization, invasion, maintenance and post-infection dispersal in the host ,22. In temotaxis  (0.101- p < 0.05). T3SS enables pathogenic bacteria to directly inject effector proteins into host cells, facilitating bacterial colonization in the host [V. parahaemolyticus ATCC17802 was significantly reduced after being treated with the CC 1 from R. madaio Makino.Interestingly, 23 DEGs encoding type III secretory system (T3SS) components were also significantly down-regulated (0.055- to 0.490 -fold) (the host . This reVP_RS00200), a thiol: disulfide interchange protein DsbA/DsbL (VP_RS21260), an ATP-binding cassette domain-containing protein (VP_RS05670), a multidrug efflux RND transporter periplasmic adaptor subunit VmeC (VP_RS00205), and a phosphoethanolamine-lipid A transferase (VP_RS21300)  resistance system, five DEGs were significantly inhibited (0.120- to 0.489-fold), including a multidrug efflux RND transporter permease subunit VmeD (RS21300) . These rVP_RS14060) and an envelope stress sensor histidine kinase CpxA (VP_RS14065) , e.g., a response regulator (RS14065) .V. parahaemolyticus B4-10 genes were expressed differently in the experimental group when compared with the control group. Among these genes, 204 showed higher transcription levels (FC ≥ 2.0), and 579 genes were down-regulated (FC ≤ 0.5). Based on the comparative transcriptome analysis, five significantly changed metabolic pathways were identified, including styrene degradation, nitrogen metabolism, QS, folate biosynthesis, and histidine metabolism (Approximately 16.75% (783/4674) of tabolism .V. alginolyticus ATCC17749, the expression of 10 DEGs in the nitrogen metabolism were significantly up-regulated (2.129- to 107.754-fold) (p < 0.05) (VP_RS05780) was greatly up-regulated (107.754-fold). This enzyme can reduce hydroxylamine analogs such as methylhydroxylamine and hydroxyquinone as a scavenger of potentially toxic by-products of nitrate metabolism [V. parahaemolyticus B4-10 after being treated by the CC 1.Similar to  < 0.05) . Notablytabolism . MoreoveB. cereus A1-1 genes were expressed differently in the experimental group. Among these genes, 178 showed higher transcription levels (FC ≥ 2.0), and 542 genes were down-regulated (FC ≤ 0.5). The comparative transcriptome analysis revealed 17 significantly changed metabolic pathways, including flagellar assembly; bacterial chemotaxis; two-component system (TCS); thiamine and nitrogen metabolisms; ABC transporters; arginine biosynthesis; fatty acid degradation; alanine, aspartate and glutamate metabolism; riboflavin metabolism; HIF-1 signaling pathway; glycolysis/gluconeogenesis; butanoate, pyrimidine, and propanoate metabolisms; benzoate degradation; and inositol phosphate metabolism (Approximately 12.57% (720/5730) of tabolism .B. cereus A1-1 (3.325- to 150.780-fold) (p < 0.05) (BCN_RS16540) was also greatly enhanced to express in B. cereus A1-1 (150.780-fold).Similar to the other bacterial strains tested, expression of 12 DEGs involved in the nitrogen metabolism and riboflavin metabolism were significantly up-regulated in  < 0.05) . SpecifiB. cereus A1-1 (0.038- to 0.487-fold) (p < 0.05) (p < 0.05); 9 DEGs in bacterial chemotaxis were significantly down-regulated (0.063- to 0.474-fold); and expression of 33 DEGs in ABC transporters were significantly inhibited (0.051- to 0.487-fold).Conversely, 69 DEGs involved in the flagellar assembly, bacterial chemotaxis, ABC transporters, and TCS were significantly down-regulated at the transcription level in  < 0.05) , similarBCN_RS24725) was also significantly down-regulated (0.191-fold). These results indicated the inhibited signal transduction systems in B. cereus A1-1.Approximately eight DEGs in the TCSs were significantly down-regulated. TCSs are widespread regulatory systems that can help bacteria to control their cellular functions and respond to a diverse range of stimuli . In thisp < 0.05)  ( < 0.05) , which sVibrio strains were inoculated in media (pH 8.4–8.5) with 3.0% NaCl, while non-Vibrios in media (pH 7.0–7.2) with 1% NaCl [Bacterial strains and culture media used in this study are listed in  1% NaCl .R. madaio Makino was collected in Lishui City , Zhejiang Province, China in September of 2020. A 500 g of fresh leaf and stem tissues of R. madaio Makino was washed clean, dried at room temperature, and then freeze-dried using ALPHA 2-4 LD Plus Freeze Dryer  at −80 °C for 48 h. The freeze-dried material was crushed using FW-135 High-Speed Crusher  and passed through 300 mesh screen. Then, 10.0 g of the powder was mixed with 99-mL chloroform: methanol  at a solid to liquid ratio of 1.10 (m/v) for 5 h [2O  was then added, fully mixed, and then sonicated using Scientz IID ULtrasonic Cell Crusher  at the following parameters: power: 300 W; ultrasonic on time: 1 s; ultrasonic off time: 1 s; working time: 20 min; and probe size: 6 mm. The sonicated mixture was filtered through 20–25 μm membrane , and the filtration was collected for the secondary extraction. The methanol phase was separated from the chloroform phase and then individually evaporated, concentrated on pasting using Rotary Evaporator . for 5 h . A 60 mLR. madaio Makino was determined according to the method issued by Clinical and Laboratory Standards Institute (CLSI)  using Mueller-Hinton (M-H) agar (CM337) and Mueller-Hinton broth (M391) . Briefly, a 10 μL of crude extracts (500 μg/mL) was added onto each blank disc  on MH ager plates. The gentamicin disc  was used as a positive control, while the methanol-phase with water and chloroform-phase with ethanol was a negative control, respectively. The plates were incubated at 37 °C for 12 h. Bacteriostatic activity was evaluated by measuring diameters of bacteriostatic circles.Susceptibility of bacterial strains  to the e6 colony-forming unit (CFU)/mL), and then incubated at 37 °C for 12 h [Broth dilution testing (microdilution)  was used to determine MICs of the extracts. Briefly, a 100 μL/well of the extracts (1024 μg/mL) was serially diluted, mixed with 100 μL/well of Mueller-Hinton broth (CM337) and 10 μL/well of bacteria strain  were centrifuged at 12,000 rpm for 20 min. The supernatant was filtered through 0.22 µm membrane , and the filtration was collected for further analysis. Prep-HPLC was run using Waters 2707  linked with UPLC Sunfire C18 column   at the following parameters: column temperature, 40 °C; injection volume, 100 μL; and mobile phase of methanol (eluent A) and water (eluent B) at a flow rate of 4 mL/min . Photo-diode array (PDA) spectra were measured in the wavelength ranging from 200 to 600 nm.Aliquots (10 mg/mL) of freeze-dried samples resolved in H2O (v/v), and mobile phase B was acetonitrile ; column temperature: 40 °C; auto-sampler temperature: 4 °C; injection volume: 2 μL. Typical ion source parameters were: IonSpray voltage: +5500/−4500 V; curtain gas: 35 psi; temperature: 400 °C; ion source Gas 1:60 psi; ion source Gas 2: 60 psi; and declustering potential (DP): ±100 V. The SCIEX Analyst Work Station Software (Version 1.6.3) was employed for multiple reaction monitoring (MRM) data acquisition and processing. In-house R program and database were applied for peak detection and annotation .The UHPLC–MS analysis was carried out using EXIONLC System  by Shanghai Hoogen Biotech, Shanghai, China using the parameters as described previously . The mobR. madaio Makino was added in bacterial culture (5 mL) at middle logarithmic growth phase (mid-LQP), and incubated at 37 °C for 2 h, 4 h and 6 h, respectively. A 1.5 mL of the cell suspension were collected, washed, fixed, and observed using SU5000 transmission electron microscope  [Samples for TEM analysis were prepared according to the method described previously . Briefly×30,000) .600 nm values of 0.55 to 0.60) and rotated for 1 min and then stood at room temperature for 30 min. The absorbance of the aqueous phase was measured at OD600 nm using BioTek Synergy 2 . To measure the membrane fluidity, a 200 μL/well of bacterial suspension was mixed with 2 μL of 10 mM 1,6-diphenyl-1,3,5-hexatriene (DPH) , and the change of fluorescence intensity of each well was measured at excitation light wavelength of 362 nm and emission light wavelength of 427 nm using BioTek Synergy 2 .Bacterial cell surface hydrophobicity and membrane fluidity were measured according to the methods by Krausova et al.  and KuhrCell membrane damage was examined according to the method described previously . BrieflyR. madaio Makino and then incubated at 37 °C for 2 h, 4 h and 6 h. Outer membrane permeability was measured according to the method described previously [Bacterial culture at the mid-LGS was mixed with 1 × MIC concentration of the CC 1 from eviously . Brieflyeviously .415 nm using BioTek Synergy 2  every 30 min for 5 h, which was marked as OD1, while OD2 generated from the untreated bacterial suspension was used as a negative control [Inner membrane permeability was measured according to the method described previously . Briefly control .R. madaio Makino for 6 h. Total RNA was prepared using RNeasy Protect Bacteria Mini Kit  and QIAGEN RNeasy Mini Kit (QIAGEN). DNA was removed from the samples using RNase-Free DNase Set (QIAGEN). Three independently prepared RNA samples were used for each Illumina RNA-sequencing analysis. Illumina sequencing was conducted by Shanghai Majorbio Bio-pharm Technology Co. Ltd.  using Illumina HiSeq 2500 platform . High quality reads that passed the Illumina quality filters were used for sequence analyses [Bacterial culture at the mid-LGP was treated with 1 × MIC concentration of the CC 1 from analyses .−ΔΔCt method was used to calculate relative expression of genes. Oligonucleotide primers used for the RT-qPCR were synthesized by Sangon, Shanghai, China.Total RNA extraction, reverse transcription reactions, and relative quantitative PCR reactions were performed using the same kits and instrument according to the method described previously . The 16Shttp://deweylab.github.io/RSEM/, accessed on 17 October 2021). Genes with the criteria, fold-changes ≥ 2.0 or ≤0.5, and p-values < 0.05 relative to the control were defined as DEGs. These DEGs were used for gene set enrichment analysis (GSEA) against the Kyoto Encyclopedia of Genes and Genomes (KEGG) database . Significantly changed GSEA were identified when the enrichment test p-value fell below 0.05 [Expression of each gene was calculated using RNA-Seq by Expectation-Maximization . The methanol-phase extract was further purified using the Prep-HPLC technique, and five separated CCs were obtained. Among these, the CC 1 from R. madaio Makino significantly increased bacterial cell surface hydrophobicity and membrane permeability and decreased membrane fluidity of Gram-positive and Gram-negative pathogens, such as V. parahaemolyticus ATCC17802, V. parahaemolyticus B4-10, V. alginolyticus ATCC17749, and B. cereus A1-1. The damaged cell surface and membrane structure integrity facilitated the CC1 to penetrate bacterial cell envelope to target intracellular processes. A total of 58 different compounds in the extract were identified using UHPLC–MS technique. Comparative transcriptomic analyses revealed a number of differentially expressed genes (DGEs) and various changed metabolic pathways mediated by the CC1 action, such as down-regulation of carbohydrate transport and/or utilization, and energy metabolism; upward regulation of amino acid and fatty acid degradation, and nitrogen metabolism; and inactive flagellar assembly and mobility in the four bacterial strains. Taken, the results in this study demonstrated that the CC1 from R. madaio Makino are promising candidates for antibacterial medicine and human health care products.In this study, we identified, for the first time, antibacterial components and action modes of methanol-phase extract from one edible herbaceous plant


Frailty, a clinical syndrome characterized by vulnerability to stressors resulting from multisystemic loss of physiological reserve, predicts future cognitive decline. However, frailty has also been proposed as a dementia risk factor, predicting future cognitive impairment. The study aim was to determine frailty in older veterans and its association with risk of dementia. Community-dwelling Veterans ≥50 years completed a mailed socio-demographic questionnaire and Self-Administered Gerocognitive Examination (SAGE), July 2019-May 2020. The information was complemented with EHR data. We calculated the CAIDE score, a validated tool predicting dementia (≥6 points= high risk 20 years later) and the 31-item VA frailty index data . After adjusting for socio-demographic characteristics, smoking, alcohol/substance abuse, OSA and anticholinergic use, odds ratio (OR) and 95% CI were calculated using BLR to assess the cross-sectional association between frailty and dementia risk (CAIDE ≥6 points and MCI). The survey response rate was 19.75% . Participants mean age was 68.38 (SD=8.49) years, 57.50% (n=617) Caucasian, 69.34% (n=744) non-Hispanic, 95.81%  male, and 36.72% (n=394) frail. 11.84%(n=127) screened positive for MCI and 15.38% (n=165) for dementia. 689 (75.88%) veterans were at high risk for dementia of whom 426 (61.83%) were non-frail and 263 (38.17%) were frail. Frailty was cross-sectionally associated with higher risk for dementia in older Veterans, adjusted OR:1.45 (95%CI:1.016-2.070), p=.041. The mailed screening was a feasible and practical approach to screen for dementia risk. Early identification of patients with frailty can help in the implementation of interventions aimed at preventing or delaying dementia.


Excessive sedentary behavior (SB) is related to deleterious health outcomes. Understanding the patterns and contexts in which SB accumulates can promote healthy aging. Daily sitting time and mean sitting bout duration (MBD) were measured by triaxial accelerometers. Participants self-reported how much time they spent sitting while: watching TV, reading, using the computer, driving, working, or taking phone calls. Data were compared across aging-related characteristics. Age-adjusted sitting time (minutes/day) for 5,838 diverse , older women (mean age 78.7±6.7) were 577.2 for Hispanic women, 630.3 for Black women, and 632.0 for White women. Those in the lowest vs. highest physical function category had the longest MBD (16.1 vs. 11.7 minutes/bout). Watching television was the most common self-reported sedentary activity. The highest vs. lowest quartile of MBD spent, on average, 30.6 and 22.3 minutes/day watching television, respectively. This presentation will illuminate critical factors associated with sitting patterns in older adults.


This study reports on the E. coli and 344 S. saprophyticus isolates were collected between 2019 and 2020 from 92 medical centers located in 25 countries. Most isolates (68%) tested were cultured from urine specimens collected from patients seen in ambulatory, emergency, family practice, and outpatient medical services. Bacterial identifications were confirmed by MALDI-TOF. Isolates were tested for susceptibility by CLSI methods at a central laboratory (JMI Laboratories). MIC results for oral antibiotics licensed for the treatment of uUTI and drug-resistant subsets were interpreted per CLSI guidelines. A total of 3,562 50/90, 2/2 mg/L) displayed good activity against 3,562 E. coli isolates, with 98.0% of all observed gepotidacin MICs ≤4 mg/L (Table). Susceptibility (S) rates for the other oral agents tested against these isolates were: amoxicillin-clavulanate (79.6% S), ampicillin (45.6% S), ciprofloxacin (72.5%S), fosfomycin (99.0% S), mecillinam (94.1%S), nitrofurantoin (97.3% S), and trimethoprim-sulfamethoxazole (68.2% S). When tested against the drug-resistant subsets, gepotidacin maintained similar MIC50/90 values (2/4 mg/L), except against isolates resistant to fosfomycin (2/8 mg/L). Against S. saprophyticus isolates, gepotidacin  inhibited all isolates at ≤0.25 mg/L. Most oral agents showed S results of >97% against S. saprophyticus isolates, except for penicillin (3.5%S).Gepotidacin  (Research Grant or Support)GlaxoSmithKline, LLC (Research Grant or Support)Melinta Therapeutics, LLC (Research Grant or Support)Nabriva Therapeutics (Research Grant or Support)Spero Therapeutics (Research Grant or Support) Deborah Butler, n/a, GlaxoSmithKline, LLC (Employee) Nicole Scangarella-Oman, MS, GlaxoSmithKline, LLC (Employee) Lindsey Paustian, BS (ASCP), GlaxoSmithKline, LLC (Research Grant or Support) Jennifer M. Streit, BS, GlaxoSmithKline, LLC (Research Grant or Support)Melinta Therapeutics, LLC (Research Grant or Support)Shionogi (Research Grant or Support)Spero Therapeutics (Research Grant or Support) Rodrigo E. Mendes, PhD, AbbVie (Research Grant or Support)AbbVie (formerly Allergan) (Research Grant or Support)Cipla Therapeutics (Research Grant or Support)Cipla USA Inc. (Research Grant or Support)ContraFect Corporation (Research Grant or Support)GlaxoSmithKline, LLC (Research Grant or Support)Melinta Therapeutics, Inc. (Research Grant or Support)Melinta Therapeutics, LLC (Research Grant or Support)Nabriva Therapeutics (Research Grant or Support)Pfizer, Inc. (Research Grant or Support)Shionogi (Research Grant or Support)Spero Therapeutics (Research Grant or Support)


Here, we describe BIOVERA-Tree, a database on tree diversity, community composition, forest structure and functional traits collected in 120 forest plots, distributed along an extensive elevational gradient in Veracruz State, Mexico. BIOVERA-Tree includes information on forest structure from three levels of forest-use intensity, namely old-growth, degraded and secondary forest, replicated across eight elevations from sea-level to near the tree line at 3500 m and on size and location of 4549 tree individuals with a diameter at breast height ≥ 5 cm belonging to 216 species, 154 genera and 80 families. We also report measurements of eight functional traits, namely wood density for 143 species, maximum height for 216 species and leaf traits including: specific leaf area, lamina density, leaf thickness, chlorophyll content and leaf area for 148 species and leaf dry matter content for 145 species.BIOVERA-Tree is a new database comprising data collected in a rigorous sampling design along forest-use intensity and elevational gradients, contributing to our understanding of how interactive effects of forest-use intensity and elevation affect tree diversity, community composition and functional traits in tropical forests. Mountains are fascinating ecosystems and natural laboratories for evolutionary and ecological research as they encompass a wide variety of different climatic conditions over short distances . MountaiLand-use change and intensification are occurring at rapid rates and are strongly impacting mountain ecosystems . For ins> 5 cm and tree height (metres) of 4548 and 4549 individuals, respectively. BIOVERA-Tree also includes functional traits for the common tree species, with data for wood density of 143 species calculated, based on 483 individuals, maximun height for 216 species calculated, based on 4549 individuals and leaf traits for 148 species, specific leaf area (n = 3148 leaves), lamina density (n = 3194 leaves), chlorophyll content (n = 3280 leaves), leaf area (n = 3214 leaves), leaf thickness (n = 3299) and leaf dry matter content for 145 species (n = 3081 leaves).BIOVERA-Tree originated from the interdisciplinary research project BIOVERA, which aims at documenting and understanding biodiversity patterns along gradients of altitude, climate, soil and disturbance along an elevational transect at the Cofre de Perote in central Veracruz, Mexico . BIOVERAThe study area is located along an elevational gradient from sea level close to the Gulf of Mexico to near the tree line at 3545 m on the eastern slopes of the Cofre de Perote volcano, in the central part of the State of Veracruz, Mexico Fig. . This reWe selected eight sites along the elevational gradient, separated by about 500 m in elevation Fig. . At each> 5 cm and identified individual trees to the highest taxonomic resolution possible. For each individual, we measured its DBH (in cm) and tree height (in m), which was measured with a Leica laser  to close to the treeline at 3545 m elevation  along the eastern slopes of Cofre de Perote volcano (4282 m) in Veracruz State, Mexico . Individuals were identified to the species level by specialists , while some individuals could only be identified to the family or genus level or could not be identified. Vouchers of specimens were deposited at the Herbarium XAL of Instituto de Ecología, A.C. at Xalapa, Mexico.Tree diversity and community compositionThe database contains information of 216 tree species Data package title BIOVERA-Tree: community, functional traits and forest structure along forest-use intensity and elevational gradients in Veracruz, Mexico.6BIOVERA-Tree forest plots description13Location of the 120 plots along the elevational gradient at the eastern slopes of Cofre de Perote in Veracruz, Mexico. Available as Suppl. material BIOVERA-Tree scientific name3List of tree species along the elevational gradient and different levels of forest-use intensity. Available as Suppl. material BIOVERA-Tree community matrix2Tree community matrix composition along eight elevational sites and three different forest-use intensity levels of 216 tree species (n = 5 plots per forest-use intensity within elevation). The numbers within the matrix are the number of individuals. Available as Suppl. material BIOVERA-Tree forest structure5> 5 cm and tree height (metres) for 216 species along the elevational gradient and different levels of forest-use intensity. Available as Suppl. material Diameter at breast height (DBH) BIOVERA-Tree functional traits7Plant functional traits measured along the elevational gradient and different levels of forest-use intensity; including leaf traits, wood density and maximum height. Available as Suppl. material BIOVERA Tree metadata3metadataDefinition and categories according with Darwin Core, Functional Diversity thesaurus and this research.https://doi.org/10.5061/dryad.ngf1vhhvb.The data underpinning the analysis reported in this paper are deposited in the Dryad Data Repository at 1BCEFC74-C760-5C41-BC74-77C909F237EB10.3897/BDJ.9.e69560.suppl1Supplementary material 1BIOVERA-Tree forest plots descriptionData typeLocation DataFile: oo_549009.csvhttps://binary.pensoft.net/file/549009María Leticia Monge-González, Patrick Weigelt, Nathaly Guerrero-Ramírez, Dylan Craven, Gonzalo Castillo-Campos, Thorsten Krömer, Holger Kreft.297517E1-0C9F-5503-B9D1-6B484B74C9E710.3897/BDJ.9.e69560.suppl2Supplementary material 2BIOVERA-Tree scientific nameData typeTaxonomyFile: oo_549044.csvhttps://binary.pensoft.net/file/549044María Leticia Monge-González, Patrick Weigelt, Nathaly Guerrero-Ramírez, Dylan Craven, Gonzalo Castillo-Campos, Thorsten Krömer, Holger Kreft.42D688AD-8284-53B7-8D58-28F37D03F2EF10.3897/BDJ.9.e69560.suppl3Supplementary material 3BIOVERA-Tree community matrixData typeSpecies community dataBrief descriptionTree community matrix with abundancesFile: oo_549021.csvhttps://binary.pensoft.net/file/549021María Leticia Monge-González, Patrick Weigelt, Nathaly Guerrero-Ramírez, Dylan Craven, Gonzalo Castillo-Campos, Thorsten Krömer, Holger Kreft.1B6E2702-97B7-5634-BDC4-A09F03A3709210.3897/BDJ.9.e69560.suppl4Supplementary material 4BIOVERA-Tree forest structureData typeForest structure, DBH, tree heightFile: oo_549014.csvhttps://binary.pensoft.net/file/549014María Leticia Monge-González, Patrick Weigelt, Nathaly Guerrero-Ramírez, Dylan Craven, Gonzalo Castillo-Campos, Thorsten Krömer, Holger Kreft.8880BA0C-CC51-5419-83D8-EA722FAC054510.3897/BDJ.9.e69560.suppl5Supplementary material 5BIOVERA-Tree functional traitsData typeFunctional trait dataFile: oo_549015.csvhttps://binary.pensoft.net/file/549015María Leticia Monge-González, Patrick Weigelt, Nathaly Guerrero-Ramírez, Dylan Craven, Gonzalo Castillo-Campos, Thorsten Krömer, Holger Kreft.BB3E33BB-DDFE-53F9-976F-4676697DF15D10.3897/BDJ.9.e69560.suppl6Supplementary material 6BIOVERA-Tree metadataData typemetadataBrief descriptionDefinition and categories according with Darwin Core, Functional Diversity thesaurus and this research.File: oo_549022.csvhttps://binary.pensoft.net/file/549022María Leticia Monge-González, Patrick Weigelt, Nathaly Guerrero-Ramírez, Dylan Craven, Gonzalo Castillo-Campos, Thorsten Krömer, Holger Kreft.


Patients (pts) with newly diagnosed acute myeloid leukemia (AML) undergoing induction chemotherapy are at increased risk for invasive fungal infections (IFI). Guidelines recommend posaconazole prophylaxis (ppx), but use is precluded by interactions and adverse effects. Micafungin (MCF) is an alternative, but data is limited by small prospective and retrospective studies. Primary objective: describe incidence of probable/proven IFI until neutrophil recovery (ANC ≥ 500 cells/µL) or 28 days after induction start date, whichever occurred first, in pts receiving MCF ppx. Secondary objective: describe incidence of clinical failure to MCF prophylaxis. Retrospective review (January 2017 to January 2020) of newly diagnosed AML adult pts undergoing 7 + 3 using idarubicin (7 + 3-ida), 7 + 3 using daunorubicin (7 + 3-dau), venetoclax/decitabine (VEN/DEC), or venetoclax/azacitadine (VEN/AZA) receiving MCF ppx for at least 7 days included. Diagnosis of IFI < 30 days prior to induction, liver function tests (LFT) 5x ULN at start of induction, or evidence of refractory disease after induction excluded. Probable/proven IFI defined by EORTC criteria. Clinical failure: changing to a different antifungal class for any reason until ANC recovery or 28 days after induction start date.Ninety-five pts included. Baseline characteristics: mean (±SD) age 57.8 (±13.0) years; 53.6% males. 62% (59/95) 7 + 3-ida, 13.7% (13/95) 7 + 3-dau, 15.8% (15/95) VEN/DEC, 8.4% (8/95) VEN/AZA. Mean (±SD): 32.5% (±26) blasts, WBC 13.2 (±23.8), ANC 2.4 (±4.6), ALC 1.9 (±1.6), platelets 92.6 (±123.2). Incidence of probable IFI 2/95 (2.1%). No proven IFI cases identified. Clinical failure occurred in 37/95 (39%): 8 persistent febrile neutropenia, 29 due to suspected IFI. No MCF discontinuation due to adverse events. Our findings suggest that prophylactic MCF is safe and effective in pts with newly diagnosed AML undergoing induction chemotherapy. Outcomes were similar to those of prophylactic posaconazole studies, indicating MCF may be considered as an alternative when interactions and adverse effects preclude use of posaconazole. Our study was limited by small numbers, retrospective, single-center design. Future opportunities include prospective trials of prophylactic MCF in this setting. All Authors: No reported disclosures


Hospitalized patients with COVID-19 have created increased demands on health care infrastructure and resources. Bacterial and fungal infections have been reported and have increased the need for antimicrobial utilization. We performed a retrospective chart review to characterize bacterial infections and antibiotic utilization during the COVID-19 surge at our tertiary care center. All patients diagnosed with COVID-19 using SARS-CoV-2 PCR admitted to MedStar Georgetown University Hospital from 01Mar2020 through 31Aug2020 were included in the analysis. Data was collected on hospital-wide antimicrobial utilization [mean days of therapy per 1000-patient-days (DOT)] during the 6-month surge and was compared to antimicrobial utilization during a 6-month period that preceded the COVID-19 surge. Clinical and microbiological data and patient outcomes were also collected and analyzed.A total of 238 patients met eligibility criteria during the observation period, of which 25.6% (n = 61) developed a bacterial, fungal, or viral co-infection. Culture-positive bacterial complications were seen in 21.8% (n = 52) with 32.8% (n = 20) having a multidrug resistant organism (MDRO). There was a statistically significant difference between COVID-19 patients with co-infection and those without for intubation (p < 0.001), vasopressor use (p < 0.001), and renal replacement therapy (p = 0.001). COVID-19 patients with co-infections had a longer mean length of stay  and greater mortality  compared to those without a co-infection, respectively.Mean antimicrobial utilization for the entire hospital population was 790.6 DOT during the COVID surge compared to 928.7 DOT during a 6-month period preceding the COVID surge (p < 0.001). For all COVID-19 patients, antimicrobial utilization was 846.9 DOT; however, this increased to 1236.4 DOT for COVID-19 patients with co-infections.Table 1. DemographicsTable 2. Antimicrobial Utilization in COVID-19 PatientsAlthough hospital-wide antimicrobial utilization had decreased during the COVID surge, COVID-19 patients with co-infections demonstrated a disproportionate use of antimicrobial agents as well as ICU resources. As MDRO infections were relatively common, antimicrobial stewardship should be prioritized in the COVID-19 population.Lan Duong, Pharm.D., Astra Zeneca (Shareholder)Eli Lilly & Co. (Shareholder)Gilead Sciences, Inc. (Shareholder)Merck & Co. (Speaker’s Bureau)Moderna, Inc. (Shareholder)Novavax, Inc. (Shareholder)Sarepta Therapeutics (Shareholder)Thermo Fisher Scientific (Shareholder) Princy N. Kumar, MD, AMGEN Eli Lilly (Grant/Research Support)Gilead GSK Merck & Co., Inc.