Salmonella spp., Escherichia coli O157:H7, and shiga toxin-producing E. coli (non-O157 STEC). Results revealed that Salmonella spp. was isolated and confirmed for the presence of invA gene in 5/36 samples (13.8%) and 5/36 samples (13.8%) at pre-evisceration in plants A and B, respectively. Salmonella spp. was isolated in 2/9 samples (5.6%) of plants A and was not detected in any sample at post-evisceration process. E. coli O157:H7 was not detected in any sample collected from plant A and B. E. coli non-O157 was isolated and confirmed for the presence of virulence genes in 4/36 samples (11.1%) and 2/36 samples (5.5%) at post-evisceration in plants A and B, respectively. Salmonella spp. was detected based on the presence of the Salmonella invA gene in the slaughter hall floor (4/4) and the offal’s table (2/4) samples using multiplex polymerase chain reaction (mPCR). In plant B, Salmonella spp. was also confirmed in the slaughter hall floor (2/4) and brisket saw (2/4) samples. On the other hand, one isolate of E. coli O157:H7 and one non-O157 STEC were obtained from the slaughter hall floor of plant A. The E. coli O157:H7 isolate was positive for stx1, stx2, eaeA, and EHEC-hly genes. Two isolates of non-O157 STEC (2/4) were detected in the environmental site samples, one from the slaughter hall floor, and one from an offal’s table sample of plant B. These data can be used to inform food safety interventions targeting halal meat operations in the southeastern United States.The aim of this study was to estimate the prevalence of pathogenic bacteria on halal beef carcasses and environmental surfaces in two halal beef slaughterhouses in the United States. To evaluate halal beef slaughter operations, 144 beef carcass samples (pre- and post-evisceration), and 24 environmental site samples  were collected in two halal beef slaughterhouses during June to September 2017. All carcass and environmental samples were analyzed for the presence of  One of the fastest-growing segments of the US food business, the US halal food market was valued at USD22.6 billion in 2016 and is anticipated to reach USD26.8 billion by 2021 . The aniIn the United States, as of 2018, there were 6500 meat and poultry slaughtering and processing facilities  ,8. SmallSalmonella spp. was attributed to seven outbreaks in poultry processing facilities and twelve in beef processing facilities  [Salmonella enterica Typhimurium [Furthermore, epidemiological data has demonstrated that improperly managed animal slaughter can result in the contamination of meat with foodborne pathogens . Nineteetbreaks) . In addihimurium .The majority of the scientific data relevant to halal slaughter operations originates from Muslim-majority nations rather than western nations. Only two microbiological studies have been conducted on halal beef, one in a halal slaughterhouse in the US and the other in a halal butcher shop in the UK, according to a systematic literature search ,13. FiveSalmonella spp., E. coli O157:H7, non-O157 STEC) on halal beef carcasses and environmental surfaces in two halal beef slaughterhouses in the United States over a four-month using multiplex PCR.Given how quickly the halal food market is expanding in the US, it is sensible to investigate this segment of production in order to identify potential food safety risks. Therefore, this study aims to determine the prevalence of pathogenic bacteria  from the eight halal slaughter operations included in our geographic sampling frame agreed to participate in the study. Samples were collected from both slaughterhouses between June and September 2017. During each site visit (every four weeks for 12 visits), three carcasses were randomly selected, and samples collected during two processing steps at three carcass sites , as recommended by United States Department of Agriculture-Food Safety Inspection Services (USDA-FSIS) . Systema2 each) were collected from each carcass using a carcass sampling kit ) from three sites on each carcass at two slaughterhouses. A sterile template 10 × 10 cm (100 cm2)  was used to mark the swabbing areas for the three carcass sites, which were taken (1) after hide removal-pre-evisceration and (2) at the end of slaughter after the final wash before chilling-post-evisceration. The swabbing procedure included 10 horizontal scrubbing motions followed by 10 vertical scrubbing motions for each site [Swabs ) were swabbed during slaughtering. The total of environmental samples for each slaughterhouse was 12 samples (4 visits × 3 surfaces). Surface samples were aseptically collected using sterile pre-moistened polyurethane foam PUR-Blue™ swabs by rubbing firmly over the surface area marked with a sterile template (10 × 10 cm) . Each swSalmonella spp., E. coli O157:H7, and non-O157 STEC.All carcass and environmental samples were analyzed for the presence of Salmonella spp. Sterile buffered peptone water  was added to each sample at a 1:10 ratio and homogenized in a stomacher for 2 min at room temperature then incubated at 35–37 °C for 24 h. After incubation, 0.1 mL and 1 mL of pre-enriched culture were transferred to 10 mL Rappaport-Vassiliadis Broth  and Tetrathionate Broth  then incubated at 41.5 °C, 37 °C for 24 h, respectively. Isolates were cultured on Brilliant Green Agar , Xylose Lysine Deoxycholate Agar , Bismuth Sulfite Agar , and Hektoen Enteric Agar  then incubated at 35–37 °C for 24–48 h [Salmonella spp. isolates were biochemically confirmed using Triple Sugar Iron Agar  and Lysine Iron Agar .A total of 168 different samples (144 beef carcass samples (9 samples × 2 slaughter processes × 2 plants × 4 visit times) and 24 environmental samples) were analyzed using the ISO 6579, 2002 (updated in 2007) standard method for the detection of  24–48 h . All preSalmonella spp. isolates were confirmed using polymerase chain reaction (PCR). Salmonella Enteritidis (H2292) was used as a positive control. A 284 bp region of the invA gene was targeted and amplified for Salmonella spp. using 139-R (5′-GTG AAA TTA TCG CCA CGT TCG GGC AA) and 141-F (5′-TCA TCG CAC CGT CAA AGG AAC C) primers designed by [igned by . Subsequ2, and 2.5 mM dNTP mixture . PCR amplifications were conducted using a DNA thermocycler . The PCR protocol consisted of an initial incubation for 2 min at 95 °C followed by 30 cycles of denaturing for 30 s at 95 °C, 30 s at 50 °C for annealing, and 45 s at 72 °C for extension then 7 min at 72 °C for the final extension. The PCR products were mixed with 6× loading dye and analyzed by electrophoresis on 1.2% agarose gel with TBE (Tris/Borate/EDTA) as the running buffers. Thereafter, the products were stained with ethidium bromide and visualized by UV illumination .Reactions were carried out in a total volume of 25 μL containing 2 μL of DNA template (60 ng of DNA), 1 μL (100 pmol) of each primer, 2U Taq Polymerase, 10× Taq buffer (100 mM Tris-HCl (pH8.3), 500 mM KCl), 25 mM MgClE. coli O157:H7 and non-O157 STEC using the ISO 16654, 2001 standard method. Samples were enriched in a modified Tryptic Soy Broth with Novobiocin  at a 1:10 ratio and homogenized in a stomacher for 2 min at room temperature  then incubated at 41.5 °C for 18–24 h. A 1.5 mL Eppendorf tube was used for separation and concentration. One ml of the enriched broth culture was treated with 20 μL immunomagnetic beads coated with anti-O157 antibody (Dynabeads™ anti-E. coli O157)  for 10 min with continuous agitation using an MPC™-S rack  to prevent the beads from settling. Multiple washing steps using a sterile wash buffer were used to avoid cross-contamination. The Eppendorf tubes were inserted onto the Magnetic plate MPC™-L  for 3 min for maximum recovery of Dynabeads® anti-E. coli O157. The sample supernatant was carefully aspirated and discarded. Dynabeads®-bacteria complex was resuspended in 100 μL of wash buffer and mixed briefly by vortex.A total of 168 unique samples (144 beef carcass samples (9 samples × 2 slaughter processes × 2 plants × 4 visit times) and 24 environmental samples) were analyzed for the detection of ®-bacteria complex was inoculated onto MacConkey Sorbitol Agar containing Cefixime-Tellurite supplement (CT-SMAC)  and CHROMagar™ O157  and incubated at 37 °C for 24 h. Colorless colonies on CT-SMAC and mauve colonies on CHROMagar™ O157 were examined by the indole tests  and specific latex agglutination test for E. coli O157:H7  to confirm the isolates before using multiplex PCR. Fifty (50) μL of Dynabeadsstx1, stx2, eaeA, and EHEC hlyA) of E. coli O157:H7 and non-O157 shiga toxin-producing E. coli. E. coli O157:H7 (F6B-2) was used as a positive control. Oligonucleotide primers were manufactured commercially   . E. coli isolates were incubated at 37 °C overnight on tryptone soya agar  plates. Subsequently, a colony from the plate was suspended in 1 mL sterilized distilled water in a 2 mL Eppendorf tube and boiled for 10 min. Thereafter, the Eppendorf tube was chilled on ice and then centrifuged at 7000 rpm for 5 min. Two (2) μL supernatant was used as template DNA in the PCR reaction. PCR assays were carried out in a total volume 50 μL containing 2 μL DNA template (60 ng of DNA), 2 μL of 2 mM concentrations of each primer, 4 U Taq Polymerase, 10× Taq buffer (100 mM Tris-HCl (pH8.3), 500 mM KCl), 25 mM MgCl2, and 2.5 mM dNTP mixture . Temperature conditions consisted of an initial incubation for 3 min at 95 °C followed by 35 cycles of 95 °C for 20 s, 58 °C for 40 s, and 72 °C for 90 s then 5 min at 72 °C for the final extension. p < 0.05.Descriptive and inferential statistics were performed using JMP Pro16.1 software . PercentSalmonella spp., E. coli O157:H7 and non-O157 STEC during four months of sampling (June–September 2017) of plant A and B. Salmonella spp. was isolated and confirmed for the presence of invA gene in 5/36 samples (13.8%) and 5/36 samples (13.8%) at pre-evisceration in plants A and B, respectively. A total of 144 different samples were tested for Salmonella spp. was isolated in 2/9 samples (5.6%) of plants A and was not detected in any sample at post-evisceration. Salmonella spp. isolates using different media and PCR.E. coli O157:H7 was not detected in any sample collected from plant A and B. E. coli non-O157 was isolated and confirmed for the presence of virulence genes in 4/36 samples and 2/36 samples at post-evisceration in plants A and B, respectively (ectively .E. coli (non-O157 STEC), and Salmonella spp. In carcass samples significantly decreased (p < 0.02) after the decontamination steps  in both plants. Most interestingly, pathogenic Salmonella spp. And non-O157 STEC. Salmonella spp. was confirmed for the presence of Salmonella invA gene in the slaughter hall floor (4/4) and the offal’s table (2/4) samples using multiplex PCR. In plant B, Salmonella spp. was also confirmed in the slaughter hall floor (2/4) and brisket saw (2/4) samples. On the other hand, one isolate of E. coli O157:H7 and one non-O157 STEC were detected in the slaughter hall floor of plant A. E. coli O157:H7 was positive to stx1, stx2, eaeA, and EHEC-hly genes. Two isolates of non-O157 STEC (2/4) were detected in the environmental site samples, one from the slaughter hall floor, and one from offal’s table sample of plant B. A total of 24 different environmental samples were tested for Salmonella spp. and E. coli O157 are common causes of foodborne illnesses. Evisceration and de-hiding processes can lead to contamination of carcasses during slaughter operations. Therefore, we aimed to estimate the prevalence of pathogenic bacteria  on halal beef carcasses and environmental surfaces in a convenience sample of two halal beef slaughterhouses in the United States. A limited number of studies describing halal beef carcass hygiene at slaughter are available, yet data are needed to characterize food safety risk factors. Salmonella spp. at post-evisceration in plant A. However, there was no isolate of Salmonella spp. in beef carcasses at post-evisceration in plant B. The results indicated that the overall percentage of Salmonella spp. positive carcass samples was higher for pre-evisceration samples and decreased following the application of carcass decontamination procedures [In the present study, we observed a carcass contamination rate of 5.6% for ocedures ,29. Thisocedures ,31.Salmonella spp. during warmer than colder months [The implementation of the food safety practices under the supervision of the USDA-FSIS showed lower microbial contamination in beef carcasses in the two halal slaughter operations (post-evisceration samples) compared to other studies, revealing higher contamination (7.1% and 7.8%) ,33. In ar months , and ourThe incidence of non-O157 STEC was higher in pre-evisceration than post-evisceration. Additionally, the plant A showed higher non-O157 STEC contamination than plant B. The reason may be the effectiveness of automatic decontamination interventions compared to manual decontamination intervention  with no set time for each treatment in plant A). Previous studies  showed that 58.3% of the beef carcasses tested in large processing plants in the United States carried at least one type of non-O157 STEC in pre-evisceration samples, however, the prevalence was reduced by using a variety of antimicrobial intervention strategies to 8.3% of the carcasses carrying non-O157 STEC at the post-evisceration process . RogerieE. coli on the floor, saw, and tables in non-halal slaughterhouse [Salmonella spp. which was isolated from 13 of 41 environmental samples (31.7%) at the end of the sampling day in non-halal slaughter activities compared with our study [Lastly, the sanitation conditions of the slaughterhouse environment findings showed low levels pathogenic microorganisms in plant B suggesting effective implementation of sanitation standard operating procedures (SSOP) to control for environmental contamination of fecal contamination. Our review of SSOPs indicates that halal slaughterhouses implemented the routine cleaning procedures properly which can remove microbiological contamination effectively. Unlike our findings, Barros et al. found a higher contamination of terhouse . Piras eur study .The results of this study were limited to the participation of two slaughterhouses in the United States, where the other six halal slaughterhouses in our geographic sampling frame refused involvement. Therefore, our findings can only be generalized to the two sites included in the study.Salmonella spp. and pathogenic E. coli infections transmissible between animals and humans, the implementation of the food safety practices (HACCP system) under the supervision of the USDA-FSIS provided significant control and prevention of microbial contamination in beef carcasses in the two halal slaughter operations in our study. Indeed, hygienic control programs, e.g., SSOP of the halal slaughterhouses, would reduce the risk of environmental contamination, which is a potential source of foodborne pathogens. The findings in this study may suggest that carcass contamination is influenced by slaughterhouse, and this could be due to variations in how the intervention is implemented. There were some sites in the slaughter environment that had varying degrees of contamination. The results of environmental samples did not show an association with carcass contamination. Our findings showed that the main contamination came from the de-hiding process compared to the post-evisceration process. Further research in non-Muslim majority countries is recommended to evaluate the microbial status of other halal animals . In order to protect human health against


Ex-vivo treatment of splenocytes with S100A8 protein activated NK cells. Our results indicate that treatment with S100A8 may favourably modify the lung microenvironment to promote an effective immune response in lungs, thereby representing a new strategy that could complement current immunotherapies in lung cancer.Lung cancer is the leading cause of cancer-related death worldwide. Increasing evidence indicates a critical role for chronic inflammation in lung carcinogenesis. S100A8 is a protein with reported pro- and anti-inflammatory functions. It is highly expressed in myeloid-derived suppressor cells (MDSC) that accumulate in the tumor microenvironment and abrogate effective anti-cancer immune responses. Mechanisms of MDSC-mediated immunosuppression include production of reactive oxygen species and nitric oxide, and depletion of L-arginine required for T cell function. Although S100A8 is expressed in MDSC, its role in the lung tumor microenvironment is largely unknown. To address this, mouse recombinant S100A8 was repeatedly administered intranasally to mice bearing orthotopic lung cancers. S100A8 treatment prolonged survival from 19 days to 28 days (p < 0.001). At midpoint of survival, whole lungs and bronchoalveolar lavage fluid (BALF) were collected and relevant genes/proteins measured. We found that S100A8 significantly lowered expression of cytokine genes and proteins that promote expansion and activation of MDSC in lungs and BALF from cancer-bearing mice. Moreover, S100A8 enhanced activities of antioxidant enzymes and suppressed production of nitrite to create a lung microenvironment conducive to cytotoxic lymphocyte expansion and function. In support of this, we found decreased MDSC numbers, and increased numbers of CD4 The host response to cancer can induce a powerful immune response that influences clinical outcomes , 2. This+ T cell activation and hinders the efficacy of PD-1 and CTLA-4 checkpoint inhibitors (A neoplastic microenvironment characterized by infiltrating myeloid-derived suppressor cells (MDSC)  and highhibitors –12. Emerhibitors . For exahibitors . Moreovehibitors . In anothibitors . This trNumerous studies report clinical associations between elevated expression of members of the S100 family of calcium-binding proteins and human malignancies , but theWe previously made the unexpected finding that when inhaled directly into the lungs of mice, S100A8 promoted anti-inflammatory responses in the airways . This suEx vivo treatment of splenocytes with S100A8 activated splenic NK cells. Taken together, our results indicate that S100A8 favourably modified the cancer microenvironment in the lungs to promote effective immune responses and may represent a new treatment option that complements current therapeutic strategies in lung cancer.Studies herein showed for the first time that intranasal delivery of S100A8 delayed aggressive lung cancer growth and increased survival in a syngeneic orthotopic lung cancer mouse model. S100A8 positively modified the lung microenvironment by decreasing expression of key genes and proteins in the lungs which are involved in regulating MDSC recruitment and activation. Moreover, protective antioxidant activities were increased whereas deleterious nitric oxide levels were decreased in lungs from S100A8-treated mice, together with reduced MDSC numbers, and increased CD4 T cell and natural killer T (NK-T) cell numbers. E. coli BL21QS cells; recombinant murine S100A8 protein was produced and cleaved by thrombin as described (A Glutagene pGEX2T-vector containing murine S100A8 and glutathione-S-transferase fusion protein was transformed into escribed . S100A8 escribed  (Suppleescribed . For int™) was purchased from American Type Culture Collection and authenticated by IDEXX BioResearch (USA) using STR profiling. LLC cells were cultured in DMEM medium supplemented with 10% (v/v) fetal bovine serum and 1% (v/v) L-glutamine  at 37°C, 5% CO2 for a maximum of 6 months, and routinely verified as mycoplasma free.Lewis Lung Carcinoma (LLC) cell line . LLC cells (4 x 105/ml) or DPBS  mixed with chilled growth factor reduced Matrigel™ (BD Biosciences) (1:1 v/v) were orthotopically injected into murine lungs as previously described  from Australian BioResources  were maintained in ventilated cages exposed to a 12-hour light or dark cycle and given autoclaved food and water escribed .Single treatment: S100A8 (10 µg) or HBSS (control) was administered intranasally at the same time of orthotopic LLC cell injection into the lungs (Day 0), and whole lungs harvested 20 days later. Multiple treatments: S100A8 (10 µg) or HBSS was intranasally administered on Days 0, 3 and 6 post-LLC cell implantation into the lungs, and whole lungs harvested 9 days later. At study endpoints lung cancer areas on hematoxylin and eosin-stained sections were quantified using the CellSens software coupled to an Olympus DP73 microscope at 100X magnification.Mice were administered intranasal S100A8 (10 µg) or HBSS on Days 3, 6 and 9 post-LLC cell implantation into the lungs, or every third day. Endpoint criteria included weight loss equal to or greater than 20%, dehydration, lethargy, and labored breathing.To examine effects of S100A8 inhalation on the lung microenvironment, mice were administered S100A8 (10 µg) or HBSS intranasally on Days 3, 6 and 9 post-LLC cell implantation into the lungs. Cancer-free mice treated with S100A8 or HBSS served as controls. Twenty-four hours after the final treatment (Day 10), lungs were homogenized for gene expression analysis or measurement of antioxidant enzymatic activities, or fixed in 10% neutral-buffered formalin for immunohistochemistry. Bronchoalveolar lavage fluid (BALF), collected by inserting a 19G cannula  into the trachea and instillation of chilled DPBS (1 ml x 2) into the lungs, was used to quantitate cytokine and nitrite production.Total RNA (1.5 µg) from lungs (≥ 4 mice per treatment group) was reversed-transcribed into cDNA using the SuperScript VILO cDNA synthesis kit . Effects of S100A8 treatment on the lung microenvironment were assessed using a panel of 98 genes that affect immune regulation, redox, cancer growth, metastasis, hypoxia and angiogenesis . PCR ampCytokine  levels in BALF were measured using enzyme-linked immunosorbent assays (ELISA) according to the manufacturer’s instructions .540nm was measured using a SpectraMax M3 plate reader.Nitric oxide production, in terms of concentration of nitrite (μM), was measured in BALF using the Griess reagent (Sigma); A-1/min/mg of protein) were calculated using molar extinction coefficients of 2.47 M-1 cm-1 (-1 cm-1 (4 M-1 cm-1 (Lung homogenates (50 mg lungs/ml buffer) were prepared in chilled lysis buffer  containing complete proteinase inhibitor cocktail mixture (Roche) using a rotating piston (500 RPM). Absorbance correlated to the activities of superoxide dismutase (SOD) , thioredoxin reductase (TXN-R) (in terms of 5-thio-2-nitrobenzoic acid formation at 412 nm) and peroxiredoxin (PRDX) (in terms of the inverse of iron (III)-xylenol orange complex formation at 560 nm) in total lung lysates was determined by the SpectraMax M3 plate reader with a path length of 1 cm. Specific enzyme activity of SOD, TXN-R (nmol/min/mg of protein) and PRDX  as previously described , followe6 cells) were prepared in 50 µl 0.1% BSA/PBS for flow cytometry analysis.To analyze changes in MDSC and/or lymphocyte populations harvested on Day 10, single cell suspensions were prepared from lungs and spleen. Lungs were digested in 0.7 mg/ml collagenase A, 30 µg/ml bovine pancreatic DNaseI and 35 U/ml hyaluronidase at 37°C for 1 hour, then mechanically disaggregated and ground using a 40 μm strainer (BD Biosciences). To obtain single cell suspensions containing the lymphocyte/monocyte fraction, Percoll centrifugation  was performed to collect the interface between the 60% and 30% gradients as described (+ population identified the CD11b+/Gr-1+ MDSC population (-/F4/80-) and monocytic MDSC  (+ population identified NK-T cells (CD3+/NK 1.1+), whereas gating on the CD3- identified NK cells (CD3-/NK 1.1+)  cells (CD4-/CD8-) , PE-Cy™7 anti-Gr-1 , FITC anti-CD14  and PE anti-F4/80 antibodies , or IgG controls. Although CD14 is a marker for human monocytic MDSC –38, the 6 cells) were treated with S100A8 (10 µg/ml) for 24 and 48 hours. Cells treated with media served as controls. Subsequently, splenocytes from each group were stained with FITC anti-CD3 , PE anti-NK1.1 , PerCP-eFluor 710 anti-granzyme B  and APC anti-granzyme A antibodies , or isotype- and fluorochrome-matched controls. To determine percentages of granzyme A and granzyme B in NK cells (CD3-/NK1.1+), flow cytometry was performed using a BD FACSCalibur machine (BD Biosciences), and data analyzed using the FlowJo software (Tree Star Inc)  were isolated and cultured in RPMI media supplemented with 10% (v/v) fetal bovine serum, 1% (v/v) MEM sodium pyruvate, 1% (v/v) MEM non-essential amino acid solution , 2 mM L-glutamine, 100 U/mL penicillin and 100 μg/mL streptomycin . Splenocytes . Mantel-Cox test compared mean survival of mice with orthotopic lung cancers treated with S100A8 or HBSS (control). For all other experiments, Student’s t-test compared changes in means between two treatment groups, and ANOVA in conjunction with Holm–Šídák’s multiple comparison test compared changes in means for more than two treatment groups. P values < 0.05 were considered statistically significant. GraphPad Prism 9.1.0 software was used for data analysis and plotting of graphs.2), whereas only small cancer nodules were present in S100A8-treated mice (8.4 ± 4.6 mm2) (p < 0.01) , and whole lungs harvested 20 days post-LLC cell implantation, the time when advanced lung disease was evident. Control mice had extensive lung cancers , or repeated S100A8 treatments every third day for up to 27 days ; controls received HBSS  as outlined in IL-4 and IFN-γ mRNA showed the highest upregulation in growing LLC cancers, with increased expression of 137-fold (p < 0.001) and 35-fold (p < 0.0001), respectively, compared to controls (lungs with no cancers)  and IFN-γ  in BALF from mice with LLC cancers to basal levels (SOD1)  and thioredoxin (TXN)  in lungs of mice with LLC cancers , although this was not altered in other treatment groups   (S100A8 and S100A9 are highly expressed in MDSC . Immunohof view) 6. Impor+/Gr-1+), PMN-MDSC (CD11b+/Gr-1+/CD14-/F4/80-) and M-MDSC (MDSC that were CD14+ and/or F4/80+) populations in lungs from mice with LLC cancers    and their absolute numbers in lungs, but not in spleen, of cancer-bearing mice compared to controls (+/NK 1.1+) cells  in lungs but not in the spleen (-/NK1.1+) ; no change was observed in the spleen   but had no effect at 48 hours  cell population that was elevated in lungs of tumor-bearing mice treated with S100A8 for which the functional significance remains to be elucidated. Somewhat surprisingly, NK cell numbers were reduced in lungs from mice with orthotopic LLC cancers treated with S100A8. Whether the reduction in NK cell numbers is also associated with impaired activation and function remains to be determined. Interestingly, our ex-vivo studies showed that S100A8 is a potent activator of NK cells, as indicated by increased expression of granzyme B  pathway show promise in eliciting a potent anti-cancer immune response in the tumor microenvironment of different cancers including lung cancer , 84. NotA limitation of this study was the use of a single lung cancer mouse model. Future studies should examine the effects of inhalation of S100A8 on growth of different lung cancer subtypes, or its effect when delivered locally to tumors which rely on an inflammatory and/or redox microenvironment for growth and lung metastasis. In conclusion, our study revealed for the first time that intranasal S100A8 administration to lungs of mice had anti-inflammatory activities, which delayed cancer growth by altering the tumor microenvironment to deplete MDSC and modulate the oxidative balance to encourage accumulation of T cells.10.6084/m9.figshare.19365713 after this. The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: GEO, GSE190817 Figshare, DOI: The animal study was reviewed and approved by Animal Care and Ethics Committee of the University of New South Wales, Australia (12/148B and 16/142B).SW, KH performed experiments. SW, JM, KH, CG and NT have analysed data. JM, CG, and NT designed experiments. SW, JM, CG, and NT wrote the manuscript text. All authors have critically reviewed, read and agreed to the published version of the manuscript.This research was partially funded by the National Health and Medical Research Council of Australia (APP1027189) and APP1144113). JM is in part supported by the Olivia Lambert Foundation.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Osteosarcoma is the most frequent primary bone tumor with a poor prognosis. Immune infiltration proved to have a strong impact on prognosis. We analyzed single-cell datasets and bulk datasets to confirm the main immune cell populations and their properties in osteosarcoma.The examples in bulk datasets GSE21257 and GSE32981 from the Gene Expression Omnibus database were divided into two immune infiltration level groups, and 34 differentially expressed genes were spotted. Then, we located these genes among nine major cell clusters and their subclusters identified from 99,668 individual cells in single-cell dataset GSE152048 including 11 osteosarcoma patients. Especially, the markers of all kinds of myeloid cells identified in single-cell dataset GSE152048 were set to gene ontology enrichment. We clustered the osteosarcoma samples in the TARGET-OS from the Therapeutically Applicable Research to Generate Effective Treatments dataset into two groups by complete component 1q positive macrophage markers and compared their survival.Compared with the low-immune infiltrated group, the high-immune infiltrated group showed a better prognosis. Almost all the 34 differentially expressed genes expressed higher or exclusively among myeloid cells. A group of complete component 1q-positive macrophages was identified from the myeloid cells. In the bulk dataset TARGET-OS, these markers and the infiltration of complete component 1q-positive macrophages related to longer survival.Complete component 1q-positive tumor-associated macrophages were the major immune cell population in osteosarcoma, which contributed to a better prognosis. The staInfiltrating immune and stromal cells are essential for OS progression . The immIL‐6), IL‐1β, and tumor necrosis factor, which facilitate a proinflammatory response. M2 can be induced by IL‐4 and IL‐13, which turn on the expression of anti‐inflammatory cytokines, such as IL‐10 and ARG1. These are considered immune suppression and pro-tumor signals  and M2-type macrophages (M2) are two major kinds of them. M1 can be induced by pathogen‐associated patterns such as lipopolysaccharides and interferon‐γ. M1 highly expresses interleukin 6 , one of the three first components of the classical pathway, modulates both inflammation and repair progress  and the Gene Expression Omnibus (GEO) database (https://www.ncbi.nlm.nih.gov/geo/). Dataset TARGET-OS contains 88 samples with both complete survival information and expression profiles. Dataset GSE21257 contains 53 OS samples with survival information and expression profiles. Dataset GSE32981 contains 23 samples with only expression profiles. Specific clinical information of 88 samples in the TARGET database and 53 samples in the GSE21257 dataset is separately listed in Clinical and transcriptome data of OS patients were downloaded from the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) database  and survminer (https://CRAN.R-project.org/package=survminer). The grouping of samples in dataset TARGET-OS was almost the same, except that the immune metagenes were replaced by C1Q+ TAM markers with a fold change larger than 0.25. Based on the normalized expression matrix, immune scores across OS specimens from the GSE21257 dataset were estimated using single-sample gene set enrichment analysis (ssGSEA). This algorithm infers the overall infiltration levels of immune cells in tumor tissues using gene expression signatures. The Kaplan–Meier overall survival curves were examined between groups, and the prognosis was compared by log-rank test.The samples in datasets GSE21257 and GSE32981 were clustered into high- and low-immune infiltrated groups by R. We used identified immune metagenes  and func2.3via the clusterProfiler package, including Gene Ontology (GO)  . Terms wogy (GO) .2.4http://satijalab.org/seurat/). Each of the 11 samples generated a Seurat object by function Read10×. Next, we filtered out the cells with less than 300 expressed genes or with mitochondrial gene expression accounting for more than 10% of total expressed genes. The top 3,000 highly variable genes were picked up for the principal component analysis. Further, the doublets in each Seurat object were cleared out by the DoubletFinder package (version 2.0.3)  . We inte2.5via molecular interactions  and its appropriate packages.CellPhoneDB 2 was used in the python 3.7 environment; the rest of the analysis was presented using R version 4.1.2  21,067 myeloid cells highly express CD74, CD14, and FCGR3A; (2) 13,667 fibroblasts highly express COL1A1 and LUM; (3) 8,089 TILs including T and NK cells highly express IL7R, CD3D, and NKG7; (4) 7,699 proliferating OS cells highly express TOP2A, PCNA, and MKI67; (5) 7,307 osteoclasts highly express MMP9 and CTSK; (6) 3,646 endothelial cells highly express vWF, (7) 129 FABP4+ macrophages highly express FCGR3A and FABP4; and (8) 125 myoblasts highly express MYPL  37,939 OS cells highly express enriched Table S13.3FABP4+ macrophages, we took the myeloid cell group to a subcluster analysis similar to the previous step. Myeloid cells were divided into nine clusters when the resolution was set as 0.5. We identified six cell groups from the nine clusters  3,516 monocytes with low C1Q expression and high G0S2 and S100A9 expression in cluster 2; (3) 1,072 C1Q+ osteoclasts with high C1Q, MMP9, and CTSK expression in cluster 5; (4) 609 C1Q+ fibroblasts with high C1Q, COL1A1, and LUM expression in cluster 7; (5) 79 FABP4+ macrophages with high FABP4 expression in cluster 8; and (6) 7,418 unknown cells in clusters 3, 4, and 6  and HMGB1 plus C1Q can respectively regulate inflammatory macrophage polarization. HMGB1 plus C1Q induced an anti-inflammatory phenotype by inhibiting IRF5, a regulator of pro-inflammatory macrophage polarization  program can regulate C1Q+ TAMs, which express multiple immunomodulatory ligands to modulate tumor-infiltrating CD8+ T cells. A low METTL14-m+6A level induces high levels of EBI3, a cytokine subunit, and finally leads to dysfunctional T cells   . They deACKR1 was regarded as a neutralizer of chemokine instead of a signal transmitter, but now it is reported that chemokines retain their biological activity after binding to DARC [spice] (ACKR1 decreased the pro-angiogenic properties of chemokines (We tried to explain the tumor-limiting function of C1Q+TAMs by cell–cell interaction. Cellphone analysis suggested that C1Q+ TAMs act mainly on endothelial cells by the ACKR1-related pathway ( [spice] , 52. Wheemokines . FurtherFABP4+ macrophages just like previous authors did (FABP4+ macrophages roared over 3,000, whereas the mean number of the rest was lower than 2,000. We thought that these cells were homologous doublets. The subcluster monocytes highly expressed S100A8 and S100A9. S100A8 and S100A9, molecular markers promoting pre-metastatic niche formation, can cause an expansion of myeloid-derived suppressor cells, thereby contributing to an immunocompromise (C1Q+ osteoclasts, whereas there were 7,307 normal osteoclasts. Osteoclasts are multinucleated members of the monocyte/macrophage family, working as skeletal remodelers. OS cells mediated bone destruction by activated osteoclasts and obtained higher OS aggressiveness (C1Q just like Kupffer cells in the liver and microglia in the brain. In turn, C1Q strongly promotes osteoclasts derived from monocytes (CD74+LUM+C1Q+ cells. It might be a distinct TAM-induced extracellular matrix molecular signature (Some other subclusters of myeloid cells are also worth paying attention. We detected hors did . They hampromise , 56. Thesiveness . Howeversiveness . Osteoclonocytes . We notiignature . In the ignature .5C1Q as an antitumor factor in osteosarcoma. C1Q+ TAMs, marked by high C1QA/B/C, APOE/C, FLOR2, SLC40A1, SEPP1, and MRC1 expression, contribute to a better prognosis in OS patients. C1Q+ TAMs are the major immune cells in the OS TME. This study provided the image of how immune cells influence prognosis in osteosarcoma and C1Q+ TAMs that can be therapeutic target cells to improve the osteosarcoma treatment.This analysis revealed that a higher immune infiltration level improves the overall survival of OS patients and most of the high expression of immune infiltration-related genes links to better survival. Especially, we report https://portal.gdc.cancer.gov/repository GSE21257:https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE21257 GSE32981:https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE32981 GSE152048:https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE152048.Publicly available datasets were analyzed in this study. This data can be found here: TARGET-OS: Ethical review and approval was not required for the study on human participants in accordance with the local legislation and institutional requirements. Written informed consent for participation was not required for this study in accordance with the national legislation and the institutional requirements.BL, JT, DW, and XZ designed this research. JT collected and analyzed datasets. DW drafted this manuscript. XZ adapted the manuscript for final submission. All authors contributed to the article and approved the submitted version.


MC1R, MC2R, and MC5R) contribute to the risk of major depressive disorder (MDD), and one melanocortin receptor gene (MC4R) contributes to the risk of type 2 diabetes (T2D). MDD increases T2D risk in drug-naïve patients; thus, MDD and T2D commonly coexist. The five melanocortin receptor genes might confer risk for both disorders. However, they have never been investigated jointly to evaluate their potential contributing roles in the MDD-T2D comorbidity, specifically within families. In 212 Italian families with T2D and MDD, we tested 11 single nucleotide polymorphisms (SNPs) in the MC1R gene, 9 SNPs in MC2R, 3 SNPs in MC3R, 4 SNPs in MC4R, and 2 SNPs in MC5R. The testing used 2-point parametric linkage and linkage disequilibrium (LD)  analysis with four models (dominant with complete penetrance (D1), dominant with incomplete penetrance (D2), recessive with complete penetrance (R1), and recessive with incomplete penetrance (R2)). We detected significant (p ≤ 0.05) linkage and/or LD  to/with MDD for one SNP in MC2R (rs111734014) and one SNP in MC5R (rs2236700), and to/with T2D for three SNPs in MC1R , one SNP in MC2R (rs104894660), two SNPs in MC3R (rs3746619 and rs3827103), and one SNP in MC4R genes (Chr18-60372302). The linkage/LD/association was significant across different linkage patterns and different modes of inheritance. All reported variants are novel in MDD and T2D. This is the first study to report risk variants in MC1R, MC2R, and MC3R genes in T2D. MC2R and MC5R genes are replicated in MDD, with one novel variant each. Within our dataset, only the MC2R gene appears to confer risk for both MDD and T2D, albeit with different risk variants. To further clarity the role of the melanocortin receptor genes in MDD-T2D, these findings should be sought among other ethnicities as well.The melanocortin receptors are G-protein-coupled receptors, which are essential components of the hypothalamic–pituitary–adrenal axis, and they mediate the actions of melanocortins  as well as the adrenocorticotropin hormone (ACTH) in skin pigmentation, adrenal steroidogenesis, and stress response. Three melanocortin receptor genes ( MC1R-MC5R)  and melanin-concentrating hormone receptor 2 [MCHR2]) [TDO2 gene is activated by glucocorticoids [MC5R-related MDD might be mediated by its role in inflammatory responses [MC5R-deficient mice display behavioral changes such as reduced aggression and more defensive behaviors [MC5R in humans might cause similar behavioral and/or mood changes, but this remains to be confirmed.The esponses . Variantwith MDD  and T2D with MDD . We detedisorder , interesrticoids  and is articoids  and alcorticoids , and infrticoids . The patesponses ; of noteehaviors . MutatedTCF7L2 [As we and others have described ,75, MDD,TCF7L2  has beenTCF7L2 , furtherMC2R shows pleiotropic effects within our familial dataset. This might be explained by the mediating effect of the HPA-axis on the MC2R of the adrenal, triggering cortisol secretion. Hypercortisolism is implicated in MDD [Our study suggests that the melanocortin receptors risk variants, detected as contributing to familial risk for MDD and/or T2D, might be part of a more complex pathway implicated in the shared comorbidity of metabolic and mental disorders ,77. Whild in MDD  and T2D d in MDD , and as d in MDD . HoweverMC1R, MC2R, MC3R, MC4R, and MC5R genes in the pathogenesis of T2D, MDD, and their comorbidity.Our aim was to investigate the potential role of the We studied previously recruited Italian families with T2D, and the dataset was deidentified and coded. The study was approved by the Jefferson Ethical Committee. The 212 families studied descended from at least three generations of Italians originating from the Italian peninsula. Families with identical twins and siblings with uncertain paternity were excluded. The families had an enriched history of T2D ,81 and wMC1R, 9 SNPs in MC2R, 3 SNPs in MC3R, 4 SNPs in MC4R, and 2 SNPs in MC5R using microarrays. We performed genotyping and Mendelian error exclusion by PLINK [p ≤ 0.05), we computed LD correlations via LD matrices among the SNPs available in the Toscani Italian population from the 1000 Genomes Project ) . The SNPs that significantly correlated  in by PLINK . Using PMC1R, MC2R, and MC3R genes in T2D. MC2R and MC5R genes are replicated in MDD; however, these appear with one novel variant each. Within our dataset, only the MC2R gene appears to confer risks for both MDD and T2D, albeit with different risk variants. To further clarify the role of the melanocortin receptor genes in MDD-T2D, these findings should be replicated in other ethnicities to improve our understanding of the comorbidity of MDD and T2D.Our study expanded the phenotypic spectra of melanocortin receptor genes. This is the first study to report risk variants in


Type 2 diabetes (T2D) onset is a complex, organized biological process with multilevel regulation, and its physiopathological mechanisms are yet to be elucidated. This study aims to find out the key drivers and pathways involved in the pathogenesis of T2D through multi-omics analysis.The datasets used in the experiments comprise three groups: (1) genomic (2) transcriptomic, and (3) epigenomic categories. Then, a series of bioinformatics technologies including Marker set enrichment analysis (MSEA), weighted key driver analysis (wKDA) was performed to identify key drivers. The hub genes were further verified by the Receiver Operator Characteristic (ROC) Curve analysis, proteomic analysis, and Real-time quantitative polymerase chain reaction (RT-qPCR). The multi-omics network was applied to the Pharmomics pipeline in Mergeomics to identify drug candidates for T2D treatment. Then, we used the drug-gene interaction network to conduct network pharmacological analysis. Besides, molecular docking was performed using AutoDock/Vina, a computational docking program.PSMB9, COL1A1, COL4A1, HLA-DQB1, COL3A1, IRF7, COL5A1, CD74, HLA-DQA1, and HLA-DRB1) were selected by wKDA analysis. Among these, COL5A1, IRF7, CD74, and HLA-DRB1 were verified to have the capability to diagnose T2D, and expression levels of PSMB9 and CD74 had significantly higher in T2D patients. We further predict the co-expression network and transcription factor (TF) binding specificity of the key driver. Besides, based on module interaction networks and key driver networks, 17 compounds are considered to possess T2D-control potential, such as sunitinib.Module-gene interaction network was constructed using MSEA, which revealed a significant enrichment of immune-related activities and glucose metabolism. Top 10 key drivers (We identified signature genes, biomolecular processes, and pathways using multi-omics networks. Moreover, our computational network analysis revealed potential novel strategies for pharmacologic interventions of T2D.The online version contains supplementary material available at 10.1186/s12967-022-03826-5. Type 2 diabetes (T2D) is a chronic metabolic disease distinguished by insulin resistance and elevated blood glucose levels. As a global endemic, recent data from the Centers for Disease Control and Prevention (CDC)  suggesteDue to the complexity of the onset and progression of T2D and the tandem with various diseases, analysis from multiple levels could exponentially augment our understanding of its pathophysiological mechanism. Including genomics, epigenomics, transcriptomics, etc., multi-omics analysis  can provIn this study, more accurate potential key genes and their regulatory mechanisms in T2D were identified by constructing gene regulatory network through multi-omics analysis, which contributes to demonstrating pathology, and identifying drug targets of T2D Fig. .Fig. 1Thhttp://www.ncbi.nlm.nih.gov/geo). Inclusion criteria were as follows: (1) Availability of islets from T2D patients in the dataset; (2) Ten or more islet samples in the dataset. Three eligible datasets were selected, including GSE38642 and GSE21232 (training set), and GSE25724 (test set). Additionally, genomics data (Nature 536(7614): 41–47.) was retrieved from Mergeomics web server. The details of the data are shown in Table Gene expression profiles and DNA methylation profiles of T2D were filtrated through the Gene Expression Omnibus (GEO) database (follows:  Availabihttp://www.ncbi.nih.gov/geo). The raw data were downloaded as MINiML files. It contains the data for all platforms, samples, and GSE records. The extracted data were normalized by log2 transformation. The microarray data were normalized by the normalized quantiles function of the preprocessCore package in R software (version 3.4.1). Probes were converted to gene symbols according to the annotation information of the normalized data in the platform. Probes matching multiple genes were removed from these datasets; the average expression value of genes measured by multiple probes was calculated as the final expression value and, as in the case of the same dataset and platform but in different batches, used the removeBatchEffect function of the limma package in the R software to remove batch effects. As in different datasets or the same dataset but in different platforms, extracting multiple data sets with common gene symbols, marking different datasets or platforms as different batches, used the removeBatchEffect function of the limma package in the R software to remove batch effects. The result of the data preprocessing was assessed by Density plot. The UMAP plot was drawn to illustrate the samples before and after batch effect.All microarray data were submitted to the GEO database  [Based on the key driver genes screened above, the Cytoscape (version 3.9.1)  softwareia.org/) , which ihttps://hiplot.com.cn/).To uncover the biological function related to the hub genes, Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were performed using Hiplot  were evaluated in this study. The total RNA was isolated using RNA extraction kit (TIANGEN) and reverse transcribed into cDNA using reverse transcription kit (ABI). Real-time quantitative PCR (RT-qPCR) analysis was performed using real‐time PCR kit (ABI). The relative mRNA expression levels of All animal studies were approved by the Animal Care and Use Committee of our institution and in accordance with relevant guidelines and regulations. Five-week-old male Sprague–Dawley (SD) rats initially weighing 160–180 g were housed individually in cages at a constant temperature of 24 ± 2 °C with a 12:12-h light–dark cycle. Rats were fed a high-fat diet and intraperitoneally injected low-dose streptozotocin (32 mg/kg) to induce T2DM model. Rats with random blood glucose ≥ 16.7 mmol/L on 3 consecutive days were selected [Pancreas tissues were grinded and then dissolved in SDT lysis buffer . The supernatant was collected and quantified after boiling and centrifuging. The extracted proteins were treated with the method of filter-aided sample preparation (FASP) enzymatic hydrolysis. The samples were labeled according to the instructions of the iTRAQ Reagents 8-plex kit . Mascot software 2.6 and Proteome Discoverer software 2.1 (Thermo Fisher Scientific) were used to process proteomic data against the rat database (Uniprot_RattusNorvegicus_36080_20180123).After merging the GSE77943 , the comparison between the hub genes expression of T2D and contorl was performed with the T-test. P-value < 0.05 was considered significant.Receiver operator characteristic (ROC) curve analysis was performed to predict the diagnostic effectiveness of biomarkers by SSPA Statistics 23. The area under the ROC curve (AUC) value was utilized to determine the diagnostic effectiveness in discriminating T2D from control samples in the GSE25724 dataset.Transcriptional Regulatory Relationships Unraveled by Sentence-based Text mining (TRRUST)  is a dathttp://cistrome.org/db) ["Homo sapiens" and "pancreas islet" were jointly searched in the Cistrome Data Browser (DB) (.org/db) . Throughhttp://bioinfogp.cnb.csic.es/tools/venny/) [The gene-disease association networks were created using DisGeNet , OMIM 1, OpenTar/venny/) .Potential drugs for the management of T2D were selected using the network based drug repositioning method from the Pharmomics pipeline in the Mergeomics web server. Drug-target interactions were used to construct a drug-target interaction network and visualized using Cytoscape v3.9.1. Gene Ontology and KEGG pathway analysis can clarify the role of potential targets by gene function and signaling pathways. The drug-disease common targets were converted into Entrez IDs, and then the “clusterProfiler” package was installed in the R software. According to the converted Entrez IDs, enrichment analysis of key target gene GO functions and analysis of KEGG signaling pathways were performed with p < 0.05.https://pubchem.ncbi.nlm.nih.gov/) [COL1A1 , COL4A1 , PSMB9 , IRF7 , HLA-DQB1  and COL3A1  were downloaded from the PDB (http://www.rcsb.org/pdb/home/home.do). For docking analysis, all protein and molecular files were converted into PDBQT format with all water molecules excluded and polar hydrogen atoms added. The grid box was centered to cover the domain of each protein and to accommodate free molecular movement. The grid box was set to 30 Å × 30 Å × 30 Å, and the grid point distance was 0.05 nm. Molecular docking studies were performed by Autodock Vina 1.2.2 (http://autodock.scripps.edu/).To analyze the binding affinities and modes of interaction between the drug candidate and their targets, AutodockVina 1.2.2, a silico protein–ligand docking software was employed [Statistical analysis was performed using GraphPad software  and R software (version 3.4.1).Mergeomics web server  and genomics data retrieved from To better understand the biological implications that relate to T2D, based on the multi-omic profile, we applied marker set enrichment analysis (MSEA) to evaluate the biological modules and functional categories. GO analysis results are mainly enriched in glucose homeostasis, carbohydrate homeostasis, and lipoprotein particle binding  and genes in T2D, module-gene network were visualized , although there were modest trends .Further expression validation of the hub genes was performed in obese diabetic mice, which was achieved by comparing proteomics data from obese diabetic mice and data from the GEO database GSE77943) after normalization and batch effect adjustment. The results show that expression levels of 3 after nNFKB1 is highly expressed in the T2D group , which coordinately participated in the regulation of four hub genes .Based on the TRRUST database, we found that 8 TFs may regulate the expression of these genes Fig. A  Results showed that each target bound to sunitinib through visible hydrogen bonds and strong electrostatic interactions, moreover, hydrophobic pockets of each target were occupied successfully by sunitinib .Elucidation of T2D driving molecular profiles through integrative multi-omic analysis, including genomic, epigenomic, and transcriptomic analysis, was the primary focus of this study. The most prominent finding to emerge from the research is that putative ten hub genes are associated with T2D. Among these, ous work –32. Chemous work , 34, sucous work ; the proous work , 37. In HLA-DQB1 [, COL1A1 [, COL3A1 [, COL4A1 [, CD74 [, and HLA-DQA1 [PSMB9, IRF7, and COL5A1 have not been previously reported to be associated with T2D in the literature. PSMB9 is a multicatalytic proteinase complex with a highly ordered ring-shaped 20S core structure, which encodes a member of the proteasome B-type family, also known as the T1B family. An essential function of a modified proteasome, the immunoproteasome, is the processing of class I MHC peptides, which have critical roles in the immune response. The activation of PSMB9 may be related to the expression of CTCF, which is shown in the results. CTCF, also called CCCTC-binding factor, encodes a transcriptional regulatory protein with 11 highly conserved zinc finger (ZF) domains. It has been revealed that CTCF-mediated chromatin accessibility changes could help to increase the transcription of genes related to important functions of pancreatic β cells, thereby increasing insulin secretion and improving T2D [PSMB9 gene in GWAS datasets from the DISEASES Experimental Gene-Disease Association Evidence Scores dataset revealed a potential association between PSMB9 and type 1 diabetes mellitus with a standardized value of 1.20753. Besides, Phenotypes associated with the PSMB9 gene by text-mining GWAS publications from the HuGE Navigator Gene-Phenotype Associations dataset also suggested the potential association between PSMB9 mutations and type 1 diabetes. IRF7 encodes interferon regulatory factor 7, a member of the interferon regulatory factor (IRF) family, which has been shown to play a role in the transcriptional activation of virus-inducible cellular genes, including interferon β chain genes. In the results of DNase-seq, the cis-acting elements located near the TSS region of IRF7 showed high chromatin accessibility. It means that the transcription of IRF7 may be regulated in many ways, such as H3K4me3, H3K27ac, and H3K9ac, which promote the activation of transcription. A previous study has reported that highly expressed IRF7 in the pancreas is implicated in immunoinflammatory diseases such as autoimmune pancreatitis [COL5A1 encodes an alpha chain for one of the low abundance fibrillar collagens. Previous work revealed that Col5A1, Nqo1, and Notch2 modulated by Ast may promote insulin-releasing balance, relieve insulin resistance, and maintain normal size in marginal-zone B cells [COL5A1 gene/protein from the curated CTD Gene-Disease Associations dataset suggested the potential association between COL5A1 and diabetes mellitus with a standardized value of 1.24401 [PSMB9, IRF7, and COL5A1 in the occurrence and development of T2D, and further exploration is needed.In accordance with the present results, previous studies have demonstrated that HLA-DQB1 , COL1A1 , COL1A1 , COL3A1 , COL3A1 , COL4A1 , COL4A1 , CD74 [4 [, CD74 , and HLAHLA-DQA1  are highving T2D . Diseasereatitis  and pancreatitis . In addi B cells . Besides 1.24401 . Hence, Due to the lack of effective/safe and less expensive drugs, drug repositioning appears to be the best tool for finding proper targets and predicting latent drugs in the therapy for T2D and related complications. Finally, 17 compounds were expected to have potential therapeutic effects on T2D. Among these, sunitinib  is a mulDue to the heterogeneity of T2D , future CD74 and PSMB9 were up-regulated in the T2D group, while the expressions of other genes were not detected  The results of GO were presented by bubble charts.Additional file 2: Figure S2. GO and KEGG analysis of sunitinib target genes. (A-C) The results of GO were presented by bubble charts. (D) Bubble graphs showed the enrichment results of the KEGG pathways.Additional file 3: Figure S3. RT-qPCR analysis of CD74. (A-H) The RT-qPCR process results of CD74.Additional file 4: Figure S4. RT-qPCR analysis of PSMB9. (A-H) The RT-qPCR process results of PSMB9.Additional file 5: Table S1: MSEA top pathways. Table S2: Scores of pathways after applying 12 topological algorithms. Table S3: Detailed information of key drivers in wKDA network. Table S4: The details of hub genes. Table S5: The details of TFs. Table S6: Detailed results of drug repositioning results.


Globally decreased histone 3, lysine 27 tri-methylation (H3K27me3) is a hallmark of H3K27-altered diffuse midline gliomas (DMGs) and group-A posterior fossa ependymomas (PFAs). H3K27-altered DMGs are largely characterized by lysine-to-methionine mutations in histone 3 at position 27 (H3K27M). Most PFAs overexpress EZH inhibitory protein (EZHIP), which possesses a region of similarity to the mutant H3K27M. Both H3K27M and EZHIP inhibit the function of the polycomb repressive complex 2 (PRC2) responsible for H3K27me3 deposition. These tumors often arise in neighboring regions of the brainstem and posterior fossa. In rare cases PFAs harbor H3K27M mutations, and DMGs overexpress EZHIP. These findings together raise the possibility that certain cell populations in the developing hindbrain/posterior fossa are especially sensitive to modulation of H3K27me3 states. We identified shared molecular features by comparing genomic, bulk transcriptomic, chromatin-based profiles, and single-cell RNA-sequencing (scRNA-seq) data from the two tumor classes. Our approach demonstrated that 1q gain, a key biomarker in PFAs, is prognostic in H3.1K27M, but not H3.3K27M gliomas. Conversely, Activin A Receptor Type 1 (ACVR1), which is associated with mutations in H3.1K27M gliomas, is overexpressed in a subset of PFAs with poor outcome. Despite diffuse H3K27me3 reduction, previous work shows that both tumors maintain genomic H3K27me3 deposition at select sites. We demonstrate heterogeneity in shared patterns of residual H3K27me3 for both tumors that largely segregated with inferred anatomic tumor origins and progenitor populations of tumor cells. In contrast, analysis of genes linked to H3K27 acetylation (H3K27ac)-marked enhancers showed higher expression in astrocytic-like tumor cells. Finally, common H3K27me3-marked genes mapped closely to expression patterns in the human developing hindbrain. Overall, our data demonstrate developmentally relevant molecular similarities between PFAs and H3K27M DMGs and support the overall hypothesis that deregulated mechanisms of hindbrain development are central to the biology of both tumors.The online version contains supplementary material available at 10.1186/s40478-023-01514-z. Advances in high-throughput sequencing have provided incredible windows into understanding the diversity and heterogeneity in cancers. Genomic, transcriptomic, and epigenomic studies have aided in molecular classification of tumors, elucidation of candidate driving events, and identification of potential cells of origin for many cancers, including pediatric brain tumors. Nevertheless, brain tumors remain the leading cause of mortality among all pediatric cancers . Unlike H3-3A and H3C2) [Group A posterior fossa ependymomas (PFAs) and diffuse midline gliomas (DMGs), H3K27-altered, are characterized by global reduction of the repressive histone modification histone 3 lysine 27 trimethylation (H3K27me3) –15. Globnd H3C2) –15 that nd H3C2) , 12, 16.nd H3C2) , 18 thatnd H3C2) –23. In and H3C2) , 22–27.With their epigenetic similarities, H3K27M DMGs and PFAs also share sensitivities to inhibitors targeting histone-modifying enzymes, including histone lysine demethylases, and histone deacetylases –30. AlthIn addition to their molecular similarities, PFAs and H3K27M DMGs arise in nearby structures associated with the hindbrain. The floor of the fourth ventricle is formed by the pons and the roof by the cerebellum. Whereas the majority of H3K27M DMGs arise from the pons, most PFAs are found in association with the fourth ventricle and cerebellum , 37. Mor. [Arm-length copy number alterations for PFAs and DMGs were obtained from supplementary data of published datasets , 37. For. .. [PFA  microarray expression data and DMG  meta-anFor samples from Bender et al. , BED filWe obtained published H3K27M DMG-specific enhancer-associated genes from Krug et al.  and PFA-. [https://cbl-dev.cells.ucsc.edu and analyzed using Seurat. AddModuleScore was used to generate a score for the H3K27me3-retained gene signature. Single-cell RNA-seq data from Eze et al. [https://cells-test.gi.ucsc.edu/?ds=early-brain and processed using Seurat as described in [CRABP1 feature plots were generated using the Seurat ViolinPlot function, ggplot2 (3.3.5), and ggprism (1.0.3). For area under the curve measurements from the Sepp et al. [Published RNA-Seq by Expectation–Maximization (RSEM) values were obtained for PFA and DMG datasets , 6 and l.  were obte et al.  was obtaribed in , and CRAp et al.  dataset,Human data were obtained from BrainSpan, and mouse data were obtained from the Allen Developing Mouse Brain map. All resulting analyses were completed with GraphPad Prism 9.1.1.Graphs were plotted and statistical analyses were performed using Prism software . Data are represented as the means ± standard deviation (S.D.) or as violin plots. The sample size (n) along with the statistical test performed and corresponding p-values are indicated in each figure or figure legend. Progression-free and overall survival metadata, when available, was analyzed comparing survival curves utilized the Log-Rank (Mantel-Cox) test for significance. Unpaired two-tailed, two-sided, Student’s t test or one-way ANOVA followed by multiple comparisons analysis using either Ordinary one-way ANOVA or Kruskal–Wallis tests were used to analyze data. Data were considered significant if p-values, adjusted where appropriate, were below 0.05 .To characterize the shared features of H3K27M DMG and PFAs, we began by comparing copy number changes in two published data sets and from Mackay et al. , H3K27M a. We valGain of 1q has previously been associated with worse overall survival outcome in PFAs in multiple studies , 49–52, In addition to recurrent 1q gain, the other alterations observed in at least 5% of each cohort were losses of 6q, 10q, and 22q  occur more frequently in H3.1 vs. H3.3 DMGs [ACVR1 mutations in 1q gain tumors vs. non-1q gain tumors , a subtype of PF ependymomas that lacks EZHIP expression [ACVR1 expression in the Pajtler et al. cohort to be associated with worse progression-free and overall survival outcomes  profiles generated by our group  and otheHOXA2, HOXA3, and HOXA4 have been identified as transcriptional markers that differentiate PFA1 and PFA2 [HOXA-family genes and hypothesized distinct anatomical origins PFA1 vs. PFA2 ependymomas [HOXA2 bulk expression revealed a bimodal expression pattern in PFAs , a mark associated with active transcription , 24–27. Due to the importance of enhancer activity in regulation of cell identity, we explored whether the enhancer signatures segregated with differentiation cell states within each tumor by assessing the single-cell RNA-seq datasets , 6. SingIn further interrogation of the single-cell datasets, we asked whether genes with retained H3K27me3 exhibited preferential expression in single-cell defined tumor cell populations. In both tumors, cells with progenitor-like states including OPC-like tumor cells in H3K27M-DMGs, and NPC-like tumor cells in PFA ependymomas, tended to have higher expression of genes in the retained H3K27me3 signature shared by H3K27M DMGs and PFAs Fig. a, b.Fig.CRABP1) was a clear outlier for both tumor subtypes  included large populations of both PFA- and DMG-derived cells  [H3K27M DMGs and PFAs are proposed to arise from cells along developmental lineages in the hindbrain –6, 8, 10. . Overall. , 65, we blished) . H3K27meCRABP1 expression in the developing hindbrain utilizing a scRNA-seq dataset generated from human brain samples from the first trimester [CRABP1 expression in stage 19, which corresponds to the peak in expression of H3-3A, the histone variant most commonly mutated in DMGs  [CRABP1 expression [ CRABP1 expression strongly correlated with HOX genes including HOXA2 and HOXD3  . The r4-6 (r4–6) , 76. Givpression , 78. WitWhile EZHIP-expressing PFAs and H3K27-altered DMGs (including H3.3 and H3.1K27M gliomas and EZHIP positive DMGs) have distinct natural histories and histologic features, evidence of their shared molecular features continues to mount as we profile broader samples of these tumor types. Furthermore, both tumors most commonly arise in neighboring hindbrain/posterior fossa-derived structures. This pattern suggests that developmental lineages in this region possess susceptibility to alterations that impede canonical PRC2 activity and therefore regulation of H3K27me3 state. Leveraging the existing molecular knowledge about each of these tumor types, we exploited genomic, bulk transcriptomic, scRNA-seq, and epigenomic profiles of both cancers to elucidate shared features that may improve our understanding of tumor biology.ACVR1—a gene frequently altered in H3.1-mutant DMGs—expression to be associated with: (a) worse survival in PFAs, and (b) upregulation of pathways that were also enriched in ACVR1-mutated gliomas.Assessment of the genomic landscape of these tumors demonstrated that H3.1K27M DMGs showed greater copy number similarity with EZHIP-expressing DMGs than did H3.3K27M tumors, with more quiescent landscapes and higher frequencies of 1q gain. These profiles of relatively lower genomic instability may point toward greater commonalities between PFAs and H3.1K27M vs. H3.3K27M DMGs. Consistent with this pattern, we identified high We additionally characterized common and unique features of H3K27M DMG and PFA chromatin landscapes based on patterns of H3K27me3/ac genomic deposition. The two tumors exhibited a considerable overlap in both H3K27me3-marked and H3K27ac-enhancer-associated genes. Exploration of the transcriptional states associated with these gene sets demonstrated inter- and intra-tumoral heterogeneity of expression patterns. Common enhancer-associated genes were enriched in cell motility and oligodendrocyte and glial differentiation pathways. Moreover, scRNA-seq demonstrated the highest levels of expression of these genes in astrocytic lineage cell types, especially in comparison to progenitor cell types. These patterns highlight the unique insights single-cell resolution transcriptomics can provide and illustrate how tumor-specific enhancers generated from bulk-sequencing methods may not fully reflect the regulatory landscapes of the tumors.HOX genes, which correlated with inferred anatomic origins of tumor, and these genes demonstrate distinct anterior–posterior boundaries of expression in the developing human hindbrain. These data suggest that developmental epigenetic profiles and origins of PFAs and H3K27M DMGs may influence the H3K27me3 landscape and gene expression programs in these tumors.Interrogation of genes associated with residual H3K27me3 also yielded important insights. The sensitivity of H3K27M DMGs and PFAs to EZH2 inhibition –35 pointCRABP1, like the HOX genes, has been shown to have highly specific expression patterns in mouse nervous system development [CRABP1 has been proposed to be a critical regulator of retinoic acid signaling, preventing canonical RA-induced transcriptional programs [CRABP1 may be associated with progenitor cells in early stages of the developing hindbrain that may be dysregulated in both PFAs and H3K27M DMGs. Further investigation is needed to elucidate whether CRABP1 contributes to maintenance of progenitor-type states in the presence of retinoids during development.elopment . In bothprograms . This roprograms –84 and tprograms . TogetheOverall, our studies further define molecular commonalities in the biology between DMGs and PFAs that express H3K27M and EZHIP and highlight similar patterns in the heterogeneity among tumors of each class. Moreover, some of these common epigenetic signatures showed specific patterns in human hindbrain development supporting the overall hypothesis that hindbrain epigenetic programs contribute to the biology of both tumors.Additional file 1: Supplementary Figures 1–5.


The following changes have been made to correct errors in this article and Correction , 2. AddiFigures and legends:The following figures have been updated to correct inconsistencies in the labeling: Figs The legends for Figs The Untreated zVAD panel in the updated A pair of fluorescence and brightfield panels for H9 Control were removed from CD95(Δ1–210) panels), Images in the following figures have been replaced with unmodified versions, with no changes to contrast or brightness: Figs Underlying image data for the western blot experiments in the following figures are provided in The Materials and Methods section is supplemented with the following information provided in Protocol for the generation of control and CD95-expressing PS120-NHE1 cells.Protocol for the transfection of Lifeact-GFP, PHAkt-GFP and GFP-Orai1.Protocol for the production of Ig-CD95.Information regarding CEM and SKW6.4 cell lines.Description of PLCg1-reconsistuted Jurkat cells.Sequences for all shRNAs, including scrambled.Suppliers for PI3K isoform selective inhibitors, zVAD.Additionally, the Flow Cytometry Analysis paragraph in  is correCD95(Δ1–210)).”In addition to the above changes in , an erroThe corresponding author apologizes for the errors in the published article and previous Correction.S1 FigS126E/L127E. Cells were lyzed (Cell) and supernatants were harvested (Sn) 5 d after transfection. Dead cells were eliminated from the supernatant by centrifugation , and then exosomes were pelleted using ultracentrifugation  and lyzed (Exo.). White arrowhead indicates cleaved-CD95L. Black arrowhead depicts full-length CD95L and the star indicates an unknown processed CD95L product. (B) The molecular size of cleaved CD95L was analyzed using size exclusion S-200-HR Sephacryl columns . Fractions were harvested and cl-CD95L was dosed by ELISA. (C) Jurkat and H9 T-cells were treated with 100 ng/ml of the home-made Ig-CD95L (dodecameric) or the naturally processed CD95L (trimeric), and activation of caspase-8 was assessed by following the cleavage of the protease using immunoblot (fragments p41/43 and p18). For each cell, long exposures of the film are depicted to detect the p18 cleaved fragment. Stars indicate irrelevant bands. (D) Indicated cells were incubated for 24 h with an engineered dodecameric Ig-CD95L or the naturally processed homotrimeric CD95L and cell death was assessed by MTT assay.(A) The human embryonic kidney epithelial cell line 293T (HEK) was transfected with wild type CD95L or the mutated CD95L(TIF)Click here for additional data file.S2 Figp<0.01 and ***p<0.001 as calculated using non-parametric and two-tailed Mann-Whitney test. (C) Leukemic T-cells H9 and CEM were untreated (supernatant of pcDNA3.1(+)-transfected HEK cells) or treated for 30 min with cleaved CD95L (100 ng/ml), and after extensive washing, cells were fixed. CD95 was stained using an anti-CD95 mAb (APO1-3) and revealed using a secondary Alexa488-conjugated goat anti-mouse antibody. Nuclei were stained with DAPI (blue). Slides were washed with PBS, dried, and mounted with Fluoromount . Red arrows depict emitted pseudopods upon CD95 engagement. Images were acquired with a confocal microscope TSC SP5  with a ApoPLAN 63× objective.(A) The leukemic T-cell line H9 was incubated for indicated times with 100 ng/ml of cleaved CD95L or a control medium (supernatant of pcDNA3.1(+)-transfected HEK cells—untreated), and after extensive washing, cells were fixed. CD95 was stained using an anti-CD95 mAb (APO1-3) and revealed using the secondary Alexa488-conjugated goat anti-mouse antibody . Number of cells harboring a CD95-Cap was counted (at least 300 cells counted for each condition). (B) The leukemic T-cell lines CEM and H9 were incubated for 30 min with 100 ng/ml of cleaved CD95L or a control medium (supernatant of pcDNA3.1(+)-transfected HEK cells -untreated), and the plasma membrane distribution of CD95 was then analyzed as described in (A). The quantity of cells harboring a CD95-Cap was assessed by counting (300 cells/condition). Values represent means and SD of three independently performed experiments. **(TIF)Click here for additional data file.S3 FigUpper panel: analysis of the CD95 expression at the surface of the indicated PS120 clones stably expressing either the CD95 wild type or its death domain truncated counterpart (Δ1–210). Cells were stained with an anti-CD95 mAb (clone DX2), washed, and a PE-coupled goat anti-mouse secondary antibody was used to reveal plasma membrane CD95 by flow cytometry. Lower panel: for each staining, the mean of the fluorescence intensity (MFI), which is correlated to the amount of plasma membrane CD95, was depicted. FACS analyses of PS120 cells shown in (A) have been partly reused in i values using a calibration curve . Right panel: Statistical analyses of the AUC values for the indicated T-cells. *** p ≤0.001 using non-parametric two-tailed Mann-Whitney test.  Left panels: Jurkat (C) and H9 (D) T-cells were loaded as previously mentioned. Cells were incubated with the IP3-R inhibitor, 2-APB (20 μM) during the recording as indicated by the unfilled rectangle or DMSO (control). Then cells were stimulated with 100 ng/ml of cl-CD95L (black arrow) and the intracellular Ca2+ was recorded every 10 s. Regions of Interest (ROIs) outlining individual cells were defined in three independently performed experiments and the mean ratio in each ROI was plotted versus time. Ratio values were converted to [Ca2+]i values using a calibration curve . Values depict the mean ± SD of the area under the curve (AUC) calculated for 1,000 s. Right panels: statistical analyses of the AUC values. The control H9 values served in 2+ response. *** p value≤0.001 using non-parametric two-tailed Mann-Whitney test.(A) The T-cell line Jurkat was incubated for the indicated times with 100 ng/ml of cl-CD95L, and then cells were lyzed. 100 μg of protein was loaded per lane and resolved in a 10% SDS-PAGE. Immunoblots were performed using indicated mAbs . (B) (TIF)Click here for additional data file.S6 Fig2+ (2 mM [Ca2+]e) or a Ca2+-free medium (0 mM [Ca2+]e) and then treated with supernatant of pcDNA3.1(+)-transfected HEK cells (Untreated) or with 100 ng/ml of cl-CD95L. Values were recorded every 10 s. Regions of Interest (ROIs) outlining individual cells were defined in three independently performed experiments and the mean ratio in each ROI was plotted versus time. Ratio values were converted to [Ca2+]i values using a calibration curve . For each treatment, the mean ± SD of the area under the curve (AUC) measured for 1,000 s in individual cells is depicted. Basal and maximal levels of [Ca2+]i are indicated by red and blue dotted lines, respectively. (B) Statistical analyses of the AUC values for indicated cells incubated in the presence of cl-CD95L in a regular (2 mM) or a Ca2+-free medium (0 mM). Using non-parametric two-tailed Mann-Whitney test, *** indicates a p value ≤0.001. (C) PBLs were untreated  or treated (lower panel) with the SOC channel inhibitor BTP-2 (500 nM) and then 100 ng/ml of cl-CD95L was added. For each condition, the mean ± SD of the area under the curve (AUC) measured for 1,000 s is depicted. Basal and maximal levels of [Ca2+]i are indicated by red and blue dotted lines, respectively. Note that individual calcium oscillations and/or synchronous calcium oscillations (upper panel) lead to apparent calcium oscillations during the plateau phase.(A) Indicated T-cells were loaded with 1 μM of the calcium probe Fura-2AM for 30 min at RT. Cells were bathed at 37°C in a medium containing 2 mM Ca(TIF)Click here for additional data file.S7 Fig2+]i values using a calibration curve . For each treatment, the mean ± SD of the area under the curve (AUC) measured for 1,000 s is depicted. (B) Statistical analyses of the AUC values for H9 T-cells treated or untreated (DMSO) with 5 mM of BAPTA-AM and stimulated with cl-CD95L. Using non-parametric two-tailed Mann-Whitney test, *** indicates a p ≤0.001. The control H9 values (DMSO) served in 2+ response.(A) H9 T-cells were loaded with 1 mM of the calcium probe Fura-2AM for 30 min at RT. Cells were pre-incubated or not (DMSO) with BAPTA-AM (5 mM) and then stimulated with 100 ng/ml of cl-CD95L. Values were recorded every 10 s. Regions of Interest (ROIs) outlining individual cells were defined in three independently performed experiments, and the mean ratio in each ROI was plotted versus time. Ratio values were converted to -pyrimidin-4-one); PI3K-γ Inh ; PI3K-δ Inh-X, IC87114; and Wortmannin and LY294002 were tested to ascertain the isoform involved in the cl-CD95L-mediated motility. All the inhibitors came from Calbiochem . Note: Data were compiled from [(TIF)Click here for additional data file.S9 FigWound healing assays: A confluent monolayer of the indicated adherent cells was wounded with a tip. Then, cells were pre-incubated for 30 min with 40 μM of zVAD-fmk or DMSO (vehicle) and treated for 24 h with 100 ng/ml of cl-CD95L or with a supernatant of pcDNA3.1(+)-transfected HEK cells (untreated) and pictures were acquired (Bars  =  50 μm). Pictures are representative of three independently performed experiments. (B) Upper panels: The CD95-expressing PS120 cells were pre-incubated with the pan-caspase inhibitor zVAD-fmk (40 μM) or DMSO (vehicle) for 30 min and then seeded in the upper compartment of the Boyden chamber in a low FCS (1%)-containing medium. Cells were incubated for 24 h with 100 ng/ml of cl-CD95L or with a supernatant of pcDNA3.1(+)-transfected HEK cells (untreated). Then the membrane was removed, the upper side containing the non-migrating cells was wiped out with cotton-tipped swabs, and migrating cells in the opposite side of the filter were fixed with methanol and stained by Giemsa (purple cells). For each experiment, five pictures of random fields were taken and a representative picture was depicted (Bars  =  70 μm). The images of PS120CD95 cells untreated/vehicle and cl-CD95L/vehicle came from the same experiment to decipher the effect of PP2 (src inhibitor) and zVAD (caspase inhibitor) and were used in Lower panel: To quantify cell motility, Giemsa-stained migrating cells from the lower side of the membrane were lyzed and absorbance was measured at 560 nm. Data represent means and SD of three independently performed experiments.(A) (TIF)Click here for additional data file.S10 FigWound healing assays: A confluent monolayer of PS120CD95 adherent cells was wounded with a tip. Cells were pre-incubated with or without (DMSO-vehicle) 10 mM of the src inhibitor PP2 and then were treated or untreated (supernatant of pcDNA3.1(+)-transfected HEK cells) for 24 h with 100 ng/ml of cl-CD95L. Pictures were acquired to assess the efficiency of the wound healing. Dotted red lines delineate the initial position of the wound. (B) PS120CD95 cells were transduced with lentiviral particles containing a c-yes-targeting shRNAmir or a scramble shRNAmir . 48 h after transduction, the confluent monolayer of cells was wounded and cell migration was analyzed in the presence of 100 ng/ml of cl-CD95L for the indicated time. Pictures are representative of three independently performed experiments.(A) (TIF)Click here for additional data file.S1 File(PPTX)Click here for additional data file.S2 File(DOCX)Click here for additional data file.