PLOD3, COL4A1, UBE2C, MIF, and PRPF5 were identified as having gene expression profiles that can be reversed by drugs. These findings support the use of a computational reversal gene expression approach to identify new drug candidates that can be used to treat GC.Treatment of gastric cancer (GC) often produces poor outcomes. Moreover, predicting which GC treatments will be effective remains challenging. Computational drug repositioning using public databases is a promising and efficient tool for discovering new uses for existing drugs. Here we used a computational reversal of gene expression approach based on effects on gene expression signatures by GC disease and drugs to explore new GC drug candidates. Gene expression profiles for individual GC tumoral and normal gastric tissue samples were downloaded from the Gene Expression Omnibus (GEO) and differentially expressed genes (DEGs) in GC were determined with a meta-signature analysis. Profiles drug activity and drug-induced gene expression were downloaded from the ChEMBL and the LINCS databases, respectively. Candidate drugs to treat GC were predicted using reversal gene expression score (RGES). Drug candidates including sorafenib, olaparib, elesclomol, tanespimycin, selumetinib, and ponatinib were predicted to be active for treatment of GC. Meanwhile, GC-related genes such as  Although GC rates have declined in most developed countries, the incidence of non-cardia GC among Caucasians aged 25–39 years has increased in the United States over the past two decades2.Gastric cancer (GC) is the fifth most common cancer worldwide and the third leading cause of cancer death, with 1.3 million incident cases and 819,000 deaths occurring globally in 20153. Moreover, GC is a highly heterogeneous disease as reflected by the numerous histological and molecular classifications4.Increased rates of early GC detection have increased survival rates for GC patients, but treatment outcomes for GC remain low and difficult to predict5. After some success in targeted therapies for the treatment of several human cancers7, research has focused more on new approaches for the identification of novel targets in cancer therapy. Although large numbers of potential targets have been identified by advanced technologies, it has proven difficult to find targets that are causally involved in the disease.The development of new drugs to treat diseases, especially cancer, is dependent on the identification of novel drug targets. In recent years, an increasing number of innovations have promised to improve our understanding of disease biology, provide novel targets, and catalyze a new era in the development of medicines. However, despite impressive advances in technologies, the situation has remained relatively static in terms of new molecular entities9. Over 90% of drugs fail during the early development stage due to safety concerns or a lack of efficacy10.The number of drugs approved by the US Food and Drug Administration has continuously declined because traditional methods of drug development do not support highly efficient drug discovery. Traditional approaches to develop of new drugs are expensive and time-consuming, with an average of 15 years and a price tag of more than $2 billion necessary to bring a drug to market11, the Cancer Cell Line Encyclopedia (CCLE)12, DrugBank13, and the Library of Integrated Network-Based Cellular Signatures (LINCS)15, which together catalog disease-specific and drug-induced gene expression signatures, offers a time-efficient approach to reposition existing drugs for new indications16. Several computational methods, such as bioinformatics, system biology, machine learning, and network analysis can be used for drug repositioning or repurposing as well as to identify new indications for drugs17.The increasing availability of large public datasets such as the Gene Expression Omnibus (GEO) at the National Center for Biotechnology Information (NCBI)18, wherein agents having similar properties are predicted to have similar effects. Many drug repositioning strategies are based on different data, including similar chemical structures, genetic variations, and gene expression profiles19. Recently, interest in the use of genomics-based drug repositioning to aid and accelerate the drug discovery process has increased9. Drug development strategies based on gene expression signatures are advantageous in that they do not require a large amount of a priori knowledge pertaining to particular diseases or drugs21.Most computational drug repositioning approaches are based on a “guilt by association” strategyThe purpose of this study is to predict drug candidates that can treat GC using a computational method that integrates publicly available gene expression profiles of GC patient tumors and GC cell lines and cellular drug response activity profiles.The study selection process is outlined in Fig. P values in the MetaDE package. Among the gene expression signatures, 136 genes showed increased expression levels in tumors compared to normal tissues , whereas 53 genes showed decreased expression levels in tumors compared to normal tissues .The degree of similarity in the gene expressions between tumor samples from the GEO and GC cell lines from the CCLE was assessed by a ranked based Spearman’s correlation test. Gene expression profiles for 40 GC cell lines were included in the CCLE  values were computed by weighting various cell lines, compound concentrations, and treatment durations. A number of known methods were used to summarize the RGES and obtain sRGES values (Supplementary Table COL4A1); (ii) procollagen-lysine 2-oxoglutarate 5-dioxygenase 3 (PLOD3); (iii) ubiquitin conjugating enzyme E2 C (UBE2C); (iv) macrophage migration inhibitory factor (MIF); and (v) pre-mRNA processing factor 4 (PRPF4)  the collagen type IV α1 chain (F4) Fig. . FifteenF4) Fig. .Figure 725. In this study, we used public cancer genomic and pharmacologic databases to demonstrate the reversal potency relationship between DEGs and drug activity and to predict potential new drug candidates for GC.Methods to identify drug candidates that can reverse the expression states of disease-related genes can complement traditional target-oriented approaches in drug discovery50 values indicated that combining disease gene expression data derived from clinical samples with drug gene expression profiles obtained from results with in vitro cell lines could be used to predict drug activity.Our results showed that the ability of drugs to reverse DEGs was correlated with drug activity in GC, although this correlation was highly dependent on the cell line as well as the drug concentration and treatment duration. The positive correlation between sRGES and ICCOL4A1, PLOD3, UBE2C, MIF, and PRPF4, showed reversed expressions in response to 15 active compounds. To the best of our knowledge, this is the first study of drug repositioning using a computational reversal gene expression approach in GC. Among these genes, PLOD326 and COL4A127 were recently shown to be overexpressed in GC. Meanwhile, the overexpression of UBE2C was related to poor prognosis in GC28 and was a potential biomarker of intestinal type GC29. MIF could also be a potential prognostic factor for GC30. These genes showed reversed expression levels and thus may be feasible as therapeutic targets for GC. Additionally, PRPF4 as a pre-mRNA splicing factor has been suggested as a potential therapeutic target for cancer therapy31.In our study, five GC genes, 32 and a poly (ADP-ribose) polymerase (PARP) inhibitor, olaparib33, have completed phase II and phase III clinical trials, respectively, for GC patients. Meanwhile, the heat shock protein 70 (Hsp70) inducer elesclomol, the novel tyrosine kinase inhibitor ponatinib, the heat shock protein 70 (Hsp90) inhibitor tanespimycin, and the mitogen-activated protein kinase inhibitor selumetinib have not been previously studied clinically for their effectiveness against GC.Among the active drugs identified by our analysis, the multiple tyrosine kinase inhibitor sorafenibGC is a heterogeneous disease that involves multiple factors associated with various molecular pathways that can function differently during the cancer development process. A limitation of this study is that the GC disease gene expression datasets from the GEO are not uniformly associated with clinical outcomes or GC etiologies. The drug activity of predicted compounds may also vary because the GC disease states varied for individual patients. Sampling time information is important, as samples obtained after the initial neo-adjuvant chemotherapy can affect the results of this meta-analysis. Nonetheless, such information was not available from some datasets.Many recent projects focus on precision medicine to provide insights between diseases and genes. A repurposing strategy based on alterations of driver genes in each tumor can be used to identify therapeutic targets. The collection of therapeutic agents targeting driver genes and determining the connection between each patient and the targeted therapies can enhance promising drug repositioning opportunities and eventually benefit patients. Therefore, RGES may improve predictions of drug candidates because it is based on the molecular characteristics of actual tumors.Therapeutic efficacy is more complex than a simple correlation of gene expression profiles with drugs and diseases. Therefore, our findings with regard to drug candidates will require further preclinical testing and demonstrations in clinical trials, although our results did validate that the method of the computational analysis of public gene expression databases is a potentially useful means of drug discovery. In summary, our computational approach combined disease gene expression with drug-induced expression profiles in GC to identify new drugs and target genes for GC therapy. This approach can also be used to predict the efficacy of new drug candidates with which to treat GC. This computational approach could be broadly applied to other diseases for which reliable gene expression data are available.https://www.ncbi.nlm.nih.gov/geo), A search of the GEO database was conducted in July of 2018 using ‘gastric cancer’ as a key search phrase. The results for deposits made since January of 2015 were filtered using the search terms Homo sapiens, expression profiling by array, and expression profiling by high-throughput sequencing. Only original experimental datasets that compared the expression levels of mRNAs between GC tumors and normal tissue controls were selected. Datasets containing more than ten sets of normal and tumor samples were retained. Additionally, gene expression profiles of human gastric adenocarcinoma cell lines were downloaded from the CCLE 12.Publicly available gene expression profiles for GC patients were downloaded from the GEO database of the NCBI , and the Gene IDs were used to cross-map genes among the different platforms. Only genes present in all selected platforms were considered. To combine the results from individual studies and to obtain a list of more robust DEGs between GC and normal control tissues, guidelines outlined by Ramasamy et al.34 for meta-analyses of gene expression microarray datasets were followed. The R packages MetaQC35 was used for quality control (QC). MetaQC uses six quantitative QC parameters: (i) measures of internal QC; (ii) measures of external QC; (iii) accuracy QC of featured genes; (iv) accuracy QC of the pathway; (v) consistency of QC in the ranking of featured genes; and (vi) consistency QC in the ranking of the pathway. The mean rank of all QC measures in each dataset was also determined as a quantitative summary score by calculating the ranks of each QC measure among all included datasets.The GEO accession number, platform, sample type, numbers of cases and controls, references and expression data were extracted from each of the identified datasets, which were then individually preprocessed using a log38 was used to the identify DEGs in GC. A moderated t-statistic was used to calculate the P values for each dataset, and a meta-analysis was conducted with a fixed-effect model39 using the MetaDE package to identify DEGs. Additionally, similarities among gene expressions profiles between tumor samples from the GEO and GC cell lines from the CCLE were assessed.MetaDEhttps://lincscloud.org/), hosted by the Broad Institute40. Cell lines described in LINCS, CCLE, and ChEMBL 41 were mapped using GC cell line names followed by manual inspections. Meta-information for compound-induced gene expressions, including the cell line types as well as the treatment durations and drug concentrations was retrieved. Only small-molecule perturbagens having high-quality gene expression profiles  were used for further analysis.Level 4 gene expression profiles consisting of 978 landmark genes (L1000 genes) from LINCS as of May of 2018 were downloaded from LINCS cloud storage (50) in GC cell lines, were retrieved from ChEMBL. As the IC50 values for a given compound could vary for the same cell line across different studies, the median IC50 value was used. Compounds included in the ChEMBL and LINCS were mapped using The International Union of Pure and Applied Chemistry International Chemical Identifier keys. Additionally, the area under the curve (AUC) values for compound activity data in GC cell lines were retrieved from the Cancer Therapeutic Response Portal 42. Sensitivity levels were measured in the form of cellular ATP levels as a surrogate for cell number and growth using CellTiter-Glo assays43. A compound-performance score was computed at each concentration of compound. The AUC using percent-viability scores was computed as a metric of sensitivity given that AUC reflects both relative potency and the total level of inhibition observed for a compound across CCLs. Median AUC values across multiple cell lines were used. Compounds were categorized into active (IC50 < 10 μM) and inactive groups (IC50 ≥ 10 μM) based on their activities in cell lines. An IC50 value of 10 μM was chosen as an activity threshold because compounds with IC50 ≥ 10 μM in primary screenings are often not pursued44.Compound response activity data, described as the half-maximal inhibitory concentrations , whereas downregulated genes were ranked low (i.e. on the bottom). Among the upregulated genes, reversal genes were defined as those that were ranked lower in the inactive group (IC50 < 10 μM) than in the inactive group (IC50 ≥ 10 μM). In contrast, among the downregulated genes, the reversal genes were defined as those that were ranked higher in the active group than in the inactive group. A leave-one-compound-out cross-validation approach was used to find genes having reversed expressions47. For each trial, one compound was removed and the reversed genes were then identified using the approach described above. Only those genes that were significantly reversed in all trials were retained. Genes having P < 0.1 in all trials were considered as reversal genes.In cases for which multiple compound activity IC50 from ChEMBL or AUC from CCLE. A Wilcoxon signed-rank test was used to assess differences between RGES across the same and different cell lines, longer  treatment durations, and higher (≥10 μM) and lower (<10 μM) drug concentrations. P values were adjusted with a Benjamini and Hochberg’s false discovery rate method to correct for multiple testing.The degrees of similarity in the gene expressions between tumor samples from the GEO and GC cell lines from the CCLE were assessed by Spearman’s rank correlation testing, as was the similarity of RGES and ICSupplementary materials


Despite extensive research on the mechanisms of HLA-mediated immune control of HIV-1 pathogenesis, it is clear that much remains to be discovered, as exemplified by protective HLA alleles like HLA-B*81 which are associated with profound protection from CD4+ T cell decline without robust control of early plasma viremia. Here, we report on additional HLA class I , and HLA class II (DQB1*02 and DRB1*15) alleles that display discordant virological and immunological phenotypes in a Zambian early infection cohort. HLA class I alleles of this nature were also associated with enhanced immune responses to conserved epitopes in Gag. Furthermore, these HLA class I alleles were associated with reduced levels of lipopolysaccharide (LPS) in the plasma during acute infection. Elevated LPS levels measured early in infection predicted accelerated CD4+ T cell decline, as well as immune activation and exhaustion. Taken together, these data suggest novel mechanisms for HLA-mediated immune control of HIV-1 pathogenesis that do not necessarily involve significant control of early viremia and point to microbial translocation as a direct driver of HIV-1 pathogenesis rather than simply a consequence. During acute HIV infection, there exists a complex interplay between the host immune response and the virus, and the balance of these interactions dramatically affects disease trajectory in infected individuals. Variations in Human Leukocyte Antigen (HLA) alleles dictate the potency of the cellular immune response to HIV, and certain well-studied alleles  are associated with control of HIV viremia. However, though plasma viral load is indicative of disease progression, the number of CD4+ T cells in the blood is a better measurement of disease severity. Through analysis of a large Zambian acute infection cohort, we identified HLA alleles that were associated with protection for CD4+ T cell loss, without dramatic affect on early plasma viremia. We further link these favorable HLA alleles to reduction in a well-known contributor to HIV pathogenesis, the presence of microbial products in the blood, which is indicative of damage to the gastrointestinal tract, a process which accelerates disease progression in HIV infected individuals. Ultimately, these results suggest a new mechanism by which the cellular immune response can combat HIV-associated pathogenesis, and further highlight the contribution of gut damage and microbial translocation to accelerating disease progression, even at early stages in HIV infection. The factors underlying HIV-1 pathogenesis are a complex interplay between the host and virus. We have previously shown that viral characteristics, such as the replicative fitness of the transmitted variant, are significant predictors of early immune activation and further disease progression in HIV-1 infected individuals –4. VariaThe most potent clinical indicator of HIV-1 pathogenesis and the severe immune depletion of AIDS is the CD4+ T cell count in the peripheral blood. CD4+ T cell decline during HIV-1 infection has been shown to be more accurately predicted by levels of chronic immune activation than viral load, underlining the importance of both host and virus in pathogenesis –15. The However, there are differences in baseline levels of detectable microbial translocation in specific populations, with evidence of ongoing immune activation in HIV seronegative MSM , suggestTo elucidate novel mechanisms of immune protection, we sought to identify additional HLA alleles associated with the phenotype of CD4+ T cell preservation, irrespective of their associations with plasma viremia. This approach allows us to uncover alternate mechanisms by which the host cellular immune response alters disease trajectory following acute infection. We identified 4 HLA class I alleles and 2 HLA class II alleles significantly associated with protection from rapid CD4+ T cell decline even when correcting for early set point viral load, sex of the infected individual, and viral replicative capacity (vRC). Furthermore, the HLA class I alleles we identified were associated with a reduction in levels of circulating LPS during acute infection, and appear to be associated with CD8+ T cell responses targeting more conserved regions of Gag. Early LPS levels were a significant predictor of CD4+ T cell decline independent of plasma viral load, and were further associated with cellular immune activation, specifically in the T cell compartment. Collectively, these data suggest that microbial translocation is a driver of immune activation rather than a consequence, establishing early gut damage and microbial translocation as a potential target for therapeutic intervention at both the acute and chronic stages of HIV infection.. Detailed clinical data was collected and plasma viral load and CD4+T cell counts measured longitudinally starting from the time of HIV-1 diagnosis (median of 44 days post estimated date of infection) and continuing at 3-month intervals thereafter up to 6 years post infection. We have previously defined viral replicative capacity (vRC) for the transmitted viruses in this cohort, as measured by an in vitro replicative fitness assay [To identify the contribution of protective immunogenetic factors to disease progression in a Zambian heterosexual transmission cohort, we performed HLA class I and class II typing to 4-digit resolution in a group of 127 acutely infected subjects ss assay  and that3. Sex of the infected participant and vRC of the transmitted virus, which represent additional host and viral characteristics previously shown to significantly affect HIV-1 pathogenesis [We employed Cox proportional hazards models coupled with a stepwise backward variable selection approach, to identify both HLA class I and II alleles associated with protection from significant CD4+ T cell decline, which we defined as the time to a CD4+ T cell count less than 300 cells/mmogenesis –31, were.In a multivariable Cox proportional hazards model, 4 HLA class I alleles  and 2 HLA class II alleles (DQB1*02 and DRB1*15) were found to be independent protective factors delaying the loss of CD4+ T cells . Furthermore, these HLA alleles generated additive scores when divided into HLA class I or class II alleles only . In contrast, a significant additive effect of the protective alleles was not observed on set point viral load , suggesting that these alleles may affect immunopathogenesis without a substantial impact on early plasma viremia in this cohort.As is implied by their independent predictive nature, HLA alleles affecting CD4+ T cell decline were additive in their effects and formed distinct profiles even in the absence of sex and vRC as covariates  assessed HLA alleles for their effect on CD4+ T decline without controlling for set point viral load we built a multivariable Cox proportional hazards model where set point viral load was added as a covariate. We found that all 6 HLA alleles remained significant predictors with set point viral load in the model . Indeed, only B*57 was associated with a statistically significant decrease in early set point viral loads when analyzed in isolation , an association which has been described previously [To further interrogate this observation, we sought to isolate the effects from the influence of viral load. Plasma viremia, and specifically viral load set point, is a consistent and strong predictor of CD4+ T cell decline in this and multiple other HIV infection cohorts ,32,33. Ceviously . These dFig 2A). This difference in LPS levels was specific for class I alleles; we observed no association between protective HLA class II alleles and circulating LPS . The grouped protective class I alleles were also associated with a moderate 0.31 log reduction in plasma viral loads at the time of LPS sampling . However, in a generalized linear model, carriage of protective class I alleles is a significant predictor of LPS levels at seroconversion (p = 0.008), whereas plasma viral loads are not (p = 0.66) .Inflammation is an alternative driver of disease progression, independent of viral load , and is  in a subset of individuals with samples available for analysis (n = 30). These data indicate that the LPS association is durable and is reinforced by other markers of gut integrity and microbial translocation. Furthermore, individuals carrying these protective HLA-I alleles, but not those lacking them, demonstrated a significant reduction in IL-10 levels over time , consistent with published literature linking the presence of circulating microbial products with increased production of IL-10 by monocytes [Protective HLA class I alleles were also associated with reduced levels of LPS, soluble CD14, and intestinal fatty acid-binding protein (I-FABP) at 6 months post seroconversion onocytes ,39.. Importantly, in a generalized linear model, excessive alcohol consumption and protective HLA class I alleles were independent predictors of plasma LPS levels near seroconversion , suggesting the association between HLA class I and LPS is not significantly confounded by alcohol consumption in this cohort.Other factors such as excessive alcohol use can significantly affect gut integrity and bacterial dysbiosis leading to increased levels of microbial translocation –42. In t2 values >0.8 . This suggests that the observed reduction in microbial translocation is likely attributable to the presence of these 4 protective HLA class I alleles and not other immune-related genes in the MHC locus.The MHC gene locus contains additional genes that play key roles in the innate and adaptive immune systems. Many of3, individuals in the lowest 50th percentile of LPS levels at seroconversion exhibited significantly slower CD4+ T cell decline . This association was independent of viral load at the time of LPS sampling . Thus, this measure of microbial translocation early during HIV-1 infection is directly associated with the kinetics of longitudinal CD4+ T cell decline.We next assessed whether a direct relationship between LPS levels and CD4+ T cell decline existed in this cohort. While translocation of microbial products, such as LPS, has been linked to immune activation and disease progression during chronic HIV-1 infection , we sougIn chronic HIV-1 infection, systemic LPS levels are correlated with increased cellular immune activation, specifically with an increased frequency of activated CD38+/HLA-DR+ CD8 T cells . The mec. Similarly, in the CD8+ T cell compartment, increased LPS in the periphery also correlated with the percentage of CD38 and HLA-DR double positive cells, increased percentages of Ki67+ cells, and CD38 expression levels (assessed by MFI) . Furthermore, the associations between LPS and these activated T cell phenotypes were independent of viral loads at the time of PBMC collection , providing further evidence for early microbial translocation as an independent predictor of immune activation and a potential driver of immunopathogenesis.In order to determine the effect of early microbial translocation on subsequent T cell activation in this cohort, cryopreserved PBMCs available from a subset of individuals collected 3 months after the estimated date of infection (median 97 days post estimated date of infection and median 65 days post systemic LPS measurements) were assessed by flow cytometry for markers of T cell activation and antigen experience/exhaustion. We found that LPS levels at seroconversion directly correlated with PD-1 expression on central memory and effector memory CD4+ T cells an average of two months later . Furthermore, we observed no differences in the breadth of Gag targeting when analyzing the number of specific epitopes targeted between HLA-I groups . However, when peptide pools were optimized for sequence conservation, we observed that individuals with the protective HLA class I alleles identified in this study exhibited significantly higher IFNγ ELISpot responses to a pool of highly conserved Gag peptides [In order to gain insight regarding the quality of the CD8+ T cell response restricted by these protective HLA class I alleles, we analyzed IFNγ ELISpot data for a subset of individuals in this cohort for which early cryopreserved PMBCs were available. When PBMCs were interrogated with potential T cell epitope (PTE) Gag pools ,50, we opeptides –53. Targpeptides –58, may In this study, we have utilized detailed clinical, virologic, and immunogenetic data from a well-defined heterosexual transmission cohort  to identThe discovery of HLA alleles associated with protection from rapid CD4+ T cell decline without significant control of early plasma viremia is not without precedent. HLA-B*81 has been shown to be highly protective in this and in other African cohorts without significant control of early set point viral loads . The impIn contrast to the class I alleles identified in this study, the protective HLA class II alleles (DQB1*02 and DRB1*15) were not associated with reduced levels of LPS in early infection. The protective mechanism of HIV-specific CD4+ T cells is less clear than CD8+ T cells, especially since HIV-specific CD4+ T cells are preferentially infected and depleted by HIV during infection . HoweverTranslocation of bacterial-derived products into the systemic circulation was first shown to be associated with pathogenesis in HIV-1 infection by Brenchley et al., specifically in the chronic stages of infection . It has It is not known if the findings presented in the current study can be generalized to other populations, such as MSM, living with HIV. Innate immune cell persistence in the gut is linked to the composition of the microbiome  and specth percentile of LPS values protected from rapid loss of CD4+ T cells. In addition, we observed correlations between circulating LPS levels at seroconversion and markers of T cell activation (CD38 MFI and CD38/HLA-DR co-expression), antigen experience/exhaustion , and proliferation (Ki67 expression) at a subsequent 3-month time point, suggesting a causal link between circulating LPS and immune activation, which itself is strongly correlated with disease progression irrespective of viral loads [We demonstrate in this current study that LPS levels in the plasma measured at or near seroconversion predict longitudinal CD4+ T cell decline, with individuals in the lowest 50al loads . The assInterestingly, we did not observed any difference between individuals in terms of magnitude or breadth of epitope recognition as measure by IFNγ ELISpot response between those that expressed or lacked CD4-protective HLA class I alleles when using broad potential T cell epitope (PTE) Gag pools ,50. HoweIn summary, these data definitively link the presence of microbial products in the circulation during acute/early infection with subsequent CD4+ T cell decline and cellular immune activation. The levels of bacterial products found in the periphery, as measured by plasma LPS levels are modulated by protective HLA class I alleles at both seroconversion and 6 months post infection. These findings demonstrate an unappreciated mechanism of protection by favorable HLA alleles, beyond simply controlling systemic viral load, and further establish microbial translocation as a driver of HIV pathogenesis. Moreover, they offer additional supporting data that efforts to ameliorate gut damage and dysbiosis, and the subsequent immune activation this dysregulation initiates in HIV-infected patients, will have positive impacts on disease outcome.All participants in the Zambia Emory HIV Research Project (ZEHRP) discordant couples cohort in Lusaka, Zambia were adults at the time of participation and enrolled in human subjects protocols approved by both the University of Zambia Research Ethics Committee and the Emory University Institutional Review Board. Prior to enrollment, individuals received counseling and signed a written informed consent form agreeing to participate.The subjects included in this study were selected from the ZEHRP cohort based on being recently infected with HIV-1. All subjects were initially seronegative partners within serodiscordant cohabitating heterosexual couples, who knew their HIV status and were provided counseling and condoms, but subsequently seroconverted. All subjects were antiretroviral therapy naïve and were identified a median of 44 (IQR = 33–49) days after the estimated date of infection (EDI). The algorithm used to determine the EDI has been previously described . AdditioEarly set point viral load (VL) for newly infected individuals was defined as the earliest stable nadir VL value measured between 3 and 9 months post infection and which did not show a significant increase in value within a 3–4 month window. HIV plasma VL was determined at the Emory Center for AIDS Research Virology Core Laboratory using the Amplicor HIV-1 Monitor Test . CD4+ T cell counts were based on T-cell immunophenotyping, with assays done using the FACScount System  in collaboration with the International AIDS Vaccine Initiative.xl DNA Analyzer (Applied Biosystems).Genomic DNA was extracted from whole blood or buffy coats . HLA genotyping relied on a combination of PCR-based techniques, involving sequence-specific primers (Invitrogen) and sequence-specific oligonucleotide probes (Innogenetics), as described previously . Ambigui−6). Data processing and quality control procedures have been described previously for this data set[SNP genotyping with the Illumina ImmunoChip was processed at a genomics core facility (University of Alabama at Birmingham); SNP alleles were inferred using the joint calling and haplotype phasing algorithm implemented in BEAGLECALL. We comp data set.gag genes were amplified using a nested second round PCR. Patient-derived gag genes were joined with the MJ4 long terminal repeat portion via splice-overlap-extension PCR, and gag-LTR amplicons were cloned into the MJ4 proviral vector using NgoMIV and BclI endonuclease restriction enzymes.Gag-MJ4 chimeras were generated from frozen plasma isolated at the seroconversion time point for 127 subjects as previously described[The in vitro replication assay used to generate viral replication capacity (vRC) values has been described extensively . BrieflyCox proportional hazards models with a backwards variable selection strategy were used to identify protective HLA alleles. All HLA alleles represented by at least 3 or more individuals in the cohort were included in the initial model. Sex of the seroconvertor as well as viral replication capacity of the transmitted founder virus  were added as static covariates present in every iteration of the model. HLA alleles contributing least significantly to the model  were iteratively excluded in a stepwise fashion until all variables in the model had p-values < 0.05. Linkage disequilibrium between specific HLA alleles was accounted for during the variable selection process.HLA scores presented in Measurement of LPS levels was performed using the LAL Chromogenic Endotoxin Quantification kit (American Diagnostica). Levels of sCD14 (R&D systems) were measured using a standard ELISA based assay according to the manufacturer’s instructions. Plasma levels of IL-10 were measured using MILLIPLEX Human Cytokine/Chemokine detection kit (EMD Millipore). Samples were run in duplicate with all individuals on the same plate and wells with low bead count or coefficient of variance >30% were excluded from subsequent analysis. Plates were read on the Bio-Plex 3D Suspension Array System (Bio-Rad).Intestinal fatty acid binding protein (I-FABP) was measured using a commercially available ELISA DuoSet assay  according to the manufacturer's instructions with minor adjustments. Plasma samples were diluted to 10% in diluent from the R&D Systems soluble CD14 ELISA kit (DC140) and plates were blocked with Sigma Blocking Buffer.th percentile.In order to maintain normal distributions and to lessen the effect of outliers, extreme positive values for each analyte measured in plasma were Winsorized to the 90Cryopreserved PBMCs were stained for flow cytometric analysis in a manner identical to that described in . The folAll flow cytometry data was collected on an LSRII (BD Biosciences) cytometer using FACSDiVa version 6.3.1 software. Analysis of the data performed using FlowJo version 9.7.5 software (TreeStar).Data on alcohol consumption was collected for individuals in the ZEHRP cohort as described previously ,44. In oCryopreserved PBMCs collected at the first study visit post infection (median 45 post estimated date of infection) from 33 individuals were thawed, rested overnight, and interrogated with two PTE Gag peptide pools  and a pool of 31 15-mer peptides spanning 3 highly conserved regions in Gag  via an IPeptide specific IFNγ ELISpot responses to measure breadth were performed on a separate subset of cryopreserved PBMCs collected a median of 339 days post infection for a total of 18 individuals in the cohort ,80. PBMCAll statistical analysis was performed using JMP Pro, version 12 . All bivariate continuous correlations were performed using standard linear regression. One-way comparison of means was performed using the Student’s t-test, and one-tailed p-values are reported. Standard ANOVA was used for the comparison of means between more than two variables. MANOVA was used to assess changes between means of two groups over time. Generalized Linear Models were used to test the predictive nature of two or more continuous or categorical variables against a single continuous outcome. Kaplan-Meier survival curves and Cox proportional hazards models were performed using an endpoint defined as a single CD4+ T cell count reading less than 300 cells/μl, unless otherwise specified, and statistics reported for survival analyses are generated from the log-rank test.S1 FigHLA class I score consist of the following alleles: HLA-B*1401, B*57, B*5801, and B*81. HLA class II scores consist of the following alleles: HLA-DQB1*02 and HLA-DRB1*15. Scores were calculated by summing the number of CD4-protective HLA class I or class II alleles for each individual for panel A and B, respectively. (A and B) Kaplan-Meier survival curves, with an endpoint defined as CD4 counts < 300/μl, demonstrating the effect of carriage of increasing numbers of protective HLA alleles on CD4+ T cell decline. P-values represent the comparison of decline trajectories between adjacent groups and were generated from a Cox proportional hazards model.(DOCX)Click here for additional data file.S2 Figt tests. ns (not significant); * p < 0.05; ** p < 0.01; *** p < 0.001.HLA alleles initially defined as being protective for CD4+ T cell decline were evaluated for differences in mean set point viral loads between those carrying the allele (light blue bars) and those not carrying the allele (gray bars). Statistics based on individual (DOCX)Click here for additional data file.S3 Figt test, two-tailed p-value. (B) The table depicts a generalized linear model with protective HLA class I alleles and excessive alcohol consumption as predictors of plasma LPS levels near seroconversion.Alcohol consumption data was collected through a revised version of the Alcohol Use Disorders Identification Test (AUDIT) questionnaire for couples in the ZEHRP cohort. Individuals with alcohol consumption data were divided into two groups, those that reported getting drunk monthly or more than monthly in the last year (n = 40), and those that reported getting drunk less than monthly or never in the last year (n = 58). (A) The graph depicts the comparison of the mean LPS levels early after HIV infection between these two groups. Statistics based on the Student’s (DOCX)Click here for additional data file.S4 Fig(A–D) Graphs depict the linkage disequilibrium (LD) between individual SNPs within the MHC locus and four HLA class I alleles: B*1401, B*57, B*5801, and B*81. The r2 values for the linkage disequilibrium association between SNPs and each HLA class I allele are plotted on the y-axis. SNPs with r2 values >0.8 (above dotted line) are considered to be in strong LD.(DOCX)Click here for additional data file.S1 Table(DOCX)Click here for additional data file.S2 Table(DOCX)Click here for additional data file.S3 Table(DOCX)Click here for additional data file.S4 Table(DOCX)Click here for additional data file.


SOX2 (Sex-determining region Y box 2) also functions as an oncoprotein and promotes cancer progression but the mechanisms involved remain largely unknown. Prostate cancer (PCa) is a common health problem in elderly. RAGE (Receptor for advanced glycation end products) is overexpressed in multiple human cancers. RAGE and SOX2 in benign and malignant prostate samples in correlation with the histopathological findings in order to evaluate their role as prognostic markers or therapeutic targets. The current study investigated the expression patterns of RAGE and SOX2 antibodies was applied on 87 prostatic biopsies . Immunohistochemical staining for RAGE and SOX2 (percentage of positive cells) was significantly higher in PCa lesions compared with prostatitis (p<0.01) and BPH (p<0.0001) and was also significantly higher in prostatitis compared with BPH lesions (p<0.01). Also, percentage of positive RAGE and SOX2 cells showed a significant stepwise increase from Gleason Grade 3 to Grade 5 and were significantly higher in high Gleason Scores (≥8) compared to lower Scores (≤7) with statistical significance (p=0.001). Expression of SOX2 were up-regulated in prostate cancer lesions, mainly in advanced grades, suggesting an active role of both antigens in the development and progression of prostate cancer and expecting the possibility of their use as therapeutic targets.RAGE and  Prostate cancer (PCa) is a common health problem that in the majority of cases starts to develop at the age of 50 years, reaching its peak at 60–70 years of age . While highly curable if localized, patients with metastatic disease have a 5-year survival rate of only 31% . As no reliable means of identifying patients at risk of metastatic dissemination are currently available . A better understanding of the mechanisms underlying the spread of prostate cancer cells should aid the development of treatment strategies that improve outcomes for patients with advanced disease .RAGE (Receptor for advanced glycation end products) is a cell surface molecule and a member immunoglobulin superfamily . RAGE has multiple ligands; its major one is advanced glycation end products (AGEs) and AGE-RAGE interaction is implicated in development, homeostasis, inflammation, neurodegenerative disorders, tumors, and pathogenesis of various diseases . It is likely that abnormal stimulation of AGE-RAGE affects cell cycle genes controlling the G1/S phase transition, increases the number of cells in the S phase of the cell cycle (DNA synthesis phase) and decreases the percentage of cells in G1 phase (cell growth phase) , so this interaction can be involved in the development, growth and metastasis of a number of tumor types including prostate cancer .SOX2 [Sex-determining region Y box 2] is one of the key members of the Sox family that encodes a series of transcription factors. SOX2 functions as an oncoprotein and promotes cancer progression and focused on its positive contribution to many physiological processes of cancer cells, such as proliferation and growth, cellular migration and invasion, maintenance of stemness, anti-apoptotic activity, chemoresistance, metastasis and tumorigenesis, however, the mechanisms involved remain largely unknown, both in prostate and in other cancers .The embryonic stemness gene SOX2 and RAGE expression in benign and malignant prostatic tissue samples and investigated their correlation with Gleason Grade, Gleason Score and Grade Group of prostate cancer cases.In this study, we analyzed immunohistochemical expression of We collected archival prostatic specimens and clinical pathological data related to 87 patients who had chronic urologic complaint and came to seek medical advice at Urology Department, Theodor Bilharz Research Institute, in the interval from January 2016 to December 2017, where clinical examination and radiological investigations were done, then cystoscopic or transrectal ultrasound-guided biopsies were taken. RAGE and SOX2 antibodies.Routine histopathological processing and diagnosis were made at Pathology Department, Theodor Bilharz Research Institute, using hematoxylin and eosin stain. Pathology reporting sheet includes diagnosis  and scoring of malignant cases according to Gleason’s criteria. Then immunohistochemical studies were applied on archival prostatic tissue using Prostate cancer patients under study did not receive hormone therapy, che¬motherapy or radiotherapy. Serum PSA (Serum prostate-specific antigen) levels were measured for all studied cases.This study was approved by our institutional ethics committee.Study groups were categorized as: Prostatitis: 16 cases; Benign prostatic hyperplasia (BPH): 20 cases; and Prostate cancer (PCa): 51 cases.Prostate cancer samples were further classified according to Gleason Classifications :• Gleason Grade (Grades 1-5): According to degree of tumor differentiation.• Gleason Score (Scores 2-10): Determined by adding up the two most prevalent Gleason Grade numbers.• Gleason Grade Group (Groups 1-5): A newly categorized grading system that is based on Gleason score.Immunohistochemical technique oC for 30 minutes. Sections were incubated in 0.03% hydrogen peroxide for 10 minutes at room temperature to remove endogenous peroxidase activity and then in blocking serum  for 30 minutes at room temperature. Sections were incubated overnight at 4°C in humid chamber with the primary antibodies: Anti-RAGE antibody (A11)  and Anti- SOX2 antibody  at an optimal dilution of 1:100. Sections were then washed three times for 5 min in PBS. Non-specific staining was blocked 5% normal serum for 30 min at room temperature. Finally, staining was developed with diaminobenzidine substrate and sections were counterstained with hematoxylin. PBS replaced RAGE and SOX2 antibody in negative controls. Tissue sections were processed into 3 μm thick sections. Unmasking of the antigen was performed with 10 ml sodium citrate buffer, pH 6.0, at 90Interpretation of immunostaining RAGE and SOX2 was semi-quantitatively estimated as brown membrano-cytoplasmic immunostaining (for RAGE) and brown nuclear immunostaining (for SOX2), which was calculated as the proportion of positive cells to the total number of cells calculated in 10 successive high power fields (percentage of positive cells). The expression of The sections were examined by using light microscope . Photomicrographs were taken using a microscope-camera . Statistical analysisRAGE or SOX2 expression with tumor grade. The comparisons of quantitative variables were performed between two groups using ANOVA and student t-tests. A p value of < 0.05 was considered of statistical significance.SPSS for Windows, version 20, was used for statistical analysis . Results were given as mean ±SD. Distribution of negative and positive cases was studied with cross tables (Pearson’s Chi square-test). Spearman rank correlation coefficient was used to investigate a possible correlation of In our study, the mean ages of PCa and BPH were 68.30 and 68.97 years respectively which showed significant difference with the mean age of prostatitis cases (65.33years) with p=0.05. Mean of serum PSA level was higher in PCa lesions (72.88 mg/ml) compared with prostatitis (20.55 mg/dl ) and PBH (10.90 mg/dl) with significant differences (p<0.01 and <0.0001 respectively) .RAGE and SOX2 (percentage of positive cells) was significantly higher in PCa lesions compared with prostatitis (p<0.01) and BPH (p<0.0001) and also was significantly higher in prostatitis compared with BPH lesions (p<0.01) (Expression of (p<0.01)    and 2SOX2 cells showed a significant stepwise increase from Gleason Grade 3 to Grade 5. Also, by comparing Gleason Score of PCa; expression of RAGE and SOX2 showed significantly increase in high Gleason Scores (≥8) compared to lower Scores (≤7) with statistical significance (p=0.001). Furthermore, by comparing the different Gleason Grade Groups of PCa according to the new Gleason classification; expression of RAGE and SOX2 was significantly increased in high Gleason Grade Groups (≥3) compared to lower Groups (≤2) with statistical significance; being tested by ANOVA , but no significant correlation with PSA level . SOX2 expression in our studied prostatic lesions.Serum prostate-specific antigen (PSA) is a protein produced by normal and malignant prostate gland and is a widely used parameter for early detection and monitoring of prostate cancer. Consistent with the fact that in addition to prostate cancer, prostatitis and BPH can cause elevation in man’s PSA level; we found an increase in mean PSA value in prostate cancer compared with prostatitis and BPH. Naz et al., (2004) observed that a majority of prostate cancer cases had serum PSA >20 ng/ml and 50% of BPH cases had serum PSA in the gray zone (4.1-20 ng/ml). Meanwhile, PSA levels did not show significant correlations with RAGE or The AGE-RAGE interaction plays a crucial part in the development of prostate cancer. Ishiguro et al., (2005) stated that inhibiting this interaction has potential as a new molecular target for cancer prevention or therapy. Several reports had revealed an association between the expression of RAGE and development of cancers, as Aboushousha et al., (2016) found that RAGE expression was up-regulated in human gastric adenocarcinoma and Kuniyasu et al., (2003) described that RAGE production was enhanced in metastatic compared to non-metastatic prostate cancer and healthy prostate tissue.In our study, we found significant increase in mean percentage of RAGE positive cells in prostate cancer tissue compared with prostatitis and BPH, which matches results of Lu et al., (2010) who reported a significant higher expression of RAGE in the prostate cancer than in normal prostate tissue by immunohistochemistry, western blot analysis and real-time quantitative PCR. Ishiguro et al., (2005) found that RAGE mRNA expression was up-regulated in prostate cancer tissues and also found that untreated primary prostate cancer tissue and hormone-refractory prostate cancer tissue showed significantly higher RAGE mRNA expression than normal prostate tissues. In a study performed by Aboushousha et al., (2018) they reported a significant higher RAGE expression in hepatic dysplasia and hepatocellular carcinoma lesions than in non-tumor hepatic tissue. Similarly, Amornsupak et al., (2017) found stronger RAGE immunoreactivity in the breast cancer tissue sections than in that in the benign disease tissue. Collectively, these findings suggest a role played by RAGE in development and growth of cancer lesions.RAGE expression in high-stage and aggressive breast cancer tumors. Sasahira et al., (2005) demonstrated higher RAGE expression in colorectal adenomas with severe atypia. In contrast; Aboushousha et al., (2018) detected higher expression of RAGE in low grades of hepatocellular carcinoma compared with high grades, moreover, Ishiguro et al., (2005) did not find a significant difference in RAGE mRNA expression according to both grade and stage in their prostate cancer samples.According to our results, RAGE expression showed a positive significant correlation with each of Gleason Grade, Gleason Score and Gleason Grade Group. We found a significant increase in mean percentage of RAGE positive cells in higher Gleason Grades (≥4) compared with lower Grade 3, and in higher Gleason Scores (≥ 8) compared with lower Scores (≤7) and also in higher Gleason Grade Groups ((≥3) compared with lower Groups (≤2). RAGE overexpression had been reported in advanced grades and stages in diverse types of malignant tumors. Aboushousha et al., (2016) found a significant correlation between RAGE expression with advanced stages and lymph node metastasis of gastric adenocarcinoma. Nankali et al., (2016) reported higher SOX2, an important transcription factor, has been shown to be over-expressed in most human cancer types, including prostate cancer. It plays a key role in cell self-renewal, differentiation, proliferation and apoptosis in stem cells and in cancer cells .SOX2 positive cells in prostate neoplastic tissue compared with BPH, which matches our results. In our study, we demonstrated a significant higher percentage of SOX2 positive cells in prostate cancer compared with prostatitis and BPH lesions; however, Ugolkov et al., (2011) reported an increase in expression of SOX2 in both benign and malignant prostate tissue, but only in a small percentage of cells (10%). So it is possible that SOX2 participates in the development and regulation of both hyperplastic and malignant prostatic tissues. Meanwhile Russo et al., (2016) demonstrated distinct SOX2 expression in the basal cell layer of normal prostate glands and its absence in most of the neoplastic epithelia, with the exception of a few foci of low-grade and high-grade prostate cancer, which agrees with the notion that prostates lose basal cells during cancer progression. Taken together, these controversial findings have indicated that the dynamics of SOX2 expression in prostate cancer are largely unknown and there is a need for further studies to elucidate its role in prostate cancer progress.Bae et al., (2010) and Jia et al., (2011) reported an increase in percentage of SOX2 expression and both of Gleason Grade and Gleason Score, we found a statistical significant increase in percentage of SOX2 positive cells in higher Gleason Grades, Gleason Scores and Gleason Grade Groups compared with lower ones. Since a higher Gleason grade indicates a worse prognosis, so it is suggested that SOX2 may contribute to the tumorigenesis of prostate cancer and may play an important role in the clinical progress of prostate cancer. Bae et al., (2016) postulated that, the expression of SOX2 in prostate tumors has been thought to promote a less differentiated embryonic stem cell tumor phenotype; that confers a worse disease prognosis. In accordance with previous studies  which reported a correlation between SOX2 positive cells and each of Gleason Grade, Gleason Score and Gleason Grade Group. Many previous studies reported an association between SOX2 expression and advanced stages in several human tumors. Yang et al., (2013) reported that SOX2 expression was associated with clinical stage and lymph node status in patients with small cell lung cancer. Tang et al., (2013) suggested that SOX2 expression was significantly associated with clinical stage, lymph node metastasis and recurrence in laryngeal squamous cell carcinoma. Zhang et al., (2010) reported that patients with strong SOX2 expression showed deeper invasion and advanced clinical stages compared to patients with low SOX2 expression in gastric cancer. Huang et al., (2014) reported a close association between SOX2 expression with clinicopathological parameters in breast cancer; including high histological grade, large tumor size, molecular subtypes, negative hormone receptor status and high proliferation index. In the present study, we found a positive significant correlation between percentage of RAGE and SOX2 were up-regulated in prostate cancer; mainly in cancers with a worse prognosis which have higher Gleason Grade, higher Gleason Score and higher Gleason Grade Group. So, we suggest a role played by RAGE and SOX2 in the progression of prostate cancer that they can be used as predictive tools or therapeutic targets in prostate cancer. In conclusion,


Recent studies have suggested that genes longer than 100 kb are more likely to be misregulated in neurological diseases associated with synaptic dysfunction, such as autism and Rett syndrome. These length-dependent transcriptional changes are modest in MeCP2-mutant samples, but, given the low sensitivity of high-throughput transcriptome profiling technology, here we re-evaluate the statistical significance of these results. We find that the apparent length-dependent trends previously observed in MeCP2 microarray and RNA-sequencing datasets disappear after estimating baseline variability from randomized control samples. This is particularly true for genes with low fold changes. We find no bias with NanoString technology, so this long gene bias seems to be particular to polymerase chain reaction amplification-based platforms. In contrast, authentic long gene effects, such as those caused by topoisomerase inhibition, can be detected even after adjustment for baseline variability. We conclude that accurate characterization of length-dependent (or other) trends requires establishing a baseline from randomized control samples. Recent studies have suggested that long genes (>100 kb) are more likely to be misregulated in some neurological diseases, such as autism and Rett syndrome. Here the authors find that the apparent length-dependent trends previously observed in MeCP2 microarray and RNA-sequencing datasets disappeared after controlling for baseline variations. This is not surprising in the case of RTT, given the role of the causative gene, MeCP2, in epigenetic regulation. But recent microarray and RNA-sequencing (RNA-seq) studies have observed a trend that is surprising: the genes dysregulated in neurodevelopmental syndromes tend to be those that are longer than 100 kb3. This intriguing length bias has been observed across both epigenetic and transcriptional datasets for Angelman syndrome4, RTT8, Fragile X syndrome9, and autism11. The degree of bias tends to be fairly mild, however, and long genes are themselves overrepresented in the brain compared to other tissues in the body2. Because this is a recurring theme in neurologic disease datasets, it is worth examining this apparent bias more closely.Large-scale analysis of transcriptional changes has transformed our understanding of many human neurological diseases. Neurodevelopmental disorders such as Rett syndrome (RTT) and Fragile X syndrome, for example, involve transcriptional alterations in thousands of genes11 partitioned the entire genome into hundreds of overlapping bins (or windows), with each bin containing hundreds of genes. Within each bin, the average fold change in wild-type (WT) or untreated brain tissue was compared to that observed in the knock-out or treatment groups, and a running average log2fold change was plotted against the average gene length. In these running average plots, long genes demonstrated a nonzero mean compared to short genes. Yet these analyses did not establish a baseline of inherent variation among samples within a given genotype, and they did not employ a statistical test to determine the significance of the length-dependent changes. Variations in measured gene expression can arise because of RNA priming13, guanine–cytosine content14, transcript length15, or library preparation16, all of which must be accounted for before drawing biological conclusions18.The aforementioned gene expression studiesWe, therefore, reanalyze a number of large datasets derived from different transcriptome profiling technologies and set out to determine the best way to enhance the signal-to-noise ratio. To this end, we develop a statistical approach to accurately estimate noise and identify statistically significant gene length-dependent changes. Upon implementing this approach, we show a genuine trend in transcriptional alterations in long genes when the fold-change values are large, such as those caused by topoisomerase inhibition. In contrast with prior studies, however, we find no preferential misregulation of long genes in MeCP2 datasets after correcting for statistical significance and baseline variability. We propose that smaller fold changes in transcription observed after polymerase chain reaction (PCR) amplification leads to overestimation of long gene expression levels.10, also known as running average plots  and vehicle-treated cortical neurons (vehicle or V), we observed a preferential downregulation of long genes  and vehicle vs. vehicle (V/V) comparisons, followed by an adjustment for multiple hypothesis testing. For consistency, these plots are referred to as overlap plots  and MECP2 duplication syndrome (caused by duplication or triplication of the locus)—have provided a wealth of transcriptome data. Experiments in mouse models of both syndromes have suggested that loss of MeCP2 function causes preferential upregulation of long genes6 and, conversely, that gain of MeCP2 function leads to preferential downregulation of long genes6. We chose to delve deeper into these datasets to explore the extent of the contribution of long genes to RTT pathology. We first applied our approach to ten publicly available MeCP2 studies  samples   Fig. —on the cels Fig. . Thus, w8 from the cortical cells of Mecp2-mutant mice bearing either of two Rett-causing mutations, T158M or R106W, which are among the most common mutations found in RTT patients27. The dataset was generated by combining an in vivo biotinylation system with Cre-loxP technology that circumvented the cellular heterogeneity of the brain and enabled examination of transcriptomic changes due to MeCP2 in specific cell types, in both male and female mice8.A recent study reported that transcripts of long genes were downregulated in nuclear and nascent RNA samplesMecp2-mutant excitatory neurons from 18-week-old T158M or R106W female mice , as it is with the long genes observed in the MeCP2 datasets, it is more likely that the observed long gene trend is due to inherent variation among samples. The reported long gene trend in the MeCP2 datasets is in the same range as the noise in the intra-sample comparison in the control groups, and this effect was seen in all the MeCP2 datasets that we assessed. This further suggests that the length-dependent variability estimated from microarray and RNA-seq platforms is not sensitive enough to capture small transcriptional changes. We therefore recommend that baseline gene-length dependency be evaluated from the control group first to understand the statistical significance of observed long gene trends in any sequencing dataset.MECP2 mutation28 , neural progenitor cells, and neurons from the fibroblasts of two independent patients, with and without a Mecp2 datasets, they have been reported to be preferentially upregulated in human RTT samples6, and we wondered if a more robust signal would be observed in postmortem human datasets. Three RTT and three normal control samples from the superior frontal gyrus were obtained from a previous study29. These samples were from three different ages: RTT samples were obtained from donors aged 8, 6, and <4 years (pooled samples from a 2- and a 4-year old), with approximate age-matched normal control samples obtained from donors aged 10, 5, and 2 years, respectively. The long gene trend was observed in a comparison of the three RTT samples to the three control samples .Although long genes do not appear to be upregulated above the level of background noise in murine les Fig. . Because31 and in-house generated RNA-seq RTT datasets. The Lin et al. dataset31 consists of postmortem brain samples from the frontal and temporal cortex of RTT patients with age-matched controls . Because the phenotypes are similar for RTT patients in this age range30, we grouped these RTT samples together and compared them to the pooled age-matched controls. We computed running average plots on the normalized dataset (Methods), and observed no overrepresentation of long genes . To further probe whether the long gene trend might be present in the early stages of the disease, we compared a RTT postmortem male sample from frontal cortex  to an age-matched control sample  and again found no significant upregulation of long genes , which increases the inherent variability in gene expression among biological replicates. Such variability could obscure the effects of a subtle bias in the sequencing process. Nevertheless, the present findings suggest that long genes are not preferentially misregulated in human RTT datasets.23. We divided the entire list of differentially expressed genes into four groups based on gene length (> or <100 kb) and fold-change direction (either up or down). Consistent with our overlap plots, we found long genes to be substantially overrepresented and downregulated in topotecan datasets , B (Human Brain Reference RNA), C (a mixture of A and B at a ratio of 3:1), and D (a mixture of A and B at a ratio of 1:3). The RNA-seq datasets generated using the Illumina HiSeq 2000 platform across six different sites were used for quality control analyses (Methods), and the raw read counts were normalized using the DESeq2 method33.To investigate whether the apparent length bias might be a function of amplification-based platforms, we next performed running average analysis on the samples from the phase-III sequencing/microarray quality control (SEQC) projectβ ratio of observed gene expression in the samples, which is obtained from the following equation: ((B–A)/(C–A))32. The value of the β ratio34 is 4:1 (or log2(4) = 2). In theory, the β ratio should be independent of gene length in the brain and nonbrain tissues. After assessing various SEQC datasets, we found that the Novartis dataset had nominal batch effects and the β ratio was close to 2. This dataset would be ideal, as it would not bias downstream analyses.To determine whether the SEQC dataset showed nominal batch effects or other nonbiological variability, we used multidimensional scaling (MDS) plots to see if the samples clustered according to RNA sample type Fig. . To asceβ ratio computed from all SEQC sample types is constant over gene length. The expected average log2β ratio should be a horizontal line along the x-axis with a y-intercept equal to two . We found, however, that the expected ratio was not maintained for long genes and was overestimated . Instead, we found that long genes deviated from the expected pattern, with the fold changes of long genes being overestimated  RNA-seq samples into two groups of 32 samples each, based on their 32. Human brain reference samples (type B) were separated into two groups based on y-axis location on the MDS plot . The MDS plot on normalized data showed that the samples clustered based on sample type   into three groups (n = 16 each) based on different library preparation ID numbers from the Novartis SEQC dataset. The principle component analysis (PCA) plot clearly clustered the brain samples based on the library preparation group to which they belonged  models when library preparations ID 2 (red) and ID 1 (green), or library preparations ID 3 (blue) and ID 1 (green), were compared6. However, we observed no differentially expressed genes when comparing two batches of brain samples from the NanoString nCounter dataset.We next assessed the influence of the fold-change threshold on differential expression analysis using brain samples (having 750 genes present in Human NanoString nCounter dataset). Although we did not expect to see a trend between replicates, we observed preferential regulation of long genes when the fold change was small . The PCA plot on normalized datasets showed that the samples clustered based on sample type  and FDR < 0.05).To determine whether a long gene trend is present only in the 2((mean(group1) + 1)/(mean(group2) + 1)) and using shrunken log fold-changes by DESeq2  suggested that the latter method yields more highly correlated fold changes  quantified by both the RNA-seq and nCounter NanoString expression analyses , which further supports our hypothesis that artefactual long gene trends are more likely to appear in amplification-based expression datasets.This analysis suggests that the genes identified as differentially expressed by RNA-seq with lower fold changes are not reproducible by NanoString. To determine whether fold-changes are inflated in RNA-seq, we compared the absolute difference of log19. To establish a statistical baseline for the length-dependent gene regulation analysis, we took advantage of a large number of SEQC consortium datasets where the relative gene expression fold change has been measured using RNA-seq and microarray. We demonstrated the power of big data analyses by uncovering major sources of technical variation, such as intra-sample variation and PCR amplification bias, that can affect the analysis of long gene expression. By contrast, NanoString nCounter technology, which does not rely on amplification, revealed no long gene bias. Our results demonstrate that amplification-based transcriptomic technologies can lead to overestimations of long gene expression changes.Several recent papers have suggested that diseases associated with synaptic dysfunction tend to preferentially involve misregulation of long genes (genes with length > 100 kb)10. By contrast, we found no bias toward long gene dysregulation in the MeCP2 datasets after baseline correction, even when we focused on only those genes that are differentially expressed to a statistically significant degree. The sole exception was the one infantile RTT case, but a single case does not allow us to draw any firm conclusions. Again, this does not rule out that MeCP2 regulates some long genes; it simply does not support a preferential misregulation of long genes in MeCP2-mutant samples.This is not to say that there is never a bias toward expression changes in long genes. The topotecan datasets showed an authentic long gene trend even after accounting for baseline variability. This sizeable effect on long gene expression is consistent with the biological function of topotecan, which inhibits topoisomerase I; long genes should, in theory, be more dependent on proper unwinding during transcription elongationApparent expression changes in long genes are clearly liable to exaggeration by biases in microarray and RNA-seq. We recommend eliminating confounds such as batch effects and properly estimating both intersample and intrasample variations; the control datasets must be carefully analyzed in order to reveal the degree of baseline variability, which then can inform further analyses of the size of the signal required to overcome background noise in sequencing datasets  were downloaded from GEO. RMA function39 in the R affy package was used to perform background correction, normalization, and summarization of core probesets. NetAffx annotation files (Release 33 for mm9) was used to map affy probes to their official gene symbols. The expression values for genes with multiple probes were obtained by taking the average log2 expression value across all the probes corresponding to each gene. The NetAffx annotation file has information about the probe location, length and gene coordinates; we calculated gene length using the gene coordinates, and we specifically used gene length in all our figures where Gene length in kb is defined on the x-axis. We also ran our analysis on the transcript length , RTT iPSC28 and RNA-seq RTT dataset28 were mapped to mm10 genome using STAR aligner v2.4.2a40 . For the hypothalamus RNA-seq dataset23 (GSE66871), we used a published list of differentially expressed genes and normalized counts. For the Johnson et al.8 RNA-seq dataset (GSE83474), we used the raw count files provided by the authors in GEO and it was normalized using DESeq233 (https://bioconductor.org/packages/release/bioc/html/DESeq2.html). Similarly, for the transcriptome analysis of frontal and temporal cortex from RTT patients, we used the normalized gene expression profile provided in GSE7530331. We generated box plots and MDS plots to check for outliers in the sample distributions. The annotation files provided by GPL10558 were obtained to map Illumina probes to official gene symbols and RefSeq hg19 annotation was used to obtain gene length information.The transcriptome dataset of the visual cortex6 to compute the running average plots. In brief, the genes were sorted by their lengths and partitioned into bins using a sliding window of 200 consecutive genes in steps of 40 genes. The log2 fold-change values for genes within each bin were averaged. For consistency with the previous studies, we used genes whose lengths are between 1 and 1000 kb for all the plots. These plots were created using the ggplot2 package (https://github.com/tidyverse/ggplot2) in R.We used the same method as described in ref. t test was applied to each of the bins between KO/WT (or Tg/WT or D/V or RTT/WT) and WT/WT (or V/V), followed by multiple hypothesis adjustment using the Benjamini–Hochberg method (FDR). The significant bins (FDR < 0.05) are denoted by red and non-significant bins are denoted by gray. The overlap plots were created using the cowplot package in R (https://github.com/wilkelab/cowplot).We define the plots used in Fig. 41 to facilitate uniform comparison between these gene lists. The genomic locations were obtained for mm10/GRCm38. The original fold change and FDR thresholds reported by respective publications were used. For the microarray datasets, genes were plotted against their length. In the case of the RNA-seq datasets, the calculation was done based on UCSC transcript IDs. Long genes (gene length > 100 kb) were represented as red dots and short genes were shown as blue dots. The numbers of the upregulated and downregulated long/short genes are shown in each respective quadrant.To measure the distribution of the long gene bias among differentially expressed genes, we extracted published lists of genes found to be significantly activated or repressed by MeCP2 across different brain regions. The published lists of differentially expressed genes were downloaded from the supplementary files in each study. Because of the frequent changes in gene name and annotation, we used MGI batch query42. The GEO accession IDs for the RNA-seq and microarray datasets are GSE47774 and GSE56457, respectively. Raw count files from the Australian Genome Research Facility (AGR), Beijing Genomics Institute (BGI), Weill Cornell Medical College (CNL), City of Hope (COH), Mayo Clinic (MAY), and Novartis (NVS) were normalized using the DESeq2 method33. Principal Component Analysis (PCA) and MDS plots (using Euclidean distance) were used to do a sanity check for a nominal amount of batch effects.We measured the long gene fold-change bias in RNA-seq and microarray benchmark SEQC consortium datasets, using the RNA-seq datasets generated by all of the Illumina HiSeq 2000 sites and the microarray datasets generated by USF using the Affymetrix Human Gene 2.0 ST Array. The RNA-seq raw count files and microarray PrimeView normalized files were accessed from the Gene Expression Omnibus database (GEO)transcripts and exon functions in GenomicFeatures Bioconductor package43 (https://bioconductor.org/packages/release/bioc/html/GenomicFeatures.html) were used to obtain the gene and transcript lengths, respectively, from the hg19 GTF file. Since a small number of genes or transcripts have multiple different genomic locations, genes or transcripts with the longest length were used. Expression values for genes with multiple transcript clusters were averaged across all transcript clusters corresponding to each gene. Similarly, for the microarray USF PrimeView dataset, sanity checks were performed using boxplot and MDS plots. Boxplots were used to check if the dataset was properly normalized and MDS plots were used to confirm that the dataset had a nominal amount of batch effects or non-biological variation.For further downstream analyses, we decided to use the Novartis dataset, as it had a minimal amount of non-biological variation. The Novartis dataset consisted of 64 technical samples each of A , B (Human Brain Reference RNA), C (3A:1B) and D (1A:3B). We did not use sample type E (Ambion ERCC Spike-In Control Mix 1) or F (Ambion ERCC Spike-In Control Mix 2) in our analysis. For consistency with the SEQC consortium, we used hg19 iGenome NCBI/RefSeq annotation (build 27.2). The 33: 1) Total Counts44 and 2) the trimmed mean of M-values (TMM) method implemented in edgeR37 (https://bioconductor.org/packages/release/bioc/html/edgeR.html). For Total Counts, scaling factors were computed such that the normalized read counts across all samples are equal. In the case of the TMM method, we used the calcNormFactors function in the edgeR Bioconductor package to get the scaling factors and normalized read counts.To ensure that our normalization methods were not obscuring a genuine long gene bias, we normalized the raw counts from the Novartis RNA-seq dataset using two other methods apart from DESeq245 in the R NanoStringNorm package (https://cran.r-project.org/web/packages/NanoStringNorm/index.html) to normalize the dataset. Boxplots and MDS plots were used to confirm the samples were properly normalized and to check for the presence of batch effects. The two-sided Wilcoxon rank sum test was used to compare the distribution of the fold-change between long and short genes across the three different platforms—RNA-seq, Microarray and NanoString.We purchased Universal Human Reference RNA from Agilent Technologies, Inc., and Human Brain Reference RNA from Life Technologies, Inc. For the nCounter experiments, we used the same RNA sample types as the SEQC consortium. We assessed RNA purity and integrity with Bioanalyzer  prior to use in the nCounter assays. Sample preparation and analysis were done using a nCounter Prep Station 5 s and a nCounter Digital Analyzer 5s. Expression of 770 genes (~730 genes with ~40 housekeeping genes and positive and negative controls) was assessed using the nCounter Human PanCancer Pathways Panel. A second PanCancer Pathways Panel was run using the same samples submitted to the first panel to assess the effect of batches on nCounter results. We used the NanoStringNorm functionMecp2-null mice) using the Aurum™ Total RNA Fatty and Fibrous Tissue Kit (Bio-Rad 7326830) per the manufacturer’s instructions. Genomic DNA was eliminated using an on-column DNase digestion step. RNA quality was assessed using the Agilent 2100 Bioanalyzer system prior to library preparation for deep sequencing or use of the total RNA for NanoString nCounter quantification.We performed RNA extraction and purification from the cerebellum of male mice 8–9 weeks of age  using STAR aligner v2.4.2a40 with default parameters. The overall mappability for all 6 samples was above 90%  was assessed using the nCounter Mouse PanCancer Pathways Panel  database with the accession codes as follows: GSE94073 [Supplementary InformationPeer Review FileDescription of Additional Supplementary FilesSupplementary Data 1Supplementary Data 2Supplementary Data 3Supplementary Data 4