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Between-sample normalization is a critical step in genomic data analysis to remove systematic bias and unwanted technical variation in high-throughput data. Global normalization methods are based on the assumption that observed variability in global properties is due to technical reasons and are unrelated to the biology of interest. For example, some methods correct for differences in sequencing read counts by scaling features to have similar median values across samples, but these fail to reduce other forms of unwanted technical variation. Methods such as quantile normalization transform the statistical distributions across samples to be the same and assume global differences in the distribution are induced by only technical variation. However, it remains unclear how to proceed with normalization if these assumptions are violated, for example, if there are global differences in the statistical distributions between biological conditions or groups, and external information, such as negative or control features, is not available. Here, we introduce a generalization of quantile normalization, referred to as smooth quantile normalization (qsmooth), which is based on the assumption that the statistical distribution of each sample should be the same (or have the same distributional shape) within biological groups or conditions, but allowing that they may differ between groups. We illustrate the advantages of our method on several high-throughput datasets with global differences in distributions corresponding to different biological conditions. We also perform a Monte Carlo simulation study to illustrate the bias-variance tradeoff and root mean squared error of qsmooth compared to other global normalization methods. A software implementation is available from https://github.com/stephaniehicks/qsmooth.

Diet- and age-dependent changes in glucose regulation in mice occur, but the temporal development, mechanisms and influence of dietary fat source remain to be defined. We followed metabolic changes in three groups of mice including a low-fat diet (LFD) reference group and two high-fat, high-sucrose diets based on either fish oil (FOD) or soybean oil (SOD), rich in ω3- and ω6-polyunsaturated fatty acids, respectively, to closely monitor the age-dependent development in glucose regulation in both obese (SOD-fed) and lean (LFD- and FOD-fed) mice. We assessed glucose homeostasis and glucose clearance at week 8, 12, 16, 24, 31, and 39 and performed an insulin tolerance test at week 40. We further analyzed correlations between the gut microbiota and key metabolic parameters. Interestingly, alterations in glucose homeostasis and glucose clearance were temporally separated, while 16S ribosomal gene amplicon sequencing revealed that gut microbial alterations formed correlation clusters with fat mass and either glucose homeostasis or glucose clearance, but rarely both. Importantly, effective glucose clearance was maintained in FOD- and even increased in LFD-fed mice, whereas SOD-fed mice rapidly developed impaired glucose clearance followed by a gradual improvement from week 8 to week 39. All groups had similar responses to insulin 40 weeks post diet initiation despite severe nonalcoholic steatohepatitis in SOD-fed mice. We conclude that age-related alterations in glucose regulation may occur in both lean and obese mice and are modulated by dietary fat as indicated by the sustained metabolic homeostasis observed in mice fed ω3-polyunsaturated fatty acids., (Copyright © 2017 Elsevier Inc. All rights reserved.)

Tuberculosis is the leading killer among infectious diseases worldwide. Increasing multidrug resistance has prompted new approaches for tuberculosis drug development, including targeted inhibition of virulence determinants and of signaling cascades that control many downstream pathways. We used a multisystem approach to determine the effects of a potent small-molecule inhibitor of the essential Mycobacterium tuberculosis Ser/Thr protein kinases PknA and PknB. We observed differential levels of phosphorylation of many proteins and extensive changes in levels of gene expression, protein abundance, cell wall lipids, and intracellular metabolites. The patterns of these changes indicate regulation by PknA and PknB of several pathways required for cell growth, including ATP synthesis, DNA synthesis, and translation. These data also highlight effects on pathways for remodeling of the mycobacterial cell envelope via control of peptidoglycan turnover, lipid content, a SigE-mediated envelope stress response, transmembrane transport systems, and protein secretion systems. Integrated analysis of phosphoproteins, transcripts, proteins, and lipids identified an unexpected pathway whereby threonine phosphorylation of the essential response regulator MtrA decreases its DNA binding activity. Inhibition of this phosphorylation is linked to decreased expression of genes for peptidoglycan turnover, and of genes for mycolyl transferases, with concomitant changes in mycolates and glycolipids in the cell envelope. These findings reveal novel roles for PknA and PknB in regulating multiple essential cell functions and confirm that these kinases are potentially valuable targets for new antituberculosis drugs. In addition, the data from these linked multisystems provide a valuable resource for future targeted investigations into the pathways regulated by these kinases in the M. tuberculosis cell. IMPORTANCE Tuberculosis is the leading killer among infectious diseases worldwide. Increasing drug resistance threatens efforts to control this epidemic; thus, new antitubercular drugs are urgently needed. We performed an integrated, multisystem analysis of Mycobacterium tuberculosis responses to inhibition of its two essential serine/threonine protein kinases. These kinases allow the bacterium to adapt to its environment by phosphorylating cellular proteins in response to extracellular signals. We identified differentially phosphorylated proteins, downstream changes in levels of specific mRNA and protein abundance, and alterations in the metabolite and lipid content of the cell. These results include changes previously linked to growth arrest and also reveal new roles for these kinases in regulating essential processes, including growth, stress responses, transport of proteins and other molecules, and the structure of the mycobacterial cell envelope. Our multisystem data identify PknA and PknB as promising targets for drug development and provide a valuable resource for future investigation of their functions., (Copyright © 2018 Carette et al.)

The lung microbiota has received less attention compared to other body sites, in part because its study carries special technological difficulties related to obtaining reliable samples as compared to other body niches. The limited number of studies on the sputum microbiota on TB patients and controls available so far have reported inconsistent, and sometimes, contradictory results. Aiming to clarify if changes in the lung microbiota composition are associated with pulmonary TB, we performed a meta-analysis of available data on microbiota of the lower respiratory tract in TB patients and healthy controls. Re-processing next generation sequencing data under uniform parameters and utilizing state-of-the-art bioinformatics analysis, we obtained distinct clusterings of microbiota between TB cases vs. controls across multiple studies. We identified Tumebacillus ginsengisoli, Propionibacterium acnes, Haemophilus parahaemolyticus as differentially abundant species signature in healthy controls while Caulobacter henricii, Actinomyces graevenitzii, Rothia mucilaginosa, in addition to Mycobacterium tuberculosis as differentially abundant species signature in TB cases, and described R. mucilaginosa as the anchoring species in a network of bacteria co-occurring with Mycobacterium tuberculosis (Mtb) infection., (Copyright © 2018. Published by Elsevier Ltd.)

Although all human tissues carry out common processes, tissues are distinguished by gene expression patterns, implying that distinct regulatory programs control tissue specificity. In this study, we investigate gene expression and regulation across 38 tissues profiled in the Genotype-Tissue Expression project. We find that network edges (transcription factor to target gene connections) have higher tissue specificity than network nodes (genes) and that regulating nodes (transcription factors) are less likely to be expressed in a tissue-specific manner as compared to their targets (genes). Gene set enrichment analysis of network targeting also indicates that the regulation of tissue-specific function is largely independent of transcription factor expression. In addition, tissue-specific genes are not highly targeted in their corresponding tissue network. However, they do assume bottleneck positions due to variability in transcription factor targeting and the influence of non-canonical regulatory interactions. These results suggest that tissue specificity is driven by context-dependent regulatory paths, providing transcriptional control of tissue-specific processes., (Copyright © 2017 The Authors. Published by Elsevier Inc. All rights reserved.)

Background: Although ultrahigh-throughput RNA-Sequencing has become the dominant technology for genome-wide transcriptional profiling, the vast majority of RNA-Seq studies typically profile only tens of samples, and most analytical pipelines are optimized for these smaller studies. However, projects are generating ever-larger data sets comprising RNA-Seq data from hundreds or thousands of samples, often collected at multiple centers and from diverse tissues. These complex data sets present significant analytical challenges due to batch and tissue effects, but provide the opportunity to revisit the assumptions and methods that we use to preprocess, normalize, and filter RNA-Seq data - critical first steps for any subsequent analysis., Results: We find that analysis of large RNA-Seq data sets requires both careful quality control and the need to account for sparsity due to the heterogeneity intrinsic in multi-group studies. We developed Yet Another RNA Normalization software pipeline (YARN), that includes quality control and preprocessing, gene filtering, and normalization steps designed to facilitate downstream analysis of large, heterogeneous RNA-Seq data sets and we demonstrate its use with data from the Genotype-Tissue Expression (GTEx) project., Conclusions: An R package instantiating YARN is available at http://bioconductor.org/packages/yarn .

Characterizing the collective regulatory impact of genetic variants on complex phenotypes is a major challenge in developing a genotype to phenotype map. Using expression quantitative trait locus (eQTL) analyses, we constructed bipartite networks in which edges represent significant associations between genetic variants and gene expression levels and found that the network structure informs regulatory function. We show, in 13 tissues, that these eQTL networks are organized into dense, highly modular communities grouping genes often involved in coherent biological processes. We find communities representing shared processes across tissues, as well as communities associated with tissue-specific processes that coalesce around variants in tissue-specific active chromatin regions. Node centrality is also highly informative, with the global and community hubs differing in regulatory potential and likelihood of being disease associated., Competing Interests: The authors declare no conflict of interest.

Background: Cell lines are an indispensable tool in biomedical research and often used as surrogates for tissues. Although there are recognized important cellular and transcriptomic differences between cell lines and tissues, a systematic overview of the differences between the regulatory processes of a cell line and those of its tissue of origin has not been conducted. The RNA-Seq data generated by the GTEx project is the first available data resource in which it is possible to perform a large-scale transcriptional and regulatory network analysis comparing cell lines with their tissues of origin., Results: We compared 127 paired Epstein-Barr virus transformed lymphoblastoid cell lines (LCLs) and whole blood samples, and 244 paired primary fibroblast cell lines and skin samples. While gene expression analysis confirms that these cell lines carry the expression signatures of their primary tissues, albeit at reduced levels, network analysis indicates that expression changes are the cumulative result of many previously unreported alterations in transcription factor (TF) regulation. More specifically, cell cycle genes are over-expressed in cell lines compared to primary tissues, and this alteration in expression is a result of less repressive TF targeting. We confirmed these regulatory changes for four TFs, including SMAD5, using independent ChIP-seq data from ENCODE., Conclusions: Our results provide novel insights into the regulatory mechanisms controlling the expression differences between cell lines and tissues. The strong changes in TF regulation that we observe suggest that network changes, in addition to transcriptional levels, should be considered when using cell lines as models for tissues.

Contact: johnq@jimmy.harvard.edu or dschlauch@fas.harvard.edu., Availability and Implementation: PandaR is provided as a Bioconductor R Package and is available at bioconductor.org/packages/pandaR., (© The Author(s) 2017. Published by Oxford University Press.)

Although cholera is a major public health concern in Mozambique, its transmission patterns remain unknown. We surveyed the genetic relatedness of 75 Vibrio cholerae isolates from patients at Manhiça District Hospital between 2002-2012 and 3 isolates from river using multilocus variable-number tandem-repeat analysis (MLVA) and whole genome sequencing (WGS). MLVA revealed 22 genotypes in two clonal complexes and four unrelated genotypes. WGS revealed i) the presence of recombination, ii) 67 isolates descended monophyletically from a single source connected to Wave 3 of the Seventh Pandemic, and iii) four clinical isolates lacking the cholera toxin gene. This Wave 3 strain persisted for at least eight years in either an environmental reservoir or circulating within the human population. Our data raises important questions related to where these isolates persist and how identical isolates can be collected years apart despite our understanding of high change rate of MLVA loci and the V. cholerae molecular clock.

Plant-associated microbes are important for the growth and health of their hosts. As a result of numerous prior studies, we know that host genotypes and abiotic factors influence the composition of plant microbiomes. However, the high complexity of these communities challenges detailed studies to define experimentally the mechanisms underlying the dynamics of community assembly and the beneficial effects of such microbiomes on plant hosts. In this work, from the distinctive microbiota assembled by maize roots, through host-mediated selection, we obtained a greatly simplified synthetic bacterial community consisting of seven strains ( Enterobacter cloacae , Stenotrophomonas maltophilia, Ochrobactrum pituitosum, Herbaspirillum frisingense, Pseudomonas putida, Curtobacterium pusillum , and Chryseobacterium indologenes ) representing three of the four most dominant phyla found in maize roots. By using a selective culture-dependent method to track the abundance of each strain, we investigated the role that each plays in community assembly on roots of axenic maize seedlings. Only the removal of E. cloacae led to the complete loss of the community, and C. pusillum took over. This result suggests that E. cloacae plays the role of keystone species in this model ecosystem. In planta and in vitro, this model community inhibited the phytopathogenic fungus Fusarium verticillioides , indicating a clear benefit to the host. Thus, combined with the selective culture-dependent quantification method, our synthetic seven-species community representing the root microbiome has the potential to serve as a useful system to explore how bacterial interspecies interactions affect root microbiome assembly and to dissect the beneficial effects of the root microbiota on hosts under laboratory conditions in the future.

Tobacco products, specifically cigarettes, are home to microbial ecosystems that may play an important role in the generation of carcinogenic tobacco-specific nitrosamines (TSNAs), as well as the onset of multiple adverse human health effects associated with the use of these products. Therefore, we conducted time-series experiments with five commercially available brands of cigarettes that were either commercially mentholated, custom-mentholated, user-mentholated, or non-mentholated. To mimic user storage conditions, the cigarettes were incubated for 14 days under three different temperatures and relative humidities (i.e., pocket, refrigerator, and room). Overall, 360 samples were collected over the course of 2 weeks and total DNA was extracted, PCR amplified for the V3V4 hypervariable region of the 16S rRNA gene and sequenced using Illumina MiSeq. A subset of samples ( n = 32) was also analyzed via liquid chromatography with tandem mass spectrometry for two TSNAs: N '-nitrosonornicotine (NNN) and 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanone (NNK). Comparative analyses of the five tobacco brands revealed bacterial communities dominated by Pseudomonas , Pantoea , and Bacillus , with Pseudomonas relatively stable in abundance regardless of storage condition. In addition, core bacterial operational taxonomic units (OTUs) were identified in all samples and included Bacillus pumilus, Rhizobium sp., Sphingomonas sp., unknown Enterobacteriaceae , Pantoea sp., Pseudomonas sp., Pseudomonas oryzihabitans , and P. putida . Additional OTUs were identified that significantly changed in relative abundance between day 0 and day 14, influenced by brand and storage condition. In addition, small but statistically significant increases in NNN levels were observed in user- and commercially mentholated brands between day 0 and day 14 at pocket conditions. These data suggest that manufacturing and user manipulations, such as mentholation and storage conditions, may directly impact the microbiome of cigarette tobacco as well as the levels of carcinogens.

Background: There is a paucity of data regarding the microbial constituents of tobacco products and their impacts on public health. Moreover, there has been no comparative characterization performed on the bacterial microbiota associated with the addition of menthol, an additive that has been used by tobacco manufacturers for nearly a century. To address this knowledge gap, we conducted bacterial community profiling on tobacco from user- and custom-mentholated/non-mentholated cigarette pairs, as well as a commercially-mentholated product. Total genomic DNA was extracted using a multi-step enzymatic and mechanical lysis protocol followed by PCR amplification of the V3-V4 hypervariable regions of the 16S rRNA gene from five cigarette products (18 cigarettes per product for a total of 90 samples): Camel Crush, user-mentholated Camel Crush, Camel Kings, custom-mentholated Camel Kings, and Newport Menthols. Sequencing was performed on the Illumina MiSeq platform and sequences were processed using the Quantitative Insights Into Microbial Ecology (QIIME) software package., Results: In all products, Pseudomonas was the most abundant genera and included Pseudomonas oryzihabitans and Pseudomonas putida, regardless of mentholation status. However, further comparative analysis of the five products revealed significant differences in the bacterial compositions across products. Bacterial community richness was higher among non-mentholated products compared to those that were mentholated, particularly those that were custom-mentholated. In addition, mentholation appeared to be correlated with a reduction in potential human bacterial pathogens and an increase in bacterial species resistant to harsh environmental conditions., Conclusions: Taken together, these data provide preliminary evidence that the mentholation of commercially available cigarettes can impact the bacterial community of these products.

Emiliania huxleyi is a model coccolithophore micro-alga that generates vast blooms in the ocean. Bacteria are not considered among the major factors influencing coccolithophore physiology. Here we show through a laboratory model system that the bacterium Phaeobacter inhibens , a well-studied member of the Roseobacter group, intimately interacts with E. huxleyi. While attached to the algal cell, bacteria initially promote algal growth but ultimately kill their algal host. Both algal growth enhancement and algal death are driven by the bacterially-produced phytohormone indole-3-acetic acid. Bacterial production of indole-3-acetic acid and attachment to algae are significantly increased by tryptophan, which is exuded from the algal cell. Algal death triggered by bacteria involves activation of pathways unique to oxidative stress response and programmed cell death. Our observations suggest that bacteria greatly influence the physiology and metabolism of E. huxleyi. Coccolithophore-bacteria interactions should be further studied in the environment to determine whether they impact micro-algal population dynamics on a global scale., Competing Interests: JC: Reviewing editor, eLife. The other authors declare that no competing interests exist.

Background: Longitudinal studies of the lung microbiome are challenging due to the invasive nature of sample collection. In addition, studies of the lung microbiome in human disease are usually performed after disease onset, limiting the ability to determine early events in the lung. We used a non-human primate model to assess lung microbiome alterations over time in response to an HIV-like immunosuppression and determined impact of the lung microbiome on development of obstructive lung disease. Cynomolgous macaques were infected with the SIV-HIV chimeric virus SHIV89.6P. Bronchoalveolar lavage fluid samples were collected pre-infection and every 4 weeks for 53 weeks post-infection. The microbiota was characterized at each time point by 16S ribosomal RNA (rRNA) sequencing., Results: We observed individual variation in the composition of the lung microbiota with a proportion of the macaques having Tropheryma whipplei as the dominant organism in their lungs. Bacterial communities varied over time both within and between animals, but there did not appear to be a systematic alteration due to SHIV infection. Development of obstructive lung disease in the SHIV-infected animals was characterized by a relative increase in abundance of oral anaerobes. Network analysis further identified a difference in community composition that accompanied the development of obstructive disease with negative correlations between members of the obstructed and non-obstructed groups. This emphasizes how species shifts can impact multiple other species, potentially resulting in disease., Conclusions: This study is the first to investigate the dynamics of the lung microbiota over time and in response to immunosuppression in a non-human primate model. The persistence of oral bacteria in the lung and their association with obstruction suggest a potential role in pathogenesis. The lung microbiome in the non-human primate is a valuable tool for examining the impact of the lung microbiome in human health and disease.

Motivation: Developing targeted therapeutics and identifying biomarkers relies on large amounts of research participant data. Beyond human DNA, scientists now investigate the DNA of micro-organisms inhabiting the human body. Recent work shows that an individual's collection of microbial DNA consistently identifies that person and could be used to link a real-world identity to a sensitive attribute in a research dataset. Unfortunately, the current suite of DNA-specific privacy-preserving analysis tools does not meet the requirements for microbiome sequencing studies., Results: To address privacy concerns around microbiome sequencing, we implement metagenomic analyses using secure computation. Our implementation allows comparative analysis over combined data without revealing the feature counts for any individual sample. We focus on three analyses and perform an evaluation on datasets currently used by the microbiome research community. We use our implementation to simulate sharing data between four policy-domains. Additionally, we describe an application of our implementation for patients to combine data that allows drug developers to query against and compensate patients for the analysis., Availability and Implementation: The software is freely available for download at: http://cbcb.umd.edu/∼hcorrada/projects/secureseq.html, Supplementary Information: Supplementary data are available at Bioinformatics online., Contact: hcorrada@umiacs.umd.edu., (© The Author 2016. Published by Oxford University Press.)

Background: Enterotoxigenic Escherichia coli (ETEC) is a major cause of diarrhea in inhabitants from low-income countries and in visitors to these countries. The impact of the human intestinal microbiota on the initiation and progression of ETEC diarrhea is not yet well understood., Results: We used 16S rRNA (ribosomal RNA) gene sequencing to study changes in the fecal microbiota of 12 volunteers during a human challenge study with ETEC (H10407) and subsequent treatment with ciprofloxacin. Five subjects developed severe diarrhea and seven experienced few or no symptoms. Diarrheal symptoms were associated with high concentrations of fecal E. coli as measured by quantitative culture, quantitative PCR, and normalized number of 16S rRNA gene sequences. Large changes in other members of the microbiota varied greatly from individual to individual, whether or not diarrhea occurred. Nonetheless the variation within an individual was small compared to variation between individuals. Ciprofloxacin treatment reorganized microbiota populations; however, the original structure was largely restored at one and three month follow-up visits., Conclusion: Symptomatic ETEC infections, but not asymptomatic infections, were associated with high fecal concentrations of E. coli. Both infection and ciprofloxacin treatment caused variable changes in other bacteria that generally reverted to baseline levels after three months.

In this study we investigated the methylome of chickens immunized with Infectious laryngotracheitis (ILT) vaccine derived from chicken embryos. Methyl-CpG binding domain protein-enriched genome sequencing (MBD-Seq) method was employed in the detection of the 1,155 differentially methylated regions (DMRs) across the entire genome. After validation, we ascertained the genomic DMRs distribution and annotated them regarding genes, transcription start sites (TSS) and CpG islands. We found that global DNA methylation decreased in vaccinated birds, presenting 704 hypomethylated and 451 hypermethylated DMRs, respectively. Additionally, we performed an enrichment analysis detecting gene networks, in which cancer and RNA post-transcriptional modification appeared in the first place, followed by humoral immune response, immunological disease and inflammatory disease. The top four identified canonical pathways were EIF2 signaling, regulation of EIF4 and p70S6K signaling, axonal guidance signaling and mTOR signaling, providing new insight regarding the mechanisms of ILT etiology. Lastly, the association between DNA methylation and differentially expressed genes was examined, and detected negative correlation in seventeen of the eighteen genes.

Pathogens in the gastrointestinal tract exist within a vast population of microbes. We examined associations between pathogens and composition of gut microbiota as they relate to Shigella spp./enteroinvasive Escherichia coli infection. We analyzed 3,035 stool specimens (1,735 nondiarrheal and 1,300 moderate-to-severe diarrheal) from the Global Enteric Multicenter Study for 9 enteropathogens. Diarrheal specimens had a higher number of enteropathogens (diarrheal mean 1.4, nondiarrheal mean 0.95; p<0.0001). Rotavirus showed a negative association with Shigella spp. in cases of diarrhea (odds ratio 0.31, 95% CI 0.17-0.55) and had a large combined effect on moderate-to-severe diarrhea (odds ratio 29, 95% CI 3.8-220). In 4 Lactobacillus taxa identified by 16S rRNA gene sequencing, the association between pathogen and disease was decreased, which is consistent with the possibility that Lactobacillus spp. are protective against Shigella spp.-induced diarrhea. Bacterial diversity of gut microbiota was associated with diarrhea status, not high levels of the Shigella spp. ipaH gene.

We introduce a methodology to assess differential abundance in sparse high-throughput microbial marker-gene survey data. Our approach, implemented in the metagenomeSeq Bioconductor package, relies on a novel normalization technique and a statistical model that accounts for undersampling-a common feature of large-scale marker-gene studies. Using simulated data and several published microbiota data sets, we show that metagenomeSeq outperforms the tools currently used in this field.

Cultivation-based assays combined with PCR or enzyme-linked immunosorbent assay (ELISA)-based methods for finding virulence factors are standard methods for detecting bacterial pathogens in stools; however, with emerging molecular technologies, new methods have become available. The aim of this study was to compare four distinct detection technologies for the identification of pathogens in stools from children under 5 years of age in The Gambia, Mali, Kenya, and Bangladesh. The children were identified, using currently accepted clinical protocols, as either controls or cases with moderate to severe diarrhea. A total of 3,610 stool samples were tested by established clinical culture techniques: 3,179 DNA samples by the Universal Biosensor assay (Ibis Biosciences, Inc.), 1,466 DNA samples by the GoldenGate assay (Illumina), and 1,006 DNA samples by sequencing of 16S rRNA genes. Each method detected different proportions of samples testing positive for each of seven enteric pathogens, enteroaggregative Escherichia coli (EAEC), enterotoxigenic E. coli (ETEC), enteropathogenic E. coli (EPEC), Shigella spp., Campylobacter jejuni, Salmonella enterica, and Aeromonas spp. The comparisons among detection methods included the frequency of positive stool samples and kappa values for making pairwise comparisons. Overall, the standard culture methods detected Shigella spp., EPEC, ETEC, and EAEC in smaller proportions of the samples than either of the methods based on detection of the virulence genes from DNA in whole stools. The GoldenGate method revealed the greatest agreement with the other methods. The agreement among methods was higher in cases than in controls. The new molecular technologies have a high potential for highly sensitive identification of bacterial diarrheal pathogens.

Background and Objectives: Chronic pelvic pain is often difficult to diagnose and treat properly. Physicians called on to treat this problem may not be able to give a specific diagnosis. The aim of this study was to see whether the physical presence of anterior vaginal wall tenderness could help narrow down and elucidate diagnoses in a practice focusing on diagnosis and treatment of chronic pelvic pain., Methods: The study cohort comprised 284 patients with chronic pelvic pain limited to gynecologic and lower urinary problems. Histories, physical examinations, and endoscopic procedures were performed on each patient. An analysis of this information was conducted., Results: Of the chronic pelvic pain patients, 78% had endometriosis, 81% had interstitial cystitis, and 61% had both concurrently. The sensitivity of anterior vaginal wall tenderness (AVWT) in patients with interstitial cystitis was 95%, and in those with only endometriosis and no interstitial cystitis, the sensitivity was 17%. The positive predictive value for interstitial cystitis was 85%, and for endometriosis it was 67%., Conclusions: Examination of the anterior vaginal wall with an empty bladder at the initial examination can lead one to suspect interstitial cystitis and possibly either concomitant or singular endometriosis and allow the physician to approach the workup accordingly.