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SummaryWe recently introduced the Gut Microbiome Health Index (GMHI), a stool-based indicator for monitoring health given the state of one’s gut microbiome. GMHI depends on health-prevalent and health-scarce species determined and validated using a pooled dataset of 5,026 stool shotgun metagenomic samples from 43 independent studies. Encouragingly, GMHI has already been utilized in various studies focusing on identifying differences in the gut microbiome between cases and controls. However, current computational barriers and logistical issues prevent researchers from computing, interpreting, and contextualizing GMHI, thereby limiting its further widespread utilization. Herein, we introduce the GMHI-webtool, a user-friendly browser application that computes GMHI, health-prevalent/scarce species, α-diversities, and taxonomic distributions of the gut microbiome from stool samples. Users of our interactive online tool can visualize their results and compare side-by-side with those from our pooled reference dataset, as well as export data in .csv format and high-resolution figures.Availability and implementationGMHI-webtool is freely available here: https://gmhi-webtool.github.io/. Source code: https://github.com/danielchang2002/GMHI-webtool.

Measuring prebiotic activities of foods on human microbiome and providing dietary guidelines on one’s health status are an emerging research topic. However, it is difficult to directly compare different foods using the prebiotic index (PI) proposed in 2003 to determine their prebiotic activities. This ambiguity can be resolved with the gut microbiome health index (GMHI), newly built in 2020, using a consortium of 50 microbial species associated with human health. As a translational application of this index, we used this formula to predict the PI of five commercial prebiotics, fructooligosaccharides (FOS), galactooligosaccharides (GOS), xylooligosaccharides (XOS), inulin (IN), and 2`-fucosyllactose (2FL), using in vitro human fecal fermentation. To calculate the GMHI-based PI values, microbial community changes were analyzed at the species level using shotgun sequencing during the fermentation of the prebiotics. As a result, the five compounds exerted a prebiotic effect, increasing the abundance of health-prevalent species, such as Bifidobacterium adolescentis and decreasing health-scarce species. In this context, the GMHI of prebiotics increased after 24 h, but that of the control group without carbon sources decreased. GMHI was highest in the order of IN (0.48 ± 0.06), FOS (0.47 ± 0.03), XOS (0.33 ± 0.02), GOS (0.26 ± 0.02), and 2FL (0.16 ± 0.06), which more accurately reflects the prebiotic effect than PI. Additionally, GMHI stratified the prebiotics more effectively than ecological metrics analyses, such as Shannon diversity, species richness, species evenness, and the inverse Simpson diversity index. This study demonstrates that GMHI-based PI can be used to measure prebiotic activities of several bifidogenic oligosaccharides and perhaps predict the health-beneficial effects of diets after collecting substantial amounts of population-scale meta-datasets.

Chemotherapy-induced cognitive impairment (CICI) has emerged as a significant medical problem without therapeutic options. Using the platinum-based chemotherapy cisplatin to model CICI, we revealed robust elevations in the adenosine A

Chemotherapy-induced cognitive impairment (CICI) has emerged as a significant medical problem without therapeutic options. Using the platinum-based chemotherapy cisplatin to model CICI, we revealed robust elevations in the adenosine A 2A receptor (A 2A R) and its downstream effectors, cAMP and CREB, by cisplatin in the adult mouse hippocampus, a critical brain structure for learning and memory. Notably, A 2A R inhibition by the Food and Drug Administration–approved A 2A R antagonist KW-6002 prevented cisplatin-induced impairments in neural progenitor proliferation and dendrite morphogenesis of adult-born neurons, while improving memory and anxiety-like behavior, without affecting tumor growth or cisplatin’s antitumor activity. Collectively, our study identifies A 2A R signaling as a key pathway that can be therapeutically targeted to prevent cisplatin-induced cognitive impairments.

OBJECTIVE: To identify hallmark genes and biomolecular processes in aortitis using high-throughput gene expression profiling. Additionally, to provide a range of potentially new drug targets (genes) and therapeutics from a pharmacogenomic network analysis. METHODS: Bulk RNA-seq was performed on surgically resected ascending aortic tissues from inflammatory (giant cell arteritis [GCA] with or without polymyalgia rheumatica [PMR] = 8; clinically isolated aortitis [CIA] = 17) and non-inflammatory (n = 25) aneurysms undergoing surgical aortic repair. Differentially expressed genes (DEGs) between the two patient groups were identified while controlling for clinical covariates. A protein-protein interaction model, drug-gene target information, and the DEGs were used to construct a pharmacogenomic network for identifying promising drug targets and potentially new treatment strategies in aortitis. RESULTS: Overall, tissue gene expression patterns were the most associated with disease state than with any other clinical characteristic. We identified 159 and 93 genes that were significantly upregulated and downregulated, respectively, in inflammatory aortic aneurysms compared to non-inflammatory aortic aneurysms. We found that the upregulated genes were enriched in immune-related functions, whereas the downregulated genes were enriched in neuronal processes. Notably, gene expression profiles of inflammatory aortic aneurysms from patients with GCA were no different than those from patients with CIA. Finally, our pharmacogenomic network analysis identified genes that could potentially be targeted by immunosuppressive drugs currently approved for other inflammatory diseases. CONCLUSION: We performed the first global transcriptomics analysis in inflammatory aortic aneurysms from surgically resected aortic tissues. We identified signature genes and biomolecular processes, while finding that CIA may be a limited presentation of GCA. Moreover, our computational network analysis revealed potential novel strategies for pharmacologic interventions, and suggests future biomarker discovery directions for the precise diagnosis and treatment of aortitis.

A key question in human gut microbiome research is what are the robust structural patterns underlying its taxonomic composition. Herein, we use whole metagenomic datasets from healthy human guts to show that such robust patterns do exist, albeit not in the conventional enterotype sense. We first introduce the concept of mixed-membership enterotypes using a network inference approach based on stochastic block models. We find that gut microbiomes across a group of people (hosts) display a nested structure, which has been observed in a number of ecological systems. This finding led us to designate distinct ecological roles to both microbes and hosts: generalists and specialists. Specifically, generalist hosts have microbiomes with most microbial species, while specialist hosts only have generalist microbes. Moreover, specialist microbes are only present in generalist hosts. From the nested structure of microbial taxonomies, we show that these ecological roles of microbes are generally conserved across datasets. Our results show that the taxonomic composition of healthy human gut microbiomes is associated with robustly structured combinations of generalist and specialist species.

Background: The combination of cyclin dependent 4/6 kinase inhibitors (CDK4/6i) with endocrine therapy (ET) has resulted in clinically significant improvements in progression-free survival (PFS) and overall survival (OS) in hormone-receptor (HR)-positive metastatic breast cancer (MBC). However, most patients’ disease ultimately progresses on CDK4/6i and ET. Therefore, further research is necessary to understand the mechanisms driving primary and secondary resistance. PROMISE is a multicenter prospective cohort study enrolling women with HR-positive MBC commencing treatment with palbociclib + letrozole (1st line) or palbociclib + fulvestrant (2nd Line). The study provides a comprehensive “omic” assessment of blood, tumor, urine and the fecal microbiome to identify molecular or cellular features associated with primary endocrine resistance (e.g. disease progression ≤ 12 months on treatment) and acquired resistance to CDK 4/6i. Additionally, patient derived xenografts and organoids are created to test new drug strategies designed to overcome resistance to CDK 4/6i and ET. Here, we present initial sequencing results from pretreatment biospecimens collected from PROMISE study participants. Methods: On-study tumor biopsies and blood samples were collected for DNA/RNA sequencing (TempusTM). The analyzed biospecimens were all obtained prior to initiation of palbociclib and ET. We correlated patient clinical characteristics (phenotypes) with molecular data and responses to protocol treatment. The data were analyzed using a series of cutting-edge bioinformatics pipelines for somatic and germline mutations in addition to copy number alterations (CNAs). The study database was locked for analysis on 06/20/2019. Results: We analyzed the somatic single nucleotide variants/INDELs (sSNV/INDEL) profiles across the tumor samples to determine the genes that were least likely to occur as a result of background mutation processes. Twenty-six patients had somatic copy number alterations (sCNA) and/or sSNV/INDEL in at least one of 18 genes with the most significant sSNV/INDEL profiles (p < 0.03) which included clinically and biologically relevant genes. The genes with the most statistically significant sSNV/INDEL mutation profiles were GATA3, PIK3CA, CDH1, and ESR1 (p < 0.0009). We observed a high percentage of tumors with somatic alterations in GATA3 (23% sSNV/INDEL, 15% sCNA), PIK3CA (38%, 12%), CDH1 (19%, 50%) and ESR1 (19%, 58%). ESR1 mutations were more frequent in patients receiving 2nd line treatment. Other frequently altered genes included TP53 (15%, 46%), MAP2K4 (8%, 50%), DNAAF1 (8%, 50%), and CDKN1B (8%, 35%). Further, ZNF317 and F3 were altered in 9 and 7 patients, respectively. Twenty-four samples had alterations in at least one of the CDK4/6 pathway genes (RB1, CCNE2, CCND1, CDK6, ESR1, CDKN2A, CCND3, CDK4, CDK2 and CCNE1). Four patients progressed on therapy; three of the four patients had mutations in PIK3CA, and one had a mutation in ESR1. Results of the RNA sequencing data (N=26) will be presented at the SABCS meeting. Conclusions: This is the first report of a prospective study designed to characterize the genomic landscape of ER+/HER2- MBC prior to palbociclib treatment. We observed high frequencies of known targetable alterations in PIK3CA and ESR1, including in patients that progress, which is consistent with previous reports. RNA sequencing data will be presented at the meeting. Citation Format: Ciara C O Sullivan, Krishna R Kalari, Vera J Suman, Peter T Vedell, Ann Moyer, Erin Carlson, Jason Sinnwell, Tejaswi Alaparthi, Xiaojia Tang, Kevin Thompson, Jaeyun Sung, Alvaro Moreno-Aspitia, Donald Northfelt, Minetta C Liu, Tufia C Haddad, Prema Peethambaram, Saranya Chumsri, Kathryn J Ruddy, Karthik V Giridhar, Roberto A Leon-Ferre, Paula Gill, Mohammad Ranginwala, Asad Javed, Sameer Batoo, Brendan P. McMenomy, Richard Weinshilboum, Liewei Wang, Matthew P Goetz. Comprehensive tumor sequencing to identify biomarkers of palbociclib response: First report of the PROMISE study [abstract]. In: Proceedings of the 2019 San Antonio Breast Cancer Symposium; 2019 Dec 10-14; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2020;80(4 Suppl):Abstract nr P2-11-07.

Predictive modeling tools for assessing microbial communities are important for realizing transformative capabilities of microbiomes in agriculture, ecology, and medicine. Constraint-based community-scale metabolic modeling is unique in its potential for making mechanistic predictions regarding both the structure and function of microbial communities. However, accessing this potential requires an understanding of key physicochemical constraints, which are typically considered on a per-species basis. What is needed is a means of incorporating global constraints relevant to microbial ecology into community models. Resource-allocation constraint, which describes how limited resources should be distributed to different cellular processes, sets limits on the efficiency of metabolic and ecological processes. In this study, we investigate the implications of resource-allocation constraints in community-scale metabolic modeling through a simple mechanism-agnostic implementation of resource-allocation constraints directly at the flux level. By systematically performing single-, two-, and multi-species growth simulations, we show that resource-allocation constraints are indispensable for predicting the structure and function of microbial communities. Our findings call for a scalable workflow for implementing a mechanistic version of resource-allocation constraints to ultimately harness the full potential of community-scale metabolic modeling tools.

Biosynthetic gene clusters (BGCs) in microbial genomes encode for bioactive secondary metabolites (SMs), which can play important roles in microbe-microbe and host-microbe interactions. Given the biological significance of SMs and the current profound interest in the metabolic functions of microbiomes, the unbiased identification of BGCs from high-throughput metagenomic data could offer novel insights into the complex chemical ecology of microbial communities. Currently available tools for predicting BGCs from shotgun metagenomes have several limitations, including the need for computationally demanding read-assembly; predicting a narrow breadth of BGC classes; and not providing the SM product. To overcome these limitations, we developed TaxiBGC (Taxonomy-guided Identification of Biosynthetic Gene Clusters), a command-line tool for predicting experimentally characterized BGCs (and inferring their known SMs) in metagenomes by first pinpointing the microbial species likely to harbor them. We benchmarked TaxiBGC on various simulated metagenomes, showing that our taxonomy-guided approach could predict BGCs with much-improved performance (mean F1 score: 0.56, mean PPV score: 0.80) compared to directly identifying BGCs by mapping sequencing reads onto the BGC genes (mean F1 score: 0.49, mean PPV score: 0.41). Next, by applying TaxiBGC on 2,650 metagenomes from the Human Microbiome Project and various case-control gut microbiome studies, we were able to associate BGCs (and their SMs) with different human body sites and with multiple gastrointestinal diseases, including Crohn’s disease and liver cirrhosis. In all, TaxiBGC provides an in silico platform to predict experimentally characterized BGCs and their SM production potential in metagenomic data while demonstrating important advantages over existing techniques.IMPORTANCECurrently available bioinformatics tools to identify BGCs from metagenomic sequencing data are limited in their predictive capability or ease of use to even computationally-oriented researchers. We present an automated computational pipeline called TaxiBGC, which predicts experimentally characterized BGCs (and infers their known SMs) in shotgun metagenomes by first considering the microbial species source. Through rigorous benchmarking techniques on simulated metagenomes, we show that TaxiBGC provides a significant advantage over existing methods. When demonstrating TaxiBGC on thousands of human microbiome samples, we associate BGCs encoding for bacteriocins with different human body sites and diseases, thereby elucidating a possible novel role of this antibiotic class in maintaining the stability of microbial ecosystems throughout the human body. Furthermore, we report for the first time gut microbial BGC associations shared amongst multiple pathologies. Ultimately, we expect our tool to facilitate future investigations into the chemical ecology of microbial communities across diverse niches and pathologies.

Applyingsingle-level statistical models to multilevel data typically producesunderestimated standard errors, which may result in misleading conclusions.This study examined the impact of ignoring multilevel data structure on theestimation of item parameters and their standard errors of the Rasch, two-, andthree- parameter logistic models in item response theory (IRT) to demonstratethe degree of such underestimation in IRT. Also, the Lord’s chi-square testusing the underestimated standard errors was used to test differential item functioning(DIF) to show the impact of such underestimation on the practical applicationsof IRT. The results of simulation studies showed that, in the most severe caseof multilevel data, the standard error estimate from the standard single-levelIRT models was about half of the minimal asymptotic standard error, and thetype I error rate of the Lord’s chi-square test was inflated up to .35. Theresults of this study suggest that standard single-level IRT models mayseriously mislead our conclusions in the presence of multilevel data, andtherefore multilevel IRT models need to be considered as alternatives., Applying single-level statistical models to multilevel data typically produces underestimated standard errors, which may result in misleading conclusions. This study examined the impact of ignoring multilevel data structure on the estimation of item parameters and their standard errors of the Rasch, two-, and three- parameter logistic models in item response theory (IRT) to demonstrate the degree of such underestimation in IRT. Also, the Lord’s chi-square test using the underestimated standard errors was used to test differential item functioning (DIF) to show the impact of such underestimation on the practical applications of IRT. The results of simulation studies showed that, in the most severe case of multilevel data, the standard error estimate from the standard single-level IRT models was about half of the minimal asymptotic standard error, and the type I error rate of the Lord’s chi-square test was inflated up to .35. The results of this study suggest that standard single-level IRT models may seriously mislead our conclusions in the presence of multilevel data, and therefore multilevel IRT models need to be considered as alternatives.

Background: Gastric carcinogenesis has been found to be associated with intragastric microbiome, gastric mucosal gene expression, and immune cells comprising the tumor microenvironment separately in independent studies. However, to date, there has been no multi-omic investigations analyzing these biological traits simultaneously. Thus, there is a paucity in our understanding of the intricate relationship between intragastric microbes and the host in gastric disease. To address this gap, we aimed to comprehensively investigate the microbiome, host gene expression, and immune cells in the stomach across a range of different states along the gastric carcinogenesis pathway. Methods: Gastric biopsy samples were obtained from healthy individuals or patients with gastritis or gastric cancer (biopsy cohort, n=30). Surgical samples (of cancer and adjacent severe gastritis tissues) were obtained from gastric cancer patients (surgery cohort, n=40). Using 16S rRNA gene sequencing, RNA-seq, and cell-type enrichment analysis of each participant’s biospecimens, we identified associations between the microbiome and host transcriptome or inferred immune cell types. Results: Microbiome composition and gene expression patterns showed significant differences between disease states (healthy, gastritis, or cancer). Expression of ‘Cell cycle’ pathway genes was enriched in gastric cancer in both biopsy and surgery cohorts. In the biopsy cohort, Helicobacteraceae, which was abundant in gastritis, was highly correlated with FAM3D, which was previously found to be involved in gastrointestinal inflammation. In the surgery cohort, Lachnospiraceae, which was abundant in gastric cancer, was highly correlated with UBD, which is known to regulate mitosis and reduce cell cycle time. Cell-type enrichment analysis revealed lower B cell infiltration in gastric cancer than in gastritis (both cohorts). Tissue-infiltrating B cells were associated with Helicobacteraceae. Conclusions: Associations between the intragastric microbiome, human gene expression, and tissue-infiltrating immune cells are unique to gastric disease state. The findings herein may aid the discovery of novel biomarkers or therapeutic targets of gastric cancer. Citation Format: Chan Hyuk Park, Changjin Hong, A-reum Lee, Jaeyun Sung, Tae Hyun Hwang. Integrated analysis of intragastric microbiome and human gene expression uncovers genetic, microbial, and immunological associations in gastritis and gastric cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 5013.

BackgroundRapid advances in the past decade have shown that dysbiosis of the gut microbiome is a key hallmark of rheumatoid arthritis (RA). Yet, the relationship between gut microbiome and clinical improvement in RA disease activity remains unclear. In this study, we explored the gut microbiome of patients with RA to identify features that are associated with, as well as predictive of, minimum clinically important improvement (MCII) in disease activity.MethodsWhole metagenome shotgun sequencing was performed on 64 stool samples, which were collected from 32 patients with RA at two separate time-points. The Clinical Disease Activity Index (CDAI) of each patient was measured at both time-points to assess achievement of MCII; depending on this clinical status, patients were distinguished into two groups. Multiple linear regression models were used to identify microbial taxa and biochemical pathways associated with MCII while controlling for potentially confounding factors. Lastly, a deep-learning neural network was trained upon gut microbiome, clinical, and demographic data at baseline to classify patients according to MCII status, thereby enabling the prediction of whether a patient will achieve MCII at follow-up.ResultsWe determined that MCII status can explain a significant proportion of the overall compositional variance in the gut microbiome (R2 = 3.8%, P = 0.005, PERMANOVA). Additionally, by looking at patients’ baseline gut microbiome profiles, we observed significantly different microbiome traits between patients who eventually showed MCII and those who did not. Taxonomic features include alpha- and beta-diversity measures, as well as several microbial taxa, such as Coprococcus, Bilophila sp. 4_1_30, and Ruminococcus sp. Functional profiling identified thirteen biochemical pathways, most of which were involved in the biosynthesis of L-arginine and L-methionine, to be differentially abundant between the MCII patient groups. In addition to these observations at baseline, we found microbiome features that vary differently in fold-change (from baseline to follow-up) between the two patient groups. These results could suggest that, depending on the clinical course, gut microbiomes not only start at different ecological states, but also are on separate trajectories. Finally, the neural network proved to be highly effective in predicting which patient will achieve MCII (balanced accuracy = 90.0%), demonstrating potential clinical utility of gut microbiome profiles.ConclusionsOur findings confirm the presence of taxonomic and functional signatures of the gut microbiome associated with MCII in RA patients. Ultimately, the gut microbiome may aid in the development of non-invasive tools for predicting future prognosis in RA.Trial registrationN/A

Background Rheumatoid arthritis (RA) is a chronic, autoimmune disorder characterized by joint inflammation and pain. In patients with RA, metabolomic approaches, i.e., high-throughput profiling of small-molecule metabolites, on plasma or serum has thus far enabled the discovery of biomarkers for clinical subgroups, risk factors, and predictors of treatment response. Despite these recent advancements, the identification of blood metabolites that reflect quantitative disease activity remains an important challenge in precision medicine for RA. Herein, we use global plasma metabolomic profiling analyses to detect metabolites associated with, and predictive of, quantitative disease activity in patients with RA. Methods Ultra-high-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) was performed on a discovery cohort consisting of 128 plasma samples from 64 RA patients and on a validation cohort of 12 samples from 12 patients. The resulting metabolomic profiles were analyzed with two different strategies to find metabolites associated with RA disease activity defined by the Disease Activity Score-28 using C-reactive protein (DAS28-CRP). More specifically, mixed-effects regression models were used to identify metabolites differentially abundant between two disease activity groups (“lower”, DAS28-CRP ≤ 3.2; and “higher”, DAS28-CRP > 3.2) and to identify metabolites significantly associated with DAS28-CRP scores. A generalized linear model (GLM) was then constructed for estimating DAS28-CRP using plasma metabolite abundances. Finally, for associating metabolites with CRP (an indicator of inflammation), metabolites differentially abundant between two patient groups (“low-CRP”, CRP ≤ 3.0 mg/L; “high-CRP”, CRP > 3.0 mg/L) were investigated. Results We identified 33 metabolites differentially abundant between the lower and higher disease activity groups (P < 0.05). Additionally, we identified 51 metabolites associated with DAS28-CRP (P < 0.05). A GLM based upon these 51 metabolites resulted in higher prediction accuracy (mean absolute error [MAE] ± SD: 1.51 ± 1.77) compared to a GLM without feature selection (MAE ± SD: 2.02 ± 2.21). The predictive value of this feature set was further demonstrated on a validation cohort of twelve plasma samples, wherein we observed a stronger correlation between predicted and actual DAS28-CRP (with feature selection: Spearman’s ρ = 0.69, 95% CI: [0.18, 0.90]; without feature selection: Spearman’s ρ = 0.18, 95% CI: [−0.44, 0.68]). Lastly, among all identified metabolites, the abundances of eight were significantly associated with the CRP patient groups while controlling for potential confounders (P < 0.05). Conclusions We demonstrate for the first time the prediction of quantitative disease activity in RA using plasma metabolomes. The metabolites identified herein provide insight into circulating pro-/anti-inflammatory metabolic signatures that reflect disease activity and inflammatory status in RA patients.

Background and Objectives: Both the Society of Teachers of Family Medicine (STFM) and the American Academy of Family Physicians have developed strategic plans to increase the training of underrepresented minority in medicine (URMM) family physicians to meet the needs of an increasingly diverse patient population in the United States. This study examines data from the 2017 Council of Academic Family Medicine (CAFM) Educational Research Alliance (CERA) Program Directors (PD12) Survey to assess whether recruitment strategies increase the diversity of underrepresented minority physicians in family medicine. Methods: Data were collected from an online electronic survey administered by the CERA of family medicine program directors in 2018. The data included specific questions about the diversity of URMM residents in family medicine programs and about initiatives that were used in their recruitment. We analyzed the data using the Pearson χ2 criteria for cause and effect of two variables. Results: Family medicine residency programs that have initiatives dedicated to increasing resident diversity have a higher percentage of URMM residents. Specifically, residency programs that have URMM recruitment strategies are 2.5 to 4 times more likely to have a diverse residency population than those programs without strategies (P

The role of our gut microbiota in health and disease is largely attributed to the collective metabolic activities of the inhabitant microbes. A system-level framework of the microbial community structure, mediated through metabolite transport, would provide important insights into the complex microbe-microbe and host-microbe chemical interactions. This framework, if adaptable to both mouse and human systems, would be useful for mechanistic interpretations of the vast amounts of experimental data from gut microbiomes in murine animal models, whether humanized or not. Here, we constructed a literature-curated, interspecies network of the mammalian gut microbiota for mouse and human hosts, called NJC19. This network is an extensive data resource, encompassing 838 microbial species (766 bacteria, 53 archaea, and 19 eukaryotes) and 6 host cell types, interacting through 8,224 small-molecule transport and macromolecule degradation events. Moreover, we compiled 912 negative associations between organisms and metabolic compounds that are not transportable or degradable by those organisms. Our network may facilitate experimental and computational endeavors for the mechanistic investigations of host-associated microbial communities.

Problem Prenatal exposure to metabolic dysregulation arising from maternal obesity can have negative health consequences in post-natal life. To date, the specific effects of maternal obesity on fetal immunity at a cellular level have not been well characterized. Method of study Using cord blood mononuclear cells (CBMCs) and cord plasma (n = 9/group) isolated from infants born to women with a high body mass index (BMI>25kg/m2 ) compared to women with a normal BMI (18-25kg/m2 ), we evaluated differences in immune cell populations using single-cell mass cytometry (CyTOF). CBMCs were matched according to potentially confounding variables, such as maternal and gestational age, ethnicity, smoking status, and gravidity. Statistical results were adjusted for fetal sex. Data were analyzed by viSNE and FlowSOM softwares in Cytobank™ . Results In newborn CBMCs from women with high BMI, we observed changes in frequency and phenotype of immune cell populations, including significant increases in CD4+ T cells and decreases in myeloid cell populations. IL-12p40 and MDC concentrations were significantly elevated in the high BMI group compared to control. Conclusion This study demonstrates an association between maternal obesity and fetal immunity. Our results warrant following long-term immunologic outcomes and associated clinical risks in children born to women with a high pre-pregnancy BMI.

The role of our gut microbiota in health and disease is largely attributed to the collective metabolic activities of the inhabitant microbes. A system-level framework of the microbial community structure, mediated through metabolite transport, would provide important insights into the complex microbe-microbe and host-microbe chemical interactions. This framework, if adaptable to both mouse and human systems, would be useful for mechanistic interpretations of the vast amounts of experimental data from gut microbiomes in murine animal models, whether humanized or not. Here, we constructed a literature-curated, interspecies network of the mammalian gut microbiota for mouse and human hosts, called NJC19. This network is an extensive data resource, encompassing 838 microbial species (766 bacteria, 53 archaea, and 19 eukaryotes) and 6 host cell types, interacting through 8,224 small-molecule transport and macromolecule degradation events. Moreover, we compiled 912 negative associations between organisms and metabolic compounds that are not transportable or degradable by those organisms. Our network may facilitate experimental and computational endeavors for the mechanistic investigations of host-associated microbial communities., Measurement(s)metabolic process • transport • macromolecule catabolic process • gut microbiome measurementTechnology Type(s)digital curation • Phylogenetic AnalysisFactor Type(s)Species of microorganisms • Type of host cellsSample Characteristic - OrganismHomo sapiens • Mus musculus • Bacteria • Archaea • Eukaryota Machine-accessible metadata file describing the reported data: 10.6084/m9.figshare.12336164

The development of a biologically-interpretable and robust metric that provides clear insight into the general health status (i.e. healthy or non-healthy) of one’s gut microbiome remains an important target in human microbiome research. We introduce the Gut Microbiome Health Index (GMHI), a mathematical formula that determines the degree to which a gut microbiome profile reflects good or adverse health. GMHI was formulated based on microbial species specific to healthy gut ecosystems. These species were identified through a multi-study, integrative analysis on 4,347 human stool metagenomes from 34 published studies across healthy and 12 different disease or abnormal bodyweight conditions. When demonstrated on our population-scale meta-dataset, GMHI is the most robust and consistent predictor of general health compared to α-diversity indices commonly considered as markers for gut health. Validation of GMHI on 679 samples from 9 additional studies resulted in remarkable reproducibility in distinguishing healthy and non-healthy groups. Our findings suggest that gut taxonomic signatures can indeed serve as robust predictors of general health, and highlight the importance of how data sharing efforts can provide broadly-applicable novel discoveries.

The relationship between Primary Biliary Cholangitis (PBC), a chronic cholestatic autoimmune liver disease, and the peripheral immune system remains to be fully understood. Herein, we performed the first mass cytometry (CyTOF)-based, immunophenotyping analysis of the peripheral immune system in PBC at single-cell resolution. CyTOF was performed on peripheral blood mononuclear cells (PBMCs) from PBC patients (n=33) and age-/sex-matched healthy controls (n=33) to obtain immune cell abundance and marker expression profiles. Hiearchical clustering methods were applied to identify immune cell types and subsets significantly associated with PBC. Subsets of gamma-delta T cells (CD3+TCRgd+), CD8+ T cells (CD3+CD8+CD161+PD1+), and memory B cells (CD3-CD19+CD20+CD24+CD27+) were found to have lower abundance in PBC than in control. In contrast, higher abundance of subsets of monocytes and naïve B cells were observed in PBC compared to control. Furthermore, several naïve B cell (CD3-CD19+CD20+CD24-CD27-) subsets were significantly higher in PBC patients with cirrhosis (indicative of late-stage disease) than in those without cirrhosis. Alternatively, subsets of CD8+CD161+ T cells and memory B cells were lower in abundance in cirrhotic relative to non-cirrhotic PBC patients. Future immunophenotyping investigations could lead to better understanding of PBC pathogenesis and progression, and also to the discovery of novel biomarkers and treatment strategies.

Background Links between colorectal cancer (CRC) and the gut microbiome have been established, but the specific microbial species and their role in carcinogenesis remain an active area of inquiry. Our understanding would be enhanced by better accounting for tumor subtype, microbial community interactions, metabolism, and ecology. Methods We collected paired colon tumor and normal-adjacent tissue and mucosa samples from 83 individuals who underwent partial or total colectomies for CRC. Mismatch repair (MMR) status was determined in each tumor sample and classified as either deficient MMR (dMMR) or proficient MMR (pMMR) tumor subtypes. Samples underwent 16S rRNA gene sequencing and a subset of samples from 50 individuals were submitted for targeted metabolomic analysis to quantify amino acids and short-chain fatty acids. A PERMANOVA was used to identify the biological variables that explained variance within the microbial communities. dMMR and pMMR microbial communities were then analyzed separately using a generalized linear mixed effects model that accounted for MMR status, sample location, intra-subject variability, and read depth. Genome-scale metabolic models were then used to generate microbial interaction networks for dMMR and pMMR microbial communities. We assessed global network properties as well as the metabolic influence of each microbe within the dMMR and pMMR networks. Results We demonstrate distinct roles for microbes in dMMR and pMMR CRC. Bacteroides fragilis and sulfidogenic Fusobacterium nucleatum were significantly enriched in dMMR CRC, but not pMMR CRC. These findings were further supported by metabolic modeling and metabolomics indicating suppression of B. fragilis in pMMR CRC and increased production of amino acid proxies for hydrogen sulfide in dMMR CRC. Conclusions Integrating tumor biology and microbial ecology highlighted distinct microbial, metabolic, and ecological properties unique to dMMR and pMMR CRC. This approach could critically improve our ability to define, predict, prevent, and treat colorectal cancers. Electronic supplementary material The online version of this article (10.1186/s13073-018-0586-6) contains supplementary material, which is available to authorized users.

Incidence rates for endometrial cancer (EC) are rising, particularly in postmenopausal and obese women. Previously, we showed that the uterine and vaginal microbiome distinguishes patients with EC from those without. Here, we sought to examine the impact of patient factors (such as menopause status, body mass index, and vaginal pH) in the microbiome in the absence of EC and how these might contribute to the microbiome signature in EC. We find that each factor independently alters the microbiome and identified postmenopausal status as the main driver of a polymicrobial network associated with EC (ECbiome). We identified Porphyromas somerae presence as the most predictive microbial marker of EC and we confirm this using targeted qPCR, which could be of use in detecting EC in high-risk, asymptomatic women. Given the established pathogenic behavior of P. somerae and accompanying network in tissue infections and ulcers, future investigation into their role in EC is warranted.

Bacterial persisters are a small fraction of quiescent cells that survive in the presence of lethal concentrations of antibiotics. They can regrow to give rise to a new population that has the same vulnerability to the antibiotics as did the parental population. Although formation of bacterial persisters in the presence of various antibiotics has been documented, the molecular mechanisms by which these persisters tolerate the antibiotics are still controversial. We found that amplification of the fumarate reductase operon ( FRD ) in Escherichia coli led to a higher frequency of persister formation. The persister frequency of E. coli was increased when the cells contained elevated levels of intracellular fumarate. Genetic perturbations of the electron transport chain (ETC), a metabolite supplementation assay, and even the toxin-antitoxin-related hipA7 mutation indicated that surplus fumarate markedly elevated the E. coli persister frequency. An E. coli strain lacking succinate dehydrogenase ( SDH ), thereby showing a lower intracellular fumarate concentration, was killed ∼1,000-fold more effectively than the wild-type strain in the stationary phase. It appears that SDH and FRD represent a paired system that gives rise to and maintains E. coli persisters by producing and utilizing fumarate, respectively.

Primary sclerosing cholangitis (PSC) is a pathogenically complex, chronic, fibroinflammatory disorder of the bile ducts without known etiology or effective pharmacotherapy. Emerging in vitro and in vivo evidence support fundamental pathophysiologic mechanisms in PSC centered on enterohepatic circulation. To date, no studies have specifically interrogated the chemical footprint of enterohepatic circulation in PSC. Herein, we evaluated the metabolome and lipidome of portal venous blood and bile obtained at the time of liver transplantation in patients with PSC (n = 7) as compared to individuals with noncholestatic, end-stage liver disease (viral, metabolic, etc. (disease control, DC, n = 19)) and to nondisease controls (NC, living donors, n = 12). Global metabolomic and lipidomic profiling was performed on serum derived from portal venous blood (portal serum) and bile using ultraperformance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) and differential mobility spectroscopy-mass spectroscopy (DMS-MS, complex lipid platform). The Mann&ndash, Whitney U test was used to identify metabolites that significantly differed between groups. Principal-component analysis (PCA) showed significant separation of both PSC and DC from NC for both portal serum and bile. Metabolite set enrichment analysis of portal serum and bile demonstrated that the liver-disease cohorts (PSC and DC) exhibited similar enrichment in several metabolite categories compared to NC. Interestingly, the bile in PSC was uniquely enriched for dipeptide and polyamine metabolites. Finally, analysis of patient-matched portal serum and biliary metabolome revealed that these biological fluids were more homogeneous in PSC than in DC or NC, suggesting aberrant bile formation and enterohepatic circulation. In summary, PSC and DC patients exhibited alterations in several metabolites in portal serum and bile, while PSC patients exhibited a unique bile metabolome. These specific alterations in PSC are amenable to hypothesis testing and, potentially, therapeutic pharmacologic manipulation.

Figure S1. Unweighted Unifrac distances between tumor and normal-associated microbiota in dMMR and pMMR. Figure S2. Venn diagram highlighting number of microbes that overlap between tumor and normal samples in relation to MMR status (dMMR = red font, pMMR = green font, red circles = tumor samples, blue circles =normal samples). Figure S3. Microbes significantly enriched in tumor as compared to normal samples (colon tissue and mucosa) in individuals with dMMR CRC: a) Bacteroides fragilis and b) Fusobacterium nucleatum. Figure S4. Differentially abundant OTUs between patient-matched tumor and normal samples in individuals with dMMR CRC from an independent cohort. Table S1. Table S1: List of PATRIC model IDs and associated sOTUs. Table S2. Microbes identified as differentially abundant in tumor as compared to normal samples (tissue and mucosa) from individuals with dMMR or pMMR CRC. Table S3. sOTUs enriched in tumor samples (colon tissue and mucosa) as compared to normal adjacent samples in individuals with dMMR CRC. Table S4. sOTUs enriched in tumor samples (colon tissue and mucosa) as compared to normal adjacent samples in individuals with pMMR CRC. Table S5. sOTUs enriched in the proximal or distal colon (colon tissue and mucosa) of individuals with dMMR CRC. Table S6. sOTUs enriched in the proximal or distal colon (colon tissue and mucosa) of individuals with pMMR CRC. Table S7. Differentially abundant microbes in individuals with dMMR CRC from an independent cohort. Table S8. Differentially abundant microbes in individuals with pMMR CRC from an independent cohort. (PDF 2790 kb)

A system-level framework of complex microbe-microbe and host-microbe chemical cross-talk would help elucidate the role of our gut microbiota in health and disease. Here we report a literature-curated interspecies network of the human gut microbiota, called NJS16. This is an extensive data resource composed of ~570 microbial species and 3 human cell types metabolically interacting through >4,400 small-molecule transport and macromolecule degradation events. Based on the contents of our network, we develop a mathematical approach to elucidate representative microbial and metabolic features of the gut microbial community in a given population, such as a disease cohort. Applying this strategy to microbiome data from type 2 diabetes patients reveals a context-specific infrastructure of the gut microbial ecosystem, core microbial entities with large metabolic influence, and frequently-produced metabolic compounds that might indicate relevant community metabolic processes. Our network presents a foundation towards integrative investigations of community-scale microbial activities within the human gut., Supplementary material is available at the journal website

Biomarkers are essential for performing early diagnosis, monitoring neurodegenerative disease progression, gauging responses to therapies, and stratifying neurodegenerative diseases into their different subtypes. A wide range of molecular markers are under investigation in tissues and biofluids as well as through imaging; moreover, many are prominent proteins present in cerebrospinal fluid. However, in more frequently and easily collected fluids such as plasma, these proteins show only a modest correlation with disease and thus lack the necessary sensitivity or specificity for clinical use. High-throughput and quantitative proteomic technologies and systems-driven approaches to biofluid analysis are now being utilized in the search for better biomarkers. Biomarker discovery involves many critical steps including study design, sample preparation, protein and peptide separation and identification, and bioinformatics and data integration issues that must be carefully controlled before independent confirmation and validation. In this review, we summarize current proteomic and nucleic acid technologies involved in the discovery of biomarkers of neurodegenerative diseases, particularly Alzheimer's, Parkinson's, Huntington's, and prion diseases.

Gene expression data from microarrays are being applied to predict preclinical and clinical endpoints, but the reliability of these predictions has not been established. In the MAQC-II project, 36 independent teams analyzed six microarray data sets to generate predictive models for classifying a sample with respect to one of 13 endpoints indicative of lung or liver toxicity in rodents, or of breast cancer, multiple myeloma or neuroblastoma in humans. In total, >30,000 models were built using many combinations of analytical methods. The teams generated predictive models without knowing the biological meaning of some of the endpoints and, to mimic clinical reality, tested the models on data that had not been used for training. We found that model performance depended largely on the endpoint and team proficiency and that different approaches generated models of similar performance. The conclusions and recommendations from MAQC-II should be useful for regulatory agencies, study committees and independent investigators that evaluate methods for global gene expression analysis.

Summary Fecal microbiota transplantation (FMT) from healthy donor to patient is a treatment for microbiome-associated diseases. Although the success of FMT requires donor bacteria to engraft in the patient's gut, the forces governing engraftment in humans are unknown. Here we use an ongoing clinical experiment, the treatment of recurrent Clostridium difficile infection, to uncover the rules of engraftment in humans. We built a statistical model that predicts which bacterial species will engraft in a given host, and developed Strain Finder, a method to infer strain genotypes and track them over time. We find that engraftment can be predicted largely from the abundance and phylogeny of bacteria in the donor and the pre-FMT patient. Furthermore, donor strains within a species engraft in an all-or-nothing manner and previously undetected strains frequently colonize patients receiving FMT. We validated these findings for metabolic syndrome, suggesting that the same principles of engraftment extend to other indications.

Background The biomarker discovery field is replete with molecular signatures that have not translated into the clinic despite ostensibly promising performance in predicting disease phenotypes. One widely cited reason is lack of classification consistency, largely due to failure to maintain performance from study to study. This failure is widely attributed to variability in data collected for the same phenotype among disparate studies, due to technical factors unrelated to phenotypes (e.g., laboratory settings resulting in “batch-effects”) and non-phenotype-associated biological variation in the underlying populations. These sources of variability persist in new data collection technologies. Methods Here we quantify the impact of these combined “study-effects” on a disease signature’s predictive performance by comparing two types of validation methods: ordinary randomized cross-validation (RCV), which extracts random subsets of samples for testing, and inter-study validation (ISV), which excludes an entire study for testing. Whereas RCV hardwires an assumption of training and testing on identically distributed data, this key property is lost in ISV, yielding systematic decreases in performance estimates relative to RCV. Measuring the RCV-ISV difference as a function of number of studies quantifies influence of study-effects on performance. Results As a case study, we gathered publicly available gene expression data from 1,470 microarray samples of 6 lung phenotypes from 26 independent experimental studies and 769 RNA-seq samples of 2 lung phenotypes from 4 independent studies. We find that the RCV-ISV performance discrepancy is greater in phenotypes with few studies, and that the ISV performance converges toward RCV performance as data from additional studies are incorporated into classification. Conclusions We show that by examining how fast ISV performance approaches RCV as the number of studies is increased, one can estimate when “sufficient” diversity has been achieved for learning a molecular signature likely to translate without significant loss of accuracy to new clinical settings.