Citations

Abstract Incidence rates for endometrial cancer (EC) are rising, particularly in postmenopausal and obese women. Our previous work has showed that the uterine and vaginal microbiome distinguishes patients with EC from those without. We now 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). Of the 17 taxa we found enriched in EC patients, 8 were also enriched by postmenopause. Because postmenopausal status is a main risk factor for endometrial cancer, this system can be thought of as an ecological succession towards a disease state. Organisms within the ECbiome have been previously identified as polymicrobial associations in chronic soft tissue infections, ulcers, morbid obesity, and bacterial vaginosis. This suggests that this microbiome network is likely involved in mechanisms of host disease. Within the ECbiome, Porphyromonas somerae stands out because of its very significant and specific association with EC (p = 5.73E-6; Q = 0.009). We had previously identified this species as significantly associated with EC, and hereby validate that finding. Because the association of P. somerae with EC was robust and specific enough to potentially be considered as a biomarker for the disease (AUC 76.7%, CI: 67.9-85.5%), we developed a P. somerae-specific qPCR assay to detect this species in vaginal swabs. This becomes of particular interest because the lower tract and uterine microbiome are significantly correlated in these patients (p = 0.01), indicating that the vaginal microbiome can be used as a proxy for the uterine environment in EC. In agreement with the 16S rRNA gene sequencing data, P. somerae was detected significantly more frequently in vaginal swabs of patients with EC than those without EC (p = 3.02E-7). Overall, the test achieved a sensitivity of 74% and specificity of 63%, with a positive predictive value of 0.86 for obese and postmenopausal patients. In summary, we identify 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, we have also explored mechanistic routes through which this microbe and associated consortia may be involved in the mechanisms of disease. Citation Format: Marina Walther-Antonio, Dana Walsh, Alexis Hokenstad, Jun Chen, Jayeun Sung, Gregory Jenkins, Nicholas Chia, Heidi Nelson, Andrea Mariani. Postmenopause as a key factor in the composition of the Endometrial Cancer Microbiome (ECbiome): Putative role of Porphyromonas somerae in the disease [abstract]. In: Proceedings of the AACR Special Conference on the Microbiome, Viruses, and Cancer; 2020 Feb 21-24; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2020;80(8 Suppl):Abstract nr PR09.

Abstract For over 10 years, ModelSEED has been a primary resource for the construction of draft genome-scale metabolic models based on annotated microbial or plant genomes. Now being released, the biochemistry database serves as the foundation of biochemical data underlying ModelSEED and KBase. The biochemistry database embodies several properties that, taken together, distinguish it from other published biochemistry resources by: (i) including compartmentalization, transport reactions, charged molecules and proton balancing on reactions; (ii) being extensible by the user community, with all data stored in GitHub; and (iii) design as a biochemical ‘Rosetta Stone’ to facilitate comparison and integration of annotations from many different tools and databases. The database was constructed by combining chemical data from many resources, applying standard transformations, identifying redundancies and computing thermodynamic properties. The ModelSEED biochemistry is continually tested using flux balance analysis to ensure the biochemical network is modeling-ready and capable of simulating diverse phenotypes. Ontologies can be designed to aid in comparing and reconciling metabolic reconstructions that differ in how they represent various metabolic pathways. ModelSEED now includes 33,978 compounds and 36,645 reactions, available as a set of extensible files on GitHub, and available to search at https://modelseed.org/biochem and KBase.

Abstract Motivation While each cancer is the result of an isolated evolutionary process, there are repeated patterns in tumorigenesis defined by recurrent driver mutations and their temporal ordering. Such repeated evolutionary trajectories hold the potential to improve stratification of cancer patients into subtypes with distinct survival and therapy response profiles. However, current cancer phylogeny methods infer large solution spaces of plausible evolutionary histories from the same sequencing data, obfuscating repeated evolutionary patterns. Results To simultaneously resolve ambiguities in sequencing data and identify cancer subtypes, we propose to leverage common patterns of evolution found in patient cohorts. We first formulate the Multiple Choice Consensus Tree problem, which seeks to select a tumor tree for each patient and assign patients into clusters in such a way that maximizes consistency within each cluster of patient trees. We prove that this problem is NP-hard and develop a heuristic algorithm, Revealing Evolutionary Consensus Across Patients (RECAP), to solve this problem in practice. Finally, on simulated data, we show RECAP outperforms existing methods that do not account for patient subtypes. We then use RECAP to resolve ambiguities in patient trees and find repeated evolutionary trajectories in lung and breast cancer cohorts. Availability and implementation https://github.com/elkebir-group/RECAP. Supplementary information Supplementary data are available at Bioinformatics online.

The vaginal microbiome is a critical component of women’s sexual and reproductive health, with variations in microbial composition, particularly the loss of Lactobacillus species, being implicated in gynecologic and obstetric diseases. Given that the vaginal microbiome is so crucial, why do vaginal microbial profiles vary strikingly from person to person and even change over time within the same person? In the present study, which tracked the daily vaginal microbiomes of young healthy women through different lifestyles, we found that use of a locally released progestin contraceptive, a vegetarian diet, and intense exercise appear to lead to vaginal microbiome alterations and loss of Lactobacillus species. The impact of these vaginal microbiome changes on immediate and long-term health remain to be investigated.

A major direction for future microbiome research is implementation of fecal sample collections in large-scale, prospective epidemiologic studies. Studying microbiome-disease associations likely requires microbial data to be pooled from multiple studies. Our findings suggest collection methods that are most optimal to be used standardly across future WGSS microbiome studies.

Colorectal adenomas are precursors of CRC. Recently, the gut microbiota, i.e., the collection of microbes residing in our gut, has been recognized as a key player in CRC development. There have been a number of gut microbiota profiling studies for colorectal adenoma and CRC; however, fewer studies have considered the gut metabolome, which serves as the chemical interface between the host and gut microbiota. Here, we conducted a gut metabolome profiling study of colorectal adenoma and CRC and analyzed the metabolomic profiles together with paired microbiota composition profiles. We found several chemical signatures of colorectal adenoma that were associated with some gut microbes and potentially indicative of future CRC. This study highlights potential early-driver metabolites in CRC pathogenesis and guides further targeted experiments and thus provides an important stepping stone toward developing better CRC prevention strategies.

Metagenomic shotgun sequencing for the identification of pathogens is being increasingly utilized as a diagnostic method. Interpretation of large and complicated data sets is a significant challenge, for which multiple commercial tools have been developed. Three commercial metagenomic shotgun sequencing tools, CosmosID, One Codex, and IDbyDNA, were compared to determine whether they result in similar interpretations of the same sequencing data.

Personalized models of the gut microbiome are valuable for disease prevention and treatment. For this, one requires a mathematical model that predicts microbial community composition and the emergent behaviour of microbial communities. We seek a modelling strategy that can capture emergent behaviour when built from sets of universal individual interactions. Our investigation reveals that species–metabolite interaction (SMI) modelling is better able to capture emergent behaviour in community composition dynamics than direct species–species modelling. Using publicly available data, we examine the ability of species–species models and species–metabolite models to predict trio growth experiments from the outcomes of pair growth experiments. We compare quadratic species–species interaction models and quadratic SMI models and conclude that only species–metabolite models have the necessary complexity to explain a wide variety of interdependent growth outcomes. We also show that general species–species interaction models cannot match the patterns observed in community growth dynamics, whereas species–metabolite models can. We conclude that species–metabolite modelling will be important in the development of accurate, clinically useful models of microbial communities.

INTRODUCTION: Primary sclerosing cholangitis (PSC) is a chronic, fibroinflammatory, cholestatic liver disease of unknown etiopathogenesis which is recognized as a major risk factor for cholangiocarcinoma (CCA). We aimed to investigate the roles of the biliary and intestinal microflora in the inflammatory process of PSC and to determine if the microflora is associated with progression to CCA. METHODS: We studied the bile and stool microbiomes in samples obtained from patients with PSC (31 stool and 35 bile samples), sporadic CCA (11 stool and 26 bile samples), and PSC coexisting with CCA (16 stool and bile 17 samples). Control bile was obtained from patients with cholelithiasis or choledocholithiasis (n = 17). DNA extraction was followed by amplification and high-throughput next generation sequencing (MiSeq) of the 16S rDNA (V3-V5) region to identify the microbiota present. We performed: 1. analysis of the stool microbiome between PSC and CCA patients; 2. analysis of the biliary microbiome in PSC and CCA patients; 3. comparison of the biliary microbiome collected before and after biliary stent placement. RESULTS: Although bile samples from study patients had significantly lower sequencing depths than stool, their sequencing depths were much higher than bile from negative controls. The microbiomes of stool and bile samples from the same patients were significantly correlated ( P = 0.022). CCA bile samples had significant difference of alpha diversity from control bile samples with less Streptococcaceae and Desulfovibrionaceae (FDR < 10%) than PSC whereas no significant change of stool microbiome was identified between CCA and PSC. Fusobacterium and Gemella were correlated to PSC duration. An increased abundance of Fusobacteria after stent placement was revealed. Fusobacteria, which is inflammation-promoting, was also increased with the number of stents in the bile duct. CONCLUSION: Our study suggests that the bile microbiome is potentially a more distinctive biomarker than the stool microbiome for PSC and CCA development. There is increased inhabitance of inflammation-promoting bacteria in the bile of CCA patients and after biliary stent placement. Increased PSC duration also increases the microbial burden in the bile, which may also promote CCA development. We further detected specific bile microbes statistically associated with CCA or stent presence in the bile ducts, which raises the possibility of their role in the CCA progression or stent obstruction. Additional analyses with more samples are needed.