Identification of shared and disease-specific host gene–microbiome associations across human diseases using multi-omic integration

While gut microbiome and host gene regulation independently contribute to gastrointestinal disorders, it is unclear how the two may interact to influence host pathophysiology. Here we developed a machine learning-based framework to jointly analyse paired host transcriptomic (n = 208) and gut microbiome (n = 208) profiles from colonic mucosal samples of patients with colorectal cancer, inflammatory bowel disease and irritable bowel syndrome. We identified associations between gut microbes and host genes that depict shared as well as disease-specific patterns. We found that a common set of host genes and pathways implicated in gastrointestinal inflammation, gut barrier protection and energy metabolism are associated with disease-specific gut microbes. Additionally, we also found that mucosal gut microbes that have been implicated in all three diseases, such as Streptococcus, are associated with different host pathways in each disease, suggesting that similar microbes can affect host pathophysiology in a disease-specific manner through regulation of different host genes. Our framework can be applied to other diseases for the identification of host gene–microbiome associations that may influence disease outcomes

Scaling Out Sound and Complete Reasoning for Conjunctive Queries on OWL Knowledge Bases

Abstract. One of the challenges the Semantic Web community is facing today is the issue of scalable reasoning that can generate responsive results to complicated queries over large-scale OWL knowledge bases. Current large-scale semantic web systems scale to billions of triples but many such systems perform no reasoning or rely on materialization. On the other hand, most state-of-the-art, sound and complete DL reasoners are main memory-based and fail when given ontologies that include enormous data graphs in addition to expressive axioms. Thus, until now, reasoning has been restricted to either limited expressivity or limited size of the data. The focus of this thesis is to develop a scalable framework to perform sound and complete reasoning on large and expressive data graphs for answering conjunctive queries over a cluster of commodity machines. In order to achieve our goal, we outline our approach to address the following challenges: partitioning large and expressive datasets across the cluster for distributed reasoning, and allocating reasoning and queryexecution tasks involved in processing conjunctive queries to nodes of the cluster. We include evaluation results for our preliminary framework. Keywords: distributed reasoning, partitioning, conjunctive queries, scalable framework Problem Statement Objective of the Research: This thesis focuses on how to scale out sound and complete reasoning for answering conjunctive queries over real-world ontologies with increasingly large data graphs over commodity clusters. Our goal is to design a framework that can scale to clusters of commodity machines for answering complex queries over large-scale knowledge bases characterized by small but expressive TBox and very large ABox. In order to reach this objective, we plan to address the following research questions: How to partition expressive datasets across the cluster such that traditional DL reasoners can answer subproblems independently at each partition? How to recombine these results to get sound and complete answers? How to co-locate partitions to reduce communication overheads? How to allocate reasoning and query-execution tasks involved in processing conjunctive queries to different nodes of the cluster? Note, we restric

Gut microbiota diversity across ethnicities in the United States.

Composed of hundreds of microbial species, the composition of the human gut microbiota can vary with chronic diseases underlying health disparities that disproportionally affect ethnic minorities. However, the influence of ethnicity on the gut microbiota remains largely unexplored and lacks reproducible generalizations across studies. By distilling associations between ethnicity and differences in two US-based 16S gut microbiota data sets including 1,673 individuals, we report 12 microbial genera and families that reproducibly vary by ethnicity. Interestingly, a majority of these microbial taxa, including the most heritable bacterial family, Christensenellaceae, overlap with genetically associated taxa and form co-occurring clusters linked by similar fermentative and methanogenic metabolic processes. These results demonstrate recurrent associations between specific taxa in the gut microbiota and ethnicity, providing hypotheses for examining specific members of the gut microbiota as mediators of health disparities

HOMINID: a framework for identifying associations between host genetic variation and microbiome composition

Recent studies have uncovered a strong effect of host genetic variation on the composition of host-associated microbiota. Here, we present HOMINID, a computational approach based on Lasso linear regression, that given host genetic variation and microbiome taxonomic composition data, identifies host single nucleotide polymorphisms (SNPs) that are correlated with microbial taxa abundances. Using simulated data, we show that HOMINID has accuracy in identifying associated SNPs and performs better compared with existing methods. We also show that HOMINID can accurately identify the microbial taxa that are correlated with associated SNPs. Lastly, by using HOMINID on real data of human genetic variation and microbiome composition, we identified 13 human SNPs in which genetic variation is correlated with microbiome taxonomic composition across body sites. In conclusion, HOMINID is a powerful method to detect host genetic variants linked to microbiome composition and can facilitate discovery of mechanisms controlling host-microbiome interactions.University of Minnesota College of Biological Sciences; Randy Shaver Cancer Research and Community Fund; American Cancer Society [124166-IRG-58-001-55-IRG53]; Alfred P. Sloan FoundationOpen Access Journal.This item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

Interaction networks among bacteria are defined by host tumor mutations.

<p>(A) SparCC analysis of the microbial abundances of the taxa identified by LEfSe for tumors with LoF mutations in APC (left) and without mutation (right) produce distinct patterns of correlations (edges) between a common set of taxa (nodes). Direct correlations are indicated as red edges and inverse correlations as blue edges (SparCC R > = 0.25, P < = 0.05 for displayed edges). (B) SparCC analysis was run simultaneously for all taxa identified by LEfSe when predicting interactions with mutations in PID pathways. There are interactions (dashed edges) between the taxa (grey nodes) associated with mutations across sets of PID pathways (green nodes). The solid edges indicate SparCC R-values (red for direct and blue for inverse correlations). The grey taxon nodes are scaled to the average abundance of the taxa in the associated tumor set. Edge color indicates the direction of the interaction, red for negative and blue for positive. Note that while several of the pathways (green nodes) have closely related general functions (<i>e</i>.<i>g</i>. “Canonical Wnt signaling pathway” and “Degradation of beta-catenin”), the underlying gene sets that comprise these pathways are distinct and result in independent correlations with microbial taxa.</p

Colorectal cancer mutational profiles correlate with defined microbial communities in the tumor microenvironment

<div><p>Variation in the gut microbiome has been linked to colorectal cancer (CRC), as well as to host genetic variation. However, we do not know whether, in addition to baseline host genetics, somatic mutational profiles in CRC tumors interact with the surrounding tumor microbiome, and if so, whether these changes can be used to understand microbe-host interactions with potential functional biological relevance. Here, we characterized the association between CRC microbial communities and tumor mutations using microbiome profiling and whole-exome sequencing in 44 pairs of tumors and matched normal tissues. We found statistically significant associations between loss-of-function mutations in tumor genes and shifts in the abundances of specific sets of bacterial taxa, suggestive of potential functional interaction. This correlation allows us to statistically predict interactions between loss-of-function tumor mutations in cancer-related genes and pathways, including MAPK and Wnt signaling, solely based on the composition of the microbiome. In conclusion, our study shows that CRC microbiomes are correlated with tumor mutational profiles, pointing towards possible mechanisms of molecular interaction.</p></div

Correlation between the microbial community at a tumor that differentiates between tumor stage.

<p>(A) Low-stage (stages 1–2) and high-stage (stages 3–4) tumors can be differentiated using a risk index classifier generated from microbial abundance data (y-axis). The central black bar indicates the median, and the thin black bars represent the 25th and 75th percentiles. (B) A receiver operating characteristic (ROC) curve was generated using a 10-fold cross-validation (blue dotted lines). The average of the 10-fold cross-validation curves is represented as a thick black line. (C) Differences in the mean abundances of a subset of the taxa predicted to interact differentially with high-stage and low-stage tumors. This subset represents those taxa that had a mean difference in abundance of greater than 0.1%, proportionally.</p