Biology subjects

Lam, T. J.

Publications and source records attributed to Lam, T. J..

3 recordsLinked to original sources

bioRxiv2022

Meta-analysis of Microbiome Association Networks Reveal Patterns of Dysbiosis in Diseased Microbiomes

The human gut microbiome is composed of a diverse and dynamic population of microbial species which play key roles in modulating host health and physiology. While individual microbial species have been found to be associated with certain disease states, increasing evidence suggests that higher-order microbial interactions may have an equal or greater contribution to host fitness. To better understand microbial community dynamics, we utilize complex networks to study interactions through a meta-analysis of microbial association networks between healthy and disease gut microbiomes. Taking advantage of the large number of metagenomes derived from healthy individuals and patients with various diseases, together with recent advances in network inference that can deal with sparse compositional data, we inferred microbial association networks based on co-occurrence of gut microbial species and made the networks publicly available as a resource (github repository named GutNet). Through our meta-analysis of inferred networks, we were able to identify network-associated features that help stratify between healthy and disease states such as the differentiation of various bacterial phyla and enrichment of Proteobacteria interactions in diseased networks. Additionally, our findings show that the contributions of taxa in microbial associations are disproportionate to their abundances and that rarer taxa of microbial species play an integral part in shaping dynamics of microbial community interactions. Overall, this meta-analysis revealed valuable insights into microbial community dynamics between healthy and disease phenotypes.

bioinformatics↗

bioRxiv2020

Pathway-based and phylogenetically adjusted quantification of metabolic interaction between microbial species

Microbial community members exhibit various forms of interactions. Taking advantage of the increasing availability of microbiome data, many computational approaches have been developed to infer bacterial interactions from the co-occurrence of microbes across diverse microbial communities. Additionally, the introduction of genome-scale metabolic models have also enabled the inference of metabolic interactions, such as competition and cooperation, between bacterial species. By nature, phylogenetically similar microbial species are likely to share common functional profiles or biological pathways due to their genomics similarity. Without properly factoring out the phylogenetic relationship, any estimation of the competition and cooperation based on functional/pathway profiles may bias downstream applications. To address these challenges, we developed a novel approach for estimating the competition and complementarity indices for a pair of microbial species, adjusted by their phylogenetic distance. An automated pipeline, PhyloMint, was implemented to construct competition and complementarity indices from genome scale metabolic models derived from microbial genomes. Application of our pipeline to 2,815 human-gut bacteria showed high correlation between phylogenetic distance and metabolic competition/cooperation indices among bacteria. Using a discretization approach, we were able to detect pairs of bacterial species with cooperation scores significantly higher than the average pairs of bacterial species with similar phylogenetic distances. A network community analysis of high metabolic cooperation but low competition reveals distinct modules of bacterial interactions. Our results suggest that niche differentiation plays a dominant role in microbial interactions, while habitat filtering also plays a role among certain clades of bacterial species. Author summaryMicrobial communities, also known as microbiomes, are formed through the interactions of various microbial species. Utilizing genomic sequencing, it is possible to infer the compositional make-up of communities as well as predict their metabolic interactions. However, because some species are more similarly related to each other, while others are more distantly related, one cannot directly compare metabolic relationships without first accounting for their phylogenetic relatedness. Here we developed a computational pipeline which predicts complimentary and competitive metabolic relationships between bacterial species, while normalizing for their phylogenetic relatedness. Our results show that phylogenetic distances are correlated with metabolic interactions, and factoring out such relationships can help better understand microbial interactions which drive community formation.

bioinformatics↗

bioRxiv2018

CRISPRs for strain tracking and its application to microbiota transplantation data analysis

CRISPR-Cas systems are adaptive immune systems naturally found in bacteria and archaea. Bacteria and archaea use these systems to defend against invaders, including phages, plasmids and other mobile genetic elements. Relying on integration of invader sequences (protospacers) into CRISPR loci (forming spacers flanked by repeats), CRISPR-Cas systems store genetic memory of past invasions. While CRISPR-Cas systems have evolved in response to invading mobile elements, invaders have also developed mechanisms to avoid detection. As a result of arms-race between CRISPR-Cas systems and their targets, the CRISPR arrays typically undergo rapid turnover of the spacers with removal of old spacers and acquisition of new ones. Additionally, different individuals rarely share spacers amongst their microbiome. In this paper, we developed a pipeline (called CRISPRtrack) for strain tracking based on CRISPR spacer content, and applied it to fecal transplantation microbiome data to study the retention of donor strains in recipients. Our results demonstrate the potential use of CRISPRs as a simple yet effective tool for donor strain tracking in fecal transplantation, and also as a general purpose tool for quantifying microbiome similarity.

bioinformatics↗