Computational methods for taxa-free microbial biomarker discovery and clinical risk stratification

About This Grant

ABSTRACT The gut microbiome has shown immense clinical potential for human disease as non-invasive biomarkers and new therapeutic interventions such as fecal microbiota transplants. A lack of replicable taxonomic associations across cohorts is currently limiting the clinical potential of the gut microbiome. Most microbiome studies focus on taxa-level associations because methods to taxonomically profile microbiomes are more mature and less computationally complex than those for functional profiling. However, the taxonomic composition of the gut microbiome is not conserved across individuals in different populations, resulting in the need for approaches that can explain the persistent relationships between different gut microbes and human health phenotypes. Given that the functional composition of the gut microbiome is more consistent across individuals and microbial proteins with the same function can originate in different species, my central hypothesis is that protein biomarkers underlie taxonomic gut microbial associations with human disease and health outcomes. There are no existing methods to quantify and efficiently map metagenomic short reads to microbial proteins, which is a necessary first step in identifying protein and peptide-level biomarkers. My objectives are to develop taxa-free frameworks for gut microbial biomarker discovery with a goal of understanding the role of gut microbial proteins in human disease. In Aim 1 I will develop and comprehensively benchmark a new tool to efficiently map whole-genome metagenomic shotgun sequencing reads to groups of homologous microbial proteins originating in different taxa. In Aim 2 I will develop non-invasive clinical risk stratification methods based on gut microbial proteins and introduce a pipeline for prioritizing specific microbial peptides for experimental validation. I will demonstrate the clinical utility of the risk stratification methods I develop by predicting risk from immune-checkpoint inhibitor therapy from metagenomic sequencing of fecal samples in published cohorts and validating the methods on a newly sequenced cohort. Successful completion of these aims will enable non-invasive clinical risk stratification and microbial biomarker discovery for a wide range of human health outcomes. Application of the developed methods will demonstrate their clinical utility and help clarify the roles of gut microbes in responses to anti-cancer immunotherapy.