Binary Metabolic Phenotypes and Phenotype Diversity Metrics for the Functional Characterization of Microbial Communities

Abstract

The profiling of 16S rRNA revolutionized the exploration of microbiomes, allowing to describe community composition by enumerating relevant taxa and their abundances. However, taxonomic profiles alone lack interpretability in terms of bacterial metabolism, and their translation into functional characteristics of microbiomes is a challenging task. This bottom-up approach minimally requires a reference collection of major metabolic traits deduced from the complete genomes of individual organisms, an accurate method of projecting these traits from a reference collection to the analyzed amplicon sequence variants (ASVs), and, ultimately, an approach to a microbiome-wide aggregation of predicted individual traits into physiologically relevant cumulative metrics to characterize and compare multiple microbiome samples. In this study, we extended a previously introduced computational approach for the functional profiling of complex microbial communities, which is based on the concept of binary metabolic phenotypes encoding the presence (“1”) or absence (“0”) of various measurable physiological properties in individual organisms that are termed phenotype carriers or non-carriers, respectively. Derived from complete genomes via metabolic reconstruction, binary phenotypes provide a foundation for the prediction of functional traits for each ASV identified in a microbiome sample. Here, we introduced three distinct mapping schemes for a microbiome-wide phenotype prediction and assessed their accuracy on the 16S datasets of mock bacterial communities representing human gut microbiome (HGM) as well as on two large HGM datasets, the American Gut Project and the UK twins study. The 16S sequence-based scheme yielded a more accurate phenotype predictions, while the taxonomy-based schemes demonstrated a reasonable performance to warrant their application for other types of input data (e.g., from shotgun metagenomics or qPCR). In addition to the abundance-weighted Community Phenotype Indices (CPIs) reflecting the fractional representation of various phenotype carriers in microbiome samples, we employ metrics capturing the diversity of phenotype carriers, Phenotype Alpha Diversity (PAD) and Phenotype Beta Diversity (PBD). In combination with CPI, PAD allows to classify the robustness of metabolic phenotypes by their anticipated stability in the face of potential environmental perturbations. PBD provides a promising approach for detecting the metabolic features potentially contributing to disease-associated metabolic traits as illustrated by a comparative analysis of HGM samples from healthy and Crohn’s disease cohorts.

Short-Chain Fatty Acids Modulate Healthy Gut Microbiota Composition and Functional Potential

“…To predict metabolic potential of microbial taxa identified by 16S rRNA analysis we utilized the Phenotype Profiler tool (PhenoBiome Inc., San Francisco, CA). To predict metabolic capabilities of 2856 reference genomes representing 690 microbial species from human gut, we used a subsystem-based approach implemented in microbial community SEED (mcSEED) platform [ 14 ], as we have described previously [ 10 , 15 , 16 ]. Each reference genome in each analyzed metabolic subsystem was assigned a binary (“1” or “0”) phenotype reflecting the presence/absence of a complete amino acid/vitamin or SCFA synthesis pathway.…”

“…Each reference genome in each analyzed metabolic subsystem was assigned a binary (“1” or “0”) phenotype reflecting the presence/absence of a complete amino acid/vitamin or SCFA synthesis pathway. The obtained binary phenotype matrix (BPM) for metabolic phenotype distributions in the reference genomes was used to calculate a probabilistic estimate P for each mapped taxa obtained from 16S analysis to possess a certain binary metabolic phenotype as previously described [ 15 ]. Community Phenotype Index (CPI) values for each 16S sample were calculated as a sum of respective p values of each taxa multiplied by their relative abundance.…”

Genomic reconstruction of short-chain fatty acid production by the human gut microbiota

“…Briefly, 16S sequences were quality-filtered, chimeric reads were removed, and the resulting reads were dereplicated into amplicon sequence variants (ASV) with default dada2 parameters. Taxonomic classification of the obtained ASV sequences was carried out using the multi-taxonomic assignment (MTA) approach ( Iablokov S. et al, 2021 ) using the joined reference NCBI 16S and RDP ( Cole et al, 2014 ) databases, with taxonomic names in RDP updated according the NCBI Taxonomy database. Finally, we renormalized the original relative abundances of ASVs by 16S rRNA gene copy numbers derived from the rrndb (version 5.6) database ( Stoddard et al, 2015 ).…”

“…ASVs with identity to the HGM reference organisms below this threshold were discarded. According to our previous analyses, PI values were calculated by phenotype averaging across all genomes mapped with 90% threshold, resulting in PI predictions of sufficient accuracy for the majority of phenotypes, including SCFAs ( Iablokov S. et al, 2021 ).…”

“…To obtain taxonomic profiles of WGS samples we used Kraken 2 ( Wood et al, 2019 ) and Bracken ( Lu et al, 2017 ) and our custom genomic database containing 2,856 reference HGM bacteria with taxonomic assignments according to the NCBI Taxonomy database. To assess the metabolic potential for SCFA production in WGS samples, we used the Phenotype Profiler tool with the relative taxonomy abundance profiles provided as an input and a taxonomy-based approach to map the respective taxonomic assignments to the reference BPM organisms (for details, see ( Iablokov S. et al, 2021 )). WGS entries with taxonomic similarity to the BPM worse than on family level were marked as “non-mapped” and discarded, with the respective relative abundances of “mapped” entries renormalized to sum to 1.…”