Evaluating bacterial and functional diversity of human gut microbiota by complementary metagenomics and metatranscriptomics

Abstract

1It is well accepted that dysbiosis of microbiota is associated with disease; however, the biological 2 mechanisms that promote susceptibility or resilience to disease remain elusive. One of the major limitations 3 of previous microbiome studies has been the lack of complementary metatranscriptomic (functional) data 4 to complement the interpretation of metagenomics (bacterial abundance). The purpose of the study was 5 twofold, first to evaluate the bacterial diversity and differential gene expression of gut microbiota using 6 complementary shotgun metagenomics (MG) and metatranscriptomics (MT) from same fecal sample. 7 Second, to compare sequence data using different Illumina platforms and with different sequencing 8 parameters as new sequencers are introduced and determine if the data are comparable on different 9 platforms. In this study, we perform ultra-deep metatranscriptomic shotgun sequencing for a sample that 10 we previously analyzed with metagenomics shotgun sequencing. We validated the sequencing and 11 analysis methods using different Illumina platform, and with different sequencing and analysis parameters. 12 Our results suggest that use of different Illumina platform did not lead to detectable bias in the sequencing 13 data. The analysis of the sample using MG and MT approach shows that some species genes are more 14 highly represented in the MT than in the MG, indicating that some species are highly metabolically active. 15 Our analysis also shows that ~52% of the genes in the metagenome are in the metatranscriptome, and 16 therefore are robustly expressed. The functions of the low and rare abundance bacterial species remain 17 poorly understood. Our observations indicate that among the low abundant species analyzed in this study 18 some were found to be more metabolically active compared to others and can contribute distinct profiles of 19 biological functions that may modulate the host-microbiota and bacteria-bacteria interactions. 20 21

Comparing Gut Microbiome in Mothers’ Own Breast Milk- and Formula-Fed Moderate-Late Preterm Infants

“…16S rRNA gene amplicon sequencing is commonly used to determine the gut microbiome, but limitations exist including uneven PCR amplification, short sequence lengths (Quince et al, 2009;Poretsky et al, 2014), and low-accuracy OTU mapping (Huse et al, 2010;Ranjan et al, 2018). In recent years, the rapid development of high-throughput sequencing technology and reduced costs has made metatranscriptome feasible in microbiome studies.…”

The Computational Diet: A Review of Computational Methods Across Diet, Microbiome, and Health

“…Whole-genome or shotgun metagenomics (Quince et al, 2017) is a recent technique that not only reveals the microbial community structure, but it can also quantify relative abundances of genes, taxa, conserved functional groups, or over-represented pathways. Within-sample (alpha) and crosssample (beta) diversity of microbiome can be calculated with respect to genetic, taxonomic, functional, or metabolic pathway profiles of samples (Turnbaugh et al, 2009;Martiny et al, 2011;Huttenhower et al, 2012;Lozupone et al, 2012;Heintz-Buschart and Wilmes, 2017;Ranjan et al, 2018). The Shannon index, Chao1, and Abundance-based Coverage Estimator (ACE) are used to measure alpha diversity while UniFrac, weighted UniFrac, and Bray-Curtis calculate beta diversity.…”