It is well accepted that dysbiosis of microbiota is associated with disease; however, the biological mechanisms that promote susceptibility or resilience to disease remain elusive. One of the major limitations of previous microbiome studies has been the lack of complementary metatranscriptomic (functional) data to complement the interpretation of metagenomics (bacterial abundance). The purpose of the study was twofold, first to evaluate the bacterial diversity and differential gene expression

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... gut microbiota using complementary shotgun metagenomics (MG) and metatranscriptomics (MT) from same fecal sample. Second, to compare sequence data using different Illumina platforms and with different sequencing parameters as new sequencers are introduced and determine if the data are comparable on different platforms. In this study, we perform ultra-deep metatranscriptomic shotgun sequencing for a sample that we previously analyzed with metagenomics shotgun sequencing. We validated the sequencing and analysis methods using different Illumina platform, and with different sequencing and analysis parameters. Our results suggest that use of different Illumina platform did not lead to detectable bias in the sequencing data. The analysis of the sample using MG and MT approach shows that some species genes are more highly represented in the MT than in the MG, indicating that some species are highly metabolically active. Our analysis also shows that ~52% of the genes in the metagenome are in the metatranscriptome, and therefore are robustly expressed. The functions of the low and rare abundance bacterial species remain poorly understood. Our observations indicate that among the low abundant species analyzed in this study some were found to be more metabolically active compared to others and can contribute distinct profiles of biological functions that may modulate the host-microbiota and bacteria-bacteria interactions.