Investigating the biological and clinical significance of human dysbioses

Abstract

Culture-independent microbiological technologies that interrogate complex microbial populations without prior axenic culture, coupled with high-throughput DNA sequencing, have revolutionized the scale, speed and economics of microbial ecological studies. Their application to the medical realm has led to a highly productive merger of clinical, experimental and environmental microbiology. The functional roles played by members of the human microbiota are being actively explored through experimental manipulation of animal model systems and studies of human populations. In concert, these studies have appreciably expanded our understanding of the composition and dynamics of human-associated microbial communities (microbiota). Of note, several human diseases have been linked to alterations in the composition of resident microbial communities, so-called dysbiosis. However, how changes in microbial communities contribute to disease etiology remains poorly defined. Correlation of microbial composition represents integration of only two datasets (phenotype and microbial composition). This article explores strategies for merging the human microbiome data with multiple additional datasets (e.g. host single nucleotide polymorphisms and host gene expression) and for integrating patient-based data with results from experimental animal models to gain deeper understanding of how host-microbe interactions impact disease.

Figure 1. Possible etiological significance of dysbiosis

Imbalances in microbial communities might be a necessary condition for disease (A) or an inconsequential result of etiological agents (B) or disease itself (C). Because of the myriad beneficial services provided by the human-associated microbiota, loss of these functions as a result of disease development could further exacerbate the severity, duration, or frequency of disease (D).

Figure 2. Metagenomic tools for studying host-microbiota interactions in human disease

Bacteria are identified through culture independent analysis of 16S rRNA genes amplified from surgical specimens by broad-range PCR. Host genotypes and phenotypes are assessed through analysis of genomic DNA and mRNA of surgical specimens. Together, these data provide insight into how host factors and enteric microbial communities interact during development and/or progression of disease. The ultimate goal of these experiments is to use this knowledge to design, deploy, and monitor novel treatment regimes. Abbreviations: RT, reverse transcription.

Figure 3. Incorporating multimodality microbiome measurements

Schematic diagram of potential determinants of human microbiome and host phenotype (e.g. disease). The central oval (microbiome) depicts an unmeasured latent variable representing the kinds and quantities of microbes that constitute the microbiome. These are inferred by various experimental techniques, including Sanger sequencing, 454 pyrosequencing (using different 16S rRNA primer sets), and quantitative PCR (qPCR). Red arrows denote influences of host and microbiome on host phenotype whereas blue arrows denote causal relationships between host factors and microbiome. The relative contributions of these factors can be modeled using structural equation modeling, as described in the text.