Tracking Strains in the Microbiome: Insights from Metagenomics and Models

Abstract

Transmission usually refers to the movement of pathogenic organisms. Yet, commensal microbes that inhabit the human body also move between individuals and environments. Surprisingly little is known about the transmission of these endogenous microbes, despite increasing realizations of their importance for human health. The health impacts arising from the transmission of commensal bacteria range widely, from the prevention of autoimmune disorders to the spread of antibiotic resistance genes. Despite this importance, there are outstanding basic questions: what is the fraction of the microbiome that is transmissible? What are the primary mechanisms of transmission? Which organisms are the most highly transmissible? Higher resolution genomic data is required to accurately link microbial sources (such as environmental reservoirs or other individuals) with sinks (such as a single person's microbiome). New computational advances enable strain-level resolution of organisms from shotgun metagenomic data, allowing the transmission of strains to be followed over time and after discrete exposure events. Here, we highlight the latest techniques that reveal strain-level resolution from raw metagenomic reads and new studies that are tracking strains across people and environments. We also propose how models of pathogenic transmission may be applied to study the movement of commensals between microbial communities.

Keywords: bacterial genomics; biological; genotyping techniques; metagenomics; microbiome; models; strain diversity.

Figure 1

Scenarios for molecular epidemiology approaches. (A) Nesting of one individuals' strain lineages within another's supports transmission from the host carrying the ancestral strain to the host carrying the more recently diverged strain, as shown here of a putative transmission event (shown in red) from person A to person B. (B) The loss of lineages can affect our ability to determine directionality. Given the same phylogeny in (A), without the gray lineages, it is unclear which person's strains are ancestral. This can occur due to the choice of gene or characterizing fewer strains in an individual than what is present. (C) An outgroup helps distinguish transmission direction. Without lineage (C), it is unclear whether (A) transmitted strains to (B) or vice versa. The inclusion of appropriate control samples can help reduce the likelihood of indirect transmission from an intermediate host or environmental source. In the 1994 case involving HIV, controls were chosen from HIV-infected individuals in the same geography, although not necessarily with the same risk factors (i.e., drug use, sexuality, hemophilia; Metzker et al., 2002). (D) Phylogenetic distances may not reflect the timing of transmission. An organism's rate of evolution may depend on factors specific to the individual, such as immunity, diet or genetics, which create different host selective pressures. (E) The rate of evolution of the marker gene is important to detect putative direct transmission. Long-term carriage of a microbe with high rates of evolution may result in long branch-lengths, upon which it becomes more difficult to exclude the possibility of indirect transmission.

Figure 2

Modeling bacterial transmission. (A) Metapopulation models. Change in island occupancy, by a microbe perhaps, is modeled as a function of migration (m) and an extinction rate (e). Other considerations such as a distance-based probability of infection may modify m. $\frac{dP}{dt}=mP(1-P)-eP$ (B) Susceptible-Infected-Resistant (SIR) models (with or without strain dynamics). Susceptible (S) individuals may become infected (I) and can recover and become immune. SIR models are similar to metapopulation models in that infection rate (β) is akin to migration between islands, as recovery (γ) is akin to extinction in the metapopulation model. Variations may include demographic processes, infection processes (latency, carriage), and alternative hosts or vectors. $\frac{dS}{dt}=-\beta SI$ $\frac{dI}{dt}=\beta SI-\gamma I$ $\frac{dR}{dt}=\gamma I$ SIR models that incorporate within-host evolution of specific strains typically are nested models that account for individuals' infection composition. (C) Landscape fate-and-transport (F&T) models. F&T models estimate microbial abundances rather than a dichotomous infection status. The models stem from traditional advection-dispersion equations. Landscape features such as the surface porosity or water flow can be incorporated. $\frac{\partial C}{\partial x}=D\frac{{\partial }^{2}C}{{\partial }^{2}x}-\nu \frac{\partial C}{\partial x}$