Hidden state prediction: a modification of classic ancestral state reconstruction algorithms helps unravel complex symbioses.

Rebecca L V Thurber,Jesse R R Zaneveld

doi:10.3389/fmicb.2014.00431

Abstract

Complex symbioses between animal or plant hosts and their associated microbiotas can involve thousands of species and millions of genes. Because of the number of interacting partners, it is often impractical to study all organisms or genes in these host-microbe symbioses individually. Yet new phylogenetic predictive methods can use the wealth of accumulated data on diverse model organisms to make inferences into the properties of less well-studied species and gene families. Predictive functional profiling methods use evolutionary models based on the properties of studied relatives to put bounds on the likely characteristics of an organism or gene that has not yet been studied in detail. These techniques have been applied to predict diverse features of host-associated microbial communities ranging from the enzymatic function of uncharacterized genes to the gene content of uncultured microorganisms. We consider these phylogenetically informed predictive techniques from disparate fields as examples of a general class of algorithms for Hidden State Prediction (HSP), and argue that HSP methods have broad value in predicting organismal traits in a variety of contexts, including the study of complex host-microbe symbioses.

Highlights

Reviewed by: Amparo Latorre, University of Valencia, Spain Anna Carolin Frank, University of California Merced, USA
Hidden State Prediction (HSP) is similar to ancestral state reconstruction (ASR) techniques, in which the properties of ancestral organisms are inferred based on traits of their living descendants
While methods that use phylogeny to predict traits have been available for decades, it is only relatively recently that these methods have been applied at high-throughput to summarize our understanding of key players in microbial symbioses

Summary

Introduction

Reviewed by: Amparo Latorre, University of Valencia, Spain Anna Carolin Frank, University of California Merced, USA. Predictive functional profiling methods use evolutionary models based on the properties of studied relatives to put bounds on the likely characteristics of an organism or gene that has not yet been studied in detail These techniques have been applied to predict diverse features of host-associated microbial communities ranging from the enzymatic function of uncharacterized genes to the gene content of uncultured microorganisms. Data from experiments on model species must generally be extrapolated to predict impacts in rare or hard to access relatives (Guénard et al, 2011) These problems of vast diversity and limited ability to study all species are apparent in the context of complex symbiotic assemblages like those between metazoans and their associated microbial communities. The human microbiome is a heavily studied system, the diversity of its constituents presents an important challenge to gaining an ecosystem-level understanding of the contribution of each member to the dynamics of the overall system

Methods

Conclusion