Application of meta-transcriptomics and -proteomics to analysis of in situ physiological state

Abstract

Analysis of the growth-limiting factor or environmental stressors affecting microbes in situ is of fundamental importance but analytically difficult. Microbes can reduce in situ limiting nutrient concentrations to sub-micromolar levels, and contaminated ecosystems may contain multiple stressors. The patterns of gene or protein expression by microbes in nature can be used to infer growth limitations, because they are regulated in response to environmental conditions. Experimental studies under controlled conditions in the laboratory provide the physiological underpinnings for developing these physiological indicators. Although regulatory networks may differ among specific microbes, there are some broad principles that can be applied, related to limiting nutrient acquisition, resource allocation, and stress responses. As technologies for transcriptomics and proteomics mature, the capacity to apply these approaches to complex microbial communities will accelerate. Global proteomics has the particular advantage that it reflects expressed catalytic activities. Furthermore, the high mass accuracy of some proteomic approaches allows mapping back to specific microbial strains. For example, at the Rifle IFRC field site in Western Colorado, the physiological status of Fe(III)-reducing populations has been tracked over time. Members of a "subsurface clade" within the Geobacter predominated during carbon amendment to the subsurface environment. At the functional level, proteomic identifications produced inferences regarding (i) temporal changes in anabolism and catabolism of acetate, (ii) the onset of N(2) fixation when N became limiting, and (iii) expression of phosphate transporters during periods of intense growth. The application of these approaches in situ can lead to discovery of novel physiological adaptations.

Keywords: marine habitats; physiological indicators; subsurface sediments.

FIGURE 1

Steps involved in the identification of physiological indicators and their application to natural microbial communities. A pure culture isolated from the environment is exposed to a range of physicochemical conditions hypothesized to be relevant to the in situ environment. Physiological responses specific to particular limitations or stressors are elucidated. Natural samples are then tested for the presence of these specific physiological indicators, to infer that the relevant limitations/stressors are acting in situ.

FIGURE 2

The discovery of novel physiological responses via data mining of transcriptome or proteome data. Replicate environmental samples are collected along an environmental gradient where a measured physicochemical factor is known to vary. Multivariate statistical analyses of the molecular sequences and the associated environmental metadata identify genes or proteins whose abundance is related to environmental characteristics. Some of these may represent annotated genes that previously have been identified as physiological indicators. If this data mining suggests that there is an annotated gene that has not previously been recognized as part of the adaptive response, laboratory experiments should be undertaken to confirm its role under well-controlled conditions. However, the most powerful outcome of this approach may be the identification of unannotated genes that contribute to specific physiological adaptations. This generates a motivation for detailed functional genomic analysis, and subsequent biochemical experimentation to elucidate its novel function.