close

Abstract

Interspecies hydrogen transfer between organisms producing and consuming hydrogen promotes the decomposition
of organic matter in most anoxic environments. Although syntrophic coupling between hydrogen
producers and consumers is a major feature of the carbon cycle, mechanisms for energy recovery at the
extremely low free energies of reactions typical of these anaerobic communities have not been established. In
this study, comparative transcriptional analysis of a model sulfate-reducing microbe, Desulfovibrio vulgaris
Hildenborough, suggested the use of alternative electron transfer systems dependent on growth modality.
During syntrophic growth on lactate with a hydrogenotrophic methanogen, numerous genes involved in
electron transfer and energy generation were upregulated in D. vulgaris compared with their expression in
sulfate-limited monocultures. In particular, genes coding for the putative membrane-bound Coo hydrogenase,
two periplasmic hydrogenases (Hyd and Hyn), and the well-characterized high-molecular-weight cytochrome
(Hmc) were among the most highly expressed and upregulated genes. Additionally, a predicted operon
containing genes involved in lactate transport and oxidation exhibited upregulation, further suggesting an
alternative pathway for electrons derived from lactate oxidation during syntrophic growth. Mutations in a
subset of genes coding for Coo, Hmc, Hyd, and Hyn impaired or severely limited syntrophic growth but had
little effect on growth via sulfate respiration. These results demonstrate that syntrophic growth and sulfate
respiration use largely independent energy generation pathways and imply that to understand microbial
processes that sustain nutrient cycling, lifestyles not captured in pure culture must be considered.

Funding Source

ESPP

Keywords

Environmental Genomics

close

Abstract

The area of transcriptomics analysis is among the more established in computational biology, having evolved in both technology and experimental design. Transcriptomics has a strong impetus to develop sophisticated computational methods due to the large amounts of available whole-genome datasets for many species and because of powerful applications in regulatory network reconstruction as well as elucidation and modeling of cellular transcriptional responses. While gene expression microarray data can be noisy and comparisons across experiments challenging, there are a number of sophisticated methods that aid in arriving at statistically and biologically significant conclusions. As such, computational transcriptomics analysis can provide guidance for analysis of results from newer experimental technologies. More recently, search methods have been developed to identify modules of genes, which exhibit coherent expression patterns in only a subset of experimental conditions. The latest advances in these methods allow to integrate multiple data types and datasets, both experimental and computational, within a single statistical framework accounting for data confidence and relevance to specific biological questions. Such frameworks provide a unified environment for the exploration of specific biological hypothesis and for the discovery of coherent data patterns along with the evidence supporting them.

Presenter

Joachimiak, Marcin P.

Funding Source

Environmental Stress Pathway Project (ESPP)

Keywords

Bioinformatics, Transcriptomics