Inferring Gene Networks for Strains of Dehalococcoides Highlights Conserved Relationships between Genes Encoding Core Catabolic and Cell-Wall Structural Proteins.
The interpretation of high-throughput gene expression data for non-model microorganisms remains obscured because of the high fraction of hypothetical genes and the limited number of methods for the robust inference of gene networks. Therefore, to elucidate gene-gene and gene-condition linkages in the bioremediation-important genus Dehalococcoides, we applied a Bayesian inference strategy called Reverse Engineering/Forward Simulation (REFS™) on transcriptomic data collected from two organohalide-respiring communities containing different Dehalococcoides mccartyi strains: the Cornell University mixed community D2 and the commercially available KB-1® bioaugmentation culture. In total, 49 and 24 microarray datasets were included in the REFS™ analysis to generate an ensemble of 1,000 networks for the Dehalococcoides population in the Cornell D2 and KB-1® culture, respectively. Considering only linkages that appeared in the consensus network for each culture (exceeding the determined frequency cutoff of ≥ 60%), the resulting Cornell D2 and KB-1® consensus networks maintained 1,105 nodes (genes or conditions) with 974 edges and 1,714 nodes with 1,455 edges, respectively. These consensus networks captured multiple strong and biologically informative relationships. One of the main highlighted relationships shared between these two cultures was a direct edge between the transcript encoding for the major reductive dehalogenase (tceA (D2) or vcrA (KB-1®)) and the transcript for the putative S-layer cell wall protein (DET1407 (D2) or KB1_1396 (KB-1®)). Additionally, transcripts for two key oxidoreductases (a [Ni Fe] hydrogenase, Hup, and a protein with similarity to a formate dehydrogenase, "Fdh") were strongly linked, generalizing a strong relationship noted previously for Dehalococcoides mccartyi strain 195 to multiple strains of Dehalococcoides. Notably, the pangenome array utilized when monitoring the KB-1® culture was capable of resolving signals from multiple strains, and the network inference engine was able to reconstruct gene networks in the distinct strain populations.
Department of Civil and Environmental Engineering, Cornell University, Ithaca, NY, United States of America.
Cresten B
CB
Mansfeldt
Department of Civil and Environmental Engineering, Cornell University, Ithaca, NY, United States of America.
Gretchen W
GW
Heavner
Field of Microbiology, Cornell University, Ithaca, NY, United States of America.
Annette R
AR
Rowe
GNS Healthcare. Cambridge, MA, United States of America.
Boris
B
Hayete
GNS Healthcare. Cambridge, MA, United States of America.
Bruce W
BW
Church
Department of Civil and Environmental Engineering, Cornell University, Ithaca, NY, United States of America.
Ruth E
RE
Richardson
D002473:Cell Wall / Q000235:genetics*; D017358:Cell Wall Skeleton / Q000235:genetics*; D041862:Chloroflexi / Q000235:genetics* / Q000378:metabolism; D016384:Consensus Sequence / Q000235:genetics; D053263:Gene Regulatory Networks / Q000235:genetics*; D008660:Metabolism / Q000235:genetics*; D020411:Oligonucleotide Array Sequence Analysis
D017358:Cell Wall Skeleton