Network analysis of transcriptomics expands regulatory landscapes inSynechococcussp. PCC 7002

Abstract

Cyanobacterial regulation of gene expression must contend with a genome organization that lacks apparent functional context, as the majority of cellular processes and metabolic pathways are encoded by genes found at disparate locations across the genome and relatively few transcription factors exist. In this study, global transcript abundance data from the model cyanobacterium Synechococcus sp. PCC 7002 grown under 42 different conditions was analyzed using Context-Likelihood of Relatedness (CLR). The resulting network, organized into 11 modules, provided insight into transcriptional network topology as well as grouping genes by function and linking their response to specific environmental variables. When used in conjunction with genome sequences, the network allowed identification and expansion of novel potential targets of both DNA binding proteins and sRNA regulators. These results offer a new perspective into the multi-level regulation that governs cellular adaptations of the fast-growing physiologically robust cyanobacterium Synechococcus sp. PCC 7002 to changing environmental variables. It also provides a methodological high-throughput approach to studying multi-scale regulatory mechanisms that operate in cyanobacteria. Finally, it provides valuable context for integrating systems-level data to enhance gene grouping based on annotated function, especially in organisms where traditional context analyses cannot be implemented due to lack of operon-based functional organization.

Global Network Analysis of Neisseria gonorrhoeae Identifies Coordination between Pathways, Processes, and Regulators Expressed during Human Infection

“…Most of the edges that tRNA genes had were to other tRNA genes, and, as described below, a large cluster of tRNAs was present in the network as a module of tightly connected nodes. The phenomenon of all, or nearly all, tRNA genes forming a tight cluster has been seen with other networks inferred for bacterial species (33). Aside from tRNA genes, other genes with high degree values included NGO0508, a phage-associated gene, NGO1506, an NTP pyrophosphohydrolase, NGO1818, RNA polymerase subunit alpha, NGO1741 and NGO1743, two NADH subunits, and NGO942, NGO1825 (rplF), and NGO1853 (rplJ), a 23s rRNA methyltransferase and two 50s ribosomal proteins, respectively.…”

“…To carry out GBA analysis, we collected the 75 genes that had the highest coexpression values (Z-scores) with a given protein. We then carried out functional enrichment on that group of genes to determine if any known functions were found (33). A putative category was assigned to a protein (i) if at least 10% of the genes in this 75-gene data set were assigned to that category, (ii) if this percentage was higher than the percentage of genes of this same category in the genome as a whole, and (iii) if this increase was significant (P value Ͻ 0.05) using Fisher's exact test.…”

Species-specific transcriptomic network inference of interspecies interactions

“…Biological replicates for a subset of conditions were averaged by mean. Networks were then inferred from the expression data using CLR [20] along with resampling methods previously described [18]. Briefly, the CLR program computes the mutual information between all gene pairs in the dataset.…”

“…Following the reverse ecology approach, we sought to use a high-throughput method that can facilitate mining and identification of interspecies molecular events in microbial communities from meta-omics data. Building on previous success with mutual information (MI) methods to characterize regulatory responses in single species [17][18][19], we tested the applicability of the Context Likelihood of Relatedness (CLR) algorithm [20] to reconstruct multi-organism gene-association network from transcriptomic data and predict interactions between organisms from coordinated changes in gene expression. Networks made using MI are able to link genes that are co-expressed across a range of conditions based on the information that one can gather about one gene (variable) compared to another.…”

“…Networks based on MI that link coexpressed genes can therefore also link genes with similar or coordinated functions [21], allowing prediction of gene function [22]. We previously demonstrated that this method is especially useful for linking genes in biological systems where genomic context is ineffective due to the localization of functionally and structurally related genes at disparate genome locations [18,23]. We hypothesized that network-based MI analysis would identify genes that are functionally linked (e.g., involved in division of labor or exchange of nutrients) but are found in separate organisms and are thus physically separated.…”