Link clustering reveals structural characteristics and biological contexts in signed molecular networks

Two-State Co-Expression Network Analysis to Identify Genes Related to Salt Tolerance in Thai rice

Khao Dawk Mali 105 (KDML105) rice is one of the most important crops of Thailand. It is a challenging task to identify the genes responding to salinity in KDML105 rice. The analysis of the gene co-expression network has been widely performed to prioritize significant genes, in order to select the key genes in a specific condition. In this work, we analyzed the two-state co-expression networks of KDML105 rice under salt-stress and normal grown conditions. The clustering coefficient was applied to both networks and exhibited significantly different structures between the salt-stress state network and the original (normal-grown) network. With higher clustering coefficients, the genes that responded to the salt stress formed a dense cluster. To prioritize and select the genes responding to the salinity, we investigated genes with small partners under normal conditions that were highly expressed and were co-working with many more partners under salt-stress conditions. The results showed that the genes responding to the abiotic stimulus and relating to the generation of the precursor metabolites and energy were the great candidates, as salt tolerant marker genes. In conclusion, in the case of the complexity of the environmental conditions, gaining more information in order to deal with the co-expression network provides better candidates for further analysis.

A hemolytic-uremic syndrome-associated strain O113:H21 Shiga toxin-producing Escherichia coli specifically expresses a transcriptional module containing dicA and is related to gene network dysregulation in Caco-2 cells

Shiga toxin-producing (Stx) Escherichia coli (STEC) O113:H21 strains are associated with human diarrhea and some of these strains may cause hemolytic uremic syndrome (HUS). The molecular mechanism underlying this capacity and the differential host cell response to HUS-causing strains are not yet completely understood. In Brazil O113:H21 strains are commonly found in cattle but, so far, were not isolated from HUS patients. Here we conducted comparative gene co-expression network (GCN) analyses of two O113:H21 STEC strains: EH41, reference strain, isolated from HUS patient in Australia, and Ec472/01, isolated from cattle feces in Brazil. These strains were cultured in fresh or in Caco-2 cell conditioned media. GCN analyses were also accomplished for cultured Caco-2 cells exposed to EH41 or Ec472/01. Differential transcriptome profiles for EH41 and Ec472/01 were not significantly changed by exposure to fresh or Caco-2 conditioned media. Conversely, global gene expression comparison of both strains cultured in conditioned medium revealed a gene set exclusively expressed in EH41, which includes the dicA putative virulence factor regulator. Network analysis showed that this set of genes constitutes an EH41 specific transcriptional module. PCR analysis in Ec472/01 and in other 10 Brazilian cattle-isolated STEC strains revealed absence of dicA in all these strains. The GCNs of Caco-2 cells exposed to EH41 or to Ec472/01 presented a major transcriptional module containing many hubs related to inflammatory response that was not found in the GCN of control cells. Moreover, EH41 seems to cause gene network dysregulation in Caco-2 as evidenced by the large number of genes with high positive and negative covariance interactions. EH41 grows slowly than Ec472/01 when cultured in Caco-2 conditioned medium and fitness-related genes are hypoexpressed in that strain. Therefore, EH41 virulence may be derived from its capacity for dysregulating enterocyte genome functioning and its enhanced enteric survival due to slow growth.

Functional Analysis and Characterization of Differential Coexpression Networks

Differential coexpression analysis is emerging as a complement to conventional differential gene expression analysis. The identified differential coexpression links can be assembled into a differential coexpression network (DCEN) in response to environmental stresses or genetic changes. Differential coexpression analyses have been successfully used to identify condition-specific modules; however, the structural properties and biological significance of general DCENs have not been well investigated. Here, we analyzed two independent Saccharomyces cerevisiae DCENs constructed from large-scale time-course gene expression profiles in response to different situations. Topological analyses show that DCENs are tree-like networks possessing scale-free characteristics, but not small-world. Functional analyses indicate that differentially coexpressed gene pairs in DCEN tend to link different biological processes, achieving complementary or synergistic effects. Furthermore, the gene pairs lacking common transcription factors are sensitive to perturbation and hence lead to differential coexpression. Based on these observations, we integrated transcriptional regulatory information into DCEN and identified transcription factors that might cause differential coexpression by gain or loss of activation in response to different situations. Collectively, our results not only uncover the unique structural characteristics of DCEN but also provide new insights into interpretation of DCEN to reveal its biological significance and infer the underlying gene regulatory dynamics.

Core and specific network markers of carcinogenesis from multiple cancer samples

Cancer is the leading cause of death worldwide and is generally caused by mutations in multiple proteins or the dysregulation of pathways. Understanding the causes and the underlying carcinogenic mechanisms can help fight this disease. In this study, a systems biology approach was used to construct the protein-protein interaction (PPI) networks of four cancers and the non-cancers by their corresponding microarray data, PPI modeling and database-mining. By comparing PPI networks between cancer and non-cancer samples to find significant proteins with large PPI changes during carcinogenesis process, core and specific network markers were identified by the intersection and difference of significant proteins, respectively, with carcinogenesis relevance values (CRVs) for each cancer. A total of 28 significant proteins were identified as core network markers in the carcinogenesis of four types of cancer, two of which are novel cancer-related proteins (e.g., UBC and PSMA3). Moreover, seven crucial common pathways were found among these cancers based on their core network markers, and some specific pathways were particularly prominent based on the specific network markers of different cancers (e.g., the RIG-I-like receptor pathway in bladder cancer, the proteasome pathway and TCR pathway in liver cancer, and the HR pathway in lung cancer). Additional validation of these network markers using the literature and new tested datasets could strengthen our findings and confirm the proposed method. From these core and specific network markers, we could not only gain an insight into crucial common and specific pathways in the carcinogenesis, but also obtain a high promising PPI target for cancer therapy.