Significant Functional Modules Research Articles

Elucidating genes and gene networks linked to individual susceptibility to milk fat depression in dairy goats.

Dietary supplementation with marine lipids modulates ruminant milk composition toward a healthier fatty acid profile for consumers, but it also causes milk fat depression (MFD). Because the dairy goat industry is mainly oriented toward cheese manufacturing, MFD can elicit economic losses. There is large individual variation in animal susceptibility with goats more (RESPO+) or less (RESPO-) responsive to diet-induced MFD. Thus, we used RNA-Seq to examine gene expression profiles in mammary cells to elucidate mechanisms underlying MFD in goats and individual variation in the extent of diet-induced MFD. Differentially expression analyses (DEA) and weighted gene co-expression network analysis (WGCNA) of RNA-Seq data were used to study milk somatic cell transcriptome changes in goats consuming a diet supplemented with marine lipids. There were 45 differentially expressed genes (DEGs) between control (no-MFD, before diet-induced MFD) and MFD, and 18 between RESPO+ and RESPO-. Biological processes and pathways such as "RNA transcription" and "Chromatin modifying enzymes" were downregulated in MFD compared with controls. Regarding susceptibility to diet-induced MFD, we identified the "Triglyceride Biosynthesis" pathway upregulated in RESPO- goats. The WGCNA approach identified 9 significant functional modules related to milk fat production and one module to the fat yield decrease in diet-induced MFD. The onset of MFD in dairy goats is influenced by the downregulation of SREBF1, other transcription factors and chromatin-modifying enzymes. A list of DEGs between RESPO+ and RESPO- goats (e.g., DBI and GPD1), and a co-related gene network linked to the decrease in milk fat (ABCD3, FABP3, and PLIN2) was uncovered. Results suggest that alterations in fatty acid transport may play an important role in determining individual variation. These candidate genes should be further investigated.

Intimate intertwining of the pathogenesis of hypoxia and systemic sclerosis: A transcriptome integration analysis.

Systemic sclerosis (SSc) is an autoimmune disease caused by various pathogenic factors, including hypoxia. Hypoxia stimulates the production of the extracellular matrix to promote fibrosis. However, the integrated function and the underlying mechanism of hypoxia in SSc are unclear. In the present study, we used Agilent SurePrint G3 Human Gene Expression v3 for the transcriptional sequencing of fibroblasts with and without hypoxia to detect differentially expressed genes (DEGs) in hypoxia. We analyzed the results with the transcriptome data of SSc lesions (GSE95065) to select the co-DEGs. Then, Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses were performed on the basis of the co-DEGs using the R package ClusterProfiler, which showed that hypoxia and cross talk of hypoxia with other pathogenic factors are involved in the pathogenesis of SSc. Furthermore, we constructed a protein-protein interaction (PPI) network of co-DEGs and screened two significant functional expression modules. We identified nine hub genes (ALDH1A1, EGF, NOX4, LYN, DNTT, PTGS2, TKT, ACAA2, and ALDH3A1). These genes affect the pentose phosphate pathway, oxidative stress, and lipolysis. Our study provides insights into the mechanisms underlying the effects of hypoxia on SSc pathogenesis, which will help to better understand SSc pathogenesis and develop new therapeutic strategies for SSc.

Identification of crucial genes involved in pathogenesis of regional weakening of the aortic wall

BackgroundThe diameter of the abdominal aortic aneurysm (AAA) is the most commonly used parameter for the prediction of occurrence of AAA rupture. However, the most vulnerable region of the aortic wall may be different from the most dilated region of AAA under pressure. The present study is the first to use weighted gene coexpression network analysis (WGCNA) to detect the coexpressed genes that result in regional weakening of the aortic wall.MethodsThe GSE165470 raw microarray dataset was used in the present study. Differentially expressed genes (DEGs) were filtered using the “limma” R package. DEGs were assessed by Gene Ontology biological process (GO-BP) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses. WGCNA was used to construct the coexpression networks in the samples with regional weakening of the AAA wall and in the control group to detect the gene modules. The hub genes were defined in the significant functional modules, and a hub differentially expressed gene (hDEG) coexpression network was constructed with the highest confidence based on protein–protein interactions (PPIs). Molecular compound detection (MCODE) was used to identify crucial genes in the hDEG coexpression network. Crucial genes in the hDEG coexpression network were validated using the GSE7084 and GSE57691 microarray gene expression datasets.ResultA total of 350 DEGs were identified, including 62 upregulated and 288 downregulated DEGs. The pathways were involved in immune responses, vascular smooth muscle contraction and cell–matrix adhesion of DEGs in the samples with regional weakening in AAA. Antiquewhite3 was the most significant module and was used to identify downregulated hDEGs based on the result of the most significant modules negatively related to the trait of weakened aneurysm walls. Seven crucial genes were identified and validated: ACTG2, CALD1, LMOD1, MYH11, MYL9, MYLK, and TPM2. These crucial genes were associated with the mechanisms of AAA progression.ConclusionWe identified crucial genes that may play a significant role in weakening of the AAA wall and may be potential targets for medical therapies and diagnostic biomarkers. Further studies are required to more comprehensively elucidate the functions of crucial genes in the pathogenesis of regional weakening in AAA.

Identification of Cancer Trait Genes and Association Analysis Under Pan-Cancer

Background: Different cancers have different sites of origin, cell types, and forms of genetic mutations that manifest different forms of cancers. Therefore, identifying genes associated with cancer traits and analyzing their functions in different cancers is important for understanding the mechanisms of cancer. Objective: The purpose of this paper is to make up for the shortcomings of single tumor analysis and realize the discovery and identification of genes related to each cancer trait at the level of multiple tumors, and their association analysis. Methods: In this paper, we use structural equation model to quantitatively identify genes associated with cancer traits for five cancers. We verify the correctness and effectiveness of the method through correlation analysis. Then we analyze the functions of genes and the biological processes involved through GO and KEGG pathways. Finally, we further analyze and verify the experimental results through protein interaction network and survival analysis. Results: Through five types of cancer data, we identify 44 genes related to cancer traits. We verify the combined effects of these genes and the biological processes they participate in. Moreover, we find key gene pathways and two significant gene functional modules. Conclusion: The results show that the structural equation model has unique advantages in quantifying the combined effects of genes. Many of the genes we have identified are tumor metastasis genes and are related to many cancers. There are strong potential commonalities among cancers. Four cancer genes are not only related to protein metabolism but also related to the regulation of nucleobase, nucleoside, nucleotide and nucleic acid metabolism, which are of great significance for cancer treatment.

Weighted gene co-expression network analysis identifies modules and functionally enriched pathways in the lactation process

The exponential growth in knowledge has resulted in a better understanding of the lactation process in a wide variety of animals. However, the underlying genetic mechanisms are not yet clearly known. In order to identify the mechanisms involved in the lactation process, various mehods, including meta-analysis, weighted gene co-express network analysis (WGCNA), hub genes identification, gene ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment at before peak (BP), peak (P), and after peak (AP) stages of the lactation processes have been employed. A total of 104, 85, and 26 differentially expressed genes were identified based on PB vs. P, BP vs. AP, and P vs. AP comparisons, respectively. GO and KEGG pathway enrichment analysis revealed that DEGs were significantly enriched in the “ubiquitin-dependent ERAD” and the “chaperone cofactor-dependent protein refolding” in BP vs. P and P vs. P, respectively. WGCNA identified five significant functional modules related to the lactation process. Moreover, GJA1, AP2A2, and NPAS3 were defined as hub genes in the identified modules, highlighting the importance of their regulatory impacts on the lactation process. The findings of this study provide new insights into the complex regulatory networks of the lactation process at three distinct stages, while suggesting several candidate genes that may be useful for future animal breeding programs. Furthermore, this study supports the notion that in combination with a meta-analysis, the WGCNA represents an opportunity to achieve a higher resolution analysis that can better predict the most important functional genes that might provide a more robust bio-signature for phenotypic traits, thus providing more suitable biomarker candidates for future studies.

Identification of protein complexes and functional modules in E. coli PPI networks

BackgroundEscherichia coli always plays an important role in microbial research, and it has been a benchmark model for the study of molecular mechanisms of microorganisms. Molecular complexes, operons, and functional modules are valuable molecular functional domains of E. coli. The identification of protein complexes and functional modules of E. coli is essential to reveal the principles of cell organization, process, and function. At present, many studies focus on the detection of E. coli protein complexes based on experimental methods. However, based on the large-scale proteomics data set of E. coli, the simultaneous prediction of protein complexes and functional modules, especially the comparative analysis of them is relatively less.ResultsIn this study, the Edge Label Propagate Algorithm (ELPA) of the complex biological network was used to predict the protein complexes and functional modules of two high-quality PPI networks of E. coli, respectively. According to the gold standard protein complexes and function annotations provided by EcoCyc dataset, most protein modules predicted in the two datasets matched highly with real protein complexes, cellular processes, and biological functions. Some novel and significant protein complexes and functional modules were revealed based on ELPA. Moreover, through a comparative analysis of predicted complexes with corresponding functional modules, we found the protein complexes were significantly overlapped with corresponding functional modules, and almost all predicted protein complexes were completely covered by one or more functional modules. Finally, on the same PPI network of E. coli, ELPA was compared with a well-known protein module detection method (MCL) and we found that the performance of ELPA and MCL is comparable in predicting protein complexes.ConclusionsIn this paper, a link clustering method was used to predict protein complexes and functional modules in PPI networks of E. coli, and the correlation between them was compared, which could help us to understand the molecular functional units of E. coli better.

Systems biology approach identifies functional modules and regulatory hubs related to secondary metabolites accumulation after transition from autotrophic to heterotrophic growth condition in microalgae.

Heterotrophic growth mode is among the most promising strategies put forth to overcome the low biomass and secondary metabolites productivity challenge. To shedding light on the underlying molecular mechanisms, transcriptome meta-analysis was integrated with weighted gene co-expression network analysis (WGCNA), connectivity analysis, functional enrichment, and hubs identification. Meta-analysis and Functional enrichment analysis demonstrated that most of the biological processes are up-regulated at heterotrophic growth condition, which leads to change of genetic architectures and phenotypic outcomes. WGNCA analysis of meta-genes also resulted four significant functional modules across logarithmic (LG), transition (TR), and production peak (PR) phases. The expression pattern and connectivity characteristics of the brown module as a non-preserved module vary across LG, TR, and PR phases. Functional analysis identified Carotenoid biosynthesis, Fatty acid metabolism and Methane metabolism as enriched pathways in the non-preserved module. Our integrated approach was applied here, identified some hubs, such as a serine hydroxymethyltransferase (SHMT1), which is the best candidate for development of metabolites accumulating strains in microalgae. Current study provided a new insight into underlying metabolite accumulation mechanisms and opens new avenue for the future applied studies in the microalgae field.

Signatures of altered long noncoding RNAs and messenger RNAs expression in the early acute phase of spinal cord injury.

Spinal cord injury (SCI) is a highly severe disease and it can lead to the destruction of the motor and sensory function resulting in temporary or permanent disability. Long noncoding RNAs (lncRNAs) are transcripts longer than 200 nt that play a critical role in central nervous system (CNS) injury. However, the exact roles of lncRNAs and messenger RNAs (mRNAs) in the early acute phase of SCI remain to be elucidated. We examined the expression of mRNAs and lncRNAs in a rat model at 2 days after SCI and identified the differentially expressed lncRNAs (DE lncRNAs) and differentially expressed mRNAs (DE mRNAs) using microarray analysis. Subsequently, a comprehensive bioinformatics analysis was also performed to clarify the interaction between DE mRNAs. A total of 3,193 DE lncRNAs and 4,308 DE mRNAs were identified between the injured group and control group. Classification, length distribution, and chromosomal distribution of the dysregulated lncRNAs were also performed. The gene ontology analysis and Kyoto Encyclopedia of Genes and Genomes enrichment analysis were performed to identify the critical biological processes and pathways. A protein-protein interaction (PPI) network indicated that IL6, TOP2A, CDK1, POLE, CCNB1, TNF, CCNA2, CDC20, ITGAM, and MYC were the top 10 core genes. The subnetworks from the PPI network were identified to further elucidate the most significant functional modules of the DE mRNAs. These data may provide novel insights into the molecular mechanism of the early acute phase of SCI. The identification of lncRNAs and mRNAs may offer potential diagnostic and therapeutic targets for SCI.

Exploring the major cross-talking edges of competitive endogenous RNA networks in human Chronic and Acute Myeloid Leukemia

BackgroundHuman Chronic and Acute Myeloid Leukemia are myeloproliferative disorders in myeloid lineage of blood cells characterized by accumulation of aberrant white blood cells. In cancer, the anomalous transcriptome includes deregulated expression of non-coding RNAs in conjunction with protein-coding mRNAs in human genome. The coding or non-coding RNA transcripts harboring miRNA-binding sites can converse with and regulate each other by explicitly contending for a limited pool of shared miRNAs and act as competitive endogenous RNAs (ceRNAs). An unifying hypothesis attributing ‘modulation of expression of transcripts’ in this fashion had been defined as ‘competitive endogenous RNA hypothesis’. Network built with ceRNAs evidently offers a platform to elucidate complex regulatory interactions at post-transcriptional level in human cancers. MethodsContemplating cancers of human myeloid lineage we constructed ceRNA networks for CML and AML coding and non-coding repertoire utilizing patient sample data. Through functional enrichment analysis we selected the significant functional modules for transcripts being differentially expressed in Blastic phases of each cancer types with respect to Normal. After retrieving free energy of binding and duplex formation of shared miRNAs on ceRNAs, we performed statistical averaging of energy values over the ensemble of populations considering cellular system as in canonical (Iso-thermal) situation. Results and conclusionsWe aimed to shed light on ‘Sibling Rivalry’ in ceRNA partners from the perspective of statistical thermodynamics, identified major cross-talking tracks and ceRNAs influencing transcripts concerned in myeloid cancer systems. General significanceInsights into ceRNA-regulation will shed light on progression and prognosis of human Chronic and Acute Myeloid Leukemia.

Systematically analyses of the common dysregulated networks to understand the common pathologies between T2D and atherosclerosis

BackgroundDiabetic macroangiopathy, atherosclerosis secondary to diabetes mellitus, causes cerebro-cardiovascular diseases (CVD), which increase the risk of death in patients with DM and significantly reduce their quality of life. Therefore, mechanisms underlying the common shared pathologies between type 2 diabetes (T2D) and atherosclerosis are key to prevention and treatment of diabetic macroangiopathy. However, the common shared pathological links between T2D and atherosclerosis are not fully understood. MethodsWe constructed a T2D and atherosclerosis associated protein interaction sub-network (TAN) to investigate common shared mechanisms between T2D and atherosclerosis. In addition, MCODE plugin of Cytoscape was applied to TAN to identify most significant functional modules. The network modules were further mapped to KEGG pathway enrichment analysis. Finally, we established a miRNA-gene regulatory network by searching disease associated miRNAs and integrated them into miRNA-gene interaction network for each module. ResultsTAN contains 1230 nodes which represent the union of T2D and atherosclerosis related genes and 3683 edges which represent the interactions of gene pairs. MCODE plugin was applied and five most significant modular clusters were identified. KEGG analysis of functional modules showed these genes were involved in several pathways including type 2 diabetes mellitus, ErbB and neurotrophin signaling pathway. miRNAgene interaction network was established and these miRNA-gene interactions mediated common shared pathologies between T2D and atherosclerosis. ConclusionsAnalysis of TAN demonstrated that modular organization of the interaction network elucidates shared pathologies of T2D and atherosclerosis. Furthermore, the disease associated miRNA-gene interaction network enriched our insight into role of miRNAs in mediating common shared pathologies between T2D and atherosclerosis. Thus, miRNAs constitute attractive targets for the development of novel therapies for treating both T2D and atherosclerosis.

Regulatory network analysis of Epstein-Barr virus identifies functional modules and hub genes involved in infectious mononucleosis.

Epstein-Barr virus (EBV) is the most common cause of infectious mononucleosis (IM) and establishes lifetime infection associated with a variety of cancers and autoimmune diseases. The aim of this study was to develop an integrative gene regulatory network (GRN) approach and overlying gene expression data to identify the representative subnetworks for IM and EBV latent infection (LI). After identifying differentially expressed genes (DEGs) in both IM and LI gene expression profiles, functional annotations were applied using gene ontology (GO) and BiNGO tools, and construction of GRNs, topological analysis and identification of modules were carried out using several plugins of Cytoscape. In parallel, a human-EBV GRN was generated using the Hu-Vir database for further analyses. Our analysis revealed that the majority of DEGs in both IM and LI were involved in cell-cycle and DNA repair processes. However, these genes showed a significant negative correlation in the IM and LI states. Furthermore, cyclin-dependent kinase 2 (CDK2) - a hub gene with the highest centralityscore - appeared to be the key player in cell cycle regulation in IM disease. The most significant functional modules in the IM and LI states were involved in the regulation of the cell cycle and apoptosis, respectively. Human-EBV network analysis revealed several direct targets of EBV proteins during IM disease. Our study provides an important first report on the response to IM/LI EBV infection in humans. An important aspect of our data was the upregulation of genes associated with cell cycle progression and proliferation.

Deciphering the cross-talking of human competitive endogenous RNAs in K562 chronic myelogenous leukemia cell line.

Chronic myelogenous leukemia (CML) is a myeloproliferative disorder characterized by increased proliferation or abnormal accumulation of granulocytic cell line without the depletion of their capacity to differentiate. A reciprocal chromosomal translocation proceeding to the 'Philadelphia chromosome', involving the ABL proto-oncogene and BCR gene residing on Chromosome 9 and 22 respectively, is observed to be attributed to CML pathogenesis. Recent studies have been unraveling the crucial role of genomic 'dark matter' or the non-coding repertoire in cancer initiation and progression. The intricate cross-talk between competitive endogenous RNAs (ceRNAs) provides a scaffold to systematically functionalize the miRNA response element harboring non-coding RNAs and incorporate them with the protein-coding RNA dimension in complex ceRNA networks. This network of coding and non-coding transcriptome linked by shared miRNAs evidently offers a platform to elucidate the complex regulatory interactions at the post-transcriptional level in human cancers. In this context, analyzing CML, from the perspective of the ceRNA hypothesis, surely craves intensive attention and a comprehensive discussion. Here, we performed RNA-seq data analysis to retrieve Lymphoblastoid and CML coding as well as non-coding repertoire and constructed a ceRNA network for the CML cell line, considering the non-cancer lymphoblastoid cell line as the control. We investigated if any alteration exists in the ceRNA landscape of the transcripts which are exhibiting differential expression across the two cell lines and observed that the major ceRNA regulators vary in cancer network when compared with the Lymphoblastoid network. The top ranked significant functional modules in the ceRNA network display cancer associated attributes and reveal putative regulators in CML pathogenesis.

Controllability and observability analysis for vertex domination centrality in directed networks.

Topological centrality is a significant measure for characterising the relative importance of a node in a complex network. For directed networks that model dynamic processes, however, it is of more practical importance to quantify a vertex's ability to dominate (control or observe) the state of other vertices. In this paper, based on the determination of controllable and observable subspaces under the global minimum-cost condition, we introduce a novel direction-specific index, domination centrality, to assess the intervention capabilities of vertices in a directed network. Statistical studies demonstrate that the domination centrality is, to a great extent, encoded by the underlying network's degree distribution and that most network positions through which one can intervene in a system are vertices with high domination centrality rather than network hubs. To analyse the interaction and functional dependence between vertices when they are used to dominate a network, we define the domination similarity and detect significant functional modules in glossary and metabolic networks through clustering analysis. The experimental results provide strong evidence that our indices are effective and practical in accurately depicting the structure of directed networks.

A novel approach to significant pathway identification using pathway interaction network from PPI data

Discovering and understanding a variety of genetic markers (e.g., SNPs, genes, pathways) related to a certain phenotype of interest is one of the fundamental challenges in recent genetic studies. For this purpose, conventional methods have usually done by detecting significantly differentially expressed genes or SNPs between case and control samples. However, such approaches often produce a large list of potential markers which contain only a few genetic markers truly associated with a given phenotype. That is, their results often include too many false positives about phenotype relevant markers. As an alternative, lately, several studies have attempted to identify significant functional modules (or pathways) each of which contains a set of genes involved in a particular biological function or process. These pathway marker findings could be better in uncovering complex disease mechanism than individual gene marker findings. This paper investigates a novel approach to significant pathway identification that exploits pathway interaction network (PIN) derived from protein-protein interaction (PPI) data. Specifically, we first construct PIN which indicates the hidden associations between biological pathways, by exploring PPI data and then prioritize pathway nodes over PIN with PIN-PageRank algorithm to identify significant pathways. In this procedure, we employ differentially expressed gene profiles for PIN node initialization. To evaluate efficacy and usability of our proposed approach, we performed experiments for the identification of breast cancer relevant pathways and compared these results with existing approaches like GSEA and DAVID. Overall, it was observed that our PIN-PageRank approach outperforms existing approaches in finding significant pathways.

Systems Biology Approach for the Identification of Diagnostic and Therapeutic Targets in Medulloblastomas

Medulloblastomas are the most aggressive pediatric brain tumors originating from self-renewing common progenitor cells and associated with high rate of invasion along with rapid spread. The hereditary origin of these tumors can be studied to define it at genome level; however, to understand the molecular mechanism and pathogenesis, systems level investigation is required. In this study we used systems level analysis to identify molecular targets, pathways, molecular networks and functional modules associated with genes/proteins with altered expression in medulloblastoma. Molecular functions, annotated functions, associated pathways, networks and functional modules were identified with PANTHER, DAVID, Ingenuity Pathway Analysis, MetaCore and GeneSpring software analysis, respectively. P53 signaling, PI3K/AKT signaling, Notch signaling, Hedgehog signaling and HGF signaling were identified as significant pathways, indicating the signature of molecules involved in medulloblastoma. Network analysis using IPA revealed new set of molecules, HNRNPK, PDE4A, HES5, GSTP1, SLC16A2, and GADD45A, which also appeared as significant functional modules in GeneSpring analysis. The systems biology approach used in this study provides a comprehensive understanding of complex molecular alterations involved in medulloblastoma and reveals potential novel targets, which could be used for future targeted or personalized therapeutic interventions.

基于共表达网络挖掘肺癌相关模块

目的:识别肺癌疾病相关功能模块，对了解肺癌疾病的发病机制至关重要。方法：本文提出一个挖掘疾病相关功能模块的整合方法。采用包括正常和肺癌样本的微阵列数据，首先，应用rank-based方法构建基因共表达网络；其次，通过Qcut挖掘基因共表达模块；然后基于肺癌差异表达基因及基因模块功能一致性的联合测度，最终筛选出疾病相关功能模块。结果：研究发现，我们的方法获得7个显著疾病相关功能模块，经文献证实都与肺癌的发生发展有着密切的联系。进一步分析发现不仅能获得与传统方法功能一致的模块，而且还发现了传统方法没有获得的病毒层面的两个模块(模块351和352)。结论：我们的方法能够有效地发现新的功能模块，为探索癌症致病机理提供新的视角及依据。 Objective: Identifying lung cancer disease-related functional modules is important to understand the mechanism of lung cancer. Methods: In this paper, we propose an integration method of mining disease-related functional mod-ule. Using microarray data of normal and lung cancer samples, firstly, rank-based method was applied to construct gene co-expression network. Secondly, gene co-expression modules were mined through Qcut, then disease-related functional modules were screened based on the joint measure of lung cancer differentially expressed genes and the functional con-sistency. Results: 7 significant disease-related functional modules were screened, which were closely linked with the development of lung cancer by literature confirmation. Further it found that our method could not only return the func-tional consistency modules, but also find two modules were associated with specific functional annotations named “virus response” that could not be identified by other methods. Conclusions: The method provided additional insights for find-ing new functional module, which will be helpful for the studies on the pathogenesis of human complex diseases.

AtBioNet– an integrated network analysis tool for genomics and biomarker discovery

BackgroundLarge amounts of mammalian protein-protein interaction (PPI) data have been generated and are available for public use. From a systems biology perspective, Proteins/genes interactions encode the key mechanisms distinguishing disease and health, and such mechanisms can be uncovered through network analysis. An effective network analysis tool should integrate different content-specific PPI databases into a comprehensive network format with a user-friendly platform to identify key functional modules/pathways and the underlying mechanisms of disease and toxicity.ResultsatBioNet integrates seven publicly available PPI databases into a network-specific knowledge base. Knowledge expansion is achieved by expanding a user supplied proteins/genes list with interactions from its integrated PPI network. The statistically significant functional modules are determined by applying a fast network-clustering algorithm (SCAN: a Structural Clustering Algorithm for Networks). The functional modules can be visualized either separately or together in the context of the whole network. Integration of pathway information enables enrichment analysis and assessment of the biological function of modules. Three case studies are presented using publicly available disease gene signatures as a basis to discover new biomarkers for acute leukemia, systemic lupus erythematosus, and breast cancer. The results demonstrated that atBioNet can not only identify functional modules and pathways related to the studied diseases, but this information can also be used to hypothesize novel biomarkers for future analysis.ConclusionatBioNet is a free web-based network analysis tool that provides a systematic insight into proteins/genes interactions through examining significant functional modules. The identified functional modules are useful for determining underlying mechanisms of disease and biomarker discovery. It can be accessed at: http://www.fda.gov/ScienceResearch/BioinformaticsTools/ucm285284.htm.

In-silico identification of phenotype-biased functional modules

BackgroundPhenotypes exhibited by microorganisms can be useful for several purposes, e.g., ethanol as an alternate fuel. Sometimes, the target phenotype maybe required in combination with other phenotypes, in order to be useful, for e.g., an industrial process may require that the organism survive in an anaerobic, alcohol rich environment and be able to feed on both hexose and pentose sugars to produce ethanol. This combination of traits may not be available in any existing organism or if they do exist, the mechanisms involved in the phenotype-expression may not be efficient enough to be useful. Thus, it may be required to genetically modify microorganisms. However, before any genetic modification can take place, it is important to identify the underlying cellular subsystems responsible for the expression of the target phenotype.ResultsIn this paper, we develop a method to identify statistically significant and phenotypically-biased functional modules. The method can compare the organismal network information from hundreds of phenotype expressing and phenotype non-expressing organisms to identify cellular subsystems that are more prone to occur in phenotype-expressing organisms than in phenotype non-expressing organisms. We have provided literature evidence that the phenotype-biased modules identified for phenotypes such as hydrogen production (dark and light fermentation), respiration, gram-positive, gram-negative and motility, are indeed phenotype-related.ConclusionThus we have proposed a methodology to identify phenotype-biased cellular subsystems. We have shown the effectiveness of our methodology by applying it to several target phenotypes. The code and all supplemental files can be downloaded from (http://freescience.org/cs/phenotype-biased-biclusters/).

Efficient and accurate greedy search methods for mining functional modules in protein interaction networks

BackgroundMost computational algorithms mainly focus on detecting highly connected subgraphs in PPI networks as protein complexes but ignore their inherent organization. Furthermore, many of these algorithms are computationally expensive. However, recent analysis indicates that experimentally detected protein complexes generally contain Core/attachment structures.MethodsIn this paper, a Greedy Search Method based on Core-Attachment structure (GSM-CA) is proposed. The GSM-CA method detects densely connected regions in large protein-protein interaction networks based on the edge weight and two criteria for determining core nodes and attachment nodes. The GSM-CA method improves the prediction accuracy compared to other similar module detection approaches, however it is computationally expensive. Many module detection approaches are based on the traditional hierarchical methods, which is also computationally inefficient because the hierarchical tree structure produced by these approaches cannot provide adequate information to identify whether a network belongs to a module structure or not. In order to speed up the computational process, the Greedy Search Method based on Fast Clustering (GSM-FC) is proposed in this work. The edge weight based GSM-FC method uses a greedy procedure to traverse all edges just once to separate the network into the suitable set of modules.ResultsThe proposed methods are applied to the protein interaction network of S. cerevisiae. Experimental results indicate that many significant functional modules are detected, most of which match the known complexes. Results also demonstrate that the GSM-FC algorithm is faster and more accurate as compared to other competing algorithms.ConclusionsBased on the new edge weight definition, the proposed algorithm takes advantages of the greedy search procedure to separate the network into the suitable set of modules. Experimental analysis shows that the identified modules are statistically significant. The algorithm can reduce the computational time significantly while keeping high prediction accuracy.

Efficient α, β-motif finder for identification of phenotype-related functional modules

BackgroundMicrobial communities in their natural environments exhibit phenotypes that can directly cause particular diseases, convert biomass or wastewater to energy, or degrade various environmental contaminants. Understanding how these communities realize specific phenotypic traits (e.g., carbon fixation, hydrogen production) is critical for addressing health, bioremediation, or bioenergy problems.ResultsIn this paper, we describe a graph-theoretical method for in silico prediction of the cellular subsystems that are related to the expression of a target phenotype. The proposed (α, β)-motif finder approach allows for identification of these phenotype-related subsystems that, in addition to metabolic subsystems, could include their regulators, sensors, transporters, and even uncharacterized proteins. By comparing dozens of genome-scale networks of functionally associated proteins, our method efficiently identifies those statistically significant functional modules that are in at least α networks of phenotype-expressing organisms but appear in no more than β networks of organisms that do not exhibit the target phenotype. It has been shown via various experiments that the enumerated modules are indeed related to phenotype-expression when tested with different target phenotypes like hydrogen production, motility, aerobic respiration, and acid-tolerance.ConclusionThus, we have proposed a methodology that can identify potential statistically significant phenotype-related functional modules. The functional module is modeled as an (α, β)-clique, where α and β are two criteria introduced in this work. We also propose a novel network model, called the two-typed, divided network. The new network model and the criteria make the problem tractable even while very large networks are being compared. The code can be downloaded from http://www.freescience.org/cs/ABClique/