Functional modules by relating protein interaction networks and gene expression

Genes and proteins are organized on the basis of their particular mutual relations or according to their interactions in cellular and genetic networks. These include metabolic or signaling pathways and protein interaction, regulatory or co-expression networks. Integrating the information from the different types of networks may lead to the notion of a functional network and functional modules. To find these modules, we propose a new technique which is based on collective, multi-body correlations in a genetic network. We calculated the correlation strength of a group of genes (e.g. in the co-expression network) which were identified as members of a module in a different network (e.g. in the protein interaction network) and estimated the probability that this correlation strength was found by chance. Groups of genes with a significant correlation strength in different networks have a high probability that they perform the same function. Here, we propose evaluating the multi-body correlations by applying the superparamagnetic approach. We compare our method to the presently applied mean Pearson correlations and show that our method is more sensitive in revealing functional relationships.     

Proteomics analysis of cytokine-induced dysfunction and death in insulin-producing INS-1E cells: new insights into the pathways involved

Cytokines released by islet-infiltrating immune cells play a crucial role in beta-cell dysfunction and apoptotic cell death in the pathogenesis of type 1 diabetes and after islet transplantation. RNA studies revealed complex pathways of genes being activated or suppressed during this beta-cell attack. The aim of the present study was to analyze protein changes in insulin-producing INS-1E cells exposed to inflammatory cytokines in vitro using two-dimensional DIGE. Within two different pH ranges we observed 2214 +/- 164 (pH 4-7) and 1641 +/- 73 (pH 6-9) spots. Analysis at three different time points (1, 4, and 24 h of cytokine exposure) revealed that the major changes were taking place only after 24 h. At this time point 158 proteins were altered in expression (4.1%, n = 4, p < or = 0.01) by a combination of interleukin-1beta and interferon-gamma, whereas only 42 and 23 proteins were altered by either of the cytokines alone, giving rise to 199 distinct differentially expressed spots. Identification of 141 of these by MALDI-TOF/TOF revealed proteins playing a role in insulin secretion, cytoskeleton organization, and protein and RNA metabolism as well as proteins associated with endoplasmic reticulum and oxidative stress/defense. We investigated the interactions of these proteins and discovered a significant interaction network (p < 1.27e-05) containing 42 of the identified proteins. This network analysis suggests that proteins of different pathways act coordinately in a beta-cell dysfunction/apoptotic beta-cell death interactome. In addition the data suggest a central role for chaperones and proteins playing a role in RNA metabolism. As many of these identified proteins are regulated at the protein level or undergo post-translational modifications, a proteomics approach, as performed in this study, is required to provide adequate insight into the mechanisms leading to beta-cell dysfunction and apoptosis. The present findings may open new avenues for the understanding and prevention of beta-cell loss in type 1 diabetes.     

Integrative Network Analysis Unveils Convergent Molecular Pathways in Parkinson's Disease and Diabetes

Background

Shared dysregulated pathways may contribute to Parkinson''s disease and type 2 diabetes, chronic diseases that afflict millions of people worldwide. Despite the evidence provided by epidemiological and gene profiling studies, the molecular and functional networks implicated in both diseases, have not been fully explored. In this study, we used an integrated network approach to investigate the extent to which Parkinson''s disease and type 2 diabetes are linked at the molecular level.

Methods and Findings

Using a random walk algorithm within the human functional linkage network we identified a molecular cluster of 478 neighboring genes closely associated with confirmed Parkinson''s disease and type 2 diabetes genes. Biological and functional analysis identified the protein serine-threonine kinase activity, MAPK cascade, activation of the immune response, and insulin receptor and lipid signaling as convergent pathways. Integration of results from microarrays studies identified a blood signature comprising seven genes whose expression is dysregulated in Parkinson''s disease and type 2 diabetes. Among this group of genes, is the amyloid precursor protein (APP), previously associated with neurodegeneration and insulin regulation. Quantification of RNA from whole blood of 192 samples from two independent clinical trials, the Harvard Biomarker Study (HBS) and the Prognostic Biomarker Study (PROBE), revealed that expression of APP is significantly upregulated in Parkinson''s disease patients compared to healthy controls. Assessment of biomarker performance revealed that expression of APP could distinguish Parkinson''s disease from healthy individuals with a diagnostic accuracy of 80% in both cohorts of patients.

Conclusions

These results provide the first evidence that Parkinson''s disease and diabetes are strongly linked at the molecular level and that shared molecular networks provide an additional source for identifying highly sensitive biomarkers. Further, these results suggest for the first time that increased expression of APP in blood may modulate the neurodegenerative phenotype in type 2 diabetes patients.     

Network Propagation with Dual Flow for Gene Prioritization

Based on the hypothesis that the neighbors of disease genes trend to cause similar diseases, network-based methods for disease prediction have received increasing attention. Taking full advantage of network structure, the performance of global distance measurements is generally superior to local distance measurements. However, some problems exist in the global distance measurements. For example, global distance measurements may mistake non-disease hub proteins that have dense interactions with known disease proteins for potential disease proteins. To find a new method to avoid the aforementioned problem, we analyzed the differences between disease proteins and other proteins by using essential proteins (proteins encoded by essential genes) as references. We find that disease proteins are not well connected with essential proteins in the protein interaction networks. Based on this new finding, we proposed a novel strategy for gene prioritization based on protein interaction networks. We allocated positive flow to disease genes and negative flow to essential genes, and adopted network propagation for gene prioritization. Experimental results on 110 diseases verified the effectiveness and potential of the proposed method.     

Predicting aging/longevity-related genes in the nematode Caenorhabditis elegans

We present a novel mathematical/computational strategy for predicting genes/proteins associated with aging/longevity. The novelty of our method arises from the topological analysis of an organismal longevity gene/protein network (LGPN), which extends the existing cellular networks. The LGPN nodes represent both genes and corresponding proteins. Links stand for all known interactions between the nodes. The LGPN of C. elegans incorporated 362 genes/proteins, 160 connecting and 202 age-related ones, from a list of 321 with known impact on aging/longevity. A 'longevity core' of 129 directly interacting genes or proteins was identified. This core may shed light on the large-scale mechanisms of aging. Predictions were made, based upon the finding that LGPN hubs and centrally located nodes have higher likelihoods of being associated with aging/longevity than do randomly selected nodes. Analysis singled-out 15 potential aging/longevity-related genes for further examination: mpk-1, gei-4, csp-1, pal-1, mkk-4, 4O210, sem-5, gei-16, 1O814, 5M722, ife-3, ced-10, cdc-42, 1O776Co, and 1O690.     

Information Flow Analysis of Interactome Networks

Recent studies of cellular networks have revealed modular organizations of genes and proteins. For example, in interactome networks, a module refers to a group of interacting proteins that form molecular complexes and/or biochemical pathways and together mediate a biological process. However, it is still poorly understood how biological information is transmitted between different modules. We have developed information flow analysis, a new computational approach that identifies proteins central to the transmission of biological information throughout the network. In the information flow analysis, we represent an interactome network as an electrical circuit, where interactions are modeled as resistors and proteins as interconnecting junctions. Construing the propagation of biological signals as flow of electrical current, our method calculates an information flow score for every protein. Unlike previous metrics of network centrality such as degree or betweenness that only consider topological features, our approach incorporates confidence scores of protein–protein interactions and automatically considers all possible paths in a network when evaluating the importance of each protein. We apply our method to the interactome networks of Saccharomyces cerevisiae and Caenorhabditis elegans. We find that the likelihood of observing lethality and pleiotropy when a protein is eliminated is positively correlated with the protein's information flow score. Even among proteins of low degree or low betweenness, high information scores serve as a strong predictor of loss-of-function lethality or pleiotropy. The correlation between information flow scores and phenotypes supports our hypothesis that the proteins of high information flow reside in central positions in interactome networks. We also show that the ranks of information flow scores are more consistent than that of betweenness when a large amount of noisy data is added to an interactome. Finally, we combine gene expression data with interaction data in C. elegans and construct an interactome network for muscle-specific genes. We find that genes that rank high in terms of information flow in the muscle interactome network but not in the entire network tend to play important roles in muscle function. This framework for studying tissue-specific networks by the information flow model can be applied to other tissues and other organisms as well.     

Grade-specific diagnostic and prognostic biomarkers in breast cancer

An integrative approach is presented to identify grade-specific biomarkers for breast cancer. Grade-specific molecular interaction networks were constructed with differentially expressed genes (DEGs) of cancer grade 1, 2, and 3. We observed that the molecular network of grade3 is predominantly associated with cancer-specific processes. Among the top ten connected DEGs in the grade3, the increase in the expression of UBE2C and CCNB2 genes was statistically significant across different grades. Along with UBE2C and CCNB2 genes, the CDK1, KIF2C, NDC80, and CCNB2 genes are also profoundly expressed in different grades and reduce the patient's survival. Gene set enrichment analysis of these six genes reconfirms their role in metastatic phenotype. Moreover, the coexpression network shows a strong association of these six genes promotes cancer specific biological processes and possibly drives cancer from lower to a higher grade. Collectively the identified genes can act as potential biomarkers for breast cancer diagnosis and prognosis.     

多组学整合网络: 一把精准解码玉米功能基因组的钥匙

高通量组学技术的快速发展使生命科学进入大数据时代。科学家们从基因组、转录组、蛋白质组和代谢组等多组学数据中剥茧抽丝, 逐步揭示生物体内复杂而巧妙的调控网络。近日, 华中农业大学李林课题组联合杨芳课题组和严建兵课题组构建了玉米(Zea mays)首个多组学整合网络。该网络包括3万个玉米基因在三维基因组水平、转录水平、翻译水平和蛋白质互作水平的调控关系, 由280万个网络连接组成, 构成1 412个调控模块。利用该整合网络, 研究团队预测并证实了5个调控玉米分蘖、侧生器官发育和籽粒皱缩的新基因。进一步结合机器学习方法, 他们预测出2 651个影响玉米开花期的候选基因, 鉴定到8条可能参与玉米开花期的调控通路, 并利用基因编辑技术和EMS突变体证实了20个候选基因的生物学功能。此外, 通过对整合调控网络的进化分析, 他们发现玉米两套亚基因组在转录组、翻译组和蛋白互作组水平上存在渐进式的功能分化。这套集合多组学数据构建的整合网络图谱是玉米功能基因组学研究的重大进展, 为玉米重要性状新基因克隆、分子调控通路解析和玉米基因组进化分析提供了新工具, 是解锁玉米功能基因组学的一把新钥匙。     

Advances in systems biology are enhancing our understanding of disease and moving us closer to novel disease treatments

With tens of billions of dollars spent each year on the development of drugs to treat human diseases, and with fewer and fewer applications for investigational new drugs filed each year despite this massive spending, questions now abound on what changes to the drug discovery paradigm can be made to achieve greater success. The high rate of failure of drug candidates in clinical development, where the great majority of these drugs fail due to lack of efficacy, speak directly to the need for more innovative approaches to study the mechanisms of disease and drug discovery. Here we review systems biology approaches that have been devised over the last several years to understand the biology of disease at a more holistic level. By integrating a diversity of data like DNA variation, gene expression, protein–protein interaction, DNA–protein binding, and other types of molecular phenotype data, more comprehensive networks of genes both within and between tissues can be constructed to paint a more complete picture of the molecular processes underlying physiological states associated with disease. These more integrative, systems-level methods lead to networks that are demonstrably predictive, which in turn provides a deeper context within which single genes operate such as those identified from genome-wide association studies or those targeted for therapeutic intervention. The more comprehensive views of disease that result from these methods have the potential to dramatically enhance the way in which novel drug targets are identified and developed, ultimately increasing the probability of success for taking new drugs through clinical development. We highlight a number of the integrative approaches via examples that have resulted not only in the identification of novel genes for diabetes and cardiovascular disease, but in more comprehensive networks as well that describe the context in which the disease genes operate.     

Time-dependent network analysis reveals molecular targets underlying the development of diet-induced obesity and non-alcoholic steatohepatitis

Prolonged high-fat diet leads to the development of obesity and multiple comorbidities including non-alcoholic steatohepatitis (NASH), but the underlying molecular basis is not fully understood. We combine molecular networks and time course gene expression profiles to reveal the dynamic changes in molecular networks underlying diet-induced obesity and NASH. We also identify hub genes associated with the development of NASH. Core diet-induced obesity networks were constructed using Ingenuity pathway analysis (IPA) based on 332 high-fat diet responsive genes identified in liver by time course microarray analysis (8 time points over 24 weeks) of high-fat diet-fed mice compared to normal diet-fed mice. IPA identified five core diet-induced obesity networks with time-dependent gene expression changes in liver. These networks were associated with cell-to-cell signaling and interaction (Network 1), lipid metabolism (Network 2), hepatic system disease (Network 3 and 5), and inflammatory response (Network 4). When we merged these core diet-induced obesity networks, Tlr2, Cd14, and Ccnd1 emerged as hub genes associated with both liver steatosis and inflammation and were altered in a time-dependent manner. Further, protein–protein interaction network analysis revealed Tlr2, Cd14, and Ccnd1 were interrelated through the ErbB/insulin signaling pathway. Dynamic changes occur in molecular networks underlying diet-induced obesity. Tlr2, Cd14, and Ccnd1 appear to be hub genes integrating molecular interactions associated with the development of NASH. Therapeutics targeting hub genes and core diet-induced obesity networks may help ameliorate diet-induced obesity and NASH.     

Architecture of basic building blocks in protein and domain structural interaction networks

MOTIVATION: The structural interaction of proteins and their domains in networks is one of the most basic molecular mechanisms for biological cells. Topological analysis of such networks can provide an understanding of and solutions for predicting properties of proteins and their evolution in terms of domains. A single paradigm for the analysis of interactions at different layers, such as domain and protein layers, is needed. RESULTS: Applying a colored vertex graph model, we integrated two basic interaction layers under a unified model: (1) structural domains and (2) their protein/complex networks. We identified four basic and distinct elements in the model that explains protein interactions at the domain level. We searched for motifs in the networks to detect their topological characteristics using a pruning strategy and a hash table for rapid detection. We obtained the following results: first, compared with a random distribution, a substantial part of the protein interactions could be explained by domain-level structural interaction information. Second, there were distinct kinds of protein interaction patterns classified by specific and distinguishable numbers of domains. The intermolecular domain interaction was the most dominant protein interaction pattern. Third, despite the coverage of the protein interaction information differing among species, the similarity of their networks indicated shared architectures of protein interaction network in living organisms. Remarkably, there were only a few basic architectures in the model (>10 for a 4-node network topology), and we propose that most biological combinations of domains into proteins and complexes can be explained by a small number of key topological motifs. CONTACT: doheon@kaist.ac.kr.     

Metabolic capabilities and systems fluctuations in Haloarcula marismortui revealed by integrative genomics and proteomics analyses

The 1310 Haloarcula marismortui proteins identified from mid-log and late-log phase soluble and membrane proteomes were analyzed in metabolic and cellular process networks to predict the available systems and systems fluctuations upon environmental stresses. When the connected metabolic reactions with identified proteins were examined, the availability of a number of metabolic pathways and a highly connected amino acid metabolic network were revealed. Quantitative spectral count analyses suggested 300 or more proteins might have expression changes in late-log phase. Among these, integrative network analyses indicated approximately 106 were metabolic proteins that might have growth-phase dependent changes. Interestingly, a large proportion of proteins in affected biomodules had the same trend of changes in spectral counts. Disregard the magnitude of changes, we had successfully predicted and validated the expression changes of nine genes including the rimK, gltCP, rrnAC0132, and argC in lysine biosynthesis pathway which were downregulated in late-log phase. This study had not only revealed the expressed proteins but also the availability of biological systems in two growth phases, systems level changes in response to the stresses in late-log phase, cellular locations of identified proteins, and the likely regulated genes to facilitate further analyses in the postgenomic era.     

Schizophrenia Gene Networks and Pathways and Their Applications for Novel Candidate Gene Selection

Background

Schizophrenia (SZ) is a heritable, complex mental disorder. We have seen limited success in finding causal genes for schizophrenia from numerous conventional studies. Protein interaction network and pathway-based analysis may provide us an alternative and effective approach to investigating the molecular mechanisms of schizophrenia.

Methodology/Principal Findings

We selected a list of schizophrenia candidate genes (SZGenes) using a multi-dimensional evidence-based approach. The global network properties of proteins encoded by these SZGenes were explored in the context of the human protein interactome while local network properties were investigated by comparing SZ-specific and cancer-specific networks that were extracted from the human interactome. Relative to cancer genes, we observed that SZGenes tend to have an intermediate degree and an intermediate efficiency on a perturbation spreading throughout the human interactome. This suggested that schizophrenia might have different pathological mechanisms from cancer even though both are complex diseases. We conducted pathway analysis using Ingenuity System and constructed the first schizophrenia molecular network (SMN) based on protein interaction networks, pathways and literature survey. We identified 24 pathways overrepresented in SZGenes and examined their interactions and crosstalk. We observed that these pathways were related to neurodevelopment, immune system, and retinoic X receptor (RXR). Our examination of SMN revealed that schizophrenia is a dynamic process caused by dysregulation of the multiple pathways. Finally, we applied the network/pathway approach to identify novel candidate genes, some of which could be verified by experiments.

Conclusions/Significance

This study provides the first comprehensive review of the network and pathway characteristics of schizophrenia candidate genes. Our preliminary results suggest that this systems biology approach might prove promising for selection of candidate genes for complex diseases. Our findings have important implications for the molecular mechanisms for schizophrenia and, potentially, other psychiatric disorders.     

Associating Genes and Protein Complexes with Disease via Network Propagation

A fundamental challenge in human health is the identification of disease-causing genes. Recently, several studies have tackled this challenge via a network-based approach, motivated by the observation that genes causing the same or similar diseases tend to lie close to one another in a network of protein-protein or functional interactions. However, most of these approaches use only local network information in the inference process and are restricted to inferring single gene associations. Here, we provide a global, network-based method for prioritizing disease genes and inferring protein complex associations, which we call PRINCE. The method is based on formulating constraints on the prioritization function that relate to its smoothness over the network and usage of prior information. We exploit this function to predict not only genes but also protein complex associations with a disease of interest. We test our method on gene-disease association data, evaluating both the prioritization achieved and the protein complexes inferred. We show that our method outperforms extant approaches in both tasks. Using data on 1,369 diseases from the OMIM knowledgebase, our method is able (in a cross validation setting) to rank the true causal gene first for 34% of the diseases, and infer 139 disease-related complexes that are highly coherent in terms of the function, expression and conservation of their member proteins. Importantly, we apply our method to study three multi-factorial diseases for which some causal genes have been found already: prostate cancer, alzheimer and type 2 diabetes mellitus. PRINCE''s predictions for these diseases highly match the known literature, suggesting several novel causal genes and protein complexes for further investigation.