Module organization and variance in protein-protein interaction networks

Inference of single-cell network using mutual information for scRNA-seq data analysis

BACKGROUND

With the advance in single-cell RNA sequencing (scRNA-seq) technology, deriving inherent biological system information from expression profiles at a single-cell resolution has become possible. It has been known that network modeling by estimating the associations between genes could better reveal dynamic changes in biological systems. However, accurately constructing a single-cell network (SCN) to capture the network architecture of each cell and further explore cell-to-cell heterogeneity remains challenging.

RESULTS

We introduce SINUM, a method for constructing the SIngle-cell Network Using Mutual information, which estimates mutual information between any two genes from scRNA-seq data to determine whether they are dependent or independent in a specific cell. Experiments on various scRNA-seq datasets with different cell numbers based on eight performance indexes (e.g., adjusted rand index and F-measure index) validated the accuracy and robustness of SINUM in cell type identification, superior to the state-of-the-art SCN inference method. Additionally, the SINUM SCNs exhibit high overlap with the human interactome and possess the scale-free property.

CONCLUSIONS

SINUM presents a view of biological systems at the network level to detect cell-type marker genes/gene pairs and investigate time-dependent changes in gene associations during embryo development. Codes for SINUM are freely available at https://github.com/SysMednet/SINUM .

Key target genes related to anti-breast cancer activity of ATRA: A network pharmacology, molecular docking and experimental investigation

All-trans retinoic acid (ATRA) has promising activity against breast cancer. However, the exact mechanisms of ATRA's anticancer effects remain complex and not fully understood. In this study, a network pharmacology and molecular docking approach was applied to identify key target genes related to ATRA's anti-breast cancer activity. Gene/disease enrichment analysis for predicted ATRA targets was performed using the Database for Annotation, Visualization and Integrated Discovery (DAVID), the Comparative Toxicogenomics Database (CTD), and the Gene Set Cancer Analysis (GSCA) database. Protein-Protein Interaction Network (PPIN) generation and analysis was conducted via Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) and cytoscape, respectively. Cancer-associated genes were evaluated using MyGeneVenn from the CTD. Differential expression analysis was conducted using the Tumor, Normal, and Metastatic (TNM) Plot tool and the Human Protein Atlas (HPA). The Glide docking program was used to predict ligand-protein binding. Treatment response predication and clinical profile assessment were performed using Receiver Operating Characteristic (ROC) Plotter and OncoDB databases, respectively. Cytotoxicity and gene expression were measured using MTT/fluorescent assays and Real-Time PCR, respectively. Molecular functions of ATRA targets (n = 209) included eicosanoid receptor activity and transcription factor activity. Some enriched pathways included inclusion body myositis and nuclear receptors pathways. Network analysis revealed 35 hub genes contributing to 3 modules, with 16 of them were associated with breast cancer. These genes were involved in apoptosis, cell cycle, androgen receptor pathway, and ESR-mediated signaling, among others. CCND1, ESR1, MMP9, MDM2, NCOA3, and RARA were significantly overexpressed in tumor samples. ATRA showed a high affinity towards CCND1/CDK4 and MMP9. CCND1, ESR1, and MDM2 were associated with poor treatment response and were downregulated after treatment of the breast cancer cell line with ATRA. CCND1 and ESR1 exhibited differential expression across breast cancer stages. Therefore, some part of ATRA's anti-breast cancer activity may be exerted through the CCND1/CDK4 complex.

Capturing the hierarchically assorted modules of protein-protein interactions in the organized nucleome

Nuclear proteins are major constituents and key regulators of nucleome topological organization and manipulators of nuclear events. To decipher the global connectivity of nuclear proteins and the hierarchically organized modules of their interactions, we conducted two rounds of cross-linking mass spectrometry (XL-MS) analysis, one of which followed a quantitative double chemical cross-linking mass spectrometry (in vivoqXL-MS) workflow, and identified 24,140 unique crosslinks in total from the nuclei of soybean seedlings. This in vivo quantitative interactomics enabled the identification of 5340 crosslinks that can be converted into 1297 nuclear protein-protein interactions (PPIs), 1220 (94%) of which were non-confirmative (or novel) nuclear PPIs compared with those in repositories. There were 250 and 26 novel interactors of histones and the nucleolar box C/D small nucleolar ribonucleoprotein complex, respectively. Modulomic analysis of orthologous Arabidopsis PPIs produced 27 and 24 master nuclear PPI modules (NPIMs) that contain the condensate-forming protein(s) and the intrinsically disordered region-containing proteins, respectively. These NPIMs successfully captured previously reported nuclear protein complexes and nuclear bodies in the nucleus. Surprisingly, these NPIMs were hierarchically assorted into four higher-order communities in a nucleomic graph, including genome and nucleolus communities. This combinatorial pipeline of 4C quantitative interactomics and PPI network modularization revealed 17 ethylene-specific module variants that participate in a broad range of nuclear events. The pipeline was able to capture both nuclear protein complexes and nuclear bodies, construct the topological architectures of PPI modules and module variants in the nucleome, and probably map the protein compositions of biomolecular condensates.

SWEET: a single-sample network inference method for deciphering individual features in disease

Recently, extracting inherent biological system information (e.g. cellular networks) from genome-wide expression profiles for developing personalized diagnostic and therapeutic strategies has become increasingly important. However, accurately constructing single-sample networks (SINs) to capture individual characteristics and heterogeneity in disease remains challenging. Here, we propose a sample-specific-weighted correlation network (SWEET) method to model SINs by integrating the genome-wide sample-to-sample correlation (i.e. sample weights) with the differential network between perturbed and aggregate networks. For a group of samples, the genome-wide sample weights can be assessed without prior knowledge of intrinsic subpopulations to address the network edge number bias caused by sample size differences. Compared with the state-of-the-art SIN inference methods, the SWEET SINs in 16 cancers more likely fit the scale-free property, display higher overlap with the human interactomes and perform better in identifying three types of cancer-related genes. Moreover, integrating SWEET SINs with a network proximity measure facilitates characterizing individual features and therapy in diseases, such as somatic mutation, mut-driver and essential genes. Biological experiments further validated two candidate repurposable drugs, albendazole for head and neck squamous cell carcinoma (HNSCC) and lung adenocarcinoma (LUAD) and encorafenib for HNSCC. By applying SWEET, we also identified two possible LUAD subtypes that exhibit distinct clinical features and molecular mechanisms. Overall, the SWEET method complements current SIN inference and analysis methods and presents a view of biological systems at the network level to offer numerous clues for further investigation and clinical translation in network medicine and precision medicine.

Co-fitness analysis identifies a diversity of signal proteins involved in the utilization of specific c-type cytochromes

Purpose

c-Type cytochromes are essential for extracellular electron transfer (EET) in electroactive microorganisms. The expression of appropriate c-type cytochromes is an important feature of these microorganisms in response to different extracellular electron acceptors. However, how these diverse c-type cytochromes are tightly regulated is still poorly understood.

Methods

In this study, we identified the high co-fitness genes that potentially work with different c-type cytochromes by using genome-wide co-fitness analysis. We also constructed and studied the co-fitness networks that composed of c-type cytochromes and the top 20 high co-fitness genes of them.

Results

We found that high co-fitness genes of c-type cytochromes were enriched in signal transduction processes in Shewanella oneidensis MR-1 cells. We then checked the top 20 co-fitness proteins for each of the 41 c-type cytochromes and identified the corresponding signal proteins for different c-type cytochromes. In particular, through the analysis of the high co-fitness signal protein for CymA, we further confirmed the cooperation between signal proteins and c-type cytochromes and identified a novel signal protein that is putatively involved in the regulation of CymA. In addition, we showed that these signal proteins form two signal transduction modules.

Conclusion

Taken together, these findings provide novel insights into the coordinated utilization of different c-type cytochromes under diverse conditions.

Network-Based Methods for Approaching Human Pathologies from a Phenotypic Point of View

Network and systemic approaches to studying human pathologies are helping us to gain insight into the molecular mechanisms of and potential therapeutic interventions for human diseases, especially for complex diseases where large numbers of genes are involved. The complex human pathological landscape is traditionally partitioned into discrete “diseases”; however, that partition is sometimes problematic, as diseases are highly heterogeneous and can differ greatly from one patient to another. Moreover, for many pathological states, the set of symptoms (phenotypes) manifested by the patient is not enough to diagnose a particular disease. On the contrary, phenotypes, by definition, are directly observable and can be closer to the molecular basis of the pathology. These clinical phenotypes are also important for personalised medicine, as they can help stratify patients and design personalised interventions. For these reasons, network and systemic approaches to pathologies are gradually incorporating phenotypic information. This review covers the current landscape of phenotype-centred network approaches to study different aspects of human diseases.

Insight into Shenqi Jiangtang Granule on the improved insulin sensitivity by integrating in silico and in vivo approaches

ETHNOPHARMACOLOGICAL RELEVANCE

Presently, insulin resistance has been a growing concern that urgently needs to be addressed, because it not only places patients at risk of developing type 2 diabetes mellitus but also results in metabolic syndrome and different aspects of cardiovascular diseases. Shenqi Jiangtang Granule (SJG) is a classic traditional Chinese medicine (TCM) prescription that is widely used to treat diabetes mellitus and its complications in clinical practice. While studies have revealed that SJG with multi-ingredients and multi-targets characteristics possesses potential anti-insulin resistance pharmacological properties, its mechanisms of action and molecular targets for the treatment of insulin resistance are still obscure, which prompt us to conduct an in-depth research.

AIM OF THE STUDY

This study was purposed to uncover the pharmacological mechanism of SJG against insulin resistance through integrating network pharmacology and experimental validation.

MATERIALS AND METHODS

The putative ingredients of SJG and its related targets were discerned from the TCMSP database. Subsequently, insulin resistance-associated targets were retrieved from GeneCard, OMIM, and GEO database. Compound-target, protein-protein interaction (PPI), and compound-target-pathway networks were established using Cytoscape software. GO and KEGG pathway analyses were performed to identify possible enrichment of genes with specific biological themes. Molecular docking was used to verify the correlation between the main active ingredients and hub targets. Optimal docking conformation was further analyzed by molecular dynamics (MD) simulation. Finally, the potential molecular mechanisms of SJG acting on insulin resistance, as predicted by the network pharmacology analyses, were validated experimentally in insulin-resistant rat model.

RESULTS

136 active compounds, 211 corresponding targets in addition to 1463 disease-related targets were collected, of which 94 intersection targets were obtained. 29 key targets including AKT1, VEGFA, IL-6, CASP3, and PTGS2 were identified through PPI network analysis. Hub module of PPI network was closely associated with inflammation. GO and KEGG analyses also revealed that inflammation-related pathways may be a central factor for SJG to modulate insulin resistance. Molecular docking test showed a good binding potency between primary active ingredients and core targets, and the binding mode of optimal docking conformation was stable in MD simulation. A rat model of insulin resistance was successfully induced by chronic high-fat diet (HFD) consumption. Through a series of in vivo studies, including HEC, ITT, and HOMA-IR measurement, it was revealed that SJG exhibited a beneficial effect on ameliorating insulin resistance, as demonstrated by a significant increase of GIR and a significant decrease of AUCITT and HOMA-IR index value. Further molecular biological analysis showed that SJG can decrease the mRNA expression level and serum concentration of inflammatory cytokines (TNF-α, IL-6, and IL-1β), along with suppressing the p-NFκB protein overexpression, indicating its anti-inflammatory activity. Also, it can contribute to the reversal of the impaired hepatic insulin signaling pathway, as evidenced by up-regulated protein expression of p-Akt and GLUT2.

CONCLUSIONS

Through in silico and in vivo approaches, the present study not only provides a unique insight into the possible mechanism of SJG in insulin resistance after successfully filtering out associated key target genes and signaling pathways, but also suggests a novel promising therapeutic strategy for curing insulin resistance.

Convolutional neural network for human cancer types prediction by integrating protein interaction networks and omics data

Many studies have proven the power of gene expression profile in cancer identification, however, the explosive growth of genomics data increasing needs of tools for cancer diagnosis and prognosis in high accuracy and short times. Here, we collected 6136 human samples from 11 cancer types, and integrated their gene expression profiles and protein-protein interaction (PPI) network to generate 2D images with spectral clustering method. To predict normal samples and 11 cancer tumor types, the images of these 6136 human cancer network were separated into training and validation dataset to develop convolutional neural network (CNN). Our model showed 97.4% and 95.4% accuracies in identification of normal versus tumors and 11 cancer types, respectively. We also provided the results that tumors located in neighboring tissues or in the same cell types, would induce machine make error classification due to the similar gene expression profiles. Furthermore, we observed some patients may exhibit better prognosis if their tumors often misjudged into normal samples. As far as we know, we are the first to generate thousands of cancer networks to predict and classify multiple cancer types with CNN architecture. We believe that our model not only can be applied to cancer diagnosis and prognosis, but also promote the discovery of multiple cancer biomarkers.

Highly heterogeneous-related genes of triple-negative breast cancer: potential diagnostic and prognostic biomarkers

Background

Triple-negative breast cancer (TNBC) is a highly heterogeneous subtype of breast cancer, showing aggressive clinical behaviors and poor outcomes. It urgently needs new therapeutic strategies to improve the prognosis of TNBC. Bioinformatics analyses have been widely used to identify potential biomarkers for facilitating TNBC diagnosis and management.

Methods

We identified potential biomarkers and analyzed their diagnostic and prognostic values using bioinformatics approaches. Including differential expression gene (DEG) analysis, Receiver Operating Characteristic (ROC) curve analysis, functional enrichment analysis, Protein-Protein Interaction (PPI) network construction, survival analysis, multivariate Cox regression analysis, and Non-negative Matrix Factorization (NMF).

Results

A total of 105 DEGs were identified between TNBC and other breast cancer subtypes, which were regarded as heterogeneous-related genes. Subsequently, the KEGG enrichment analysis showed that these genes were significantly enriched in ‘cell cycle’ and ‘oocyte meiosis’ related pathways. Four (FAM83B, KITLG, CFD and RBM24) of 105 genes were identified as prognostic signatures in the disease-free interval (DFI) of TNBC patients, as for progression-free interval (PFI), five genes (FAM83B, EXO1, S100B, TYMS and CFD) were obtained. Time-dependent ROC analysis indicated that the multivariate Cox regression models, which were constructed based on these genes, had great predictive performances. Finally, the survival analysis of TNBC subtypes (mesenchymal stem-like [MSL] and mesenchymal [MES]) suggested that FAM83B significantly affected the prognosis of patients.

Conclusions

The multivariate Cox regression models constructed from four heterogeneous-related genes (FAM83B, KITLG, RBM24 and S100B) showed great prediction performance for TNBC patients’ prognostic. Moreover, FAM83B was an important prognostic feature in several TNBC subtypes (MSL and MES). Our findings provided new biomarkers to facilitate the targeted therapies of TNBC and TNBC subtypes.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-021-08318-1.

Systemic Bioinformatic Analyses of Nuclear-Encoded Mitochondrial Genes in Hypertrophic Cardiomyopathy

Hypertrophic cardiomyopathy (HCM) is an autosomal dominant disease and mitochondria plays a key role in the progression in HCM. Here, we analyzed the expression pattern of nuclear-encoded mitochondrial genes (NMGenes) in HCM and found that the expression of NMGenes was significantly changed. A total of 316 differentially expressed NMGenes (DE-NMGenes) were identified. Pathway enrichment analyses showed that energy metabolism-related pathways such as "pyruvate metabolism" and "fatty acid degradation" were dysregulated, which highlighted the importance of energy metabolism in HCM. Next, we constructed a protein-protein interaction network based on 316 DE-NMGenes and identified thirteen hubs. Then, a total of 17 TFs (transcription factors) were predicted to potentially regulate the expression of 316 DE-NMGenes according to iRegulon, among which 8 TFs were already found involved in pathological hypertrophy. The remaining TFs (like GATA1, GATA5, and NFYA) were good candidates for further experimental verification. Finally, a mouse model of transverse aortic constriction (TAC) was established to validate the genes and results showed that DDIT4, TKT, CLIC1, DDOST, and SNCA were all upregulated in TAC mice. The present study represents the first effort to evaluate the global expression pattern of NMGenes in HCM and provides innovative insight into the molecular mechanism of HCM.

Characterization of cadmium-responsive transcription factors in wolf spider Pardosa pseudoannulata

Transcription factors (TFs) act on the regulation of gene expression, which is prevalent in all organisms, and their characterization may provide important clues for understanding the regulatory mechanism of gene expression. In this research, inhibited growth (delayed developmental time and decreased body weight) and increased activities of antioxidant enzymes (peroxidase, superoxide dismutase, and catalase) were recorded in Pardosa pseudoannulata in response to cadmium burden. Expression profiles of TFs were analyzed based on the transcriptome profiling of P. pseudoannulata, and 1711 TFs genes were differentially expressed with 995 up-regulated and 716 down-regulated. Most of the differentially expressed TFs belonged to zf-C2H2, ZBTB, Homeobox, and bHLH families. Interestingly, hub genes smads, TCF7L2, EGR1, and GATA5 were identified to be the candidate Cd-responsive TFs related to growth of spider. The expression level of Sod2 (superoxide dismutase) was regulated by the up-regulated TF foxo3, implying its important role in the antioxidant defense of spider. Moreover, sequence analysis demonstrated that smads and foxo3 were conserved among spiders and insects. This study revealed for the first time the role of TFs in molecular response of P. pseudoannulata to Cd stress, providing the basis for the protection of tarantula under Cd stress.

Membrane protein-regulated networks across human cancers

Alterations in membrane proteins (MPs) and their regulated pathways have been established as cancer hallmarks and extensively targeted in clinical applications. However, the analysis of MP-interacting proteins and downstream pathways across human malignancies remains challenging. Here, we present a systematically integrated method to generate a resource of cancer membrane protein-regulated networks (CaMPNets), containing 63,746 high-confidence protein-protein interactions (PPIs) for 1962 MPs, using expression profiles from 5922 tumors with overall survival outcomes across 15 human cancers. Comprehensive analysis of CaMPNets links MP partner communities and regulated pathways to provide MP-based gene sets for identifying prognostic biomarkers and druggable targets. For example, we identify CHRNA9 with 12 PPIs (e.g., ERBB2) can be a therapeutic target and find its anti-metastasis agent, bupropion, for treatment in nicotine-induced breast cancer. This resource is a study to systematically integrate MP interactions, genomics, and clinical outcomes for helping illuminate cancer-wide atlas and prognostic landscapes in tumor homo/heterogeneity.

Deep learning with evolutionary and genomic profiles for identifying cancer subtypes

Cancer subtype identification is an unmet need in precision diagnosis. Recently, evolutionary conservation has been indicated to contain informative signatures for functional significance in cancers. However, the importance of evolutionary conservation in distinguishing cancer subtypes remains largely unclear. Here, we identified the evolutionarily conserved genes (i.e. core genes) and observed that they are primarily involved in cellular pathways relevant to cell growth and metabolisms. By using these core genes, we developed two novel strategies, namely a feature-based strategy (FES) and an image-based strategy (IMS) by integrating their evolutionary and genomic profiles with the deep learning algorithm. In comparison with the FES using the random set and the strategy using the PAM50 classifier, the core gene set-based FES achieved a higher accuracy for identifying breast cancer subtypes. The IMS and FES using the core gene set yielded better performances than the other strategies, in terms of classifying both breast cancer subtypes and multiple cancer types. Moreover, the IMS is reproducible even using different gene expression data (i.e. RNA-seq and microarray). Comprehensive analysis of eight cancer types demonstrates that our evolutionary conservation-based models represent a valid and helpful approach for identifying cancer subtypes and the core gene set offers distinguishable clues of cancer subtypes.

NETWORK ANALYSIS OF COMMON DIFFERENTIAL GENES IDENTIFIES KEY GENES AND IMPORTANT MODULES UNDERLYING EXTRACELLULAR ELECTRON TRANSFER PROCESSES

Electricigens can transfer electrons that produced in intracellular metabolic processes to cellular surface to restore extracellular insoluble electron acceptors (extracellular electron transfer, EET). To uncover the molecular mechanisms underlying EET processes, we integrated transcriptome changes accompanying such processes with molecular network. Firstly, time-series expression datasets for Shewanella oneidensis MR-1 under limited/changed [Formula: see text] conditions were obtained from the GEO database, and a total of 336 common differentially expressed genes (DEGs) were identified. Then, we constructed the protein–protein interaction (PPI) network that involved in EET processes from these DEGs. Furthermore, by using centralization analysis and community detection, network analysis of the PPI network was performed. Although the fundamental EET genes are similar to previous studies, important new genes have been discovered. Taking together, our study identified many literature-validated genes critical to EET processes, and also proposed some novel genes that were putatively involved in EET processes.

Functional homogeneity and specificity of topological modules in human proteome

BACKGROUND

Functional modules in protein-protein interaction networks (PPIN) are defined by maximal sets of functionally associated proteins and are vital to understanding cellular mechanisms and identifying disease associated proteins. Topological modules of the human proteome have been shown to be related to functional modules of PPIN. However, the effects of the weights of interactions between protein pairs and the integration of physical (direct) interactions with functional (indirect expression-based) interactions have not been investigated in the detection of functional modules of the human proteome.

RESULTS

We investigated functional homogeneity and specificity of topological modules of the human proteome and validated them with known biological and disease pathways. Specifically, we determined the effects on functional homogeneity and heterogeneity of topological modules (i) with both physical and functional protein-protein interactions; and (ii) with incorporation of functional similarities between proteins as weights of interactions. With functional enrichment analyses and a novel measure for functional specificity, we evaluated functional relevance and specificity of topological modules of the human proteome.

CONCLUSIONS

The topological modules ranked using specificity scores show high enrichment with gene sets of known functions. Physical interactions in PPIN contribute to high specificity of the topological modules of the human proteome whereas functional interactions contribute to high homogeneity of the modules. Weighted networks result in more number of topological modules but did not affect their functional propensity. Modules of human proteome are more homogeneous for molecular functions than biological processes.

Analysis of protein-protein interaction network based on transcriptome profiling of ovine granulosa cells identifies candidate genes in cyclic recruitment of ovarian follicles

After pubertal, cohort of small antral follicles enters to gonadotrophin-sensitive development, called recruited follicles. This study was aimed to identify candidate genes in follicular cyclic recruitment via analysis of protein-protein interaction (PPI) network. Differentially expressed genes (DEGs) in ovine granulosa cells of small antral follicles between follicular and luteal phases were accumulated among gene/protein symbols of the Ensembl annotation. Following directed graphs, PTPN6 and FYN have the highest indegree and outdegree, respectively. Since, these hubs being up-regulated in ovine granulosa cells of small antral follicles during the follicular phase, it represents an accumulation of blood immune cells in follicular phase in comparison with luteal phase. By contrast, the up-regulated hubs in the luteal phase including CDK1, INSRR and TOP2A which stimulated DNA replication and proliferation of granulosa cells, they known as candidate genes of the cyclic recruitment.

Mutational Pleiotropy and the Strength of Stabilizing Selection Within and Between Functional Modules of Gene Expression

Variational modules, sets of pleiotropically covarying traits, affect phenotypic evolution, and therefore are predicted to reflect functional modules, such that traits within a variational module also share a common function. Such an alignment of function and pleiotropy is expected to facilitate adaptation by reducing the deleterious effects of mutations, and by allowing coordinated evolution of functionally related sets of traits. Here, we adopt a high-dimensional quantitative genetic approach using a large number of gene expression traits in Drosophila serrata to test whether functional grouping, defined by gene ontology (GO terms), predicts variational modules. Mutational or standing genetic covariance was significantly greater than among randomly grouped sets of genes for 38% of our functional groups, indicating that GO terms can predict variational modularity to some extent. We estimated stabilizing selection acting on mutational covariance to test the prediction that functional pleiotropy would result in reduced deleterious effects of mutations within functional modules. Stabilizing selection within functional modules was weaker than that acting on randomly grouped sets of genes in only 23% of functional groups, indicating that functional alignment can reduce deleterious effects of pleiotropic mutation but typically does not. Our analyses also revealed the presence of variational modules that spanned multiple functions.

Introducing Potential Key Proteins and Pathways in Human Laryngeal Cancer: A System Biology Approach

The most common malignant neoplasm of the head and neck region is laryngeal cancer which presents a significant international health problem. The present study aims to screen potential proteins related to laryngeal cancer by network analysis to further understanding disease pathogenesis and biomarker discovery. Differentially expressed proteins were extracted from literatures of laryngeal cancer that compare proteome profiling of patient›s tissue with healthy controls. The PPI network analyzed for up and down regulated proteins with Cytoscape Version 3.4. After PPI construction, topological properties of the two networks have been analyzed. Besides, by using MCODE. the Gene Ontology (GO) analysis, the related modules and pathways were examined. Our study screened 275 differentially changed proteins, including 136 up- and 139 down-regulated proteins. For each network, it has been considered 20 key proteins as hub and 20 as bottleneck. A number of 26 hub-bottleneck nodes is introduced for the two networks. A total of 11 modules including 6 downregulated and 5 upregulated network modules were obtained. The most significant GO function in the significant upregulated module was the RNA processing, and the most significant one in the downregulated module with highest score was the respiratory electron transport chain. Among 275 investigated proteins, 12 crucial proteins are determined that 4 of them can be introduce as a possible biomarker panel including YWHAZ, PPP2R1A, HSP90AA1, and CALM3 for human laryngeal cancer.

Functional diversity of topological modules in human protein-protein interaction networks

A large-scale molecular interaction network of protein-protein interactions (PPIs) enables the automatic detection of molecular functional modules through a computational approach. However, the functional modules that are typically detected by topological community detection algorithms may be diverse in functional homogeneity and are empirically considered to be default functional modules. Thus, a significant challenge that has been described but not elucidated is investigating the relationship between topological modules and functional modules. We systematically investigated this issue by initially using seven widely used community detection algorithms to partition the PPI network into communities. Four homogeneity measures were subsequently implemented to evaluate the functional homogeneity of protein community. We determined that a significant portion of topological modules with heterogeneous functionality exists and should be further investigated; moreover, these findings indicated that topologically based functional module detection approaches must be reconsidered. Furthermore, we found that the functional homogeneity of topological modules is positively correlated with their edge densities, degree of association with diseases and general Gene Ontology (GO) terms. Thus, topologically based module detection approaches should be used with caution in the identification of functional modules with high homogeneity

Neighbor affinity based algorithm for discovering temporal protein complex from dynamic PPI network

Detection of temporal protein complexes would be a great aid in furthering our knowledge of the dynamic features and molecular mechanism in cell life activities. Most existing clustering algorithms for discovering protein complexes are based on static protein interaction networks in which the inherent dynamics are often overlooked. We propose a novel algorithm DPC-NADPIN (Discovering Protein Complexes based on Neighbor Affinity and Dynamic Protein Interaction Network) to identify temporal protein complexes from the time course protein interaction networks. Inspired by the idea of that the tighter a protein's neighbors inside a module connect, the greater the possibility that the protein belongs to the module, DPC-NADPIN algorithm first chooses each of the proteins with high clustering coefficient and its neighbors to consolidate into an initial cluster, and then the initial cluster becomes a protein complex by appending its neighbor proteins according to the relationship between the affinity among neighbors inside the cluster and that outside the cluster. In our experiments, DPC-NADPIN algorithm is proved to be reasonable and it has better performance on discovering protein complexes than the following state-of-the-art algorithms: Hunter, MCODE, CFinder, SPICI, and ClusterONE; Meanwhile, it obtains many protein complexes with strong biological significance, which provide helpful biological knowledge to the related researchers. Moreover, we find that proteins are assembled coordinately to form protein complexes with characteristics of temporality and spatiality, thereby performing specific biological functions.

PTIR: Predicted Tomato Interactome Resource

Protein-protein interactions (PPIs) are involved in almost all biological processes and form the basis of the entire interactomics systems of living organisms. Identification and characterization of these interactions are fundamental to elucidating the molecular mechanisms of signal transduction and metabolic pathways at both the cellular and systemic levels. Although a number of experimental and computational studies have been performed on model organisms, the studies exploring and investigating PPIs in tomatoes remain lacking. Here, we developed a Predicted Tomato Interactome Resource (PTIR), based on experimentally determined orthologous interactions in six model organisms. The reliability of individual PPIs was also evaluated by shared gene ontology (GO) terms, co-evolution, co-expression, co-localization and available domain-domain interactions (DDIs). Currently, the PTIR covers 357,946 non-redundant PPIs among 10,626 proteins, including 12,291 high-confidence, 226,553 medium-confidence, and 119,102 low-confidence interactions. These interactions are expected to cover 30.6% of the entire tomato proteome and possess a reasonable distribution. In addition, ten randomly selected PPIs were verified using yeast two-hybrid (Y2H) screening or a bimolecular fluorescence complementation (BiFC) assay. The PTIR was constructed and implemented as a dedicated database and is available at http://bdg.hfut.edu.cn/ptir/index.html without registration.

Mining Temporal Protein Complex Based on the Dynamic PIN Weighted with Connected Affinity and Gene Co-Expression

The identification of temporal protein complexes would make great contribution to our knowledge of the dynamic organization characteristics in protein interaction networks (PINs). Recent studies have focused on integrating gene expression data into static PIN to construct dynamic PIN which reveals the dynamic evolutionary procedure of protein interactions, but they fail in practice for recognizing the active time points of proteins with low or high expression levels. We construct a Time-Evolving PIN (TEPIN) with a novel method called Deviation Degree, which is designed to identify the active time points of proteins based on the deviation degree of their own expression values. Owing to the differences between protein interactions, moreover, we weight TEPIN with connected affinity and gene co-expression to quantify the degree of these interactions. To validate the efficiencies of our methods, ClusterONE, CAMSE and MCL algorithms are applied on the TEPIN, DPIN (a dynamic PIN constructed with state-of-the-art three-sigma method) and SPIN (the original static PIN) to detect temporal protein complexes. Each algorithm on our TEPIN outperforms that on other networks in terms of match degree, sensitivity, specificity, F-measure and function enrichment etc. In conclusion, our Deviation Degree method successfully eliminates the disadvantages which exist in the previous state-of-the-art dynamic PIN construction methods. Moreover, the biological nature of protein interactions can be well described in our weighted network. Weighted TEPIN is a useful approach for detecting temporal protein complexes and revealing the dynamic protein assembly process for cellular organization.

Comparison of module detection algorithms in protein networks and investigation of the biological meaning of predicted modules

Background

It is generally acknowledged that a functional understanding of a biological system can only be obtained by an understanding of the collective of molecular interactions in form of biological networks. Protein networks are one particular network type of special importance, because proteins form the functional base units of every biological cell. On a mesoscopic level of protein networks, modules are of significant importance because these building blocks may be the next elementary functional level above individual proteins allowing to gain insight into fundamental organizational principles of biological cells.

Results

In this paper, we provide a comparative analysis of five popular and four novel module detection algorithms. We study these module prediction methods for simulated benchmark networks as well as 10 biological protein interaction networks (PINs). A particular focus of our analysis is placed on the biological meaning of the predicted modules by utilizing the Gene Ontology (GO) database as gold standard for the definition of biological processes. Furthermore, we investigate the robustness of the results by perturbing the PINs simulating in this way our incomplete knowledge of protein networks.

Conclusions

Overall, our study reveals that there is a large heterogeneity among the different module prediction algorithms if one zooms-in the biological level of biological processes in the form of GO terms and all methods are severely affected by a slight perturbation of the networks. However, we also find pathways that are enriched in multiple modules, which could provide important information about the hierarchical organization of the system.

Dynamic identifying protein functional modules based on adaptive density modularity in protein-protein interaction networks

BACKGROUND

The identification of protein functional modules would be a great aid in furthering our knowledge of the principles of cellular organization. Most existing algorithms for identifying protein functional modules have a common defect -- once a protein node is assigned to a functional module, there is no chance to move the protein to the other functional modules during the follow-up processes, which lead the erroneous partitioning occurred at previous step to accumulate till to the end.

RESULTS

In this paper, we design a new algorithm ADM (Adaptive Density Modularity) to detect protein functional modules based on adaptive density modularity. In ADM algorithm, according to the comparison between external closely associated degree and internal closely associated degree, the partitioning of a protein-protein interaction network into functional modules always evolves quickly to increase the density modularity of the network. The integration of density modularity into the new algorithm not only overcomes the drawback mentioned above, but also contributes to identifying protein functional modules more effectively.

CONCLUSIONS

The experimental result reveals that the performance of ADM algorithm is superior to many state-of-the-art protein functional modules detection techniques in aspect of the accuracy of prediction. Moreover, the identified protein functional modules are statistically significant in terms of "Biological Process" annotated in Gene Ontology, which provides substantial support for revealing the principles of cellular organization.