Network compression as a quality measure for protein interaction networks

With the advent of large-scale protein interaction studies, there is much debate about data quality. Can different noise levels in the measurements be assessed by analyzing network structure? Because proteomic regulation is inherently co-operative, modular and redundant, it is inherently compressible when represented as a network. Here we propose that network compression can be used to compare false positive and false negative noise levels in protein interaction networks. We validate this hypothesis by first confirming the detrimental effect of false positives and false negatives. Second, we show that gold standard networks are more compressible. Third, we show that compressibility correlates with co-expression, co-localization, and shared function. Fourth, we also observe correlation with better protein tagging methods, physiological expression in contrast to over-expression of tagged proteins, and smart pooling approaches for yeast two-hybrid screens. Overall, this new measure is a proxy for both sensitivity and specificity and gives complementary information to standard measures such as average degree and clustering coefficients.

Overexpression of ribosome binding protein 1 (RRBP1) in breast cancer

The molecular events that lead to malignant transformation and subsequent metastasis of breast carcinoma include alterations in the cells at genome, transcriptome and proteome levels. In this study, we used publicly available gene expression databases to identify those candidate genes which are upregulated at the mRNA level in breast cancers but have not been systematically validated at the protein level. Based on an extensive literature search, we identified ribosome binding protein 1 (RRBP1) as a candidate that is upregulated at the mRNA level in five different studies but its protein expression had not been investigated. Immunohistochemical labeling of breast cancer tissue microarrays was carried out to determine the expression of RRBP1 in a large panel of breast cancers. We found that RRBP1 was overexpressed in 84% (177/219) of breast carcinoma cases tested. The subcellular localization of RRBP1 was mainly observed to be in the cytoplasm with intense staining in the perinuclear region. Our findings suggest that RRBP1 is an interesting molecule that can be further studied for its potential to serve as a breast cancer biomarker. This study also demonstrates how the integration of biological data from available resources in conjunction with systematic evaluation approaches can be successfully applied to clinical proteomics.

Prioritizing cancer-related key miRNA-target interactions by integrative genomics

Accumulating evidence indicates that microRNAs (miRNAs) can function as oncogenes or tumor suppressor genes by controlling few key targets, which in turn contribute to the pathogenesis of cancer. The identification of cancer-related key miRNA-target interactions remains a challenge. We performed a systematic analysis of known cancer-related key interactions manually curated from published papers based on different aspects including sequence, expression and function. Known cancer-related key interactions show more miRNA binding sites (especially for 8mer binding sites), more reliable binding of miRNA to the target region, higher expression associations and broader functional coverage when compared to non-disease-related interactions. Through integrating these sequence, expression and function features, we proposed a bioinformatics approach termed PCmtI to prioritize cancer-related key interactions. Ten-fold cross-validation of our approach revealed that it can achieve an area under the receiver operating characteristic curve of 93.9%. Subsequent leave-one-miRNA-out cross-validation also demonstrated the performance of our approach. Using miR-155 as a case, we found that the top ranked interactions can account for most functions of miR-155. In addition, we further demonstrated the power of our approach by 23 recently identified cancer-related key interactions. The approach described here offers a new way for the discovery of novel cancer-related key miRNA-target interactions.

Matrix metalloproteinase-2 and -9 in the sera and in the urine of human oncocytoma and renal cell carcinoma

Matrix metalloproteinases (MMPs) are a family of zinc-dependent endopeptidases, capable of degrading all the molecular components of extracellular matrix. MMPs have been shown to play critical roles in tumor cell invasion and metastasis. We verified the activity of MMPs in the sera and in the urine of patients with kidney carcinoma by gelatin zymography. Of these patients, 16 had clear cell renal carcinoma (ccRCC) and 4 patients had oncocytoma. The sera and the urine of 16 healthy subjects were used as controls. In the sera, zymography analysis showed gelatinolytic bands at 72 kDa (gelatinase A) at 92, 130 and 240 kDa (gelatinase B). MMP-9 activity was slightly enhanced in sera from ccRCC compared with oncocytoma patients. Serum MMP-2 activity was similar in ccRCC and in oncocytoma patients. In the urine, 2 oncocytoma patients and 3 (33%) of the ccRCC patients showed gelatinolytic activity, whereas MMPs could not be detected in the concentrated urine of healthy subjects. The most abundant lytic activity was at 92 kDa, whereas MMP-2 was present in lesser quantities. However, there was broad overlap of the data and we did not find any correlation to type, stage or grade. Therefore, despite previous evidence, MMP-2 and -9 activity in serum and urine may not be useful biomarker for kidney carcinomas.

Cancer core modules identification through genomic and transcriptomic changes correlation detection at network level

BACKGROUND

Identification of driver mutations among numerous genomic alternations remains a critical challenge to the elucidation of the underlying mechanisms of cancer. Because driver mutations by definition are associated with a greater number of cancer phenotypes compared to other mutations, we hypothesized that driver mutations could more easily be identified once the genotype-phenotype correlations are detected across tumor samples.

RESULTS

In this study, we describe a novel network analysis to identify the driver mutation through integrating both cancer genomes and transcriptomes. Our method successfully identified a significant genotype-phenotype change correlation in all six solid tumor types and revealed core modules that contain both significantly enriched somatic mutations and aberrant expression changes specific to tumor development. Moreover, we found that the majority of these core modules contained well known cancer driver mutations, and that their mutated genes tended to occur at hub genes with central regulatory roles. In these mutated genes, the majority were cancer-type specific and exhibited a closer relationship within the same cancer type rather than across cancer types. The remaining mutated genes that exist in multiple cancer types led to two cancer type clusters, one cluster consisted of three neural derived or related cancer types, and the other cluster consisted of two adenoma cancer types.

CONCLUSIONS

Our approach can successfully identify the candidate drivers from the core modules. Comprehensive network analysis on the core modules potentially provides critical insights into convergent cancer development in different organs.

Differential secretome analysis reveals CST6 as a suppressor of breast cancer bone metastasis

Bone metastasis is a frequent complication of breast cancer and a common cause of morbidity and mortality from the disease. During metastasis secreted proteins play crucial roles in the interactions between cancer cells and host stroma. To characterize the secreted proteins that are associated with breast cancer bone metastasis, we preformed a label-free proteomic analysis to compare the secretomes of four MDA-MB-231 (MDA231) derivative cell lines with varied capacities of bone metastasis. A total of 128 proteins were found to be consistently up-/down-regulated in the conditioned medium of bone-tropic cancer cells. The enriched molecular functions of the altered proteins included receptor binding and peptidase inhibition. Through additional transcriptomic analyses of breast cancer cells, we selected cystatin E/M (CST6), a cysteine protease inhibitor down-regulated in bone-metastatic cells, for further functional studies. Our results showed that CST6 suppressed the proliferation, colony formation, migration and invasion of breast cancer cells. The suppressive function against cancer cell motility was carried out by cancer cell-derived soluble CST6. More importantly, ectopic expression of CST6 in cancer cells rescued mice from overt osteolytic metastasis and deaths in the animal study, while CST6 knockdown markedly enhanced cancer cell bone metastasis and shortened animal survival. Overall, our study provided a systemic secretome analysis of breast cancer bone tropism and established secreted CST6 as a bona fide suppressor of breast cancer osteolytic metastasis.

Recent approaches to the prioritization of candidate disease genes

Many efforts are still devoted to the discovery of genes involved with specific phenotypes, in particular, diseases. High-throughput techniques are thus applied frequently to detect dozens or even hundreds of candidate genes. However, the experimental validation of many candidates is often an expensive and time-consuming task. Therefore, a great variety of computational approaches has been developed to support the identification of the most promising candidates for follow-up studies. The biomedical knowledge already available about the disease of interest and related genes is commonly exploited to find new gene-disease associations and to prioritize candidates. In this review, we highlight recent methodological advances in this research field of candidate gene prioritization. We focus on approaches that use network information and integrate heterogeneous data sources. Furthermore, we discuss current benchmarking procedures for evaluating and comparing different prioritization methods.

BIPS: BIANA Interolog Prediction Server. A tool for protein-protein interaction inference

Protein–protein interactions (PPIs) play a crucial role in biology, and high-throughput experiments have greatly increased the coverage of known interactions. Still, identification of complete inter- and intraspecies interactomes is far from being complete. Experimental data can be complemented by the prediction of PPIs within an organism or between two organisms based on the known interactions of the orthologous genes of other organisms (interologs). Here, we present the BIANA (Biologic Interactions and Network Analysis) Interolog Prediction Server (BIPS), which offers a web-based interface to facilitate PPI predictions based on interolog information. BIPS benefits from the capabilities of the framework BIANA to integrate the several PPI-related databases. Additional metadata can be used to improve the reliability of the predicted interactions. Sensitivity and specificity of the server have been calculated using known PPIs from different interactomes using a leave-one-out approach. The specificity is between 72 and 98%, whereas sensitivity varies between 1 and 59%, depending on the sequence identity cut-off used to calculate similarities between sequences. BIPS is freely accessible at http://sbi.imim.es/BIPS.php.

Identifying dysregulated pathways in cancers from pathway interaction networks

Background

Cancers, a group of multifactorial complex diseases, are generally caused by mutation of multiple genes or dysregulation of pathways. Identifying biomarkers that can characterize cancers would help to understand and diagnose cancers. Traditional computational methods that detect genes differentially expressed between cancer and normal samples fail to work due to small sample size and independent assumption among genes. On the other hand, genes work in concert to perform their functions. Therefore, it is expected that dysregulated pathways will serve as better biomarkers compared with single genes.

Results

In this paper, we propose a novel approach to identify dysregulated pathways in cancer based on a pathway interaction network. Our contribution is three-fold. Firstly, we present a new method to construct pathway interaction network based on gene expression, protein-protein interactions and cellular pathways. Secondly, the identification of dysregulated pathways in cancer is treated as a feature selection problem, which is biologically reasonable and easy to interpret. Thirdly, the dysregulated pathways are identified as subnetworks from the pathway interaction networks, where the subnetworks characterize very well the functional dependency or crosstalk between pathways. The benchmarking results on several distinct cancer datasets demonstrate that our method can obtain more reliable and accurate results compared with existing state of the art methods. Further functional analysis and independent literature evidence also confirm that our identified potential pathogenic pathways are biologically reasonable, indicating the effectiveness of our method.

Conclusions

Dysregulated pathways can serve as better biomarkers compared with single genes. In this work, by utilizing pathway interaction networks and gene expression data, we propose a novel approach that effectively identifies dysregulated pathways, which can not only be used as biomarkers to diagnose cancers but also serve as potential drug targets in the future.

Integrating the Alzheimer's disease proteome and transcriptome: a comprehensive network model of a complex disease

Network models combined with gene expression studies have become useful tools for studying complex diseases like Alzheimer's disease. We constructed a "Core" Alzheimer's disease protein interaction network by human curation of the primary literature. The Core network consisted of 775 nodes and 2,204 interactions. To our knowledge, this is the most comprehensive and accurate protein interaction network yet constructed for Alzheimer's disease. An "Expanded" network was computationally constructed by adding additional proteins that interacted with Core network proteins, and consisted of 4,945 nodes and 26,064 interactions. We then mapped existing gene expression studies to the Core network. This combined data model identified the MAPK/ERK pathway and clathrin-mediated receptor endocytosis as key pathways in Alzheimer's disease. Important proteins in the MAPK/ERK pathway that interacted in the Core network formed a downregulated cluster of nodes, whereas clathrin and several clathrin accessory proteins that interacted in the Core network formed an upregulated cluster of nodes. The MAPK/ERK pathway is a key component in synaptic plasticity and learning, processes disrupted in Alzheimer's. Clathrin and clathrin adaptor proteins are involved in the endocytosis of the APP protein that can lead to increased intracellular levels of amyloid beta peptide, contributing to the progression of Alzheimer's.

Proteomic analysis of 17β-estradiol degradation by Stenotrophomonas maltophilia

Microbial degradation plays a critical role in determining the environmental fate of steroid hormones, such as 17β-estradiol (E2). The molecular mechanisms governing the microbial transformation of E2 and its primary degradation intermediate, estrone (E1), are largely unknown. The objective of this study was to identify metabolism pathways that might be involved in microbial estrogen degradation. To achieve the objective, Stenotrophomonas maltophilia strain ZL1 was used as a model estrogen degrading bacterium and its protein expression level during E2/E1 degradation was studied using quantitative proteomics. During an E2 degradation experiment, strain ZL1 first converted E2 to E1 stoichiometrically. At 16 h E1 reached its peak concentration, and microbial growth started. At the same time, enzymes involved in certain catabolic and anabolic pathways were most highly expressed compared to the other time points tested. Among those enzymes, the ones involved in protein and lipid biosyntheses were observed to be particularly active. Based on the metabolite information from a previous study and the proteomic data from this study, we hypothesized that S. maltophilia strain ZL1 was able to convert E1 to amino acid tyrosine through ring cleavage on a saturated ring of the E1 molecule and then utilize tyrosine in protein biosynthesis.

Transcriptome and Coexpression Network Analysis of the Human Glioma Cell Line Hs683 Exposed to Candoxin

OBJECTIVE: Gliomas are the most common primary tumours of the central nervous system. Snake venom, such as candoxin (CDX) isolated from Bungarus candidus, inhibits glioma cell proliferation. This study explored the gene regulation profile of CDX-treated human glioma Hs683 cells. METHODS: Using microarray technology and bioinformatics analyses the underlying molecular mechanism of action of CDX was evaluated by constructing gene regulation and protein—protein interaction coexpression networks. RESULTS: CDX treatment induced a large number of related genes at the transcriptional level. The MYC gene (v-myc myelocytomatosis viral oncogene homologue [avian]) had a key role in the response of Hs683 cells to CDX treatment. Activation of MYC upregulated NDRG1 (N-myc downstream regulated 1), WNT10B (wingless-type mouse mammary tumour virus integration site family, member 10B), CASP9 (caspase 9, apoptosis-related cysteine peptidase) and CDKN2A (cyclin-dependent kinase inhibitor 2A), and downregulated ID3 (inhibitor of DNA binding 3, dominant negative helix—loop—helix protein) and SLC1A4 (solute carrier family 1 [glutamate/neutral amino acid transporter], member 4). In addition, a subnetwork was constructed among SPP1 (secreted phosphoprotein 1), SDC1 (syndecan 1) and CD44 based on protein—protein interactions, and these genes were predicted to be involved in glioma cell invasion. CONCLUSION: These findings might provide novel therapeutic targets for glioma chemotherapy.

Dynamic network of transcription and pathway crosstalk to reveal molecular mechanism of MGd-treated human lung cancer cells

Recent research has revealed various molecular markers in lung cancer. However, the organizational principles underlying their genetic regulatory networks still await investigation. Here we performed Network Component Analysis (NCA) and Pathway Crosstalk Analysis (PCA) to construct a regulatory network in human lung cancer (A549) cells which were treated with 50 uM motexafin gadolinium (MGd), a metal cation-containing chemotherapeutic drug for 4, 12, and 24 hours. We identified a set of key TFs, known target genes for these TFs, and signaling pathways involved in regulatory networks. Our work showed that putative interactions between these TFs (such as ESR1/Sp1, E2F1/Sp1, c-MYC-ESR, Smad3/c-Myc, and NFKB1/RELA), between TFs and their target genes (such as BMP41/Est1, TSC2/Myc, APE1/Sp1/p53, RARA/HOXA1, and SP1/USF2), and between signaling pathways (such as PPAR signaling pathway and Adipocytokines signaling pathway). These results will provide insights into the regulatory mechanism of MGd-treated human lung cancer cells.

Conserved and quickly evolving immunome genes have different evolutionary paths

Genetic, transcript, and protein level variations have important functional and evolutionary consequences. We performed systematic data collection and analysis of copy-number variations, single-nucleotide polymorphisms, disease-causing variations, messenger RNA splicing variants, and protein posttranslational modifications for the genes and proteins essential for human immune system. Information about polymorphic and evolutionarily fixed genetic variations was used to group immunome genes to the most conserved and the most quickly changing ones under directed selection during the recent immunome evolution. Gene Ontology terms related to adaptive immunity are associated with gene groups subject to recent directing selection. In addition, several other characteristics of the immunome genes and proteins in these two categories have statistically significant differences. The presented findings question the usability of directed mouse genes as models for human diseases and conditions and shed light on the fine tuning of human immunity and its diverse functions.

NetAligner--a network alignment server to compare complexes, pathways and whole interactomes

The many ongoing genome sequencing initiatives are delivering comprehensive lists of the individual molecular components present in an organism, but these reveal little about how they work together. Follow-up initiatives are revealing thousands of interrelationships between gene products that need to be analyzed with novel bioinformatics approaches able to capture their complex emerging properties. Recently, we developed NetAligner, a novel network alignment tool that allows the identification of conserved protein complexes and pathways across organisms, providing valuable hints as to how those interaction networks evolved. NetAligner includes the prediction of likely conserved interactions, based on evolutionary distances, to counter the high number of missing interactions in current interactome networks, and a fast assessment of the statistical significance of individual alignment solutions, which increases its performance with respect to existing tools. The web server implementation of the NetAligner algorithm presented here features complex, pathway and interactome to interactome alignments for seven model organisms, namely Homo sapiens, Mus musculus, Drosophila melanogaster, Caenorhabditis elegans, Arabidopsis thaliana, Saccharomyces cerevisiae and Escherichia coli. The user can query complexes and pathways of arbitrary topology against a target species interactome, or directly compare two complete interactomes to identify conserved complexes and subnetworks. Alignment solutions can be downloaded or directly visualized in the browser. The NetAligner web server is publicly available at http://netaligner.irbbarcelona.org/.

SH3 domain tyrosine phosphorylation--sites, role and evolution

Background

SH3 domains are eukaryotic protein domains that participate in a plethora of cellular processes including signal transduction, proliferation, and cellular movement. Several studies indicate that tyrosine phosphorylation could play a significant role in the regulation of SH3 domains.

Results

To explore the incidence of the tyrosine phosphorylation within SH3 domains we queried the PhosphoSite Plus database of phosphorylation sites. Over 100 tyrosine phosphorylations occurring on 20 different SH3 domain positions were identified. The tyrosine corresponding to c–Src Tyr-90 was by far the most frequently identified SH3 domain phosphorylation site. A comparison of sequences around this tyrosine led to delineation of a preferred sequence motif ALYD(Y/F). This motif is present in about 15% of human SH3 domains and is structurally well conserved. We further observed that tyrosine phosphorylation is more abundant than serine or threonine phosphorylation within SH3 domains and other adaptor domains, such as SH2 or WW domains. Tyrosine phosphorylation could represent an important regulatory mechanism of adaptor domains.

Conclusions

While tyrosine phosphorylation typically promotes signaling protein interactions via SH2 or PTB domains, its role in SH3 domains is the opposite - it blocks or prevents interactions. The regulatory function of tyrosine phosphorylation is most likely achieved by the phosphate moiety and its charge interfering with binding of polyproline helices of SH3 domain interacting partners.

TMEM16A induces MAPK and contributes directly to tumorigenesis and cancer progression

Frequent gene amplification of the receptor-activated calcium-dependent chloride channel TMEM16A (TAOS2 or ANO1) has been reported in several malignancies. However, its involvement in human tumorigenesis has not been previously studied. Here, we show a functional role for TMEM16A in tumor growth. We found TMEM16A overexpression in 80% of head and neck squamous cell carcinoma (SCCHN), which correlated with decreased overall survival in patients with SCCHN. TMEM16A overexpression significantly promoted anchorage-independent growth in vitro, and loss of TMEM16A resulted in inhibition of tumor growth both in vitro and in vivo. Mechanistically, TMEM16A-induced cancer cell proliferation and tumor growth were accompanied by an increase in extracellular signal-regulated kinase (ERK)1/2 activation and cyclin D1 induction. Pharmacologic inhibition of MEK/ERK and genetic inactivation of ERK1/2 (using siRNA and dominant-negative constructs) abrogated the growth effect of TMEM16A, indicating a role for mitogen-activated protein kinase (MAPK) activation in TMEM16A-mediated proliferation. In addition, a developmental small-molecule inhibitor of TMEM16A, T16A-inh01 (A01), abrogated tumor cell proliferation in vitro. Together, our findings provide a mechanistic analysis of the tumorigenic properties of TMEM16A, which represents a potentially novel therapeutic target. The development of small-molecule inhibitors against TMEM16A may be clinically relevant for treatment of human cancers, including SCCHN.

LDGIdb: a database of gene interactions inferred from long-range strong linkage disequilibrium between pairs of SNPs

Background

Complex human diseases may be associated with many gene interactions. Gene interactions take several different forms and it is difficult to identify all of the interactions that are potentially associated with human diseases. One approach that may fill this knowledge gap is to infer previously unknown gene interactions via identification of non-physical linkages between different mutations (or single nucleotide polymorphisms, SNPs) to avoid hitchhiking effect or lack of recombination. Strong non-physical SNP linkages are considered to be an indication of biological (gene) interactions. These interactions can be physical protein interactions, regulatory interactions, functional compensation/antagonization or many other forms of interactions. Previous studies have shown that mutations in different genes can be linked to the same disorders. Therefore, non-physical SNP linkages, coupled with knowledge of SNP-disease associations may shed more light on the role of gene interactions in human disorders. A user-friendly web resource that integrates information about non-physical SNP linkages, gene annotations, SNP information, and SNP-disease associations may thus be a good reference for biomedical research.

Findings

Here we extracted the SNPs located within the promoter or exonic regions of protein-coding genes from the HapMap database to construct a database named the Linkage-Disequilibrium-based Gene Interaction database (LDGIdb). The database stores 646,203 potential human gene interactions, which are potential interactions inferred from SNP pairs that are subject to long-range strong linkage disequilibrium (LD), or non-physical linkages. To minimize the possibility of hitchhiking, SNP pairs inferred to be non-physically linked were required to be located in different chromosomes or in different LD blocks of the same chromosomes. According to the genomic locations of the involved SNPs (i.e., promoter, untranslated region (UTR) and coding region (CDS)), the SNP linkages inferred were categorized into promoter-promoter, promoter-UTR, promoter-CDS, CDS-CDS, CDS-UTR and UTR-UTR linkages. For the CDS-related linkages, the coding SNPs were further classified into nonsynonymous and synonymous variations, which represent potential gene interactions at the protein and RNA level, respectively. The LDGIdb also incorporates human disease-association databases such as Genome-Wide Association Studies (GWAS) and Online Mendelian Inheritance in Man (OMIM), so that the user can search for potential disease-associated SNP linkages. The inferred SNP linkages are also classified in the context of population stratification to provide a resource for investigating potential population-specific gene interactions.

Conclusion

The LDGIdb is a user-friendly resource that integrates non-physical SNP linkages and SNP-disease associations for studies of gene interactions in human diseases. With the help of the LDGIdb, it is plausible to infer population-specific SNP linkages for more focused studies, an avenue that is potentially important for pharmacogenetics. Moreover, by referring to disease-association information such as the GWAS data, the LDGIdb may help identify previously uncharacterized disease-associated gene interactions and potentially lead to new discoveries in studies of human diseases.

Keywords

Gene interaction, SNP, Linkage disequilibrium, Systems biology, Bioinformatics

Overexpression of Kinesin Associated Protein 3 (KIFAP3) in Breast Cancer

Gene expression profiling studies on breast cancer have generated catalogs of differentially expressed genes. However, many of these genes have not been investigated for their expression at the protein level. It is possible to systematically evaluate such genes in a high-throughput fashion for their overexpression at the protein level using breast cancer tissue microarrays. Our strategy involved integration of information from publicly available repositories of gene expression to prepare a list of genes upregulated at the mRNA level in breast cancer followed by curation of the published literature to identify those genes that were not tested for overexpression at the protein level. We identified Kinesin Associated Protein 3 (KIFAP3) as one such molecule for further validation at the protein level. Immunohistochemical labeling of KIFAP3 using breast cancer tissue microarrays revealed overexpression of KIFAP3 protein in 84% (240/285) of breast cancers indicating the utility of our integrated approach of combining computational analysis with experimental biology.

Protein disulfide isomerase in redox cell signaling and homeostasis

Thiol proteins may potentially act as redox signaling adaptor proteins, adjusting reactive oxygen species intermediates to specific signals and redox signals to cell homeostasis. In this review, we discuss redox effects of protein disulfide isomerase (PDI), a thioredoxin superfamily oxidoreductase from the endoplasmic reticulum (ER). Abundantly expressed PDI displays ubiquity, interactions with redox and nonredox proteins, versatile effects, and several posttranslational modifications. The PDI family contains >20 members with at least some apparent complementary actions. PDI has oxidoreductase, isomerase, and chaperone effects, the last not directly dependent on its thiols. PDI is a converging hub for pathways of disulfide bond introduction into ER-processed proteins, via hydrogen peroxide-generating mechanisms involving the oxidase Ero1α, as well as hydrogen peroxide-consuming reactions involving peroxiredoxin IV and the novel peroxidases Gpx7/8. PDI is a candidate pathway for coupling ER stress to oxidant generation. Emerging information suggests a convergence between PDI and Nox family NADPH oxidases. PDI silencing prevents Nox responses to angiotensin II and inhibits Akt phosphorylation in vascular cells and parasite phagocytosis in macrophages. PDI overexpression spontaneously enhances Nox activation and expression. In neutrophils, PDI redox-dependently associates with p47phox and supports the respiratory burst. At the cell surface, PDI exerts transnitrosation, thiol reductase, and apparent isomerase activities toward targets including adhesion and matrix proteins and proteases. Such effects mediate redox-dependent adhesion, coagulation/thrombosis, immune functions, and virus internalization. The route of PDI externalization remains elusive. Such multiple redox effects of PDI may contribute to its conspicuous expression and functional role in disease, rendering PDI family members putative redox cell signaling adaptors.

Identification of molecular target proteins in berberine-treated cervix adenocarcinoma HeLa cells by proteomic and bioinformatic analyses

In this study, the apoptosis of HeLa cells induced by berberine was investigated. Fifty-one differentially expressed proteins were identified before and after berberine treatment by a proteomic method, which either interacted with each other directly or through one intermediate protein to form a connected protein interaction sub-network. Nine of them were selected and validated. Compared with the cells in the control group, the expressions of 14-3-3σ and lamin-A/C of the cells treated by berberine for 48 h increased by 94.12 and 5.24 times, respectively, and the expressions of annexin A5, cytokeratin 17, prohibitin, heat shock cognate 71 kDa protein (HSPA8), programmed cell death 6 and vimentin decreased by 4.1, 1.34, 23.8, 11.85, 4.63 and 5.24 times, respectively. In addition, tubulin-β decreased from 9537 to 6908 ng/L. Furthermore, the inverse dock program (INVDOCK) was used to predict the possible drug-target of berberine's anticancer activity, and the results showed that HSPA8 and annexin A5 might be the drug targets. This study suggests that the anticancer effect of berberine on HeLa cells was a complex process based on affecting multiple protein expression and acting on an interaction network. Our work could be helpful to elucidate the mechanism of berberine's anticancer activity on HeLa cells.

Resequencing and association analysis of the KALRN and EPHB1 genes and their contribution to schizophrenia susceptibility

BACKGROUND

Our genome-wide association study of schizophrenia found association signals at the Kalirin gene (KALRN) and EPH receptor B1 gene (EPHB1) in a Japanese population. The importance of these synaptogenic pathway genes in schizophrenia is gaining independent supports. Although there has been growing interest in rare (<1%) missense mutations as potential contributors to the unexplained heritability of schizophrenia, there are no population-based studies targeting rare (<1%) coding mutations with a larger effect size (eg, OR >1.5) in KALRN or EPHB1.

METHODS AND RESULTS

The present study design consisted of 3 phases. At the discovery phase, we conducted resequencing analyses for all exon regions of KALRN and EPHB1 using a DNA microarray-based method. Seventeen rare (<1%) missense mutations were discovered in the first sample set (320 schizophrenic patients). After the prioritization phase based on frequencies in the second sample set (729 cases and 562 controls), we performed association analyses for each selected mutation using the third sample set (1511 cases and 1517 controls), along with a combined association analysis across all selected mutations. In KALRN, we detected a significant association between schizophrenia and P2255T (OR = 2.09, corrected P = .048, 1 tailed); this was supported in the combined association analysis (OR = 2.07, corrected P = .006, 1 tailed). We found no evidence of association of EPHB1 with schizophrenia. In silico analysis indicated the functional relevance of these rare missense mutations.

CONCLUSION

We provide evidence that multiple rare (<1%) missense mutations in KALRN may be genetic risk factors for schizophrenia.

Networks of ProteinProtein Interactions: From Uncertainty to Molecular Details

Proteins are the bricks and mortar of cells. The work of proteins is structural and functional, as they are the principal element of the organization of the cell architecture, but they also play a relevant role in its metabolism and regulation. To perform all these functions, proteins need to interact with each other and with other bio-molecules, either to form complexes or to recognize precise targets of their action. For instance, a particular transcription factor may activate one gene or another depending on its interactions with other proteins and not only with DNA. Hence, the ability of a protein to interact with other bio-molecules, and the partners they have at each particular time and location can be crucial to characterize the role of a protein. Proteins rarely act alone; they rather constitute a mingled network of physical interactions or other types of relationships (such as metabolic and regulatory) or signaling cascades. In this context, understanding the function of a protein implies to recognize the members of its neighborhood and to grasp how they associate, both at the systemic and atomic level. The network of physical interactions between the proteins of a system, cell or organism, is defined as the interactome. The purpose of this review is to deepen the description of interactomes at different levels of detail: from the molecular structure of complexes to the global topology of the network of interactions. The approaches and techniques applied experimentally and computationally to attain each level are depicted. The limits of each technique and its integration into a model network, the challenges and actual problems of completeness of an interactome, and the reliability of the interactions are reviewed and summarized. Finally, the application of the current knowledge of protein-protein interactions on modern network medicine and protein function annotation is also explored.

Molecular biology techniques for the surgeon

New technologies are constantly being introduced into the medical and surgical fields. These technologies come in the form of newer medicines, imaging methods and prognostic tools, among others, and allow clinicians to make more rational and informed decisions on the care of their patients. Many of these technologies utilize advanced techniques which are at the forefront of many research fields and represent a transition of bench advances into the clinical realm. This review will highlight four technologies that are at the forefront in the treatment of oncology patients treated by surgeons on a daily basis. Circulating tumor cells, microarray analysis, proteomic studies and rapid sequencing technologies will be highlighted. These technologies will be reviewed and their potential use in the care of surgical patients will be discussed.

An overview of esophageal squamous cell carcinoma proteomics

Esophageal squamous cell carcinoma (ESCC) still remains the leading cancer-caused mortality in northern China, in particular in areas nearby Taihang Mountain. Late-stage diagnosis of ESCC increases the mortality and morbidity of ESCC. Therefore, it is imperative to identify biomarkers for early diagnosis, monitoring of tumor progression and identifying potential therapeutic targets of ESCC. Proteomics provides a functional translation of the genome and represents a richer source for the functional description of diseases and biomarkers implicated in cancer. In this review, we discuss the dysregulated proteins associated with ESCC identified by proteomic approaches and aim to enhance our understanding of molecular mechanisms implicated in ESCC development and progression from a proteomics perspective and discuss the potential biomarkers of ESCC as well.

A critical evaluation of network and pathway-based classifiers for outcome prediction in breast cancer

Recently, several classifiers that combine primary tumor data, like gene expression data, and secondary data sources, such as protein-protein interaction networks, have been proposed for predicting outcome in breast cancer. In these approaches, new composite features are typically constructed by aggregating the expression levels of several genes. The secondary data sources are employed to guide this aggregation. Although many studies claim that these approaches improve classification performance over single genes classifiers, the gain in performance is difficult to assess. This stems mainly from the fact that different breast cancer data sets and validation procedures are employed to assess the performance. Here we address these issues by employing a large cohort of six breast cancer data sets as benchmark set and by performing an unbiased evaluation of the classification accuracies of the different approaches. Contrary to previous claims, we find that composite feature classifiers do not outperform simple single genes classifiers. We investigate the effect of (1) the number of selected features; (2) the specific gene set from which features are selected; (3) the size of the training set and (4) the heterogeneity of the data set on the performance of composite feature and single genes classifiers. Strikingly, we find that randomization of secondary data sources, which destroys all biological information in these sources, does not result in a deterioration in performance of composite feature classifiers. Finally, we show that when a proper correction for gene set size is performed, the stability of single genes sets is similar to the stability of composite feature sets. Based on these results there is currently no reason to prefer prognostic classifiers based on composite features over single genes classifiers for predicting outcome in breast cancer.

Linking proteins to signaling pathways for experiment design and evaluation

Biomedical experimental work often focuses on altering the functions of selected proteins. These changes can hit signaling pathways, and can therefore unexpectedly and non-specifically affect cellular processes. We propose PathwayLinker, an online tool that can provide a first estimate of the possible signaling effects of such changes, e.g., drug or microRNA treatments. PathwayLinker minimizes the users' efforts by integrating protein-protein interaction and signaling pathway data from several sources with statistical significance tests and clear visualization. We demonstrate through three case studies that the developed tool can point out unexpected signaling bias in normal laboratory experiments and identify likely novel signaling proteins among the interactors of known drug targets. In our first case study we show that knockdown of the Caenorhabditis elegans gene cdc-25.1 (meant to avoid progeny) may globally affect the signaling system and unexpectedly bias experiments. In the second case study we evaluate the loss-of-function phenotypes of a less known C. elegans gene to predict its function. In the third case study we analyze GJA1, an anti-cancer drug target protein in human, and predict for this protein novel signaling pathway memberships, which may be sources of side effects. Compared to similar services, a major advantage of PathwayLinker is that it drastically reduces the necessary amount of manual literature searches and can be used without a computational background. PathwayLinker is available at http://PathwayLinker.org. Detailed documentation and source code are available at the website.

Caveolin-1, caveolae, and glioblastoma

Glioblastoma multiforme (GBM) is the most common malignant brain tumor and is characterized by high invasiveness, poor prognosis, and limited therapeutic options. Biochemical and morphological experiments have shown the presence of caveolae in glioblastoma cells. Caveolae are flask-shaped plasma membrane subdomains that play trafficking, mechanosensing, and signaling roles. Caveolin-1 is a membrane protein that participates in the formation of caveolae and binds a multitude of signaling proteins, compartmentalizing them in caveolae and often directly regulating their activity via binding to its scaffolding domain. Caveolin-1 has been proposed to behave either as a tumor suppressor or as an ongogene depending on the tumor type and progress. This review discusses the existing information on the expression and function of caveolin-1 and caveolae in GBM and the role of this organelle and its defining protein on cellular signaling, growth, and invasiveness of GBM. We further analyze the available data suggesting caveolin-1 could be a target in GBM therapy.

Protein-protein interaction network-based detection of functionally similar proteins within species

Although functionally similar proteins across species have been widely studied, functionally similar proteins within species showing low sequence similarity have not been examined in detail. Identification of these proteins is of significant importance for understanding biological functions, evolution of protein families, progression of co-evolution, and convergent evolution and others which cannot be obtained by detection of functionally similar proteins across species. Here, we explored a method of detecting functionally similar proteins within species based on graph theory. After denoting protein-protein interaction networks using graphs, we split the graphs into subgraphs using the 1-hop method. Proteins with functional similarities in a species were detected using a method of modified shortest path to compare these subgraphs and to find the eligible optimal results. Using seven protein-protein interaction networks and this method, some functionally similar proteins with low sequence similarity that cannot detected by sequence alignment were identified. By analyzing the results, we found that, sometimes, it is difficult to separate homologous from convergent evolution. Evaluation of the performance of our method by gene ontology term overlap showed that the precision of our method was excellent.

Evolutionary conservation and disease gene association of the human genes composing pseudogenes

Pseudogenes, the 'genomic fossils' present portrayal of evolutionary history of human genome. The human genes configuring pseudogenes are also now coming forth as important resources in the study of human protein evolution. In this communication, we explored evolutionary conservation of the genes forming pseudogenes over the genes lacking any pseudogene and delving deeper, we probed an evolutionary rate difference between the disease genes in the two groups. We illustrated this differential evolutionary pattern by gene expressivity, number of regulatory miRNA targeting per gene, abundance of protein complex forming genes and lesser percentage of protein intrinsic disorderness. Furthermore, pseudogenes are observed to harbor sequence variations, over their entirety, those become degenerative disease-causing mutations though the disease involvement of their progenitors is still unexplored. Here, we unveiled an immense association of disease genes in the genes casting pseudogenes in human. We interpreted the issue by disease associated miRNA targeting, genes containing polymorphisms in miRNA target sites, abundance of genes having disease causing non-synonymous mutations, disease gene specific network properties, presence of genes having repeat regions, affluence of dosage sensitive genes and the presence of intrinsically unstructured protein regions.

Chk1 phosphorylates the tumour suppressor Mig-6, regulating the activation of EGF signalling

The tumour suppressor gene product Mig-6 acts as an inhibitor of epidermal growth factor (EGF) signalling. However, its posttranslational modifications and regulatory mechanisms have not been elucidated. Here, we investigated the phosphorylation of human Mig-6 and found that Chk1 phosphorylated Mig-6 in vivo as well as in vitro. Moreover, EGF stimulation promoted phosphorylation of Mig-6 without DNA damage and the phosphorylation was inhibited by depletion of Chk1. EGF also increased Ser280-phosphorylated Chk1, a cytoplasmic-tethering form, via PI3K pathway. Mass spectrometric analyses suggested that Ser 251 of Mig-6 was a major phosphorylation site by Chk1 in vitro and in vivo. Substitution of Ser 251 to alanine increased inhibitory activity of Mig-6 against EGF receptor (EGFR) activation. Moreover, EGF-dependent activation of EGFR and cell growth were inhibited by Chk1 depletion, and were rescued by co-depletion of Mig-6. Our results suggest that Chk1 phosphorylates Mig-6 on Ser 251, resulting in the inhibition of Mig-6, and that Chk1 acts as a positive regulator of EGF signalling. This is a novel function of Chk1.

Beyond hairballs: The use of quantitative mass spectrometry data to understand protein-protein interactions

The past 10 years have witnessed a dramatic proliferation in the availability of protein interaction data. However, for interaction mapping based on affinity purification coupled with mass spectrometry (AP-MS), there is a wealth of information present in the datasets that often goes unrecorded in public repositories, and as such remains largely unexplored. Further, how this type of data is represented and used by bioinformaticians has not been well established. Here, we point out some common mistakes in how AP-MS data are handled, and describe how protein complex organization and interaction dynamics can be inferred using quantitative AP-MS approaches.

Context-specific protein network miner--an online system for exploring context-specific protein interaction networks from the literature

BACKGROUND

Protein interaction networks (PINs) specific within a particular context contain crucial information regarding many cellular biological processes. For example, PINs may include information on the type and directionality of interaction (e.g. phosphorylation), location of interaction (i.e. tissues, cells), and related diseases. Currently, very few tools are capable of deriving context-specific PINs for conducting exploratory analysis.

RESULTS

We developed a literature-based online system, Context-specific Protein Network Miner (CPNM), which derives context-specific PINs in real-time from the PubMed database based on a set of user-input keywords and enhanced PubMed query system. CPNM reports enriched information on protein interactions (with type and directionality), their network topology with summary statistics (e.g. most densely connected proteins in the network; most densely connected protein-pairs; and proteins connected by most inbound/outbound links) that can be explored via a user-friendly interface. Some of the novel features of the CPNM system include PIN generation, ontology-based PubMed query enhancement, real-time, user-queried, up-to-date PubMed document processing, and prediction of PIN directionality.

CONCLUSIONS

CPNM provides a tool for biologists to explore PINs. It is freely accessible at http://www.biotextminer.com/CPNM/.

Inhibition of pyrimidine synthesis reverses viral virulence factor-mediated block of mRNA nuclear export

The NS1 protein of influenza virus is a major virulence factor essential for virus replication, as it redirects the host cell to promote viral protein expression. NS1 inhibits cellular messenger ribonucleic acid (mRNA) processing and export, down-regulating host gene expression and enhancing viral gene expression. We report in this paper the identification of a nontoxic quinoline carboxylic acid that reverts the inhibition of mRNA nuclear export by NS1, in the absence or presence of the virus. This quinoline carboxylic acid directly inhibited dihydroorotate dehydrogenase (DHODH), a host enzyme required for de novo pyrimidine biosynthesis, and partially reduced pyrimidine levels. This effect induced NXF1 expression, which promoted mRNA nuclear export in the presence of NS1. The release of NS1-mediated mRNA export block by DHODH inhibition also occurred in the presence of vesicular stomatitis virus M (matrix) protein, another viral inhibitor of mRNA export. This reversal of mRNA export block allowed expression of antiviral factors. Thus, pyrimidines play a necessary role in the inhibition of mRNA nuclear export by virulence factors.

Identification of colorectal cancer related genes with mRMR and shortest path in protein-protein interaction network

One of the most important and challenging problems in biomedicine and genomics is how to identify the disease genes. In this study, we developed a computational method to identify colorectal cancer-related genes based on (i) the gene expression profiles, and (ii) the shortest path analysis of functional protein association networks. The former has been used to select differentially expressed genes as disease genes for quite a long time, while the latter has been widely used to study the mechanism of diseases. With the existing protein-protein interaction data from STRING (Search Tool for the Retrieval of Interacting Genes), a weighted functional protein association network was constructed. By means of the mRMR (Maximum Relevance Minimum Redundancy) approach, six genes were identified that can distinguish the colorectal tumors and normal adjacent colonic tissues from their gene expression profiles. Meanwhile, according to the shortest path approach, we further found an additional 35 genes, of which some have been reported to be relevant to colorectal cancer and some are very likely to be relevant to it. Interestingly, the genes we identified from both the gene expression profiles and the functional protein association network have more cancer genes than the genes identified from the gene expression profiles alone. Besides, these genes also had greater functional similarity with the reported colorectal cancer genes than the genes identified from the gene expression profiles alone. All these indicate that our method as presented in this paper is quite promising. The method may become a useful tool, or at least plays a complementary role to the existing method, for identifying colorectal cancer genes. It has not escaped our notice that the method can be applied to identify the genes of other diseases as well.

Phosphoproteomic studies of receptor tyrosine kinases: future perspectives

In the last decade, large-scale mass spectrometry-based phosphoproteomic studies of receptor tyrosine kinases (RTKs) have generated a compendium of signalling networks that are activated downstream of these receptors. In this article, a brief summary of previous phosphoproteomic studies on epidermal growth factor receptor (EGFR) signalling will be presented together with a perspective on the importance for the field to keep pace with new advances in RTK biology. Using examples drawn primarily from studies on the EGFR, c-Met and Flt3 receptors, areas in RTK biology which will greatly benefit from the power of phosphoproteomics will be discussed, including (a) validating oncogenic RTK mutants identified in cancer genome sequencing efforts, (b) spatial RTK signalling networks and (c) understanding crosstalk and co-activation between members of the RTK superfamily.

A systematic characterization of genes underlying both complex and Mendelian diseases

Traditionally, genetic disorders have been classified as either Mendelian diseases or complex diseases. This nosology has greatly benefited genetic counseling and the development of gene mapping strategies. However, based on two well-established databases, we identified that 54% (524 of 968) of the Mendelian disease genes were also involved in complex diseases, and this kind of genes has not been systematically analyzed. Here, we classified human genes into five categories: Mendelian and complex disease (MC) genes, Mendelian but not complex disease (MNC) genes, complex but not Mendelian disease (CNM) genes, essential genes and OTHER genes. First, we found that MC genes were associated with more diseases and phenotypes, and were involved in more complex protein-protein interaction network than MNC or CNM genes on average. Secondly, MC genes encoded the longest proteins and had the highest transcript count among all gene categories. Especially, tissue specificity of MC genes was much higher than that of any other gene categories (P < 7.5 × 10(-5)), although their expression level was similar to that of essential genes. Thirdly, evidences from different aspects supported that MC genes have been subjected to both purifying and positive selection. Interestingly, functions of some human disease genes might be different from those of their orthologous genes in non-primate mammalians since they were even less conserved than OTHER genes. The significant over-representation of copy number variations (CNVs) in CNM genes suggested the important roles of CNVs in complex diseases. In brief, our study not only revealed the characteristics of MC genes, but also provided new insights into the other four gene categories.

Improving biomarker list stability by integration of biological knowledge in the learning process

BACKGROUND

The identification of robust lists of molecular biomarkers related to a disease is a fundamental step for early diagnosis and treatment. However, methodologies for biomarker discovery using microarray data often provide results with limited overlap. It has been suggested that one reason for these inconsistencies may be that in complex diseases, such as cancer, multiple genes belonging to one or more physiological pathways are associated with the outcomes. Thus, a possible approach to improve list stability is to integrate biological information from genomic databases in the learning process; however, a comprehensive assessment based on different types of biological information is still lacking in the literature. In this work we have compared the effect of using different biological information in the learning process like functional annotations, protein-protein interactions and expression correlation among genes.

RESULTS

Biological knowledge has been codified by means of gene similarity matrices and expression data linearly transformed in such a way that the more similar two features are, the more closely they are mapped. Two semantic similarity matrices, based on Biological Process and Molecular Function Gene Ontology annotation, and geodesic distance applied on protein-protein interaction networks, are the best performers in improving list stability maintaining almost equal prediction accuracy.

CONCLUSIONS

The performed analysis supports the idea that when some features are strongly correlated to each other, for example because are close in the protein-protein interaction network, then they might have similar importance and are equally relevant for the task at hand. Obtained results can be a starting point for additional experiments on combining similarity matrices in order to obtain even more stable lists of biomarkers. The implementation of the classification algorithm is available at the link: http://www.math.unipd.it/~dasan/biomarkers.html.

A multilevel layout algorithm for visualizing physical and genetic interaction networks, with emphasis on their modular organization

BACKGROUND

Graph drawing is an integral part of many systems biology studies, enabling visual exploration and mining of large-scale biological networks. While a number of layout algorithms are available in popular network analysis platforms, such as Cytoscape, it remains poorly understood how well their solutions reflect the underlying biological processes that give rise to the network connectivity structure. Moreover, visualizations obtained using conventional layout algorithms, such as those based on the force-directed drawing approach, may become uninformative when applied to larger networks with dense or clustered connectivity structure.

METHODS

We implemented a modified layout plug-in, named Multilevel Layout, which applies the conventional layout algorithms within a multilevel optimization framework to better capture the hierarchical modularity of many biological networks. Using a wide variety of real life biological networks, we carried out a systematic evaluation of the method in comparison with other layout algorithms in Cytoscape.

RESULTS

The multilevel approach provided both biologically relevant and visually pleasant layout solutions in most network types, hence complementing the layout options available in Cytoscape. In particular, it could improve drawing of large-scale networks of yeast genetic interactions and human physical interactions. In more general terms, the biological evaluation framework developed here enables one to assess the layout solutions from any existing or future graph drawing algorithm as well as to optimize their performance for a given network type or structure.

CONCLUSIONS

By making use of the multilevel modular organization when visualizing biological networks, together with the biological evaluation of the layout solutions, one can generate convenient visualizations for many network biology applications.

PTRF/cavin-1 and MIF proteins are identified as non-small cell lung cancer biomarkers by label-free proteomics

With the completion of the human genome sequence, biomedical sciences have entered in the “omics” era, mainly due to high-throughput genomics techniques and the recent application of mass spectrometry to proteomics analyses. However, there is still a time lag between these technological advances and their application in the clinical setting. Our work is designed to build bridges between high-performance proteomics and clinical routine. Protein extracts were obtained from fresh frozen normal lung and non-small cell lung cancer samples. We applied a phosphopeptide enrichment followed by LC-MS/MS. Subsequent label-free quantification and bioinformatics analyses were performed. We assessed protein patterns on these samples, showing dozens of differential markers between normal and tumor tissue. Gene ontology and interactome analyses identified signaling pathways altered on tumor tissue. We have identified two proteins, PTRF/cavin-1 and MIF, which are differentially expressed between normal lung and non-small cell lung cancer. These potential biomarkers were validated using western blot and immunohistochemistry. The application of discovery-based proteomics analyses in clinical samples allowed us to identify new potential biomarkers and therapeutic targets in non-small cell lung cancer.

Interactomic and pharmacological insights on human sirt-1

Sirt-1 is defined as a nuclear protein involved in the molecular mechanisms of inflammation and neurodegeneration through the de-acetylation of many different substrates even if experimental data in mouse suggest both its cytoplasmatic presence and nucleo-cytoplasmic shuttling upon oxidative stress. Since the experimental structure of human Sirt-1 has not yet been reported, we have modeled its 3D structure, highlighted that it is composed by four different structural regions: N-terminal region, allosteric site, catalytic core and C-terminal region, and underlined that the two terminal regions have high intrinsic disorder propensity and numerous putative phosphorylation sites. Many different papers report experimental studies related to its functional activators because Sirt-1 is implicated in various diseases and cancers. The aim of this article is (i) to present interactomic studies based human Sirt-1 to understand its most important functional relationships in the light of the gene–protein interactions that control major metabolic pathways and (ii) to show by docking studies how this protein binds some activator molecules in order to evidence structural determinants, physico-chemical features and those residues involved in the formation of complexes.

FUSE: a profit maximization approach for functional summarization of biological networks

BACKGROUND

The availability of large-scale curated protein interaction datasets has given rise to the opportunity to investigate higher level organization and modularity within the protein interaction network (PPI) using graph theoretic analysis. Despite the recent progress, systems level analysis of PPIS remains a daunting task as it is challenging to make sense out of the deluge of high-dimensional interaction data. Specifically, techniques that automatically abstract and summarize PPIS at multiple resolutions to provide high level views of its functional landscape are still lacking. We present a novel data-driven and generic algorithm called FUSE (Functional Summary Generator) that generates functional maps of a PPI at different levels of organization, from broad process-process level interactions to in-depth complex-complex level interactions, through a pro t maximization approach that exploits Minimum Description Length (MDL) principle to maximize information gain of the summary graph while satisfying the level of detail constraint.

RESULTS

We evaluate the performance of FUSE on several real-world PPIS. We also compare FUSE to state-of-the-art graph clustering methods with GO term enrichment by constructing the biological process landscape of the PPIS. Using AD network as our case study, we further demonstrate the ability of FUSE to quickly summarize the network and identify many different processes and complexes that regulate it. Finally, we study the higher-order connectivity of the human PPI.

CONCLUSION

By simultaneously evaluating interaction and annotation data, FUSE abstracts higher-order interaction maps by reducing the details of the underlying PPI to form a functional summary graph of interconnected functional clusters. Our results demonstrate its effectiveness and superiority over state-of-the-art graph clustering methods with GO term enrichment.

High accordance in prognosis prediction of colorectal cancer across independent datasets by multi-gene module expression profiles

A considerable portion of patients with colorectal cancer have a high risk of disease recurrence after surgery. These patients can be identified by analyzing the expression profiles of signature genes in tumors. But there is no consensus on which genes should be used and the performance of specific set of signature genes varies greatly with different datasets, impeding their implementation in the routine clinical application. Instead of using individual genes, here we identified functional multi-gene modules with significant expression changes between recurrent and recurrence-free tumors, used them as the signatures for predicting colorectal cancer recurrence in multiple datasets that were collected independently and profiled on different microarray platforms. The multi-gene modules we identified have a significant enrichment of known genes and biological processes relevant to cancer development, including genes from the chemokine pathway. Most strikingly, they recruited a significant enrichment of somatic mutations found in colorectal cancer. These results confirmed the functional relevance of these modules for colorectal cancer development. Further, these functional modules from different datasets overlapped significantly. Finally, we demonstrated that, leveraging above information of these modules, our module based classifier avoided arbitrary fitting the classifier function and screening the signatures using the training data, and achieved more consistency in prognosis prediction across three independent datasets, which holds even using very small training sets of tumors.

Systematic identification of common functional modules related to heart failure with different etiologies

The development of heart failure (HF) is a complex process that can be initiated by multiple etiologies. Identifying common functional modules associated with HF is a challenging task. Here, we developed a systems method to identify these common functional modules by integrating multiple expression profiles, protein interactions from four species, gene function annotations, and text information. We identified 1439 consistently differentially expressed genes (CDEGs) across HF with different etiologies by applying three meta-analysis methods to multiple HF-related expression profiles. Using a weighted human interaction network constructed by combining interaction data from multiple species, we extracted 60 candidate CDEG modules. We further evaluated the functional relevance of each module by using expression, interaction network, functional annotations, and text information together. Finally, five functional modules with significant biological relevance were identified. We found that almost half of the genes in these modules are hubs in the weighted network, and that these modules can accurately classify HF patients from healthy subjects. We also identified many significantly enriched biological processes that contribute to the pathophysiology of HF, including two new ones, RNA splicing and vesicle-mediated protein transport. In summary, we proposed a novel framework to analyze common functional modules related to HF with different etiologies. Our findings provide important insights into the complex mechanism of HF. Further biological experimentations should be required to validate these novel biological processes.

Predicting the lethal phenotype of the knockout mouse by integrating comprehensive genomic data

MOTIVATION

The phenotypes of knockout mice provide crucial information for understanding the biological functions of mammalian genes. Among various knockout phenotypes, lethality is of great interest because those involved genes play essential roles. With the availability of large-scale genomic data, we aimed to assess how well the integration of various genomic features can predict the lethal phenotype of single-gene knockout mice.

RESULTS

We first assembled a comprehensive list of 491 candidate genomic features derived from diverse data sources. Using mouse genes with a known phenotype as the training set, we integrated the informative genomic features to predict the knockout lethality through three machine learning methods. Based on cross-validation, our models could achieve a good performance (accuracy = 73% and recall = 63%). Our results serve as a valuable practical resource in the mouse genetics research community, and also accelerate the translation of the knowledge of mouse genes into better strategies for studying human disease.