Functional Protein Interaction Networks Research Articles

Downregulation of coding transmembrane protein 35 gene inhibits cell proliferation, migration and cell cycle arrest in osteosarcoma cells.

Osteosarcoma (OSA) is the most common primary tumor of the bone. Resistance to chemotherapy and the fast rapid development of metastatic lesions are major issues responsible for treatment failure and poor survival rates in OSA patients. Tetraspanins comprise a family of transmembrane receptor glycoproteins that affect tumor cell migration through tetraspanin-integrin interaction. The present study focused on a four-pass transmembrane protein gene, transmembrane protein 35 (TMEM35) gene, and examined its role in the growth, migration and cell cycle progression of OSA cells. In addition, the study discussed whether the TMEM35 gene, which encodes the TMEM35 protein, may be a potential therapeutic target for OSA. In the current study, reverse transcription-quantitative polymerase chain reaction was performed to examine TMEM35 expression in OSA and matched healthy tissues. Small interfering RNAs (siRNAs) were transfected into SaOS2 and U2OS cells to knockdown the TMEM35 expression. Soft-agar colony formation assay was performed to evaluate cell growth, and cell cycle progression was analyzed by flow cytometry. Wound-healing and Boyden chamber assays were also performed to investigate cell invasion and migration by the SaOS2 and U2OS cells. TMEM35 protein was analyzed in a functional protein interaction networks database (STRING database) to predict the functional interaction partner proteins of TMEM35. The results indicated that TMEM35 was abnormally expressed in OSA tissues. Of the 37 examined patients, TMEM35 expression was significantly increased in the OSA tissues of 24 patients (64.86%; P<0.05), when compared with the expression in normal tissues. Furthermore, TMEM35 knockdown following transfection with siRNAs inhibited the colony formation ability of SaOS2 and U2OS cells in soft agar. Flow cytometric analysis also revealed that TMEM35 knockdown by RNA interference may result in G1 phase arrest and a decreased cell population at the S phase. TMEM35 knockdown inhibited cell migration in SaOS2 and U2OS cells in wound-healing assays. In conclusion, TMEM35, a member of the tetraspanin family, serves an important role in the growth of OSA cells.

Berberine induces neuronal differentiation through inhibition of cancer stemness and epithelial-mesenchymal transition in neuroblastoma cells

BackgroundBerberine, a plant alkaloid, has been used since many years for treatment of gastrointestinal disorders. It also shows promising medicinal use against metabolic disorders, neurodegenerative disorders and cancer; however its efficacy in neuroblastoma (NB) is poorly explored. HypothesisEMT is important in cancer stemness and metastasis resulting in failure to differentiate; thus targeting EMT and related pathways can have clinical benefits. Study designPotential of berberine was investigated for (i) neuronal differentiation and cancer stemness inhibition, (ii) underlying molecular mechanisms regulating cancer-stemness and (iii) EMT reversal. MethodsUsing neuro2a (N2a) neuroblastoma cells (NB); we investigated effect of berberine on neuronal differentiation, cancer-stemness, EMT and underlying signalling by immunofluorescence, RT-PCR, Western blot. High glucose-induced TGF-β mediated EMT model was used to test EMT reversal potential by Western blot and RT-PCR. STRING analysis was done to determine and validate functional protein-interaction networks. ResultsWe demonstrate berberine induces neuronal differentiation accompanying increased neuronal differentiation markers like MAP2, β-III tubulin and NCAM; generated neurons were viable. Berberine attenuated cancer stemness markers CD133, β-catenin, n-myc, sox2, notch2 and nestin. Berberine potentiated G0/G1 cell cycle arrest by inhibiting proliferation, cyclin dependent kinases and cyclins resulting in apoptosis through increased bax/bcl-2 ratio. Restoration of tumor suppressor proteins, p27 and p53, indicate promising anti-cancer property. The induction of NCAM and reduction in its polysialylation indicates anti-migratory potential which is supported by down regulation of MMP-2/9. It increased epithelial marker laminin and smad and increased Hsp70 levels also suggest its protective role. Molecular insights revealed that berberine regulates EMT via downregulation of PI3/Akt and Ras-Raf-ERK signalling and subsequent upregulation of p38-MAPK. TGF-β secretion from N2a cells was potentiated by high glucose and negatively regulated by berberine through modulation of TGF-β receptors II and III. Berberine reverted mesenchymal markers, vimentin and fibronectin, with restoration of epithelial marker E-cadherin, highlighting the role of berberine in reversal of EMT. ConclusionCollectively, the study demonstrates prospective use of berberine against neuroblastoma as elucidated through inhibition of fundamental characteristics of cancer stem cells: tumorigenicity and failure to differentiation and instigates reversal in the EMT.

CisPath: an R/Bioconductor package for cloud users for visualization and management of functional protein interaction networks.

BackgroundWith the burgeoning development of cloud technology and services, there are an increasing number of users who prefer cloud to run their applications. All software and associated data are hosted on the cloud, allowing users to access them via a web browser from any computer, anywhere. This paper presents cisPath, an R/Bioconductor package deployed on cloud servers for client users to visualize, manage, and share functional protein interaction networks.ResultsWith this R package, users can easily integrate downloaded protein-protein interaction information from different online databases with private data to construct new and personalized interaction networks. Additional functions allow users to generate specific networks based on private databases. Since the results produced with the use of this package are in the form of web pages, cloud users can easily view and edit the network graphs via the browser, using a mouse or touch screen, without the need to download them to a local computer. This package can also be installed and run on a local desktop computer. Depending on user preference, results can be publicized or shared by uploading to a web server or cloud driver, allowing other users to directly access results via a web browser.ConclusionsThis package can be installed and run on a variety of platforms. Since all network views are shown in web pages, such package is particularly useful for cloud users. The easy installation and operation is an attractive quality for R beginners and users with no previous experience with cloud services.

Alkylation Damage by Lipid Electrophiles Targets Functional Protein Systems

Protein alkylation by reactive electrophiles contributes to chemical toxicities and oxidative stress, but the functional impact of alkylation damage across proteomes is poorly understood. We used Click chemistry and shotgun proteomics to profile the accumulation of proteome damage in human cells treated with lipid electrophile probes. Protein target profiles revealed three damage susceptibility classes, as well as proteins that were highly resistant to alkylation. Damage occurred selectively across functional protein interaction networks, with the most highly alkylation-susceptible proteins mapping to networks involved in cytoskeletal regulation. Proteins with lower damage susceptibility mapped to networks involved in protein synthesis and turnover and were alkylated only at electrophile concentrations that caused significant toxicity. Hierarchical susceptibility of proteome systems to alkylation may allow cells to survive sublethal damage while protecting critical cell functions.

Protein interaction networks as metric spaces: a novel perspective on distribution of hubs

BackgroundIn the post-genomic era, a central and overarching question in the analysis of protein-protein interaction networks continues to be whether biological characteristics and functions of proteins such as lethality, physiological malfunctions and malignancy are intimately linked to the topological role proteins play in the network as a mathematical structure. One of the key features that have implicitly been presumed is the existence of hubs, highly connected proteins considered to play a crucial role in biological networks. We explore the structure of protein interaction networks of a number of organisms as metric spaces and show that hubs are non randomly positioned and, from a distance point of view, centrally located.ResultsBy analysing how the human functional protein interaction network, the human signalling network, Saccharomyces cerevisiae, Arabidopsis thaliana and Escherichia coli protein-protein interaction networks from various databases are distributed as metric spaces, we found that proteins interact radially through a central node, high degree proteins coagulate in the centre of the network, and those far away from the centre have low degree. We further found that the distribution of proteins from the centre is in some hierarchy of importance and has biological significance.ConclusionsWe conclude that structurally, protein interaction networks are mathematical entities that share properties between organisms but not necessarily with other networks that follow power-law. We therefore conclude that (i) if there are hubs defined by degree, they are not distributed randomly; (ii) zones closest to the centre of the network are enriched for critically important proteins and are also functionally very specialised for specific 'house keeping’ functions; (iii) proteins closest to the network centre are functionally less dispensable and may present good targets for therapy development; and (iv) network biology requires its own network theory modelled on actual biological evidence and that simply adopting theories from the social sciences may be misleading.

The cell morphogenesis ANGUSTIFOLIA (AN) gene, a plant homolog of CtBP/BARS, is involved in abiotic and biotic stress response in higher plants

BackgroundANGUSTIFOLIA (AN), one of the CtBP family proteins, plays a major role in microtubule-dependent cell morphogenesis. Microarray analysis of mammalian AN homologs suggests that AN might function as a transcriptional activator and regulator of a wide range of genes. Genetic characterization of AN mutants suggests that AN might be involved in multiple biological processes beyond cell morphology regulation.ResultsUsing a reverse genetic approach, we provide in this paper the genetic, biochemical, and physiological evidence for ANGUSTIFOLIA’s role in other new biological functions such as abiotic and biotic stress response in higher plants. The T-DNA knockout an-t1 mutant exhibits not only all the phenotypes of previously described angustifolia null mutants, but also copes better than wild type under dehydration and pathogen attack. The stress tolerance is accompanied by a steady-state modulation of cellular H2O2 content, malondialdehyde (MDA) derived from cellular lipid peroxidation, and over-expression of stress responsive genes. Our results indicate that ANGUSTIFOLIA functions beyond cell morphology control through direct or indirect functional protein interaction networks mediating other biological processes such as drought and pathogen attacks.ConclusionsOur results indicate that the ANGUSTIFOLIA gene participates in several biochemical pathways controlling cell morphogenesis, abiotic, and biotic stress responses in higher plants. Our results suggest that the in vivo function of plant ANGUSTIFOLIA has been overlooked and it needs to be further studied beyond microtubule-dependent cell morphogenesis.

Graphical Identification of Cancer-Associated Gene Subnetworks Based on Small Proteomics Data Sets

Proteomics is a rapidly emerging frontier in post-genomics medicine and biology, but the quantitative analysis and validation of proteomic data are in need of further improvements. Before selecting potential candidate proteomic biomarkers, it is important to understand the broader context of how biological processes are regulated under different conditions or in different phenotypes. The enrichment of proteomic data consists of extracting as much biological meaning as possible from curated, pathway-based, functional protein interaction networks. Currently, most of the enrichment tools are intended for microarray data and require parametric data, whereas proteomic data are often nonparametric. In this study, we aimed to select a suite of interactive tools that can enrich proteomic results with a graphical overview. This facilitated diagnosis and interpretation prior to further analysis. From a list of proteins, a network was constructed using a map of the most severely disrupted biological process, and the disease entity was then identified on the basis of clinical data. Taken together, this graphical and interactive method ranks potential proteins via functional analysis in order to improve the choice of biomarkers for validation with the following advantages: 1) It adds neighbor proteins that are not selected by mass spectrometry analysis, but could in fact be key proteins; 2) pinpoints the biological process most often involved; and 3) predicts the most likely disease on the basis of clinical data.

Proteomic Profiling of Bronchoalveolar Lavage Fluid in Critically Ill Patients with Ventilator-Associated Pneumonia

RationaleVentilator-associated pneumonia (VAP) is a common complication in patients with acute lung injury (ALI) and can lead to increased morbidity and mortality. Identifying protein profiles specific to VAP in bronchoalveolar lavage fluid (BALF) may aid in earlier diagnosis, elucidate mechanisms of disease, and identify putative targets for therapeutic intervention.MethodsBALF was obtained from 5 normal subjects and 30 ALI patients: 14 with VAP (VAP+) and 16 without VAP (VAP–). Each sample underwent shotgun proteomic analysis based on tandem mass spectrometry. Differentially expressed proteins between the groups were identified using statistical methods based on spectral counting. Mechanisms of disease were explored using functional annotation and protein interaction network analysis. Supervised classification algorithms were implemented to discover a proteomic classifier for identifying critically ill patients with VAP.ResultsALI patients had distinct BALF proteomic profiles compared to normal controls. Within the ALI group, we identified 76 differentially expressed proteins between VAP+ and VAP–. Functional analysis of these proteins suggested activation of pro-inflammatory pathways during VAP. We identified and validated a limited proteomic signature that discriminated VAP+ from VAP– patients comprised of three proteins: S100A8, lactotransferrin (LTF), and actinin 1 (ACTN1).ConclusionsCombining proteomic with computational analyses is a powerful approach to study the BALF proteome during lung injury and development of VAP. This integrative methodology is a promising strategy to differentiate clinically relevant subsets of ALI patients, including those suffering from VAP.

Fetal metabolic programming and epigenetic modifications: a systems biology approach

A growing body of evidence supports the notion that epigenetic changes such as DNA methylation and histone modifications, both involving chromatin remodeling, contribute to fetal metabolic programming. We use a combination of gene-protein enrichment analysis resources along with functional annotations and protein interaction networks for an integrative approach to understanding the mechanisms underlying fetal metabolic programming. Systems biology approaches suggested that fetal adaptation to an impaired nutritional environment presumes profound changes in gene expression that involve regulation of tissue-specific patterns of methylated cytosine residues, modulation of the histone acetylation-deacetylation switch, cell differentiation, and stem cell pluripotency. The hypothalamus and the liver seem to be differently involved. In addition, new putative explanations have emerged about the question of whether in utero overnutrition modulates fetal metabolic programming in the same fashion as that of a maternal environment of undernutrition, suggesting that the mechanisms behind these two fetal nutritional imbalances are different. In conclusion, intrauterine growth restriction is most likely to be associated with the induction of persistent changes in tissue structure and functionality. Conversely, a maternal obesogenic environment is most probably associated with metabolic reprogramming of glucose and lipid metabolism, as well as future risk of metabolic syndrome (MS), fatty liver, and insulin (INS) resistance.

GLAD4U: deriving and prioritizing gene lists from PubMed literature

BackgroundAnswering questions such as "Which genes are related to breast cancer?" usually requires retrieving relevant publications through the PubMed search engine, reading these publications, and creating gene lists. This process is not only time-consuming, but also prone to errors.ResultsWe report GLAD4U (Gene List Automatically Derived For You), a new, free web-based gene retrieval and prioritization tool. GLAD4U takes advantage of existing resources of the NCBI to ensure computational efficiency. The quality of gene lists created by GLAD4U for three Gene Ontology (GO) terms and three disease terms was assessed using corresponding "gold standard" lists curated in public databases. For all queries, GLAD4U gene lists showed very high recall but low precision, leading to low F-measure. As a comparison, EBIMed's recall was consistently lower than GLAD4U, but its precision was higher. To present the most relevant genes at the top of a list, we studied two prioritization methods based on publication count and the hypergeometric test, and compared the ranked lists and those generated by EBIMed to the gold standards. Both GLAD4U methods outperformed EBIMed for all queries based on a variety of quality metrics. Moreover, the hypergeometric method allowed for a better performance by thresholding genes with low scores. In addition, manual examination suggests that many false-positives could be explained by the incompleteness of the gold standards. The GLAD4U user interface accepts any valid queries for PubMed, and its output page displays the ranked gene list and information associated with each gene, chronologically-ordered supporting publications, along with a summary of the run and links for file export and functional enrichment and protein interaction network analysis.ConclusionsGLAD4U has a high overall recall. Although precision is generally low, the prioritization methods successfully rank truly relevant genes at the top of the lists to facilitate efficient browsing. GLAD4U is simple to use, and its interface can be found at: http://bioinfo.vanderbilt.edu/glad4u.

AtPID: the overall hierarchical functional protein interaction network interface and analytic platform for Arabidopsis

Protein interactions are involved in important cellular functions and biological processes that are the fundamentals of all life activities. With improvements in experimental techniques and progress in research, the overall protein interaction network frameworks of several model organisms have been created through data collection and integration. However, most of the networks processed only show simple relationships without boundary, weight or direction, which do not truly reflect the biological reality. In vivo, different types of protein interactions, such as the assembly of protein complexes or phosphorylation, often have their specific functions and qualifications. Ignorance of these features will bring much bias to the network analysis and application. Therefore, we annotate the Arabidopsis proteins in the AtPID database with further information (e.g. functional annotation, subcellular localization, tissue-specific expression, phosphorylation information, SNP phenotype and mutant phenotype, etc.) and interaction qualifications (e.g. transcriptional regulation, complex assembly, functional collaboration, etc.) via further literature text mining and integration of other resources. Meanwhile, the related information is vividly displayed to users through a comprehensive and newly developed display and analytical tools. The system allows the construction of tissue-specific interaction networks with display of canonical pathways. The latest updated AtPID database is available at http://www.megabionet.org/atpid/.

Functional Features, Biological Pathways, and Protein Interaction Networks of Addiction‐Related Genes

Addictions are chronic and common brain disorders affected by many genetic, environmental, and behavioral factors. Recent genome-wide linkage and association studies have revealed several promising genomic regions and multiple genes relating to addictions. To explore the underlying biological processes in the development of addictions, we used 62 genes recently reviewed by Li and Burmeister (2009) as representative addiction-related genes, and then we investigated their features in gene function, pathways, and protein interaction networks. We performed enrichment tests of their Gene Ontology (GO) annotations and of their pathways in the Ingenuity Pathways Analysis (IPA) system. The tests revealed that these addiction-related genes were highly enriched in neurodevelopment-related processes. Interestingly, we found circadian rhythm signaling in one of the enriched pathways. Moreover, these addiction-related genes tended to have higher connectivity and shorter characteristic shortest-path distances compared to control genes in the protein-protein interaction (PPI) network. This investigation is the first of such kind in addiction studies, and it is useful for further addiction candidate-gene prioritization and verification, thus helping us to better understand molecular mechanisms of addictions.

A human functional protein interaction network and its application to cancer data analysis

A high-quality human functional protein interaction network is constructed. Its utility is demonstrated in the identification of cancer candidate genes.

Modularity and evolutionary constraint on proteins

Modularity, which has been found in the functional and physical protein interaction networks of many organisms, has been postulated to affect both the mode and tempo of evolution. Here I show that in the yeast Saccharomyces cerevisiae, protein interaction hubs situated in single modules are highly constrained, whereas those connecting different modules are more plastic. This pattern of change could reflect a tendency for evolutionary innovations to occur by altering the proteins and interactions between rather than within modules, in a manner somewhat similar to the evolution of new proteins through the shuffling of conserved protein domains.