Defining the E-Cadherin repressor interactome in epithelial-mesenchymal transition : the PMC42 model as a case study

Epithelial-mesenchymal transition (EMT) is a feature of migratory cellular processes in all stages of life, including embryonic development and wound healing. Importantly, EMT features cluster with disease states such as chronic fibrosis and cancer. The dissolution of the E-cadherin-mediated adherens junction (AJ) is a key preliminary step in EMT and may occur early or late in the growing epithelial tumour. This is a first step for tumour cells towards stromal invasion, intravasation, extravasation and distant metastasis. The AJ may be inactivated in EMT by directed E-cadherin cleavage; however, it is increasingly evident that the majority of AJ changes are transcriptional and mediated by an expanding group of transcription factors acting directly or indirectly to repress E-cadherin expression. A review of the current literature has revealed that these factors may regulate each other in a hierarchical pattern where Snail1 (formerly Snail) and Snail2 (formerly Slug) are initially induced, leading to the activation of Zeb family members, TCF3, TCF4, Twist, Goosecoid and FOXC2. Within this general pathway, many inter-regulatory relationships have been defined which may be important in maintaining the EMT phenotype. This may be important given the short half-life of Snail1 protein. We have investigated these inter-regulatory relationships in the mesenchymal breast carcinoma cell line PMC42 (also known as PMC42ET) and its epithelial derivative, PMC42LA. This review also discusses several newly described regulators of E-cadherin repressors including oestrogen receptor-α and new discoveries in hypoxia- and growth factor-induced EMT. Finally, we evaluated how these findings may influence approaches to current cancer treatment.

Exhaustive search of the SNP-sNP interactome identifies epistatic effects on brain volume in two cohorts

The SNP-SNP interactome has rarely been explored in the context of neuroimaging genetics mainly due to the complexity of conducting approximately 10(11) pairwise statistical tests. However, recent advances in machine learning, specifically the iterative sure independence screening (SIS) method, have enabled the analysis of datasets where the number of predictors is much larger than the number of observations. Using an implementation of the SIS algorithm (called EPISIS), we used exhaustive search of the genome-wide, SNP-SNP interactome to identify and prioritize SNPs for interaction analysis. We identified a significant SNP pair, rs1345203 and rs1213205, associated with temporal lobe volume. We further examined the full-brain, voxelwise effects of the interaction in the ADNI dataset and separately in an independent dataset of healthy twins (QTIM). We found that each additional loading in the epistatic effect was associated with approximately 5% greater brain regional brain volume (a protective effect) in both the ADNI and QTIM samples.

A "Candidate-Interactome" Aggregate Analysis of Genome-Wide Association Data in Multiple Sclerosis

Though difficult, the study of gene-environment interactions in multifactorial diseases is crucial for interpreting the relevance of non-heritable factors and prevents from overlooking genetic associations with small but measurable effects. We propose a "candidate interactome" (i.e. a group of genes whose products are known to physically interact with environmental factors that may be relevant for disease pathogenesis) analysis of genome-wide association data in multiple sclerosis. We looked for statistical enrichment of associations among interactomes that, at the current state of knowledge, may be representative of gene-environment interactions of potential, uncertain or unlikely relevance for multiple sclerosis pathogenesis: Epstein-Barr virus, human immunodeficiency virus, hepatitis B virus, hepatitis C virus, cytomegalovirus, HHV8-Kaposi sarcoma, H1N1-influenza, JC virus, human innate immunity interactome for type I interferon, autoimmune regulator, vitamin D receptor, aryl hydrocarbon receptor and a panel of proteins targeted by 70 innate immune-modulating viral open reading frames from 30 viral species. Interactomes were either obtained from the literature or were manually curated. The P values of all single nucleotide polymorphism mapping to a given interactome were obtained from the last genome-wide association study of the International Multiple Sclerosis Genetics Consortium & the Wellcome Trust Case Control Consortium, 2. The interaction between genotype and Epstein Barr virus emerges as relevant for multiple sclerosis etiology. However, in line with recent data on the coexistence of common and unique strategies used by viruses to perturb the human molecular system, also other viruses have a similar potential, though probably less relevant in epidemiological terms. © 2013 Mechelli et al.

targetTB: A target identification pipeline for Mycobacterium tuberculosis through an interactome, reactome and genome-scale structural analysis

Background: Tuberculosis still remains one of the largest killer infectious diseases, warranting the identification of newer targets and drugs. Identification and validation of appropriate targets for designing drugs are critical steps in drug discovery, which are at present major bottle-necks. A majority of drugs in current clinical use for many diseases have been designed without the knowledge of the targets, perhaps because standard methodologies to identify such targets in a high-throughput fashion do not really exist. With different kinds of 'omics' data that are now available, computational approaches can be powerful means of obtaining short-lists of possible targets for further experimental validation. Results: We report a comprehensive in silico target identification pipeline, targetTB, for Mycobacterium tuberculosis. The pipeline incorporates a network analysis of the protein-protein interactome, a flux balance analysis of the reactome, experimentally derived phenotype essentiality data, sequence analyses and a structural assessment of targetability, using novel algorithms recently developed by us. Using flux balance analysis and network analysis, proteins critical for survival of M. tuberculosis are first identified, followed by comparative genomics with the host, finally incorporating a novel structural analysis of the binding sites to assess the feasibility of a protein as a target. Further analyses include correlation with expression data and non-similarity to gut flora proteins as well as 'anti-targets' in the host, leading to the identification of 451 high-confidence targets. Through phylogenetic profiling against 228 pathogen genomes, shortlisted targets have been further explored to identify broad-spectrum antibiotic targets, while also identifying those specific to tuberculosis. Targets that address mycobacterial persistence and drug resistance mechanisms are also analysed. Conclusion: The pipeline developed provides rational schema for drug target identification that are likely to have high rates of success, which is expected to save enormous amounts of money, resources and time in the drug discovery process. A thorough comparison with previously suggested targets in the literature demonstrates the usefulness of the integrated approach used in our study, highlighting the importance of systems-level analyses in particular. The method has the potential to be used as a general strategy for target identification and validation and hence significantly impact most drug discovery programmes.

Construction of a Plasmodium falciparum Rab-interactome identifies CK1 and PKA as Rab-effector kinases in malaria parasites

Background information. The pathology causing stages of the human malaria parasite Plasmodium falciparum reside within red blood cells that are devoid of any regulated transport system. The parasite, therefore, is entirely responsible for mediating vesicular transport within itself and in the infected erythrocyte cytoplasm, and it does so in part via its family of 11 Rab GTPases. Putative functions have been ascribed to Plasmodium Rabs due to their homology with Rabs of yeast, particularly with Saccharomyces that has an equivalent number of rab/ypt genes and where analyses of Ypt function is well characterized. Results. Rabs are important regulators of vesicular traffic due to their capacity to recruit specific effectors. In order to identify P. falciparum Rab (PfRab) effectors, we first built a Ypt-interactome by exploiting genetic and physical binding data available at the Saccharomyces genome database (SGD). We then constructed a PfRab-interactome using putative parasite Rab-effectors identified by homology to Ypt-effectors. We demonstrate its potential by wet-bench testing three predictions; that casein kinase-1 (PfCK1) is a specific Rab5B interacting protein and that the catalytic subunit of cAMP-dependent protein kinase A (PfPKA-C) is a PfRab5A and PfRab7 effector. Conclusions. The establishment of a shared set of physical Ypt/PfRab-effector proteins sheds light on a core set Plasmodium Rab-interactants shared with yeast. The PfRab-interactome should benefit vesicular trafficking studies in malaria parasites. The recruitment of PfCK1 to PfRab5B+ and PfPKA-C to PfRab5A+ and PfRab7+ vesicles, respectively, suggests that PfRab-recruited kinases potentially play a role in early and late endosome function in malaria parasites.

Crowd Sourcing a New Paradigm for Interactome Driven Drug Target Identification in Mycobacterium tuberculosis

A decade since the availability of Mycobacterium tuberculosis (Mtb) genome sequence, no promising drug has seen the light of the day. This not only indicates the challenges in discovering new drugs but also suggests a gap in our current understanding of Mtb biology. We attempt to bridge this gap by carrying out extensive re-annotation and constructing a systems level protein interaction map of Mtb with an objective of finding novel drug target candidates. Towards this, we synergized crowd sourcing and social networking methods through an initiative `Connect to Decode' (C2D) to generate the first and largest manually curated interactome of Mtb termed `3interactome pathway' (IPW), encompassing a total of 1434 proteins connected through 2575 functional relationships. Interactions leading to gene regulation, signal transduction, metabolism, structural complex formation have been catalogued. In the process, we have functionally annotated 87% of the Mtb genome in context of gene products. We further combine IPW with STRING based network to report central proteins, which may be assessed as potential drug targets for development of drugs with least possible side effects. The fact that five of the 17 predicted drug targets are already experimentally validated either genetically or biochemically lends credence to our unique approach.

Interactome analyses of Salmonella pathogenicity islands reveal SicA indispensable for virulence

Background: Serovars of Salmonella enterica, namely Typhi and Typhimurium, reportedly, are the bacterial pathogens causing systemic infections like gastroenteritis and typhoid fever. To elucidate the role and importance in such infection, the proteins of the Type III secretion system of Salmonella pathogenicity islands and two component signal transduction systems, have been mainly focused. However, the most indispensable of these virulent ones and their hierarchical role has not yet been studied extensively. Results: We have adopted a theoretical approach to build an interactome comprising the proteins from the Salmonella pathogeneicity islands (SPI) and two component signal transduction systems. This interactome was then analyzed by using network parameters like centrality and k-core measures. An initial step to capture the fingerprint of the core network resulted in a set of proteins which are involved in the process of invasion and colonization, thereby becoming more important in the process of infection. These proteins pertained to the Inv, Org, Prg, Sip, Spa, Ssa and Sse operons along with chaperone protein SicA. Amongst them, SicA was figured out to be the most indispensable protein from different network parametric analyses. Subsequently, the gene expression levels of all these theoretically identified important proteins were confirmed by microarray data analysis. Finally, we have proposed a hierarchy of the proteins involved in the total infection process. This theoretical approach is the first of its kind to figure out potential virulence determinants encoded by SPI for therapeutic targets for enteric infection. Conclusions: A set of responsible virulent proteins was identified and the expression level of their genes was validated by using independent, published microarray data. The result was a targeted set of proteins that could serve as sensitive predictors and form the foundation for a series of trials in the wet-lab setting. Understanding these regulatory and virulent proteins would provide insight into conditions which are encountered by this intracellular enteric pathogen during the course of infection. This would further contribute in identifying novel targets for antimicrobial agents. (C) 2014 Elsevier Ltd. All rights reserved.

SInCRe-structural interactome computational resource for Mycobacterium tuberculosis

We have developed an integrated database for Mycobacterium tuberculosis H37Rv (Mtb) that collates information on protein sequences, domain assignments, functional annotation and 3D structural information along with protein-protein and protein-small molecule interactions. SInCRe (Structural Interactome Computational Resource) is developed out of CamBan (Cambridge and Bangalore) collaboration. The motivation for development of this database is to provide an integrated platform to allow easily access and interpretation of data and results obtained by all the groups in CamBan in the field of Mtb informatics. In-house algorithms and databases developed independently by various academic groups in CamBan are used to generate Mtb-specific datasets and are integrated in this database to provide a structural dimension to studies on tuberculosis. The SInCRe database readily provides information on identification of functional domains, genome-scale modelling of structures of Mtb proteins and characterization of the small-molecule binding sites within Mtb. The resource also provides structure-based function annotation, information on small-molecule binders including FDA (Food and Drug Administration)-approved drugs, protein-protein interactions (PPIs) and natural compounds that bind to pathogen proteins potentially and result in weakening or elimination of host-pathogen protein-protein interactions. Together they provide prerequisites for identification of off-target binding.

Computational analysis of the LRRK2 interactome

LRRK2 was identified in 2004 as the causative protein product of the Parkinson’s disease locus designated PARK8. In the decade since then, genetic studies have revealed at least 6 dominant mutations in LRRK2 linked to Parkinson’s disease, alongside one associated with cancer. It is now well established that coding changes in LRRK2 are one of the most common causes of Parkinson’s. Genome-wide association studies (GWAs) have, more recently, reported single nucleotide polymorphisms (SNPs) around the LRRK2 locus to be associated with risk of developing sporadic Parkinson’s disease and inflammatory bowel disorder. The functional research that has followed these genetic breakthroughs has generated an extensive literature regarding LRRK2 pathophysiology; however, there is still no consensus as to the biological function of LRRK2. To provide insight into the aspects of cell biology that are consistently related to LRRK2 activity, we analysed the plethora of candidate LRRK2 interactors available through the BioGRID and IntAct data repositories. We then performed GO terms enrichment for the LRRK2 interactome. We found that, in two different enrichment portals, the LRRK2 interactome was associated with terms referring to transport, cellular organization, vesicles and the cytoskeleton. We also verified that 21 of the LRRK2 interactors are genetically linked to risk for Parkin- son’s disease or inflammatory bowel disorder. The implications of these findings are discussed, with particular regard to potential novel areas of investigation.

Reconstruction and analysis of the NF-kB pathway interactome: a systems biology approach

Background. One of the phenomena observed in human aging is the progressive increase of a systemic inflammatory state, a condition referred to as “inflammaging”, negatively correlated with longevity. A prominent mediator of inflammation is the transcription factor NF-kB, that acts as key transcriptional regulator of many genes coding for pro-inflammatory cytokines. Many different signaling pathways activated by very diverse stimuli converge on NF-kB, resulting in a regulatory network characterized by high complexity. NF-kB signaling has been proposed to be responsible of inflammaging. Scope of this analysis is to provide a wider, systemic picture of such intricate signaling and interaction network: the NF-kB pathway interactome. Methods. The study has been carried out following a workflow for gathering information from literature as well as from several pathway and protein interactions databases, and for integrating and analyzing existing data and the relative reconstructed representations by using the available computational tools. Strong manual intervention has been necessarily used to integrate data from multiple sources into mathematically analyzable networks. The reconstruction of the NF-kB interactome pursued with this approach provides a starting point for a general view of the architecture and for a deeper analysis and understanding of this complex regulatory system. Results. A “core” and a “wider” NF-kB pathway interactome, consisting of 140 and 3146 proteins respectively, were reconstructed and analyzed through a mathematical, graph-theoretical approach. Among other interesting features, the topological characterization of the interactomes shows that a relevant number of interacting proteins are in turn products of genes that are controlled and regulated in their expression exactly by NF-kB transcription factors. These “feedback loops”, not always well-known, deserve deeper investigation since they may have a role in tuning the response and the output consequent to NF-kB pathway initiation, in regulating the intensity of the response, or its homeostasis and balance in order to make the functioning of such critical system more robust and reliable. This integrated view allows to shed light on the functional structure and on some of the crucial nodes of thet NF-kB transcription factors interactome. Conclusion. Framing structure and dynamics of the NF-kB interactome into a wider, systemic picture would be a significant step toward a better understanding of how NF-kB globally regulates diverse gene programs and phenotypes. This study represents a step towards a more complete and integrated view of the NF-kB signaling system.

Identification of SNPs in the cystic fibrosis interactome influencing pulmonary progression in cystic fibrosis

There is growing evidence that the great phenotypic variability in patients with cystic fibrosis (CF) not only depends on the genotype, but apart from a combination of environmental and stochastic factors predominantly also on modifier gene effects. It has been proposed that genes interacting with CF transmembrane conductance regulator (CFTR) and epithelial sodium channel (ENaC) are potential modifiers. Therefore, we assessed the impact of single-nucleotide polymorphisms (SNPs) of several of these interacters on CF disease outcome. SNPs that potentially alter gene function were genotyped in 95 well-characterized p.Phe508del homozygous CF patients. Linear mixed-effect model analysis was used to assess the relationship between sequence variants and the repeated measurements of lung function parameters. In total, we genotyped 72 SNPs in 10 genes. Twenty-five SNPs were used for statistical analysis, where we found strong associations for one SNP in PPP2R4 with the lung clearance index (P ≤ 0.01), the specific effective airway resistance (P ≤ 0.005) and the forced expiratory volume in 1 s (P ≤ 0.005). In addition, we identified one SNP in SNAP23 to be significantly associated with three lung function parameters as well as one SNP in PPP2R1A and three in KRT19 to show a significant influence on one lung function parameter each. Our findings indicate that direct interacters with CFTR, such as SNAP23, PPP2R4 and PPP2R1A, may modify the residual function of p.Phe508del-CFTR while variants in KRT19 may modulate the amount of p.Phe508del-CFTR at the apical membrane and consequently modify CF disease.

Proteomic characterization of the FUS/TLS interactome

FUS/TLS (fused in sarcoma/translocated in liposarcoma) is a ubiquitously expressed RNA-binding protein, that has been discovered as fused to transcription factors in several human sarcomas and found in protein aggregates in neurons of patients with an inherited form of Amyotrophic Lateral Sclerosis [1]. To date, FUS has been implicated in a variety of cellular processes such as gene expression control, transcriptional regulation, pre-mRNA splicing and miRNA processing [2]. In addition, some evidences link FUS to genome stability control and DNA damage response. In fact, mice lacking FUS are hypersensitive to ionizing radiation and show high levels of chromosome instability and in response to double-strand breaks, FUS gets phosphorylated by the protein kinase ATM [3, 4, 5]. Moreover, upon DNA damage stress, FUS mediates Ebp1 (ErbB3 receptor-binding protein) SUMOylation, a post-translational modification that is required for its onco-suppressive activity, by acting as SUMO E3 ligase [6]. The study aims to investigate the role of FUS in DNA damage response and SUMOylation, two cellular pathways tightly interconnected to each other. Moreover, we will exploit biochemical and mass spectrometry-based approaches in order to identify other potential substrates of the E3 SUMO ligase activity of FUS. Preliminary results of mass spectrometric identification of FUS interacting proteins, in HEK293 and SHSY5Y cells, highlighted the interaction of FUS with several proteins involved in DNA damage response and many of those have been described already as target of SUMOylation, such as XRCC5, DDX5, PARP1, Nucleophosmin, and others. These evidences strengthen the hypothesis that FUS might represent a link between these pathways, even thou its exact role still needs to be clearly addressed. [1] Vance C. et al. (2009) Science 323(5918): p. 1208-11 [2] Fiesel FC., Kahle PJ. (2011) FEBS J. 278(19): p. 3550-68 [3] Kuroda M. et al. (2000) Embo J. 19(3): p. 453-62 [4] Hicks GG. et al. (2000) Nat Genet. 24(2):p. 175-9 [5] Gardiner M. et al. (2008) Biochem J. 415(2): p. 297-307 [6] Oh SM. et al. (2010) Oncogene 29(7): p. 1017-30

A comprehensive two-hybrid analysis to explore the yeast protein interactome

Protein–protein interactions play crucial roles in the execution of various biological functions. Accordingly, their comprehensive description would contribute considerably to the functional interpretation of fully sequenced genomes, which are flooded with novel genes of unpredictable functions. We previously developed a system to examine two-hybrid interactions in all possible combinations between the ≈6,000 proteins of the budding yeast Saccharomyces cerevisiae. Here we have completed the comprehensive analysis using this system to identify 4,549 two-hybrid interactions among 3,278 proteins. Unexpectedly, these data do not largely overlap with those obtained by the other project [Uetz, P., et al. (2000) Nature (London) 403, 623–627] and hence have substantially expanded our knowledge on the protein interaction space or interactome of the yeast. Cumulative connection of these binary interactions generates a single huge network linking the vast majority of the proteins. Bioinformatics-aided selection of biologically relevant interactions highlights various intriguing subnetworks. They include, for instance, the one that had successfully foreseen the involvement of a novel protein in spindle pole body function as well as the one that may uncover a hitherto unidentified multiprotein complex potentially participating in the process of vesicular transport. Our data would thus significantly expand and improve the protein interaction map for the exploration of genome functions that eventually leads to thorough understanding of the cell as a molecular system.