Near-Neighbor Interactions in the NS3-4A Protease of HCV Impact Replicative Fitness of Drug-Resistant Viral Variants.

A variety of amino acid substitutions in the NS3-4A protease of the hepatitis C virus lead to protease inhibitor (PI) resistance. Many of these significantly impair the replication fitness of the resistant variants in a genotype- and subtype-dependent manner, a critical factor in determining the probability with which resistant variants will persist. However, the underlying molecular mechanisms are unknown. Here, we present a novel residue-interaction network approach to determine how near-neighbor interactions of PI resistance mutations in NS3-4A can impact protease functional sites dependent on their genomic background. We constructed subtype-specific consensus residue networks for subtypes 1a and 1b from protease structure ensembles combined with biological properties of protein residues and evolutionary amino acid conservation. By applying local and global network topology analysis and visual exploration, we characterize PI resistance-associated sites and outline differences in near-neighbor interactions. We find local residue-interaction patterns and features at protease functional sites that are subtype specific. The noncovalent bonding patterns indicate higher fitness costs conferred by PI resistance mutations in a subtype 1b genomic background and explain the prevalence of Q80K and R155K in subtype 1a. Based on local residue interactions, we predict a subtype-specific role for the protease residue NS3-Q80 in molecular mechanisms related to the assembly of infectious virus particles that is supported by experimental data on the capacity of Q80K variants to replicate and produce infectious virus in subtype 1a and 1b cell culture.

CytoGEDEVO-global alignment of biological networks with Cytoscape.

MOTIVATION: In the systems biology era, high-throughput omics technologies have enabled the unraveling of the interplay of some biological entities on a large scale (e.g. genes, proteins, metabolites or RNAs). Huge biological networks have emerged, where nodes correspond to these entities and edges between them model their relations. Protein-protein interaction networks, for instance, show the physical interactions of proteins in an organism. The comparison of such networks promises additional insights into protein and cell function as well as knowledge-transfer across species. Several computational approaches have been developed previously to solve the network alignment (NA) problem, but only a few concentrate on the usability of the implemented tools for the evaluation of protein-protein interactions by the end users (biologists and medical researchers). RESULTS: We have created CytoGEDEVO, a Cytoscape app for visual and user-assisted NA. It extends the previous GEDEVO methodology for global pairwise NAs with new graphical and functional features. Our main focus was on the usability, even by non-programmers and the interpretability of the NA results with Cytoscape. AVAILABILITY AND IMPLEMENTATION: CytoGEDEVO is publicly available from the Cytoscape app store at http://apps.cytoscape.org/apps/cytogedevo In addition, we provide stand-alone command line executables, source code, documentation and step-by-step user instructions at http://cytogedevo.compbio.sdu.dk CONTACT: malek@tugraz.at SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

A FYVE zinc finger domain protein specifically links mRNA transport to endosome trafficking.

An emerging theme in cellular logistics is the close connection between mRNA and membrane trafficking. A prominent example is the microtubule-dependent transport of mRNAs and associated ribosomes on endosomes. This coordinated process is crucial for correct septin filamentation and efficient growth of polarised cells, such as fungal hyphae. Despite detailed knowledge on the key RNA-binding protein and the molecular motors involved, it is unclear how mRNAs are connected to membranes during transport. Here, we identify a novel factor containing a FYVE zinc finger domain for interaction with endosomal lipids and a new PAM2-like domain required for interaction with the MLLE domain of the key RNA-binding protein. Consistently, loss of this FYVE domain protein leads to specific defects in mRNA, ribosome, and septin transport without affecting general functions of endosomes or their movement. Hence, this is the first endosomal component specific for mRNP trafficking uncovering a new mechanism to couple mRNPs to endosomes.

Abundant genetic overlap between blood lipids and immune-mediated diseases indicates shared molecular genetic mechanisms.

Epidemiological studies suggest a relationship between blood lipids and immune-mediated diseases, but the nature of these associations is not well understood. We used genome-wide association studies (GWAS) to investigate shared single nucleotide polymorphisms (SNPs) between blood lipids and immune-mediated diseases. We analyzed data from GWAS (n~200,000 individuals), applying new False Discovery Rate (FDR) methods, to investigate genetic overlap between blood lipid levels [triglycerides (TG), low density lipoproteins (LDL), high density lipoproteins (HDL)] and a selection of archetypal immune-mediated diseases (Crohn's disease, ulcerative colitis, rheumatoid arthritis, type 1 diabetes, celiac disease, psoriasis and sarcoidosis). We found significant polygenic pleiotropy between the blood lipids and all the investigated immune-mediated diseases. We discovered several shared risk loci between the immune-mediated diseases and TG (n = 88), LDL (n = 87) and HDL (n = 52). Three-way analyses differentiated the pattern of pleiotropy among the immune-mediated diseases. The new pleiotropic loci increased the number of functional gene network nodes representing blood lipid loci by 40%. Pathway analyses implicated several novel shared mechanisms for immune pathogenesis and lipid biology, including glycosphingolipid synthesis (e.g. FUT2) and intestinal host-microbe interactions (e.g. ATG16L1). We demonstrate a shared genetic basis for blood lipids and immune-mediated diseases independent of environmental factors. Our findings provide novel mechanistic insights into dyslipidemia and immune-mediated diseases and may have implications for therapeutic trials involving lipid-lowering and anti-inflammatory agents.

Identification of HNRNPK as regulator of hepatitis C virus particle production.

Hepatitis C virus (HCV) is a major cause of chronic liver disease affecting around 130 million people worldwide. While great progress has been made to define the principle steps of the viral life cycle, detailed knowledge how HCV interacts with its host cells is still limited. To overcome this limitation we conducted a comprehensive whole-virus RNA interference-based screen and identified 40 host dependency and 16 host restriction factors involved in HCV entry/replication or assembly/release. Of these factors, heterogeneous nuclear ribonucleoprotein K (HNRNPK) was found to suppress HCV particle production without affecting viral RNA replication. This suppression of virus production was specific to HCV, independent from assembly competence and genotype, and not found with the related Dengue virus. By using a knock-down rescue approach we identified the domains within HNRNPK required for suppression of HCV particle production. Importantly, HNRNPK was found to interact specifically with HCV RNA and this interaction was impaired by mutations that also reduced the ability to suppress HCV particle production. Finally, we found that in HCV-infected cells, subcellular distribution of HNRNPK was altered; the protein was recruited to sites in close proximity of lipid droplets and colocalized with core protein as well as HCV plus-strand RNA, which was not the case with HNRNPK variants unable to suppress HCV virion formation. These results suggest that HNRNPK might determine efficiency of HCV particle production by limiting the availability of viral RNA for incorporation into virions. This study adds a new function to HNRNPK that acts as central hub in the replication cycle of multiple other viruses.

Integrative visual analysis of protein sequence mutations.

BACKGROUND: An important aspect of studying the relationship between protein sequence, structure and function is the molecular characterization of the effect of protein mutations. To understand the functional impact of amino acid changes, the multiple biological properties of protein residues have to be considered together. RESULTS: Here, we present a novel visual approach for analyzing residue mutations. It combines different biological visualizations and integrates them with molecular data derived from external resources. To show various aspects of the biological information on different scales, our approach includes one-dimensional sequence views, three-dimensional protein structure views and two-dimensional views of residue interaction networks as well as aggregated views. The views are linked tightly and synchronized to reduce the cognitive load of the user when switching between them. In particular, the protein mutations are mapped onto the views together with further functional and structural information. We also assess the impact of individual amino acid changes by the detailed analysis and visualization of the involved residue interactions. We demonstrate the effectiveness of our approach and the developed software on the data provided for the BioVis 2013 data contest. CONCLUSIONS: Our visual approach and software greatly facilitate the integrative and interactive analysis of protein mutations based on complementary visualizations. The different data views offered to the user are enriched with information about molecular properties of amino acid residues and further biological knowledge.

Deciphering the molecular profile of plaques, memory decline and neuron loss in two mouse models for Alzheimer's disease by deep sequencing.

One of the central research questions on the etiology of Alzheimer's disease (AD) is the elucidation of the molecular signatures triggered by the amyloid cascade of pathological events. Next-generation sequencing allows the identification of genes involved in disease processes in an unbiased manner. We have combined this technique with the analysis of two AD mouse models: (1) The 5XFAD model develops early plaque formation, intraneuronal Aß aggregation, neuron loss, and behavioral deficits. (2) The Tg4-42 model expresses N-truncated Aß4-42 and develops neuron loss and behavioral deficits albeit without plaque formation. Our results show that learning and memory deficits in the Morris water maze and fear conditioning tasks in Tg4-42 mice at 12 months of age are similar to the deficits in 5XFAD animals. This suggested that comparative gene expression analysis between the models would allow the dissection of plaque-related and -unrelated disease relevant factors. Using deep sequencing differentially expressed genes (DEGs) were identified and subsequently verified by quantitative PCR. Nineteen DEGs were identified in pre-symptomatic young 5XFAD mice, and none in young Tg4-42 mice. In the aged cohort, 131 DEGs were found in 5XFAD and 56 DEGs in Tg4-42 mice. Many of the DEGs specific to the 5XFAD model belong to neuroinflammatory processes typically associated with plaques. Interestingly, 36 DEGs were identified in both mouse models indicating common disease pathways associated with behavioral deficits and neuron loss.

setsApp for Cytoscape: Set operations for Cytoscape Nodes and Edges.

setsApp ( http://apps.cytoscape.org/apps/setsapp) is a relatively simple Cytoscape 3 app for users to handle groups of nodes and/or edges. It supports several important biological workflows and enables various set operations. setsApp provides basic tools to create sets of nodes or edges, import or export sets, and perform standard set operations (union, difference, intersection) on those sets. Automatic set partitioning and layout functions are also provided. The sets functionality is also exposed to users and app developers in the form of a set of commands that can be used for scripting purposes or integrated in other Cytoscape apps.

Profiling of Parkin-binding partners using tandem affinity purification.

Parkinson's disease (PD) is a progressive neurodegenerative disorder affecting approximately 1-2% of the general population over age 60. It is characterized by a rather selective loss of dopaminergic neurons in the substantia nigra and the presence of α-synuclein-enriched Lewy body inclusions. Mutations in the Parkin gene (PARK2) are the major cause of autosomal recessive early-onset parkinsonism. The Parkin protein is an E3 ubiquitin ligase with various cellular functions, including the induction of mitophagy upon mitochondrial depolarizaton, but the full repertoire of Parkin-binding proteins remains poorly defined. Here we employed tandem affinity purification interaction screens with subsequent mass spectrometry to profile binding partners of Parkin. Using this approach for two different cell types (HEK293T and SH-SY5Y neuronal cells), we identified a total of 203 candidate Parkin-binding proteins. For the candidate proteins and the proteins known to cause heritable forms of parkinsonism, protein-protein interaction data were derived from public databases, and the associated biological processes and pathways were analyzed and compared. Functional similarity between the candidates and the proteins involved in monogenic parkinsonism was investigated, and additional confirmatory evidence was obtained using published genetic interaction data from Drosophila melanogaster. Based on the results of the different analyses, a prioritization score was assigned to each candidate Parkin-binding protein. Two of the top ranking candidates were tested by co-immunoprecipitation, and interaction to Parkin was confirmed for one of them. New candidates for involvement in cell death processes, protein folding, the fission/fusion machinery, and the mitophagy pathway were identified, which provide a resource for further elucidating Parkin function.

Characterization of the EGFR interactome reveals associated protein complex networks and intracellular receptor dynamics.

Growth factor receptor mediated signaling is meanwhile recognized as a complex signaling network, which is initiated by recruiting specific patterns of adaptor proteins to the intracellular domain of epidermal growth factor receptor (EGFR). Approaches to globally identify EGFR-binding proteins are required to elucidate this network. We affinity-purified EGFR with its interacting proteins by coprecipitation from lysates of A431 cells. A total of 183 proteins were repeatedly detected in high-resolution MS measurements. For 15 of these, direct interactions with EGFR were listed in the iRefIndex interaction database, including Grb2, shc-1, SOS1 and 2, STAT 1 and 3, AP2, UBS3B, and ERRFI. The newly developed Cytoscape plugin ModuleGraph allowed retrieving and visualizing 93 well-described protein complexes that contained at least one of the proteins found to interact with EGFR in our experiments. Abundances of 14 proteins were modulated more than twofold upon EGFR activation whereof clathrin-associated adaptor complex AP-2 showed 4.6-fold enrichment. These proteins were further annotated with different cellular compartments. Finally, interactions of AP-2 proteins and the newly discovered interaction of CIP2A could be verified. In conclusion, a powerful technique is presented that allowed identification and quantitative assessment of the EGFR interactome to provide further insight into EGFR signaling.

Association between variants of PRDM1 and NDP52 and Crohn's disease, based on exome sequencing and functional studies.

BACKGROUND & AIMS: Genome-wide association studies (GWAS) have identified 140 Crohn's disease (CD) susceptibility loci. For most loci, the variants that cause disease are not known and the genes affected by these variants have not been identified. We aimed to identify variants that cause CD through detailed sequencing, genetic association, expression, and functional studies. METHODS: We sequenced whole exomes of 42 unrelated subjects with CD and 5 healthy subjects (controls) and then filtered single nucleotide variants by incorporating association results from meta-analyses of CD GWAS and in silico mutation effect prediction algorithms. We then genotyped 9348 subjects with CD, 2868 subjects with ulcerative colitis, and 14,567 control subjects and associated variants analyzed in functional studies using materials from subjects and controls and in vitro model systems. RESULTS: We identified rare missense mutations in PR domain-containing 1 (PRDM1) and associated these with CD. These mutations increased proliferation of T cells and secretion of cytokines on activation and increased expression of the adhesion molecule L-selectin. A common CD risk allele, identified in GWAS, correlated with reduced expression of PRDM1 in ileal biopsy specimens and peripheral blood mononuclear cells (combined P = 1.6 × 10(-8)). We identified an association between CD and a common missense variant, Val248Ala, in nuclear domain 10 protein 52 (NDP52) (P = 4.83 × 10(-9)). We found that this variant impairs the regulatory functions of NDP52 to inhibit nuclear factor κB activation of genes that regulate inflammation and affect the stability of proteins in Toll-like receptor pathways. CONCLUSIONS: We have extended the results of GWAS and provide evidence that variants in PRDM1 and NDP52 determine susceptibility to CD. PRDM1 maps adjacent to a CD interval identified in GWAS and encodes a transcription factor expressed by T and B cells. NDP52 is an adaptor protein that functions in selective autophagy of intracellular bacteria and signaling molecules, supporting the role of autophagy in the pathogenesis of CD.

NetworkPrioritizer: a versatile tool for network-based prioritization of candidate disease genes or other molecules.

SUMMARY: The prioritization of candidate disease genes is often based on integrated datasets and their network representation with genes as nodes connected by edges for biological relationships. However, the majority of prioritization methods does not allow for a straightforward integration of the user's own input data. Therefore, we developed the Cytoscape plugin NetworkPrioritizer that particularly supports the integrative network-based prioritization of candidate disease genes or other molecules. Our versatile software tool computes a number of important centrality measures to rank nodes based on their relevance for network connectivity and provides different methods to aggregate and compare rankings. AVAILABILITY: NetworkPrioritizer and the online documentation are freely available at http://www.networkprioritizer.de

Specificity of linear motifs that bind to a common mitogen-activated protein kinase docking groove.

Mitogen-activated protein kinases (MAPKs) have a docking groove that interacts with linear "docking" motifs in binding partners. To determine the structural basis of binding specificity between MAPKs and docking motifs, we quantitatively analyzed the ability of 15 docking motifs from diverse MAPK partners to bind to c-Jun amino-terminal kinase 1 (JNK1), p38α, and extracellular signal-regulated kinase 2 (ERK2). Classical docking motifs mediated highly specific binding only to JNK1, and only those motifs with a sequence pattern distinct from the classical MAPK binding docking motif consensus differentiated between the topographically similar docking grooves of ERK and p38α. Crystal structures of four complexes of MAPKs with docking peptides, representing JNK-specific, ERK-specific, or ERK- and p38-selective binding modes, revealed that the regions located between consensus positions in the docking motifs showed conformational diversity. Although the consensus positions in the docking motifs served as anchor points that bound to common MAPK surface features and mostly contributed to docking in a nondiscriminatory fashion, the conformation of the intervening region between the anchor points mostly determined specificity. We designed peptides with tailored MAPK binding profiles by rationally changing the length and amino acid composition of intervening regions located between anchor points. These results suggest a coherent structural model for MAPK docking specificity that reveals how short linear motifs binding to a common kinase docking groove can mediate diverse interaction patterns and contribute to correct MAPK partner selection in signaling networks.

Mining GO annotations for improving annotation consistency.

Despite the structure and objectivity provided by the Gene Ontology (GO), the annotation of proteins is a complex task that is subject to errors and inconsistencies. Electronically inferred annotations in particular are widely considered unreliable. However, given that manual curation of all GO annotations is unfeasible, it is imperative to improve the quality of electronically inferred annotations. In this work, we analyze the full GO molecular function annotation of UniProtKB proteins, and discuss some of the issues that affect their quality, focusing particularly on the lack of annotation consistency. Based on our analysis, we estimate that 64% of the UniProtKB proteins are incompletely annotated, and that inconsistent annotations affect 83% of the protein functions and at least 23% of the proteins. Additionally, we present and evaluate a data mining algorithm, based on the association rule learning methodology, for identifying implicit relationships between molecular function terms. The goal of this algorithm is to assist GO curators in updating GO and correcting and preventing inconsistent annotations. Our algorithm predicted 501 relationships with an estimated precision of 94%, whereas the basic association rule learning methodology predicted 12,352 relationships with a precision below 9%.

Functional characterization of human genes from exon expression and RNA interference results.

Complex biological systems comprise a large number of interacting molecules. The identification and detailed characterization of the functions of the involved genes and proteins are crucial for modeling and understanding such systems. To interrogate the various cellular processes, high-throughput techniques such as the Affymetrix Exon Array or RNA interference (RNAi) screens are powerful experimental approaches for functional genomics. However, they typically yield long gene lists that require computational methods to further analyze and functionally annotate the experimental results and to gain more insight into important molecular interactions. Here, we focus on bioinformatics software tools for the functional interpretation of exon expression data to discover alternative splicing events and their impact on gene and protein architecture, molecular networks, and pathways. We additionally demonstrate how to explore large lists of candidate genes as they also result from RNAi screens. In particular, our exemplary application studies show how to analyze the function of human genes that play a major role in human stem cells or viral infections.

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.

Topological analysis and interactive visualization of biological networks and protein structures.

Computational analysis and interactive visualization of biological networks and protein structures are common tasks for gaining insight into biological processes. This protocol describes three workflows based on the NetworkAnalyzer and RINalyzer plug-ins for Cytoscape, a popular software platform for networks. NetworkAnalyzer has become a standard Cytoscape tool for comprehensive network topology analysis. In addition, RINalyzer provides methods for exploring residue interaction networks derived from protein structures. The first workflow uses NetworkAnalyzer to perform a topological analysis of biological networks. The second workflow applies RINalyzer to study protein structure and function and to compute network centrality measures. The third workflow combines NetworkAnalyzer and RINalyzer to compare residue networks. The full protocol can be completed in ∼2 h.

Novel search method for the discovery of functional relationships.

MOTIVATION: Numerous annotations are available that functionally characterize genes and proteins with regard to molecular process, cellular localization, tissue expression, protein domain composition, protein interaction, disease association and other properties. Searching this steadily growing amount of information can lead to the discovery of new biological relationships between genes and proteins. To facilitate the searches, methods are required that measure the annotation similarity of genes and proteins. However, most current similarity methods are focused only on annotations from the Gene Ontology (GO) and do not take other annotation sources into account. RESULTS: We introduce the new method BioSim that incorporates multiple sources of annotations to quantify the functional similarity of genes and proteins. We compared the performance of our method with four other well-known methods adapted to use multiple annotation sources. We evaluated the methods by searching for known functional relationships using annotations based only on GO or on our large data warehouse BioMyn. This warehouse integrates many diverse annotation sources of human genes and proteins. We observed that the search performance improved substantially for almost all methods when multiple annotation sources were included. In particular, our method outperformed the other methods in terms of recall and average precision.

Measuring and analyzing tissue specificity of human genes and protein complexes.

Proteins and their interactions are essential for the survival of each human cell. Knowledge of their tissue occurrence is important for understanding biological processes. Therefore, we analyzed microarray and high-throughput RNA-sequencing data to identify tissue-specific and universally expressed genes. Gene expression data were used to investigate the presence of proteins, protein interactions and protein complexes in different tissues. Our comparison shows that the detection of tissue-specific genes and proteins strongly depends on the applied measurement technique. We found that microarrays are less sensitive for low expressed genes than high-throughput sequencing. Functional analyses based on microarray data are thus biased towards high expressed genes. This also means that previous biological findings based on microarrays might have to be re-examined using high-throughput sequencing results.