Francisco S. Domingues

Sequence variants in IL10, ARPC2 and multiple other loci contribute to ulcerative colitis susceptibility

Inflammatory bowel disease (IBD) typically manifests as either ulcerative colitis (UC) or Crohns disease (CD). Systematic identification of susceptibility genes for IBD has thus far focused mainly on CD, and little is known about the genetic architecture of UC. Here we report a genome-wide association study with 440,794 SNPs genotyped in 1,167 individuals with UC and 777 healthy controls. Twenty of the most significantly associated SNPs were tested for replication in three independent European case-control panels comprising a total of 1,855 individuals with UC and 3,091 controls. Among the four consistently replicated markers, SNP rs3024505 immediately flanking the IL10 (interleukin 10) gene on chromosome 1q32.1 showed the most significant association in the combined verification samples (P = 1.35 × 10−12; OR = 1.46 (1.31–1.62)). The other markers were located in ARPC2 and in the HLA-BTNL2 region. Association between rs3024505 and CD (1,848 cases, 1,804 controls) was weak (P = 0.013; OR = 1.17 (1.01–1.34)). IL10 is an immunosuppressive cytokine that has long been proposed to influence IBD pathophysiology. Our findings strongly suggest that defective IL10 function is central to the pathogenesis of the UC subtype of IBD.

A new measure for functional similarity of gene products based on Gene Ontology.

BackgroundGene Ontology (GO) is a standard vocabulary of functional terms and allows for coherent annotation of gene products. These annotations provide a basis for new methods that compare gene products regarding their molecular function and biological role.ResultsWe present a new method for comparing sets of GO terms and for assessing the functional similarity of gene products. The method relies on two semantic similarity measures; simReland funSim. One measure (simRel) is applied in the comparison of the biological processes found in different groups of organisms. The other measure (funSim) is used to find functionally related gene products within the same or between different genomes. Results indicate that the method, in addition to being in good agreement with established sequence similarity approaches, also provides a means for the identification of functionally related proteins independent of evolutionary relationships. The method is also applied to estimating functional similarity between all proteins in Saccharomyces cerevisiae and to visualizing the molecular function space of yeast in a map of the functional space. A similar approach is used to visualize the functional relationships between protein families.ConclusionThe approach enables the comparison of the underlying molecular biology of different taxonomic groups and provides a new comparative genomics tool identifying functionally related gene products independent of homology. The proposed map of the functional space provides a new global view on the functional relationships between gene products or protein families.

Characterization of resistance to the protease inhibitor boceprevir in hepatitis C virus–infected patients

Boceprevir is a hepatitis C virus (HCV) nonstructural protein (NS) 3/4A protease inhibitor that is currently being evaluated in combination with peginterferon alfa‐2b and ribavirin in phase 3 studies. The clinical resistance profile of boceprevir is not characterized in detail so far. The NS3 protease domain of viral RNA was cloned from HCV genotype 1–infected patients (n = 22). A mean number of 47 clones were sequenced before, at the end, and after treatment with 400 mg boceprevir twice or three times daily for 14 days for genotypic, phenotypic, and viral fitness analysis. At the end of treatment, a wild‐type an NS3 protease sequence was observed with a mean frequency of 85.9%. In the remaining isolates, five previously observed resistance mutations (V36M/A, T54A/S, R155K/T, A156S, V170A) and one mutation (V55A) with unknown resistance to boceprevir were detected either alone or in combination. Phenotypic analysis in the HCV replicon assay showed low (V36G, T54S, R155L; 3.8‐ to 5.5‐fold 50% inhibitory concentration [IC50]), medium (V55A, R155K, V170A, T54A, A156S; 6.8‐ to 17.7‐fold IC50) and high level (A156T; >120‐fold IC50) resistance to boceprevir. The overall frequency of resistant mutations and the level of resistance increased with greater declines in mean maximum HCV RNA levels. Two weeks after the end of treatment, the frequency of resistant variants declined and the number of wild‐type isolates increased to 95.5%. With the exception of V36 and V170 variants all resistant mutations declined by more than 50%. Mathematical modeling revealed impaired replicative fitness for all single mutations, whereas for combined mutations a relative increase of replication efficiency was suggested. Conclusion: During boceprevir monotherapy, resistance mutations at six positions within the NS3 protease were detected by way of clonal sequence analysis. All mutations are associated with reduced replicative fitness estimated by mathematical modeling and show cross‐resistance to telaprevir. (HEPATOLOGY 2009.)

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.

NOXclass: prediction of protein-protein interaction types

BackgroundStructural models determined by X-ray crystallography play a central role in understanding protein-protein interactions at the molecular level. Interpretation of these models requires the distinction between non-specific crystal packing contacts and biologically relevant interactions. This has been investigated previously and classification approaches have been proposed. However, less attention has been devoted to distinguishing different types of biological interactions. These interactions are classified as obligate and non-obligate according to the effect of the complex formation on the stability of the protomers. So far no automatic classification methods for distinguishing obligate, non-obligate and crystal packing interactions have been made available.ResultsSix interface properties have been investigated on a dataset of 243 protein interactions. The six properties have been combined using a support vector machine algorithm, resulting in NOXclass, a classifier for distinguishing obligate, non-obligate and crystal packing interactions. We achieve an accuracy of 91.8% for the classification of these three types of interactions using a leave-one-out cross-validation procedure.ConclusionNOXclass allows the interpretation and analysis of protein quaternary structures. In particular, it generates testable hypotheses regarding the nature of protein-protein interactions, when experimental results are not available. We expect this server will benefit the users of protein structural models, as well as protein crystallographers and NMR spectroscopists. A web server based on the method and the datasets used in this study are available at http://noxclass.bioinf.mpi-inf.mpg.de/.

Analyzing and visualizing residue networks of protein structures

The study of individual amino acid residues and their molecular interactions in protein structures is crucial for understanding structure-function relationships. Recent work has indicated that residue networks derived from 3D protein structures provide additional insights into the structural and functional roles of interacting residues. Here, we present the new software tools RINerator and RINalyzer for the automatized generation, 2D visualization, and interactive analysis of residue interaction networks, and highlight their use in different application scenarios.

Genome-Wide Association Study Identifies Novel Loci Associated with Circulating Phospho- and Sphingolipid Concentrations

Phospho- and sphingolipids are crucial cellular and intracellular compounds. These lipids are required for active transport, a number of enzymatic processes, membrane formation, and cell signalling. Disruption of their metabolism leads to several diseases, with diverse neurological, psychiatric, and metabolic consequences. A large number of phospholipid and sphingolipid species can be detected and measured in human plasma. We conducted a meta-analysis of five European family-based genome-wide association studies (N = 4034) on plasma levels of 24 sphingomyelins (SPM), 9 ceramides (CER), 57 phosphatidylcholines (PC), 20 lysophosphatidylcholines (LPC), 27 phosphatidylethanolamines (PE), and 16 PE-based plasmalogens (PLPE), as well as their proportions in each major class. This effort yielded 25 genome-wide significant loci for phospholipids (smallest P-value = 9.88×10−204) and 10 loci for sphingolipids (smallest P-value = 3.10×10−57). After a correction for multiple comparisons (P-value<2.2×10−9), we observed four novel loci significantly associated with phospholipids (PAQR9, AGPAT1, PKD2L1, PDXDC1) and two with sphingolipids (PLD2 and APOE) explaining up to 3.1% of the variance. Further analysis of the top findings with respect to within class molar proportions uncovered three additional loci for phospholipids (PNLIPRP2, PCDH20, and ABDH3) suggesting their involvement in either fatty acid elongation/saturation processes or fatty acid specific turnover mechanisms. Among those, 14 loci (KCNH7, AGPAT1, PNLIPRP2, SYT9, FADS1-2-3, DLG2, APOA1, ELOVL2, CDK17, LIPC, PDXDC1, PLD2, LASS4, and APOE) mapped into the glycerophospholipid and 12 loci (ILKAP, ITGA9, AGPAT1, FADS1-2-3, APOA1, PCDH20, LIPC, PDXDC1, SGPP1, APOE, LASS4, and PLD2) to the sphingolipid pathways. In large meta-analyses, associations between FADS1-2-3 and carotid intima media thickness, AGPAT1 and type 2 diabetes, and APOA1 and coronary artery disease were observed. In conclusion, our study identified nine novel phospho- and sphingolipid loci, substantially increasing our knowledge of the genetic basis for these traits.

Calculating the Statistical Significance of Changes in Pathway Activity From Gene Expression Data

We present a statistical approach to scoring changes in activity of metabolic pathways from gene expression data. The method identifies the biologically relevant pathways with corresponding statistical significance. Based on gene expression data alone, only local structures of genetic networks can be recovered. Instead of inferring such a network, we propose a hypothesis-based approach. We use given knowledge about biological networks to improve sensitivity and interpretability of findings from microarray experiments. Recently introduced methods test if members of predefined gene sets are enriched in a list of top-ranked genes in a microarray study. We improve this approach by defining scores that depend on all members of the gene set and that also take pairwise co-regulation of these genes into account. We calculate the significance of co-regulation of gene sets with a nonparametric permutation test. On two data sets the method is validated and its biological relevance is discussed. It turns out that useful measures for co-regulation of genes in a pathway can be identified adaptively. We refine our method in two aspects specific to pathways. First, to overcome the ambiguity of enzyme-to-gene mappings for a fixed pathway, we introduce algorithms for selecting the best fitting gene for a specific enzyme in a specific condition. In selected cases, functional assignment of genes to pathways is feasible. Second, the sensitivity of detecting relevant pathways is improved by integrating information about pathway topology. The distance of two enzymes is measured by the number of reactions needed to connect them, and enzyme pairs with a smaller distance receive a higher weight in the score calculation.

Comparative Analysis of Protein Structure Alignments

BackgroundSeveral methods are currently available for the comparison of protein structures. These methods have been analysed regarding the performance in the identification of structurally/evolutionary related proteins, but so far there has been less focus on the objective comparison between the alignments produced by different methods.ResultsWe analysed and compared the structural alignments obtained by different methods using three sets of pairs of structurally related proteins. The first set corresponds to 355 pairs of remote homologous proteins according to the SCOP database (ASTRAL40 set). The second set was derived from the SISYPHUS database and includes 69 protein pairs (SISY set). The third set consists of 40 pairs that are challenging to align (RIPC set). The alignment of pairs of this set requires indels of considerable number and size and some of the proteins are related by circular permutations, show extensive conformational variability or include repetitions. Two standard methods (CE and DALI) were applied to align the proteins in the ASTRAL40 set. The extent of structural similarity identified by both methods is highly correlated and the alignments from the two methods agree on average in more than half of the aligned positions. CE, DALI, as well as four additional methods (FATCAT, MATRAS, Cα-match and SHEBA) were then compared using the SISY and RIPC sets. The accuracy of the alignments was assessed by comparison to reference alignments. The alignments generated by the different methods on average match more than half of the reference alignments in the SISY set. The alignments obtained in the more challenging RIPC set tend to differ considerably and match reference alignments less successfully than the SISY set alignments.ConclusionThe alignments produced by different methods tend to agree to a considerable extent, but the agreement is lower for the more challenging pairs. The results for the comparison to reference alignments are encouraging, but also indicate that there is still room for improvement.

Structural Descriptors of gp120 V3 Loop for the Prediction of HIV-1 Coreceptor Usage

HIV-1 cell entry commonly uses, in addition to CD4, one of the chemokine receptors CCR5 or CXCR4 as coreceptor. Knowledge of coreceptor usage is critical for monitoring disease progression as well as for supporting therapy with the novel drug class of coreceptor antagonists. Predictive methods for inferring coreceptor usage based on the third hypervariable (V3) loop region of the viral gene coding for the envelope protein gp120 can provide us with these monitoring facilities while avoiding expensive phenotypic tests. All simple heuristics (such as the 11/25 rule) as well as statistical learning methods proposed to date predict coreceptor usage based on sequence features of the V3 loop exclusively. Here, we show, based on a recently resolved structure of gp120 with an untruncated V3 loop, that using structural information on the V3 loop in combination with sequence features of V3 variants improves prediction of coreceptor usage. In particular, we propose a distance-based descriptor of the spatial arrangement of physicochemical properties that increases discriminative performance. For a fixed specificity of 0.95, a sensitivity of 0.77 was achieved, improving further to 0.80 when combined with a sequence-based representation using amino acid indicators. This compares favorably with the sensitivities of 0.62 for the traditional 11/25 rule and 0.73 for a prediction based on sequence information as input to a support vector machine and constitutes a statistically significant improvement. A detailed analysis and interpretation of structural features important for classification shows the relevance of several specific hydrogen-bond donor sites and aliphatic side chains to coreceptor specificity towards CCR5 or CXCR4. Furthermore, an analysis of side chain orientation of the specificity-determining residues suggests a major role of one side of the V3 loop in the selection of the coreceptor. The proposed method constitutes the first approach to an improved prediction of coreceptor usage based on an original integration of structural bioinformatics methods with statistical learning.