Dmitry Kuznetsov

ExPASy: SIB bioinformatics resource portal

ExPASy (http://www.expasy.org) has worldwide reputation as one of the main bioinformatics resources for proteomics. It has now evolved, becoming an extensible and integrative portal accessing many scientific resources, databases and software tools in different areas of life sciences. Scientists can henceforth access seamlessly a wide range of resources in many different domains, such as proteomics, genomics, phylogeny/evolution, systems biology, population genetics, transcriptomics, etc. The individual resources (databases, web-based and downloadable software tools) are hosted in a ‘decentralized’ way by different groups of the SIB Swiss Institute of Bioinformatics and partner institutions. Specifically, a single web portal provides a common entry point to a wide range of resources developed and operated by different SIB groups and external institutions. The portal features a search function across ‘selected’ resources. Additionally, the availability and usage of resources are monitored. The portal is aimed for both expert users and people who are not familiar with a specific domain in life sciences. The new web interface provides, in particular, visual guidance for newcomers to ExPASy.

OrthoDB v9.1: cataloging evolutionary and functional annotations for animal, fungal, plant, archaeal, bacterial and viral orthologs

OrthoDB is a comprehensive catalog of orthologs, genes inherited by extant species from a single gene in their last common ancestor. In 2016 OrthoDB reached its 9th release, growing to over 22 million genes from over 5000 species, now adding plants, archaea and viruses. In this update we focused on usability of this fast-growing wealth of data: updating the user and programmatic interfaces to browse and query the data, and further enhancing the already extensive integration of available gene functional annotations. Collating functional annotations from over 100 resources, and enabled us to propose descriptive titles for 87% of ortholog groups. Additionally, OrthoDB continues to provide computed evolutionary annotations and to allow user queries by sequence homology. The OrthoDB resource now enables users to generate publication-quality comparative genomics charts, as well as to upload, analyze and interactively explore their own private data. OrthoDB is available from http://orthodb.org.

Measuring the diurnal pattern of leaf hyponasty and growth in Arabidopsis – a novel phenotyping approach using laser scanning

Plants forming a rosette during their juvenile growth phase, such as Arabidopsis thaliana (L.) Heynh., are able to adjust the size, position and orientation of their leaves. These growth responses are under the control of the plants circadian clock and follow a characteristic diurnal rhythm. For instance, increased leaf elongation and hyponasty - defined here as the increase in leaf elevation angle - can be observed when plants are shaded. Shading can either be caused by a decrease in the fluence rate of photosynthetically active radiation (direct shade) or a decrease in the fluence rate of red compared with far-red radiation (neighbour detection). In this paper we report on a phenotyping approach based on laser scanning to measure the diurnal pattern of leaf hyponasty and increase in rosette size. In short days, leaves showed constitutively increased leaf elevation angles compared with long days, but the overall diurnal pattern and the magnitude of up and downward leaf movement was independent of daylength. Shade treatment led to elevated leaf angles during the first day of application, but did not affect the magnitude of up and downward leaf movement in the following day. Using our phenotyping device, individual plants can be non-invasively monitored during several days under different light conditions. Hence, it represents a proper tool to phenotype light- and circadian clock-mediated growth responses in order to better understand the underlying regulatory genetic network.

A multiplicity of factors contributes to selective RNA polymerase III occupancy of a subset of RNA polymerase III genes in mouse liver

The genomic loci occupied by RNA polymerase (RNAP) III have been characterized in human culture cells by genome-wide chromatin immunoprecipitations, followed by deep sequencing (ChIP-seq). These studies have shown that only ∼40% of the annotated 622 human tRNA genes and pseudogenes are occupied by RNAP-III, and that these genes are often in open chromatin regions rich in active RNAP-II transcription units. We have used ChIP-seq to characterize RNAP-III-occupied loci in a differentiated tissue, the mouse liver. Our studies define the mouse liver RNAP-III-occupied loci including a conserved mammalian interspersed repeat (MIR) as a potential regulator of an RNAP-III subunit-encoding gene. They reveal that synteny relationships can be established between a number of human and mouse RNAP-III genes, and that the expression levels of these genes are significantly linked. They establish that variations within the A and B promoter boxes, as well as the strength of the terminator sequence, can strongly affect RNAP-III occupancy of tRNA genes. They reveal correlations with various genomic features that explain the observed variation of 81% of tRNA scores. In mouse liver, loci represented in the NCBI37/mm9 genome assembly that are clearly occupied by RNAP-III comprise 50 Rn5s (5S RNA) genes, 14 known non-tRNA RNAP-III genes, nine Rn4.5s (4.5S RNA) genes, and 29 SINEs. Moreover, out of the 433 annotated tRNA genes, half are occupied by RNAP-III. Transfer RNA gene expression levels reflect both an underlying genomic organization conserved in dividing human culture cells and resting mouse liver cells, and the particular promoter and terminator strengths of individual genes.

OpenFluDB, a database for human and animal influenza virus

Although research on influenza lasted for more than 100 years, it is still one of the most prominent diseases causing half a million human deaths every year. With the recent observation of new highly pathogenic H5N1 and H7N7 strains, and the appearance of the influenza pandemic caused by the H1N1 swine-like lineage, a collaborative effort to share observations on the evolution of this virus in both animals and humans has been established. The OpenFlu database (OpenFluDB) is a part of this collaborative effort. It contains genomic and protein sequences, as well as epidemiological data from more than 27 000 isolates. The isolate annotations include virus type, host, geographical location and experimentally tested antiviral resistance. Putative enhanced pathogenicity as well as human adaptation propensity are computed from protein sequences. Each virus isolate can be associated with the laboratories that collected, sequenced and submitted it. Several analysis tools including multiple sequence alignment, phylogenetic analysis and sequence similarity maps enable rapid and efficient mining. The contents of OpenFluDB are supplied by direct user submission, as well as by a daily automatic procedure importing data from public repositories. Additionally, a simple mechanism facilitates the export of OpenFluDB records to GenBank. This resource has been successfully used to rapidly and widely distribute the sequences collected during the recent human swine flu outbreak and also as an exchange platform during the vaccine selection procedure. Database URL: http://openflu.vital-it.ch.

HLA photoaffinity labeling reveals overlapping binding of homologous melanoma-associated gene peptides by HLA-A1, HLA-A29, and HLA-B44

Melanoma-associated genes (MAGEs) encode tumor-specific antigens that can be recognized by CD8 cytotoxic T lymphocytes. To investigate the interaction of the HLA-A1-restricted MAGE-1 peptide 161-169 (EADPTGHSY) with HLA class I molecules, photoreactive derivatives were prepared by single amino acid substitution with N-[iodo-4-azidosalicyloyl]-L-2,3-diaminopropionic acid. These derivatives were tested for their ability to bind to, and to photoaffinity-label, HLA-A1 on C1R.A1 cells. Only the derivatives containing the photoreactive amino acid in position 1 or 7 fulfilled both criteria. Testing the former derivative on 14 lymphoid cell lines expressing over 44 different HLA class I molecules indicated that it efficiently photoaffinity-labeled not only HLA-A1, but possibly also HLA-A29 and HLA-B44. MAGE peptide binding by HLA-A29 and HLA-B44 was confirmed by photoaffinity labeling with photoreactive MAGE-3 peptide derivatives on C1R.A29 and C1R.B44 cells, respectively. The different photoaffinity labeling systems were used to assess the ability of the homologous peptides derived from MAGE-1, −2, −3, −4a, −4b, −6, and −12 to bind to HLA-A1, HLA-A29, and HLA-B44. All but the MAGE-2 and MAGE-12 nonapeptides efficiently inhibited photoaffinity labeling of HLA-A1, which is in agreement with the known HLA-A1 peptide-binding motif (acidic residue in P3 and C-terminal tyrosine). In contrast, photoaffinity labeling of HLA-A29 was efficiently inhibited by these as well as by the MAGE-3 and MAGE-6 nonapeptides. Finally, the HLA-B44 photoaffinity labeling, unlike the HLA-A1 and HLA-A29 labeling, was inhibited more efficiently by the corresponding MAGE decapeptides, which is consistent with the reported HLA-B44 peptide-binding motif (glutamic acid in P2, and C-terminal tyrosine or phenylalanine). The overlapping binding of homologous MAGE peptides by HLA-A1, A29, and B44 is based on different binding principles and may have implications for immunotherapy of MAGE-positive tumors.

Differential Roles of T Cell Receptor α and β Chains in Ligand Binding Among H-2Kd-restricted Cytolytic T Lymphocyte Clones Specific for a Photoreactive Plasmodium berghei Circumsporozoite Peptide Derivative

To study the interaction of T cell receptor with its ligand, a complex of a major histocompatibility complex molecule and a peptide, we derived H-2Kd-restricted cytolytic T lymphocyte clones from mice immunized with a Plasmodium berghei circumsporozoite peptide (PbCS) 252-260 (SYIPSAEKI) derivative containing photoreactive Nγ-[4-azidobenzoyl] lysine in place of Pro-255. This residue and Lys-259 were essential parts of the epitope recognized by these clones. Most of the clones expressed BV1S1A1 encoded β chains along with specific complementary determining region (CDR) 3β regions but diverse α chain sequences. Surprisingly, all T cell receptors were preferentially photoaffinity labeled on the α chain. For a representative T cell receptor, the photoaffinity labeled site was located in the Vα C-strand. Computer modeling suggested the presence of a hydrophobic pocket, which is formed by parts of the Vα/Jα C-, F-, and G-strands and adjacent CDR3α residues and structured to be able to avidly bind the photoreactive ligand side chain. We previously found that a T cell receptor specific for a PbCS peptide derivative containing this photoreactive side chain in position 259 similarly used a hydrophobic pocket located between the junctional CDR3 loops. We propose that this nonpolar domain in these locations allow T cell receptors to avidly and specifically bind epitopes containing non-peptidic side chains.

The SwissLipids knowledgebase for lipid biology

Motivation: Lipids are a large and diverse group of biological molecules with roles in membrane formation, energy storage and signaling. Cellular lipidomes may contain tens of thousands of structures, a staggering degree of complexity whose significance is not yet fully understood. High-throughput mass spectrometry-based platforms provide a means to study this complexity, but the interpretation of lipidomic data and its integration with prior knowledge of lipid biology suffers from a lack of appropriate tools to manage the data and extract knowledge from it. Results: To facilitate the description and exploration of lipidomic data and its integration with prior biological knowledge, we have developed a knowledge resource for lipids and their biology—SwissLipids. SwissLipids provides curated knowledge of lipid structures and metabolism which is used to generate an in silico library of feasible lipid structures. These are arranged in a hierarchical classification that links mass spectrometry analytical outputs to all possible lipid structures, metabolic reactions and enzymes. SwissLipids provides a reference namespace for lipidomic data publication, data exploration and hypothesis generation. The current version of SwissLipids includes over 244 000 known and theoretically possible lipid structures, over 800 proteins, and curated links to published knowledge from over 620 peer-reviewed publications. We are continually updating the SwissLipids hierarchy with new lipid categories and new expert curated knowledge. Availability: SwissLipids is freely available at http://www.swisslipids.org/. Contact: alan.bridge@isb-sib.ch Supplementary information: Supplementary data are available at Bioinformatics online.

The EMPRES-i genetic module: a novel tool linking epidemiological outbreak information and genetic characteristics of influenza viruses

Combining epidemiological information, genetic characterization and geomapping in the analysis of influenza can contribute to a better understanding and description of influenza epidemiology and ecology, including possible virus reassortment events. Furthermore, integration of information such as agroecological farming system characteristics can provide new knowledge on risk factors of influenza emergence and spread. Integrating viral characteristics into an animal disease information system is therefore expected to provide a unique tool to trace-and-track particular virus strains; generate clade distributions and spatiotemporal clusters; screen for distribution of viruses with specific molecular markers; identify potential risk factors; and analyze or map viral characteristics related to vaccines used for control and/or prevention. For this purpose, a genetic module was developed within EMPRES-i (FAO’s global animal disease information system) linking epidemiological information from influenza events with virus characteristics and enabling combined analysis. An algorithm was developed to act as the interface between EMPRES-i disease event data and publicly available influenza virus sequences in OpenfluDB. This algorithm automatically computes potential links between outbreak event and sequences, which are subsequently manually validated by experts. Subsequently, other virus characteristics such as antiviral resistance can then be associated to outbreak data. To visualize such characteristics on a geographic map, shape files with virus characteristics to overlay on other EMPRES-i map layers (e.g. animal densities) can be generated. The genetic module allows export of associated epidemiological and sequence data for further analysis. FAO has made this tool available for scientists and policy makers. Contributions are expected from users to improve and validate the number of linked influenza events and isolate information as well as the quality of information. Possibilities to interconnect with other influenza sequence databases or to expand the genetic module to other viral diseases (e.g. foot and mouth disease) are being explored. Database OpenfluDB URL: http://openflu.vital-it.ch Database EMPRES-i URL: http://EMPRES-i.fao.org/

Molecular phenotyping of multiple mouse strains under metabolic challenge uncovers a role for Elovl2 in glucose-induced insulin secretion.

Objective In type 2 diabetes (T2D), pancreatic β cells become progressively dysfunctional, leading to a decline in insulin secretion over time. In this study, we aimed to identify key genes involved in pancreatic beta cell dysfunction by analyzing multiple mouse strains in parallel under metabolic stress. Methods Male mice from six commonly used non-diabetic mouse strains were fed a high fat or regular chow diet for three months. Pancreatic islets were extracted and phenotypic measurements were recorded at 2 days, 10 days, 30 days, and 90 days to assess diabetes progression. RNA-Seq was performed on islet tissue at each time-point and integrated with the phenotypic data in a network-based analysis. Results A module of co-expressed genes was selected for further investigation as it showed the strongest correlation to insulin secretion and oral glucose tolerance phenotypes. One of the predicted network hub genes was Elovl2, encoding Elongase of very long chain fatty acids 2. Elovl2 silencing decreased glucose-stimulated insulin secretion in mouse and human β cell lines. Conclusion Our results suggest a role for Elovl2 in ensuring normal insulin secretory responses to glucose. Moreover, the large comprehensive dataset and integrative network-based approach provides a new resource to dissect the molecular etiology of β cell failure under metabolic stress.