From 1989 to 1997, the yeast genome was sequenced by a worldwide international consortium initiated and conducted by André Goffeau (1935–2018). The article describes the pioneering collaboration of ...yeast scientists from a bioinformatics perspective. Indeed, the yeast genome has turned bioinformatics from an exotic hobby of few nerds into a discipline indispensable for answering biological questions using computational methods.
Serum metabolite concentrations provide a direct readout of biological processes in the human body, and they are associated with disorders such as cardiovascular and metabolic diseases. We present a ...genome-wide association study (GWAS) of 163 metabolic traits measured in human blood from 1,809 participants from the KORA population, with replication in 422 participants of the TwinsUK cohort. For eight out of nine replicated loci (FADS1, ELOVL2, ACADS, ACADM, ACADL, SPTLC3, ETFDH and SLC16A9), the genetic variant is located in or near genes encoding enzymes or solute carriers whose functions match the associating metabolic traits. In our study, the use of metabolite concentration ratios as proxies for enzymatic reaction rates reduced the variance and yielded robust statistical associations with P values ranging from 3 × 10−24 to 6.5 × 10−179. These loci explained 5.6%-36.3% of the observed variance in metabolite concentrations. For several loci, associations with clinically relevant parameters have been reported previously.
There is an increasing need to use genome and transcriptome sequencing to genetically diagnose patients suffering from suspected monogenic rare diseases. The proper detection of compound heterozygous ...variant combinations as disease-causing candidates is a challenge in diagnostic workflows as haplotype information is lost by currently used next-generation sequencing technologies. Consequently, computational tools are required to phase, or resolve the haplotype of, the high number of heterozygous variants in the exome or genome of each patient. Here we present SmartPhase, a phasing tool designed to efficiently reduce the set of potential compound heterozygous variant pairs in genetic diagnoses pipelines. The phasing algorithm of SmartPhase creates haplotypes using both parental genotype information and reads generated by DNA or RNA sequencing and is thus well suited to resolve the phase of rare variants. To inform the user about the reliability of a phasing prediction, it computes a confidence score which is essential to select error-free predictions. It incorporates existing haplotype information and applies logical rules to determine variants that can be excluded as causing a recessive, monogenic disease. SmartPhase can phase either all possible variant pairs in predefined genetic loci or preselected variant pairs of interest, thus keeping the focus on clinically relevant results. We compared SmartPhase to WhatsHap, one of the leading comparable phasing tools, using simulated data and a real clinical cohort of 921 patients. On both data sets, SmartPhase generated error-free predictions using our derived confidence score threshold. It outperformed WhatsHap with regard to the percentage of resolved pairs when parental genotype information is available. On the cohort data, SmartPhase enabled on average the exclusion of approximately 22% of the input variant pairs in each singleton patient and 44% in each trio patient. SmartPhase is implemented as an open-source Java tool and freely available at http://ibis.helmholtz-muenchen.de/smartphase/.
Metabolomics is the rapidly evolving field of the comprehensive measurement of ideally all endogenous metabolites in a biological fluid. However, no single analytic technique covers the entire ...spectrum of the human metabolome. Here we present results from a multiplatform study, in which we investigate what kind of results can presently be obtained in the field of diabetes research when combining metabolomics data collected on a complementary set of analytical platforms in the framework of an epidemiological study.
40 individuals with self-reported diabetes and 60 controls (male, over 54 years) were randomly selected from the participants of the population-based KORA (Cooperative Health Research in the Region of Augsburg) study, representing an extensively phenotyped sample of the general German population. Concentrations of over 420 unique small molecules were determined in overnight-fasting blood using three different techniques, covering nuclear magnetic resonance and tandem mass spectrometry. Known biomarkers of diabetes could be replicated by this multiple metabolomic platform approach, including sugar metabolites (1,5-anhydroglucoitol), ketone bodies (3-hydroxybutyrate), and branched chain amino acids. In some cases, diabetes-related medication can be detected (pioglitazone, salicylic acid).
Our study depicts the promising potential of metabolomics in diabetes research by identification of a series of known and also novel, deregulated metabolites that associate with diabetes. Key observations include perturbations of metabolic pathways linked to kidney dysfunction (3-indoxyl sulfate), lipid metabolism (glycerophospholipids, free fatty acids), and interaction with the gut microflora (bile acids). Our study suggests that metabolic markers hold the potential to detect diabetes-related complications already under sub-clinical conditions in the general population.
The rapidly evolving field of metabolomics aims at a comprehensive measurement of ideally all endogenous metabolites in a cell or body fluid. It thereby provides a functional readout of the ...physiological state of the human body. Genetic variants that associate with changes in the homeostasis of key lipids, carbohydrates, or amino acids are not only expected to display much larger effect sizes due to their direct involvement in metabolite conversion modification, but should also provide access to the biochemical context of such variations, in particular when enzyme coding genes are concerned. To test this hypothesis, we conducted what is, to the best of our knowledge, the first GWA study with metabolomics based on the quantitative measurement of 363 metabolites in serum of 284 male participants of the KORA study. We found associations of frequent single nucleotide polymorphisms (SNPs) with considerable differences in the metabolic homeostasis of the human body, explaining up to 12% of the observed variance. Using ratios of certain metabolite concentrations as a proxy for enzymatic activity, up to 28% of the variance can be explained (p-values 10(-16) to 10(-21)). We identified four genetic variants in genes coding for enzymes (FADS1, LIPC, SCAD, MCAD) where the corresponding metabolic phenotype (metabotype) clearly matches the biochemical pathways in which these enzymes are active. Our results suggest that common genetic polymorphisms induce major differentiations in the metabolic make-up of the human population. This may lead to a novel approach to personalized health care based on a combination of genotyping and metabolic characterization. These genetically determined metabotypes may subscribe the risk for a certain medical phenotype, the response to a given drug treatment, or the reaction to a nutritional intervention or environmental challenge.
The type III secretion system (TTSS) is a key mechanism for host cell interaction used by a variety of bacterial pathogens and symbionts of plants and animals including humans. The TTSS represents a ...molecular syringe with which the bacteria deliver effector proteins directly into the host cell cytosol. Despite the importance of the TTSS for bacterial pathogenesis, recognition and targeting of type III secreted proteins has up until now been poorly understood. Several hypotheses are discussed, including an mRNA-based signal, a chaperon-mediated process, or an N-terminal signal peptide. In this study, we systematically analyzed the amino acid composition and secondary structure of N-termini of 100 experimentally verified effector proteins. Based on this, we developed a machine-learning approach for the prediction of TTSS effector proteins, taking into account N-terminal sequence features such as frequencies of amino acids, short peptides, or residues with certain physico-chemical properties. The resulting computational model revealed a strong type III secretion signal in the N-terminus that can be used to detect effectors with sensitivity of approximately 71% and selectivity of approximately 85%. This signal seems to be taxonomically universal and conserved among animal pathogens and plant symbionts, since we could successfully detect effector proteins if the respective group was excluded from training. The application of our prediction approach to 739 complete bacterial and archaeal genome sequences resulted in the identification of between 0% and 12% putative TTSS effector proteins. Comparison of effector proteins with orthologs that are not secreted by the TTSS showed no clear pattern of signal acquisition by fusion, suggesting convergent evolutionary processes shaping the type III secretion signal. The newly developed program EffectiveT3 (http://www.chlamydiaedb.org) is the first universal in silico prediction program for the identification of novel TTSS effectors. Our findings will facilitate further studies on and improve our understanding of type III secretion and its role in pathogen-host interactions.
In recent years, the Munich Information Center for Protein Sequences (MIPS) yeast protein–protein interaction (PPI) dataset has been used in numerous analyses of protein networks and has been called ...a gold standard because of its quality and comprehensiveness H. Yu, N. M. Luscombe, H. X. Lu, X. Zhu, Y. Xia, J. D. Han, N. Bertin, S. Chung, M. Vidal and M. Gerstein (2004) Genome Res., 14, 1107–1118. MPact and the yeast protein localization catalog provide information related to the proximity of proteins in yeast. Beside the integration of high-throughput data, information about experimental evidence for PPIs in the literature was compiled by experts adding up to 4300 distinct PPIs connecting 1500 proteins in yeast. As the interaction data is a complementary part of CYGD, interactive mapping of data on other integrated data types such as the functional classification catalog A. Ruepp, A. Zollner, D. Maier, K. Albermann, J. Hani, M. Mokrejs, I. Tetko, U. Güldener, G. Mannhaupt, M. Münsterkötter and H. W. Mewes (2004) Nucleic Acids Res., 32, 5539–5545 is possible. A survey of signaling proteins and comparison with pathway data from KEGG demonstrates that based on these manually annotated data only an extensive overview of the complexity of this functional network can be obtained in yeast. The implementation of a web-based PPI-analysis tool allows analysis and visualization of protein interaction networks and facilitates integration of our curated data with high-throughput datasets. The complete dataset as well as user-defined sub-networks can be retrieved easily in the standardized PSI-MI format. The resource can be accessed through http://mips.gsf.de/genre/proj/mpact.
The MIPS mammalian protein–protein interaction database (MPPI) is a new resource of high-quality experimental protein interaction data in mammals. The content is based on published experimental ...evidence that has been processed by human expert curators. We provide the full dataset for download and a flexible and powerful web interface for users with various requirements. Availability: The MPPI database is located at http://mips.gsf.de/proj/ppi/ Contact: d.frishman@wzw.tum.de
Chlamydiae are the major cause of preventable blindness and sexually transmitted disease. Genome analysis of a chlamydia-related symbiont of free-living amoebae revealed that it is twice as large as ...any of the pathogenic chlamydiae and had few signs of recent lateral gene acquisition. We showed that about 700 million years ago the last common ancestor of pathogenic and symbiotic chlamydiae was already adapted to intracellular survival in early eukaryotes and contained many virulence factors found in modern pathogenic chlamydiae, including a type III secretion system. Ancient chlamydiae appear to be the originators of mechanisms for the exploitation of eukaryotic cells.
There have recently been developments in the methods used to access the accuracy of the prediction and applicability domain of absorption, distribution, metabolism, excretion and toxicity models, and ...also in the methods used to predict the physicochemical properties of compounds in the early stages of drug development. The methods are classified into two main groups: those based on the analysis of similarity of molecules, and those based on the analysis of calculated properties. An analysis of octanol-water distribution coefficients is used to exemplify the consistency of estimated and calculated accuracy of the ALOGPS program (
http://www.vcclab.org) to predict in-house and publicly available datasets.
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