Hans-Werner Mewes

Life with 6000 Genes

The genome of the yeast Saccharomyces cerevisiae has been completely sequenced through a worldwide collaboration. The sequence of 12,068 kilobases defines 5885 potential protein-encoding genes, approximately 140 genes specifying ribosomal RNA, 40 genes for small nuclear RNA molecules, and 275 transfer RNA genes. In addition, the complete sequence provides information about the higher order organization of yeasts 16 chromosomes and allows some insight into their evolutionary history. The genome shows a considerable amount of apparent genetic redundancy, and one of the major problems to be tackled during the next stage of the yeast genome project is to elucidate the biological functions of all of these genes.

Analysis of 1.9 Mb of contiguous sequence from chromosome 4 of Arabidopsis thaliana

The plant Arabidopsis thaliana (Arabidopsis) has become an important model species for the study of many aspects of plant biology. The relatively small size of the nuclear genome and the availability of extensive physical maps of the five chromosomes provide a feasible basis for initiating sequencing of the five chromosomes. The YAC (yeast artificial chromosome)-based physical map of chromosome 4 was used to construct a sequence-ready map of cosmid and BAC (bacterial artificial chromosome) clones covering a 1.9-megabase (Mb) contiguous region, and the sequence of this region is reported here. Analysis of the sequence revealed an average gene density of one gene every 4.8 kilobases (kb), and 54% of the predicted genes had significant similarity to known genes. Other interesting features were found, such as the sequence of a disease-resistance gene locus, the distribution of retroelements, the frequent occurrence of clustered gene families, and the sequence of several classes of genes not previously encountered in plants.

Deciphering the evolution and metabolism of an anammox bacterium from a community genome

Anaerobic ammonium oxidation (anammox) has become a main focus in oceanography and wastewater treatment. It is also the nitrogen cycles major remaining biochemical enigma. Among its features, the occurrence of hydrazine as a free intermediate of catabolism, the biosynthesis of ladderane lipids and the role of cytoplasm differentiation are unique in biology. Here we use environmental genomics—the reconstruction of genomic data directly from the environment—to assemble the genome of the uncultured anammox bacterium Kuenenia stuttgartiensis from a complex bioreactor community. The genome data illuminate the evolutionary history of the Planctomycetes and allow us to expose the genetic blueprint of the organisms special properties. Most significantly, we identified candidate genes responsible for ladderane biosynthesis and biological hydrazine metabolism, and discovered unexpected metabolic versatility.

Human metabolic individuality in biomedical and pharmaceutical research

Genome-wide association studies (GWAS) have identified many risk loci for complex diseases, but effect sizes are typically small and information on the underlying biological processes is often lacking. Associations with metabolic traits as functional intermediates can overcome these problems and potentially inform individualized therapy. Here we report a comprehensive analysis of genotype-dependent metabolic phenotypes using a GWAS with non-targeted metabolomics. We identified 37 genetic loci associated with blood metabolite concentrations, of which 25 show effect sizes that are unusually high for GWAS and account for 10–60% differences in metabolite levels per allele copy. Our associations provide new functional insights for many disease-related associations that have been reported in previous studies, including those for cardiovascular and kidney disorders, type 2 diabetes, cancer, gout, venous thromboembolism and Crohn’s disease. The study advances our knowledge of the genetic basis of metabolic individuality in humans and generates many new hypotheses for biomedical and pharmaceutical research.

Genetics Meets Metabolomics: A Genome-Wide Association Study of Metabolite Profiles in Human Serum

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.

A genome-wide perspective of genetic variation in human metabolism

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.

Metabolic footprint of diabetes: a multiplatform metabolomics study in an epidemiological setting.

Background 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. Methodology/Principal Findings 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). Conclusions/Significance 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.

MIPS: a database for protein sequences, homology data and yeast genome information

The MIPS group (Martinsried Institute for Protein Sequences) at the Max-Planck-Institute for Biochemistry, Martinsried near Munich, Germany, collects, processes and distributes protein sequence data within the framework of the tripartite association of the PIR-International Protein Sequence Database (,). MIPS contributes nearly 50% of the data input to the PIR-International Protein Sequence Database. The database is distributed on CD-ROM together with PATCHX, an exhaustive supplement of unique, unverified protein sequences from external sources compiled by MIPS. Through its WWW server (http://www.mips.biochem.mpg.de/ ) MIPS permits internet access to sequence databases, homology data and to yeast genome information. (i) Sequence similarity results from the FASTA program () are stored in the FASTA database for all proteins from PIR-International and PATCHX. The database is dynamically maintained and permits instant access to FASTA results. (ii) Starting with FASTA database queries, proteins have been classified into families and superfamilies (PROT-FAM). (iii) The HPT (hashed position tree) data structure () developed at MIPS is a new approach for rapid sequence and pattern searching. (iv) MIPS provides access to the sequence and annotation of the complete yeast genome (), the functional classification of yeast genes (FunCat) and its graphical display, the Genome Browser (). A CD-ROM based on the JAVA programming language providing dynamic interactive access to the yeast genome and the related protein sequences has been compiled and is available on request.

Complete DNA sequence of yeast chromosome II

In the framework of the EU genome‐sequencing programmes, the complete DNA sequence of the yeast Saccharomyces cerevisiae chromosome II (807 188 bp) has been determined. At present, this is the largest eukaryotic chromosome entirely sequenced. A total of 410 open reading frames (ORFs) were identified, covering 72% of the sequence. Similarity searches revealed that 124 ORFs (30%) correspond to genes of known function, 51 ORFs (12.5%) appear to be homologues of genes whose functions are known, 52 others (12.5%) have homologues the functions of which are not well defined and another 33 of the novel putative genes (8%) exhibit a degree of similarity which is insufficient to confidently assign function. Of the genes on chromosome II, 37‐45% are thus of unpredicted function. Among the novel putative genes, we found several that are related to genes that perform differentiated functions in multicellular organisms of are involved in malignancy. In addition to a compact arrangement of potential protein coding sequences, the analysis of this chromosome confirmed general chromosome patterns but also revealed particular novel features of chromosomal organization. Alternating regional variations in average base composition correlate with variations in local gene density along chromosome II, as observed in chromosomes XI and III. We propose that functional ARS elements are preferably located in the AT‐rich regions that have a spacing of approximately 110 kb. Similarly, the 13 tRNA genes and the three Ty elements of chromosome II are found in AT‐rich regions. In chromosome II, the distribution of coding sequences between the two strands is biased, with a ratio of 1.3:1. An interesting aspect regarding the evolution of the eukaryotic genome is the finding that chromosome II has a high degree of internal genetic redundancy, amounting to 16% of the coding capacity.

The PIR-International Protein Sequence Database

The Protein Information Resource (PIR; http://www-nbrf.georgetown. edu/pir/) supports research on molecular evolution, functional genomics, and computational biology by maintaining a comprehensive, non-redundant, well-organized and freely available protein sequence database. Since 1988 the database has been maintained collaboratively by PIR-International, an international association of data collection centers cooperating to develop this resource during a period of explosive growth in new sequence data and new computer technologies. The PIR Protein Sequence Database entries are classified into superfamilies, families and homology domains, for which sequence alignments are available. Full-scale family classification supports comparative genomics research, aids sequence annotation, assists database organization and improves database integrity. The PIR WWW server supports direct on-line sequence similarity searches, information retrieval, and knowledge discovery by providing the Protein Sequence Database and other supplementary databases. Sequence entries are extensively cross-referenced and hypertext-linked to major nucleic acid, literature, genome, structure, sequence alignment and family databases. The weekly release of the Protein Sequence Database can be accessed through the PIR Web site. The quarterly release of the database is freely available from our anonymous FTP server and is also available on CD-ROM with the accompanying ATLAS database search program.