Changing metabolic signatures of amino acids and lipids during the prediabetic period in a pig model with impaired incretin function and reduced β-cell mass

Diabetes is generally diagnosed too late. Therefore, biomarkers indicating early stages of β-cell dysfunction and mass reduction would facilitate timely counteraction. Transgenic pigs expressing a dominant-negative glucose-dependent insulinotropic polypeptide receptor (GIPR(dn)) reveal progressive deterioration of glucose control and reduction of β-cell mass, providing a unique opportunity to study metabolic changes during the prediabetic period. Plasma samples from intravenous glucose tolerance tests of 2.5- and 5-month-old GIPR(dn) transgenic and control animals were analyzed for 163 metabolites by targeted mass spectrometry. Analysis of variance revealed that 26 of 163 parameters were influenced by the interaction Genotype × Age (P ≤ 0.0001) and thus are potential markers for progression within the prediabetic state. Among them, the concentrations of seven amino acids (Phe, Orn, Val, xLeu, His, Arg, and Tyr) were increased in 2.5-month-old but decreased in 5-month-old GIPR(dn) transgenic pigs versus controls. Furthermore, specific sphingomyelins, diacylglycerols, and ether phospholipids were decreased in plasma of 5-month-old GIPR(dn) transgenic pigs. Alterations in plasma metabolite concentrations were associated with liver transcriptome changes in relevant pathways. The concentrations of a number of plasma amino acids and lipids correlated significantly with β-cell mass of 5-month-old pigs. These metabolites represent candidate biomarkers of early phases of β-cell dysfunction and mass reduction.

Bayesian independent component analysis recovers pathway signatures from blood metabolomics data

Interpreting the complex interplay of metabolites in heterogeneous biosamples still poses a challenging task. In this study, we propose independent component analysis (ICA) as a multivariate analysis tool for the interpretation of large-scale metabolomics data. In particular, we employ a Bayesian ICA method based on a mean-field approach, which allows us to statistically infer the number of independent components to be reconstructed. The advantage of ICA over correlation-based methods like principal component analysis (PCA) is the utilization of higher order statistical dependencies, which not only yield additional information but also allow a more meaningful representation of the data with fewer components. We performed the described ICA approach on a large-scale metabolomics data set of human serum samples, comprising a total of 1764 study probands with 218 measured metabolites. Inspecting the source matrix of statistically independent metabolite profiles using a weighted enrichment algorithm, we observe strong enrichment of specific metabolic pathways in all components. This includes signatures from amino acid metabolism, energy-related processes, carbohydrate metabolism, and lipid metabolism. Our results imply that the human blood metabolome is composed of a distinct set of overlaying, statistically independent signals. ICA furthermore produces a mixing matrix, describing the strength of each independent component for each of the study probands. Correlating these values with plasma high-density lipoprotein (HDL) levels, we establish a novel association between HDL plasma levels and the branched-chain amino acid pathway. We conclude that the Bayesian ICA methodology has the power and flexibility to replace many of the nowadays common PCA and clustering-based analyses common in the research field.

On the hypothesis-free testing of metabolite ratios in genome-wide and metabolome-wide association studies

Background

Genome-wide association studies (GWAS) with metabolic traits and metabolome-wide association studies (MWAS) with traits of biomedical relevance are powerful tools to identify the contribution of genetic, environmental and lifestyle factors to the etiology of complex diseases. Hypothesis-free testing of ratios between all possible metabolite pairs in GWAS and MWAS has proven to be an innovative approach in the discovery of new biologically meaningful associations. The p-gain statistic was introduced as an ad-hoc measure to determine whether a ratio between two metabolite concentrations carries more information than the two corresponding metabolite concentrations alone. So far, only a rule of thumb was applied to determine the significance of the p-gain.

Results

Here we explore the statistical properties of the p-gain through simulation of its density and by sampling of experimental data. We derive critical values of the p-gain for different levels of correlation between metabolite pairs and show that B/(2*α) is a conservative critical value for the p-gain, where α is the level of significance and B the number of tested metabolite pairs.

Conclusions

We show that the p-gain is a well defined measure that can be used to identify statistically significant metabolite ratios in association studies and provide a conservative significance cut-off for the p-gain for use in future association studies with metabolic traits.

Mesenchymal cell interaction with ovarian cancer cells triggers pro-metastatic properties

Tumor microenvironement is an important actor of ovarian cancer progression but the relations between mesenchymal cells and ovarian cancer cells remain unclear. The objective of this study was to determine the ovarian cancer cells' biological modifications induced by mesenchymal cells. To address this issue, we used two different ovarian cancer cell lines (NIH:OVCAR3 and SKOV3) and co-cultured them with mesenchymal cells. Upon co-culture the different cell populations were sorted to study their transcriptome and biological properties. Transcriptomic analysis revealed three biological-function gene clusters were enriched upon contact with mesenchymal cells. These were related to the increase of metastatic abilities (adhesion, migration and invasion), proliferation and chemoresistance in vitro. Therefore, contact with the mesenchymal cell niche could increase metastatic initiation and expansion through modification of cancer cells. Taken together these findings suggest that pathways involved in hetero-cellular interaction may be targeted to disrupt the acquired pro-metastatic profile.

Serum metabolite concentrations and decreased GFR in the general population

BACKGROUND

Metabolites such as creatinine and urea are established kidney function markers. High-throughput metabolomic studies have not been reported in large general population samples spanning normal kidney function and chronic kidney disease (CKD).

STUDY DESIGN

Cross-sectional observational studies of the general population.

SETTING AND PARTICIPANTS

2 independent samples: KORA F4 (discovery sample, n = 3,011) and Twins UK (validation sample, n = 984). EXPOSURE FACTORS: 151 serum metabolites, quantified by targeted mass spectrometry.

OUTCOMES AND MEASUREMENTS

Metabolites and their 22,650 ratios were analyzed by multivariable-adjusted linear regression for their association with glomerular filtration rate (eGFR), estimated separately from creatinine and cystatin C levels by CKD-EPI (CKD Epidemiology Collaboration) equations. After correction for multiple testing, significant metabolites (P < 3.3 × 10(-4) for single metabolites; P < 2.2 × 10(-6) for ratios) were meta-analyzed with independent data from the TwinsUK Study.

RESULTS

Replicated associations with eGFR were observed for 22 metabolites and 516 metabolite ratios. Pooled P values ranged from 7.1 × 10(-7) to 1.8 × 10(-69) for the replicated single metabolites. Acylcarnitines such as glutarylcarnitine were associated inversely with eGFR (-3.73 mL/min/1.73 m(2) per standard deviation [SD] increase, pooled P = 1.8 × 10(-69)). The replicated ratio with the strongest association was the ratio of serine to glutarylcarnitine (P = 3.6 × 10(-81)). Almost all replicated phenotypes associated with decreased eGFR (<60 mL/min/1.73 m(2); n = 172 cases) in KORA F4: per 1-SD increment, ORs ranged from 0.29-2.06. Across categories of a metabolic score consisting of 3 uncorrelated metabolites, the prevalence of decreased eGFR increased from 3% to 53%.

LIMITATIONS

Cross-sectional study design, GFR was estimated, limited number of metabolites.

CONCLUSIONS

Distinct metabolic phenotypes were reproducibly associated with eGFR in 2 separate population studies. They may provide novel insights into renal metabolite handling, improve understanding of pathophysiology, or aid in the diagnosis of kidney disease. Longitudinal studies are needed to clarify whether changes in metabolic phenotypes precede or result from kidney function impairment.

Childhood obesity is associated with changes in the serum metabolite profile

OBJECTIVE

The human serum metabolite profile is reflective of metabolic processes, including pathophysiological changes characteristic of diseases. Therefore, investigation of serum metabolite concentrations in obese children might give new insights into biological mechanisms associated with childhood obesity.

METHODS

Serum samples of 80 obese and 40 normal-weight children between 6 and 15 years of age were analyzed using a mass spectrometry-based metabolomics approach targeting 163 metabolites. Metabolite concentrations and metabolite ratios were compared between obese and normal-weight children as well as between children of different pubertal stages.

RESULTS

Metabolite concentration profiles of obese children could be distinguished from those of normal-weight children. After correction for multiple testing, we observed 14 metabolites (glutamine, methionine, proline, nine phospholipids, and two acylcarnitines, p < 3.8 × 10⁻⁴) and 69 metabolite ratios (p < 6.0 × 10⁻⁶) to be significantly altered in obese children. The identified metabolite markers are indicative of oxidative stress and of changes in sphingomyelin metabolism, in β-oxidation, and in pathways associated with energy expenditure. In contrast, pubertal stage was not associated with metabolite concentration differences.

CONCLUSION

Our study shows that childhood obesity influences the composition of the serum metabolome. If replicated in larger studies, the altered metabolites might be considered as potential biomarkers in the generation of new hypotheses on the biological mechanisms behind obesity.

Genetic associations with lipoprotein subfractions provide information on their biological nature

Adverse levels of lipoproteins are highly heritable and constitute risk factors for cardiovascular outcomes. Hitherto, genome-wide association studies revealed 95 lipid-associated loci. However, due to the small effect sizes of these associations large sample numbers (>100 000 samples) were needed. Here we show that analyzing more refined lipid phenotypes, namely lipoprotein subfractions, can increase the number of significantly associated loci compared with bulk high-density lipoprotein and low-density lipoprotein analysis in a study with identical sample numbers. Moreover, lipoprotein subfractions provide novel insight into the human lipid metabolism. We measured 15 lipoprotein subfractions (L1-L15) in 1791 samples using (1)H-NMR (nuclear magnetic resonance) spectroscopy. Using cluster analyses, we quantified inter-relationships among lipoprotein subfractions. Additionally, we analyzed associations with subfractions at known lipid loci. We identified five distinct groups of subfractions: one (L1) was only marginally captured by serum lipids and therefore extends our knowledge of lipoprotein biochemistry. During a lipid-tolerance test, L1 lost its special position. In the association analysis, we found that eight loci (LIPC, CETP, PLTP, FADS1-2-3, SORT1, GCKR, APOB, APOA1) were associated with the subfractions, whereas only four loci (CETP, SORT1, GCKR, APOA1) were associated with serum lipids. For LIPC, we observed a 10-fold increase in the variance explained by our regression models. In conclusion, NMR-based fine mapping of lipoprotein subfractions provides novel information on their biological nature and strengthens the associations with genetic loci. Future clinical studies are now needed to investigate their biomedical relevance.

Role of medium- and short-chain L-3-hydroxyacyl-CoA dehydrogenase in the regulation of body weight and thermogenesis

Dysregulation of fatty acid oxidation plays a pivotal role in the pathophysiology of obesity and insulin resistance. Medium- and short-chain-3-hydroxyacyl-coenzyme A (CoA) dehydrogenase (SCHAD) (gene name, hadh) catalyze the third reaction of the mitochondrial β-oxidation cascade, the oxidation of 3-hydroxyacyl-CoA to 3-ketoacyl-CoA, for medium- and short-chain fatty acids. We identified hadh as a putative obesity gene by comparison of two genome-wide scans, a quantitative trait locus analysis previously performed in the polygenic obese New Zealand obese mouse and an earlier described small interfering RNA-mediated mutagenesis in Caenorhabditis elegans. In the present study, we show that mice lacking SCHAD (hadh(-/-)) displayed a lower body weight and a reduced fat mass in comparison with hadh(+/+) mice under high-fat diet conditions, presumably due to an impaired fuel efficiency, the loss of acylcarnitines via the urine, and increased body temperature. Food intake, total energy expenditure, and locomotor activity were not altered in knockout mice. Hadh(-/-) mice exhibited normal fat tolerance at 20 C. However, during cold exposure, knockout mice were unable to clear triglycerides from the plasma and to maintain their normal body temperature, indicating that SCHAD plays an important role in adaptive thermogenesis. Blood glucose concentrations in the fasted and postprandial state were significantly lower in hadh(-/-) mice, whereas insulin levels were elevated. Accordingly, insulin secretion in response to glucose and glucose plus palmitate was elevated in isolated islets of knockout mice. Therefore, our data indicate that SCHAD is involved in thermogenesis, in the maintenance of body weight, and in the regulation of nutrient-stimulated insulin secretion.

High TCR diversity ensures optimal function and homeostasis of Foxp3+ regulatory T cells

Dominant tolerance to self-antigen requires the presence of sufficient numbers of CD4(+) Foxp3(+) Treg cells with matching antigen specificity. However, the size and role of TCR repertoire diversity for antigen-specific immuno-regulation through Treg cells is not clear. Here, we developed and applied a novel high-throughput (HT) TCR sequencing approach to analyze the TCR repertoire of Treg cells and revealed the importance of high diversity for Treg-cell homeostasis and function. We found that highly polyclonal Treg cells from WT mice vigorously expanded after adoptive transfer into non-lymphopenic TCR-transgenic recipients with low Treg-cell diversity. In that system, we identified specific Treg-cell TCR preferences in distinct anatomic locations such as the mesenteric LN indicating that Treg cells continuously compete for MHC class-II-presented self-, food-, or flora-antigen. Functionally, we showed that high TCR diversity was required for optimal suppressive function of Treg cells in experimental acute graft versus host disease (GvHD). In conclusion, we suggest that efficient immuno-regulation by Treg cells requires high TCR diversity. Thereby, continuous competition of peripheral Treg cells for limited self-antigen shapes an organ-optimized, yet highly diverse, local TCR repertoire.

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.

Discovery of sexual dimorphisms in metabolic and genetic biomarkers

Metabolomic profiling and the integration of whole-genome genetic association data has proven to be a powerful tool to comprehensively explore gene regulatory networks and to investigate the effects of genetic variation at the molecular level. Serum metabolite concentrations allow a direct readout of biological processes, and association of specific metabolomic signatures with complex diseases such as Alzheimer's disease and cardiovascular and metabolic disorders has been shown. There are well-known correlations between sex and the incidence, prevalence, age of onset, symptoms, and severity of a disease, as well as the reaction to drugs. However, most of the studies published so far did not consider the role of sexual dimorphism and did not analyse their data stratified by gender. This study investigated sex-specific differences of serum metabolite concentrations and their underlying genetic determination. For discovery and replication we used more than 3,300 independent individuals from KORA F3 and F4 with metabolite measurements of 131 metabolites, including amino acids, phosphatidylcholines, sphingomyelins, acylcarnitines, and C6-sugars. A linear regression approach revealed significant concentration differences between males and females for 102 out of 131 metabolites (p-values<3.8×10(-4); Bonferroni-corrected threshold). Sex-specific genome-wide association studies (GWAS) showed genome-wide significant differences in beta-estimates for SNPs in the CPS1 locus (carbamoyl-phosphate synthase 1, significance level: p<3.8×10(-10); Bonferroni-corrected threshold) for glycine. We showed that the metabolite profiles of males and females are significantly different and, furthermore, that specific genetic variants in metabolism-related genes depict sexual dimorphism. Our study provides new important insights into sex-specific differences of cell regulatory processes and underscores that studies should consider sex-specific effects in design and interpretation.

Differences between human plasma and serum metabolite profiles

BACKGROUND

Human plasma and serum are widely used matrices in clinical and biological studies. However, different collecting procedures and the coagulation cascade influence concentrations of both proteins and metabolites in these matrices. The effects on metabolite concentration profiles have not been fully characterized.

METHODOLOGY/PRINCIPAL FINDINGS

We analyzed the concentrations of 163 metabolites in plasma and serum samples collected simultaneously from 377 fasting individuals. To ensure data quality, 41 metabolites with low measurement stability were excluded from further analysis. In addition, plasma and corresponding serum samples from 83 individuals were re-measured in the same plates and mean correlation coefficients (r) of all metabolites between the duplicates were 0.83 and 0.80 in plasma and serum, respectively, indicating significantly better stability of plasma compared to serum (p = 0.01). Metabolite profiles from plasma and serum were clearly distinct with 104 metabolites showing significantly higher concentrations in serum. In particular, 9 metabolites showed relative concentration differences larger than 20%. Despite differences in absolute concentration between the two matrices, for most metabolites the overall correlation was high (mean r = 0.81±0.10), which reflects a proportional change in concentration. Furthermore, when two groups of individuals with different phenotypes were compared with each other using both matrices, more metabolites with significantly different concentrations could be identified in serum than in plasma. For example, when 51 type 2 diabetes (T2D) patients were compared with 326 non-T2D individuals, 15 more significantly different metabolites were found in serum, in addition to the 25 common to both matrices.

CONCLUSIONS/SIGNIFICANCE

Our study shows that reproducibility was good in both plasma and serum, and better in plasma. Furthermore, as long as the same blood preparation procedure is used, either matrix should generate similar results in clinical and biological studies. The higher metabolite concentrations in serum, however, make it possible to provide more sensitive results in biomarker detection.

Identification of a potential biomarker for FABP4 inhibition: the power of lipidomics in preclinical drug testing

The fatty acid binding protein 4 (FABP4) belongs to the family of lipid chaperones that control intracellular fluxes and compartmentalization of their respective ligands (e.g., fatty acids). FABP4, which is almost exclusively expressed in adipocytes and macrophages, contributes to the development of insulin resistance and atherosclerosis in mice. Lack of FABP4 protects against the development of insulin resistance associated with genetic or diet-induced obesity in mice. Furthermore, total or macrophage-specific FABP4 deficiency is protective against atherosclerosis in apolipoprotein E-deficient mice. The FABP4 small-molecule inhibitor BMS309403 has demonstrated efficacy in mouse models for type 2 diabetes mellitus and atherosclerosis, resembling phenotypes of mice with FABP4 deficiency. However, despite the therapeutically attractive long-term effects of FABP4 inhibition, an acute biomarker for drug action is lacking. The authors applied mass spectrometry lipidomics analysis to in vitro and in vivo (plasma and adipose tissue) samples upon inhibitor treatment. They report the identification of a potential biomarker for acute in vivo FABP4 inhibition that is applicable for further investigations and can be implemented in simple and fast-flow injection mass spectrometry assays. In addition, this approach can be considered a proof-of-principle study that can be applied to other lipid-pathway targeting mechanisms.

Gaussian graphical modeling reconstructs pathway reactions from high-throughput metabolomics data

Background

With the advent of high-throughput targeted metabolic profiling techniques, the question of how to interpret and analyze the resulting vast amount of data becomes more and more important. In this work we address the reconstruction of metabolic reactions from cross-sectional metabolomics data, that is without the requirement for time-resolved measurements or specific system perturbations. Previous studies in this area mainly focused on Pearson correlation coefficients, which however are generally incapable of distinguishing between direct and indirect metabolic interactions.

Results

In our new approach we propose the application of a Gaussian graphical model (GGM), an undirected probabilistic graphical model estimating the conditional dependence between variables. GGMs are based on partial correlation coefficients, that is pairwise Pearson correlation coefficients conditioned against the correlation with all other metabolites. We first demonstrate the general validity of the method and its advantages over regular correlation networks with computer-simulated reaction systems. Then we estimate a GGM on data from a large human population cohort, covering 1020 fasting blood serum samples with 151 quantified metabolites. The GGM is much sparser than the correlation network, shows a modular structure with respect to metabolite classes, and is stable to the choice of samples in the data set. On the example of human fatty acid metabolism, we demonstrate for the first time that high partial correlation coefficients generally correspond to known metabolic reactions. This feature is evaluated both manually by investigating specific pairs of high-scoring metabolites, and then systematically on a literature-curated model of fatty acid synthesis and degradation. Our method detects many known reactions along with possibly novel pathway interactions, representing candidates for further experimental examination.

Conclusions

In summary, we demonstrate strong signatures of intracellular pathways in blood serum data, and provide a valuable tool for the unbiased reconstruction of metabolic reactions from large-scale metabolomics data sets.

MIPS: curated databases and comprehensive secondary data resources in 2010

The Munich Information Center for Protein Sequences (MIPS at the Helmholtz Center for Environmental Health, Neuherberg, Germany) has many years of experience in providing annotated collections of biological data. Selected data sets of high relevance, such as model genomes, are subjected to careful manual curation, while the bulk of high-throughput data is annotated by automatic means. High-quality reference resources developed in the past and still actively maintained include Saccharomyces cerevisiae, Neurospora crassa and Arabidopsis thaliana genome databases as well as several protein interaction data sets (MPACT, MPPI and CORUM). More recent projects are PhenomiR, the database on microRNA-related phenotypes, and MIPS PlantsDB for integrative and comparative plant genome research. The interlinked resources SIMAP and PEDANT provide homology relationships as well as up-to-date and consistent annotation for 38 000 000 protein sequences. PPLIPS and CCancer are versatile tools for proteomics and functional genomics interfacing to a database of compilations from gene lists extracted from literature. A novel literature-mining tool, EXCERBT, gives access to structured information on classified relations between genes, proteins, phenotypes and diseases extracted from Medline abstracts by semantic analysis. All databases described here, as well as the detailed descriptions of our projects can be accessed through the MIPS WWW server (http://mips.helmholtz-muenchen.de).

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.

Metabolomic profiles indicate distinct physiological pathways affected by two loci with major divergent effect on Bos taurus growth and lipid deposition

Identifying trait-associated genetic variation offers new prospects to reveal novel physiological pathways modulating complex traits. Taking advantage of a unique animal model, we identified the I442M mutation in the non-SMC condensin I complex, subunit G ( NCAPG) gene and the Q204X mutation in the growth differentiation factor 8 ( GDF8) gene as substantial modulators of pre- and/or postnatal growth in cattle. In a combined metabolomic and genotype association approach, which is the first respective study in livestock, we surveyed the specific physiological background of the effects of both loci on body-mass gain and lipid deposition. Our data provided confirming evidence from two historically and geographically distant cattle populations that the onset of puberty is the key interval of divergent growth. The locus-specific metabolic patterns obtained from monitoring 201 plasma metabolites at puberty mirror the particular NCAPG I442M and GDF8 Q204X effects and represent biosignatures of divergent physiological pathways potentially modulating effects on proportional and disproportional growth, respectively. While the NCAPG I442M mutation affected the arginine metabolism, the 204X allele in the GDF8 gene predominantly raised the carnitine level and had concordant effects on glycerophosphatidylcholines and sphingomyelins. Our study provides a conclusive link between the well-described growth-regulating functions of arginine metabolism and the previously unknown specific physiological role of the NCAPG protein in mammalian metabolism. Owing to the confirmed effect of the NCAPG/LCORL locus on human height in genome-wide association studies, the results obtained for bovine NCAPG might add valuable, comparative information on the physiological background of genetically determined divergent mammalian growth.

Mouse phenotyping

Model organisms like the mouse are important tools to learn more about gene function in man. Within the last 20 years many mutant mouse lines have been generated by different methods such as ENU mutagenesis, constitutive and conditional knock-out approaches, knock-down, introduction of human genes, and knock-in techniques, thus creating models which mimic human conditions. Due to pleiotropic effects, one gene may have different functions in different organ systems or time points during development. Therefore mutant mouse lines have to be phenotyped comprehensively in a highly standardized manner to enable the detection of phenotypes which might otherwise remain hidden. The German Mouse Clinic (GMC) has been established at the Helmholtz Zentrum München as a phenotyping platform with open access to the scientific community (www.mousclinic.de; [1]). The GMC is a member of the EUMODIC consortium which created the European standard workflow EMPReSSslim for the systemic phenotyping of mouse models (http://www.eumodic.org/[2]).

Variation in the human lipidome associated with coffee consumption as revealed by quantitative targeted metabolomics

The effect of coffee consumption on human health is still discussed controversially. Here, we report results from a metabolomics study of coffee consumption, where we measured 363 metabolites in blood serum of 284 male participants of the Cooperative Health Research in the Region of Augsburg study population, aged between 55 and 79 years. A statistical analysis of the association of metabolite concentrations and the number of cups of coffee consumed per day showed that coffee intake is positively associated with two classes of sphingomyelins, one containing a hydroxy-group (SM(OH)) and the other having an additional carboxy-group (SM(OH,COOH)). In contrast, long- and medium-chain acylcarnitines were found to decrease with increasing coffee consumption. It is noteworthy that the concentration of total cholesterol also rises with an increased coffee intake in this study group. The association observed here between these hydroxylated and carboxylated sphingolipid species and coffee intake may be induced by changes in the cholesterol levels. Alternatively, these molecules may act as scavengers of oxidative species, which decrease with higher coffee intake. In summary, we demonstrate strong positive associations between coffee consumption and two classes of sphingomyelins and a negative association between coffee consumption and long- and medium-chain acylcarnitines.

Characteristics of mutants designed to incorporate a new ion pair into the structure of a cold adapted subtilisin-like serine proteinase

Structural comparisons of VPR, a subtilisin-like serine proteinase from a psychrotrophic Vibrio species and a thermophilic homologue, aqualysin I, have led us to hypothesize about the roles of different residues in the temperature adaptation of the enzymes. Some of these hypotheses are now being examined by analysis of mutants of the enzymes. The selected substitutions are believed to increase the stability of the cold adapted enzyme based on structural analysis of the thermostable structure. We report here on mutants, which were designed to incorporate an ion pair into the structure of VPR. The residues Asp17 and Arg259 are assumed to form an ion pair in aqualysin I. The cold adapted VPR contains Asn (Asn15) and Lys (Lys257) at corresponding sites in its structure. In VPR, Asn 15 is located on a surface loop with its side group pointing towards the side chain of Lys257. By substituting Asn15 by Asp (N15D) it was considered feasible that a salt bridge would form between the oppositely charged groups. To mimic further the putative salt bridge from the thermophile enzyme the corresponding double mutant (N15D/K257R) was also produced. The N15D mutation increased the thermal stability of VPR by approximately 3 degrees C, both in T(50%) and T(m). Addition of the K257R mutation did not however, increase the stability of the double mutant any further. Despite this stabilization of the VPR mutants the catalytic activity (k(cat)) against the substrate Suc-AAPF-NH-Np was increased in the mutants. Molecular dynamics simulations on wild type and the two mutant proteins suggested that indeed a salt bridge was formed in both cases. Furthermore, a truncated form of the N15D mutant (N15DDeltaC) was produced, lacking a 15 residue long C-terminal extended sequence not present in the thermophilic enzyme. In wild type VPR this supposedly moveable, negatively charged arm on the protein molecule might interfere with the new salt bridge introduced as a result of the N15D mutation. Removal of the C-terminal arm improved the thermal stability (T(m) approximately +1.5 degrees C) of the truncated enzyme (VPRDeltaC) as compared to the wild type VPR. Introduction of the N15D substitution into VPRDeltaC improved the thermal stability further by about 3 degrees C, or to about the same extent as in the wild type. However, contrary to what was observed for the wild type, the introduction of the putative salt bridge did not affect the catalytic properties (k(cat)) of the C-terminal truncated enzyme.

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.

Bioinformatics analysis of targeted metabolomics--uncovering old and new tales of diabetic mice under medication

Metabolomics is a powerful tool for identifying both known and new disease-related perturbations in metabolic pathways. In preclinical drug testing, it has a high potential for early identification of drug off-target effects. Recent advances in high-precision high-throughput mass spectrometry have brought the metabolomic field to a point where quantitative, targeted, metabolomic measurements with ready-to-use kits allow for the automated in-house screening for hundreds of different metabolites in large sets of biological samples. Today, the field of metabolomics is, arguably, at a point where transcriptomics was about 5 yr ago. This being so, the field has a strong need for adapted bioinformatics tools and methods. In this paper we describe a systematic analysis of a targeted quantitative characterization of more than 800 metabolites in blood plasma samples from healthy and diabetic mice under rosiglitazone treatment. We show that known and new metabolic phenotypes of diabetes and medication can be recovered in a statistically objective manner. We find that concentrations of methylglutaryl carnitine are oppositely impacted by rosiglitazone treatment of both healthy and diabetic mice. Analyzing ratios between metabolite concentrations dramatically reduces the noise in the data set, allowing for the discovery of new potential biomarkers of diabetes, such as the N-hydroxyacyloylsphingosyl-phosphocholines SM(OH)28:0 and SM(OH)26:0. Using a hierarchical clustering technique on partial eta(2) values, we identify functionally related groups of metabolites, indicating a diabetes-related shift from lysophosphatidylcholine to phosphatidylcholine levels. The bioinformatics data analysis approach introduced here can be readily generalized to other drug testing scenarios and other medical disorders.

Computing multiple sequence/structure alignments with the T-coffee package

The FASTA package provides a comprehensive set of similarity searching programs, similar to those provided by the BLAST package, and some additional programs for searching with short peptides and oligonucleotides that are not provided by BLAST. The FASTA programs work with a wide variety of database formats, including mySQL sequence databases. FASTA provides very accurate statistical significance estimates, and is more sensitive than BLASTN when comparing DNA sequences. These protocols describe how to use the FASTA programs to characterize protein and DNA sequences, using protein:protein, protein:DNA, and DNA:DNA comparisons.

MassTRIX: mass translator into pathways

Recent technical advances in mass spectrometry (MS) have brought the field of metabolomics to a point where large numbers of metabolites from numerous prokaryotic and eukaryotic organisms can now be easily and precisely detected. The challenge today lies in the correct annotation of these metabolites on the basis of their accurate measured masses. Assignment of bulk chemical formula is generally possible, but without consideration of the biological and genomic context, concrete metabolite annotations remain difficult and uncertain. MassTRIX responds to this challenge by providing a hypothesis-driven approach to high precision MS data annotation. It presents the identified chemical compounds in their genomic context as differentially colored objects on KEGG pathway maps. Information on gene transcription or differences in the gene complement (e.g. samples from different bacterial strains) can be easily added. The user can thus interpret the metabolic state of the organism in the context of its potential and, in the case of submitted transcriptomics data, real enzymatic capacities. The MassTRIX web server is freely accessible at http://masstrix.org

Reductive genome evolution from the mother of Rickettsia

The Rickettsia genus is a group of obligate intracellular α-proteobacteria representing a paradigm of reductive evolution. Here, we investigate the evolutionary processes that shaped the genomes of the genus. The reconstruction of ancestral genomes indicates that their last common ancestor contained more genes, but already possessed most traits associated with cellular parasitism. The differences in gene repertoires across modern Rickettsia are mainly the result of differential gene losses from the ancestor. We demonstrate using computer simulation that the propensity of loss was variable across genes during this process. We also analyzed the ratio of nonsynonymous to synonymous changes (Ka/Ks) calculated as an average over large sets of genes to assay the strength of selection acting on the genomes of Rickettsia, Anaplasmataceae, and free-living γ-proteobacteria. As a general trend, Ka/Ks were found to decrease with increasing divergence between genomes. The high Ka/Ks for closely related genomes are probably due to a lag in the removal of slightly deleterious nonsynonymous mutations by natural selection. Interestingly, we also observed a decrease of the rate of gene loss with increasing divergence, suggesting a similar lag in the removal of slightly deleterious pseudogene alleles. For larger divergence (Ks > 0.2), Ka/Ks converge toward similar values indicating that the levels of selection are roughly equivalent between intracellular α-proteobacteria and their free-living relatives. This contrasts with the view that obligate endocellular microorganisms tend to evolve faster as a consequence of reduced effectiveness of selection, and suggests a major role of enhanced background mutation rates on the fast protein divergence in the obligate intracellular α-proteobacteria.

Genome downsizing and fast sequence divergence are frequently observed in bacteria living exclusively within the cells of higher eukaryotes. However, the driving forces and contributions of these processes to the genome diversity of the microorganisms remain poorly understood. The genus Rickettsia, a group of small obligate intracellular pathogens of humans, provides a fascinating model to study the genome downsizing process. In this article, we used seven Rickettsia genomes to reconstruct the genome of their ancestor and inferred the origin and fate of the genes found in today's species. We identify the process of gene loss as the main cause of genome diversification within the genus and show that the rate of gene loss, sequence divergence, and genome rearrangements are highly variable across the various Rickettsia lineages. This heterogeneity likely reflects the intricate effects of specialization to distinct arthropod hosts and critical alterations of the gene repertoire, such as the losses of DNA repair genes and the amplification of mobile genes. In contrast, we did not find evidence for the role of reduced population sizes on the long-term acceleration of sequence evolution. Overall, the data presented in this article shed new light on the fundamental evolutionary processes that drive the evolution of obligate intracellular bacteria.

Inference of gene function based on gene fusion events: the rosetta-stone method

The method described in this chapter can be used to infer putative functional links between two proteins. The basic idea is based on the principle of "guilt by association." It is assumed that two proteins, which are found to be transcribed by a single transcript in one (or several) genomes are likely to be functionally linked, for example by acting in a same metabolic pathway or by forming a multiprotein complex. This method is of particular interest for studying genes that exhibit no, or only remote, homologies with already well-characterized proteins. Combined with other non-homology based methods, gene fusion events may yield valuable information for hypothesis building on protein function, and may guide experimental characterization of the target protein, for example by suggesting potential ligands or binding partners. This chapter uses the FusionDB database (http://www.igs.cnrs-mrs.fr/FusionDB/) as source of information. FusionDB provides a characterization of a large number of gene fusion events at hand of multiple sequence alignments. Orthologous genes are included to yield a comprehensive view of the structure of a gene fusion event. Phylogenetic tree reconstruction is provided to evaluate the history of a gene fusion event, and three-dimensional protein structure information is used, where available, to further characterize the nature of the gene fusion. For genes that are not comprised in FusionDB, some instructions are given as how to generate a similar type of information, based solely on publicly available web tools that are listed here.

Estimation of prokaryote genomic DNA G+C content by sequencing universally conserved genes

Determination of the DNA G+C content of prokaryotic genomes using traditional methods is time-consuming and results may vary from laboratory to laboratory, depending on the technique used. We explored the possibility of extrapolating the genomic DNA G+C content of prokaryotes from gene sequences. For this, 127 universally conserved genes were studied from 50 prokaryotic genomes in the Clusters of Orthologous Groups database. Of these, 57 genes were present as a single copy in the genomes of 157 different prokaryote species available in GenBank. There was a strong correlation [coefficient of determination (r2) >95 %] between the DNA G+C contents of 20 genes and their corresponding genomes. For each of the 157 prokaryotic genomes studied, the DNA G+C content of the 20 genes was used to determine a 'calculated' genome DNA G+C content (CGC) and this value was compared with the 'real' genome DNA G+C content (RGC). In order to select the most suitable gene for the determination of CGC values, we compared the r2 and median mol% difference between CGC and RGC as well as the sensitivity of each gene to provide CGC values for prokaryotic genomes that differ by less than 5 mol% from their RGC. The highly conserved ftsY gene (median size 1144 nucleotides), a vertically inherited member of the GTPase superfamily, showed the highest r2 value of 0.98, the smallest median mol% difference between CGC and RGC of 1.06 and a sensitivity of 100 %. Using ftsY DNA G+C content values, the CGC values of 100 genomes not included in the calculation of r2 differed by less than 5 mol% from their RGC values. These data suggest that the genomic DNA G+C content of prokaryotes may be estimated easily and reliably from the ftsY gene sequence.

GFscore: a general nonlinear consensus scoring function for high-throughput docking

Most of the recent published works in the field of docking and scoring protein/ligand complexes have focused on ranking true positives resulting from a Virtual Library Screening (VLS) through the use of a specified or consensus linear scoring function. In this work, we present a methodology to speed up the High Throughput Screening (HTS) process, by allowing focused screens or for hitlist triaging when a prohibitively large number of hits is identified in the primary screen, where we have extended the principle of consensus scoring in a nonlinear neural network manner. This led us to introduce a nonlinear Generalist scoring Function, GFscore, which was trained to discriminate true positives from false positives in a data set of diverse chemical compounds. This original Generalist scoring Function is a combination of the five scoring functions found in the CScore package from Tripos Inc. GFscore eliminates up to 75% of molecules, with a confidence rate of 90%. The final result is a Hit Enrichment in the list of molecules to investigate during a research campaign for biological active compounds where the remaining 25% of molecules would be sent to in vitro screening experiments. GFscore is therefore a powerful tool for the biologist, saving both time and money.

NORMA: a tool for flexible fitting of high-resolution protein structures into low-resolution electron-microscopy-derived density maps

This paper describes a freely available software suite that allows the modelling of large conformational changes of high-resolution three-dimensional protein structures under the constraint of a low-resolution electron-density map. Typical applications are the interpretation of electron-microscopy data using atomic scale X-ray structural models. The software package provided should enable the interested user to perform flexible fitting on new cases without encountering major technical difficulties. The NORMA software suite including three fully executable reference cases and extensive user instructions are available at http://www.elnemo.org/NORMA/.

Isolation, characterization, and bioinformatic analysis of calmodulin-binding protein cmbB reveals a novel tandem IP22 repeat common to many Dictyostelium and Mimivirus proteins

A novel calmodulin-binding protein cmbB from Dictyostelium discoideum is encoded in a single gene. Northern analysis reveals two cmbB transcripts first detectable at 4 h during multicellular development. Western blotting detects an approximately 46.6 kDa protein. Sequence analysis and calmodulin-agarose binding studies identified a "classic" calcium-dependent calmodulin-binding domain (179IPKSLRSLFLGKGYNQPLEF198) but structural analyses suggest binding may not involve classic alpha-helical calmodulin-binding. The cmbB protein is comprised of tandem repeats of a newly identified IP22 motif ([I,L]Pxxhxxhxhxxxhxxxhxxxx; where h = any hydrophobic amino acid) that is highly conserved and a more precise representation of the FNIP repeat. At least eight Acanthamoeba polyphaga Mimivirus proteins and over 100 Dictyostelium proteins contain tandem arrays of the IP22 motif and its variants. cmbB also shares structural homology to YopM, from the plague bacterium Yersenia pestis.

Determination of strongly overlapping signaling activity from microarray data

BACKGROUND

As numerous diseases involve errors in signal transduction, modern therapeutics often target proteins involved in cellular signaling. Interpretation of the activity of signaling pathways during disease development or therapeutic intervention would assist in drug development, design of therapy, and target identification. Microarrays provide a global measure of cellular response, however linking these responses to signaling pathways requires an analytic approach tuned to the underlying biology. An ongoing issue in pattern recognition in microarrays has been how to determine the number of patterns (or clusters) to use for data interpretation, and this is a critical issue as measures of statistical significance in gene ontology or pathways rely on proper separation of genes into groups.

RESULTS

Here we introduce a method relying on gene annotation coupled to decompositional analysis of global gene expression data that allows us to estimate specific activity on strongly coupled signaling pathways and, in some cases, activity of specific signaling proteins. We demonstrate the technique using the Rosetta yeast deletion mutant data set, decompositional analysis by Bayesian Decomposition, and annotation analysis using ClutrFree. We determined from measurements of gene persistence in patterns across multiple potential dimensionalities that 15 basis vectors provides the correct dimensionality for interpreting the data. Using gene ontology and data on gene regulation in the Saccharomyces Genome Database, we identified the transcriptional signatures of several cellular processes in yeast, including cell wall creation, ribosomal disruption, chemical blocking of protein synthesis, and, critically, individual signatures of the strongly coupled mating and filamentation pathways.

CONCLUSION

This works demonstrates that microarray data can provide downstream indicators of pathway activity either through use of gene ontology or transcription factor databases. This can be used to investigate the specificity and success of targeted therapeutics as well as to elucidate signaling activity in normal and disease processes.

Mimivirus and the emerging concept of "giant" virus

The recently discovered Acanthamoeba polyphaga Mimivirus is the largest known DNA virus. Its particle size (750 nm), genome length (1.2 million bp) and large gene repertoire (911 protein coding genes) blur the established boundaries between viruses and parasitic cellular organisms. In addition, the analysis of its genome sequence identified many types of genes never before encountered in a virus, including aminoacyl-tRNA synthetases and other central components of the translation machinery previously thought to be the signature of cellular organisms. In this article, we examine how the finding of such a giant virus might durably influence the way we look at microbial biodiversity, and lead us to revise the classification of microbial domains and life forms. We propose to introduce the word "girus" to recognize the intermediate status of these giant DNA viruses, the genome complexity of which makes them closer to small parasitic prokaryotes than to regular viruses.

Gene and genome duplication in Acanthamoeba polyphaga Mimivirus

Gene duplication is key to molecular evolution in all three domains of life and may be the first step in the emergence of new gene function. It is a well-recognized feature in large DNA viruses but has not been studied extensively in the largest known virus to date, the recently discovered Acanthamoeba polyphaga Mimivirus. Here, I present a systematic analysis of gene and genome duplication events in the mimivirus genome. I found that one-third of the mimivirus genes are related to at least one other gene in the mimivirus genome, either through a large segmental genome duplication event that occurred in the more remote past or through more recent gene duplication events, which often occur in tandem. This shows that gene and genome duplication played a major role in shaping the mimivirus genome. Using multiple alignments, together with remote-homology detection methods based on Hidden Markov Model comparison, I assign putative functions to some of the paralogous gene families. I suggest that a large part of the duplicated mimivirus gene families are likely to interfere with important host cell processes, such as transcription control, protein degradation, and cell regulatory processes. My findings support the view that large DNA viruses are complex evolving organisms, possibly deeply rooted within the tree of life, and oppose the paradigm that viral evolution is dominated by lateral gene acquisition, at least in regard to large DNA viruses.

Conformational flexibility of Mycobacterium tuberculosis thioredoxin reductase: crystal structure and normal-mode analysis

The thioredoxin system exists ubiquitously and participates in essential antioxidant and redox-regulation processes via a pair of conserved cysteine residues. In Mycobacterium tuberculosis, which lacks a genuine glutathione system, the thioredoxin system provides reducing equivalents inside the cell. The three-dimensional structure of thioredoxin reductase from M. tuberculosis has been determined at 3 A resolution. TLS refinement reveals a large libration axis, showing that NADPH-binding domain has large anisotropic disorder. The relative rotation of the NADPH domain with respect to the FAD domain is necessary for the thioredoxin reduction cycle, as it brings the spatially distant reacting sites close together. Normal-mode analysis carried out based on the elastic network model shows that the motion required to bring about the functional conformational change can be accounted for by motion along one single mode. TLS refinement and normal-mode analysis thus enhance our understanding of the associated conformational changes.

Phydbac "Gene Function Predictor": a gene annotation tool based on genomic context analysis

Background

The large amount of completely sequenced genomes allows genomic context analysis to predict reliable functional associations between prokaryotic proteins. Major methods rely on the fact that genes encoding physically interacting partners or members of shared metabolic pathways tend to be proximate on the genome, to evolve in a correlated manner and to be fused as a single sequence in another organism.

Results

The new "Gene Function Predictor", linked to the web server Phydbac proposes putative associations between Escherichia coli K-12 proteins derived from a combination of these methods. We show that associations made by this tool are more accurate than linkages found in the other established databases. Predicted assignments to GO categories, based on pre-existing functional annotations of associated proteins are also available. This new database currently holds 9,379 pairwise links at an expected success rate of at least 80%, the 6,466 functional predictions to GO terms derived from these links having a level of accuracy higher than 70%.

Conclusion

The "Gene Function Predictor" is an automatic tool that aims to help biologists by providing them hypothetical functional predictions out of genomic context characteristics. The "Gene Function predictor" is available at .

Mimivirus gene promoters exhibit an unprecedented conservation among all eukaryotes

The initial analysis of the recently sequenced genome of Acanthamoeba polyphaga Mimivirus, the largest known double-stranded DNA virus, predicted a proteome of size and complexity more akin to small parasitic bacteria than to other nucleocytoplasmic large DNA viruses and identified numerous functions never before described in a virus. It has been proposed that the Mimivirus lineage could have emerged before the individualization of cellular organisms from the three domains of life. An exhaustive in silico analysis of the noncoding moiety of all known viral genomes now uncovers the unprecedented perfect conservation of an AAAATTGA motif in close to 50% of the Mimivirus genes. This motif preferentially occurs in genes transcribed from the predicted leading strand and is associated with functions required early in the viral infectious cycle, such as transcription and protein translation. A comparison with the known promoter of unicellular eukaryotes, amoebal protists in particular, strongly suggests that the AAAATTGA motif is the structural equivalent of the TATA box core promoter element. This element is specific to the Mimivirus lineage and may correspond to an ancestral promoter structure predating the radiation of the eukaryotic kingdoms. This unprecedented conservation of core promoter regions is another exceptional feature of Mimivirus that again raises the question of its evolutionary origin.

Bayesian decomposition analysis of bacterial phylogenomic profiles

BACKGROUND

The past two decades have seen the appearance of new infectious diseases and the reemergence of old diseases previously thought to be under control. At the same time, the effectiveness of the existing antibacterials is rapidly decreasing due to the spread of multidrug-resistant pathogens.

AIM

The aim of this study was to the identify candidate molecular targets (e.g. enzymes) within essential metabolic pathways specific to a significant subset of bacterial pathogens as the first step in the rational design of new antibacterial drugs.

METHODS

We constructed a dataset of phylogenomic profiles (vectors that encode the similarity, measured by BLAST scores, of a gene across many species) for a series of 31 pathogenic bacteria of interest with 1073 genes taken from the reference organisms Escherichia coli and Mycobacterium tuberculosis. We applied Bayesian Decomposition, a matrix decomposition algorithm, to identify functional metabolic units comprising overlapping sets of genes in this dataset.

RESULTS

Although no information on phylogeny was provided to the system, Bayesian Decomposition retrieved the known bacteria phylogenic relationships on the basis of the proteins necessary for survival. In addition, a set of genes required by all bacteria was identified, as well as components and enzymes specific to subsets of bacteria.

CONCLUSION

The use of phylogenomic profiles and Bayesian Decomposition provide important insights for the design of new antibacterial therapeutics.

Mimivirus TyrRS: preliminary structural and functional characterization of the first amino-acyl tRNA synthetase found in a virus

The amoeba-infecting Mimivirus is the largest known double-stranded DNA virus, with a 400 nm particle size, comparable to that of mycoplasma. The complete sequence of its 1.2 Mbp genome has recently been determined [Raoult et al. (2004), Science, 306, 1344-1350] and revealed numerous genes that were not expected to be found in a virus, such as genes encoding translation components, including 4-amino-acyl tRNA synthetases and homologues to various translation initiation, elongation and termination factors. A comprehensive structural and functional study of these Mimivirus gene products was initiated, as they may hold important clues about the origin of DNA viruses. Here, the first preliminary crystallographic and functional results obtained on one of these targets, Mimivirus TyrRS, are reported. Preliminary phasing was obtained using an original combination of homology modelling and normal mode analysis. Experimental evidence that Mimivirus tyrosyl tRNA synthetase recombinant gene product does indeed activate tyrosine is also presented.

Annotation of bacterial genomes using improved phylogenomic profiles

MOTIVATION

Phylogenomic profiling is a large-scale comparative genomic method used to infer protein function from evolutionary information first described in a binary form by Pellegrini et al. (1999). Here, we propose improvements of this approach including the use of normalized Blastp bit scores, a normalization of the matrix of profiles to take into account the evolutionary distances between bacteria, the definition of a phylogenomic neighborhood based on continuous pairwise distances between genes and an original annotation procedure including the computation of a p-value for each functional assignment.

RESULTS

The method presented here increases the number of Ecocyc enzymes identified as being evolutionarily related by about 25% with respect to the original binary form (absent/present) method. The fraction of 'false' positives is shown to be smaller than 20%. Based on their phylogenomic relationships, genes of unknown function can then be automatically related to annotated genes. Each gene annotation predicted is associated with a p-value, i.e. its probability to be obtained by chance. The validity of this method was extensively tested on a large set of genes of known function using the MultiFun database. We find that 50% of 3122 function attributions that can be made at a p-value level of 10(-11) correspond to the actual gene annotation. The method can be readily applied to any newly sequenced microbial genome. In contrast to earlier work on the same topic, our approach avoids the use of arbitrary cut-off values, and provides a reliability estimate of the functional predictions in form of p-values.

3DCoffee@igs: a web server for combining sequences and structures into a multiple sequence alignment

This paper presents 3DCoffee@igs, a web-based tool dedicated to the computation of high-quality multiple sequence alignments (MSAs). 3D-Coffee makes it possible to mix protein sequences and structures in order to increase the accuracy of the alignments. Structures can be either provided as PDB identifiers or directly uploaded into the server. Given a set of sequences and structures, pairs of structures are aligned with SAP while sequence-structure pairs are aligned with Fugue. The resulting collection of pairwise alignments is then combined into an MSA with the T-Coffee algorithm. The server and its documentation are available from http://igs-server.cnrs-mrs.fr/Tcoffee/.

Phydbac2: improved inference of gene function using interactive phylogenomic profiling and chromosomal location analysis

Phydbac (phylogenomic display of bacterial genes) implemented a method of phylogenomic profiling using a distance measure based on normalized BLAST scores. This method was able to increase the predictive power of phylogenomic profiling by about 25% when compared to the classical approach based on Hamming distances. Here we present a major extension of Phydbac (named here Phydbac2), that extends both the concept and the functionality of the original web-service. While phylogenomic profiles remain the central focus of Phydbac2, it now integrates chromosomal proximity and gene fusion analyses as two additional non-similarity-based indicators for inferring pairwise gene functional relationships. Moreover, all presently available (January 2004) fully sequenced bacterial genomes and those of three lower eukaryotes are now included in the profiling process, thus increasing the initial number of reference genomes (71 in Phydbac) to 150 in Phydbac2. Using the KEGG metabolic pathway database as a benchmark, we show that the predictive power of Phydbac2 is improved by 27% over the previous version. This gain is accounted for on one hand, by the increased number of reference genomes (11%) and on the other hand, as a result of including chromosomal proximity into the distance measure (16%). The expanded functionality of Phydbac2 now allows the user to query more than 50 different genomes, including at least one member of each major bacterial group, most major pathogens and potential bio-terrorism agents. The search for co-evolving genes based on consensus profiles from multiple organisms, the display of Phydbac2 profiles side by side with COG information, the inclusion of KEGG metabolic pathway maps the production of chromosomal proximity maps, and the possibility of collecting and processing results from different Phydbac queries in a common shopping cart are the main new features of Phydbac2. The Phydbac2 web server is available at http://igs-server.cnrs-mrs.fr/phydbac/.

CaspR: a web server for automated molecular replacement using homology modelling

Molecular replacement (MR) is the method of choice for X-ray crystallography structure determination when structural homologues are available in the Protein Data Bank (PDB). Although the success rate of MR decreases sharply when the sequence similarity between template and target proteins drops below 35% identical residues, it has been found that screening for MR solutions with a large number of different homology models may still produce a suitable solution where the original template failed. Here we present the web tool CaspR, implementing such a strategy in an automated manner. On input of experimental diffraction data, of the corresponding target sequence and of one or several potential templates, CaspR executes an optimized molecular replacement procedure using a combination of well-established stand-alone software tools. The protocol of model building and screening begins with the generation of multiple structure-sequence alignments produced with T-COFFEE, followed by homology model building using MODELLER, molecular replacement with AMoRe and model refinement based on CNS. As a result, CaspR provides a progress report in the form of hierarchically organized summary sheets that describe the different stages of the computation with an increasing level of detail. For the 10 highest-scoring potential solutions, pre-refined structures are made available for download in PDB format. Results already obtained with CaspR and reported on the web server suggest that such a strategy significantly increases the fraction of protein structures which may be solved by MR. Moreover, even in situations where standard MR yields a solution, pre-refined homology models produced by CaspR significantly reduce the time-consuming refinement process. We expect this automated procedure to have a significant impact on the throughput of large-scale structural genomics projects. CaspR is freely available at http://igs-server.cnrs-mrs.fr/Caspr/.

ElNemo: a normal mode web server for protein movement analysis and the generation of templates for molecular replacement

Normal mode analysis (NMA) is a powerful tool for predicting the possible movements of a given macromolecule. It has been shown recently that half of the known protein movements can be modelled by using at most two low-frequency normal modes. Applications of NMA cover wide areas of structural biology, such as the study of protein conformational changes upon ligand binding, membrane channel opening and closure, potential movements of the ribosome, and viral capsid maturation. Another, newly emerging field of NMA is related to protein structure determination by X-ray crystallography, where normal mode perturbed models are used as templates for diffraction data phasing through molecular replacement (MR). Here we present ElNémo, a web interface to the Elastic Network Model that provides a fast and simple tool to compute, visualize and analyse low-frequency normal modes of large macro-molecules and to generate a large number of different starting models for use in MR. Due to the 'rotation-translation-block' (RTB) approximation implemented in ElNémo, there is virtually no upper limit to the size of the proteins that can be treated. Upon input of a protein structure in Protein Data Bank (PDB) format, ElNémo computes its 100 lowest-frequency modes and produces a comprehensive set of descriptive parameters and visualizations, such as the degree of collectivity of movement, residue mean square displacements, distance fluctuation maps, and the correlation between observed and normal-mode-derived atomic displacement parameters (B-factors). Any number of normal mode perturbed models for MR can be generated for download. If two conformations of the same (or a homologous) protein are available, ElNémo identifies the normal modes that contribute most to the corresponding protein movement. The web server can be freely accessed at http://igs-server.cnrs-mrs.fr/elnemo/index.html.