The Saliva Metabolome in Association to Oral Health Status.

Periodontitis is one of the most prevalent oral diseases worldwide and is caused by multifactorial interactions between host and oral bacteria. Altered cellular metabolism of host and microbes releases a number of intermediary end products known as metabolites. There is an increasing interest in identifying metabolites from oral fluids such as saliva to widen the understanding of the complex pathogenesis of periodontitis. It is believed that some metabolites might serve as indicators toward early detection and screening of periodontitis and perhaps even for monitoring its prognosis in the future. Because contemporary periodontal screening methods are deficient, there is an urgent need for novel approaches in periodontal screening procedures. To this end, we associated oral parameters (clinical attachment level, periodontal probing depth, supragingival plaque, supragingival calculus, number of missing teeth, and removable denture) with a large set of salivary metabolites ( n = 284) obtained by mass spectrometry among a subsample ( n = 909) of nondiabetic participants from the Study of Health in Pomerania (SHIP-Trend-0). Linear regression analyses were performed in age-stratified groups and adjusted for potential confounders. A multifaceted image of associated metabolites ( n = 107) was revealed with considerable differences according to age groups. In the young (20 to 39 y) and middle-aged (40 to 59 y) groups, metabolites were predominantly associated with periodontal variables, whereas among the older subjects (≥60 y), tooth loss was strongly associated with metabolite levels. Metabolites associated with periodontal variables were clearly linked to tissue destruction, host defense mechanisms, and bacterial metabolism. Across all age groups, the bacterial metabolite phenylacetate was significantly associated with periodontal variables. Our results revealed alterations of the salivary metabolome in association with age and oral health status. Among our comprehensive panel of metabolites, periodontitis was significantly associated with the bacterial metabolite phenylacetate, a promising substance for further biomarker research.

A network-based conditional genetic association analysis of the human metabolome.

Background: Genome-wide association studies have identified hundreds of loci that influence a wide variety of complex human traits; however, little is known regarding the biological mechanism of action of these loci. The recent accumulation of functional genomics ("omics"), including metabolomics data, has created new opportunities for studying the functional role of specific changes in the genome. Functional genomic data are characterized by their high dimensionality, the presence of (strong) statistical dependency between traits, and, potentially, complex genetic control. Therefore, the analysis of such data requires specific statistical genetics methods. Results: To facilitate our understanding of the genetic control of omics phenotypes, we propose a trait-centered, network-based conditional genetic association (cGAS) approach for identifying the direct effects of genetic variants on omics-based traits. For each trait of interest, we selected from a biological network a set of other traits to be used as covariates in the cGAS. The network can be reconstructed either from biological pathway databases (a mechanistic approach) or directly from the data, using a Gaussian graphical model applied to the metabolome (a data-driven approach). We derived mathematical expressions that allow comparison of the power of univariate analyses with conditional genetic association analyses. We then tested our approach using data from a population-based Cooperative Health Research in the region of Augsburg (KORA) study (n = 1,784 subjects, 1.7 million single-nucleotide polymorphisms) with measured data for 151 metabolites. Conclusions: We found that compared to single-trait analysis, performing a genetic association analysis that includes biologically relevant covariates can either gain or lose power, depending on specific pleiotropic scenarios, for which we provide empirical examples. In the context of analyzed metabolomics data, the mechanistic network approach had more power compared to the data-driven approach. Nevertheless, we believe that our analysis shows that neither a prior-knowledge-only approach nor a phenotypic-data-only approach is optimal, and we discuss possibilities for improvement.

The human metabolic profile reflects macro- and micronutrient intake distinctly according to fasting time.

Although the impact of dietary patterns on human serum metabolites has been examined, the fasting effect on the metabolic profile has not yet been considered. The aim of this cross-sectional study is to investigate the influence of fasting regarding the association between dietary patterns, reflected by macro- and micronutrient intake, and human serum metabolites in a population-based cohort. A total 1197 non-diabetic German adults aged 45 to 83 years, who participated in baseline of the CARLA study 2002-2006 and had metabolite quantification were selected for this study. Macro- and micronutrient intakes were estimated from a food frequency questionnaire (FFQ). Concentrations of 134 serum metabolites were measured by targeted metabolomics AbsoluteIDQ p150 Kit. The association of dietary patterns with serum metabolites was calculated by means of linear regression and the influence of the fasting status was considered by including interaction terms with each macro- and micronutrient. Higher self-reported intake of alcohol and lower self-reported intake of organic acids were associated with higher concentrations of acylcarnitines and phosphatidylcholines. Mainly the associations between dietary patterns and acylcarnitines and hexose were altered after including interaction terms, suggesting effect modification by fasting status. No effect from fasting time was seen for amino acids and saturated, mono- and polyunsaturated phosphatidylcholines.

Instability of personal human metabotype is linked to all-cause mortality.

Disruption of metabolic homeostasis is an important factor in many diseases. Various metabolites have been linked to higher risk of morbidity and all-cause mortality using metabolomics in large population-based cohorts. In these studies, baseline metabolite levels were compared across subjects to identify associations with health outcomes, implying the existence of 'healthy' concentration ranges that are equally applicable to all individuals. Here, we focused on intra-individual changes in metabolite levels over time and their link to mortality, potentially allowing more personalized risk assessment. We analysed targeted metabolomics data for 134 blood metabolites from 1409 participants in the population-based CARLA cohort at baseline and after four years. Metabotypes of the majority of participants (59%) were extremely stable over time indicated by high correlation between the subjects' metabolite profiles at the two time points. Metabotype instability and, in particular, decrease of valine were associated with higher risk of all-cause mortality in 7.9 years of follow-up (hazard ratio (HR) = 1.5(95%CI = 1.0-2.3) and 0.2(95%CI = 0.1-0.3)) after multifactorial adjustment. Excluding deaths that occurred in the first year after metabolite profiling showed similar results (HR = 1.8(95%CI = 1.1-2.8)). Lower metabotype stability was also associated with incident cardiovascular disease (OR = 1.2(95%CI = 1.0-1.3)). Therefore, changes in the personal metabotype might be a valuable indicator of pre-clinical disease.

Serum metabolomic profiling highlights pathways associated with liver fat content in a general population sample.

BACKGROUND/OBJECTIVES: Fatty liver disease (FLD) is an important intermediate trait along the cardiometabolic disease spectrum and strongly associates with type 2 diabetes. Knowledge of biological pathways implicated in FLD is limited. An untargeted metabolomic approach might unravel novel pathways related to FLD. SUBJECTS/METHODS: In a population-based sample (n=555) from Northern Germany, liver fat content was quantified as liver signal intensity using magnetic resonance imaging. Serum metabolites were determined using a non-targeted approach. Partial least squares regression was applied to derive a metabolomic score, explaining variation in serum metabolites and liver signal intensity. Associations of the metabolomic score with liver signal intensity and FLD were investigated in multivariable-adjusted robust linear and logistic regression models, respectively. Metabolites with a variable importance in the projection >1 were entered in in silico overrepresentation and pathway analyses. RESULTS: In univariate analysis, the metabolomics score explained 23.9% variation in liver signal intensity. A 1-unit increment in the metabolomic score was positively associated with FLD (n=219; odds ratio: 1.36; 95% confidence interval: 1.27-1.45) adjusting for age, sex, education, smoking and physical activity. A simplified score based on the 15 metabolites with highest variable importance in the projection statistic showed similar associations. Overrepresentation and pathway analyses highlighted branched-chain amino acids and derived gamma-glutamyl dipeptides as significant correlates of FLD. CONCLUSIONS: A serum metabolomic profile was associated with FLD and liver fat content. We identified a simplified metabolomics score, which should be evaluated in prospective studies.

Increased amino acids levels and the risk of developing of hypertriglyceridemia in a 7-year follow-up.

BACKGROUND: Recently, five branched-chain and aromatic amino acids were shown to be associated with the risk of developing type 2 diabetes (T2D). AIM: We set out to examine whether amino acids are also associated with the development of hypertriglyceridemia. MATERIALS AND METHODS: We determined the serum amino acids concentrations of 1,125 individuals of the KORA S4 baseline study, for which follow-up data were available also at the KORA F4 7 years later. After exclusion for hypertriglyceridemia (defined as having a fasting triglyceride level above 1.70 mmol/L) and diabetes at baseline, 755 subjects remained for analyses. RESULTS: Increased levels of leucine, arginine, valine, proline, phenylalanine, isoleucine and lysine were significantly associated with an increased risk of hypertriglyceridemia. These associations remained significant when restricting to those individuals who did not develop T2D in the 7-year follow-up. The increase per standard deviation of amino acid level was between 26 and 40 %. CONCLUSIONS: Seven amino acids were associated with an increased risk of developing hypertriglyceridemia after 7 years. Further studies are necessary to elucidate the complex role of these amino acids in the pathogenesis of metabolic disorders.

CYGD: the Comprehensive Yeast Genome Database.

The Comprehensive Yeast Genome Database (CYGD) compiles a comprehensive data resource for information on the cellular functions of the yeast Saccharomyces cerevisiae and related species, chosen as the best understood model organism for eukaryotes. The database serves as a common resource generated by a European consortium, going beyond the provision of sequence information and functional annotations on individual genes and proteins. In addition, it provides information on the physical and functional interactions among proteins as well as other genetic elements. These cellular networks include metabolic and regulatory pathways, signal transduction and transport processes as well as co-regulated gene clusters. As more yeast genomes are published, their annotation becomes greatly facilitated using S.cerevisiae as a reference. CYGD provides a way of exploring related genomes with the aid of the S.cerevisiae genome as a backbone and SIMAP, the Similarity Matrix of Proteins. The comprehensive resource is available under http://mips.gsf.de/genre/proj/yeast/.

The HIB database of annotated UniGene clusters.

SUMMARY: The HumanInfoBase (HIB) is a database of putative human gene transcripts. UniGene clusters are assembled, and the resulting consensus sequences are submitted to the PEDANT software system (Frishman,D., Albermann,K., Hani,J., Heumann,K., Metanomski,A., Zollner,A. and Mewes,H.-W., 2001, Bioinformatics, 17, 44--57) for fully automatic sequence analysis and annotation. Predicted transcripts are classified using a variety of functional and structural categories, and hyperlinks to various databases are provided for additional information. A WWW-based graphical user interface represents the assembly process as well as functionally important sites in the putative transcripts.

Nearest neighbor classification in 3D protein databases.

In molecular databases, structural classification is a basic task that can be successfully approached by nearest neighbor methods. The underlying similarity models consider spatial properties such as shape and extension as well as thematic attributes. We introduce 3D shape histograms as an intuitive and powerful approach to model similarity for solid objects such as molecules. Errors of measurement, sampling, and numerical rounding may result in small displacements of atomic coordinates. These effects may be handled by using quadratic form distance functions. An efficient processing of similarity queries based on quadratic forms is supported by a filter-refinement architecture. Experiments on our 3D protein database demonstrate the high classification accuracy of more than 90% and the good performance of the technique.