Connecting the epigenome, metabolome and proteome for a deeper understanding of disease.

Epigenome-wide association studies (EWAS) identify genes that are dysregulated by the studied clinical endpoints, thereby indicating potential new diagnostic biomarkers, drug targets and therapy options. Combining EWAS with deep molecular phenotyping, such as approaches enabled by metabolomics and proteomics, allows further probing of the underlying disease-associated pathways. For instance, methylation of the TXNIP gene is associated robustly with prevalent type 2 diabetes and further with metabolites that are short-term markers of glycaemic control. These associations reflect TXNIP's function as a glucose uptake regulator by interaction with the major glucose transporter GLUT1 and suggest that TXNIP methylation can be used as a read-out for the organism's exposure to glucose stress. Another case is the association between DNA methylation of the AHRR and F2RL3 genes with smoking and a protein that is involved in the reprogramming of the bronchial epithelium. These examples show that associations between DNA methylation and intermediate molecular traits can open new windows into how the body copes with physiological challenges. This knowledge, if carefully interpreted, may indicate novel therapy options and, together with monitoring of the methylation state of specific methylation sites, may in the future allow the early diagnosis of impending disease. It is essential for medical practitioners to recognize the potential that this field holds in translating basic research findings to clinical practice. In this review, we present recent advances in the field of EWAS with metabolomics and proteomics and discuss both the potential and the challenges of translating epigenetic associations, with deep molecular phenotypes, to biomedical applications.

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.

Non-truncating LIFR mutation: causal for prominent congenital pain insensitivity phenotype with progressive vertebral destruction?

We present a Qatari family with two children who displayed a characteristic phenotype of congenital marked pain insensitivity with hypohidrosis and progressive aseptic destruction of joints and vertebrae resembling that of hereditary sensory and autonomic neuropathies (HSANs). The patients, aged 10 and 14, remained of uncertain genetic diagnosis until whole genome sequencing was pursued. Genome sequencing identified a novel homozygous C65S mutation in the LIFR gene that is predicted to markedly destabilize and alter the structure of a particular domain and consequently to affect the functionality of the whole multi-domain LIFR protein. The C65S mutant LIFR showed altered glycosylation and an elevated expression level that might be attributed to a slow turnover of the mutant form. LIFR mutations have been reported in Stüve-Wiedemann syndrome (SWS), a severe autosomal recessive skeletal dysplasia often resulting in early death. Our patients share some clinical features of rare cases of SWS long-term survivors; however, they also phenocopy HSAN due to the marked pain insensitivity phenotype and progressive bone destruction. Screening for LIFR mutations might be warranted in genetically unresolved HSAN phenotypes.

Where cancer genomics should go next: a clinician's perspective.

Large-scale, genomic studies of specific tumors such as The Cancer Genome Atlas have provided a better understanding of the alterations of pathways involved in the development of solid tumors including glioblastoma, breast cancer, ovarian and endometrial cancers, colon cancer and lung squamous cell carcinoma. This tremendous effort of the scientific community has confirmed the view that cancer actually represents a wide variety of diseases originating from different organs. These studies showed that TP53 and PI3KCA are the two most mutated genes in all types of cancers and that 30-70% of all solid tumors harbor potentially 'actionable' mutations that can be exploited for patient stratification or treatment optimization. Translation of this huge oncogenomic data set to clinical application in personalized medicine programs is now the main challenge for the future. The gap between our basic knowledge and clinical application is still wide. Closing the gap will require translational personalized trials, which may initiate a radical change in our routine clinical practice in oncology.

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.

Alcohol-induced metabolomic differences in humans.

Alcohol consumption is one of the world's major risk factors for disease development. But underlying mechanisms by which moderate-to-heavy alcohol intake causes damage are poorly understood and biomarkers are sub-optimal. Here, we investigated metabolite concentration differences in relation to alcohol intake in 2090 individuals of the KORA F4 and replicated results in 261 KORA F3 and up to 629 females of the TwinsUK adult bioresource. Using logistic regression analysis adjusted for age, body mass index, smoking, high-density lipoproteins and triglycerides, we identified 40/18 significant metabolites in males/females with P-values <3.8E-04 (Bonferroni corrected) that differed in concentrations between moderate-to-heavy drinkers (MHD) and light drinkers (LD) in the KORA F4 study. We further identified specific profiles of the 10/5 metabolites in males/females that clearly separated LD from MHD in the KORA F4 cohort. For those metabolites, the respective area under the receiver operating characteristic curves were 0.812/0.679, respectively, thus providing moderate-to-high sensitivity and specificity for the discrimination of LD to MHD. A number of alcohol-related metabolites could be replicated in the KORA F3 and TwinsUK studies. Our data suggests that metabolomic profiles based on diacylphosphatidylcholines, lysophosphatidylcholines, ether lipids and sphingolipids form a new class of biomarkers for excess alcohol intake and have potential for future epidemiological and clinical studies.

Integrative genetic and metabolite profiling analysis suggests altered phosphatidylcholine metabolism in asthma.

BACKGROUND: Genome-wide association studies (GWAS) have identified many risk loci for asthma, but effect sizes are small, and in most cases, the biological mechanisms are unclear. Targeted metabolite quantification that provides information about a whole range of pathways of intermediary metabolism can help to identify biomarkers and investigate disease mechanisms. Combining genetic and metabolic information can aid in characterizing genetic association signals with high resolution. This work aimed to investigate the interrelation of current asthma, candidate asthma risk alleles and a panel of metabolites. METHODS: We investigated 151 metabolites, quantified by targeted mass spectrometry, in fasting serum of asthmatic and nonasthmatic individuals from the population-based KORA F4 study (N = 2925). In addition, we analysed effects of single-nucleotide polymorphisms (SNPs) at 24 asthma risk loci on these metabolites. RESULTS: Increased levels of various phosphatidylcholines and decreased levels of various lyso-phosphatidylcholines were associated with asthma. Likewise, asthma risk alleles from the PDED3 and MED24 genes at the asthma susceptibility locus 17q21 were associated with increased concentrations of various phosphatidylcholines with consistent effect directions. CONCLUSIONS: Our study demonstrated the potential of metabolomics to infer asthma-related biomarkers by the identification of potentially deregulated phospholipids that associate with asthma and asthma risk alleles.

Comparison of metabolic profiles of acutely ill and short-term weight recovered patients with anorexia nervosa reveals alterations of 33 out of 163 metabolites.

Starvation represents an extreme physiological state and entails numerous endocrine and metabolic adaptations. The large-scale application of metabolomics to patients with acute anorexia nervosa (AN) should lead to the identification of state markers characteristic of starvation in general and of the starvation specifically associated with this eating disorder. Novel metabolomics technology has not yet been applied to this disorder. Using a targeted metabolomics approach, we analysed 163 metabolite concentrations in 29 patients with AN in the acute stage of starvation (T0) and after short-term weight recovery (T1). Of the 163 metabolites of the respective kit, 112 metabolites were quantified within restrictive quality control limits. We hypothesized that concentrations are different in patients in the acute stage of starvation (T0) and after weight gain (T1). Furthermore, we compared all 112 metabolite concentrations of patients at the two time points (T0, T1) with those of 16 age and gender matched healthy controls. Thirty-three of the metabolite serum levels were found significantly different between T0 and T1. At the acute stage of starvation (T0) serum concentrations of 90 metabolites differed significantly from those of healthy controls. Concentrations of controls mostly differed even more strongly from those of AN patients after short-term weight recovery than at the acute stage of starvation. We conclude that AN entails profound and longer lasting alterations of a large number of serum metabolites. Further studies are warranted to distinguish between state and trait related alterations and to establish diagnostic sensitivity and specificity of the thus altered metabolites.

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.