A Community Challenge for Inferring Genetic Predictors of Gene Essentialities through Analysis of a Functional Screen of Cancer Cell Lines.

We report the results of a DREAM challenge designed to predict relative genetic essentialities based on a novel dataset testing 98,000 shRNAs against 149 molecularly characterized cancer cell lines. We analyzed the results of over 3,000 submissions over a period of 4 months. We found that algorithms combining essentiality data across multiple genes demonstrated increased accuracy; gene expression was the most informative molecular data type; the identity of the gene being predicted was far more important than the modeling strategy; well-predicted genes and selected molecular features showed enrichment in functional categories; and frequently selected expression features correlated with survival in primary tumors. This study establishes benchmarks for gene essentiality prediction, presents a community resource for future comparison with this benchmark, and provides insights into factors influencing the ability to predict gene essentiality from functional genetic screens. This study also demonstrates the value of releasing pre-publication data publicly to engage the community in an open research collaboration.

Metabolite profiling of the carnivorous pitcher plants Darlingtonia and Sarracenia.

Sarraceniaceae is a New World carnivorous plant family comprising three genera: Darlingtonia, Heliamphora, and Sarracenia. The plants occur in nutrient-poor environments and have developed insectivorous capability in order to supplement their nutrient uptake. Sarracenia flava contains the alkaloid coniine, otherwise only found in Conium maculatum, in which its biosynthesis has been studied, and several Aloe species. Its ecological role and biosynthetic origin in S. flava is speculative. The aim of the current research was to investigate the occurrence of coniine in Sarracenia and Darlingtonia and to identify common constituents of both genera, unique compounds for individual variants and floral scent chemicals. In this comprehensive metabolic profiling study, we looked for compound patterns that are associated with the taxonomy of Sarracenia species. In total, 57 different Sarracenia and D. californica accessions were used for metabolite content screening by gas chromatography-mass spectrometry. The resulting high-dimensional data were studied using a data mining approach. The two genera are characterized by a large number of metabolites and huge chemical diversity between different species. By applying feature selection for clustering and by integrating new biochemical data with existing phylogenetic data, we were able to demonstrate that the chemical composition of the species can be explained by their known classification. Although transcriptome analysis did not reveal a candidate gene for coniine biosynthesis, the use of a sensitive selected ion monitoring method enabled the detection of coniine in eight Sarracenia species, showing that it is more widespread in this genus than previously believed.

Crowdsourced assessment of common genetic contribution to predicting anti-TNF treatment response in rheumatoid arthritis.

Rheumatoid arthritis (RA) affects millions world-wide. While anti-TNF treatment is widely used to reduce disease progression, treatment fails in ∼one-third of patients. No biomarker currently exists that identifies non-responders before treatment. A rigorous community-based assessment of the utility of SNP data for predicting anti-TNF treatment efficacy in RA patients was performed in the context of a DREAM Challenge (http://www.synapse.org/RA_Challenge). An open challenge framework enabled the comparative evaluation of predictions developed by 73 research groups using the most comprehensive available data and covering a wide range of state-of-the-art modelling methodologies. Despite a significant genetic heritability estimate of treatment non-response trait (h(2)=0.18, P value=0.02), no significant genetic contribution to prediction accuracy is observed. Results formally confirm the expectations of the rheumatology community that SNP information does not significantly improve predictive performance relative to standard clinical traits, thereby justifying a refocusing of future efforts on collection of other data.

Noninvasive Detection of Nonalcoholic Steatohepatitis Using Clinical Markers and Circulating Levels of Lipids and Metabolites.

BACKGROUND & AIMS: Use of targeted mass spectrometry (MS)-based methods is increasing in clinical chemistry laboratories. We investigate whether MS-based profiling of plasma improves noninvasive risk estimates of nonalcoholic steatohepatitis (NASH) compared with routinely available clinical parameters and patatin-like phospholipase domain-containing protein 3 (PNPLA3) genotype at rs738409. METHODS: We used MS-based analytic platforms to measure levels of lipids and metabolites in blood samples from 318 subjects who underwent a liver biopsy because of suspected NASH. The subjects were divided randomly into estimation (n = 223) and validation (n = 95) groups to build and validate the model. Gibbs sampling and stepwise logistic regression, which fulfilled the Bayesian information criterion, were used for variable selection and modeling. RESULTS: Features of the metabolic syndrome and the variant in PNPLA3 encoding I148M were significantly more common among subjects with than without NASH. We developed a model to identify subjects with NASH based on clinical data and PNPLA3 genotype (NASH Clin Score), which included aspartate aminotransferase (AST), fasting insulin, and PNPLA3 genotype. This model identified subjects with NASH with an area under the receiver operating characteristic of 0.778 (95% confidence interval, 0.709-0.846). We then used backward stepwise logistic regression analyses of variables from the NASH Clin Score and MS-based factors associated with NASH to develop the NASH ClinLipMet Score. This included glutamate, isoleucine, glycine, lysophosphatidylcholine 16:0, phosphoethanolamine 40:6, AST, and fasting insulin, along with PNPLA3 genotype. It identified patients with NASH with an area under the receiver operating characteristic of 0.866 (95% confidence interval, 0.820-0.913). The NASH ClinLipMet score identified patients with NASH with significantly higher accuracy than the NASH Clin Score or MS-based profiling alone. CONCLUSIONS: A score based on MS (glutamate, isoleucine, glycine, lysophosphatidylcholine 16:0, phosphoethanolamine 40:6) and knowledge of AST, fasting insulin, and PNPLA3 genotype is significantly better than a score based on clinical or metabolic profiles alone in determining the risk of NASH.

Accumulated Metabolites of Hydroxybutyric Acid Serve as Diagnostic and Prognostic Biomarkers of Ovarian High-Grade Serous Carcinomas.

Ovarian cancer is a heterogeneous disease of low prevalence, but poor survival. Early diagnosis is critical for survival, but it is often challenging because the symptoms of ovarian cancer are subtle and become apparent only during advanced stages of the disease. Therefore, the identification of robust biomarkers of early disease is a clinical priority. Metabolomic profiling is an emerging diagnostic tool enabling the detection of biomarkers reflecting alterations in tumor metabolism, a hallmark of cancer. In this study, we performed metabolomic profiling of serum and tumor tissue from 158 patients with high-grade serous ovarian cancer (HGSOC) and 100 control patients with benign or non-neoplastic lesions. We report metabolites of hydroxybutyric acid (HBA) as novel diagnostic and prognostic biomarkers associated with tumor burden and patient survival. The accumulation of HBA metabolites caused by HGSOC was also associated with reduced expression of succinic semialdehyde dehydrogenase (encoded by ALDH5A1), and with the presence of an epithelial-to-mesenchymal transition gene signature, implying a role for these metabolic alterations in cancer cell migration and invasion. In conclusion, our findings represent the first comprehensive metabolomics analysis in HGSOC and propose a new set of metabolites as biomarkers of disease with diagnostic and prognostic capabilities.

From drug response profiling to target addiction scoring in cancer cell models.

Deconvoluting the molecular target signals behind observed drug response phenotypes is an important part of phenotype-based drug discovery and repurposing efforts. We demonstrate here how our network-based deconvolution approach, named target addiction score (TAS), provides insights into the functional importance of druggable protein targets in cell-based drug sensitivity testing experiments. Using cancer cell line profiling data sets, we constructed a functional classification across 107 cancer cell models, based on their common and unique target addiction signatures. The pan-cancer addiction correlations could not be explained by the tissue of origin, and only correlated in part with molecular and genomic signatures of the heterogeneous cancer cells. The TAS-based cancer cell classification was also shown to be robust to drug response data resampling, as well as predictive of the transcriptomic patterns in an independent set of cancer cells that shared similar addiction signatures with the 107 cancers. The critical protein targets identified by the integrated approach were also shown to have clinically relevant mutation frequencies in patients with various cancer subtypes, including not only well-established pan-cancer genes, such as PTEN tumor suppressor, but also a number of targets that are less frequently mutated in specific cancer types, including ABL1 oncoprotein in acute myeloid leukemia. An application to leukemia patient primary cell models demonstrated how the target deconvolution approach offers functional insights into patient-specific addiction patterns, such as those indicative of their receptor-type tyrosine-protein kinase FLT3 internal tandem duplication (FLT3-ITD) status and co-addiction partners, which may lead to clinically actionable, personalized drug treatment developments. To promote its application to the future drug testing studies, we have made available an open-source implementation of the TAS calculation in the form of a stand-alone R package.

Circulating triacylglycerol signatures in nonalcoholic fatty liver disease associated with the I148M variant in PNPLA3 and with obesity.

We examined whether relative concentrations of circulating triacylglycerols (TAGs) between carriers compared with noncarriers of PNPLA3(I148M) gene variant display deficiency of TAGs, which accumulate in the liver because of defective lipase activity. We also analyzed the effects of obesity-associated nonalcoholic fatty liver disease (NAFLD) independent of genotype, and of NAFLD due to either PNPLA3(I148M) gene variant or obesity on circulating TAGs. A total of 372 subjects were divided into groups based on PNPLA3 genotype or obesity. Absolute and relative deficiency of distinct circulating TAGs was observed in the PNPLA3(148MM/148MI) compared with the PNPLA3(148II) group. Obese and 'nonobese' groups had similar PNPLA3 genotypes, but the obese subjects were insulin-resistant. Liver fat was similarly increased in obese and PNPLA3(148MM/148MI) groups. Relative concentrations of TAGs in the obese subjects versus nonobese displayed multiple changes. These closely resembled those between obese subjects with NAFLD but without PNPLA3(I148M) versus those with the I148M variant and NAFLD. The etiology of NAFLD influences circulating TAG profiles. 'PNPLA3 NAFLD' is associated with a relative deficiency of TAGs, supporting the idea that the I148M variant impedes intrahepatocellular lipolysis rather than stimulates TAG synthesis. 'Obese NAFLD' is associated with multiple changes in TAGs, which can be attributed to obesity/insulin resistance rather than increased liver fat content per se.

Prediction of non-alcoholic fatty-liver disease and liver fat content by serum molecular lipids.

AIMS/HYPOTHESIS: We examined whether analysis of lipids by ultra-performance liquid chromatography (UPLC) coupled to MS allows the development of a laboratory test for non-alcoholic fatty-liver disease (NAFLD), and how a lipid-profile biomarker compares with the prediction of NAFLD and liver-fat content based on routinely available clinical and laboratory data. METHODS: We analysed the concentrations of molecular lipids by UPLC-MS in blood samples of 679 well-characterised individuals in whom liver-fat content was measured using proton magnetic resonance spectroscopy ((1)H-MRS) or liver biopsy. The participants were divided into biomarker-discovery (n = 287) and validation (n = 392) groups to build and validate the diagnostic models, respectively. RESULTS: Individuals with NAFLD had increased triacylglycerols with low carbon number and double-bond content while lysophosphatidylcholines and ether phospholipids were diminished in those with NAFLD. A serum-lipid signature comprising three molecular lipids ('lipid triplet') was developed to estimate the percentage of liver fat. It had a sensitivity of 69.1% and specificity of 73.8% when applied for diagnosis of NAFLD in the validation series. The usefulness of the lipid triplet was demonstrated in a weight-loss intervention study. CONCLUSIONS/INTERPRETATION: The liver-fat-biomarker signature based on molecular lipids may provide a non-invasive tool to diagnose NAFLD, in addition to highlighting lipid molecular pathways involved in the disease.

Cord serum lipidome in prediction of islet autoimmunity and type 1 diabetes.

Previous studies show that children who later progress to type 1 diabetes (T1D) have decreased preautoimmune concentrations of multiple phospholipids as compared with nonprogressors. It is still unclear whether these changes associate with development of ß-cell autoimmunity or specifically with clinical T1D. Here, we studied umbilical cord serum lipidome in infants who later developed T1D (N = 33); infants who developed three or four (N = 31) islet autoantibodies, two (N = 31) islet autoantibodies, or one (N = 48) islet autoantibody during the follow-up; and controls (N = 143) matched for sex, HLA-DQB1 genotype, city of birth, and period of birth. The analyses of serum molecular lipids were performed using the established lipidomics platform based on ultra-performance liquid chromatography coupled to mass spectrometry. We found that T1D progressors are characterized by a distinct cord blood lipidomic profile that includes reduced major choline-containing phospholipids, including sphingomyelins and phosphatidylcholines. A molecular signature was developed comprising seven lipids that predicted high risk for progression to T1D with an odds ratio of 5.94 (95% CI, 1.07-17.50). Reduction in choline-containing phospholipids in cord blood therefore is specifically associated with progression to T1D but not with development of ß-cell autoimmunity in general.

Characterization of microbial metabolism of Syrah grape products in an in vitro colon model using targeted and non-targeted analytical approaches.

PURPOSE: Syrah red grapes are used in the production of tannin-rich red wines. Tannins are high molecular weight molecules, proanthocyanidins (PAs), and poorly absorbed in the upper intestine. In this study, gut microbial metabolism of Syrah grape phenolic compounds was investigated. METHODS: Syrah grape pericarp was subjected to an enzymatic in vitro digestion model, and red wine and grape skin PA fraction were prepared. Microbial conversion was screened using an in vitro colon model with faecal microbiota, by measurement of short-chain fatty acids by gas chromatography (GC) and microbial phenolic metabolites using GC with mass detection (GC-MS). Red wine metabolites were further profiled using two-dimensional GC mass spectrometry (GCxGC-TOFMS). In addition, the effect of PA structure and dose on conversion efficiency was investigated by GC-MS. RESULTS: Red wine exhibited a higher degree of C1-C3 phenolic acid formation than PA fraction or grape pericarp powders. Hydroxyphenyl valeric acid (flavanols and PAs as precursors) and 3,5-dimethoxy-4-hydroxybenzoic acid (anthocyanin as a precursor) were identified from the red wine metabolite profile. In the absence of native grape pericarp or red wine matrix, the isolated PAs were found to be effective in the dose-dependent inhibition of microbial conversions and short-chain fatty acid formation. CONCLUSIONS: Metabolite profiling was complementary to targeted analysis. The identified metabolites had biological relevance, because the structures of the metabolites resembled fragments of their grape phenolic precursors or were in agreement with literature data.

Mitofusin 2 (Mfn2) links mitochondrial and endoplasmic reticulum function with insulin signaling and is essential for normal glucose homeostasis.

Mitochondria are dynamic organelles that play a key role in energy conversion. Optimal mitochondrial function is ensured by a quality-control system tightly coupled to fusion and fission. In this connection, mitofusin 2 (Mfn2) participates in mitochondrial fusion and undergoes repression in muscle from obese or type 2 diabetic patients. Here, we provide in vivo evidence that Mfn2 plays an essential role in metabolic homeostasis. Liver-specific ablation of Mfn2 in mice led to numerous metabolic abnormalities, characterized by glucose intolerance and enhanced hepatic gluconeogenesis. Mfn2 deficiency impaired insulin signaling in liver and muscle. Furthermore, Mfn2 deficiency was associated with endoplasmic reticulum stress, enhanced hydrogen peroxide concentration, altered reactive oxygen species handling, and active JNK. Chemical chaperones or the antioxidant N-acetylcysteine ameliorated glucose tolerance and insulin signaling in liver-specific Mfn2 KO mice. This study provides an important description of a unique unexpected role of Mfn2 coordinating mitochondria and endoplasmic reticulum function, leading to modulation of insulin signaling and glucose homeostasis in vivo.

Insulin signaling regulates fatty acid catabolism at the level of CoA activation.

The insulin/IGF signaling pathway is a highly conserved regulator of metabolism in flies and mammals, regulating multiple physiological functions including lipid metabolism. Although insulin signaling is known to regulate the activity of a number of enzymes in metabolic pathways, a comprehensive understanding of how the insulin signaling pathway regulates metabolic pathways is still lacking. Accepted knowledge suggests the key regulated step in triglyceride (TAG) catabolism is the release of fatty acids from TAG via the action of lipases. We show here that an additional, important regulated step is the activation of fatty acids for beta-oxidation via Acyl Co-A synthetases (ACS). We identify pudgy as an ACS that is transcriptionally regulated by direct FOXO action in Drosophila. Increasing or reducing pudgy expression in vivo causes a decrease or increase in organismal TAG levels respectively, indicating that pudgy expression levels are important for proper lipid homeostasis. We show that multiple ACSs are also transcriptionally regulated by insulin signaling in mammalian cells. In sum, we identify fatty acid activation onto CoA as an important, regulated step in triglyceride catabolism, and we identify a mechanistic link through which insulin regulates lipid homeostasis.

Heterogeneous biological network visualization system: case study in context of medical image data.

We have developed a system called megNet for integrating and visualizing heterogeneous biological data in order to enable modeling biological phenomena using a systems approach. Herein we describe megNet, including a recently developed user interface for visualizing biological networks in three dimensions and a web user interface for taking input parameters from the user, and an in-house text mining system that utilizes an existing knowledge base. We demonstrate the software with a case study in which we integrate lipidomics data acquired in-house with interaction data from external databases, and then find novel interactions that could possibly explain our previous associations between biological data and medical images. The flexibility of megNet assures that the tool can be applied in diverse applications, from target discovery in medical applications to metabolic engineering in industrial biotechnology.

Metabolic regulation in progression to autoimmune diabetes.

Recent evidence from serum metabolomics indicates that specific metabolic disturbances precede ß-cell autoimmunity in humans and can be used to identify those children who subsequently progress to type 1 diabetes. The mechanisms behind these disturbances are unknown. Here we show the specificity of the pre-autoimmune metabolic changes, as indicated by their conservation in a murine model of type 1 diabetes. We performed a study in non-obese prediabetic (NOD) mice which recapitulated the design of the human study and derived the metabolic states from longitudinal lipidomics data. We show that female NOD mice who later progress to autoimmune diabetes exhibit the same lipidomic pattern as prediabetic children. These metabolic changes are accompanied by enhanced glucose-stimulated insulin secretion, normoglycemia, upregulation of insulinotropic amino acids in islets, elevated plasma leptin and adiponectin, and diminished gut microbial diversity of the Clostridium leptum group. Together, the findings indicate that autoimmune diabetes is preceded by a state of increased metabolic demands on the islets resulting in elevated insulin secretion and suggest alternative metabolic related pathways as therapeutic targets to prevent diabetes.

Association of lipidome remodeling in the adipocyte membrane with acquired obesity in humans.

Identification of early mechanisms that may lead from obesity towards complications such as metabolic syndrome is of great interest. Here we performed lipidomic analyses of adipose tissue in twin pairs discordant for obesity but still metabolically compensated. In parallel we studied more evolved states of obesity by investigating a separated set of individuals considered to be morbidly obese. Despite lower dietary polyunsaturated fatty acid intake, the obese twin individuals had increased proportions of palmitoleic and arachidonic acids in their adipose tissue, including increased levels of ethanolamine plasmalogens containing arachidonic acid. Information gathered from these experimental groups was used for molecular dynamics simulations of lipid bilayers combined with dependency network analysis of combined clinical, lipidomics, and gene expression data. The simulations suggested that the observed lipid remodeling maintains the biophysical properties of lipid membranes, at the price, however, of increasing their vulnerability to inflammation. Conversely, in morbidly obese subjects, the proportion of plasmalogens containing arachidonic acid in the adipose tissue was markedly decreased. We also show by in vitro Elovl6 knockdown that the lipid network regulating the observed remodeling may be amenable to genetic modulation. Together, our novel approach suggests a physiological mechanism by which adaptation of adipocyte membranes to adipose tissue expansion associates with positive energy balance, potentially leading to higher vulnerability to inflammation in acquired obesity. Further studies will be needed to determine the cause of this effect.

MPEA--metabolite pathway enrichment analysis.

UNLABELLED: We present metabolite pathway enrichment analysis (MPEA) for the visualization and biological interpretation of metabolite data at the system level. Our tool follows the concept of gene set enrichment analysis (GSEA) and tests whether metabolites involved in some predefined pathway occur towards the top (or bottom) of a ranked query compound list. In particular, MPEA is designed to handle many-to-many relationships that may occur between the query compounds and metabolite annotations. For a demonstration, we analysed metabolite profiles of 14 twin pairs with differing body weights. MPEA found significant pathways from data that had no significant individual query compounds, its results were congruent with those discovered from transcriptomics data and it detected more pathways than the competing metabolic pathway method did. AVAILABILITY: The web server and source code of MPEA are available at http://ekhidna.biocenter.helsinki.fi/poxo/mpea/.

Drug metabolome of the simvastatin formed by human intestinal microbiota in vitro.

The human colon contains a diverse microbial population which contributes to degradation and metabolism of food components. Drug metabolism in the colon is generally poorly understood. Metabolomics techniques and in vitro colon models are now available which afford detailed characterization of drug metabolites in the context of colon metabolism. The aim of this work was to identify novel drug metabolites of Simvastatin (SV) by using an anaerobic human in vitro colon model at body temperature coupled with systems biology platform, excluding the metabolism of the host liver and intestinal epithelia. Comprehensive two-dimensional gas chromatography with a time-of-flight mass spectrometry (GC×GC-TOFMS) was used for the metabolomic analysis. Metabolites showing the most significant differences in the active faecal suspension were elucidated in reference with SV fragmentation and compared with controls: inactive suspension or buffer with SV, or with active suspension alone. Finally, time courses of selected metabolites were investigated. Our data suggest that SV is degraded by hydrolytic cleavage of methylbutanoic acid from the SV backbone. Metabolism involves demethylation of dimethylbutanoic acid, hydroxylation/dehydroxylation and ß-oxidation resulting in the production of 2-hydroxyisovaleric acid (3-methyl-2-hydroxybutanoic acid), 3-hydroxybutanoic acid and lactic acid (2-hydroxypropanoic acid), and finally re-cyclisation of heptanoic acid (possibly de-esterified and cleaved methylpyranyl arm) to produce cyclohexanecarboxylic acid. Our study elucidates a pathway of colonic microbial metabolism of SV as well as demonstrates the applicability of the in vitro colon model and metabolomics to the discovery of novel drug metabolites from drug response profiles.

The gut microbiota modulates host energy and lipid metabolism in mice.

The gut microbiota has recently been identified as an environmental factor that may promote metabolic diseases. To investigate the effect of gut microbiota on host energy and lipid metabolism, we compared the serum metabolome and the lipidomes of serum, adipose tissue, and liver of conventionally raised (CONV-R) and germ-free mice. The serum metabolome of CONV-R mice was characterized by increased levels of energy metabolites, e.g., pyruvic acid, citric acid, fumaric acid, and malic acid, while levels of cholesterol and fatty acids were reduced. We also showed that the microbiota modified a number of lipid species in the serum, adipose tissue, and liver, with its greatest effect on triglyceride and phosphatidylcholine species. Triglyceride levels were lower in serum but higher in adipose tissue and liver of CONV-R mice, consistent with increased lipid clearance. Our findings show that the gut microbiota affects both host energy and lipid metabolism and highlights its role in the development of metabolic diseases.

Detection of molecular paths associated with insulitis and type 1 diabetes in non-obese diabetic mouse.

Recent clinical evidence suggests important role of lipid and amino acid metabolism in early pre-autoimmune stages of type 1 diabetes pathogenesis. We study the molecular paths associated with the incidence of insulitis and type 1 diabetes in the Non-Obese Diabetic (NOD) mouse model using available gene expression data from the pancreatic tissue from young pre-diabetic mice. We apply a graph-theoretic approach by using a modified color coding algorithm to detect optimal molecular paths associated with specific phenotypes in an integrated biological network encompassing heterogeneous interaction data types. In agreement with our recent clinical findings, we identified a path downregulated in early insulitis involving dihydroxyacetone phosphate acyltransferase (DHAPAT), a key regulator of ether phospholipid synthesis. The pathway involving serine/threonine-protein phosphatase (PP2A), an upstream regulator of lipid metabolism and insulin secretion, was found upregulated in early insulitis. Our findings provide further evidence for an important role of lipid metabolism in early stages of type 1 diabetes pathogenesis, as well as suggest that such dysregulation of lipids and related increased oxidative stress can be tracked to beta cells.