The Immunogenomic Landscape of Peripheral High-Dose IL-2 Pharmacodynamics in Patients with Metastatic Renal Cell Carcinoma: A Benchmark for Next-Generation IL-2-Based Immunotherapies.

High-dose (HD) IL-2 was the first immuno-oncology agent approved for treating advanced renal cell carcinoma and metastatic melanoma, but its use was limited because of substantial toxicities. Multiple next-generation IL-2 agents are being developed to improve tolerability. However, a knowledge gap still exists for the genomic markers that define the target pharmacology for HD IL-2 itself. In this retrospective observational study, we collected PBMC samples from 23 patients with metastatic renal cell carcinoma who were treated with HD IL-2 between 2009 and 2015. We previously reported the results of flow cytometry analyses. In this study, we report the results of our RNA-sequencing immunogenomic survey, which was performed on bulk PBMC samples from immediately before (day 1), during (day 3), and after treatment (day 5) in cycle 1 and/or cycle 2 of the first course of HD IL-2. As part of a detailed analysis of immunogenomic response to HD IL-2 treatment, we analyzed the changes in individual genes and immune gene signatures. By day 3, most lymphoid cell types had transiently decreased, whereas myeloid transcripts increased. Although most genes and/or signatures generally returned to pretreatment expression levels by day 5, certain ones representative of B cell, NK cell, and T cell proliferation and effector functions continued to increase, along with B cell (but not T cell) oligoclonal expansion. Regulatory T cells progressively expanded during and after treatment. They showed strong negative correlation with myeloid effector cells. This detailed RNA-sequencing immunogenomic survey of IL-2 pharmacology complements results of prior flow cytometry analyses. These data provide valuable pharmacological context for assessing PBMC gene expression data from patients dosed with IL-2-related compounds that are currently in development.

Association of Antifolate Response Signature Status and Clinical Activity of Pemetrexed-Platinum Chemotherapy in Non-Small Cell Lung Cancer: The Piedmont Study.

PURPOSE: The Piedmont study is a prospectively designed retrospective evaluation of a new 48-gene antifolate response signature (AF-PRS) in patients with locally advanced/metastatic nonsquamous (NS) non-small cell lung cancer (NSCLC) treated with pemetrexed-containing platinum doublet chemotherapy (PMX-PDC). The study tested the hypothesis that AF-PRS identifies patients with NS-NSCLC who have a higher likelihood of responding positively to PMX-PDC. The goal was to gather clinical evidence supporting AF-PRS as a potential diagnostic test. EXPERIMENTAL DESIGN: Residual pretreatment FFPE tumor samples and clinical data were analyzed from 105 patients treated with first-line (1L) PMX-PDC. Ninety-five patients had sufficient RNA sequencing (RNA-seq) data quality and clinical annotation for inclusion in the analysis. Associations between AF-PRS status and associate genes and outcome measures including progression-free survival (PFS) and clinical response were evaluated. RESULTS: Overall, 53% of patients were AF-PRS(+), which was associated with extended PFS, but not overall survival, versus AF-PRS(-) (16.6 months vs. 6.6 months; P = 0.025). In patients who were stage I to III patients at the time of treatment, PFS was further extended in AF-PRS(+) versus AF-PRS(-) (36.2 months vs. 9.3 months; P = 0.03). Complete response (CR) to therapy was noted in 14 of 95 patients. AF-PRS(+) preferentially selected a majority (79%) of CRs, which were evenly split between patients stage I to III (six of seven) and stage IV (five of seven) at the time of treatment. CONCLUSIONS: AF-PRS identified a significant population of patients with extended PFS and/or clinical response following PMX-PDC treatment. AF-PRS may be a useful diagnostic test for patients indicated for systemic chemotherapy, especially when determining the optimal PDC regimen for locally advanced disease.

Distinct Predictive Immunogenomic Profiles of Response to Immune Checkpoint Inhibitors and IL2: A Real-world Evidence Study of Patients with Advanced Renal Cancer.

Recombinant human high-dose IL2 (HD-IL2; aldesleukin) was one of the first approved immune-oncology agents based upon clinical activity in renal cell carcinoma (RCC) and metastatic melanoma but use was limited due to severe toxicity. Next-generation IL2 agents designed to improve tolerability are in development, increasing the need for future identification of genomic markers of clinical benefit and/or clinical response. In this retrospective study, we report clinical and tumor molecular profiling from patients with metastatic RCC (mRCC) treated with HD-IL2 and compare findings with patients with RCC treated with anti-PD-1 therapy. Genomic characteristics common and unique to IL2 and/or anti-PD-1 therapy response are presented, with insight into rational combination strategies for these agents. Residual pretreatment formalin-fixed paraffin embedded tumor samples from n = 36 patients with HD-IL2 mRCC underwent RNA-sequencing and corresponding clinical data were collected. A de novo 40-gene nearest centroid IL2 treatment response classifier and individual gene and/or immune marker signature differences were correlated to clinical response and placed into context with a separate dataset of n = 35 patients with anti-PD-1 mRCC. Immune signatures and genes, comprising suppressor and effector cells, were increased in patients with HD-IL2 clinical benefit. The 40-gene response classifier was also highly enriched for immune genes. While several effector immune signatures and genes were common between IL2 and anti-PD-1 treated patients, multiple inflammatory and/or immunosuppressive genes, previously reported to predict poor response to anti-PD-L1 immunotherapy, were only increased in IL2-responsive tumors. These findings suggest that common and distinct immune-related response markers for IL2 and anti-PD-1 therapy may help guide their use, either alone or in combination. Significance: Next-generation IL2 agents, designed for improved tolerability over traditional HD-IL2 (aldesleukin), are in clinical development. Retrospective molecular tumor profiling of patients treated with HD-IL2 or anti-PD-1 therapy provides insights into genomic characteristics of therapy response. This study revealed common and distinct immune-related predictive response markers for IL2 and anti-PD-1 therapy which may play a role in therapy guidance, and rational combination strategies for these agents.

Genome-wide scan identifies novel genetic loci regulating salivary metabolite levels.

Saliva, as a biofluid, is inexpensive and non-invasive to obtain, and provides a vital tool to investigate oral health and its interaction with systemic health conditions. There is growing interest in salivary biomarkers for systemic diseases, notably cardiovascular disease. Whereas hundreds of genetic loci have been shown to be involved in the regulation of blood metabolites, leading to significant insights into the pathogenesis of complex human diseases, little is known about the impact of host genetics on salivary metabolites. Here we report the first genome-wide association study exploring 476 salivary metabolites in 1419 subjects from the TwinsUK cohort (discovery phase), followed by replication in the Study of Health in Pomerania (SHIP-2) cohort. A total of 14 distinct locus-metabolite associations were identified in the discovery phase, most of which were replicated in SHIP-2. While only a limited number of the loci that are known to regulate blood metabolites were also associated with salivary metabolites in our study, we identified several novel saliva-specific locus-metabolite associations, including associations for the AGMAT (with the metabolites 4-guanidinobutanoate and beta-guanidinopropanoate), ATP13A5 (with the metabolite creatinine) and DPYS (with the metabolites 3-ureidopropionate and 3-ureidoisobutyrate) loci. Our study suggests that there may be regulatory pathways of particular relevance to the salivary metabolome. In addition, some of our findings may have clinical significance, such as the utility of the pyrimidine (uracil) degradation metabolites in predicting 5-fluorouracil toxicity and the role of the agmatine pathway metabolites as biomarkers of oral health.

Hypoxia-mediated impaired erythrocyte Lands' Cycle is pathogenic for sickle cell disease.

Although Lands' cycle was discovered in 1958, its function and cellular regulation in membrane homeostasis under physiological and pathological conditions remain largely unknown. Nonbiased high throughput metabolomic profiling revealed that Lands' cycle was impaired leading to significantly elevated erythrocyte membrane lysophosphatidylcholine (LysoPC) content and circulating and erythrocyte arachidonic acid (AA) in mice with sickle cell disease (SCD), a prevalent hemolytic genetic disorder. Correcting imbalanced Lands' cycle by knockdown of phospholipase 2 (cPLA2) or overexpression of lysophosphatidycholine acyltransferase 1 (LPCAT1), two key enzymes of Lands' cycle in hematopoietic stem cells, reduced elevated erythrocyte membrane LysoPC content and circulating AA levels and attenuated sickling, inflammation and tissue damage in SCD chimeras. Human translational studies validated SCD mouse findings and further demonstrated that imbalanced Lands' cycle induced LysoPC production directly promotes sickling in cultured mouse and human SCD erythrocytes. Mechanistically, we revealed that hypoxia-mediated ERK activation underlies imbalanced Lands' cycle by preferentially inducing the activity of PLA2 but not LPCAT in human and mouse SCD erythrocytes. Overall, our studies have identified a pathological role of imbalanced Lands' cycle in SCD erythrocytes, novel molecular basis regulating Lands' cycle and therapeutic opportunities for the disease.

Plasma metabolomic profiles enhance precision medicine for volunteers of normal health.

Precision medicine, taking account of human individuality in genes, environment, and lifestyle for early disease diagnosis and individualized therapy, has shown great promise to transform medical care. Nontargeted metabolomics, with the ability to detect broad classes of biochemicals, can provide a comprehensive functional phenotype integrating clinical phenotypes with genetic and nongenetic factors. To test the application of metabolomics in individual diagnosis, we conducted a metabolomics analysis on plasma samples collected from 80 volunteers of normal health with complete medical records and three-generation pedigrees. Using a broad-spectrum metabolomics platform consisting of liquid chromatography and GC coupled with MS, we profiled nearly 600 metabolites covering 72 biochemical pathways in all major branches of biosynthesis, catabolism, gut microbiome activities, and xenobiotics. Statistical analysis revealed a considerable range of variation and potential metabolic abnormalities across the individuals in this cohort. Examination of the convergence of metabolomics profiles with whole-exon sequences (WESs) provided an effective approach to assess and interpret clinical significance of genetic mutations, as shown in a number of cases, including fructose intolerance, xanthinuria, and carnitine deficiency. Metabolic abnormalities consistent with early indications of diabetes, liver dysfunction, and disruption of gut microbiome homeostasis were identified in several volunteers. Additionally, diverse metabolic responses to medications among the volunteers may assist to identify therapeutic effects and sensitivity to toxicity. The results of this study demonstrate that metabolomics could be an effective approach to complement next generation sequencing (NGS) for disease risk analysis, disease monitoring, and drug management in our goal toward precision care.

Untargeted metabolomic analysis for the clinical screening of inborn errors of metabolism.

Global metabolic profiling currently achievable by untargeted mass spectrometry-based metabolomic platforms has great potential to advance our understanding of human disease states, including potential utility in the detection of novel and known inborn errors of metabolism (IEMs). There are few studies of the technical reproducibility, data analysis methods, and overall diagnostic capabilities when this technology is applied to clinical specimens for the diagnosis of IEMs. We explored the clinical utility of a metabolomic workflow capable of routinely generating semi-quantitative z-score values for ~900 unique compounds, including ~500 named human analytes, in a single analysis of human plasma. We tested the technical reproducibility of this platform and applied it to the retrospective diagnosis of 190 individual plasma samples, 120 of which were collected from patients with a confirmed IEM. Our results demonstrate high intra-assay precision and linear detection for the majority compounds tested. Individual metabolomic profiles provided excellent sensitivity and specificity for the detection of a wide range of metabolic disorders and identified novel biomarkers for some diseases. With this platform, it is possible to use one test to screen for dozens of IEMs that might otherwise require ordering multiple unique biochemical tests. However, this test may yield false negative results for certain disorders that would be detected by a more well-established quantitative test and in its current state should be considered a supplementary test. Our findings describe a novel approach to metabolomic analysis of clinical specimens and demonstrate the clinical utility of this technology for prospective screening of IEMs.

Bladder cancer biomarker discovery using global metabolomic profiling of urine.

Bladder cancer (BCa) is a common malignancy worldwide and has a high probability of recurrence after initial diagnosis and treatment. As a result, recurrent surveillance, primarily involving repeated cystoscopies, is a critical component of post diagnosis patient management. Since cystoscopy is invasive, expensive and a possible deterrent to patient compliance with regular follow-up screening, new non-invasive technologies to aid in the detection of recurrent and/or primary bladder cancer are strongly needed. In this study, mass spectrometry based metabolomics was employed to identify biochemical signatures in human urine that differentiate bladder cancer from non-cancer controls. Over 1000 distinct compounds were measured including 587 named compounds of known chemical identity. Initial biomarker identification was conducted using a 332 subject sample set of retrospective urine samples (cohort 1), which included 66 BCa positive samples. A set of 25 candidate biomarkers was selected based on statistical significance, fold difference and metabolic pathway coverage. The 25 candidate biomarkers were tested against an independent urine sample set (cohort 2) using random forest analysis, with palmitoyl sphingomyelin, lactate, adenosine and succinate providing the strongest predictive power for differentiating cohort 2 cancer from non-cancer urines. Cohort 2 metabolite profiling revealed additional metabolites, including arachidonate, that were higher in cohort 2 cancer vs. non-cancer controls, but were below quantitation limits in the cohort 1 profiling. Metabolites related to lipid metabolism may be especially interesting biomarkers. The results suggest that urine metabolites may provide a much needed non-invasive adjunct diagnostic to cystoscopy for detection of bladder cancer and recurrent disease management.

Elevated sphingosine-1-phosphate promotes sickling and sickle cell disease progression.

Sphingosine-1-phosphate (S1P) is a bioactive lipid that regulates multicellular functions through interactions with its receptors on cell surfaces. S1P is enriched and stored in erythrocytes; however, it is not clear whether alterations in S1P are involved in the prevalent and debilitating hemolytic disorder sickle cell disease (SCD). Here, using metabolomic screening, we found that S1P is highly elevated in the blood of mice and humans with SCD. In murine models of SCD, we demonstrated that elevated erythrocyte sphingosine kinase 1 (SPHK1) underlies sickling and disease progression by increasing S1P levels in the blood. Additionally, we observed elevated SPHK1 activity in erythrocytes and increased S1P in blood collected from patients with SCD and demonstrated a direct impact of elevated SPHK1-mediated production of S1P on sickling that was independent of S1P receptor activation in isolated erythrocytes. Together, our findings provide insights into erythrocyte pathophysiology, revealing that a SPHK1-mediated elevation of S1P contributes to sickling and promotes disease progression, and highlight potential therapeutic opportunities for SCD.

An atlas of genetic influences on human blood metabolites.

Genome-wide association scans with high-throughput metabolic profiling provide unprecedented insights into how genetic variation influences metabolism and complex disease. Here we report the most comprehensive exploration of genetic loci influencing human metabolism thus far, comprising 7,824 adult individuals from 2 European population studies. We report genome-wide significant associations at 145 metabolic loci and their biochemical connectivity with more than 400 metabolites in human blood. We extensively characterize the resulting in vivo blueprint of metabolism in human blood by integrating it with information on gene expression, heritability and overlap with known loci for complex disorders, inborn errors of metabolism and pharmacological targets. We further developed a database and web-based resources for data mining and results visualization. Our findings provide new insights into the role of inherited variation in blood metabolic diversity and identify potential new opportunities for drug development and for understanding disease.

Serum metabolomics indicate altered cellular energy metabolism in children with cystic fibrosis.

BACKGROUND: Cystic fibrosis (CF) is a multi-system disease affecting multiple organs and cells besides the respiratory system. Metabolomic profiling allows simultaneous detection of biochemicals originating from cells, organs, or exogenous origin that may be valuable for monitoring of disease severity or in diagnosis. AIM: We hypothesized that metabolomics using serum from children would differentiate CF from non-CF lung disease subjects and would provide insight into metabolism in CF. METHODS: Serum collected from children with CF (n = 31) and 31 age and gender matched children with other lung diseases was used for metabolomic profiling by gas- and liquid-chromatography. Relative concentration of metabolites was compared between the groups using partial least square discriminant analyses (PLS-DA) and linear modeling. RESULTS: A clear separation of the two groups was seen in PLS-DA. Linear model found that among the 459 detected metabolites 92 differed between CF and non-CF. These included known biochemicals in lipid metabolism, oxidants, and markers consistent with abnormalities in bile acid processing. Bacterial metabolites were identified and differed between the groups indicating intestinal dysbiosis in CF. As a novel finding several pathways were markedly different in CF, which jointly point towards decreased activity in the ß-oxidation of fatty acids. These pathways include low ketone bodies, low medium chain carnitines, elevated di-carboxylic acids and decreased 2-hydroxybutyrate from amino acid metabolism in CF compared to non-CF. CONCLUSION: Serum metabolomics discriminated CF from non-CF and show altered cellular energy metabolism in CF potentially reflecting mitochondrial dysfunction. Future studies are indicated to examine their relation to the underlying CF defect and their use as biomarkers for disease severity or for cystic fibrosis transmembrane regulator (CFTR) function in an era of CFTR modifying drugs.

Metabolomic signatures of aggressive prostate cancer.

BACKGROUND: Current diagnostic techniques have increased the detection of prostate cancer; however, these tools inadequately stratify patients to minimize mortality. Recent studies have identified a biochemical signature of prostate cancer metastasis, including increased sarcosine abundance. This study examined the association of tissue metabolites with other clinically significant findings. METHODS: A state of the art metabolomics platform analyzed prostatectomy tissues (331 prostate tumor, 178 cancer-free prostate tissues) from two independent sites. Biochemicals were analyzed by gas chromatography-mass spectrometry and ultrahigh performance liquid chromatography-tandem mass spectrometry. Statistical analyses identified metabolites associated with cancer aggressiveness: Gleason score, extracapsular extension, and seminal vesicle and lymph node involvement. RESULTS: Prostate tumors had significantly altered metabolite profiles compared to cancer-free prostate tissues, including biochemicals associated with cell growth, energetics, stress, and loss of prostate-specific biochemistry. Many metabolites were further associated with clinical findings of aggressive disease. Aggressiveness-associated metabolites stratified prostate tumor tissues with high abundances of compounds associated with normal prostate function (e.g., citrate and polyamines) from more clinically advanced prostate tumors. These aggressive prostate tumors were further subdivided by abundance profiles of metabolites including NAD+ and kynurenine. When added to multiparametric nomograms, metabolites improved prediction of organ confinement (AUROC from 0.53 to 0.62) and 5-year recurrence (AUROC from 0.53 to 0.64). CONCLUSIONS: These findings support and extend earlier metabolomic studies in prostate cancer and studies where metabolic enzymes have been associated with carcinogenesis and/or outcome. Furthermore, these data suggest that panels of analytes may be valuable to translate metabolomic findings to clinically useful diagnostic tests.

Metabolomic signatures in lipid-loaded HepaRGs reveal pathways involved in steatotic progression.

OBJECTIVES: A spectrum of disorders including simple steatosis, nonalcoholic steatohepatitis, fibrosis, and cirrhosis is described by nonalcoholic fatty liver disease (NAFLD). With the increased prevalence of obesity, and consequently NAFLD, there is a need for novel therapeutics in this area. To facilitate this effort, a cellular model of hepatic steatosis was developed using HepaRG cells and the resulting biochemical alterations were determined. DESIGN AND METHODS: Using global metabolomic profiling, by means of a novel metabolite extraction procedure, the metabolic profiles in response to the saturated fatty acid palmitate, and a mixture of saturated and unsaturated fatty acids, palmitate and oleate (1:2) were examined. RESULTS: We observed elevated levels of the branched chain amino acids, tricarboxylic acid cycle intermediates, sphingosine and acylcarnitines, and reduced levels of carnitine in the steatotic HepaRG model with both palmitate and palmitate:oleate treatments. In addition, elevated levels of diacylglycerols and monoacylglycerols as well as altered bile acid metabolism were selectively displayed by palmitate-induced steatotic cells. CONCLUSIONS: Biochemical changes in pathways important in the transition to hepatic steatosis including insulin resistance, altered mitochondrial metabolism, and oxidative stress are revealed by this global metabolomic approach. Moreover, the utility of this in vitro model for investigating the mechanisms of steatotic progression, insulin resistance, and lipotoxicity in NAFLD was demonstrated.

Perturbation of bile acid homeostasis is an early pathogenesis event of drug induced liver injury in rats.

Drug-induced liver injury (DILI) is a significant consideration for drug development. Current preclinical DILI assessment relying on histopathology and clinical chemistry has limitations in sensitivity and discordance with human. To gain insights on DILI pathogenesis and identify potential biomarkers for improved DILI detection, we performed untargeted metabolomic analyses on rats treated with thirteen known hepatotoxins causing various types of DILI: necrosis (acetaminophen, bendazac, cyclosporine A, carbon tetrachloride, ethionine), cholestasis (methapyrilene and naphthylisothiocyanate), steatosis (tetracycline and ticlopidine), and idiosyncratic (carbamazepine, chlorzoxasone, flutamide, and nimesulide) at two doses and two time points. Statistical analysis and pathway mapping of the nearly 1900 metabolites profiled in the plasma, urine, and liver revealed diverse time and dose dependent metabolic cascades leading to DILI by the hepatotoxins. The most consistent change induced by the hepatotoxins, detectable even at the early time point/low dose, was the significant elevations of a panel of bile acids in the plasma and urine, suggesting that DILI impaired hepatic bile acid uptake from the circulation. Furthermore, bile acid amidation in the hepatocytes was altered depending on the severity of the hepatotoxin-induced oxidative stress. The alteration of the bile acids was most evident by the necrosis and cholestasis hepatotoxins, with more subtle effects by the steatosis and idiosyncratic hepatotoxins. Taking together, our data suggest that the perturbation of bile acid homeostasis is an early event of DILI. Upon further validation, selected bile acids in the circulation could be potentially used as sensitive and early DILI preclinical biomarkers.

Application of metabolomics to diagnosis of insulin resistance.

Metabolomics, the global interrogation of the biochemical components in a biological sample, has become an important complement to genomics and proteomics to aid in the understanding of pathophysiology. Major advantages of metabolomics are the size of the metabolome relative to the genome or proteome and the fact that it provides a view of the existing biochemical phenotype. As such, metabolomics is fast becoming an important discovery tool for new diagnostic and prognostic biomarkers. Although many methods exist for performing metabolomics, relatively few have led to successful development of new diagnostic tests. This review will aid the reader in understanding various metabolomic methods and their applications, as well as some of their inherent advantages and disadvantages. In addition, we present one example of the application of metabolomics to the identification of new fasting blood biomarkers for the diagnosis and monitoring of insulin resistance.

Metabolomic profiles in individuals with negative affectivity and social inhibition: a population-based study of Type D personality.

BACKGROUND: Individuals with negative affectivity who are inhibited in social situations are characterized as distressed, or Type D, and have an increased risk of cardiovascular disease (CVD). The underlying biomechanisms that link this psychological affect to a pathological state are not well understood. This study applied a metabolomic approach to explore biochemical pathways that may contribute to the Type D personality. METHODS: Type D personality was determined by the Type D Scale-14. Small molecule biochemicals were measured using two complementary mass-spectrometry based metabolomics platforms. Metabolic profiles of Type D and non-Type D participants within a population-based study in Southern Germany were compared in cross-sectional regression analyses. The PHQ-9 and GAD-7 instruments were also used to assess symptoms of depression and anxiety, respectively, within this metabolomic study. RESULTS: 668 metabolites were identified in the serum of 1502 participants (age 32-77); 386 of these individuals were classified as Type D. While demographic and biomedical characteristics were equally distributed between the groups, a higher level of depression and anxiety was observed in Type D individuals. Significantly lower levels of the tryptophan metabolite kynurenine were associated with Type D (p-value corrected for multiple testing=0.042), while no significant associations could be found for depression and anxiety. A Gaussian graphical model analysis enabled the identification of four potentially interesting metabolite networks that are enriched in metabolites (androsterone sulfate, tyrosine, indoxyl sulfate or caffeine) that associate nominally with Type D personality. CONCLUSIONS: This study identified novel biochemical pathways associated with Type D personality and demonstrates that the application of metabolomic approaches in population studies can reveal mechanisms that may contribute to psychological health and disease.

Early metabolic markers of the development of dysglycemia and type 2 diabetes and their physiological significance.

Metabolomic screening of fasting plasma from nondiabetic subjects identified α-hydroxybutyrate (α-HB) and linoleoyl-glycerophosphocholine (L-GPC) as joint markers of insulin resistance (IR) and glucose intolerance. To test the predictivity of α-HB and L-GPC for incident dysglycemia, α-HB and L-GPC measurements were obtained in two observational cohorts, comprising 1,261 nondiabetic participants from the Relationship between Insulin Sensitivity and Cardiovascular Disease (RISC) study and 2,580 from the Botnia Prospective Study, with 3-year and 9.5-year follow-up data, respectively. In both cohorts, α-HB was a positive correlate and L-GPC a negative correlate of insulin sensitivity, with α-HB reciprocally related to indices of ß-cell function derived from the oral glucose tolerance test (OGTT). In follow-up, α-HB was a positive predictor (adjusted odds ratios 1.25 [95% CI 1.00-1.60] and 1.26 [1.07-1.48], respectively, for each standard deviation of predictor), and L-GPC was a negative predictor (0.64 [0.48-0.85] and 0.67 [0.54-0.84]) of dysglycemia (RISC) or type 2 diabetes (Botnia), independent of familial diabetes, sex, age, BMI, and fasting glucose. Corresponding areas under the receiver operating characteristic curve were 0.791 (RISC) and 0.783 (Botnia), similar in accuracy when substituting α-HB and L-GPC with 2-h OGTT glucose concentrations. When their activity was examined, α-HB inhibited and L-GPC stimulated glucose-induced insulin release in INS-1e cells. α-HB and L-GPC are independent predictors of worsening glucose tolerance, physiologically consistent with a joint signature of IR and ß-cell dysfunction.

Mining the unknown: a systems approach to metabolite identification combining genetic and metabolic information.

Recent genome-wide association studies (GWAS) with metabolomics data linked genetic variation in the human genome to differences in individual metabolite levels. A strong relevance of this metabolic individuality for biomedical and pharmaceutical research has been reported. However, a considerable amount of the molecules currently quantified by modern metabolomics techniques are chemically unidentified. The identification of these "unknown metabolites" is still a demanding and intricate task, limiting their usability as functional markers of metabolic processes. As a consequence, previous GWAS largely ignored unknown metabolites as metabolic traits for the analysis. Here we present a systems-level approach that combines genome-wide association analysis and Gaussian graphical modeling with metabolomics to predict the identity of the unknown metabolites. We apply our method to original data of 517 metabolic traits, of which 225 are unknowns, and genotyping information on 655,658 genetic variants, measured in 1,768 human blood samples. We report previously undescribed genotype-metabotype associations for six distinct gene loci (SLC22A2, COMT, CYP3A5, CYP2C18, GBA3, UGT3A1) and one locus not related to any known gene (rs12413935). Overlaying the inferred genetic associations, metabolic networks, and knowledge-based pathway information, we derive testable hypotheses on the biochemical identities of 106 unknown metabolites. As a proof of principle, we experimentally confirm nine concrete predictions. We demonstrate the benefit of our method for the functional interpretation of previous metabolomics biomarker studies on liver detoxification, hypertension, and insulin resistance. Our approach is generic in nature and can be directly transferred to metabolomics data from different experimental platforms.

Cancer detection and biopsy classification using concurrent histopathological and metabolomic analysis of core biopsies.

BACKGROUND: Metabolomics, the non-targeted interrogation of small molecules in a biological sample, is an ideal technology for identifying diagnostic biomarkers. Current tissue extraction protocols involve sample destruction, precluding additional uses of the tissue. This is particularly problematic for high value samples with limited availability, such as clinical tumor biopsies that require structural preservation to histologically diagnose and gauge cancer aggressiveness. To overcome this limitation and increase the amount of information obtained from patient biopsies, we developed and characterized a workflow to perform metabolomic analysis and histological evaluation on the same biopsy sample. METHODS: Biopsies of ten human tissues (muscle, adrenal gland, colon, lung, pancreas, small intestine, spleen, stomach, prostate, kidney) were placed directly in a methanol solution to recover metabolites, precipitate proteins, and fix tissue. Following incubation, biopsies were removed from the solution and processed for histology. Kidney and prostate cancer tumor and benign biopsies were stained with hemotoxylin and eosin and prostate biopsies were subjected to PIN-4 immunohistochemistry. The methanolic extracts were analyzed for metabolites on GC/MS and LC/MS platforms. Raw mass spectrometry data files were automatically extracted using an informatics system that includes peak identification and metabolite identification software. RESULTS: Metabolites across all major biochemical classes (amino acids, peptides, carbohydrates, lipids, nucleotides, cofactors, xenobiotics) were measured. The number (ranging from 260 in prostate to 340 in colon) and identity of metabolites were comparable to results obtained with the current method requiring 30 mg ground tissue. Comparing relative levels of metabolites, cancer tumor from benign kidney and prostate biopsies could be distinguished. Successful histopathological analysis of biopsies by chemical staining (hematoxylin, eosin) and antibody binding (PIN-4, in prostate) showed cellular architecture and immunoreactivity were retained. CONCLUSIONS: Concurrent metabolite extraction and histological analysis of intact biopsies is amenable to the clinical workflow. Methanol fixation effectively preserves a wide range of tissues and is compatible with chemical staining and immunohistochemistry. The method offers an opportunity to augment histopathological diagnosis and tumor classification with quantitative measures of biochemicals in the same tissue sample. Since certain biochemicals have been shown to correlate with disease aggressiveness, this method should prove valuable as an adjunct to differentiate cancer aggressiveness.