metabCombiner 2.0: Disparate Multi-Dataset Feature Alignment for LC-MS Metabolomics

Liquid chromatography-high-resolution mass spectrometry (LC-HRMS), as applied to untargeted metabolomics, enables the simultaneous detection of thousands of small molecules, generating complex datasets. Alignment is a crucial step in data processing pipelines, whereby LC-MS features derived from common ions are assembled into a unified matrix amenable to further analysis. Variability in the analytical factors that influence liquid chromatography separations complicates data alignment. This is prominent when aligning data acquired in different laboratories, generated using non-identical instruments, or between batches from large-scale studies. Previously, we developed metabCombiner for aligning disparately acquired LC-MS metabolomics datasets. Here, we report significant upgrades to metabCombiner that enable the stepwise alignment of multiple untargeted LC-MS metabolomics datasets, facilitating inter-laboratory reproducibility studies. To accomplish this, a "primary" feature list is used as a template for matching compounds in "target" feature lists. We demonstrate this workflow by aligning four lipidomics datasets from core laboratories generated using each institution's in-house LC-MS instrumentation and methods. We also introduce batchCombine, an application of the metabCombiner framework for aligning experiments composed of multiple batches. metabCombiner is available as an R package on Github and Bioconductor, along with a new online version implemented as an R Shiny App.

CorrelationCalculator and Filigree: Tools for Data-Driven Network Analysis of Metabolomics Data

A significant challenge in the analysis of omics data is extracting actionable biological knowledge. Metabolomics is no exception. The general problem of relating changes in levels of individual metabolites to specific biological processes is compounded by the large number of unknown metabolites present in untargeted liquid chromatography-mass spectrometry (LC-MS) studies. Further, secondary metabolism and lipid metabolism are poorly represented in existing pathway databases. To overcome these limitations, our group has developed several tools for data-driven network construction and analysis. These include CorrelationCalculator and Filigree. Both tools allow users to build partial correlation-based networks from experimental metabolomics data when the number of metabolites exceeds the number of samples. CorrelationCalculator supports the construction of a single network, while Filigree allows building a differential network utilizing data from two groups of samples, followed by network clustering and enrichment analysis. We will describe the utility and application of both tools for the analysis of real-life metabolomics data.

Trichloroethylene Metabolite S-(1,2-Dichlorovinyl)-l-cysteine Stimulates Changes in Energy Metabolites and Amino Acids in the BeWo Human Placental Trophoblast Model during Syncytialization

Syncytialization, the fusion of cytotrophoblasts into an epithelial barrier that constitutes the maternal-fetal interface, is a crucial event of placentation. This process is characterized by distinct changes to amino acid and energy metabolism. A metabolite of the industrial solvent trichloroethylene (TCE), S-(1,2-dichlorovinyl)-l-cysteine (DCVC), modifies energy metabolism and amino acid abundance in HTR-8/SVneo extravillous trophoblasts. In the current study, we investigated DCVC-induced changes to energy metabolism and amino acids during forskolin-stimulated syncytialization in BeWo cells, a human villous trophoblastic cell line that models syncytialization in vitro. BeWo cells were exposed to forskolin at 100 μM for 48 h to stimulate syncytialization. During syncytialization, BeWo cells were also treated with DCVC at 0 (control), 10, or 20 μM. Following treatment, the targeted metabolomics platform, "Tricarboxylic Acid Plus", was used to identify changes in energy metabolism and amino acids. DCVC treatment during syncytialization decreased oleic acid, aspartate, proline, uridine diphosphate (UDP), UDP-d-glucose, uridine monophosphate, and cytidine monophosphate relative to forskolin-only treatment controls, but did not increase any measured metabolite. Notable changes stimulated by syncytialization in the absence of DCVC included increased adenosine monophosphate and guanosine monophosphate, as well as decreased aspartate and glutamate. Pathway analysis revealed multiple pathways in amino acid and sugar metabolisms that were altered with forskolin-stimulated syncytialization alone and DCVC treatment during syncytialization. Analysis of ratios of metabolites within the pathways revealed that DCVC exposure during syncytialization changed metabolite ratios in the same or different direction compared to syncytialization alone. Building off our oleic acid findings, we found that extracellular matrix metalloproteinase-2, which is downstream in oleic acid signaling, underwent the same changes as oleic acid. Together, the metabolic changes stimulated by DCVC treatment during syncytialization suggest changes in energy metabolism and amino acid abundance as potential mechanisms by which DCVC could impact syncytialization and pregnancy.

Sustained Perturbation of Metabolism and Metabolic Subphenotypes Are Associated With Mortality and Protein Markers of the Host Response

Perturbed host metabolism is increasingly recognized as a pillar of sepsis pathogenesis, yet the dynamic alterations in metabolism and its relationship to other components of the host response remain incompletely understood. We sought to identify the early host-metabolic response in patients with septic shock and to explore biophysiological phenotyping and differences in clinical outcomes among metabolic subgroups.

DESIGN

We measured serum metabolites and proteins reflective of the host-immune and endothelial response in patients with septic shock.

SETTING

We considered patients from the placebo arm of a completed phase II, randomized controlled trial conducted at 16 U.S. medical centers. Serum was collected at baseline (within 24 hr of the identification of septic shock), 24-hour, and 48-hour postenrollment. Linear mixed models were built to assess the early trajectory of protein analytes and metabolites stratified by 28-day mortality status. Unsupervised clustering of baseline metabolomics data was conducted to identify subgroups of patients.

PATIENTS

Patients with vasopressor-dependent septic shock and moderate organ dysfunction that were enrolled in the placebo arm of a clinical trial.

INTERVENTIONS

None.

MEASUREMENTS AND MAIN RESULTS

Fifty-one metabolites and 10 protein analytes were measured longitudinally in 72 patients with septic shock. In the 30 patients (41.7%) who died prior to 28 days, systemic concentrations of acylcarnitines and interleukin (IL)-8 were elevated at baseline and persisted at T24 and T48 throughout early resuscitation. Concentrations of pyruvate, IL-6, tumor necrosis factor-α, and angiopoietin-2 decreased at a slower rate in patients who died. Two groups emerged from clustering of baseline metabolites. Group 1 was characterized by higher levels of acylcarnitines, greater organ dysfunction at baseline and postresuscitation (p < 0.05), and greater mortality over 1 year (p < 0.001).

CONCLUSIONS

Among patients with septic shock, nonsurvivors exhibited a more profound and persistent dysregulation in protein analytes attributable to neutrophil activation and disruption of mitochondrial-related metabolism than survivors.

Body mass index associates with amyotrophic lateral sclerosis survival and metabolomic profiles

INTRODUCTION/AIMS

Body mass index (BMI) is linked to amyotrophic lateral sclerosis (ALS) risk and prognosis, but additional research is needed. The aim of this study was to identify whether and when historical changes in BMI occurred in ALS participants, how these longer term trajectories associated with survival, and whether metabolomic profiles provided insight into potential mechanisms.

METHODS

ALS and control participants self-reported body height and weight 10 (reference) and 5 years earlier, and at study entry (diagnosis for ALS participants). Generalized estimating equations evaluated differences in BMI trajectories between cases and controls. ALS survival was evaluated by BMI trajectory group using accelerated failure time models. BMI trajectories and survival associations were explored using published metabolomic profiling and correlation networks.

RESULTS

Ten-year BMI trends differed between ALS and controls, with BMI loss in the 5 years before diagnosis despite BMI gains 10 to 5 years beforehand in both groups. An overall 10-year drop in BMI associated with a 27.1% decrease in ALS survival (P = .010). Metabolomic networks in ALS participants showed dysregulation in sphingomyelin, bile acid, and plasmalogen subpathways.

DISCUSSION

ALS participants lost weight in the 5-year period before enrollment. BMI trajectories had three distinct groups and the group with significant weight loss in the past 10 years had the worst survival. Participants with a high BMI and increase in weight in the 10 years before symptom onset also had shorter survival. Certain metabolomics profiles were associated with the BMI trajectories. Replicating these findings in prospective cohorts is warranted.

Metabolomic signatures associated with weight gain and psychosis spectrum diagnoses: A pilot study

Psychosis spectrum disorders (PSDs), as well as other severe mental illnesses where psychotic features may be present, like bipolar disorder, are associated with intrinsic metabolic abnormalities. Antipsychotics (APs), the cornerstone of treatment for PSDs, incur additional metabolic adversities including weight gain. Currently, major gaps exist in understanding psychosis illness biomarkers, as well as risk factors and mechanisms for AP-induced weight gain. Metabolomic profiles may identify biomarkers and provide insight into the mechanistic underpinnings of PSDs and antipsychotic-induced weight gain. In this 12-week prospective naturalistic study, we compared serum metabolomic profiles of 25 cases within approximately 1 week of starting an AP to 6 healthy controls at baseline to examine biomarkers of intrinsic metabolic dysfunction in PSDs. In 17 of the case participants with baseline and week 12 samples, we then examined changes in metabolomic profiles over 12 weeks of AP treatment to identify metabolites that may associate with AP-induced weight gain. In the cohort with pre-post data (n = 17), we also compared baseline metabolomes of participants who gained ≥5% baseline body weight to those who gained <5% to identify potential biomarkers of antipsychotic-induced weight gain. Minimally AP-exposed cases were distinguished from controls by six fatty acids when compared at baseline, namely reduced levels of palmitoleic acid, lauric acid, and heneicosylic acid, as well as elevated levels of behenic acid, arachidonic acid, and myristoleic acid (FDR < 0.05). Baseline levels of the fatty acid adrenic acid was increased in 11 individuals who experienced a clinically significant body weight gain (≥5%) following 12 weeks of AP exposure as compared to those who did not (FDR = 0.0408). Fatty acids may represent illness biomarkers of PSDs and early predictors of AP-induced weight gain. The findings may hold important clinical implications for early identification of individuals who could benefit from prevention strategies to reduce future cardiometabolic risk, and may lead to novel, targeted treatments to counteract metabolic dysfunction in PSDs.

Metabolomics identifies shared lipid pathways in independent amyotrophic lateral sclerosis cohorts

Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease lacking effective treatments. This is due, in part, to a complex and incompletely understood pathophysiology. To shed light, we conducted untargeted metabolomics on plasma from two independent cross-sectional ALS cohorts versus control participants to identify recurrent dysregulated metabolic pathways. Untargeted metabolomics was performed on plasma from two ALS cohorts (cohort 1, n = 125; cohort 2, n = 225) and healthy controls (cohort 1, n = 71; cohort 2, n = 104). Individual differential metabolites in ALS cases versus controls were assessed by Wilcoxon, adjusted logistic regression and partial least squares-discriminant analysis, while group lasso explored sub-pathway level differences. Adjustment parameters included age, sex and body mass index. Metabolomics pathway enrichment analysis was performed on metabolites selected using the above methods. Additionally, we conducted a sex sensitivity analysis due to sex imbalance in the cohort 2 control arm. Finally, a data-driven approach, differential network enrichment analysis (DNEA), was performed on a combined dataset to further identify important ALS metabolic pathways. Cohort 2 ALS participants were slightly older than the controls (64.0 versus 62.0 years, P = 0.009). Cohort 2 controls were over-represented in females (68%, P < 0.001). The most concordant cohort 1 and 2 pathways centred heavily on lipid sub-pathways, including complex and signalling lipid species and metabolic intermediates. There were differences in sub-pathways that were enriched in ALS females versus males, including in lipid sub-pathways. Finally, DNEA of the merged metabolite dataset of both ALS and control cohorts identified nine significant subnetworks; three centred on lipids and two encompassed a range of sub-pathways. In our analysis, we saw consistent and important shared metabolic sub-pathways in both ALS cohorts, particularly in lipids, further supporting their importance as ALS pathomechanisms and therapeutics targets.

Alignment and Analysis of a Disparately Acquired Multibatch Metabolomics Study of Maternal Pregnancy Samples

Untargeted liquid chromatography-mass spectrometry metabolomics studies are typically performed under roughly identical experimental settings. Measurements acquired with different LC-MS protocols or following extended time intervals harbor significant variation in retention times and spectral abundances due to altered chromatographic, spectrometric, and other factors, raising many data analysis challenges. We developed a computational workflow for merging and harmonizing metabolomics data acquired under disparate LC-MS conditions. Plasma metabolite profiles were collected from two sets of maternal subjects three years apart using distinct instruments and LC-MS procedures. Metabolomics features were aligned using metabCombiner to generate lists of compounds detected across all experimental batches. We applied data set-specific normalization methods to remove interbatch and interexperimental variation in spectral intensities, enabling statistical analysis on the assembled data matrix. Bioinformatics analyses revealed large-scale metabolic changes in maternal plasma between the first and third trimesters of pregnancy and between maternal plasma and umbilical cord blood. We observed increases in steroid hormones and free fatty acids from the first trimester to term of gestation, along with decreases in amino acids coupled to increased levels in cord blood. This work demonstrates the viability of integrating nonidentically acquired LC-MS metabolomics data and its utility in unconventional metabolomics study designs.

Trichloroethylene modifies energy metabolites in the amniotic fluid of Wistar rats

Exposure to trichloroethylene (TCE), an industrial solvent, is associated with several adverse pregnancy outcomes in humans and decreased fetal weight in rats. However, effects of TCE on energy metabolites in amniotic fluid, which have associations with pregnancy outcomes, has not been published previously. In the current exploratory study, timed-pregnant Wistar rats were exposed to 480 mg TCE/kg/day via vanilla wafer or to vehicle (wafer) alone from gestational day (GD) 6-16. Amniotic fluid collected on GD 16 was analyzed for metabolites important in energy metabolism using short chain fatty acid and tricarboxylic acid plus platforms (N = 4 samples/sex/treatment). TCE decreased concentrations of the following metabolites in amniotic fluid for both fetal sexes: 6-phosphogluconate, guanosine diphosphate, adenosine diphosphate, adenosine triphosphate, and flavin adenine dinucleotide. TCE decreased fructose 1,6-bisphosphate and guanosine triphosphate concentrations in amniotic fluid of male but not female fetuses. Moreover, TCE decreased uridine diphosphate-D-glucuronate concentrations, and increased arginine and phosphocreatine concentrations, in amniotic fluid of female fetuses only. No metabolites were increased in amniotic fluid of male fetuses. Pathway analysis suggested that TCE altered folate biosynthesis and pentose phosphate pathway in both sexes. Using metabolite ratios to investigate changes within specific pathways, some ratio alterations, including those in arginine metabolism and phenylalanine metabolism, were detected in females only. Ratio analysis also suggested enzymes, including gluconokinase, as potential TCE targets. Together, results from this exploratory study suggest that TCE differentially modified energy metabolites in amniotic fluid based on sex. These findings may inform future studies of TCE reproductive toxicity.

Comparing the Fasting and Random-Fed Metabolome Response to an Oral Glucose Tolerance Test in Children and Adolescents: Implications of Sex, Obesity, and Insulin Resistance

As the incidence of obesity and type 2 diabetes (T2D) is occurring at a younger age, studying adolescent nutrient metabolism can provide insights on the development of T2D. Metabolic challenges, including an oral glucose tolerance test (OGTT) can assess the effects of perturbations in nutrient metabolism. Here, we present alterations in the global metabolome in response to an OGTT, classifying the influence of obesity and insulin resistance (IR) in adolescents that arrived at the clinic fasted and in a random-fed state. Participants were recruited as lean (n = 55, aged 8–17 years, BMI percentile 5–85%) and overweight and obese (OVOB, n = 228, aged 8–17 years, BMI percentile ≥ 85%). Untargeted metabolomics profiled 246 annotated metabolites in plasma at t0 and t60 min during the OGTT. Our results suggest that obesity and IR influence the switch from fatty acid (FA) to glucose oxidation in response to the OGTT. Obesity was associated with a blunted decline of acylcarnitines and fatty acid oxidation intermediates. In females, metabolites from the Fasted and Random-Fed OGTT were associated with HOMA-IR, including diacylglycerols, leucine/isoleucine, acylcarnitines, and phosphocholines. Our results indicate that at an early age, obesity and IR may influence the metabolome dynamics in response to a glucose challenge.

Pharmacometabolomics identifies candidate predictor metabolites of an L-carnitine treatment mortality benefit in septic shock

Sepsis‐induced metabolic dysfunction contributes to organ failure and death. L‐carnitine has shown promise for septic shock, but a recent phase II study of patients with vasopressor‐dependent septic shock demonstrated a non‐significant reduction in mortality. We undertook a pharmacometabolomics study of these patients (n = 250) to identify metabolic profiles predictive of a 90‐day mortality benefit from L‐carnitine. The independent predictive value of each pretreatment metabolite concentration, adjusted for L‐carnitine dose, on 90‐day mortality was determined by logistic regression. A grid‐search analysis maximizing the Z‐statistic from a binomial proportion test identified specific metabolite threshold levels that discriminated L‐carnitine responsive patients. Threshold concentrations were further assessed by hazard ratio and Kaplan‐Meier estimate. Accounting for L‐carnitine treatment and dose, 11 ¹H‐NMR metabolites and 12 acylcarnitines were independent predictors of 90‐day mortality. Based on the grid‐search analysis numerous acylcarnitines and valine were identified as candidate metabolites of drug response. Acetylcarnitine emerged as highly viable for the prediction of an L‐carnitine mortality benefit due to its abundance and biological relevance. Using its most statistically significant threshold concentration, patients with pretreatment acetylcarnitine greater than or equal to 35 µM were less likely to die at 90 days if treated with L‐carnitine (18 g) versus placebo (p = 0.01 by log rank test). Metabolomics also identified independent predictors of 90‐day sepsis mortality. Our proof‐of‐concept approach shows how pharmacometabolomics could be useful for tackling the heterogeneity of sepsis and informing clinical trial design. In addition, metabolomics can help understand mechanisms of sepsis heterogeneity and variable drug response, because sepsis induces alterations in numerous metabolite concentrations.

Serum Levels of Branched Chain Amino Acids Predict Duration of Cardiovascular Organ Failure in Septic Shock

BACKGROUND

Sepsis shifts cardiac metabolic fuel preference and this disruption may have implications for cardiovascular function. A greater understanding of the role of metabolism in the development and persistence of cardiovascular failure in sepsis could serve to identify novel pharmacotherapeutic approaches.

METHODS

Secondary analysis of prospective quantitative proton nuclear magnetic resonance (1H-NMR) metabolomic data from patients enrolled in a phase II randomized control trial of L-carnitine in septic shock. Participants with a sequential organ failure assessment (SOFA) score of > = 5, lactate > = 2, and requiring vasopressor support for at least 4 h were eligible for enrollment. The independent prognostic value of metabolites to predict survival with shock resolution within 48 h and vasopressor free days were assessed. Concentrations of predictive metabolites were compared between participants with and without shock resolution at 48 h.

RESULTS

Serum 1H-NMR metabolomics data from 228 patients were analyzed. Eighty-one (36%) patients met the primary outcome; 33 (14%) died prior to 48 h. The branched chain amino acids (BCAA), valine, leucine, and isoleucine were univariate predictors of the primary outcome after adjusting for multiple hypothesis testing, while valine remained significant after controlling for SOFA score. Similar results were observed when analyzed based on vasopressor free days, and persisted after controlling for confounding variables and excluding non-survivors. BCAA concentrations at 48 h significantly discriminated between those with shock resolution versus persistent shock.

CONCLUSIONS

Among patients with septic shock, BCAA concentrations independently predict time to shock resolution. This study provides hypothesis generating data into the potential contribution of BCAAs to the pathophysiology of cardiovascular failure in sepsis, opening areas for future investigations.

Metabolomic Profiling in Response to an Oral Glucose Tolerance Test Reveals Pathways Associated With Obesity and Insulin Resistance During the Pubertal Transition

Objectives

To reveal alterations in metabolic pathways in response to an oral glucose tolerance test (OGTT) underlying the development of insulin resistance during the pubertal transition.

Methods

Participants were recruited as healthy controls (HC, n = 55, aged 8.3–18.0 years, BMI percentile 5–85%) and overweight and obese individuals (OVOB, n = 228, aged 8.1–17.9 years, BMI percentile ≥ 85%). Participants were grouped based on their peak insulin response to the OGTT, stratified by peak at t30 (Group 1, n = 163), t60 (Group 2, n = 75), and t120 minutes (Group 3,

n = 44). Untargeted metabolomics profiled 267 annotated metabolites and &gt; 3000 unannotated features in plasma at t0 and t60 minutes. Regression classified changes in metabolites across the time-course, assessing the influence of BMI and insulin response (FDR &lt; 0.05). The connectivity of the metabolome was determined using differential network enrichment analysis (DNEA), stratified by insulin response group.

Results

At fasting, 32% of the metabolites differed between HC and OVOB, including elevated kynurenine, leucine/isoleucine, methionine, tyrosine, short-chain acylcarnitines, and diacylglycerols in OVOB. At t60, only 4% of the metabolites differed between HC and OVOB participants, suggesting a “normalization” of the metabolome, with exceptions of acylcarnitines and FA oxidation (FAO) intermediates. Although no metabolites differed significantly between insulin response group, differential subnetworks were observed, including increased connectivity between FA and FAO intermediates in Group 1 at t60, suggesting differential regulation in post-prandial FAs.

Conclusions

Profiling the metabolome response to an OGTT may highlight metabolic dysfunction prior to type 2 diabetes and will be used in future longitudinal analyses predicting insulin resistance trajectory.

Funding Sources

The National Institute of Child Health and Human Development, the National Institute of Diabetes and Digestive and Kidney Diseases, and the National Institutes of Health

metabCombiner: Paired Untargeted LC-HRMS Metabolomics Feature Matching and Concatenation of Disparately Acquired Data Sets

LC-HRMS experiments detect thousands of compounds, with only a small fraction of them identified in most studies. Traditional data processing pipelines contain an alignment step to assemble the measurements of overlapping features across samples into a unified table. However, data sets acquired under nonidentical conditions are not amenable to this process, mostly due to significant alterations in chromatographic retention times. Alignment of features between disparately acquired LC-MS metabolomics data could aid collaborative compound identification efforts and enable meta-analyses of expanded data sets. Here, we describe metabCombiner, a new computational pipeline for matching known and unknown features in a pair of untargeted LC-MS data sets and concatenating their abundances into a combined table of intersecting feature measurements. metabCombiner groups features by mass-to-charge (m/z) values to generate a search space of possible feature pair alignments, fits a spline through a set of selected retention time ordered pairs, and ranks alignments by m/z, mapped retention time, and relative abundance similarity. We evaluated this workflow on a pair of plasma metabolomics data sets acquired with different gradient elution methods, achieving a mean absolute retention time prediction error of roughly 0.06 min and a weighted per-compound matching accuracy of approximately 90%. We further demonstrate the utility of this method by comprehensively mapping features in urine and muscle metabolomics data sets acquired from different laboratories. metabCombiner has the potential to bridge the gap between otherwise incompatible metabolomics data sets and is available as an R package at https://github.com/hhabra/metabCombiner and Bioconductor.

Pharmacologic modulation of brain metabolism by valproic acid can induce a neuroprotective environment

OBJECTIVE

Traumatic brain injury (TBI) is a leading cause of trauma-related morbidity and mortality. Valproic acid (VPA) has been shown to attenuate brain lesion size and swelling within the first few hours following TBI. Because injured neurons are sensitive to metabolic changes, we hypothesized that VPA treatment would alter the metabolic profile in the perilesional brain tissues to create a neuroprotective environment.

METHODS

We subjected swine to combined TBI (12-mm cortical impact) and hemorrhagic shock (40% blood volume loss and 2 hours of hypotension) and randomized them to two groups (n = 5/group): (1) normal saline (NS; 3× hemorrhage volume) and (2) NS-VPA (NS, 3× hemorrhage volume; VPA, 150 mg/kg). After 6 hours, brains were harvested, and 100 mg of the perilesional tissue was used for metabolite extraction. Samples were analyzed using reversed-phase liquid chromatography-mass spectrometry in positive and negative ion modes, and data were analyzed using MetaboAnalyst software (McGill University, Quebec, Canada).

RESULTS

In untargeted reversed-phase liquid chromatography-mass spectrometry analysis, we detected 3,750 and 1,955 metabolites in positive and negative ion modes, respectively. There were no significantly different metabolites in positive ion mode; however, 167 metabolite features were significantly different (p < 0.05) in the negative ion mode, which included VPA derivates. Pathway analysis showed that several pathways were affected in the treatment group, including the biosynthesis of unsaturated fatty acids (p = 0.001). Targeted amino acid analysis on glycolysis/tricarboxylic acid (TCA) cycle revealed that VPA treatment significantly decreased the levels of the excitotoxic amino acid serine (p = 0.001).

CONCLUSION

Valproic acid can be detected in perilesional tissues in its metabolized form. It also induces metabolic changes in the brains within the first few hours following TBI to create a neuroprotective environment.

Genetic and Metabolite Variability in One-Carbon Metabolism Applied to an Insulin Resistance Model in Patients With Schizophrenia Receiving Atypical Antipsychotics

Background: Patients with schizophrenia are at high risk of pre-mature mortality due to cardiovascular disease (CVD). Our group has completed studies in pharmacogenomics and metabolomics that have independently identified perturbations in one-carbon metabolism as associated with risk factors for CVD in this patient population. Therefore, this study aimed to use genetic and metabolomic data to determine the relationship between folate pharmacogenomics, one-carbon metabolites, and insulin resistance as measured using the homeostatic model assessment for insulin resistance (HOMA-IR) as a marker of CVD. Methods: Participants in this pilot analysis were on a stable atypical antipsychotic regimen for at least 6 months, with no diabetes diagnosis or use of antidiabetic medications. Participant samples were genotyped for MTHFR variants rs1801131 (MTHFR A1298C) and rs1801133 (MTHFR C677T). Serum metabolite concentrations were obtained with NMR. A least squares regression model was used to predict log(HOMA-IR) values based on the following independent variables: serum glutamate, glycine, betaine, serine, and threonine concentrations, and carrier status of the variant alleles for the selected genotypes. Results: A total of 67 participants were included, with a median age of 47 years old (IQR 42-52), 39% were female, and the median BMI was 30.3 (IQR 26.3-37.1). Overall, the model demonstrated an ability to predict log(HOMA-IR) values with an adjusted R ² of 0.44 and a p-value of < 0.001. Glutamate, threonine, and carrier status of the MTHFR 1298 C or MTHFR 677 T allele were positively correlated with log(HOMA-IR), whereas glycine, serine, and betaine concentrations trended inversely with log(HOMA-IR). All factors included in this final model were considered as having a possible effect on predicting log(HOMA-IR) as measured with a p-value < 0.1. Conclusions: Presence of pharmacogenomic variants that decrease the functional capacity of the MTHFR enzyme are associated with increased risk for cardiovascular disease, as measured in this instance by log(HOMA-IR). Furthermore, serine, glycine, and betaine concentrations trended inversely with HOMA-IR, suggesting that increased presence of methyl-donating groups is associated with lower measures of insulin resistance. Ultimately, these results will need to be replicated in a significantly larger population.

Deep annotation of untargeted LC-MS metabolomics data with Binner

MOTIVATION

When metabolites are analyzed by electrospray ionization (ESI)-mass spectrometry, they are usually detected as multiple ion species due to the presence of isotopes, adducts and in-source fragments. The signals generated by these degenerate features (along with contaminants and other chemical noise) obscure meaningful patterns in MS data, complicating both compound identification and downstream statistical analysis. To address this problem, we developed Binner, a new tool for the discovery and elimination of many degenerate feature signals typically present in untargeted ESI-LC-MS metabolomics data.

RESULTS

Binner generates feature annotations and provides tools to help users visualize informative feature relationships that can further elucidate the underlying structure of the data. To demonstrate the utility of Binner and to evaluate its performance, we analyzed data from reversed phase LC-MS and hydrophilic interaction chromatography (HILIC) platforms and demonstrated the accuracy of selected annotations using MS/MS. When we compared Binner annotations of 75 compounds previously identified in human plasma samples with annotations generated by three similar tools, we found that Binner achieves superior performance in the number and accuracy of annotations while simultaneously minimizing the number of incorrectly annotated principal ions. Data reduction and pattern exploration with Binner have allowed us to catalog a number of previously unrecognized complex adducts and neutral losses generated during the ionization of molecules in LC-MS. In summary, Binner allows users to explore patterns in their data and to efficiently and accurately eliminate a significant number of the degenerate features typically found in various LC-MS modalities.

AVAILABILITY AND IMPLEMENTATION

Binner is written in Java and is freely available from http://binner.med.umich.edu.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Using l-Carnitine as a Pharmacologic Probe of the Interpatient and Metabolic Variability of Sepsis

OBJECTIVE

The objective of this review is to discuss the therapeutic use and differential treatment response to Levo-carnitine (l-carnitine) treatment in septic shock, and to demonstrate common lessons learned that are important to the advancement of precision medicine approaches to sepsis. We propose that significant interpatient variability in the metabolic response to l-carnitine and clinical outcomes can be used to elucidate the mechanistic underpinnings that contribute to sepsis heterogeneity.

METHODS

A narrative review was conducted that focused on explaining interpatient variability in l-carnitine treatment response. Relevant biological and patient-level characteristics considered include genetic, metabolic, and morphomic phenotypes; potential drug interactions; and pharmacokinetics (PKs).

MAIN RESULTS

Despite promising results in a phase I study, a recent phase II clinical trial of l-carnitine treatment in septic shock showed a nonsignificant reduction in mortality. However, l-carnitine treatment induces significant interpatient variability in l-carnitine and acylcarnitine concentrations over time. In particular, administration of l-carnitine induces a broad, dynamic range of serum concentrations and measured peak concentrations are associated with mortality. Applied systems pharmacology may explain variability in drug responsiveness by using patient characteristics to identify pretreatment phenotypes most likely to derive benefit from l-carnitine. Moreover, provocation of sepsis metabolism with l-carnitine offers a unique opportunity to identify metabolic response signatures associated with patient outcomes. These approaches can unmask latent metabolic pathways deranged in the sepsis syndrome and offer insight into the pathophysiology, progression, and heterogeneity of the disease.

CONCLUSIONS

The compiled evidence suggests there are several potential explanations for the variability in carnitine concentrations and clinical response to l-carnitine in septic shock. These serve as important confounders that should be considered in interpretation of l-carnitine clinical studies and broadly holds lessons for future clinical trial design in sepsis. Consideration of these factors is needed if precision medicine in sepsis is to be achieved.

Mitochondrial Nutrient Utilization Underlying the Association Between Metabolites and Insulin Resistance in Adolescents

CONTEXT

A person's intrinsic metabolism, reflected in the metabolome, may describe the relationship between nutrient intake and metabolic health.

OBJECTIVES

Untargeted metabolomics was used to identify metabolites associated with metabolic health. Path analysis classified how habitual dietary intake influences body mass index z-score (BMIz) and insulin resistance (IR) through changes in the metabolome.

DESIGN

Data on anthropometry, fasting metabolites, C-peptide, and dietary intake were collected from 108 girls and 98 boys aged 8 to 14 years. Sex-stratified linear regression identified metabolites associated with BMIz and homeostatic model assessment of IR using C-peptide (HOMA-CP), accounting for puberty, age, and muscle and fat area. Path analysis identified clusters of metabolites that underlie the relationship between energy-adjusted macronutrient intake with BMIz and HOMA-CP.

RESULTS

Metabolites associated with BMIz include positive associations with diglycerides among girls and positive associations with branched chain and aromatic amino acids in boys. Intermediates in fatty acid metabolism, including medium-chain acylcarnitines (AC), were inversely associated with HOMA-CP. Carbohydrate intake is positively associated with HOMA-CP through decreases in levels of AC, products of β-oxidation. Approaching significance, fat intake is positively associated with HOMA-CP through increases in levels of dicarboxylic fatty acids, products of omega-oxidation.

CONCLUSIONS

This cross-sectional analysis suggests that IR in children is associated with reduced fatty acid oxidation capacity. When consuming more grams of fat, there is evidence for increased extramitochondrial fatty acid metabolism, while higher carbohydrate intake appears to lead to decreases in intermediates of β-oxidation. Thus, biomarkers of IR and mitochondrial oxidative capacity may depend on macronutrient intake.

Differential network enrichment analysis reveals novel lipid pathways in chronic kidney disease

MOTIVATION

Functional enrichment testing methods can reduce data comprising hundreds of altered biomolecules to smaller sets of altered biological 'concepts' that help generate testable hypotheses. This study leveraged differential network enrichment analysis methodology to identify and validate lipid subnetworks that potentially differentiate chronic kidney disease (CKD) by severity or progression.

RESULTS

We built a partial correlation interaction network, identified highly connected network components, applied network-based gene-set analysis to identify differentially enriched subnetworks, and compared the subnetworks in patients with early-stage versus late-stage CKD. We identified two subnetworks 'triacylglycerols' and 'cardiolipins-phosphatidylethanolamines (CL-PE)' characterized by lower connectivity, and a higher abundance of longer polyunsaturated triacylglycerols in patients with severe CKD (stage ≥4) from the Clinical Phenotyping Resource and Biobank Core. These finding were replicated in an independent cohort, the Chronic Renal Insufficiency Cohort. Using an innovative method for elucidating biological alterations in lipid networks, we demonstrated alterations in triacylglycerols and cardiolipins-phosphatidylethanolamines that precede the clinical outcome of end-stage kidney disease by several years.

AVAILABILITY AND IMPLEMENTATION

A complete list of NetGSA results in HTML format can be found at http://metscape.ncibi.org/netgsa/12345-022118/cric_cprobe/022118/results_cric_cprobe/main.html. The DNEA is freely available at https://github.com/wiggie/DNEA. Java wrapper leveraging the cytoscape.js framework is available at http://js.cytoscape.org.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Intrinsic Mitochondrial Nutrient Utilization May Underlie the Association of Metabolite Levels with BMIz and Insulin Resistance (FS03-02-19)

Objectives

To identify metabolites associated with BMIz and insulin resistance (IR) among 108 girls and 98 boys aged 8–14 years. We sought evidence of whether altered mitochondrial nutrient utilization, as indicated by mitochondrial-derived metabolites, mediates the relationship between diet, IR and obesity.

Methods

Anthropometry, fasting untargeted-liquid chromatography/mass spectrometry-derived metabolites and C-Peptide, and semi-quantitative food frequency questionnaires were collected from adolescents in the Early Life Exposure in Mexico to ENvironmental Toxicants (ELEMENT) birth cohort. Sex-stratified generalized linear models were used to identify metabolites that are marginally associated BMIz and HOMA C-peptide (HOMA-CP), accounting for puberty, age and muscle and fat area (FDR < 0.1). Assessed the relationship between energy-adjusted macronutrient intake with HOMA-CP and BMIz. Structural equation models coupled with hierarchical clustering identified clusters of metabolites that may mediate the relationship between macronutrient intake with BMIz and HOMA-CP.

Results

Stratification by sex demonstrated sex-specific associations with BMIz. Most notable were girl's positive association with diacylglycerols and boy's positive association with branched chain and aromatic amino acids, independent of HOMA-CP. Intermediates in fatty acid metabolism, including medium chain acylcarnitines (acylCN), were inversely associated with HOMA-CP. No direct relationship was observed between macronutrient intake with BMIz and IR. Using mediation analyses, fat intake is positively associated with BMIz and HOMA-CP through increases in levels of dicarboxylic fatty acids (DiC-FA), products of omega-oxidation. Carbohydrate intake is positively associated with HOMA-CP through decreases in levels of medium chain acylCN, products of β-oxidation.

Conclusions

Insulin resistance in children appears to be associated with reduced fatty acid oxidation capacity. When consuming more grams of fat, there is evidence for increased extra-mitochondrial fatty acid metabolism (DiC-FA), while higher carbohydrate intake appears to lead to accumulation of intermediates of β-oxidation. Thus, biomarkers of IR and mitochondrial oxidative capacity may depend on macronutrient intake.

Funding Sources

This work was supported by the NIEHS, EPA and NIDDK.

Untargeted Metabolomics Differentiates l-Carnitine Treated Septic Shock 1-Year Survivors and Nonsurvivors

l-Carnitine is a candidate therapeutic for the treatment of septic shock, a condition that carries a ≥40% mortality. Responsiveness to l-carnitine may hinge on unique metabolic profiles that are not evident from the clinical phenotype. To define these profiles, we performed an untargeted metabolomic analysis of serum from 21 male sepsis patients enrolled in a placebo-controlled l-carnitine clinical trial. Although treatment with l-carnitine is known to induce changes in the sepsis metabolome, we found a distinct set of metabolites that differentiated 1-year survivors from nonsurvivors. Following feature alignment, we employed a new and innovative data reduction strategy followed by false discovery correction, and identified 63 metabolites that differentiated carnitine-treated 1-year survivors versus nonsurvivors. Following identification by MS/MS and database search, several metabolite markers of vascular inflammation were determined to be prominently elevated in the carnitine-treated nonsurvivor cohort, including fibrinopeptide A, allysine, and histamine. While preliminary, these results corroborate that metabolic profiles may be useful to differentiate l-carnitine treatment responsiveness. Furthermore, these data show that the metabolic signature of l-carnitine-treated nonsurvivors is associated with a severity of illness (e.g., vascular inflammation) that is not routinely clinically detected.

Atypical Antipsychotic Exposure May Not Differentiate Metabolic Phenotypes of Patients with Schizophrenia

STUDY OBJECTIVE

Patients with schizophrenia are known to have higher rates of metabolic disease than the general population. Contributing factors likely include lifestyle and atypical antipsychotic (AAP) use, but the underlying mechanisms are unknown. The objective of this study was to identify metabolomic variability in adult patients with schizophrenia who were taking AAPs and grouped by fasting insulin concentration, our surrogate marker for metabolic risk.

DESIGN

Metabolomics analysis PARTICIPANTS: Ninety-four adult patients with schizophrenia who were taking an AAP for at least 6 months, with no changes in their antipsychotic regimen for the previous 8 weeks, and who did not require treatment with insulin, participated in the study. Twenty age- and sex-matched nonobese (10 subjects) and obese (10 subjects) controls without cardiovascular disease or mental health diagnoses were used to match the body mass index (BMI) range of the patients with schizophrenia to account for metabolite concentration differences attributable to BMI.

MEASUREMENTS AND MAIN RESULTS

Existing serum samples were used to identify aqueous metabolites (to differentiate fasting insulin concentration quartiles) and fatty acids with quantitative nuclear magnetic resonance and gas chromatography methods, respectively. To exclude metabolites from our pathway mapping analysis that were due to variability in weight, we also subjected serum samples from the nonobese and obese controls to the same analyses. Patients with schizophrenia had a median age of 47.0 years (interquartile range 41.0-52.0 years). Using a false discovery rate threshold of less than 25%, 10 metabolites, not attributable to weight, differentiated insulin concentration quartiles in patients with schizophrenia and identified variability in one-carbon metabolism between groups. Patients with higher fasting insulin concentrations (quartiles 3 and 4) also trended toward higher levels of saturated fatty acids compared with patients with lower fasting insulin concentrations (quartiles 1 and 2).

CONCLUSION

Our results illustrate the utility of metabolomics to identify pathways underlying variable fasting insulin concentration in patients with schizophrenia. Importantly, no significant difference in AAP exposure was observed among groups, suggesting that current antipsychotic use may not be a primary factor that differentiates middle-aged adult patients with schizophrenia by fasting insulin concentration.

Sparse network modeling and metscape-based visualization methods for the analysis of large-scale metabolomics data

Motivation

Recent technological advances in mass spectrometry, development of richer mass spectral libraries and data processing tools have enabled large scale metabolic profiling. Biological interpretation of metabolomics studies heavily relies on knowledge-based tools that contain information about metabolic pathways. Incomplete coverage of different areas of metabolism and lack of information about non-canonical connections between metabolites limits the scope of applications of such tools. Furthermore, the presence of a large number of unknown features, which cannot be readily identified, but nonetheless can represent bona fide compounds, also considerably complicates biological interpretation of the data.

Results

Leveraging recent developments in the statistical analysis of high-dimensional data, we developed a new Debiased Sparse Partial Correlation algorithm (DSPC) for estimating partial correlation networks and implemented it as a Java-based CorrelationCalculator program. We also introduce a new version of our previously developed tool Metscape that enables building and visualization of correlation networks. We demonstrate the utility of these tools by constructing biologically relevant networks and in aiding identification of unknown compounds.

Availability and Implementation

http://metscape.med.umich.edu.

Supplementary information

Supplementary data are available at Bioinformatics online.

Glycolytic Enzymes Coalesce in G Bodies under Hypoxic Stress

Glycolysis is upregulated under conditions such as hypoxia and high energy demand to promote cell proliferation, although the mechanism remains poorly understood. We find that hypoxia in Saccharomyces cerevisiae induces concentration of glycolytic enzymes, including the Pfk2p subunit of the rate-limiting phosphofructokinase, into a single, non-membrane-bound granule termed the "glycolytic body" or "G body." A yeast kinome screen identifies the yeast ortholog of AMP-activated protein kinase, Snf1p, as necessary for G-body formation. Many G-body components identified by proteomics are required for G-body integrity. Cells incapable of forming G bodies in hypoxia display abnormal cell division and produce inviable daughter cells. Conversely, cells with G bodies show increased glucose consumption and decreased levels of glycolytic intermediates. Importantly, G bodies form in human hepatocarcinoma cells in hypoxia. Together, our results suggest that G body formation is a conserved, adaptive response to increase glycolytic output during hypoxia or tumorigenesis.

Lipidomic Signature of Progression of Chronic Kidney Disease in the Chronic Renal Insufficiency Cohort

Introduction

Human studies report conflicting results on the predictive power of serum lipids on the progression of chronic kidney disease. We aimed to systematically identify the lipids that predict progression to end-stage kidney disease.

Methods

From the Chronic Renal Insufficiency Cohort, 79 patients with chronic kidney disease stages 2 to 3 who progressed to end-stage kidney disease over 6 years of follow-up were selected and frequency matched by age, sex, race, and diabetes with 121 nonprogressors with less than 25% decline in estimated glomerular filtration rate during the follow-up. The patients were randomly divided into training and test sets. We applied liquid chromatography-mass spectrometry-based lipidomics on visit year 1 samples.

Results

We identified 510 lipids, of which the top 10 coincided with false discovery threshold of 0.058 in the training set. From the top 10 lipids, the abundance of diacylglycerols and cholesteryl esters was lower, but that of phosphatidic acid 44:4 and monoacylglycerol 16:0 was significantly higher in progressors. Using logistic regression models, a multimarker panel consisting of diacylglycerols and monoacylglycerol independently predicted progression. The c-statistic of the multimarker panel added to the base model consisting of estimated glomerular filtration rate and urine protein-to-creatinine ratio as compared with that of the base model was 0.92 (95% confidence interval: 0.88–0.97) and 0.83 (95% confidence interval: 0.76–0.90, P < 0.01), respectively, an observation that was validated in the test subset.

Discussion

We conclude that a distinct panel of lipids may improve prediction of progression of chronic kidney disease beyond estimated glomerular filtration rate and urine protein-to-creatinine ratio when added to the base model.

Metabolomics and Its Application to Acute Lung Diseases

Metabolomics is a rapidly expanding field of systems biology that is gaining significant attention in many areas of biomedical research. Also known as metabonomics, it comprises the analysis of all small molecules or metabolites that are present within an organism or a specific compartment of the body. Metabolite detection and quantification provide a valuable addition to genomics and proteomics and give unique insights into metabolic changes that occur in tangent to alterations in gene and protein activity that are associated with disease. As a novel approach to understanding disease, metabolomics provides a "snapshot" in time of all metabolites present in a biological sample such as whole blood, plasma, serum, urine, and many other specimens that may be obtained from either patients or experimental models. In this article, we review the burgeoning field of metabolomics in its application to acute lung diseases, specifically pneumonia and acute respiratory disease syndrome (ARDS). We also discuss the potential applications of metabolomics for monitoring exposure to aerosolized environmental toxins. Recent reports have suggested that metabolomics analysis using nuclear magnetic resonance (NMR) and mass spectrometry (MS) approaches may provide clinicians with the opportunity to identify new biomarkers that may predict progression to more severe disease, such as sepsis, which kills many patients each year. In addition, metabolomics may provide more detailed phenotyping of patient heterogeneity, which is needed to achieve the goal of precision medicine. However, although several experimental and clinical metabolomics studies have been conducted assessing the application of the science to acute lung diseases, only incremental progress has been made. Specifically, little is known about the metabolic phenotypes of these illnesses. These data are needed to substantiate metabolomics biomarker credentials so that clinicians can employ them for clinical decision-making and investigators can use them to design clinical trials.

ConceptMetab: exploring relationships among metabolite sets to identify links among biomedical concepts

MOTIVATION

Capabilities in the field of metabolomics have grown tremendously in recent years. Many existing resources contain the chemical properties and classifications of commonly identified metabolites. However, the annotation of small molecules (both endogenous and synthetic) to meaningful biological pathways and concepts still lags behind the analytical capabilities and the chemistry-based annotations. Furthermore, no tools are available to visually explore relationships and networks among functionally related groups of metabolites (biomedical concepts). Such a tool would provide the ability to establish testable hypotheses regarding links among metabolic pathways, cellular processes, phenotypes and diseases.

RESULTS

Here we present ConceptMetab, an interactive web-based tool for mapping and exploring the relationships among 16 069 biologically defined metabolite sets developed from Gene Ontology, KEGG and Medical Subject Headings, using both KEGG and PubChem compound identifiers, and based on statistical tests for association. We demonstrate the utility of ConceptMetab with multiple scenarios, showing it can be used to identify known and potentially novel relationships among metabolic pathways, cellular processes, phenotypes and diseases, and provides an intuitive interface for linking compounds to their molecular functions and higher level biological effects.

AVAILABILITY AND IMPLEMENTATION

http://conceptmetab.med.umich.edu

CONTACTS

akarnovsky@umich.edu or sartorma@umich.edu

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Proteomics analysis of rough endoplasmic reticulum in pancreatic beta cells

Pancreatic beta cells have well-developed ER to accommodate for the massive production and secretion of insulin. ER homeostasis is vital for normal beta cell function. Perturbation of ER homeostasis contributes to beta cell dysfunction in both type 1 and type 2 diabetes. To systematically identify the molecular machinery responsible for proinsulin biogenesis and maintenance of beta cell ER homeostasis, a widely used mouse pancreatic beta cell line, MIN6 cell was used to purify rough ER. Two different purification schemes were utilized. In each experiment, the ER pellets were solubilized and analyzed by 1D SDS-PAGE coupled with HPLC-MS/MS. A total of 1467 proteins were identified in three experiments with ≥95% confidence, among which 1117 proteins were found in at least two separate experiments and 737 proteins found in all three experiments. GO analysis revealed a comprehensive profile of known and novel players responsible for proinsulin biogenesis and ER homeostasis. Further bioinformatics analysis also identified potential beta cell specific ER proteins as well as ER proteins present in the risk genetic loci of type 2 diabetes. This dataset defines a molecular environment in the ER for proinsulin synthesis, folding and export and laid a solid foundation for further characterizations of altered ER homeostasis under diabetes-causing conditions. All MS data have been deposited in the ProteomeXchange with identifier PXD001081 (http://proteomecentral.proteomexchange.org/dataset/PXD001081).

Metabolomics and diabetes: analytical and computational approaches

Diabetes is characterized by altered metabolism of key molecules and regulatory pathways. The phenotypic expression of diabetes and associated complications encompasses complex interactions between genetic, environmental, and tissue-specific factors that require an integrated understanding of perturbations in the network of genes, proteins, and metabolites. Metabolomics attempts to systematically identify and quantitate small molecule metabolites from biological systems. The recent rapid development of a variety of analytical platforms based on mass spectrometry and nuclear magnetic resonance have enabled identification of complex metabolic phenotypes. Continued development of bioinformatics and analytical strategies has facilitated the discovery of causal links in understanding the pathophysiology of diabetes and its complications. Here, we summarize the metabolomics workflow, including analytical, statistical, and computational tools, highlight recent applications of metabolomics in diabetes research, and discuss the challenges in the field.

MetDisease--connecting metabolites to diseases via literature

MOTIVATION

In recent years, metabolomics has emerged as an approach to perform large-scale characterization of small molecules in biological systems. Metabolomics posed a number of bioinformatics challenges associated in data analysis and interpretation. Genome-based metabolic reconstructions have established a powerful framework for connecting metabolites to genes through metabolic reactions and enzymes that catalyze them. Pathway databases and bioinformatics tools that use this framework have proven to be useful for annotating experimental metabolomics data. This framework can be used to infer connections between metabolites and diseases through annotated disease genes. However, only about half of experimentally detected metabolites can be mapped to canonical metabolic pathways. We present a new Cytoscape 3 plug-in, MetDisease, which uses an alternative approach to link metabolites to disease information. MetDisease uses Medical Subject Headings (MeSH) disease terms mapped to PubChem compounds through literature to annotate compound networks.

AVAILABILITY AND IMPLEMENTATION

MetDisease can be downloaded from http://apps.cytoscape.org/apps/metdisease or installed via the Cytoscape app manager. Further information about MetDisease can be found at http://metdisease.ncibi.org

CONTACT

akarnovs@med.umich.edu

SUPPLEMENTARY INFORMATION

Supplementary Data are available at Bioinformatics online.

Epithelial-mesenchymal transition-associated secretory phenotype predicts survival in lung cancer patients

In cancer cells, the process of epithelial-mesenchymal transition (EMT) confers migratory and invasive capacity, resistance to apoptosis, drug resistance, evasion of host immune surveillance and tumor stem cell traits. Cells undergoing EMT may represent tumor cells with metastatic potential. Characterizing the EMT secretome may identify biomarkers to monitor EMT in tumor progression and provide a prognostic signature to predict patient survival. Utilizing a transforming growth factor-β-induced cell culture model of EMT, we quantitatively profiled differentially secreted proteins, by GeLC-tandem mass spectrometry. Integrating with the corresponding transcriptome, we derived an EMT-associated secretory phenotype (EASP) comprising of proteins that were differentially upregulated both at protein and mRNA levels. Four independent primary tumor-derived gene expression data sets of lung cancers were used for survival analysis by the random survival forests (RSF) method. Analysis of 97-gene EASP expression in human lung adenocarcinoma tumors revealed strong positive correlations with lymph node metastasis, advanced tumor stage and histological grade. RSF analysis built on a training set (n = 442), including age, sex and stage as variables, stratified three independent lung cancer data sets into low-, medium- and high-risk groups with significant differences in overall survival. We further refined EASP to a 20 gene signature (rEASP) based on variable importance scores from RSF analysis. Similar to EASP, rEASP predicted survival of both adenocarcinoma and squamous carcinoma patients. More importantly, it predicted survival in the early-stage cancers. These results demonstrate that integrative analysis of the critical biological process of EMT provides mechanism-based and clinically relevant biomarkers with significant prognostic value.

Signal intensities derived from different NMR probes and parameters contribute to variations in quantification of metabolites

We discovered that serious issues could arise that may complicate interpretation of metabolomic data when identical samples are analyzed at more than one NMR facility, or using slightly different NMR parameters on the same instrument. This is important because cross-center validation metabolomics studies are essential for the reliable application of metabolomics to clinical biomarker discovery. To test the reproducibility of quantified metabolite data at multiple sites, technical replicates of urine samples were assayed by 1D-¹H-NMR at the University of Alberta and the University of Michigan. Urine samples were obtained from healthy controls under a standard operating procedure for collection and processing. Subsequent analysis using standard statistical techniques revealed that quantitative data across sites can be achieved, but also that previously unrecognized NMR parameter differences can dramatically and widely perturb results. We present here a confirmed validation of NMR analysis at two sites, and report the range and magnitude that common NMR parameters involved in solvent suppression can have on quantitated metabolomics data. Specifically, saturation power levels greatly influenced peak height intensities in a frequency-dependent manner for a number of metabolites, which markedly impacted the quantification of metabolites. We also investigated other NMR parameters to determine their effects on further quantitative accuracy and precision. Collectively, these findings highlight the importance of and need for consistent use of NMR parameter settings within and across centers in order to generate reliable, reproducible quantified NMR metabolomics data.

Untargeted LC-MS metabolomics of bronchoalveolar lavage fluid differentiates acute respiratory distress syndrome from health

Acute respiratory distress syndrome (ARDS) remains a significant hazard to human health and is clinically challenging because there are no prognostic biomarkers and no effective pharmacotherapy. The lung compartment metabolome may detail the status of the local environment that could be useful in ARDS biomarker discovery and the identification of drug target opportunities. However, neither the utility of bronchoalveolar lavage fluid (BALF) as a biofluid for metabolomics nor the optimal analytical platform for metabolite identification is established. To address this, we undertook a study to compare metabolites in BALF samples from patients with ARDS and healthy controls using a newly developed liquid chromatography (LC)-mass spectroscopy (MS) platform for untargeted metabolomics. Following initial testing of three different high-performance liquid chromatography (HPLC) columns, we determined that reversed phase (RP)-LC and hydrophilic interaction chromatography (HILIC) were the most informative chromatographic methods because they yielded the most and highest quality data. Following confirmation of metabolite identification, statistical analysis resulted in 37 differentiating metabolites in the BALF of ARDS compared with health across both analytical platforms. Pathway analysis revealed networks associated with amino acid metabolism, glycolysis and gluconeogenesis, fatty acid biosynthesis, phospholipids, and purine metabolism in the ARDS BALF. The complementary analytical platforms of RPLC and HILIC-LC generated informative, insightful metabolomics data of the ARDS lung environment.

LRpath analysis reveals common pathways dysregulated via DNA methylation across cancer types

Background

The relative contribution of epigenetic mechanisms to carcinogenesis is not well understood, including the extent to which epigenetic dysregulation and somatic mutations target similar genes and pathways. We hypothesize that during carcinogenesis, certain pathways or biological gene sets are commonly dysregulated via DNA methylation across cancer types. The ability of our logistic regression-based gene set enrichment method to implicate important biological pathways in high-throughput data is well established.

Results

We developed a web-based gene set enrichment application called LRpath with clustering functionality that allows for identification and comparison of pathway signatures across multiple studies. Here, we employed LRpath analysis to unravel the commonly altered pathways and other gene sets across ten cancer studies employing DNA methylation data profiled with the Illumina HumanMethylation27 BeadChip. We observed a surprising level of concordance in differential methylation across multiple cancer types. For example, among commonly hypomethylated groups, we identified immune-related functions, peptidase activity, and epidermis/keratinocyte development and differentiation. Commonly hypermethylated groups included homeobox and other DNA-binding genes, nervous system and embryonic development, and voltage-gated potassium channels. For many gene sets, we observed significant overlap in the specific subset of differentially methylated genes. Interestingly, fewer DNA repair genes were differentially methylated than expected by chance.

Conclusions

Clustering analysis performed with LRpath revealed tightly clustered concepts enriched for differential methylation. Several well-known cancer-related pathways were significantly affected, while others were depleted in differential methylation. We conclude that DNA methylation changes in cancer tend to target a subset of the known cancer pathways affected by genetic aberrations.

Metab2MeSH: annotating compounds with medical subject headings

SUMMARY

Progress in high-throughput genomic technologies has led to the development of a variety of resources that link genes to functional information contained in the biomedical literature. However, tools attempting to link small molecules to normal and diseased physiology and published data relevant to biologists and clinical investigators, are still lacking. With metabolomics rapidly emerging as a new omics field, the task of annotating small molecule metabolites becomes highly relevant. Our tool Metab2MeSH uses a statistical approach to reliably and automatically annotate compounds with concepts defined in Medical Subject Headings, and the National Library of Medicine's controlled vocabulary for biomedical concepts. These annotations provide links from compounds to biomedical literature and complement existing resources such as PubChem and the Human Metabolome Database.

Convergence of genetic influences in comorbidity

Background

Predisposition to complex diseases is explained in part by genetic variation, and complex diseases are frequently comorbid, consistent with pleiotropic genetic variation influencing comorbidity. Genome Wide Association (GWA) studies typically assess association between SNPs and a single-disease phenotype. Fisher meta-analysis combines evidence of association from single-disease GWA studies, assuming that each study is an independent test of the same hypothesis. The Rank Product (RP) method overcomes limitations posed by Fisher assumptions, though RP was not designed for GWA data.

Methods

We modified RP to accommodate GWA data, and we call it modRP. Using p-values output from GWA studies, we aggregate evidence for association between SNPs and related phenotypes. To assess significance, RP randomly samples the observed ranks to develop the null distribution of the RP statistic, and then places the observed RPs into the null distribution. ModRP eliminates the effect of linkage disequilibrium and controls for differences in power at tested SNPs, to meet RP assumptions in application to GWA data.

Results

After validating modRP based on both positive and negative control studies, we searched for pleiotropic influences on comorbid substance use disorders in a novel study, and found two SNPs to be significantly associated with comorbid cocaine, opium, and nicotine dependence. Placing these SNPs into biological context, we developed a protein network modeling the interaction of cocaine, nicotine, and opium with these variants.

Conclusions

ModRP is a novel approach to identifying pleiotropic genetic influences on comorbid complex diseases. It can be used to assess association for related phenotypes where raw data is unavailable or inappropriate for analysis using other approaches. The method is conceptually simple and produces statistically significant, biologically relevant results.

Metscape 2 bioinformatics tool for the analysis and visualization of metabolomics and gene expression data

MOTIVATION

Metabolomics is a rapidly evolving field that holds promise to provide insights into genotype-phenotype relationships in cancers, diabetes and other complex diseases. One of the major informatics challenges is providing tools that link metabolite data with other types of high-throughput molecular data (e.g. transcriptomics, proteomics), and incorporate prior knowledge of pathways and molecular interactions.

RESULTS

We describe a new, substantially redesigned version of our tool Metscape that allows users to enter experimental data for metabolites, genes and pathways and display them in the context of relevant metabolic networks. Metscape 2 uses an internal relational database that integrates data from KEGG and EHMN databases. The new version of the tool allows users to identify enriched pathways from expression profiling data, build and analyze the networks of genes and metabolites, and visualize changes in the gene/metabolite data. We demonstrate the applications of Metscape to annotate molecular pathways for human and mouse metabolites implicated in the pathogenesis of sepsis-induced acute lung injury, for the analysis of gene expression and metabolite data from pancreatic ductal adenocarcinoma, and for identification of the candidate metabolites involved in cancer and inflammation.

AVAILABILITY

Metscape is part of the National Institutes of Health-supported National Center for Integrative Biomedical Informatics (NCIBI) suite of tools, freely available at http://metscape.ncibi.org. It can be downloaded from http://cytoscape.org or installed via Cytoscape plugin manager.

CONTACT

metscape-help@umich.edu; akarnovs@umich.edu

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Abstract C24: LRpath analysis reveals common pathways dysregulated via DNA methylation across cancer types

Since the introduction of the Illumina HumanMethylation27 BeadArray platform, which assesses the % methylation of over 27,000 sites across the genome, several studies have been published testing for genes with aberrant methylation in their promoter regions. Interestingly, a majority of these publicly available datasets are studying cancer, making the time ripe to perform an integrative analysis of these results. Our hypothesis is that during the pathogenesis of cancer, certain pathways or biological gene groups are commonly dysregulated via DNA methylation across cancer types. The ability of our logistic regression-based gene set enrichment method (LRpath) to implicate important biological pathways and groupings has previously been demonstrated and published. Recently, we developed a web-based application for LRpath with greatly expanded and novel gene set annotations, including transcription factor and microRNA target sets not yet published, and clustering analysis functionality, allowing one to identify and compare biological concept signatures across multiple studies. We employed LRpath and clustering analysis to unravel the commonly altered pathways and other biological concepts across multiple cancer studies using DNA methylation data profiled using the Illumina Infinium HumanMethylation27 BeadArray.

Using six independent cancer datasets (five solid tumors including breast, colorectal, glioblastoma, lung, and prostate, and one myeloma), we identified genes harboring abberant promoter methylation between normal and cancer samples using an empirical Bayes method. Each gene was assigned a p-value and change in % methylation indicating the level of hyper- or hypomethylation. The gene list in each study was then subjected to LRpath enrichment analysis using multiple annotation databases. To identify the biological concepts commonly altered across various types of cancer, concepts satisfying defined significance criteria were subjected to the clustering analysis using Euclidean distance matrix and ward linkage method.

Clustering analysis performed on the results of LRpath identified tightly clustered hypermethylated concepts involved in neurogenesis and epidermis development. It has been previously reported that the group of genes regulating early development is occupied by polycomb repressive complex 2 subunit SUZ12 in human embryonic stem cells. Another tightly clustered group involves voltage-gated potassium channels, which play a role in cell proliferation processes. This result supports previously reported promoter DNA methylation events in breast and pancreas adenocarcinomas. The majority of hypomethylated concepts identified across various cancer studies were related to immune response concepts identified by GO terms and KEGG pathways. These immune-related concepts range from chemokine and cytokine activity, responses to stimulus and inflammation, to receptor binding activities. The DNA hypomethylation events often result in gene upregulation. Thus, this result suggests elevated immune responses as a commonly affected mechanism across multiple cancer types.

Interestingly, cell cycle activity, one of the most commonly affected pathways in cancer development, was found to be significantly depleted in both hyper- and hypomethylation across cancer types. Based on this observation, we speculate that genes involved in cell cycle tend to be dysregulated by alternative mechanisms such as genomic aberrations, mutations, and histone modifications, rather than DNA methylation. Here, we performed an integrative analysis of commonly affected biological concepts across six different cancer types. We were able to clearly identify concepts that are affected in cancer regardless of its type and further support biologically important findings.

Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the Second AACR International Conference on Frontiers in Basic Cancer Research; 2011 Sep 14-18; San Francisco, CA. Philadelphia (PA): AACR; Cancer Res 2011;71(18 Suppl):Abstract nr C24.

Molecular characterization of the endoplasmic reticulum: insights from proteomic studies

The endoplasmic reticulum (ER) is a multifunctional intracellular organelle responsible for the synthesis, processing and trafficking of a wide variety of proteins essential for cell growth and survival. Therefore, comprehensive characterization of the ER proteome is of great importance to the understanding of its functions and has been actively pursued in the past decade by scientists in the proteomics field. This review summarizes major proteomic studies published in the past decade that focused on the ER proteome. We evaluate the data sets obtained from two different organs, liver and pancreas each of which contains a primary cell type (hepatocyte and acinar cell) with specialized functions. We also discuss how the nature of the proteins uncovered is related to the methods of organelle purification, organelle purity and the techniques used for protein separation prior to MS. In addition, this review also puts emphasis on the biological insights gained from these studies regarding the molecular functions of the ER including protein synthesis and translocation, protein folding and quality control, ER-associated degradation and ER stress, ER export and membrane trafficking, calcium homeostasis and detoxification and drug metabolism.

Metabolic consequences of sepsis-induced acute lung injury revealed by plasma ¹H-nuclear magnetic resonance quantitative metabolomics and computational analysis

Metabolomics is an emerging component of systems biology that may be a viable strategy for the identification and validation of physiologically relevant biomarkers. Nuclear magnetic resonance (NMR) spectroscopy allows for establishing quantitative data sets for multiple endogenous metabolites without preconception. Sepsis-induced acute lung injury (ALI) is a complex and serious illness associated with high morbidity and mortality for which there is presently no effective pharmacotherapy. The goal of this study was to apply ¹H-NMR based quantitative metabolomics with subsequent computational analysis to begin working towards elucidating the plasma metabolic changes associated with sepsis-induced ALI. To this end, this pilot study generated quantitative data sets that revealed differences between patients with ALI and healthy subjects in the level of the following metabolites: total glutathione, adenosine, phosphatidylserine, and sphingomyelin. Moreover, myoinositol levels were associated with acute physiology scores (APS) (ρ = -0.53, P = 0.05, q = 0.25) and ventilator-free days (ρ = -0.73, P = 0.005, q = 0.01). There was also an association between total glutathione and APS (ρ = 0.56, P = 0.04, q = 0.25). Computational network analysis revealed a distinct metabolic pathway for each metabolite. In summary, this pilot study demonstrated the feasibility of plasma ¹H-NMR quantitative metabolomics because it yielded a physiologically relevant metabolite data set that distinguished sepsis-induced ALI from health. In addition, it justifies the continued study of this approach to determine whether sepsis-induced ALI has a distinct metabolic phenotype and whether there are predictive biomarkers of severity and outcome in these patients.

Quantitative organellar proteomics analysis of rough endoplasmic reticulum from normal and acute pancreatitis rat pancreas

The rough endoplasmic reticulum (RER) is a central organelle for synthesizing and processing digestive enzymes and alteration of ER functions may participate in the pathogenesis of acute pancreatitis (AP). To comprehensively characterize the normal and diseased RER subproteome, this study quantitatively compared the protein compositions of pancreatic RER between normal and AP animals using isobaric tags (iTRAQ) and 2D LC-MALDI-MS/MS. A total of 469 unique proteins were revealed from four independent experiments using two different AP models. These proteins belong to a large number of functional categories including ribosomal proteins, translocon subunits, chaperones, secretory proteins, and glyco- and lipid-processing enzymes. A total of 37 RER proteins (25 unique in arginine-induced, 6 unique in caerulein-induced and 6 common in both models of AP) showed significant changes during AP including translational regulators and digestive enzymes, whereas only mild changes were found in some ER chaperones. The six proteins common to both AP models included a decrease in pancreatic triacylglycerol lipase precursor, Erp27, and prolyl 4-hydroxylase beta polypeptide as well as a dramatic increase in fibrinogen alpha, beta and gamma chains. These results suggest that the early stages of AP involve changes of multiple RER proteins that may affect the synthesis and processing of digestive enzymes.

Metscape: a Cytoscape plug-in for visualizing and interpreting metabolomic data in the context of human metabolic networks

SUMMARY

Metscape is a plug-in for Cytoscape, used to visualize and interpret metabolomic data in the context of human metabolic networks. We have developed a metabolite database by extracting and integrating information from several public sources. By querying this database, Metscape allows users to trace the connections between metabolites and genes, visualize compound networks and display compound structures as well as information for reactions, enzymes, genes and pathways. Applying the pathway filter, users can create subnetworks that consist of compounds and reactions from a given pathway. Metscape allows users to upload experimental data, and visualize and explore compound networks over time, or experimental conditions. Color and size of the nodes are used to visualize these dynamic changes. Metscape can display the entire metabolic network or any of the pathway-specific networks that exist in the database.

AVAILABILITY

Metscape can be installed from within Cytoscape 2.6.x under 'Network and Attribute I/O' category. For more information, please visit http://metscape.ncibi.org/tryplugin.html.

ConceptGen: a gene set enrichment and gene set relation mapping tool

MOTIVATION

The elucidation of biological concepts enriched with differentially expressed genes has become an integral part of the analysis and interpretation of genomic data. Of additional importance is the ability to explore networks of relationships among previously defined biological concepts from diverse information sources, and to explore results visually from multiple perspectives. Accomplishing these tasks requires a unified framework for agglomeration of data from various genomic resources, novel visualizations, and user functionality.

RESULTS

We have developed ConceptGen, a web-based gene set enrichment and gene set relation mapping tool that is streamlined and simple to use. ConceptGen offers over 20,000 concepts comprising 14 different types of biological knowledge, including data not currently available in any other gene set enrichment or gene set relation mapping tool. We demonstrate the functionalities of ConceptGen using gene expression data modeling TGF-beta-induced epithelial-mesenchymal transition and metabolomics data comparing metastatic versus localized prostate cancers.

Anterior axis duplication in Xenopus induced by the over-expression of the cadherin-binding protein plakoglobin

Plakoglobin interacts with both classical and desmosomal cadherins. It is closely related to Drosophila aramadillo (arm) gene product; arm acts in the wingless (wg)-signaling pathway to establish segment polarity. In Xenopus, homologs of wg--i.e., wnts, can produce anterior axis duplications by inducing dorsal mesoderm. Studies in Drosophila suggest that wnt acts by increasing the level of cytoplasmic armadillo protein (arm). To test whether simply increasing the level of plakoglobin mimics the effects of exogenous wnts in Xenopus, we injected fertilized eggs with RNA encoding an epitope-tagged form of plakoglobin; this induced both early radial gastrulation and anterior axis duplication. Exogenous plakoglobin accumulates in the nuclei of embryonic cells. Plakoglobin binds to the tail domain of the desmosomal cadherin desmoglein 1. When RNA encoding the tail domain of desmoglein was coinjected with plakoglobin RNA, both the dorsalizing effect and nuclear accumulation of plakoglobin were suppressed. Mutational analysis indicates that the central arm repeat region of plakoglobin is sufficient to induce axis duplication and that this polypeptide accumulates in the nuclei of embryonic cells. These data show that increased plakoglobin levels can, by themselves, generate the intracellular signals involved in the specification of dorsal mesoderm.

Morphogenesis and the cytoskeleton: studies of the Xenopus embryo

The morphological transformation from oocyte to embryo is brought about by the structural components of the cell, the cytoskeleton. Cytoskeletal elements act to generate and maintain cellular asymmetries, cellular movement and morphologies, and to integrate signals and forces into morphogenetically coherent behavior. Because of its unique experimental accessibility, the Xenopus embryo provides a powerful model system in which to study the "body language" of early embryonic development.