Cysteine induces mitochondrial reductive stress in glioblastoma through hydrogen peroxide production.

Glucose and amino acid metabolism are critical for glioblastoma (GBM) growth, but little is known about the specific metabolic alterations in GBM that are targetable with FDA-approved compounds. To investigate tumor metabolism signatures unique to GBM, we interrogated The Cancer Genome Atlas for alterations in glucose and amino acid signatures in GBM relative to other human cancers and found that GBM exhibits the highest levels of cysteine and methionine pathway gene expression of 32 human cancers. Treatment of patient-derived GBM cells with the FDA-approved single cysteine compound N-acetylcysteine (NAC) reduced GBM cell growth and mitochondrial oxygen consumption, which was worsened by glucose starvation. Normal brain cells and other cancer cells showed no response to NAC. Mechanistic experiments revealed that cysteine compounds induce rapid mitochondrial H2O2 production and reductive stress in GBM cells, an effect blocked by oxidized glutathione, thioredoxin, and redox enzyme overexpression. From analysis of the clinical proteomic tumor analysis consortium (CPTAC) database, we found that GBM cells exhibit lower expression of mitochondrial redox enzymes than four other cancers whose proteomic data are available in CPTAC. Knockdown of mitochondrial thioredoxin-2 in lung cancer cells induced NAC susceptibility, indicating the importance of mitochondrial redox enzyme expression in mitigating reductive stress. Intraperitoneal treatment of mice bearing orthotopic GBM xenografts with a two-cysteine peptide induced H2O2 in brain tumors in vivo. These findings indicate that GBM is uniquely susceptible to NAC-driven reductive stress and could synergize with glucose-lowering treatments for GBM.

Breast adipose tissue-derived extracellular vesicles from obese women alter tumor cell metabolism.

Breast adipose tissue is an important contributor to the obesity-breast cancer link. Extracellular vesicles (EVs) are nanosized particles containing selective cargo, such as miRNAs, that act locally or circulate to distant sites to modulate target cell functions. Here, we find that long-term education of breast cancer cells with EVs obtained from breast adipose tissue of women who are overweight or obese (O-EVs) results in increased proliferation. RNA-seq analysis of O-EV-educated cells demonstrates increased expression of genes involved in oxidative phosphorylation, such as ATP synthase and NADH: ubiquinone oxidoreductase. O-EVs increase respiratory complex protein expression, mitochondrial density, and mitochondrial respiration in tumor cells. The mitochondrial complex I inhibitor metformin reverses O-EV-induced cell proliferation. Several miRNAs-miR-155-5p, miR-10a-3p, and miR-30a-3p-which promote mitochondrial respiration and proliferation, are enriched in O-EVs relative to EVs from lean women. O-EV-induced proliferation and mitochondrial activity are associated with stimulation of the Akt/mTOR/P70S6K pathway, and are reversed upon silencing of P70S6K. This study reveals a new facet of the obesity-breast cancer link with human breast adipose tissue-derived EVs causing metabolic reprogramming of breast cancer cells.

Multi-omic comparative analysis of COVID-19 and bacterial sepsis-induced ARDS.

BACKGROUND: Acute respiratory distress syndrome (ARDS), a life-threatening condition characterized by hypoxemia and poor lung compliance, is associated with high mortality. ARDS induced by COVID-19 has similar clinical presentations and pathological manifestations as non-COVID-19 ARDS. However, COVID-19 ARDS is associated with a more protracted inflammatory respiratory failure compared to traditional ARDS. Therefore, a comprehensive molecular comparison of ARDS of different etiologies groups may pave the way for more specific clinical interventions. METHODS AND FINDINGS: In this study, we compared COVID-19 ARDS (n = 43) and bacterial sepsis-induced (non-COVID-19) ARDS (n = 24) using multi-omic plasma profiles covering 663 metabolites, 1,051 lipids, and 266 proteins. To address both between- and within- ARDS group variabilities we followed two approaches. First, we identified 706 molecules differently abundant between the two ARDS etiologies, revealing more than 40 biological processes differently regulated between the two groups. From these processes, we assembled a cascade of therapeutically relevant pathways downstream of sphingosine metabolism. The analysis suggests a possible overactivation of arginine metabolism involved in long-term sequelae of ARDS and highlights the potential of JAK inhibitors to improve outcomes in bacterial sepsis-induced ARDS. The second part of our study involved the comparison of the two ARDS groups with respect to clinical manifestations. Using a data-driven multi-omic network, we identified signatures of acute kidney injury (AKI) and thrombocytosis within each ARDS group. The AKI-associated network implicated mitochondrial dysregulation which might lead to post-ARDS renal-sequalae. The thrombocytosis-associated network hinted at a synergy between prothrombotic processes, namely IL-17, MAPK, TNF signaling pathways, and cell adhesion molecules. Thus, we speculate that combination therapy targeting two or more of these processes may ameliorate thrombocytosis-mediated hypercoagulation. CONCLUSION: We present a first comprehensive molecular characterization of differences between two ARDS etiologies-COVID-19 and bacterial sepsis. Further investigation into the identified pathways will lead to a better understanding of the pathophysiological processes, potentially enabling novel therapeutic interventions.

Bidirectional modulation of TCA cycle metabolites and anaplerosis by metformin and its combination with SGLT2i.

BACKGROUND: Metformin and sodium-glucose-cotransporter-2 inhibitors (SGLT2i) are cornerstone therapies for managing hyperglycemia in diabetes. However, their detailed impacts on metabolic processes, particularly within the citric acid (TCA) cycle and its anaplerotic pathways, remain unclear. This study investigates the tissue-specific metabolic effects of metformin, both as a monotherapy and in combination with SGLT2i, on the TCA cycle and associated anaplerotic reactions in both mice and humans. METHODS: Metformin-specific metabolic changes were initially identified by comparing metformin-treated diabetic mice (MET) with vehicle-treated db/db mice (VG). These findings were then assessed in two human cohorts (KORA and QBB) and a longitudinal KORA study of metformin-naïve patients with Type 2 Diabetes (T2D). We also compared MET with db/db mice on combination therapy (SGLT2i + MET). Metabolic profiling analyzed 716 metabolites from plasma, liver, and kidney tissues post-treatment, using linear regression and Bonferroni correction for statistical analysis, complemented by pathway analyses to explore the pathophysiological implications. RESULTS: Metformin monotherapy significantly upregulated TCA cycle intermediates such as malate, fumarate, and α-ketoglutarate (α-KG) in plasma, and anaplerotic substrates including hepatic glutamate and renal 2-hydroxyglutarate (2-HG) in diabetic mice. Downregulated hepatic taurine was also observed. The addition of SGLT2i, however, reversed these effects, such as downregulating circulating malate and α-KG, and hepatic glutamate and renal 2-HG, but upregulated hepatic taurine. In human T2D patients on metformin therapy, significant systemic alterations in metabolites were observed, including increased malate but decreased citrulline. The bidirectional modulation of TCA cycle intermediates in mice influenced key anaplerotic pathways linked to glutaminolysis, tumorigenesis, immune regulation, and antioxidative responses. CONCLUSION: This study elucidates the specific metabolic consequences of metformin and SGLT2i on the TCA cycle, reflecting potential impacts on the immune system. Metformin shows promise for its anti-inflammatory properties, while the addition of SGLT2i may provide liver protection in conditions like metabolic dysfunction-associated steatotic liver disease (MASLD). These observations underscore the importance of personalized treatment strategies.

Metabolomics profiling reveals distinct, sex-specific signatures in serum and brain metabolomes in mouse models of Alzheimer's disease.

INTRODUCTION: Increasing evidence suggests that metabolic impairments contribute to early Alzheimer's disease (AD) mechanisms and subsequent dementia. Signals in metabolic pathways conserved across species can facilitate translation. METHODS: We investigated differences in serum and brain metabolites between the early-onset 5XFAD and late-onset LOAD1 (APOE4.Trem2*R47H) mouse models of AD to C57BL/6J controls at 6 months of age. RESULTS: We identified sex differences for several classes of metabolites, such as glycerophospholipids, sphingolipids, and amino acids. Metabolic signatures were notably different between brain and serum in both mouse models. The 5XFAD mice exhibited stronger differences in brain metabolites, whereas LOAD1 mice showed more pronounced differences in serum. DISCUSSION: Several of our findings were consistent with results in humans, showing glycerophospholipids reduction in serum of apolipoprotein E (apoE) ε4 carriers and replicating the serum metabolic imprint of the APOE ε4 genotype. Our work thus represents a significant step toward translating metabolic dysregulation from model organisms to human AD. HIGHLIGHTS: This was a metabolomic assessment of two mouse models relevant to Alzheimer's disease. Mouse models exhibit broad sex-specific metabolic differences, similar to human study cohorts. The early-onset 5XFAD mouse model primarily alters brain metabolites while the late-onset LOAD1 model primarily changes serum metabolites. Apolipoprotein E (apoE) ε4 mice recapitulate glycerophospolipid signatures of human APOE ε4 carriers in both brain and serum.

Urine-based multi-omic comparative analysis of COVID-19 and bacterial sepsis-induced ARDS.

BACKGROUND: Acute respiratory distress syndrome (ARDS), a life-threatening condition during critical illness, is a common complication of COVID-19. It can originate from various disease etiologies, including severe infections, major injury, or inhalation of irritants. ARDS poses substantial clinical challenges due to a lack of etiology-specific therapies, multisystem involvement, and heterogeneous, poor patient outcomes. A molecular comparison of ARDS groups holds the potential to reveal common and distinct mechanisms underlying ARDS pathogenesis. METHODS: We performed a comparative analysis of urine-based metabolomics and proteomics profiles from COVID-19 ARDS patients (n = 42) and bacterial sepsis-induced ARDS patients (n = 17). To this end, we used two different approaches, first we compared the molecular omics profiles between ARDS groups, and second, we correlated clinical manifestations within each group with the omics profiles. RESULTS: The comparison of the two ARDS etiologies identified 150 metabolites and 70 proteins that were differentially abundant between the two groups. Based on these findings, we interrogated the interplay of cell adhesion/extracellular matrix molecules, inflammation, and mitochondrial dysfunction in ARDS pathogenesis through a multi-omic network approach. Moreover, we identified a proteomic signature associated with mortality in COVID-19 ARDS patients, which contained several proteins that had previously been implicated in clinical manifestations frequently linked with ARDS pathogenesis. CONCLUSION: In summary, our results provide evidence for significant molecular differences in ARDS patients from different etiologies and a potential synergy of extracellular matrix molecules, inflammation, and mitochondrial dysfunction in ARDS pathogenesis. The proteomic mortality signature should be further investigated in future studies to develop prediction models for COVID-19 patient outcomes.

SGI: automatic clinical subgroup identification in omics datasets.

SUMMARY: The 'Subgroup Identification' (SGI) toolbox provides an algorithm to automatically detect clinical subgroups of samples in large-scale omics datasets. It is based on hierarchical clustering trees in combination with a specifically designed association testing and visualization framework that can process an arbitrary number of clinical parameters and outcomes in a systematic fashion. A multi-block extension allows for the simultaneous use of multiple omics datasets on the same samples. In this article, we first describe the functionality of the toolbox and then demonstrate its capabilities through application examples on a type 2 diabetes metabolomics study as well as two copy number variation datasets from The Cancer Genome Atlas. AVAILABILITY AND IMPLEMENTATION: SGI is an open-source package implemented in R. Package source codes and hands-on tutorials are available at https://github.com/krumsieklab/sgi. The QMdiab metabolomics data is included in the package and can be downloaded from https://doi.org/10.6084/m9.figshare.5904022. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

maplet: an extensible R toolbox for modular and reproducible metabolomics pipelines.

This article presents maplet, an open-source R package for the creation of highly customizable, fully reproducible statistical pipelines for metabolomics data analysis. It builds on the SummarizedExperiment data structure to create a centralized pipeline framework for storing data, analysis steps, results and visualizations. maplet's key design feature is its modularity, which offers several advantages, such as ensuring code quality through the maintenance of individual functions and promoting collaborative development by removing technical barriers to code contribution. With over 90 functions, the package includes a wide range of functionalities, covering many widely used statistical approaches and data visualization techniques. AVAILABILITY AND IMPLEMENTATION: The maplet package is implemented in R and freely available at https://github.com/krumsieklab/maplet.

Angiopoietin 2 Is Associated with Vascular Necroptosis Induction in Coronavirus Disease 2019 Acute Respiratory Distress Syndrome.

Vascular injury is a well-established, disease-modifying factor in acute respiratory distress syndrome (ARDS) pathogenesis. Recently, coronavirus disease 2019 (COVID-19)-induced injury to the vascular compartment has been linked to complement activation, microvascular thrombosis, and dysregulated immune responses. This study sought to assess whether aberrant vascular activation in this prothrombotic context was associated with the induction of necroptotic vascular cell death. To achieve this, proteomic analysis was performed on blood samples from COVID-19 subjects at distinct time points during ARDS pathogenesis (hospitalized at risk, N = 59; ARDS, N = 31; and recovery, N = 12). Assessment of circulating vascular markers in the at-risk cohort revealed a signature of low vascular protein abundance that tracked with low platelet levels and increased mortality. This signature was replicated in the ARDS cohort and correlated with increased plasma angiopoietin 2 levels. COVID-19 ARDS lung autopsy immunostaining confirmed a link between vascular injury (angiopoietin 2) and platelet-rich microthrombi (CD61) and induction of necrotic cell death [phosphorylated mixed lineage kinase domain-like (pMLKL)]. Among recovery subjects, the vascular signature identified patients with poor functional outcomes. Taken together, this vascular injury signature was associated with low platelet levels and increased mortality and can be used to identify ARDS patients most likely to benefit from vascular targeted therapies.

Validation of disease-specific biomarkers for the early detection of bronchopulmonary dysplasia.

OBJECTIVE: To demonstrate and validate the improvement of current risk stratification for bronchopulmonary dysplasia (BPD) early after birth by plasma protein markers (sialic acid-binding Ig-like lectin 14 (SIGLEC-14), basal cell adhesion molecule (BCAM), angiopoietin-like 3 protein (ANGPTL-3)) in extremely premature infants. METHODS AND RESULTS: Proteome screening in first-week-of-life plasma samples of n = 52 preterm infants <32 weeks gestational age (GA) on two proteomic platforms (SomaLogic®, Olink-Proteomics®) confirmed three biomarkers with significant predictive power: BCAM, SIGLEC-14, and ANGPTL-3. We demonstrate high sensitivity (0.92) and specificity (0.86) under consideration of GA, show the proteins' critical contribution to the predictive power of known clinical risk factors, e.g., birth weight and GA, and predicted the duration of mechanical ventilation, oxygen supplementation, as well as neonatal intensive care stay. We confirmed significant predictive power for BPD cases when switching to a clinically applicable method (enzyme-linked immunosorbent assay) in an independent sample set (n = 25, p < 0.001) and demonstrated disease specificity in different cohorts of neonatal and adult lung disease. CONCLUSION: While successfully addressing typical challenges of clinical biomarker studies, we demonstrated the potential of BCAM, SIGLEC-14, and ANGPTL-3 to inform future clinical decision making in the preterm infant at risk for BPD. TRIAL REGISTRATION: Deutsches Register Klinische Studien (DRKS) No. 00004600; https://www.drks.de . IMPACT: The urgent need for biomarkers that enable early decision making and personalized monitoring strategies in preterm infants with BPD is challenged by targeted marker analyses, cohort size, and disease heterogeneity. We demonstrate the potential of the plasma proteins BCAM, SIGLEC-14, and ANGPTL-3 to identify infants with BPD early after birth while improving the predictive power of clinical variables, confirming the robustness toward proteome assays and proving disease specificity. Our comprehensive analysis enables a phase-III clinical trial that allows full implementation of the biomarkers into clinical routine to enable early risk stratification in preterms with BPD.

Comparison of genetic risk prediction models to improve prediction of coronary heart disease in two large cohorts of the MONICA/KORA study.

It is still unclear how genetic information, provided as single-nucleotide polymorphisms (SNPs), can be most effectively integrated into risk prediction models for coronary heart disease (CHD) to add significant predictive value beyond clinical risk models. For the present study, a population-based case-cohort was used as a trainingset (451 incident cases, 1488 noncases) and an independent cohort as testset (160 incident cases, 2749 noncases). The following strategies to quantify genetic information were compared: A weighted genetic risk score including Metabochip SNPs associated with CHD in the literature (GRSMetabo ); selection of the most predictive SNPs among these literature-confirmed variants using priority-Lasso (PLMetabo ); validation of two comprehensive polygenic risk scores: GRSGola based on Metabochip data, and GRSKhera (available in the testset only) based on cross-validated genome-wide genotyping data. We used Cox regression to assess associations with incident CHD. C-index, category-free net reclassification index (cfNRI) and relative integrated discrimination improvement (IDIrel ) were used to quantify the predictive performance of genetic information beyond Framingham risk score variables. In contrast to GRSMetabo and PLMetabo , GRSGola significantly improved the prediction (delta C-index [95% confidence interval]: 0.0087 [0.0044, 0.0130]; IDIrel : 0.0509 [0.0131, 0.0894]; cfNRI improved only in cases: 0.1761 [0.0253, 0.3219]). GRSKhera yielded slightly worse prediction results than GRSGola .

Association of circulating cell-free double-stranded DNA and metabolic derangements in idiopathic pulmonary fibrosis.

Idiopathic pulmonary fibrosis (IPF) is a progressive and fatal lung disease with unclear aetiology and poorly understood pathophysiology. Although plasma levels of circulating cell-free DNA (ccf-DNA) and metabolomic changes have been reported in IPF, the associations between ccf-DNA levels and metabolic derangements in lung fibrosis are unclear. Here, we demonstrate that ccf-double-stranded DNA (dsDNA) is increased in patients with IPF with rapid progression of disease compared with slow progressors and healthy controls and that ccf-dsDNA associates with amino acid metabolism, energy metabolism and lipid metabolism pathways in patients with IPF.

The Colorectal Cancer Lipidome: Identification of a Robust Tumor-Specific Lipid Species Signature.

OBJECTIVE: Lipidomic changes were causally linked to metabolic diseases, but the scenario for colorectal cancer (CRC) is less clear. We investigated the CRC lipidome for putative tumor-specific alterations through analysis of 3 independent retrospective patient cohorts from 2 clinical centers, to derive a clinically useful signature. DESIGN: Quantitative comprehensive lipidomic analysis was performed using direct infusion electrospray ionization coupled with tandem mass spectrometry (ESI-MS/MS) and high-resolution mass spectrometry (HR-MS) on matched nondiseased mucosa and tumor tissue in a discovery cohort (n = 106). Results were validated in 2 independent cohorts (n = 28, and n = 20), associated with genomic and clinical data, and lipidomic data from a genetic mouse tumor model (Apc1638N). RESULTS: Significant differences were found between tumor and normal tissue for glycero-, glycerophospho-, and sphingolipids in the discovery cohort. Comparison to the validation collectives unveiled that glycerophospholipids showed high interpatient variation and were strongly affected by preanalytical conditions, whereas glycero- and sphingolipids appeared more robust. Signatures of sphingomyelin and triacylglycerol (TG) species significantly differentiated cancerous from nondiseased tissue in both validation studies. Moreover, lipogenic enzymes were significantly up-regulated in CRC, and FASN gene expression was prognostically detrimental. The TG profile was significantly associated with postoperative disease-free survival and lymphovascular invasion, and was essentially conserved in murine digestive cancer, but not associated with microsatellite status, KRAS or BRAF mutations, or T-cell infiltration. CONCLUSION: Analysis of the CRC lipidome revealed a robust TG-species signature with prognostic potential. A better understanding of the cancer-associated glycerolipid and sphingolipid metabolism may lead to novel therapeutic strategies.

A metabolome-wide association study in the general population reveals decreased levels of serum laurylcarnitine in people with depression.

Depression constitutes a leading cause of disability worldwide. Despite extensive research on its interaction with psychobiological factors, associated pathways are far from being elucidated. Metabolomics, assessing the final products of complex biochemical reactions, has emerged as a valuable tool for exploring molecular pathways. We conducted a metabolome-wide association analysis to investigate the link between the serum metabolome and depressed mood (DM) in 1411 participants of the KORA (Cooperative Health Research in the Augsburg Region) F4 study (discovery cohort). Serum metabolomics data comprised 353 unique metabolites measured by Metabolon. We identified 72 (5.1%) KORA participants with DM. Linear regression tests were conducted modeling each metabolite value by DM status, adjusted for age, sex, body-mass index, antihypertensive, cardiovascular, antidiabetic, and thyroid gland hormone drugs, corticoids and antidepressants. Sensitivity analyses were performed in subcohorts stratified for sex, suicidal ideation, and use of antidepressants. We replicated our results in an independent sample of 968 participants of the SHIP-Trend (Study of Health in Pomerania) study including 52 (5.4%) individuals with DM (replication cohort). We found significantly lower laurylcarnitine levels in KORA F4 participants with DM after multiple testing correction according to Benjamini/Hochberg. This finding was replicated in the independent SHIP-Trend study. Laurylcarnitine remained significantly associated (p value < 0.05) with depression in samples stratified for sex, suicidal ideation, and antidepressant medication. Decreased blood laurylcarnitine levels in depressed individuals may point to impaired fatty acid oxidation and/or mitochondrial function in depressive disorders, possibly representing a novel therapeutic target.

The landscape of metabolic brain alterations in Alzheimer's disease.

INTRODUCTION: Alzheimer's disease (AD) is accompanied by metabolic alterations both in the periphery and the central nervous system. However, so far, a global view of AD-associated metabolic changes in the brain has been missing. METHODS: We metabolically profiled 500 samples from the dorsolateral prefrontal cortex. Metabolite levels were correlated with eight clinical parameters, covering both late-life cognitive performance and AD neuropathology measures. RESULTS: We observed widespread metabolic dysregulation associated with AD, spanning 298 metabolites from various AD-relevant pathways. These included alterations to bioenergetics, cholesterol metabolism, neuroinflammation, and metabolic consequences of neurotransmitter ratio imbalances. Our findings further suggest impaired osmoregulation as a potential pathomechanism in AD. Finally, inspecting the interplay of proteinopathies provided evidence that metabolic associations were largely driven by tau pathology rather than amyloid beta pathology. DISCUSSION: This work provides a comprehensive reference map of metabolic brain changes in AD that lays the foundation for future mechanistic follow-up studies.

Integrative metabolomics-genomics approach reveals key metabolic pathways and regulators of Alzheimer's disease.

Metabolites, the biochemical products of the cellular process, can be used to measure alterations in biochemical pathways related to the pathogenesis of Alzheimer's disease (AD). However, the relationships between systemic abnormalities in metabolism and the pathogenesis of AD are poorly understood. In this study, we aim to identify AD-specific metabolomic changes and their potential upstream genetic and transcriptional regulators through an integrative systems biology framework for analyzing genetic, transcriptomic, metabolomic, and proteomic data in AD. Metabolite co-expression network analysis of the blood metabolomic data in the Alzheimer's Disease Neuroimaging Initiative (ADNI) shows short-chain acylcarnitines/amino acids and medium/long-chain acylcarnitines are most associated with AD clinical outcomes, including episodic memory scores and disease severity. Integration of the gene expression data in both the blood from the ADNI and the brain from the Accelerating Medicines Partnership Alzheimer's Disease (AMP-AD) program reveals ABCA1 and CPT1A are involved in the regulation of acylcarnitines and amino acids in AD. Gene co-expression network analysis of the AMP-AD brain RNA-seq data suggests the CPT1A- and ABCA1-centered subnetworks are associated with neuronal system and immune response, respectively. Increased ABCA1 gene expression and adiponectin protein, a regulator of ABCA1, correspond to decreased short-chain acylcarnitines and amines in AD in the ADNI. In summary, our integrated analysis of large-scale multiomics data in AD systematically identifies novel metabolites and their potential regulators in AD and the findings pave a way for not only developing sensitive and specific diagnostic biomarkers for AD but also identifying novel molecular mechanisms of AD pathogenesis.

Seasonal Regulation of Metabolism: The Effect of Wintertime Fasting and Autumnal Fattening on Key Central Regulators of Metabolism and the Metabolic Profile of the Raccoon Dog (Nyctereutes Procyonoides).

Investigations into the mechanisms regulating obesity are frantic and novel translational approaches are needed. The raccoon dog (Nyctereutes procyonoides) is a canid species representing a promising model to study metabolic regulation in a species undergoing cycles of seasonal obesity and fasting. To understand the molecular mechanisms of metabolic regulation in seasonal adaptation, we analyzed key central nervous system and peripheral signals regulating food intake and metabolism from raccoon dogs after autumnal fattening and winter fasting. Expressions of neuropeptide Y (NPY), orexin-2 receptor (OX2R), pro-opiomelanocortin (POMC) and leptin receptor (ObRb) were analyzed as examples of orexigenic and anorexigenic signals using qRT-PCR from raccoon dog hypothalamus samples. Plasma metabolic profiles were measured with 1H NMR-spectroscopy and LC-MS. Circulating hormones and cytokines were determined with canine specific antibody assays. Surprisingly, NPY and POMC were not affected by the winter fasting nor autumn fattening and the metabolic profiles showed a remarkable equilibrium, indicating conserved homeostasis. However, OX2R and ObRb expression changes suggested seasonal regulation. Circulating cytokine levels were not increased, demonstrating that the autumn fattening did not induce subacute inflammation. Thus, the raccoon dog developed seasonal regulatory mechanisms to accommodate the autumnal fattening and prolonged fasting making the species unique in coping with the extreme environmental challenges.

MoDentify: phenotype-driven module identification in metabolomics networks at different resolutions.

Summary: Associations of metabolomics data with phenotypic outcomes are expected to span functional modules, which are defined as sets of correlating metabolites that are coordinately regulated. Moreover, these associations occur at different scales, from entire pathways to only a few metabolites; an aspect that has not been addressed by previous methods. Here, we present MoDentify, a free R package to identify regulated modules in metabolomics networks at different layers of resolution. Importantly, MoDentify shows higher statistical power than classical association analysis. Moreover, the package offers direct interactive visualization of the results in Cytoscape. We present an application example using complex, multifluid metabolomics data. Due to its generic character, the method is widely applicable to other types of data. Availability and implementation: https://github.com/krumsieklab/MoDentify (vignette includes detailed workflow). Supplementary information: Supplementary data are available at Bioinformatics online.

Proteome-wide assessment of diabetes mellitus in Qatari identifies IGFBP-2 as a risk factor already with early glycaemic disturbances.

BACKGROUND: Proteomics is expected to provide novel insights in the underlying pathophysiology of type 2 diabetes mellitus. In the present study, we aimed to identify and biochemically characterize proteins associated with diabetes mellitus in a Qatari population. METHODS: In a diabetes case-control study (175 cases, 164 controls; Arab, South Asian and Philippine ethnicities), we conducted a discovery study to screen 1141 blood protein levels for associations with diabetes mellitus. Additional analyses were done in controls in relation to Hb1Ac, and biochemical characterization of the main findings was performed with metabolomics (501 metabolites). We performed two-sample Mendelian Randomization to provide evidence of potential causality using data from European descent of the DIAGRAM consortium (74,124 cases of diabetes mellitus and 824,006 controls) for the identified proteins for T2D and Hb1Ac. RESULTS: After accounting for multiple testing, 30 protein levels were different (p-values<8.6e-5) between cases and controls. Of these, a higher Hb1Ac in controls was associated with a lower IGFBP-2 level (p-value = 4.1e-6). IGFBP-2 protein level was found lower among cases compared with controls across all ethnicities. In controls, IGFBP-2 was associated with 21 metabolite levels, but specifically connected to the metabolite citrulline in network analyses. We observed no evidence, however, that the association between IGFBP-2 and diabetes mellitus was causal. CONCLUSIONS: We specifically identified IGFBP-2 to be associated with diabetes mellitus, although with no evidence for causality, which was specifically connected to citrulline metabolism.

A Novel Metabolic Signature To Predict the Requirement of Dialysis or Renal Transplantation in Patients with Chronic Kidney Disease.

Identification of chronic kidney disease patients at risk of progressing to end-stage renal disease (ESRD) is essential for treatment decision-making and clinical trial design. Here, we explored whether proton nuclear magnetic resonance (NMR) spectroscopy of blood plasma improves the currently best performing kidney failure risk equation, the so-called Tangri score. Our study cohort comprised 4640 participants from the German Chronic Kidney Disease (GCKD) study, of whom 185 (3.99%) progressed over a mean observation time of 3.70 ± 0.88 years to ESRD requiring either dialysis or transplantation. The original four-variable Tangri risk equation yielded a C statistic of 0.863 (95% CI, 0.831-0.900). Upon inclusion of NMR features by state-of-the-art machine learning methods, the C statistic improved to 0.875 (95% CI, 0.850-0.911), thereby outperforming the Tangri score in 94 out of 100 subsampling rounds. Of the 24 NMR features included in the model, creatinine, high-density lipoprotein, valine, acetyl groups of glycoproteins, and Ca2+-EDTA carried the highest weights. In conclusion, proton NMR-based plasma fingerprinting improved markedly the detection of patients at risk of developing ESRD, thus enabling enhanced patient treatment.