Validation and Automation of a High-Throughput Multitargeted Method for Semiquantification of Endogenous Metabolites from Different Biological Matrices Using Tandem Mass Spectrometry

Gene expression in the dorsal root ganglion and the cerebrospinal fluid metabolome in polyneuropathy and opioid tolerance in rats

First-line pharmacotherapy for peripheral neuropathic pain (NP) of diverse pathophysiology consists of antidepressants and gabapentinoids, but only a minority achieve sufficient analgesia with these drugs. Opioids are considered third-line analgesics in NP due to potential severe and unpredictable adverse effects in long-term use. Also, opioid tolerance and NP may have shared mechanisms, raising further concerns about opioid use in NP. We set out to further elucidate possible shared and separate mechanisms after chronic morphine treatment and oxaliplatin-induced and diabetic polyneuropathies, and to identify potential diagnostic markers and therapeutic targets. We analysed thermal nociceptive behaviour, the transcriptome of dorsal root ganglia (DRG) and the metabolome of cerebrospinal fluid (CSF) in these three conditions, in rats. Several genes were differentially expressed, most following oxaliplatin and least after chronic morphine treatment, compared with saline-treated rats. A few genes were differentially expressed in the DRGs in all three models (e.g. Csf3r and Fkbp5). Some, e.g. Alox15 and Slc12a5, were differentially expressed in both diabetic and oxaliplatin models. Other differentially expressed genes were associated with nociception, inflammation, and glial cells. The CSF metabolome was most significantly affected in the diabetic rats. Interestingly, we saw changes in nicotinamide metabolism, which has been associated with opioid addiction and withdrawal, in the CSF of morphine-tolerant rats. Our results offer new hypotheses for the pathophysiology and treatment of NP and opioid tolerance. In particular, the role of nicotinamide metabolism in opioid addiction deserves further study.

Loss of the histone chaperone UNC-85/ASF1 inhibits the epigenome-mediated longevity and modulates the activity of one-carbon metabolism

Histone H3/H4 chaperone anti-silencing function 1 (ASF1) is a conserved factor mediating nucleosomal assembly and disassembly, playing crucial roles in processes such as replication, transcription, and DNA repair. Nevertheless, its involvement in aging has remained unclear. Here, we utilized the model organism Caenorhabditis elegans to demonstrate that the loss of UNC-85, the homolog of ASF1, leads to a shortened lifespan in a multicellular organism. Furthermore, we show that UNC-85 is required for epigenome-mediated longevity, as knockdown of the histone H3 lysine K4 methyltransferase ash-2 does not extend the lifespan of unc-85 mutants. In this context, we found that the longevity-promoting ash-2 RNA interference enhances UNC-85 activity by increasing its nuclear localization. Finally, our data indicate that the loss of UNC-85 increases the activity of one-carbon metabolism, and that downregulation of the one-carbon metabolism component dao-3/MTHFD2 partially rescues the short lifespan of unc-85 mutants. Together, these findings reveal UNC-85/ASF1 as a modulator of the central metabolic pathway and a factor regulating a pro-longevity response, thus shedding light on a mechanism of how nucleosomal maintenance associates with aging.

Buyang Huanwu Decoction Alleviates Atherosclerosis by Regulating gut Microbiome and Metabolites in Apolipoprotein E-deficient Mice fed with High-fat Diet

ABSTRACT

The traditional Chinese herbal prescription Buyang Huanwu decoction (BHD), effectively treats atherosclerosis. However, the mechanism of BHD in atherosclerosis remains unclear. We aimed to determine whether BHD could alleviate atherosclerosis by altering the microbiome-associated metabolic changes in atherosclerotic mice. An atherosclerotic model was established in apolipoprotein E-deficient mice fed high-fat diet, and BHD was administered through gavage for 12 weeks at 8.4 g/kg/d and 16.8 g/kg/d. The atherosclerotic plaque size, composition, serum lipid profile, and inflammatory cytokines, were assessed. Mechanistically, metabolomic and microbiota profiles were analyzed by liquid chromatography-mass spectrometry and 16S rRNA gene sequencing, respectively. Furthermore, intestinal microbiota and atherosclerosis-related metabolic parameters were correlated using Spearman analysis. Atherosclerotic mice treated with BHD exhibited reduced plaque area, aortic lumen occlusion, and lipid accumulation in the aortic root. Nine perturbed serum metabolites were significantly restored along with the relative abundance of microbiota at the family and genus levels but not at the phylum level. Gut microbiome improvement was strongly negatively correlated with improved metabolite levels. BHD treatment effectively slows the progression of atherosclerosis by regulating altered intestinal microbiota and perturbed metabolites.

Machine learning identifies fatigue as a key symptom of fibromyalgia reflected in tyrosine, purine, pyrimidine, and glutaminergic metabolism

Fibromyalgia patients vary in clinical phenotype and treatment can be challenging. The pathophysiology of fibromyalgia is incompletely understood but appears to involve metabolic changes at rest or in response to stress. We enrolled 54 fibromyalgia patients and 31 healthy controls to this prospective study. Symptoms were assessed using the Fibromyalgia Impact Questionnaire (FIQ) and blood samples were collected for metabolomics analysis at baseline and after an oral glucose tolerance test and a cardiopulmonary exercise test. We identified key symptoms of fibromyalgia and related them to changes in metabolic pathways with supervised and unsupervised machine learning methods. Algorithms trained with the FIQ information assigned the fibromyalgia diagnosis in new data with balanced accuracy of 88% while fatigue alone already provided the diagnosis with 86% accuracy. Supervised analyses reduced the metabolomic information from 77 to 13 key markers. With these metabolites, fibromyalgia could be identified in new cases with 79% accuracy. In addition, 5-hydroxyindole-3-acetic acid and glutamine levels correlated with the severity of fatigue. Patients differed from controls at baseline in tyrosine and purine pathways, and in the pyrimidine pathway after the stress challenges. Several key markers are involved in glutaminergic neurotransmission. This data-driven analysis highlights fatigue as a key symptom of fibromyalgia. Fibromyalgia is associated with metabolic changes which also reflect the degree of fatigue. Responses to metabolic and physical stresses result in a metabolic pattern that allows discrimination of fibromyalgia patients from controls and narrows the focus on key pathophysiological processes in fibromyalgia as treatment targets.

Alcohol use-associated alterations in the circulating metabolite profile in the general population and in individuals with major depressive disorder

Our aim was to evaluate whether alcohol use is associated with changes in the circulating metabolite profile similar to those present in persons with depression. If so, these findings could partially explain the link between alcohol use and depression. We applied a targeted liquid chromatography mass spectrometry method to evaluate correlates between concentrations of 86 circulating metabolites and self-reported alcohol use in a cohort of the non-depressed general population (GP) (n = 247) and a cohort of individuals with major depressive disorder (MDD) (n = 99). Alcohol use was associated with alterations in circulating concentrations of metabolites in both cohorts. Our main finding was that self-reported alcohol use was negatively correlated with serum concentrations of hippuric acid in the GP cohort. In the GP cohort, consumption of six or more doses per week was associated with low hippuric acid concentrations, similar to those observed in the MDD cohort, but in these individuals it was regardless of their level of alcohol use. Reduced serum concentrations of hippuric acid suggest that already-moderate alcohol use is associated with depression-like changes in the serum levels of metabolites associated with gut microbiota and liver function; this may be one possible molecular level link between alcohol use and depression.

Serum levels of carnosine may be associated with the duration of MDD episodes

BACKGROUND

Major depressive disorder (MDD) is a recurrent disorder that incurs a high societal burden. However, the etiology of MDD remains unclear. The functioning of several systems associated with the etiopathogenesis of MDD, such as inflammatory and stress systems, is partially modulated by the dipeptide carnosine.

METHODS

The study comprised 99 MDD patients and 253 non-depressed controls aged 20-71 years. Fasting serum samples were analyzed using ultra-performance liquid chromatography coupled to mass spectrometry to determine the serum levels of carnosine and its constituent, histidine. We compared these metabolites in three different settings: 1) MDD patients vs. non-depressed controls and 2) remitted vs. non-remitted MDD patients, as well as 3) changes in the metabolite levels during the follow-up period within a) the remitted group and b) the non-remitted group. In addition, we assessed the possible effect of medications on the measured metabolites.

RESULTS

We observed higher serum levels of carnosine in the MDD group compared to the control group at baseline (OR = 1.895, 95%CI = 1.223-2.937, p = 0.004). Elevated serum levels of carnosine were also associated with a longer duration of the depressive episode (Z = 0.406, p = 0.001). However, the use of any antipsychotic medication (n = 36) was associated with lowered carnosine levels (p = 0.010 for use vs. non-use). At the follow-up, remitted and non-remitted participants displayed no significant differences in their carnosine levels (Z = -0.14, p = 0.891) or histidine (Z = -1.39 p = 0.164).

CONCLUSIONS

An increase in circulating carnosine may characterize depressive episodes and may represent a protective homeostatic reaction against MDD-related oxidative stress and inflammation.

An Optimised Monophasic Faecal Extraction Method for LC-MS Analysis and Its Application in Gastrointestinal Disease

Liquid chromatography coupled with mass spectrometry (LC-MS) metabolomic approaches are widely used to investigate underlying pathogenesis of gastrointestinal disease and mechanism of action of treatments. However, there is an unmet requirement to assess faecal metabolite extraction methods for large-scale metabolomics studies. Current methods often rely on biphasic extractions using harmful halogenated solvents, making automation and large-scale studies challenging. The present study reports an optimised monophasic faecal extraction protocol that is suitable for untargeted and targeted LC-MS analyses. The impact of several experimental parameters, including sample weight, extraction solvent, cellular disruption method, and sample-to-solvent ratio, were investigated. It is suggested that a 50 mg freeze-dried faecal sample should be used in a methanol extraction (1:20) using bead beating as the means of cell disruption. This is revealed by a significant increase in number of metabolites detected, improved signal intensity, and wide metabolic coverage given by each of the above extraction parameters. Finally, we addressed the applicability of the method on faecal samples from patients with Crohn's disease (CD) and coeliac disease (CoD), two distinct chronic gastrointestinal diseases involving metabolic perturbations. Untargeted and targeted metabolomic analysis demonstrated the ability of the developed method to detect and stratify metabolites extracted from patient groups and healthy controls (HC), highlighting characteristic changes in the faecal metabolome according to disease. The method developed is, therefore, suitable for the analysis of patients with gastrointestinal disease and can be used to detect and distinguish differences in the metabolomes of CD, CoD, and HC.

Metabolomics as a Prospective Tool for Soybean (Glycine max) Crop Improvement

Global demand for soybean and its products has stimulated research into the production of novel genotypes with higher yields, greater drought and disease tolerance, and shorter growth times. Genetic research may be the most effective way to continue developing high-performing cultivars with desirable agronomic features and improved nutritional content and seed performance. Metabolomics, which predicts the metabolic marker for plant performance under stressful conditions, is rapidly gaining interest in plant breeding and has emerged as a powerful tool for driving crop improvement. The development of increasingly sensitive, automated, and high-throughput analytical technologies, paired with improved bioinformatics and other omics techniques, has paved the way for wide characterization of genetic characteristics for crop improvement. The combination of chromatography (liquid and gas-based) with mass spectrometry has also proven to be an indisputable efficient platform for metabolomic studies, notably plant metabolic fingerprinting investigations. Nevertheless, there has been significant progress in the use of nuclear magnetic resonance (NMR), capillary electrophoresis, and Fourier-transform infrared spectroscopy (FTIR), each with its own set of benefits and drawbacks. Furthermore, utilizing multivariate analysis, principal components analysis (PCA), discriminant analysis, and projection to latent structures (PLS), it is possible to identify and differentiate various groups. The researched soybean varieties may be correctly classified by using the PCA and PLS multivariate analyses. As metabolomics is an effective method for evaluating and selecting wild specimens with desirable features for the breeding of improved new cultivars, plant breeders can benefit from the identification of metabolite biomarkers and key metabolic pathways to develop new genotypes with value-added features.

Machine Learning and Pathway Analysis-Based Discovery of Metabolomic Markers Relating to Chronic Pain Phenotypes

Recent scientific evidence suggests that chronic pain phenotypes are reflected in metabolomic changes. However, problems associated with chronic pain, such as sleep disorders or obesity, may complicate the metabolome pattern. Such a complex phenotype was investigated to identify common metabolomics markers at the interface of persistent pain, sleep, and obesity in 71 men and 122 women undergoing tertiary pain care. They were examined for patterns in d = 97 metabolomic markers that segregated patients with a relatively benign pain phenotype (low and little bothersome pain) from those with more severe clinical symptoms (high pain intensity, more bothersome pain, and co-occurring problems such as sleep disturbance). Two independent lines of data analysis were pursued. First, a data-driven supervised machine learning-based approach was used to identify the most informative metabolic markers for complex phenotype assignment. This pointed primarily at adenosine monophosphate (AMP), asparagine, deoxycytidine, glucuronic acid, and propionylcarnitine, and secondarily at cysteine and nicotinamide adenine dinucleotide (NAD) as informative for assigning patients to clinical pain phenotypes. After this, a hypothesis-driven analysis of metabolic pathways was performed, including sleep and obesity. In both the first and second line of analysis, three metabolic markers (NAD, AMP, and cysteine) were found to be relevant, including metabolic pathway analysis in obesity, associated with changes in amino acid metabolism, and sleep problems, associated with downregulated methionine metabolism. Taken together, present findings provide evidence that metabolomic changes associated with co-occurring problems may play a role in the development of severe pain. Co-occurring problems may influence each other at the metabolomic level. Because the methionine and glutathione metabolic pathways are physiologically linked, sleep problems appear to be associated with the first metabolic pathway, whereas obesity may be associated with the second.

Frankincense vinegar-processing improves the absorption of boswellic acids by regulating bile acid metabolism

BACKGROUND

Boswellic acids in Olibanum (known as frankincense) are potent anti-inflammatory properties in treating ulcerative colitis (UC), but its low bioavailability limited drug development. Evidence accumulated that vinegar processing of frankincense exerts positive effects on improving absorption of compositions. The underlying mechanism is unknown. In recent decades, spectacular growth and multidisciplinary integration of metabolic application were witnessed. The relationship between drug absorption and curative effect has been more or less established. However, it remains a knowledge gap in the field between drug absorption and endocrine metabolism.

PURPOSE

To investigate the enhancement mechanism of vinegar processing in the absorption of boswellic acids via the aspect of bile acid metabolism.

METHODS

The effects of raw frankincense (RF) and processed frankincense (PF) were compared by the UC model of rats. The plasma concentration of boswellic acids and the hepatic and colonic bile acids contents were quantified by UPLC-TQ-MS. The levels of mRNA and protein associated with bile acid metabolism were also compared.

RESULTS

The results showed that PF exhibited re-markable mitigating effects on UC with the elevated plasma level of boswellic acid and upregulated expression of the absorption-related protein multidrug resistance-associated protein 2 (MRP2) and organic anion transporting polypeptide 1B3 (OATP1B3) in the liver and colon. It improved colonic lithocholic acid (LCA), which promoted the expression of bile acid nuclear receptors constitutive androstane receptor (CAR) and pregnane X receptor (PXR), resulting in the upregulation of MRP2 and OATP1B3.

CONCLUSION

This paper revealed the mechanisms behind the absorption promotion effects of processing. Bile acids metabolism exhibits potential status in pharmaceutical development. The results shed light on the interdisciplinary collaboration between the metabolism and drug absorption fields.

Interactive effects of aging and aerobic capacity on energy metabolism-related metabolites of serum, skeletal muscle, and white adipose tissue

Aerobic capacity is a strong predictor of longevity. With aging, aerobic capacity decreases concomitantly with changes in whole body metabolism leading to increased disease risk. To address the role of aerobic capacity, aging, and their interaction on metabolism, we utilized rat models selectively bred for low and high intrinsic aerobic capacity (LCRs/HCRs) and compared the metabolomics of serum, muscle, and white adipose tissue (WAT) at two time points: Young rats were sacrificed at 9 months of age, and old rats were sacrificed at 21 months of age. Targeted and semi-quantitative metabolomics analysis was performed on the ultra-pressure liquid chromatography tandem mass spectrometry (UPLC-MS) platform. The effects of aerobic capacity, aging, and their interaction were studied via regression analysis. Our results showed that high aerobic capacity is associated with an accumulation of isovalerylcarnitine in muscle and serum at rest, which is likely due to more efficient leucine catabolism in muscle. With aging, several amino acids were downregulated in muscle, indicating more efficient amino acid metabolism, whereas in WAT less efficient amino acid metabolism and decreased mitochondrial β-oxidation were observed. Our results further revealed that high aerobic capacity and aging interactively affect lipid metabolism in muscle and WAT, possibly combating unfavorable aging-related changes in whole body metabolism. Our results highlight the significant role of WAT metabolism for healthy aging.

Metabolomics-Guided Elucidation of Plant Abiotic Stress Responses in the 4IR Era: An Overview

Plants are constantly challenged by changing environmental conditions that include abiotic stresses. These are limiting their development and productivity and are subsequently threatening our food security, especially when considering the pressure of the increasing global population. Thus, there is an urgent need for the next generation of crops with high productivity and resilience to climate change. The dawn of a new era characterized by the emergence of fourth industrial revolution (4IR) technologies has redefined the ideological boundaries of research and applications in plant sciences. Recent technological advances and machine learning (ML)-based computational tools and omics data analysis approaches are allowing scientists to derive comprehensive metabolic descriptions and models for the target plant species under specific conditions. Such accurate metabolic descriptions are imperatively essential for devising a roadmap for the next generation of crops that are resilient to environmental deterioration. By synthesizing the recent literature and collating data on metabolomics studies on plant responses to abiotic stresses, in the context of the 4IR era, we point out the opportunities and challenges offered by omics science, analytical intelligence, computational tools and big data analytics. Specifically, we highlight technological advancements in (plant) metabolomics workflows and the use of machine learning and computational tools to decipher the dynamics in the chemical space that define plant responses to abiotic stress conditions.

Mitochondrial spongiotic brain disease: astrocytic stress and harmful rapamycin and ketosis effect

Astrocyte-specific mtDNA depletion causes spongiotic encephalopathy, aggravated by ketogenic diet or rapamycin. Astrocytes, but not neurons, drive mitochondrial integrated stress response in the CNS.

Mitochondrial DNA (mtDNA) depletion syndrome (MDS) is a group of severe, tissue-specific diseases of childhood with unknown pathogenesis. Brain-specific MDS manifests as devastating spongiotic encephalopathy with no curative therapy. Here, we report cell type–specific stress responses and effects of rapamycin treatment and ketogenic diet (KD) in mice with spongiotic encephalopathy mimicking human MDS, as these interventions were reported to improve some mitochondrial disease signs or symptoms. These mice with astrocyte-specific knockout of Twnk gene encoding replicative mtDNA helicase Twinkle (TwKO^astro) show wide-spread cell-autonomous astrocyte activation and mitochondrial integrated stress response (ISR^mt) induction with major metabolic remodeling of the brain. Mice with neuronal-specific TwKO show no ISR^mt. Both KD and rapamycin lead to rapid deterioration and weight loss of TwKO^astro and premature trial termination. Although rapamycin had no robust effects on TwKO^astro brain pathology, KD exacerbated spongiosis, gliosis, and ISR^mt. Our evidence emphasizes that mitochondrial disease treatments and stress responses are tissue- and disease specific. Furthermore, rapamycin and KD are deleterious in MDS-linked spongiotic encephalopathy, pointing to a crucial role of diet and metabolism for mitochondrial disease progression.

Niacin Cures Systemic NAD+ Deficiency and Improves Muscle Performance in Adult-Onset Mitochondrial Myopathy

NAD⁺ is a redox-active metabolite, the depletion of which has been proposed to promote aging and degenerative diseases in rodents. However, whether NAD⁺ depletion occurs in patients with degenerative disorders and whether NAD⁺ repletion improves their symptoms has remained open. Here, we report systemic NAD⁺ deficiency in adult-onset mitochondrial myopathy patients. We administered an increasing dose of NAD⁺-booster niacin, a vitamin B3 form (to 750-1,000 mg/day; clinicaltrials.govNCT03973203) for patients and their matched controls for 10 or 4 months, respectively. Blood NAD⁺ increased in all subjects, up to 8-fold, and muscle NAD⁺ of patients reached the level of their controls. Some patients showed anemia tendency, while muscle strength and mitochondrial biogenesis increased in all subjects. In patients, muscle metabolome shifted toward controls and liver fat decreased even 50%. Our evidence indicates that blood analysis is useful in identifying NAD⁺ deficiency and points niacin to be an efficient NAD⁺ booster for treating mitochondrial myopathy.

Fibroblast Growth Factor 21 Drives Dynamics of Local and Systemic Stress Responses in Mitochondrial Myopathy with mtDNA Deletions

Mitochondrial dysfunction elicits stress responses that safeguard cellular homeostasis against metabolic insults. Mitochondrial integrated stress response (ISR^mt) is a major response to mitochondrial (mt)DNA expression stress (mtDNA maintenance, translation defects), but the knowledge of dynamics or interdependence of components is lacking. We report that in mitochondrial myopathy, ISR^mt progresses in temporal stages and development from early to chronic and is regulated by autocrine and endocrine effects of FGF21, a metabolic hormone with pleiotropic effects. Initial disease signs induce transcriptional ISR^mt (ATF5, mitochondrial one-carbon cycle, FGF21, and GDF15). The local progression to 2^nd metabolic ISR^mt stage (ATF3, ATF4, glucose uptake, serine biosynthesis, and transsulfuration) is FGF21 dependent. Mitochondrial unfolded protein response marks the 3^rd ISR^mt stage of failing tissue. Systemically, FGF21 drives weight loss and glucose preference, and modifies metabolism and respiratory chain deficiency in a specific hippocampal brain region. Our evidence indicates that FGF21 is a local and systemic messenger of mtDNA stress in mice and humans with mitochondrial disease.

Disruption of the mouse Shmt2 gene confers embryonic anaemia via foetal liver-specific metabolomic disorders

In a previous study, we proposed that age-related mitochondrial respiration defects observed in elderly subjects are partially due to age-associated downregulation of nuclear-encoded genes, including serine hydroxymethyltransferase 2 (SHMT2), which is involved in mitochondrial one-carbon (1C) metabolism. This assertion is supported by evidence that the disruption of mouse Shmt2 induces mitochondrial respiration defects in mouse embryonic fibroblasts generated from Shmt2-knockout E13.5 embryos experiencing anaemia and lethality. Here, we elucidated the potential mechanisms by which the disruption of this gene induces mitochondrial respiration defects and embryonic anaemia using Shmt2-knockout E13.5 embryos. The livers but not the brains of Shmt2-knockout E13.5 embryos presented mitochondrial respiration defects and growth retardation. Metabolomic profiling revealed that Shmt2 deficiency induced foetal liver-specific downregulation of 1C-metabolic pathways that create taurine and nucleotides required for mitochondrial respiratory function and cell division, respectively, resulting in the manifestation of mitochondrial respiration defects and growth retardation. Given that foetal livers function to produce erythroblasts in mouse embryos, growth retardation in foetal livers directly induced depletion of erythroblasts. By contrast, mitochondrial respiration defects in foetal livers also induced depletion of erythroblasts as a consequence of the inhibition of erythroblast differentiation, resulting in the manifestation of anaemia in Shmt2-knockout E13.5 embryos.

Integrated targeted metabolomic and lipidomic analysis: A novel approach to classifying early cystic precursors to invasive pancreatic cancer

Pancreatic cystic neoplasms (PCNs) are a highly prevalent disease of the pancreas. Among PCNs, Intraductal Papillary Mucinous Neoplasms (IPMNs) are common lesions that may progress from low-grade dysplasia (LGD) through high-grade dysplasia (HGD) to invasive cancer. Accurate discrimination of IPMN-associated neoplastic grade is an unmet clinical need. Targeted (semi)quantitative analysis of 100 metabolites and >1000 lipid species were performed on peri-operative pancreatic cyst fluid and pre-operative plasma from IPMN and serous cystic neoplasm (SCN) patients in a pancreas resection cohort (n = 35). Profiles were correlated against histological diagnosis and clinical parameters after correction for confounding factors. Integrated data modeling was used for group classification and selection of the best explanatory molecules. Over 1000 different compounds were identified in plasma and cyst fluid. IPMN profiles showed significant lipid pathway alterations compared to SCN. Integrated data modeling discriminated between IPMN and SCN with 100% accuracy and distinguished IPMN LGD or IPMN HGD and invasive cancer with up to 90.06% accuracy. Free fatty acids, ceramides, and triacylglycerol classes in plasma correlated with circulating levels of CA19-9, albumin and bilirubin. Integrated metabolomic and lipidomic analysis of plasma or cyst fluid can improve discrimination of IPMN from SCN and within PMNs predict the grade of dysplasia.

Lipoxygenase drives lipidomic and metabolic reprogramming in ischemic heart failure

BACKGROUND

After myocardial infarction (MI), delayed progression or reversal of cardiac remodeling is a prime target to limit advanced chronic heart failure (HF). However, the temporal kinetics of lipidomic and systemic metabolic signaling is unclear in HF. There is no consensus on metabolic and lipidomic signatures that influence structure, function, and survival in HF. Here we use genetic knock out model to delineate lipidomic, and metabolic changes to describe the role of lipoxygenase in advancing ischemic HF driven by leukocyte activation with signs of non-resolving inflammation. Bioactive lipids and metabolites are implicated in acute and chronic HF, and the goal of this study was to define the role of lipoxygenase in temporal kinetics of lipidomic and metabolic reprogramming in HF.

MATERIALS AND METHODS

To address this question, we used a permanent coronary ligation mouse model which showed profound metabolic and lipidomic reprogramming in acute HF. Additionally, we defined the lipoxygenase-mediated changes in cardiac pathophysiology in acute and chronic HF. For this, we quantitated systemic metabolic changes and lipidomic profiling in infarcted heart tissue with obvious structural remodeling and cardiac dysfunction progressing from acute to chronic HF in the survival cohort.

RESULTS

After MI, lipoxygenase-derived specialized pro-resolving mediators were quantitated and showed lipoxygenase-deficient mice (12/15LOX^-/-) biosynthesize epoxyeicosatrienoic acid (EETs; cypoxins) to facilitate cardiac healing. Lipoxygenase-deficient mice reduced diabetes risk biomarker 2-aminoadipic acid with profound alterations of plasma metabolic signaling of hexoses, amino acids, biogenic amines, acylcarnitines, glycerophospholipids, and sphingolipids in acute HF, thereby improved survival.

CONCLUSION

Specific lipoxygenase deletion alters lipidomic and metabolic signatures, with modified leukocyte profiling that delayed HF progression and improved survival. Future studies are warranted to define the molecular network of lipidome and metabolome in acute and chronic HF patients.

Computational Methods for the Discovery of Metabolic Markers of Complex Traits

Metabolomics uses quantitative analyses of metabolites from tissues or bodily fluids to acquire a functional readout of the physiological state. Complex diseases arise from the influence of multiple factors, such as genetics, environment and lifestyle. Since genes, RNAs and proteins converge onto the terminal downstream metabolome, metabolomics datasets offer a rich source of information in a complex and convoluted presentation. Thus, powerful computational methods capable of deciphering the effects of many upstream influences have become increasingly necessary. In this review, the workflow of metabolic marker discovery is outlined from metabolite extraction to model interpretation and validation. Additionally, current metabolomics research in various complex disease areas is examined to identify gaps and trends in the use of several statistical and computational algorithms. Then, we highlight and discuss three advanced machine-learning algorithms, specifically ensemble learning, artificial neural networks, and genetic programming, that are currently less visible, but are budding with high potential for utility in metabolomics research. With an upward trend in the use of highly-accurate, multivariate models in the metabolomics literature, diagnostic biomarker panels of complex diseases are more recently achieving accuracies approaching or exceeding traditional diagnostic procedures. This review aims to provide an overview of computational methods in metabolomics and promote the use of up-to-date machine-learning and computational methods by metabolomics researchers.

Metabolomes of mitochondrial diseases and inclusion body myositis patients: treatment targets and biomarkers

Mitochondrial disorders (MDs) are inherited multi-organ diseases with variable phenotypes. Inclusion body myositis (IBM), a sporadic inflammatory muscle disease, also shows mitochondrial dysfunction. We investigated whether primary and secondary MDs modify metabolism to reveal pathogenic pathways and biomarkers. We investigated metabolomes of 25 mitochondrial myopathy or ataxias patients, 16 unaffected carriers, six IBM and 15 non-mitochondrial neuromuscular disease (NMD) patients and 30 matched controls. MD and IBM metabolomes clustered separately from controls and NMDs. MDs and IBM showed transsulfuration pathway changes; creatine and niacinamide depletion marked NMDs, IBM and infantile-onset spinocerebellar ataxia (IOSCA). Low blood and muscle arginine was specific for patients with m.3243A>G mutation. A four-metabolite blood multi-biomarker (sorbitol, alanine, myoinositol, cystathionine) distinguished primary MDs from others (76% sensitivity, 95% specificity). Our omics approach identified pathways currently used to treat NMDs and mitochondrial stroke-like episodes and proposes nicotinamide riboside in MDs and IBM, and creatine in IOSCA and IBM as novel treatment targets. The disease-specific metabolic fingerprints are valuable "multi-biomarkers" for diagnosis and promising tools for follow-up of disease progression and treatment effect.