Metabolomics as a Tool to Understand Pathophysiological Processes

Metabolomic profile and its association with the diagnosis of prostate cancer: a systematic review

OBJECTIVE

To determine the association of a metabolomic profile with the diagnosis of localized prostate cancer.

METHODS

We conducted a search strategy in MEDLINE (OVID), EMBASE, LILACS, and the Cochrane Central Register of Controlled Trials (CENTRAL) from 2008 to the present. We included Clinical trials and analytical and descriptive observational studies that reported metabolite results and metabolite profiles in serum, tissue, urine, and seminal fluid. All studies used metabolomic techniques such as MS and MRI to identify patients with localized prostate cancer compared with patients without cancer. We used QUADAS 2 to assess the risk of bias.

RESULTS

We found 1248 studies with the search strategy. Finally, 14 case-control studies were included. Serum was the primary sample to identify the metabolites. Low concern was found regarding applying the index test and the reference standard in assessing the risk of bias. The metabolites of interest associated with establishing a metabolomic profile in the diagnosis of localized prostate cancer were amino acids, lipids, androgens, estrogens, nucleotides, and histidine metabolism.

CONCLUSION

Disturbances in the metabolism of fatty acids, amino acids, nucleotides, and steroid hormones were identified, suggesting the presence of localized prostate cancer. Importantly, serum samples showed an increase in amino acid levels. Glutamate and aspartic acid stand out among the amino acids that register high levels. In addition, glycine and serine were consistently decreased metabolites in the three kinds of biological samples analyzed.

Novel metabolic and lipidomic biomarkers of sarcopenia

BACKGROUND

The pathophysiology of sarcopenia is complex and multifactorial and has not been fully elucidated. The impact of resistance training and nutritional support (RTNS) on metabolomics and lipodomics in older adults with sarcopenia remains uncertain. This study aimed to explore potential biomarkers of sarcopenia and clinical indicators of RTNS in older sarcopenic adults.

METHODS

Older individuals diagnosed with sarcopenia through routine health checkups at a community hospital were recruited for a 12-week randomized controlled trial focusing on RTNS. Plasma metabolomic and lipidomic profiles of 45 patients with sarcopenia and 47 matched controls were analysed using 1H-nuclear magnetic resonance (1H-NMR) and liquid chromatography-mass spectrometer (LC-MS).

RESULTS

At baseline, the patient and control groups had similar age, sex, and height distribution. The patient group had significantly lower weight, BMI, grip strength, gait speed, skeletal muscle index, lean mass of both the upper and lower limbs, and lower limb bone mass. There was a significant difference in 12 metabolites between the control and patient groups. They are isoleucine (patient/control fold change [FC] = 0.86 ± 0.04, P = 0.0005), carnitine (FC = 1.05 ± 0.01, P = 0.0110), 1-methylhistamine/3-methylhistamine (FC = 1.24 ± 0.14, P = 0.0039), creatinine (FC = 0.71 ± 0.04, P < 0.0001), carnosine (FC = 0.71 ± 0.04, P = 0.0007), ureidopropionic acid (FC = 0.61 ± 0.10, P = 0.0107), uric acid (FC = 0.88 ± 0.03, P = 0.0083), PC (18:2/20:0) (FC = 0.69 ± 0.03, P = 0.0010), PC (20:2/18:0) (FC = 0.70 ± 0.06, P = 0.0014), PC (18:1/20:1) (FC = 0.74 ± 0.05, P = 0.0015), PI 32:1 (FC = 4.72 ± 0.17, P = 0.0006), and PI 34:3 (FC = 1.88 ± 0.13, P = 0.0003). Among them, carnitine, 1-methylhistamine/3-methylhistamine, creatinine, ureidopropionic acid, uric acid, PI 32:1, and PI 34:3 were first identified. Notably, PI 32:1 had highest diagnostic accuracy (0.938) for sarcopenia. 1-Methylhistamine/3-methylhistamine, carnosine, PC (18:2/20:0), PI 32:1, and PI 34:3 levels were not different from the control group after RTNS. These metabolites are involved in amino acid metabolism, lipid metabolism, and the PI3K-AKT/mTOR signalling pathway through the ingenuity pathway analysis.

CONCLUSIONS

These findings provide information on metabolic changes, lipid perturbations, and the role of RTNS in patients with sarcopenia. They reveal new insights into its pathological mechanisms and potential therapies.

Serum metabolomics analysis of malnutrition in patients with gastric cancer: a cross sectional study

BACKGROUND

Although malnutrition is common in cancer patients, its molecular mechanisms has not been fully clarified. This study aims to identify significantly differential metabolites, match the corresponding metabolic pathways, and develop a predictive model of malnutrition in patients with gastric cancer.

METHODS

In this cross-sectional study, we applied non-targeted metabolomics using liquid chromatography-mass spectrometry to explore the serum fingerprinting of malnutrition in patients with gastric cancer. Malnutrition-specific differential metabolites were identified by orthogonal partial least-squares discriminant analysis and t-test and matched with the Human Metabolome Database and the LIPID Metabolites and Pathways Strategy. We matched the corresponding metabolic pathways of malnutrition using pathway analysis at the MetaboAnalyst 5.0. We used random forest analyses to establish the predictive model.

RESULTS

We recruited 220 malnourished and 198 non-malnourished patients with gastric cancer. The intensities of 25 annotated significantly differential metabolites were lower in patients with malnutrition than those without, while two others were higher in patients with malnutrition than those without, including newly identified significantly differential metabolites such as indoleacrylic acid and lysophosphatidylcholine(18:3/0:0). We matched eight metabolic pathways associated with malnutrition, including aminoacyl-tRNA biosynthesis, tryptophan metabolism, and glycerophospholipid metabolism. We established a predictive model with an area under the curve of 0.702 (95% CI: 0.651-0.768) based on four annotated significantly differential metabolites, namely indoleacrylic acid, lysophosphatidylcholine(18:3/0:0), L-tryptophan, and lysophosphatidylcholine(20:3/0:0).

CONCLUSIONS

We identified 27 specific differential metabolites of malnutrition in malnourished compared to non-malnourished patients with gastric cancer. We also matched eight corresponding metabolic pathways and developed a predictive model. These findings provide supportive data to better understand molecular mechanisms of malnutrition in patients with gastric cancer and new strategies for the prediction, diagnosis, prevention, and treatment for those malnourished.

The interaction between insulin resistance and Alzheimer's disease: a review article

Insulin serves multiple functions as a growth-promoting hormone in peripheral tissues. It manages glucose metabolism by promoting glucose uptake into cells and curbing the production of glucose in the liver. Beyond this, insulin fosters cell growth, drives differentiation, aids protein synthesis, and deters degradative processes like glycolysis, lipolysis, and proteolysis. Receptors for insulin and insulin-like growth factor-1 are widely expressed in the central nervous system. Their widespread presence in the brain underscores the varied and critical functions of insulin signaling there. Insulin aids in bolstering cognition, promoting neuron extension, adjusting the release and absorption of catecholamines, and controlling the expression and positioning of gamma-aminobutyric acid (GABA). Importantly, insulin can effortlessly traverse the blood-brain barrier. Furthermore, insulin resistance (IR)-induced alterations in insulin signaling might hasten brain aging, impacting its plasticity and potentially leading to neurodegeneration. Two primary pathways are responsible for insulin signal transmission: the phosphoinositide 3-kinase (PI3K)/protein kinase B (AKT) pathway, which oversees metabolic responses, and the mitogen-activated protein kinase (MAPK) pathway, which guides cell growth, survival, and gene transcription. This review aimed to explore the potential shared metabolic traits between Alzheimer's disease (AD) and IR disorders. It delves into the relationship between AD and IR disorders, their overlapping genetic markers, and shared metabolic indicators. Additionally, it addresses existing therapeutic interventions targeting these intersecting pathways.

Deliverables from Metabolomics in Kidney Disease: Adenine, New Insights, and Implication for Clinical Decision-Making

BACKGROUND

Chronic kidney disease (CKD) presents a persistent global health challenge, characterized by complex pathophysiology and diverse progression patterns. Metabolomics has emerged as a valuable tool in unraveling the intricate molecular mechanisms driving CKD progression.

SUMMARY

This comprehensive review provides a summary of recent progress in the field of metabolomics in kidney disease with a focus on spatial metabolomics to shed important insights to enhancing our understanding of CKD progression, emphasizing its transformative potential in early disease detection, refined risk assessment, and the development of targeted interventions to improve patient outcomes.

KEY MESSAGE

Through an extensive analysis of metabolic pathways and small-molecule fluctuations, bulk and spatial metabolomics offers unique insights spanning the entire spectrum of CKD, from early stages to advanced disease states. Recent advances in metabolomics technology have enabled spatial identification of biomarkers to provide breakthrough discoveries in predicting CKD trajectory and enabling personalized risk assessment. Furthermore, metabolomics can help decipher the complex molecular intricacies associated with kidney diseases for exciting novel therapeutic approaches. A recent example is the identification of adenine as a key marker of kidney fibrosis for diabetic kidney disease using both untargeted and targeted bulk and spatial metabolomics. The metabolomics studies were critical to identify a new biomarker for kidney failure and to guide new therapeutics for diabetic kidney disease. Similar approaches are being pursued for acute kidney injury and other kidney diseases to enhance precision medicine decision-making.

Metabolomics profile of plasma in acute diquat-poisoned patients using gas chromatography-mass spectrometry

Diquat (DQ) has been confirmed to be toxic to humans and responsible for severe health impairment. While to date, very little is known about the toxicological mechanisms of DQ. Thus, investigations to discover the toxic targets and potential biomarkers of DQ poisoning are urgently needed. In this study, a metabolic profiling analysis was conducted to reveal the changes of metabolites of plasma and find out the potential biomarkers of DQ intoxication by GC-MS. First, multivariate statistical analysis demonstrated that acute DQ poisoning can lead to metabolomic changes in human plasma. Then, metabolomics studies showed that 31 of the identified metabolites were significantly altered by DQ. Pathway analysis indicated that three primarily metabolic pathways including phenylalanine, tyrosine and tryptophan biosynthesis, taurine and hypotaurine metabolism, and phenylalanine metabolism were affected by DQ, resulting in the perturbations of phenylalanine, tyrosine, taurine, and cysteine. Finally, the results of receiver operating characteristic analysis showed the above four metabolites could be used as reliable tools for the diagnosis and severity assessments of DQ intoxication. These data provided the theoretical basis for basic research to understand the potential mechanisms of DQ poisoning, and also identified the desirable biomarkers with great potential for clinical applications.

Stepwise solid phase extraction integrated with chemical derivatization for all-in-one injection LC-MS/MS analysis of metabolome and lipidome

The metabolome and lipidome are critical components in illustrating biological processes and pathological mechanisms. Generally, two or more independent methods are required to analyze the two compound panels due to their distinct chemical properties and polarity differences. Here, a novel strategy integrating stepwise solid-phase extraction (SPE) and dansyl chemical derivatization was proposed for all-in-one injection LC-MS/MS analysis of serum metabolome and lipidome. In this workflow, a stepwise elution procedure was firstly optimized to separate the metabolome and lipidome fractions using an Ostro plate. Dansyl chemical derivatization was then applied to label amine/phenol, carboxyl, and carbonyl-containing sub-metabolomes. Our results demonstrated that the dansyl labeling could significantly improve chromatographic separation, enhance the MS response, and overcome the matrix effect of co-eluting lipids. Ultimately, an all-in-one injection LC-MS/MS method measuring 256 lipids (covering 20 subclasses) and 212 metabolites (including amino acids, bile acids, fatty acids, acylcarnitines, indole derivatives, ketones and aldehydes, nucleic acid metabolism, polyamines, etc.) was established. This method was applied to investigate the metabolic changes in cisplatin-induced nephrotoxicity in rats and the results were compared with previous untargeted metabolomics. The presented strategy could predominantly improve the analytical coverage and throughput and can be of great use in discovering reliable potential biomarkers in various applications.

The Immune System as a Therapeutic Target for Alzheimer’s Disease

Alzheimer’s disease (AD) is a heterogeneous neurodegenerative disorder and is the most common cause of dementia. Furthermore, aging is considered the most critical risk factor for AD. However, despite the vast amount of research and resources allocated to the understanding and development of AD treatments, setbacks have been more prominent than successes. Recent studies have shown that there is an intricate connection between the immune and central nervous systems, which can be imbalanced and thereby mediate neuroinflammation and AD. Thus, this review examines this connection and how it can be altered with AD. Recent developments in active and passive immunotherapy for AD are also discussed as well as suggestions for improving these therapies moving forward.

Dysregulation in Sphingolipid Signaling Pathways is Associated With Symptoms and Functional Connectivity of Pain Processing Brain Regions in Provoked Vestibulodynia

Provoked vestibulodynia (PVD) is a chronic pain disorder characterized by local hypersensitivity and severe pain with pressure localized to the vulvar vestibule. Despite decades of study, the lack of identified biomarkers has slowed the development of effective therapies. The primary aim of this study was to use metabolomics to identify novel biochemical mechanisms in vagina and blood underlying brain biomarkers and symptoms in PVD, thereby closing this knowledge gap. Using a cross-sectional case-control observational study design, untargeted and unbiased metabolomic profiling of vaginal fluid and plasma was performed in women with PVD compared to healthy controls. In women with PVD, we also obtained assessments of vulvar pain, vestibular and vaginal muscle tenderness, and 24-hour symptom intensity alongside resting-state brain functional connectivity of brain regions involved in pain processing and modulation. Compared to healthy controls, women with PVD demonstrated differences primarily in vaginal (but not plasma) concentrations of metabolites of the sphingolipid signaling pathways, suggesting localized effects in vagina and vulvar vestibule rather than systemic effects. Our findings reveal that dysregulation of sphingolipid metabolism in PVD is associated with increased vulvar pain and muscle tenderness, sexual dysfunction, and decreased functional connectivity strength in pain processing/modulatory brain regions. This data collectively suggests that alterations in sphingolipid signaling pathways are likely an important molecular biomarker in PVD that could lead to new targets for therapeutic intervention. PERSPECTIVE: This manuscript presents the results of a robust, unbiased molecular assessment of plasma and vaginal fluid samples in women with provoked vestibulodynia compared to healthy controls. The findings suggest that alterations in sphingolipid signaling pathways are associated with symptoms and brain biomarkers and may be an important molecular marker that could provide new targets for therapeutic intervention.

The Metabolic Signature of Cardiorespiratory Fitness: A Systematic Review

Background

Cardiorespiratory fitness (CRF) is a potent health marker, the improvement of which is associated with a reduced incidence of non-communicable diseases and all-cause mortality. Identifying metabolic signatures associated with CRF could reveal how CRF fosters human health and lead to the development of novel health-monitoring strategies.

Objective

This article systematically reviewed reported associations between CRF and metabolites measured in human tissues and body fluids.

Methods

PubMed, EMBASE, and Web of Science were searched from database inception to 3 June, 2021. Metabolomics studies reporting metabolites associated with CRF, measured by means of cardiopulmonary exercise test, were deemed eligible. Backward and forward citation tracking on eligible records were used to complement the results of database searching. Risk of bias at the study level was assessed using QUADOMICS.

Results

Twenty-two studies were included and 667 metabolites, measured in plasma (n = 619), serum (n = 18), skeletal muscle (n = 16), urine (n = 11), or sweat (n = 3), were identified. Lipids were the metabolites most commonly positively (n = 174) and negatively (n = 274) associated with CRF. Specific circulating glycerophospholipids (n = 85) and cholesterol esters (n = 17) were positively associated with CRF, while circulating glycerolipids (n = 152), glycerophospholipids (n = 42), acylcarnitines (n = 14), and ceramides (n = 12) were negatively associated with CRF. Interestingly, muscle acylcarnitines were positively correlated with CRF (n = 15).

Conclusions

Cardiorespiratory fitness was associated with circulating and muscle lipidome composition. Causality of the revealed associations at the molecular species level remains to be investigated further. Finally, included studies were heterogeneous in terms of participants’ characteristics and analytical and statistical approaches.

PROSPERO Registration Number

CRD42020214375.

Supplementary Information

The online version contains supplementary material available at 10.1007/s40279-021-01590-y.

Diagnosing Overtraining Syndrome: A Scoping Review

Context:

Overtraining syndrome (OTS) is a condition characterized by a long-term performance decrement, which occurs after a persisting imbalance between training-related and nontraining-related load and recovery. Because of the lack of a gold standard diagnostic test, OTS remains a diagnosis of exclusion.

Objective:

To systematically review and map biomarkers and tools reported in the literature as potentially diagnostic for OTS.

Data Sources:

PubMed, Web of Science, and SPORTDiscus were searched from database inception to February 4, 2021, and results screened for eligibility. Backward and forward citation tracking on eligible records were used to complement results of database searching.

Study Selection:

Studies including athletes with a likely OTS diagnosis, as defined by the European College of Sport Science and the American College of Sports Medicine, and reporting at least 1 biomarker or tool potentially diagnostic for OTS were deemed eligible.

Study Design:

Scoping review following the guidelines of the Joanna Briggs Institute and PRISMA Extension for Scoping Reviews (PRISMA-ScR).

Level of Evidence:

Level 4.

Data Extraction:

Athletes’ population, criteria used to diagnose OTS, potentially diagnostic biomarkers and tools, as well as miscellaneous study characteristics were extracted.

Results:

The search yielded 5561 results, of which 39 met the eligibility criteria. Three diagnostic scores, namely the EROS-CLINICAL, EROS-SIMPLIFIED, and EROS-COMPLETE scores (EROS = Endocrine and Metabolic Responses on Overtraining Syndrome study), were identified. Additionally, basal hormone, neurotransmitter and other metabolite levels, hormonal responses to stimuli, psychological questionnaires, exercise tests, heart rate variability, electroencephalography, immunological and redox parameters, muscle structure, and body composition were reported as potentially diagnostic for OTS.

Conclusion:

Specific hormones, neurotransmitters, and metabolites, as well as psychological, electrocardiographic, electroencephalographic, and immunological patterns were identified as potentially diagnostic for OTS, reflecting its multisystemic nature. As exemplified by the EROS scores, combinations of these variables may be required to diagnose OTS. These scores must now be validated in larger samples and within female athletes.

Sarcopenic metabolomic profile reflected a sarcopenic phenotype associated with amino acid and essential fatty acid changes

INTRODUCTION

Although sarcopenia greatly affects health and quality of life in older people, its pathophysiological causes are not fully elucidated. To face this challenge, omics technologies can be used. The metabolome gives a vision of the interaction between the genome and the environment through metabolic networks, thus contributing in clarifying the pathophysiology of the sarcopenic phenotype.

OBJECTIVES

The main goal of this study was to compare the plasma metabolome of sarcopenic and non-sarcopenic older people.

METHODS

Cross-sectional study of 20 sarcopenic and 21 non-sarcopenic older subjects with available frozen plasma samples. Non-targeted metabolomic study by ultra-high-performance liquid chromatography-electrospray ionization tandem mass spectrometry (UHPLC-ESI-MS/MS) analysis with later bioinformatics data analysis. Once the significantly different metabolites were identified, the KEGG database was used on them to establish which were the metabolic pathways mainly involved.

RESULTS

From 657 features identified, 210 showed significant differences between the study groups, and 30 had a FoldChangeLog₂ > 2. The most interesting metabolic pathways found with the KEGG database were the biosynthesis of amino acids, arginine and proline metabolism, the biosynthesis of alkaloids derived from ornithine, linoleic acid metabolism, and the biosynthesis of unsaturated fatty acids.

CONCLUSIONS

The study results allowed us to confirm that the concept of "sarcopenic phenotype" is also witnessed at the plasma metabolite levels. The non-targeted metabolomics study can open a wide view of the sarcopenic features changes at the plasma level, which would be linked to the sarcopenic physiopathological alterations.

METABOLOMICS REVEALS BIOMARKERS IN HUMAN URINE AND PLASMA TO PREDICT CYP2D6 ACTIVITY

Background and Purpose

Individualized assessment of cytochrome P450 2D6 (CYP2D6) activity is usually performed through phenotyping following administration of a probe drug to measure the enzyme's activity. To avoid any iatrogenic harm (allergic drug reaction, dosing error) related to the probe drug, the development of non‐burdensome tools for real‐time phenotyping of CYP2D6 could significantly contribute to precision medicine. This study focuses on the identification of markers of the CYP2D6 enzyme in human biofluids using an LC‐high‐resolution mass spectrometry‐based metabolomic approach.

Experimental Approach

Plasma and urine samples from healthy volunteers were analysed before and after intake of a daily dose of paroxetine 20 mg over 7 days. CYP2D6 genotyping and phenotyping, using single oral dose of dextromethorphan 5 mg, were also performed in all participants.

Key Results

We report four metabolites of solanidine and two unknown compounds as possible novel CYP2D6 markers. Mean relative intensities of these features were significantly reduced during the inhibition session compared with the control session (n = 37). Semi‐quantitative analysis showed that the largest decrease (−85%) was observed for the ion m/z 432.3108 normalized to solanidine (m/z 398.3417). Mean relative intensities of these ions were significantly higher in the CYP2D6 normal–ultrarapid metabolizer group (n = 37) compared with the poor metabolizer group (n = 6). Solanidine intensity was more than 15 times higher in CYP2D6‐deficient individuals compared with other volunteers.

Conclusion and Implications

The applied untargeted metabolomic strategy identified potential novel markers capable of semi‐quantitatively predicting CYP2D6 activity, a promising discovery for personalized medicine.

Metabolomics meets lipidomics: Assessing the small molecule component of metabolism

Metabolomics, including lipidomics, is emerging as a quantitative biology approach for the assessment of energy flow through metabolism and information flow through metabolic signaling; thus, providing novel insights into metabolism and its regulation, in health, healthy ageing and disease. In this forward-looking review we provide an overview on the origins of metabolomics, on its role in this postgenomic era of biochemistry and its application to investigate metabolite role and (bio)activity, from model systems to human population studies. We present the challenges inherent to this analytical science, and approaches and modes of analysis that are used to resolve, characterize and measure the infinite chemical diversity contained in the metabolome (including lipidome) of complex biological matrices. In the current outbreak of metabolic diseases such as cardiometabolic disorders, cancer and neurodegenerative diseases, metabolomics appears to be ideally situated for the investigation of disease pathophysiology from a metabolite perspective.

Metabolic profiling links cardiovascular risk and vascular end organ damage

BACKGROUND AND AIMS

An untargeted metabolomics approach allows for a better understanding and identification of new candidate metabolites involved in the etiology of vascular disease. We aimed to investigate the associations of cardiovascular (CV) risk factors with the metabolic fingerprint and macro- and microvascular health in an untargeted metabolomic approach in predefined CV risk groups of aged individuals.

METHODS

The metabolic fingerprint and the macro- and microvascular health from 155 well-characterized aged (50-80 years) individuals, based on the EXAMIN AGE study, were analysed. Nuclear magnetic resonance spectroscopy was used to analyse the metabolic fingerprint. Carotid-femoral pulse wave velocity and retinal vessel diameters were assessed to quantify macro- and microvascular health.

RESULTS

The metabolic fingerprint became more heterogeneous with an increasing number of risk factors. There was strong evidence for higher levels of glutamine [estimate (95% CI): -14.54 (-17.81 to -11.27), p < 0.001], glycine [-5.84 (-7.88 to -3.79), p < 0.001], histidine [-0.73 (-0.96 to -0.50), p < 0.001], and acetate [-1.68 (-2.91 to -0.46), p = 0.007] to be associated with a lower CV risk profile. Tryptophan, however, was positively associated with higher CV risk [0.31 (0.06-0.56), p = 0.015]. The combination of a priori defined CV risk factors explained up to 45.4% of the metabolic variation. The metabolic fingerprint explained 20% of macro- and 23% of microvascular variation.

CONCLUSIONS

Metabolic profiling has the potential to improve CV risk stratification by identifying new underlying metabolic pathways associated with atherosclerotic disease development, from cardiovascular risk to metabolites, to vascular end organ damage.

DBnorm as an R package for the comparison and selection of appropriate statistical methods for batch effect correction in metabolomic studies

As a powerful phenotyping technology, metabolomics provides new opportunities in biomarker discovery through metabolome-wide association studies (MWAS) and the identification of metabolites having a regulatory effect in various biological processes. While mass spectrometry-based (MS) metabolomics assays are endowed with high throughput and sensitivity, MWAS are doomed to long-term data acquisition generating an overtime-analytical signal drift that can hinder the uncovering of real biologically relevant changes. We developed “dbnorm”, a package in the R environment, which allows for an easy comparison of the model performance of advanced statistical tools commonly used in metabolomics to remove batch effects from large metabolomics datasets. “dbnorm” integrates advanced statistical tools to inspect the dataset structure not only at the macroscopic (sample batches) scale, but also at the microscopic (metabolic features) level. To compare the model performance on data correction, “dbnorm” assigns a score that help users identify the best fitting model for each dataset. In this study, we applied “dbnorm” to two large-scale metabolomics datasets as a proof of concept. We demonstrate that “dbnorm” allows for the accurate selection of the most appropriate statistical tool to efficiently remove the overtime signal drift and to focus on the relevant biological components of complex datasets.

Detecting early myocardial ischemia in rat heart by MALDI imaging mass spectrometry

Diagnostics of myocardial infarction in human post-mortem hearts can be achieved only if ischemia persisted for at least 6–12 h when certain morphological changes appear in myocardium. The initial 4 h of ischemia is difficult to diagnose due to lack of a standardized method. Developing a panel of molecular tissue markers is a promising approach and can be accelerated by characterization of molecular changes. This study is the first untargeted metabolomic profiling of ischemic myocardium during the initial 4 h directly from tissue section. Ischemic hearts from an ex-vivo Langendorff model were analysed using matrix assisted laser desorption/ionization imaging mass spectrometry (MALDI IMS) at 15 min, 30 min, 1 h, 2 h, and 4 h. Region-specific molecular changes were identified even in absence of evident histological lesions and were segregated by unsupervised cluster analysis. Significantly differentially expressed features were detected by multivariate analysis starting at 15 min while their number increased with prolonged ischemia. The biggest significant increase at 15 min was observed for m/z 682.1294 (likely corresponding to S-NADHX—a damage product of nicotinamide adenine dinucleotide (NADH)). Based on the previously reported role of NAD⁺/NADH ratio in regulating localization of the sodium channel (Na_v1.5) at the plasma membrane, Na_v1.5 was evaluated by immunofluorescence. As expected, a fainter signal was observed at the plasma membrane in the predicted ischemic region starting 30 min of ischemia and the change became the most pronounced by 4 h. Metabolomic changes occur early during ischemia, can assist in identifying markers for post-mortem diagnostics and improve understanding of molecular mechanisms.

Reduced concentrations of vaginal metabolites involved in steroid hormone biosynthesis are associated with increased vulvar vestibular pain and vaginal muscle tenderness in provoked vestibulodynia: An exploratory metabolomics study

Provoked vestibulodynia (PVD) is a chronic vulvar pain disorder characterized by hypersensitivity and severe pain with pressure localized to the vulvar vestibule. Knowledge regarding pathophysiological mechanisms contributing to the etiology and production of symptoms in PVD remains incomplete but is considered multifactorial. Using a cross-sectional observational study design, data from untargeted metabolomic profiling of vaginal fluid and plasma in women with PVD and healthy women was combined with pain testing and brain imaging in women with PVD to test the hypotheses that women with PVD compared to healthy women show differences in vaginal and plasma metabolites involved in steroid hormone biosynthesis. Steroid hormone metabolites showing group differences were correlated with vulvar vestibular pain and vaginal muscle tenderness and functional connectivity of brain regions involved in pain processing in women with PVD to provide insight into the functional mechanisms linked to the identified alterations. Sensitivity analyses were also performed to determine the impact of hormonal contraceptive use on the study findings. Women with PVD compared to healthy controls had significant reductions primarily in vaginal fluid concentrations of androgenic, pregnenolone and progestin metabolites involved in steroidogenesis, suggesting localized rather than systemic effects in vagina and vulvar vestibule. The observed reductions in androgenic metabolite levels showed large effect size associations with increased vulvar vestibular pain and vulvar muscle tenderness and decreases in androgenic and progestin metabolites were associated with decreased connectivity strength in primary sensorimotor cortices. Women with PVD showed symptom-associated reductions in vaginal fluid concentrations of metabolites involved in the biosynthesis of steroid hormones previously shown to affect the integrity of vulvar and vaginal tissue and nociceptive processing. Deficiency of certain steroids may be an important mechanism contributing to the pathophysiology of symptoms in PVD may provide potential diagnostic markers that could lead to new targets for therapeutic intervention.

Human umbilical cord mesenchymal stem cells restore the ovarian metabolome and rescue premature ovarian insufficiency in mice

Background

Premature ovarian insufficiency (POI) is an ovarian dysfunction that seriously affects a woman’s physiological health and reproduction. Mesenchymal stem cell (MSC) transplantation offers a promising treatment option for ovarian restoration in rodent POI models. However, the efficacy and mechanism of it remain unclear.

Methods

POI mice model was generated by cyclophosphamide and busulfan, followed with the treatment of tail-vein injection of the human umbilical cord mesenchymal stem cells (hUCMSCs). Maternal physiological changes and offspring behavior were detected. To reveal the pathogenesis and therapeutic mechanisms of POI, we first compared the metabolite profiles of healthy and POI ovarian tissues using untargeted metabolomics analyses. After stem cell therapy, we then collected the ovaries from control, POI, and hUCMSC-treated POI groups for lipid metabolomics and pseudotargeted metabolomics analysis.

Results

Our results revealed remarkable changes of multiple metabolites, especially lipids, in ovarian tissues after POI generation. Following the transplantation of clinical-grade hUCMSCs, POI mice exhibited significant improvements in body weight, sex hormone levels, estrous cycles, and reproductive capacity. Lipid metabolomics and pseudotargeted metabolomics analyses for the ovaries showed that the metabolite levels in the POI group, mainly lipids, glycerophospholipids, steroids, and amino acids changed significantly compared with the controls’, and most of them returned to near-healthy levels after receiving hUCMSC treatment. Meanwhile, we also observed an increase of monosaccharide levels in the ovaries from POI mice and a decrease after stem cell treatment.

Conclusions

hUCMSCs restore ovarian function through activating the PI3K pathway by promoting the level of free amino acids, consequently improving lipid metabolism and reducing the concentration of monosaccharides. These findings provide potential targets for the clinical diagnosis and treatment of POI.

Systemic and central nervous system metabolic alterations in Alzheimer's disease

Background

Metabolic alterations, related to cerebral glucose metabolism, brain insulin resistance, and age-induced mitochondrial dysfunction, play an important role in Alzheimer’s disease (AD) on both the systemic and central nervous system level. To study the extent and significance of these alterations in AD, quantitative metabolomics was applied to plasma and cerebrospinal fluid (CSF) from clinically well-characterized AD patients and cognitively healthy control subjects. The observed metabolic alterations were associated with core pathological processes of AD to investigate their relation with amyloid pathology and tau-related neurodegeneration.

Methods

In a case-control study of clinical and biomarker-confirmed AD patients (n = 40) and cognitively healthy controls without cerebral AD pathology (n = 34) with paired plasma and CSF samples, we performed metabolic profiling, i.e., untargeted metabolomics and targeted quantification. Targeted quantification focused on identified deregulated pathways highlighted in the untargeted assay, i.e. the TCA cycle, and its anaplerotic pathways, as well as the neuroactive tryptophan and kynurenine pathway.

Results

Concentrations of several TCA cycle and beta-oxidation intermediates were higher in plasma of AD patients, whilst amino acid concentrations were significantly lower. Similar alterations in these energy metabolism intermediates were observed in CSF, together with higher concentrations of creatinine, which were strongly correlated with blood-brain barrier permeability. Alterations of several amino acids were associated with CSF Amyloidβ1–42. The tryptophan catabolites, kynurenic acid and quinolinic acid, showed significantly higher concentrations in CSF of AD patients, which, together with other tryptophan pathway intermediates, were correlated with either CSF Amyloidβ1–42, or tau and phosphorylated Tau-181.

Conclusions

This study revealed AD-associated systemic dysregulation of nutrient sensing and oxidation and CNS-specific alterations in the neuroactive tryptophan pathway and (phospho)creatine degradation. The specific association of amino acids and tryptophan catabolites with AD CSF biomarkers suggests a close relationship with core AD pathology.

Our findings warrant validation in independent, larger cohort studies as well as further investigation of factors such as gender and APOE genotype, as well as of other groups, such as preclinical AD, to identify metabolic alterations as potential intervention targets.

Untargeted metabolomics analysis of rat hippocampus subjected to sleep fragmentation

Sleep fragmentation (SF) commonly occurs in several pathologic conditions and is especially associated with impairments of hippocampus-dependent neurocognitive functions. Although the effects of SF on hippocampus in terms of protein or gene levels were examined in several studies, the impact of SF at the metabolite level has not been investigated. Thus, in this study, the differentially expressed large-scale metabolite profiles of hippocampus in a rat model of SF were investigated using untargeted metabolomics approaches. Forty-eight rats were divided into the following 4 groups: 4-day SF group, 4-day exercise control (EC) group, 15-day SF group, and 15-day EC group (n = 12, each). SF was accomplished by forced exercise using a walking wheel system with 30-s on/90-s off cycles, and EC condition was set at 10-min on/30-min off. The metabolite profiles of rat hippocampi in the SF and EC groups were analyzed using liquid chromatography/mass spectrometry. Multivariate analysis revealed distinctive metabolic profiles and marker signals between the SF and corresponding EC groups. Metabolic changes were significant only in the 15-day SF group. In the 15-day SF group, L-tryptophan, myristoylcarnitine, and palmitoylcarnitine were significantly increased, while adenosine monophosphate, hypoxanthine, L-glutamate, L-aspartate, L-methionine, and glycerophosphocholine were decreased compared to the EC group. The alanine, aspartate, and glutamate metabolism pathway was observed as the common key pathway in the 15-day SF groups. The results from this untargeted metabolomics study provide a perspective on metabolic impact of SF on the hippocampus.

Sex Differences in Age-Associated Type 2 Diabetes in Rats-Role of Estrogens and Oxidative Stress

Females live longer than males, and the estrogens are one of the reasons for this difference. We reported some years ago that estrogens are able to protect rats against oxidative stress, by inducing antioxidant genes. Type 2 diabetes is an age-associated disease in which oxidative stress is involved, and moreover, some studies show that the prevalence is higher in men than in women, and therefore there are sex-associated differences. Thus, the aim of this study was to evaluate the role of estrogens in protecting against oxidative stress in type 2 diabetic males and females. For this purpose, we used Goto-Kakizaki rats, which develop type 2 diabetes with age. We found that female diabetic rats showed lower glycaemia levels with age than did diabetic males and that estrogens enhanced insulin sensitivity in diabetic females. Moreover, glucose uptake, measured by positron emission tomography, was higher in the female brain, cerebellum, and heart than in those from male diabetic rats. There were also sex-associated differences in the plasma metabolic profile as determined by metabolomics. The metabolic profile was similar between estrogen-replaced and control diabetic rats and different from ovariectomized diabetic rats. Oxidative stress is involved in these differences. We showed that hepatic mitochondria from females produced less hydrogen peroxide levels and exhibited lower xanthine oxidase activity. We also found that hepatic mitochondrial glutathione oxidation and lipid oxidation levels were lower in diabetic females when compared with diabetic males. Ovariectomy induced oxidative stress, and estrogen replacement therapy prevented it. These findings provide evidence for estrogen beneficial effects in type 2 diabetes and should be considered when prescribing estrogen replacement therapy to menopausal women.

Liquid chromatography-high resolution mass spectrometry for broad-spectrum drug screening of dried blood spot as microsampling procedure

BACKGROUND

Hyphenation of liquid chromatography (LC) with high-resolution mass spectrometry (HRMS) offers the potential to develop broad-spectrum screening procedures from low volumes of biological matrices. In parallel, dried blood spot (DBS) has become a valuable tool in the bioanalysis landscape to overcome conventional blood collection issues. Herein, we demonstrated the applicability of DBS as micro-sampling procedure for broad-spectrum toxicological screening.

METHODS

A method was developed on a HRMS system in data dependant acquisition (DDA) mode using an extensive inclusion list to promote collection of relevant data. 104 real toxicology cases were analysed, and the results were cross-validated with one published and one commercial screening procedures. Quantitative MRM analyses were also performed on identified substances on a triple quadrupole instrument as a complementary confirmation procedure.

RESULTS

The method showed limits of identification (LOIs) in appropriateness with therapeutic ranges for all the classes of interest. Applying the three screening approaches on 104 real cases, 271 identifications were performed including 14 and 6 classes of prescribed and illicit drugs, respectively. Among the detected substances, 23% were only detected by the proposed method. Based on confirmatory analyses, we demonstrated that the use of blood micro-samples did not impair the sensitivity allowing more identifications in the low concentration ranges.

CONCLUSION

A LC-HRMS assay was successfully developed for toxicological screening of blood microsamples demonstrating a high identification power at low concentration ranges. The validation procedure and the analysis of real cases demonstrated the potential of this assay by supplementing screening approaches of reference.

Promising Metabolite Profiles in the Plasma and CSF of Early Clinical Parkinson's Disease

Parkinson's disease (PD) shows high heterogeneity with regard to the underlying molecular pathogenesis involving multiple pathways and mechanisms. Diagnosis is still challenging and rests entirely on clinical features. Thus, there is an urgent need for robust diagnostic biofluid markers. Untargeted metabolomics allows establishing low-molecular compound biomarkers in a wide range of complex diseases by the measurement of various molecular classes in biofluids such as blood plasma, serum, and cerebrospinal fluid (CSF). Here, we applied untargeted high-resolution mass spectrometry to determine plasma and CSF metabolite profiles. We semiquantitatively determined small-molecule levels (≤1.5 kDa) in the plasma and CSF from early PD patients (disease duration 0-4 years; n = 80 and 40, respectively), and sex- and age-matched controls (n = 76 and 38, respectively). We performed statistical analyses utilizing partial least square and random forest analysis with a 70/30 training and testing split approach, leading to the identification of 20 promising plasma and 14 CSF metabolites. These metabolites differentiated the test set with an AUC of 0.8 (plasma) and 0.9 (CSF). Characteristics of the metabolites indicate perturbations in the glycerophospholipid, sphingolipid, and amino acid metabolism in PD, which underscores the high power of metabolomic approaches. Further studies will enable to develop a potential metabolite-based biomarker panel specific for PD.