Metabolomics for laboratory diagnostics

Peripheral blood and urine metabolites and biological functions in post-stroke depression

Post-stroke depression (PSD) is the most common and severe neuropsychiatric complication after stroke. However, the molecular mechanism of PSD is still unclear. Previous studies have identified peripheral blood and urine metabolites associated with PSD using metabolomics techniques. We searched and systematically summarized metabolites that may be involved in metabolic changes in peripheral blood and urine of patients with PSD from the Metabolite Network of Depression Database (MENDA) and other biomedical databases. MetaboAnalyst5.0 software was used for pathway analysis and enrichment analysis of differential metabolites, and subgroup analyses were performed according to tissue types and metabolomics techniques. We identified 47 metabolites that were differentially expressed between patients with and without PSD. Five differential metabolites were found in both plasma and urine, including L-glutamic acid, pyroglutamic acid, palmitic acid, L-phenylalanine, and L-tyrosine. We integrated these metabolites into metabolic pathways, and six pathways were significantly altered. These pathways could be roughly divided into three modules including amino acid metabolism, nucleotide metabolism, and glucose metabolism. Among them, the most significantly altered pathway was "phenylalanine metabolism" and the pathway containing the most associated metabolites was "aminoacyl-tRNA biosynthesis", which deserve further study to elucidate their role in the molecular mechanism of PSD. In summary, metabolic changes in peripheral blood and urine are associated with PSD, especially the disruption of "phenylalanine metabolism" and "aminoacyl-tRNA biosynthesis" pathways. This study provides clues to the metabolic characteristics of patients with PSD, which may help to elucidate the molecular pathogenesis of PSD.

Duodenal Metabolic Profile Changes in Heat-Stressed Broilers

Simple Summary

Heat stress (HS) represents an environmental and socio-economic burden to the poultry industry worldwide. However, the underpinning mechanisms for HS responses are still not well defined. Here, we used a high-throughput analysis to determine the metabolite profiles in acute and chronic heat-stressed broilers in comparison with thermoneutral and pair-fed birds. The results showed that HS altered several duodenal metabolites in a duration-dependent manner and identified potential metabolite signatures.

Abstract

Heat stress (HS) is devastating to poultry production sustainability worldwide. In addition to its adverse effects on growth, welfare, meat quality, and mortality, HS alters the gut integrity, leading to dysbiosis and leaky gut syndrome; however, the underlying mechanisms are not fully defined. Here, we used a high-throughput mass spectrometric metabolomics approach to probe the metabolite profile in the duodenum of modern broilers exposed to acute (AHS, 2 h) or chronic cyclic (CHS, 8 h/day for 2 weeks) HS in comparison with thermoneutral (TN) and pair-fed birds. Ultra high performance liquid chromatography coupled with high resolution mass spectrometry (UHPLC–HRMS) identified a total of 178 known metabolites. The trajectory analysis of the principal component analysis (PCA) score plots (both 2D and 3D maps) showed clear separation between TN and each treated group, indicating a unique duodenal metabolite profile in HS birds. Within the HS groups, partial least squares discriminant analysis (PLS-DA) displayed different clusters when comparing metabolite profiles from AHS and CHS birds, suggesting that the metabolite signatures were also dependent on HS duration. To gain biologically related molecule networks, the above identified duodenal metabolites were mapped into the Ingenuity Pathway Analysis (IPA) knowledge-base and analyzed to outline the most enriched biological functions. Several common and specific top canonical pathways were generated. Specifically, the adenosine nucleotide degradation and dopamine degradation pathways were specific for the AHS group; however, the UDP-D-xylose and UDP-D-glucuronate biosynthesis pathways were generated only for the CHS group. The top diseases enriched by the IPA core analysis for the DA metabolites, including cancer, organismal (GI) injury, hematological, cardiovascular, developmental, hereditary, and neurological disorders, were group-specific. The top altered molecular and cellular functions were amino acid metabolism, molecular transport, small molecule biochemistry, protein synthesis, cell death and survival, and DNA damage and repair. The IPA-causal network predicted that the upstream regulators (carnitine palmitoyltransferase 1B, CPT1B; histone deacetylase 11, HDAC11; carbonic anhydrase 9, CA9; interleukin 37, IL37; glycine N-methyl transferase, GNMT; GATA4) and the downstream mediators (mitogen-activated protein kinases, MAPKs; superoxide dismutase, SOD) were altered in the HS groups. Taken together, these data showed that, independently of feed intake depression, HS induced significant changes in the duodenal metabolite profile in a duration-dependent manner and identified a potential duodenal signature for HS.

Sphingolipid metabolism plays a key role in diabetic peripheral neuropathy

INTRODUCTION

As the most common chronic complication of diabetes mellitus (DM), diabetic peripheral neuropathy (DPN) seriously affects the quality of life of DM patients. So, it is of great significance for the diagnosis and treatment of DPN. In recent years, there have been numerous studies on pathogenesis and biomarkers of DM, but there are few studies on the biomarkers of DPN.

OBJECTIVES

This research is intended to identify abnormal metabolic pathways, search for potential biomarkers of DPN, and provide a metabolic basis for the diagnosis and mechanism of DPN.

METHODS

Serum samples from 23 healthy controls (HC), 42 DM patients and 30 DPN patients and urine samples from 42 HC, 40 DM patients, and 30 DPN patients were collected. UPLC-Q-TOF/MS was used to analyze the samples. Potential biomarkers were screened from principal component analysis (PCA) to orthogonal partial least squares discriminant analysis (OPLS-DA) and further evaluated by receiver operating characteristic analysis (ROC). The biomarkers were then enriched and pathway analyzed.

RESULTS

12 potential DPN biomarkers were identified from patient's serum. 11 potential DPN biomarkers were identified from the patient's urine. Among them, the diagnostic ability of gluconic acid, lipoic acid, sphinganine, bilirubin, sphingosine and 4-hydroxybenzoic acid was increased by ROC analysis. Potential biomarkers suggest that the disorder of DPN metabolism may be linked to sphingolipid metabolism.

CONCLUSIONS

This research laid a theoretical foundation for the diagnosis and pathogenesis of DPN.

Flos Carthami Exerts Hepatoprotective Action in a Rat Model of Alcoholic Liver Injury via Modulating the Metabolomics Profile

This study was intended to identify the shifts in the metabolomics profile of the hepatic tissue damaged by alcohol consumption and verify the potential restorative action of flos carthami (the flowers of Carthamus tinctorius, FC) in the protection of alcohol-induced injury by attenuating the level of identified metabolites. Rats were treated with FC and subsequently subjected to alcohol administration. The serum samples were subjected to liquid chromatography-mass spectrometry (LC-MS)-based metabolomics followed by statistical and bioinformatics analyses. The clustering of the samples showed an obvious separation in the principal component analysis (PCA) plot, and the scores plot of the orthogonal partial least squares-discriminant analysis (OPLS-DA) model allowed the distinction among the three groups. Among the 3211 total metabolites, 1088 features were significantly different between the control and alcohol-treated groups, while 367 metabolites were identified as differential metabolites between the alcohol- and FC-treated rat groups. Time series clustering approach indicated that 910 metabolites in profile 6 were upregulated by alcohol but subsequently reversed by FC treatment; among them, the top 10 metabolites based on the variable importance in projection (VIP) scores were 1-methyladenine, phenylglyoxylic acid, N-acetylvaline, mexiletine, L-fucose, propylthiouracil, dopamine 4-sulfate, isoleucylproline, (R)-salsolinol, and monomethyl phthalate. The Pearson correlation analysis and network construction revealed 96 hub metabolites that were upregulated in the alcohol liver injury model group but were downregulated by FC. This study confirmed the hepatoprotective effects of FC against alcohol-induced liver injury and the related changes in the metabolic profiles, which will contribute to the understanding and the treatment of alcohol-induced acute liver injury.

Intracellular and extracellular untargeted metabolomics reveal the effect of acute uranium exposure in HK-2 cells

Uranium exposure poses a serious threat to the health of occupational populations and the public. Although metabolomics is a promising research approach to study the toxicological mechanisms of uranium exposure, in vitro studies using human cells are scarce. Applying cultured cell metabolomics, we exhaustively analyzed the intracellular and extracellular differential metabolites upon uranium exposure and characterized the possible biological effects of uranium exposure on human kidney cells. Uranium exposure significantly induced disturbance in the amino acid biosynthesis and linoleic acid metabolism of the cells. Cells exposed to uranium produce excessive amounts of arachidonic acid, which has the potential to cause oxidative stress and damage cells. The results provide new evidence for an oxidative stress mechanism of uranium-induced renal cell injury. Cell metabolomics has proven to be a useful diagnostic tool to study the molecular mechanisms of uranium poisoning.

Residue of thiram in food, suppresses immune system stress signals and disturbs sphingolipid metabolism in chickens

Thiram, a well-known sulfur containing organic compound is frequently and extensively used in agriculture because of high biological activity to control different pests. In certain cases, due to long persistence in the environment pesticides and other environmental contaminants induce undesirable toxic impacts to public health and environment. To ascertain the potential mechanisms of toxicity of thiram on different immune organs of broilers, a total of 100 one-day-old chicks were obtained and randomly divided into two groups including thiram group (50 mg/kg) and untreated control group. Thymus and spleen tissues were collected at the age of 14 days from the experimental birds. At necropsy level, thymus was congested, enlarged and hyperemic while spleen had no obvious lesions. The results on mechanisms (apoptosis and autophagy) of immunotoxicity showed significantly increased expression of bax, caspase3, cytc, ATG5, beclin1 and p62 in spleen of treated mice. Results indicated significantly decreased expression of m-TOR and bcl2 to activate apoptosis and autophagy. The expressions of bax, p53 and m-TOR were up-regulated in the thymus while the expressions of ATG5 and Beclin1 were down-regulated to mediate cell apoptosis and inhibit autophagy. The results on different metabolome investigation showed that the sphingolipid metabolism in the thymus of chicks exposed to thiram was disrupted resulting in up-regulation of metabolites related to cell membrane components such as SM, galactosylceramide and lactosylceramide. The results of our experimental research suggest that thiram can interfere with the sphingolipid metabolism in thymus and angiogenesis, inhibit the proliferation of vascular endothelial cells to induce potential toxic effects in chicken.

Metabolomics Research in Periodontal Disease by Mass Spectrometry

Periodontology is a newer field relative to other areas of dentistry. Remarkable progress has been made in recent years in periodontology in terms of both research and clinical applications, with researchers worldwide now focusing on periodontology. With recent advances in mass spectrometry technology, metabolomics research is now widely conducted in various research fields. Metabolomics, which is also termed metabolomic analysis, is a technology that enables the comprehensive analysis of small-molecule metabolites in living organisms. With the development of metabolite analysis, methods using gas chromatography–mass spectrometry, liquid chromatography–mass spectrometry, capillary electrophoresis–mass spectrometry, etc. have progressed, making it possible to analyze a wider range of metabolites and to detect metabolites at lower concentrations. Metabolomics is widely used for research in the food, plant, microbial, and medical fields. This paper provides an introduction to metabolomic analysis and a review of the increasing applications of metabolomic analysis in periodontal disease research using mass spectrometry technology.

Breast cancer in the era of integrating “Omics” approaches

Worldwide, breast cancer is the leading cause of cancer-related deaths in women. Breast cancer is a heterogeneous disease characterized by different clinical outcomes in terms of pathological features, response to therapies, and long-term patient survival. Thus, the heterogeneity found in this cancer led to the concept that breast cancer is not a single disease, being very heterogeneous both at the molecular and clinical level, and rather represents a group of distinct neoplastic diseases of the breast and its cells. Indubitably, in the past decades we witnessed a significant development of innovative therapeutic approaches, including targeted and immunotherapies, leading to impressive results in terms of increased survival for breast cancer patients. However, these multimodal treatments fail to prevent recurrence and metastasis. Therefore, it is urgent to improve our understanding of breast tumor and metastasis biology. Over the past few years, high-throughput “omics” technologies through the identification of novel biomarkers and molecular profiling have shown their great potential in generating new insights in the study of breast cancer, also improving diagnosis, prognosis and prediction of response to treatment. In this review, we discuss how the implementation of “omics” strategies and their integration may lead to a better comprehension of the mechanisms underlying breast cancer. In particular, with the aim to investigate the correlation between different “omics” datasets and to define the new important key pathway and upstream regulators in breast cancer, we applied a new integrative meta-analysis method to combine the results obtained from genomics, proteomics and metabolomics approaches in different revised studies.

Plasma Quantitative Lipid Profiles: Identification of CarnitineC18:1-OH, CarnitineC18:2-OH and FFA (20:1) as Novel Biomarkers for Pre-warning and Prognosis in Acute Myocardial Infarction

This study was aimed to determine the association between potential plasma lipid biomarkers and early screening and prognosis of Acute myocardial infarction (AMI). In the present study, a total of 795 differentially expressed lipid metabolites were detected based on ultra-performance liquid chromatography tandem mass spectrometry (UPLC-MS/MS). Out of these metabolites, 25 lipid metabolites were identified which showed specifical expression in the AMI group compared with the healthy control (HC) group and unstable angina (UA) group. Then, we applied the least absolute shrinkage and selection operator (LASSO) and support vector machine-recursive feature elimination (SVM-RFE) methods to obtain three lipid molecules, including CarnitineC18:1-OH, CarnitineC18:2-OH and FFA (20:1). The three lipid metabolites and the diagnostic model exhibited well predictive ability in discriminating between AMI patients and UA patients in both the discovery and validation sets with an area under the curve (AUC) of 0.9. Univariate and multivariate logistic regression analyses indicated that the three lipid metabolites may serve as potential biomarkers for diagnosing AMI. A subsequent 1-year follow-up analysis indicated that the three lipid biomarkers also had prominent performance in predicting re-admission of patients with AMI due to cardiovascular events. In summary, we used quantitative lipid technology to delineate the characteristics of lipid metabolism in patients with AMI, and identified potential early diagnosis biomarkers of AMI via machine learning approach.

Comprehensive Transcriptomic and Metabolomic Analysis of the Litopenaeus vannamei Hepatopancreas After WSSV Challenge

Litopenaeus vannamei is the major farmed shrimp species worldwide. White spot disease due to white spot syndrome virus (WSSV) is severely affecting shrimp worldwide, causing extensive economic losses in L. vannamei culture. This is the first study that applied combined transcriptomic and metabolomic analysis to study the effects on the L. vannamei hepatopancreas after WSSV challenge. Our transcriptomic data revealed differentially expressed genes (DEGs) associated with immunity, apoptosis, the cytoskeleton and the antioxidant system in the hepatopancreas of L. vannamei. Metabolomic results showed that WSSV disrupts metabolic processes including amino acid metabolism, lipid metabolism and nucleotide metabolism. After challenged by WSSV, immune-related DEGs and differential metabolites (DMs) were detected in the hepatopancreas of L. vannamei, indicating that WSSV may damage the immune system and cause metabolic disorder in the shrimp. In summary, these results provide new insights into the molecular mechanisms underlying L. vannamei's response to WSSV.

A Distinct Metabolite Signature in Military Personnel Exposed to Repetitive Low-Level Blasts

Military Breachers and Range Staff (MBRS) are subjected to repeated sub-concussive blasts, and they often report symptoms that are consistent with a mild traumatic brain injury (mTBI). Biomarkers of blast injury would potentially aid blast injury diagnosis, surveillance and avoidance. Our objective was to identify plasma metabolite biomarkers in military personnel that were exposed to repeated low-level or sub-concussive blast overpressure. A total of 37 military members were enrolled (18 MBRS and 19 controls), with MBRS having participated in 8–20 breaching courses per year, with a maximum exposure of 6 blasts per day. The two cohorts were similar except that the number of blast exposures were significantly higher in the MBRS, and the MBRS cohort suffered significantly more post-concussive symptoms and poorer health on assessment. Metabolomics profiling demonstrated significant differences between groups with 74% MBRS classification accuracy (CA). Feature reduction identified 6 metabolites that resulted in a MBRS CA of 98%, and included acetic acid (23.7%), formate (22.6%), creatine (14.8%), acetone (14.2%), methanol (12,7%), and glutamic acid (12.0%). All 6 metabolites were examined with individual receiver operating characteristic (ROC) curve analyses and demonstrated areas-under-the-curve (AUCs) of 0.82–0.91 (P ≤ 0.001) for MBRS status. Several parsimonious combinations of three metabolites increased accuracy of ROC curve analyses to AUCs of 1.00 (P < 0.001), while a combination of volatile organic compounds (VOCs; acetic acid, acetone and methanol) yielded an AUC of 0.98 (P < 0.001). Candidate biomarkers for chronic blast exposure were identified, and if validated in a larger cohort, may aid surveillance and care of military personnel. Future point-of-care screening could be developed that measures VOCs from breath, with definitive diagnoses confirmed with plasma metabolomics profiling.

Fast and Sensitive Quantification of AccQ-Tag Derivatized Amino Acids and Biogenic Amines by UHPLC-UV Analysis from Complex Biological Samples

Metabolomic analysis of different body fluids bears high importance in medical sciences. Our aim was to develop and validate a fast UHPLC-UV method for the analysis of 33 amino acids and biogenic amines from complex biological samples. AccQ-Tag derivatization was conducted on target molecules and the derivatized targets were analyzed by UHPLC-UV. The detection of the analytes was carried out with UV analysis and by Selected Reaction Monitoring (SRM)-based targeted mass spectrometry. The method was validated according to the FDA guidelines. Serum and non-stimulated tear samples were collected from five healthy individuals and the samples were analyzed by the method. The method was successfully validated with appropriate accuracy and precision for all 33 biomolecules. A total of 29 analytes were detected in serum samples and 26 of them were quantified. In the tears, 30 amino acids and biogenic amines were identified and 20 of them were quantified. The developed and validated UHPLC-UV method enables the fast and precise analysis of amino acids and biogenic amines from complex biological samples.

Microalgae show a range of responses to exometabolites of foreign species

Studies on microalgae interspecific interactions have so far focused either on nutrient competition or allelopathic effects due to excreted substances from Harmful Algal Bloom (HAB) species. Evidence from plants, bacteria and specific microalgae groups, point to a range of responses mediated by sensing or direct chemical impact of exometabolites from foreign species. Such processes remain under-investigated, especially in non-HAB microalgae, despite the importance of such knowledge in ecology and industrial applications. Here, we study the directional effect of exometabolites of 4 "foreign" species Heterosigma akashiwo, Phaeocystis sp., Tetraselmis sp. and Thalassiosira sp. to each of three "target" species across a total of 12 treatments. We disentangle these effects from nutrient competition by adding cell free medium of each "foreign" species into our treatment cultures. We measured the biomass response, to the foreign exometabolites, as cell number and photosynthetic biomass (Chla), whereas nutrient use was measured as residual phosphorus (PO₄) and intracellular phosphorus (P). Exometabolites from filtrate of foreign species were putatively annotated by untargeted metabolomics analysis and were discussed in association to observed responses of target species. Among others, these metabolites included L-histidinal, Tiliacorine and dimethylsulfoniopropionate (DMSP). Our findings show that species show a range of responses with the most common being biomass suppression, and less frequent biomass enhancement and intracellular P storage. Filtrate from the green microalgae Tetraselmis caused the most pronounced negative effects suggesting that non-HAB species can also cause negative chemical interference. A candidate metabolite inducing this response is L-histidinal which was measured in high abundance uniquely in Tetraselmis and its L-histidine form derived from bacteria was previously confirmed as a microalgal algicidal. H. akashiwo also induced biomass suppression on other microalgae and a candidate metabolite for this response is Tiliacorine, a plant-derived alkaloid with confirmed cytotoxic activity.

Using Artificial Intelligence to Better Predict and Develop Biomarkers

Advancements in technology have improved biomarker discovery in the field of heart failure (HF). What was once a slow and laborious process has gained efficiency through use of high-throughput omics platforms to phenotype HF at the level of genes, transcripts, proteins, and metabolites. Furthermore, improvements in artificial intelligence (AI) have made the interpretation of large omics data sets easier and improved analysis. Use of omics and AI in biomarker discovery can aid clinicians by identifying markers of risk for developing HF, monitoring care, determining prognosis, and developing druggable targets. Combined, AI has the power to improve HF patient care.

Metabolomic Profiling Reveals That 5-Hydroxylysine and 1-Methylnicotinamide Are Metabolic Indicators of Keloid Severity

Background: Keloid is a skin fibroproliferative disease with unknown pathogenesis. Metabolomics provides a new perspective for revealing biomarkers related to metabolites and their metabolic mechanisms. Method: Metabolomics and transcriptomics were used for data analysis. Quality control of the data was performed to standardize the data. Principal component analysis (PCA), PLS-DA, OPLS-DA, univariate analysis, CIBERSORT, neural network model, and machine learning correlation analysis were used to calculate differential metabolites. The molecular mechanisms of characteristic metabolites and differentially expressed genes were identified through enrichment analysis and topological analysis. Result: Compared with normal tissue, lipids have a tendency to decrease in keloids, while peptides have a tendency to increase in keloids. Significantly different metabolites between the two groups were identified by random forest analysis, including 1-methylnicotinamide, 4-hydroxyproline, 5-hydroxylysine, and l-prolinamide. The metabolic pathways which play important roles in the pathogenesis of keloids included arachidonic acid metabolism and d-arginine and d-ornithine metabolism. Metabolomic profiling reveals that 5-hydroxylysine and 1-methylnicotinamide are metabolic indicators of keloid severity. The high-risk early warning index for 5-hydroxylysine is 4 × 10⁸-6.3×10⁸ (p = 0.0008), and the high-risk predictive index for 1-methylnicotinamide is 0.95 × 10⁷-1.6×10⁷ (p = 0.0022). Conclusion: This study was the first to reveal the metabolome profile and transcriptome of keloids. Differential metabolites and metabolic pathways were calculated by machine learning. Metabolomic profiling reveals that 5-hydroxylysine and 1-methylnicotinamide may be metabolic indicators of keloid severity.

Study on the levels of N-nitrosamine compounds and untargeted metabolomics in patients with colorectal cancer

Plasma samples were collected from 34 patients with advanced CRC and 92 healthy persons (control group), and the levels of 9 VNAs were measured using GC-MS. Untargeted metabolomics analysis was performed using LC-MS/MS. Partial least squares discriminant analysis (PLS-DA) and hierarchical cluster analysis were used to determine differential metabolites between the 2 groups. Receiver operating characteristic (ROC) curve analysis and Kyoto Encyclopedia of Genes and Genomes pathway analysis were performed on the differential metabolites. It turned out that the detection rates of N-nitrosodimethylamine (NDMA) and N-nitrosopyrrolidine (NPYR) in patients with CRC were higher than in the control group (P < 0.05). N-nitrosomethylethylamine (NMEA) and N-nitrosodiphenylamine (NDPhA) were not detected in CRC patients. NDMA, N-nitrosodibutylamine (NDBA), N-nitrosopiperidine (NPIP), and NPYR were detected in male and female patients with CRC. There was no difference in VNAs exposure between the sexes of CRC patients. In the positive and negative ion mode, a total of 132 differential metabolites and 6 differential metabolic pathways were detected. Adenosine 5'-monophosphate, hypoxanthine, 11,12-epoxy-(5Z,8Z,11Z)-icosatrienoic acid, 16(R)-HETE, acetylcarnitine, and lysophosphatidic acid (LPA 20:5, LPA 20:4) were candidate biomarkers with higher predictive value. Hypoxanthine and xanthine metabolic pathways were associated with changes in VNAs in CRC patients. In summary, the effects of changes of VNAs in the plasma of CRC patients (especially NDMA and NPYR) on the progression of CRC should attract attention. Abnormalities of adenine and guanine and downstream hypoxanthine-xanthine metabolic pathways were closely related to changes of VNAs and metabolomics in CRC patients.

Rapidity and Precision of Steroid Hormone Measurement

Steroids are present in all animals and plants, from mammals to prokaryotes. In the medical field, steroids are commonly classified as glucocorticoids, mineralocorticoids, and gonadal steroid hormones. Monitoring of hormones is useful in clinical and research fields for the assessment of physiological changes associated with aging, disease risk, and the diagnostic and therapeutic effects of various diseases. Since the discovery and isolation of steroid hormones, measurement methods for steroid hormones in biological samples have advanced substantially. Although immunoassays (IAs) are widely used in daily practice, mass spectrometry (MS)-based methods have been reported to be more specific. Steroid hormone measurement based on MS is desirable in clinical practice; however, there are several drawbacks, including the purchase and maintenance costs of the MS instrument and the need for specialized training of technicians. In this review, we discuss IA- and MS-based methods currently in use and briefly present the history of steroid hormone measurement. In addition, we describe recent advances in IA- and MS-based methods and future applications and considerations.

Methods to Improve Molecular Diagnosis in Genomic Cold Cases in Pediatric Neurology

During the last decade, genetic testing has emerged as an important etiological diagnostic tool for Mendelian diseases, including pediatric neurological conditions. A genetic diagnosis has a considerable impact on disease management and treatment; however, many cases remain undiagnosed after applying standard diagnostic sequencing techniques. This review discusses various methods to improve the molecular diagnostic rates in these genomic cold cases. We discuss extended analysis methods to consider, non-Mendelian inheritance models, mosaicism, dual/multiple diagnoses, periodic re-analysis, artificial intelligence tools, and deep phenotyping, in addition to integrating various omics methods to improve variant prioritization. Last, novel genomic technologies, including long-read sequencing, artificial long-read sequencing, and optical genome mapping are discussed. In conclusion, a more comprehensive molecular analysis and a timely re-analysis of unsolved cases are imperative to improve diagnostic rates. In addition, our current understanding of the human genome is still limited due to restrictions in technologies. Novel technologies are now available that improve upon some of these limitations and can capture all human genomic variation more accurately. Last, we recommend a more routine implementation of high molecular weight DNA extraction methods that is coherent with the ability to use and/or optimally benefit from these novel genomic methods.

Correlation between metabolomic profile constituents and feline pancreatic lipase immunoreactivity

Background

Feline pancreatic lipase immunoreactivity (fPLI) is commonly used to diagnose pancreatitis in cats (FP). Untargeted metabolomics has been extensively applied in human and veterinary medicine, but no metabolomic studies regarding FP have been conducted.

Objectives

To identify metabolites significantly associated with increased fPLI.

Animals

Forty‐nine client‐owned cats: 11 clinically healthy and 38 with various clinical conditions.

Methods

Analytical cross‐sectional study with convenience sampling. A panel of 630 metabolites belonging to 26 biochemical classes was quantified in plasma using a commercial metabolomic assay. The correlation between plasma metabolite concentrations and serum fPLI was evaluated using Spearman's rank correlation coefficient (R_s) with Bonferroni correction. Multivariable analysis then was performed to control for glomerular filtration rate, liver damage, and blood glucose concentration. The accuracy of selected metabolites in discriminating between cats with normal (≤3.5 μg/L) and increased (>5.3 μg/L) fPLI was estimated using the area under the receiver operating characteristic curve (AUROC).

Results

Four hundred and seven of 630 metabolites (64.6%) were quantified in all cats. When controlled for potential confounders only 3 sphingolipids were significantly positively correlated with fPLI: 2 cerebrosides: HexCer(d18:1/24:0); (R_s = .56), and HexCer(d18:1/24:1); (R_s = .58) and 1 sphingomyelin: SM C18:0 (R_s = .55). Their AUROCs in identifying cats with increased fPLI were 82% (95% confidence interval [CI 95%], 70%‐94%), 84% (CI 95%, 72%‐96%), and 78% (CI 95%, 65%‐92%), respectively.

Conclusions and Clinical Importance

Selected sphingolipids are moderately positively correlated with fPLI and appear to have fair to moderate diagnostic accuracy in discriminating between cats with normal and increased fPLI.

Discovery of Potential Biomarkers for Postmenopausal Osteoporosis Based on Untargeted GC/LC-MS

PURPOSE

As an important public health problem, osteoporosis (OP) in China is also in an upward trend year by year. As a standard method for diagnosing OP, dual-energy X-ray absorptiometry (DXA) cannot analyze the pathological process but only see the results. It is difficult to evaluate the early diagnosis of OP. Our study was carried out through a serum metabolomic study of OP in Chinese postmenopausal women on untargeted gas chromatography (GC)/liquid chromatography (LC)-mass spectrometry (MS) to find possible diagnostic markers.

MATERIALS AND METHODS

50 Chinese postmenopausal women with osteoporosis and 50 age-matched women were selected as normal controls. We first used untargeted GC/LC-MS to analyze the serum of these participants and then combined it with a large number of multivariate statistical analyses to analyze the data. Finally, based on a multidimensional analysis of the metabolites, the most critical metabolites were considered to be biomarkers of OP in postmenopausal women. Further, biomarkers identified relevant metabolic pathways, followed by a map of metabolic pathways found in the database.

RESULTS

We found that there may be metabolic pathway disorders like glucose metabolism, lipid metabolism, and amino acid metabolism in postmenopausal women with OP. 18 differential metabolites are considered to be potential biomarkers of OP in postmenopausal women which are a major factor in metabolism and bone physiological function.

CONCLUSION

These findings can be applied to clinical work through further validation studies. It also shows that metabonomic analysis has great potential in the application of early diagnosis and recurrence monitoring in postmenopausal OP women.

A Contemporary Insight of Metabolomics Approach for Type 1 Diabetes: Potential for Novel Diagnostic Targets

High-throughput omics has been widely applied in metabolic disease, type 1 diabetes (T1D) was one of the most typical diseases. Effective prevention and early diagnosis are very important because of infancy and persistent characteristics of T1D. The occurrence and development of T1D is a chronic and continuous process, in which the production of autoantibodies (ie serum transformation) occupies the central position. Metabolomics can evaluate the metabolic characteristics of serum before seroconversion, the changes with age and T1D complications. And the addition of natural drug metabolomics is more conducive to the systematic and comprehensive diagnosis and treatment of T1D. This paper reviewed the metabolic changes and main pathogenesis from pre-diagnosis to treatment in T1D. The metabolic spectrum of significant abnormal energy and glucose-related metabolic pathway, down-regulation of lipid metabolism and up-regulation of some antioxidant pathways has appeared before seroconversion, indicating that the body has been in the dual state of disease progression and disease resistance before T1D onset. Some metabolites (such as methionine) are closely related to age, and the types of autoantibodies produced are age-specific. Some metabolites may jointly predict DN with eGFR, and metabolomics can further contribute to the pathogenesis based on the correlation between DN and DR. Many natural drug components have been proved to act on abnormal metabolic pathways of T1D and have a positive impact on some metabolic levels, which is very important for further finding therapeutic targets and developing new drugs with small side effects. Metabolomics can provide auxiliary value for the diagnosis of T1D and provide a new direction to reveal the pathogenesis of T1D and find new therapeutic targets. The development of T1D metabolomics shows that high-throughput research methods are expected to be introduced into clinical practice.

Association Between Plasma Vitamin D2 and Type 2 Diabetes Mellitus

OBJECTIVE

To investigate the relationship between plasma vitamin D2(VD2) and type 2 diabetes(T2DM).

METHOD

Data from electronic medical records of 797 inpatients treated at Sun Yat Sen Memorial Hospital, Sun Yat-sen University between June 24, 2019 and December 24, 2020 were collected, and a total of 596 patients were enrolled after screening based on inclusion and exclusion criteria. Patients were divided into diabetic and non-diabetic groups according to whether they had T2DM. The Wilcoxon rank sum test was finally selected for the analysis of differences between groups according to the distribution of patients' plasma VD2, and logistic regression models were used to find the corresponding influencing factors.

RESULT

Of the 596 hospitalized patients, 138 (23.15%) were diagnosed with T2DM. The Wilcoxon test showed no statistically significant difference in plasma VD2 concentrations between the T2DM and non-T2DM groups (p=0.833). After adjustment for confounders by multivariate logistic regression, there was still no significant difference in plasma VD2 concentrations between the two groups (P=0.316, OR: 1.15 (0.88,1.49)). The uncorrelated relationship between VD2 and T2DM was not found to change after incorporating 12 indicators, including demographic characteristics, laboratory indicators and complications, into the logistic regression model by 3 steps, even the OR (1.08 (0.92,1.26)) did not change in the 3 models. Similarly, the adjusted ORs agreed that there was no statistical association between VD2 and T2DM.

CONCLUSION

VD2 levels are similar in patients with T2DM compared to those without T2DM. Clinical caution should be exercised in giving VD2 supplementation to patients with T2DM unless other diseases requiring VD2 supplementation (e.g., rickets, osteoporosis) are present.

NMR spectroscopy spotlighting immunogenicity induced by COVID-19 vaccination to mitigate future health concerns

In this review, the disease and immunogenicity affected by COVID-19 vaccination at the metabolic level are described considering the use of nuclear magnetic resonance (NMR) spectroscopy for the analysis of different biological samples. Consistently, we explain how different biomarkers can be examined in the saliva, blood plasma/serum, bronchoalveolar-lavage fluid (BALF), semen, feces, urine, cerebrospinal fluid (CSF) and breast milk. For example, the proposed approach for the given samples can allow one to detect molecular biomarkers that can be relevant to disease and/or vaccine interference in a system metabolome. The analysis of the given biomaterials by NMR often produces complex chemical data which can be elucidated by multivariate statistical tools, such as PCA and PLS-DA/OPLS-DA methods. Moreover, this approach may aid to improve strategies that can be helpful in disease control and treatment management in the future.

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NMR analysis of various bio-samples can explore disease course and vaccine interaction.

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Immunogenicity and reactogenicity caused by COVID-19 vaccination can be studied by NMR.

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Vaccine interaction alters metabolic pathway(s) at a certain stage, and this mechanism can be probed at the metabolic level.

Inborn Errors of Metabolism Screening in Neonates: Current Perspective with Diagnosis and Therapy

Inborn errors of metabolism (IEMs) are rare hereditary or acquired disorders resulting from an enzymatic deformity in biochemical and metabolic pathways influencing proteins, fats, carbohydrate metabolism, or hampered some organelle function. Even though individual IEMs are uncommon, together, they represent a diverse class of genetic diseases, with new issues and disease mechanisms being portrayed consistently. IEM includes the extraordinary multifaceted nature of the fundamental pathophysiology, biochemical diagnosis, molecular level investigation, and complex therapeutic choices. However, due to the molecular, biochemical, and clinical heterogeneity of IEM, screening alone will not detect and diagnose all illnesses included in newborn screening programs. Early diagnosis prevents the emergence of severe clinical symptoms in the majority of IEM cases, lowering morbidity and death. The appearance of IEM disease can vary from neonates to adult people, with the more serious conditions showing up in juvenile stages along with significant morbidity as well as mortality. Advances in understanding the physiological, biochemical, and molecular etiologies of numerous IEMs by means of modalities, for instance, the latest molecular-genetic technologies, genome engineering knowledge, entire exome sequencing, and metabolomics, have prompted remarkable advancement in detection and treatment in modern times. In this review, we analyze the biochemical basis of IEMs, clinical manifestations, the present status of screening, ongoing advances, and efficiency of diagnosis in treatment for IEMs, along with prospects for further exploration as well as innovation.

Metabolic characterization and biomarkers screening for visceral leishmaniasis in golden hamsters

A better understanding of the changes in metabolic molecules during visceral leishmaniasis (VL) is essential to develop new strategies for diagnosis and treatment. Previous metabolomics studies on Leishmania have increased our knowledge of leishmaniasis and its causative pathogen. As these studies were mainly carried out in vitro, to go further, we conducted this global metabolomics analysis on the serum of golden hamsters. Serum samples were detected over a time course of 2, 4, 8 and 12 weeks post infection. Our results revealed that under extensively disturbed metabolomes between the infection group and controls, glycerophospholipid (GPL) metabolism was most affected over the infection time, followed by α-linoleic acid metabolism and arachidonic acid metabolism. Within GPLs, phosphatidylcholine (PC) and phosphatidylethanolamine (PE) were found to be significantly increased, while their enzyme-catalysed metabolites lysophosphatidylcholine (LPC) and lysophosphatidylethanolamine (LPE) showed no significant changes. Moreover, eight differential metabolites were selected. The ability of these metabolites to be used as a diagnostic biomarker panel was supported by receiver operating characteristic (ROC) analysis. Our findings revealed that GPL metabolism might play an important role in the response of the host to Leishmania infection. The metabolism of PC and PE might be crucial in the in vivo progression of VL. The panel of eight potential biomarkers might contribute to the diagnosis of VL.

A Hypothesis From Metabolomics Analysis of Diabetic Retinopathy: Arginine-Creatine Metabolic Pathway May Be a New Treatment Strategy for Diabetic Retinopathy

Diabetic retinopathy is one of the serious complications of diabetes, which the leading causes of blindness worldwide, and its irreversibility renders the existing treatment methods unsatisfactory. Early detection and timely intervention can effectively reduce the damage caused by diabetic retinopathy. Metabolomics is a branch of systems biology and a powerful tool for studying pathophysiological processes, which can help identify the characteristic metabolic changes marking the progression of diabetic retinopathy, discover potential biomarkers to inform clinical diagnosis and treatment. This review provides an update on the known metabolomics biomarkers of diabetic retinopathy. Through comprehensive analysis of biomarkers, we found that the arginine biosynthesis is closely related to diabetic retinopathy. Meanwhile, creatine, a metabolite with arginine as a precursor, has attracted our attention due to its important correlation with diabetic retinopathy. We discuss the possibility of the arginine-creatine metabolic pathway as a therapeutic strategy for diabetic retinopathy.

Metabolomics as an Important Tool for Determining the Mechanisms of Human Skeletal Muscle Deconditioning

Muscle deconditioning impairs both locomotor function and metabolic health, and is associated with reduced quality life and increased mortality rates. Despite an appreciation of the existence of phenomena such as muscle anabolic resistance, mitophagy, and insulin resistance with age and disease in humans, little is known about the mechanisms responsible for these negative traits. With the complexities surrounding these unknowns and the lack of progress to date in development of effective interventions, there is a need for alternative approaches. Metabolomics is the study of the full array of metabolites within cells or tissues, which collectively constitute the metabolome. As metabolomics allows for the assessment of the cellular metabolic state in response to physiological stimuli, any chronic change in the metabolome is likely to reflect adaptation in the physiological phenotype of an organism. This, therefore, provides a holistic and unbiased approach that could be applied to potentially uncover important novel facets in the pathophysiology of muscle decline in ageing and disease, as well as identifying prognostic markers of those at risk of decline. This review will aim to highlight the current knowledge and potential impact of metabolomics in the study of muscle mass loss and deconditioning in humans and will highlight key areas for future research.

Altered Plasma Metabolic Profiles in Chinese Patients With Multiple Sclerosis

Multiple sclerosis (MS) is an autoimmune disease that leads to the demyelination of nerve axons. An increasing number of studies suggest that patients with MS exhibit altered metabolic profiles, which might contribute to the course of MS. However, the alteration of metabolic profiles in Chinese patients with MS and their potential roles in regulating the immune system remain elusive. In this study, we performed a global untargeted metabolomics approach in plasma samples from 22 MS-affected Chinese patients and 21 healthy subjects. A total of 42 differentially abundant metabolites (DAMs) belonging to amino acids, lipids, and carbohydrates were identified in the plasma of MS patients and compared with those in healthy controls. We observed an evident reduction in the levels of amino acids, such as L-tyrosine, L-isoleucine, and L-tryptophan, whereas there was a great increase in the levels of L-glutamic acid and L-valine in MS-affected patients. The levels of lipid and carbohydrate metabolites, such as sphingosine 1-phosphate and myo-inositol, were also reduced in patients with MS. In addition, the concentrations of proinflammatory cytokines, such as IL-17 and TNF-α, were significantly increased, whereas those of several anti-inflammatory cytokines and chemokines, such as IL-1ra, IL-7, and MIP-1α, were distinctly reduced in the plasma of MS patients compared with those in healthy subjects. Interestingly, some DAMs, such as L-tryptophan and sphingosine 1-phosphate, showed an evident negative correlation with changes in the level of TNF-α and IL-17, while tightly positively correlating with altered concentrations of anti-inflammatory cytokines and chemokines, such as MIP-1α and RANTES. Our results revealed that altered metabolomic profiles might contribute to the pathogenesis and course of MS disease by modulating immuno-inflammatory responses in the peripheral system, which is essential for eliciting autoimmune responses in the central nervous system, thus resulting in the progression of MS. This study provides potential clues for developing therapeutic strategies for MS in the near future.

Development of computational models using omics data for the identification of effective cancer metabolic biomarkers

Identification of novel biomarkers has been an active area of study for the effective diagnosis, prognosis and treatment of cancers. Among various types of cancer biomarkers, metabolic biomarkers, including enzymes, metabolites and metabolic genes, deserve attention as they can serve as a reliable source for diagnosis, prognosis and treatment of cancers. In particular, efforts to identify novel biomarkers have been greatly facilitated by a rapid increase in the volume of multiple omics data generated for a range of cancer cells. These omics data in turn serve as ingredients for developing computational models that can help derive deeper insights into the biology of cancer cells, and identify metabolic biomarkers. In this review, we provide an overview of omics data generated for cancer cells, and discuss recent studies on computational models that were developed using omics data in order to identify effective cancer metabolic biomarkers.

Lipid Metabolism in the Development and Progression of Vascular Cognitive Impairment: A Systematic Review

Background: Vascular cognitive impairment (VCI) is a major public health problem. The current diagnosis of VCI is made based on the assessment of clinical symptoms and neuropsychological measurements, and is supported by neuroimaging. These methods are both time-consuming and expensive, which leads to needs for alternative biomarkers for VCI. Metabolomics is an emerging and powerful tool to discover of new biomarkers of disease, which can investigate variations in different metabolic processes such as lipid, since the brain is highly enriched in lipids and that lipid changes may lead to pathology in the brain. Vascular cognitive impairment is vulnerable to the disturbance of lipid metabolism. Furthermore, blood samples, which could be identified as reliable clinical biomarkers are relatively convenient to obtain and provide a non-invasive assessment. Therefore, our study aims to understand whether peripheral lipid biomarkers can be used as diagnostic biomarkers and monitor the progression of VCI.

Methods: We systematically searched the PubMed, Embase, CNKI, and VIP databases to find VCI and lipid metabolism in reports from inception through February 2021. Studies meeting the following criteria were eligible: (1) original studies in humans; (2) lipid metabolites in blood; (3) reports of VCI.

Results: Through our review, nine original articles were eligible. Blood-based metabolites that might be potential biomarkers were identified. Most of them including PC, PE, Cers, and ChEs were significantly lower, while elevation of FAs and DGs were associated with VCI. Most importantly, these blood-based metabolites might be proposed as potential biomarkers for VCI, which provides direction for further validation.

Discussion and Conclusion: To the best of our knowledge, this is the first systemic review concerning the relationship of lipid metabolism and VCI. It identifies potential biomarkers and provides insights into the disease pathobiology. However, more advanced studies and researches on a lipidomic platform must be done to understand the exact pathology behind and identify potential lipid biomarkers, which might help achieve the goal of discovering novel therapeutics.

Slit2-Mediated Metabolic Reprogramming in Bone Marrow-Derived Macrophages Enhances Antitumor Immunity

Slit2 exerts antitumor effects in various cancers; however, the underlying mechanism, especially its role in regulating the immune, especially in the bone marrow niche, system is still unknown. Elucidating the behavior of macrophages in tumor progression can potentially improve immunotherapy. Using a spontaneous mammary tumor virus promoter-polyoma middle T antigen (PyMT) breast cancer mouse model, we observed that Slit2 increased the abundance of antitumor M1 macrophage in the bone marrow upon differentiation in vitro. Moreover, myeloablated PyMT mice injected with Slit2-treated bone marrow allografts showed a marked reduction in tumor growth, with enhanced recruitment of M1 macrophage in their tumor stroma. Mechanistic studies revealed that Slit2 significantly enhanced glycolysis and reduced fatty acid oxidation in bone marrow-derived macrophages (BMDMs). Slit2 treatment also altered mitochondrial respiration metabolites in macrophages isolated from healthy human blood that were treated with plasma from breast cancer patients. Overall, this study, for the first time, shows that Slit2 increases BMDM polarization toward antitumor phenotype by modulating immune-metabolism. Furthermore, this study provides evidence that soluble Slit2 could be developed as novel therapeutic strategy to enhance antitumor immune response.

Metabolomics reveals the mechanism of Antarctic yeast Rhodotorula mucliaginosa AN5 to cope with cadmium stress

Heavy metal pollution in Antarctica has far exceeded expectations. Antarctic yeast is widely present in polar marine environment. The mechanisms of metabolomics effect of heavy metal on polar yeast have not been reported previously. In this study, gas chromatography-mass spectrometry (GC-MS) wascarried out to performed the metabolite profiling analysis of Antarctic sea-ice yeast Rhodotorula mucilaginosa AN5 exposed to different cadmium (Cd) stresses of 5 mM (HM5), 10 mM (HM10) and 20 mM (HM20), respectively. Metabolic profile analysis showed that the composition and contents of cellular metabolites have been altered by cadmium. 93 different metabolites were identified altogether, among which 23, 58 and 81 different metabolites were found in HM5, HM10 and HM20 group respectively. MetaboAnalyst analysis showed that in HM5, HM10 and HM20 groups, 12, 24 and 31 metabolic pathways were involved in the stress of cadmium to R. mucilaginosa, respectively. By contrasting with Kyoto Encyclopedia of Genes and Genomes database, we discovered that exposure of yeast AN5 to Cd stress resulted in profound biochemical changes including amino acids, organic acids and saccharides. These results will supply a nonnegligible basis of studying the adaptive resistance mechanism of Antarctic yeast Rhodotorula mucilaginosa to heavy metal.

Comparison of bacteria disintegration methods and their influence on data analysis in metabolomics

Metabolomic experiments usually contain many different steps, each of which can strongly influence the obtained results. In this work, metabolic analyses of six bacterial strains were performed in light of three different bacterial cell disintegration methods. Three strains were gram-negative (Pseudomonas aeruginosa, Escherichia coli, and Klebsiella pneumoniae), and three were gram-positive (Corynebacterium glutamicum, Bacillus cereus, and Enterococcus faecalis). For extraction, the methanol–water extraction method (1:1) was chosen. To compare the efficiency of different cell disintegration methods, sonication, sand mill, and tissue lyser were used. For bacterial extract metabolite analysis, ¹H NMR together with univariate and multivariate analyses were applied. The obtained results showed that metabolite concentrations are strongly dependent on the cell lysing methodology used and are different for various bacterial strains. The results clearly show that one of the disruption methods gives the highest concentration for most identified compounds (e. g. sand mill for E. faecalis and tissue lyser for B. cereus). This study indicated that the comparison of samples prepared by different procedures can lead to false or imprecise results, leaving an imprint of the disintegration method. Furthermore, the presented results showed that NMR might be a useful bacterial strain identification and differentiation method. In addition to disintegration method comparison, the metabolic profiles of each elaborated strain were analyzed, and each exhibited its metabolic profile. Some metabolites were identified by the ¹H NMR method in only one strain. The results of multivariate data analyses (PCA) show that regardless of the disintegration method used, the strain group can be identified. Presented results can be significant for all types of microbial studies containing the metabolomic targeted and non-targeted analysis.

Personal Metabolomics: A Global Challenge

Today, the introduction of metabolomics, like other omics sciences, into clinical practice as a personal omics test that realizes the perfect analytical capabilities of this science has become an important subject. The assembled data show that the metabolome of biosamples is a collection of highly informative and accurate signatures of virtually all diseases that are widespread in the population. However, we have not seen the emergence of personalized metabolomics in clinical practice. This article analyzes the causes of this problem. The complexity of personal metabolic data analysis and its incompatibility with widely accepted data treatment in metabolomics are shown. As a result, the impossibility of translating metabolic signatures accumulated in databases into a personal test is revealed. Problem-solving strategies that may radically change the situation and realize the analytical capabilities of metabolomics in medical laboratory practice are discussed.

Contribution of Metabolomics to Multiple Sclerosis Diagnosis, Prognosis and Treatment

Metabolomics-based technologies map in vivo biochemical changes that may be used as early indicators of pathological abnormalities prior to the development of clinical symptoms in neurological conditions. Metabolomics may also reveal biochemical pathways implicated in tissue dysfunction and damage and thus assist in the development of novel targeted therapeutics for neuroinflammation and neurodegeneration. Metabolomics holds promise as a non-invasive, high-throughput and cost-effective tool for early diagnosis, follow-up and monitoring of treatment response in multiple sclerosis (MS), in combination with clinical and imaging measures. In this review, we offer evidence in support of the potential of metabolomics as a biomarker and drug discovery tool in MS. We also use pathway analysis of metabolites that are described as potential biomarkers in the literature of MS biofluids to identify the most promising molecules and upstream regulators, and show novel, still unexplored metabolic pathways, whose investigation may open novel avenues of research.

Serum metabolic profile of postoperative acute kidney injury following infant cardiac surgery with cardiopulmonary bypass

BACKGROUND

We sought to determine differences in the circulating metabolic profile of infants with or without acute kidney injury (AKI) following cardiothoracic surgery with cardiopulmonary bypass (CPB).

METHODS

We performed a secondary analysis of preoperative and 24-h postoperative serum samples from infants ≤ 120 days old undergoing CPB. Metabolic profiling of the serum samples was performed by targeted analysis of 165 serum metabolites via tandem mass spectrometry. We then compared infants who did or did not develop AKI in the first 72 h postoperatively to determine global differences in the preoperative and 24-h metabolic profiles in addition to specific differences in individual metabolites.

RESULTS

A total of 57 infants were included in the study. Six infants (11%) developed KDIGO stage 2/3 AKI and 13 (23%) developed stage 1 AKI. The preoperative metabolic profile did not differentiate between infants with or without AKI. Infants with severe AKI could be moderately distinguished from infants without AKI by their 24-h metabolic profile, while infants with stage 1 AKI segregated into two groups, overlapping with either the no AKI or severe AKI groups. Differences in these 24-h metabolic profiles were driven by 21 metabolites significant at an adjusted false discovery rate of < 0.05. Prominently altered pathways include purine, methionine, and kynurenine/nicotinamide metabolism.

CONCLUSION

Moderate-to-severe AKI after infant cardiac surgery is associated with changes in the serum metabolome, including prominent changes to purine, methionine, and kynurenine/nicotinamide metabolism. A portion of infants with mild AKI demonstrated similar metabolic changes, suggesting a potential role for metabolic analysis in the evaluation of lower stage injury.

Metabolic Phenotypes in Asthmatic Adults: Relationship with Inflammatory and Clinical Phenotypes and Prognostic Implications

Bronchial asthma is a chronic disease that affects individuals of all ages. It has a high prevalence and is associated with high morbidity and considerable levels of mortality. However, asthma is not a single disease, and multiple subtypes or phenotypes (clinical, inflammatory or combinations thereof) can be detected, namely in aggregated clusters. Most studies have characterised asthma phenotypes and clusters of phenotypes using mainly clinical and inflammatory parameters. These studies are important because they may have clinical and prognostic implications and may also help to tailor personalised treatment approaches. In addition, various metabolomics studies have helped to further define the metabolic features of asthma, using electronic noses or targeted and untargeted approaches. Besides discriminating between asthma and a healthy state, metabolomics can detect the metabolic signatures associated with some asthma subtypes, namely eosinophilic and non-eosinophilic phenotypes or the obese asthma phenotype, and this may prove very useful in point-of-care application. Furthermore, metabolomics also discriminates between asthma and other “phenotypes” of chronic obstructive airway diseases, such as chronic obstructive pulmonary disease (COPD) or Asthma–COPD Overlap (ACO). However, there are still various aspects that need to be more thoroughly investigated in the context of asthma phenotypes in adequately designed, homogeneous, multicentre studies, using adequate tools and integrating metabolomics into a multiple-level approach.

Pursuing Multiple Biomarkers for Early Idiopathic Parkinson's Disease Diagnosis

Parkinson's disease (PD) ranks first in the world as a neurodegenerative movement disorder and occurs most commonly in an idiopathic form. PD patients may have motor symptoms, non-motor symptoms, including cognitive and behavioral changes, and symptoms related to autonomic nervous system (ANS) failures, such as gastrointestinal, urinary, and cardiovascular symptoms. Unfortunately, the diagnostic accuracy of PD by general neurologists is relatively low. Currently, there is no objective molecular or biochemical test for PD; its diagnosis is based on clinical criteria, mainly by cardinal motor symptoms, which manifest when patients have lost about 60-80% of dopaminergic neurons. Therefore, it is urgent to establish a panel of biomarkers for the early and accurate diagnosis of PD. Once the disease is accurately diagnosed, it may be easier to unravel idiopathic PD's pathogenesis, and ultimately, finding a cure. This review discusses several biomarkers' potential to set a panel for early idiopathic PD diagnosis and future directions.

Sex-Specific Relationship between the Cardiorespiratory Fitness and Plasma Metabolite Patterns in Healthy Humans-Results of the KarMeN Study

Cardiorespiratory fitness (CRF) represents a strong predictor of all-cause mortality and is strongly influenced by regular physical activity (PA). However, the biological mechanisms involved in the body's adaptation to PA remain to be fully elucidated. The aim of this study was to systematically examine the relationship between CRF and plasma metabolite patterns in 252 healthy adults from the cross-sectional Karlsruhe Metabolomics and Nutrition (KarMeN) study. CRF was determined by measuring the peak oxygen uptake during incremental exercise. Fasting plasma samples were analyzed by nuclear magnetic resonance spectroscopy and mass spectrometry coupled to one- or two-dimensional gas chromatography or liquid chromatography. Based on this multi-platform metabolomics approach, 427 plasma analytes were detected. Bi- and multivariate association analyses, adjusted for age and menopausal status, showed that CRF was linked to specific sets of metabolites primarily indicative of lipid metabolism. However, CRF-related metabolite patterns largely differed between sexes. While several phosphatidylcholines were linked to CRF in females, single lyso-phosphatidylcholines and sphingomyelins were associated with CRF in males. When controlling for further assessed clinical and phenotypical parameters, sex-specific CRF tended to be correlated with a smaller number of metabolites linked to lipid, amino acid, or xenobiotics-related metabolism. Interestingly, sex-specific CRF explanation models could be improved when including selected plasma analytes in addition to clinical and phenotypical variables. In summary, this study revealed sex-related differences in CRF-associated plasma metabolite patterns and proved known associations between CRF and risk factors for cardiometabolic diseases such as fat mass, visceral adipose tissue mass, or blood triglycerides in metabolically healthy individuals. Our findings indicate that covariates like sex and, especially, body composition have to be considered when studying blood metabolic markers related to CRF.

Baicalein Delays H2O2-Induced Astrocytic Senescence through Inhibition of Senescence-Associated Secretory Phenotype (SASP), Suppression of JAK2/STAT1/NF-κB Pathway, and Regulation of Leucine Metabolism

Baicalein is an active ingredient extracted from the dried roots of the Scutellaria baicalensis Georgi. It has been demonstrated to improve memory impairment in multiple animal models; however, the underlying mechanisms remain ambiguous. The accumulation of senescent astrocytes and senescence-associated secretory phenotype (SASP) secreted by senescent astrocytes has been deemed as potential contributors to neurodegenerative diseases. Therefore, this study explored the protective effects of baicalein against astrocyte senescence and investigated the molecular mechanisms and metabolic mechanisms of baicalein against astrocyte senescence. Our results demonstrated that treatment with baicalein protects T98G cells from H₂O₂-induced damage, delays cell senescence, inhibits the secretion of SASP (IL-6, IL-8, TNF-α, CXCL1, and MMP-1), and inhibits SASP-related pathways NF-κB and JAK2/STAT1. ¹H NMR metabolomics analysis and correlation analysis revealed that leucine was significantly correlated with SASP factors. Further study demonstrated that supplement with leucine could restrain SASP secretion, and baicalein could significantly increase leucine level through down-regulation of BCAT1 and up-regulation of SLC7A5 expression. The above results revealed that baicalein exerted protective and antisenescence effects in H₂O₂-induced T98G cells possibly through inhibition of SASP, suppression of JAK2/STAT1/NF-κB pathway, and regulation of leucine metabolism. Consistent results were obtained in primary astrocytes of newborn SD rats, which suggests that baicalein significantly increases viabilities, delays senescence, inhibits IL-6 secretion, and increases leucine level in H₂O₂-induced primary astrocytes.

Hepatic lipid metabolomics in response to heat stress in local broiler chickens breed (Huaixiang chickens)

High-temperature environment-induced heat stress (HS) is a hazard environmental element for animals, leading to dramatic changes in physiological and metabolic function. However, the metabolomic-level mechanisms underlying lipid metabolism in liver of slow-growing broilers are still obscure. The present study investigated the effects of HS on hepatic lipidomics in Chinese indigenous slow-growing broilers (Huaixiang chickens). The study includes two treatments, each treatment had 5 replicates with 4 broilers per cage, where a total of 40 eight-week-old female Huaixiang chickens (average initial body weight of 840.75 ± 20.79 g) were randomly divided into normal temperature (NT) and HS groups for 4 weeks, and the broilers of NT and HS groups were exposed to 21.3 ± 1.2℃ and 32.5 ± 1.4℃ respectively. The relative humidity of the two groups was maintained at 55%-70%. The liquid chromatography-mass spectrometry (LC-MS)-based metabolomics were conducted to evaluate the changes in hepatic lipidomics of broilers. The results showed that there were 12 differential metabolites between the two treatments. Compared with the NT group, HS group reduced the levers of hepatic phosphatidylcholine (PC) (16:0/16:0), PC (16:0/18:2), triglyceride (TG) (16:0/16:1/18:1), TG (18:0/18:1/20:4) (VIP > 1 and p < 0.05), while increased PC (18:1/20:3), PC (18:0/18:1), PC (18:1/18:1), PC (18:0/22:5), dimethyl-phosphatidyl ethanolamine (dMePE) (14:0/18:3), dMePE (18:0/18:1) and dMePE (16:0/20:3) levels (Variable Importance in the Projection; VIP > 1 and p < 0.05). In addition, according to the analysis of metabolic pathway, the pathways of linoleic acid, alpha-linolenic acid, glycerolipid and glycerophospholipid metabolism were involved in the effects of HS on hepatic lipid metabolism of broilers (p < 0.05). In conclusion, HS altered the hepatic lipid metabolism mainly through linoleic acid, alpha-linolenic acid, glycerolipid and glycerophospholipid metabolism pathway in indigenous broilers. These findings provided novel insights into the role of HS on hepatic lipidomics in Chinese indigenous broiler chickens.

Serum metabolomics in chickens infected with Cryptosporidium baileyi

Background

Cryptosporidium baileyi is an economically important zoonotic pathogen that causes serious respiratory symptoms in chickens for which no effective control measures are currently available. An accumulating body of evidence indicates the potential and usefulness of metabolomics to further our understanding of the interaction between pathogens and hosts, and to search for new diagnostic or pharmacological biomarkers of complex microorganisms. The aim of this study was to identify the impact of C. baileyi infection on the serum metabolism of chickens and to assess several metabolites as potential diagnostic biomarkers for C. baileyi infection.

Methods

Ultraperformance liquid chromatography-mass spectrometry (UPLC-MS) and subsequent multivariate statistical analysis were applied to investigate metabolomics profiles in the serum samples of chickens infected with C. baileyi, and to identify potential metabolites that can be used to distinguish chickens infected with C. baileyi from non-infected birds.

Results

Multivariate statistical analysis identified 138 differential serum metabolites between mock- and C. baileyi-infected chickens at 5 days post-infection (dpi), including 115 upregulated and 23 downregulated compounds. These metabolites were significantly enriched into six pathways, of which two pathways associated with energy and lipid metabolism, namely glycerophospholipid metabolism and sphingolipid metabolism, respectively, were the most enriched. Interestingly, some important immune-related pathways were also significantly enriched, including the intestinal immune network for IgA production, autophagy and cellular senescence. Nine potential C. baileyi-responsive metabolites were identified, including choline, sirolimus, all-trans retinoic acid, PC(14:0/22:1(13Z)), PC(15:0/22:6(4Z,7Z,10Z,13Z,16Z,19Z)), PE(16:1(9Z)/24:1(15Z)), phosphocholine, SM(d18:0/16:1(9Z)(OH)) and sphinganine.

Conclusions

This is the first report on serum metabolic profiling of chickens with early-stage C. baileyi infection. The results provide novel insights into the pathophysiological mechanisms of C. baileyi in chickens.

Graphic abstract

Supplementary Information

The online version contains supplementary material available at 10.1186/s13071-021-04834-y.

Application of metabolomics in clinical and laboratory gastrointestinal oncology

Metabolites are versatile bioactive molecules. They are not only the substrates and/or the products of enzymatic reactions but also act as the regulators in the systemic metabolism. Metabolomics is a high-throughput analytical strategy to qualify or quantify as many metabolites as possible in the metabolomes. It is an indispensable part of systems biology. The leading techniques in this field are mainly based on mass spectrometry and nuclear magnetic resonance spectroscopy. The metabolomic analysis has gained wide use in bioscience fields. In the tumor research arena, metabolomics can be employed to identify biomarkers for prediction, diagnosis, and prognosis. Chemotherapeutic effect evaluation and personalized medicine decision-making can also benefit from metabolomic analysis of patient biofluid or biopsy samples. Many cell-level studies can help in disease exploration. In this review, the basic features and principles of varied metabolomic analysis are introduced. The value of metabolomics in clinical and laboratory gastrointestinal cancer studies is discussed, especially for mass spectrometry applications. Besides, combined use of metabolomics and other tools to solve problems in cancer practice is briefly illustrated. In summary, metabolomics paves a new way to explore cancerous diseases in the light of small molecules.

Metabolic profiling of liver and faeces in mice infected with echinococcosis

Background

Echinococcosis is a severe zoonotic parasitic disease which severely affects the health of the hosts. The diagnosis of echinococcosis depends mainly on imaging examination. However, the patient is often in the late stage of the disease when the symptoms appear, thus limiting the early diagnosis of echinococcosis. The treatment and prognosis of the patients are hampered because of long-term asymptomatic latency. Metabolomics is a new discipline developed in the late 1990s. It reflects a series of biological responses in pathophysiological processes by demonstrating the changes in metabolism under the influence of internal and external factors. When the organism is invaded by pathogens, the alteration in the characteristics of metabolites in cells becomes extremely sensitive. Here, we used a metabolomics approach involving liquid chromatography coupled with tandem mass spectrometry (LC–MS/MS) to determine the molecular mechanism of cystic echinococcosis (CE) and to develop an effective method for CE diagnosis.

Methods

Twenty 8-week-old female BALB/c mice were divided into normal and Echinococcus granulosus infection groups. To develop the E. granulosus infection model, mice were infected with protoscoleces. Six weeks later, the abdomens of the mice showed significant bulging. An LC–MS/MS system-based metabolomics approach was used to analyse the liver and faeces to reveal the metabolic profiles of mice with echinococcosis.

Results

We found that the metabolism of nucleotides, alkaloids, amino acids, amides, and organic acids in mice is closely interrelated with E. granulosus infection. In the liver, the metabolic pathways of tyrosine and tryptophan biosynthesis; phenylalanine, valine, leucine and isoleucine biosynthesis; and phenylalanine metabolism were notably associated with the occurrence and development of hydatid disease, and in the faeces, pantothenate and CoA biosynthesis are thought to be closely associated with the development of CE.

Conclusion

The metabolomics approach used in this study provides a reference for a highly sensitive and specific diagnostic and screening method for echinococcosis.

Graphic Abstract

Supplementary Information

The online version contains supplementary material available at 10.1186/s13071-021-04807-1.

Differential modulation of polyunsaturated fatty acids in patients with myocardial infarction treated with ticagrelor or clopidogrel

Untargeted metabolomics is used to refine the development of biomarkers for the diagnosis of cardiovascular disease. Myocardial infarction (MI) has major individual and societal consequences for patients, who remain at high risk of secondary events, despite advances in pharmacological therapy. To monitor their differential response to treatment, we performed untargeted plasma metabolomics on 175 patients from the platelet inhibition and patient outcomes (PLATO) trial treated with ticagrelor and clopidogrel, two common P₂Y₁₂ inhibitors. We identified a signature that discriminates patients, which involves polyunsaturated fatty acids (PUFAs) and particularly the omega-3 fatty acids docosahexaenoate and eicosapentaenoate. The known cardiovascular benefits of PUFAs could contribute to the efficacy of ticagrelor. Our work, beyond pointing out the high relevance of untargeted metabolomics in evaluating response to treatment, establishes PUFA metabolism as a pathway of clinical interest in the recovery path from MI.

We detect an extreme metabolomic signature of myocardial infarction (MI) in plasma

Polyunsaturated fatty acids (PUFAs) are upregulated in patients taking ticagrelor

PUFA metabolism is a pathway of clinical interest in the recovery path from MI

Data science methods detect biologically meaningful patterns in metabolite signals

Mass spectrometry with derivatization method for concurrent measurement of amino acids and acylcarnitines in plasma of diabetic type 2 patients with diabetic nephropathy

BACKGROUND

Amino acids (AAs) and acylcarnitines play a key role in metabolic disease and can be used as biomarkers of various diseases such as malignancies, type 2 diabetes (T2D), insulin resistance, and cardiovascular diseases, therefore, designing an accurate and simple laboratory method that simultaneously measure both groups of substances, could improve the process of analytes quantification. In this research, a flow injection tandem mass spectrometry (FI-MS/MS) method for simultaneous measurement of AAs and acylcarnitines in addition to results of validation is explained.

METHODS

Samples were mixed with internal standards and after derivatization (with butanolic-HCL), AAs, and acylcarnitines were quantified by tandem mass spectrometry (SCIEX API 3200). Analytical performance studies were designed based on the Clinical and Laboratory Standards Institute (CLSI) guidelines including precision, accuracy, linearity, and limit of detection-quantification (LOD-LOQ) experiments. Samples from patients with T2D in different stages of kidney disease were also analyzed to ensure the clinical usage of the method.

RESULTS

Performance evaluation of the method demonstrated adequate results. The mean of estimated inter-assay precision (reported as a coefficient variation) for AAs and acylcarnitines were less than 8.7% and 12.3%, the estimated mean bias was below 8.8% and 10.2% respectively. LOD of analytes ranged between 0.6-10 μmol per liter (μmol/L) for AAs and 0.02-1 μmol/L for acylcarnitines. LOQ analytes showed a range of 2-25 μmol/L and 0.05-5 μmol/L for AAs and carnitine/acylcarnitines respectively. In diabetic patients sample analysis, a significant increase in acylcarnitines (C2, C4, C5DC, C6, C8, C10, C14) and citrulline with a significant decrease in valine were seen in patients with severely increased albuminuria.

CONCLUSION

FI-MS/MS method with pre-injection derivatization with butanolic-HCL can be used for concurrent measurement of AAs and carnitine/acylcarnitines in a short time and it satisfies the analytical performance requirements. This method is applied for AAs and carnitine/acylcarnitines measurement in patient with T2DM and results show some of the acylcarnitines and AAs can be involved in diabetic nephropathy development.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s40200-021-00786-3.