Liquid chromatography-mass spectrometry untargeted metabolomics reveals increased levels of tryptophan indole metabolites in urine of metabolic syndrome patients

Age-Related Gut Microbiota Transplantation Disrupts Myocardial Energy Homeostasis and Induces Oxidative Damage

BACKGROUND

Aging-related energy homeostasis significantly affects normal heart function and disease development. The relationship between the gut microbiota and host energy metabolism has been well established. However, the influence of an aged microbiota on energy metabolism in the heart remains unclear.

OBJECTIVE

The objective of this was to explore the effects of age-related microbiota composition on energy metabolism in the heart.

METHODS

In this study, we used the fecal microbiota transplantation (FMT) method. The fecal microbiota from young (2-3 mo) and aged (18-22 mo) donor mice were transplanted into separate groups of young (2-3 mo) recipient mice. The analysis utilized whole 16S rRNA sequencing and plasma metabolomics to assess changes in the gut microbiota composition and metabolic potential. Energy changes were monitored by performing an oral glucose tolerance test, biochemical testing, body composition analysis, and metabolic cage measurements. Metabolic markers and markers of DNA damage were assessed in heart samples.

RESULTS

FMT of an aged microbiota changed the composition of the recipient's gut microbiota, leading to an elevated Firmicutes-to-Bacteroidetes ratio. It also affected overall energy metabolism, resulting in elevated plasma glucose concentrations, impaired glucose tolerance, and epididymal fat accumulation. Notably, FMT of an aged microbiota increased the heart weight and promoted cardiac hypertrophy. Furthermore, there were significant associations between heart weight and cardiac hypertrophy indicators, epididymal fat weight, and fasting glucose concentrations. Mechanistically, FMT of an aged microbiota modulated the glucose metabolic pathway and induced myocardial oxidative damage.

CONCLUSIONS

Our findings suggested that an aged microbiota can modulate metabolism and induce cardiac injury. This highlights the possible role of the gut microbiota in age-related metabolic disorders and cardiac dysfunction.

Microbiome metabolite quantification methods enabling insights into human health and disease

Many of the health-associated impacts of the microbiome are mediated by its chemical activity, producing and modifying small molecules (metabolites). Thus, microbiome metabolite quantification has a central role in efforts to elucidate and measure microbiome function. In this review, we cover general considerations when designing experiments to quantify microbiome metabolites, including sample preparation, data acquisition and data processing, since these are critical to downstream data quality. We then discuss data analysis and experimental steps to demonstrate that a given metabolite feature is of microbial origin. We further discuss techniques used to quantify common microbial metabolites, including short-chain fatty acids (SCFA), secondary bile acids (BAs), tryptophan derivatives, N-acyl amides and trimethylamine N-oxide (TMAO). Lastly, we conclude with challenges and future directions for the field.

Unraveling biomarkers of exposure for tenuazonic acid through urinary metabolomics

Mycotoxins are secondary metabolites produced by fungi such as Aspergillus, Alternaria, and Penicillium, affecting nearly 80% of global food crops. Tenuazonic acid (TeA) is the major mycotoxin produced by Alternaria alternata, a prevalent pathogen affecting plants, fruits, and vegetables. TeA is notably prevalent in European diets, however, TeA biomarkers of exposure and metabolites remain unknown. This research aims to bridge this knowledge-gap by gaining insights about human TeA exposure and metabolization. Nine subjects were divided into two groups. The first group received a single bolus of TeA at the Threshold of Toxicological Concern (TTC) to investigate the presence of TeA urinary biomarkers, while the second group served as a control. Sixty-nine urinary samples were prepared and analyzed using UPLC-Xevo TQ-XS for TeA quantification and UPLC-Orbitrap Exploris for polar metabolome acquisition. TeA was rapidly excreted during the first 13 h and the fraction extracted was 0.39 ± 0.22. The polar metabolome compounds effectively discriminating the two groups were filtered using Orthogonal Partial Least Squares-Discriminant Analysis and subsequently annotated (n = 122) at confidence level 4. Finally, the urinary metabolome was compared to in silico predicted TeA metabolites. Nine metabolites, including oxidized, N-alkylated, desaturated, glucuronidated, and sulfonated forms of TeA were detected.

Small molecule metabolites: discovery of biomarkers and therapeutic targets

Metabolic abnormalities lead to the dysfunction of metabolic pathways and metabolite accumulation or deficiency which is well-recognized hallmarks of diseases. Metabolite signatures that have close proximity to subject's phenotypic informative dimension, are useful for predicting diagnosis and prognosis of diseases as well as monitoring treatments. The lack of early biomarkers could lead to poor diagnosis and serious outcomes. Therefore, noninvasive diagnosis and monitoring methods with high specificity and selectivity are desperately needed. Small molecule metabolites-based metabolomics has become a specialized tool for metabolic biomarker and pathway analysis, for revealing possible mechanisms of human various diseases and deciphering therapeutic potentials. It could help identify functional biomarkers related to phenotypic variation and delineate biochemical pathways changes as early indicators of pathological dysfunction and damage prior to disease development. Recently, scientists have established a large number of metabolic profiles to reveal the underlying mechanisms and metabolic networks for therapeutic target exploration in biomedicine. This review summarized the metabolic analysis on the potential value of small-molecule candidate metabolites as biomarkers with clinical events, which may lead to better diagnosis, prognosis, drug screening and treatment. We also discuss challenges that need to be addressed to fuel the next wave of breakthroughs.

New Advances in Metabolic Syndrome, from Prevention to Treatment: The Role of Diet and Food

The definition of metabolic syndrome (MetS) has undergone several changes over the years due to the difficulty in establishing universal criteria for it. Underlying the disorders related to MetS is almost invariably a pro-inflammatory state related to altered glucose metabolism, which could lead to elevated cardiovascular risk. Indeed, the complications closely related to MetS are cardiovascular diseases (CVDs) and type 2 diabetes (T2D). It has been observed that the predisposition to metabolic syndrome is modulated by complex interactions between human microbiota, genetic factors, and diet. This review provides a summary of the last decade of literature related to three principal aspects of MetS: (i) the syndrome's definition and classification, pathophysiology, and treatment approaches; (ii) prediction and diagnosis underlying the biomarkers identified by means of advanced methodologies (NMR, LC/GC-MS, and LC, LC-MS); and (iii) the role of foods and food components in prevention and/or treatment of MetS, demonstrating a possible role of specific foods intake in the development of MetS.

A multiple-metabolites model to predict preliminary renal injury induced by iodixanol based on UHPLC/Q-Orbitrap-MS and 1H-NMR

BACKGROUND & AIMS

There are some problems, such as unclear pathological mechanism, delayed diagnosis, and inaccurate therapeutic target of Contrast-induced acute kidney injury (CI-AKI). It is significantly important to find biomarkers and therapeutic targets that can indicate renal injury in the early stage of CI-AKI. This study aims to establish a multiple-metabolites model to predict preliminary renal injury induced by iodixanol and explore its pathogenesis.

METHODS

Both UHPLC/Q-Orbitrap-MS and ¹H-NMR methods were applied for urine metabolomics studies on two independent cohorts who suffered from a preliminary renal injury caused by iodixanol, and the multivariate statistical analysis and random forest (RF) algorithm were used to process the related date.

RESULTS

In the discovery cohort (n = 169), 6 metabolic markers (leucine, indole, 5-hydroxy-L-tryptophan, N-acetylvaline, hydroxyhexanoycarnine, and kynurenic acid) were obtained by the cross-validation between the RF and liquid chromatography-mass spectrometry (LC-MS). Secondly, the 6 differential metabolites were confirmed by comparison of standard substance and structural identification of ¹H-NMR. Subsequently, the multiple-metabolites model composed of the 6 biomarkers was validated in a validation cohort (n = 165).

CONCLUSIONS

The concentrations of leucine, indole, N-acetylvaline, 5-hydroxy-L-tryptophan, hydroxyhexanoycarnitine and kynurenic acid in urine were proven to be positively correlated with the degree of renal injury induced by iodixanol. The multiple-metabolites model based on these 6 biomarkers has a good predictive ability to predict early renal injury caused by iodixanol, provides treatment direction for injury intervention and a reference for reducing the incidence of clinical CI-AKI further.

Efficacy and mechanism of Jiedu Tongluo Tiaogan Formula in treating type 2 diabetes mellitus combined with non-alcoholic fatty liver disease: Study protocol for a parallel-armed, randomized controlled trial

Background: The incidence of Type 2 diabetes mellitus (T2DM) combined with non-alcoholic fatty liver disease (NAFLD) has risen over the years. This comorbid condition significantly increases the probability of cirrhosis, liver cancer, and mortality compared to the disease alone. The multi-targeted, holistic treatment efficacy of traditional Chinese medicine (TCM) plays a vital role in the treatment of T2DM and NAFLD. Jiedu Tongluo Tiaogan Formula (JTTF), based on TCM theory, is widely used in clinical treatment, and its effectiveness in lowering glucose, regulating lipids, improving insulin resistance, and its pathways of action have been demonstrated in previous studies. However, the mechanism of this formula has not been investigated from a metabolomics perspective. Moreover, high-quality clinical studies on T2DM combined with NAFLD are lacking. Therefore, we aim to conduct a clinical trial to investigate the clinical efficacy, safety, and possible pathways of JTTF in the treatment of T2DM combined with NAFLD using metabolomics techniques.

Methods: A total of 98 participants will be recruited to this clinical trial and randomly assigned to either a treatment group (JTTF + conventional basic treatment) or control group (conventional basic treatment) in a 1:1 ratio. Both groups will have received the same lifestyle interventions in the preceding 12 weeks. The primary outcome will be change in visceral fat area and total score on the TCM syndromes efficacy score scale. The secondary outcome will include changes in ultrasound steatosis grade, fibrosis 4 score (FIB-4), metabolic parameters, anthropometric parameters, visceral fat area. In addition, serum and urine samples collected at baseline and at the end of 12 weeks of treatment will be sequentially tested for untargeted and targeted metabolomics.

Discussion: This study will evaluate the efficacy and safety of JTTF, as well as investigate the differential metabolites and possible mechanisms of JTTF treatment in T2DM combined with NAFLD. We hypothesize that patients will benefit from JTTF, which may provide strong evidence for the clinical use of JTTF in the treatment of T2DM and NAFLD, leading to the possibility of further mechanistic exploration.

Clinical Trial Registration: This clinical trial has been registered in China Clinical Trial Registry (ChiCTR 2100051174).

Acylcarnitines: Nomenclature, Biomarkers, Therapeutic Potential, Drug Targets, and Clinical Trials

Acylcarnitines are fatty acid metabolites that play important roles in many cellular energy metabolism pathways. They have historically been used as important diagnostic markers for inborn errors of fatty acid oxidation and are being intensively studied as markers of energy metabolism, deficits in mitochondrial and peroxisomal β -oxidation activity, insulin resistance, and physical activity. Acylcarnitines are increasingly being identified as important indicators in metabolic studies of many diseases, including metabolic disorders, cardiovascular diseases, diabetes, depression, neurologic disorders, and certain cancers. The US Food and Drug Administration-approved drug L-carnitine, along with short-chain acylcarnitines (acetylcarnitine and propionylcarnitine), is now widely used as a dietary supplement. In light of their growing importance, we have undertaken an extensive review of acylcarnitines and provided a detailed description of their identity, nomenclature, classification, biochemistry, pathophysiology, supplementary use, potential drug targets, and clinical trials. We also summarize these updates in the Human Metabolome Database, which now includes information on the structures, chemical formulae, chemical/spectral properties, descriptions, and pathways for 1240 acylcarnitines. This work lays a solid foundation for identifying, characterizing, and understanding acylcarnitines in human biosamples. We also discuss the emerging opportunities for using acylcarnitines as biomarkers and as dietary interventions or supplements for many wide-ranging indications. The opportunity to identify new drug targets involved in controlling acylcarnitine levels is also discussed. SIGNIFICANCE STATEMENT: This review provides a comprehensive overview of acylcarnitines, including their nomenclature, structure and biochemistry, and use as disease biomarkers and pharmaceutical agents. We present updated information contained in the Human Metabolome Database website as well as substantial mapping of the known biochemical pathways associated with acylcarnitines, thereby providing a strong foundation for further clarification of their physiological roles.

Multi-stage metabolomics and genetic analyses identified metabolite biomarkers of metabolic syndrome and their genetic determinants

Background

Metabolic syndrome (MetS) is a cluster of multiple cardiometabolic risk factors that increase the risk of type 2 diabetes and cardiovascular diseases. Identifying novel biomarkers of MetS and their genetic associations could provide insights into the mechanisms of cardiometabolic diseases.

Methods

Potential MetS-associated metabolites were screened and internally validated by untargeted metabolomics analyses among 693 patients with MetS and 705 controls. External validation was conducted using two well-established targeted metabolomic methods among 149 patients with MetS and 253 controls. The genetic associations of metabolites were determined by linear regression using our previous genome-wide SNP data. Causal relationships were assessed using a one-sample Mendelian Randomization (MR) approach.

Findings

Nine metabolites were ultimately found to be associated with MetS or its components. Five metabolites, including LysoPC(14:0), LysoPC(15:0), propionyl carnitine, phenylalanine, and docosapentaenoic acid (DPA) were selected to construct a metabolite risk score (MRS), which was found to have a dose-response relationship with MetS and metabolic abnormalities. Moreover, MRS displayed a good ability to differentiate MetS and metabolic abnormalities. Three SNPs (rs11635491, rs7067822, and rs1952458) were associated with LysoPC(15:0). Two SNPs, rs1952458 and rs11635491 were found to be marginally correlated with several MetS components. MR analyses showed that a higher LysoPC(15:0) level was causally associated with the risk of overweight/obesity, dyslipidaemia, high uric acid, high insulin and high HOMA-IR.

Interpretation

We identified five metabolite biomarkers of MetS and three SNPs associated with LysoPC(15:0). MR analyses revealed that abnormal LysoPC metabolism may be causally linked the metabolic risk.

Funding

This work was supported by grants from the National Key Research and Development Program of China (2017YFC0907004).