Metabolomics for laboratory diagnostics

Metabolomics as a Tool for Unraveling the Impact of Enantioselectivity in Cellular Metabolism

Metabolomics is an emerging interdisciplinary field focused on the comprehensive analysis of all metabolites within biological samples, making it valuable for areas such as drug development, and environmental analysis. Many compounds, including pharmaceuticals and agrochemicals that have been extensively studied by metabolomics are chiral. The intrinsic chirality of biological targets can lead to a selective recognition of enantiomers resulting in distinct pharmacokinetic, pharmacodynamic and/or toxicological profiles (enantioselectivity). Given that metabolomics captures an instant snapshot of an organism's metabolic state, it serves as a powerful tool to investigate chiral compounds and understand enantioselective effects. Herein, a systematic compilation of scientific literature was performed and 48 enantioselectivity studies using metabolomics were selected. These studies revealed an increasing focus on chiral pesticides (77%), the use of animal models (59%), reliance on LC-MS techniques (52%), and predominantly untargeted approaches (83%). Enantioselective effects were described in most studies. This review describes significant advances in this emerging field and highlights the use of metabolomics to unravel the role of stereochemistry in cellular metabolism by the examination of enantiomer-specific metabolic effects. Furthermore, it elucidates enantioselectivity mechanism that can be further applied to other groups of chiral compounds.

Current progress and future perspectives in personal identification of monozygotic twins in forensic medicine

The personal identification of monozygotic (MZ) twins is of great importance in forensic medicine. Due to the extreme similarity in genetic between MZ twins, it is challenging to differentiate them using autosomal STR genotyping. Forensic experts are striving to explore available genetic markers that can differentiate between MZ twins. With the advent of next-generation sequence (NGS), an increasing number of genetic markers have been demonstrated to effectively differentiate between MZ twins. Here, we summarized for the relevant studies on MZ twins' differentiation and discussed the limitations of the underlying markers. In details, single-nucleotide variants (SNVs), copy number variation (CNV), mitochondrial DNA (mtDNA), DNA methylation, and non-coding RNA have been demonstrated considerable value. Furthermore, the utilization of proteomics, metabolomics, and microbiomics has shed light on MZ twin differentiation. Additionally, we introduce the methodologies for MZ differentiation based on external morphological variations observed in the human body. Looking to the future, the process of aging may represent a novel avenue for the differentiation of MZ twins.

Transcriptomic and metabolomic analysis revealed potential mechanisms of growth and disease resistance dimorphism in male and female common carp (Cyprinus carpio)

Sexual dimorphism is well-documented in aquaculture, particularly regarding growth differences, wherein one sex often grows faster than the other. However, despite the phenomenon being so widely documented, its underlying molecular mechanisms remain poorly understood. As an important digestive and immune organ, the gut plays key roles in the regulation of fish growth. In this study, we conducted RNA-seq and metabolomic analysis on the gut of female and male common carp. We discovered that growth-related pathways, such as "Glycolysis/Gluconeogenesis" and "Riboflavin metabolism" are significantly enriched in the gut of female carp. Conversely, pathways linked to disease resistance, such as "Th17 cell differentiation" and "Autophagy-animal" are predominantly enriched in male carp. Following intraperitoneal injection of spring viraemia of carp virus (SVCV) into both male and female carp, quantitative reverse transcription polymerase chain reaction (RT-qPCR) analysis and histopathological staining confirmed that male carp exhibit greater disease resistance compared to females. This study identified the disease resistance dimorphism in common carp and specific mechanisms underlying growth differences. Our findings offer valuable insights for the application of growth dimorphism and disease-resistant breeding in fish.

Discrimination of superficial lymph nodes using ultrasonography and tissue metabolomics coupled with machine learning

Introduction

Diagnosing the types of malignant lymphoma could help determine the most suitable treatment, anticipate the probability of recurrence and guide long-term monitoring and follow-up care.

Methods

We evaluated the differences in benign, lymphoma and metastasis superficial lymph nodes using ultrasonography and tissue metabolomics.

Results

Our findings indicated that three ultrasonographic features, blood supply pattern, cortical echo, and cortex elasticity, hold potential in differentiating malignant lymph nodes from benign ones, and the shape and corticomedullary boundary emerged as significant indicators for distinguishing between metastatic and lymphoma groups. Metabolomics revealed the difference in metabolic profiles among lymph nodes. We observed significant increases in many amino acids, organic acids, lipids, and nucleosides in both lymphoma and metastasis groups, compared to the benign group. Specifically, the lymphoma group exhibited higher levels of nucleotides (inosine monophosphate and adenosine diphosphate) as well as glutamic acid, and the metastasis group was characterized by higher levels of carbohydrates, acylcarnitines, glycerophospholipids, and uric acid. Linear discriminant analysis coupled with these metabolites could be used for differentiating lymph nodes, achieving recognition rates ranging from 87.4% to 89.3%, outperforming ultrasonography (63.1% to 75.4%).

Discussion

Our findings could contribute to a better understanding of malignant lymph node development and provide novel targets for therapeutic interventions.

Characterization of Metabolic Patterns in Mouse Spermatogenesis and Its Clinical Implications in Humans

Spermatogenesis is a complex process requiring precisely controlled metabolic adaptations. Although the genetic and cellular aspects of spermatogenesis have been extensively studied, the underlying metabolic mechanisms remain largely underexplored. In this study, we utilized STA-PUT technology to separate three key cell types involved in mouse spermatogenesis: pachytene spermatocytes (PAC), round spermatids (RS), and elongated spermatids (ES). A comprehensive untargeted metabolomic analysis revealed significant metabolic changes during spermatogenesis, such as reduced methylation-related metabolites and increased glycolytic intermediates and TCA cycle metabolites during ES. Moreover, metabolic differences between germ cells and somatic cells (Leydig and Sertoli cells) were highlighted, particularly in steroidogenesis and lipid metabolism. To investigate clinical relevance, we analyzed human seminal plasma. Samples from individuals with azoospermia displayed significant metabolic abnormalities, including reduced methionine, tryptophan, and arginine, which play vital roles in sperm development. Pathway enrichment analysis revealed disturbances in the metabolism of nucleotide, amino acid, and energy in azoospermia, suggesting potential biomarkers of male infertility. Our findings provide a comprehensive metabolic profile of spermatogenesis and suggest that metabolic alterations may be significant contributors to male infertility, particularly in cases of azoospermia.

Metabolomics in the Diagnosis of Bacterial Infections

One of the essential factors in the appropriate treatment of infections is accurate and timely laboratory diagnosis. The correct diagnosis of infections plays a vital role in determining desirable therapy and controlling the spread of pathogens. Traditional methods of infection diagnosis are limited by several factors such as insufficient sensitivity and specificity, being time-consuming and laborious, having a low ability to distinguish infection from non-infectious inflammatory conditions and a low potential to predict treatment outcomes. Therefore, it is necessary to find innovative strategies for detecting specific biomarkers in order to diagnose infections. The rapid advancement of metabolomics makes it possible to determine the pattern of metabolite changes in the both of pathogen and the host during an infection. Metabolomics is a method used to assess the levels and type of metabolites in an organism. Metabolites are of low-molecular-weight compounds produced as a result of metabolic processes and pathways within cells. Metabolomics provides valuable data to detect accurate biomarkers of specific biochemical features directly related to certain phenotypes or conditions. This study aimed to review the applications and progress of metabolomics as a biomarker for the diagnosis of bacterial infections.

Venoms and Extracellular Vesicles: A New Frontier in Venom Biology

Extracellular vesicles (EVs) are nanoparticle-sized vesicles secreted by nearly all cell types under normal physiological conditions. In toxicological research, EVs have emerged as a crucial link between public health and multi-omics approaches, offering insights into cellular responses to disease-causing injury agents such as environmental and biological toxins, contaminants, and drugs. Notably, EVs present a unique opportunity to deepen our understanding of the pathophysiology of envenomation by natural toxins. Recent advancements in isolating and purifying EV cargo, mass spectrometry techniques, and bioinformatics have positioned EVs as potential biomarkers that could elucidate biological signaling pathways and provide valuable information on the relationship between venomous toxins, their mechanisms of action, and the effectiveness of antivenoms. Additionally, EVs hold promise as proxies for various aspects of envenomation, including the toxin dosage, biological characterization, injury progression, and prognosis during therapeutic interventions. These aspects can be explored through multi-omics technology applied to EV contents from the plasma, saliva, or urine samples of envenomated individuals, offering a comprehensive integrative approach to understanding and managing envenomation cases.

Uncovering the Metabolic Footprint of New Psychoactive Substances by Metabolomics: A Systematic Review

New psychoactive substances (NPSs) emerged in the 2000s as legal alternatives to illicit drugs and quickly became a huge public health threat due to their easy accessibility online, limited information, and misleading labels. Synthetic cannabinoids and synthetic cathinones are the most reported groups of NPSs. Despite NPSs being widely studied, due to their structural diversity and the constant emergence of novel compounds with unknown properties, the development of new techniques is required to clarify their mode of action and evaluate their toxicological effects. Metabolomics has been a useful tool to evaluate the metabolic effects of several xenobiotics. Herein, a systematic review was performed, following PRISMA guidelines, regarding metabolomic studies on synthetic cathinones and synthetic cannabinoids to evaluate their effects in cellular metabolism. In the studies, in vivo models were the most employed (86%) and the analysis mostly followed untargeted approaches (75%) using LC-MS techniques (67%). Both groups of NPSs seem to primarily interfere with energy metabolism-related pathways. Even though this type of study is still limited, metabolomics holds great promise as a tool to clarify mechanisms of actions, identify biomarkers of exposure, and explain the toxicological effects of NPSs.

Metagenomics and metabolomics to investigate the effect of Amygdalus mongolica oil on intestinal microbiota and serum metabolites in rats

BACKGROUND

Renal fibrosis (RF) is an inevitable consequence of multiple manifestations of progressive chronic kidney diseases (CKDs). Mechanism of Amygdalus mongolica (Maxim.) in the treatment of RF needs further investigation.

PURPOSE

The study further investigated the potential mechanism of A. mongolica in the treatment of RF.

METHODS

A rat model of RF was induced by unilateral ureteral obstruction (UUO), followed by treatment with varying dosages of A. mongolica oil for 4 weeks. Body weight was measured weekly. We detected serum levels of interleukin (IL)-6, IL-1β, type Ⅲ procollagen (Col-Ⅲ), type IV collagen (Col-Ⅳ), laminin (LN), hyaluronidase (HA), and tissue levels of albumin (ALB), blood urea nitrogen (BUN), creatinine (Cre), superoxide dismutase (SOD), malondialdehyde (MDA), and hydroxyproline (HYP). Shotgun metagenomics analyzed the composition of the intestinal microbiota. High-performance liquid chromatography coupled with a quadrupole-exactive mass spectrometer (HPLC-Q-Exactive-MS) monitored changes in metabolite levels in serum and gut. Multiple reaction monitoring-mass spectrometry (MRM-MS) determined the levels of amino acids in serum.

RESULTS

A. mongolica oil significantly alleviated indicators related to RF (p < 0.05). A. mongolica oil reduced the ratio of Firmicutes to Bacteroidetes and restored the balance of intestinal microbiota in rats with RF. A. mongolica oil modulated levels of metabolites in gut content and serum. It regulated 11 metabolic pathways including arachidonic acid metabolism. Targeted metabolomics of amino acids showed that 17 amino acids were significantly changed by A. mongolica oil, including L-glycine, L-serine and L-glutamine.

CONCLUSION

A. mongolica oil regulates intestinal microbiota and metabolites, restoring amino acid metabolism to treat RF.

Metabolomics in Depression: What We Learn from Preclinical and Clinical Evidences

Depression is one of the predominant common mental illnesses that affects millions of people of all ages worldwide. Random mood changes, loss of interest in routine activities, and prevalent unpleasant senses often characterize this common depreciated mental illness. Subjects with depressive disorders have a likelihood of developing cardiovascular complications, diabesity, and stroke. The exact genesis and pathogenesis of this disease are still questionable. A significant proportion of subjects with clinical depression display inadequate response to antidepressant therapies. Hence, clinicians often face challenges in predicting the treatment response. Emerging reports have indicated the association of depression with metabolic alterations. Metabolomics is one of the promising approaches that can offer fresh perspectives into the diagnosis, treatment, and prognosis of depression at the metabolic level. Despite numerous studies exploring metabolite profiles post-pharmacological interventions, a quantitative understanding of consistently altered metabolites is not yet established. The article gives a brief discussion on different biomarkers in depression and the degree to which biomarkers can improve treatment outcomes. In this review article, we have systemically reviewed the role of metabolomics in depression along with current challenges and future perspectives.

Comprehensive plasma metabolomics analysis of berberine treatment in ulcerative colitis rats by LC-MS/MS

Background

Ulcerative colitis (UC) is a chronic inflammatory bowel disease (IBD) influenced by multiple factors. Berberine, an isoquinoline alkaloid derived from the root and bark of Coptis chinensis Franch., has shown promise in managing UC, but its underlying mechanisms remain unclear.

Methods

To elucidate the relationship between berberine, ulcerative colitis (UC), and the organism's metabolome, we established a dextran sulfate sodium (DSS)-induced UC model in rats. Colonic tissue was collected for histopathological examination, while plasma samples were analyzed using liquid chromatography-tandem mass spectrometry (LC-MS/MS) with dynamic Multiple Reaction Monitoring (dMRM). This approach, characterized by its short analysis time of 20 min per sample, excellent reproducibility, and straightforward data processing, allowed for the comprehensive detection of a wide array of metabolites, including amino acids, lipids, and organic acids, many of which are implicated in the pathophysiology of UC.

Results

Our results showed that berberine modulated the metabolic disturbances of 33 compounds in the plasma of UC rats, primarily including amino acids, pyrimidines, organic phosphoric acids, fatty acyls, and organonitrogen compounds. These altered metabolites were associated with various pathways, such as amino acid metabolism, glutathione metabolism, nicotinate and nicotinamide metabolism, taurine and hypotaurine metabolism, pyrimidine metabolism, glyoxylate and dicarboxylate metabolism, and the citrate cycle (TCA cycle). Notably, 3-hydroxyproline, homocysteic acid, L-threonine, L-lysine, carbamoyl phosphate, O-phosphoethanolamine, taurine, leucine, and phosphorylcholine exhibited significant differences between the Treatment and Model groups, with levels reverting to those of the Control group (p < 0.001). These findings suggested that these compounds may serve as potential plasma biomarkers for UC.

Conclusion

This study provided valuable insights into the mechanism by which berberine exerted its therapeutic effects on UC through metabolomics. Our results highlighted berberine's potential to modulate key metabolic pathways and restore the levels of several metabolites, suggesting its utility as a therapeutic agent for UC. These findings underscored the importance of metabolomics in understanding the pathophysiology and treatment of UC.

Integrating metabolomics into the diagnosis and investigation of anthelmintic resistance

Anthelmintic resistance (AR) in parasitic nematodes poses a global health problem in livestock and domestic animals and is an emerging problem in humans. Consequently, we must understand the mechanisms of AR, including target-site resistance (TSR), in which mutations affect drug binding, and non-target site resistance (NTSR), which involves alterations in drug metabolism and detoxification processes. Because much of the focus has been on TSR, NTSR has received less attention. Here, we describe how metabolomics approaches using Caenorhabditis elegans offer the ability to disentangle nematode drug metabolism, identify metabolic changes associated with resistance, uncover novel biomarkers, and enhance diagnostic methods.

Assessment of progression of pulmonary fibrosis based on metabonomics and analysis of intestinal microbiota

The main purpose of this study was to explore the changes of biomarkers in different developmental stages of bleomycin-induced pulmonary fibrosis (PF) in rats via comprehensive pathophysiology, UPLC-QTOF/MS metabonomic technology, and 16S rRNA gene sequencing of intestinal microbiota. The rats were randomly divided into normal control and 1-, 2- and 4-week model group. The rat model of PF was established by one-time intratracheal instillation of bleomycin. The levels of inflammatory and fibrosis-related factors such as hydroxyproline (HYP), type III procollagen (COL-III), type IV collagen (COL-IV), hyaluronidase (HA), laminin (LN), interleukin (IL)-1β, IL-6, malondialdehyde (MDA) increased and superoxide dismutase (SOD) decreased as the PF cycle progressed. In the 1-, 2- and 4-week model group, 2, 19 and 18 potential metabolic biomarkers and 3, 16 and 12 potential microbial biomarkers were detected, respectively, which were significantly correlated. Glycerophospholipid metabolism pathway was observed to be an important pathway affecting PF at 1, 2 and 4 weeks; arginine and proline metabolism pathways significantly affected PF at 2 weeks. Linoleic acid metabolism pathway exhibited clear metabolic abnormalities at 2 and 4 weeks of PF, and alpha-linolenic acid metabolism pathway significantly affected PF at 4 weeks.

Metabolomics: Implication in cardiovascular research and diseases

Cellular metabolism influences all aspects of cellular function and is crucial for overall organismal health. Metabolic disorders related to cardiovascular health can lead to cardiovascular diseases (CVDs). Moreover, associated comorbidities may also damage cardiovascular metabolism, exacerbating CVD and perpetuating a vicious cycle. Given the prominent role of metabolic alterations in CVD, metabolomics has emerged as an imperative technique enabling a comprehensive assessment of metabolites and metabolic architecture within the body. Metabolite profile and metabolic pathways help to deepen and broaden our understanding of complex genomic landscape and pathophysiology of CVD. Here in this review, we aim to overview the experimental and clinical applications of metabolomics in pathogenesis, diagnosis, prognosis, and management of various CVD plus future perspectives and limitations.

Unique Gut Microbiome and Metabolic Profiles in Chinese Workers Exposed to Dust: Insights From a Case-Control Study

OBJECTIVES

This study aimed to identify distinct gut microbiome and serum metabolic features in workers exposed to dust compared to healthy controls.

METHODS

A case-control study was conducted with dust-exposed workers without silicosis and age-matched healthy controls. Gut microbiome composition was analyzed using 16S rRNA sequencing, and serum and fecal metabolomic profiles were assessed by LC-MS.

RESULTS

Dust-exposed workers showed higher levels of Blautia and Trichoderma and lower levels of Anaplasma , Aspergillus , Plasmodiophoromycetes, and Escherichia coli-Shigella . Metabolites such as indole-3-acetate and gentamicin C1a were downregulated, while adenine, 2-phenylacetamide, and 4-pyridoxic acid were upregulated.

CONCLUSIONS

Blautia spp. were linked to altered metabolites in dust-exposed workers, suggesting microbiome-metabolite interactions that may affect silicosis progression. However, the small sample size and cross-sectional design limit generalizability, and further longitudinal studies are needed.

Metabolomics as a promising technology for investigating external therapy of traditional Chinese medicine: A review

To demonstrate the potential for connecting metabolomics with traditional Chinese medicine (TCM) external therapies such as acupuncture and moxibustion, we conducted a literature review on metabolomics as a measurement tool for determining the efficacy of various TCM external therapies. Human research and animal models published in the last 10 years were summarized. The investigation can be classified as follows: Using metabolomics to study metabolic profile changes produced by stimulation of a specific acupoint ST36 indicates the perturbation of metabolites produced by stimulation of acupoints by external TCM treatments can be characterized by metabolomics; and Using metabolomics to reveal the molecular mechanism of various TCM external therapy methods to treat specific diseases such as digestive system disease, cardiovascular disease, neurological disorder, bone disease, and muscle fatigue. We conclude that metabolomics has considerable potential for comprehending TCM external treatment interventions, particularly from a systems perspective. Linking TCM external therapy research with metabolomics can further bridge detailed biological mechanisms with the systematic effect of TCM external therapy, hence providing new paths for gaining a deeper knowledge of the importance of TCM in the treatment and maintenance of health.

Integrative analysis of metabolites and microbial diversity revealed metabolic mechanism of coarse feeding tolerance in Songliao Black sows during gestation

Dietary fiber is a key nutritional regulatory factor that has been studied intensively for its role in improving reproduction in sows during gestation. However, the metabolic mechanism underlying the effect of interactions between metabolites and gut microbes on coarse feeding tolerance in indigenous sows remains to be elucidated. Therefore, the present study aimed to investigate the effects of dietary supplementation with alfalfa at different content ratios on the reproductive performance of pregnant Songliao Black sows. In total, 40 Songliao Black sows at 30 days of gestation were allocated to four treatments, which received the following diets: (1) a corn-soybean meal basal diet with no alfalfa meal (CON group), (2) a corn-soybean meal basal diet +10% alfalfa meal (Treatment 1 group), (3) a corn-soybean meal basal diet +20% alfalfa meal (Treatment 2 group), and (4) a corn-soybean meal basal diet +30% alfalfa meal (Treatment 3 group). Untargeted metabolomics, 16S rDNA sequencing, and enzyme-linked immunosorbent assay (ELISA) were performed to determine the possible effects of metabolites, the microbial communities in fecal samples and their functional potential, and the effects of dietary fiber on serum biochemical parameters, oxidative stress, and reproductive hormones in Songliao Black sows during gestation. The results revealed that the meals with 10 and 20% alfalfa had a beneficial effect on sows in terms of improving the reproductive performance of these sows. Bacterial 16S rDNA sequencing of the fecal samples revealed that the 10% alfalfa meal group had a higher α-diversity and higher abundance of probiotics. Bacteroidetes, Firmicutes, Proteobacteria, and Actinobacteria were revealed as the most abundant groups at the phylum level and Lactobacillus, Prevotella, Ruminococcus, Streptococcus, and Clostridium were the most abundant at the genus level in the sows fed with diets containing higher fiber levels. A total of 239 differential metabolites were identified in the sows fed with alfalfa meals. These metabolites were enriched mainly in the cAMP signaling pathway, biosynthesis of amino acids, and steroid biosynthesis. Pearson correlation analysis revealed significant positive correlations between Blautia and Daizein, Fibrobacter and 5-alpha-Cholestanone, Sphaerochaeta, Sutterella, and Metaraminol. Negative correlations were revealed between Sphaerochaeta and Erucic acid, Prevotellaceae and Harmaline, and Streptococcus and 5-alpha-Cholestanone. Collectively, these findings provide novel insights into the application of dietary fiber in sow diets.

Metabolomic profiling of infants undergoing cardiopulmonary bypass and association with clinical outcomes: a systematic review

Introduction

The incidence of adverse short-term outcomes for infants who undergo complex congenital heart disease (CHD) surgery with cardiopulmonary bypass (CPB) is still high. Early identification and treatment of high-risk patients remain challenging, especially because clinical risk factors often fail to explain the different outcomes of this vulnerable population. Metabolomics offers insight into the phenotype of the patient and the complex interplay between the genetic substrate and the environmental influences at the time of sampling. For these reasons, it may be helpful to identify the mechanisms of physio-pathological disruptions experienced in neonates undergoing congenital heart surgery and to identify potential therapeutic targets.

Methods

We conducted a systematic review (PROSPERO: ID 565112) of studies investigating the association between targeted or untargeted metabolomic analysis of infants undergoing elective surgery with CPB for CHD and clinical outcomes. The PRISMA guidelines were followed. We searched MEDLINE via PubMed, EMBASE via Ovid, the Cochrane Central Register of Controlled Trials, the Cochrane Library, ClinicalTrials.gov and the World Health Organization's International Trials Registry and Platform.

Results

Seven studies involving 509 children (aged 1 day to 21.3 months), all of whom underwent cardiac surgery requiring CPB, were included for qualitative analysis. We found associations between metabolomic profiles and various clinical outcomes, such as mortality, acute kidney injury (AKI), and neurological outcomes. Specific metabolites (mainly amino acids, their metabolic products and fatty acids) were identified as potential biomarkers for these outcomes, demonstrating the utility of metabolomics in predicting certain postoperative complications.

Conclusion

The quality of the evidence was limited due to heterogeneity in study designs and small sample sizes, but the findings are promising and suggest that further research is warranted to confirm these associations.

Systematic Review Registration

https://www.crd.york.ac.uk/prospero/, PROSPERO ID 565112.

Omics Studies in CKD: Diagnostic Opportunities and Therapeutic Potential

Omics technologies have significantly advanced the prediction and therapeutic approaches for chronic kidney disease (CKD) by providing comprehensive molecular insights. This is a review of the current state and future prospects of integrating biomarkers into the clinical practice for CKD, aiming to improve patient outcomes by targeted therapeutic interventions. In fact, the integration of genomic, transcriptomic, proteomic, and metabolomic data has enhanced our understanding of CKD pathogenesis and identified novel biomarkers for an early diagnosis and targeted treatment. Advanced computational methods and artificial intelligence (AI) have further refined multi-omics data analysis, leading to more accurate prediction models for disease progression and therapeutic responses. These developments highlight the potential to improve CKD patient care with a precise and individualized treatment plan .

ENDOTHELIAL-DEPENDENT VASCULAR REACTIVITY AFTER CARDIOPULMONARY BYPASS IS ASSOCIATED WITH UNIQUE METABOLOMIC SIGNATURES

ABSTRACT

Cardiopulmonary bypass (CPB), an extracorporeal method necessary for the surgical correction of complex congenital heart defects, incites significant inflammation that affects vascular function. These changes are associated with alterations in cellular metabolism that promote energy production to deal with this stress. Utilizing laser Doppler perfusion monitoring coupled with iontophoresis in patients undergoing corrective heart surgery, we hypothesized that temporal, untargeted metabolomics could be performed to assess the link between metabolism and vascular function. Globally, we found 2,404 unique features in the plasma of patients undergoing CPB. Metabolites related to arginine biosynthesis were the most altered by CPB. Correlation of metabolic profiles with endothelial-dependent (acetylcholine [ACh]) or endothelial-independent (sodium nitroprusside [SNP]) vascular reactivity identified purine metabolism being most consistently associated with either vascular response. Concerning ACh-mediated responses, acetylcarnitine levels were most strongly associated, while glutamine levels were associated with both ACh and SNP responsiveness. These data provide insight into the metabolic landscape of children undergoing CPB for corrective heart surgery and provide detail into how these metabolites relate to physiological aberrations in vascular function.

Metabolomics efficiently discriminates monozygotic twins in peripheral blood

Monozygotic (MZ) twins cannot be distinguished using conventional forensic STR typing because they present identical STR genotypings. However, MZ twins do not always live in the same environment and often have different dietary and other lifestyle habits. Metabolic profiles are deyermined by individual characteristics and are also influenced by the environment in which they live. Therefore, they are potential markers capable of identifying MZ twins. Moreover, the production of proteins varies from organism to organism and is influenced by both the physiological state of the body and the external environment. Hence, we used metabolomics and proteomics to identify metabolites and proteins in peripheral blood to discriminate MZ twins. We identified 1749 known metabolites and 622 proteins in proteomic analysis. The metabolic profiles of four pairs of MZ twins revealed minor differences in intra-MZ twins and major differences in inter-MZ twins. Each pair of MZ twins exhibited distinct characteristics, and four metabolites-methyl picolinate, acesulfame, paraxanthine, and phenylbenzimidazole sulfonic acid-were observed in all four MZ twin pairs. These four differential exogenous metabolites conincidently show that the different external environments and life styles can be well distinguished by metabolites, considering that twins do not all have the same eating habits and living environments. Moreover, MZ twins showed different protein profiles in serum but not in whole blood. Thus, our results indicate that differential metabolites provide potential biomarkers for the personal identification of MZ twins in forensic medicine.

Serum metabolite and metal ions profiles for breast cancer screening

Enhancing early-stage breast cancer detection requires integrating additional screening methods with current diagnostic imaging. Omics screening, using easily collectible serum samples, could serve as an initial step. Alongside biomarker identification capabilities, omics analysis allows for a comprehensive analysis of prevalent histological types-DCIS and IDC. Employing metabolomics, metallomics, and machine learning, could yield accurate screening models with valuable insights into organism responses. Serum samples of confirmed breast cancer patients were utilized to analyze metabolite and metal ion profiles, using two distinct analysis methods, proton NMR for metabolomics and ICP-OES for metallomics. The resulting responses were then subjected to discriminant analysis, progression biomarker exploration, examination of correlations between patients' metabolites and metal ions, and the impact of age and menopause status. Measured NMR spectra and metabolite relative integrals were used to achieve statistically significant discrimination through MVA between breast cancer and control groups. The analysis identified 24 metabolites and 4 metal ions crucial for discrimination. Furthermore, four metabolites were associated with disease progression. Additionally, there were important correlations and relationships between metabolite relative integrals, metal ion concentrations, and age/menopausal status subgroups. Quantified relative integrals allowed for discrimination between studied subgroups, validated with a holdout set. Feature importance and statistical analysis for metabolomics and metallomics extracted a set of common entities which in combination provides valuable insights into ongoing molecular disturbances and disease progression.

Using widely targeted metabolomics profiling to explore differences in constituents of three Bletilla species

Bletilla striata has been used in traditional Chinese medicine for thousands of years to treat a variety of health diseases. Currently, metabolic causes of differences in medicinal values are unknown, due to the lack of a large-scale and comprehensive investigation of metabolites in Bletilla species. In order to gain a better understanding of the major chemical constituents responsible for the medicinal value, this study aimed to explore the metabolomic differences among three Bletilla species (Bletilla striata: Bs, Bletilla ochracea: Bo and Bletilla formosana: Bf). There were 258 different metabolites between 'Bo' and 'Bf', the contents of 109 metabolites had higher abundance, while 149 metabolites showed less accumulation. There were 165 different metabolites between the 'Bs' and 'Bf', content of 72 metabolites was increased and content of 93 metabolites was decreased. There were 239 different metabolites between the 'Bs' and 'Bo', content of 145 metabolites was increased and content of 94 metabolites was decreased. In the Bo_vs_Bf, Bs_vs_Bf and Bs_vs_Bo groups, the major differential categories were flavonoids, phenolic acids, organic acids and alkaloids. Moreover, the differential metabolites were clustered into clear and distinct profiles via K-means analysis. In addition, the major differential categories were flavonoids, phenolic acids, organic acids and alkaloids. The 'Flavonoid biosynthesis' (ko00941) and 'Phenylalanine metabolism' (ko00360) pathways were significantly enriched in Bo_vs_Bf, Bs_vs_Bf and Bs_vs_Bo comparisons. These results clarify the metabolomics in different Bletilla species, as well as providing basis for the phamaceutical value of novel species of Bletilla.

Metabolomic analysis reveals potential role of immunometabolism dysregulation in recurrent pregnancy loss

Background

Recurrent pregnancy loss (RPL) affects women's reproductive health seriously, with immune dysfunction playing a key role in its cause, yet the exact mechanisms remain elusive. We aim to investigate potential mechanisms and identify biomarkers linked to RPL.

Methods

Immune cytokine testing and metabolomic profiling were conducted on the serum of 34 RPL patients and 30 healthy individuals. The metabolic pathways of the differential metabolites were analyzed, and specific metabolites were validated through targeted profiling. Potential biomarkers were identified, and the relationships between immune cytokines and differential metabolites were explored.

Results

In the RPL group, serum interleukin-6 and interleukin-10 levels were significantly higher, while interleukin-2 and interferon-γ were significantly lower. A total of 296 differential metabolites were detected by untargeted metabolomic profiling between the RPL and control groups, with most linked to amino acid metabolism. Targeted metabolomic profiling of amino acid metabolism revealed upregulation of indole-3-acetic acid, tyrosine, glycine, isoleucine, tryptophan, lysine, aspartic acid, arginine, leucine, threonine, glutamic acid, cystine, and phenylpyruvic acid (PPA) in the RPL group. Moreover, PPA and 5-hydroxy-L-tryptophan showed great potential in predicting RPL in a diagnostic model. Cystine and tyrosine were associated with immune cytokines in correlation analysis.

Conclusion

The study highlights the role of amino acid metabolism in RPL pathogenesis, suggesting that PPA and 5-HTP may be potential predictive indicators, while cystine and tyrosine may potentially regulate immune responses related to RPL. Further investigation into the molecular mechanisms underlying these findings could potentially result in the creation of novel diagnostic and therapeutic approaches for RPL.

Analyzing how SiMiao Wan regulates ferroptosis to prevent RA-ILD using metabolomics and cyberpharmacology

BACKGROUND

Interstitial lung disease (ILD) is a common complication of rheumatoid arthritis (RA) that plays a significant role in the morbidity and mortality of individuals with this condition. In clinical settings, Si Miao Wan (SMW), a traditional Chinese medicine, is often utilized for the management of RA, as it is believed to possess properties that aid in reducing inflammation, eliminating excess moisture, and alleviating joint pain.

PURPOSE

The primary objective of this investigation was to elucidate the potential mechanism of RA-ILD prevention from the perspective of ferroptosis mediated by SMW.

METHODS

UPLC-Q-TOF/MS and network pharmacology were employed to forecast the potential targets of SMW for the early prevention of RA-ILD. Following this, HE staining, metabolomics, and RT-PCR were utilized to investigate the mechanism by which SMW prevents RA-ILD at an early stage.

RESULTS

Following six weeks of continuous administration of SMW extract at a dosage of 2.16 g/kg/day, it was observed that SMW exhibited early preventive effects against RA-ILD. Metabolomics analysis revealed seven potential biomarkers linked to the pharmacological efficacy of SMW in the early prevention of RA-ILD. Additionally, network pharmacology analysis suggested that SMW may exert its therapeutic effects on RA-ILD by modulating signaling pathways associated with lipid metabolism, atherosclerosis, TNF, and IL-17. Ultimately, through the integration of metabolomics and network pharmacology analysis, along with subsequent verification, it was determined that the early prevention of rheumatoid arthritis-associated interstitial lung disease (RA-ILD) by Shenmai injection (SMW) is associated with the ferroptosis pathway.

CONCLUSION

This research offers preliminary insights into the potential mechanism by which traditional Chinese medicine Shen Mai Wan (SMW) may mitigate the early onset of Rheumatoid Arthritis-Interstitial Lung Disease (RA-ILD) via the process of ferroptosis. Furthermore, it establishes a theoretical framework for the development of innovative SMW-based pharmaceuticals for the management of RA-ILD. The signal proteins implicated in this process are anticipated to emerge as crucial targets for the prevention of RA-ILD.

Non-target metabolomics unravels the effect and mechanism of Lianpu Drink on spleen-stomach damp-heat syndrome

BACKGROUND

Lianpu Drink (LPY) is a classic prescription for treating spleen-stomach damp-heat syndrome (SSDHS), known for its ability to clear heat and eliminate dampness. However, the underlying mechanisms of LPY in treating SSDHS remain unclear.

OBJECTIVES

This study aims to use non-target metabolomics to unravel the effects and mechanisms of LPY on SSDHS.

METHODS

A metabolomics technique based on ultra-high-performance liquid chromatography-tandem quadrupole time-of-flight mass spectrometry (UPLC-Q-TOF/MS) was used to identify the endogenous small-molecule metabolites in the urine of SSDHS model rats and find the metabolites associated with the LPY treatment of SSDHS. Furthermore, a network pharmacological analysis and molecular docking experiments were used to screen and validate the key metabolic pathways regulated by LPY.

RESULTS

LPY exerted therapeutic effects on SSDHS by increasing the levels of motilin and gastrin, reducing the rectal temperature, alleviating the pathological changes in gastric and colonic tissues, and regulating the metabolic pattern in SSDHS rats. A total of 25 different metabolites, including L-histidine, citric acid and isocitric acid, were identified as the potential biomarkers for SSDHS via metabolomics. Among them, 11 metabolites were substantially reversed by LPY, including L-histidine, citric acid, isocitric acid, pantothenic acid, homovanillic acid sulfate, hippuric acid, indole-3-carboxilic acid-O-sulphate, 6-hydroxy-5-methoxyindole glucuronide, 2-phenylethan-ol glucuronide, 3-hydroxydodecanedioic acid and 3-methoxy-4-hydroxy-phenylethyleneglyclol sulfate. The results of network pharmacological analysis and molecular docking experiments validated that LPY ameliorated SSDHS by regulating the citrate cycle and histidine metabolism.

CONCLUSION

We preliminarily investigated the effects and mechanisms of LPY on SSDHS at the level of endogenous small-molecule metabolites. Furthermore, this study provides a novel perspective for objectively evaluating the therapeutic effects, and exploring the mechanisms of Chinese medicinal formulas on SSDHS.

Integration of proteomics and metabolomics analysis investigate mechanism of As-induced immune injury in rat spleen

Arsenic (As) is a widespread metalloid and human carcinogen found in the natural environment, and multiple toxic effects have been shown to be associated with As exposure. As can be accumulated in the spleen, the largest peripheral lymphatic organ, and long-term exposure to As can lead to splenic injury. In this study, a Sprague-Dawley (SD) rat model of As-poisoned was established, aiming to explore the molecular mechanism of As-induced immune injury through the combined analysis of proteomics and metabolomics of rats' spleen. After feeding the rats with As diet (50 mg/kg) for 90 days, the spleen tissue of the rats in the As-poisoned group was damaged, the level of As was significantly higher than that of the control group (P < 0.001), and the level of inflammatory cytokine interleukin-6 (IL-6) was decreased (P < 0.01). Proteomics and metabolomics results showed that a total of 134 differentially expressed proteins (DEPs) (P < 0.05 and fold change > 1.2) and 182 differentially expressed metabolites (DEMs) (VIP >1 and P < 0.05) were identified in the spleens of the As poisoned group compared to the control group (As/Ctrl). The proteomic results highlight the role of hypoxia-inducible factors (HIF), natural killer cell mediated cytotoxicity, and ribosomes. The major pathways of metabolic disruption included arachidonic acid (AA) metabolism, glycerophospholipid metabolism and folate single-carbon pool. The integrated analysis of these two omics suggested that Hmox1, Stat3, arachidonic acid, phosphatidylcholine and leukotriene B4 may play key roles in the mechanism of immune injury to the spleen by As exposure. The results indicate that As exposure can cause spleen damage in rats. Through proteomic and metabolomic analysis, the key proteins and metabolites and their associated mechanisms were obtained, which provided a basis for further understanding of the molecular mechanism of spleen immune damage caused by As exposure.

Engineered Bimetallic MOF-Crafted Bullet Aids in Penetrating Serum Metabolic Traits of Chronic Obstructive Pulmonary Disease

Metabolomics analysis based on body fluids, combined with high-throughput laser desorption and ionization mass spectrometry (LDI-MS), holds great potential and promising prospects for disease diagnosis and screening. On the other hand, chronic obstructive pulmonary disease (COPD) currently lacks innovative and powerful diagnostic and screening methods. In this work, CoFeNMOF-D, a metal-organic framework (MOF)-derived metal oxide nanomaterial, was synthesized and utilized as a matrix to assist LDI-MS for extracting serum metabolic fingerprints of COPD patients and healthy controls (HC). Through machine learning algorithms, successful discrimination between the COPD and HC was achieved. Furthermore, four potential biomarkers significantly downregulated in COPD were screened out. The disease diagnostic models based on the biomarkers demonstrated excellent diagnostic performance across different algorithms, with area under the curve (AUC) values reaching 0.931 and 0.978 in the training and validation sets, respectively. Finally, the potential metabolic pathways and disease mechanisms associated with the identified markers were explored. This work advances the application of LDI-based molecular diagnostics in clinical settings.

Integration of Transcriptomics and Metabolomics Reveals the Antitumor Mechanism of Protopanaxadiol Triphenylphosphate Derivative in Non-Small-Cell Lung Cancer

The objective of this study was to enhance the membrane permeability and anticancer effectiveness of (20S)-protopanaxadiol (PPD) by introducing triphenylphosphonium into the OH group at the C-3 site. This study shows that the anti-proliferation activity of CTPPPPD, with an IC50 value of 1.65 ± 0.10 μmol/L, was 33-times better than that of PPD (with an IC50 value of 54.56 ± 4.56 μmol/L) and superior to that of cisplatin (with an IC50 value of 1.82 ± 0.25 μmol/L) against A549 cells. Biological examinations suggested that CTPPPPD treatment reduced the growth rate of A549 cells, increased the permeability of cell membranes, and changed the structure of chromosomal DNA in a concentration-dependent manner. Annexin V/PI assay and flow cytometry were employed to detect the effect of CTPPPPD on the apoptosis of A549 cells. The results showed that CTPPPPD could induce the apoptosis of A549 cells, and the apoptosis rate of A549 cells treated with 0, 1.0, 2.0, and 4.0 μM of CTPPPPD for 24 h was 0%, 4.9%, 12.7%, and 31.0%, respectively. The integration of transcriptomics and metabolomics provided a systematic and detailed perspective on the induced antitumor mechanisms. A combined analysis of DEGs and DAMs suggested that they were primarily involved in the central carbon metabolism pathway in cancer, as well as the metabolism of aminoacyl-tRNA biosynthesis, alanine, aspartate, and glutamate. Central carbon metabolism in cancer-related genes, i.e., SLC16A3, FGFR3, LDHA, PGAM1, and SLC2A1, significantly reduced after treatment with CTPPPPD. In particular, the dominant mechanism responsible for total antitumor activity may be attributed to perturbations in the PI3K-AKT, MAPK, and P53 pathways. The findings derived from transcriptomics and metabolomics were empirically confirmed through q-PCR and molecular docking. Further analyses revealed that CTPPPPD could be a promising lead for the development of protopanaxadiol for non-small-cell lung cancer (NSCLC) drugs.

Plasma metabolites associated with biopsychosocial parameters in overweight/obese women with severe knee osteoarthritis

Introduction

Obesity aligned with quadriceps muscle weakness contributes to the high incidence of knee osteoarthritis (KOA), which is prevalent in women. Although molecular signatures of KOA have been suggested, the association between biopsychosocial responses and the plasma metabolomic profile in overweight/ obese women with KOA remains in its early stages of investigation. This study aims to associate the plasma metabolome with biopsychosocial parameters of overweight/obese women diagnosed with KOA.

Methods

Twenty-eight overweight/obese women (Control-n = 14; KOA-n = 14) underwent two visits to the laboratory. Functional tests and questionnaires assessing biopsychosocial parameters were administered during the first visit. After 48 h, the participants returned to the laboratory for blood collection. Specific to the KOA condition, the Numerical Pain Rating Scale (NPRS), Tampa Scale for Kinesiophobia (TSK), and Knee injury and Osteoarthritis Outcome Score (KOOS) were applied.

Results

Thirteen molecules were different between groups, and four correlated with KOA's biopsychosocial parameters. DG 22:4-2OH and gamma-Glutamylvaline were inversely associated with KOSS leisure and TSK score, respectively. LysoPE 18:0 and LysoPE 20:5 were positively associated with KOSS symptoms and TSK score, respectively.

Discussion

While the correlations of LysoPE 18:0 and gamma-Glutamylvaline are supported by existing literature, this is not the case for DG 22:4-2OH and LysoPE 20:5. Further studies are recommended to better elucidate these correlations before dismissing their potential involvement in the biopsychosocial factors of the disease.

Clinical metabolomics: current state and prospects in Russia

Using analytical technologies it is possible now to measure the entire diversity of molecules even in a small amount of biological samples. Metabolomic technologies simultaneously analyze thousands of low-molecular substances in a single drop of blood. Such analytical performance opens new possibilities for clinical laboratory diagnostics, still relying on the measurement of only a limited number of clinically significant substances. However, there are objective difficulties hampering introduction of metabolomics into clinical practice. The Institute of Biomedical Chemistry (IBMC), consolidating the efforts of leading scientific and medical organizations, has achieved success in this area by developing a clinical blood metabogram (CBM). CBM opens opportunities to obtain overview on the state of the body with the detailed individual metabolic characteristics of the patient. A number of scientific studies have shown that the CBM is an effective tool for monitoring the state of the body, and based on the CBM patterns (signatures), it is possible to diagnose and monitor the treatment of many diseases. Today, the CBM creation determines the current state and prospects of clinical metabolomics in Russia. This article, dedicated to the 80th anniversary of IBMC, is a review of these achievements focused on a discussion of their implementation in clinical practice.

The Future of Obesity Management through Precision Nutrition: Putting the Individual at the Center

PURPOSE OF REVIEW: The prevalence of obesity continues to rise steadily. While obesity management typically relies on dietary and lifestyle modifications, individual responses to these interventions vary widely. Clinical guidelines for overweight and obesity stress the importance of personalized approaches to care. This review aims to underscore the role of precision nutrition in delivering tailored interventions for obesity management. RECENT FINDINGS: Recent technological strides have expanded our ability to detect obesity-related genetic polymorphisms, with machine learning algorithms proving pivotal in analyzing intricate genomic data. Machine learning algorithms can also predict postprandial glucose, triglyceride, and insulin levels, facilitating customized dietary interventions and ultimately leading to successful weight loss. Additionally, given that adherence to dietary recommendations is one of the key predictors of weight loss success, employing more objective methods for dietary assessment and monitoring can enhance sustained long-term compliance. Biomarkers of food intake hold promise for a more objective dietary assessment. Acknowledging the multifaceted nature of obesity, precision nutrition stands poised to transform obesity management by tailoring dietary interventions to individuals' genetic backgrounds, gut microbiota, metabolic profiles, and behavioral patterns. However, there is insufficient evidence demonstrating the superiority of precision nutrition over traditional dietary recommendations. The integration of precision nutrition into routine clinical practice requires further validation through randomized controlled trials and the accumulation of a larger body of evidence to strengthen its foundation.

Volatile organic compounds in exhaled breath of newborns: a pilot study

OBJECTIVE

To assess volatile organic compounds (VOCs) in breath samples collected non-invasively from preterm and full-term infants.

METHODS

This was a pilot study included preterm and full-term infants who were not intubated or suspected or diagnosed with metabolic or gastrointestinal disorders. The samples were analyzed for VOCs using a selected-ion flow-tube mass spectrometer.

RESULTS

Twenty infants were included; ten preterm and ten full-term infants. Twenty-two VOCs were detected and measurable in all samples. There was a significant difference between preterm and full-term infants for the 2-propanol, acetaldehyde, acetone, acetonitrile, benzene, ethanol, isoprene, pentane, 3-methylhexane, 2-nonene, ethane, triethylamine, and trimethylamine compounds.

CONCLUSION

It is feasible to measure VOCs in breath samples of preterm and full-term non-intubated infants. Full-term infants express different concentrations than preterm infants. Further studies are needed to examine the utility and reproducibility of measuring VOCs to identify neonatal diseases and predict outcomes.

Tolerance enhancement of Dendrobium officinale by salicylic acid family-related metabolic pathways under unfavorable temperature

BACKGROUND

Unfavorable temperatures significantly constrain the quality formation of Dendrobium officinale, severely limiting its food demand. Salicylic acid (SA) enhances the resistance of D. officinale to stress and possesses various analogs. The impact and mechanism of the SA family on improving the quality of D. officinale under adverse temperature conditions remains unclear.

RESULTS

Combined with molecular docking analysis, chlorophyll fluorescence and metabolic analysis after treatments with SA analogues or extreme temperatures are performed in this study. The results demonstrate that both heat and cold treatments impede several main parameters of chlorophyll fluorescence of D. officinale, including the ΦPSII parameter, a sensitive growth indicator. However, this inhibition is mitigated by SA or its chemically similar compounds. Comprehensive branch imaging of ΦPSII values revealed position-dependent improvement of tolerance. Molecular docking analysis using a crystal structure model of NPR4 protein reveals that the therapeutic effects of SA analogs are determined by their binding energy and the contact of certain residues. Metabolome analysis identifies 17 compounds are considered participating in the temperature-related SA signaling pathway. Moreover, several natural SA analogs such as 2-hydroxycinnamic acid, benzamide, 2-(formylamino) benzoic acid and 3-o-methylgallic acid, are further found to have high binding ability to NPR4 protein and probably enhance the tolerance of D. officinale against unfavorable temperatures through flavone and guanosine monophosphate degradation pathways.

CONCLUSIONS

These results reveal that the SA family with a high binding capability of NPR4 could improve the tolerance of D. officinale upon extreme temperature challenges. This study also highlights the collaborative role of SA-related natural compounds present in D. officinale in the mechanism of temperature resistance and offers a potential way to develop protective agents for the cultivation of D. officinale.

Combination of Transcriptomics and Metabolomics Analyses Provides Insights into the Mechanisms of Growth Differences in Spotted Seabass (Lateolabrax maculatus) Fed a Low-Phosphorus Diet

To analyze the potential mechanisms of growth differences in spotted seabass (Lateolabrax maculatus) fed a low-phosphorus diet, a total of 150 spotted seabass with an initial body weight of 4.49 ± 0.01 g were used (50 fish per tank) and fed a low-phosphorus diet for eight weeks. At the end of the experiment, five of the heaviest and five of the lightest fish were selected from each tank as fast-growing spotted seabass (FG) and slow-growing spotted seabass (SG), respectively, and their livers were analyzed by metabolomics and transcriptomics. The hepatic antioxidant capacity of the FG fed a low-phosphorus diet was significantly higher than that of the SG. A total of 431 differentially expressed genes (DEGs) were determined in the two groups, and most of the DEGs were involved in metabolism-related pathways such as steroid biosynthesis, glycolysis/gluconeogenesis, and protein digestion and absorption. Substance transport-related regulators and transporters were predominantly up-regulated. Furthermore, a large number of metabolites in the liver of FG were significantly up-regulated, especially amino acids, decanoyl-L-carnitine and dehydroepiandrosterone. The integration analysis of differential metabolites and genes further revealed that the interaction between protein digestion and absorption, as well as phenylalanine metabolism pathways were significantly increased in the liver of FG compared to those of the SG. In general, FG fed a low-phosphorus diet induced an enhancement in hepatic immune response, substance transport, and amino acid metabolism. This study provides new information on genetic mechanisms and regulatory pathways underlying differential growth rate and provides a basis for the foundation of efficient utilization of low-phosphorus diets and selective breeding programs for spotted seabass.

Neuroprotective Mechanism of MOTS-c in TBI Mice: Insights from Integrated Transcriptomic and Metabolomic Analyses

Background

Traumatic brain injury (TBI) is a condition characterized by structural and physiological disruptions in brain function caused by external forces. However, as the highly complex and heterogenous nature of TBI, effective treatments are currently lacking. Mitochondrial open reading frame of the 12S rRNA-c (MOTS-c) has shown notable antinociceptive and anti-inflammatory effects, yet its detailed neuroprotective effects and mode of action remain incompletely understood. This study investigated the neuroprotective effects and the underlying mechanisms of MOTS-c.

Methods

Adult male C57BL/6 mice were randomly divided into three groups: control (CON) group, MOTS-c group and TBI group. Enzyme-linked immunosorbent assay (ELISA) kit method was used to measure the expression levels of MOTS-c in different groups. Behavioral tests were conducted to assess the effects of MOTS-c. Then, transcriptomics and metabolomics were performed to search Differentially Expressed Genes (DEGs) and Differentially Expressed Metabolites (DEMs), respectively. Moreover, the integrated transcriptomics and metabolomics analysis were employed using R packages and online Kyoto Encyclopedia of Genes and Genomes (KEGG) database.

Results

ELISA kit method showed that TBI resulted in a decrease in the expression of MOTS-c. and peripheral administration of MOTS-c could enter the brain tissue after TBI. Behavioral tests revealed that MOTS-c improved memory, learning, and motor function impairments in TBI mice. Additionally, transcriptomic analysis screened 159 differentially expressed genes. Metabolomic analysis identified 491 metabolites with significant differences. Integrated analysis found 14 KEGG pathways, primarily related to metabolic pathways. Besides, several signaling pathways were enriched, including neuroactive ligand-receptor interaction and retrograde endocannabinoid signaling.

Conclusion

TBI reduced the expression of MOTS-c. MOTS-c reduced inflammatory responses, molecular damage, and cell death by down-regulating macrophage migration inhibitory factor (MIF) expression and activating the retrograde endocannabinoid signaling pathway. In addition, MOTS-c alleviated the response to hypoxic stress and enhanced lipid β-oxidation to provide energy for the body following TBI. Overall, our study offered new insights into the neuroprotective mechanisms of MOTS-c in TBI mice.

Navigating Challenges and Opportunities in Multi-Omics Integration for Personalized Healthcare

The field of multi-omics has witnessed unprecedented growth, converging multiple scientific disciplines and technological advances. This surge is evidenced by a more than doubling in multi-omics scientific publications within just two years (2022-2023) since its first referenced mention in 2002, as indexed by the National Library of Medicine. This emerging field has demonstrated its capability to provide comprehensive insights into complex biological systems, representing a transformative force in health diagnostics and therapeutic strategies. However, several challenges are evident when merging varied omics data sets and methodologies, interpreting vast data dimensions, streamlining longitudinal sampling and analysis, and addressing the ethical implications of managing sensitive health information. This review evaluates these challenges while spotlighting pivotal milestones: the development of targeted sampling methods, the use of artificial intelligence in formulating health indices, the integration of sophisticated n-of-1 statistical models such as digital twins, and the incorporation of blockchain technology for heightened data security. For multi-omics to truly revolutionize healthcare, it demands rigorous validation, tangible real-world applications, and smooth integration into existing healthcare infrastructures. It is imperative to address ethical dilemmas, paving the way for the realization of a future steered by omics-informed personalized medicine.

Machine Learning-Driven SERS Nanoendoscopy and Optophysiology

A frontier of analytical sciences is centered on the continuous measurement of molecules in or near cells, tissues, or organs, within the biological context in situ, where the molecular-level information is indicative of health status, therapeutic efficacy, and fundamental biochemical function of the host. Following the completion of the Human Genome Project, current research aims to link genes to functions of an organism and investigate how the environment modulates functional properties of organisms. New analytical methods have been developed to detect chemical changes with high spatial and temporal resolution, including minimally invasive surface-enhanced Raman scattering (SERS) nanofibers using the principles of endoscopy (SERS nanoendoscopy) or optical physiology (SERS optophysiology). Given the large spectral data sets generated from these experiments, SERS nanoendoscopy and optophysiology benefit from advances in data science and machine learning to extract chemical information from complex vibrational spectra measured by SERS. This review highlights new opportunities for intracellular, extracellular, and in vivo chemical measurements arising from the combination of SERS nanosensing and machine learning.

Genetics, transcriptomics, metagenomics, and metabolomics in the pathogenesis and prediction of atrial fibrillation

The primary cellular substrates of atrial fibrillation (AF) and the mechanisms underlying AF onset remain poorly characterized and therefore, its risk assessment lacks precision. While the use of omics may enable discovery of novel AF risk factors and narrow down the cellular pathways involved in AF pathogenesis, the work is far from complete. Large-scale genome-wide association studies and transcriptomic analyses that allow an unbiased, non-candidate-gene-based delineation of molecular changes associated with AF in humans have identified at least 150 genetic loci associated with AF. However, only few of these loci have been thoroughly mechanistically dissected, indicating that much remains to be discovered for targeted diagnostics and therapeutics. Metabolomics and metagenomics, on the other hand, add to the understanding of AF downstream of the primary substrate and integrate the signalling of environmental and host factors, respectively. These two rapidly developing fields have already provided several correlates of prevalent and incident AF that require additional validation in external cohorts and experimental studies. In this review, we take a look at the recent developments in genetics, transcriptomics, metagenomics, and metabolomics and how they may aid in improving the discovery of AF risk factors and shed light into the molecular mechanisms leading to AF onset.

[Characteristics and clinical value of intestinal metabolites in children aged 4-6 years with obstructive sleep apnea-hypopnea syndrome]

OBJECTIVES

To study the characteristics and clinical value of intestinal metabolites in children aged 4-6 years with obstructive sleep apnea-hypopnea syndrome (OSAHS).

METHODS

A total of 31 children aged 4-6 years with OSAHS were prospectively enrolled as the test group, and 24 healthy children aged 4-6 years were included as the control group. Relevant clinical indicators were recorded. Fecal samples were collected, and non-targeted metabolomics analysis using liquid chromatography-mass spectrometry was performed to detect all metabolites.

RESULTS

A total of 206 metabolites were detected, mainly amino acids and their derivatives. There was a significant difference in the overall composition of intestinal metabolites between the test and control groups (P<0.05). Eighteen different metabolites were selected, among which six (N-acetylmethionine, L-methionine, L-lysine, DL-phenylalanine, L-tyrosine, and L-isoleucine) had receiver operating characteristic curve areas greater than 0.7 for diagnosing OSAHS. Among them, N-acetylmethionine had the largest area under the curve, which was 0.807, with a sensitivity of 70.83% and a specificity of 80.65%. Correlation analysis between different metabolites and clinical indicators showed that there were positive correlations between the degree of tonsil enlargement and enterolactone, between uric acid and phenylacetaldehyde, between blood glucose and acetylmethionine, and between cholesterol and 9-bromodiphenyl and procaine (P<0.05). There were negative correlations between the degree of tonsil enlargement and N-methyltyramine, aspartate aminotransferase and indolepropionic acid and L-isoleucine, between alanine aminotransferase and DL-phenylalanine, between indolepropionic acid and L-isoleucine, between uric acid and hydroxyquinoline, and between urea nitrogen and N,N-dicyclohexylurea (P<0.05). The metabolic functional pathways affected by differential metabolites mainly included riboflavin metabolism, arginine and proline metabolism, pantothenic acid and coenzyme A biosynthesis, cysteine and methionine metabolism, lysine degradation and glutathione metabolism.

CONCLUSIONS

Intestinal metabolites and metabolic functions are altered in children aged 4-6 years with OSAHS, primarily involving amino acid metabolism disorders. The screened differential intestinal metabolites have potential screening and diagnostic value as biomarkers for OSAHS.

Stereoselective Amine-omics Using Heavy Atom Isotope Labeled l- and d-Marfey's Reagents

Biological amines and amino acids play essential roles in many biochemical processes. The chemical complexity of biological samples is challenging, and the selective identification and quantification of amines and amino acid stereoisomers would be very useful for amine-focused "amino-omics" studies. Many amines and amino acids are chiral, and their stereoisomers cannot be resolved on achiral media without chiral derivatization. In prior studies, we demonstrated the use of Marfey's reagent─a chiral derivatization reagent for amines and phenolic OH groups─for the LC-MS/MS resolution and quantification of amines and amino acid stereoisomers. In this study, a heavy atom isotope labeled Marfey's reagent approach for the stereoselective detection and quantification of amines and amino acids was developed. Heavy (13C2) l-Marfey's (Hl-Mar) and heavy (2H3) d-Marfey's (Hd-Mar) were synthesized from 13C2-l-Ala and 2H3-d-Ala, respectively. Both light and heavy Marfey's reagents were used to derivatize standard amine mixtures, which were analyzed by LC-QToF-HRMS. Aligned peak lists were comparatively analyzed by light vs heavy Mar mass differences to identify mono-, di-, and tri-Marfey's adducts and then by the retention time difference between l- and d-Mar derivatives to identify stereoisomers. This approach was then applied to identify achiral and chiral amine and amino acid components in a methicillin-resistant Staphylococcus aureus (MRSA) extract. This approach shows high analytical selectivity and reproducibility.

Metabolic profiling during COVID-19 infection in humans: Identification of potential biomarkers for occurrence, severity and outcomes using machine learning

BACKGROUND

After its emergence in China, the coronavirus SARS-CoV-2 has swept the world, leading to global health crises with millions of deaths. COVID-19 clinical manifestations differ in severity, ranging from mild symptoms to severe disease. Although perturbation of metabolism has been reported as a part of the host response to COVID-19 infection, scarce data exist that describe stage-specific changes in host metabolites during the infection and how this could stratify patients based on severity.

METHODS

Given this knowledge gap, we performed targeted metabolomics profiling and then used machine learning models and biostatistics to characterize the alteration patterns of 50 metabolites and 17 blood parameters measured in a cohort of 295 human subjects. They were categorized into healthy controls, non-severe, severe and critical groups with their outcomes. Subject's demographic and clinical data were also used in the analyses to provide more robust predictive models.

RESULTS

The non-severe and severe COVID-19 patients experienced the strongest changes in metabolite repertoire, whereas less intense changes occur during the critical phase. Panels of 15, 14, 2 and 2 key metabolites were identified as predictors for non-severe, severe, critical and dead patients, respectively. Specifically, arginine and malonyl methylmalonyl succinylcarnitine were significant biomarkers for the onset of COVID-19 infection and tauroursodeoxycholic acid were potential biomarkers for disease progression. Measuring blood parameters enhanced the predictive power of metabolic signatures during critical illness.

CONCLUSIONS

Metabolomic signatures are distinctive for each stage of COVID-19 infection. This has great translation potential as it opens new therapeutic and diagnostic prospective based on key metabolites.

Development and trends in metabolomics studies in psoriasis: A bibliometric analysis of related research from 2011 to 2024

Background

Psoriasis is a chronic, inflammatory skin disease with autoimmune characteristics. Recent research has made significant progress in the field of psoriasis metabolomics. However, there is a lack of bibliometric analysis on metabolomics of psoriasis. The objective of this study is to utilize bibliometrics to present a comprehensive understanding of the knowledge structure and research hotspots in psoriasis within the field of metabolomics.

Methods

We conducted a bibliometric analysis by searching the Web of Science Core Collection database for publications on metabolomics in psoriasis from 2011 to 2024. To perform this analysis, we utilized tools such as VOSviewers, CiteSpace, and the R package "bibliometrix".

Results

A total of 307 articles from 47 countries, with the United States and China leading the way, were included in the analysis. The publications focusing on metabolomics in psoriasis have shown a steady year-on-year growth. The Medical University of Bialystok is the main research institution. The International Journal of Molecular Sciences emerges as the prominent journal in the field, while the Journal of Investigative Dermatology stands out as the highly co-cited publication. A total of 2029 authors contributed to these publications, with Skrzydlewska Elzbieta, Baran Anna, Flisiak Iwona, Murakami Makoto being the most prolific contributors. Notably, Armstrong April W. received the highest co-citation. Investigating the mechanisms of metabolomics in the onset and progression of psoriasis, as well as exploring therapeutic strategies, represents the primary focus of this research area. Emerging research hotspots encompass inflammation, lipid metabolism, biomarker, metabolic syndrome, obesity, and arthritis.

Conclusion

The results of this study indicate that metabolism-related research is thriving in psoriasis, with a focus on the investigation of metabolic targets and interventions within the metabolic processes. Metabolism is expected to be a hot topic in future psoriasis research.

Biomarkers of bipolar disorder based on metabolomics: A systematic review

Bipolar disorder (BD) is a severe affective disorder characterized by recurrent episodes of depression or mania/hypomania, which significantly impair cognitive function, life skills, and social abilities of patients. There is little understanding of the neurobiological mechanisms of BD. The diagnosis of BD is primarily based on clinical assessment and psychiatric examination, highlighting the urgent need for objective markers to facilitate the diagnosis of BD. Metabolomics can be used as a diagnostic tool for disease identification and evaluation. This study summarized the altered metabolites in BD and analyzed aberrant metabolic pathways, which might contribute to the diagnosis of BD. Search of PubMed and Web of science for human BD studies related to metabolism to identify articles published up to November 19, 2022 yielded 987 articles. After screening and applying the inclusion and exclusion criteria, 16 untargeted and 11 targeted metabolomics studies were included. Pathway analysis of the potential differential biometabolic markers was performed using the Kyoto encyclopedia of genes and genomes (KEGG). There were 72 upregulated and 134 downregulated biomarkers in the untargeted metabolomics studies using blood samples. Untargeted metabolomics studies utilizing urine specimens revealed the presence of 78 upregulated and 54 downregulated metabolites. The targeted metabolomics studies revealed abnormalities in the metabolism of glutamate and tryptophan. Enrichment analysis revealed that the differential metabolic pathways were mainly involved in the metabolism of glucose, amino acid and fatty acid. These findings suggested that certain metabolic biomarkers or metabolic biomarker panels might serve as a reference for the diagnosis of BD.

Metabolomics for the diagnosis of bladder cancer: A systematic review

Objective

Metabolomics has been extensively utilized in bladder cancer (BCa) research, employing mass spectrometry and nuclear magnetic resonance spectroscopy to compare various variables (tissues, serum, blood, and urine). This study aimed to identify potential biomarkers for early BCa diagnosis.

Methods

A search strategy was designed to identify clinical trials, descriptive and analytical observational studies from databases such as Medline, Embase, Cochrane Central Register of Controlled Trials, and Latin American and Caribbean Literature in Health Sciences. Inclusion criteria comprised studies involving BCa tissue, serum, blood, or urine profiling using widely adopted metabolomics techniques like mass spectrometry and nuclear magnetic resonance. Primary outcomes included description of metabolites and metabolomics profiling in BCa patients and the association of metabolites and metabolomics profiling with BCa diagnosis compared to control patients. The risk of bias was assessed using the Quality Assessment of Studies of Diagnostic Accuracy.

Results

The search strategy yielded 2832 studies, of which 30 case-control studies were included. Urine was predominantly used as the primary sample for metabolite identification. Risk of bias was often unclear inpatient selection, blinding of the index test, and reference standard assessment, but no applicability concerns were observed. Metabolites and metabolomics profiles associated with BCa diagnosis were identified in glucose, amino acids, nucleotides, lipids, and aldehydes metabolism.

Conclusion

The identified metabolites in urine included citric acid, valine, tryptophan, taurine, aspartic acid, uridine, ribose, phosphocholine, and carnitine. Tissue samples exhibited elevated levels of lactic acid, amino acids, and lipids. Consistent findings across tissue, urine, and serum samples revealed downregulation of citric acid and upregulation of lactic acid, valine, tryptophan, taurine, glutamine, aspartic acid, uridine, ribose, and phosphocholine.

Unbiased serum metabolomic analysis in cats with naturally occurring chronic enteropathies before and after medical intervention

Chronic enteropathies (CE) are common disorders in cats and the differentiation between the two main underlying diseases, inflammatory bowel disease (IBD) and low-grade intestinal T-cell lymphoma (LGITL), can be challenging. Characterization of the serum metabolome could provide further information on alterations of disease-associated metabolic pathways and may identify diagnostic or therapeutic targets. Unbiased metabolomics analysis of serum from 28 cats with CE (14 cats with IBD, 14 cats with LGITL) and 14 healthy controls identified 1,007 named metabolites, of which 129 were significantly different in cats with CE compared to healthy controls at baseline. Random Forest analysis revealed a predictive accuracy of 90% for differentiating controls from cats with chronic enteropathy. Metabolic pathways found to be significantly altered included phospholipids, amino acids, thiamine, and tryptophan metabolism. Several metabolites were found to be significantly different between cats with IBD versus LGITL, including several sphingolipids, phosphatidylcholine 40:7, uridine, pinitol, 3,4-dihydroxybenzoic acid, and glucuronic acid. However, random forest analysis revealed a poor group predictive accuracy of 60% for the differentiation of IBD from LGITL. Of 129 compounds found to be significantly different between healthy cats and cats with CE at baseline, 58 remained different following treatment.

Gentiopicroside improves non-alcoholic steatohepatitis by activating PPARα and suppressing HIF1

Gentiopicroside (GPS) is a highly water-soluble small-molecule drug and the main bioactive secoiridoid glycoside of Gentiana scabra that has been shown to have hepatoprotective effects against non-alcoholic steatohepatitis (NASH), a form of non-alcoholic fatty liver disease (NAFLD) that can progress to cirrhosis and hepatocellular carcinoma. However, the effects of GPS on NASH and the underlying mechanisms remain obscure. Firstly, a high-fat, high-cholesterol (HFHC) diet and a high-sugar solution containing d-fructose and d-glucose were used to establish a non-alcoholic steatohepatitis (NASH) mice model. Secondly, we confirmed GPS supplementation improve metabolic abnormalities and reduce inflammation in NASH mice induced by HFHC and high-sugar solution. Then we used metabolomics to investigate the mechanisms of GPS in NASH mice. Metabolomics analysis showed GPS may work through the Peroxisome Proliferator-Activated Receptor (PPAR) signaling pathway and glycine, serine, and threonine metabolism. Functional metabolites restored by GPS included serine, glycine, eicosapentaenoic acid (EPA), and docosahexaenoic acid (DHA). Western blot and qRT-PCR analysis confirmed GPS improve NASH by regulating PPARα and Hypoxia-Inducible Factor-1α (HIF-1α) signaling pathways. In vitro, studies further demonstrated EPA and DHA enhance fatty acid oxidation through the PPARα pathway, while serine and glycine inhibit oxidative stress through the HIF-1α pathway in palmitic acid-stimulated HepG2 cells. Our results suggest GPS's anti-inflammatory and anti-steatosis effects in NASH progression are related to the suppression of HIF-1α through the restoration of L-serine and glycine and the activation of PPARα through increased EPA and DHA.

Profiling canine mammary tumors: A potential model for studying human breast cancer

Despite all clinical progress recorded in the last decades, human breast cancer (HBC) remains a major challenge worldwide both in terms of its incidence and its management. Canine mammary tumors (CMTs) share similarities with HBC and represent an alternative model for HBC. The utility of the canine model in studying HBC relies on their common features, include spontaneous development, subtype classification, mutational profile, alterations in gene expression profile, and incidence/prevalence. This review describes the similarities between CMTs and HBC regarding genomic landscape, microRNA expression alteration, methylation, and metabolomic changes occurring during mammary gland carcinogenesis. The primary purpose of this review is to highlight the advantages of using the canine model as a translational animal model for HBC research and to investigate the challenges and limitations of this approach.

Determining predictive metabolomic biomarkers of meniscal injury in dogs with cranial cruciate ligament rupture

OBJECTIVES

This study used hydrogen nuclear magnetic resonance spectroscopy for the first time to examine differences in the metabolomic profile of stifle joint synovial fluid from dogs with cranial cruciate ligament rupture with and without meniscal injuries, in order to identify biomarkers of meniscal injury. Identifying a biomarker of meniscal injury could then ultimately be used to design a minimally invasive diagnostic test for meniscal injuries in dogs.

MATERIALS AND METHODS

Stifle joint synovial fluid was collected from dogs undergoing stifle joint surgery or arthrocentesis for lameness investigations. We used multi-variate statistical analysis using principal component analysis and univariate statistical analysis using one-way analysis of variance and analysis of co-variance to identify differences in the metabolomic profile between dogs with cranial cruciate ligament rupture and meniscal injury, cranial cruciate ligament rupture without meniscal injury, and neither cranial cruciate ligament rupture nor meniscal injury, taking into consideration clinical variables.

RESULTS

A total of 154 samples of canine synovial fluid were included in the study. Sixty-four metabolites were annotated to the hydrogen nuclear magnetic resonance spectroscopy spectra. Six spectral regions were found to be significantly altered (false discovery rate adjusted P-value <0.05) between groups with cranial cruciate ligament rupture with and without meniscal injury, including three attributed to nuclear magnetic resonance mobile lipids [mobile lipid -CH3 (P=0.016), mobile lipid -n(CH3 )3 (P=0.017), mobile unsaturated lipid (P=0.031)].

CLINICAL SIGNIFICANCE

We identified an increase in nuclear magnetic resonance mobile lipids in the synovial fluid of dogs with meniscal injury which are of interest as potential biomarkers of meniscal injury.

Exploiting volatile fingerprints for bladder cancer diagnosis: A scoping review of metabolomics and sensor-based approaches

Bladder cancer (BC) represents a significant global health concern, for which early detection is essential to improve patient outcomes. This review evaluates the potential of the urinary volatile organic compounds (VOCs) as biomarkers for detecting and staging BC. The methods used include gas chromatography-mass spectrometry (GC-MS)-based metabolomics and electronic-nose (e-nose) sensors. The GC-MS studies that have been published reveal diverse results in terms of diagnostic performance. The sensitivities range from 27 % to an impressive 97 %, while specificities vary between 43 % and 94 %. Furthermore, the accuracies reported in these studies range from 80 to 89 %. In the urine of BC patients, a total of 80 VOCs were discovered to be significantly altered when compared to controls. These VOCs encompassed a variety of chemical classes such as alcohols, aldehydes, alkanes, aromatic compounds, fatty acids, ketones, and terpenoids, among others. Conversely, e-nose-based studies displayed sensitivities from 60 to 100 %, specificities from 53 to 96 %, and accuracies from 65 to 97 %. Interestingly, conductive polymer-based sensors performed better, followed by metal oxide semiconductor and optical sensors. GC-MS studies have shown improved performance in detecting early stages and low-grade tumors, providing valuable insights into staging. Based on these findings, VOC-based diagnostic tools hold great promise for early BC detection and staging. Further studies are needed to validate biomarkers and their classification performance. In the future, advancements in VOC profiling technologies may significantly contribute to improving the overall survival and quality of life for BC patients.