Evaluating different extraction solvents for GC-MS based metabolomic analysis of the fecal metabolome of adult and baby giant pandas

Integration of metabolomics and network pharmacology technology to explain the effect mechanisms of Danggui Buxue decoction in vascular dementia

Danggui Buxue decoction (DBD) is a traditional Chinese medicine herbal decoction that has a good therapeutic effect on vascular dementia (VaD). However, its pharmacodynamic substances and underlying mechanisms are ambiguous. The work aimed to decipher the pharmacodynamic substances and molecular mechanisms of DBD against VaD rats based on gas chromatography-mass spectrometry metabonomics, network pharmacology, molecular docking, and experimental verification. The results indicated that DBD significantly improved the learning abilities and cognitive impairment in the VaD rat model. Integration analysis of the metabolomics and network pharmacology approach revealed that DBD might primarily affect arachidonic acid (AA) and inositol phosphate metabolic pathways by regulating the platelet activation signaling pathways. Six core targets (TNF [tumor necrosis factor], IL-6 [interleukin 6], PTGS2 [prostaglandin-endoperoxide synthase 2], MAPK1, MAPK3, and TP53) in the platelet activation signaling pathways also had a good affinity to seven main active components (saponins, organic acids, flavonoids, and phthalides) of DBD through the verification of molecular docking. Enzyme-linked immunosorbent assay results (ELISA) showed that the levels of TNF, IL-6, PTGS2, thromboxane B2, and caspase-3 in the platelet activation signaling pathway can be regulated by DBD. Our results indicated that DBD treated VaD mainly by modulating the platelet activation signaling pathway, and AA and inositol phosphate metabolism.

Gas-Phase Ion/Ion Reactions to Enable Radical-Directed Dissociation of Fatty Acid Ions: Application to Localization of Methyl Branching

Methyl branching on the carbon chains of fatty acids and fatty esters is among the structural variations encountered with fatty acids and fatty esters. Branching in fatty acid/ester chains is particularly prominent in bacterial species and, for example, in vernix caseosa and sebum. The distinction of branched chains from isomeric straight-chain species and the localization of branching can be challenging to determine by mass spectrometry (MS). Condensed-phase derivatization strategies, often used in conjunction with separations, are most commonly used to address the identification and characterization of branched fatty acids. In this work, a gas-phase ion/ion strategy is presented that obviates condensed-phase derivatization and introduces a radical site into fatty acid ions to facilitate radical-directed dissociation (RDD). The gas-phase approach is also directly amenable to fatty acid anions generated via collision-induced dissociation from lipid classes that contain fatty esters. Specifically, divalent magnesium complexes bound to two terpyridine ligands that each incorporate a ((2,2,6,6-tetramethyl-1-piperidine-1-yl)oxy) (TEMPO) moiety are used to charge-invert fatty acid anions. Following the facile loss of one of the ligands and the TEMPO group of the remaining ligand, a radical site is introduced into the complex. Subsequent collision-induced dissociation (CID) of the complex exhibits preferred cleavages that localize the site(s) of branching. The approach is illustrated with iso-, anteiso-, and isoprenoid branched-chain fatty acids and an intact glycerophospholipid and is applied to a mixture of branched- and straight-chain fatty acids derived from Bacillus subtilis.

Bacterial community and diversity in the rumen of 11 Mongolian cattle as revealed by 16S rRNA amplicon sequencing

Through microorganism in the rumen of ruminant, plant fiber can be converted to edible food such as meat and milk. Ruminants had a rich and complex microbial community within the rumen, and the bacteria comprised the dominant proportion of the ruminal microbes. High-throughput sequencing offered a viable solution for the study of rumen microbes. In this study, rumen fluid samples were taken from 11 cattle from Inner Mongolian, the DNA of 11 rumen fluid samples were extracted and bacterial amplicons of the V4 regions of 16S rRNA were subjected to Illumina sequencing. More than 90,000 raw reads and 60,000 effect Tags per sample were obtained. 28,122 operational taxonomic units (OTUs) were observed from 11 samples, in average 2557 ± 361 OTUs for each sample. Bacteroidetes (44.41 ± 7.31%), Firmicutes (29.07 ± 3.78%), and Proteobacteria (7.18 ± 5.63%) were the dominant phyla among the bacteria of rumen, accounting for 82%. At the genus level, the highest relative abundance was Prevotella. Their functions were predicted using the Kyoto Encyclopedia of Genes and Genomes (KEGG). The results showed that they included metabolism, genetic information processing, environmental information processing and cellular processes. It explored the bacterial community diversity and composition of the rumen of Mongolian cattle. On the whole, our research showed that there was a high diversity as well as rich bacterial flora function of rumen bacteria in Mongolian cattle. Meanwhile, these findings provided information for further studies on the relationship between the community, diversity, functions of rumen bacteria and the nutritional physiological functions of the host.

Machine-Learning Classification of Bacteria Using Two-Dimensional Tandem Mass Spectrometry

Biothreat detection has continued to gain attention. Samples suspected to fall into any of the CDC's biothreat categories require identification by processes that require specialized expertise and facilities. Recent developments in analytical instrumentation and machine learning algorithms offer rapid and accurate classification of Gram-positive and Gram-negative bacterial species. This is achieved by analyzing the negative ions generated from bacterial cell extracts with a modified linear quadrupole ion-trap mass spectrometer fitted with two-dimensional tandem mass spectrometry capabilities (2D MS/MS). The 2D MS/MS data domain of a bacterial cell extract is recorded within five s using a five-scan average after sample preparation by a simple extraction. Bacteria were classified at the species level by their lipid profiles using the random forest, k-nearest neighbor, and multilayer perceptron machine learning models. 2D MS/MS data can also be treated as image data for use with image recognition algorithms such as convolutional neural networks. The classification accuracy of all models tested was greater than 99%. Adding to previously published work on the 2D MS/MS analysis of bacterial growth and the profiling of sporulating bacteria, this study demonstrates the utility and information-rich nature of 2D MS/MS in the identification of bacterial pathogens at the species level when coupled with machine learning.

Impact of humid climate on rheumatoid arthritis faecal microbiome and metabolites

Studies have shown that high humidity is a condition that aggravates the pain of rheumatoid arthritis (RA), but the relevant mechanism is controversial. Currently, there is a lack of experimental animal studies on high humidity as an adverse factor related to the pathogenesis of RA. We used healthy SD rats and collagen-induced arthritis (CIA) rats to investigate the effects of high humidity on arthritis. Integrated metabolomics analyses of faeces and 16S rRNA sequencing of the faecal microbiota were performed to comprehensively assess the diversity of the faecal microbiota and metabolites in healthy and CIA rats. In this study, high humidity aggravated arthritis in CIA rats, which manifested as articular cartilage lesions, increased arthritis scores, and an increase in proinflammatory cytokines. High humidity had a certain effect on the articular cartilage extent, arthritis score and proinflammatory cytokines of healthy rats as well. Furthermore, high humidity caused significant changes in faecal microbes and metabolites in both healthy and CIA rats. 16S rRNA sequencing of faecal samples showed that high humidity increased the amount of inflammation-related bacteria in healthy and CIA rats. Faecal metabolomics results showed that high humidity significantly altered the level of faecal metabolites in healthy rats and CIA rats, and the changes in biological functions were mainly related to the inflammatory response and oxidative stress. Combined analysis showed that there was a strong correlation between the faecal microbiota and faecal metabolites. High humidity is an adverse factor for the onset and development of RA, and its mechanism is related to the inflammatory response and oxidative stress. However, the question of how high humidity impacts RA pathogenesis needs to be further investigated.

Optimization of fecal sample homogenization for untargeted metabolomics

INTRODUCTION

Fecal samples are highly complex and heterogeneous, containing materials at various stages of digestion. The heterogeneity and complexity of feces make stool metabolomics inherently challenging. The level of homogenization influences the outcome of the study, affecting the metabolite profiles and reproducibility; however, there is no consensus on how fecal samples should be prepared to overcome the topographical discrepancy and obtain data representative of the stool as a whole.

OBJECTIVES

Various combinations of homogenization conditions were compared to investigate the effects of bead size, addition of solvents and the differences between wet-frozen and lyophilized feces.

METHODS

The homogenization parameters were systematically altered to evaluate the solvent usage, bead size, and whether lyophilization is required in homogenization. The metabolic coverage and reproducibility were compared among the different conditions.

RESULTS

The current work revealed that a combination of mechanical and chemical lysis obtained by bead-beating with a mixture of big and small sizes of beads in an organic solvent is an effective way to homogenize fecal samples with adequate reproducibility and metabolic coverage. Lyophilization is required when bead-beating is not available.

CONCLUSIONS

A comprehensive and systematical evaluation of various fecal matter homogenization conditions provides a profound understanding for the effects of different homogenization methods. Our findings would be beneficial to assist with standardization of fecal sample homogenization protocol.

Lignocellulose Fermentation Products Generated by Giant Panda Gut Microbiomes Depend Ultimately on pH Rather than Portion of Bamboo: A Preliminary Study

Giant pandas feed almost exclusively on bamboo but miss lignocellulose-degrading genes. Their gut microbiome may contribute to their nutrition; however, the limited access to pandas makes experimentation difficult. In vitro incubation of dung samples is used to infer gut microbiome activity. In pandas, such tests indicated that green leaves are largely fermented to ethanol at neutral pH and yellow pith to lactate at acidic pH. Pandas may feed on either green leaves or yellow pith within the same day, and it is unclear how pH, dung sample, fermentation products and supplied bamboo relate to one another. Additionally, the gut microbiome contribution to solid bamboo digestion must be appropriately assessed. Here, gut microbiomes derived from dung samples with mixed colors were used to ferment green leaves, also by artificially adjusting the initial pH. Gut microbiomes digestion of solid lignocellulose accounted for 30–40% of the detected final fermentation products. At pH 6.5, mixed-color dung samples had the same fermentation profile as green dung samples (mainly alcohols), while adjusting the initial pH to 4.5 resulted in the profile of yellow dung samples (mainly lactate). Metaproteomics confirmed that gut microbiomes attacked hemicellulose, and that the panda’s alpha amylase was the predominant enzyme (up to 75%).

Sodium Humate Alleviates Enterotoxigenic Escherichia coli-Induced Intestinal Dysfunction via Alteration of Intestinal Microbiota and Metabolites in Mice

Enterotoxigenic Escherichia coli (ETEC) can damage intestinal epithelial barrier function and lead to serious intestinal diarrhea in newborns and young animals. Sodium humate (HNa) is natural organic bioactive compound possessing antibacterial, anti-inflammatory, and anti-diarrheal properties. This study investigated the alleviative potential of HNa on the impaired intestinal barrier and intestinal inflammation, and regulatory effects on gut microbiota and metabolites in ETEC K88 infected mice. A total of 30 female mice were randomly assigned into three groups. The mice in the control and ETEC groups were gavaged with 0.2 mL of sterile saline, while the mice in the ETEC + HNa group were gavaged with 0.2 mL of 5% HNa, daily. On day 8, the mice in ETEC and ETEC + HNa group were challenged with ETEC K88. The trial lasted for 12 days. HNa administration elevated ETEC K88-induced body weight loss and ameliorated jejunum and colon pathological injury. HNa also reduced the levels of pro-inflammatory cytokines in the serum, jejunum, and colon. Additionally, HNa reduced intestinal barrier damage by up-regulating the expression of tight junction proteins (TJPs) and mucosal repair factors. 16s rDNA gene sequencing results showed that HNa increased the abundance of beneficial bacteria Lactobacillus, Prevotella_9, and Odoribacter but decreased the abundance of pathogenic bacteria Escherichia and Gastranaerophilales in the feces of mice. Moreover, metabolomic analysis revealed that the concentrations of 15 metabolites, the pathways of protein digestion and absorption, and propanoic acid metabolism were changed by HNa administration. In conclusion, HNa could alleviate ETEC K88-induced intestinal dysfunction through restoring intestinal barrier integrity, modulating gut microbiota, and metabolites.

The relationship between hair metabolites, air pollution exposure and gestational diabetes mellitus: A longitudinal study from pre-conception to third trimester

BACKGROUND

Gestational diabetes mellitus (GDM) is a metabolic condition defined as glucose intolerance with first presentation during pregnancy. Many studies suggest that environmental exposures, including air pollution, contribute to the pathogenesis of GDM. Although hair metabolite profiles have been shown to reflect pollution exposure, few studies have examined the link between environmental exposures, the maternal hair metabolome and GDM. The aim of this study was to investigate the longitudinal relationship (from pre-conception through to the third trimester) between air pollution exposure, the hair metabolome and GDM in a Chinese cohort.

METHODS

A total of 1020 women enrolled in the Complex Lipids in Mothers and Babies (CLIMB) birth cohort were included in our study. Metabolites from maternal hair segments collected pre-conception, and in the first, second, and third trimesters were analysed using gas chromatography-mass spectrometry (GC-MS). Maternal exposure to air pollution was estimated by two methods, namely proximal and land use regression (LUR) models, using air quality data from the air quality monitoring station nearest to the participant's home. Logistic regression and mixed models were applied to investigate associations between the air pollution exposure data and the GDM associated metabolites.

RESULTS

Of the 276 hair metabolites identified, the concentrations of fourteen were significantly different between GDM cases and non-GDM controls, including some amino acids and their derivatives, fatty acids, organic acids, and exogenous compounds. Three of the metabolites found in significantly lower concentrations in the hair of women with GDM (2-hydroxybutyric acid, citramalic acid, and myristic acid) were also negatively associated with daily average concentrations of PM_2.5, PM₁₀, SO₂, NO₂, CO and the exposure estimates of PM_2.5 and NO_2, and positively associated with O₃.

CONCLUSIONS

This study demonstrated that the maternal hair metabolome reflects the longitudinal metabolic changes that occur in response to environmental exposures and the development of GDM.

Efficacy of Zhuyu Pill Intervention in a Cholestasis Rat Model: Mutual Effects on Fecal Metabolism and Microbial Diversity

Cholestasis is a clinical condition resulting from impaired bile flow. Currently, patients with cholestasis face several barriers in seeking diagnosis and treatment. Zhuyu Pill (ZYP) is an ancient classic formula of the Coptis-Evodia herb couples (CEHC), and has been used for cholestasis treatment in the clinic, however, its underlying biological activity in cholestasis remain to be clarified. In this study, an α-naphthyl-isothiocyanate (ANIT, 50 mg/kg)-induced rat model of cholestasis was treated with ZYP. Serum biochemical indices and histopathological evaluation was performed, together with the metabolomics analyses of feces and 16S rDNA sequencing of the fecal microbiota. We evaluated the anti-cholestatic activity of ZYP and investigated the mechanisms underlying its correlation with fecal microbiota and fecal metabolite regulation. The relationships between biochemical indices and changes in gene expression associated with liver injury, levels fecal metabolites, and composition of fecal microbiota were analyzed. The results showed that both high (1.2 g/kg) and low (0.6 g/kg) doses of ZYP could effectively improve biochemical parameters in the blood of cholestasis-induced rat models; the intervention effect of high dose ZYP was superior to that that of lower dose ZYP. Based on a metabolomics test of fecal samples, significantly altered metabolites in the ANIT and ZYP treatment group were identified. In total, 734 metabolites were differentially expressed, and whose biological functions were mainly associated with amino acid metabolism, steroid hormone biosynthesis, and bile secretion. In addition, sequencing of the 16S rDNA unit in fecal samples revealed that the ZYP could improve the fecal microbiota dysbiosis that ANIT had induced. Therefore, we conclude that ANIT altering of blood biochemical and metabolic profiles and of fecal microbiota could effectively be alleviated with ZYP treatment. This study contributes to the “TCM wisdom” applied in clinical diagnosis and treatment of cholestasis.

High Throughput Analysis Reveals Changes in Gut Microbiota and Specific Fecal Metabolomic Signature in Hematopoietic Stem Cell Transplant Patients

There is mounting evidence for the emerging role of gut microbiota (GM) and its metabolites in profoundly impacting allogenic hematopoietic stem cell transplantation (allo-HSCT) and its subsequent complications, mainly infections and graft versus host-disease (GvHD). The present study was performed in order to investigate changes in GM composition and fecal metabolic signature between transplant patients (n = 15) and healthy controls (n = 18). The intestinal microbiota was characterized by NGS and gas chromatography-mass spectrometry was employed to perform untargeted analysis of fecal metabolites. We found lower relative abundances of Actinobacteria, Firmicutes, and Bacteroidetes and a higher abundance of Proteobacteria phylum after allo-HSCT. Particularly, the GvHD microbiota was characterized by a lower relative abundance of the short-chain fatty acid-producing bacteria, namely, the Feacalibacterium, Akkermansia, and Veillonella genera and the Lachnospiraceae family, and an enrichment in multidrug-resistant bacteria belonging to Escherichia, Shigella, and Bacteroides. Moreover, network analysis showed that GvHD was linked to a higher number of positive interactions of Blautia and a significant mutual-exclusion rate of Citrobacter. The fecal metabolome was dominated by lipids in the transplant group when compared with the healthy individuals (p < 0.05). Overall, 76 metabolites were significantly altered within transplant recipients, of which 24 were selected as potential biomarkers. Furthermore, the most notable altered metabolic pathways included the TCA cycle; butanoate, propanoate, and pyruvate metabolisms; steroid biosynthesis; and glycolysis/gluconeogenesis. Specific biomarkers and altered metabolic pathways were correlated to GvHD onset. Our results showed significant shifts in gut microbiota structure and fecal metabolites characterizing allo-HSCT.

Comparison of microbial diversity in rumen and small intestine of Xinong Saanen dairy goats using 16S rRNA gene high-throughput sequencing

Context Gastrointestinal microorganisms play an important role in ruminant digestion and metabolism, immune regulation and disease prevention and control. Different parts of the digestive tract have different functions and microbial community structures. Aims This study aims to explore the microbial diversity in the rumen and the small intestine of Xinong Saanen dairy goats. Methods Rumen fluid and jejunum fluid from three Xinong Saanen dairy bucks with the average slaughter weight of 33.93 ± 0.68 kg were collected and analysed for microbial diversity, by using 16S rRNA gene high-throughput sequencing. Key results In total, 1118 operational taxonomic units (OTUs) were identified, with 1020 OTUs and 649 OTUs being clustered to rumen and jejunum samples respectively. Alpha-diversity indices were significantly (P < 0.05) different between rumen and jejunum, as indicated by the fact that the rumen microbial community diversity, richness and uniformity/evenness were higher than those of jejunum. At the phylum level, the dominant phyla in the rumen were Bacteroidetes (66.7%) and Firmicutes (25.1%), accounting for 91.8% of the rumen microorganisms. The dominant phylum in the jejunum was Firmicutes, accounting for 73.0% of the jejunum microorganisms. At the genus level, the dominant bacteria in the rumen were Prevotella_1, norank_f_Bacteroidales_BS11_gut_group, Rikenellaceae_RC9_gut_group, Christensenellaceae_R-7_group and Family_XIII_AD3011_group, whereas the dominant bacteria in the jejunum were Omboutsia, Aeriscardovia, Intestinibacter, unclassified_f_Peptostreptococcaceae and unclassified_f_Bifidobacteriaceae. Clusters of Orthologous Groups (COG) and Kyoto Encyclopedia of Genes and Genomes (KEGG) results showed that the major functions of microorganisms in the rumen and jejunum were carbohydrate metabolism, amino acid metabolism, nucleotide metabolism, membrane transport and translation. Interestingly, fructose and mannose metabolism and peptidoglycan biosynthesis were abundant in the rumen, while homologous recombination and nucleotide excision repair were abundant in the jejunum. Conclusions Our study clarified the differences in microbial diversity and community structure between the rumen and the jejunum in Xinong Saanen dairy goats. Prevotella was the most predominant genus in the rumen, compared with Romboutsia, Bifidobacterium as well as Peptostreptococcaceae genera, which were the predominant genera in the jejunum. Implications In combination with the functional prediction of microorganisms and the metabolic characteristics of different parts of the digestive tract in ruminants, our findings provided information for further exploring the relationship among genes, species and functions of microorganisms and their hosts’ nutritional and physiological functions.

Altered Fecal Metabolites and Colonic Glycerophospholipids Were Associated With Abnormal Composition of Gut Microbiota in a Depression Model of Mice

The microbiota–gut–brain axis has been considered to play an important role in the development of depression, but the underlying mechanism remains unclear. The gastrointestinal tract is home to trillions of microbiota and the colon is considered an important site for the interaction between microbiota and host, but few studies have been conducted to evaluate the alterations in the colon. Accordingly, in this study, we established a chronic social defeated stress (CSDS) mice model of depression. We applied 16S rRNA gene sequencing to assess the gut microbial composition and gas and liquid chromatography–mass spectroscopy to identify fecal metabolites and colonic lipids, respectively. Meanwhile, we used Spearman’s correlation analysis method to evaluate the associations between the gut microbiota, fecal metabolites, colonic lipids, and behavioral index. In total, there were 20 bacterial taxa and 18 bacterial taxa significantly increased and decreased, respectively, in the CSDS mice. Further, microbial functional prediction demonstrated a disturbance of lipid, carbohydrate, and amino acid metabolism in the CSDS mice. We also found 20 differential fecal metabolites and 36 differential colonic lipids (in the category of glycerolipids, glycerophospholipids, and sphingolipids) in the CSDS mice. Moreover, correlation analysis showed that fecal metabolomic signature was associated with the alterations in the gut microbiota composition and colonic lipidomic profile. Of note, three lipids [PC(16:0/20:4), PG(22:6/22:6), and PI(18:0/20:3), all in the category of glycerophospholipids] were significantly associated with anxiety- and depression-like phenotypes in mice. Taken together, our results indicated that the gut microbiota might be involved in the pathogenesis of depression via influencing fecal metabolites and colonic glycerophospholipid metabolism.

Dried Blood Spot (DBS) Methodology Study for Biomarker Discovery in Lysosomal Storage Disease (LSD)

Lysosomal storage diseases (LSDs) are a heterogeneous group of inherited metabolic diseases caused by mutations in genes encoding for proteins involved in the lysosomal degradation of macromolecules. They occur in approximately 1 in 5000 live births and pose a lifelong risk. Therefore, to achieve the maximum benefit from LSDs therapies, a fast and early diagnosis of the disease is required. In this framework, biomarker discovery is a significant factor in disease diagnosis and in predicting its outcomes. On the other hand, the dried blood spot (DBS) based metabolomics platform can open up new pathways for studying non-directional hypothesis approaches to biomarker discovery. This study aims to increase the efficiency of the developed methods for biomarker development in the context of rare diseases, with an improved impact on the reliability of the detected compounds. Thereby, we conducted two independent experiments and integrated them into the screening of the human blood metabolome: (1) comparison of EDTA blood and filter cards in terms of their suitability for metabolomics studies; (2) optimization of the extraction method: a side-by-side comparison of a series of buffers to the best utility to the disease of interest. The findings were compared to previous studies across parameters such as metabolite coverage, sample type suitability, and stability. The results indicate that measurements of metabolites are susceptible to differences in pre-analytical conditions and extraction solvents. This proposed approach can increase the positive rate of the future development of biomarkers. Altogether, the procedure can be easily adapted and applied to other studies, where the limited number of samples is a common barrier.

Multi-Solvent Extraction Procedure for the Pioneer Fecal Metabolomic Analysis-Identification of Potential Biomarkers in Stable Kidney Transplant Patients

Metabolic alteration plays a functional role in kidney allograft complications. Metabolomics is a promising high-throughput approach in nephrology but is still limited by the lack of overlap in metabolite coverage. We performed an untargeted fecal metabolomic analysis of forty stable kidney allograft recipients and twenty non-transplant controls. First, we applied the ultra-high performance liquid chromatography (UHPLC) analysis coupled with the Diod Array detector. The potential biomarkers were then collected and identified by gas chromatography-mass spectrometry (GCMS). In order to allow for complete coverage of the fecal polar and non-polar metabolites, the performance of five organic solvents with increasing polarity was investigated successively. UHPLC analysis revealed that the fecal metabolite profiles following the five extractions were significantly different between controls and kidney allografts. GC-MS analysis showed that the best predictors’ metabolites belonged mainly to long-chain fatty acids, phenolic compounds, and amino acids. Collectively, our results showed the efficiency of our pioneer method to successfully discriminate stable kidney-transplant recipients from controls. These findings suggest that distinct metabolic profiles mainly affect fatty acid biosynthesis and amino acid metabolism. In such a context, the novel insights into metabolomic investigation may be a valuable tool that could provide useful new relevant biomarkers for preventing kidney transplant complications.

Fecal Metabolomics Reveals Distinct Profiles of Kidney Transplant Recipients and Healthy Controls

Monitoring graft recipients remains dependent on traditional biomarkers and old technologies lacking specificity, sensitivity, or accuracy. Recently, metabolomics is becoming a promising approach that may offer to kidney transplants a more effective and specific monitoring. Furthermore, emerging evidence suggested a fundamental role of gut microbiota as an important determinant of patients’ metabolomes. In the current study, we enrolled forty stable renal allografts recipients compared to twenty healthy individuals. Samples were taken at different time points from patient to patient following transplantation surgery, which varied from 3 months to 22 years post-graft. All patients started the immunosuppression therapy immediately following kidney graft (Day 0). Gas chromatography–mass spectrometry (GC–MS) was employed to perform untargeted analysis of fecal metabolites. Globally, the fecal metabolic signature was significantly different between kidney transplants and the control group. Fecal metabolome was dominated by lipids (sterols and fatty acids) in the stable transplant group compared to the controls (p < 0.05). Overall, 18 metabolites were significantly altered within kidney transplant recipients. Furthermore, the most notable altered metabolic pathways in kidney transplants include ubiquinone and other terpenoid-quinone biosynthesis, tyrosine metabolism, tryptophan biosynthesis, and primary bile acid biosynthesis. Fecal metabolites could effectively distinguish stable transplant recipients from controls, supporting the potential utility of metabolomics in rapid and non-invasive diagnosis to produce relevant biomarkers and to help clinicians in monitoring kidney transplants. Further investigations are needed to clarify the physiological relevance of fecal metabolome and to assess the impact of microbiota modulation.

Mass spectrometry-based metabolomics in health and medical science: a systematic review

Metabolomics is the study of the investigation of small molecules derived from cellular and organism metabolism, which reflects the outcomes of the complex network of biochemical reactions in living systems.