Mass spectrometry-based metabolomics in microbiome investigations

Post sleeve gastrectomy-enriched gut commensal Clostridia promotes secondary bile acid increase and weight loss

The gut microbiome is altered after bariatric surgery and is associated with weight loss. However, the commensal bacteria involved and the underlying mechanism remain to be determined. We performed shotgun metagenomic sequencing in obese subjects before and longitudinally after sleeve gastrectomy (SG), and found a significant enrichment in microbial species in Clostridia and bile acid metabolizing genes after SG treatment. Bile acid profiling further revealed decreased primary bile acids (PBAs) and increased conjugated secondary bile acids (C-SBAs) after SG. Specifically, glycodeoxycholic acid (GDCA) and taurodeoxycholic acid (TDCA) were increased at different follow-ups after SG, and were associated with the increased abundance of Clostridia and body weight reduction. Fecal microbiome transplantation with post-SG feces increased SBA levels, and alleviated body weight gain in the recipient mice. Furthermore, both Clostridia-enriched spore-forming bacteria and GDCA supplementation increased the expression of genes responsible for lipolysis and fatty acid oxidation in adipose tissue and reduced adiposity via Takeda G-protein-coupled receptor 5 (TGR5) signaling. Our findings reveal post-SG gut microbiome and C-SBAs as contributory to SG-induced weight loss, in part via TGR5 signaling, and suggest SBA-producing gut microbes as a potential therapeutic target for obesity intervention.

Artificial intelligence in bacterial diagnostics and antimicrobial susceptibility testing: Current advances and future prospects

Recently, artificial intelligence (AI) has emerged as a transformative tool, enhancing the speed, accuracy, and scalability of bacterial diagnostics. This review explores the role of AI in revolutionizing bacterial detection and antimicrobial susceptibility testing (AST) by leveraging machine learning models, including Random Forest, Support Vector Machines (SVM), and deep learning architectures such as Convolutional Neural Networks (CNNs) and transformers. The integration of AI into these methods promises to address the current limitations of traditional techniques, offering a path toward more efficient, accessible, and reliable diagnostic solutions. In particular, AI-based approaches have demonstrated significant potential in resource-limited settings by enabling cost-effective and portable diagnostic solutions, reducing dependency on specialized infrastructure, and facilitating remote bacterial detection through smartphone-integrated platforms and telemedicine applications. This review highlights AI's transformative role in automating data analysis, minimizing human error, and delivering real-time diagnostic results, ultimately improving patient outcomes and optimizing healthcare efficiency. In addition, we not only examine the current advances in machine learning and deep learning but also review their applications in plate counting, mass spectrometry, morphology-based and motion-based microscopic detection, holographic microscopy, colorimetric and fluorescence detection, electrochemical sensors, Raman and Surface-Enhanced Raman Spectroscopy (SERS), and Atomic Force Microscopy (AFM) for bacterial diagnostics and AST. Finally, we discuss the future directions and potential advancements in AI-driven bacterial diagnostics.

Exploring sub-species variation in food microbiomes: a roadmap to reveal hidden diversity and functional potential

Within-species diversity of microorganisms in food systems significantly shapes community function. While next-generation sequencing (NGS) methods have advanced our understanding of microbiomes at the community level, it is essential to recognize the importance of within-species variation for understanding and predicting the functional activities of these communities. This review highlights the substantial variation observed among microbial species in food systems and its implications for their functionality. We discuss a selection of key species in fermented foods and food systems, highlighting examples of strain-level variation and its influence on quality and safety. We present a comprehensive roadmap of methodologies aimed at uncovering this often overlooked underlying diversity. Technologies like long-read marker-gene or shotgun metagenome sequencing offer enhanced resolution of microbial communities and insights into the functional potential of individual strains and should be integrated with techniques such as metabolomics, metatranscriptomics, and metaproteomics to link strain-level microbial community structure to functional activities. Furthermore, the interactions between viruses and microbes that contribute to strain diversity and community stability are also critical to consider. This article highlights existing research and emphasizes the importance of incorporating within-species diversity in microbial community studies to harness their full potential, advance fundamental science, and foster innovation.

Identifying metabolic biomarkers and pathways in pulpitis: a metabolomic study using ultra-high-performance liquid chromatography/orbitrap mass spectrometry

BACKGROUND

Pulpitis, which is often triggered by caries and trauma, is a significant clinical challenge due to its prevalence. This research aims to uncover potential metabolic biomarkers for pulpitis and map out the implicated metabolic pathways, thereby laying a foundation for enhanced diagnostic and preventive strategies.

METHODOLOGY

We analyzed pulp samples from 12 participants (six who had pulpitis and six who had healthy teeth) using serum metabolomics via ultra-high-performance liquid chromatography coupled with Orbitrap mass spectrometry. Important biomarkers were pinpointed via multivariate analysis and orthogonal partial least squares discriminant analysis. Additionally, correlation and biomarker pathway enrichment analyses were conducted to explore the relations between differentially expressed biomarkers and their associated biological pathways. Specific metabolites of interest were further examined via alkaline phosphatase (ALP) staining, Alizarin Red staining, and RT-qPCR analysis.

RESULTS

We identified 22 significant biomarkers (13 increased, nine decreased) related to 18 metabolic pathways in pulpitis cases. Key biomarkers included ascorbic acid, inosine, allopurinol riboside, and L-asparagine, in which ascorbic acid and inosine showed the most substantial downregulation and strongest association with pulpitis. Notably, aminoacyl-tRNA biosynthesis and retrograde endocannabinoid signaling pathways were closely linked with pulpitis. Ascorbic acid enhanced the osteogenic differentiation, calcium deposition, as well as the expression of osteogenic genes of human dental pulp stem cells (DPSCs).Conclusions: The identified biomarkers and metabolic pathways offer insights into the pathogenesis of pulpitis and have potential applications in developing preventive treatments.

Dissecting inflammation in the immunemetabolomic era

The role of immune metabolism, specific metabolites and cell-intrinsic and -extrinsic metabolic states across the time course of an inflammatory response are emerging knowledge. Targeted and untargeted metabolomic analysis is essential to understand how immune cells adapt their metabolic program throughout an immune response. In addition, metabolomic analysis can aid to identify pathophysiological patterns in inflammatory disease. Here, we discuss new metabolomic findings within the transition from inflammation to resolution, focusing on three key programs of immunity: Efferocytosis, IL-10 signaling and trained immunity. Particularly the tryptophan-derived metabolite kynurenine was identified as essential for efferocytosis and inflammation resolution as well as a potential biomarker in diverse inflammatory conditions. In summary, metabolomic analysis and integration with transcriptomic and proteomic data, high resolution imaging and spatial information is key to unravel metabolic drivers and dependencies during inflammation and progression to tissue-repair.

The single degree of polymerization influences the efficacy of xylooligosaccharides in shaping microbial and metabolite profiles in chicken gut to combat avian pathogenic Escherichia coli

BACKGROUND

Avian pathogenic Escherichia coli (APEC) threatens both poultry production and human health. Xylooligosaccharides (XOS) may suppress pathogenic bacteria through prebiotic actions. However, the influences of single degree of polymerization (DP) on the inhibition of APEC by XOS remain unknown. This study aimed to probe if XOS and their major monomers (xylobiose, xylotriose and xylotetraose) could differentially combat APEC via prebiotic actions using an in vitro fermentation model with chicken cecal microbiota.

METHODS

Microbiota were randomly divided into 7 groups (5 replicate tubes/group). Control group (CON) received no treatment; XOS group received commercial XOS mixtures; APEC group received APEC; XA, X2, X3 and X4 groups received APEC combined with commercial XOS mixtures, xylobiose, xylotriose and xylotetraose, respectively.

RESULTS

XOS and their major monomers mitigated APEC-induced decline (p < 0.05) in gut microbial α-diversity, with xylotetrose showing the least effect. Gut microbiota in XA, X2, X3 and X4 groups clustered together, with a relative separation observed in X4 group. XOS and their monomers elevated (p < 0.05) the abundances of Firmicutes, Bacteroidota and several probiotics (Lactobacillus, Bacteroides and Megamonas), but reduced (p < 0.05) the abundances of Proteobacteria and Escherichia-Shigella, with xylotetraose exhibiting the least efficacy. Besides, xylotriose and xylotetrose had an advantage over xylotetraose in promoting microbial production of short-chain fatty acids. Metabolomics analysis revealed that APEC challenge mainly downregulated (p < 0.05) several amino acids metabolism pathways of gut microbiota, while xylotriose had an inferiority to XOS in upregulating (p < 0.05) histidine metabolism pathway. Furthermore, microbial fermentation metabolites of all XOS monomers lowered (p < 0.05) certain virulence genes expression in APEC, with xylotriose being the most advantageous.

CONCLUSIONS

XOS and their major monomers differentially improved gut microbiota and metabolite profiles in chicken gut against APEC challenge. Overall, xylotriose exhibited the greatest inhibition against APEC abundance and virulence. Our findings underscore the role of single DP in influencing the prebiotic actions of XOS against APEC, providing a basis for the reasonable application of XOS in diets to combat bacterial challenge.

The metabolic dialogue between intratumoural microbes and cancer: implications for immunotherapy

The tumour microenvironment (TME) exerts a profound influence on cancer progression and treatment outcomes. Recent investigations have elucidated the crucial role of intratumoural microbiota and their metabolites in shaping the TME and modulating anti-tumour immunity. This review critically assesses the influence of intratumoural microbial metabolites on the TME and cancer immunotherapy. We systematically analyse how microbial-derived glucose, amino acid, and lipid metabolites modulate immune cell function, cytokine secretion, and tumour growth. The roles of specific metabolites, including lactate, short-chain fatty acids, bile acids, and tryptophan derivatives, are comprehensively examined in regulating immune responses and tumour progression. Furthermore, we investigate the potential of these metabolites to augment the efficacy of cancer immunotherapies, with particular emphasis on immune checkpoint inhibitors. By delineating the mechanisms through which microbial metabolites influence the TME, this review provides insights into novel microbiome-based therapeutic strategies, thereby highlighting a promising frontier in personalised cancer medicine.

Profiling electric signals of electrogenic probiotic bacteria using self-attention analysis

We fabricated a self-assembled electric circuit to detect the electrical signals produced by two electrogenic probiotic bacteria [Leuconostoc mesenteroides (L. mesenteroides) and Lactococcus lactis (L. lactis)] on chicken egg chorioallantoic membranes as well as in the intestine lumen of mice. Inoculation of L. mesenteroides or L. lactis plus glucose onto a ferrozine assay triggered the reduction of ferric ions to ferrous ions and the formation of ferrozine complexes, indicating the bacterial electron production. In the presence of glucose, L. lactis yielded higher electricity, measured by voltage changes, than L. mesenteroides in vitro. The spectra of the electrical signals generated by these two probiotic bacteria were highly distinguishable. We evaluated the importance of these differences with the application of a self-attention mechanism, a deep learning-based module, revealing several unique signals in the electrical spectra of L. mesenteroides as well as L. lactis bacteria. The specific electrical spectrum for each probiotic bacterium provided a dynamic signature for evaluation of the efficacy of various therapies using probiotics, antibiotics, and fecal microbiota transplantation in the future. KEY POINTS: • The electrical signals produced by probiotic bacteria L. mesenteroides and L. lactis on chicken egg chorioallantoic membranes and in the mouse intestine lumen were detectable. • In the presence of glucose, L. lactis yielded higher electricity than L. mesenteroides in vitro. Furthermore, the electrical spectra generated by these two bacteria were different. • The importance of these differences with the application of a self-attention mechanism revealed several unique signals in the electrical spectra.

Quality evaluation of metabolite annotation based on comprehensive simulation of MS/MS data from high-resolution mass spectrometry (HRMS) and similarity scoring

Metabolite annotation is a critical step in discovery metabolomics, but remains a significant challenge. In this study, the accuracy of metabolite annotation was systematically evaluated by leveraging the proposed strategies for simulation of tandem mass spectrometry (MS/MS) data from high-resolution mass spectrometry (HRMS) and then construction of a large-scale virtual database. Furthermore, various similarity scoring methods were comprehensively compared to assess the performance for annotation. First, three key characteristics that are essential for simulating MS/MS spectra to closely resemble experimental data were identified: (i) the number of mass-to-charge ratio (m/z) features, (ii) the differences between neighboring m/z values, and (iii) the intensity distribution of MS/MS features. These factors were employed to generate representative MS/MS spectra for subsequent study. A meticulously designed virtual MS/MS database was constructed to facilitate accurate annotation assessment, which covered over 100,000 metabolites with diverse structural similarities and differences. To evaluate annotation quality, two simulation strategies on the basis of strong and weak data inference were respectively proposed to replicate MS/MS spectra for unknown metabolites. These simulated spectra were then compared with the virtual database, which provided insights into the expected variations in experimental MS/MS data. Furthermore, eight similarity evaluation methods, including entropy similarity (ES) and weighted dot product (W/DP) algorithms, were rigorously evaluated for their effectiveness in metabolite annotation. The results revealed that some methods, such as ES, exhibited strong resistance to interference and broad adaptability across different MS/MS patterns, whereas others selectively yielded reliable outcomes under specific conditions. This study provided a systematic framework for quality evaluation in metabolite annotation and offered strategies to mitigate false-positive identifications. The findings held great significance for advancing metabolomics research and further improving annotation reliability in complex biological samples.

Targeting the Gut Microbiota's Role in Host Energy Absorption With Precision Nutrition Interventions for the Prevention and Treatment of Obesity

The field of precision nutrition aims to develop dietary approaches based on individual biological factors such as genomics or the gut microbiota. The gut microbiota, which is the highly individualized and complex community of microbes residing in the colon, is a key contributor to human physiology. Although gut microbes play multiple roles in the metabolism of nutrients, their role in modulating the absorption of dietary energy from foods that escape digestion in the small intestine has the potential to variably affect energy balance and, thus, body weight. The fate of this energy, and its subsequent impact on body weight, is well described in rodents and is emerging in humans. This narrative review is focused on recent clinical evidence of the role of the gut microbiota in human energy balance, specifically its impact on energy available to the human host. Despite recent progress, remaining gaps in knowledge present opportunities for developing and implementing strategies to understand causal microbial mechanisms related to energy balance. We propose that implementing rigorous microbiota-focused measurements in the context of innovative clinical trial designs will elucidate integrated diet-host-gut microbiota mechanisms. These mechanisms are primed to be targets for precision nutrition interventions to optimize energy balance to achieve desired weight outcomes. Given the magnitude and impact of the obesity epidemic, implementing these interventions within comprehensive weight management paradigms has the potential to be of public health significance.

Group IV Bimetallic MOFs Engineering Enhanced Metabolic Profiles Co-Predict Liposarcoma Recognition and Classification

The rarity and heterogeneity of liposarcomas (LPS) pose significant challenges in their diagnosis and management. In this work, a series of metal-organic frameworks (MOFs) engineering is designed and implemented. Through comprehensive characterization and performance evaluations, such as stability, thermal-driven desorption efficiency, as well as energy- and charge-transfer capacity, the engineering of group IV bimetallic MOFs emerges as particularly noteworthy. This is especially true for their derivative products, which exhibit superior performance across a range of laser desorption/ionization mass spectrometry (LDI MS) performance tests, including those involving practical sample assessments. The top-performing product is utilized to enable high-throughput recording of LPS metabolic fingerprints (PMFs) within seconds using LDI MS. With machine learning on PMFs, both the LPSrecognizer and LPSclassifier are developed, achieving accurate recognition and classification of LPS with area under the curves (AUCs) of 0.900-1.000. Simplified versions are also developed of the LPSrecognizer and LPSclassifier by screening metabolic biomarker panels, achieving considerable predictive performance, and conducting basic pathway exploration. The work highlights the MOFs engineering for the matrix design and their potential application in developing metabolic analysis and screening tools for rare diseases in clinical settings.

Mass spectral database-based methodologies for the annotation and discovery of natural products

Natural products (NPs) have long held a significant position in various fields such as medicine, food, agriculture, and materials. The chemical space covered by NPs is extensive but often underexplored. Therefore, high-throughput and efficient methodologies for the annotation and discovery of NPs are desired to address the complexity and diversity of NP-based systems. Mass spectrometry (MS) has emerged as a powerful platform for the annotation and discovery of NPs. MS databases provide vital support for the structural characterization of NPs by integrating extensive mass spectral data and sample information. Additionally, the released annotation methodologies, based on a variety of informatics tools, continuously improve the ability to annotate the structure and properties of compounds. This review examines the current mainstream databases and annotation methodologies, focusing on their advantages and limitations. Prospects for future technological advancements are then discussed in terms of novel applications and research objectives. Through a systematic overview, this review aims to provide valuable insights and a reference for MS-based NPs annotation, thereby promoting the discovery of novel natural entities.

Urinary biochemical ecology reveals microbiome-metabolite interactions and metabolic markers of recurrent urinary tract infection

Recurrent urinary tract infections (rUTIs) are a major clinical challenge and their increasing prevalence underscores the need to define host-microbiome interactions underlying susceptibility. How the urinary microbiota engages with the biochemical environment of the urogenital tract is yet to be fully defined. Here, we leverage paired metagenomic and quantitative metabolomic data to establish a microbe-metabolite association network of the female urinary microbiome and define metabolic signatures of rUTI. We observe unique metabolic networks of uropathogens and uroprotective species, highlighting potential metabolite-driven ecological shifts influencing rUTI susceptibility. We find distinct metabolites are associated with urinary microbiome diversity and identify a lipid signature of active rUTI that accurately distinguishes cases from controls. Finally, we identify deoxycholic acid as a prognostic indicator for UTI recurrence. Together these findings provide insight into microbiome-metabolite interactions within the female urinary tract and highlight new biomarkers for the development of new diagnostic tools to improve patient outcomes.

Operationalizing Team Science at the Academic Cancer Center Network to Unveil the Structure and Function of the Gut Microbiome

Oncologists increasingly recognize the microbiome as an important facilitator of health as well as a contributor to disease, including, specifically, cancer. Our knowledge of the etiologies, mechanisms, and modulation of microbiome states that ameliorate or promote cancer continues to evolve. The progressive refinement and adoption of "omic" technologies (genomics, transcriptomics, proteomics, and metabolomics) and utilization of advanced computational methods accelerate this evolution. The academic cancer center network, with its immediate access to extensive, multidisciplinary expertise and scientific resources, has the potential to catalyze microbiome research. Here, we review our current understanding of the role of the gut microbiome in cancer prevention, predisposition, and response to therapy. We underscore the promise of operationalizing the academic cancer center network to uncover the structure and function of the gut microbiome; we highlight the unique microbiome-related expert resources available at the City of Hope of Comprehensive Cancer Center as an example of the potential of team science to achieve novel scientific and clinical discovery.

Human Untargeted Metabolomics in High-Throughput Gut Microbiome Research: Ethanol vs Methanol

Untargeted metabolomics is frequently performed on human fecal samples in conjunction with sequencing to unravel the gut microbiome functionality. As sample collection efforts are rapidly expanding, with individuals often collecting specimens at home, metabolomics experiments should adapt to accommodate the safety and needs of bulk off-site collections and improve high throughput. Here, we show that a 95% ethanol, safe to be shipped and handled, extraction part of the Matrix Method pipeline recovers comparable amounts of metabolites as a validated 50% methanol extraction, preserving metabolic profile differences between investigated subjects. Additionally, we show that the fecal metabolome remains relatively stable when stored in 95% ethanol for up to 1 week at room temperature. Finally, we suggest a metabolomics data analysis workflow based on robust centered log ratio transformation, which removes the variance introduced by possible different sample weights and concentrations, allowing for reliable and integration-ready untargeted metabolomics experiments in gut microbiome research.

Root exudates and microbial metabolites: signals and nutrients in plant-microbe interactions

Plant roots meticulously select and attract particular microbial taxa from the surrounding bulk soil, thereby establishing a specialized and functionally diverse microbial community within the rhizosphere. Rhizosphere metabolites, including root exudates and microbial metabolites, function as both signals and nutrients that govern the assembly of the rhizosphere microbiome, playing crucial roles in mediating communications between plants and microbes. The environment and their feedback loops further influence these intricate interactions. However, whether and how specific metabolites shape plant-microbe interactions and facilitate diverse functions remains obscure. This review summarizes the current progress in plant-microbe communications mediated by chemical compounds and their functions in plant fitness and ecosystem functioning. Additionally, we raise some prospects on future directions for manipulating metabolite-mediated plant-microbe interactions to enhance crop productivity and health. Unveiling the biological roles of specific metabolites produced by plants and microbes will bridge the gap between fundamental research and practical applications.

The bronchiectasis microbiome: current understanding and treatment implications

PURPOSE OF REVIEW

Advances in DNA sequencing and analysis of the respiratory microbiome highlight its close association with bronchiectasis phenotypes, revealing fresh opportunities for diagnosis, stratification, and personalized clinical intervention. An under-recognized condition, bronchiectasis is increasingly the subject of recent large-scale, multicentre, and longitudinal clinical studies including detailed analysis of the microbiome. In this review, we summarize recent progress in our understanding of the bronchiectasis microbiome within the context of its potential use in treatment decisions.

RECENT FINDINGS

Diverse microbiome profiles exist in bronchiectasis, in line with the established disease heterogeneity including treatment response. Classical microbiology has established Pseudomonas aeruginosa and Haemophilus influenza as two microbial markers of disease, while holistic microbiome analysis has uncovered important associations with less common bacterial taxa including commensal an/or pathobiont species, including the emerging role of the fungal mycobiome, virome, and interactome. Integration of airway microbiomes with other high-dimensional biological and clinical datasets holds significant promise to determining treatable traits and mechanisms of disease related to the microbiome.

SUMMARY

The bronchiectasis microbiome is an emerging and key area of study with significant implications for understanding bronchiectasis, influencing treatment decisions and ultimately improving patient outcomes.

Potential role of indole-3-propionic acid in tuberculosis: current perspectives and future prospects

INTRODUCTION

Indole-3-propionic acid (IPA), a tryptophan catabolite derived from gut bacterial metabolism, has been identified as a functional link between the gut microbiome and tuberculosis.

AREA COVERED

IPA has gained ample attention over the past two decades on account of its multiple physiological roles, besides being both detectable and quantifiable. IPA is well studied across different health conditions, including cardiovascular and neurological conditions. IPA blocks tryptophan synthesis in Mycobacterium by binding to the allosteric tryptophan-binding site of TrpE, thereby threatening Mycobacterium survival due to tryptophan deficit.

EXPERT OPINION

Characterizing IPA would enable its use as a tool to investigate the pathophysiology of tuberculosis. Integrating 'OMICS' techniques (through next-generation sequencing) along with targeted microbial metabolomics may help explore the possible association of serum IPA levels with TB in patients. This will aid in identifying IPA-producing gut microbes and selecting probiotic strains as a microbiome-targeting adjunct therapy, eventually enhancing our understanding of the molecular dynamics of the pathophysiology of tuberculosis in the context of the microbiome.

Metabolomic analysis of the intrinsic resistance mechanisms of Microtus fortis against Schistosoma japonicum infection

Microtus fortis (M. fortis) is the only mammal known in China that is intrinsically resistant to Schistosoma japonicum (S. japonicum) infection. Nevertheless, the underlying resistance mechanism of M. fortis against schistosomes are still unclear. In this study, we detected and compared colon aqueous extracts and serum metabolic profiles between M. fortis and ICR mice before and after S. japonicum infection using liquid chromatography-mass spectrometry (LC-MS). We identified 232 specific colon aqueous extract metabolites and 79 specific serum metabolites of M. fortis infected with or without S. japonicum at two weeks compared with those of ICR mice, which might be closely correlated with the time-course of schistosomiasis progression and could also be used as indicators for the M. fortis against S. japonicum, for example, nonadecanoic acid, hesperetin, glycocholic acid, 2-Aminobenzoic acid, 6-hydroxydaidzein and spermidine. And the enriched pathways were further identified, our findings revealed that S. japonicum infection induced the metabolic changes involved in a variety of metabolic pathways including amino acid metabolism, lipid metabolism, ABC transporters, central carbon metabolism in cancer and bile secretion. These results indicated that the colon aqueous extracts and serum metabolic profiles were significantly different between M. fortis and ICR mice before and after S. japonicum infection and will provide new insights into the underlying resistance mechanism of M. fortis against S. japonicum infection and identify promising candidates for the use of drugs against schistosomes.

Integrated cell metabolomics and network pharmacology approach deciphers the mechanisms of Astragali Radix MIX in repairing podocyte injury

To elucidate the molecular mechanisms of the MIX (combined in proportion to the content in Astragali Radix (AR), named the MIX) repairing podocyte damage to ameliorate nephropathy. MTT assay and western blot analysis were used to evaluate the protective effects of MIX on MPC5 cells induced by Adriamycin (ADR). Screening of potential pharmacodynamic markers, relevant drugs and disease targets were conducted by using metabolomics combined with bioinformatics, and the most relevant metabolic pathways were identified by analyzing shared target and KEGG pathways. The key mechanism was subsequently validated by cell adhesion assays, western blot assays, and immunofluorescence staining. The results showed that the MIX has the capacity to repair adriamycin-induced damage in MPC5 cells, as evidenced by enhanced cell viability and synaptopodin expression. Additionally, the MIX shows promise in potentially reinstating the podocyte adhesion through the modulation of the expression of podocyte adhesion-related proteins linked to nucleotide metabolites. The MIX has the potential to beneficially affect podocyte injury by modulating the cell adhesion pathway, contributing to one of the pharmacodynamic mechanisms of AR treatment of nephrotic syndrome. This has implications for the development and utilization of AR resources and achievement the social benefit of empowering rural revitalization.

Metagenomic and Metabolomic Analyses Reveal the Role of a Bacteriocin-Producing Strain of Enterococcus faecalis DH9003 in Regulating Gut Microbiota in Mice

To investigate the regulatory effect of a bacteriocin-producing strain of Enterococcus faecalis DH9003 on the gut microbiota of mice, 15 healthy C57 male mice were randomly administered an equal volume of sterile normal saline (HD, control group, n = 7) and E. faecalis DH9003 (YD, treatment group, n = 8) via gavage. Metagenomic and metabolomic analyses were performed to determine the composition and metabolic function of the intestinal microbiota in mice. The results showed that the relative abundance of Firmicutes continuously increased over time in YD compared to HD. The number of E. faecalis DH9003 increased slowly and remained steady from days 7 to 28, indicating that E. faecalis DH9003 could colonize a considerable number of mouse guts via intragastric administration. Supplementation with E. faecalis DH9003 demonstrated a regulatory effect on the intestinal microbiota composition of mice, causing a shift in the relative abundance of Bacteroidetes and Firmicutes at the phylum level. In addition, a total of 2426 different metabolites were found in mouse feces, including 1286 and 1140 metabolites in positive and negative modes, respectively. Vitamin B6 and succinate were the most regulated and downregulated metabolites in negative ion mode, and the most upregulated and downregulated metabolites in positive ion mode were N-methyl-glutamic acid and N-octanoyl sphingosine. In conclusion, E. faecalis DH9003 can colonize mice gut, affecting the gut microbiota and metabolic competence. This strain therefore offers considerable potential for application as a probiotic.

Antimicrobial Activity and Metabolomic Analysis of Linalool Against Pathogenic Bacteria Methicillin-Resistant Staphylococcus aureus

Purpose

The purpose of this study was to evaluate the antibacterial activity and mechanism of linalool against Methicillin-resistant Staphylococcus aureus (MRSA).

Methods

The determination of the antibacterial activity of linalool against clinically isolated MRSA strains was based on the minimum inhibitory concentration (MIC) and growth curve analysis. Finally, the inhibition mechanism of linalool was elucidated through metabolomic analysis and molecular docking.

Results

Among the isolated strains, penicillin resistance was found to be the highest, while resistance to daptomycin/quinupristin-dalfopristin, linezolid, vancomycin, tetracycline, telithromycin, and levofloxacin was not observed. The MIC range of linalool was 211.24-1.65 μg/mL, with MIC50 and MIC90 values of 13.2 μg/mL and 105.62 μg/mL, respectively. Metabolomic analysis revealed that linalool interferes with various substance metabolisms and energy metabolism in MRSA, with the glutathione pathway potentially being a key pathway affected by linalool. Molecular docking revealed that linalool exhibited good binding potential to the target glutathione.

Conclusion

This study suggests that linalool could be developed as a drug or preservative to inhibit MRSA growth.

Global metabolite profiling in feces, serum, and urine yields insights into energy balance phenotypes induced by diet-driven microbiome remodeling

Background

Preclinical literature and behavioral human data suggest that diet profoundly impacts the human gut microbiome and energy absorption-a key determinant of energy balance. To determine whether these associations are causal, domiciled controlled feeding studies with precise measurements of dietary intake and energy balance are needed. Metabolomics-a functional readout of microbiome modulation-can help identify putative mechanisms mediating these effects. We previously demonstrated that a high-fiber, minimally processed Microbiome Enhancer Diet (MBD) fed at energy balance decreased energy absorption and increased microbial biomass relative to a calorie-matched fiber-poor, highly processed Western Diet (WD).

Objective

To identify metabolic signatures distinguishing MBD from WD feeding and potential metabolomic mechanisms mediating the MBD-induced negative energy balance.

Methods

We deployed global metabolomics in feces, serum, and urine using samples collected at the end of a randomized crossover controlled feeding trial delivering 22 days of an MBD and a WD to 17 persons without obesity. Samples were collected while participants were domiciled on a metabolic ward and analyzed using Ultrahigh Performance Liquid Chromatography-Tandem Mass Spectroscopy. Linear mixed effects models tested metabolite changes by diet. Weighted gene network correlation analysis identified metabolite modules correlated with energy balance phenotypes.

Results

Numerous metabolites consistently altered in the feces, fasting serum, and/or urine may serve as putative dietary biomarkers of MBD feeding. Fecal diet-microbiota co-metabolites decreased by an MBD correlated with reduced energy absorption and increased microbial biomass. An MBD shifted the urinary metabolome from sugar degradation to ketogenesis-evidence of negative energy balance.

Conclusions

Precisely controlled diets disparate in microbiota-accessible substrates led to distinct metabolomic signatures in feces, fasting serum, and/or urine. These diet-microbiota co-metabolites may be biomarkers of a "fed" (MBD) or "starved" (WD) gut microbiota associated with energy balance. These findings lay the foundation for unveiling causal pathways linking diet-microbiota co-metabolism to energy absorption.

Microbiota governs host chenodeoxycholic acid glucuronidation to ameliorate bile acid disorder induced diarrhea

BACKGROUND

Disorder in bile acid (BA) metabolism is known to be an important factor contributing to diarrhea. However, the pathogenesis of BA disorder-induced diarrhea remains unclear.

METHODS

The colonic BA pool and microbiota between health piglets and BA disorder-induced diarrheal piglets were compared. Fecal microbiota transplantation and various cell experiments further indicated that chenodeoxycholic acid (CDCA) metabolic disorder produced CDCA-3β-glucuronide, which is the main cause of BA disorder diarrhea. Non-targeted metabolomics uncovered the inhibition of the BA glucuronidation by Lactobacillus reuteri (L. reuteri) is through deriving indole-3-carbinol (I3C). In vitro, important gene involved in the reduction of BA disorder induced-diarrhea were screened by RNA transcriptomics sequencing, and activation pathway of FXR-SIRT1-LKB1 to alleviate BA disorder diarrhea and P53-mediated apoptosis were proposed in vitro by multifarious siRNA interference, CO-IP, immunofluorescence, and so on, which mechanism was also verified in a variety of mouse models.

RESULTS

Here, we reveal for the first time that core microbiota derived I3C represses gut epithelium glucuronidation, particularly 3β-glucuronic CDCA production, which reaction is mediated by host UDP glucuronosyltransferase family 1 member A4 (UGT1A4) and necessary of BA disorder induced diarrhea. Mechanistically, L. reuteri derived I3C activates aryl hydrocarbon receptor to decrease UGT1A4 transcription and CDCA-3β-glucuronide content, thereby upregulating FXR-SIRT1-LKB1 signal. LKB1 binds with P53 based on protein interaction, ultimately resists to apoptosis and diarrhea. Moreover, I3C assists CDCA to attain the ameliorative effects of FXR activation in BA disorder diarrhea, through reversion of abnormal metabolism pathway, improving the outcomes of CDCA supplement.

CONCLUSION

These findings uncover the crucial interplay between gut epithelial cells and microbes, highlighting UGT1A4-mediated conversion of CDCA-3β-glucuronide as a key target for ameliorating BA disorder-induced diarrhea. Video Abstract.

Immunoassay-mass spectrometry to identify Brucella melitensis

Two factors frequently impede accurate bacterial identification using matrix-assisted laser desorption ionization time-of-flight mass spectrometry (MALDI-TOF MS): inadequate bacterial abundance in real samples and bacterial combinations. For MALDI-TOF MS analysis and libraries for bacterial identification, time-consuming culture procedures are necessary to achieve sufficient concentration and isolation of a single bacterium. When dealing with hazardous bacteria like Brucella, which are more difficult to handle and cure, this problem becomes even more crucial. To overcome these obstacles, Fe3O4 magnetic nanoparticles (MNPs) linked with Brucella-specific antibodies and MALDI-TOF MS analysis have been used to create a quick and accurate technique for direct bacterial separation and identification in complex samples. This method allows MNPs to immune-selectively collect Brucella cells, which are then deactivated and ready for MALDI-TOF MS analysis by a formic acid/acetonitrile wash. Rabbits were used to manufacture brucella antibodies, which have effectively adsorbed onto the MNPs-protein A. Any particular Brucella bacteria found in the media might be absorbed by this MNPs-protein A-antibody immunoprobe. The concentration of Brucella bacterial cells increases the protein spectrum's visibility by a factor of 103, making it possible to quickly identify Brucella spp. without first growing them in cultural conditions. This method has been successfully used to achieve a limit of detection (LOD) of 50 CFU/mL in an aqueous medium and genuine sample-milk. The diagnostic time for this harmful bacterium is greatly decreased because the entire procedure from bacterial isolation to species identification is finished in less than 60 min. High sensitivity and specificity are demonstrated by the immunoassay-MS approach, as the spectral pattern it produces matches well-known databases like SPECLUST and Ribopeaks.

Bacterial metabolomics: current applications for human welfare and future aspects

An imbalanced microbiome is linked to several diseases, such as cancer, inflammatory bowel disease, obesity, and even neurological disorders. Bacteria and their by-products are used for various industrial and clinical purposes. The metabolites under discussion were chosen based on their biological impacts on host and gut microbiota interactions as established by metabolome research. The separation of bacterial metabolites by using statistics and machine learning analysis creates new opportunities for applications of bacteria and their metabolites in the environmental and medical sciences. Thus, the metabolite production strategies, methodologies, and importance of bacterial metabolites for human well-being are discussed in this review.

Effective analysis of thyroid toxicity and mechanisms of acetyltributyl citrate using network toxicology, molecular docking, and machine learning strategies

The growing prevalence of environmental pollutants has raised concerns about their potential role in thyroid dysfunction and related disorders. Previous research suggests that various chemicals, including plasticizers like acetyl tributyl citrate (ATBC), may adversely affect thyroid health, yet the precise mechanisms remain poorly understood. The objective of this study was to elucidate the complex effects of acetyl tributyl citrate (ATBC) on the thyroid gland and to clarify the potential molecular mechanisms by which environmental pollutants influence the disease process. Through an exhaustive exploration of databases such as ChEMBL, STITCH, and GEO, we identified a comprehensive list of 19 potential targets closely associated with ATBC and the thyroid gland. After rigorous screening using the STRING platform and Cytoscape software, we narrowed this list to 15 candidate targets, ultimately identifying five core targets: CBX5, HADHB, TRIM33, TP53, and CUL4A, utilizing three well-established machine learning methods. In-depth Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses conducted in the DAVID database revealed that the primary pathways through which ATBC affects the thyroid gland involve key signaling cascades, including the FoxO signaling pathway and metabolic pathways such as fatty acid metabolism. Furthermore, molecular docking simulations using Molecular Operating Environment software confirmed strong binding interactions between ATBC and these core targets, enhancing our understanding of their interactions. Overall, our findings provide a theoretical framework for comprehending the intricate molecular mechanisms underlying ATBC's effects on thyroid damage and pave the way for the development of preventive and therapeutic strategies against thyroid disorders caused by exposure to ATBC-containing plastics or overexposure to ATBC.

Precision Dietary Intervention: Gut Microbiome and Meta-metabolome as Functional Readouts

Gut microbiome, the group of commensals residing within the intestinal tract, is closely associated with dietary patterns by interacting with food components. The gut microbiome is modifiable by the diet, and in turn, it utilizes the undigested food components as substrates and generates a group of small molecule-metabolites that addressed as "meta-metabolome" in this review. Profiling and mapping of meta-metabolome could yield insightful information at higher resolution and serve as functional readouts for precision nutrition and formation of personalized dietary strategies. For assessing the meta-metabolome, sample preparation is important, and it should aim for retrieval of gut microbial metabolites as intact as possible. The meta-metabolome can be investigated via untargeted and targeted meta-metabolomics with analytical platforms such as nuclear magnetic resonance spectroscopy and mass spectrometry. Employing flux analysis with meta-metabolomics using available database could further elucidate metabolic pathways that lead to biomarker discovery. In conclusion, integration of gut microbiome and meta-metabolomics is a promising supplementary approach to tailor precision dietary intervention. In this review, relationships among diet, gut microbiome, and meta-metabolome are elucidated, with an emphasis on recent advances in alternative analysis techniques proposed for nutritional research. We hope that this review will provide information for establishing pipelines complementary to traditional approaches for achieving precision dietary intervention.

How the early life microbiome shapes immune programming in childhood asthma and allergies

Despite advances in our understanding of their diagnosis and treatment, pediatric allergies impose substantial burdens on affected children, families, and healthcare systems. Further, the prevalence of allergic diseases has dramatically increased over the past half-century, leading to additional concerns and concerted efforts to identify the origins, potential predictors and preventions, and therapies of allergic diseases. Together with the increase in allergic diseases, changes in lifestyle and early-life environmental influences have corresponded with changes in colonization patterns of the infant gut microbiome. The gut microbiome plays a key role in developing the immune system, thus greatly influencing the development of allergic disease. In this review, we specifically highlight the importance of the proper maturation and composition of the gut microbiome as an essential step in healthy child development or disease progression. By exploring the intertwined development of the immune system and microbiome across pediatric allergic diseases, we provide insights into potential novel strategies for their prevention and management.

iTASO: A Novel Photosensitive Probe for High-Throughput and Selective Submetabolomic Analysis via Flow Injection-Mass Spectrometry

Flow injection mass spectrometry (FI-MS) is widely employed for high-throughput metabolome analysis, yet the absence of prior separation leads to significant matrix effects, thereby limiting the metabolome coverage. In this study, we introduce a novel photosensitive MS probe, iTASO-ONH2, integrated with FI-MS to establish a high-throughput strategy for submetabolome analyses. The iTASO probe features a conjugated-imino sulfonate moiety for efficient photolysis under 365 nm irradiation and a reactive group for selective metabolite labeling. The iTASO-ONH2 probe effectively and selectively labels carbonyl compounds, forming highly stable labeled products. Upon UV exposure, the labeled products rapidly release sulfonic acid-containing photolysis products, detectable with high sensitivity in ESI-negative mode and low matrix effect, offering femtomole-level detection sensitivity. The iTASO-ONH2-based FI-MS strategy was applied to fecal samples from chronic sleep-deprived and control mice, revealing 192 potential carbonyl compounds of which 37 exhibited significant alterations. Additionally, three other photosensitive probes─iTASO-NH2, iTASO-NHS, and iTASO-MAL─were synthesized to selectively label carboxyl, amino, and thiol metabolites, respectively, underscoring the versatility of the iTASO-based FI-MS strategy for submetabolomic analysis across diverse metabolite classes.

Fungal Metabolomics: A Comprehensive Approach to Understanding Pathogenesis in Humans and Identifying Potential Therapeutics

Metabolomics has emerged as a transformative tool in the study of microbes, including pathogenic fungi, facilitating the identification of unique metabolic profiles that elucidate their pathogenic mechanisms, host interactions, and treatment resistance. This review highlights key applications of metabolomics in understanding fungal metabolites essential for human virulence, such as mycotoxins produced by various fungal species, including Aspergillus fumigatus (gliotoxin, fumagillins) and Candida species (phenylethyl alcohol, TCA cycle metabolites), and secondary metabolites that contribute to pathogenicity. It also explores the metabolic adaptations of fungi in relation to drug resistance and biofilm formation, revealing alterations in key metabolic pathways during infection, as seen in C. albicans and C. auris. Furthermore, metabolomics aids in deciphering host-pathogen interactions, showcasing how fungi like Cryptococcus neoformans and Candida modify host metabolism to promote survival and evade immune responses. The study of antifungal resistance mechanisms has also benefited from metabolomic approaches, identifying specific metabolite patterns that signify resistance, such as in Candida albicans and Candidozyma (Candida) auris, and informing new therapeutic strategies. The integration of metabolomics with other omics technologies is paving the way for a comprehensive understanding of fungal biology and pathogenesis. Such multi-omics approaches are crucial for discovering new therapeutic targets and developing innovative antifungal treatments. Thus, the purpose of this review is to provide an overview of how metabolomics is revolutionizing our understanding of fungal pathogenesis, drug resistance, and host interactions, and to highlight its potential for identifying new therapeutic targets and improving antifungal strategies.

Integrating the milk microbiome signatures in mastitis: milk-omics and functional implications

Mammalian milk contains a variety of complex bioactive and nutritional components and microorganisms. These microorganisms have diverse compositions and functional roles that impact host health and disease pathophysiology, especially mastitis. The advent and use of high throughput omics technologies, including metagenomics, metatranscriptomics, metaproteomics, metametabolomics, as well as culturomics in milk microbiome studies suggest strong relationships between host phenotype and milk microbiome signatures in mastitis. While single omics studies have undoubtedly contributed to our current understanding of milk microbiome and mastitis, they often provide limited information, targeting only a single biological viewpoint which is insufficient to provide system-wide information necessary for elucidating the biological footprints and molecular mechanisms driving mastitis and milk microbiome dysbiosis. Therefore, integrating a multi-omics approach in milk microbiome research could generate new knowledge, improve the current understanding of the functional and structural signatures of the milk ecosystem, and provide insights for sustainable mastitis control and microbiome management.

Chemical interplay between gut microbiota and epigenetics: Implications in circadian biology

Circadian rhythms are intrinsic molecular mechanisms that synchronize biological functions with the day/night cycle. The mammalian gut is colonized by a myriad of microbes, collectively named the gut microbiota. The microbiota impacts host physiology via metabolites and structural components. A key mechanism is the modulation of host epigenetic pathways, especially histone modifications. An increasing number of studies indicate the role of the microbiota in regulating host circadian rhythms. However, the mechanisms remain largely unknown. Here, we summarize studies on microbial regulation of host circadian rhythms and epigenetic pathways, highlight recent findings on how the microbiota employs host epigenetic machinery to regulate circadian rhythms, and discuss its impacts on host physiology, particularly immune and metabolic functions. We further describe current challenges and resources that could facilitate research on microbiota-epigenetic-circadian rhythm interactions to advance our knowledge of circadian disorders and possible therapeutic avenues.

Human AKR1C3 binds agonists of GPR84 and participates in an expanded polyamine pathway

Altered human aldo-keto reductase family 1 member C3 (AKR1C3) expression has been associated with poor prognosis in diverse cancers, ferroptosis resistance, and metabolic diseases. Despite its clinical significance, the endogenous biochemical roles of AKR1C3 remain incompletely defined. Using untargeted metabolomics, we identified a major transformation mediated by AKR1C3, in which a spermine oxidation product "sperminal" is reduced to "sperminol." Sperminal causes DNA damage and activates the DNA double-strand break response, whereas sperminol induces autophagy in vitro. AKR1C3 also pulls down acyl-pyrones and pyrone-211 inhibits AKR1C3 activity. Through G protein-coupled receptor ligand screening, we determined that pyrone-211 is also a potent agonist of the semi-orphan receptor GPR84. Strikingly, mammalian fatty acid synthase produces acyl-pyrones in vitro, and this production is modulated by NADPH. Taken together, our studies support a regulatory role of AKR1C3 in an expanded polyamine pathway and a model linking fatty acid synthesis and NADPH levels to GPR84 signaling.

Omics detection and treatment of syphilis

Treponema pallidum is the source of the chronic systemic sexually transmitted illness syphilis. T. pallidum can evade immunity and spread. A hard chancre, enlarged lymph nodes, and a syphilis rash are the primary clinical signs. The condition may affect the nervous or cardiovascular system and even become fatal after being neglected. Omics technology is a cutting-edge technique that maps the entire regulatory network of gene and protein metabolism using high-throughput sequencing and other techniques, such as transcriptomics, proteomics, metabolomics, and genomics, to perform more efficient and methodical research on biological samples. Owing to the diverse and intricate biological roles and gene expression of T. pallidum, a single omics study is frequently insufficient and limited. This review focused on and summarized the use of several omics methods for investigating T. pallidum by referencing several different studies in the literature.

Angelica sinensis polysaccharide as potential protectants against recurrent spontaneous abortion: focus on autophagy regulation

Introduction

Recurrent spontaneous abortion (RSA) represents a significant clinical challenge, with its underlying mechanisms yet to be fully elucidated. Despite advances in understanding, the precise pathophysiology driving RSA remains unclear. Angelica sinensis, a traditional herbal remedy, is frequently used as an adjunctive treatment for miscarriage. However, it remains uncertain whether its primary active component, Angelica sinensis polysaccharide (ASP), plays a definitive role in its therapeutic effects. The specific function and mechanism of ASP in the context of RSA require further investigation.

Methods

In this study, we sought to evaluate autophagy levels at the maternal-fetal interface in RSA patients and in an RSA mouse model treated with ASP, complemented by a comprehensive metabolomic analysis. Autophagy flux in the decidua was compared between eight RSA patients and eight healthy pregnant women. Additionally, changes in autophagy flux were assessed in an RSA mouse model following ASP treatment, with embryos and placental tissues collected for subsequent metabolomic profiling.

Results

Our results revealed a significant reduction in Beclin 1 protein levels in the decidua of RSA patients compared to the normal pregnancy group. Conversely, ASP treatment in the RSA mouse model restored autophagy-related protein expression, including ATG7, ATG16L, and Beclin 1, to levels higher than those observed in the untreated RSA group. Metabolomic analyses further identified significant changes in phosphatidylethanolamine levels between ASP-treated and control groups, with differential metabolites enriched in pathways related to glycolysis/gluconeogenesis, glycerolipid metabolism, and glycine, serine, and threonine metabolism. Functional assays revealed that ASP enhances trophoblast cell proliferation, migration, and invasion.

Conclusion

In summary, our findings demonstrate diminished autophagy activity in RSA patients, while ASP appears to restore autophagy and regulate key metabolic pathways, including glycolysis/gluconeogenesis. These results provide new insights into the protective mechanisms of ASP in RSA, suggesting its potential as a therapeutic intervention for this condition.

Toward Automated Preprocessing of Untargeted LC-MS-Based Metabolomics Feature Lists from Human Biofluids

Maximizing the extraction of true, high-quality, nonredundant features from biofluids analyzed via LC-MS systems is challenging. Here, the R packages IPO and AutoTuner were used to optimize XCMS parameter settings for the retrieval of metabolite or lipid features in both ionization modes from either faecal or urine samples from two cohorts (n = 621). The feature lists obtained were compared with those where the parameter values were selected manually. Three categories were used to compare feature lists: 1) feature quality through removing false positives, 2) tentative metabolite identification using the Human Metabolome Database (HMDB) and 3) feature utility such as analyzing the proportion of features within intensity threshold bins. Furthermore, a PCA-based approach to feature filtering using QC samples and variable loadings was also explored under this category. Overall, more features were observed after automated selection of parameter values for all data sets (1.3- to 3.7-fold), which propagated through comparative exercises. For example, a greater number of features (on average 51 vs 45%) had a coefficient of variation (CV) < 30%. Additionally, there was a significant increase (7.6-10.4%) in the number of faecal metabolites that could be tentatively annotated, and more features were present in higher intensity threshold bins. Considering the overlap across all three categories, a greater number of features were also retained. Automated approaches that guide selection of optimal parameter values for preprocessing are important to decrease the time invested for this step, while taking advantage of the wealth of data that LC-MS systems provide.

Effect of leucine on mitochondria and oxidative stress to reduce virulence and pathogenicity of Acinetobacter baumannii

Elucidating the virulence mechanisms of A. baumannii is essential for developing strategies to mitigate pathogenicity. Although high-virulent strains are associated with increased mortality rate in severely infected patients, the underlying mechanisms remains not well understood. Our analysis revealed leucine as a pivotal biomarker, with the 11dP and paaK being significant contributors to virulence. The ATP-dependent activity and antioxidant activity were identified as the most important pathways in distinguishing the virulence of A. baumannii. Exogenous leucine was found to modulate mitochondria dysfunction and oxidative stress, thereby diminishing the pathogenicity of A. baumannii towards Beas 2B cells. Moreover, leucine reduced the virulence of A. baumannii to Galleria mellonella (G. mellonella) and alleviated pathological damage to lung tissues in mice. Our study offers a novel treatment strategy based on metabolomics, which may assist in the exploration and management of infections caused by highly virulent pathogens. It sets a new course for reducing the impact of highly virulent A. baumannii infections and has significant implications for the development of future therapeutic interventions.

Exosomes derived from umbilical cord mesenchymal stem cells promote healing of complex perianal fistulas in rats

BACKGROUND

Complex perianal fistulas, challenging to treat and prone to recurrence, often require surgical intervention that may cause fecal incontinence and lower quality of life due to large surgical wounds and potential sphincter damage. Human umbilical cord-derived MSCs (hUC-MSCs) and their exosomes (hUCMSCs-Exo) may promote wound healing.

METHODS

This study assessed the efficacy, mechanisms, and safety of these exosomes in treating complex perianal fistulas in SD rats. We established a rat model, divided rats with fistulas into the control and the exosome groups. We assessed treatment efficacy through ultrasound, clinical observations, and histopathological analysis. We also evaluated the activation of the HIF-1α/TGF-β/Smad signaling pathway via PCR and Western blot and assessed serological markers for HIF-1α and inflammatory indices through ELISA. We analyzed gut microbiota and the systemic metabolic environment via untargeted metabolomics.

RESULTS

The hUCMSCs-Exo effectively promoted healing of wound, regulated the immune balance enhanced collagen synthesis and angiogenesis in the perianal fistulas model of rats, and regulated the gut microbiota and metabolomic profiles. Results of PCR and Western blot analyses indicated that the exosomes activated HIF-1α/TGF-β/Smad signaling pathways. To the dosages tested, the 10ug/100ul concentration (medium dose) was found to be the most effective to the treatment of complex perianal fistulas.

CONCLUSIONS

The hUCMSCs-Exo significantly promoted the healing of wound in perianal fistulas of rats and demonstrated higher safety. The underlying mechanism facilitating the healing process was likely associated with the activation of the HIF-1α/TGF-β/Smad signaling pathway.

Influence of gut flora on diabetes management after kidney transplantation

Post-transplant diabetes mellitus (PTDM) is a common complication following renal transplantation, and its incidence has been gradually increasing in recent years, posing a significant public health challenge. Managing PTDM is complex, as studies suggest that it involves changes in the microbial flora across multiple organs. Recent research highlights the critical role of gut flora metabolism in the development of diabetes among post-renal transplant patients. This paper reviews the alterations in gut flora observed in PTDM patients and explores how gut flora influences PTDM. These findings may offer new perspectives on targeting gut flora metabolites for the prevention and treatment of PTDM.

Microbiota-related metabolites correlated with the severity of COVID-19 patients

BACKGROUND

Coronavirus disease 2019 (COVID-19) is a global pandemic with high mortality, and the treatment options for the severe patients remain limited. Previous studies reported the altered gut microbiota in severe COVID-19. But there are no comprehensive data on the role of microbial metabolites in COVID-19 patients.

METHODS

We identified 153 serum microbial metabolites and assessed the changes in 72 COVID-19 patients upon admission and one-month after their discharge, comparing these changes to those in 133 healthy control individuals from the outpatient department during the same period.

RESULTS

Our study revealed that microbial metabolites varied across different stages and severity of COVID-19 patients. These altered microbial metabolites included tryptophan, bile acids, fatty acids, amino acids, vitamins and those containing benzene. A total of 13 distinct microbial metabolites were identified in COVID-19 patients compared to healthy controls. Notably, correlations were found among these disrupted metabolites and organ injury and inflammatory responses related to COVID-19. Furthermore, these metabolites did not restore to the normal levels one month after discharge. Importantly, two microbial metabolites were the core microbial metabolites related to the severity of COVID-19 patients.

CONCLUSIONS

The microbial metabolites were altered in the acute and recovery stage, correlating with disease severity of COVID-19. These results indicated the important role of gut microbiota in the progression of COVID-19, and facilitated the potential therapeutic microbial target for severe COVID-19 patients.

Integration of metabolomics and other omics: from microbes to microbiome

Metabolomics is a cutting-edge omics technology that identifies metabolites in organisms and their environments and tracks their fluctuations. This field has been extensively utilized to elucidate previously unknown metabolic pathways and to identify the underlying causes of metabolic changes, given its direct association with phenotypic alterations. However, metabolomics inherently has limitations that can lead to false positives and false negatives. First, most metabolites function as intermediates in multiple biochemical reactions, making it challenging to pinpoint which specific reaction is responsible for the observed changes in metabolite levels. Consequently, metabolic processes that are anticipated to vary with metabolite concentrations may not exhibit significant changes, generating false positives. Second, the range of metabolites identified is contingent upon the analytical conditions employed. Until now, no analytical instrument or protocol has been developed that can capture all metabolites simultaneously. Therefore, some metabolites are changed but are not detected, generating false negatives. In this review, we offer a novel and systematic assessment of the limitations of omics technologies and propose-specific strategies to minimize false positives and false negatives through multi-omics approaches. Additionally, we provide examples of multi-omics applications in microbial metabolic engineering and host-microbiome interactions, helping other researchers gain a better understanding of these strategies. KEY POINTS: • Metabolomics identifies metabolic shifts but has inherent false positive/negatives. • Multi-omics approaches help overcome metabolomics' inherent limitations.

Rapid and Robust Workflows Using Different Ionization, Computation, and Visualization Approaches for Spatial Metabolome Profiling of Microbial Natural Products in Pseudoalteromonas

Ambient mass spectrometry (MS) technologies have been applied to spatial metabolomic profiling of various samples in an attempt to both increase analysis speed and reduce the length of sample preparation. Recent studies, however, have focused on improving the spatial resolution of ambient approaches. Finer resolution requires greater analysis times and commensurate computing power for more sophisticated data analysis algorithms and larger data sets. Higher resolution provides a more detailed molecular picture of the sample; however, for some applications, this is not required. A liquid microjunction surface sampling probe (LMJ-SSP) based MS platform combined with unsupervised multivariant analysis based hyperspectral visualization is demonstrated for the metabolomic analysis of marine bacteria from the genus Pseudoalteromonas to create a rapid and robust spatial profiling workflow for microbial natural product screening. In our study, metabolomic profiles of different Pseudoalteromonas species are quickly acquired without any sample preparation and distinguished by unsupervised multivariant analysis. Our robust platform is capable of automated direct sampling of microbes cultured on agar without clogging. Hyperspectral visualization-based rapid spatial profiling provides adequate spatial metabolite information on microbial samples through red-green-blue (RGB) color annotation. Both static and temporal metabolome differences can be visualized by straightforward color differences and differentiating m/z values identified afterward. Through this approach, novel analogues and their potential biosynthetic pathways are discovered by applying results from the spatial navigation to chromatography-based metabolome annotation. In this current research, LMJ-SSP is shown to be a robust and rapid spatial profiling method. Unsupervised multivariant analysis based hyperspectral visualization is proven straightforward for facile/rapid data interpretation. The combination of direct analysis and innovative data visualization forms a powerful tool to aid the identification/interpretation of interesting compounds from conventional metabolomics analysis.

An interpretable machine learning model for precise prediction of biomarkers for intermittent fasting pattern

Intermittent fasting is currently a highly sought-after dietary pattern. To explore the potential biomarkers of intermittent fasting, untargeted metabolomics analysis of fecal metabolites in two groups of mice, intermittent fasting and normal feeding, was conducted using UPLC-HRMS. The data was further analyzed through interpretable machine learning (ML) to data mine the biomarkers for two dietary patterns. We developed five machine learning models and results showed that under three-fold cross-validation, Random Forest model was the most suitable for distinguishing the two dietary patterns. Finally, Shapely Additive exPlanations (SHAP) were explored to perform a weighted explanatory analysis on the Random Forest model, and the contribution of each metabolite to the model was calculated. Results indicated that Ganoderenic Acid C is the potential biomarkers to distinguish the two dietary patterns. Our work provides new insights for metabolic biomarker analysis and lays a theoretical foundation for the selection of a healthieir dietary lifestyle.

The Potential of Metabolomics in Colorectal Cancer Prognosis

Colorectal cancer (CRC) is one of the most common cancers worldwide, posing a serious threat to human health. Metabolic reprogramming represents a critical feature in the process of tumor development and progression, encompassing alterations in sugar metabolism, lipid metabolism, amino acid metabolism, and other pathways. Metabolites hold promise as innovative prognostic biomarkers for cancer patients, which is crucial for targeted follow-up care and interventions. This review aims to provide an overview of the progress in research on metabolic biomarkers for predicting the prognosis of CRC. We also discuss the future trends and challenges in this area.

Comparison of Fecal Microbiota and Metabolites Between Captive and Grazing Male Reindeer

The reindeer (Rangifer tarandus) is a circumpolar member of the Cervidae family, and has adapted to a harsh environment. Summer is a critical period for reindeer, with peak digestibility facilitating body fat accumulation. The gut microbiota plays a pivotal role in nutrient metabolism, and is affected by captivity. However, differences in the composition of the gut microbiota and metabolites between captive and grazing reindeer during summer remain poorly understood. Here, we conducted a comparative study of the fecal microbiota and metabolites between captive (n = 6) and grazing (n = 6) male reindeer, using full-length 16S rRNA gene sequencing and gas chromatography-time-of-flight mass spectrometry, respectively. Our results indicated that Prevotella, Phocaeicola, Papillibacter, Muribaculum, and Bacteroides were the predominant genera in the feces of reindeer. However, microbial diversity was significantly higher in captive reindeer compared to their grazing counterparts. Principal coordinate analysis revealed significant differences in the fecal microbiota between captive and grazing reindeer. In captive reindeer, the relative abundances of the genera Clostridium, Paraprevotella, Alistipes, Paludibacter, Lentimicrobium, Paraclostridium, and Anaerovibrio were significantly higher, while those of the genera Prevotella, Phocaeicola, Pseudoflavonifractor, and Lactonifactor were significantly lower. A comparison of predicted functions indicated that pathways involved in fat digestion and absorption, histidine metabolism, lysine biosynthesis, and secondary bile acid biosynthesis were more abundant in captive reindeer, whereas the pathways of fructose and mannose metabolism and propanoate metabolism were less abundant. An untargeted metabolomic analysis revealed that 624 metabolites (e.g., amino acids, lipids, fatty acids, and bile acids) and 645 metabolites (e.g., carbohydrates and purines) were significantly increased in the feces of captive and grazing reindeer, respectively. In conclusion, we unveiled significant differences in fecal microbiota and metabolites between captive and grazing male reindeer, with the results suggesting a potentially enhanced ability to utilize plant fibers in grazing reindeer.

Autologous DNA mobilization and multiplication expedite natural products discovery from bacteria

The transmission of antibiotic-resistance genes, comprising mobilization and relocation events, orchestrates the dissemination of antimicrobial resistance. Inspired by this evolutionarily successful paradigm, we developed ACTIMOT, a CRISPR-Cas9-based approach to unlock the vast chemical diversity concealed within bacterial genomes. ACTIMOT enables the efficient mobilization and relocation of large DNA fragments from the chromosome to replicative plasmids within the same bacterial cell. ACTIMOT circumvents the limitations of traditional molecular cloning methods involving handling and replicating large pieces of genomic DNA. Using ACTIMOT, we mobilized and activated four cryptic biosynthetic gene clusters from Streptomyces, leading to the discovery of 39 compounds across four distinct classes. This work highlights the potential of ACTIMOT for accelerating the exploration of biosynthetic pathways and the discovery of natural products.

Predicting Tandem Mass Spectra of Small Molecules Using Graph Embedding of Precursor-Product Ion Pair Graph

Liquid chromatography-mass spectrometry (LC-MS)-based metabolomics identification relies heavily on high-quality MS/MS data; MS/MS prediction is a good way to address this issue. However, the accuracy of the prediction, resolution, and correlation with chemical structures have not been well-solved. In this study, we have developed a MS/MS prediction method, PPGB-MS2, which transforms the MS/MS prediction into fragment intensity prediction, and the concept of precursor-product ion pair graph bags (PPGBs) was introduced to represent fragments, achieving uniform representation of precursor and product ion structures and MS/MS fragmentation information. The chemical structure information is kept before it is incorporated into machine learning models. Due to the PPGB representation, graph neural networks (GNNs) can be utilized to achieve MS/MS fragment intensity prediction. The system was trained and evaluated using [M+H]+ and [M-H]- data acquired by an Agilent QTOF 6530 in the NIST 20 tandem MS database. Results demonstrated that the average cosine similarity is 0.71 in the test set, which is higher than classical MS/MS prediction methods. PPGB-MS2 also achieves high-resolution MS/MS prediction due to its effective management of the correspondence between fragments and structures.

Gold Flake-Enabled Miniature Capacitive Picobalances

Measurement of masses of microscale objects or weak force with ultrahigh sensitivity (down to nanogram/piconewton level) and compact configuration is highly desired for fundamental research and applications in various disciplines. Here, by using freestanding gold flakes with high reflectivity (≈98% at 980 nm) as the sample tray and silica microfibers with extremely low spring constant (≈0.05 mN m-1) as the cantilever beams, miniature capacitive balances are reported with piconewton-level detection limit (picobalances) and reliable radiation force-based calibration. In the design, the gold flake is suspended by two silica microfibers, which also functions as an electrode to form a capacitor with an underneath gold electrode. Benefitting from the high reflectivity of the gold flake, the performance of picobalances can be precisely calibrated by exerting piconewton-level radiation pressure on the gold flake (working as a mirror) with a laser, showing a detection limit as low as 6.9 pN. Finally, using a fiber taper-assisted micromanipulation technique, masses of various types of pollens (with weights ranging from 4.6 to 96.3 ng) are readily measured by a picobalance at single-particle level. The miniature picobalances should find applications in precise measurement of masses of micro or nanoscale objects and various types of weak forces.

Advance in the application of metabolomics technology in poultry

Metabolomics is a science that takes small molecular metabolites in organisms as the research object and determines the dynamic changes of metabolites at the overall level through a variety of modern analytical techniques. At present, metabolomics technology has been widely used in biological significance interpretation, food safety and quality, breeding, disease diagnosis, functional compound identification, and other fields. Its application in poultry science has also become the focus of widespread attention. With the sustainable development of analytical techniques, metabolomics has great potential in the application of poultry science. In this paper, the research progress of metabolomics in poultry growth and development, genetics and breeding, egg quality, meat quality, and disease is reviewed and concluded, which is expected to provide scientific ideas for the research of metabolomics in poultry.