PubMed

Chem Biodivers. 2024 Dec 23:e202402311. doi: 10.1002/cbdv.202402311. Online ahead of print.ABSTRACTBlanchetia heterotricha is a species popularly used for its medicinal properties. However, few scientific records report the investigation of its chemical composition and biological activity. Herein, a metabolomics and multivariate statistical analysis approach was used to assess the chemical composition, antioxidant, and antimicrobial activity of B. heterotricha essential oil and fixed extracts (non-volatile). Thirty-six metabolites were identified in the essential oil by GC-MS, whereas 59 metabolites were identified in the fixed extracts by HPLC-HRMS. The essential oil and fixed extracts showed varying degrees of antioxidant and antimicrobial activities. The antioxidant activity varied from 7.27 ± 0.95 μg.mL-1 for the root methanolic extract extract to 513.25 ± 7.77 μg.mL-1 for the seed hexane extract. Extracts obtained with ethyl acetate showed the most promising antimicrobial activity, followed by extracts obtained with methanol and hexane. Multivariate statistical analysis allowed the identification of possible contributors to the observed activities within the extracts. This is the first study to assess the antimicrobial activity in any Blanchetia species. The combination of metabolomics and multivariate statistical analysis was a powerful tool to identify bioactive compounds, highlighting the potential of B. heterotricha essential oil and fixed extracts as sources of metabolites with antioxidant and antimicrobial properties.PMID:39714403 | DOI:10.1002/cbdv.202402311

Mol Omics. 2024 Dec 23. doi: 10.1039/d4mo00187g. Online ahead of print.ABSTRACTCancer remains the second leading cause of death worldwide, surpassed only by cardiovascular disease. From the different types of cancer, pancreatic cancer (PaC) has one of the lowest survival rates, with a survival rate of about 20% after the first year of diagnosis and about 8% after 5 years. The lack of highly sensitive and specific biomarkers, together with the absence of symptoms in the early stages, determines a late diagnosis, which is associated with a decrease in the effectiveness of medical intervention, regardless of its nature - surgery and/or chemotherapy. This review provides an updated overview of recent studies combining multi-OMICs approaches (e.g., proteomics, metabolomics) with analytical tools, highlighting the synergy between high-throughput molecular data generation and precise analytical tools such as LC-MS, GC-MS and MALDI-TOF MS. This combination significantly improves the detection, quantification and identification of biomolecules in complex biological systems and represents the latest advances in understanding PaC management and the search for effective diagnostic tools. Large-scale data analysis coupled with bioinformatics tools enables the identification of specific genetic mutations, gene expression patterns, pathways, networks, protein modifications and metabolic signatures associated with PaC pathogenesis, progression and treatment response through the integration of multi-OMICs data.PMID:39714229 | DOI:10.1039/d4mo00187g

mSystems. 2024 Dec 23:e0045224. doi: 10.1128/msystems.00452-24. Online ahead of print.ABSTRACTDietary fiber confers multiple health benefits originating from the expansion of beneficial gut microbial activity. However, very few studies have established the metabolic consequences of interactions among specific fibers, microbiome composition, and function in either human or representative animal models. In a study design reflective of realistic population dietary variation, fecal metagenomic and metabolomic profiles were analyzed from healthy dogs fed 12 test foods containing different fiber sources and quantities (5-13% as-fed basis). Taxa and functions were identified whose abundances were associated either with overall fiber intake or with specific fiber compositions. Fourteen microbial species were significantly enriched in response to ≥1 specific fiber source; enrichment of fiber-derived metabolites was more pronounced in response to these fiber sources. Positively associated fecal metabolites, including short-chain fatty acids, acylglycerols, fiber bound sugars, and polyphenols, co-occurred with microbes enriched in specific food groups. Critically, the specific metabolite pools responsive to differential fiber intake were dependent on differences both in individual microbial community membership and in overall ecological configuration. This helps to explain, for the first time, differences in microbiome-diet associations observed in companion animal epidemiology. Thus, our study corroborates findings in human cohorts and reinforces the role of personalized microbiomes even in seemingly phenotypically homogeneous subjects.IMPORTANCE: Consumption of dietary fiber changes the composition of the gut microbiome and, to a larger extent, the associated metabolites. Production of health-relevant metabolites such as short-chain fatty acids from fiber depends both on the consumption of a specific fiber and on the enrichment of beneficial metabolite-producing species in response to it. Even in a seemingly homogeneous population, the benefit received from fiber consumption is personalized and emphasizes specific fiber-microbe-host interactions. These observations are relevant for both population-wide and personalized nutrition applications.PMID:39714168 | DOI:10.1128/msystems.00452-24

mSystems. 2024 Dec 23:e0118924. doi: 10.1128/msystems.01189-24. Online ahead of print.ABSTRACTOnions are well-known vegetables that offer various health benefits. This study explores the impact of onion extracts on gut microbiome using an in vitro fecal incubation model and metabolome analysis. Fecal samples were collected from 19 healthy donors and incubated in the presence or absence of onion extracts for 24 h. To reduce inter-individual variability in the gut microbiome, we employed enterotyping based on baseline fecal microbiota: 14 subjects with a Bacteroides-dominant type (enterotype B) and 5 subjects with Prevotella-dominant type (enterotype P). Alpha diversity was significantly reduced in the onion-treated group compared to the non-treated control group in both Bacteroides- and Prevotella-dominant types. However, significant structural differences in bacterial communities were observed based on weighted UniFrac distance. Notably, short-chain fatty acid (SCFA)-producing bacteria, such as Bifidobacterium_388775, Feacalibacterium, and Fusicatenibacter, were overrepresented in response to onion extracts in enterotype B. Furthermore, genes related to butyrate production were significantly overrepresented in the onion-treated group within enterotype B. Consistent with the enriched taxa and the predicted metabolic pathways, SCFAs and their related metabolites were significantly enriched in the onion-treated group. Additionally, tryptophan metabolism-derived metabolites, including indolelactate (ILA) and indolepropionate (IPA), were elevated by 4- and 32-fold, respectively, in the onion-treated group compared to the control group. In vitro growth assays showed an increase in lactobacilli strains in the presence of onion extracts. These results provide evidence that onion extracts could serve as promising prebiotics by altering gut microbial structure and promoting the production of beneficiary metabolites, including SCFAs and indole derivatives, and enhancing the growth of probiotics.IMPORTANCEThis study is significant as it provides compelling evidence that onion extracts have the potential to serve as effective prebiotics. Utilizing an in vitro fecal incubation model and enterotyping to reduce inter-individual variability, the research demonstrates how onion extracts can alter gut microbial structure and promote the production of beneficial metabolites, including SCFAs and indole derivatives like ILA and IPA. Additionally, onion extract treatment enhances the growth of beneficial probiotics. The findings underscore the potential of onion extracts to improve gut health by enriching specific beneficial bacteria and metabolic pathways, thereby supporting the development of functional foods aimed at improving gut microbiota composition and metabolic health.PMID:39714164 | DOI:10.1128/msystems.01189-24

Front Nutr. 2024 Dec 6;11:1509341. doi: 10.3389/fnut.2024.1509341. eCollection 2024.ABSTRACTGiven the abundance of beneficial properties and enzymes secreted by edible oyster mushrooms, their mycelium could serve as a starter for fermented foods to enhance their nutritional and bioactive quality. This study aimed to investigate the effects on the nutritional ingredients, antioxidant activity, and non-volatile metabolites during solid-state fermentation (SSF) of soybeans by Pleurotus ostreatus mycelium. The results indicated that the contents of dietary fiber and starch in fermented soybeans decreased, while the amounts of protein and lipid increased after SSF (P < 0.05). Analysis of the total phenolic content (TPC) and antioxidant activities of the fermented soybeans revealed that the methanolic extracts significantly increased TPC and antioxidant activities against intracellular reactive oxygen species (ROS) in lipopolysaccharide (LPS)-stimulated RAW 264.7 macrophages, as well as against DPPH and ABTS radicals in vitro. A total 154 differential metabolites were identified after SSF, and a Spearman correlation study revealed a direct relationship between antioxidant activities and certain metabolites including phenolic compounds, oligopeptides, and free fatty acids etc. Among these metabolites, phenolic compounds produced by the shikimic acid pathway were diverse in variety and had the greatest multiple differences. The study discovered that a potential mechanism involving SSF with P. ostreatus mycelium increased the antioxidant activity of soybeans.PMID:39713777 | PMC:PMC11660803 | DOI:10.3389/fnut.2024.1509341

J Inflamm Res. 2024 Dec 18;17:11197-11209. doi: 10.2147/JIR.S495512. eCollection 2024.ABSTRACTINTRODUCTION: Sepsis-induced acute lung injury (ALI), a critical sequela of systemic inflammation, often progresses to acute respiratory distress syndrome, conferring high mortality. Although UMI-77 has demonstrated efficacy in mitigating lung injury in sepsis, the molecular mechanisms underlying its action have not yet been fully elucidated.METHODS: This study aimed to delineate the mechanism by which UMI-77 counteracts sepsis-induced ALI using comprehensive transcriptomic and metabolomic analyses.RESULTS: UMI-77 significantly ameliorated histopathological changes in the lungs of mice with sepsis-induced ALI Transcriptomic analysis revealed that 124 differentially expressed genes were modulated by UMI-77 and were predominantly implicated in chemokine-mediated signaling pathways, apoptosis regulation, and inflammatory responses. Integrated metabolomic analysis identified Atp4a, Ido1, Ctla4, and Cxcl10 as key genes, and inosine 5'-monophosphate (IMP), thiamine monophosphate, thymidine 3',5'-cyclic monophosphate (dTMP) as key differential metabolites. UMI-77 may regulate key genes (Atp4a, Ido1, Ctla4, and Cxcl10) to affect key metabolites (IMP, thiamine monophosphate, and dTMP) and their target genes (Entpd2, Entpd1, Nt5e, and Hprt) involved in cytokine-cytokine receptor interaction, gastric acid secretion, pyrimidine, and purine metabolism in the treatment of sepsis-induced ALI.CONCLUSION: UMI-77 exerts its therapeutic effect in sepsis-induced ALI through intricate modulation of pivotal genes and metabolites, thereby influencing critical biological pathways. This study lays the groundwork for further development and clinical translation of UMI-77 as a potential therapeutic agent for sepsis-associated lung injuries.PMID:39713715 | PMC:PMC11663390 | DOI:10.2147/JIR.S495512

bioRxiv [Preprint]. 2024 Dec 14:2024.12.13.628212. doi: 10.1101/2024.12.13.628212.ABSTRACTProteolysis of hydrophobic helices is required for complete breakdown of every transmembrane protein trafficked to the lysosome and sustains high rates of endocytosis. However, the lysosomal mechanisms for degrading hydrophobic domains remain unknown. Combining lysosomal proteomics with functional genomic data mining, we identify Lysosomal Leucine Aminopeptidase (LyLAP; formerly Phospholipase B Domain-Containing 1) as the hydrolase most tightly associated with elevated endocytic activity. Untargeted metabolomics and biochemical reconstitution demonstrate that LyLAP is not a phospholipase, but a processive monoaminopeptidase with strong preference for N-terminal leucine - an activity necessary and sufficient for breakdown of hydrophobic transmembrane domains. LyLAP is upregulated in pancreatic ductal adenocarcinoma (PDA), which relies on macropinocytosis for nutrient uptake, and its ablation led to buildup of undigested hydrophobic peptides, which compromised lysosomal membrane integrity and inhibited PDA cell growth. Thus, LyLAP enables lysosomal degradation of membrane proteins, and may represent a vulnerability in highly endocytic cancer cells.ONE SENTENCE SUMMARY: LyLAP degrades transmembrane proteins to sustain high endocytosis and lysosomal membrane stability in pancreatic cancer.PMID:39713462 | PMC:PMC11661280 | DOI:10.1101/2024.12.13.628212

bioRxiv [Preprint]. 2024 Dec 14:2024.12.13.628441. doi: 10.1101/2024.12.13.628441.ABSTRACTMetabolite production, consumption, and exchange are intimately involved with host health and disease, as well as being key drivers of host-microbiome interactions. Despite the increasing prevalence of datasets that jointly measure microbiome composition and metabolites, computational tools for linking these data to the status of the host remain limited. To address these limitations, we developed MMETHANE, an open-source software package that implements a purpose-built deep learning model for predicting host status from paired microbial sequencing and metabolomic data. MMETHANE incorporates prior biological knowledge, including phylogenetic and chemical relationships, and is intrinsically interpretable, outputting an English-language set of rules that explains its decisions. Using a compendium of six datasets with paired microbial composition and metabolomics measurements, we showed that MMETHANE always performed at least on par with existing methods, including blackbox machine learning techniques, and outperformed other methods on >80% of the datasets evaluated. We additionally demonstrated through two cases studies analyzing inflammatory bowel disease gut microbiome datasets that MMETHANE uncovers biologically meaningful links between microbes, metabolites, and disease status.PMID:39713330 | PMC:PMC11661223 | DOI:10.1101/2024.12.13.628441

bioRxiv [Preprint]. 2024 Dec 14:2024.12.06.627264. doi: 10.1101/2024.12.06.627264.ABSTRACTMetabolic reprogramming is a hallmark of cancer, enabling tumor cells to adapt to and exploit their microenvironment for sustained growth. The liver is a common site of metastasis, but the interactions between tumor cells and hepatocytes remain poorly understood. In the context of liver metastasis, these interactions play a crucial role in promoting tumor survival and progression. This study leverages multiomics coverage of the microenvironment via liquid chromatography and high-resolution, high-mass accuracy mass spectrometry-based untargeted metabolomics, 13 C-stable isotope tracing, and RNA sequencing to uncover the metabolic impact of co-localized primary hepatocytes and a colon adenocarcinoma cell line, SW480, using a 2D co-culture model. Metabolic profiling revealed disrupted Warburg metabolism with an 80% decrease in glucose consumption and 94% decrease in lactate production by hepatocyte-SW480 co-cultures relative to SW480 control cultures. Decreased glucose consumption was coupled with alterations in glutamine and ketone body metabolism, suggesting a possible fuel switch upon co-culturing. Further, integrated multiomic analysis indicates that disruptions in metabolic pathways, including nucleoside biosynthesis, amino acids, and TCA cycle, correlate with altered SW480 transcriptional profiles and highlight the importance of redox homeostasis in tumor adaptation. Finally, these findings were replicated in 3-dimensional microtissue organoids. Taken together, these studies support a bioinformatic approach to study metabolic crosstalk and discovery of potential therapeutic targets in preclinical models of the tumor microenvironment.PMID:39713297 | PMC:PMC11661097 | DOI:10.1101/2024.12.06.627264

ADMET DMPK. 2024 Dec 8;12(6):957-970. doi: 10.5599/admet.2476. eCollection 2024.ABSTRACTBACKGROUND AND PURPOSE: Glyphosate-based herbicides, extensively utilized worldwide, raise concerns regarding potential human risks due to the detection of glyphosate (GLY) in human body fluids. This study aims to address critical knowledge gaps regarding whether GLY undergoes metabolism in humans, particularly considering the limited information available on human metabolism.EXPERIMENTAL APPROACH: The study investigated GLY and its metabolites in eight amenity horticultural workers using proton nuclear magnetic resonance (1H-NMR) data analysis. Multiple spot urine samples were collected before and after herbicide applications.KEY RESULTS: Findings reveal the presence of GLY and its metabolites (AMPA, formaldehyde, sarcosine, glyoxylic acid, and methylamine). Results demonstrate a moderate correlation between median GLY concentration and its metabolites within the studied population.CONCLUSION: Persuasive evidence suggests the potential metabolism of GLY in humans. 1H-NMR data analysis might be a promising technique for determining the metabolism of GLY in humans, offering valuable insights into urinary excretion patterns.PMID:39713254 | PMC:PMC11661807 | DOI:10.5599/admet.2476

Food Chem X. 2024 May 31;23:101525. doi: 10.1016/j.fochx.2024.101525. eCollection 2024 Oct 30.ABSTRACTIn this study, three fermentation treatments of spontaneous fermentation (SF), direct inoculation of CECA (YF), and inoculation with CECA after addition of dimethyl dicarbonate (YDF) were carried out. Multivariate statistical analysis approved that CECA inoculation significantly influenced the composition of 141 metabolites (15 volatile organic compounds (VOCs) and 126 non-VOCs), mainly consisting of 36 acids and derivatives and 25 lipids and lipid-like molecules. YF and YDF wines exhibited similar correlations with aroma types, while there were differences in the kinds and number of VOCs. Moreover, CECA-inoculated fermentation was more favorable to the formation of aftertaste-A, umami, sourness, and richness. The KEGG metabolic pathway analysis indicated that the inoculation strategy significantly affected the amino acid metabolism. The antimicrobial treatment effectively enhanced bitterness, astringency, umami and saltiness while reducing acidity. Further studies are needed to assess the effects of antimicrobial treatment on lipid metabolism.PMID:39713187 | PMC:PMC11662240 | DOI:10.1016/j.fochx.2024.101525

World J Gastroenterol. 2024 Dec 21;30(47):5076-5080. doi: 10.3748/wjg.v30.i47.5076.ABSTRACTIn this manuscript, we comment on the article by Liu et al published in the recent issue of the journal. Hyperuricemia (HUA) has become the second most common metabolic disease after type 2 diabetes mellitus and is the most important risk factor for gout. This discussion focuses on the targets and clinical application value of traditional Chinese medicine (TCM) extracts in the treatment of HUA and gout, emphasizing the role of gut microbiota. Liu et al's study demonstrated that Poecilobdella manillensis protein extract alleviated HUA through multiple mechanisms, including inhibition of uric acid (UA) reabsorption, promotion of UA excretion, repair of intestinal barrier function, and regulation of gut microbiota and metabolome. Unlike the commonly used urate-lowering drugs such as allopurinol and febuxostat, which have clear and single targets, many TCMs have multi-target effects. However, the active components and mechanisms of TCMs are not fully understood, limiting their clinical application in the treatment of HUA and gout. Additionally, the role of gut microbiota in UA metabolic homeostasis needs to be further explored.PMID:39713159 | PMC:PMC11612855 | DOI:10.3748/wjg.v30.i47.5076

Front Bioeng Biotechnol. 2024 Dec 6;12:1493766. doi: 10.3389/fbioe.2024.1493766. eCollection 2024.ABSTRACTIn this study, a strain isolated from the surface of flue-cured tobacco leaves, identified as Bacillus velezensis HJ-16, was applied in the solid-state fermentation of tobacco leaves. This strain, known for producing thermally stable enzymes, including amylase, cellulase, and protease, significantly improved the sensory qualities of tobacco, enhancing aromatic intensity, density, and softness, while reducing irritation. Whole-genome sequencing and functional annotation revealed that B. velezensis HJ-16 possesses a single circular chromosome containing genes associated with enzyme production and metabolic activities, particularly in carbohydrate metabolism and amino acid metabolism. Untargeted metabolomics analysis identified significant changes in non-volatile metabolites induced by fermentation. These metabolites were enriched in pathways related to flavonoid biosynthesis, alkaloid biosynthesis, aromatic amino acid metabolism, lipid metabolism, and carbon metabolism. Metagenomic analysis showed that Bacillus became the dominant genus on the tobacco leaf surface following inoculation with B. velezensis HJ-16, altering the microbial community composition, reducing diversity and evenness, and enhancing microbial metabolic activity. These findings underscore the potential of B. velezensis HJ-16 as a biotechnological tool to improve tobacco leaf quality.PMID:39713101 | PMC:PMC11659759 | DOI:10.3389/fbioe.2024.1493766

ACS Meas Sci Au. 2024 Oct 21;4(6):668-677. doi: 10.1021/acsmeasuresciau.4c00035. eCollection 2024 Dec 18.ABSTRACTAmbient 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.PMID:39713036 | PMC:PMC11659995 | DOI:10.1021/acsmeasuresciau.4c00035

ACS Meas Sci Au. 2024 Oct 4;4(6):695-701. doi: 10.1021/acsmeasuresciau.4c00044. eCollection 2024 Dec 18.ABSTRACTAssessing the quality of wheat, one of humanity's most important crops, in a straightforward manner, is essential. In this study, analysis of variance (ANOVA) simultaneous component analysis (ASCA) paired with near-infrared spectroscopy (NIRS) was used as an easy-to-implement and environmentally friendly tool for this purpose. The capabilities of combining NIRS with ASCA were demonstrated by studying the effects of sampling site and year on the quality of 180 Austrian wheat samples across four sites over 3 years. It was found that the year, sample site, and their combination significantly (p < 0.001) affect the NIR spectra of wheat. NIR spectral preprocessing tools, usually employed in chemometric workflows, notably influence the results obtained by ASCA, particularly in terms of the variance attributed to annual and regional effects. The influence of the year was identified as the dominant factor, followed by region and the combined effect of year and sampling site. Interpretation of the loading plots obtained by ASCA demonstrates that wheat components such as proteins, carbohydrates, moisture, or fat contribute to annual and regional differences. Additionally, the protein, starch, moisture, fat, fiber, and ash contents of wheat samples obtained using a NIR-based calibration were found to be significantly influenced by year, sampling site, or their combination using ANOVA. This study shows that the combination of ASCA with NIRS simplifies NIR-based quality assessment of wheat without the need for time- and chemical-consuming calibration development.PMID:39713033 | PMC:PMC11659997 | DOI:10.1021/acsmeasuresciau.4c00044

ACS Meas Sci Au. 2024 Oct 14;4(6):702-711. doi: 10.1021/acsmeasuresciau.4c00047. eCollection 2024 Dec 18.ABSTRACTSample normalization is a crucial step in metabolomics for fair quantitative comparisons. It aims to minimize sample-to-sample variations due to differences in the total metabolite amount. When samples lack a specific metabolic quantity to accurately represent their total metabolite amounts, post-acquisition sample normalization becomes essential. Despite many proposed normalization algorithms, understanding remains limited of their differences, hindering the selection of the most suitable one for a given metabolomics study. This study bridges this knowledge gap by employing data simulation, experimental simulation, and real experiments to elucidate the differences in the mechanism and performance among common post-acquisition sample normalization methods. Using public datasets, we first demonstrated the dramatic discrepancies between the outcomes of different sample normalization methods. Then, we benchmarked six normalization methods: sum, median, probabilistic quotient normalization (PQN), maximal density fold change (MDFC), quantile, and class-specific quantile. Our results show that most normalization methods are biased when there is unbalanced data, a phenomenon where the percentages of up- and downregulated metabolites are unequal. Notably, unbalanced data can be sourced from the underlying biological differences, experimental perturbations, and metabolic interference. Beyond normalization algorithms and data structure, our study also emphasizes the importance of considering additional factors contributed by data quality, such as background noise, signal saturation, and missingness. Based on these findings, we propose an evidence-based normalization strategy to maximize sample normalization outcomes, providing a robust bioinformatic solution for advancing metabolomics research with a fair quantitative comparison.PMID:39713024 | PMC:PMC11659990 | DOI:10.1021/acsmeasuresciau.4c00047

EFSA J. 2024 Dec 20;22(Suppl 1):e221115. doi: 10.2903/j.efsa.2024.e221115. eCollection 2024 Dec.ABSTRACTThis study provides a comprehensive proteomic and metabolomic analysis of novel anthocyanin- and carotenoid-rich wheat varieties to assess their immunogenicity in the context of Celiac Disease. Using (semi)-quantitative mass spectrometry, the research found that gliadin expression and peptide release, particularly those containing immunostimulatory γ-gliadin epitopes, vary significantly across different wheat varieties. While non-targeted mass spectrometry provided valuable insights, the study acknowledged potential methodological biases, such limitations of ion current intensity as a measure of peptide abundance. Despite promising results, further research is required to determine the safety and efficacy of coloured wheat varieties for Celiac Disease patients, considering the complex interplay of gluten proteins, food processing, digestion and matrix effects. The ongoing studies hold potential for developing nutritionally beneficial wheat alternatives for Celiac Disease management.PMID:39712908 | PMC:PMC11659743 | DOI:10.2903/j.efsa.2024.e221115

Front Microbiol. 2024 Dec 5;15:1486566. doi: 10.3389/fmicb.2024.1486566. eCollection 2024.ABSTRACTThis study investigated age-related changes in the gut microbiota and metabolome of Sapsaree dogs through metagenomic and metabolomic analyses. Using Illumina (short-read) and Nanopore (long-read) sequencing technologies, we identified both common and unique bacterial genera in the dogs across different age groups. In metagenomic analysis, Firmicutes were predominant at the family level. At the genus level, Lactobacillus, Streptococcus, Romboutsia, and Clostridium XI were the most abundant, and the bacterial genera typically considered beneficial were less prevalent in senior dogs, whereas the genera associated with pathogenicity were more abundant. These findings suggest age-related shifts in gut microbiota composition. Metabolomic analysis showed distinct clustering of metabolites based on the age group, with changes in metabolite profiles correlating with metagenomic findings. Although Illumina and Nanopore methods provided distinctive results, the genera detected by both methods exhibited similar trends across all age groups in Sapsaree dogs. These findings highlight the relationship between ages, metabolite profiles and gut microbiota composition in dogs, suggesting the need for further research to explore this relation in greater depth.PMID:39712896 | PMC:PMC11659757 | DOI:10.3389/fmicb.2024.1486566

Front Microbiol. 2024 Dec 6;15:1514825. doi: 10.3389/fmicb.2024.1514825. eCollection 2024.ABSTRACTUnderstanding the impact of antibiotics on bacterial metabolism is crucial for elucidating their mechanisms of action and developing more effective therapeutic strategies. β-lactam antibiotics, distinguished by their distinctive β-lactam ring structure, are widely used as antimicrobial agents. This study investigates the global metabolic alterations induced by three β-lactam antibiotics-meropenem (a carbapenem), ampicillin (a penicillin), and ceftazidime (a cephalosporin)-in Escherichia coli. Our comprehensive metabolic profiling revealed significant perturbations in bacterial metabolism, particularly in pathways such as glutathione metabolism, pantothenate and CoA biosynthesis, pyrimidine metabolism, and purine metabolism. Antibiotic treatment markedly increased reactive oxygen species levels, with meropenem reaching nearly 200 ± 7%, ampicillin at 174 ± 11%, and ceftazidime at 152 ± 7%. Additionally, β-lactam antibiotics elevated 8-OHdG levels to 4.73 ± 0.56-fold for meropenem, 2.49 ± 0.19-fold for ampicillin, and 3.19 ± 0.34-fold for ceftazidime; 8-OHG levels increased to 5.57 ± 0.72-fold for meropenem, 3.08 ± 0.31-fold for ampicillin, and 4.45 ± 0.66-fold for ceftazidime, indicating that oxidative stress enhances oxidative damage to bacterial DNA and RNA. Notably, we observed a selective upregulation of specific amino acids associated with cellular repair mechanisms, indicating a metabolic adaptation to counteract oxidative damage. These findings illustrate that β-lactam antibiotics induce a complex metabolic perturbations associated with ROS production, potentially compromising critical cellular components. This study enhances our understanding of the intricate relationship between antibiotic action and bacterial metabolism, providing valuable insights for developing effective strategies against antibiotic-resistant pathogens.PMID:39712889 | PMC:PMC11659197 | DOI:10.3389/fmicb.2024.1514825

Front Pharmacol. 2024 Dec 6;15:1470142. doi: 10.3389/fphar.2024.1470142. eCollection 2024.ABSTRACTBACKGROUND: Acid-sensing ion channels are activated during myocardial ischemia and are implicated in the mechanism of myocardial ischemia-reperfusion injury (MIRI). Acid-sensing ion channel 3 (ASIC3), the most pH-sensitive member of the ASIC family, is highly expressed in myocardial tissues. However, the role of ASIC3 in MIRI and its precise effects on the myocardial metabolome remain unclear. These unknowns might be related to the cardioprotective effects observed with APETx2 post-conditioning.METHOD: Rat hearts subjected to Langendorff perfusion were randomly assigned to the normal (Nor) group, ischemia/reperfusion (I/R) group, ASIC3 blockade (AP) group. Rat hearts in group AP were treated with the ASIC3-specific inhibitor APETx2 (630 nM). Molecular and morphological changes were observed to elucidate the role of ASIC3 in MIRI. Bioinformatics analyses identified differential metabolites and pathways associated with APETx2 post-conditioning.RESULTS: APETx2 post-conditioning stabilized hemodynamics in the isolated rat heart model of MIRI. It also reduced myocardial infarct size, mitigated mitochondrial damage at the ultrastructural level, and improved markers of myocardial injury and oxidative stress. Further more, we observed that phosphatidylcholine, phosphatidylethanolamine, citric acid, cyanidin 5-O-beta-D-glucoside, and L-aspartic acid decreased after MIRI. The levels of these metabolites were partially restored by APETx2 post-conditioning. These metabolites are primarily involved in autophagy and endogenous cannabinoid signaling pathways.CONCLUSION: ASIC3 is potentially a key player in MIRI. APETx2 post-conditioning may improve MIRI through specific metabolic changes. This study provides valuable data for future research on the metabolic mechanisms underlying the effects of APETx2 post-conditioning in MIRI.PMID:39712499 | PMC:PMC11658994 | DOI:10.3389/fphar.2024.1470142