PubMed

Cells. 2024 May 7;13(10):795. doi: 10.3390/cells13100795.ABSTRACTMyeloid-derived suppressor cells (MDSCs) play an essential role in suppressing the antitumor activity of T lymphocytes in solid tumors, thus representing an attractive therapeutic target to enhance the efficacy of immunotherapy. However, the differences in protein expression between MDSCs and their physiological counterparts, particularly polymorphonuclear neutrophils (PMNs), remain inadequately characterized, making the specific identification and targeting of MDSCs difficult. PMNs and PMN-MDSCs share markers such as CD11b+CD14-CD15+/CD66b+, and some MDSC-enriched markers are emerging, such as LOX-1 and CD84. More proteomics studies are needed to identify the signature and markers for MDSCs. Recently, we reported the induced differentiation of isogenic PMNs or MDSCs (referred to as iPMNs and iMDSCs, respectively) from the human promyelocytic cell line HL60. Here, we profiled the global proteomics and membrane proteomics of these cells with quantitative mass spectrometry, which identified a 41-protein signature ("cluster 6") that was upregulated in iMDSCs compared with HL60 and iPMN. We further integrated our cell line-based proteomics data with a published proteomics dataset of normal human primary monocytes and monocyte-derived MDSCs induced by cancer-associated fibroblasts. The analysis identified a 38-protein signature that exhibits an upregulated expression pattern in MDSCs compared with normal monocytes or PMNs. These signatures may provide a hypothesis-generating platform to identify protein biomarkers that phenotypically distinguish MDSCs from their healthy counterparts, as well as potential therapeutic targets that impair MDSCs without harming normal myeloid cells.PMID:38786019 | DOI:10.3390/cells13100795

Biomolecules. 2024 Apr 29;14(5):532. doi: 10.3390/biom14050532.ABSTRACTMyocardial infarction (MI), including ST-segment elevation MI (STEMI) and non-ST-segment elevation MI (NSTEMI), is still a leading cause of death worldwide. Metabolomics technology was used to explore differential metabolites (DMs) as potential biomarkers for early diagnosis of STEMI and NSTEMI. In the study, 2531 metabolites, including 1925 DMs, were discovered. In the selected 27 DMs, 14 were successfully verified in a new cohort, and the AUC values were all above 0.8. There were 10 in STEMI group, namely L-aspartic acid, L-acetylcarnitine, acetylglycine, decanoylcarnitine, hydroxyphenyllactic acid, ferulic acid, itaconic acid, lauroylcarnitine, myristoylcarnitine, and cis-4-hydroxy-D-proline, and 5 in NSTEMI group, namely L-aspartic acid, arachidonic acid, palmitoleic acid, D-aspartic acid, and palmitelaidic acid. These 14 DMs may be developed as biomarkers for the early diagnosis of MI with high sensitivity and specificity. These findings have particularly important clinical significance for NSTEMI patients because these patients have no typical ECG changes.PMID:38785939 | DOI:10.3390/biom14050532

Biology (Basel). 2024 May 11;13(5):334. doi: 10.3390/biology13050334.ABSTRACTFritillaria cirrhosa is an important cash crop, and its industrial development is being hampered by continuous cropping obstacles, but the composition and changes of rhizosphere soil microorganisms and metabolites in the cultivation process of Fritillaria cirrhosa have not been revealed. We used metagenomics sequencing to analyze the changes of the microbiome in rhizosphere soil during a three-year cultivation process, and combined it with LC-MS/MS to detect the changes of metabolites. Results indicate that during the cultivation of Fritillaria cirrhosa, the composition and structure of the rhizosphere soil microbial community changed significantly, especially regarding the relative abundance of some beneficial bacteria. The abundance of Bradyrhizobium decreased from 7.04% in the first year to about 5% in the second and third years; the relative abundance of Pseudomonas also decreased from 6.20% in the first year to 2.22% in the third year; and the relative abundance of Lysobacter decreased significantly from more than 4% in the first two years of cultivation to 1.01% in the third year of cultivation. However, the relative abundance of some harmful fungi has significantly increased, such as Botrytis, which increased significantly from less than 3% in the first two years to 7.93% in the third year, and Talaromyces fungi, which were almost non-existent in the first two years of cultivation, significantly increased to 3.43% in the third year of cultivation. The composition and structure of Fritillaria cirrhosa rhizosphere metabolites also changed significantly, the most important of which were carbohydrates represented by sucrose (48.00-9.36-10.07%) and some amino acid compounds related to continuous cropping obstacles. Co-occurrence analysis showed that there was a significant correlation between differential microorganisms and differential metabolites, but Procrustes analysis showed that the relationship between bacteria and metabolites was closer than that between fungi and metabolites. In general, in the process of Fritillaria cirrhosa cultivation, the beneficial bacteria in the rhizosphere decreased, the harmful bacteria increased, and the relative abundance of carbohydrate and amino acid compounds related to continuous cropping obstacles changed significantly. There is a significant correlation between microorganisms and metabolites, and the shaping of the Fritillaria cirrhosa rhizosphere's microecology by bacteria is more relevant.PMID:38785816 | DOI:10.3390/biology13050334

Curr Issues Mol Biol. 2024 May 2;46(5):4234-4250. doi: 10.3390/cimb46050258.ABSTRACTThe activity of dental caries, combined with its multifactorial etiology, alters salivary molecule composition. The present systematic review was developed to answer the following question: "Are salivary biomarkers reliable for diagnosis of dental caries?". Following the "Preferred Reporting Item for Systematic Reviews and Meta-analysis" (PRISMA) guidelines, the review was conducted using multiple database research (Medline, Web of Science, and Scopus). Studies performed on healthy subjects with and without dental caries and providing detailed information concerning the clinical diagnosis of caries (Decayed, Missing, Filled Teeth-DMFT and International Caries Detection and Assessment System-ICDAS criteria) were included. The quality assessment was performed following a modified version of the Joanna Briggs Institute Prevalence Critical Appraisal Checklist. The protocol was registered in the International Prospective Register of Systematic Reviews (PROSPERO, ID: CRD42022304505). Sixteen papers were included in the review. All studies reported statistically significant differences in the concentration of salivary molecules between subjects with and without caries (p < 0.05). Proteins were the most investigated molecules, in particular alpha-amylase and mucins. Some studies present a risk of bias, such as identifying confounding factors and clearly defining the source population. Nevertheless, the 16 papers were judged to be of moderate to high quality. There is evidence that some salivary compounds studied in this review could play an important diagnostic role for dental caries, such as salivary mucins, glycoproteins (sCD14), interleukins (IL-2RA, 4,-13), urease, carbonic anhydrase VI, and urea.PMID:38785526 | DOI:10.3390/cimb46050258

Curr Issues Mol Biol. 2024 Apr 30;46(5):4133-4146. doi: 10.3390/cimb46050254.ABSTRACTToday, colorectal cancer (CRC) diagnosis is performed using colonoscopy, which is the current, most effective screening method. However, colonoscopy poses risks of harm to the patient and is an invasive process. Recent research has proven metabolomics as a potential, non-invasive detection method, which can use identified biomarkers to detect potential cancer in a patient's body. The aim of this study is to develop a machine-learning (ML) model based on chemical descriptors that will recognize CRC-associated metabolites. We selected a set of metabolites found as the biomarkers of CRC, confirmed that they participate in cancer-related pathways, and used them for training a machine-learning model for the diagnostics of CRC. Using a set of selective metabolites and random compounds, we developed a range of ML models. The best performing ML model trained on Stage 0-2 CRC metabolite data predicted a metabolite class with 89.55% accuracy. The best performing ML model trained on Stage 3-4 CRC metabolite data predicted a metabolite class with 95.21% accuracy. Lastly, the best-performing ML model trained on Stage 0-4 CRC metabolite data predicted a metabolite class with 93.04% accuracy. These models were then tested on independent datasets, including random and unrelated-disease metabolites. In addition, six pathways related to these CRC metabolites were also distinguished: aminoacyl-tRNA biosynthesis; glyoxylate and dicarboxylate metabolism; glycine, serine, and threonine metabolism; phenylalanine, tyrosine, and tryptophan biosynthesis; arginine biosynthesis; and alanine, aspartate, and glutamate metabolism. Thus, in this research study, we created machine-learning models based on metabolite-related descriptors that may be helpful in developing a non-invasive diagnosis method for CRC.PMID:38785522 | DOI:10.3390/cimb46050254

Glycobiology. 2024 May 24:cwae036. doi: 10.1093/glycob/cwae036. Online ahead of print.ABSTRACTAberrant glycosylation is a key mechanism employed by cancer cells to evade immune surveillance, induce angiogenesis and metastasis, among other hallmarks of cancer. Sialic acids, distinctive terminal glycan structures located on glycoproteins or glycolipids, are prominently upregulated across various tumor types, including colorectal cancer (CRC). Sialylated glycans modulate anti-tumor immune responses through their interactions with Siglecs, a family of glycan-binding receptors with specificity for sialic acid-containing glycoconjugates, often resulting in immunosuppression. In this paper, we investigated the immunomodulatory function of ST3Gal5, a sialyltransferase that catalyzes the addition of α2-3 sialic acids to glycosphingolipids, since lower expression of ST3Gal5 is associated with better survival of CRC patients. We employed CRISPR/Cas9 to knock out the ST3Gal5 gene in two murine CRC cell lines MC38 and CT26. Glycomics analysis confirmed the removal of sialic acids on glycolipids, with no discernible impact on glycoprotein sialylation. Although knocking out ST3Gal5 in both cell lines did not affect in vivo tumor growth, we observed enhanced levels of regulatory T cells in CT26 tumors lacking ST3Gal5. Moreover, we demonstrate that the absence of ST3Gal5 affected size and blood vessel density only in MC38 tumors. In summary, we ascertain that sialylation of glycosphingolipids has a limited influence on the anti-tumor immune response in CRC, despite detecting alterations in the tumor microenvironment, possibly due to a shift in ganglioside abundance.PMID:38785323 | DOI:10.1093/glycob/cwae036

Ren Fail. 2024 Dec;46(1):2356021. doi: 10.1080/0886022X.2024.2356021. Epub 2024 May 24.ABSTRACTOBJECTIVE: This study aims to assess the clinical efficacy and safety of CHF-II in combination with RG for treating AKI on CKD (A on C), and to explore potential therapeutic mechanisms through lipidomics analysis.METHODS: 98 patients were enrolled and randomly assigned to the RG or RG + CHF groups. Both groups received RG therapy, with RG + CHF group additionally receiving CHF-II treatment over a duration of two weeks. Evaluation endpoints included changes in renal function, blood lipid profiles, urinary AKI biomarkers, and TCM symptoms before and after treatment. Serum samples were collected for lipid metabolite analysis.RESULTS: The total clinical effective rate in RG + CHF group was 73.5%, and that of RG group was 40.8%. TCM syndrome scores in RG + CHF group showed a more pronounced decrease (p < 0.05). Scr, BUN, and UA levels decreased while eGFR levels increased in both groups (p < 0.05), with a greater magnitude of change observed in the RG + CHF group. Urinary AKI biomarkers decreased more in RG + CHF group (p < 0.05). No serious adverse events occurred during the trial. 58 different lipid metabolites and 48 lipid biomarkers were identified. According to the KEGG database, the possible metabolic pathways involved triglyceride metabolic pathway and fat digestion and absorption metabolic pathways.CONCLUSION: CHF-II effectively alleviated kidney injury and improved TCM syndrome scores in patients with A on C. Lipid differential metabolites could serve as diagnostic indicators for AKI in patients with CKD. The possible metabolic pathways might be implicated in therapeutic action of CHF-II in the prevention and treatment of patients with A on C.PMID:38785301 | DOI:10.1080/0886022X.2024.2356021

Expert Rev Anticancer Ther. 2024 May 24. doi: 10.1080/14737140.2024.2344649. Online ahead of print.ABSTRACTINTRODUCTION: APC-associated polyposis is a rare hereditary disorder characterized by the development of multiple adenomas in the digestive tract. Individuals with APC-associated polyposis need to be managed by specialized multidisciplinary teams in dedicated centers.AREAS COVERED: The study aimed to review the literature on Familial adenomatous polyposis (FAP) to provide an update on diagnostic and surgical management while focusing on strategies to minimize the risk of desmoid-type fibromatosis, cancer in anorectal remnant, and postoperative complications. FAP individuals require a comprehensive approach that includes diagnosis, surveillance, preventive surgery, and addressing specific extracolonic concerns such as duodenal and desmoid tumors. Management should be personalized considering all factors: genotype, phenotype, and personal needs. Total colectomy and ileo-rectal anastomosis have been shown to yield superior QoL results when compared to Restorative Procto colectomy and ileopouch-anal anastomosis with acceptable oncological risk of developing cancer in the rectal stump if patients rigorously adhere to lifelong endoscopic surveillance. Additionally, a low-inflammatory diet may prevent adenomas and cancer by modulating systemic and tissue inflammatory indices.EXPERT OPINION: FAP management requires a multidisciplinary and personalized approach. Integrating genetic advances, innovative surveillance techniques, and emerging therapeutic modalities will contribute to improving outcomes and quality of life for FAP individuals.PMID:38785081 | DOI:10.1080/14737140.2024.2344649

Front Microbiol. 2024 May 3;15:1387957. doi: 10.3389/fmicb.2024.1387957. eCollection 2024.ABSTRACTThe host genes play a crucial role in shaping the composition and structure of the gut microbiome. Red deer is listed as an endangered species by the International Union for the Conservation of Nature, and its pilose antlers have good medicinal value. Hybridization can lead to heterosis, resulting in increased pilose antler production and growth performance in hybrid deer. However, the role of the gut microbiome in hybrid deer remains largely unknown. In this study, alpha and beta diversity analysis showed that hybridization altered the composition and structure of the gut microbiome of the offspring, with the composition and structure of the hybrid offspring being more similar to those of the paternal parents. Interestingly, the LefSe differential analysis showed that there were some significantly enriched gut microbiome in the paternal parents (such as g_Prevotellaceae UCG-003, f_Bacteroidales RF16 group; Ambiguous_taxa, etc.) and the maternal parents (including g_Alistipes, g_Anaerosporobacter, etc.), which remained significantly enriched in the hybrid offspring. Additionally, the hybrid offspring exhibited a significant advantage over the parental strains, particularly in taxa that can produce short-chain fatty acids, such as g_Prevotellaceae UCG-003, g_Roseburia, g_Succinivibrio, and g_Lachnospiraceae UCG-006. Similar to bacterial transmission, metagenomic analysis showed that some signaling pathways related to pilose antler growth ("Wnt signaling pathway," "PI3K Akt signaling pathway," "MAPK signaling pathway") were also enriched in hybrid red deer after hybridization. Furthermore, metabolomic analysis revealed that compared with the paternal and maternal parents, the hybrid offspring exhibited significant enrichment in metabolites related to "Steroid hormone biosynthesis," "Tryptophan metabolism," "Valine, leucine and isoleucine metabolism," and "Vitamin B metabolism." Notably, the metagenomic analysis also showed that these metabolic pathways were significantly enriched in hybrid deer. Finally, a correlation analysis between the gut microbiome and metabolites revealed a significant positive correlation between the enriched taxa in hybrid deer, including the Bacteroidales RF16 group, Prevotellaceae, and Succinivibrio, and metabolites, such as 7α-hydroxytestosterone, L-kynurenine, indole, L-isoleucine, and riboflavin. The study contributes valuable data toward understanding the role of the gut microbiome from red deer in hybridization and provides reference data for further screening potential probiotics and performing microbial-assisted breeding that promotes the growth of red deer pilose antlers and bodies, development, and immunity.PMID:38784815 | PMC:PMC11112572 | DOI:10.3389/fmicb.2024.1387957

Front Microbiol. 2024 May 9;15:1386150. doi: 10.3389/fmicb.2024.1386150. eCollection 2024.ABSTRACTChanges in climatic factors and rhizosphere microbiota led plants to adjust their metabolic strategies for survival under adverse environmental conditions. Changes in plant metabolites can mediate crop growth and development and interact with rhizosphere microbiota of the plant rhizosphere. To understand the interactions among environmental factors, rhizosphere microbiota, and metabolites of tobacco, a study was conducted by using integrated metagenomic and metabolomic strategies at four typical representative tobacco planting sites in Yunnan, China. The results showed that the agronomical and biochemical traits were significantly affected by temperature, precipitation (PREP), soil pH, and altitude. Correlation analyses revealed a significant positive correlation of temperature with length, width, and area of the leaf, while PREP correlated with plant height and effective leaf numbers. Furthermore, total sugar and reducing sugar contents of baked leaves were significantly higher, while the total nitrogen and total alkaloid levels were lower in tobacco leaves at site with low PREP. A total of 770 metabolites were detected with the highest number of different abundant metabolites (DMs) at Chuxiong (CX) with low PREP as compared to the other three sites, in which secondary metabolites were more abundant in both leaves and roots of tobacco. A total of 8,479 species, belonging to 2,094 genera with 420 individual bins (including 13 higher-quality bins) harboring 851,209 CDSs were detected. The phyla levels of microorganisms such as Euryarchaeota, Myxococcota, and Deinococcota were significantly enriched at the CX site, while Pseudomonadota was enriched at the high-temperature site with good PREP. The correlation analyses showed that the metabolic compounds in low-PREP site samples were positively correlated with Diaminobutyricimonas, Nissabacter, Alloactinosynnema, and Catellatospora and negatively correlated with Amniculibacterium, Nordella, Noviherbaspirillum, and Limnobacter, suggesting that the recruitment of Diaminobutyricimonas, Nissabacter, Alloactinosynnema, and Catellatospora in the rhizosphere induces the production and accumulation of secondary metabolites (SMs) (e.g., nitrogen compounds, terpenoids, and phenolics) for increasing drought tolerance with an unknown mechanism. The results of this study may promote the production and application of microbial fertilizers and agents such as Diaminobutyricimonas and Alloactinosynnema to assemble synthetic microbiota community or using their gene resources for better cultivation of tobacco as well as other crops in drought environments.PMID:38784812 | PMC:PMC11112021 | DOI:10.3389/fmicb.2024.1386150

Front Microbiol. 2024 May 9;15:1389311. doi: 10.3389/fmicb.2024.1389311. eCollection 2024.ABSTRACTINTRODUCTION: Cardiovascular diseases, including myocardial infarction, remain a leading cause of death globally. Emerging evidence suggests the gut microbiota plays a crucial role in cardiovascular health. This study aims to explore the impact of gut microbiota on myocardial infarction using a mouse model.METHODS: The research utilizes a multi-omics approach, including 16S rDNA sequencing and LC-MS-based metabolomics to analyze fecal and serum samples from mice modeled to mimic myocardial infarction. This methodology allows for a comprehensive analysis of microbial populations and their metabolic output.RESULTS: The findings reveal a significant reduction in gut microbiota α-diversity in mice with induced myocardial infarction compared to healthy controls. Notably, there is an increase in populations of Fusobacteria and Clostridia. Metabolomic analysis indicates disruptions in amino acid and energy metabolism, suggesting a metabolic dysregulation linked to myocardial health.DISCUSSION: The study proposes a novel microbiota-metabolite-myocardium axis, where specific microbial metabolites may directly affect heart health. This connection points to the gut microbiota as a potential player in the pathogenesis of myocardial infarction and may open new therapeutic avenues targeting the gut microbiome to combat cardiovascular diseases.PMID:38784809 | PMC:PMC11112089 | DOI:10.3389/fmicb.2024.1389311

PeerJ. 2024 May 20;12:e17414. doi: 10.7717/peerj.17414. eCollection 2024.ABSTRACTBACKGROUND: Sepsis-induced myocardial injury, as one of the important complications of sepsis, can significantly increase the mortality of septic patients. Our previous study found that nucleolin affected mitochondrial function in energy synthesis and had a protective effect on septic cardiomyopathy in mice. During sepsis, glucose metabolism disorders aggravated myocardial injury and had a negative effect on septic patients.OBJECTIVES: We investigated whether nucleolin could regulate glucose metabolism during endotoxemia-induced myocardial injury.METHODS: The study tested whether the nucleolin cardiac-specific knockout in the mice could affect glucose metabolism through untargeted metabolomics, and the results of metabolomics were verified experimentally in H9C2 cells. The ATP content, lactate production, and oxygen consumption rate (OCR) were evaluated.RESULTS: The metabolomics results suggested that glycolytic products were increased in endotoxemia-induced myocardial injury, and that nucleolin myocardial-specific knockout altered oxidative phosphorylation-related pathways. The experiment data showed that TNF-α combined with LPS stimulation could increase the lactate content and the OCR values by about 25%, and decrease the ATP content by about 25%. However, interference with nucleolin expression could further decrease ATP content and OCR values by about 10-20% and partially increase the lactate level in the presence of TNF-α and LPS. However, nucleolin overexpression had the opposite protective effect, which partially reversed the decrease in ATP content and the increase in lactate level.CONCLUSION: Down-regulation of nucleolin can exacerbate glucose metabolism disorders in endotoxemia-induced myocardial injury. Improving glucose metabolism by regulating nucleolin was expected to provide new therapeutic ideas for patients with septic cardiomyopathy.PMID:38784400 | PMC:PMC11114111 | DOI:10.7717/peerj.17414

Front Plant Sci. 2024 May 9;15:1413866. doi: 10.3389/fpls.2024.1413866. eCollection 2024.NO ABSTRACTPMID:38784061 | PMC:PMC11112109 | DOI:10.3389/fpls.2024.1413866

Front Pharmacol. 2024 May 9;15:1372449. doi: 10.3389/fphar.2024.1372449. eCollection 2024.ABSTRACTIschemia/reperfusion (IR) can induce deleterious responses such as apoptosis, inflammation, and oxidative stress; however, there are currently no efficient therapeutics to treat IR brain injury. Dragon's blood (DB) plays a significant role in treating ischemic stroke in China. Borneol (B) is an upper ushering drug that guides drugs to the target organs, including the brain. Therefore, we hypothesized that the combination of DB and B (DB + B) would provide cooperative therapeutic benefits for IR brain injury. To confirm this, we first investigated the protective effect of DB + B in an IR brain injury rat model using the modified neurological severity score (mNSS), infarction size measure, HE staining, and laser speckle contrast imaging. Then, we comprehensively evaluated the mechanism of DB + B in ameliorating IR brain injury based on RNA sequencing, serum untargeted metabolomics, and 16S rRNA sequencing. We have confirmed that DB + B enhanced the efficacy of the ischemic stroke treatment compared to DB or B alone for the first time. Our study provisionally confirms that the mechanism by which DB + B prevents IR brain injury is related to the maintenance of intestinal microecological balance and regulation of metabolic dysfunction, thereby suppressing inflammation and regulating immunity. DB + B may effectively regulate intestinal flora including o_Pseudomonadales, s_Bacteroides_caecimuris, o_unidentified_Bacilli, f-Pseudomonadaceae, and g-Pseudomonas, mainly regulate serum metabolites including improve the protective benefit of IR brain injury lysoPCs and lysoPEs, thus inhibiting TLR4/MyD88/NF-κB and IL-17 signing pathway to reduce inflammatory reactions. hat this mechanism is associated with the maintenance of intestinal flora balance and the regulation of metabolic dysfunction, thereby suppressing inflammation and regulating immunity. This provides scientific support for the clinical translation of DB + B in the prevention and treatment of ischemic stroke and establishes a basis for further investigation of its therapeutic mechanism.PMID:38783945 | PMC:PMC11112420 | DOI:10.3389/fphar.2024.1372449

Anal Chem. 2024 May 24. doi: 10.1021/acs.analchem.4c00630. Online ahead of print.ABSTRACTThe application of machine learning (ML) to -omics research is growing at an exponential rate owing to the increasing availability of large amounts of data for model training. Specifically, in metabolomics, ML has enabled the prediction of tandem mass spectrometry and retention time data. More recently, due to the advent of ion mobility, new ML models have been introduced for collision cross-section (CCS) prediction, but those have been trained with different and relatively small data sets covering a few thousands of small molecules, which hampers their systematic comparison. Here, we compared four existing ML-based CCS prediction models and their capacity to predict CCS values using the recently introduced METLIN-CCS data set. We also compared them with simple linear models and with ML models that used fingerprints as regressors. We analyzed the role of structural diversity of the data on which the ML models are trained with and explored the practical application of these models for metabolite annotation using CCS values. Results showed a limited capability of the existing models to achieve the necessary accuracy to be adopted for routine metabolomics analysis. We showed that for a particular molecule, this accuracy could only be improved when models were trained with a large number of structurally similar counterparts. Therefore, we suggest that current annotation capabilities will only be significantly altered with models trained with heterogeneous data sets composed of large homogeneous hubs of structurally similar molecules to those being predicted.PMID:38783786 | DOI:10.1021/acs.analchem.4c00630

J Microbiol Biotechnol. 2024 Apr 29;34(6):1-8. doi: 10.4014/jmb.2402.02021. Online ahead of print.ABSTRACTThe accumulating evidence substantiates the indispensable role of gut microbiota in modulating the pathogenesis of type 2 diabetes. Uncovering the intricacies of the mechanism is imperative in aiding disease control efforts. Revealing key bacterial species, their metabolites and/or metabolic pathways from the vast array of gut microorganisms can significantly contribute to precise treatment of the disease. With a high prevalence of type 2 diabetes in Inner Mongolia, China, we recruited volunteers from among the Mongolian population to investigate the relationship between gut microbiota and the disease. Fecal samples were collected from the Volunteers of Mongolia with Type 2 Diabetes group and a Control group, and detected by metagenomic analysis and untargeted metabolomics analysis. The findings suggest that Firmicutes and Bacteroidetes phyla are the predominant gut microorganisms that exert significant influence on the pathogenesis of type 2 diabetes in the Mongolian population. In the disease group, despite an increase in the quantity of most gut microbial metabolic enzymes, there was a concomitant weakening of gut metabolic function, suggesting that the gut microbiota may be in a compensatory state during the disease stage. β-Tocotrienol may serve as a pivotal gut metabolite produced by gut microorganisms and a potential biomarker for type 2 diabetes. The metabolic biosynthesis pathways of ubiquinone and other terpenoid quinones could be the crucial mechanism through which the gut microbiota regulates type 2 diabetes. Additionally, certain Clostridium gut species may play a pivotal role in the progression of the disease.PMID:38783719 | DOI:10.4014/jmb.2402.02021

Clin Transl Sci. 2024 May;17(5):e13828. doi: 10.1111/cts.13828.ABSTRACTAs a treatment for relapsed or refractory multiple myeloma (MM), carfilzomib has been associated with a significant risk of cardiovascular adverse events (CVAE). The goals of our study were to evaluate the metabolomic profile of MM patients to identify those at high risk prior to carfilzomib treatment and to explore the mechanisms of carfilzomib-CVAE to inform potential strategies to protect patients from this cardiotoxicity. Global metabolomic profiling was performed on the baseline and post-baseline plasma samples of 60 MM patients treated with carfilzomib-based therapy, including 31 who experienced CVAE, in a prospective cohort study. Baseline metabolites and post-baseline/baseline metabolite ratios that differ between the CVAE and no-CVAE patients were identified using unadjusted and adjusted methods. A baseline metabolomic risk score was created to stratify patients. We observed a lower abundance of tauroursodeoxycholic acid (T-UDCA) in CVAE patients at baseline (odds ratio [OR] = 0.47, 95% confidence interval [CI] = 0.21-0.94, p = 0.044) compared with the no-CVAE patients. A metabolite risk score was able to stratify patients into three risk groups. The area under the receiver-operating curve of the model with clinical predictors and metabolite risk score was 0.93. Glycochenodeoxycholic acid (OR = 0.56, 95% CI = 0.31-0.87, p = 0.023) was significantly lower in post-baseline/baseline ratios of CVAE patients compared with no-CVAE patients. Following metabolomic analysis, we created a baseline metabolite risk score that can stratify MM patients into different risk groups. The result also provided intriguing clues about the mechanism of carfilzomib-CVAE and potential cardioprotective strategies.PMID:38783568 | DOI:10.1111/cts.13828

J Cheminform. 2024 May 23;16(1):58. doi: 10.1186/s13321-024-00851-y.ABSTRACTEffective visualization of small molecules is paramount in conveying concepts and results in cheminformatics. Scalable vector graphics (SVG) are preferred for creating such visualizations, as SVGs can be easily altered in post-production and exported to other formats. A wide spectrum of software applications already exist that can visualize molecules, and customize these visualizations, in many ways. However, software packages that can output projected 3D models onto a 2D canvas directly as SVG, while being programmatically accessible from Python, are lacking. Here, we introduce CineMol, which can draw vectorized approximations of three-dimensional small molecule models in seconds, without triangulation or ray tracing, resulting in files of around 50-300 kilobytes per molecule model for compounds with up to 45 heavy atoms. The SVGs outputted by CineMol can be readily modified in popular vector graphics editing software applications. CineMol is written in Python and can be incorporated into any existing Python cheminformatics workflow, as it only depends on native Python libraries. CineMol also provides programmatic access to all its internal states, allowing for per-atom and per-bond-based customization. CineMol's capacity to programmatically create molecular visualizations suitable for post-production offers researchers and scientists a powerful tool for enhancing the clarity and visual impact of their scientific presentations and publications in cheminformatics, metabolomics, and related scientific disciplines.Scientific contributionWe introduce CineMol, a Python-based tool that provides a valuable solution for cheminformatics researchers by enabling the direct generation of high-quality approximations of two-dimensional SVG visualizations from three-dimensional small molecule models, all within a programmable Python framework. CineMol offers a unique combination of speed, efficiency, and accessibility, making it an indispensable tool for researchers in cheminformatics, especially when working with SVG visualizations.PMID:38783386 | DOI:10.1186/s13321-024-00851-y

Anal Chem. 2024 May 23. doi: 10.1021/acs.analchem.3c04436. Online ahead of print.ABSTRACTMetabolites from feces provide important insights into the functionality of the gut microbiome. As immediate freezing is not always feasible in gut microbiome studies, there is a need for sampling protocols that provide the stability of the fecal metabolome and microbiome at room temperature (RT). Here, we investigated the stability of various metabolites and the microbiome (16S rRNA) in feces collected in 95% ethanol (EtOH) and commercially available sample collection kits with specific preservatives OMNImet•GUT/OMNIgene•GUT. To simulate field-collection scenarios, the samples were stored at different temperatures at varying durations (24 h + 4 °C, 24 h RT, 36 h RT, 48 h RT, and 7 days RT) and compared to aliquots immediately frozen at -80 °C. We applied several targeted and untargeted metabolomics platforms to measure lipids, polar metabolites, endocannabinoids, short-chain fatty acids (SCFAs), and bile acids (BAs). We found that SCFAs in the nonstabilized samples increased over time, while a stable profile was recorded in sample aliquots stored in 95% EtOH and OMNImet•GUT. When comparing the metabolite levels between aliquots stored at room temperature and at +4 °C, we detected several changes in microbial metabolites, including multiple BAs and SCFAs. Taken together, we found that storing samples at RT and stabilizing them in 95% EtOH yielded metabolomic results comparable to those from flash freezing. We also found that the overall composition of the microbiome did not vary significantly between different storage types. However, notable differences were observed in the α diversity. Altogether, the stability of the metabolome and microbiome in 95% EtOH provided results similar to those of the validated commercial collection kits OMNImet•GUT and OMNIgene•GUT, respectively.PMID:38782403 | DOI:10.1021/acs.analchem.3c04436

J Proteome Res. 2024 May 23. doi: 10.1021/acs.jproteome.4c00049. Online ahead of print.ABSTRACTExtracellular chemical cues constitute much of the language of life among marine organisms, from microbes to mammals. Changes in this chemical pool serve as invisible signals of overall ecosystem health and disruption to this finely tuned equilibrium. In coral reefs, the scope and magnitude of the chemicals involved in maintaining reef equilibria are largely unknown. Processes involving small, polar molecules, which form the majority components of labile dissolved organic carbon, are often poorly captured using traditional techniques. We employed chemical derivatization with mass spectrometry-based targeted exometabolomics to quantify polar dissolved phase metabolites on five coral reefs in the U.S. Virgin Islands. We quantified 45 polar exometabolites, demonstrated their spatial variability, and contextualized these findings in terms of geographic and benthic cover differences. By comparing our results to previously published coral reef exometabolomes, we show the novel quantification of 23 metabolites, including central carbon metabolism compounds (e.g., glutamate) and novel metabolites such as homoserine betaine. We highlight the immense potential of chemical derivatization-based exometabolomics for quantifying labile chemical cues on coral reefs and measuring molecular level responses to environmental stressors. Overall, improving our understanding of the composition and dynamics of reef exometabolites is vital for effective ecosystem monitoring and management strategies.PMID:38782401 | DOI:10.1021/acs.jproteome.4c00049