PubMed

Adv Exp Med Biol. 2023;1412:311-335. doi: 10.1007/978-3-031-28012-2_17.ABSTRACTCurrently, methods in machine learning have opened a significant number of applications to construct classifiers with capacities to recognize, identify, and interpret patterns hidden in massive amounts of data. This technology has been used to solve a variety of social and health issues against coronavirus disease 2019 (COVID-19). In this chapter, we present some supervised and unsupervised machine learning techniques that have contributed in three aspects to supplying information to health authorities and diminishing the deadly effects of the current worldwide outbreak on the population. First is the identification and construction of powerful classifiers capable of predicting severe, moderate, or asymptomatic responses in COVID-19 patients starting from clinical or high-throughput technologies. Second is the identification of groups of patients with similar physiological responses to improve the triage classification and inform treatments. The final aspect is the combination of machine learning methods and schemes from systems biology to link associative studies with mechanistic frameworks. This chapter aims to discuss some practical applications in the use of machine learning techniques to handle data coming from social behavior and high-throughput technologies, associated with COVID-19 evolution.PMID:37378775 | DOI:10.1007/978-3-031-28012-2_17

Adv Exp Med Biol. 2023;1412:197-209. doi: 10.1007/978-3-031-28012-2_10.ABSTRACTCOVID-19 stands for Corona Virus Disease 2019, which starts as a viral infection that provokes illness with different symptoms and severity. The infected individuals can be asymptomatic or present with mild, moderate, severe, and critical illness with acute respiratory distress syndrome (ARDS), acute cardiac injury, and multiorgan failure. When the virus enters the cells, it replicates and provokes responses. Most diseased individuals resolve the problems in a short time but unfortunately, some may die, and almost 3 years after the first reported cases, COVID-19 still kills thousands per day worldwide. One of the problems in not curing the viral infection is that the virus passes by undetected in cells. This can be caused by the lack of pathogen-associated molecular patterns (PAMPs) that start an orchestrated immune response, such as activation of type 1 interferons (IFNs), inflammatory cytokines, chemokines, and antiviral defenses. Before all of these events can happen, the virus uses the infected cells and numerous small molecules as sources of energy and building blocks for newly synthesized viral nanoparticles that travel to and infect other host cells. Therefore, studying the cell metabolome and metabolomic changes in biofluids might give insights into the state of the viral infection, viral loads, and defense response. NMR-metabolomics can help in solving the real-time host interactions by monitoring concentration changes in metabolites. This chapter addresses the state of the art of COVIDomics by NMR analyses and presents exemplified biomolecules identified in different world regions and gravities of illness as potential biomarkers.PMID:37378768 | DOI:10.1007/978-3-031-28012-2_10

Eur J Nutr. 2023 Jun 28. doi: 10.1007/s00394-023-03186-w. Online ahead of print.ABSTRACTBACKGROUND: Maternal dietary choline has a central role in foetal brain development and may be associated with later cognitive function. However, many countries are reporting lower than recommended intake of choline during pregnancy.METHODS: Dietary choline was estimated using food frequency questionnaires in pregnant women participating in population-derived birth cohort, the Barwon Infant Study (BIS). Dietary choline is reported as the sum of all choline-containing moieties. Serum total choline-containing compounds (choline-c), phosphatidylcholine and sphingomyelin were measured using nuclear magnetic resonance metabolomics in the third trimester. The main form of analysis was multivariable linear regression.RESULTS: The mean daily dietary choline during pregnancy was 372 (standard deviation (SD) 104) mg/day. A total of 236 women (23%) had adequate choline intake (440 mg/day) based on the Australian and New Zealand guidelines, and 27 women (2.6%) took supplemental choline ([Formula: see text] 50 mg/dose) daily during pregnancy. The mean serum choline-c in pregnant women was 3.27 (SD 0.44) mmol/l. Ingested choline and serum choline-c were not correlated (R2) = - 0.005, p = 0.880. Maternal age, maternal weight gain in pregnancy, and a pregnancy with more than one infant were associated with higher serum choline-c, whereas gestational diabetes and environmental tobacco smoke during preconception and pregnancy were associated with lower serum choline-c. Nutrients or dietary patterns were not associated with variation in serum choline-c.CONCLUSION: In this cohort, approximately one-quarter of women met daily choline recommendations during pregnancy. Future studies are needed to understand the potential impact of low dietary choline intake during pregnancy on infant cognition and metabolic intermediaries.PMID:37378694 | DOI:10.1007/s00394-023-03186-w

Metabolomics. 2023 Jun 28;19(7):64. doi: 10.1007/s11306-023-02027-5.ABSTRACTINTRODUCTION: Interpretation and analysis of NMR-based metabolic profiling studies is limited by substantially incomplete commercial and academic databases. Statistical significance tests, including p-values, VIP scores, AUC values and FC values, can be largely inconsistent. Data normalization prior to statistical analysis can cause erroneous outcomes.OBJECTIVES: The objectives were (1) to quantitatively assess consistency among p-values, VIP scores, AUC values and FC values in representative NMR-based metabolic profiling datasets, (2) to assess how data normalization can impact statistical significance outcomes, (3) to determine resonance peak assignment completion potential using commonly used databases and (4) to analyze intersection and uniqueness of metabolite space in these databases.METHODS: P-values, VIP scores, AUC values and FC values, and their dependence on data normalization, were determined in orthotopic mouse model of pancreatic cancer and two human pancreatic cancer cell lines. Completeness of resonance assignments were evaluated using Chenomx, the human metabolite database (HMDB) and the COLMAR database. The intersection and uniqueness of the databases was quantified.RESULTS: P-values and AUC values were strongly correlated compared to VIP or FC values. Distributions of statistically significant bins depended strongly on whether or not datasets were normalized. 40-45% of peaks had either no or ambiguous database matches. 9-22% of metabolites were unique to each database.CONCLUSIONS: Lack of consistency in statistical analyses of metabolomics data can lead to misleading or inconsistent interpretation. Data normalization can have large effects on statistical analysis and should be justified. About 40% of peak assignments remain ambiguous or impossible with current databases. 1D and 2D databases should be made consistent to maximize metabolite assignment confidence and validation.PMID:37378680 | DOI:10.1007/s11306-023-02027-5

Mol Carcinog. 2023 Jun 28. doi: 10.1002/mc.23597. Online ahead of print.ABSTRACTMany cancers, including melanoma, have a higher requirement for l-methionine in comparison with noncancerous cells. In this study, we show that administration of an engineered human methionine-γ-lyase (hMGL) significantly reduced the survival of both human and mouse melanoma cells in vitro. A multiomics approach was utilized to identify global changes in gene expression and in metabolite levels with hMGL treatment in melanoma cells. There was considerable overlap in the perturbed pathways identified in the two data sets. Common pathways were flagged for further investigation to understand their mechanistic importance. In this regard, hMGL treatment induced S and G2 phase cell cycle arrest, decreased nucleotide levels, and increased DNA double-strand breaks suggesting an important role for replication stress in the mechanism of hMGL effects on melanoma cells. Further, hMGL treatment resulted in increased cellular reactive oxygen species levels and increased apoptosis as well as uncharged transfer RNA pathway upregulation. Finally, treatment with hMGL significantly inhibited the growth of both mouse and human melanoma cells in orthotopic tumor models in vivo. Overall, the results of this study provide a strong rationale for further mechanistic evaluation and clinical development of hMGL for the treatment of melanoma skin cancer and other cancers.PMID:37378415 | DOI:10.1002/mc.23597

Front Cardiovasc Med. 2023 Jun 12;10:1191303. doi: 10.3389/fcvm.2023.1191303. eCollection 2023.ABSTRACTProprotein convertase subtilisin/kexin type 9 (PCSK9), one of the key regulators of the low-density lipoprotein receptor (LDLR), can play a direct role in atheroma development. Although advances in the understandings of genetic PCSK9 polymorphisms have enabled to reveal the role of PCSK9 in the complex pathophysiology of cardiovascular diseases (CVDs), increasing lines of evidence support non-cholesterol-related processes mediated by PCSK9. Owing to major improvements in mass spectrometry-based technologies, multimarker proteomic and lipidomic panels hold the promise to identify novel lipids and proteins potentially related to PCSK9. Within this context, this narrative review aims to provide an overview of the most significant proteomics and lipidomics studies related to PCSK9 effects beyond cholesterol lowering. These approaches have enabled to unveil non-common targets of PCSK9, potentially leading to the development of novel statistical models for CVD risk prediction. Finally, in the era of precision medicine, we have reported the impact of PCSK9 on extracellular vesicles (EVs) composition, an effect that could contribute to an increased prothrombotic status in CVD patients. The possibility to modulate EVs release and cargo could help counteract the development and progression of the atherosclerotic process.PMID:37378405 | PMC:PMC10291627 | DOI:10.3389/fcvm.2023.1191303

iScience. 2023 May 23;26(6):106960. doi: 10.1016/j.isci.2023.106960. eCollection 2023 Jun 16.ABSTRACTBy a survey of metagenome-wide association studies (MWAS), we found a robust depletion of Bacteroides cellulosilyticus, Faecalibacterium prausnitzii, and Roseburia intestinalis in individuals with atherosclerotic cardiovascular disease (ACVD). From an established collection of bacteria isolated from healthy Chinese individuals, we selected B. cellulosilyticus, R. intestinalis, and Faecalibacterium longum, a bacterium related to F. prausnitzii, and tested the effects of these bacteria in an Apoe/- atherosclerosis mouse model. We show that administration of these three bacterial species to Apoe-/- mice robustly improves cardiac function, reduces plasma lipid levels, and attenuates the formation of atherosclerotic plaques. Comprehensive analysis of gut microbiota, plasma metabolome, and liver transcriptome revealed that the beneficial effects are associated with a modulation of the gut microbiota linked to a 7α-dehydroxylation-lithocholic acid (LCA)-farnesoid X receptor (FXR) pathway. Our study provides insights into transcriptional and metabolic impact whereby specific bacteria may hold promises for prevention/treatment of ACVD.PMID:37378328 | PMC:PMC10291474 | DOI:10.1016/j.isci.2023.106960

Front Immunol. 2023 Jun 12;14:1188257. doi: 10.3389/fimmu.2023.1188257. eCollection 2023.ABSTRACTIdiopathic inflammatory myopathy (IIM) is a heterogeneous group of autoimmune diseases with various clinical manifestations, treatment responses, and prognoses. According to the clinical manifestations and presence of different myositis-specific autoantibodies (MSAs), IIM is classified into several major subgroups, including PM, DM, IBM, ASS, IMNM, and CADM. However, the pathogenic mechanisms of these subgroups remain unclear and need to be investigated. Here, we applied MALDI-TOF-MS to examine the serum metabolome of 144 patients with IIM and analyze differentially expressed metabolites among IIM subgroups or MSA groups. The results showed that the DM subgroup had lower activation of the steroid hormone biosynthesis pathway, while the non-MDA5 MSA group had higher activation of the arachidonic acid metabolism pathway. Our study may provide some insights into the heterogeneous mechanisms of IIM subgroups, potential biomarkers, and management of IIM.PMID:37377960 | PMC:PMC10291268 | DOI:10.3389/fimmu.2023.1188257

Front Pharmacol. 2023 Jun 12;14:1228923. doi: 10.3389/fphar.2023.1228923. eCollection 2023.ABSTRACT[This corrects the article DOI: 10.3389/fphar.2023.1021535.].PMID:37377931 | PMC:PMC10292008 | DOI:10.3389/fphar.2023.1228923

Front Pharmacol. 2023 Jun 12;14:1203349. doi: 10.3389/fphar.2023.1203349. eCollection 2023.ABSTRACTBackground: Alzheimer's disease (AD), the most prevalent form of dementia, is a debilitating, progressive neurodegeneration. Amino acids play a wide variety of physiological and pathophysiological roles in the nervous system, and their levels and disorders related to their synthesis have been related to cognitive impairment, the core feature of AD. Our previous multicenter trial showed that hachimijiogan (HJG), a traditional Japanese herbal medicine (Kampo), has an adjuvant effect for Acetylcholine estelase inhibitors (AChEIs) and that it delays the deterioration of the cognitive dysfunction of female patients with mild AD. However, there are aspects of the molecular mechanism(s) by which HJG improves cognitive dysfunction that remain unclear. Objectives: To elucidate through metabolomic analysis the mechanism(s) of HJG for mild AD based on changes in plasma metabolites. Methods: Sixty-seven patients with mild AD were randomly assigned to either an HJG group taking HJG extract 7.5 g/day in addition to AChEI or to a control group treated only with AChEI (HJG:33, Control:34). Blood samples were collected before, 3 months, and 6 months after the first drug administration. Comprehensive metabolomic analyses of plasma samples were done by optimized LC-MS/MS and GC-MS/MS methods. The web-based software MetaboAnalyst 5.0 was used for partial least square-discriminant analysis (PLS-DA) to visualize and compare the dynamics of changes in the concentrations of the identified metabolites. Results: The VIP (Variable Importance in Projection) score of the PLS-DA analysis of female participants revealed a significantly higher increase in plasma metabolite levels after HJG administration for 6 months than was seen in the control group. In univariate analysis, the aspartic acid level of female participants showed a significantly higher increase from baseline after HJG administration for 6 months when compared with the control group. Conclusion: Aspartic acid was a major contributor to the difference between the female HJG and control group participants of this study. Several metabolites were shown to be related to the mechanism of HJG effectiveness for mild AD.PMID:37377927 | PMC:PMC10292017 | DOI:10.3389/fphar.2023.1203349

Front Mol Biosci. 2023 Jun 12;10:1161036. doi: 10.3389/fmolb.2023.1161036. eCollection 2023.ABSTRACTBackground: Chronic kidney disease (CKD) is characterized by the progressive and irreversible deterioration of kidney function and structure with the appearance of renal fibrosis. A significant decrease in mitochondrial metabolism, specifically a reduction in fatty acid oxidation (FAO) in tubular cells, is observed in tubulointerstitial fibrosis, whereas FAO enhancement provides protection. Untargeted metabolomics offers the potential to provide a comprehensive analysis of the renal metabolome in the context of kidney injury. Methodology: Renal tissue from a carnitine palmitoyl transferase 1a (Cpt1a) overexpressing mouse model, which displays enhanced FAO in the renal tubule, subjected to folic acid nephropathy (FAN) was studied through a multiplatform untargeted metabolomics approach based on LC-MS, CE-MS and GC-MS analysis to achieve the highest coverage of the metabolome and lipidome affected by fibrosis. The expression of genes related to the biochemical routes showing significant changes was also evaluated. Results: By combining different tools for signal processing, statistical analysis and feature annotation, we were able to identify variations in 194 metabolites and lipids involved in many metabolic routes: TCA cycle, polyamines, one-carbon metabolism, amino acid metabolism, purine metabolism, FAO, glycerolipids and glycerophospholipids synthesis and degradation, glycosphingolipids interconversion, and sterol metabolism. We found several metabolites strongly altered by FAN, with no reversion induced by Cpt1a overexpression (v.g. citric acid), whereas other metabolites were influenced by CPT1A-induced FAO (v.g. glycine-betaine). Conclusion: It was implemented a successful multiplatform metabolomics approach for renal tissue analysis. Profound metabolic changes accompany CKD-associated fibrosis, some associated with tubular FAO failure. These results highlight the importance of addressing the crosstalk between metabolism and fibrosis when undertaking studies attempting to elucidate the mechanism of CKD progression.PMID:37377862 | PMC:PMC10291237 | DOI:10.3389/fmolb.2023.1161036

Front Plant Sci. 2023 Jun 12;14:1172028. doi: 10.3389/fpls.2023.1172028. eCollection 2023.ABSTRACTCereal grains have been domesticated largely from food grains to feed and malting grains. Barley (Hordeum vulgare L.) remains unparalleled in its success as a primary brewing grain. However, there is renewed interest in "alternative" grains for brewing (and distilling) due to attention being placed on flavor, quality, and health (i.e., gluten issues) aspects that they may offer. This review covers basic and general information on "alternative grains" for malting and brewing, as well as an in-depth look at several major biochemical aspects of these grains including starch, protein, polyphenols, and lipids. These traits are described in terms of their effects on processing and flavor, as well as the prospects for improvement through breeding. These aspects have been studied extensively in barley, but little is known about the functional properties in other crops for malting and brewing. In addition, the complex nature of malting and brewing produces a large number of brewing targets but requires extensive processing, laboratory analysis, and accompanying sensory analysis. However, if a better understanding of the potential of alternative crops that can be used in malting and brewing is needed, then significantly more research is required.PMID:37377804 | PMC:PMC10291334 | DOI:10.3389/fpls.2023.1172028

Front Plant Sci. 2023 Jun 12;14:1166813. doi: 10.3389/fpls.2023.1166813. eCollection 2023.ABSTRACTStem rust caused by the pathogen Puccinia graminis f. sp. tritici is a destructive fungal disease-causing major grain yield losses in wheat. Therefore, understanding the plant defence regulation and function in response to the pathogen attack is required. As such, an untargeted LC-MS-based metabolomics approach was employed as a tool to dissect and understand the biochemical responses of Koonap (resistant) and Morocco (susceptible) wheat varieties infected with two different races of P. graminis (2SA88 [TTKSF] and 2SA107 [PTKST]). Data was generated from the infected and non-infected control plants harvested at 14- and 21- days post-inoculation (dpi), with 3 biological replicates per sample under a controlled environment. Chemo-metric tools such as principal component analysis (PCA), orthogonal projection to latent structures-discriminant analysis (OPLS-DA) were used to highlight the metabolic changes using LC-MS data of the methanolic extracts generated from the two wheat varieties. Molecular networking in Global Natural Product Social (GNPS) was further used to analyse biological networks between the perturbed metabolites. PCA and OPLS-DA analysis showed cluster separations between the varieties, infection races and the time-points. Distinct biochemical changes were also observed between the races and time-points. Metabolites were identified and classified using base peak intensities (BPI) and single ion extracted chromatograms from samples, and the most affected metabolites included flavonoids, carboxylic acids and alkaloids. Network analysis also showed high expression of metabolites from thiamine and glyoxylate, such as flavonoid glycosides, suggesting multi-faceted defence response strategy by understudied wheat varieties towards P. graminis pathogen infection. Overall, the study provided the insights of the biochemical changes in the expression of wheat metabolites in response to stem rust infection.PMID:37377801 | PMC:PMC10292758 | DOI:10.3389/fpls.2023.1166813

J Comput Graph Stat. 2023;32(2):730-743. doi: 10.1080/10618600.2022.2099404. Epub 2022 Aug 30.ABSTRACTModern data often take the form of a multiway array. However, most classification methods are designed for vectors, i.e., 1-way arrays. Distance weighted discrimination (DWD) is a popular high-dimensional classification method that has been extended to the multiway context, with dramatic improvements in performance when data have multiway structure. However, the previous implementation of multiway DWD was restricted to classification of matrices, and did not account for sparsity. In this paper, we develop a general framework for multiway classification which is applicable to any number of dimensions and any degree of sparsity. We conducted extensive simulation studies, showing that our model is robust to the degree of sparsity and improves classification accuracy when the data have multiway structure. For our motivating application, magnetic resonance spectroscopy (MRS) was used to measure the abundance of several metabolites across multiple neurological regions and across multiple time points in a mouse model of Friedreich's ataxia, yielding a four-way data array. Our method reveals a robust and interpretable multi-region metabolomic signal that discriminates the groups of interest. We also successfully apply our method to gene expression time course data for multiple sclerosis treatment. An R implementation is available in the package MultiwayClassification at http://github.com/lockEF/MultiwayClassification.PMID:37377729 | PMC:PMC10292743 | DOI:10.1080/10618600.2022.2099404

Front Cell Infect Microbiol. 2023 Jun 12;13:1167312. doi: 10.3389/fcimb.2023.1167312. eCollection 2023.ABSTRACTFructus gardeniae (FG) is a traditional Chinese medicine and health food for thousands of years of application throughout Chinese history and is still widely used in clinical Chinese medicine. FG has a beneficial impact on anxiety, depression, insomnia, and psychiatric disorders; however, its mechanism of action requires further investigation. This study aimed to investigate the effects and mechanisms of FG on sleep deprivation (SD)-induced anxiety-like behavior in rats. A model of SD-induced anxiety-like behavior in rats was established by intraperitoneal injection of p-chlorophenylalanine (PCPA). This was accompanied by neuroinflammation and metabolic abnormalities in the hippocampus and disturbance of intestinal microbiota. However reduced SD-induced anxiety-like behavior and decreased levels of pro-inflammatory cytokines including TNF-α and IL-1β were observed in the hippocampus of rats after 7 days of FG intervention. In addition, metabolomic analysis demonstrated that FG was able to modulate levels of phosphatidylserine 18, Phosphatidylinositol 18, sn-glycero-3-phosphocholine, deoxyguanylic acid, xylose, betaine and other metabolites in the hippocampus. The main metabolic pathways of hippocampal metabolites after FG intervention involve carbon metabolism, glycolysis/gluconeogenesis, pentose phosphate, and glycerophospholipid metabolism. 16S rRNA sequencing illustrated that FG ameliorated the dysbiosis of gut microbiota in anxious rats, mainly increased the abundance of Muribaculaceae and Lactobacillus, and decreased the abundance of Lachnospiraceae_NK4A136_group. In addition, the correlation analysis demonstrated that there was a close relationship between hippocampal metabolites and intestinal microbiota. In conclusion, FG improved the anxiety behavior and inhibited of neuroinflammation in sleep-deprived rats, and the mechanism may be related to the FG regulation of hippocampal metabolites and intestinal microflora composition.PMID:37377643 | PMC:PMC10291143 | DOI:10.3389/fcimb.2023.1167312

Cancer Res Commun. 2023 Jun 13;3(6):1026-1040. doi: 10.1158/2767-9764.CRC-22-0415. eCollection 2023 Jun.ABSTRACTResistance to immune checkpoint blockade remains challenging in patients with non-small cell lung cancer (NSCLC). Tumor-infiltrating leukocyte (TIL) quantity, composition, and activation status profoundly influence responsiveness to cancer immunotherapy. This study examined the immune landscape in the NSCLC tumor microenvironment by analyzing TIL profiles of 281 fresh resected NSCLC tissues. Unsupervised clustering based on numbers and percentages of 30 TIL types classified adenocarcinoma (LUAD) and squamous cell carcinoma (LUSQ) into the cold, myeloid cell-dominant, and CD8+ T cell-dominant subtypes. These were significantly correlated with patient prognosis; the myeloid cell subtype had worse outcomes than the others. Integrated genomic and transcriptomic analyses, including RNA sequencing, whole-exome sequencing, T-cell receptor repertoire, and metabolomics of tumor tissue, revealed that immune reaction-related signaling pathways were inactivated, while the glycolysis and K-ras signaling pathways activated in LUAD and LUSQ myeloid cell subtypes. Cases with ALK and ROS1 fusion genes were enriched in the LUAD myeloid subtype, and the frequency of TERT copy-number variations was higher in LUSQ myeloid subtype than in the others. These classifications of NSCLC based on TIL status may be useful for developing personalized immune therapies for NSCLC.SIGNIFICANCE: The precise TIL profiling classified NSCLC into novel three immune subtypes that correlates with patient outcome, identifying subtype-specific molecular pathways and genomic alterations that should play important roles in constructing subtype-specific immune tumor microenvironments. These classifications of NSCLC based on TIL status are useful for developing personalized immune therapies for NSCLC.PMID:37377611 | PMC:PMC10263066 | DOI:10.1158/2767-9764.CRC-22-0415

Cancer Res Commun. 2023 Jun 14;3(6):1041-1056. doi: 10.1158/2767-9764.CRC-23-0144. eCollection 2023 Jun.ABSTRACTGlioblastomas (GBM) are heterogeneous tumors with high metabolic plasticity. Their poor prognosis is linked to the presence of glioblastoma stem cells (GSC), which support resistance to therapy, notably to temozolomide (TMZ). Mesenchymal stem cells (MSC) recruitment to GBM contributes to GSC chemoresistance, by mechanisms still poorly understood. Here, we provide evidence that MSCs transfer mitochondria to GSCs through tunneling nanotubes, which enhances GSCs resistance to TMZ. More precisely, our metabolomics analyses reveal that MSC mitochondria induce GSCs metabolic reprograming, with a nutrient shift from glucose to glutamine, a rewiring of the tricarboxylic acid cycle from glutaminolysis to reductive carboxylation and increase in orotate turnover as well as in pyrimidine and purine synthesis. Metabolomics analysis of GBM patient tissues at relapse after TMZ treatment documents increased concentrations of AMP, CMP, GMP, and UMP nucleotides and thus corroborate our in vitro analyses. Finally, we provide a mechanism whereby mitochondrial transfer from MSCs to GSCs contributes to GBM resistance to TMZ therapy, by demonstrating that inhibition of orotate production by Brequinar (BRQ) restores TMZ sensitivity in GSCs with acquired mitochondria. Altogether, these results identify a mechanism for GBM resistance to TMZ and reveal a metabolic dependency of chemoresistant GBM following the acquisition of exogenous mitochondria, which opens therapeutic perspectives based on synthetic lethality between TMZ and BRQ.SIGNIFICANCE: Mitochondria acquired from MSCs enhance the chemoresistance of GBMs. The discovery that they also generate metabolic vulnerability in GSCs paves the way for novel therapeutic approaches.PMID:37377608 | PMC:PMC10266428 | DOI:10.1158/2767-9764.CRC-23-0144

Mol Nutr Food Res. 2023 Jun 28:e2300017. doi: 10.1002/mnfr.202300017. Online ahead of print.ABSTRACTSCOPE: Vitamin D is vital to cardiovascular health. This study examines the association between plasma 25-hydroxyvitamin D (25[OH]D) and the progression of carotid intima-media thickness (cIMT) and identifies the potential mediating biomarkers of gut microbiota and metabolites in adults.METHODS AND RESULTS: This 9-year prospective study includes 2975 subjects with plasma 25(OH)D at baseline and determined cIMT every 3 years. Higher circulating 25(OH)D is associated with decreased odds of higher (≥median) 9-year cIMT changes at the common carotid artery (hΔCCA-cIMT) (p-trend < 0.001). Multivariable-adjusted OR (95%CI) of hΔCCA-cIMT for tertiles 2 and 3 (vs. 1) of 25(OH)D is 0.87 (0.73-1.04) and 0.68 (0.57-0.82). Gut microbiome and metabolome analysis identify 18 biomarkers significantly associated with both 25(OH)D and hΔCCA-cIMT, including three microbial genera, seven fecal metabolites, eight serum metabolites, and pathway of synthesis and degradation of ketone bodies. Mediation/path analyses show the scores generated from the overlapped differential gut microbiota, fecal and serum metabolites, and serum acetoacetic acid alone could mediate the beneficial association between 25(OH)D and hΔCCA-cIMT by 10.8%, 23.1%, 59.2%, and 62.0% (all p < 0.05), respectively.CONCLUSIONS: These findings show a beneficial association between plasma 25(OH)D and the CCA-cIMT progression. The identified multi-omics biomarkers provide novel mechanistic insights for the epidemiological association.PMID:37377073 | DOI:10.1002/mnfr.202300017

Food Funct. 2023 Jun 28. doi: 10.1039/d3fo00260h. Online ahead of print.ABSTRACTChanges in light/dark cycles and obesogenic diets are related to the disruption of circadian rhythms and metabolic disorders. Grape seed flavanols have shown beneficial effects on metabolic diseases and, recently, a circadian system modulation has been suggested to mediate their health-enhancing properties. Therefore, the aim of this study was to evaluate the grape seed (poly)phenol extract (GSPE) effects in healthy and obese rats after a light/dark cycle disruption. Forty-eight rats were fed a standard (STD) or cafeteria (CAF) diet for 6 weeks under STD conditions of a light/dark cycle (12 h light per day, L12). Then, animals were switched to a long (18 h light per day, L18) or short (6 h light per day, L6) photoperiod and administered a vehicle (VH) or GSPE (25 mg kg-1) for 1 week. The results showed changes in serum lipids and insulin and metabolomic profiles dependent on the photoperiod and animal health status. GSPE administration improved serum parameters and increased the Nampt gene expression in CAF rats and modified the metabolomic profile in a photoperiod-dependent manner. Metabolic effects of light/dark disturbance depend on the health status of the rats, with diet-induced CAF-induced obese rats being more affected. Grape seed flavanols improve the metabolic status in a photoperiod-dependent manner and their effects on the circadian system suggest that part of their metabolic effects could be mediated by their action on biological rhythms.PMID:37377055 | DOI:10.1039/d3fo00260h

Proteomics Clin Appl. 2023 Jun 27:e2200081. doi: 10.1002/prca.202200081. Online ahead of print.ABSTRACTPURPOSE: Stroke is the sudden death of brain cells in a localized area due to an inadequate blood flow or blood vessel rupture, and it seriously affects the quality of life. The metabolite biomarkers are needed for predicting the functional outcome of acute ischemic stroke (AIS).EXPERIMENTAL DESIGN: To identify biomarkers for AIS, untargeted LC/MS metabolomics was performed on plasma samples from subjects with favorable prognosis (mRS ≤ 2) and unfavorable prognosis (mRS > 2). The identified markers were further absolutely quantified by a targeted MRM approach.RESULTS: There were 10 upregulated and 26 downregulated markers. Among these candidates, one was successfully identified as glycocholic acid and then absolutely quantified in plasma samples. Glycocholic acid could discriminate between subjects with favorable and unfavorable prognosis with an area under the curve (AUC) of 0.68 and odds ratio of 5.88.CONCLUSIONS AND CLINICAL RELEVANCE: Glycocholic acid was identified as a potential plasma metabolite marker of non-progressive outcomes after ischemic stroke and could serve as predictive prognostic markers for clinical acute stroke outcomes.PMID:37376802 | DOI:10.1002/prca.202200081