PubMed

Elife. 2024 May 3;12:RP88510. doi: 10.7554/eLife.88510.ABSTRACTAdenine phosphoribosyltransferase (APRT) and hypoxanthine-guanine phosphoribosyltransferase (HGPRT) are two structurally related enzymes involved in purine recycling in humans. Inherited mutations that suppress HGPRT activity are associated with Lesch-Nyhan disease (LND), a rare X-linked metabolic and neurological disorder in children, characterized by hyperuricemia, dystonia, and compulsive self-injury. To date, no treatment is available for these neurological defects and no animal model recapitulates all symptoms of LND patients. Here, we studied LND-related mechanisms in the fruit fly. By combining enzymatic assays and phylogenetic analysis, we confirm that no HGPRT activity is expressed in Drosophila melanogaster, making the APRT homolog (Aprt) the only purine-recycling enzyme in this organism. Whereas APRT deficiency does not trigger neurological defects in humans, we observed that Drosophila Aprt mutants show both metabolic and neurobehavioral disturbances, including increased uric acid levels, locomotor impairments, sleep alterations, seizure-like behavior, reduced lifespan, and reduction of adenosine signaling and content. Locomotor defects could be rescued by Aprt re-expression in neurons and reproduced by knocking down Aprt selectively in the protocerebral anterior medial (PAM) dopaminergic neurons, the mushroom bodies, or glia subsets. Ingestion of allopurinol rescued uric acid levels in Aprt-deficient mutants but not neurological defects, as is the case in LND patients, while feeding adenosine or N6-methyladenosine (m6A) during development fully rescued the epileptic behavior. Intriguingly, pan-neuronal expression of an LND-associated mutant form of human HGPRT (I42T), but not the wild-type enzyme, resulted in early locomotor defects and seizure in flies, similar to Aprt deficiency. Overall, our results suggest that Drosophila could be used in different ways to better understand LND and seek a cure for this dramatic disease.PMID:38700995 | DOI:10.7554/eLife.88510

STAR Protoc. 2024 May 2;5(2):103051. doi: 10.1016/j.xpro.2024.103051. Online ahead of print.ABSTRACTPhospholipids are important biomolecules for the study of lipidomics, signal transduction, biodiesel, and synthetic biology; however, it is difficult to synthesize and analyze phospholipids in a defined in vitro condition. Here, we present a protocol for in vitro production and quantification of phospholipids. We describe steps for preparing a cell-free system consisting of fatty acid synthesis and a gene expression system that synthesizes acyltransferases on liposomes. The whole reaction can be completed within a day and the products are quantified by liquid chromatography-mass spectrometry. For complete details on the use and execution of this protocol, please refer to Eto et al.1.PMID:38700978 | DOI:10.1016/j.xpro.2024.103051

STAR Protoc. 2024 May 2;5(2):103052. doi: 10.1016/j.xpro.2024.103052. Online ahead of print.ABSTRACTGenetic variations influence the levels of blood metabolites. We present analytical pipelines for assessing genetic influences on human blood metabolites. We describe steps for the normalization of metabolome data, genome-wide association studies, and the identification of metabolite quantitative trait loci (mQTLs). We then detail procedures for functional enrichment analysis of mQTLs. This protocol could be applicable to other quantitative traits, such as clinical measurements or proteome data. For complete details on the use and execution of this protocol, please refer to Iwasaki et al.1.PMID:38700977 | DOI:10.1016/j.xpro.2024.103052

AMB Express. 2024 May 3;14(1):50. doi: 10.1186/s13568-024-01705-4.ABSTRACTCoccidia of the genus Eimeria are important pathogens that cause coccidiosis in livestock and poultry. Due to the expansion of intensive farming, coccidiosis has become more difficult to control. In addition, the continued use of anti-coccidiosis drugs has led to drug resistance and residue. Some herbs used in traditional Chinese medicine (TCM) have been shown to alleviate the clinical symptoms of coccidiosis, while enhancing immunity and growth performance (GP) of livestock and poultry. Previous in vitro and in vivo studies have reported that the TCM herb Portulaca oleracea exhibited anti-parasitic activities. In total, 36 female Hu lambs were equally divided into six treatment groups: PL (low-dose P. oleracea), PH (high-dose P. oleracea), PW (P. oleracea water extract), PE (P. oleracea ethanol extract), DIC (diclazuril), and CON (control). The treatment period was 14 days. The McMaster counting method was used to evaluate the anti-coccidiosis effects of the different treatments. Untargeted metabolomics and 16S rRNA gene sequencing were used to investigate the effects of treatment on the gut microbiota (GM) and GP. The results showed that P. oleracea ameliorated coccidiosis, improved GP, increased the abundances of beneficial bacteria, and maintained the composition of the GM, but failed to completely clear coccidian oocysts. The Firmicutes to Bacteroides ratio was significantly increased in the PH group. P. oleracea increased metabolism of tryptophan as well as some vitamins and cofactors in the GM and decreased the relative content of arginine, tryptophan, niacin, and other nutrients, thereby promoting intestinal health and enhancing GP. As an alternative to the anti-coccidiosis drug DIC, P. oleracea effectively inhibited growth of coccidia, maintained the composition of the GM, promoted intestinal health, and increased nutrient digestibility.PMID:38700828 | DOI:10.1186/s13568-024-01705-4

Cell Mol Life Sci. 2024 May 3;81(1):204. doi: 10.1007/s00018-023-05043-9.ABSTRACTThe silent information regulator T1 (SIRT1) is linked to longevity and is a crucial mediator of osteoblast function. We investigated the direct role of Sirt1 during bone modeling and remodeling stages in vivo using Tamoxifen-inducible osteoblast-specific Sirt1 conditional knockout (cKO) mice. cKO mice exhibited lower trabecular and cortical bone mass in the distal femur. These phenotypes were coupled with lower bone formation and bone resorption. Metabolomics analysis revealed that the metabolites involved in glycolysis were significantly decreased in cKO mice. Further analysis of the quantitative acetylome revealed 11 proteins with upregulated acetylation levels in both the femur and calvaria of cKO mice. Cross-analysis identified four proteins with the same upregulated lysine acetylation site in both the femur and calvaria of cKO mice. A combined analysis of the metabolome and acetylome, as well as immunoprecipitation, gene knockout, and site-mutation experiments, revealed that Sirt1 deletion inhibited glycolysis by directly binding to and increasing the acetylation level of Glutamine oxaloacetic transaminase 1 (GOT1). In conclusion, our study suggested that Sirt1 played a crucial role in regulating osteoblast metabolism to maintain bone homeostasis through its deacetylase activity on GOT1. These findings provided a novel insight into the potential targeting of osteoblast metabolism for the treatment of bone-related diseases.PMID:38700532 | DOI:10.1007/s00018-023-05043-9

Cancer Epidemiol Biomarkers Prev. 2024 May 3. doi: 10.1158/1055-9965.EPI-24-0017. Online ahead of print.ABSTRACTBACKGROUND: Studies investigating the associations of self-reported aspirin use and mammographic breast density (MBD) have reported conflicting results. We, therefore, investigated the associations of aspirin metabolites, with MBD in premenopausal women.METHODS: We performed this study on 705 premenopausal women who had fasting blood draw for metabolomic profiling. We performed covariate-adjusted linear regression models to calculate the least squares means of volumetric measures of MBD (volumetric percent density (VPD), dense volume (DV), and non-dense volume (NDV)) by quartiles of aspirin metabolites (salicyluric glucuronide, 2-hydroxyhippurate (salicylurate), salicylate, and 2,6-dihydroxybenzoic acid).RESULTS: Approximately 13% of participants reported taking aspirin in the past 12 months. Aspirin users had higher levels of 2-hydroxyhippurate (salicylurate), salicylate, and salicyluric glucuronide (peak area) than non-users, but only mean peak area of salicyluric glucuronide increased by both dose (1-2 tabs per day=1,140,663.7, and ≥3 tabs per day=1,380,476.0) and frequency (days per week: 1 day=888,129.3, 2-3 days=1,199,897.9 and ≥4 days=1,654,637.0). Aspirin metabolites were not monotonically associated with VPD, DV, or NDV.CONCLUSIONS: Given the null results, additional research investigating the associations of aspirin metabolites in breast tissue and MBD is necessary.IMPACT: Elucidating the determinants of MBD, a strong risk factor for breast cancer, can play an important role in breast cancer prevention. Future studies should determine the associations of non-aspirin non-steroidal anti-inflammatory drug metabolites with MBD.PMID:38700429 | DOI:10.1158/1055-9965.EPI-24-0017

Front Neurosci. 2024 Apr 18;18:1362239. doi: 10.3389/fnins.2024.1362239. eCollection 2024.ABSTRACTINTRODUCTION: Aging is a complex, time-dependent biological process that involves a decline of overall function. Over the past decade, the field of intestinal microbiota associated with aging has received considerable attention. However, there is limited information surrounding microbiota-gut-brain axis (MGBA) to further reveal the mechanism of aging.METHODS: In this study, locomotory function and sensory function were evaluated through a series of behavioral tests.Metabolic profiling were determined by using indirect calorimetry.16s rRNA sequence and targeted metabolomics analyses were performed to investigate alterations in the gut microbiota and fecal short-chain fatty acids (SCFAs). The serum cytokines were detected by a multiplex cytokine assay.The expression of proinflammatory factors were detected by western blotting.RESULTS: Decreased locomotor activity, decreased pain sensitivity, and reduced respiratory metabolic profiling were observed in aged mice. High-throughput sequencing revealed that the levels of genus Lactobacillus and Dubosiella were reduced, and the levels of genus Alistipes and Bacteroides were increased in aged mice. Certain bacterial genus were directly associated with the decline of physiological behaviors in aged mice. Furthermore, the amount of fecal SCFAs in aged mice was decreased, accompanied by an upregulation in the circulating pro-inflammatory cytokines and increased expression of inflammatory factors in the brain.DISCUSSION: Aging-induced microbial dysbiosis was closely related with the overall decline in behavior, which may attribute to the changes in metabolic products, e.g., SCFAs, caused by an alteration in the gut microbiota, leading to inflammaging and contributing to neurological deficits. Investigating the MGBA might provide a novel viewpoint to exploring the pathogenesis of aging and expanding appropriate therapeutic targets.PMID:38699678 | PMC:PMC11063250 | DOI:10.3389/fnins.2024.1362239

Food Chem X. 2024 Apr 18;22:101396. doi: 10.1016/j.fochx.2024.101396. eCollection 2024 Jun 30.ABSTRACTWith the proliferation of the consumer's awareness of wine provenance, wines with unique origin characteristics are increasingly in demand. This study aimed to investigate the influence of geographical origins and climatological characteristics on grapes and wines. A total of 94 anthocyanins and 78 non-anthocyanin phenolic compounds in grapes and wines from five Chinese viticultural vineyards (CJ, WH, QTX, WW, and XY) were identified by UHPLC-QqQ-MS/MS. Chemometric methods PCA and OPLS-DA were established to select candidate differential metabolites, including flavonols, stilbenes, hydroxycinnamic acids, peonidin derivatives, and malvidin derivatives. CCA showed that malvidin-3-O-glucoside had a positive correlation with mean temperature, and quercetin-3-O-glucoside had a negative correlation with precipitation. In addition, enrichment analysis elucidated that the metabolic diversity in different origins mainly occurred in flavonoid biosynthesis. This study would provide some new insights to understand the effect of geographical origins and climatological characteristics on phenolic compounds in grapes and wines.PMID:38699585 | PMC:PMC11063387 | DOI:10.1016/j.fochx.2024.101396

medRxiv [Preprint]. 2024 Apr 16:2024.04.13.24305771. doi: 10.1101/2024.04.13.24305771.ABSTRACTScalable identification of patients with the post-acute sequelae of COVID-19 (PASC) is challenging due to a lack of reproducible precision phenotyping algorithms and the suboptimal accuracy, demographic biases, and underestimation of the PASC diagnosis code (ICD-10 U09.9). In a retrospective case-control study, we developed a precision phenotyping algorithm for identifying research cohorts of PASC patients, defined as a diagnosis of exclusion. We used longitudinal electronic health records (EHR) data from over 295 thousand patients from 14 hospitals and 20 community health centers in Massachusetts. The algorithm employs an attention mechanism to exclude sequelae that prior conditions can explain. We performed independent chart reviews to tune and validate our precision phenotyping algorithm. Our PASC phenotyping algorithm improves precision and prevalence estimation and reduces bias in identifying Long COVID patients compared to the U09.9 diagnosis code. Our algorithm identified a PASC research cohort of over 24 thousand patients (compared to about 6 thousand when using the U09.9 diagnosis code), with a 79.9 percent precision (compared to 77.8 percent from the U09.9 diagnosis code). Our estimated prevalence of PASC was 22.8 percent, which is close to the national estimates for the region. We also provide an in-depth analysis outlining the clinical attributes, encompassing identified lingering effects by organ, comorbidity profiles, and temporal differences in the risk of PASC. The PASC phenotyping method presented in this study boasts superior precision, accurately gauges the prevalence of PASC without underestimating it, and exhibits less bias in pinpointing Long COVID patients. The PASC cohort derived from our algorithm will serve as a springboard for delving into Long COVID's genetic, metabolomic, and clinical intricacies, surmounting the constraints of recent PASC cohort studies, which were hampered by their limited size and available outcome data.PMID:38699316 | PMC:PMC11065031 | DOI:10.1101/2024.04.13.24305771

PeerJ. 2024 Apr 29;12:e17272. doi: 10.7717/peerj.17272. eCollection 2024.ABSTRACTBACKGROUND: Esophageal squamous cell carcinoma (ESCC) is highly prevalent and has a high mortality rate. Traditional diagnostic methods, such as imaging examinations and blood tumor marker tests, are not effective in accurately diagnosing ESCC due to their low sensitivity and specificity. Esophageal endoscopic biopsy, which is considered as the gold standard, is not suitable for screening due to its invasiveness and high cost. Therefore, this study aimed to develop a convenient and low-cost diagnostic method for ESCC using plasma-based lipidomics analysis combined with machine learning (ML) algorithms.METHODS: Plasma samples from a total of 40 ESCC patients and 31 healthy controls were used for lipidomics study. Untargeted lipidomics analysis was conducted through liquid chromatography-mass spectrometry (LC-MS) analysis. Differentially expressed lipid features were filtered based on multivariate and univariate analysis, and lipid annotation was performed using MS-DIAL software.RESULTS: A total of 99 differential lipids were identified, with 15 up-regulated lipids and 84 down-regulated lipids, suggesting their potential as diagnostic targets for ESCC. In the single-lipid plasma-based diagnostic model, nine specific lipids (FA 15:4, FA 27:1, FA 28:7, FA 28:0, FA 36:0, FA 39:0, FA 42:0, FA 44:0, and DG 37:7) exhibited excellent diagnostic performance, with an area under the curve (AUC) exceeding 0.99. Furthermore, multiple lipid-based ML models also demonstrated comparable diagnostic ability for ESCC. These findings indicate plasma lipids as a promising diagnostic approach for ESCC.PMID:38699187 | PMC:PMC11064858 | DOI:10.7717/peerj.17272

Front Physiol. 2024 Apr 18;15:1248276. doi: 10.3389/fphys.2024.1248276. eCollection 2024.ABSTRACTIntroduction: It may take decades to develop cardiovascular dysfunction following exposure to high doses of ionizing radiation from medical therapy or from nuclear accidents. Since astronauts may be exposed continually to a complex space radiation environment unlike that experienced on Earth, it is unresolved whether there is a risk to cardiovascular health during long-term space exploration missions. Previously, we have described that mice exposed to a single dose of simplified Galactic Cosmic Ray (GCR5-ion) develop cardiovascular dysfunction by 12 months post-radiation. Methods: To investigate the biological basis of this dysfunction, here we performed a quantitative mass spectrometry-based proteomics analysis of heart tissue (proteome and phosphoproteome) and plasma (proteome only) from these mice at 8 months post-radiation. Results: Differentially expressed proteins (DEPs) for irradiated versus sham irradiated samples (fold-change ≥1.2 and an adjusted p-value of ≤0.05) were identified for each proteomics data set. For the heart proteome, there were 87 significant DEPs (11 upregulated and 76 downregulated); for the heart phosphoproteome, there were 60 significant differentially phosphorylated peptides (17 upregulated and 43 downregulated); and for the plasma proteome, there was only one upregulated protein. A Gene Set Enrichment Analysis (GSEA) technique that assesses canonical pathways from BIOCARTA, KEGG, PID, REACTOME, and WikiPathways revealed significant perturbation in pathways in each data set. For the heart proteome, 166 pathways were significantly altered (36 upregulated and 130 downregulated); for the plasma proteome, there were 73 pathways significantly altered (25 upregulated and 48 downregulated); and for the phosphoproteome, there were 223 pathways significantly affected at 0.1 adjusted p-value cutoff. Pathways related to inflammation were the most highly perturbed in the heart and plasma. In line with sustained inflammation, neutrophil extracellular traps (NETs) were demonstrated to be increased in GCR5-ion irradiated hearts at 12-month post irradiation. NETs play a fundamental role in combating bacterial pathogens, modulating inflammatory responses, inflicting damage on healthy tissues, and escalating vascular thrombosis. Discussion: These findings suggest that a single exposure to GCR5-ion results in long-lasting changes in the proteome and that these proteomic changes can potentiate acute and chronic health issues for astronauts, such as what we have previously described with late cardiac dysfunction in these mice.PMID:38699144 | PMC:PMC11063234 | DOI:10.3389/fphys.2024.1248276

JHEP Rep. 2024 Mar 12;6(6):101067. doi: 10.1016/j.jhepr.2024.101067. eCollection 2024 Jun.ABSTRACTBACKGROUND & AIMS: Patients with intrahepatic cholangiocarcinoma can now be managed with targeted therapies directed against specific molecular alterations. Consequently, tissue samples submitted to the pathology department must produce molecular information in addition to a diagnosis or, for resection specimens, staging information. The pathologist's role when evaluating these specimens has therefore changed to accommodate such personalised approaches.METHODS: We developed recommendations and guidance for pathologists by conducting a systematic review of existing guidance to generate candidate statements followed by an international Delphi process. Fifty-nine pathologists from 28 countries in six continents rated statements mapped to all elements of the specimen pathway from receipt in the pathology department to authorisation of the final written report. A separate survey of 'end-users' of the report including surgeons, oncologists, and gastroenterologists was undertaken to evaluate what information should be included in the written report to enable appropriate patient management.RESULTS: Forty-eight statements reached consensus for inclusion in the guidance including 10 statements about the content of the written report that also reached consensus by end-user participants. A reporting proforma to allow easy inclusion of the recommended data points was developed.CONCLUSIONS: These guiding principles and recommendations provide a framework to allow pathologists reporting on patients with intrahepatic cholangiocarcinoma to maximise the informational yield of specimens required for personalised patient management.IMPACT AND IMPLICATIONS: Biopsy or resection lesional tissue from intrahepatic cholangiocarcinoma must yield information about the molecular abnormalities within the tumour that define suitability for personalised therapies in addition to a diagnosis and staging information. Here, we have developed international consensus guidance for pathologists that report such cases using a Delphi process that sought the views of both pathologists and 'end-users of pathology reports. The guide highlights the need to report cases in a way that preserves tissue for molecular testing and emphasises that reporting requires interpretation of histological characteristics within the broader clinical and radiological context. The guide will allow pathologists to report cases of intrahepatic cholangiocarcinoma in a uniform manner that maximises the value of the tissue received to facilitate optimal multidisciplinary patient management.PMID:38699072 | PMC:PMC11060959 | DOI:10.1016/j.jhepr.2024.101067

Heliyon. 2024 Apr 16;10(9):e29809. doi: 10.1016/j.heliyon.2024.e29809. eCollection 2024 May 15.ABSTRACTAnal fistula is a common anal and intestinal disease. The wound of anal fistula surgery is open and polluting, which is the most difficult to heal among all surgical incisions. To investigate the mechanism of Huanglian ointment (HLO) on wound healing after anal fistula incision. The S. aureus infected wound in SD rats were used to imitate poor healing wound after anal fistula surgery. SD rats with wound sites (n = 24) were randomly divided into four groups (Control group, Model group, Potassium permanganate (PP) treatment group, and HLO treatment group). The wound healing rate was evaluated, HE staining was used to evaluate the pathological changes of each group, ELISA was used to detect the secretion of inflammatory factors in each group, and the mechanism was explored through metabolomics and proteomics in plasma rat. Compared to other groups, the rate of wound healing in the HLO group was higher on days 7 and 14. Histological analysis showed that collagen and fibroblast in HLO rats were significantly increased, inflammatory cells were reduced, and vascular endothelial permeability was increased. ELISA results showed that the secretion of inflammatory factors in HLO rats was significantly lower. Significant proteins and metabolites were identified in the wound tissues of the infected rats and HLO-treated rats, which were mainly attributed to Cdc42, Ctnnb1, Actr2, Actr3, Arpc1b, Itgam, Itgb2, Cttn, Linoleic acid metabolism, d-Glutamine and d-glutamate metabolism, Phenylalanine, tyrosine and tryptophan biosynthesis, Phenylalanine metabolism, alpha-Linolenic acid metabolism, and Ascorbate and aldarate metabolism. In conclusion, this study showed that HLO can promote S. aureus infected wound healing, and the data provide a theoretical basis for the treatment of wounds after anal fistula surgery with HLO.PMID:38699024 | PMC:PMC11064137 | DOI:10.1016/j.heliyon.2024.e29809

Front Immunol. 2024 Apr 18;15:1364329. doi: 10.3389/fimmu.2024.1364329. eCollection 2024.ABSTRACTINTRODUCTION: Within tumor microenvironment, the presence of preexisting antitumor CD8+ T Q7 cells have been shown to be associated with a favorable prognosis in most solid cancers. However, in the case of prostate cancer (PCa), they have been linked to a negative impact on prognosis.METHODS: To gain a deeper understanding of the contribution of infiltrating CD8+ T cells to poor prognosis in PCa, the infiltration levelsof CD8+ T cells were estimated using the TCGA PRAD (The Cancer Genome Atlas Prostate Adenocarcinoma dataset) and MSKCC (Memorial Sloan Kettering Cancer Center) cohorts.RESULTS: Bioinformatic analyses revealed that CD8+ T cells likely influence PCa prognosis through increased expression of immune checkpoint molecules and enhanced recruitment of regulatory T cells. The MLXIPL was identified as the gene expressed in response to CD8+ T cell infiltration and was found to be associated with PCa prognosis. The prognostic role of MLXIPL was examined in two cohorts: TCGA PRAD (p = 2.3E-02) and the MSKCC cohort (p = 1.6E-02). Subsequently, MLXIPL was confirmed to be associated with an unfavorable prognosis in PCa, as evidenced by an independent cohort study (hazard ratio [HR] = 2.57, 95% CI: 1.42- 4.65, p = 1.76E-03).DISCUSSION: In summary, the findings suggested that MLXIPL related to tumor-infiltrating CD8+ T cells facilitated a poor prognosis in PCa.PMID:38698844 | PMC:PMC11063283 | DOI:10.3389/fimmu.2024.1364329

Front Mol Biosci. 2024 Apr 18;11:1341290. doi: 10.3389/fmolb.2024.1341290. eCollection 2024.ABSTRACTObjective: This study aimed to explore the risk factors, metabolic characteristics, and potential biomarkers of mild cognitive impairment in type 2 diabetes mellitus (T2DM-MCI) and to provide potential evidence for the diagnosis, prevention, and treatment of mild cognitive impairment (MCI) in patients with type 2 diabetes mellitus (T2DM). Methods: A total of 103 patients with T2DM were recruited from the Endocrinology Department of The Second Affiliated Hospital of Dalian Medical University for inclusion in the study. The Montreal Cognitive Assessment (MoCA) was utilized to evaluate the cognitive functioning of all patients. Among them, 50 patients were categorized into the T2DM-MCI group (MoCA score < 26 points), while 53 subjects were classified into the T2DM without cognitive impairment (T2DM-NCI) group (MoCA score ≥ 26 points). Serum samples were collected from the subjects, and metabolomics profiling data were generated by Ultra-high performance liquid chromatography-mass spectrometry (UHPLC-MS). These groups were analyzed to investigate the differences in expression of small molecule metabolites, metabolic pathways, and potential specific biomarkers. Results: Comparison between the T2DM-MCI group and T2DM-NCI group revealed significant differences in years of education, history of insulin application, insulin resistance index, insulin-like growth factor-binding protein-3 (IGFBP-3), and creatinine levels. Further binary logistic regression analysis of the variables indicated that low educational level and low serum IGFBP-3 were independent risk factor for T2DM-MCI. Metabolomics analysis revealed that differential expression of 10 metabolites between the T2DM-MCI group and T2DM-NCI group (p < 0.05 and FDR<0.05, VIP>1.5). Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment pathway analysis revealed that fatty acid degradation was the most significant pathway. Receiver operating characteristic (ROC) analysis shows that lysophosphatidylcholine (LPC) 18:0 exhibited greater diagnostic efficiency. Conclusion: This study revealed that a shorter duration of education and lower serum IGFBP-3 levels are independent risk factors for T2DM-MCI. Serum metabolites were found to be altered in both T2DM-MCI and T2DM-NCI groups. T2DM patients with or without MCI can be distinguished by LPC 18:0. Abnormal lipid metabolism plays a significant role in the development of MCI in T2DM patients.PMID:38698772 | PMC:PMC11063278 | DOI:10.3389/fmolb.2024.1341290

BMC Med Inform Decis Mak. 2024 May 2;24(1):116. doi: 10.1186/s12911-024-02521-3.ABSTRACTBACKGROUND: Machine learning (ML) classifiers are increasingly used for predicting cardiovascular disease (CVD) and related risk factors using omics data, although these outcomes often exhibit categorical nature and class imbalances. However, little is known about which ML classifier, omics data, or upstream dimension reduction strategy has the strongest influence on prediction quality in such settings. Our study aimed to illustrate and compare different machine learning strategies to predict CVD risk factors under different scenarios.METHODS: We compared the use of six ML classifiers in predicting CVD risk factors using blood-derived metabolomics, epigenetics and transcriptomics data. Upstream omic dimension reduction was performed using either unsupervised or semi-supervised autoencoders, whose downstream ML classifier performance we compared. CVD risk factors included systolic and diastolic blood pressure measurements and ultrasound-based biomarkers of left ventricular diastolic dysfunction (LVDD; E/e' ratio, E/A ratio, LAVI) collected from 1,249 Finnish participants, of which 80% were used for model fitting. We predicted individuals with low, high or average levels of CVD risk factors, the latter class being the most common. We constructed multi-omic predictions using a meta-learner that weighted single-omic predictions. Model performance comparisons were based on the F1 score. Finally, we investigated whether learned omic representations from pre-trained semi-supervised autoencoders could improve outcome prediction in an external cohort using transfer learning.RESULTS: Depending on the ML classifier or omic used, the quality of single-omic predictions varied. Multi-omics predictions outperformed single-omics predictions in most cases, particularly in the prediction of individuals with high or low CVD risk factor levels. Semi-supervised autoencoders improved downstream predictions compared to the use of unsupervised autoencoders. In addition, median gains in Area Under the Curve by transfer learning compared to modelling from scratch ranged from 0.09 to 0.14 and 0.07 to 0.11 units for transcriptomic and metabolomic data, respectively.CONCLUSIONS: By illustrating the use of different machine learning strategies in different scenarios, our study provides a platform for researchers to evaluate how the choice of omics, ML classifiers, and dimension reduction can influence the quality of CVD risk factor predictions.PMID:38698395 | DOI:10.1186/s12911-024-02521-3

BMC Plant Biol. 2024 May 3;24(1):358. doi: 10.1186/s12870-024-05030-7.ABSTRACTBACKGROUND: Astragalus membranaceus var. mongholicus (Astragalus), acknowledged as a pivotal "One Root of Medicine and Food", boasts dual applications in both culinary and medicinal domains. The growth and metabolite accumulation of medicinal roots during the harvest period is intricately regulated by a transcriptional regulatory network. One key challenge is to accurately pinpoint the harvest date during the transition from conventional yield content of medicinal materials to high and to identify the core regulators governing such a critical transition. To solve this problem, we performed a correlation analysis of phenotypic, transcriptome, and metabolome dynamics during the harvesting of Astragalus roots.RESULTS: First, our analysis identified stage-specific expression patterns for a significant proportion of the Astragalus root genes and unraveled the chronology of events that happen at the early and later stages of root harvest. Then, the results showed that different root developmental stages can be depicted by co-expressed genes of Astragalus. Moreover, we identified the key components and transcriptional regulation processes that determine root development during harvest. Furthermore, through correlating phenotypes, transcriptomes, and metabolomes at different harvesting periods, period D (Nov.6) was identified as the critical period of yield and flavonoid content increase, which is consistent with morphological and metabolic changes. In particular, we identified a flavonoid biosynthesis metabolite, isoliquiritigenin, as a core regulator of the synthesis of associated secondary metabolites in Astragalus. Further analyses and experiments showed that HMGCR, 4CL, CHS, and SQLE, along with its associated differentially expressed genes, induced conversion of metabolism processes, including the biosynthesis of isoflavones and triterpenoid saponins substances, thus leading to the transition to higher medicinal materials yield and active ingredient content.CONCLUSIONS: The findings of this work will clarify the differences in the biosynthetic mechanism of astragaloside IV and calycosin 7-O-β-D-glucopyranoside accumulation between the four harvesting periods, which will guide the harvesting and production of Astragalus.PMID:38698337 | DOI:10.1186/s12870-024-05030-7

Sci Rep. 2024 May 2;14(1):10097. doi: 10.1038/s41598-024-60750-y.ABSTRACTTo explore the impacts of continuous Ganoderma lucidum cultivation on soil physicochemical factors, soil enzyme activity, and the metabolome of Ganoderma lucidum fruiting bodies, this study conducted two consecutive years of cultivation on the same plot of land. Soil physicochemical factors and enzyme activity were assessed, alongside non-targeted metabolomic analysis of the Ganoderma lucidum fruiting bodies under continuous cultivation. The findings unveiled that in the surface soil layer (0-15 cm), there was a declining trend in organic matter, ammonium nitrogen, available phosphorus, available potassium, pH, polyphenol oxidase, peroxidase, alkaline phosphatase, and sucrase, whereas nitrate nitrogen, electrical conductivity (EC), and salt content exhibited an upward trend. Conversely, in the deeper soil layer (15-30 cm), organic matter, ammonium nitrogen, available potassium, alkaline phosphatase, and sucrase demonstrated a decreasing trend, while nitrate nitrogen, available phosphorus, pH, EC, salt content, polyphenol oxidase, and soil peroxidase showed an increasing trend. Metabolomic analysis of Ganoderma lucidum fruiting bodies distinguished 64 significantly different metabolites between the GCK and GT groups, with 39 components having markedly higher relative contents in GCK and 25 components having significantly lower relative contents in GCK compared to GT. Moreover, among these metabolites, there were more types with higher contents in the fruiting bodies harvested in the first year (GCK) compared to those harvested in the second year (GT), with pronounced differences. KEGG pathway analysis revealed that GCK exhibited more complex metabolic pathways compared to GT. The metabolites of Ganoderma lucidum fruiting bodies were predominantly influenced by soil physicochemical factors and soil enzyme activity. In the surface soil layer (0-15 cm), the metabolome was significantly affected by soil pH, soil organic matter, available phosphorus, and soil alkaline phosphatase, while in the deeper soil layer (15-30 cm), differences in the Ganoderma lucidum metabolome were more influenced by soil alkaline phosphatase, soil catalase, pH, nitrate nitrogen, and soil sucrase.PMID:38698154 | DOI:10.1038/s41598-024-60750-y

Anal Chem. 2024 May 2. doi: 10.1021/acs.analchem.4c00291. Online ahead of print.ABSTRACTDuring the coronavirus disease 2019 (COVID-19) pandemic, which has witnessed over 772 million confirmed cases and over 6 million deaths globally, the outbreak of COVID-19 has emerged as a significant medical challenge affecting both affluent and impoverished nations. Therefore, there is an urgent need to explore the disease mechanism and to implement rapid detection methods. To address this, we employed the desorption separation ionization (DSI) device in conjunction with a mass spectrometer for the efficient detection and screening of COVID-19 urine samples. The study encompassed patients with COVID-19, healthy controls (HC), and patients with other types of pneumonia (OP) to evaluate their urine metabolomic profiles. Subsequently, we identified the differentially expressed metabolites in the COVID-19 patients and recognized amino acid metabolism as the predominant metabolic pathway involved. Furthermore, multiple established machine learning algorithms validated the exceptional performance of the metabolites in discriminating the COVID-19 group from healthy subjects, with an area under the curve of 0.932 in the blind test set. This study collectively suggests that the small-molecule metabolites detected from urine using the DSI device allow for rapid screening of COVID-19, taking just three minutes per sample. This approach has the potential to expand our understanding of the pathophysiological mechanisms of COVID-19 and offers a way to rapidly screen patients with COVID-19 through the utilization of machine learning algorithms.PMID:38697955 | DOI:10.1021/acs.analchem.4c00291

Klin Onkol. 2024;38(2):102-109. doi: 10.48095/ccko2024102.ABSTRACTBACKGROUND: Endometrial carcinoma (EC) is the most common cancer of the female reproductive tract in developed countries. The prognosis and 5-year survival rates are closely tied to the stage diagnosis. Current routine diagnostic methods of EC are either lacking specificity or are uncomfortable, invasive and painful for the patient. As of now, the gold diagnostic standard is endometrial biopsy. Early and non-invasive diagnosis of EC requires the identification of new biomarkers of disease and a screening test applicable to routine laboratory diagnostics. The application of untargeted metabolomics combined with artificial intelligence and biostatistics tools has the potential to qualitatively and quantitatively represent the metabolome, but its introduction into routine diagnostics is currently unrealistic due to the financial, time and interpretation challenges. Fluorescence spectral analysis of body fluids utilizes autofluorescence of certain metabolites to define the composition of the metabolome under physiological conditions.PURPOSE: This review highlights the potential of fluorescence spectroscopy in the early detection of EC. Data obtained by three-dimensional fluorescence spectroscopy define the quantitative and qualitative composition of the complex fluorescent metabolome and are useful for identifying biochemical metabolic changes associated with endometrial carcinogenesis. Autofluorescence of biological fluids has the prospect of providing new molecular markers of EC. By integrating machine learning and artificial intelligence algorithms in the data analysis of the fluorescent metabolome, this technique has great potential to be implemented in routine laboratory diagnostics.PMID:38697818 | DOI:10.48095/ccko2024102