Metabolomics Data Research Articles

Nuclear magnetic resonance-based metabolomics with machine learning for predicting progression from prediabetes to diabetes.

Identification of individuals with prediabetes who are at high risk of developing diabetes allows for precise interventions. We aimed to determine the role of nuclear magnetic resonance (NMR)-based metabolomic signature in predicting the progression from prediabetes to diabetes. This prospective study included 13,489 participants with prediabetes who had metabolomic data from the UK Biobank. Circulating metabolites were quantified via NMR spectroscopy. Cox proportional hazard (CPH) models were performed to estimate the associations between metabolites and diabetes risk. Supporting vector machine, random forest, and extreme gradient boosting were used to select the optimal metabolite panel for prediction. CPH and random survival forest (RSF) models were utilized to validate the predictive ability of the metabolites. During a median follow-up of 13.6 years, 2525 participants developed diabetes. After adjusting for covariates, 94 of 168 metabolites were associated with risk of progression to diabetes. A panel of nine metabolites, selected by all three machine-learning algorithms, was found to significantly improve diabetes risk prediction beyond conventional risk factors in the CPH model (area under the receiver-operating characteristic curve, 1 year: 0.823 for risk factors + metabolites vs 0.759 for risk factors, 5 years: 0.830 vs 0.798, 10 years: 0.801 vs 0.776, all p < 0.05). Similar results were observed from the RSF model. Categorization of participants according to the predicted value thresholds revealed distinct cumulative risk of diabetes. Our study lends support for use of the metabolite markers to help determine individuals with prediabetes who are at high risk of progressing to diabetes and inform targeted and efficient interventions. Shanghai Municipal Health Commission (2022XD017). Innovative Research Team of High-level Local Universities in Shanghai (SHSMU-ZDCX20212501). Shanghai Municipal Human Resources and Social Security Bureau (2020074). Clinical Research Plan of Shanghai Hospital Development Center (SHDC2020CR4006). Science and Technology Commission of Shanghai Municipality (22015810500).

Statistical analysis of feature-based molecular networking results from non-targeted metabolomics data

Statistical analysis of feature-based molecular networking results from non-targeted metabolomics data

The chemical composition of secondary organic aerosols regulates transcriptomic and metabolomic signaling in an epithelial-endothelial in vitro coculture

BackgroundThe formation of secondary organic aerosols (SOA) by atmospheric oxidation reactions substantially contributes to the burden of fine particulate matter (PM2.5), which has been associated with adverse health effects (e.g., cardiovascular diseases). However, the molecular and cellular effects of atmospheric aging on aerosol toxicity have not been fully elucidated, especially in model systems that enable cell-to-cell signaling.MethodsIn this study, we aimed to elucidate the complexity of atmospheric aerosol toxicology by exposing a coculture model system consisting of an alveolar (A549) and an endothelial (EA.hy926) cell line seeded in a 3D orientation at the air‒liquid interface for 4 h to model aerosols. Simulation of atmospheric aging was performed on volatile biogenic (β-pinene) or anthropogenic (naphthalene) precursors of SOA condensing on soot particles. The similar physical properties for both SOA, but distinct differences in chemical composition (e.g., aromatic compounds, oxidation state, unsaturated carbonyls) enabled to determine specifically induced toxic effects of SOA.ResultsIn A549 cells, exposure to naphthalene-derived SOA induced stress-related airway remodeling and an early type I immune response to a greater extent. Transcriptomic analysis of EA.hy926 cells not directly exposed to aerosol and integration with metabolome data indicated generalized systemic effects resulting from the activation of early response genes and the involvement of cardiovascular disease (CVD) -related pathways, such as the intracellular signal transduction pathway (PI3K/AKT) and pathways associated with endothelial dysfunction (iNOS; PDGF). Greater induction following anthropogenic SOA exposure might be causative for the observed secondary genotoxicity.ConclusionOur findings revealed that the specific effects of SOA on directly exposed epithelial cells are highly dependent on the chemical identity, whereas non directly exposed endothelial cells exhibit more generalized systemic effects with the activation of early stress response genes and the involvement of CVD-related pathways. However, a greater correlation was made between the exposure to the anthropogenic SOA compared to the biogenic SOA. In summary, our study highlights the importance of chemical aerosol composition and the use of cell systems with cell-to-cell interplay on toxicological outcomes.Graphical

Metabolomics, phytohormone and transcriptomics strategies to reveal the mechanism of barley heading date regulation to responds different photoperiod

BackgroundThe correlation between heading date and flowering time significantly regulates grain filling and seed formation in barley and other crops, ultimately determining crop productivity. In this study, the transcriptome, hormone content detection, and metabolome analysis were performed systematically to analyze the regulatory mechanism of heading time in highland barley under different light conditions. The heading date of D18 (winter highland barley variety, Dongqing18) was later than that of K13 (vernal highland barley variety) under normal growth conditions or long-day (LD) treatment, while this situation will reverse with short-day (SD) treatment.ResultsThe circadian rhythm plant, plant hormone signaling transduction, starch and sucrose metabolism, and photosynthesis-related pathways are significantly enriched in barley under SD and LD to influence heading time. In the plant circadian rhythm pathway, the key genes GI (Gigantea), PRR (Pesudoresponseregulator), FKF1 (Flavin-binding kelch pepeat F-Box 1), and FT (Flowering locus T) are identified as highly expressed in D18SD3 and K13SD2, while they are significantly down-regulated in K13SD3. These genes play an important role in regulating the heading date of D18 earlier than that of K13 under SD conditions. In photosynthesis-related pathways, a-b binding protein and RBS were highly expressed in K13LD3, while NADP-dependent malic enzyme, phosphoenolpyruvate carboxylase, fructose-bisphosphate aldolase, and triosephosphate isomerase were significantly expressed in D18SD3. In the starch and sucrose metabolism pathway, 41 DEGs (differentially expressed genes) and related metabolites were identified as highly expressed and accumulated in D18SD3. The DEGs SAUR (Small auxin-up RNA), ARF (Auxin response factor), TIR1 (Transport inhibitor response 1), EIN3 (Ethylene-insensitive 3), ERS1 (Ethylene receptor gene), and JAZ1 (Jasmonate ZIM-domain) in the plant hormone pathway were significantly up-regulated in D18SD3. Compared with D18LD3, the content of N6-isopentenyladenine, indole-3-carboxylic acid, 1-aminocyclopropanecarboxylic acid, trans-zeatin, indole-3-carboxaldehyde, 1-O-indol-3-ylacetylglucose, and salicylic acid in D18SD3 also increased. The expression levels of vernalization genes (HvVRN1, HvVRN2, and HvVRN3), photoperiod genes (PPD), and PPDK (Pyruvate phosphate dikinase) that affect photosynthetic efficiency in barley are also analyzed, which play important regulatory roles in barley heading date. The WGCNA analysis of the metabolome data and circadian regulatory genes identified the key metabolites and candidate genes to regulate the heading time of barley in response to the photoperiod.ConclusionThese studies will provide a reference for the regulation mechanism of flowering and the heading date of highland barley.

Metabolic phenotypes and vitamin D response in the critically ill: A metabolomic cohort study

Background & AimsAlthough vitamin D deficiency is common in critically ill patients, randomized controlled trials fail to demonstrate benefits of supplementation. We aimed to identify distinct vitamin D3 responsive metabolic phenotypes prior to trial intervention of high-dose vitamin D3 by applying machine learning clustering method to metabolomics data from the Correction of Vitamin D Deficiency in Critically Ill Patients (VITdAL-ICU) trial. MethodsIn the randomized, placebo-controlled VITdAL-ICU trial, critically ill adults received placebo or high-dose vitamin D3. To distinguish vitamin D3 responsive metabolic phenotypes prior to intervention, we implemented consensus clustering with partitioning around medoids algorithm to the plasma metabolome data before randomization. Individual metabolite differences were determined utilizing linear mixed-effects regression models stratified for metabolomic phenotypes with false discovery rate adjustment. The association between vitamin D3 supplementation and 180-day mortality was evaluated in each metabolic phenotype, applying multivariable logistic regression analysis. ResultsIn 453 critically ill adults, the study identified 4 distinct metabolic phenotypes (clusters A. N=134; B. N=123; C. N=92; D. N=104). We found differential metabolic pathway patterns in the four clusters. Specifically, branched chain amino acid catabolic metabolites, long-chain acylcarnitines and diacylglycerol species are significantly increased in a specific metabolic phenotype (cluster D) following high-dose vitamin D3. Further, in cluster D high-dose vitamin D3 supplementation had a significantly lower adjusted odds of 180-day mortality after controlling age, sex, Simplified Acute Physiology Score II, admission diagnosis, and baseline 25-hydroxyvitamin D (OR 0.28 (95%CI, 0.09–0.89); P=0.03). In metabotype A, B, and C, high-dose vitamin D3 supplementation was not significantly associated with lower 180-day mortality following multivariable adjustment. ConclusionIn this post-hoc cohort study of the VITdAL-ICU trial, the clustering analysis of plasma metabolome data identified biologically distinct metabolic phenotypes. Among clusters, we found the different associations between high-dose vitamin D3 supplementation and specific metabolite pathways as well as 180-day mortality. Our findings facilitate further research to validate metabolic phenotype-targeted strategies for critical illness treatments.

A type 2 immune circuit and arachidonic acid metabolism role in anti-nematode infection: evidence from transcriptome and targeted metabolome data in goat

The gastrointestinal nematode infection poses a covert threat to both humans and domestic animals worldwide, eliciting a type 2 immune response within the small intestine. Intestinal tuft cells detect the nematode and activated group 2 innate lymphoid cells. Tuft cell−derived leukotrienes (one of the metabolites of arachidonic acid) were found to drive rapid anti-helminth immunity, but it is still poorly understood whether the tuft cell−mediated type 2 immune circuit and arachidonic acid metabolism modulate anti-parasitic immunity in the gastric epithelium. This study was designed to evaluate the immunological responses of goats inoculated with or without H. contortus. Results showed that H. contortus infection induced a systemic type 2 immune response, characterised by lymphocyte proliferation and greater eosinophils both in peripheral blood and abomasal mucosa, as well as increased type 2 cytokines IL-4, IL-5, and IL-13. Infection of H. contortus altered the transcriptome of the abomasum epithelium, especially tuft cell−mediated circuit-key genes. The infection also influenced the abomasal microbiota, arachidonic acid metabolism and related lipid metabolites, accompanying with great increases in the secretion of leukotrienes and prostaglandins. These findings demonstrate the role of tuft cells mediated circuit in sensing H. contortus infection and immune activation, reveal the candidate function of arachidonic acid involved in anti-helminth immunity, and suggest novel strategies for the control of parasitic diseases in livestock and humans.

Pharmacometabolomics of sulfonylureas in patients with type 2 diabetes: a cross-sectional study.

Sulfonylureas have been a longstanding pharmacotherapy in the management of type 2 diabetes, with potential benefits beyond glycemic control. Although sulfonylureas are effective, interindividual variability exists in drug response. Pharmacometabolomics is a potent method for elucidating variations in individual drug response. Identifying unique metabolites associated with treatment response can improve our ability to predict outcomes and optimize treatment strategies for individual patients. Our objective is to identify metabolic signatures associated with good and poor response to sulfonylureas, which could enhance our capability to anticipate treatment outcome. In this cross-sectional study, clinical and metabolomics data for 137 patients with type 2 diabetes who are taking sulfonylurea as a monotherapy or a combination therapy were obtained from Qatar Biobank. Patients were empirically categorized according to their glycosylated hemoglobin levels into poor and good responders to sulfonylureas. To examine variations in metabolic signatures between the two distinct groups, we have employed orthogonal partial least squares discriminant analysis and linear models while correcting for demographic confounders and metformin usage. Good responders showed increased levels of acylcholines, gamma glutamyl amino acids, sphingomyelins, methionine, and a novel metabolite 6-bromotryptophan. Conversely, poor responders showed increased levels of metabolites of glucose metabolism and branched chain amino acid metabolites. The results of this study have the potential to empower our knowledge of variability in patient response to sulfonylureas, and carry significant implications for advancing precision medicine in type 2 diabetes management.

Using integrated transcriptomics and metabolomics to explore the effects of infant formula on the growth and development of small intestinal organoids.

Infant formulas are designed to provide sufficient energy and the necessary nutrients to support the growth and development of newborns. Currently, research on the functions of formula milk powder focuses on clinical research and cell experiments, and there were many cell experiments that investigated the effect of infant formulas on cellular growth. However, most of the cells used are tumor cell lines, which are unable to simulate the real digestion process of an infant. In this study, we innovatively proposed a method that integrates human small intestinal organoids (SIOs) with transcriptomics and metabolomics analysis. We induced directed differentiation of human embryonic stem cells into SIOs and simulated the intestinal environment of newborns with them. Then, three kinds of 1-stage infant formulas from the same brand were introduced to simulate the digestion, absorption, and metabolism of the infant intestine. The nutritional value of each formula milk powder was examined by multi-omics sequencing methods, including transcriptomics and metabolomics analysis. Results showed that there were significant alterations in gene expression and metabolites in the three groups of SIOs after absorbing different infant formulas. By analyzing transcriptome and metabolome data, combined with GO, KEGG, and GSEA analysis, we demonstrated the ability of SIOs to model the different aspects of the developing process of the intestine and discovered the correlation between formula components and their effects, including Lactobacillus lactis and lactoferrin. The study reveals the effect and mechanisms of formula milk powder on the growth and development of infant intestines and the formation of immune function. Furthermore, our method can help to construct a multi-level assessment model, detect the effects of nutrients, and evaluate the interactions between nutrients, which is helpful for future research and development of infant powders.

Integrated transcriptomic and metabolic analyses reveal the early response mechanism of Pinus tabulaeformis to pine wood nematodes

Pine wilt disease (PWD) is a devastating disease of pine trees caused by the pine wood nematode (Bursapherenchus xylophilus, PWN). To study how Pinus tabulaeformis responds to PWD infection, we collected 3-year-old P. tabulaeformis seedlings at 2 days, 5 days, and 8 days after being infected with B. xylophilus. We identified genes and metabolites early responding to infection using transcriptome and metabolomic data obtained by high-throughput mRNA sequencing (RNA-seq) and liquid chromatography-tandem mass spectrometry (LC-MS/MS)-based assays, respectively. The following results were obtained: (1) After inoculation with PWN, the average number of days taken for 3-year-old P. tabulaeformis seedlings to develop symptoms was 8 days. (2) Combined transcriptome and metabolome analysis revealed that phenylpropanoid biosynthesis and flavonoid biosynthesis are critically important pathways for P. tabulaeformis to respond to PWD. (3) The response of P. tabulaeformis to stress was mainly through positive regulation of gene expression, including some key genes related to plant hormones or transcription factors that have been widely studied. Genes related to pathways such as photosynthesis, plant-pathogen interactions, and DNA replication were downregulated. (4) Terpenoid biosynthesis genes involved during the development of pine wilt disease. This study demonstrated the defence and pathogenic mechanisms of P. tabulaeformis against PWD, providing a reference for the early diagnosis of PWD.

Abstract C049: Computational Methods for Central and Lipid Metabolism Networks Analysis to Reveals Flux Change in PDAC

Abstract Pancreatic ductal adenocarcinoma (PDAC) is among the most challenging cancers to treat due to its aggressive nature and the difficulties in early detection. The disease progresses rapidly from stage T1 to T4, leaving a narrow window for effective intervention. This swift progression is closely tied to significant metabolic alterations, and current studies have highlighted specific metabolic changes. This includes elevated glutamine metabolism and altered aspartate and alpha-ketoglutarate production to support uncontrolled tumor growth; and elevated lysophosphatidylcholines secretion by cancer-associated fibroblasts to support tumor progression. These insights underscore the need for a holistic understanding of PDAC metabolism. Hence, there is an urgent need for a comprehensive and high-resolution analysis of PDAC's metabolic landscape remains unexplored. To computationally characterize the PDAC metabolism and fully leverage the large-scale transcriptomics with matched or unmatched metabolomics data, we developed an algorithm based on the Michaelis-Menten equation to estimate distribution-wise variations in mass-carrying metabolic networks using gene expression and metabolomics data. We start by manually curating a PDAC-specific metabolic network that focuses on central metabolism, glutamine-glutamate metabolism, and lipid metabolism. We then gathered comprehensive kinetic parameters, predicted enzyme activity from transcriptomics data, and conducted metabolomic profiling to determine metabolic compound concentrations. By applying Monte Carlo sampling methods, we achieved an overall balance of metabolic flux, enabling us to model reaction rates accurately and predict metabolic shifts. This approach holds potential to provide a comprehensive perspective on the rewired metabolism in PDAC, inspiring the development of metabolic targets or diet interventions for therapeutic treatment. Citation Format: Sha Cao, Haiqi Zhu, Yijie Wang, Melissa Fishel, Chi Zhang. Computational Methods for Central and Lipid Metabolism Networks Analysis to Reveals Flux Change in PDAC [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Advances in Pancreatic Cancer Research; 2024 Sep 15-18; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2024;84(17 Suppl_2):Abstract nr C049.

TRPA1 Influences Staphylococcus aureus Skin Infection in Mice and Associates with HIF-1a and MAPK Pathway Modulation.

Infections caused by methicillin-resistant Staphylococcus aureus (MRSA) are a major public health burden. Emerging antibiotic resistance has heightened the need for new treatment approaches for MRSA infection such as developing novel antimicrobial agents and enhancing the host's defense response. The thermo-ion channels Transient Receptor Potential (TRP-) A1 and V1 have been identified as modulators of S. aureus quorum sensing in cell culture models. However, their effects on in vivo infection control are unknown. In this study, we investigated the therapeutic effect of natural TRP ion channel inhibitors on MRSA skin infection in mice. While deletion of TRPV1 did not affect lesion size or inflammatory markers, TRPA1-/- mice demonstrated significantly reduced infection severity and abscess size. Treatment with natural inhibitors of TRPA1 with or without blockade of TRPV1 also reduced abscess size. Tissue transcriptomic data coupled with immunohistochemistry revealed that TRPA1 inhibition impacted heat shock protein expression (HSP), modulated the HIF-1a and MAPK pathways, and reduced IL4 expression. Additionally, metabolomics data showed an impact on purine and glycosaminoglycan pathways. Multi-omic integration of transcriptomic and metabolic data revealed that diacylglycerol metabolism was the likely bridge between metabolic and immunological impacts. Our findings suggest that TRPA1 antagonism could provide a promising and cost-effective therapeutic approach for reducing the severity of MRSA infection, and presents a novel underlying molecular mechanism.

PSIII-7 Effects of Saccharomyces cerevisiae fermentation product on total tract digestibility, ruminal fermentation, and plasma metabolome of beef steers fed a finishing diet

Abstract Saccharomyces cerevisiae fermentation product (SCFP) has been widely studied; however, its mode of action on high-concentrate diets has not been fully elucidated. This study evaluated the impact of SCFP on total tract digestibility, fecal excretions of N, P, Zn, and Cu, rumen fermentation, and plasma metabolome of finishing beef steers. Ruminally cannulated Holstein steers [n = 8; body weight (BW) = 580 ± 29.2 kg] were enrolled in a crossover design study with 25-d treatment periods and a 24-d washout period. Steers were fed ad libitum a finishing diet consisting of 20% corn silage and 80% concentrate (on DM basis). Steers were blocked based on initial BW and randomly assigned to two treatments in period 1 and switched to the other treatment in period 2. The treatments were CON (Control, basal diet only) and SCFP (basal diet top-dressed with 12 gּ steer-1ּ ּ d-1 SCFP, NaturSafe, Diamond V, Cedar Rapids, IA). Feed and fecal samples were collected during d 22 to 24 for determination of digestibility. Blood was collected before morning feeding on d 25. Plasma samples were analyzed for metabolome with liquid chromatography-mass spectrometry (LC-MS). Rumen fluid was collected via the rumen cannula at 0, 4, 8, and 12 h post-morning feeding on d 25 and analyzed for pH, volatile fatty acids (VFA), and NH3-N. Performance and ruminal fermentation characteristics data were analyzed using the GLIMMIX procedure of SAS 9.4. Metabolomic data analysis was conducted with Metaboanalyst 6.0. Supplementing steers with SCFP reduced DMI (10.6 vs. 11.4 kg/d, P = 0.05) without affecting (P &amp;gt; 0.05) average daily gain, feed efficiency, and digestibility of organic matter, crude protein, and neutral detergent fiber. Treatment did not affect (P &amp;gt; 0.10) fecal excretions of N, P, Cu, and Zn. Feeding SCFP increased ruminal pH (6.29 vs. 6.01, P = 0.01), numerically reduced concentrations of NH3-N (5.01 vs 6.25 mM, P = 0.16), and total VFA (96.8 vs. 102.5 mM, P = 0.15). SCFP also reduced the ruminal molar proportion of butyrate (11.2 vs. 12.8 molar %, P = 0.01) and tended to increase those of isobutyrate (1.03 vs. 0.90, molar %, P = 0.06) and isovalerate (2.62 vs. 2.29, molar %, P = 0.10). Supplementation of SCFP enriched (P ≤ 0.05) purine metabolism, amino sugar and nucleotide sugar metabolism, and lysine degradation pathways in the plasma. Additionally, tryptophan metabolism and pyrimidine metabolism pathways tended to be enriched (0.05 &amp;lt; P ≤ 0.10) by SCFP. Our findings indicate that feeding SCFP improved the ruminal pH of finishing cattle, which may be beneficial for preventing subacute ruminal acidosis in feedlot cattle. Additionally, SCFP enriched several important plasma nitrogen metabolism pathways, potentially associated with microbial nitrogen metabolism in the rumen, warranting further research.

Metabolomics-Based Study of the Protective Effect of 4-Hydroxybenzyl Alcohol on Ischemic Astrocytes.

Ischemic stroke is a common and dangerous disease in clinical practice. Astrocytes (ASs) are essential for maintaining the metabolic balance of the affected regions during the disease process. 4-Hydroxybenzyl alcohol (4HBA) from Gastrodia elata Bl. has potential neuroprotective properties due to its ability to cross the blood-brain barrier. In an in vitro experiment, we replicated the oxygen-glucose deprivation/reoxygenation model, and used methyl thiazoly tertrazolium, flow cytometry, kits, and other technical means to clarify the protective effect of 4HBA on primary ASs. In in vivo experiments, the 2VO model was replicated, and immunofluorescence and immunohistochemistry techniques were used to clarify the protective effect of 4HBA on ASs and the maintenance of the blood-brain barrier. Differential metabolites and related pathways were screened and verified using metabolomics analysis and western blot. 4HBA noticeably amplified AS cell survival, reduced mitochondrial dysfunction, and mitigated oxidative stress. It demonstrated a protective effect on ASs in both environments and was instrumental in stabilizing the blood-brain barrier. Metabolomic data indicated that 4HBA regulated nucleic acid and glutathione metabolism, influencing purines, pyrimidines, and amino acids, and it activated the N-methyl-D-aspartate/p-cAMP-response element binding protein/brain-derived neurotrophic factor signaling pathway via N-methyl-D-aspartate R1/N-methyl-D-aspartate 2C receptors. Our findings suggest that 4HBA is a potent neuroprotective agent against ischemic stroke, enhancing AS cell survival and function while stabilizing the blood-brain barrier. The N-methyl-D-aspartate/p-cAMP-response element binding protein/brain-derived neurotrophic factor signaling pathway is activated by 4HBA.

Metabolomic and Transcriptomic Analyses Reveal the Potential Mechanisms of Dynamic Ovarian Development in Goats during Sexual Maturation.

The ovary is a crucial reproductive organ in mammals, and its development directly influences an individual's sexual maturity and reproductive capacity. To comprehensively describe ovarian sexual maturation in goats, we integrated phenotypic, hormonal, metabolomic, and transcriptomic data from four specific time points: after birth (D1), at 2 months old (M2), at 4 months old (M4), and at 6 month old (M6). The study showed that during the early stage (D1-M2), ovarian growth was the most rapid, with weight and morphology increasing by 284% and 65%, respectively, and hormone levels rose significantly, with estradiol increasing by 57%. Metabolomic analysis identified 1231 metabolites, primarily lipids, lipid molecules, and organic acids, which can support hormone balance and follicle development by providing energy and participating in signaling transduction. Transcriptomic analysis identified 543 stage-specific differentially expressed genes, mainly enriched in steroid biosynthesis, amino acid metabolism, and the PI3K/AKT pathway, which are key factors influencing ovarian cell proliferation, apoptosis, hormone secretion, and metabolism. The integrated analysis revealed the key processes in the ovarian steroid hormone biosynthesis pathway and gene/metabolite networks associated with ovarian phenotypes and hormone levels, ultimately highlighting scavenger receptor class B type 1 (SCARB1), Cytochrome P450 Family 1 Subfamily A Member 1 (CYP11A1), 3beta-hydroxysteroid dehydrogenase (3BHSD), progesterone, estradiol, and L-phenylalanine as key regulators of ovarian morphological and functional changes at different developmental stages. This study is the first to reveal the metabolic changes and molecular regulatory mechanisms during ovarian sexual maturation in goats, providing valuable insights for understanding reproductive system development and optimizing reproductive performance and breeding efficiency.

330 Growth performance, incidence of scours, and plasma metabolites in beef × dairy crossbred calves transported at 2, 4, 8 and 14 d of age

Abstract Transportation can compromise health and production performance in young calves. Although widely accepted that the younger calves are, the greater the probability of negative outcomes, there still is a lack of evidence-based recommendations on the minimum age at transportation. Our objective was to evaluate the impact of transport age on growth performance, incidence of scours, and plasma metabolites over the first 28 d post-transport in Charolais × Holstein crossbred calves. Calves (n = 36) ages 2, 4, 8 and 14-d old were transported in summer from a commercial farm to an on-campus calf rearing facility (410 miles). Calf body weight (BW) was recorded before departure, on arrival and d 28 post-transport. Dry matter intake (DMI) and fecal scores were recorded daily. Blood samples collected prior to transportation, on arrival, and d 7 and 28 post-transport were used for targeted metabolomics analysis (Biocrates MxP Quant 500 kit). Growth performance and fecal score data were analyzed using PROC MIXED (SAS) whereas R was used for predictive modeling of metabolomics data. Over the 28-d period post-transport, DMI was greater (P &amp;lt; 0.01) for 14 d than 2 and 4 d old calves; however, it did not differ between 2, 4, and 8 d old calves. Average daily gain (ADG) and feed efficiency (FE) were also greater (P &amp;lt; 0.01) for 14 d than 2, 4, and 8 d old calves. During wk 1 post-transport, the total number of days calves had scours was least (P &amp;lt; 0.01) for 2 and 14 d, intermediate for 4 d, and greatest for 8 d old calves. During wk 2, the total number of days with scours was less (P &amp;lt; 0.01) for 8 and 14 d (average of 2 d) compared with 2 and 4 d old calves (5 d). Additionally, the total number of days calves had scours during the first 2 wk post-transport tended (P = 0.08) to be greater for 2, 4, and 8 d than 14 d old calves. However, during wk 3 and 4 post-transport, there were no differences (P ≥ 0.58) in the total number of days with scours across transport age. Arginine methylation, d 7 of measurements, carnitine uptake defect, phosphatidic acid 16:0:18:2, methylenetetrahydrofolate reductase, sodium, triglyceride 18:2:34:4, threonine, α-aminobutyric acid, and glycolithocholic acid 3-sulfate were the most important features in terms of predicting the occurrence of scours. Overall, DMI, ADG and FE were less for calves transported at 2, 4, and 8 d than at 14 d of age, which could partially be attributed to differences in the incidence rate of scours. In addition, several plasma metabolites could be good predictors of scours in transported calves.

Multi-Omics Exploration of ABA Involvement in Identifying Unique Molecular Markers for Single and Combined Stresses in tomato plants.

Over the past decade, our research group has found that plant responses to combined abiotic stresses are unique and cannot be inferred from studying plants exposed to individual stresses. Understanding how adaptative plant mechanisms integrate from stress perception to biochemical and physiological adjustments is a major challenge in abiotic stress signaling studies. Considering abscisic acid (ABA) as a key regulator in plant abiotic stress responses, in our study, ABA-deficient plants (flc) exposed to single or combined salinity and heat stresses were evaluated and different -omics analyses were conducted. Significant changes in biomass, photosynthesis, ions, transcripts, and metabolites occurred in mutant plants under single or combined stresses. Exogenous ABA application in flc mutants did not fully recover plant phenotypes or metabolic levels but induced cellular reprogramming with changes in specific markers. Multi-omics analysis aimed to identify ABA-dependent, ABA-independent, or stress-dependent markers in plant responses to single or combined stresses. We demonstrated that studying different -omics as a whole led to the identification of specific markers for each stress condition that were not detectable when each -omic was studied individually. This resource article provides an important and novel reference for scientists working in the field of plant abiotic stress. Future exploration of the transcriptomic, ionomic and metabolomic data presented in this study could lead to the identification of new pathways and genes associated with ABA signaling processes. These findings may be utilized to enhance crop resilience to heat waves, salinity, and their combination, contributing to addressing food security challenges in a climate change scenario.

MetMiner: A user-friendly pipeline for large-scale plant metabolomics data analysis.

The utilization of metabolomics approaches to explore the metabolic mechanisms underlying plant fitness and adaptation to dynamic environments is growing, highlighting the need for an efficient and user-friendly toolkit tailored for analyzing the extensive datasets generated by metabolomics studies. Current protocols for metabolome data analysis often struggle with handling large-scale datasets or require programming skills. To address this, we present MetMiner (https://github.com/ShawnWx2019/MetMiner), a user-friendly, full-functionality pipeline specifically designed for plant metabolomics data analysis. Built on R shiny, MetMiner can be deployed on servers to utilize additional computational resources for processing large-scale datasets. MetMiner ensures transparency, traceability, and reproducibility throughout the analytical process. Its intuitive interface provides robust data interaction and graphical capabilities, enabling users without prior programming skills to engage deeply in data analysis. Additionally, we constructed and integrated a plant-specific mass spectrometry database into the MetMiner pipeline to optimize metabolite annotation. We have also developed MDAtoolkits, which include a complete set of tools for statistical analysis, metabolite classification, and enrichment analysis, to facilitate the mining of biological meaning from the datasets. Moreover, we propose an iterative weighted gene co-expression network analysis strategy for efficient biomarker metabolite screening in large-scale metabolomics data mining. In two case studies, we validated MetMiner's efficiency in data mining and robustness in metabolite annotation. Together, the MetMiner pipeline represents a promising solution for plant metabolomics analysis, providing a valuable tool for the scientific community to use with ease.

Multi-omics analysis reveals the transcription factor AtuMYB306 improves drought tolerance by regulating flavonoid metabolism in Chinese chive (Allium tuberosum Rottler)

Drought is one of the most detrimental stresses that severely constrains plant growth and productivity. Although Chinese chive (Allium tuberosum Rottler) is a vegetable species that is cultivated and consumed worldwide, few studies have investigated how this species responds to drought. In this study, we conducted transcriptomics, metabolomics, and proteomics analyses on chive seedlings exposed to different water availability conditions (mild drought, moderate drought, severe drought, and re-watering) and found that the accumulation of flavonoids in chive leaves was substantially altered under drought stress. Gene co-expression regulatory network analysis, conducted by integrating transcriptome and metabolome data, revealed a chive R2R3-MYB transcription factor, AtuMYB306, as a central regulator of flavonoid synthesis. Overexpression of AtuMYB306 significantly improved osmotic stress tolerance and enhanced flavonoid content in Arabidopsis. We further demonstrated that AtuMYB306 directly binds to the promoters of three flavonoid biosynthetic genes (Atu4CL, AtuF3H, and AtuF3’H) and activates their expression. These results suggest that AtuMYB306 improves drought tolerance in Chinese chive by enhancing flavonoid biosynthesis to scavenge reactive oxygen species (ROS) generated under water-deficit conditions. Thus, our findings provide evidence that AtuMYB306 playing a pivotal role in improving drought resistance in Chinese chive.

Gut microbiota-derived Metabolite, Shikimic Acid, inhibits vascular smooth muscle cell proliferation and migration

Gut microbiota dysbiosis is linked to vascular wall disease, but the mechanisms by which gut microbiota cross-talk with the host vascular cells remain largely unknown. Shikimic acid (SA) is a biochemical intermediate synthesized in plants and microorganisms, but not mammals. Surprisingly, recent metabolomic profiling data demonstrate that SA is detectable in human and murine blood. In this study, analyzing data from germ-free rats, we provide evidence in support of SA as a bona fide gut microbiota-derived metabolite, emphasizing its biological relevance. Since vascular cells are the first cells exposed to circulating metabolites, in this study, we examined, for the first time, the effects and potential underlying molecular mechanisms of SA on vascular smooth muscle cell (VSMC) proliferation and migration, which play a key role in occlusive vascular diseases, such as post-angioplasty restenosis and atherosclerosis. We found that SA inhibits the proliferation and migration of human coronary artery SMCs. At the molecular level, unexpectedly, we found that SA activates, rather than inhibits, multiple pro-mitogenic signaling pathways in VSMCs, such as ERK1/2, AKT, and mTOR/p70S6K. Conversely, we found that SA activates the anti-proliferative AMP-activated protein kinase (AMPK) in VSMCs, a key cellular energy sensor and regulator. However, loss-of-function experiments demonstrate that AMPK does not mediate the inhibitory effects of SA on VSMC proliferation. In conclusion, these studies demonstrate that a microbiota-derived metabolite, SA, inhibits VSMC proliferation and migration in vitro and prompt further evaluation of the possible underlying molecular mechanisms and the potential protective role in VSMC-related vascular wall disease in vivo.

Molecular and cellular level analysis of the mechanism of nutritional intervention in preventing epidemic virus infection

Molecular nutrition encompasses a wider range of investigations than nutritional genomics, which can be considered a scientific investigation of how different nutrients and dietary components influence the cellular and molecular mechanisms of the body and health. In effect, increasing antioxidant elements in daily diets can assist with combating the inflammatory response caused by cytokines in the human body. Antioxidants can impede the oxidation process and hence avoid the creation of free radicals in the cytoplasm that can damage the cells via chain reactions. Contamination sequences, batch effects, uneven sampling, unreported taxa, technological biases, and heteroscedasticity are all significant issues in molecular microbial biology. Artificial Intelligence (AI) methodologies to uncover hidden patterns, connections, and interactions within metabolomics data, allowing them to provide personalized food recommendations based on individual health profiles. A multi-scale convolutional neural network (MCNN) classifies cellular images into phenotypes in a single coherent step utilizing the image’s pixel intensity values. Hence, the proposed AI-MCNN has been an essential nutrient that assists the immune system in various ways, including acting as an antioxidant to protect healthy cells, promoting immune cell development and function, and creating antibodies. According to epidemiological research, people who are undernourished are more likely to get bacterial, viral, and other diseases. Nutritional epidemiology investigates dietary or nutritional parameters concerning illness prevalence in communities. Nutritional epidemiology results often add to the evidence that provides dietary recommendations for preventing disease and related disorders.