Background: This study aimed to identify distinct metabolic signatures associated with disease progression by integrating high-resolution computed tomography (HRCT) visual scoring with comprehensive metabolomic profiling. Materials and Methods: This single-center, cross-sectional study enrolled 60 idiopathic pulmonary fibrosis/interstitial lung disease (IPF/ILD) patients with usual interstitial pneumonia pattern. Participants underwent standardized pulmonary function testing, HRCT imaging, and peripheral blood collection for metabolomic analysis using one-dimensional hydrogen nuclear magnetic resonance spectroscopy and ultra-high-performance liquid chromatography coupled to tandem mass spectrometry. Linear regression analysis integrated radiographic scores with metabolomic profiles, adjusted for multiple covariates. Results: Stable IPF/ILD exhibited moderate negative correlations between the six most significant metabolites and HRCT scores (r = -0.27 to -0.51), along with a high abundance of specific phospholipids (triacylglycerol, monoacylglycerol, phosphatidylglycerol, phosphatidylethanolamine, diacylglycerol), sphingomyelin, ceramide, and acylcarnitine. In contrast, progressive disease showed weak positive correlations between the six most significant metabolites and HRCT scores (r = 0.19-0.26), and moderate negative correlation between specific triacylglycerol species and HRCT scores (r = -0.37-0.4). Furthermore, metabolomic analysis in individuals with progressive disease revealed both high and low abundances of specific phospholipid species (including high and low triacylglycerol species, as well as low levels of phosphatidylglycerol, phosphatidylethanolamine, phosphatidylcholine, phosphatidylserine, and phosphatidylinositol), along with high levels of certain sphingomyelin, ceramide, taurine, and purine bases, and low levels of xanthine and lactic acid observed. Conclusions: Integration of systematic HRCT semi-quantitative scoring with metabolomic profiling successfully differentiated stable from progressive IPF/ILD through distinct molecular-radiographic signatures.

Nuclear magnetic resonance (NMR) and Mass spectrometry (MS) are two mainly used techniques in metabolomics that can measure abundance of hundreds of metabolites. The data generated by either techniques have already allowed a huge progress in various research fields from personalized medicine to pharmaceutical sciences. Combining measurements from both platforms through a simple concatenation also led valuable discoveries. In this work, however, we propose an alternative data fusion approach based on deep learning that aims to make use of each type of measurement and combine the derived information in a different layer for disease prediction. Rather than feeding in all data as a single vector, we create dual "pipelines", operating on NMR and MS datasets separately, and then combine the outputs into a single prediction. We measure the performance of our dual-pipeline approach by using Alzheimer’s and Parkinson’s data for diagnostic prediction. We not only build custom deep learning (DL) architectures for both data sets, but also differentiate pipelines as to how to handle NMR and MS, i.e., separately or concatenated. Our results show that the dual-pipeline approach performs better than a single traditional DL pipeline which relies on traditional data fusion approach when both NMR and MS data are available.

Background/Objectives: Metabolomics has emerged as a tool to gain insight into the body’s biological responses to therapeutic interventions. Bariatric surgery remains the most effective treatment for severe obesity and associated comorbidities, leading to significant weight and metabolic improvements. Using a multi-platform, multi-compartment metabolomics approach, this study systematically characterizes longitudinal changes in fecal and serum metabolomes of 45 patients following sleeve gastrectomy (SG). Participants were stratified by weight loss outcomes to identify metabolic signatures associated with differential responses, which may serve as predictors of weight loss and provide mechanistic insights in developing targeted therapeutic strategies. Methods: Metabolomic and lipidomic responses to SG were analyzed using multivariable linear mixed-effects models based on data collected pre-operatively and at 3 and 12 months post-operatively. Results: The percentage total weight loss for the highest versus lowest weight loss tertiles (T3 vs. T1) at twelve months was 35.81 + 5.4% and 19.49 + 2.62%, p < 0.001, respectively. Substantial alterations in fecal metabolites and lipid species were observed among T3 after twelve months, including aspartate, tyrosine, carnitine, glycine, PC.ae.C36.4, PC.aa.C38.0, PC.ae.C44.4, PC.ae.C40.2, and PC.aa.C40:5. Specifically, changes in serum lipid species including SM.OH.C22:1, PC.ae.C32:1, PC.aa.C34:4, PC.aa.C36:6, PC.ae.C34:3, PC.ae.C34:1, and PC.ae.C32:2, support serum lipidomics as a minimally invasive marker of gut remodeling and adaptation following SG. Sex-stratified analysis revealed unique fecal and serum metabolic changes, highlighting the significance of personalized metabolic monitoring and obesity treatment. Conclusions: Our findings identify metabolic alterations associated with response variability following SG and highlight the potential utility of machine learning to predict weight-loss trajectories and inform personalized interventions.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a highly transmissible RNA betacoronavirus, causing coronavirus disease-19 (COVID-19). Infection with SARS-CoV-2 can result in a broad spectrum of clinical outcomes, ranging from asymptomatic or mild to a severe, deadly illness. Emerging evidence suggests SARS-CoV-2 affects host gene regulation through epigenetic mechanisms, such as DNA methylation, potentially contributing to immune dysregulation and post-acute sequelae, including neurological and psychiatric disorders. However, the extent and functional relevance of these epigenetic changes remain uncertain. We employed whole-genome methylation sequencing (WGMS) to profile DNA methylation in peripheral blood from SARS-CoV-2-positive patients across a spectrum of symptom severity, ranging from asymptomatic to severe (n = 101), in comparison to SARS-CoV-2-negative individuals (n = 105). We observed a widespread hypomethylation in the genomes of infected individuals, which was more pronounced in severe cases. Notably, we identified differentially methylated genes in patients with mild (19 genes), moderate (19 genes), and severe (35 genes) symptoms. These genes included those involved in canonical immune responses as well as known to be linked to neurodegenerative diseases. Subsequent pathway enrichment analysis further supported the significant association between the differentially methylated genes and those implicated in Alzheimer’s and Parkinson’s disease, as well as neuropsychiatric conditions, suggesting potential epigenetic links between acute SARS-CoV-2 infection and long-term neurological outcomes. Our WGMS comprehensively mapped severity-stratified genome-wide DNA methylation changes in COVID-19 patients. Our findings underscore the potential importance of epigenetic regulation in the acute responses to SARS-CoV-2 infection and highlight an overlap with epigenetic mechanisms relevant for neuropsychiatric disease processes.

Dietary biomarkers may help objectively assessing dietary pattern adherence. This study performed K-means clustering analysis on quantitative food diary data from a dietary intervention study. Standardised dietary data (134 food diaries) from 57 participants were K-means clustered stepwise until fully optimised and cross-validated. The primary endpoint was to develop distinct dietary clusters and to evaluate the performanceof 90 plasma metabolites. The secondary endpoint was to analyse the biomarker-food groups relationships from those distinct dietary patterns. The final two cluster models comprised of 6 specific food types. Cluster 1 included participants with higher intake of fruit and vegetables, legumes, fish and whole grain cereals, and lower intake of meat and sweet foods than Cluster 2. Ten plasma metabolites significantly differed between the clusters (p < 0.05; q < 0.05) with reasonable biomarker performance (receiver operating characteristic (ROC): 0.64-0.72). Docosahexaenoic acid (DHA), eicosapentaenoic acid (EPA), α-linolenic acid, citric acid and vitamin C were significantly higher in Cluster 1, whereas adrenic acid, osbond acid, cholesterol, dihomo-γ-linolenic acid (DGLA) and triglycerides were higher in Cluster 2. Five additional metabolites also showed significant differences (p < 0.02; q < 0.11) and were included: palmitic acid, tyrosine, β-carotene, α-carotene and betaine. The DHA-to-Osbond acid ratio was an optimal indicator distinguishing healthy from unhealthy dietary patterns (ROC: 0.78). Combining clustering and metabolite profiling methods effectively identifies biomarkers of particular dietary patterns and highlights several robust food-metabolite correlations.

Uterine luminal fluid influences embryonic development and the subsequent phenotype of offspring, yet its detailed metabolomic composition remains poorly characterized. Here, minimally invasive transcervical techniques were employed to collect neat uterine fluid from postpartum dairy cows and cyclic beef cows to allow for metabolomic profiling via targeted mass spectrometry. Objectives were to 1) compare the metabolomic profile of uterine fluid with plasma in dairy cows and 2) assess the impact of dietary rumen-protected methionine and stage of estrous cycle (day 0 vs 7) on plasma and uterine fluid metabolomic profile in beef cows. Results revealed that the concentrations of many metabolites, including amino acids, signaling molecules (e.g. dopamine, gamma-aminobutyric acid) and lipids (e.g. ceramides, diacylglycerols), were higher in uterine fluid than in plasma. An oral bolus of rumen-protected methionine increased uterine concentration of methionine on day 0 of the estrous cycle. The uterine metabolome remained relatively stable between days 0 and 7 although there was temporal variability for a select number of metabolites (cysteine, methionine, methionine sulfoxide, asymmetric dimethylarginine, ceramides, and glycerophospholipids). Correlations between plasma and uterine fluid concentrations were strong or moderate for many amino acids. Collectively, these findings highlight that the uterine lumen is a specialized, selectively regulated biochemical compartment.

Alzheimer’s disease (AD), a neurodegenerative, irreversible, and progressive brain disorder, is characterized by memory loss and cognitive dysfunction. In neurodegeneration, gut microbiota and their metabolites have an impact on behavior and brain function. This study aimed to identify fecal candidate discriminant metabolites in 5XFAD mice assosciated with AD as compared to an age-matched wild-type (WT) healthy mice groups at three different time points (3, 6, and 9 month) using Proton Nuclear Magnetic Resonance (1H NMR) spectroscopy. A total of 18 male mice (n = 9 for 5XFAD mice and n = 9 for WT, with three animals per age group) were used to determine the fecal candidate discriminant metabolites. As a result of the study, it was identified a total of 67 metabolites in fecal samples. Among these, 19 metabolites were found significantly up-regulated or down-regulated in 5XFAD mice compared to WT mice during the disease progress. According to the volcano plot analysis (FC ≥ 1.2 and p-value ≤ 0.05), the potential candidate discriminant metabolites were fumarate and malate in 3-month-old 5XFAD mice, 3-methylxanthine, glucose, cholate, phenylacetate, and trimethylamine N-oxide (TMAO) in 6-month-old 5XFAD mice and cholate and succinate in 9-month-old 5XFAD mice. These findings highlight the potential of fecal metabolomics as a non-invasive exploratory approach for understanding AD disease mechanisms and progression. Furthermore, to the best of our knowledge, this pilot study is the first to utilize NMR spectroscopy to investigate age-stratified cross-sectional changes in fecal metabolites in 5xFAD mice, thereby advancing our understanding of disease progression and potentially informing future therapeutic strategies.

Abstract This 3-yr study evaluated the effects of access to artificial shade during late gestation on the plasma metabolome of beef cows before calving and their offspring at birth. On day 0 of each year (92 ± 20 d prepartum), 176 multiparous, pregnant Brangus cows were stratified by body condition score and body weight and allocated into 16 bahiagrass (Paspalum notatum) pastures (8 ha and 11 cows per pasture). Gestational treatments were randomly assigned to pastures and consisted of cows provided (SH) or not (NSH) access to artificial shade (4.5 m2 per cow) from day 0 to 90. Cows and calves were managed similarly from calving to day 330 (weaning). Blood samples were collected from 3 cows per pasture on days 0, 30, and 60 and from 3 calves per pasture at birth to determine the plasma metabolome using liquid chromatography. Principal component and partial least squares-discriminant analyses were performed within each day to discriminate the treatments. Single metabolite concentrations were compared between SH and NSH groups using the MIXED procedure in SAS, and differentially expressed metabolites were analyzed for canonical pathways using the Ingenuity Pathway Analysis. A clear separation was observed on days 30 (R² = 0.76) and 60 (R² = 0.67) for the plasma metabolome of SH and NSH cows. Effects of treatment (P ≤ 0.05) were detected for 23 metabolites on day 30 and 17 metabolites on day 60 from the top 100 variable importance in projection. Twelve pathways related to putrescine, glycine, and arginine metabolism were downregulated (P ≤ 0.04), whereas tryptophan degradation X was upregulated (P = 0.02) in SH vs. NSH cows on day 30. On day 60, 25 pathways, mostly related to cysteine, serine, and methionine biosynthesis, were upregulated (P ≤ 0.05), whereas 4 pathways related to amino acid degradation were downregulated (P ≤ 0.01) in SH vs. NSH cows. A clear separation was observed for the calf plasma metabolome at birth (R² = 0.73). At birth, effects of treatment (P ≤ 0.03) were detected for 10 metabolites, and 50 metabolic pathways were upregulated in calves born from SH vs. NSH cows. Most metabolic pathways impacted were related to amino acid metabolism, specifically glutamate, histidine, and alanine, as well as the antioxidant response. Thus, access to shade for pregnant beef cows altered their amino acid metabolism compared with no access to shade. Prenatal access to shade programmed the calf metabolism at birth, improving amino acid metabolism and antioxidant responses.

The etiology of Alzheimer's disease (AD) remains unclear but is likely driven by gene-environment interactions. We present a multi-organ untargeted metabolomics atlas (n = 2,271) paired with metagenomics data (n = 666) from two AD transgenic mouse models (3xTg and 5xFAD) under colonized and germ-free conditions. Systems-level analyses revealed clusters of dysregulated molecules across tissues, including carnitines, bile acids, B vitamins, neurotransmitters, and N-acyl lipids. Metabolic shifts were associated with the depletion of Akkermansia muciniphila and enrichment of Mucispirillum schaedleri in the 3xTg model. We identify previously unexplored carnitines linked to microbial metabolism of phenylalanine. Using tissueMASST-a mass spectrometry search tool we developed to translate animal-model findings into a human clinical context-we trace phenylacetyl-carnitine in human plasma and serum samples (n = 1,470) from independent cohorts, revealing associations with aging, cognitive impairment, and diminished memory performance. This public resource and associated tools will aid future research in AD etiology.

In cattle, the second week after estrus encompasses critical changes in uterine function. The endometrium will either prepare for the release of luteolytic pulses of prostaglandin-F2 alpha or for the support of an eventual pregnancy. We hypothesized that concentrations of amino acids and lipids in the uterine luminal fluid (ULF), and the gene expression in luminal epithelial cells (LE), change across the second week of the estrous cycle. The objective was to compare amino acid and lipid concentrations in ULF and target gene expression in LE 7 (D7), 10 and 14 d after estrus. The ULF and LE samples were collected from the uterine body of five primiparous, non-lactating, cyclic Bos indicus-influenced crossbred cows using a cytological brush on each day after synchronized estrus. Targeted metabolomics of ULF was performed using mass spectrometry, and gene expression in LE was assessed using RNA sequencing. Data were analyzed by uni- and multivariate statistics. Multivariate analyses separated D7, D10, and D14, with amino acid metabolism and lipid biosynthesis as enriched pathways. Luminal concentrations of amino acids (e.g., arginine, histidine) and lipids (e.g., CE(17:1), PC aa C34:1) increased from D7 to D10, but from D10 to D14 concentrations of amino acids (e.g., glutamine, glutamic acid) decreased while that of lipids (e.g., CE(18:2), SM C16:0) continued to increase. Transcriptomic profiling revealed temporal regulation of 186 amino acid-related and 133 lipid-related genes. Between D7 and D10, there was increased expression of genes for oxidative phosphorylation and extracellular secretion pathways supporting secretory capacity, while decreased expression of genes in arginine-proline metabolism and solute carrier-mediated transport pathways promoted luminal metabolite accumulation. From D10 to D14, there was increased expression of genes in fatty acid elongation and sphingolipid metabolism pathways likely driving lipid synthesis and amino acid catabolism, while decreased expression of genes in protein digestion/absorption and PI3K/AKT signaling pathways reduced nutrient export to the lumen. ULF composition and LE gene expression changed markedly during the second week of the estrous cycle. It is plausible that the dynamic shifts in amino acid and lipid metabolites in the uterine lumen reflect changing responses to sex steroids. The influence of such changes on embryo development and pregnancy success in cattle warrants investigation.

Postoperative delirium (POD) is a common complication in older surgical patients, linked to long-term cognitive decline and progression to dementia, yet its mechanisms remain unclear. We investigated arginine-related metabolites (ARMs) in cerebrospinal fluid (CSF) from 248 patients undergoing elective-surgery: 25 developed POD. Targeted mass spectrometry, gene expression profiling, and machine learning were applied to identify metabolic predictors. POD patients showed significant correlations with citrulline, ornithine, and glutamine, while models highlighted glutamine, glutamic acid, putrescine, N1-acetylspermidine, and spermidine as key biomarkers, achieving >77% predictive accuracy. Cluster and pathway analyses revealed POD-specific shifts in GABA synthesis and polyamine metabolism, contrasting with urea cycle dominance in non-POD cases. Associations persisted after adjusting for age and CSF Aβ42. Preoperative profiles in polyamine metabolism, ammonia detoxification, and neurotransmitter regulation suggest underlying neuroinflammatory and oxidative stress vulnerabilities that reduce resilience. Targeting polyamine biosynthesis may offer novel preventative and therapeutic strategies to mitigate POD and dementia risk.

BackgroundVascular factors contribute to dementia in approximately 20 million individuals, notably in vascular contributions to cognitive impairment and dementia (VCI). However, the lack of specific molecular biomarkers to differentiate VCI from normal aging and Alzheimer's disease (AD) impedes early diagnosis and treatment.ObjectiveTo date the use of saliva for VCI diagnosis has not been previously reported. In this small proof-of-concept study, we aim to explore the feasibility of screening novel salivary diagnostic biomarkers for VCI.MethodsUsing both proton nuclear magnetic resonance (1H NMR) spectroscopy and liquid chromatography coupled with mass spectrometry (LC-MS) we biochemically profiled saliva samples collected from individuals with VCI (n = 26) and compared them with cognitively healthy controls (n = 37).ResultsOf the 167 salivary metabolites 56 of them are found to be at significantly different concentrations in the saliva of individuals with VCI as compared to controls. Subsequently, we developed predictive models capable of distinguishing VCI from controls with 0.92 accuracy. Moreover, sex-stratified analysis revealed the perturbation of different metabolic pathways in the saliva of individuals with VCI.ConclusionsThis study underscores the promising role of salivary metabolomics as a non-invasive tool for the early detection of VCI. Our findings suggest that oral microbiome dysbiosis may contribute to VCI pathogenesis, offering novel mechanistic insights. Given the accessibility of saliva, further validation of these robust salivary biomarkers could facilitate scalable, cost-effective screening for VCI, aiding in timely intervention strategies.

Background:The aging global population faces significant challenges from Alzheimer's disease (AD). Metabolomics offers a non-invasive, cost-effective diagnostic alternative. Researchers identified significant variations in the level of salivary and urinary metabolites between AD patients and cognitively healthy controls. However, it's unclear whether differences in metabolites between AD patients and healthy controls are due to the disease or medications. Objective:This study aims to investigate the impact of common AD medications (cholinesterase inhibitors) and hypertension medications (ARBs) on the AD patient metabolome. Methods:A retrospective metabolomics analysis was conducted to elucidate whether observed metabolic perturbations were attributable to AD pathology or polypharmacy, with a specific focus on cholinesterase inhibitors and ARBs. Results:Linear models revealed that cholinesterase inhibitors did not significantly modulate potential urinary and salivary biomarkers for AD. However, these inhibitors were associated with significant changes in urinary metabolite concentrations, including increased isovaleric acid and L-leucine and decreased formate and L-histidine (q = 0.027 for all). Analysis of ARB treatment revealed significantly reduced urinary tryptophan levels (q = 0.001) in AD patients and increased salivary acetone and isopropyl alcohol concentrations (q = 0.028) across all groups. Further analysis of saliva metabolite ratios revealed notable differences in asymmetric arginine methylation between the AD group and the control group (q = 0.021). Additionally, variations in citrate synthesis were observed between the mild cognitive impairment group and the control group on ARBs (q = 0.024). Conclusions:Our research confirms that distinct biomarker profiles characteristic of AD remain present regardless of cholinesterase inhibitor and ARB treatment, providing a foundation for future clinical studies and therapeutic development by providing better diagnostic tools.

This study evaluated the impacts of access to artificial shade during prepartum and postpartum periods on the plasma metabolome of heat-stressed cow-calf pairs. On day 0, 64 pregnant Brangus crossbred beef heifers (<25% Bos indicus; 20 to 22 mo of age) were stratified by body weight (BW) (454 ± 37 kg) and body condition score (6.3 ± 0.28) and allocated to 1 of the 16 bahiagrass pastures (1 ha and 4 heifers per pasture). Treatments were randomly assigned to pastures (8 pastures per treatment) and consisted of heifers provided (SH) or not provided (NSH) access to artificial shade from day 0 to 133 (83 ± 4 days prepartum until 50 ± 4 d postpartum). Calves were weaned on day 203 (120 d of age) and limit-fed the same concentrate at 3.25% of BW until day 268. Calves were vaccinated against pathogens associated with bovine respiratory disease on day 222. Blood samples were collected from all heifers 30 d before calving (day 55) and from calves on days 222 and 223 to determine the plasma metabolome using liquid chromatography. Principal component and partial least squares-discriminant analyses were conducted daily to distinguish treatment groups. Metabolite concentrations were compared between SH and NSH groups using SAS MIXED, and differentially expressed metabolites were analyzed for canonical pathways via Ingenuity Pathway Analysis. A clear separation was observed on day 55 for SH and NSH heifers. Effects of treatment (P ≤ 0.05) were detected for 16 metabolites and tended to be detected (P ≤ 0.10) for 9 metabolites from the top 100 variable importance in projection. Nine pathways related to glutamate, alanine, and aspartate metabolism were upregulated (P ≤ 0.03), whereas glutamate degradation I was downregulated (P = 0.02) in SH versus NSH heifers. Clear separations were observed for calf metabolome on days 222 and 223. Plasma concentrations of 12 and 5 metabolites associated with amino acid and lipid metabolism increased (P ≤ 0.05) in SH versus NSH offspring on days 222 and 223, respectively. Shade access upregulated (P ≤ 0.01) 9 calf metabolic pathways related to amino acid and antioxidant metabolism on day 222 but did not impact (P > 0.10) calf metabolic pathways on day 223. Thus, access to shade for pregnant heifers altered their glutamate metabolism and appeared to decrease lipolysis compared with no access to shade. Access to shade programmed the calf metabolism to increase primarily glutamate utilization and reduce oxidative stress markers.

This study evaluated the effects of maternal supplementation of a Bacillus-based direct-fed microbial (DFM) on the plasma metabolome of cow-calf pairs. At the start of the study (day 0), 72 pregnant Brangus crossbred beef heifers (20 to 22 mo of age) were stratified by body weight (BW; 431 ± 31 kg) and body condition score (6.0 ± 0.36) and randomly allocated into 12 bahiagrass pastures (1 ha and six heifers/pasture). Treatments were assigned to pastures (six pastures/treatment) and consisted of heifers supplemented with 1 kg/head/d (dry matter basis) of soybean hulls, either alone (CON) or combined (BAC) with DFM containing a mix of Bacillus subtilis and B. licheniformis (Bovacillus; Novonesis, Hørsholm, Denmark) from day 0 to 242 (139 ± 4 d prepartum until 104 ± 4 d postpartum). Calves were weaned on day 242 and then allocated to drylot pens and fed the same diet until day 319. On days 271 and 287, calves were vaccinated against pathogens associated with bovine respiratory disease. Blood samples were collected from all heifers on days 0 and 63 (prepartum) and from all calves on days 271 (pre-vaccination), 274 (during the inflammatory response), and 287 (post-inflammatory response) to assess the plasma concentration of metabolites. There was a separation on day 63 (R2 = 0.96) for the plasma metabolome profile of BAC and CON heifers. Heifers fed BAC had increased (P ≤ 0.05) plasma concentration of 17 metabolites, including glycerophospholipids and amino acids, but decreased (P ≤ 0.05) plasma concentration of 4 triacylglycerols. Eight pathways related to amino acids metabolism were increased (P ≤ 0.01) in BAC vs. CON heifers. For the calf metabolome, a separation was observed on days 271 (R2 = 0.95), 274 (R2 = 0.95), and 287 (R2 = 0.99). Supplementation with BAC decreased (P ≤ 0.05) plasma concentrations of three and six metabolites from amino acids and triacylglycerols on days 271 and 274, respectively, and increased (P ≤ 0.05) plasma concentrations of 9, 10, and 28 metabolites associated with amino acids and lipids metabolism on days 271, 274, and 287, respectively. Maternal supplementation with Bacillus-based DFM altered calf amino acid metabolism before vaccination (P ≤ 0.03) but enhanced pathways associated with immune response after vaccination (P ≤ 0.05). Thus, maternal supplementation of a Bacillus-based DFM modified the maternal prepartum metabolome and the calf metabolome before, during, and after a vaccination-induced inflammatory response.

To compare the metabolomic profile differences between ILD (interstitial lung disease) and chronic obstructive pulmonary disease (COPD) controls, and to distinguish profiles between progressive and stable idiopathic pulmonary fibrosis (IPF)/ILD subjects. This single-center prospective study enrolled n = 71 (progressive IPF/ILD: n = 33, stable IPF/ILD: n = 27, COPD: n = 11) participants between December 2021 and October 2022. Metabolite quantification was performed using the liquid chromatography-mass spectrometry (LC–MS platform), and nuclear magnetic resonance spectroscopy (1H NMR). Further, pathway enrichment analysis was performed to identify biochemical pathways associated with the disease. 715 metabolites were accurately quantified to investigate (a) differences between the combined groups of stable and progressive idiopathic pulmonary fibrosis IPF/ILD and COPD controls, and (b) differences between progressive IPF/ILD and stable IPF/ILD controls. The most notable metabolites distinguishing fibrotic lung disease (both stable and progressive IPF/ILD) from COPD were glycerolipids (GL). Enrichment analysis of IPF/ILD versus COPD revealed significant disruptions in lipid metabolic pathways, particularly glycerophospholipids, and sphingolipids (false discovery rate FDR q-value < 0.05). In addition, significant disruptions in TG species were found in progressive IPF/ILD with enrichment analysis revealing dysregulation of metabolic pathways associated with glycerophospholipids (FDR q-value < 0.05). These findings emphasize the dysregulation of lipid metabolism in fibrotic lung diseases, involving glycerolipids, glycerophospholipids, and sphingolipids. The distinct lipid alterations identified through metabolomic profiling provide valuable insight into lipid metabolism in IPF/ILD, warranting further research to explore their potential as biomarkers.

Parkinson's disease (PD) is the second most common neurodegenerative disorder following Alzheimer's disease, with a 1.5 times higher prevalence in males. Several lipid-related genetic risk factors for PD have been identified, and the brain lipid signature of PD patients is distinguishable from controls. To elucidate the molecular mechanisms underlying PD and its sex differences, we conducted a lipidomic analysis of postmortem brain samples from the primary motor cortex (Brodmann area 4) of 40 PD patients and 43 age- and sex-matched matched controls. Mass spectrometry based lipidomics analysis revealed notable differences in 95 lipid species, especially Triacylglycerols and Lysophosphatidylcholines. Notably, sex-stratified analysis suggested that mitochondrial dysfunction may explain the higher prevalence of PD in males. These findings highlight lipid dysregulation in PD and point to potential biomarkers for diagnosis, warranting further validation.

In cattle, embryonic elongation requires an increased uptake of fatty acids supplied by the uterine environment. During the peri elongation period, the concentration of specific phospholipids, sphingolipids, ceramides, and oxylipins composed of unsaturated fatty acids and choline are increased in the uterine luminal fluid. Therefore, our objective was to provide a dietary supplementation strategy containing rumen-inert mono- and polyunsaturated fatty acids and rumen-protected choline to support embryonic elongation and embryo-maternal communication. One hundred suckled multiparous Angus cows were randomly assigned on d -30 to receive either TARG) 100 g of a rumen-inert mono- and polyunsaturated fatty acid source (Essentiom; Church and Dwight Co., Inc., Princeton, NJ) plus 60 g of a rumen-protected choline source (ReaShure; Balchem, Montvale, NJ) or CON) 114 g of a saturated fatty acid source (Energy Booster 100; Milk Specialties, Eden Prairie, MN). Treatments were top-dressed daily into a similar total mixed ration until d 30. All cows were synchronized using a 7-day CO-synch+CIDR protocol and received timed artificial insemination by the same technician on d 0. On d 16, uterine flushing was conducted in a subset of cows (CON = 20 and TARG = 23) to determine the presence and length of the embryo, and uterine luminal fluid was analyzed for the concentration of interferon tau (IFNT). Only samples with fully recovered embryos were analyzed (CON = 6 and TARG = 6). In addition, blood was collected to determine the concentration of progesterone (P4) and for RNA extraction and further RNAseq from peripheral white blood cells (PWBC). Cows subjected to uterine flushing were excluded from the experiment, and the remaining cows (CON = 29 and TARG = 26) received a blood collection on d 19 for further P4 and RNAseq analyses. On d 30, pregnancy diagnosis was conducted and only samples from cows deemed pregnant were analyzed. The effects of treatment, group, and their interaction on embryonic length, P4, and IFNT were analyzed by ANOVA. Embryonic length, P4, and IFNT did not differ between treatments, lengths averaged 6.8 ± 2.05 cm and 5.19 ± 1.79 on TARG and CON, respectively (P = 0.57). However, gene expression in PWBC was altered between treatments. On d 16, there were 22 differentially expressed genes (DEGs; FDR ≤ 0.05) in PWBC, with 11 genes being upregulated in TARG, such as PTGR1, TMEM38A, RAPGEF3, and AP3M2. On d 19, there were 702 DEGs (FDR ≤ 0.05) in PWBC with 413 downregulated and 289 upregulated in TARG with a major downregulation in canonical pathways involved in immune responses. Altogether, these preliminary results show major differences in the gene expression in PWBC during maternal recognition of pregnancy despite the lack of changes in embryonic parameters.

Alzheimer's Disease (AD) is a complex, multifactorial, progressive, and irreversible neurodegenerative disorder characterized by cognitive, functional, and behavioral impairments. The diagnosis of AD is based on the presence of amyloid plaques and intracellular neurofibrillary tangles, with pathological changes beginning 20 to 30 years before symptoms appear. Current treatments only slow disease progression and manage symptoms, while research remains focused on single omics approaches such as genomics, metabolomics, proteomics, and lipidomics, with the high cost of multi-omics integration limiting deeper insight into its neuropathology. This study's novelty lies in integrating metabolomics and methylation analysis to investigate the etiology and pathogenesis of AD using post-mortem brain samples from individuals with AD and mild-AD, compared to age and gender-matched controls. A targeted LC-MS/MS, 1 H NMR and the Illumina Infinium Methylation EPIC Bead Chip assay, we identified differentially abundant metabolites and differentially methylated cytosines using robust linear regression. We further examined the correlation between methylation and metabolite in brain samples from individuals with AD ( n  = 30), mild-AD ( n  = 14), and age and gender-matched controls ( n  = 30). 20 metabolites were significantly different concentrations when we compared AD against controls (FDR q < 0.05). Similarly, 17 metabolites were identified as being at significantly different concentrations when we compared mild-AD against controls (FDR q < 0.05). We identified 18 differentially methylated CpGs when comparing AD to controls and 48 CpGs when comparing Mild AD to control. Epimetabolome analysis corroborated our initial metabolomics analysis highlighting specific CpGs associated with the proteins of interested to be either hypo or hypermethylated. Inflammatory regulators, serotonergic synapse, and sphingolipid metabolism were all upregulated metabolic pathways in mild-AD which could be directly linked to disease development. We also report significant perturbation in the biosynthesis of amino acids, 2-Oxocarboxyclic acid metabolism, Starch, and sucrose metabolism those individuals who died from AD. Overall, our findings demonstrate intricate relationship between methylation changes and metabolite concentrations which underlines the utility of combining metabolomics and other omics-based platforms such as epigenetics for the study of AD and related dementias.

INTRODUCTION:Traumatic brain injury (TBI) remains a leading cause of death and long-term disability in children worldwide. Despite its impact, current clinical management is limited to supportive care, with no FDA-approved therapies to reduce mortality or mitigate lasting neurological consequences. This study presents, to our knowledge, the first integrated multi-omics analysis combining transcriptomic and metabolomic data from pediatric patients with severe TBI spanning both acute and subacute phases offering novel insights into the molecular pathways underlying injury and recovery. METHODS:In this prospective, observational cohort study seventeen severe pediatric TBI patients (median age of 13.1 years, median Glasgow Coma Scale (GCS) of 3, and median Injury Severity Score (ISS) of 29) with no pre-existing neurological comorbidities or non-accidental trauma, and corresponding sex and age-matched controls were enrolled between May 2022 and November 2023. The longitudinal bulk transcriptomic analysis of whole blood and metabolomic profiling of serum were performed at three distinct timepoints. The resulting multi-omics datasets were subsequently integrated with validated clinical severity scoring systems to assess changes over a nine-day period of care in the pediatric intensive care unit (PICU). RESULTS:We showed that despite the heterogeneity of mechanism and presentation, there was overlap in the transcriptomic and metabolic signatures at each timepoint. There were immediate signs of inflammatory and immune activation, metabolic dysregulation, disturbance of the gut-brain axis in the acute phase. Early markers of T-cell infiltration, such as TRAV35 and ANXA2R, are highly correlated with GCS, and lysophosphatidylcholine 18:0 is highly correlated with NK-cell activation. Multiple gut metabolites, such as indole-3-propionic acid (IPA), and RNA signatures of gut flora are elevated in blood early after TBI. Putrescine elevation at time point one highly correlates with Day 9 red blood cell stimulation. At Day9, multiple lipid species in the metabolome are associated with length of stay and Glasgow Outcome Scale-Extended (GOS-E Peds). By Day 9, both the metabolome and transcriptome show incomplete recovery, marked by highly specific TBI IGH, IGK, and IGL clonal expansion. CONCLUSIONS:Despite the heterogeneity in injury mechanisms and clinical presentations, our findings reveal a convergent host response, characterized by shared transcriptomic and metabolic signatures across all time points. This convergence highlights potentially targetable biological pathways and opens the door to the development of novel therapeutic strategies for severe pediatric TBI.