Nadia Ashrafi

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.

Background: 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. Results: 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. Conclusions: 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.

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.

Background: 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. Method: 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). Result: 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. Conclusion: 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.

Enhanced lipid metabolism, which involves the active import, storage, and utilization of fatty acids from the tumor microenvironment, plays a contributory role in malignant glioma transformation, thereby serving as an important gain of function. In this work, through studies initially designed to understand and reconcile possible mechanisms underlying the antitumor activity of a high-fat ketogenic diet, we discovered that this phenotype of enhanced lipid metabolism observed in glioblastoma may also serve as a metabolic vulnerability to diet modification. Specifically, exogenous polyunsaturated fatty acids (PUFAs) demonstrate the unique ability of short-circuiting lipid homeostasis in glioblastoma cells. This leads to lipolysis-mediated lipid droplet breakdown, an accumulation of intracellular free fatty acids, and lipid peroxidation–mediated cytotoxicity, which was potentiated when combined with radiation therapy. Leveraging these data, we formulated a PUFA-rich modified diet that does not require carbohydrate restriction, which would likely improve long-term adherence when compared with a ketogenic diet. The modified PUFA-rich diet demonstrated both antitumor activity and potent synergy when combined with radiation therapy in mouse glioblastoma models. Collectively, this work offers both a mechanistic understanding and a potentially translatable approach of targeting this metabolic phenotype in glioblastoma through diet modification and/or nutritional supplementation that may be readily integrated into clinical practice.

Background: Huntington’s disease (HD) is a multifaceted neurological disorder characterized by the progressive deterioration of motor, cognitive, and psychiatric functions. Despite a limited understanding of its pathogenesis, research has implicated abnormal trinucleotide cytosine-adenine-guanine CAG repeat expansion in the huntingtin gene (HTT) as a critical factor. The development of innovative strategies is imperative for the early detection of predictive biomarkers, enabling timely intervention and mitigating irreversible cellular damage. Lipidomics, a comprehensive analytical approach, has emerged as an indispensable tool for systematically characterizing lipid profiles and elucidating their role in disease pathology. Method: A MedLine search was performed to identify studies that use lipidomics for the characterization of HD. Search terms included “Huntington disease”; “lipidomics”; “biomarker discovery”; “NMR”; and “Mass spectrometry”. Results: This review highlights the significance of lipidomics in HD diagnosis and treatment, exploring changes in brain lipids and their functions. Recent breakthroughs in analytical techniques, particularly mass spectrometry and NMR spectroscopy, have revolutionized brain lipidomics research, enabling researchers to gain deeper insights into the complex lipidome of the brain. Conclusions: A comprehensive understanding of the broad spectrum of lipidomics alterations in HD is vital for precise diagnostic evaluation and effective disease management. The integration of lipidomics with artificial intelligence and interdisciplinary collaboration holds promise for addressing the clinical variability of HD.

Background: Metabolic dysfunction–associated steatotic liver disease (MASLD) is strongly associated with obesity. Sex and age affect MASLD prevalence and pathophysiology. The use of animal models fed Western-style diets is vital for investigating the molecular mechanisms contributing to metabolic dysregulation and for facilitating novel drug target identification. However, the sex-associated and age-associated mechanisms underlying the pathophysiology remain poorly understood. This knowledge gap limits the development of personalized sex-specific and age-specific drug treatments. Methods: Young (7 wk) and aged (52 wk) male and female mice were fed a high-fat diet (HFD) or low-fat diet. Liver metabolome (>600 molecules) and transcriptome profiles were analyzed. Results: Male and female mice fed an HFD developed obesity, glucose intolerance, and hepatic steatosis. However, fasting blood glucose, insulin, and serum alanine aminotransferase levels were higher in males fed an HFD, indicating a more severe metabolic disease. In addition, males showed significant increases in liver diacylglycerides and glycosylceramides (known mediators of insulin resistance and fibrosis), and more changes in the transcriptome: extracellular matrix organization and proinflammatory genes were elevated only in males. In contrast, no major increase in damaging lipid classes was observed in females fed an HFD. However, aging affected the liver to a greater extent in females. Acylcarnitine levels were significantly reduced, suggestive of changes in fatty acid oxidation, and broad changes in the transcriptome were observed, including reduced oxidative stress response gene expression and alterations in lipid partitioning genes. Conclusions: Here, we show distinct responses to an HFD between males and females. Our study underscores the need for using both sexes in drug target identification studies, and characterizing the molecular mechanisms contributing to the MASLD pathophysiology in aging animals. Export

Huntington’s disease (HD) is a progressive, fatal neurodegenerative disease characterized by motor, cognitive, and psychiatric symptoms. The precise mechanisms of HD progression are poorly understood; however, it is known that there is an expansion of the trinucleotide cytosine-adenine-guanine (CAG) repeat in the Huntingtin gene. Important new strategies are of paramount importance to identify early biomarkers with predictive value for intervening in disease progression at a stage when cellular dysfunction has not progressed irreversibly. Metabolomics is the study of global metabolite profiles in a system (cell, tissue, or organism) under certain conditions and is becoming an essential tool for the systemic characterization of metabolites to provide a snapshot of the functional and pathophysiological states of an organism and support disease diagnosis and biomarker discovery. This review briefly highlights the historical progress of metabolomic methodologies, followed by a more detailed review of the use of metabolomics in HD research to enable a greater understanding of the pathogenesis, its early prediction, and finally the main technical platforms in the field of metabolomics.