Comprehensive metabolomics analysis reveals novel biomarkers and pathways in falsely suspected glutaric aciduria Type-1 newborns.

BACKGROUND: Glutaric aciduria type-1 (GA-1) is a rare metabolic disorder due to glutaryl coenzyme A dehydrogenase deficiency, causing elevated levels of glutaryl-CoA and its derivatives. GA-1 exhibits symptoms like macrocephaly, developmental delays, and movement disorders. Timely diagnosis through genetic testing and newborn screening is crucial. However, in some cases, transiently elevated level of glutarylcarnitine (C5DC) challenges accurate diagnosis, highlighting the need for alternative diagnostic methods, like mass spectrometry-based untargeted metabolomics, to identify additional biomarkers for distinguishing falsely suspected GA-1 from healthy newborns. METHODOLOGY: DBS samples from falsely suspected GA-1 newborns (n = 47) and matched control were collected through the NBS program. Untargeted metabolomics using liquid chromatography-high-resolution mass spectrometry (LC-HRMS) was performed to enable biomarker and pathway investigations for significantly altered metabolites. RESULTS: 582 and 546 were up- and down-regulated metabolites in transient GA-1. 155 endogenous metabolites displayed significant variations compared to the control group. Furthermore, our data identified novel altered metabolic biomarkers, such as N-palmitoylcysteine, heptacarboxyporphyrin, 3-hydroxylinoleoylcarnitine, and monoacylglyceride (MG) (0:0/20:1/0:0), along with perturbed metabolic pathways like sphingolipid and thiamine metabolism associated with the transient elevated C5DC levels in DBS samples. CONCLUSIONS: A distinct metabolic pattern linked to the transient C5DC elevation in newborns was reported to enhance the prediction of the falsely positive cases, which could help avoiding unnecessary medical treatments and minimizing the financial burdens in the health sector.

Metabolomic Profiling of Blood Plasma in Females with Hyperplasia and Endometrial Cancer.

Uterine cancer is the most prevalent gynecologic malignancy in women worldwide. Endometrial cancer (EC) has an 81% five-year survival rate, depending on disease stage and time of diagnosis. While endometrial cancer is largely treatable when detected early, no established screening techniques are available in clinical practice. As a result, one of the most significant issues in the medical field is the development of novel ways for early cancer identification, which could boost treatment success rates. Liquid chromatography-high-resolution mass spectrometry (LC-HRMS)-based metabolomics was employed to explore the metabolomic markers and pathways unique to this cancer type and link them to the benign endometrial hyperplasia that may progress to cancer in 5% to 25% of patients. The study involved 59 postmenopausal participants, 20 with EC type 1, 20 with benign hyperplasia, and 19 healthy participants. Metabolite distribution changes were analyzed, and 338 of these features were dysregulated and significant. The first two main components, PC1 and PC2, were responsible for 11.5% and 12.2% of the total metabolites, respectively. Compared with the control group (CO), EC samples had 203 differentially expressed metabolites (180 upregulated and 23 downregulated); in hyperplasia (HP), 157 metabolites were dysregulated (127 upregulated and 30 downregulated) compared to the CO group while 21 metabolites exhibited differential regulation (16 upregulated and 5 downregulated) in EC plasma samples compared to the HP group. Hyperplasia samples exhibited similar metabolic changes to those reported in cancer, except for alterations in triglyceride levels, 7a,12 b-dihydroxy-5b-Cholan-24-oic acid, and Hept-2-enedioyl carnitine levels. The metabolites N-heptanoyl glycine and -(Methylthio)-2,3-isopentyl phosphate and formimino glutamic acid can be specific markers for hyperplasia conditions and dimethyl phosphatidyl ethanolamine and 8-isoprostaglandin E2 can be specific markers for EC conditions. Metabolic activities rely on mitochondrial oxidative phosphorylation for energy generation. The changes in metabolites identified in our study indicate that endometrial cancer cells adopt alternative strategies to increase energy production to meet the energy demand, thereby supporting proliferation.

Further delineation of the phenotypic and metabolomic profile of ALDH1L2-related neurodevelopmental disorder.

ALDH1L2, a mitochondrial enzyme in folate metabolism, converts 10-formyl-THF (10-formyltetrahydrofolate) to THF (tetrahydrofolate) and CO2. At the cellular level, deficiency of this NADP+-dependent reaction results in marked reduction in NADPH/NADP+ ratio and reduced mitochondrial ATP. Thus far, a single patient with biallelic ALDH1L2 variants and the phenotype of a neurodevelopmental disorder has been reported. Here, we describe another patient with a neurodevelopmental disorder associated with a novel homozygous missense variant in ALDH1L2, Pro133His. The variant caused marked reduction in the ALDH1L2 enzyme activity in skin fibroblasts derived from the patient as probed by 10-FDDF, a stable synthetic analog of 10-formyl-THF. Additional associated abnormalities in these fibroblasts include reduced NADPH/NADP+ ratio and pool of mitochondrial ATP, upregulated autophagy and dramatically altered metabolomic profile. Overall, our study further supports a link between ALDH1L2 deficiency and abnormal neurodevelopment in humans.

Lipidomics Profiling of Metformin-Induced Changes in Obesity and Type 2 Diabetes Mellitus: Insights and Biomarker Potential.

Metformin is the first-line oral medication for treating type 2 diabetes mellitus (T2DM). In the current study, an untargeted lipidomic analytical approach was used to investigate the alterations in the serum lipidome of a cohort of 89 participants, including healthy lean controls and obese diabetic patients, and to examine the alterations associated with metformin administration. A total of 115 lipid molecules were significantly dysregulated (64 up-regulated and 51 down-regulated) in the obese compared to lean controls. However, the levels of 224 lipid molecules were significantly dysregulated (125 up-regulated and 99 down-regulated) in obese diabetic patients compared to the obese group. Metformin administration in obese diabetic patients was associated with significant dysregulation of 54 lipid molecule levels (20 up-regulated and 34 down-regulated). Levels of six molecules belonging to five lipid subclasses were simultaneously dysregulated by the effects of obesity, T2DM, and metformin. These include two putatively annotated triacylglycerols (TGs), one plasmenyl phosphatidylcholine (PC), one phosphatidylglycerol (PGs), one sterol lipid (ST), and one Mannosyl-phosphoinositol ceramide (MIPC). This study provides new insights into our understanding of the lipidomics alterations associated with obesity, T2DM, and metformin and offers a new platform for potential biomarkers for the progression of diabetes and treatment response in obese patients.

Demaghi, a polyherbal formulation, mitigates aluminum chloride-induced neurological impairment in mice: Insights from phytochemical analysis and behavioral assessment.

Herbal products have been very popular in Pakistan for their curative significance against various disorders. Demaghi (DEMG) is a widely used herbal product claimed to own natural substances having neuroprotective potential. The current study aims to scientifically validate the chemical composition as well as its neuroprotective claims of this widely used herbal tonic. The commercially available Demaghi product was chemically characterized for its phytocomposition. The mice were treated with two doses of Demaghi (DEMG 50 mg and 100 mg/kg/day), and the effects of its prolonged exposure on animal anxiety, memory, and depression were noted through a series of behavioral tests in the AlCl3-induced memory deficient mice model. Besides that, dissected brains were biochemically analyzed for oxidative stress markers and acetylcholinesterase activity, as well as histopathological changes. The study outcomes showed that DEMG (100 mg/kg/day) has prominent anti-anxiety effects, memory-enhancing properties, and anti-depressants effects observed in the AlCl3-induced memory-deficient mice model. Biochemical assays also showed a greater decrease in oxidative stress of tested animals treated with 100 mg/kg/day of DEMG. The histopathological analysis also revealed that administration of DEMG reduced the AlCl3-induced toxicity. UPLC-MS results revealed the presence of many phytoconstituents, which showed to support cholinergic signaling in in-silico studies. The current research validates the neurological benefits of Demaghi for memory-boosting properties. The phytocompounds present in Demaghi exert neuroprotective effects, possibly by enhancing the cholinergic neurotransmission and combating the neurotoxin-induced oxidative stress.

Label-free quantitative proteomics analysis for type 2 diabetes mellitus early diagnostic marker discovery using data-independent acquisition mass spectrometry (DIA-MS).

Type-2 diabetes mellitus (T2DM) therapy requires early diagnosis and complication avoidance. Unfortunately, current diagnostic markers do not meet these needs. Data-independent acquisition mass spectrometry (DIA-MS) offers a solution for clinical diagnosis, providing reliable and precise sample quantification. This study utilized DIA-MS to investigate proteomic differential expression in the serum of recently diagnosed T2DM patients. The study conducted a comparative protein expression analysis between healthy and recently diagnosed T2DM groups (discovery cohort). A candidate protein was then validated using enzyme-linked immune assay (ELISA) on serum samples collected from T2DM patients (n = 87) and healthy control (n = 60) (validation cohort). A total of 1074 proteins were identified, and 90 were significantly dysregulated between the two groups, including 32 newly associated with T2DM. Among these proteins, the expression of S100 calcium-binding protein A6 (S100A6) was validated by ELISA. It showed a significant increase in T2DM samples compared to the control group. It was evaluated as a biomarker using the receiver operating characteristic (ROC) curve, consistent with the DIA-MS results. Novel proteins are reported to be involved in the development and progression of T2DM. Further studies are required to investigate the differential expression of candidate marker proteins in a larger population of T2DM patients.

A diagnostic electrochemical aptasensor development for sCD80 protein detection in human serum.

Elevating soluble CD80 (sCD80) in human serum is a natural response to autoimmune diseases such as rheumatoid arthritis (RA). The level of sCD80 is associated with RA development and prognosis; therefore, it is potentially used as a biomarker. sCD80 is commonly measured in human serum using immunoassays (e.g., ELISA) with multiple drawbacks, mainly cross-reactivity. Aptamer-based biosensors (aptasensors) development for quantifying and detecting different biological molecules demonstrates applicability in next-generation medicine and biomarker detection. Herein, we selected a specific aptamer for sCD80 by conventional in-vitro selection process (SELEX) with the high-affinity aptamer (Kd = 47.69 nM). A sensitive aptasensor, for the first time, was developed on a screen-printed gold electrode (AuSPE) platform compatible with easy-to-use label-free electrochemical impedance spectroscopy. The immobilization of the aptamer on the gold surface and the presence of sCD80 in a complex with the aptamer were characterized by photo-induced force microscopy, which revealed the uniform assembly of the aptamer monolayer and the distribution of sCD80 on the electrode surface. The developed aptasensor showed a linear performance (0.025-10.0 nM of protein) with a detection limit of 8.0 pM. Furthermore, the aptasensor was tested in a biological matrix, where a linear signal was observed for the increased amount of spiked sCD80 (R2 = 0.9887). The recovery of the spiked amounts ranged from 105 to 125% with coefficient of variation (CV%) <7%, which supported the applicability of this sensor in detecting sCD80 for diagnosis.

Proteomics Investigation of the Impact of the Enterococcus faecalis Secretome on MCF-7 Tumor Cells.

Breast cancer is the most prevalent form of cancer among women. The microenvironment of a cancer tumor is surrounded by various cells, including the microbiota. An imbalance between microbes and their host may contribute to the development and spread of breast cancer. Therefore, the objective of this study is to investigate the influence of Enterococcus faecalis on a breast cancer cell line (MCF-7) to mimic the luminal A subtype of breast cancer, using an untargeted proteomics approach to analyze the proteomic profiles of breast cancer cells after their treatment with E. faecalis in order to understand the microbiome and its role in the development of cancer. The breast cancer cell line MCF-7 was cultured and then treated with a 10% bacterial supernatant at two time points (24 h and 48 h) at 37 °C in a humidified incubator with 5% CO2. Proteins were then extracted and separated using two-dimensional difference (2D-DIGE) gel electrophoresis, and the statistically significant proteins (p-value < 0.05, fold change > 1.5) were identified via matrix-assisted laser desorption/ionization-time-of-flight mass spectrometry (MALDI-TOF-MS). The protein fingerprints showed a differential protein expression pattern in the cells treated with E. faecalis for 24 and 48 h compared with the control. We found 58 statistically significant proteins changes in the MCF-7 breast cancer cells affected by E. faecalis. Kilin and transgelin were upregulated after 24 h of treatment and could be used as diagnostic and prognostic markers for breast cancer. In addition, another protein involved in the inhibition of cell proliferation was coiled-coil domain-containing protein 154. The protein markers identified in this study may serve as possible biomarkers for breast cancer progression. This promotes their future uses as important therapeutic goals in the treatment and diagnosis of cancer and increases our understanding of the breast microbiome and its role in the development of cancer.

Proteomic Profiling Identifies Distinct Regulation of Proteins in Obese Diabetic Patients Treated with Metformin.

Background: Obesity and type 2 diabetes mellitus (T2DM) are characterized by underlying low-grade chronic inflammation. Metformin has been used as the first line of therapy in T2DM as it decreases hepatic glucose production and glucose intestinal absorption, enhances insulin sensitivity and weight loss, and is known to ameliorate inflammation. The mechanisms through which metformin exerts its effect remain unclear. Proteomics has emerged as a unique approach to explore the biological changes associated with diseases, including T2DM. It provides insight into the circulating biomarkers/mediators which could be utilized for disease screening, diagnosis, and prognosis. Methods: This study evaluated the proteomic changes in obese (Ob), obese diabetics (OD), and obese diabetic patients on metformin (ODM) using a 2D DIGE MALDI-TOF mass spectrometric approach. Results: Significant changes in sixteen plasma proteins (15 up and 1 down, ANOVA, p ≤ 0.05; fold change ≥ 1.5) were observed in the ODM group when compared to the Ob and OD groups. Bioinformatic network pathway analysis revealed that the majority of these altered plasma proteins are involved in distinct pathways involving acute-phase response, inflammation, and oxidative response and were centered around HNF4A, ERK, JNK, and insulin signaling pathways. Conclusions: Our study provides important information about the possible biomarkers altered by metformin treatment in obese patients with and without T2DM. These altered plasma proteins are involved in distinct pathways involving acute-phase response, inflammation, and oxidative response and were centered around HNF4A, ERK, JNK, and insulin signaling pathways. The presented proteomic profiling approach may help in identifying potential biomarkers/mediators affected by metformin treatment in T2DM and inform the understanding of metformin's mechanisms of action.

Targeted Metabolomics Analysis of Individuals Carrying the ANGPTL8 R59W Variant.

ANGPTL8 is recognized as a regulator of lipid metabolism through its role in inhibiting lipoprotein lipase activity. ANGPTL8 gene variants, particularly rs2278426 leading to the R59W variant in the protein, have been associated with lipid traits in various ethnicities. We aimed to use metabolomics to understand the impact of the ANGPTL8 R59W variant on metabolites in humans. We used the Biocrates-p400 kit to quantify 408 plasma metabolites in 60 adult male Arab individuals from Kuwait and identify differences in metabolite levels between individuals carrying reference genotypes and those with carrier genotypes at ANGPTL8 rs2278426. Individuals with carrier genotypes (CT+TT) compared to those carrying the reference genotype (CC) showed statistically significant differences in the following metabolites: acylcarnitine (perturbs metabolic pathways), phosphatidylcholine (supports liver function and cholesterol levels), cholesteryl ester (brings chronic inflammatory response to lipoprotein depositions in arteries), α-aminoadipic acid (modulates glucose homeostasis), histamine (regulates glucose/lipid metabolism), sarcosine (links amino acid and lipid metabolism), diacylglycerol 42:1 (regulates homeostasis of cellular lipid stores), and lysophosphatidylcholine (regulates oxidative stress and inflammatory response). Functional aspects attributed to these metabolites indicate that the ANGPTL8 R59W variant influences the concentrations of lipid- and inflammation-related metabolites. This observation further highlights the role of ANGPTL8 in lipid metabolism.

Recent advances in proteomic-based diagnostics of cystic fibrosis.

INTRODUCTION: Cystic fibrosis (CF) is a genetic disease characterized by thick and sticky mucus accumulation, which may harm numerous internal organs. Various variables such as gene modifiers, environmental factors, age of diagnosis, and CF transmembrane conductance regulator (CFTR) gene mutations influence phenotypic disease diversity. Biomarkers that are based on genomic information may not accurately represent the underlying mechanism of the disease as well as its lethal complications. Therefore, recent advancements in mass spectrometry (MS)-based proteomics may provide deep insights into CF mechanisms and cellular functions by examining alterations in the protein expression patterns from various samples of individuals with CF. AREAS COVERED: We present current developments in MS-based proteomics, its application, and findings in CF. In addition, the future roles of proteomics in finding diagnostic and prognostic novel biomarkers. EXPERT OPINION: Despite significant advances in MS-based proteomics, extensive research in a large cohort for identifying and validating diagnostic, prognostic, predictive, and therapeutic biomarkers for CF disease is highly needed.

Metabolic Alteration of MCF-7 Cells upon Indirect Exposure to E. coli Secretome: A Model of Studying the Microbiota Effect on Human Breast Tissue.

According to studies, the microbiome may contribute to the emergence and spread of breast cancer. E. coli is one of the Enterobacteriaceae family recently found to be present as part of the breast tissue microbiota. In this study, we focused on the effect of E. coli secretome free of cells on MCF-7 metabolism. Liquid chromatography-mass spectrometry (LC-MS) metabolomics was used to study the E. coli secretome and its role in MCF-7 intra- and extracellular metabolites. A comparison was made between secretome-exposed cells and unexposed controls. Our analysis revealed significant alterations in 31 intracellular and 55 extracellular metabolites following secretome exposure. Several metabolic pathways, including lactate, aminoacyl-tRNA biosynthesis, purine metabolism, and energy metabolism, were found to be dysregulated upon E. coli secretome exposure. E. coli can alter the breast cancer cells' metabolism through its secretome which disrupts key metabolic pathways of MCF-7 cells. These microbial metabolites from the secretome hold promise as biomarkers of drug resistance or innovative approaches for cancer treatment, either as standalone therapies or in combination with other medicines.

Quantitative analysis of soluble costimulatory molecules as potential diagnostic biomarkers for rheumatoid arthritis using LC-MS/MS in MRM mode.

BACKGROUND AND AIMS: Rheumatoid arthritis (RA) is a chronic autoimmune disease. RA-induced immunological responses are coordinated by T-cell stimulation. The costimulatory signal CD28-B7 is essential for T-cell activation by interacting CD28 with CD80 and CD86 costimulatory proteins. CTLA4 is another costimulatory protein that binds to CD80 and CD86 to inhibit T-cell activity. The soluble costimulatory proteins: sCD80, sCD86, sCD28, and sCTLA-4 were detected and quantified in human plasma and correlated with RA development. As potential diagnostic biomarkers for RA, developing a sensitive, specific, and reproducible method for quantifying these costimulatory molecules in human plasma and establishing quantitative ranges for each protein in healthy and RA patients' plasma is essential for advancing the clinical diagnostic and health outcomes. MATERIALS AND METHODS: A novel quantitative liquid chromatography-tandem spectrometry (LC-MS/MS) technique using multiple reaction monitoring (MRM) modes was developed and validated to measure soluble costimulatory molecules sCTLA4, sCD28, sCD80, and sCD86 in human plasma samples. Furthermore, the method was applied to determine sCTLA4, sCD28, sCD80, and sCD86 levels in plasma samples from RA patients (n = 23) and healthy controls (n = 21). RESULTS: The method was successfully developed and validated according to international inter- and intra-assay precision and accuracy guidelines. The linearity of the method was achieved between 0.5 nM and 100 nM for each protein with a correlation coefficient of > 0.998. The plasma level of sCTLA4, sCD80, and sCD86 in RA patients was significantly elevated compared to controls. RA patients had 63.32 ± 17.63 nM sCTLA4 and controls 36.05 ± 18.83 nM; p < 0.0001. The performance of the four proteins was determined using ROC curves, where sCTLA4 showed the highest diagnostic and clinical performance compared to the others. CONCLUSIONS: This study reports the first use of LC-MS/MS in MRM mode to accurately quantify soluble costimulatory molecules in plasma samples as potential RA diagnostic biomarkers. Determination of the reference range for each protein with high selectivity and sensitivity increases the potential for utilizing this method as a clinical diagnostic.

Untargeted Metabolomics Identifies Biomarkers for MCADD Neonates in Dried Blood Spots.

Medium-chain acyl-CoA dehydrogenase deficiency (MCADD) is the most common inherited mitochondrial metabolic disease of fatty acid ß-oxidation, especially in newborns. MCADD is clinically diagnosed using Newborn Bloodspot Screening (NBS) and genetic testing. Still, these methods have limitations, such as false negatives or positives in NBS and the variants of uncertain significance in genetic testing. Thus, complementary diagnostic approaches for MCADD are needed. Recently, untargeted metabolomics has been proposed as a diagnostic approach for inherited metabolic diseases (IMDs) due to its ability to detect a wide range of metabolic alterations. We performed an untargeted metabolic profiling of dried blood spots (DBS) from MCADD newborns (n = 14) and healthy controls (n = 14) to discover potential metabolic biomarkers/pathways associated with MCADD. Extracted metabolites from DBS samples were analyzed using UPLC-QToF-MS for untargeted metabolomics analyses. Multivariate and univariate analyses were used to analyze the metabolomics data, and pathway and biomarker analyses were also performed on the significantly identified endogenous metabolites. The MCADD newborns had 1034 significantly dysregulated metabolites compared to healthy newborns (moderated t-test, no correction, p-value ≤ 0.05, FC 1.5). A total of 23 endogenous metabolites were up-regulated, while 84 endogenous metabolites were down-regulated. Pathway analyses showed phenylalanine, tyrosine, and tryptophan biosynthesis as the most affected pathways. Potential metabolic biomarkers for MCADD were PGP (a21:0/PG/F1alpha) and glutathione, with an area under the curve (AUC) of 0.949 and 0.898, respectively. PGP (a21:0/PG/F1alpha) was the first oxidized lipid in the top 15 biomarker list affected by MCADD. Additionally, glutathione was chosen to indicate oxidative stress events that could happen during fatty acid oxidation defects. Our findings suggest that MCADD newborns may have oxidative stress events as signs of the disease. However, further validations of these biomarkers are needed in future studies to ensure their accuracy and reliability as complementary markers with established MCADD markers for clinical diagnosis.

Metabolomic profile of patients on levothyroxine treatment for hypothyroidism.

Background: Hypothyroidism is clinically characterized by a decrease in levels of the circulating thyroid hormones namely thyroxine and triiodothyronine. The main treatment for hypothyroidism is thyroid hormone replacement using levothyroxine to normalize serum thyroid hormone levels. Objectives: In this study, we explored the metabolic changes in the plasma of patients with hypothyroidism after reaching a euthyroid state with levothyroxine treatment. Methods: Plasma samples from 18 patients diagnosed as overt hypothyroidism were collected before and after levothyroxine treatment upon reaching a euthyroid state and were analyzed by high-resolution mass spectrometry-based metabolomics. Multivariate and univariate analyses evaluated data to highlight potential metabolic biomarkers. Results: Liquid chromatography-mass spectrometry-based metabolomics revealed a significant decrease in the levels of ceramide, phosphatidylcholine, triglycerides, acylcarnitine, and peptides after levothyroxine treatment; this could indicate a change in the fatty acid transportation system and an enhanced ß-oxidation, compared with a hypothyroid state. At the same time, the decrease in the peptides suggested a shift in protein synthesis. In addition, there was a considerable rise in glycocholic acid following therapy, suggesting the involvement of thyroid hormones in stimulating bile acid production and secretion. Conclusions: A metabolomic analysis of patients with hypothyroidism revealed significant changes in several metabolites and lipids after treatment. This study showed the value of the metabolomics technique in providing a complementary understanding of the pathophysiology of hypothyroidism and as a crucial tool for examining the molecular impact of levothyroxine treatment on hypothyroidism. It was an important tool for investigating the therapeutic effects of levothyroxine on hypothyroidism at the molecular level.

A Distinctive Metabolomics Profile and Potential Biomarkers for Very Long Acylcarnitine Dehydrogenase Deficiency (VLCADD) Diagnosis in Newborns.

Very long-chain acylcarnitine dehydrogenase deficiency (VLCADD) is a rare inherited metabolic disorder associated with fatty acid ß-oxidation and characterized by genetic mutations in the ACADVL gene and accumulations of acylcarnitines. VLCADD, developed in neonates or later adults, can be diagnosed using newborn bloodspot screening (NBS) or genetic sequencing. These techniques have limitations, such as a high false discovery rate and variants of uncertain significance (VUS). As a result, an extra diagnostic tool is needed to deliver improved performance and health outcomes. As VLCADD is linked with metabolic disturbance, we postulated that newborn patients with VLCADD could display a distinct metabolomics pattern compared to healthy newborns and other disorders. Herein, we applied an untargeted metabolomics approach using liquid chromatography-high resolution mass spectrometry (LC-HRMS) to measure the global metabolites in dried blood spot (DBS) cards collected from VLCADD newborns (n = 15) and healthy controls (n = 15). Two hundred and six significantly dysregulated endogenous metabolites were identified in VLCADD, in contrast to healthy newborns. Fifty-eight and one hundred and eight up- and down-regulated endogenous metabolites were involved in several pathways such as tryptophan biosynthesis, aminoacyl-tRNA biosynthesis, amino sugar and nucleotide sugar metabolism, pyrimidine metabolism and pantothenate, and CoA biosynthesis. Furthermore, biomarker analyses identified 3,4-Dihydroxytetradecanoylcarnitine (AUC = 1), PIP (20:1)/PGF1alpha) (AUC = 0.982), and PIP2 (16:0/22:3) (AUC = 0.978) as potential metabolic biomarkers for VLCADD diagnosis. Our findings showed that compared to healthy newborns, VLCAADD newborns exhibit a distinctive metabolic profile, and identified potential biomarkers that can be used for early diagnosis, which improves the identification of the affected patients earlier. This allows for the timely administration of proper treatments, leading to improved health. However, further studies with large independent cohorts of VLCADD patients with different ages and phenotypes need to be studied to validate our potential diagnostic biomarkers and their specificity and accuracy during early life.

Biomarker discovery in galactosemia: Metabolomics with UPLC/HRMS in dried blood spots.

Introduction:Galactosemia (GAL) is a genetic disorder that results in disturbances in galactose metabolism and can lead to life-threatening complications. However, the underlying pathophysiology of long-term complications in GAL remains poorly understood. Methods: In this study, a metabolomics approach using ultra-performance liquid chromatography coupled with high-resolution mass spectrometry was used to investigate metabolomic changes in dried blood spots of 15 patients with GAL and 39 healthy individuals. Results: The study found that 2,819 metabolites underwent significant changes in patients with GAL compared to the control group. 480 human endogenous metabolites were identified, of which 209 and 271 were upregulated and downregulated, respectively. PA (8:0/LTE4) and ganglioside GT1c (d18:0/20:0) metabolites showed the most significant difference between GAL and the healthy group, with an area under the curve of 1 and 0.995, respectively. Additionally, the study identified potential biomarkers for GAL, such as 17-alpha-estradiol-3-glucuronide and 16-alpha-hydroxy DHEA 3-sulfatediphosphate. Conclusion: This metabolomics study deepened the understanding of the pathophysiology of GAL and presented potential biomarkers that might serve as prognostic biomarkers to monitor the progression or support the clinical diagnosis of GAL.

Multi-Omics Profiling in PGM3 and STAT3 Deficiencies: A Tale of Two Patients.

Hyper-IgE Syndrome (HIES) is a heterogeneous group of primary immune-deficiency disorders characterized by elevated levels of IgE, eczema, and recurrent skin and lung infections. HIES that is autosomally dominant in the signal transducer and activator of transcription 3 (STAT3), and autosomal recessive mutations in phosphoglucomutase 3 (PGM3) have been reported in humans. An early diagnosis, based on clinical suspicion and immunological assessments, is challenging. Patients' metabolomics, proteomics, and cytokine profiles were compared to DOCK 8-deficient and atopic dermatitis patients. The PGM3 metabolomics profile identified significant dysregulation in hypotaurine, hypoxanthine, uridine, and ribothymidine. The eight proteins involved include bifunctional arginine demethylase and lysyl hydroxylase (JMJD1B), type 1 protein phosphatase inhibitor 4 (PPI 4), and platelet factor 4 which aligned with an increased level of the cytokine GCSF. Patients with STAT3 deficiency, on the other hand, showed significant dysregulation in eight metabolites, including an increase in protocatechuic acid, seven proteins including ceruloplasmin, and a plasma protease C1 inhibitor, in addition to cytokine VEGF being dysregulated. Using multi-omics profiling, we identified the dysregulation of endothelial growth factor (EGFR) and tumor necrosis factor (TNF) signaling pathways in PGM3 and STAT3 patients, respectively. Our findings may serve as a stepping stone for larger prospective HIES clinical cohorts to validate their future use as biomarkers.

The metabolomics approach revealed a distinctive metabolomics pattern associated with hyperthyroidism treatment.

Background: Hyperthyroidism is characterized by increased thyroid hormone production, which impacts various processes, including metabolism and energy expenditure. Yet, the underlying mechanism and subsequent influence of these changes are unknown. Metabolomics is a broad analytical method that enables qualitative and quantitative examination of metabolite level changes in biological systems in response to various stimuli, pathologies, or treatments. Objectives: This study uses untargeted metabolomics to explore the potential pathways and metabolic patterns associated with hyperthyroidism treatment. Methods: The study consisted of 20 patients newly diagnosed with hyperthyroidism who were assessed at baseline and followed up after starting antithyroid treatment. Two blood samples were taken from each patient, pre (hyperthyroid state) and post-treatment (euthyroid state). Hyperthyroid and euthyroid states were identified based on thyroxine and thyroid-stimulating hormone levels. The metabolic alteration associated with antithyroid therapy was investigated using liquid chromatography- high-resolution mass spectrometry. The untargeted metabolomics data was analyzed using both univariate and multivariate analyses using MetaboAnalyst v5.0. The significant metabolic pattern was identified using the lab standard pipeline, which included molecular annotation in the Human Metabolome Database, LipidMap, LipidBlast, and METLIN. The identified metabolites were examined using pathway and network analyses and linked to cellular metabolism. Results: The results revealed a strong group separation between the pre- and post-hyperthyroidism treatment (Q2 = 0.573, R2 = 0.995), indicating significant differences in the plasma metabolome after treatment. Eighty-three mass ions were significantly dysregulated, of which 53 and 30 characteristics were up and down-regulated in the post-treatment compared to the pre-treatment group, respectively. The medium-chain acylcarnitines, octanoylcarnitine, and decanoylcarnitine, previously found to rise in hyperthyroid patients, were among the down-regulated metabolites, suggesting that their reduction could be a possible biomarker for monitoring euthyroid restoration. Kynurenine is a downregulated tryptophan metabolite, indicating that the enzyme kynurenine 3-hydroxylase, inhibited in hyperthyroidism, is back functioning. L-cystine, a cysteine dimer produced from cysteine oxidation, was among the down-regulated metabolites, and its accumulation is considered a sign of oxidative stress, which was reported to accompany hyperthyroidism; L-cystine levels dropped, this suggests that the plasma level of L-cystine can be used to monitor the progress of euthyroid state restoration. Conclusion: The plasma metabolome of patients with hyperthyroidism before and after treatments revealed differences in the abundance of several small metabolites. Our findings add to our understanding of hyperthyroidism's altered metabolome and associated metabolic processes and shed light on acylcarnitines as a new biomarker for treatment monitoring in conjunction with thyroxine and thyroid-stimulating hormone.