Project description:Cystic fibrosis (CF) is a life-shortening disease caused by a mutation in the cystic fibrosis transmembrane conductance regulator (CFTR) gene. To gain an understanding of the epithelial dysfunction associated with CF mutations and discover biomarkers for therapeutics development, untargeted metabolomic analysis was performed on primary human airway epithelial cell cultures from three separate cohorts of CF patients and non-CF subjects. Statistical analysis revealed a set of reproducible and significant metabolic differences between the CF and non-CF cells. Aside from changes that were consistent with known CF effects, such as diminished cellular regulation against oxidative stress and osmotic stress, new observations on the cellular metabolism in the disease were generated. In the CF cells, the levels of various purine nucleotides, which may function to regulate cellular responses via purinergic signaling, were significantly decreased. Furthermore, CF cells exhibited reduced glucose metabolism in glycolysis, pentose phosphate pathway, and sorbitol pathway, which may further exacerbate oxidative stress and limit the epithelial cell response to environmental pressure. Taken together, these findings reveal novel metabolic abnormalities associated with the CF pathological process and identify a panel of potential biomarkers for therapeutic development using this model system.
Project description:Spinocerebellar ataxia 3, also known as Machado-Joseph disease (SCA3/MJD), is a rare autosomal-dominant neurodegenerative disease caused by an abnormal expansion of CAG repeats in the ATXN3 gene. In the present study, we performed a global metabolomic analysis to identify pathogenic biochemical pathways and novel biomarkers implicated in SCA3 patients. Metabolic profiling of serum samples from 13 preclinical SCA3 patients, 13 symptomatic SCA3 patients, and 15 healthy controls were mapped using ultra-high-performance liquid chromatography-mass spectrometry and gas chromatography-mass spectrometry techniques. The symptomatic SCA3 patients showed a metabolic profile significantly distinct from those of the preclinical SCA3 patients and healthy controls. The principal differential metabolites were involved in the amino acid (AA) metabolism and fatty acid metabolism pathways. In addition, four candidate serum biomarkers, FFA 16:1 (palmitoleic acid), FFA 18:3 (linolenic acid), L-Proline and L-Tryptophan, were selected to discriminate between symptomatic SCA3 patients and healthy controls by receiver operator curve analysis with an area under the curve of 0.979. Our study demonstrates that symptomatic SCA3 patients present distinct metabolic profiles with perturbed AA metabolism and fatty acid metabolism, and FFA 16:1, FFA 18:3, L-Proline and L-Tryptophan are identified as potential disease biomarkers.
Project description:Cerebral palsy (CP) is one of the most common causes of motor disability in childhood, with complex and heterogeneous etiopathophysiology and clinical presentation. Understanding the metabolic processes associated with the disease may aid in the discovery of preventive measures and therapy. Tissue samples (caudate nucleus) were obtained from post-mortem CP cases (n = 9) and age- and gender-matched control subjects (n = 11). We employed a targeted metabolomics approach using both ¹H NMR and direct injection liquid chromatography-tandem mass spectrometry (DI/LC-MS/MS). We accurately identified and quantified 55 metabolites using ¹H NMR and 186 using DI/LC-MS/MS. Among the 222 detected metabolites, 27 showed significant concentration changes between CP cases and controls. Glycerophospholipids and urea were the most commonly selected metabolites used to develop predictive models capable of discriminating between CP and controls. Metabolomics enrichment analysis identified folate, propanoate, and androgen/estrogen metabolism as the top three significantly perturbed pathways. We report for the first time the metabolomic profiling of post-mortem brain tissue from patients who died from cerebral palsy. These findings could help to further investigate the complex etiopathophysiology of CP while identifying predictive, central biomarkers of CP.
Project description:BackgroundSingle-stranded DNA aptamers are oligonucleotides of ≈50 base pairs in length selected for their ability to bind proteins with high specificity and affinity. Emerging DNA aptamer-based technologies may address limitations of existing proteomic techniques, including low sample throughput, which have hindered proteomic analyses of large cohorts.MethodsTo identify early biomarkers of myocardial injury, we applied an aptamer-based proteomic platform that measures 1129 proteins to a clinically relevant perturbational model of planned myocardial infarction (PMI), patients undergoing septal ablation for hypertrophic cardiomyopathy. Blood samples were obtained before and at 10 and 60 minutes after PMI, and protein changes were assessed by repeated-measures analysis of variance. The generalizability of our PMI findings was evaluated in a spontaneous myocardial infarction cohort (Wilcoxon rank-sum). We then tested the platform's ability to detect associations between proteins and Framingham Risk Score components in the Framingham Heart Study, performing regression analyses for each protein versus each clinical trait.ResultsWe found 217 proteins that significantly changed in the peripheral vein blood after PMI in a derivation cohort (n=15; P<5.70E-5). Seventy-nine of these proteins were validated in an independent PMI cohort (n=15; P<2.30E-4); >85% were directionally consistent and reached nominal significance. We detected many protein changes that are novel in the context of myocardial injury, including Dickkopf-related protein 4, a WNT pathway inhibitor (peak increase 124%, P=1.29E-15) and cripto, a growth factor important in cardiac development (peak increase 64%, P=1.74E-4). Among the 40 validated proteins that increased within 1 hour after PMI, 23 were also elevated in patients with spontaneous myocardial infarction (n=46; P<0.05). Framingham Heart Study analyses revealed 156 significant protein associations with the Framingham Risk Score (n=899), including aminoacylase 1 (β=0.3386, P=2.54E-22) and trigger factor 2 (β=0.2846, P=5.71E-17). Furthermore, we developed a novel workflow integrating DNA-based immunoaffinity with mass spectrometry to analytically validate aptamer specificity.ConclusionsOur results highlight an emerging proteomics tool capable of profiling >1000 low-abundance analytes with high sensitivity and high precision, applicable both to well-phenotyped perturbational studies and large human cohorts, as well.
Project description:The miRBase-21 database currently lists 1881 microRNA (miRNA) precursors and 2585 unique mature human miRNAs. Since their discovery, miRNAs have proved to present a new level of epigenetic post-transcriptional control of protein synthesis. Initial results point to a possible involvement of miRNA in Alzheimer's disease (AD). We applied OpenArray technology to profile the expression of 1178 unique miRNAs in cerebrospinal fluid (CSF) samples of AD patients (n = 22) and controls (n = 28). Using a Cq of 34 as cut-off, we identified positive signals for 441 miRNAs, while 729 miRNAs could not be detected, indicating that at least 37% of miRNAs are present in the brain. We found 74 miRNAs being down- and 74 miRNAs being up-regulated in AD using a 1.5 fold change threshold. By applying the new explorative "Measure of relevance" method, 6 reliable and 9 informative biomarkers were identified. Confirmatory MANCOVA revealed reliable miR-100, miR-146a and miR-1274a as differentially expressed in AD reaching Bonferroni corrected significance. MANCOVA also confirmed differential expression of informative miR-103, miR-375, miR-505#, miR-708, miR-4467, miR-219, miR-296, miR-766 and miR-3622b-3p. Discrimination analysis using a combination of miR-100, miR-103 and miR-375 was able to detect AD in CSF by positively classifying controls and AD cases with 96.4% and 95.5% accuracy, respectively. Referring to the Ingenuity database we could identify a set of AD associated genes that are targeted by these miRNAs. Highly predicted targets included genes involved in the regulation of tau and amyloid pathways in AD like MAPT, BACE1 and mTOR.
Project description:ObjectiveAmeloblastoma is a benign odontogenic tumor that may lead to ameloblastic carcinoma. This study aimed to determine potential signaling pathways and biological processes, critical genes and their regulating transcription factors (TFs), and miRNAs, as well as protein kinases involved in the etiology of primary ameloblastoma.MethodsThe dataset GSE132472 was obtained from the GEO database, and multivariate statistical analyses were applied to identify differentially expressed genes (DEGs) in primary ameloblastoma tissues compared to the corresponding normal gingiva samples. A protein-protein interaction (PPI) map was built using the STRING database. The Cytoscape software identified significant modules and the hub genes within the PPI network. Gene Ontology annotation and signaling pathway analyses were executed by employing the DAVID and Reactome databases, respectively. Significant TFs and miRNAs acting on the hub genes were identified using the iRegulon plugin and MiRWalk 2.0 database, respectively. A protein kinase enrichment analysis was conducted using the online Kinase Enrichment Analysis 2 (KEA2) web server. The approved drugs acting on the hub genes were also found.ResultsA total of 1,629 genes were differentially expressed in primary ameloblastoma (P value <0.01 and |Log2FC| > 1). HRAS, CDK1, MAPK3, ERBB2, COL1A1, CYCS, and BRCA1 demonstrated high degree and betweenness centralities in the PPI network. E2F4 was the most significant TF acting on the hub genes. BTK was the protein kinase significantly enriched by the TFs. Cholesterol biosynthesis was considerably involved in primary ameloblastoma.ConclusionsThis study provides an intuition into the potential mechanisms involved in the etiology of ameloblastoma.
Project description:Metabolomics has been reported as an efficient tool to screen biomarkers that are related to esophageal cancer. However, the metabolic biomarkers identifying malignant degrees and therapeutic efficacy are still largely unknown in the disease. Here, GC-MS-based metabolomics was used to understand metabolic alteration in 137 serum specimens from patients with esophageal cancer, which is approximately two- to fivefold as many plasma specimens as the previous reports. The elevated amino acid metabolism is in sharp contrast to the reduced carbohydrate as a characteristic feature of esophageal cancer. Comparative metabolomics showed that most metabolic differences were determined between the early stage (0-II) and the late stage (III and IV) among the 0-IV stages of esophageal cancer and between patients who received treatment and those who did not receive treatment. Glycine, serine, and threonine metabolism and glycine were identified as the potentially overlapped metabolic pathway and metabolite, respectively, in both disease progress and treatment effect. Glycine, fructose, ornithine, and threonine can be a potential array for the evaluation of disease prognosis and therapy in esophageal cancer. These results highlight the means of identifying previously unknown biomarkers related to esophageal cancer by a metabolomics approach.
Project description:Chronic obstructive pulmonary disease is the third leading cause of death worldwide. Gene expression profiling across multiple regions of the same lung identified genes significantly related to emphysema. We sought to determine whether the lung and epithelial expression of 127 emphysema-related genes was also related to lung function in independent cohorts, and whether any of these genes could be used as biomarkers in the peripheral blood of patients with chronic obstructive pulmonary disease. To that end, we examined whether the expression levels of these genes were under genetic control in lung tissue (n = 1,111). We then determined whether the mRNA levels of these genes in lung tissue (n = 727), small airway epithelial cells (n = 238), and peripheral blood (n = 620) were significantly related to lung function measurements. The expression of 63 of the 127 genes (50%) was under genetic control in lung tissue. The lung and epithelial mRNA expression of a subset of the emphysema-associated genes, including ASRGL1, LPHN2, and EDNRB, was strongly associated with lung function. In peripheral blood, the expression of 40 genes was significantly associated with lung function. Twenty-nine of these genes (73%) were also associated with lung function in lung tissue, but with the opposite direction of effect for 24 of the 29 genes, including those involved in hypoxia and B cell-related responses. The integrative genomics approach uncovered a significant overlap of emphysema genes associations with lung function between lung and blood with opposite directions between the two. These results support the use of peripheral blood to detect disease biomarkers.
Project description:IntroductionChronic kidney disease (CKD) is a global public health problem, and the absence of reliable and accurate diagnostic and monitoring tools contributes to delayed treatment, impacting patients' quality of life and increasing treatment costs in public health. Proteomics using saliva is a key strategy for identifying potential disease biomarkers.MethodsWe analyzed the untargeted proteomic profiles of saliva samples from 20 individuals with end-stage kidney disease (ESKD) (n = 10) and healthy individuals (n = 10) using liquid chromatography-tandem mass spectrometry (LC-MS/MS) to identify potential biomarkers for CKD. A volcano plot was generated using a p-value of ≤0.05 and a fold change (FC) ≥ 2.0. Multivariate analysis was performed to generate the orthogonal partial least squares discriminant analysis (OPLS-DA) model and the variable importance in projection (VIP) scores. The accuracy of candidate biomarker proteins was evaluated using receiver operating characteristic (ROC) curves.ResultsIn total, 431 proteins were identified in the salivary proteomic profile, and 3 proteins were significantly different between the groups: apoptosis inhibitor 5 (API5), phosphoinositide phospholipase C (PI-PLC), and small G protein signaling modulator 2 (Sgsm2). These proteins showed good accuracy based on the ROC curve and a VIP score of >2.0. During pathway enrichment, PI-PLC participates in the synthesis of IP3 and IP4 in the cytosol. Gene ontology (GO) analysis revealed data on molecular functions, biological processes, cellular components, and protein classes.ConclusionWe can conclude that the salivary API5, PI-PLC, and Sgsm2 can be potential biomarker candidates for CKD detection. These proteins may participate in pathways related to renal fibrosis and other associated diseases, such as mineral and bone disorders.