Project description:The aim of this project was the development of a predicitive model combining clinical variables and a panel of proteins measured by targeted-MS able to distinguish PCa from BPH. In particular, a total of 32 formerly N-glycosylated peptides were quantified by PRM.
Project description:Purpose: In this study, we performed RNA-seq analysis as a screening strategy to identify EV-miRNAs derived from serum of well clinically annotated breast cancer (BC) patients from South of Brazil. Methods: EVs from three groups of samples, healthy controls (CT), luminal A (LA), and triple negative (TNBC), were isolated from serum using a precipitation method and analyzed by RNA-seq (screening phase). Subsequently, four EV-miRNAs (miR-142-5p, miR-150-5p, miR-320a, and miR-4433b-5p) were selected to be quantified by RT-qPCR in individual samples (test phase). Results: A panel composed of miR-142-5p, miR-320a, and miR-4433b-5p discriminated BC patients from CT with an AUC of 0.8387 (93.33% sensitivity, 68.75% specificity). In addition, the combination of miR-142-5p and miR-320a, presented an AUC of 0.941 (100% sensitivity, 93.80% specificity) in distinguishing LA patients from CT. Interestingly, decrease expression of miR-142-5p and miR-150-5p were significantly associated with more advanced tumor grades (grade III), while the decrease expression of miR-142-5p and miR-320a with larger tumor size. Conclusion: These results provide insights into the potential application of EVs-miRNAs from serum as novel specific markers for early diagnosis of BC.
Project description:Targeted LC/MS Metabolomics. Cecal contents were lyophilized overnight and approximately 2.5-5 mg was homogenized in 250 µL of 50% acidified acetonitrile (0.3% formic acid) for targeted LC/MS/MS metabolomics (acylcarnitines, amino acids, organic acids, nucleotides, and CoAs) according to validated, optimized protocols in our previously published study (Previs et al.). Separate aliquots of homogenates were extracted with solvents for each class of metabolites, and then each class was analyzed with a unique LC/MS/MS method to optimize their chromatographic resolution and sensitivity. Quantitation of metabolites in each assay module was achieved using multiple reaction monitoring of calibration solutions and study samples with isotopically-labelled internal standards on an Agilent 1290 Infinity UHPLC/6495 triple quadrupole mass spectrometer. Raw data was processed using Mass Hunter quantitative analysis software (Agilent). Calibration curves (R2 = 0.99 or greater) are either fitted with a linear or a quadratic curve with a 1/X or 1/X2 weighting. Metabolic and Proteomic Defects in Human Hypertrophic Cardiomyopathy Michael J. Previs, Thomas S. O’Leary, Neil B. Wood, Michael P. Morley, Brad Palmer, Martin LeWinter, Jaime Yob, Francis D. Pagani, Christopher Petucci, Min-Soo Kim, Kenneth B. Margulies, Zoltan Arany, Daniel P. Kelly, Sharlene M. Day bioRxiv 2021.08.18.455967; doi: https://doi.org/10.1101/2021.08.18.455967
Project description:In this study, we used a comprehensive approach involving endogenous peptidome along with bioinformatics analysis, to identify and evaluate potential biomarkers for HCC. Serum samples from 40 subjects, comprising 20 HCC cases and 20 patients with liver cirrhosis (CIRR), were analyzed. Among 2568 endogenous peptides, 68 showed significant differential expression between the HCC vs. CIRR. Further analysis revealed three endogenous peptides (VMHEALHNHYTQKSLSLSPG, NRFTQKSLSLSPG, and SARQSTLDKEL) that showed far better performance compared to AFP in terms of area under the receiver operating characteristic curve (AUC), showcasing their potential as biomarkers for HCC. Additionally, endogenous peptide IAVEWESNGQPENNYKT that belongs to precursor protein Immunoglobulin heavy constant gamma 4, was detected in 100% of the HCC group and completely absent in all samples of the CIRR group, suggesting a promising diagnostic biomarker. Gene ontology and pathway analysis revealed the potential involvement of these dysregulated peptides in HCC.
Project description:Gallbladder cancer (GBC) is the most common biliary tract malignancy worldwide. Although a growing number of studies have been devoted to the mechanism’s exploration of GBC, few molecules can be utilized as a specific biomarker for GBC early diagnosis and therapeutic treatment. Recent studies have shown that exosomes not only participate in the progression of tumors, but also carry the specific information which can define multiple cancer types. The present study was carried out to investigate the expression profile of coding and non-coding RNAs (lncRNAs and circRNAs) in the plasma-derived exosomes from GBC patients. By using high-throughput RNA sequencing and subsequently bioinformatic analysis, we identified a number of differentially expressed (DE) mRNAs, lncRNAs and circRNAs in GBC-exosomes comparing to xantho-granulomatous cholecystitis (XGC)-exosomes. Then we applied Gene ontology (GO) and Kyoto Encyclopedia of Gene and Genome (KEGG) analysis to investigate the potential function of these DE RNAs. Furthermore, we explored the interaction networks and competing endogenous RNA networks of these DE RNAs and their targeted genes, which revealed a complex regulatory network among coding and non-coding RNAs. In summary, this study will give evidence on the diagnostic worth of plasma-derived exosomes in GBC and provide a new perspective on the novel mechanism of GBC.
Project description:We developed a straightforward targeted sequencing approach as extension to high-throughput droplet-based scRNA-seq. By overlaying standard gene expression data with genotype information at the transcript level, we created a powerful method for evaluating the impact of genetic variants. We systematically analysed the effect of technical parameters on the targeted sequencing method and demonstrated how the targeted sequencing method can be used.
Project description:In this study, we developed an approach using Zirconium (IV)-grafted mesoporous beads to enrich phosphopeptides followed by analysis with a high resolution nanoRPLC-MS/MS system. The new method was first tested with tryptic digests of standard phosphoproteins and HeLa cell lysates, with excellent enrichment performance achieved. The method was further used for endogenous phosphopeptidomic analysis of serum samples from pancreatic ductal adenocarcinoma (PDAC) patients and controls. In total, 329 endogenous phosphopeptides (containing 113 high confidence sites) were identified across samples, by far the largest endogenous phosphorylation dataset catalogued to date. In addition, the method was readily applied for phosphoproteomics of the same set of samples, with 172 phosphopeptides identified and significant changes in dozens of phosphopeptides observed. Taken together, this method serves as a benchmark for analyzing serum phosphorylation events. Given the simplicity and robustness of the proposed method, we envision that it can be readily used for comprehensive phosphorylation studies (i.e., simultaneous endogenous phosphopeptidomics and phosphoproteomics) of serum and other biofluid samples.
Project description:The Illumina Infinium 27k Human DNA methylation Beadchip v1.2 was used to obtain DNA methylation profiles across approximately 27,000 CpGs in serum DNA samples from a total of 282 healthy postmenopausal women of which 134 developed breast cancer within a 5-year follow-up period
Project description:The endocrine changes and gene regulation patterns responding to sexual maturation and spawning have not been clarified in fish species. To identify the potential metabolites and genes regulating the ovarian development and spawning, we employed the approach integrating the metabolic and RNA-seq techniques to investigate the metabolites and candidate genes participating in the sexual maturation and spawning of female blunt snout bream Megalobrama amblycephala. This data descriptor provides metabolic and transcriptomic information for the blood tissue at different developmental stages of female M. amblycephala. With UPLC-MS/MS method, a total of 763 and 173 differential ions were detected from the pairwise comparisons of the four groups in positive and negative mode, respectively. Meanwhile, 191.65 Mb raw reads were generated using illumina Hiseq platform with 50 bp single-end strategy. We then analysed the differential metabolites and differentially expressed genes and their functions among the four groups. This data descriptor will contribute to understanding the dynamics of metabolites and gene regulation in ovarian development and spawning and reuse to support physiological and genetic researches in aquaculture.
Project description:The collection of metabolites circulating in the human blood, termed the serum metabolome, contains a plethora of biomarkers and causative agents. Although the origin of specific compounds is known, we have a poor understanding of the key determinants of most metabolites. Here, we measured the levels of 1251 circulating metabolites in serum samples from a healthy human cohort of 491 individuals, and devised machine learning algorithms to predict their levels in held-out subjects based on a comprehensive profile consisting of host genetics, gut microbiome, clinical parameters, diet, lifestyle, anthropometric measurements and medication data. Notably, we obtained statistically significant predictions for over 76% of the profiled metabolites. Despite using the strict out-of-sample prediction metric, which is a lower bound for the explained variance, diet and microbiome each explained hundreds of metabolites, with over 50% of the variance explained in some metabolites. We further validated the robustness of the microbiome related associations by showing a high replication rate in two geographically independent cohorts that were not available to us when developing the algorithms. We also demonstrate that some of these interactions are causal, as some metabolites we predicted to be positively associated with bread increased in level following a randomized clinical trial of bread intervention. Microbiome-explained metabolites were enriched with unnamed metabolites, and we devised an algorithm that accurately predicts their biological pathway, finding that they mainly include food components, aromatic amino acids and secondary bile acid derivatives. Overall, our results unravel potential determinants of over 800 metabolites, paving the way towards mechanistic understanding of alterations in metabolites under different conditions and to designing interventions for manipulating circulating metabolite levels.