Project description:Pancreatic Cancer (PC) has the worst 5-year survival rate of any cancer as of 2024, at just 13%. The late-stage diagnosis of these patients limits their treatment options, further compounding the problem. Early detection of PC, therefore, is the primary concern of most PC research, as it has the potential to make a substantial difference to the treatment and survival of these patients. Pancreatic cystic lesions (PCLs) are fluid-filled sacs, on or inside the pancreas, that have the potential to become premalignant. While some PCLs are completely benign, others have been shown to have malignant potential and could therefore play a role in the progression to PC. Using the 2018 European evidence-based guidelines for pancreatic cystic neoplasms, patients were classified as being either at a low- or high-risk of PC development. In this study, we profile the proteome of pancreatic cyst fluid from low-risk (n=15) and high-risk (n=17) patients with PCLs and identify differentially expressed proteins between these two risk classifications. We show that these PCF-based differentially expressed proteins have potential utility as biomarkers of risk stratification in this setting.
Project description:Asthma is a complex syndrome associated with episodic decompensations provoked by aeroaller-gen exposures. The underlying pathophysiological states driving exacerbations are latent in the resting state and do not adequately inform biomarker-driven therapy. A better understanding of the pathophysiological pathways driving allergic exacerbations is needed. We hypothesized that disease-associated pathways could be identified in humans by unbiased metabolomics of bron-choalveolar fluid (BALF) during the peak inflammatory response provoked by a bronchial aller-gen challenge. We analyzed BALF metabolites in samples from 12 volunteers who underwent segmental bronchial antigen provocation (SBP-Ag). Metabolites were quantified using liquid chromatography-tandem mass spectrometry (LC–MS/MS) followed by pathway analysis and cor-relation with airway inflammation. SBP-Ag induced statistically significant changes in 549 fea-tures that mapped to 72 uniquely identified metabolites. From these features, two distinct induci-ble metabolic phenotypes were identified by the principal component analysis, partitioning around medoids (PAM) and k-means clustering. Ten index metabolites were identified that in-formed the presence of asthma-relevant pathways, including unsaturated fatty acid produc-tion/metabolism, mitochondrial beta oxidation of unsaturated fatty acid, and bile acid metabolism. Pathways were validated using proteomics in eosinophils. A segmental bronchial allergen chal-lenge induces distinct metabolic responses in humans, providing insight into pathogenic and pro-tective endotypes in allergic asthma.
Project description:Microbial Dynamics of Acute Pancreatitis: Integrating Culture, Sequencing, and Bile Impact on Bacterial Populations and Gaseous Metabolites.
Project description:Exosomes/microvesicles (hereafter referred to as extracellular vesicles) were isolated from the ULF of day 14 cyclic and pregnant ewes using ExoQuick-TC. Extracellular vesicle RNA was pooled (n=4 per status) and analyzed for small RNAs by sequencing on the Ion Torrent PGM platform and analysis with CLC Genomics Workbench small RNA workflow based on the miRBase (Release 19) Bos taurus database. Small RNA analysis of day 14 uterine luminal fluid extracellular vesicles isolated from pregnant and cyclic ewes.
Project description:Pancreatic cancer is one of the most lethal cancer types worldwide, with the majority of the diagnostics being pancreatic adenocarcinomas (PDAC). Early and accurate diagnosis are among the best alternatives to improve treatment effectiveness and survival of PDCA patients; however, plasmatic biomarkers molecules are scarce. Here, we describe a proteomic screening on depleted plasma from 29 PDAC patients and 30 healthy controls, aiming to identify potential biomarkers for PDAC diagnosis. We observed 121 differentially expressed proteins (DEPs), which were associated with the tumor biology and clinical behavior. We emphasize the potential of three over-expressed proteins (ORM1, ORM2, and SERPINA1), and four down-expressed proteins (ALDOA, PPIA, VCL, and SPARC) as biomarkers in PDAC, which were also correlated with disease staging. It was also demonstrated that some proteins could be used as complementary biomarkers associated with gold-standard CA19-9. Our high throughput proteomics approach suggests a set of proteins with potential use in PDAC diagnosis, giving light to future validation and application in clinical practice, enabling target proteomics and other serological assays.