Project description:Background: Macrophage-based immune dysregulation plays a critical role in development of delayed gastric emptying in animal models of diabetes. Human studies have also revealed loss of anti-inflammatory macrophages and increased expression of genes associated with pro-inflammatory macrophages in full thickness gastric biopsies from gastroparesis patients. Aim: We aimed to determine broader protein expression (proteomics) and protein-based signaling pathways in full thickness gastric biopsies of diabetic (DG) and idiopathic gastroparesis (IG) patients. Additionally, we determined correlations between protein expressions, gastric emptying and symptoms. Methods: Full-thickness gastric antrum biopsies were obtained from nine DG, seven IG patients and five non-diabetic controls. Aptamer-based SomaLogic tissue scan that quantitatively identifies 1300 human proteins was used. Protein fold changes were computed, and differential expressions were calculated using Limma. Ingenuity Pathway Analysis and correlations were carried out. Multiple-testing corrected p-values <0.05 were considered statistically significant. Results: 73 proteins were differentially expressed in DG, 132 proteins in IG and 40 proteins were common to DG and IG. In both DG and IG, “Role of Macrophages, Fibroblasts and Endothelial Cells” was the most statistically significant altered pathway (DG FDR: 7.9x10-9; IG FDR: 6.3x10-12). In DG, properdin expression correlated with GCSI-bloating (r: -0.99, FDR: 0.02) and expressions of prostaglandin G/H synthase 2, protein kinase C zeta type and complement C2 correlated with 4 hr gastric retention (r: -0.97, FDR: 0.03 for all). No correlations were found between proteins and symptoms or gastric emptying in IG. Conclusions: Protein expression changes suggest a central role of macrophage-driven immune dysregulation and complement activation in gastroparesis.
Project description:Ribosome profiling is a widespread tool for studying translational dynamics in human cells. Its central assumption is that ribosome footprint density on a transcript quantitatively reflects protein synthesis. Here, we test this assumption using pulsed-SILAC (pSILAC) high-accuracy targeted proteomics. We focus on multiple myeloma cells exposed to bortezomib, a first-line chemotherapy and proteasome inhibitor. In the absence of drug effects, we found that direct measurement of protein synthesis by pSILAC correlated well with indirect measurement of synthesis from ribosome footprint density. This correlation, however, broke down under bortezomib-induced stress. By developing a statistical model integrating longitudinal proteomic and mRNA-seq measurements, we found that proteomics could directly detect global alterations in translational rate caused by bortezomib; these changes are not detectable by ribosomal profiling alone. Further, by incorporating pSILAC data into a gene expression model, we predict cell-stress specific proteome remodeling events. These results demonstrate that pSILAC provides an important complement to ribosome profiling in measuring proteome dynamics. Timecourse experiment with six points over 48hr after bortezomib exposure in MM.1S myeloma cells. mRNA-seq and ribosome profiling data at each time point.
Project description:Ribosome profiling is a widespread tool for studying translational dynamics in human cells. Its central assumption is that ribosome footprint density on a transcript quantitatively reflects protein synthesis. Here, we test this assumption using pulsed-SILAC (pSILAC) high-accuracy targeted proteomics. We focus on multiple myeloma cells exposed to bortezomib, a first-line chemotherapy and proteasome inhibitor. In the absence of drug effects, we found that direct measurement of protein synthesis by pSILAC correlated well with indirect measurement of synthesis from ribosome footprint density. This correlation, however, broke down under bortezomib-induced stress. By developing a statistical model integrating longitudinal proteomic and mRNA-seq measurements, we found that proteomics could directly detect global alterations in translational rate caused by bortezomib; these changes are not detectable by ribosomal profiling alone. Further, by incorporating pSILAC data into a gene expression model, we predict cell-stress specific proteome remodeling events. These results demonstrate that pSILAC provides an important complement to ribosome profiling in measuring proteome dynamics.
Project description:Quercetin has been shown to act as an anti-carcinogen in experimental colorectal cancer (CRC). The aim of the present study was to characterise transcriptome and proteome changes occurring in the distal colon mucosa of rats supplemented with 10 g quercetin/kg diet for 11 weeks. Transcriptome data analysed with Gene Set Enrichment Analysis showed that quercetin significantly downregulated the potentially oncogenic mitogen-activated protein kinase (Mapk) pathway. In addition, quercetin enhanced expression of tumor suppressor genes, including Pten, Tp53 and Msh2, and of cell cycle inhibitors, including Mutyh. Furthermore, dietary quercetin enhanced genes involved in phase I and II metabolism, including Fmo5, Ephx1, Ephx2 and Gpx2. Quercetin increased PPARα target genes, and concomitantly enhanced expression genes in volved in of mitochondrial fatty acid degradation. Proteomics performed in the same samples revealed 33 affected proteins, of which 4 glycolysis enzymes and 3 heatshock proteins were decreased. A proteome-transcriptome comparison showed a low correlation, but both pointed out towards altered energy metabolism. In conclusion, transcriptomics combined with proteomics showed that dietary quercetin evoked changes contrary to those found in colorectal carcinogenesis. These tumor-protective mechanisms were associated with a shift in energy production pathways, pointing at decreased glycolysis in the cytoplasm towards increased fatty acid degradation in the mitochondria. Keywords: Transscriptomics, proteomics, quercetin-exposed and control rats
Project description:In this study, we evaluated the utility of proteomics to identify plasma proteins in healthy participants from a phase I clinical trial with IFNβ-1a and pegIFNβ-1a biologics to identify potential pharmacodynamic (PD) biomarkers. Using a linear mixed-effects model with repeated measurement for product-time interaction, we found that 248 and 528 analytes detected by the SOMAscan® assay were differentially expressed (p-value < 6.86E-06) between therapeutic doses of IFNβ-1a or pegIFNβ-1a, and placebo, respectively. We further prioritized signals based on peak change, area under the effect curve over the study duration, and overlap in signals from the two products. Analysis of prioritized datasets indicated activation of IFNB1 signaling and an IFNB signaling node with IL-6 as upstream regulators of the plasma protein patterns from both products. Increased TNF, IL-1B, IFNG, and IFNA signaling also occurred early in response to each product suggesting a direct link between each product and these upstream regulators. In summary, we identified longitudinal global PD changes in a large array of new and previously reported circulating proteins in healthy participants treated with IFNβ-1a and pegIFNβ-1a that may help identify novel single proteomic PD biomarkers and/or composite PD biomarker signatures as well as provide insight into the mechanism of action of these products. Independent replication is needed to confirm present proteomic results and to support further investigation of the identified candidate PD biomarkers for biosimilar product development.
Project description:Parkinson disease (PD) is a neurodegenerative disease characterized by the accumulation of alpha-synuclein (SNCA) and other proteins in aggregates termed âLewy Bodiesâ within neurons. PD has both genetic and environmental risk factors, and while processes leading to aberrant protein aggregation are unknown, past work points to abnormal levels of SNCA and other proteins. Although several genome-wide studies have been performed for PD, these have focused on DNA sequence variants by genome-wide association studies (GWAS) and on RNA levels (microarray transcriptomics), while genome-wide proteomics analysis has been lacking. After appropriate filters, proteomics identified 3,558 unique proteins and 283 of these (7.9%) were significantly different between PD and controls (q-value<0.05). RNA-sequencing identified 17,580 protein-coding genes and 1,095 of these (6.2%) were significantly different (FDR p-value<0.05), but only 166 of the FDR significant protein-coding genes (0.94%) were present among the 3,558 proteins characterized. Of these 166, eight genes (4.8%) were significant in both studies, with the same direction of effect. Functional enrichment analysis of the proteomics results strongly supports mitochondrial-related pathways, while comparable analysis of the RNA-sequencing results implicates protein folding pathways and metallothioneins. Ten of the implicated genes or proteins co-localized to GWAS loci. Evidence implicating SNCA was stronger in proteomics than in RNA-sequencing analyses. Notably, differentially expressed protein-coding genes were more likely to not be characterized in the proteomics analysis, which lessens the ability to compare across platforms. Combining multiple genome-wide platforms offers novel insights into the pathological processes responsible for this disease by identifying pathways implicated across methodologies. The study consists of mRNA-Seq (29 PD, 44 neurologically normal controls) and three-stage Mass Spectrometry Tandem Mass Tag Proteomics (12 PD, 12 neurologically normal controls) performed in post-mortem BA9 brain tissue. The proteomics samples are a subset of the RNA-Seq samples.