Project description:The Family Colorectal Cancer Awareness and Risk Education Project (Family CARE Project) is designed to determine whether a personalized telephone plus mailed print cancer risk assessment and behavior change counseling intervention is more effective than a targeted mailed print intervention in promoting risk appropriate screening in individuals with a family history of the disease. The project targets people residing in both rural and urban areas, allowing an examination of differential intervention effects with regard to place of residence.
Project description:Coregulator proteins (CoRegs) are part of multi-protein complexes that transiently assemble with transcription factors and chromatin modifiers to regulate gene expression. In this study we analyzed data from 3,290 immuno-precipitations (IP) followed by mass spectrometry (MS) applied to human cell lines aimed at identifying CoRegs complexes. Using the semi-quantitative spectral counts, we scored binary protein-protein and domain-domain associations with several equations. Unlike previous applications, our methods scored prey-prey protein-protein interactions regardless of the baits used. We also predicted domain-domain interactions underlying predicted protein-protein interactions. The quality of predicted protein-protein and domain-domain interactions was evaluated using known binary interactions from the literature, whereas one protein-protein interaction, between STRN and CTTNBP2NL, was validated experimentally; and one domain-domain interaction, between the HEAT domain of PPP2R1A and the Pkinase domain of STK25, was validated using molecular docking simulations. The scoring schemes presented here recovered known, and predicted many new, complexes, protein-protein, and domain-domain interactions. The networks that resulted from the predictions are provided as a web-based interactive application at http://maayanlab.net/HT-IP-MS-2-PPI-DDI/.
Project description:Our project focuses on the effects of an in utero TCDD exposure on the male reproductive function. Keywords: transcriptionnal analysis
Project description:Our project focuses on the effects of an in utero TCDD exposure on the male reproductive function. Keywords: transcriptionnal analysis
Project description:LC-MS/MS-based proteomics studies rely on stable analytical system performance that can be evaluated by objective criteria. The National Institute of Standards and Technology (NIST) introduced the MSQC software to compute diverse metrics from experimental LC-MS/MS data, enabling quality analysis and quality control (QA/QC) of proteomics instrumentation. In practice, however, several attributes of the MSQC software prevent its use for routine instrument monitoring. Here, we present QuaMeter, an open-source tool that improves MSQC in several aspects. QuaMeter can directly read raw data from instruments manufactured by different vendors. The software can work with a wide variety of peptide identification software for improved reliability and flexibility. Finally, QC metrics implemented in QuaMeter are rigorously defined and tested. The source code and binary versions of QuaMeter are available under Apache 2.0 License at http://fenchurch.mc.vanderbilt.edu.
Project description:Immunoprecipitation is among the most widely utilized methods in biomedical research, with applications that include the identification of antibody targets and associated proteins. The path to identifying these targets is not straightforward, however, and often requires the use of chemical cross-linking and/or gel electrophoresis to separate targets from an overabundance of immunoglobulin protein. Such experiments are labor intensive and often yield long lists of candidate antibody targets. Here, we describe an unbiased immunoprecipitation-to-mass spectrometry (IP-to-MS) method that relies on a novel protein tag to separate low abundance immunoprecipitated proteins from overwhelmingly abundant immunoglobulins. We demonstrate that the IP-to-MS serotyping workflow is highly reproducible and can be used for the identification of novel, patient-specific antigen targets in multiple disease states. Furthermore, we show that IP-to-MS may outperform conventional methods of antibody detection, including enzyme-linked immunosorbent assay, while also enabling patient stratification beyond what is possible with traditional approaches.
Project description:Highly specialized cells are fundamental for proper functioning of complex organs. Variations in cell-type specific gene expression and protein composition have been linked to a variety of diseases. Although single cell technologies have emerged as valuable tools to address this cellular heterogeneity, a majority of these workflows lack sufficient in situ resolution for functional classification of cells and are associated with extremely long analysis time, especially when it comes to in situ proteomics. In addition, lack of understanding of single cell dynamics within their native environment limits our ability to explore the altered physiology in disease development. This limitation is particularly relevant in the mammalian brain, where different cell types perform unique functions and exhibit varying sensitivities to insults. The hippocampus, a brain region crucial for learning and memory, is of particular interest due to its obvious involvement in various neurological disorders. Here, we present a combination of experimental and data integration approaches for investigation of cellular heterogeneity and functional disposition within the mouse brain hippocampus using MALDI Imaging mass spectrometry (MALDI-IMS) and shotgun proteomics (LC-MS/MS) coupled with laser-capture microdissection (LCM) along with spatial transcriptomics. Within the dentate gyrus granule cells we identified two proteomically distinct cellular subpopulations that are characterized by a substantial number of discriminative proteins. These cellular clusters contribute to the overall functionality of the dentate gyrus by regulating redox homeostasis, mitochondrial organization, RNA processing, and microtubule organization. Importantly, most of the identified proteins matched their transcripts, verifying the in situ protein identification and supporting their functional analyses. By combining high-throughput spatial proteomics with transcriptomics, our approach enables reliable near-single-cell scale identification of proteins and profiling of inter-cellular heterogeneity within similar cell-types in tissues. This methodology has the potential to be applied to different biological conditions and tissues, providing a deeper understanding of cellular subpopulations in situ.
Project description:Fatal COVID-19 is often complicated by hypoxemic respiratory failure and acute respiratory distress syndrome (ARDS). Mechanisms governing lung injury and repair in ARDS remain poorly understood because there are no biomarker-targeted therapeutics for patients with ARDS. We hypothesized that plasma proteomics may uncover unique biomarkers that correlate with disease severity in COVID-19 ARDS. We analyzed the circulating plasma proteome from 32 patients with ARDS and COVID-19 using an aptamer-based platform, which measures 7289 proteins, and correlated protein measurements with sequential organ failure assessment (SOFA) scores at 2 time points (Days 1 and 7 following ICU admission). We compared differential protein abundance and SOFA scores at each individual time point and identified 119 proteins at Day 1 and 46 proteins at Day 7 that correlated with patient SOFA scores. We modeled the relationship between dynamic protein abundance and changes in SOFA score between Days 1 and 7 and identified 39 proteins that significantly correlated with changes in SOFA score. Using Ingenuity Pathway Analysis, we identified increased ephrin signaling and acute phase response signaling correlated with increased SOFA scores over time, while pathways related to pulmonary fibrosis signaling and wound healing had an inverse relationship with SOFA scores between Days 1 and 7. These findings suggest that persistent inflammation may drive worsened disease severity, while repair processes correlate with improvements in organ dysfunction over time. This approach is generalizable to more diverse ARDS cohorts for identification of protein biomarkers and disease mechanisms as we strive towards targeted therapies in ARDS.