Project description:LIMD1 is a tumour suppressor commonly found to have loss-of-function mutations in lung adenocarcinoma patients. Here we explore potential vulnerabilities of a lung cancer cell line specific to LIMD1 loss.
Project description:This project aims to compare gRNA design algorithms using a benchmark CRISPR-knockout library composed of gRNAs targeting essential and non-essential genes.
Project description:This project also aims to compare gRNA design algorithms using a benchmark CRISPR-knockout library composed of gRNAs targeting essential and non-essential genes.
Project description:This project aims to compare gRNA design algorithms and single- versus dual-targeting using a benchmark CRISPR-knockout library composed of gRNAs targeting essential and non-essential genes.
Project description:A genome-wide CRISPR-Cas9 knockout screen was performed in the breast cancer cell lines T47D, MCF7, and CAMA-1 to identify genes modulating sensitivity and resistance to capivasertib, a selective AKT inhibitor. Cells were transduced with a CRISPR library, followed by treatment with capivasertib or vehicle control (DMSO). Guide RNA (sgRNA) enrichment and depletion were assessed via next-generation sequencing to determine gene-level effects on cell viability and drug response. Gene-level data are provided for the initial plasmid library, baseline, DMSO-treated controls, and post-capivasertib treatment across replicates.
Project description:The goal of the project is to identify deubiquitinases (DUBs) networks that could be targetable in cancer. To identify functionally interacting DUBs, we performed a pooled combinatorial knockout screen using a CRISPR/Cas12a system. Here we transduced a custom guide library targeting any 2-gene combination derived from 160 genes (including ~90 DUBs and other genes of interest) into Cas12a-expressing HCT116 cells. While the cells are subjected to puromycin selection, a reference sample (Day 4 post-transduction) was taken and sequenced. Two replicates were derived from this pool that were expanded and sequenced at Day 11 and Day 18 post-transduction.
Project description:Objective: This study examined the ability of established brain biomarkers, glial fibrillary acid protein (GFAP), neuro-filament light chain (NFL), ubiquitin carboxy hydrolase-L1(UCH-L1), tau, and phosphorylated tau (p-tau), and novel biomark- ers from blood collected sub-acutely after concussion to determine prediction of persisting post-concussion symptoms (PPCS) beyond 3 months. We hypoth- esized that a combination of established and novel proteins would predict high PPCS burden. Participants: Adolescents 11 to 17.99 years with a concussion based on Concussion In Sport Group (CISG) criteria were eligible. Participants were assessed 7–35 days post-injury (baseline) and then reassessed at 3 months (follow-up) for persistent post-concussion symptoms (PPCS) using the Post- Concussion Symptom Inventory, 2nd Edition (PCSI-2). A total of 155 participants (78 females, 77 males) with both blood biomarkers and 3-month symptom data were analyzed. Design: Plasma proteins collected sub-acutely (7–35 days post concussion) were quantified by both Quanterix and the Olink Explore platform, and compared between participants with the highest and lowest quartiles of PPCS severity at follow-up (85–95 days post concussion) using the PCSI-2. An exhaustive best- subsets logistic regression strategy was executed following clinical and biologi- cal pre-filtering to identify parsimonious multivariable configurations of protein biomarkers distinguishing individuals with high and low PPCS at follow-up. A stratified 10-fold cross-validation framework was implemented to evaluate model generalizability and safeguard against overfitting, while bootstrapping was used to calculate confidence intervals. Ingenuity Pathway Analysis (IPA) was performed to generate hypotheses of molecular pathways implicated in PPCS pathogenesis. Results: None of the brain biomarkers collected sub-acutley were signifi- cantly different in PPCS-high and PPCS-low groups at 90-day follow-up. No Olink proteins survived multiple testing correction, but a cross-validated mul- tivariable model, including Tripartite Motif Containing 39 (TRIM39) + Sclerostin (SOST) + Transcription factor Dp family member 3 (TFDP3) + TNF receptor superfamily member 9 (TNFRSF9) + Leptin (LEP) + Prune Homolog 2 With BCH Domain (PRUNE2) + Trimethylguanosine Synthase 1 (TGS1) + EPCAM (Epithelial Cell Adhesion Molecule) distinguished high PPCS at follow-up with an area under the curve (AUC) of 0.86 (95% CI 0.80–0.92). IPA identified three significant net- works associated with PPCS: (a) cardiovascular and neurological disease, organ- ismal injury, and abnormalities (score = 45; 28 focus molecules); (b) connective tissue disorders, inflammatory disease, and organismal injury and abnormalities (score = 41; 26 focus molecules); and (c) connective tissue development and function, embryonic development, and organismal development (score = 41; 26 focus molecules). Conclusion: This study suggests the possibility to utilize novel biomarkers discov- ered by high throughput proteomic analysis to predict high PPCS. Future research should further develop precision of unique biomarker profiles and prolonged symptomatology in adolescents’ post-concussion.
Project description:Introduction: Post-traumatic stress disorder (PTSD) is highly prevalent among U.S. service members and veterans (SMV) and has lasting impacts on health and well-being. However, the biological underpinnings of PTSD remain poorly characterized. This study aimed to discover novel candidate proteins and protein pathways associated with PTSD using unbiased, high-throughput proteomics profiling. Methods: A cross-sectional study was conducted using a subset of SMV participants from a clinical cohort undergoing evaluations at the National Intrepid Center of Excellence, Walter Reed National Military Medical Center, who consented to a research blood draw and use of their clinical data in research. The cohort with available blood samples was classified into PTSD-Present and PTSD-Absent groups based on the PTSD Checklist- Civilian Version. Olink high-throughput proteomic profiling examined 5400 human plasma proteins, and differentially expressed proteins were examined. Ingenuity pathway analysis was used to identify proteomic pathways among the significant differentially expressed proteins between the PTSD-Present and PTSD-Absent groups. Results: We included 208 samples in our analysis, 126 with and 82 without PTSD. We identified 366 proteins that were significantly differentially expressed between groups, with the 3 most significant being LMOD2 (Leiomodin- 2), ATP5F1D (ATP synthase δ-subunit), and CASKIN1 (CASK-interacting protein). Extracellular matrix organization pathways and Vascular Endothelial Growth Factor (VEGF) signaling were downregulated in PTSD, suggesting possible vascular inflammation and remodeling. Data- driven protein networks suggest reduced immune cell activation. Discussion: Determining differential protein expression and identifying the associated protein pathways linked to PTSD may provide new insights into the biological basis of chronic PTSD symptoms and help identify novel candidate protein biomarkers of PTSD and PTSD symptoms for validation in separate and larger cohorts. From this discovery cohort, we report that elevated PTSD symptoms may be associated with downregulation of extracellular matrix organization, inflammation signaling, and vascular remodeling pathways. Future research is necessary to validate this novel group of biomarkers and pathways.
Project description:Elucidating the role of gut microbiota in physiological and pathological processes has recently emerged as a key research aim in life sciences. In this respect, metaproteomics (the study of the whole protein complement of a microbial community) can provide a unique contribution by revealing which functions are actually being expressed by specific microbial taxa. However, its wide application to gut microbiota research has been hindered by challenges in data analysis, especially related to the choice of the proper sequence databases for protein identification. Here we present a systematic investigation of variables concerning database construction and annotation, and evaluate their impact on human and mouse gut metaproteomic results. We found that both publicly available and experimental metagenomic databases lead to the identification of unique peptide assortments, suggesting parallel database searches as a mean to gain more complete information. Taxonomic and functional results were revealed to be strongly database-dependent, especially when dealing with mouse samples. As a striking example, in mouse the Firmicutes/Bacteroidetes ratio varied up to 10-fold depending on the database used. Finally, we provide recommendations regarding metagenomic sequence processing aimed at maximizing gut metaproteome characterization, and contribute to identify an optimized pipeline for metaproteomic data analysis.