Targeting p130Cas- and microtubule-dependent MYC regulation sensitizes pancreatic cancer to ERK MAPK inhibition.

To identify therapeutic targets for KRAS mutant pancreatic cancer, we conduct a druggable genome small interfering RNA (siRNA) screen and determine that suppression of BCAR1 sensitizes pancreatic cancer cells to ERK inhibition. Integrative analysis of genome-scale CRISPR-Cas9 screens also identify BCAR1 as a top synthetic lethal interactor with mutant KRAS. BCAR1 encodes the SRC substrate p130Cas.

Dexamethasone inhibits respiratory syncytial virus-driven mucus production while increasing viral replication without altering antiviral interferon signaling.

Respiratory syncytial virus (RSV) infection can cause mucus overproduction and bronchiolitis in infants leading to severe disease and hospitalization. As a therapeutic strategy, immune modulatory agents may help prevent RSV-driven immune responses that cause severe airway disease. We developed a high throughput screen to identify compounds that reduced RSV-driven mucin 5AC (Muc5AC) expression and identified dexamethasone.

Metascape provides a biologist-oriented resource for the analysis of systems-level datasets.

A critical component in the interpretation of systems-level studies is the inference of enriched biological pathways and protein complexes contained within OMICs datasets. Successful analysis requires the integration of a broad set of current biological databases and the application of a robust analytical pipeline to produce readily interpretable results. Metascape is a web-based portal designed to provide a comprehensive gene list annotation and analysis resource for experimental biologists.

Identification of a Xist silencing domain by Tiling CRISPR.

Despite essential roles played by long noncoding RNAs (lncRNAs) in development and disease, methods to determine lncRNA cis-elements are lacking. Here, we developed a screening method named "Tiling CRISPR" to identify lncRNA functional domains. Using this approach, we identified Xist A-Repeats as the silencing domain, an observation in agreement with published work, suggesting Tiling CRISPR feasibility.

Mapping the malaria parasite druggable genome by using in vitro evolution and chemogenomics.

Chemogenetic characterization through in vitro evolution combined with whole-genome analysis can identify antimalarial drug targets and drug-resistance genes. We performed a genome analysis of 262 parasites resistant to 37 diverse compounds. We found 159 gene amplifications and 148 nonsynonymous changes in 83 genes associated with drug-resistance acquisition, where gene amplifications contributed to one-third of resistance acquisition events.

A broad analysis of resistance development in the malaria parasite.

Microbial resistance to chemotherapy has caused countless deaths where malaria is endemic. Chemotherapy may fail either due to pre-existing resistance or evolution of drug-resistant parasites. Here we use a diverse set of antimalarial compounds to investigate the acquisition of drug resistance and the degree of cross-resistance against common resistance alleles.

High-Throughput Assay and Discovery of Small Molecules that Interrupt Malaria Transmission.

Preventing transmission is an important element of malaria control. However, most of the current available methods to assay for malaria transmission blocking are relatively low throughput and cannot be applied to large chemical libraries. We have developed a high-throughput and cost-effective assay, the Saponin-lysis Sexual Stage Assay (SaLSSA), for identifying small molecules with transmission-blocking capacity.

Phylogeny-based classification of microbial communities.

Motivation: Next-generation sequencing coupled with metagenomics has led to the rapid growth of sequence databases and enabled a new branch of microbiology called comparative metagenomics. Comparative metagenomic analysis studies compositional patterns within and between different environments providing a deep insight into the structure and function of complex microbial communities. It is a fast growing field that requires the development of novel supervised learning techniques for addressing challenges associated with metagenomic data, e.

Separating metagenomic short reads into genomes via clustering.

Background: The metagenomics approach allows the simultaneous sequencing of all genomes in an environmental sample. This results in high complexity datasets, where in addition to repeats and sequencing errors, the number of genomes and their abundance ratios are unknown. Recently developed next-generation sequencing (NGS) technologies significantly improve the sequencing efficiency and cost.