SUsPECT: a pipeline for variant effect prediction based on custom long-read transcriptomes for improved clinical variant annotation.

Our incomplete knowledge of the human transcriptome impairs the detection of disease-causing variants, in particular if they affect transcripts only expressed under certain conditions. These transcripts are often lacking from reference transcript sets, such as Ensembl/GENCODE and RefSeq, and could be relevant for establishing genetic diagnoses. We present SUsPECT (Solving Unsolved Patient Exomes/gEnomes using Custom Transcriptomes), a pipeline based on the Ensembl Variant Effect Predictor (VEP) to predict variant impact on custom transcript sets, such as those generated by long-read RNA-sequencing, for downstream prioritization. Our pipeline predicts the functional consequence and likely deleteriousness scores for missense variants in the context of novel open reading frames predicted from any transcriptome. We demonstrate the utility of SUsPECT by uncovering potential mutational mechanisms of pathogenic variants in ClinVar that are not predicted to be pathogenic using the reference transcript annotation. In further support of SUsPECT's utility, we identified an enrichment of immune-related variants predicted to have a more severe molecular consequence when annotating with a newly generated transcriptome from stimulated immune cells instead of the reference transcriptome. Our pipeline outputs crucial information for further prioritization of potentially disease-causing variants for any disease and will become increasingly useful as more long-read RNA sequencing datasets become available.

Personalized Proteome: Comparing Proteogenomics and Open Variant Search Approaches for Single Amino Acid Variant Detection.

Discovery of variant peptides such as a single amino acid variant (SAAV) in shotgun proteomics data is essential for personalized proteomics. Both the resolution of shotgun proteomics methods and the search engines have improved dramatically, allowing for confident identification of SAAV peptides. However, it is not yet known if these methods are truly successful in accurately identifying SAAV peptides without prior genomic information in the search database. We studied this in unprecedented detail by exploiting publicly available long-read RNA sequences and shotgun proteomics data from the gold standard reference cell line NA12878. Searching spectra from this cell line with the state-of-the-art open modification search engine ionbot against carefully curated search databases resulted in 96.7% false-positive SAAVs and an 85% lower true positive rate than searching with peptide search databases that incorporate prior genetic information. While adding genetic variants to the search database remains indispensable for correct peptide identification, inclusion of long-read RNA sequences in the search database contributes only 0.3% new peptide identifications. These findings reveal the differences in SAAV detection that result from various approaches, providing guidance to researchers studying SAAV peptides and developers of peptide spectrum identification tools.

Imaging the Different Mechanisms of Prostate Cancer Cell-killing by Tumor-targeting Salmonella typhimurium A1-R.

BACKGROUND/AIMS: We previously developed a genetically-modified bacterial strain of Salmonella typhimurium, auxotrophic for leucine and arginine, which also expresses green fluorescent protein (GFP), termed S. typhimurium A1-R. S. typhimurium A1-R was found to be effective against metastatic human prostate, breast, pancreatic, cervical and ovarian cancer, as well as osteosarcoma, fibrosarcoma and glioma, in clinically relevant nude mouse models. MATERIALS AND METHODS: To understand the tumor cell-killing mechanism of S. typhimurium A1-R-GFP, we studied the interaction of S. typhimurium A1-R-GFP with three different prostate cancer cell lines in vitro. S. typhimurium-GFP invasion, proliferation, and means of killing in three different human prostate cancer cell lines were visualized by confocal fluorescence microscopy with the Olympus FV1000. RESULTS: We found that S. typhimurium A1-R-induced cancer-cell death had different mechanisms in different prostate cancer cell lines, occurring through apoptosis and necrosis in the PC-3 prostate cancer cell line, and by cell bursting in the LNCaP and DU-145 prostate cancer cell lines. The time required for S. typhimurium A1-R-GFP to kill the majority of cancer cells varied from line to line, ranging from 2 hours to 48 hours. CONCLUSION: Understanding the various mechanisms of cancer-cell killing by S. typhimurium A1-R will be important for its use as a general therapeutic for cancer.