Fast and scalable querying of eukaryotic linear motifs with gget elm.

MOTIVATION: Eukaryotic linear motifs (ELMs), or Short Linear Motifs, are protein interaction modules that play an essential role in cellular processes and signaling networks and are often involved in diseases like cancer. The ELM database is a collection of manually curated motif knowledge from scientific papers. It has become a crucial resource for investigating motif biology and recognizing candidate ELMs in novel amino acid sequences. Users can search amino acid sequences or UniProt Accessions on the ELM resource web interface. However, as with many web services, there are limitations in the swift processing of large-scale queries through the ELM web interface or API calls, and, therefore, integration into protein function analysis pipelines is limited. RESULTS: To allow swift, large-scale motif analyses on protein sequences using ELMs curated in the ELM database, we have extended the gget suite of Python and command line tools with a new module, gget elm, which does not rely on the ELM server for efficiently finding candidate ELMs in user-submitted amino acid sequences and UniProt Accessions. gget elm increases accessibility to the information stored in the ELM database and allows scalable searches for motif-mediated interaction sites in the amino acid sequences. AVAILABILITY AND IMPLEMENTATION: The manual and source code are available at https://github.com/pachterlab/gget.

Efficient and accurate detection of viral sequences at single-cell resolution reveals putative novel viruses perturbing host gene expression.

There are an estimated 300,000 mammalian viruses from which infectious diseases in humans may arise. They inhabit human tissues such as the lungs, blood, and brain and often remain undetected. Efficient and accurate detection of viral infection is vital to understanding its impact on human health and to make accurate predictions to limit adverse effects, such as future epidemics. The increasing use of high-throughput sequencing methods in research, agriculture, and healthcare provides an opportunity for the cost-effective surveillance of viral diversity and investigation of virus-disease correlation. However, existing methods for identifying viruses in sequencing data rely on and are limited to reference genomes or cannot retain single-cell resolution through cell barcode tracking. We introduce a method that accurately and rapidly detects viral sequences in bulk and single-cell transcriptomics data based on highly conserved amino acid domains, which enables the detection of RNA viruses covering up to 1012 virus species. The analysis of viral presence and host gene expression in parallel at single-cell resolution allows for the characterization of host viromes and the identification of viral tropism and host responses. We applied our method to identify putative novel viruses in rhesus macaque PBMC data that display cell type specificity and whose presence correlates with altered host gene expression.

The impact of package selection and versioning on single-cell RNA-seq analysis.

Standard single-cell RNA-sequencing analysis (scRNA-seq) workflows consist of converting raw read data into cell-gene count matrices through sequence alignment, followed by analyses including filtering, highly variable gene selection, dimensionality reduction, clustering, and differential expression analysis. Seurat and Scanpy are the most widely-used packages implementing such workflows, and are generally thought to implement individual steps similarly. We investigate in detail the algorithms and methods underlying Seurat and Scanpy and find that there are, in fact, considerable differences in the outputs of Seurat and Scanpy. The extent of differences between the programs is approximately equivalent to the variability that would be introduced in benchmarking scRNA-seq datasets by sequencing less than 5% of the reads or analyzing less than 20% of the cell population. Additionally, distinct versions of Seurat and Scanpy can produce very different results, especially during parts of differential expression analysis. Our analysis highlights the need for users of scRNA-seq to carefully assess the tools on which they rely, and the importance of developers of scientific software to prioritize transparency, consistency, and reproducibility for their tools.

PSCA-CAR T cell therapy in metastatic castration-resistant prostate cancer: a phase 1 trial.

Despite recent therapeutic advances, metastatic castration-resistant prostate cancer (mCRPC) remains lethal. Chimeric antigen receptor (CAR) T cell therapies have demonstrated durable remissions in hematological malignancies. We report results from a phase 1, first-in-human study of prostate stem cell antigen (PSCA)-directed CAR T cells in men with mCRPC. The starting dose level (DL) was 100 million (M) CAR T cells without lymphodepletion (LD), followed by incorporation of LD. The primary end points were safety and dose-limiting toxicities (DLTs). No DLTs were observed at DL1, with a DLT of grade 3 cystitis encountered at DL2, resulting in addition of a new cohort using a reduced LD regimen + 100 M CAR T cells (DL3). No DLTs were observed in DL3. Cytokine release syndrome of grade 1 or 2 occurred in 5 of 14 treated patients. Prostate-specific antigen declines (>30%) occurred in 4 of 14 patients, as well as radiographic improvements. Dynamic changes indicating activation of peripheral blood endogenous and CAR T cell subsets, TCR repertoire diversity and changes in the tumor immune microenvironment were observed in a subset of patients. Limited persistence of CAR T cells was observed beyond 28 days post-infusion. These results support future clinical studies to optimize dosing and combination strategies to improve durable therapeutic outcomes. ClinicalTrials.gov identifier NCT03873805 .