No abstract

Endemic Burkitt lymphoma (eBL) is the most common pediatric cancer in malaria-endemic equatorial Africa and nearly always contains Epstein-Barr virus (EBV), unlike sporadic Burkitt Lymphoma (sBL) that occurs with a lower incidence in developed countries. Given these differences and the variable clinical presentation and outcomes, we sought to further understand pathogenesis by investigating transcriptomes using RNA sequencing (RNAseq) from multiple primary eBL tumors compared to sBL tumors. Within eBL tumors, minimal expression differences were found based on: anatomical presentation site, in-hospital survival rates, and EBV genome type; suggesting that eBL tumors are homogeneous without marked subtypes. The outstanding difference detected using surrogate variable analysis was the significantly decreased expression of key genes in the immunoproteasome complex (PSMB9/β1i, PSMB10/β2i, PSMB8/β5i, and PSME2/PA28β) in eBL tumors carrying type 2 EBV compared to type 1 EBV. Secondly, in comparison to previously published pediatric sBL specimens, the majority of the expression and pathway differences was related to the PTEN/PI3K/mTOR signaling pathway and was correlated most strongly with EBV status rather than geographic designation. Third, common mutations were observed significantly less frequently in eBL tumors harboring EBV type 1, with mutation frequencies similar between tumors with EBV type 2 and without EBV. In addition to the previously reported genes, a set of new genes mutated in BL including TFAP4, MSH6, PRRC2C, BCL7A, FOXO1, PLCG2, PRKDC, RAD50, and RPRD2 were identified. Overall, these data establish that EBV, particularly EBV type 1, supports BL oncogenesis alleviating the need for certain driver mutations in the human genome.

Maternal symptoms of preeclampsia (PE) are primarily driven by excess anti-angiogenic factors originating from the placenta. Chief among these are soluble Flt1 proteins (sFlt1s) produced from alternatively polyadenylated mRNA isoforms. Here we used polyadenylation site sequencing (PAS-Seq) of RNA from normal and PE human placentae to interrogate transcriptome-wide gene expression and alternative polyadenylation signatures associated with early-onset PE (EO-PE; symptom onset < 34 weeks) and late-onset PE (LO-PE; symptom onset > 34 weeks) cohorts. While we observed no general shift in alternative polyadenylation associated with PE, the EO-PE and LO-PE cohorts do exhibit gene expression profiles distinct from both each other and from normal placentae. The only two genes upregulated across all transcriptome-wide PE analyses to date (microarray, RNA-Seq and PAS-Seq) are NRIP1 (RIP140), a transcriptional co-regulator linked to metabolic syndromes associated with obesity, and Flt1. Consistent with sFlt1 overproduction being a significant driver of clinical symptoms, placental Flt1 mRNA levels strongly correlate with maternal blood pressure. For Flt1, just three mRNA isoforms account for > 94% of all transcripts, with increased transcription of the entire locus driving Flt1 upregulation in both EO-PE and LO-PE. These three isoforms thus represent potential targets for therapeutic RNA interference (RNAi) in both early and late presentations.

Motivation One major goal of single-cell RNA sequencing (scRNAseq) experiments is to identify novel cell types. With increasingly large scRNAseq datasets, unsupervised clustering methods can now produce detailed catalogues of transcriptionally distinct groups of cells in a sample. However, the interpretation of these clusters is challenging for both technical and biological reasons. Popular clustering algorithms are sensitive to parameter choices, and can produce different clustering solutions with even small changes in the number of principal components used, the k nearest neighbor, and the resolution parameters, among others. Results Here, we present a set of tools to evaluate cluster stability by subsampling, which can guide parameter choice and aid in biological interpretation. The R package scclusteval and the accompanying Snakemake workflow implement all steps of the pipeline: subsampling the cells, repeating the clustering with Seurat, and estimation of cluster stability using the Jaccard similarity index and providing rich visualizations. Availability R package scclusteval: https://github.com/crazyhottommy/scclusteval Snakemake workflow: https://github.com/crazyhottommy/pyflow_seuratv3_parameter Tutorial: https://crazyhottommy.github.io/EvaluateSingleCellClustering/ Supplementary information Supplementary data are available at Bioinformatics online.