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- Alternative splicing (2)
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Institute
Resistance to CD19-directed immunotherapies in lymphoblastic leukemia has been attributed, among other factors, to several aberrant CD19 pre-mRNA splicing events, including recently reported excision of a cryptic intron embedded within CD19 exon 2. While “exitrons” are known to exist in hundreds of human transcripts, we discovered, using reporter assays and direct long-read RNA sequencing (dRNA-seq), that the CD19 exitron is an artifact of reverse transcription. Extending our analysis to publicly available datasets, we identified dozens of questionable exitrons, dubbed “falsitrons,” that appear only in cDNA-seq, but never in dRNA-seq. Our results highlight the importance of dRNA-seq for transcript isoform validation.
Mutations causing aberrant splicing are frequently implicated in human diseases including cancer. Here, we establish a high-throughput screen of randomly mutated minigenes to decode the cis-regulatory landscape that determines alternative splicing of exon 11 in the proto-oncogene MST1R (RON). Mathematical modelling of splicing kinetics enables us to identify more than 1000 mutations affecting RON exon 11 skipping, which corresponds to the pathological isoform RON∆165. Importantly, the effects correlate with RON alternative splicing in cancer patients bearing the same mutations. Moreover, we highlight heterogeneous nuclear ribonucleoprotein H (HNRNPH) as a key regulator of RON splicing in healthy tissues and cancer. Using iCLIP and synergy analysis, we pinpoint the functionally most relevant HNRNPH binding sites and demonstrate how cooperative HNRNPH binding facilitates a splicing switch of RON exon 11. Our results thereby offer insights into splicing regulation and the impact of mutations on alternative splicing in cancer.
Non-coding variations located within regulatory elements may alter gene expression by modifying Transcription Factor (TF) binding sites and thereby lead to functional consequences like various traits or diseases. To understand these molecular mechanisms, different TF models are being used to assess the effect of DNA sequence variations, such as Single Nucleotide Polymorphisms (SNPs). However, few statistical approaches exist to compute statistical significance of results but they often are slow for large sets of SNPs, such as data obtained from a genome-wide association study (GWAS) or allele-specific analysis of chromatin data.
Results We investigate the distribution of maximal differential TF binding scores for general computational models that assess TF binding. We find that a modified Laplace distribution can adequately approximate the empirical distributions. A benchmark on in vitro and in vivo data sets showed that our new approach improves on an existing method in terms of performance and speed. In applications on large sets of eQTL and GWAS SNPs we could illustrate the usefulness of the novel statistic to highlight cell type specific regulators and TF target genes.
Conclusions Our approach allows the evaluation of DNA changes that induce differential TF binding in a fast and accurate manner, permitting computations on large mutation data sets. An implementation of the novel approach is freely available at https://github.com/SchulzLab/SNEEP.
Tolerizing CTL by sustained hepatic PD-L1 expression provides a new therapy spproach in mouse sepsis
(2019)
Cytotoxic T lymphocyte (CTL) activation contributes to liver damage during sepsis, but the mechanisms involved are largely unknown. Understanding the underlying principle will permit interference with CTL activation and thus, provide a new therapeutic option.
Methods: To elucidate the mechanism leading to CTL activation we used the Hepa1-6 cell line in vitro and the mouse model of in vivo polymicrobial sepsis, following cecal-ligation and -puncture (CLP) in wildtype, myeloid specific NOX-2, global NOX2 and NOX4 knockout mice, and their survival as a final readout. In this in vivo setting, we also determined hepatic mRNA and protein expression as well as clinical parameters of liver damage - aspartate- and alanine amino-transaminases. Hepatocyte specific overexpression of PD-L1 was achieved in vivo by adenoviral infection and transposon-based gene transfer using hydrodynamic injection.
Results: We observed downregulation of PD-L1 on hepatocytes in the murine sepsis model. Adenoviral and transposon-based gene transfer to restore PD-L1 expression, significantly improved survival and reduced the release of liver damage, as PD-L1 is a co-receptor that negatively regulates T cell function. Similar protection was observed during pharmacological intervention using recombinant PD-L1-Fc. N-acetylcysteine blocked the downregulation of PD-L1 suggesting the involvement of reactive oxygen species. This was confirmed in vivo, as we observed significant upregulation of PD-L1 expression in NOX4 knockout mice, following sham operation, whereas its expression in global as well as myeloid lineage NOX2 knockout mice was comparable to that in the wild type animals. PD-L1 expression remained high following CLP only in total NOX2 knockouts, resulting in significantly reduced release of liver damage markers.
Conclusion: These results suggest that, contrary to common assumption, maintaining PD-L1 expression on hepatocytes improves liver damage and survival of mice during sepsis. We conclude that administering recombinant PD-L1 or inhibiting NOX2 activity might offer a new therapeutic option in sepsis.
Highlights
• Single nucleotide variants (SNVs) may affect transcription factor (TF) binding
• Fast statistical approach to assess significance of differential TF binding for SNVs
• Validate new approach on in vitro and in vivo TF binding assays
• Applications on GWAS SNVs and large eQTL studies illustrate utility
Summary
Non-coding variants located within regulatory elements may alter gene expression by modifying transcription factor (TF) binding sites, thereby leading to functional consequences. Different TF models are being used to assess the effect of DNA sequence variants, such as single nucleotide variants (SNVs). Often existing methods are slow and do not assess statistical significance of results. We investigated the distribution of absolute maximal differential TF binding scores for general computational models that affect TF binding. We find that a modified Laplace distribution can adequately approximate the empirical distributions. A benchmark on in vitro and in vivo datasets showed that our approach improves upon an existing method in terms of performance and speed. Applications on eQTLs and on a genome-wide association study illustrate the usefulness of our statistics by highlighting cell type-specific regulators and target genes. An implementation of our approach is freely available on GitHub and as bioconda package.