Comprehensive and scalable quantification of splicing differences with MntJULiP

5-ethynyluridine perturbs nuclear RNA metabolism to promote the nuclear accumulation of TDP-43 and other RNA binding proteins

TDP-43, an essential nucleic acid binding protein and splicing regulator, is broadly disrupted in neurodegeneration. TDP-43 nuclear localization and function depend on the abundance of its nuclear RNA targets and its recruitment into large ribonucleoprotein complexes, which restricts TDP-43 nuclear efflux. To further investigate the interplay between TDP-43 and nascent RNAs, we aimed to employ 5-ethynyluridine (5EU), a widely used uridine analog for 'click chemistry' labeling of newly transcribed RNAs. Surprisingly, 5EU induced the nuclear accumulation of TDP-43 and other RNA-binding proteins and attenuated TDP-43 mislocalization caused by disruption of the nuclear transport apparatus. RNA FISH demonstrated 5EU-induced nuclear accumulation of polyadenylated and GU-repeat-rich RNAs, suggesting increased retention of both processed and intronic RNAs. TDP-43 eCLIP confirmed that 5EU preserved TDP-43 binding at predominantly GU-rich intronic sites. RNAseq revealed significant 5EU-induced changes in alternative splicing, accompanied by an overall reduction in splicing diversity, without any major changes in RNA stability or TDP-43 splicing regulatory function. These data suggest that 5EU may impede RNA splicing efficiency and subsequent nuclear RNA processing and export. Our findings have important implications for studies utilizing 5EU and offer unexpected confirmation that the accumulation of endogenous nuclear RNAs promotes TDP-43 nuclear localization.

Multimodal genome-wide survey of progressing and non-progressing breast ductal carcinoma in-situ

BACKGROUND

Ductal carcinoma in-situ (DCIS) is a pre-invasive form of invasive breast cancer (IBC). Due to improved breast cancer screening, it now accounts for ~ 25% of all breast cancers. While the treatment success rates are over 90%, this comes at the cost of considerable morbidity, considering that the majority of DCIS never become invasive and our understanding of the molecular changes occurring in DCIS that predispose to invasive disease is limited. The aim of this study is to characterize molecular changes that occur in DCIS, with the goal of improving DCIS risk stratification.

METHODS

We identified and obtained a total of 197 breast tissue samples from 5 institutions (93 DCIS progressors, 93 DCIS non-progressors, and 11 adjacent normal breast tissues) that had at least 10-year follow-up. We isolated DNA and RNA from archival tissue blocks and characterized genome-wide mRNA expression, DNA methylation, DNA copy number variation, and RNA splicing variation.

RESULTS

We obtained all four genomic data sets in 122 of the 197 samples. Our intrinsic expression subtype-stratified analyses identified multiple molecular differences both between DCIS subtypes and between DCIS and IBC. While there was heterogeneity in molecular signatures and outcomes within intrinsic subtypes, several gene sets that differed significantly between progressing and non-progressing DCIS were identified by Gene Set Enrichment Analysis.

CONCLUSION

DCIS is a molecularly highly heterogenous disease with variable outcomes, and the molecular events determining DCIS disease progression remain poorly defined. Our genome-wide multi-omic survey documents DCIS-associated alterations and reveals molecular heterogeneity within the intrinsic DCIS subtypes. Further studies investigating intrinsic subtype-stratified characteristics and molecular signatures are needed to determine if these may be exploitable for risk assessment and mitigation of DCIS progression. The highly significant associations of specific gene sets with IBC progression revealed by our Gene Set Enrichment Analysis may lend themselves to the development of a prognostic molecular score, to be validated on independent DCIS cohorts.

MntJULiP and Jutils: Differential splicing analysis of RNA-seq data with covariates

Differences in alternative splicing patterns can reveal important markers of phenotypic differentiation, including biomarkers of disease. Emerging large and complex RNA-seq datasets from disease and population studies include multiple confounders such as sex, age, ethnicity and clinical attributes, which demand highly specialized data analysis tools. However, few methods are equipped to handle the new challenges. We describe an implementation of our programs MntJULiP and Jutils for differential splicing detection and visualization from RNA-seq data that takes into account covariates. MntJULiP detects intron-level differences in alternative splicing from RNA-seq data using a Bayesian mixture model. Jutils visualizes alternative splicing variation with heatmaps, PCA and sashimi plots, and Venn diagrams. Our tools are scalable and can process thousands of samples within hours. We applied our methods to the collection of GTEx brain RNA-seq samples to deconvolute the effects of sex and age at death on the splicing patterns. In particular, clustering of covariate adjusted data identifies a subgroup of individuals undergoing a distinct splicing program during aging. MntJULiP and Jutils are implemented in Python and are available from https://github.com/splicebox/.