Snazarus and its human ortholog SNX25 modulate autophagic flux

Macroautophagy, the degradation and recycling of cytosolic components in the lysosome, is an important cellular mechanism. It is a membrane-mediated process that is linked to vesicular trafficking events. The sorting nexin (SNX) protein family controls the sorting of a large array of cargoes, and various SNXs impact autophagy. To improve our understanding of their functions in vivo, we screened all Drosophila SNXs using inducible RNA interference in the fat body. Significantly, depletion of snazarus (snz) led to decreased autophagic flux. Interestingly, we observed altered distribution of Vamp7-positive vesicles with snz depletion, and snz's roles were conserved in human cells. SNX25, the closest human ortholog to snz, regulates both VAMP8 endocytosis and lipid metabolism. Through knockout-rescue experiments, we demonstrate that these activities are dependent on specific SNX25 domains and that the autophagic defects upon SNX25 loss can be rescued by ethanolamine addition. We also demonstrate the presence of differentially spliced forms of SNX14 and SNX25 in cancer cells. This work identifies a conserved role for snz/SNX25 as regulators of autophagic flux and reveals differential isoform expression between paralogs.

Combining Multiple RNA-Seq Data Analysis Algorithms Using Machine Learning Improves Differential Isoform Expression Analysis

RNA sequencing has become the standard technique for high resolution genome-wide monitoring of gene expression. As such, it often comprises the first step towards understanding complex molecular mechanisms driving various phenotypes, spanning organ development to disease genesis, monitoring and progression. An advantage of RNA sequencing is its ability to capture complex transcriptomic events such as alternative splicing which results in alternate isoform abundance. At the same time, this advantage remains algorithmically and computationally challenging, especially with the emergence of even higher resolution technologies such as single-cell RNA sequencing. Although several algorithms have been proposed for the effective detection of differential isoform expression from RNA-Seq data, no widely accepted golden standards have been established. This fact is further compounded by the significant differences in the output of different algorithms when applied on the same data. In addition, many of the proposed algorithms remain scarce and poorly maintained. Driven by these challenges, we developed a novel integrative approach that effectively combines the most widely used algorithms for differential transcript and isoform analysis using state-of-the-art machine learning techniques. We demonstrate its usability by applying it on simulated data based on several organisms, and using several performance metrics; we conclude that our strategy outperforms the application of the individual algorithms. Finally, our approach is implemented as an R Shiny application, with the underlying data analysis pipelines also available as docker containers.

Mapping and modeling the genomic basis of differential RNA isoform expression at single-cell resolution with LR-Split-seq

Alternative RNA isoforms are defined by promoter choice, alternative splicing, and polyA site selection. Although differential isoform expression is known to play a large regulatory role in eukaryotes, it has proved challenging to study with standard short-read RNA-seq because of the uncertainties it leaves about the full-length structure and precise termini of transcripts. The rise in throughput and quality of long-read sequencing now makes it possible, in principle, to unambiguously identify most transcript isoforms from beginning to end. However, its application to single-cell RNA-seq has been limited by throughput and expense. Here, we develop and characterize long-read Split-seq (LR-Split-seq), which uses a combinatorial barcoding-based method for sequencing single cells and nuclei with long reads. We show that LR-Split-seq can associate isoforms with cell types with relative economy and design flexibility. We characterize LR-Split-seq for whole cells and nuclei by using the well-studied mouse C2C12 system in which mononucleated myoblast cells differentiate and fuse into multinucleated myotubes. We show that the overall results are reproducible when comparing long- and short-read data from the same cell or nucleus. We find substantial evidence of differential isoform expression during differentiation including alternative transcription start site (TSS) usage. We integrate the resulting isoform expression dynamics with snATAC-seq chromatin accessibility to validate TSS-driven isoform choices. LR-Split-seq provides an affordable method for identifying cluster-specific isoforms in single cells that can be further quantified with companion deep short-read scRNA-seq from the same cell populations.

Snazarus and its human ortholog SNX25 regulate autophagic flux by affecting VAMP8 endocytosis

Autophagy, the degradation and recycling of cytosolic components in the lysosome, is an essential cellular mechanism. It is a membrane-mediated process that is linked to vesicular trafficking events. The sorting nexin (SNX) protein family controls the sorting of a large array of cargoes, and various SNXs can impact autophagy. To gain a better understanding of their functions in vivo under nutrient starvation, we screened all Drosophila SNXs by RNAi in the fat body. Significantly, depletion of snazarus (snz) strongly impacted autolysosome formation and led to decreased autophagic flux. Interestingly, we observed altered distribution of Vamp7-positive vesicles with snz depletion and snz roles were conserved in human cells. SNX25 is the closest ortholog to snz, and we demonstrate a role for it in VAMP8 trafficking. We found that this activity was dependent on the SNX25 PX domain, and independent of SNX25 anchoring at the ER. We also demonstrate that differentially spliced forms of SNX14 and SNX25 are present in cancer cells. This work identifies a conserved role for snz/SNX25 as regulators of autophagic flux, and show differential isoform expression between orthologs.

A benchmarking of workflows for detecting differential splicing and differential expression at isoform level in human RNA-seq studies

ABSTRACTOver the last few years, RNA-seq has been used to study alterations in alternative splicing related to several diseases. Bioinformatics workflows used to perform these studies can be divided into two groups, those finding changes in the absolute isoform expression and those studying differential splicing. Many computational methods for transcriptomics analysis have been developed, evaluated and compared; however, there are not enough reports of systematic and objective assessment of processing pipelines as a whole. Moreover, comparative studies have been performed considering separately the changes in absolute or relative isoform expression levels. Consequently, no consensus exists about the best practices and appropriate workflows to analyse alternative and differential splicing. To assist the adequate pipeline choice, we present here a benchmarking of nine commonly used workflows to detect differential isoform expression and splicing. We evaluated the workflows performance over three different experimental scenarios where changes in absolute and relative isoform expression occurred simultaneously. In addition, the effect of the number of isoforms per gene, and the magnitude of the expression change over pipeline performances were also evaluated. Our results suggest that workflow performance is influenced by the number of replicates per condition and the conditions heterogeneity. In general, workflows based on DESeq, DEXSeq, Limma and NOISeq performed well over a wide range of transcriptomics experiments. In particular, we suggest the use of workflows based on Limma when high precision is required, and DESeq2 and DEXseq pipelines to prioritize sensitivity. When several replicates per condition are available, NOISeq and Limma pipelines are indicated.

Identification and visualisation of differential isoform expression in RNA-seq time series

As sequencing technologies improve their capacity to detect distinct transcripts of the same gene and to address complex experimental designs such as longitudinal studies, there is a need to develop statistical methods for the analysis of isoform expression changes in time series data. Iso-maSigPro is a new functionality of the R package maSigPro for transcriptomics time series data analysis. Iso-maSigPro identifies genes with a differential isoform usage across time. The package also includes new clustering and visualization functions that allow grouping of genes with similar expression patterns at the isoform level, as well as those genes with a shift in major expressed isoform. The package is freely available under the LGPL license from the Bioconductor web site (http://bioconductor.org).