Unknown,Transcriptomics,Genomics,Proteomics

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Simulation-based assessment of differential transcript usage using RNA-seq data: a matter of counting

ABSTRACT: 'Background: Large-scale sequencing of cDNA (RNA-seq) has been a boon to the quantitative analysis of transcriptomes. A notable application of significant biomedical relevance is the detection of changes in transcript usage between experimental conditions. For example, discovery of pathological alternative splicing may allow the development of new treatments or better management of patients. From an analysis perspective, there are several ways to represent RNA-seq data to unravel differential transcript usage, such as annotation-based exon-level counting, differential analysis of the `percent spliced in'' measure or quantitative analysis of assembled transcripts. The goal of this research is to compare and contrast current state-of-the-art methods, as well as to suggest improvements to commonly used workflows. Results: We assess the performance of representative workflows using synthetic data, and explore the effect of using non-standard counting bin definitions as input to a state-of-the-art inference engine (DEXSeq). Although the canonical counting provided the best results overall, several non-canonical approaches were as good or better in specific aspects, and most counting approaches outperformed the evaluated event- and assembly-based methods. We show that an incomplete annotation catalog can have a detrimental effect on the ability to detect differential transcript usage in transcriptomes with few isoforms per gene, and that isoform-level pre-filtering can considerably improve the false discovery rate (FDR) control. Conclusion: Count-based methods generally perform well in detection of differential transcript usage. Controlling the FDR at the imposed threshold is difficult, mainly in complex organisms, but can be improved by pre-filtering of the annotation catalog.'

INSTRUMENT(S): unspecified

ORGANISM(S): synthetic construct

SUBMITTER: Mark Robinson

PROVIDER: E-MTAB-3766 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

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Project description:1.1 Introduction Cardiac fibrosis occurs in a wide range of cardiac diseases and is characterised by the transdifferentiation of cardiac fibroblasts into myofibroblasts these cells produce large quantities of extracellular matrix, resulting in myocardial scar. The profibrotic process is multi-factorial, meaning identification of effective treatments has been limited. The antifibrotic effect of the bile acid ursodeoxycholic acid (UDCA) is established in cases of liver fibrosis however its mechanism and role in cardiac fibrosis is less well understood. 1.2 Methods In this study, we used cellular models of cardiac fibrosis and living myocardial slices to characterise the macroscopic and cellular responses of the myocardium to UDCA treatment. We complemented this approach by conducting RNA-seq on cardiac fibroblasts isolated from dilated cardiomyopathy patients. This allowed us to gain insights into the mechanism of action and explore whether the IL-11 and TGFβ/ WWP2 profibrotic networks are influenced by UDCA. Finally, we used fibroblasts from a TGR5 KO mouse to confirm the mechanism of action. 1.3 Results and Discussion We found that UDCA reduced myofibroblast markers in rat and human fibroblasts and in living myocardial slices, indicating its antifibrotic action. Furthermore, we demonstrated that the treatment of UDCA successfully reversed the profibrotic IL-11 and TGFβ/ WWP2 gene networks. We also show that TGR5 is the most highly expressed UDCA receptor in cardiac fibroblasts. Utilising cells isolated from a TGR5 knock-out mouse, we identified that the antifibrotic effect of UDCA is attenuated in the KO fibroblasts. This study combines cellular studies with RNA-seq and state-of-the-art living myocardial slices to offer new perspectives on cardiac fibrosis. Our data confirm that TGR5 agonists, such as UDCA, offer a unique pathway of action for the treatment of cardiac fibrosis. Medicines for cardiac fibrosis have been slow to clinic and have the potential to be used in the treatment of multiple cardiac diseases. UDCA is well tolerated in the treatment of other diseases, indicating it is an excellent candidate for further in-human trials.

Project description:In the era of open-modification search engines, more post-translational modifications than ever can be detected by LC-MS/MS-based proteomics. This development can switch proteomics research into a higher gear, as PTMs are key in many cellular pathways important in cell proliferation, migration, metastasis and ageing. However, despite these advances in modification identification, statistical methods for PTM-level quantification and differential analysis have yet to catch up. This absence can partly be explained by the inherently low abundance of many PTMs and the confounding of PTM intensities with its parent protein abundance. Therefore, we have developed msqrob2PTM, a new workflow in the msqrob2 universe capable of differential abundance analysis at the PTM, and at the peptidoform level. The latter is important for validating significantly found PTMs. Indeed, as our method can deal with multiple PTMs per peptidoform, there is a possibility that significant PTMs stem from one significant peptidoform carrying another PTM, hinting that it might the other PTM driving the perceived differential abundance. Our workflows can flag both Differential Peptidoform (PTM) Abundance (DPA) and Differential Peptidoform (PTM) Usage (DPU). This enables a distinction between direct assessment of differential abundance of peptidoforms (DPA), and differences in the relative usage of peptidoforms corrected for corresponding protein abundances (DPU). For DPA, we directly model the log2-transformed peptidoform (PTM) intensities, while for DPU, we correct for parent protein abundance by an intermediate normalisation step which calculates the log2-ratio of the peptidoform (PTM) intensities to their summarized parent protein intensities. We demonstrated the utility and performance of msqrob2PTM by applying it on datasets with known ground truth, as well as on biological PTM-rich datasets. Our results show that msqrob2PTM is on par with, or surpassing the performance of, the current state-of-the-art method, MSstatsPTM. Moreover, msqrob2PTM is currently unique in providing output at the peptidoform level.

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Simulation-based assessment of differential transcript usage using RNA-seq data: a matter of counting

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