Biology subjects

Sparks, R.

Publications and source records attributed to Sparks, R..

2 recordsLinked to original sources

bioRxiv2023

Conserved long noncoding RNA TILAM promotes liver fibrosis through interaction with PML in hepatic stellate cells

Background & AimsFibrosis is the common endpoint for all forms of chronic liver injury, and progression of fibrosis leads to the development of end-stage liver disease. Activation of hepatic stellate cells (HSCs) and their transdifferentiation to myofibroblasts results in the accumulation of extracellular matrix (ECM) proteins that form the fibrotic scar. Long noncoding (lnc) RNAs regulate the activity of HSCs and may provide targets for fibrotic therapies. MethodsWe identified lncRNA TILAM as expressed near COL1A1 in human HSCs and performed loss-of-function studies in human HSCs and liver organoids. Transcriptomic analyses of HSCs isolated from mice defined the murine ortholog of TILAM. We then generated Tilam-deficient GFP reporter mice and quantified fibrotic responses to carbon tetrachloride (CCl4) and choline-deficient L-amino acid defined high fat diet (CDA-HFD). Co-precipitation studies, mass spectrometry, and gene expression analyses identified protein partners of TILAM. ResultsTILAM is conserved between human and mouse HSCs and regulates expression of ECM proteins, including collagen. Tilam is selectively induced in HSCs during the development of fibrosis in vivo. In both male and female mice, loss of Tilam results in reduced fibrosis in the setting of CCl4 and CDA-HFD injury models. TILAM interacts with promyelocytic leukemia protein (PML) to stabilize PML protein levels and promote the fibrotic activity of HSCs. ConclusionTILAM is activated in HSCs and interacts with PML to drive the development of liver fibrosis. Depletion of TILAM may serve as a therapeutic approach to combat the development of end stage liver disease.

cell biology↗

bioRxiv2022

Singletrome: A method to analyze and enhance the transcriptome with long noncoding RNAs for single cell analysis

Single cell RNA sequencing (scRNA-seq) has revolutionized the study of gene expression in individual cell types, but scRNA-seq studies have focused primarily on expression of protein-coding genes. Long noncoding RNAs (lncRNAs) are more diverse than protein-coding genes, yet remain underexplored in part because they are under-represented in reference annotations applied to scRNA-seq. Merging annotations containing protein-coding and lncRNA genes is not sufficient, because the addition of lncRNA genes that overlap in sense and antisense with protein-coding genes will affect how reads are counted for both protein-coding and lncRNA genes. Here, we introduce Singletrome, a Singularity image that integrates protein-coding and lncRNA gene transfer format (GTF) annotations to generate enhanced annotations that take into account the sense and antisense overlap of annotated genes, maps scRNA-seq data, and produces files for downstream analysis and visualization. With Singletrome, we observed an increase in the number of reads mapped to exons, detected thousands of lncRNAs not included in GENCODE, and observed a decrease in uniquely mapped reads, indicating improved mapping specificity. Moreover, we were able to cluster cell types based solely on lncRNAs expression, and lncRNAs alone were able to predict cell types and human disease pathology through machine learning. This comprehensive annotation will allow mapping of lncRNA expression across cell types of the human body, facilitating the development of an atlas of human lncRNAs in health and disease with the ability to integrate new lncRNA annotations as they become available.

bioinformatics↗