Cell Type- and Age-Specific Expression of lncRNAs across Kidney Cell Types

BACKGROUND

Accumulated evidence demonstrates that long non-coding RNAs (lncRNAs) regulate cell differentiation and homeostasis, influencing kidney aging and disease. Despite their versatility, the function of lncRNA remains poorly understood due to the lack of a reference map of lncRNA transcriptome in various cell types.

METHODS

In this study, we employed a targeted single-cell RNA sequencing (scRNA-seq) method to enrich and characterize lncRNAs in individual cells. We applied this method to various mouse tissues, including normal and aged kidneys.

RESULTS

Through tissue-specific clustering analysis, we identified cell type-specific lncRNAs that showed a high correlation with known cell-type marker genes. Furthermore, we constructed gene regulatory networks (GRNs) to explore the functional roles of differentially expressed lncRNAs in each cell type. In the kidney, we observed dynamic expression changes of lncRNAs during aging, with specific changes in glomerular cells. These cell type- and age-specific expression patterns of lncRNAs provide insights into their potential roles in regulating cellular processes, such as immune response and energy metabolism, during kidney aging.

CONCLUSIONS

Our study sheds light on the comprehensive landscape of lncRNA expression and function and provides a valuable resource for future analysis of lncRNAs (https://gist-fgl.github.io/sc-lncrna-atlas/).

Chromatin accessibility analysis and architectural profiling of human kidneys reveal key cell types and a regulator of diabetic kidney disease

Diabetes is the leading cause of kidney disease that progresses to kidney failure. However, the key molecular and cellular pathways involved in diabetic kidney disease (DKD) pathogenesis are largely unknown. Here, we performed a comparative analysis of adult human kidneys by examining cell type-specific chromatin accessibility by single-nucleus ATAC-seq (snATAC-seq) and analyzing three-dimensional chromatin architecture via high-throughput chromosome conformation capture (Hi-C method) of paired samples. We mapped the cell type-specific and DKD-specific open chromatin landscape and found that genetic variants associated with kidney diseases were significantly enriched in the proximal tubule- (PT) and injured PT-specific open chromatin regions in samples from patients with DKD. BACH1 was identified as a core transcription factor of injured PT cells; its binding target genes were highly associated with fibrosis and inflammation, which were also key features of injured PT cells. Additionally, Hi-C analysis revealed global chromatin architectural changes in DKD, accompanied by changes in local open chromatin patterns. Combining the snATAC-seq and Hi-C data identified direct target genes of BACH1, and indicated that BACH1 binding regions showed increased chromatin contact frequency with promoters of their target genes in DKD. Thus, our multi-omics analysis revealed BACH1 target genes in injured PTs and highlighted the role of BACH1 as a novel regulator of tubular inflammation and fibrosis.

CRESSP: a comprehensive pipeline for prediction of immunopathogenic SARS-CoV-2 epitopes using structural properties of proteins

The development of autoimmune diseases following SARS-CoV-2 infection, including multisystem inflammatory syndrome, has been reported, and several mechanisms have been suggested, including molecular mimicry. We developed a scalable, comparative immunoinformatics pipeline called cross-reactive-epitope-search-using-structural-properties-of-proteins (CRESSP) to identify cross-reactive epitopes between a collection of SARS-CoV-2 proteomes and the human proteome using the structural properties of the proteins. Overall, by searching 4 911 245 proteins from 196 352 SARS-CoV-2 genomes, we identified 133 and 648 human proteins harboring potential cross-reactive B-cell and CD8+ T-cell epitopes, respectively. To demonstrate the robustness of our pipeline, we predicted the cross-reactive epitopes of coronavirus spike proteins, which were recognized by known cross-neutralizing antibodies. Using single-cell expression data, we identified PARP14 as a potential target of intermolecular epitope spreading between the virus and human proteins. Finally, we developed a web application (https://ahs2202.github.io/3M/) to interactively visualize our results. We also made our pipeline available as an open-source CRESSP package (https://pypi.org/project/cressp/), which can analyze any two proteomes of interest to identify potentially cross-reactive epitopes between the proteomes. Overall, our immunoinformatic resources provide a foundation for the investigation of molecular mimicry in the pathogenesis of autoimmune and chronic inflammatory diseases following COVID-19.