bioRxiv Science⌕ Search

bioRxiv · 10.1101/2021.02.22.432314

Characterizing gene expression responses to biomechanical strain in an in vitro model of osteoarthritis

Abstract

Osteoarthritis (OA) is a common chronic degenerative joint disease affecting articular cartilage and underlying bone. Both genetic and environmental factors appear to contribute to the development of this disease. Specifically, pathological levels of biomechanical stress on joints play a notable role in disease initiation and progression. Population-level gene expression studies of cartilage cells experiencing biomechanical stress may uncover gene-by-environment interactions relevant to OA and human joint health. To build a foundation for such studies, we applied differentiation protocols to develop an in vitro system of chondrogenic cell lines (iPSC-chondrocytes). We characterized gene regulatory responses of three human iPSC-chondrocyte lines to cyclic tensile strain treatment. We measured the contribution of biological and technical factors to gene expression variation in this system and, even in this small sample, found several genes that exhibit inter-individual expression differences in response to mechanical strain, including genes previously implicated in OA. Expanding this system to include iPSC-chondrocytes from a larger number of individuals will allow us to characterize and better understand gene-by-environment interactions related to OA and joint health.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Hung, A., Housman, G., Briscoe, E., Cuevas, C., Gilad, Y.. 2021-02-22. Characterizing gene expression responses to biomechanical strain in an in vitro model of osteoarthritis. https://doi.org/10.1101/2021.02.22.432314

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

SPACE-Tag Enables High-Throughput Spatial Chromatin Profiling With CUT&Tag

Spatial epigenomics maps chromatin states within intact tissues, yet current methods remain limited by accessibility and scale. We developed SPACE-Tag, a spatial CUT&Tag framework that converts epigenetic marks-associated DNA into polyadenylated RNA through T7 based linear amplification, enabling direct integration with standard spatial transcriptomic workflows. Applied to the mouse brain, SPACETag resolved activating and repressive chromatin landscapes, recovered region-specific gene regulatory programs across anatomical domains, and showed strong correlation with existing bulk and single cell reference data. We generated an integrated multimodal atlas of the aging mouse brain, profiling five histone modifications and the transcriptome in parallel across the adult lifespan to establish the largest spatial multimodal dataset to date. This analysis uncovered sharp regional divergence with aging in the brain: fiber tracts showed widespread transcriptional downregulation, often without nearby chromatin state transitions, whereas the isocortex showed coordinated chromatin transcriptome changes, including an H3K4me1 peak module that increased with age, was enriched at super-enhancers and linked to upregulation of neuronal plasticity regulators. These findings highlight the power of multimodal and high throughput spatial genomics in uncovering the divergent regulatory logic that governs tissue-specific vulnerability and resilience in the aging brain.

genomics↗

Long-read DNA sequencing reveals global hypomethylation and focal hypermethylation in SDHx-deficient pheochromocytoma and paraganglioma

Pheochromocytomas and paragangliomas (PPGL) are highly heritable neuroendocrine tumors of the autonomic ganglia. Genomic DNA in most cancer cells is hypomethylated. However, DNA in PPGLs secondary to mutations in succinate dehydrogenase complex (SDHx) genes is reportedly hypermethylated due to succinate-mediated inhibition of DNA demethylases. We used nanopore long-read sequencing to characterize the methylome of tumor and white blood cell DNA from SDHx and non-SDHx PPGL patients, alongside normal adrenal medulla. We find that both SDHx and non-SDHx PPGLs are globally hypomethylated, particularly at centromeres. In normal adrenal medulla, CpG islands marked by H3K4 methylation are hypomethylated, whereas CpG islands lacking H3K4 methylation are hypermethylated. In SDHx-related PPGL, some H3K4me-marked CpG islands are hypermethylated, but most are not. We propose that DNA methylation in SDHx-related PPGL reflects the global hypomethylation generally observed in tumors, partly offset by succinate-mediated inhibition of TET DNA demethylases. Opposing effects of succinate may alter the balance between DNA methylation (indirect inhibition of the DNMT3A/B DNA methylases via inhibition of H3K4me demethylases) and DNA demethylation (inhibition of TET DNA demethylases) at active CpG islands.

genomics↗

High piRNA abundance is a hallmark of evolutionarily persistent piRNA-producing loci.

PIWI-interacting RNAs (piRNAs) silence transposons in the germline, and the loci that produce them diverge rapidly between species. Yet, some loci retain both their genomic location and their piRNA production across large evolutionary distances, and the basis for this conservation is unclear. To identify the features that distinguish these persistent loci, we compared piRNA production at orthologous loci in three closely related mouse species, contrasting loci that produce piRNAs in all three species with those that do so in only one. Transposon content and UTR features, often linked to piRNA production, did not distinguish the two groups. Transcription extending beyond gene ends (readthrough) was more frequent at conserved loci, but did not explain conservation once the amount of piRNAs produced was taken into account. Instead, we found that the conserved loci are those that produce the most piRNAs. The same association holds true in human and two other primate species. We speculate that most piRNAs expressed in testis are tolerated molecular noise, and that only the highly expressed minority are likely to be functional.

genomics↗