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Orozco, G.

Publications and source records attributed to Orozco, G..

5 recordsLinked to original sources

Injury-related cell death and proteoglycan loss in articular cartilage: Numerical model combining necrosis, reactive oxygen species, and inflammatory cytokines

Osteoarthritis (OA) is a common musculoskeletal disease that leads to deterioration of articular cartilage, joint pain, and decreased quality of life. When OA develops after a joint injury, it is designated as post-traumatic OA (PTOA). The etiology of PTOA remains poorly understood, but it is known that proteoglycan (PG) loss, cell dysfunction, and cell death in cartilage are among the first signs of the disease. These processes, influenced by biomechanical and inflammatory stimuli, disturb the normal cell-regulated balance between tissue synthesis and degeneration. Previous computational mechanobiological models have not explicitly incorporated the cell-mediated degradation mechanisms triggered by an injury that eventually can lead to tissue-level compositional changes. Here, we developed a 2-D mechanobiological finite element model to predict necrosis, apoptosis following excessive production of reactive oxygen species (ROS), and inflammatory cytokine (interleukin-1)-driven apoptosis in cartilage explant. The resulting PG loss over 30 days was simulated. Biomechanically triggered PG degeneration, associated with cell necrosis, excessive ROS production, and cell apoptosis, was predicted to be localized near a lesion, while interleukin-1 diffusion-driven PG degeneration was manifested more globally. The numerical predictions were supported by several previous experimental findings. Furthermore, the ROS and inflammation mechanisms had longer-lasting effects (over 3 days) on the PG content than localized necrosis. Interestingly, the model also showed proteolytic activity and PG biosynthesis closer to the levels of healthy tissue when pro-inflammatory cytokines were rapidly inhibited or cleared from the culture medium, leading to partial recovery of PG content. The mechanobiological model presented here may serve as a numerical tool for assessing early cartilage degeneration mechanisms and the efficacy of interventions to mitigate PTOA progression. Author summaryOsteoarthritis is one of the most common musculoskeletal diseases. When osteoarthritis develops after a joint injury, it is designated as post-traumatic osteoarthritis. A defining feature of osteoarthritis is degeneration of articular cartilage, which is partly driven by cartilage cells after joint injury, and further accelerated by inflammation. The degeneration triggered by these biomechanical and biochemical mechanisms is currently irreversible. Thus, early prevention/mitigation of disease progression is a key to avoiding PTOA. Prior computational models have been developed to provide insights into the complex mechanisms of cartilage degradation, but they rarely include cell-level cartilage degeneration mechanisms. Here, we present a novel approach to simulate how the early post-traumatic biomechanical and inflammatory effects on cartilage cells eventually influence tissue composition. Our model includes the key regulators of early post-traumatic osteoarthritis: chondral lesions, cell death, reactive oxygen species, and inflammatory cytokines. The model is supported by several experimental explant culture findings. Interestingly, we found that when post-injury inflammation is mitigated, cartilage composition can partially recover. We suggest that mechanobiological models including cell-tissue-level mechanisms can serve as future tools for evaluating high-risk lesions and developing new intervention strategies.

biophysics↗

An active chromatin interactome elucidates the biological mechanisms underlying genetic risk factors of dermatological conditions in disease relevant cell lines

Chromatin looping between regulatory elements and gene promoters presents a potential mechanism whereby disease risk variants affect their target genes. Here we use H3K27ac HiChIP, a method for assaying the active chromatin interactome in two cell lines: keratinocytes and skin derived CD8+ T cells. We integrate public datasets for a lymphoblastoid cell line and primary CD4+ T cells and identify gene targets at risk loci for skin-related disorders. Interacting genes enrich for pathways of known importance in each trait, such as cytokine response (psoriatic arthritis, psoriasis) and replicative senescence (melanoma). We show examples of how our analysis can inform changes in the current understanding of multiple psoriasis associated risk loci. For example, the variant rs10794648, which is generally assigned to IFNLR1, was linked to GRHL3 in our dataset, a gene essential in skin repair and development. Our findings, therefore, indicate a renewed importance of skin related factors in the risk of disease. O_FIG O_LINKSMALLFIG WIDTH=158 HEIGHT=200 SRC="FIGDIR/small/973271v2_ufig1.gif" ALT="Figure 1"> View larger version (36K): org.highwire.dtl.DTLVardef@99cd40org.highwire.dtl.DTLVardef@1725b6dorg.highwire.dtl.DTLVardef@d05d21org.highwire.dtl.DTLVardef@1924d07_HPS_FORMAT_FIGEXP M_FIG Graphical Abstract In this article we take disease associated variants from 5 dermatological conditions and use cell type specific datasets to map genes that could be affected by these variants, providing insight into disease mechanisms. C_FIG

genomics↗

Chromatin-based techniques map DNA interaction landscapes in psoriasis susceptibility loci and highlight KLF4 as a target gene in 9q31

Genome-wide association studies (GWAS) have uncovered many genetic risk loci for psoriasis, yet many remain uncharacterised in terms of the causal gene and their biological mechanism in disease. Here, we use a disease-focused Capture Hi-C experiment to link psoriasis-associated variants with their target genes in psoriasis-relevant cell lines (HaCaT keratinocytes and My-La CD8+ T cells). We confirm previously assigned genes, suggest novel candidates and provide evidence for complexity at psoriasis GWAS loci. In the 9q31 risk locus we combine further epigenomic evidence to demonstrate how the psoriasis association forms a functional interaction with the distant (>500 kb) KLF4 gene. We use CRISPR activation coupled with RNA-seq to demonstrate how activation of psoriasis-associated enhancers upregulates KLF4 in HaCaT cells. Our study design provides a robust pipeline for following up on GWAS disease-associated variants, paving the way for functional translation of genetic findings into clinical benefit.

genomics↗

Exploring the overlap between rheumatoid arthritis susceptibility loci and long non-coding RNA annotations

Whilst susceptibility variants for many complex diseases, such as rheumatoid arthritis (RA), have been well characterised, the mechanism by which risk is mediated is still unclear for many loci. This is especially true for the majority of variants that do not affect protein-coding regions. lncRNA represent a group of molecules that have been shown to be enriched amongst variants associated with RA and other complex diseases, compared to random variants. In order to establish to what degree direct disruption of lncRNA may represent a potential mechanism for mediating RA susceptibility, we chose to further explore this overlap. By testing the ability of annotated features to improve a model of disease susceptibility, we were able to demonstrate a local enrichment of enhancers from immune-relevant cell types amongst RA susceptibility variants (log2 enrichment 3.40). This was not possible for lncRNA annotations in general, however a small, but significant enrichment was observed for immune-enriched lncRNA (log2 enrichment 0.867002). This enrichment was no longer apparent when the model was conditioned on immune-relevant enhancers (log2 enrichment -0.372734), suggesting that direct disruption of lncRNA sequence, independent of enhancer disruption, does not represent a major mechanism by which susceptibility to complex diseases is mediated. Furthermore, we demonstrated that, in keeping with general lncRNA characteristics, immune-enriched lncRNA are expressed at low levels that may not be amenable to functional characterisation.

genetics↗

HiChIP-Peaks: A HiChIP peak calling algorithm

MotivationHiChIP is a powerful tool to interrogate 3D chromatin organization. Current tools to analyse chromatin looping mechanisms using HiChIP data require the identification of loop anchors to work properly. However, current approaches to discover these anchors from HiChIP data are not satisfactory, having either a very high false discovery rate or strong dependence on sequencing depth. Moreover, these tools do not allow quantitative comparison of peaks across different samples, failing to fully exploit the information available from HiChIP datasets.\n\nResultsWe develop a new tool based on a representation of HiChIP data centred on the re-ligation sites to identify peaks from HiChIP datasets, which can subsequently be used in other tools for loop discovery. This increases the reliability of these tools and improves recall rate as sequencing depth is reduced. We also provide a method to count reads mapping to peaks across samples, which can be used for differential peak analysis using HiChIP data.\n\nAvailabilityHiChIP-Peaks is freely available at https://github.com/ChenfuShi/HiChIP_peaks\n\nContact: chenfu.shi@postgrad.manchester.ac.uk

bioinformatics↗