PARP-1 inhibition attenuates the inflammatory response in the cartilage of a rat model of osteoarthritis

Liu, Zili; Wang, Honglin; Wang, Shaoqian; Gao, Jie; Niu, Lei

doi:10.1302/2046-3758.107.bjr-2020-0200.r2

“…Furthermore, another novel prediction is 1,5-isoquinolinediol, a PARP-1 inhibitor. In accordance with our prediction, a previous study also reported that PARP-1 inhibitors are able to decrease the inflammatory response in the cartilage of OA rat model ( 65 ). Finally, we applied the algorithm also to the sane four mouse models of NASH used in the previous section (i.e.…”

Section: Resultssupporting

confidence: 93%

ChemPert: mapping between chemical perturbation and transcriptional response for non-cancer cells

Zheng

¹

,

Okawa

²

,

Bravo

³

et al. 2022

Nucleic Acids Research

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Prior knowledge of perturbation data can significantly assist in inferring the relationship between chemical perturbations and their specific transcriptional response. However, current databases mostly contain cancer cell lines, which are unsuitable for the aforementioned inference in non-cancer cells, such as cells related to non-cancer disease, immunology and aging. Here, we present ChemPert (https://chempert.uni.lu/), a database consisting of 82 270 transcriptional signatures in response to 2566 unique perturbagens (drugs, small molecules and protein ligands) across 167 non-cancer cell types, as well as the protein targets of 57 818 perturbagens. In addition, we develop a computational tool that leverages the non-cancer cell datasets, which enables more accurate predictions of perturbation responses and drugs in non-cancer cells compared to those based onto cancer databases. In particular, ChemPert correctly predicted drug effects for treating hepatitis and novel drugs for osteoarthritis. The ChemPert web interface is user-friendly and allows easy access of the entire datasets and the computational tool, providing valuable resources for both experimental researchers who wish to find datasets relevant to their research and computational researchers who need comprehensive non-cancer perturbation transcriptomics datasets for developing novel algorithms. Overall, ChemPert will facilitate future in silico compound screening for non-cancer cells.

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“…Furthermore, another novel prediction is 1,5-isoquinolinediol, a PARP-1 inhibitor. In accordance with our prediction, a previous study also reported that PARP-1 inhibitors are able to decrease the inflammatory response in the cartilage of OA rat model (46).…”

Section: Resultssupporting

confidence: 93%

“…Furthermore, another novel prediction is 1,5-isoquinolinediol, a PARP-1 inhibitor. In accordance with our prediction, a previous study also reported that PARP-1 inhibitors are able to decrease the inflammatory response in the cartilage of OA rat model (46). To summarize, ChemPert not only recapitulated the known perturbagens, but also provided novel predictions as potential therapies for the treatment of OA.…”

Section: Use Case -Chempert Predicts Novel Perturbagens For the Treat...supporting

confidence: 89%

ChemPert: mapping between chemical perturbation and transcriptional response for non-cancer cells

Zheng

¹

,

Okawa

²

,

Bravo

³

et al. 2022

Preprint

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Prior knowledge of perturbation data can significantly assist in inferring the relationship between chemical perturbations and their specific transcriptional response. However, current databases mostly contain cancer cell lines, which are unsuitable for the aforementioned inference in non-cancer cells. Here we present ChemPert (https://chempert.uni.lu/), a database consisting of 82270 transcriptional signatures across 167 non-cancer cell types, enabling more accurate predictions of perturbation responses and drugs compared to cancer databases in non-cancer cells. In particular, ChemPert correctly predicted drug effects for treating non-alcoholic steatohepatitis and novel drugs for osteoarthritis. Overall, ChemPert provides a valuable resource for drug discovery in non-cancer diseases.

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“…In addition, it is reported that age and sex are considered to relate to OA prevalence. 46 However, some of the demographic information such as sex and age was missing from the selected datasets. The possible differences in the age and the sex distribution of the normal compared to OA samples might lead to unreliability in the results.…”

Section: Discussionmentioning

confidence: 99%

ATF3 as a potential diagnostic marker of early-stage osteoarthritis and its correlation with immune infiltration through bioinformatics analysis

Yang

¹

,

Fan

²

,

Liu

³

2022

Bone & Joint Research

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Aims This study aimed, through bioinformatics analysis, to identify the potential diagnostic markers of osteoarthritis, and analyze the role of immune infiltration in synovial tissue. Methods The gene expression profiles were downloaded from the Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) were identified by R software. Functional enrichment analyses were performed and protein-protein interaction networks (PPI) were constructed. Then the hub genes were screened. Biomarkers with high value for the diagnosis of early osteoarthritis (OA) were validated by GEO datasets. Finally, the CIBERSORT algorithm was used to evaluate the immune infiltration between early-stage OA and end-stage OA, and the correlation between the diagnostic marker and infiltrating immune cells was analyzed. Results A total of 88 DEGs were identified. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses indicated that DEGs were significantly enriched in leucocyte migration and interleukin (IL)-17 signalling pathways. Disease ontology (DO) indicated that DEGs were mostly enriched in rheumatoid arthritis. Six hub genes including FosB proto-oncogene, AP-1 transcription factor subunit (FOSB); C-X-C motif chemokine ligand 2 (CXCL2); CXCL8; IL-6; Jun proto-oncogene, AP-1 transcription factor subunit (JUN); and Activating transcription factor 3 (ATF3) were identified and verified by GEO datasets. ATF3 (area under the curve = 0.975) turned out to be a potential biomarker for the diagnosis of early OA. Several infiltrating immune cells varied significantly between early-stage OA and end-stage OA, such as resting NK cells (p = 0.016), resting dendritic cells (p = 0.043), and plasma cells (p = 0.043). Additionally, ATF3 was significantly correlated with resting NK cells (p = 0.034), resting dendritic cells (p = 0.026), and regulatory T cells (Tregs, p = 0.018). Conclusion ATF3 may be a potential diagnostic marker for early diagnosis and treatment of OA, and immune cell infiltration provides new perspectives for understanding the mechanism during OA progression. Cite this article: Bone Joint Res 2022;11(9):679–689.

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PARP-1 inhibition attenuates the inflammatory response in the cartilage of a rat model of osteoarthritis

Cited by 13 publications

References 35 publications

ChemPert: mapping between chemical perturbation and transcriptional response for non-cancer cells

ChemPert: mapping between chemical perturbation and transcriptional response for non-cancer cells

ChemPert: mapping between chemical perturbation and transcriptional response for non-cancer cells

ATF3 as a potential diagnostic marker of early-stage osteoarthritis and its correlation with immune infiltration through bioinformatics analysis

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