Previous studies showed that non-calcemic 20(OH)D3, a product of CYP11A1 action on vitamin D3, has antifibrotic activity in human dermal fibroblasts and in a bleomycin mouse model of scleroderma. In this study we tested the role of RORγ, which is expressed in skin, in the action of CYP11A1-derived secosteroids using murine fibroblasts isolated from the skin of wild type (RORg^+/+), knock out (RORg^-/-) and heterozygote (RORg^+/-) mice. CYP11A1-derived 20(OH)D3, 20,23(OH)₂D3, 1,20(OH)₂D3, and 1,20,23(OH)₃D3 inhibited proliferation of RORγ^+/+ fibroblasts in a dose-dependent manner with a similar potency to 1,25(OH)₂D3. Surprisingly, this effect was reversed in RORγ^+/- and RORγ^-/- fibroblasts with the most pronounced stimulatory effect seen in RORγ^-/- fibroblasts. All of the analogs tested inhibited TGF-β1-induced collagen synthesis in RORγ^+/+ fibroblasts and the expression of other fibrosis-related genes. This effect was curtailed or reversed in RORγ^-/- fibroblasts. These results show that the antiproliferative and antifibrotic activities of the vitamin D hydroxy-derivatives are dependent on a functional RORγ. The dramatic changes in the transcriptomes of fibroblasts of RORg^-/- versus wild type mice following treatment with 20(OH)D3 or 1,20(OH)₂D3 provide a molecular basis to explain, at least in part, the observed phenotypic differences

Data were analyzed with Ingenuity Pathway Analysis (Ingenuity® Systems, www.ingenuity.com). For generating networks, a data set containing gene identifiers and corresponding expression values was uploaded into the application. Each identifier was mapped to its corresponding object in Ingenuity’s Knowledge Base. A fold change cutoff of +/-2 was set to identify molecules whose expression was significantly differentially regulated. These molecules, called Network Eligible molecules, were overlaid onto a global molecular network developed from information contained in Ingenuity’s Knowledge Base. Networks of Network Eligible Molecules were then algorithmically generated based on their connectivity. The Functional Analysis identified the biological functions and/or diseases that were most significant to the entire data set. Molecules from the dataset that met the fold change cutoff of +/-2 and were associated with biological functions and/or diseases in Ingenuity’s Knowledge Base were considered for the analysis. Right-tailed Fisher’s exact test was used to calculate the p-value determining the probability that each biological function and/or disease assigned to that data set is due to chance alone.