Project description:Many cytokines are involved in the pathogenesis of autoimmune diseases and are recognized as relevant therapeutic targets to attenuate inflammation, such as TNFα in RA and IFNα/γ in SLE. To relate the transcriptional imprinting of cytokines in a cell type-specific and disease-specific manner, we generated gene-expression profiles from peripheral monocytes of SLE and RA patients and compared them to in vitro-generated signatures induced by TNFα, IFNα2a and IFNγ. Monocytes from SLE and RA patients revealed disease-specific gene-expression profiles. In vitro-generated signatures induced by IFNα2a and IFNγ showed similar profiles that only partially overlapped with those induced by TNFα. Comparisons between disease-specific and in vitro-generated signatures identified cytokine-regulated genes in SLE and RA with qualitative and quantitative differences. The IFN-responses in SLE and RA were found to be regulated in a STAT1-dependent and STAT1-independent manner, respectively. Similarly, genes recognized as TNFα-regulated were clearly distinguishable between RA and SLE patients. While the activity of SLE monocytes was mainly driven by IFN, the activity from RA monocytes showed a dominance of TNFα that was characterized by STAT1 down-regulation. The responses to specific cytokines were revealed to be disease-dependent and reflected the interplay of cytokines within various inflammatory milieus. This study has demonstrated that monocytes from RA and SLE patients exhibit disease-specific gene-expression profiles, which can be molecularly dissected when compared to in vitro-generated cytokine signatures. The results suggest that an assessment of cytokine-response status in monocytes may be helpful for improvement of diagnosis and selection of the best cytokine target for therapeutic intervention. Expression profiles of human peripheral blood monocytes activated in vivo and stimulated in vitro. Monocytes from patients with SLE and RA and from healthy donors were used for generating disease-specific gene-expression profiles, where these profiles represent in vivo activation of monocytes. In addition, monocytes from healthy donors were stimulated in vitro by cytokines: TNFα, IFNα2a and IFNγ. Cytokine-specific gene-expression profiles were generated by comparing stimulated monocytes with unstimulated ones. TNFα-, IFNα2a- and IFNγ as cytokine-specific gene-expression profiles were compared with RA and SLE, as disease-specific gene-expression profiles.

Project description:High-throughput transcriptomics (HTTr) uses gene expression profiling to characterize the biological activity of chemicals in in vitro cell-based test systems. As an extension of a previous study testing 44 chemicals, HTTr was used to screen an additional 1751 unique chemicals from the EPA’s ToxCast collection in MCF7 cells using eight concentrations and an exposure duration of 6 hours. We hypothesized that concentration-response modeling of signature scores could be used to identify putative molecular targets and cluster chemicals with similar bioactivity. Clustering and enrichment analyses were conducted based on signature catalog annotations and ToxPrint chemotypes to facilitate molecular target prediction and grouping of chemicals with similar bioactivity profiles. Enrichment analysis based on signature catalog annotation identified known mechanisms-of-action (MeOAs) associated with well-studied chemicals and generated putative MeOAs for other active chemicals. Chemicals with predicted MeOAs included those targeting estrogen receptor (ER), glucocorticoid receptor (GR), retinoic acid receptor (RAR), the NRF2/KEAP/ARE pathway, AP-1 activation and others. Using reference chemicals for ER modulation, the study demonstrated that HTTr in MCF7 cells was able to stratify chemicals in terms of agonist potency, distinguish ER agonists from antagonists, and cluster chemicals with similar activities as predicted by the ToxCast ER Pathway model. Uniform manifold approximation and projection (UMAP) embedding of signature-level results identified novel ER modulators with no ToxCast ER Pathway model predictions. Finally, UMAP combined with ToxPrint chemotype enrichment was used to explore the biological activity of structurally-related chemicals. The study demonstrates that HTTr can be used to inform chemical risk assessment by determining in vitro points-of-departure, predicting chemicals’ molecular mechanism(s)-of-action (MeOA) and grouping chemicals with similar bioactivity profiles.

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