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Although current antiretroviral therapies (ART) are successful in controlling HIV-1 infection, a stable viral reservoir reactivates when ART is discontinued. Consequently, there is a major research effort to develop approaches to disrupt the latent viral reservoir and enhance the immune system's ability to clear HIV-1. A number of small molecules, termed latency reversal agents (LRAs), have been identified which can reactivate latent HIV-1 in cell lines and patients' cells ex vivo. However, clinical trials have suggested that combinations of LRAs will be required to efficiently reactivate HIV-1 in vivo, especially LRAs that act synergistically by functioning through distinct pathways. To identify novel LRAs, we used an image-based assay to screen a natural compound library for the ability to induce a low level of aggregation of resting primary CD4+ T cells from healthy donors. We identified celastrol as a novel LRA. Celastrol functions synergistically with other classes of LRA to reactivate latent HIV-1 in a Jurkat cell line, suggesting a novel mechanism in its LRA activity. Additionally, celastrol does not appear to activate resting CD4+ T cells at levels at which it can reactivate latent HIV-1. Celastrol appears to represent a novel class of LRAs and it therefore can serve as a lead compound for LRA development. [ABSTRACT FROM AUTHOR]

We recently discovered that steroid receptor coactivators (SRCs) SRCs-1, 2 and 3, are abundantly expressed in cardiac fibroblasts (CFs) and their activation with the SRC small molecule stimulator MCB-613 improves cardiac function and dramatically lowers pro-fibrotic signaling in CFs post-myocardial infarction. These findings suggest that CF-derived SRC activation could be beneficial in the mitigation of chronic heart failure after ischemic insult. However, the cardioprotective mechanisms by which CFs contribute to cardiac pathological remodeling are unclear. Here we present studies designed to identify the molecular and cellular circuitry that governs the anti-fibrotic effects of an MCB-613 derivative, MCB-613-10-1, in CFs. We performed cytokine profiling and whole transcriptome and proteome analyses of CF-derived signals in response to MCB-613-10-1. We identified the NRF2 pathway as a direct MCB-613-10-1 therapeutic target for promoting resistance to oxidative stress in CFs. We show that MCB-613-10-1 promotes cell survival of anti-fibrotic CFs exposed to oxidative stress by suppressing apoptosis. We demonstrate that an increase in HMOX1 expression contributes to CF resistance to oxidative stress-mediated apoptosis via a mechanism involving SRC co-activation of NRF2, hence reducing inflammation and fibrosis. We provide evidence that MCB-613-10-1 acts as a protectant against oxidative stress-induced mitochondrial damage. Our data reveal that SRC stimulation of the NRF2 transcriptional network promotes resistance to oxidative stress and highlights a mechanistic approach toward addressing pathologic cardiac remodeling. [Display omitted] • Steroid receptor activation by MCB-613-10-1 inhibits fibroblast activation. • Steroid receptor activation enhances antioxidant and cytoprotective signaling. • MCB-613-10-1 protects fibroblast mitochondria from oxidative stress-induced damage. [ABSTRACT FROM AUTHOR]

In recent years, protein kinases have become some of the most significant drug targets in cancer patients. Kinases are known to regulate the activity of many human proteins, and consequently their inhibition has been used to control cancer proliferation. A significant challenge in drug discovery is the rapid and efficient identification of new small molecules. In this study, we propose a novel in silico drug discovery approach to identify kinase targets that impinge on nuclear receptor signaling with data generated using high-content analysis (HCA). A high-throughput imaging dataset was generated from an siRNA human kinome screen on engineered cells that allow direct visualization of effects on estrogen receptor-α or a chimeric progesterone receptor B binding to specific DNA. Two types of kinase descriptors are extracted from these imaging data: first, a population-median-based descriptor and second a bag-of-words (BoW) descriptor that can capture heterogeneity information in the imaging data. Using these descriptors, we provide prediction results of drug-kinase-target interactions based on single-task learning, multi-task learning, and collaborative filtering methods. The best performing model in target-based drug discovery gives an area under the receiver operating characteristic curve (AUC) of 0.86, whereas the best model in ligand-based discovery gives an AUC of 0.79. These promising results suggest that imaging-based information can be used as an additional source of information to existing virtual screening methods, thereby making the drug discovery process more time and cost efficient. [ABSTRACT FROM AUTHOR]