Pharmacological validation of targets regulating CD14 during macrophage differentiation.

The signalling receptor for LPS, CD14, is a key marker of, and facilitator for, pro-inflammatory macrophage function. Pro-inflammatory macrophage differentiation remains a process facilitating a broad array of disease pathologies, and has recently emerged as a potential target against cytokine storm in COVID19. Here, we perform a whole-genome CRISPR screen to identify essential nodes regulating CD14 expression in myeloid cells, using the differentiation of THP-1 cells as a starting point. This strategy uncovers many known pathways required for CD14 expression and regulating macrophage differentiation while additionally providing a list of novel targets either promoting or limiting this process. To speed translation of these results, we have then taken the approach of independently validating hits from the screen using well-curated small molecules. In this manner, we identify pharmacologically tractable hits that can either increase CD14 expression on non-differentiated monocytes or prevent CD14 upregulation during macrophage differentiation. An inhibitor for one of these targets, MAP2K3, translates through to studies on primary human monocytes, where it prevents upregulation of CD14 following M-CSF induced differentiation, and pro-inflammatory cytokine production in response to LPS. Therefore, this screening cascade has rapidly identified pharmacologically tractable nodes regulating a critical disease-relevant process.

Computationally efficient algorithm to identify matched molecular pairs (MMPs) in large data sets.

Modern drug discovery organizations generate large volumes of SAR data. A promising methodology that can be used to mine this chemical data to identify novel structure-activity relationships is the matched molecular pair (MMP) methodology. However, before the full potential of the MMP methodology can be utilized, a MMP identification method that is capable of identifying all MMPs in large chemical data sets on modest computational hardware is required. In this paper we report an algorithm that is capable of systematically generating all MMPs in chemical data sets. Additionally, the algorithm is computationally efficient enough to be applied on large data sets. As an example the algorithm was used to identify the MMPs in the approximately 300k NIH MLSMR set. The algorithm identified approximately 5.3 million matched molecular pairs in the set. These pairs cover approximately 2.6 million unique molecular transformations.