Machine learning-powered antibiotics phenotypic drug discovery.

Identification of novel antibiotics remains a major challenge for drug discovery. The present study explores use of phenotypic readouts beyond classical antibacterial growth inhibition adopting a combined multiparametric high content screening and genomic approach. Deployment of the semi-automated bacterial phenotypic fingerprint (BPF) profiling platform in conjunction with a machine learning-powered dataset analysis, effectively allowed us to narrow down, compare and predict compound mode of action (MoA). The method identifies weak antibacterial hits allowing full exploitation of low potency hits frequently discovered by routine antibacterial screening. We demonstrate that BPF classification tool can be successfully used to guide chemical structure activity relationship optimization, enabling antibiotic development and that this approach can be fruitfully applied across species. The BPF classification tool could be potentially applied in primary screening, effectively enabling identification of novel antibacterial compound hits and differentiating their MoA, hence widening the known antibacterial chemical space of existing pharmaceutical compound libraries. More generally, beyond the specific objective of the present work, the proposed approach could be profitably applied to a broader range of diseases amenable to phenotypic drug discovery.

White-to-brown metabolic conversion of human adipocytes by JAK inhibition.

The rising incidence of obesity and related disorders such as diabetes and heart disease has focused considerable attention on the discovery of new therapeutics. One promising approach has been to increase the number or activity of brown-like adipocytes in white adipose depots, as this has been shown to prevent diet-induced obesity and reduce the incidence and severity of type 2 diabetes. Thus, the conversion of fat-storing cells into metabolically active thermogenic cells has become an appealing therapeutic strategy to combat obesity. Here, we report a screening platform for the identification of small molecules capable of promoting a white-to-brown metabolic conversion in human adipocytes. We identified two inhibitors of Janus kinase (JAK) activity with no precedent in adipose tissue biology that stably confer brown-like metabolic activity to white adipocytes. Importantly, these metabolically converted adipocytes exhibit elevated UCP1 expression and increased mitochondrial activity. We further found that repression of interferon signalling and activation of hedgehog signalling in JAK-inactivated adipocytes contributes to the metabolic conversion observed in these cells. Our findings highlight a previously unknown role for the JAK-STAT pathway in the control of adipocyte function and establish a platform to identify compounds for the treatment of obesity.

Chemical conversion of human fibroblasts into functional Schwann cells.

Direct transdifferentiation of somatic cells is a promising approach to obtain patient-specific cells for numerous applications. However, conversion across germ-layer borders often requires ectopic gene expression with unpredictable side effects. Here, we present a gene-free approach that allows efficient conversion of human fibroblasts via a transient progenitor stage into Schwann cells, the major glial cell type of peripheral nerves. Using a multikinase inhibitor, we transdifferentiated fibroblasts into transient neural precursors that were subsequently further differentiated into Schwann cells. The resulting induced Schwann cells (iSCs) expressed numerous Schwann cell-specific proteins and displayed neurosupportive and myelination capacity in vitro. Thus, we established a strategy to obtain mature Schwann cells from human postnatal fibroblasts under chemically defined conditions without the introduction of ectopic genes.