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Quigley, I. K.

Publications and source records attributed to Quigley, I. K..

2 recordsLinked to original sources

Functional immune mapping with deep-learning enabled phenomics applied to immunomodulatory and COVID-19 drug discovery

Development of accurate disease models and discovery of immune-modulating drugs is challenged by the immune systems highly interconnected and context-dependent nature. Here we apply deep-learning-driven analysis of cellular morphology to develop a scalable "phenomics" platform and demonstrate its ability to identify dose-dependent, high-dimensional relationships among and between immunomodulators, toxins, pathogens, genetic perturbations, and small and large molecules at scale. High-throughput screening on this platform demonstrates rapid identification and triage of hits for TGF-{beta}- and TNF--driven phenotypes. We deploy the platform to develop phenotypic models of active SARS-CoV-2 infection and of COVID-19-associated cytokine storm, surfacing compounds with demonstrated clinical benefit and identifying several new candidates for drug repurposing. The presented library of images, deep learning features, and compound screening data from immune profiling and COVID-19 screens serves as a deep resource for immune biology and cellular-model drug discovery with immediate impact on the COVID-19 pandemic.

immunology↗

Rfx2 stabilizes Foxj1 binding at chromatin loops to enable multiciliated cell gene expression

Cooperative transcription factor binding at cis-regulatory sites in the genome drives robust eukaryotic gene expression, and many such sites must be coordinated to produce coherent transcriptional programs. The transcriptional program leading to motile cilia formation requires members of the DNA-binding forkhead (Fox) and Rfx transcription factor families and these factors co-localize to cilia gene promoters, but it is not clear how many cilia genes are regulated by these two factors, whether these factors act directly or indirectly, or how these factors act with specificity in the context of a 3-dimensional genome. Here, we use genome-wide approaches to show that cilia genes reside at the boundaries of topological domains and that these areas have low enhancer density. We show that the transcription factors Foxj1 and Rfx2 binding occurs in the promoters of more cilia genes than other known cilia transcription factors and that while Rfx2 binds directly to promoters and enhancers equally, Foxj1 prefers direct binding to enhancers and is stabilized at promoters by Rfx2. Finally, we show that Rfx2 and Foxj1 lie at the anchor endpoints of chromatin loops, suggesting that target genes are activated when Foxj1 bound at distal sites is recruited via a loop created by Rfx2 binding at both sites. We speculate that the primary function of Rfx2 is to stabilize distal enhancers with proximal promoters by operating as a scaffolding factor, bringing key regulatory domains bound by Foxj1 into close physical proximity and enabling coordinated cilia gene expression.\n\nAuthor SummaryThe multiciliated cell extends hundreds of motile cilia to produce fluid flow in the airways and other organ systems. The formation of this specialized cell type requires the coordinated expression of hundreds of genes in order to produce all the protein parts motile cilia require. While a relatively small number of transcription factors has been identified that promote gene expression during multiciliate cell differentiation, it is not clear how they work together to coordinate the expression of genes required for multiple motile ciliation. Here, we show that two transcription factors known to drive cilia formation, Foxj1 and Rfx2, play complementary roles wherein Foxj1 activates target genes but tends not to bind near them in the genome, whereas Rfx2 cant activate target genes by itself but instead acts as a scaffold by localizing Foxj1 to the proper targets. These results suggest not only a mechanism by which complex gene expression is coordinated in multiciliated cells, but also how transcriptional programs in general could be modular and deployed across different cellular contexts with the same basic promoter configuration.

genomics↗