Title: Functional genomics and ARDS: in-silico and in-vitro gene editing to dissect genome wide associations in patients with Acute Respiratory Distress Syndrome
Funder: The Academy of Medical Sciences

Abstract: Therapeutics have improved the chance of survival for those with COVID-19 who develop Acute Respiratory Distress Syndrome (ARDS). Large genome-wide association studies (GWAS) have identified drug targets, such as Baricitinib. However, there are no effective drug treatments for non-COVID ARDS, which accounts for 10% of all ICU admissions and has a mortality >35%. The application of genomic techniques to non-COVID ARDS has the potential to shift the landscape. I aim to develop a programme which takes information from GWAS and uses gene editing to dissect associations. Ultimately, producing druggable targets for ARDS. In this proposal I will build the foundations of this programme and generate data to support a future fellowship application Focusing on a gene associated with severe COVID-19 and also implicated in non-COVID ARDS, Mucin 1 (MUC1). First, I will use an algorithm based on wild-type single-cell RNA-seq data to perform in-silico knockout (KO) of MUC1 in human alveolar cells, producing a prediction of which genes are co-expressed. Second, in a novel SpCas9-expressing porcine precision cut lung slice model, I will use CRISPR to KO MUC1 and then perform RNA-seq on alveolar epithelial cells to identify co-expressed genes. These data will provide us with a clearer picture of the MUC 1 gene regulatory network and enhance our ability to find associated druggable targets. These experiments will serve as a demonstration of the feasibility of using Cas9-expressing tissues for CRISPR-based lung editing, de-risking future in-vivo work, and I will test the utility of in-silico tools for prioritising target genes.

Lay abstract: In its most severe form COVID-19 causes lungs to fail, leading to a state named the Acute Respiratory Distress Syndrome (ARDS). Many other conditions can cause ARDS, including bacterial pneumonia, influenza, and major trauma. However, in this case there are no effective treatments. As a result, ~35% of these patients will die and those who survive are often left with disability. A key difference between COVID and non-COVID ARDS is our understanding of the genetics of susceptibility. For COVID, we have amassed a detailed picture of genetic variants present in those who develop severe disease. Several of these have pointed directly to treatments. My aim is to develop a programme which takes information from these large genetic association studies and uses gene-editing techniques to unravel the findings. Ultimately, producing novel drug targets for non-COVID ARDS. As a proof of principle, I will focus on a gene implicated in COVID and non-COVID ARDS, called Mucin 1 (MUC1). I will adopt two approaches to better understand its function and identify related genes. First, I will use a novel machine learning algorithm to virtually knockout (KO) MUC1 in a simulated human lung, producing predictions as to which other genes are perturbed. Second, I will use CRISPR-based editing to KO MUC1 in a sophisticated 'lung in a dish' model and use in-depth sequencing of gene transcripts to establish which genes are co-expressed. These data will improve our understanding of the function of MUC1 and identify associated genes which may be targetable with existing drugs.

Research activity codes: 2.1 BIOLOGICAL AND ENDOGENOUS FACTORS
Health categories: RESPIRATORY;INFECTION