An immune dysfunction score for stratification of patients with acute infection based on whole-blood gene expression

Dysregulated host responses to infection can lead to organ dysfunction and sepsis, causing millions of global deaths each year. To alleviate this burden, improved prognostication and biomarkers of response are urgently needed. We investigated the use of whole-blood transcriptomics for stratification of patients with severe infection by integrating data from 3149 samples from patients with sepsis due to community-acquired pneumonia or fecal peritonitis admitted to intensive care and healthy individuals into a gene expression reference map. We used this map to derive a quantitative sepsis response signature (SRSq) score reflective of immune dysfunction and predictive of clinical outcomes, which can be estimated using a 7- or 12-gene signature. Last, we built a machine learning framework, SepstratifieR, to deploy SRSq in adult and pediatric bacterial and viral sepsis, H1N1 influenza, and COVID-19, demonstrating clinically relevant stratification across diseases and revealing some of the physiological alterations linking immune dysregulation to mortality. Our method enables early identification of individuals with dysfunctional immune profiles, bringing us closer to precision medicine in infection.peer-reviewe

Understanding the role of CD4+ T cells in common immune-mediated diseases

Immune-mediated diseases such as autoimmunity are complex traits which collectively affect around 10% of the European population. Genome-wide association studies (GWAS) have demonstrated that genetic susceptibility to these diseases is explained by thousands of loci spread throughout the genome, most of which lie within non-coding DNA. Moreover, these loci are enriched in CD4+ T cell regulatory elements, which suggests they might disrupt the expression of nearby genes in T cells. Nonetheless, the target genes of most immune disease loci have yet to be discovered. In this dissertation, I describe three studies designed to further our understanding of the relationship between genetic variation, CD4+ T cell function, and disease risk. I first introduce a large epigenetic study which profiled active promoters and enhancers in 55 different CD4+ T cell and macrophage states. By integrating these data with GWAS loci with a novel statistical approach, I conclude that immune disease loci are enriched in enhancers and promoters specifically active during early memory T cell activation. In a second study, I proceed to characterize memory CD4+ T cells at single-cell resolution by profiling cells in the resting state and after stimulation with 11 different cytokine combinations. My observations reveal that CD4+ T cells are formed of a continuum of cell states which reflect a naïve-to-memory progression, and that as cells advance in this progression they express increasingly higher levels of cytokines, chemokines, and other effector molecules. Finally, I describe the results from a single-cell expression quantitative trait locus (sc-eQTL) mapping study performed on CD4+ T cells undergoing activation. I identify over 6,000 genes regulated by an eQTL, of which approximately 2,000 show evidence of a gene-by-environment interaction, where the eQTL effect size changes as a function of T cell activation time. Integration with GWAS associations demonstrates that immune disease loci alter the expression of genes in cis at specific stages of T cell activation. This results in the prioritization of 139 candidate disease genes which could be relevant for drug target identification. These results expand our understanding of CD4+ T cells and suggest that dysregulation of gene expression dynamics during T cell activation could be a hallmark of disease.Gates Cambridge Scholarship (Grant code: OPP1144) Open Targets (Grant code: OTAR040

Reproducibility of fluorescent expression from engineered biological constructs in E. coli

We present results of the first large-scale interlaboratory study carried out in synthetic biology, as part of the 2014 and 2015 International Genetically Engineered Machine (iGEM) competitions. Participants at 88 institutions around the world measured fluorescence from three engineered constitutive constructs in E. coli. Few participants were able to measure absolute fluorescence, so data was analyzed in terms of ratios. Precision was strongly related to fluorescent strength, ranging from 1.54-fold standard deviation for the ratio between strong promoters to 5.75-fold for the ratio between the strongest and weakest promoter, and while host strain did not affect expression ratios, choice of instrument did. This result shows that high quantitative precision and reproducibility of results is possible, while at the same time indicating areas needing improved laboratory practices.Peer reviewe