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Parnell, G. P.

Publications and source records attributed to Parnell, G. P..

2 recordsLinked to original sources

Genetic evidence that the latency III stage of Epstein-Barr Virus infection is a therapeutic target for Multiple Sclerosis

Genome wide association studies have identified >200 susceptibility loci accounting for much of the heritability of Multiple Sclerosis (MS). Epstein Barr virus (EBV), a memory B cell tropic virus, has been identified as necessary but not sufficient for development of MS, with evidence for disease causation. The molecular and immunological basis for this has not been established. LCL proliferation is driven by signalling through the EBV produced cell surface protein LMP1, a homologue of the MS risk gene CD40. We show that the CD40 ligand, CD40L, potentially through competitive signalling with LMP1, reduces LCL proliferation (p<0.001). The MS risk variants of the LMP1 signalling inhibitor, TRAF3, had altered expression in B cells and LCLs. Both CD40 and TRAF3 risk SNPs are in binding sites for the EBV transcription factor EBNA2. We have investigated transcriptomes of B cells and EBV infected B cells at Latency III (LCLs) and identified 47 MS risk genes with altered expression, associated with the risk genotype. Overall these MS risk SNPs were overrepresented in target loci of the EBV transcription factor EBNA2 (p<10-16), in genes dysregulated between B and LCLs (p<10-5), and as targets for EBV miRNAs (p<10-4). The risk gene ZC3HAV1 is the putative target for multiple EBV miRNAs. It amplifies the interferon response, and was shown to have reduced expression in LCLs for the risk allele. These data indicate targeting EBV EBNA2, miRNAs, and MS risk genes on the LMP1/LMP2 pathways, and the pathways themselves, may be of therapeutic benefit in MS.

genomics

Mortality prediction in sepsis via gene expression analysis: a community approach

Improved risk stratification and prognosis in sepsis is a critical unmet need. Clinical severity scores and available assays such as blood lactate reflect global illness severity with suboptimal performance, and do not specifically reveal the underlying dysregulation of sepsis. Here three scientific groups were invited to independently generate prognostic models for 30-day mortality using 12 discovery cohorts (N=650) containing transcriptomic data collected from primarily community-onset sepsis patients. Predictive performance was validated in 5 cohorts of community-onset sepsis patients (N=189) in which the models showed summary AUROCs ranging from 0.765-0.89. Similar performance was observed in 4 cohorts of hospital-acquired sepsis (N=282). Combining the new gene-expression-based prognostic models with prior clinical severity scores led to significant improvement in prediction of 30-day mortality (p<0.01). These models provide an opportunity to develop molecular bedside tests that may improve risk stratification and mortality prediction in patients with sepsis, improving both resource allocation and prognostic enrichment in clinical trials.

bioinformatics