👤 Nancy J Mendelsohn

Multisystem inflammatory syndrome in children (MIS-C) is a severe acute inflammatory reaction to SARS-CoV-2 infection in children. There is a lack of data describing differential expression of immune genes in MIS-C compared to healthy children or those with other inflammatory conditions and how expression changes over time. In this study, we investigated expression of immune-related genes in South African MIS-C patients and controls. The cohort included 30 pre-treatment MIS-C cases and 54 healthy non-inflammatory paediatric controls. Other controls included 34 patients with juvenile systemic lupus erythematosus, Kawasaki disease or other inflammatory conditions. Longitudinal post-treatment MIS-C specimens were available at various timepoints. Expression of 80 immune-related genes was determined by real-time quantitative PCR. A total of 29 differentially expressed genes were identified in pre-treatment MIS-C compared to healthy controls. Up-regulated genes were found to be overrepresented in innate immune pathways including interleukin-1 processing and pyroptosis. Post-treatment follow-up data were available for up to 1,200 hours after first treatment. All down-regulated genes and 17/18 up-regulated genes resolved to normal levels in the timeframe, and all patients clinically recovered. When comparing MIS-C to other febrile conditions, only These data indicate a unique 29-gene signature of MIS-C in South African children. The up-regulation of interleukin-1 and pyroptosis pathway genes highlights the role of the innate immune system in MIS-C. IL-27 is a potent anti-inflammatory and antiviral cytokine that may distinguish MIS-C from other conditions in our setting. Show less

Familial involvement is common in dilated cardiomyopathy (DCM) and >40 genes have been implicated in causing disease. However, the role of genetic testing in clinical practice is not well defined. We examined the experience of clinical genetic testing in a diverse DCM population to characterize the prevalence and predictors of gene mutations. We studied 264 unrelated adult and pediatric DCM index patients referred to 1 reference lab for clinical genetic testing. Up to 10 genes were analyzed (MYH7, TNNT2, TNNI3, TPM1, MYBPC3, ACTC, LMNA, PLN, TAZ, and LDB3), and 70% of patients were tested for all genes. The mean age was 26.6 ± 21.3 years, and 52% had a family history of DCM. Rigorous criteria were used to classify DNA variants as clinically relevant (mutations), variants of unknown clinical significance (VUS), or presumed benign. Mutations were found in 17.4% of patients, commonly involving MYH7, LMNA, or TNNT2 (78%). An additional 10.6% of patients had VUS. Genetic testing was rarely positive in older patients without a family history of DCM. Conversely in pediatric patients, family history did not increase the sensitivity of genetic testing. Using rigorous criteria for classifying DNA variants, mutations were identified in 17% of a diverse group of DCM index patients referred for clinical genetic testing. The low sensitivity of genetic testing in DCM reflects limitations in both current methodology and knowledge of DCM-associated genes. However, if mutations are identified, genetic testing can help guide family management. Show less

Bicuspid Aortic Valve (BAV) is a highly heritable congenital heart defect. The low frequency of BAV (1% of general population) limits our ability to perform genome-wide association studies. We present the application of four a priori SNP selection techniques, reducing the multiple-testing penalty by restricting analysis to SNPs relevant to BAV in a genome-wide SNP dataset from a cohort of 68 BAV probands and 830 control subjects. Two knowledge-based approaches, CANDID and STRING, were used to systematically identify BAV genes, and their SNPs, from the published literature, microarray expression studies and a genome scan. We additionally tested Functionally Interpolating SNPs (fitSNPs) present on the array; the fourth consisted of SNPs selected by Random Forests, a machine learning approach. These approaches reduced the multiple testing penalty by lowering the fraction of the genome probed to 0.19% of the total, while increasing the likelihood of studying SNPs within relevant BAV genes and pathways. Three loci were identified by CANDID, STRING, and fitSNPS. A haplotype within the AXIN1-PDIA2 locus (p-value of 2.926x10(-06)) and a haplotype within the Endoglin gene (p-value of 5.881x10(-04)) were found to be strongly associated with BAV. The Random Forests approach identified a SNP on chromosome 3 in association with BAV (p-value 5.061x10(-06)). The results presented here support an important role for genetic variants in BAV and provide support for additional studies in well-powered cohorts. Further, these studies demonstrate that leveraging existing expression and genomic data in the context of GWAS studies can identify biologically relevant genes and pathways associated with a congenital heart defect. Show less