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We explored the putative kinase activity of ALPK3 and the consequences of damaging variants using isogenic human induced pluripotent stem cell-derived cardiomyocytes, mice, and human patient tissues.Multiple sequence alignment of all human α-kinase domains and their orthologs revealed 4 conserved residues that were variant only in ALPK3, demonstrating evolutionary divergence of the ALPK3 α-kinase domain sequence. Phosphoproteomic evaluation of both ALPK3 kinase domain inhibition and overexpression failed to detect significant changes in catalytic activity, establishing ALPK3 as a pseudokinase. Investigations into alternative functions revealed that ALPK3 colocalized with myomesin proteins (MYOM1, MYOM2) at both the nuclear envelope and the sarcomere M-band.ALPK3 is an essential cardiac pseudokinase that inserts in the nuclear envelope and the sarcomere M-band. Loss of ALPK3 causes mislocalization of myomesins, critical force-buffering proteins in cardiomyocytes, and also dysregulates M-band proteins necessary for sarcomere protein turnover. We conclude that

The well-established manifestation of mitochondrial mutations in functional cardiac disease (e.g., mitochondrial cardiomyopathy) prompted the hypothesis that mitochondrial DNA (mtDNA) sequence and/or copy number (mtDNAcn) variation contribute to cardiac defects in congenital heart disease (CHD). MtDNAcns were calculated and rare, non-synonymous mtDNA mutations were identified in 1,837 CHD-affected proband-parent trios, 116 CHD-affected singletons, and 114 paired cardiovascular tissue/blood samples. The variant allele fraction (VAF) of heteroplasmic variants in mitochondrial RNA from 257 CHD cardiovascular tissue samples was also calculated. On average, mtDNA from blood had 0.14 rare variants and 52.9 mtDNA copies per nuclear genome per proband. No variation with parental age at proband birth or CHD-affected proband age was seen. mtDNAcns in valve/vessel tissue (320 ± 70) were lower than in atrial tissue (1,080 ± 320, p= 6.8E-21), which were lower than in ventricle tissue (1,340 ± 280, p= 1.4E-4). The frequency of rare variants in CHD-affected individual DNA was indistinguishable from the frequency in an unaffected cohort, and proband mtDNAcns did not vary from those of CHD cohort parents. In both the CHD and the comparison cohorts, mtDNAcns were significantly correlated between mother-child, father-child, and mother-father. mtDNAcns among people with European (mean= 52.0), African (53.0), and Asian haplogroups (53.5) were calculated and were significantly different for European and Asian haplogroups (p= 2.6E-3). Variant heteroplasmic fraction (HF) in blood correlated well with paired cardiovascular tissue HF (r= 0.975) and RNA VAF (r= 0.953), which suggests blood HF is a reasonable proxy for HF in heart tissue. We conclude that mtDNA mutations and mtDNAcns are unlikely to contribute significantly to CHD risk.

Background The contribution of somatic mosaicism, or genetic mutations arising after oocyte fertilization, to congenital heart disease (CHD) is not well understood. Further, the relationship between mosaicism in blood and cardiovascular tissue has not been determined. Methods We developed a new computational method, EM-mosaic (Expectation-Maximization-based detection of mosaicism), to analyze mosaicism in exome sequences derived primarily from blood DNA of 2530 CHD proband-parent trios. To optimize this method, we measured mosaic detection power as a function of sequencing depth. In parallel, we analyzed our cohort using MosaicHunter, a Bayesian genotyping algorithm-based mosaic detection tool, and compared the two methods. The accuracy of these mosaic variant detection algorithms was assessed using an independent resequencing method. We then applied both methods to detect mosaicism in cardiac tissue-derived exome sequences of 66 participants for which matched blood and heart tissue was available. Results EM-mosaic detected 326 mosaic mutations in blood and/or cardiac tissue DNA. Of the 309 detected in blood DNA, 85/97 (88%) tested were independently confirmed, while 7/17 (41%) candidates of 17 detected in cardiac tissue were confirmed. MosaicHunter detected an additional 64 mosaics, of which 23/46 (50%) among 58 candidates from blood and 4/6 (67%) of 6 candidates from cardiac tissue confirmed. Twenty-five mosaic variants altered CHD-risk genes, affecting 1% of our cohort. Of these 25, 22/22 candidates tested were confirmed. Variants predicted as damaging had higher variant allele fraction than benign variants, suggesting a role in CHD. The estimated true frequency of mosaic variants above 10% mosaicism was 0.14/person in blood and 0.21/person in cardiac tissue. Analysis of 66 individuals with matched cardiac tissue available revealed both tissue-specific and shared mosaicism, with shared mosaics generally having higher allele fraction. Conclusions We estimate that ~ 1% of CHD probands have a mosaic variant detectable in blood that could contribute to cardiac malformations, particularly those damaging variants with relatively higher allele fraction. Although blood is a readily available DNA source, cardiac tissues analyzed contributed ~ 5% of somatic mosaic variants identified, indicating the value of tissue mosaicism analyses.

Mosaicism due to somatic mutations can cause multiple diseases including cancer, developmental and overgrowth syndromes, neurodevelopmental disorders, autoinflammatory diseases, and atrial fibrillation. With the increased use of next generation sequencing technology, multiple tools have been developed to identify low-frequency variants, specifically from matched tumor-normal tissues in cancer studies. To investigate whether mosaic variants are implicated in congenital heart disease (CHD), we developed a pipeline using the cancer somatic variant caller MuTect to identify mosaic variants in whole-exome sequencing (WES) data from a cohort of parent/affected child trios (n = 715) and a cohort of healthy individuals (n = 416). This is a novel application of the somatic variant caller designed for cancer to WES trio data. We identified two cases with mosaic KMT2D mutations that are likely pathogenic for CHD, but conclude that, overall, mosaicism detectable in peripheral blood or saliva does not account for a significant portion of CHD etiology.

We describe a variation of droplet digital PCR (ddPCR) for DNA structural variant analysis, where expensive fluorescent probes are replaced by low-cost EvaGreen, a DNA binding dye, hence reducing cost without reducing sensitivity. ddPCR is a method that employs water-oil microfluidics and fluorescence technology to quantify target nucleic acids with extreme precision and sensitivity. Traditional ddPCR uses standard Taqman probe assays to determine target DNA concentrations, however these two-color fluorescent oligonucleotide probes are expensive and require extensive optimization. EvaGreen’s DNA binding properties allow target products to be effectively quantified at a significantly lower cost. This protocol employs the EvaGreen ddPCR assay to confirm structural variants on a ddPCR system and produces results comparable to the standard, far more expensive Taqman approach.

Multiple tools have been developed to identify copy number variants (CNVs) from whole exome (WES) and whole genome sequencing (WGS) data. Current tools such as XHMM for WES and CNVnator for WGS identify CNVs based on changes in read depth. For WGS, other methods to identify CNVs include utilizing discordant read pairs and split reads and genome-wide local assembly with tools such as Lumpy and SvABA, respectively. Here, we introduce a new method to identify deletion CNVs from WES and WGS trio data based on the clustering of Mendelian errors (MEs). Using our Mendelian Error Method (MEM), we identified 127 deletions (inherited and de novo) in 2,601 WES trios from the Pediatric Cardiac Genomics Consortium, with a validation rate of 88% by digital droplet PCR. MEM identified additional de novo deletions compared to XHMM, and also identified sample switches, DNA contamination, a significant enrichment of 15q11.2 deletions compared to controls and eight cases of uniparental disomy. We applied MEM to WGS data from the Genome In A Bottle Ashkenazi trio and identified deletions with 97% specificity. MEM provides a robust, computationally inexpensive method for identifying deletions, and an orthogonal approach for verifying deletions called by other tools.

The mechanisms by which truncating mutations in