Exome sequencing in multiplex families with left-sided cardiac defects has high yield for disease gene discovery.

Congenital heart disease (CHD) is a common group of birth defects with a strong genetic contribution to their etiology, but historically the diagnostic yield from exome studies of isolated CHD has been low. Pleiotropy, variable expressivity, and the difficulty of accurately phenotyping newborns contribute to this problem. We hypothesized that performing exome sequencing on selected individuals in families with multiple members affected by left-sided CHD, then filtering variants by population frequency, in silico predictive algorithms, and phenotypic annotations from publicly available databases would increase this yield and generate a list of candidate disease-causing variants that would show a high validation rate. In eight of the nineteen families in our study (42%), we established a well-known gene/phenotype link for a candidate variant or performed confirmation of a candidate variant's effect on protein function, including variants in genes not previously described or firmly established as disease genes in the body of CHD literature: BMP10, CASZ1, ROCK1 and SMYD1. Two plausible variants in different genes were found to segregate in the same family in two instances suggesting oligogenic inheritance. These results highlight the need for functional validation and demonstrate that in the era of next-generation sequencing, multiplex families with isolated CHD can still bring high yield to the discovery of novel disease genes.

A model of pulldown alignments from SssI-treated DNA improves DNA methylation prediction.

BACKGROUND: Protein pulldown using Methyl-CpG binding domain (MBD) proteins followed by high-throughput sequencing is a common method to determine DNA methylation. Algorithms have been developed to estimate absolute methylation level from read coverage generated by affinity enrichment-based techniques, but the most accurate one for MBD-seq data requires additional data from an SssI-treated Control experiment. RESULTS: Using our previous characterizations of Methyl-CpG/MBD2 binding in the context of an MBD pulldown experiment, we build a model of expected MBD pulldown reads as drawn from SssI-treated DNA. We use the program BayMeth to evaluate the effectiveness of this model by substituting calculated SssI Control data for the observed SssI Control data. By comparing methylation predictions against those from an RRBS data set, we find that BayMeth run with our modeled SssI Control data performs better than BayMeth run with observed SssI Control data, on both 100 bp and 10 bp windows. Adapting the model to an external data set solely by changing the average fragment length, our calculated data still informs the BayMeth program to a similar level as observed data in predicting methylation state on a pulldown data set with matching WGBS estimates. CONCLUSION: In both internal and external MBD pulldown data sets tested in this study, BayMeth used with our modeled pulldown coverage performs better than BayMeth run without the inclusion of any estimate of SssI Control pulldown, and is comparable to - and in some cases better than - using observed SssI Control data with the BayMeth program. Thus, our MBD pulldown alignment model can improve methylation predictions without the need to perform additional control experiments.

Methyl-CpG/MBD2 Interaction Requires Minimum Separation and Exhibits Minimal Sequence Specificity.

Determining the pattern of methylation at CpG dinucleotides in a cell remains an essential component of epigenetic profiling. The correlations among methylation, gene expression, and accompanying disease have just begun to be explored. Many experiments for sensing methylation use a relatively inexpensive, high-throughput approach with a methyl-binding domain (MBD) protein that preferentially binds to methylated CpGs. Here, we characterize the cooperativity and sequence specificity of MBD2-DNA binding in a pulldown experiment revealing three potential biases in such experiments. The first is caused by steric clashes between two MBD2 proteins at mCpGs separated by 2 bp or less, which suggests that simultaneous binding at these sites is inhibited. This is confirmed by comparing input versus pulldown high-throughput sequencing data on M.SssI-treated samples, from which we also find that pulldown efficiency sharply increases for DNA fragments with four or more mCpGs. Analysis of these two data sets was again employed to investigate MBD2's sequence preferences surrounding a methylated CpG (mCpG). In comparing the distributions of bases at positions with respect to an mCpG, statistically significant preferences for certain bases were found, although the corresponding biases in pulldown efficiency were all <5%. While this suggests that mCpG sequence context can mostly be ignored in MBD2 binding, the statistical certainty exhibited by our high-throughput approach bodes well for future applications.

5'-End sequencing in Saccharomyces cerevisiae offers new insights into 5'-ends of tRNAHis and snoRNAs.

tRNAHis guanylyltransferase (Thg1) specifies eukaryotic tRNAHis identity by catalysing a 3'-5' non-Watson-Crick (WC) addition of guanosine to the 5'-end of tRNAHis . Thg1 family enzymes in Archaea and Bacteria, called Thg1-like proteins (TLPs), catalyse a similar but distinct 3'-5' addition in an exclusively WC-dependent manner. Here, a genetic system in Saccharomyces cerevisiae was employed to further assess the biochemical differences between Thg1 and TLPs. Utilizing a novel 5'-end sequencing pipeline, we find that a Bacillus thuringiensis TLP sustains the growth of a thg1Δ strain by maintaining a WC-dependent addition of U-1 across from A73 . Additionally, we observe 5'-end heterogeneity in S. cerevisiae small nucleolar RNAs (snoRNAs), an observation that may inform methods of annotation and mechanisms of snoRNA processing.