H3K9me3 (methylation of lysine 9
of histone H3) is an epigenetic
modification that acts as a repressor mark. Several diseases, including
cancers and neurological disorders, have been associated with aberrant
changes in H3K9me3 levels. Different tools have been developed to
enable detection and quantification of H3K9me3 levels in cells. Most
techniques, however, lack live cell compatibility. To address this
concern, we have engineered recombinant protein sensors for probing
H3K9me3 in situ. A heterodimeric sensor containing a chromodomain
and chromo shadow domain from HP1a was found to be optimal in recognizing
H3K9me3 and exhibited similar spatial resolution to commercial antibodies.
Our sensor offers similar quantitative accuracy in characterizing
changes in H3K9me3 compared to antibodies but claims single cell resolution.
The sensor was applied to evaluate changes in H3K9me3 responding to
environmental chemical atrazine (ATZ). ATZ was found to result in
significant reductions in H3K9me3 levels after 24 h of exposure. Its
impact on the distribution of H3K9me3 among cell populations was also
assessed and found to be distinctive. We foresee the application of
our sensors in multiple toxicity and drug-screening applications.
The SNP heritability of a trait is the proportion of its variance explained by the additive effects of the genome-wide single nucleotide polymorphisms (SNPs). The existing approaches to estimate SNP heritability can be broadly classified into two categories. One set of approaches model the SNP effects as fixed effects and the other treats the SNP effects as random effects. These methods make certain assumptions about the dependency among individuals (familial relationship) as well as the dependency among markers (linkage disequilibrium, LD) to provide consistent estimates of SNP heritability as the number of individuals increases. While various approaches have been proposed to account for such dependencies, it remains unclear which estimates reported in the literature are more robust against various model misspecifications. Here we investigate the impact of different structures of LD and familial relatedness on heritability estimation. We show that the performance of different methods for heritability estimation depends heavily on the structure of the underlying pattern of LD and the degree of relatedness among sampled individuals. Moreover, we establish the equivalence between the two method-of-moments estimators, one using a fixed-SNP-effects approach, and another using a random-SNP-effects approach.
The epigenetic signature of cancer cells varies with disease progression and drug treatment, necessitating the study of these modifications with single cell resolution over time. The rapid detection and sorting of cells based on their underlying epigenetic modifications by flow cytometry can enable single cell measurement and tracking to understand tumor heterogeneity and progression warranting the development of a live‐cell compatible epigenome probes. In this work, we developed epigenetic probes based on bimolecular fluorescence complementation (BiFC) and demonstrated their capabilities in quantifying and sorting cells based on their epigenetic modification contents. The sorted cells are viable and exhibit distinctive responses to chemo‐therapy drugs. Notably, subpopulations of MCF7 cells with higher H3K9me3 levels are more likely to develop resistance to Doxorubicin. Subpopulations with higher 5mC levels, on the other hand, tend to be more responsive. Overall, we report for the first time, the application of novel split probes in flow cytometry application and elucidated the potential role of 5mC and H3K9me3 in determining drug responses.
Single-molecule chromatin fiber sequencing is based on the single-nucleotide resolution identification of DNA N6-methyladenine (m6A) along individual sequencing reads. We present fibertools, a semi-supervised convolutional neural network that permits the fast and accurate identification of both endogenous and exogenous m6A-marked bases using single-molecule long-read sequencing. Fibertools enables highly accurate (>90% precision and recall) m6A identification along multi-kilobase DNA molecules with a ~1,000-fold improvement in speed and the capacity to generalize to new sequencing chemistries.
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