DNA-m6A calling and integrated long-read epigenetic and genetic analysis with fibertools.

Long-read DNA sequencing has recently emerged as a powerful tool for studying both genetic and epigenetic architectures at single-molecule and single-nucleotide resolution. Long-read epigenetic studies encompass both the direct identification of native cytosine methylation and the identification of exogenously placed DNA N 6 -methyladenine (DNA-m6A). However, detecting DNA-m6A modifications using single-molecule sequencing, as well as coprocessing single-molecule genetic and epigenetic architectures, is limited by computational demands and a lack of supporting tools. Here, we introduce fibertools, a state-of-the-art toolkit that features a semisupervised convolutional neural network for fast and accurate identification of m6A-marked bases using Pacific Biosciences (PacBio) single-molecule long-read sequencing, as well as the coprocessing of long-read genetic and epigenetic data produced using either the PacBio or Oxford Nanopore Technologies (ONT) sequencing platforms. We demonstrate accurate DNA-m6A identification (>90% precision and recall) along >20 kb long DNA molecules with an ∼1000-fold improvement in speed. In addition, we demonstrate that fibertools can readily integrate genetic and epigenetic data at single-molecule resolution, including the seamless conversion between molecular and reference coordinate systems, allowing for accurate genetic and epigenetic analyses of long-read data within structurally and somatically variable genomic regions.

Matrix prior for data transfer between single cell data types in latent Dirichlet allocation.

Single cell ATAC-seq (scATAC-seq) enables the mapping of regulatory elements in fine-grained cell types. Despite this advance, analysis of the resulting data is challenging, and large scale scATAC-seq data are difficult to obtain and expensive to generate. This motivates a method to leverage information from previously generated large scale scATAC-seq or scRNA-seq data to guide our analysis of new scATAC-seq datasets. We analyze scATAC-seq data using latent Dirichlet allocation (LDA), a Bayesian algorithm that was developed to model text corpora, summarizing documents as mixtures of topics defined based on the words that distinguish the documents. When applied to scATAC-seq, LDA treats cells as documents and their accessible sites as words, identifying "topics" based on the cell type-specific accessible sites in those cells. Previous work used uniform symmetric priors in LDA, but we hypothesized that nonuniform matrix priors generated from LDA models trained on existing data sets may enable improved detection of cell types in new data sets, especially if they have relatively few cells. In this work, we test this hypothesis in scATAC-seq data from whole C. elegans nematodes and SHARE-seq data from mouse skin cells. We show that nonsymmetric matrix priors for LDA improve our ability to capture cell type information from small scATAC-seq datasets.

Development and application of novel BiFC probes for cell sorting based on epigenetic modification.

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.

Predicting chromatin conformation contact maps.

Over the past 15 years, a variety of next-generation sequencing assays have been developed for measuring the 3D conformation of DNA in the nucleus. Each of these assays gives, for a particular cell or tissue type, a distinct picture of 3D chromatin architecture. Accordingly, making sense of the relationship between genome structure and function requires teasing apart two closely related questions: how does chromatin 3D structure change from one cell type to the next, and how do different measurements of that structure differ from one another, even when the two assays are carried out in the same cell type? In this work, we assemble a collection of chromatin 3D datasets-each represented as a 2D contact map- spanning multiple assay types and cell types. We then build a machine learning model that predicts missing contact maps in this collection. We use the model to systematically explore how genome 3D architecture changes, at the level of compartments, domains, and loops, between cell type and between assay types.

DNA-m6A calling and integrated long-read epigenetic and genetic analysis with fibertools.

Long-read DNA sequencing has recently emerged as a powerful tool for studying both genetic and epigenetic architectures at single-molecule and single-nucleotide resolution. Long-read epigenetic studies encompass both the direct identification of native cytosine methylation as well as the identification of exogenously placed DNA N6-methyladenine (DNA-m6A). However, detecting DNA-m6A modifications using single-molecule sequencing, as well as co-processing single-molecule genetic and epigenetic architectures, is limited by computational demands and a lack of supporting tools. Here, we introduce fibertools, a state-of-the-art toolkit that features a semi-supervised convolutional neural network for fast and accurate identification of m6A-marked bases using PacBio single-molecule long-read sequencing, as well as the co-processing of long-read genetic and epigenetic data produced using either PacBio or Oxford Nanopore sequencing platforms. We demonstrate accurate DNA-m6A identification (>90% precision and recall) along >20 kilobase long DNA molecules with a ~1,000-fold improvement in speed. In addition, we demonstrate that fibertools can readily integrate genetic and epigenetic data at single-molecule resolution, including the seamless conversion between molecular and reference coordinate systems, allowing for accurate genetic and epigenetic analyses of long-read data within structurally and somatically variable genomic regions.

Comparing heritability estimators under alternative structures of linkage disequilibrium.

The single nucleotide polymorphism heritability of a trait is the proportion of its variance explained by the additive effects of the genome-wide single nucleotide polymorphisms. The existing approaches to estimate single nucleotide polymorphism heritability can be broadly classified into 2 categories. One set of approaches models the single nucleotide polymorphism effects as fixed effects and the other treats the single nucleotide polymorphism 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) to provide consistent estimates of single nucleotide polymorphism 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 linkage disequilibrium 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 linkage disequilibrium and the degree of relatedness among sampled individuals. Moreover, we establish the equivalence between the 2 method-of-moments estimators, one using a fixed-single nucleotide polymorphism-effects approach, and another using a random-single nucleotide polymorphism-effects approach.

Clinical Pharmacokinetics and Pharmacodynamics of Anti-Tubercular Drugs in Pregnancy.

The objectives of this qualitative review were to critically evaluate and summarize the currently available data on the use of anti-tuberculosis (TB) drugs during pregnancy, with a focus on treatment outcomes, safety, and pharmacokinetics. This qualitative, narrative review was based on literature searches in Medline, Pubmed, Embase, and Google Scholar (from their inception to 13 August 2020). Our search identified 22 papers related to treatment outcomes and 14 papers related to pharmacokinetic exposures and fetal distributions. While it is challenging to study this patient population, current evidence supports treatment of drug-susceptible TB, multidrug-resistant TB and latent TB infections. However, decisions regarding initiating, continuing, or discontinuing anti-tubercular medications while pregnant should be individualized and discussed with a specialist. Similarly, the pharmacokinetic data of anti-TB agents were mainly derived from small scale, observational studies many of which lacked high quality controls. Based on these data, it does not appear that pregnancy has an extensive impact on the pharmacokinetics of the majority of first-line and second-line agents, although caution (discussed in the review) should be exercised in data interpretation. Fetal drug exposure can also be significant and should be considered when selecting an anti-TB agent for longer term treatment. Overall, it is generally difficult to predict pregnancy-associated pharmacokinetic changes based only on drug's physiochemical characteristics.

Monitoring Histone Methylation (H3K9me3) Changes in Live Cells.

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.