Aaron Taudt

Rate, spectrum, and evolutionary dynamics of spontaneous epimutations

Significance Changes in the methylation status of cytosine nucleotides are a source of heritable epigenetic and phenotypic diversity in plants. Here we derive robust estimates of the rate at which cytosine methylation is spontaneously gained (forward epimutation) or lost (backward epimutation) in the genome of the model plant Arabidopsis thaliana. We show that the forward–backward dynamics of selectively neutral epimutations have a major impact on methylome evolution and shape genome-wide patterns of methylation diversity among natural populations in this species. The epimutation rates presented here can serve as reference values in future empirical and theoretical population epigenetic studies in plants. Stochastic changes in cytosine methylation are a source of heritable epigenetic and phenotypic diversity in plants. Using the model plant Arabidopsis thaliana, we derive robust estimates of the rate at which methylation is spontaneously gained (forward epimutation) or lost (backward epimutation) at individual cytosines and construct a comprehensive picture of the epimutation landscape in this species. We demonstrate that the dynamic interplay between forward and backward epimutations is modulated by genomic context and show that subtle contextual differences have profoundly shaped patterns of methylation diversity in A. thaliana natural populations over evolutionary timescales. Theoretical arguments indicate that the epimutation rates reported here are high enough to rapidly uncouple genetic from epigenetic variation, but low enough for new epialleles to sustain long-term selection responses. Our results provide new insights into methylome evolution and its population-level consequences.

Genetic sources of population epigenomic variation

The field of epigenomics has rapidly progressed from the study of individual reference epigenomes to surveying epigenomic variation in populations. Recent studies in a number of species, from yeast to humans, have begun to dissect the cis- and trans-regulatory genetic mechanisms that shape patterns of population epigenomic variation at the level of single epigenetic marks, as well as at the level of integrated chromatin state maps. We show that this information is paving the way towards a more complete understanding of the heritable basis underlying population epigenomic variation. We also highlight important conceptual challenges when interpreting results from these genetic studies, particularly in plants, in which epigenomic variation can be determined both by genetic and epigenetic inheritance.

Single-cell whole genome sequencing reveals no evidence for common aneuploidy in normal and Alzheimer's disease neurons

BackgroundAlzheimer’s disease (AD) is a neurodegenerative disease of the brain and the most common form of dementia in the elderly. Aneuploidy, a state in which cells have an abnormal number of chromosomes, has been proposed to play a role in neurodegeneration in AD patients. Several studies using fluorescence in situ hybridization have shown that the brains of AD patients contain an increased number of aneuploid cells. However, because the reported rate of aneuploidy in neurons ranges widely, a more sensitive method is needed to establish a possible role of aneuploidy in AD pathology.ResultsIn the current study, we used a novel single-cell whole genome sequencing (scWGS) approach to assess aneuploidy in isolated neurons from the frontal cortex of normal control individuals (n = 6) and patients with AD (n = 10). The sensitivity and specificity of our method was shown by the presence of three copies of chromosome 21 in all analyzed neuronal nuclei of a Down’s syndrome sample (n = 36). Very low levels of aneuploidy were found in the brains from control individuals (n = 589) and AD patients (n = 893). In contrast to other studies, we observe no selective gain of chromosomes 17 or 21 in neurons of AD patients.ConclusionscWGS showed no evidence for common aneuploidy in normal and AD neurons. Therefore, our results do not support an important role for aneuploidy in neuronal cells in the pathogenesis of AD. This will need to be confirmed by future studies in larger cohorts.

Single-cell sequencing reveals karyotype heterogeneity in murine and human malignancies

BackgroundChromosome instability leads to aneuploidy, a state in which cells have abnormal numbers of chromosomes, and is found in two out of three cancers. In a chromosomal instable p53 deficient mouse model with accelerated lymphomagenesis, we previously observed whole chromosome copy number changes affecting all lymphoma cells. This suggests that chromosome instability is somehow suppressed in the aneuploid lymphomas or that selection for frequently lost/gained chromosomes out-competes the CIN-imposed mis-segregation.ResultsTo distinguish between these explanations and to examine karyotype dynamics in chromosome instable lymphoma, we use a newly developed single-cell whole genome sequencing (scWGS) platform that provides a complete and unbiased overview of copy number variations (CNV) in individual cells. To analyse these scWGS data, we develop AneuFinder, which allows annotation of copy number changes in a fully automated fashion and quantification of CNV heterogeneity between cells. Single-cell sequencing and AneuFinder analysis reveals high levels of copy number heterogeneity in chromosome instability-driven murine T-cell lymphoma samples, indicating ongoing chromosome instability. Application of this technology to human B cell leukaemias reveals different levels of karyotype heterogeneity in these cancers.ConclusionOur data show that even though aneuploid tumours select for particular and recurring chromosome combinations, single-cell analysis using AneuFinder reveals copy number heterogeneity. This suggests ongoing chromosome instability that other platforms fail to detect. As chromosome instability might drive tumour evolution, karyotype analysis using single-cell sequencing technology could become an essential tool for cancer treatment stratification.

Histone propionylation is a mark of active chromatin

Histones are highly covalently modified, but the functions of many of these modifications remain unknown. In particular, it is unclear how histone marks are coupled to cellular metabolism and how this coupling affects chromatin architecture. We identified histone H3 Lys14 (H3K14) as a site of propionylation and butyrylation in vivo and carried out the first systematic characterization of histone propionylation. We found that H3K14pr and H3K14bu are deposited by histone acetyltransferases, are preferentially enriched at promoters of active genes and are recognized by acylation-state-specific reader proteins. In agreement with these findings, propionyl-CoA was able to stimulate transcription in an in vitro transcription system. Notably, genome-wide H3 acylation profiles were redefined following changes to the metabolic state, and deletion of the metabolic enzyme propionyl-CoA carboxylase altered global histone propionylation levels. We propose that histone propionylation, acetylation and butyrylation may act in combination to promote high transcriptional output and to couple cellular metabolism with chromatin structure and function.

histoneHMM: Differential analysis of histone modifications with broad genomic footprints

BackgroundChIP-seq has become a routine method for interrogating the genome-wide distribution of various histone modifications. An important experimental goal is to compare the ChIP-seq profiles between an experimental sample and a reference sample, and to identify regions that show differential enrichment. However, comparative analysis of samples remains challenging for histone modifications with broad domains, such as heterochromatin-associated H3K27me3, as most ChIP-seq algorithms are designed to detect well defined peak-like features.ResultsTo address this limitation we introduce histoneHMM, a powerful bivariate Hidden Markov Model for the differential analysis of histone modifications with broad genomic footprints. histoneHMM aggregates short-reads over larger regions and takes the resulting bivariate read counts as inputs for an unsupervised classification procedure, requiring no further tuning parameters. histoneHMM outputs probabilistic classifications of genomic regions as being either modified in both samples, unmodified in both samples or differentially modified between samples. We extensively tested histoneHMM in the context of two broad repressive marks, H3K27me3 and H3K9me3, and evaluated region calls with follow up qPCR as well as RNA-seq data. Our results show that histoneHMM outperforms competing methods in detecting functionally relevant differentially modified regions.ConclusionhistoneHMM is a fast algorithm written in C++ and compiled as an R package. It runs in the popular R computing environment and thus seamlessly integrates with the extensive bioinformatic tool sets available through Bioconductor. This makeshistoneHMM an attractive choice for the differential analysis of ChIP-seq data. Software is available from http://histonehmm.molgen.mpg.de.

chromstaR: Tracking combinatorial chromatin state dynamics in space and time

Background Post-translational modifications of histone residue tails are an important component of genome regulation. It is becoming increasingly clear that the combinatorial presence and absence of various modifications define discrete chromatin states which determine the functional properties of a locus. An emerging experimental goal is to track changes in chromatin state maps across different conditions, such as experimental treatments, cell-types or developmental time points. Results Here we present chromstaR, an algorithm for the computational inference of combinatorial chromatin state dynamics across an arbitrary number of conditions. ChromstaR uses a multivariate Hidden Markov Model to determine the number of discrete combinatorial chromatin states using multiple ChIP-seq experiments as input and assigns every genomic region to a state based on the presence/absence of each modification in every condition. We demonstrate the advantages of chromstaR in the context of three common experimental data scenarios. First, we study how different histone modifications combine to form combinatorial chromatin states in a single tissue. Second, we infer genome-wide patterns of combinatorial state differences between two cell types or conditions. Finally, we study the dynamics of combinatorial chromatin states during tissue differentiation involving up to six differentiation points. Our findings reveal a striking sparcity in the combinatorial organization and temporal dynamics of chromatin state maps. Conclusions chromstaR is a versatile computational tool that facilitates a deeper biological understanding of chromatin organization and dynamics. The algorithm is implemented as an R-package and freely available from http://bioconductor.org/packages/chromstaR/.

Histone methylation changes are required for life cycle progression in the human parasite Schistosoma mansoni

Epigenetic mechanisms and chromatin structure play an important role in development. Their impact is therefore expected to be strong in parasites with complex life cycles and multiple, strikingly different, developmental stages, i.e. developmental plasticity. Some studies have already described how the chromatin structure, through histone modifications, varies from a developmental stage to another in a few unicellular parasites. While H3K4me3 profiles remain relatively constant, H3K27 trimethylation and bivalent methylation show strong variation. Inhibitors (A366 and GSK343) of H3K27 histone methyltransferase activity in S. mansoni efficiently blocked miracidium to sporocyst transition indicating that H3K27 trimethylation is required for life cycle progression. As S. mansoni is a multicellular parasite that significantly affects both the health and economy of endemic areas, a better understanding of fluke developmental processes within the definitive host will likely highlight novel disease control strategies. Towards this goal, we also studied H4K20me1 in female cercariae and adults. In particular, we found that bivalent trimethylation of H3K4 and H3K27 at the transcription start site of genes is a landmark of the cercarial stage. In cercariae, H3K27me3 presence and strong enrichment in H4K20me1 over long regions (10–100 kb) is associated with development related genes. Here, we provide a broad overview of the chromatin structure of a metazoan parasite throughout its most important lifecycle stages. The five developmental stages studied here present distinct chromatin structures, indicating that histone methylation plays an important role during development. Hence, components of the histone methylation (and demethylation) machinery may provide suitable Schistosomiasis control targets.

METHimpute: imputation-guided construction of complete methylomes from WGBS data

BackgroundWhole-genome bisulfite sequencing (WGBS) has become the standard method for interrogating plant methylomes at base resolution. However, deep WGBS measurements remain cost prohibitive for large, complex genomes and for population-level studies. As a result, most published plant methylomes are sequenced far below saturation, with a large proportion of cytosines having either missing data or insufficient coverage.ResultsHere we present METHimpute, a Hidden Markov Model (HMM) based imputation algorithm for the analysis of WGBS data. Unlike existing methods, METHimpute enables the construction of complete methylomes by inferring the methylation status and level of all cytosines in the genome regardless of coverage. Application of METHimpute to maize, rice and Arabidopsis shows that the algorithm infers cytosine-resolution methylomes with high accuracy from data as low as 6X, compared to data with 60X, thus making it a cost-effective solution for large-scale studies.ConclusionsMETHimpute provides methylation status calls and levels for all cytosines in the genome regardless of coverage, thus yielding complete methylomes even with low-coverage WGBS datasets. The method has been extensively tested in plants, but should also be applicable to other species. An implementation is available on Bioconductor.

Single-cell whole genome sequencing reveals no evidence for common aneuploidy in normal and Alzheimer's disease neurons (vol 17, 116, 2016)

Alzheimer’s disease (AD) is a neurodegenerative disease of the brain and the most common form of dementia in the elderly. Aneuploidy, a state in which cells have an abnormal number of chromosomes, has been proposed to play a role in neurodegeneration in AD patients. Several studies using fluorescence in situ hybridization have shown that the brains of AD patients contain an increased number of aneuploid cells. However, because the reported rate of aneuploidy in neurons ranges widely, a more sensitive method is needed to establish a possible role of aneuploidy in AD pathology. In the current study, we used a novel single-cell whole genome sequencing (scWGS) approach to assess aneuploidy in isolated neurons from the frontal cortex of normal control individuals (n = 6) and patients with AD (n = 10). The sensitivity and specificity of our method was shown by the presence of three copies of chromosome 21 in all analyzed neuronal nuclei of a Down’s syndrome sample (n = 36). Very low levels of aneuploidy were found in the brains from control individuals (n = 589) and AD patients (n = 893). In contrast to other studies, we observe no selective gain of chromosomes 17 or 21 in neurons of AD patients. scWGS showed no evidence for common aneuploidy in normal and AD neurons. Therefore, our results do not support an important role for aneuploidy in neuronal cells in the pathogenesis of AD. This will need to be confirmed by future studies in larger cohorts.