Background

In eukaryotes, DNA is packaged into chromatin by wrapping around histone proteins. Histone post-translational modifications (hPTMs) are key regulators of gene expression, epigenetic memory, and transposable element (TE) repression. Several hPTMs, such as H3K4me3, H3K9me3, and H3K27me3, are found across animals, plants, fungi, and even unicellular eukaryotes.

However, the conservation of a mark does not necessarily imply conservation of function. Traditional chromatin immunoprecipitation followed by sequencing (ChIP-seq) requires large amounts of cells and antibodies, and experiments must be performed separately for each species, making cross-species comparisons difficult. Due to the focus of research on a limited number of model organisms such as humans, mice, yeast, and Arabidopsis, the chromatin states of amoebozoa, rhizaria, dictyostelia, and cryptomonads are largely unknown. This Nature Genetics paper aimed to compare the location and function of hPTMs in distantly related eukaryotes using a consistent experimental framework.

Key Findings

The researchers developed iChIP2, a low-input combinatorial indexing ChIP-seq method. In this method, the first barcode is attached to the chromatin of each species, and then the samples are pooled for immunoprecipitation, followed by the addition of a second index that distinguishes the antibodies. By processing multiple species in the same reaction, the method reduces antibody performance and batch effects. The researchers simultaneously evaluated 25 anti-hPTM antibodies and selected the optimal antibody for each mark, then mapped 12 hPTMs in 12 phylogenetically diverse eukaryotic species. The method was also validated using 110 and 440 nanograms of chromatin to test the barcoding conditions.

The ChIP-seq data for each species was combined with RNA sequencing and gene/TE annotations. The researchers then applied the ChromHMM algorithm, a model for predicting chromatin states, and compared the common hPTM combinations across species, organizing them into 19 'meta-states'. Meta-states 1-9 were primarily composed of histone acetylation and H3K4me2/H3K4me3 around promoters, while 10-16 consisted of H3K36me3 and H3K79 methylation in the body of active genes. The distribution of these active chromatin states was relatively consistent across lineages.

In contrast, repressive chromatin states differed significantly. Silent genes and TEs showed different combinations of H3K9me3, H3K27me3, and H3K79me1/me2/me3 depending on the species. In the amoebozoan Acanthamoeba castellanii, two distinct repressive states centered on H3K9me3 were identified, while in Dictyostelium discoideum, three states including H3K79 methylation were found. The cryptomonad Guillardia theta showed an H3K9me1 state associated with TEs. In Naegleria gruberi, the H3K9me3/H3K27me3 combination was focused on TEs, but in Biggwellia natans, the same combination was associated with both TEs and lowly expressed genes.

Significance and Implications

These results suggest that the 'histone code' of eukaryotes is more like a grammar that combines ancient chemical marks in species-specific ways, rather than a fixed dictionary. In particular, the regulation of active genes appears to have deep evolutionary roots, while repressive systems may have been rapidly reorganized in response to competition between TEs and their hosts. It is also possible that TE repression mechanisms have been co-opted for species-specific gene regulation.

iChIP2 can be used as a platform to screen the epigenomes of multiple species at once, including parasitic protists, microalgae, and environmental microorganisms that have low biomass or are difficult to obtain. For example, comparing hPTMs around TEs and virulence genes in pathogenic protists under normal and drug-treated conditions can help narrow down the chromatin targets associated with dormancy or drug resistance. In industrial microorganisms, it can be used to identify candidate strains for improvement by tracking the state of TEs that cause gene silencing and genome instability. However, this study did not directly present human disease markers, so target-specific validation and evaluation of species-specific antibody performance should follow before clinical diagnosis or therapeutic application.