Chongfan Technology
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28
2024
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The team of Chen Baiyang introduced the QHistone of using epigenome analysis to predict protein synergy in Arabidopsis.
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Histone modifications can epigenetically regulate transcription by marking specific genomic sites, which can be located using chromatin immunoprecipitation sequencing (ChIP-seq).
On October 24, 2024, Chen Baiyang of the Academia Sinica of Taiwan, China, as the corresponding author, was inNature CommunicationsOnline is titled“Predicting protein synergistic effect in Arabidopsis using epigenome profiling”The research paper,The studyQHistone, a predicted database of 1534 ChIP sequences from 27 histone modifications in Arabidopsis, providing three key functions, is presented.
First, QHistone use machine learning to predict the epigenomic features of the query protein, characterized by its most relevant histone modifications, and use these modifications to infer the protein's role in transcriptional regulation. Second, it predicts the synergistic regulatory activity between two proteins by comparing their characteristics. Finally, it detects previously unexplored pairs of co-regulatory proteins by screening all known proteins. The QHistone accurately identifies histone modifications associated with specific known proteins and allows users to computationally validate their results using gene expression data from various plant tissues.These features demonstrate a useful approach to gene regulation analysis using epigenomic data, making QHistone a valuable resource (https://qhistone.paoyang.ipmb.sinica.edu.tw) for the scientific community.
It is well known that histone modifications, such as methylation, acetylation, phosphorylation, and ubiquitination, typically affect transcription by changing chromatin state.For example, acetylation of histones is often associated with activating transcription by reducing the positive charge on the histones, thereby allowing the protein to access DNA. Conversely, methylation can change the overall charge of histones, affecting their interaction with negatively charged DNA, thereby affecting the interaction dynamics between histones and DNA, and ultimately affecting the compression of chromatin. For example, histone 3 lysine 9 dimethylation (H3K9me2) and histone 3 lysine 27 monomethylation (H3P27me1) are known to be rich in constitutively silent heterochromatin and to repressibly regulate transcription due to the dense structure of the heterochromosome. In plants, histone modifications may affect the strength of histone binding to DNA, depending on plant conditions, including stress, developmental stage, tissue, and genotype.Overall, the types of histone modifications and their specific preferences for marker locations on the genome can provide valuable insights into transcriptional regulation, and these preferences are sensitive to plant conditions.
Sequencing after chromatin immunoprecipitation (ChIP-seq) and DNA affinity purification (DAP-seq) are commonly used high-throughput methods for analyzing genome-wide binding between genomic DNA and proteins of interest.Several databases have been established to collect ChIP sequences in Arabidopsis. PlantPAN3.0 incorporated 54 transcription factors (TFs) and 19 histone modifications and variants into its database and studied cis-and trans-regulatory motifs in plant promoters primarily through transcription factor binding sites (TFBS). ReMap2022 integrated ChIP-seq data for 423 TFs and 33 histone modifications and histone variants in Arabidopsis. It enables the identification of histone markers that are enriched in the genome binding region of the query protein. A higher percentage indicates a stronger association with the protein.These databases did not study plant conditions associated with histone modification data, nor did they use rigorous statistical tests or the latest machine learning techniques to approach epigenomic regulation, and machine learning is still limited in predicting histone modifications at specific sites.
Regulatory activity of proteins, such as activation or inhibition, is usually associated with histone modifications.For example, the binding site of the activator typically coincides with the position of the histone acetylation tag. Thus, the set of histone modifications that typically mark the binding positions of a particular regulatory protein can be used to assess the regulatory function of that protein, and this unique set of histone modifications can be viewed as an epigenomic map of that protein. Depending on the regulatory protein, the relevant epigenetic marks in the epigenomic map can be activated, repressed or combined, reflecting the complex regulatory dynamics involved in biological processes. Heterochromatin-like protein 1(LHP1) mediates the diffusion of histone H3 lysine 27(H3K27me3) trimethylation and is required to maintain inhibition of the purified inducible gene FLOWERING LOCUS C(FLC).Therefore, by examining the set of epigenetic marks frequently observed at regulatory protein binding sites (I. e., their epigenomic map), their regulatory activity can be predicted.
In addition, regulatory proteins can perform their regulatory functions synergistically. They can all perform similar functions to activate or inhibit the expression of target genes.Alternatively, they can play opposite roles in transcriptional control. For example, histone deacetylase 6(HDA6) works synergistically with the histone demethylase lysine-specific demethylase 1-LIKE 1 and 2(LDL1/2) to jointly inhibit the expression of genes and TEs. Multimeric inhibitory complex 2(PRC2) catalyzes H3K27me3 and maintains the repression of the target gene. In contrast, increased BONSAI methylation 1(IBM1) and LDL2 have opposite functions in regulating transcription. Loss of IBM1 function results in ectopic accumulation of H3K9me2, an inhibitory marker, in the genome. Another example includes relatively early flowering 6(REF6) and LHP1, where REF6 can alter the repressed state maintained by LHP1, reactivating the gene by inhibitory modification of histone H2A monoubiquitination (H2Aub1).In summary, these examples show that by evaluating the similarity of their epigenomic features, it is possible to determine whether the two regulatory proteins have similar functions or opposite functions in regulation
The study developed QHistone, a predictive database of histone modifications in Arabidopsis (https://qhistone.paoyang.ipmb.sinica.edu.tw). It first predicts epigenomic features of proteins that can be used to assess their regulatory activity.Using a machine learning model trained on a large ChIP-seq dataset of various histone markers, QHistone can accurately cluster regulatory proteins with histone modifications to which they are closely related. In addition, between the two regulatory proteins, QHistone compared the epigenomic map between the proteins to assess their possible coordinated regulation. Pair-wise comparisons of epigenomic maps of 616 proteins were also performed using available ChIP-seq data from NCBI, identifying previously unexplored pairs of functionally linked proteins. The prediction was validated by comparing it to known regulatory proteins associated with specific histone modifications and pairs of proteins known to have similar or different activities. QHistone provide gene expression analysis tools that allow users to computationally validate their results and provide an overview of histone modifications associated with various tissues, stress responses, and genotypes.By integrating comprehensive epigenomic data sets and bioinformatics analysis, QHistone provide a new perspective for gene regulation in plant epigenomics research.
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