利用表观基因组分析预测拟南芥的蛋白质协同效应-动脉网

Predicting protein synergistic effect in Arabidopsis using epigenome profiling

Nature 等信源发布 2024-10-24 15:58

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AbstractHistone modifications can regulate transcription epigenetically by marking specific genomic loci, which can be mapped using chromatin immunoprecipitation sequencing (ChIP-seq). Here we present QHistone, a predictive database of 1534 ChIP-seqs from 27 histone modifications in Arabidopsis, offering three key functionalities.

摘要组蛋白修饰可以通过标记特定的基因组位点来表观遗传调节转录，可以使用染色质免疫沉淀测序（ChIP-seq）对其进行定位。在这里，我们介绍了QHistone，一个来自拟南芥27个组蛋白修饰的1534个ChIP-seq的预测数据库，提供了三个关键功能。

Firstly, QHistone employs machine learning to predict the epigenomic profile of a query protein, characterized by its most associated histone modifications, and uses these modifications to infer the protein’s role in transcriptional regulation. Secondly, it predicts synergistic regulatory activities between two proteins by comparing their profiles.

首先，QHistone使用机器学习来预测查询蛋白的表观基因组谱，其特征在于其最相关的组蛋白修饰，并使用这些修饰来推断蛋白质在转录调控中的作用。其次，它通过比较两种蛋白质的概况来预测两种蛋白质之间的协同调节活性。

Lastly, it detects previously unexplored co-regulating protein pairs by screening all known proteins. 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.

。QHistone准确识别与特定已知蛋白质相关的组蛋白修饰，并允许用户使用来自各种植物组织的基因表达数据计算验证其结果。

These functions demonstrate an useful approach to utilizing epigenome data for gene regulation analysis, making QHistone a valuable resource for the scientific community (https://qhistone.paoyang.ipmb.sinica.edu.tw)..

这些功能证明了利用表观基因组数据进行基因调控分析的有用方法，使QHistone成为科学界的宝贵资源(https://qhistone.paoyang.ipmb.sinica.edu.tw)。。

IntroductionHistone modifications such as methylation, acetylation, phosphorylation, and ubiquitylation are known to affect transcription commonly through altering chromatin status1. For example, acetylated histones are often associated with activating transcription by reducing the positive charges on histones, thus allowing proteins to gain access to the DNA2,3,4,5.

引言已知组蛋白修饰如甲基化，乙酰化，磷酸化和泛素化通常通过改变染色质状态来影响转录1。例如，乙酰化组蛋白通常通过减少组蛋白上的正电荷来激活转录，从而使蛋白质获得DNA2,3,4,5。

Conversely, methylation can change the overall charge of the histone proteins, affecting their interaction with the negatively charged DNA, hence influencing the interaction dynamics between histones and DNA, that ultimately impacts the chromatin compaction2,3,4,5. For example, histone 3 lysine 9 dimethylation (H3K9me2) and histone 3 lysine 27 monomethylation (H3K27me1) are known to be enriched in constitutive silenced heterochromatin, and repressively regulate transcription due to the dense structure of heterochromatin4,5,6,7,8,9,10 (See Supplementary Table 1 for a list of histone modifications that are often active or repressive).

相反，甲基化可以改变组蛋白的总电荷，影响它们与带负电荷的DNA的相互作用，从而影响组蛋白和DNA之间的相互作用动力学，最终影响染色质的紧密性2,3,4,5。例如，已知组蛋白3赖氨酸9二甲基化（H3K9me2）和组蛋白3赖氨酸27单甲基化（H3K27me1）富含组成型沉默的异染色质，并且由于异染色质4,5,6,7,8,9,10的致密结构而抑制性调节转录（参见补充表1以获得通常具有活性或抑制性的组蛋白修饰列表）。

In plants, histone modifications may affect the strength of histone binding to DNA, and such influence is dependent on the plant conditions including stresses, developmental stages, tissues and genotypes11,12. Overall, the types of histone modifications and their specific preferences for marking locations on the genome can provide valuable insights into transcriptional regulation, and these preferences are sensitive to plant conditions.Chromatin immunoprecipitation followed by sequencing (ChIP-seq) and DNA affinity purification sequencing (DAP-seq) are commonly used high-throughput methods for profiling the genome-wide binding between genomic DNA and proteins of interests13,14.

在植物中，组蛋白修饰可能会影响组蛋白与DNA结合的强度，这种影响取决于植物条件，包括胁迫，发育阶段，组织和基因型11,12。总体而言，组蛋白修饰的类型及其在基因组上标记位置的特定偏好可以为转录调控提供有价值的见解，并且这些偏好对植物条件敏感。染色质免疫沉淀然后测序（ChIP-seq）和DNA亲和纯化测序（DAP-seq）是常用的高通量方法，用于分析基因组DNA和感兴趣蛋白质之间的全基因组结合13,14。

A couple of databases have been built to collect ChIP-seqs in Arabidopsis (Supplementary table 2). Pla.

已经建立了几个数据库来收集拟南芥中的ChIP-seq（补充表2）。解放军。

(1)

Where P(x) is the probabilistic outputs of the first query, and Q(x) is the second query59.In addition, Mutual information (MI) is another score for estimating the amount of information shared between the two epigenome profiles. The formula of MI is as below,$$I(X{;}Y)={\sum}_{x\in Y}{\sum}_{x\in X}{P}_{\left(X,Y\right)}(x,y)\log \frac{{P}_{\left(X,Y\right)}(x,y)}{{P}_{\left(X\right)}(x){P}_{\left(Y\right)}(y)}$$.

其中P（x）是第一个查询的概率输出，Q（x）是第二个查询59。此外，互信息（MI）是估计两个表观基因组图谱之间共享信息量的另一个得分。MI的公式如下，$$I（X{；}Y）={\ sum}{X \ in Y}{\ sum}{X \ in X}{P}_{\左（X，Y \右）}（X，Y）\log\frac{{P}_{\左（X，Y \右）}（X，Y）}{{P}_{P}_{\左（Y \右）}（Y）}$$。

(2)

Where X is ranking order of first query, Y is ranking order of second query, and P(X,Y) is the joint probability mass function60. We evaluated these scores from known partners or functionally associated pairs of functional consistency and functionally opposite (Supplementary Table 7).

其中X是第一个查询的排名顺序，Y是第二个查询的排名顺序，P（X，Y）是联合概率质量函数60。我们从已知的合作伙伴或功能相关的功能一致性对和功能相反的对中评估了这些分数（补充表7）。

The functionally consistent pairs would display a low KLD score and a high MI value. In contrast, the opposite pairs would exhibit a high KLD score and a low MI value.Gene expression analysisAs part of validating the use of an epigenome profile to predict the regulatory activity of a query protein, a gene expression analysis is implemented.

功能一致的对将显示低KLD分数和高MI值。相反，相反的对将表现出高KLD分数和低MI值。基因表达分析作为验证使用表观基因组谱预测查询蛋白调控活性的一部分，实施了基因表达分析。

By comparing the overall expression levels of promoters (the genomic regions between the TSS and 2 kilobases upstream) overlapping with peaks from ChIP-seq of the query protein with those that do not overlap, the result can be used to assess whether the query protein is likely to function as an activator or repressor.

通过比较与查询蛋白的ChIP-seq峰重叠的启动子（TSS和上游2千碱基之间的基因组区域）与不重叠的启动子的总体表达水平，结果可用于评估查询蛋白是否可能起激活剂或阻遏物的作用。

This analysis is performed across 42 plant tissues, with results detailed in Supplementary Table 11 along with the RNA-seq data sources. These results can be used as a validation for predictions based on epigenome profiling (See Fig. 4E SDG2 and S2Lb for examples of activating transcription, and Supplementary Fig. 5E CLF and SWN for examples of repressive transcription).

该分析在42个植物组织中进行，结果详见补充表11以及RNA-seq数据源。这些结果可以用作基于表观基因组分析的预测的验证（参见图4E SDG2和S2Lb以获得激活转录的例子，以及补充图5E CLF和SWN以获得抑制转录的例子）。

Moreover, there might be cases when a regulatory protein may change its regulatory functions by tissues (Supplementary Fig. 6C IBM1) and may function differently between a protein pair (Supplementary Fig. 6C IBM1 and LDL1).GO enrichment analysisThe protein binding regions were functionally classified via Gene Ontology enrichment analysis (GOEA) using GOATOOLS61 with FDR < 0.05 as the threshold.

此外，可能存在调节蛋白可能通过组织改变其调节功能的情况（补充图6C IBM1），并且在蛋白质对之间可能具有不同的功能（补充图6C IBM1和LDL1）。GO富集分析使用GOATOOLS61以FDR<0.05为阈值，通过基因本体富集分析（GOEA）对蛋白质结合区进行功能分类。

In “Epigenome Profiling “, it is performed on the genes overlap.

在“表观基因组分析”中，它是在基因重叠上进行的。

(3)

where A is the length of overlapping between the specific structure and the protein binding regions, B is the total length of protein binding regions, C is the total length of the genomic regions containing the genomic structure, and D is the genome size. The enrichment score measures the level of enrichment of a specific histone modification in a genomic structure, normalized by the overall distribution of this structure throughout the genome.

其中A是特定结构和蛋白质结合区域之间的重叠长度，B是蛋白质结合区域的总长度，C是包含基因组结构的基因组区域的总长度，D是基因组大小。富集评分测量基因组结构中特定组蛋白修饰的富集水平，通过该结构在整个基因组中的总体分布进行归一化。

The score is log2 scaled, where a score above zero suggests a higher presence of the histone mark at this structure compared to random, and a score below zero suggests a lower presence.Reporting summaryFurther information on research design is available in the Nature Portfolio Reporting Summary linked to this article..

分数是log2标度的，其中高于零的分数表明与随机相比，该结构中组蛋白标记的存在更高，低于零的分数表明存在更低。报告摘要有关研究设计的更多信息，请参阅本文链接的Nature Portfolio Reporting Summary。。

Data availability

数据可用性

The data that support the findings of this study are openly available at Zenodo: https://doi.org/10.5281/zenodo.11228366.

支持本研究结果的数据可在Zenodo公开获得：https://doi.org/10.5281/zenodo.11228366.

Code availability

代码可用性

Source codes are publicly available at:Zenodo: https://doi.org/10.5281/zenodo.10649327. Docker images of peak-calling pipeline is available at: https://hub.docker.com/r/dppss90008/qhistone-pipeline.

https://doi.org/10.5281/zenodo.10649327.peak calling pipeline的Docker图像位于：https://hub.docker.com/r/dppss90008/qhistone-pipeline.

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Download referencesAcknowledgementsWe thank Chiao-Yu Lyra Sheu for the preliminary data analysis, Nien Yang for the art painting of Arabidopsis. We also thank Zheng-Zhong Huang and Jimmy Lin from the IPMB IT/Network service for the IT support. This work was supported by grants from Academia Sinica (NTU-AS Innovative Joint Program (AS-NTU-112-12)), and National Science and Technology Council, Taiwan 109-2313-B-001-009-MY3 and 111 −2927-I-001 −505 and 112-2311-B-001 −007 to P.-Y.C.Author informationAuthors and AffiliationsInstitute of Plant and Microbial Biology, Academia Sinica, Taipei, 115201, TaiwanChih-Hung Hsieh, Ya-Ting Sabrina Chang, Ming-Ren Yen, Jo-Wei Allison Hsieh & Pao-Yang ChenAuthorsChih-Hung HsiehView author publicationsYou can also search for this author in.

下载参考文献致谢我们感谢焦裕琴的初步数据分析，感谢杨年的拟南芥艺术绘画。我们还要感谢IPMB IT/网络服务的郑忠煌和吉米·林对IT的支持。

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PubMed Google ScholarContributionsP.-Y.C. conceived the project. C.-H.H. majorly carried out the data analysis and developed the website. Y.-T.S.C. and M.-R.Y. also contribute to the data analysis. C.-H.H., J.-W.A.H., and P.-Y.C. wrote and edited the paper with inputs from all authors.Corresponding authorCorrespondence to.

PubMed谷歌学术贡献SP-Y、 C.构思了这个项目。C、 -H.H.主要进行了数据分析并开发了网站。Y、。C、 -H.H.，J.-W.A.H。和P.-Y.C.在所有作者的投入下撰写和编辑了这篇论文。对应作者对应。

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Reprints and permissionsAbout this articleCite this articleHsieh, CH., Chang, YT.S., Yen, MR. et al. Predicting protein synergistic effect in Arabidopsis using epigenome profiling.

转载和许可本文引用本文Hsieh，CH.，Chang，YT.S.，Yen，MR。等人使用表观基因组分析预测拟南芥中的蛋白质协同效应。

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