选择性剪接在调节转录调控中的作用资源-CSDN文库

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选择性剪接是生物体内一种重要的基因表达调控机制，它通过改变前体mRNA的剪接方式，影响成熟mRNA的组成，进而改变蛋白质的氨基酸序列、结构和功能。选择性剪接在调节转录调控中扮演着重要角色，它不仅可以改变转录因子的活性，还能够调节靶基因的表达，进而影响细胞的生理和病理过程。从给出的文件内容来看，文章主要探讨了选择性剪接事件如何影响转录因子活性，并以肾细胞癌为例进行了深入研究。文章中指出，转录因子的调控能力是由多种遗传和表观遗传机制共同调节的。选择性剪接能够通过增加或去除特定蛋白质结构域，进而调节基因的功能。研究团队构建了一个统计模型，用来推断调控蛋白的选择性剪接事件是否会影响关键转录因子调控其靶基因表达水平的能力。研究利用了从癌症基因组图谱（The Cancer Genome Atlas，TCGA）下载的肾细胞癌（Kidney Renal Clear Cell Carcinoma，KIRC）的RNA-seq数据。研究结果发现，转录因子（如GR，即糖皮质激素受体）的活性水平可以被多种蛋白质的剪接状态所影响，这些蛋白质包括TP53、MDM2（小鼠双微体2同源物）、RBM14（RNA结合蛋白14）和SLK（STE20样激酶）。受到影响的GR靶基因富集了包括p53信号通路、TR/RXR激活、CAR/RXR激活、G1/S检查点调控通路和G2/M DNA损伤检查点调控通路在内的关键癌症相关通路。研究结果表明，某些调控蛋白的外显子包含水平能够影响其他转录因子的活性。此外，通过统计模型的构建和应用，研究揭示了选择性剪接事件与转录调控网络之间的逻辑关系，从而为理解蛋白质之间相互作用及其在转录调控中的作用提供了新的视角。这项研究强调了选择性剪接在转录调控网络中的潜在作用，它不仅影响单一蛋白质的功能，还可能影响蛋白质之间的相互作用和整个网络的动态。了解这些机制对于揭示细胞内复杂的调控网络和疾病的发生机制具有重要意义，特别是在癌症等疾病的治疗和预防策略开发方面。此外，这项研究还表明，利用基因表达谱的反向工程可以有效地解析转录调控中多个蛋白质之间的逻辑关系。选择性剪接是基因表达调控的重要组成部分，其研究不仅仅局限于理论探讨，还可以应用于疾病的诊断、预后评估以及治疗策略的制定。随着转录组学、蛋白质组学及系统生物学技术的不断发展，我们可以期待未来将会有更多关于选择性剪接在调节转录调控中作用的深入研究，以及其在精准医疗和个性化治疗中潜力的实现。

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RES E A R C H Open Access

Roles of alternative splicing in modulating

transcriptional regulation

Jin Li

1†

, Yang Wang

1,2†

, Xi Rao

2,3

, Yue Wang

, Weixing Feng

, Hong Liang

and Yunlong Liu

1,2,3,4*

From The International Conference on Intelligent Biology and Medicin e (ICIBM) 2016

Houston, TX, USA. 08-10 December 2016

Abstract

Background: The ability of a transcription factor to regulate its targets is modulated by a variety of genetic and

epigenetic mechanisms. Alternative splicing can modulate gene function by adding or removing certain protein

domains, and therefore affect the activity of protein. Reverse engineering of gene regulatory networks using gene

expression profiles has proven valuable in dissecting the logical relationships among multiple proteins during the

transcriptional regulation. However, it is unclear whether alternative splicing of certain proteins affects the activity of

other transcription factors.

Results: In order to investigate the roles of alternative splicing during transcriptional regulation, we constructed a

statistical model to infer whether the alternative splicing events of modulator proteins can affect the ability of key

transcription factors in regulating the expression levels of their transcriptional targets. We tested our strategy in

KIRC (Kidney Renal Clear Cell Carcinoma) using the RNA-seq data downloaded from TCGA (the Cancer Genomic

Atlas). We identified 828of modulation relationships between the splicing levels of modulator proteins and activity

levels of transcription factors. For instance, we found that the activity levels of GR (glucocorticoid receptor) protein,

a key transcription factor in kidney, can be influenced by the splicing status of multiple proteins, including TP53,

MDM2 (mouse double minute 2 homolog), RBM14 (RNA-binding protein 14) and SLK (STE20 like kinase). The

influenced GR-targets are enriched by key cancer-related pathways, including p53 signaling pathway, TR/RXR

activation, CAR/RXR activation, G1/S checkpoint regulation pathway, and G2/M DNA damage checkpoint regulation

pathway.

Conclusions: Our analysis suggests, for the first time, that exon inclusion levels of certain regulatory proteins can

affect the activities of many transcription factors. Such analysis can potentially unravel a novel mechanism of how

splicing variation influences the cellular function and provide important insights for how dysregulation of splicing

outcome can lead to various diseases.

Keywords: Kidney cancer, Alternative splicing, Transcriptional regulation, Linear regression, GR, MDM2, TP53

Background

Regulation of gene expression is one of the most impo rt-

ant biological processes in cellular systems. Previously

studies suggest that transcriptional regulation can be

affected by many factors, such as post-translational

modifications of transcription factors, and competitive

binding of multiple Tran scription factor (TF) [1, 2]. It

has been observed that alternative splicing could regu-

late gene function by adding or removing protein

domains, affecting protein activity, or altering the

stability of the transcript of the resulting protein [3–6].

However, the role of alternative splicing on the modula-

tions of transcriptional regulation has not been system-

atically investigated.

Alternative splicing is a critical step of gene regulation

and it enables individual genes to generate multiple

protein products with different structures and functions

* Correspondence: lh@hrbeu.edu.cn; yunliu@iu.edu

†

Equal contributors

College of Automation, Harbin Engineering University, Harbin, Heilongjiang

150001, China

Full list of author information is available at the end of the article

International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and

reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to

the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver

(http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

The Author(s) BMC Systems Biology 2017, 11(Suppl 5):89

DOI 10.1186/s12918-017-0465-6

by the insertion or deletion of important functional do-

mains encoded by alternatively spliced exons [7]. Differ-

ences in the gene expression levels of splicing regulatory

factor have been observed in many cancers, and these

proteins often affect the splicing patterns of many genes

that function in certain cancer-specific biological path-

ways , including cell cycle progression, cellular prolifera-

tion and migration, and RNA processing [8–10].

Although alternative splicing is one of the most wide-

spread mechanisms involved in gene regulation, their

roles of acting as modulators to regulate the activity of

transcription factor have not been explored.

As demonstrated in Fig. 1, splicing-centric modulation

relationship is defined as the ability of one transcription

factor (TF) regulating the expression levels of its target

genes (T) is influenced by the percentage of inclusion

(PSI) of certain alternatively spliced exons of the modu-

lator protein (M). One example is that when the PSI

value of a specific exon in M is high, the expression

levels of the putative transcription factor correlates with

its targets, while such correlation relation is lost when

the PSI value is low.

In this study, we develop a simple regression-ba sed

statistical model for evaluating whether the interactions

of the percentage of inclusion level of a putative modula-

tor protein and the expression levels of transcription

factors jointly contribute to the expression levels of the

target genes. A significant interaction term indicates a

potentially functional modulation relationship. Tested in

KIRC (Kidney Renal Clear Cell Carcinoma) data in the

TCGA database [11], our model ha s the power to

identify hundreds of statistically significant modulation

relationships using RNA-seq data from only a moderate

number of samples. Further investigation suggested that

the activ ity of GR (glucocorticoid receptor) protein has

been influenced by the splicing outcomes of p53 and

MDM2. This result suggested a potential novel mechan-

ism of how these two proteins influenced cell prolifera-

tion and growth in cancer.

Results

Model framework

In this study, we construct a regression-based linear

model to infer the interactions between the activity of

transcription factor (TF), evaluated by correlation be-

tween the expression levels of the TF and its targets (T),

and the percentage of inclusion of a puta tive exon in a

modulator protein (M). A schematic diagram of work-

flow is provided in Fig. 2, using the RNA-seq data of the

479 KIRC samples from the TCGA database [11], and

the TF-target relationships , derived from the ENCODE

(The Encyclopedia of DNA Elements) database. Briefly,

the gene expression levels of transcription factors (TF)

and their targets (T) were downloaded from the TCGA

data portal, which provides the 18,802 values for 20,531

genes. The percentage of inclusion (PSI) of 165 exons

was directly derived from.bam files of the RNA-seq data,

using a probabilistic model called Mixture of Isoforms

(MISO) [12]. These 165 exons were selected from 42,485

annotated skipped exons that are derived using the gene

structures of ENSEMBL database, for their correlations

with the overall survival outcomes. A complete list of the

165 exons is included in the supplementary information

(Additional file 1: Table S1). The splicing outcomes of

these 165 exons are considered putative modulators.

In addition to RNA level datasets, the relationships

between TFs and their targets were downloaded from

the factorbook table, disseminated from the ENCODE

database [13]. We focus our analysis on transcription

factors and putative targets that with the expression

samples over 400. This analysis obtained 226,025 TF-

target pairs composed of 83 TFs and 15,597 targets.

Fig. 1 Splicing-centric modulation relationship. The ability of one transcription factor (TF) regulating the expression levels of its target genes (T) is