IDPT: Insights into potential intrinsically disordered proteins through transcriptomic analysis of genes for prostate carcinoma epigenetic data

    IDPT: Insights into potential intrinsically disordered proteins through transcriptomic analysis of genes for prostate carcinoma epigenetic data Saurav Mallik, Sagnik Sen, Ujjwal Maulik PII: DOI: Reference:

S0378-1119(16)30246-3 doi: 10.1016/j.gene.2016.03.056 GENE 41262

To appear in:

Gene

Received date: Revised date: Accepted date:

2 December 2015 22 February 2016 30 March 2016

Please cite this article as: Mallik, Saurav, Sen, Sagnik, Maulik, Ujjwal, IDPT: Insights into potential intrinsically disordered proteins through transcriptomic analysis of genes for prostate carcinoma epigenetic data, Gene (2016), doi: 10.1016/j.gene.2016.03.056

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ACCEPTED MANUSCRIPT

IDPT: Insights into Potential Intrinsically

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Disordered Proteins Through Transcriptomic

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Epigenetic Data

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Analysis of Genes for Prostate Carcinoma

Intelligence Unit, Indian Statistical Institute, Kolkata-700108, India.

b Department

of Computer Science and Engineering, Jadavpur University,

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a Machine

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Saurav Mallik a , Sagnik Sen b , and Ujjwal Maulik c

Kolkata-700032, India. of Computer Science and Engineering, Jadavpur University,

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c Department

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Abstract

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Kolkata-700032, India.

Involvement of intrinsically disordered proteins (IDPs) with various dreadful diseases like cancer is an interesting research topic. In order to gain novel insights into the regulation of IDPs, in this article, we perform a transcriptomic analysis of mRNAs (genes) for transcripts encoding IDPs on a human multi-omics prostate carcinoma dataset having both gene expression and methylation data. In this regard, firstly the genes that consist of both the expression and methylation data, and that are corresponding to the cancer-related prostate-tissue-specific disordered proteins of M obiDb database, are selected. We apply standard t-test for determining differentially expressed genes as well as differentially methylated genes. A network having these genes and their targeter miRNAs from Diana Tarbase v7.0 database and corresponding Transcription Factors from TRANSFAC and ITFP databases, is then built. Thereafter, we perform literature search, and KEGG pathway and gene ontology analyses using DAVID database. Finally, we report several significant po-

Preprint submitted to Elsevier

22 February 2016

ACCEPTED MANUSCRIPT tential gene-markers (with the corresponding IDPs) that have inverse relationship between differential expression and methylation patterns, and that are hub genes of the TF-miRNA-gene network.

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Key words: Intrinsically Disordered proteins, Epigenetic genemarkers,

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dataset, t-test, TF-miRNA-gene network, hub genes.

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Transcriptomic analysis of genes, Multi-omics Prostate Carcinoma Epigenetic

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Introduction

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Intrinsically Disordered Proteins (IDP s) are a type of proteins without any

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stable ordered structure [1], [2], [3]. IDP s consist of a specific disorder region.

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Alternatively, it might have a complete structural disorder. IDP s are of differ-

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ent categories of proteins (viz., membrane, soluble, aqueous proteins etc.) [4].

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IDP s are indicators of evolutionary rate [5]. IDP s may involve different types

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of functional activities. In regulatory and signaling pathways, IDP s that treat

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like lead compounds, interact with other proteins by its disordered region. It

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is known that many proteins need to acquire a well-defined structure in order

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to perform their function, while a higher significant portion of the proteome

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of any organism has polypeptide segments which are not likely to design a

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defined 3D structure, but are still functional. These segments of proteins are

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stated as intrinsically disordered regions (IDRs) [6]. The Proteins that do

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not have IDRs, are denoted as structured proteins, whereas the proteins with

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completely disordered sequences which do not acquire any tertiary structure,

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are stated as intrinsically disordered proteins (IDP s) [7]. IDP is a complete

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disordered structure or containing Intrinsically Disordered regions (IDRs) of

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Email addresses: [email protected], chasaurav [email protected] (Saurav Mallik), [email protected] (Sagnik Sen), [email protected] (Ujjwal Maulik).

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ACCEPTED MANUSCRIPT different lengths at different positions [8]. It is observed that there are several

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IDRs which are predicted from some ordered proteins. Approximately, 30%

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of eukaryotic proteins have disordered regions. But it is not necessary that

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they are differentially expressed.

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Due to their unusual structural characteristics and important functional char-

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acteristics, the presence of IDP s in a cell needs to be observed very carefully.

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Different dreadful diseases like cancers, carcinomas, cardiac diseases and neu-

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rodegenerative diseases can be formed by the differentially expressed IDP s

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[8].

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Now a days, gene expression is not a singular matter [9]. Different epigenetic

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factors (like DNA methylation [10], [11], [12]) can affect the regulation of genes.

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It signifies addition of the methyl group to 5th cytosine pyrimidine ring or 6th

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nitrogen of the adenine purine ring in genomic DNA. Methylation in general

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decreases the gene expression levels. Until now, IDP s are not analyzed with

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multi-omics epigenetic dataset. Methylation has two states (hyper-methylation

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and hypo-methylation). Approximately, 80-90% of CpG sites are methylated

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across the whole genome, in human body.

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Transcription Factors (T F s) [13] also play an important role in case of gene

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regulation. T F s might regulate genes and miRNAs either positively or nega-

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tively. It maintains the flow of genetic information either alone or with other

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proteins in a group from DNA to mRN A by either promoting, or blocking the

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hiring of RNA polymerase to the specified genes. T F s have significant role in

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case of gene expression levels as well as methylation levels.

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Prostate Carcinoma is a type of cancers that generally affects the male pro-

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static glands. It is observed that prostate cancer is generally common factor

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for the old aged people. It contains three risk groups (viz., genetic, obesity

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and aged). Prostate cancer is uncommon for the age less than 45. Average age

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of the prostate cancers might be 70. It has not have any clear symptoms. It

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ACCEPTED MANUSCRIPT has some symptoms just like the benign prostatic hyperplasia. Some of the

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common symptoms are hematuria (i.e., blood in urine), painful urination etc.

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In case of advanced stages of prostate carcinoma, it can be possible that it

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might affect other body organs like spinal chord, femer etc.

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Now, the equilibrium level of a protein [14] is based on the rate of its creation

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relative to the rate of its degradation. The production-quantity of a protein

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in the cell is surely affected by the level of expression of its mRNA (gene)

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transcript. If it is necessary for the proteins to keep themselves in the disor-

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dered state for any length of time, they have to either bypass the pathways

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related to endogenous degradation (e.g., ATP-dependent proteolytic 26S pro-

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teasome) which target unfolded-proteins or be created in sufficient quantity

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to overload the pathways associated with protein degradation in temporary

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fashion. In order to determine the expression patterns of mRNA (gene) for

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transcripts encoding IDPs and to discover novel insights into the biomolec-

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ular mechanisms of transcriptional regulation, some bioinformatics-analysis

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might be carried out on any publicly available tissue-specific mRNA (gene)

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expression data having experimental (diseased) and control (normal) sam-

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ples. Until now, no epigenetic study has been tried for this purpose. Thus, if

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some multi-omics epigenetic data can be utilized for this purpose, then the

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analysis will be surely enhanced. Therefore, for finding novel insights into

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the regulation of IDPs, in this article, we perform a transcriptomic anal-

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ysis of mRNAs (genes) for transcripts encoding IDPs on a publicly avail-

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able human multi-omics dataset having both gene expression and methyla-

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tion data. In this regard, we identify some epigenetic gene-markers having

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their diffential expression and methylation patterns by integrating statistical

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and TF-miRNA-gene network analyses for a prostate carcinoma multi-omics

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(epigenetic) dataset having both the gene expression and methylation data.

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Thereafter, the corresponding IDPs related to the resulting gene-markers are

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obtained. These resulting IDPs are called as potential IDPs since their cor-

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ACCEPTED MANUSCRIPT responding mRNA transcripts are both differentially expressed as well as dif-

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ferentially methylated, and are also hub-genes (centralized genes) in corre-

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sponding TF-miRNA-gene network that are associated with higher number of

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other biomolecules (TF and miRNAs). In brief, for this regard, at first, from

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M obiDb database [15], we collect the IDP s which are associated with the

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cancers in different tissue types of human body (viz., prostate tissue, ovarian

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tissue, lung tissue, oral tissue, cardiac tissue, lymph tissue, pancreatic tissue,

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breast tissue). Since we know that protein is a translational product of mRNA

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(gene), and epigenetic data is not available for the protein, therefore for the

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experimental purposes, we take a multi-omics prostate carcinoma epigenetic

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dataset having both the gene expression and methylation data. Thereafter, we

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consider matched genes (say, Gmatch ) that are common to both the datasets

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(i.e., gene expression and methylation datasets). We then perform intersec-

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tion between Gmatch and the genes corresponding to the collected IDP s from

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M obiDb database (=Gintegrated ). Notably, the matched samples between ex-

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pression and methylation datasets are only considered from the intersected

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genes and are utilized in our experiment. After that, we run standard t-test

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[16] on the gene expression data as well as the methylated data of the selected

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genes to identify differentially expressed genes (DEgenes ) as well as differen-

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tially methylated genes (DMgenes ), respectively. Volcanoplot is applied to find

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the genes whether they are up-regulated (DEup ) or down-regulated (DEdown ),

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and hyper-methylated (DMhyper ) or hypo-methylated (DMhypo ). We also de-

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termine the genes that have inverse relationship between the expression and

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methylation patterns (i.e. (DEup ∩DMhypo ) or (DEdown ∩DMhyper )) that are

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together denoted as Ginv . Besides this, we consider all the intersected genes

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(Gintegrated ) and identify the corresponding targeter miRN As of them by Di-

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ana Tarbase v7.0. Simultaneously, we determine the corresponding Transcrip-

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tion Factors (T F s) that regulate these genes through TRANSFAC [16], [17]

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and ITFP [16], [18] databases. Thereafter, we build a network comprising of

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these genes, T F s and miRN As. In this case, we also highlight the genes which

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ACCEPTED MANUSCRIPT are associated with higher number of T F s and/or miRN As. Thereafter, we

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compute the degree centrality of the genes, and determine the hub genes of the

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network (denoted as Ghub ). Thereafter, we perform intersection between the

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Ginv and Ghub , and the resultant genes are stated as epigenetic markers for the

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disease of prostate carcinoma. In addition, we perform literature search, and

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Gene Ontology analysis using DAVID database. Finally, the top epigenetic

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gene-markers, and corresponding potential IDP s are reported.

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The rest of article is organized as follows. In Section 2, literature survey of

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the IDP s and related works are described. Section 3 demonstrates the our

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method whereas Section 4 represents the dataset as well as the experimental

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results and discussion, consecutively. Finally, Section 5 concludes the article.

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2

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The area of research related to IDP is bound by In-Vivo techniques [19]. In

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the area of In-silico studies, only a few computational methods have been

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developed. Defining the structure of a protein is one of the important ob-

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jectives in computational biology. In earlier, computational works related to

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IDP s are based on X-Ray crystallography and Nuclear Magnetic Resonance

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(N M R) predictions [20], [21]. In recent studies, differentially expressed IDP s

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are causing diseases like cancers, neurodegenerative diseases, HIV [22], and

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heart diseases. The computational techniques basically are used to predict

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the disorder regions [20], [21]. As mentioned before, earlier ages of compu-

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tational researches are depending on core structural studies. Later, different

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ad-hoc techniques and energy computation are performed for the prediction of

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IDRs. Some of the well known optimal tools are developed and designed. One

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of them is DISOPRED viz., DISOPRED2 [23] and DISOPRED3 [24]. This

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tool is designed for IDR predictions. Now a days, the relationship between

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the diseases and IDP s are one of the important research platforms [1], [19].

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Literature Review

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ACCEPTED MANUSCRIPT The proteins with IDRs can cause different neurodegenerative diseases (e.g.,

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Alzheimer’s disease). Sometimes the gene regulatory networks or pathways [1]

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are completely or partially effected by the IDP s. Basically, if the IDP is a

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lead compound, IDR becomes an active site. As a result, proteins with IDRs

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behave differently during interaction which is affecting the normal verse of

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protein-protein interactions as well as regulatory networks and pathways. The

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functional classes associated with IDP s have been proposed to cover the wider

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range of biological processes rather than the classes related to structured pro-

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teins. The concept of IDP s does not belong to the classical structure theory of

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proteins. The traditional structure-function relationship of proteins is applied

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to the area of stable and folded proteins whereas the structured features are

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identified for interaction of neighbor molecules [8], [25].

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Besides the above information, [26] has provided a study of significant highly

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disordered proteins in finding their functions in human cells by analyzing gene

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expression data. The association of the IDP s in cell signaling and cancer is

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demonstrated through a corresponding study in [1] and [27]. Furthermore,

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several works have been performed on the transcriptome level of Nef-expressing

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U937 cells and their exosomes [28], [29].

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From the literature, it is observed that no epigenetic study has been performed

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previously for determining IDP s. It might be helpful to refine the previous

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findings regarding IDP s and additionally able to produce some novel results.

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Therefore, for finding novel insights into the regulation of IDPs, in this article,

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we perform a transcriptomic analysis of mRNAs (genes) for transcripts encod-

155

ing IDPs on a publicly available human multi-omics dataset having both gene

156

expression and methylation data. In this regard, we identify some epigenetic

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gene-markers having their differential expression and methylation patterns by

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integrating statistical and TF-miRNA-gene network analyses for a prostate

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carcinoma multi-omics (epigenetic) dataset having both the gene expression

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and methylation data. Here, the epigenetic gene-markers must have both dif-

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ACCEPTED MANUSCRIPT ferential expression patterns as well as differential methylation patterns, and

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they also behave as the hub genes of the network. Finally, the corresponding

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IDPs related to the resulting gene-markers are obtained, and then reported.

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These resulting IDPs are stated as potential IDPs as their corresponding

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mRNA transcripts are both differentially expressed as well as differentially

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methylated, and are also hub-genes (centralized genes) of TF-miRNA-gene

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network.

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3

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In this article, we carry out a transcriptomic analysis of mRNAs (genes) for

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transcripts encoding IDPs on a publicly available human multi-omics epi-

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genetic dataset having both gene expression and methylation data. In this

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regard, we determine some epigenetic gene-markers having their diffential ex-

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pression and methylation patterns by integrating statistical and TF-miRNA-

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gene network analyses for a prostate carcinoma multi-omics (epigenetic) dataset

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having both the gene expression and methylation data. The corresponding

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IDPs related to the resulting gene-markers are also identified. The steps of

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the transcriptomic analysis are described as follows.

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3.1 Initial set of IDP s from M obiDb database

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M obiDb [15] is a structural database of IDP s combining the data from

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Disprot [37], P DB [38], EM BL, U niprot and other structural databases.

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M obiDb basically provides tissue specificity and annotation for a IDP . The

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IDP s are chosen from the M obiDb based on their cancer specific tissue types

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(viz., prostate, lung, ovarian, oral, pancreatic, cardiac, lymph, and breast tis-

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sues).

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Materials and Methods

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ACCEPTED MANUSCRIPT In this article, we utilize M obiDb database to collect the IDP s related to one

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of the tissues (viz., prostate tissue).

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3.2 Initial geneset determination

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We initially consider the common genes having both the values and the (matched)

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common samples from multi-omics dataset (= Gmatch ). We thereafter perform

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the intersection between the Gmatch and the geneset of the corresponding IDP s

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(= Gintegrated ). This intersected genes will be used for further study.

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3.3 Data-normalization

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Microarray technique is one of the useful tools for measuring gene expression

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data across different experimental and control samples. Before using any statistical test, we need to scale all the values of each gene. Therefore, initially we have to utilize a normalization technique on each of the Gintegrated genes for the expression data as well as methylation data. To normalize the expression/methylation dataset, we perform zero-mean normalization [11]. The zero-mean normalization is formulated in such a way that mean value of gene is converted to zero which is formulated as follows. norm = yjk

(yjk − µ) , σ

(1)

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where µ and σ stand for the mean and standard deviation, respectively for

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expression/methylation data of j-th gene before normalization, whereas yjk

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norm and yjk denote the values of j-th gene across k-th sample before and after

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normalization, respectively. 9

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3.4 Identification of Differentially expressed and differentially methylated genes

Statistical test is important for determining differentially expressed genes (DE)

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as well as differentially methylated genes (DM). The t-test [9] is a popular test that is widely used in microarray data for determing DE/DM genes.

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In (two-sample) t-test, the difference between the means of the two groups

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is compared where the variation of the data is also considered. The p-value is the probability of observing a t-value as large or larger than the actual

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observed t-value in which the null hypothesis is considered as true. The p-value is computed from either t-table or cumulative distribution function (cdf). Let, for each gene n, group 1 consists of m1 diseased samples, with mean a1 and

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standard deviation sm1 , and group 2 contains m2 control samples, with mean

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a2 and standard deviation sm2 . Therefore, t-test is defined as follows. (a1 − a2 ) , em

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t=

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where em refers to the standard error of the groups’ mean, which is formulated

em = eP ooled ∗

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1 1 + . m1 m2

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Here, ePooled is the pooled estimate of the population standard deviation; i.e.,

v u u (m1 − 1) ∗ s2m1 + (m2 − 1) ∗ s2m2 epooled = t ,

dof

(4)

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where dof refers to the degree of freedom of the test (i.e., dof = (m1 +m2 −2)).

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In this article, we apply the t-test on the normalized expression data of each

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gene belongs to the Gintegrated genes to identify DE genes. Similarly, we per-

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form the t-test on the normalized methylation data of each gene belongs to

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the Gintegrated genes to produce DM genes. If the p-value of a gene for its

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(normalized) expression data is less than 5%, it can be said that the gene

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becomes DE. Similarly, the p-value of a gene for its (normalized) methylation 10

ACCEPTED MANUSCRIPT data is less than 5%, the gene is DM .

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After obtaining the DE/DM genes, we apply volcanoplot to identify up-

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regulated/over-expressed genes (DEup ) and down-regulated/under-expressed

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genes (DEdown ) from the expression data. Similarly, from the methylation

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data, hyper-methylated genes (DMhyper ) and hypo-methylated genes (DMhypo )

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are obtained by the use of the volcanoplot.

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3.5 Integrated study of methylation on expression

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Now, we identify the genes of which differential expression status is inverse of

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the status of differential methylation (say, Ginv ). In other words, we consider

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the genes having (i) DEup ∩ DMhypo , and (ii) DEdown ∩ DMhyper patterns.

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Later, these genes will be utilized for selecting the disease-markers.

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3.6 Accumulation of T F s and miRN As of the genes

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We thereafter collect the targeter miRNAs of the genes belongs to the Gintegrated

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set using DIANA Tarbase V.07 database [30], a web server which provides val-

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idated interactions between the genes and the targeter miRN As. Besides this,

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we have obtained the corresponding TFs of the genes belongs to the Gintegrated

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set using TRANSFAC [16], [17] and ITFP [16], [18] databases.

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3.7 T F -miRN A-gene regulatory network formation and Hub gene selection

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A network comprising the genes, the corresponding TFs and the targeter miR-

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NAs is then built. The TF-miRNA-gene network is analyzed by a well-known

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topological measure, degree centrality (DC) [36], [40], [41] since the whole

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network is considered as a symmetric network.

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ACCEPTED MANUSCRIPT In the degree centrality measure, the degree of a vertex (gene/TF/miRNA)

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is only considered whereas the degree of the vertex refers to the number of

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vertices that are directly connected to it (i.e., nearest neighbors of the ver-

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tex). In the degree centrality measure, each neighbour of the vertex has equal

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importance for calculating the degree centrality of the vertex. Here, from the

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final network, the degree centrality of a gene is observed to count the total

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number of interactions of the gene to the corresponding miRN As and T F s.

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After computing the DC values, we choose some top genes with respect to

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their DC values from highest (best) to lowest (worst) cases. These top genes are

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stated as Hub genes (centralized genes) of the network. These hub genes (say,

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Ghub ) are utilized in epigenetic marker discovery in the next step. However,

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the Tf-miRNA-gene network is visualized using Cytoscape 3.2.1 software.

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3.8 Epigenetic marker identification

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We thereafter perform intersection between Ginv genes and Ghub genes. Finally,

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the intersected genes are the epigenetic gene-markers that are the hub genes

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as well as they have inverse relationships between their differential expression

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and differential methylation status. In addition, the corresponding IDP s of

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these resulting epigenetic gene-markers are mentioned.

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3.9 Biological validation

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Furthermore, we have performed literature search, as well as Kegg pathway

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and gene ontology (GO) analyses using DAVID database in order to validate

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the gene-markers. However, we have provided a flowchart in Fig. 1 that rep-

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resents site map of the proposed framework.

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Fig. 1. A Flow chart to describe the proposed framework.

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ACCEPTED MANUSCRIPT 4

Epigenetic Dataset and Experimental Result

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In this section, we describe the real epigenetic data that is used for the purpose

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of experiment. Thereafter, we provide the experimental result and related

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discussion.

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4.1 Dataset Information

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In this article, we use a multi-omics Prostate Carcinoma (epigenetic) dataset

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(NCBI Ref. id: GSE55599) which consists of 18,309 common genes having

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both gene expression and methylation values (=(Gmatch ). It has 32 common

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Prostate Carcinoma (experimental) samples and 16 common control (normal)

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samples.

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4.2 Experimental Results and Discussion

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Here, we discuss about the experimental result.

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4.2.1 Determination of Differentially Expressed and Differentially Methy-

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lated genes

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At first, we accumulate 18,309 genes as Gmatch , and then identify 40 genes as

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Gintegrated . Thereafter, as mentioned in Section 3, we perform data-normalization

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and t-test consecutively on the expression/methylation data of Gintegrated genes.

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We obtain three genes under DEup category, and eleven genes under DEdown

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data (see Table 1). Similarly, four genes are identified as DMhyper genes,

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whereas three genes are DMhypo (see Table 2 and Fig. 2). Thereafter, we

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identify only one gene having DEup ∩ DMhypo pattern, and four genes belongs

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to DEdown ∩ DMhyper category (see Fig. 3). We provide the p-values, and the 14

ACCEPTED MANUSCRIPT status of the differential expression and differential methylation of these five

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(=1+4) genes in Table 3.

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4.2.2 Resultant Network and Hub gene identification

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We build a network consisting of the genes of Gintegrated set, the targeter

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miRNA collected from the DIANA Tarbase V.07 database, and the corre-

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sponding TFs accumulated from the TRANSFAC and ITFP databases. Fig. 4

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shows the corresponding whole network whereas Fig. 5 shows another view of

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Fig. 4 in which all the genes and all the miRNAs are kept in the left-top and

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right sides, respectively of the network, whereas all the TFs are put in the

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middle-bottom portion of the network.

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In the TF-miRNA-gene network, we observe that four specific genes, named

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PHF19, HTRA1, SPSB1 and CCDC151 have higher degree centrality (DC)

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values consecutively than the others (see Table 4). According to Table 4, it

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has been observed that DC values of the genes PHF19, HTRA1, SPSB1 and

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CCDC151 are 38, 11, 10, and 3, respectively. Depending on degree central-

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ity, we identify the hub genes from Figure 4. PHF19, HTRA1, SPSB1 and

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CCDC151 are those four hub genes (Ghub ) which have highest degree central-

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ity 38, 11, 10 and 3. Now, the genes which are differentially expressed as well

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as differentially methylated, have been observed.

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Besides this, we have performed further critical analysis. We observe that

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very few number of targeter miRNAs of the corresponding genes are common.

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E.g., PHF19 and HTRA1 have three common miRNAs (viz., hsa-miR-107,

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hsa-miR-103a-3p and hsa-miR-16-5p). Similarly, PHF19 and SPSB1 have two

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common miRNAs (viz., hsa-miR-522-5p and hsa-miR-96-5p), whereas only one

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miRNA is common for the case of PHF19 and CCDC151, as well as the case of

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SPSB1 and HTRA1 (viz., hsa-miR-424-5p and hsa-miR-335-5p, respectively).

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In addition, three genes have relationship with three different TFs. E.g., SMN1

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ACCEPTED MANUSCRIPT gene has associated with the TF CEBPB, whereas KLHDC4 is connected with

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the TF OTX1, and SF3B2 is related with TF MESP2. See Table 4 for details.

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4.2.3 Epigenetic gene markers

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We consider every possible combinations between differentially expressed and

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differentially methylated status of genes (i.e., DEup ∩DMhyper , DEup ∩DMhypo ,

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DEdown ∩DMhyper , and DEdown ∩DMhypo ). From Table 3, we provide those

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genes that consist of the combinations having inverse relationship between

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expression and methylation values. Here five genes are listed with inverse

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relationship (i.e, DEup ∩DMhyper , DEup ∩DMhyper ), denoted as Ginv . These

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genes are PHF19, SPSB1, HTRA1, CCDC151 and STX19. Among them, only

309

STX19 is hypo-methylated as well as up-regulated. Other four genes are hyper-

310

methylated as well as down-regulated. Thereafter, we perform intersection be-

311

tween Ghub and Ginv genes, and identify the resultant genes (viz., PHF19,

312

HTRA1, SPSB1 and CCDC151) or epigenetic marker. Furthermore, the cor-

313

responding IDP s of the markers are mentioned in column 1 of Table 4 as well

314

as column 1 of Table 5.

315

4.2.4 Biological Validation of genes and related IDP s

316

We further perform literature search, and pathway and Gene-Ontology anal-

317

yses. The association between gene HTRA1 and prostate carcinoma is de-

318

scribed in [31]. Similarly, [43] represents the association of gene SPSB1 in

319

the prostate carcinoma, whereas the relation between the gene HTRA1 and

320

the prostate carcinoma is described in [35]. Besides these, gene SPSB1 is en-

321

riched with a prostate-carcinoma-related GO:BP (viz., GO:0007242 intracellu-

322

lar signaling cascade (p-value=0.017996325)). Now, the relationship between

323

the GO:BP of GO:0007242 intracellular signaling cascade and the prostate

324

carcinoma is found in [33]. Therefore, gene SPSB1 involves indirectly with

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Fig. 2. Volcanoplot for identifying hyper-methylated and hypo-methylated genes (DMhyper and DMhypo , respectively) through simultaneous verification of significance level of p-value and fold change in log scale for methylation data.

the corresponding disease through the gene-ontology analyses. Similarly, gene

326

PHF19 is enriched with some prostate-carcinoma-related GO:BP-terms like

327

GO:0006350 transcription (p-value=5.85E-04) [32], [39], and GO:0045449 reg-

328

ulation of transcription (p-value=0.002157192) [42]. HTRA1 is associated with

329

related GO:BPs of GO:0010648 negative regulation of cell communication (p-

330

value=0.046487613) [34] and GO:0009968 negative regulation of signal trans-

331

duction (p-value= 0.034877541).

332

Among the four gene-markers, three markers (viz., PHF19, HTRA1, and

333

SPSB1) are already known through either literature search or Gene Ontology

334

analyses or both, whereas the remaining marker (viz., CCDC151) is stated as

335

a novel epigenetic marker since no information is available through the liter-

336

ature search and Gene-Ontology analyses. Moreover, the corresponding IDPs

337

of the resulting gene-markers are provided in Table 5.

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ACCEPTED MANUSCRIPT Table 1 The list of up-regulated and down-regulated genes with p-values for the expression data of the Prostate Carcinoma dataset.

P-value

Status of gene

STX19

0.0001056

DEup

MED25

0.009166984

DEup

SNRPB

0.016748568

DEup

PHF19

8.32E-05

DEdown

SAMD3

0.000100528

CCDC151

0.000245824

FYCO1

0.000337727

DEdown

HTRA1

0.001240815

DEdown

RXRA

0.001511319

DEdown

HNRNPA1

0.005020765

DEdown

SF3B2

0.038755606

DEdown

0.040726192

DEdown

SMAD9

0.043757832

DEdown

CDCA4

0.045839458

DEdown

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Gene Name

DEdown

Table 2 The list of hyper-methylated and hypo-methylated genes with p-values for the methylation data of the Prostate Carcinoma dataset.

Gene Name

P-value

Status of gene

SPSB1

5.93E-08

DMhyper

PHF19

3.20E-05

DMhyper

HTRA1

0.001479375

DMhyper

CCDC151

0.002158725

DMhyper

APP

0.000387211

DMhypo

STX19

0.000517349

DMhypo

LOXL4

0.000953542

DMhypo

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Fig. 3. Venn diagram that shows the intersections between each pair of different patterns of differential expression and differential methylation (viz., DEup , DEdown , DMhyper , DMhypo ).

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Table 3 The list of genes having inverse relationship between their differential expression and methylation status for the integrated Prostate Carcinoma epigenetic dataset where “exp” and “methyl” denote the expression and methylation data, respectively.

Gene Name STX19

P-value 0.0001056 (exp)

0.000517349 (methyl)

PHF19

8.32E-05 (exp)

5.93E-08 (methyl) CCDC151

0.000245824 (exp) 3.20E-05 (methyl)

HTRA1

0.001240815 (exp) 0.001479375 (methyl)

SPSB1

0.040726192 (exp) 0.002158725 (methyl)

19

Status of gene (DEup ∩ DMhypo )

(DEdown ∩ DMhyper )

(DEdown ∩DMhyper )

(DEdown ∩DMhyper )

(DEdown ∩DMhyper )

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Fig. 4. A simple view of the complete T F -miRN A-gene network for the experiment, specially highlighting (enlarging) the four genes, PHF19, HTRA1, CCDC151, and SPSB1. Here, OV AL shape with light orange color denotes genes, P ARALLELOGRAM shape with pink color shows the T F s, whereas T RIAN GLE shape with light orange color represents miRs or miRN As.

20

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Fig. 5. Second view of the above figure (Fig. 4) in which all the genes are kept together in the left-top side of the network, and all the miRNAs are arranged together in the right side, whereas all the TFs are shown in the middle-bottom portion of the network.

21

ACCEPTED MANUSCRIPT Table 4 The forty IDP s and their corresponding transcripts (genes) of Gintegrated set, with their corresponding targeter miRN As and T F s. IDP s

Gene Names

Degree

Targeter miRN As

TFs

centrality

PHF19

38

Serine protease HTRA1

HTRA1

11

miR-107,miR-103a-3p,miR-16-5p,miR-196a-3p,miR-99a-3p,miR-31, miR-190a,miR-197-3p,miR-155-5p,miR-26b-5p,miR-335-5p.

SPRY domain-containing SOCS box protein 1

SPSB1

10

miR-522-5p,miR-96-5p,miR-27a-3p,miR-26a-5p,miR-567, miR-122-5p,miR-24-3p,miR-941,miR-185-5p,miR-335-5p.

CCDC151

3

STX19

1

SMN1

1

KLHDC4

1

SF3B2

1

APP

1

CDCA4

RXRA protein Mothers against decapentaplegic homolog 9 Zinc finger protein SNAI1 Vacuolar-sorting protein SNF8 U1 small nuclear ribonucleoprotein A Small nuclear ribonucleoproteinassociated proteins B and B’ Putative Polycomb group protein ASXL1 Small nuclear ribonucleoprotein Sm D3 Suppressor of cytokine signaling 7 Signal transducer and activator of transcription 1-alpha/beta Splicing factor U2AF 65 kDa subunit

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kshv-miR-K12-6-5p,miR-10a-5p,miR-424-5p.

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ARHGEF1

1

miR-335-5p CEBPB

OTX1 MESP2 miR-298 miR-124

1

miR-424

1

miR-486-3p

1

miR-138

DNAJA3

1

miR-129-5p

FYCO1

1

miR-218-5p

GSTCD

1

miR-570

HDAC3

1

miR-1261

HIGD1A

1

miR-641

CHD3

D

CLNS1A

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Kelch domain containing protein 4 Splicing factor 3B subunit 2 Amyloid beta A4 protein ARHGEF1 protein Cell division cycleassociated protein 4 Chromodomain-helicaseDNA-binding protein 3 Methylosome subunit pICln DnaJ homolog subfamily A member 3, mitochondrial FYVE and coiled-coil domain-containing protein 1 Glutathione S-transferase C-terminal domain -containing protein Histone deacetylase 3 HIG1 domain family member 1A, mitochondrial Histone H3.1t MICOS complex subunit MIC60 Lysyl oxidase homolog 4 Nuclear receptor coactivator 1 Glycylpeptide Ntetradecanoyltransferase 1 Oxysterols receptor LXR-alpha Phosphatidylinositol 3-kinase regulatory subunit alpha Peroxisome proliferatoractivated receptor delta Retinoic acid receptor alpha Ras association domaincontaining protein 2 REST corepressor 1

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Coiled-coil domain -containing protein 151 Syntaxin-19 Survival motor neuron protein

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PHD finger protein 19

let-7g-5p,miR-182-5p,miR-105-5p,miR-23a-3p,miR-18a-5p,let-7f, let-7d-5p,let-7b-5p,let-7a-5p,miR-1301,miR-590-3p,miR-BART5, miR-571,miR-328,miR-107,miR-98,let-7c,miR-9-3p, let-7e-5p,miR-1283,miR-423-5p,miR-522-5p,miR-92b-3p,miR-138-5p, miR-218-5p,miR-34a-5p,miR-103a-3p,miR-96-5p,miR-16-5p,miR-192-5p, miR-193b-3p,miR-124-3p,miR-33a-3p,miR-29a-5p,miR-15a-3p, miR-18b-5p,miR-424-5p,miR-23b-3p.

HIST3H3

1

miR-1276p

IMMT

1

miR-199a-3p

LOXL4

1

miR-455-3p

NCOA1

1

miR-498

NMT1

1

miR-623

NR1H3

1

miR-622

PIK3R1

1

miR-455-3p

PPARD

1

miR-1270

RARA

1

miR-939

RASSF2

1

miR-302c

RCOR1

1

miR-198p

RXRA

1

miR-574-3p

SMAD9

1

miR-512-3p

SNAI1

1

miR-199a-5p

SNF8

1

miR-506

SNRPA

1

miR-1108

SNRPB

1

miR-1207-5p

ASXL1

1

miR-1207-5p

SNRPD3

1

miR-570

SOCS7

1

miR-145

STAT1

1

miR-1303

U2AF2

1

miR-145

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PHD finger protein 19

Gene

Gene Ontology

Literature

Status

name

search

of

GO:BP of GO:0006350 transcription (p-value=5.85E-04) [39]* , [32]* ,

[35]

Existing

[31]

Existing

[43]

Existing

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IDP

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Table 5 Biological validations of the epigenetic gene-markers, and the corresponding (potential) IDPs.

PHF19

marker

GO:0045449 regulation of transcription (p-value=0.002157192) [42]* . HTRA1

GO:BP of GO:0010648 negative regulation of cell communication

D

Serine protease HTRA1

SPSB1

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SPRY domain -containing SOCS box protein 1

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(p-value=0.046487613)

Coiled-coil domaincontaining protein 151

[34]* ,

GO:0009968 negative regulation of

signal transduction (p-value=0.034877541).

GO:BP of GO:0007242 intracellular signaling cascade (p-value=0.017996325) [33] * .

CCDC151

Novel

The reference represents that the corresponding GO-term has a relation with Prostate Carcinoma. *

23

ACCEPTED MANUSCRIPT 5

Conclusion

339

In this article, we perform a transcriptomic analysis of genes for transcripts

340

encoding IDP s on a multi-omics prostate cancer dataset that consists of both

341

gene expression and methylation data. As a result, we identify some potential

342

epigenetic gene-markers (having inverse relationship between their diffential

343

expression and methylation patterns) through integrating statistical and TF-

344

miRNA-gene network centrality methods.

345

Notably, in this study, we have two hypotheses. First one is that the integrated

346

method of associations of methylation patterns with the gene expression pat-

347

terns, and hub gene selection together has a better significance of identify-

348

ing potential genemarkers (and corresponding IDPs) for the disease rather

349

than considering only the expression pattern. Now, methylation generally de-

350

creases the gene expression level. Since we utilize this phenomena (i.e., inverse

351

relationship between expression and methylation patterns), thus our study

352

is surely the enhanced version rather than considering only the expression

353

pattern. The functional significance is that the expression patterns of these

354

biomarkers are completely depended on their methylation patterns rather than

355

other factors. The second hypothesis is that here we have identified four epige-

356

netic gene-markers (viz., PHF19, HTRA1, SPSB1, and CCDC151). However,

357

for testing the second hypothesis, we initially perform literature search, and

358

gene ontology analyses on the genes using DAVID database for finding as-

359

sociations between the genes and the disease. Among these markers, three

360

markers (viz., PHF19, HTRA1, and SPSB1) are existing since some associa-

361

tions between the genes and the disease are found through either literature

362

evidences or Gene Ontology analysis or both, whereas the remaining marker

363

(viz., CCDC151) is a novel marker. It is expected that the novel marker may

364

have a significant therapeutic value for the disease. Finally, these markers and

365

their corresponding IDP s are reported.

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ACCEPTED MANUSCRIPT List of Abbreviations IDPs

- Intrinsically Disordered Proteins

TF

- Transcription Factors

PT

miRNA - MicroRNA

HTRA1 - serine Protease HTRA1

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SPSB1 - SPRY domain-containing SOCS box protein 1

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PHF19 - PHD finger protein 19

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CCDC151 - Coiled-coil domain-containing protein 151 - CEBPB protein 1

OTX1

- Homeobox protein OTX1

MESP2

- Mesoderm posterior protein 2

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CEBPB

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ITPT - Insights into Potential Intrinsically Disordered Proteins Through Transcriptomic Analysis of Genes

ACCEPTED MANUSCRIPT Highlights

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We perform a transcriptomic analysis of genes for transcripts encoding IDPs on a Prostate cancer multi-omics dataset. We obtain differentially expressed and differentially methylated genes using t-test. We determine hub-genes from TF-miRNA-gene network using degree centrality. We identify some epigenetic gene-markers by integrating statistical and TF-miRNAgene network analyses.

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