Prediction and Validation of Transcription Factors Modulating the Expression of Sestrin3 Gene Using an Integrated Computational and Experimental Approach

Rajneesh Srivastava; Yang Zhang; Xiwen Xiong; Xiaoning Zhang; Xiaoyan Pan; X. Charlie Dong; Suthat Liangpunsakul; Sarath Chandra Janga

doi:10.1371/journal.pone.0160228

Abstract

SESN3 has been implicated in multiple biological processes including protection against oxidative stress, regulation of glucose and lipid metabolism. However, little is known about the factors and mechanisms controlling its gene expression at the transcriptional level. We performed in silico phylogenetic footprinting analysis of 5 kb upstream regions of a diverse set of human SESN3 orthologs for the identification of high confidence conserved binding motifs (BMo). We further analyzed the predicted BMo by a motif comparison tool to identify the TFs likely to bind these discovered motifs. Predicted TFs were then integrated with experimentally known protein-protein interactions and experimentally validated to delineate the important transcriptional regulators of SESN3. Our study revealed high confidence set of BMos (integrated with DNase I hypersensitivity sites) in the upstream regulatory regions of SESN3 that could be bound by transcription factors from multiple families including FOXOs, SMADs, SOXs, TCFs and HNF4A. TF-TF network analysis established hubs of interaction that include SMAD3, TCF3, SMAD2, HDAC2, SOX2, TAL1 and TCF12 as well as the likely protein complexes formed between them. We show using ChIP-PCR as well as over-expression and knock out studies that FOXO3 and SOX2 transcriptionally regulate the expression of SESN3 gene. Our findings provide an important roadmap to further our understanding on the regulation of SESN3.

Citation: Srivastava R, Zhang Y, Xiong X, Zhang X, Pan X, Dong XC, et al. (2016) Prediction and Validation of Transcription Factors Modulating the Expression of Sestrin3 Gene Using an Integrated Computational and Experimental Approach. PLoS ONE 11(7): e0160228. https://doi.org/10.1371/journal.pone.0160228

Editor: Jun-Tao Guo, The University of North Carolina at Charlotte, UNITED STATES

Received: January 4, 2016; Accepted: July 16, 2016; Published: July 28, 2016

Copyright: © 2016 Srivastava et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All relevant data are within the paper and its Supporting Information files.

Funding: This work was supported in part by the following grants and awards: R01 DK091592 grant (XCD) from the National Institute of Diabetes and Digestive and Kidney Diseases, Showalter Scholar Award (XCD), Indiana CTSA (SL and XCD), R01 DK107682-01 (SL and XCD), R21 1R21AA024935-01 (SL), VA Merit Award 1I01CX000361 (SL), NIH U01AA021840 (SL), and US DOD W81XWH-12-1-0497 (SL). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors have declared that no competing interests exist.

Introduction

Sestrins belong to a small family of evolutionally conserved proteins. They are distinct from any other characterized eukaryotic protein families because they do not have any previously identified domain structures[1]. Mammals express three sestrin genes (SESN1/2/3), while most invertebrates contain only a single sestrin gene[2]. Sestrins do not contain any known structural domains/catalytic motifs; only a partial homologous sequence to bacterial oxidoreductases is identified, suggesting an antioxidant function of this protein[1]. Sestrins regulate multiple signaling pathways for metabolic and cellular homeostasis[3]. First, sestrins reduce oxidative stress through either their intrinsic oxidoreductase activity or NRF2 (nuclear factor erythroid derived 2 like 2)-regulated pathway [4,5]. Second, sestrins modulate glucose and lipid metabolism through AMPK (AMP-activated protein kinase) and mTORC1 (mechanistic target of rapamycin complex 1)[1]. Third, Sestrins regulate autophagy through activation of AMPK and inhibition of mTORC1 [2]. Deletion of a single SESN gene in fruit fly leads to triglyceride accumulation in its body [2], equivalent to the liver in mammals. We have observed that ethanol suppresses SESN3 gene expression and function in hepatocytes and mouse livers. Over expression of SESN3 dramatically reduces the ethanol-induced hepatic steatosis [6]. In addition, SESN2 and SESN3 have also been shown to regulate insulin sensitivity and glucose homeostasis [7,8]. However, to date, the factors that control SESN3 expression are not well studied. Understanding the complex regulatory mechanisms that regulate the SESN3 is of importance, as new therapeutic targets for metabolic diseases might be discovered.

Transcription factors (TFs) are known to bind specifically to gene’s promoters at the regulatory positions (binding motifs) and thus contribute to its transcriptional regulation and cellular function. Various in vitro [9], in vivo [10] and in silico [11] approaches have been developed for identifying TF binding motifs. Typically, potential TFs bind to its high affinity binding sites (represented as a weight matrix), however, little is known about the tissue specific binding pattern of most TFs in higher eukaryotes [12].

In this study, we used the upstream regulatory regions of human SESN3 orthologs from a diverse set of primates and rodents (with at least 85% sequence homology with human) to perform phylogenetic footprinting [13]. We employed the MEME-SUITE of tools [14,15] which allowed the identification of high confidence conserved binding motifs and corresponding position specific weight matrices. We also tested the feasibility (i.e. TF binding tendency) of these binding motifs (BMo) in open chromatin region of human cell lines and mouse liver using DNase Hypersensitive Sites (DHS) in SESN3 upstream region. Predicted binding motifs were further analyzed by Tomtom (a motif comparison tool from MEME-SUITE) to identify motif specific potential transcription factors. Predicted TFs were integrated with documented protein-protein interaction in BioGRID [16] to decipher the important regulators and the network of interactors controlling the expression of the SESN3 gene.

Materials and Methods

Human-SESN3 orthologs and their upstream regulatory regions were extracted (FASTA sequences) from ENSEMBL. These SESN3 sequences from human and its 10 orthologs (Primates and Rodents) were taken and executed using MEME-SUITE, an open source hub of bioinformatics tools. Prediction of novel regulatory motifs was performed by using phylogenetic footprinting, an in silico method coupled with downstream computational analysis. Based on this, consensus sequences in upstream region were discovered by MEME analysis. These consensus sequences were further analyzed using the Tomtom tool which enables the comparison of predicted motifs with Position Weight Matrices (PWM) of TFs for overlap. Further, protein-protein interaction network was constructed between the potential TFs by utilizing the available physical interactions in BioGRID to delineate the important regulators and the network of interactors controlling the expression of SESN3 gene.

Sestrin 3 transcripts and their expression profile

Human SESN3 gene is located on chromosome 11. We obtained DNA sequences for the human SESN3 gene (Ensembl ID ENSG00000149212) from the ENSEMBL database. There are 5 transcripts reported for the human SESN3 gene (Table 1); of which 4 have been reported to be protein coding. Expression profile of this gene was obtained from open source database–GeneCards [17], for further examination.

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Table 1. Transcripts of Human SESN3 gene reported in ENSEMBL database.

https://doi.org/10.1371/journal.pone.0160228.t001

Identification of human SESN3 orthologs and their upstream regulatory regions for phylogenetic footprinting

Phylogenetic footprinting is one of the classical methods applied for DNA binding motif discovery [13,18,19]. It involves the upstream regulatory sequence of a gene of interest across possible orthologs to search for highly conserved consensus DNA binding sites. We selected orthologs of the human SESN3 gene from primates and rodents using Ensembl Compara gene trees [20]. These dataset allows the identification of orthologous sequences across species with high sequence resemblance as shown in S1 Table. Gene expression is controlled by various cis-acting transcriptional regulatory factors by binding mostly in close proximity to the transcription start sites (TSS) in the promoter regions of a gene [21]. Based on previous studies from others [22,23] and our group, we found that most functional TF binding sites occur within the 5kb upstream region of the gene TSS (data not shown). So we focused our study on 5kb upstream regions of the SESN3 gene for motif discovery. Upstream regulatory regions for human and its 10 selected SESN3 orthologs were obtained from Ensembl database (S1 Table).

MEME analysis for discovering DNA binding motifs

DNA binding motif discovery using the in silico phylogenetic footprinting approach covered regulatory regions in the promoters of orthologous genes from multiple species. This is under the notion that regulatory elements would be conserved in the background of non-functional sequences and hence could be discriminated as footprints contributing to regulatory control. To facilitate the motif finding in these regions, we used the MEME-suite of tools [14, 15]. MEME is a tool for discovering motifs in a group of related DNA or protein sequences, which detects the frequently occurring conserved sequence across a group of related DNA sequences, using expectation maximization[24]. These motifs are typically represented as position-dependent letter-probability matrices in logos which describe the probability of each possible letter at each position in the pattern to incorporate the variation in the detected motif instances across sequences. In this study, we used 5kb upstream sequences of human SESN3 and its 11 orthologs compiled as a FASTA file and used as an input data for MEME to identify significantly over-represented motifs (E-value < e^-34). Here we limit the width of discovered binding motifs in MEME analysis to reflect the widths of most established PWMs—which typically vary in length between 4bp to 30bp [25–28].

Prediction of TFs associated with discovered motifs

Transcription Factors (TFs) are thought to bind specifically to their corresponding binding motif (BMo)[18] and regulate the expression of a target gene. DNA binding motifs were represented as PWM (Position-Specific Weight Matrix) based logos. Nucleotide constituent of each consensus motif has its own probability of occurrence within the site. Since PWMs for various TFs have already been reported in JASPAR [25], UniPROBE [26], Jolma et al [27] and TRANSFAC [28] public databases, based on a comparison of the similarity between the reported PWM of a TF to the footprinted PWM in the orthologous upstream regions, it is possible to predict the TFs which are most likely to bind to these predicted binding sites. Tomtom [29] is a tool in the MEME-suite which compares discovered DNA motifs to known motifs of such databases.

We used a set of 2201 DNA motifs ranging between 4bp and 30bp in length (average length 12.7) from TRANSFAC, 843 DNA motifs ranging between 7bp and 23bp in length (average length 12.7) in Jolma et al and 979 DNA motifs ranging between 5bp and 30bp in length (average length 13.0) in JASPAR CORE and UniPROBE Mouse. Hence, we rationalized that a motif length between 4bp to 30bp for the discovered motifs, would be able to capture most of these recognition sequences in the SESN3 upstream regions.

PWMs of various discovered motifs were used as input file for Tomtom and compared with already reported PWMs of TFs in the above described databases to identify the potential TFs binding to the SESN3 upstream regions. Only the TF associations which are identified at p ≤ 1e^-03 with E-value < 10 were considered as statistically significant for the 5kb upstream regions.

Analysis of DNase I hypersensitive site in SESN3 upstream region

DNase I hypersensitive sites (DHS) are open chromatin region of DNA, sensitive to DNase I cleavage. It is believed that, the occurrence of DHS, notably in the promoter region [30] is an indicator of potential binding site for transcription factor. We extracted the available DHS data in various human cell lines and mouse (14.5 days and 8 week) liver from ENCODE project [31] and visualized them for upstream regions of SESN3 genes in UCSC genome browser (http://genome.ucsc.edu/cgi-bin/hgFileUi?db=mm9&g=wgEncodeUwDnase). The images generated from the browser were positioned according to the coordinate of the SESN3 upstream region of block diagram and studied for active BMo.

Experimental validation of potential transcription factors

Human HEK293 cells were transfected with plasmid DNAs carrying coding sequences for control GFP (green fluorescent protein), human FOXO3 and SOX2 genes. The constructs also contained FLAG tag sequence on the N-terminus. After 48 hours of transfection, cells were processed for chromatin immunoprecipitation (ChIP) analysis for the predicted TF binding sequences as previously described [32]. The sequences for the PCR primers are: FOXO3 ChIP forward primer 5’-ACAAATCCTGGTACGCTGGA-3’, reverse primer 5’–CAGGACTGTGCATTATGACATCA– 3’; SOX2 ChIP forward primer 5’–CCAGTAGGCGATGCAAGTTA– 3’, and reverse primer 5’–CTAGACGCCCGCAACCTG– 3’.

CRISPR/Cas9 gene knockout

Human FOXO3 and SOX2 CRISPR/Cas9 single guide RNA (sgRNA) sequences were designed using an online program at crispr.mit.edu (Dr. Feng Zhang lab) for gene knockout. The selected two sgRNA sequences for the human FOXO3 and SOX2 genes are: 5’-CACTTCGAGCGGAGAGAGCG-3’ (FOXO3 sgRNA1), 5’-TCCACTTCGAGCGGAGAGAG-3’ (FOXO3 sgRNA2), 5’-TGGGCCGCTTGACGCGGTCC-3’ (SOX2 sgRNA1), and 5’-ATGGGCCGCTTGACGCGGTC-3’ (SOX2 sgRNA2). The DNA oligonucleotides were cloned into a lentiCRISPRv2 vector (a gift from Dr. Feng Zhang, Addgene plasmid #52961) as described previously [33,34]. To generate gene knockout stable cell lines, we transfected HEK293T cells with control GFP, FOXO3, or SOX2 sgRNA plasmids. The transfected cells were selected using puromycin (1 μg/ml) for 7 days, and then maintained in the culture medium containing 0.5 μg/ml puromycin.

DNA constructs preparation

The coding sequences for GFP, human FOXO3, and SOX2 genes were cloned into a pcDNA3 vector using PCR amplification and restriction digestion.

Cell culture and transfection

Human HEK293T and HepG2 cells were cultured in DMEM/high glucose medium containing 10% FBS. HEK293T cells were transfected with plasmid DNA using polyethylenimine and HepG2 cells were transfected using TurboFect reagent (Thermo Fisher Scientific).

mRNA analysis

Total RNAs were isolated from cultured cells using TRI Reagent (Sigma). mRNA levels for selected genes were analyzed by real-time PCR. Peptidylprolyl isomerase A (PPIA) was chosen as an internal control gene. Primer sequences are listed as follows: human PPIA forward primer: 5’- AGGTCCCAAAGACAGCAGAA-3’, human PPIA reverse primer: 5’-GAAGTCACCACCCTGACACA-3’, human SESN3 forward primer: 5’-GTACCAACTGCCGGAAAGTG-3’, and human SESN3 reverse primer: 5’- CCACTGTGTTTGCTTGGACA.

Mapping protein interactions between the potential TFs

Eukaryotic TFs often regulate the expression of genes by forming protein complexes and several examples have been documented in the literature including that of FOXOs interacting with SMAD3 [35], HNF4a[36] etc to modulate the transcription of their target genes. We employed the currently available manually curated set of protein-protein interactions for the human genome available from the BioGRID database [37] to map the physical associations between the predicted TFs from the Tomtom analysis for the 5kb upstream region. This not only allowed the construction of a protein interaction network between the predicted TFs but allowed the dissection of the major TFs based on their number of protein interactions in the network.

Results and Discussion

SESN3 has similar pattern of expression (RNA seq based) across most of the body fluids like blood, liver secretome, and multiple tissue types (S1 Fig, GeneCards[17]) indicating the consistent and universal transcriptional regulation of this gene. However, little is known about the factors and mechanisms controlling its expression. Our study attempts to identify the cis-regulatory binding sites controlling SESN3 and all possible regulatory proteins which may be involved in regulating the expression of SESN3 gene at transcriptional level.

Identification of potential binding motifs by in silico phylogenetic footprinting in the regulatory regions of SESN3 across primates and rodents

Human SESN3 consists of multiple protein coding transcripts as outlined in Table 1 extracted from ENSEMBL database. In Ensembl, a gene start refers to the earliest start co-ordinate of all the transcripts associated with a given gene. Phylogenetic footprinting analysis facilitates the search for regions of conserved chromosomal fragments where the likelihood of transcription factor binding is high. These protein-binding sites, which are short fragments of DNA, often range from 6–30 bp in length[18, 38–40]. We identified the set of binding sites and corresponding TFs controlling the SESN3 gene by performing motif discovery based on phylogenetic alignments of orthologous sequences from a diverse set of primates and rodents using the human SESN3 gene as a reference (see Materials and Methods, S1 Table). In silico phylogenetic footprinting [18], was applied for identifying the best conserved motifs in those orthologous regions [13]. This approach has its shortcoming as it may miss some of the binding motifs which are not conserved in upstream region of SESN3. However, this approach has several advantages because of the limited information currently available regarding the transcription regulators controlling this gene. Further, this analysis also limits the false discovery of motifs as well as associated TFs. Briefly, 5kb upstream sequences of SESN3 gene for human and its orthologs (S1 Table) were analyzed by MEME, an expectation maximization-based motif-finding algorithm, to identify the potential binding sites conserved across the species. We have used the gene start as the reference to obtain the 5kb upstream. Based on the alignments, PWMs representing each of the 20 most significant BMo enriched across the analyzed sequences were identified. We observed that most of the established binding motif PWMs in publically available databases ranges in length between 4 bp to 30 bp (See Materials and Methods) therefore, we believe that the discovered motifs in current study would be able to capture most of these recognition sequences, including large co-complex TF binding sites or palindrome motifs, if they are present in the SESN3 upstream. Motif logos[41] corresponding to each of these 20 significantly conserved ones along with the number of occurrences of the motifs across the 11 sequences were shown in Fig 1. Consensus sequences representing these discovered motifs were shown in S2 Table.

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Fig 1. Identification of potential binding motifs by phylogenetic footprinting of 5 kb upstream regulatory regions of SESN3 gene.

Twenty phylogenetically conserved and statistically significant (indicated by e-value) novel motifs with the number of sites contributing to their identification were shown for SESN3 5kb upstream. These motifs were displayed as sequence LOGOs representing position weight matrices of each possible letter code occuring at particular position of motif and its height representing the probability of the letter at that position multiplied by the total information content of the stack in bits.

https://doi.org/10.1371/journal.pone.0160228.g001

Distribution of binding motifs for SESN3 across species

Genes of many eukaryotes display a more complex architecture of associated regulatory elements, including cis-promoter elements with binding sites for basal transcription factors, and distal /trans elements with host specific transcription factors binding sites [42]. Several elegant studies on developmentally regulated [43] and immune-response genes [44,45] have revealed an important role for combinatorial interactions between different transcription factors (TFs) in establishing the complex sequential patterns of gene expression. Hence, increasing evidence now suggests the importance of not only knowing the binding location of a eukaryotic TF [46] but also the complex combinatorial interplay between them [47]. Therefore, we first mapped the identified conserved novel motif sites across multiple species. These binding motifs were quite different from each other; as indicated by the Pearson correlation coefficient values (S3 Table) obtained using MAST from MEME-suite[15, 48]. Relative positions of the discovered binding sites in the 5kb upstream regulatory sequences across the species, organized by phylogenetic distance along with the combined significance of motif co-occurrence, were shown as a block diagram (Fig 2A). The conservation of motifs was observed high in the region between -1 and -2.5 kb of the SESN3 gene promoter.

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Fig 2. Block diagram showing occurrence of conserved motifs.

(A) Location of twenty motifs identified and their distribution in 5 kb upstream sequences across human-SESN3 & its other primate/rodent orthologous species were shown in the block diagram. The combined best matches of a sequence to a group of motifs were shown by combined p value. Sequence strand specified as “+” (input sequence was read from left to right) and “-” (input sequence was read on its complementary strand from right to left) with respect to the occurrence of motifs. Coordinates of each motif across species is shown as a sequence scale (from left to right, in blue) below the diagram. DNase I hypersensitive region was shown in 5kb upstream region of SESN3 in (B) human cell lines and (C) mouse liver (8 week adult and 14.5 days embryo) using ENCODE project, represented by UCSC browser visualization tool. An overlap of DHS signal was found and shown as dark band over respective motifs in block diagram. The two coordinates on x-axis represents the 5kb upstream regions as base distance (in blue) and genic distance (with respect to gene start site, in red) of SESN3 gene.

https://doi.org/10.1371/journal.pone.0160228.g002

DNase I hypersensitive sites (DHSs) are DNase I enzyme sensitive regions of chromatin, where chromatin has less condensed structure due to chromatin remodeling for facilitating transcriptional activation and other downstream events [49]. We used the DHS data available for human cell lines and mouse liver (14.5 days and 8 weeks), generated from University of Washington as part of the ENCODE project [50]. Our analysis strongly suggested several predicted motifs (Fig 2B and 2C) in 5 kb upstream region of the SESN3 genes to be active and open for transcription factor binding, especially within 1 kb of the gene promoter.

Prediction and validation of transcriptional regulatory apparatus targeting discovered motifs of SESN3 upstream region

We downloaded the motif databases viz. JASPAR CORE 2014, TRANSFAC, UniPROBE mouse and Jolma 2013 (See Materials and Methods) separately and then combined all together to perform the motif comparison analysis using Tomtom with proper filtering criteria (p-value ≤ 1e^-03 and E-value <10). All possible TFs predicted to bind to the discovered motifs were catalogued and shown in S4 Table. High confidence set of TFs predicted to regulate the expression of SESN3 via Tomtom [29] included FOXOs, SMADs, SOXs, HNF4A, and TCFs (see S4 Table, Fig 3A–3D). We validated binding motifs which corresponded to high confidence TFs overlapping with DHS signals viz. SOX2 and FOXO3 using ChIP-PCR approach in HEK293 cells (See Materials and Methods). SOX2 and FOXO3 transcription factors were found to exhibit significantly enriched binding to the predicted location in the human SESN3 promoter region compared to a negative control GFP (Green Fluorescent Protein) (Fig 3E and 3F). Thus, this validation confirms the active BMos discovered for FOXO3 and SOX2 in the promoter region of the human SESN3 gene. To further verify the functional relevance of these TFs in the regulation of the SESN3 gene, we also performed overexpression and knockout of FOXO3 and SOX2 in human cell lines. We found that overexpression of FOXO3 or SOX2 could significantly activate the SESN3 gene in human HepG2 hepatoma cells (Fig 3G), but they did not significantly affect the SESN3 gene expression in human HEK293 cells (embryonic kidney cell line) (S2 Fig), suggesting that there might be cell-type-specific effects. Nevertheless, knockout of either FOXO3 or SOX2 downregulated the SESN3 gene expression (Fig 3H).

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Fig 3. Tomtom analysis results for conserved motifs and experimental validation.

(A-D) Transcription factors predicted for 20 consensus sequences (as query motif) by Tomtom analysis. Selected set of DHS overlapped motif aligning with their TF’s PWM (top) and query motif (bottom) with binding specificity mentioned by p-values. (E-F) Validation of FOXO3 and SOX2 binding to predicted BMo location in SESN3 upstream region by ChIP analysis. (G) Overexpression of FOXO3 and SOX2 activated the SESN3 gene expression in human HepG2 hepatoma cells. (H) Knockout of FOXO3 or SOX2 using CRISPR/Cas9 approach downregulated the SESN3 gene in human HEK293 cells. (* p<0.05).

https://doi.org/10.1371/journal.pone.0160228.g003

There are different isoforms of SESN3 as shown in Table 1. Therefore, it is possible to have alternative regulatory elements in the first intron of the gene. In addition to the previous analysis, we also performed insilico phylogenetic foot printing with 3 kb upstream and 2 kb instream query sequence of the primates and rodents for motif discovery and potential TFs binding to these new motifs. The new analysis might not produce the same set of motifs similar to the previously identified consensus sequences because the sequence search spaces are different, however we believe, motifs which overlap fully or partially with common DHS signals to the previous analysis, should produce reproducible results. We identified a set of 20 overrepresented consensus motifs (E-value < e^-44) among which, motifs overlapping with the DHS signals (See S3 and S4 Figs), and their corresponding potential binding transcription factors are documented in S5 Table. We observed that ~64% of the previously detected TFs (whose binding motifs were supported with DHS) were still detected in the new analysis including SOXs, FOXOs, SMADs, TCFs, HAP1, LEF1, GATA1, POU3F4, POU5F1, EKLF and TFAP4. Hence, inclusion of instream region increased the coverage of predicted TFs in our analysis corresponding to the newly identified motifs. Additionally, we examined the length distribution of TF sites overlapping with the width of discovered motifs. We observed that most TF binding sites exhibited an overlap between 4–28 bp with the query motifs as shown in S5 Fig, suggesting that employing a 6–30 bp motif width is an ideal threshold to capture most potential TFs likely to bind to the upstream regions of SESN3.

Further, in order to prioritize these predicted TFs (S4 Table) and to know potential protein complexes that might be responsible for regulation, we integrated the currently available human protein interaction network from the BioGRID[16] to construct a network of physical associations between TFs predicted to be binding to the SESN3 gene regulatory regions (see Materials and Methods). This resulted in a network of 67 TFs with 125 associations among them, with TFs like SMAD3, HDAC2, TCF3, SMAD2, CEBPA, SOX2, SMAD1 and TAL1 exhibiting high degree of associations (S6 Fig). Such physically interacting TF-TF network could provide potential co-complex interactions contributing to the regulation of SESN3 gene. While it is possible to argue that Tomtom algorithm we applied for motif comparison, might result in false positives, increasing evidence from large-scale analysis suggests that most of the transcription factors with similar binding sequences tend to regulate genes with similar biological functions[27,51]. It indicates that several of the TFs with very similar binding affinities might be competing to bind to the target sites to result in the final transcriptional outcome. Therefore, in an attempt to identify a high confidence list of TFs, this network was further filtered to include only the TFs which were predicted to bind the BMos with a high confidence (p<e^-03) from Tomtom analysis and their corresponding motifs overlapping with DHS signals thereby resulting in a subset of TF-TF interactions which are likely to control SESN3 promoter. The resulting network of 30 nodes with 60 interactions is shown in Fig 4. We found that the hubs of this TF-TF interaction network included SMAD3, TCF3, SMAD2, HDAC2, SOX2, TAL1 and TCF12. FOXOs, which have been documented to regulate the SESN3 gene transcription[52] were also found to interact with SMAD3, suggesting their interplay to combinatorially control SESN3.

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Fig 4. Interaction network of high confidence transcription factors.

Protein interaction network between TFs constructed for high confident (p ≤ 0.001, E-value < 10) transcription factors using BioGRID database with TFs belongs to DHS signal overlapped BMo were shown.

https://doi.org/10.1371/journal.pone.0160228.g004

SOX2 contains highly conserved DNA binding domains known as HMG (High-mobility group) box domains which facilitate the binding with DNA for transcriptional control [53]. Our study predicted its significant binding (p = 1.75e^-05, E-value = 0.07) to motif 4. This TF was found physically interacting with TCF3, POU5F1 (OCT-4), SOX6, HDAC2 and in addition to that it is also interacting with TCF12, thus indirectly bridging with SMAD3- another major hub of TF-TF interaction network (Fig 4).

Hepatocytes nuclear factor 4 alpha (HNF4A) belongs to the HNF4 family. It is known to bind to DNA either as a homodimer or as a heterodimer with other transcription factors such as SMAD3 [54]^, [55]. This protein was predicted to be significantly binding to motif 10 (p = 1.66e^-03, E-Value = 6.67) Protein-protein interaction network data suggests its assistive role in regulating the SESN3 gene along with SMADs.

SMADs are signaling cascade associated proteins that act as transcriptional mediators of multiple signaling pathways. For instance–they modulate the transcriptional activity of target genes by transforming growth factor-beta-1[56]. They are reported to bind to CAGA box [57,58] and in some cases to the reverse palindromic sequence ‘GTCTAGAC’ known as SBE (Smad Binding Element) [59] in the upstream regions of target genes by co-complexing with proteins like SP1[60], FOXOs [54,61], HNF4A [55] etc. Motif 4 identified in our analysis was predicted (p = 9.34e^-04, E-value = 3.76) to be bound by SMAD3 further supporting the binding specificity of this TF to SESN3 regulatory regions. SMAD3 works as a master regulator consistent with our observation that it forms a hub with most other high confident TFs as is evident from our interaction network analysis.

Conclusions

This work is among the first efforts to identify transcription factor binding sites in the SESN3 gene promoter using an unbiased computational approach. We found high confidence set of TFs correspond to these identified novel BMos and obtained hubs of TF-TF interaction network that include SMADs, SOXs and TCFs. FOXOs, which have been documented to regulate the SESN3 gene transcription[52] were also found to interact with SMAD3, suggesting their interplay to combinatorially control SESN3. Some of them including FOXO3 and SOX2 have also been experimentally validated for their binding affinity in identified BMos using ChIP-PCR technique. Our findings can form a roadmap to further our understanding on the regulation of the SESN3 gene.

Supporting Information

S1 Fig. Sestrin 3 is stress response protein, secreted in most of body fluid and liver secretome as shown by mRNA expression pattern of the gene in reference expression data set i.e. mRNA expression profile using genecards.org survey of diverse anatomic regions.

https://doi.org/10.1371/journal.pone.0160228.s001

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S2 Fig. Overexpression of FOXO3 and SOX2 activated the SESN3 gene expression in HEK293 cells.

https://doi.org/10.1371/journal.pone.0160228.s002

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S3 Fig. Identification of potential binding motifs by phylogenetic footprinting of 3 kb upstream and 2 kb instream regulatory regions of SESN3 gene.

https://doi.org/10.1371/journal.pone.0160228.s003

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S4 Fig. DNase I hypersensitive sites and block diagram showing the occurrence of conserved motifs in human and mouse.

DNase I hypersensitive region was shown in 3 kb upstream and 2 kb instream sequences of SESN3 in (A) human cell lines and (B) mouse liver (8 week adult and 14.5 days embryo) using ENCODE project, represented by UCSC browser visualization tool. An overlap of DHS signal was found and shown as dark band over respective motifs in block diagram. The combined best matches of a sequence to a group of motifs were shown by combined p value. Sequence strand specified as “+” (input sequence was read from left to right) and “-” (input sequence was read on its complementary strand from right to left) with respect to the occurrence of motifs. The two coordinates on x-axis represents the 3 kb upstream and 2 kb instream regions as base distance (in blue) and genic distance (with respect to gene start site, in red) of SESN3 gene.

https://doi.org/10.1371/journal.pone.0160228.s004

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S5 Fig. Length distribution of TF sites overlapping with the width of discovered motifs.

https://doi.org/10.1371/journal.pone.0160228.s005

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S6 Fig. Interaction network of predicted transcription factors.

Protein interaction network between TFs constructed for all possible predicted transcription factors using BioGRID database with TFs belongs to DHS signaled BMo were shown in asterisk “*”.

https://doi.org/10.1371/journal.pone.0160228.s006

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S1 Table. This table enlists human-SESN3 in orthologous species (primates and rodents) with its location (coordinates) and % query, target matching.

https://doi.org/10.1371/journal.pone.0160228.s007

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S2 Table. Motifs identified in 5kb upstream region of SESN3 in occuring species was shown.

For each motif, significance, location from start (0 in block diagram) and sequence at location for each species was documented.

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S3 Table. Correlation indices of motifs identified in analysis for 5 kb upstream (20 motifs) were shown.

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S4 Table. This table enlists TFs associated to 20 motifs identified in 5kb upstreams region of SESN3 predicted by Tomtom analysis using Jolma et al, Jaspar & UniPROBE Mouse_2014 and TRANSFAC databases (Motifs supported by DHS signal were color coded; green in mouse and yellow in human).

https://doi.org/10.1371/journal.pone.0160228.s010

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S5 Table. This table enlists TFs associated to DHS supported motifs identified in 3kb upstream and 2 kb instreams regulatory region of SESN3 predicted by Tomtom analysis using Jolma et al, Jaspar & UniPROBE Mouse_2014 and TRANSFAC databases (Motifs supported by DHS signal were color coded; green in mouse, yellow in human and peach in both).

https://doi.org/10.1371/journal.pone.0160228.s011

(XLSX)

Author Contributions

Conceived and designed the experiments: RS XCD SCJ SL. Performed the experiments: RS YZ XX XZ XP XCD. Analyzed the data: RS SL XCD SCJ. Contributed reagents/materials/analysis tools: RS XCD SCJ SL. Wrote the paper: RS SL XCD SCJ.

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Figures

Abstract

Introduction

Materials and Methods

Sestrin 3 transcripts and their expression profile

Identification of human SESN3 orthologs and their upstream regulatory regions for phylogenetic footprinting

MEME analysis for discovering DNA binding motifs

Prediction of TFs associated with discovered motifs

Analysis of DNase I hypersensitive site in SESN3 upstream region

Experimental validation of potential transcription factors

CRISPR/Cas9 gene knockout

DNA constructs preparation

Cell culture and transfection

mRNA analysis

Mapping protein interactions between the potential TFs

Results and Discussion

Identification of potential binding motifs by in silico phylogenetic footprinting in the regulatory regions of SESN3 across primates and rodents

Distribution of binding motifs for SESN3 across species

Prediction and validation of transcriptional regulatory apparatus targeting discovered motifs of SESN3 upstream region

Conclusions

Supporting Information

S1 Fig. Sestrin 3 is stress response protein, secreted in most of body fluid and liver secretome as shown by mRNA expression pattern of the gene in reference expression data set i.e. mRNA expression profile using genecards.org survey of diverse anatomic regions.

S2 Fig. Overexpression of FOXO3 and SOX2 activated the SESN3 gene expression in HEK293 cells.

S3 Fig. Identification of potential binding motifs by phylogenetic footprinting of 3 kb upstream and 2 kb instream regulatory regions of SESN3 gene.

S4 Fig. DNase I hypersensitive sites and block diagram showing the occurrence of conserved motifs in human and mouse.

S5 Fig. Length distribution of TF sites overlapping with the width of discovered motifs.

S6 Fig. Interaction network of predicted transcription factors.

S1 Table. This table enlists human-SESN3 in orthologous species (primates and rodents) with its location (coordinates) and % query, target matching.

S2 Table. Motifs identified in 5kb upstream region of SESN3 in occuring species was shown.

S3 Table. Correlation indices of motifs identified in analysis for 5 kb upstream (20 motifs) were shown.

S4 Table. This table enlists TFs associated to 20 motifs identified in 5kb upstreams region of SESN3 predicted by Tomtom analysis using Jolma et al, Jaspar & UniPROBE Mouse_2014 and TRANSFAC databases (Motifs supported by DHS signal were color coded; green in mouse and yellow in human).

Author Contributions

References