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Ramezani M, Javan B, Jafari M H, Banisadr A, pourafshar M, Raeissadati S S, et al . In Silico Identification of Housekeeping Genes by Expressed Sequence Tags and Assessment of Short Tandem Repeats in Their Promoters. jcbr 2021; 5 (4) :31-43
URL: http://jcbr.goums.ac.ir/article-1-335-en.html
1- Student Research Committee, Golestan University of Medical Sciences, Gorgan, Iran /Department of Medical Genetics, School of Advanced Technologies in Medicine, Golestan University of Medical Sciences, Gorgan, Iran
2- Medical Cellular and Molecular Research Center, Golestan University of Medical Sciences, Gorgan, Iran
3- Department of Medical Biotechnology, School of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran.
4- Student Research Committee, Golestan University of Medical Sciences, Gorgan, Iran /Department of Medical Biotechnology, School of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran/Department of Medical Biotechnology, School of Advanced Technologies in Medicine, Golestan University of Medical Sciences, Gorgan, Iran
5- Student Research Committee, Golestan University of Medical Sciences, Gorgan, Iran /Department of Medical Biotechnology, School of Advanced Technologies in Medicine, Golestan University of Medical Sciences, Gorgan, Iran.
6- Deputy Of Research and Technology, Golestan University of Medical Sciences, Gorgan, Iran
7- Department of Medical Genetics, School of Advanced Technologies in Medicine, Golestan University of Medical Sciences, Gorgan, Iran/Ischemic Disorders Research Center, Golestan University of Medical Sciences, Gorgan, Iran / Gorgan Congenital Malformations Research Center, Golestan University of Medical Sciences, Gorgan, Iran , oladnabidozin@yahoo.com
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Introduction
  A quantitative study of gene expression based on the amount of RNA produced by a specific gene in biological conditions is necessary to understand the structure and function of the gene.  Quantitative real-time PCR (qRT-PCR) is considered as an accurate, powerful, and reliable technique for gene expression studies. However, several factors can affect its outcomes. Therefore, qRT-PCR data need to be normalized by internal controls to remove the non-specific variability related to differences in the quantity and quality of the RNA (1).
  In gene expression studies, accuracy of normalized data is highly dependent on the reliability of reference genes. Failure to select an appropriate reference gene may introduce bias in the gene expression data (2). An ideal internal control gene should be ubiquitous and constitutively expression in different cell types and tissues, regardless of the type of tissue, disease condition, developmental stages, or experimental conditions (3). These conditions concerned with reference genes are more consistent with the housekeeping genes (HKGs) as critical genes to maintain basal cellular functions and existence of cells (4). However, there is no HKG capable of stable expression in all tissue types under all experimental conditions (5). For instance, studies have indicated that commonly known HKGs, such as β-actin (ACTB) and glyceraldehyde-3- phosphate dehydrogenase (GAPDH) have variable expression levels in various  developmental stages and different physiological and pathological conditions (68). Therefore, it is suggested to consider validation of the expression stability of HKGs prior to comparison and normalization with the target gene.
One of the efficient ways to obtain information about gene expression is by expressed sequence tags (ESTs), a high-throughput method for gene expression analysis representing the expression profile, including complexity and abundance levels of transcripts from different tissues, celltypes, and developmental stages (9,10). Furthermore, short tandem repeats (STRs) are among the elements affecting gene expression. These are repetitive short sequences consisting of 1-6 bp motifs covering approximately 3% of the human genome (11,12). Studies have demonstrated that most of the STRs are located near and flanking the transcription start site (TSS) or within the gene, playing a decisive role in gene expression (13,14). Other evidence suggests that genes containing a high density of repeat sequences have a higher rate of transcriptional divergence and gene expression (15). In the present study, we introduced a new category of HKGs based on the criteria mentioned for these genes by EST data and compared the nucleotide composition, abundance, and type of STRs in these genes with those of human protein-encoding transcripts.
 
MATERIALS AND METHODS
  In the present study, all human class II (protein-coding) genes were selected from the GeneCards database (http://www.genecards.org/List). Then, EST profiles of the genes were extracted from the UniGene database (https://www.ncbi.nlm.nih.gov/unigene ). In this database, only 17,242 genes amongst the total of 130,062 genes have an EST profile. Overall, 81550 sequences corresponding to the the first 120 nucleotides of the coding genes were obtained from the ENSEMBLE database. These sequences were also evaluated for STR, type, and copy number as well as nucleotide percentage in In Silico (http://insilico.ehu.es/mini_tools/microsatellites) and ALGGEN(http://alggen.lsi.upc.es/cgibin/promo_v3/promo/promoinit.cgi?driDB=TF_8.3) databases, respectively.
  In the next step, Log2 (transcript count per million + 2) scale was used to normalize EST counts. Then, the ratio of cancer-to-normal tissue expression was calculated for each tissue type. Genes that exhibited their expression variations in the range of 1± 0.01 were selected as housekeeping genes for the examined tissues. In addition, EST data on the UniGene database were divided into three categories: 45 normal tissues, 25 tumor tissues, and seven developmental stages. Finally, the genes with expression in all of these items and no variability of gene expression level between normal and tumor tissues were selected as HKGs. Next, the gene ontology (GO) analysis was performed using the Web Gestalt database (http://webgestalt.org) to determine the molecular and biological function as well as cellular component. Data were analyzed by one-way ANOVA using the SPSS 16.0 software. P-values ≥0.05 were considered statistically significant.
 
RESULTS
  Based on the EST data on the UniGene database, only 100 genes from total of 17,242 genes were expressed in all normal and tumor tissues types. Among them, only 16 normal and tumor tissues were comparable to each other. Eventually, 93 genes with no expression variation between normal and tumor tissues as well as in different developmental conditions were selected as candidate HKGs (Figure 1). Common HKGs such as ACTB and GAPDH showed variable expression. Among 45 normal tissues in the UniGene database, GAPDH is expressed in 44 ones. However, it was not among the genes that are expressed in both normal and tumor tissues. Therefore, it was discarded at this stage of our study. Although ACTB expression was observed in all studied tissues, it expression changes were within our desirable range (1±0.01) only in three tissues. In other words, ACTB expression had significant variability in other tissues. Three genes including CALM1, MORF4L1 and HNRNPK covered more tissues. These genes are GC rich in most of their transcripts.

 
Figure 1. Some candidate HKGs in different tissues. Among the normal and tumor tissue data, only 16 normal and tumor tissues were comparable to each other. Some of HKGs are common in different tissues; however, some tissues have their own genes.

 
 
STR analysis  
  First, transcripts of 93 genes were identified, and then the 120 bp up-stream sequences to the +1TSSs of these 661 transcripts were analyzed based on STRs. Among these genes, 40.7% had no STR and 59.3% had STRs, most of which had just one (Table 1). The STRs were also studied
 
based on the type and copy number (Table 2). The binary repetitions were the most frequent, and STRs with three copy numbers had the highest frequency. Study of the STRs nucleotide composition revealed that those with three GC repetitions had the highest percentage
                            
Table 1. Frequency of STRs in 120 bp up-stream sequences of TSSs
Number of STRs Frequency Percent
0 269 40.7
1 228 34.5
2 107 16.2
3 40 6.1
4 11 1.7
5 5 0.8
6 1 0.2
 
Table 2. Frequency of various STRs types and the number of STR repeats
Type of STRs Frequency Percent Repeats in STRs Frequency Percent
MONO 132 20.7 3 repeats 412 64.7
DI 393 61.7 4-5 repeats 84 13.2
TRI 69 10.8 6-7 repeats 104 16.3
TETRA 16 2.5 8-10 repeats 26 4.1
PENTA 26 4.1 >10 repeats 11 1.7
HEXA 1 0.2      
 
GC content
In this stage, 120 bp up-stream of each transcript were analyzed based on the percentage of purine, pyrimidine, and GC content as well as purine/pyrimidine ratio. The results indicated that almost half of the transcripts had a purine/pyrimidine ratio of higher than one (data not shown). The GC-ratio was evaluated using the following formula: G+C/A+T+C+G×100. (Figure 2) demonstrates the frequency of genes based on the GC percentage. A total of 191 transcripts belonging to 49 genes had a high GC content. Based on the results, GNAS with the highest GC content (> 90%) is suggested as the HKG in the pancreatic and skintissues.
Physical interaction and pathway analysis
  Evaluation of the physical interaction of candidate HKGs was performed in the STRING and GENEMANIA databases.  (Figure 3) illustrates the interaction between genes according to the STRING database.
 Investigation of the relationship between these genes in terms of co-expression, physical interaction, etc. in the GENEMANIA database revealed that more than half of these genes were co-expressed, which could confirm our findings

 

 
Figure 3. The output of interactions between candidate HKGs according to the STRING database. The line thickness indicates the strength of data support at high confidence (0.7). PPI enrichment p-value < 1.0e-16.
 
Biological process 
  Evaluation of molecular and biological function as well as cellular component of candidate HKGs were performed in the Web Gestalt database (Figure 4). These genes encode proteins that are often involved in the structure of ribosomal subunits, RNA processing, transcription, translation, protein localization, ubiquitination, cellular structure, and skeletal proteins. Most of these proteins are located in the cytoplasm, nucleus, and membranes that are involved in transporting materials. Among all candidate HKGs in this study, CALM1, HNRNPK, and MORF4L1 covered more tissues. These genes are involved in formation of calmodulin-calcium complex, regulation of transcription, and chromatin and protein N-terminus binding, respectively





 
Figure 4. The gene ontology analysis of candidate genes. The Web Gestalt database was used to perform the GO enrichment analysis and to categorize genes in biological process (Red bar charts), cellular component (Blue bar charts), and molecular function ontology (Green bar charts).
 
 Table 3. List of the candidate HKGs found in this study
Gene Ensemble Transcript ID GO Term (biological process) pu/py STR
ADAR ENSG00000160710
 
 RNA processing 0.85 3(CCT), 6(A),10(T)
ANAPC5 ENST00000261819 Protein phosphatase binding 0.67  
ANXA2 ENST00000396024 Membrane raft assembly 1.45 4(GGCGG)
ARF1 ENST00000541182 Post-Golgi vesicle-mediated transport 0.43  
ARPC2 ENST00000295685 Positive regulation of actin filament polymerization 1.93 3(CG)
ATP5C1 ENST00000356708 ATP biosynthetic process 2.08 3(AG)
ATP6V1E1 ENST00000253413 ATP hydrolysis coupled proton transport 1.26 6(A)
3(CT)
B2M ENST00000558401 ATP hydrolysis coupled proton transport 1.11 4(CT)
BTF3 ENST00000380591 Transcription by RNA polymerase II 0.94 3(CG)
CALM1 ENST00000356978 Detection of calcium ion 1.45 3(GC)
CAPRIN1 ENST00000341394 Negative regulation of translation 0.94  
CCT3 ENST00000295688 Protein folding 0.90 3(AC)
CCT5 ENST00000280326 Protein folding 0.79 3(CCT), 22(A)
CIRBP ENST00000589710 Negative regulation of translation 1.22 3(GC)
CNBP ENST00000422453 Negative regulation of transcription by RNA polymerase II 1.07 3(GC), 3(CG)
COX4I1 ENST00000562336 Electron transport chain 0.67  
CSDE1 ENST00000610726 Regulation of transcription, DNA-templated 1.07  
DDX24 ENST00000621632 RNA secondary structure unwinding/ ATP-dependent RNA helicase activity 1.07  
DDX5 ENST00000225792 Alternative mRNA splicing, via spliceosome 1.40  
EIF3A ENST00000369144 Translation initiation factor activity 0.82 3(GC), 3(GC)
EIF3D ENST00000216190 Translation initiation factor activity 1.55 3(GC)
EIF3L ENST00000624234 Translation initiation factor activity 1.07  
EIF4A1 ENST00000293831 Nucleic acid binding and hydrolase activity 1.35  
EIF4B ENST00000262056 Nucleic acid binding and RNA binding 0.79 6(A), 3(AGC)
EIF4G2 ENST00000526148 Regulation of translation 1.07  
ENO1 ENST00000234590 Transcription corepressor activity 1.79  
GHITM ENST00000372134 Apoptotic process 0.67  
GLO1 ENST00000373365 Carbohydrate metabolic process 0.94 3(AG)
GNAI2 ENST00000313601 GTP binding 0.67  
GNAS ENST00000371100 GTP binding and obsolete signal transducer activity 1.07  
HDLBP ENST00000391975 Lipid transport 1.22 6(A), 3(GGGC)
HMGN1 ENST00000380749 Chromatin binding and nucleosomal DNA binding. 1.73 16(G)
HNRNPA1 ENST00000546500 mRNA processing 0.62 3(CCG), 6(T)
HNRNPA2B1 ENST00000354667 mRNA processing 1.50 6(G)
HNRNPA3 ENST00000411529 mRNA processing 0.54 3(GC)
HNRNPK ENST00000376263 Regulation of transcription 0.87  
HNRNPU ENST00000640218 Chromatin organization 1.03 3(GC)
HSP90AA1 ENST00000334701 G2/M transition of mitotic cell cycle 0.97  
HSP90AB1 ENST00000371554 Protein folding 1.00  
HSPA8 ENST00000534624 Ubiquitin protein ligase binding 0.97 4(CG)
ILF2 ENST00000615950 Double-stranded RNA binding 1.18 6(T)
MAPRE1 ENST00000375571 Cell cycle 0.97 3(GC), 3(CT)
MATR3 ENST00000394805 Posttranscriptional regulation of gene expression 2.00 4(GC), 4(GC)
MORF4L1 ENST00000331268 Chromatin binding and protein N-terminus binding 1.31 3(GGGGT), 16(G)
NACA ENST00000550952 Protein transport/transcription coactivator activity and TBP-class protein binding. 0.74  
NCL ENST00000322723 Nucleic acid binding and identical protein binding 0.58  
NPM1 ENST00000296930 Ribosomal large subunit export from nucleus 2.16 3(GC)
P4HB ENST00000331483 Protein folding 1.50  
PABPC1 ENST00000318607 Nuclear-transcribed mRNA poly(A) tail shortening 1.07 3(CG)
PDIA6 ENST00000404371 Isomerase activity and protein disulfide 0.97  
PKM ENST00000319622 Glucose metabolic process/ATP biosynthetic process 0.82  
PPIA ENST00000468812 RNA-dependent DNA biosynthetic process 1.61  
PTTG1IP ENST00000330938 Positive regulation of protein ubiquitination 0.45  
RAB7A ENST00000265062 Protein targeting to lysosome 1.18  
RAC1 ENST00000348035 GTP binding and enzyme binding 2.08 3(GCG)
RHOA ENST00000418115 Cell morphogenesis 1.26  
RPL10 ENST00000424325 rRNA processing 0.76 3(CT)
RPL11 ENST00000374550 Ribosomal large subunit assembly 0.82 3(CT)
RPL12 ENST00000361436 Translational initiation 0.90 3(CG)
RPL13A ENST00000391857 Regulation of translation 1.18  
RPL15 ENST00000456530 Cytoplasmic translation 1.31 3(AG), 3(TC)
RPL19 ENST00000579260 Structural constituent of ribosome 1.26  
RPL27A ENST00000314138 Translational initiation 1.45  
RPL28 ENST00000344063 Signal recognition particle-dependent co-translational protein targeting to membrane 0.90  
RPL3 ENST00000216146 Structural constituent of ribosome. 0.60 3(GT)
RPL35A ENST00000464167 Translational initiation 0.76  
RPL4 ENST00000307961 Structural constituent of ribosome 1.07 3(AG), 6(T)
RPS20 ENST00000519807 Structural constituent of ribosome 0.76 3(TA)
RPS23 ENST00000296674 Structural constituent of ribosome 0.79  
RPS24 ENST00000440692 Structural constituent of ribosome 1.07  
RPS3 ENST00000531188 Nuclear-transcribed mRNA catabolic process 0.52  
RPS3A ENST00000274065 Structural constituent of ribosome 1.22 3(CG), 3(CA), 3(CT)
RPS4X ENST00000316084 Structural constituent of ribosome 1.22 3(GC)
RPS5 ENST00000596046 Structural constituent of ribosome 0.82 3(GC)
RPS6 ENST00000380394 Structural constituent of ribosome 0.60  
RPS8 ENST00000396651 Structural constituent of ribosome 1.03  
RPSA ENST00000301821 Ribosome binding 0.67  
SARS ENST00000369923 RNA binding and aminoacyl-tRNA ligase activity. 1.03 3(GC)
SERBP1 ENST00000370994 Regulation of mRNA stability 1.03 3(TG)
SERP1 ENST00000479209 Cellular protein modification process 1.03  
SLC25A6 ENST00000381401 Transporter activity 1.31 3(AG)
8(C)
SQSTM1 ENST00000389805 Homodimerization activity 1.93 3(AG)
SRSF2 ENST00000392485 Termination of RNA polymerase II transcription 1.40  
SRSF3 ENST00000373715 mRNA export from nucleus 0.97  
TPT1 ENST00000616577 Transcription factor binding 0.52 3(CG)
TUBA1B ENST00000336023 Cytoskeleton organization 1.00 3(GC), 3(GCCC)
UBC ENST00000536769 MAPK cascade 0.64 3(TG)
6(T)
UQCRC2 ENST00000268379 Mitochondrial electron transport 0.76 3(AC)
VCP ENST00000358901 Signaling receptor binding 0.67 3(TG)
WDR1 ENST00000499869 Cytoskeleton organization 1.18 3(TA), 4(CG)
XRCC5 ENST00000392133 Transcription regulatory region DNA binding 1.07 5(CA)
XRCC6 ENST00000359308 Protein C-terminus binding. 1.11  
YWHAB ENST00000372839 Protein targeting 1.14  
 

DISCUSSION
  Ideally, a HKG should be expressed in all normal and tumor tissues as well as in different developmental conditions, with a fairly constant expression level (3). Given that the EST frequency can be an approximate index of gene expression level in a specific cell or tissue, EST data can be an appropriate resource for gene expression analysis (16). In the present study, based on the EST data of 16 normal and tumor tissues, we identified 93 HKGs that are expressed in all tissues. In previous studies, HKGs have been selected only based on identical expression in both tumor and
 
normal tissue. However, in this research, the third criterion i.e., identicial expression in different stages of development was also considered. Our findings showed that some of HKGs are common between tissues and some of them (18 genes) are exclusive for one tissue. Among the studied tissues, esophagus with 30 genes and brain with four genes had the highest and lowest number of HKGs candidates, respectively. Moreover, CALM1, MORF4L1, and HNRNPK covered more tissues, so they can be considered as the most suitable candidate HKGs. In contrast, our results indicated that common HKGs such as ACTB and GAPDH had variable expression in different tissue types and are not a suitable candidate to normalize expression data in gene expression studies. Previous studies have also reported the variable expression of these genes in tissues (7,8,18).
  In addition, the type and nucleotide composition of STRs in the promoter region of these genes were investigated for the first time in this study. The core promoter sequences contain fundamental motifs for the expression of the downstream genes. Several studies have demonstrated that STRs affect chromatin organization, regulation of gene activity, modulation of gene expression, etc. (11,15,19). In our previous study, we analyzed the nucleotide composition of the 120-bp flanking sequences to the +1 TSS of human protein-coding genes and showed that approximately 25% of these genes have at least STR of 3-repeats in their core promoters and GA-repeats play a decisive role in the regulation of transcription (20,21).
  In the present study, we analyzed the 120-bp immediate upstream sequences to the +1TSSs of candidate HKGs and compared it with other protein-coding genes in order to find a particular pattern for predicting HKGs according to their core promoter STRs. However, we found no difference between HKGs and other protein-coding genes in their core promoter STRs composition. In both groups, most genes had only one STR of which the binary repetitions were the most frequent. In addition, most genes had STRs with three copy numbers and a high-GC content.
 
CONCLUSION
  Our results suggest a novel set of genes as potential HKGs and confirm that some of the common HKGs such as GAPDH and ACTB are not suitable for gene expression normalization. In addition, some genes can be suggested as potential HKGs for a specific tissue; however, this requires further investigations and other high-throughput data since low abundance is a limitation of EST. In addition, core promoter STRs composition and frequency of these genes are the same as other protein-coding genes and GC repeats have the highest frequency in core promoter of these genes.
 
ACKNOWLEDGMENTS
  We would like to acknowledge the Student Research Committee of Golestan University of Medical Sciences for funding this study.
 
DECLARATIONS
Funding
  This research has been supported and funded by the Student Research Committee, Golestan University of Medical Sciences, Gorgan, Iran (project code: 110634).
 
Ethics approvals and consent to participate
  The study was approved by the ethics committee of the Golestan University of Medical Sciences, Iran (ethical code: IR.GOUMS.REC.1397.297).
 
Conflict of interest
  The authors declare that there is no conflict of interest regarding the publication of this article.
 
 
 
 
 
 
 
Article Type: Research | Subject: Genetics

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