Promoter analysis identified transcription factors (TFs) that are common and unique to each group of genotypes. The candidate TFs that are common in all genotypes are MYB, bZIP, AP2/ERF, ARF, WRKY, ZnF, MADS-box, NAC, AS2, DOF, E2F, ARR-B, and HSF. However, the highly tolerant genotypes interestingly possess binding sites associated with HY5 (bZIP), GBF3, GBF4 and GBF5 (bZIP), DPBF-3 (bZIP), ABF2, ABI5, bHLH, and BES/BZR, in addition to the common TFs. Besides, the extremely tolerant genotypes possess binding sites associated with bHLH TFs such as BEE2, BIM1, BIM3, BM8 and BAM8, and ABF1, in addition to the TFs identified in the tolerant and highly tolerant genotypes. The transcriptional regulation of these TFs could be linked to phenotypic variation in coleoptile elongation in response to submergence tolerance. Moreover, the results indicate a cross-talk between the key TFs and phytohormones such as gibberellic acid, abscisic acid, ethylene, auxin, jasmonic acid, and brassinosteroids, for an altered transcriptional regulation leading to differences in germination and coleoptile elongation under submergence. The information derived from the current in silico analysis can potentially assist in developing new rice breeding targets for direct seeding.Rice has the natural morphological adaptation to germinate and elongate its coleoptile under submerged flooding conditions. The phenotypic deviation associated with the tolerance to submergence at the germination stage could be due to natural variation. However, the molecular basis of this variation is still largely unknown. A comprehensive understanding of gene regulation of different genotypes that have diverse rates of coleoptile elongation can provide significant insights into improved rice varieties. To do so, publicly available transcriptome data of five rice genotypes, which have different lengths of coleoptile elongation under submergence tolerance, were analyzed. The aim was to identify the correlation between promoter architecture, associated with transcriptional and hormonal regulation, in diverse genotype groups of rice that have different rates of coleoptile elongation. This was achieved by identifying the putative  Likewis(OsARF1) . High acmergence .cis-acting elements in the promoters of specific genes  and 8753 from Indonesia (IRGC 54313)]-were used for the promoter architecture analysis. To identify the promoter cis-element content of the common upregulated genes of different rice genotypes based on their extent of tolerance in terms of elongation of coleoptiles, common genes upregulated in different groups of genotypes  that have 24 upregulated genes  were anacis-elements were identified in the promoter regions of each set of common upregulated genes by the “The Dragon Motif Builder program” having EM2 option with a threshold value of 0.875 , PLACE database (2), AGRIS (3), and PlantPAN 3.0 (4). Putative cis-elements in the promoters of the upregulated genes were identified based on their sites for different plant TFs present in plant genes with a minimum sequence length of five nucleotides. The cutoffs for core and matrix similarities were more than 75%. TF genes, significantly upregulated, were identified from the RNA-sequencing data (5). The methods used to analyze the data have been summarized and presented in Putative of 0.875 , which iof 0.875 . The proTRANSFAC 1, PLACE database 2, AGRIS ), AGRIS 3, and PltPAN 3.0 4. Putatiing data  and annoing data 5. The mecis-elements/TF binding sites that are responsible for the regulation of genes associated with various hormone signaling as well as metabolic pathways. Our analysis identified a number of common putative cis-elements in the promoter regions of the upregulated genes in tolerant , highly tolerant , and extremely tolerant (8391 and 8753) genotypes. Putative cis-elements that are present in all genotypes are found to be associated with many known TFs such as MYB, bZIP, AP2/ERF, ARF, WRKY, ZnF, MADS-box, NAC, AS2, DOF, E2F, ARR-B, and HSF -like, and MYB-box-like. The MYB-box-like elements were highly enriched in the highly tolerant genotypes and, besides the promoters, also possess MYB-box related-like elements associated with MYB-like (CDC5) and MYB-like  TFs (Os12g0125000 (MYB-like DNA-binding domain-containing protein), Os11g0128500 (MYB-like DNA-binding domain-containing protein), Os05g0553400 (Similar to MYB-related TF), Os01g0298400 , Os12g0567300 , Os01g0874300 , and Os05g0140100 (R2R3 MYB TF)  TFs -3. TheseA-1) TFs  such as  MYB TF) .in silico analysis that MYB TFs that bind to MYB-box/GT-element-like elements play a key role in the transcriptional regulation of rice during germination under submergence and anoxia  could also be playing a role together with other hormones in the germination and coleoptile elongation of rice under submergence.It has been highlighted that MYB TFs represent a major protein in rice and are involved in the transcriptional regulation of many developmental processes as well as abiotic and biotic stress conditions . Additiod anoxia . In thisd anoxia . It actid anoxia . Under sr anoxia . It appepression . MYBS1 aOs12g0547600 , Os11g0152700 , and Os05g0129300 (bZIP transcription factor)  binding bZIP TF, has been shown to be induced during different abiotic stresses, such as anoxia, drought, cold, salinity, and ABA in rice seedlings  . bZIP TFand rice . Althoug in rice . Moreove hypoxia . Later, eedlings . ABA plaeedlings . Promoteeedlings . Recentleedlings . In the eedlings , glycolyeedlings , and in eedlings . In riceeedlings . High enInterestingly, binding sites associated with bZIPs such as HY5, DPBF-3, and GBF 3, 5, and 6 were identified in both highly tolerant and extremely tolerant rice genotypes. HY5 is a key integrating factor for light and ABA pathways, and it stimulates ABA signaling pathway by binding to the promoter of ABI5 . It alsocis-elements associated with different groups of ERF TFs were identified in the promoter regions of the upregulated genes in tolerant, highly tolerant, and extremely tolerant genotypes in response to submergence (cis-elements related to the ERF group is less compared to their presence in tolerant genotypes (Os01g0968800 (DREB transcription factor-like), Os06g0127100 (Dehydration-responsive element-binding protein 1C), Os01g0868000 (AP2/ERF transcription factor-like), Os09g0522100 (Dehydration-responsive element-binding protein 1H), Os01g0140700 (Similar to RAV2: AP2/ERF and B3 domain-containing protein), Os02g0677300 (Similar to CRT/DRE binding factor 1), Os02g0654700 , Os02g0656600 (DRE binding factor 2B), Os03g0184500 , and Os01g0693400 (RAV family protein)  .SUBMERGENCE 1A (Sub1A), an ERF-type TF, regulates submergence/flooding tolerance in rice. Although the japonica cultivar “Nipponbare” lacks this gene  (Os05g0549800 (AP2/ERF and B3 domain-containing protein: similar to DNA-binding protein RAV1). In Arabidopsis, during seed germination and early seedling development, RAV1 plays a key role in ABA signaling by repressing the expression of ABI3, ABI4, and ABI5 by binding to the 5′−CAACA−3′ motif within the promoter of ABI3, ABI4, and ABI5. These results suggest that RAV1 could also be acting as a repressor of ABA signaling. In addition to ABA inhibition, ethylene also inhibits the biosynthesis of jasmonate (JA) during coleoptile elongation of etiolated rice seedlings (In nditions . This meRAP2.12) . In thiseedlings . AlthougArabidopsis  binding-site-like motifs associated with ABR1 TF belonging to AP2-domain-containing protein group X in rice. TF ABR1 functions as a negative regulator of ABA response during seed germination. The expression of this TF is induced during various abiotic stresses, such as drought, cold, and salt in bidopsis . In ricebidopsis  and show hypoxia . Later, , and 40 . Signifi, and 40 . This sh, and 40 . This TFArabidopsis   -4. The pn IAA31) . Moreoven IAA31) , whereasn IAA31) . Auxin mn IAA31) . OsARF, eoptiles . Recentlmergence . DiffereOs05g0583000 (Similar to WRKY transcription factor 8), Os05g0537100 , and Os01g0750100 (WRKY transcription factor 13)  by preserving carbohydrates, mainly sucrose and starch, through the repression of fermentation pathways -deficient mutant of rice where WRKY could be playing a key role in cell survival rather than elongation  -4. Identctor 13) . WRKY plctor 13) . Increasbidopsis  and ricebidopsis  in respopathways . Upregulpathways . Similarongation . Interesmergence . This shArabidopsis, three WRKY genes such as AtWRKY18, AtWRKY40, and AtWRKY60 negatively regulate ABA signaling. Among them, AtWRKY 40 binds to the promoter of ABI4 and ABI5 and represses the expression of ABA-responsive genes , Os03g0820300 (C2H2 transcription factor), Os12g0113700 , Os02g0646200 , Os10g0456800 (CHY zinc finger family protein), Os01g0303600 , Os06g0340200 , Os03g0329200 (Zinc finger CCCH domain-containing protein 23), Os03g0764100 (Zinc finger transcription factor ZF1), Os09g0486500 (Zinc finger A20 and AN1 domain-containing stress-associated protein 1), and Os05g0128200 (Zinc finger CCCH domain-containing protein 33)  . The enrtein 33) . In ricetein 33) . In our tein 33) . We alsotein 33) . Besidesa/anoxia . In ricea/anoxia . A B-boxa/anoxia .Os11g0154500 , Os03g0815100 (Similar to OsNAC6 protein), Os01g0884300 (NAC domain-containing protein 6), Os07g0684800 (Similar to NAM/CUC2-like protein), and Os07g0225300 (OsNAC3 protein)  . Memberser crops , b. In mer crops . In addir plants . It alsobidopsis . It coulbidopsis .OsDof3 suggested that it interacts with GAMYB to induce the expression of RAmy1A to facilitate GA signaling during the germination of rice seeds  TFs. These TFs are mainly plant-specific  and are ce seeds . In thisOs08g0471000 , Os09g0526600 , Os09g0456800 (Heat stress transcription factor B-1), and Os02g0232000   -4. HSFs , HSF 5) . Inductiand rice . HSF proand rice  and alsoArabidopsis during flooding stress  family TF AS2 -4. Howevg stress . ARF7 anbidopsis . Recentlbidopsis , althougA moderate enrichment of E2F-binding site-like elements associated with E2F TFs was identified in the upregulated genes of the tolerant genotypes . E2F TF Arabidopsis, the activity of AtTCP14 is necessary for seed germination and there is a functional relationship between this TF and GA , Os05g0129700 (Homeobox-leucine zipper protein HOX28), Os03g0198600 (Homeodomain-leucine zipper transcription factor), Os06g0140700 (Homeobox-leucine zipper protein HOX2), Os03g0188900 (Homeobox-leucine zipper protein HOX13), and Os09g0528200 (Similar to Homeobox-leucine zipper protein HOX6)  . HD-ZIP in HOX6) . Howeverlarly BR . AtHB4 ay plants . The preARR-B binding element-like associated with type-B ARRs was identified among upregulated genes in all tolerant, highly tolerant, and extremely tolerant genotypes -3. Type-Os04g0580700 (MADS-box transcription factor 17)  TF were identified in the upregulated genes of all tolerant, highly tolerant, and extremely tolerant genotypes -3, and tctor 17) . MADS boctor 17) . Howeverd AGL 21 . AGL25 inditions .Arabidopsis  signaling pathway were identified only in the upregulated genes of highly tolerant and extremely tolerant genotypes that had longer coleoptile elongation in response to submergence , 3. BR ibidopsis . It alsobidopsis . They habidopsis . Althoug of ABI5 .BR also activates different stress adaptive signaling pathways by directly or indirectly regulating different stress-responsive TFs such as bZIP, MYB, WRKY, NAC, DREB, etc. through BIN2 and key BZR1/BES1 TFs . GeneticOs01g0773800 (bHLH protein 185), Os03g0188400 (bHLH protein), Os07g0628500 (bHLH dimerization region bHLH domain-containing protein), Os03g0135700 (bHLH transcription factor), and Os07g0143200 (Phytochrome-interacting bHLH factor) (E-box-like/G-box-like elements associated with bHLH (Gr. III and VII) including PIFs were identified in both highly tolerant and extremely tolerant genotypes , 3. Inte factor) . bHLH TF factor)  such as  factor) , light s factor) , hormone factor) , respons factor) . They alcoleoptile photomorphogenesis 1 (cpm1) mutant suggest that this gene has a role in phytochrome-mediated inhibition of coleoptile growth. Later studies on phototropism rice coleoptile demonstrates that auxin is involved in this process (Phytochrome-interacting factors (PIFs) are bHLH TFs. Besides the bHLH domain, they have active phytochrome A/B binding domains. They are involved in various physiological processes such as seed germination, photomorphogenesis, shade responses, flowering time, and leaf senescence . Besides process .cis-elements in tolerant, highly tolerant, and extremely tolerant genotype groups, upregulated genes in the intermediate group, which has two highly tolerant genotypes, F291 and F274-2a, were also analyzed , highly tolerant genotypes with longer coleoptile elongation , and extremely tolerant natural genotypes with the longest coleoptile elongation (8391 and 8753). The promoter architecture analysis of upregulated genes for tolerant genotypes including O. sativa japonica identified the presence of putative cis-elements that are associated with TFs such as MYB, bZIP, AP2/ERF, ARF, EIN3, ABI3, ABR1,WRKY, ZnF, MADS-box, NAC, AS2, DOF, HD-ZIP, E2F, ARR-B, and HSF  in the aleurone layer for starch degradation to provide substrates for the germination and coleoptile elongation of rice seeds  and higher expression of LOC_Os07g47790 in the tolerant genotypes]  and MYB (R2R3) during the initial stage, i.e., imbibition and germination where both hormones ABA and GA play a key role. Upon imbibition of rice seed, the GA content gradually increases and breaks dormancy by inducing the secretion of hydrolytic enzymes and the endogenous ABA level decreases rapidly due to the induction of ABA catabolic genes . GAMYB ice seeds  under suignaling . The higignaling . High enignaling . This isnotypes] . The rolnotypes]  – 3. EIN and MYB notypes] . Besidesnotypes] -3. Identromoters . The enrromoters . Interacromoters  and playromoters . Auxin uromoters . Moreoveromoters .Arabidopsis  is induced by sucrose and can regulate sugar metabolism. It is most probably involved in adaptations to carbon starvation in bidopsis . bZIP albidopsis . In riceongation . Howeverongation . Identifbidopsis . High enbidopsis .Arabidopsis  TFs indicate a probable role of AGL as an environmental surveillance integrator to safeguard seed germination process by inducing ABA signaling through ABI5 TF  gene acts downstream of P1F1 and regulates activation of hypocotyl cell wall composition and elongation through hormone-mediated development, particularly BR . Howevernd MYB30 . MYB30 cnd MYB30 . Moreovend MYB30 . It seemnd MYB30 .Interestingly, BBRE-element-like/G-box-like/E-box-like elements associated with TF BIM1, BIM3, and BEH1 are highly enriched in the extremely tolerant natural genotypes that have maximum coleoptile elongation . These gBesides these binding sites, there were identifications of binding sites for TFs such as ABR1, HD-ZIP (ATHB4), TCP, ZNF, E2F, AS2 (LBD), NAC, and HSF -3, 5, 6.Germination and coleoptile elongation of diverse rice genotypes vary in response to submergence. Promoter architecture of upregulated genes associated with different tolerant genotypes suggests a fine-tuning at the transcriptional level that affects the phenotype of different genotypes. The germination and elongation of rice coleoptiles in response to submergence tolerance in all three genotype groups could be due to a combination of mechanisms involving different TFs such as MYB, bZIP, AP2/ERF, ARF, WRKY, ZnF, MADS-box, NAC, AS2, DOF, E2F, ARR-B, and HSF, and hormonal regulation by GA, ABA, ethylene, JA, and auxin. There could be re-balance between GA and ABA to activate other TFs and stress-responsive genes for the elongation of coleoptile to escape the submergence stress. Interestingly, the longer coleoptile elongation in the highly tolerant and extremely tolerant genotypes could be due to the involvement of additional TFs such as bHLH and BES/BZR along with BR signaling and a cross-talk with other signaling pathways. Moreover, the maximum elongation in the two extremely tolerant natural genotypes might be due to the additional transcriptional regulatory mechanism governed by TFs such as BEE2, BIM1, BIM3, and BAM8, which are not present in the other two genotype groups. The variation in coleoptile elongation in different groups of genotypes is certainly due to the difference in transcriptional regulatory mechanism controlled by specific TFs along with a synergistic cross-talk interaction between different hormones, which needs further experimental validation. This analysis provides a potential mechanism of transcriptional regulation across rice genotypes from diverse backgrounds, which may be helpful for rice breeding targets for direct seeding to improve rice production.The original contributions presented in the study are included in the article/BM designed and analyzed the data, and wrote the manuscript.The author declares that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Whole genome and whole exome sequencing technologies play a very important role in the studies of the genetic aspects of the pathogenesis of various diseases. The ample use of genome-wide and exome-wide association studymethodology (GWAS and EWAS) made it possible to identify a large number of genetic variants associated with diseases.This information is accumulated in the databases like GWAS central, GWAS catalog, OMIM, ClinVar, etc. Most of the variants identified by the GWAS technique are located in the noncoding regions of the human genome. According to theENCODE project, the fraction of regions in the human genome potentially involved in transcriptional control is many timesgreater than the fraction of coding regions. Thus, genetic variation in noncoding regions of the genome can increase thesusceptibility to diseases by disrupting various regulatory elements . However, identification of the mechanisms of influence of pathogenic genetic variants on the diseases risk is difficultdue to a wide variety of regulatory elements. The present review focuses on the molecular genetic mechanisms by whichpathogenic genetic variants affect gene expression. At the same time, attention is concentrated on the transcriptional levelof regulation as an initial step in the expression of any gene. A triggering event mediating the effect of a pathogenic geneticvariant on the level of gene expression can be, for example, a change in the functional activity of transcription factor binding sites (TFBSs) or DNA methylation change, which, in turn, affects the functional activity of promoters or enhancers. Dissecting the regulatory roles of polymorphic loci have been impossible without close integration of modern experimentalapproaches with computer analysis of a growing wealth of genetic and biological data obtained using omics technologies.The review provides a brief description of a number of the most well-known public genomic information resources containing data obtained using omics technologies, including (1) resources that accumulate data on the chromatin states and theregions of transcription factor binding derived from ChIP-seq experiments; (2) resources containing data on genomic loci,for which allele-specific transcription factor binding was revealed based on ChIP-seq technology; (3) resources containingin silico predicted data on the potential impact of genetic variants on the transcription factor binding sites Experimental datasets of comparable volumehave been accumulated in a number of other databases ongenotype-phenotype associations .At present, largely due to the widespread use of the technologyof genome-wide and exome-wide association study (GWASand EWAS), a large number of polymorphisms associatedwith diseases have been identified. For example, GWAScentral ,but the molecular mechanisms underlying these associationsare extremely poorly understood. This is due to the fact thatonly a relatively small proportion of pathogenic GVs is locatedin the coding regions of the human genome, changes in thenucleotide sequence of which disrupt the structure and function of proteins. A huge mass of polymorphic loci associatedwith diseases is located in non-coding regions of the genome. For example, according to GWAS data, ~90 % of thetotal number of variants associated with diseases are locatedin noncoding regions of the human genome .It is known that non-coding regions of the genome containregions that perform a wide range of regulatory functions:promoter regions, enhancers, negative regulatory elements,nuclear matrix attachment regions, regions that determinethe structure of topologically associating domains (TADs),and other features of 3D organization of genome .The proportion of regions in the human genome potentiallyinvolved in the transcriptional regulation is extremely high.According to the ENCODE project, the chromatin regionscorresponding to the peaks of transcription factor(TF) bindingidentified by the ChIP-seq occupy ~8.1 % of the total genomicDNA , which is significantly higher than the proportion of coding regions of thehuman genome (~1.2 %). Considering that not all known TFsand not all cell lines were studied in the ENCODE project,an obviously larger fraction of genomic DNA is involved inthe interaction with TFs. The total length of human genomeregions with enhancer-associated chromatin features alsosignificantly exceeds the total size of the coding regions: forexample, in only one cell type studied (H1-ES), enhancerregions occupy ~3.2 % .Studies aimed at identifying the mechanisms of the influence of pathogenic GVs on the predisposition to diseases arecarried out very actively, which is reflected in a number ofreview publications .The most discussed effect of pathogenic GVs is a change inthe binding activity of TFBSs . It has also been shown that polymorphic loci can be associated with alteration of DNA methylationpatterns  and modifications of histoneproteins , with structural change in chromatinloops  and, as one ofthe manifestations of this process, with changes in the TADsstructure . Examples ofsuch effects will be discussed below (Table 1).The effects of genetic variants on the functionalactivity of transcription factor binding sitesThe key role in the transcriptional regulation is played bytranscription factors – proteins that can specifically bind toDNA of the regulatory regions of genes and to initiate the transcription complexes formation. The human genome containsmore than 1500 genes encoding TFs .TF binding sites, as a rule, have a length of 10–25 nucleotides.Nucleotide substitutions, as well as short insertions/deletions at polymorphic loci, can disrupt TFBSs or create themde novo (see Table 1), and this, in turn, can have both negativeand positive effects on the level of gene transcription . Such GVs (and the corresponding polymorphic loci) that affect the transcriptionalactivity of genes are usually called regulatory variants .Pathological  can be bothan allelic variant of the DNA sequence containing a disruptedTFBS and an allelic variant, leading to creation of TFBS de novo (see Table 1).Pathological GVs, affecting the binding activity of TFBSs,can be located not only in promoter regions, but also in regulatory regions located at considerable distance from transcription start sites (TSSs) of genes: enhancers , regulatoryregions with repressive function , and TAD boundary regions  (see Table 1).For example, the rs1421085 T→C substitution associatedwith obesity impairs the functioning of the negative regulatory region controlling expression of the IRX3 and IRX5 genes. The rs1421085 locus is located inthe intron of the FTO gene  at a considerable distancefrom the transcription start sites of IRX3 and IRX5 . Normally, the DNA region containingallele T interacts with a repressor factor ARID5B, leadingto a decrease in transcriptional activity of IRX3 and IRX5genes. In carriers of the mutant variant of the DNA sequence, the binding site of the ARID5B repressor factor isdisrupted, which causes an excessively high expression of theIRX3 and IRX5 genes and activates adipogenesis .Occasionally a nucleotide substitution at a polymorphiclocus disrupts the TFBS and this, in turn, affects the functionalactivity of the TAD (see Table 1). This effect was found inthe case of A→G (rs2001389), associated with the risk ofpancreatic cancer . The rs2001389 locus is located inthe region that determines the structure of chromatin loopswithin the TAD. This TAD contains 91 genes and is formedby spatially adjacent chromatin regions . TheDNA region containing the risk allele G is characterized by areduced ability to interact with CTCF, which in this case actsas a structural protein of chromatin. Normally, CTCF bindingensures the functioning of one of the regions that determinesthe structure of chromatin loops within the considered TAD.The pathogenic allele G alters the activity of CTCF bindingmotif within TAD boundary disrupting the stability of corresponding 3D structure of chromatin. As a result, the expression of the genes within this TAD is impaired. In this case,the greatest decrease in MFSD13A expression is observed.The effects of genetic variability on DNAmethylation and gene transcriptional activityDNA methylation doesn’t change the nucleotide sequenceand is the addition of a methyl group to the fifth carbon atomof cytosine . An increase inthe level of DNA methylation, as a rule, leads to a long-terminactivation of the expression of genes lying in the methylatedregion, since, according to the generally accepted concept,methylation of a DNA region facilitates recruiting proteincomplexes, containing histone deacetylase (HDAC) . DNA methylation can alsodecrease the ability of some TFs to interact with DNA: it isknown that CTCF factors and factors from the ETS familyhave such sensitivity to methylation . Incontrast, another transcription factor, ZFP57, binds only tomethylated DNA . Thus, cytosinemethylation can activate different mechanisms of gene transcription regulation, and not always an increase in the methylation level of the regulatory DNA region is associated witha decrease in the expression of the corresponding gene .Genetic variability affects significantly the methylation ofDNA regions that have regulatory potential. Thus, a genomewide analysis of the methylation patterns of DNA collectedfrom 24 subjects from Norfolk Island genetic isolate , identified 12,761 regions containing at least twoCpG dinucleotides and having an allele-specific methylationlevel. In most cases (98 %), regions with allele-specific methylation level are co-localized with single nucleotide variantspresented in dbSNP .This study  also analyzed the locationof allele-specific methylation regions relative to the set ofpolymorphic loci associated with human diseases extractedfrom the GWAS catalog database. It turned out that polymorphic loci associated with diseases overlap with regions ofallele-specific methylation twice more often than it would beexpected by chance. This means that the change in methylation levels due to genetic variability is one of the factors thatincrease the risk of disease.As an example, consider the rs174537 (G→T) polymorphic locus located in the enhancer of the FADS1 and FADS2genes encoding fatty acid desaturases 1 and 2. The T variantof the rs174537 locus is associated with an increased risk ofpathological disturbances of lipid metabolism (see Table 1).It was shown that individuals that have rs174537(T) allelehad a higher methylation level of the regulatory region of theFADS1 and FADS2 genes in human liver ,which led to the suppression of the transcriptional activity ofFADS1 and FADS2Occasionally, in one of the allelic variants, DNA demethylation occurs, initiated by TF binding to DNA (see Table 1).For example, such a mechanism was revealed for rs612529T→C. This locus is located in the promoter region of theVSTM1 . The low expression of VSTM1 in monocytesprovokes the development of atopic dermatitis. In this celltype, the promoter region containing the protective variant Tinteracts with the transcription factor PU.1 more actively than the other one containing variant C. PU.1 initiates DNAdemethylation by recruiting DNA demethylases . As a result, carriers of the T allele have completelydemethylated VSTM1 promoter, and VSTM1 expression isactivated. In carriers of pathogenic variant C, the interactionof PU.1 with DNA is disrupted, as a result, methylation levelof the VSTM1 promoter is elevated, and this is accompaniedby a decrease in VSTM1 expression .The effects of the genetic variabilityon the chromatin states and chromatinspatial organizationPathogenic GVs may impaire the chromatin state . There are cases when the presence of a pathogenic GV was accompanied by a change in the patterns ofhistone modification and the appearance (or disappearance)of DNase I hypersensitive sites . In thesecases, allele-specific contacts between promoters and enhancers were identified, the number of which correlated with theactivity of the enhancer regions.There are also known cases when structural variations ofthe genome  lead to a changein the spatial organization of chromatin, thereby disrupting the expression of genes associated with pathologicalprocesses . Forexample, the expansion of CGG trinucleotide repeats in the5′-untranslated region (5′-UTR) of the FMR1 gene, associatedwith the fragile X syndrome, disrupts the structure of TAD,that includes FMR1 . Normally, FMR1 isvery close to the 5′-boundary region of TAD . The DNA region corresponding to this 5′-boundaryis hypomethylated and is occupied by CTCF. In individualswith mutation-length CGG triplet repeats (more than 100),this boundary is ablated (this region is hypermethylated andits CTCF occupancy is lost). As a result, TAD1 dissolves andthe boundary of the other TAD  shifts to the 3′-region of FMR1. Therefore, FMR1 iswithin the TAD2, which normally does not contain this gene.In this case, FMR1 promoter is hypermethylated, and FMR1expression is inactivated .To study molecular-genetic mechanisms of the effect of genome variability on the 3D chromatin structure, it is necessaryto reconstruct the spatial genome organization. The followingbasic levels of the 3D genome organization have been identified: (1) regulatory DNA loops that bring together promotersand enhancers; (2) topologically associating domains(TADs),within which DNA regions have more contacts with eachother than with neighboring domains; (3) A and B compartments corresponding to transcriptionally active and condensedchromatin; and finally (4) chromosome territories . Disruption of 3D contactsbetween promoters and enhancers within the TAD, caused, forexample, by chromosomal rearrangements, can significantlyaffect the transcriptional activity of a gene, increasing risk ofdiseases .The Institute of Cytology and Genetics SB RAS has developed an experimental computer approach for prediction physical contacts between promoters and enhancers withinthe 3D chromatin structure . The approach is based on the following information: (1) cell type;(2) cell-specific localization of enhancers in the linear genome(from the ENCODE database); (3) transcriptional activity ofpromoters (from RNA-seq experiments); (4) boundaries ofchromatin loop extrusion (based on ChIP-seq mapping ofCTCF occupancy in a definite cell type); (5) orientation ofCTCF binding motifs (based on motif prediction pipeline);(6)Aor B chromatin compartment (according to Hi-C experiments). Analysis of these data using the original 3DPredictorprogram , developed on the basisof machine learning algorithms, allows to predict the frequencies of physical contacts between promoters and enhancers inthe 3D genome structure with an accuracy that exceeds theaccuracy of other known prediction methods.The 3DPredictor was used to analyze the 3D genome structure in homozygous DelB/DelB mice that have a deletion ofthe 1.5 Mb genomic region containing Epha4. This deletionis accompanied by the appearance of additional contacts between Pax3 gene and Epha4 enhancer region, altering Pax3expression and leading to brachydactyly. Mice with the DelB/DelB genotype are a genetic model of human pathology accompanied by limb malformations .Testing 3DPredictor on this model has demonstrated the highefficiency of the program: in homozygous DelB/DelB mice,ectopic contacts between the Pax3 gene and Epha4 enhancers cluster were predicted , andthese predictions were in good agreement with the experimental data. Genetic variability: combined analysisof heterogeneous big biological and genetic dataAs noted above, many polymorphic loci associated with diseases are located at a considerable distance from the codingregions of genes . Additional studies are needed to identify themolecular-genetic mechanisms of the influence of such GVson the predisposition to diseases. The purpose of such studiesis to clarify the regulatory role of GVs. A typical example isthe work , which made it possible to finda functionally active regulatory variant rs4321755 associatedwith the risk of breast cancer. The rs4321755 locus is locatedin a distant enhancer that regulates the expression of theMRPS30 and RP11-53O19.1 genes (see Table 1). It turned outthat in the presence of the pathogenic variant rs4321755(T),a new GATA3 binding site is created. The transcription factorGATA3 increases the functional activity of the enhancer, thisleads to the formation of more contacts between the enhancerand the divergent promoter of the MRPS30 and RP11-53O19.1genes, and increased expression level of these genes. To identify this functionally significant regulatory variant, the authorsdeveloped an integrated experimental computer method basedon a combined analysis of heterogeneous big biological andgenetic data, including: (1) data on allele-specific expression obtained from RNA-seq in combination with data onhaplotypes; (2) expression quantitative trait loci (eQTL);(3) genomic distribution of DNAse I hypersensitive sites;(4) localization of ChIP-seq peaks from ENCODE and GEOdatabases; (5) localization of regulatory motives predicted bycomputer programs. Similar scenarios for integrated experimental computer research have been implemented in the otherstudies .This kind of research became possible due to (1) the development of modern high-throughput experimental approachesthat allow producing data of different types on a genome-widescale ; (2) development of public informationresources accumulating such experimental data. Table 2 provides a brief description of information resources containinggenomic data obtained on the basis of omics technologies andused to study the mechanisms by which GVs alter the level oftranscription. These resources present (1) the human genomeannotation (GENCODE); (2) genome variability in human populations ;(3) GVs associated with diseases ; (4) modificationsof the chromatin ; (5) expression quantitative trait loci; (6) profilingof transcription factor binding events by ChIP-seq ; (7) allele-specific bindingof TFs, identified using ChIP-seq data in combination withthe data on the genotypes of the studied cells ; (8) the effects of genetic variability on TFBSspredicted in silico by computer programs .https://genome.ucsc.edu/) and (2) the genome browser of the Ensembl databasewhich is a joint research project of the European Bioinformatics Institute and the Wellcome Trust Sanger Institute .These genome browsers integrate data on genome sequencesand its features obtained by different research groups usinga wide range of experimental methods . The websites of these browsers provide access tothe primary DNA sequences and genome annotations for manyorganisms . Browser’s graphical interfaces allow to obtain scalablemaps of genomic regions and to visualize interactively a largenumber of annotations and features .A separate category of information resources includes:(1) the genome browser of the University of California, SantaCruz, USA  and BioMart data mining tool(https://www.ensembl.org/info/data/biomart/index.html).The websites of the UCSC Genome Browser and EnsemblGenome Browser provide access to software tools for extraction data as text files: UCSC table browser . These approaches are based on the analysis of theChIP-seq data in combination with the sequencing data, whichallow to find heterozygous loci within a single genome and tophase genotypes of the studied cells. Thus, for each type ofcells examined, its own set of genomic loci interacting witha specific transcription factor in an allele-specific mannercan be identified. For example, in , theChIP-seq data for 55 TFs in the HepG2 cells and 57 TFs in theHeLa-S3 cells were analyzed. In HepG2 cells, 3001 genomicloci with allele-specific signals were found, and 712 loci werefound in HeLa-S3 cells. The authors note the pronouncedtissue-specific nature of allele-specific TF binding: of theentire set of identified loci, only 34 were found in both celllines .http://alleleseq.gersteinlab.org/)  (see Table 2),as well as in the supplemental files to publications .The data on allele-specific binding of TFs are collected inthe following information resources: AlleleDB http://alleledb.gersteinlab.org/ , AlleleSeq  and from 12 to 55 for cancer cell lines  .https://pubs.broadinstitute.org/mammals/haploreg/haploreg.php) ,SNP2TFBS  , rSNPBase (http://rsnp3.psych.ac.cn/index.do) , rVarBase (http://rv.psych.ac.cn) (see Table 2).When generating hypotheses on the mechanisms that mediate the effect of GVs on disease risk, one can also use the dataon the effects of genetic variants on the functional activity ofTFBSs predicted in silico. 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Background: Chief among mechanisms of telomerase reverse transcriptase (TERT) reactivation is the appearance of mutations in the TERT promoter. The two main TERT promoter mutations are C>T transitions located −146C>T and −124C>T upstream from the translational start site. They generate a novel Ets/TCF binding site. Both mutations are mutually exclusive and −124C>T is strikingly overrepresented in most cancers. We investigated whether this mutational bias and mutual exclusion could be due to transcriptional constraints. Methods: We compared sense and antisense transcription of a panel of TERT promoter-luciferase vectors harboring the −124C>T and -146C>T mutations alone or together. lncRNA TAPAS levels were measured by RT-PCR. Results: Both mutations generally increased TERT transcription by 2–4-fold regardless of upstream and downstream regulatory elements. The double mutant increased transcription in an additive fashion, arguing against a direct transcriptional constraint. The −146C>T mutation, alone or in combination with −124C>T, also unleashed antisense transcription. In line with this finding, lncRNA TAPAS was higher in cells with mutated TERT promoter (T98G and U87) than in cells with wild-type promoter, suggesting that lncRNA TAPAS may balance the effect of TERT promoter mutations. Conclusions: −146C>T and −124C>T TERT promoter mutations increase TERT sense and antisense transcription, and the double mutant features higher transcription levels. Increased antisense transcription may contain TERT expression within sustainable levels. Telomeres are DNA–protein complexes which act as protective caps for chromosomal ends. They consist of tandem repeats of the sequence TTAGGG bound by Shelterin enzymes. They shield chromosomal ends from inappropriate DNA damage responses, end-to-end fusion events and erosion, maintaining genomic integrity ,2,3,4. TTERT transcription is tightly regulated. Promoter characterization studies have localized the core promoter within 300 base pairs (bp) uptream from the translational start site (TSS). The TERT core promoter is essential for transcriptional activation as it harbors binding sites for numerous activating transcription factors including c-Myc (E-box), Sp1, HIF-1α, STAT, Ets/CTF, and HSP90 [TERT hypermethylated oncological region  allows binding of transcription factors and is associated with TERT reactivation in cancer cell lines [TERT promoter activity [TERT mRNA [TERT promoter is bidirectional and antisense activity is silenced by the upstream proximal promoter region [TAPAS, which has been suggested to negatively regulate TERT expression in a negative feedback loop [nd HSP90 ,13,14. Bnd HSP90 ,16. The nd HSP90 ,17. Hypoll lines . Others activity . FinallyERT mRNA . Like mar region . Antisenack loop . TERT reactivation is due to mutations in the TERT promoter [TERT promoter mutations are located at positions 1295228 (C228T) and 1295250 (C250T) on chromosome 5 and generate C>T transitions −124 and −146 base pairs upstream from the TERT TSS. They are highly prevalent in primary gliomas and glioblastoma multiforme (GBM), oligodendrogliomas and astrocytomas [In 31% of cancers, promoter . The mosocytomas ,38,39,40ocytomas ,48,49, mocytomas ,50,51,52ocytomas ,54,55,56ocytomas ,63,64,65ocytomas ,66,67. Oocytomas ,49. For ocytomas .TERT transcription by ~2–6 fold by qRT-PCR and reporter assays in vitro [TERT promoter mutations. GABP selectively binds the −124C>T and −146C>T mutations in GBM, melanoma, and urothelial bladder cancer cell lines [TERT promoter mutations have been associated with epigenetically active chromatin [Both mutations generate identical sequences and increase in vitro ,68 and iin vitro ,67,68,69in vitro ,71,72. All lines ,71,72. Ull lines ,74,75,76ll lines , or brouhromatin ,70,77,78TERT promoter mutations and TERT reactivation are generally monoallelic [TERT reactivation on one allele is sufficient to counteract telomere attrition in cancer cells [TERT promoter mutations are mutually exclusive [TERT transcription is reactivated by other mechanisms such as copy number duplications [TERT reactivation tends to rely on one single mechanism remain elusive. In this work, we asked whether a transcriptional constraint could restrict the appearance of both mutations concomitantly. To address this question, we engineered a series of TERT promoter-luciferase reporter constructs spanning different upstream and downstream regulatory regions of the TERT promoter and carrying the −124C>T and −146C>T mutations alone or in combination (−124C>T + −146C>T). We found that the −124C>T and −146C>T mutations lead to similar increases in transcription, in line with previous reports [TAPAS [oallelic ,27, suggxclusive ,41. Theyications ,80 or viications ,82,83,84ications ,80. The  reports  and thats [TAPAS , the −14All cell lines  were maintained in DMEM supplemented with 10% fetal calf serum (FCS) , 2 mM L-Glutamine, 50 µg/mL Penicillin and 50 µg/mL Streptomycin .TERT promoter upstream of the firefly luciferase gene were constructed. To that aim, a DNA fragment spanning the TERT promoter from position −1822 to +359 relative to the TSS was amplified from HEK293T genomic DNA (gDNA) using the following forward primer: TERTpF-1822: 5′-GAAGCTCACCCCACTCAAGTGTT-3′ and reverse primer: TERTpR+359: 5′-CACTCGGGCCACCAGCTCCTT-3′ and TA-cloned in pCR2.1 . The −146C>T and −124C>T mutations were introduced in the TERT promoter construct individually or in combination by overlapping PCR using the same external primers and internal primers: TERT-Fmut−146: 5′-CGACCCCTTCCGGGTCCC-3′ and TERT-Rmut-146C>T: 5′-GGGACCCGGAAGGGGTCG-3′ for −146C>T and TERT- Fmut-124: 5′-CCCAGCCCCTTCCGGGCC -3′ and TERT-Rmut-124: 5′-GGCCCGGAAGGGGCTGGG-3′ for −124C>T. The mutated base is highlighted in bold. Smaller PCR fragments spanning different portions of the TERT promoter were amplified using different combinations of forward and reverse primers. The complete list of primers used for cloning and mutagenesis is reported in TERT promoter regions were amplified: TERT −1822 to +359 encompassing the full promoter; TERT −1822 to −23 spanning the core promoter and the distal and proximal upstream regulatory regions, TERT −499 to −23 spanning the core promoter and part of the proximal upstream promoter, TERT −297 to −23 spanning the core promoter, TERT −297 to +81 spanning the core promoter and exon 1, TERT −297 to +359 spanning the core promoter and the downstream regulatory region (exon 1 + intron 1 + 38 nucleotides of exon 2), TERT −499 to +81 spanning the proximal upstream promoter down to exon 1 and TERT −499 to +359 spanning the proximal upstream promoter and the downstream regulatory region. The PCR products were TA-sub-cloned into pCR2.1 and sequenced. Finally, the fragments were then cloned into pGL4.10 Luc2  upstream of the firefly luciferase reporter gene in sense and antisense (AS) orientation, using Kpn I and EcoR V or Xho I and Hind III, depending on the orientation of the promoter in pCR2.1. All TERT promoter-luciferase constructs are summarized in (luciferase TSS was kept in order to assess the contribution of mutations to antisense transcription.A panel of reporter vectors expressing different portions of the wild type (wt) or mutated rized in A–C. The 5/well in a 48-well plate) or HeLa cells (5 × 104 cells/well in a 48-well plate) were transfected with 200 ng of each of the wt or mutated pGL4.F10Luc-TERT promoter-luciferase constructs using JetPRIME  following the manufacturer’s recommendations. To normalize for transfection efficiency, a plasmid expressing HSV-TK-Renilla-luciferase (Promega pGL4.74 hRLuc) (10 ng in HEK293T and 20 ng in HeLa cells respectively) was included in all experiments. Using 10 ng of pGL4.74 hRLuc gave high signals in HEK293T but too low signals in HeLa and thus had to be increased. For some experiments, T98G (8 × 104 cells/well in a 48-well plate) and U87 (5 × 104 cells/well in a 48-well plate) were used. They were transfected with 400 ng of each of the wt plasmids and 100 ng of HSV-TK-Renilla-luciferase for normalization. Lower amounts of DNA did not give exploitable Fluc or RLuc signals in these cell lines. Cells were lysed after 48 h and firefly luciferase induction was normalized to the corresponding Renilla Luciferase signal measured using the Dual Glo Luciferase Assay  using a Mithras LB950 luminometer . For mutant clones, the fold-change relative to the corresponding wt construct was calculated. All transfections were performed in duplicate wells and repeated in at least 3 independent experiments for HEK293T and HeLa and two independent experiments for T98G and U87. Error bars represent standard error.HEK293T cells (1.2 × 105 cells/well), HeLa (2 × 105 cells/well), T98G (2 × 105 cells/well) and U87 (2 × 105 cells/well) cells cultured in 24-well plates for 48 h using Trizol. One µg of total RNA was reverse transcribed with the QuantiTect reverse transcription kit . qPCR for lncRNA TAPAS and GAPDH was performed in duplicate using Takyon Rox dTT Blue 2X Master Mix , and primers described in [GAPDH. lncRNA TAPAS was normalized to GAPDH according to the 2−ΔΔCt method [Total RNA was extracted from HEK293T , the core promoter with upstream regulatory regions (intermediate upstream promoter (TERT−499 to −23) or distal upstream promoter (TERT−1822 to −23), the core promoter with downstream regulatory regions , or the core promoter with both upstream and downstream regions (TERT−1822 to +359). The regulatory regions of the TERT promoter are shown in Most studies to date addressing the impact of mutations −146C>T and −124C>T on promoter ,41,68,70coreTERT promoter alone or the core promoter and its upstream regulatory regions as long as they lacked downstream regions (TERT−1822 to −23) and for the proximal upstream promoter (TERT−499 to −23). In contrast, all TERTp-luciferase constructs containing regions downstream from the core promoter, including the full TERT promoter (TERT−1822 to +359), led to very low or undetectable transcription levels (p < 0.001 in both cell lines)  (p < 0.001 for HEK293T and HeLa cells) (TERT−499 to −295) and distal upstream (TERT−1822 to −295) regions in HEK293T cells (p < 0.001). Therefore, the regions encompassing nucleotides down to position −23 relative to the TSS contain the positive regulatory elements enabling TERT expression, while the region spanning exon 1 + intron 1  acts to negatively regulate TERT expression. Notably, the distal upstream 5′ region (TERT−1822 to −295) moderately decreased expression compared with the TERT core promoter in HeLa cells only (p < 0.05) (TERT promoter mutations (T98G and U87), displayed similar profiles  D,E. In aa cells) D,E and c < 0.05) E to leveprofiles , in agret manner . Slight eviously ,86 and lTERT promoter was reported to be bidirectional [TAPAS in antisense direction, which negatively regulates TERT expression [TERT promoter regions described above in antisense orientation upstream of the firefly luciferase reporter   F,G, indiTERT promoter is bidirectional, that the region spanning exon 1 + intron 1 + the first 38 nucleotides of exon 2 leashes TERT transcription while the proximal upstream promoter (−499 to −295 from the TSS) represses antisense transcription from the TERT promoter.Taken together, these results confirm that the TERT promoter mutations −146C>T + −124C>T on TERT transcription, we introduced the mutations alone and in combination in each of the sense TERTp-luciferase reporter vectors, generating 4 constructs for each TERT promoter region: wt, −146C>T mutant, −124C>T mutant and −146C>T + −124C>T (double mutant). As shown in TERT promoter-driven expression by 1.5 to 2.4-fold and the −124C>T mutation by 1.6 to 3.2-fold in HEK293T cells  (To assess the impact of the two main 3T cells A. This i3T cells B. Despith cases) A,B. SincTERT promoter (p < 0.01 or p < 0.001) and compared to each of the single mutants for all constructs in both cell lines , had similar or lower transcription when the two mutations were present compared with the single mutants  and from the coreTERT promoter was upregulated by the −146C>T mutation, but not by the −124C>T mutation in both cell lines, suggesting that the latter mutation only impacts sense transcription. In the presence of downstream regulatory elements (exon 1 only or exon 1 + intron 1 + 38 bp of exon 2), the −124C>T slightly increased antisense transcription compared with the wt promoter, but less than the −146C>T. However, when the upstream regulatory elements were present, antisense transcription was increased comparably by both mutations, to levels ranging from 2-fold to 63-fold , which harbors both upstream and downstream regulatory elements, and the coreTERT promoter consistently show upregulation of antisense transcription by the −146C>T mutation only, it is likely that in vivo the −146C>T mutation more efficiently allows an overcoming of the repression of antisense transcription.Next, we investigated whether  63-fold A,B. RemaAs for sense transcription, the double mutant led to an additive transcriptional increase in both cell lines C. In linTERT promoter by TERT promoter mutations translates into increased lncRNA TAPAS levels. To that aim, we measured lncRNA TAPAS levels in two GBM cell lines harboring the monoallelic −146C>T (T98G) and −124C>T (U87) TERT promoter mutations. We detected much lower levels of lncRNA TAPAS in HeLa than in HEK293T cells, again highlighting cell-type specificities, and significantly higher levels of lncRNA TAPAS in cells with TERT promoter mutations compared with HEK293T and HeLa , this binding site seems to no longer be exploited or accessible. One possible reason could be DNA looping resulting in ternary DNA structures that do not allow the binding of transcription factors to GGAA at position −124 and even at neighboring GGAA sites. In this scenario, the –146 position would, a contrario, still remain accessible, and thus not be impacted by the DNA looping. Alternatively, access to RNA Pol II to the antisense transcriptional initiation site may be affected by the ternary structure adopted by DNA in the presence of the −124C>T mutation. Answers to these questions warrant further investigation. These observations suggest that the −146C>T mutation upregulates antisense transcription and thereby lncRNA TAPAS concomitantly to TERT, and might thus negatively affect TERT levels. How lncRNA TAPAS impacts TERT levels remains elusive, as only an inverse correlation has been described to date [TAPAS decrease TERT and ultimately contribute to the selective advantage of −124C>T and its strong overrepresentation across cancers. In this study, we only investigated the effect of the two most frequent TERT promoter mutations, −146C>T and −124C>T. Other mutations which create novel Ets/TCF binding sites pepper the TERT promoter [TERT transcription [TAPAS levels.Our findings further reveal that the −146C>T mutation also unleashes antisense transcription from the promoter A,B and tutations D. In a ppromoter . Our reselements . Moreovebe GABPA . NF-κB ibe GABPA . In this to date . Yet, itpromoter ,49. Theycription ,69,87,88TERT promoter mutations, alone or in combination, on antisense transcription was recorded for constructs containing the proximal upstream promoter region appended to the TERT core promoter in both cell lines (core-AS-luciferase to TERTp−499 to −23-AS-luciferase or TERTp−295 to +359-AS-luciferase to TERTp−499 to +359-AS-luciferase). This region hosts binding sites for strong repressors such as CTCF [TERT promoter , but its impact is much less directly affected by the presence of TERT promoter mutations. Together, these results suggest that repressors binding to the region encompassing the proximal upstream regulatory region (−499 to −295 from the TSS) can be displaced by transcription factors such as GABPA or Ets/TCF binding to mutations in the TERT promoter, while the distal upstream regulatory region is less affected by such a mechanism. It is noteworthy that epigenetic tuning of transcription cannot be investigated using TERTp-luciferase reporters, and only the balance between activator and repressor transcription factors is measured using this system. Further studies would be needed to ascertain whether methylation patterns and lncRNA TAPAS expression are affected by TERT promoter mutations.The strongest impact of both  as CTCF ,16, and romoter  and a single genetic mechanism of telomere elongation enable sufficient telomere healing to avoid telomere crisis and cell death [TERT promoter mutations are generally found in solid cancers with relatively low self-renewal rate [TERT expression, such as hematopoietic, lymphoid, or gastrointestinal malignancies [TERT promoter mutations increase both sense (TERT) and antisense (lncRNA TAPAS) transcription to maintain TERT protein levels within a sustainable threshold. Disproportionate levels of one transcription product would shift the balance towards insufficient or excessive TERT, eventually leading to cell death by apoptosis or starvation. Such a self-regulatory mechanism where TERT promoter reactivation promotes TERT expression together with a TERT auto-regulatory mechanism is an extremely elegant means to ensure this equilibrium is maintained. Further studies will be needed to assess whether lncRNA TAPAS is the only mechanism ensuring TERT transcriptional homeostasis.ch other  as well ch other ,80, virach other ,82,83,84ch other ,33,34,57ch other . TERT protential ,90,91,92otential ,94, c-Myotential ,96, and otential ,98, therotential ,100. Thell death ,100,101,wal rate , while tgnancies ,80,102.