Article

ISSN 0006-2979, Biochemistry (Moscow), 2024, Vol. 89, No. 6, pp. 1002-1013 © The Author(s) 2024. This article is an open access publication.

1002

REVIEW

Methods for Functional Characterization

of Genetic Polymorphisms of Non-Coding Regulatory

Regions of the Human Genome

Aksinya N. Uvarova

1,a

*, Elena A. Tkachenko

2,3

, Ekaterina M. Stasevich

1,4

Elina A. Zheremyan

, Kirill V. Korneev

, and Dmitry V. Kuprash

1,3

Center for Precision Genome Editing and Genetic Technologies for Biomedicine,

Engelhardt Institute of Molecular Biology, Russian Academy of Sciences, 119991 Moscow, Russia

Faculty of Biology, Lomonosov Moscow State University, 119234 Moscow, Russia

Moscow Institute of Physics and Technology, 141700 Dolgoprudny, Moscow Region, Russia

e-mail: uvarowww@gmail.com

Received November 20, 2023

Revised March 27, 2024

Accepted April 11, 2024

Abstract— Currently, numerous associations between genetic polymorphisms and various diseases have been

characterized through the Genome-Wide Association Studies. Majority of the clinically significant polymorphisms

are localized in non-coding regions of the genome. While modern bioinformatic resources make it possible

topredict molecular mechanisms that explain influence of the non-coding polymorphisms on gene expression,

such hypotheses require experimental verification. This review discusses the methods for elucidating molecu-

lar mechanisms underlying dependence of the disease pathogenesis on specific genetic variants within the

non-coding sequences. A particular focus is on the methods for identification of transcription factors with binding

efficiency dependent on polymorphic variations. Despite remarkable progress in bioinformatic resources enabling

prediction of the impact of polymorphisms on the disease pathogenesis, there is still the need for experimental

approaches to investigate this issue.

DOI: 10.1134/S0006297924060026

Keywords: functional polymorphisms, regulatory genomic regions, transcription factors, reporter analysis,

CRISPR-Cas

Abbreviations: ChIP, chromatin immunoprecipitation; CRISPR,clustered regularly interspaced short palindromic repeats;

HDR,homology-directed repair; MPRA,massively parallel reporter assay; QTL,quantitative trait locus; raQTL,reporter assay

quantitative trait locus; SNP,single nucleotide polymorphism; TF,transcription factor; UTR,untranslated region.

* To whom correspondence should be addressed.

INTRODUCTION

In spite of the fact that human genomes are iden-

tical by 99.9%, it is precisely the remaining 0.1% of

genetic variants that underlie phenotypic differences,

including susceptibility to diseases [1]. These genet-

ic variations are Single Nucleotide Variation (SNV) or

Single Nucleotide Polymorphism (SNP), insertion/de-

letion(indel), and Structural Variation of more than

50 b.p. in length (SV) [2]. The most widespread ge-

netic variation is SNP, i.e., a DNA sequence variation

(avariant allele) of one nucleotide in size in the mem-

bers of the same species, which occurs within a pop-

ulation ata frequency of at least 1% [3]. SNPs occur

every 200-300 b.p. in the genome, being localized in its

coding and regulatory parts (promoters, enhancers, in-

trons, and untranslated regions)[4,5]. Importance of

studying SNP lies in the fact that such genetic variants

are often associated with different diseases, as it has

been shown by numerous Genome-Wide Association

Studies (GWAS). About 95% of the clinically significant

SNPs are localized in non-coding genome regions[6],

METHODS FOR FUNCTIONAL CHARACTERIZATION OF POLYMORPHISMS 1003

BIOCHEMISTRY (Moscow) Vol. 89 No. 6 2024

and their functional significance is probably associ-

ated with the changes in the regulatory characteris-

tics of the regions surrounding the polymorphism[7].

Such regulatory regions of the eukaryotic genome may

be promoters, enhancers, 5′- and 3′-untranslated re-

gions(UTR) of protein-coding genes, gene regions of

non-coding RNA(ncRNA), and splicing regulatory el-

ements (SRE)[5, 8]. Promoters initiate gene transcrip-

tion and enhancer elements increase the rate of this

initiation[9]. Promoters are preferred sites for bind-

ing transcription factors (TFs) and RNA polymerase II

to DNA and include the region of the first transcribed

nucleotide of the transcript (transcription start site,

TSS) [10]. Enhancers, which have been identified for

the first time with the help of reporter analysis as ele-

ments capable of enhancing the reporter gene expres-

sion[11], are the platforms for TF binding that can act

irrespective of orientation, distance, and localization

relative to the target gene [12]. The 5′- and 3′-UTRs play

an important role in post-transcriptional regulation of

gene expression and are part of mature coding mRNA.

For example, 5′-UTRs contain different regulatory com-

ponents influencing translation initiation, and 3′-UTRs

comprise the sequences that bind microRNA and lead

to transcript degradation[5]. In addition, it should be

noted that the non-coding polymorphisms within UTR

could also be involved in transcription regulation, be-

cause the 5′-UTR sequence usually overlaps with the

promoter regions of the genes, while the 3′-UTR se-

quence could overlap with other regulatory elements

of the genes, e.g., enhancers[13]. Non-coding polymor-

phisms are also localized in ncRNA; in recent years,

a lot of information has been obtained about their

effects on RNA maturation, transcription regulation,

chromatin remodeling, and post-transcriptional mod-

ificationsof RNA[14].

Being the most frequently occurring class of genetic

variants, SNPs are the major genetic marker for Quan-

titative Trait Loci (QTL) mapping; they further could be

conditionally divided into those regulating gene expres-

sion directly at the transcriptional and chromatin levels,

exerting effect on the mRNA level (eQTL– expression

QTL regulating gene expression at the transcriptional

level), and those influencing post-transcriptional pro-

cesses (sQTL– splicing QTL regulating alternative splic-

ing of pre-mRNA; pQTL– protein QTL regulating protein

expression)[15]. The following mechanism of function-

al effects of polymorphisms at the genomic level could

be suggested: functions of the regulatory elements are

impaired due to the change in the sequence of the sites

for TF–DNA interaction (both decrease and increase in

binding efficiency)[16]. At the post-transcriptional lev-

el, non-coding polymorphisms could affect activity of

the 5′- and 3′-UTR mRNA, which play a key role in trans-

lation regulation and mRNA stability, including due to

the change in the regulatory microRNA binding[17-19].

In addition, SNPs in the sequence of immature

microRNAs could affect efficiency of microRNA matu-

ration and change efficiency of mRNA binding[20, 21],

and allele variants within the lnRNA (long non-coding

RNA) could modulate, with different efficiency, concen-

tration of the complementary microRNA[22]. Consid-

erable number of functional genetic variants classified

as sQTL is localized in the splicing regulatory elements,

directly changing sequence of the splicing sites or mod-

ifying binding sites for the RNA-binding proteins[23].

The main mechanisms of effects of non-coding poly-

morphisms on gene expression regulation are shown

in Fig.1.

The present review describes main experimental

approaches to the analysis of functional non-coding

allele variations, including the methods for determi-

nation of TF with binding efficiency dependent on the

allele variation.

ANALYSIS OF THE EFFECTS

OF GENETIC POLYMORPHISMS ON GENE

EXPRESSION USING REPORTER GENES

Experimental methods used for studying the ef-

fects of polymorphisms on gene expression can be di-

vided into two large groups: the studies with involve-

ment of genetic reporter constructs and the studies of

polymorphisms directly in the native genomic context.

The former group of methods involves reporter

genetic constructs, where the effect of genetic vari-

ant on a regulatory element is determined by the re-

porter gene activity (reporter assay QTL, raQTL). They

also involve classical technique of luciferase reporter

analysis, where the allele variants of the regulatory se-

quence under study (a promoter or an enhancer) are

integrated into the reporter construct, and activities of

the reporter genes in the resultant constructs are com-

pared after their transfection into a physiologically

relevant cell types[24]. The method of dual-luciferase

assay was used to describe numerous raQTL in differ-

ent types of cells and regulatory elements. For exam-

ple, the previously proposed molecular mechanisms ex-

plain relationship between the development of diseases

and genetic polymorphisms localized in the regulatory

regions of different genes: promoters[25-28], closely

located enhancer regions[29,30], and enhancers locat-

ed at distant intergenic loci [31,32].

Search for sQTL in the systems using reporter

genes is usually limited by the size of the gene under

study, if it exceeds capacity of the reporter plasmid.

In such situation, the so-called reporter minigenes are

used. A minigene construct includes a fragment of the

studied locus containing a polymorphism and suffi-

cient for reproduction of the natural pattern of splicing

between the splicing reporters (as a rule, between two

UVAROVA et al.1004

BIOCHEMISTRY (Moscow) Vol. 89 No. 6 2024

Fig. 1. Main mechanisms of the effects of non-coding polymorphisms on regulation of gene expression (the image was produced

using BioRender.com).

exons). Ability of the region under study to influence

splicing efficiency is measured based on expression of

the target transcript, or, in a nuclear extract, by quan-

titative PCR analysis(qPCR), or, in live cells, if the re-

porter protein encoded by the minigene allows it[33].

Forexample, the reporter analysis of minigenes was

used to characterize polymorphisms regulating gene

splicing of the SCN1A subunit of the calcium channel

associated with epilepsy[34], the RAD51C component of

DNA repair system acting as a tumor suppressor[35],etc.

HIGH-THROUGHPUT REPORTER ASSAYS

Over the past decade, numerous modifications of

high-throughput reporter assays have been developed.

They can be categorized according to the regulatory

regions studying of which they allow, as well as ac-

cording to technological features used. For example,

the Massively Parallel Reporter Assay (MPRA) proto-

col involves synthesis of DNA sequences (potential en-

hancers/promoters, 5′-UTR or 3′-UTR) with addition of

unique barcodes and cloning of these sequences into

reporter plasmids, which are next transfected into the

cell types ofinterest. Activity of the regulatory regions

is analyzed using high-throughput sequencing and

quantification of barcodes, which unambiguously de-

termine a particular regulatory sequence and correlate

with the RNA level of the reporter gene[36, 37]. MPRA

is obviously suitable not only for studying functional-

ity of the regulatory elements but also for assessing

functional effects of their genetic variants[38]. For ex-

ample, MPRA was used for screening polymorphisms

located in the non-coding regions of the genomes and

associated with schizophrenia and Alzheimer’s disease.

It is interesting that only 9 out of 148SNPs with the

allele differences in the K562 and 53 cells have ex-

hibited allele differences in both SK-SY5Y cell lines,

clearly demonstrating that genetic variants usually

exert their regulatory effects only in certain types of

cells[39]. MPRA applied to the library of human gene

5′-UTRs made it possible to reveal 45 disease-associat-

ed allele variations exerting significant effects on the

process of loading mRNAs onto ribosomes; however,

it is interesting that the data on most of the revealed

variants proved to be insufficient to change classifica-

tion of pathogenicity in the Clinvar database, and the

most striking effect was demonstrated by 3polymor-

phisms generating a new start codon, i.e., affecting pro-

tein structure[40]. In another work, Griesemer stud-

ied more than 12,000 3′-UTR variants from 6 human

cell lines, which were associated with human diseas-

es and/or were under positive pressure in the human

population[41]. It turned out that several hundreds of

them had significant effects on the level of reporter

transcript in at least one cell line, and several tens

METHODS FOR FUNCTIONAL CHARACTERIZATION OF POLYMORPHISMS 1005

BIOCHEMISTRY (Moscow) Vol. 89 No. 6 2024

of them coincided with the previously character-

ized variants with any level of clinical significance.

Interestingly, only for two SNPs present in the TRIM14

gene ofviral defense and in the PILRB gene associated

with age-related macular degeneration, combination

of novelty and degree of influence on the level of the

transcript proved to be sufficient to verify the hypoth-

esis using the Cas9-mediated allele substitution in the

genomic context [41]. Technological limitations of

MPRA also include length of the tested DNA fragments

(up to 130-230 b.p.) and number of the tested constructs

(up to 100-200 thousand sequences)[42].

There are high-throughput approaches involving

sequences obtained from the genomic DNA. For exam-

ple, SuRE (Survey of Regulatory Elements) was devel-

oped with the involvement of sequencing data for the

genomes of cell lines originating from four different

ethnic groups and optimized to study potential effect of

a single-nucleotide substitution on activity of the reg-

ulatory elements[43]. Random fragments of genomic

DNA of several hundred b.p. in length are cloned in

the reporter plasmid without a promoter, which, when

transfected into the cultured cells, produce a transcript

only if the inserted fragment carries a functional tran-

scription start site. Since transcripts can produce both

active promoters and enhancers, this method makes it

possible to analyze activities of both types of regula-

tory elements. Like in the MPRA technique, the tran-

scripts are analyzed by high-throughput sequencing

and quantified with involvement of a barcode unique

for each genomic fragment under study. This approach

makes it possible to test activity of the regulatory ele-

ments containing alternative alleles of several millions

of different SNPs (i.e., most of the known ones).

One another method that allows raQTL identifica-

tion– High-resolution Dissection of Regulatory Activity

(HiDRA) [42]– also involves fragmentation of genomic

DNA and is a combination of the ATAC-seq and STARR-

seq techniques. ATAC-seq (Assay for Transposase-Acces-

sible Chromatin using sequencing) makes it possible

to enrich samples with the transposase-accessible,

i.e.,open, chromatin, while STARR-seq (Self-Transcrib-

ing Active Regulatory Region sequencing) is a reporter

assay, where putative regulatory elements (which are

able to enhance transcriptional activity of the reporter)

are cloned in 3′-UTR of the reporter gene and thereby

promote their own transcription. Next, active DNA se-

quences are identified and quantified by high-through-

put RNA sequencing[44]. For example, HiDRA made

it possible to identify a 76-b.p. driver element in the

IKZF3 gene locus, which included rs12946510 associ-

ated with multiple sclerosis; hence, this SNP can be

identified as potentially functional[42]. Indeed, fur-

ther functional testing showed that presence of the

risk allele rs12946510 reduced activation of T helpers

and expression of the IKZF3 and ORMDL3 genes[45].

An important stage of processing the results of each

of the high-throughput methods described above is

using probabilistic mathematical models such as, e.g.,

the SHARPR-RE algorithm (Systematic High-resolution

Activation and Repression Prediction from Reporter as-

says with Random Endpoints)[46], for analysis of the

sequence overlap and assessment of the effects of par-

ticular nucleotides on activity of these sequences.

High-throughput reporter assays of polymor-

phic variants include Massively Parallel Splicing As-

say (MaPSY)[47], which was used to study impaired

splicing in the case of autism spectrum disorders.

Thescreening results were used to characterize genetic

variants in the TNRC6C, MAPK8IP1, and USP45 genes,

and it has been shown that the proteins of TNRC6 fam-

ily could increase the risk of autism development[48].

Recently, the method of Cre-dependent MPRA in vivo

has been proposed for functional analysis of the library

of 3′-UTRs with genetic variants associated with autism.

Quantification of the transcripts depending on activity

of the regulatory element was performed in particular

types of neurons by transduction of the libraries into

the brain tissues of mice with tissue-specific expression

of Cre recombinase. This method makes it possible to

study regulatory effect in a more relevant cellular

context, because neurons have an absolutely differ-

ent expression profile of trans-acting factors (e.g., TF

andmicroRNA) compared to other cell lines[49].

Main limitation of the methods based on reporter

assays is absence of the relevant chromatin context,

which accompanies the regulatory element in the na-

tive genome. This limitation is partially eliminated in

the lentiMPRA technique, when library with the reg-

ulatory elements under study is created in a lentivi-

ral vector, which is integrated into the genome, facil-

itating analysis of transcription within the chromatin

context[50].

FUNCTIONAL ANALYSIS

OF GENETIC POLYMORPHISMS

IN THE NATIVE GENOMIC CONTEXT

With regard to the effects of genetic variants

on pathogenesis of a disease, it is important to take

into account chromatin context which, in turn, var-

ies between the different types and functional states

of the cells. The eQTL mapping perse makes it possi-

ble to relate a particular genotype to the changes in

mRNA levels of potential target genes in the native

genomic context, including tissue specificity[51,52].

Functional relationship between the genes and dis-

tant regulatory loci can be found by determining 3D

chromatin organization using methods such as Hi-C

(high-throughput chromosome conformation capture),

ChIA-PET (chromatin interaction analysis with paired-

UVAROVA et al.1006

BIOCHEMISTRY (Moscow) Vol. 89 No. 6 2024

end tag sequencing), and their modifications[53,54].

Comparison of the 3D tissue-specific genomic maps

with disease-associated regulatory SNPs makes it pos-

sible to identify the most probable genes involved in

pathogenesis. Hence, the most accurate method for ver-

ification of hypotheses constructed is genome editing

and producing of cells with the desired combinations

of variants. Precise and efficient editing of particular

nucleotides in the human genome has become a daunt-

ing but realistic challenge due to the RNA-programma-

ble bacterial nucleases found in the CRISPR(clustered

regularly interspaced short palindromic repeats)-Cas

system [55]. The double-strand break (DSB) in DNA

induced in the target site by the Cas9 nuclease from

Streptococcus pyogenes (currently, the most popular

genome editor) triggers cellular mechanisms of DNA

repair, including homology-directed repair (HDR)[56],

which is used in the CRISPR-HDR methods, when the tar-

get region is repaired in the presence of a homologous

DNA sequence containing the necessary allelevariant.

This method used in many polymorphism stud-

ies [57, 58] has a significant limitation with respect

to efficiency, because DSB repair in mammals occurs

mainly with involvement of nonhomologous end

joining (NHEJ) [59]. Due to these peculiarities, the

CRISPR-HDR editing takes a lot of efforts and could

lead to the impaired expression of the neighboring

genes[45]. Another approach to precise genome ed-

iting, which performs well in a proper nucleotide

context, is base editing (BE) with involvement of the

catalytically inactive dCas9 (dead Cas9) or Cas9 with

nickase activity(nCas9) fused with deaminase. With

respect to enzyme specificity, there are cytosine (con-

verting C•G into T•A) and adenine (converting A•T into

G•C) editors, as well as an editor based on cytidine de-

aminase and uracil-DNA glycosylase (converting C•G

into G•C)[60-63]. For example, the cytosine base editing

system was used to study polymorphism rs12603332

associated with the risk of asthma and demonstrated

its effect on expression of the genes of sphingolipid

biosynthesis regulator ORMDL3 and cellular stress re-

sponse modulator ATF6α in the Jurkat T-cell line[64].

Due to the absence of the stage of DSB formation, the

BE technique is safer for the cells than CRISPR-HDR;

however, it has limitations with respect to enzyme

activities and off-target editing of the neighboring

nucleotides[60, 61]. Another recently developed and

promising approach to genome editing is prime edit-

ing. The editor is based on the mutant nuclease Cas9

inserting single-strand breaks(nCas9) fused with the

reverse transcriptase (MMLVRT) and uses a modified

guide RNA (pegRNA), which simultaneously determines

the target site fornCas9, acts as a primer for MMLVRT,

and is an RNA template for the synthesis of a new DNA

sequence. The edited DNA strand is then included into

the genome by endogenous cellular processes[65].

Due to the high-precision of editing and wider

area of its application compared to the standard base

editors, prime editing has a great potential for working

with single-nucleotide polymorphisms. Single-nucle-

otide substitutions are used for directed evolution in

the selection of agricultural crops[66,67]. Mouse mod-

els were used to demonstrate low off-target activity of

prime editing when changing the variant of non-coding

polymorphism compared to CRISPR-HDR[68]. Inaddi-

tion, prime editing in human myoblasts was used to

correct mutation in the protein-coding region of the

calcium channel gene RYR1 associated with motor im-

pairments[69]. Major fundamental limitation of prime

editing is large size of the editor and difficulties with its

delivery into the cells[70]. In the case of high-through-

put screening systems development, the problem of

delivery could be solved using lentiviral transduction

of the target cells by the constructs encoding the edi-

tor and pegRNAs. Subsequent cultivation of the cells

for several weeks makes it possible to achieve editing

efficiency sufficient for studying functional effects of

hundreds and even thousands of single-nucleotide sub-

stitutions in a single experiment[71].

IDENTIFICATION OF TRANSCRIPTION

FACTORS MEDIATING THE ALLELE-SPECIFIC

DIFFERENCE IN THE ACTIVITIES

OF REGULATORY ELEMENTS

Identification of different types of QTL (eQTL,

raQTL,etc.) does not provide information about par-

ticular molecular mechanism affected by a particular

genetic variant; therefore, further functional annota-

tion remains relevant. As mentioned before, the mech-

anisms of effects of polymorphisms on the functions of

regulatory element include changes in the properties

of promoter and enhancer regions, 5′-UTR and 3′-UTR,

ncRNA, as well as impaired splicing. The best studied

cause of the dependence of the properties of regulato-

ry elements on SNPs localized in them is capability of

the single-nucleotide substitution to influence affinity

to the functional transcription factor.

There are various insilico approaches to predict

the preferred TF binding motifs, in most cases based

on Positional Weight Matrices (PWM) formed by the

multiple alignment of TF-binding sequences[72, 73].

Inturn, information about the particular TF-binding

sequences can be obtained by high-throughput genome-

wide mapping of binding sites in vivo, e.g., methods

based on Chromatin Immunoprecipitation(ChIP) or on

high-throughput systemic evolution of ligands by ex-

ponential enrichment (HT-SELEX) for the selection of

TF-binding sequences in vitro[74]. Genome sequences

associated with the specific proteins in their native

chromatin context are identified by the ChIP-seq tech-

METHODS FOR FUNCTIONAL CHARACTERIZATION OF POLYMORPHISMS 1007

BIOCHEMISTRY (Moscow) Vol. 89 No. 6 2024

Fig. 2. Methods for identification of functional transcription factors with allele-specific binding to the region of polymorphism

(the image was produced using BioRender.com).

nique combining chromatin immunoprecipitation with

subsequent high-throughput DNA sequencing [75].

Sequences optimal for binding of a particular TF

(probably not existing in nature) are found with the

involvement of SELEX methods for enrichment of the

libraries of randomly generated oligonucleotides with

specific sequences exhibiting high affinity to a given

TF[76]. The are well-known PWM motif databases in-

cluding TRANSFAC[77], HOCOMOCO[78], JASPAR[79],

HOMER[80], iRegulon[81],etc. Application of bioinfor-

matics makes it possible to assess potential changes in

the strength of TF binding depending on the variant of

polymorphism. Efficiency of the allele-specific TF bind-

ing can be estimated directly by the ChIP-Seq data, if

sequencing depth allows detection of the statistically

significant deviations in the frequencies of alternative

SNP alleles in the binding site[82,83]. Combination of

ChIP with quantification of alleles, ChIP-AS-qPCR(ChIP-

based allele-specific quantitative PCR), makes it possi-

ble to measure effects of the allele variants on efficien-

cy of TF binding in a living cell[57]. A high-throughput

variant of the analysis of TF binding with polymor-

phisms in the regulatory regions, SNP-SELEX, based on

the HT-SELEX has been proposed. This method allows

analysis of the effects of about 100,000 allele variants

of the potentially regulatory (GWAS-annotated) SNPs

on binding of several hundreds of TFs[84]. Classical

method of analysis of DNA–protein interactions based

on the shifts in electrophoretic mobility (electropho-

retic mobility shift assay, EMSA) can also be consid-

ered as an experimental approach to TF identification.

During EMSA, proteins under study specifically bind to

the labeled oligonucleotide probes, which is followed

by analysis of mobility of such fragments using elec-

trophoresis in polyacrylamide gel under native condi-

tions; relative strength of the binding could be assessed

based on the amount of the formed complex [85]. Spec-

ificity of determination of protein components in the

complexes is achieved by adding antibodies against a

specific protein in the reaction: EMSA–supershift[86].

There are also high-throughput methods for analysis of

large amounts of SNP allowing to find out effects of the

allele variants on TF binding based on incubation of

the SNP-containing oligonucleotides with a nuclear ex-

tract from the particular cell type, followed by sequenc-

ing of the enriched libraries; such methods are SNPs-

Seq[57] and Reel-Seq[87]. Neither of these methods

perse makes it possible to establish, which TF binds to

a particular allele variant; however, such information

could be obtained by mass spectrometry and/or using a

purified TF instead of the nuclear extract[24,88].

Bioinformatics databases suitable for analysis of

SNP of interest include the on-line resource PERFECTOS-

ARE https://opera.autosome.org/perfectosape[76], where

the predicted TF binding motifs are collected from

various databases: HOCOMOCO [78], JASPAR [79],

HT-SELEX [89], etc. Another bioinformatics resource,

ADASTRA [82], that provides comprehensive data on

the allele-specific TF binding with allele variants in

different types of cells, is based on the HOCOMOCO

and SPRy-SARUS data [90], as well as on the allele-

specific data of the DNase footprinting assay[91]. The

ANANASTRA resource [92] based on the systematic

analysis of allelic imbalance in the ChIP-Seq experi-

ments, makes it possible to annotate a great number of

genetic variants in parallel.

One of the examples of using such annotation

could be functional characterization of the SNPs

rs7873784 and rs71327024 localized in the regulato-

ry regions of the TLR4 and CXCR6 genes, respective-

ly [13, 31]. According to the results of GWAS, both

SNPs are disease-associated: the minor C allele of

rs7873784 is associated with rheumatoid arthritis and

the minor T allele of rs71327024 is associated with

UVAROVA et al.1008

BIOCHEMISTRY (Moscow) Vol. 89 No. 6 2024

severe COVID-19. The reporter assays have shown

that both SNPs are raQTL; therefore, bioinformatics

analysis was used to find TFs PU.1 (rs7873784) and

c-Myb(rs71327024) relevant for the respective types

of cells characterized by the allele-dependent bind-

ing to SNP-containing sites. This hypothesis was veri-

fied using the genetic knockdown of TF with involve-

ment of small interfering RNA (siRNA), as well as the

DNA pull-down immunoprecipitation technique[93].

The latter includes incubation of oligonucleotides

containing alternative SNP variants with the nuclear

extract from the relevant cells and immunoprecip-

itation with the specific antibodies against the pre-

dicted TF, followed by quantification of the enriched

oligonucleotides. The described methods for iden-

tification of transcription factors with binding effi-

ciency depending on the allele of polymorphism are

shown in Fig.2.

Due to continuously increasing amounts of data

and modern machine learning models, bioinformatic

computations provide a more precise annotation of the

candidate TFs with allele-specific binding to the SNP re-

gion[94-96]. However, clinical validation and a fortiori

application of these data in diagnostics and probably

treatment of the diseases are possible only after exper-

imental validation in different types of cells in the rel-

evant functional context.

CONCLUSIONS

To date, meta-analysis of large amounts of experi-

mental data makes it possible to develop bioinformat-

ics tools for searching for the most probable functional

genetic variants, as well as for prediction of particu-

lar mechanisms of their effects on pathogenesis of the

diseases. Overwhelming majority of the genetic vari-

ants are localized in the non-coding regions of the ge-

nome; they affect functions of the genes by regulating

their expression. Such regulation could vary widely

depending on the type and functional state of cells,

which is not always taken into consideration in the

case of insilico methods involving statistical general-

izations. In view of the above, it is still relevant to use

versatile experimental techniques for characterization

of particular genetic variants. The most informative

method for studying effects of the genetic variants on

phenotype is development of precise genetic models

using genome editing techniques. However, due to the

difficult procedure of precise genome editing, prelim-

inary characterization of allele variants under study

by the reporter assays remains relevant.

Contributions. A.N.U. concept and supervision of

the work; A.N.U., E.A.T., E.M.S., and E.A.Zh. writing the

manuscript; K.V.K. and D.V.K. editing the manuscript.

Funding. The work was financially supported by

the Russian Science Foundation (project no.22-24-00987).

Ethics declarations. This work does not contain

any studies involving human and animal subjects.

Theauthors of this work declare that they have nocon-

flicts of interest.

Open access. This article is licensed under a Cre-

ative Commons Attribution4.0 International License,

which permits use, sharing, adaptation, distribution,

and reproduction in any medium or format, as long

as you give appropriate credit to the original author(s)

and the source, provide a link to the Creative Commons

license, and indicate if changes were made. Theimages

or other third-party material in this article are includ-

ed in the article’s Creative Commons license, unless

indicated otherwise in a credit line to the material.

Ifmaterial is not included in the article’s Creative Com-

mons license and your intended use is not permitted

by statutory regulation or exceeds the permitted use,

you will need to obtain permission directly from the

https://creativecommons.org/licenses/by/4.0/.

REFERENCES

1. Ahmed, Z., Zeeshan, S., Mendhe, D., and Dong, X.

(2020) Human gene and disease associations for clin-

ical‐genomics and precision medicine research, Clin.

Transl. Med., 10, 297-318, doi:10.1002/ctm2.28.

2. Lappalainen, T., Scott, A. J., Brandt, M., and Hall,

I. M. (2019) Genomic analysis in the age of human

genome sequencing, Cell, 177, 70-84, doi: 10.1016/

j.cell.2019.02.032.

3. Wright, A.F. (2005) Genetic variation: polymorphisms

and mutations, in eLS, doi:10.1038/npg.els.0005005.

4. Salisbury, B. A., Pungliya, M., Choi, J. Y., Jiang, R.,

Sun, X. J., and Stephens, J. C. (2003) SNP and haplo-

type variation in the human genome, Mutat. Res., 526,

53-61, doi:10.1016/S0027-5107(03)00014-9.

5. Fabo,T., and Khavari,P. (2023) Functional characteri-

zation of human genomic variation linked to polygen-

ic diseases, Trends Genet., 39, 462-490, doi: 10.1016/

j.tig.2023.02.014.

6. Orozco, G., Schoenfelder, S., Walker, N., Eyre, S.,

and Fraser, P. (2022) 3D genome organization links

non-coding disease-associated variants to genes,

Front. Cell Dev. Biol., 10, 995388, doi: 10.3389/

FCELL.2022.995388/BIBTEX.

7. Johnston, A. D., Simões-Pires, C. A., Thompson, T. V.,

Suzuki,M., and Greally, J.M. (2019) Functional genetic

variants can mediate their regulatory effects through

alteration of transcription factor binding, Nat. Com-

mun., 10, 3472, doi:10.1038/s41467-019-11412-5.

8. Grodecká,L., Buratti,E., and Freiberger,T. (2017) Mu-

tations of pre-mRNA splicing regulatory elements: Are

METHODS FOR FUNCTIONAL CHARACTERIZATION OF POLYMORPHISMS 1009

BIOCHEMISTRY (Moscow) Vol. 89 No. 6 2024

predictions moving forward to clinical diagnostics?

Int.J. Mol. Sci., 18, 1668, doi:10.3390/ijms18081668.

9. Andersson,R., and Sandelin,A. (2020) Determinants

of enhancer and promoter activities of regulatory

elements, Nat. Rev. Genet., 21, 71-87, doi: 10.1038/

S41576-019-0173-8.

10. Carninci, P., Sandelin, A., Lenhard, B., Katayama, S.,

Shimokawa,K., Ponjavic,J., Semple, C. A. M., Taylor,

M. S., Engström, P. G., Frith, M. C., Forrest, A. R. R.,

Alkema, W. B., Tan, S. L., Plessy, C., Kodzius, R.,

Ravasi,T., Kasukawa, T., Fukuda, S., Kanamori-Kata-

yama,M., Kitazume,Y., Kawaji,H., Kai,C., Nakamu-

ra, M., Konno, H., Nakano, K., Mottagui-Tabar, S.,

Arner, P., Chesi, A., Gustincich, S., Persichetti, F., Su-

zuki, H., Grimmond, S. M., Wells, C. A., Orlando, V.,

Wahlestedt, C., Liu, E. T., Harbers, M., Kawai, J., Ba-

jic, V. B., Hume, D. A., and Hayashizaki, Y. (2006)

Genome-wide analysis of mammalian promoter ar-

chitecture and evolution, Nat. Genet., 38, 626-635,

doi:10.1038/NG1789.

11. Banerji, J., Rusconi, S., and Schaffner, W. (1981) Ex-

pression of a β-globin gene is enhanced by remote

SV40 DNA sequences, Cell, 27, 299-308, doi: 10.1016/

0092-8674(81)90413-X.

12. Krivega,I., and Dean,A. (2012) Enhancer and promot-

er interactions-long distance calls, Curr. Opin. Genet.

Dev., 22, 79-85, doi:10.1016/j.gde.2011.11.001.

13. Korneev, K. V., Sviriaeva, E. N., Mitkin, N. A., Gor-

bacheva, A.M., Uvarova, A. N., Ustiugova, A. S., Po-

lanovsky, O.L., Kulakovskiy, I.V., Afanasyeva, M.A.,

Schwartz, A.M., and Kuprash, D.V. (2020) Minor Cal-

lele of the SNP rs7873784 associated with rheumatoid

arthritis and type-2 diabetes mellitus binds PU.1 and

enhances TLR4 expression., Biochim. Biophys. Acta

Mol. Basis Dis., 1866, 165626, doi: 10.1016/j.bbadis.

2019.165626.

14. Panni,S., Lovering, R. C., Porras,P., and Orchard, S.

(2020) Non-coding RNA regulatory networks, Bio-

chim. Biophys. Acta Gene Regul. Mech., 1863, 194417,

doi:10.1016/j.bbagrm.2019.194417.

15. Lappalainen, T., and MacArthur, D. G. (2021) From

variant to function in human disease genetics, Sci-

ence, 373, 1464-1468, doi:10.1126/science.abi8207.

16. Tseng, C. C., Wong, M. C., Liao, W. T., Chen, C. J.,

Lee, S.C., Yen, J.H., and Chang, S.J. (2021) Genetic

variants in transcription factor binding sites in hu-

mans: triggered by natural selection and triggers

of diseases, Int. J. Mol. Sci., 22, 4187, doi: 10.3390/

ijms22084187.

17. Pan, X., Zhao, J., Zhou, Z., Chen, J., Yang, Z., Wu, Y.,

Bai, M., Jiao, Y., Yang, Y., Hu, X., Cheng, T., Lu, Q.,

Wang, B., Li, C. L., Lu, Y. J., Diao, L., Zhong, Y. Q.,

Pan,J., Zhu,J., Xiao, H.S., Qiu, Z. L., Li, J., Wang,Z.,

Hui, J., Bao, L., and Zhang, X. (2021) 5′-UTR SNP of

FGF13 causes translational defect and intellectual dis-

ability, eLife, 10, e63021, doi:10.7554/eLife.63021.

18. Cui, Y., Peng, F., Wang, D., Li, Y., Li, J. S., Li, L., and

Li,W. (2022) 3′aQTL-atlas: Anatlas of 3′UTR alterna-

tive polyadenylation quantitative trait loci across hu-

man normal tissues, Nucleic Acids Res., 50, D39-D45,

doi:10.1093/nar/gkab740.

19. Chhichholiya,Y., Suryan, A.K., Suman,P., Munshi,A.,

and Singh, S. (2021) SNPs in miRNAs and target se-

quences: role in cancer and diabetes, Front. Genet.,

12, 793523, doi:10.3389/fgene.2021.793523.

20. Hrdlickova, B., de Almeida, R. C., Borek, Z., and

Withoff, S. (2014) Genetic variation in the non-cod-

ing genome: Involvement of micro-RNAs and long

non-coding RNAs in disease, Biochim. Biophys.

Acta Mol. Basis Dis., 1842, 1910-1922, doi: 10.1016/

j.bbadis.2014.03.011.

21. Rykova,E., Ershov,N., Damarov,I., and Merkulova,T.

(2022) SNPs in 3′UTR miRNA target sequences asso-

ciated with individual drug susceptibility, Int.J. Mol.

Sci., 23, 13725, doi:10.3390/ijms232213725.

22. Feng,T., Feng,N., Zhu,T., Li,Q., Zhang,Q., Wang,Y.,

Gao, M., Zhou, B., Yu, H., Zheng, M., and Qian, B.

(2020) ASNP-mediated lncRNA (LOC146880) and mi-

croRNA (miR-539-5p) interaction and its potential

impact on the NSCLC risk, J. Exp. Clin. Cancer Res.,

39, 157, doi:10.1186/s13046-020-01652-5.

23. Garrido-Martín, D., Borsari, B., Calvo, M., Revert-

er, F., and Guigó, R. (2021) Identification and analy-

sis of splicing quantitative trait loci across multiple

tissues in the human genome, Nat. Commun., 12,

727, doi:10.1038/s41467-020-20578-2.

24. Degtyareva, A. O., Antontseva, E. V., and Merkulo-

va, T. I. (2021) Regulatory snps: Altered transcrip-

tion factor binding sites implicated in complex traits

and diseases, Int. J. Mol. Sci., 22, 6454, doi: 10.3390/

ijms22126454.

25. Gorbacheva, A. M., Korneev, K. V., Kuprash, D. V.,

and Mitkin, N.A. (2018) Therisk G allele of the sin-

gle-nucleotide polymorphism rs928413 creates a

CREB1-binding site that activates IL33 promot-

er in lung epithelial cells, Int. J. Mol. Sci., 19, 2911,

doi:10.3390/ijms19102911.

26. Putlyaeva, L.V., Demin, D.E., Korneev, K.V., Kasyan-

ov, A. S., Tatosyan, K.A., Kulakovskiy, I.V., Kuprash,

D. V., and Schwartz, A. M. (2018) Potential markers

of autoimmune diseases, alleles rs115662534(T) and

rs548231435(C), disrupt the binding of transcription

factors STAT1 and EBF1 to the regulatory elements of

human CD40 gene, Biochemistry (Moscow), 83, 1534-

1542, doi:10.1134/S0006297918120118.

27. Zhou, J., To, K. K. W., Dong, H., Cheng, Z. S., Lau,

C.C.Y., Poon, V.K.M., Fan, Y.H., Song, Y.Q., Tse,H.,

Chan, K.H., Zheng, B.J., Zhao, G.P., and Yuen, K.Y.

(2012) A functional variation in CD55 increases the

severity of 2009 pandemic H1N1 influenza a virus

infection, J. Infect. Dis., 206, 495-503, doi: 10.1093/

infdis/jis378.

UVAROVA et al.1010

BIOCHEMISTRY (Moscow) Vol. 89 No. 6 2024

28. Matveeva, M. Y., Kashina, E. V., Reshetnikov, V. V.,

Bryzgalov, L.O., Antontseva, E.V., Bondar, N.P., and

Merkulova, T. I. (2016) Regulatory single nucleotide

polymorphisms (rSNPs) at the promoters 1A and 1B

of the human APC gene, BMC Genet., 17, 127-135,

doi:10.1186/s12863-016-0460-8.

29. Mitkin, N. A., Muratova, A. M., Korneev, K. V.,

Pavshintsev, V. V., Rumyantsev, K. A., Vagida, M. S.,

Uvarova, A. N., Afanasyeva, M. A., Schwartz, A. M.,

and Kuprash, D.V. (2018) Protective C allele of the sin-

gle-nucleotide polymorphism rs1335532 is associated

with strong binding of Ascl2 transcription factor and

elevated CD58 expression in B-cells, Biochim. Biophys.

Acta Mol. Basis Dis., 1864, 3211-3220, doi: 10.1016/

j.bbadis.2018.07.008.

30. Uvarova, A. N., Ustiugova, A. S., Mitkin, N. A.,

Schwartz, A. M., Korneev, K. V., and Kuprash, D. V.

(2022) The minor T allele of the single nucleotide

polymorphism rs13360222 decreases the activity of

the HAVCR2 gene enhancer in a cell model of hu-

man macrophages, Mol. Biol., 56, 90-96, doi:10.1134/

S0026893322010095.

31. Uvarova, A. N., Stasevich, E. M., Ustiugova, A. S.,

Mitkin, N. A., Zheremyan, E. A., Sheetikov, S. A.,

Zornikova, K. V., Bogolyubova, A. V., Rubtsov, M. A.,

Kulakovskiy, I. V., Kuprash, D.V., Korneev, K.V., and

Schwartz, A. M. (2023) rs71327024 Associated with

COVID-19 hospitalization reduces CXCR6 promot-

er activity in human CD4

T cells via disruption of

c-Myb binding, Int.J. Mol. Sci., 24, 13790, doi:10.3390/

IJMS241813790.

32. Ustiugova, A. S., Korneev, K. V., Kuprash, D. V., and

Afanasyeva, M.A. (2019) Functional SNPs in the hu-

man autoimmunity-associated locus 17q12-21, Genes,

10, 77, doi:10.3390/GENES10020077.

33. Cooper, T.A. (2005) Use of minigene systems to dissect

alternative splicing elements, Methods, 37, 331-340,

doi:10.1016/J.YMETH.2005.07.015.

34. Sparber,P., Sharova,M., Davydenko,K., Pyankov,D.,

Filatova, A., and Skoblov, M. (2023) Deciphering the

impact of coding and non-coding SCN1A gene variants

on RNA splicing, Brain, 147, 1278-1293, doi:10.1093/

BRAIN/AWAD383.

35. Sanoguera-Miralles, L., Bueno-Martínez, E., Valenzu-

ela-Palomo, A., Esteban-Sánchez, A., Llinares-Bur-

guet, I., Pérez-Segura, P., García-Álvarez, A., de

la Hoya, M., and Velasco-Sampedro, E. A. (2022)

Minigene splicing assays identify 20 spliceogenic

variants of the breast/ovarian cancer susceptibil-

ity gene RAD51C, Cancers, 14, 2960, doi: 10.3390/

CANCERS14122960.

36. Nguyen, T. A., Jones, R. D., Snavely, A. R., Pfenning,

A.R., Kirchner,R., Hemberg,M., and Gray, J.M. (2016)

High-throughput functional comparison of promoter

and enhancer activities, Genome Res., 26, 1023-1033,

doi:10.1101/GR.204834.116.

37. Melnikov, A., Murugan, A., Zhang, X., Tesileanu, T.,

Wang,L., Rogov,P., Feizi,S., Gnirke,A., Callan, C.G.,

Kinney, J.B., Kellis,M., Lander, E.S., and Mikkelsen,

T.S. (2012) Systematic dissection and optimization of

inducible enhancers in human cells using a massively

parallel reporter assay, Nat. Biotechnol., 30, 271-277,

doi:10.1038/nbt.2137.

38. Tewhey,R., Kotliar,D., Park, D.S., Liu,B., Winnicki,S.,

Reilly, S. K., Andersen, K. G., Mikkelsen, T. S., Land-

er, E.S., Schaffner, S.F., and Sabeti, P.C. (2016) Direct

identification of hundreds of expression-modulating

variants using a multiplexed reporter assay, Cell, 172,

1519-1529, doi:10.1016/j.cell.2018.02.021.

39. Myint, L., Wang, R., Boukas, L., Hansen, K. D., Goff,

L. A., and Avramopoulos, D. (2020) A screen of

1,049 schizophrenia and 30 Alzheimer’s-associated

variants for regulatory potential, Am. J. Med. Genet.

Part B Neuropsychiatr. Genet., 183, 61-73, doi:10.1002/

AJMG.B.32761.

40. Sample, P. J., Wang, B., Reid, D. W., Presnyak, V.,

McFadyen, I.J., Morris, D.R., and Seelig,G. (2019) Hu-

man 5′ UTR design and variant effect prediction from

a massively parallel translation assay, Nat. Biotech-

nol., 37, 803-809, doi:10.1038/s41587-019-0164-5.

41. Griesemer, D., Xue, J. R., Reilly, S. K., Ulirsch, J. C.,

Kukreja,K., Davis, J.R., Kanai,M., Yang, D.K., Butts,

J. C., Guney, M. H., Luban, J., Montgomery, S. B., Fi-

nucane, H. K., Novina, C. D., Tewhey, R., and Sabeti,

P.C. (2021) Genome-wide functional screen of 3′UTR

variants uncovers causal variants for human dis-

ease and evolution, Cell, 184, 5247-5260, doi:10.1016/

j.cell.2021.08.025.

42. Wang, X., He, L., Goggin, S. M., Saadat, A., Wang, L.,

Sinnott-Armstrong,N., Claussnitzer,M., and Kellis,M.

(2018) High-resolution genome-wide functional dissec-

tion of transcriptional regulatory regions and nucleo-

tides in human, Nat. Commun., 9, 5380, doi:10.1038/

s41467-018-07746-1.

43. van Arensbergen, J., Pagie, L., FitzPatrick, V. D., de

Haas,M., Baltissen, M.P., Comoglio,F., van der Weide,

R. H., Teunissen, H., Võsa, U., Franke, L., de Wit, E.,

Vermeulen,M., Bussemaker, H.J., and van Steensel,B.

(2019) High-throughput identification of human SNPs

affecting regulatory element activity, Nat. Genet.,

51, 1160-1169, doi:10.1038/s41588-019-0455-2.

44. Arnold, C. D., Gerlach, D., Stelzer, C., Boryń, Ł. M.,

Rath, M., and Stark, A. (2013) Genome-wide quanti-

tative enhancer activity maps identified by STARR-

seq, Science, 339, 1074-1077, doi: 10.1126/SCIENCE.

1232542.

45. Ustiugova, A.S., Dvorianinova, E.M., Melnikova, N.V.,

Dmitriev, A.A., Kuprash, D.V., and Afanasyeva, M.A.

(2023) CRISPR/Cas9 genome editing demonstrates

functionality of the autoimmunity-associated SNP

rs12946510, Biochim. Biophys. Acta Mol. Basis Dis.,

1869, 166599, doi:10.1016/j.bbadis.2022.166599.

METHODS FOR FUNCTIONAL CHARACTERIZATION OF POLYMORPHISMS 1011

BIOCHEMISTRY (Moscow) Vol. 89 No. 6 2024

46. Ernst,J., Melnikov,A., Zhang,X., Wang,L., Rogov,P.,

Mikkelsen, T. S., and Kellis, M. (2016) Genome-scale

high-resolution mapping of activating and repressive

nucleotides in regulatory regions, Nat. Biotechnol.,

34, 1180-1190, doi:10.1038/nbt.3678.

47. Soemedi, R., Cygan, K. J., Rhine, C. L., Wang, J., Bu-

lacan,C., Yang, J., Bayrak-Toydemir, P., McDonald, J.,

and Fairbrother, W.G. (2017) Pathogenic variants that

alter protein code often disrupt splicing, Nat. Genet.,

49, 848-855, doi:10.1038/ng.3837.

48. Rhine, C.L., Neil,C., Wang,J., Maguire,S., Buerer,L.,

Salomon, M., Meremikwu, I. C., Kim, J., Strande,

N. T., and Fairbrother, W. G. (2022) Massively paral-

lel reporter assays discover de novo exonic splicing

mutants in paralogs of Autism genes, PLoS Genet.,

18, e1009884, doi:10.1371/journal.pgen.1009884.

49. Lagunas,T., Plassmeyer, S.P., Fischer, A.D., Friedman,

R. Z., Rieger, M. A., Selmanovic, D., Sarafinovska, S.,

Sol, Y.K., Kasper, M.J., Fass, S.B., Aguilar Lucero, A.F.,

An, J. Y., Sanders, S. J., Cohen, B. A., and Dougherty,

J.D. (2023) ACre-dependent massively parallel report-

er assay allows for cell-type specific assessment of the

functional effects of non-coding elements invivo, Com-

mun. Biol., 6, 1151, doi:10.1038/s42003-023-05483-w.

50. Gordon, M. G., Inoue, F., Martin, B., Schubach, M.,

Agarwal,V., Whalen,S., Feng,S., Zhao,J., Ashuach,T.,

Ziffra,R., Kreimer,A., Georgakopoulous-Soares,I., Yo-

sef,N., Ye, C.J., Pollard, K.S., Shendure,J., Kircher,M.,

and Ahituv, N. (2020) lentiMPRA and MPRAflow for

high-throughput functional characterization of gene

regulatory elements, Nat. Protoc., 15, 2387-2412,

doi:10.1038/s41596-020-0333-5.

51. GTEx Consortium (2020) TheGTEx Consortium atlas

of genetic regulatory effects across human tissues,

Science, 369, 1318-1330, doi:10.1126/science.aaz1776.

52. Bryois, J., Calini, D., Macnair, W., Foo, L., Urich, E.,

Ortmann, W., Iglesias, V. A., Selvaraj, S., Nutma, E.,

Marzin,M., Amor,S., Williams,A., Castelo-Branco,G.,

Menon,V., De Jager,P., and Malhotra,D. (2022) Cell-

type-specific cis-eQTLs in eight human brain cell

types identify novel risk genes for psychiatric and

neurological disorders, Nat. Neurosci., 25, 1104-1112,

doi:10.1038/s41593-022-01128-z.

53. Capurso,D., Tang,Z., and Ruan,Y. (2020) Methods for

comparative ChIA-PET and Hi-C data analysis, Meth-

ods, 170, 69-74, doi:10.1016/J.YMETH.2019.09.019.

54. Huang, L., Yang, Y., Li, G., Jiang, M., Wen, J., Abnou-

si,A., Rosen, J.D., Hu,M., and Li,Y. (2022) Asystem-

atic evaluation of Hi-C data enhancement methods

for enhancing PLAC-seq and HiChIP data, Brief. Bioin-

form., 23, bbac145, doi:10.1093/BIB/BBAC145.

55. Khalil, A. M. (2020) The genome editing revolution:

review, J.Genet. Eng. Biotechnol., 18, 68, doi:10.1186/

S43141-020-00078-Y.

56. Moon, S. B., Kim, D. Y., Ko, J. H., and Kim, Y. S.

(2019) Recent advances in the CRISPR genome ed-

iting tool set, Exp. Mol. Med., 51, 1-11, doi: 10.1038/

s12276-019-0339-7.

57. Zhang,P., Xia, J.H., Zhu,J., Gao,P., Tian, Y.J., Du,M.,

Guo, Y. C., Suleman, S., Zhang, Q., Kohli, M., Till-

mans, L. S., Thibodeau, S. N., French, A. J., Cerhan,

J. R., Wang, L. D., Wei, G. H., and Wang, L. (2018)

High-throughput screening of prostate cancer risk loci

by single nucleotide polymorphisms sequencing, Nat.

Commun., 9, 2022, doi:10.1038/s41467-018-04451-x.

58. Rodríguez-Rodríguez, D.R., Ramírez-Solís, R., Garza-

Elizondo, M. A., Garza-Rodríguez, M. D. L., and Bar-

rera-Saldaña, H.A. (2019) Genome editing: a perspec-

tive on the application of CRISPR/Cas9 to study human

diseases (Review), Int. J. Mol. Med., 43, 1559-1574,

doi:10.3892/ijmm.2019.4112.

59. Yang,H., Ren,S., Yu,S., Pan,H., Li,T., Ge,S., Zhang,J.,

and Xia, N. (2020) Methods favoring homology-di-

rected repair choice in response to CRISPR/Cas9

Induced-double strand breaks, Int. J. Mol. Sci., 21,

6461, doi:10.3390/IJMS21186461.

60. Rees, H.A., and Liu, D.R. (2018) Base editing: precision

chemistry on the genome and transcriptome ofliving

cells, Nat. Rev. Genet., 19, 770-788, doi:10.1038/s41576-

018-0059-1.

61. Komor, A.C., Kim, Y.B., Packer, M.S., Zuris, J.A., and

Liu, D.R. (2016) Programmable editing of a target base

in genomic DNA without double-stranded DNA cleav-

age, Nature, 533, 420-424, doi:10.1038/nature17946.

62. Gaudelli, N.M., Komor, A.C., Rees, H.A., Packer, M.S.,

Badran, A. H., Bryson, D. I., and Liu, D. R. (2017)

Programmable base editing of A·T to G·C in genom-

ic DNA without DNA cleavage, Nature, 551, 464-471,

doi:10.1038/nature24644.

63. Zhao,D., Li,J., Li,S., Xin,X., Hu,M., Price, M.A., Ross-

er, S. J., Bi,C., and Zhang,X. (2021) Glycosylase base

editors enable C-to-A and C-to-G base changes, Nat.

Biotechnol., 39, 35-40, doi:10.1038/s41587-020-0592-2.

64. Weng, N., Miller, M., Pham, A. K., Komor, A. C., and

Broide, D.H. (2022) Single-base editing of rs12603332

on chromosome 17q21 with a cytosine base editor

regulates ORMDL3 and ATF6α expression, Allergy, 77,

1139-1149, doi:10.1111/ALL.15092.

65. Anzalone, A. V., Randolph, P. B., Davis, J. R., Sousa,

A.A., Koblan, L.W., Levy, J.M., Chen, P.J., Wilson,C.,

Newby, G. A., Raguram, A., and Liu, D. R. (2019)

Search-and-replace genome editing without dou-

ble-strand breaks or donor DNA, Nature, 576, 149-157,

doi:10.1038/s41586-019-1711-4.

66. Jiang, Y., Chai, Y., Qiao,D., Wang, J., Xin, C., Sun, W.,

Cao,Z., Zhang,Y., Zhou,Y., Wang, X.C., and Chen, Q.J.

(2022) Optimized prime editing efficiently generates

glyphosate-resistant rice plants carrying homozygous

TAP-IVS mutation in EPSPS, Mol. Plant, 15, 1646-1649,

doi:10.1016/j.molp.2022.09.006.

67. Hassan, M. M., Yuan, G., Chen, J. G., Tuskan, G. A.,

and Yang, X. (2020) Prime editing technology and

UVAROVA et al.1012

BIOCHEMISTRY (Moscow) Vol. 89 No. 6 2024

its prospects for future applications in plant biology

research, BioDes. Res., 2020, 9350905, doi: 10.34133/

2020/9350905.

68. Gao,P., Lyu,Q., Ghanam, A.R., Lazzarotto, C.R., New-

by, G.A., Zhang,W., Choi,M., Slivano, O.J., Holden,K.,

Walker, J.A., Kadina, A.P., Munroe, R.J., Abratte, C.M.,

Schimenti, J. C., Liu, D. R., Tsai, S. Q., Long, X., and

Miano, J.M. (2021) Prime editing in mice reveals the

essentiality of a single base in driving tissue-specific

gene expression, Genome Biol., 22, 83, doi: 10.1186/

s13059-021-02304-3.

69. Godbout, K., Rousseau, J., and Tremblay, J. P. (2023)

Successful correction by prime editing of a mutation

in the RYR1 gene responsible for a myopathy, Cells,

13, 31, doi:10.3390/CELLS13010031.

70. Petrova, I. O., and Smirnikhina, S. A. (2023) The de-

velopment, optimization and future of prime editing,

Int.J. Mol. Sci., 24, 17045, doi:10.3390/IJMS242317045.

71. Ren, X., Yang, H., Nierenberg, J. L., Sun, Y., Chen, J.,

Beaman, C., Pham, T., Nobuhara, M., Takagi, M. A.,

Narayan, V., Li, Y., Ziv, E., and Shen, Y. (2023)

High-throughput PRIME-editing screens identify func-

tional DNA variants in the human genome, Mol. Cell,

83, 4633-4645.e9, doi:10.1016/J.MOLCEL.2023.11.021.

72. Ambrosini, G., Vorontsov, I., Penzar, D., Groux, R.,

Fornes, O., Nikolaeva, D. D., Ballester, B., Grau, J.,

Grosse, I., Makeev, V., Kulakovskiy, I., and Bucher,P.

(2020) Insights gained from a comprehensive all-

against-all transcription factor binding motif bench-

marking study, Genome Biol., 21, 114, doi: 10.1186/

s13059-020-01996-3.

73. Lambert, S. A., Jolma, A., Campitelli, L. F., Das, P. K.,

Yin,Y., Albu,M., Chen,X., Taipale,J., Hughes, T.R., and

Weirauch, M.T. (2018) Thehuman transcription fac-

tors, Cell, 172, 650-665, doi:10.1016/J.CELL.2018.01.029.

74. Tognon, M., Giugno, R., and Pinello, L. (2023) A sur-

vey on algorithms to characterize transcription

factor binding sites, Brief Bioinform., 24, bbad156,

doi:10.1093/bib/bbad156.

75. Mundade, R., Ozer, H. G., Wei, H., Prabhu, L., and

Lu, T. (2014) Role of ChIP-seq in the discovery of

transcription factor binding sites, differential gene

regulation mechanism, epigenetic marks and be-

yond, Cell Cycle, 13, 2847-2852, doi:10.4161/15384101.

2014.949201.

76. Vorontsov, I. E., Kulakovskiy, I. V., Khimulya, G., Ni-

kolaeva, D. D., and Makeev, V. J. (2015) PERFECTOS-

APE: Predicting regulatory functional effect of

SNPs by approximate P-value estimation, Bioinfor-

ma. 2015 – 6th Int. Conf. Bioinforma. Model. Meth-

ods Algorithms, Proceedings; Part 8th Int. Jt. Conf.

Biomed. Eng. Syst. Technol., BIOSTEC 2015, 2, 102-108,

doi:10.5220/0005189301020108.

77. Wingender, E., Chen, X., Fricke, E., Geffers, R.,

Hehl,R., Liebich,I., Krull, M., Matys, V., Michael, H.,

Ohnhäuser,R., Prüß,M., Schacherer,F., Thiele,S., and

Urbach,S. (2001) TheTRANSFAC system on gene ex-

pression regulation, Nucleic Acids Res., 29, 281-283,

doi:10.1093/nar/29.1.281.

78. Vorontsov, I. E., Eliseeva, I. A., Zinkevich, A., Nikon-

ov, M., Abramov, S., Boytsov, A., Kamenets, V., Ka-

sianova, A., Kolmykov, S., Yevshin, I. S., Favorov, A.,

Medvedeva, Y.A., Jolma,A., Kolpakov,F., Makeev, V.J.,

and Kulakovskiy, I. V. (2024) HOCOMOCO in 2024: a

rebuild of the curated collection of binding models for

human and mouse transcription factors, Nucleic Acids

Res., 52, D154-D163, doi:10.1093/NAR/GKAD1077.

79. Castro-Mondragon, J. A., Riudavets-Puig, R., Raulu-

seviciute, I., Berhanu Lemma, R., Turchi, L., Blanc-

Mathieu, R., Lucas, J., Boddie, P., Khan, A., Perez,

N.M., Fornes,O., Leung, T.Y., Aguirre,A., Hammal,F.,

Schmelter,D., Baranasic,D., Ballester,B., Sandelin,A.,

Lenhard,B., Vandepoele,K., Wasserman, W.W., Par-

cy,F., and Mathelier,A. (2022) JASPAR2022: the9th re-

lease of the open-access database of transcription fac-

tor binding profiles, Nucleic Acids Res., 50, D165-D173,

doi:10.1093/NAR/GKAB1113.

80. Heinz, S., Benner, C., Spann, N., Bertolino, E., Lin,

Y. C., Laslo, P., Cheng, J. X., Murre, C., Singh, H.,

and Glass, C. K. (2010) Simple combinations of lin-

eage-determining transcription factors prime cis-reg-

ulatory elements required for macrophage and

B cell identities, Mol. Cell, 38, 576-589, doi: 10.1016/

j.molcel.2010.05.004.

81. Janky,R., Verfaillie,A., Imrichová,H., van de Sande,B.,

Standaert, L., Christiaens, V., Hulselmans, G., Her-

ten,K., Naval Sanchez,M., Potier,D., Svetlichnyy,D.,

Kalender Atak,Z., Fiers,M., Marine, J.C., and Aerts,S.

(2014) iRegulon: from a gene list to a gene regu-

latory network using large motif and track collec-

tions, PLoS Comput. Biol., 10, e1003731, doi:10.1371/

journal.pcbi.1003731.

82. Abramov, S., Boytsov, A., Bykova, D., Penzar, D. D.,

Yevshin,I., Kolmykov, S.K., Fridman, M.V., Favorov,

A. V., Vorontsov, I. E., Baulin, E., Kolpakov, F., Ma-

keev, V.J., and Kulakovskiy, I.V. (2021) Landscape of

allele-specific transcription factor binding in the hu-

man genome, Nat. Commun., 12, 2751, doi: 10.1038/

s41467-021-23007-0.

83. Li, Y., Zhang, X. O., Liu, Y., and Lu, A. (2023) Al-

lele-specific binding (ASB) analyzer for annotation

of allele-specific binding SNPs, BMC Bioinform., 24,

464, doi:10.1186/S12859-023-05604-6.

84. Yan, J., Qiu, Y., Ribeiro dos Santos, A. M., Yin, Y., Li,

Y.E., Vinckier,N., Nariai,N., Benaglio,P., Raman,A.,

Li,X., Fan,S., Chiou,J., Chen,F., Frazer, K.A., Gaulton,

K. J., Sander, M., Taipale, J., and Ren, B. (2021) Sys-

tematic analysis of binding of transcription factors to

noncoding variants, Nature, 591, 147-151, doi:10.1038/

s41586-021-03211-0.

85. Hellman, L.M., and Fried, M.G. (2007) Electrophoret-

ic mobility shift assay (EMSA) for detecting protein–

METHODS FOR FUNCTIONAL CHARACTERIZATION OF POLYMORPHISMS 1013

BIOCHEMISTRY (Moscow) Vol. 89 No. 6 2024

nucleic acid interactions, Nat. Protoc., 2, 1849-1861,

doi:10.1038/nprot.2007.249.

86. Parés-Matos, E. I. (2013) Electrophoretic mobility-

shift and super-shift assays for studies and char-

acterization of protein-DNA complexes, Meth-

ods Mol. Biol., 977, 159-167, doi: 10.1007/978-1-

62703-284-1_12.

87. Zhao, Y., Wu, D., Jiang, D., Zhang, X., Wu, T., Cui, J.,

Qian,M., Zhao, J., Oesterreich,S., Sun,W., Finkel,T.,

and Li, G. (2020) A sequential methodology for the

rapid identification and characterization of breast

cancer-associated functional SNPs, Nat. Commun.,

11, 3340, doi:10.1038/s41467-020-17159-8.

88. Butter, F., Davison, L., Viturawong, T., Scheibe, M.,

Vermeulen,M., Todd, J.A., and Mann,M. (2012) Pro-

teome-wide analysis of disease-associated SNPs that

show allele-specific transcription factor binding,

PLoS Genet., 8, e1002982, doi: 10.1371/journal.pgen.

1002982.

89. Jolma, A., Kivioja,T., Toivonen,J., Cheng,L., Wei,G.,

Enge,M., Taipale,M., Vaquerizas, J.M., Yan,J., Sillan-

pää, M.J., Bonke,M., Palin,K., Talukder, S., Hughes,

T. R., Luscombe, N. M., Ukkonen, E., and Taipale, J.

(2010) Multiplexed massively parallel SELEX for char-

acterization of human transcription factor binding

specificities, Genome Res., 20, 861-873, doi: 10.1101/

gr.100552.109.

90. Mille, M., Ripoll, J., Cazaux, B., and Rivals, E. (2023)

dipwmsearch: a Python package for searching di-

PWM motifs, Bioinformatics, 39, btad141, doi:10.1093/

BIOINFORMATICS/BTAD141.

91. Maurano, M.T., Haugen,E., Sandstrom,R., Vierstra,J.,

Shafer, A., Kaul, R., and Stamatoyannopoulos, J. A.

(2015) Large-scale identification of sequence variants

influencing human transcription factor occupancy

invivo, Nat. Genet., 47, 1393-1401, doi:10.1038/ng.3432.

92. Boytsov, A., Abramov, S., Aiusheeva, A. Z., Kasiano-

va, A.M., Baulin,E., Kuznetsov, I.A., Aulchenko, Y.S.,

Kolmykov, S., Yevshin, I., Kolpakov, F., Vorontsov,

I. E., Makeev, V. J., and Kulakovskiy, I. V. (2022)

ANANASTRA: annotation and enrichment analysis of

allele- specific transcription factor binding at SNPs,

Nucleic Acids Res., 50, W51-W56, doi: 10.1093/nar/

gkac262.

93. Mitkin, N. A., Korneev, K.V., Gorbacheva, A.M., and

Kuprash, D.V. (2019) Relative efficiency of transcrip-

tion factor binding to allelic variants of regulatory

regions of human genes in immunoprecipitation and

real-time PCR, Mol. Biol., 53, 346-353, doi: 10.1134/

S0026893319030117.

94. Yevshin,I., Sharipov,R., Valeev,T., Kel,A., and Kolpa-

kov, F. (2017) GTRD: a database of transcription fac-

tor binding sites identified by ChIP-seq experiments,

Nucleic Acids Res., 45, D61-D67, doi: 10.1093/nar/

GKW951.

95. Zhang, Y., Mo, Q., Xue,L., and Luo, J. (2021) Evalua-

tion of deep learning approaches for modeling tran-

scription factor sequence specificity, Genomics, 113,

3774-3781, doi:10.1016/J.YGENO.2021.09.009.

96. Chen,C., Hou,J., Shi,X., Yang,H., Birchler, J.A., and

Cheng, J. (2021) DeepGRN: prediction of transcrip-

tion factor binding site across cell-types using atten-

tion-based deep neural networks, BMC Bioinformatics,

22, 38, doi:10.1186/S12859-020-03952-1.

Publisher’s Note. Pleiades Publishing remains

neutral with regard to jurisdictional claims in pub-

lished maps and institutional affiliations.