SNPs at 3'UTR of APOL1 and miR-6741-3p target sites associated with Kidney Diseases more susceptible to SARS-COV-2 infection

doi:10.21203/rs.3.rs-55912/v1

Download PDF

Research

SNPs at 3'UTR of APOL1 and miR-6741-3p target sites associated with Kidney Diseases more susceptible to SARS-COV-2 infection

https://doi.org/10.21203/rs.3.rs-55912/v1

This work is licensed under a CC BY 4.0 License

Version 1

posted

You are reading this latest preprint version

Background: Since APOL1 is expressed by proximal tubules and podocytes, the kidney is likely a target for SARS-CoV-2. This increases the likelihood that African American people with the high-risk genotype APOL1 are at increased risk for kidney disease in the COVID-19 environment. Single nucleotide polymorphisms (SNPs) are found in various microRNAs (miRNAs) and target genes are changed the miRNA activity that leads to different diseases. Evidence has shown that SNPs increase/decrease the effectiveness of the interaction between miRNAs and disease-related target genes. The aim of this study is not only to identify miRSNPs on the APOL1 gene and SNPs in miRNA genes targeting 3'UTR but also to evaluate the effect of these gene variations in kidney patients and their association with SARS-COV-2 infection.

Methods: In 3'UTR of the APOL1 gene, we detected 96 miRNA binding sites and 35 different SNPs with 10 different online software in the binding sites of the miRNA (in silico). Also we studied gene expression of patients and control samples by using qRT-PCR (in vitro).

Results: In silico study, the binding site of miR-6741-3p on APOL1 has two SNPs (rs1288875001, G> C; rs1452517383, A> C) on APOL1 3'UTR, and its genomic sequence is the same nucleotide as rs1288875001. Similarly, two other SNPs (rs1142591, T> A; rs376326225, G> A) were identified in the binding sites of miR-6741-3p at the first position. Here, the miRSNP (rs1288875001) in APOL1 3'UTR and SNP (rs376326225) in the miR-6741-3p genomic sequence are cross-matched in the same binding region. In vitro study, the relative expression levels were calculated by the 2^-ΔΔCtmethod. The expression of APOL1 gene was different in chronic kidney patients along with COVID-19. By these results, APOL1 expression was found lower in patients than healthy (p<0.05) in kidney patients along with COVID-19. In addition, miR-6741-3p targets many APOL1-related genes (TLR7, SLC6A19, IL-6,10,18, chemokine (C-C motif) ligand 5, SWT1, NFYB, BRF1, HES2, NFYB, MED12L, MAFG, GTF2H5, TRAF3, angiotensin II receptor-associated protein, PRSS23) by evaluating online software in the binding sites of the miR-6741-3p. miR-6741-3p has not previously shown any association with kidney diseases and SARS-COV-2 infection.

Conclusions: It assures that APOL1 can have a significant consequence in kidney associated diseases by different pathways. Henceforth, this study represents and demonstrates an effective association between miR-6741-3p and kidney diseases, i.e collapsing glomerulopathy, chronic kidney disease (CKD), acute kidney injury (AKI), and tubulointerstitial lesions susceptibility to SARS-COV-2 infection via in silico and in vitro exploration and recommended to have better insight.

Translational Medicine

Urology & Nephrology

APOL1

miR-6741-3p

SNPs

3′UTR region

Kidney Diseases

SARS-COV-2

COVID-19

Coronaviruses (CoV) are serious health problems linked to enteric, respiratory, hepatic and central nervous diseases in humans and animals. Severe acute respiratory syndrome-coronavirus (SARS-CoV) and Middle East respiratory syndrome coronavirus (MERS-CoV) were identified as the source of epidemics in 2002 and 2012 with rates of high mortality due to severe respiratory syndrome [1, 2]. Respiratory pneumonia (COVID-19) caused by Severe acute respiratory syndrome 2 (SARS-CoV-2) has been detected in Wuhan, where China has spread worldwide since December 2019.

Recently, two cases of focal segmental glomeruloscleroses (FSGS) and tubulointerstitial lesions have been reported in African patients with COVID-19 and APOL1 polymorphism. They also highlighted the potentially important role of the risk alleles APOL1 G1 and G2 in the formation of collapsed FSGS linked to SARS-CoV-2 [3]. Larsen et al. reported CG variants of the APOL1 gene associated with COVID-19 in kidney disease patient [4]. CG has not been reported in outbreaks in China and Europe, perhaps the high-risk APOL1 genotypes are only available in populations of African strains [4, 5]. High risk genotypes have been reported in 10–15% of African American individuals. Given the expected attack rates of 50–80% of COVID-19 in the general population, a significant portion of the West African population is the COVID-19 epidemic in America and Africa [4, 5].

Numerous studies have reported that miRNAs act not only as signatures of tissue expression and function, but also as potential biomarkers that play an important role in regulating the pathophysiology of diseases [6]. In viral infections, host antiviral miRNAs play an important role in regulating the immune response to viral infection caused by the viral substance. It appears that many known human miRNAs can target viral genes and their functions, such as replication, translation and interference with expression [7–9]. Irregularity of miRNAs expression plays an important role in the development of kidney disease susceptibility to SARS-COV-2 infection [10, 11]. Some structural variations of human APOL1 and miRNA genes targeting 3'UTRs have been identified, characterized by lower binding affinity with a pointed viral protein, which has potential protective effects [12].

In this study, we focus on the relationship among the 3'UTR of APOL1 and miRNAs target sites associated with kidney diseases susceptibility to COVID-19 and aim to facilitate the exploration of new therapeutic medicine to control kidney diseases i.e. collapsing glomerulopathy, chronic kidney disease (CKD), acute kidney injury (AKI) and tubulointerstitial lesions and decrease susceptibility to SARS-COV-2 infection.

2.1. Screening of miRNA targeting 3’UTR of APOL1 gene

Ten unique online databases including DIANA Tools, miRTarBase, Miranda, TargetScan, miRDB, miRecords, miRcode, miRO, miRWalk, MiCosm were used for the detection of miRNA targeting the 3'UTR of the APOL1 gene, and then we followed several algorithms and calculations below methodologies provided [13]. Accordingly, the scanning of all miRNA targets can be performed without missing any of them using the multiple databases below.

DIANA tools

To obtain estimates about in-silico-miRNA-mRNA interactions, a server used DIANA-microT containing specific databases, so that each communication and further estimation of each target site for estimation of miRNA-target gene interactions. The 3'UTR and coding regions have a positive and negative set of miRNA recognition elements, so these positive and negative clusters are locations where this tool operates. A fantastic increase in sensitivity by DIANA-microT-CDS (http://diana.imis.athena-innovation.gr/DianaTools/index.php) compared to experimental proteomic efficiency [14, 15].

miRTarBase

miRTarbase not only provides fifty thousand miRNA-target interactions to scan research articles related to functional miRNA studies, but also provides interaction information for manual review of appropriate literature after investigation of text consistency. Commonly, experiments confirm western blot, microarray, a reporter assay, next-generation sequencing, and obtained MTIs. Compared to pre-built databases, the most current and greatest amount of data is provided by miRTarBase (http://mirtarbase.mbc.nctu.edu.tw/php/index.php) [16].

MiRanda

In a comprehensive assessment, miRanda competes with other target prediction databases, not only because of the specification of the target genes, but also the downregulation prediction at the translational or transcriptional level. Experimental identification of a large number of unprotected and non-canonical areas is determined by this database (http://www.microrna.org/microrna/home.do) [17].

TargetScan

To estimate the computational targets of miRNAs, filtration of the protected 8 and 7 occurrences per region matching the seed portion of all miRNAs, and then another prediction of unprotected sites, target scanning was used. The context and scores of places in mammals make calculations, and these calculations also help align the target productivity estimates. The ordering of forecasts is also made with target probabilities in protected areas (http://www.targetscan.org/vert_72/) [18].

miRDB

miRDB is an online analytical database tool used to predict miRNA targets and explain their functions. Another bioinformatics tool, miR target, was used to predict all targets of miRDB created after high-output sequencing research by studying thousands of miRNA-target interactions. MiRNA target binding has some well-known common features. These features are used to predict the target of miRNA by the machine learning method (http://www.mirdb.org/). miRDB provides miRNA targets of five species; mouse, human, rat, chicken, and dog. The latest update of this software makes it easy for the user to predict customized target sequences [19].

miRecords

miRecords is a source of miRNA-target interaction in animals. This online database tool is divided into two components. The main component consists of authenticated targets and a high inferiority database, and these databases use experimental miRNA targets after certain literature improvements. Another component of MiRecords (http://c1.accurascience.com/miRecords/) is to estimate targets, which are the assimilation of expected miRNA targets produced by 11 verified miRNA target computation series. Since April 27, 2013, this software's authenticated targets component has been recording approximately 2705 interactions between 644 miRNAs of 9 animal species and 1901 target genes [20]. Among these records, it was handled from 2028 “low input” trials. The predicted targets of miRNA target estimation tools include HedefScan / TargertScanS, MiInspector, miRanda, MirTarget2, miTarget, NBmiRTar, RNA hybrid, NBmiRTar, PicTar, PITA, RNA22, and DIANA-miT.

miRcode

miRcode (http://www.mircode.org/) contains more than 10,000 non-encoding RNA genes. The miRNA target estimates in miRcode provided by "full transcriptome" are based on extensive GENCODE gene detailing. This tool also includes coding genes and these coding genes also contain unusual regions such as 5'UTR and CDS. The names and definitions in the MiRNA family are consistent with TargetScan. Vertebrate species form the basis of evaluation for the protection of the site [21].

2.2. Screening of miRSNPs

Regions targeted by miRNAs in 3'UTR of the APOL1 gene were taken from these ten databases and a target site list was constructed. Meanwhile, direct matching (A-U, G-C) and wobble matching (G-U) were well thought out in target prediction. GU wobble in seed matching indicates that a G match with a U is allowed instead of C. SNPs in the 3 'UTR of APOL1 were obtained from dbSNP build 96 databases (Table 1). SNPs related to APOL1 activity that do not match these matching areas were identified and evaluated. Scanning SNPs of miRNA genes targeting the 3 'UTR of APOL1 on the miRNA gene targeting the 3' UTR of APOL1 was scanned from the NCBI database. SNPs in the genes of miRNAs and miRSNPs in the 3 'UTR of APOL1 were matched (Table 2). Cross-match SNPs were searched as done by [13]. Matched and unmatched SNPs are shown and interpreted for APOL1 activity with kidney diseases that has susceptibility to COVID-19.

Table 1. List of miRNA target sequences and SNPs in binding sites in the 3’ UTR of APOL1.
miRNAs	SNPs	Databases	Gene Sequences
hsa-miR-584-3p	rs368925197	DianaTool miRDB STarMirDB TargetScan	GCCTGCAATAAGG[G/A/T]AAAAATGGGAACTGG*
hsa-miR-1273a	rs61436784	DianaTool miRDB TargetScan	TTCTG*AGACAGAGTCTTGCTCTGT[C/T]GCCA
hsa-miR-597-3p	rs367781604	DianaTool miRDB TargetScan	AAGAATAGAGAGGAGGCT*TG[A/C]AGGAACCA
hsa-miR-3913-5p	rs9610473	DianaTool TargetScan	TCTTGATC[T/C]GCCCACCTTGGCCTCCCAAA
hsa-miR-557	rs373553101	DianaTool TargetScan	CCTGGCAGGGGCCAGGAC[-/A]AAAATGCAAAC
hsa-miR-3922-3p	rs367781604	DianaTool STarMirDB TargetScan	TTG[A/C]AGGAACCAGCAATGAGAAGGCCAGG
hsa-miR-3680-3p	rs112920649	DianaTool miRDB TargetScan	CCTG*GCAGGGGCCAG[G/A]ACAAAATGCAAAC
hsa-miR-612	rs1142542	DianaTool TargetScan	GGTGGTG[G/A]GCCATGGCCATG*GTCCCCAGC
hsa-miR-6768-5p	rs1142593	DianaTool TargetScan	C[T/C]TGTCG*CCGCCCAGGATTGACCTGTGTG
hsa-miR-4755-5p	rs368925197	DianaTool miRDB STarMirDB TargetScan	AGGAACATTGGAGCCTGCAATAAGG[G/A/T]AAA
hsa-miR-8052	rs3075462	DianaTool miRDB TargetScan	GCCCAGGATTGACC[GT/-]TG*TGTAAGTCCCA
hsa-miR-6783-3p	rs1142594	DianaTool TargetScan	TCTAGAGCTGTCTTGTCGC[C/T]GCCCAGGAT
hsa-miR-7154-3p	rs370345495	DianaTool miRDB TargetScan	AGACCCAGCCCCAG*GTTCAATGTCCTCC[G/A]
hsa-miR-6784-3p	rs1142564	DianaTool TargetScan	AACCCAAACTTCCCAGAGAGTAT[G/A/C]TGAGA
hsa-miR-6780a-5p	rs9610473	DianaTool miRDB TargetScan	CTCTT*GATC[T/C]GCCCACCTTGGCCTCCCAA
hsa-miR-5193	rs180731649	DianaTool TargetScan	AGCATGAAAGCAGTTTAGCA[T/C]TG*GGAGGA
hsa-miR-136-5p	rs61436784	DianaTool miRDB STarMirDB TargetScan	AGAGTCTTGCTCTGT[C/T]GCCAAG*TTGGAGT
hsa-miR-365b-5p	rs1142550	DianaTool miRDB STarMirDB TargetScan	T[G/A]GTCCCCAGCTGAGGAG*CAGGTGTCCCTGAGAACCCA
hsa-miR-7977	rs183351145	DianaTool TargetScan	CCCACCTTGGCCTCCCAAA[G/C]TGCTGGGAT
hsa-miR-1302	rs9610474	DianaTool TargetScan	CTCTTGATCTGCCCACCT[T/C]G*GCCTCCCAA
hsa-miR-3136-5p	rs1065088	DianaTool TargetScan	TTGTCCT[C/T]CTGGGGGCATATCTCAGTCAG
hsa-miR-6865-3p	rs185083415	DianaTool TargetScan	ACTAAAGAATATATTGGGGGG*C[C/T]GGGTGT
hsa-miR-365a-5p	rs1142550	DianaTool miRDB STarMirDB TargetScan	T[G/A]GTCCCCAGCTGAGGAGCAGGTGTCCCT
hsa-miR-4768-3p	rs151210481	DianaTool TargetScan	ACGAG[G/C]TCAGGAGATCGAGACCATCCTGG
hsa-miR-3199	rs1142550	DianaTool miRDB TargetScan	T[G/A]GTCCCCAGCTG*AGGAGCAGGTGTCCCT
hsa-miR-3144-5p	rs186069172	DianaTool miRDB TargetScan	TCCAG[G/T]TTACTAAAGGGTGCATGTCCCCT
hsa-miR-3187-5p	rs191472094	DianaTool TargetScan	GACTACAGGC[G*/A]CCTACCACCATGCCCAGC
hsa-miR-6787-3p	rs1142542	DianaTool miRDB TargetScan	TG[G/A]GCCATGGCCATGGTCCCCAGCTGAGG
hsa-miR-6884-3p	rs78523	DianaTool TargetScan	GGTC[A/G/T]TTGGGGT*GGTTGTCATGTGATGGG
hsa-miR-519e-5p	rs368925197	DianaTool STarMirDB TargetScan miRcode	GCAATAAG*G[G/A/T]AAAAATGGGAACTGGAGAG
hsa-miR-892c-3p	rs1142587	DianaTool TargetScan	CATCGCTCTTA[C/G]CCGGTAAGTAAACAGTC
hsa-miR-515-5p	rs368925197	DianaTool STarMirDB TargetScan	GCAATAAGG[G/A/T]AAAAATGGGAACTGGAGAG*
hsa-miR-361-3p	rs66473469	DianaTool TargetScan	TACTTTAGACT[A/C]AAGAATATATTGGGGGG
hsa-miR-887-5p	rs9610474	DianaTool TargetScan	TCTTGATCTG*CCCACCT[T/C]GGCCTCCCAAA
hsa-miR-1343-3p	rs151210481	DianaTool STarMirDB TargetScan	CCAAGGCGGGCGGATCACGAG[G/C]TCAGGAG
hsa-miR-452-5p	rs1142587	DianaTool TargetScan	CATCGCTCTTA[C/G]CCGGTAAGT*AAACAGTC
hsa-miR-4284	rs188783876	miRTarBase TargetScan	CTGGGATTACAGGC[G/A]TGAGCCA
hsa-miR-2276-3p	rs367781604	miRTarBase TargetScan	GAATAGAGAGGAGG*CTTG[A/C]AGG
hsa-miR-3653-5p	rs151210481	miRTarBase TargetScan	GCGGGCGG*ATCACGAG[G/C]TCAGGAGA
hsa-miR-122-3p	rs185153491	MirTarBase TargetScan	GCTGAGGCAGGAGAATGG[C/T]GTG
hsa-miR-122-3p	rs187862099	MirTarBase TargetScan	AAGTTGGAGTGCAATGGTG[C/T]G
hsa-miR-1306-5p	rs1010201478	MirTarBase TargetScan	ATGG*CGTGAACCTGGGAGG[T/C/G]GG
hsa-miR-1306-5p	rs192517180	MirTarBase TargetScan	CTGAGCACAGCAGGGGAGGG[G/T]T
hsa-miR-6890-3p	rs546529559	MirTarBase TargetScan	GGTTA[A/G]TGCAGATGGCAGTGC
hsa-miR-6890-3p	rs555542726	MirTarBase TargetScan	CCTGGGG[G/A]CATATCTCAGTCAGGCAGCGG
hsa-miR-6840-3p	rs1001914693	MirTarBase TargetScan	TCCCTTGT*CCTCCTGGG[G/A]
hsa-miR-6840-3p	rs138141669	MirTarBase TargetScan	AGAATGGC[G/A]TG*AACCTGGGA
hsa-miR-1915-3p	rs138141669	STarMirDB MirTarBase	AGAATGGC[G/A]TGAACCTGGGA
hsa-miR-6764-5p	rs1065088	MirTarBase TargetScan	CCACTCTCCCTTGTCCT[C/T]CTGGGG
hsa-miR-6764-5p	rs560542572	MirTarBase TargetScan	CGCCACTGCACTCCAGC[C/A/T]TGGGT
hsa-miR-4726-3p	rs138141669	MirTarBase TargetScan	TGAGGCAG*GAGAATGGC[G/A]TGAACCTGGGA
hsa-miR-4726-3p	rs540237398	MirTarBase TargetScan	GCCA[C/T]TGCACTCCAG*CCTGGGT
hsa-miR-4722-3p	rs575199310	MirTarBase TargetScan	CACCAGGAGAGATATGCCTGGC[A/G]GGG
hsa-miR-4722-3p	rs192517180	MirTarBase TargetScan	CAGCAGGGGAGGG[G/T]TTAATGCAGATGGCAGTG
hsa-miR-4722-3p	rs377572847	MirTarBase TargetScan	TAGCCGGGCATGGTGG[C/T]GGGC
hsa-miR-6747-3p	rs140768119	MirTarBase TargetScan	ACTTTGGGAGGCC[A/G]AGGCGGGC
hsa-miR-6747-3p	rs142061305	MirTarBase TargetScan	ACTTTGGGAGGCCAAGG[C/T]GGGC
hsa-miR-6741-3p	rs971803255	MirTarBase TargetScan	GGAGGTGGAGCTTGCAGTGAGCC[G/A]A
hsa-miR-6741-3p	rs1142591	MirTarBase TargetScan	GCATTGGGAGGAAGC[T/A]CAGATCTCTAGAGCT*GT
hsa-miR-1304-3p	rs189436505	MirTarBase TargetScan	GACCATCCTGGCTAACAC[A/C/G/T]GTGAAA
hsa-miR-1304-3p	rs367781604	MirTarBase	GAGGCTTG[A/C]AGGAACCAGCAATGAGA
hsa-miR-1976	rs997752974	TargetScan MirTarBase	TGGGC[G/A]GCTGAGGCAGGAGA
hsa-miR-4279	rs986058987	STarMirDB MirTarBase	CCAGC[T*/A]GAGGAGCA
hsa-miR-4279	rs377750095	STarMirDB MirTarBase	GCAG[C/T]CACCAGGAGAG
hsa-miR-4459	rs1065088	TargetScan	TCCACTCTCCCTTGTCCT[C/T]CTGG
hsa-miR-4456	rs377750095	TargetScan	CACAGGGCAGGGCAG[C/T]CACCAGG
hsa-miR-6845-3p	rs377750095	TargetScan	GGCAGGGCAG[C/T]CACCAGGAGAG
hsa-miR-3675-3p	rs377750095	TargetScan	AGGGCAG[C/T]CACCAGGAGAGATAT
hsa-miR-4673	rs748673128	TargetScan	CCACCAGGAGAGATA[T/C]GCCTGGC
hsa-miR-4645-5p	rs748673128	TargetScan	CCACCAGGAGAGATA[T/C]GCCTGGC
hsa-miR-6752-3p	rs575199310	TargetScan	AGGAGAGATATGCCTGGC[A/G]GGGG
hsa-miR-6801-3p	rs748673128	TargetScan	GGAGAGATA[T/C]GCCTGGCAGGGGC
hsa-miR-874-5p	rs1007382357	TargetScan	GAGATATGCCTGGCAG[G/C]GGCCAG
hsa-miR-221-5p	rs544109054	STarMirDB TargetScan	ATATGCCTGGCAGG[G/C]GCCAGGAC
hsa-miR-2355-3p	rs575199310	TargetScan	TGCCTGGC[A/G]GGGGCCAGGACAAA
hsa-miR-32-5p	rs998630616	TargetScan	TGTCGCCAAGTT[G/C]GAGTGCAATG
hsa−miR−26a−1−3p	rs992720706	STarMirDB TargetScaan	TTGA[C/A/T]GGAAGAATAGA
hsa−miR−4769−3p	rs192517180	STarMirDB TargetScan	AGGGGAGGG[G*/T]TTAATGCAGATGGCAGT
hsa−miR−5006−3p	rs541982833	STarMirDB TargetScan	AGGCAGGAACATTGGAG[C/T]CTGCAATAAGGGAAAA
hsa−miR−4723−3p	rs992720706	STarMirDB TargetScan	ATTGA[C/A/T]GAAGAAT*AGAGAGGA
hsa−miR−3183	rs992720706	STarMirDB TargetScan	ATTGA[C/A/T]GAAGAATAGAGAGGA
hsa−miR−3137	rs562669277	STarMirDB TargetScan	ACAGCGGCTCC[A/G]CTACAGAC
hsa−miR−4655−5p	rs542535764	STarMirDB TargetScan	AGTCCCATC[G/A/C/T]CT*CTTACCCGGTAA
hsa−miR−4646−5p	rs1014094016	STarMirDB TargetScan	GTCCCTGA[G/C]AACCCAAACTTCCCAGA
hsa−miR−3688−3p	rs557809907	STarMirDB TargetScan	TAGG*GACTTTGGCATTT[C/T]CATAG
hsa−miR−4753−3p	rs6000221	STarMirDB	GAGAAGGCAGGAAC[A/G]TTGGAGC
hsa−miR−363−3p	rs559887789	STarMirDB TargetScan	GGCAGGAACA[T/C]TGGAG*CCTGCAATA
hsa−miR−4492	rs562669277	STarMirDB TargetScan	AGCGGCTCC[A/G]CTACAGACCCAGCCCCA
hsa−miR−3941	rs1142594	STarMirDB TargetScan	TTGTCGC[C/T]GCCCAGGATTGACCTGTGTGTAA
hsa−miR−941	rs550530468	STarMirDB TargetScan	ATATATT[G/T]GGGGGCCGGGTGT
hsa−miR−4800−3p	rs992720706	STarMirDB	AAGAATATATTGA[C/A/T]GAA
hsa−miR−5001−5p	rs562669277	STarMirDB TargetScan	CGGCTCC[A/G]CTACAGACCCAGCCCC
hsa−miR−4762−3p	rs1142587	STarMirDB TargetScan	TTA[C/G]CCGGTAAGTAAACAGTCAGAAA
hsa−miR−3152−3p	rs184571030	STarMirDB TargetScan	ATCC[T/C]GGCTAACACAG
hsa−miR−330−5p	rs1014094016	STarMirDB TargetScan	CCCTG*A[G/C]AACCCAAACTTCCCAGAGAG
hsa−miR−33a−5p	rs112920649	STarMirDB TargetScan	GGCAGGGGCCAG[G/A]ACAAAATGCAA

Table 2. List of miRNAs having SNP in their genomic sequences at target site to their 3’ UTR of APOL1
miRNAs	SNPs	Sequences
Hsa-miR-557	rs1472716872	GTTT[G/A]CACGGGTGGGCCTTGTCT
Hsa-miR-943	rs766475819	CTGGA*GGAC[G/A]GCAACAGTCAG
Hsa-miR-6747-3p	rs1392252916	AGCTGGTGCAGAG[G/A]AAGGCAGGA*
Hsa-miR-597-3p	rs990067796	TGGTTCTCTTGTG*[G/T]CTCA
Hsa-miR-136-5p	rs1190567087	ACTCCATTTGTTTTGATGAT[G/A]GA
Hsa-miR-7977	rs905530916	TCCCAGCCAA[C/G/T]GCAC
Hsa-miR-6736-3p	rs782751082	TGTGGGTA*GAGA[G/A/C]GAGCTGA
Hsa-miR-3136-5p	rs912074637	ACCCTAC[C/T]TATTCAGTCAG
Hsa-miR-6865-3p	rs989222017	GTAGG[G/T]AA*AGAGGGTGT
Hsa-miR-3122	rs748154848	GTTGGGACAAGAGGAC[G/A/C]G*TCT
Hsa-miR-4518	rs767411469	GCTCA[G/C]GGATGATAACTGTGCTGAGA
Hsa-miR-4722-5p	rs938540081	CCTGGCA[C/A]AGCCCTCCTGCC
Hsa-miR-4314	rs1301006314	CTCTG[G/A]GAAATGGGACAG
Hsa-miR-6875-3p	rs999511124	ATTCTTCCTG[C/G/T]CCTGGC
Hsa-miR-1342-3p	rs1335261302	CTCCTGGGGCCC[G/A]CACTCTCG
Hsa-miR-584-3p	rs766864122	AGCCTGG[T/A/C]TGGC*CTGGAACTGA
Hsa-miR-3913-5p	rs1297472396	AGACATCAAGAT[C/A/G]AGTCCCAAA
Hsa-miR-3922-3p	rs1277436741	TCTGGC[C/G]TTGACTTGACTCT*T
Hsa-miR-3680-3p	rs749689078	CCTA*CTCCCAGGGTCATGC[-/A]AAAA
Hsa-miR-612	rs1418535165	GCTGGGCAGGGCTTCTGA[G/A]C
Hsa-miR-330-5p	rs761051145	GCCTA*AG[CA/-]ACAGGCCCAGAGA
Hsa-miR-6768-5p	rs1396880489	CACACAGGAAAAGCGG[G/A]GCCCT*G
Hsa-miR-4755-5p	rs886558705	AAAGC[C/T]AGGCTCTGAAGGGAAA
Hsa-miR-8052	rs899347852	CGG[G/A]ACTGTAGAGGGC
Hsa-miR-6783-3p	rs557187234	ACAGAGGA[G/A/C]AAGCCCAGGAA
Hsa-miR-7154-3p	rs765189209	TCCCACAA*CTTGTCCTC[C/A/T]T
Hsa-miR-6784-3p	rs755577607	TGGGGCAGA[G/A]TTGGGGTGAGA*
Hsa-miR-5006-3p	rs759936519	CAGGAT[G/A]GAAAGGGAA
Hsa-miR-5193	rs1478481783	AC*T[G/A]GGATGAGGTAGAGGAGG
Hsa-miR-4768-3p	rs752997118	CCAG[G/A]AGATCCAGAGAGAAT*
Hsa-miR-3199	rs532455179	AGGGACTGCCTT*AG[G/T]AGA
Hsa-miR-3144-5p	rs756052592	AGGGGACCAAAGA[G/A]ATA
Hsa-miR-3187-5p	rs1419100120	CCTGGGCAGCGT[G/A]T*GGCTGAAGG
Hsa-miR-6787-3p	rs528876687	TCT[C/G/T]AGCTG*CTGCCCTCTCCA
Hsa-miR-6884-3p	rs372598387	GAGACG[G/T]AAA*GGTGATGGG

Samples Collection, RNA isolation and cDNA Conversion

A total of 40 samples of blood were collected from Erbil. There were 20 samples of kidney disease as well as COVID-19 patients and 20 were healthy controls. RNA isolation was achieved using a kit (Trizol, Sigma-Aldrich, Life Sciences Technologies) reagent as per direction of the manufacturer and stored at -20 C. The cDNA was synthezied using the kit (QuantiTect Reverse Transcription kit, Qiagen) as per the instructions of manufacturer.

Relative gene expression using qRT-PCR

We used Rotor Gene qReal-Time PCR (qRT-PCR) to measure the expression level of miR-6741-3p and APOL1 gene in patient (kidney disease + COVID-19) and healthy subjects. Expression values of APOL1 in healthy and patients subjects were shown in Figs. 2 & 3. The relative expression levels were calculated by the 2^−ΔΔCt method. By the results, APOL1 expression was found statistically lower in the patients than healthy (p < 0.05). As our hypothesis, we thought that miR-6741-3p targeting APOL1 gene may be used to inhibit complications in kidney patients with SARS-COV-2 infection. Because APOL1 gene inhibit partially transcription with miR-6741-3p since miR-6741-3p targets 3'UTR region of APOL1 according to computational analysis.

Finding binding and targeting sites on MiRNA is expensive and time-consuming. Estimation of miRNA/gene interaction by computational analysis is a valuable tool in a wet lab for experimental transmission. Also, as researchers face difficulties in predicting the mysterious basic mechanisms of miRNA-targets, finding a comparative figure of miRNA-mediated target binding is a powerful activity because it discourages the trouble of recognizing the potential miRNA-mRNA interaction of millions of miRNA-gene combinations [22]. In addition, by suppressing mRNAs, various biological functions are targeted by the miRNA, and the representation of miRNA-mediated effects is problematic, even when they are attached to large-scale regulatory systems [23]. One of the benefits of existing databases is that it mostly includes miRNA-gene interaction in the 3'UTR regions.

In this study, we used ten different tools for analysis to detect miRSNPs in the APOL1 gene and SNPs on miRNA genes targeting ^APOL1 with COVID-19 sensitive kidney diseases. Online databases were used to find the 3 'UTR of APOL1 gene targeted by miRNAs. Instead of just one database, ten different databases were used, followed by various algorithms and computational approaches. Accordingly, scanning of all miRNA targets can be performed without missing any of them using various databases. So we detected 96 miRNAs binding sites and 35 different SNPs in binding sites of miRNA in 3'UTR of the APOL1 gene (Tables 1&2). Interestingly, miR-6741-3p 's binding site on APOL1 has two SNPs (rs1288875001, G > C; rs1452517383, A > C) on APOL1 3'UTR, and its genomic sequence has an SNP (rs376326225, G > A) at the same nucleotide with rs1288875001. Similarly, two other SNPs (rs1142591, T > A; rs376326225, G > A) were identified at the first position binding sites of miR-6741-3p. Here, miRSNP (rs1288875001) at APOL1 3'UTR and SNP (rs376326225) at miR-6741-3p genomic sequence cross-matches at the same site of binding region (Fig. 1). These variations may assist the entry of SARS-COV-2 and develop infection in patients and damage lungs along with kidneys. Previously, some researchers found variants in APOL1 at stage G1 (rs73885319 and rs609101) and G2 (rs71785313). They also found varients in other related genes which have strong relation with kidney diseases i.e. rs10854687 (APOL2), rs73641143 (APOL4) and rs4821481 and rs3752462 (MYH9) in kidney patients. Two variants of APOL1 gene at stage G1 and G2 showed strong association with FSGS. By reduction of G1 its not show elimination of FSGS but by reduction of both G1 and G2 with respect to G0 show reduction in FSGS [24, 25]. In our in vitro study, when we analyze miR-6741-3p and APOL1 expression profiles in healthy and patient subjects (co-infection of kidney disease and COVID-19), miR-6741-3p expression was found higher in patients than healthy according to Fig. 3 (p < 0.05). At the same time, APOL1 expression was found lower in patients than healthy subjects but this difference was not statistically significant according to Fig. 2 (p > 0.05). These results show that miR-6741-3p may have a role on regulation of APOL1 expression because of the fact that miR-6741-3p expression level increases while APOL1 expression level decreases in patients. However, at this point, it is not possible to comment APOL1 expression profile upon mechanism.

Recently, the researchers have been focused on the genetic variations in APOL1 gene and found its relationship between COVID-19 and kidney diseases, especially collapsing glomerulopathy, chronic kidney disease (CKD), acute kidney injury (AKI) and tubulointerstitial lesions that lead to increase rate of COVID-19 in Americans and Africans instead of Chineese [3–5]. The relationship between the levels of miR-15b and miR-17 in kidney tissue and acute kidney damage in human and animal models has been reported [26]. Since gene expression levels are highly tissue specific, it is not clear whether gene expression studies in one tissue can be generalized to a different tissue [27–29].

In addition, miR-6741-3p targets many APOL1-related genes (TLR7, SLC6A19, IL-6,10,18, chemokine (C-C motif) ligand 5, SWT1, NFYB, BRF1, HES2, NFYB, MED12L, MAFG, GTF2H5, TRAF3, angiotensin II recepter associated protein, PRSS23) (Table 3) that have direct interaction with kidney diseases which may lead to SARS-COV-2 [30–35]. Formerly, miR-6741-3p had not demonstrated any association towards kidney diseases and SARS-COV-2, but these genes have been studied intensively that explains different pathways for kidney diseases and SARS-COV-2 infection [3–5]. It assures that APOL1 could have a significant consequence of kidney diseases and its associated diseases through different pathways and could be hoping to be the association between miR-6741-3p and APOL1 gene because their SNPs are located in a splicing sites that needs to be investigated further in vivo studies.

	Table 3 List of kidney diseases-related genes (other than Apol1) targeted miR-6741-3p and associated with kidney disease pathways susceptibility to SARS-CoV-2 infection.
Sr#	Genes	Relationship with Kidney diseases and SARS-COV-2
1.	Angiotensin II recepter associated protein, PRSS23SWT1, NFYB, BRF1, HES2, NFYB, MED12L, MAFG, GTF2H5	Viral gene structure variations and human genes relationship with drugs
2	TLR7 and TRAF3	SARS Coronavirus Papain-Like Protease Inhibits the TLR7 Signaling Pathway through Removing Lys63-Linked Polyubiquitination of TRAF3 and TRAF6
3	SLC6A19	Functional association of mutant SLC6A19 transporters with APOL1 in the intestine
4	IL-6, 10,18 & chemokine (C-C motif) ligand 5	APOL1 showed enhancement of pro-inflammatory cytokines, i.e. IL6,10,18 and chemokine (C-C motif) ligand 5

The single nucleotide polymorphisms (SNPs) and targets found in various microRNA (miRNA) genes are a change of miRNA activity leading to various diseases. Evidence reported that SNPs increase/decrease the effectiveness of the interaction between miRNAs and disease-related target genes. Therefore, there was a need to find the actual mechanisms of miRSNPs on the APOL1 gene and SNPs in miRNA genes targeting 3'UTR to evaluate the effect of these gene variations in kidney diseases and their associated COVID-19. This study advances information by showing that the effects of miRSNPs on the APOL1 gene and SNPs in miRNA genes targeting 3'UTR are related to changes in kidney diseases and their associated SARS-COV-2 infection. After this, a validated in silico and in vitro analysis emerges that this study has better prediction and validation of miRNA targets and that the genomic sequences of SNPs and miRNAs in target regions are of great importance, especially in the 3'UTR region of APOL1 to develop new drugs for COVID-19.

Ethics approval and consent to participate

It is certified that consent has been and study was approved by committee.

Consent for publication

Not applicable.

Availability of data and materials

All data analyzed or generated during this study are included in this published and its additional files.

Competing interests

The authors declare that they have no competing interests.

Funding

Not applicable.

Authors' contributions

The initial writing was carried out by MS and SZ; the other authors contributed to the complementation, critical review and final of the manuscript. All authors read and approved the fnal manuscript.

Acknowledgements

We would like to thank CUVAS, BWP, for logistic support on this paper.

Chang L, Yan Y, Wang L: Coronavirus disease 2019: coronaviruses and blood safety. Transfusion medicine reviews 2020.
de Wilde AH, Snijder EJ, Kikkert M, van Hemert MJ: Host factors in coronavirus replication. In: Roles of Host Gene and Non-coding RNA Expression in Virus Infection. edn.: Springer; 2017: 1-42.
Couturier A, Ferlicot S, Chevalier K, Guillet M, Essig M, Jauréguiberry S, Collarino R, Dargelos M, Michaut A, Geri G: Indirect effects of severe acute respiratory syndrome coronavirus 2 on the kidney in coronavirus disease patients. Clinical Kidney Journal 2020.
Larsen C, Bourne T, Wilson J: Collapsing glomerulopathy in a patient with coronavirus disease 2019 (COVID-19)[e-pub ahead of print]. Kidney Int Rep, 10.
Peleg Y, Kudose S, D’Agati V, Siddall E, Ahmad S, Kisselev S, Gharavi A, Canetta P: Acute kidney injury due to collapsing glomerulopathy following COVID-19 infection. Kidney International Reports 2020.
A Macha M, Seshacharyulu P, Ram Krishn S, Pai P, Rachagani S, Jain M, K Batra S: MicroRNAs (miRNAs) as biomarker (s) for prognosis and diagnosis of gastrointestinal (GI) cancers. Current pharmaceutical design 2014, 20(33):5287-5297.
Bátkai S, Thum T: MicroRNAs in hypertension: mechanisms and therapeutic targets. Current hypertension reports 2012, 14(1):79-87.
Bronze-da-Rocha E: MicroRNAs expression profiles in cardiovascular diseases. BioMed research international 2014, 2014.
Song X, Shan D, Chen J, Jing Q: miRNAs and lncRNAs in vascular injury and remodeling. Science China Life Sciences 2014, 57(8):826-835.
Demirci MDS, Adan A: Computational analysis of microRNA-mediated interactions in SARS-CoV-2 infection. PeerJ 2020, 8:e9369.
Mallick B, Ghosh Z, Chakrabarti J: MicroRNome analysis unravels the molecular basis of SARS infection in bronchoalveolar stem cells. PloS one 2009, 4(11).
Kontaraki JE, Marketou ME, Zacharis EA, Parthenakis FI, Vardas PE: MicroRNA-9 and microRNA-126 expression levels in patients with essential hypertension: potential markers of target-organ damage. Journal of the American Society of Hypertension 2014, 8(6):368-375.
Ergun S, Oztuzcu S: Computational analysis of 3′ UTR Region of CASP3 with respect to miRSNPs and SNPs in targetting miRNAs. Computational biology and chemistry 2014, 53:235-241.
Paraskevopoulou MD, Georgakilas G, Kostoulas N, Vlachos IS, Vergoulis T, Reczko M, Filippidis C, Dalamagas T, Hatzigeorgiou AG: DIANA-microT web server v5. 0: service integration into miRNA functional analysis workflows. Nucleic acids research 2013, 41(W1):W169-W173.
Vlachos IS, Kostoulas N, Vergoulis T, Georgakilas G, Reczko M, Maragkakis M, Paraskevopoulou MD, Prionidis K, Dalamagas T, Hatzigeorgiou AG: DIANA miRPath v. 2.0: investigating the combinatorial effect of microRNAs in pathways. Nucleic acids research 2012, 40(W1):W498-W504.
Hsu S-D, Tseng Y-T, Shrestha S, Lin Y-L, Khaleel A, Chou C-H, Chu C-F, Huang H-Y, Lin C-M, Ho S-Y: miRTarBase update 2014: an information resource for experimentally validated miRNA-target interactions. Nucleic acids research 2014, 42(D1):D78-D85.
Betel D, Koppal A, Agius P, Sander C, Leslie C: Comprehensive modeling of microRNA targets predicts functional non-conserved and non-canonical sites. Genome biology 2010, 11(8):R90.
Bommer GT, Gerin I, Feng Y, Kaczorowski AJ, Kuick R, Love RE, Zhai Y, Giordano TJ, Qin ZS, Moore BB: p53-mediated activation of miRNA34 candidate tumor-suppressor genes. Current biology 2007, 17(15):1298-1307.
Wong N, Wang X: miRDB: an online resource for microRNA target prediction and functional annotations. Nucleic acids research 2015, 43(D1):D146-D152.
Wang R, Wang S-T, Wang Y-D, Wu G, Du Y, Qian M-Q, Liang X-G, Elbatreek MH, Yang H-Y, Liu Z-R: Stress-responsive heme oxygenase-1 isoenzyme participates in Toll-like receptor 4-induced inflammation during brain ischemia. Neuroreport 2016, 27(6):445-454.
Jeggari A, Marks DS, Larsson E: miRcode: a map of putative microRNA target sites in the long non-coding transcriptome. Bioinformatics 2012, 28(15):2062-2063.
Huang Y, Zou Q, Song H, Song F, Wang L, Zhang G, Shen X: A study of miRNAs targets prediction and experimental validation. Protein & cell 2010, 1(11):979-986.
Gosline SJ, Gurtan AM, JnBaptiste CK, Bosson A, Milani P, Dalin S, Matthews BJ, Yap YS, Sharp PA, Fraenkel E: Elucidating MicroRNA regulatory networks using transcriptional, post-transcriptional, and histone modification measurements. Cell reports 2016, 14(2):310-319.
Genovese G, Friedman DJ, Pollak MR: APOL1 variants and kidney disease in people of recent African ancestry. Nature reviews Nephrology 2013, 9(4):240.
Reidy KJ, Hjorten R, Parekh RS: Genetic risk of APOL1 and kidney disease in children and young adults of African ancestry. Current opinion in pediatrics 2018, 30(2):252.
Fan P-C, Chen C-C, Chen Y-C, Chang Y-S, Chu P-H: MicroRNAs in acute kidney injury. Human genomics 2016, 10(1):29.
Consortium G: The Genotype-Tissue Expression (GTEx) pilot analysis: multitissue gene regulation in humans. Science 2015, 348(6235):648-660.
Fu J, Wolfs MG, Deelen P, Westra H-J, Fehrmann RS, Te Meerman GJ, Buurman WA, Rensen SS, Groen HJ, Weersma RK: Unraveling the regulatory mechanisms underlying tissue-dependent genetic variation of gene expression. PLoS genetics 2012, 8(1).
Rodwell GE, Sonu R, Zahn JM, Lund J, Wilhelmy J, Wang L, Xiao W, Mindrinos M, Crane E, Segal E: A transcriptional profile of aging in the human kidney. PLoS biology 2004, 2(12).
Guo Y-R, Cao Q-D, Hong Z-S, Tan Y-Y, Chen S-D, Jin H-J, Tan K-S, Wang D-Y, Yan Y: The origin, transmission and clinical therapies on coronavirus disease 2019 (COVID-19) outbreak–an update on the status. Military Medical Research 2020, 7(1):1-10.
Li S-W, Wang C-Y, Jou Y-J, Huang S-H, Hsiao L-H, Wan L, Lin Y-J, Kung S-H, Lin C-W: SARS coronavirus papain-like protease inhibits the TLR7 signaling pathway through removing Lys63-linked polyubiquitination of TRAF3 and TRAF6. International journal of molecular sciences 2016, 17(5):678.
Moreno-Eutimio MA, Lopez-Macias C, Pastelin-Palacios R: Bioinformatic analysis and identification of single-stranded RNA sequences recognized by TLR7/8 in the SARS-CoV-2, SARS-CoV, and MERS-CoV genomes. Microbes and Infection 2020.
Naicker S, Yang C-W, Hwang S-J, Liu B-C, Chen J-H, Jha V: The Novel Coronavirus 2019 epidemic and kidneys. Kidney International 2020, 97(5):824-828.
Vellingiri B, Jayaramayya K, Iyer M, Narayanasamy A, Govindasamy V, Giridharan B, Ganesan S, Venugopal A, Venkatesan D, Ganesan H: COVID-19: A promising cure for the global panic. Science of The Total Environment 2020:138277.
Zhang Y-m, Zhang H: Genetic Roadmap for Kidney Involvement of Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) Infection. Clinical Journal of the American Society of Nephrology 2020.

Download PDF

Version 1

posted

You are reading this latest preprint version

SNPs at 3'UTR of APOL1 and miR-6741-3p target sites associated with Kidney Diseases more susceptible to SARS-COV-2 infection

Status:

Version 1

Abstract

Figures

1. Background

2. Material And Methods

3. Results And Discussion

4. Conclusion

Declarations

References

Status:

Version 1