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R E S E A R C H Open Access
Candidate SNP markers of reproductive
potential are predicted by a significant
change in the affinity of TATA-binding
protein for human gene promoters
Irina V. Chadaeva
1,2
, Petr M. Ponomarenko
3
, Dmitry A. Rasskazov
1
, Ekaterina B. Sharypova
1
, Elena V. Kashina
1
,
Dmitry A. Zhechev
1
, Irina A. Drachkova
1
, Olga V. Arkova
1,4
, Ludmila K. Savinkova
1
, Mikhail P. Ponomarenko
1,2*
,
Nikolay A. Kolchanov
1,2
, Ludmila V. Osadchuk
1,5
and Alexandr V. Osadchuk
1
From Belyaev Conference
Novosibirsk, Russia. 07-10 August 2017
Abstract
Background: The progress of medicine, science, technology, education, and culture improves, year by year, quality
of life and life expectancy of the populace. The modern human has a chance to further improve the quality and
duration of his/her life and the lives of his/her loved ones by bringing their lifestyle in line with their sequenced
individual genomes. With this in mind, one of genome-based developments at the junction of personalized
medicine and bioinformatics will be considered in this work, where we used two Web services: (i) SNP_TATA_
Comparator to search for alleles with a single nucleotide polymorphism (SNP) that alters the affinity of TATA-
binding protein (TBP) for the TATA boxes of human gene promoters and (ii) PubMed to look for retrospective
clinical reviews on changes in physiological indicators of reproductive potential in carriers of these alleles.
Results: A total of 126 SNP markers of female reproductive potential, capable of altering the affinity of TBP for gene
promoters, were found using the two above-mentioned Web services. For example, 10 candidate SNP markers of
thrombosis (e.g., rs563763767) can cause overproduction of coagulation inducers. In pregnant women, Hughes
syndrome provokes thrombosis with a fatal outcome although this syndrome can be diagnosed and eliminated
even at the earliest stages of its development. Thus, in women carrying any of the above SNPs, preventive
treatment of this syndrome before a planned pregnancy can reduce the risk of death. Similarly, seven SNP markers
predicted here (e.g., rs774688955) can elevate the risk of myocardial infarction. In line with Bowles’lifespan theory,
women carrying any of these SNPs may modify their lifestyle to improve their longevity if they can take under
advisement that risks of myocardial infarction increase with age of the mother, total number of pregnancies, in
multiple pregnancies, pregnancies under the age of 20, hypertension, preeclampsia, menstrual cycle irregularity, and
in women smokers.
(Continued on next page)
* Correspondence: pon@bionet.nsc.ru
1
Brain Neurobiology and Neurogenetics Center, Institute of Cytology and
Genetics, Siberian Branch of Russian Academy of Sciences, 10 Lavrentyev
Ave, Novosibirsk 630090, Russia
2
Novosibirsk State University, Novosibirsk 630090, Russia
Full list of author information is available at the end of the article
© The Author(s). 2018 Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0
International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and
reproduction in any medium, provided 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 Creative Commons Public Domain Dedication waiver
(http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
Chadaeva et al. BMC Genomics 2018, 19(Suppl 3):0
DOI 10.1186/s12864-018-4478-3
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
(Continued from previous page)
Conclusions: According to Bowles’lifespan theory—which links reproductive potential, quality of life, and life
expectancy—the above information was compiled for those who would like to reduce risks of diseases
corresponding to alleles in own sequenced genomes. Candidate SNP markers can focus the clinical analysis of
unannotated SNPs, after which they may become useful for people who would like to bring their lifestyle in line
with their sequenced individual genomes.
Keywords: Reproductive potential, Gene, Promoter, TATA box, TATA-binding protein, Single nucleotide
polymorphism, SNP marker, Keyword-based search, Prediction, Verification
Background
Incessant progress in medical and biological sciences, ad-
vancement of technology, and education year in and year
out improve quality of life and life expectancy of the popu-
lation, creating comfortable conditions for active living.
Nonetheless, there are numerous factors that adversely
affect human health. They can include, for example, differ-
ent kinds of environmental pollution, an increase in popu-
lation density, which leads to the rapid spread of infections
and parasitoses, and an increase in psychological stress.
This situation not only reduces the quality of life and lon-
gevity of the individual but also has a deferred, long-term
effect on the next generation, by acting as a mutagen [1].
The accumulating mutational load often worsens health
and reduces the subsequent generation’s survival and adap-
tation to their habitat that ultimately reduces the chances
of sustainable population reproduction.
The effects of the above factors limit individual repro-
ductive potential: a concept used in population ecology
to assess the evolutionary success of an individual [2]or
a population [3]. In the 1970s, Eric Pianka defined re-
productive potential as the most important conditional
indicator reflecting a population’s ability to reproduce,
survive, and develop under optimal ecological conditions
[2–5]. In the context of human society, in the term “re-
productive potential,”researchers can also include the
mental state and physical state that allow a person to pro-
duce healthy offspring when social and physical maturity
is achieved. Consequently, reproductive potential depends
not only on physiological readiness for reproduction (pri-
marily the reproductive system), but also on the general
physical condition (with the exception of existing diseases
that are incompatible with the implementation of
reproduction) and on socio-economic status. With this in
mind, everything is focused on individual ability for
reproduction until the next generation becomes repro-
ductive. In particular, not only the phenotype plays a role
here, but so does the genotype, where most abilities of a
given individual are encoded, both normal and mutational
as well as epigenetic ones. It should also be noted that re-
productive potential varies throughout the life cycle and
does so in different ways for men and women. Ideally, the
evaluation of reproductive potential would include not
only the direct material and energy costs of reproduction
but also the price of the risk associated with future repro-
ductive attempts [5].
Predictive-preventive personalized medicine may help
to improve individual reproductive success. Its methods
include prediction (based on the analysis of the genome)
of the probability of a specific disease, analysis of indi-
vidual indicators, biomarkers (such as single nucleotide
polymorphisms, SNPs [6,7]), and the development of
preventive and therapeutic measures for changing the
physiological parameters of the reproductive potential in
patients [8]. In particular, the analysis of SNP biomarkers
allows a physician not only to make a prognosis for a pa-
tient regarding possible diseases that can reduce repro-
ductive potential but also to adjust the prescribed
treatment, taking into account individual characteristics
and reactions to medicines.
In addition, according to Bowles’lifespan theory [9],
which links reproductive potential, quality of life, and life
expectancy of an individual, it is possible timely to pre-
vent diseases, which correspond to the alleles of the
decoded genotype.
Within the framework of the biggest modern scientific
project “1000 Genomes”, 10545 individual genomes have
already been sequenced [10]. The “reference human gen-
ome”is publicly available via the Ensembl database [11]
using the Web service UCSC Genome Browser [12]. A
total of 100,877,027 SNPs have been experimentally
identified and stored in the dbSNP database [6]. Data-
base dbWGFP [13] containing 8.58 billion possible hu-
man whole-genome SNPs has already been created for
accumulation of predictions, experimental data, clinical
observations, and any other information relevant for bio-
medical analysis of individual genomes. For such an ana-
lysis, the most valuable biomedical SNP markers—within
the framework of personalized medicine—are those that
can differ between the individual human genomes of pa-
tients having some pathology and the reference human
genome [14]. To find such markers, cohorts of patients
with a given disease and healthy volunteers (as a control)
are compared in a clinical study (e.g., [15]).
As far as human health is concerned, the clinical search
for biomedical SNP markers is the only acceptable
Chadaeva et al. BMC Genomics 2018, 19(Suppl 3):0 Page 16 of 141
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
method. Nevertheless, it is so laborious and expensive that
its application to all 8.58 billion potentially possible SNPs
[13] and all known human pathologies is rather unlikely.
Moreover, both Haldane’s dilemma [16] and Kimura’sthe-
ory of neutral evolution [17] independently predict that
the absolute majority of SNPs in humans are neutral and
do not affect health in any way; thus, it is unclear why it is
necessary to verify them clinically. With this in mind, the
mainstream clinical search for SNP markers of a given dis-
ease is currently limited by the simplest idea about heuris-
tic handmade selection of candidate SNPs for clinical
testing among unannotated SNPs on the basis of their lo-
cation near the human genes that are already clinically as-
sociated with this disease (e.g., [18,19]). Accordingly,
computer-based preliminary analysis of unannotated
SNPs can eliminate the absolute majority of neutral
SNPs to make the clinical cohort-based search for
biomedical SNP markers faster, cheaper, and more
targeted [20]. There are many public Web services
[21–38] that facilitate the computer-based search for
candidate SNP markers using various similarity mea-
sures based on whole-genome data in health [39],
after treatment [40], and during a disease [41]orin-
fection [42] to eliminate unannotated SNPs that bear
the least resemblance to known biomedical SNP
markers (i.e. to eliminate the most probable neutral
SNPs). The Central Limit Theorem predicts that the
accuracy of this similarity-based elimination of unan-
notated neutral SNPs increases with the increase in
thesizeanddiversityofwhole-genomedataunder
study [43].
Now, the best accuracy of this mainstream search cor-
responds to SNPs in protein-coding regions of genes
[44], i.e., SNPs that damage proteins [45] whose defects
are uncorrectable by treatment or lifestyle changes. On
the contrary, the worst accuracy of this kind of search is
seen for regulatory SNPs [11], which alter concentra-
tions of proteins without any damage to the proteins,
and such problems are correctable by medication and
lifestyle. The best balance between the predictability and
biomedical usefulness corresponds to the regulatory
SNPs between nucleotide positions -70 and –20 up-
stream of a transcription start site (TSS) [46,47] where
TATA-binding protein (TBP) binds to the promoter at
the very beginning of transcription initiation. This TBP–
promoter complex is obligatory for any TSSes because
the TBP knockout model animals (TBP
−/−
) are always
inviable since their development cannot proceed past
the blastula stage because their maternal supply of TBP
is exhausted [48,49]. Moreover, the TBP–promoter af-
finity linearly correlates with the transcription magni-
tude of the human gene containing this promoter [50].
This notion has been repeatedly confirmed experimen-
tally (for review, see [51]). The canonical form of the
TBP-binding site (TATA box, synonyms: Hogness box
and Goldberg-Hogness box [52]) is the best-studied
regulatory element among human gene promoters [47].
In our previous studies, we developed public Web
service SNP_TATA_Comparator (http://beehive.bionet.n-
sc.ru/cgi-bin/mgs/tatascan/start.pl)[53] and applied it to
predict candidate SNP markers within TATA boxes of hu-
man genes associated with obesity [54], autoimmune dis-
eases [55], chronopathology [56], aggressiveness [57,58],
Alzheimer’s disease [59], and efficacy of anticancer
chemotherapy [60] (for review, see [20]). In the present
work, we applied our Web service [53] in the same way to
human reproductive potential as the most common con-
cept of population ecology dealing with the evolutionary
success of either individuals [2] or populations [3].
Results
Tables 1,2,3,4,5,6and 7present the results obtained
by our Web service [53] for the 126 known and candi-
date reproductive-potential-related SNP markers in the
TBP-binding sites of human gene promoters (see
Methods: Supplementary Method, Additional file 1).
First, we analyzed all SNPs mapped within [−70; −20]
regions upstream of transcription start sites for the hu-
man genes containing the known biomedical SNP
markers that alter TBP’s binding to promoters of these
genes (Tables 1,2,3,4,5and 6). Let us first describe in
more detail only one human gene in order to briefly re-
view all the others.
Known and candidate reproductivity-related SNP markers
of cancers
The human ESR2 gene (estrogen receptor β) contains a
known SNP marker (Fig. 1a: rs35036378) of an ESR2-
deficient primary pT1 breast tumor, which is needed in
tamoxifen-based prophylaxis of cancer [61] as shown in
Table 1. The prediction of our Web service [53] is con-
sistent with this independent clinical observation (Fig.
1b: text box “Results”, line “Decision”contains the label
“deficiency: significant”).
Next, near this known biomedical SNP marker
rs35036378, we found the unannotated SNP rs766797386,
which can also decrease expression of the human ESR2
gene (Fig. 1c) and thus cause an ESR2-deficient primary
pT1 tumor requiring prophylaxis by tamoxifen against
breast cancer [61]. This result allowed us to suggest
rs766797386 as a candidate SNP marker of a higher risk
of breast cancer reducing reproductive potential.
Finally, using our secondary keyword search for these
two SNP markers (hereinafter: see Methods: Additional
file 2: Figure S1. dotted-line box, Additional file 2), we
learned (hereinafter: see Table S1, Additional file 3) that
cadmium (Cd) elevates the risk of a primary tumor’s
Chadaeva et al. BMC Genomics 2018, 19(Suppl 3):0 Page 17 of 141
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
becoming malignant [62], whereas mothers undergoing
tamoxifen-based treatment should not breastfeed [63].
The human HSD17B1,PGR,andGSTM3 genes en-
code hydroxysteroid (17-β) dehydrogenase 1, proges-
terone receptor, and glutathione S-transferase μ3,
respectively. Their promoters have the known SNP
markers rs201739205, rs10895068, and rs1332018,
which elevate risks of breast [64]andendometrial
[65] cancers; a brain tumor in a fetus, newborn, or a
child [66], respectively; as well as renal cancer and
Alzheimer’sdisease[67](Table1). Near these known
biomedical SNP markers, there are four unannotated
SNPs rs201739205, rs748743528, rs200209906, and
rs750789679, which can similarly alter expression
levels of the same genes according to the predictions
of our Web service [53] (Table 1). Hence, we pro-
posed them as the candidate SNP markers of the
same diseases.
Besides, within the same promoters, we found four
other unannotated SNPs rs755636251, rs544843047,
rs748231432, and rs763859166, which can cause the op-
posite alterations in the expression of the corresponding
genes (Table 1). Using our primary keyword search
(hereinafter: see Methods, Additional file 2: Figure S1.
two dashed-line boxes, Additional file 2), we found that
both HSD17B1 overexpression and deficiency can ele-
vate the risk of breast cancer [68], whereas GSTM3 defi-
ciency can reduce these risks in people who never drink
alcohol [69] (Table 1). In addition, Searles Nielsen and
colleagues [66] suggested that another mechanism of
GSTM3 overexpression can reduce the risk of a brain
tumor in some children, as can rs748231432 and
rs763859166 according to our results shown in Table 1.
Finally, using our secondary keyword search, we found
eight retrospective clinical reviews [70–76]. The most in-
teresting among them, in our opinion, is a report on a
Table 1 Known and candidate SNP markers of tumors in reproductive organs
Gene dbSNP [6]
rel. 147 or
see [Ref]
5′flank wt
mut
3′flank K
D
, nM Known diseases (SNP markers) or hypothetical disease
(candidate SNP markers)
[Ref]
or
[this
work]
wt
mut
ΔZαρ
ESR2 rs35036378 cctctcggtc t
g
ttaaaaggaa 6
8
↓510
-3
B ESR2-deficient pT1 breast tumor needing tamoxifen
prophylaxis against cancer
[61]
rs766797386 ttaaaaggaa g
t
aaggggctta 6
7
↓310
-2
C(hypothetically) the same disease [this
work]
HSD17B1 rs201739205 aggtgatatc a
c
agcccagagc 13
18
↓510
-6
A higher risk of breast cancer [64]
rs201739205 agcaggtgat a
t
tcaagcccag 13
35
↓18 10
-6
A(hypothetically) the same disease [this
work]
rs748743528 gcaggtgata t
c
caagcccaga 13
28
↓13 10
-6
A
rs755636251 ggcgaagcag g
t
tgatatcaag 13
11
↑2 0.05 D (hypothetically) higher risk of breast cancer [68]
PGR rs10895068 gggagataaa g
a
gagccgcgtg 10
6
↑810
-6
A endometrial cancer caused by the spurious TATA box and
its TSS disbalancing both αand βisoforms of progesterone
receptor
[65]
rs544843047 agtcgggaga t
c
aaaggagccg 10
22
↓14 10
-6
A(hypothetically) health as the norm without the above-
mentioned spurious TATA box
[this
work]
GSTM3 rs1332018 ccccttatgt c
a
gggtataaag 4
3
= 2 1 E maternal “c”(Wb: TF-binding site damaged, not TATA box),
elevates risk of a brain tumor in her child, renal cancer, and
Alzheimer’s disease
[66,
67]
rs200209906 gtataaagcc c
t,a
ctcccgctca 3.6
4.3
↓21 E(hypothetically) the same disease and low risks of breast
cancer in those who never drink alcohol and lesser Hg-
resistance during reproduction
[this
work],
[69]
rs750789679 cgggtataaa g
c
cccctcccgc 3.6
4.5
↓310
-2
C
rs748231432 cccttatgtc g
c,t
ggtataaagc 3.6
3.0
↑3 0.05 D (hypothetically) lower risk of a brain tumor in a child whose
mother has “c”-allele of rs1332018
[this
work],
[66]
rs763859166 gggtataaag c
t
ccctcccgct 3.6
2.9
↑310
-2
C
Hereinafter, ancestral (wt) and minor (mut) alleles; K
D
, dissociation constant of TBP–DNA interaction; Δ, a change: overexpression (↑), deficit (↓), norm (=); α=1–
p, significance {where p value is shown in Fig. 1;α= 1 denotes insignificance}; ρ, heuristic rank of candidate SNP markers varying in alphabetical order from the
“best”(A) to the “worst”(E); the CETP gene: 18bp, the 18-bp deletion 5’-gggcggacatacatatac-3’; the F3 gene: 30bp, 17bp, and 18bp as the insertions 5’-agaccttca-
taagaaataatcctgatccaa-3’,5’-tgctgcgtactggcaaa-3’, and 5’-acggcgtagagactggga-3’of 30 bp, 17 bp, and 18 bp in length, respectively; EMSA, electrophoretic mobility
shift assay; Hg, mercury; LUC, luciferase reporter assay; TF, transcription factor; Wb: western blot.
Chadaeva et al. BMC Genomics 2018, 19(Suppl 3):0 Page 18 of 141
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
nontrivial balance between reproductive potential and
the risk of cancers of reproductive organs [70]. It is in-
teresting that only one SNP marker (rs605059; protein-
coding region, HSD17B1) of a positive correlation be-
tween the lifespan and number of children in women is
known so far [71]. It is also noteworthy that one of
current theories is that aging is a stepwise reduction in
reproductive potential of individuals where one of these
steps is under the control of the luteinizing hormone,
whose suppression by smoking can reduce the risk of
Alzheimer’s disease [9].
The human IL1B,CYP2A6,CYP2B6, and DHFR genes
encode interleukin 1β, xenobiotic monooxygenase, 1,4-
cineole 2-exo-monooxygenase, and dihydrofolate reduc-
tase, respectively. Their promoters contain the known
SNP markers (rs1143627 [77–85], rs28399433 [86,87])
of nonreproductive organ cancer, as well as SNP markers
(rs34223104 [88] and rs10168 [89]) of bioactivation and
resistance to anticancer drugs, as shown in Table 2.Near
these known SNP markers, we detected three unanno-
tated SNPs, rs761592914, rs563558831, and rs750793297,
which can alter expression levels of the same genes in the
same manner (Table 2) and may be candidate SNP
markers in this regard.
In addition, in the same gene regions, we found four
other unannotated SNPs rs549858786, rs766799008,
rs764508464, and rs754122321 that can have the oppos-
ite effect on the expression of the corresponding genes
(Table 2). Using our primary keyword search, we
found four articles [90–93] similar to those that
were in the case of the known SNPs, where we
learned about the correlations between the intensity
of physiological and clinical manifestations under
study [85–89](Table2). Finally, our secondary key-
word search yielded 12 reviews [93–105], among
which, the most relevant for us was the notion that
Helicobacter pylori infection can cause not only can-
cer of non-reproductive organs, but can directly re-
duce human reproductive potential in both men and
women [101].
Looking through Tables 1,2, and Additional file 3:
Table S1, one can see that a person increases his/her
Table 2 Known and candidate SNP markers of tumors in nonreproductive organs
Gene dbSNP [6]
rel. 147 or
see [Ref]
5′flank wt
mut
3′flank K
D
, nM Known diseases (SNP markers) or hypothetical disease
(candidate SNP markers)
[Ref] or
[this work]
wt
mut
ΔZαρ
IL1B rs1143627 ttttgaaagc c
t
ataaaaacag 5
2
↑15 10
-
6
A high risks of gastric, liver, and non–small cell lung cancers;
gastric ulcer, chronic gastritis, recurrent major depression,
obesity, Graves’disease, pre-eclampsia, (hypothetically)
short time-to-delivery in pregnancy and childbirth
[77–85]
[this work]
rs549858786 tgaaagccat a
t
aaaacagcga 5
7
↓810
-
6
A(hypothetically) lesser risk of the same diseases [60]
CYP2A6 rs28399433 tcaggcagta t
g
aaaggcaaac 2
9
↓21 10
-
6
A low risk of lung cancer in smokers (LUC: “-34g”
corresponds to 50% of “-34t”), (hypothetically) lesser
damage from secondhand smoke in pregnant women who
are nonsmokers
[86,87],
[this work],
[90–92]
rs761592914 tttttcaggc a
c
gtataaaggc 2
3
↓310
-
3
B(hypothetically) the same disease [this work]
CYP2B6 rs34223104 gatgaaattt t
c
ataacagggt 4
10
↓15 10
-
6
A TATA
WT
→USF
SNP,
TSS
WT
→TATA
SNP
, and de-novo TSS
SNP
can cause overexpression of this gene of a bioactivator of
immunosuppressive and antitumor prodrug
cyclophosphamide
[88]
rs563558831 tgaaatttta t
c
aacagggtgc 4
10
↓13 10
-
6
A(hypothetically) the same problem [this work]
DHFR rs10168 ctgcacaaat g
a
gggacgaggg 15
9
↑910
-
6
A resistance to methotrexate treatment of leukemia and
(hypothetically) that in the cases of ectopic pregnancy,
metastatic choriocarcinoma, and gestational trophoblastic
disease
[89], [this
work], [93]
rs750793297 tgcacaaatg g
t
ggacgagggg 15
13
↑310
-
2
C(hypothetically) the same diseases [this work]
rs766799008 ctgcacaaat a
g
tggggacgag 15
19
↓310
-
3
B(hypothetically) greater bioactivity of methotrexate during
treatment of leukemia, ectopic pregnancy, metastatic
choriocarcinoma, and gestational trophoblastic disease
[this work],
[60]
rs764508464 ctgcacaaat a
-
tggggacgag 15
37
↓17 10
-
6
A
rs754122321 ctcgcctgca c
g
aaatggggac 15
25
↓910
-
3
B
See “Note”under Table 1
Chadaeva et al. BMC Genomics 2018, 19(Suppl 3):0 Page 19 of 141
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lifespan and reproductive potential when this person re-
duces the encounters with cancer risk factors.
Known and candidate reproductivity-related SNP markers
of metabolism
Human LEP,GCG,GH1, and INS genes encode hor-
mones leptin, glucagon, somatotropin, and insulin, re-
spectively. There are four known biomedical SNP
markers: rs201381696 (obesity [54,106]), rs183433761
(resistance to obesity during a high-fat diet [54]),
rs11568827 (short stature [107]), and rs5505 (type 1 dia-
betes after neonatal diabetes mellitus [108]) as presented
in Table 3.
Near these known SNP markers, 10 candidate SNP
markers rs200487063, rs34104384, rs757035851, rs7962
37787, rs768454929, rs761695685, rs774326004, rs7770
03420, rs563207167, and rs11557611 were first predicted
by our Web service [53] and, then, were characterized by
our primary keyword search (Table 3). The most interest-
ing among these predictions [109–116], in our opinion, is
the candidate SNP marker rs563207167 of neonatal
macrosomia whose known clinical marker is hyperinsuli-
nemia [115], which can be caused by the minor allele of
this SNP according to our calculations (Table 3).
Finally, our secondary keyword search produced 31
original articles [105,117–146], e.g., showing that a ma-
ternal high-fat diet elevates the risk of hypertrophy in
offspring via fetal hyperinsulinemia programmed epige-
netically [141]. It is also relevant that bupropion used as
an antidepressant against smoking in pregnancy can
cause hyperinsulinemia in newborn children [142].
Human genes NOS2,STAR,APOA1,CETP,SOD1,
TPI1,andGJA5 code for inducible nitric oxide synthase
2, steroidogenic acute regulatory protein, apolipoprotein
A1, cholesteryl ester transfer protein, Cu/Zn superoxide
dismutase, triosephosphate isomerase, and connexin 40,
respectively. Their promoters contain eight known bio-
medical SNP markers shown in Table 4.
Around these known biomedical SNP markers, we
found six unannotated SNPs rs544850971, rs17231520,
Table 3 Known and candidate reproductivity-related SNP markers in genes of hormones
Gene dbSNP [6]
rel. 147 or
see [Ref]
5′flank wt
mut
3′flank K
D
, nM Known diseases (SNP markers) or hypothetical disease
(candidate SNP markers)
[Ref] or
[this
work]
wt
mut
ΔZαρ
LEP rs201381696 tcgggccgct a
g
taagaggggc 4
12
↓17 10
-6
Ahypoleptinemia elevates risk of obesity [54,106]
rs200487063 tgatcgggcc g
a
ctataagagg 4
2
↑610
-6
A(hypothetically) hyperleptinemia elevates risk of hypertension in
obesity
[this
work],
[107,
110]
rs34104384 ccgctataag a
t
ggggcgggca 4
3
↑410
-2
C
GCG rs183433761 gctggagagt a
g
tataaaagca 0.9
1.6
↓17 10
-6
A resistance to obesity during a high-fat diet [54]
(hypothetically) hypoglucogonemia decreases pregnancy
probability, serum insulin in pregnancy, and during late
gestational period
[this
work],
[111,
112]
rs757035851 tatataaaag cag
-
tgcgccttgg 0.9
1.1
↓310
-3
B
GH1 rs11568827 aggggccagg g
-
tataaaaagg 1.5
1.4
= 1 1 E short stature (EMSA: unknown TF-binding site lost, not TATA
box)
[107]
(hypothetically) higher risk of GH1 deficiency as clinical
syndrome whose symptoms are increased central adiposity,
atherogenesis, as well as cerebrovascular and cardiac
morbidity (and mortality), and, also, decreased lean body
mass, bone mineral density, quality of life
[this
work],
[113]
rs796237787 gaaggggcca g
-
ggtataaaaa
rs768454929 agggtataaa a
c
agggcccaca 1.5
2.6
↓710
-6
A
rs761695685 gccagggtat a
g
aaaagggccc 1.5
5.8
↓19 10
-6
A
rs774326004 ccagggtata a
t
aaagggccca 1.5
0.9
↑710
-6
A(hypothetically) higher risks of acromegaly [this
work],
[114]
rs777003420 aaggggccag g
t
gtataaaaag 1.5
1.3
↑3 0.05 D
INS rs5505 agatcactgt c
t
cttctgccat 53
44
↑410
-3
B type 1 diabetes after neonatal diabetes mellitus [108]
(hypothetically) hyperinsulinemia elevates the placental leptin
which causes neonatal macrosomia
[this
work],
[115]
rs563207167 tcagccctgc c
t
tgtctcccag 53
44
↑410
-3
B
rs11557611 gatcactgtc c
t
ttctgccatg 53
60
↓2 0.05 D (hypothetically) hypoinsulinemia slows down fetal growth [this
work],
[116]
See “Note”under Table 1
Chadaeva et al. BMC Genomics 2018, 19(Suppl 3):0 Page 20 of 141
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
rs569033466, rs757176551, rs781835924, and rs58774
5372, which can alter expression levels of the human
genes containing them according to in silico predictions
of our Web service [53] (Table 4). Next, we carried out
our primary keyword search where [147–165] the most
interesting finding (in our opinion) is the clinical associ-
ation between a SOD1 deficiency and asthenospermia
[151], as one can see in Table 4. Finally, we performed
our secondary keyword search, which yielded 21 literary
sources [155–175]. For instance, bisphenol A pollution
in men can increase the risk of congenital heart mor-
phogenesis disorders in their offspring as Lobmo and
colleagues [174] have reported.
As readers can see in Tables 3,4,andAdditional
file 3: Table S1, deviations from normal metabolism
in parents (e.g., starvation, stress, dietary changes,
and polluted environment) can epigenetically program
pathologies of the development in their offspring (e.g.,
[141]). Therefore, a person can increase his/her
reproductive potential and lifespan by keeping one’s
metabolism normal.
Known and candidate reproductivity-related SNP markers
related to blood
Human genes HBB,HBD,HBG2,ACKR1,MBL2,
MMP12, and F2 encode subunits β,δ, and γ2 (fetal) of
hemoglobin as well as glycoprotein D, mannan-binding
lectin, macrophage elastase, and serine protease, respect-
ively. Table 5shows 10 known SNP markers (rs397
509430, rs33980857, rs34598529, rs33931746, rs339
81098, rs34500389, and rs35518301) of both malaria re-
sistance and thalassemia [176] as well as rs2814778 (both
malaria resistance and low white-blood-cell count [177,
178]), rs72661131 (variable immunodeficiency [179], pre-
eclampsia [180], and stroke [181]), and rs2276109 (lower
risks of psoriasis [182], systemic sclerosis [183], and
asthma [184]).
Table 4 Known and candidate reproductivity-related SNP markers in genes of other metabolic proteins
Gene dbSNP [6]
rel. 147 or
see [Ref]
5′flank wt
mut
3′flank K
D
, nM Known diseases (SNP markers) or hypothetical disease
(candidate SNP markers)
[Ref] or
[this work]
wt
mut
ΔZαρ
NOS2 -51t→c
[288]
gtataaatac t
c
tcttggctgc 2
1
↑310
-
2
C resistance to malaria and epilepsy (hypothetically) higher
risk of gestational diabetes mellitus
[288–290],
[this work],
[147]
STAR rs16887226 cagccttcag c
t
gggggacatt 10
10
= 0 1 E hypertensive diabetic patients, (EMSA: unknown TF-
binding site lost rather than TATA box)
[291]
rs544850971 tcagcggggg a
g
catttaagac 10
12
↓510
-
2
C(hypothetically) lower risk of the same disease and
congenital adrenal hyperplasia
[this work],
[148]
APOA1 35a→c
[292]
tgcagacata a
c
ataggccctg 3
4
↓510
-
6
A fatty liver (hypothetically) high risk of polycystic ovary
syndrome in young women
[292], [this
work],
[149]
CETP DEL-51(18
bp) [293]
cgtgggggct 18bp
-
gggctccagg 4
7
↓710
-
6
A hyperalphalipoproteinemia reducing risk of
atherosclerosis
[293]
rs17231520 ggggctgggc g
a
gacatacata 4
2
↑10 10
-
6
A(hypothetically) biomarker of late pregnancy when plasma
triglyceride, high-density lipoprotein, and cholesterol concen-
trations are significantly increased
[this work],
[150]
rs569033466 atacatatac g
a
ggctccaggc 4
3
↑410
-
3
B
rs757176551 catatacggg c
g
tccaggctga 4
2
↑10 10
-
6
A
SOD1 rs7277748 ggtctggcct a
g
taaagtagtc 2
7
↓17 10
-
6
A amyotrophic lateral sclerosis, (hypothetically),
asthenospermia, lower female fertility via progesterone
deficiency
[294], [this
work],
[151,152]
TPI1 rs1800202 gcgctctata t
g
aagtgggcag 1
4
↓17 10
-
6
B hemolytic anemia, neuromuscular diseases [295,296]
(hypothtically) higher risk of asthenospermia [this work],
[153]rs781835924 cgcggcgctc t
c
atataagtgg 1
2
↓10 10
-
6
B
GJA5 rs10465885 caactaagat g
a
tattaaacac 3
3
= 1 1 E arrhythmia, cardiovascular events (LUC: TF-binding site
damaged, not TATA box)
[297]
(hypothetically) the same disease and higher risk of heart
morphogenesis disorders
[this work],
[154]
rs587745372 ggcgacagat a
t
cgattaaaaa 6
7
↓310
-
3
B
rs35594137 gaggagggaa g
a
gcgacagata 6
6
= 0 1 E arrhythmia, cardiovascular events (LUC: TF-binding site
damaged, not TATA box)
[298]
See “Note”under Table 1
Chadaeva et al. BMC Genomics 2018, 19(Suppl 3):0 Page 21 of 141
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Using our Web service [53], we found seven candidate
SNP markers rs63750953, rs281864525, rs117785782,
rs34166473, rs745580140, rs562962093, and rs572527
200, which can alter expression of the human genes
containing them, as is the case for the above SNP
markers, which can affect the human reproductive
potential [185,186] (Table 5). In addition, using our pri-
mary keyword search, we identified three more candi-
date SNP markers: rs567653539 (reduced risks of
recurrent vulvovaginal infections [187]), rs572527200
(high risk of ovarian hyper stimulation syndrome [188]),
rs564528021, and rs752364393 (high risk of pre-
eclampsia [189]). Finally, we performed our secondary
keyword search, which yielded 22 reviews [162,190–
210], the most important of which (in our opinion) men-
tions pre-eclampsia as a leading cause of maternal and
fetal mortality and morbidity worldwide [162], as readers
can see in Additional file 3: Table S1.
Table 5 Known and candidate reproductivity-related SNP markers related to blood proteins
Gene dbSNP [6]
rel. 147 or
see [Ref]
5′flank wt
mut
3′flank K
D
, nM Known diseases (SNP markers) or hypothetical disease
(candidate SNP markers)
[Ref] or
[this work]
wt
mut
ΔZαρ
HBB rs397509430 gggctgggca t
-
atacaacagt 5
29
↓34 10
-
6
A malaria resistance and thalassemia [176]
rs33980857 gggctgggca t
a,g,c
atacaacagt 5
21
↓27 10
-
6
A
rs34598529 ggctgggcat a
g
aaagtcaggg 5
18
↓24 10
-
6
A
rs33931746 gctgggcata a
g,c
aagtcagggc 5
11
↓14 10
-
6
A
rs33981098 agggctgggc a
g,c
taaaagtcag 5
9
↓10 10
-
6
A
rs34500389 cagggctggg c
a,t,g
ataaaagtca 5
6
↓310
-
2
C(hypothetically) the same disease; heterozygotes “wt-mut”
are still more viable according to most of clinical indicators
in comparison with both homozygotes “wt-wt”and “mut-
mut”
[this work],
[185]
rs63750953 ctgggcataa aa
-
gtcagggcag 5
8
↓910
-
6
A
rs281864525 tgggcataaa a
c
gtcagggcag 5
7
↓710
-
6
A
rs117785782 ggctgagggt t
c
tgaagtccaa 28
39
↓710
-
6
A
HBD rs35518301 caggaccagc a
g
taaaaggcag 4
8
↓11 10
-
6
A malaria resistance and thalassemia [176]
(hypothetically) the same disease; heterozygotes “wt-mut”
are still more viable according to most of clinical indicators
[this work],
[185]rs34166473 aggaccagca t
c
aaaaggcagg 4
8
↓18 10
-
6
A
HBG2 rs745580140 ggagttgctc ta
-
cacaagctct 11
22
↓10 10
-
6
A
ACKR1 rs2814778 ttggctctta t
c
cttggaagca 10
12
↓410
-
3
B low white-blood-cell count and resistance to malaria,
(hypothetically) pre-eclampsia
[177,178],
[this work],
[186]
MBL2 rs72661131 tctatttcta t
c
atagcctgca 2
4
↓12 10
-
6
A variable immunedefici-ency, pre-eclampsia, stroke, [190–192]
(hypothetically) the same disease; higher risks of recurrent
vulvovaginal infections
[this work],
[187]rs562962093 atctatttct a
g
tatagcctgc 2
5
↓15 10
-
6
A
rs567653539 tttctatata g
a
cctgcaccca 2
1
↑12 10
-
6
A(hypothetically) reduced risks of recurrent vulvovaginal
infections
MMP12 rs2276109 gatatcaact a
g
tgagtcactc 11
14
↓310
-
2
C lower risk of psoriasis, systemic sclerosis, asthma [193–195]
(hypothetically), higher risk of ovarian hyper-stimulation
syndrome
[this work],
[188]rs572527200 gatgatatca a
g
ctatgagtca 11
14
↓310
-
2
C
F2 rs564528021 agttcaacat t
c
aacccagagg 13
9
↑710
-
6
A(hypothetically) high risk of pre-eclampsia [this work],
[189]
rs752364393 caacattaac c
t
cagaggggtc 13
11
↑310
-
3
B
See “Note”under Table 1
Chadaeva et al. BMC Genomics 2018, 19(Suppl 3):0 Page 22 of 141
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Table 6 Known and candidate reproductivity-related SNP markers related to coagulation of blood
Gene dbSNP [6]
rel. 147 or
see [Ref]
5′flank wt
mut
3′flank K
D
, nM Known diseases (SNP markers) or hypothetical
disease (candidate SNP markers)
[Ref] or [this
work]
wt
mut
ΔZαρ
PROC rs528817178 cctttcattc c
t
gcttccacct 27
21
↑510
-6
A(hypothetically) higher risk of tumor cell invasion [this work],
[214]
rs539608065 ctttcattcc g
a
cttccacctg 27
22
↑410
-3
B
rs539731824 ttgtggttat g
a
gattaactcg 10
6
↑810
-6
A
rs756414294 ggcgcggcac c
t
agcaccagct 121
27
↑25 10
-6
A
rs777687270 ggcaccagca c
t
cagctgcccg 121
59
↑13 10
-6
A
rs746382956 tgcccgcaga g
a
gtgagcttcc 121
44
↑19 10
-6
A
rs542626506 cacacaggga c
t
agccctttca 27
31
↓310
-2
C(hypothetically) high risks of thrombosis, inflammation,
and pregnancy loss
[this work],
[215]
rs61731661 ccctttcatt c
t
cgcttccacc 27
29
↓5 0.05 D
F8 rs781855957 acggcggcag c
t
ggaagaggga 75
49
↑810
-6
A(hypothetically) higher risk of thrombosis [this work]
[216]
THBD rs13306848 agggagggcc g
a
ggcacttata 2
2
= 1 1 E thrombosis (LUC: TF site damaged, not TATA) [211]
(hypothetically) higher risks of placental failure
and fetal loss
[this work],
[217]rs568801899 caatccgagt g
a
tgcggcatca 45
70
↓610
-6
A
F3 rs563763767 ccctttatag c
t
gcgcggggca 3
2
↑610
-6
A myocardial infarction; thrombosis; [212]
(hypothetically) higher risk of ovarian cancer [this work],
[218]rs779755900 atctcgccgc -
30bp
caactggtag 90
10
↑43 10
-6
A
rs749456955 gatctcgccg c
a
caactggtag 90
75
↑410
-3
B
rs746842194 cgatctcgcc -
17bp
gccaactggt 90
31
↑15 10
-6
A
rs754815577 ctcgatctcg -
18bp
ccgccaactg 90
32
↑17 10
-6
A
rs768753666 ggaacccgct c
g
gatctcgccg 90
117
↓510
-6
A(hypothetically) lower risk of ovarian cancer
rs774688955 cgccacggaa c
t
ccgctcgatc 90
101
↓2 0.05 D
F7 -33a→c
[213]
ccttggaggc a
c
gagaactttg 53
62
↓310
-2
C moderate bleeding [213]
(hypothetically) lower risk of ovarian cancer [this work],
[218]rs749691733 agaactttgc c
t
cgtcagtccc 53
66
↓410
-3
B
rs367732974 aactttgccc g
a
tcagtcccat 53
47
↑2 0.05 D (hypothetically) higher risk of ovarian cancer
rs549591993 gcccgtcagt c
a
ccatggggaa 53
25
↑13 10
-6
A
rs777947114 agagaacttt g
a
cccgtcagtc 53
19
↑19 10
-6
A
rs770113559 gtcacccttg g
a
aggcagagaa 53
41
↑510
-6
A
rs754814507 cctcccccat c
t
cctctgtcac 53
45
↑310
-3
B
F11 rs754739433 tctgggaatt a
g
tttttagtaa 4
5
↓2 0.05 D (hypothetically) hereditary factor XI deficiency,
high risk of spontaneous primary hemorrhage
[this work],
[219]
Chadaeva et al. BMC Genomics 2018, 19(Suppl 3):0 Page 23 of 141
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Human genes THBD,PROC,F8,F3,F7,F9, and F11
code for thrombomodulin, and blood coagulation factors
XIV, 8, 3, 7, 9, and 11, respectively (Table 6). There are
three known SNP markers rs13306848 (thrombosis
[211]), rs563763767 (myocardial infarction and throm-
bosis [212]), and F7:-33a→c (moderate bleeding [213])
located within the promoters of these genes, which are
listed in Table 6.
Within 90-bp proximal regions of these promoters, we
selected 30 candidate SNP markers of tumor invasion
[214], thrombosis, inflammation and pregnancy loss [215–
217], ovarian cancer [218], hemorrhage [219], angioneur-
otic edema [220], hemophilia B [221], and myocardial fi-
brosis [222] (Table 6). We predicted them using our Web
service [53] and a primary keyword search, as described
above in detail. Finally, our secondary keyword search
produced 29 reviews [101,223–250]. The most interesting
among them, in our opinion, is the fact that Homo sapiens
is the longest-lived species among great apes (Hominidae)
in the postreproductive period. Most often, this period in
the life of a human is accompanied by various types of de-
mentia and atherosclerosis, whereas cardiomyopathy and
myocardial fibrosis predominate in great apes [248].
Looking through Tables 5,6, and Additional file 3:
Table S1, readers can see that by reducing the risk of
blood diseases, a person can increase his/her lifespan
and reproductive potential.
Candidate SNP markers of reproductivity-related genes
In addition, using a standard keyword search in the
PubMed database, we found articles on human reproduct-
ive potential. On this basis, we selected a set of 22 human
genes—AR,CAT,CLCA4,CYP1B1,CYP17A1,DAZ1,
DAZ2,DAZ3,DAZ4,DEFB126,DNMT1,GNRH1,
LHCGR, MTHFR,NR5A1,PARP1,PYGO2,SRD5A2, SRY,
TACR3,TET1,andTSSK2—whose promoters do not con-
tain known biomedical SNP markers. This gene set repre-
sents a wide variety of known reproductivity-related
physiological markers, such as enzymes, transcription fac-
tors, hormones, and their receptors. Table 7presents the
results obtained using our Web service [53].
None of the SNPs can statistically significantly alter
TBP’s affinity for the promoters of human genes CAT,
CLCA4,CYP1B1,DAZ1,DAZ2,DAZ3,DAZ4,DEF
B126,GNRH1,LHCGR, PARP1,PYGO2,SRD5A2, SRY,
TACR3,TET1, and TSSK2 being analyzed (data not
shown). Within promoters of five remaining genes (AR,
MTHFR,DNMT1,CYP17A1, and NR5A1), in the same
way, we found 24 candidate SNP markers (Table 7). Our
primary keyword search associated them with androge-
netic alopecia and androgen-induced premature senes-
cence in adult men [251], preeclampsia [252], adverse
pregnancy outcomes [253], epigenetic disorders of fetal/
newborn brain development [254,255], activation of
protooncogenes in cancer [256], hyperandrogenism in
polycystic ovary syndrome [257], fertility impairments
[258], adrenal tumors and endometriosis [259] (Table 7).
As a cross-validation test, we unexpectedly found the
ratio 5:19 of the candidate SNP markers in the
reproductivity-related genes (Table 7) decreasing versus
increasing TBP-promoter affinity. In contrast, the well-
known whole-genome ratio 2:1 of SNPs reducing versus
SNPs increasing affinity of the transcription factors for
human gene promoters has been identified by two inde-
pendent teams [260,261]. According to binomial distri-
bution, this difference between the candidate SNP
markers in the reproductivity-related genes (Table 7)
and all SNPs of the human genome is statistically signifi-
cant (α< 0.000005). This statistical significance reflects
the stronger pressure of natural selection against
Table 6 Known and candidate reproductivity-related SNP markers related to coagulation of blood (Continued)
Gene dbSNP [6]
rel. 147 or
see [Ref]
5′flank wt
mut
3′flank K
D
, nM Known diseases (SNP markers) or hypothetical
disease (candidate SNP markers)
[Ref] or [this
work]
wt
mut
ΔZαρ
rs780731761 ttatttttag t
a
aaaggaaatt 4
7
↑810
-6
A
rs747652067 tatttttagt a
g
aaggaaattt 4
7
↑910
-6
A
rs374761594 catttgtcta c
t
tgaagcacac 13
10
↑310
-3
B(hypothetically) higher risk of angioneurotic edema [this work],
[220]
rs759231858 acaccaacca g
t
aataacgaag 13
4
↑17 10
-6
A
rs752308147 ccagaataac g
a
aagctcgata 13
9
↑610
-6
A
F9 rs371045754 tggtacaact a
c
atcgacctta 6
10
↓510
-6
A(hypothetically) higher risk of hemophilia B [this work],
[221]
rs750827465 tttggtacaa c
t
taatcgacct 6
4
↑710
-6
A(hypothetically) higher risk of myocardial fibrosis [this work],
[222]
See “Note”under Table 1
Chadaeva et al. BMC Genomics 2018, 19(Suppl 3):0 Page 24 of 141
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
Table 7 Candidate SNP markers of reproductivity-related genes
Gene dbSNP [6]
rel. 147 or
see [Ref]
5′flank wt
mut
3′flank K
D
,nM hypothetical disease (candidate SNP markers) [this
work],
[Ref]
wt
mut
ΔZαρ
AR rs763353257 aagggaagta g
-
gtggaagatt 30
21
↑610
-6
A(hypothetically) higher risks of androgenetic alopecia and
androgen-induced premature senescence in adult men
[251]
rs749306567 aagggaagta g
a
gtggaagatt 30
15
↑11 10
-6
A
rs377711437 cagcactgca g
a
ccacgacccg 75
66
↑2 0.05 D
MTHFR rs780207553 cacgcactct g
a
ggcctgagct 74
38
↑12 10
-6
A(hypothetically) higher risk of pre-eclampsia [252]
rs749532075 tccctcccca c
t
*)
gcactctggg 74
50
↑710
-6
A
rs771960561 cctctgttcc c
t
tccccacgca 74
66
↑310
-2
B
rs773214376 tgcctctgtt c
t
cctccccacg 74
66
↑2 0.05 D
rs566478202 ggtgcctctg t
g
tccctcccca 74
85
↓2 0.05 D (hypothetically) higher risk of adverse pregnancy outcomes [253]
rs752181249 gaggatctac a
c
gccatcagct 27
35
↓410
-3
B
DNMT1 rs570287204 gtgggggggg -
gtg
tgtgtgcccg 52
23
↑11 10
-6
A(hypothetically) under stress, higher risk of epigenetic disorders
of fetal and newborn brain development causing long-term
neurobehavioral problems that may be reversible in
adolescence
[254,
255]
rs534819409 cgtggggggg g
t
ggcctgagct 52
30
↑710
-6
A
rs553454792 gcgtgggggg g
t
gtgtgtgccc 52
23
↑11 10
-6
A
rs558447661 cgtggagctt g
t
gacgagccca 72
29
↑15 10
-6
A
rs535899986 cccagcaaac c
t
gtggagcttg 72
58
↑510
-3
B
rs143796354 cacctcccag c
a
aaaccgtgga 72
26
↑20 10
-6
A
rs756103340 gcggcgcgca g
a
cggcagttgg 92
79
↑310
-3
B
rs758026532 ccagcaaacc g
t
*)
tggagcttgg 72
88
↓410
-3
B(hypothetically) higher risks of activation of protooncogenes in
cancer
[256]
rs772821225 gtctccaata a
c
atgcagctgg 7
8
↓2 0.05 D
CYP17A1 rs758657961 ctggagttga g
a
ccagcccttg 56
30
↑11 10
-6
A(hypothetically) higher risk of hyperandrogenism in polycystic
ovary syndrome
[257]
rs373488849 tgccctggag t
c
tgagccagcc 56
70
↓410
-3
B(hypothetically) higher risk of fertility impairments [258]
NR5A1 rs147497093 gttcagcaag c
t
acaagagaaa 19
6
↑17 10
-6
A(hypothetically) higher risks of adrenal tumors and
endometriosis
[259]
rs535432539 cgctgcttcc g
a
cttcgtaagt 31
18
↑910
-6
A
rs553326158 gcgctgcttc c
t
gcttcgtaag 31
26
↑310
-2
C
rs143242438 caccctcatc c
t
ggtgtgagag 31
21
↑610
-6
A
See the footnote of Table 1;
*)
this SNP includes one more minor neutral allele: “a.”
Chadaeva et al. BMC Genomics 2018, 19(Suppl 3):0 Page 25 of 141
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
underexpression of the reproductivity-related genes. This
unexpected finding indicates higher robustness of this
specific sort of human genes on a whole-genome scale
and is consistent with the commonly accepted meaning of
the term “reproductive potential”as a mainstream concept
in population ecology, which defines this term as a meas-
ure of evolutionary success of either human individuals
[2] or populations [3]. This match between our predic-
tions (Table 7) and one of the mainstream biomedical
concepts [2,3] support the plausibility of the candidate
SNP markers predicted here.
Verification procedures for the selected candidate SNP
markers predicted here
Different public Web services [21–38,53]havetheiradvan-
tages and disadvantages in eliminating unannotated neutral
SNPs. To optimize such knowledge, a comparison between
the results of these Web services and experimental data as
an independent commonly accepted uniform platform
seems to be a necessary step for prediction of candidate
SNP markers in silico [15,20,59]. Keeping this in mind, we
selected some of the 126 candidate SNP markers predicted
here—rs563763767, rs33981098, rs35518301, rs1143627,
rs72661131, rs1800202, and rs7277748—and measured
equilibrium dissociation constant K
D
of TBP–DNA com-
plexes using an electrophoretic mobility shift assay (EMSA)
in vitro (see Methods). The results are shown in Fig. 2,for
example, panels A and B present electropherograms and
their graphical representation in the case of ancestral and
minor alleles, respectively, of the candidate SNP marker
rs33981098 within the human HBB gene promoter. Here,
readers can see that this SNP reduces the TBP–DNA affin-
ity in half: from 44 nM in the norm (wt) to 90 nM in path-
ology (mut); this finding supports our prediction, namely,
thetwofolddecreaseintheestimateofTBP–DNA affinity
from 5 to 9 nM (Table 5). Overall, panel C shows the co-
ordinate plane of the predicted (axis X) and the measured
(axis Y) ratio of K
D;MUT
/K
D;WT
values of minor versus
Fig. 1 The result produced by SNP_TATA_Comparator [53] for reproductive potential-related SNP markers in the human ESR2 gene. Legend: aUnanno-
tated SNPs (analyzed in this study) in the region [-70; -20] (where all proven TBP-binding sites (boxed) are located; double-headed arrow, ↔)ofthehuman
ESR2 gene promoter retrieved from dbSNP, rel. 147 [6] using the UCSC Genome Browser [12]. Dash-and-double-dot arrows: known and candidate SNP
markers of reproductive potential are predicted by a significant change in the affinity of TBP for the human ESR2 gene promoter. band cThe results from
our Web service SNP_TATA_Comparator [53] for the two SNP markers of reproductive potential: known marker rs35036378 [61] and candidate marker
rs766797386 near the known TBP-binding site (boxed) of the human ESR2 gene promoter. Solid, dotted, and dashed arrows indicate queries in the
reference human genome [10] by means of the BioPerl library [265]. Dash-and-dot arrows: estimates of significance of the alteration of gene product
abundance in patients carrying the minor allele (mut) relative to the norm (ancestral allele, wt) expressed as a Z-score using package R [266]. Circles indicate
the ancestral (wt) and minor (mut) alleles of the SNP marker labeled by its dbSNP ID [6]
Chadaeva et al. BMC Genomics 2018, 19(Suppl 3):0 Page 26 of 141
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
ancestral alleles of each SNP being verified. As one can see
in this figure, there is a significant correlation between
our predictions in silico and our measurements in vitro
in four statistical tests, namely: linear correlation (r),
Spearman’s rank correlation (R), Kendall’srankcorrel-
ation (τ), and Goodman–Kruskal generalized correl-
ation (γ) test, which confirm one another’sresults.
Therefore, the correlations between our predictions
and experimental data are robust in terms of the vari-
ation of statistical criteria that supports the candidate
reproductive-potential-related SNP markers predicted
here.
Besides the conventional EMSA, we used two modern
high-performance methods. Figure 3shows the results of
high-resolution spectrometry on SX.20 (Applied Photophy-
sics, UK), where a stopped-flow fluorescence assay in vitro
in real-time mode was applied to the selected candidate
SNP marker rs1800202 (see Methods). As readers can see
in Table 4, we predicted in silico that the K
D
value of TBP’s
binding affinity for this gene’s wild-type promoter (ancestral
alleles), 1 nM, can be weakened by the minor allele of this
SNP to 4 nM, in agreement with the experimental data: 1
versus 6 nM, respectively (Table 4). This is one more argu-
ment in favor of the significance of the candidate
reproductive-potential-related SNP markers predicted here.
Finally, we conducted transfection of the human cell
line hTERT-BJ1 (human fibroblasts) in culture, using the
pGL 4.10 vector carrying a reporter LUC gene whose
Fig. 2 Experimental verification of the selected candidate SNP markers by an electrophoretic mobility shift assay (EMSA) in vitro. Legend: aand b
Examples of electropherograms in the case of ancestral (panel A: norm, wild-type, wt) and minor (panel b: minor) alleles of the candidate SNP
marker rs33981098 within the human HBB gene promoter and the corresponding diagrams of experimental values. cThe significant correlations
between the ratio of K
D
values of the equilibrium dissociation constant of the TBP–ODN complex, which were either measured in vitro (Y-axis) or
in silico predicted (X-axis). Solid and dashed lines or curves denote the linear regression and boundaries of its 95% confidence interval, calculated
using software Statistica (Statsoft
TM
, USA). Circles denote the ancestral and minor alleles of the candidate SNP markers rs563763767, rs33981098,
rs35518301, rs1143627, rs72661131, rs1800202, and rs7277748 being verified; r, R, τ,γ, and αare linear correlation, Spearman’s rank correlation,
Kendall’s rank correlation, Goodman–Kruskal generalized correlation, and their significance, respectively.
Fig. 3 The kinetics of binding to and bending of the ODN corresponding to the selected SNP marker rs1800202. Legend: aThe ancestral allele,
ODN 5′-ctcTATATAAgtggg-3′.bThe minor allele, ODN 5′-ctcTATAgAAgtggg-3′. ODN concentration was 0.1 μM. TBP concentration was between
0.1 and 1.0 μM as indicated near the corresponding curve of the time series. K
D
values, a1 nM and b6 nM, were obtained as the output of the
Dynafit software (Biokin, USA) when we used the corresponding time-series data as input for this software
Chadaeva et al. BMC Genomics 2018, 19(Suppl 3):0 Page 27 of 141
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
transcription is initiated by either ancestral or minor al-
leles of the selected candidate SNP marker rs28399433
of the human CYP2A6 promoter (Table 2). The results
are depicted in Fig. 4. As shown in Table 2, the low af-
finity of TBP for the minor allele of this SNP relative to
the norm (ancestral allele) is consistent with the ex vivo
underexpression of a reporter LUC gene carrying the
minor allele of this SNP within the pGL 4.10 vector.
This ex vivo observation independently confirms our
prediction that this SNP can reduce the affinity of TBP
for the promoter of the human CYP2A6 gene (Table 2).
Thus, three independent experiments indicate that the
candidate reproductive-potential-related SNP markers
predicted here using our Web-service [53] seem to have
statistically significant effects and are not neutral.
Discussion
In this work, we limited our research to SNPs altering
TBP’s affinity for human gene promoters (according to
predictions made by our Web service [53]) and thereby
altering the expression of these genes; this is because the
TBP-binding site is the best-studied transcription-
regulatory element [47]. Using our Web service [53], we
analyzed over 1000 SNPs between nucleotide positions
-70 and -20 upstream of more than 50 protein-coding
regions documented in the Ensembl database [11] and
found only 126 candidate reproductive-potential-related
SNP markers (Tables 1,2,3,4,5,6and 7). This 8-fold re-
duction in the number of possible SNPs can make the
clinical cohort-based search for such biomedical SNP
markers faster, cheaper, and more targeted, indeed.
For clinical verification of the candidate SNP markers
predicted here, we heuristically set up their prioritization
based on Fisher’s Z-tests as rank ρ-values from the
“best”(A) to the “worst”(E) in alphabetical order (Tables
1,2,3,4,5,6and 7). With this in mind, our findings do
not mean that all the eliminated SNPs (data not shown)
cannot be considered candidate reproductive-potential-
related SNP markers. This is because they may alter
transcription factor-binding sites without disrupting the
TBP-binding site (e.g., rs11568827, rs796237787, and
rs16887226). To perform this sort of analysis for any of
them, there are many public Web services [21–38]
whose research capabilities may be enhanced when they
are used in combination with our Web service [53].
It is also worth mentioning that 126 candidate SNP
markers predicted here are whole-genome landmarks indi-
cative of either elevated or reduced reproductive potential
relativetothenormandcanbeexpectedtobepresentin
patients as minor alleles of these SNPs [20]. For example,
10 candidate SNP markers of thrombosis (rs563763767,
rs781855957, rs13306848, rs568801899, rs779755900,
rs749456955, rs746842194, rs754815577, rs768753666,
rs774688955) cause overproduction of coagulation inducers
(Table 6). In pregnant women, Hughes syndrome provokes
thrombosis with a fatal outcome, although this syndrome
can be diagnosed and cured even at the earliest stages of its
development [230–232](Additionalfile3:TableS1).Thus,
in women carrying any of the above SNPs, preventive treat-
ment of this syndrome [230–232]before a planned preg-
nancy can reduce the risk of death. Table 6shows that
seven SNPs (rs563763767, rs779755900, rs749456955,
rs746842194, rs754815577, rs768753666, rs774688955)
among the 10 mentioned above elevate the risk of myocar-
dial infarction. Hence, a woman with some of these SNPs
can improve her longevity by bringing her lifestyle in line
with the knowledge that the risk of myocardial infarction
elevates with total number of pregnancies, the age of the
mother, as well as in pregnancy under the age of 20, in mul-
tiple pregnancies, in menstrual cycle irregularity, hyperten-
sion, preeclampsia, and in women smokers [233–236]
(Additional file 3:TableS1).
Finally, during our keyword search in the PubMed data-
base, we encountered a large variety of research articles,
clinical cases, laboratory data, retrospective reviews, and
empirical findings—on human reproductive potential in
various life situations—from sociologists, geneticists, legal
scholars, clinicians, bioinformaticians, pharmacists, psy-
chologists, pedagogues, physiologists, economists, and
other relevant experts such as specialists on management,
insurance, environmental protection, health care, and law
enforcement (Tables 1,2,3,4,5,6and 7,andAdditional
file 3: Table S1). This observation means that this vital
knowledge is very much in demand for the general popula-
tion, but it is too scattered for practice use. As one can see
Fig. 4 Cell culture verification of the selected candidate SNP marker
rs28399433 in cell line hTERT-BJ1 (human fibroblasts) transfected with
the pGL 4.10 vector carrying a reporter LUC gene. Legend: Dark gray
bar, the original vector pGL 4.10 (Promega, USA) without any insertions,
which served as an independent control; open bars, ancestral allele
(wild type, WT); light gray bar, minor allele (rs28399433). The height of
the bars and their error bars correspond to the mean estimates and
boundaries of their 95% confidence intervals calculated from five
independent experiments. All differences are statistically significant at
the confidence level of α<0.05
Chadaeva et al. BMC Genomics 2018, 19(Suppl 3):0 Page 28 of 141
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
in Tables 1,2,3,4,5,6and 7and Additional file 3:Table
S1, 126 candidate reproductive-potential-related SNP
markers predicted here may serve as valid whole-genome
landmarks near which the above authors can organize their
main research on how the evolutionary success of an indi-
vidual [2] or a population [3] could be enhanced. Conse-
quently, the results of these studies can be directly
addressed to people who would like to change their lifestyle
in view of the possible risks of diseases. This approach be-
comes possible within the framework of predictive-
preventive personalized medicine based on the sequenced
individual genomes.
Conclusions
In keeping with Bowles’lifespan theory [9], a large body of
useful literature can be packaged into readable portions
relevant to candidate reproductive-potential-related SNP
markers for people who would like to reduce the risks of
diseases corresponding to known alleles in own sequenced
genome. After clinical validation, these candidate SNP
markers may become useful for physicians (to improve
treatment of patients) and for the general population (life-
style choices improving longevity).
Methods
DNA sequences
We analyzed SNPs retrieved from the dbSNP database,
v.147 [6] between nucleotide positions -70 and -20 up-
stream of the protein-coding regions documented by the
Ensembl database [11] using the public Web service
“UCSC Genome Browser”[12] as shown in Fig. 1a.
Synthetic double-helical deoxyoligonucleotides (ODNs)
The ODNs identical to ancestral and minor alleles of the
selected SNPs—rs563763767, rs33981098, rs35518301,
rs1143627, rs72661131, rs1800202, and rs7277748—were
synthesized and purified (BIOSYN, Novosibirsk, Russia).
Preparation and purification of recombinant full-length
human TBP
Recombinant human TBP (full-length native amino acid
sequence) was expressed in Escherichia coli BL21 (DE3)
cells transformed with the pAR3038-TBP plasmid (a
generous gift from Prof. B. Pugh, Pennsylvania State
University) as described elsewhere [262] with two modi-
fications: the IPTG concentration was 1.0 instead of 0.1
mM, and the induction time was 3 instead of 1.5 h (for
more details, see [263]).
EMSA
The above ODNs were labeled with
32
P on both strands by
means of T4 polynucleotide kinase (SibEnzyme, Novosi-
birsk) with subsequent annealing by heating to 95°C (at
equimolar concentrations) and slow cooling (no less than 3
h) to room temperature. Equilibrium dissociation constants
(K
D
) for each TBP–ODN complex were measured using a
conventional protocol [263] including titration of a fixed
amount of the above-mentioned recombinant TBP, 0.3 nM,
with the increasing concentrations of each ODN to reach
an equilibrium, whose timing was determined independ-
ently for each ODN in advance. The binding experiments
were conducted at 25°C in a buffer consisting of 20 mM
HEPES-KOH pH 7.6, 5 mM MgCl
2
,70mMKCl,1mM
EDTA, 100 μg/ml BSA, 0.01% of NP-40, and 5% of glycerol.
The ТВР–ODN complexes were separated from the un-
bound ODN using an EMSA, and their abundance levels
were measured. The results of these measurements were
input into conventional software OriginPro 8, whose output
was a K
D
value expressed in nanomoles per liter, nM.
Stopped-flow fluorescence measurements
The ODNs identical to both ancestral and minor alleles
of the selected SNP rs1800202, (i.e., 5′-ctcTATA-
TAAgtggg-3′and 5′-ctcTATAgAAgtggg-3′, respectively)
were labeled at their 5′-termini with fluorescent dyes
TAMRA and FAM (BIOSYN, Novosibirsk, Russia).
Combining a fixed concentration (0.1 μM) of ODNs
with various concentrations (0.1, 0.2, 0.4, 0.6, 0.8, or 1.0
μM) of the above TBP, we analyzed six time-series of the
fluorescence expressed in conventional units using high-
resolution spectrometer SX.20 (Applied Photophysics,
UK). The results of these measurements served as input
into the Dynafit software (Biokin, USA), whose output
was the above K
D
values (for more details, see [264]).
Cell culture, transfection, and reporter assays
Cell line hTERT-BJ1 (human fibroblasts) was cultivated in
a complete medium consisting of Dulbecco’s modified Ea-
gle’s medium/Nutrient mixture F-12 Ham, supplemented
with 10% (v/v) of fetal bovine serum (Sigma), penicillin
(100 U/mL), and streptomycin (100 μg/mL; BioloT). The
culture was maintained at 37°C in a humidified atmos-
phere containing 5% of CO
2
until the desired degree of
confluence. The proximal core promoter (177 bp long)
containing either the ancestral allele or minor allele of the
selected candidate SNP marker rs28399433 (5′-tcaggcag-
TATAA Aggca aac-3′or 5′- tcaggcagTAgAAAggcaaac-3′,
respectively) was cloned into the pGL 4.10 vector (Pro-
mega, USA) and cotransfected with pRL-TK using Screen
Fect A (InCella) as described elsewhere [265]. Next, the
cells were cultured in 6-well plates for 24 h. Luciferase ac-
tivity was determined using the Dual-Luciferase Reporter
Assay Kit (Promega, USA) All the experiments were con-
ducted five times independently at 80–85% confluence.
DNA sequence analysis in silico
We analyzed DNA sequences between nucleotide posi-
tions -70 and -20 upstream of the protein-coding regions
Chadaeva et al. BMC Genomics 2018, 19(Suppl 3):0 Page 29 of 141
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
in the human genes retrieved from the human reference
genome using the standard BioPerl library [266] via our
Web service [53] in the case of ancestral alleles of SNPs
under study, as described in Fig. 1b. In the case of minor
alleles of these SNPs, we created sequences by hand using
the above DNA sequences according to the description of
these alleles from database dbSNP [6]asdescribedinFig.
1c. Next, clicking on the “Calculate”button (Fig. 1b, and
c), we computed the maximal –ln(K
D
) value and its stand-
ard deviation ± εof the affinity of TBP for the [–70; -20]
region (where all the known sites are located) for both an-
cestral and minor alleles of the human gene promoter be-
ing analyzed. On this basis, using a package R [267], our
Web service [54] made its statistical decision whether the
analyzed SNP can alter the expression of the human gene
under study as described in Additional file 1[268–274].
Earlier, we tested these estimates using independent data
from more than a hundred our own experiments [275–
285] and the experiments of other authors (for review, see
[51]). Finally, as soon as we predicted either SNP-caused
significant overexpression or SNP-driven significant un-
derexpression of the human genes being analyzed (as clin-
ically relevant physiological markers), we conducted a
manual two-step keyword search in NCBI databases [286]
as described in detail in Additional file 2[287].
Additional files
Additional file 1: Supplementary method. A sequence-based statistical
estimate of the SNP-caused alteration in the affinity of TATA box binding
protein (TBP) for the human gene promoter containing this SNP within
its region [-70; -20]. (PDF 220 kb)
Additional file 2: Supplementary method. Keyword search in the
PubMed database. (PDF 221 kb)
Additional file 3: Table S1. Clinically known dependences between
reproductive potential and hereditary diseases whose SNP markers were
predicted in this work. (PDF 198 kb)
Abbreviations
ACKR1:atypical chemokine receptor 1; APOA1: apolipoprotein A1;
AR: androgen receptor; CAT: catalase; CETP: cholesteryl ester transfer protein;
CLCA4: chloride channel accessory 4; CYP17A1: cytochrome p450 family 17
subfamily A member 1; CYP1B1: cytochrome P450 family 1 subfamily B
member 1; CYP2A6: cytochrome P450 family 2 subfamily A member 6;
CYP2B6: cytochrome P450 family 2 subfamily B Member 6; DAZ1 (2, 3,
4): deleted in azoospermia 1 (2, 3, 4, respectively); DEFB126: defensin β126;
DHFR: dihydrofolate reductase; DNMT1: DNA methyltransferase 1;
EMSA: electrophoretic mobility shift assay; ESR2: estrogen receptor 2; F2 (3, 7,
8, 9, 11): coagulation factor II (III, VII, VIII, IX, XI, respectively); GCG: glucagon;
GH1: growth hormone 1; GJA5: gap junction protein α5;
GNRH1: gonadotropin releasing hormone 1; GSTM3: glutathione S-transferase
μ3; HBB: hemoglobin subunit β;HBD: hemoglobin subunit δ;
HBG2: hemoglobin subunit γ2; HSD17B1: hydroxysteroid 17-βdehydrogenase
1; IL1B: interleukin 1 β;INS: insulin; K
d
: equilibrium dissociation constant;
LEP: leptin; LHCGR: luteinizing hormone (choriogonadotropin receptor);
Ln: natural logarithm; MBL2: mannose binding lectin 2; MMP12: matrix
metallopeptidase 12; MTHFR: methylenetetrahydrofolate reductase;
Mut: minor allele of SNPs. Genes; NOS2: nitric oxide synthase 2;
NR5A1: nuclear receptor subfamily 5 group A member 1; PARP1: poly(ADP-
ribose) polymerase 1; PGR: progesterone receptor; PROC: protein C
(inactivator of coagulation factors Va and VIIIa); PYGO2: pygopus family PHD
finger 2; SNP: single nucleotide polymorphism; SOD1: superoxide dismutase
1; SRD5A2: steroid 5 α-reductase 2; SRY: sex determining region Y;
STAR: steroidogenic acute regulatory protein; TACR3: tachykinin receptor 3;
TBP: TATA-binding protein; TET1: Tet methylcytosine dioxygenase 1;
TF: transcription factor; THBD: thrombomodulin; TPI1: triosephosphate
isomerase 1; TSS: transcription start site; TSSK2: testis specific serine kinase 2;
WT: wild type (norm)
Acknowledgments
We are grateful to Shevchuk Editing (Brooklyn, NY, USA; URL: http://
www.shevchuk-editing.com) for English editing.
Funding
The publication costs for this article were funded by grant #14.B25.31.0033
from the government of the Russian Federation, Resolution No. 220 (to
Professor E.I. Rogaev as principal investigator, PI). The data compilation was
supported by Russian Ministry of Science and Education under 5-100 Excel-
lence Programme (for IVC and MPP). The experiment ex vivo was supported
by project #0324-2016-0002 (for EVK, LVO, and AVO), the experiments in vitro
were supported by project #0324-2016-0003 (for LKS, IAD, EBS, OVA, and
DAZ), the software development was financed by projects #0324-2016-0008
(for DAR and NAK).
Availability of data and materials
Web service SNP_TATA_Comparator is public available (URL=http://
beehive.bionet.nsc.ru/cgi-bin/mgs/tatascan/start.pl).
About this supplement
This article has been published as part of BMC Genomics Volume 19
Supplement 3, 2018: Selected articles from Belyaev Conference 2017:
genomics. The full contents of the supplement are available online at
https://bmcgenomics.biomedcentral.com/articles/supplements/volume-19-
supplement-3.
Authors’contributions
NAK conceived of and supervised the study. LKS, IAD, EBS, OVA, and DAZ
conducted the in vitro experiments. EVK conducted the ex vivo experiments.
PMP and DAR designed, developed, maintained, adapted, and tuned the
software for sequence analysis. IVC analyzed data in silico. LVO and AVO
interpreted the computer-based predictions in comparison with experimen-
tal data. MPP wrote the manuscript. All the coauthors read and approved
the final version of the manuscript.
Ethics approval and consent to participate
Not applicable
Consent for publication
Not applicable
Competing interests
The authors declare that they have no competing interests.
Publisher’sNote
Springer Nature remains neutral with regard to jurisdictional claims in
published maps and institutional affiliations.
Author details
1
Brain Neurobiology and Neurogenetics Center, Institute of Cytology and
Genetics, Siberian Branch of Russian Academy of Sciences, 10 Lavrentyev
Ave, Novosibirsk 630090, Russia.
2
Novosibirsk State University, Novosibirsk
630090, Russia.
3
Department of Biology, University of La Verne, La Verne, CA
91750, USA.
4
Vector-Best Inc., Koltsovo, Novosibirsk Region 630559, Russia.
5
Novosibirsk State Agricultural University, Novosibirsk 630039, Russia.
Published: 9 February 2018
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