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Citation: Zámbó, V.; Orosz, G.; Szabó,
L.; Tibori, K.; Sipeki, S.; Molnár, K.;
Csala, M.; Kereszturi, É. A Single
Nucleotide Polymorphism
(rs3811792) Affecting Human SCD5
Promoter Activity Is Associated with
Diabetes Mellitus. Genes 2022,13,
1784. https://doi.org/10.3390/
genes13101784
Academic Editor: Gil Atzmon
Received: 3 September 2022
Accepted: 29 September 2022
Published: 3 October 2022
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genes
G C A T
T A C G
G C A T
Article
A Single Nucleotide Polymorphism (rs3811792) Affecting
Human SCD5 Promoter Activity Is Associated with
Diabetes Mellitus
Veronika Zámbó, Gabriella Orosz, Luca Szabó, Kinga Tibori, Szabolcs Sipeki, Krisztina Molnár, Miklós Csala
and Éva Kereszturi *
Department of Molecular Biology, Semmelweis University, H-1085 Budapest, Hungary
*Correspondence: kereszturi.eva@med.semmelweis-univ.hu; Tel.: +36-1-2662615
Abstract:
The combined prevalence of type 1 (T1DM) and type 2 (T2DM) diabetes mellitus is
10.5% worldwide and this is constantly increasing. The pathophysiology of the diseases include
disturbances of the lipid metabolism, in which acyl-CoA desaturases play a central role as they
synthesize unsaturated fatty acids, thereby providing protection against lipotoxicity. The stearoyl-
CoA desaturase-5 (SCD5) isoform has received little scientific attention. We aimed to investigate
the SCD5 promoter and its polymorphisms
in vitro
, in silico and in a case-control study. The SCD5
promoter region was determined by a luciferase reporter system in HepG2, HEK293T and SK-N-FI
cells and it was proved to be cell type-specific, but it was insensitive to different fatty acids. The effect
of the SCD5 promoter polymorphisms rs6841081 and rs3811792 was tested in the transfected cells.
The T allele of rs3811792 single nucleotide polymorphism (SNP) significantly reduced the activity
of the SCD5 promoter
in vitro
and modified several transcription factor binding sites in silico. A
statistically significant association of rs3811792 SNP with T1DM and T2DM was also found, thus
supporting the medical relevance of this variation and the complexity of the molecular mechanisms in
the development of metabolic disorders. In conclusion, the minor allele of rs3811792 polymorphism
might contribute to the development of diabetes by influencing the SCD5 promoter activity.
Keywords:
type 1 diabetes mellitus (T1DM); type 2 diabetes mellitus (T2DM); stearoyl-CoA desaturase-5
(SCD5); single nucleotide polymorphism; lipotoxicity; fatty acid; lipid metabolism; metabolic disorder
1. Introduction
About 537 million people worldwide have diabetes, the majority are living in high-
and middle-income countries, and 1.5 million deaths are directly attributed to diabetes each
year. Both the number of cases and the prevalence of diabetes have been steadily increasing
over the past few decades [
1
,
2
]. Diabetes belongs to a group of metabolic disorders that
are characterized by a long-term high blood glucose level due to either the inadequate
production of insulin (type 1 diabetes mellitus, T1DM) or a poor response of the recipient
cells to insulin (type 2 diabetes mellitus, T2DM). While the former is considered to be a
chronic autoimmune disease [
3
], the latter is a condition that develops on the basis of obesity
and a sedentary lifestyle [
4
]. However, in either type, the role of genetic determination
in addition to environmental factors is established beyond doubt [
5
,
6
]. However, despite
some overlap, the genetic factors of T1DM and T2DM currently appear to be distinct [
7
].
The accelerator hypothesis argues that T1DM and T2DM are the same disorder of insulin
resistance, which are set against different genetic backgrounds. The hypothesis does not
deny the role of autoimmunity, only its primacy in the process. It assumes that obesity, the
excessive nutrient intake, and an imbalance of lipid metabolism are the main accelerating
factors for the appearance of diabetic symptoms [8].
The balance of lipid metabolism in our body depends on a functionally appropriate
combination of saturated (SFA), monounsaturated (MUFA) and polyunsaturated (PUFA)
Genes 2022,13, 1784. https://doi.org/10.3390/genes13101784 https://www.mdpi.com/journal/genes
Genes 2022,13, 1784 2 of 17
fatty acids (FAs) with different carbon chain lengths. Accordingly, changes in the FA
distribution and composition have a significant impact on lipid metabolism and home-
ostasis, energy storage and many other lipid-related processes [
9
]. In mammalian cells,
SFAs and MUFAs are the most abundant FAs, accounting for 80% of total FA content. The
MUFA-producing human stearoyl-CoA desaturases (SCDs) are endoplasmic reticulum
membrane-bound enzymes that introduce the first double bond in the cis-
∆
9 position of
a saturated fatty acyl-CoA, which are mainly palmitoyl-CoA and stearoyl-CoA that pro-
duce palmitoleyl-CoA and oleyl-CoA, respectively [
10
]. Two isoforms of SCDs have been
identified in humans. Numerous studies have demonstrated the inevitable importance of
stearoyl-CoA desaturase-1 (SCD1) in the metabolic and signaling pathways, making its
role unquestionable in several prevalent human diseases, including obesity, diabetes, fatty
liver, and cardiovascular diseases [
11
–
15
]. Much is known about the lipid-sensitive expres-
sion [
16
–
21
], rapid protein degradation [
22
,
23
], and complex transcriptional regulation [
24
]
of SCD1.
Unlike SCD1, the second human SCD isoform, stearoyl-CoA desaturase-5 (SCD5),
has received considerably less attention. In contrast to SCD1, which is expressed in the
tissues of major importance in lipid metabolism (such as liver and adipose tissue) [
25
],
SCD5 is more abundant in the brain, pancreas, and gonads [
26
,
27
]. Although the actual
promoter region of the SCD5 gene is largely unidentified, several transcription factor (TF)
binding sites in the putative 5
0
regulatory region have been predicted in silico that have
previously been proved to regulate SCD1 [
16
,
28
]. The multilevel FA-sensitive regulation,
which has already been revealed for SCD1 [
24
,
29
], is still an open question in the case of
SCD5 [
30
,
31
]. Despite the numerous studies focusing on the role of SCD5 in the regulation
of lipid metabolism [
32
–
35
], the importance of this gene product in the mechanism of
human metabolic diseases such as diabetes and obesity has been poorly characterized.
Although SCD5 appears to be a major regulator of visceral fat deposition and distribution
in a zebrafish model system [
36
], its polymorphisms have not been investigated at all in
the research on the genetic basis of obesity-related conditions. Accordingly, our primary
aims were to map the functional upstream regulatory region of the SCD5 gene, to test the
possible FA-sensitivity of its transcription, and to examine the SNPs in the region in silico,
in vitro, and in an association analysis involving T1DM and T2DM patients.
In the present study, we identified the functional promoter region of the SCD5 gene and
examined the functional impact of rs6841081 and rs3811792 SCD5 promoter polymorphisms
and their potential association with diabetes. The SCD5 gene expression was found to
be insensitive to FAs, and it showed a pronounced tissue-specificity with a much higher
promoter activity in the neural cells. The presence of the minor variant of rs3811792 SNP
significantly reduced the activity of the SCD5 promoter
in vitro
and modified the binding
probability of several TFs in silico. A statistically significant association of rs3811792 SNP
with both T1DM and T2DM was also found, thus supporting the medical relevance of this
polymorphism and the complexity of the molecular mechanisms in the development of
metabolic disorders.
2. Materials and Methods
2.1. Chemicals and Reagents
Culture medium and supplements were purchased from Thermo Fisher Scientific
(Waltham, MA, USA). Oleate, palmitate, stearate, linoleate, elaidate, vaccenate, bovine
serum albumin, HepG2, HEK293T, and SK-N-FI cells were purchased from Sigma-Aldrich
(St. Louis, MO, USA). All of the chemicals that were used in this study were of analytical
grade. All of the experiments and measurements were carried out by using Millipore
ultrapure water.
2.2. Web-Based Tools for in Silico Transcription Factor Binding Analysis
The JASPAR (http://jaspar.genereg.net/, accessed on 30 June 2022) [37] open-access,
non-redundant TF biding profile database was used to predict potential TF binding to
Genes 2022,13, 1784 3 of 17
the SCD5 promoter that was affected by rs6841081 or rs3811792 polymorphism. Two
other online available prediction programs (ALGGN-PROMO, http://alggen.lsi.upc.es/
cgi-bin/promo_v3/promo/promoinit.cgi?dirDB=TF_8.3 [
38
], accessed on 18 July 2022 and
LASAGNA-Search 2.0, https://biogrid-lasagna.engr.uconn.edu/lasagna_search/index.
php [39]), accessed on 20 July 2022 were also used to confirm TF binding hits.
2.3. Reporter Plasmid Construction and Mutagenesis
Different sized fragments of SCD5 upstream regulatory region were amplified using
iProof
™
High-Fidelity DNA Polymerase (Bio-Rad, Hercules, CA, USA) from human ge-
nomic DNA template using primers that contain Kpn I and Hind III restriction endonuclease
recognition sites. After purification and restriction endonuclease digestion (Thermo Fisher
Scientific, Waltham, MA, USA), the amplicons were ligated (T4 Ligase, Thermo Fisher
Scientific, Waltham, MA, USA) into pGL3B vector (Promega, Madison, WI, USA) that was
upstream of the luciferase reporter gene. The studied natural variants were generated using
Q5
®
Site-Directed Mutagenesis Kit (New England BioLabs, Ipswich, MA, USA) following
the manufacturer’s instruction. Mutagenic primers were designed using the online NEB
primer design software, NEBaseChanger
™
. After digesting the original nonmutated and
methylated plasmid by KLD reaction, an aliquot of constructs was transformed into XL10-
Gold
®
Ultracompetent Cells (Agilent, Santa Clara, CA, USA), which were then screened
for positive colonies by single cell PCR. The cloning and mutagenic primers are listed in
Table S1. All of the constructs were verified by Sanger sequencing them.
2.4. Cell Culture and Transfection
Human embryonic kidney (HEK293T), hepatocellular carcinoma (HepG2) and neurob-
lastoma (SK-N-FI) cells were cultured in 12-well plates (5
×
10
5
cells per well) in Dulbecco’s
modified Eagle medium (DMEM) which was supplemented with 10% fetal bovine serum
and 1% penicillin/streptomycin solution at 37
◦
C in a humidified atmosphere containing
5% CO
2
. HEK293T cells were transfected with 0.5
µ
g pGL3B-SCD5_P1-P4 promoter con-
structs using 3
µ
L Lipofectamine 2000 (Invitrogen, Carlsbad, CA, USA) in 1 mL DMEM.
HepG2 and SK-N-FI cells were transfected with 1
µ
g pGL3B-SCD5_P1-P4 promoter con-
structs using 3
µ
L Lipofectamine 3000 that was supplemented with 2
µ
L P3000 (Invitrogen,
Carlsbad, CA, USA) in 1 mL DMEM. As a transfection control, 0.5
µ
g pCMV-
β
-gal plasmid
was cotransfected into each sample. Cells were harvested and processed 24-30 h after
transfection.
2.5. Cell Treatment
Oleate, palmitate, stearate, linoleate, elaidate, and vaccenate were diluted in ethanol
(Molar Chemicals, Halásztelek, Hungary) to a final concentration of 50 mM and conjugated
with 20% FA free BSA in 1:4 ratio at 50
◦
C for 1 h. The working solution for FA treatments
was prepared freshly in FBS-free and antibiotic-free medium at 100
µ
M final concentration.
The culture medium was replaced 5 h after transfection. The FA treatment was carried out
for 24 h in 12-well plates.
2.6. Preparation of Cell Lysates
Cells were washed twice with PBS and harvested in 100
µ
L Reporter lysis buffer
(Promega, Madison, WI, USA) was scraping and briefly vortexed. A single freeze–thaw
cycle was followed by centrifuging in a benchtop centrifuge (5 min, max speed, 4
◦
C).
Supernatants were used for enzyme activity determination.
2.7. Luciferase Assay
Luciferase activity was detected using the Luciferase Assay System kit (Promega,
Madison, WI, USA) by adding 15
µ
L Luciferin reagent to 5
µ
L of all of the cell extracts.
β
-galactosidase activity of 20
µ
L cell lysates was measured by using o-nitrophenyl-
β
-D-
galactopyranoside (at a final concentration of 3 mM) cleavage rate. Luminescence was
Genes 2022,13, 1784 4 of 17
detected using a Varioskan multi-well plate reader (Thermo Fisher Scientific, Waltham,
Massachusetts, USA). Values for luciferase activity were normalized to
β
-galactosidase ac-
tivity (measured by standard protocol using the same Varioskan plate reader in photometry
mode). Each experiment was repeated three times independently, and each sample was
studied in triplicate.
2.8. qPCR Analysis
Total RNA was purified from HepG2, HEK293T and SK-N-FI cells by using RNeasy
Plus Mini Kit (Qiagen, Hilden, Germany) following the manufacturer’s instruction. Con-
centrations were measured using NanoDrop1000 spectrophotometer. To assess the integrity
and purity of the isolated total mRNA samples, the ratios of their absorbance at 260/280
and 260/220 nm were determined, and they were also analyzed by agarose gel electrophore-
sis to visualize bands corresponding to 28S and 18S rRNAs, respectively. Human tissue
RNAs were purchased from Thermo Fisher Scientific (Waltham, Massachusetts, USA).
cDNA samples were produced by reverse transcription of 0.5
µ
g DNA-free RNA using
SuperScript III First-Strand Synthesis System for RT-PCR Kit (Thermo Fisher Scientific,
Waltham, Massachusetts, USA). Quantitative qPCR assay was performed in 20
µ
L final
volume containing 5
µ
L 20
×
diluted cDNA, 1
×
PowerUp
TM
SYBR
TM
Green Master Mix,
and 0.5
µ
M forward and reverse primers using QuantStudio 12K Flex Real-Time PCR
System (Thermo Fisher Scientific, Waltham, Massachusetts, USA). SCD5 sequences were
amplified by 5
0
ATG GAA ACC GGC CCT ATG AC 3
0
and 5
0
CCC CAG CCA GCA CAT
GAA AT 3
0
primer pairs. GAPDH cDNA was also amplified as an endogenous control
using 5
0
GTC CAC TGG CGT CTT CAC CA 3
0
and 5
0
GTG GCA GTG ATG GCA TGG AC 3
0
primers. The first step of the thermocycle was an initial denaturation and enzyme activation
at 95
◦
C for 2 min. It was followed by 40 cycles of 95
◦
C for 15 s, 55
◦
C for 15 s, and 72
◦
C for
1 min
; measurement of the fluorescent signal was carried out during annealing. Reactions
were performed in triplicates, and a reaction mixture with RNase-free water instead of
template cDNA was employed as non-template control. Relative expression levels were
calculated as 2
−∆CT
, where
∆
C
T
values corresponded to the difference of the C
T
-values of
the endogenous control and target genes.
2.9. Subjects
One hundred and forty-five patients that were diagnosed with type 1 diabetes mellitus
(49.7% female, 50.3% male, disease onset at the age of 35.5
±
13.1 years, 1-
β
for rs6841081:
0.0690, 1-
β
for rs3811792: 0.2866) and 253 patients that were diagnosed with type 2 dia-
betes mellitus (51.8% female, 48.2% male, disease onset at the age of 62.7
±
12.1 years,
1-
β
for rs6841081: 0.0761, 1-
β
for rs3811792: 0.3749) in the 2
nd
Department of Internal
Medicine, Semmelweis University were recruited in the study. The control group consisted
of 350 volunteers without medical history of any metabolic disease (61.0% female, 39.0%
male, mean age: 31.1
±
20.1 years). The diagnosis of diabetes was made based on fast-
ing blood sugar values, oral glucose tolerance test (OGTT), and HbA
1C
value according
to WHO regulations. Individuals with autoimmune, infectious, or metabolic disorders
other than type 1 or 2 diabetes were excluded from the study. Genetic analysis of the
participants was approved by the Local Ethical Committee (ETT-TUKEB ad.328/KO/2005,
ad.323-86/2005-1018EKU from the Scientific and Research Ethics Committee of the Medical
Research Council). Participants signed written informed consent documents before sample
collection was performed for genetic analysis to take place. In order to avoid the risk of
spurious association due to population stratification, only subjects of Hungarian origin
were included to ensure the comparison of homogenous populations. Buccal epithelial cells
were collected using swabs. The first step of the DNA isolation was the incubation of the
buccal samples at 56
◦
C overnight in 0.2 mg/mL Proteinase K cell lysis buffer. Subsequently,
proteins were denatured using saturated NaCl solution. DNA was then precipitated using
isopropanol and 70% ethanol. DNA pellet was resuspended in 100
µ
L 0.5
×
TE buffer
Genes 2022,13, 1784 5 of 17
(1
×
TE: 10 mM Tris pH = 8.0; 1 mM EDTA). Concentrations of the samples were measured
using NanoDrop1000 spectrophotometer.
2.10. Genotyping
Rs6841081 and rs3811792 polymorphisms of the SCD5 gene were genotyped using
pre-designed TaqMan assays (C_34192814_10 and C_27029625_20, Thermo Fisher Scientific,
Waltham, MA, USA). qPCR assay was performed in 5
µ
L final volume containing approxi-
mately 4 ng genomic DNA, 1
×
TaqPath
™
ProAmp
™
Master Mix, and 1
×
TaqMan
®
SNP
Genotyping Assay using QuantStudio 12K Flex Real-Time PCR System (Thermo Fisher
Scientific, Waltham, MA, USA). Thermocycle was started by activating the hot start DNA-
polymerase and denaturing genomic DNA at 95
◦
C for 10 min. This was followed by 40
cycles of denaturation at 95
◦
C for 15 sec, and combined annealing and extension at 60
◦
C
for 1 min. Real-time detection was carried out during the latter step to verify the results of
the subsequent post-PCR plate reads and automatic genotype calls.
2.11. Statistical Analysis
Relative luciferase activities and mRNA levels are presented in the diagrams as mean
values
±
S.D. and were compared by ANOVA with Tukey’s multiple comparison post hoc
test using GraphPad Prism 6 software. Differences with a p< 0.05 value were considered
to be statistically significant. Genotype–phenotype association was assessed by
χ2
-test
comparing the genotype distribution of the patient and the control groups (i.e., additive
model). Odds ratios were calculated by comparing the groups with and without the risk
allele. The level of statistical significance was adjusted after Bonferroni correction. The
statistical power for both patient groups and for both SNPs was calculated based on a
likelihood ratio test framework using the additive model option [40].
3. Results
3.1. Cell Type-Specific Promoter Activity of Human Stearoyl-CoA Desaturase-5
The first aim of our work was to identify the functional promoter of SCD5. Four
increasingly larger sizes of DNA segments of the 5
0
regulatory region were cloned into
the pGL3B luciferase reporter vector, the exact lengths and positions of these are shown in
Figure 1A.
The SCD5 promoter constructs and the pCMV-
β
-gal vector were transiently transfected
into HepG2, HEK293T and SK-N-FI cells, and the luciferase activity of the cell lysates was
measured and normalized to
β
-galactosidase 24-30 h after the transfection. All four of
the promoter constructs presented significantly higher relative luciferase activity than the
promoterless pGL3B did, but the 1040 bp construct SCD5_P3 showed the highest activity
in each cell line (Figure 1B). However, the increment in the relative promoter activity
differed greatly among the cell lines. While the SCD5_P3 construct showed only a 4.5-fold
increase in HepG2, it caused a nearly 10-fold elevation in HEK293T (Figure 1B) and an
even more pronounced, approximately 26-fold increase in the SK-N-FI cells of neural origin
(Figure 1B).
An analysis of the endogenous SCD5 expression in the three cell lines revealed that
there were almost completely undetectable traces of SCD5 mRNA in the liver-derived
HepG2 cells, while the HEK293T and SK-N-FI cells showed moderate and high mRNA
levels of it, respectively (Figure 2A).
Genes 2022,13, 1784 6 of 17
Genes 2022, 13, x FOR PEER REVIEW 6 of 18
Figure 1. Relative promoter activity of different constructs of human SCD5 gene promoter in three
different human cell lines. The length and position of the subcloned SCD5 5′ regulatory sequences
are numbered from the translation start site (+1) (A) and their relative luciferase activity that is
measured in HepG2, HEK293T, and SK-N-FI cell lines is presented. (B) pCMV-β-gal vector served
as transfection control. Luciferase and β-galactosidase enzyme activities were measured as indicated
in Section 2, and their relative ratios are shown as bar graphs. The diagram depicts the results of
three independent measurements that are normalized to pGL3B “empty” promoterless vector. Data
are shown as mean values ± S.D. Statistical analysis was performed by using the Tukey–Kramer
Multiple Comparisons Test. * or #: p < 0.05; ** or ##: p < 0.01; ***, !!!, ###: p < 0.001.
The SCD5 promoter constructs and the pCMV-β-gal vector were transiently trans-
fected into HepG2, HEK293T and SK-N-FI cells, and the luciferase activity of the cell ly-
sates was measured and normalized to β-galactosidase 24‒30 h after the transfection. All
four of the promoter constructs presented significantly higher relative luciferase activity
than the promoterless pGL3B did, but the 1040 bp construct SCD5_P3 showed the highest
activity in each cell line (Figure 1B). However, the increment in the relative promoter ac-
tivity differed greatly among the cell lines. While the SCD5_P3 construct showed only a
4.5-fold increase in HepG2, it caused a nearly 10-fold elevation in HEK293T (Figure 1B)
and an even more pronounced, approximately 26-fold increase in the SK-N-FI cells of neu-
ral origin (Figure 1B).
An analysis of the endogenous SCD5 expression in the three cell lines revealed that
there were almost completely undetectable traces of SCD5 mRNA in the liver-derived
HepG2 cells, while the HEK293T and SK-N-FI cells showed moderate and high mRNA
levels of it, respectively (Figure 2A).
Figure 1.
Relative promoter activity of different constructs of human SCD5 gene promoter in three
different human cell lines. The length and position of the subcloned SCD5 5
0
regulatory sequences
are numbered from the translation start site (+1) (
A
) and their relative luciferase activity that is
measured in HepG2, HEK293T, and SK-N-FI cell lines is presented. (
B
) pCMV-
β
-gal vector served as
transfection control. Luciferase and
β
-galactosidase enzyme activities were measured as indicated
in Section 2, and their relative ratios are shown as bar graphs. The diagram depicts the results of
three independent measurements that are normalized to pGL3B “empty” promoterless vector. Data
are shown as mean values
±
S.D. Statistical analysis was performed by using the Tukey–Kramer
Multiple Comparisons Test. * or #: p< 0.05; ** or ##: p< 0.01; ***, !!!, ###: p< 0.001.
Genes 2022, 13, x FOR PEER REVIEW 7 of 18
Figure 2. SCD5 mRNA expression in three different cell lines (A) and human tissues. (B) The mRNA
expression was measured in HepG2, HEK293T, and SK-N-FI cells, as well as in human liver, kidney,
and brain samples. Samples were prepared as described in Section 2. qPCR was performed using
GAPDH and SCD5 sequence specific primers as indicated in Section 2. The diagram depicts the
results of three independent measurements. Statistical analysis was performed by using the Tukey–
Kramer Multiple Comparisons Test. Data are shown as mean values ± S.D. ***: p < 0.001.
A very similar pattern was observed when performing the comparison of the human
tissue RNA samples (Figure 2B). In contrast to the minimal hepatic SCD5 expression, the
mRNA levels were one order of magnitude higher in the kidney tissue and two orders of
magnitude higher in the brain tissue. These findings are in accordance with the results of
our luciferase assays, which indicate that there is a cell type-specific promoter activity in
the SCD5 gene.
3.2. Fatty Acid Insensitive Promoter Activity of Stearoyl-CoA Desaturase-5
As the expression of the SCD1 gene has been shown to be FA sensitive [16–21], this
possibility arose in the case of SCD5 as well. We aimed to test this hypothesis by using six
different FAs (oleate, palmitate, stearate, linoleate, elaidate, and vaccinate) in the lucifer-
ase assay, but to ensure the reliability of the model, we first tested the possible effect of
these FAs on the luciferase activity using HEK293T cells that were transfected with the
control pGL3B vector. A mild FA sensitivity was observed, and a significantly higher lu-
ciferase activity was detected while it was in the presence of stearate in comparison to that
of the untreated sample (Figure S1); therefore, we decided to normalize the luciferase/β-
galactosidase activity of each sample to that of the control pGL3B which was treated with
the same FA. When the SCD5_P3 construct was tested for FA sensitivity in the optimized
experimental system, none of the six FAs affected the relative luciferase activity when the
promoter activity was compared to that of the untreated cells (Figure 3A).
Figure 2.
SCD5 mRNA expression in three different cell lines (
A
) and human tissues. (
B
) The mRNA
expression was measured in HepG2, HEK293T, and SK-N-FI cells, as well as in human liver, kidney,
and brain samples. Samples were prepared as described in Section 2. qPCR was performed using
GAPDH and SCD5 sequence specific primers as indicated in Section 2. The diagram depicts the results
of three independent measurements. Statistical analysis was performed by using the Tukey–Kramer
Multiple Comparisons Test. Data are shown as mean values ±S.D. ***: p< 0.001.
Genes 2022,13, 1784 7 of 17
A very similar pattern was observed when performing the comparison of the human
tissue RNA samples (Figure 2B). In contrast to the minimal hepatic SCD5 expression, the
mRNA levels were one order of magnitude higher in the kidney tissue and two orders of
magnitude higher in the brain tissue. These findings are in accordance with the results of
our luciferase assays, which indicate that there is a cell type-specific promoter activity in
the SCD5 gene.
3.2. Fatty Acid Insensitive Promoter Activity of Stearoyl-CoA Desaturase-5
As the expression of the SCD1 gene has been shown to be FA sensitive [
16
–
21
], this
possibility arose in the case of SCD5 as well. We aimed to test this hypothesis by using six
different FAs (oleate, palmitate, stearate, linoleate, elaidate, and vaccinate) in the luciferase
assay, but to ensure the reliability of the model, we first tested the possible effect of these
FAs on the luciferase activity using HEK293T cells that were transfected with the control
pGL3B vector. A mild FA sensitivity was observed, and a significantly higher luciferase
activity was detected while it was in the presence of stearate in comparison to that of
the untreated sample (Figure S1); therefore, we decided to normalize the luciferase/
β
-
galactosidase activity of each sample to that of the control pGL3B which was treated with
the same FA. When the SCD5_P3 construct was tested for FA sensitivity in the optimized
experimental system, none of the six FAs affected the relative luciferase activity when the
promoter activity was compared to that of the untreated cells (Figure 3A).
Genes 2022, 13, x FOR PEER REVIEW 8 of 18
Figure 3. Effect of different fatty acids on relative SCD5 promoter activity (A) and on mRNA level
(B). (A) Transfection and FA treatment were performed as described in Section 2. pCMV-β-gal vec-
tor served as transfection control. Luciferase and β-galactosidase enzyme activities were measured
as indicated in Section 2 and their relative ratios are shown as bar graphs. The diagram depicts the
results of three independent measurements that were normalized to pGL3B “empty” promoterless
vector. (B) The mRNA expression was measured in FA treated HEK293T cells. Samples were treated
and prepared as described in Section 2. qPCR was performed using GAPDH and SCD5 sequence
specific primers as indicated in Section 2. The diagram presents the results of three independent
measurements. Data are shown as mean values ± S.D. Statistical analysis was performed by using
the Tukey–Kramer Multiple Comparisons Test.
Furthermore, these FAs had no effect on the endogenous SCD5 mRNA levels either
(Figure 3B). Collectively, these data imply that, unlike in case of SCD1, the expression of
SCD5 may be FA independent.
3.3. Effect of rs3811792 Polymorphism on Stearoyl-CoA Desaturase-5 Promoter Activity
Based on the NCBI database, two SNPs (rs6841081, rs3811792, minor allele frequency
> 1%) are located in the promoter of SCD5, and their position is depicted in Figure 4A.
All four of the possible combinations (i.e., haplotypes) of the two polymorphisms
were created by a site-directed mutagenesis in the SCD5_P3 promoter construct, and their
impact on the relative luciferase activity was examined in the HEK293T and SK-N-FI cells
(Figure 4B,C). The plasmid carrying the higher frequency allele of both of the SNPs
(rs6841081_G/rs3811792_C) resulted in the highest promoter activity in both of the cell
lines. Although the presence of the minor allele of rs6841081 polymorphism
(rs6841081_T/rs3811792_C) caused a mild reduction in the luciferase enzyme activity
which was compared to that of the frequent haplotype version
(rs6841081_G/rs3811792_C), this was not significant in either the HEK293T or the SK-N-
FI cells (Figure 4B,C). In contrast, the minor allele of rs3811792 (rs6841081_G/rs3811792_T)
resulted in a significantly lower promoter activity when it was compared to that of the
frequent haplotype construct (rs6841081_G/rs3811792_C), and the reduction was of 30%
in the human embryonic kidney-derived cell line (Figure 4B), and it was more than 50%
in the neuroblastoma cells (Figure 4C). The haplotype of both of the minor alleles
(rs6841081_T/rs3811792_T) showed a minimal further reduction of the SCD5 promoter ac-
tivity when it was compared to that of rs6841081_G/rs3811792_T, but this was statistically
significant only in the HEK293T cell line (Figure 4B). In summary, it was the rs3811792
promoter SNP that negatively influenced the SCD5 promoter activity in both of the cell
lines that we examined in vitro in the luciferase reporter system.
Figure 3.
Effect of different fatty acids on relative SCD5 promoter activity (
A
) and on mRNA level
(
B
). (
A
) Transfection and FA treatment were performed as described in Section 2. pCMV-
β
-gal vector
served as transfection control. Luciferase and
β
-galactosidase enzyme activities were measured as
indicated in Section 2and their relative ratios are shown as bar graphs. The diagram depicts the
results of three independent measurements that were normalized to pGL3B “empty” promoterless
vector. (
B
) The mRNA expression was measured in FA treated HEK293T cells. Samples were treated
and prepared as described in Section 2. qPCR was performed using GAPDH and SCD5 sequence
specific primers as indicated in Section 2. The diagram presents the results of three independent
measurements. Data are shown as mean values
±
S.D. Statistical analysis was performed by using
the Tukey–Kramer Multiple Comparisons Test.
Furthermore, these FAs had no effect on the endogenous SCD5 mRNA levels either
(Figure 3B). Collectively, these data imply that, unlike in case of SCD1, the expression of
SCD5 may be FA independent.
3.3. Effect of rs3811792 Polymorphism on Stearoyl-CoA Desaturase-5 Promoter Activity
Based on the NCBI database, two SNPs (rs6841081, rs3811792, minor allele frequency
> 1%) are located in the promoter of SCD5, and their position is depicted in Figure 4A.
Genes 2022,13, 1784 8 of 17
Genes 2022, 13, x FOR PEER REVIEW 9 of 18
Figure 4. Position (A) and effect of two polymorphisms on relative SCD5 promoter activity in
HEK293T (B) and SK-N-FI (C) cells. (A) The position of two polymorphisms is marked on SCD5 5′
regulatory region and numbered from the translation start site (+1). Transfection was performed as
described in Section 2. pCMV-β-gal vector served as transfection control. Luciferase and β-galacto-
sidase enzyme activities were measured as indicated in Section 2 and their relative ratios are shown
as bar graphs. The diagram depicts the results of three independent measurements normalized to
the SCD5_P3 with the highest activity. Data are shown as mean values ± S.D. Statistical analysis was
performed by using the Tukey–Kramer Multiple Comparisons Test. #: p < 0.05; ##: p < 0.01; *** or
###: p < 0.001; ns: not significant.
3.4. Effect of rs6841081 and rs3811792 Promoter Polymorphisms on Transcription Factor
Binding Sites in Silico
The possible impact of the two SNPs on the TF binding sites in the promoter of the
SCD5 gene was analyzed in silico using the JASPAR transcription factor binding site pre-
diction program. Specifically, we addressed the question of whether the exchange of the
two nucleotides that were affected by the polymorphisms could cause a predictable
change in the binding probability of any of the TFs in this region. During the analysis, two
41-nucleotide long DNA sections were compared for each SNP, in which either version of
the polymorphic nucleotide was located at position 21.
In the first step, the matrix of all (949) of the TFs that were included in the JASPAR
database was compared with the DNA sequences that are described above. In order to
find all of the possible TF binding sites, the TF binding probability was set to the lowest
value (1%) that was allowed by the software. More than 56,300 hits were obtained (Table
1) for both of the allelic variants in case of each polymorphism.
Figure 4.
Position (
A
) and effect of two polymorphisms on relative SCD5 promoter activity in
HEK293T (
B
) and SK-N-FI (
C
) cells. (
A
) The position of two polymorphisms is marked on SCD5 5
0
regulatory region and numbered from the translation start site (+1). Transfection was performed
as described in Section 2. pCMV-
β
-gal vector served as transfection control. Luciferase and
β
-
galactosidase enzyme activities were measured as indicated in Section 2and their relative ratios are
shown as bar graphs. The diagram depicts the results of three independent measurements normalized
to the SCD5_P3 with the highest activity. Data are shown as mean values
±
S.D. Statistical analysis
was performed by using the Tukey–Kramer Multiple Comparisons Test. #: p< 0.05; ##: p< 0.01; *** or
###: p< 0.001; ns: not significant.
All four of the possible combinations (i.e., haplotypes) of the two polymorphisms
were created by a site-directed mutagenesis in the SCD5_P3 promoter construct, and
their impact on the relative luciferase activity was examined in the HEK293T and SK-
N-FI cells (Figure 4B,C). The plasmid carrying the higher frequency allele of both of the
SNPs (rs6841081_G/rs3811792_C) resulted in the highest promoter activity in both of
the cell lines. Although the presence of the minor allele of rs6841081 polymorphism
(rs6841081_T/rs3811792_C) caused a mild reduction in the luciferase enzyme activity
which was compared to that of the frequent haplotype version (rs6841081_G/rs3811792_C),
this was not significant in either the HEK293T or the SK-N-FI cells (Figure 4B,C). In con-
trast, the minor allele of rs3811792 (rs6841081_G/rs3811792_T) resulted in a significantly
lower promoter activity when it was compared to that of the frequent haplotype con-
struct (rs6841081_G/rs3811792_C), and the reduction was of 30% in the human embryonic
kidney-derived cell line (Figure 4B), and it was more than 50% in the neuroblastoma cells
(Figure 4C). The haplotype of both of the minor alleles (rs6841081_T/rs3811792_T) showed
a minimal further reduction of the SCD5 promoter activity when it was compared to that of
rs6841081_G/rs3811792_T, but this was statistically significant only in the HEK293T cell line
(Figure 4B). In summary, it was the rs3811792 promoter SNP that negatively influenced the
SCD5 promoter activity in both of the cell lines that we examined
in vitro
in the luciferase
reporter system.
Genes 2022,13, 1784 9 of 17
3.4. Effect of rs6841081 and rs3811792 Promoter Polymorphisms on Transcription Factor Binding
Sites in Silico
The possible impact of the two SNPs on the TF binding sites in the promoter of
the SCD5 gene was analyzed in silico using the JASPAR transcription factor binding site
prediction program. Specifically, we addressed the question of whether the exchange of
the two nucleotides that were affected by the polymorphisms could cause a predictable
change in the binding probability of any of the TFs in this region. During the analysis, two
41-nucleotide long DNA sections were compared for each SNP, in which either version of
the polymorphic nucleotide was located at position 21.
In the first step, the matrix of all (949) of the TFs that were included in the JASPAR
database was compared with the DNA sequences that are described above. In order to find
all of the possible TF binding sites, the TF binding probability was set to the lowest value
(1%) that was allowed by the software. More than 56,300 hits were obtained (Table 1) for
both of the allelic variants in case of each polymorphism.
Table 1.
Three-step transcription factor binding site screening. The position of SNPs is counted
upstream from the ATG start codon.
SNP ID Position Allele
All Hits (above
1% Relative
Score)
At Least 15%
Relative Score
Difference
between Alleles
Relative Score
Grater than 80%
at Least for One
Allele
rs6841081 −254 G 56,353 438 6
T 56,356
rs3811792 −316 C 56,359 372 9
T 56,362
By definition, the vast majority of these were very low probability hits. In the second
step, the hit list was narrowed to identify the most relevant TFs. The TFs that were retained
were those whose binding probability showed at least 15% difference between the two
variations of the given polymorphism. Thus, the list of TFs was reduced to 438 for rs6841081
SNP and to 372 for rs3811792 SNP (Table 1). However, some of these hits could have a
low binding probability, although they were significantly different for the two alleles. In
the third step, therefore, only the TF hits were kept when they had a relative score that
was above 80% for at least one allele (Table 1). The three-step filtering identified six TFs
for rs6841081 SNP (Tables 1and 2) and nine TFs for rs3811792 SNP (Tables 1and 3) that
are predicted to bind to the promoter sequence with a substantially high (at least 80%)
probability, and their binding probability is remarkably (by at least 15%) different for the
two allelic versions of the polymorphism.
Table 2.
List of transcription factors that were affected by rs6841081 polymorphism. Positive values of
relative score differences on a red background indicate that the minor allele increased the TF binding
probability, negative values on a green background indicate that the minor allele decreased the TF
binding probability. A darker shade indicates that there is a larger difference.
Name TF ID Strand Relative Score (%)
G Allele T Allele Difference
SPI1 MA0080.1 - 61.12 81.85 20.73
MEIS3 MA0775.1 - 64.05 81.49 17.44
SOX18 MA1563.1 + 67.49 83.78 16.28
NFIA MA0670.1 + 66.42 82.29 15.87
TFE3 MA0831.1 - 80.86 64.07 −16.79
TFAP2A MA0003.1 - 86.43 67.53 −18.90
Genes 2022,13, 1784 10 of 17
Table 3.
List of transcription factors that were affected by rs3811792 polymorphism. Positive values of
relative score differences on a red background indicate that the minor allele increased the TF binding
probability, negative values on a green background indicate that the minor allele decreased the TF
binding probability. A darker shade indicates that there is a larger difference.
Name TF ID Strand Relative Score (%)
C Allele T Allele Difference
SOX10 MA0442.1 + 61.69 80.51 18.83
SOX2 MA0143.4 - 69.17 85.12 15.95
MEIS2 MA0774.1 + 68.63 83.88 15.25
RBPJ MA1116.1 - 72.83 87.87 15.04
NFATC2 MA0152.1 + 91.46 75.36 −16.10
ZNF354C MA0130.1 + 86.87 68.98 −17.89
NFATC3 MA0625.2 - 93.30 74.36 −18.94
MZF1 MA0056.1 - 81.31 61.97 −19.34
ETS1 MA0098.1 + 93.09 71.50 −21.60
Table 2lists the names and IDs of the TFs that were affected by rs6841081 SNP as well
as their relative binding probabilities to the two alleles and their differences.
The binding probabilities of the SPI1, MEIS3, SOX18, and NFIA factors are increased
by the presence of the minor polymorphic T allele (Table 2, a red color and a darker shade
means that there is a larger probability difference), while TFE3 and TFAP2A prefer to bind
to the major polymorphic sequence (Table 2, a green color and a darker shade means that
there is a larger probability difference). The results that were obtained with rs3811792 SNP
are summarized in Table 3.
Out of the nine TFs, four of them (SOX10, SOX2, MEIS2, RBPJ, shown in red) are
predicted to bind more strongly to the minor alleles containing the T nucleotide, while in
the case of five TFs (NFATC2, ZNF354C, NFATC3, MZF1, ETS1, marked in green), the same
T allele reduces the likelihood of an interaction.
The exact binding positions of the TFs that were affected by the two SCD5 polymor-
phisms according to the in silico analyses are shown in Figure 5.
Genes 2022, 13, x FOR PEER REVIEW 11 of 18
Table 3. List of transcription factors that were affected by rs3811792 polymorphism. Positive values
of relative score differences on a red background indicate that the minor allele increased the TF
binding probability, negative values on a green background indicate that the minor allele decreased
the TF binding probability. A darker shade indicates that there is a larger difference.
Name TF ID Strand Relative Score (%)
C Allele T Allele Difference
SOX10 MA0442.1 + 61.69 80.51 18.83
SOX2 MA0143.4 - 69.17 85.12 15.95
MEIS2 MA0774.1 + 68.63 83.88 15.25
RBPJ MA1116.1 - 72.83 87.87 15.04
NFATC2 MA0152.1 + 91.46 75.36 −16.10
ZNF354C MA0130.1 + 86.87 68.98 −17.89
NFATC3 MA0625.2 - 93.30 74.36 −18.94
MZF1 MA0056.1 - 81.31 61.97 −19.34
ETS1 MA0098.1 + 93.09 71.50 −21.60
Out of the nine TFs, four of them (SOX10, SOX2, MEIS2, RBPJ, shown in red) are
predicted to bind more strongly to the minor alleles containing the T nucleotide, while in
the case of five TFs (NFATC2, ZNF354C, NFATC3, MZF1, ETS1, marked in green), the
same T allele reduces the likelihood of an interaction.
The exact binding positions of the TFs that were affected by the two SCD5 polymor-
phisms according to the in silico analyses are shown in Figure 5.
Figure 5. Transcription factor binding sites in the SCD5 promoter that were affected by rs6841081
(A) and rs3811792 (B) polymorphisms. The major alleles of the SNPs are marked in blue, and the
minor polymorphic versions are in red in the DNA sequences. The exact binding sites of the tran-
scription factors that were identified by the JASPAR online prediction program and filtered as de-
scribed in Section 3 are plotted. TFs that are typed above the sequence are more likely to bind to the
major allele, while TFs that are below the sequence prefer to bind to the minor polymorphic allele.
Asterisk indicates results confirmed by PROMO, double cross shows hits confirmed by LASAGNA.
In the case of the rs6841081 SNP, TFAP2A was also confirmed to be present by the
PROMO TF binding site search database, which is available online (Figure 5A). The
NFATC2 binding site that was affected by the rs3811792 polymorphism was also pre-
dicted by two other programs (PROMO and LASAGNA) (Figure 5B).
3.5. Association of rs3811792 Polymorphism with T1DM and T2DM
The putative associations between the type 1 or type 2 diabetes mellitus and the
rs6841081_G/T or rs3811792_C/T SNP were assessed by case–control setup. The observed
Figure 5.
Transcription factor binding sites in the SCD5 promoter that were affected by rs6841081 (
A
)
and rs3811792 (
B
) polymorphisms. The major alleles of the SNPs are marked in blue, and the minor
polymorphic versions are in red in the DNA sequences. The exact binding sites of the transcription
factors that were identified by the JASPAR online prediction program and filtered as described in
Section 3are plotted. TFs that are typed above the sequence are more likely to bind to the major allele,
while TFs that are below the sequence prefer to bind to the minor polymorphic allele. * indicates
results confirmed by PROMO, # shows hits confirmed by LASAGNA.
Genes 2022,13, 1784 11 of 17
In the case of the rs6841081 SNP, TFAP2A was also confirmed to be present by the
PROMO TF binding site search database, which is available online (Figure 5A). The NFATC2
binding site that was affected by the rs3811792 polymorphism was also predicted by two
other programs (PROMO and LASAGNA) (Figure 5B).
3.5. Association of rs3811792 Polymorphism with T1DM and T2DM
The putative associations between the type 1 or type 2 diabetes mellitus and the
rs6841081_G/T or rs3811792_C/T SNP were assessed by case–control setup. The observed
genotype distributions of the control group for both of the polymorphisms were in ac-
cordance with the expected values that were calculated based on the Hardy–Weinberg
equilibrium (
χ2
-test: p= 0.9769 for rs6841081, p= 0.9669 for rs3811792). The frequency of
the minor rs6841081 SNP allele was 1.1% in the control population, which is in agreement
with the European population average as per the NCBI database (0.9-2.8%). The 15.7%
frequency of the minor rs3811792 SNP allele was slightly below the European population
average (19%).
The genotype frequencies of the two SNPs that were measured in the healthy control
population were compared with those that were found in the T1DM and T2DM groups
using the
χ2
-test. Since this meant we had to conduct four different tests (two patient
groups and two SNPs), the limit of the statistical significance was lowered from p< 0.05
to 0.0125 due to the Bonferroni correction. No association was found for the rs6841081
polymorphism either with T1DM or with T2DM (Table 4).
Table 4.
Comparison of genotype frequencies of rs6841081 and rs3811792 polymorphisms in control,
T1DM and T2DM populations. MAF: minor allele frequency; OR: odds ratio; CI confidence interval.
Control (N = 350)T1DM (N = 145)T2DM (N = 253)
rs6841081
N % N % N %
GG 342 98 141 97 243 96
GT 8 2 4 3 10 4
TT 0 0 0 0 0 0
χ2p= 0.7555 p= 0.1119
MAF (T) 8 1 4 1 10 2
OR with 95% CI 1.2128 (0.3594–4.0922) 1.7593 (0.6844–4.5224)
Control (N = 350) T1DM (N = 143) T2DM (N = 248)
rs3811792
N % N % N %
CC 248 71 97 68 162 65
CT 94 27 33 23 79 32
TT 8 2 13 9 7 3
χ2p= 0.0029 p= 0.0114
MAF (T) 110 16 59 21 93 19
OR with 95% CI 4.275 (1.7318–10.5527) 1.2907 (0.9108–1.8291)
In contrast, the frequency of the rs3811792 SNP genotype was significantly different
in both the T1DM (p= 0.0029, Table 4) and T2DM (p= 0.0114, Table 4) groups when they
were compared to those of the control population. It is noteworthy that this difference
can be largely attributed to the different frequency of the CT heterozygous genotype in
the T2DM group (32% vs. 27%, Table 4) and to that of the TT homozygous genotype
in the patients with T1DM (9% vs. 2%, Table 4). For this reason, different genotype
categorizations were used for the determination of the odds ratios in the two patient
groups. For T1DM, the patients with the C allele and the patients without the C allele
were placed into separate categories, while in the case of T2DM, the T allele carriers and T
allele non-carriers were grouped separately. In the absence of the C allele of rs3811792, the
chance of developing T1DM was found to be increased by more than four times, although
this is with the occurrence of a large confidence interval (Table 4). Despite there being
Genes 2022,13, 1784 12 of 17
significantly different genotype frequencies between the T2DM and control groups, the OR
remained close to one for this SNP.
4. Discussion
Despite the progress that has been made in the research on SCD5 in recent years,
many aspects of the transcriptional, nutritional, and hormonal regulation of this desaturase
are still to be elucidated. To understand the exact function of SCD5 and its role in lipid
metabolism, it would be necessary to clarify the regulation of its gene expression. The long
overdue step toward the identification of the valid regulatory molecules that are involved
in it is the mapping of the functional SCD5 promoter region. In the work that is presented
here, roughly 1000 base pair section in the 5
0
regulatory region of SCD5 were identified,
which showed the highest activity in each cell line that we examined
in vitro
in a luciferase
reporter system. These findings are in line with the preliminary expectations that we had
since the average promoter length of human genes is between 300 and 1000 bp [
41
]. In
addition, very similar results were obtained for SCD1, the other human desaturase isoform
that was characterized previously in detail in a similar reporter system, with a sequence
of nearly 1000 bp (between 882 and 150 nucleotides) that provides a maximum amount
of transcriptional activity and was identified as the functional promoter [
24
]. Although
several specific transcription factors influencing the SCD1 gene expression have been
already identified [
24
], and a lot is known about the hormonal and nutritional regulation of
the SCD1 gene [
16
], only in silico prediction data on SCD5 are available. A number of TF
binding sites at the 5
0
regulatory region of human SCD5 indicate that C/EBP-
α
, AP1, SP1,
NF-1, NF-Y, T3R, PPAR-a, and SREBP1 may bind to the promoter sequence of the gene [
28
].
As the binding of the TFs to the aforementioned binding sites was known to control the
expression of SCD1 [
16
], and earlier studies assumed that there was both a similar function
and a similar regulation for the two SCD isoforms, in silico searches have focused mainly
on the similarities with SCD1.
However, them having a similar function seems less and less likely in light of the
increasingly detailed mapping of the differences between the regulation of the two isoforms.
The likelihood of the different transcriptional regulatory mechanisms of the two SCDs
is also supported by the fact that the promoter activity of SCD5 was not sensitive to the
presence of different FAs in our experimental system. This observation is not without
precedent, as the diets containing various unsaturated FAs, which have been shown to
enhance the expression of SCD1 in cows, left the SCD5 mRNA level unchanged [
30
]. A
similar phenomenon was also described in human tumor cell lines, where, in contrast
to SCD1, neither the change in the serum lipid level [
42
] nor the presence of retinoic
acid [35] affected the expression of the SCD5 gene. The regulation of SCD1, but not SCD5,
is affected significantly by the lipid-derived factors at different levels. It is known that
the promoter activity of SCD1 is influenced positively by the SFAs, and it is negatively
influenced by the MUFAs and PUFAs [
16
–
21
,
24
]; in addition, oleate also enhances the
intracellular degradation of the SCD1 protein [
29
,
43
]. Furthermore, a common missense
polymorphism in the SCD1 gene can stabilize the protein in an FA-dependent manner [
23
].
Although the two desaturases catalyze the same reaction, increasing evidence suggests that
SCD5 may be less sensitive to the regulation by lipid factors in comparison to SCD1.
The different regulation also raises the possibility of it having a different function,
which might be further supported by the significantly different tissue expression of the two
isoforms, as well as the cell line-specific promoter activity of SCD5. It is well known that
SCD1 is ubiquitously found in all tissues, but it is more predominantly present in adipose
tissue, liver tissue, brain tissue, heart tissue, breast tissue, and lung tissue [
25
], and while
this is in agreement with our findings, SCD5 is primarily expressed in the fetal and adult
brain, the pancreas, the gonads, and to a lesser amount, in the kidney tissue, lung tissue,
and adipose tissue [
26
,
27
]. Details of the evident tissue-specific transcriptional regulation
are still unclear, and their discovery may shed light on the exact function and role of the
enzyme in lipid metabolism and its related disorders.
Genes 2022,13, 1784 13 of 17
Polymorphisms in the promoter region can modify gene expression. The present
work is the first to functionally examine the two promoter SNPs of human SCD5, and it
revealed that the T allele of rs3811792 reduces the SCD5 promoter activity in the kidney-
derived and neuronal cell lines. Interestingly, not only the SCD5, but also the human SCD1
polymorphisms have been quite neglected, and only a few variants have been characterized
so far, and there is no promoter SNP among them, instead there is only the stabilizing
effect of a missense variant [
23
] and the microRNA binding site-modifying role of a 3
0
untranslated region (30UTR) polymorphism [44] have been described.
Just as certain polymorphisms can affect the microRNA binding sites (see above),
other variations may largely modify the TF binding sites in the promoter. The in silico
three-step analysis that we performed in the case of both polymorphisms identified that
there are TFs whose binding probability is significantly affected by the exchange of the
given nucleotide. Among these, NFATC2, which prefers the C allele of the rs3811792
polymorphic SCD5 promoter, was confirmed by several predictive programs, and this
polymorphism was also shown to modify the promoter activity and to be associated
with diabetes. NFATC2 is a member of the nuclear factor of the activated T-cells family
which plays a central role in inducing gene transcription during the immune response.
Obesity and obesity-related conditions are associated with changes in the immune system
that significantly hinder its ability to mount efficient immune responses [
45
]. Moreover,
increased lipid loads can further dysregulate the defective T-cell responses, and based on
our results, NFATC2, which is one of the potential mediators that is involved, may act in an
allele-specific manner. In addition, an analysis of the 3
0
UTR of the SCD5 gene suggests
the presence of several conserved regions for the microRNA families among vertebrates,
including two sites for mammals [
28
]. Importantly, these microRNA clusters are associated
with several diseases including non-alcoholic fatty liver disease, schizophrenia, autism,
as well as brain and pancreatic cancers [
28
]. The T-cells from patients with rheumatoid
arthritis are characterized by increased levels of microRNA 34b and a decrease in the level
of SCD5, which is a target for this microRNA [
31
], thereby suggesting a further connection
between the regulation of the desaturase levels and the immune-inflammatory functions in
humans.
The present work is pioneering regarding not only the functional characterization
of these factors, but also the association analysis of them, since no one has investigated
the association of a single SCD5 polymorphism with diabetes before, although it would
be plausible given the function of SCD1. Based on our result, the genotype frequency of
rs3811792 SNP significantly differs from the healthy population both in T1DM, which is
primarily caused by autoimmune processes, and in T2DM, which is obesity-related and
also characterized by immune response abnormalities and chronic inflammation. It is
proved that SCD5 may contribute to the development of T2DM, since SCD5 was identified
as a master regulator of fat distribution, which plays a significant role in determining
the visceral adipose tissue accumulation, which is a major risk factor for diabetes [
36
].
On the other hand, the connection of SCD5 with T1DM may be also relevant due to the
significantly higher expression of the enzyme in the pancreas in comparison to that in
other tissues [
26
,
27
,
46
], even though a precise functional explanation is not yet available.
At the same time, the association that we have demonstrated raises several additional
questions. One such question that is yet to be answered is how the same allele of the same
polymorphism can be related to both T1DM and T2DM. A possible explanation is offered
by Wilkin’s accelerator hypothesis, according to which T1DM and T2DM are distinguished
only by the time of the onset of the symptoms, with an earlier-onset diabetes reflecting a
more susceptible genotype [
8
]. This may be reflected by the stronger association that is
measured with the rs3811792 SNP in the T1DM population in comparison to that in the
T2DM population (p= 0.0029 vs. p= 0.0114). It is also worth noting that in T1DM, the
frequency of the TT genotype containing the minor allele increases significantly, while in
T2DM, the frequency of CT heterozygotes differs from that of the control group. This can be
further evidence for the spectrum nature of diabetes according to the accelerator hypothesis
Genes 2022,13, 1784 14 of 17
since it is conceivable that while the CT genotype leads to the late onset of the condition
in association with other environmental factors, while the TT genotype manifests itself
phenotypically at a younger age. In any case, it is certain that the two types of diabetes
are complex multifactorial conditions that develop on the basis of several genetic and
environmental factors, and so the rs3811792 SNP of the SCD5 promoter may only be one
factor in this complex system.
The study that is presented here aimed to shed light on SCD5, the so far neglected
isoform of human stearoyl-CoA desaturases of potential pathological importance. Outlining
the functional upstream promoter region was a necessary first step to investigate the
transcriptional control of the gene expression. The maximal degree of promoter activity
that was ensured by an approximately 1 kb segment was highly dependent on the cell
type, and the pattern closely resembled the tissue specificity of
in vivo
SCD5 expression.
Although these results strongly indicate that the TFs that are responsible for the tissue
specific activation or repression of SCD5 bind to the upstream promoter, the contribution
of possible downstream regulatory elements cannot be ruled out, and a thorough search for
the relevant response elements should be extended accordingly. Since the study of the SCD5
promoter and its polymorphisms was performed in a cellular system
in vitro
, it would be
necessary to prove the effect of these human variations on transcription
in vivo
, as well as to
confirm the in silico predicted allele-dependent TF binding by direct methods either
in vitro
or
in vivo
. Our findings also showed that SCD5 expression is not sensitive to the different
FAs, which is a remarkable difference in comparison to SCD1 expression, and it is likely
related to the somewhat different roles of the two isoforms at the cellular and particularly at
the organismal level. Although apparently SCD1 and SCD5 catalyze the same biochemical
reactions, the formation of unsaturated fatty acyl chains might have significantly diverse
metabolic functions in various cells and tissues. Nevertheless, our
in vitro
findings do
not pinpoint the exact nature of these differences, so this intriguing area requires further
research. Due to the rather low number of cases that we used and in order to increase the
statistical power of the study, the extension of the association analysis to larger control
and patient groups should also be considered. Nevertheless, the association that we found
between a common SCD5 promoter polymorphism and diabetes mellitus not only extends
the list of diabetes-related genes with another item because the cellular defense against
lipotoxicity provides a reasonable, yet currently partly speculative functional relationship
between FA desaturation and the development of diabetes. Experimental data support
that a limited cellular capacity to convert SFAs to unsaturated ones may render
β
-cells
sensitive to FA-induced damages and increase the risk of
β
-cell failure [
47
,
48
]. This is
because
β
-cells are particularly sensitive to SFAs [
49
,
50
], and SCD enzymes allow cells
to convert these highly deleterious molecules into less harmful UFAs, thus providing an
intrinsic defense mechanism against lipotoxicity [
51
,
52
]. A genetic predisposition to lower
the SCD expression, therefore, might contribute to the development of diabetes mellitus,
however, to the extent that this may occur is not yet clear.
In conclusion, the cell line-specific activity and FA insensitivity of the SCD5 promoter,
as well as its tissue expression pattern that is different from that of SCD1, together imply that
there is significantly different transcriptional regulation between the two human desaturase
isoforms, thereby emphasizing their likely different role in lipid metabolism. In addition,
the SCD5 promoter and its polymorphisms may represent a common denominator between
T1DM and T2DM, which otherwise have mostly non-overlapping genetic backgrounds, but
in a not yet fully elucidated manner. At the same time, these results raise several additional
questions that may open new avenues in research to understand SCD5. The identification
of protein and lipid components that regulate the tissue- and cell line-specific expression of
SCD5 may also be a new research area. Although the role of SCD1 in the development of
obesity-related conditions is a relatively clear and widely investigated topic, the potential
relationship of SCD5 in diabetes is only now beginning to be noticed.
Genes 2022,13, 1784 15 of 17
Supplementary Materials:
The following supporting information can be downloaded at: https:
//www.mdpi.com/article/10.3390/genes13101784/s1, Figure S1: Effect of different fatty acids on
pGL3B luciferase activity in HEK293T cells; Table S1: The list of cloning and mutagenic primers used
in SCD5 promoter and SNP analysis.
Author Contributions:
Conceptualization, É.K.; Methodology, V.Z, L.S., and É.K.; Software: É.K.;
Validation, V.Z., G.O., and L.S.; Formal Analysis, K.M. and É.K.; Investigation: V.Z., G.O., L.S., S.S.,
and K.T.; Resources: M.C., K.M., and É.K.; Data curation, V.Z., K.M. and M.C.; Writing-Original Draft
Preparation, V.Z., É.K., and M.C.; Writing, Review, and Editing, É.K. and M.C.; Visualization, V.Z.
and É.K.; Supervision, É.K. and M.C.; Project Administration, É.K.; Funding Acquisition, É.K. and
M.C. All authors have read and agreed to the published version of the manuscript.
Funding:
This work was supported by the Hungarian National Research, Development and Inno-
vation Office (NKFIH grant number: FK138115 and K125201) and by the INKUBÁTOR program of
the Department of Molecular Biology, Semmelweis University, Budapest. Project no. TKP2021-EGA-
24 was implemented with the support provided by the Ministry of Innovation and Technology of
Hungary from the National Research, Development and Innovation Fund, and financed under the
TKP2021-EGA funding scheme.
Institutional Review Board Statement:
The study was conducted according to the guidelines of the
Declaration of Helsinki and approved by the Local Ethical Committee (ETT-TUKEB ad.328/KO/2005,
ad.323-86/2005-1018EKU from the Scientific and Research Ethics Committee of the Medical Research
Council).
Informed Consent Statement:
Informed consent was obtained from all subjects involved in the
study.
Data Availability Statement: Date is contained within the article.
Acknowledgments:
We thank Valéria Mile, Helga Németh and Viktória Molnár for their skillful
technical assistance.
Conflicts of Interest: The authors declare no conflict of interest.
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