(2024) 22:357
Qian et al. BMC Medicine
https://doi.org/10.1186/s12916-024-03583-w

BMC Medicine

RESEARCH

Open AccessA functional variant rs912304 for lateonset T1D risk contributes to islet dysfunction
by regulating proinflammatory cytokineresponsive gene STXBP6 expression
Yu Qian1†, Shu Chen1,2†, Yan Wang1†, Yuyue Zhang1†, Jie Zhang1, Liying Jiang1, Hao Dai1, Min Shen1,
Yunqiang He1, Hemin Jiang1, Tao Yang1, Qi Fu1* and Kuanfeng Xu1*

Abstract
Background Our previous genome‑wide association studies (GWAS) have suggested rs912304 in 14q12 as a suggestive risk variant for type 1 diabetes (T1D). However, the association between this risk region and T1D subgroups
and related clinical risk features, the underlying causal functional variant(s), putative candidate gene(s), and related
mechanisms are yet to be elucidated.
Methods We assessed the association between variant rs912304 and T1D, as well as islet autoimmunity and islet
function, stratified by the diagnosed age of 12. We used epigenome bioinformatics analyses, dual luciferase reporter
assays, and expression quantitative trait loci (eQTL) analyses to prioritize the most likely functional variant and potential causal gene. We also performed functional experiments to evaluate the role of the causal gene on islet function
and its related mechanisms.
Results We identified rs912304 as a risk variant for T1D subgroups with diagnosed age ≥ 12 but not < 12. This variant
is associated with residual islet function but not islet-specific autoantibody positivity in T1D individuals. Bioinformatics
analysis indicated that rs912304 is a functional variant exhibiting spatial overlaps with enhancer active histone marks(H3K27ac and H3K4me1) and open chromatin status (ATAC-seq) in the human pancreas and islet tissues. Luciferase
reporter gene assays and eQTL analyses demonstrated that the biallelic sites of rs912304 had differential allele-specificenhancer activity in beta cell lines and regulated STXBP6 expression, which was defined as the most putative causal
gene based on Open Targets Genetics, GTEx v8 and Tiger database. Moreover, Stxbp6 was upregulated by T1D-related
proinflammatory cytokines but not high glucose/fat. Notably, Stxbp6 over-expressed INS-1E cells exhibited decreasing insulin secretion and increasing cell apoptosis through Glut1 and Gadd45β, respectively.
Conclusions This study expanded the genomic landscape regarding late-onset T1D risk and supported islet function
mechanistically connected to T1D pathogenesis.
Keywords T1D, Stxbp6, Islet function, Islet autoimmunity, Variant†

Yu Qian, Shu Chen, Yan Wang, and Yuyue Zhang contributed equally.

*Correspondence:
Qi Fu
drfuqi@njmu.edu.cn
Kuanfeng Xu
kuanfengxu@njmu.edu.cn
Full list of author information is available at the end of the article© The Author(s) 2024. Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0
International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as longas you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if
you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article orparts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicatedotherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not
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Qian et al. BMC Medicine(2024) 22:357

Background
Type 1 diabetes (T1D) is an autoimmune disease causedby the destruction of pancreatic β cells and the absoluteinsufficiency of insulin [1], which has been increasing
worldwide for several decades [2]. T1D is a multifactorialdisease, with immune, genetic, viral, and environmental
factors contributing to its development [3]. As the initial
trigger of T1D [4, 5], over 80 non-HLA regions have beenreported to be associated with T1D risk [5–7] except for
the HLA risk region (especially HLA-DR-DQ on 6p21)[5]. Studies also indicate that multiple non-HLA risk
regions influence the risk for islet autoimmunity [8–10].
As an autoimmune disease, most T1D candidate genes
were functionally related to immune responses, suchas CTLA4, IL-2RA, PTPN2, and CD226 [5]. However,cumulative evidence supports a role for islet β-cell function in T1D pathogenesis [11]. Several candidate genes
in T1D non-HLA risk regions, such as INS, GLIS3,
CTSH, and CLEC16A, were reported to affect islet function [12]. Our previous study [13] also indicated that thegenetic risk score (GRS) affected fasting C-peptide levels
in newly diagnosed T1D individuals.
Syntaxin binding protein 6 (STXBP6), the sixth member of the syntaxin-binding protein family, is locatedin 14q12, which is a vital part of the SNAP receptor(SNARE) complex and is crucial in neuronal vesicle
trafficking [14]. A prior study has reported that SOX4
affected extracellular insulin delivery by regulating
STXBP6 expression [15, 16], which is related to glucose-stimulated insulin secretion by participating in
the expansion of fusion pores during the insulin exocytosis process [15, 16]. Vujkovic et al. demonstrated that
14q12 was a T2D risk region, with the leading GWASsignificant variant rs11159347 near STXBP6 [17]. Scanning from our previous T1D GWAS [13], rs912304,
another independent leading variant in STXBP6, wasfound to be associated with T1D risk. However, itremains to be determined whether rs912304 or its highlinkage disequilibrium (LD) variant is functionallyrelated to T1D and its related mechanism.
Therefore, this study investigated the association
between the variant rs912304 and T1D subgroups andrelated clinical indicators, including islet autoimmunity
and islet function. We further assessed the functionalvariant(s) and implacted gene(s) in 14q12. Finally, weevaluated the role of the putative causal gene on islet
function and its related mechanism.
Methods
Study participants

A total of 2423 T1D and 5082 subjects were enrolled in the
study. As previously described [13], we recruited 1045 T1D
and 1308 healthy individuals for GWAS scanning, and thePage 2 of 14

validation subjects consisted of 1378 patients and 3774
controls recruited from southeastern, central, and southern China. Only patients diagnosed with insulin-dependent diabetes mellitus within six months and having at least
one positive autoantibody were included in the T1D subjects. Controls were outpatients from the same region withno diabetes and no family history of diabetes. All samples
were collected with appropriate informed consent from allparticipants or guardians. The study was approved by the
Ethics Committee of the First Affiliated Hospital of Nanjing Medical University and was conducted following theprinciples of the Declaration of Helsinki.
Laboratory measurements for glycemic traits
and islet‑specific autoantibodies

A total of 2353 non-diabetic controls from the validation
stage measured plasma glucose and serum insulin levels at
fasting, 30 min, and 120 min based on an oral glucose tolerance test (OGTT). The plasma glucose and serum insulinlevels were measured using a fully automated biochemical analyzer (Roche, Switzerland) and insulin radioimmunoassay kit (BMIBT, China). Insulinogenic Index (IGI) andcorrected insulin response (CIR) were used as indicatorsof insulin release stimulated by oral glucose, while insulin
sensitivity was evaluated using ISI Matsuda and other indices. Next, a mixed meal glucose tolerance test (MMTT)
was performed on the 202 newly diagnosed T1D individuals. This test was not required for T1D patients with fasting
C-peptide levels ≤ 25 pmol/L, but only the fasting C-peptide
level was measured by chemiluminescence (Roche Diagnostics, Switzerland). In addition, islet-specific autoantibodies,including ZnT8A, GADA, and IA-2A, were measured when
T1D subjects enrolled, which was detected separately byradioimmunoassay, as previously described [13].
Genotyping assay

Genomic DNA was extracted from peripheral blood
lymphocytes using the QIAamp DNA blood extraction kit (QIAGEN, Germany). Genotyping of rs912304
in STXBP6 was from two aspects: 1045 T1D and 1308
healthy individuals were extracted for this variant in the
T1D GWAS screening section [13]. The validated 1378
T1D and 3774 subjects were genotyped using TaqMan
assays (Applied Biosystems). The distributions of genotypes in healthy and T1D subjects were in accordancewith the Hardy–Weinberg equilibrium (P > 0.05).
In silico annotation of the functional variant and casualgene

For the potential functional variants, we performed
with RegulomeDB v.2 [18], a tool incorporating

Qian et al. BMC Medicine(2024) 22:357

genomic assays (TF ChIP-seq and DNase-seq from the
ENCODE database and those overlapping the footprints and QTL data), to prioritize functional variant(s)located within 14q12. We also verified the results offunctional predictions using HaploReg v4.2 [19], which
utilized epigenomics data (chromatin states and histone H3 marks) primarily from Roadmap Epigenomics
and ENCODE projects.
To prioritize the most causal gene in 14q12, we firstannotated using our Variant-to-Gene (V2G) pipeline
using Open Targets Genetics [20], which integrates
molecular phenotype quantitative trait loci experiments, chromatin interaction experiments, variant
effect predictor (VEP) from Ensembl, the distance
between the variant and each gene’s canonical transcription start site (TSS), etc. We further identified the
top-ranked eQTL gene using a meta-analysis P value ofmulti-tissues from GTEx v8 [21], confirmed by Tiger,
Translational human pancreatic Islet Genotype tissueExpression Resource [22].
Dual‑luciferase reporter gene assay

Dual luciferase reporter gene assays were performed
to assess whether the double-stranded oligonucleotide
containing either G or T allele at the variant rs912304
showed different enhancer activity. DNA fragments covering the G or T allele at rs912304 (hg38
Chr14:25,006,964) were amplified, and constructed
into the pGL3-promoter plasmids. The pGL3-promoter plasmid with DNA fragment containing either
G or T allele, or empty pGL3-promoter vector in equalamounts was transiently co-transfected into HEK-239
T, INS-1E and MIN6 cell lines with pRL-TK plasmidusing Lipofectamine 3000 transfection reagent (Invitrogen). Twenty-four hours post-transfection, cells
were collected, and luciferase activity was measured
by Dual-Luciferase Reporter Assay System (Promega).
Firefly luciferase activity was expressed as relative luciferase units (RLU) after correction for Renilla luciferase
activity to adjust for transfection efficiency. Data werenormalized to those cells transfected with empty pGL3Promoter vector. All assays were performed with a minimum of four replications in at least three independent
experiments.
Cell culture and treatment

Isolation and culture of primary mouse islets, mouse
β-cell lines (MIN6 and β-TC6), and HEK-293 T cells were
performed as previously described [23]. The rat β-cellline INS-1E (donated by Prof. Xiao Han from Nanjing

Page 3 of 14

Medical University) was cultured in RPMI-1640 medium(11 mM glucose) containing 10% FBS, sodium pyruvate(1 mmol/L), HEPES (10 mmol/L), penicillin (100 U/mL), streptavidin (100 μg/mL), β-mercaptoethanol (50μmol/L), and L-glutamine (2 mmol/L).
T1D-related cytokines consisted of 5 ng/ml IL-1β,
100 ng/ml INF-γ and 10 ng/ml TNF-α, respectively. A
mixture of the three cytokine mixes was added to thecomplete medium of MIN6 cells, INS-1E cells, and
β-TC-6 cells, respectively, according to the amountneeded. The cells were spread in the culture plate at adensity of about 1 × ­106 cells/ml and then incubated in aconstant temperature and humidity incubator (containing 5% ­CO2) at 37 °C. Upon reaching approximately 70%
confluency, the cell culture medium was replaced withcytokines or free medium. In addition, high glucose (20mM glucose) and high FFA (0.5 mM palmitic acid) weretreated in INS-1E with a similar procedure. RNA andprotein samples were accordingly collected after 48 h ofincubation.
siRNA transfection and recombinant adenovirus infusion

MIN6 cells were seeded in 24-well plates and cultured
until they reached 70% confluence. Transient transfection
with siRNA targeting the Stxbp6 gene was done using
Lipo3000 (Life Technologies, USA). All assays were performed 48 h after transfection. INS-1E cells were seededin 6-well or 24-well plates and cultured until they reached
70% confluence. The cell culture medium was replaced
the next day by one containing empty or Stxbp6 recombinant adenovirus (AdMax system), the amount of virus
added per ml of cell culture medium = MOI × cell numers × ­103/virus titer), and the medium was changed after
6 h of transfection according to the cell status. After 48
h of incubation, the samples were collected, and the corresponding RNA and protein were extracted. SiRNA andrecombinant adenovirus were purchased from Shanghai
GenePharma CO. LTD and Shanghai OBio Tech CO.
LTD, respectively.
GSIS functional assay

Transfected INS-1E were grown in 24-well plates andtreated for 48 h accordingly. In brief, we added glucose
to Kreb’s buffer to make 2 mM and 20 mM glucose solution and then discarded the solution after incubatingthem with glucose-free Kreb’s buffer for 60 min. Next,
we added 500 μl 2 mM and 20 mM glucose solution perwell and incubated for 60 min, collected the supernatant,centrifuged at 4 ℃, 3000 rpm, 15 min, and aspirated thesupernatant, and stored at – 80 ℃. Finally, the cell culture supernatant was measured using insulin ELISA kits
(MS300, Ezassay, China).

Qian et al. BMC Medicine(2024) 22:357

Page 4 of 14

For quantifying cell apoptosis after adenovirus transfection, TUNEL staining was performed with YF594
TUNEL Assay Apoptosis Detection Kit (US Everbright,
China) in INS-1E cells according to the instructions.

GLUT1 Polyclonal antibody, 21,829–1-AP, Proteintech;
GADD45B Antibody, DF2375, Affinity). Protein blots
were visualized using SuperSignal West Pico Chemiluminescent Substrate (Pierce Biotechnology) in a ChemiDoc
XRS + system (Bio-Rad Laboratories).

Bulk RNA sequencing

Statistical analysis

RNA-seq methods have been previously described [23].
Briefly, total RNA was extracted from INS-1E using
TRIzol reagent (Invitrogen, CA, USA) according to the
manufacturer’s instructions. On-chip electrophoresis
was used to measure the quality and quantity of extracted
RNA using the Agilent RNA 6000 Nano Kit and Agilent 2100 Bioanalyzer (Agilent Technologies, CA, USA).
Library preparation was performed on samples exhibited
1.9 ≤ A260/A280 ≤ 2.2, RNA integrity number > 8.0, and
28S/18S > 1.0. By enriching poly (A) mRNA with magnetic oligo (dT) beads, total RNA was purified. With therandom hexamer (N6) primers, double-stranded cDNA
fragments were synthesized and subjected to end repair
and adapter ligation. Next, PCR amplification was used
to construct cDNA libraries sequenced on BGISEQ-500
by BGI CO. LTD (Wuhan, China). Gene expression
was quantified using RSEM 1.1.12 software and normalized using the method of fragments per kilobase ofexon model per million reads mapped (FPKM). DEseq2(logFC ≥ 0.5 and adjusted p ≤ 0.05) was used to identify
differentially expressed genes (DEGs) between the Stxbp6
over-expressed and control groups.

The frequencies of allele and genotype were assessed bydirect counting. Associations between the variant andT1D risk in the case–control study and departures fromthe Hardy–Weinberg equilibrium (HWE) were analyzed
by the χ2 test. After testing for heterogeneity, a combined
meta-analysis was performed using the Mantel–Haenszel (fixed-effects) and DerSimonian and Laird (randomeffects) tests. The association of different genotypes with
T1D risk was estimated using odds ratio (OR) tests with
95% CI. The heterogeneity between T1D subgroups was
performed using Cochran’s Q test, considered significant
with I2 ≥ 75% and P ≤ 0.05. The additive model examinedassociations for glycemic quantitative traits using logistic
regression analysis. Values of serum insulin, HOMA-B,
IGI, CIR, HOMA-IR, and I­SIMatsuda were logarithmicallytransformed. The student’s unpaired two-tailed t-test or
Mann–Whitney test was used for pairwise comparisonsdepending on whether the data followed a Gaussian distribution. One-way ANOVA, or the Kruskal–Wallis test,
was used for more than two groups. Error bars represent
mean ± SD. P values were considered statistically significant with a P value of < 0.05. These statistical analyses
were performed using SPSS 18.0, Stata11.0, and GraphPad Prism 9.0 software.

TUNEL assay

q‑PCR

Total mRNA was extracted from tissues and cells using
TRIzol reagent (Life Technologies). RNA from eachsample was reverse transcribed using PrimeScript™ RT
reagent kit (Takara Bio, Kusatsu, Japan). qRT-PCR was
performed with SYBR Green PCR Master Mix (Takara
Biotechnology) and analyzed using a real-time PCR system (StepOneplus, ABI). The relative gene expression was
calculated using the comparative CT (2 − ΔΔCT) methodwith GAPDH as a reference gene. The primer sequencesused are listed in Additional file 1: Table S1.
Western blotting

Cell lysates were prepared using RIPA lysis buffer on
ice. The protein concentration was measured using thebicinchoninic acid (BCA) Assay Kit (Beyotime). WB
was performed using the indicated antibodies (STXBP6
Polyclonal antibody, 10,976–4-AP, Proteintech; β-tubulin
Polyclonal antibody, 10,068–1-AP, Proteintech; Caspase 3
Polyclonal antibody, 19,677–1-AP, Proteintech; Cleaved
Caspase 3 Polyclonal antibody, 25,128–1-AP, Proteintech;
ENO2 Monoclonal antibody, 66,150–1-Ig, Proteintech;

Results
Rs912304 in 14q12 is a risk variant for T1D subgroups
with diagnosed age ≥ 12 years old

In our previous T1D GWAS, 14q12 regions were associated with T1D risk with the leading variant rs912304(Additional file 1: Table S2). In a further case–control
study, we first replicated and validated the association
between rs912304 and T1D (P = 0.0177). Combined
results with the GWAS scanning showed this variant
was associated with T1D risk (OR (95% CI) = 1.18 (1.09–1.27), P = 1.29E − 05) (Table 1). We further analyzed
T1D subjects stratified by the age of T1D diagnosis andfound that this variant was a suggestive risk variant for
those diagnosed age ≥ 12 years old (OR (95% CI) = 1.21
(1.12–1.31), P = 2.54E − 06), but not those diagnosed
age < 12 (OR (95% CI) = 1.05 (0.94–1.17), P = 0.377). A
significant heterogeneity was observed between these
two subgroups (I2 = 76.6%, P = 0.039). We also stratified
T1D subjects according to the number of autoantibodies
and found that rs912304 was associated with single (ORQian et al. BMC Medicine(2024) 22:357

Page 5 of 14

Table 1 The association between rs912304 in STXBP6 and T1D risk
Stage

Genotype distribution (GG/GT/TT)

MAF(%)

Cases

Cases

Controls

Additive model
Controls

OR (95%CI)

P value

Discovery

278/512/215

435/603/219

46.9

41.4

1.28 (1.13-1.45)

9.59E-05

Replication

458/759/302

946/1389/510

44.9

42.3

1.11 (1.01-1.21)

0.0177

Combined736/1271/517

1381/1992/729

45.7

42.1

1.18 (1.09-1.27)

1.29E-05

MAF minor allele frequency. G allele, the reference allele; T allele, the effect allele(95% CI) = 1.17 (1.07–1.27), P = 1.75E − 04) and multiple(OR (95% CI) = 1.14 (1.04 − 1.26), P = 1.03E − 02) antibody-positive T1D individuals without heterogeneity(I2 = 0, P = 0.692) (Table 2). In addition, some T2D subjects might be misclassified as late-onset T1D [24]. T1Dsubjects diagnosed age ≥ 12 were stratified by autoantibody status, and we found that both the subjects withmultiple autoantibodies and those with single autoantibodies showed significant association with the variant
rs912304 without any heterogeneity (Additional file 1:
Table S3).
Rs912304 affects residual islet function in T1D individualsof diagnosed age ≥ 12 years old

We next evaluated the impact of the variant rs912304
on islet function indexes in healthy individuals basedon OGTT. This variant was significantly correlated with
CIR and DI (P = 0.007 and 0.047, respectively) (Table 3).
Subsequently, we assessed its effect on residual β-cellfunction in newly diagnosed T1D individuals, and it
was correlated with fasting C-peptide level (P = 0.0289)(Fig. 1A) not the AUC of 2-h C-peptide level (Fig. 1D).
After further stratified by the age of T1D diagnosis, thisvariant was significantly associated with fasting and
AUC of 2-h C-peptide level (P = 0.0186, and 0.0298,
respectively) (Fig. 1C, F) in T1D subjects with diagnosed
age ≥ 12, which was consistent with their association withresidual β-cell function (≥ 200 pmol/L) in this subgroup(P = 0.037) (Additional file 1: Table S4). However, nosuch correlation was found in those diagnosed age < 12
(Fig. 1B, E; Additional file 1: Table S4). In addition, we
also investigated whether this variant affects the positive
rate of islet-specific autoantibodies in T1D subjects, but
no significant correlation was observed (Fig. 1G–I, Additional file 1: Table S5).
Rs912304 is a functional variant regulating STXBP6
expression in islets

We evaluated the potential functional variant(s) in 14q12
risk regions. We first used RegulomeDB to prioritize
functionally important variants within high linkage disequilibrium (LD, r2 ≥ 0.8) to rs912304. Among the twelve
high LD variants, six (rs912304 and the other five variants) were ranked 1d-1f with high posterior probability (scores > 0.5) (Additional file 1: Table S6). Next, weverified the results of functional predictions using HaploReg v4.2 [19], which utilized epigenomics data (chromatin states and histone H3 marks) primarily from
Roadmap Epigenomics and ENCODE projects. In thisdataset, we found that only rs912304 exhibited spatial
overlap with enhancer, an active chromatin state, in tissues related to islet function (both pancreas and pancreatic islets) (Fig. 2A, Additional file 1: Table S7). It
co-localized more specifically with an enhancer region
marked by H3K4me1 broad peaks in the pancreas andpancreatic islets (Fig. 2A). Further functional follow-up
in the published bulk islets dataset [25, 26], we also foundthat rs912304 mapped to ATAC-Seq peaks (Fig. 2B).

Table 2 The association between rs912304 in STXBP6 and T1D subgroups stratified by islet-specific autoantibody status and age at
T1D diagnosis
GroupsSubgroups

Control
T1D

Genotypes

Additive model

GG

GT

TT

Heterogeneity

OR(95%CI)

P value

I2 (%)

P value

0

0.692

76.6

0.039

1381

1922

729

Multi Abs+

313

497

217

1.14 [1.04,1.26]

1.03E-02

Single Ab+

423

774

300

1.17 [1.07,1.27]

1.75E-04

≥12

504

889

387

1.21 [1.12,1.31]

2.54E-06

232

382

130

1.05 [0.94,1.17]

0.377

＜12

Multi Ab+, at least two autoantibodies were positive among ZnT8A, GADA, and IA-2A; Single Ab+, only one autoantibody-positive among ZnT8A, GADA, and IA-2A.
≥12 and <12 mean the T1D subjects were diagnosed with age ≥12 and <12 years old, respectively

Qian et al. BMC Medicine(2024) 22:357

Page 6 of 14

Table 3 The correlation between rs912304 and glycemic quantitative traits in 2353 healthy individuals based on OGTT​
Traits

GG

GT

TT

β

SE

P

P-adj

n (men/women)

775 (207/568)

1,148 (297/851)

430 (100/330)

0.017

0.013

0.187

Age (years)

54.2 ± 8.8

54.3 ± 9.2

54.3 ± 8.8

0.054

0.265

0.84

BMI (kg/m2)

22.3 ± 2.0

22.2 ± 2.0

22.3 ± 2.0-0.047

0.059

0.425

Fasting

5.17 ± 0.27

5.17 ± 0.27

5.15 ± 0.28

-0.009

0.008

0.179

0.239

30 min post OGTT​8.30 ± 1.39

8.21 ± 1.41

8.09 ± 1.42

-0.094

0.041

0.015

0.021

120 min post OGTT​

6.06 ± 0.98

5.99 ± 0.98

5.98 ± 0.99

-0.045

0.028

0.136

0.109

Fasting

9.44 (6.89-12.63)

9.38 (7.19-12.76)

9.74 (7.12-13.04)

0.007

0.006

0.332

0.29

30 min post OGTT​52.86 (34.87-80.7)

53.48 (34.90-81.71)

53.68 (36.92-83.77)

0.008

0.008

0.342

0.304

120 min post OGTT​38.85 (24.98-58.15)

38.02 (24.82-57.79)

37.14 (24.01-58.62)-0.007

0.009

0.46

0.433

HOMA-β111.3 (84.3-158.9)

115.6 (85.1-156.2)

118.6 (84.7-163.1)

0.01

0.007

0.169

0.16

IGI

14.48 (8.44-24.9)

15.03 (9.15-25.48)

15.88 (8.99-27.14)

0.019

0.011

0.09

0.085

CIR

153.92 (91.93-247.67)

161.45 (99.25-265.84)

177.05 (102.71-294.23)

0.026

0.01

0.007

0.007

HOMA-IR

2.19 (1.59-2.93)

2.18 (1.63-2.92)

2.21 (1.61-2.98)

0.006

0.007

0.411

0.354

ISIc

5.19 (3.89-7.06)

5.25 (3.95-7.03)

5.22 (3.86-6.79)-0.003

0.006

0.682

0.616

67.90 (42.76-115.13)

72.72 (44.13-124.33)

73.56 (46.69-126.39)

0.02

0.01

0.038

0.047

Plasma glucose (mmol/l)Serum insulin (mIU/l)

Islet function

Insulin resistance

Disposition indexes

The risk allele beta coefficients (SE) corresponding to the effect sizes were calculated using a linear regression analysis under an additive model. The analysis of insulinlevels and islet function-related indicators at different time points were logarithmically transformed before analysis
IGI the insulinogenic index, CIR corrected insulin response, ISIc Matsuda’s insulin sensitivity index, DI disposition indexes, derived from IR/HOMA-IR

Collectively, rs912304 is the most potential functionalvariant in pancreatic tissue and islets.
As intron variants likely affect gene regulation [7], we
further assessed the most putative candidate gene regulated by rs912304. Predicted by Open Targets Genetics[20], we found that among the six assigned candidate
genes, STXBP6 was functionally implicated as the topranked potential causal gene with the highest variant
to gene (V2G) score (Additional file 1: Table S8). Multitissue eQTL comparison from the GTEx v8 databaserevealed that STXBP6 is the unique eQTL gene regulated
by rs912304, with a meta-analysis P-value = 1.32871e − 77
(Additional file 2: Fig. S1). Tiger, an integrative datasetwith 404 human islets [22], also showed that STXBP6
is the most significant eQTL gene (P = 8.6e − 3) affectedby rs912304 (Fig. 2C). Then, we performed luciferase
reporter assays in HEK-293 T cells and two widely used
cellular models of the β-cell (rat INS-1E and mouse
MIN6). The construct containing the G allele of rs912304
exhibited higher enhancer activity of the STXBP6 genethan that of the T allele in HEK-293 T (P < 0.001), INS1E (P < 0.01), and MIN6 (P < 0.01) (Fig. 2D–G). These data
indicate that rs912304 was the most functional variant
in pancreatic islets, which regulated the candidate gene
STXBP6 as an enhancer.

T1D‑related proinflammatory cytokine, not high glucose
or FFA, up‑regulates Stxbp6 expression on islet beta cells

To investigate the effects of different stress on Stxbp6
expression in islet β cells, we simulated them with three
conditions in vitro: inflammatory cytokine, high glucose(20 mM glucose), and high fat (FFA). Under high glucose
or FFA, Stxbp6 expression was not altered in cultured
INS-1E and MIN6 cell lines (Fig. 3A, B). The results
were similar to those observed under similar conditions derived from the published GEO database (Additional file 2: Fig. S2). However, we reanalyzed single-cell
RNA sequencing (scRNA-seq) in the Human Pancreas
Analysis Program (HPAP) [27]. The results showed that
STXBP6 expression was upregulated in islet beta cellsof T1D subjects (Fig. 3C). Meanwhile, after stimulatingwith T1D-related proinflammatory cytokine (TNF-α,
IL-1β, and IFN-γ), we observed that Stxbp6 mRNA level
was increased in primary mouse islets, and three β-celllines (INS-1E, MIN6, and β-TC6) (all P < 0.05), (Fig. 3D–
G). These were consistent with the fact that the Stxbp6
protein level was upregulated in the islet β-cell line
stressed with cytokines (P < 0.05) (Fig. 3H, I). These datasuggested that Stxbp6 was specifically stimulated in islet
beta cells by T1D-related cytokines but not high glucose
or lipids.

Qian et al. BMC Medicine(2024) 22:357

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Fig. 1 The relationship between the variant rs912304 and residual islet function and islet autoimmunity in T1D individuals. A-C The association
between rs912304 and fasting C-peptide level in total newly diagnosed T1D subjects, those diagnosed age <12 and ≥12 years old, respectively. D-F
The association between rs912304 and 2-hour C-peptide AUC in total newly diagnosed T1D subjects, those diagnosed age <12 and ≥12 years old,
respectively. G-I The association between rs912304 and the positive rate of ZnT8A, GADA, and IA-2A in total T1D subjects. The genotype distributionof rs912304 in newly diagnosed T1D subjects: GG = 52, TG = 102, TT = 48; T1D subjects with diagnosed age <12: GG = 10, TG = 26, TT = 8; and T1Dsubjects with diagnosed age ≥12: GG= 42, TG = 76, TT = 40. The genotype distribution of rs912304 in islet autoimmunity: GG=616, GT=1004,
TT=420 for ZnT8A; GG=644, GT=1061, TT=443 for GADA; GG=639, GT=1059, TT=443 for IA-2A. The above correlations were analyzed by linear
regression analysis and were corrected for sex and duration of T1D disease. Data are mean with 95%CI. *P<0.05 was considered as significant

Stxbp6 promotes β‑cell apoptosis and inhibits insulin
secretion via regulating Gadd45β and Glut1 expression
respectively

We first over-expressed Stxbp6 in INS-1E cells withrecombinant adenovirus to assess its effect on apoptosis.
Over-expression of Stxbp6 did not affect apoptosis-associated protein Caspase 3 but significantly increased theexpression of cleaved Caspase 3 (Fig. 4A). The apoptosis detection by TUNEL assay also revealed a significant
increase in the Stxbp6 overexpression group (Fig. 4B)
in INS-1E cells, suggesting that the Stxbp6 has a role inpromoting apoptosis in pancreatic β-cells. Furthermore,
although no significant change in the transcript levels of
Ins1 and Ins2 was observed in the Stxbp6 overexpressed
INS-1E cells (Fig. 4C), insulin secretion, not intracellular
insulin content, was suppressed under high glucose stimulation conditions (P < 0.05) (Fig. 4D, E).

Further RNA sequencing revealed that 67 and 285
genes were significantly down-regulated or up-regulated in Stxbp6 overexpressed INS-1E cells (RPKM > 1)
(Fig. 5A, B). Pathway enrichment analysis revealed
that the downstream genes regulated by the Stxbp6
gene were significantly enriched in insulin secretionand the p53 signaling pathway (Fig. 5C, Additional
file 1: Table S9). We validated the RNA-seq results
and showed that about half of the downstream genes
were associated with cell survival and apoptosis (e.g.,Cdk2, Gadd45β, and Tp73; Fig. 5D), insulin secretion(e.g., Eno2 and Slc2a1; Fig. 5E), and other related genes(e.g., Cxcl12, Cygb, and Mgarp; Additional file 2: Fig.
S3A, B). Knockdown of Stxbp6 with SiRNA (Si-332)
in cytokine-stimulated MIN6 cells (Additional file 2:
Fig. S3C), we found that mRNA levels of genes related
to apoptosis (Cdk2 and Gadd45β, Fig. 5F), endocrineQian et al. BMC Medicine(2024) 22:357

Page 8 of 14

Fig. 2 Rs912304 is a functional variant regulating STXBP6 expression in islet β-cells. A The overlaps between rs912304 and chromatin state, histone
H3 marks in Roadmap Epigenomics and ENCODE projects. The color red means rs912304 exhibited spatial overlaps with peaks of epigenomics
data (chromatin states or histone H3 marks), blue means no spatial overlaps, and grey means the data were missing. B UCSC genome browser
data showed that ATAC-Seq peaks overlapped with a genomic sequence containing rs912304 in published human islets. The location of rs912304
is highlighted with the red line. C Stxbp6 is the most significant eQTL gene regulated by rs912304 in 404 human islets derived from the Tiger
database, highlighted in red. D The plasmid constructs containing different alleles of rs912304 enhancer and STXBP6 gene promoter. E-G The
transcriptional activity of STXBP6 promoter after transfected to HEK-293T, islet β cell line INS-1E, and MIN6, respectively. The transcriptional activity
was detected by the luciferase dual reporter gene method. Data are mean±SD. P-values were calculated by t-test, and at least three independent
replicates were performed in each group. **P<0.01;***P<0.001

system (Slc2a1, Fig. 5G), immune system, and othersignaling pathways (e.g., Cxcl12, Additional file 2: Fig.
S3D, E). We further verified the apoptosis- and isletfunction-related genes, consistent in both overexpression and knockdown experiments at the protein level.
We found that the expression level of the Gadd45β
was significantly increased in INS-1E cells after theoverexpression of the Stxbp6 (P < 0.01), suggesting that
Gadd45β was associated with Stxbp6 induced apoptosis in INS-1E cells. In addition, while Eno2 did notchange significantly, the expression of Glut1 decreased
significantly (P < 0.01), suggesting that the Glut1 was
associated with Stxbp6-mediated reduction of insulin
secretion in INS-1E cells (Fig. 5H).

Qian et al. BMC Medicine(2024) 22:357

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Fig. 3 Stxbp6 is significantly upregulated in pancreatic islet β-cells stimulated with T1D-related proinflammatory cytokines. A-B The alternationof Stxbp6 mRNA levels in INS-1E and MIN6 under high glucose (20mM glucose) and high lipid (0.5mM palmitic acid) stimulation.C The differenceof STXBP6 mRNA expression in human islets from nPOD. AAB means islet antibody-positive normoglycemic population. D-G The mRNA levelof Stxbp6 in primary mouse islets, INS-1E, MIN6, and β-TC6 cell lines after stimulation with mouse/rat cytokines (5 ng/ml IL-1β, 100 ng/ml INF-γ
and 10 ng/ml TNF-α) for different periods. H-I Alternation of Stxbp6 protein level in INS-1E and MIN6 cell lines after 6 and 24 h stimulation
with the above proinflammatory cytokines. Data are mean±SD. P-values were calculated by t-test, and at least three independent replicates were
performed in each group. *P<0.05;**P<0.01; ***P<0.001

Discussion
In this study, we first reveal that rs912304 in STXBP6 is a
T1D risk variant associated with fasting and 2-h C-peptide levels in newly diagnosed T1D individuals diagnosed
age over 12 years old. Next, rs912304, as an enhancer,
functionally regulates STXBP6 expression in islet beta
cells. Finally, Stxbp6 can inhibit insulin secretion andpromote islet β-cell apoptosis by regulating Glut1 andGadd45β expression.
Studies have observed an increasing incidence of adultT1D in multiple ethnicities [28] and revealed significant
heterogeneities in T1D subgroups of age at T1D diagnosis [29]. Although LD score regression analysis showeda highly correlated genetic basis in early- and late-onset
T1D individuals [13], our previous study demonstrated
that HLA regions showed stronger effect sizes in earlyonset T1D individuals [13]. Studies also identified uniquerisk regions and the same risk regions with more effectsize in early-onset T1D individuals [30, 31]. Steck et al.
[32] further found that non-HLA risk variants, such as
rs6476839 in GLIS3, and rs4900384 near VRK1 weresignificantly associated with progression to T1D in individuals < 12 years old, whereas rs3184504 in SH2B3
was significant in those ≥ 12. This study revealed that
rs912304 in STXBP6 is a suggestive non-HLA risk variant for late-onset T1D individuals (diagnosed age ≥ 12).

Qian et al. BMC Medicine(2024) 22:357

Page 10 of 14

Fig. 4 Effect of Stxbp6 overexpression on apoptosis, insulin synthesis, and secretion in INS-1E cells. A Protein expression of Stxbp6, Caspase 3
and cleaved- Caspase 3 in INS-1E cells treated with Stxbp6 recombinant adenovirus. The relative change of protein was calculated on the right.
B Apoptosis detection by TUNEL assay. Scale bar 100μm. C Changes in insulinogenic gene Ins1/Ins2 mRNA levels after overexpression of Stxbp6
in INS-1E cell. D Insulin secretion at glucose concentrations of 2 mM and 20 mM, respectively; E, Intracellular insulin levels after overexpressionof Stxbp6. Data are mean±SD. P-values were calculated by t-test, and at least three independent replicates were performed in each group.
*P<0.05;**P<0.01; ***P<0.001

These suggested further exploration with GWAS to hunt
for more potential non-HLA risk regions for T1D subgroups with different diagnosed ages.
Prior studies demonstrated a shared genetic basis for
T1D and islet-specific autoantibody positivity, such as
rs1990760 in IFIH1 for IA-2A and rs12722495 in IL2RA
for GADA positive rate [33, 34]. However, we found no
significant association between rs912304 and the positivity of any islet-specific autoantibodies (ZnT8A, GADA,
or IA-2A), indicating the variant did not affect islet
autoimmunity. Multiple T1D genetic risk variants also
affected islet function, for instance, rs3825932 in CTSH

affected insulin secretion [35]. For the variant rs912304,
prior studies have revealed its effect on 2-h glucose challenge [36] and HbA1C levels [37]. Our study found that
rs912304 was associated with glucose-stimulated insulin
secretion in healthy individuals based on OGTT, and further affected residual islet function in newly diagnosed
T1D individuals, especially of diagnosed age ≥ 12. Thus,
we speculated that rs912304 might contribute to T1D byregulating islet function.
For complex common diseases, multi-risk variants
reaching GWAS significance are usually observed ina certain risk region, and the lead variant is not always

Qian et al. BMC Medicine(2024) 22:357

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Fig. 5 Gadd45β and Glut1 are two key downstream genes regulated by Stxbp6 in islet β-cell lines. A Volcano plot of recombinant adenovirus
overexpressing Stxbp6 gene in INS-1E cells with downstream differential gene expression. Red dots represent genes with rising differential
expression, and blue dots represent genes with falling differential expression. DEGs were screened using logFC>0.5, FPKM>1, P-adj<0.05, Q
value<0.1. B Heatmap of Stxbp6 gene downstream related genes based on RNA-Seq hints. C Analysis of pathway enrichment of DEGs using
the KEGG database. D Effect of overexpression of Stxbp6 gene in INS-1E cells on cell proliferation and apoptosis genes. E Effect of overexpressionof Stxbp6 gene in INS-1E cells on genes related to endocrine system. F-G Effect of knocking down Stxbp6 gene in MIN6 cells on genes related
to the endocrine system and apoptosis. H Western blotting detection of protein expression of Gadd45β, Glut1, and Eno2 in INS-1E cells treatedwith Stxbp6 recombinant adenovirus. The relative change of protein was calculated on the right. Data are mean±SD. P-values were calculatedby t-test, and at least three independent replicates were performed in each group. *P<0.05;**P<0.01; ****P<0.0001

the most likely one to be functional. Moreover, mostrisk regions contain multiple risk variants and candidate
genes, making it crucial to identify the most functionalvariant and causal gene within these regions. Prior studies [7, 38, 39] have discovered the most functional variants and putative implicated genes for T1D risk using
fine mapping, epigenetic data, etc. Studies also found that
14q12 was the risk region for white blood cell and monocyte count [40], rs2332462 and rs2038700 in STXBP6(in high LD to rs912304) were predicted to be the mostcausal variants for these two counts, respectively. However, our study indicated that rs912304 is the mostcasual variant in pancreatic tissues or islets by overlapping with DNase I hotspots, H3K4me1, and ATAC-Seq.
These implicated that different tissues might have different functional variants. In addition, from the ChIPSeq experiments integrating into HaploReg v4.2 [19],DNA regions spanning rs912304 were found to bind two
small MAF proteins (MAFF and MAFK), which regulate
islet beta cell survival [41]. Regulatory motifs prediction
by Position Weight Matrix (PWM) showed a different
allele-specific alternation of this variant to pdx1, which
involves the early development of the pancreas and regulates glucose-dependent insulin gene expression [42].

Qian et al. BMC Medicine(2024) 22:357

Collectively, these data further demonstrated rs912304 as
an islet-specific functional variant.
A prior study revealed that STXBP6 induced by needleshaped hydroxyapatite (n-nHA)-treated macrophages
triggers autophagy, which markedly promotes macrophage fusion into multinucleated giant cells (MNGCs)[43], which implicated STXBP6 as an immune function
related gene. Moreover, 14q12 was reported to be a T2D
risk region with the leading variant rs11159347 [17].
Using locus-to-gene pipeline (L2G) from Open Targets
Genetics [20] prioritized STXBP6 as the most potential
causal gene for T2D with the highest L2G score. This
study identified STXBP6 as the most putative causal
gene in islet beta cells, regulated by the functional T1D
risk variant rs912304, independent of rs11159347. Prior
studies have demonstrated that the proinflammatory
cytokines IFN-γ, IL-1β, and TNF-α are produced mainlyby islet-infiltrating immune cells and affect beta cell function and apoptosis, contributing to T1D development[44, 45]. Our study further revealed that STXBP6 was
upregulated in islet β-cells under such proinflammatory
cytokine stimulation. Consistently, human islets scRNAseq analysis in the HPAP dataset [27] showed that
STXBP6 was increased in islet beta cells of T1D subjects.
Despite its relatively low expression in islet beta cells,
STXBP6 could participate in the expansion of fusion
pores during the insulin exocytosis process [15, 16] andinhibit glucose-stimulated insulin secretion regulated by
SOX4 [15, 16]. Although Glut2 is well recognized as the
glucose transporter contributing to insulin secretion [46],this gene was not regulated by Stxbp6 in mouse pancreatic beta cells. However, this study indicated that Glut1,
another glucose transporter isoform, was down-regulated by Stxbp6 in islet beta cells, facilitating glucosestimulated insulin secretion [47]. Moreover, our study
suggested that Stxbp6 also promoted islet β cell apoptosis, consistent with the downstream genes of Stxbp6 thatwere enriched in the p53 pathway. Gadd45β, a typicalgene in the p53 pathway, was upregulated by Stxbp6 inislet beta cells and is involved in the positive regulationof the apoptotic process [48, 49]. Interaction networks
from GeneMANIA (http://​genem​ania.​org/) and STRING(https://​cn.​string-​db.​org/) revealed that Stxbp6 might
regulate Glut1 and Gadd45β indirectly. As cumulative
evidence supported a critical role for β-cells contributing
to T1D pathogenesis [11], we speculated that STXBP6
might contribute to T1D pathogenesis via regulating islet
function and beta cell apoptosis.
Our study also has several limitations. First, due to thedifferences in minor allele frequency, LD patterns, andeffect size in different populations, no single population
is sufficient for fully uncovering the variants underlying T1D in all populations. Thus, studies should further

Page 12 of 14

explore the association between rs912304 and late-onset
T1D risk in other populations. Second, although Stxbp6
regulated Glut1 and Gadd45β in islet beta cells, the exact
contributions of these two genes in mediating the effectsof Stxbp6 on beta cell function and survival still need further investigation. Finally, the function of Stxbp6 in the
immune regulation related to T1D risk should be further
studied, as both innate and adaptive immune abnormalities play critical roles in T1D pathogenesis [50].

Conclusions
In summary, we have identified rs912304 as a functional variant for late-onset T1D risk, affecting residual
islet function in newly diagnosed T1D individuals. Thisvariant regulates STXBP6 expression in islet beta cells,
a causal gene promoting islet β-cell apoptosis and inhibiting insulin secretion. These findings expanded the
genomic landscape regarding T1D risk and prompted
the genetic heterogeneity in T1D subgroups with different diagnosed ages. This study also showed the evidence
supported a role for β-cells in T1D pathogenesis, whichneeds further exploration.
Abbreviations
ATAC-seq	The assay for transposase-accessible chromatin using
sequencing
CD226	DNAX accessory molecule-1
Cdk2	Cyclin-dependent kinase 2
CI	Confidence interval
CIR	Corrected insulin response
CLEC16A	C-type lectin domain containing 16A
CTLA4	Cytotoxic T lymphocyte-associated antigen-4
CTSH	Cathepsin H
Cxcl12	C-X-C motif chemokine ligand 12
Cygb	Cytoglobin
DEGs	Differentially expressed genes
DI	Disposition indexes
ENO2	Enolase 2
eQTL	Expression quantitative trait loci
FFA	Free fatty acid
FPKM	
Fragments per kilobase of exon model per million reads
mappedGADA	Glutamic acid decarboxylase antibodies
Gadd45β	Growth arrest and DNA damage-inducible 45Β
GAPDH	Glyceraldehyde-3-phosphate dehydrogenase
GLIS3	Glis family zinc finger 3
Glut1	The glucose transporter type 1
GRS	Genetic risk score
GSIS	Glucose-stimulated insulin secretion test
GWAS	Genome-wide association studies
H3K27ac	Histone H3 lysine 27 acetylation
H3K4me1	Histone H3 lysine 4 monomethylation
HbA1C	Hemoglobin A1C
HLA	Human leukocyte antigen
HOMA-B	Homeostasis model assessment of beta-cell function
HOMA-IR	Homeostasis model assessment of insulin resistance
HPAP	Human pancreas analysis program
HWE	Hardy-Weinberg equilibrium
IA-2A	Islet antigen 2 antibodies
IFIH1	Interferon induced with helicase C domain 1
IGI	Insulinogenic index
IL-1β	Interleukin-1Β
IL-2RA	Interleukin 2 receptor subunit alphaQian et al. BMC Medicine(2024) 22:357

Page 13 of 14

INF-γ	Interferon-ΓINS	Insulin
ISIMatsuda	Insulin sensitivity index of Matsuda
L2G	Locus-to-gene pipeline
LD	Linkage disequilibrium
MAF	Minor allele frequency
MAFF	Maf Bzip transcription factor F
MAFK	Maf Bzip transcription factor K
Mgarp	Mitochondria localized glutamic acid-rich protein
MMTT	Mixed meal glucose tolerance test
nPOD	The Network For Pancreatic Organ Donors With Diabetes
OGTT​	Oral glucose tolerance testOR	Odds ratio
PTPN2	Protein tyrosine phosphatase non-receptor type 2
PWM	Position weight matrix
RIPA	Radio immunoprecipitation assay
scRNA-seq	Single-cell RNA sequencing
SD	Standard deviation
SEM	Standard error of mean
SH2B3	Sh2B adaptor protein 3
siRNA	Small/short interfering RNA
Slc2a1	Solute carrier family 2 member 1
SNARE	Soluble N-ethylmaleimide-sensitive factor attachment protein
receptor
SOX4	SRY-box transcription factor 4
STXBP6	Syntaxin binding protein 6
T1D	Type 1 diabetes
T2D	Type 2 diabetes
TNF-α	Tumor necrosis factor-ΑTp73	Tumor protein P73
TSS	Transcription start site
V2G	Variant-to-gene
VEP	Variant effect predictor
VRK1 	VRK serine/threonine kinase 1
ZnT8A	Zinc transporter 8 autoantibody

Authors’ contributions
K.X. directed the study design, performed statistical analyses, and drafted theinitial manuscript. Q.F. and T.Y. participated in the study design and revised
the manuscript. Y.Q., S.C., Y.W. and Y.Z. performed the cell and molecularexperiments, reviewed and edited the final manuscript. J.Z., L.J., and Y.H.
conducted part of the statistical analyses and revised the manuscript. H.D.,
M.S. and H.J. performed overall project management and sample processing
and carried out laboratory measurements. All authors read and approved thefinal manuscript.

Supplementary Information

Author details
1
Department of Endocrinology and Metabolism, the First Affiliated Hospitalof Nanjing Medical University, Nanjing 210029, China. 2 Department of Endocrinology, Affiliated Hospital 2 of Nantong University, Nantong First People’s
Hospital, Nantong 226000, China.

The online version contains supplementary material available at https://​doi.​
org/​10.​1186/​s12916-​024-​03583-w.
Additional file 1: Table S1-Table S9. Table S1. The list of primer sequences
for q-PCR used in this study. Table S2. The lists of the variants in 14q12
regions associated with T1D risk. Table S3. The association between
rs912304 in STXBP6 and late-onset T1D subjects stratified by islet-specificautoantibody status. Table S4. Correlation of rs912304 with residual islet
function in newly diagnosed T1D individuals. Table S5. The association
between rs912304 and the positive rate of GADA, ZnT8A and IA-2A.
Table S6. Identification of the potential functional variants in the suggestive T1D risk regions 14q12 using RegulomeDB. Table S7. The spatial overlaps for potential functional variants in 14q12 with epigenomics datain
pancreas and islets. Table S8. The overall variant to genescores implicatedby this variant rs912304 by Open Targets Genetics. Table S9. The lists of therepresentive enriched KEGG and GO pathway in Stxbp6 over-expressed
INS-1E cells
Additional file 2: Figures S1-S3. FigS1. Multi-tissue eQTL Plot for rs912304
derived from the GTEx Analysis Release V8 (dbGaP Accession phs000424.
v8.p2). FigS2. Expression of STXBP6 gene in pancreas or islets under
different conditions in GEO database. FigS3. Validation with qPCR for the
potential downstream genes identified by RNA-Seq
Additional file 3. Original blot images
Acknowledgements
We thank all study participants and the research staff who participated inthis work. We appreciate the assistance provided by the Core Facility of theFirst Affiliated Hospital of Nanjing Medical University in conducting theexperiment.

Funding
This work was supported by the National Natural Science Foundation of China(82370839, 81670715, 82070803, and 82200888), Jiangsu Province Youth Medical Talents Project (QNRC2016584), the Natural Science Foundation of Jiangsu
Province (BK20220714 and BK2012486).
Availability of data and materials
All data is provided in the manuscript and in the supplementary tables.
Data availability
No datasets were generated or analysed during the current study.

Declarations
Ethics approval and consent to participate
All experiments were approved by the Medical Ethics Committee of the First
Affiliated Hospital of Nanjing Medical University (2021-SR-298). Experiments
were conducted in accordance with relevant institutional guidelines andregulations.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.

Received: 20 March 2024 Accepted: 23 August 2024

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