(2022) 24:26
Chen et al. Biological Procedures Online
https://doi.org/10.1186/s12575-022-00189-5Open Access

RESEARCH

The Critical Biomarkers Identificationof Insulin Signaling Involved in Initiating cAMP
Signaling Mediated Salivary Secretion in Sjogren
Syndrome: Transcriptome Sequencing in NOD
Mice Model
Bo Chen1 , Jiannan Zhou1, Tianjiao Mao1, Tingting Cao1, Shilin Hu1, Wenqi Zhang2, Xueyang Li1, Xiuni Qin3,
Xintong Liu4,5, Nobumoto Watanabe4,5 and Jiang Li1*

Abstract
Background: Sjogren’s syndrome (SS) is an autoimmune disorder characterized by the destruction of exocrine
glands, resulting in dry mouth and eyes. Currently, there is no effective treatment for SS, and the mechanisms
associated with inadequate salivary secretion are poorly understood.
Methods: In this study, we used NOD mice model to monitor changes in mice’s salivary secretion andwater consumption. Tissue morphology of the submandibular glands was examined by H&E staining, andImmunohistochemical detected the expression of AQP5 (an essential protein in salivary secretion). Global gene
expression profiling was performed on submandibular gland tissue of extracted NOD mice model using RNA-seq.
Subsequently, a series of bioinformatics analyses of transcriptome sequencing was performed, including differentiallyexpressed genes (DEGs) identification, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG)
enrichment analysis, PPI network construction, hub gene identification, and the validity of diagnostic indicators using
the dataset GSE40611. Finally, IFN-γ was used to treat the cells, the submandibular gland tissue of NOD mice model
was extracted, and RT-qPCR was applied to verify the expression of hub genes.
Results: We found that NOD mice model had reduced salivary secretion and increased water consumption. H&E
staining suggests acinar destruction and basement membrane changes in glandular tissue. Immunohistochemistry
detects a decrease in AQP5 immunostaining within acinar. In transcriptome sequencing, 42 overlapping DEGs wereidentified, and hub genes (REN, A2M, SNCA, KLK3, TTR, and AZGP1) were identified as initiating targets for insulin
signaling. In addition, insulin signaling and cAMP signaling are potential pathways for regulating salivary secretionand constructing a regulatory relationship between target-cAMP signaling-salivary secretion.

*Correspondence: ljiang@gzhmu.edu.cn
1
Guangdong Engineering Research Center of Oral Restoration
and Reconstruction, Affiliated Stomatology Hospital of Guangzhou Medical
University, #195 Dongfeng West Road, Guangzhou 510140, Guangdong,
China
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Chen et al. Biological Procedures Online(2022) 24:26

Page 2 of 16

Conclusion: The new potential targets and signal axes for regulating salivary secretion provide a strategy for SS
therapy in a clinical setting.
Keywords: Sjogren syndrome (SS), Insulin signaling, cAMP signaling, Salivary secretion, Biomarkers, NOD mice model

Introduction
Sjogren’s syndrome (SS) is a multi-system rheumatoid
inflammatory disease characterized by dry mouth, dryeyes, dry skin, and systemic multi-organ secretion disorders, causing multi-organ system damage and tumors.
The high incidence of xerostomia caused by decreased
salivary secretion is the acute lesion of the disease andthe initiating factor of body system damage [1–5]. Salivary gland secretion dysfunction and structural degeneration lead to SS, which produces not only dry mouth, dryeyes, and systemic dryness, but is also often secondary tosystemic diseases such as pulmonary fibrosis, gastritis,
liver cirrhosis, pancreatitis, renal insufficiency, osteoarthritis, and neurological disorders, often accompanied
by malignant tumors such as thyroid cancer, oral cancer,
thymic cancer, and gastric cancer, significantly lymphoma’s risk is increased by more than 40 times [6, 7].
SS prevalence varies among populations ranging from
0.1 to 0.72%, a common rheumatic disease [8, 9]. It is estimated that SS is about 10 times more frequent in womenthan in men [10]. SS may extend from autoimmune
endocrinopathy to affect a variety of extraducular manifestations such as muscle, lungs, blood, and skin [11].
Disease increases mortality compared to systemic lupus
erythematosus [12]. Unfortunately, all therapies tested
to date have proven ineffective in reversing the course ofSS, and research specific to SS in the field of rheumatology is relatively lacking. Therefore, SS significantly harms
human health, and an in-depth analysis of its mechanism
is of great scientific significance.
Despite significant differences in immune defense
mechanisms, evolutionary distance, and living
environment, mouse models have been widely usedin biomedical research to avoid ethical challenges inhuman research [13–15]. The non-obese diabetic strain(NOD), a spontaneous autoimmune disease mouse, isa good mouse model for studying SS [16–19]. Salivary
flow rate and relative protein concentration in saliva
were reduced compared to control mice [20]. Comparedwith C57BL/6 mice, tear secretion, conjunctival goblet
cells, and lymphocyte infiltration were changed [21].
Transcriptome sequencing is a second-generation
sequencing platform to obtain almost quickly andcomprehensively all transcripts and gene sequences ofa specific cell or tissue of a species in a particular state,
which can be used to study gene expression, function,
structure, alternative splicing, and new transcript

prediction. At present, transcriptome sequencing has
been widely used in basic research, clinical diagnosis,
and drug discovery [22–25].
Here, NOD mice models were selected for animal-levelstudies to characterize the clinical features of SS. ICR
mice were chosen as controls to monitor salivary secretion and water consumption dynamically. The submandibular gland tissues of mice are dissected and extracted
to assess morphological changes of the submandibular
gland tissues by H&E staining. In contrast, the expressionof AQP5, an essential protein secreted by saliva, is evaluated by Immunohistochemical analysis. We obtained the
expected experimental results from animal experiments:
NOD mice model showed reduced salivary secretion andincreased water consumption, H&E staining suggested acinar destruction and changes in the basement membrane,
and Immunohistochemical detected a decrease in AQP5
staining in the cell membrane of the acinus. The experimental results also suggest symptoms of xerostomia.
To investigate the regulatory mechanism of salivary
secretion in SS, we performed transcriptome sequencing analysis of submandibular gland tissue from extracted
NOD mice model using RNA-seq. Subsequently, a seriesof bioinformatics analyses were performed on RNA-seq,
and hub genes (REN, A2M, SNCA, KLK3, TTR, and
AZGP1) were identified as the initiating targets of insulin
signaling. The effectiveness of the targets was confirmedby analysis and in vitro experiments. In addition, insulin
signaling and cAMP signaling are potential pathways forregulating salivary secretion and constructing a regulatory relationship between target-cAMP signaling-salivary
secretion.
In conclusion, this study explores a promising novel
targeting molecule for predicting SS development andanalyzes that insulin signaling and cAMP signaling may
be potential pathways for regulating salivary secretion.
Hub genes (REN, A2M, SNCA, KLK3, TTR, and AZGP1)serve as initiating targets for insulin signaling and construct a regulatory relationship between target-cAMPsignaling-salivary secretion. In SS development, targetingthese hub genes may represent a novel strategy for therapeutic interventions in SS patients.

Materials and Methods
Animal Study

Female ICR and NOD mice models (6 weeks old, 16-20 g)
were purchased from Gempharmatech Co., Ltd. (Jiangsu,

Chen et al. Biological Procedures Online(2022) 24:26

China). At a constant temperature of 25 ± 1 °C, all micefreely acquire food and water for 12/12 h in a light/darkcycle. ICR mice and NOD mice, four each, dynamicallymonitored the indicators of mice. At the observation
endpoint, mice were anesthetized to remove submandibular gland tissue for H&E staining and immunohistochemical assays. All procedures involving animals wereconducted in accordance with the ethical standards ofthe Institutional Animal Care and Use Committee of
Guangzhou Medical University (approval No. GY2022119, approved on June 26, 2022).
Salivary Flow and Water Consumption Assessment

Previously described methods measured salivary secretion [26]. Mice fasted overnight before saliva was collected. Mice were routinely anesthetized with sodium
pentobarbital (40 mg/kg) intraperitoneally, and pilocarpine (1.0 mg/kg) was injected subcutaneously. Saliva
was collected for 10 minutes: a glass capillary wasplaced on the side of the mouth under the tongue andheld steadily during a 10-min period to collect saliva
into a tube. The weight difference in the tubes before
and after saliva collection was calculated. The salivary
secretion index was calculated by the increase (ug)/body weight (g). The water consumption of NOD and
ICR mice for 18 days was recorded. Each mouse’s average water consumption index was calculated as water
consumption (ml)/body weight (g), and the changes
among mice were observed.
Hematoxylin and Eosin Staining

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xylene and hydrated in an ethanol gradient. After rinsing
with distilled water, a hydrogen oxide solution blocked
endogenous peroxidase activity. Tissue sections wereincubated with primary antibody AQP5 (1:200) overnightat 4 °C. Tissue sections were then incubated with goatanti-rabbit IgG antibody for 60 min at room temperature.
Sections were dehydrated in graded ethanol series andsealed with neutral resin. Finally, the positively stained
cells were observed under a light microscope.
Transcriptome Sequencing and Microarray Data

Submandibular gland tissue from ICR and NOD micemodels was extracted, and total RNA was extracted from
fresh tissue using Trizol reagent, followed by strict quality control of RNA samples. mRNA with polyA tail isenriched by Oligo (dT) magnetic beads, and the obtainedmRNA is randomly interrupted with divalent cations in
the NEB Fragmentation Buffer. The library is built according to the NEB standard or strand-specific library building
method. Qubit2.0 Fluorometer was used for preliminary
quantification, Agilent 2100 bioanalyzer was used to detect
the insert size of the library, and qPCR was used to quantify the effective concentration of the library accurately.
Based on the HiSeq platform, all mRNAs transcribedfrom tissues were sequenced, and the sequencing experiment was constructed using the Illumina TruseqTM RNA
sample prep kit method. In addition, we downloaded thegene expression profile data GSE40611 [27] from the GEO(http://​www.​ncbi.​nlm.​nih.​gov/​geo/) database as a reference for comparison with our RNA-seq results.

Hematoxylin and Eosin (H&E) staining is used to observe
general histopathology. Use xylene to dewax submandibular gland sections (5 μM). Gradient ethanol is then used tohydrate the submandibular gland sections. Next, stain thesubmandibular glands with H&E reagent (Wuhan Servicebio Technology Co. LTD). The tissue morphology of thesubmandibular glands is examined using light microscopy.

Bidirectional hierarchical cluster analysis (BHCA) was
performed using the “Pheatmap” package in R (v1.0.2,
http://​ c ran.r-​ p roje​ c t.​ o rg/​ w eb/​ p acka​ g es/​ p heat​ m ap/).
Based on the Euclidean distance between expression
values, BHCA separately clustered DEGs with similar
expression patterns.

Immunohistochemical Assays

Identification of Differentially Expressed Genes

Paraffin-embedded tissue sections were prepared as 5 μM
tissue sections. Tissue sections were then dewaxed in

Raw data were processed using the robust multiarray mean (RMA) algorithm in the “Affy” package in

Hierarchical Clustering Analysis

Table 1 The primer sequences of qPCR
Primer Name

Forward primer (5′–3′)

Reverse primer (5′–3′)

REN

AGA​ATG​CCC​TCA​ATC​CGA​GAA​AGC​

TGT​TGC​CAA​GTG​TCA​GCC​TCTTC​

A2M

CAC​TGT​GTC​GCC​T TC​GCT​GTC​

TTC​T TG​GGT​TGG​TCC​T TT​CAC​T TG​G

SNCA

GTG​GCA​ACA​GTG​GCT​GAG​AAGAC​

TCT​GGG​C TA​C TG​C TG​TCA​CACC​KLK3TCC​CAC​ACC​CGC​TCT​ACG​ATATG​GTC​CAT​GAC​C TT​CAC​AGC​ATCCG​

TTR​

CTT​ACT​GGA​AGG​CAC​T TG​GCA​TCT​C

ACA​GCC​GTG​GTG​GAA​TAG​GAG​TAG​

AZGP1

GGC​TCA​C TC​AAT​GAC​C TC​CAG​T TC​

CTG​C TT​CCA​ATC​C TC​CAT​TCC​T TC​C

β-actinATC​ACT​ATT​GGC​AAC​GAG​CGG​T TC​

CAG​CAC​TGT​GTT​GGC​ATA​GAG​GTC​

Chen et al. Biological Procedures Online(2022) 24:26

the R language (http://​cran.r-​proje​ct.​org/), including
background correction, normalization, and probesummarization. Statistically significant differentiallyexpressed genes (DEGs) were mined based on the
difference in expression values between NOD and ICR

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samples using linear models from the “LIMMA” package
in R language. Volcano plots were generated using the“ggplot2” package in the R language, and BenjaminiHochberg’s method was used to control for the false

Fig. 1 Results of NOD mice model measuring salivary secretion-related indicators. A Monitoring of salivary secretion index between NOD and ICR
mice. B Measurement of water consumption between NOD and ICR mice. C Histological images (H&E stained) of mice submandibular gland tissues.
D Immunohistochemistry images of AQP5 of mice submandibular gland tissues

Chen et al. Biological Procedures Online(2022) 24:26

discovery rate (FDR). “P-value < 0.05 and |log2FC| >1”
were critical values for screening DEGs.
Functional and Pathway Enrichment Analyses

DAVID (http://​david.​abcc.​ncifc​rf.​gov/) is an annotation,
visualization, and synthesis discovery database that is
an online tool for classifying gene function and evaluating the biological function of genes [28]. GO and KEGG
enrichment analysis was performed using the DAVID
database to study the role of DEGs. P < 0.05 was taken asa statistically significant cut-off point.
PPI Network Construction and Module Analysis

STRING (v11.5, https://​string-​db.​org) is an online tool
for retrieving interacting genes and proteins and entering genes into a database to build interaction networks[29]. In this study, genes with a total score of > 0.4 wereselected for network construction. In addition, Cytoscape(v3.8.2, https://​cytos​cape.​org) is used to visualize the networks [30]. Finally, the hub modules in the network are
identified by the MCODE plug-in and the degree plug-in
in cytoHumba for screening hub genes.
Predictive Value of Biomarkers

ROC analysis is performed by the “pROC” package of the
R language to predict the diagnostic validity of biomarkers. The area under the ROC curve value (AUC) was used
to determine the sensitivity of the pSS in the dataset tothe diagnosis of the control sample.
Chemicals and Reagents

The A253 cells were obtained from Zhejiang Meisen Cell
Biotechnology Co., Ltd. Fetal bovine serum (Ocamar
Technologies Co., Ltd., Shanghai, China). 1640 medium(Ocamar Technologies Co., Ltd., Shanghai, China). Total
RNA extraction kit (Shanghai Hengyuan biochemical
reagent Co., Ltd., China), IFN-γ (Guangzhou Ruiqian
Biotechnology Co., Ltd., China), anti-rabbit monoclonal
AQP5 antibody (Abcam, UK, 1:200). Anti-rabbit secondary antibody (Proteintech, China, 1:5000). Hematoxylin
and Eosin Staining kit (Wuhan Servicebio Technology
Co. Ltd., China). RT-qPCR kit (Invitrogen, USA). RTqPCR primers (Sangon Biotech, Shanghai, China).

Page 5 of 16

Cell Culture and Handling

A253 cells were cultured in RPMI 1640 medium containing 10% fetal bovine serum, and the cells were cultured
in an incubator at 37 °C, 5% CO2, and saturated humidity. The experiments used cells in the logarithmic growthphase.
Take the cell suspension containing 4 × ­105 cells/ml,
inoculate it in a 6 cm dish, and use RPMI 1640 medium
containing glutamine. Treatment was performed after
the cells had adhered. Cells were treated with 100 nM
IFN-γ. After 48 h, cells were collected to extract RNA,and the mRNA level of the target gene was detected by
RT-qPCR.
Real‑Time Quantitative PCR

Extract total RNA using kit reagents using the GoScript™ Reverse Transcription System Kit (Promega, UK).
Use SYBR premix Ex Taq™II (TaKaRa Clontech) following the manufacturer’s instructions, post-transcriptional
cDNA for qPCR. Analysis was performed using the 2(−
ΔΔCt) method. Detect the gene of interest using primers(Table 1). Analysis using the ΔΔCt method with the geneof interest mRNA = 2-ΔΔCt.
Statistical Analysis

Statistical analysis was performed using GraphPad
9. Data are presented as mean ± standard deviation(X ± SD). The data between the two groups were compared using the t-test method. p < 0.05 was considered
statistically significant.

Results
Decreased Salivary Secretion and Increased Water
Consumption Rates Were Observed in NOD Mice Model

To evaluate phenotypic characteristics of SS, we
compared between NOD mice and ICR mice. The
salivary secretion of the mice was monitored at 0,
3, 6, 9, 12, 15, and 18 days, respectively, as shown in
Fig. 1A. Compared with the ICR mice, the salivary
secretion of the NOD mice was significantly reduced,
and the salivary secretion index was 0.19-2.83 μg/g. In
the statistics of tear secretion in mice, the results alsosuggested that compared with the ICR mice, the tearsecretion of the NOD mice was significantly reduced,
and the tear secretion index was 0.04-0.25 μg/g

(See figure on next page.)
Fig. 2 Processing of transcriptome sequencing. A Normalization of the transcriptome sequencing data. The black line in each box represents eachdata group’s median, which determines the degree of normalization of the data through its distribution. The upside is the expression value data
before normalization, and the normalized expression value data is the downside. B PCA diagram of samples based on expression abundance. C
The volcano of DEGs. The blue points indicate the screened down-regulated DEGs, the red points indicate the screened up-regulated DEGs, andthe black points indicate genes with no significant differences. D The heatmap for all DEGs. All DEGs are screened based on P-value < 0.05 and |foldchange| > 1. DEGs differentially expressed genes; PCA, Principal Component Analysis

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Fig. 2 (See legend on previous page.)(2022) 24:26

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Chen et al. Biological Procedures Online(2022) 24:26

Page 7 of 16(Supplementary Fig. 1). To further illustrate the results,
we dynamically monitored the water consumptionof the NOD and ICR mice over 18 days. The results
showed no significant change in water consumption ineach stage of ICR, and the water consumption index
was 0.32-0.68 ml/g. While the water consumption in the
NOD mice increased to 3 times that of the ICR mice,
the water consumption increased slowly. It remained
at a high level during the observation period (Fig. 1B).
These results suggest that NOD mice have decreased
salivary secretion and increased water consumption,suggesting symptoms of xerostomia.

acinar destruction and basement membrane changes(Fig. 1C). Studies have shown that acinar cell apical andbasolateral membranes show positive AQP5 labelingin mice [31, 32]. In addition, the expression of AQP5
was also detected in intercalated ducts [33]. As shownin Fig. 1D, AQP5 immunostaining in the ICR mice wasdistributed on the acinar cell membrane. In the NOD
mice, AQP5 immunostaining of the cell membrane andapical membrane of the acinus was severely decreased.
These data suggest that acinar destruction and AQP5
expression are reduced in the submandibular gland tissue of NOD mice model.

Acinar Destruction and Decreased AQP5 Expression
in the Submandibular Gland of NOD Mice

Processing of Transcriptome Sequencing

Since salivary secretion is impaired in NOD micemodel, we next investigated the expression of a criticalprotein AQP5 involved in submandibular gland salivary secretion. The results of H&E staining indicatedthat, compared with the ICR mice, the histology of thesubmandibular gland tissue in the NOD mice showed

To better understand the molecular mechanisms ofsalivary secretion regulation, we performed transcriptome
sequencing analysis of submandibular gland tissues
extracted from 6-week-old ICR and NOD mice modelusing RNA-seq. The dataset was preprocessed using
the “Affy” package in R to remove systematicallybiased genes in the original data. Figure 2A shows geneFig. 3 GO and KEGG enrichment analysis. A-C GO enrichment analysis of the DEGs. D KEGG enrichment analysis of the DEGs. GO, Gene Ontology;
KEGG, Kyoto Encyclopedia of Genes and Genomes; DEGs, differentially expressed genes

Chen et al. Biological Procedures Online(2022) 24:26

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Fig. 4 Analysis and screening of hub genes. A Venn diagram of 42 overlapped DEGs of insulin signaling, cAMP signaling, salivary secretion,
and DEGs. B The PPI network of overlapped DEGs was constructed in Cytoscape. C MCODE analysis. D Degree score sorting. DEGs, differentiallyexpressed genes

Chen et al. Biological Procedures Online(2022) 24:26

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Fig. 5 Diagnostic Effectiveness of the Biomarkers. A Expression levels of the hub genes between pSS and control samples in GSE40611. B The
expression heatmap of the hub gene in GSE40611. C ROC analysis of hub genes. ROC, Receiver Operator Characteristic CurveChen et al. Biological Procedures Online(2022) 24:26

expression before and after normalization. Based on thenormalized data, the NOD and ICR mice were completely
distinguished by PCA analysis (Fig. 2B). The DEGs werescreened using the “limma” package in the R language,
with “P-value < 0.05 and |log2FC|>1” as filter conditions.
834 DEGs were obtained, including 505 up-regulated
and 329 down-regulated genes (Fig. 2C). In addition, aheatmap was used to show the expression of all DEGs
(Fig. 2D).
GO and KEGG Enrichment Analysis

To further understand the molecular functions andpathways involved in DEGs, we performed an enrichment
analysis using the DAVID database. The GO enrichment
results showed that these DEGs play critical roles inregulating water transport, insulin secretion, and cell-cellsignaling (Fig. 3A-C). Further KEGG enrichment analysis
showed that insulin signaling is essential in regulating
DEGs. Furthermore, salivary secretion and cAMP signaling
significantly enriched KEGG (Fig. 3D).
Identification of Hub GenesFirst, an integrated analysis of insulin signaling, cAMPsignaling, and salivary secretion related genes and DEGs
was performed using the Venn tool, and overlapping
parts of the four datasets were taken. In our study,
42 overlapping DEGs were identified, including 26
up-regulated DEGs and 16 down-regulated DEGs
(Fig. 4A).
To establish protein-protein interactions, we constructed a PPI network for overlapping DEGs using the
STRING database, consisting of 42 nodes and 37 edges.
After exporting the TSV file, Cytoscape filters out noninteracting proteins and visualizes them (Fig. 4B). In
addition, the algorithm of the MCODE plug-in was used
for analysis, and the highest-scoring module was selected
as the hub module (Fig. 4C). The Degree plug-in identified the network of the top five genes in cytoHubba(Fig. 4D). Finally, we integrated two key networks ofgenes (REN, A2M, SNCA, KLK3, TTR, and AZGP1) as
our hub genes, which are also considered to be initiating
regulators of insulin signaling.
Diagnostic Effectiveness of the Biomarkers

To ensure the reliability of transcriptome sequencing
analysis results, we used the dataset GSE40611 fromthe GEO database to examine the expression of hub

Page 10 of 16

genes between the pSS and the control. The numberof samples in the pSS group was 17, and the numberin the control group was 18. The results showed thatthe expression levels of hub genes REN and A2M
were significantly higher than in the control group(P < 0.001). In comparison, the expression levels of
the hub genes SNCA, KLK3, TTR, and AZGP1 in thepSS were significantly lower than those in the control
group (P < 0.05) (Fig. 5A). We further demonstrated theexpression of hub genes between pSS and control groups
through clustering heatmaps (Fig. 5B). In addition, we
performed ROC analysis to detect the diagnostic validityof hub genes as pSS biomarkers, and “AUC > 0.7” isconsidered to have good sensitivity for pSS diagnosis. As
shown in Fig. 5C, in pSS, the AUC values for REN, A2M,
SNCA, KLK3, TTR, and AZGP1 are 0.704, 0.709, 0.827,
0.827, 0.703, and 0.752, respectively.
Construct a Regulatory Relationship between Hub Genesand cAMP Signaling and Salivary Secretion

To investigate the relationship between hub genes andcAMP signaling and salivary secretion, we analyzed
them using Venn and Cytoscape. First, we integratedcAMP signaling-related genes and DEGs, took theiroverlapping parts, and showed the up- and downregulated overlapping genes through cluster analysis
heatmaps (Fig. 6A). Next, we constructed regulatory
networks between upregulated hub genes (REN andA2M) and downregulated hub genes (SNCA, KLK3,
TTR, and AZGP1) and overlapping genes (Fig. 6B),
respectively. From this, we can see that the hub geneplays a crucial role in regulating the cAMP signaling
gene. In addition, we also integrated salivary secretionrelated genes and DEGs (Fig. 7A) and constructed aregulatory network between hub genes and overlapping
genes (Fig. 7B). The results also suggest that hub genes
are crucial in regulating salivary secretion genes. Finally,
based on these 42 co-expressed genes, we established theregulatory relationship between the hub genes, cAMPsignaling, and salivary secretion. CATSPER3, DCPS,OTOF, AGR2, and FOXC2 are the top five-degree genes
in cAMP signaling (Fig. 8A), while ASCL2, SLC52A3,
MMP12, AGR2, and MMP27 are the top five-degree
genes in the salivary secretion (Fig. 8B).

(See figure on next page.)
Fig. 6 Construct a regulatory relationship between hub genes and cAMP signaling. A The cluster heatmap showed the up- and down-regulated
overlapping genes (cAMP signaling and DEGs common genes). B The regulatory network between the hub and overlapping genes. The red triangle
represents the upregulation of common DEGs, the green triangle represents the downregulation of common DEGs, the red circle represents theupregulation of hub genes, and the green circle represents the downregulation of hub genes

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Fig. 6 (See legend on previous page.)(2022) 24:26

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Fig. 7 Construct a regulatory relationship between hub genes and salivary secretion. A The cluster heatmap showed the up- and down-regulated
overlapping genes (salivary secretion and DEGs common genes). B The regulatory network between the hub and overlapping genes. The
red triangle represents the upregulation of common DEGs, the green triangle represents the downregulation of common DEGs, the red circle
represents the upregulation of hub genes, and the green circle represents the downregulation of hub genes

Experimental Verification of Hub Genes

To further validate the results of our transcriptome
sequencing analysis, we treated A253 cells with IFN-γ at
100 nM and used PBS as the control group. Changes inthe expression of hub genes at the mRNA level are thendetected. The results showed that after IFN-γ treatment,
the expression of REN and A2M was increased at themRNA level, while the expression of SNCA, KLK3, TTR,
and AZGP1 was decreased at the mRNA level (Fig. 9A).

In addition, we selected submandibular gland tissue from
NOD and ICR mice, respectively. The results also showedthat the expression of hub genes REN and A2M waselevated at the mRNA level. In contrast, the expressionof SNCA, KLK3, TTR, and AZGP1 was decreased at themRNA level (Fig. 9B), which further verified the reliabilityof the results. These data suggest that the hub gene has

Chen et al. Biological Procedures Online(2022) 24:26

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Fig. 8 The regulatory relationship between the hub genes, cAMP signaling, and salivary secretion. A Construction of regulatory networks between
upregulated hub genes and DEGs of cAMP signaling and salivary secretion. B Construction of regulatory networks between downregulated
hub genes and DEGs of cAMP signaling and salivary secretion. The red triangle represents the upregulation of common DEGs, the green triangle
represents the downregulation of common DEGs, the red circle represents the upregulation of hub genes, the green circle represents thedownregulation of hub genes, and the blue circle represents the pathway

some validity in diagnosing SS, which is consistent withthe results predicted by sequencing analysis.

Discussion
Sjogren’s syndrome (SS) is a high-rate, common, andharmful global disease with higher potential morbidity and is an essential menopausal and aging disease[34–36]. The disease is characterized by xerostomia with
dysfunction of salivary gland secretion as the initiating
factor, which affects the whole body’s health [37–39].
Although SS is a refractory autoimmune disease, thenumber of studies focusing on essential genes and pathways associated with SS is far less than in systemic lupus
erythematosus and rheumatoid arthritis. Molecular andcellular events that occur during SS pathogenesis need
to be characterized. The treatment of SS patients is stilla clinical challenge, so it is an essential medical topic tostudy the pathogenesis of the disease and elucidate theregulatory mechanism of abnormal salivation secretion.
In this study, SS was characterized by NOD micemodel. We dynamically monitored salivary secretion
and water consumption in mice, and NOD mice modelexperienced decreased salivary secretion and increased
water consumption compared with the ICR mice. This
result also demonstrates that SS is a disease characterized
by dysfunction of salivation. After reviewing all available data on SS mouse models, Ghada et al. noted that
the NOD strain is the model that best encapsulates disease characteristics. NOD mice exhibit heterogeneous
clinical and laboratory features comparable to SS patients[13]. This also provides good support for our experimental animal research. To further interpret SS at the histological level, we selected submandibular glandular tissue
from NOD mice model and applied H&E staining to

examine the changes in gland histological morphology.
We found that compared with the ICR mice, the submandibular gland tissue of NOD mice model had histology of acinar destruction, basement membrane changes,
and lymphocyte infiltration. Studies have shown that SS,
in addition to epithelial dysfunction, has several extraglandular manifestations associated with lymphocyte infiltration and B cell overactivity in other organs [40–42].
Numerous studies have shown that aquaporin AQP5 isa crucial target for determining salivary gland secretionand tissue formation [43–45]. Furthermore, the expression of AQP5, an essential protein in salivary secretion,
was detected by Immunohistochemical assay. Our study
found that the AQP5 immunostaining of the cell and apical membrane of the submandibular gland tissue of NOD
mice was severely reduced. These results suggest symptoms of xerostomia.
Next, we performed transcriptome sequencing analysisof extracted submandibular gland tissue from NOD micemodel using RNA-seq. Subsequently, differential genes
were identified by RNA-seq, and a total of 834 DEGs
were identified, including 505 up-regulated genes and 329
down-regulated genes. The biological functions of these
DEGs were investigated by DAVID enrichment analysis.
The NOD mice significantly enriched water transport,
regulation of insulin secretion, and cell-cell signaling. In
addition, the KEGG results also suggested that insulin
signaling plays an essential role in regulating DEGs, salivary secretion, cAMP signaling, and insulin signaling were
the top three significantly enriched results. Insulin signaling is a critical, flexible, and pleiotropic pathway that plays
a vital role in maintaining glucose homeostasis. Whenthe glucose homeostasis in the body is disrupted, glucose
metabolism decreases, blood glucose rises, and diabetes

Chen et al. Biological Procedures Online(2022) 24:26

Page 14 of 16

Fig. 9 Experimental verification of hub genes. A The mRNA levels of REN, A2M, SNCA, KLK3, TTR, and AZGP1 were detected after cells were treatedwith 100 nM IFN-γ. B The mRNA levels of REN, A2M, SNCA, KLK3, TTR, and AZGP1 were detected between NOD and ICR mice submandibular glandtissueChen et al. Biological Procedures Online(2022) 24:26

develops. Studies have shown that diabetes is a disease thatcauses xerostomia, and there is a significant correlation
between the degree of xerostomia and glucose levels insaliva [46]. Hiramatsu et al. have also shown insulin receptor studies that glucose intolerance occurs in Sjogren’s
syndrome [47]. These results suggest that insulin signaling
may contribute to the critical initiating signal of xerostomia. Numerous studies have shown that cAMP signaling isvital in regulating insulin secretion [48–51]. This provides
insights into the regulatory relationship between insulin
signaling, cAMP signaling, and salivary secretion.
We used the Venn to analyze insulin signaling, cAMPsignaling, and salivary secretion related genes and DEGs,
and a total of 42 overlapping genes were identified. Further
PPI network construction and modular analysis of these 42
genes identified the hub genes REN, A2M, SNCA, KLK3,
TTR, and AZGP1, which are also considered initiating
regulators of insulin signaling. By constructing the regulatory network between the hub gene and cAMP signaling
and salivary secretion, we found that the hub gene is in a
critical position in the network, which provides favorable support for our view. In addition, we downloaded thedataset GSE40611 containing pSS from the GEO database,which further validated our results and ensured the reliability of the sequencing analysis metrics. Finally, to further validate the results of our transcriptome sequencing
analysis, we treated cells with IFN-γ and then examinedchanges in the expression of hub genes at the mRNA level.
In addition, we selected the submandibular gland tissues of
NOD and ICR mice, respectively, to detect the hub genes.
The results indicated that the expressions of hub genes
REN and A2M were increased at the mRNA level, while
the expressions of SNCA, KLK3, TTR, and AZGP1 weredecreased at the mRNA level. These data suggest that hub
genes have some validity in diagnosing SS, which is consistent with the results predicted by sequencing analysis.
In interpreting our results, the following limitations
need to be carefully discussed: on the one hand,
although this study is based on NOD mice model andtranscriptome sequencing of submandibular glandmaterial, the sample size is small, and there may besome bias; On the other hand, although we identified
the critical targets of insulin signaling and proposed
the target-cAMP signaling-salivary secretion regulatory
relationship, only these targets were validated at themRNA level on cells and tissues. Therefore, further
precisely designed studies are needed to verify
these key targets and regulatory relationships. In
conclusion, our analysis reveals previously unknown
transcriptional changes in SS and demonstrates the roleof transcriptome sequencing-based expression profilingin characterizing disease biomarkers. Our findings may
provide new therapeutic targets for SS. Therefore, thisPage 15 of 16

analysis can guide future experimental research andclinical translation.

Supplementary Information
The online version contains supplementary material available at https://​doi.​
org/​10.​1186/​s12575-​022-​00189-5.
Additional file 1.
Acknowledgements
We thank the support from the National Key Research and Development
Program of China (2021YFE0108000).
Authors’ Contributions
Bo Chen contributed to the study design, collection, and interpretation of thedata and the writing of the manuscript; Jiannan Zhou, Tianjiao Mao, Tingting
Cao, Shilin Hu, Wenqi Zhang, Xueyang Li, Xiuni Qin, Xintong Liu, and Nobumoto Watanabe contributed to the study design, and interpretation of thedata; Jiang Li contributed to the study design, interpretation of the data, thewriting of the manuscript, and the submission of the manuscript for publication. The author(s) read and approved the final manuscript.
Funding
This study was supported by grants from the National Key Research andDevelopment Program of China (2021YFE0108000).
Availability of Data and Materials
All the data supporting the results are included in the article.

Declarations
Ethics Approval and Consent to Participate
Not applicable.
Consent for Publication
Not applicable.
Competing Interests
The authors declare that they have no competing interests.
Author details
1
Guangdong Engineering Research Center of Oral Restoration and Reconstruction, Affiliated Stomatology Hospital of Guangzhou Medical University,#195 Dongfeng West Road, Guangzhou 510140, Guangdong, China. 2 Schoolof Basic Medicine, Guangzhou Medical University, Guangzhou, Guangdong,
China. 3 Guangzhou Concord Cancer Center, Guangzhou, Guangdong, China.
4
Chemical Biology Research Group, RIKEN Center for Sustainable Resource
Science, Wako, Saitama 351‑0198, Japan. 5 Bio‑Active Compounds Discovery
Unit, RIKEN Center for Sustainable Resource Science, Wako, Saitama 351‑0198,
Japan.
Received: 5 November 2022 Accepted: 14 December 2022

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