Dysregulation of c-Jun (JUN) and FBJ murine osteosarcoma viral oncogene homolog B (FOSB) in obese people and their predictive values for metabolic syndrome

Abstract

The incidences of metabolic syndrome (MetS), denoting insulin resistance-associated various metabolic disorders, are increasing. This study aimed to identify new biomarkers for predicting MetS and provide a novel diagnostic approach. Herein, the expression profiles of c-Jun (JUN) and FBJ murine osteosarcoma viral oncogene homolog B (FOSB) in individuals with obesity and patients with MetS from the Gene Expression Omnibus database. Quantitative reverse transcription polymerase chain reaction (RT-qPCR) was used to evaluate the messenger RNA levels of JUN and FOSB in the peripheral blood of healthy volunteers (lean and obese) and patients with MetS (lean and obese), along with that in the adipose tissue and peripheral blood of obese mouse model. Furthermore, receiver operating characteristic (ROC) curve and logistic regression analyses were performed to determine the diagnostic value of JUN and FOSB in MetS. The expression profiles and RT-qPCR results showed that JUN and FOSB were highly expressed in individuals with obesity, obese mouse models, and patients with MetS. The ROC analysis results showed an area under the curve values of 0.872 and 0.879 for JUN, 0.802 and 0.962 for FOSB, and 0.946 and 0.979 for JUN–FOSB in the lean group and the group with obesity, respectively, in predicting MetS. Logistic regression analysis showed that the p-values of both JUN and FOSB as MetS-affecting factors were <0.05. Altogether, the findings of this study indicate that both JUN and FOSB, abnormally expressed in individuals with obesity, are good biomarkers of MetS.

1. Introduction

Owing to the changing lifestyles (including sedentary and high-calorie diets), the incidence of obesity has been gradually increasing in society. From 1980 to 2013, the body mass index (BMI) ratio of >25 kg/m² increased from 28.8% to 36.9% in men and from 29.8% to 38% in women globally [1]. In 2013, in countries of the Middle East, North Africa, and Oceania, >50% of the adult population presented obesity [2]. Obesity not only damages appearance but also threatens the health of the individual, as excess fat accumulation can alter the levels of inflammatory factors and the structure of the cardiovascular system, resulting in hypertension and hyperlipidemia, and thus, a notable increase in cardiovascular disease-associated morbidity and mortality [3]. Obesity-induced hypertension and insulin resistance (IR) are risk factors for chronic kidney disease (CKD), and obesity mediates CKD occurrence by inducing intrarenal inflammation [4]. Additionally, obesity has been associated with infertility in both men and women [5, 6]. Maternal obesity may increase the risk of diabetes in the offspring [7]. Notably, obesity has been reported to cause a series of metabolic disorders in the body, referred to as metabolic syndrome (MetS).

The World Health Organization (WHO) defines MetS as concurrent impairment of glucose tolerance/fasting blood glucose or IR to meet any two of the following characteristics: abnormal blood pressure (blood pressure ≥140/90 mmHg); dyslipidemia (triglyceride ≥1.695 mmol/L, high-density lipoprotein cholesterol ≤0.9 mmol/L in male and ≤1.0 mmol/L in female); central obesity (waist–hip ratio >0.90 in men and >0.85 in women or BMI >30 kg/m²); and microalbuminuria (urinary albumin excretion ratio ≥20 μg/min or albumin/creatinine ≥30 mg/g) [8]. Patients with MetS are considerably susceptible to cardiovascular disease and type 2 diabetes [9]. IR is considered to play a central role in the pathophysiology of MetS [10]. Reportedly, activated protein 1 (AP-1) can regulate IR. Hu et al. showed that AP-1 small interference RNA transfection alleviated IR and hepatocyte metabolic abnormalities in high-fat diet-fed obese mice [11]. AP-1 is a transcription complex composed of c-Jun (JUN), c-Fos, and ATF proteins and is involved in nearly all associated physiological functions [12]. However, the diagnostic value of AP-1 components JUN and FBJ murine osteosarcoma viral oncogene homolog B (FOSB; a member of the c-Fos family) in MetS has not been elucidated.

This study aimed to investigate the diagnostic potential of JUN and FOSB, based on their expression in individuals with obesity and patients with MetS, in predicting MetS.

2. Materials and methods

2.1 Study participants and samples

Herein, peripheral blood samples were obtained from 53 healthy lean individuals, 53 healthy individuals with obesity, 53 lean patients with MetS, and 53 obese patients with MetS; BMI >30 kg/m² was considered “obese” and BMI <30 kg/m² was considered “lean” [13] (details characteristics presented in Table 1). The RNA from the blood samples was extracted to detect the messenger RNA (mRNA) levels of genes through quantitative reverse transcription polymerase chain reaction (RT-qPCR).

Table 1 The characteristics of subjects

	Samples	Age	Smoking	Drinking	Gender	FBG	BMI	TG	HDL-C	BP
Groups	n	years	%	%	M/F	mmol/L	kg/m2	mmol/L	mmol/L	SBP/DBP (mmHg)
HL	53	41.1 ± 8.6	47.2	43.4	24/29	5.1 ± 0.6	21.3 ± 1.5	1.02 ± 0.3	1.36 ± 0.2	111.0 ± 8.5/76.1 ± 8.2
HO	53	43.1 ± 8.4	54.7	49.1	30/23	5.0 ± 0.4	34.9 ± 2.7	1.15 ± 0.3	1.39 ± 0.2	112.6 ± 8.8/76.7 ± 8.3
ML	53	40.3 ± 9.0	39.6	37.7	29/24	6.4 ± 0.2	26.1 ± 2.0	1.89 ± 0.1	0.77 ± 0.1	169.6 ± 6.2/99.0 ± 5.4
MO	53	40.8 ± 8.1	52.8	50.9	31/22	6.5 ± 0.2	34.8 ± 3.0	1.93 ± 0.1	0.76 ± 0.1	167.9 ± 5.7/99.6 ± 4.8

Groups: HL, healthy, lean volunteers; HO, healthy, obese volunteers; ML, lean volunteers with metabolic syndrome (MetS); MO, obese volunteers with (MetS).

Notes: M, Male; F, Female; FBG, Fasting Blood Glucose; BMI, Body Mass Index; TG, Triglyceride; HDL-C, High Density Lipoprotein Cholesterol; BP, Blood Pressure; SBP, Systolic Blood Pressure; DBP, Diastolic Blood Pressure.

Written informed consent was obtained from the study participants before beginning the study, and the study was approved by the ethics committee of The Affiliated Chuzhou Hospital of Anhui Medical University.

2.2 Bioinformatics-based data retrieval

The GSE235696 and GSE98895 datasets were obtained from the Gene Expression Omnibus (GEO) database (https://www.ncbi.nlm.nih.gov/geo/). The GSE235696 dataset contained a gene expression dataset sequenced from the visceral adipose tissue of four lean and four individuals with obesity [14]. The GSE98895 dataset presented the gene set of differentially expressed genes in the peripheral blood monocytes of healthy subjects and patients with MetS [15].

2.3 Obese mouse model

For this study, 12 mice (3-week-old) were purchased from the Charles River Laboratory (Beijing, China). The mice were randomly divided into two groups after 1 week of normal feeding [16], and fed as follows: one group was fed a normal diet (XTCON50J) (Jiangsu Xietong, Nanjing, China) and the other group was fed a high-fat diet (XTHF60) (Jiangsu Xietong, Nanjing, China) for 12 weeks. All research procedures followed the guidelines of the Animal Care and Use Committee of The Affiliated Chuzhou Hospital of Anhui Medical University.

2.4 RT-qPCR

Total RNA was extracted from the peripheral blood of the participants and mice using the RNAprep Pure Blood Total RNA Extraction Kit (DP443) (TIANGEN, Beijing, China). The total RNA of the adipose tissue from mice was extracted using the RNA Easy Fast Animal Tissue/Cell Total RNA Extraction Kit (DP451) (TIANGEN, Beijing, China). Following this, the total RNA was reverse-transcribed into complementary DNA using the BeyoRT™ III cDNA first strand synthesis kit (Beyotime, Shanghai, China) for quantitative polymerase chain reaction. The primers used in this study were listed in Supplementary Table 1.

2.5 Statistical analyses

The experimental data in this study (repeated more than thrice) were statistically analyzed using the GraphPad Prism 9 software and expressed as mean ± standard deviation. The t-test was used to detect the differences of mRNA expression in the peripheral blood from healthy lean and healthy individuals with obesity, healthy individuals and patients with MetS (lean and obese), and in the peripheral blood and adipose tissue from healthy and obese mice. The Statistical Package for Social Sciences (SPSS) software was used to construct the receiver operating characteristic (ROC) curves and perform logistic regression analyses to determine the value of genes in predicting MetS. The p-value of <0.05 was considered statistically significant.

3. Results

3.1 JUN and FOSB contribute to obesity development

Using the GSE235696 and GSE98895 datasets, JUN and FOSB were identified as the highly expressed genes in individuals with obesity and patients with MetS (p < 0.05, log fold change >1) (Fig. 1A); the expression maps of JUN and FOSB are shown in Fig. 1 B and C. The RT-qPCR of the peripheral blood samples of 53 lean and 53 individuals with obesity revealed that the mRNA levels of JUN and FOSB were markedly upregulated in the group with obesity (Fig. 1D and E). Next, an obese mouse model was constructed, and their visceral adipose tissue and peripheral blood were collected to extract RNA for RT-qPCR. Notably, the expression of JUN and FOSB in the adipose tissue and peripheral blood of obese mice was considerably high (Fig. 1F and G). These results suggest that JUN and FOSB play a promoting role in obesity.

Fig. 1  JUN and FOSB contribute to the development of obesity. (A) The Venn diagram of the genes that were highly expressed in individuals with obesity (GSE235696) and patients with metabolic syndrome (GSE98895). (B and C) The expression profiles of JUN and FOSB in lean and individuals with obesity from the GSE235696 dataset. (D and E) Relative mRNA levels of JUN and FOSB in the peripheral blood derived from lean and individuals with obesity. (F and G) Relative mRNA levels of JUN and FOSB in the visceral adipose tissue and peripheral blood of healthy and obese mice. ***p < 0.001.

3.2 JUN and FOSB are highly expressed in patients with MetS

The gene expression maps from the GSE98895 dataset of the GEO database revealed that JUN and FOSB were highly expressed in patients with MetS (Fig. 2A and B), which is consistent with the RT-qPCR results of this study. JUN and FOSB mRNA levels were markedly increased in the peripheral blood of lean and obese patients with MetS (Fig. 2C and D), indicating JUN and FOSB as pathogenic factors of MetS and their potential as biomarkers for MetS diagnosis.

Fig. 2  High expression of JUN and FOSB in patients with MetS. (A and B) The expression maps of JUN and FOSB in healthy individuals and patients with MetS from the GSE98895 dataset. (C and D) Relative mRNA levels of JUN and FOSB in the peripheral blood of lean and individuals with obesity without or with MetS. ***p < 0.001.

3.3 JUN and FOSB are good biomarkers for MetS

The SPSS software was used to construct ROC curves of JUN, FOSB, and MetS in the lean group and the group with obesity. The ROC analysis results indicated that the area under the curve (AUC) values of JUN were 0.872 (lean) and 0.879 (obese), and those of FOSB were 0.802 (lean) and 0.962 (obese) when diagnosing MetS alone (Fig. 3A and B). This suggested that JUN and FOSB levels are good predictors of MetS. In the lean group and the group with obesity, the JUN–FOSB combination exhibited higher accuracy in predicting MetS compared with single-factor diagnosis. The AUC values were 0.946 and 0.979; sensitivities were 84.9% and 94.3%; specificities were 96.2% and 94.3%, and the cutoff values were 0.608 or 0.522, for the lean group and the group with obesity, respectively (Fig. 3A and B). Furthermore, the logistic regression analysis of the predictors in the model revealed that both JUN (p < 0.05) and FOSB (p < 0.05) were independent influencing factors for MetS, with odds ratio values of 4.573 and 13.110, respectively (Table 2). This indicated that the risk of MetS will increase by 3.573 and 12.110 times, respectively, per unit increase in JUN and FOSB levels. These results indicate that JUN and FOSB are potential biomarkers of MetS.

Fig. 3  JUN and FOSB are valuable biomarkers of MetS. The ROC curves of JUN and FOSB levels for MetS prediction in (A) lean individuals and (B) individuals with obesity.

Table 2 Binary logistic regression analysis of factors in MetS

Group	Lean				Obese
	p values	OR	95% CI		p values	OR	95% CI
			Lower	Upper			Lower	Upper
JUN	0.000	19.761	5.168	75.557	0.003	4.573	1.678	12.466
FOSB	0.000	9.898	3.029	32.340	0.000	13.110	3.596	47.796
Age	0.272	2.254	0.529	9.600	0.282	0.580	0.459	14.489
Gender	0.615	1.437	0.350	5.907	0.926	1.091	0.174	6.843
Smoking	0.259	2.290	0.544	9.651	0.679	1.469	0.238	9.075
Drinking	0.309	2.264	0.469	10.940	0.216	3.293	0.499	21.717

Notes: MetS, Metabolic syndrome; OR, odds ratio; CI, confidence interval.

4. Discussion

MetS is a complex disease, which is manifested as a series of metabolic disorders. The WHO defines MetS as a condition of impaired glucose tolerance/fasting glucose or IR, with other symptoms including hypertension, hyperlipidemia, central obesity, and microalbuminuria [8]. Consequently, the molecular mechanisms of MetS pathogenesis are linked to various physiological processes and are extremely complex. Blood interlinks every organ in the body and plays an important role in inflammatory response, immunity, and physiological homeostasis. Hence, changes in gene expression profiles in the blood can reflect the pathological status of the entire organ [17]. Reportedly, >80% of genes are expressed in the same pattern in the blood as that in different tissues [18, 19], suggesting the potential reliability of circulating blood as a source of disease biomarkers, where changes in gene expression can provide initial insights into disease-related molecular mechanisms. Herein, by detecting the changes of gene expression in the peripheral blood of patients with MetS, JUN, and FOSB were identified as the potential key pathogenic genes, providing a preliminary theoretical basis for investigating MetS pathogenesis.

JUN plays an important role in obesity-induced MetS. Reportedly, non-alcoholic fatty liver disease is an important characteristic of MetS, and JUN can promote the development of non-alcoholic steatohepatitis [20]. Furthermore, uric acid has been reported to induce liver fat accumulation through the reactive oxygen species/JUN N-terminal kinase/AP-1 pathway [21]. JUN is closely associated with IR. Overexpressing aldehyde dehydrogenase-2 could alleviate high-fat diet-induced IR by inhibiting JUN expression [22]. Herein, mRNA levels of JUN were markedly increased in the peripheral blood of healthy individuals with obesity and patients with MetS (lean and obese), and in the peripheral blood and adipose tissue of obese mice, confirming the promoting role of JUN in obesity-induced MetS. Although FOSB has been associated with obesity, its role in MetS has not been elucidated. FOSB expression has been reported to increase in the prefrontal limbic subcortex in high-fat/high-sugar diet-fed mice [23]. Pre-pregnancy maternal obesity can lead to markedly increased FOSB levels in the umbilical cord, resulting in low sensibility to insulin [24]. These findings suggest the role of FOSB in MetS occurrence. Herein, RT-qPCR results showed that the mRNA levels of FOSB markedly increased in the peripheral blood of healthy individuals with obesity and patients with MetS (lean and obese), and in the peripheral blood and adipose tissue of obese mice, indicating the positive regulatory role of FOSB in obesity-induced MetS. The constructed ROC curves for JUN and FOSB to predict MetS indicated that both JUN and FOSB were good predictors of MetS in both lean group and the group with obesity. Furthermore, JUN combined with FOSB exhibited a more accurate prediction effect. Additionally, the logistic regression analysis revealed that JUN and FOSB were not affected by other factors in predicting MetS, suggesting them as independent influencing factors of MetS. These findings indicate that JUN and FOSB are biomarkers for MetS.

Altogether, the findings of this study indicate that JUN and FOSB, which are abnormally expressed in individuals with obesity, can well predict the occurrence of MetS as independent factors. Furthermore, they may be the pathogenic factors of MetS. These findings provide a novel approach and theoretical basis for utilizing JUN and FOSB to investigate the mechanisms underlying MetS pathogenesis.

Author’s contribution

Conceptualization, C.Y., Y.C., G.T. and T.S.; Data curation, C.Y., Y.C., G.T. and T.S.; Formal analysis, C.Y., Y.C., Y.C., G.T. and T.S.; Funding acquisition, T.S.; Investigation, L.L. and T.S.; Methodology G.T. and T.S.; Project administration, T.S. and L.L.; Resources, C.Y., Y.C. and G.T.; Software, C.Y., Y.C. and G.T.; Supervision, T.S. and L.L.; Validation G.T. and T.S.; Visualization, G.T. and T.S.; Roles/Writing - original draft, C.Y., Y.C. and G.T.; Writing - review & editing, T.S. and L.L.

Disclosure

None of the authors have any potential conflicts of interest associated with this research.

Supplementary Table 1 The primers for RT-qPCR

Gene	Forward or Reverse	Primer sequence
JUN	Forward	5'-AGATGAACTCTTTCTGGCCTGCCT-3'
	Reverse	5'-ACACTGGGCAGGATACCCAAACAA-3'
FOSB	Forward	5'-GCTGCAAGATCCCCTACGAAG-3'
	Reverse	5'-ACGAAGAAGTGTACGAAGGGTT-3'
β-actin	Forward	5'-CCTCTCCCAAGTCCACACAG-3'
	Reverse	5'-GGGCACGAAGGCTCATCATT-3'

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