The Change of Interleukin-6 Level-Related Genes and Pathways Induced by Exercise in Sedentary Individuals

Abstract

Sedentary behavior increases the risk of many chronic disorders, in addition, these chronic diseases are associated with elevated markers interleukin-6 (IL-6). Increasing evidence indicates that physical activity can prevent chronic inflammatory disease. However, the effect of exercise on sedentary individuals with disparate basal serum IL-6 level was not well elucidated. In this study, the gene expression profile of GES12384 was downloaded from the Gene Expression Omnibus (GEO) database. This data set contained 12 sedentary middle-aged men (6 high IL-6 and 6 low IL-6 level), and their blood samples were taken in the pre-exercise period and at the end of 24 weeks of exercise. The differentially expressed genes (DEGs) of 24 weeks group were identified, followed by functional enrichment analysis. Subsequently, protein–protein interaction (PPI) network and transcription factors (TFs)-DEGs network were constructed. A total of 193 DEGs were identified between high and low IL-6 level in the 24 weeks group. Functional enrichment analysis showed that DEGs were mainly involved in African trypanosomiasis pathway. PPI network revealed that the hub genes included C-C motif chemokine receptor 7 (CCR7), hemoglobin subunit delta (HBD), and interferon gamma (IFNG). Subnetworks analysis indicated that these genes were relevant to immune response, and participated in African trypanosomiasis pathway. The TF targets network found that myocyte enhancer factor 2A (MEF2A) was a key regulatory factor. In conclusion, the inflammation-related genes (CCR7, HBD, and IFNG) in sedentary individuals could be affected by exercise, and the identified DEGs and TFs in this study promoted our understanding of exercise inhibited the development of chronic disease.

Color images are available online

Introduction

Many chronic and immune-related diseases are associated with elevated markers of inflammation, such as interleukin-6 (IL-6) (Starr and others 2015). IL-6 is a pleiotropic cytokine, which is closely related with immune response, acute phase response, and hematopoietic regulation (Larkin III and others 2013; Teles and others 2013). IL-6 has been reported to regulate the differentiation and activation of T cells (Yuk and others 2017). Besides, IL-6 is involved in the pathogenesis of many chronic inflammatory and autoimmune diseases. A study reported by Ferrucci and others (1999) indicated that higher circulating levels of IL-6 could predict disability onset in older persons, suggesting IL-6 plays pathophysiologic role in specific diseases.

In addition, Cohen and others (1997) observed that IL-6 level might be a measurement index of health, and dysregulation of IL-6 was associated with the functional disability seen with aging. Moreover, high serum level of IL-6 is related to high degree of tumor progression, systemic weakness, and several chronic inflammatory diseases (Miura and others 2015; Vafa 2019). Thus, IL-6 inhibitors have become a new idea for the autoimmune and chronic inflammatory diseases treatment (Neurath and Finotto 2011; Kim and others 2015).

Reportedly, physical exercise, as a natural and strong anti-inflammatory strategy, is also widely used in alleviating chronic progression, such as cardiovascular disease, type-2 diabetes, and obesity (Ambrose and Golightly 2015; Pedersen 2017). Exercise can change the inflammatory cycle biomarkers (Papini and others 2014). Farinha found that after 12 weeks aerobic exercise, the level of inflammatory biomarkers, including IL-6, interleukin-1β (IL-1β), tumor necrosis factor α (TNF-α), and interferon-γ (IFN-γ), was decreased in women with metabolic syndrome (Farinha and others 2015). Starkie and others (2003) also found exercise could inhibit the production of TNF-α in the body. Therefore, moderate exercise training can improve systemic inflammation and reduce the risk of low-grade inflammation (McCarthy and others 2015; Sathyan and others 2015).

Sedentary behaviors, such as sitting, watching TV, using a computer, and driving, are characterized by little physical movement and low energy expenditure. Convincing evidence indicates that sedentary behavior can increase the risk of many chronic disorders, such as type 2 diabetes (De Greef and others 2010), metabolic syndrome (Edwardson and others 2012), acute coronary syndrome (Zhu and others 2018), and obesity (Biddle and others 2017). A systematic review showed that self-directed exercise programs in sedentary middle-aged individuals were successful in improving cardiorespiratory fitness, physical characteristics, and metabolism (Byrne and others 2018). However, changes in gene expression in sedentary individuals with different basal serum IL-6 levels following an organized physical activity program has not been elucidated.

In this study, the gene expression profile (GSE12384) was downloaded from Gene Expression Omnibus (GEO) database, then differentially expressed genes (DEGs) between basal and 24 weeks exercise were identified, followed by functional enrichment. Subsequently, the protein–protein interaction (PPI) network and transcription factor (TF)-DEGs network were constructed. This study aimed to identify the genes related to both IL-6 level and exercise, revealing the effects of exercise on sedentary individuals with different basal serum IL-6 levels.

Materials and Methods

Affymetrix microarray data

The gene expression profile of GSE12384 was downloaded from the National Center for Biotechnology Information (NCBI) GEO database (Starr and others 2015), which was based on the GPL4133 Agilent-014850 Whole Human Genome Microarray 4x44K G4112F (Feature Number version) platform. This data set comprised 24 sedentary middle-aged men (mean age 52.6 years and body mass index 29.1 kg/m²), who undertook a 24-week physical activity program, and their blood samples were taken in the pre-exercise period and at the end of 24 weeks exercise. The samples were divided into 2 groups, pre-exercise (basal group) and 24 weeks physical activity (24 weeks group). Each group includes 6 low IL-6 level (0.59 pg/mL) and 6 high IL-6 level (2.13 pg/mL), respectively.

Screening of DEGs

The raw data were normalized using the R software limma package (version 3.10.3) (Smyth and others 2005), including format conversion, missing value complement, background correction (MicroArray Suite method), and quantile method. The classical Bayesian method in the limma package was used to identify the DEGs between high IL-6 and low IL-6 level samples in the 2 groups, respectively. P < 0.05 and |logFC| > 0.585 were considered significant.

The bidirectional hierarchical cluster was conducted by pheatmap package based on the DEGs. Then, the DEGs in basal and 24 weeks groups were integrated, the common DEGs between basal and 24 weeks were considered as set 1, the unique DEGs in 24 weeks as set 2. DEGs in set 1 were related to IL-6, whereas DEGs in set 2 were associated with both exercise and IL-6 level.

Functional enrichment analysis

The database for annotation, visualization, and integrated discovery (DAVID, version 6.8, https://david-d.ncifcrf.gov) (Huang and others 2009) was utilized to analyze the Gene Ontology (GO) function (Ashburner and others 2000) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway (Kanehisa and Goto 2000) enrichment of DEGs in set 1 and set 2. The GO terms contained biological process (BP), cellular component (CC), and molecular function (MF). The count >2 and P < 0.05 were defined as the cutoff criteria.

PPI network and module analysis

The interaction between proteins encoded by DEGs was predicted by the search tool for the retrieval of interacting genes/proteins (STRING) database (version 10.0) (Szklarczyk and others 2014). To explore the change of IL-6 level-related DEGs, we chose set 2 for further study. The PPIs with a confidence of 0.15 were selected, and PPI network was structured using Cytoscape (version: 3.2.0).

The CytoNCA plugin (version 2.1.6) was applied to analyze the topology properties of the node in the network (Tang and others 2015). Important nodes that participated in the protein interaction relationship in the PPI network were obtained by referring to the ranking of each node's score (Bandettini and others 2012). In addition, Cytoscape MCODE plug-in (version 1.4.2) was used for searching clustered subnetwork, score >5 was considered as the threshold.

TF target regulation prediction

The TFs of the target genes were predicted by the iRegulon plugin of Cytoscape (Verfaillie and others 2014) (version 1.3). The parameters were set as follows: minimum identity between orthologous genes, 0.05; maximum false discovery rate on motif similarity, 0.001. A gene–TF pair with normalized enrichment score >4 was retained.

Results

DEGs screening and VENN analysis

According to the screening criteria, total 65 upregulated and 113 downregulated DEGs were obtained in the basal group (Fig. 1A), and a total of 193 DEGs (85 upregulated and 108 downregulated DEGs) were identified in the 24 weeks group (Fig. 1B). VENN analysis was performed based on the DEGs in the basal group and the 24 weeks group. There were 68 common DEGs and 125 unique DEGs in the 24 weeks group (Fig. 2).

FIG. 1.

Heat map of DEGs. (A: basal B: 24 weeks). Black and gray represents genes from up to down regulation, the upper tree diagram represents clustering analysis results for different samples from different groups, the left-hand tree diagram represents clustering analysis results for different genes from different samples. DEGs, differentially expressed genes.

FIG. 2.

VENN analysis chart.

Functional enrichment analysis

The top 5 GO terms and KEGG pathways of the 24 weeks DEGs are listed in Table 1. The DEGs were mainly enriched in BP terms, such as oxygen transport, bicarbonate transport, response to hydrogen peroxide, and hydrogen peroxide catabolic process. Moreover, the DEGs participated in African trypanosomiasis, malaria, and cytokine–cytokine receptor interaction pathways.

Table 1.

The Kyoto Encyclopedia of Genes and Genomes Pathway and Top 5 Enriched Gene Ontology Terms for 24 Weeks Differentially Expressed Genes

Category	KEGG-ID	Term	P	Genes
KEGG	hsa05143	African trypanosomiasis	0.000374	IFNG, HBA2, HBA1, HBB
	hsa05144	Malaria	0.001205	IFNG, HBA2, HBA1, HBB
	hsa04060	Cytokine–cytokine receptor interaction	0.020224	CCR7, IFNG, EDAR, CCL5, CXCL11
GO_BP	GO:0015671	Oxygen transport	6.78493E-11	HBQ1, HBG1, HBA2, HBA1, HBE1, HBB, HBD
	GO:0015701	Bicarbonate transport	0.001427195	HBA2, SLC4A9, HBA1, HBB
	GO:0042542	Response to hydrogen peroxide	0.002187724	HBA2, HBA1, AREG, HBB
	GO:0042744	Hydrogen peroxide catabolic process	0.004534704	HBA2, HBA1, HBB
	GO:0051291	Protein hetero-oligomerization	0.004748732	HBA2, HBA1, HBE1, HBB
GO_CC	GO:0005833	Hemoglobin complex	1.44E-11	HBQ1, HBG1, HBA2, HBA1, HBE1, HBB, HBD
	GO:0071682	Endocytic vesicle lumen	0.000070887	HBA2, SCGB3A2, HBA1, HBB
	GO:0031838	Haptoglobin–hemoglobin complex	0.000156881	HBA2, HBA1, HBB
	GO:0045177	Apical part of cell	0.000609794	MYO6, EDAR, SLC4A9, DCHS1, VASH1
	GO:0072562	Blood microparticle	0.001142631	SERPING1, HBA2, HBA1, HBE1, HBB, HBD
GO_MF	GO:0005344	Oxygen transporter activity	4.2558E-11	HBQ1, HBG1, HBA2, HBA1, HBE1, HBB, HBD
	GO:0019825	Oxygen binding	1.33053E-07	HBQ1, HBG1, HBA2, HBA1, HBE1, HBB, HBD
	GO:0020037	Heme binding	0.000070675	HBQ1, HBG1, HBA2, HBA1, HBE1, HBB, HBD
	GO:0031720	Haptoglobin binding	0.000076705	HBA2, HBA1, HBB
	GO:0005506	Iron ion binding	0.000130032	HBQ1, HBG1, HBA2, HBA1, HBE1, HBB, HBD

BP, biological process; CC, cellular component; CCR7, C-C motif chemokine receptor 7; GO, Gene Ontology; HBA1, hemoglobin subunit alpha 1; HBD, hemoglobin subunit delta; IFNG, interferon gamma; KEGG, Kyoto Encyclopedia of Genes and Genomes; MF, molecular function.

PPI network and subnetwork module

According to the information in the STRING database, a PPI network consisted of 91 nodes and 260 interaction pairs were constructed (Fig. 3). Using MCODE to analyze the most significantly enriched modules, we observed there were 9 nodes and 36 interaction pairs in the subnetwork. The top 10 nodes are shown in Table 2.

FIG. 3.

(A,B) PPI netowrk and module. Circle nodes represent upregulated DEGs, diamond nodes represent downregulated DEGs, and the node size indicates the degree value. The higher the degree, the longer the node.

Table 2.

Degree Value (Top 10) and Module Gene List

Type	Nodes	Description	Degree
PPI-degree top10	IFNG	Up	27
	ADTRP	Down	18
	CCL5	Up	18
	SOX8	Down	15
	CCR7	Down	13
	KLF4	Up	13
	HBD	Down	11
	HEMGN	Down	11
	UCHL1	Up	11
	NEFL	Down	11
Module	ADTRP	Down	18
	HEMGN	Down	11
	HBD	Down	11
	HBA1	Down	10
	HBQ1	Down	10
	HBA2	Down	10
	HBE1	Down	10
	ALAS2	Down	9
	HBG1	Down	8

ADTRP, androgen-dependent TFPI regulating protein; HEMGN, hemogen; PPI, protein–protein interaction; SOX8, SRY-box transcription factor 8.

In the PPI network, interferon gamma (IFNG, degree = 27), androgen-dependent TFPI regulating protein (ADTRP, degree = 18), C-C motif chemokine ligand 5 (CCL5, degree = 18), SRY-box transcription factor 8 (SOX8, degree = 15), and C-C motif chemokine receptor 7 (CCR7, degree = 13) might be considered as hub proteins. In the subnetwork, genes such as ADTRP (degree = 18), hemogen (HEMGN, degree = 11), hemoglobin subunit delta (HBD, degree = 11), and hemoglobin subunit alpha 1 (HBA1, degree = 10) with higher degrees, and might be recognized as key nodes. Furthermore, functional enrichment analysis of PPI and subnetwork was performed, respectively (Tables 3 and 4). The submodule was significantly involved in African trypanosomiasis and malaria.

Table 3.

The Kyoto Encyclopedia of Genes and Genomes Pathway and Gene Ontology Enrich Terms of Protein–Protein Interaction (Top 10)

Category	ID	Term	P	Genes
KEGG	hsa04060	Cytokine–cytokine receptor interaction	0.01158041	CCR7, IFNG, CCL5
GO_BP	GO:0019896	Axonal transport of mitochondrion	0.00285553	UCHL1, NEFL
	GO:0002407	Dendritic cell chemotaxis	0.00807213	CCR7, CCL5
	GO:0032735	Positive regulation of interleukin-12 production	0.01185101	CCR7, IFNG
	GO:0048662	Negative regulation of smooth muscle cell proliferation	0.01373573	IFNG, KLF4
	GO:0006955	Immune response	0.01588632	CCR7, IFNG, CCL5
GO_CC	GO:0044798	Nuclear transcription factor complex	0.00542049	SOX8, KLF4
	GO:1904115	Axon cytoplasm	0.01715644	UCHL1, NEFL

Table 4.

The Kyoto Encyclopedia of Genes and Genomes Pathway and Gene Ontology Enrich Terms of Module

Category	ID	Term	P	Genes
KEGG	hsa05143	African trypanosomiasis	0.009572099	HBA2, HBA1
KEGG	hsa05144	Malaria	0.014196546	HBA2, HBA1
GO_BP	GO:0015671	Oxygen transport	5.6648E-15	HBQ1, HBG1, HBA2, HBA1, HBE1, HBD
	GO:0051291	Protein hetero-oligomerization	0.000325125	HBA2, HBA1, HBE1
	GO:0007596	Blood coagulation	0.00241873	HBG1, HBE1, HBD
	GO:0042744	Hydrogen peroxide catabolic process	0.008309052	HBA2, HBA1
	GO:0010942	Positive regulation of cell death	0.012028774	HBA2, HBA1
GO_CC	GO:0005833	Hemoglobin complex	2.64614E-15	HBQ1, HBG1, HBA2, HBA1, HBE1, HBD
	GO:0072562	Blood microparticle	3.08952E-05	HBA2, HBA1, HBE1, HBD
	GO:0031838	Haptoglobin–hemoglobin complex	0.001754915	HBA2, HBA1
	GO:0071682	Endocytic vesicle lumen	0.007003501	HBA2, HBA1
	GO:0022627	Cytosolic small ribosomal subunit	0.020881862	HBA2, HBA1
GO_MF	GO:0005344	Oxygen transporter activity	3.67847E-15	HBQ1, HBG1, HBA2, HBA1, HBE1, HBD
	GO:0019825	Oxygen binding	2.80977E-12	HBQ1, HBG1, HBA2, HBA1, HBE1, HBD
	GO:0020037	Heme binding	6.78197E-10	HBQ1, HBG1, HBA2, HBA1, HBE1, HBD
	GO:0005506	Iron ion binding	1.18551E-09	HBQ1, HBG1, HBA2, HBA1, HBE1, HBD
	GO:0031720	Haptoglobin binding	0.00124356	HBA2, HBA1

TF target regulatory network

The iRegulon was used to predict and analyze the TF target gene relationship pairs in the PPI network, 3 TFs, 49 genes, and 66 pairs were identified (Fig. 4). Estrogen Receptor 1 (ESR1), LIM Homeobox 9 (LHX9), and Myocyte Enhancer Factor 2A (MEF2A) were TFs.

FIG. 4.

TF Target regulates the network analysis map. Circles represent the upregulated DEGs, prisms represent the downregulated DEGs, represents the TF, and the arrows connecting the lines represent the regulatory direction. TF, transcription factor.

Discussion

In this study, a total of 125 DEGs related to both IL-6 level and exercise were identified. IFNG, CCR7, and HBD genes were considered as hub genes in the PPI network, and might play crucial role in exercise among sedentary individuals. The DEGs were mainly enriched in oxygen transport, blood coagulation, and immune response. Besides, the KEGG pathways analysis showed that DEGs were involved in cytokine–cytokine receptor interaction, African trypanosomiasis, and malaria. According to the TF-DEGs network, we found ESR1, LHX9, and MEF2A were the key regulators.

During exercise, inflammatory factor IL-6 was active (Catoire and Kersten 2015); therefore, we studied the DEGs after exercise and revealed the specific effects of exercise on different IL-6 levels in sedentary individuals. In this study, we found that IFNG might be the hub gene and significantly enriched in immunoregulatory function.

IFNG is a member of the type II interferon class that encodes a soluble cytokine (Teles and others 2013). Based on its pleiotropic functions, IFNG plays a pivotal role in orchestrating immune system homeostasis (Larkin III and others 2013). Both in vitro and in vivo studies indicate that IFNG has been considered as a proinflammatory factor and showed pathogenic function in several chronic diseases (McCarthy and others 2015; Sathyan and others 2015). However, growing studies have demonstrated that IFNG also plays a protective role in immunological diseases, such as multiple sclerosis and its animal model experimental autoimmune encephalomyelitis (Billiau and others 1988; Duong and others 1992).

Ottum and others (2015) indicated that IFNG served protective effect on experimental autoimmune encephalomyelitis in low doses, whereas induced disease deterioration in high doses. These findings revealed the dual roles of IFNG in inflammation. Shaw and others (2020) observed that exercise induced the release of IFNG that modulates the immune responses of individuals. In addition, Vijayaraghava (2017) confirmed that regular physical activity of moderate nature protected against inflammation by increasing levels of IFNG. Similarly, we also found that IFNG was significantly upregulated in sedentary individuals after 24 weeks of exercise. Taken together, we speculated that moderate exercise training might induce beneficial effects on health through decreasing proinflammatory status.

Another gene, CCR7, was observed significantly downregulated in the 24 weeks exercise group. The proteins encoded by CCR7 belong to the G protein-coupled receptor family, and its receptor is expressed in various lymphoid tissues, as well as activates B and T lymphocytes (Fleige and others 2018). In addition, CCR7 has been reported to control the migration of memory T cells to inflamed tissues (Moschovakis and Förster 2012), and CCR7 promoter is involved in many inflammatory diseases through binding with nuclear factor-κB (Kumar and others 2004). Liu and others (2016) indicated that a decrease in CCR7 messenger RNA (mRNA) level could attenuate inflammatory cell infiltration into the lungs. Wei and others (2017) also found that CCR7 reduction weakened inflammatory response in serum.

Based on these findings, we suspected that CCR7 might play an important role in exercise of sedentary individuals by affecting inflammatory processes. However, follow-up investigation is needed to better understand the role of CCR7 in exercise. In the TF-DEGs regulatory network, MEF2A regulated involvement in oxygen transport and immune-related genes, such as HBD and CCR7. The protein encoded by MEF2A is involved in several cellular processes, such as cell growth control and apoptosis (Xiong and others 2019). Prior studies have suggested that through exercise training, MEF2A can be induced to be highly acetylated and enhance DNA-binding ability (Yuan and others 2014; Widmann and others 2019).

In addition, we found CCR7 and HBD were induced by MEF2A through 24 weeks exercise. James and others (2018) found that HBD was closely associated with inflammation, and upregulation of HBD was observed during infection and inflammation (Özdemir and others 2020). Furthermore, the expression of HBD-2 was inducible by inflammation in various tissues (Hosokawa and others 2006). In this study, we found that GO annotations related to HBD, including oxygen transport, iron ion binding, blood coagulation, and combination with oxygen. In the study of cardiopulmonary function and respiratory infections, HBD and inflammatory factors such as IL-6 and IFNG are involved in lung defense and repair (Smith and others 2019). Taken together, we revealed that MEF2A had turned out to be importantly involved in exercise by regulating inflammation-related genes.

In this study, the African trypanosomiasis was the most significantly enriched pathway. African trypanosomiasis is caused by infection with the tsetse fly-transmitted protozoan hemoflagellates trypanosoma brucei rhodesiense and T.b.gambiense (Duong and others 1992). Kuriakose and others (2012) found that the African trypanosomiasis caused the activation of macrophages, which might result in an inflammatory response. The T congolense-induced infection activates macrophage to produce various proinflammatory cytokines, such as IL-6, IL-12, and TNF, and collectively activates the adaptive immune response of the body (Moschovakis and Förster 2012).

Moreover, the relationship between African trypanosomiasis and inflammation has been reported in many diseases, such as African trypanosomiasis-associated anemia (Yuk and others 2017), central nervous system inflammation, and inflamed gingival tissue (Vijayaraghava 2017; Fleige and others 2018). In our study, we observed the DEGs mainly participated in the African trypanosomiasis pathway, suggesting that the inflammation-related genes enriched in African trypanosomiasis were altered by exercise. Thus, exercise plays an active role in preventing inflammation.

Sedentary behavior not only increases the chronic inflammatory diseases risk, but also increases cancer risk (Lynch and others 2018; Patterson and others 2018). In addition, the level of IL-6 in inflammatory tissues were higher than that of healthy organizations (Sindhu and others 2015). Therefore, we speculated that people who were sedentary and had high levels of IL-6 might be more likely to suffer from various chronic diseases. The genes obtained in this study were principally associated with immunity and inflammation, hence exercise might prevent the chronic inflammatory disease in sedentary people.

Conclusion

In summary, our data provided a systematic bioinformatics analysis of DEGs, which might be related to both exercise and IL-6 level. CCR7, HBD, and IFNG might serve vital role in reducing inflammation by exercise, and these genes might prevent the risk of chronic diseases in sedentary individuals. However, further experimental studies are urgently demanded to validate our findings.

Footnotes

Author Disclosure Statement

The authors declare that they have no conflict of interest.

Funding Information

No funding statement was provided.

References

Ambrose

, Golightly

. 2015. Physical exercise as non-pharmacological treatment of chronic pain: why and when. Best Pract Res Clin Rheumatol, 29(1):120–130.

Ashburner

, Ball

, Blake

, Botstein

, Butler

, Cherry

, Davis

, Dolinski

, Dwight

, Eppig

. 2000. Gene ontology: tool for the unification of biology. Nat Genet, 25(1):25.

Bandettini

, Kellman

, Mancini

, Booker

, Vasu

, Leung

, Wilson

, Shanbhag

, Chen

, Arai

. 2012. MultiContrast Delayed Enhancement (MCODE) improves detection of subendocardial myocardial infarction by late gadolinium enhancement cardiovascular magnetic resonance: a clinical validation study. J Cardiovasc Magn Reson, 14(1):83.

Biddle

, García

, Pedisic

, Bennie

, Vergeer

, Wiesner

. 2017. Screen time, other sedentary behaviours, and obesity risk in adults: a review of reviews. Curr Obes Rep, 6(2):134–147.

Billiau

, Heremans

, Vandekerckhove

, Dijkmans

, Sobis

, Meulepas

, Carton

. 1988. Enhancement of experimental allergic encephalomyelitis in mice by antibodies against IFN-gamma. J Immunol, 140(5):1506–1510.

Byrne

, Caulfield

, De Vito

. 2018. Self-directed exercise programmes in sedentary middle-aged individuals in good overall health; a systematic review. Prev Med, 114:156–163.

Catoire

, Kersten

. 2015. The search for exercise factors in humans. FASEB J, 29(5):1615–1628.

Cohen

, Pieper

, Harris

, Rao

KMK

, Currie

. 1997. The association of plasma IL-6 levels with functional disability in community-dwelling elderly. J Gerontol A Biol Sci Med Sci, 52(4):M201–M208.

De Greef

, Deforche

, Tudor-Locke

, De Bourdeaudhuij

. 2010. A cognitive-behavioural pedometer-based group intervention on physical activity and sedentary behaviour in individuals with type 2 diabetes. Health Educ Res, 25(5):724–736.

10.

Duong

, Louis

, Gilbert

, Finkelman

, Strejan

. 1992. Effect of anti-interferon-γ and anti-interleukin-2 monoclonal antibody treatment on the development of actively and passively induced experimental allergic encephalomyelitis in the SJL/J mouse. J Neuroimmunol, 36(2–3):105–115.

11.

Edwardson

, Gorely

, Davies

, Gray

, Khunti

, Wilmot

, Yates

, Biddle

. 2012. Association of sedentary behaviour with metabolic syndrome: a meta-analysis. PLoS One, 7(4):e34916.

12.

Farinha

, Steckling

, Stefanello

, Cardoso

, Nunes

, Barcelos

, Duarte

, Kretzmann

, Mota

, Bresciani

. 2015. Response of oxidative stress and inflammatory biomarkers to a 12-week aerobic exercise training in women with metabolic syndrome. Sports Med Open, 1(1):19.

13.

Ferrucci

, Harris

, Guralnik

, Tracy

, Corti

, Cohen

, Penninx

, Pahor

, Wallace

, Havlik

. 1999. Serum IL-6 level and the development of disability in older persons. J Am Geriatr Soc, 47(6):639–646.

14.

Fleige

, Bosnjak

, Permanyer

, Ristenpart

, Bubke

, Willenzon

, Sutter

, Luther

, Förster

. 2018. Manifold roles of CCR7 and its ligands in the induction and maintenance of bronchus-associated lymphoid tissue. Cell Rep, 23(3):783–795.

15.

Hosokawa

, Hosokawa

, Komatsuzawa

, Goncalves

, Karimbux

, Napimoga

, Seki

, Ouhara

, Sugai

, Taubman

. 2006. Innate immune peptide LL-37 displays distinct expression pattern from beta-defensins in inflamed gingival tissue. Clin Exp Immunol, 146(2):218–225.

16.

Huang

, Sherman

, Lempicki

. 2009. Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat Protoc, 4(1):44.

17.

James

, Bajaj-Elliott

, Abujaber

, Forya

, Klein

, David

, Hollox

, Peebles

. 2018. Human beta defensin (HBD) gene copy number affects HBD2 protein levels: impact on cervical bactericidal immunity in pregnancy. Eur J Hum Genet, 26(3):434.

18.

Kanehisa

, Goto

. 2000. KEGG: Kyoto Encyclopedia of Genes and Genomes. Nucleic Acids Res, 28(1):27–30.

19.

Kim

, Lee

, Pi

, Lim

, Lee

, Jeong

, Chung

. 2015. IL-6 inhibitors for treatment of rheumatoid arthritis: past, present, and future. Arch Pharm Res, 38(5):575–584.

20.

Kumar

, Takada

, Boriek

, Aggarwal

. 2004. Nuclear factor-κB: its role in health and disease. J Mol Med, 82(7):434–448.

21.

Kuriakose

, Muleme

, Onyilagha

, Singh

, Jia

, Uzonna

. 2012. Diminazene aceturate (Berenil) modulates the host cellular and inflammatory responses to Trypanosoma congolense infection. PLoS One, 7(11):e48696.

22.

Larkin III

, Ahmed

, Wilson

, Johnson

. 2013. Regulation of interferon gamma signaling by suppressors of cytokine signaling and regulatory T cells. Front Immunol, 4:469.

23.

Liu

, Wei

, Luo

, Xu

, Zhao

, Zhang

, Lu

, Sun

, Liu

, Du

. 2016. Baicalin attenuates inflammation in mice with OVA-induced asthma by inhibiting NF-κB and suppressing CCR7/CCL19/CCL21. Int J Mol Med, 38(5):1541–1548.

24.

Lynch

, Mahmood

, Boyle

. 2018. Sedentary behaviour and cancer. In: Leitzman

, Jochem

, Schmid

, eds. Springer Series on Epidemiology and Public Health. Cham, Switzerland: Springer, pp. 245–298.

25.

McCarthy

, Procario

, Twisselmann

, Wilkinson

, Archambeau

, Michele

, Day

, Weinberg

. 2015. Proinflammatory effects of interferon gamma in mouse adenovirus 1 myocarditis. J Virol, 89(1):468–479.

26.

Miura

, Mitsunaga

, Ikeda

, Shimizu

, Ohno

, Takahashi

, Furuse

, Inagaki

, Higashi

, Kato

. 2015. Characterization of patients with advanced pancreatic cancer and high serum interleukin-6 levels. Pancreas, 44(5):756–763.

27.

Moschovakis

, Förster

. 2012. Multifaceted activities of CCR7 regulate T-cell homeostasis in health and disease. Eur J Immunol, 42(8):1949–1955.

28.

Neurath

, Finotto

. 2011. IL-6 signaling in autoimmunity, chronic inflammation and inflammation-associated cancer. Cytokine Growth Factor Rev, 22(2):83–89.

29.

Ottum

, Arellano

, Reyes

, Iruretagoyena

, Naves

. 2015. Opposing roles of interferon-gamma on cells of the central nervous system in autoimmune neuroinflammation. Front Immunol, 6:539.

30.

Özdemir

, Caglayan

, Bikker

, Pussinen

, Könönen

, Yamalik

, Gürsoy

, Fteita

, Nazmi

, Güncü

, Pietiäinen

, Tolvanen

, Gürsoy

. 2020. Gingival tissue human beta-defensin levels in relation to infection and inflammation. J Clin Periodontol, 47(3):309–318.

31.

Papini

, Nakamura

, Zorzetto

, Thompson

, Phillips

, Kokubun

. 2014. The effect of a community-based, primary health care exercise program on inflammatory biomarkers and hormone levels. Mediators Inflamm, 2014(2):185707.

32.

Patterson

, McNamara

, Tainio

, de Sá

, Smith

, Sharp

, Edwards

, Woodcock

, Brage

, Wijndaele

. 2018. Sedentary behaviour and risk of all-cause, cardiovascular and cancer mortality, and incident type 2 diabetes: a systematic review and dose response meta-analysis. Eur J Epidemiol, 33(9):811–829.

33.

Pedersen

. 2017. Anti-inflammatory effects of exercise: role in diabetes and cardiovascular disease. Eur J Clin Invest, 47(8):600–611.

34.

Sathyan

, Koshy

, Srinivas

, Easwer

, Premkumar

, Nair

, Bhattacharya

, Alapatt

, Banerjee

. 2015. Pathogenesis of intracranial aneurysm is mediated by proinflammatory cytokine TNFA and IFNG and through stochastic regulation of IL10 and TGFB1 by comorbid factors. J Neuroinflamm, 12(1):135.

35.

Shaw

, Merien

, Braakhuis

, Keaney

, Dulson

. 2020. Acute hyperketonaemia alters T-cell-related cytokine gene expression within stimulated peripheral blood mononuclear cells following prolonged exercise. Eur J Appl Physiol, 120(1):191–202.

36.

Sindhu

, Thomas

, Shihab

, Sriraman

, Behbehani

, Ahmad

. 2015. Obesity is a positive modulator of IL-6R and IL-6 expression in the subcutaneous adipose tissue: significance for metabolic inflammation. PLoS One, 10(7):e0133494.

37.

Smyth

, Ritchie

, Thorne

, Wettenhall

. 2005. LIMMA: linear models for microarray data. In: Gentleman

, Corey

, Huber

, Irizarry

, Duboit

, eds. Bioinformatics and computational biology solutions using R and bioconductor. Statistics for Biology and Health. New York: Springer.

38.

Stark

, Mueller

. 2012. Lung defenses: intrinsic, innate, and adaptive. In: Wilmott

, Boat

, Bush

, Chernick

, Detending

, Ratjen

, eds. Kendig & Chernick's Disorders of the Respiratory Tract in Children, 8th ed. Philadelphia, PA: W.B. Saunders, pp. 89–109.

39.

Starkie

, Ostrowski

, Jauffred

, Febbraio

, Pedersen

. 2003. Exercise and IL-6 infusion inhibit endotoxin-induced TNF-alpha production in humans. FASEB J, 17(8):884–886.

40.

Starr

, Saito

, Evers

, Saito

. 2015. Age-associated increase in cytokine production during systemic inflammation-II: the role of IL-1beta in age-dependent IL-6 upregulation in adipose tissue. J Gerontol A Biol Sci Med Sci, 70(12):1508–1515.

41.

Szklarczyk

, Franceschini

, Wyder

, Forslund

, Heller

, Huerta-Cepas

, Simonovic

, Roth

, Santos

, Tsafou

. 2014. STRING v10: protein–protein interaction networks, integrated over the tree of life. Nucleic Acids Res, 43(D1):D447–D452.

42.

Tang

, Li

, Wang

, Pan

, Wu

F-X

. 2015. CytoNCA: a cytoscape plugin for centrality analysis and evaluation of protein interaction networks. Biosystems, 127:67–72.

43.

Teles

, Graeber

, Krutzik

, Montoya

, Schenk

, Lee

, Komisopoulou

, Kelly-Scumpia

, Chun

, Iyer

. 2013. Type I interferon suppresses type II interferon–triggered human anti-mycobacterial responses. Science, 339(6126):1448–1453.

44.

Vafa

. 2019. Can coenzyme Q10 supplementation effectively reduce human tumor necrosis factor-α and interleukin-6 levels in chronic inflammatory diseases? A systematic review and meta-analysis of randomized controlled trials. Pharmacol Res, 148:104290.

45.

Verfaillie

, Imrichová

, Van de Sande

, Standaert

, Christiaens

, Hulselmans

, Herten

, Sanchez

, Potier

, Svetlichnyy

. 2014. iRegulon: from a gene list to a gene regulatory network using large motif and track collections. PLoS Comput Biol, 10(7):e1003731.

46.

Vijayaraghava

. 2017. Behavior of plasma interferon-gamma with graded exercise in individuals with varied body mass index and age: risk stratification of predisposition to inflammation. Natl J Physiol Pharm Pharmacol, 7(1):131.

47.

Wei

, Jie

, Haibo

, Chaoneng

, Dong

, Jianbing

, Junjie

, Leilei

, Hongtao

, Yunzeng

. 2017. Dendritic cells derived exosomes migration to spleen and induction of inflammation are regulated by CCR7. Sci Rep, 7:42996.

48.

Widmann

, Nieß

, Munz

. 2019. Physical exercise and epigenetic modifications in skeletal muscle. Sports Med, 49(4):509–523.

49.

Xiong

, Wang

, Jiang

, Pang

, Liu

, Li

, Zhong

, Ou

, Liu

. 2019. MEF2A alters the proliferation, inflammation-related gene expression profiles and its silencing induces cellular senescence in human coronary endothelial cells. BMC Mol Biol, 20(1):8.

50.

Yuan

, Niu

, Liu

, Fu

. 2014. Exercise increases the binding of MEF 2A to the Cpt1b promoter in mouse skeletal muscle. Acta Physiol, 212(4):283–292.

51.

Yuk

, Park

, Kwon

B-I

, Lah

, Chang

, Kim

J-Y

, Lee

K-M

, Park

S-H

, Hong

, Lee

S-H

. 2017. Basophil-derived IL-6 regulates T H 17 cell differentiation and CD4 T cell immunity. Sci Rep, 7:41744.

52.

Zhu

, Blumenthal

, Shi

, Jiang

, Patel

, Zhang

, Yu

, Gao

, Wu

. 2018. Sedentary behavior and the risk of depression in patients with acute coronary syndromes. Am J Cardiol, 121(12):1456–1460.