Genomic Expression Profiling and Bioinformatics Analysis of Chronic Recurrent Multifocal Osteomyelitis

Hindawi BioMed Research International Volume 2021, Article ID 6898093, 10 pages https://doi.org/10.1155/2021/6898093

Research Article Genomic Expression Profiling and Bioinformatics Analysis of Chronic Recurrent Multifocal Osteomyelitis

Kai Huang , Bingyuan Lin , Yiyang Liu , Qiaofeng Guo, and Haiyong Ren

Department of Orthopaedics, Tongde Hospital of Zhejiang Province, Hangzhou, China

Correspondence should be addressed to Haiyong Ren; [email protected]

Received 5 August 2020; Revised 31 December 2020; Accepted 12 January 2021; Published 9 February 2021

Academic Editor: R. K. Tripathy

Copyright © 2021 Kai Huang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Objective. Chronic nonbacterial osteomyelitis (CNO) is an autoinflammatory bone disorder. Its most severe form is referred to as chronic recurrent multifocal osteomyelitis (CRMO). Currently, the exact molecular pathophysiology of CNO/CRMO remains unknown. No uniform diagnostic standard and treatment protocol were available for this disease. The aim of this study was to identify the differentially expressed genes (DEGs) in CRMO tissues compared to normal control tissues to investigate the mechanisms of CRMO. Materials. Microarray data from the GSE133378 (12 CRMO and 148 matched normal tissue samples) data sets were downloaded from the Gene Expression Omnibus (GEO) database. DEGs were identified using the limma package in the R software. Gene Ontology (GO) analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, and protein-protein interaction (PPI) network analysis were performed to further investigate the function of the identified DEGs. Results. This study identified a total of 1299 differentially expressed mRNAs, including1177 upregulated genes and 122 downregulated genes, between CRMO and matched normal tissue samples. GO analyses showed that DEGs were enriched in immune-related terms. KEGG pathway enrichment analyses showed that the DEGs were mainly related to oxidative phosphorylation, ribosome, and Parkinson disease. Eight modules were extracted from the gene expression network, including one module constituted with immune-related genes and one module constituted with ribosomal-related genes. Conclusion.Oxidative phosphorylation, ribosome, and Parkinson disease pathways were significantly associated with CRMO. The immune-related genes including IRF5, OAS3, and HLA-A, as well as numerous ribosomal-related genes, might be implicated in the pathogenesis of CRMO. The identification of these genes may contribute to the development of early diagnostic tools, prognostic markers, or therapeutic targets in CRMO.

1. Introduction no apparent infectious agents are detectable at the site of the bone lesion, and antibiotic treatment does not affect clin- Chronic nonbacterial osteomyelitis (CNO) is an autoinflam- ical signs or patient symptoms. It is suggested that CRMO matory bone disorder that primarily affects children and can be genetically driven. It has also been suggested that adults [1]. It can generally occur in all age groups while the CRMO may constitute a pediatric form of SAPHO (synovitis, peak age-of-onset was between 7 and 12 years [2]. The clini- acne, pustulosis, hyperostosis, and osteitis) syndrome [7, 8]. cal presentation of CNO varies widely, from mild, time-lim- Findings indicated that an imbalance between proinflamma- ited, and unifocal asymptomatic bone lesions up to severe, tory (TNF-α, IL-6) and anti-inflammatory (IL-10, IL-1β) multifocal bone lesions with chronically active or recurrent cytokines may be centrally involved in the molecular pathol- features [3, 4]. These severe manifestations are referred to ogy of CNO [9, 10]. Moreover, mutations in IL1RN, LPIN2, as chronic recurrent multifocal osteomyelitis (CRMO) [5]. FBLIM1, and Pstpip2 have been found in human or murine Despite the intense scientificefforts that had contributed models of CRMO [6]. to a better understanding of the underlying molecular mech- There are no widely accepted diagnostic criteria or dis- anism of CNO/CRMO, the exact molecular pathophysiology ease biomarkers; CNO/CRMO remains a diagnosis of exclu- remains unknown [2, 6]. An infectious origin is excluded as sion [1]. As the clinical presentation is greatly variable and 2 BioMed Research International

Principal component analysis biplot 20

10000 15

–10000 PC2 (34.93%) −Log10( P value)

5 –20000

0 –30000 –20000 01000010000 20000 30000 –5.0 –2.5 0.0 2.5 5.0 PC1 (48.13%) Log2(fold change) Group Down Control Nosig Osteomyelitis Up (a) (b)

Sample

–1

–2

Sample Control Osteomyelitis (c)

Figure 1: Identification of 1299 DEGs in CRMO. (a) The plot of PCA analysis between CRMO samples and control samples after normalized gene expression. (b) Volcano plots of the gene expression between CRMO samples and control samples from GSE133378 data sets. Cutoff: ∣log 2ðFCÞ ∣ >1, P value < 0.05. The blue dots represent upregulated DEGs, the red dots represent downregulated DEGs, and the green dots indicated no statistically significant genes. (c) The heat map of the gene expression between CRMO samples and control samples from GSE133378 data sets. Cutoff: ∣log 2ðFCÞ ∣ >1, P value < 0.05. the clinical symptoms were commonly unspecific, the diag- ment were unavoidably persistent among the patients. The nosis is frequently delayed or even missed [11]. Misdiagnosis, estimated prevalence of CNO/CRMO is reported to be 1-2 prolonged antibiotic treatment, and delays in optimal treat- per million [12]. Although CNO is still deemed as a rare BioMed Research International 3

Table 1: The top 10 DEGs between CRMO and matched normal tissues within GSE133378 data sets.

Gene symbol Base mean LogFC lfcSE Stat P value FDR PSMB1 492.5637 3.23197 0.339503 9.519701 <0.000001 <0.000000001 CSTB 117.7362 4.06927 0.490468 8.296703 <0.000001 <0.000000001 FTL 1339.244 2.43132 0.324784 7.485968 <0.000001 <0.000000001 LAMP2 297.502 2.382048 0.339133 7.023935 <0.000001 0.000000007 RPS2P7 86.70384 3.342528 0.478375 6.987258 <0.000001 0.000000007 BCL9L 50.66015 -2.16449 0.442774 -4.88848 0.000001 0.000254625 LOC728535 88.75019 -1.68466 0.386537 -4.35833 0.000013 0.001401282 RBMX 249.9677 -1.36433 0.337952 -4.03706 0.000054 0.004287488 LOC613037 168.746 -1.61427 0.409896 -3.93825 0.000082 0.005758674 RRAS2 90.51448 -1.32056 0.381311 -3.46321 0.000534 0.024275822 disorder, it is believed that the incidence would be underesti- and false discovery rate ðFDRÞ <0:05 were selected as differ- mated [10]. According to several case series, CNO/CRMO entially expressed genes (DEGs). may be almost as common as infectious osteomyelitis [1, 13, 14]. Currently, in the absence of an exact treatment pro- 2.2. GO Term and KEGG Pathway Enrichment Analyses. To fi tocol for patients with CNO/CRMO, treatment of this disease conduct enrichment analyses, the package clusterPro ler is largely based on expert opinion, case reports, and also per- (version 3.8.1) of R software (version 3.4.0) was used for ana- sonal experience [1, 10]. Nonsteroidal anti-inflammatory lyzing the KEGG pathways and GO processes in this study. fi drugs (NSAIDs) are usually deemed as the first-line treat- The identi ed DEGs were enriched for terms in the GO bio- ment, which have been demonstrated to be useful for logical process (BP), molecular function (MF), and cellular pain control and inducing remission [10]. Corticosteroids, component (CC) categories [15]. KEGG pathway analysis disease-modifying antirheumatic drugs (such as methotrex- was utilized to clarify the potential functions and signaling ate or sulfasalazine), anti-TNF agents, or bisphosphonates pathways of the DEGs. had also been reported to be effective [1, 3]. However, longi- 2.3. Protein-Protein Interaction (PPI) Network Analysis. The tudinal, placebo-control large-scale studies on the different Search Tool for the Retrieval of Interacting Genes/Proteins possible therapeutic strategies are absent, and the molecular (STRING, https://stringdb.org/) was used to generate the pathophysiology of CNO/CRMO remains incompletely PPI network of proteins encoded by the DEGs. A combined understood; the optimal treatment strategy for CNO/CRMO score > 0 was selected to construct the coexpression PPI net- remains difficult to determine. The discovery of gene network work. The PPI network was constructed by the Cytoscape interactions could provide important insights to advance our Software 3.8 (http://cytoscape.org/). understanding of the pathogenesis of CNO/CRMO, and the analysis of abnormal gene expression in the patients would help explore new molecular targets for development of diag- 3. Results nostic tools or drugs against CNO/CRMO. 3.1. Identification of 1299 DEGs in CRMO. The relationship between CRMO samples and control samples was analyzed by PCA, and the results showed obvious bias between CRMO 2. Materials and Methods samples and control samples; moreover, the biological repe- tition of three CRMO samples was consistent (Supplemen- 2.1. Collection of Gene Expression Data Sets. Microarray data tary Figure S1). Thus, after the data was normalized from the GSE133378 data sets (https://www.ncbi.nlm.nih (Supplementary Figure S2), three CRMO samples (R_BEN- .gov/geo/query/acc.cgi?acc=GSE133378) were downloaded 012_S19_L001, R_UZG_6_S27_L001, and R_UZG_4_S25_ from the GEO (https://www.ncbi.nlm.nih.gov/geo) of NCBI. L001) and three control samples (AZT14C_S38_L001, All data were freely accessible online. GSE133378 includes 49 AZT15C_S40_L001, and AZT1C_S20_L001) were used for viral and 7 bacterial cases. Three CRMO samples (R_BEN- subsequent analyses (Figure 1(a)). By the criteria of FDR 012_S19_L001, R_UZG_6_S27_L001, and R_UZG_4_S25_ <0:05 and fold change > 1, we identified 1299 mRNAs L001) with four biological repetitions and three control sam- differentially expressed in CRMO compared with matched ples (AZT14C_S38_L001, AZT15C_S40_L001, and AZT1C_ normal tissues. As a result, 1177 mRNAs were upregulated, S20_L001) were randomly selected from 148 control sam- and 122 mRNAs were downregulated (Figures 1(b) and ples. The relationship of the intersamples was analyzedThe 1(c)). The top 10 DEGs according to the value of log2FC inter-relationship of the samples was analyzed by principal are listed in Table 1. The detailed DEGs are summarized in component analysis (PCA). The array Quality package was Supplementary File 1. used for quality control, and the limma package was used to apply raw data in R software. The normalization criteria were 3.2. Analyses of Biological Function. The results of GO term quantile normalization. Genes that met fold changes ðFCÞ >1 analysis illustrated that in terms of biological processes 4 BioMed Research International

Upregulated DEGs FDR 0.0020 GO:0019884~antigen processing and presentation of exogenous antigen [GO_BP] GO:0048002~antigen processing and presentation of peptide antigen [GO_BP] GO:0002475~antigen processing and presentation via MHC class Ib [GO_BP] GO:0019883~antigen processing and presentation of endogenous antigen [GO_BP] GO:0022900~electron transport chain [GO_BP] GO:0006413~translational initiation [GO_BP] GO:0002218~activation of innate immune response [GO_BP] GO:0002504~antigen processing and presentation of peptide or polysaccharide antigen via MHC class II [GO_BP] GO:0015980~energy derivation by oxidation of organic compounds [GO_BP] GO:0090269~fbroblast growth factor production [GO_BP] 0.0015 GO:0098803_respiratory chain complex [GO_CC] GO:0070469~respiratory chain [GO_CC] GO:0030666~endocytic vesicle membrane [GO_CC] GO:0031983~vesicle lumen [GO_CC] GO:1990204~oxidoreductase complex [GO_CC] GO:0005747~mitochondrial respiratory chain complex I [GO_CCİ GO:0030964~NADH dehydrogenase complex [GO_CC] GO:0005746~mitochondrial respiratory chain [GO_CC] GO:0031227~intrinsic component of endoplasmic reticulum membrane [GO_CC] GO:0044391~ribosomal subunit [GO_CC] 0.0010 GO:0030881~beta~2~microglobulin binding [GO_MF] GO:0023023~MHC protein complex binding [GO_MF] GÖ:0046977~TAP binding [GO_MF] GO:0042605~peptide antigen binding [GO_MF] GO:0016651~oxidoreductase activity, acting on NAD(P)H [GO_MF] GO:0042277~peptide binding [GO_MF] GO:0042887~amide transmembrane transporter activity [GO_MF] GO:0071889–14~3~3 protein binding [GO_MF] GO:0044183~protein binding involved in protein folding [GO_MF] 0.0005 GO:0015002~heme~copper terminal oxidase activity [GO_MF]

246810 Fold enrichment

Counts 20 60 40 80 (a)

Downregulated DEGs FDR GO:1901722~regulation of cell proliferation involved in kidney development [GO_BP] 0.4 GO:0035112~genitalia morphogenesis [GO_BP] GO:0046500~S~adenosylmethionine metabolic process [GO_BP] GO:1900119~positive regulation of execution phase of apoptosis [GO_BP] GO:0019226_transmission of nerve impulse [GO_BP] GO:0035019~somatic stem cell population maintenance [GO_BP] GO:0008360~regulation of cell shape [GO_BP] GO:0006730~one~carbon metabolic process [GO_BP] GO:0019827~stem cell population maintenance [GO_BP] GO:0051302~regulation of cell division [GO_BP] 0.3 GO:0045211~postsynaptic membrane [GO_CC] GO:0014069_postsynaptic density [GO_CC] GO:0097060~synaptic membrane [GO_CC] GO:0032279~asymmetric synapse [GO_CC] GO:0099572_postsynaptic specialization [GO_CC] GO:0005938~cell cortex [GO_CC] GO:0031252~cell leading edge [GO_CC] GO:0000151~ubiquitin ligase complex [GO_CC] GO:0043197~dendritic spine [GO_CC] 0.2 GO:0031256~leading edge membrane [GO_CC] GO:0051184~cofactor transmembrane transporter activity [GO_MF] GO:0015238~drug transmembrane transporter activity [GO_MF] GO:1901682~sulfur compound transmembrane transporter activity [GO_MF] GO:0005527~macrolide binding [GO_MF] GO:0016885~ligase activity, forming carbon~carbon bonds [GO_MF] GO:0048029~monosaccharide binding [GO_MF] GO:0042910~xenobiotic transmembrane transporter activity [GO_MF] GO:0005516~calmodulin binding [GO_MF] GO:0017081~chloride channel regulator activity [GO_MF] 0.1 GO:0097602~cullin family protein binding [GO_MF]

10 20 30 Fold enrichment

Counts 1 5 2 6 3 7 4 (b)

Figure 2: GO function analysis of biological function. (a) The top 30 GO function (BP, CC, and MF) analyses of upregulated DEGs. Cutoﬀ: P value < 0.05. (b) The top 30 GO function (BP, CC, and MF) analyses of downregulated DEGs. Cutoﬀ: P value < 0.05.

(BP), the upregulated genes were mainly enriched in “antigen upregulated genes were mainly enriched in the “respiratory processing and presentation of exogenous antigen,”“antigen chain complex,”“respiratory chain,” and “endocytic vesicle processing and presentation of peptide antigen,” and “antigen membrane” areas (Figure 2(a)), whereas downregulated genes processing and presentation via MHC class Ib” (Figure 2(a)). were mainly enriched in “postsynaptic membrane,”“postsyn- Downregulated genes were mainly enriched in “regulation of aptic density,” and “synaptic membrane” (Figure 2(b)). In cell proliferation involved in kidney development,”“genitalia terms of molecular function (MF), upregulated genes were morphogenesis,” and “S-adenosylmethionine metabolic pro- mainly enriched in “beta-2-microglobulin binding,”“MHC cess” (Figure 2(b)). In terms of cellular components (CC), protein complex binding,” and “TAP binding” (Figure 2(a)), BioMed Research International 5

Upregulated DEGs FDR hsa00190~oxidative phosphorylation 0.0015

hsa03010~ribosome

hsa05012~Parkinson disease

hsa04932~nonalcoholic fatty liver disease (NAFLD)

hsa05010~Alzheimer disease 0.0010 hsa04145~phagosome

hsa05016~Huntington disease

hsa04714~thermogenesis

hsa05140~leishmaniasis

hsa04612~antigen processing and presentation 0.0005

2.5 3.0 3.5 Fold enrichment Counts 15 35 20 40 25 45 30 (a) Downregulated DEGs FDR 0.48 hsa01523~antifolate resistance

hsa01200~carbon metabolism

hsa00670~one carbon pool by folate 0.44 hsa00515~mannose type O~glycan biosynthesis

hsa04010~MAPK signaling pathway

hsa04072~phospholipase D signaling pathway 0.40

hsa04514~cell adhesion molecules (CAMs)

hsa04392~Hippo signaling pathway–multiple species 0.36 hsa00630~glyoxylate and dicarboxylate metabolism

hsa00640~propanoate metabolism

51015 Fold enrichment Counts 1.0 2.5 1.5 3.0 2.0 (b)

Figure 3: KEGG pathway enrichment analysis of biological function. (a) The top 10 enriched KEGG pathways of upregulated DEGs. Cutoﬀ: P value < 0.05. (b) The top 10 enriched KEGG pathways of downregulated DEGs. Cutoﬀ: P value < 0.05. whereas downregulated genes were mainly enriched in GO:0048002 antigen processing and presentation of peptide “cofactor transmembrane transporter activity,”“drug trans- antigen, GO:0002475 antigen processing and presentation membrane transporter activity,” and “sulfur compound trans- via MHC class Ib, GO:0019883 antigen processing and pre- membrane transporter activity” (Figure 2(b)). As shown in the sentation of endogenous antigen, and GO:0002218 activa- GO analysis, numerous genes were enriched in GO terms that tion of innate immune response. The detailed results of associated with autoimmune function, such as GO:0019884 the GO enrichment analyses are provided in Supplementary antigen processing and presentation of exogenous antigen, File 2. 6 BioMed Research International

Module 1 Module 2

Module 7

Module 6

Module 8 Module 5 Module 4

Module 3

Figure 4: Establishment of PPI network based on DEGs. The PPI network of the DEGs after screening the proteins with degree > 6. The PPI network can be divided into 8 modules.

Furthermore, based on KEGG pathway enrichment (degree = 27), TPT1 (degree = 24), SPCS3 (degree = 31), and analysis, the upregulated genes were enriched in pathways EEF1A1 (degree =33). In module 5 (Figure 5(e)), the DEGs such as “oxidative phosphorylation,”“ribosome,” and “Par- were found to be immune-related, including IRF5, IFI6, kinson disease,” whereas downregulated genes were enriched BST2, IFITM2, RSAD2, ISG15, STAT1, DDX58, IFIT1, OAS3, in pathways such as “antifolate resistance,”“carbon metabo- HLA-A, IFI30, HLA-A, IFI30, HLA-DRB1, FCGR1B, HLA- lism,” and “one carbon pool by folate.” The top 10 enriched DQA1, and HLA-DRA. Among them, IRF5 (degree = 14), pathways of upregulated DEGs and downregulated DEGs OAS3 (degree = 14), and HLA-A (degree = 14)hadthe are shown in Figure 3. The detailed results of the KEGG path- highest connectivity with other proteins. way analyses are provided in Supplementary File 3. 4. Discussion 3.3. Establishment of PPI Network. STRING was used to generate the PPI network, which can illustrate interactions Chronic nonbacterial osteomyelitis (CNO) is an autoinflam- between identified DEGs. This PPI network contained 317 matory bone disorder. The more severe form of CNO is nodes and 1432 edges (Supplementary Figure S3). We found referred to as chronic recurrent multifocal osteomyelitis numerous DEGs with high connectivity with other proteins. (CRMO). The molecular pathophysiology remains unknown, In order to identify protein interaction modules with strong and no exact treatment protocol for this disease has been interaction, we screened the proteins with degree > 6 for established. An understanding of the mechanisms underlying further analysis. And then a network with 137 nodes and the pathophysiology of CNO/CRMO is vital for the develop- 1165 edges was obtained (Figure 4), while 8 modules were ment of effective novel therapeutics and diagnostic tools. selected from the PPI network by the MCODE plugin in In the present study, the gene expression levels between Cytoscape (Figure 5). Further GO term and KEGG pathway CRMO and matched normal tissues were compared. A total analyses for DEGs in each module were performed to clarify of 1299 DEGs, including 1177 upregulated and 122 downreg- the potential functions and signaling pathways of the genes ulated mRNAs, were identified to be associated with CRMO. (Supplementary Figure S4 and S5). In module 2 DEGs were observed and analyzed using GO term and (Figure 5(b)), 40 proteins were found to own high KEGG pathway analyses. The PPI network analysis was connectivity with other proteins. 31 of 40 proteins encoded applied to identify key genes and significant modules associ- ribosomal proteins (RBs) and another 9 proteins including ated with CRMO pathogenesis. FAU (degree = 38), EIF3G (degree = 33), UPF1 (degree = 32 As shown in the GO analysis, in terms of biological pro- ), MAGOH (degree = 38), EIF3F (degree = 32), EEF1B2 cesses, numerous genes were enriched in terms that were BioMed Research International 7

RPL30 RPL6 RPS13 SDCBP RPS5 RPL31 GCA RPS6 RPL9 FTL FAU RPS28 STK111P RPS8 RPL39 MPO RPL18 MAGOH TRAPPC1 RPSA RPS3A LYZ RPL24 TPT1 PTGES2 RPS21 RPLP0 JUP RPS17 RNASE2 UPF1 TOLLIP RPS4X RPS15A

RETN RPS29 EIF3G CFP ARSA NEU1 EEF1A1 RPL18A RPS27 RPS9

RPL37A RPL8

EEF1B2 RPL15 RPL27 RPSAP58 RPL29 SPCS3 RPS7 EIF3F RPS14

(a) (b)

SIGLEC14 ATP5G1 MT-ATP6

ADAM8 SVIP ATP6V1F MT-ND3 DGAT1 GAA MT-ND1

NDUFA5 AGPAT2 NBEAL2 NDUFB2

CEACAM3 ATP6AP2 MT-ND2 UQCRQ PLAUR CD33 NDUFA4 GRMC1 NDUFA7

IRF5 HLA-DQ SNRNP200 HLA-DRA IFI6 BST2 CWC15 U2AF2 HLA-DQA1 IFITM2 FCGR1B RBM22 POLR2F RSAD2 HLA-DRB1 ISG15 POLE4 SNRNP27 IF130 STAT1 POLR2J HLA-A DDX58 OAS3 IFIT1

(e) (f)

Figure 5: Continued. 8 BioMed Research International

RAB5B SPSB3 FBXL18 UBC RPC3 KLHL21 TRAF7 LONRF1 NFKBIE PSMB10 FBXL16 UBE3D COPS7A PSMC4 CD40LG UBC DCUN1D3 DVL2 PSMB1 KLHL9 SOCS5 KCTD7 ARRE PSME2 COMMD6 NEKBIA MKRN1 FBXW5 RNF 126

(g) (h)

Figure 5: Eight sub-PPI networks of PPI network. (a–h) Eight strong connected sub-PPI networks from the PPI network. associated with immune function, such as GO:0019884 anti- It had been indicated that OAS3 is associated with the early gen processing and presentation of exogenous antigen, inflammatory response to viruses or bacteria and may also GO:0048002 antigen processing and presentation of peptide play a role in cell growth, apoptosis, differentiation, or gene antigen, GO:0002475 antigen processing and presentation regulation [19–21]. HLA-A is one of three major types of via MHC class Ib, GO:0019883 antigen processing and pre- the human major histocompatibility complex (MHC) class sentation of endogenous antigen, and GO:0002218 activation I cell surface receptors. It is known that HLA-A plays a vital of innate immune response. The GO analysis of the DEGs in role within the immune system through its involvement in module 5 showed that genes were enriched in GO terms such adaptive and innate immune responses [22]. The exact role as GO:0019884 antigen processing and presentation of exog- of these genes in CRMO pathogenesis calls for further enous antigen, GO:0048002 antigen processing and presenta- exploration. tion of peptide antigen, GO:0002504 antigen processing and In terms of biological processes, the DEGs were also presentation of peptide or polysaccharide antigen via MHC found to be enriched in “electron transport chain,”“transla- class II, and GO:0002699 positive regulation of immune tional initiation,” and “energy derivation by oxidation of effector process, which were also intensely related with organic compounds” in the GO analysis. In terms of cellular immune function. These GO terms that DEGs were enriched components, the DEGs were mainly enriched in “respiratory in were similar with the global GO analysis results. Autoin- chain complex,”“respiratory chain,”“oxidoreductase com- flammatory osteopathies, including CRMO, are suggested plex,”“mitochondrial respiratory chain complex I,”“NADH to be the result of a dysregulated innate immune system dehydrogenase complex,”“mitochondrial respiratory chain,” [9]. It is suggested that the genes in module 5 might be pri- and “ribosomal subunit.” In terms of molecular function, the marily involved in the disease progression of CRMO. The DEGs were enriched in “oxidoreductase activity” and “acting DEGs in module 5 included IRF5, IFI6, BST2, IFITM2, on NAD(P)H.” In addition, based on KEGG pathway analy- RSAD2, ISG15, STAT1, DDX58, IFIT1, OAS3, HLA-A, sis, genes were enriched in pathways such as “oxidative phos- IFI30, HLA-A, IFI30, HLA-DRB1, FCGR1B, HLA-DQA1, phorylation” and “ribosome.” It is suggested genes or and HLA-DRA. Among them, IRF5 (degree = 14), OAS3 pathways relating to mitochondria and ribosomes might (degree = 14), and HLA-A (degree = 14) owned the highest have important roles in the pathophysiology of CRMO. connectivity with other proteins. The interferon regulatory And in the PPI network analysis, 40 genes were found to factor 5 (IRF5) gene, as a member of the interferon regulatory own high connectivity with other genes in module 2. Among factor (IRF) family, participates in the type I interferon (IFN) them, 31 encoded ribosomal proteins (RBs), while 9 other signaling pathway and mediates induction of proinflamma- genes, including FAU, EIF3G, UPF1, MAGOH, EIF3F, tory cytokines such as interleukin-6 (IL-6), IL-12, IL-23, EEF1B2, TPT1, SPCS3, and EEF1A1, were also associated and TNF-α [16, 17]. A number of studies had indicated that with the function of ribosomes. It has indicated that DEGs the IRF5 gene contributes to the pathogenesis of varied in module 2 were intensely connected with each other. Accu- inflammatory and autoimmune diseases, such as rheumatoid mulating evidences have shown that mitochondria are a hub arthritis, human lupus, systemic sclerosis, and inflammatory of the immune system [23]. By regulating metabolic states as bowel disease [16, 18]. The IRF5 gene might be associated well as mitochondrial reactive oxygen species production, with the pathogenesis of CRMO, which is believed to be an mitochondria are involved in many immune cell functions autoinflammatory bone disorder. The 2′,5′-oligoadenylate [24]. In stressed or damaged cells, mitochondrial compo- synthetase (OAS) family is an IFN-induced enzyme that is nents are released and are also a source of immunogenicity. involved in the activation of ribonuclease L (RNase L) [19]. Ribosomal proteins have also been shown to be involved in BioMed Research International 9 immune signaling and play diverse roles in host immune Supplementary 3. Supplementary Figure 3 The PPI network response [25]. For example, TPT1 can induce the release of based on DEGs with degree > 6. cytokines and other signaling molecules from multiple types Supplementary 4. Supplementary Figure 4 The top 30 of immune cells [26]. Moreover, upregulation of EEF1A1 in – – fi enriched GO terms of DEGs in modules 1 8(ah, Sahiwal cows signi cantly associates with immune function respectively). and inflammatory response [27]. Mitochondria and ribosomes are also involved in a variety of cellular phenomena, Supplementary 5. Supplementary Figure 5 The top 10 signif- such as apoptosis, cell cycle, proliferation, and differentiation icant KEGG signaling pathways that DEGs in modules 1–8 [25, 28]. The exact molecular mechanisms of mitochondria (a–h, respectively) were enriched in. and ribosomes in CRMO still remain unknown and call for Supplementary 6. Supplementary File 1 The details of all the further exploration. DEGs. Certain limitations are associated with the present study. The sample size in our study was relatively small. In addition, Supplementary 7. Supplementary File 2 The detailed results only bioinformatics approaches were used in the study due to of the GO enrichment analyses of all the DEGs. the rarity of this condition. Larger numbers of samples are Supplementary 8. Supplementary File 3 The detailed results also required for further investigation to validate the results of the KEGG pathway analyses of all the DEGs. obtained. References 5. Conclusion [1] S. R. Hofmann, F. Kapplusch, H. J. Girschick et al., “Chronic Oxidative phosphorylation, ribosome, and Parkinson disease recurrent multifocal osteomyelitis (CRMO): presentation, pathways were significantly associated with CRMO. The pathogenesis, and treatment,” Current osteoporosis reports, immune-related genes including IRF5, OAS3, and HLA-A, as vol. 15, no. 6, pp. 542–554, 2017. well as numerous ribosomal-related genes, might be implicated [2] S. R. Hofmann, F. Kapplusch, K. Mäbert, and C. M. 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