Comprehensive analyses of transcriptomes induced by Lyme spirochete infection to CNS model system

Comprehensive analyses of transcriptomes induced by Lyme spirochete infection to CNS model system | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Comprehensive analyses of transcriptomes induced by Lyme spirochete infection to CNS model system Shiyuan Wen, Xin Xu, Jing Kong, Lisha Luo, Peng Yue, Wenjing Cao, and 16 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-822329/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Lyme disease is a zoonotic disease caused by infection with Borrelia burgdorferi ( Bb ), the involvement of the nervous system in Lyme disease is usually referred to as Lyme neuroborreliosis (LNB). LNB has diverse clinical manifestations, most commonly including meningitis, Bell’s palsy, and encephalitis. However, the molecular pathogenesis of neuroborreliosis is still poorly understood. Comprehensive transcriptomic analysis following Bb infection could provide new insights into the pathogenesis of LNB and may identify novel biomarkers or therapeutic targets for LNB diagnosis and treatment. Methods In the present study, we pooled transcriptomic datasets (transcriptomic rhesus data from our laboratory and the GSE85143 dataset from the Gene Expression Omnibus database) to screen common differentially expressed genes (DEGs) in the Bb infection group and the control group. Functional and enrichment analyses were conducted using the Database of Annotation Visualization and Integrated Discovery database, Protein-Protein Interaction network, and hub genes were identified using the Search Tool for the Retrieval of Interaction Genes database and the CytoHubba plugin. In addition, in vitro and ex vivo assays were performed to verify the above findings. The mRNA expression levels of these genes were verified by quantitative real-time PCR (qPCR). Results A total of 80 upregulated DEGs and 32 downregulated DEGs were identified. Among them, 11 hub genes were selected. Upregulated genes in the Gene Ontology analysis were significantly enriched in cell adhesion processes. The pathway enrichment analyses revealed that the PI3K-Akt signaling pathway was significantly enriched. The mRNA levels of ANGPT1 , TLR6 , SREBF1 , LDLR , TNC , and ITGA2 in U251 cells and/or rhesus brain explants by exposure to Bb were validated by qPCR. Conclusion Our study suggested that TLR6 , ANGPT1 , LDLR , SREBF1 , TNC , and ITGA were differentially highly expressed in Bb- infected astrocytes compared to normal controls, and overexpression of LDLR might be a favorable prognostic factor of LNB patients. Further study is needed to explore the value of TLR6 , ANGPT1 , LDLR , SREBF1 , TNC , and ITGA in LNB pathogenesis. Cellular & Molecular Neuroscience Lyme neuroborreliosis pathogenesis transcriptomic analysis Borrelia burgdorferi candidate biomarker Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Introduction Lyme disease (also known as Lyme borreliosis), mainly caused by Borrelia burgdorferi ( Bb ) spirochetes, is the most common vector-borne disease in the northern hemisphere. While frequently occurring in the United States, it is also endemic in Europe and parts of Asia(Schotthoefer and Frost 2015 ). It has showed increasing growth and expansion in geographic extent over the past two decades, with an estimated 300,000 people infected with Lyme spirochetes annually(Hinckley et al. 2014 , Kugeler et al. 2015 , Nawrocki and Hinckley 2020 ). Lyme disease is a multistage and multisystem disorder predominantly affecting the skin, but it also involves the joints, heart, and nervous system. Transmitted by Ixodes ticks, its causative agent Bb initially propagates locally in the skin before it disseminates hematogenously to other organ systems. Lyme disease which results in neurological manifestation, such as painful meningoradiculitis (Bannwarth syndrome), cranial neuritis, lymphocytic meningitis, and rarely encephalitis or cerebral vasculitis at a later period (stage 2), is called Lyme neuroborreliosis (LNB)(Reik et al. 1979 , Stanek et al. 2012 ). Bb is a highly neurophilic pathogen which remains latent in the central or peripheral nervous system, and the clinical presentation varies with disease stages. It has been reported that the incidence of LNB in Lyme disease patients is not less than 10%(Koedel et al. 2015 , Steere et al. 2016 ). Pain is generally the first neurological symptom of meningoradiculitis, and approximately one-quarter of LNB patients will show no other symptoms. More than half of patients with meningoradiculitis also show cranial nerve symptoms, with unilateral or bilateral facial nerve involvement being the most common(Garcia-Monco and Benach 2019 , Hansen and Lebech 1992 ). Neurological symptoms occur concomitantly with lymphocytic cerebrospinal fluid (CSF) pleocytosis (10–1000 cells per µL), which is a diagnostic criterion for definite LNB in Europe(Mygland et al. 2010 , Pfister et al. 1984 ). Intravenous antibiotics have been proven to be an effective therapy for LNB, however, in a few patients Bb has been reported to persist in the CSF after antibiotic therapy(Preac-Mursic et al. 1989 ). Moreover, a proportion of patients still have symptoms following appropriate antibiotic therapy, which are defined as post-treatment Lyme disease syndrome(Kaplan et al. 2003 , Koedel, Fingerle and Pfister 2015 ). Atypical symptoms and the lack of clinically specific diagnostic indicators often prolongs the diagnosis of LNB or leads to its misdiagnosis. If LNB is left untreated or is improperly treated, neurological symptoms of patients can persist for many years(Feder et al. 2007 ). Nonetheless, the molecular mechanisms associated with the pathogenesis of LNB remain poorly understood. In recent years, high-throughput sequencing and bioinformatics technologies have rapidly becoming critical tools in the study of diseases, including Lyme disease(Bouquet et al. 2016 , Ding et al. 2020 , Mansfield et al. 2017 , Petzke et al. 2020 ). In particular, the study of transcriptomics is more economical and informative than genomics. Transcriptome sequencing of mRNA using high-throughput sequencing technologies can quickly and comprehensively obtain information on almost all transcripts of a specific species tissue or cell in a certain state, including base sequences and gene expressions. Although mRNA only comprises approximately 4% of the total RNA in mammalian cells, it has been the focus of research, because it is ultimately translated into protein and participates in the expression of the cell phenotype(Wu et al. 2014 ). In addition, the Gene Expression Omnibus (GEO) database developed by the National Center for Biotechnology Information (NCBI) also provides vast valuable datasets. At present, however, there are limited transcriptomic studies associated with Lyme spirochete infection to CNS. Thus, we conducted this study to explore the pathogenesis of LNB and to provide valuable clues for future research. We obtained the transcriptome data of frontal cortex brain explants that were exposed to Bb and the human astrocytes following Bb infection transcriptome dataset (GSE85143), and screened the data using the GEO database for comprehensive analysis (Fig. 1 ). The results of the present study will improve our understanding of the underlying mechanisms of LNB and will identify biomarker candidates for LNB diagnosis and prognosis. Materials And Methods Data resources To identify the host candidate hub genes in models of LNB, we comprehensively analyzed our previous transcriptome raw sequencing data(Ding et al. 2019) which has been uploaded to NCBI in the format of.fastq (http://www.ncbi.nlm.nih.gov/bioproject/723671) and the GSE85143 dataset from the GEO database (GEO, https://www.ncbi.nlm.nih.gov/geo/ )(Clough and Barrett 2016). Frontal cortex brain explants from three healthy rhesus macaques (2 males and 1 female) were used for ex vivo experiments in our previous study(Ding, Ma, Tao, Peng, Han, Sun, Dai, Ji, Bai, Jian, Chen, Luo, Wang, Bi, Liu and Bao 2019, Ding, Sun, Bi, Zhang, Yue, Xu, Cao, Luo, Chen, Li, Ji, Jian, Lu, Abi, Liu and Bao 2020), the explants were collected for transcriptomic analysis after co-culture with live Bb ( Bb 4680, DSMZ, A1511358-1) or culture containing normal medium as controls. The platform used for this dataset was Illumina HiSeq 2500. We referred to this data as ‘Rhesus dataset’ in this paper. The GSE85143 dataset was based on GPL11154 Illumina HiSeq 2000 (Homo sapiens) platform. The experiment contained 9 samples consisting of 6 Bb -infected samples and 3 uninfected samples, which used human astrocytes infected with Bb and uninfected astrocytes as controls. Differential Expression Analyses Differential expression analysis was performed using the online analysis tool BioJupies in the GSE85143 dataset. BioJupies ( https://amp.pharm.mssm.edu/biojupies/ ) is an easy-to-use web application for RNA-seq data analyses, the datasets published in GEO can be processed by ARCHS4 directly(Torre et al. 2018). The cut-off criteria to select the differentially expressed genes (DEGs) were the adjusted P-value <0.05 and fold-change (FC)≥2. For the GSE85143 dataset, the volcano plot was visualized using R package ggplot2. The differential analysis of the Rhesus dataset has been described in detail in our previous study(Ding, Ma, Tao, Peng, Han, Sun, Dai, Ji, Bai, Jian, Chen, Luo, Wang, Bi, Liu and Bao 2019). We merged all DEGs from three time points in the Rhesus dataset. Statistical analysis was performed for each dataset using the Venn diagram webtool ( http://bioinformatics.psb.ugent.be/webtools/Venn/ ) to identify the intersecting DEGs. Functional and Pathway Enrichment Analyses The Gene ontology (GO) term enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of DEGs were performed using DAVID (the Database for Annotation, Visualization, and Integration Discovery). DAVID (version 6.8, https://david.ncifcrf.gov/ ) is a web server which provides a comprehensive set of functional annotation tools to help researchers understand the biological significance of a large list of genes(Huang et al. 2007). GO is an important biological tool that enables investigators to turn data into knowledge. GO comprises three categories including Biological process (BP), Molecular function (MF), and Cellular component (CC)(Ashburner et al. 2000). A P-value of <0.05 was used as the threshold for significance. Protein–Protein Interaction Network Analysis and Identification of Hub Genes The protein–protein interaction (PPI) network was analyzed and visualized using the online resource of Search Tool for the Retrieval of Interaction Genes (STRING) 11.0 ( https://string-db.org/ ), which collects, scores, and integrates functional interactions among proteins(Szklarczyk et al. 2019). We mapped previously identified DEGs to STRING to identify potential interactions between them. In our study, the minimum interaction score required was 0.4 (medium confidence). The PPI network was visualized by Cytoscape 3.8.2 and analyzed in Cytoscape software with the cytoHubba plugin. Eleven hub genes closely linked to models of LNB were identified using MCC, DMNC, MNC, Degree, EPC, Bottleneck, Eccentricity, Closeness, Radiality, Betweenness, Stress, Clustering Coefficient methods. Multi-dimensional Venn diagrams were generated and visualized using the R package UpSetR(Conway et al. 2017). Spirochetes Culture Borrelia burgdorferi sensu stricto strain 4680 (DSMZ, A1511358-1) culture was carried out as described previously with little modification(Ding, Ma, Tao, Peng, Han, Sun, Dai, Ji, Bai, Jian, Chen, Luo, Wang, Bi, Liu and Bao 2019). For co-culture experiments, spirochetes were centrifuged (2500×g; 10 min; 4℃) and the Barbour-Stoenner-Kelly II (BSK II) medium supplemented with 6% rabbit serum (Thermo Fisher Scientific, United States) was removed and washed three times with sterilized PBS. The spirochetes were resuspended in antibiotic-free cell culture medium. Bacterial counts were determined using a counting plate under dark-field microscopy. Co-culture of the Human Astrocyte Cell Line with Bb The human astrocyte cell line U251 was obtained from the Kunming Cell Bank, Kunming Institute of Zoology, Chinese Academy of Sciences (Kunming, China). U251 cells were cultured in Dulbecco’s Modified Eagle Medium (DMEM) (Thermo Fisher Scientific, United States) supplemented with 10% fetal bovine serum (FBS) (Thermo Fisher Scientific, United States) at 37℃ with 5% CO 2 . U251 cells were seeded in 6-well plates at a cell density of 1.2×10 5 cells per well. After attaching to plates, cells were exposed to Bb at a multiplicity of infection (MOI) of 0.1 or 1 for 6 h, 12 h, 24 h, and 48 h. Co-culture of Frontal Cortex Brain Explants with Bb Fresh rhesus frontal cortex tissue sections were collected during the animal necropsy, and the repeated specimens were co-cultured with live spirochetes as described in our previously published studies(Ding, Ma, Tao, Peng, Han, Sun, Dai, Ji, Bai, Jian, Chen, Luo, Wang, Bi, Liu and Bao 2019). The brain explants were stored with Trizol reagent (Invitrogen, United States) at -80℃. RNA Isolation and Reverse Transcription Total brain RNA was extracted from brain explants by phenol and chloroform extraction as well as isopropanol precipitation, and then washed with ice-cold 75% ethanol. The total cell RNA was extracted from U251 using a Trizol reagent. Total RNA was reverse-transcribed into cDNA using the PrimeScript RTreagent kit with gDNA Eraser (Takara, Japan) according to the manufacturer’s instructions. The final reaction mixture volume was 20 μl. Specific details were referenced to our previous work(Ding, Ma, Tao, Peng, Han, Sun, Dai, Ji, Bai, Jian, Chen, Luo, Wang, Bi, Liu and Bao 2019). Validation of Result by Quantitative Real-Time Polymerase Chain Reaction (qPCR) The resulting cDNA was used to perform qPCR on the CFX Connect System (Bio‑Rad Laboratories, Inc.). The total qPCR reaction volume was 25 µL and included Takara SYBR Green II (12.5 µL), forward and reverse primers (1 µL of each), RNase-free water (8.5 µL) and cDNA template (2 µL). The cycling conditions were as follows: 95°C for 30 sec, followed by 40 cycles of 95°C for 5 sec, and 58.5°C for 30 sec. The sequences of the specific primers used for the above reactions are shown in Table 1 . Human β-Actin and rhesus GAPDH were used as the internal references. The relative expression of target genes was calculated with the 2 −ΔΔCt method(Livak and Schmittgen 2001). Statistical Analysis All data were reported as the mean±standard error of the mean (SEM) and were analyzed using GraphPad Prism 8 (GraphPad Software, San Diego, CA, USA). All experiments were repeated in triplicate. Two-Way ANOVA was applied for group comparisons. A P-value <0.05 was considered statistically significant. Results Identification of DEGs between Unexposed and Bb- infected Groups We used the gene expression profiles of the GSE85143 and the Rhesus dataset from our previous study(Ding, Ma, Tao, Peng, Han, Sun, Dai, Ji, Bai, Jian, Chen, Luo, Wang, Bi, Liu and Bao 2019). In our previous study, we co-cultured rhesus macaques’ frontal cortex brain explants with living spirochetes. There were 6 control samples and 6 Bb infected samples. The GSE85143 contains 6 Bb infected samples and 3 untreated samples. According to the cut-off criteria P-value <0.05 and FC ≥2, from the Rhesus dataset, we identified 3075 DEGs, including 2116 upregulated genes and 959 downregulated genes. Based on the RNA-seq GSE85143 dataset, a total of 1450 DEGs were identified, and comprised 724 upregulated genes and 726 downregulated genes ( Supplemental File1 ). For the GSE85143 dataset, the DEGs are shown in the volcano plot and in the heatmap, whose clustering was performed using BioJupies ( Fig 2 ). Moreover, the principal component analysis (PCA) was also performed to examine differences between Bb infected and untreated groups ( Fig. S1 ). All DEGs were defined by comparison of untreated samples and Bb infected samples. The Venn diagram showed the intersection of the DEGs in the two sets ( Fig. 3 ). The results indicated there was a total of 112 matched DEGs between the two datasets, of which 80 were upregulated genes ( Supplemental File 2 ) and 32 were downregulated genes ( Supplemental File 3 ). GO Annotations and KEGG Pathways The functional annotation analysis and KEGG pathway enrichment analysis of DEGs were performed using web tool DAVID. GO analysis indicated the annotation of DEGs from three categories: BP, CC, and MF ( Fig. 4 ). The results revealed that the upregulated DEGs in the BP category were mainly enriched in cell adhesion, negative regulation of endopeptidase activity, single organismal cell-cell adhesion or leukocyte migration, and response to hypoxia. While the downregulated DEGs in the BP were mainly involved in extracellular matrix organization and cell proliferation. CC analysis showed that the upregulated DEGs were significantly enriched in plasma membrane, extracellular region, extracellular space and the cytoskeleton, while the downregulated DEGs in CC were mainly in involved in integral components of the membrane, extracellular space, extracellular region, endoplasmic reticulum membrane, and the proteinaceous extracellular matrix. For MF analysis, the upregulated DEGs were mainly enriched in heparin binding and carbohydrate binding, the downregulated DEGs were enriched in extracellular matrix structural constituents and binding. In addition, the upregulated DEGs were significantly mapped on complement and coagulation cascades, hematopoietic cell lineage, Staphylococcus aureus infection, and arrhythmogenic right ventricular cardiomyopathy (ARVC) in the KEGG enrichment analysis ( Fig. 5 ). PPI Network Analysis and Identification of Hub genes To investigate the potential relationships between DEGs, we attempted to construct a PPI network using the STRING database. In total, 117 nodes and 95 edges were presented in this PPI (PPI enrichment P-value was lower than 1.44e-07) network ( Fig. 6 ). The DEGs were comprehensively calculated to identify the intersections among 12 different algorithms, and the most notable 11 genes were considered as hub genes ( Table 2 ). The multi-dimensional Venn diagram revealed the intersection of 12 algorithms for DEGs ( Fig. 7A ). The heat map showed the differential expression of hub genes in our Rhesus samples ( Fig. 7B ). A PPI (PPI enrichment P-value <2.17e-08) network of the hub genes was constructed using STRING, a total of 11 nodes and 13 edges were involved ( Fig. 7C ). Functional analysis of hub genes was performed using DAVID. The results showed that the hub genes were mainly enriched in the extracellular matrix organization, plasma membrane, and receptor activity ( Table 3 ). The KEGG pathway analysis showed the hub genes were significantly enriched in PI3K-Akt signaling pathway ( Table 3 ). Validation of Hub Genes in vitro Among the identified hub genes SELP, CD93, ANGPT1, TLR6, and SERPIND1 were upregulated, while SREBF1, LDLR, MATN3, TNC , ITGA2, and CSF1R, were downregulated. To verify these results, we conducted validation experiments using qPCR in vitro . The U251 cells were co-cultured with live Bb for 6 h, 12 h, 24 h, and 48 h at MOI of 0.1 or 1, and cells cultured in normal medium served as control. Subsequently, total RNA was isolated and qPCR was performed as mentioned above. For upregulated hub genes, the mRNA level of TLR6 and ANGPT 1 were significantly upregulated in both MOI=0.1 and MOI=1 groups as compared to the controls ( Fig. 8A, B ). For downregulated hub genes, we verified that LDLR, SREBF 1, TNC, and ITGA , presented a downward trend at different time points after cells were stimulated by live Bb ( Fig. 8C - F ). Based on the above results, we speculated that the hub genes TLR6, ANGPT 1, LDLR, SREBF1, TNC, and ITGA2 were potentially associated with the pathogenesis of LNB. Validation of Hub Genes in ex vivo To further verify the expression of these hub genes, we also verified their expression in the ex vivo model by qPCR. We found that the mRNA expression level of TLR6 in the Bb -infected group was upregulated compared to the untreated group, especially at the 24 h timepoint ( Fig. 9A ). For downregulated hub genes, the results showed that the mRNA expression level of LDLR in the Bb -infected group had decreased compared with the control group ( P = 0.08) at 12 h, whereas no significant differences were observed at the other two timepoints ( Fig. 9B ). In the Bb group, the mRNA expression level of TNC showed a downward trend over time, however the results were not statistically significant ( Fig. 9C ). These results indicated that TLR6 was most likely a critical molecule involved in the pathogenesis of LNB. Discussion LNB is a nervous system disorder caused by spirochete Bb infection. At present, the efficacy of antibiotics in the treatment of LNB has been defined. Early detection and prompt anti-pathogen treatment can improve the prognosis of patients(Rauer et al. 2018 ). However, due to the lack of specific clinical manifestations, sporadic cases in non-endemic areas are usually missed and often misdiagnosed. Patients with LNB can experience serious consequences such as dementia or personality disorders in the absence of timely treatment, which greatly impacts on their quality of life. Therefore, it is crucial to study the pathogenic biomarkers of LNB that may provide evidence for early diagnosis and treatment. Recently, bioinformatics analysis has become the conventional means for medical research into disease diagnosis or treatment, and it has significantly accelerated the utilization and integration of public biomedical resources. Nonetheless, to date, only a few omics studies have pointed to models of LNB. Our previous study established a dataset of the transcriptional changes induced by Bb in fresh rhesus frontal cortex tissues(Ding, Ma, Tao, Peng, Han, Sun, Dai, Ji, Bai, Jian, Chen, Luo, Wang, Bi, Liu and Bao 2019). In the present study, for the first time we conducted a comprehensive analysis of transcriptional mRNA changes induced by Bb both in rhesus brain tissue and primary human astrocytes(Casselli et al. 2017 ). Similarly, DEGs from astrocytes co-cultured with Bb and control astrocytes were identified and integrated with the gene expression dataset GSE85143 from the GEO database. Since the two datasets generated on different sequencing platforms (Illumina HiSeq 2500 & Illumina HiSeq 2000), the DEGs associated with Lyme spirochete infection to CNS were identified by taking the intersection analysis on each dataset. Some original data used in the current study have been used in prior publications, however, we generated a set of DEGs by taking the union from all time points. The sample species are different between our rhesus dataset and GSE85143, the DAVID Gene ID Conversion Tool was used. After deleting duplicate and invalid genes, we finally identified 3,075 differentially expressed genes in this study. In our previous study, the number of DEGs identified were 2,249 (6 h post-exposure), 1,064 (12 h post-exposure), and 420 (24 h post-exposure)(Ding, Ma, Tao, Peng, Han, Sun, Dai, Ji, Bai, Jian, Chen, Luo, Wang, Bi, Liu and Bao 2019). In the study conducted by Casselli et al ., GSE85143 is a part of superseries, GSE85143 is RNA-seq dataset which reveals changes in the astrocyte transcriptome following Bb infection and the other parts were about non-coding RNA profiling. We only focused on the analysis of mRNA expression profile for comprehensive analysis. We used the samples infected with Bb at 24 h and 48 h as test group and the three uninfected as control. Accordingly, there are 1450 DEGs in GSE85143 which could be compared to previously published results(Casselli, Qureshi, Peterson, Perley, Blake, Jokinen, Abbas, Nechaev, Watt, Dhasarathy and Brissette 2017). Herein, we screened and identified 80 upregulated DEGs ( Supplemental File 2 ) and 32 downregulated DEGs ( Supplemental File 3 ). Among the upregulated DEGs, most GO terms in the BP group were associated with cell adhesion, while for downregulated DEGs, the significantly enriched transcripts in BP GO terms were related to extracellular matrix organization. The KEGG pathway analysis indicated that these upregulated DEGs were significantly enriched in pathways involved in the complement and coagulation cascades, hematopoietic cell lineage, Staphylococcus aureus infection and ARVC. There were no significantly enriched KEGG pathways associated with downregulated DEGs. We constructed a PPI network by screening 11 hub genes. To obtain more reliable results, the 112 DEGs of 12 algorithms were investigated using the CytoHubba plugin. The results of top 30 DEGs of each algorithm were sorted by rank and the following intersection genes were considered as hub genes ( Supplemental File 4 ): SREBF1 , LDLR , SELP , CD93 , ANGPT1 , TLR6 , MATN3 , SERPIND1 , TNC, ITGA2 , and CSF1R . Among these hub genes, SELP , CD93 , ANGPT1 , TLR6 , and SERPIND1 were upregulated, while SREBF1 , LDLR , MATN3 , TNC , ITGA2 , and CSF1R were downregulated. Furthermore, we performed KEGG pathway enrichment analyses on hub genes and the results revealed that the PI3K-Akt signaling pathway showed the most significant difference (P-value < 0.01). Finally, we validated these results using ex vivo samples of rhesus frontal cortex brain explants and in vitro in human U251 cell lines using qPCR at the transcription level. These results suggested that TLR6 , ANGPT1 , SREBF1 , LDLR , TNC , and ITGA2 might be involved in the pathogenesis of LNB. TLR6 is a member of the Toll-like receptor (TLR) family, which plays a key role in pathogen-associated molecular patterns (PAMPs) recognition and in innate immune responses. TLR1, TLR2, and TLR6 are all located on the cell membrane. They share high protein homology in their transmembrane and cytoplasmic regions, while their extracellular region is diverse. Thus, they can recognize specific pathogens and activate common intracellular signaling pathways(Takeuchi et al. 1999 ). It has been shown that TLR is a driving force behind Bb infection, and that the TLR signaling pathway plays an important role in inducing inflammatory responses(Koening et al. 2009 , Singh and Girschick 2006 , Thomas and Fikrig 2002 ). Specifically, TLR2 plays an important role in the recognition of Lyme spirochetes(Hirschfeld et al. 1999 ). TLR6 forms heterodimer with TLR2 and distributes along the cell surface to specifically recognize diacylated lipopeptides and partly triacylated lipopeptides(Kang et al. 2009 ). A previous study showed that blocking TLR6 on human peripheral blood cells after exposure to Bb did not reduce the production of cytokines, while blocking TLR1 significantly decreased cytokine production(Oosting et al. 2011 ). Further, the Bb- induced secretion of IFN-γ in murine cells depends on TLR6(Oosting, Ter Hofstede, Sturm, Adema, Kullberg, van der Meer, Netea and Joosten 2011). Our previous studies have also shown that the TLR1/TLR2 ratio plays an important role in initiating of proinflammatory chemokine storm in Lyme disease(Zhao et al. 2019 ). However, due to gene expression specificities in different species, tissues or cell lineages, the role of TLR6 in LNB needs to be further explored. In the present study, TLR6 is the only hub gene that was verified to be elevated in both the human U251 cell line and in rhesus brain explants when exposed to Bb. The transcription level of TLR6 began to increase at 12 h after human astrocytes were exposed to Bb , and the increased expression was observed at only 6 h after rhesus brain explants were exposed to Bb . This may be because other immune cells in the brain tissue, such as microglia, are also involved in the pathophysiological process in the early stages of LBN. Additionally, further analysis of KEGG enrichment analysis of hub genes suggested that the PI3K-AKT pathway may play a key role in the pathogenesis of LNB. Interestingly, we found that the TLR6 signals could activate this pathway using the KEGG pathway mapping tool ( Fig. S2 ). Therefore, TLR6 may take part in the pathogenesis of LNB. With regard to the upregulated hub genes, in addition to TLR6, another gene that has been verified to be upregulated in human astrocytes exposed to Bb is ANGPT1 . This result is consistent with the results of the study by Casselli et al. (Casselli, Qureshi, Peterson, Perley, Blake, Jokinen, Abbas, Nechaev, Watt, Dhasarathy and Brissette 2017). They confirmed the elevation of angiopoietin-like 4 (ANGPTL4) in human primary astrocytes exposed to Bb by enzyme-linked immunosorbent assays. Both ANGPT1 and ANGPTL4 belong to the angiopoietin family and encode secreted glycoproteins that play critical roles in development and in disorders including in angiogenesis, inflammation, cell proliferation, apoptosis, lipid and glucose metabolism, cell migration, and cancer(Carbone et al. 2018 , Ehrlich et al. 2019 , Snipstad et al. 2010 , Zhang et al. 2018 ). Compared with other ANGPT/ANGPTL genes which are embedded in the introns of larger genes, ANGPT1 and ANGPTL4 are stand-alone genes whose expression correlated with tissue-specific enhancer or promoter chromatin sequences(Ehrlich, Lacey and Ehrlich 2019 ). ANGPT1 is the ligand of the transmembrane tyrosine kinase TIE2 receptor. Phosphorylation of tyrosine kinase can activate the PI3K/AKT pathway ( Fig. S3 ), which was a core signaling pathway identified in our previous studies(Fukuhara et al. 2008 , Huang et al. 2010 ). ANGPT2, which is highly homologous to ANGPT1, has a completely different effect on TIE2(Parikh 2017 ). The involvement of ANGPT1 and ANGPT2 in pathological inflammation is clear. In several diseases that have a strong association with inflammation, such as sepsis and malignant tumors, angiopoietins can be used as prognostic indicators(Fiedler and Augustin 2006 , Park et al. 2009 , Seol et al. 2020 ). In our previous study(Zhao et al. 2018 ), we found that the NF-κB pathway plays a key role in the pathogenesis of LNB, and other studies have reported that ANGPT can also influence the NF-κB pathway(Fiedler and Augustin 2006 , Huang, Bhat, Woodnutt and Lappe 2010 ). These results suggested the possible potential of ANGPT either as novel diagnostic and prognostic indicators, or as a therapeutic target for LNB. In addition, upregulation of SERPIN in the upregulated hub genes were verified in the study by Casselli et al(Casselli, Qureshi, Peterson, Perley, Blake, Jokinen, Abbas, Nechaev, Watt, Dhasarathy and Brissette 2017) . Thus, they have not been discussed in detail here. Among the downregulated hub genes, we verified that the expression of LDLR, SREBF1, TNC , and ITGA showed a statistically significant decrease in human astrocytes on exposure to Bb . Following exposure of rhesus brain explants to Bb , we found there was a decrease in both LDLR and TNC levels, although there the differences were not statistically significant (P-values of LDLR at 6 h, 12 h and 24 h Bb- exposed astrocytes vs controls were 0.8, 0.08, and 0.37, respectively). LDLR is a cell surface glycoprotein that can be expressed on various cell types including astrocytes in many tissues. LDLR can specifically recognize and bind lipoproteins containing apolipoprotein (Apo)E or ApoB100 to mediate cholesterol metabolism, thus it is a central component for the maintenance of cholesterol homeostasis(Brown and Goldstein 1986 ). Studies have elucidated that LNB may promote the neuro-inflammatory response and endow susceptibility to Alzheimer’s disease (AD), which can not only lead to dementia, but also may induce pathological features typical of AD, such as amyloid-beta (Aβ) deposits(Miklossy et al. 2004 , Miklossy et al. 2006 ). Conversely, upregulating the expression of LDLR can improve the brain clearance of Aβ, reduce amyloid-deposition and attenuate the neuroinflammatory response(Basak et al. 2012 , Yao et al. 2016 ). A recent study revealed that lower LDLR expression can aggravate the neuronal inflammatory response, which might occur through NF-κB signaling and NLRP3-ASC caspase-1 inflammasome assembly(Sun et al. 2020 ). Thus, LDLR may serve as a protective factor for LNB. Meanwhile, the reduction of SREBF1 expression at the transcriptional level has been verified in human U251 cells at exposure to Bb . The proteins encoded by SREBFs are transcription factors that control cholesterol homeostasis. LDLR is one of the target genes regulated by sterol regulatory element-binding proteins (SREBPs)(Horton et al. 2003 ). We observed a decrease in TNC transcriptional expression of U251 cells after stimulation by Bb , and we also found a downward trend over time in rhesus brain explants, albeit there was no statistically significant difference. The protein encoded by TNC belongs to the tenascin family and is widely involved in pathological processes such as inflammation and malignancy. TNC plays a key role in the proliferation of primary astrocytes(Ikeshima-Kataoka et al. 2008 , Roll and Faissner 2019 ). There have been many reports that TNC is highly expressed when the CNS is injured(Roll and Faissner 2019 ). However, brain injured TNC knock-out mice showed higher expression of inflammatory factors such as TNF-α, IL-6, and IL-1β than wild-type mice(Ikeshima-Kataoka, Shen, Eto, Saito and Yuasa 2008 ). The expression level of TNC varies under different pathological conditions. It may help regulate the production of inflammatory factors in the damaged brain. In addition, ITGA2 reduction was observed at 6 h and 48 h in U251 cells after exposure to Bb. ITGA2 is a glycoprotein of the integrin family, which mediates cell–cell interactions and those of the cell-extracellular matrix. It also participates in the pathophysiological process of inflammation and the immune response. However, studies have reported that ITGA2 can aggravate the destruction of inflammatory cartilage in rheumatoid arthritis, and has been implicated in cell growth and apoptosis in tumors(Penrose et al. 2019 , Peters et al. 2012 ). The roles of ITGA2 in LNB remain to be clarified, therefore, further studies are needed. All in all, in present study, we combined two different datasets related to LNB and identified a group of commonly affected transcripts. Using the commonly affected genes from two very different experimental systems to conduct detailed functional and network analyses. Among them, 11 hub genes were identified from network analysis. We also conducted validation experiments and validated some of the hub genes. There are some limitations in our current study. First, in vivo experimental validation is needed to validate these findings. In addition, although we have verified the mRNA expression of hub genes using a cell line in vitro and in ex vivo rhesus brain explants, we did not carry out in-depth mechanism research. Additionally, due to the different sample sources and species of the two datasets, the intersection of the DEGs could provide only limited information. Moreover, the sufficient validation by alternate methods or by measuring the protein levels for biological relevance to LNB will need to be performed in future work. And the number of samples is relatively small. The underlying mechanisms need to be further studied in our future studies. In conclusion, the present study suggests that TLR6 , ANGPT1 , LDLR , SREBF1 , TNC , and ITGA were differentially highly expressed in Bb- infected astrocytes compared to control astrocytes. These candidate genes are clinically promising biomarkers that can be used for LNB diagnosis or treatment. In addition, LDLR was found to be significantly associated with favorable outcomes in neuroinflammatory diseases, which may provide a better understanding of molecular mechanisms and novel targets as therapeutic strategies in the future. Thus, our findings may provide valuable insights into the study of LNB pathogenesis and provides guidance for designing further follow-up studies. Declarations a ETHICAL STATEMENT: Ethics approval and consent to participate This experiment was performed according to the Guide for the Care and Use of Laboratory Animals and ARRIVE Guidelines for Reporting Animal Research. The Animal Ethics and Welfare Committee of Kunming Medical University reviewed and approved this study. The animal permit number: SCXK (DIAN) K2015-0004. Consent for publication Not applicable. Availability of data and materials All data files described in this manuscript for analysis, are available at the NCBI site: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE85143 http://www.ncbi.nlm.nih.gov/bioproject/723671 All data generated or analyzed during this study are included in this published article [and its supplementary information files]. Competing interests The authors declare that they have no competing interests. Funding This work was supported by grants from the National Natural Science Foundation of China (No. 32060180, 81860644, 81560596, and 31560051) and the Natural Foundation of Yunnan Province [No. 2019FE001(-002) and 2017FE467(-001)]. The funding institutions had no involvement in the design of the study or review of the manuscript. Authors’ contributions All authors contributed to the study conception and design. Fukai Bao, Aihua Liu, and Shiyuan Wen conceived and designed the experiments. Shiyuan Wen, Xin Xu, Jing Kong, Lisha Luo, Yu Zhang, Mingbiao Ma, Lvyan Tao and Yun Peng developed the methodology. Shiyuan Wen, Xin Xu, Wenjing Cao, Peng Yue, Yuxin Fan, Meixiao Liu and Jingjing Chen performed all experiments. Yan Dong, Suyi Luo, Bingxue Li, Feng Wang, Guozhong Zhou, Taigui Chen and Lianbao Li analyzed and discussed the data. Shiyuan Wen wrote the manuscript. Shiyuan Wen, Fukai Bao and Aihua Liu edited and revised the manuscript. All authors read and approved the final manuscript. Acknowledgements The authors thank the Yunnan Province Key Laboratory for Tropical Infectious Diseases in Universities; Yunnan Province Integrative Innovation Center for Public Health, Diseases Prevention and Control; Kunming Medical University; and Yunnan Demonstration Base of International Science and Technology Cooperation for Tropical Diseases (all located in Kunming, China) for supporting this study. Declarations Ethics approval and consent to participate This experiment was performed according to the Guide for the Care and Use of Laboratory Animals and ARRIVE Guidelines for Reporting Animal Research. The Animal Ethics and Welfare Committee of Kunming Medical University reviewed and approved this study. The animal permit number: SCXK (DIAN) K2015-0004. Consent for publication Not applicable. Declarations Competing interests The authors declare that they have no competing interests. Funding This work was supported by grants from the National Natural Science Foundation of China (No. 32060180, 81860644, 81560596, and 31560051) and the Natural Foundation of Yunnan Province [No. 2019FE001(-002) and 2017FE467(-001)]. The funding institutions had no involvement in the design of the study or review of the manuscript. Authors’ contributions All authors contributed to the study conception and design. Fukai Bao, Aihua Liu, and Shiyuan Wen conceived and designed the experiments. Shiyuan Wen, Xin Xu, Jing Kong, Lisha Luo, Yu Zhang, Mingbiao Ma, Lvyan Tao and Yun Peng developed the methodology. Shiyuan Wen, Xin Xu, Wenjing Cao, Peng Yue, Yuxin Fan, Meixiao Liu and Jingjing Chen performed all experiments. Yan Dong, Suyi Luo, Bingxue Li, Feng Wang, Guozhong Zhou, Taigui Chen and Lianbao Li analyzed and discussed the data. Shiyuan Wen wrote the manuscript. Shiyuan Wen, Fukai Bao and Aihua Liu edited and revised the manuscript. All authors read and approved the final manuscript. Acknowledgements The authors thank the Yunnan Province Key Laboratory for Tropical Infectious Diseases in Universities; Yunnan Province Integrative Innovation Center for Public Health, Diseases Prevention and Control; Kunming Medical University; and Yunnan Demonstration Base of International Science and Technology Cooperation for Tropical Diseases (all located in Kunming, China) for supporting this study. References Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM, Davis AP, Dolinski K, Dwight SS, Eppig JT et al (2000) Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat Genet May 25:25–29. Epub 2000/05/10 Basak JM, Verghese PB, Yoon H, Kim J, Holtzman DM (2012) Low-density lipoprotein receptor represents an apolipoprotein E-independent pathway of Abeta uptake and degradation by astrocytes. J Biol Chem Apr 20:287:13959–13971. 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Epub 2018/02/03 Tables Table 1 Primer Sequences Used in Quantitative Real-Time Polymerase Chain Reaction Gene Forward (5' ® 3') Reverse (5' ® 3') species TLR6 TGAATGCAAAAACCCTTCACCT CCAAGTCGTTTCTATGTGGTTGA Homo sapiens ANGPT1 TCGTGAGAGTACGACAGACCA TCTCCGACTTCATGTTTTCCAC Homo sapiens LDLR TGAACTGGTGTGAGAGGACC TTTAGCCTGACGGTGGATGT Homo sapiens SREBF1 CTCAGATACCACCAGCGTCT TTGCGATGCCTCCAGAAGTA Homo sapiens TNC CTCCTCCCAAAGACCTCGTT AAGTACTCCACACCAGGCTC Homo sapiens ITGA TGGTCATCAGGGCACTATCC CACTTGTCCAAAGGCACCAA Homo sapiens β-Actin TGGCATCCACGAAACTACCT CAATGCCAGGGTACATGGTG Homo sapiens TLR6 GACAAGGACACGGATTCAGCCATC CAGGTTGACACGGTGACAGTTCC Macaca mulatta LDLR CTGGAAGAACTGGCGGCTGAAG TGCGGCAAATGTGGACCTCATC Macaca mulatta TNC GACAAGGACACGGATTCAGCCATC CAGGTTGACACGGTGACAGTTCC Macaca mulatta GAPDH GCACCACCAACTGCTTAGCAC TCTTCTGGGTGGCAGTGATG Macaca mulatta Table 2 The Hub Genes and their Description. Gene symbol Gene description Differentially expression SELP Selectin P Upregulated CD93 CD93 Molecule Upregulated ANGPT1 Angiopoietin 1 Upregulated TLR6 Toll Like Receptor 6 Upregulated SERPIND1 Serpin Family D Member 1 Upregulated SREBF1 Sterol Regulatory Element Binding Transcription Factor 1 Downregulated LDLR Low Density Lipoprotein Receptor Downregulated MATN3 Matrilin 3 Downregulated TNC Tenascin C Downregulated ITGA2 Integrin Subunit Alpha 2 Downregulated CSF1R Colony Stimulating Factor 1 Receptor Downregulated Table 3 Significantly enriched GO terms and KEGG pathways of the Hub DEG Category Term Description Count P-Value - In(P - Value) BP GO:0030198 Extracellular matrix organization 3 0.004623509 2.335028301 BP GO:0010867 Positive regulation of triglyceride biosynthetic process 2 0.005881637 2.230501758 BP GO:0002687 Positive regulation of leukocyte migration 2 0.006414804 2.192816627 BP GO:0031589 Cell-substrate adhesion 2 0.009076825 2.04206604 BP GO:0071398 Cellular response to fatty acid 2 0.012262881 1.911407495 BP GO:0006954 Inflammatory response 3 0.016468787 1.78333838 BP GO:0007162 Negative regulation of cell adhesion 2 0.019661627 1.706380557 BP GO:0045785 Positive regulation of cell adhesion 2 0.022817408 1.641733698 BP GO:0007155 Cell adhesion 3 0.023632805 1.62648472 BP GO:0030097 Hemopoiesis 2 0.031188723 1.506002404 BP GO:0014068 Positive regulation of phosphatidylinositol 3-kinase signaling 2 0.034311493 1.464560382 BP GO:0008203 Cholesterol metabolic process 2 0.03586952 1.445274431 BP GO:0050729 Positive regulation of inflammatory response 2 0.038461267 1.414976408 BP GO:0046718 Viral entry into host cell 2 0.042079309 1.3759314 CC GO:0009986 Cell surface 4 0.001922057 2.716233687 CC GO:0009897 External side of plasma membrane 3 0.004637261 2.333738458 CC GO:0005886 Plasma membrane 7 0.005894117 2.229581248 CC GO:0005887 Integral component of plasma membrane 4 0.027504704 1.560593032 MF GO:0004872 Receptor activity 3 0.00557956 2.253400069 MF GO:0001948 Glycoprotein binding 2 0.034133334 1.466821294 MF GO:0001618 Virus receptor activity 2 0.036715556 1.435149885 KEGG hsa04151 PI3K-Akt signaling pathway 4 0.003763234 2.424438754 GO, Gene Ontology. BP, biological process. CC, cellular component. MF, molecular function. KEGG, Kyoto Encyclopedia of Genes and Genomes. DEG, differentially expressed gene. Supplementary Files FigureS1.png Fig. S1 Principal Component Analysis results for the GSE85143 dataset. The figure displays an interactive, three-dimensional scatter plot of the first three Principal Components of the data. Each point represents an RNA-seq sample in GSE85143. Samples with similar gene expression profiles are closer in the three-dimensional space. Red points indicate Bb infected samples, blue points indicate untreated samples. FigureS2hsa04620.png Fig. S2 KEGG hsa 04620 Toll-like receptor signaling pathway. TLR6 stimulation activates the PI3K/Akt signaling pathway. Abbreviations: KEGG, Kyoto Encyclopedia of Genes and Genomes. FigureS3hsa04151.png Fig. S3 KEGG hsa 04151 PI3K-Akt signaling pathway. Abbreviations: KEGG, Kyoto Encyclopedia of Genes and Genomes. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-822329","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":50064832,"identity":"b1004f0a-a4e7-4ace-a119-1be2c2f0ccd0","order_by":0,"name":"Shiyuan Wen","email":"","orcid":"","institution":"Kunming Medical University","correspondingAuthor":false,"prefix":"","firstName":"Shiyuan","middleName":"","lastName":"Wen","suffix":""},{"id":50064833,"identity":"725ed800-71d9-40b0-a342-91a51908d343","order_by":1,"name":"Xin Xu","email":"","orcid":"","institution":"Kunming Medical 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University","correspondingAuthor":false,"prefix":"","firstName":"Peng","middleName":"","lastName":"Yue","suffix":""},{"id":50064837,"identity":"7012c94a-320e-44dd-86a6-97fab138d10a","order_by":5,"name":"Wenjing Cao","email":"","orcid":"","institution":"Kunming Medical University","correspondingAuthor":false,"prefix":"","firstName":"Wenjing","middleName":"","lastName":"Cao","suffix":""},{"id":50064838,"identity":"75d5ee81-78a8-4c18-b002-24747eddae88","order_by":6,"name":"Yu Zhang","email":"","orcid":"","institution":"Kunming Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yu","middleName":"","lastName":"Zhang","suffix":""},{"id":50064839,"identity":"8e6cb452-df86-4543-988e-5172032bb877","order_by":7,"name":"Meixiao Liu","email":"","orcid":"","institution":"Kunming Medical University","correspondingAuthor":false,"prefix":"","firstName":"Meixiao","middleName":"","lastName":"Liu","suffix":""},{"id":50064840,"identity":"79ed8c1a-e4b4-4d5e-82ee-1d32e8883bed","order_by":8,"name":"Yuxin Fan","email":"","orcid":"","institution":"Kunming Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yuxin","middleName":"","lastName":"Fan","suffix":""},{"id":50064841,"identity":"e6fa8d69-54ea-436c-a7f9-effcfb41c0cb","order_by":9,"name":"Jingjing Chen","email":"","orcid":"","institution":"Kunming Medical University","correspondingAuthor":false,"prefix":"","firstName":"Jingjing","middleName":"","lastName":"Chen","suffix":""},{"id":50064842,"identity":"d3240983-c87a-4288-a9cd-70473db88ef4","order_by":10,"name":"Mingbiao Ma","email":"","orcid":"","institution":"Kunming Medical University","correspondingAuthor":false,"prefix":"","firstName":"Mingbiao","middleName":"","lastName":"Ma","suffix":""},{"id":50064843,"identity":"aa156598-8f76-4edd-87de-4f3accdc0221","order_by":11,"name":"Lvyan Tao","email":"","orcid":"","institution":"Kunming Medical University","correspondingAuthor":false,"prefix":"","firstName":"Lvyan","middleName":"","lastName":"Tao","suffix":""},{"id":50064844,"identity":"a77674e4-8d8a-4353-b97f-39e1845ec2f5","order_by":12,"name":"Yun Peng","email":"","orcid":"","institution":"Kunming Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yun","middleName":"","lastName":"Peng","suffix":""},{"id":50064845,"identity":"27cffbc3-66ba-49b6-acde-e18b40693270","order_by":13,"name":"Yan Dong","email":"","orcid":"","institution":"Kunming Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yan","middleName":"","lastName":"Dong","suffix":""},{"id":50064846,"identity":"70ff8cc0-a69a-41a0-b635-ec2bd483bfda","order_by":14,"name":"Bingxue Li","email":"","orcid":"","institution":"Kunming Medical University","correspondingAuthor":false,"prefix":"","firstName":"Bingxue","middleName":"","lastName":"Li","suffix":""},{"id":50064847,"identity":"330b395f-5875-46be-ae91-83ce41a752de","order_by":15,"name":"Suyi Luo","email":"","orcid":"","institution":"Kunming Medical University","correspondingAuthor":false,"prefix":"","firstName":"Suyi","middleName":"","lastName":"Luo","suffix":""},{"id":50064848,"identity":"d5fe276c-e243-4276-8bbd-355cf0821cb9","order_by":16,"name":"Feng Wang","email":"","orcid":"","institution":"Kunming Medical University","correspondingAuthor":false,"prefix":"","firstName":"Feng","middleName":"","lastName":"Wang","suffix":""},{"id":50064849,"identity":"b8df0f54-8682-423e-a828-421e50240c0f","order_by":17,"name":"Guozhong Zhou","email":"","orcid":"","institution":"Kunming Medical University","correspondingAuthor":false,"prefix":"","firstName":"Guozhong","middleName":"","lastName":"Zhou","suffix":""},{"id":50064850,"identity":"1e1b181a-3f77-4459-af3c-ff75d2a2ef82","order_by":18,"name":"Taigui Chen","email":"","orcid":"","institution":"Kunming Medical University","correspondingAuthor":false,"prefix":"","firstName":"Taigui","middleName":"","lastName":"Chen","suffix":""},{"id":50064851,"identity":"0ce0e2f1-a8b6-4b7a-9ff4-ff6987e621d4","order_by":19,"name":"Lianbao Li","email":"","orcid":"","institution":"Kunming Medical University","correspondingAuthor":false,"prefix":"","firstName":"Lianbao","middleName":"","lastName":"Li","suffix":""},{"id":50064852,"identity":"dc21d130-db0d-43e6-89f9-3f967705dff1","order_by":20,"name":"Aihua Liu","email":"","orcid":"","institution":"Kunming Medical University","correspondingAuthor":false,"prefix":"","firstName":"Aihua","middleName":"","lastName":"Liu","suffix":""},{"id":50064853,"identity":"176f9082-e36b-4310-93c5-531238a5a828","order_by":21,"name":"Fukai Bao","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA3ElEQVRIiWNgGAWjYBACAyD+wHCAgYefByLA2ECEFsYZIC2SPaRqYTA4Q6wWc/bDB5t5ztjJGJ85nbqZh8FGdsMB5mcP8Gmx7ElLbOa5kcxjdrZ3220ehjTjDQfYzA3wOuxAjvljng/MPGbneUFaDiduOMDDJoFXy/k3hs08H+p5jPvBWv4ToeVGDlDLjcM8Brxghx0grMVyxrPExjlnjvNInDm77eYcg2TjmYfZzPBqMedPPtjw5li1PX9P7rYbbyrsZPuONz/DqwXdnUDMTIL6UTAKRsEoGAXYAQDHH02X4EVNNgAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0003-2652-6660","institution":"Institute of Tropical Medicine","correspondingAuthor":true,"prefix":"","firstName":"Fukai","middleName":"","lastName":"Bao","suffix":""}],"badges":[],"createdAt":"2021-08-18 04:05:04","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-822329/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-822329/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":13875362,"identity":"32f8c3e4-a6b3-4017-bbf3-61d0e086b62c","added_by":"auto","created_at":"2021-09-22 15:52:41","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":159198,"visible":true,"origin":"","legend":"Workflow of the study design. \nAbbreviations: DEGs, differentially expressed genes. PPI, protein-protein interaction. Bb, Borrelia burgdorferi. qRT-PCR, quantitative real-time polymerase chain reaction.\n","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-822329/v1/a1c3c2f9e8feb9345dfb9026.png"},{"id":13874699,"identity":"54434b70-c744-478e-a5ef-436debd0f44a","added_by":"auto","created_at":"2021-09-22 15:49:41","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":324811,"visible":true,"origin":"","legend":"Volcano plot and Heatmap of differentially expressed genes identified in GSE85143. \n(A) The figure shows an interactive scatter plot displaying the log2-fold changes and statistical significance of each gene calculated by performing a differential gene expression analysis. Every point in the plot represents a gene. Red points indicate significantly upregulated genes, blue points indicate downregulated genes. (B) The figure contains an interactive heatmap displaying gene expression for each sample in the GSE85143 dataset. Each row of the heatmap represents a gene, every column represents a sample, and every cell displays normalized gene expression values. The heatmap additionally features color bars beside each column which represent prior knowledge of each sample, such as the tissue of origin or experimental treatment. \n","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-822329/v1/9f357f9ca2b55a672f4def28.png"},{"id":13874107,"identity":"577f3b7d-d9e9-466c-b392-eb8225cb938d","added_by":"auto","created_at":"2021-09-22 15:46:41","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":63682,"visible":true,"origin":"","legend":"Venn diagram of DEGs common to the Rhesus and GEO datasets. \n(A) Eighty shared upregulated genes in both the Rhesus and GEO dataset. (B) Thirty-two shared downregulated genes in both the Rhesus and GEO datasets. Abbreviations: DEG, differentially expressed gene. GEO, Gene Expression Omnibus.\n\n","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-822329/v1/e78290cad001ff6adfdf1054.png"},{"id":13874104,"identity":"8371cfae-4348-4b72-b479-0fa4cdbde18c","added_by":"auto","created_at":"2021-09-22 15:46:41","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":138268,"visible":true,"origin":"","legend":"GO annotation analysis of upregulated and downregulated DEGs. \n(A) GO annotation for upregulated DEGs. (B) GO annotation for downregulated DEGs. \nAbbreviations: GO, Gene Ontology. BP, biological process. CC, cellular component. MF, molecular function. DEG, differentially expressed gene. TFA, transcription factor activity.\n\n","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-822329/v1/88074c41dc2de8816c43b255.png"},{"id":13874102,"identity":"11d35841-6b6f-418e-858b-b278cfedd014","added_by":"auto","created_at":"2021-09-22 15:46:41","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":46263,"visible":true,"origin":"","legend":"KEGG pathway enrichment analysis of upregulated DEGs.\nAbbreviations: KEGG, Kyoto Encyclopedia of Genes and Genomes. DEG, differentially expressed gene.\n","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-822329/v1/28411ef8848990bc5aec7996.png"},{"id":13875364,"identity":"c249ab07-913c-4308-ae93-850fa6717665","added_by":"auto","created_at":"2021-09-22 15:52:41","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":82065,"visible":true,"origin":"","legend":"The PPI network of DEGs constructed using Cytoscape. Pink nodes indicate upregulated DEGs and green nodes indicate downregulated DEGs.\nAbbreviations: PPI, Protein-protein interaction. DEG, differentially expressed gene.\n\n","description":"","filename":"Figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-822329/v1/024567f8544c85da24a9e8e3.png"},{"id":13874116,"identity":"2e6ad205-4b63-4d8b-a0e4-9e3e1cc6d23a","added_by":"auto","created_at":"2021-09-22 15:46:42","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":543347,"visible":true,"origin":"","legend":"Identification of Hub DEGs. (A) The multi-dimensional Venn diagram shows the intersection of 12 algorithms for DEGs. (B) The heat map depicting the Hub DEGs in the Rhesus dataset. (C) The PPI network of the Hub DEGs. Red represents upregulated hubgenes and green represents downregulated hubgenes.\nAbbreviations: PPI, Protein-protein interaction. DEG, differentially expressed gene.\n","description":"","filename":"Figure7.png","url":"https://assets-eu.researchsquare.com/files/rs-822329/v1/86f521fecb4302d13bd39ea8.png"},{"id":13874114,"identity":"6acca695-bf93-4b39-b134-0d20b86ab1d1","added_by":"auto","created_at":"2021-09-22 15:46:42","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":620877,"visible":true,"origin":"","legend":"Expression levels of indicated mRNA by qPCR comparing human U251 cells co-cultured with live Bb with the controls at 6 h, 12 h, 24 h, and 48 h. (A-B) TLR6 and ANGPT1 were demonstrated upregulated in Bb group compared to Control group at 12 h, 24 h, and 48 h. (C) LDLR was demonstrated downregulated in Bb group compared to Control group at 6 h, 12 h, and 48 h. (D) SREBF1 was demonstrated downregulated in Bb group compared to Control group at 6 h, 12 h, 24 h, and 48 h. (E) TNC was demonstrated downregulated in Bb group compared to Control group at 12 h and 48 h. (F) ITGA was demonstrated downregulated in Bb group compared to Control group at 48 h. Asterisks denote.*P-Value \u003c0.05, **P-Value \u003c0.01. \nAbbreviations: Bb, Borrelia burgdorferi. qPCR, quantitative real-time polymerase chain reaction.\n","description":"","filename":"Figure8.png","url":"https://assets-eu.researchsquare.com/files/rs-822329/v1/8b00561b823be3a29761e13f.png"},{"id":13874110,"identity":"38c97387-df8a-4234-bb60-e2d36af9bc3a","added_by":"auto","created_at":"2021-09-22 15:46:41","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":41040,"visible":true,"origin":"","legend":"The expression levels of indicated mRNA by qPCR comparing explants from the frontal cortex of rhesus brains co-cultured with live Bb with the controls at 6 h, 12 h, and 24 h. (A) TLR6 was demonstrated upregulated in Bb group compared to Control group at 24 h. (B-C) The expression levels of LDLR and TNC mRNA comparing explants from the frontal cortex of rhesus brains co-cultured with live Bb with the controls at 6 h, 12 h, and 24 h. Asterisks denote. **P-Value \u003c0.01. \nAbbreviations: Bb, Borrelia burgdorferi. qPCR, quantitative real-time polymerase chain reaction.\n","description":"","filename":"Figure9.png","url":"https://assets-eu.researchsquare.com/files/rs-822329/v1/9428410bde456f30583cf526.png"},{"id":15047220,"identity":"82832be0-776f-47b8-bd01-5fdfb8b0c5bd","added_by":"auto","created_at":"2021-10-30 04:46:17","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1819712,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-822329/v1/05008649-d313-456e-bee9-f5c15d472f74.pdf"},{"id":13875551,"identity":"87c2fb84-1ce5-4bb3-9f34-09a40475ed37","added_by":"auto","created_at":"2021-09-22 15:55:41","extension":"png","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":43972,"visible":true,"origin":"","legend":"Fig. S1 Principal Component Analysis results for the GSE85143 dataset. \nThe figure displays an interactive, three-dimensional scatter plot of the first three Principal Components of the data. Each point represents an RNA-seq sample in GSE85143. Samples with similar gene expression profiles are closer in the three-dimensional space. Red points indicate Bb infected samples, blue points indicate untreated samples.\n","description":"","filename":"FigureS1.png","url":"https://assets-eu.researchsquare.com/files/rs-822329/v1/e6cde6364d19c8f19610f444.png"},{"id":13874701,"identity":"3b0cf4bd-c2c9-41f5-873f-89cd5918ed5b","added_by":"auto","created_at":"2021-09-22 15:49:41","extension":"png","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":75812,"visible":true,"origin":"","legend":"Fig. S2 KEGG hsa 04620 Toll-like receptor signaling pathway. \nTLR6 stimulation activates the PI3K/Akt signaling pathway. \nAbbreviations: KEGG, Kyoto Encyclopedia of Genes and Genomes.\n","description":"","filename":"FigureS2hsa04620.png","url":"https://assets-eu.researchsquare.com/files/rs-822329/v1/89f0415acfcb5ee462564452.png"},{"id":13874706,"identity":"112b04ac-d1d8-42d3-8736-b8ee1ef269aa","added_by":"auto","created_at":"2021-09-22 15:49:42","extension":"png","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":98775,"visible":true,"origin":"","legend":"Fig. S3 KEGG hsa 04151 PI3K-Akt signaling pathway. \nAbbreviations: KEGG, Kyoto Encyclopedia of Genes and Genomes.\n","description":"","filename":"FigureS3hsa04151.png","url":"https://assets-eu.researchsquare.com/files/rs-822329/v1/36493561a974231f0fc71949.png"},{"id":13874705,"identity":"9628e1fd-f798-4ab7-b949-c1df4f828529","added_by":"auto","created_at":"2021-09-22 15:49:41","extension":"xlsx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":166451,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementalFile1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-822329/v1/02c94bfe64d92fbd0f97f512.xlsx"},{"id":13874703,"identity":"0492ebe0-45a7-4adb-b9f1-960eff422d18","added_by":"auto","created_at":"2021-09-22 15:49:41","extension":"txt","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":24051,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementalFile2.txt","url":"https://assets-eu.researchsquare.com/files/rs-822329/v1/d112ca8371c37a5b5b22d4d5.txt"},{"id":13874112,"identity":"47712442-bdc0-4c6c-9eca-400f1b0f8ec5","added_by":"auto","created_at":"2021-09-22 15:46:42","extension":"txt","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":14329,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementalFile3.txt","url":"https://assets-eu.researchsquare.com/files/rs-822329/v1/e4c8390aad2a55628a433d6e.txt"},{"id":13874707,"identity":"f03054eb-e8ee-4870-9d4e-421bf55bf616","added_by":"auto","created_at":"2021-09-22 15:49:42","extension":"xlsx","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":11282,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementalFile4.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-822329/v1/1ed6937606792f9c92bd68b9.xlsx"}],"financialInterests":"","formattedTitle":"\u003cp\u003eComprehensive analyses of transcriptomes induced by Lyme spirochete infection to CNS model system \u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eLyme disease (also known as Lyme borreliosis), mainly caused by \u003cem\u003eBorrelia burgdorferi\u003c/em\u003e (\u003cem\u003eBb\u003c/em\u003e) spirochetes, is the most common vector-borne disease in the northern hemisphere. While frequently occurring in the United States, it is also endemic in Europe and parts of Asia(Schotthoefer and Frost \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). It has showed increasing growth and expansion in geographic extent over the past two decades, with an estimated 300,000 people infected with Lyme spirochetes annually(Hinckley et al. \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2014\u003c/span\u003e, Kugeler et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2015\u003c/span\u003e, Nawrocki and Hinckley \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Lyme disease is a multistage and multisystem disorder predominantly affecting the skin, but it also involves the joints, heart, and nervous system. Transmitted by \u003cem\u003eIxodes\u003c/em\u003e ticks, its causative agent \u003cem\u003eBb\u003c/em\u003e initially propagates locally in the skin before it disseminates hematogenously to other organ systems. Lyme disease which results in neurological manifestation, such as painful meningoradiculitis (Bannwarth syndrome), cranial neuritis, lymphocytic meningitis, and rarely encephalitis or cerebral vasculitis at a later period (stage 2), is called Lyme neuroborreliosis (LNB)(Reik et al. \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e1979\u003c/span\u003e, Stanek et al. \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). \u003cem\u003eBb\u003c/em\u003e is a highly neurophilic pathogen which remains latent in the central or peripheral nervous system, and the clinical presentation varies with disease stages. It has been reported that the incidence of LNB in Lyme disease patients is not less than 10%(Koedel et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2015\u003c/span\u003e, Steere et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003cp\u003ePain is generally the first neurological symptom of meningoradiculitis, and approximately one-quarter of LNB patients will show no other symptoms. More than half of patients with meningoradiculitis also show cranial nerve symptoms, with unilateral or bilateral facial nerve involvement being the most common(Garcia-Monco and Benach \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2019\u003c/span\u003e, Hansen and Lebech \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e1992\u003c/span\u003e). Neurological symptoms occur concomitantly with lymphocytic cerebrospinal fluid (CSF) pleocytosis (10\u0026ndash;1000 cells per \u0026micro;L), which is a diagnostic criterion for definite LNB in Europe(Mygland et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2010\u003c/span\u003e, Pfister et al. \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e1984\u003c/span\u003e). Intravenous antibiotics have been proven to be an effective therapy for LNB, however, in a few patients \u003cem\u003eBb\u003c/em\u003e has been reported to persist in the CSF after antibiotic therapy(Preac-Mursic et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e1989\u003c/span\u003e). Moreover, a proportion of patients still have symptoms following appropriate antibiotic therapy, which are defined as post-treatment Lyme disease syndrome(Kaplan et al. \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2003\u003c/span\u003e, Koedel, Fingerle and Pfister \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Atypical symptoms and the lack of clinically specific diagnostic indicators often prolongs the diagnosis of LNB or leads to its misdiagnosis. If LNB is left untreated or is improperly treated, neurological symptoms of patients can persist for many years(Feder et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). Nonetheless, the molecular mechanisms associated with the pathogenesis of LNB remain poorly understood.\u003c/p\u003e \u003cp\u003eIn recent years, high-throughput sequencing and bioinformatics technologies have rapidly becoming critical tools in the study of diseases, including Lyme disease(Bouquet et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2016\u003c/span\u003e, Ding et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2020\u003c/span\u003e, Mansfield et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2017\u003c/span\u003e, Petzke et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). In particular, the study of transcriptomics is more economical and informative than genomics. Transcriptome sequencing of mRNA using high-throughput sequencing technologies can quickly and comprehensively obtain information on almost all transcripts of a specific species tissue or cell in a certain state, including base sequences and gene expressions. Although mRNA only comprises approximately 4% of the total RNA in mammalian cells, it has been the focus of research, because it is ultimately translated into protein and participates in the expression of the cell phenotype(Wu et al. \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). In addition, the Gene Expression Omnibus (GEO) database developed by the National Center for Biotechnology Information (NCBI) also provides vast valuable datasets.\u003c/p\u003e \u003cp\u003eAt present, however, there are limited transcriptomic studies associated with Lyme spirochete infection to CNS. Thus, we conducted this study to explore the pathogenesis of LNB and to provide valuable clues for future research. We obtained the transcriptome data of frontal cortex brain explants that were exposed to \u003cem\u003eBb\u003c/em\u003e and the human astrocytes following \u003cem\u003eBb\u003c/em\u003e infection transcriptome dataset (GSE85143), and screened the data using the GEO database for comprehensive analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The results of the present study will improve our understanding of the underlying mechanisms of LNB and will identify biomarker candidates for LNB diagnosis and prognosis.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Materials And Methods","content":"\u003cp\u003e\u003cstrong\u003eData resources\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo identify the host candidate hub genes in models of LNB, we comprehensively analyzed our previous transcriptome raw sequencing data(Ding et al. 2019) which has been uploaded to NCBI in the format of.fastq (http://www.ncbi.nlm.nih.gov/bioproject/723671) and the GSE85143 dataset from the GEO database (GEO, \u003ca href=\"https://www.ncbi.nlm.nih.gov/geo/\"\u003ehttps://www.ncbi.nlm.nih.gov/geo/\u003c/a\u003e)(Clough and Barrett 2016). Frontal cortex brain explants from three healthy rhesus macaques (2 males and 1 female) were used for \u003cem\u003eex vivo\u003c/em\u003e experiments in our previous study(Ding, Ma, Tao, Peng, Han, Sun, Dai, Ji, Bai, Jian, Chen, Luo, Wang, Bi, Liu and Bao 2019, Ding, Sun, Bi, Zhang, Yue, Xu, Cao, Luo, Chen, Li, Ji, Jian, Lu, Abi, Liu and Bao 2020), the explants were collected for transcriptomic analysis after co-culture with live \u003cem\u003eBb\u003c/em\u003e (\u003cem\u003eBb\u003c/em\u003e4680, DSMZ, A1511358-1) or culture containing normal medium as controls. The platform used for this dataset was Illumina HiSeq 2500. We referred to this data as \u0026lsquo;Rhesus dataset\u0026rsquo; in this paper. The GSE85143 dataset was based on GPL11154 Illumina HiSeq 2000 (Homo sapiens) platform. The experiment contained 9 samples consisting of 6 \u003cem\u003eBb\u003c/em\u003e-infected samples and 3 uninfected samples, which used human astrocytes infected with \u003cem\u003eBb\u003c/em\u003e and uninfected astrocytes as controls.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDifferential Expression Analyses\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDifferential expression analysis was performed using the online analysis tool BioJupies in the GSE85143 dataset. BioJupies (\u003ca href=\"https://amp.pharm.mssm.edu/biojupies/\"\u003ehttps://amp.pharm.mssm.edu/biojupies/\u003c/a\u003e) is an easy-to-use web application for RNA-seq data analyses, the datasets published in GEO can be processed by ARCHS4 directly(Torre et al. 2018). The cut-off criteria to select the differentially expressed genes (DEGs) were the adjusted P-value \u0026lt;0.05 and fold-change (FC)\u0026ge;2. For the GSE85143 dataset, the volcano plot was visualized using R package ggplot2. The differential analysis of the Rhesus dataset has been described in detail in our previous study(Ding, Ma, Tao, Peng, Han, Sun, Dai, Ji, Bai, Jian, Chen, Luo, Wang, Bi, Liu and Bao 2019). We merged all DEGs from three time points in the Rhesus dataset.\u0026nbsp;Statistical analysis was performed for each dataset using the Venn diagram webtool (\u003ca href=\"http://bioinformatics.psb.ugent.be/webtools/Venn/\"\u003ehttp://bioinformatics.psb.ugent.be/webtools/Venn/\u003c/a\u003e) to identify the intersecting DEGs.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunctional and Pathway Enrichment Analyses\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Gene ontology (GO) term enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of DEGs were performed using DAVID (the Database for Annotation, Visualization, and Integration Discovery). DAVID (version 6.8, \u003ca href=\"https://david.ncifcrf.gov/\"\u003ehttps://david.ncifcrf.gov/\u003c/a\u003e) is a web server which provides a comprehensive set of functional annotation tools to help researchers understand the biological significance of a large list of genes(Huang et al. 2007). GO is an important biological tool that enables investigators to turn data into knowledge. GO comprises three categories including Biological process (BP), Molecular function (MF), and Cellular component (CC)(Ashburner et al. 2000). A P-value of \u0026lt;0.05 was used as the threshold for significance.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eProtein\u0026ndash;Protein Interaction Network Analysis and Identification of Hub Genes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003eprotein\u0026ndash;protein interaction (PPI) network was analyzed and visualized using the online resource of Search Tool for the Retrieval of Interaction Genes (STRING) 11.0 (\u003ca href=\"https://string-db.org/\"\u003ehttps://string-db.org/\u003c/a\u003e), which collects, scores, and integrates functional interactions among proteins(Szklarczyk et al. 2019). We mapped previously identified DEGs to STRING to identify potential interactions between them. In our study, the minimum interaction score required was 0.4 (medium confidence). The PPI network was visualized by Cytoscape 3.8.2 and analyzed in Cytoscape software with the cytoHubba plugin. Eleven hub genes closely linked to models of LNB were identified using MCC, DMNC, MNC, Degree, EPC, Bottleneck, Eccentricity, Closeness, Radiality, Betweenness, Stress, Clustering Coefficient methods. Multi-dimensional Venn diagrams were generated and visualized using the R package UpSetR(Conway et al. 2017).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSpirochetes Culture\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eBorrelia\u0026nbsp;burgdorferi\u003c/em\u003e \u003cem\u003esensu stricto\u003c/em\u003e strain 4680 (DSMZ, A1511358-1) culture was carried out as described previously with little modification(Ding, Ma, Tao, Peng, Han, Sun, Dai, Ji, Bai, Jian, Chen, Luo, Wang, Bi, Liu and Bao 2019). For co-culture experiments, spirochetes were centrifuged (2500\u0026times;g; 10 min; 4℃) and the Barbour-Stoenner-Kelly II (BSK II) medium supplemented with 6% rabbit serum (Thermo Fisher Scientific, United States) was removed and washed three times with sterilized PBS. The spirochetes were resuspended in antibiotic-free cell culture medium. Bacterial counts were determined using a counting plate under dark-field microscopy.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCo-culture of the Human Astrocyte Cell Line with \u003cem\u003eBb\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003ehuman astrocyte cell line U251 was obtained from the Kunming Cell Bank, Kunming Institute of Zoology, Chinese Academy of Sciences (Kunming, China). U251 cells were cultured in Dulbecco\u0026rsquo;s Modified Eagle Medium (DMEM) (Thermo Fisher Scientific, United States) supplemented with 10% fetal bovine serum (FBS) (Thermo Fisher Scientific, United States) at 37℃ with 5% CO\u003csub\u003e2\u003c/sub\u003e. U251 cells were seeded in 6-well plates at a cell density of 1.2\u0026times;10\u003csup\u003e5\u003c/sup\u003e cells per well. After attaching to plates, cells were exposed to \u003cem\u003eBb\u0026nbsp;\u003c/em\u003eat a multiplicity of infection (MOI) of 0.1 or 1 for 6 h, 12 h, 24 h, and 48 h.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCo-culture of Frontal Cortex Brain Explants with \u003cem\u003eBb\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFresh rhesus frontal cortex tissue sections were collected during the animal necropsy, and the repeated specimens were co-cultured with live spirochetes as described in our previously published studies(Ding, Ma, Tao, Peng, Han, Sun, Dai, Ji, Bai, Jian, Chen, Luo, Wang, Bi, Liu and Bao 2019). The brain explants were stored with Trizol reagent (Invitrogen, United States) at -80℃.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRNA Isolation and Reverse Transcription\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTotal brain RNA was extracted from brain explants by phenol and chloroform extraction as well as isopropanol precipitation, and then washed with ice-cold 75% ethanol. The total cell RNA was extracted from U251 using a Trizol reagent. Total RNA was reverse-transcribed into cDNA using the PrimeScript RTreagent kit with gDNA Eraser (Takara, Japan) according to the manufacturer\u0026rsquo;s instructions. The final reaction mixture volume was 20 \u0026mu;l. Specific details were referenced to our previous work(Ding, Ma, Tao, Peng, Han, Sun, Dai, Ji, Bai, Jian, Chen, Luo, Wang, Bi, Liu and Bao 2019).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eValidation of Result\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eby Quantitative Real-Time Polymerase Chain Reaction\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;(qPCR)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe resulting cDNA was used to perform qPCR on the CFX Connect System (Bio‑Rad Laboratories, Inc.). The total qPCR reaction volume was 25 \u0026micro;L and included Takara SYBR Green II (12.5 \u0026micro;L), forward and reverse primers (1 \u0026micro;L of each), RNase-free water (8.5 \u0026micro;L) and cDNA template (2 \u0026micro;L). The cycling conditions were as follows: 95\u0026deg;C for 30 sec, followed by 40 cycles of 95\u0026deg;C for 5 sec, and 58.5\u0026deg;C for 30 sec. The sequences of the specific primers used for the above reactions are shown in \u003cstrong\u003eTable 1\u003c/strong\u003e. Human \u003cem\u003e\u0026beta;-Actin\u003c/em\u003e and rhesus \u003cem\u003eGAPDH\u003c/em\u003e were used as the internal references. The relative expression of target genes was calculated with the 2\u003csup\u003e\u0026minus;\u0026Delta;\u0026Delta;Ct\u003c/sup\u003e method(Livak and Schmittgen 2001).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data were reported as the mean\u0026plusmn;standard error of the mean (SEM) and were analyzed using GraphPad Prism 8 (GraphPad Software, San Diego, CA, USA). All experiments were repeated in triplicate. Two-Way ANOVA was applied for group comparisons. A P-value \u0026lt;0.05 was considered statistically significant.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eIdentification of DEGs between Unexposed and \u003cem\u003eBb-\u003c/em\u003einfected Groups\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe used the gene expression profiles of the GSE85143 and the Rhesus dataset from our previous study(Ding, Ma, Tao, Peng, Han, Sun, Dai, Ji, Bai, Jian, Chen, Luo, Wang, Bi, Liu and Bao 2019). In our previous study, we co-cultured rhesus macaques\u0026rsquo; frontal cortex brain explants with living spirochetes. There were 6 control samples and 6 \u003cem\u003eBb\u003c/em\u003e infected samples. The GSE85143 contains 6 \u003cem\u003eBb\u003c/em\u003e infected samples and 3 untreated samples. According to the cut-off criteria P-value \u0026lt;0.05 and FC \u0026ge;2, from the Rhesus dataset, we identified 3075 DEGs, including 2116 upregulated genes and 959 downregulated genes. Based on the RNA-seq GSE85143 dataset, a total of 1450 DEGs were identified, and comprised 724 upregulated genes and 726 downregulated genes (\u003cstrong\u003eSupplemental File1\u003c/strong\u003e). For the GSE85143 dataset, the DEGs are shown in the volcano plot and in the heatmap, whose clustering was performed using BioJupies (\u003cstrong\u003eFig 2\u003c/strong\u003e). Moreover, the principal component analysis (PCA) was also performed to examine differences between \u003cem\u003eBb\u003c/em\u003e infected and untreated groups (\u003cstrong\u003eFig. S1\u003c/strong\u003e). All DEGs were defined by comparison of untreated samples and \u003cem\u003eBb\u003c/em\u003e infected samples. The Venn diagram showed the intersection of the DEGs in the two sets (\u003cstrong\u003eFig. 3\u003c/strong\u003e). The results indicated there was a total of 112 matched DEGs between the two datasets, of which 80 were upregulated genes (\u003cstrong\u003eSupplemental File 2\u003c/strong\u003e) and 32 were downregulated genes (\u003cstrong\u003eSupplemental File 3\u003c/strong\u003e).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGO Annotations and KEGG Pathways\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe functional annotation analysis and KEGG pathway enrichment analysis of DEGs were performed using web tool DAVID. GO analysis indicated the annotation of DEGs from three categories: BP, CC, and MF\u0026nbsp;(\u003cstrong\u003eFig. 4\u003c/strong\u003e). The results revealed that the upregulated DEGs in the BP category were mainly enriched in cell adhesion, negative regulation of endopeptidase activity, single organismal cell-cell adhesion or leukocyte migration, and response to hypoxia. While the downregulated DEGs in the BP were mainly involved in extracellular matrix organization and cell proliferation. CC analysis showed that the upregulated DEGs were significantly enriched in plasma membrane, extracellular region, extracellular space and the cytoskeleton, while the downregulated DEGs in CC were mainly in involved in integral components of the membrane, extracellular space, extracellular region, endoplasmic reticulum membrane, and the proteinaceous extracellular matrix. For MF analysis, the upregulated DEGs were mainly enriched in heparin binding and carbohydrate binding, the downregulated DEGs were enriched in extracellular matrix structural constituents and binding. In addition, the upregulated DEGs were significantly mapped on complement and coagulation cascades, hematopoietic cell lineage, \u003cem\u003eStaphylococcus aureus\u0026nbsp;\u003c/em\u003einfection, and arrhythmogenic right ventricular cardiomyopathy (ARVC) in the KEGG enrichment analysis (\u003cstrong\u003eFig. 5\u003c/strong\u003e).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePPI Network Analysis and Identification of\u0026nbsp;Hub genes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo investigate the potential relationships between DEGs, we attempted to construct a PPI network using the STRING database. In total, 117 nodes and 95 edges were presented in this PPI (PPI enrichment P-value was lower than 1.44e-07) network (\u003cstrong\u003eFig. 6\u003c/strong\u003e). The DEGs were comprehensively calculated to identify the intersections among 12 different algorithms, and the most notable 11 genes were considered as hub genes (\u003cstrong\u003eTable 2\u003c/strong\u003e). The multi-dimensional Venn diagram revealed the intersection of 12 algorithms for DEGs (\u003cstrong\u003eFig. 7A\u003c/strong\u003e). The heat map showed the differential expression of hub genes in our Rhesus samples (\u003cstrong\u003eFig. 7B\u003c/strong\u003e). A PPI (PPI enrichment P-value \u0026lt;2.17e-08) network of the hub genes was constructed using STRING, a total of 11 nodes and 13 edges were involved (\u003cstrong\u003eFig. 7C\u003c/strong\u003e). Functional analysis of hub genes was performed using DAVID. The results showed that the hub genes were mainly enriched in the extracellular matrix organization, plasma membrane, and receptor activity (\u003cstrong\u003eTable 3\u003c/strong\u003e). The KEGG pathway analysis showed the hub genes were significantly enriched in PI3K-Akt signaling pathway (\u003cstrong\u003eTable 3\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eValidation of Hub Genes \u003cem\u003ein vitro\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAmong the identified hub genes \u003cem\u003eSELP, CD93, ANGPT1, TLR6,\u0026nbsp;\u003c/em\u003eand \u003cem\u003eSERPIND1\u0026nbsp;\u003c/em\u003ewere upregulated, while \u003cem\u003eSREBF1, LDLR, MATN3, TNC\u003c/em\u003e, \u003cem\u003eITGA2,\u0026nbsp;\u003c/em\u003eand \u003cem\u003eCSF1R,\u003c/em\u003e were downregulated. To verify these results, we conducted validation experiments using qPCR \u003cem\u003ein vitro\u003c/em\u003e. The U251 cells were co-cultured with live \u003cem\u003eBb\u0026nbsp;\u003c/em\u003efor 6 h, 12 h, 24 h, and 48 h at MOI of 0.1 or 1, and cells cultured in normal medium served as control. Subsequently, total RNA was isolated and qPCR was performed as mentioned above. For upregulated hub genes, the mRNA level of \u003cem\u003eTLR6\u003c/em\u003e and\u003cem\u003e\u0026nbsp;ANGPT 1\u0026nbsp;\u003c/em\u003ewere significantly upregulated in both MOI=0.1 and MOI=1 groups as compared to the controls (\u003cstrong\u003eFig. 8A, B\u003c/strong\u003e). For downregulated hub genes, we verified that \u003cem\u003eLDLR, SREBF 1, TNC,\u003c/em\u003e and \u003cem\u003eITGA\u003c/em\u003e, presented a downward trend at different time points after cells were stimulated by live \u003cem\u003eBb\u003c/em\u003e (\u003cstrong\u003eFig. 8C\u003c/strong\u003e-\u003cstrong\u003eF\u003c/strong\u003e). Based on the above results, we speculated that the hub genes \u003cem\u003eTLR6, ANGPT 1, LDLR, SREBF1, TNC,\u0026nbsp;\u003c/em\u003eand \u003cem\u003eITGA2\u0026nbsp;\u003c/em\u003ewere potentially associated with the pathogenesis of LNB.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eValidation of Hub Genes in \u003cem\u003eex vivo\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo further verify the expression of these hub genes, we also verified their expression in the \u003cem\u003eex vivo\u003c/em\u003e model by qPCR. We found that the mRNA expression level of \u003cem\u003eTLR6\u003c/em\u003e in the \u003cem\u003eBb\u003c/em\u003e-infected group was upregulated compared to the untreated group, especially at the 24 h timepoint (\u003cstrong\u003eFig. 9A\u003c/strong\u003e). For downregulated hub genes, the results showed that the mRNA expression level of \u003cem\u003eLDLR\u003c/em\u003e in the \u003cem\u003eBb\u003c/em\u003e-infected group had decreased compared with the control group (\u003cem\u003eP =\u003c/em\u003e 0.08) at 12 h, whereas no significant differences were observed at the other two timepoints (\u003cstrong\u003eFig. 9B\u003c/strong\u003e). In the \u003cem\u003eBb\u003c/em\u003e group, the mRNA expression level of \u003cem\u003eTNC\u003c/em\u003e showed a downward trend over time, however the results were not statistically significant (\u003cstrong\u003eFig. 9C\u003c/strong\u003e). These results indicated that \u003cem\u003eTLR6\u003c/em\u003e was most likely a critical molecule involved in the pathogenesis of LNB.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eLNB is a nervous system disorder caused by spirochete \u003cem\u003eBb\u003c/em\u003e infection. At present, the efficacy of antibiotics in the treatment of LNB has been defined. Early detection and prompt anti-pathogen treatment can improve the prognosis of patients(Rauer et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). However, due to the lack of specific clinical manifestations, sporadic cases in non-endemic areas are usually missed and often misdiagnosed. Patients with LNB can experience serious consequences such as dementia or personality disorders in the absence of timely treatment, which greatly impacts on their quality of life. Therefore, it is crucial to study the pathogenic biomarkers of LNB that may provide evidence for early diagnosis and treatment.\u003c/p\u003e \u003cp\u003eRecently, bioinformatics analysis has become the conventional means for medical research into disease diagnosis or treatment, and it has significantly accelerated the utilization and integration of public biomedical resources. Nonetheless, to date, only a few omics studies have pointed to models of LNB. Our previous study established a dataset of the transcriptional changes induced by \u003cem\u003eBb\u003c/em\u003e in fresh rhesus frontal cortex tissues(Ding, Ma, Tao, Peng, Han, Sun, Dai, Ji, Bai, Jian, Chen, Luo, Wang, Bi, Liu and Bao 2019). In the present study, for the first time we conducted a comprehensive analysis of transcriptional mRNA changes induced by \u003cem\u003eBb\u003c/em\u003e both in rhesus brain tissue and primary human astrocytes(Casselli et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Similarly, DEGs from astrocytes co-cultured with \u003cem\u003eBb\u003c/em\u003e and control astrocytes were identified and integrated with the gene expression dataset GSE85143 from the GEO database.\u003c/p\u003e \u003cp\u003eSince the two datasets generated on different sequencing platforms (Illumina HiSeq 2500 \u0026amp; Illumina HiSeq 2000), the DEGs associated with Lyme spirochete infection to CNS were identified by taking the intersection analysis on each dataset. Some original data used in the current study have been used in prior publications, however, we generated a set of DEGs by taking the union from all time points. The sample species are different between our rhesus dataset and GSE85143, the DAVID Gene ID Conversion Tool was used. After deleting duplicate and invalid genes, we finally identified 3,075 differentially expressed genes in this study. In our previous study, the number of DEGs identified were 2,249 (6 h post-exposure), 1,064 (12 h post-exposure), and 420 (24 h post-exposure)(Ding, Ma, Tao, Peng, Han, Sun, Dai, Ji, Bai, Jian, Chen, Luo, Wang, Bi, Liu and Bao 2019). In the study conducted by Casselli \u003cem\u003eet al\u003c/em\u003e., GSE85143 is a part of superseries, GSE85143 is RNA-seq dataset which reveals changes in the astrocyte transcriptome following \u003cem\u003eBb\u003c/em\u003e infection and the other parts were about non-coding RNA profiling. We only focused on the analysis of mRNA expression profile for comprehensive analysis. We used the samples infected with \u003cem\u003eBb\u003c/em\u003e at 24 h and 48 h as test group and the three uninfected as control. Accordingly, there are 1450 DEGs in GSE85143 which could be compared to previously published results(Casselli, Qureshi, Peterson, Perley, Blake, Jokinen, Abbas, Nechaev, Watt, Dhasarathy and Brissette 2017).\u003c/p\u003e \u003cp\u003eHerein, we screened and identified 80 upregulated DEGs (\u003cb\u003eSupplemental File 2\u003c/b\u003e) and 32 downregulated DEGs (\u003cb\u003eSupplemental File 3\u003c/b\u003e). Among the upregulated DEGs, most GO terms in the BP group were associated with cell adhesion, while for downregulated DEGs, the significantly enriched transcripts in BP GO terms were related to extracellular matrix organization. The KEGG pathway analysis indicated that these upregulated DEGs were significantly enriched in pathways involved in the complement and coagulation cascades, hematopoietic cell lineage, \u003cem\u003eStaphylococcus aureus\u003c/em\u003e infection and ARVC. There were no significantly enriched KEGG pathways associated with downregulated DEGs. We constructed a PPI network by screening 11 hub genes. To obtain more reliable results, the 112 DEGs of 12 algorithms were investigated using the CytoHubba plugin. The results of top 30 DEGs of each algorithm were sorted by rank and the following intersection genes were considered as hub genes (\u003cb\u003eSupplemental File 4\u003c/b\u003e): \u003cem\u003eSREBF1\u003c/em\u003e, \u003cem\u003eLDLR\u003c/em\u003e, \u003cem\u003eSELP\u003c/em\u003e, \u003cem\u003eCD93\u003c/em\u003e, \u003cem\u003eANGPT1\u003c/em\u003e, \u003cem\u003eTLR6\u003c/em\u003e, \u003cem\u003eMATN3\u003c/em\u003e, \u003cem\u003eSERPIND1\u003c/em\u003e, \u003cem\u003eTNC, ITGA2\u003c/em\u003e, and \u003cem\u003eCSF1R\u003c/em\u003e. Among these hub genes, \u003cem\u003eSELP\u003c/em\u003e, \u003cem\u003eCD93\u003c/em\u003e, \u003cem\u003eANGPT1\u003c/em\u003e, \u003cem\u003eTLR6\u003c/em\u003e, and \u003cem\u003eSERPIND1\u003c/em\u003e were upregulated, while \u003cem\u003eSREBF1\u003c/em\u003e, \u003cem\u003eLDLR\u003c/em\u003e, \u003cem\u003eMATN3\u003c/em\u003e, \u003cem\u003eTNC\u003c/em\u003e, \u003cem\u003eITGA2\u003c/em\u003e, and \u003cem\u003eCSF1R\u003c/em\u003e were downregulated. Furthermore, we performed KEGG pathway enrichment analyses on hub genes and the results revealed that the PI3K-Akt signaling pathway showed the most significant difference (P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.01). Finally, we validated these results using \u003cem\u003eex vivo\u003c/em\u003e samples of rhesus frontal cortex brain explants and \u003cem\u003ein vitro\u003c/em\u003e in human U251 cell lines using qPCR at the transcription level. These results suggested that \u003cem\u003eTLR6\u003c/em\u003e, \u003cem\u003eANGPT1\u003c/em\u003e, \u003cem\u003eSREBF1\u003c/em\u003e, \u003cem\u003eLDLR\u003c/em\u003e, \u003cem\u003eTNC\u003c/em\u003e, and \u003cem\u003eITGA2\u003c/em\u003e might be involved in the pathogenesis of LNB.\u003c/p\u003e \u003cp\u003e \u003cem\u003eTLR6\u003c/em\u003e is a member of the Toll-like receptor (TLR) family, which plays a key role in pathogen-associated molecular patterns (PAMPs) recognition and in innate immune responses. TLR1, TLR2, and TLR6 are all located on the cell membrane. They share high protein homology in their transmembrane and cytoplasmic regions, while their extracellular region is diverse. Thus, they can recognize specific pathogens and activate common intracellular signaling pathways(Takeuchi et al. \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e1999\u003c/span\u003e). It has been shown that TLR is a driving force behind \u003cem\u003eBb\u003c/em\u003e infection, and that the TLR signaling pathway plays an important role in inducing inflammatory responses(Koening et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2009\u003c/span\u003e, Singh and Girschick \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2006\u003c/span\u003e, Thomas and Fikrig \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). Specifically, TLR2 plays an important role in the recognition of Lyme spirochetes(Hirschfeld et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e1999\u003c/span\u003e). TLR6 forms heterodimer with TLR2 and distributes along the cell surface to specifically recognize diacylated lipopeptides and partly triacylated lipopeptides(Kang et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). A previous study showed that blocking TLR6 on human peripheral blood cells after exposure to \u003cem\u003eBb\u003c/em\u003e did not reduce the production of cytokines, while blocking TLR1 significantly decreased cytokine production(Oosting et al. \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Further, the \u003cem\u003eBb-\u003c/em\u003einduced secretion of IFN-γ in murine cells depends on TLR6(Oosting, Ter Hofstede, Sturm, Adema, Kullberg, van der Meer, Netea and Joosten 2011). Our previous studies have also shown that the TLR1/TLR2 ratio plays an important role in initiating of proinflammatory chemokine storm in Lyme disease(Zhao et al. \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). However, due to gene expression specificities in different species, tissues or cell lineages, the role of \u003cem\u003eTLR6\u003c/em\u003e in LNB needs to be further explored. In the present study, \u003cem\u003eTLR6\u003c/em\u003e is the only hub gene that was verified to be elevated in both the human U251 cell line and in rhesus brain explants when exposed to \u003cem\u003eBb.\u003c/em\u003e The transcription level of \u003cem\u003eTLR6\u003c/em\u003e began to increase at 12 h after human astrocytes were exposed to \u003cem\u003eBb\u003c/em\u003e, and the increased expression was observed at only 6 h after rhesus brain explants were exposed to \u003cem\u003eBb\u003c/em\u003e. This may be because other immune cells in the brain tissue, such as microglia, are also involved in the pathophysiological process in the early stages of LBN. Additionally, further analysis of KEGG enrichment analysis of hub genes suggested that the PI3K-AKT pathway may play a key role in the pathogenesis of LNB. Interestingly, we found that the TLR6 signals could activate this pathway using the KEGG pathway mapping tool (\u003cb\u003eFig. S2\u003c/b\u003e). Therefore, \u003cem\u003eTLR6\u003c/em\u003e may take part in the pathogenesis of LNB.\u003c/p\u003e \u003cp\u003eWith regard to the upregulated hub genes, in addition to TLR6, another gene that has been verified to be upregulated in human astrocytes exposed to \u003cem\u003eBb\u003c/em\u003e is \u003cem\u003eANGPT1\u003c/em\u003e. This result is consistent with the results of the study by Casselli \u003cem\u003eet al.\u003c/em\u003e(Casselli, Qureshi, Peterson, Perley, Blake, Jokinen, Abbas, Nechaev, Watt, Dhasarathy and Brissette 2017). They confirmed the elevation of angiopoietin-like 4 (ANGPTL4) in human primary astrocytes exposed to \u003cem\u003eBb\u003c/em\u003e by enzyme-linked immunosorbent assays. Both \u003cem\u003eANGPT1\u003c/em\u003e and \u003cem\u003eANGPTL4\u003c/em\u003e belong to the angiopoietin family and encode secreted glycoproteins that play critical roles in development and in disorders including in angiogenesis, inflammation, cell proliferation, apoptosis, lipid and glucose metabolism, cell migration, and cancer(Carbone et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2018\u003c/span\u003e, Ehrlich et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2019\u003c/span\u003e, Snipstad et al. \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2010\u003c/span\u003e, Zhang et al. \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Compared with other ANGPT/ANGPTL genes which are embedded in the introns of larger genes, \u003cem\u003eANGPT1\u003c/em\u003e and \u003cem\u003eANGPTL4\u003c/em\u003e are stand-alone genes whose expression correlated with tissue-specific enhancer or promoter chromatin sequences(Ehrlich, Lacey and Ehrlich \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). ANGPT1 is the ligand of the transmembrane tyrosine kinase TIE2 receptor. Phosphorylation of tyrosine kinase can activate the PI3K/AKT pathway (\u003cb\u003eFig. S3\u003c/b\u003e), which was a core signaling pathway identified in our previous studies(Fukuhara et al. \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2008\u003c/span\u003e, Huang et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). ANGPT2, which is highly homologous to ANGPT1, has a completely different effect on TIE2(Parikh \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The involvement of ANGPT1 and ANGPT2 in pathological inflammation is clear. In several diseases that have a strong association with inflammation, such as sepsis and malignant tumors, angiopoietins can be used as prognostic indicators(Fiedler and Augustin \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2006\u003c/span\u003e, Park et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2009\u003c/span\u003e, Seol et al. \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). In our previous study(Zhao et al. \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), we found that the NF-κB pathway plays a key role in the pathogenesis of LNB, and other studies have reported that ANGPT can also influence the NF-κB pathway(Fiedler and Augustin \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2006\u003c/span\u003e, Huang, Bhat, Woodnutt and Lappe \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). These results suggested the possible potential of \u003cem\u003eANGPT\u003c/em\u003e either as novel diagnostic and prognostic indicators, or as a therapeutic target for LNB. In addition, upregulation of \u003cem\u003eSERPIN\u003c/em\u003e in the upregulated hub genes were verified in the study by Casselli \u003cem\u003eet al(Casselli, Qureshi, Peterson, Perley, Blake, Jokinen, Abbas, Nechaev, Watt, Dhasarathy and Brissette 2017)\u003c/em\u003e. Thus, they have not been discussed in detail here.\u003c/p\u003e \u003cp\u003eAmong the downregulated hub genes, we verified that the expression of \u003cem\u003eLDLR, SREBF1, TNC\u003c/em\u003e, and \u003cem\u003eITGA\u003c/em\u003e showed a statistically significant decrease in human astrocytes on exposure to \u003cem\u003eBb\u003c/em\u003e. Following exposure of rhesus brain explants to \u003cem\u003eBb\u003c/em\u003e, we found there was a decrease in both \u003cem\u003eLDLR\u003c/em\u003e and \u003cem\u003eTNC\u003c/em\u003e levels, although there the differences were not statistically significant (P-values of \u003cem\u003eLDLR\u003c/em\u003e at 6 h, 12 h and 24 h \u003cem\u003eBb-\u003c/em\u003eexposed astrocytes vs controls were 0.8, 0.08, and 0.37, respectively). LDLR is a cell surface glycoprotein that can be expressed on various cell types including astrocytes in many tissues. LDLR can specifically recognize and bind lipoproteins containing apolipoprotein (Apo)E or ApoB100 to mediate cholesterol metabolism, thus it is a central component for the maintenance of cholesterol homeostasis(Brown and Goldstein \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e1986\u003c/span\u003e). Studies have elucidated that LNB may promote the neuro-inflammatory response and endow susceptibility to Alzheimer\u0026rsquo;s disease (AD), which can not only lead to dementia, but also may induce pathological features typical of AD, such as amyloid-beta (Aβ) deposits(Miklossy et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2004\u003c/span\u003e, Miklossy et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2006\u003c/span\u003e). Conversely, upregulating the expression of LDLR can improve the brain clearance of Aβ, reduce amyloid-deposition and attenuate the neuroinflammatory response(Basak et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2012\u003c/span\u003e, Yao et al. \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). A recent study revealed that lower LDLR expression can aggravate the neuronal inflammatory response, which might occur through NF-κB signaling and NLRP3-ASC caspase-1 inflammasome assembly(Sun et al. \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Thus, LDLR may serve as a protective factor for LNB.\u003c/p\u003e \u003cp\u003eMeanwhile, the reduction of \u003cem\u003eSREBF1\u003c/em\u003e expression at the transcriptional level has been verified in human U251 cells at exposure to \u003cem\u003eBb\u003c/em\u003e. The proteins encoded by \u003cem\u003eSREBFs\u003c/em\u003e are transcription factors that control cholesterol homeostasis. LDLR is one of the target genes regulated by sterol regulatory element-binding proteins (SREBPs)(Horton et al. \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). We observed a decrease in \u003cem\u003eTNC\u003c/em\u003e transcriptional expression of U251 cells after stimulation by \u003cem\u003eBb\u003c/em\u003e, and we also found a downward trend over time in rhesus brain explants, albeit there was no statistically significant difference. The protein encoded by \u003cem\u003eTNC\u003c/em\u003e belongs to the tenascin family and is widely involved in pathological processes such as inflammation and malignancy. TNC plays a key role in the proliferation of primary astrocytes(Ikeshima-Kataoka et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2008\u003c/span\u003e, Roll and Faissner \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). There have been many reports that TNC is highly expressed when the CNS is injured(Roll and Faissner \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). However, brain injured TNC knock-out mice showed higher expression of inflammatory factors such as TNF-α, IL-6, and IL-1β than wild-type mice(Ikeshima-Kataoka, Shen, Eto, Saito and Yuasa \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). The expression level of TNC varies under different pathological conditions. It may help regulate the production of inflammatory factors in the damaged brain. In addition, \u003cem\u003eITGA2\u003c/em\u003e reduction was observed at 6 h and 48 h in U251 cells after exposure to \u003cem\u003eBb.\u003c/em\u003e ITGA2 is a glycoprotein of the integrin family, which mediates cell\u0026ndash;cell interactions and those of the cell-extracellular matrix. It also participates in the pathophysiological process of inflammation and the immune response. However, studies have reported that ITGA2 can aggravate the destruction of inflammatory cartilage in rheumatoid arthritis, and has been implicated in cell growth and apoptosis in tumors(Penrose et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2019\u003c/span\u003e, Peters et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). The roles of ITGA2 in LNB remain to be clarified, therefore, further studies are needed.\u003c/p\u003e \u003cp\u003eAll in all, in present study, we combined two different datasets related to LNB and identified a group of commonly affected transcripts. Using the commonly affected genes from two very different experimental systems to conduct detailed functional and network analyses. Among them, 11 hub genes were identified from network analysis. We also conducted validation experiments and validated some of the hub genes.\u003c/p\u003e \u003cp\u003eThere are some limitations in our current study. First, \u003cem\u003ein vivo\u003c/em\u003e experimental validation is needed to validate these findings. In addition, although we have verified the mRNA expression of hub genes using a cell line \u003cem\u003ein vitro\u003c/em\u003e and in \u003cem\u003eex vivo\u003c/em\u003e rhesus brain explants, we did not carry out in-depth mechanism research. Additionally, due to the different sample sources and species of the two datasets, the intersection of the DEGs could provide only limited information. Moreover, the sufficient validation by alternate methods or by measuring the protein levels for biological relevance to LNB will need to be performed in future work. And the number of samples is relatively small. The underlying mechanisms need to be further studied in our future studies.\u003c/p\u003e \u003cp\u003eIn conclusion, the present study suggests that \u003cem\u003eTLR6\u003c/em\u003e, \u003cem\u003eANGPT1\u003c/em\u003e, \u003cem\u003eLDLR\u003c/em\u003e, \u003cem\u003eSREBF1\u003c/em\u003e, \u003cem\u003eTNC\u003c/em\u003e, and \u003cem\u003eITGA\u003c/em\u003e were differentially highly expressed in \u003cem\u003eBb-\u003c/em\u003einfected astrocytes compared to control astrocytes. These candidate genes are clinically promising biomarkers that can be used for LNB diagnosis or treatment. In addition, \u003cem\u003eLDLR\u003c/em\u003e was found to be significantly associated with favorable outcomes in neuroinflammatory diseases, which may provide a better understanding of molecular mechanisms and novel targets as therapeutic strategies in the future. Thus, our findings may provide valuable insights into the study of LNB pathogenesis and provides guidance for designing further follow-up studies.\u003c/p\u003e "},{"header":"Declarations","content":"a\u003cp\u003e\u003cstrong\u003eETHICAL STATEMENT:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis experiment was performed according to the Guide for the Care and Use of Laboratory Animals and ARRIVE Guidelines for Reporting Animal Research. The Animal Ethics and Welfare Committee of Kunming Medical University reviewed and approved this study. The animal permit number: SCXK (DIAN) K2015-0004.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data files described in this manuscript for analysis, are available at the NCBI site:\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE85143\"\u003ehttps://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE85143\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003e\u003ca href=\"http://www.ncbi.nlm.nih.gov/bioproject/723671\"\u003ehttp://www.ncbi.nlm.nih.gov/bioproject/723671\u003c/a\u003e\u003c/p\u003e\n\u003cp\u003eAll data generated or analyzed during this study are included in this published article [and its supplementary information files].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by grants from the National Natural Science Foundation of China\u0026nbsp;(No. 32060180, 81860644, 81560596, and 31560051) and the Natural Foundation of Yunnan Province [No. 2019FE001(-002) and 2017FE467(-001)].\u0026nbsp;The funding institutions had no involvement in the design of the study or review of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors contributed to the study conception and design. Fukai Bao, Aihua Liu, and Shiyuan Wen conceived and designed the experiments. Shiyuan Wen, Xin Xu, Jing Kong, Lisha Luo, Yu Zhang, Mingbiao Ma, Lvyan Tao and Yun Peng developed the methodology. Shiyuan Wen, Xin Xu, Wenjing Cao, Peng Yue, Yuxin Fan, Meixiao Liu and Jingjing Chen performed all experiments. Yan Dong, Suyi Luo, Bingxue Li, Feng Wang, Guozhong Zhou, Taigui Chen and Lianbao Li analyzed and discussed the data. Shiyuan Wen wrote the manuscript. Shiyuan Wen, Fukai Bao and Aihua Liu edited and revised the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors thank the Yunnan Province Key Laboratory for Tropical Infectious Diseases in Universities; Yunnan Province Integrative Innovation Center for Public Health, Diseases Prevention and Control; Kunming Medical University; and Yunnan Demonstration Base of International Science and Technology Cooperation for Tropical Diseases (all located in Kunming, China) for supporting this study.\u003c/p\u003e\u003ch2\u003eDeclarations\u003c/h2\u003e \u003cp\u003e \u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e \u003cp\u003e This experiment was performed according to the Guide for the Care and Use of Laboratory Animals and ARRIVE Guidelines for Reporting Animal Research. The Animal Ethics and Welfare Committee of Kunming Medical University reviewed and approved this study. The animal permit number: SCXK (DIAN) K2015-0004.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent for publication\u003c/strong\u003e \u003cp\u003eNot applicable.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eDeclarations\u003c/h2\u003e \u003cp\u003e \u003cstrong\u003eCompeting interests\u003c/strong\u003e \u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis work was supported by grants from the National Natural Science Foundation of China (No. 32060180, 81860644, 81560596, and 31560051) and the Natural Foundation of Yunnan Province [No. 2019FE001(-002) and 2017FE467(-001)]. The funding institutions had no involvement in the design of the study or review of the manuscript.\u003c/p\u003e\u003ch2\u003eAuthors\u0026rsquo; contributions\u003c/h2\u003e \u003cp\u003eAll authors contributed to the study conception and design. Fukai Bao, Aihua Liu, and Shiyuan Wen conceived and designed the experiments. Shiyuan Wen, Xin Xu, Jing Kong, Lisha Luo, Yu Zhang, Mingbiao Ma, Lvyan Tao and Yun Peng developed the methodology. Shiyuan Wen, Xin Xu, Wenjing Cao, Peng Yue, Yuxin Fan, Meixiao Liu and Jingjing Chen performed all experiments. Yan Dong, Suyi Luo, Bingxue Li, Feng Wang, Guozhong Zhou, Taigui Chen and Lianbao Li analyzed and discussed the data. Shiyuan Wen wrote the manuscript. Shiyuan Wen, Fukai Bao and Aihua Liu edited and revised the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e \u003cp\u003eThe authors thank the Yunnan Province Key Laboratory for Tropical Infectious Diseases in Universities; Yunnan Province Integrative Innovation Center for Public Health, Diseases Prevention and Control; Kunming Medical University; and Yunnan Demonstration Base of International Science and Technology Cooperation for Tropical Diseases (all located in Kunming, China) for supporting this study.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAshburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM, Davis AP, Dolinski K, Dwight SS, Eppig JT et al (2000) Gene ontology: tool for the unification of biology. The Gene Ontology Consortium. Nat Genet May 25:25\u0026ndash;29. 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Epub 2018/02/03\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1\u0026nbsp;\u003c/strong\u003ePrimer Sequences Used in Quantitative Real-Time Polymerase Chain Reaction\u003c/p\u003e\n\u003ctable border=\"0\" cellpadding=\"0\" cellspacing=\"0\" width=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"11.640211640211641%\"\u003e\n \u003cp\u003e\u003cstrong\u003eGene\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"37.43386243386244%\"\u003e\n \u003cp\u003e\u003cstrong\u003eForward (5\u0026apos;\u003c/strong\u003e\u003cstrong\u003e\u0026reg;\u003c/strong\u003e\u003cstrong\u003e3\u0026apos;)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"35.18518518518518%\"\u003e\n \u003cp\u003e\u003cstrong\u003eReverse (5\u0026apos;\u003c/strong\u003e\u003cstrong\u003e\u0026reg;\u003c/strong\u003e\u003cstrong\u003e3\u0026apos;)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.74074074074074%\"\u003e\n \u003cp\u003e\u003cstrong\u003especies\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"11.640211640211641%\"\u003e\n \u003cp\u003e\u003cem\u003eTLR6\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"37.43386243386244%\"\u003e\n \u003cp\u003eTGAATGCAAAAACCCTTCACCT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"35.18518518518518%\"\u003e\n \u003cp\u003eCCAAGTCGTTTCTATGTGGTTGA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.74074074074074%\"\u003e\n \u003cp\u003eHomo sapiens\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"11.640211640211641%\"\u003e\n \u003cp\u003e\u003cem\u003eANGPT1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"37.43386243386244%\"\u003e\n \u003cp\u003eTCGTGAGAGTACGACAGACCA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"35.18518518518518%\"\u003e\n \u003cp\u003eTCTCCGACTTCATGTTTTCCAC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.74074074074074%\"\u003e\n \u003cp\u003eHomo sapiens\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"11.640211640211641%\"\u003e\n \u003cp\u003e\u003cem\u003eLDLR\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"37.43386243386244%\"\u003e\n \u003cp\u003eTGAACTGGTGTGAGAGGACC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"35.18518518518518%\"\u003e\n \u003cp\u003eTTTAGCCTGACGGTGGATGT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.74074074074074%\"\u003e\n \u003cp\u003eHomo sapiens\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"11.640211640211641%\"\u003e\n \u003cp\u003e\u003cem\u003eSREBF1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"37.43386243386244%\"\u003e\n \u003cp\u003eCTCAGATACCACCAGCGTCT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"35.18518518518518%\"\u003e\n \u003cp\u003eTTGCGATGCCTCCAGAAGTA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.74074074074074%\"\u003e\n \u003cp\u003eHomo sapiens\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"11.640211640211641%\"\u003e\n \u003cp\u003e\u003cem\u003eTNC\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"37.43386243386244%\"\u003e\n \u003cp\u003eCTCCTCCCAAAGACCTCGTT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"35.18518518518518%\"\u003e\n \u003cp\u003eAAGTACTCCACACCAGGCTC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.74074074074074%\"\u003e\n \u003cp\u003eHomo sapiens\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"11.640211640211641%\"\u003e\n \u003cp\u003e\u003cem\u003eITGA\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"37.43386243386244%\"\u003e\n \u003cp\u003eTGGTCATCAGGGCACTATCC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"35.18518518518518%\"\u003e\n \u003cp\u003eCACTTGTCCAAAGGCACCAA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.74074074074074%\"\u003e\n \u003cp\u003eHomo sapiens\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"11.640211640211641%\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026beta;-Actin\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"37.43386243386244%\"\u003e\n \u003cp\u003eTGGCATCCACGAAACTACCT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"35.18518518518518%\"\u003e\n \u003cp\u003eCAATGCCAGGGTACATGGTG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.74074074074074%\"\u003e\n \u003cp\u003eHomo sapiens\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"11.640211640211641%\"\u003e\n \u003cp\u003e\u003cem\u003eTLR6\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"37.43386243386244%\"\u003e\n \u003cp\u003eGACAAGGACACGGATTCAGCCATC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"35.18518518518518%\"\u003e\n \u003cp\u003eCAGGTTGACACGGTGACAGTTCC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.74074074074074%\"\u003e\n \u003cp\u003eMacaca mulatta\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"11.640211640211641%\"\u003e\n \u003cp\u003e\u003cem\u003eLDLR\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"37.43386243386244%\"\u003e\n \u003cp\u003eCTGGAAGAACTGGCGGCTGAAG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"35.18518518518518%\"\u003e\n \u003cp\u003eTGCGGCAAATGTGGACCTCATC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.74074074074074%\"\u003e\n \u003cp\u003eMacaca mulatta\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"11.640211640211641%\"\u003e\n \u003cp\u003e\u003cem\u003eTNC\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"37.43386243386244%\"\u003e\n \u003cp\u003eGACAAGGACACGGATTCAGCCATC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"35.18518518518518%\"\u003e\n \u003cp\u003eCAGGTTGACACGGTGACAGTTCC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.74074074074074%\"\u003e\n \u003cp\u003eMacaca mulatta\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" width=\"11.640211640211641%\"\u003e\n \u003cp\u003e\u003cem\u003eGAPDH\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"37.43386243386244%\"\u003e\n \u003cp\u003eGCACCACCAACTGCTTAGCAC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"35.18518518518518%\"\u003e\n \u003cp\u003eTCTTCTGGGTGGCAGTGATG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" width=\"15.74074074074074%\"\u003e\n \u003cp\u003eMacaca mulatta\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2\u0026nbsp;\u003c/strong\u003eThe Hub Genes and their Description.\u003c/p\u003e\n\u003ctable border=\"1\" cellpadding=\"0\" cellspacing=\"0\" width=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"18.63173216885007%\"\u003e\n \u003cp\u003e\u003cstrong\u003eGene symbol\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"52.838427947598255%\"\u003e\n \u003cp\u003e\u003cstrong\u003eGene description\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"28.529839883551674%\"\u003e\n \u003cp\u003e\u003cstrong\u003eDifferentially expression\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"18.63173216885007%\"\u003e\n \u003cp\u003e\u003cem\u003eSELP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"52.838427947598255%\"\u003e\n \u003cp\u003eSelectin P\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"28.529839883551674%\"\u003e\n \u003cp\u003eUpregulated\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"18.63173216885007%\"\u003e\n \u003cp\u003e\u003cem\u003eCD93\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"52.838427947598255%\"\u003e\n \u003cp\u003eCD93\u0026nbsp;Molecule\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"28.529839883551674%\"\u003e\n \u003cp\u003eUpregulated\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"18.63173216885007%\"\u003e\n \u003cp\u003e\u003cem\u003eANGPT1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"52.838427947598255%\"\u003e\n \u003cp\u003eAngiopoietin 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"28.529839883551674%\"\u003e\n \u003cp\u003eUpregulated\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"18.63173216885007%\"\u003e\n \u003cp\u003e\u003cem\u003eTLR6\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"52.838427947598255%\"\u003e\n \u003cp\u003eToll Like Receptor 6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"28.529839883551674%\"\u003e\n \u003cp\u003eUpregulated\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"18.63173216885007%\"\u003e\n \u003cp\u003e\u003cem\u003eSERPIND1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"52.838427947598255%\"\u003e\n \u003cp\u003eSerpin Family D Member 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"28.529839883551674%\"\u003e\n \u003cp\u003eUpregulated\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"18.63173216885007%\"\u003e\n \u003cp\u003e\u003cem\u003eSREBF1\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"52.838427947598255%\"\u003e\n \u003cp\u003eSterol Regulatory Element Binding Transcription Factor 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"28.529839883551674%\"\u003e\n \u003cp\u003eDownregulated\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"18.63173216885007%\"\u003e\n \u003cp\u003e\u003cem\u003eLDLR\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"52.838427947598255%\"\u003e\n \u003cp\u003eLow Density Lipoprotein Receptor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"28.529839883551674%\"\u003e\n \u003cp\u003eDownregulated\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"18.63173216885007%\"\u003e\n \u003cp\u003e\u003cem\u003eMATN3\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"52.838427947598255%\"\u003e\n \u003cp\u003eMatrilin 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"28.529839883551674%\"\u003e\n \u003cp\u003eDownregulated\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"18.63173216885007%\"\u003e\n \u003cp\u003e\u003cem\u003eTNC\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"52.838427947598255%\"\u003e\n \u003cp\u003eTenascin C\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"28.529839883551674%\"\u003e\n \u003cp\u003eDownregulated\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"18.63173216885007%\"\u003e\n \u003cp\u003e\u003cem\u003eITGA2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"52.838427947598255%\"\u003e\n \u003cp\u003eIntegrin Subunit Alpha 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"28.529839883551674%\"\u003e\n \u003cp\u003eDownregulated\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" width=\"18.63173216885007%\"\u003e\n \u003cp\u003e\u003cem\u003eCSF1R\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"52.838427947598255%\"\u003e\n \u003cp\u003eColony Stimulating Factor 1 Receptor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" width=\"28.529839883551674%\"\u003e\n \u003cp\u003eDownregulated\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3\u0026nbsp;\u003c/strong\u003eSignificantly enriched GO terms and KEGG pathways of the Hub DEG\u003c/p\u003e\n\u003ctable border=\"0\" cellpadding=\"0\" cellspacing=\"0\" width=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.051546391752577%\"\u003e\n \u003cp\u003e\u003cstrong\u003eCategory\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.824742268041238%\"\u003e\n \u003cp\u003e\u003cstrong\u003eTerm\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"46.134020618556704%\"\u003e\n \u003cp\u003e\u003cstrong\u003eDescription\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.6340206185567%\"\u003e\n \u003cp\u003e\u003cstrong\u003eCount\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.824742268041238%\"\u003e\n \u003cp\u003e\u003cstrong\u003eP-Value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.530927835051546%\"\u003e\n \u003cp\u003e-\u003cstrong\u003eIn(P\u003c/strong\u003e-\u003cstrong\u003eValue)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.051546391752577%\"\u003e\n \u003cp\u003eBP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.824742268041238%\"\u003e\n \u003cp\u003eGO:0030198\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"46.134020618556704%\"\u003e\n \u003cp\u003eExtracellular matrix organization\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.6340206185567%\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.824742268041238%\"\u003e\n \u003cp\u003e0.004623509\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.530927835051546%\"\u003e\n \u003cp\u003e2.335028301\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.051546391752577%\"\u003e\n \u003cp\u003eBP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.824742268041238%\"\u003e\n \u003cp\u003eGO:0010867\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"46.134020618556704%\"\u003e\n \u003cp\u003ePositive regulation of triglyceride biosynthetic process\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.6340206185567%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.824742268041238%\"\u003e\n \u003cp\u003e0.005881637\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.530927835051546%\"\u003e\n \u003cp\u003e2.230501758\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.051546391752577%\"\u003e\n \u003cp\u003eBP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.824742268041238%\"\u003e\n \u003cp\u003eGO:0002687\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"46.134020618556704%\"\u003e\n \u003cp\u003ePositive regulation of leukocyte migration\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.6340206185567%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.824742268041238%\"\u003e\n \u003cp\u003e0.006414804\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.530927835051546%\"\u003e\n \u003cp\u003e2.192816627\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.051546391752577%\"\u003e\n \u003cp\u003eBP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.824742268041238%\"\u003e\n \u003cp\u003eGO:0031589\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"46.134020618556704%\"\u003e\n \u003cp\u003eCell-substrate adhesion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.6340206185567%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.824742268041238%\"\u003e\n \u003cp\u003e0.009076825\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.530927835051546%\"\u003e\n \u003cp\u003e2.04206604\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.051546391752577%\"\u003e\n \u003cp\u003eBP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.824742268041238%\"\u003e\n \u003cp\u003eGO:0071398\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"46.134020618556704%\"\u003e\n \u003cp\u003eCellular response to fatty acid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.6340206185567%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.824742268041238%\"\u003e\n \u003cp\u003e0.012262881\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.530927835051546%\"\u003e\n \u003cp\u003e1.911407495\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.051546391752577%\"\u003e\n \u003cp\u003eBP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.824742268041238%\"\u003e\n \u003cp\u003eGO:0006954\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"46.134020618556704%\"\u003e\n \u003cp\u003eInflammatory response\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.6340206185567%\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.824742268041238%\"\u003e\n \u003cp\u003e0.016468787\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.530927835051546%\"\u003e\n \u003cp\u003e1.78333838\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.051546391752577%\"\u003e\n \u003cp\u003eBP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.824742268041238%\"\u003e\n \u003cp\u003eGO:0007162\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"46.134020618556704%\"\u003e\n \u003cp\u003eNegative regulation of cell adhesion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.6340206185567%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.824742268041238%\"\u003e\n \u003cp\u003e0.019661627\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.530927835051546%\"\u003e\n \u003cp\u003e1.706380557\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.051546391752577%\"\u003e\n \u003cp\u003eBP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.824742268041238%\"\u003e\n \u003cp\u003eGO:0045785\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"46.134020618556704%\"\u003e\n \u003cp\u003ePositive regulation of cell adhesion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.6340206185567%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.824742268041238%\"\u003e\n \u003cp\u003e0.022817408\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.530927835051546%\"\u003e\n \u003cp\u003e1.641733698\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.051546391752577%\"\u003e\n \u003cp\u003eBP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.824742268041238%\"\u003e\n \u003cp\u003eGO:0007155\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"46.134020618556704%\"\u003e\n \u003cp\u003eCell adhesion\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.6340206185567%\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.824742268041238%\"\u003e\n \u003cp\u003e0.023632805\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.530927835051546%\"\u003e\n \u003cp\u003e1.62648472\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.051546391752577%\"\u003e\n \u003cp\u003eBP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.824742268041238%\"\u003e\n \u003cp\u003eGO:0030097\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"46.134020618556704%\"\u003e\n \u003cp\u003eHemopoiesis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.6340206185567%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.824742268041238%\"\u003e\n \u003cp\u003e0.031188723\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.530927835051546%\"\u003e\n \u003cp\u003e1.506002404\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.051546391752577%\"\u003e\n \u003cp\u003eBP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.824742268041238%\"\u003e\n \u003cp\u003eGO:0014068\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"46.134020618556704%\"\u003e\n \u003cp\u003ePositive regulation of phosphatidylinositol 3-kinase signaling\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.6340206185567%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.824742268041238%\"\u003e\n \u003cp\u003e0.034311493\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.530927835051546%\"\u003e\n \u003cp\u003e1.464560382\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.051546391752577%\"\u003e\n \u003cp\u003eBP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.824742268041238%\"\u003e\n \u003cp\u003eGO:0008203\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"46.134020618556704%\"\u003e\n \u003cp\u003eCholesterol metabolic process\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.6340206185567%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.824742268041238%\"\u003e\n \u003cp\u003e0.03586952\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.530927835051546%\"\u003e\n \u003cp\u003e1.445274431\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.051546391752577%\"\u003e\n \u003cp\u003eBP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.824742268041238%\"\u003e\n \u003cp\u003eGO:0050729\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"46.134020618556704%\"\u003e\n \u003cp\u003ePositive regulation of inflammatory response\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.6340206185567%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.824742268041238%\"\u003e\n \u003cp\u003e0.038461267\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.530927835051546%\"\u003e\n \u003cp\u003e1.414976408\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.051546391752577%\"\u003e\n \u003cp\u003eBP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.824742268041238%\"\u003e\n \u003cp\u003eGO:0046718\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"46.134020618556704%\"\u003e\n \u003cp\u003eViral entry into host cell\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.6340206185567%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.824742268041238%\"\u003e\n \u003cp\u003e0.042079309\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.530927835051546%\"\u003e\n \u003cp\u003e1.3759314\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.051546391752577%\"\u003e\n \u003cp\u003eCC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.824742268041238%\"\u003e\n \u003cp\u003eGO:0009986\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"46.134020618556704%\"\u003e\n \u003cp\u003eCell surface\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.6340206185567%\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.824742268041238%\"\u003e\n \u003cp\u003e0.001922057\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.530927835051546%\"\u003e\n \u003cp\u003e2.716233687\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.051546391752577%\"\u003e\n \u003cp\u003eCC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.824742268041238%\"\u003e\n \u003cp\u003eGO:0009897\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"46.134020618556704%\"\u003e\n \u003cp\u003eExternal side of plasma membrane\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.6340206185567%\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.824742268041238%\"\u003e\n \u003cp\u003e0.004637261\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.530927835051546%\"\u003e\n \u003cp\u003e2.333738458\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.051546391752577%\"\u003e\n \u003cp\u003eCC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.824742268041238%\"\u003e\n \u003cp\u003eGO:0005886\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"46.134020618556704%\"\u003e\n \u003cp\u003ePlasma membrane\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.6340206185567%\"\u003e\n \u003cp\u003e7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.824742268041238%\"\u003e\n \u003cp\u003e0.005894117\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.530927835051546%\"\u003e\n \u003cp\u003e2.229581248\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.051546391752577%\"\u003e\n \u003cp\u003eCC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.824742268041238%\"\u003e\n \u003cp\u003eGO:0005887\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"46.134020618556704%\"\u003e\n \u003cp\u003eIntegral component of plasma membrane\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.6340206185567%\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.824742268041238%\"\u003e\n \u003cp\u003e0.027504704\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.530927835051546%\"\u003e\n \u003cp\u003e1.560593032\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.051546391752577%\"\u003e\n \u003cp\u003eMF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.824742268041238%\"\u003e\n \u003cp\u003eGO:0004872\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"46.134020618556704%\"\u003e\n \u003cp\u003eReceptor activity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.6340206185567%\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.824742268041238%\"\u003e\n \u003cp\u003e0.00557956\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.530927835051546%\"\u003e\n \u003cp\u003e2.253400069\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.051546391752577%\"\u003e\n \u003cp\u003eMF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.824742268041238%\"\u003e\n \u003cp\u003eGO:0001948\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"46.134020618556704%\"\u003e\n \u003cp\u003eGlycoprotein binding\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.6340206185567%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.824742268041238%\"\u003e\n \u003cp\u003e0.034133334\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.530927835051546%\"\u003e\n \u003cp\u003e1.466821294\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.051546391752577%\"\u003e\n \u003cp\u003eMF\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.824742268041238%\"\u003e\n \u003cp\u003eGO:0001618\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"46.134020618556704%\"\u003e\n \u003cp\u003eVirus receptor activity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.6340206185567%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.824742268041238%\"\u003e\n \u003cp\u003e0.036715556\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.530927835051546%\"\u003e\n \u003cp\u003e1.435149885\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"10.051546391752577%\"\u003e\n \u003cp\u003eKEGG\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.824742268041238%\"\u003e\n \u003cp\u003ehsa04151\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"46.134020618556704%\"\u003e\n \u003cp\u003ePI3K-Akt signaling pathway\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.6340206185567%\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.824742268041238%\"\u003e\n \u003cp\u003e0.003763234\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.530927835051546%\"\u003e\n \u003cp\u003e2.424438754\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\" width=\"100%\"\u003e\n \u003cp\u003eGO, Gene Ontology. BP, biological process. CC, cellular component. MF, molecular function. KEGG, Kyoto Encyclopedia of Genes and Genomes. DEG, differentially expressed gene.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Lyme neuroborreliosis, pathogenesis, transcriptomic analysis, Borrelia burgdorferi, candidate biomarker","lastPublishedDoi":"10.21203/rs.3.rs-822329/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-822329/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eLyme disease is a zoonotic disease caused by infection with \u003cem\u003eBorrelia burgdorferi\u003c/em\u003e (\u003cem\u003eBb\u003c/em\u003e), the involvement of the nervous system in Lyme disease is usually referred to as Lyme neuroborreliosis (LNB). LNB has diverse clinical manifestations, most commonly including meningitis, Bell\u0026rsquo;s palsy, and encephalitis. However, the molecular pathogenesis of neuroborreliosis is still poorly understood. Comprehensive transcriptomic analysis following \u003cem\u003eBb\u003c/em\u003e infection could provide new insights into the pathogenesis of LNB and may identify novel biomarkers or therapeutic targets for LNB diagnosis and treatment.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eIn the present study, we pooled transcriptomic datasets (transcriptomic rhesus data from our laboratory and the GSE85143 dataset from the Gene Expression Omnibus database) to screen common differentially expressed genes (DEGs) in the \u003cem\u003eBb\u003c/em\u003e infection group and the control group. Functional and enrichment analyses were conducted using the Database of Annotation Visualization and Integrated Discovery database, Protein-Protein Interaction network, and hub genes were identified using the Search Tool for the Retrieval of Interaction Genes database and the CytoHubba plugin. In addition, \u003cem\u003ein vitro\u003c/em\u003e and \u003cem\u003eex vivo\u003c/em\u003e assays were performed to verify the above findings. The mRNA expression levels of these genes were verified by quantitative real-time PCR (qPCR).\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eA total of 80 upregulated DEGs and 32 downregulated DEGs were identified. Among them, 11 hub genes were selected. Upregulated genes in the Gene Ontology analysis were significantly enriched in cell adhesion processes. The pathway enrichment analyses revealed that the PI3K-Akt signaling pathway was significantly enriched. The mRNA levels of \u003cem\u003eANGPT1\u003c/em\u003e, \u003cem\u003eTLR6\u003c/em\u003e, \u003cem\u003eSREBF1\u003c/em\u003e, \u003cem\u003eLDLR\u003c/em\u003e, \u003cem\u003eTNC\u003c/em\u003e, and \u003cem\u003eITGA2\u003c/em\u003e in U251 cells and/or rhesus brain explants by exposure to \u003cem\u003eBb\u003c/em\u003e were validated by qPCR.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eOur study suggested that \u003cem\u003eTLR6\u003c/em\u003e, \u003cem\u003eANGPT1\u003c/em\u003e, \u003cem\u003eLDLR\u003c/em\u003e, \u003cem\u003eSREBF1\u003c/em\u003e, \u003cem\u003eTNC\u003c/em\u003e, and \u003cem\u003eITGA\u003c/em\u003e were differentially highly expressed in \u003cem\u003eBb-\u003c/em\u003einfected astrocytes compared to normal controls, and overexpression of \u003cem\u003eLDLR\u003c/em\u003e might be a favorable prognostic factor of LNB patients. Further study is needed to explore the value of \u003cem\u003eTLR6\u003c/em\u003e, \u003cem\u003eANGPT1\u003c/em\u003e, \u003cem\u003eLDLR\u003c/em\u003e, \u003cem\u003eSREBF1\u003c/em\u003e, \u003cem\u003eTNC\u003c/em\u003e, and \u003cem\u003eITGA\u003c/em\u003e in LNB pathogenesis.\u003c/p\u003e","manuscriptTitle":"Comprehensive analyses of transcriptomes induced by Lyme spirochete infection to CNS model system","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2021-09-22 15:46:39","doi":"10.21203/rs.3.rs-822329/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"1a07dcfd-919c-4f7a-b3c2-660a661ab0d2","owner":[],"postedDate":"September 22nd, 2021","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":7359647,"name":"Cellular \u0026 Molecular Neuroscience"}],"tags":[],"updatedAt":"2021-10-30T04:46:12+00:00","versionOfRecord":[],"versionCreatedAt":"2021-09-22 15:46:39","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-822329","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-822329","identity":"rs-822329","version":["v1"]},"buildId":"_2-kVJe1T_tPrBINL-cwx","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}