Characterization of circular RNAs in bovine mammary epithelial cells induced by Escherichia coli LPS

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LPS, as a component of the outer wall of E. coli cell wall, is a common endotoxin in the construction of inflammatory models. The objective of this study is to identified and compared circular RNAs (circRNAs) from bovine mammary epithelial cells (bMECs) between the control and LPS groups. The expression profiles of circRNAs were obtained by high-throughput sequencing (RNA-seq) based on the construction of bMECs - LPS inflammation model, with control group (n = 3) and LPS group (n = 3) cell RNA as samples. After analysis, we identified 4323 circRNAs, ranging from 63 bp to 96387 bp. Chromosome 5 had most circRNAs, containing 259 circRNAs. Furthermore, 87.42% of the circRNAs belonged to sense-overlapping circRNA. CircRNAs contains different number of exons, ranging from 1 to 43, and most of cirsRNAs harbored 1 to 5 exons. Compared with the negative control (NC) group, 841 circRNAs with significantly different expressions (DE) in the LPS group (10 μg/mL), including 400 upregulated and 441 downregulated circRNAs. Enrichment analysis revealed the enrichment of circRNAs in methylation, such as positive regulation of G1/S transition of the mitotic cell cycle, histone methyltransferase activity (H3-K27 specific), and DNA methylation. The significantly enriched pathways further indicate that circRNAs play important roles in immunoreaction, such as hippo signaling pathway – fly, AMPK signaling pathway, and Fc gamma R-mediated phagocytosis. This study revealed the expression profile and characteristics of circRNAs in bMECs induced by LPS, and providing information for studying circRNA functions and mechanisms underlying mastitis, which suggesting a new avenue to investigate the regulatory mechanisms of mastitis. circular RNA bovine mammary epithelial cells methylation peak Holstein cows Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1. Introduction Mastitis is one of the most common diseases in dairy cows and the costliest to the dairy industry. One of the pathogenic bacteria that causes acute clinical mastitis includes Escherichia coli 1 . Lipopolysaccharide (LPS) is a component of the outer wall of E. coli cell wall, and its physiological effects are primarily manifested via Toll-like receptor 4 (TLR4), present on the surface of host cells. The rise of multi-omics techniques in recent years has facilitated the in-depth study of the pathogenesis of bovine mastitis. Xu et al. for example, analyzed the m6A methylation of circular RNAs (circRNA) from mammary epithelial MAC-T cells injured by Staphylococcus aureus and E. coli and made predictions on differentiallyN6-methyladenosine (m6A)-methylated circRNA 2 . Some studies have investigated the immune response of S. aureus and E. coli intramammary infection 3 – 6 . However, the regulatory function of noncoding RNA and epigenetic modification has not been thoroughly investigated. The circRNAs are a newly discovered class of endogenous non-coding RNA molecules formed by covalent bonds 7 . Compared with traditional linear RNA, circRNAs do not have 5' and 3' ends because they are covalently bonded to each other in a circular atresia structure, which makes them more resistant to RNase degradation 8 . CircRNAs can be divided into different categories according to their nucleotide source 9 , and these different categories can have different biological functions. Some can sequester miRNAs and, therefore, regulate the expression of target genes by acting as miRNA sponges 10 . CircRNAs can also bind to transcriptional regulatory elements and interact with proteins to regulate gene transcription, and can also play a role in m6A modifications to promote the effective initiation of protein translation 11 , 12 . Bovine mammary epithelial cells (bMECs) are the main cell type found in the bovine udder, and in addition to having lactation functions, they are also involved in regulating the mammary gland innate immune response to pathogen challenge 13 . The proliferation and apoptosis of bMECs are regulated by various molecules such as cytokines, hormones and enzymes, which is a cellular metabolic process of breast tissue in a specific physiological environment, affecting the development and function of the mammary gland. Studies have shown that simulation of bMECs with LPS can generate immune solid responses, upregulating the expression of pathogen-associated molecular patterns (PAMPs), activating the nuclear factor-κB (NF-κB) signaling pathway, and increasing the secretion of inflammatory cytokines 14 , 15 . Given that bMECs can produce energy-producing metabolites such as pro-inflammatory, anti-inflammatory, and antioxidant in response to inflammatory stimuli stimulated by LPS, and the potential of circRNA to regulate gene expression indirectly, it is necessary to identify and characterize circRNAs in bMECs induced by LPS since this circRNA may be involved in the epigenetic and genetic regulation of bMECs functions. Therefore, this study used high-throughput RNA sequencing (RNA-seq) to investigate the expression profile of cirRNAs in bMECs induced by LPS, and identified the differential expressed cicrRNAs. In addition, gene ontology (GO) and KEGG pathway enrichment analysis were performed for the parental mRNA genes of the differentially expressed cirRNA to investigate their potential roles. Through the characteristics of circRNAs after LPS induced, circRNAs are expected to become novel molecular targets for udder inflammation in dairy cows and provide new research ideas for the treatment of mastitis. 2. Materials and Methods 2.1 bMECs injury induced by LPS Bovine mammary epithelial cells (bMECs) were cultured in Gibco DMEM F-12 (Thermo Fisher Scientific, US) containing 10% FBS Green season FBS (Tian hang Biotechnology, China) at 37 ℃ with 5% CO 2 . The bMECs were seeded into 6-well plates (Corning, US) at 2×10 5 cells/well. After 12 h of maintenance culture, media was replaced with either fresh control media, or treatment media containing 10 µg/mL LPS (Solarbio, Escherichia coli 055:B5, China). After 6 h of culture, the bMECs were washed thrice with PBS (HyClone, China) before adding TRIzol (Invitrogen, US). The plates were set up to include three replicate control and three treatment samples. 2.2 mRNA preparation Total RNA from the above 6 samples was isolated according to the instructions of the commercial reagent manufacturer (Vazyme, Nanjing, China). The quantity and quality of RNAs were measured using a NanoDrop® ND-1000 (Thermo Scientific, DE). The quantity of total RNA was greater than 400 ng/µL and the 260/280 requirement was between 1.9 ~ 2.0. Denaturing agarose gel electrophoresis was used to validate RNA integrity and genomic DNA contamination. The samples were stored at − 80°C for later use. The RNA samples were reverse transcribed into cDNA using Vazyme HiScript II Reverse Transcriptase (+ gDNA wiper) (Vazyme, Nanjing, China), and the expression of target mRNA was detected by Vazyme AceQ® SYBR qPCR Master Mix (Vazyme) in a ViiA7 Real-time PCR System (Applied Biosystems Inc., Foster City, CA, United States). The 2 −△△ct method was used to analyze the fluorescence quantitative data, and GraphPad Prism 7.0 was used to process the data. Related primer sequences are shown at Table 1 . Table 1 Primer sequence Gene Forward (5′→3′) Reverse (5′→3′) GAPDH CTGAGAATCTCCTGACTT TTATTGATGGTACACAAGG IL6 AGAACGAGTATGAGGGAAAT TGGCTGGAGTGGTTATTAG IL8 AAGAATTGAGAGTTATTGAGAGT CAGACCTCGTTTCCATTG NF-kB ATACGTCGGCCGTGTCTAT GGAACTGTGATCCGTGTAG TLR4 GGACCCTTGCGTACAGGTTG GGAAGCTGGAGAAGTTATGGC 2.3 circRNA sequencing Firstly, the ribosomal RNA was removed from the bMECs RNA samples using a transcriptome isolation kit (Ribominus Bacteria 2.0, Thermo Fisher). The remaining RNA was used for RNA sequencing library construction by using TruSeq RNA Library Preparation Kit (Illumina Inc., San Diego, US), and paired-end sequencing was carried out using an Illumina HiSeq Xten (Illumina Inc., San Diego, CA) sequencer by the Shanghai OE Biotechnology Company Ltd. (Shanghai, China). After sequencing, the raw data were cleaned by filtering the following low-quality reads: (a) extreme reads of signal strength, caused by the sequencing instrument hardware, were removed; (b) reads with low overall quality (Q = 20, base proportion less than 50%) were removed; (c) the proportion of read bases with an error rate less than 1% were removed; (d) reads with N-base ambiguity caused by insufficient sequencing fluorescence intensity were removed; (e) reads with a length of fewer than 20 bases and containing adaptor sequences were removed; and (f) ribosomal RNA reads were removed. The clean reads obtained through the above preprocessing were used to identify the circRNAs using the software FIND_CIRC ( https://github.com/marvin-jens/find_circ ) 16 . The known circRNAs and the newly predicted circRNAs were obtained using the CIRI software and by comparing data with the circBase database ( http://circrna.org/cgi-bin/singlerecord.cgi?id = mmu_circ_0001771) 17 . The circRNA expression level and density distribution were simultaneously read per million mapped reads (RPM) for each sample. The chromosome distribution and length distribution of the identified circRNAs were analyzed according to the FIND_CIRC search Deseq software, which was used to conduct standardized processing on the number of junction read counts of circRNA in each sample (mean base value was used to estimate the expression level), the difference multiple was calculated, and NB (negative binomial distribution test) was used to test the different significance of reads numbers 18 . Finally, the differentially expressed circRNAs (DE circRNAs) were screened according to the difference multiple and difference significance test results. The default criteria for filtering differences were p < 0.05, and the variance multiple was greater than 2. 2.4 Enrichment analysis Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) enrichment analyses for the DE circRNAs source gene were performed using the DAVID biometric analysis tool 19 , 20 . 3. Results 3.1 Inflammation in bMECs induced with LPS To confirm that LPS activated the bMECs, candidate mRNAs were assessed after 6 h of LPS stimulation. The results shown below demonstrate cell activation based on IL6, IL8, NF-kB, and TLR4 being significantly upregulated in the LPS treatment compared to the control group (Fig. 1 ). 3.2 Identification and sequence characteristics of circRNAs in bMECs from Holstein cows [A total of 4323 circRNAs were identified from the bMEC RNA. Chromosome 5 contained most of the circRNAs (n = 259 (Fig. 2 -A). The size of circRNAs ranged from 63 bp to 96387 bp and the average size was 2310 bp. The circRNA lengths were mainly in the 201–700 bp, and also in greater than 2000 bp (Fig. 2 -B). The circRNAs contained different numbers of exons, ranging from 1 to 43, and most of the circRNAs harbored 1 to 5 exons (Fig. 2 -C). Most circRNAs (n = 877) contain two exons. The CG content of circRNA was distributed in the range of 30%-80%, mainly concentrated in the range of 40%-50% (Fig. 2 -D). There were five types of circRNA identified as following: antisense (1.11%), exonic (7.01%), intergenic (3.40%), intronic (1.06%), and sense-overlapping (87.42%) (Fig. 2 -E). 3.3 Differential expression of circRNAs analysis in bMECs induced by E. coli LPS Compared with NC group, 841 significant DE circRNAs were identified in the LPS group, 400 of which were upregulated and 441 were downregulated (Fig. 3 -A). The expression of differential circRNA was analyzed according to the criteria of |log2(Fold Change)| equal to 0.58 and p value less than 0.05, red indicated significant upregulation, green indicated significant downregulation (Fig. 3 -B). Unsupervised hierarchical clustering of DE circRNAs was carried out, and the distance between pairs of multiple samples was calculated to form a distance matrix, and the expression of selected differential circRNAs was used to calculate the direct correlation of samples. The two clusters are up- and down- regulated circRNAs, clustered in the same cluster may have similar biological functions (Fig. 3 -C). 3.4 circRNA enrichment analysis To investigate the possible functions of differentially expressed circRNAs, GO enrichment analysis was performed on the target genes of differentially expressed circRNAs. The GO analysis identified positive regulation of G1/S transition of the mitotic cell cycle, histone methyltransferase activity (H3-K27 specific), and DNA methylation (Fig. 4 ). KEGG pathway analysis mainly focused on the hippo signaling pathway – fly, AMPK signaling pathway, and Fc gamma R-mediated phagocytosis (Fig. 5 ). 4. Discussion Cows are significant source of dairy products and an ideal large animal model to study the transcriptome and expression characteristics of the bMECs. As a new non-coding RNA, circRNA has recently become a new research hotspot. Many studies have reported that circRNA is widely present in humans 21 , mice 22 , pigs 23 , cattle 24 , sheep 25 , and other species. LPS is used extensively as an endotoxin to induce an inflammatory response in cells 26 – 28 . In this study, we first constructed bMECs model by LPS and verified whether cytokines such as IL-6, IL-8, NF-kB, and TLR4 changed after LPS infection by qRT-PCR. After 6 hours of stimulation of cells by adding LPS at a concentration of 10 µg/mL, there was a significant increase in IL-6, IL-8, NF-kB, and TLR4 in the cellular RNA. This is also consistent with many research results on the changes in gene expression after LPS infection 29 – 31 . On this basis, cellular RNA is extracted for high-throughput sequencing. High-throughput sequencing was used to explore the presence and expression of circRNAs in bMECs from Holstein cows induced by LPS and to screen and identify circRNAs that may play an important role in lactation. Through systematic identification and analysis of circRNAs, it was found that 1196 and 1407 unique circRNAs were predicted in the bMECs of Holstein cows in the LPS group and NC group. These differential circRNAs may be related to changes in cytokines secreted by cells after LPS infects cells. We previously identified circRNAs in Holstein cows' mammary tissues during early lactating and non-lactating and found 3250 and 3359 circRNAs were predicted in the mammary tissue of Holstein cows in two group mammary tissues 9 . Hao et al. identified 4906 circRNAs in two sheep mammary gland tissues with different lactation performances and 33 of these were differentially expressed between breeds 32 . Another study showed that 6621 circRNAs were differentially expressed in the mammary tissue of Holstein cows at postpartum 90 days and 250 days, of which 2231 were coexpressed 33 . These different results may be due to the influence of hormones in bovine mammary epithelial cells on gene expression related to milk component synthesis under different conditions 34 . Similarly, under the stimulation of LPS, the expression level of some cytokine changes, thereby affecting the expression of mRNA of related genes and the formation of circRNA. A total of 4323 circRNAs were identified from RNA in bMECs from Holstein cows by library construction, sequencing, and bioinformatics analysis. Most circRNAs are short in length and are concentrated between 201–700 bp, and longer than 2000 bp. This is also consistent with many research findings 9 , 35 . In addition, we found five different types of circRNAs in bMECs, of which 87.42% belonged to sense-overlapping, while only three types of circRNAs were identified in mammary tissue RNA at different lactation stages in the previous study 9 . This may also be related to the mechanism of circRNA formation. Sense-overlapping circRNAs (sometimes defined as EIciRNA) have overlapping regions with mRNA exons and are transcribed in the same direction. Related studies have shown that long flank introns are considered crucial to exon cyclization, and they contain ALU repeats 36 and possibly help determine the production rate of circRNAs 37 . Finally, introns between the encircled exons are retained, which Li termed EIciRNAs 38 . Most of the circRNAs in this study are sense overlapping, which may be related to the above reasons. Compared with the NC group, 841 circRNAs with significantly different expressions in the LPS group, 400 upregulated and 441 downregulated. GO and KEGG enrichment analysis can illustrate the related functions of genes. In this study, the GO analysis identified positive regulation of G1/S transition of mitotic cell cycle, histone methyltransferase activity (H3-K27 specific), and DNA methylation. Studies have shown that LPS-mediated inflammation plays a role in cell cycle reactivation and apoptosis in differentiated neuronal cells 39 . Histone methylation is an essential epigenetic mechanism, while LPS-induced gene-specific histone methylation affects the expression and release of the pro-inflammatory cytokines IL-6 and TNF-α 40 . Meanwhile, Jhamat et al. found that LPS altered the DNA methylation patterns of bovine endometrial epithelial cells 41 . The above studies have shown that LPS infection causes changes in the expression of autoinflammatory factors but also causes changes in cell cycle and methylation. In addition, the KEGG pathway analysis mainly revealed the hippo signaling pathway – fly, AMPK signaling pathway, and Fc gamma R-mediated phagocytosis. The Hippo signaling pathway was initially discovered in Drosophila melanogaster as a critical regulator of tissue growth 42 , while LPS could activate the hippo signaling pathway and induce an inflammatory response 43 . Similarly, the AMPK signaling pathway and Fc gamma R-mediated phagocytosis are classical inflammatory or immune-related signaling pathways that can be activated by LPS stimulation 44 , 45 . From the above analysis, we speculate that these differentially expressed circRNAs are mainly related to methylation, immune responses, and autophagy. These studies show that circRNAs has become a new research hotspot and provides new research ideas for us to study the molecular mechanism of cows' mastitis. In the next study, we will focus on the function of methylation modifications (such as methylation, acetylation, ubiquitination) on circRNAs in LPS-induced inflammatory bMECs in hopes of understanding how they regulate LPS-induced bMECs. 5. Conclusions In this study, through high-throughput sequencing of circRNAs in bMECs induced by E. coli LPS, 4323 circRNAs were detected, mainly sense-overlapping. Among the 841 DE circRNAs detected, enrichment analysis found that they mainly concentrated on methylation, immune responses, and autophagy. This study revealed the expression profile and characteristics of circRNAs in bMECs injured by LPS and provided a wealth of information for studying the function and mechanism of circRNAs. Declarations Ethics approval and consent to participate Not Applicable. Consent for publication Not Applicable. Availability of data and materials The RNA seq data are available only to the collaborating scientists from the respective participating centers. The data may be available upon request for some of the participating centers but not for all due to relevant data protection laws. Competing interests The authors declare no conflict of interest. Funding The research received financial support from the National Natural Science Foundation of China (31972555), the Postgraduate Research and Practice Innovation Program of Yangzhou University (KYCX21_3264), Yangzhou University International Academic Exchange Fund. Authors' Contributions Conceptualization, M.Y. and Y.Z.; methodology, L.M.; software, L.M.; validation, L.Y., and X.Y.; formal analysis, L.Y. and W.M.; data curation, L.Y.; writing original draft preparation, L.Y.; writing—review and editing, N.A.; visualization, L.M.; supervision, M.Y.; project administration, M.Y.; funding acquisition, M.Y. 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Alveolar macrophage-derived exosomal tRF-22-8BWS7K092 activates Hippo signaling pathway to induce ferroptosis in acute lung injury. Int Immunopharmacol. 2022;107:108690. Ran X, Yan Z, Yang Y, Hu G, Liu J, Hou S, Guo W, Kan X, Fu S. Dioscin Improves Pyroptosis in LPS-Induced Mice Mastitis by Activating AMPK/Nrf2 and Inhibiting the NF-κB Signaling Pathway. Oxid Med Cell Longev. 2020;2020:8845521. Fronhofer V, Lennartz MR, Loegering DJ. Role of PKC isoforms in the Fc(gamma)R-mediated inhibition of LPS-stimulated IL-12 secretion by macrophages. J Leukoc Biol. 2006;79(2):408-15. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies 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-2857377","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":195519653,"identity":"a2f0e988-918b-47ec-9d8f-092de66e07b7","order_by":0,"name":"YAN LIANG","email":"","orcid":"","institution":"Yangzhou University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"YAN","middleName":"","lastName":"LIANG","suffix":""},{"id":195519655,"identity":"ee052830-69f0-4ea6-94cf-a7b587e94421","order_by":1,"name":"Yuxin Xia","email":"","orcid":"","institution":"Yangzhou University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Yuxin","middleName":"","lastName":"Xia","suffix":""},{"id":195519657,"identity":"1a037874-1f5b-4845-9c8a-6fe08874be60","order_by":2,"name":"Mengqi Wang","email":"","orcid":"","institution":"Laval University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Mengqi","middleName":"","lastName":"Wang","suffix":""},{"id":195519660,"identity":"b252213c-35bd-4d93-8424-14597f84acb4","order_by":3,"name":"Mingxun Li","email":"","orcid":"","institution":"Yangzhou University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Mingxun","middleName":"","lastName":"Li","suffix":""},{"id":195519663,"identity":"c59d103f-1d7e-413b-9912-4a8dabff5de4","order_by":4,"name":"Zhangping Yang","email":"","orcid":"","institution":"Yangzhou University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Zhangping","middleName":"","lastName":"Yang","suffix":""},{"id":195519666,"identity":"b22723d9-8dd8-4af7-936e-46928c63e998","order_by":5,"name":"Niel A. Karrow","email":"","orcid":"","institution":"University of Guelph","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Niel","middleName":"A.","lastName":"Karrow","suffix":""},{"id":195519667,"identity":"231a5e9d-e4b0-42db-94ac-ba43b228d4de","order_by":6,"name":"Yongjiang Mao","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAxUlEQVRIiWNgGAWjYLCCBww2EAYPceqZGRsSGNJI13KYBC387eePP0ioOJ84f0YC44O3bQzy5oS0SJxJBtpy5nZi44wEZsO5bQyGOxsIaDFgAGpJbLud2CyRwCbN28aQYHCAkBb+x0At/84ltkkksP8mTosEyJaGA4k9QFuYidIiceOx4YyEY8nGM3geNkvOOSdhuIGQFv7+xAcfPtTYyc5vTz744U2ZjTxBW2DAsYGBsQFkK5HqgcCeeKWjYBSMglEw4gAAdTxAC6oSlIkAAAAASUVORK5CYII=","orcid":"","institution":"Yangzhou University","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Yongjiang","middleName":"","lastName":"Mao","suffix":""}],"badges":[],"createdAt":"2023-04-25 04:59:22","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-2857377/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-2857377/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":36540407,"identity":"843de87d-567c-4e81-a4ff-22372bc8189e","added_by":"auto","created_at":"2023-05-02 19:40:28","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":257679,"visible":true,"origin":"","legend":"\u003cp\u003eExpression of bMEC inflammatory factors induced by 10 μg/mL of \u003cem\u003eE. coli\u003c/em\u003e LPS for 6 h.\u003c/p\u003e\n\u003cp\u003eLPS significantly induced IL6, IL8, NF-kB and TLR4 mRNA expression (**\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01) compared to control cells (NC).\u003c/p\u003e","description":"","filename":"figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-2857377/v1/e1993d1013c2d3c8b9b842c1.jpg"},{"id":36540189,"identity":"ae229563-0ec6-40da-a7be-79b2f0d4f54a","added_by":"auto","created_at":"2023-05-02 19:32:28","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":943520,"visible":true,"origin":"","legend":"\u003cp\u003eIdentification and sequence characteristics of circRNAs from bMECs induced by 10 μg/mL of \u003cem\u003eE. coli\u003c/em\u003e LPS for 6 h.\u003c/p\u003e\n\u003cp\u003e(A) Distribution of circRNAs numbers on individual chromosomes or scaffolds. (B) CircRNAs sequence length distribution plot. (C) The number of exons by circRNAs. (D) The Y axis is the frequence of circRNAs, the X axis shows the CG content of circRNAs. (E) Categorical percentage of different types of circRNAs.\u003c/p\u003e","description":"","filename":"figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-2857377/v1/423fb4c06027ce22d1ab5909.jpg"},{"id":36540190,"identity":"6d8da6d6-34e7-4d5d-b89f-20c4efdf3a58","added_by":"auto","created_at":"2023-05-02 19:32:28","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":3729645,"visible":true,"origin":"","legend":"\u003cp\u003eDifferential expression of circRNAs analysis in bMECs induced by 10 μg/mL of \u003cem\u003eE. coli\u003c/em\u003e LPS for 6 h.(A) The LPS group differed in expressing the overall number of circRNAs compared to the NC group. (B) Differential expression volcano chart. The differences resulting from the comparison are reflected in the volcano chart, with gray for non-significant differences, red and green for significant differences; The X axis is the display of log\u003csub\u003e2\u003c/sub\u003e Fold Change, and the Y axis direction is the display of -log\u003csub\u003e10\u003c/sub\u003e \u003cem\u003ep\u003c/em\u003e value. (C) DE circRNAs level clustering. Red indicates high expression and blue indicates low expression.\u003c/p\u003e","description":"","filename":"figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-2857377/v1/fe60bc14288589f28a857a7f.jpg"},{"id":36540187,"identity":"edc53415-6afb-40ef-b074-8f387324a6e5","added_by":"auto","created_at":"2023-05-02 19:32:28","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":2086111,"visible":true,"origin":"","legend":"\u003cp\u003eGO enrichment analysis of differential circRNAs\u003c/p\u003e\n\u003cp\u003e(A) Differential gene GO distribution. The X-axis is the name of the entry, and the Y axis represents the number of genes and their percentages of the corresponding entry. (B) GO enrichment analysis of differential genes. The X axis is -log10\u003cem\u003e p\u003c/em\u003e value, and the Y axis is the GO entry name.\u003c/p\u003e","description":"","filename":"figure4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-2857377/v1/92bb0f72b060501339949b1c.jpg"},{"id":36540186,"identity":"16ebe0e3-51ab-4ace-ad28-10ceda81fccb","added_by":"auto","created_at":"2023-05-02 19:32:28","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":2049006,"visible":true,"origin":"","legend":"\u003cp\u003eKEGG enrichment analysis of differential circRNAs\u003c/p\u003e\n\u003cp\u003e(A) Differential circRNAs distribution at KEGG Level 2. The X-axis is the ratio (%) of circRNAs annotated to the differences in each metabolic pathway, and the Y axis represents the name of the pathway. (B) KEGG enrichment top 30. The X-axis Enrichment_Score is the enrichment score, the larger the bubble, the larger the entry, the more differential genes are contained, the color of the bubble changes from gray to red, and the enrichment \u003cem\u003ep\u003c/em\u003e value gradually decreases, indicating that the significance gradually increases.\u003c/p\u003e","description":"","filename":"figure5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-2857377/v1/df6438f87364563e09b3c99a.jpg"},{"id":76663232,"identity":"8a9baeb7-3f78-477b-a880-752a5e4b8bdb","added_by":"auto","created_at":"2025-02-19 12:17:04","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":9677775,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-2857377/v1/1855aead-dafc-499c-9d32-2a1d92e07f1a.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Characterization of circular RNAs in bovine mammary epithelial cells induced by Escherichia coli LPS","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eMastitis is one of the most common diseases in dairy cows and the costliest to the dairy industry. One of the pathogenic bacteria that causes acute clinical mastitis includes \u003cem\u003eEscherichia coli\u003c/em\u003e \u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. Lipopolysaccharide (LPS) is a component of the outer wall of \u003cem\u003eE. coli\u003c/em\u003e cell wall, and its physiological effects are primarily manifested via Toll-like receptor 4 (TLR4), present on the surface of host cells.\u003c/p\u003e \u003cp\u003eThe rise of multi-omics techniques in recent years has facilitated the in-depth study of the pathogenesis of bovine mastitis. Xu et al. for example, analyzed the m6A methylation of circular RNAs (circRNA) from mammary epithelial MAC-T cells injured by \u003cem\u003eStaphylococcus aureus\u003c/em\u003e and \u003cem\u003eE. coli\u003c/em\u003e and made predictions on differentiallyN6-methyladenosine (m6A)-methylated circRNA\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. Some studies have investigated the immune response of \u003cem\u003eS. aureus\u003c/em\u003e and \u003cem\u003eE. coli\u003c/em\u003e intramammary infection \u003csup\u003e\u003cspan additionalcitationids=\"CR4 CR5\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. However, the regulatory function of noncoding RNA and epigenetic modification has not been thoroughly investigated.\u003c/p\u003e \u003cp\u003eThe circRNAs are a newly discovered class of endogenous non-coding RNA molecules formed by covalent bonds \u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. Compared with traditional linear RNA, circRNAs do not have 5' and 3' ends because they are covalently bonded to each other in a circular atresia structure, which makes them more resistant to RNase degradation\u003csup\u003e\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. CircRNAs can be divided into different categories according to their nucleotide source \u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e, and these different categories can have different biological functions. Some can sequester miRNAs and, therefore, regulate the expression of target genes by acting as miRNA sponges\u003csup\u003e\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e. CircRNAs can also bind to transcriptional regulatory elements and interact with proteins to regulate gene transcription, and can also play a role in m6A modifications to promote the effective initiation of protein translation\u003csup\u003e\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e,\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eBovine mammary epithelial cells (bMECs) are the main cell type found in the bovine udder, and in addition to having lactation functions, they are also involved in regulating the mammary gland innate immune response to pathogen challenge\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. The proliferation and apoptosis of bMECs are regulated by various molecules such as cytokines, hormones and enzymes, which is a cellular metabolic process of breast tissue in a specific physiological environment, affecting the development and function of the mammary gland. Studies have shown that simulation of bMECs with LPS can generate immune solid responses, upregulating the expression of pathogen-associated molecular patterns (PAMPs), activating the nuclear factor-κB (NF-κB) signaling pathway, and increasing the secretion of inflammatory cytokines \u003csup\u003e\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e,\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eGiven that bMECs can produce energy-producing metabolites such as pro-inflammatory, anti-inflammatory, and antioxidant in response to inflammatory stimuli stimulated by LPS, and the potential of circRNA to regulate gene expression indirectly, it is necessary to identify and characterize circRNAs in bMECs induced by LPS since this circRNA may be involved in the epigenetic and genetic regulation of bMECs functions.\u003c/p\u003e \u003cp\u003eTherefore, this study used high-throughput RNA sequencing (RNA-seq) to investigate the expression profile of cirRNAs in bMECs induced by LPS, and identified the differential expressed cicrRNAs. In addition, gene ontology (GO) and KEGG pathway enrichment analysis were performed for the parental mRNA genes of the differentially expressed cirRNA to investigate their potential roles. Through the characteristics of circRNAs after LPS induced, circRNAs are expected to become novel molecular targets for udder inflammation in dairy cows and provide new research ideas for the treatment of mastitis.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 bMECs injury induced by LPS\u003c/h2\u003e \u003cp\u003eBovine mammary epithelial cells (bMECs) were cultured in Gibco DMEM F-12 (Thermo Fisher Scientific, US) containing 10% FBS Green season FBS (Tian hang Biotechnology, China) at 37 ℃ with 5% CO\u003csub\u003e2\u003c/sub\u003e. The bMECs were seeded into 6-well plates (Corning, US) at 2\u0026times;10\u003csup\u003e5\u003c/sup\u003e cells/well. After 12 h of maintenance culture, media was replaced with either fresh control media, or treatment media containing 10 \u0026micro;g/mL LPS (Solarbio, \u003cem\u003eEscherichia coli\u003c/em\u003e 055:B5, China). After 6 h of culture, the bMECs were washed thrice with PBS (HyClone, China) before adding TRIzol (Invitrogen, US). The plates were set up to include three replicate control and three treatment samples.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 mRNA preparation\u003c/h2\u003e \u003cp\u003eTotal RNA from the above 6 samples was isolated according to the instructions of the commercial reagent manufacturer (Vazyme, Nanjing, China). The quantity and quality of RNAs were measured using a NanoDrop\u0026reg; ND-1000 (Thermo Scientific, DE). The quantity of total RNA was greater than 400 ng/\u0026micro;L and the 260/280 requirement was between 1.9\u0026thinsp;~\u0026thinsp;2.0. Denaturing agarose gel electrophoresis was used to validate RNA integrity and genomic DNA contamination. The samples were stored at \u0026minus;\u0026thinsp;80\u0026deg;C for later use.\u003c/p\u003e \u003cp\u003eThe RNA samples were reverse transcribed into cDNA using Vazyme HiScript II Reverse Transcriptase (+\u0026thinsp;gDNA wiper) (Vazyme, Nanjing, China), and the expression of target mRNA was detected by Vazyme AceQ\u0026reg; SYBR qPCR Master Mix (Vazyme) in a ViiA7 Real-time PCR System (Applied Biosystems Inc., Foster City, CA, United States). The 2\u003csup\u003e\u0026minus;△△ct\u003c/sup\u003e method was used to analyze the fluorescence quantitative data, and GraphPad Prism 7.0 was used to process the data. Related primer sequences are shown at Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePrimer sequence\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGene\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eForward (5\u0026prime;\u0026rarr;3\u0026prime;)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReverse (5\u0026prime;\u0026rarr;3\u0026prime;)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGAPDH\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCTGAGAATCTCCTGACTT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTTATTGATGGTACACAAGG\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIL6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAGAACGAGTATGAGGGAAAT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTGGCTGGAGTGGTTATTAG\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIL8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAAGAATTGAGAGTTATTGAGAGT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCAGACCTCGTTTCCATTG\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNF-kB\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eATACGTCGGCCGTGTCTAT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGGAACTGTGATCCGTGTAG\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTLR4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGGACCCTTGCGTACAGGTTG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGGAAGCTGGAGAAGTTATGGC\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 circRNA sequencing\u003c/h2\u003e \u003cp\u003eFirstly, the ribosomal RNA was removed from the bMECs RNA samples using a transcriptome isolation kit (Ribominus Bacteria 2.0, Thermo Fisher). The remaining RNA was used for RNA sequencing library construction by using TruSeq RNA Library Preparation Kit (Illumina Inc., San Diego, US), and paired-end sequencing was carried out using an Illumina HiSeq Xten (Illumina Inc., San Diego, CA) sequencer by the Shanghai OE Biotechnology Company Ltd. (Shanghai, China). After sequencing, the raw data were cleaned by filtering the following low-quality reads: (a) extreme reads of signal strength, caused by the sequencing instrument hardware, were removed; (b) reads with low overall quality (Q\u0026thinsp;=\u0026thinsp;20, base proportion less than 50%) were removed; (c) the proportion of read bases with an error rate less than 1% were removed; (d) reads with N-base ambiguity caused by insufficient sequencing fluorescence intensity were removed; (e) reads with a length of fewer than 20 bases and containing adaptor sequences were removed; and (f) ribosomal RNA reads were removed. The clean reads obtained through the above preprocessing were used to identify the circRNAs using the software FIND_CIRC (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/marvin-jens/find_circ\u003c/span\u003e\u003cspan address=\"https://github.com/marvin-jens/find_circ\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e)\u003csup\u003e16\u003c/sup\u003e. The known circRNAs and the newly predicted circRNAs were obtained using the CIRI software and by comparing data with the circBase database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://circrna.org/cgi-bin/singlerecord.cgi?id\u003c/span\u003e\u003cspan address=\"http://circrna.org/cgi-bin/singlerecord.cgi?id\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u0026thinsp;=\u0026thinsp;mmu_circ_0001771)\u003csup\u003e17\u003c/sup\u003e. The circRNA expression level and density distribution were simultaneously read per million mapped reads (RPM) for each sample. The chromosome distribution and length distribution of the identified circRNAs were analyzed according to the FIND_CIRC search Deseq software, which was used to conduct standardized processing on the number of junction read counts of circRNA in each sample (mean base value was used to estimate the expression level), the difference multiple was calculated, and NB (negative binomial distribution test) was used to test the different significance of reads numbers\u003csup\u003e\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e. Finally, the differentially expressed circRNAs (DE circRNAs) were screened according to the difference multiple and difference significance test results. The default criteria for filtering differences were \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, and the variance multiple was greater than 2.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Enrichment analysis\u003c/h2\u003e \u003cp\u003eKyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) enrichment analyses for the DE circRNAs source gene were performed using the DAVID biometric analysis tool \u003csup\u003e\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e,\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003e3.1 Inflammation in bMECs induced with LPS\u003c/h2\u003e\n \u003cp\u003eTo confirm that LPS activated the bMECs, candidate mRNAs were assessed after 6 h of LPS stimulation. The results shown below demonstrate cell activation based on IL6, IL8, NF-kB, and TLR4 being significantly upregulated in the LPS treatment compared to the control group (Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\n \u003ch2\u003e3.2 Identification and sequence characteristics of circRNAs in bMECs from Holstein cows\u003c/h2\u003e\n \u003cp\u003e[A total of 4323 circRNAs were identified from the bMEC RNA. Chromosome 5 contained most of the circRNAs (n\u0026thinsp;=\u0026thinsp;259 (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e-A). The size of circRNAs ranged from 63 bp to 96387 bp and the average size was 2310 bp. The circRNA lengths were mainly in the 201\u0026ndash;700 bp, and also in greater than 2000 bp (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e-B). The circRNAs contained different numbers of exons, ranging from 1 to 43, and most of the circRNAs harbored 1 to 5 exons (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e-C). Most circRNAs (n\u0026thinsp;=\u0026thinsp;877) contain two exons. The CG content of circRNA was distributed in the range of 30%-80%, mainly concentrated in the range of 40%-50% (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e-D). There were five types of circRNA identified as following: antisense (1.11%), exonic (7.01%), intergenic (3.40%), intronic (1.06%), and sense-overlapping (87.42%) (Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e-E).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n \u003ch2\u003e\u003cspan type=\"BoldItalic\" class=\"BoldItalic\" name=\"Emphasis\"\u003e3.3 Differential expression of circRNAs analysis in bMECs induced by E. coli LPS\u003c/span\u003e\u003c/h2\u003e\n \u003cp\u003eCompared with NC group, 841 significant DE circRNAs were identified in the LPS group, 400 of which were upregulated and 441 were downregulated (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e-A). The expression of differential circRNA was analyzed according to the criteria of |log2(Fold Change)| equal to 0.58 and \u003cem\u003ep\u003c/em\u003e value less than 0.05, red indicated significant upregulation, green indicated significant downregulation (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e-B). Unsupervised hierarchical clustering of DE circRNAs was carried out, and the distance between pairs of multiple samples was calculated to form a distance matrix, and the expression of selected differential circRNAs was used to calculate the direct correlation of samples. The two clusters are up- and down- regulated circRNAs, clustered in the same cluster may have similar biological functions (Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e-C).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003e3.4 circRNA enrichment analysis\u003c/h2\u003e\n \u003cp\u003eTo investigate the possible functions of differentially expressed circRNAs, GO enrichment analysis was performed on the target genes of differentially expressed circRNAs. The GO analysis identified positive regulation of G1/S transition of the mitotic cell cycle, histone methyltransferase activity (H3-K27 specific), and DNA methylation (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e). KEGG pathway analysis mainly focused on the hippo signaling pathway \u0026ndash; fly, AMPK signaling pathway, and Fc gamma R-mediated phagocytosis (Fig. \u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e\n\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eCows are significant source of dairy products and an ideal large animal model to study the transcriptome and expression characteristics of the bMECs. As a new non-coding RNA, circRNA has recently become a new research hotspot. Many studies have reported that circRNA is widely present in humans\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e, mice\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e, pigs\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e, cattle\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e, sheep\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e, and other species. LPS is used extensively as an endotoxin to induce an inflammatory response in cells\u003csup\u003e\u003cspan additionalcitationids=\"CR27\" citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e. In this study, we first constructed bMECs model by LPS and verified whether cytokines such as IL-6, IL-8, NF-kB, and TLR4 changed after LPS infection by qRT-PCR. After 6 hours of stimulation of cells by adding LPS at a concentration of 10 \u0026micro;g/mL, there was a significant increase in IL-6, IL-8, NF-kB, and TLR4 in the cellular RNA. This is also consistent with many research results on the changes in gene expression after LPS infection\u003csup\u003e\u003cspan additionalcitationids=\"CR30\" citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. On this basis, cellular RNA is extracted for high-throughput sequencing.\u003c/p\u003e \u003cp\u003eHigh-throughput sequencing was used to explore the presence and expression of circRNAs in bMECs from Holstein cows induced by LPS and to screen and identify circRNAs that may play an important role in lactation. Through systematic identification and analysis of circRNAs, it was found that 1196 and 1407 unique circRNAs were predicted in the bMECs of Holstein cows in the LPS group and NC group. These differential circRNAs may be related to changes in cytokines secreted by cells after LPS infects cells. We previously identified circRNAs in Holstein cows' mammary tissues during early lactating and non-lactating and found 3250 and 3359 circRNAs were predicted in the mammary tissue of Holstein cows in two group mammary tissues\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. Hao et al. identified 4906 circRNAs in two sheep mammary gland tissues with different lactation performances and 33 of these were differentially expressed between breeds\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. Another study showed that 6621 circRNAs were differentially expressed in the mammary tissue of Holstein cows at postpartum 90 days and 250 days, of which 2231 were coexpressed\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. These different results may be due to the influence of hormones in bovine mammary epithelial cells on gene expression related to milk component synthesis under different conditions\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. Similarly, under the stimulation of LPS, the expression level of some cytokine changes, thereby affecting the expression of mRNA of related genes and the formation of circRNA.\u003c/p\u003e \u003cp\u003eA total of 4323 circRNAs were identified from RNA in bMECs from Holstein cows by library construction, sequencing, and bioinformatics analysis. Most circRNAs are short in length and are concentrated between 201\u0026ndash;700 bp, and longer than 2000 bp. This is also consistent with many research findings\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e. In addition, we found five different types of circRNAs in bMECs, of which 87.42% belonged to sense-overlapping, while only three types of circRNAs were identified in mammary tissue RNA at different lactation stages in the previous study\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. This may also be related to the mechanism of circRNA formation. Sense-overlapping circRNAs (sometimes defined as EIciRNA) have overlapping regions with mRNA exons and are transcribed in the same direction. Related studies have shown that long flank introns are considered crucial to exon cyclization, and they contain ALU repeats\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e and possibly help determine the production rate of circRNAs\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e. Finally, introns between the encircled exons are retained, which Li termed EIciRNAs\u003csup\u003e\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e\u003c/sup\u003e. Most of the circRNAs in this study are sense overlapping, which may be related to the above reasons.\u003c/p\u003e \u003cp\u003eCompared with the NC group, 841 circRNAs with significantly different expressions in the LPS group, 400 upregulated and 441 downregulated. GO and KEGG enrichment analysis can illustrate the related functions of genes. In this study, the GO analysis identified positive regulation of G1/S transition of mitotic cell cycle, histone methyltransferase activity (H3-K27 specific), and DNA methylation. Studies have shown that LPS-mediated inflammation plays a role in cell cycle reactivation and apoptosis in differentiated neuronal cells\u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e. Histone methylation is an essential epigenetic mechanism, while LPS-induced gene-specific histone methylation affects the expression and release of the pro-inflammatory cytokines IL-6 and TNF-α\u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e. Meanwhile, Jhamat et al. found that LPS altered the DNA methylation patterns of bovine endometrial epithelial cells\u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e. The above studies have shown that LPS infection causes changes in the expression of autoinflammatory factors but also causes changes in cell cycle and methylation. In addition, the KEGG pathway analysis mainly revealed the hippo signaling pathway \u0026ndash; fly, AMPK signaling pathway, and Fc gamma R-mediated phagocytosis. The Hippo signaling pathway was initially discovered in Drosophila melanogaster as a critical regulator of tissue growth\u003csup\u003e\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u003c/sup\u003e, while LPS could activate the hippo signaling pathway and induce an inflammatory response\u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e. Similarly, the AMPK signaling pathway and Fc gamma R-mediated phagocytosis are classical inflammatory or immune-related signaling pathways that can be activated by LPS stimulation\u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e,\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e. From the above analysis, we speculate that these differentially expressed circRNAs are mainly related to methylation, immune responses, and autophagy.\u003c/p\u003e \u003cp\u003eThese studies show that circRNAs has become a new research hotspot and provides new research ideas for us to study the molecular mechanism of cows' mastitis. In the next study, we will focus on the function of methylation modifications (such as methylation, acetylation, ubiquitination) on circRNAs in LPS-induced inflammatory bMECs in hopes of understanding how they regulate LPS-induced bMECs.\u003c/p\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003eIn this study, through high-throughput sequencing of circRNAs in bMECs induced by \u003cem\u003eE. coli\u003c/em\u003e LPS, 4323 circRNAs were detected, mainly sense-overlapping. Among the 841 DE circRNAs detected, enrichment analysis found that they mainly concentrated on methylation, immune responses, and autophagy. This study revealed the expression profile and characteristics of circRNAs in bMECs injured by LPS and provided a wealth of information for studying the function and mechanism of circRNAs.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot Applicable.\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\u003eThe RNA seq data are available only to the collaborating scientists from the respective participating centers. The data may be available upon request for some of the participating centers but not for all due to relevant data protection laws.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe research received financial support from the National Natural Science Foundation of China (31972555), the Postgraduate Research and Practice Innovation Program of Yangzhou University (KYCX21_3264), Yangzhou University International Academic Exchange Fund.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization, M.Y. and Y.Z.; methodology, L.M.; software, L.M.; validation, L.Y., and X.Y.; formal analysis, L.Y. and W.M.; data curation, L.Y.; writing original draft preparation, L.Y.; writing\u0026mdash;review and editing, N.A.; visualization, L.M.; supervision, M.Y.; project administration, M.Y.; funding acquisition, M.Y. All authors have read and agreed to the published version of the manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col class=\"decimal_type\"\u003e\n \u003cli\u003eGuo M, Gao Y, Xue Y, Liu Y, Zeng X, Cheng Y, Ma J, Wang H, Sun J, Wang Z, Yan Y. Bacteriophage Cocktails Protect Dairy Cows Against Mastitis Caused By Drug Resistant Escherichia coli Infection. Front Cell Infect Microbiol. 2021; 11:690377.\u003c/li\u003e\n \u003cli\u003eXu H, Lin C, Li T, Zhu Y, Yang J, Chen S, Chen J, Chen X, Chen Y, Guo A, Hu C. N6-Methyladenosine-Modified circRNA in the Bovine Mammary Epithelial Cells Injured by Staphylococcus aureus and Escherichia coli. Front Immunol. 2022; 13:873330.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eIbeagha-Awemu EM, Ibeagha AE, Messier S, Zhao X. Proteomics, Genomics, and Pathway Analyses of Escherichia Coli and Staphylococcus Aureus Infected Milk Whey Reveal Molecular Pathways and Networks Involved in Mastitis. 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J Leukoc Biol. 2006;79(2):408-15.\u0026nbsp;\u003c/li\u003e\n\u003c/ol\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":"circular RNA, bovine mammary epithelial cells, methylation peak, Holstein cows","lastPublishedDoi":"10.21203/rs.3.rs-2857377/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-2857377/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe rise of multi-omics technology in recent years provides convenient for in-depth study of the pathogenesis of dairy cow mastitis, and circRNAs, as endogenous non-coding RNAs, are expected to become molecular targets to study the pathogenesis of dairy cow mastitis. LPS, as a component of the outer wall of \u003cem\u003eE. coli\u003c/em\u003e cell wall, is a common endotoxin in the construction of inflammatory models. The objective of this study is to identified and compared circular RNAs (circRNAs) from bovine mammary epithelial cells (bMECs) between the control and LPS groups. The expression profiles of circRNAs were obtained by high-throughput sequencing (RNA-seq) based on the construction of bMECs - LPS inflammation model, with control group (n = 3) and LPS group (n = 3) cell RNA as samples. After analysis, we identified 4323 circRNAs, ranging from 63 bp to 96387 bp. Chromosome 5\u003cstrong\u003e \u003c/strong\u003ehad most circRNAs, containing 259 circRNAs. Furthermore, 87.42% of the circRNAs belonged to sense-overlapping circRNA. CircRNAs contains different number of exons, ranging from 1 to 43, and most of cirsRNAs harbored 1 to 5 exons. Compared with the negative control (NC) group, 841 circRNAs with significantly different expressions (DE) in the LPS group (10 μg/mL), including 400 upregulated and 441 downregulated circRNAs. Enrichment analysis revealed the enrichment of circRNAs in methylation, such as positive regulation of G1/S transition of the mitotic cell cycle, histone methyltransferase activity (H3-K27 specific), and DNA methylation. The significantly enriched pathways further indicate that circRNAs play important roles in immunoreaction, such as hippo signaling pathway – fly, AMPK signaling pathway, and Fc gamma R-mediated phagocytosis. This study revealed the expression profile and characteristics of circRNAs in bMECs induced by LPS, and providing information for studying circRNA functions and mechanisms underlying mastitis, which suggesting a new avenue to investigate the regulatory mechanisms of mastitis.\u003c/p\u003e","manuscriptTitle":"Characterization of circular RNAs in bovine mammary epithelial cells induced by Escherichia coli LPS","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-05-02 19:32:23","doi":"10.21203/rs.3.rs-2857377/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":"113877a6-43db-4ad7-a380-5937a82756de","owner":[],"postedDate":"May 2nd, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-02-19T12:08:54+00:00","versionOfRecord":[],"versionCreatedAt":"2023-05-02 19:32:23","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-2857377","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-2857377","identity":"rs-2857377","version":["v1"]},"buildId":"wLkW0s4AflPzk-lpfg-fK","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}