Genome-wide identification of circular RNAs to elucidate the genetic basis of disease resistance to Salmonella infection in Chicken

Genome-wide identification of circular RNAs to elucidate the genetic basis of disease resistance to Salmonella infection in Chicken | 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 Genome-wide identification of circular RNAs to elucidate the genetic basis of disease resistance to Salmonella infection in Chicken Mahak Khan, Mashooq Ahmad Dar, Junaid Nazir, Sahar Saleem Bhat, and 8 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5757781/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 Salmonella infections have far-reaching implications for public health due to their potential to cause various human illnesses, ranging from gastroenteritis to more severe conditions like enteric fever. These infections are predominantly food-borne and are often linked to consuming contaminated meat products, with poultry being a common transmission source. Consequently, Salmonellosis ranks among the most prevalent food-borne illnesses, leading to substantial morbidity, hospitalizations, and even fatalities. In this study, we investigated the role of circular RNAs (circRNAs) in mediating the response to Salmonella Typhimurium infection in poultry, focusing on two chicken breeds: broiler (Cobb 430) (susceptible) and Kashmir Faverolla (resistant). Through high-throughput RNA sequencing, we identified and analyzed circRNA expression patterns in liver and spleen samples from both breeds. Results reveal a comprehensive catalog of circRNAs, with 26 differentially expressed (DE) circRNAs identified across various comparison groups. Our study revealed that the circRNAs associated with genes conferring resistance to Kashmir Faverolla are predominantly located on chromosome 1, with a notable presence also observed on chromosome 4. Moreover, genes FGB, FGG , and ALB could play pivotal roles in mediating the response to Salmonella infection in poultry. Network analyses and protein-protein interaction networks shed light on the interconnectedness of genes like FGB, FGG , and ALB , suggesting their collaborative roles in mediating the response to Salmonella infection. Moreover, functional analyses uncover significant biological processes associated with target mRNAs, emphasizing their involvement in immune responses, infectious diseases, and molecular pathways. Keywords: Circular RNA; Salmonella; Infection; Chicken; Disease resistance. Figures Figure 1 Figure 2 1. Introduction Salmonella enterica serovar Typhimurium ( Salmonella Typhimurium) bacteria belongs to the Enterobacteriaceae family and is known to cause various disease conditions, ranging from gastroenteritis to potentially life-threatening enteric fevers. Salmonellosis is the most common food-borne zoonotic disease, affecting a wide range of hosts and posing a significant public health concern (Baron 1996). The main infection sources for humans include consuming contaminated meat products, including poultry meat (Antunes, Mourão et al. 2016). Developing countries lack effective vaccination programs against Salmonellosis in poultry, and as such, this disease incurs huge economic losses in terms of mortality and morbidity. Understanding the pathogenesis of this disease is crucial in developing therapeutic and preventive strategies against this infection. Disease resistance is primarily determined by the genetic interaction between the pathogen and the host. When bacteria are identified, the bactericidal activity of host macrophages triggers the maturation and migration of dendritic cells, along with the production of inflammatory chemokines, cytokines, and interleukins for effective elimination of bacteria by the immune system (Wang, Zhu et al. 2020). Salmonella has evolved mechanisms to bypass host defense barriers and suppress the activation of the immune response through its virulence genes called Salmonella pathogenicity islands 1 and 2 (Wang, Zhu et al. 2020), which harbors two distinct virulence-related T3SSs (Type III secretion systems) that operate at different stages during infection. These are important in inducing and activating intestinal inflammatory responses, causing diarrhea, establishing intestinal colonization and initiating systemic disease (Hansen-Wester, Hensel et al. 2001, Haraga, Ohlson et al. 2008). In developing countries, genetic disease resistance is particularly important, as indigenous breeds tend to be more resistant to local diseases (Pal, Chakravarty et al. 2020). The Kashmir Faverolla , a notable indigenous chicken breed from the northern Indian state of Jammu and Kashmir, exhibits high disease resistance than other breeds like commercial broilers (Iqbal and Pampori 2008). This resistance is attributed by the enrichment of certain genes and signaling pathways involved in both innate and adaptive immune responses against bacterial infections, including interleukins, cytokines, NOS2, Avβ-defensins, toll-like receptors, and other immune-related gene families. Pathway analysis revealed significant enrichment in pathways such as MAPK signaling, PPAR signaling, NOD-like receptor signaling, TLR signaling, and endocytosis in Kashmir Faverolla . Conversely, some TLR genes show upregulation in susceptible chicken breeds (Dar, Ahmad et al. 2022). Following infection with Salmonella enterica serovar typhimurium, Kashmir Favorella chicken exhibit three genes with altered expression levels namely, Nuclear Factor Kappa B (NF-κB1), Forkhead Box Protein O3 (FOXO3) and Paired box 5 (Pax5). The three differentially expressed genes (NF-κB1, FOXO3, and PaX5) impact 12 interacting proteins and 16 transcription factors (TFs). Among these, cyclic adenosine monophosphate Response Element Binding protein (CREBBP), erythroblast transformation-specific (ETSI), Tumour-protein 53 (TP53I), IKKBK, lymphoid enhancer-binding factor-1 (LEF1), and interferon regulatory factor-4 (IRF4) are notably involved in immune responses (Ahmad, Bhat et al. 2023). Kashmir Favorella chickens have high-impact SNPs in immune-related pathways, whereas broilers have SNPs in metabolic pathways, suggesting these genetic differences contribute to their respective disease resistance and susceptibility to Salmonella (Dar, Bhat et al. 2023). Role of circular RNAs (circRNAs) in mediating resistance to bacterial diseases in poultry is reported in diseases like New Castle Disease (Chen, Ruan et al. 2023), Salmonella enterica serovar Enteritidis (SE) (Zheng, Liu et al. 2019), Avian pathogenic E. coli (APEC) (Sun, Yang et al. 2022). These studies collectively indicate that circRNAs play significant roles in mediating the immune response and resistance to bacterial infections in poultry. CircRNAs have emerged as key regulators of gene expression and have been implicated in various biological processes, including immune responses and infectious diseases (Jeck, Sorrentino et al. 2013, Memczak and JenS 2013, Barrett, Wang et al. 2015). The varying expression of circRNAs in response to infections suggests they could be useful as biomarkers for disease resistance and as targets for therapeutic treatments. In this study, we aimed to investigate the role of circular RNAs (circRNAs) in mediating resistance to Salmonella Typhimurium infection in two distinct chicken breeds: broilers (Cobb 430) (known for their susceptibility) and Kashmir Faverolla (recognized for their resistance). CircRNA influences the progression and evolution of various diseases by sequestering miRNAs as sponges. Multiple studies indicate that circRNA competitively binds miRNA, thereby fostering processes like proliferation, migration, and inflammation in mononuclear macrophages (Zhang, Zhang et al. 2018). Studies have aimed to build circRNA-miRNA-mRNA networks for predicting molecular functions and pathways (Kong, Sun et al. 2021). By profiling circRNA expression patterns in response to Salmonella infection across different chicken breeds, we attempt to uncover breed-specific variations in circRNA expression and their potential implications for disease resistance. 2. Materials and Methods The study utilizes RNA sequencing data from our prior research (GEO ID: GSE168060) where we analyzed gene expression and pathways related to bacterial infection defense in Kashmir Faverolla chickens. Building on this, we further examined circRNAs from the same set of samples with the purpose of identifying role of circRNAs in regulation of the host’s immune response to bacterial pathogens. This study is exploratory in nature and provides a foundational report on circRNAs related to disease resistance in Salmonella infections. Data collection and sequencing protocols were detailed in the previous study (4). The experimental trial took place at the animal facility center at the Faculty of Veterinary Sciences and Animal Husbandry, Sher-e-Kashmir University of Agricultural Sciences and Technology of Kashmir, India. One hundred one-day-old chicks, fifty each of Kashmir Faverolla and broiler (Cobb 430) breeds, were obtained. They were divided into four groups (two breeds × two treatment groups) and housed in controlled environments. The infected groups were orally challenged with Salmonella Typhimurium, while control groups received nutrient broth. Daily monitoring and fecal swab collection confirmed Salmonella colonization. On the fifth day post-infection, liver and spleen samples were collected for RNA extraction. Total RNA was extracted, treated for DNA contamination, and assessed for quality with the Revert Aid First Strand cDNA synthesis kit (Thermo Scientific, USA). RNA meeting quality criteria underwent cDNA library construction and sequencing using the Illumina NovaSeq platform, generating paired-end reads with a length of 150 base pairs [4]. 2.1 Data Analysis: a)Quality Control and Preprocessing Raw sequencing reads underwent quality control checks using FastQC program version 0.11.2 (Andrews 2010 ), followed by adapter trimming and quality filtering using tools such as Trimmomatic(Paya-Milans, Olmstead et al. 2018 ). b) Identification of DE circRNAs : A dual methodology was engaged to identify differentially expressed (DE) circRNAs. In the first approach, they seekCRIT tool version 1.0.0.b(Chaabane, Andreeva et al. 2020 ) was executed on the filtered reads to pinpoint DE circRNAs. In seekCRIT pipeline, STAR alignment tools was utilized to detect back-splice junctions (BSJs) which is a key characteristic of circRNA. STAR alignment implementation helps to minimize false positives by accurately distinguishing between linear and circular RNA reads. Also, astringent quality filters were used for the raw reads to exclude low-quality sequences. Only high-quality reads with a specified minimum quality (Q Score > 30) score were used in the analysis to reduce the likelihood of including sequencing errors.The second strategy for DE circRNA identification entailed a process of quantification using CIRI and CYCLER (Gao, Wang et al. 2015 , Stefanov and Meyer 2023 ), followed by the application of the edgeR software (Robinson, McCarthy et al. 2010 ). The quasi-likelihood F-test method from the edgeR program was employed. This statistical approach enabled the identification of circRNAs that exhibited significant differential expression. The criteria for DE circRNA selection included a P-value threshold of less than 0.05 and a log-fold change (logFC) > 1. The final ensemble of DE circRNAs encompassed those circRNAs that were consistent between the seekCRIT dataset and the edgeR dataset. Specifically, circRNAs exhibiting a log2FC exceeding 1 and a p-value below 0.05 in both datasets were classified as differentially expressed in the two distinct breeds. All identified DE circRNAs were cross-referenced against established circRNAs from diverse species, including Homo sapiens , Mus musculus , Susscrofa , Gallus gallus , and Canis lupus from circRNA databases to ascertain conservation. 2.2 Functional analysis To deepen our comprehension of the functions of messenger RNAs (mRNAs) targeted by differentially expressed circular RNAs (DE circRNAs), we conducted Gene Ontology (GO) and Pathway enrichment analyses. These analyses provided insights into the potential biological functions and pathways associated with these mRNAs. Employing the well-established KOBAS 3 server, we identified enriched Gene Ontology (GO) terms and pathways among the set of mRNAs targeted by DE circRNAs. Network analysis of target mRNAs was performed using STRING-DB, a resource for protein-protein interaction (PPI) analysis (using a strict confidence-score of 0.9). We utilized Maximal Clique Centrality (MCC) and Degree metrics to pinpoint key nodes in the PPI network, visualizing it through Cytoscape software. Additionally, we delved into drug-gene interactions using the DrugBank database and disease gene interactions via the DisGeNET database. This multifaceted approach unveiled connections between genes, drugs, and diseases, enriching our understanding of their complex interplay. 3. Results A total of 2114 circular RNAs (circRNAs) were identified from samples under study. Most of these were circRNAs, accounting for approximately 63.97% of the total, while a smaller proportion comprised cirRNAs, making up about 35.98%. When looking at their chromosomal distribution, a significant portion of circRNAs were found on chromosome 1, followed by chromosome 4. In contrast, the fewest circRNAs were observed on chromosome 36 and chromosome number above 39 (Figure A). 3.1. Analysis of DE circRNAs A total of 26 DE circRNAs were found across six distinct comparison groups under our study's purview. We found that 12 mRNAs are targeted by the differentially expressed circRNAs (Table 2 ). Our investigation revealed intricate interactions between circRNAs and mRNAs, particularly noting that PIT54, FGG, ADK and CR1 engage with multiple circRNAs, implying a varied regulatory role. Notably, SPINK5 and FGB displayed intriguing associations with circRNAs, with SPINK5 being regulated by four circRNAs and FGB by six, suggesting a heightened impact across diverse groups. Additionally, our analysis of the protein-protein interaction (PPI) network revealed FGB's interaction with both FGG and ALB genes (Supplementary file one), hinting at potential collaborative functions in combating Salmonella infection. Moreover, the broader context provided by the PPI network underscores the pivotal roles of FGB, FGG , and ALB in mediating the host's response to Salmonella infection in poultry, inviting further exploration into their intricate mechanisms of action in defense against this bacterial pathogen (Fig. 1 ). Table 1 Summary of the sample groups involved in the study and the DE circRNAs that were identified. In the 'Groups' column, the entries represent the sample types and the number of samples utilized in the analysis. Group_ID Groups Number of DE circRNAs G1 Broiler Liver Infected (BL – 3 Samples) Vs Broiler Liver Control (BLC – 2 Samples) Total 7 DE circRNAs 7 upregulated in BL G2 Broiler Spleen Infected (BS – 3 Samples) Vs Broiler Spleen Control (BSC – 2 Samples) Total 2 DE circRNAs. 2 upregulated in BS G3 Broiler Liver Infected (BL – 3 Samples) Vs Faverolla Infected Liver (FL – 3 Samples) Total 10 DE circRNAs 2 upregulated in BL & 8 upregulated in FL G4 Broiler Spleen Infected (BS – 3 Samples) Vs Faverolla Spleen Infected (FS – 2 Samples) Total 2 DE circRNAs 2 upregulated in BS G5 Faverolla Liver Infected (FL – 3 Samples) Vs Faverolla Liver Control (FLC – 2 Samples) Total 2DE circRNAs 2 upregulated in FL G6 Faverolla Spleen Infected (FS – 2 Samples) Vs Faverolla Spleen Control (FSC – 2 Samples) Total 1 DE circRNAs 1 upregulated in FS G1 to G6 represent number of groups of birds taken in the study. BL: Broiler Liver Infected, BS: Broiler Spleen Infected, BSC: Broiler Spleen Control, FL: Faverolla Infected Liver, FLC: Faverolla Liver Contro, FS: Faverolla Spleen Infected, FSC: Faverolla Spleen Control, DE cirRNAs: Differentially Expressed cirRNAs Table 2 DE circRNAs and their corresponding target mRNAs across all contrast groups (G1 to G6). This table presents a comprehensive overview of the interplay between DE circRNAs and their respective target mRNAs within multiple contrast groups (G1 to G6). A total of 24 DE circRNAs have been identified, collectively targeting 12 distinct mRNAs. The circRNA ID is listed in the " DE_circRNAs " column, while the comparison group information for each circRNAis located in the " group " column. G1 to G6 represent 6 different contrast groups, detail in Table 1 . The "Annotation" column provides details about the location of circRNAs. Within the "Annotation" column, the "+" and "-" symbols indicate the positive and negative DNA strands, respectively. The "Location" information represents the chromosome number, starting position, and ending position of the circRNAs. S. No Target mRNAs DE circRNAs Group Annotation 1. FGB ciRNA_4_130 G5 Location: Chr4:19781130–19781271(+) circRNA_4_514 G3 Location: Chr4:19779514–19779921(+) ciRNA_4_136 G3 Location: Chr4:19781136–19781272(+) ciRNA_4_136 G3 Location: Chr4:19781136–19781167(+) circRNA_4_984 G3 Location: Chr4:19780984–19781144(+) ciRNA_4_136 G1 Location: Chr4:19781136–19781167 (+) circRNA_4_139 G1 Location: Chr4:19779516–19781139 (+) 2. SPINK5 circRNA_13_940 G1 Location: Chr13: 10161940–10164094(+) circRNA_13_618 G1 Location: Chr13: 10161618–10162074(+) circRNA_13_039 G2 Location: Chr13: 10164039–10165289(+) circRNA_13_758 G5 Location: Chr13: 10164758–10166642(+) 3. PIT54 circRNA_31_605 G1 Location: Chr31: 244605–245598(-) circRNA_31_605 G3 Location: Chr31: 244605–245598(-) 4. NPM1 circRNA_13_683 G1 Location: Chr13: 2916683–2916779(-) 5. ALB circRNA_4_481 G1 Location: Chr4: 50349481–50349691(+) 6. RPL27 ciRNA_27_720 G2 Location: Chr27: 5097720–5097816(+) 7. RPL6 circRNA_15_622 G2 Location: Chr15: 6400622–6400826(+) 8. FGG circRNA_4_481 G3 Location: Chr4: 19800481–19801585(-) circRNA_4_581 G5 Location: Chr4: 19800481–19801585(-) 9. ADK circRNA_6_264 G3 Location: Chr6: 16025264–16029254(-) circRNA_6_264 G5 Location: Chr6: 16025264–16029254(-) 10. ADH1C circRNA_4_951 G3 Location: Chr4: 59499915–59500044(-) 11. CR1 circRNA_26_872 G4 Location: Chr26: 2706872–2715091(+) circRNA_26_872 G6 Location: Chr26: 2706872–2715091(+) 12. RPL31 circRNA_1_087 G4 Location: Chr1: 133477087–133477203(+) Exploring the depths of this network, Fig. 1 unveils another layer of potential regulation. Transcription factors (TFs) emerge as key players, including KLF9, KLF11, GATAD2A, SSRP1 , and SOX13 . The presence of these Transcription factors (TFs) in the network hints at their possible involvement in orchestrating the expression of genes like FGB, FGG , and ALB in response to Salmonella infection. 3.2. Analysis of Enriched Terms The results from the Gene Ontology (GO) and pathway enrichment analysis offer valuable insights, emphasizing the importance of specific target mRNAs. Notably, the analysis underscores the essential functions of nine target mRNAs: RPL6, RPL27, ALB, RPL31, FGG, NPM1, FGB, CR1 and ADH1C (Supplementary File one). These genes are implicated in a range of vital biological processes, as evidenced by the enrichment of pathways they participate in. The evidence indicates their participation in the key process like Infectious diseases (p-value = 2.59E-06), MAP2K and MAPK activation (p-value = 0.00006), Innate Immune System (p-value = 0.002), immune system (p-value = 0.01), and Drug Metabolism (p-value = 0.02), complement and coagulation cascades (p-value = 1.43E-06), platelet degranulation (p-value 5.88E-06), seven amino acid metabolism (p-value = 4.52E-06), Platelet activation (p-value = 0.00055), TP53 regulates transcription of cell cycle genes (p-value = 0.013). 3.3. Enrichment analysis of differentially expressed genes The association between circRNAs and the control of gene expression was further studied using the KEGG pathway enrichment and cluster analysis of gene ontology (GO) for differentially expressed circRNAs. DE circRNA-enriched Gene Ontology (GO) terms were significantly (P-value ≤ 0.05) enriched in specific number of biological processes. Studying the drug interaction pathways, we found the interaction of gene ALB with cefotaxime, vancomycin, and Rifampicin; interaction of FGB with Alfimeprase, sucralfate; interaction of FGG with sucralfate and interaction of ADH1C with pyrazole. Elucidating the chemical interactions of the upregulated genes in our study, we found their association with many hormones, vitamins, and chemical compounds (Table 2 ). Further, genes ADK and ALB were seen in many disease interactions. ADK dysfunction was seen to cause abnormality in liver enzymes, liver dysfunction, decreased liver function, liver dysfunction, muscle degeneration, abnormal LFT, and failure to gain weight. ALB dysfunction causes hypoalbuminemia and kidney diseases. Table 2 The table provides an in-depth analysis of how differentially expressed circular RNAs (circRNAs) and various small molecules interact during a Salmonella infection. The table provides a detailed view of how circRNAs coordinate molecular interactions and potential regulatory networks during Salmonella infection. These connections provide insights for managing infections and developing therapeutic strategies. S.No. Genes targeted by DE circRNAs Interacting Molecules 1. SPINK5 Estradiol 2 FGG Estradiol, Norethindrone 3 FGB Flurouracil, Vitamin k 4 ALB Vitamin E, Flurouracil, Estradiol, Vitamin k, Zinc, Acetaminophen, Vorinostat. 5 RPL31 Estradiol, Vincristine 6 CR1 Zinc, Methotrexate 7 RPL6 Flurouracil, Estradiol, Acetaminophen, Raloxifene hydrochloride 8 NPM1 Flurouracil, Estradiol, Raloxifene hydrochloride 9 ADK Estradiol, Vitamin k, Vorinostat 10 ADH1C Estradiol, Acetaminophen 4. Discussion When the host encounters an infection, its immune system activates to eliminate the pathogen and repair any damage, striking a balance between resisting the infection and tolerating its effects. This balance is essential in managing pathogen invasion effectively (Piñero, Queralt-Rosinach et al. 2015 ). CircRNAs have been implicated in various infectious and inflammatory diseases (Yu, Mi et al. 2021 ). Circular RNAs (circRNAs) are recognized as potent diagnostic and therapeutic biomarkers across a variety of diseases. In the present study, we utilized the RNA sequencing data from our previous study (GEO ID: GSE168060) and examined cirRNAs from the same samples in order to identify their role in regulating host immune response against bacterial infection. In our previous study, we found Kashmir Favorella chicken have higher enrichment of genes and pathways that protect it against bacterial infections (Dar, Ahmad et al. 2022 ). Many studies have been carried out to identify the differentially expressed (DE) cirRNAs in different diseased condition like in community-aquired pneumonia (CAP) (Zhao, Zheng et al. 2019 ), in pulmonary tuberculosis (TB) diagnosis (Qian, Liu et al. 2018 ), in meningitic E. coli infection (Yang, Xu et al. 2018 ), in Salmonella Enteritidis (SE) infection (Zheng, Liu et al. 2019 ). Our study investigates the mechanisms of disease resistance in broilers (susceptible) and Kashmir Favorella (resistant) chickens by analyzing their circRNA expression. We found that differentially expressed circRNAs interact with proteins like FGG, SPINK5 , and ADK , suggesting a role in regulating disease resistance. (Yu, Mi et al. 2021 ). Also, expression of certain host genes get altered during inflammatory phase of Salmonella infection. These include Acute phase proteins ( APP ), including alpha1-acid glycoprotein ( AGP ), Serum Amyloid A ( SAA ), PIT54 , C-reactive protein ( CR1 ), and ovotransferrin (OVT) (Marques, Nordio et al. 2017 ). Understanding these mechanisms can aid in developing strategies for managing pathogen invasion effectively. Earlier reports explain the role of CR1 in mediating complement-mediated inflammatory reactions (Khera and Das 2009) and role of ADK gene in mediating infection and inflammation by regulating adenosine levels (Park, Jo et al. 2022 ). SPINK5 is also known to influence the immune response in host upon pathogen invasion (Park, Jo et al. 2022 ). In our study, PPI network analyses results show the interaction of FGB with FGG and ALB , affirming their mutual connection in functioning and mediation of Salmonella infection. Our study highlights the expression of FGG, FGB , and ALB genes mediated by transcription factors (TFs), namely, KLF2, KLF9, KLF11, GATAD2A, SSRP1 , and SOX13 . The differential expression of these transcription factors and their crucial role in genetic resistance in Salmonella infectious conditions is in accordance with previous study (Ahmad, Bhat et al. 2023 ). In the present study, Gene ontology (GO) and KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway analysis was done to understand the detailed functional pathways and biological processes of this disease. Differentially expressed (DE) circRNA in host genes reveal their involvement in critical biological processes such as infectious diseases, MAPK activation, and immune system responses. This analysis highlights the roles of nine target mRNAs, namely RPL6, RPL27, ALB, RPL31, FGG, NPM1, FGB, CR1 , and ADH1C in Salmonella infection. These genes are involved in various biological processes like infectious diseases, MAP2K and MAPK activation, innate immune system, immune system, and drug metabolism. Consistent with previous studies, we found that Salmonella organism manipulates host cell functions, inhibits ribosomal gene transcription (Simpson-Haidaris, Courtney et al. 1998 ), and activates MAPK and AKT pathways to enhance virulence (Liu, Lu et al. 2010 , Scanu, Spaapen et al. 2015 ). Additionally, we found numerous circRNAs to be associated with amino acid metabolism pathways and pathways related to complement and coagulation as supported by earlier studies (Yeaman and sciences 2010, Hitchcock, Cook et al. 2015 , Ryan, Pati et al. 2015 , Barrila, Sarker et al. 2021 ). Collectively, our findings highlight the complex regulatory roles of circRNAs in host responses to Salmonella infection, particularly in immune response pathways, metabolism, and cell cycle regulation. Further, our study depicts interaction of specific circRNAs with molecules like estradiol, vitamin E, vitamin K and zinc, suggesting these molecules' significant roles in modulating immune responses to Salmonella (Gourdy, Araujo et al. 2005 , Dalia, Loh et al. 2018 ) (Liu, Mosenthin et al. 2021 ). This study provides a comprehensive understanding of the molecular interactions during Salmonella infection and opens avenues for potential therapeutic targets. 5. Conclusions The study aims to explore circRNA expression patterns in response to Salmonella infection across different chicken breeds, revealing breed-specific distributions and variations, particularly in Kashmir Faverolla and commercial broilers, with 26 differentially expressed circRNAs identified. Network analyses highlighted collaborative roles of genes like FGB, FGG, and ALB in mediating the response to Salmonella, while functional analyses emphasized the involvement of various biological processes in immune responses and infectious diseases 6. Limitations While our study elucidates circRNAs' roles in Salmonella immune responses, it is constrained by a limited sample size of specific chicken breeds, a cross-sectional design, and a focus solely on Salmonella Typhimurium. Functional validation, temporal dynamics, broader pathogen inclusion, in vivo complexities, and environmental factors were not fully explored. Further research integrating diverse breeds, longitudinal designs, functional assays, and environmental considerations is crucial for validating and broadening the implications of our findings in circRNA-mediated immune regulation. Declarations Author Contributions: Conceptualization, S.A.; Formal analysis, B.B. and M.K.; Investigation, S.A; Methodology, M.K., B.B, M.D., Z.H., N.S., R.S. and S.A.; Supervision, S.A.; Validation, B.B.; Visualization, M.K., N.S., S.F., H.A. and S.A.; Writing – original draft, M.K., B.B. and S.A.; Writing – review & editing, M.K., B.B., M.D., J.N., S.B., S.F. and H.B. All authors have read and agreed to the published version of the manuscript. Funding: None. Acknowledgments: We are highly thankful to DST for “PURSE-2023 to carry out this work. We also acknowledge The Science and Engineering Research Board (SERB), Department of Science and Technology, Government of India. The authors acknowledge and extent their appreciation to the Researchers Supporting Project Number (RSPD2025R783), King Saud University, Riyadh, Saudi Arabia for their support to carry out this work. Declarations: Institutional Review Board Statement- All experimental procedures strictly adhered to the guidelines outlined by the Institutional Animal Ethics Committee. Approval from the institutional animal ethics committee on ethical standards in animal experimentation was granted under reference number AU/FVSc/PS-57/16021. Consent to Participate: Not applicable. Consent to Publish declarations: Not applicable. Informed Consent Statement: Not applicable. Data Availability Statement: None. Conflicts of Interest: The authors declare that the research was conducted in absence of any commercial or financial relationships that could be construed as a potential conflict of interest. References Ahmad SM, Bhat SS, Shafi S, Dar MA, Saleem A, Haq Z, Farooq N (2023) J. Nazir and B. J. B. g. 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T.- XJ (2018) Circular RNA transcriptomic analysis of primary human brain microvascular endothelial cells infected with meningitic Escherichia coli. 13:651–664 Yeaman MRJC (2010) and m. l. sciences Platelets in defense against bacterial pathogens. 67: 525–544 Yu H, Mi C, Wang Q, Zou W, Dai G, Zhang T, Zhang G, Xie K, Wang J (2021) H. J. F. i. C. Shi and I. Microbiology Comprehensive analyses of circRNA expression profiles and function prediction in chicken cecums after eimeria tenella infection. 11: 628667 Zhang F, Zhang R, Zhang X, Wu Y, Li X, Zhang S, Hou W, Ding Y, Tian J, Sun LJA (2018) Comprehensive analysis of circRNA expression pattern and circRNA-miRNA-mRNA network in the pathogenesis of atherosclerosis in rabbits. 10(9):2266 Zhao T, Zheng Y, Hao D, Jin X, Luo Q, Guo Y, Li D, Xi W, Xu Y, Chen (2019) Blood circRNAs as biomarkers for the diagnosis of community-acquired pneumonia. 120(10):16483–16494 Zheng L, Liu L, Lin L, Tang H, Fan X, H. Lin and X. J. F. i. I., Li (2019) Cecal circRNAs are associated with the response to Salmonella enterica serovar enteritidis inoculation in the chicken. 10: 1186 Additional Declarations No competing interests reported. Supplementary Files Supplementaryfileone.xlsx 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-5757781","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":398876411,"identity":"ae75d415-078a-463c-9651-2553db25dfd6","order_by":0,"name":"Mahak Khan","email":"","orcid":"","institution":"FVSc \u0026 AH, SKUAST-Kashmir, SKUAST-K","correspondingAuthor":false,"prefix":"","firstName":"Mahak","middleName":"","lastName":"Khan","suffix":""},{"id":398876412,"identity":"8e20dc14-d790-449c-9cf1-3920e25a5b4d","order_by":1,"name":"Mashooq Ahmad Dar","email":"","orcid":"","institution":"Nencki Institute of Experimental Biology of Polish Academy of Sciences","correspondingAuthor":false,"prefix":"","firstName":"Mashooq","middleName":"Ahmad","lastName":"Dar","suffix":""},{"id":398876413,"identity":"77d39a68-d07e-446c-b9d4-3bb4c4d8800e","order_by":2,"name":"Junaid Nazir","email":"","orcid":"","institution":"FVSc \u0026 AH, SKUAST-Kashmir, SKUAST-K","correspondingAuthor":false,"prefix":"","firstName":"Junaid","middleName":"","lastName":"Nazir","suffix":""},{"id":398876415,"identity":"6e09c13d-07c9-4978-81a6-7394170146ec","order_by":3,"name":"Sahar Saleem Bhat","email":"","orcid":"","institution":"FVSc \u0026 AH, SKUAST-Kashmir, SKUAST-K","correspondingAuthor":false,"prefix":"","firstName":"Sahar","middleName":"Saleem","lastName":"Bhat","suffix":""},{"id":398876417,"identity":"85790e34-031a-4b90-b34f-ab2c7faa2c5a","order_by":4,"name":"Zulfqarul Haq","email":"","orcid":"","institution":"F.V.Sc \u0026 AH, SKUAST-K","correspondingAuthor":false,"prefix":"","firstName":"Zulfqarul","middleName":"","lastName":"Haq","suffix":""},{"id":398876419,"identity":"40106fcb-ff3e-40f4-b33d-317071f4c630","order_by":5,"name":"Nadeem Shabir","email":"","orcid":"","institution":"FVSc \u0026 AH, SKUAST-Kashmir, SKUAST-K","correspondingAuthor":false,"prefix":"","firstName":"Nadeem","middleName":"","lastName":"Shabir","suffix":""},{"id":398876420,"identity":"43b6dd23-a4a8-4f5e-8173-f83524a34791","order_by":6,"name":"Hina Fayaz Bhat","email":"","orcid":"","institution":"FVSc \u0026 AH, SKUAST-Kashmir, SKUAST-K","correspondingAuthor":false,"prefix":"","firstName":"Hina","middleName":"Fayaz","lastName":"Bhat","suffix":""},{"id":398876421,"identity":"d5f91047-bb53-4925-94d1-a85fd7657376","order_by":7,"name":"Riaz A. Shah","email":"","orcid":"","institution":"FVSc \u0026 AH, SKUAST-Kashmir, SKUAST-K","correspondingAuthor":false,"prefix":"","firstName":"Riaz","middleName":"A.","lastName":"Shah","suffix":""},{"id":398876422,"identity":"fe7e0d2e-55b0-4e7f-a714-394ce512637d","order_by":8,"name":"Sheikh F. Ahmad","email":"","orcid":"","institution":"King Saud University","correspondingAuthor":false,"prefix":"","firstName":"Sheikh","middleName":"F.","lastName":"Ahmad","suffix":""},{"id":398876423,"identity":"5d2e83a9-0442-4146-8be3-47b9ef89c755","order_by":9,"name":"Haneen A. Al-Mazoura","email":"","orcid":"","institution":"King Saud University","correspondingAuthor":false,"prefix":"","firstName":"Haneen","middleName":"A.","lastName":"Al-Mazoura","suffix":""},{"id":398876424,"identity":"8905281e-8587-4991-a65a-eea05e8b3f0d","order_by":10,"name":"Basharat Bhat","email":"","orcid":"","institution":"SKUAST-Kashmir","correspondingAuthor":false,"prefix":"","firstName":"Basharat","middleName":"","lastName":"Bhat","suffix":""},{"id":398876425,"identity":"f805697b-0ca7-4197-b678-235450d9c8bf","order_by":11,"name":"Syed Mudasir Ahmad","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA0UlEQVRIiWNgGAWjYHACAxBKYGNgPgDkSMiQooUtAaSFh0gtDAxA5TxgBmEtujOSN378UXAvj4+95/OrGzUWPAzsh49uwKfF7EZasTSPQXExG8/ZbdY5x4AO40lLu4FfS46BNINBQmKbRO424xw2oBYJHjNCWox//gBryXlmnPOPOC1mEjwQLcyPc9uI0XLmWZk1UAvQL8fMmHP7JHjYCPrlePLmmz/+JOTJtzc//pzzrU6On/3wMbxakAGbBJgkVjkIMH8gRfUoGAWjYBSMHAAAQKpEXCUJL6UAAAAASUVORK5CYII=","orcid":"","institution":"FVSc \u0026 AH, SKUAST-Kashmir, SKUAST-K","correspondingAuthor":true,"prefix":"","firstName":"Syed","middleName":"Mudasir","lastName":"Ahmad","suffix":""}],"badges":[],"createdAt":"2025-01-03 11:08:26","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5757781/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5757781/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":73309165,"identity":"a22f11c9-7187-4863-84cb-f3cac1e21693","added_by":"auto","created_at":"2025-01-08 17:51:22","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":92612,"visible":true,"origin":"","legend":"\u003cp\u003eFigure A: Comprehensive Genomic Mapping of circRNAs in Chickens by providing the detailed insight into the extensive distribution of circRNAs across the chicken genome. The x-axis corresponds to the chromosome number, while the y-axis represents the number of circRNAs on each chromosome.\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-5757781/v1/abf22cac772ec6b34af9d149.jpeg"},{"id":73309168,"identity":"f5b08e11-5835-457a-bf87-0cd9be31ffe8","added_by":"auto","created_at":"2025-01-08 17:51:22","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":53482,"visible":true,"origin":"","legend":"\u003cp\u003eFigure 1. Genes targeted by circRNAs and their regulatory links with transcription factors in \u003cem\u003eGallus gallus\u003c/em\u003egenome\u003cem\u003e.\u003c/em\u003eThis figure represents relationships between genes targeted by circRNAs and their influence on transcription factors in the \u003cem\u003eGallus gallus\u003c/em\u003e genome. In the figure, circular nodes represent genes targeted by DE circRNAs, while rhombus-shaped nodes represent target transcription factors (TFs). The color of circular nodes indicates the direction of regulation: red circular nodes represent downregulation, and green circular nodes represent upregulation.The intensity of the color in circular nodes corresponds to the level of gene expression: darker colors represent higher expression, while lighter colors represent lower expression. The interactions were identified from interactome database and figure was developed using Cytoscape program.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-5757781/v1/30061da12084eae9cc7dae15.png"},{"id":77130996,"identity":"571ffd7f-df0b-482e-afcc-ac33a7315edf","added_by":"auto","created_at":"2025-02-25 12:09:09","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":991033,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5757781/v1/9f983375-fb3e-4fa3-8ce3-d65f58492e68.pdf"},{"id":73309838,"identity":"50d2cd40-de7f-41f3-bd11-990727984325","added_by":"auto","created_at":"2025-01-08 17:59:22","extension":"xlsx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":344468,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementaryfileone.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-5757781/v1/629738f81760cf7815d0e5f5.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Genome-wide identification of circular RNAs to elucidate the genetic basis of disease resistance to Salmonella infection in Chicken","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003e\u003cem\u003eSalmonella\u0026nbsp;\u003c/em\u003eenterica serovar Typhimurium (\u003cem\u003eSalmonella\u0026nbsp;\u003c/em\u003eTyphimurium) bacteria belongs to the Enterobacteriaceae family and is known to cause various disease conditions, ranging from gastroenteritis to potentially life-threatening enteric fevers. Salmonellosis is the most common food-borne zoonotic disease, affecting a wide range of hosts and posing a significant public health concern (Baron 1996). The main infection sources for humans include consuming contaminated meat products, including poultry meat (Antunes, Mour\u0026atilde;o et al. 2016). Developing countries lack effective vaccination programs against Salmonellosis in poultry, and as such, this disease incurs huge economic losses in terms of mortality and morbidity. Understanding the pathogenesis of this disease is crucial in developing therapeutic and preventive strategies against this infection. Disease resistance is primarily determined by the genetic interaction between the pathogen and the host. When bacteria are identified, the bactericidal activity of host macrophages triggers the maturation and migration of dendritic cells, along with the production of inflammatory chemokines, cytokines, and interleukins for effective elimination of bacteria by the immune system (Wang, Zhu et al. 2020). Salmonella has evolved mechanisms to bypass host defense barriers and suppress the activation of the immune response through its virulence genes called Salmonella pathogenicity islands 1 and 2 (Wang, Zhu et al. 2020), which harbors two distinct virulence-related T3SSs (Type III secretion systems) that operate at different stages during infection. These are important in inducing and activating intestinal inflammatory responses, causing diarrhea, establishing intestinal colonization and initiating systemic disease (Hansen-Wester, Hensel et al. 2001, Haraga, Ohlson et al. 2008). In developing countries, genetic disease resistance is particularly important, as indigenous breeds tend to be more resistant to local diseases (Pal, Chakravarty et al. 2020). The \u003cem\u003eKashmir Faverolla\u003c/em\u003e, a notable indigenous chicken breed from the northern Indian state of Jammu and Kashmir, exhibits high disease resistance than other breeds like commercial broilers (Iqbal and Pampori 2008). This resistance is attributed by the enrichment of certain genes and signaling pathways involved in both innate and adaptive immune responses against bacterial infections, including interleukins, cytokines, NOS2, Av\u0026beta;-defensins, toll-like receptors, and other immune-related gene families. Pathway analysis revealed significant enrichment in pathways such as MAPK signaling, PPAR signaling, NOD-like receptor signaling, TLR signaling, and endocytosis in \u003cem\u003eKashmir Faverolla\u003c/em\u003e. Conversely, some \u003cem\u003eTLR\u003c/em\u003e genes show upregulation in susceptible chicken breeds (Dar, Ahmad et al. 2022). Following infection with Salmonella enterica serovar typhimurium, \u003cem\u003e\u0026nbsp;Kashmir Favorella\u003c/em\u003e chicken exhibit three genes with altered expression levels namely, Nuclear Factor Kappa B (NF-\u0026kappa;B1), Forkhead Box Protein O3 (FOXO3) and Paired box 5 (Pax5). The three differentially expressed genes (NF-\u0026kappa;B1, FOXO3, and PaX5) impact 12 interacting proteins and 16 transcription factors (TFs). Among these, cyclic adenosine monophosphate Response Element Binding protein (CREBBP), erythroblast transformation-specific (ETSI), Tumour-protein 53 (TP53I), IKKBK, lymphoid enhancer-binding factor-1 (LEF1), and interferon regulatory factor-4 (IRF4) are notably involved in immune responses (Ahmad, Bhat et al. 2023). \u003cem\u003e\u0026nbsp;Kashmir Favorella\u0026nbsp;\u003c/em\u003echickens have high-impact SNPs in immune-related pathways, whereas broilers have SNPs in metabolic pathways, suggesting these genetic differences contribute to their respective disease resistance and susceptibility to \u003cem\u003eSalmonella\u0026nbsp;\u003c/em\u003e(Dar, Bhat et al. 2023). Role of circular RNAs (circRNAs) in mediating resistance to bacterial diseases in poultry is reported in diseases like New Castle Disease (Chen, Ruan et al. 2023), \u003cem\u003eSalmonella enterica\u003c/em\u003e serovar Enteritidis (SE) (Zheng, Liu et al. 2019), Avian pathogenic \u003cem\u003eE. coli\u0026nbsp;\u003c/em\u003e(APEC) (Sun, Yang et al. 2022). These studies collectively indicate that circRNAs play significant roles in mediating the immune response and resistance to bacterial infections in poultry. CircRNAs have emerged as key regulators of gene expression and have been implicated in various biological processes, including immune responses and infectious diseases (Jeck, Sorrentino et al. 2013, Memczak and JenS 2013, Barrett, Wang et al. 2015). The varying expression of circRNAs in response to infections suggests they could be useful as biomarkers for disease resistance and as targets for therapeutic treatments.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn this study, we aimed to investigate the role of circular RNAs (circRNAs) in mediating resistance to \u003cem\u003eSalmonella\u003c/em\u003e Typhimurium infection in two distinct chicken breeds: broilers (Cobb 430) (known for their susceptibility) and \u003cem\u003eKashmir Faverolla\u003c/em\u003e (recognized for their resistance). CircRNA influences the progression and evolution of various diseases by sequestering miRNAs as sponges. Multiple studies indicate that circRNA competitively binds miRNA, thereby fostering processes like proliferation, migration, and inflammation in mononuclear macrophages (Zhang, Zhang et al. 2018). Studies have aimed to build circRNA-miRNA-mRNA networks for predicting molecular functions and pathways (Kong, Sun et al. 2021). By profiling circRNA expression patterns in response to \u003cem\u003eSalmonella\u003c/em\u003e infection across different chicken breeds, we attempt to uncover breed-specific variations in circRNA expression and their potential implications for disease resistance.\u0026nbsp;\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cp\u003eThe study utilizes RNA sequencing data from our prior research (GEO ID: GSE168060) where we analyzed gene expression and pathways related to bacterial infection defense in \u003cem\u003eKashmir Faverolla\u003c/em\u003e chickens. Building on this, we further examined circRNAs from the same set of samples with the purpose of identifying role of circRNAs in regulation of the host\u0026rsquo;s immune response to bacterial pathogens. This study is exploratory in nature and provides a foundational report on circRNAs related to disease resistance in Salmonella infections. Data collection and sequencing protocols were detailed in the previous study (4).\u003c/p\u003e \u003cp\u003eThe experimental trial took place at the animal facility center at the Faculty of Veterinary Sciences and Animal Husbandry, Sher-e-Kashmir University of Agricultural Sciences and Technology of Kashmir, India. One hundred one-day-old chicks, fifty each of \u003cem\u003eKashmir Faverolla\u003c/em\u003e and broiler (Cobb 430) breeds, were obtained. They were divided into four groups (two breeds \u0026times; two treatment groups) and housed in controlled environments. The infected groups were orally challenged with \u003cem\u003eSalmonella\u003c/em\u003e Typhimurium, while control groups received nutrient broth. Daily monitoring and fecal swab collection confirmed Salmonella colonization. On the fifth day post-infection, liver and spleen samples were collected for RNA extraction. Total RNA was extracted, treated for DNA contamination, and assessed for quality with the Revert Aid First Strand cDNA synthesis kit \u003cem\u003e(Thermo Scientific, USA).\u003c/em\u003e RNA meeting quality criteria underwent cDNA library construction and sequencing using the Illumina NovaSeq platform, generating paired-end reads with a length of 150 base pairs [4].\u003c/p\u003e \u003cdiv id=\"Sec2\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Data Analysis:\u003c/h2\u003e \u003cp\u003e \u003cstrong\u003ea)Quality Control and Preprocessing\u003c/strong\u003e \u003cp\u003eRaw sequencing reads underwent quality control checks using FastQC program version 0.11.2 (Andrews \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2010\u003c/span\u003e), followed by adapter trimming and quality filtering using tools such as Trimmomatic(Paya-Milans, Olmstead et al. \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eb) Identification of DE circRNAs\u003c/em\u003e: A dual methodology was engaged to identify differentially expressed (DE) circRNAs. In the first approach, they seekCRIT tool version 1.0.0.b(Chaabane, Andreeva et al. \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) was executed on the filtered reads to pinpoint DE circRNAs. In seekCRIT pipeline, STAR alignment tools was utilized to detect back-splice junctions (BSJs) which is a key characteristic of circRNA. STAR alignment implementation helps to minimize false positives by accurately distinguishing between linear and circular RNA reads. Also, astringent quality filters were used for the raw reads to exclude low-quality sequences. Only high-quality reads with a specified minimum quality (Q Score\u0026thinsp;\u0026gt;\u0026thinsp;30) score were used in the analysis to reduce the likelihood of including sequencing errors.The second strategy for DE circRNA identification entailed a process of quantification using CIRI and CYCLER (Gao, Wang et al. \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2015\u003c/span\u003e, Stefanov and Meyer \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), followed by the application of the edgeR software (Robinson, McCarthy et al. \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). The quasi-likelihood F-test method from the edgeR program was employed. This statistical approach enabled the identification of circRNAs that exhibited significant differential expression. The criteria for DE circRNA selection included a P-value threshold of less than 0.05 and a log-fold change (logFC)\u0026thinsp;\u0026gt;\u0026thinsp;1. The final ensemble of DE circRNAs encompassed those circRNAs that were consistent between the seekCRIT dataset and the edgeR dataset. Specifically, circRNAs exhibiting a log2FC exceeding 1 and a p-value below 0.05 in both datasets were classified as differentially expressed in the two distinct breeds. All identified DE circRNAs were cross-referenced against established circRNAs from diverse species, including \u003cem\u003eHomo sapiens\u003c/em\u003e, \u003cem\u003eMus musculus\u003c/em\u003e, \u003cem\u003eSusscrofa\u003c/em\u003e, \u003cem\u003eGallus gallus\u003c/em\u003e, and \u003cem\u003eCanis lupus\u003c/em\u003e from circRNA databases to ascertain conservation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Functional analysis\u003c/h2\u003e \u003cp\u003eTo deepen our comprehension of the functions of messenger RNAs (mRNAs) targeted by differentially expressed circular RNAs (DE circRNAs), we conducted Gene Ontology (GO) and Pathway enrichment analyses. These analyses provided insights into the potential biological functions and pathways associated with these mRNAs. Employing the well-established KOBAS 3 server, we identified enriched Gene Ontology (GO) terms and pathways among the set of mRNAs targeted by DE circRNAs. Network analysis of target mRNAs was performed using STRING-DB, a resource for protein-protein interaction (PPI) analysis (using a strict confidence-score of 0.9). We utilized Maximal Clique Centrality (MCC) and Degree metrics to pinpoint key nodes in the PPI network, visualizing it through Cytoscape software. Additionally, we delved into drug-gene interactions using the DrugBank database and disease gene interactions via the DisGeNET database. This multifaceted approach unveiled connections between genes, drugs, and diseases, enriching our understanding of their complex interplay.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cp\u003eA total of 2114 circular RNAs (circRNAs) were identified from samples under study. Most of these were circRNAs, accounting for approximately 63.97% of the total, while a smaller proportion comprised cirRNAs, making up about 35.98%. When looking at their chromosomal distribution, a significant portion of circRNAs were found on chromosome 1, followed by chromosome 4. In contrast, the fewest circRNAs were observed on chromosome 36 and chromosome number above 39 (Figure A).\u003c/p\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Analysis of DE circRNAs\u003c/h2\u003e \u003cp\u003eA total of 26 DE circRNAs were found across six distinct comparison groups under our study's purview. We found that 12 mRNAs are targeted by the differentially expressed circRNAs (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Our investigation revealed intricate interactions between circRNAs and mRNAs, particularly noting that \u003cem\u003ePIT54, FGG, ADK\u003c/em\u003e and \u003cem\u003eCR1\u003c/em\u003e engage with multiple circRNAs, implying a varied regulatory role. Notably, \u003cem\u003eSPINK5\u003c/em\u003e and \u003cem\u003eFGB\u003c/em\u003e displayed intriguing associations with circRNAs, with \u003cem\u003eSPINK5\u003c/em\u003e being regulated by four circRNAs and FGB by six, suggesting a heightened impact across diverse groups. Additionally, our analysis of the protein-protein interaction (PPI) network revealed FGB's interaction with both FGG and \u003cem\u003eALB\u003c/em\u003e genes (Supplementary file one), hinting at potential collaborative functions in combating Salmonella infection. Moreover, the broader context provided by the PPI network underscores the pivotal roles of \u003cem\u003eFGB, FGG\u003c/em\u003e, and \u003cem\u003eALB\u003c/em\u003e in mediating the host's response to \u003cem\u003eSalmonella\u003c/em\u003e infection in poultry, inviting further exploration into their intricate mechanisms of action in defense against this bacterial pathogen (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" 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\u003eSummary of the sample groups involved in the study and the DE circRNAs that were identified. In the \u003cem\u003e'Groups'\u003c/em\u003e column, the entries represent the sample types and the number of samples utilized in the analysis.\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\u003eGroup_ID\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGroups\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNumber of DE circRNAs\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eG1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eBroiler Liver Infected (BL \u0026ndash; 3 Samples)\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eVs\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eBroiler Liver Control (BLC \u0026ndash; 2 Samples)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eTotal 7 DE circRNAs\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003e 7 upregulated in BL\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eG2\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eBroiler Spleen Infected (BS \u0026ndash; 3 Samples)\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eVs\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eBroiler Spleen Control (BSC \u0026ndash; 2 Samples)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eTotal 2 DE circRNAs.\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003e2 upregulated in BS\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eG3\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eBroiler Liver Infected (BL \u0026ndash; 3 Samples)\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eVs\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eFaverolla Infected Liver (FL \u0026ndash; 3 Samples)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eTotal 10 DE circRNAs\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003e2 upregulated in BL \u0026amp;\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003e8 upregulated in FL\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eG4\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eBroiler Spleen Infected (BS \u0026ndash; 3 Samples)\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eVs\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eFaverolla Spleen Infected (FS \u0026ndash; 2 Samples)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eTotal 2 DE circRNAs\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003e2 upregulated in BS\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eG5\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eFaverolla Liver Infected (FL \u0026ndash; 3 Samples)\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eVs\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eFaverolla Liver Control (FLC \u0026ndash; 2 Samples)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eTotal 2DE circRNAs\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003e2 upregulated in FL\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eG6\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eFaverolla Spleen Infected (FS \u0026ndash; 2 Samples)\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eVs\u003c/em\u003e\u003c/p\u003e \u003cp\u003e\u003cem\u003eFaverolla Spleen Control (FSC \u0026ndash; 2 Samples)\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eTotal 1 DE circRNAs\u003c/em\u003e\u003c/p\u003e \u003cp\u003e1 \u003cem\u003eupregulated in FS\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003e\u003cem\u003eG1 to G6 represent number of groups of birds taken in the study. BL: Broiler Liver Infected, BS: Broiler Spleen Infected, BSC: Broiler Spleen Control, FL: Faverolla Infected Liver, FLC: Faverolla Liver Contro, FS: Faverolla Spleen Infected, FSC: Faverolla Spleen Control, DE cirRNAs: Differentially Expressed cirRNAs\u003c/em\u003e\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDE circRNAs and their corresponding target mRNAs across all contrast groups (G1 to G6). This table presents a comprehensive overview of the interplay between DE circRNAs and their respective target mRNAs within multiple contrast groups (G1 to G6). A total of 24 DE circRNAs have been identified, collectively targeting 12 distinct mRNAs. The circRNA ID is listed in the \"\u003cem\u003eDE_circRNAs\u003c/em\u003e\" column, while the comparison group information for each circRNAis located in the \"\u003cem\u003egroup\u003c/em\u003e\" column. G1 to G6 represent 6 different contrast groups, detail in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The \"Annotation\" column provides details about the location of circRNAs. Within the \"Annotation\" column, the \"+\" and \"-\" symbols indicate the positive and negative DNA strands, respectively. The \"Location\" information represents the chromosome number, starting position, and ending position of the circRNAs.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eS. No\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTarget mRNAs\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDE circRNAs\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGroup\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAnnotation\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"6\" rowspan=\"7\"\u003e \u003cp\u003e1.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"6\" rowspan=\"7\"\u003e \u003cp\u003e\u003cem\u003eFGB\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eciRNA_4_130\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eG5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLocation: Chr4:19781130\u0026ndash;19781271(+)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ecircRNA_4_514\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eG3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLocation: Chr4:19779514\u0026ndash;19779921(+)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eciRNA_4_136\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eG3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLocation: Chr4:19781136\u0026ndash;19781272(+)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eciRNA_4_136\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eG3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLocation: Chr4:19781136\u0026ndash;19781167(+)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ecircRNA_4_984\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eG3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLocation: Chr4:19780984\u0026ndash;19781144(+)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eciRNA_4_136\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eG1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLocation: Chr4:19781136\u0026ndash;19781167 (+)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ecircRNA_4_139\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eG1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLocation: Chr4:19779516\u0026ndash;19781139 (+)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e2.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u003cem\u003eSPINK5\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ecircRNA_13_940\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eG1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLocation: Chr13: 10161940\u0026ndash;10164094(+)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ecircRNA_13_618\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eG1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLocation: Chr13: 10161618\u0026ndash;10162074(+)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ecircRNA_13_039\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eG2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLocation: Chr13: 10164039\u0026ndash;10165289(+)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ecircRNA_13_758\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eG5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLocation: Chr13: 10164758\u0026ndash;10166642(+)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e3.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003ePIT54\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ecircRNA_31_605\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eG1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLocation: Chr31: 244605\u0026ndash;245598(-)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ecircRNA_31_605\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eG3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLocation: Chr31: 244605\u0026ndash;245598(-)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eNPM1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ecircRNA_13_683\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eG1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLocation: Chr13: 2916683\u0026ndash;2916779(-)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eALB\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ecircRNA_4_481\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eG1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLocation: Chr4: 50349481\u0026ndash;50349691(+)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eRPL27\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eciRNA_27_720\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eG2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLocation: Chr27: 5097720\u0026ndash;5097816(+)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eRPL6\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ecircRNA_15_622\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eG2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLocation: Chr15: 6400622\u0026ndash;6400826(+)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e8.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eFGG\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ecircRNA_4_481\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eG3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLocation: Chr4: 19800481\u0026ndash;19801585(-)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ecircRNA_4_581\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eG5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLocation: Chr4: 19800481\u0026ndash;19801585(-)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e9.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eADK\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ecircRNA_6_264\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eG3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLocation: Chr6: 16025264\u0026ndash;16029254(-)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ecircRNA_6_264\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eG5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLocation: Chr6: 16025264\u0026ndash;16029254(-)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eADH1C\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ecircRNA_4_951\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eG3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLocation: Chr4: 59499915\u0026ndash;59500044(-)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e11.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eCR1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ecircRNA_26_872\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eG4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLocation: Chr26: 2706872\u0026ndash;2715091(+)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ecircRNA_26_872\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eG6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLocation: Chr26: 2706872\u0026ndash;2715091(+)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e12.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eRPL31\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ecircRNA_1_087\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eG4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLocation: Chr1: 133477087\u0026ndash;133477203(+)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eExploring the depths of this network, Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e unveils another layer of potential regulation. Transcription factors (TFs) emerge as key players, including \u003cem\u003eKLF9, KLF11, GATAD2A, SSRP1\u003c/em\u003e, and \u003cem\u003eSOX13\u003c/em\u003e. The presence of these Transcription factors (TFs) in the network hints at their possible involvement in orchestrating the expression of genes like \u003cem\u003eFGB, FGG\u003c/em\u003e, and \u003cem\u003eALB\u003c/em\u003e in response to \u003cem\u003eSalmonella\u003c/em\u003e infection.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Analysis of Enriched Terms\u003c/h2\u003e \u003cp\u003eThe results from the Gene Ontology (GO) and pathway enrichment analysis offer valuable insights, emphasizing the importance of specific target mRNAs. Notably, the analysis underscores the essential functions of nine target mRNAs: \u003cem\u003eRPL6, RPL27, ALB, RPL31, FGG, NPM1, FGB, CR1\u003c/em\u003e and \u003cem\u003eADH1C\u003c/em\u003e (Supplementary File one). These genes are implicated in a range of vital biological processes, as evidenced by the enrichment of pathways they participate in. The evidence indicates their participation in the key process like \u003cem\u003eInfectious diseases\u003c/em\u003e (p-value\u0026thinsp;=\u0026thinsp;2.59E-06), \u003cem\u003eMAP2K and MAPK activation\u003c/em\u003e (p-value\u0026thinsp;=\u0026thinsp;0.00006), \u003cem\u003eInnate Immune System\u003c/em\u003e (p-value\u0026thinsp;=\u0026thinsp;0.002), \u003cem\u003eimmune system\u003c/em\u003e (p-value\u0026thinsp;=\u0026thinsp;0.01), and \u003cem\u003eDrug Metabolism\u003c/em\u003e (p-value\u0026thinsp;=\u0026thinsp;0.02), complement and coagulation cascades (p-value\u0026thinsp;=\u0026thinsp;1.43E-06), platelet degranulation (p-value 5.88E-06), seven amino acid metabolism (p-value\u0026thinsp;=\u0026thinsp;4.52E-06), Platelet activation (p-value\u0026thinsp;=\u0026thinsp;0.00055), TP53 regulates transcription of cell cycle genes (p-value\u0026thinsp;=\u0026thinsp;0.013).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e3.3. Enrichment analysis of differentially expressed genes\u003c/h2\u003e \u003cp\u003eThe association between circRNAs and the control of gene expression was further studied using the KEGG pathway enrichment and cluster analysis of gene ontology (GO) for differentially expressed circRNAs. DE circRNA-enriched Gene Ontology (GO) terms were significantly (P-value\u0026thinsp;\u0026le;\u0026thinsp;0.05) enriched in specific number of biological processes. Studying the drug interaction pathways, we found the interaction of gene \u003cem\u003eALB\u003c/em\u003e with cefotaxime, vancomycin, and Rifampicin; interaction of \u003cem\u003eFGB\u003c/em\u003e with Alfimeprase, sucralfate; interaction of \u003cem\u003eFGG\u003c/em\u003e with sucralfate and interaction of \u003cem\u003eADH1C\u003c/em\u003e with pyrazole. Elucidating the chemical interactions of the upregulated genes in our study, we found their association with many hormones, vitamins, and chemical compounds (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Further, genes \u003cem\u003eADK\u003c/em\u003e and \u003cem\u003eALB\u003c/em\u003e were seen in many disease interactions. \u003cem\u003eADK\u003c/em\u003e dysfunction was seen to cause abnormality in liver enzymes, liver dysfunction, decreased liver function, liver dysfunction, muscle degeneration, abnormal LFT, and failure to gain weight. \u003cem\u003eALB\u003c/em\u003e dysfunction causes hypoalbuminemia and kidney diseases.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe table provides an in-depth analysis of how differentially expressed circular RNAs (circRNAs) and various small molecules interact during a \u003cem\u003eSalmonella\u003c/em\u003e infection. The table provides a detailed view of how circRNAs coordinate molecular interactions and potential regulatory networks during \u003cem\u003eSalmonella\u003c/em\u003e infection. These connections provide insights for managing infections and developing therapeutic strategies.\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\u003eS.No.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGenes targeted by DE circRNAs\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eInteracting Molecules\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eSPINK5\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEstradiol\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eFGG\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEstradiol, Norethindrone\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eFGB\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFlurouracil, Vitamin k\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eALB\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVitamin E, Flurouracil, Estradiol, Vitamin k, Zinc, Acetaminophen, Vorinostat.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eRPL31\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEstradiol, Vincristine\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eCR1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eZinc, Methotrexate\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eRPL6\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFlurouracil, Estradiol, Acetaminophen, Raloxifene hydrochloride\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eNPM1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFlurouracil, Estradiol, Raloxifene hydrochloride\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eADK\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEstradiol, Vitamin k, Vorinostat\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eADH1C\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEstradiol, Acetaminophen\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"},{"header":"4. Discussion","content":"\u003cp\u003eWhen the host encounters an infection, its immune system activates to eliminate the pathogen and repair any damage, striking a balance between resisting the infection and tolerating its effects. This balance is essential in managing pathogen invasion effectively (Pi\u0026ntilde;ero, Queralt-Rosinach et al. \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). CircRNAs have been implicated in various infectious and inflammatory diseases (Yu, Mi et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Circular RNAs (circRNAs) are recognized as potent diagnostic and therapeutic biomarkers across a variety of diseases. In the present study, we utilized the RNA sequencing data from our previous study (GEO ID: GSE168060) and examined cirRNAs from the same samples in order to identify their role in regulating host immune response against bacterial infection. In our previous study, we found \u003cem\u003eKashmir Favorella\u003c/em\u003e chicken have higher enrichment of genes and pathways that protect it against bacterial infections (Dar, Ahmad et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Many studies have been carried out to identify the differentially expressed (DE) cirRNAs in different diseased condition like in community-aquired pneumonia (CAP) (Zhao, Zheng et al. \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), in pulmonary tuberculosis (TB) diagnosis (Qian, Liu et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), in meningitic \u003cem\u003eE. coli\u003c/em\u003e infection (Yang, Xu et al. \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2018\u003c/span\u003e), in \u003cem\u003eSalmonella\u003c/em\u003e Enteritidis (SE) infection (Zheng, Liu et al. \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Our study investigates the mechanisms of disease resistance in broilers (susceptible) and \u003cem\u003eKashmir Favorella\u003c/em\u003e (resistant) chickens by analyzing their circRNA expression. We found that differentially expressed circRNAs interact with proteins like \u003cem\u003eFGG, SPINK5\u003c/em\u003e, and \u003cem\u003eADK\u003c/em\u003e, suggesting a role in regulating disease resistance. (Yu, Mi et al. \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Also, expression of certain host genes get altered during inflammatory phase of \u003cem\u003eSalmonella\u003c/em\u003e infection. These include Acute phase proteins (\u003cem\u003eAPP\u003c/em\u003e), including alpha1-acid glycoprotein (\u003cem\u003eAGP\u003c/em\u003e), Serum Amyloid A (\u003cem\u003eSAA\u003c/em\u003e), \u003cem\u003ePIT54\u003c/em\u003e, C-reactive protein (\u003cem\u003eCR1\u003c/em\u003e), and ovotransferrin\u003cem\u003e(OVT)\u003c/em\u003e (Marques, Nordio et al. \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Understanding these mechanisms can aid in developing strategies for managing pathogen invasion effectively. Earlier reports explain the role of \u003cem\u003eCR1\u003c/em\u003e in mediating complement-mediated inflammatory reactions (Khera and Das 2009) and role of \u003cem\u003eADK\u003c/em\u003e gene in mediating infection and inflammation by regulating adenosine levels (Park, Jo et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). \u003cem\u003eSPINK5\u003c/em\u003e is also known to influence the immune response in host upon pathogen invasion (Park, Jo et al. \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). In our study, PPI network analyses results show the interaction of \u003cem\u003eFGB\u003c/em\u003e with \u003cem\u003eFGG\u003c/em\u003e and \u003cem\u003eALB\u003c/em\u003e, affirming their mutual connection in functioning and mediation of \u003cem\u003eSalmonella\u003c/em\u003e infection. Our study highlights the expression of \u003cem\u003eFGG, FGB\u003c/em\u003e, and \u003cem\u003eALB\u003c/em\u003e genes mediated by transcription factors (TFs), namely, \u003cem\u003eKLF2, KLF9, KLF11, GATAD2A, SSRP1\u003c/em\u003e, and \u003cem\u003eSOX13\u003c/em\u003e. The differential expression of these transcription factors and their crucial role in genetic resistance in \u003cem\u003eSalmonella\u003c/em\u003e infectious conditions is in accordance with previous study (Ahmad, Bhat et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). In the present study, Gene ontology (GO) and KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway analysis was done to understand the detailed functional pathways and biological processes of this disease. Differentially expressed (DE) circRNA in host genes reveal their involvement in critical biological processes such as infectious diseases, \u003cem\u003eMAPK\u003c/em\u003e activation, and immune system responses. This analysis highlights the roles of nine target mRNAs, namely \u003cem\u003eRPL6, RPL27, ALB, RPL31, FGG, NPM1, FGB, CR1\u003c/em\u003e, and \u003cem\u003eADH1C\u003c/em\u003e in \u003cem\u003eSalmonella\u003c/em\u003e infection. These genes are involved in various biological processes like infectious diseases, \u003cem\u003eMAP2K\u003c/em\u003e and \u003cem\u003eMAPK\u003c/em\u003e activation, innate immune system, immune system, and drug metabolism. Consistent with previous studies, we found that \u003cem\u003eSalmonella\u003c/em\u003e organism manipulates host cell functions, inhibits ribosomal gene transcription (Simpson-Haidaris, Courtney et al. \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e1998\u003c/span\u003e), and activates \u003cem\u003eMAPK\u003c/em\u003e and \u003cem\u003eAKT\u003c/em\u003e pathways to enhance virulence (Liu, Lu et al. \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2010\u003c/span\u003e, Scanu, Spaapen et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). Additionally, we found numerous circRNAs to be associated with amino acid metabolism pathways and pathways related to complement and coagulation as supported by earlier studies (Yeaman and sciences 2010, Hitchcock, Cook et al. \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2015\u003c/span\u003e, Ryan, Pati et al. \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2015\u003c/span\u003e, Barrila, Sarker et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Collectively, our findings highlight the complex regulatory roles of circRNAs in host responses to \u003cem\u003eSalmonella\u003c/em\u003e infection, particularly in immune response pathways, metabolism, and cell cycle regulation. Further, our study depicts interaction of specific circRNAs with molecules like estradiol, vitamin E, vitamin K and zinc, suggesting these molecules' significant roles in modulating immune responses to \u003cem\u003eSalmonella\u003c/em\u003e (Gourdy, Araujo et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2005\u003c/span\u003e, Dalia, Loh et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) (Liu, Mosenthin et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). This study provides a comprehensive understanding of the molecular interactions during Salmonella infection and opens avenues for potential therapeutic targets.\u003c/p\u003e"},{"header":"5. Conclusions","content":"\u003cp\u003eThe study aims to explore circRNA expression patterns in response to Salmonella infection across different chicken breeds, revealing breed-specific distributions and variations, particularly in \u003cem\u003eKashmir Faverolla\u003c/em\u003e and commercial broilers, with 26 differentially expressed circRNAs identified. Network analyses highlighted collaborative roles of genes like FGB, FGG, and ALB in mediating the response to Salmonella, while functional analyses emphasized the involvement of various biological processes in immune responses and infectious diseases\u003c/p\u003e"},{"header":"6. Limitations","content":"\u003cp\u003eWhile our study elucidates circRNAs' roles in \u003cem\u003eSalmonella\u003c/em\u003e immune responses, it is constrained by a limited sample size of specific chicken breeds, a cross-sectional design, and a focus solely on \u003cem\u003eSalmonella\u003c/em\u003e Typhimurium. Functional validation, temporal dynamics, broader pathogen inclusion, \u003cem\u003ein vivo\u003c/em\u003e complexities, and environmental factors were not fully explored. Further research integrating diverse breeds, longitudinal designs, functional assays, and environmental considerations is crucial for validating and broadening the implications of our findings in circRNA-mediated immune regulation.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor Contributions:\u003c/strong\u003e Conceptualization, S.A.; Formal analysis, B.B. and M.K.; Investigation, S.A; Methodology, M.K., B.B, M.D., Z.H., N.S., R.S. and S.A.; Supervision, S.A.; Validation, B.B.; Visualization, M.K., N.S., S.F., H.A. and S.A.; Writing \u0026ndash; original draft, M.K., B.B. and S.A.; Writing \u0026ndash; review \u0026amp; editing, M.K., B.B., M.D., J.N., S.B., S.F. and H.B.\u003c/p\u003e\n\u003cp\u003eAll authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e None.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments:\u003c/strong\u003e We are highly thankful to DST for \u0026ldquo;PURSE-2023 to carry out this work. We also acknowledge The Science and Engineering Research Board (SERB), Department of Science and Technology, Government of India. The authors acknowledge and extent their appreciation to the Researchers Supporting Project Number (RSPD2025R783), King Saud University, Riyadh, Saudi Arabia for their support to carry out this work.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclarations: Institutional Review Board Statement-\u003c/strong\u003e All experimental procedures strictly adhered to the guidelines outlined by the Institutional Animal Ethics Committee. Approval from the institutional animal ethics committee on ethical standards in animal experimentation was granted under reference number \u003cstrong\u003eAU/FVSc/PS-57/16021.\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Participate:\u0026nbsp;\u003c/strong\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Publish declarations:\u0026nbsp;\u003c/strong\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInformed Consent Statement:\u003c/strong\u003e Not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement:\u003c/strong\u003e None.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of Interest:\u003c/strong\u003e The authors declare that the research was conducted in absence of any commercial or financial relationships that could be construed as a potential conflict of interest.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAhmad SM, Bhat SS, Shafi S, Dar MA, Saleem A, Haq Z, Farooq N (2023) J. 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I., Li (2019) Cecal circRNAs are associated with the response to Salmonella enterica serovar enteritidis inoculation in the chicken. 10: 1186\u003c/span\u003e\u003c/li\u003e\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":"","lastPublishedDoi":"10.21203/rs.3.rs-5757781/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5757781/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cem\u003eSalmonella\u003c/em\u003e infections have far-reaching implications for public health due to their potential to cause various human illnesses, ranging from gastroenteritis to more severe conditions like enteric fever. These infections are predominantly food-borne and are often linked to consuming contaminated meat products, with poultry being a common transmission source. Consequently, Salmonellosis ranks among the most prevalent food-borne illnesses, leading to substantial morbidity, hospitalizations, and even fatalities. In this study, we investigated the role of circular RNAs (circRNAs) in mediating the response to \u003cem\u003eSalmonella\u003c/em\u003e Typhimurium infection in poultry, focusing on two chicken breeds: broiler (Cobb 430) (susceptible) and \u003cem\u003eKashmir Faverolla\u003c/em\u003e (resistant). Through high-throughput RNA sequencing, we identified and analyzed circRNA expression patterns in liver and spleen samples from both breeds. Results reveal a comprehensive catalog of circRNAs, with 26 differentially expressed (DE) circRNAs identified across various comparison groups. Our study revealed that the circRNAs associated with genes conferring resistance to \u003cem\u003eKashmir Faverolla\u003c/em\u003e are predominantly located on chromosome 1, with a notable presence also observed on chromosome 4. Moreover, genes \u003cem\u003eFGB, FGG\u003c/em\u003e, and \u003cem\u003eALB\u003c/em\u003e could play pivotal roles in mediating the response to \u003cem\u003eSalmonella\u003c/em\u003e infection in poultry. Network analyses and protein-protein interaction networks shed light on the interconnectedness of genes like \u003cem\u003eFGB, FGG\u003c/em\u003e, and \u003cem\u003eALB\u003c/em\u003e, suggesting their collaborative roles in mediating the response to \u003cem\u003eSalmonella\u003c/em\u003e infection. Moreover, functional analyses uncover significant biological processes associated with target mRNAs, emphasizing their involvement in immune responses, infectious diseases, and molecular pathways. Keywords: Circular RNA; \u003cem\u003eSalmonella;\u003c/em\u003e Infection; Chicken; Disease resistance.\u003c/p\u003e","manuscriptTitle":"Genome-wide identification of circular RNAs to elucidate the genetic basis of disease resistance to Salmonella infection in Chicken","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-01-08 17:51:17","doi":"10.21203/rs.3.rs-5757781/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":"6e45daca-f610-4d96-b6d0-15eb3618cf5a","owner":[],"postedDate":"January 8th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-02-25T12:08:40+00:00","versionOfRecord":[],"versionCreatedAt":"2025-01-08 17:51:17","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5757781","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5757781","identity":"rs-5757781","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}