Transcriptomic Insights into Mycobacterium orygis Infection-associated Pulmonary Granulomas Reveal Multicellular Immune Networks and Tuberculosis Biomarkers in Cattle.

Transcriptomic Insights into Mycobacterium orygis Infection-associated Pulmonary Granulomas Reveal Multicellular Immune Networks and Tuberculosis Biomarkers in Cattle. | 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 Transcriptomic Insights into Mycobacterium orygis Infection-associated Pulmonary Granulomas Reveal Multicellular Immune Networks and Tuberculosis Biomarkers in Cattle. Rishi Kumar, Sripratyusha Gandham, Vinay Bhaskar, Manas Praharaj, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5184037/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 Mycobacterium orygis , a member of the Mycobacterium tuberculosis complex (MTBC), has emerged as a significant contributor to tuberculosis (TB) in cattle, wildlife, and humans. However, understanding about its pathogenesis and severity is limited, compounded by the lack of reliable TB biomarkers in cattle. This study delves into the comparative pathology and transcriptomic landscape of pulmonary granulomas in cattle naturally infected with M. orygis , using high-throughput RNA sequencing. Histopathological analysis revealed extensive, multistage granulomatous, necrotic, and cavitary lesions, indicative of severe lung pathology induced by M. orygis . Transcriptomic profiling highlighted numerous differentially expressed genes and dysregulated pathways related to immune response modulation and extracellular matrix remodeling. Additionally, cell type enrichment analysis provided insights into the multicellularity of the granulomatous niche, emphasizing complex cell-cell interactions within TB granulomas. Comparative transcriptomics leveraging publicly available bovine and human TB omics datasets, 14 key immuno-modulators (SOD2, IL1α/β, IL15, IL18, CCL2/MCP-1, CCL3/MIP-1α, CCL4/MIP-1β, CCL8/MCP-2, CCL20/MIP-3α, CXCL2/MIP-2, CXCL10/IP-10, CXCL11, and IFN-γ) were identified as potential biomarkers for active TB in cattle. These findings significantly advance our understanding of M. orygis pathogenesis in bovine TB and highlight potential targets for the development of diagnostic tools for managing and controlling the disease. Tuberculosis Bovine tuberculosis Mycobacterium orygis Granuloma Transcriptome Biomarkers Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Introduction Bovine tuberculosis (bTB) is a persistent threat affecting both cattle and wildlife and serving as a potential source of zoonotic TB in humans globally. Although primarily associated with Mycobacterium bovis , bTB can also be caused by other members of the Mycobacterium tuberculosis complex (MTBC), including the human tubercle bacillus- M. tuberculosis [ 1 ]. Recent epidemiological studies have highlighted a surge in TB cases in cattle and wildlife attributed to M. orygis , an emerging member of the MTBC [ 2 ]. Moreover, M. orygis has emerged as a primary cause of zoonotic TB in humans in India, surpassing M. bovis , presenting novel challenges to TB control efforts [ 3 ]. Despite considerable research efforts, the identification of reliable biomarkers for confirming bovine TB remains challenging, hindering global disease management and control strategies [ 4 ]. Consequently, there is a need for a more thorough investigation into the molecular mechanisms underlying TB infection in cattle. Prior studies on various cellular models such as bovine PBMC, PBMC-derived macrophages, and bovine alveolar macrophages have deepened our understanding of bTB immunopathogenesis, highlighting the major immune responses mounted against mycobacterial invasion in the bovine host [ 5 – 7 ]. However, except our recent study of early host responses to TB infection in the bovine using a bovine 3D pulmosphere model [ 8 ], knowledge on the multicellular intricacies of pulmonary granuloma and complexity of the immune networks during active TB in the bovine is limited. Similarly, despite the growing recognition of M. orygis as a leading cause of TB, our understanding of its pathogenesis and host immune responses, particularly in cattle, remains inadequate. Understanding the host transcriptome during M. orygis infection is crucial for several reasons. Firstly, it provides insights into the molecular mechanisms underlying host-pathogen interactions, including the activation of innate and adaptive immune responses, inflammatory pathways, and tissue remodeling processes. Secondly, transcriptomic analysis enables the identification of candidate biomarkers for detection, diagnosis, and monitoring of bTB in cattle populations. These biomarkers have the potential to revolutionize bTB surveillance and control programs, facilitating timely intervention and disease management. Furthermore, transcriptomic profiling offers a foundation for the development of host directed therapeutic interventions and vaccine strategies tailored to combat M. orygis infection in cattle. Addressing this gap, our research endeavors to delineate the transcriptional landscape of pulmonary granuloma of M. orygis -infected cattle. We aim to elucidate the complex interactions between different types of cells and the core immune networks operative within pulmonary granulomas, the hallmark lesions of TB infection [ 9 ]. Furthermore, this research goes beyond mere descriptive analysis by leveraging comparative transcriptomic data from publicly available repositories encompassing bovine and human TB datasets. By juxtaposing our findings with existing knowledge, we aim to uncover commonalities in immune response pathways and molecular signatures across species. Such comparative analyses hold the promise of identifying conserved biomarkers with the potential to transcend species boundaries and serve as universal indicators of active TB infection. Materials and Methods Ethics statement All experiments were reviewed and approved by the Institutional Biological Safety Committee (IBSC) of the National Institute of Animal Biotechnology, Hyderabad (Approval No. IBSC/2018/NIAB/BD/001), and by the Animal Ethics Committee of the West Bengal University of Animal and Fishery Sciences, Kolkata, India (Approval No. IAEC/22 (B), CPCSEA Reg. No.763/GO/Re/SL/03/CPCSEA). The collection of cattle lung tissues during post-mortem evaluations at government-approved abattoirs was conducted by certified veterinarians, adhering to relevant government guidelines and regulations. Sample collection Lung tissues from both healthy cattle and those displaying suspected granulomatous lesions were collected during post-mortem inspections of the viscera at an approved abattoir, under the supervision of a veterinarian. The resected lung tissues were immediately washed with phosphate-buffered saline (PBS) and divided into three sections. One section was fixed in 10% buffered formalin, another was preserved in RNA stabilizing and storage solution (RNAlater), and the third section was kept in PBS. Total DNA was extracted from the tissues preserved in PBS and subjected to PCR-based confirmation for the presence of Mycobacterial DNA [ 10 ]. Tissue samples that tested positive for M. orygis through species-specific PCR and amplicon sequencing were further analyzed via histopathology and RNA sequencing [ 3 ]. In this study, lung tissues from crossbred Sahiwal x Holstein Friesian (SHF) cattle were evaluated (n = 3 each for healthy and diseased). Histopathology Five-micron-thick sections were prepared from lung tissues fixed in 10% buffered formalin, using standard histopathological slide preparation methods. The tissue sections were stained with haematoxylin and eosin, and images were captured using a light microscope for further evaluation by a trained veterinary pathologist. RNA extraction RNA isolation was performed using a combination of TRIZOL (Sigma) and the RNeasy Mini Plus Kit (Qiagen). Briefly, approximately 100 mg of tissue was added to 1 ml of TRIZOL reagent and homogenized in a tube containing zirconia beads using a BeadBug microtube homogenizer (Benchmark Scientific). Subsequently, 200 µL of chloroform was added to the homogenate, which was then shaken for 15 seconds, incubated at room temperature for 3 minutes, and centrifuged at 12,000g for 15 minutes at 4°C. The upper aqueous layer was carefully separated for further RNA extraction using the RNeasy Mini Kit, following the manufacturer’s instructions. Total RNA was eluted with 30 µL of RNase-free water and stored at − 80°C. The concentration and purity of the RNA were assessed using a Nanodrop 1000 (Thermo Fisher), and the RNA-seq was outsourced to Nucleome Informatics Pvt. Ltd., Hyderabad, India. qRT-PCR cDNA was synthesised from RNA using the Prime script 1st -strand cDNA synthesis kit (Takara) as per the manufacturer's instructions and using a mixture of random hexamer and oligo dT primers. Primers were designed for bovine gene targets (IL1β, IFN-γ, SOD2, CCL2/MCP-1, CCL3/MIP-1α, CCL8/MCP-2, CCL20/MIP-3α, CXCL2/MIP-2, CXCL10/IP-10, and CXCL11) ( Supplementary table 1 ) using Primer-BLAST (NCBI) and real-time PCR was performed using a CFX96 Touch System (Biorad). Real time PCR protocol started with an initial denaturation and enzyme activation at 95℃ for 2 minutes followed by 40 cycles of denaturation at 95℃ for 15 seconds, annealing and extension was carried out for 1 minute at a temperature ranging from 55°C to 65°C (based on the target gene). Melt curve analysis was performed by heating the samples from 65°C to 95℃ with an increment of 0.5 and fluorescence was recorded. Relative gene expression of the target genes was calculated using the 2 –ΔΔCT method with RPLP0 as an internal control [ 11 ]. Whole Transcriptome Sequencing The assessment of RNA quantity was performed using the Qubit fluorometer (Thermofisher #Q33238) with the RNA HS assay kit (Thermofisher #Q32851), adhering to the manufacturer’s guidelines. Subsequently, the RNA Integrity Number (RIN) values were determined on a TapeStation 4150 using the HS RNA screen tape (Thermo Fisher). This meticulous process ensures accurate quantification of RNA and provides insights into its quality, establishing a robust foundation for downstream analyses. Library preparation utilized the TruSeq® Stranded Total RNA kit (Illumina #15032618, Illumina #20020596). Subsequent to preparation, the final libraries were quantified with precision using the Qubit 4.0 fluorometer (Thermofisher #Q33238) and a DNA HS assay kit (Thermofisher #Q32851), strictly following the manufacturer's protocol. To ascertain the insert size of the library, a thorough examination was conducted on TapeStation 4150 (Agilent) utilizing the highly sensitive D1000 screentape (Agilent #5067–5582) according to the manufacturer's guidelines. The quality assessment of the raw FASTQ reads from the sample was executed using FastQC v.0.11.9 with default parameters [ 12 ]. Subsequently, the raw FASTQ reads underwent meticulous preprocessing using Fastp v.0.20.1, implementing specific parameters (--trim_front1 9 --trim_front2 9 --length_required 50 –correction --trim_poly_g --qualified_quality_phred 30) [ 13 ]. Following this preprocessing step, a comprehensive quality re-assessment was performed using FastQC, and the results were summarized using MultiQC [ 14 ]. Mapping of processed sequencing reads to the reference genome and analysis The processed reads were aligned to the STAR-indexed Bos taurus ARS-UCD1.2 genome using the STAR aligner v 2.7.9a with specific parameters (‘--outSAMtype’ BAM SortedByCoordinate, ‘--outSAMunmapped’ Within, ‘--quantMode’ TranscriptomeSAM, '--outSAMattributes' Standard) for Bos Taurus (ARS-UCD1.2) [ 15 ]. To enhance the specificity of the alignment, rRNA features were excluded from the GTF file of the Bos taurus genome ARS-UCD1.2. Subsequently, the resulting alignment files (sorted BAM) from individual samples were quantified using featureCounts v. 0.46.1, relying on the rRNA-filtered GTF file, to derive gene counts [ 16 ]. The obtained gene counts were employed as inputs for DESeq2, facilitating the estimation of differential gene expression. The analysis was performed with specific parameters, including a threshold of statistical significance (--alpha 0.05) and the Benjamini-Hochberg (BH) method for p-value adjustment [ 17 ]. Functional enrichment analysis was conducted using ShinyGO 0.77 [ 18 ], and the results were cross-verified with g:Profiler [ 19 ] to ensure robustness. Hierarchical clustering was applied to generate heatmaps, allowing the visualization of expression patterns. Discriminating variables between comparison groups were identified based on a stringent false discovery rate, with a threshold set at p < 0.05. Cell type enrichment analysis Cell type enrichment analysis is a crucial tool for discerning the prevalence of specific cell types within a set of genes. xCell, a sophisticated web tool, specializes in performing cell type enrichment analysis on gene expression data, focusing on 64 immune and stroma cell types. This powerful method is grounded in gene signatures derived from a wealth of knowledge acquired from thousands of pure cell types from diverse sources. xCell employs a cutting-edge technique designed to minimize associations between closely related cell types, enhancing the precision of its analysis. In the cell type enrichment analysis, we employed normalized read counts to ensure a consistent and unbiased assessment across samples. The use of raw scores in representing the outcomes ensures transparency and preserves the integrity of the original analysis results [ 20 ]. The xCell tool provides 64 cell types, including lymphoid, myeloid, stromal cells, stem cells, and other cells. Hence, the xCell score analysis using the R package “xCell” ( https://github.com/dviraran/xCell ) allowed us to obtain 64 immune cell type abundance scores. Web-based cell-type specific enrichment analysis (WebCSEA) available at https://bioinfo.uth.edu/webcsea/ is application that provides a comprehensive exploration of the tissue cell type (TC) specificity of gene among human major TC map. This dataset comprises a total of 111 scRNA-seq panels of human tissues and 1355 TVs from 61 different general tissues across 11 human organ system. It provides a user-friendly interactive platform for a wide group of investigators to explore the cellular context of any gene list [ 21 ]. Network analysis Differentially expressed genes identified as both up-regulated and down-regulated in the transcriptome were utilized to construct a Protein-Protein Interaction (PPI) network using the STRING database (version 12.0). The full range of string network types, encompassing both physical and functional associations, was retained with a medium confidence level set at 0.4, following default settings. The generated network was then imported into Cytoscape software (version 3.9.1) for in-depth analysis of the PPI network structure and dynamics. During network analysis, highly connected clusters were identified using the MCODE clustering method [ 22 ]. This method helps uncover densely connected regions within the network. The cluster with the highest MCODE score in the network was selected for further analysis. Additionally, important genes or nodes were identified using six different topological analysis methods such as degree, closeness, radiality, betweenness, stress, and maximum neighbourhood component (MNC), were used to pinpoint individual nodes that play crucial roles in connecting different parts of the network or regulating network dynamics. Subsequently, the maximal clique centrality (MCC) method was applied to identify the important hub genes based on the MCC Score [ 23 ]. This comprehensive approach allowed for a thorough exploration of the PPI network structure and prioritize the selection of key functional modules and hub genes. Comparative transcriptomics for validation of potential biomarker for bovine TB We executed a comprehensive validation protocol to evaluate a set of genes identified as potential biomarkers for active TB in cattle. The validation process involved a meticulous comparison of these key genes with publicly available TB infection transcriptome datasets. Our specific emphasis was on TB disease cohort studies accessible through the NCBI Gene Expression Omnibus (GEO) database ( https://www.ncbi.nlm.nih.gov/geo/ ). Detailed information, including the list of selected studies and their corresponding GEO accession numbers, can be found in Supplementary Data File S1 . Our selection criteria were confined to two species: Bovine and Human, as detailed in our previous article. The dataset inclusion criteria encompassed studies involving M. tuberculosis and M. bovis infections specifically in peripheral encompassed blood mononuclear cells (PBMC), whole blood, lung, and alveolar macrophages (AM). In broad terms, the analyses centered around comparing groups infected with M. tuberculosis and M. bovis with their respective healthy control groups. Based on the above inclusion factors 21 studies were considered for further comparison. The identification of DEGs was conducted using GEO2R, integrated with the limma package, with a focus on genes exhibiting statistically significant differences between pairwise groups (adjusted p value < 0.05, FDR < 0.05). In instances where GEO2R was not accessible, Log 2 FC values were employed, provided they were available in the supplementary data of the respective articles. Statistical Analysis The statistical analysis for the study was conducted using a combination of bioinformatics tools and statistical software, and described under the respective sub-sections in the Materials and Methods section or in the figure legends. Especially, differential expression analysis was performed using DESeq2, where genes with a false discovery rate (FDR) of less than 0.05 and a log2 fold change greater than ± 2 were considered significantly differentially expressed. A principal component analysis (PCA) was conducted to assess the variance and clustering of samples. Heatmaps and volcano plots were generated to visualize the expression patterns and the distribution of differentially expressed genes. The significance of the enriched GO terms and pathways was evaluated using a hypergeometric test, with a p-value threshold of less than 0.05 considered statistically significant, and graphs are made using R (version: R 4.3.2). GraphPad Prism 9 was also used for data analysis and graph generation in selective cases as indicated in the corresponding figure legend. All statistical tests were carried out with a significance threshold of p < 0.05, corresponding to a 95% confidence interval. Results Severe disseminated granulomatous lung pathology in cattle infected with Mycobacterium orygis In order to enhance our understanding of the pathogenesis of M. orygis infection in cattle, we evaluated the gross and histopathological characteristics of lung tissue post-necropsy (Fig. 1 A-H ) . Gross examination revealed the presence of numerous small to large tubercles dispersed throughout all lung lobes, indicative of an advanced stage of TB with disseminated granulomatous lung lesions (Fig. 1 A ). Formalin fixed gross sections of lung revealed presence of various types of granulomas, including caseous, and cavitary lesions (Fig. 1 B ) . Histological analysis demonstrated the presence of well-organized classical granulomatous lesions representing various stages of granuloma formation, including caseous, liquefied, necrotic, and cavitary lesions, which are hallmark features of advanced TB disease (Fig. 1 C, D). Cavitary lesions of varying sizes, ranging from small size of a few millimeters to large size of several centimeters, were observed. The cellular composition within these granulomas was characterized by the presence of classical immune cells, including macrophages, lymphocytes, neutrophils, and multinucleated giant cells. In contrast, the gross and histopathology of the healthy cow lungs demonstrated normal lung tissue morphology and histology in case of healthy cows (Fig. 1 E-H). The observed pathologies in the case of M. orygis infected lungs underscore the intricate interplay between the pathogen and host immune responses, offering insights into the progression and pathogenesis of bovine TB lung granulomas associated with M. orygis infection. Significantly altered transcriptome profile in granulomatous lungs compared to healthy lungs With our maiden approach to study the molecular immune responses underlying the severe granulomatous lung pathology observed in M. orygis infected cattle, we next performed whole transcriptome analysis of diseased lungs tissue and compared it with healthy cattle lungs. Post-processing of the RNA-Seq reads, alignment, and mapping to Bos taurus reference genome ARS-UCD1.2, normalized read counts were generated and the transcriptome data was deposited to the NCBI GEO database GEO273969. The transcriptome data quality parameters are provided in the Supplementary data file S2. Subsequently, following the DESeq2 analysis, a stringent analytical framework was employed, wherein genes demonstrating at least a 2-fold change, and with an adjusted p-value of < 0.05 were deemed differentially expressed (DE). The transcriptome data analysis workflow was provided in Supplementary Figure S1 . The principal component analysis (PCA) has revealed distinct transcriptome cluster gene expressions in the healthy cow lungs and cow infected with M. orygis , as illustrated in Fig. 2 A (PCA). The PCA plot represents the sample-to-sample distances within and between groups, emphasizing the variation between these two groups. While the within group variation (PC2) was 3.97%, the between group variation (PC1) is 92.56% indicating a significant clustering of the diseased samples from the healthy lung samples. Further, as depicted in Fig. 2 B, samples belonging to the same phylogenetic clades cluster closely, reinforcing the findings from the PCA analysis. Moreover, hierarchical clustering of the top 50 variable genes reinforces the clear separation between groups (Fig. 2 C). The hierarchical clustering analysis enhances our understanding of the molecular distinctions associated with M. orygis infection, highlighting the robustness of the observed separation between infected and healthy samples. A total of 27607 variably expressed genes are plotted in the volcano plot depicting a significant number of differentially expressed genes (DEGs) in M. orygis infected cow lung granulomas relative to healthy cow lungs (Fig. 2 D). A total of 8385 DEGs (padj 2log 2 FC) have been identified in the diseased lungs, of which 3045 DEGs are up-regulated, 5341 genes are down-regulated ( Supplementary data file S3 ). These DEGs were subsequently subjected to transcriptome signature-based granuloma cellular composition analysis and functional enrichment analysis. Inflammatory and immune-regulatory cell infiltration in the bovine pulmonary granulomas Understanding the cellular composition of TB granulomas is critical as it provides insights into the disease's mechanisms and progression indicating the phase of infection [ 24 ]. Each cell type within the granuloma plays a specific role, influencing everything from disease control to progression towards active disease. Thus, dissecting the cellular architecture of TB granulomas is essential for advancing our understanding of the disease and enhancing our ability to combat it. For the cell type enrichment analysis, using xCell-web based tool [ 20 ], we used normalized read counts to ensure a consistent and unbiased assessment of global call-type analysis across samples ( Fig. 3 A-C). This analysis identified 64 types of cells in both healthy and infected lung tissues however with considerable differences in their proportions underscoring the diverse cellular landscape present in the healthy and diseased lungs (Fig. 3 A-B, and Supplementary data file S4.1 ). A comparative analysis with the healthy lungs revealed a significant enrichment of multiple cell types within the M. orygis infected granulomatous lung tissues (Fig. 3 C, and Supplementary data file S4.2 ). Remarkably, several immune logically relevant cell types including, T-cells (Th2 cells, Tregs, CD4 + and CD8 + Tcm and Tem, and γδ T cells), B-cells (pro B-cells, memory B-cells, naïve B-cells, total B- cell, and plasma cells), CLP (common lymphoid progenitor cells), NK cells, and myeloid cells (DC, pDC, GMP, Megakaryocytes, Erythrocytes, Platelets, neutrophils, MPP, CMP and MEP) were found to be highly abundant in M. orygis infected granulomatous lungs compared to healthy lungs in cows. Notably, certain cell types unrelated to classical lung cells were also found to be enriched in the diseased tissue such as Neurons, Myocytes, Melanocyte, Hepatocytes, Sebocytes, and Keratinocytes (Fig. 3 C). Further, using the DEGs of M. orygis infected cattle lungs over the healthy control, and a publicly available human TB lung granuloma transcriptome DEG data set, we performed a lung tissue-specific cell typing using the web-based tool WebCSEA ( Supplementary Figure S2 , and Supplementary data file S4.3 ) [ 27 ]. While, the lung tissue-specific cell typing of bovine pulmonary granulomas identified 28 different cells types to be significantly enriched, human lung TB granuloma showed significant enrichment of 10 different cell types. Presence of highly significant number of a plethora of immune cells in the bovine lung granulomatous tissue indicate a highly active inflammatory state of the lungs highlighting active TB disease in the M. orygis naturally infected cattle. Functional enrichment analysis of differentially expressed gene To gain insights into the biological implications of the observed differential gene expression in the case of granulomatous lung tissues compared to the healthy lungs, we performed Gene Ontology (GO) analyses on the upregulated and downregulated genes and proteins. Functional enrichment of the upregulated DEGs into the biological process reveals four major themes within the top GO terms: immune response, membrane transport, signalling pathway, and cellular homeostasis were up-regulated in the infected lung tissue compared with the healthy lungs (Fig. 4 A-E). Among the upregulated immune response related pathways, Granulocyte-Macrophage Colony-Stimulating Factor (GM-CSF) activity, kynurenine pathway, Receptor Signaling Pathways through JAK-STAT signaling, Interleukin-17 (IL-17) production, terpenoid metabolic process, and positive regulation of lymphocyte proliferation were the leading pathways (Fig. 4 A). In addition, significant up-regulation of several membrane transport pathways were observed including such as chloride, calcium, sodium, potassium, and mono- and dicarboxylic acid transport (Fig. 4 B). Moreover, the upregulated DEGs participate in diverse signaling pathways including GABA, cAMP and calcium signalling (Fig. 4 C), contribute to homeostatic regulatory biological processes within the immune system (Fig. 4 D), and Extracellular matrix remodeling (Fig. 4 E). Furthermore, down-regulated DEGs exhibit associations with various immune response pathways, including cytokine production, acute inflammatory response, and interleukin production, negative regulation of cytokines, chemokine production, defence response, leukocyte, myeloid cell, T cell activation, and differentiation (Fig. 5 A). Additionally, pathways related cell death (Fig. 5 B), and lipid metabolism (Fig. 5 C) are also identified as top-downregulated pathways. For detailed information regarding the gene list associated with each pathway, please refer to Supplementary data file S5 . Protein-protein interaction (PPI) network analysis of differentially expressed genes To comprehensively explore the functional interactions among all up-regulated DEGs, we performed the construction of PPI maps utilizing STRING software. Through this approach, we identified a potential network of interacting proteins. Subsequently, we subjected the significantly interacting proteins from the STRING analysis to further analysis using the molecular complex detection (MCODE) algorithm within Cytoscape (Fig. 6 ). The top relevant MCODE clusters includes: cluster-1 (Cell Cycle), cluster-2 (Immune response), cluster-3 (Redox signaling), and cluster-4 (Hemostasis), respectively (Fig. 6 A-D). Further, we expanded the analysis to include the examination of associated pathways within each MCODE clusters using the CLueGO plug-in within Cytoscape. This comprehensive exploration encompassed pivotal pathways under cluster-1, including Meiotic chromosome segregation, Meiotic cell cycle checkpoint, nuclear chromosome segregation, regulation of chromosome segregation, mitotic spindle organization, and Attachment of spindle microtubules to kinetochore (Fig. 6 E). Pathways such as Activated T cell proliferation, IL-1b production, cytokine-cytokine receptor interaction, Cellular response to IFNγ, TH17 cell differentiation, Toll-like receptor signaling pathway, TNF signaling pathways, and Myeloid Leukocyte activation were identified under cluster-2 (Fig. 6 F). Under cluster-3, major pathways were related to oxidoreduction-driven active transmembrane transporter activity, NADH dehydrogenase (ubiquinone) activity, and aerobic electron transport chain (Fig. 6 G), while under cluster-4, fibrinolysis, and regulation of blood coagulation are two major pathways (Fig. 6 H). In silico identification and validation of potential biomarker for active tuberculosis From the total upregulated DEGs, a gene list was curated specifically targeting immune response-related genes associated with M. tuberculosis infection or TB diseases. This refined list aims to capture genes important for enhancing effective immune defence against TB, thereby providing additional insight into potential therapeutic targets and pathways for further investigation and intervention. In addition, Cytohubba analysis was conducted utilizing six algorithms (MNC, Degree, Closeness, Radiality, Betweenness, Stress), revealing the top 50 PPI networks within each category ( Supplementary data file S6 ). Next, the selection of 25 genes from MCODE analysis, and 97 genes through Cytohubba analysis, resulted in a total of 122 unique genes after removal of duplicates (Fig. 7 A). Subsequently, additional analysis was conducted to assess the presence of these proteins in serum/plasma using the Human Body Fluid Proteome (HBFP) and the Human Protein Atlas database. Furthermore, each of these protein’s secretion status in plasma was thoroughly examined via extensive literature search, and 55 genes were selected for further analysis ( Fig. 7 B). For in silico validation, 21-selected publicly available datasets (including this study) on lung and PBMC transcriptomics were compared, of which 4 datasets are from bovine studies and 17 datasets are from human studies ( Supplementary data file S1 ). Of the 55 total genes, genes that are represented in less than 5 studies are excluded, and 27 genes were further shortlisted for checker board analysis. Figure 7 C depicts a heat-map illustrating the expression pattern of these 27 genes associated with 21 transcriptomic studies along with their detection (presence or absence). Considering the selection of a bovine specific biomarker, first we shortlisted 14 genes (SOD2, IL1α/β, IL15, IL18, CCL2/MCP-1, CCL3/MIP-1α, CCL4/MIP-1β, CCL8/MCP-2, CCL20/MIP-3α, CXCL2/MIP-2, CXCL10/IP-10, CXCL11, and IFN-γ) that are upregulated in at least 3 of the 4 datasets from bovine studies including the current study. Interestingly, all these 14 genes were found to show upregulation in majority of the human transcriptome datasets highlighting the potential of the selected genes as transcriptional biomarker of active TB in the bovine and humans (Fig. 7 D). Further, co-expression analysis of 14 genes using STRING confirmed that the selected genes are expressed together in different experimental conditions representing an optimized combination for developing multiple target-based biomarkers in active tuberculosis in the bovine (Fig. 7 E). qRT-PCR based validation of RNA-seq data To confirm the RNA-seq data, we performed qRT-PCR on selected genes. Our qRT-PCR confirmed the significant upregulation of all the randomly selected 10 genes - IL1β, IFN-γ, SOD2, CCL2/MCP-1, CCL3/MIP-1α, CCL8/MCP-2, CCL20/MIP-3α, CXCL2/MIP-2, CXCL10/IP-10, and CXCL11, which was in agreement with the RNA-seq data (Fig. 8 ). Discussion The study provides a comprehensive transcriptomic analysis of pulmonary granulomas in cattle infected with M. orygis , a less-studied member of the MTBC known for its zoonotic potential. Our findings not only enhance the understanding of M. orygis pathogenesis in the case of bovine pulmonary TB but also propose potential biomarkers for bovine TB, which could augment diagnostics and contribute to better disease management. Granulomas harbouring the tubercle bacilli serve as a distinctive niche where host immune defences intersect with bacterial survival strategies. Our study confirmed the presence of severe granulomatous, necrotic, and cavitary lesions in the lungs of cattle infected with M. orygis , which are indicative of an active but prolonged immune-mediated damage typical of TB [25]. These findings also highlights that the development and characteristics of TB granulomas due to M. orygis natural infection in cattle is similar to that reported in the case of M. bovis infection in cattle as well as M. tuberculosis infection in humans [26]. Our study provides unprecedented insights into the cellular composition of bovine TB granulomas, revealing a diverse array of cell types, including unexpected lung-unrelated cells. This comprehensive cellular landscape underscores the complex immune environment within granulomas and its role in disease progression. The identification of 64 distinct cell types, with significant differences in their proportions between healthy and infected lung tissues, highlights the intricate and dynamic nature of the granulomatous response to M. orygis infection. Remarkably, we observed an enrichment of various immunologically relevant cell types within the granulomatous tissues. These included T-cells (Th2 cells, Tregs, CD4+ and CD8+ Tcm and Tem, and γδ T cells), B-cells (pro B-cells, memory B-cells, naïve B-cells, total B-cells, and plasma cells), common lymphoid progenitor cells (CLP), NK cells, and diverse myeloid cells (DC, pDC, GMP, megakaryocytes, erythrocytes, platelets, neutrophils, MPP, CMP, and MEP). The presence of these cell types suggests a highly active inflammatory state and indicates the granuloma's role in containing the infection and preventing its dissemination. Our findings are supported by previous studies that have emphasized the critical role of various immune cells in the formation and maintenance of granulomas and their importance in the immune response to TB [27]. The enrichment of these cell types within bovine granulomas aligns with existing literature and adds new dimensions to our understanding of the cellular dynamics in bovine TB. The comparative analysis with human TB lung granuloma transcriptome data, which identified a significantly smaller number of enriched cell types, further highlights the unique aspects of the bovine immune response to TB [9]. This comparison underscores the value of species-specific studies in understanding the pathogenesis of TB and developing targeted interventions. Using transcriptomic data for cell type analysis is a new and powerful approach, providing an unbiased assessment of cellular composition. Tools like xCell and WebCSEA enabled detailed cellular typing, demonstrating the value of this method in understanding host-pathogen interactions within granulomas [28]. Of particular interest is the identification of cell types unrelated to classical lung cells, such as neurons, myocytes, melanocytes, hepatocytes, sebocytes, and keratinocytes, within the granulomatous tissues. These findings suggest a more complex interaction between the immune system and other physiological systems than previously understood, potentially indicating systemic effects or migration of cells from other tissues in response to infection [29]. These findings align with emerging literature suggesting that TB granulomas are not merely local immune responses but are systemically influenced structures [30]. The results of our study demonstrate a complex interplay of molecular signaling in the pathogenesis of bovine TB, highlighting the significant upregulation of both signaling networks and PPI networks in granulomatous lung tissues. The functional enrichment of DEGs identified crucial pathways such as Granulocyte-Macrophage Colony-Stimulating Factor (GM-CSF) activity, JAK-STAT signaling, and Interleukin-17 (IL-17) production. These pathways are instrumental in orchestrating a robust immune response, as evidenced by the activation and proliferation of lymphocytes, which are vital for the immune system's ability to combat TB infection. Particularly noteworthy is the role of the JAK-STAT pathway, which has been extensively documented for its involvement in inflammatory and immune responses in various diseases including TB [31]. The upregulation of this pathway suggests an enhanced activation state within the M. orygis -infected lung tissue, potentially facilitating the persistent inflammation characteristic of active TB granulomas [32]. Furthermore, GM-CSF is known to play a pivotal role in the survival and function of tissue macrophages, which are key players in the pathogen survival and the host's defense mechanism [33]. Further, IL-17, which is predominantly produced by Th17 cells, and has been implicated in promoting the formation and maintenance of granulomas via orchestrating the recruitment and activation of various immune cells to the site of infection [34]. Studies suggest that IL-17 enhances the recruitment of neutrophils and monocytes/macrophages to the granulomatous lesions, facilitating the encapsulation and isolation of the bacteria [35]. While, this response is vital for the initial containment of the pathogen but can also contribute to exacerbated inflammation and pathology if not properly regulated, highlighting its dual role in TB pathogenesis. Our analysis using STRING software and the MCODE algorithm in Cytoscape revealed significant clusters within the PPI networks that correspond to critical biological processes. Notably, the immune response cluster highlighted interactions that enhance cytokine production and T-cell activation, essential for an effective adaptive immune response against TB. This is supported by the identification of pathways such as Toll-like receptor and TNF signaling pathways, which are integral to initiating and sustaining the immune response in TB [36]. Moreover, the redox-mediated membrane transport and hemostasis clusters underline the metabolic shifts and vascular changes occurring in response to chronic infection [37]. These findings suggest that M. orygis infection in bovine lungs not only triggers a robust immune response but also induces significant metabolic and physiological adaptations that may influence disease outcome. The identification of 14 key immuno-modulatory molecules (SOD2, IL1α/β, IL15, IL18, CCL2/MCP-1, CCL3/MIP-1α, CCL4/MIP-1β, CCL8/MCP-2, CCL20/MIP-3α, CXCL2/MIP-2, CXCL10/IP-10, CXCL11, and IFN-g) as potential biomarkers is particularly noteworthy. These immuno-mediators are known to play pivotal roles in the recruitment and activation of various immune cells, reflecting the active immune surveillance and response in infected tissues [38]. In the context of TB, SOD2, which is an antioxidant enzyme, may mitigate the oxidative damage caused by reactive oxygen species (ROS) produced during the immune response to M. orygis infection [39]. SOD2 was previously shown to differentiate between TB associated pleural effusions and malignant pleural effusion, suggesting its potential as a diagnostic biomarker for TB [40]. Additionally, SOD1 was also proposed as a diagnostic marker for severe secondary pulmonary TB, along with S100A9, ORM2, and IL1F6 proteins [41]. IL1α and IL1β are pro-inflammatory cytokines involved in the activation of macrophages and induction of other cytokines and chemokines, and are essential for the containment of M. tuberculosis infection and the formation of granulomas [42]. Prior studies in human TB patients showed enhanced levels of IL1α in serum, and IL1β in saliva [38, 43]. Further, given their roles in TB pathogenesis especially macrophage activation and IFN-γ production, both IL-15 and IL-18 have been investigated as potential biomarkers for TB [44, 45]. Elevated levels of these cytokines in the serum have been associated with active TB disease, suggesting that they could be used to differentiate between active and latent TB infections. CCL2/MCP-1, CCL3/MIP-1α, CCL4/MIP-1β, CCL8/MCP-2, CCL20/MIP-3α, CXCL2/MIP-2, CXCL10/IP-10, and CXCL11 are chemokines critical for the recruitment of monocytes, macrophages, and lymphocytes to the site of M. tuberculosis infection [46, 47]. Particularly, Th1 cells are recruited by CXCL10, NK cells by CXCL11, and neutrophils by CXCL2 to the lungs [48, 49]. Further, while CCL8 is involved in the recruitment of monocytes and T cells, CCL20 is known to be involved in the recruitment of dendritic cells and lymphocytes, contributing to the adaptive immune response [50]. In addition, CCL20 was highly expressed in the M. tuberculosis infected monocytes, and TB patients exhibited the up-regulated expression of CCL20, via MAPK/NF-κB-mediated transcriptional mechanisms [38, 50]. CCL3, and CCL4 are known to be involved in the early stages of granuloma formation, while CCL2 plays a role in sustaining the granulomatous response and CCL4 was associated with disease severity [51]. Additionally, CCL4 was also detected in plasma and proposed as potential diagnostic biomarker, and with the combination of IP-10 [52]. CXCL2 was identified as a potential biomarker for accurately diagnosing active TB from latent infection in outbred mice population and other lungs disease in human [53]. In addition, higher expression of CXCL2 was also reported in human TB patients, with reduction following treatment [54]. CXCL10 was reported to distinguish between different stages of TB infection, as well as drug-sensitive and drug-resistant TB cases [55]. Moreover, the CXCL10 release assay showed considerable sensitivity and specificity comparable to traditional IFN-g release assay in TB patients with HIV co-infection and in immunosuppressed individuals [56]. In case of bovine TB, several studies have not only reported heightened CXCL10 levels in both mRNA and protein levels in M. bovis infected cattle, but also proposed CXCL10 based bovine TB diagnostic platforms [57, 58]. In addition, CCL4 was also reported as a potential diagnostic candidate for bovine TB [59]. CXCL11 plays a crucial role in TB by being a functional ligand of the CXCR3 receptor, contributing to macrophage and NK cell recruitment to infectious foci [48, 60]. Higher level of CXCL11 is reported in individuals with TB compared to healthy individuals indicating its diagnostic potential [61]. IFN-g is a key cytokine in the immune response to TB, driving the activation of macrophages and the production of reactive nitrogen and oxygen species [62]. It is essential for the containment of M. tuberculosis within granulomas [63]. IFN-g is the most sought-after cytokine, and was used in several formats for TB diagnosis in humans along with the standard ESAT-6/CFP-10 antigen specific IGRA test [64]. In a similar line, IGRA tests and other diagnostic platforms based on the same antigens as well as novel antigens have been developed for diagnosis of bovine TB to replace the age old tuberculin skin test with DIVA capability [65]. Altogether, elevated levels of majority of these cytokines and chemokines have been associated with active TB in both humans and cattle [46, 59]. Given their roles in immune signaling, these molecules hold promise not only for diagnosing bovine TB but also for monitoring disease progression and response to therapy. The comparative transcriptomic analysis leveraging human and bovine TB datasets highlights the conserved nature of host responses to MTBC pathogens, suggesting that insights gained from bovine models may be applicable to human TB. This cross-species understanding could facilitate the development of universal diagnostic tools and therapeutic strategies, potentially benefiting TB control efforts both in animals and humans [66, 67]. While this study significantly advances our understanding of M. orygis -induced pulmonary granulomas, it is not without limitations. As this study is based on total RNA isolated from granulomatous tissues involving a mixture of tissues involving various stages of granuloma development, the transcriptome does not address the complex nature of granuloma biology and the inter-granuloma variability of each stage of granuloma formation. This demands further investigation using tissue samples from different stages of granuloma formation as well as different phases of TB diseases. Additionally, the potential systemic implications suggested by the presence of non-immune cells within granulomas warrant further exploration to fully understand their roles in TB pathogenesis [68]. In conclusion, our study not only elucidates the intricate transcriptomic landscape of pulmonary granuloma in bovine TB due to M. orygis infection but also provides a foundation for future research aimed at unravelling the complex immune dynamics at play. By identifying potential biomarkers and highlighting the multicellular nature of granulomatous inflammation, this study contributes to the ongoing efforts to combat bovine TB and zoonotic TB, and pave the way for the development of novel diagnostic and therapeutic strategies for this economically significant disease. Statements and Declarations Competing Interests The authors have no financial or non-financial interests to disclose. Funding This study received financial support from the NIAB intramural grant, and the Department of Biotechnology (DBT), Govt. of India Grant No. BT/PR31378/AAQ/1/745/2019. Financial support by DBT for providing Junior and Senior Research Fellowship (JRF/SRF) to RK and VB, Department of Science and Technology (DST), Govt. of India for providing the Inspire fellowship (JRF/SRF) to SG, Council for Scientific and Industrial Research (CSIR) for providing JRF/SRF to MRP are thankfully acknowledged. Data Availability The transcriptome data generated in this study are publicly accessible through the NCBI GEO accession number (GSE273969). Additionally, any supplementary data supporting the findings of the study are available from the corresponding author upon reasonable request. Software and Database Several publicly available databases were used to aid in the in-depth analysis of cell types, networks, and pathways based on the transcriptome data. Some of the major databases include NCBI-GEO, Human Body Fluid Proteome (HBFP) and the Human Protein Atlas database. All the databases and software information are provided in the Supplementary file S7 . Author Contributions : The project was conceptualized and designed by BD. Experiments were performed by RK, SG, HKM, US, and BD. Data analysis was carried out by RK, SG, VB, MRP, US, and BD. Contributed reagents, materials, analysis tools, and facilities: US and BD. Manuscript written by RK, SG, VB, and BD. All authors reviewed and edited the manuscript. Overall supervision of the study: BD. Funding: We gratefully acknowledge the financial support received from the NIAB intramural grant, and the Department of Biotechnology (DBT), Govt. of India (Grant No. BT/PR31378/AAQ/1/745/2019). Support by DBT for providing Junior and Senior Research Fellowship (JRF/SRF) to RK and VB; Department of Science and Technology (DST), Govt. of India for providing the Inspire fellowship (JRF/SRF) to SG, Council for Scientific and Industrial Research (CSIR) for providing JRF/SRF to MRP. Acknowledgments: Prof. Sharmistha Banerjee, coordinator of the University of Hyderabad - NIAB BSL3/ABSL3 facility, University of Hyderabad, India, and the technical support staff are thankfully acknowledged for facilitating the BSL-3 based experiments. Supplementary information Please see the supplementary information file. References J. Sawyer, S. Rhodes, G. J. Jones, P. J. Hogarth, and H. M. Vordermeier, "Mycobacterium bovis and its impact on human and animal tuberculosis," J Med Microbiol, vol. 72, no. 11, Nov 2023, doi: 10.1099/jmm.0.001769. A. Jawahar, G. Dhinakar Raj, N. Pazhanivel, and K. Karthik, "Gross and histopathological features of tuberculosis in cattle, buffalo and spotted deer (Axis axis) caused by Mycobacterium orygis," J Comp Pathol, vol. 208, pp. 15-19, Jan 2024, doi: 10.1016/j.jcpa.2023.10.010. L. J. 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Supplementary Files SupplementarydatafileS1.xlsx SupplementarydatafileS2.xlsx SupplementarydatafileS3.xlsx SupplementarydatafileS4.xlsx SupplementarydatafileS5.xlsx SupplementarydatafileS6.xlsx SupplementarydatafileS7.xlsx Supplementaryinformation.pdf 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-5184037","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":363360024,"identity":"adb82ddf-8072-43b8-90f0-521d29d94b51","order_by":0,"name":"Rishi Kumar","email":"","orcid":"","institution":"National Institute of Animal Biotechnology","correspondingAuthor":false,"prefix":"","firstName":"Rishi","middleName":"","lastName":"Kumar","suffix":""},{"id":363360025,"identity":"271b5d18-fd0f-4a93-ae8a-065a96ffde34","order_by":1,"name":"Sripratyusha Gandham","email":"","orcid":"","institution":"National Institute of Animal Biotechnology","correspondingAuthor":false,"prefix":"","firstName":"Sripratyusha","middleName":"","lastName":"Gandham","suffix":""},{"id":363360026,"identity":"002de5fa-cc3b-465c-a50b-c6ef76100a89","order_by":2,"name":"Vinay Bhaskar","email":"","orcid":"","institution":"National Institute of Animal Biotechnology","correspondingAuthor":false,"prefix":"","firstName":"Vinay","middleName":"","lastName":"Bhaskar","suffix":""},{"id":363360027,"identity":"b09deffa-7529-4842-9c99-0f782886f84d","order_by":3,"name":"Manas Praharaj","email":"","orcid":"","institution":"National Institute of Animal Biotechnology","correspondingAuthor":false,"prefix":"","firstName":"Manas","middleName":"","lastName":"Praharaj","suffix":""},{"id":363360028,"identity":"cbf6a493-f467-432f-afcd-350130d09eb3","order_by":4,"name":"Hemanta Kumar Maity","email":"","orcid":"","institution":"West Bengal University of Animal and Fishery Sciences","correspondingAuthor":false,"prefix":"","firstName":"Hemanta","middleName":"Kumar","lastName":"Maity","suffix":""},{"id":363360029,"identity":"1a1647ce-dd4a-4ab0-a05f-3467f6cb7bcd","order_by":5,"name":"Uttam Sarkar","email":"","orcid":"","institution":"West Bengal University of Animal and Fishery Sciences","correspondingAuthor":false,"prefix":"","firstName":"Uttam","middleName":"","lastName":"Sarkar","suffix":""},{"id":363360030,"identity":"75ac3fc0-c474-4f04-b549-5a43eb3c88cf","order_by":6,"name":"Bappaditya Dey","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAv0lEQVRIiWNgGAWjYBACA4YEBoYPDBJABhhIEKeFcQbJWph5wAxigTl78jFp2x0W8uYMzMc+fmGwyCOoxbLnWZp07hkJw50NbMmzZRgkigk77EaOmXRumwTjhgM8xswSDBKJDYS15H+TtmyTsCdFSw6bNGObRCJIC+MHYrQA/WJs2dsmkbyzmS2ZmcGACC3AEHt442dbne129ubDjD8q6ghrAQIWSPQxAxEPkbHD/AHGYvxBnI5RMApGwSgYYQAAShk1b27oRxgAAAAASUVORK5CYII=","orcid":"","institution":"National Institute of Animal Biotechnology","correspondingAuthor":true,"prefix":"","firstName":"Bappaditya","middleName":"","lastName":"Dey","suffix":""}],"badges":[],"createdAt":"2024-10-01 02:23:08","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5184037/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5184037/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":66931655,"identity":"6521392b-fead-4380-a803-ac2af0c61f2e","added_by":"auto","created_at":"2024-10-18 07:09:43","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":697427,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePathological features of lungs of cattle infected with \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eMycobacterium orygis\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e.\u003c/strong\u003eRepresentative gross and histopathological images of (A-D) \u003cem\u003eM. orygis\u003c/em\u003e-infected, and (E-H) healthy cattle lungs. Photographs of (A, E) cow lung tissues, (B, F) formalin-fixed tissue sections, (C, D \u0026amp; G, H) H\u0026amp;E-stained histopathological tissue sections from infected and healthy cattle, respectively. Gross and microscopy examination revealed typical granulomatous tissue morphology with the presence of caseous, necrotic, and cavitary lesions in the lungs of \u003cem\u003eM. orygis-infected\u003c/em\u003e cattle compared to healthy cattle depicting intact lung parenchyma and alveolar structures. The bar depicts 2000 μm (C,G), and 500 μm (D,H).\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-5184037/v1/f1d10cb085b5950519fb6cc9.png"},{"id":66931660,"identity":"e1e04312-e8cf-4aab-9aa8-418b1f437bfe","added_by":"auto","created_at":"2024-10-18 07:09:43","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":203560,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eComparative transcriptome analysis of \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eMycobacterium orygis\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e infected and healthy cattle lungs. \u003c/strong\u003e(A) PCA plot generated using DESeq2 data, showing variation within and between healthy and diseased groups. (B) Hierarchical clustering based on Euclidean distance using regularized log-transformed count data. (C) Heatmap of the top 50 differentially expressed genes, with colors indicating gene abundance from dark red (high) to light blue (low). (D) Volcano plot showing upregulated (red) and downregulated (blue) DEGs in \u003cem\u003eM. orygis\u003c/em\u003e-infected versus healthy lung tissue, with FDR \u0026lt; 0.05 and Log2FC \u0026gt; 2.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-5184037/v1/c1b3c4c3aba61c27a3bbafcd.png"},{"id":66931806,"identity":"b50c4de4-121c-4f9c-8347-fe8a0f5cf6b5","added_by":"auto","created_at":"2024-10-18 07:17:44","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":298298,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAlteration of the cellular composition of lungs following \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eMycobacterium orygis\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e infection. \u003c/strong\u003eGlobal cell type enrichment analysis was performed using xCell on normalized read counts. Pie charts display the proportions of various cell types in (A) healthy and (B) \u003cem\u003eM. orygis\u003c/em\u003e-infected cattle lung tissues. Colors in the pie charts are assigned randomly, representing cell proportions from highest to lowest. (C) Comparative analysis of cell type proportions between infected and healthy lungs.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-5184037/v1/bacb1637c4574821e0243c58.png"},{"id":66931658,"identity":"e9e69141-9560-46aa-827a-3bd85511a8ed","added_by":"auto","created_at":"2024-10-18 07:09:43","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":172117,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePathway enrichment analysis of the up-regulated DEGs in \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eM. orygis \u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003einfected granulomatous lungs\u003c/strong\u003e\u003cem\u003e\u003cstrong\u003e.\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e \u003c/strong\u003eUpregulated DEGs are enriched in the following biological pathways: (A) Immune Response, (B) Transmembrane Transport, (C) Signaling Pathway, (D) Homeostasis, and (E) Extracellular Matrix. Analysis was performed using ShinyGO, and figures were prepared using R-Studio.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-5184037/v1/87ac8799a05d8cb9d8b302b2.png"},{"id":66931665,"identity":"ff5bd01c-7cfb-40a6-be3a-2b0784772230","added_by":"auto","created_at":"2024-10-18 07:09:44","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":278896,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePathway enrichment analysis of the down-regulated DEGs in \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eM. orygis \u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003einfected granulomatous lungs\u003c/strong\u003e\u003cem\u003e.\u003c/em\u003eDown-regulated DEGs are enriched in the following biological pathways: (A) Immune system, (B) Cell death, and (C) Lipid metabolism. Analysis was performed using ShinyGO, and figures were prepared using R-Studio.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-5184037/v1/3d0adb5972489ace2f5c34b9.png"},{"id":66931661,"identity":"2e2801c3-28de-4f66-9c93-42a5efbd6a04","added_by":"auto","created_at":"2024-10-18 07:09:44","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":298208,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCritical gene network analysis of the up-regulated DEGs in the \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eM. orygis \u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003einfected cattle lungs:\u003c/strong\u003e\u003cem\u003e\u003cstrong\u003e \u003c/strong\u003e\u003c/em\u003e(A-D) Four key gene network clusters identified by MCODE in Cytoscape: (A) Cluster 1: Cell Cycle, (B) Cluster 2: Immune Response, (C) Cluster 3: Redox Signaling, and (D) Cluster 4: Hemostasis. (E-H) Corresponding pathways for each gene network cluster were identified using the ClueGO plugin in Cytoscape.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-5184037/v1/2968f9b137d17d51eb118442.png"},{"id":66931666,"identity":"1de8f869-5683-477c-b021-7b03d8e66f60","added_by":"auto","created_at":"2024-10-18 07:09:44","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":338805,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePotential biomarkers of active TB in cattle. (A)\u003c/strong\u003e Identification of 122 genes via MCODE and CytoHubba analysis. \u003cstrong\u003e(B)\u003c/strong\u003e Detection frequency of 55 serum/plasma proteins across transcriptome studies. \u003cstrong\u003e(C)\u003c/strong\u003e Heatmap of Log2FC values for key genes detected in over 5 studies, with associated bar graphs showing the number of studies with gene upregulation (right) and the number of genes detected per study (top). \u003cstrong\u003e(D)\u003c/strong\u003e Dot plot with mean ± SD representing Log2FC values for selected genes across 21 transcriptome studies. \u003cstrong\u003e(E) \u003c/strong\u003eCo-expression analysis of 14 shortlisted genes using STRING software.\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-5184037/v1/9f9860b98218130e0dc8a25e.png"},{"id":66931804,"identity":"9642e248-0cb8-42fd-891c-885205ccc5d5","added_by":"auto","created_at":"2024-10-18 07:17:43","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":30404,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRelative mRNA expression of selected genes by qRT-PCR. \u003c/strong\u003eReal-time RT-PCR was conducted on 10 genes (IFN-g, IL-1β, CCL2, CCL20, CCL3, CCL8, CXCL10, CXCL11, CXCL2, and SOD2). Fold expression was calculated using the 2\u003csup\u003e-ΔΔCT\u003c/sup\u003e method. Bars represent the Log\u003csub\u003e2\u003c/sub\u003e fold change values from qRT-PCR and RNA-seq analysis, comparing \u003cem\u003eM. orygis\u003c/em\u003e-infected cattle lung tissues to healthy lungs.\u003c/p\u003e","description":"","filename":"8.png","url":"https://assets-eu.researchsquare.com/files/rs-5184037/v1/a5002f21b07e183e4cc1fffb.png"},{"id":66932749,"identity":"c986d5a4-7a46-411c-8e01-6f069c47fc26","added_by":"auto","created_at":"2024-10-18 07:25:45","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3546509,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5184037/v1/168df70a-4921-4eb0-bf88-dfa0c09ae01e.pdf"},{"id":66931803,"identity":"2960a468-4053-45e7-aa79-ffddea1f0bdc","added_by":"auto","created_at":"2024-10-18 07:17:43","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":12278,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementarydatafileS1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-5184037/v1/449c63955fe9ed5caa51dbe4.xlsx"},{"id":66931657,"identity":"a16c801c-68be-4d79-a25e-7d83feb73f0e","added_by":"auto","created_at":"2024-10-18 07:09:43","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":13415,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementarydatafileS2.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-5184037/v1/f022c81c8b0aaec95955a1f7.xlsx"},{"id":66931807,"identity":"b24e6c47-15b6-44ba-b2a0-18863a176a50","added_by":"auto","created_at":"2024-10-18 07:17:44","extension":"xlsx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":894999,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementarydatafileS3.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-5184037/v1/252e8a169139d9d1344d31fe.xlsx"},{"id":66931805,"identity":"f05dd2f4-82eb-4f8f-9cee-ffa480a3c4a9","added_by":"auto","created_at":"2024-10-18 07:17:43","extension":"xlsx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":32542,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementarydatafileS4.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-5184037/v1/417c7bff5aa460cc737402c7.xlsx"},{"id":66931670,"identity":"97e55dcd-60ed-4af6-839a-85075c5f7604","added_by":"auto","created_at":"2024-10-18 07:09:45","extension":"xlsx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":23580,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementarydatafileS5.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-5184037/v1/cec3289a4fcc9837888ce22f.xlsx"},{"id":66931669,"identity":"b8c37e2e-c5ef-4d04-b1c7-8e384846ab6f","added_by":"auto","created_at":"2024-10-18 07:09:45","extension":"xlsx","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":11646,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementarydatafileS6.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-5184037/v1/c316310399a137a434db4ff5.xlsx"},{"id":66931671,"identity":"7497d07c-af02-4bf2-9695-73f31b1bd3b1","added_by":"auto","created_at":"2024-10-18 07:09:45","extension":"xlsx","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":10567,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementarydatafileS7.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-5184037/v1/8bd7b58449240a9759ab1b41.xlsx"},{"id":66931668,"identity":"9f4f0859-da17-4849-b502-c16b4399c61d","added_by":"auto","created_at":"2024-10-18 07:09:44","extension":"pdf","order_by":8,"title":"","display":"","copyAsset":false,"role":"supplement","size":153622,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementaryinformation.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5184037/v1/43356dcd7e1401ee2c328251.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Transcriptomic Insights into Mycobacterium orygis Infection-associated Pulmonary Granulomas Reveal Multicellular Immune Networks and Tuberculosis Biomarkers in Cattle.","fulltext":[{"header":"Introduction","content":"\u003cp\u003eBovine tuberculosis (bTB) is a persistent threat affecting both cattle and wildlife and serving as a potential source of zoonotic TB in humans globally. Although primarily associated with \u003cem\u003eMycobacterium bovis\u003c/em\u003e, bTB can also be caused by other members of the Mycobacterium tuberculosis complex (MTBC), including the human tubercle bacillus- \u003cem\u003eM. tuberculosis\u003c/em\u003e [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Recent epidemiological studies have highlighted a surge in TB cases in cattle and wildlife attributed to \u003cem\u003eM. orygis\u003c/em\u003e, an emerging member of the MTBC [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Moreover, \u003cem\u003eM. orygis\u003c/em\u003e has emerged as a primary cause of zoonotic TB in humans in India, surpassing \u003cem\u003eM. bovis\u003c/em\u003e, presenting novel challenges to TB control efforts [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eDespite considerable research efforts, the identification of reliable biomarkers for confirming bovine TB remains challenging, hindering global disease management and control strategies [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Consequently, there is a need for a more thorough investigation into the molecular mechanisms underlying TB infection in cattle. Prior studies on various cellular models such as bovine PBMC, PBMC-derived macrophages, and bovine alveolar macrophages have deepened our understanding of bTB immunopathogenesis, highlighting the major immune responses mounted against mycobacterial invasion in the bovine host [\u003cspan additionalcitationids=\"CR6\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. However, except our recent study of early host responses to TB infection in the bovine using a bovine 3D pulmosphere model [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], knowledge on the multicellular intricacies of pulmonary granuloma and complexity of the immune networks during active TB in the bovine is limited. Similarly, despite the growing recognition of \u003cem\u003eM. orygis\u003c/em\u003e as a leading cause of TB, our understanding of its pathogenesis and host immune responses, particularly in cattle, remains inadequate.\u003c/p\u003e \u003cp\u003eUnderstanding the host transcriptome during \u003cem\u003eM. orygis\u003c/em\u003e infection is crucial for several reasons. Firstly, it provides insights into the molecular mechanisms underlying host-pathogen interactions, including the activation of innate and adaptive immune responses, inflammatory pathways, and tissue remodeling processes. Secondly, transcriptomic analysis enables the identification of candidate biomarkers for detection, diagnosis, and monitoring of bTB in cattle populations. These biomarkers have the potential to revolutionize bTB surveillance and control programs, facilitating timely intervention and disease management. Furthermore, transcriptomic profiling offers a foundation for the development of host directed therapeutic interventions and vaccine strategies tailored to combat \u003cem\u003eM. orygis\u003c/em\u003e infection in cattle.\u003c/p\u003e \u003cp\u003eAddressing this gap, our research endeavors to delineate the transcriptional landscape of pulmonary granuloma of \u003cem\u003eM. orygis\u003c/em\u003e-infected cattle. We aim to elucidate the complex interactions between different types of cells and the core immune networks operative within pulmonary granulomas, the hallmark lesions of TB infection [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Furthermore, this research goes beyond mere descriptive analysis by leveraging comparative transcriptomic data from publicly available repositories encompassing bovine and human TB datasets. By juxtaposing our findings with existing knowledge, we aim to uncover commonalities in immune response pathways and molecular signatures across species. Such comparative analyses hold the promise of identifying conserved biomarkers with the potential to transcend species boundaries and serve as universal indicators of active TB infection.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003e \u003cstrong\u003eEthics statement\u003c/strong\u003e \u003cp\u003eAll experiments were reviewed and approved by the Institutional Biological Safety Committee (IBSC) of the National Institute of Animal Biotechnology, Hyderabad (Approval No. IBSC/2018/NIAB/BD/001), and by the Animal Ethics Committee of the West Bengal University of Animal and Fishery Sciences, Kolkata, India (Approval No. IAEC/22 (B), CPCSEA Reg. No.763/GO/Re/SL/03/CPCSEA). The collection of cattle lung tissues during post-mortem evaluations at government-approved abattoirs was conducted by certified veterinarians, adhering to relevant government guidelines and regulations.\u003c/p\u003e \u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eSample collection\u003c/h2\u003e \u003cp\u003eLung tissues from both healthy cattle and those displaying suspected granulomatous lesions were collected during post-mortem inspections of the viscera at an approved abattoir, under the supervision of a veterinarian. The resected lung tissues were immediately washed with phosphate-buffered saline (PBS) and divided into three sections. One section was fixed in 10% buffered formalin, another was preserved in RNA stabilizing and storage solution (RNAlater), and the third section was kept in PBS. Total DNA was extracted from the tissues preserved in PBS and subjected to PCR-based confirmation for the presence of Mycobacterial DNA [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Tissue samples that tested positive for \u003cem\u003eM. orygis\u003c/em\u003e through species-specific PCR and amplicon sequencing were further analyzed via histopathology and RNA sequencing [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. In this study, lung tissues from crossbred Sahiwal x Holstein Friesian (SHF) cattle were evaluated (n\u0026thinsp;=\u0026thinsp;3 each for healthy and diseased).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eHistopathology\u003c/h3\u003e\n\u003cp\u003eFive-micron-thick sections were prepared from lung tissues fixed in 10% buffered formalin, using standard histopathological slide preparation methods. The tissue sections were stained with haematoxylin and eosin, and images were captured using a light microscope for further evaluation by a trained veterinary pathologist.\u003c/p\u003e\n\u003ch3\u003eRNA extraction\u003c/h3\u003e\n\u003cp\u003eRNA isolation was performed using a combination of TRIZOL (Sigma) and the RNeasy Mini Plus Kit (Qiagen). Briefly, approximately 100 mg of tissue was added to 1 ml of TRIZOL reagent and homogenized in a tube containing zirconia beads using a BeadBug microtube homogenizer (Benchmark Scientific). Subsequently, 200 \u0026micro;L of chloroform was added to the homogenate, which was then shaken for 15 seconds, incubated at room temperature for 3 minutes, and centrifuged at 12,000g for 15 minutes at 4\u0026deg;C. The upper aqueous layer was carefully separated for further RNA extraction using the RNeasy Mini Kit, following the manufacturer\u0026rsquo;s instructions. Total RNA was eluted with 30 \u0026micro;L of RNase-free water and stored at \u0026minus;\u0026thinsp;80\u0026deg;C. The concentration and purity of the RNA were assessed using a Nanodrop 1000 (Thermo Fisher), and the RNA-seq was outsourced to Nucleome Informatics Pvt. Ltd., Hyderabad, India.\u003c/p\u003e\n\u003ch3\u003eqRT-PCR\u003c/h3\u003e\n\u003cp\u003ecDNA was synthesised from RNA using the Prime script 1st -strand cDNA synthesis kit (Takara) as per the manufacturer's instructions and using a mixture of random hexamer and oligo dT primers. Primers were designed for bovine gene targets (IL1β, IFN-γ, SOD2, CCL2/MCP-1, CCL3/MIP-1α, CCL8/MCP-2, CCL20/MIP-3α, CXCL2/MIP-2, CXCL10/IP-10, and CXCL11) (\u003cb\u003eSupplementary table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u003c/b\u003e) using Primer-BLAST (NCBI) and real-time PCR was performed using a CFX96 Touch System (Biorad). Real time PCR protocol started with an initial denaturation and enzyme activation at 95℃ for 2 minutes followed by 40 cycles of denaturation at 95℃ for 15 seconds, annealing and extension was carried out for 1 minute at a temperature ranging from 55\u0026deg;C to 65\u0026deg;C (based on the target gene). Melt curve analysis was performed by heating the samples from 65\u0026deg;C to 95℃ with an increment of 0.5 and fluorescence was recorded. Relative gene expression of the target genes was calculated using the 2\u003csup\u003e\u0026ndash;ΔΔCT\u003c/sup\u003e method with RPLP0 as an internal control [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e\n\u003ch3\u003eWhole Transcriptome Sequencing\u003c/h3\u003e\n\u003cp\u003eThe assessment of RNA quantity was performed using the Qubit fluorometer (Thermofisher #Q33238) with the RNA HS assay kit (Thermofisher #Q32851), adhering to the manufacturer\u0026rsquo;s guidelines. Subsequently, the RNA Integrity Number (RIN) values were determined on a TapeStation 4150 using the HS RNA screen tape (Thermo Fisher). This meticulous process ensures accurate quantification of RNA and provides insights into its quality, establishing a robust foundation for downstream analyses. Library preparation utilized the TruSeq\u0026reg; Stranded Total RNA kit (Illumina #15032618, Illumina #20020596). Subsequent to preparation, the final libraries were quantified with precision using the Qubit 4.0 fluorometer (Thermofisher #Q33238) and a DNA HS assay kit (Thermofisher #Q32851), strictly following the manufacturer's protocol. To ascertain the insert size of the library, a thorough examination was conducted on TapeStation 4150 (Agilent) utilizing the highly sensitive D1000 screentape (Agilent #5067\u0026ndash;5582) according to the manufacturer's guidelines. The quality assessment of the raw FASTQ reads from the sample was executed using FastQC v.0.11.9 with default parameters [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Subsequently, the raw FASTQ reads underwent meticulous preprocessing using Fastp v.0.20.1, implementing specific parameters (--trim_front1 9 --trim_front2 9 --length_required 50 \u0026ndash;correction --trim_poly_g --qualified_quality_phred 30) [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Following this preprocessing step, a comprehensive quality re-assessment was performed using FastQC, and the results were summarized using MultiQC [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eMapping of processed sequencing reads to the reference genome and analysis\u003c/h2\u003e \u003cp\u003eThe processed reads were aligned to the STAR-indexed Bos taurus ARS-UCD1.2 genome using the STAR aligner v 2.7.9a with specific parameters (\u0026lsquo;--outSAMtype\u0026rsquo; BAM SortedByCoordinate, \u0026lsquo;--outSAMunmapped\u0026rsquo; Within, \u0026lsquo;--quantMode\u0026rsquo; TranscriptomeSAM, '--outSAMattributes' Standard) for Bos Taurus (ARS-UCD1.2) [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. To enhance the specificity of the alignment, rRNA features were excluded from the GTF file of the Bos taurus genome ARS-UCD1.2. Subsequently, the resulting alignment files (sorted BAM) from individual samples were quantified using featureCounts v. 0.46.1, relying on the rRNA-filtered GTF file, to derive gene counts [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. The obtained gene counts were employed as inputs for DESeq2, facilitating the estimation of differential gene expression. The analysis was performed with specific parameters, including a threshold of statistical significance (--alpha 0.05) and the Benjamini-Hochberg (BH) method for p-value adjustment [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Functional enrichment analysis was conducted using ShinyGO 0.77 [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], and the results were cross-verified with g:Profiler [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] to ensure robustness. Hierarchical clustering was applied to generate heatmaps, allowing the visualization of expression patterns. Discriminating variables between comparison groups were identified based on a stringent false discovery rate, with a threshold set at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eCell type enrichment analysis\u003c/h3\u003e\n\u003cp\u003eCell type enrichment analysis is a crucial tool for discerning the prevalence of specific cell types within a set of genes. xCell, a sophisticated web tool, specializes in performing cell type enrichment analysis on gene expression data, focusing on 64 immune and stroma cell types. This powerful method is grounded in gene signatures derived from a wealth of knowledge acquired from thousands of pure cell types from diverse sources. xCell employs a cutting-edge technique designed to minimize associations between closely related cell types, enhancing the precision of its analysis. In the cell type enrichment analysis, we employed normalized read counts to ensure a consistent and unbiased assessment across samples. The use of raw scores in representing the outcomes ensures transparency and preserves the integrity of the original analysis results [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. The xCell tool provides 64 cell types, including lymphoid, myeloid, stromal cells, stem cells, and other cells. Hence, the xCell score analysis using the R package \u0026ldquo;xCell\u0026rdquo; (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/dviraran/xCell\u003c/span\u003e\u003cspan address=\"https://github.com/dviraran/xCell\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) allowed us to obtain 64 immune cell type abundance scores. Web-based cell-type specific enrichment analysis (WebCSEA) available at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://bioinfo.uth.edu/webcsea/\u003c/span\u003e\u003cspan address=\"https://bioinfo.uth.edu/webcsea/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e is application that provides a comprehensive exploration of the tissue cell type (TC) specificity of gene among human major TC map. This dataset comprises a total of 111 scRNA-seq panels of human tissues and 1355 TVs from 61 different general tissues across 11 human organ system. It provides a user-friendly interactive platform for a wide group of investigators to explore the cellular context of any gene list [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e\n\u003ch3\u003eNetwork analysis\u003c/h3\u003e\n\u003cp\u003eDifferentially expressed genes identified as both up-regulated and down-regulated in the transcriptome were utilized to construct a Protein-Protein Interaction (PPI) network using the STRING database (version 12.0). The full range of string network types, encompassing both physical and functional associations, was retained with a medium confidence level set at 0.4, following default settings. The generated network was then imported into Cytoscape software (version 3.9.1) for in-depth analysis of the PPI network structure and dynamics. During network analysis, highly connected clusters were identified using the MCODE clustering method [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. This method helps uncover densely connected regions within the network. The cluster with the highest MCODE score in the network was selected for further analysis. Additionally, important genes or nodes were identified using six different topological analysis methods such as degree, closeness, radiality, betweenness, stress, and maximum neighbourhood component (MNC), were used to pinpoint individual nodes that play crucial roles in connecting different parts of the network or regulating network dynamics. Subsequently, the maximal clique centrality (MCC) method was applied to identify the important hub genes based on the MCC Score [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. This comprehensive approach allowed for a thorough exploration of the PPI network structure and prioritize the selection of key functional modules and hub genes.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eComparative transcriptomics for validation of potential biomarker for bovine TB\u003c/h2\u003e \u003cp\u003eWe executed a comprehensive validation protocol to evaluate a set of genes identified as potential biomarkers for active TB in cattle. The validation process involved a meticulous comparison of these key genes with publicly available TB infection transcriptome datasets. Our specific emphasis was on TB disease cohort studies accessible through the NCBI Gene Expression Omnibus (GEO) database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/geo/\u003c/span\u003e\u003cspan address=\"https://www.ncbi.nlm.nih.gov/geo/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Detailed information, including the list of selected studies and their corresponding GEO accession numbers, can be found in \u003cb\u003eSupplementary Data File S1\u003c/b\u003e. Our selection criteria were confined to two species: Bovine and Human, as detailed in our previous article. The dataset inclusion criteria encompassed studies involving \u003cem\u003eM. tuberculosis\u003c/em\u003e and \u003cem\u003eM. bovis\u003c/em\u003e infections specifically in peripheral encompassed blood mononuclear cells (PBMC), whole blood, lung, and alveolar macrophages (AM). In broad terms, the analyses centered around comparing groups infected with \u003cem\u003eM. tuberculosis\u003c/em\u003e and \u003cem\u003eM. bovis\u003c/em\u003e with their respective healthy control groups. Based on the above inclusion factors 21 studies were considered for further comparison. The identification of DEGs was conducted using GEO2R, integrated with the limma package, with a focus on genes exhibiting statistically significant differences between pairwise groups (adjusted p value\u0026thinsp;\u0026lt;\u0026thinsp;0.05, FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05). In instances where GEO2R was not accessible, Log\u003csub\u003e2\u003c/sub\u003eFC values were employed, provided they were available in the supplementary data of the respective articles.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eThe statistical analysis for the study was conducted using a combination of bioinformatics tools and statistical software, and described under the respective sub-sections in the \u003cspan refid=\"Sec2\" class=\"InternalRef\"\u003eMaterials and Methods\u003c/span\u003e section or in the figure legends. Especially, differential expression analysis was performed using DESeq2, where genes with a false discovery rate (FDR) of less than 0.05 and a log2 fold change greater than \u0026plusmn;\u0026thinsp;2 were considered significantly differentially expressed. A principal component analysis (PCA) was conducted to assess the variance and clustering of samples. Heatmaps and volcano plots were generated to visualize the expression patterns and the distribution of differentially expressed genes. The significance of the enriched GO terms and pathways was evaluated using a hypergeometric test, with a p-value threshold of less than 0.05 considered statistically significant, and graphs are made using R (version: R 4.3.2). GraphPad Prism 9 was also used for data analysis and graph generation in selective cases as indicated in the corresponding figure legend. All statistical tests were carried out with a significance threshold of p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, corresponding to a 95% confidence interval.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003e \u003cb\u003eSevere disseminated granulomatous lung pathology in cattle infected with\u003c/b\u003e \u003cb\u003eMycobacterium orygis\u003c/b\u003e\u003c/p\u003e \u003cp\u003eIn order to enhance our understanding of the pathogenesis of \u003cem\u003eM. orygis\u003c/em\u003e infection in cattle, we evaluated the gross and histopathological characteristics of lung tissue post-necropsy (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA-H\u003cb\u003e)\u003c/b\u003e. Gross examination revealed the presence of numerous small to large tubercles dispersed throughout all lung lobes, indicative of an advanced stage of TB with disseminated granulomatous lung lesions (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA\u003cb\u003e).\u003c/b\u003e Formalin fixed gross sections of lung revealed presence of various types of granulomas, including caseous, and cavitary lesions (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB\u003cb\u003e)\u003c/b\u003e. Histological analysis demonstrated the presence of well-organized classical granulomatous lesions representing various stages of granuloma formation, including caseous, liquefied, necrotic, and cavitary lesions, which are hallmark features of advanced TB disease (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC, D). Cavitary lesions of varying sizes, ranging from small size of a few millimeters to large size of several centimeters, were observed. The cellular composition within these granulomas was characterized by the presence of classical immune cells, including macrophages, lymphocytes, neutrophils, and multinucleated giant cells. In contrast, the gross and histopathology of the healthy cow lungs demonstrated normal lung tissue morphology and histology in case of healthy cows (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eE-H). The observed pathologies in the case of \u003cem\u003eM. orygis\u003c/em\u003e infected lungs underscore the intricate interplay between the pathogen and host immune responses, offering insights into the progression and pathogenesis of bovine TB lung granulomas associated with \u003cem\u003eM. orygis\u003c/em\u003e infection.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eSignificantly altered transcriptome profile in granulomatous lungs compared to healthy lungs\u003c/h2\u003e \u003cp\u003eWith our maiden approach to study the molecular immune responses underlying the severe granulomatous lung pathology observed in \u003cem\u003eM. orygis\u003c/em\u003e infected cattle, we next performed whole transcriptome analysis of diseased lungs tissue and compared it with healthy cattle lungs. Post-processing of the RNA-Seq reads, alignment, and mapping to \u003cem\u003eBos taurus\u003c/em\u003e reference genome ARS-UCD1.2, normalized read counts were generated and the transcriptome data was deposited to the NCBI GEO database GEO273969. The transcriptome data quality parameters are provided in the \u003cb\u003eSupplementary data file S2.\u003c/b\u003e Subsequently, following the DESeq2 analysis, a stringent analytical framework was employed, wherein genes demonstrating at least a 2-fold change, and with an adjusted p-value of \u0026lt;\u0026thinsp;0.05 were deemed differentially expressed (DE). The transcriptome data analysis workflow was provided in \u003cb\u003eSupplementary Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e\u003c/b\u003e.\u003c/p\u003e \u003cp\u003eThe principal component analysis (PCA) has revealed distinct transcriptome cluster gene expressions in the healthy cow lungs and cow infected with \u003cem\u003eM. orygis\u003c/em\u003e, as illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA (PCA).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe PCA plot represents the sample-to-sample distances within and between groups, emphasizing the variation between these two groups. While the within group variation (PC2) was 3.97%, the between group variation (PC1) is 92.56% indicating a significant clustering of the diseased samples from the healthy lung samples. Further, as depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB, samples belonging to the same phylogenetic clades cluster closely, reinforcing the findings from the PCA analysis. Moreover, hierarchical clustering of the top 50 variable genes reinforces the clear separation between groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC). The hierarchical clustering analysis enhances our understanding of the molecular distinctions associated with \u003cem\u003eM. orygis\u003c/em\u003e infection, highlighting the robustness of the observed separation between infected and healthy samples. A total of 27607 variably expressed genes are plotted in the volcano plot depicting a significant number of differentially expressed genes (DEGs) in \u003cem\u003eM. orygis\u003c/em\u003e infected cow lung granulomas relative to healthy cow lungs (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD). A total of 8385 DEGs (padj\u0026thinsp;\u0026lt;\u0026thinsp;0.05, \u0026gt;2log\u003csub\u003e2\u003c/sub\u003e FC) have been identified in the diseased lungs, of which 3045 DEGs are up-regulated, 5341 genes are down-regulated (\u003cb\u003eSupplementary data file S3\u003c/b\u003e). These DEGs were subsequently subjected to transcriptome signature-based granuloma cellular composition analysis and functional enrichment analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eInflammatory and immune-regulatory cell infiltration in the bovine pulmonary granulomas\u003c/h2\u003e \u003cp\u003eUnderstanding the cellular composition of TB granulomas is critical as it provides insights into the disease's mechanisms and progression indicating the phase of infection [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Each cell type within the granuloma plays a specific role, influencing everything from disease control to progression towards active disease. Thus, dissecting the cellular architecture of TB granulomas is essential for advancing our understanding of the disease and enhancing our ability to combat it. For the cell type enrichment analysis, using xCell-web based tool [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], we used normalized read counts to ensure a consistent and unbiased assessment of global call-type analysis across samples \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA-C). This analysis identified 64 types of cells in both healthy and infected lung tissues however with considerable differences in their proportions underscoring the diverse cellular landscape present in the healthy and diseased lungs (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA-B, \u003cb\u003eand Supplementary data file S4.1\u003c/b\u003e). A comparative analysis with the healthy lungs revealed a significant enrichment of multiple cell types within the \u003cem\u003eM. orygis\u003c/em\u003e infected granulomatous lung tissues (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC, \u003cb\u003eand Supplementary data file S4.2\u003c/b\u003e). Remarkably, several immune logically relevant cell types including, T-cells (Th2 cells, Tregs, CD4\u0026thinsp;+\u0026thinsp;and CD8\u0026thinsp;+\u0026thinsp;Tcm and Tem, and γδ T cells), B-cells (pro B-cells, memory B-cells, na\u0026iuml;ve B-cells, total B- cell, and plasma cells), CLP (common lymphoid progenitor cells), NK cells, and myeloid cells (DC, pDC, GMP, Megakaryocytes, Erythrocytes, Platelets, neutrophils, MPP, CMP and MEP) were found to be highly abundant in \u003cem\u003eM. orygis\u003c/em\u003e infected granulomatous lungs compared to healthy lungs in cows. Notably, certain cell types unrelated to classical lung cells were also found to be enriched in the diseased tissue such as Neurons, Myocytes, Melanocyte, Hepatocytes, Sebocytes, and Keratinocytes (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC). Further, using the DEGs of \u003cem\u003eM. orygis\u003c/em\u003e infected cattle lungs over the healthy control, and a publicly available human TB lung granuloma transcriptome DEG data set, we performed a lung tissue-specific cell typing using the web-based tool WebCSEA (\u003cb\u003eSupplementary Figure \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e, and Supplementary data file S4.3\u003c/b\u003e) [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. While, the lung tissue-specific cell typing of bovine pulmonary granulomas identified 28 different cells types to be significantly enriched, human lung TB granuloma showed significant enrichment of 10 different cell types. Presence of highly significant number of a plethora of immune cells in the bovine lung granulomatous tissue indicate a highly active inflammatory state of the lungs highlighting active TB disease in the \u003cem\u003eM. orygis\u003c/em\u003e naturally infected cattle.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eFunctional enrichment analysis of differentially expressed gene\u003c/h2\u003e \u003cp\u003eTo gain insights into the biological implications of the observed differential gene expression in the case of granulomatous lung tissues compared to the healthy lungs, we performed Gene Ontology (GO) analyses on the upregulated and downregulated genes and proteins. Functional enrichment of the upregulated DEGs into the biological process reveals four major themes within the top GO terms: immune response, membrane transport, signalling pathway, and cellular homeostasis were up-regulated in the infected lung tissue compared with the healthy lungs (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA-E). Among the upregulated immune response related pathways, Granulocyte-Macrophage Colony-Stimulating Factor (GM-CSF) activity, kynurenine pathway, Receptor Signaling Pathways through JAK-STAT signaling, Interleukin-17 (IL-17) production, terpenoid metabolic process, and positive regulation of lymphocyte proliferation were the leading pathways (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). In addition, significant up-regulation of several membrane transport pathways were observed including such as chloride, calcium, sodium, potassium, and mono- and dicarboxylic acid transport (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). Moreover, the upregulated DEGs participate in diverse signaling pathways including GABA, cAMP and calcium signalling (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC), contribute to homeostatic regulatory biological processes within the immune system (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eD), and Extracellular matrix remodeling (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eE).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFurthermore, down-regulated DEGs exhibit associations with various immune response pathways, including cytokine production, acute inflammatory response, and interleukin production, negative regulation of cytokines, chemokine production, defence response, leukocyte, myeloid cell, T cell activation, and differentiation (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). Additionally, pathways related cell death (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB), and lipid metabolism (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC) are also identified as top-downregulated pathways. For detailed information regarding the gene list associated with each pathway, please refer to \u003cb\u003eSupplementary data file S5\u003c/b\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eProtein-protein interaction (PPI) network analysis of differentially expressed genes\u003c/h2\u003e \u003cp\u003eTo comprehensively explore the functional interactions among all up-regulated DEGs, we performed the construction of PPI maps utilizing STRING software. Through this approach, we identified a potential network of interacting proteins. Subsequently, we subjected the significantly interacting proteins from the STRING analysis to further analysis using the molecular complex detection (MCODE) algorithm within Cytoscape (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe top relevant MCODE clusters includes: cluster-1 (Cell Cycle), cluster-2 (Immune response), cluster-3 (Redox signaling), and cluster-4 (Hemostasis), respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA-D). Further, we expanded the analysis to include the examination of associated pathways within each MCODE clusters using the CLueGO plug-in within Cytoscape. This comprehensive exploration encompassed pivotal pathways under cluster-1, including Meiotic chromosome segregation, Meiotic cell cycle checkpoint, nuclear chromosome segregation, regulation of chromosome segregation, mitotic spindle organization, and Attachment of spindle microtubules to kinetochore (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eE). Pathways such as Activated T cell proliferation, IL-1b production, cytokine-cytokine receptor interaction, Cellular response to IFNγ, TH17 cell differentiation, Toll-like receptor signaling pathway, TNF signaling pathways, and Myeloid Leukocyte activation were identified under cluster-2 (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eF). Under cluster-3, major pathways were related to oxidoreduction-driven active transmembrane transporter activity, NADH dehydrogenase (ubiquinone) activity, and aerobic electron transport chain (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eG), while under cluster-4, fibrinolysis, and regulation of blood coagulation are two major pathways (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eH).\u003c/p\u003e \u003cp\u003e \u003cb\u003eIn silico\u003c/b\u003e \u003cb\u003eidentification and validation of potential biomarker for active tuberculosis\u003c/b\u003e\u003c/p\u003e \u003cp\u003eFrom the total upregulated DEGs, a gene list was curated specifically targeting immune response-related genes associated with \u003cem\u003eM. tuberculosis\u003c/em\u003e infection or TB diseases. This refined list aims to capture genes important for enhancing effective immune defence against TB, thereby providing additional insight into potential therapeutic targets and pathways for further investigation and intervention. In addition, Cytohubba analysis was conducted utilizing six algorithms (MNC, Degree, Closeness, Radiality, Betweenness, Stress), revealing the top 50 PPI networks within each category (\u003cb\u003eSupplementary data file S6\u003c/b\u003e). Next, the selection of 25 genes from MCODE analysis, and 97 genes through Cytohubba analysis, resulted in a total of 122 unique genes after removal of duplicates (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA). Subsequently, additional analysis was conducted to assess the presence of these proteins in serum/plasma using the Human Body Fluid Proteome (HBFP) and the Human Protein Atlas database. Furthermore, each of these protein\u0026rsquo;s secretion status in plasma was thoroughly examined via extensive literature search, and 55 genes were selected for further analysis \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003eFor \u003cem\u003ein silico\u003c/em\u003e validation, 21-selected publicly available datasets (including this study) on lung and PBMC transcriptomics were compared, of which 4 datasets are from bovine studies and 17 datasets are from human studies (\u003cb\u003eSupplementary data file S1\u003c/b\u003e). Of the 55 total genes, genes that are represented in less than 5 studies are excluded, and 27 genes were further shortlisted for checker board analysis. Figure\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eC depicts a heat-map illustrating the expression pattern of these 27 genes associated with 21 transcriptomic studies along with their detection (presence or absence). Considering the selection of a bovine specific biomarker, first we shortlisted 14 genes (SOD2, IL1α/β, IL15, IL18, CCL2/MCP-1, CCL3/MIP-1α, CCL4/MIP-1β, CCL8/MCP-2, CCL20/MIP-3α, CXCL2/MIP-2, CXCL10/IP-10, CXCL11, and IFN-γ) that are upregulated in at least 3 of the 4 datasets from bovine studies including the current study. Interestingly, all these 14 genes were found to show upregulation in majority of the human transcriptome datasets highlighting the potential of the selected genes as transcriptional biomarker of active TB in the bovine and humans (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eD). Further, co-expression analysis of 14 genes using STRING confirmed that the selected genes are expressed together in different experimental conditions representing an optimized combination for developing multiple target-based biomarkers in active tuberculosis in the bovine (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eE).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eqRT-PCR based validation of RNA-seq data\u003c/h2\u003e \u003cp\u003eTo confirm the RNA-seq data, we performed qRT-PCR on selected genes. Our qRT-PCR confirmed the significant upregulation of all the randomly selected 10 genes - IL1β, IFN-γ, SOD2, CCL2/MCP-1, CCL3/MIP-1α, CCL8/MCP-2, CCL20/MIP-3α, CXCL2/MIP-2, CXCL10/IP-10, and CXCL11, which was in agreement with the RNA-seq data (Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe study provides a comprehensive transcriptomic analysis of pulmonary granulomas in cattle infected with \u003cem\u003eM. orygis\u003c/em\u003e, a less-studied member of the MTBC known for its zoonotic potential. Our findings not only enhance the understanding of \u003cem\u003eM. orygis\u003c/em\u003e pathogenesis in the case of bovine pulmonary TB but also propose potential biomarkers for bovine TB, which could augment diagnostics and contribute to better disease management.\u003c/p\u003e\n\u003cp\u003eGranulomas harbouring the tubercle bacilli serve as a distinctive niche where host immune defences intersect with bacterial survival strategies. Our study confirmed the presence of severe granulomatous, necrotic, and cavitary lesions in the lungs of cattle infected with\u003cem\u003e\u0026nbsp;M. orygis\u003c/em\u003e, which are indicative of an active but prolonged immune-mediated damage typical of TB\u0026nbsp;[25]. These findings also highlights that the development and characteristics of TB granulomas due to \u003cem\u003eM. orygis\u003c/em\u003e natural infection in cattle is similar to that reported in the case of \u003cem\u003eM. bovis\u0026nbsp;\u003c/em\u003einfection in cattle as well as \u003cem\u003eM. tuberculosis\u003c/em\u003e infection in humans\u0026nbsp;[26].\u003c/p\u003e\n\u003cp\u003eOur study provides unprecedented insights into the cellular composition of bovine TB granulomas, revealing a diverse array of cell types, including unexpected lung-unrelated cells. This comprehensive cellular landscape underscores the complex immune environment within granulomas and its role in disease progression. The identification of 64 distinct cell types, with significant differences in their proportions between healthy and infected lung tissues, highlights the intricate and dynamic nature of the granulomatous response to \u003cem\u003eM. orygis\u003c/em\u003e infection. Remarkably, we observed an enrichment of various immunologically relevant cell types within the granulomatous tissues. These included T-cells (Th2 cells, Tregs, CD4+ and CD8+ Tcm and Tem, and γδ T cells), B-cells (pro B-cells, memory B-cells, naïve B-cells, total B-cells, and plasma cells), common lymphoid progenitor cells (CLP), NK cells, and diverse myeloid cells (DC, pDC, GMP, megakaryocytes, erythrocytes, platelets, neutrophils, MPP, CMP, and MEP). The presence of these cell types suggests a highly active inflammatory state and indicates the granuloma's role in containing the infection and preventing its dissemination. Our findings are supported by previous studies that have emphasized the critical role of various immune cells in the formation and maintenance of granulomas and their importance in the immune response to TB\u0026nbsp;[27]. The enrichment of these cell types within bovine granulomas aligns with existing literature and adds new dimensions to our understanding of the cellular dynamics in bovine TB. The comparative analysis with human TB lung granuloma transcriptome data, which identified a significantly smaller number of enriched cell types, further highlights the unique aspects of the bovine immune response to TB\u0026nbsp;[9]. This comparison underscores the value of species-specific studies in understanding the pathogenesis of TB and developing targeted interventions.\u003c/p\u003e\n\u003cp\u003eUsing transcriptomic data for cell type analysis is a new and powerful approach, providing an unbiased assessment of cellular composition. Tools like xCell and WebCSEA enabled detailed cellular typing, demonstrating the value of this method in understanding host-pathogen interactions within granulomas\u0026nbsp;[28]. Of particular interest is the identification of cell types unrelated to classical lung cells, such as neurons, myocytes, melanocytes, hepatocytes, sebocytes, and keratinocytes, within the granulomatous tissues. These findings suggest a more complex interaction between the immune system and other physiological systems than previously understood, potentially indicating systemic effects or migration of cells from other tissues in response to infection\u0026nbsp;[29]. These findings align with emerging literature suggesting that TB granulomas are not merely local immune responses but are systemically influenced structures\u0026nbsp;[30].\u003c/p\u003e\n\u003cp\u003eThe results of our study demonstrate a complex interplay of molecular signaling in the pathogenesis of bovine TB, highlighting the significant upregulation of both signaling networks and PPI networks in granulomatous lung tissues. The functional enrichment of DEGs identified crucial pathways such as Granulocyte-Macrophage Colony-Stimulating Factor (GM-CSF) activity, JAK-STAT signaling, and Interleukin-17 (IL-17) production. These pathways are instrumental in orchestrating a robust immune response, as evidenced by the activation and proliferation of lymphocytes, which are vital for the immune system's ability to combat TB infection. Particularly noteworthy is the role of the JAK-STAT pathway, which has been extensively documented for its involvement in inflammatory and immune responses in various diseases including TB\u0026nbsp;[31]. The upregulation of this pathway suggests an enhanced activation state within the \u003cem\u003eM. orygis\u003c/em\u003e-infected lung tissue, potentially facilitating the persistent inflammation characteristic of active TB granulomas\u0026nbsp;[32]. Furthermore, GM-CSF is known to play a pivotal role in the survival and function of tissue macrophages, which are key players in the pathogen survival and the host's defense mechanism\u0026nbsp;[33]. Further, IL-17, which is predominantly produced by Th17 cells, and has been implicated in promoting the formation and maintenance of granulomas via orchestrating the recruitment and activation of various immune cells to the site of infection\u0026nbsp;[34]. Studies suggest that IL-17 enhances the recruitment of neutrophils and monocytes/macrophages to the granulomatous lesions, facilitating the encapsulation and isolation of the bacteria\u0026nbsp;[35]. While, this response is vital for the initial containment of the pathogen but can also contribute to exacerbated inflammation and pathology if not properly regulated, highlighting its dual role in TB pathogenesis. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOur analysis using STRING software and the MCODE algorithm in Cytoscape revealed significant clusters within the PPI networks that correspond to critical biological processes. Notably, the immune response cluster highlighted interactions that enhance cytokine production and T-cell activation, essential for an effective adaptive immune response against TB. This is supported by the identification of pathways such as Toll-like receptor and TNF signaling pathways, which are integral to initiating and sustaining the immune response in TB\u0026nbsp;[36]. Moreover, the redox-mediated membrane transport and hemostasis clusters underline the metabolic shifts and vascular changes occurring in response to chronic infection\u0026nbsp;[37]. These findings suggest that \u003cem\u003eM. orygis\u003c/em\u003e infection in bovine lungs not only triggers a robust immune response but also induces significant metabolic and physiological adaptations that may influence disease outcome.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe identification of 14 key immuno-modulatory molecules (SOD2, IL1α/β, IL15, IL18, CCL2/MCP-1, CCL3/MIP-1α, CCL4/MIP-1β, CCL8/MCP-2, CCL20/MIP-3α, CXCL2/MIP-2, CXCL10/IP-10, CXCL11, and IFN-g) as potential biomarkers is particularly noteworthy. These immuno-mediators are known to play pivotal roles in the recruitment and activation of various immune cells, reflecting the active immune surveillance and response in infected tissues\u0026nbsp;[38]. In the context of TB, SOD2, which is an antioxidant enzyme, may mitigate the oxidative damage caused by reactive oxygen species (ROS) produced during the immune response to \u003cem\u003eM. orygis\u003c/em\u003e infection\u0026nbsp;[39]. SOD2 was previously shown to differentiate between TB associated pleural effusions and malignant pleural effusion, suggesting its potential as a diagnostic biomarker for TB\u0026nbsp;[40]. Additionally, SOD1 was also proposed as a diagnostic marker for severe secondary pulmonary TB, along with S100A9, ORM2, and IL1F6 proteins\u0026nbsp;[41]. IL1α and IL1β are pro-inflammatory cytokines involved in the activation of macrophages and induction of other cytokines and chemokines, and are essential for the containment of \u003cem\u003eM. tuberculosis\u003c/em\u003e infection and the formation of granulomas\u0026nbsp;[42]. Prior studies in human TB patients showed enhanced levels of IL1α in serum, and IL1β in saliva\u0026nbsp;[38, 43]. \u0026nbsp;Further, given their roles in TB pathogenesis especially macrophage activation and IFN-γ production, both IL-15 and IL-18 have been investigated as potential biomarkers for TB\u0026nbsp;[44, 45]. Elevated levels of these cytokines in the serum have been associated with active TB disease, suggesting that they could be used to differentiate between active and latent TB infections. CCL2/MCP-1, CCL3/MIP-1α, CCL4/MIP-1β, CCL8/MCP-2, CCL20/MIP-3α, CXCL2/MIP-2, CXCL10/IP-10, and CXCL11 are chemokines critical for the recruitment of monocytes, macrophages, and lymphocytes to the site of \u003cem\u003eM. tuberculosis\u003c/em\u003e infection\u0026nbsp;[46, 47]. Particularly, Th1 cells are recruited by CXCL10, NK cells by CXCL11, and neutrophils by CXCL2 to the lungs\u0026nbsp;[48, 49]. Further, while CCL8 is involved in the recruitment of monocytes and T cells, CCL20 is known to be involved in the recruitment of dendritic cells and lymphocytes, contributing to the adaptive immune response\u0026nbsp;[50]. In addition, CCL20 was highly expressed in the \u003cem\u003eM. tuberculosis\u003c/em\u003e infected monocytes, and TB patients exhibited the up-regulated expression of CCL20, via MAPK/NF-κB-mediated transcriptional mechanisms\u0026nbsp;[38, 50]. CCL3, and CCL4 are known to be involved in the early stages of granuloma formation, while CCL2 plays a role in sustaining the granulomatous response and CCL4 was associated with disease severity\u0026nbsp;[51]. Additionally, CCL4 was also detected in plasma and proposed as potential diagnostic biomarker, and with the combination of IP-10\u0026nbsp;[52]. CXCL2 was identified as a potential biomarker for accurately diagnosing active TB from latent infection in outbred mice population and other lungs disease in human\u0026nbsp;[53]. In addition, higher expression of CXCL2 was also reported in human TB patients, with reduction following treatment\u0026nbsp;[54]. CXCL10 was reported to distinguish between different stages of TB infection, as well as drug-sensitive and drug-resistant TB cases\u0026nbsp;[55]. Moreover, the CXCL10 release assay showed considerable sensitivity and specificity comparable to traditional IFN-g\u0026nbsp;release assay in TB patients with HIV co-infection and in immunosuppressed individuals\u0026nbsp;[56]. In case of bovine TB, several studies have not only reported heightened CXCL10 levels in both mRNA and protein levels in \u003cem\u003eM. bovis\u003c/em\u003e infected cattle, but also proposed CXCL10 based bovine TB diagnostic platforms\u0026nbsp;[57, 58]. In addition, CCL4 was also reported as a potential diagnostic candidate for bovine TB\u0026nbsp;[59]. CXCL11 plays a crucial role in TB by being a functional ligand of the CXCR3 receptor, contributing to macrophage and NK cell recruitment to infectious foci\u0026nbsp;[48, 60]. Higher level of CXCL11 is reported in individuals with TB compared to healthy individuals indicating its diagnostic potential\u0026nbsp;[61].\u003c/p\u003e\n\u003cp\u003eIFN-g\u0026nbsp;is a key cytokine in the immune response to TB, driving the activation of macrophages and the production of reactive nitrogen and oxygen species\u0026nbsp;[62]. It is essential for the containment of \u003cem\u003eM. tuberculosis\u003c/em\u003e within granulomas\u0026nbsp;[63]. IFN-g\u0026nbsp;is the most sought-after cytokine, and was used in several formats for TB diagnosis in humans along with the standard ESAT-6/CFP-10 antigen specific IGRA test\u0026nbsp;[64]. In a similar line, IGRA tests and other diagnostic platforms based on the same antigens as well as novel antigens have been developed for diagnosis of bovine TB to replace the age old tuberculin skin test with DIVA capability\u0026nbsp;[65]. Altogether, elevated levels of majority of these cytokines and chemokines have been associated with active TB in both humans and cattle\u0026nbsp;[46, 59]. Given their roles in immune signaling, these molecules hold promise not only for diagnosing bovine TB but also for monitoring disease progression and response to therapy.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe comparative transcriptomic analysis leveraging human and bovine TB datasets highlights the conserved nature of host responses to MTBC pathogens, suggesting that insights gained from bovine models may be applicable to human TB. This cross-species understanding could facilitate the development of universal diagnostic tools and therapeutic strategies, potentially benefiting TB control efforts both in animals and humans\u0026nbsp;[66, 67].\u003c/p\u003e\n\u003cp\u003eWhile this study significantly advances our understanding of \u003cem\u003eM. orygis\u003c/em\u003e-induced pulmonary granulomas, it is not without limitations. As this study is based on total RNA isolated from granulomatous tissues involving a mixture of tissues involving various stages of granuloma development, the transcriptome does not address the complex nature of granuloma biology and the inter-granuloma variability of each stage of granuloma formation. This demands further investigation using tissue samples from different stages of granuloma formation as well as different phases of TB diseases. Additionally, the potential systemic implications suggested by the presence of non-immune cells within granulomas warrant further exploration to fully understand their roles in TB pathogenesis\u0026nbsp;[68].\u003c/p\u003e\n\u003cp\u003eIn conclusion, our study not only elucidates the intricate transcriptomic landscape of pulmonary granuloma in bovine TB due to \u003cem\u003eM. orygis\u003c/em\u003e infection but also provides a foundation for future research aimed at unravelling the complex immune dynamics at play. By identifying potential biomarkers and highlighting the multicellular nature of granulomatous inflammation, this study contributes to the ongoing efforts to combat bovine TB and zoonotic TB, and pave the way for the development of novel diagnostic and therapeutic strategies for this economically significant disease.\u0026nbsp;\u003c/p\u003e"},{"header":"Statements and Declarations","content":"\u003cp\u003e\u003cstrong\u003e\u003cem\u003eCompeting Interests\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no financial or non-financial interests to disclose.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eFunding\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study received financial support from the NIAB intramural grant, and the Department of Biotechnology (DBT), Govt. of India Grant No. BT/PR31378/AAQ/1/745/2019. Financial support by DBT for providing Junior and Senior Research Fellowship (JRF/SRF) to RK and VB, Department of Science and Technology (DST), Govt. of India for providing the Inspire fellowship (JRF/SRF) to SG, Council for Scientific and Industrial Research (CSIR) for providing JRF/SRF to MRP are thankfully acknowledged.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe transcriptome data generated in this study are publicly accessible through the NCBI GEO accession number (GSE273969). Additionally, any supplementary data supporting the findings of the study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSoftware and Database\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSeveral publicly available databases were used to aid in the in-depth analysis of cell types, networks, and pathways based on the transcriptome data. Some of the major databases include NCBI-GEO, Human Body Fluid Proteome (HBFP) and the Human Protein Atlas database. All the databases and software information are provided in the \u003cstrong\u003eSupplementary file S7\u003c/strong\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e: The project was conceptualized and designed by BD. Experiments were performed by RK, SG, HKM, US, and BD. Data analysis was carried out by RK, SG, VB, MRP, US, and BD. Contributed reagents, materials, analysis tools, and facilities: US and BD. Manuscript written by RK, SG, VB, and BD. All authors reviewed and edited the manuscript. Overall supervision of the study: BD.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e We gratefully acknowledge the financial support received from the NIAB intramural grant, and the Department of Biotechnology (DBT), Govt. of India (Grant No. BT/PR31378/AAQ/1/745/2019). Support by DBT for providing Junior and Senior Research Fellowship (JRF/SRF) to RK and VB; Department of Science and Technology (DST), Govt. of India for providing the Inspire fellowship (JRF/SRF) to SG, Council for Scientific and Industrial Research (CSIR) for providing JRF/SRF to MRP.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments:\u003c/strong\u003e Prof. Sharmistha Banerjee, coordinator of the University of Hyderabad - NIAB BSL3/ABSL3 facility, University of Hyderabad, India, and the technical support staff are thankfully acknowledged for facilitating the BSL-3 based experiments.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSupplementary information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePlease see the supplementary information file.\u003cbr\u003e\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eJ. Sawyer, S. Rhodes, G. J. Jones, P. J. Hogarth, and H. M. Vordermeier, \u0026quot;Mycobacterium bovis and its impact on human and animal tuberculosis,\u0026quot; \u003cem\u003eJ Med Microbiol,\u0026nbsp;\u003c/em\u003evol. 72, no. 11, Nov 2023, doi: 10.1099/jmm.0.001769.\u003c/li\u003e\n \u003cli\u003eA. Jawahar, G. Dhinakar Raj, N. Pazhanivel, and K. Karthik, \u0026quot;Gross and histopathological features of tuberculosis in cattle, buffalo and spotted deer (Axis axis) caused by Mycobacterium orygis,\u0026quot; \u003cem\u003eJ Comp Pathol,\u0026nbsp;\u003c/em\u003evol. 208, pp. 15-19, Jan 2024, doi: 10.1016/j.jcpa.2023.10.010.\u003c/li\u003e\n \u003cli\u003eL. J. Sumanth\u003cem\u003e\u0026nbsp;et al.\u003c/em\u003e, \u0026quot;Clinical features of human tuberculosis due to Mycobacterium orygis in Southern India,\u0026quot; \u003cem\u003eJ Clin Tuberc Other Mycobact Dis,\u0026nbsp;\u003c/em\u003evol. 32, p. 100372, Aug 2023, doi: 10.1016/j.jctube.2023.100372.\u003c/li\u003e\n \u003cli\u003eM. 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Gunosewoyo, \u0026quot;Tuberculosis: Pathogenesis, Current Treatment Regimens and New Drug Targets,\u0026quot; \u003cem\u003eInt J Mol Sci,\u0026nbsp;\u003c/em\u003evol. 24, no. 6, Mar 8 2023, doi: 10.3390/ijms24065202.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"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":"Tuberculosis, Bovine tuberculosis, Mycobacterium orygis, Granuloma, Transcriptome, Biomarkers","lastPublishedDoi":"10.21203/rs.3.rs-5184037/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5184037/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cem\u003eMycobacterium orygis\u003c/em\u003e, a member of the \u003cem\u003eMycobacterium tuberculosis complex\u003c/em\u003e (MTBC), has emerged as a significant contributor to tuberculosis (TB) in cattle, wildlife, and humans. However, understanding about its pathogenesis and severity is limited, compounded by the lack of reliable TB biomarkers in cattle. This study delves into the comparative pathology and transcriptomic landscape of pulmonary granulomas in cattle naturally infected with \u003cem\u003eM. orygis\u003c/em\u003e, using high-throughput RNA sequencing. Histopathological analysis revealed extensive, multistage granulomatous, necrotic, and cavitary lesions, indicative of severe lung pathology induced by \u003cem\u003eM. orygis\u003c/em\u003e. Transcriptomic profiling highlighted numerous differentially expressed genes and dysregulated pathways related to immune response modulation and extracellular matrix remodeling. Additionally, cell type enrichment analysis provided insights into the multicellularity of the granulomatous niche, emphasizing complex cell-cell interactions within TB granulomas. Comparative transcriptomics leveraging publicly available bovine and human TB omics datasets, 14 key immuno-modulators (SOD2, IL1α/β, IL15, IL18, CCL2/MCP-1, CCL3/MIP-1α, CCL4/MIP-1β, CCL8/MCP-2, CCL20/MIP-3α, CXCL2/MIP-2, CXCL10/IP-10, CXCL11, and IFN-γ) were identified as potential biomarkers for active TB in cattle. These findings significantly advance our understanding of \u003cem\u003eM. orygis\u003c/em\u003epathogenesis in bovine TB and highlight potential targets for the development of diagnostic tools for managing and controlling the disease.\u003c/p\u003e","manuscriptTitle":"Transcriptomic Insights into Mycobacterium orygis Infection-associated Pulmonary Granulomas Reveal Multicellular Immune Networks and Tuberculosis Biomarkers in Cattle.","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-10-18 07:09:38","doi":"10.21203/rs.3.rs-5184037/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":"26cff11d-6c26-488c-83df-ca0f02ab9233","owner":[],"postedDate":"October 18th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-10-18T07:09:38+00:00","versionOfRecord":[],"versionCreatedAt":"2024-10-18 07:09:38","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5184037","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5184037","identity":"rs-5184037","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}