Weighted gene co-expression network analysis reveals key genes and lncRNAs in desi cattle chronically infected with Johne’s disease

Weighted gene co-expression network analysis reveals key genes and lncRNAs in desi cattle chronically infected with Johne’s disease | bioRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (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];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-M677548'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search New Results Weighted gene co-expression network analysis reveals key genes and lncRNAs in desi cattle chronically infected with Johne’s disease View ORCID Profile Abhisek Sahu , View ORCID Profile Mohd Abdullah , View ORCID Profile Saurabh Gupta , View ORCID Profile Shoor Vir Singh , View ORCID Profile Ankush Dhillon , View ORCID Profile Prabhati Yadav , View ORCID Profile Sarwar Azam doi: https://doi.org/10.1101/2025.01.17.633561 Abhisek Sahu a National Institute of Animal Biotechnology , Hyderabad, India Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Abhisek Sahu Mohd Abdullah b GLA University, Inst Appl Sci & Humanities, Dept Biotechnology , Mathura, Uttar Pradesh, India Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Mohd Abdullah Saurabh Gupta b GLA University, Inst Appl Sci & Humanities, Dept Biotechnology , Mathura, Uttar Pradesh, India Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Saurabh Gupta Shoor Vir Singh b GLA University, Inst Appl Sci & Humanities, Dept Biotechnology , Mathura, Uttar Pradesh, India Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Shoor Vir Singh Ankush Dhillon b GLA University, Inst Appl Sci & Humanities, Dept Biotechnology , Mathura, Uttar Pradesh, India Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Ankush Dhillon Prabhati Yadav b GLA University, Inst Appl Sci & Humanities, Dept Biotechnology , Mathura, Uttar Pradesh, India Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Prabhati Yadav Sarwar Azam a National Institute of Animal Biotechnology , Hyderabad, India c Indian Institute of Technology Hyderabad , Sangareddy, India Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Sarwar Azam For correspondence: sarwar{at}niab.org.in Abstract Full Text Info/History Metrics Supplementary material Preview PDF Abstract Johne’s disease (JD), caused by Mycobacterium avium subsp. paratuberculosis (MAP), is a chronic enteric disease with significant economic impacts on the dairy industry. To elucidate molecular mechanisms underlying MAP infection in desi cattle ( Bos indicus ), we conducted a comprehensive transcriptomic study. This revealed 1,905 protein-coding and 3,123 lncRNA genes as differentially expressed in MAP-infected samples, alongside the first comprehensive annotation of 45,947 lncRNAs in desi cattle. Weighted Gene Co-expression Network Analysis identified 11 co-expressed gene modules, with the turquoise module comprising 870 protein- coding genes and 934 lncRNAs, highly correlated with clinical traits. Functional enrichment analysis of this key module revealed significant involvement in defense response, inflammatory processes, and NK cell-mediated immunity and cytotoxicity. GSE analysis revealed suppressed pathways, including NK cell immunity and lectin response, facilitating bacterial persistence in chronic infection, while activated pathways included G-protein-coupled receptor signaling and metabolic pathways. Network approach identified 12 hub genes ( IL7R, TLR4, KLRK1, IFNG, TGFB1, CD68, CXCR6, GZMB, KLRG1, MMP9, GZMA, SELL ) associated with immune suppression, inflammation, and tissue remodeling. Additionally, 22 lncRNAs co-expressed with hub genes suggesting roles in modulating immune regulation. By identifying critical genes and lncRNAs, our study offers potential targets for developing innovative diagnostic and therapeutic strategies for JD. 1. Introduction Paratuberculosis, also known as Johne’s disease (JD), is a chronic, insidious, and infectious disease characterized by incurable enteritis. It significantly affects the productivity of domestic ruminants, including cattle, while also infecting wild ruminants and a wide range of other animals, including primates and humans [ 1 , 2 ]. Symptoms of the disease are weakness, diarrhea, and a partial or complete loss of productivity [ 3 , 4 ]. The causative agent, Mycobacterium avium subsp. paratuberculosis (MAP), a robust, gram-positive, intracellular pathogen, exhibits remarkable resilience against diverse environmental and physical challenges [ 5 , 6 ]. It can endure adverse climatic conditions for extended durations outside the host and persists up to 55 weeks in dry and shaded environments [ 6 ]. Infected cattle experience a progressive disease course with four stages, leading to symptoms such as weight loss (with or without diarrhea), reduced milk and meat productivity, lethargy, hypoproteinemia, and emaciation [ 7 – 9 ]. These symptoms contribute to significant economic losses for dairy producers [ 10 ]. In India, the economic burden is exacerbated by the ban on cow slaughter in many states, rendering infected cattle unsalvageable. High morbidity rates and premature culling further amplify the losses [ 11 , 12 ]. Estimates from global studies highlight the financial impact, with annual losses per MAP-infected cattle ranging from $21 to $79 in the United States and approximately $40 in Canada [ 13 ]. Additionally, losses of $366 per lactating cow shedding MAP bacilli and up to $1,644 per 100 cows in herds with a 7% JD prevalence have been reported in the U.S., emphasizing the substantial economic implications of JD for dairy farming [ 14 – 16 ]. Research on MAP infection has focused on the transcriptomic responses of infected cattle to identify biomarkers and pathways involved in the disease. For instance, Marino et al. [ 17 ] utilized an in vitro model to investigate the early response to MAP infection using RNA-seq. Machugh et al. [ 18 ] analyzed gene expression in bovine monocyte-derived macrophages in response to in vitro MAP infection. David et al. [ 19 ] profiled gene expression in calves at 6 and 9 months post-inoculation with MAP, while Thirunavukkarasu et al. [ 20 ] identified novel pathways related to cholesterol and lipid metabolism in the early stages of MAP pathogenesis in cattle. Further studies by Shin et al. [ 21 ] and Hempel et al. [ 22 ] have examined immune responses, lipid metabolism, and the expression of immune regulatory genes in MAP-infected cattle. However, all of these studies have primarily focused on Bos taurus cattle. While these studies have provided valuable insights, their approaches have mainly employed differential gene expression analysis, potentially uncovering the host response and biomarkers for JD. However, they have not extensively explored co-expression modules of highly related genes, which are crucial for predicting gene function and identifying key genes involved in disease progression. Weighted Gene Co-expression Network Analysis (WGCNA) offers a robust method for integrating gene expression with phenotypic data, clustering genes with similar expression patterns into modules [ 23 , 24 ]. Genes within these modules often participate in related functions or pathways, making WGCNA a valuable tool for studying complex diseases [ 25 ]. This approach has been instrumental in identifying biomarker genes associated with various biological issues, including cancer [ 26 , 27 ], type I diabetes [ 28 ], rheumatoid arthritis [ 29 ], feed efficiency [ 30 ], and meat quality [ 31 ]. Its effectiveness in grouping genes into functional modules and uncovering regulatory mechanisms underlying complex traits has been emphasized in numerous recent studies. India has a substantial cattle population of approximately 1.9 billion ( https://dahd.nic.in/ ), primarily comprising a mix of breeds, with the majority being Bos indicus , also known as desi cattle [ 32 ]. Desi cattle are considered to possess better disease resilience compared to Bos taurus . However, JD is endemic among desi cattle, and high prevalence has been reported from many regions of the country [ 33 – 35 ]. Despite its substantial negative impact on dairy farming economics and potential public health implications, research on JD in desi cattle remains relatively limited. Previous transcriptomic studies in Bos taurus have provided valuable insights into host responses to MAP infection. However, a comprehensive understanding of the molecular mechanisms underlying MAP pathogenesis in desi cattle remains unexplored. This study leverages a weighted gene co-expression network analysis of transcriptomic data from chronically infected desi cattle to identify key regulatory genes and pathways involved in the host immune response to MAP infection. By integrating network analysis and functional enrichment analysis, we aim to uncover novel insights into the pathogenesis of MAP infection, which ultimately leads to JD. These findings may contribute to the development of targeted strategies for the prevention and control of MAP infection in desi cattle, mitigating its significant impact on animal health and the dairy industry. 2. Materials and methods 2.1. Phenotyping, Sample collection, and RNA sequencing Careful selection of desi cattle was a critical aspect of this study to ensure accurate transcriptome profiling. Diseased and healthy female desi cattle were meticulously chosen based on specific exclusion and inclusion criteria as mentioned in Additional file 1 . Each cattle recruited for the project underwent phenotyping for body condition score (BCS), clinical condition, and production parameters [ 36 ]. Samples were collected from phenotype cattle as per the guidelines of the Committee for the Purpose of Control and Supervision on Experiments on Animals (CPCSEA), India, with the Institutional Animal Ethics Committee (IAEC) approval. RNA sequencing methods are detailed in Additional File 1 . Raw data were preprocessed using Fastp [ 37 ], processed with SortMeRNA [ 38 ], and resultant non-rRNA paired-end reads were retained as cleaned reads for further analysis. 2.2. Quantification and differential expression of mRNA The Bos indicus reference genome (UOA_Brahman_1) and annotation (Ensembl v110) were used to extract protein-coding transcripts with GffRead [ 39 ]. Cleaned reads were aligned to these transcripts using Salmon [ 40 ] for transcript quantification, followed by gene-level quantification via the “tximport” R package [ 41 ]. Differential expression analysis was conducted using DESeq2 [ 42 ], with genes considered differentially expressed mRNAs (DEmRNAs) if p-adj 2 or < -2. PCA, heatmaps, MA, and volcano plots were generated using R packages to visualize gene expression patterns. 2.3. LncRNA analysis For long non-coding RNA (lncRNA) identification, a customized LncEvo pipeline [ 43 ] based on Nextflow was used with raw reads and the annotated Bos indicus reference genome (UOA_Brahman_1). Cleaned reads were aligned to the reference genome using STAR [ 44 ], and transcript structures were assembled using Stringtie [ 45 ]. These were merged into a unified GTF file with Stringtie’s merge function and compared with the reference GTF using CuffCompare to exclude transcripts with error-prone class codes (c, e, p, s). Stringent filtering steps, detailed in Additional File 1 , were then applied to refine the transcript set. The pipeline ultimately produced a curated set of potential lncRNAs in GTF and FASTA formats, ensuring reliable identification. LncRNA abundance was quantified using Salmon, followed by data processing with the tximport R package to create a count matrix. LncRNAs with mapped read counts ≥10 were retained, and DESeq2 identified differentially expressed lncRNAs (DElncRNAs) based on p.adj < 0.05 and log2fc 2. PCA, MA, and volcano plots were used to visualize expression patterns. 2.4. Module trait analysis The WGCNA R package [ 24 ] was used to identify significant gene modules and hub genes from DEmRNAs and DElncRNAs. Using VST transformed, normalized DESeq2 output and phenotypic trait data, a scale-free network was constructed by determining the optimal soft threshold power. Pearson correlation was used to create a similarity matrix, converted to an adjacency matrix, and then to topological overlap matrices (TOM). Gene modules were detected with the ’DynamicTreeCut’ function, and closely related modules were merged. Module Eigengenes (ME) were calculated, and their relationships with traits were visualized via heatmaps. Modules strongly correlated with traits were identified, and intramodular analysis pinpointed genes with high geneModuleMembership and geneTraitSignificance, offering insights into module-trait associations. 2.5. Functional enrichment analysis of key module To identify biologically relevant Gene Ontology (GO) terms associated with MAP infection, key module genes were analyzed for GO enrichment using the clusterProfiler R package [ 46 ] with the Bos indicus database. A significance threshold of adjusted p-value < 0.05 was applied. The enriched GO terms were visualized and plotted using clusterProfiler package. 2.6. Identification of hub genes Genes within the key module were identified based on high module membership values, measured by intramodular connectivity (KME or KIM) as described by Langfelder et al. [ 47 ]. Hub genes were selected with thresholds of ModuleMembership (|MM|) > 0.7 and geneSignificance (|GS|) > 0.3. A protein-protein interaction (PPI) network was constructed using the STRING database [ 48 ], retaining interactions with a confidence score ≥ 0.4. The cytoHubba plugin [ 49 ] in Cytoscape [ 50 ] ranked genes based on Degree, Maximum Clique Centrality (MCC), and Maximum Neighborhood Component (MNC), identifying the top 20 genes for each measure. Additionally, the MCODE plugin [ 51 ] identified densely connected regions (modules) with scores > 0.5. Hub genes common to both cytoHubba and MCODE analyses were considered the final set of hub genes. 2.7. Co-expression analysis of DEmRNA and DElncRNA in key module To investigate co-expression between DElncRNAs and DEmRNAs, we identified cis- regulated DEmRNAs for each DElncRNA within the key module [ 52 , 53 ]. These cis-targets were defined as DEmRNAs located within a 100 kb window upstream or downstream of the DElncRNA [ 54 ] or overlapping with it, as identified using the ’bedtools window’ tool [ 55 ]. DElncRNAs co-expressed with their target DEmRNAs and present within the same key module (node weight > 0.05) were considered as co-expressed DElncRNAs and included in the network analysis. 3. Results 3.1. Phenotyping of control and clinically infected animals In this study, animals were recruited based on specific inclusion and exclusion criteria outlined in the methods section. The phenotyping process involved assessing multiple criteria, including body condition score (BCS), clinical condition, and ZN (Ziehl-Neelsen) staining status for each animal. For the infected group, cattle underwent a phenotyping period of at least six months before selecting 20 cattle for blood sample extraction. These cattle consistently displayed positive ZN staining results, loose feces, and low BCS, confirming their infection status. Similarly, the healthy/control group was composed of animals that were phenotyped for more than 1 year before blood sample extraction. To be included in the control group, cattle had to consistently maintain good health and meet the criteria for being classified as healthy throughout the monitoring period. A total of 22 animals from the same gaushalas (cattle shelters) where infected animals were identified were used for blood sample extraction in the control group ( Table 1 ). This careful selection and monitoring process ensured that the two groups, infected and healthy, were distinct and suitable for the experiment’s objectives. View this table: View inline View popup Table 1: Sample phenotypes and clinical details included in this study 3.2. Genome wide identification of expressed transcripts in healthy and infected animals Transcripts expressed in PBMCs from phenotyped animals were captured for the RNA- seq experiment. Sequencing was performed on 22 healthy and 20 diseased samples using illumina short reads. A total of 3,438.61 million reads were generated from these 42 samples and were initially processed through a quality filter, resulting in 3,225.84 million clean reads ( Table S1 ). After the removal of ribosomal RNA, 2,790.64 million reads were obtained ( Table S2 ). As a result, each sample had non-rRNA, high-quality cleaned data with a minimum of 24 million and a median of 64.18 million paired-end reads. 3.3. Identification of differentially expressed genes To explore gene expression alterations in MAP-infected cattle, we analyzed the expression profiles of coding genes. RNA-seq data was mapped to mRNA transcripts (Fig. S1A) , resulting in the identification of 18,524 expressed genes. Differential expression analysis revealed 1,905 significantly altered genes (|log2FC| ≥ 2, adjusted p-value < 0.05) between diseased and healthy cattle. Hereafter these differentially expressed coding genes are referred to as DEmRNA. Among these, 1,588 DEmRNAs were upregulated and 317 were downregulated in the diseased condition. Principal component analysis (PCA) and hierarchical clustering demonstrated clear separation between diseased and healthy samples ( Fig. 1A , S1B) . The diseased group exhibited greater variability compared to the healthy group, which clustered very tightly. Volcano and MA plots ( Fig. 1B , S2A) visualized the extent of differential gene expression, highlighting the significant transcriptional response to MAP infection. A heatmap (Fig. S2B ) further illustrated the expression patterns of the DEGs. Download figure Open in new tab Figure 1: Clustering and differential gene expression analysis. (A) PCA plot of RNA-seq gene expression profiles. Triangles represent healthy individuals, while circles represent diseased individuals. (B) Volcano plot of differential gene expression analysis, with red points indicating significantly upregulated and downregulated genes based on log2 fold changes and p-value. (C) Bar plot of the top 10 enriched GO terms across categories: Biological Process (BP), Cellular Component (CC), and Molecular Function (MF). A comprehensive GO analysis of 1905 DEmRNAs revealed insights into their biological processes (BP), cellular components (CC), and molecular functions (MF) ( Fig. 1C ) . Enriched BP terms were predominantly associated with immune responses, including inflammation, cell morphogenesis, and adhesion. Key MF terms were related to immune receptor and chemokine receptor activities. Gene Set Enrichment (GSE) analysis identified significant activation of signaling pathways, such as cGMP-mediated, cyclic nucleotide-mediated, and G protein-coupled receptor signaling pathways, and suppression of leukocyte-mediated cytotoxicity, lymphocyte- mediated immunity, natural killer cell mediated immunity and cell killing-related pathways in infected cattle. 3.4. Genome-wide identification and characterization of lncRNAs A modified lncRNA prediction pipeline was employed to identify 58,621 lncRNA transcripts in cattle samples. Among these, 192 were known lncRNAs (class code "="), and the remaining were novel lncRNA candidates. These novel candidates were distributed in five classes based on their genomic location and overlap with annotated genes: intergenic (u), intronic (i), antisense (x), novel isoforms (j), and overlapping (o) ( Fig. 2A ) . The identified lncRNAs exhibited a wide range of lengths, from 200 bp to 22 kb. While many were multi-exonic, a significant number (11,042) were single-exonic. Among multi-exonic lncRNAs, the majority contained two exons ( Fig. 2B ). Only a small fraction exhibited a high exon count, with the maximum being 22 exons. The distribution of lncRNAs across chromosomes was uneven, with the X chromosome and chromosome 1 harboring the highest number of lncRNAs ( Fig. 2C ) . Download figure Open in new tab Figure 2: Genomic characteristics of lncRNAs in diseased vs. healthy desi cattle samples. (A) Distribution of predicted lncRNAs categorized by class code. (B) Exon count distribution among predicted lncRNA transcripts, illustrating the frequency of single and multi-exonic lncRNAs. (C) Chromosomal distribution of predicted lncRNA transcripts, showing the number of lncRNAs identified on each chromosome. 3.5. Identification of differentially expressed lncRNAs For identification of differentially expressed lncRNAs, we quantified the expression levels of 58,621 lncRNA transcripts that corresponded to 45,947 genes using Salmon. After filtering for genes with a minimum read count threshold, 42,291 lncRNA genes were considered expressed. Differential expression analysis revealed 3,123 significantly altered lncRNA genes (|log2FC| ≥ 2, adjusted p-value < 0.05) between diseased and healthy cattle. These differentially expressed lncRNA genes will hereafter be denoted as DElncRNAs. Among these, 2,459 DElncRNAs were upregulated and 664 DElncRNAs were downregulated in the diseased condition. Volcano and MA plots ( Fig. S4A, S4B ) visualized the extent of DElncRNAs expression, highlighting the significant transcriptional response to MAP infection. A heatmap ( Fig. S4C ) further illustrated the expression patterns of the DElncRNAs, emphasizing the distinct clustering of diseased and healthy samples. 3.6. Construction of co-expression modules A co-expression network was constructed using 1,905 DEmRNAs and 3,123 DElncRNAs with the WGCNA. After clustering all samples to identify outliers, no samples were excluded ( Fig. 3A ). A soft-threshold (β) power of 7 was chosen for a scale-free network ( Fig. 3B ), and genes were grouped into 18 modules through hierarchical clustering ( Table S3 ). To refine module similarity, modules with a correlation >0.2 were merged, resulting in 11 final co- expression modules containing between 21 and 1,960 genes ( Table 2 , Fig. 3C ). To investigate interactions within these co-expression modules, we generated a network heatmap illustrating their correlations, which indicated a relatively high degree of independence among clusters ( Fig. 4A ). To quantify the co-expression similarity across entire modules, we calculated their eigengenes and clustered them based on correlation. The resulting dendrogram showed that the 11 modules could be divided into two large subclusters, one comprising five modules and another with three modules, while two modules remained independent of either subcluster. This finding was further supported by the adjacency heatmap plot ( Fig. 4B ). Download figure Open in new tab Figure 3: Construction of weighted gene co-expression network. (A) Sample dendrogram with an associated trait heatmap, where colors indicate the relative association with clinical traits. (B) Scale-free topology analysis, showing the fit index for various soft-thresholding powers (left) and mean connectivity for each soft-thresholding power (right). (C) Gene clustering dendrogram based on topological overlap, with colors representing distinct co- expression modules. Each module color indicates a specific gene cluster, with branches above representing individual genes. The top row displays the initial module assignment, while the bottom row shows the merged modules. Download figure Open in new tab Figure 4: Module trait enrichment analysis.(A) Heatmap of the Topological Overlap Matrix (TOM) representing gene similarity in the weighted co-expression network. Lighter colors indicate lower overlap, while red indicates higher overlap. The left panel shows the TOM before module merging, and the right panel shows the TOM after merging modules. (B) Heatmap of eigengene adjacency, with color bars on the left and bottom indicating module assignments for each row and column. (C) Module-trait relationships, with each row representing a module eigengene and each column a trait. Each cell contains the correlation coefficient (R) and significance (p-value), with red indicating a positive correlation and blue indicating a negative correlation. View this table: View inline View popup Table 2: Gene counts in co-expressed merged modules 3.7. Identifying key modules To identify key modules, we defined the representative expression profile for each module using the module eigengene. To explore relationships between these module eigengenes and specific traits of interest, we calculated correlation coefficients ( Fig. S5 ). The resulting data were used to create a heatmap, visually depicting the correlation strength (R) and significance (p-value) between each module and the investigated traits ( Fig. 4C ). This heatmap highlights significant associations between modules and specific traits. Notably, the turquoise module, comprising 1,804 genes (870 protein-coding and 934 lncRNA), showed the most significant positive correlations with diseased conditions ( Fig. S6A ), fecal condition (Fig. S6B) , and ZN staining (Fig. S6C) , while displaying a negative correlation with BCS (Fig. S6D) . GS and MM analysis for the turquoise module ( Fig. S7 ) confirmed the strong association between GS and MM. Based on its significant correlations with specific traits, the turquoise module was identified as the key module of interest for subsequent analysis. 3.8. Functional enrichment analysis of the turquoise module GO enrichment analysis was performed on the 870 protein-coding genes in the turquoise module. The analysis revealed significant enrichment in BPs related to the regulation of immune responses, underscoring the module’s relevance to immune function. To provide a broader overview of BP enrichment, a word cloud was generated to highlight frequently enriched terms ( Fig. S8A ), while an induced GO enrichment graph demonstrated relationships among all enriched BP terms, revealing hierarchical patterns within immune response pathways ( Fig. S8B ). At the molecular level, genes within this module showed significant enrichment for processes such as defense response, inflammatory response, and natural killer cell-mediated immunity and cytotoxicity ( Fig. 5A ). To visually represent these enrichments, an enrichment map plot was created to organize the top 20 enriched BP terms into a network, with edges representing overlapping gene sets. The plot highlighted the interconnection of top enriched GO terms within the module ( Fig. 5B ). Additionally, a cnet plot was constructed to depict the distribution of specific genes contributing to key immune functions, offering a clear visualization of gene involvement ( Fig. 5C ). Several genes from the turquoise module, including TLR4 and IFT2, were enriched in all the top 10 GO terms. Download figure Open in new tab Figure 5: Functional enrichment analysis of the turquoise module. (A) Dot plot displaying the top 20 enriched GO BP terms associated with protein-coding genes in the turquoise module. (B) Enrichment map of the top 20 enriched BP terms, represented as a network, with edges indicating gene set overlap. (C) Category network plot (cnetplot) showing the relationships and overlap of genes across the top 10 GO biological process terms. GSE analysis identified pathways significantly enriched in both upregulated and downregulated gene sets during infection. The top 30 enriched pathways are visualized using a GSE plot (Fig. S9A) and a ridge plot (Fig. S9B) . Suppressed pathways were predominantly associated with natural killer (NK) cell-mediated immunity, lectin response, nucleosome organization, and protein-DNA complex assembly. Conversely, activated pathways were linked to G-protein-coupled receptor signaling, circulatory system processes, metabolic processes, and other key biological functions. 3.9. Identification of hub genes and co-expressed lncRNA To identify key regulatory (hub) genes in the turquoise module, a network-based approach was applied to the 870 protein-coding genes identified in the module. Filtering by MM and GS reduced the gene set to 526 genes. A high-confidence protein-protein interaction (PPI) network was then built using the STRING database configured for Bos indicus hybrids, resulting in 384 genes with interaction scores ≥0.4. The cytoHubba plugin ranked the top 20 most interconnected genes, while the MCODE plugin identified 34 genes from densely connected subnetworks. Combining these results, 12 hub genes (IL7R, SELL, TLR4, KLRK1, IFNG, TGFB1, CD68, CXCR6, GZMB, KLRG1, MMP9, and GZMA) were identified as central regulators in MAP infection, with high connectivity within the network ( Fig. 6A ) . Download figure Open in new tab Figure 6: Identification of hub genes and their associated network of co-expressed lncRNAs and enriched GO terms. (A) Venn diagram illustrating the identification of a common set of hub genes across four algorithms. The overlapping region represents the hub genes shared by all four algorithms. (B) Network diagram depicting the interactions among hub genes, their co- expressed lncRNAs, and enriched GO biological process (BP) terms. Hub genes are shown as oval shapes, with upregulated genes highlighted in red and downregulated genes in green. Co- expressed lncRNAs are represented by diamond shapes in cyan, while enriched GO BP terms are displayed as square shapes in violet. Recognizing the regulatory potential of lncRNAs, 934 DElncRNA genes from the turquoise module were investigated for their co-expression with hub genes. This analysis revealed 22 DElncRNAs co-expressed with five hub genes: IFNG (17 DElncRNAs), CD68 (3 DElncRNAs), CXCR6, GZMB, and SELL (1 DElncRNA each).These findings suggest that DElncRNAs may regulate hub gene expression, highlighting their potential roles in modulating immune response pathways during MAP infection ( Fig. 6B ). 4. Discussion Previous transcriptomic studies on MAP infection in Bos taurus have primarily focused on identifying DEGs and candidate biomarkers [ 3 , 56 , 57 ]. While informative, these studies have often been limited in their ability to pinpoint core regulatory genes and pathways. This study advances our understanding of MAP infection in Bos indicus , a desi cattle known for its resilience. Leveraging a multi-layered approach, integrating phenotypic observations, transcriptomic data (mRNA and lncRNA), and WGCNA, we identified key regulatory hub genes and pathways associated with JD. These findings provide new insights into the complex host- pathogen interactions and offer a foundation for developing targeted therapeutic strategies. The phenotypic characterization of the animals provided clear and clinically relevant distinctions between the chronically infected and control groups. The infected group exhibited consistent signs of MAP infection, including low BCS, loose feces, and positive ZN staining. This aligns with previous studies, which have identified these symptoms as hallmark indicators of clinical Johne’s disease [ 36 ]. RNA-seq analysis of infected and control groups revealed a robust transcriptional response to MAP infection in desi cattle, with 1,905 genes differentially expressed. Genes associated with enriched GO terms highlighted both activated and suppressed pathways in infected cattle. Activated signaling pathways included cGMP-mediated signaling, cyclic nucleotide-mediated signaling, and G protein-coupled receptor signaling. These pathways potentially assist the bacteria in persisting within the host by enhancing tolerance and antimicrobial resistance [ 58 , 59 ]. The cGMP signaling pathway, for instance, plays a crucial role in host survival against Gram-positive bacterial infections in Drosophila [ 60 ]. Elevated GPCR signaling has also been observed in Mycobacterium tuberculosis (Mtb) infections [ 61 ]. In contrast, suppressed pathways were primarily involved in adaptive and innate immune responses. Pathways such as leukocyte-mediated cytotoxicity, lymphocyte-mediated immunity, natural killer cell mediated immunity and cell-killing processes are essential components of cell- mediated immunity. The suppression of these pathways aids intracellular bacteria like MAP in evading key defense mechanisms that protect the host against intracellular pathogens [ 62 ]. Altogether, the altered transcriptomic response in chronically infected cattle suggests that MAP employs immune evasion strategies to persist within host cells, as previously reported [ 63 ]. LncRNAs are emerging as critical regulators of gene expression, particularly in response to diseases and stress [ 64 ]. However, identifying and functionally characterizing lncRNAs remains a significant challenge in livestock [ 65 ]. The current Ensembl reference genome annotation for Bos indicus lacks comprehensive lncRNA annotations. Previously, the only study by Sabara et al. [ 66 ] reported 10,360 lncRNAs in desi cattle using RNA-seq data from control and horn cancer samples. Our study provides a more comprehensive annotation by analyzing both MAP-infected diseased and healthy cattle. We identified 58,621 lncRNA transcripts in Bos indicus using 42 desi cattle. This number is comparable to the 47,683 lncRNA transcripts reported by Marete et al. [ 4 ] in Bos taurus in response to MAP infection. Further, differential expression analysis identified 3,123 DElncRNAs in MAP-infected cattle. Interestingly, a higher number of lncRNAs were upregulated in diseased conditions, suggesting a potential compensatory or regulatory mechanism activated during infection [ 67 ]. DEmRNAs and DElncRNAs exhibiting similar expression patterns were grouped into modules using co-expression network analysis. WGCNA, a widely employed co-expression network tool, has been successfully applied in numerous studies. A key advantage of WGCNA is its ability to assign an eigengene to each module, representing the overall expression profile. This eigengene can then be correlated with phenotypic data or clinical parameters, leading to highly reliable and biologically significant results. Using 1,905 DEmRNAs and 3,123 DElncRNAs, we discovered 11 modules of co-expressed genes. Associating these modules with specific traits requires accurate phenotypic data. An earlier study by Heidari et al. [ 56 ] on MAP infection in Bos taurus identified modules but lacked detailed phenotypic records, preventing module-trait analysis. In contrast, our study included clinical phenotype data, enabling us to identify the turquoise module as the key module most strongly associated with disease traits in desi cattle. This finding suggests that the genes within the turquoise module play a pivotal role in the pathological changes observed in MAP-infected desi cattle. The strong correlation between GS and MM for the turquoise module indicates that many of these genes are not only co- expressed but also highly relevant to the disease process. Characterization of the turquoise module for biological significance revealed that 870 DEmRNAs were associated with top GO terms related to defense response and cell-mediated immunity, like other studies [ 56 , 68 , 69 ]. Additionally, GSE analysis highlighted the suppression of pathways involved in cell-mediated immunity. In contrast, activated pathways within the module included G-protein-coupled receptor signaling, circulatory system processes, and metabolic processes. The suppressed and activated pathways identified by GSE analysis from genes within the turquoise module were consistent with the enriched pathways identified using the overall set of DEmRNAs. This suggests that the turquoise module genes represent a core set of DEGs that effectively capture the key cellular changes associated with the disease. The observed activated pathways likely favor bacterial growth and persistence within the host, while the suppressed pathways represent the host’s defensive mechanisms that MAP attempts to evade. We employed a network-based approach to identify key regulatory genes within the turquoise module, revealing 12 hub genes that emerged as central regulators of the network: IL7R , TGFB1, IFNG , KLRK1 , GZMB , GZMA , TLR4 , CD68 , MMP9 , CXCR6 , KLRG1 , and SELL . These genes exhibited high |MM| > 0.7, |GS| > 0.3, and extensive protein-protein interactions (confidence score ≥ 0.4), underscoring their roles in the host-pathogen interplay during MAP infection. IL7R plays a pivotal role in immune suppression, with decreased expression linked to MAP susceptibility and inflammatory diseases like UC and IBD [ 4 , 70 ] . Its association with increased IFN-γ production upon MAP antigen stimulation further underscores its potential as a therapeutic target. Similarly, TGFB1 , a pleiotropic cytokine, is essential for maintaining immune homeostasis. Its upregulation in MAP infection reflects a dual role: limiting excessive inflammation while contributing to immunosuppression, paralleling findings in TB- infected cattle [ 71 , 72 ]. IFNG , a critical cytokine for intracellular pathogen defense. It plays a pivotal role in activating macrophages and promoting a Th1-type immune response, which is essential for controlling mycobacterial infections [ 73 , 74 ]. As the disease progresses, a decline in IFN-γ levels is commonly observed, including in this study, resulting in a weakened immune response and increased susceptibility to opportunistic infections [ 75 , 76 ]. KLRK1 (NKG2D), a receptor on NK cells and cytotoxic T cells, also exhibited downregulation, suggesting impaired cytotoxic immunity and favoring MAP persistence [ 77 , 78 ]. The serine proteases GZMB and GZMA , vital for immune-mediated pathogen clearance, displayed differential regulation [ 79 ] ; GZMB was downregulated, while GZMA , less studied in MAP, may have extracellular functions in immune modulation [ 80 , 81 ]. TLR4 , an innate immune receptor, was upregulated, reinforcing its role in pathogen recognition and immune activation [ 82 , 83 ]. Genetic variations in TLR4 have been linked to MAP susceptibility, positioning it as a potential biomarker for diagnostic and therapeutic applications [ 84 , 85 ]. CD68 , a macrophage marker, showed increased expression, indicative of macrophage activation and infiltration, though its association with granulomatous lesions reveals a complex role in disease progression [ 86 – 88 ]. MMP9 , known for extracellular matrix remodeling and immune cell migration, was significantly upregulated, suggesting its utility as a transcriptional biomarker for JD [ 89 , 90 ]. Similarly, CXCR6 , a G protein-coupled receptor, demonstrated context-dependent regulation, balancing immune cell recruitment with potential contributions to inflammation [ 91 , 92 ]. KLRG1 , an immune checkpoint receptor associated with immune cell exhaustion, was identified as a novel hub gene, highlighting its likely involvement in MAP pathogenesis [ 93 , 94 ]. Lastly, SELL (L-selectin), crucial for leukocyte recruitment during inflammation [ 95 ], was identified as a hub gene, though its specific role in MAP infection warrants further exploration. Together, these findings highlight the intricate regulatory network underpinning MAP infection and offer potential targets for future research and therapeutic interventions. Previous studies have emphasized the regulatory role of lncRNAs in gene expression, often mediated through their interactions with protein-coding genes. In bovine paratuberculosis induced by MAP, lncRNAs have been reported to influence the expression of adjacent genes related to immune response [ 56 , 96 ]. In our study, we identified 22 key lncRNAs that are differentially expressed and located near five hub genes, suggesting their role as molecular switches that fine-tune gene expression in response to MAP infection. These lncRNAs exhibit a high correlation with disease-associated traits, as indicated by their elevated GS scores, demonstrating their strong association with MAP infection-related traits. Among these, 17 lncRNAs are co-expressed with IFNG, a cytokine critical for macrophage activation and the promotion of Th1-type immune responses. This co-expression suggests that these lncRNAs may play a role in regulating IFN-γ expression, potentially influencing the host’s immune response to MAP infection. LncRNAs identified in the study could not be directly compared with those reported in previous studies on lncRNA involvement in MAP infection in cattle due to the absence of a comprehensive lncRNA database for cattle. This limitation represents a significant gap in the study, as such a resource would enable more robust comparisons, validation, and integration of findings across different studies. Conclusion This study provides a detailed transcriptomic analysis of chronically MAP-infected desi cattle using RNA sequencing, identifying differentially expressed genes to explore the molecular mechanisms of MAP infection. WGCNA highlighted key gene modules, especially the turquoise module, strongly linked to clinical traits. GSE analysis revealed both activated and suppressed pathways in infected animals, reflecting the complex interaction between host and pathogen. The identification of hub genes such as IL7R, TLR4, KLRK1, IFNG, TGFB1, CD68, CXCR6, GZMB, KLRG1, MMP9, GZMA, and SELL provides insights into immune response regulation, immune suppression, inflammation, immune cell recruitment, and tissue remodeling in MAP infection. These genes may contribute to immune evasion and tissue damage during the disease process. Moreover, the study annotated a comprehensive set of lncRNAs in desi cattle and identified differentially expressed lncRNAs co-expressed with hub genes, suggesting their involvement in MAP pathogenesis. Overall, our findings suggest that MAP employs various strategies to evade host immune responses and establish persistent infection. The identified hub genes and lncRNAs represent potential biomarkers and therapeutic targets for the control of Johne’s disease. Further, validation and research into the exact mechanisms of these genes and lncRNAs in MAP pathogenesis are essential for advancing our understanding and treatment strategies. CRediT authorship contribution statement Abhisek Sahu: Data curation, Formal analysis, Software, Visualization, Writing – original draft. Mohd Abdullah: Investigation, Resources, Validation. Saurabh Gupta: Project administration, Supervision. Shoor Vir Singh: Conceptualization, Project administration, Supervision, Funding acquisition, Writing – review and editing. Ankush Dhillon: Resources, Validation. Prabhati Yadav: Resources, Validation. Sarwar Azam: Conceptualization, Data curation, Formal analysis, Funding acquisition, Project administration, Supervision, Writing – original draft, Writing – review and editing. Funding This project, titled ’Identification of key molecular factors involved in resistance/susceptibility to paratuberculosis infection in indigenous breeds of cows,’ was fully funded by the Department of Biotechnology (DBT), India, under grant BT/PR32758/AAQ/1/760/2019. Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Data availability The short-read sequencing dataset generated from the Illumina platform and used in this study has been submitted to the Indian Biological Data Centre (IBDC) under study id INRP000128. The detailed accession numbers for the RNA-seq data are provided in Table S4 . All the data can also be accessed from the NCBI. Additional Files Additional File 1: Additional methods Download figure Open in new tab Figure S1: Data normalization and sample clustering. (A) Boxplot of transformed and normalized counts for each sample, illustrating consistent distribution across samples. (B) Hierarchical clustering dendrogram of samples based on gene expression patterns, showing distinct clusters that separate diseased and healthy samples. Download figure Open in new tab Figure S2: Differential expression of mRNA. (A) MA plot showing the relationship between the average expression (log-transformed mean counts) and the log2 fold change of differentially expressed mRNAs between disease and healthy conditions. (B) Heatmap displaying the expression patterns of differentially expressed genes across disease and healthy samples. Each row represents a gene, and each column represents a sample. Download figure Open in new tab Figure S3: Enrichment of differentially expressed genes. (A) GSE plot displaying the top 20 pathways activated or suppressed in response to MAP infection. (B) Cnet plot showing the top five enriched GO terms and the genes associated with BP. Download figure Open in new tab Figure S4: Differential expression of lncRNAs. (A) Volcano plot showing differentially expressed lncRNAs, with red points indicating upregulated and downregulated genes based on the log2 fold change (log2(FC)) between disease and healthy samples. (B) MA plot displaying the relationship between average expression (log-transformed mean counts) and the log2 fold change of differentially expressed lncRNAs between disease and healthy conditions. (C) Heatmap of differentially expressed lncRNAs in healthy versus diseased samples. Download figure Open in new tab Figure S5: Eigengene heatmap showing correlated modules with traits. Heatmap of module eigengenes to identify clusters of correlated eigengenes (modules) in relation to each trait. (A) Condition (B) BCS (C) Fecal (D) ZN staining Download figure Open in new tab Figure S6: Heatmap of MM and GS in the turquoise module. (A) MM vs. GS with condition trait (B) MM vs. GS with fecal trait (C) MM vs. GS with ZN staining trait (D) MM vs. GS with BCS trait Download figure Open in new tab Figure S7: Relationship between MM and GS for MAP infection in turquoise module. Scatter plot showing the correlation between MM and GS in the turquoise module for MAP infection, comparing diseased and healthy cattle. Download figure Open in new tab Figure S8: GO enrichment visualization of protein coding genes in turquoise module. (A) Word cloud of all enriched GO BP terms, with font size representing the frequency and significance of biological processes associated with protein coding genes. Larger terms indicate higher enrichment significance. (B) Enriched GO network graph showing the relationships among the top GO terms in BP. Nodes represent enriched GO terms, with edges indicating functional relationships based on shared genes. Node size reflects significance, and color intensity represents gene count. This graph highlights interconnected pathways. Download figure Open in new tab Figure S9: Gene set enrichment analysis. (A) GSE plot illustrating the top 30 enriched pathways, highlighting those that are activated or suppressed in the dataset. (B) Ridge plot showing the distribution of the top 25 upregulated and downregulated pathways, providing insight into the variability and significance of pathway changes. Acknowledgement The authors gratefully acknowledge the financial support provided by the Department of Biotechnology (DBT), Ministry of Science, New Delhi, India. We also extend our sincere thanks to the National Institute of Animal Biotechnology (NIAB) for their invaluable support throughout this study. In particular, S.A. wishes to express gratitude to Dr. G. Taru Sharma, Director of NIAB, for her support. SVS is deeply indebted to the support of Dr. A.K.Gupta, Vice Chancellor and GLA university for this study. References [1]. ↵ L.S. Zwick , T.F. Walsh , R. Barbiers , M.T. Collins , M.J. Kinsel , R.D. Murnane , Paratuberculosis in a mandrill (Papio sphinx) , J. Vet. Diagn. Invest . 14 ( 2002 ) 326 – 328 . doi: 10.1177/104063870201400409 . OpenUrl CrossRef PubMed [2]. ↵ P.M. Beard , M.J. Daniels , D. Henderson , A. Pirie , K. Rudge , D. Buxton , S. Rhind , A. Greig , M.R. Hutchings , I. McKendrick , K. Stevenson , J.M. Sharp , Paratuberculosis infection of nonruminant wildlife in Scotland , J. Clin. Microbiol . 39 ( 2001 ) 1517 – 1521 . doi: 10.1128/JCM.39.4.1517-1521.2001 . OpenUrl Abstract / FREE Full Text [3]. ↵ H.-T. Park , H.-E. Park , S. Shim , S. Kim , M.-K. Shin , H.S. Yoo , Epithelial processed Mycobacterium avium subsp. paratuberculosis induced prolonged Th17 response and suppression of phagocytic maturation in bovine peripheral blood mononuclear cells , Sci. Rep . 10 ( 2020 ) 21048 . doi: 10.1038/s41598-020-78113-8 . OpenUrl CrossRef PubMed [4]. ↵ A. Marete , O. Ariel , E. Ibeagha-Awemu , N. Bissonnette , Identification of long non-coding RNA isolated from naturally infected macrophages and associated with bovine Johne’s disease in Canadian Holstein using a combination of neural networks and logistic regression , Front. Vet. Sci . 8 ( 2021 ) 639053 . doi: 10.3389/fvets.2021.639053 . OpenUrl CrossRef PubMed [5]. ↵ I.R. Grant , Zoonotic potential of Mycobacterium avium ssp. paratuberculosis : the current position , J. Appl. Microbiol . 98 ( 2005 ) 1282 – 1293 . doi: 10.1111/j.1365-2672.2005.02598.x . OpenUrl CrossRef PubMed [6]. ↵ R.J. Whittington , D.J. Marshall , P.J. Nicholls , I.B. Marsh , L.A. Reddacliff , Survival and dormancy of Mycobacterium avium subsp. paratuberculosis in the environment , Appl. Environ. Microbiol . 70 ( 2004 ) 2989 – 3004 . doi: 10.1128/AEM.70.5.2989-3004.2004 . OpenUrl Abstract / FREE Full Text [7]. ↵ R.H. Whitlock , C. Buergelt , Preclinical and clinical manifestations of paratuberculosis (including pathology) , Vet. Clin. North Am. Food Anim. Pract . 12 ( 1996 ) 345 – 356 . doi: 10.1016/s0749-0720(15)30410-2 . OpenUrl CrossRef PubMed Web of Science [8]. S.H. Hendrick , D.F. Kelton , K.E. Leslie , K.D. Lissemore , M. Archambault , T.F. Duffield , Effect of paratuberculosis on culling, milk production, and milk quality in dairy herds , J. Am. Vet. Med. Assoc . 227 ( 2005 ) 1302 – 1308 . doi: 10.2460/javma.2005.227.1302 . OpenUrl CrossRef PubMed Web of Science [9]. ↵ M.G. Gonda , Y.M. Chang , G.E. Shook , M.T. Collins , B.W. Kirkpatrick , Effect of Mycobacterium paratuberculosis infection on production, reproduction, and health traits in US Holsteins , Prev. Vet. Med . 80 ( 2007 ) 103 – 119 . doi: 10.1016/j.prevetmed.2007.01.011 . OpenUrl CrossRef PubMed [10]. ↵ S.L. Ott , S.J. Wells , B.A. Wagner , Herd-level economic losses associated with Johne’s disease on US dairy operations , Prev. Vet. Med . 40 ( 1999 ) 179 – 192 . doi: 10.1016/s0167-5877(99)00037-9 . OpenUrl CrossRef PubMed Web of Science [11]. ↵ R.W. Shephard , S.H. Williams , S.D. Beckett , Farm economic impacts of bovine Johne’s disease in endemically infected Australian dairy herds , Aust. Vet. J . 94 ( 2016 ) 232 – 239 . doi: 10.1111/avj.12455 . OpenUrl CrossRef PubMed [12]. ↵ A.B. Garcia , L. Shalloo , Invited review: The economic impact and control of paratuberculosis in cattle , J. Dairy Sci . 98 ( 2015 ) 5019 – 5039 . doi: 10.3168/jds.2014-9241 . OpenUrl CrossRef PubMed [13]. ↵ R.B. Pillars , D.L. Grooms , C.A. Wolf , J.B. Kaneene , Economic evaluation of Johne’s disease control programs implemented on six Michigan dairy farms , Prev. Vet. Med . 90 ( 2009 ) 223 – 232 . doi: 10.1016/j.prevetmed.2009.04.009 . OpenUrl CrossRef PubMed [14]. ↵ E.A. Raizman , J.P. Fetrow , S.J. Wells , Loss of income from cows shedding Mycobacterium avium subspecies paratuberculosis prior to calving compared with cows not shedding the organism on two Minnesota dairy farms , J. Dairy Sci . 92 ( 2009 ) 4929 – 4936 . doi: 10.3168/jds.2009-2133 . OpenUrl CrossRef PubMed Web of Science [15]. B. Bhattarai , G.T. Fosgate , J.B. Osterstock , C.P. Fossler , S.C. Park , A.J. Roussel , Perceptions of veterinarians in bovine practice and producers with beef cow-calf operations enrolled in the US Voluntary Bovine Johne’s Disease Control Program concerning economic losses associated with Johne’s disease , Prev. Vet. Med . 112 ( 2013 ) 330 – 337 . doi: 10.1016/j.prevetmed.2013.08.009 . OpenUrl CrossRef PubMed [16]. ↵ P. Rasmussen , H.W. Barkema , S. Mason , E. Beaulieu , D.C. Hall , Economic losses due to Johne’s disease (paratuberculosis) in dairy cattle , J. Dairy Sci . 104 ( 2021 ) 3123 – 3143 . doi: 10.3168/jds.2020-19381 . OpenUrl CrossRef PubMed [17]. ↵ R. Marino , R. Capoferri , S. Panelli , G. Minozzi , F. Strozzi , E. Trevisi , G.G.M. Snel , P. Ajmone-Marsan , J.L. Williams , Johne’s disease in cattle: an in vitro model to study early response to infection of Mycobacterium avium subsp. paratuberculosis using RNA-seq , Mol. Immunol . 91 ( 2017 ) 259 – 271 . doi: 10.1016/j.molimm.2017.08.017 . OpenUrl CrossRef PubMed [18]. ↵ D.E. Machugh , M. Taraktsoglou , K.E. Killick , N.C. Nalpas , J.A. Browne , S. DE Park , K. Hokamp , E. Gormley , D.A. Magee , Pan-genomic analysis of bovine monocyte-derived macrophage gene expression in response to in vitro infection with Mycobacterium avium subspecies paratuberculosis , Vet. Res . 43 ( 2012 ) 25 . doi: 10.1186/1297-9716-43-25 . OpenUrl CrossRef PubMed [19]. ↵ J. David , H.W. Barkema , L.L. Guan , J. De Buck , Gene-expression profiling of calves 6 and 9 months after inoculation with Mycobacterium avium subspecies paratuberculosis , Vet. Res . 45 ( 2014 ) 96 . doi: 10.1186/s13567-014-0096-5 . OpenUrl CrossRef PubMed [20]. ↵ S. Thirunavukkarasu , K.M. Plain , K. de Silva , D. Begg , R.J. Whittington , A.C. Purdie , Expression of genes associated with cholesterol and lipid metabolism identified as a novel pathway in the early pathogenesis of Mycobacterium avium subspecies paratuberculosis- infection in cattle , Vet. Immunol. Immunopathol . 160 ( 2014 ) 147 – 157 . doi: 10.1016/j.vetimm.2014.04.002 . OpenUrl CrossRef PubMed [21]. ↵ M.-K. Shin , H.-T. Park , S.W. Shin , M. Jung , Y.B. Im , H.-E. Park , Y.-I. Cho , H.S. Yoo , Whole-blood gene-expression profiles of cows infected with Mycobacterium avium subsp. paratuberculosis reveal changes in immune response and lipid metabolism , J. Microbiol. Biotechnol . 25 ( 2015 ) 255 – 267 . doi: 10.4014/jmb.1408.08059 . OpenUrl CrossRef PubMed [22]. ↵ R.J. Hempel , J.P. Bannantine , J.R. Stabel , Transcriptional Profiling of Ileocecal Valve of Holstein Dairy Cows Infected with Mycobacterium avium subsp. Paratuberculosis , PLoS One 11 ( 2016 ) e0153932 . doi: 10.1371/journal.pone.0153932 . OpenUrl CrossRef PubMed [23]. ↵ B. Zhang , S. Horvath , A general framework for weighted gene co-expression network analysis , Stat. Appl. Genet. Mol. Biol . 4 ( 2005 ) Article17. doi: 10.2202/1544-6115.1128 . OpenUrl CrossRef PubMed [24]. ↵ P. Langfelder , S. Horvath , WGCNA: an R package for weighted correlation network analysis , BMC Bioinformatics 9 ( 2008 ) 559 . doi: 10.1186/1471-2105-9-559 . OpenUrl CrossRef PubMed [25]. ↵ M. Niemira , F. Collin , A. Szalkowska , A. Bielska , K. Chwialkowska , J. Reszec , J. Niklinski , M. Kwasniewski , A. Kretowski , Molecular signature of subtypes of non-small- cell lung cancer by large-scale transcriptional profiling: Identification of key modules and genes by weighted gene co-expression network analysis (WGCNA) , Cancers (Basel ) 12 ( 2019 ) 37 . doi: 10.3390/cancers12010037 . OpenUrl CrossRef PubMed [26]. ↵ X. Liu , A.-X. Hu , J.-L. Zhao , F.-L. Chen , Identification of key gene modules in human osteosarcoma by co-expression analysis weighted gene co-expression network analysis (WGCNA) , J. Cell. Biochem . 118 ( 2017 ) 3953 – 3959 . doi: 10.1002/jcb.26050 . OpenUrl CrossRef PubMed [27]. ↵ Y. Di , D. Chen , W. Yu , L. Yan , Bladder cancer stage-associated hub genes revealed by WGCNA co-expression network analysis , Hereditas 156 ( 2019 ) 7 . doi: 10.1186/s41065-019-0083-y . OpenUrl CrossRef PubMed [28]. ↵ H. Li , X. Hu , J. Li , W. Jiang , L. Wang , X. Tan , Identification of key regulatory genes and their working mechanisms in type 1 diabetes , BMC Med. Genomics 16 ( 2023 ) 8 . doi: 10.1186/s12920-023-01432-y . OpenUrl CrossRef PubMed [29]. ↵ F. Jiang , H. Zhou , H. Shen , Identification of critical biomarkers and immune infiltration in rheumatoid arthritis based on WGCNA and LASSO algorithm , Front. Immunol . 13 ( 2022 ) 925695 . doi: 10.3389/fimmu.2022.925695 . OpenUrl CrossRef PubMed [30]. ↵ S.M. Salleh , G. Mazzoni , P. Løvendahl , H.N. Kadarmideen , Gene co-expression networks from RNA sequencing of dairy cattle identifies genes and pathways affecting feed efficiency , BMC Bioinformatics 19 ( 2018 ) 513 . doi: 10.1186/s12859-018-2553-z . OpenUrl CrossRef PubMed [31]. ↵ X. Liu , J. Zhang , X. Xiong , C. Chen , Y. Xing , Y. Duan , S. Xiao , B. Yang , J. Ma , An integrative analysis of transcriptome and GWAS data to identify potential candidate genes influencing meat quality traits in pigs , Front. Genet . 12 ( 2021 ) 748070 . doi: 10.3389/fgene.2021.748070 . OpenUrl CrossRef PubMed [32]. ↵ R. Sharma , S. Ahlawat , H. Sharma , R.L. Bhagat , P.K. Singh , M.S. Tantia , Identification of a new potential native Indian cattle breed by population differentiation based on microsatellite markers , Mol. Biol. Rep . 47 ( 2020 ) 6429 – 6434 . doi: 10.1007/s11033-020-05639-5 . OpenUrl CrossRef PubMed [33]. ↵ A.V. Singh , D.S. Chauhan , S.V. Singh , V. Kumar , A. Singh , A. Yadav , V.S. Yadav , Current status of Mycobacterium avium subspecies paratuberculosis infection in animals & humans in India: What needs to be done? , Indian J. Med. Res . 144 ( 2016 ) 661 – 671 . doi: 10.4103/ijmr.IJMR_1401_14 . OpenUrl CrossRef PubMed [34]. K.K. Chaubey , S.V. Singh , S. Gupta , M. Singh , J.S. Sohal , N. Kumar , M.K. Singh , A.K. Bhatia , K. Dhama , Mycobacterium avium subspecies paratuberculosis - an important food borne pathogen of high public health significance with special reference to India: an update , Vet. Q . 37 ( 2017 ) 282 – 299 . doi: 10.1080/01652176.2017.1397301 . OpenUrl CrossRef PubMed [35]. ↵ S. Sharma , A. Gautam , S.V. Singh , K.K. Chaubey , R. Mehta , S. Gupta , M. Sharma , M.K. Rose , V.K. Jain , Prevalence of Mycobacterium avium subspecies paratuberculosis (MAP) infection in suspected diarrhoeic buffaloes and cattle reporting at Veterinary University in India, Comp. Immunol . Microbiol. Infect. Dis . 73 ( 2020 ) 101533. doi: 10.1016/j.cimid.2020.101533 . OpenUrl CrossRef [36]. ↵ R.L. Smith , Y.H. Schukken , Y.T. Gröhn , A new compartmental model of Mycobacterium avium subsp. paratuberculosis infection dynamics in cattle , Prev. Vet. Med . 122 ( 2015 ) 298 – 305 . doi: 10.1016/j.prevetmed.2015.10.008 . OpenUrl CrossRef PubMed [37]. ↵ S. Chen , Y. Zhou , Y. Chen , J. Gu, fastp: an ultra-fast all-in-one FASTQ preprocessor , Bioinformatics 34 ( 2018 ) i884 – i890 . doi: 10.1093/bioinformatics/bty560 . OpenUrl CrossRef PubMed [38]. ↵ E. Kopylova , L. Noé , H. Touzet , SortMeRNA: fast and accurate filtering of ribosomal RNAs in metatranscriptomic data , Bioinformatics 28 ( 2012 ) 3211 – 3217 . doi: 10.1093/bioinformatics/bts611 . OpenUrl CrossRef PubMed Web of Science [39]. ↵ G. Pertea , M. Pertea , GFF utilities: GffRead and GffCompare , F1000Res . 9 ( 2020 ) 304 . doi: 10.12688/f1000research.23297.2 . OpenUrl CrossRef [40]. ↵ R. Patro , G. Duggal , M.I. Love , R.A. Irizarry , C. Kingsford , Salmon provides fast and bias- aware quantification of transcript expression , Nat. Methods 14 ( 2017 ) 417 – 419 . doi: 10.1038/nmeth.4197 . OpenUrl CrossRef PubMed [41]. ↵ C. Soneson , M.I. Love , M.D. Robinson , Differential analyses for RNA-seq: transcript-level estimates improve gene-level inferences , F1000R es. 4 ( 2015 ) 1521. doi: 10.12688/f1000research.7563.1 . OpenUrl CrossRef PubMed [42]. ↵ M.I. Love , W. Huber , S. Anders , Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2 , Genome Biol . 15 ( 2014 ) 550 . doi: 10.1186/s13059-014-0550-8 . OpenUrl CrossRef PubMed [43]. ↵ O. Bryzghalov , I. Makałowska , M.W. Szcześniak , lncEvo: automated identification and conservation study of long noncoding RNAs , BMC Bioinformatics 22 ( 2021 ) 59 . doi: 10.1186/s12859-021-03991-2 . OpenUrl CrossRef [44]. ↵ A. Dobin , C.A. Davis , F. Schlesinger , J. Drenkow , C. Zaleski , S. Jha , P. Batut , M. Chaisson , T.R. Gingeras , STAR: ultrafast universal RNA-seq aligner , Bioinformatics 29 ( 2013 ) 15 – 21 . doi: 10.1093/bioinformatics/bts635 . OpenUrl CrossRef PubMed Web of Science [45]. ↵ M. Pertea , G.M. Pertea , C.M. Antonescu , T.-C. Chang , J.T. Mendell , S.L. Salzberg , StringTie enables improved reconstruction of a transcriptome from RNA-seq reads , Nat. Biotechnol . 33 ( 2015 ) 290 – 295 . doi: 10.1038/nbt.3122 . OpenUrl CrossRef PubMed [46]. ↵ T. Wu , E. Hu , S. Xu , M. Chen , P. Guo , Z. Dai , T. Feng , L. Zhou , W. Tang , L. Zhan , X. Fu , S. Liu , X. Bo , G. Yu , clusterProfiler 4.0: A universal enrichment tool for interpreting omics data , Innovation (Camb ) 2 ( 2021 ) 100141 . doi: 10.1016/j.xinn.2021.100141 . OpenUrl CrossRef PubMed [47]. ↵ P. Langfelder , R. Luo , M.C. Oldham , S. Horvath , Is my network module preserved and reproducible? , PLoS Comput. Biol . 7 ( 2011 ) e1001057 . doi: 10.1371/journal.pcbi.1001057 . OpenUrl CrossRef PubMed [48]. ↵ D. Szklarczyk , R. Kirsch , M. Koutrouli , K. Nastou , F. Mehryary , R. Hachilif , A.L. Gable , T. Fang , N.T. Doncheva , S. Pyysalo , P. Bork , L.J. Jensen , C. von Mering , The STRING database in 2023: protein-protein association networks and functional enrichment analyses for any sequenced genome of interest , Nucleic Acids Res . 51 ( 2023 ) D638 – D646 . doi: 10.1093/nar/gkac1000 . OpenUrl CrossRef PubMed [49]. ↵ C.-H. Chin , S.-H. Chen , H.-H. Wu , C.-W. Ho , M.-T. Ko , C.-Y. Lin , cytoHubba: identifying hub objects and sub-networks from complex interactome , BMC Syst. Biol . 8 Suppl 4 ( 2014 ) S11 . doi: 10.1186/1752-0509-8-S4-S11 . OpenUrl CrossRef PubMed [50]. ↵ P. Shannon , A. Markiel , O. Ozier , N.S. Baliga , J.T. Wang , D. Ramage , N. Amin , B. Schwikowski , T. Ideker , Cytoscape: a software environment for integrated models of biomolecular interaction networks , Genome Res . 13 ( 2003 ) 2498 – 2504 . doi: 10.1101/gr.1239303 . OpenUrl Abstract / FREE Full Text [51]. ↵ G.D. Bader , C.W.V. Hogue , An automated method for finding molecular complexes in large protein interaction networks , BMC Bioinformatics 4 ( 2003 ) 2 . doi: 10.1186/1471-2105-4-2 . OpenUrl CrossRef PubMed [52]. ↵ X.-B. Mo , L.-F. Wu , X. Lu , X.-W. Zhu , W. Xia , L. Wang , P. He , P.-F. Bing , Y.-H. Zhang , F.-Y. Deng , S.-F. Lei , Detection of lncRNA-mRNA interaction modules by integrating eQTL with weighted gene co-expression network analysis , Funct. Integr. Genomics 19 ( 2019 ) 217 – 225 . doi: 10.1007/s10142-018-0638-4 . OpenUrl CrossRef PubMed [53]. ↵ Y. Song , X. Wang , A. Hou , H. Li , J. Lou , Y. Liu , J. Cao , W. Mi , Integrative analysis of lncRNA and mRNA and profiles in postoperative delirium patients , Front. Aging Neurosci . 13 ( 2021 ) 665935 . doi: 10.3389/fnagi.2021.665935 . OpenUrl CrossRef PubMed [54]. ↵ T. Zhang , Q. Liang , C. Li , S. Fu , J.K. Kundu , X. Zhou , J. Wu , Transcriptome analysis of rice reveals the lncRNA-mRNA regulatory network in response to rice black-streaked dwarf virus infection , Viruses 12 ( 2020 ) 951 . doi: 10.3390/v12090951 . OpenUrl CrossRef PubMed [55]. ↵ A.R. Quinlan , I.M. Hall , BEDTools: a flexible suite of utilities for comparing genomic features , Bioinformatics 26 ( 2010 ) 841 – 842 . doi: 10.1093/bioinformatics/btq033 . OpenUrl CrossRef PubMed Web of Science [56]. ↵ M. Heidari , A. Pakdel , M.R. Bakhtiarizadeh , F. Dehghanian , Integrated analysis of lncRNAs, mRNAs, and TFs to identify regulatory networks underlying MAP infection in cattle , Front. Genet . 12 ( 2021 ) 668448 . doi: 10.3389/fgene.2021.668448 . OpenUrl CrossRef PubMed [57]. ↵ E. Raffo , P. Steuer , C. Tomckowiack , C. Tejeda , B. Collado , M. Salgado , More insights about the interfering effect of Mycobacterium avium subsp. paratuberculosis (MAP) infection on Mycobacterium bovis (M. bovis) detection in dairy cattle , Trop. Anim. Health Prod . 52 ( 2020 ) 1479 – 1485 . doi: 10.1007/s11250-019-02151-2 . OpenUrl CrossRef PubMed [58]. ↵ C.H. Serezani , M.N. Ballinger , D.M. Aronoff , M. Peters-Golden , Cyclic AMP , Am. J . Respir . Cell Mol. Biol . 39 ( 2008 ) 127 – 132 . doi: 10.1165/rcmb.2008-0091tr . OpenUrl CrossRef [59]. ↵ N. Arshad , S.S. Visweswariah , The multiple and enigmatic roles of guanylyl cyclase C in intestinal homeostasis , FEBS Lett . 586 ( 2012 ) 2835 – 2840 . doi: 10.1016/j.febslet.2012.07.028 . OpenUrl CrossRef PubMed Web of Science [60]. ↵ H. Kanoh , S. Iwashita , T. Kuraishi , A. Goto , N. Fuse , H. Ueno , M. Nimura , T. Oyama , C. Tang , R. Watanabe , A. Hori , Y. Momiuchi , H. Ishikawa , H. Suzuki , K. Nabe , T. Takagaki , M. Fukuzaki , L.-L. Tong , S. Yamada , Y. Oshima , T. Aigaki , J.A.T. Dow , S.-A. Davies , S. Kurata , cGMP signaling pathway that modulates NF-κB activation in innate immune responses , iScience 24 ( 2021 ) 103473 . doi: 10.1016/j.isci.2021.103473 . OpenUrl CrossRef PubMed [61]. ↵ H. Yang , H. Liu , H. Chen , H. Mo , J. Chen , X. Huang , R. Zheng , Z. Liu , Y. Feng , F. Liu , B. Ge , G protein-coupled receptor160 regulates mycobacteria entry into macrophages by activating ERK , Cell. Signal . 28 ( 2016 ) 1145 – 1151 . doi: 10.1016/j.cellsig.2016.05.022 . OpenUrl CrossRef PubMed [62]. ↵ L. Tian , W. Zhou , X. Wu , Z. Hu , L. Qiu , H. Zhang , X. Chen , S. Zhang , Z. Lu , CTLs: Killers of intracellular bacteria , Front. Cell. Infect. Microbiol . 12 ( 2022 ) 967679 . doi: 10.3389/fcimb.2022.967679 . OpenUrl CrossRef PubMed [63]. ↵ H.-E. Park , H.-T. Park , Y.H. Jung , H.S. Yoo , Gene expression profiles of immune- regulatory genes in whole blood of cattle with a subclinical infection of Mycobacterium avium subsp. paratuberculosis , PLoS One 13 ( 2018 ) e0196502 . doi: 10.1371/journal.pone.0196502 . OpenUrl CrossRef PubMed [64]. ↵ M. Obaid , S.M.N. Udden , P. Deb , N. Shihabeddin , M.H. Zaki , S.S. Mandal , LncRNA HOTAIR regulates lipopolysaccharide-induced cytokine expression and inflammatory response in macrophages , Sci. Rep . 8 ( 2018 ) 15670 . doi: 10.1038/s41598-018-33722-2 . OpenUrl CrossRef [65]. ↵ R. Weikard , W. Demasius , C. Kuehn , Mining long noncoding RNA in livestock , Anim. Genet . 48 ( 2017 ) 3 – 18 . doi: 10.1111/age.12493 . OpenUrl CrossRef PubMed [66]. ↵ P.H. Sabara , S.J. Jakhesara , K.J. Panchal , C.G. Joshi , P.G. Koringa , Transcriptomic analysis to affirm the regulatory role of long non-coding RNA in horn cancer of Indian zebu cattle breed Kankrej (Bos indicus) , Funct. Integr. Genomics 20 ( 2020 ) 75 – 87 . doi: 10.1007/s10142-019-00700-4 . OpenUrl CrossRef PubMed [67]. ↵ M. Santoro , V. Nociti , M. Lucchini , C. De Fino , F.A. Losavio , M. Mirabella , Expression profile of long non-coding RNAs in serum of patients with multiple sclerosis , J. Mol. Neurosci . 59 ( 2016 ) 18 – 23 . doi: 10.1007/s12031-016-0741-8 . OpenUrl CrossRef PubMed [68]. ↵ A.C. Purdie , K.M. Plain , D.J. Begg , K. de Silva , R.J. Whittington , Expression of genes associated with the antigen presentation and processing pathway are consistently regulated in early Mycobacterium avium subsp. paratuberculosis infection , Comp. Immunol. Microbiol. Infect. Dis . 35 ( 2012 ) 151 – 162 . doi: 10.1016/j.cimid.2011.12.007 . OpenUrl CrossRef PubMed [69]. ↵ S. Khare , K.L. Drake , S.D. Lawhon , J.E.S. Nunes , J.F. Figueiredo , C.A. Rossetti , T. Gull , R.E. Everts , H.A. Lewin , L.G. Adams , Systems Analysis of Early Host Gene Expression Provides Clues for Transient Mycobacterium avium ssp avium vs. Persistent Mycobacterium avium ssp paratuberculosis Intestinal Infections , PLoS One 11 ( 2016 ) e0161946 . doi: 10.1371/journal.pone.0161946 . OpenUrl CrossRef PubMed [70]. ↵ G. Badia-Bringué , M. Canive , P. Vázquez , J.M. Garrido , A. Fernández , R.A. Juste , J.A. Jiménez , O. González-Recio , M. Alonso-Hearn , Association between high interferon- gamma production in avian tuberculin-stimulated blood from Mycobacterium avium subsp. Paratuberculosis-infected cattle and candidate genes implicated in necroptosis , Microorganisms 11 ( 2023 ). doi: 10.3390/microorganisms11071817 . OpenUrl CrossRef [71]. ↵ C.P. Verschoor , S.D. Pant , Q. You , F.S. Schenkel , D.F. Kelton , N.A. Karrow , Polymorphisms in the gene encoding bovine interleukin-10 receptor alpha are associated with Mycobacterium avium ssp. paratuberculosis infection status , BMC Genet . 11 ( 2010 ) 23 . doi: 10.1186/1471-2156-11-23 . OpenUrl CrossRef PubMed [72]. ↵ A. Facciuolo , A.H. Lee , P. Gonzalez Cano , H.G.G. Townsend , R. Falsafi , V. Gerdts , A. Potter , S. Napper , R.E.W. Hancock , L.M. Mutharia , P.J. Griebel , Regional Dichotomy in Enteric Mucosal Immune Responses to a Persistent Mycobacterium avium ssp. paratuberculosis Infection , Front. Immunol . 11 ( 2020 ) 1020 . doi: 10.3389/fimmu.2020.01020 . OpenUrl CrossRef PubMed [73]. ↵ S. Thirunavukkarasu , K.M. Plain , A.C. Purdie , R.J. Whittington , K. de Silva , IFN-γ fails to overcome inhibition of selected macrophage activation events in response to pathogenic mycobacteria , PLoS One 12 ( 2017 ) e0176400 . doi: 10.1371/journal.pone.0176400 . OpenUrl CrossRef PubMed [74]. ↵ M.Y. Bhat , H.S. Solanki , J. Advani , A.A. Khan , T.S. Keshava Prasad , H. Gowda , S. Thiyagarajan , A. Chatterjee , Comprehensive network map of interferon gamma signaling , J. Cell Commun. Signal . 12 ( 2018 ) 745 – 751 . doi: 10.1007/s12079-018-0486-y . OpenUrl CrossRef PubMed [75]. ↵ N.B. Harris , R.G. Barletta , Mycobacterium avium subsp. paratuberculosis in Veterinary Medicine , Clin. Microbiol. Rev . 14 ( 2001 ) 489 – 512 . doi: 10.1128/CMR.14.3.489-512.2001 . OpenUrl Abstract / FREE Full Text [76]. ↵ S. Thirunavukkarasu , K. de Silva , R.J. Whittington , K.M. Plain , In vivo and in vitro expression pattern of Toll-like receptors in Mycobacterium avium subspecies paratuberculosis infection , Vet. Immunol. Immunopathol . 156 ( 2013 ) 20 – 31 . doi: 10.1016/j.vetimm.2013.08.008 . OpenUrl CrossRef PubMed [77]. ↵ B. Zafirova , F.M. Wensveen , M. Gulin , B. Polić , Regulation of immune cell function and differentiation by the NKG2D receptor , Cell. Mol. Life Sci . 68 ( 2011 ) 3519 – 3529 . doi: 10.1007/s00018-011-0797-0 . OpenUrl CrossRef PubMed [78]. ↵ F.M. Wensveen , V. Jelenčić , B. Polić , NKG2D: A master regulator of immune cell responsiveness , Front. Immunol . 9 ( 2018 ) 441 . doi: 10.3389/fimmu.2018.00441 . OpenUrl CrossRef PubMed [79]. ↵ M. Malvisi , F. Palazzo , N. Morandi , B. Lazzari , J.L. Williams , G. Pagnacco , G. Minozzi , Responses of Bovine Innate Immunity to Mycobacterium avium subsp. paratuberculosis Infection Revealed by Changes in Gene Expression and Levels of MicroRNA , PLoS One 11 ( 2016 ) e0164461 . doi: 10.1371/journal.pone.0164461 . OpenUrl CrossRef PubMed [80]. ↵ F.C. Blanco , M. Soria , M.V. Bianco , F. Bigi , Transcriptional response of peripheral blood mononuclear cells from cattle infected with Mycobacterium bovis , PLoS One 7 ( 2012 ) e41066 . doi: 10.1371/journal.pone.0041066 . OpenUrl CrossRef PubMed [81]. ↵ V. Rasi , D.C. Wood , C.S. Eickhoff , M. Xia , N. Pozzi , R.L. Edwards , M. Walch , N. Bovenschen , D.F. Hoft , Granzyme A produced by γ9δ2 T cells activates ER stress responses and ATP production, and protects against intracellular Mycobacterial replication independent of enzymatic activity , Front. Immunol . 12 ( 2021 ) 712678 . doi: 10.3389/fimmu.2021.712678 . OpenUrl CrossRef PubMed [82]. ↵ R. Mucha , M.R. Bhide , E.B. Chakurkar , M. Novak , I. Mikula Sr , Toll-like receptors TLR1, TLR2 and TLR4 gene mutations and natural resistance to Mycobacterium avium subsp. paratuberculosis infection in cattle, Vet. Immunol . Immunopathol . 128 ( 2009 ) 381 – 388 . doi: 10.1016/j.vetimm.2008.12.007 . OpenUrl CrossRef PubMed Web of Science [83]. ↵ U.K. Shandilya , X. Wu , C. McAllister , L. Mutharia , N.A. Karrow , Role of Toll-Like Receptor 4 in Mycobacterium avium subsp. paratuberculosis Infection of Bovine Mammary Epithelial (MAC-T) Cells In Vitro , Microbiol. Spectr . ( 2023 ) e0439322 . doi: 10.1128/spectrum.04393-22 . OpenUrl CrossRef [84]. ↵ S. Kumar , S. Kumar , R.V. Singh , A. Chauhan , A. Kumar , S. Sulabh , J. Bharati , S.V. Singh , Genetic association of polymorphisms in bovine TLR2 and TLR4 genes with Mycobacterium avium subspecies paratuberculosis infection in Indian cattle population , Vet. Res. Commun . 43 ( 2019 ) 105 – 114 . doi: 10.1007/s11259-019-09750-2 . OpenUrl CrossRef PubMed [85]. ↵ B.S. Sharma , M.K. Abo-Ismail , F.S. Schenkel , Q. You , C.P. Verschoor , S.D. Pant , N.A. Karrow , Association of TLR4 polymorphisms with Mycobacterium avium subspecies paratuberculosis infection status in Canadian Holsteins , Anim. Genet . 46 ( 2015 ) 560 – 565 . doi: 10.1111/age.12333 . OpenUrl CrossRef PubMed [86]. ↵ E.M. Ibeagha-Awemu , N. Bissonnette , D.N. Do , P.-L. Dudemaine , M. Wang , A. Facciuolo , P. Griebel , Regionally distinct immune and metabolic transcriptional responses in the bovine small intestine and draining lymph nodes during a subclinical Mycobacterium avium subsp. Paratuberculosis infection , Front. Immunol . 12 ( 2021 ) 760931 . doi: 10.3389/fimmu.2021.760931 . OpenUrl CrossRef PubMed [87]. T.L.T. Wherry , M. Heggen , A.L. Shircliff , S. Mooyottu , J.R. Stabel , Stage of infection with Mycobacterium avium subsp. paratuberculosis impacts expression of Rab5, Rab7, and CYP27B1 in macrophages within the ileum of naturally infected cows, Front . Vet. Sci . 10 ( 2023 ) 1117591 . doi: 10.3389/fvets.2023.1117591 . OpenUrl CrossRef [88]. ↵ M.G. Hamed , J. Gómez-Laguna , F. Larenas-Muñoz , A.Z. Mahmoud , F.A.Z. Ali , S.K. Abd- Elghaffar , Monitoring the immune response of macrophages in tuberculous granuloma through the expression of CD68, iNOS and HLA-DR in naturally infected beef cattle , BMC Vet. Res . 19 ( 2023 ) 220 . doi: 10.1186/s12917-023-03763-5 . OpenUrl CrossRef PubMed [89]. ↵ H.-E. Park , H.-T. Park , Y.H. Jung , H.S. Yoo , Establishment a real-time reverse transcription PCR based on host biomarkers for the detection of the subclinical cases of Mycobacterium avium subsp. paratuberculosis , PLoS One 12 ( 2017 ) e0178336 . doi: 10.1371/journal.pone.0178336 . OpenUrl CrossRef PubMed [90]. ↵ L.I. Klepp , M.A. Colombatti , R.D. Moyano , M.I. Romano , T. Malovrh , M. Ocepek , F.C. Blanco , F. Bigi , Assessment of tuberculosis biomarkers in paratuberculosis-infected cattle, J . Vet. Res . 67 ( 2023 ) 55 – 60 . doi: 10.2478/jvetres-2023-0007 . OpenUrl CrossRef [91]. ↵ A.C. Purdie , K.M. Plain , D.J. Begg , K. de Silva , R.J. Whittington , Gene expression profiles during subclinical Mycobacterium avium subspecies paratuberculosis infection in sheep can predict disease outcome , Sci. Rep . 9 ( 2019 ) 8245 . doi: 10.1038/s41598-019-44670-w . OpenUrl CrossRef PubMed [92]. ↵ A.S. Ashhurst , M. Flórido , L.C.W. Lin , D. Quan , E. Armitage , S.A. Stifter , J. Stambas , W.J. Britton , CXCR6-deficiency improves the control of pulmonary Mycobacterium tuberculosis and influenza infection independent of T-lymphocyte recruitment to the lungs , Front. Immunol . 10 ( 2019 ) 339 . doi: 10.3389/fimmu.2019.00339 . OpenUrl CrossRef PubMed [93]. ↵ M.C. Boer , K.E. van Meijgaarden , D. Goletti , V. Vanini , C. Prins , T.H.M. Ottenhoff , S.A. Joosten , KLRG1 and PD-1 expression are increased on T-cells following tuberculosis- treatment and identify cells with different proliferative capacities in BCG-vaccinated adults , Tuberculosis (Edinb .) 97 ( 2016 ) 163 – 171 . doi: 10.1016/j.tube.2015.11.008 . OpenUrl CrossRef PubMed [94]. ↵ Z. Hu , H.-M. Zhao , C.-L. Li , X.-H. Liu , D. Barkan , D.B. Lowrie , S.-H. Lu , X.-Y. Fan , The role of KLRG1 in human CD4+ T-cell immunity against tuberculosis , J. Infect. Dis . 217 ( 2018 ) 1491 – 1503 . doi: 10.1093/infdis/jiy046 . OpenUrl CrossRef PubMed [95]. ↵ Z. Muslimova , A. Abdualiyeva , N. Shaugimbayeva , K. Orynkhanov , Y. Ussenbekov , Genotyping of Holstein cows by SELL, MX1 and CXCR1 gene loci associated with mastitis resistance, Reprod . Domest. Anim . 59 ( 2024 ) e14713 . doi: 10.1111/rda.14713 . OpenUrl CrossRef [96]. ↵ P. Gupta , S. Peter , M. Jung , A. Lewin , G. Hemmrich-Stanisak , A. Franke , M. von Kleist , C. Schütte , R. Einspanier , S. Sharbati , J.Z. Bruegge , Analysis of long non-coding RNA and mRNA expression in bovine macrophages brings up novel aspects of Mycobacterium avium subspecies paratuberculosis infections , Sci. Rep . 9 ( 2019 ) 1571 . doi: 10.1038/s41598-018-38141-x . OpenUrl CrossRef PubMed View the discussion thread. Back to top Previous Next Posted January 22, 2025. Download PDF Supplementary Material Email Thank you for your interest in spreading the word about bioRxiv. NOTE: Your email address is requested solely to identify you as the sender of this article. Your Email * Your Name * Send To * Enter multiple addresses on separate lines or separate them with commas. You are going to email the following Weighted gene co-expression network analysis reveals key genes and lncRNAs in desi cattle chronically infected with Johne’s disease Message Subject (Your Name) has forwarded a page to you from bioRxiv Message Body (Your Name) thought you would like to see this page from the bioRxiv website. 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Share Weighted gene co-expression network analysis reveals key genes and lncRNAs in desi cattle chronically infected with Johne’s disease Abhisek Sahu , Mohd Abdullah , Saurabh Gupta , Shoor Vir Singh , Ankush Dhillon , Prabhati Yadav , Sarwar Azam bioRxiv 2025.01.17.633561; doi: https://doi.org/10.1101/2025.01.17.633561 Share This Article: Copy Citation Tools Weighted gene co-expression network analysis reveals key genes and lncRNAs in desi cattle chronically infected with Johne’s disease Abhisek Sahu , Mohd Abdullah , Saurabh Gupta , Shoor Vir Singh , Ankush Dhillon , Prabhati Yadav , Sarwar Azam bioRxiv 2025.01.17.633561; doi: https://doi.org/10.1101/2025.01.17.633561 Citation Manager Formats BibTeX Bookends EasyBib EndNote (tagged) EndNote 8 (xml) Medlars Mendeley Papers RefWorks Tagged Ref Manager RIS Zotero Tweet Widget Facebook Like Google Plus One Subject Area Systems Biology Subject Areas All Articles Animal Behavior and Cognition (7626) Biochemistry (17654) Bioengineering (13878) Bioinformatics (41895) Biophysics (21430) Cancer Biology (18569) Cell Biology (25471) Clinical Trials (138) Developmental Biology (13366) Ecology (19876) Epidemiology (2067) Evolutionary Biology (24294) Genetics (15593) Genomics (22480) Immunology (17719) Microbiology (40331) Molecular Biology (17155) Neuroscience (88500) Paleontology (666) Pathology (2829) Pharmacology and Toxicology (4818) Physiology (7635) Plant Biology (15116) Scientific Communication and Education (2044) Synthetic Biology (4286) Systems Biology (9817) Zoology (2269)