Inter-species SNPs from buffalo liver transcriptome indicate diversity in immune pathways between buffalo and cattle

Inter-species SNPs from buffalo liver transcriptome indicate diversity in immune pathways between buffalo and 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 Short Report Inter-species SNPs from buffalo liver transcriptome indicate diversity in immune pathways between buffalo and cattle Chaitanya Kumar Thota Venkata, Sudhakar Singh, Gowdar Veerapa Vedamurthy, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4709928/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Graphical Abstract Abstract Graphical abstract Abstract: The duration of negative energy balance (NEB), a physiological adaptation in females during early postpartum, and its coping mechanisms vary among animals, breeds and species because of genetic differences. However, genetic variations that may influence the NEB differences between cattle and buffaloes were not reported. Therefore, the present study aims to identify such genetic differences between cattle and buffaloes by identifying inter-species single nucleotide polymorphisms (SNPs) by mapping the available liver RNA-seq data earlier obtained from three early postpartum buffaloes and three heifers to the cattle genome (UMD 3.1) using the SNP eff tool. The liver transcriptome data were chosen, as it is the major metabolic organ in balancing metabolic milieu to meet different physiological demands during early postpartum. Using the identified interspecies SNPs, intragenus SNPs (Buffalo) were derived from six different Murrah buffaloes and mapped them to the Bubalus bubalis (ASM312139v1) genome. Further, functional annotation and biological pathway analyses were performed for those genes harbouring the putative SNPs. We identified a total of 1,40,056 interspecies and 188 intragenus SNPs. Functional annotation and pathway analyses revealed that most of the putative interspecies SNPs between the cattle (Bos taurus UMD 3.1) and buffalo (Bubalis bubalus) were in the protein metabolism genes (>1900) predominantly involved in the immune system (>1800 genes). Similarly, most of the putative intragenus SNPs among buffaloes were in the genes of lipid metabolic pathways. In conclusion, our study suggests that cattle and buffaloes might differ in protein metabolism genes involved in immunity at the deoxyribonucleic acid (DNA) level. Single Nucleotide Polymorphisms Liver transcriptome Functional annotation Cattle genome Buffalo Figures Figure 1 Figure 2 1. Introduction Understanding genetic variation among species and breeds of dairy animals is utmost important for selection of efficient animals that could balance the energy needs during transition period. During transition period, the energy demands for milk production surpass the energy intake, leading to negative energy balance (NEB), which is common in high yielding dairy cows and buffaloes. However, the genetic variations between cows and buffaloes influencing NEB were not reported. Therefore, the present study is targeted to identify such genetic differences. Liver is the principal organ that balances the metabolic milieu during postpartum to meet the milk production and reproduction demands. Particularly, liver is the gluconeogenic organ in ruminants for supplying the inputs for lactose production (Drackley et al. 2001 ). During NEB, the liver induces lipolysis in adipose tissue through angiopoietin like-4 production (Loor et al. 2007 ; McCarthy et al. 2010 ). It also controls ovarian follicular development through molecules like IGF-1 (Leroy et al. 2008 ). Therefore, liver transcriptome during early postpartum has tremendous importance to explore the genetic variants in its expressed gene repertoire for understanding the genetic variation influencing NEB. Hence, the present study aims to identify interspecies and intragenus SNPs from the liver transcriptome data of the early postpartum buffaloes and to annotate their functional importance through bioinformatics approaches. 2. Materials and Methods 2.1 Buffalo liver transcriptome data The liver transcriptome data from our previous study (Singh et al. 2019 ) were analysed in the present study to identify interspecies and intragenus SNPs. The liver transcriptome data were obtained by performing RNA-seq on the liver biopsy samples (NDRI Institutional Animal Ethics Committee approval no. 95/16) collected from three early postpartum buffaloes and three heifers. From the lactating buffaloes, transcriptome data were obtained twice, once on the 15th day and another on the 30th day. Overall, the present study utilized nine transcriptome datasets (Singh et al. 2019 ) to obtain the interspecies and intragenus SNPs. 2.2 Identification of interspecies SNPs All the transcriptome datasets were mapped to the cattle reference genome using SNP eff software integrated to GATK software ( https://software.broadinstitute.org/gatk/ ) to obtain interspecies SNPs. 2.3 Verification and finalization of interspecies SNPs obtained from the liver transcriptome data of lactating animals As two liver transcriptome data sets (15th and 30th day of early postpartum) were available from one lactating animal, two gene lists containing interspecies SNPs were obtained by the SNP-eff software for each lactating animal. The two gene lists from each lactating animal were compared by interactivenn tool ( http://www.interactivenn.net/ ) to identify the unique and common list of genes containing interspecies SNPs between these two datasets. Later, the unique and common list of genes were pooled for each animal to avoid duplicity of the gene name. For each gene in this pooled list of each lactating animal, its corresponding nucleotide base was extracted from two arrays of the gene lists along with their interspecies SNPs, which were obtained from the transcriptome data of the same animal on the 15th and 30th day of lactation by using VLOOKUP function in the MS-Excel software. The unique genes had an interspecies SNP with a single nucleotide base difference from the cattle genome. This strategy gave us the list of the unique nucleotide base for each common gene containing interspecies SNPs for one lactating animal based on transcriptome datasets of the 15th and 30th days of lactation. The total list of unique genes and common genes with their corresponding interspecies SNPs were considered together as interspecies SNPs specific to that lactating animal. Likewise, the list of genes containing interspecies SNPs were verified and finalized for each lactating animal. 2.4 Derivation of intragenus SNPs The list of interspecies SNPs obtained from six Murrah buffaloes were compared to get intragenus SNPs. This comparison was done by using the functions available in MS-Excel software. For this, the list of genes containing interspecies SNP nucleotide base (NB) of all individual six animals under study were arranged in an ascending order in different excel sheets. Then, the common genes containing interspecies SNPs between all the six animals were identified using the “VLOOKUP” function by a process developed in our lab. various functions available in MS-Excel. The NBs from six animals were compared to derive intragenus SNPs. 2.5. Mapping of the putative SNPs to the buffalo genome As the intragenus and interspecies SNPs were identified from the liver transcriptome data of six Murrah buffaloes based on cattle genome (UMD 3.1) using SNPeff software, the fifty bases of the flanking sequences at both the upstream and downstream of the SNP position were extracted from the Bos taurus (UMD 3.1.1) genome to identify their syntenic positions in the buffalo genome. These sequences containing putative SNPs were aligned to the Bubalus bubalis (ASM312139 v 1) genome by the NCBI local Blast software ( https://blast.ncbi.nlm.nih.gov/Blast.cgi ) by considering the sequences containing putative SNPs as the query sequence and the Bubalus bubalis (ASM312139 v 1) genome as a subject sequence. 2.6. Functional annotation of the genes containing interspecies SNPs The genes containing inter-genus SNPs were functionally annotated initially by analysing the data with CPDB (consensus pathway database: http://www.consensuspathdb.org/ ). The pathways with highest numbers of candidate genes were considered for further analysis by the STRING ( https://string-db.org ) and CYTOSCAPE software version 3.6.1. The .tsv file obtained on the network analysis containing node 1 and node 2 genes and their interaction score were further analysed by the CYTOSCAPE software ( https://www.cytoscape.org/php ) to visualize the networks. In the CYTOSCAPE, cluster one plugin was used to obtain the significant (P < 0.05) sub networks. Functional annotation of the list of genes in each sub network was carried by the clustering option in the DAVID (Database for annotation, visualization, and integrated discovery ( https://www.david.ncifcrf.gov/ ) software. 2.7. Functional annotation of the genes containing intragenus SNPs Functional importance of the genes having the intragenus SNPs were analysed using CPDB. Further, synonymous, or non-synonymous nature of intragenus SNPs present in the coding regions were analysed. For this, the sequence of fifty bases upstream and the fifty bases downstream to the SNP position from Bubalus bubalis genome was blasted against non-redundant protein sequences of Bubalus bubalis using the blast X software ( https://blast.ncbi.nlm.nih.gov/Blast.cgi?PROGRAM=blastx & PAGE_TYPE = Blast Search & LINK_LOC = blasthome). The triplet codons with amino acids for the two allelic forms of SNPs were determined to know whether the SNPs were synonymous or non-synonymous. Further, the point allele mutation scores for non-synonymous SNPs identified were determined using PAM 250 log odds substitution matrix 3. Results and Discussion 3.1 Intragenus and interspecies SNPs identified from buffalo liver transcriptome data A total of 1,40,056 interspecies SNPs were found in 13801 genes based on Bos taurus genome. By considering an average gene size of 1200 base pairs (bp) in eukaryotes, the 13801 genes would cover nearly 16,561,200 bp of the genome. As 1,40,056 locations showed interspecies SNPs between cattle and buffaloes, the variation between these two species could be 0.84% in the genes specific to early postpartum liver transcriptome in buffaloes. However, the variation at genomic level between the two species was reported to be 3% (Moaeen-ud-Din M and Bilal G 2015) indicating a lesser variation in the coding region of the genome than in the non-coding regions between the cattle and buffaloes. Although these SNPs were distributed throughout the genome, much variation was on the chromosome No. 3, which is syntenic to the buffalo Chromosome No. 6 (Fig. 1 a). From these 1,40,056 interspecies SNPs, 188 intra-genus buffalo SNPs were identified in 156 genes. Among these 188 SNPs, 163 has been successfully mapped to different chromosomes of Bubalus bubalis (breed Mediterranean ASM312139 v 1) (Fig. 2 a). The remaining SNPs were indicated as unplaced or not matched or shown many matches with the buffalo genome. Maximum number of intragenus SNPs were mapped to the chromosomes, 1, 2, 3 and 4 in buffaloes (Fig. 2 a). The variation in the coding region between cattle and buffaloes appear to be high on cattle chromosomes 3 (syntenic position on chromosome number 6 in buffaloes) and 19 (syntenic position on chromosome number 3 in buffaloes). 3.2 Bioinformatics analysis of the identified intragenus and interspecies SNPs Pathway analysis of the genes containing interspecies SNPs found that majority of the genes were annotated to be involved in top five pathways, such as metabolism of proteins (1999), metabolism (1960), immune system (1827), post-translational modification (1378), gene expression (1364), RNA polymerase II transcription (1228) and innate immunity (1065) (Fig. 1 b). Variation in innate immune genes between cattle and buffalo may explain their differential susceptibility to certain diseases. The SNPs in the innate immune genes may affect their differential expression of these genes between the two species. For instance, differential expression of immune genes among the two species was reported to understand the less susceptibility of water buffalo to Schistosoma japonicum than yellow cattle (Yang et al. 2015 ). As the innate immunity pathway was annotated to be functionally defined pathway by CPDB analysis, the gene list from this pathway was used for the network analysis by the STRING and CYTOSCAPE software. This network analysis resulted in eleven significant (P < 0.05) sub networks (Fig. 1 c and Fig. 1 d). DAVID functional annotation of the genes in these sub-networks revealed that majority of these genes were involved in cytoskeletal organization, protein catabolism, and membrane specific signalling involved in innate immunity. This observation based on liver transcriptome generated interspecies SNPs suggest that the two species vary at the basic molecular signalling response repertoire involved in the defence mechanism against infections. Pathway analysis of the successfully mapped 163 intragenus SNPs in 156 genes found that the SNPs were found majorly in the genes involved in metabolic pathways, vesicle mediated transport, and disease signalling (Fig. 2 b). Among the genes involved in the metabolic pathways, the genes related to lipid metabolism explains the importance of their SNPs and the liver in handling lipids. During NEB, the liver and its lipid metabolizing genes play an important role in meeting the energy demand for milk synthesis (Drackley et al. 2001 ), especially in high yielders by initiating lipolysis in adipose tissue (Loor et al. 2007 ; McCarthy et al. 2010 ) and adapting liver’s metabolism (Veshkini et al. 2022 ). Therefore, variations imposed by gene polymorphisms in lipid metabolic pathways in the liver may affect the animal’s ability to sustain the stress caused by the NEB during early post-partum and resume their normal physiological activities like reproduction. These differences in animal’s capability will also influence their differential susceptibility to post-partum diseases. Similarly, among the genes annotated to be involved in vesicle mediated transport and disease signalling, the genes related to the TGF (Tumor growth factor) beta signalling and EGFR1 (Epidermal growth factor receptor 1) signalling showed variation among buffaloes. Vesicle mediated transport and signalling are important during cross-talk among the liver, mammary, adipose, and reproductive organs, which manage the reciprocal control of metabolic activities and immune adaptation among these organs (Bu et al. 2017 ) during post-partum. Among eight intragenus SNPs found in the coding regions of the genes, six SNPs in the genes, Interferon alpha/beta receptor 1 (INFAR1B), Low molecular weight phosphotyrosine phosphatase isoform x2 (LMPTP), Ribosome binding protein 1 isoform X2 (RBP1), Leukocyte specific transcript 1 (LST1), Complement 4 like and N-fatty-acyl-amino acid synthase/hydrolase (PM20D1) are non-synonymous for the two allelic forms (Table 1 ). Among these six SNPs, the maximum point mutation score (-4) for amino acid change was observed in LMPTP as per PAM 250 log odds substitution matrix. The polymorphisms in this gene were reported to be associated with the conception season and insulin resistance in humans (Bottini et al. 2002 ; Stanford et al. 2021 ). Insulin resistance during NEB may affect lipolysis and lipid mobility, which are very crucial for regaining reproductive efficacy. Hence, future studies are needed to explore its association with reproductive efficiency in Murrah buffaloes. Table 1 Intragenus SNPs present in coding regions : Eight SNPs are present in coding regions of the genes out of 163 intragenus SNPs, of these six SNPs are nonsynonymous and two are synonymous. PAM score determined for non synonymous SNPs shows LMPTP gene non synomous SNP with maximum score S.No Gene Intra-genus SNP and type of SNP Codon change with SNP Amino acid change with SNP PAM Score 1 Interferon alpha/beta receptor 1 A/T and Non synonymous SNP TTA/TTT Leucine/ Phenylalanine 2 2 SGTA (small glutamine rich tetratricopeptide repeat containing protein alpha isoform X2) C/T and Synonymous SNP CCC/CCT Proline NA 3 Low molecular weight phosphotyrosine phosphatase isoform x2 G/C/A and Non synonymous SNP TGC/TTC/TAC Cysteine/ Phenylalanine -4 4 Ribosome binding protein 1 isoform X2 C/G and Non Synonymous SNP CGT/TGT Alanine/ Threonine 1 5 Fatty acyl amino acid synthase / hydrolase G/T/C and Non synonymous SNP TCG/TCT/TCC Alanine/Serine /Proline 1 6 Complement C4 like A/G and Non synonymous SNP GG/CAG Arginine/Glutamine 1 7 HSP1A C/A and Synonymous SNP CGG/AGG Glycine NA 8 LST1 A/G/C and Non synonymous SNP AAG/GAG/CAG Lysine/Glutamic acid 0 Conclusion The identified interspecies SNPs based on early postpartum buffalo liver transcriptome data suggests that cattle and buffalo vary in their innate immunity during this period. Likewise, intragenus SNPs in buffalo indicate variation in the expressed genes of metabolism and in particular lipid metabolism during early lactation. Declarations Funding: “The grants for conduct of experimentation included in this manuscript was supported by National Agricultural Science Fund, India (Grant No. NASF/GTR-5005/2015-16). We the authors declare that no funds, grants, or other support were received during the preparation of this manuscript” Competing Interests Authors do not have any conflict of interests. Author Contributions: “ Suneel Kumar Onteru, Dheer Singh and Gowdar Veerapa Vedamurthy contributed to the study conception and design. Material preparation, data collection and analysis were performed by Chaitanya Kumar Thota Venkata, Sudhakar Singh and Suneel Kumar Onteru. The first draft of the manuscript was written by Chaitanya Kumar Thota Venkata, Sudhakar Singh and Suneel Kumar Onteru . All authors commented and reviewed on previous versions of the manuscript. All authors read and approved the final manuscript .” Ethics approval: The study is performed in guidelines and norms of NDRI Institutional Animal Ethics Committee with approval no. 95/16 Consent to participate: No human subjects were used in present study. So this section is not applicable for present manuscript Consent to publish: No human subjects were used in present study. So this section is not applicable for present manuscript Data available statement: Data available within the article or its supplementary materials References Bottini N, Bottini E, Gloria-Bottini F et al (2002) Low-molecular-weight protein tyrosine phosphatase and human disease: in search of biochemical mechanisms. Arch Immunol Ther Exp (Warsz) 50(2):95–104 PMID: 12022706 Bu D, Bionaz M, Wang M et al (2017) Transcriptome difference and potential crosstalk between liver and mammary tissue in mid-lactation primiparous dairy cows. PLoS ONE 12(3):e0173082. https://doi.org/10.1371%2Fjournal.pone.0173082 Drackley JK, Overton TR, Douglas GN (2001) Adaptations of glucose and long-chain fatty acid metabolism in liver of dairy cows during the periparturient period. J Dairy Sci 84:E100–E112. https://doi.org/10.3168/jds.S0022-0302(01)70204-4 Leroy JLMR, Van Soom A, Opsomer G et al (2008) Reduced Fertility in High-yielding Dairy Cows: Are the Oocyte and Embryo in Danger? Part II Mechanisms Linking Nutrition and Reduced Oocyte and Embryo Quality in High‐yielding Dairy Cows. Reprod Domest Anim 43(5):623–632. https://doi.org/10.1111/j.1439-0531.2007.00961.x Loor JJ, Everts RE, Bionaz M et al (2007) Nutrition-induced ketosis alters metabolic and signalling gene networks in liver of periparturient dairy cows. Physiol Genomic 32(1):105–116. https://doi.org/10.1152/physiolgenomics.00188.2007 McCarthy SD, Waters SM, Kenny DA et al (2010) Negative energy balance and hepatic gene expression patterns in high-yielding dairy cows during the early postpartum period: a global approach. Physiol Genomics 42(3):188–199. https://doi.org/10.1152/physiolgenomics.00118.2010 Moaeen-ud‐Din M, Bilal G (2015) Sequence diversity and molecular evolutionary rates between buffalo and cattle. J Anim Breed Genet 132(1):74–84. https://doi.org/10.1111/jbg.12100 Singh S, Golla N, Sharma D et al (2019) Buffalo liver transcriptome analysis suggests immune tolerance as its key adaptive mechanism during early postpartum negative energy balance. Funct Integr Genomics 19(5):759–773. https://doi.org/10.1007/s10142-019-00676-1 Stanford SM, Diaz MA, Ardecky RJ et al (2021) Discovery of Orally Bioavailable Purine-Based Inhibitors of the Low-Molecular-Weight Protein Tyrosine Phosphatase. J Med Chem 13(9):5645–5653. https://doi.org/10.1021/acs.jmedchem.0c02126 Veshkini A, Hammon M, Sauerwein H et al (2022) Longitudinal liver proteome profiling in dairy cows during the transition from gestation to lactation: Investigating metabolic adaptations and their interactions with fatty acids supplementation via repeated measurements ANOVA-simultaneous component analysis. J Proteom 252:104435. https://doi.org/10.1016/j.jprot.2021.104435 Yang J, Fu Z, Hong Y et al (2015) The differential expression of immune genes between water buffalo and yellow cattle determines species-specific susceptibility to Schistosoma japonicum infection. PLoS ONE 10(6):e0130344. https://doi.org/10.1371/journal.pone.0130344 Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4709928","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Short Report","associatedPublications":[],"authors":[{"id":331508292,"identity":"803986c4-8c3b-4b0c-b996-7520da316ca4","order_by":0,"name":"Chaitanya Kumar Thota Venkata","email":"","orcid":"","institution":"ICAR-National Dairy Research Institute","correspondingAuthor":false,"prefix":"","firstName":"Chaitanya","middleName":"Kumar Thota","lastName":"Venkata","suffix":""},{"id":331508294,"identity":"2e9d9f10-e72e-4220-a4b8-0ba8b6d3e101","order_by":1,"name":"Sudhakar Singh","email":"","orcid":"","institution":"ICAR-National Dairy Research Institute","correspondingAuthor":false,"prefix":"","firstName":"Sudhakar","middleName":"","lastName":"Singh","suffix":""},{"id":331508296,"identity":"3b424ef2-4775-4295-853e-08faacf5e12e","order_by":2,"name":"Gowdar Veerapa Vedamurthy","email":"","orcid":"","institution":"ICAR-National Dairy Research Institute","correspondingAuthor":false,"prefix":"","firstName":"Gowdar","middleName":"Veerapa","lastName":"Vedamurthy","suffix":""},{"id":331508297,"identity":"8c60204e-124c-4b3b-b99e-09b335704cd5","order_by":3,"name":"Dheer Singh","email":"","orcid":"","institution":"ICAR-National Dairy Research Institute","correspondingAuthor":false,"prefix":"","firstName":"Dheer","middleName":"","lastName":"Singh","suffix":""},{"id":331508298,"identity":"ab293d0a-4694-4629-ac51-ac993311ecb1","order_by":4,"name":"Suneel Kumar Onteru","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA2ElEQVRIiWNgGAWjYNCCAiBmbwASBhbEajFgYODhOQBiSJCiRSIBxCJCi/yM3IMPPhgclrOXfH51w48CCQb+9u4E/ObfyEs2nGFw2JhHOqfsZg/QYRJnzm7Ar0Uix0yaxyAtsUc6J+0GD1CLgUQufi3yM3LMf/8xSKvvkTyTdvMPMVoYbuSYMTMY2CTwSLAfu02ULQZn3hhL9hjYGPacyWG7LWMgwUPQL/LtOYYfflRIyLO3H392880fGzn+9l4CDkMAHgMwSaxyEGB/QIrqUTAKRsEoGEEAAOTEQYrktAmuAAAAAElFTkSuQmCC","orcid":"","institution":"ICAR-National Dairy Research Institute","correspondingAuthor":true,"prefix":"","firstName":"Suneel","middleName":"Kumar","lastName":"Onteru","suffix":""}],"badges":[],"createdAt":"2024-07-09 07:22:19","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4709928/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4709928/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":61777515,"identity":"d6fc18b6-6d55-4464-a68d-063e154128b4","added_by":"auto","created_at":"2024-08-05 12:56:32","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":140742,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ea) Histogram of the distribution of inter-species SNPs on the cattle chromosomes. \u003c/strong\u003eThe six color bars under each chromosome indicate the number of inter-species SNPs identified from six animals. The SNPs were distributed among all the chromosomes with the major variation on the chromosome number 3. \u003cstrong\u003e\u0026nbsp;b) Pathway analysis for the genes containing inter-species SNPs (Cattle \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003evs\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e. Buffalo). \u003c/strong\u003eThe Y-axis shows the pathways annotated to be enriched for the genes containing inter genus SNPs by CPDB database.\u003cstrong\u003e \u003c/strong\u003eThe X-axis indicates the number of genes (effective size), P-values and q values related to the annotated pathways. The highest number of genes were enriched in the pathways involved in the metabolism of proteins, immune system, post-translational modification and innate immune system. \u003cstrong\u003ec)\u003c/strong\u003e \u003cstrong\u003eProtein-protein interaction network for the genes containing inter-species SNPs being annotated to be involved in innate immunity. \u003c/strong\u003eMaximum number of genes containing inter-genus SNPs were annotated to be involved in innate immunity by CPDB database. This network was designed among these genes by using protein-protein interaction network algorithm available in the STRING software. \u003cstrong\u003ed)\u003c/strong\u003e \u003cstrong\u003eEleven significant sub networks (P\u0026lt;0.05) among the genes containing inter-species SNPs being annotated to be involved in innate immunity. \u003c/strong\u003eThe sub-networks were obtained by the CYTOSCAPE (cluster one plugin) software by considering the interaction scores among the genes in the PPI network derived by the STRING software. The sub-networks explain that the genes containing inter-genus SNPs between cattle and buffaloes show much variation in cytoskeleton organization, protein catabolism and membrane specific signalling involved in innate immune response.\u003c/p\u003e","description":"","filename":"Picture1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4709928/v1/aedfe0dda82ad57f08d7f6b6.jpg"},{"id":61777513,"identity":"2e25b812-b325-418e-ac6e-51a0fa545b36","added_by":"auto","created_at":"2024-08-05 12:56:32","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":73747,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ea) Histogram of the distribution of the identified intra-genus SNPs on the buffalo chromosomes. \u003c/strong\u003e\u0026nbsp;The SNPs were distributed among all the chromosomes with the major variation is on the first four chromosomes. \u003cstrong\u003eb)\u003c/strong\u003e \u003cstrong\u003ePathway analysis for the genes containing intra-genus SNPs (Buffaloes). \u003c/strong\u003eThe Y-axis shows the pathways annotated to be enriched for the genes containing inter genus SNPs by CPDB database.\u003cstrong\u003e \u003c/strong\u003eThe X-axis indicates the number of genes (effective size), P-values and q valuesrelated to the annotated pathways. The highest number of genes were in the pathways involved in the metabolism (lipid metabolism and lipid associated metabolisms), vesicle mediated transport and, disease and signalling\u003c/p\u003e","description":"","filename":"Picture2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4709928/v1/99b54d15e73c18468990c7f0.jpg"},{"id":61777983,"identity":"9de00ee8-f2d1-4e46-ba7a-98165521844f","added_by":"auto","created_at":"2024-08-05 13:04:32","extension":"png","order_by":7,"title":"","display":"","copyAsset":false,"role":"graphical-abstract","size":106136,"visible":true,"origin":"","legend":"The duration of negative energy balance (NEB), a physiological adaptation in females during early postpartum, and its coping mechanisms vary among animals, breeds and species because of genetic differences. However, genetic variations that may influence the NEB differences between cattle and buffaloes were not reported. Therefore, the present study aims to identify such genetic differences between cattle and buffaloes by identifying inter-species single nucleotide polymorphisms (SNPs) by mapping the available liver RNA-seq data earlier obtained from three early postpartum buffaloes and three heifers to the cattle genome (UMD 3.1) using the SNP eff tool. The liver transcriptome data were chosen, as it is the major metabolic organ in balancing metabolic milieu to meet different physiological demands during early postpartum. Using the identified interspecies SNPs, intragenus SNPs (Buffalo) were derived from six different Murrah buffaloes and mapped them to the (ASM3121391) genome. Further, functional annotation and biological pathway analyses were performed for those genes harbouring the putative SNPs. We identified a total of 1,40,056 interspecies and 188 intragenus SNPs. Functional annotation and pathway analyses revealed that most of the putative interspecies SNPs between the cattle ( UMD 3.1) and buffalo () were in the protein metabolism genes (\u0026gt;\u0026thinsp;1900) predominantly involved in the immune system (\u0026gt;\u0026thinsp;1800 genes). Similarly, most of the putative intragenus SNPs among buffaloes were in the genes of lipid metabolic pathways. In conclusion, our study suggests that cattle and buffaloes might differ in protein metabolism genes involved in immunity at the deoxyribonucleic acid (DNA) level.","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-4709928/v1/61d22b9a61d8cf33353d8199.png"},{"id":65874138,"identity":"590e10f8-0707-4d4e-8da3-59a508f4ded2","added_by":"auto","created_at":"2024-10-03 21:31:25","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":881767,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4709928/v1/8e0d88db-b62c-40bb-ab4e-eb639cde1a48.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Inter-species SNPs from buffalo liver transcriptome indicate diversity in immune pathways between buffalo and cattle","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eUnderstanding genetic variation among species and breeds of dairy animals is utmost important for selection of efficient animals that could balance the energy needs during transition period. During transition period, the energy demands for milk production surpass the energy intake, leading to negative energy balance (NEB), which is common in high yielding dairy cows and buffaloes. However, the genetic variations between cows and buffaloes influencing NEB were not reported. Therefore, the present study is targeted to identify such genetic differences.\u003c/p\u003e \u003cp\u003eLiver is the principal organ that balances the metabolic milieu during postpartum to meet the milk production and reproduction demands. Particularly, liver is the gluconeogenic organ in ruminants for supplying the inputs for lactose production (Drackley et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2001\u003c/span\u003e). During NEB, the liver induces lipolysis in adipose tissue through angiopoietin like-4 production (Loor et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; McCarthy et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). It also controls ovarian follicular development through molecules like IGF-1 (Leroy et al. \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). Therefore, liver transcriptome during early postpartum has tremendous importance to explore the genetic variants in its expressed gene repertoire for understanding the genetic variation influencing NEB. Hence, the present study aims to identify interspecies and intragenus SNPs from the liver transcriptome data of the early postpartum buffaloes and to annotate their functional importance through bioinformatics approaches.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Buffalo liver transcriptome data\u003c/h2\u003e \u003cp\u003eThe liver transcriptome data from our previous study (Singh et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) were analysed in the present study to identify interspecies and intragenus SNPs. The liver transcriptome data were obtained by performing RNA-seq on the liver biopsy samples (NDRI Institutional Animal Ethics Committee approval no. 95/16) collected from three early postpartum buffaloes and three heifers. From the lactating buffaloes, transcriptome data were obtained twice, once on the 15th day and another on the 30th day. Overall, the present study utilized nine transcriptome datasets (Singh et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) to obtain the interspecies and intragenus SNPs.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e\u003cb\u003e2.2 Identification of interspecies SNPs\u003c/b\u003e\u003c/h2\u003e \u003cp\u003eAll the transcriptome datasets were mapped to the cattle reference genome using SNP eff software integrated to GATK software (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://software.broadinstitute.org/gatk/\u003c/span\u003e\u003cspan address=\"https://software.broadinstitute.org/gatk/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) to obtain interspecies SNPs.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e\u003cb\u003e2.3 Verification and finalization of interspecies SNPs obtained from the liver transcriptome data of lactating animals\u003c/b\u003e\u003c/h2\u003e \u003cp\u003eAs two liver transcriptome data sets (15th and 30th day of early postpartum) were available from one lactating animal, two gene lists containing interspecies SNPs were obtained by the SNP-eff software for each lactating animal. The two gene lists from each lactating animal were compared by interactivenn tool (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.interactivenn.net/\u003c/span\u003e\u003cspan address=\"http://www.interactivenn.net/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) to identify the unique and common list of genes containing interspecies SNPs between these two datasets. Later, the unique and common list of genes were pooled for each animal to avoid duplicity of the gene name. For each gene in this pooled list of each lactating animal, its corresponding nucleotide base was extracted from two arrays of the gene lists along with their interspecies SNPs, which were obtained from the transcriptome data of the same animal on the 15th and 30th day of lactation by using VLOOKUP function in the MS-Excel software. The unique genes had an interspecies SNP with a single nucleotide base difference from the cattle genome. This strategy gave us the list of the unique nucleotide base for each common gene containing interspecies SNPs for one lactating animal based on transcriptome datasets of the 15th and 30th days of lactation. The total list of unique genes and common genes with their corresponding interspecies SNPs were considered together as interspecies SNPs specific to that lactating animal. Likewise, the list of genes containing interspecies SNPs were verified and finalized for each lactating animal.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e\u003cb\u003e2.4 Derivation of intragenus SNPs\u003c/b\u003e\u003c/h2\u003e \u003cp\u003eThe list of interspecies SNPs obtained from six Murrah buffaloes were compared to get intragenus SNPs. This comparison was done by using the functions available in MS-Excel software. For this, the list of genes containing interspecies SNP nucleotide base (NB) of all individual six animals under study were arranged in an ascending order in different excel sheets. Then, the common genes containing interspecies SNPs between all the six animals were identified using the \u0026ldquo;VLOOKUP\u0026rdquo; function by a process developed in our lab. various functions available in MS-Excel. The NBs from six animals were compared to derive intragenus SNPs.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5. Mapping of the putative SNPs to the buffalo genome\u003c/h2\u003e \u003cp\u003eAs the intragenus and interspecies SNPs were identified from the liver transcriptome data of six Murrah buffaloes based on cattle genome (UMD 3.1) using SNPeff software, the fifty bases of the flanking sequences at both the upstream and downstream of the SNP position were extracted from the \u003cem\u003eBos taurus\u003c/em\u003e (UMD 3.1.1) genome to identify their syntenic positions in the buffalo genome. These sequences containing putative SNPs were aligned to the \u003cem\u003eBubalus bubalis\u003c/em\u003e (ASM312139\u003cem\u003ev\u003c/em\u003e1) genome by the NCBI local Blast software (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://blast.ncbi.nlm.nih.gov/Blast.cgi\u003c/span\u003e\u003cspan address=\"https://blast.ncbi.nlm.nih.gov/Blast.cgi\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) by considering the sequences containing putative SNPs as the query sequence and the \u003cem\u003eBubalus bubalis\u003c/em\u003e (ASM312139\u003cem\u003ev\u003c/em\u003e1) genome as a subject sequence.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6. Functional annotation of the genes containing interspecies SNPs\u003c/h2\u003e \u003cp\u003eThe genes containing inter-genus SNPs were functionally annotated initially by analysing the data with CPDB (consensus pathway database: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.consensuspathdb.org/\u003c/span\u003e\u003cspan address=\"http://www.consensuspathdb.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The pathways with highest numbers of candidate genes were considered for further analysis by the STRING (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://string-db.org\u003c/span\u003e\u003cspan address=\"https://string-db.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and CYTOSCAPE software version 3.6.1. The .tsv file obtained on the network analysis containing node 1 and node 2 genes and their interaction score were further analysed by the CYTOSCAPE software (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.cytoscape.org/php\u003c/span\u003e\u003cspan address=\"https://www.cytoscape.org/php\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) to visualize the networks. In the CYTOSCAPE, cluster one plugin was used to obtain the significant (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) sub networks. Functional annotation of the list of genes in each sub network was carried by the clustering option in the DAVID (Database for annotation, visualization, and integrated discovery (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.david.ncifcrf.gov/\u003c/span\u003e\u003cspan address=\"https://www.david.ncifcrf.gov/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) software.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.7. Functional annotation of the genes containing intragenus SNPs\u003c/h2\u003e \u003cp\u003eFunctional importance of the genes having the intragenus SNPs were analysed using CPDB. Further, synonymous, or non-synonymous nature of intragenus SNPs present in the coding regions were analysed. For this, the sequence of fifty bases upstream and the fifty bases downstream to the SNP position from \u003cem\u003eBubalus bubalis\u003c/em\u003e genome was blasted against non-redundant protein sequences of \u003cem\u003eBubalus bubalis\u003c/em\u003e using the blast X software (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://blast.ncbi.nlm.nih.gov/Blast.cgi?PROGRAM=blastx\u003c/span\u003e\u003cspan address=\"https://blast.ncbi.nlm.nih.gov/Blast.cgi?PROGRAM=blastx\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e \u0026amp; PAGE_TYPE\u0026thinsp;=\u0026thinsp;Blast Search \u0026amp; LINK_LOC\u0026thinsp;=\u0026thinsp;blasthome). The triplet codons with amino acids for the two allelic forms of SNPs were determined to know whether the SNPs were synonymous or non-synonymous. Further, the point allele mutation scores for non-synonymous SNPs identified were determined using PAM 250 log odds substitution matrix\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results and Discussion","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Intragenus and interspecies SNPs identified from buffalo liver transcriptome data\u003c/h2\u003e \u003cp\u003eA total of 1,40,056 interspecies SNPs were found in 13801 genes based on \u003cem\u003eBos taurus\u003c/em\u003e genome. By considering an average gene size of 1200 base pairs (bp) in eukaryotes, the 13801 genes would cover nearly 16,561,200 bp of the genome. As 1,40,056 locations showed interspecies SNPs between cattle and buffaloes, the variation between these two species could be 0.84% in the genes specific to early postpartum liver transcriptome in buffaloes. However, the variation at genomic level between the two species was reported to be 3% (Moaeen-ud-Din M and Bilal G 2015) indicating a lesser variation in the coding region of the genome than in the non-coding regions between the cattle and buffaloes. Although these SNPs were distributed throughout the genome, much variation was on the chromosome No. 3, which is syntenic to the buffalo Chromosome No. 6 (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea). From these 1,40,056 interspecies SNPs, 188 intra-genus buffalo SNPs were identified in 156 genes. Among these 188 SNPs, 163 has been successfully mapped to different chromosomes of \u003cem\u003eBubalus bubalis (breed Mediterranean\u003c/em\u003e ASM312139\u003cem\u003ev\u003c/em\u003e1) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea). The remaining SNPs were indicated as unplaced or not matched or shown many matches with the buffalo genome. Maximum number of intragenus SNPs were mapped to the chromosomes, 1, 2, 3 and 4 in buffaloes (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea). The variation in the coding region between cattle and buffaloes appear to be high on cattle chromosomes 3 (syntenic position on chromosome number 6 in buffaloes) and 19 (syntenic position on chromosome number 3 in buffaloes).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Bioinformatics analysis of the identified intragenus and interspecies SNPs\u003c/h2\u003e \u003cp\u003ePathway analysis of the genes containing interspecies SNPs found that majority of the genes were annotated to be involved in top five pathways, such as metabolism of proteins (1999), metabolism (1960), immune system (1827), post-translational modification (1378), gene expression (1364), RNA polymerase II transcription (1228) and innate immunity (1065) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb). Variation in innate immune genes between cattle and buffalo may explain their differential susceptibility to certain diseases. The SNPs in the innate immune genes may affect their differential expression of these genes between the two species. For instance, differential expression of immune genes among the two species was reported to understand the less susceptibility of water buffalo to \u003cem\u003eSchistosoma japonicum\u003c/em\u003e than yellow cattle (Yang et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). As the innate immunity pathway was annotated to be functionally defined pathway by CPDB analysis, the gene list from this pathway was used for the network analysis by the STRING and CYTOSCAPE software. This network analysis resulted in eleven significant (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) sub networks (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ec and Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ed). DAVID functional annotation of the genes in these sub-networks revealed that majority of these genes were involved in cytoskeletal organization, protein catabolism, and membrane specific signalling involved in innate immunity. This observation based on liver transcriptome generated interspecies SNPs suggest that the two species vary at the basic molecular signalling response repertoire involved in the defence mechanism against infections.\u003c/p\u003e \u003cp\u003ePathway analysis of the successfully mapped 163 intragenus SNPs in 156 genes found that the SNPs were found majorly in the genes involved in metabolic pathways, vesicle mediated transport, and disease signalling (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb). Among the genes involved in the metabolic pathways, the genes related to lipid metabolism explains the importance of their SNPs and the liver in handling lipids. During NEB, the liver and its lipid metabolizing genes play an important role in meeting the energy demand for milk synthesis (Drackley et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2001\u003c/span\u003e), especially in high yielders by initiating lipolysis in adipose tissue (Loor et al. \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; McCarthy et al. \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) and adapting liver\u0026rsquo;s metabolism (Veshkini et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Therefore, variations imposed by gene polymorphisms in lipid metabolic pathways in the liver may affect the animal\u0026rsquo;s ability to sustain the stress caused by the NEB during early post-partum and resume their normal physiological activities like reproduction. These differences in animal\u0026rsquo;s capability will also influence their differential susceptibility to post-partum diseases. Similarly, among the genes annotated to be involved in vesicle mediated transport and disease signalling, the genes related to the TGF (Tumor growth factor) beta signalling and EGFR1 (Epidermal growth factor receptor 1) signalling showed variation among buffaloes. Vesicle mediated transport and signalling are important during cross-talk among the liver, mammary, adipose, and reproductive organs, which manage the reciprocal control of metabolic activities and immune adaptation among these organs (Bu et al. \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2017\u003c/span\u003e) during post-partum.\u003c/p\u003e \u003cp\u003eAmong eight intragenus SNPs found in the coding regions of the genes, six SNPs in the genes, Interferon alpha/beta receptor 1 (INFAR1B), Low molecular weight phosphotyrosine phosphatase isoform x2 (LMPTP), Ribosome binding protein 1 isoform X2 (RBP1), Leukocyte specific transcript 1 (LST1), Complement 4 like and N-fatty-acyl-amino acid synthase/hydrolase (PM20D1) are non-synonymous for the two allelic forms (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Among these six SNPs, the maximum point mutation score (-4) for amino acid change was observed in LMPTP as per PAM 250 log odds substitution matrix. The polymorphisms in this gene were reported to be associated with the conception season and insulin resistance in humans (Bottini et al. \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Stanford et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Insulin resistance during NEB may affect lipolysis and lipid mobility, which are very crucial for regaining reproductive efficacy. Hence, future studies are needed to explore its association with reproductive efficiency in Murrah buffaloes.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cb\u003eIntragenus SNPs present in coding regions\u003c/b\u003e: Eight SNPs are present in coding regions of the genes out of 163 intragenus SNPs, of these six SNPs are nonsynonymous and two are synonymous. PAM score determined for non synonymous SNPs shows LMPTP gene non synomous SNP with maximum score\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eS.No\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGene\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIntra-genus SNP and type of SNP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCodon change with SNP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAmino acid change with SNP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ePAM Score\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInterferon alpha/beta receptor 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eA/T and Non synonymous SNP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTTA/TTT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLeucine/ Phenylalanine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSGTA (small glutamine rich tetratricopeptide repeat containing protein alpha isoform X2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eC/T and Synonymous SNP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCCC/CCT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eProline \u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLow molecular weight phosphotyrosine phosphatase isoform x2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eG/C/A and Non synonymous SNP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTGC/TTC/TAC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCysteine/\u003c/p\u003e \u003cp\u003ePhenylalanine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRibosome binding protein 1 isoform X2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eC/G and Non Synonymous SNP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCGT/TGT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAlanine/\u003c/p\u003e \u003cp\u003eThreonine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFatty acyl amino acid synthase / hydrolase\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eG/T/C and Non synonymous SNP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTCG/TCT/TCC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAlanine/Serine\u003c/p\u003e \u003cp\u003e/Proline\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eComplement C4 like\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eA/G and Non\u003c/p\u003e \u003cp\u003esynonymous SNP\u003c/p\u003e\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGG/CAG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eArginine/Glutamine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHSP1A\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eC/A and Synonymous \u003c/p\u003e \u003cp\u003eSNP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCGG/AGG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eGlycine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLST1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eA/G/C and Non\u003c/p\u003e \u003cp\u003esynonymous SNP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAAG/GAG/CAG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLysine/Glutamic acid\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":" \u003cp\u003eThe identified interspecies SNPs based on early postpartum buffalo liver transcriptome data suggests that cattle and buffalo vary in their innate immunity during this period. Likewise, intragenus SNPs in buffalo indicate variation in the expressed genes of metabolism and in particular lipid metabolism during early lactation.\u003c/p\u003e "},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding: \u003c/strong\u003e\u0026ldquo;The grants for conduct of experimentation included in this manuscript was supported by National Agricultural Science Fund, India (Grant No. NASF/GTR-5005/2015-16). \u003cem\u003eWe the authors declare that no funds, grants, or other support were received during the preparation of this manuscript\u0026rdquo;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAuthors do not have any conflict of interests.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003e\u0026ldquo;\u003c/em\u003e\u003cem\u003eSuneel Kumar Onteru,\u0026nbsp;\u003c/em\u003eDheer Singh and Gowdar Veerapa Vedamurthy\u003cem\u003e\u0026nbsp;contributed to the study conception and design. Material preparation, data collection and analysis were performed by\u0026nbsp;\u003c/em\u003eChaitanya Kumar Thota Venkata, Sudhakar Singh and \u0026nbsp;Suneel Kumar Onteru.\u003cem\u003e\u0026nbsp;The first draft of the manuscript was written by\u003c/em\u003e Chaitanya Kumar Thota Venkata, Sudhakar Singh and \u0026nbsp;Suneel Kumar Onteru\u003cem\u003e. All authors commented and reviewed on previous versions of the manuscript. All authors read and approved the final manuscript\u003c/em\u003e\u003cem\u003e.\u0026rdquo;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study is performed in guidelines and norms of\u0026nbsp;NDRI Institutional Animal Ethics Committee with approval no. 95/16\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to participate:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo human subjects were used in present study. So this section is not applicable for present manuscript\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to publish:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo human subjects were used in present study. So this section is not applicable for present manuscript\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData available statement:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData available within the article or its supplementary materials\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBottini N, Bottini E, Gloria-Bottini F et al (2002) Low-molecular-weight protein tyrosine phosphatase and human disease: in search of biochemical mechanisms. Arch Immunol Ther Exp (Warsz) 50(2):95\u0026ndash;104 PMID: 12022706\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBu D, Bionaz M, Wang M et al (2017) Transcriptome difference and potential crosstalk between liver and mammary tissue in mid-lactation primiparous dairy cows. 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PLoS ONE 10(6):e0130344. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1371/journal.pone.0130344\u003c/span\u003e\u003cspan address=\"10.1371/journal.pone.0130344\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Single Nucleotide Polymorphisms, Liver transcriptome, Functional annotation, Cattle genome, Buffalo","lastPublishedDoi":"10.21203/rs.3.rs-4709928/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4709928/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Graphical abstract\nAbstract:\nThe duration of negative energy balance (NEB), a physiological adaptation in females during early postpartum, and its coping mechanisms vary among animals, breeds and species because of genetic differences. However, genetic variations that may influence the NEB differences between cattle and buffaloes were not reported. Therefore, the present study aims to identify such genetic differences between cattle and buffaloes by identifying inter-species single nucleotide polymorphisms (SNPs) by mapping the available liver RNA-seq data earlier obtained from three early postpartum buffaloes and three heifers to the cattle genome (UMD 3.1) using the SNP eff tool. The liver transcriptome data were chosen, as it is the major metabolic organ in balancing metabolic milieu to meet different physiological demands during early postpartum. Using the identified interspecies SNPs, intragenus SNPs (Buffalo) were derived from six different Murrah buffaloes and mapped them to the Bubalus bubalis (ASM312139v1) genome. Further, functional annotation and biological pathway analyses were performed for those genes harbouring the putative SNPs. We identified a total of 1,40,056 interspecies and 188 intragenus SNPs. Functional annotation and pathway analyses revealed that most of the putative interspecies SNPs between the cattle (Bos taurus UMD 3.1) and buffalo (Bubalis bubalus) were in the protein metabolism genes (\u003e1900) predominantly involved in the immune system (\u003e1800 genes). Similarly, most of the putative intragenus SNPs among buffaloes were in the genes of lipid metabolic pathways. In conclusion, our study suggests that cattle and buffaloes might differ in protein metabolism genes involved in immunity at the deoxyribonucleic acid (DNA) level.","manuscriptTitle":"Inter-species SNPs from buffalo liver transcriptome indicate diversity in immune pathways between buffalo and cattle","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-08-05 12:56:27","doi":"10.21203/rs.3.rs-4709928/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":"a7c380ef-6d1d-4f0d-acc6-5499a4837dca","owner":[],"postedDate":"August 5th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-10-03T21:23:09+00:00","versionOfRecord":[],"versionCreatedAt":"2024-08-05 12:56:27","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4709928","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4709928","identity":"rs-4709928","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}