Expression and SNP profiling of HSP70 gene associated with thermotolerance traits in Munjal Sheep

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Bangar This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4423115/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 04 Jun, 2025 Read the published version in Journal of Thermal Biology → Version 1 posted You are reading this latest preprint version Abstract An investigation was carried out to identify the polymorphism of heat shock protein 70 (HSP70) gene and its expression profiling in Munjal sheep. Blood samples from 40 female Munjal sheep were collected for DNA extraction. After standardization of PCR for ovine HSP 70 gene, sequencing was carried out for single nucleotide polymorphism (SNP) profiling. Multiple sequence alignment was performed to screen SNP in the resource population and subsequently, A to G mutation was observed at c.459 position. Two genotypes viz., AG and AA were obtained. Association study revealed no significant association of identified genotypes with growth and thermotolerance traits. HSP70 expression analysis in different seasons was carried out using enzyme-linked immunosorbent assay (ELISA) kits. After blood collection, serum was separated for studying the gene expression and expression analysis of different groups was compared using t-test. Animals were also grouped in to two groups viz., heat stress susceptible (HSS) and heat stress tolerant (HST) based on heat tolerance coefficient (HTC) i.e. HST having HTC2.40. Statistical analysis revealed that expression of HSP70 gene varied markedly among the summer and winter season. Furthermore, a significant variation was observed between adults and lambs (P<0.05) and between HSS and HST sheep (P<0.05) for HSP70 gene expression. The expression of HSP70 was higher in adults in comparison to lambs in both summer and winter season. Selection of heat resistant animals is essential for better livestock performance amid challenging climatic conditions ahead and HSPs can be used as candidate markers for selecting resistant animals Heat stress Munjal sheep rectal temperature heat tolerance coefficient adaptation Figures Figure 1 Figure 2 Figure 3 Introduction Sheep rearing is an important economic activity worldwide for various purpose viz., meat, wool and hide production and especially in developing countries, it is one of the sources of livelihood and food security for small and marginal farmers. India boasts a wide diversity of sheep genetic resources and presently there are 45 registered sheep breeds in India, having their home tract in various agroclimatic zones of the country (Anonymous 2024). The number of sheep in India is estimated to be 74.26 million as of the 20 th livestock census (2019), and their contribution to the country's total meat production in 2022–23 was 10.51% (BAHS 2023). Munjal sheep are tall mutton type sheep and are generally found in the regions of Punjab, Haryana, and Rajasthan states of the country. It features a long head, a narrow forehead, a huge stature, and a rectangular shape. It has a tan or brown face that reaches to the middle of the neck which usually lacks wool. The ears are longer and leaf-like, while the nose is Roman. The udder is medium in size with medium-sized teats, and the tail is lengthy (Yadav et al. 2011). The climate of a particular region, especially the air temperature and relative humidity, directly influences the animal’s production potential. Heat stress is a major factor limiting the development and production of animals in tropical regions characterized by high temperatures and higher levels of solar radiation (McManus et al. 2014). When small ruminants are exposed to elevated temperatures, it results in decreased feed intake, growth rate, average daily gain, decreased fertility (Abdel-Hafez 2002, Van Wettere et al. 2021) and compromised performance (Tüfekci and Sejian, 2023). This leads to severe economic losses to the farmers. Animals adapted to hot or cold climatic conditions should show the least variation in traits when raised under such conditions. The animal’s response to heat stress can be measured by variations in body temperature, respiratory rate and heart rate, which tend to increase under thermal stress and subsequently result in hematological parameter changes because of increase in water and ion losses (Beede and Collier 1986). With a lack of thermal comfort, the animal seeks ways to lose heat. This involves a series of adaptations of the respiratory, circulatory, excretory, endocrine and nervous systems of animals reared in warm regions (McManus et al. 2009). At the cellular level, adaptations are also possible as part of the cell’s heat tolerance mechanisms. The cellular response to heat stress involves the synthesis of a family of proteins of diverse molecular weights, also known as heat shock proteins (HSP), among which HSP-70 is the most abundant (Daugaard et al. 2007). Coordination of various body systems to maintain productive potential varies between species, breeds and individuals within breed (Marai and Haeeb 2010) and animals often differ in their resilience and susceptibility to the climatic stress. Therefore, in order to recommend breeds for a given area, information about heat tolerance and adaptability is required. This variability can be used to select animals that are resistant to heat or cold stress by identifying the genes or alleles underlying such qualities of adaptation at the DNA level. So far, only a few association studies have been conducted between HSP gene polymorphisms and physiological changes in Indian sheep breeds namely Chokla, Magra, Marwari, and Madras Red sheep breeds (Singh et al. 2016; Singh et al. 2017). The ability of Munjal sheep to regulate body temperature under seasonal climatic fluctuations has been scarcely studied. So, the present study was undertaken with objectives to detect polymorphism in HSP 70 gene in Munjal sheep and to study association between identified variants of HSP 70 gene with performance and thermo-tolerance traits and expression analysis of HSP70 gene in Munjal sheep in various seasons. Materials and methods The experimental plan of study was duly approved by the Institutional Animal Ethics Committee (No. VCC/IAEC/1714-44), Lala Lajpat Rai University of Veterinary and Animal Sciences (LUVAS), Hisar, Haryana (India). Resource population The present study was conducted on 40 female Munjal Sheep (20 lambs and 20 adults) maintained at Sheep Breeding Farm, Department of Animal Genetics and Breeding, LUVAS, Hisar, Haryana, India. Traits studied Various physiological/thermotolerance traits, viz., respiration rate (RR), rectal temperature (RT), and pulse rate (PR) were recorded. RR (breath/min), RT (°F), and PR (beats/min) were measured by flank movement, digital thermometer, and the palpation of the femoral artery, respectively. Heat Tolerance coefficient (HTC): HTC was calculated using formula developed by Benezra (1954) based on respiration rate and rectal temperature, . DNA isolation: Five ml blood was collected from Munjal animals aseptically in EDTA vacutainer and transferred immediately to Animal Genomics laboratory, LUVAS, Hisar. Thereafter, DNA was isolated from these samples using Phenol Chloroform method. Quality and quantity of extracted DNA was assessed by agarose gel electrophoresis and Scandrop Nano-Volume Spectrophotometer (Analytika Jena). After checking quality and quantity of DNA, PCR was standardized for ovine HSP 70 gene using reported set of primers. Primer used and PCR amplification In the present study, 609 bp fragment of HSP70 gene was amplified using reported set of primers as described by Singh et al. (2017) and the primer details are given in Table 1. Table 1 Primer sets used for amplification of the target region of HSP70 Primer Sequence (5’-3’) Annealing Temp. (ºC) Amplicon size HSP 70F GCCTACTTCAACGACTCGCAG 61 609 bp HSP 70R CAGCAGCTTCTGCACCTTGG A reaction mixture of 25 μl was used for performing PCR reaction and it consisted of 0.5 μl forward primer, 0.5 μl reverse primer, 12.5 μl PCR master mix, 9.5 μl Milli Q water and 2 μl DNA template (100 ng/μl). The contents were thoroughly mixed by vortexing followed by PCR amplification in Thermal cycler (BIO-RAD T100) after standardizing the PCR parameters. The PCR protocol consisted of initial denaturation at 95°C for 3 min followed by 34 cycles of 95°C for 30 s, annealing at 62°C for 30 s and extension at 72°C for 1 min, and a final extension at 72°C for 5 min. Resulting PCR product was checked on 2% agarose gel including 0.5 μg/ml of ethidium bromide for validation of the amplified product. Sequencing Sequencing was carried out for all samples under study in both forward and reverse direction in order to identify any changes in nucleotide sequence. Version 2.5.1 of the Chromas software was utilized to visualize the chromatogram. According to the manufacturer's instructions, PCR amplicons were purified using the DNA Clean and Concentrator kit (Zymo Research) and then cycle sequenced using the BigDye Terminator v3.1 Cycle Sequencing Kit (Applied Biosystems). BigDye XTerminator Purification Kit (Applied Biosystems) was used to exclude extra buffers and dNTPs from the sequenced PCR products. The purified products were then subjected to capillary electrophoresis using the SeqStudio Genetic Analyzer (Applied Biosystems) from both 5’ and 3’ ends. Results The targeted region of the HSP70 gene was amplified to a product of 609 bp (Fig. 1). Subsequently, sequencing was carried out for identification of SNPs. Multiple sequence alignment was performed to screen SNP in our resource population and A to G mutation (at c.459 position) was observed in the studied population (Fig. 2). Only two genotypes viz., AG and AA were obtained. Distribution of AA (85%) genotype was found to be higher at target loci in comparison to AG (15%). Allele A was found to be predominant in our native sheep (Table 2) and allelic frequency of A and G allele were 0.925 and 0.075, respectively. Table 2 Genotype and allele Frequency of HSP 70 gene in studied population Sheep Genotype frequency Total Allele frequency Chi-Square value AA AG GG A G Munjal 0.85 (34) 0.15 (06) - 40 0.925 0.075 0.26 Association with thermo-tolerance traits Association of identified genotypes with various thermo-tolerance traits was studied after adjusting the effect of age group and time of measurement using general linear model. The means of different thermo-tolerance traits viz., rectal temperature, respiration rate and pulse rate pertaining to the HSP 70 genotypes in various months are presented in Table 3. Table 3 Effect of genotype on thermo-tolerance traits in Munjal sheep Rectal Temperature Respiration Rate Pulse Rate Genotype AA AG AA AG AA AG N 34 06 34 06 34 06 1 January 102.51±0.08 102.40±0.19 28.01±0.58 26.08±1.38 77.28±1.26 78.69±1.26 February 102.43±0.10 102.36±0.23 34.49±0.65 34.50±1.55 81.43±1.44 76.62±1.44 March 102.50±0.08 102.52±0.2 34.62±0.84 37.54±2.00 79.08±1.76 74.70±1.76 April 102.56±0.08 102.47±0.19 37.90±0.94 39.63±2.24 81.64±1.54 82.52±1.54 May 103.28±0.07 103.26±0.17 44.63±0.98 44.67±2.33 83.96±1.14 83.01±1.14 June 103.41±0.07 103.55±0.18 53.04±1.16 54.75±2.75 84.41±1.18 83.76±1.18 July 103.61±0.08 103.51±0.19 49.79±1.17 44.67±2.80 88.39±1.67 81.29±1.67 August 103.59±0.07 103.46±0.16 48.95±1.00 47.46±2.39 88.35±1.51 84.49±1.51 September 103.00±0.06 102.82±0.15 40.21±0.90 37.92±2.15 82.38±1.10 79.93±1.10 October 102.88±0.07 102.66±0.16 35.70±0.88 32.50±2.10 82.20±1.57 77.63±1.57 November 102.49±0.08 102.55±0.19 33.18±0.61 32.54±1.46 81.26±1.25 77.76±1.25 December 102.36±0.09 102.41±0.22 29.48±0.62 29.94±1.55 81.24±1.68 79.46±1.64 Results revealed that there was no significant association between genotype and various thermo-tolerance traits under study. It might be due to the fact that only four animals were found to have AG genotype in present study. Association with growth traits Various growth traits viz., Birth weight (B-WT), three-month body weight (3M-WT), six-month body weight (6M-WT), nine-month body weight (9M-WT) and one-year body weight (Y-WT) were recorded for all the animals under study. Association of genotype with various growth traits was studied using general linear model. Effect of genotype on various growth traits is presented in Table 4. Table 4 Effect of genotype on growth traits in Munjal sheep Genotype N B-WT 3M-WT 6M-WT 9M-WT 12M-WT AA 34 3.70±0.09 14.81±0.31 19.23±0.34 21.11±0.38 23.65±0.48 AG 06 3.66±0.22 15.00±0.74 19.77±0.82 21.41±0.92 23.96±1.14 Effect of genotype was found to be non-significant on growth traits in Munjal sheep. However, except birth weight, all other growth traits were comparatively higher for heterozygous genotype (AG) in comparison to homozygous genotype (AA). HSP70 expression profile HSP70 expression analysis in different seasons was carried out using ELISA kits (Bioassay Technology laboratory- Sheep heat shock protein 70 ELISA kit). The expression was studied as per manufacturers’ instructions. After blood collection, serum was obtained for studying the expression. Animals were also grouped in to two groups viz., heat stress susceptible and heat stress tolerant based on heat tolerance coefficient i.e. heat stress tolerant (HST) having HTC2.40. In present study, animals having low HTC showed higher expression of HSP70. Statistical analysis revealed that expression of HSP70 gene varied markedly among the summer and winter season and a significant variation between adults and lambs (P<0.05) and between HSS and HST sheep (P<0.05) was observed for HSP70 expression (Table 5). The expression of HSP70 was higher in adults in comparison to lambs in both summer and winter season. During summer, HSP70 expression was significantly higher (P<0.05) in adult sheep in comparison to lambs (Fig. 3). Table 5: HSP 70 expression in summer and winter in Munjal sheep Summer Winter Genotype AA 0.436±0.06 (18) 0.405±0.03 (25) AG 0.308±0.01 (03) 0.322±0.09 (03) Age group * Adult 0.521±0.09 (10) 0.417±0.04 (18) Lamb 0.313±0.01 (11) 0.358±0.03 (10) Heat stress susceptible/tolerant * HST 0.536±0.10 (09) 0.409±0.05 (14) HSS 0.319±0.04 (12) 0.383±0.03 (14) Overall 0.426±0.06 (21) 0.396±0.05 (28) Figures in parenthesis are the number of animals Discussion In agreement to our findings of allelic frequency, Singh et al. (2017) also reported similar allelic frequencies of A and G allele at the same locus in four different sheep breeds of India i.e. 0.83 and 0.17 in Chokla, 0.95 and 0.04 in Marwari, 0.82 and 0.17 in Magra and 1.0 and 0.0 for Madras Red. Overall genotypic frequency of AA and AG in their study was also similar (0.80 and 0.19, respectively) to genotypic frequencies observed in present study (0.85 and 0.15, respectively). Effect of genotype was found to be non-significant on various thermotolerance and growth traits in Munjal sheep. It might be attributed to the fact that in present study, only four animals out of 40 animals were found to have AG genotype. However, Bhat et al. (2016) investigated variants in HSP70 and reported that allele A of HSP70 is positively correlated with thermotolerance in Tharparkar cattle and genotype AA is superior with the highest HTC. While studying expression analysis, it was observed that Munjal animals having low HTC showed higher HSP70 expression. The expression of HSP70 was higher in adults in comparison to lambs in both summer and winter season indicating that adult animals are more capable of withstanding stress conditions in comparison to lambs. The HSP70 response when exposed to heat stress and other stimuli is quite important and considered as a cellular thermometer (Rout et al. 2016). Stress-induced HSP accumulation is associated with thermo-tolerance, the ability to survive otherwise lethal heat stress, and provide protection against the adverse effects of heat and chemical or abnormal stresses (De Maio 1999). Banerjee et al. (2014), studied the expression of various HSP70 genes viz., HSPA8, HSPA6, HSPA1A, HSPA1L, and HSPA2 and reported a significant variation between different seasons (P<0.01). Variation between different Indian goat breeds was also observed by the authors in relation to gene expression. During summer, the relative expressions of these genes were found to be higher in Gaddi and Chegu (cold-adapted breeds) in comparison to Sirohi and Barbari (heat-adapted breeds). The authors attributed this difference in relative expression of HSP70 genes to breed difference and their respective adaptation to different environmental conditions i.e. cold-tolerant or heat-tolerant. Therefore, both kind of stresses viz., heat and cold induced the over expression of HSP70 genes. While studying the expression of various HSP genes in different age groups of tropical and temperate region goats, Dangi et al. (2012) reported that expression of HSP90, HSP60, and ubiquitin was significantly higher (P < 0.05) in all age groups during peak summer season as compared to peak winter season in both tropical and temperate region goat whereas significantly higher (P<0.05) expression of HSP70 mRNA was observed in all age groups of tropical goats during summer season in comparison to winter season. In present study also, the overall HSP70 expression was comparatively higher in summer season as compared to winter season though the differences were non-significant. In agreement to present findings, Dangi et al. (2012) also reported non-significant difference of HSP70 expression during winter and summer seasons in various age groups of temperate region goats. Animals with AA genotype were found to have comparatively higher HSP 70 expression than AG in both summer and winter. However, the differences were non-significant. Similar to our findings, animals of AG genotypes were found to have higher expression than AA genotypes in another study (Singh et al. 2017). The authors reported that the median boot strap ratio was higher in winter than in summer and emphasized that animals of superior genotype with better adaptability (AA) had lower expression of HSPA1A than the animals of AG genotypes. Younis (2020) reported that the expression of the HSP 70 and HSP 90 genes was significantly higher in Abu Dlik as compared to Barki sheep indicating that Abu Dlik breed is more heat tolerant. Kaushik et al. (2022) analysed the relative m-RNA expression level of HSP70 in the different age groups of Jamunapari goats during summer. They observed that HSP70 gene expression was significantly higher at 9-month age as compared to 12 months and adult age in Jamunapari goats. They attributed this to maturity of goats during 9 months of age and at that time maturation of the immune system also occurs. In present study, heat stress tolerant animals showed higher expression of HSP70 during both summer and winter. In summer, significantly high HSP70 gene expression (P <0.05) was observed in HST sheep compared to HSS sheep. In a study on various Indian goats, HST individuals showed higher HSP expression than HSS individuals during peak heat stress periods in the Jamunapari, Barbari, Jakhrana and Sirohi breeds and the expression was 22.3, 22.1, 24.3 and 95.6-fold higher in HST animals compared to HSS individuals, respectively in above mentioned breeds (Rout et al., 2016). HSP-70 average concentrations for Pelibuey and Suffolk sheep were significantly increased under heat stress at 43 °C (Romero et al. 2013). When cells undergo heat stress, expression of heat shock genes and chaperones gets increased and it helps in cell survival by preventing protein aggregation and misfolding (Srikanth et al. 2017). In conclusion, under stress conditions, adult Munjal sheep regulate their body temperature more efficiently than young sheep. HSP 70 expression was correlated with heat stress tolerance as HST animals showed higher HSP70 expression in comparison to HSS animals. Expression of HSP70 was comparatively higher in summer indicating more stress during summer. Selection of heat resistant animals is essential for better livestock performance amid challenging climatic conditions ahead due to global warming and HSPs can be used as candidate markers for selecting resistant animals. The positive influence of HSP gene with heat tolerance in present study confirms that HSP70 gene in Munjal sheep is particularly vital and further studies on large population size and association with physiological and thermotolerance traits are required. Declarations Acknowledgements The authors are thankful to the Director of Research, LUVAS, Hisar, for providing the necessary facilities. Authors’ contributions SK and AM conceived and designed the study, SK and NK contributed in sample collection and lab work, YCB and SK analyzed the data, and wrote the manuscript, all authors have approved final version of the paper. Funding The authors declare that no funds, grants, or other support were received during the preparation of this manuscript. Ethics declarations The experimental plan of study was duly approved by the Institutional Animal Ethics Committee LUVAS, Hisar, Haryana (India). Consent for publication The authors give their consent for publication. 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J Animal Sci Biotechnol 12:1-8. https://doi.org/10.1186/s40104-020-00537-z Yadav DK, Arora R, Bhatia S, Singh G (2011) Morphological characterization, production and reproduction status of Munjal–A threatened sheep population of North-West India. Indian J Anim Sci 81(9):943-45. Younis F (2020) Expression pattern of heat shock protein genes in sheep. Mansoura Vet Med J 21(1):1-5. https://doi.org/10.21608/mvmj.2020.21.001 Cite Share Download PDF Status: Published Journal Publication published 04 Jun, 2025 Read the published version in Journal of Thermal Biology → Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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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-4423115","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":317601269,"identity":"c50bbc99-3fd5-455f-a16c-37e2e9b42905","order_by":0,"name":"Sunil Kumar","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+UlEQVRIiWNgGAWjYNACNhDBfPDBBxCbnXgtbMmGM0AUM/FaeMykecC2EVAsPyP32GOeMrvE+f1nDKRtfm2T52NmYPzwMQe3FoMbeenGPOeSExsbjhUY5/bdNmxjZmCWnLkNjxaJHDNp3jbmxGbG5g3JuT23GYFa2Jh58WiRnwHWUp8IVGlw2LLntj1BLQw3wFoOJ/awsRg2M/y4nUhQi8GZN2aSc84dN57Bw5bM2NtwO7mNmbEZr1/k23PMJN6UVcvO7z98/MePP7dt57c3H/zwEZ/DgIAJGB2ODSAWYxuYbMCvHqTkBwODPYT5h6DiUTAKRsEoGIEAAOeFTYzNTWW6AAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0001-9549-9673","institution":"Lala Lajpat Rai University of Veterinary and Animal Sciences","correspondingAuthor":true,"prefix":"","firstName":"Sunil","middleName":"","lastName":"Kumar","suffix":""},{"id":317601270,"identity":"ae38bbd9-cb5e-4f3a-b24c-7197f50121ec","order_by":1,"name":"Ankit Magotra","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Ankit","middleName":"","lastName":"Magotra","suffix":""},{"id":317601271,"identity":"65e1de3b-10e3-4a68-8c81-7703e776963a","order_by":2,"name":"Narender Kumar","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Narender","middleName":"","lastName":"Kumar","suffix":""},{"id":317601272,"identity":"b43bb926-ad78-4243-997c-a8172d5b69c0","order_by":3,"name":"Yogesh C. Bangar","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Yogesh","middleName":"C.","lastName":"Bangar","suffix":""}],"badges":[],"createdAt":"2024-05-15 06:52:37","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4423115/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4423115/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1016/j.jtherbio.2025.104151","type":"published","date":"2025-06-05T00:00:00+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":60140364,"identity":"11be211b-bf59-4486-8926-b80354014499","added_by":"auto","created_at":"2024-07-12 08:54:07","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":137863,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePCR amplicons of HSP70 gene in Munjal Sheep\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4423115/v1/861e6e65bbeb66075781b697.png"},{"id":60140361,"identity":"96853653-1d35-4d5e-8488-095c3c751a2e","added_by":"auto","created_at":"2024-07-12 08:54:06","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":63983,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eChromatograph of identified genotype (HSP70) in Munjal sheep\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-4423115/v1/a60844fe0d6ace0335028383.png"},{"id":60141715,"identity":"44806958-780a-47e3-b339-580901b6a3f9","added_by":"auto","created_at":"2024-07-12 09:10:07","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":16958,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eHSP70 expression in summer in Munjal sheep\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-4423115/v1/da772513465faff3b8a794d4.png"},{"id":84002702,"identity":"01c24aa4-f29d-4971-855b-86f63ea7d87a","added_by":"auto","created_at":"2025-06-05 15:02:18","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1014921,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4423115/v1/6afd0611-3cdf-4adb-ab28-1f8b1d20b6f7.pdf"}],"financialInterests":"","formattedTitle":"Expression and SNP profiling of HSP70 gene associated with thermotolerance traits in Munjal Sheep","fulltext":[{"header":"Introduction ","content":"\u003cp\u003eSheep rearing is an important economic activity worldwide for various purpose viz., meat, wool and hide production and especially in developing countries, it is one of the sources of livelihood and food security for small and marginal farmers.\u0026nbsp;India boasts a wide diversity of sheep genetic resources and presently there are 45 registered sheep breeds in India, having their home tract in various agroclimatic zones of the country (Anonymous 2024). The number of sheep in India is estimated to be 74.26 million as of the 20\u003csup\u003eth\u0026nbsp;\u003c/sup\u003elivestock census (2019), and their contribution to the country\u0026apos;s total meat production in 2022\u0026ndash;23 was 10.51% (BAHS 2023).\u0026nbsp;Munjal sheep are tall mutton type sheep and are generally found in the regions of Punjab, Haryana, and Rajasthan states of the country. It features a long head, a narrow forehead, a huge stature, and a rectangular shape. It has a tan or brown face that reaches to the middle of the neck which usually lacks wool. The ears are longer and leaf-like, while the nose is Roman. The udder is medium in size with medium-sized teats, and the tail is lengthy (Yadav et al. 2011).\u003c/p\u003e\n\u003cp\u003eThe climate of a particular region, especially the air temperature and relative humidity, directly influences the animal\u0026rsquo;s production potential. Heat stress is a major factor limiting the development and production of animals in tropical regions characterized by high temperatures and higher levels of solar radiation (McManus et al. 2014). When\u0026nbsp;small ruminants are exposed to elevated temperatures, it results in decreased feed intake, growth rate, average daily gain, decreased fertility (Abdel-Hafez 2002, Van Wettere et al. 2021) and compromised performance (T\u0026uuml;fekci and Sejian, 2023). This leads to severe economic losses to the farmers.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAnimals adapted to hot or cold climatic conditions should show the least variation in traits when raised under such conditions. The animal\u0026rsquo;s response to heat stress can be measured by variations in body temperature, respiratory rate and heart rate, which tend to increase under thermal stress and subsequently result in hematological parameter changes because of increase in water and ion losses (Beede and Collier 1986). With a lack of thermal comfort, the animal seeks ways to lose heat. This involves a series of adaptations of the respiratory, circulatory, excretory, endocrine and nervous systems of animals reared in warm regions\u0026nbsp;(McManus et al. 2009). At the cellular level, adaptations are also possible as part of the cell\u0026rsquo;s heat tolerance mechanisms. The cellular response to heat stress involves the synthesis of a family of proteins of diverse molecular weights, also known as heat shock proteins (HSP), among which HSP-70 is the most abundant (Daugaard et al. 2007).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCoordination of various body systems to maintain productive potential varies between species, breeds and individuals within breed (Marai and Haeeb 2010) and animals often differ in their resilience and susceptibility to the climatic stress. Therefore, in order to recommend breeds for a given area, information about heat tolerance and adaptability is required. This variability can be used to select animals that are resistant to heat or cold stress by identifying the genes or alleles underlying such qualities of adaptation at the DNA level. So far, only a few association studies have been conducted between HSP gene polymorphisms and physiological changes in Indian sheep breeds namely Chokla, Magra, Marwari, and Madras Red sheep breeds (Singh et al. 2016; Singh et al. 2017). The ability of Munjal sheep to regulate body temperature under seasonal climatic fluctuations has been scarcely studied. So, the present study was undertaken with objectives to detect polymorphism in HSP 70 gene in Munjal sheep and to study association between identified variants of HSP 70 gene with performance and thermo-tolerance traits and expression analysis of HSP70 gene in Munjal sheep in various seasons.\u0026nbsp;\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cp\u003eThe experimental plan of study was duly approved by the Institutional Animal Ethics Committee (No. VCC/IAEC/1714-44),\u0026nbsp;Lala Lajpat Rai University of Veterinary and Animal Sciences (LUVAS), Hisar, Haryana (India).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResource population\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe present study was conducted on 40 female Munjal Sheep (20 lambs and 20 adults) maintained at\u0026nbsp;Sheep Breeding Farm, Department of Animal Genetics and Breeding, LUVAS, Hisar,\u0026nbsp;Haryana, India.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTraits studied\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eVarious physiological/thermotolerance traits, viz., respiration rate (RR), rectal temperature (RT), and pulse rate (PR) were recorded.\u0026nbsp;RR (breath/min), RT (°F), and PR (beats/min) were measured by flank movement, digital thermometer, and the palpation of the femoral artery, respectively. Heat Tolerance coefficient (HTC): HTC was calculated using formula developed by Benezra (1954) based on respiration rate and rectal temperature,\u0026nbsp;\u003cimg src=\"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAIUAAAAtCAIAAAAP9z/oAAADw0lEQVR4Ae2a7bWjIBCGpwVqoAV6oARqoAU7oAM7sAIqsAEboAN6mD1n37OTOeoaE02u7pI/lygfw/swA0wucftcSQG6kjHNFm48rrUIGo/G41oKXMua5h+Nx7UUuJY1zT8aj2spcC1rmn80HtdS4FrW3Ns/Ukr052OM6boO6v559vg7juPnhH8MQ+Scm6aJmcdx1M9DCLXWpzbcmwczO+dSSsyccyYiaOG9Z+aUEgpd132UB4Zm5lqr9z6EAN27roMBpRRjTN/3/z4P773wMMboNSg8nqpwsAJcQXjAHr0gwGPPmri9f3jvERaMMaUUreyXecAMiZngISEr56xt+1v5X+CRUqq1WmtjjHqeX+bBzH3fG2MQM7V/hBCcc9p3tZ26fHsesn8Mw0BEwzDI9Lquc87J188VZP9gZmutSK/3DyKaLZdVex48rLVEJHinaTLGEJG1duZ64oNEhP0KXeecnXN465zb6aGrZu18KOcr1EfsQpiW441E8519oto4jnpq220xZQyEcb33YgDsgalPjXnwQHu95wA7vCylZK1FmYhQTfjDVYkIR4hSinNu/3y2Z/sjb1/icaKFWzxACIOllCQUCA8c75i5lEJEGn7OufF4g9NeHrpr4SEPEbtlH5PnGwVxZx39pKzddKOTz726in+IIlJYznnJA8FxWfO+T67CQy9MHa+0sj/LQxbK2wU9F12W08Fqz7qmLq9W3nio2y7L58QrzGR2HVsOpp+0eKXVkPI5PKZpksMVum77uUj8UmGLhz7vSqe11tm1C6+898YYhLt23hW5VgvQavW6/uCxfR+UflFtiaTWGmPEFZKIQghfuA+KVacXPrqfhxDkMjez/MFj9uI///pRHhva3pKHJGaMMeKFXdfBO3WGFTMvpcQY9XV1Q5H3XmEIJJzEJH3K2pO8Yr7n/4umlJCYkYTNMAyIAEgZSCqBmadpSimFED7KY2kSckigG2Pcefi8pX/Iqg8hICnQ971kVVddIf3+vLf2X2o1DMMMPFxnZyd35YEbj/cePOAWyEZLBl5L8B0etVb5sVZG3+8cd41XMtXVX5xWU/1f4IET5uwUW0p5Ka96S//w3mPP7PtezxaK4DeoWbInhLDc54Xr8YKGoQeKMeoU1NOBbskj54xrkPxzDfZtY0yMEStUeMwSM08Vea9CjFFOU7JEXnWO28er97S7cqtb+seVBT1oW+NxUMCTmzceJwt6sLvG46CAJzdvPE4W9GB3jcdBAU9u3nicLOjB7hqPgwKe3LzxOFnQg939AgBIEXEadaN1AAAAAElFTkSuQmCC\"\u003e.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDNA isolation:\u0026nbsp;\u003c/strong\u003eFive ml blood was collected from Munjal animals aseptically in EDTA vacutainer and transferred immediately to Animal Genomics laboratory, LUVAS, Hisar. Thereafter, DNA was isolated from these samples using Phenol Chloroform method. Quality and quantity of extracted DNA was assessed by agarose gel electrophoresis and Scandrop Nano-Volume Spectrophotometer (Analytika Jena). After checking quality and quantity of DNA, PCR was standardized for ovine HSP 70 gene using reported set of primers. \u0026nbsp;\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePrimer used and PCR amplification\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn the present study, 609 bp fragment of HSP70 gene was amplified using reported set of primers as described by\u0026nbsp;Singh et al.\u0026nbsp;(2017) and the primer details are given in Table 1.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1 Primer sets used for amplification of the target region of HSP70\u003c/strong\u003e\u003c/p\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003ePrimer\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.775510204081634%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSequence (5’-3’)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.53061224489796%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAnnealing Temp. (ºC)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.408163265306122%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAmplicon size\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"14.285714285714286%\" valign=\"top\"\u003e\n \u003cp\u003eHSP 70F\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"38.775510204081634%\" valign=\"top\"\u003e\n \u003cp\u003eGCCTACTTCAACGACTCGCAG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.53061224489796%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"20.408163265306122%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e609 bp\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.923076923076923%\" valign=\"top\"\u003e\n \u003cp\u003eHSP 70R\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"73.07692307692308%\" valign=\"top\"\u003e\n \u003cp\u003eCAGCAGCTTCTGCACCTTGG\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eA reaction mixture of 25 μl was used for performing PCR reaction and it consisted of 0.5 μl forward primer, 0.5 μl reverse primer, 12.5 μl PCR master mix, 9.5 μl Milli Q water and 2 μl DNA template (100 ng/μl). The contents were thoroughly mixed by vortexing followed by PCR amplification in Thermal cycler (BIO-RAD T100) after standardizing the PCR parameters.\u0026nbsp;The PCR protocol consisted of initial denaturation at 95°C for 3 min followed by 34 cycles\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003eof 95°C for 30 s, annealing at 62°C for 30 s and extension at 72°C for 1 min, and a final\u003cstrong\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/strong\u003eextension at 72°C for 5 min. Resulting PCR product was checked on 2% agarose gel including 0.5 μg/ml of ethidium bromide for validation of the amplified product.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSequencing\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSequencing was carried out for all samples under study in both forward and reverse direction in order to identify any changes in nucleotide sequence. Version 2.5.1 of the Chromas software was utilized to visualize the chromatogram. According to the manufacturer's instructions, PCR amplicons were purified using the DNA Clean and Concentrator kit (Zymo Research) and then cycle sequenced using the BigDye Terminator v3.1 Cycle Sequencing Kit (Applied Biosystems). BigDye XTerminator Purification Kit (Applied Biosystems) was used to exclude extra buffers and dNTPs from the sequenced PCR products. The purified products were then subjected to capillary electrophoresis using the SeqStudio Genetic Analyzer (Applied Biosystems) from both 5’ and 3’ ends.\u003c/p\u003e"},{"header":"Results ","content":"\u003cp\u003eThe targeted region of the HSP70 gene was amplified to a product of 609 bp (Fig. 1). \u0026nbsp; Subsequently, sequencing was carried out for identification of SNPs. Multiple sequence alignment was performed to screen SNP in our resource population and A to G mutation (at c.459 position) was observed in the studied population (Fig. 2). Only two genotypes viz., AG and AA were obtained. Distribution of AA (85%) genotype was found to be higher at target loci in comparison to AG (15%). Allele A was found to be predominant in our native sheep (Table 2) and allelic frequency of A and G allele were 0.925 and 0.075, respectively.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2 Genotype and allele Frequency of HSP 70 gene in studied population\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.541666666666666%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSheep\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"30.208333333333332%\" colspan=\"3\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eGenotype frequency\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.375%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.958333333333332%\" colspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAllele frequency\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.916666666666668%\" rowspan=\"2\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eChi-Square value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"16%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAG\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eGG\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eG\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.829787234042554%\" valign=\"top\"\u003e\n \u003cp\u003eMunjal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"8.51063829787234%\" valign=\"top\"\u003e\n \u003cp\u003e0.85\u003c/p\u003e\n \u003cp\u003e(34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.574468085106384%\" valign=\"top\"\u003e\n \u003cp\u003e0.15\u003c/p\u003e\n \u003cp\u003e(06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.702127659574469%\" valign=\"top\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.574468085106384%\" valign=\"top\"\u003e\n \u003cp\u003e40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.702127659574469%\" valign=\"top\"\u003e\n \u003cp\u003e0.925\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.702127659574469%\" valign=\"top\"\u003e\n \u003cp\u003e0.075\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"23.404255319148938%\" valign=\"top\"\u003e\n \u003cp\u003e0.26\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cstrong\u003eAssociation with thermo-tolerance traits\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAssociation of identified genotypes with various thermo-tolerance traits was studied after adjusting the effect of age group and time of measurement using general linear model. The means of different thermo-tolerance traits viz., rectal temperature, respiration rate and pulse rate pertaining to the HSP 70 genotypes in various months are presented in Table 3.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3 Effect of genotype on thermo-tolerance traits in Munjal sheep\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.371134020618557%\" valign=\"bottom\" style=\"width: 13.5984%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"27.835051546391753%\" colspan=\"2\" valign=\"bottom\" style=\"width: 29.5037%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRectal Temperature\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.77319587628866%\" colspan=\"2\" valign=\"bottom\" style=\"width: 27.8039%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRespiration Rate\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.77319587628866%\" colspan=\"2\" valign=\"bottom\" style=\"width: 23.7972%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePulse Rate\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.333333333333334%\" valign=\"bottom\" style=\"width: 13.5984%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGenotype\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.444444444444445%\" valign=\"bottom\" style=\"width: 14.8126%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.444444444444445%\" valign=\"bottom\" style=\"width: 14.8126%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAG\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.333333333333334%\" valign=\"bottom\" style=\"width: 14.3269%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.333333333333334%\" valign=\"bottom\" style=\"width: 13.5984%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAG\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.333333333333334%\" valign=\"bottom\" style=\"width: 14.0841%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAA\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.333333333333334%\" valign=\"bottom\" style=\"width: 9.7132%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAG\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.76595744680851%\" valign=\"bottom\" style=\"width: 13.5984%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eN\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.829787234042554%\" valign=\"bottom\" style=\"width: 14.8126%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e34\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.829787234042554%\" valign=\"bottom\" style=\"width: 14.8126%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e06\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.76595744680851%\" valign=\"bottom\" style=\"width: 14.3269%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e34\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.76595744680851%\" valign=\"bottom\" style=\"width: 13.5984%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e06\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.76595744680851%\" valign=\"bottom\" style=\"width: 14.0841%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e34\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.76595744680851%\" valign=\"bottom\" style=\"width: 9.7132%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e06\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.76595744680851%\" style=\"width: 13.5984%;\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.829787234042554%\" valign=\"bottom\" style=\"width: 14.8126%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"13.829787234042554%\" valign=\"bottom\" style=\"width: 14.8126%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"12.76595744680851%\" valign=\"bottom\" style=\"width: 14.3269%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"12.76595744680851%\" valign=\"bottom\" style=\"width: 13.5984%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"12.76595744680851%\" valign=\"bottom\" style=\"width: 14.0841%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"12.76595744680851%\" valign=\"bottom\" style=\"width: 9.7132%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.76595744680851%\" valign=\"bottom\" style=\"width: 13.5984%;\"\u003e\n \u003cp\u003eJanuary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.829787234042554%\" valign=\"bottom\" style=\"width: 14.8126%;\"\u003e\n \u003cp\u003e102.51\u0026plusmn;0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.829787234042554%\" valign=\"bottom\" style=\"width: 14.8126%;\"\u003e\n \u003cp\u003e102.40\u0026plusmn;0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.76595744680851%\" valign=\"bottom\" style=\"width: 14.3269%;\"\u003e\n \u003cp\u003e28.01\u0026plusmn;0.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.76595744680851%\" valign=\"bottom\" style=\"width: 13.5984%;\"\u003e\n \u003cp\u003e26.08\u0026plusmn;1.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.76595744680851%\" valign=\"bottom\" style=\"width: 14.0841%;\"\u003e\n \u003cp\u003e77.28\u0026plusmn;1.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.76595744680851%\" valign=\"bottom\" style=\"width: 9.7132%;\"\u003e\n \u003cp\u003e78.69\u0026plusmn;1.26\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.76595744680851%\" valign=\"bottom\" style=\"width: 13.5984%;\"\u003e\n \u003cp\u003eFebruary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.829787234042554%\" valign=\"bottom\" style=\"width: 14.8126%;\"\u003e\n \u003cp\u003e102.43\u0026plusmn;0.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.829787234042554%\" valign=\"bottom\" style=\"width: 14.8126%;\"\u003e\n \u003cp\u003e102.36\u0026plusmn;0.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.76595744680851%\" valign=\"bottom\" style=\"width: 14.3269%;\"\u003e\n \u003cp\u003e34.49\u0026plusmn;0.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.76595744680851%\" valign=\"bottom\" style=\"width: 13.5984%;\"\u003e\n \u003cp\u003e34.50\u0026plusmn;1.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.76595744680851%\" valign=\"bottom\" style=\"width: 14.0841%;\"\u003e\n \u003cp\u003e81.43\u0026plusmn;1.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.76595744680851%\" valign=\"bottom\" style=\"width: 9.7132%;\"\u003e\n \u003cp\u003e76.62\u0026plusmn;1.44\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.76595744680851%\" valign=\"bottom\" style=\"width: 13.5984%;\"\u003e\n \u003cp\u003eMarch\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.829787234042554%\" valign=\"bottom\" style=\"width: 14.8126%;\"\u003e\n \u003cp\u003e102.50\u0026plusmn;0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.829787234042554%\" valign=\"bottom\" style=\"width: 14.8126%;\"\u003e\n \u003cp\u003e102.52\u0026plusmn;0.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.76595744680851%\" valign=\"bottom\" style=\"width: 14.3269%;\"\u003e\n \u003cp\u003e34.62\u0026plusmn;0.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.76595744680851%\" valign=\"bottom\" style=\"width: 13.5984%;\"\u003e\n \u003cp\u003e37.54\u0026plusmn;2.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.76595744680851%\" valign=\"bottom\" style=\"width: 14.0841%;\"\u003e\n \u003cp\u003e79.08\u0026plusmn;1.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.76595744680851%\" valign=\"bottom\" style=\"width: 9.7132%;\"\u003e\n \u003cp\u003e74.70\u0026plusmn;1.76\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.76595744680851%\" valign=\"bottom\" style=\"width: 13.5984%;\"\u003e\n \u003cp\u003eApril\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.829787234042554%\" valign=\"bottom\" style=\"width: 14.8126%;\"\u003e\n \u003cp\u003e102.56\u0026plusmn;0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.829787234042554%\" valign=\"bottom\" style=\"width: 14.8126%;\"\u003e\n \u003cp\u003e102.47\u0026plusmn;0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.76595744680851%\" valign=\"bottom\" style=\"width: 14.3269%;\"\u003e\n \u003cp\u003e37.90\u0026plusmn;0.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.76595744680851%\" valign=\"bottom\" style=\"width: 13.5984%;\"\u003e\n \u003cp\u003e39.63\u0026plusmn;2.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.76595744680851%\" valign=\"bottom\" style=\"width: 14.0841%;\"\u003e\n \u003cp\u003e81.64\u0026plusmn;1.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.76595744680851%\" valign=\"bottom\" style=\"width: 9.7132%;\"\u003e\n \u003cp\u003e82.52\u0026plusmn;1.54\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.76595744680851%\" valign=\"bottom\" style=\"width: 13.5984%;\"\u003e\n \u003cp\u003eMay\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.829787234042554%\" valign=\"bottom\" style=\"width: 14.8126%;\"\u003e\n \u003cp\u003e103.28\u0026plusmn;0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.829787234042554%\" valign=\"bottom\" style=\"width: 14.8126%;\"\u003e\n \u003cp\u003e103.26\u0026plusmn;0.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.76595744680851%\" valign=\"bottom\" style=\"width: 14.3269%;\"\u003e\n \u003cp\u003e44.63\u0026plusmn;0.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.76595744680851%\" valign=\"bottom\" style=\"width: 13.5984%;\"\u003e\n \u003cp\u003e44.67\u0026plusmn;2.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.76595744680851%\" valign=\"bottom\" style=\"width: 14.0841%;\"\u003e\n \u003cp\u003e83.96\u0026plusmn;1.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.76595744680851%\" valign=\"bottom\" style=\"width: 9.7132%;\"\u003e\n \u003cp\u003e83.01\u0026plusmn;1.14\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.76595744680851%\" valign=\"bottom\" style=\"width: 13.5984%;\"\u003e\n \u003cp\u003eJune\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.829787234042554%\" valign=\"bottom\" style=\"width: 14.8126%;\"\u003e\n \u003cp\u003e103.41\u0026plusmn;0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.829787234042554%\" valign=\"bottom\" style=\"width: 14.8126%;\"\u003e\n \u003cp\u003e103.55\u0026plusmn;0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.76595744680851%\" valign=\"bottom\" style=\"width: 14.3269%;\"\u003e\n \u003cp\u003e53.04\u0026plusmn;1.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.76595744680851%\" valign=\"bottom\" style=\"width: 13.5984%;\"\u003e\n \u003cp\u003e54.75\u0026plusmn;2.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.76595744680851%\" valign=\"bottom\" style=\"width: 14.0841%;\"\u003e\n \u003cp\u003e84.41\u0026plusmn;1.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.76595744680851%\" valign=\"bottom\" style=\"width: 9.7132%;\"\u003e\n \u003cp\u003e83.76\u0026plusmn;1.18\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.76595744680851%\" valign=\"bottom\" style=\"width: 13.5984%;\"\u003e\n \u003cp\u003eJuly\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.829787234042554%\" valign=\"bottom\" style=\"width: 14.8126%;\"\u003e\n \u003cp\u003e103.61\u0026plusmn;0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.829787234042554%\" valign=\"bottom\" style=\"width: 14.8126%;\"\u003e\n \u003cp\u003e103.51\u0026plusmn;0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.76595744680851%\" valign=\"bottom\" style=\"width: 14.3269%;\"\u003e\n \u003cp\u003e49.79\u0026plusmn;1.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.76595744680851%\" valign=\"bottom\" style=\"width: 13.5984%;\"\u003e\n \u003cp\u003e44.67\u0026plusmn;2.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.76595744680851%\" valign=\"bottom\" style=\"width: 14.0841%;\"\u003e\n \u003cp\u003e88.39\u0026plusmn;1.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.76595744680851%\" valign=\"bottom\" style=\"width: 9.7132%;\"\u003e\n \u003cp\u003e81.29\u0026plusmn;1.67\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.76595744680851%\" valign=\"bottom\" style=\"width: 13.5984%;\"\u003e\n \u003cp\u003eAugust\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.829787234042554%\" valign=\"bottom\" style=\"width: 14.8126%;\"\u003e\n \u003cp\u003e103.59\u0026plusmn;0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.829787234042554%\" valign=\"bottom\" style=\"width: 14.8126%;\"\u003e\n \u003cp\u003e103.46\u0026plusmn;0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.76595744680851%\" valign=\"bottom\" style=\"width: 14.3269%;\"\u003e\n \u003cp\u003e48.95\u0026plusmn;1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.76595744680851%\" valign=\"bottom\" style=\"width: 13.5984%;\"\u003e\n \u003cp\u003e47.46\u0026plusmn;2.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.76595744680851%\" valign=\"bottom\" style=\"width: 14.0841%;\"\u003e\n \u003cp\u003e88.35\u0026plusmn;1.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.76595744680851%\" valign=\"bottom\" style=\"width: 9.7132%;\"\u003e\n \u003cp\u003e84.49\u0026plusmn;1.51\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.76595744680851%\" valign=\"bottom\" style=\"width: 13.5984%;\"\u003e\n \u003cp\u003eSeptember\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.829787234042554%\" valign=\"bottom\" style=\"width: 14.8126%;\"\u003e\n \u003cp\u003e103.00\u0026plusmn;0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.829787234042554%\" valign=\"bottom\" style=\"width: 14.8126%;\"\u003e\n \u003cp\u003e102.82\u0026plusmn;0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.76595744680851%\" valign=\"bottom\" style=\"width: 14.3269%;\"\u003e\n \u003cp\u003e40.21\u0026plusmn;0.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.76595744680851%\" valign=\"bottom\" style=\"width: 13.5984%;\"\u003e\n \u003cp\u003e37.92\u0026plusmn;2.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.76595744680851%\" valign=\"bottom\" style=\"width: 14.0841%;\"\u003e\n \u003cp\u003e82.38\u0026plusmn;1.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.76595744680851%\" valign=\"bottom\" style=\"width: 9.7132%;\"\u003e\n \u003cp\u003e79.93\u0026plusmn;1.10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.76595744680851%\" valign=\"bottom\" style=\"width: 13.5984%;\"\u003e\n \u003cp\u003eOctober\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.829787234042554%\" valign=\"bottom\" style=\"width: 14.8126%;\"\u003e\n \u003cp\u003e102.88\u0026plusmn;0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.829787234042554%\" valign=\"bottom\" style=\"width: 14.8126%;\"\u003e\n \u003cp\u003e102.66\u0026plusmn;0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.76595744680851%\" valign=\"bottom\" style=\"width: 14.3269%;\"\u003e\n \u003cp\u003e35.70\u0026plusmn;0.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.76595744680851%\" valign=\"bottom\" style=\"width: 13.5984%;\"\u003e\n \u003cp\u003e32.50\u0026plusmn;2.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.76595744680851%\" valign=\"bottom\" style=\"width: 14.0841%;\"\u003e\n \u003cp\u003e82.20\u0026plusmn;1.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.76595744680851%\" valign=\"bottom\" style=\"width: 9.7132%;\"\u003e\n \u003cp\u003e77.63\u0026plusmn;1.57\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.76595744680851%\" valign=\"bottom\" style=\"width: 13.5984%;\"\u003e\n \u003cp\u003eNovember\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.829787234042554%\" valign=\"bottom\" style=\"width: 14.8126%;\"\u003e\n \u003cp\u003e102.49\u0026plusmn;0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.829787234042554%\" valign=\"bottom\" style=\"width: 14.8126%;\"\u003e\n \u003cp\u003e102.55\u0026plusmn;0.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.76595744680851%\" valign=\"bottom\" style=\"width: 14.3269%;\"\u003e\n \u003cp\u003e33.18\u0026plusmn;0.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.76595744680851%\" valign=\"bottom\" style=\"width: 13.5984%;\"\u003e\n \u003cp\u003e32.54\u0026plusmn;1.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.76595744680851%\" valign=\"bottom\" style=\"width: 14.0841%;\"\u003e\n \u003cp\u003e81.26\u0026plusmn;1.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.76595744680851%\" valign=\"bottom\" style=\"width: 9.7132%;\"\u003e\n \u003cp\u003e77.76\u0026plusmn;1.25\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"12.76595744680851%\" valign=\"bottom\" style=\"width: 13.5984%;\"\u003e\n \u003cp\u003eDecember\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.829787234042554%\" valign=\"bottom\" style=\"width: 14.8126%;\"\u003e\n \u003cp\u003e102.36\u0026plusmn;0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"13.829787234042554%\" valign=\"bottom\" style=\"width: 14.8126%;\"\u003e\n \u003cp\u003e102.41\u0026plusmn;0.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.76595744680851%\" valign=\"bottom\" style=\"width: 14.3269%;\"\u003e\n \u003cp\u003e29.48\u0026plusmn;0.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.76595744680851%\" valign=\"bottom\" style=\"width: 13.5984%;\"\u003e\n \u003cp\u003e29.94\u0026plusmn;1.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.76595744680851%\" valign=\"bottom\" style=\"width: 14.0841%;\"\u003e\n \u003cp\u003e81.24\u0026plusmn;1.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"12.76595744680851%\" valign=\"bottom\" style=\"width: 9.7132%;\"\u003e\n \u003cp\u003e79.46\u0026plusmn;1.64\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eResults revealed that there was no significant association between genotype and various thermo-tolerance traits under study. It might be due to the fact that only four animals were found to have AG genotype in present study.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAssociation with growth traits\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eVarious growth traits viz., Birth weight (B-WT), three-month body weight (3M-WT), six-month body weight (6M-WT), nine-month body weight (9M-WT) and one-year body weight (Y-WT) were recorded for all the animals under study. Association of genotype with various growth traits was studied using general linear model. Effect of genotype on various growth traits is presented in Table 4.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4 Effect of genotype on growth traits in Munjal sheep\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.504823151125402%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eGenotype\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.752411575562701%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eN\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.39871382636656%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eB-WT\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.524115755627008%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e3M-WT\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.27331189710611%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e6M-WT\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.27331189710611%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e9M-WT\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.27331189710611%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003e12M-WT\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.504823151125402%\" valign=\"top\"\u003e\n \u003cp\u003eAA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.752411575562701%\" valign=\"top\"\u003e\n \u003cp\u003e34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.39871382636656%\" valign=\"top\"\u003e\n \u003cp\u003e3.70\u0026plusmn;0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.524115755627008%\" valign=\"top\"\u003e\n \u003cp\u003e14.81\u0026plusmn;0.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.27331189710611%\" valign=\"top\"\u003e\n \u003cp\u003e19.23\u0026plusmn;0.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.27331189710611%\" valign=\"top\"\u003e\n \u003cp\u003e21.11\u0026plusmn;0.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.27331189710611%\" valign=\"top\"\u003e\n \u003cp\u003e23.65\u0026plusmn;0.48\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"13.504823151125402%\" valign=\"top\"\u003e\n \u003cp\u003eAG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.752411575562701%\" valign=\"top\"\u003e\n \u003cp\u003e06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"16.39871382636656%\" valign=\"top\"\u003e\n \u003cp\u003e3.66\u0026plusmn;0.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.524115755627008%\" valign=\"top\"\u003e\n \u003cp\u003e15.00\u0026plusmn;0.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.27331189710611%\" valign=\"top\"\u003e\n \u003cp\u003e19.77\u0026plusmn;0.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.27331189710611%\" valign=\"top\"\u003e\n \u003cp\u003e21.41\u0026plusmn;0.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.27331189710611%\" valign=\"top\"\u003e\n \u003cp\u003e23.96\u0026plusmn;1.14\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eEffect of genotype was found to be non-significant on growth traits in Munjal sheep. However, except birth weight, all other growth traits were comparatively higher for heterozygous genotype (AG) in comparison to homozygous genotype (AA).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHSP70 expression profile\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHSP70 expression analysis in different seasons was carried out using ELISA kits (Bioassay Technology laboratory- Sheep heat shock protein 70 ELISA kit). The expression was studied as per manufacturers\u0026rsquo; instructions. After blood collection, serum was obtained for studying the expression. Animals were also grouped in to two groups viz., heat stress susceptible and heat stress tolerant based on heat tolerance coefficient i.e. heat stress tolerant (HST) having HTC\u0026lt;2.40 and heat stress susceptible (HSS) with HTC\u0026gt;2.40. In present study, animals having low HTC showed higher expression of HSP70.\u0026nbsp;Statistical analysis revealed that expression of HSP70 gene varied markedly among the summer and winter season and a significant variation between adults and lambs (P\u0026lt;0.05) and between HSS and HST sheep (P\u0026lt;0.05) was observed for HSP70 expression (Table 5). The expression of HSP70 was higher in adults in comparison to lambs in both summer and winter season. During summer, HSP70 expression was significantly higher\u0026nbsp;(P\u0026lt;0.05)\u0026nbsp;in adult sheep in comparison to lambs (Fig. 3).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 5: HSP 70 expression in summer and winter in Munjal sheep\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.272727272727273%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"36.36363636363637%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eSummer\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"36.36363636363637%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eWinter\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.272727272727273%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eGenotype\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"36.36363636363637%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"36.36363636363637%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.272727272727273%\" valign=\"top\"\u003e\n \u003cp\u003eAA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"36.36363636363637%\" valign=\"top\"\u003e\n \u003cp\u003e0.436\u0026plusmn;0.06 (18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"36.36363636363637%\" valign=\"top\"\u003e\n \u003cp\u003e0.405\u0026plusmn;0.03 (25)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.272727272727273%\" valign=\"top\"\u003e\n \u003cp\u003eAG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"36.36363636363637%\" valign=\"top\"\u003e\n \u003cp\u003e0.308\u0026plusmn;0.01 (03)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"36.36363636363637%\" valign=\"top\"\u003e\n \u003cp\u003e0.322\u0026plusmn;0.09 (03)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.272727272727273%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge group\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"36.36363636363637%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"36.36363636363637%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.272727272727273%\" valign=\"top\"\u003e\n \u003cp\u003eAdult\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"36.36363636363637%\" valign=\"top\"\u003e\n \u003cp\u003e0.521\u0026plusmn;0.09 (10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"36.36363636363637%\" valign=\"top\"\u003e\n \u003cp\u003e0.417\u0026plusmn;0.04 (18)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.272727272727273%\" valign=\"top\"\u003e\n \u003cp\u003eLamb\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"36.36363636363637%\" valign=\"top\"\u003e\n \u003cp\u003e0.313\u0026plusmn;0.01 (11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"36.36363636363637%\" valign=\"top\"\u003e\n \u003cp\u003e0.358\u0026plusmn;0.03 (10)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.272727272727273%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eHeat stress susceptible/tolerant\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"36.36363636363637%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; *\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"36.36363636363637%\" valign=\"top\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.272727272727273%\" valign=\"top\"\u003e\n \u003cp\u003eHST\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"36.36363636363637%\" valign=\"top\"\u003e\n \u003cp\u003e0.536\u0026plusmn;0.10 (09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"36.36363636363637%\" valign=\"top\"\u003e\n \u003cp\u003e0.409\u0026plusmn;0.05 (14)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.272727272727273%\" valign=\"top\"\u003e\n \u003cp\u003eHSS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"36.36363636363637%\" valign=\"top\"\u003e\n \u003cp\u003e0.319\u0026plusmn;0.04 (12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"36.36363636363637%\" valign=\"top\"\u003e\n \u003cp\u003e0.383\u0026plusmn;0.03 (14)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"27.272727272727273%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cstrong\u003eOverall\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"36.36363636363637%\" valign=\"top\"\u003e\n \u003cp\u003e0.426\u0026plusmn;0.06 (21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"36.36363636363637%\" valign=\"top\"\u003e\n \u003cp\u003e0.396\u0026plusmn;0.05 (28)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eFigures in parenthesis are the number of animals\u0026nbsp;\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn agreement to our findings of allelic frequency,\u0026nbsp;Singh et al. (2017) also reported similar allelic frequencies of A and G allele at the same locus in four different sheep breeds of India i.e. 0.83 and 0.17 in Chokla, 0.95 and 0.04 in Marwari, 0.82 and 0.17 in Magra and 1.0 and 0.0 for Madras Red.\u0026nbsp;Overall genotypic frequency of AA and AG in their study was also similar (0.80 and 0.19, respectively) to genotypic frequencies observed in present study (0.85 and 0.15, respectively).\u0026nbsp;Effect of genotype was found to be non-significant on various thermotolerance and growth traits in Munjal sheep. It\u0026nbsp;might be attributed to the fact that in present study, only four animals out of 40 animals were found to have AG genotype. However, Bhat et al. (2016) investigated variants in HSP70 and reported that allele A of HSP70 is positively correlated with thermotolerance in Tharparkar cattle and genotype AA is superior with the highest HTC.\u003c/p\u003e\n\u003cp\u003eWhile studying expression analysis, it was observed that Munjal animals having low HTC showed higher HSP70 expression.\u0026nbsp;The expression of HSP70 was higher in adults in comparison to lambs in both summer and winter season indicating that adult animals are more capable of withstanding stress conditions in comparison to lambs.\u0026nbsp;The HSP70 response when exposed to heat stress and other stimuli is quite important and considered as a cellular thermometer (Rout et al. 2016).\u0026nbsp;Stress-induced HSP accumulation is associated with thermo-tolerance, the ability to survive otherwise lethal heat stress, and provide protection against the adverse effects of heat and chemical or abnormal stresses (De Maio 1999).\u0026nbsp;Banerjee et al. (2014), studied the expression of various HSP70 genes viz., HSPA8, HSPA6, HSPA1A, HSPA1L, and HSPA2 and reported a significant variation between different seasons (P\u0026lt;0.01). Variation between different Indian goat breeds was also observed by the authors in relation to gene expression. During summer, the relative expressions of these genes were found to be higher in Gaddi and Chegu (cold-adapted breeds) in comparison to Sirohi and Barbari (heat-adapted breeds). The authors attributed this\u0026nbsp;difference in relative expression of HSP70 genes to breed difference and their respective adaptation to different environmental conditions i.e. cold-tolerant or heat-tolerant.\u0026nbsp;Therefore, both kind of stresses viz., heat and cold induced the over expression of HSP70 genes. While studying the expression of various HSP genes in different age groups of tropical and temperate region goats, Dangi et al. (2012) reported that expression of HSP90, HSP60, and ubiquitin was significantly higher (P \u0026lt; 0.05) in all age groups during peak summer season as compared to peak winter season in both tropical and temperate region goat whereas significantly higher (P\u0026lt;0.05) expression of HSP70 mRNA was observed in all age groups of tropical goats during summer season in comparison to winter season. In present study also, the overall HSP70 expression was comparatively higher in summer season as compared to winter season though the differences were non-significant. In agreement to present findings, Dangi et al. (2012) also reported non-significant difference of HSP70 expression during winter and summer seasons in various age groups of temperate region goats.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAnimals with AA genotype were found to have comparatively higher HSP 70 expression than AG in both summer and winter. However, the differences were non-significant. Similar to our findings,\u0026nbsp;animals of AG genotypes were found to have higher expression than AA genotypes in another study\u0026nbsp;(Singh et al. 2017). The authors reported that the median boot strap ratio was higher in winter than in summer and emphasized that animals of superior genotype with better adaptability (AA) had lower expression of HSPA1A than the animals of AG genotypes.\u003c/p\u003e\n\u003cp\u003eYounis (2020) reported that the expression of the HSP 70 and HSP 90 genes was significantly higher in Abu Dlik as compared to Barki sheep indicating that Abu Dlik breed is more heat tolerant. Kaushik et al. (2022) analysed the relative m-RNA expression level of HSP70 in the different age groups of Jamunapari goats during summer.\u0026nbsp;They observed that HSP70 gene expression was significantly higher at 9-month age as compared to 12 months and adult age in Jamunapari goats. They attributed this to maturity of goats during 9 months of age and at that time maturation of the immune system also occurs.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn present study, heat stress tolerant animals showed higher expression of HSP70 during both summer and winter.\u0026nbsp;In summer, significantly high HSP70 gene expression (P \u0026lt;0.05) was observed in HST sheep compared to HSS sheep. In a study on various Indian goats,\u0026nbsp;HST individuals showed higher HSP expression than HSS individuals during peak heat stress periods in the Jamunapari, Barbari, Jakhrana and Sirohi breeds and the expression was 22.3, 22.1, 24.3 and 95.6-fold higher in HST animals compared to HSS individuals, respectively in above mentioned breeds (Rout et al., 2016). HSP-70 average concentrations for Pelibuey and Suffolk sheep were significantly increased under heat stress at 43 \u0026deg;C (Romero et al. 2013). When cells undergo heat stress, expression of heat shock genes and chaperones gets increased and it helps in cell survival by preventing protein aggregation and misfolding (Srikanth et al. 2017).\u003c/p\u003e\n\u003cp\u003eIn conclusion, under stress conditions, adult Munjal sheep regulate their body temperature more efficiently than young sheep. HSP 70 expression was correlated with heat stress tolerance as HST animals showed higher HSP70 expression in comparison to HSS animals. Expression of HSP70 was comparatively higher in summer indicating more stress during summer. Selection of heat resistant animals is essential for better livestock performance amid challenging climatic conditions ahead due to global warming and HSPs can be used as candidate markers for selecting resistant animals. The positive influence of HSP gene with heat tolerance in present study confirms that HSP70 gene in Munjal sheep is particularly vital and further studies on large population size and association with physiological and thermotolerance traits are required.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors are thankful to the Director of Research, LUVAS, Hisar, for providing the necessary facilities.\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u0026nbsp; SK and AM conceived and designed the study, SK and NK contributed in sample collection and lab work, YCB and SK analyzed the data, and wrote the manuscript, all authors have approved final version of the paper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e The authors declare that no funds, grants, or other support were received during the preparation of this manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics declarations\u003c/strong\u003e The experimental plan of study was duly approved by the Institutional Animal Ethics Committee LUVAS, Hisar, Haryana (India).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e The authors give their consent for publication.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e The authors declare that they have no conflict of interest.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003e20\u003csup\u003eth\u003c/sup\u003e livestock census 2019. Department of Animal Husbandry and Dairying. Govt. of India\u003c/li\u003e\n\u003cli\u003eAbdel-Hafez MAM (2002) Studies on the reproductive performance in sheep. Ph.D. thesis, Faculty of Agriculture, Zagazig University, Zagazig\u003c/li\u003e\n\u003cli\u003eAnonymous (2024) New Breeds Registered, ICAR- National Bureau of Animal Genetic Resources. https://icar.org.in/icar-nbagr-registered-8-new-livestock-and-poultry-breeds Accessed on 23 April, 2024\u003c/li\u003e\n\u003cli\u003eBAHS (2023) Basic Animal Husbandry Statistics Ministry of Fisheries, Animal Husbandry and Dairying, Govt. of India\u003c/li\u003e\n\u003cli\u003eBanerjee D, Upadhyay RC, Chaudhary UB, Kumar R, Singh S, Ashutosh, Polley S, Mukherjee A, Das TK, De S (2014) Seasonal variation in expression pattern of genes under HSP70: Seasonal variation in expression pattern of genes under HSP70 family in heat-and cold-adapted goats (Capra hircus). Cell Stress Chaperones 19:401-408. DOI: 10.1007/s12192-013-0469-0\u003c/li\u003e\n\u003cli\u003eBeede DK, Collier RJ (1986) Potential nutritional strategies for intensively managed cattle during thermal stress. J Anim Sci 62(2):543-54 https://doi.org/10.2527/jas1986.622543x \u003c/li\u003e\n\u003cli\u003eBenezra MV (1954) A new index for measuring the adaptibility of cattle to tropical conditions. J Anim Sci 13: 1015.\u003c/li\u003e\n\u003cli\u003eBhat S, Kumar P, Kashyap N, Deshmukh B, Dige MS, Bhushan B, Chauhan A, Kumar A, Singh G (2016) Effect of heat shock protein 70 polymorphism on thermotolerance in Tharparkar cattle. Vet world 9(2):113 doi: 10.14202/vetworld.2016.113-117\u003c/li\u003e\n\u003cli\u003eDangi SS, Gupta M, Maurya D, Yadav VP, Panda RP, Singh G, Mohan NH, Bhure SK, Das BC, Bag S, Mahapatra R (2012) Expression profile of HSP genes during different seasons in goats (Capra hircus). Trop Anim Health Prod 44:1905-1912. DOI: 10.1007/s11250-012-0155-8\u003c/li\u003e\n\u003cli\u003eDaugaard M, Rohde M and J\u0026auml;\u0026auml;ttel\u0026auml; M (2007) The heat shock protein 70 family: Highly homologous proteins with overlapping and distinct functions. FEBS letters, 581(19): 3702-3710 https://doi.org/10.1016/j.febslet.2007.05.039 \u003c/li\u003e\n\u003cli\u003eDe Maio A (1999). Heat shock proteins: facts, thoughts, and dreams. Shock, 11(1):1-12. DOI: 10.1097/00024382-199901000-00001\u003c/li\u003e\n\u003cli\u003eKaushik R, Goel A, Rout PK (2022) Differential expression and regulation of HSP70 gene during growth phase in ruminants in response to heat stress. Sci Rep 12(1): 18310. https://doi.org/10.1038/s41598-022-22728-6 \u003c/li\u003e\n\u003cli\u003eMarai IF, Haeeb AA (2010) Buffalo\u0026apos;s biological functions as affected by heat stress\u0026mdash;A review. Livest Sci 127(2-3):89-109 https://doi.org/10.1016/j.livsci.2009.08.001 \u003c/li\u003e\n\u003cli\u003eMcManus C, Hermuche P, Paiva SR, Ferrugem Moraes JC, de Melo CB, Mendes C (2014) Geographical distribution of sheep breeds in Brazil and their relationship with climatic and environmental factors as risk classification for conservation. Braz J Sci Tech 1:1-5. https://doi.org/10.1186/2196-288X-1-3 \u003c/li\u003e\n\u003cli\u003eMcManus C, Paludo GR, Louvandini H, Gugel R, Sasaki LC, Paiva SR (2009) Heat tolerance in Brazilian sheep: physiological and blood parameters. Trop Anim Health Prod 41:95-101. https://doi.org/10.1007/s11250-008-9162-1 \u003c/li\u003e\n\u003cli\u003eRomero RD, Montero Pardo A, Montaldo HH, Rodr\u0026iacute;guez AD, Hern\u0026aacute;ndez Cer\u0026oacute;n J (2013) Differences in body temperature, cell viability, and HSP-70 concentrations between Pelibuey and Suffolk sheep under heat stress. Trop Anim Health Prod 45:1691-6. https://doi.org/10.1007/s11250-013-0416-1 \u003c/li\u003e\n\u003cli\u003eRout PK, Kaushik R, Ramachandran N (2016) Differential expression pattern of heat shock protein 70 gene in tissues and heat stress phenotypes in goats during peak heat stress period. Cell Stress Chaperones 21(4):645-51. https://doi.org/10.1007/s12192-016-0689-1\u003c/li\u003e\n\u003cli\u003eSingh KM, Singh S, Ganguly I, Ganguly A, Nachiappan RK, Chopra A, Narula HK (2016) Evaluation of Indian sheep breeds of arid zone under heat stress condition. Small Rumin Res 141:113-117. https://doi.org/10.1016/j.smallrumres.2016.07.008 \u003c/li\u003e\n\u003cli\u003eSingh KM, Singh S, Ganguly I, Nachiappan RK, Ganguly A, Venkataramanan R, Chopra A, (2017) Association of heat stress protein 90 and 70 gene polymorphism with adaptability traits in Indian sheep (Ovis aries). Cell Stress Chaperones, 22(5): 675-684. DOI: 10.1007/s12192-017-0770-4 \u003c/li\u003e\n\u003cli\u003eSrikanth K, Kwon A, Lee E, Chung H (2017) Characterization of genes and pathways that respond to heat stress in Holstein calves through transcriptome analysis. Cell Stress and Chaperones. 22(1):29-42. DOI 10.1007/s12192-016-0739-8\u003c/li\u003e\n\u003cli\u003eT\u0026uuml;fekci H, Sejian V (2023) Stress Factors and Their Effects on Productivity in Sheep. Animals, 13(17): 2769. DOI: 10.3390/ani13172769 \u003c/li\u003e\n\u003cli\u003eVan Wettere WH, Kind KL, Gatford KL, Swinbourne AM, Leu ST, Hayman PT, Kelly JM, Weaver AC, Kleemann DO, Walker SK (2021) Review of the impact of heat stress on reproductive performance of sheep. J Animal Sci Biotechnol 12:1-8. https://doi.org/10.1186/s40104-020-00537-z \u003c/li\u003e\n\u003cli\u003eYadav DK, Arora R, Bhatia S, Singh G (2011) Morphological characterization, production and reproduction status of Munjal\u0026ndash;A threatened sheep population of North-West India. Indian J Anim Sci 81(9):943-45.\u003c/li\u003e\n\u003cli\u003eYounis F (2020) Expression pattern of heat shock protein genes in sheep. Mansoura Vet Med J 21(1):1-5. https://doi.org/10.21608/mvmj.2020.21.001 \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"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":"Heat stress, Munjal sheep, rectal temperature, heat tolerance coefficient, adaptation","lastPublishedDoi":"10.21203/rs.3.rs-4423115/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4423115/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"An investigation was carried out to identify the polymorphism of heat shock protein 70 (HSP70) gene and its expression profiling in Munjal sheep. Blood samples from 40 female Munjal sheep were collected for DNA extraction. After standardization of PCR for ovine HSP 70 gene, sequencing was carried out for single nucleotide polymorphism (SNP) profiling. Multiple sequence alignment was performed to screen SNP in the resource population and subsequently, A to G mutation was observed at c.459 position. Two genotypes viz., AG and AA were obtained. Association study revealed no significant association of identified genotypes with growth and thermotolerance traits. HSP70 expression analysis in different seasons was carried out using enzyme-linked immunosorbent assay (ELISA) kits. After blood collection, serum was separated for studying the gene expression and expression analysis of different groups was compared using t-test. Animals were also grouped in to two groups viz., heat stress susceptible (HSS) and heat stress tolerant (HST) based on heat tolerance coefficient (HTC) i.e. HST having HTC\u0026lt;2.40 and HSS with HTC\u0026gt;2.40. Statistical analysis revealed that expression of HSP70 gene varied markedly among the summer and winter season. Furthermore, a significant variation was observed between adults and lambs (P\u0026lt;0.05) and between HSS and HST sheep (P\u0026lt;0.05) for HSP70 gene expression. The expression of HSP70 was higher in adults in comparison to lambs in both summer and winter season. Selection of heat resistant animals is essential for better livestock performance amid challenging climatic conditions ahead and HSPs can be used as candidate markers for selecting resistant animals","manuscriptTitle":"Expression and SNP profiling of HSP70 gene associated with thermotolerance traits in Munjal Sheep","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-07-12 08:54:02","doi":"10.21203/rs.3.rs-4423115/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":"71f56ed9-0f57-4357-8072-d9cf0111cebc","owner":[],"postedDate":"July 12th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-06-05T15:02:13+00:00","versionOfRecord":{"articleIdentity":"rs-4423115","link":"https://doi.org/10.1016/j.jtherbio.2025.104151","journal":{"identity":"journal-of-thermal-biology","isVorOnly":true,"title":"Journal of Thermal Biology"},"publishedOn":"2025-06-05 00:00:00","publishedOnDateReadable":"June 5th, 2025"},"versionCreatedAt":"2024-07-12 08:54:02","video":"","vorDoi":"10.1016/j.jtherbio.2025.104151","vorDoiUrl":"https://doi.org/10.1016/j.jtherbio.2025.104151","workflowStages":[]},"version":"v1","identity":"rs-4423115","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4423115","identity":"rs-4423115","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}