ArticleTropical animal health and production2026
Saliva-based RT-LAMP assays support heat shock protein 70 as a promising transcript marker for estrus identification in buffaloes.
Article in Tropical animal health and production, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Buffaloes do not exhibit overt estrus signs particularly during summer, leading to a significant economic loss to farmers. Previous studies have identified several candidate transcripts (HSP70, TIMP1, TLR4 and HSD17B1), abundant in buffalo saliva during estrus stage. However, there is no widely applicable technology for estrus detection targeting these transcripts. Therefore, the present study aimed to develop reverse transcription loop mediated isothermal amplification (RT-LAMP) assays for these candidate transcripts using buffalo saliva. Saliva samples were collected from 10 cyclic buffaloes and RT-LAMP assays were optimized for salivary RNA as well as direct saliva. Among the four candidate transcripts, HSP70 showed a statistically significant colour change (p-value = 0.0191) at the estrus stage compared to the diestrus stage. This abundance of HSP70 was also supported in large simulated population datasets (10,000 animals) generated using R. Further, the RT-LAMP assays were tested using direct saliva without RNA isolation, and the colour change in the samples during estrus suggested the feasibility of estrus identification using direct saliva, overcoming the tedious step of RNA isolation. The detection of HSP70 using either direct saliva or salivary RNA indicated its potential as a marker for estrus identification. Similarly, TLR4 appeared to be another potential biomarker for RT-LAMP reaction using direct saliva, but it needs further validation in both RNA and direct saliva samples. Overall, the proof-of-concept on RT-LAMP assays optimized for salivary transcripts in the present study would be useful for estrus identification in tropical production systems following further validation on a larger sample size.
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