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. 2021 May 5;26(4):657–669. doi: 10.1007/s12192-021-01209-1

Delineation of temperature-humidity index (THI) as indicator of heat stress in riverine buffaloes (Bubalus bubalis) of a sub-tropical Indian region

Sofi Imran Ul Umar 1, Dipanjali Konwar 1, Asma Khan 1, Mohammad Altaf Bhat 2, Faizan Javid 2, Rakhshan Jeelani 1, Burhan Nabi 3, Afaq Amin Najar 3, Dhirendra Kumar 4, Biswajit Brahma 1,✉
PMCID: PMC8275759  PMID: 33950472

Abstract

The erstwhile developed temperature-humidity index (THI) has been popularly used to indicate heat stress in dairy cattle and often in buffaloes. However, scientific literature suggests differences in thermotolerance and physiological responses to heat stress between cattle and buffalo. Therefore, THI range used to indicate degree of heat stress (mild, moderate, and severe) in cattle should be recalibrated for indicating heat stress in buffaloes. The present study was carried out to delineate THI range to indicate onset and severity of heat stress in buffaloes based on physiological, biochemical, and expression profiling of heat shock response (HSR) genes in animals at different THI. The result indicated early onset of heat stress in buffaloes as compared to cattle. Physiological and biochemical parameters indicated onset of mild signs of heat stress in buffaloes at THI 68-69. Significant deviation in these parameters was again observed at THI range 73-76. At THI 77-80, the physiological and biochemical responses of animals were further intensified indicating extreme alteration in homeostasis. The in vivo expression profiling of HSR genes indicated that members of Hsp70 gene family are expressed in a temporal pattern over different THIs, whereas expressions of Hsf genes were evident during intense heat stress. Overall, the study established that amplitude of heat shock response and THI range for indicating severity of thermal stress for buffaloes are not in unison to cattle. The study also suggests skin temperature of the poll region could be used as non-invasive tool for monitoring heat stress in dairy buffaloes.

Supplementary Information

The online version contains supplementary material available at 10.1007/s12192-021-01209-1.

Keywords: Heat stress, Buffalo, THI, Infrared thermography, Hsp70, Hsf

Introduction

Indian buffalo (Bubalus bubalis) with more than 57% of world’s population contributes near half of the country’s milk production and one-third of meat export (DADH 2017; APEDA 2018). Buffaloes with inherited advantages such as better feed conversion efficiency, sustainable on poor feed and forage quality, rich nutritive value of milk, better adaptability to harsh environment, and higher disease resistance in comparison to crossbred cows are preferred by livestock farmers in India (Balhara et al. 2017). However, dairy productivity in India is still sub-optimal and climate is one of the limiting factors in dairy bovine production. Buffaloes are homeothermic animals and therefore, when the environmental temperature rises or falls abnormally, the animals are subjected to stress. Tropical to sub-tropical climate with prolonged summer subject animals to heat stress, defined as animal’s inability to dissipate sufficient heat to maintain homoeothermy, caused primarily due to high air temperature but intensified by high humidity, thermal radiation, and metabolic heat. With unprecedented rate in change of earth’s climate in recent decades (Hansen et al. 2010), it has been predicted that heat stress-associated productive and reproductive losses in bovines will be aggravated in near future (Upadhyay et al. 2012).

During the course of evolution, cells have developed highly conserved complex dynamic mechanisms termed as heat shock response (HSR) to environmental insults (Sonna et al. 2002; Collier et al. 2008; Bryantsev et al. 2007; Brown Jr et al. 2010). Cellular changes to heat stress include synthesis of a group of molecular chaperones called heat shock proteins (HSP) and among different classes of HSPs, Hsp70 family play an important role in HSR (Verghese et al. 2012; Kim et al. 2013). Physiological changes in heat-stressed animal include reducing the metabolic heat production through thermoregulatory mechanisms, which in turn affects feed conversion efficiency and leads to decreased milk production. Inability of dairy animals to adapt these stressors can result in reduction in feed consumption rate, milk production, and reproductive success rate (Collier et al. 2008).

A variety of indices were used to estimate the degree of heat stress affecting dairy animals. The temperature-humidity index (THI), first proposed by Thom (1958), has been extensively applied for assessing heat stress in moderate to hot conditions. However, there are no unified critical thresholds of THI for delineating different degrees of heat stress (Du Preez et al. 1990; Brown-Brandl et al. 2003; Broucek et al. 2009). It appears that the threshold of THI describing different degrees of heat stress is dependent upon geographical location as well as breed (Gaughan et al. 1999), age (Gebremedhin et al. 1981), and physiological status (Kolacz and Dobrzański 2006) of animal. The physiology of heat exchange in buffaloes is different from cattle. Dark skin, sparse hair coat, a less efficient evaporative cooling system, and poor sweating ability render buffaloes more prone to distress when exposed to direct solar radiation or during hot weather. Therefore, the heat exchange rate and sensitivity to heat stress in buffaloes would be different as compared to cattle. This necessitates that the THI used for cattle should be recalibrated for measuring heat stress in buffaloes. The present study was undertaken with the objectives of identifying the accurate THI ranges, based on physiological, biochemical, and HSR gene expressions, to indicate onset and severity of heat stress in buffaloes.

Materials and methods

Animals and sampling

The study was carried out at Jammu, India (32.7266° N, 74.85707° E, 300 m MSL) having a predominant sub-tropical climate. The summer season in the region has typically two spells i.e. dry-hot period (Mar-Jun) and hot-humid period (Jul-Sep) with the environmental temperature reaching as high as 46°C. All the animal experiments were approved by the Institutional Animal Ethics Committee (IEAC). Random sampling of 96 Murrah and graded Murrah buffaloes (Murrah × non-descript) in their second to fifth parity was done from different households and private dairy farms of the region. The sampled animals were reared under semi-intensive system in which animals were stall fed, allowed to graze (6-7 h/day) and were provided with ad-lib water. All the observations on animals and sample collection were done under natural environment. The sampling spanned over 1 year (Aug 2018-Sep 2019) covering a THI range 65-80 (16 intervals). Prior to actual sample collection, weather information from web-based weather forecasting services (https://www.accuweather.com; https://www.timeanddate.com; https://www.weather.com) was used as forecast guide about possible temperature and relative humidity (RH) of collection site. For each THI interval, a minimum of six samples were taken from six different and randomly selected animals and parameters were recorded. Actual THI at time of sample collection was calculated from real-time meteorological data recorded in weather data logger and wireless data monitoring system (Testo-Saveris, Testo SE & Co. KGaA, Germany). THI was calculated from environmental variables using the following equation (NRC 1971):

THI=1.8×Tdb+32−0.55−0.0055×RH×1.8×Tdb−26.8

where Tdb = dry bulb temperature (°C); RH = relative humidity (%).

Measurement of physiological parameters

On sampling days, physiological parameters of experimental animals were recorded before blood collection. Respiration rate (breaths/min) was recorded by visual observations of the flank movement. Pulse rate (beats/min) was recorded by a pulse oximeter (DRE Waveline Nano V2, DRE, KY, USA). Rectal temperature (°C) was recorded with a clinical thermometer. Infrared thermography for recording skin temperatures of different regions of animals was done through a thermal imager (Testo 875-1i Thermal Imager; Testo SE & Co. KGaA, Germany) as per methodology described earlier (Jeelani et al. 2019). Briefly, thermal images of animals were captured at a distance of 1.5 m from frontal, later, and dorsal planes using the laser point guide of the imager. The thermal images were adjusted to an emittance coefficient equal to 0.97 and analyzed by the IR-Soft software (Testo SE & Co. KGaA, Germany). Different regions of the animal body were marked with the polygonal selection tool of the software. Average temperature, temperature profile histogram, hot spot, and cold spot of the corresponding area were then analyzed with the software.

Measurement of hematological and biochemical parameters

Following proper restraining, blood (10 ml) samples were collected from jugular veins of animals in vacutainers containing either EDTA or sodium fluoride (for estimation of blood glucose) as anticoagulant and were transported to the laboratory under refrigeration. Hematological parameters (hemoglobin (Hb%), packed cell volume (PCV%), and total leucocyte count (TLC)) and blood glucose (g/ml) were measured in a semi-automated blood analyzer (Shenzhen Mind Ray Biomedical Electronics Ltd). For estimation of different biochemical parameters, plasma was collected through centrifugation (1500g × 15 min) of 5 ml blood and stored at −20°C until further analysis. Estimation of oxidative markers (superoxide dismutase (SOD) and glutathione peroxidase (GPX)) was performed within 24 h or immediately after sample collection by the previously described methods (Marklund and Marklund 1974; Hafeman et al. 1974). Concentration of plasma sodium, potassium, and chloride concentrations and activities of aspartate aminotransferase (AST) and alanine aminotransferase (ALT) were measured in a semi-automated chemistry analyzer (Medsource Ozone Biomedicals Pvt. Ltd., HR, India) using commercially available kits (Beacon Diagnostic Pvt Ltd., India; Erba Diagnostics, GmbH, Germany). Cortisol concentration was determined by ELISA method using commercial kit (BT Lab, Shanghai, China).

RNA isolation

Total RNA from peripheral blood mononuclear cells (PBMCs) was isolated by a modified trizol method. Briefly, the PBMC-enriched buffy coat from whole blood was isolated by density centrifugation (1500g for 30 min) by layering whole blood on Histopaque-1077 (Sigma-Aldrich, MO, USA). The PBMC-enriched interphase fraction was aspirated and washed twice with DPBS. The pellet was resuspended by gentle tapping and 500 μl trizol was added to this cell suspension and stored at −20°C until further processing. For RNA isolation, 200 μl chloroform was added to this 500 μl trizol containing lysed PBMCs. The suspension was uniformly mixed and then centrifuged (10,000g for 10 min) for phase separation. The aqueous phase at the top layer was carefully aspirated in a microcentrifuge tube and 0.8 volume chilled isopropanol (Sigma-Aldrich, MO, USA) was added to this. The solution was transferred to an RNA binding column (Zymo Research, CA, USA) and kept for 2 min. to allow binding of RNA to silica column. The column with collection tube was centrifuged at 5000g for 2 min and RNA bound to silica pads were washed twice with chilled ethanol (80%). Residual ethanol was removed by centrifugation (12,000g for 5 min) and RNA was eluted in 20 μl of diethyl pyrocarbonate (DEPC)-treated water. The integrity of the RNA was checked by agarose gel electrophoresis (1.5%). The RNA was stored at −20°C until further analysis.

End point and real-time PCR

Previously designed primers of HspA1A, HspA1L, HspA6, HspA8, HspA2, Hsf1, and Hsf4 genes were used for relative quantification of Hsp70 and Hsf genes (Jeelani et al. 2019; Supplementary Information, Table S1). The specificity of these primers was tested by end point PCR. Each PCR reaction cocktail contained 2.5 μl 10× buffer, 200 μM of dNTPs, 0.5 μl of each primer (10 pM/μl), 0.5 U of Taq DNA polymerase, about 100 ng of template cDNA, and nuclease free water to bring the total volume to 25 μl. Thermal cycling parameters were optimized for different genes with a touchdown protocol. The PCR products were resolved on a 2.0% agarose gel.

All real-time PCR (qRT-PCR) reactions were performed on a CFX Connect Real-Time PCR machine (Bio-Rad Laboratories, Inc., CA, USA). Each reaction consisted of 2 μl cDNA template, 5 μl of 2× SYBR Green PCR Master Mix, 0.25 μl each of forward and reverse primers (10 pmol/μl), and nuclease free water for a final volume of 10 μl. At least six biological replicates and two technical replicates were included in each analysis. Expression data of RPS18 gene was used for normalization and that of GAPDH as calibration of HSR gene expression data. Analysis of real-time PCR (qRT-PCR) was performed by delta-delta-Ct (−ΔΔCt) method (Livak and Schmittgen 2001).

Statistical analysis

Statistical analyses were carried out in the SYSTAT v12.02 software (Systat Software, Inc., CA, USA). Analysis of variance (ANOVA) (Snedecor and Cochran 1994) was used to test between group intervals. Linear regression analysis was performed using THI as independent and physiological, hemato-biochemical, hormonal parameters and skin temperature of different body regions as dependent variables. Post hoc analysis of pairwise comparison of factors having significant (p < 0.05) effect was carried out using Fischer’s restricted least significant differences (LSD). Bivariate correlation analysis was carried out for measurement of Pearson correlation coefficient, and two-tailed test of significance was used to find significance level of correlation.

Result

Effect of THI on physiological parameters

The major objectives of this study were to assess the differences in thresholds of THI as indicator of heat stress in buffalo from that of cattle and delineate the THI ranges for different degrees of heat stress (mild, moderate, and severe) in buffaloes. To analyze this, data on animals’ physiological, biochemical, and HSR gene expressions were recorded at different THI (65-80). Among physiological parameters, THI had significant (p < 0.05) effect on rectal temperature (RT), respiration rate (RR), and pulse rate (PR) of experimental animals (Fig. 1). RT increased progressively in response to incremental THI and showed first significant (p < 0.05) deviation from normal at THI 69 (Fig. 1a). THI-associated changes in RT followed a phasic pattern (Supplementary Information, Figure S1). For example, there was a linear and significant (p < 0.05) increase of RT within the THI range of 69-73. However, increase in RT was minimal (p > 0.05) in THI range 74-77 and showed significant (p < 0.05) increase after THI reached 78. Statistical analysis revealed significant (p < 0.01) positive correlation (r2 = 0.920) and high dependence (R = 0.856) of RT with the THI.

Fig. 1.

Fig. 1

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Effect of different THI on rectal temperature (RT), pulse rate (PR), and respiration rate (RR) of experimental animals

Similar to core body temperature, PR also followed a phase-wise increase in response to increased THI. First significant (p < 0.05) rise of PR was at THI 69 (Fig. 1b) and thereafter, at THI 73, THI 77, and THI 80. Increase in PR within THI ranges 69-72, 73-76, and 77-79 was non-significant (p > 0.05). High and significant correlation (p < 0.01, r2 = 0.898) was found between PR and THI. Regression analysis showed that increase in PR had high dependence on increasing THI (R = 0.840). The RR was significantly (p < 0.05) affected by the THI in experimental animals. The first significant (p < 0.05) increase of RR was at THI 71 (Fig. 1c). The rise of RR was non-significant (p > 0.05) within the THI ranges 65-70, 71-73, and 74-78 indicating RR also followed the phase-wise increase albeit THI thresholds were different to that of RT and PR. RR had significant correlation (p < 0.01; r2 = 0.825) with THI.

THI had significant effect on skin temperature of different body regions of the experimental animals (Fig. 2). Skin temperature of the head region showed progressive increase in response to incremental THI and showed significant (p < 0.01) rise at THI 68 and thereafter at THI 73-74 and at THI 78 (Fig. 2a). Within the THI ranges of 68-72 and 74-77, increase in skin temperature of the head region was minimal and non-significant (p > 0.05). Head skin temperature had high dependence and significant correlation with the THI (Supplementary Information, Table S2). The statistical analysis also revealed significant correlation between RT and skin temperature of the head region (Supplementary Information, Figure S2). For majority of the animals, the poll in the head region was the point where maximum temperature was recorded. THI had significant (p < 0.05) effect on skin temperature of the neck region in experimental animals (Fig. 2b). THI also had significant (p < 0.05) effect on skin temperature of other body regions that showed first significant increase at THI 68, THI 69, and THI 70 for the neck, chest, and abdomen regions, respectively. However, increase in skin temperature of these regions followed a non-linear and undulant pattern with the increase of THI (Fig. 2b-d). Correlation of skin temperature of these regions showed moderate correlation with THI (Supplementary Information, Figure S2). THI-mediated changes in skin temperature of the head, neck, chest, and abdomen regions were also obvious in respective temperature histograms (Supplementary Information, Figure S3).

Fig. 2.

Fig. 2

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Effect of different THI on surface temperature hotspot of different body regions of experimental animals

Effect of THI on hematology and biochemical parameters

Hematological parameters of experimental animals were significantly affected by increase in THI (Fig. 3). First significant deviance of Hb (%), TLC, and PCV (%) was observed at THI 69-70 that steadily and significantly (p < 0.05) increased with rise of THI. Correlation and regression analysis showed significant association of hematological parameters with THI (Supplementary Information; Table S2).

Fig. 3.

Fig. 3

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Effect of different THI on hematological parameters of experimental animals

Wide categories of biochemical responses, viz. electrolyte concentrations (Na+, K+, and Cl−), blood glucose, oxidative stress indicators (SOD and GPX), and enzymes and hormones (AST, ALT, and cortisol) of experimental animals, were considered for heat stress-mediated responses of experimental animals. The THI had significant (p < 0.05) effect on plasma electrolyte concentrations in experimental animals. The level of plasma Na+ concentration decreased significantly (p < 0.05) first at THI 69 (Fig. 4a) and continued to decline further in THI range 69-73; there was a linear and significant (p < 0.05) decrease of plasma Na+ concentration. Decrease in plasma Na+ concentration was minimal and non-significant (p > 0.05) in THI 74-77. THI beyond 77 again led significant (p < 0.05) decrease in Na+ concentration. Plasma K+ level showed a slightly different trend in that the concentration first increased at THI range 65-68 (significant increase at THI 68) and then continued declining in THI range 69-80 (Fig. 4b). Plasma Cl− concentration showed a similar pattern to that of K+ concentration that increased from THI 65 to 68 and then decreased in response to incremental THI (Fig. 4c).

Fig. 4.

Fig. 4

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Effect of different THI on blood electrolyte concentrations of experimental animals

The THI had significant (p < 0.05) effect on blood glucose level in experimental animals. Blood glucose level showed progressive decrease in response to incremental THI (Fig. 5a). It showed first significant (p < 0.05) deviation from normal at THI value of 70. Within the THI range of 70-73, there was a non-significant (p > 0.05) decrease of blood glucose level. At THI >74, there was a steady decline in blood glucose level that reached minimal at THI 80. Plasma AST and ALT activities maintained a converse relationship with increase in THI (Fig. 5b-c). Compared to other biochemical parameters, plasma AST and ALT levels were relatively stable in the THI range 65-72. Thereafter, AST level decreased but ALT level increased significantly (p < 0.05) in response to increased THI (>73). First significant (p < 0.05) increase of plasma cortisol was at THI 69 that increased progressively in response to incremental THI and followed a reverse pattern to that of blood glucose (Fig. 5d).

Fig. 5.

Fig. 5

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Effect of different THI on biochemical parameters of experimental animals

THI had significant (p < 0.05) effect on oxidative stress parameters of experimental animals. SOD level showed first significant (p < 0.01) decrease at THI 69 and continued to decrease within the THI range 69-73 (Fig. 5e). SOD level was relatively stable in THI 74-77 but again decreased (p < 0.05) at THI >77. GPX level decreased significantly at THI 70 and followed a similar progressive decrease like SOD in response to incremental THI with an intermittent and transient stability in THI range 74-78 (Fig. 5f).

Statistical analysis revealed significant (p < 0.05) high correlation of THI and hematological (Hb%, PCV%, TLC) and biochemical (glucose, cortisol) parameters. Regression analysis showed high dependence of these parameters on THI (Supplementary Information Table S2).

Effect of THI on HSR gene expression levels

The relative fold change of mRNA expression levels of Hsp70 and Hsf genes at different THI intervals was done using RPS18 and GAPDH as housekeeping genes (Fig. 6). THI had significant (p < 0.05) effect on HspA2 expression and upregulation of mRNA was observed at THI 77, 79, and 80. Significant (p < 0.05) upregulation of HspA1A and HspA1L mRNA was observed at THI 69 and then again at THI 77. The significant (p < 0.05) upregulation of HspA8 mRNA expression was found at THI 72, 77, and 80. Among Hsf genes, significant (p < 0.05) upregulation of Hsf1 mRNA was observed at THI 77 and 80. A significant (p < 0.05) increase in Hsf4 mRNA expression was observed at THI 77.

Fig. 6.

Fig. 6

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Relative mRNA expression levels of heat shock responsive (HSR) genes at different THI

Discussion

Deleterious effects of heat stress on the production and health of dairy animals including buffalo are well documented (Marai and Haeeb 2010; Dash et al. 2016). Accurate detection of onset of heat stress, well in time, is imperative for effecting ameliorative measures for reduction of economic losses and animal welfare. Buffalo is an important species for heat stress study in that it contributes more than half of total milk production of India, and has less efficient heat dissipation mechanism rendering it more susceptible to adverse effects of thermal stress. Based on concurrent literature (Armstrong 1994; De Rensis et al. 2015; Polsky et al. 2017), we hypothesized that onset and severity of heat stress could vary in dairy animals depending on species, breed, geographical location, and etc. and hence, heat stress indicator such as THI thresholds should be recalibrated for different categories. Furthermore, we contended behavioral signs as late indicators of heat stress and investigated physiological, biochemical, and gene expression studies for precise delineation of THI thresholds and identification of early and preferably non-invasive indicators of heat stress in buffaloes.

Physiological parameters such as core body temperature (RT), RR, and PR showed significant association with incremental heat loads in buffaloes as previously reported (Vaidya et al. 2010; Bhan et al. 2012; Chaudhary et al. 2015; Kumar et al. 2018; Shenhe et al. 2018). However, the analysis in the study revealed some additional observations. First, the change in these physiological characters in response to increased THI is non-linear and with intermediary zones showing a state of stabilization of these parameters. We previously had reported this phasic pattern of increase of vital parameters with incremental THI for crossbred cattle (Jeelani et al. 2019). Core body temperature, pulse rate, and respiration rate, besides important components of thermoregulatory mechanisms, are also indicators of homeostasis. Evidences suggest that adaptive thermoregulatory changes to heat stress occur in a temporal phasic pattern (Horowitz et al. 1996; Hahn 1999; Horowitz 2002). Initial heat acclimation involves cellular signaling pathways (Horowitz et al. 1996) and physiological responses (Gaughan et al. 2010) to restore cellular homeostasis at a new phase. This acclimatization homeostasis is transient and again disrupted when THI exceeds certain upper threshold where heat acclimation is characterized by reprogrammed gene expression and cellular response resulting in enhanced efficiency of signaling pathways and metabolic processes (Horowitz 2001; Horowitz 2002). This phase is largely controlled by endocrine changes and heat shock proteins (Maloyan and Horowitz 2002). Overall, this suggests changes in RT, RR, and PR followed a biphasic pattern where these parameters as part of thermoregulatory mechanism undergone changes during acclimation period (initial and late) to heat stress but maintained intermediary stabilization states during transient homeostasis during acclimatization. Based on result, a primary delineation of THI phases would be <68, 68-72, 73-76, and 77-80 for dynamic responses of RT, RR, and PR in this study. Another important observation in this study was pattern of change in skin temperature of animals, particularly of the head region, in response to increased THI. The skin has multiple roles in thermoregulation including sensory reception of thermal changes, thermogenesis, and heat loss by radiation and evaporative mechanisms (Singh et al. 2013). Anatomical and physiological properties of the skin, viz. density of arteriovenous anastomoses, hair and skin coat color, thickness, and hair density, contribute to the effectiveness of heat loss (Finch et al. 1984; Gebremedhin et al. 2010). We previously had reported the dependence of skin temperature on increased THI in crossbred cattle (Jeelani et al. 2019); however, there exist major anatomical and physical differences of the skin between cattle and buffalo. Dark coat color of buffalo absorbs greater solar radiation while lesser density of the sweat glands and thick epidermis reduces the capacity of cutaneous evaporation (Koga et al. 2004). Nonetheless, high dependence of skin temperature on THI in the present study suggests key role of the skin in thermoregulatory mechanism of buffalo. High correlation of skin temperature of the poll region with core body temperature (RT) and THI has implication of utilizing poll skin temperature as non-invasive and telemetric means for monitoring heat stress in animals.

Assessment of hematological parameters suggested hemoconcentration in animals with increase of THI. Numerous studies had reported the summer season and THI mediated hemoconcentration in buffaloes, apparently as a result of water loss from the body (Chaudhary et al. 2015; Haque et al. 2013). Increased water loss also could elicit blood electrolyte equilibrium (Kumar et al. 2010; Wankar et al. 2014) as experienced in the study. Increased excretion of Na+, K+, and Cl− in urine, sweat, and other secretions (Kumar et al. 2010) along with reduced electrolyte absorption (Wankar et al. 2014) could decrease plasma concentration of these electrolytes during heat stress. The initial increase in plasma Cl− concentration with increased THI could be associated with low base in blood and extracellular fluid (Korde et al. 2007), respiratory alkalosis, and renal excretion of base (Chaiyabutr et al. 1990). The reports on effect of heat stress on serum AST level are inconclusive as certain studies indicated declined (Ronchi et al. 1999; Srikandakumar and Johnson 2004) or no change in AST activity (Chaudhary et al. 2015), while others (Bhan et al. 2012; Wankar et al. 2014) reported increased ALT activities. It has been suggested that heat stress induced increased ALT activity as a result of slowdown of the liver function activity (Srikandakumar and Johnson 2004) and not attributed to leakage (liver damage). Heat stress-induced hypoglycemia has been reported in other studies as well (O’Brien et al. 2010; Das et al. 2016; Gao et al. 2017). Heat stress is associated with increased blood insulin activity (Rhoads et al. 2009; Baumgard and Rhoads 2013) leading to increased whole-body glucose utilization in different species (Baumgard et al. 2011; Okoruwa 2014; Victoria Sanz Fernandez et al. 2015). Activation of hypothalamo-pituitary-adrenal axis leading to increased cortisol concentration is a well-studied phenomenon (Kumar et al. 2010, Aggarwal and Singh 2010, Wankar et al. 2014, Chaudhary et al. 2015) in response to thermal stress. Oxidative stress is another detrimental consequence to heat stress that leads to mobilization of cellular antioxidant defenses (SOD, GPX) to scavenge and detoxify free radicals (Belhadj Slimen et al. 2016). Decrease in basal SOD and GPX levels in response to incremental THI indicates continuing engagement of anti-oxidative defenses, as suggested studies (Bhat et al. 2008; Kumar et al. 2010; Belhadj Slimen et al. 2016). It is noteworthy that majority of the previous studies had considered two or three contrasting environmental conditions and compared these parameters for comfortable and stressed environments. The present study, however, included a continuous range of environmental conditions from comfort zone to extreme heat stress and revealed the exact pattern of changes in physiological and biochemical parameters of the animals. The important information obtained from the study is that the physiological and biochemical responses of animals to incremental heat load occur in phases i.e. acclimation stage where values of these parameters change as adaptive measure to shift homeostasis to a new level followed by a transient stabilization state until the heat threshold crosses limits where the cycle is repeated. The results of the present study suggest adaptive changes to heat stress in buffaloes occur in at least three phases albeit there were minor variation in THI thresholds for delineation of phases for different parameters. Perceptive changes in physiological and biochemical parameters in response to heat stress start at THI 68 (THI range 68-70 for majority of the parameters) and continued until THI 72 (THI range 72-74). This was followed by a state of stabilization, where changes in these parameters were nominal. This stable phase was again transient and when THI crossed 77, the parameters changed again. To summarize, physiological and biochemical responses of buffalo to incremental heat stress are not linear and follow a phasic pattern with recurrent and transient stabilization stages.

The protective role of Hsp70 as molecular chaperon against deleterious effects of thermal stress has been recognized in livestock species including goats (Dangi et al. 2014), buffalo (Kishore et al. 2014), sheep (Romero et al. 2013), and cattle (Mishra and Palai 2014). Hsp70 genes are expressed constitutively and are induced as a part of heat shock response contributing to thermal stress tolerance (Leppa and Sistonen 1997; Hansen 2004). Heat stress-mediated induced expression of Hsp70 in buffaloes has been reported by previous studies (Mishra et al. 2011; Manjari et al. 2015; Kumar et al. 2015; Kumar et al. 2019). However, these studies have reported seasonal changes in Hsp70 expression pattern where differences in climatic conditions (THI) at collection points were wide. Also, majority of these studies considered Hsp70 as single gene while the concurrent literature suggests it is a family with at least 10 member genes in bovines (UCSC genome bowser, bosTau9 assembly). Our previous study with cattle suggests expression patterns of member genes of Hsp70 in response to heat stress are not uniform (Jeelani et al. 2019). In the present study, we focused on monitoring expression patterns of major Hsp70 genes and their regulators (Hsf genes; Akerfelt et al. 2007) over a continually changing THI ranging from comfort zone to severe heat stress. HSR genes in response to increased THI showed a temporal expression pattern i.e. no progressive increase but upregulation over certain (range) of THI followed by returning to basal level and increase again when THI crossed certain threshold. Among Hsp70 genes, expressions of HspA1A and HspA1L were predominant over the studied THI range with intermittent pauses. Expression of HspA2 was evident at later stages of thermal stress when THI exceeded 77. In cattle, we suggested HspA8 as an important indicator of heat stress that showed a gradual increase over incremental THI (Jeelani et al. 2019). In buffalo, however, its expression followed a biphasic pattern with upregulation over certain THIs that interestingly coincided with phase shift of important physiological parameters (Supplementary Information, Figure S4). Expression of Hsf genes was evident only after THI reached 77. In mammalians, expression of Hsp70 genes is tightly regulated (Deka and Saha 2018) with Hsp70 itself as one key regulator of expression via a feedback loop (Åkerfelt et al. 2010). Higher accumulation of Hsp70 in the cell interacts with transactivation domain of Hsf1 (another important regulator of Hsp70 expression) preventing its trimerization, required for transcription of Hsp70 genes (Åkerfelt et al. 2010). Also, regulation of Hsp70 expression is diverse in diverse cell types depending on the metabolic (Miyata et al. 2012) transcriptome and proteome state of the cell (Minsky and Roeder 2015; Xu et al. 2016). To summarize, the present study suggested important role of Hsp70 genes in thermal stress of buffalo. However, further study is warranted for finer dissection of exact role of different members of Hsp70 genes in different stages of heat stress.

To conclude, physiological and biochemical parameters and HSR gene expression profile suggest that onset of heat stress ensues at around THI 68 in buffaloes. There exists variation in THI threshold to heat stress depending on species, breed, and geographical locations; however, our previous and present studies indicate buffaloes are susceptible to heat stress sooner than cattle. Based on findings of the study, a primary delineation of THI as indicator of heat stress in buffalo would be THI 68-72 as mild heat stress, THI 73-76 as moderate stress, and THI ≥77 for severe heat stress. Significant and high correlation of head skin temperature with core body temperature implies that the former could be a reliable non-invasive monitoring tool for prediction of heat stress in buffaloes.

Supplementary information

ESM 1 (506KB, pdf)

(PDF 506 kb)

Abbreviations

HSF

Heat shock factor

HSP

Heat shock protein

HSR

Heat shock response

PBMCs

Peripheral blood mononuclear cells

THI

Temperature-humidity index

Funding

The study was supported by funding from Science and Engineering Research Board, Department of Science and Technology, Government of India (Grant No.: EMR/2016/002845/AS).

Footnotes

Publisher’s note

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