Gyanendra Kumar Gaur

Lameness is an economically significant, production-limiting syndrome that adversely affects the (re)production performance of animals besides deteriorating the quantity and quality aspects of milk in dairy cattle. The present study aimed to explore the potential biomarkers for painful foot lesions in indigenous Tharparkar and crossbred Vrindavani cattle affected with lameness. The differentially expressed genes in lame versus healthy animals were elucidated using microarray analysis and validated them by qRT-PCR. On microarray analysis, 504 genes were differentially expressed in lame crossbred cattle as compared to healthy counterparts. Similarly, 991 genes were differentially expressed in lame crossbred cattle as compared to the healthy Tharparkar animals. Various genes such as BOLA-DQA3, BOLA-DQA1, CCL4, CCR1, CCRL2, CXCL2, CXCL3, CXCL8, IL1A, IL1B, MMP-9 and SLC11A1 were common between both the comparisons (crossbred lame vs. crossbred normal cattle; and crossbred lame vs. normal Tharparkar cattle). The results revealed downregulation of multiple pro-inflammatory cytokines. Validation using qRT-PCR showed high correlation with the microarray results, except for the IRAK1 gene. The functional annotation and gene network analysis revealed involvement of various processes including inflammation, immunology, apoptosis, cell proliferation and cytoskeleton organization. The Ingenuity pathway analysis revealed three inhibited pathways in the comparison between lame and normal (healthy) crossbred cattle i.e., HMGB1-signalling pathway, Aryl hydrocarbon receptor signalling pathway, and Mitotic roles of pol-like kinase. Whereas, on comparison of lame crossbred with healthy Tharparkar cattle, the Situin signalling pathway was inhibited; the LxR/RxR activation pathway was activated. The results from microarray analysis, identifying differential expressed genes provides valuable insights into the development of molecular biomarkers for early detection of lameness-affected animals.

The present study aimed to elucidate the population structure and genetic diversity along with the estimation of genealogical parameters in Vrindavani cattle using pedigree data. The study was based on pedigree data on 12,718 animals, spread across multiple generations during a 52-year period (between 1971 and 2023). The pedigree data was used to estimate different population genealogical parameters including the generation interval; pedigree completeness; rate and level of inbreeding; effective population size; and parameters characterizing the probabilities of gene origin. The ENDOG program was used for estimation of different parameters while using population after 2010 as reference cohort. The results revealed the maximum number of generations (MG) to be 13, while the numbers of completed (CG) and equivalent generations (EqG) were 3.23 and 1.95, respectively. The mean generation interval for the population was 6.9 years. The average inbreeding coefficient of animals in the whole and reference population was 1.11 and 3.44%, respectively; with 0.68% rate of inbreeding per generation. The average additive relationship among all the animals and those in the reference population was 1.16 and 5.49%, respectively. The average effective population sizes for the maximum, equivalent, and complete generations were 115.56, 56.42, and 46.02, respectively. The effective population size on the basis of regression and log-regression on birth date was 77.40 and 71.24, respectively. The probabilities of gene origin were estimated by the effective number of founders (f_e) and ancestors (f_a), which was 115 and 78, respectively. The f_e/f_a ratio in the reference population was 1.20, indicating that occasional bottlenecks may have occurred in the population. The analysis revealed a loss of 5.3% of total heterozygosity as compared to base population, though significant variability exists in the latest generations. The results revealed that considerable genetic variability exists within the population that may be exploited through appropriate breed improvement programs targeting various economic traits.

The present study was aimed to evaluate the influence of non-genetic factors on several first lactation and lifetime performance traits and elucidate their genetic parameters in an organized Vrindavani cattle population. Data on eight first-lactation and thirteen lifetime traits were collected on 2400 cows with pedigree records that were reared during 33-year period (1989–2021). The first-lactation traits included age at first calving (AFC), total milk yield (FTMY), standard milk yield (FSMY305), peak yield (FPY), lactation length (FLL), dry period (FDP), service period (FSP) and calving interval (FCI). Whereas, the lifetime traits mainly included total lifetime milk yield (TLMY), total standard milk yield (TSMY), number of lactations completed (NL), total lactation length (TLL), herd life (HL), productive life (PL), average milk yield per day of herd life (TLMY/HL), average milk yield per day of productive life (TLMY/PL), average milk yield per day of productive life (TLMY/TLL). Other lifetime production traits included average service period (ASP), average dry period (ADP), average calving interval (ACI) and unproductive days (UD). The heritability estimates of first-lactation traits ranged between 0.026 and 0.228 and were found to be low for AFC (0.180 ± 0.042), FCI (0.191 ± 0.125), FSMY305 (0.145 ± 0.061), FTMY (0.165 ± 0.048), FDP (0.052 ± 0.049) and FSP (0.026 ± 0.033); however, FLL (0.229 ± 0.044) and FPY (0.202 ± 0.046) showed moderate heritability. Positive phenotypic correlation (p < 0.001) was revealed among FTMY, TLMY, TLL, HL and PL. The AFC produced a significant effect (p < 0.05) on several traits i,e, TLL, TLMY/HL, FSMY305, FPY, TLMY, HL and TLMY/PL. Lower AFC was associated with higher TLMY, TLL and TLMY/HL; while FSMY305, FPY, HL and TLMY/PL were higher in heifers that calved late in their life. The results revealed that AFC may be optimized with first lactation and lifetime traits for this population.

Artificial neural networks (ANNs) in conjugation with genetic algorithms (GAs) have been demonstrated to be an effective tool for system modelling and optimization in a variety of applications. The current communique is about assessing the capacity of ANN to predict investment on cattle till age at first calving (AFC) and milk production based on the data of 340 Vrindavani crossbreed cattle developed at the ICAR-Indian Veterinary Research Institute in Izatnagar, India. Three distinct artificial neural network (ANN) algorithms, namely, LevenbergMarquardt (LM), Bayesian regularization (BR), and gradient descent momentum with adaptive learning rate backpropagation (GDX) were used to train the ANN infrastructure for determining milk production and investment based on body weight and AFC as input variables. The results showed that BR with 2 hidden layer neurons showed excellent prediction ability (R²=0.999, MSE−6) and was therefore used as an objective function by GA for optimization. The optimized results revealed that higher milk production is achievable at lower investment if the age at first calving is 768days with a body weight of 281kg. The information generated by this investigation will aid in ensuring food security in terms of higher milk production while making the dairy business more sustainable and profitable for the farmers.

Lumpy skin disease (LSD) is a notifiable re-emerging transboundary viral disease of bovines that inflicts heavy losses in affected livestock farms. Genetic variations contribute substantially to the inter-individual differences in the immune response against disease agents. The present study aimed to evaluate the genetic basis of differential immune response in Vrindavani cattle by comparing the hematological, biochemical and cytokine genes’ expression profiles of LSD-affected and unaffected animals. After 21 days of the outbreak at the farm, the animals were grouped as affected (those who developed symptoms) and unaffected/healthy (those who did not). Standard hematological and biochemical parameters were evaluated in both the groups. Expression profiling of important Th1 (IL2, INFG and GMCSF) and Th2 (IL4, IL6 and IL10) cytokines was also performed via a relative quantification approach using real-time PCR. Erythrogram and leucogram analyses revealed significant differences in total leucocyte count (TLC: 14.18 ± 0.74 versus 11.38 ± 0.68 x10³/µL), hemoglobin (Hb: 8.66 ± 0.42 versus 10.84 ± 0.17 g%) and percentage of neutrophils (46.40 ± 1.98 versus 35.40 ± 2.11%), lymphocytes (49.40 ± 1.99 versus 62.40 ± 1.86) and monocytes (4.20 ± 0.37 versus 2.40 ± 0.40) between the affected and healthy animals, respectively. The production of liver enzymes (SGOT and SGPT) was significantly higher in affected animals (74.18 ± 4.76 and 59.51 ± 2.75) when compared to the healthy counterparts (65.95 ± 9.18 and 39.21 ± 3.31). The expression profiling of Th1 and Th2 cytokines revealed significant differences between the two groups, except IL10. The expression of IL2, GMCSF and IL6 were upregulated in healthy animals while that of INFG, IL4 and IL10 were upregulated in LSD-affected animals. The highest abundance was observed for IL2 transcripts in healthy animals among all assessed cytokines with log₂fold change of 1.61 as compared to affected counterparts. Overall, the immune response in healthy animals (after exposure to LSD virus) was predominated by the expression of Th1 cell proliferation and there was an increased production of pro-inflammatory cytokines as compared to the affected counterparts. The results revealed that the effective immune response to LSD in cattle consists of changes in hematological and biochemical parameters and altered expression profile of cytokines with enhanced phagocytosis and lymphocyte recruitment. Furthermore, optimal expression of Th1 cytokines is required for maintaining optimal health against infectious insult with LSD virus in cattle.

Reference gene with stable copy number is essential for normalization in qPCR based copy number assay. Present study aims to identify a suitable reference gene in pigs for qPCR based relative copy number profiling of chromosomal genes. A total of 30 crossbred pigs of both sexes were cyto-screened and gDNA was extracted from the pigs having numerically normal karyotypes. The copy number stability was studied for 7 genes (FSHB, IL4, IGF1R, TCF24, BRMS1L, ARMC1 and SRSF4) selected on the basis of the chromosomal location, reports of single copy and lack of involvement in structural chromosomal abnormalities. The copy number was estimated from Ct values in 3 technical replicates using 6 animals from either sex for each gene. The stability was evaluated from the variations in Ct values using different (Delta Ct, geNorm, BestKeeper and normFinder) algorithms. While the moderate variation was observed among relative copy number stabilities among the genes, comprehensive ranking revealed the most stable gene for normalization (IGF1R > FSHB > TCF24 > IL4 > ARMC1> SRSF4 > BRMS1L) across the samples. The selected reference gene was validated using DNA of cyto-screened pigs to find out ratio of X and Y chromosome fragments using qPCR based copy number analysis.

The present study was undertaken to estimate the (co)variance components and genetic parameters of body weights recorded in Landlly piglets from birth to weaning at weekly intervals (w0 to w6). The data pertained to body weights of 2462 piglets, born to 91 sires and 159 dams across different generations during a 7-year period from 2014 to 2020. Five animal models (I–V), differentiated by inclusion or exclusion of maternal effects with or without covariance between maternal and direct genetic effects, were fitted on the data using the Bayesian algorithm. The analyses were implemented by Gibbs sampling in the BLUPF90 program and Markov chain Monte Carlo (MCMC) methodology was used to draw samples of posterior distribution pertaining to (co)variance components. Based on deviance information criteria (DIC), model V with inclusion of direct additive genetic, direct maternal genetic and permanent environmental effect of dam as random factors along with covariance between direct additive and maternal effects best fitted the data on pre-weaning traits (w0 to w5). Whereas, model I incorporating only the direct additive genetic effect best fitted the weaning weight (w6) data in Landlly piglets. The posterior mean estimates of direct heritability under the best models for W0 to W6 were 0.13, 0.19, 0.29, 0.13, 0.26, 0.32 and 0.46, respectively. Inclusion of the maternal component helped in better partitioning of variance for different body weights in Landlly piglets. The maternal heritability ranged from 0.06 to 0.14, while the litter heritability ranged from 0.11 to 0.15 for pre-weaning weights (W0 to W5) under the best-fit models. The influence of maternal environment was greater than maternal genetic effect from birth to 4th week of age. The results implied that variations in body weight of Landlly pigs were genetically controlled to moderate levels (especially w2 and w4) with contributions from direct additive and maternal genotype that can be exploited by designing efficient breeding programmes.

In the present study, Porcine 60K SNP genotype data from 69 Landlly pigs were used to explore Copy Number Variations (CNVs) across the autosomes. A total of 386 CNVs were identified using Hidden Markov Model (HMM) in PennCNV software, which were subsequently aggregated to 115 CNV regions (CNVRs). Among the total detected CNVRs, 58 gain, 49 were loss type while remaining 8 events were both gain and loss types. Identified CNVRs covered 12.5 Mb (0.55%) of Sus scrofa reference 11.1 genome. Comparison of our results with previous investigations on pigs revealed that approximately 75% CNVRs were novel, which may be due to differences in genetic background, environment and implementation of artificial selection in Landlly pigs. Functional annotation and pathway analysis showed the significant enrichment of 267 well-annotated Sus scrofa genes in CNVRs. These genes were involved in different biological functions like sensory perception, meat quality traits, back fat thickness and immunity. Additionally, KIT and FUT1 were two major genes detected on CNVR in our population. This investigation provided a comprehensive overview of CNV distribution in the Indian porcine genome for the first time, which may be useful for further investigating the association of important quantitative traits in Landlly pigs.

The present investigation was performed to compare the global gene expression profile in peripheral blood mononuclear cells (PBMCs) of Bos indicus and crossbred (Bos taurus × B. indicus) cattle. Previously, several studies revealed the disease tolerance potential of B. indicus cattle but underlying genetic mechanism is still not fully explored. The PBMCs model was used for this investigation as it plays crucial role in the immune system regulation. Transcriptomic analysis revealed total 6767 significantly differentially expressed transcripts (fold change (absolute) >2.0, p < .05). In addition, 4149 transcripts were upregulated, 2618 transcripts were downregulated and fold change (absolute) of differentially expressed transcript varied from −223.32 to 213.63. Functional annotation analysis of differentially expressed genes confirmed their role in various molecular pathways viz. innate immune response, antigen processing and presentation, MHC protein complex, defense response to bacterium, regulation of immune response, positive regulation of JAK-STAT cascade, cytoskeletal protein binding, etc. Protein–protein interaction network analysis provided understanding of inter-relationship of immune genes with differentially expressed genes. In conclusion, this study could provide comprehensive information about the dysregulated genes and biological pathways in PBMCs which might be responsible for disease tolerance in B. indicus cattle.

The aim of the study was to evaluate 16 novel morphometric traits of Landlly piglets at weaning (6 weeks) and post weaning (8 weeks) stage and to predict corresponding body weight from the measurements. A total of 279 Landlly piglets (n = 279, 75% Landrace + 25% Ghurrah crosses) were enrolled in this study. Body length, heart girth, paunch girth, height at wither, height at back, rump width, thigh circumference, neck circumference, and body depth had high correlation coefficients (0.8–0.97) with body weight at both the stages. Stepwise regression showed that body length and heart girth contributed most in prediction of both body weights while height at wither for body weight at 6 weeks and neck circumference for body weight at 8 weeks was the next highest contributing trait. Akaike’s information criterion, Bayesian information criteria, adjusted R², concordance correlation coefficient, bias correction factor, modeling efficiency, and coefficient of model determination were used to determine the most appropriate model for the prediction of body weight. Model containing body length and heart girth was fitted best to data for prediction of body weight at both weaning and post weaning stage with adjusted R² values of 0.94 and 0.96, respectively. Hence, 2 different models were proposed for accurately predicting body weight in Landlly pigs at 6 and 8 weeks.