Sheikh Firdous Ahmad

The reliability and accuracy of relative quantification of transcripts through quantitative real-time PCR (qPCR) is dependent on the use of stable reference genes for normalization. However, the expression stability of various reference genes may differ across the tissues and patho-physiological conditions. The present study investigated the stability of multiple candidate reference genes in Japanese quail ( Coturnix japonica ) embryos, subjected to embryonic thermal conditioning (ETC). Fertilized quail eggs ( n = 200) were incubated under standard conditions and heat exposure (ETC) of incubated eggs was undertaken at 39.5 °C for 10 h daily in the embryonic days 6–8 and 12–14, maintaining constant humidity (55%). Four embryos were randomly selected from each group for experimentation. The expression levels of eight candidate reference genes i.e., 18 S rRNA , β-ACTIN , 28 S rRNA , HPRT1 , GAPDH , PGK1 , RPS8 , and TBP were assessed across four tissues (liver, intestine, brain, and heart) under ETC. Gene expression stability was assessed using five different algorithms: geNorm, NormFinder, BestKeeper, Delta Ct, and RefFinder (Comprehensive gene stability). Variations in gene stability were evident across different tissues and algorithms. The HPRT1 , GAPDH and RPS8 emerged as the most stable reference genes for qPCR analyses in liver tissue, making them suitable internal controls. Similarly, PGK1 , RPS8 , and HPRT1 genes exhibited higher expression stability in intestine. On the other hand, TBP and HPRT1 were identified as the most reliable reference genes for normalization in heart tissue, whereas HPRT1 and GAPDH were found to be the most reliable reference genes in brain tissue. On considering all tissues combined (liver, intestine, brain, and heart), TBP and RPS8 emerged as the most suitable gene set for normalization. This present study provides crucial insights into selecting appropriate reference genes for relative expression quantification studies in quail embryos under heat treatment (ETC), thereby improving the reliability and accuracy of qPCR data in avian developmental and stress physiology research.

India possesses a rich diversity of indigenous cattle that are well adapted to varied agro-climatic regions. These populations exhibit remarkable variation in stature (height at withers), ranging from short-statured types such as Vechur, Punganur, Malnad Gidda, and Khariar to tall and heavy breeds like Kankrej, Ongole and other milch breeds (Sahiwal, Gir, etc.). The short-statured breeds offer potential advantages in feed efficiency, disease resilience, and cultural value besides being economical to maintain. However, their genetic basis for stature remains underexplored. This study leverages whole-genome resequencing (WGS) data on short-statured (n = 19) and tall (Kankrej as representative; n = 19) Indian cattle to delineate the copy number variation (CNV) landscape and selection signatures underpinning stature divergence. Post-quality control, CNVs were detected from duplicate-marked bam files using CNVnator with read-depth methodology, filtered (q0 < 0.5, p < 0.01, size 1 kb–5 Mb), and concatenated into CNV regions (CNVRs). Selection signatures were identified using cross population extended haplotype homozygosity (XP-EHH) methodology for inter-population comparison of short-statured cattle with tall cohort. Genes harboured under CNVRs and sweep windows were annotated using GALLO, with functional mining from literature databases. In short-statured cattle, 41,913 CNVs were concatenated into 10,075 CNVRs, with 8.01% genomic coverage. A total of 25 genes were found to be common across two analyses i.e., unique (non-overlapping) CN regions in 70% short-statured individuals and scan of selection signature. Key genes across the analyses included IGF1R (cell proliferation), FGFR3 (skeletal growth), SOX6 (body size), EXT2/LGR4 (bone density), PRKCD (developmental regulation), ADAMTSL2 (extracellular matrix integrity), SLC25A6 (glucose metabolism), and SDHA (energy supply). Unique non-overlapping copy number regions (e.g., 78 regions found in 100% of dwarf individuals) harbored several genes, including ARL13B (osteogenesis), AXIN2 (bone remodeling), CCND2 (myogenesis), and TNNT1 (muscle contraction). This comprehensive CNV map and scan for signatures of selection unveil stature-associated genomic variants, informing conservation strategies for threatened short-statured breeds by enhancing their socio-economic value through targeted breeding. The findings underscore CNVs as pivotal drivers of phenotypic diversity in cattle populations, with implications for livestock genomics and sustainable agriculture.

Microsatellites are highly polymorphic, codominant, and reproducible molecular markers that are fairly evenly distributed across the eukaryotic genomes. Al

Mastitis is a major health and economic threat to the dairy industry, causing massive losses every year worldwide. Staphylococcus aureus is the chief pathogen, responsible for most of the subclinical as well as a considerable portion of the clinical cases. Conventional control strategies, including antibiotic treatment and genetic selection for resistance, have shown limited success due to antimicrobial resistance and low heritability of mastitis resilience traits. This calls for the exploration of novel approaches such as epigenetics, that offers insights into host–pathogen interactions beyond the genetic variations. This review focuses on DNA methylation changes in the mammary gland that occur during S. aureus mastitis. Recent research has identified immune suppression and pathogen persistence in relation with DNA methylation during the disease. The microbe has been reported to alter the methylation status of regulatory regions for many immune genes such as CXCR1 , TNF‐α , IL6R , IL10 , and C3 , resulting in dysregulation of immune responses in the host, thereby facilitating pathogen persistence and chronic infection. Along with its own virulence factors, differential DNA methylation status of such genes during infection helps the pathogen to escape host defence, and decreases the intensity of inflammation. Thus, understanding these mechanisms can open new avenues in the field of disease diagnosis, animal selection, and immunotherapy among others. Such an integrative approach offers a revolution in mastitis control strategies, ensuring better health and productivity in dairy animals. © 2025 Stichting International Foundation for Animal Genetics.

Genetic and phenotypic parameters of traits related to growth, production and reproduction are crucial in the formulation of successful breeding programmes. Given the diversity in the cattle population of India, meta‐analysis offers a comprehensive insight into the overall performance of the crossbred population for the efficient implementation of animal improvement programmes. The present study aimed to undertake meta‐analysis and estimate the genetic and phenotypic parameters of performance traits in Indian crossbred cattle. A total of 130 articles were included in this study after data editing and quality control. The heterogeneity index reached 91.2% for phenotypic parameters, 99.4% for heritability estimates, and 99.4% for phenotypic correlations. Pooled least squares mean ranged from 20.54 to 25.84 kg for body weight at birth, 1542.05 to 2691.44 kg for first lactation milk yield, 996.05 to 1259.38 days for age at first calving in crossbred cattle. Pooled heritability estimates ranged from 0.13 to 0.69 for growth traits, 0.09 to 0.76 for production traits, and 0.05 to 0.25 for reproduction traits. The pooled repeatability estimates were moderate and ranged from 0.22 to 0.42. Pooled genetic correlations were positive and ranged from 0.09 to 0.97. This is the first attempt to estimate the genetic and phenotypic parameters of performance traits in Indian crossbred cattle using a meta‐analytical approach. Meta‐analysis revealed that the heritability estimate of production and reproduction performance was low to moderate in all the crossbred populations. This suggested the influence of environment and other non‐additive genetic effects, wherein improvement is possible through the integration of effective selection and optimum management strategies. Pooled estimates of genetic and phenotypic parameters from this investigation may be used for the effective implementation of breeding programmes in the herds/populations, where these parameters are not estimated/available for any reason.

Mastitis is a major health and economic threat to the dairy industry, causing massive losses every year worldwide. Staphylococcus aureus is the chief pathogen responsible for most of the subclinical as well as a considerable portion of the clinical cases. Conventional control strategies, including antibiotic treatment and genetic selection for resistance, have limited success due to antimicrobial resistance (AMR) and low heritability of mastitis susceptibility. This calls for the exploration of novel approaches like epigenetics, which offers insights into host-pathogen interactions beyond the genetic variations. This review focuses on DNA methylation changes in the mammary gland that occur during S. aureus mastitis. Recent research works have identified immune suppression and pathogen persistence in relation with DNA methylation during the disease. The microbe has been reported to alter the methylation status of regulatory regions for many immune genes like CXCR1, TNF-α, IL6R, IL10, and C3 , resulting in dysregulation of immune responses in the host, and thereby facilitating pathogen persistence and chronic infection. Along with its own virulence factors, differential DNA methylation status of such genes during infection helps the pathogen to escape host defence, and decreases the intensity of inflammation. Thus, understanding these mechanisms can open new avenues in the field of disease detection, animal selection, and immunotherapy among others. Such an integrative approach offers a revolutionization of mastitis control strategies, ensuring better health and productivity in dairy animals.

The present study delves into the relationship between single nucleotide polymorphisms (SNPs) and production performance, employing genome-wide association study (GWAS) approach. A total of 96 randomly selected Vrindavani cows were genotyped with Illumina Bovine 50K BeadChip platform. The study employed a linear regression model within the PLINK program, with an attempt to associate genome-wide SNP markers with key production traits i.e., total lactation milk yield (TLMY), lactation length (LL), and peak yield (PY) across the first three lactations. The study involved mining relevant databases to uncover biological pathways linked to genes and quantitative trait loci (QTLs) affecting production performance of cows. The results revealed 70 SNP markers dispersed across various chromosomes that showed profound impact on the variation in TLMY (21 SNPs), LL (10 SNPs), and PY (39 SNPs). The GWAS approach uncovered novel/ potential candidate genes such as PTPRT, RBMS3, CENPE, IFNT, ESR1, ARMC1, LCORL, MED28, NCAPG, LAP3, MYH9, ITPR2, IFNT, ETV6, PARVB, ARNTL2, and PLA2G12A that showed association with different economic traits. These significant SNPs and genes hold relevance for production traits, besides offering valuable insights into potential biomarkers for enhancing production performance in bovine populations.

The present study was undertaken to elucidate the population structure and differentiation of Indian yak from Chinese and wild cohorts on genome-wide scale by identifying the selection sweeps and genomic basis of their adaptation across different comparisons while analyzing whole genome sequencing (WGS) data using latest bioinformatics tools. The study included 105 individuals from three distinct yak populations i.e., Indian yak (n = 29); Chinese yak (n = 61) and wild yak (n = 15), hypothesized to be related along the evolutionary timescale. Efficient variant calling and quality control in GATK and PLINK programs resulted in around 1 million (1,002,970) high-quality (LD-independent) SNPs with an average genotyping rate of 96.55%. The PCA, ADMIXTURE and TREEMIX analysis revealed stratification of the yak groups into three distinct clusters. The empirical distribution pattern of minor allele frequency (MAF) of SNPs on genome-wide scale was also elucidated for three yak cohorts revealing unique distribution across five different bins. The selection signature analysis revealed candidate genes that are important for the adaptation of Indian yak against harsh environmental conditions in their habitats. Under iHS analysis, several genes were identified to be under selection pressure in Indian yak including ABCA12, EXOC1, JUNB, KLF1, PRDX2, NANOS3, RFX1, RFX2, and CACNG7. On the other hand, across population analysis revealed the genes like NR2F2, OSBPL10, CIDEC, WFIKKN2, ADCY, THSD7A, ADGRB3, TRPC1, VASH2, and ABHD5 to be part of selective sweeps under these comparisons. A total of 53 genes were found common between intra- and inter-population selection signature analysis of Indian yak. Notably, the genes harbouring the SNPs under selection pressure were significant for adaptation traits including lipidogenesis, energy metabolism, thermogenesis, hair follicle formation, oxidation–reduction reactions, hypoxia and reproduction. These genes may be evaluated as candidate genes for livestock adaptation to harsh environmental conditions and to further the research and application in the present era of climate change.

Background Litter size in mice is an important fitness and economic feature that is controlled by several genes and influenced by non-genetic factors too. High positive selection pressure in each generation for Litter size at birth (LSB), resulted in the development of high and low prolific lines of inbred Swiss albino mice (SAM). Despite uniform management conditions, these lines showed variability in LSB across the generation. Methods and results Variation in estrous-phased ovarian gene expression between high (LSB ≥ 12) and low prolific lines (LSB ≤ 3) of F₄ inbred SAM, was explored using RNA-Seq. Estrous phase assessment was done using vaginal cytology. A total of 870 differentially expressed genes (DEGs) were identified; among which, 287 genes were significantly up-regulated while 583 genes were down-regulated in HLS as compared to the LLS group. DEGs were assigned to 166 Gene Ontology (GO) terms and KEGG pathways. In HLS, the significantly up-regulated DEGs were involved in ovarian cell-cell signaling, regulation of biological activity and ovarian metabolic-associated pathways. Most down-regulated DEGs were expressed in immune-related pathways, indicating that immunological dampening is associated with a high ovulation rate and higher level of progesterone concentration leading to physiological changes responsible for higher fecundity. Conclusions The present study, based on bulk RNA-seq analysis reflects the aggregate gene expression of the whole ovarian tissue, and reveals 24 DEGs that could be used as candidates for litter size attributes in future polymorphism and functional studies to gain further insights into the mechanisms underlying litter size variations in animals.