ABSTRACT
The milk, meat, skins, and draft power of domestic water buffalo (Bubalus bubalis) provide substantial contributions to the global agricultural economy. The world's water buffalo population is primarily found in Asia, and the buffalo supports more people per capita than any other livestock species. For evaluating the workflow, output rate, and completeness of transcriptome assemblies within and between reference-free (RF) de novo transcriptome and reference-based (RB) datasets, abundant bioinformatics studies have been carried out to date. However, comprehensive documentation of the degree of consistency and variability of the data produced by comparing gene expression levels using these two separate techniques is lacking. In the present study, we assessed the variations in the number of differentially expressed genes (DEGs) attained with RF and RB approaches. In light of this, we conducted a study to identify, annotate, and analyze the genes associated with four economically important traits of buffalo, viz., milk volume, age at first calving, post-partum cyclicity, and feed conversion efficiency. A total of 14,201 and 279 DEGs were identified in RF and RB assemblies. Gene ontology (GO) terms associated with the identified genes were allocated to traits under study. Identified genes improve the knowledge of the underlying mechanism of trait expression in water buffalo which may support improved breeding plans for higher productivity. The empirical findings of this study using RNA-seq data-based assembly may improve the understanding of genetic diversity in relation to buffalo productivity and provide important contributions to answer biological issues regarding the transcriptome of non-model organisms.
ABSTRACT
Machine learning algorithms were employed for predicting the feed conversion efficiency (FCE), using the blood parameters and average daily gain (ADG) as predictor variables in buffalo heifers. It was observed that isotonic regression outperformed other machine learning algorithms used in study. Further, we also achieved the best performance evaluation metrics model with additive regression as the meta learner and isotonic regression as the base learner on 10-fold cross-validation and leaving-one-out cross-validation tests. Further, we created three separate partial least square regression (PLSR) models using all 14 parameters of blood and ADG as independent (explanatory) variables and FCE as the dependent variable, to understand the interactions of blood parameters, ADG with FCE each by inclusion of all FCE values (i), only higher FCE values (negative RFI) (ii), and inclusion of only lower FCE (positive RFI) values (iii). The PLSR model including only the higher FCE values was concluded the best, based on performance evaluation metrics as compared to PLSR models developed by inclusion of the lower FCE values and all types of FCE values. IGF1 and its interactions with the other blood parameters were found highly influential for higher FCE measures. The strength of the estimated interaction effects of the blood parameter in relation to FCE may facilitate understanding of intricate dynamics of blood parameters for growth.
ABSTRACT
Single Nucleotide Polymorphism (SNP) is one of the important molecular markers widely used in animal breeding program for improvement of any desirable genetic traits. Considering this, the present study was carried out to identify, annotate and analyze the SNPs related to four important traits of buffalo viz. milk volume, age at first calving, post-partum cyclicity and feed conversion efficiency. We identified 246,495, 168,202, 74,136 and 194,747 genome-wide SNPs related to mentioned traits, respectively using ddRAD sequencing technique based on 85 samples of Murrah Buffaloes. Distribution of these SNPs were highest (61.69%) and lowest (1.78%) in intron and exon regions, respectively. Under coding regions, the SNPs for the four traits were further classified as synonymous (4697) and non-synonymous (3827). Moreover, Gene Ontology (GO) terms of identified genes assigned to various traits. These characterized SNPs will enhance the knowledge of cellular mechanism for enhancing productivity of water buffalo through molecular breeding.
Subject(s)
Buffaloes/genetics , Polymorphism, Single Nucleotide , Animals , Female , Milk , Molecular Sequence Annotation , Sequence Analysis, DNAABSTRACT
OBJECTIVES: Granulosa cells are associated with steroidogenesis and ovarian function in females. Aims of the study are to understand the effects of gold nanoparticles (AuNP) on steroidogenesis and apoptotic pathway associated genes in buffalo granulosa cells. RESULTS: The AuNP were prepared chemically and thereby characterized by transmission electron microscope (TEM) imaging, absorbance and dynamic light scattering (DLS) measurements for hydrodynamic diameter and zeta potential. The cultured buffalo granulosa cells (BGC) were co-incubated with AuNP in two concentrations (2 × 109 and 2 × 1010 AuNP/ml) for 24 h. Treatment of BGC with AuNP significantly modulated the steroidogenesis associated genes (3ß-Hsd and Cyp19A1) expression and progesterone accumulation in the culture fluid. AuNP affected the apoptotic pathway in BGC by affecting the gene expression of Caspase-3, Bad and Bax. The AuNP did not exert oxidative stress through anti-oxidant induction & lipid peroxidation in the buffalo GC. CONCLUSIONS: AuNP may modulate the endocrine system by having impact on the steroidogenesis pathway and also have the potential to affect apoptotic pathway in a buffalo granulosa cell model.
