Comparative expression analysis of water buffalo (Bubalus bubalis) to identify genes associated with economically important traits

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.

[1]  Jun-Mo Kim,et al.  Vetinformatics from functional genomics to drug discovery: Insights into decoding complex molecular mechanisms of livestock systems in veterinary science , 2022, Frontiers in Veterinary Science.

[2]  Y. Chamba,et al.  Transcriptomics-Based Study of Differentially Expressed Genes Related to Fat Deposition in Tibetan and Yorkshire Pigs , 2022, Frontiers in Veterinary Science.

[3]  V. Pedrosa,et al.  Identification of novel candidate genes for age at first calving in Nellore cows using a SNP chip specifically developed for Bos taurus indicus cattle. , 2021, Theriogenology.

[4]  K. K. Chaturvedi,et al.  SNPRBb: economically important trait specific SNP resources of buffalo (Bubalus bubalis) , 2021, Conservation Genetics Resources.

[5]  A. Kumaresan,et al.  Supplementation of a combination of herbs improves immunity, uterine cleansing and facilitate early resumption of ovarian cyclicity: A study on post-partum dairy buffaloes. , 2021, Journal of ethnopharmacology.

[6]  Emalie J. Clement,et al.  Ctdp1 deficiency leads to early embryonic lethality in mice and defects in cell cycle progression in MEFs , 2021, Biology Open.

[7]  Toshiko Tanaka,et al.  Differentially expressed genes reflect disease-induced rather than disease-causing changes in the transcriptome , 2020, Nature Communications.

[8]  K. K. Chaturvedi,et al.  Inferring Relationship of Blood Metabolic Changes and Average Daily Gain With Feed Conversion Efficiency in Murrah Heifers: Machine Learning Approach , 2020, Frontiers in Veterinary Science.

[9]  Wei Cui,et al.  Identification of transcriptome differences in goat ovaries at the follicular phase and the luteal phase using an RNA-Seq method. , 2020, Theriogenology.

[10]  K. K. Chaturvedi,et al.  Identification and characterization of trait-specific SNPs using ddRAD sequencing in water buffalo. , 2020, Genomics.

[11]  I. D. Gupta,et al.  Genomewide identification and annotation of SNPs in Bubalus bubalis. , 2019, Genomics.

[12]  Daniel Jordan de Abreu Santos,et al.  Genome-wide association study applied to type traits related to milk yield in water buffaloes (Bubalus bubalis). , 2019, Journal of dairy science.

[13]  Y. Zhou,et al.  Systematic analyses for candidate genes of milk production traits in water buffalo (Bubalus Bubalis). , 2019, Animal genetics.

[14]  A. Liang,et al.  Identifying Hub Genes for Heat Tolerance in Water Buffalo (Bubalus bubalis) Using Transcriptome Data , 2019, Front. Genet..

[15]  Xiaoya Ma,et al.  Integrative Analysis of Transcriptome and GWAS Data to Identify the Hub Genes Associated With Milk Yield Trait in Buffalo , 2019, Front. Genet..

[16]  G. Oikonomou,et al.  Associations between age at first calving and subsequent lactation performance in UK Holstein and Holstein-Friesian dairy cows , 2018, PloS one.

[17]  M. Crowe,et al.  Integrated ovarian mRNA and miRNA transcriptome profiling characterizes the genetic basis of prolificacy traits in sheep (Ovis aries) , 2018, BMC Genomics.

[18]  Joshua B. Singer,et al.  Fundamental properties of the mammalian innate immune system revealed by multispecies comparison of type I interferon responses , 2017, PLoS biology.

[19]  F. Schenkel,et al.  Genome-wide association studies and genomic prediction of breeding values for calving performance and body conformation traits in Holstein cattle , 2017, Genetics Selection Evolution.

[20]  Jihong Pan,et al.  Interferon-stimulated gene 20-kDa protein (ISG20) in infection and disease: Review and outlook. , 2017, Intractable & rare diseases research.

[21]  S. Pyne,et al.  Sphingosine 1-Phosphate Receptor 1 Signaling in Mammalian Cells , 2017, Molecules.

[22]  K. K. Chaturvedi,et al.  Prediction of novel putative miRNAs and their targets in buffalo , 2017, The Indian Journal of Animal Sciences.

[23]  F. Ferrè,et al.  RNA-Sequencing for profiling goat milk transcriptome in colostrum and mature milk , 2016, BMC Veterinary Research.

[24]  P. Stothard,et al.  Tissues, Metabolic Pathways and Genes of Key Importance in Lactating Dairy Cattle , 2016, Springer Science Reviews.

[25]  M. Goddard,et al.  Genetics of complex traits: prediction of phenotype, identification of causal polymorphisms and genetic architecture , 2016, Proceedings of the Royal Society B: Biological Sciences.

[26]  R. Alex,et al.  Characterization and validation of point mutation in Exon 19 of Calcium Channel, voltage-dependent, Alpha-2/Delta subunit 1(CACNA2D1)gene and its relationship with mastitis traits in Sahiwal , 2016 .

[27]  M. Mukesh,et al.  Genetic diversity analysis of buffalo fatty acid synthase (FASN) gene and its differential expression among bovines. , 2016, Gene.

[28]  S. M. Nahas,et al.  AcuI identifies water buffalo CSN3 genotypes by RFLP analysis , 2015, Journal of Genetics.

[29]  A. Reverter,et al.  Systems Biology Analysis Merging Phenotype, Metabolomic and Genomic Data Identifies Non-SMC Condensin I Complex, Subunit G (NCAPG) and Cellular Maintenance Processes as Major Contributors to Genetic Variability in Bovine Feed Efficiency , 2015, PloS one.

[30]  R. Bush,et al.  A Review of Recent Developments in Buffalo Reproduction — A Review , 2015, Asian-Australasian journal of animal sciences.

[31]  R. Veerkamp,et al.  Whole-genome sequencing of 234 bulls facilitates mapping of monogenic and complex traits in cattle , 2014, Nature Genetics.

[32]  Björn Usadel,et al.  Trimmomatic: a flexible trimmer for Illumina sequence data , 2014, Bioinform..

[33]  Huijiang Gao,et al.  Genome-wide detection of selective signatures in Simmental cattle , 2014, Journal of Applied Genetics.

[34]  Colin N. Dewey,et al.  De novo transcript sequence reconstruction from RNA-seq using the Trinity platform for reference generation and analysis , 2013, Nature Protocols.

[35]  I. Hulsegge,et al.  Prioritization of candidate genes for cattle reproductive traits, based on protein-protein interactions, gene expression, and text-mining. , 2013, Physiological genomics.

[36]  Cole Trapnell,et al.  TopHat2: accurate alignment of transcriptomes in the presence of insertions, deletions and gene fusions , 2013, Genome Biology.

[37]  B. Corl,et al.  Transcriptional regulation of lipid synthesis in bovine mammary epithelial cells by sterol regulatory element binding protein-1. , 2012, Journal of dairy science.

[38]  J. Behl,et al.  The Major Histocompatibility Complex in Bovines: A Review , 2012, ISRN veterinary science.

[39]  M. P. Faylon,et al.  Improved RAPD-PCR for Discriminating Breeds of Water Buffalo , 2012, Biochemical Genetics.

[40]  Steven L Salzberg,et al.  Fast gapped-read alignment with Bowtie 2 , 2012, Nature Methods.

[41]  Mukesh Jain,et al.  NGS QC Toolkit: A Toolkit for Quality Control of Next Generation Sequencing Data , 2012, PloS one.

[42]  J. Medrano,et al.  Transcriptional profiling of bovine milk using RNA sequencing , 2012, BMC Genomics.

[43]  C. Kühn,et al.  The SNP c.1326T>G in the non-SMC condensin I complex, subunit G (NCAPG) gene encoding a p.Ile442Met variant is associated with an increase in body frame size at puberty in cattle. , 2011, Animal genetics.

[44]  Colin N. Dewey,et al.  RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome , 2011, BMC Bioinformatics.

[45]  Changfa Wang,et al.  Single nucleotide polymorphisms, haplotypes and combined genotypes of LAP3 gene in bovine and their association with milk production traits , 2011, Molecular Biology Reports.

[46]  N. Friedman,et al.  Trinity: reconstructing a full-length transcriptome without a genome from RNA-Seq data , 2011, Nature Biotechnology.

[47]  S. El-Shibiny,et al.  A comprehensive review on the composition and properties of buffalo milk , 2011 .

[48]  N. Friedman,et al.  Trinity : reconstructing a full-length transcriptome without a genome from RNA-Seq data , 2016 .

[49]  A. Di Rienzo,et al.  Allele-Specific Down-Regulation of RPTOR Expression Induced by Retinoids Contributes to Climate Adaptations , 2010, PLoS genetics.

[50]  L. Pariset,et al.  Genetic variation and relationships among Turkish water buffalo populations. , 2010, Animal genetics.

[51]  Mark D. Robinson,et al.  edgeR: a Bioconductor package for differential expression analysis of digital gene expression data , 2009, Bioinform..

[52]  H. Simianer,et al.  Analysis of relationship between bovine lymphocyte antigen DRB3.2 alleles, somatic cell count and milk traits in Iranian Holstein population. , 2009, Journal of animal breeding and genetics = Zeitschrift fur Tierzuchtung und Zuchtungsbiologie.

[53]  J. Patton,et al.  Negative energy balance alters global gene expression and immune responses in the uterus of postpartum dairy cows , 2009, Physiological genomics.

[54]  Danielle G. Lemay,et al.  The bovine lactation genome: insights into the evolution of mammalian milk , 2009, Genome Biology.

[55]  M. Robles,et al.  University of Birmingham High throughput functional annotation and data mining with the Blast2GO suite , 2022 .

[56]  Danielle G. Lemay,et al.  Gene regulatory networks in lactation: identification of global principles using bioinformatics , 2007, BMC Systems Biology.

[57]  B. S. Prakash,et al.  Effects of growth hormone-releasing factor on growth hormone response, growth and feed conversion efficiency in buffalo heifers (Bubalus bubalis). , 2007, Veterinary journal.

[58]  B. Mallard,et al.  Association of bovine leukocyte antigen (BoLA) DRB3.2 with immune response, mastitis, and production and type traits in Canadian Holsteins. , 2007, Journal of dairy science.

[59]  Sarah Spiegel,et al.  Sphingosine-1-phosphate: an enigmatic signalling lipid , 2003, Nature Reviews Molecular Cell Biology.

[60]  K. K. Chaturvedi,et al.  MicroRNA-related markers associated with corpus luteum tropism in buffalo (Bubalus bubalis). , 2019, Genomics.

[61]  Y. Uemoto,et al.  Genome-wide association study for carcass traits, fatty acid composition, chemical composition, sugar, and the effects of related candidate genes in Japanese Black cattle. , 2017, Animal science journal = Nihon chikusan Gakkaiho.

[62]  V. Lefebvre,et al.  Control of cell fate and differentiation by Sry-related high-mobility-group box (Sox) transcription factors. , 2007, The international journal of biochemistry & cell biology.