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Article

Non-Synonymous Variants in Fat QTL Genes among High- and Low-Milk-Yielding Indigenous Breeds

by
Neelam A. Topno
1,2,
Veerbhan Kesarwani
1,
Sandeep Kumar Kushwaha
1,
Sarwar Azam
1,
Mohammad Kadivella
1,
Ravi Kumar Gandham
3,* and
Subeer S. Majumdar
1,*
1
DBT—National Institute of Animal Biotechnology (NIAB), Hyderabad 500032, India
2
RCB—Regional Centre of Biotechnology, Delhi 121001, India
3
ICAR—Indian Veterinary Research Institute, Bareilly 243122, India
*
Authors to whom correspondence should be addressed.
Animals 2023, 13(5), 884; https://doi.org/10.3390/ani13050884
Submission received: 13 October 2022 / Revised: 17 December 2022 / Accepted: 25 December 2022 / Published: 28 February 2023
(This article belongs to the Special Issue Effect of Genetic and Nongenetic Factors on Functional and Milk Production Traits in Livestock)
Browse Figures
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Abstract

:

Highlights

What are the main findings?
  • Differentially expressed milk fat QTL genes explored with whole genome se-quencing for variant analysis.
  • Identified non-synonymous SNPs for hub and bottleneck QTL genes associated with milk fat traits.
What is the implication of the main finding?
  • Identified differential pattern(s) of SNPs in fat QTLs between high and low milk yield breeds.
  • Impact of the identified SNP pattern(s) on milk fat traits can be further explored.

Simple Summary

Milk fat is a crucial trait that varies significantly among cattle breeds and determines the milk quality and pricing value. Indigenous breeds have disparity in milk quantity and quality. Our study is one of a kind which helps to decipher the variations at the genetic level correlated with transcriptional level among high and low milk-yielding cattle breeds exploring the fat QTLs. We assessed and unveiled a few key differences between the high and low-milk-yield breeds.

Abstract

The effect of breed on milk components—fat, protein, lactose, and water—has been observed to be significant. As fat is one of the major price-determining factors for milk, exploring the variations in fat QTLs across breeds would shed light on the variable fat content in their milk. Here, on whole-genome sequencing, 25 differentially expressed hub or bottleneck fat QTLs were explored for variations across indigenous breeds. Out of these, 20 genes were identified as having nonsynonymous substitutions. A fixed SNP pattern in high-milk-yielding breeds in comparison to low-milk-yielding breeds was identified in the genes GHR, TLR4, LPIN1, CACNA1C, ZBTB16, ITGA1, ANK1, and NTG5E and, vice versa, in the genes MFGE8, FGF2, TLR4, LPIN1, NUP98, PTK2, ZTB16, DDIT3, and NT5E. The identified SNPs were ratified by pyrosequencing to prove that key differences exist in fat QTLs between the high- and low-milk-yielding breeds.

1. Introduction

India has become the largest milk producer in the world [1]. Several schemes involving crossbreeding have been implemented to enhance milk production in the country. As a result, the number of crossbred cattle increased and contributed to around 28 percent of total milk production in India (ca. 188 million tons), surpassing the contribution of indigenous cattle [2]. However, indigenous cattle breeds are well known for their heat tolerance and disease resistance [3], and the crossbreds have been found to be susceptible to tropical diseases and harsh climatic conditions and require constant good management practices. To strike a balance between increasing demand for milk and the change in the environment due to global warming, exploring the genomic merit of indigenous cattle/breeds becomes even more important.
Milk being a polygenic trait with medium heritability, the majority of animal breeding research has centered on quantitative trait loci (QTLs) with moderate to large effects on milk production traits. The DGAT1 on chromosome 14 [4,5,6], the growth hormone receptor (GHR) on chromosome 20 [7], and the ABCG2 [8] or SPP1 (Osteopontin) on chromosome 6 [9] are well-known QTL genes that have been fully characterized with a strong putative or well-confirmed causal mutation. The two QTLs DGAT1 (K232A) and ABCG2 (Y581S) in Bos taurus have been suggested to be associated with increased fat yield and fat and protein percent in milk with a decrease in milk yield [6,8,10,11,12,13]. The GHR mutation F279Y has been observed to have a significant effect on milk composition (fat and protein percentage) and milk yield [14]. The locus c.8514C > T in the intronic region of SPP1 has also been found to have a significant effect on milk production and milk composition [15]. However, the DGAT1 and ABCG2 genes have been found to be fixed among Indian breeds Sahiwal, Rathi, Deoni, Tharparkar, Red Kandhari, and Punganur [16]. Currently, the animal QTL database (QTLdb) contains 1,93,216 QTLs for different bovine traits, out of which 83,458 QTLs have been reported for milk traits [17].
Milk is the primary source of nutrition for infants, as well as adults. Besides its nutritional value, it has a major role in imparting growth and immunity through intrinsic milk components such as growth factors, chemokines, anti-inflammatory molecules, antioxidants, prebiotics, and probiotics [8,9]. Milk has four major components, fat (3.6%), protein (3.2%), lactose (4.7%), and water (87%), along with other various kinds of minerals, enzymes, vitamins, and dissolved gases. Various research studies have shown that several factors, such as lactation stage, genetics, environmental factors, and diet management, influence milk quality. The variability of milk composition among popular dairy breeds Brown Swiss, Holstein Friesian, Jersey, Simmental, Grey Alpine, and Rendena under the same dairy management practices has been explored, and Holstein Friesian had higher milk yield with lower fat content (27.45 kg/d, 4.04%) [18], whereas Jersey had lower milk yield with relatively higher fat content (17.27 kg/d, 5.65%) [13]. A low fat percentage has also been reported for Ayrshire, Brown Swiss, Guernsey, Holstein Friesian, and Jersey breeds in the United States [19]. Furthermore, the effect of breed has been found to significantly influence the water (p ≤ 0.0001), protein (p ≤ 0.05), total solids (p ≤ 0.05), fat (p ≤ 0.05), milk urea nitrogen (p ≤ 0.001), and ash (p ≤ 0.0001) content of milk [20].
India, with a huge diversity of 50 cattle breeds, forms an ideal ground to study genetic variation at the genomic level vis-à-vis milk traits [21]. The cost of the milk world-over varies with the percentage of fat present in the milk. Exploring the variations in fat QTLs across breeds would shed light on the variable fat content in their milk. No such studies have been reported in the past for indigenous cattle breeds to evaluate the variation across indigenous breeds within the fat QTLs. Therefore, the objective of the present study was to explore genomic variation(s) within the fat QTLs that were identified to be differentially expressed in lactation across indigenous breeds, which were divided into high- (Sahiwal and Gir) and low-milk-yield (Gaolao, Deoni, Pulikulam, Hallikar, Dangi, and Amritmahal) groups.

2. Materials and Methods

2.1. Data Retrieval from the Public Repository

QTL genes associated with milk fat traits (milk fat content and percentage) and metabolism were extracted from the Animal QTLdb database [22], and the duplicates were removed. QTL genes (286 and 256 (total of 542)) were identified for milk fat yield and milk fat percentage, respectively, with 417 unique genes for both traits (Supplementary File 1). Functional annotation of the 125 QTL genes commonly associated with milk fat yield and milk fat percentage was performed in g:Profiler [23] and ShinyGO [24] to identify the enriched biological processes. Protein interaction network analysis of the 417 genes was performed using the Search Tool for the Retrieval of Interacting Genes Search Tool for the Retrieval of Interacting Genes 11.0 (STRING 11.0) database at a confidence score value of 0.5 against model species Bos taurus [25]. The interaction network was imported into the Cytoscape 3.8.0 software (Institute for System Biology, CA, USA) for visualization. The hub and bottleneck genes were identified in the interaction network using the Cytohubba plugin of Cytoscape [26] considering the degree of association between the genes and by taking the bottleneck approach, which takes into account the top 20% of the degree of distribution of the proteins in the network [26]. A total of 74 QTL genes were identified to be hub/ bottleneck genes. The hub genes were the genes that had the highest degree of association, and the bottleneck genes were the key connectors having a high betweenness (measure the centrality of the nodes) among different clusters in protein interactions [27].

2.2. Bioinformatics Analysis of Milk Transcriptome

The publicly available milk transcriptome bioproject ID (PRJNA419906) was used to analyze the expression of QTL genes associated with milk fat traits. This bioproject was considered in this study as it has data generated from Jersey (a breed with high fat content ranging from 4.10–4.86%) and Kashmiri (a breed with low fat content ranging from 3.20–3.94%) [28]. The data were generated from mammary epithelial cells (MECs) collected on Day 15 (D15), D90, and D250 from six lactating cows (three Jersey and three Kashmiri cattle). These days represent early, mid-, and late lactation, respectively [28]. The data were downloaded from the Sequence Read Archive (SRA) of the NCBI database, and the fastq-dump program of SRAtoolkit [29] was used to extract the fastq reads. Quality assessment and control of RNA-seq data were performed through Fast QC Version 0.11.5 [30], MultiQC Version 1.8 [31], and trimmomatic Version 0.39 [32]. All the high-quality reads were mapped to the Bos indicus genome (GCF_003369695.1) using STAR Version 2.5.4b with the default parameters [33]. Gene expression was estimated using RSEM [34], and differential gene expression was performed through the DESeq2-R package [35]. The differentially expressed genes among the 74 fat QTL hub and bottleneck genes were identified.

2.3. Breed Selection, Sampling, Genomic DNA Extraction, and Whole-Genome Sequencing

Indigenous cattle breeds for the proposed study were grouped into high- and low-milk-yield-breed groups. The high-milk-yield group (avg milk yield per day 8 kg) included Sahiwal (n = 4) and Gir (n = 4), and the low-milk-yield group (avg milk yield per day 2.5 kg) included 6 animals representing the breeds Gaolao, Deoni, Hallikar, Dangi, Pulikulam, and Amritmahal [36]. Animals of different breeds were considered in the study to have a true representation of both the high- and low-milk-yield groups. Genomic DNA (gDNA) was extracted from the blood samples of the animals from these breeds using the nucleospin blood L-kit (Macherey-Nagel), and the integrity of the genomic DNA was checked on agarose. After estimating the concentration of gDNA (Nanodrop2000, ThermoFischer Scientific), DNA libraries were prepared as per the manufacturer’s protocol (Illumina sequencing platform) for paired-end sequencing (2150 bp).

2.4. Bioinformatic Analysis (Variant Calling, SNP Annotation, and Functional Enrichment)

Sequencing data generated on an Illumina platform were pre-processed for quality assessment and improvement (base quality, nucleotide distribution, GC content, adaptor sequence, duplication, length distribution, etc.) by FastP [37]. All high-quality reads were mapped to the Bos indicus reference genome (Brahman-GCF_003369695.1) using BWA aligner [38]. Variant calling was performed from the aligned data using freebayes [39] and GATK [40]. For the GATK- and freebayes-generated vcf files, only SNPs were selected, leaving aside all indels and insertions. After freebayes variant calling, the low-quality variants were filtered by vcftools Version 1.10 [41] for Q > 20. In the GATK pipeline, the paired-end Illumina Hi-Seq raw reads for each individual were first converted into an unaligned bam; the illumina adapters were marked; the sam was converted back to FASTQ; the reads were mapped to the reference Brahman genome (GCF 003369695.1); the unaligned and mapped bams were combined. Finally, the duplicate marked clean bam was used to generated GVCF for each animal using GATK haplotypecaller. The GVCFs generated for all the animals were combined to call the variants using the genotypeGVCF module. The parameters for GATK VariantRecalibration to generate the VQSLOD score were QD < 2.0, MQRankSum < −8.5, ReadPosRankSum < −8.0, FS > 60.0, MQ < 40.0, SOR > 3.0, and DP 30x (depth or coverage). The final set of SNPs after recalibration by GATK included the selection of SNPs that passed. We further used the GATK-filtered set and the freebayes-filtered set to identify the common SNPs across these variant callers. From this vcf file, the SNPs in the differentially expressed hub and bottleneck genes (i.e., genes that are hub/bottleneck and are differentially expressed as identified in Section 2.2) were extracted using an in-house perl script. The non-synonymous SNPs (nsSNPs) in the coding regions of these were identified through the SnpEff tool [42].

2.5. SNP Validation through Real-Time Sequence-Based Pyrosequencing

Three nsSNPs that were found to be distinctly different between the high- and low-milk-yield groups were selected for validation and were genotyped in PyroMark Q48 (Qiagen) as per the manufacturer’s protocol. These nsSNPs were found in the differentially expressed hub and bottleneck genes GHR, LPIN1, and TLR4. GHR was one of the genes having the maximum number of interactions in the network. LPIN1 had the maximum SNP count of 10, whereas TLR4 was one of the top 10 highly upregulated genes. PCR and sequencing primers were designed using PyroMark Assay Design Software 2.0 (Qiagen). The PCR amplification was performed in a 20 µL reaction, with the thermal cycling conditions, which included an initial denaturation of 95 °C for 3 min followed by 40 cycles of 95 °C for 30 s, 65 °C for 30 s, 72 °C for 1 min, and a final extension of 72 °C for 10 min. Sequence analysis was performed by PyroMark Q48 Autoprep software Version 2.4.2 in SNP analysis assay mode for 14 animals.
The schematic representation of the study is given in Supplementary File 1: Figure S1.

3. Results

3.1. Meta-Analysis of Cattle-Milk-Fat-Component-Associated QTLs and Related Genes

Animal QTLdb was used to extract fat-trait-associated QTL genes. In the QTLdb, after the removal of duplicate genes (Supplementary File 1), 286 and 256 genes were found associated with milk fat yield and percentage, respectively, whereas 125 common genes were found between milk fat yield and percentage (Figure 1A). A total of 417 unique genes were found to be associated with milk fat and other milk traits. These genes were also found to be annotated for other milk traits such as milk yield, protein yield, and percentage.
Among all genes, 24 genes were found to be associated with both fat yield and fat percentage traits only (Supplementary File 1). The common genes (125) were found enriched in biosynthetic-, catabolic-, regulatory-, transportation-, and cellular-response-associated metabolic processes. Among the metabolic genes associated with milk fat traits, the genes associated with milk fatty acid metabolism were FASN, GPAT4, DGKG, ELOVL6, and LIPIN1 (Supplementary File 5: Table S1).

3.2. Milk Transcriptome Data Processing and Gene Expression Analysis of Fat QTL Genes

The publicly available RNA-seq bioproject (PRJNA419906) has library sizes ranging from 7764992200–13682843400 bp and 6842583600–12088807400 bp, for Jersey and Kashmiri, respectively (Supplementary File 5: Figure S2A). Further, gene expression counts per million (CPM), principal component analysis (PCA), and multidimensional scaling (MDS) (Supplementary File 5: Figure S2A,C,D) of the samples were assessed. The PCA and MDS plots of sequenced RNA-seq libraries showed a high level of similarity within breeds and relatively low variation between the lactation stages of breeds.
A total of 70 genes were found to be upregulated and 52 genes downregulated in the Jersey breed in comparison with the Kashmiri breed. Differentially expressed transcripts (DETs) were also explored for fat QTL genes (Supplementary File 2). The volcano plots of differentially expressed genes (DEGs) and DETs depicting the distribution of upregulated and downregulated genes are shown in Figure 2.
In both the Jersey and Kashmiri breeds, Beta-lactoglobulin (LOC113901792), Casein beta (CSN2), and Casein alpha s1 (CSN1S1) were identified to be among the highly expressed top 20 genes (Supplementary File 5: Table S2). The DETs are listed in Supplementary File 2. In the Jersey breed, CXCL-8, TLR4, and OLR1 were among the highly upregulated genes. CXCL-8/IL8, produced by macrophages, epithelial cells, and airway smooth muscle cells, is a neutrophil chemotactic factor that induces chemotaxis in target cells and other granulocytes to initiate movement toward infection sites, whereas OLR1, a receptor on macrophages, epithelial cells, and airway smooth muscle cells, is involved in rapid oxidization of low-density lipoprotein (LDL), which is more readily recognized by the TLR4 receptor. The list of top 10 upregulated and downregulated DEGs is given in Supplementary File 5: Table S3.

3.3. Protein Interaction Network Analysis of Milk Fat QTL Genes

A protein interaction network analysis was performed among 417 milk fat QTL genes, and the interaction network was generated with 403 nodes and 671 edges. Based on the degree of association, 50 hub and 50 bottleneck genes were selected from the network (Supplementary File 2). A total of 74 QTL genes were found to be either hub or bottleneck genes (Figure 1B). Out of these, 25 genes (which accounted for 18 hubs/17 bottleneck genes) were differentially expressed in Jersey and Kashmiri (Figure 1C, Supplementary File 2). Out of these genes, ten genes possessed both hub and bottleneck gene characteristics. The SRC and DGAT1 genes were among the top differentially expressed hub and bottleneck genes (Table 1). SRC had the highest degree of association (30) with a log2 fold change of 1.480587, followed by DGAT1 with a degree of 25 and a log2FC of 0.921104.

3.4. Variant Analysis of Hub and Bottleneck Genes among Indigenous Breeds

Illumina short read (Paired end) data of 14 samples from both groups of high- and-low-milk-yield breeds had 12.77 billion reads. After preprocessing, clean data included 11.02 billion reads, which is ca. 1516 Gb data. Each dataset had a minimum sequencing depth of ≥30x with an average GC content of 45.26%. The processed datasets contained on average 97.91% Q20 bases and 93.97% Q30 bases. The high-quality trimmed data aligned to the Brahman reference genome with an overall alignment rate of >95%. Initial variant calling on the aligned data provided 63,357,363 variants, which were filtered for high quality. After quality filtering on Q20, a total of 33,976,892 SNPs were identified across the genomes (Supplementary File 3). Upon GATK analysis, 39,625,917 variants were found to pass the variant calibration. A total of 25,956,231 SNPs were found to be common among the variant callers. From these, SNPs in the 25 differentially expressed hub and bottleneck milk fat QTL genes were extracted, out of which 20 genes were found to have non-synonymous substitutions in the coding regions (Table 2).
The variants identified in these 20 genes were further explored for two kinds of genomic variant patterns, i.e., fixed SNP pattern in the cattle of the high-milk-yield group vs. variable SNP pattern in cattle of the low-milk-yield group, or vice versa. The fixed SNP pattern in high-milk-yield breeds in comparison to low-milk-yield breeds was observed in the genes GHR, TLR4, LPIN1, CACNA1C, ZBTB16, ITGA1, ANK1, and NTG5E (Table 3), and the opposite was observed in the genes MFGE8, FGF2, TLR4, LPIN1, NUP98, PTK2, ZTB16, DDIT3, and NT5E (Table 4). SNPs C/G, C/A, and G/A were confirmed in GHR, TLR4, and LPIN1 in the Amritmahal, Pulikulam, and Dangi breeds (low-milk-yield) as against SNPs C/C, C/C, and G/G in the Gir and Sahiwal breed (high-milk-yield), respectively (Figure 3) (Supplementary File 4). In the TLR4 gene, variant g.107083326A>C was found in the low-milk-yield group, but the same variant was fixed in the high-milk-yield group. Similarly, in the LPIN1 gene variant, g.85211528C>G was found in the low-milk-yield group, but this variant was fixed in the high-milk-yield group. In the NUP98 gene and LPIN1 variants, g.32707374G>A and g.85205642T>G, respectively, were found in the high-milk-yield group, but were found to be fixed (g.32707374G>G; g.85205642T>T) in the low-milk-yield group. Another LPIN1 variant, g.85205642T>G, was observed in the high-milk-yield group, but was fixed in the low-milk-yield group. LPIN1 and ITGA1 had the maximum SNP count of 10, and the genes PTK2, IGF1R, DDIT3, CXCL8, and LPL had the least SNP count (Table 2).

4. Discussion

The Bos indicus genome is an interesting model to study the genomic potential of different indigenous cattle breeds such as Sahiwal, Gir, Amritmahal, Dangi, Gaolao, Deoni, Pulikulam, and Hallikar, which are highly adapted to different tropical conditions with varying milking potential. The availability of bovine QTL resources such as the Animal QTL database and the collection of QTLs for different traits have provided the opportunity to investigate genomic variation among indigenous breeds for milk-associated traits. Milk quality such as fat yield and percentage are highly variable traits among breeds. Jersey is one among the high-milk-producing breeds worldwide, whereas Kashmiri is one of the poorly performing breeds in the Kashmir region of India. Therefore, we aimed at differences in the expression of fat QTL genes between the two contrasting breeds, Jersey and Kashmiri. In this study, significantly expressed hub and bottleneck fat QTL genes were further analyzed to identify the genomic variants from the whole-genome sequence data between high- (Sahiwal and Gir) and low- (Amritmahal, Dangi, Gaolao, Deoni, Pulikulam, and Hallikar) milk-yield indigenous breeds. This understanding of low- and high-milk-yield breeds for milk fat quality may help in enhancing the quality of milk in the long run.
To explore the fat QTL genes, MEC RNA-seq data were processed and analyzed. The high level of similarity within breeds and relatively low variation between lactation confirmed the selection of RNA-seq data to explore differences between breeds rather than to explore difference in lactation stages of breeds. Among the highly expressed genes identified, it was observed that the Jersey breed has allocated more resources for the immune system, whereas the Kashmiri breed for regulation of ribosomal proteins. Among the top 10 upregulated genes, CXC motif chemokine ligand 8 was the most-upregulated gene. It is reported to be involved in various biological pathways such as increased insulin resistance, uncoupling of the GH/IGF1 axis, and an increase in mammary cell proliferation to improve metabolic health and milk yield [43]. Diacylglycerol kinase gamma (DGKG), another upregulated gene, is a member of the type I diacylglycerol kinases and is highly upregulated (log2FC = 4.03) in Jersey. It plays a role in lipid metabolism by modulating the balance between diacylglycerol and phosphatidic acid. Phosphatidic acid is a lipid second messenger to activate protein kinase C isoforms, ras guanyl nucleotide-releasing proteins, and some transient receptor potential channels [44]. Most of the top-upregulated genes in Jersey have been found to have a role in adipogenesis (ETS2) [45], adipocyte differentiation (OLR1, PARM1) [46,47], glucose transport (SLC6A9) (log2FC = 4.49) [48], glucose uptake (SLC45A4) [49], thyroid hormone synthesis (TG) [50], and aldosterone secretion (KCNK9) [51]. The upregulation of these genes in Jersey indicates their involvement in lipid biosynthesis in the mammary gland during lactation. Furthermore, UDP-glucose 6-dehydrogenase (UGDH), which is involved in the biosynthesis of glycosaminoglycans, hyaluronan, chondroitin sulfate, and heparan sulfate, has been found (log2FC = 0.75) to be upregulated in the Jersey breed. UGDH’s expression pattern in liver cells has been associated with an indispensable role in the metabolism of carbohydrates, fats, and proteins in dairy cattle [52]. Moreover, UGDH has been found close to two reported QTLs for fat yield, fat percentage, and protein yield [53].
During lactation, various morphological changes happen in the mammary tissue to support cellular differentiation, tissue elasticity, and reduced fat storage capacity in the animal. Upregulated GRH13 is a transcription factor that mediates the proliferation of epithelial cells [54]. Similarly, MTMR3/3-PAP, a catalytically inactive member of the myotubularin gene family that coprecipitates the activity of lipid phosphatidylinositol 3-phosphate-3-phosphatase, is upregulated [55] in Jersey. Matrilin 2 (MATN2) and prolyl 4 hydroxylase (P4HA3), which are important to maintaining the newly synthesized collagen’s stability, are also upregulated [56,57]. P4HA3 catalyzes the formation of 4-hydroxyproline (Hyp), which ensures the proper folding of procollagens during post-translational modification. The upregulation of MATN2 and P4HA3 probably may help in increasing the elasticity of the udder gland in Jersey during lactation. Further, the downregulation of MFGE8 and ELOVL16 may be responsible for the high fat content in milk and the shift to C18 from C16 fatty acids, respectively, in Jersey. MFGE8 regulates the absorption of free fatty acids and increases intracellular triglycerides’ hydrolase activity, thereby restricting the storage of fat [58], whereas ELOVL fatty acid elongase (ELOVL16) elongates C16 saturated and monounsaturated fatty acids to C18 fatty acids [59]. Further, IDH1, which catalyzes the conversion of isocitrate to α-ketoglutarate and generates the primary source of NADPH for de novo fatty acid synthesis [60], has been to be found downregulated in Jersey (log2FC = −1.632). The dysregulation of the genes MFGE8, EVOLVL16, and IDH1 might be responsible for the variation in the fat yield and composition in Jersey and Kashmiri cows. In addition, the decreased expression of these genes may be linked to the increased expression of genes involved in metabolism, glucose transport, and other transport activities, leading to higher milk production performance in Jersey.
Among the ten QTLdb milk and milk trait genes that were differentially expressed and had hub and bottleneck gene characteristics, four genes were found enriched in metabolic pathways (Supplementary File 5: Table S1). SRC was identified as the top hub gene interacting with several genes in the protein–protein interaction network. SRC, a non-receptor tyrosine kinase, performs a wide variety of cellular functions in terms of metabolism and is primarily involved in impaired glucose uptake [61]. Genes Diacylglycerol O-acyltransferase 1 (DGAT1) and ecto-5′-nucleotidase (NT5E) possess hub and bottleneck gene features. DGAT1 encodes a protein that catalyzes the conversion of diacylglycerol and fatty acyl CoA to triacylglycerol. DGAT1 is one of the highly studied genes for milk yield and fat quality [62,63]. The NT5E gene encodes a plasma membrane protein that catalyzes the conversion of extracellular nucleotides to membrane-permeable nucleosides. SRC and DGAT1 were found to be highly upregulated in Jersey with log2FC = 1.48 and log2FC = 0.92, respectively. In the network, DGAT1 was found to interact with Glycerol-3-phosphate acyltransferase 4 (GPAT4) (also known as AGPAT6). This gene has been found to be involved in triglyceride biosynthesis and is comprised of acyltransferase motifs, which are essential for binding to substrates and catalyzing acyltransferase reactions. The AGPAT6 gene, which is highly expressed in mammary gland epithelium during lactation [64], was also observed to be upregulated. NT5E was found to interact with BDNF, NTRK2, and ZNRF4. NT5E is involved in various biological process such as adenosine biosynthetic process, AMP catabolic process, leukocyte cell–cell adhesion, and negative regulation of inflammatory response [65]. DGKG, which was upregulated, was found to be a bottleneck gene in the network and had interactions with PRKCG and RNT. Phosphatidate phosphatase (LPIN1), an enzyme involved in lipid metabolism [66], was upregulated and found as a hub gene in the network having interactions with PPARA and PPARGC1A. LPIN1 is involved in various biological processes related to lipid metabolism such as the triglyceride biosynthetic process, the fatty acid catabolic process, and the regulation of transcription by RNA polymerase II [66]. The gene PTK2 is well known for its association with milk production traits [67], and CACNA1A has a role in hormone regulation of lactation [68]. Similarly, ZBTB16 is involved in bovine adipogenesis [69]. NUP98 is associated with protein percentage [70]. TLR4 is a mastitis-associated marker [71]. FGF2 expression is reported to be associated with milk production traits [72]. In this study, breed (high-milk-yield and low-milk-yield groups) differences in nsSNPs within these hub and bottleneck genes (GHR, TLR4, LPIN1, CACNA1C, MFGE8, PTK2, ZBTB16, FGF2, and NUP98) were identified. These fat QTL nsSNPs may have a role in the existing fat and milk yield differences between the breed groups. However, further studies to evaluate the impact of these SNPs on fat yield/percentage or milk yield need to be carried out.

5. Conclusions

In this study, initially, DEGs in Jersey epithelial cells were identified, and these were further explored in the QTL database as being the major hub and bottleneck genes. The transcriptome in Jersey indicated higher expression of genes involved in metabolism, glucose transport, and other transport activities, leading to higher milk production performance. The 20 differentially expressed hub and bottleneck fat QTL genes were explored for non-synonymous genomic variants in the whole-genome sequence data, which were generated from fourteen animals. The fixed SNP pattern in high-milk-yield breeds in comparison to low-milk-yield breeds was observed in the genes GHR, TLR4, LPIN1, CACNA1C, ZBTB16, ITGA1, ANK1, and NTG5E, and the opposite was observed in the genes MFGE8, FGF2, TLR4, LPIN1, NUP98, PTK2, ZTB16, DDIT3, and NT5E. The role of these SNPs needs to be further explored.

Supplementary Materials

The following Supporting Information can be downloaded at: https://www.mdpi.com/article/10.3390/ani13050884/s1, File 1: Meta-data of milk QTL genes, biological process, and pathways; File 2: Detailed description of differentially expressed QTL, hub, and bottleneck genes; File 3: Features of sequenced data before and after filtering; File 4: Detailed description of the performed variant analysis; File 5: Tables consisting of metabolic gene details, highly expressed genes, a list of top ten upregulated and downregulated differentially expressed gene between the Jersey and Kashmiri breeds, along with the QTL id and trait name; schematic representation of the study and figure of quality evaluation of transcriptome samples.

Author Contributions

S.S.M., R.K.G., S.A. and S.K.K.: conceived of the idea as a team; N.A.T. and V.K.: contributed to the first draft of the manuscript; N.A.T.: performed the Q48 experiments; S.A.: generated the VCF file from the sequenced genotype for the variant analysis; R.K.G.: finalized the final draft; S.K.K. and M.K.: analyzed the transcriptome data, generated the plots, and helped in the first draft of the manuscript. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded from the project “Genomics for conservation of indigenous cattle breeds and for enhancing milk yield (BT/PR26466/AAQ/1/704/2017)”.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

All the used RNA-seq data are available at NCBI SRA under the project accession number PRJNA419906. Genomics for conservation of indigenous cattle breeds and for enhancing milk yield BT/PR26466/AAQ/1/704/2017.

Acknowledgments

We are grateful to the Director of NIAB for providing support to carry out the study. The bioinformatics facility NIAB is acknowledged for the bioinformatics data analysis. S.S.M. is thankful for the support of the JCBOSE fellowship (Grant No. SERB-JCB/2017/000027).

Conflicts of Interest

The authors have declared that they have no competing interest.

References

  1. Dairying, D.O.A.H.A.; Ministry of Fisheries, A.H.A.D.; India, G.O. Annual Report. Department of Animal Husbandry and Dairying, 2021–2022. Available online: https://dahd.nic.in/sites/default/filess/Annual%20Report%20English.pdf (accessed on 19 November 2020).
  2. Ministry of Fisheries; Animal Husbandry & Dairying. 20th Livestock Census; PIB Delhi: New Delhi, India, 2019. [Google Scholar]
  3. Madalena, F.E.; Toledo-Alvarado, H.; Cala-Moreno, N. Bos indicus Breeds and Bos indicus × Bos taurus Crosses☆. In Encyclopedia of Dairy Sciences, 3rd ed.; McSweeney, P.L.H., McNamara, J.P., Eds.; Academic Press: Cambridge, MA, USA, 2019; pp. 30–47. [Google Scholar]
  4. Grisart, B.; Coppieters, W.; Farnir, F.; Karim, L.; Ford, C.; Berzi, P.; Cambisano, N.; Mni, M.; Reid, S.; Simon, P.; et al. Positional candidate cloning of a QTL in dairy cattle: Identification of a missense mutation in the bovine DGAT1 gene with major effect on milk yield and composition. Genome Res. 2002, 12, 222–231. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  5. Weikard, R.; Kuhn, C.; Goldammer, T.; Freyer, G.; Schwerin, M. The bovine PPARGC1A gene: Molecular characterization and association of an SNP with variation of milk fat synthesis. Physiol Genom. 2005, 21, 1–13. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  6. Winter, A.; Krämer, W.; Werner, F.A.O.; Kollers, S.; Kata, S.; Durstewitz, G.; Buitkamp, J.; Womack, J.E.; Thaller, G.; Fries, R. Association of a lysine-232/alanine polymorphism in a bovine gene encoding acyl-CoA:diacylglycerol acyltransferase (DGAT1) with variation at a quantitative trait locus for milk fat content. Proc. Natl. Acad. Sci. USA 2002, 99, 9300–9305. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  7. Blott, S.; Kim, J.-J.; Moisio, S.; Schmidt-Küntzel, A.; Cornet, A.; Berzi, P.; Cambisano, N.; Ford, C.; Grisart, B.; Johnson, D.; et al. Molecular Dissection of a Quantitative Trait Locus: A Phenylalanine-to-Tyrosine Substitution in the Transmembrane Domain of the Bovine Growth Hormone Receptor Is Associated With a Major Effect on Milk Yield and Composition. Genetics 2003, 163, 253–266. [Google Scholar] [CrossRef]
  8. Cohen-Zinder, M.; Seroussi, E.; Larkin, D.M.; Loor, J.J.; Everts-van der Wind, A.; Lee, J.-H.; Drackley, J.K.; Band, M.R.; Hernandez, A.G.; Shani, M.; et al. Identification of a missense mutation in the bovine ABCG2 gene with a major effect on the QTL on chromosome 6 affecting milk yield and composition in Holstein cattle. Genome Res. 2005, 15, 936–944. [Google Scholar] [CrossRef] [Green Version]
  9. Salehi, A.; Nasiri, K.; Aminafshar, M.; Sayaadnejad, M.B.; Sobhani, R. The Association of Bovine Osteopontin (OPN) Gene with Milk Production Traits in Iranian Holstein Bulls. Iran. J. Biotechnol. 2015, 13, 43–48. [Google Scholar] [CrossRef] [Green Version]
  10. Olsen, H.G.; Nilsen, H.; Hayes, B.; Berg, P.R.; Svendsen, M.; Lien, S.; Meuwissen, T. Genetic support for a quantitative trait nucleotide in the ABCG2 gene affecting milk composition of dairy cattle. BMC Genet 2007, 8, 32. [Google Scholar] [CrossRef] [Green Version]
  11. Sheehy, P.A.; Riley, L.G.; Raadsma, H.W.; Williamson, P.; Wynn, P.C. A functional genomics approach to evaluate candidate genes located in a QTL interval for milk production traits on BTA6. Anim Genet. 2009, 40, 492–498. [Google Scholar] [CrossRef]
  12. Zhou, C.; Li, C.; Cai, W.; Liu, S.; Yin, H.; Shi, S.; Zhang, Q.; Zhang, S. Genome-wide association study for milk protein composition traits in a Chinese Holstein population using a single-step approach. Front. Genet. 2019, 10, 72. [Google Scholar] [CrossRef] [Green Version]
  13. Schennink, A.; Stoop, W.M.; Visker, M.H.P.W.; Heck, J.M.L.; Bovenhuis, H.; Van Der Poel, J.J.; van Valenberg, H.; van Arendonk, J. DGAT1 underlies large genetic variation in milk-fat composition of dairy cows. Anim. Genet. 2007, 38, 467–473. [Google Scholar] [CrossRef]
  14. Viitala, S.; Szyda, J.; Blott, S.; Schulman, N.; Lidauer, M.; Mäki-Tanila, A.; Georges, M.; Vilkki, J. The Role of the Bovine Growth Hormone Receptor and Prolactin Receptor Genes in Milk, Fat and Protein Production in Finnish Ayrshire Dairy Cattle. Genetics 2006, 173, 2151–2164. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  15. Kułaj, D.; Pokorska, J.; Ochrem, A.; Dusza, M.; Makulska, J. Effects of the c.8514C > T polymorphism in the osteopontin gene (OPN) on milk production, milk composition and disease susceptibility in Holstein-Friesian cattle. Ital. J. Anim. Sci. 2019, 18, 546–553. [Google Scholar] [CrossRef]
  16. Tantia, M.S.; Vijh, R.K.; Mishra, B.P.; Mishra, B.; Kumar, S.T.B.; Sodhi, M. DGAT1 and ABCG2 polymorphism in Indian cattle (Bos indicus) and buffalo (Bubalus bubalis) breeds. BMC Veter-Res. 2006, 2, 32. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  17. Hu, Z.-L.; Park, C.A.; Reecy, J.M. Building a livestock genetic and genomic information knowledgebase through integrative developments of Animal QTLdb and CorrDB. Nucleic Acids Res. 2019, 47, D701–D710. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  18. Bobbo, T.; Cecchinato, A.; Cipolat-Gotet, C.; Stocco, G.; Bittante, G. Effect of breed and dairy system on milk composition and udder health traits in multi-breed dairy herds. In Proceedings of the 22nd International Symposium "Animal Science Days”, Keszthely, Hungary, September 2014. [Google Scholar] [CrossRef]
  19. Gaunt, S. Genetic Variation in the Yields and Contents of Milk Constituents [Quantitative Differences; Milk Proteins, Lactose, Ash, Non-Fat Solids; Dairy Cows Breeds; Ayrshire; Brown Swiss; Guernsey; Holstein Friesian; Jersey]; International Dairy Federation: Schaerbeek, Belgium, 1980. [Google Scholar]
  20. Kebede, E. Effect of cattle breed on milk composition in the same management conditions. Ethiop. J. Agric. Sci. 2018, 28, 53–64. [Google Scholar]
  21. Srivastava, A.; Patel, J.; Ankuya, K.; Chauhan, H.; Pawar, M.; Gupta, J. Conservation of indigenous cattle breeds. J. Anim. Res. 2019, 9, 1–12. [Google Scholar] [CrossRef]
  22. Hu, Z.-L.; Park, C.A.; Wu, X.-L.; Reecy, J.M. Animal QTLdb: An improved database tool for livestock animal QTL/association data dissemination in the post-genome era. Nucleic Acids Res. 2012, 41, D871–D879. [Google Scholar] [CrossRef] [Green Version]
  23. Reimand, J.; Arak, T.; Adler, P.; Kolberg, L.; Reisberg, S.; Peterson, H.; Vilo, J. g:Profiler—A web server for functional interpretation of gene lists (2016 update). Nucleic Acids Res. 2016, 44, W83–W89. [Google Scholar] [CrossRef]
  24. Ge, S.X.; Jung, D.; Yao, R. ShinyGO: A graphical gene-set enrichment tool for animals and plants. Bioinformatics 2020, 36, 2628–2629. [Google Scholar] [CrossRef]
  25. Szklarczyk, D.; Gable, A.L.; Nastou, K.C.; Lyon, D.; Kirsch, R.; Pyysalo, S.; Doncheva, N.T.; Legeay, M.; Fang, T.; Bork, P. The STRING database in 2021: Customizable protein–protein networks, and functional characterization of user-uploaded gene/measurement sets. Nucleic Acids Res. 2021, 49, D605–D612. [Google Scholar] [CrossRef]
  26. Chin, C.-H.; Chen, S.-H.; Wu, H.-H.; Ho, C.-W.; Ko, M.-T.; Lin, C.-Y. cytoHubba: Identifying hub objects and sub-networks from complex interactome. BMC Syst. Biol. 2014, 8 (Suppl. 4), S11. [Google Scholar] [CrossRef] [Green Version]
  27. Yu, H.; Kim, P.M.; Sprecher, E.; Trifonov, V.; Gerstein, M. The importance of bottlenecks in protein networks: Correlation with gene essentiality and expression dynamics. PLoS Comput. Biol. 2007, 3, e59. [Google Scholar] [CrossRef]
  28. Bhat, S.A.; Ahmad, S.M.; Ibeagha-Awemu, E.M.; Bhat, B.A.; Dar, M.A.; Mumtaz, P.T.; Shah, R.; Ganai, N. Comparative transcriptome analysis of mammary epithelial cells at different stages of lactation reveals wide differences in gene expression and pathways regulating milk synthesis between Jersey and Kashmiri cattle. PLoS ONE 2019, 14, e0211773. [Google Scholar] [CrossRef] [Green Version]
  29. Kawakami, E.; Nakaoka, S.; Ohta, T.; Kitano, H. Weighted enrichment method for prediction of transcription regulators from transcriptome and global chromatin immunoprecipitation data. Nucleic Acids Res. 2016, 44, 5010–5021. [Google Scholar] [CrossRef] [Green Version]
  30. Andrews, S. FastQC Version 0.11. 5. A Quality Control Tool for High Throughput Sequence Data, 2016. Available online: www.bioinformatics.babraham.ac.uk (accessed on 19 November 2020).
  31. Ewels, P.; Magnusson, M.; Lundin, S.; Käller, M. MultiQC: Summarize analysis results for multiple tools and samples in a single report. Bioinformatics 2016, 32, 3047–3048. [Google Scholar] [CrossRef] [Green Version]
  32. Bolger, A.M.; Lohse, M.; Usadel, B. Trimmomatic: A flexible trimmer for Illumina sequence data. Bioinformatics 2014, 30, 2114–2120. [Google Scholar] [CrossRef] [Green Version]
  33. Dobin, A.; Gingeras, T.R. Mapping RNA-seq reads with STAR. Curr. Protoc. Bioinform. 2015, 51, 11.14.1–11.14.19. [Google Scholar] [CrossRef] [Green Version]
  34. Li, B.; Dewey, C.N. RSEM: Accurate transcript quantification from RNA-Seq data with or without a reference genome. BMC Bioinform. 2011, 12, 323. [Google Scholar] [CrossRef] [Green Version]
  35. Love, M.I.; Huber, W.; Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 2014, 15, 1–21. [Google Scholar] [CrossRef] [Green Version]
  36. Aggarwal, A.; Singh, S.; Badrealam, K.; Renuka, A.K.; Anil, K. Haematological and hormonal profile of various breeds of cattle and buffalo under varied seasons and environmental conditions. ICAR-National Dairy Research Institute, Karnal. NDRI Publ. Np 2016, 146, 2016. [Google Scholar]
  37. Chen, S.; Zhou, Y.; Chen, Y.; Gu, J. fastp: An ultra-fast all-in-one FASTQ preprocessor. Bioinformatics 2018, 34, i884–i890. [Google Scholar] [CrossRef]
  38. Li, H.; Durbin, R. Fast and accurate short read alignment with Burrows—Wheeler transform. Bioinformatics 2009, 25, 1754–1760. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  39. Garrison, E.; Marth, G. Haplotype-based variant detection from short-read sequencing. arXiv 2012, preprint. arXiv:1207.3907 2012. [Google Scholar]
  40. Van Der Auwera, G.A.; Carneiro, M.O.; Hartl, C.; Poplin, R.; Del Angel, G.; Levy-Moonshine, A.; Jordan, T.; Shakir, K.; Roazen, D.; Thibault, J.; et al. From FastQ Data to High-Confidence Variant Calls: The Genome Analysis Toolkit Best Practices Pipeline. Curr. Protoc. Bioinform. 2013, 43, 11.10.1–11.10.33. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  41. Danecek, P.; Auton, A.; Abecasis, G.; Albers, C.A.; Banks, E.; DePristo, M.A.; Handsaker, R.E.; Lunter, G.; Marth, G.T.; Sherry, S.T.; et al. The variant call format and VCFtools. Bioinformatics 2011, 27, 2156–2158. [Google Scholar] [CrossRef]
  42. Cingolani, P.; Platts, A.; Wang, L.L.; Coon, M.; Nguyen, T.; Wang, L.; Land, S.J.; Lu, X.; Ruden, D.M. A program for annotating and predicting the effects of single nucleotide polymorphisms, SnpEff: SNPs in the genome of Drosophila melanogaster strain w1118; iso-2; iso-3. Fly 2012, 6, 80–92. [Google Scholar] [CrossRef] [Green Version]
  43. Zinicola, M.; Bicalho, M.; Santin, T.; Marques, E.; Bisinotto, R.; Bicalho, R. Effects of recombinant bovine interleukin-8 (rbIL-8) treatment on health, metabolism, and lactation performance in Holstein cattle II: Postpartum uterine health, ketosis, and milk production. J. Dairy Sci. 2019, 102, 10316–10328. [Google Scholar] [CrossRef]
  44. Goto, K.; Hozumi, Y.; Nakano, T.; Saino-Saito, S.; Martelli, A.M. Lipid Messenger, Diacylglycerol, and its Regulator, Diacylglycerol Kinase, in Cells, Organs, and Animals: History and Perspective. Tohoku J. Exp. Med. 2008, 214, 199–212. [Google Scholar] [CrossRef] [Green Version]
  45. Birsoy, K.; Berry, R.; Wang, T.; Ceyhan, O.; Tavazoie, S.; Friedman, J.M.; Rodeheffer, M.S. Analysis of gene networks in white adipose tissue development reveals a role for ETS2 in adipogenesis. Development 2011, 138, 4709–4719. [Google Scholar] [CrossRef] [Green Version]
  46. Manisha, D.; Rank, D.; Vataliya, P.; Joshi, C. Oxidized low density lipoprotein receptor 1 (OLR1) gene polymorphism in Mehsana buffaloes (Bubalus bubalis). Buffalo Bull. 2013, 32, 260–264. [Google Scholar]
  47. Park, J.Y.; Jang, H.; Curry, T.E.; Sakamoto, A.; Jo, M. Prostate androgen-regulated mucin-like protein 1: A novel regulator of progesterone metabolism. Mol. Endocrinol. 2013, 27, 1871–1886. [Google Scholar] [CrossRef] [Green Version]
  48. Alfadhel, M.; Nashabat, M.; Al Qahtani, H.; Alfares, A.; Al Mutairi, F.; Al Shaalan, H.; Douglas, G.V.; Wierenga, K.; Juusola, J.; Alrifai, M.T.; et al. Mutation in SLC6A9 encoding a glycine transporter causes a novel form of non-ketotic hyperglycinemia in humans. Qual. Life Res. 2016, 135, 1263–1268. [Google Scholar] [CrossRef] [Green Version]
  49. Vitavska, O.; Wieczorek, H. Putative role of an SLC45 H+/sugar cotransporter in mammalian spermatozoa. PflÜGers Arch.-Eur. J. Physiol. 2017, 469, 1433–1442. [Google Scholar] [CrossRef] [Green Version]
  50. Dunn, J.T.; Dunn, A.D. The importance of thyroglobulin structure for thyroid hormone biosynthesis. Biochimie 1999, 81, 505–509. [Google Scholar] [CrossRef]
  51. Nagy, D.; Gönczi, M.; Dienes, B.; Szöőr, Á.; Fodor, J.; Nagy, Z.; Tóth, A.; Fodor, T.; Bai, P.; Szücs, G.; et al. Silencing the KCNK9 potassium channel (TASK-3) gene disturbs mitochondrial function, causes mitochondrial depolarization, and induces apoptosis of human melanoma cells. Arch. Dermatol. Res. 2014, 306, 885–902. [Google Scholar] [CrossRef]
  52. Jiang, J.; Liu, L.; Gao, Y.; Shi, L.; Li, Y.; Liang, W.; Sun, D. Determination of genetic associations between indels in 11 candidate genes and milk composition traits in Chinese Holstein population. BMC Genet. 2019, 20, 48. [Google Scholar] [CrossRef] [Green Version]
  53. Xu, Q.; Mei, G.; Sun, D.; Zhang, Q.; Zhang, Y.; Yin, C.; Chen, H.; Ding, X.; Liu, J. Detection of genetic association and functional polymorphisms of UGDH affecting milk production trait in Chinese Holstein cattle. BMC Genom. 2012, 13, 590. [Google Scholar] [CrossRef] [Green Version]
  54. Krzywinska, E.; Zorawski, M.D.; Taracha, A.; Kotarba, G.; Kikulska, A.; Mlacki, M.; Kwiatkowska, K.; Wilanowski, T. Threonine 454 phosphorylation in Grainyhead-like 3 is important for its function and regulation by the p38 MAPK pathway. Biochim. et Biophys. Acta 2018, 1865, 1002–1011. [Google Scholar] [CrossRef]
  55. Lahiri, A.; Hedl, M.; Abraham, C. MTMR3 risk allele enhances innate receptor-induced signaling and cytokines by decreasing autophagy and increasing caspase-1 activation. Proc. Natl. Acad. Sci. USA 2015, 112, 10461–10466. [Google Scholar] [CrossRef] [Green Version]
  56. Deák, F.; Mátés, L.; Korpos, É.; Zvara, Á.; Szénási, T.; Kiricsi, M.; Mendler, L.; Keller-Pintér, A.; Ózsvári, B.; Juhász, H.; et al. Extracellular deposition of matrilin-2 controls the timing of the myogenic program during muscle regeneration. J. Cell Sci. 2014, 127, 3240. [Google Scholar] [CrossRef] [Green Version]
  57. Song, H.; Liu, L.; Song, Z.; Ren, Y.; Li, C.; Huo, J. P4HA3 is Epigenetically Activated by Slug in Gastric Cancer and its Deregulation is Associated With Enhanced Metastasis and Poor Survival. Technol Cancer Res Treat 2018, 17, 1533033818796485. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  58. Khalifeh-Soltani, A.; Gupta, D.; Ha, A.; Iqbal, J.; Hussain, M.; Podolsky, M.J.; Atabai, K. Mfge8 regulates enterocyte lipid storage by promoting enterocyte triglyceride hydrolase activity. JCI Insight 2016, 1, e87418. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  59. Matsuzaka, T.; Kuba, M.; Koyasu, S.; Yamamoto, Y.; Motomura, K.; Arulmozhiraja, S.; Ohno, H.; Sharma, R.; Shimura, T.; Okajima, Y.; et al. Hepatocyte ELOVL Fatty Acid Elongase 6 Determines Ceramide Acyl-Chain Length and Hepatic Insulin Sensitivity in Mice. Hepatology 2019, 71, 1609–1625. [Google Scholar] [CrossRef] [PubMed]
  60. Nichols, K.; Dijkstra, J.; van Laar, H.; Kim, J.; Cant, J.; Bannink, A. Expression of genes related to energy metabolism and the unfolded protein response in dairy cow mammary cells is affected differently during dietary supplementation with energy from protein and fat. J. Dairy Sci. 2019, 102, 6603–6613. [Google Scholar] [CrossRef]
  61. Sato, H.; Nagashima, K.; Ogura, M.; Sato, Y.; Tahara, Y.; Ogura, K.; Yamano, G.; Sugizaki, K.; Fujita, N.; Tatsuoka, H.; et al. Src regulates insulin secretion and glucose metabolism by influencing subcellular localization of glucokinase in pancreatic β-cells. J. Diabetes Investig. 2015, 7, 171–178. [Google Scholar] [CrossRef]
  62. Argov-Argaman, N.; Mida, K.; Cohen, B.C.; Visker, M.; Hettinga, K. Milk fat content and DGAT1 genotype determine lipid composition of the milk fat globule membrane. PLoS ONE 2013, 8, e68707. [Google Scholar] [CrossRef] [Green Version]
  63. Bovenhuis, H.; Visker, M.H.; van Valenberg, H.J.; Buitenhuis, A.J.; van Arendonk, J.A. Effects of the DGAT1 polymorphism on test-day milk production traits throughout lactation. J. Dairy Sci. 2015, 98, 6572–6582. [Google Scholar] [CrossRef] [Green Version]
  64. Yu, J.; Loh, K.; Song, Z.-Y.; Yang, H.-Q.; Zhang, Y.; Lin, S. Update on glycerol-3-phosphate acyltransferases: The roles in the development of insulin resistance. Nutr. Diabetes 2018, 8, 1–10. [Google Scholar] [CrossRef]
  65. Kordaß, T.; Osen, W.; Eichmüller, S.B. Controlling the Immune Suppressor: Transcription Factors and MicroRNAs Regulating CD73/NT5E. Front. Immunol. 2018, 9, 813. [Google Scholar] [CrossRef] [Green Version]
  66. Han, B.; Yuan, Y.; Liang, R.; Li, Y.; Liu, L.; Sun, D. Genetic Effects of LPIN1 Polymorphisms on Milk Production Traits in Dairy Cattle. Genes 2019, 10, 265. [Google Scholar] [CrossRef] [Green Version]
  67. Wang, H.; Jiang, L.; Liu, X.; Yang, J.; Wei, J.; Xu, J.; Zhang, Q.; Liu, J.-F. A Post-GWAS Replication Study Confirming the PTK2 Gene Associated with Milk Production Traits in Chinese Holstein. PLoS ONE 2013, 8, e83625. [Google Scholar] [CrossRef] [Green Version]
  68. Ma, Y.; Khan, M.Z.; Xiao, J.; Alugongo, G.M.; Chen, X.; Chen, T.; Liu, S.; He, Z.; Wang, J.; Shah, M.K.; et al. Genetic Markers Associated with Milk Production Traits in Dairy Cattle. Agriculture 2021, 11, 1018. [Google Scholar] [CrossRef]
  69. Wei, S.; Zhang, M.; Zheng, Y.; Yan, P. ZBTB16 Overexpression Enhances White Adipogenesis and Induces Brown-Like Adipocyte Formation of Bovine White Intramuscular Preadipocytes. Cell. Physiol. Biochem. 2018, 48, 2528–2538. [Google Scholar] [CrossRef]
  70. Pedrosa, V.B.; Schenkel, F.S.; Chen, S.-Y.; Oliveira, H.R.; Casey, T.M.; Melka, M.G.; Brito, L.F. Genomewide Association Analyses of Lactation Persistency and Milk Production Traits in Holstein Cattle Based on Imputed Whole-Genome Sequence Data. Genes 2021, 12, 1830. [Google Scholar] [CrossRef]
  71. Ogorevc, J.; Kunej, T.; Razpet, A.; Dovc, P. Database of cattle candidate genes and genetic markers for milk production and mastitis. Anim. Genet. 2009, 40, 832–851. [Google Scholar] [CrossRef] [Green Version]
  72. Wang, X.; Maltecca, C.; Tal-Stein, R.; Lipkin, E.; Khatib, H. Association of Bovine Fibroblast Growth Factor 2 (FGF2) Gene with Milk Fat and Productive Life: An Example of the Ability of the Candidate Pathway Strategy to Identify Quantitative Trait Genes. J. Dairy Sci. 2008, 91, 2475–2480. [Google Scholar] [CrossRef] [Green Version]
Animals 13 00884 g001 550
Figure 1. Venn diagram showing the hub and bottleneck genes: (A) QTL genes of milk fat percentage (256) and milk fat yield (286), (B) genes of top 50 hub and bottleneck genes, (C) genes of 17 hub and 18 bottleneck differentially expressed genes. MFP = milk fat percentage, MFY = milk fat yield.
Figure 1. Venn diagram showing the hub and bottleneck genes: (A) QTL genes of milk fat percentage (256) and milk fat yield (286), (B) genes of top 50 hub and bottleneck genes, (C) genes of 17 hub and 18 bottleneck differentially expressed genes. MFP = milk fat percentage, MFY = milk fat yield.
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Figure 2. Differentially expressed milk-trait-associated QTL genes and transcripts in the milk transcriptome of Jersey and Kashmiri breeds (A) DEGs and (B) DETs. Volcano plot showing the significance versus the fold change. Blue dots (p < 0.05 and log2FC > 2): all Bos indicus genes; green dots (p < 0.05): QTL genes; orange dots (p < 0.05): bottleneck genes; yellow dots (p < 0.05): hub genes; red dots (p < 0.05): genes possessing hub and bottleneck features.
Figure 2. Differentially expressed milk-trait-associated QTL genes and transcripts in the milk transcriptome of Jersey and Kashmiri breeds (A) DEGs and (B) DETs. Volcano plot showing the significance versus the fold change. Blue dots (p < 0.05 and log2FC > 2): all Bos indicus genes; green dots (p < 0.05): QTL genes; orange dots (p < 0.05): bottleneck genes; yellow dots (p < 0.05): hub genes; red dots (p < 0.05): genes possessing hub and bottleneck features.
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Figure 3. Confirmation of mutations shown in pyrograms with PyroMark SNP analysis assays: (A) GHR Gy392Ala (C>G) in Pulikulam (low-milk-yield breed) and (C/C) in Gir (high-milk-yield breed); (B) LPIN1 a Arg772Lys (G>A) in Amritmahal (low-milk-yield breed) and (G/G) in Sahiwal (high-milk-yield breed); (C) TLR4 a Asn347Gly (A>C) in Dangi (low-milk-yield breed) and (C/C) in Gir (high-milk-yield breed); the pyroseq sequencing primer is in reverse.
Figure 3. Confirmation of mutations shown in pyrograms with PyroMark SNP analysis assays: (A) GHR Gy392Ala (C>G) in Pulikulam (low-milk-yield breed) and (C/C) in Gir (high-milk-yield breed); (B) LPIN1 a Arg772Lys (G>A) in Amritmahal (low-milk-yield breed) and (G/G) in Sahiwal (high-milk-yield breed); (C) TLR4 a Asn347Gly (A>C) in Dangi (low-milk-yield breed) and (C/C) in Gir (high-milk-yield breed); the pyroseq sequencing primer is in reverse.
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Table
Table 1. Differentially expressed hub and bottleneck QTL genes of the protein interaction network of fat QTL genes.
Table 1. Differentially expressed hub and bottleneck QTL genes of the protein interaction network of fat QTL genes.
Gene IDlog2FCp-ValueTrait NameDescription
Hub genes
ENSBTAG00000008938 (SRC)1.4805870.002561MFY; MPP; MPY; TDMYSRC proto-oncogene, non-receptor tyrosine kinase
ENSBTAG00000026356 (DGAT1)0.9211040.032625MFP; MFY; MPP.
MPY; MKCP; MY		Diacylglycerol O-acyltransferase 1
ENSBTAG00000007867 (STAT1)1.9382080.0000648MFP; MPP; MPY; MYSignal transducer and activator of transcription 1
ENSBTAG00000005691 (FGF2)−1.558710.042228MFY; MYFibroblast growth factor 2
ENSBTAG00000019716 (CXCL8)7.0973270.000000000000077MFY; MPP; MPY;TDMYC-X-C motif chemokine ligand 8
ENSBTAG00000006240 (TLR4)3.9183830.0000000123MFP; MPP; MYToll like receptor 4
ENSBTAG00000001335 (GHR)−1.971710.02566MFP; MFY; MPP; MPY; MYGrowth hormone receptor
ENSBTAG00000012855 (LPL)−1.613770.008224MFP; MFY; MPPLipoprotein lipase
ENSBTAG00000009578 (PTK2)−0.976170.043672MFP; MFY; MPP; MPY; MYProtein tyrosine kinase 2
ENSBTAG00000000546 (ERBB2)−1.895350.048867MFP; MFY; MPP; MPY; MYErb-b2 receptor tyrosine kinase 2
ENSBTAG00000021527 (IGF1R)1.7985660.003947MFP; MFY; MPP; MPY; MYInsulin like growth factor 1 receptor
ENSBTAG00000007476 (BTRC)2.6989190.0000384MFYBeta-transducin repeat containing E3 ubiquitin protein ligase
ENSBTAG00000014357 (SDC2)−1.963120.029226MFP; MPPSyndecan 2
ENSBTAG00000048655 (NT5E)−2.970690.000136MFP; MFY; MPY5′-nucleotidase ecto
ENSBTAG00000007689 (LPIN1)−2.387990.001822MFP; MFY; MPP; MPY; TDMYLipin 1
ENSBTAG00000003300 (MFGE8)−1.904260.015698MFP; MPPMilk fat globule EGF and factor V/VIII domain containing
ENSBTAG00000020536 (HERC6)1.378830.00206MFP; MFY; MPP; MPYHECT and RLD domain containing E3 ubiquitin protein ligase family member 6
Bottleneck Genes
ENSBTAG00000026356 (DGAT1)0.9211040.032625MFP; MFY; MPP; MPY; MKCP; MYDiacylglycerol O-acyltransferase 1
ENSBTAG00000008938 (SRC)1.4805870.002561MFY; MPP; MPY; TDMYSRC proto-oncogene, non-receptor tyrosine kinase
ENSBTAG00000008432 (NUP98)1.3076270.033165MFPNucleoporin 98 and 96 precursor
ENSBTAG00000027629 (ANK1)−2.39490.000752MFY; MPYAnkyrin 1
ENSBTAG00000011266 (ZBTB16)−1.849460.011504MFYZinc finger and BTB domain containing 16
ENSBTAG00000016525 (ITGA1)−2.105830.004815MFY; MPP; MPY; MYIntegrin subunit alpha 1
ENSBTAG00000019716 (CXCL8)7.0973270.000000000000077MFY; MPP; MPY; TDMYC-X-C motif chemokine ligand 8
ENSBTAG00000007867 (STAT1)1.9382080.0000648MFP; MPP; MPY; MYSignal transducer and activator of transcription 1
ENSBTAG00000000546 (ERBB2)−1.895350.048867MFP; MFY; MPP; MPY; MYErb-b2 receptor tyrosine kinase 2
ENSBTAG00000007476 (BTRC)2.6989190.0000384MFYBeta-transducin repeat containing E3 ubiquitin protein ligase
ENSBTAG00000010106 (CCND3)−1.782270.000135MFP; MPP; MPY; MYCyclin D3
ENSBTAG00000001335 (GHR)−1.971710.02566MFP; MFY; MPP; MPY; MYGrowth hormone receptor
ENSBTAG00000006240 (TLR4)3.9183830.0000000123MFP; MPP; MYToll like receptor 4
ENSBTAG00000010660 (CACNA1C)−2.824450.0000723MFYCalcium voltage-gated channel subunit alpha1 C
ENSBTAG00000012855 (LPL)−1.613770.008224MFP; MFY; MPPLipoprotein lipase
ENSBTAG00000031544 (DDIT3)2.3673420.0000047MFP; MFY; MPP; MPY; MYDNA damage inducible transcript 3
ENSBTAG00000005091 (DGKG)4.0324640.000000442MFP; MPP; MYDiacylglycerol kinase gamma
ENSBTAG00000048655 (NT5E)−2.970690.000136MFP; MFY; MPY5′-nucleotidase ecto
MFP, milk fat percentage; MFY, milk fat yield; MPP, milk protein percentage; MPY, milk protein yield; MKCP, milk kappa casein protein; MY, milk yield.
Table
Table 2. Summary of variant analysis of differentially expressed hub and bottleneck genes.
Table 2. Summary of variant analysis of differentially expressed hub and bottleneck genes.
Gene IDChrStartEndSNP CountBiSNP
ENSBTAG00000007689 (LPIN1)NC_040086.18516899085299051109
ENSBTAG00000009578 (PTK2)NC_040089.12793205298617911
ENSBTAG00000026356 (DGAT1)NC_040089.154733655867322
ENSBTAG00000014357 (SDC2)NC_040089.1678851396800699322
ENSBTAG00000008432 (NUP98)NC_040090.1326622353274900022
ENSBTAG00000011266 (ZBTB16)NC_040090.1595191695972388733
ENSBTAG00000005691 (FGF2)NC_040092.1379009673798058222
ENSBTAG00000005091 (DGKG)NC_040076.1805954578082197633
ENSBTAG00000016525 (ITGA1)NC_040095.12593401326116012109
ENSBTAG00000001335 (GHR)NC_040095.1316830093199338666
ENSBTAG00000003300 (MFGE8)NC_040096.1205161162054064244
ENSBTAG00000021527 (IGF1R)NC_040096.17856996816185611
ENSBTAG00000027629 (ANK1)NC_040102.1360352553627659554
ENSBTAG00000010660 (CACNA1C)NC_040080.1112686731165982733
ENSBTAG00000031544 (DDIT3)NC_040080.1643463236435054011
ENSBTAG00000020536 (HERC6)NC_040081.1364923843654997499
ENSBTAG00000019716 (CXCL8)NC_040081.1883649338836871311
ENSBTAG00000006240 (TLR4)NC_040083.110707509910708612665
ENSBTAG00000012855 (LPL)NC_040083.1667175766674413111
ENSBTAG00000048655 (NT5E)NC_040084.1640296576410265966
BiSNP, biallelic SNP.
Table
Table 3. Genes with a fixed SNP pattern in high-milk-yield breeds (Sahiwal and Gir) and the respective variable SNP pattern in low-milk-yield breeds (Gaolao, Deoni, Pulikulam, Hallikar, Dangi, and Amritmahal).
Table 3. Genes with a fixed SNP pattern in high-milk-yield breeds (Sahiwal and Gir) and the respective variable SNP pattern in low-milk-yield breeds (Gaolao, Deoni, Pulikulam, Hallikar, Dangi, and Amritmahal).
GeneGHRGHRTLR4TLR4LPIN1LPIN1LPIN1CACNA1CZBTB16ITGA1ANK1ANK1NT5ENT5E
Genomic location316857733168598410708332610708391485187074852093098521152811271411597177092598312136054194360760376403509064065719
Genomic VariantRefCAACGCCCCCGGCG
AltGGCAATGTTTAATA
Protein Variant
Pos(Ref/Alt)		392
(G/A)		462
(N/D)		151
(A/T)		347
(N/G)		772
(R/K)		631
(A/E)		542
(R/P)		204
(E/K)		393
(V/M)		588
(V/M)		111
(P/S)		127
(R/H)		475
(C/F)		151
(A/V)
Sahiwal 1C/CA/AA/AC/CG/GC/CC/CC/CC/CC/CG/GG/GC/CG/G
Sahiwal 2C/CA/AA/AC/CG/GC/CC/CC/CC/CC/CG/GG/GC/CG/G
Sahiwal 3C/CA/AA/AC/CG/GC/CC/CC/CC/CC/CG/GG/GC/CG/G
Sahiwal 4C/CA/AA/AC/CG/GC/CC/CC/CC/CC/CG/GG/GC/CG/G
Gir 1C/CA/AA/AC/CG/GC/CC/CC/CC/CC/CG/GG/GC/CG/G
Gir 2C/CA/AA/AC/CG/GC/CC/CC/CC/CC/CG/GG/GC/CG/G
Gir 3C/CA/AA/AC/CG/GC/CC/CC/CC/CC/CG/GG/GC/CG/G
Gir 4C/CA/AA/AC/CG/GC/CC/CC/CC/CC/CG/GG/GC/CG/G
AmritmahalC/CA/AA/AC/CG/AC/CC/CC/CC/CC/CG/GG/GC/CG/G
DangiC/GA/GA/CC/AG/GC/CC/GC/CC/TC/CG/GG/GC/CG/G
GaolaoC/GA/AA/CC/CG/GC/TC/CC/CC/CC/CG/GG/GT/TG/G
DeoniC/CA/AA/CC/CG/GC/CC/GC/TC/TC/CG/GG/GC/CG/A
PulikulamC/GA/AA/AC/CG/AC/CC/TC/CC/CC/TG/AG/GC/CG/G
HallikarC/CA/AA/AC/CG/AC/CC/GC/CC/CC/CG/GG/AC/CG/G
Table
Table 4. Genes with fixed SNP pattern in low-milk-yield breeds (Sahiwal and Gir) and respective variable SNP pattern in high-milk-yield breeds (Gaolao, Deoni, Publicum, Hallikar, Dangi and Amritmahal).
Table 4. Genes with fixed SNP pattern in low-milk-yield breeds (Sahiwal and Gir) and respective variable SNP pattern in high-milk-yield breeds (Gaolao, Deoni, Publicum, Hallikar, Dangi and Amritmahal).
GeneMFGE8FGF2TLR4LPIN1NUP98PTK2ZBTB16ZBTB16DDIT3NT5E
Genomic location20518217379207461070803268520564232707374297394259717979597182066434657664102580
Genomic
Variant		RefAGGTGATGCG
AltTAAGACCATT
Protein Variant
Pos(Ref/Alt)		328
(S/R)		19
(G/R)		67
(R/K)		766
(S/P)		548
(H/Y)		904
(D/A)		598
(G/R)		627
(A/V)		87
(S/L)		8
(T/N)
Sahiwal 1A/AG/GG/AT/TG/GA/AT/TG/GC/TG/G
Sahiwal 2A/AG/GG/GT/GG/GA/AT/TG/GC/CG/T
Sahiwal 3A/AG/AG/GT/TG/AA/AT/TG/AC/CG/G
Sahiwal 4A/A-G/GT/TG/AA/AT/TG/GC/CG/G
Gir 1A/T-G/GT/TG/GA/AT/TG/GC/CG/G
Gir 2A/A-G/GT/TG/GA/AT/TG/GC/CG/G
Gir 3A/AG/GG/GT/TG/GA/CT/CG/GC/CG/G
Gir 4A/AG/GG/GT/TG/GA/AT/TG/GC/CG/G
AmritmahalA/AG/GG/GT/TG/GA/AT/TG/GC/CG/G
DangiA/AG/GG/GT/TG/GA/AT/TG/GC/CG/G
GaolaoA/AG/GG/GT/TG/GA/AT/TG/GC/CG/G
DeoniA/AG/GG/GT/TG/GA/AT/TG/GC/CG/G
PulikulamA/AG/GG/GT/TG/GA/AT/TG/GC/CG/G
HallikarA/AG/GG/GT/TG/GA/AT/TA/AC/CG/G
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Topno, N.A.; Kesarwani, V.; Kushwaha, S.K.; Azam, S.; Kadivella, M.; Gandham, R.K.; Majumdar, S.S. Non-Synonymous Variants in Fat QTL Genes among High- and Low-Milk-Yielding Indigenous Breeds. Animals 2023, 13, 884. https://doi.org/10.3390/ani13050884

AMA Style

Topno NA, Kesarwani V, Kushwaha SK, Azam S, Kadivella M, Gandham RK, Majumdar SS. Non-Synonymous Variants in Fat QTL Genes among High- and Low-Milk-Yielding Indigenous Breeds. Animals. 2023; 13(5):884. https://doi.org/10.3390/ani13050884

Chicago/Turabian Style

Topno, Neelam A., Veerbhan Kesarwani, Sandeep Kumar Kushwaha, Sarwar Azam, Mohammad Kadivella, Ravi Kumar Gandham, and Subeer S. Majumdar. 2023. "Non-Synonymous Variants in Fat QTL Genes among High- and Low-Milk-Yielding Indigenous Breeds" Animals 13, no. 5: 884. https://doi.org/10.3390/ani13050884

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