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Open Access

Peer-reviewed

Research Article

SNV discovery and functional candidate gene identification for milk composition based on whole genome resequencing of Holstein bulls with extremely high and low breeding values

  • Shan Lin,

    Roles Data curation, Formal analysis, Methodology, Software, Validation, Writing – original draft

    Affiliation Department of Animal Genetics and Breeding, College of Animal Science and Technology, Key Laboratory of Animal Genetics and Breeding of the Ministry of Agriculture, National Engineering Laboratory for Animal Breeding, China Agricultural University, Beijing, China

    ⨯
  • Hongyan Zhang,

    Roles Software, Visualization

    Affiliation Department of Animal Genetics and Breeding, College of Animal Science and Technology, Key Laboratory of Animal Genetics and Breeding of the Ministry of Agriculture, National Engineering Laboratory for Animal Breeding, China Agricultural University, Beijing, China

    ⨯
  • Yali Hou,

    Roles Methodology, Software

    Affiliation Laboratory of Disease Genomics and Individualized Medicine, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, China

    ⨯
  • Lin Liu,

    Roles Data curation, Formal analysis, Resources

    Affiliation Beijing Dairy Cattle Center, Beijing, China

    ⨯
  • Wenhui Li,

    Roles Resources, Software

    Affiliation Department of Animal Genetics and Breeding, College of Animal Science and Technology, Key Laboratory of Animal Genetics and Breeding of the Ministry of Agriculture, National Engineering Laboratory for Animal Breeding, China Agricultural University, Beijing, China

    ⨯
  • Jianping Jiang,

    Roles Investigation, Methodology, Validation

    Affiliation Department of Animal Genetics and Breeding, College of Animal Science and Technology, Key Laboratory of Animal Genetics and Breeding of the Ministry of Agriculture, National Engineering Laboratory for Animal Breeding, China Agricultural University, Beijing, China

    ⨯
  • Bo Han,

    Roles Resources, Validation

    Affiliation Department of Animal Genetics and Breeding, College of Animal Science and Technology, Key Laboratory of Animal Genetics and Breeding of the Ministry of Agriculture, National Engineering Laboratory for Animal Breeding, China Agricultural University, Beijing, China

    ⨯
  • Shengli Zhang,

    Roles Conceptualization, Project administration

    Affiliation Department of Animal Genetics and Breeding, College of Animal Science and Technology, Key Laboratory of Animal Genetics and Breeding of the Ministry of Agriculture, National Engineering Laboratory for Animal Breeding, China Agricultural University, Beijing, China

    ⨯
  • Dongxiao Sun

    Roles Conceptualization, Funding acquisition, Project administration, Writing – review & editing

    sundx@cau.edu.cn

    Affiliation Department of Animal Genetics and Breeding, College of Animal Science and Technology, Key Laboratory of Animal Genetics and Breeding of the Ministry of Agriculture, National Engineering Laboratory for Animal Breeding, China Agricultural University, Beijing, China

    ⨯

Correction

20 Nov 2019: Lin S, Zhang H, Hou Y, Liu L, Li W, et al. (2019) Correction: SNV discovery and functional candidate gene identification for milk composition based on whole genome resequencing of Holstein bulls with extremely high and low breeding values. PLOS ONE 14(11): e0225747. https://doi.org/10.1371/journal.pone.0225747 View correction

Abstract

We have sequenced the whole genomes of eight proven Holstein bulls from the four half-sib or full-sib families with extremely high and low estimated breeding values (EBV) for milk protein percentage (PP) and fat percentage (FP) using Illumina re-sequencing technology. Consequently, 2.3 billion raw reads were obtained with an average effective depth of 8.1×. After single nucleotide variant (SNV) calling, total 10,961,243 SNVs were identified, and 57,451 of them showed opposite fixed sites between the bulls with high and low EBVs within each family (called as common differential SNVs). Next, we annotated the common differential SNVs based on the bovine reference genome, and observed that 45,188 SNVs (78.70%) were located in the intergenic region of genes and merely 11,871 SNVs (20.67%) located within the protein-coding genes. Of them, 13,099 common differential SNVs that were within or close to protein-coding genes with less than 5 kb were chosen for identification of candidate genes for milk compositions in dairy cattle. By integrated analysis of the 2,657 genes with the GO terms and pathways related to protein and fat metabolism, and the known quantitative trait loci (QTLs) for milk protein and fat traits, we identified 17 promising candidate genes: ALG14, ATP2C1, PLD1, C3H1orf85, SNX7, MTHFD2L, CDKN2D, COL5A3, FDX1L, PIN1, FIG4, EXOC7, LASP1, PGS1, SAO, GPLD1 and MGEA5. Our findings provided an important foundation for further study and a prompt for molecular breeding of dairy cattle.

Citation: Lin S, Zhang H, Hou Y, Liu L, Li W, Jiang J, et al. (2019) SNV discovery and functional candidate gene identification for milk composition based on whole genome resequencing of Holstein bulls with extremely high and low breeding values. PLoS ONE 14(8): e0220629. https://doi.org/10.1371/journal.pone.0220629

Editor: Sujan Mamidi, HudsonAlpha Institute for Biotechnology, UNITED STATES

Received: January 4, 2019; Accepted: July 19, 2019; Published: August 1, 2019

Copyright: © 2019 Lin et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All relevant data are within the manuscript and its Supporting Information files.

Funding: This work was financially supported by the National Natural Science Foundation of China (31872330, 31802041), Beijing Dairy Industry Innovation Team (BAIC06-2017/2018), Beijing Science and Technology Program (D171100002417001), National Science and Technology Programs of China (2013AA102504), earmarked fund for Modern Agro-industry Technology Research System (CARS-36), and the Program for Changjiang Scholar and Innovation Research Team in University (IRT_15R62).

Competing interests: The authors have declared that no competing interests exist.

Introduction

Milk yield, milk protein and fat traits are main economic traits and important breeding goals of dairy industry. Compared to the standard phenotypic data based methods, marker-assisted selection is expected to lead faster genetic progress by using information at the DNA level. Of note, genomic selection (GS) with the application of high density SNP chips has become the most popular and efficient technology in dairy cattle breeding since the first report of GS in 2001 by Meuwissen et al [1, 2]. Using publicly available quantitative trait loci (QTLs) and genome-wide association study (GWAS) data can improve the accuracy of whole genome prediction (WGP) compared to the chip-based GBLUP and BayesB methods et al[3]. Over the last few decades, with linkage (LA) or linkage and linkage disequilibrium (LA/LD) analysis, candidate genes approach and genome-wide association analysis (GWAS)[4], a great amount of QTLs and genetic associations for milk yield and milk composition have been identified in dairy cattle since the first report of QTL mapping in Holstein by Georges et al [5]. So far, the Cattle QTL database contains 3,996, 17,677, and 19,895 loci for milk yield, milk protein and fat, respectively (December 23, 2018, http://www.animalgenome.org/cgi-bin/QTLdb/). Nonetheless, merely DGAT1, GHR, and ABCG2 gene have been validated to be true major genes for milk composition traits until now [6–11].

In recent years, the development of bioinformatics software and cost reduction of next generation sequencing (NGS) has opened a new era for genomics and molecular biology[12]. Compared to the traditional Sanger capillary electrophoresis sequencing method [13, 14], NGS technologies that is massively parallel DNA sequencing methods, provide higher throughput data with lower cost and make population-scale genome research possible[15–17]. Moreover, NGS can detect rare mutations, solve the disequilibrium between the rare causal mutations, genotype SNPs and distinguish structural variants [15, 18]. As for all kinds of variants, single nucleotide polymorphisms (SNPs) are the most widespread and wide-used in identification of genes for complex traits [19, 20]. Some whole genome resequencing studies in cattle have been reported on SNPs and copy number variations (CNVs) for genetic differences between the Black Angus and Holstein[21], Hanwoo-specific structural variations and selection signatures for meat quality and disease resistance traits in Hanwoo [22], haplotype under selection in USA Holstein [23] and evolutionary analysis in Japanese Kuchinoshima-Ushi [24]. In our previous studies, we detected some CNVs and insertions and deletions (indels) associated with milk protein and fat in Chinese Holstein [25, 26]. In the present study, we searched for differential SNVs between the Holstein bulls with extremely high and low estimated breeding values (EBVs) for milk protein percentage (PP) and fat percentage (FP) traits based on whole genome sequencing data, and identified candidate genes for milk compositions by integrating biological functions and the known QTL data.

Materials and methods

Sample selection and resequencing

Eight Holstein bulls were selected from the Beijing Dairy Cattle Center (http://www.bdcc.com.cn/) that consisted of four full-sib and/or half-sib families, and each family contain s two bulls who have extremely high and low EBVs for milk protein percentage (PP) and fat percentage (FP) with reliabilities of more than 0.85. The detailed information of the 8 bulls were described previously [25, 26].

The frozen semen samples were used for genomic DNA collection with the standard phenol/chloroform extraction method. 1% agarose gels and Nano Drop 2000 (Thermo Scientific Inc. Waltham, DE, USA) were performed for the DNA concentration and purity control. The purified DNAs were then used for library construction. Eight paired-end libraries (read length = 2×100 bp) with one library for each bull were constructed, and subsequently sequenced on Illumina Hiseq2000 instruments (Illumina Inc., San Diego, CA, USA).

Read mapping and SNV calling

By using the Burrows–Wheeler Alignment tool (BWA ver. 0.6.2)[27], the sequenced reads were aligned to the bovine reference genome assembly UMD3.1.69 (ftp://ftp.ensembl.org/pub/release-69/fasta/bos_taurus/dna/) with the default parameters. NGS QC Toolkit with default parameters was applied to reduce mapping error rate [28]. By comparing 8 individual sequence to the bovine reference genome respectively, we called SNVs for each bull based on SAM tools (ver. 0.1.19)[29] with following criteria: base quality score ≥20; read depth <100 for each individual; and non-reference allele supporting reads >3. Based on this, 8 sets of SNV data for 8 bulls could be obtained.

Functional annotation and SNV filtering

After SNV calling, the SNVs were annotated by ANNOVAR[30] using the RefSeq gene sets (14,912 genes; the gene sets is available from the UCSC download site http://hgdownload.cse.ucsc.edu/goldenPath/bosTau6/database/). The region that was close to a gene with less than 1kb was defined as upstream/downstream and that with more than 1kb was defined as intergenic region.

Afterwards, every single nucleotide which was polymorphic between the two bulls with high and low EBVs within each family was preserved. Then the SNVs with opposite fixed sites across four families were chosen and defined as ‘common differential SNVs’. Fixed sites, which were SNVs with opposite fixed alleles in the high and low group were used for identification of candidate genes.

Functional enrichment analysis

After annotation, we selected the genes that included or were closed to the common differential SNVs with less than 5 kb. Then, we performed Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and Medical Subject Headings (MeSH) enrichment for these genes. KOBAS tool(http://kobas.cbi.pku.edu.cn/) was used for GO and KEGG pathway enrichment and MeSH ORA was applied for MeSH enrichement[31–33].All packages used in MeSH analysis are available in the releases of BIOCONDUCTOR (http://bioconductor.org/). P value of <0.05 determined by Fisher’s exact test was set as the criteria for significance.

Apart from the genes that were referred in the significantly enriched pathways, we also remained the genes that were not significantly enriched but involved in the 8 well-known pathways related to protein, fat, and fatty acid metabolisms based on the KEGG pathway website (http://www.kegg.jp/), including mTOR, insulin, AMPK, PPAR, Jak-STAT, PI3K-Akt, MAPK, and TGF-β.

Positions comparison with known QTL database

Afterwards, we obtained the genetic position of each gene based on its physical position and compared with the confidence intervals and the peak positions of the previously reported QTLs that have been shown to be associated with milk composition traits (http://www.animalgenome.org/cgi-bin/QTLdb). The genes that were close to the peaks of QTLs with less than 1 cM were remained.

Results

Read mapping and SNV detection

With Illumina HiSeq 2000, we sequenced the genomic DNA samples of the eight Holstein bulls with extremely high and low EBVs for milk protein percentage and milk fat percentage [25, 26]. As a result, a total of 2,303,781,449 raw reads were obtained. Of these, 2,055,337,835 reads (91.71%) were finally mapped to the reference genome (UMD 3.1.69) while the average proportion of uniquely mapped reads was 82.62% with an average depth of 8.1×, and the genome coverage was approximately 98% in each individual.

By using SAM tools with variant filtration process, 10,961,243 SNVs were identified in total after removing duplicates among 8 bulls at an average of 4,560,713 within each individual (Table 1).

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Table 1. Summary of the sequencing result and SNV counts for 8 extreme Holstein bulls.

https://doi.org/10.1371/journal.pone.0220629.t001

SNV annotation and genomic distribution

The 10,945,507 SNVs were annotated based on the bovine gene set in RefSeq database (including 14,912 genes) (S1 Table). Most of the SNVs (79.63%) were detected in intergenic region, and 2,156,190 (19.70%) SNVs were located within genes including introns (19.13%), exons (0.32%) and untranslated regions (UTR) (0.24%), and other region (0.68%) (ncRNA_exonic, ncRNA_intronic and up/downstream) (Table 2 and Fig 1). Of the total 35,505 exonic SNVs, 11,843 nonsynonymous nucleotide substitutions were included.

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Fig 1. Distribution of the 10,945,507 annotated SNVs according to the functional category.

After annotation of all SNVs among eight bulls, we found 8,715,765 intergenic SNVs (away from protein-coding genes more than 1 kb), 70,979 up/downstream SNVs, 2,573 ncRNA SNVs, 2,094,358 intronic SNVs, 26,327 untranslated regions (UTRs), 19,617 synonymous SNVs, 11,843 nonsynonymous substitutions, 75 stop gain SNVs, 5 stop loss SNVs and 3,965 unknown.

https://doi.org/10.1371/journal.pone.0220629.g001

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Table 2. Annotation of 10,945,507 SNVs across 8 bulls.

https://doi.org/10.1371/journal.pone.0220629.t002

Identification of common differential SNVs

Out of the 10,945,507 annotated SNVs, 57,451 that were fixed sites between the bulls with extremely high and low EBVs across four families were chosen for the further analysis, which number across chromosomes ranged from 761 to 5,044 (Fig 2). As a result of annotation, 57,419 common differential SNVs were successfully classified into 9 functional categories: the majority was found in intergenic and intronic regions (78.70% and 20.10%, respectively), whereas fewer SNVs were located in exon (0.31%), exonic ncRNA (0.003%), UTR (0.26%) and up/downstream (0.63%) (Table 3 and Fig 3).

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Fig 2. The number of common differential SNVs in each chromosome.

Each point represents the location of a SNV on chromosome and the number above every chromosome represents the counts of SNV in this chromosome.

https://doi.org/10.1371/journal.pone.0220629.g002

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Fig 3. Distribution of the common differential SNVs in nine functional categories.

After annotation, we found 45,188 intergenic SNVs (away from protein coding genes more than 1kb), 358 up/downstream SNVs, 2 ncRNA SNVs, 11,541 intronic SNVs, 154 untranslated regions (UTRs), 116 synonymous SNVs, 45 nonsynonymous substitutions and 15 unknown.

https://doi.org/10.1371/journal.pone.0220629.g003

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Table 3. Annotation of 57,419 common differential SNVs across 8 bulls.

https://doi.org/10.1371/journal.pone.0220629.t003

Subsequently, we further identified 2,657 protein-coding genes that included or were nearby the common differential SNVs with less than 5 kb. Of these, 13,099 SNVs were remained, including 11,498 (87.78%) in intron, 176 (1.34%) in exon, 154 (1.18%) in UTR, and 355 (2.71%) in upstream/downstream while only 6.99% were detected in intergenic region (Table 4 and Fig 4).

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Fig 4. Distribution of the 13,099 SNVs in eight functional categories.

A total of 13,099 SNVs was included or nearby the protein-coding genes with less than 5 kb. Of these, 916 SNVs were located in intergenic region (away from protein coding genes more than 1kb), 355 in up/downstream, 11,498 in intron, 154 untranslated regions (UTRs), 176 in exon (116 synonymous SNVs, 45 nonsynonymous substitutions and 15 unknown).

https://doi.org/10.1371/journal.pone.0220629.g004

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Table 4. Annotation of 13,099 common differential SNVs that were included or nearby 2,657 protein-coding genes.

https://doi.org/10.1371/journal.pone.0220629.t004

Genes Ontology and pathway analyses

To further identify candidate genes for milk protein and fat traits, we performed functional analysis on the above-mentioned 2,657 genes with KOBAS online tool and MeSH ORA. A total of 6,819 GO terms and 286 KEGG pathways were observed, among them with 1,011 terms and 73 pathways were significantly enriched (P<0.05; S2 Table). 23 significant MeSH terms were detected by MeSH ORA(P<0.05; S3 Table). Of these, 29 genes was enriched in Mesh term of Amino Acids (MeSH:D000596) in the Chemicals and Drugs category which was associated with protein synthesis and metabolism Thereby, we identified 1,354 genes that were involved in 133 significant GO terms, pathways and Mesh terms relevant to protein, lipid, and fatty acid synthesis and metabolism such as protein metabolic, cellular protein modification, lipid modification, phospholipid metabolic, glycerophospholipid metabolic, sphingolipid metabolism, glycerolipid metabolic, fat cell differentiation, insulin resistance, insulin secretion and MAPK signaling pathways.

Besides the genes which were significantly enriched in pathways associated with protein and fat synthesis and metabolism, we selected 23 additional genes that were not significantly enriched but participated in six well-known pathways such as mTOR, AMPK, Jak-STAT, PI3K-Akt, PPAR and TGF-β.Thus, 1,377 candidate genes were obtained for milk protein and fat traits.

Position comparison with known QTLs and identification of promising candidates associated with milk protein and fat traits

We further compared the physical positions of the 1,377 candidate genes with the previously reported QTLs for milk fat and protein in dairy cattle (http://www.animalgenome.org/cgi-bin/QTLdb). Consequently, 94 genes were found to be adjacent to the peak positions of QTLs with less than 1.0 cM. Of these, 17 genes with 21 common differential SNVs in exon, UTR, upstream and downsteam were identified as promising candidates affecting milk protein and fat traits. They included UDP-N-acetylglucosaminyltransferase subunit (ALG14), ATPase secretory pathway Ca2+ transporting 1 (ATP2C1), phosphatidylcholine-specific (PLD1), glycosylated lysosomal membrane protein (C3H1orf85), sorting nexin 7 (SNX7), methylenetetrahydrofolate dehydrogenase (NADP+ dependent) 2 like (MTHFD2L), cyclin dependent kinase inhibitor 2D (CDKN2D), alpha3 (V) collagen chain (COL5A3), ferredoxin 1-like(FDX1L), NIMA-interacting 1(PIN1), phosphoinositide 5-phosphatase (FIG4), exocyst complex component 7 (EXOC7), LIM and SH3 protein 1 (LASP1), phosphatidylglycerophosphate synthase 1 (PGS1), primary amine oxidase, liver isozyme (SAO), glycosylphosphatidylinositol specific phospholipase D1 (GPLD1) and OGA O-GlcNAcase (MGEA5). The alleles of the common differential SNV in high and low groups and the adjacent QTLs of 17 candidate genes were shown in Tables 5 and 6, respectively.

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Table 5. The information about the SNV allele in high and low groups of 17 candidate genes.

https://doi.org/10.1371/journal.pone.0220629.t005

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Table 6. The detailed information of 17 candidate genes and the related QTLs.

https://doi.org/10.1371/journal.pone.0220629.t006

Discussion

In this study, based on the whole genome resequencing data of 8 proven Holstein bulls from the four half-sib or full-sib families with extremely high and low EBVs for milk protein and fat percentages, we obtained 57,419 common differential SNVs between high and low groups, and further identified 17 promising candidate genes for milk composition traits by integrating the positions of SNVs in gene regions, the known QTLs and the biological functions of genes.

Since the first report of SNV detection by the whole genome resequencing in cattle, a number of SNVs have been detected in different cattle breeds. In this study, a total of 10,961,243 SNVs were identified in 8 Holstein bulls (average 4,560,713 for each), which was much more than those in Holstein bulls reported by Paul et al. (SNP = 3,755,663) [21], but fewer than other Holstein bulls studies(SNP = 12,434,860 and 26.7 million, respectively)[23, 47]. This was probably due to the different sequencing depth and coverage.

Candidate genes

The 17 identified promising candidate genes that contained one or two common differential SNVs were specifically illustrated follow.

Candidate genes with SNV in exon.

SNV in exon of a gene, especially nonsynonymous variants, potentially had a bigger influence on gene function. COL5A3 with a nonsynonymous SNV in exon 2 encodes collagen type V alpha 3 belong to a superfamily of proteins. COL5A3 takes part in protein digestion, absorption and PI3K-Akt signaling pathway. Previous study found that obese black women exhibited higher expression of COL5A1 (collagen Valpha1), and COL6A1 (collagen VIalpha1) than obese white women in gluteal [48]. In our previous RNA sequencing study among 3 milking period Holstein cattle, Collagen VI was found involved in regulating fat metabolism[49]. COL5A3 is an important element of the microenvironment of certain highly specialized cell types in white adipose tissue and have profound effects on function of such cells [50]. Actually, nonsynonymous and synonymous coding SNPs show similar likelihood and effect size of traits [51–54]. ATP2C1 with 2 synonymous SNVs encodes a protein belongs to the family of P-type cation transport ATPases which catalyzes the hydrolysis of ATP coupled with the transport of calcium ions. ATP2C1 activity is associated with the sphingomyelin content of the trans-Golgi network membrane and it regulates proteases within the trans-Golgi network that require for virus glycoprotein maturation [55, 56]. The study of rat demonstrated that ATP2C1 played a role in the control of beta-cell Ca (2+) homeostasis and insulin secretion [57]. In addition, Golgi Ca2+/H+ antiporter as a contributor to mammary Golgi calcium transport needs was related to the role of ATP2C1 and ATP2C2 [58]. PLD1 encodes a phosphatidylcholine-specific phospholipase which catalyzes the hydrolysis of phosphatidylcholine in order to yield phosphatidic acid and choline. The deficiency of PLD1 or PLD2 activity promotes elevated free fatty acids (FFA) levels and are insulin as well as glucose intolerant[59]. Besides, PLD1 regulates COPII vesicle transport from the endoplasmic reticulum (ER) to the Golgi apparatus by regulating Sec13/31 recruitment from the cytosol to the ER membrane during COPII vesicle formation [60]. EXOC7 encodes a protein which is a component of the exocyst complex that plays a critical role in vesicular trafficking and the secretory pathway by targeting post-Golgi vesicles to the plasma membrane. EXOC7 is a direct substrate of the extracellular signal-regulated kinases 1/2, their phosphorylation enhances the binding of EXOC7 to other exocyst components and promotes the assembly of the exocyst complex [61, 62]. PIPKIgamma and phosphatidyl inositol phosphate pools at nascent E-cadherin contacts cue EXOC7 targeting and orient the tethering of exocyst-associated E-cadherin [62]. The protein encoded by FIG4 belongs to the SAC domain-containing protein gene family. FIG4 binds to hepatitis C virus and modulates particle formation in a cholesteryl ester-related manner [63].

Candidate genes with SNV in regulatory regions.

SNV in regulatory regions probably regulates the translation processes of a gene. ALG14 with a SNV in 5’UTR is a member of the glycosyltransferase 1 family. The protein encoded by ALG14 and ALG13 are thought to be subunits of UDP-GlcNAc transferase, which catalyzes the first two committed steps in endoplasmic reticulum N-linked glycosylation. ALG14 coordinate recruitment of catalytic ALG7 and ALG13 to the endoplasmic reticulum membrane for initiating lipid-linked oligosaccharide biosynthesis at the N- and C-termini and interacted formation of the active UDP-N-acetylglucosamine transferase complex at the C terminus mediates[64, 65]. CDKN2D, LASP1 and PIN1 respectively contained 1, 1 and 2 SNVs in 3’UTR. CDKN2D encoded a protein which is a member of the INK4 family of cyclin-dependent kinase inhibitors that form a stable complex with CDK4 or CDK6, and prevent the activation of the CDK kinases, thus function as a cell growth regulator that controls cell cycle G1 progression. FDX1L encodes a member of the ferredoxin family. The mutation of genes that encoded proteins involved in either the lipoic acid (LIPT1 and LIPT2) or mitochondrial ISC biogenesis (FDX1L, ISCA2, IBA57, NFU1, BOLA3) pathway leaded a heterogeneous group of diseases with a wide variety of clinical symptoms and combined enzymatic defects [66]. The protein encoded by LASP1 is a subfamily of LIM proteins and also a member of the nebulin family of actin-binding proteins. LASP1 activates the PI3K/AKT signaling pathway which is well-known pathways for protein and fat synthesis and metabolism [67]. LASP1 was significantly upregulated in breast cancer tissues and cell lines and identified as a target gene of miR-133a [68]. Comparing gene expression profiles of lactating bovine mammary tissue against nonlactating tissue on the BMAM microarray, LASP1 exhibited differential expression [69]. PIN1 encodes one of the PPIases, which specifically binds to phosphorylated ser/thr-pro motifs to catalytically regulate the post-phosphorylation conformation of its substrates and involved in the regulation of cell growth. Besides, PIN1 can enhance adipocyte differentiation by regulating the function of PPAR gamma [70]. Another study suggested that PIN1 expression in pancreatic beta-cells was obviously changed in obese knockout mice from diet high in fat or sucrose [71].

Candidate genes with SNV in upstream and downstream.

Transcription factors interact with specific nucleotide sequences known as transcription factor binding site and these interactions are implicated in regulation of the gene expression. The upstream and downstream regions of genes contain variety of elements/binding sites, which apparently infer on a particular gene the inducibility. C3H1orf85, GPLD1, MTHFD2L, LASP1, MGEA5, PGS1, SAO, SNX7 contained at least one SNV located in upstream and downstream (<1000 bp) of these gene. C3H1orf85 encodes glycosylated lysosomal membrane protein and was also known as GLMP. Data indicated that increased flux of glucose, increased de novo lipogenesis and lipid accumulation were detected in lysosomal protein NCU-G1 (GLMP) gt/gt primary hepatocytes[72]. Compared with the wild-type myotubes, myotubes from GLMP (gt/gt) mice metabolized glucose faster and had a larger pool of intracellular glycogen, while oleic acid uptake, storage and oxidation were significantly reduced [73]. The nuclear proteins by O-linked N-acetylglucosamine (MGEA5) addition and removal on serine and threonine residues is catalyzed by OGT (MIM 300255), which adds O-GlcNAc, and MGEA5, a glycosidase that removes O-GlcNAc modifications[74]. PGS1 encodes a phosphatidylglycero-phosphate synthase. In cancer cachexia, TNFalpha induces a higher energy wasting in liver mitochondria by increasing cardiolipin content via upregulation of phosphatidylglycerophosphate synthase (PGPS) expression [75]. PGS1 gene in Saccharomyces cerevisiae played a vital role in cells impaired in the mitochondrial DNA, is localized in the mitochondria and expressed in response to inositol and choline[76]. The protein encoded by SAO is part of the anion exchanger (AE) family and is expressed in the erythrocyte plasma membrane. MTHFD2L encodes a mitochondrial methylenetetrahydrofolate dehydrogenase isozyme expressed in adult tissues. SNX7 encodes a member of the sorting nexin family that contain a phox (PX) domain, which is a phosphoinositide binding domain, and are involved in intracellular trafficking. In zebrafish, SNX7 is a liver-enriched anti-apoptotic protein and indispensible for the liver development[77]. The protein encoded by GPLD1 is a GPI degrading enzyme. GPLD1 hydrolyzes the inositol phosphate linkage in proteins anchored by phosphatidylinositol glycans, thereby releasing the attached protein from the plasma membrane. AMPK suppresses PLD activity, and PLD suppresses AMPK via mTOR [78]. Additionally, GPLD1 influences triglyceride-rich lipoprotein metabolism [79]. Overexpressing GPLD1 in an insulinoma cell line enhanced glucose-stimulated insulin secretion [79].

The 17 candidate genes and 21 SNVs identified in this study still need further in vivo and in vitro experiments to validate their biological function and to explore molecular mechanisms for formation of milk protein and fat traits.

The interpretation of the findings from the present study still has limitations. When performed function enrichment for genes that included or were closed to the common differential SNVs with less than 5 kb, non-coding RNAs and genes could be disregarded because the current software and tools can only annotate limited protein-coding genes. Therefore, the omission of genes that haven not been studied yet is a general problem in present function study.

Conclusions

In this study, by resequencing the whole genome of eight proven Holstein bulls with extremely high and low EBVs of milk protein percentage and fat percentage, we successfully identified 10,961,243 SNVs and detected 57,451 common differential SNVs with opposite fixed sites between high and low groups. Subsequently, 2,657 genes that included or were nearby the common differential SNVs were obtained. Further, through integrating GO, KEGG pathways and Mesh enrichment results, the known QTLs for milk composition and common differential SNVs located in exon and flanking regions, we identified 17 promising candidate genes for milk protein and fat, including ALG14, ATP2C1, PLD1, C3H1orf85, SNX7, MTHFD2L, CDKN2D, COL5A3, FDX1L, PIN1, FIG4, EXOC7, LASP1, PGS1, SAO, GPLD1 and MGEA5. And the 17 genes identified in this study will provide a useful resource for future genomic selection (GS) in dairy cattle.

Ethics statement

All protocols for semen samples of China Holstein bulls were reviewed and approved by the Institutional Animal Care and Use Committee (IACUC) at China Agricultural University. Semen samples were collected specifically for this study following standard procedures with the full agreement of the Beijing Dairy Cattle Center who owned the animals.

Supporting information

S1 Table. Functional annotation of each identified SNVs.

https://doi.org/10.1371/journal.pone.0220629.s001

(ZIP)

S2 Table. GO and KEGG pathways enrichment results for 2,657 genes by KOBAS tool.

https://doi.org/10.1371/journal.pone.0220629.s002

(XLSX)

S3 Table. Mesh enrichment results for 2,657 genes by MeSH ORA.

https://doi.org/10.1371/journal.pone.0220629.s003

(XLSX)

References

  1. 1. Meuwissen THE, Hayes BJ, Goddard ME. Prediction of total genetic value using genome-wide dense marker maps. Genetics. 2001;157(4):1819–29. WOS:000168223400036. pmid:11290733
  2. 2. Matukumalli LK, Lawley CT, Schnabel RD, Taylor JF, Allan MF, Heaton MP, et al. Development and characterization of a high density SNP genotyping assay for cattle. PLoS One. 2009;4(4):e5350. pmid:19390634; PubMed Central PMCID: PMCPMC2669730.
  3. 3. Zhang Z, Ober U, Erbe M, Zhang H, Gao N, He JL, et al. Improving the Accuracy of Whole Genome Prediction for Complex Traits Using the Results of Genome Wide Association Studies. Plos One. 2014;9(3). ARTN e93017 WOS:000333459900143. pmid:24663104
  4. 4. Andersson L. Genome-wide association analysis in domestic animals: a powerful approach for genetic dissection of trait loci. Genetica. 2009;136(2):341–9. pmid:18704695.
  5. 5. Georges M, Nielsen D, Mackinnon M, Mishra A, Okimoto R, Pasquino AT, et al. Mapping quantitative trait loci controlling milk production in dairy cattle by exploiting progeny testing. Genetics. 1995;139(2):907–20. pmid:7713441; PubMed Central PMCID: PMCPMC1206390.
  6. 6. Jiang L, Liu JF, Sun DX, Ma PP, Ding XD, Yu Y, et al. Genome Wide Association Studies for Milk Production Traits in Chinese Holstein Population. Plos One. 2010;5(10). ARTN e13661 WOS:000283537000030. pmid:21048968
  7. 7. Schopen GCB, Visker MHPW, Koks PD, Mullaart E, van Arendonk JAM, Bovenhuis H. Whole-genome association study for milk protein composition in dairy cattle. Journal of Dairy Science. 2011;94(6):3148–58. WOS:000290777800049. pmid:21605784
  8. 8. Mai MD, Sahana G, Christiansen FB, Guldbrandtsen B. A genome-wide association study for milk production traits in Danish Jersey cattle using a 50K single nucleotide polymorphism chip. J Anim Sci. 2010;88(11):3522–8. pmid:20656975.
  9. 9. Grisart B, Coppieters W, Farnir F, Karim L, Ford C, Berzi 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(2):222–31. pmid:11827942.
  10. 10. Minozzi G, Nicolazzi EL, Stella A, Biffani S, Negrini R, Lazzari B, et al. Genome wide analysis of fertility and production traits in Italian Holstein cattle. PLoS One. 2013;8(11):e80219. pmid:24265800; PubMed Central PMCID: PMCPMC3827211.
  11. 11. Blott S, Kim JJ, Moisio S, Schmidt-Kuntzel A, Cornet A, Berzi P, 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(1):253–66. WOS:000181039700023. pmid:12586713
  12. 12. Park ST, Kim J. Trends in Next-Generation Sequencing and a New Era for Whole Genome Sequencing. International neurourology journal. 2016;20(Suppl 2):S76–83. pmid:27915479; PubMed Central PMCID: PMC5169091.
  13. 13. Sanger F, Nicklen S, Coulson AR. DNA sequencing with chain-terminating inhibitors. Proc Natl Acad Sci U S A. 1977;74(12):5463–7. pmid:271968; PubMed Central PMCID: PMCPMC431765.
  14. 14. Maxam AM, Gilbert W. A new method for sequencing DNA. Proc Natl Acad Sci U S A. 1977;74(2):560–4. pmid:265521; PubMed Central PMCID: PMCPMC392330.
  15. 15. Clark AG HM, Bustamante CD, Williamson SH, Nielsen R. Ascertainment bias in studies of human genome-wide polymorphism. Genome Res. 2005;15(11).
  16. 16. Shendure J, Ji H. Next-generation DNA sequencing. Nature biotechnology. 2008;26(10):1135–45. pmid:18846087.
  17. 17. Miller JR, Koren S, Sutton G. Assembly algorithms for next-generation sequencing data. Genomics. 2010;95(6):315–27. pmid:20211242; PubMed Central PMCID: PMC2874646.
  18. 18. Nielsen R, Hubisz MJ, Clark AG. Reconstituting the frequency spectrum of ascertained single-nucleotide polymorphism data. Genetics. 2004;168(4):2373–82. pmid:15371362; PubMed Central PMCID: PMCPMC1448751.
  19. 19. Lu D MS, Sargolzaei M Kelly M, Vander Voort G, Caldwell T, Wang Z, Plastow G, and Moore S. Genome-wide association analyses for growth and feed efficiency traits in beef cattle. J Anim Sci. 2013;91.
  20. 20. Utsunomiya YT, O’Brien A.M.P., Sonstegard T.S., Van Tassell C.P., do Carmo A.S., Meszaros G., Soelkner J., and Garcia J.F. Detecting loci under recent positive selection in dairy and beef cattle by combining different genome-wide scan method. Plos One. 2013;8.
  21. 21. Stothard P, Choi JW, Basu U, Sumner-Thomson JM, Meng Y, Liao X, et al. Whole genome resequencing of black Angus and Holstein cattle for SNP and CNV discovery. BMC genomics. 2011;12:559. pmid:22085807; PubMed Central PMCID: PMC3229636.
  22. 22. Choi JW, Choi BH, Lee SH, Lee SS, Kim HC, Yu D, et al. Whole-Genome Resequencing Analysis of Hanwoo and Yanbian Cattle to Identify Genome-Wide SNPs and Signatures of Selection. Mol Cells. 2015;38(5):466–73. pmid:26018558; PubMed Central PMCID: PMC4443289.
  23. 23. Larkin DM, Daetwyler HD, Hernandez AG, Wright CL, Hetrick LA, Boucek L, et al. Whole-genome resequencing of two elite sires for the detection of haplotypes under selection in dairy cattle. P Natl Acad Sci USA. 2012;109(20):7693–8. WOS:000304369800034. pmid:22529356
  24. 24. Kawahara-Miki R, Tsuda K, Shiwa Y, Arai-Kichise Y, Matsumoto T, Kanesaki Y, et al. Whole-genome resequencing shows numerous genes with nonsynonymous SNPs in the Japanese native cattle Kuchinoshima-Ushi. BMC Genomics. 2011;12:103. pmid:21310019; PubMed Central PMCID: PMC3048544.
  25. 25. Gao YH, Jiang JP, Yang SH, Hou YL, Liu GE, Zhang SG, et al. CNV discovery for milk composition traits in dairy cattle using whole genome resequencing. Bmc Genomics. 2017;18. 26510.1186/s12864-017-3636-3. WOS:000397755800001.
  26. 26. Jiang JP, Gao YH, Hou YL, Li WH, Zhang SL, Zhang Q, et al. Whole-Genome Resequencing of Holstein Bulls for Indel Discovery and Identification of Genes Associated with Milk Composition Traits in Dairy Cattle. Plos One. 2016;11(12). ARTN e0168946 WOS:000391222000088. pmid:28030618
  27. 27. Li H, Durbin R. Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics. 2009;25(14):1754–60. pmid:19451168; PubMed Central PMCID: PMCPMC2705234.
  28. 28. Patel RK, Jain M. NGS QC Toolkit: a toolkit for quality control of next generation sequencing data. PLoS One. 2012;7(2):e30619. pmid:22312429; PubMed Central PMCID: PMCPMC3270013.
  29. 29. Li H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N, et al. The Sequence Alignment/Map format and SAMtools. Bioinformatics. 2009;25(16):2078–9. pmid:19505943; PubMed Central PMCID: PMCPMC2723002.
  30. 30. Wang K, Li M, Hakonarson H. ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data. Nucleic Acids Res. 2010;38(16):e164. pmid:20601685; PubMed Central PMCID: PMCPMC2938201.
  31. 31. Xie C, Mao XZ, Huang JJ, Ding Y, Wu JM, Dong S, et al. KOBAS 2.0: a web server for annotation and identification of enriched pathways and diseases. Nucleic Acids Res. 2011;39:W316–W22. WOS:000292325300051. pmid:21715386
  32. 32. Morota G, Penagaricano F, Petersen JL, Ciobanu DC, Tsuyuzaki K, Nikaido I. An application of MeSH enrichment analysis in livestock. Anim Genet. 2015;46(4):381–7. pmid:26036323; PubMed Central PMCID: PMCPMC5032990.
  33. 33. Morota G, Beissinger TM, Penagaricano F. MeSH-Informed Enrichment Analysis and MeSH-Guided Semantic Similarity Among Functional Terms and Gene Products in Chicken. G3-Genes Genom Genet. 2016;6(8):2447–53. WOS:000381282300019.
  34. 34. Marete AG, Guldbrandtsen B, Lund MS, Fritz S, Sahana G, Boichard D. A Meta-Analysis Including Pre-selected Sequence Variants Associated With Seven Traits in Three French Dairy Cattle Populations. Front Genet. 2018;9:522. pmid:30459810; PubMed Central PMCID: PMCPMC6232291.
  35. 35. Russo V, Fontanesi L, Dolezal M, Lipkin E, Scotti E, Zambonelli P, et al. A whole genome scan for QTL affecting milk protein percentage in Italian Holstein cattle, applying selective milk DNA pooling and multiple marker mapping in a daughter design. Animal Genetics. 2012;43:72–86. WOS:000305788600009. pmid:22742505
  36. 36. Nadesalingam J, Plante Y, Gibson JP. Detection of QTL for milk production on Chromosomes 1 and 6 of Holstein cattle. Mamm Genome. 2001;12(1):27–31. pmid:11178740.
  37. 37. Ashwell MS, Heyen DW, Sonstegard TS, Van Tassell CP, Da Y, VanRaden PM, et al. Detection of quantitative trait loci affecting milk production, health, and reproductive traits in Holstein cattle. J Dairy Sci. 2004;87(2):468–75. pmid:14762090.
  38. 38. Cole JB, Wiggans GR, Ma L, Sonstegard TS, Lawlor TJ Jr., Crooker BA, et al. Genome-wide association analysis of thirty one production, health, reproduction and body conformation traits in contemporary U.S. Holstein cows. Bmc Genomics. 2011;12:408. pmid:21831322; PubMed Central PMCID: PMCPMC3176260.
  39. 39. Olsen HG, Knutsen TM, Lewandowska-Sabat AM, Grove H, Nome T, Svendsen M, et al. Fine mapping of a QTL on bovine chromosome 6 using imputed full sequence data suggests a key role for the group-specific component (GC) gene in clinical mastitis and milk production. Genet Sel Evol. 2016;48(1):79. pmid:27760518; PubMed Central PMCID: PMCPMC5072345.
  40. 40. Zhou Y, Connor EE, Wiggans GR, Lu Y, Tempelman RJ, Schroeder SG, et al. Genome-wide copy number variant analysis reveals variants associated with 10 diverse production traits in Holstein cattle. Bmc Genomics. 2018;19(1):314. pmid:29716533; PubMed Central PMCID: PMCPMC5930521.
  41. 41. Ron M, Feldmesser E, Golik M, Tager-Cohen I, Kliger D, Reiss V, et al. A complete genome scan of the Israeli Holstein population for quantitative trait loci by a daughter design. Journal of Dairy Science. 2004;87(2):476–90. WOS:000189076500025. pmid:14762091
  42. 42. Schnabel RD, Sonstegard TS, Taylor JF, Ashwell MS. Whole-genome scan to detect QTL for milk production, conformation, fertility and functional traits in two US Holstein families. Anim Genet. 2005;36(5):408–16. pmid:16167984.
  43. 43. Boichard D, Grohs C, Bourgeois F, Cerqueira F, Faugeras R, Neau A, et al. Detection of genes influencing economic traits in three French dairy cattle breeds. Genet Sel Evol. 2003;35(1):77–101. pmid:12605852; PubMed Central PMCID: PMCPMC2732691.
  44. 44. Bennewitz J, Reinsch N, Guiard V, Fritz S, Thomsen H, Looft C, et al. Multiple quantitative trait loci mapping with cofactors and application of alternative variants of the false discovery rate in an enlarged granddaughter design. Genetics. 2004;168(2):1019–27. pmid:15514072; PubMed Central PMCID: PMCPMC1448815.
  45. 45. Viitala SM, Schulman NF, de Koning DJ, Elo K, Kinos R, Virta A, et al. Quantitative trait loci affecting milk production traits in Finnish Ayrshire dairy cattle. Journal of Dairy Science. 2003;86(5):1828–36. WOS:000182985600032. pmid:12778594
  46. 46. Plante Y, Gibson JP, Nadesalingam J, Mehrabani-Yeganeh H, Lefebvre S, Vandervoort G, et al. Detection of quantitative trait loci affecting milk production traits on 10 chromosomes in Holstein cattle. J Dairy Sci. 2001;84(6):1516–24. pmid:11417712.
  47. 47. Daetwyler HD, Capitan A, Pausch H, Stothard P, van Binsbergen R, Brondum RF, et al. Whole-genome sequencing of 234 bulls facilitates mapping of monogenic and complex traits in cattle. Nat Genet. 2014;46(8):858–65. pmid:25017103.
  48. 48. Kotze-Horstmann LM, Keswell D, Adams K, Dlamini T, Goedecke JH. Hypoxia and extra-cellular matrix gene expression in adipose tissue associates with reduced insulin sensitivity in black South African women. Endocrine. 2017;55(1):144–52. pmid:27628582.
  49. 49. Liang RB, Han B, Li Q, Yuan YW, Li JG, Sun DX. Using RNA sequencing to identify putative competing endogenous RNAs (ceRNAs) potentially regulating fat metabolism in bovine liver. Sci Rep-Uk. 2017;7. ARTN 6396 WOS:000406279200003. pmid:28743867
  50. 50. Huang GR, Ge GX, Wang DY, Gopalakrishnan B, Butz DH, Colman RJ, et al. alpha 3(V) Collagen is critical for glucose homeostasis in mice due to effects in pancreatic islets and peripheral tissues. J Clin Invest. 2011;121(2):769–83. WOS:000286913800038. pmid:21293061
  51. 51. Chen R, Davydov EV, Sirota M, Butte AJ. Non-Synonymous and Synonymous Coding SNPs Show Similar Likelihood and Effect Size of Human Disease Association. Plos One. 2010;5(10). ARTN e13574 WOS:000283419100014. pmid:21042586
  52. 52. Chamary JV, Parmley JL, Hurst LD. Hearing silence: non-neutral evolution at synonymous sites in mammals. Nat Rev Genet. 2006;7(2):98–108. WOS:000234714000012. pmid:16418745
  53. 53. Duret L. Evolution of synonymous codon usage in metazoans. Curr Opin Genet Dev. 2002;12(6):640–9. WOS:000179003900003. pmid:12433576
  54. 54. Comeron JM. Selective and mutational patterns associated with gene expression in humans: Influences on synonymous composition and intron presence. Genetics. 2004;167(3):1293–304. WOS:000223109300023. pmid:15280243
  55. 55. Hoffmann HH, Schneider WM, Blomen VA, Scull MA, Hovnanian A, Brummelkamp TR, et al. Diverse Viruses Require the Calcium Transporter SPCA1 for Maturation and Spread. Cell Host Microbe. 2017;22(4):460–+. WOS:000412768900009. pmid:29024641
  56. 56. Deng YQ, Pakdel M, Blank B, Sundberg EL, Burd CG, von Blume J. Activity of the SPCA1 Calcium Pump Couples Sphingomyelin Synthesis to Sorting of Secretory Proteins in the Trans-Golgi Network. Dev Cell. 2018;47(4):464–+. WOS:000450620800012. pmid:30393074
  57. 57. Mitchell KJ, Tsuboi T, Rutter GA. Role for plasma membrane-related Ca2+-ATPase-1 (ATP2C1) in pancreatic beta-cell Ca2+ homeostasis revealed by RNA silencing. Diabetes. 2004;53(2):393–400. WOS:000188739600016. pmid:14747290
  58. 58. Reinhardt TA, Lippolis JD, Sacco RE. The Ca2+/H+ antiporter TMEM165 expression, localization in the developing, lactating and involuting mammary gland parallels the secretory pathway Ca2+ ATPase (SPCA1). Biochem Bioph Res Co. 2014;445(2):417–21. WOS:000333228700026. pmid:24530912
  59. 59. Viera JT, El-Merahbi R, Nieswandt B, Stegner D, Sumara G. Phospholipases D1 and D2 Suppress Appetite and Protect against Overweight. Plos One. 2016;11(6). ARTN e0157607 WOS:000377822200066. pmid:27299737
  60. 60. Nakagawa H, Hazama K, Ishida K, Komori M, Nishimura K, Matsuo S. Inhibition of PLD1 activity causes ER stress via regulation of COPII vesicle formation. Biochem Bioph Res Co. 2017;490(3):895–900. WOS:000406820600047. pmid:28648601
  61. 61. Ren JQ, Guo W. ERK1/2 Regulate Exocytosis through Direct Phosphorylation of the Exocyst Component Exo70. Dev Cell. 2012;22(5):967–78. WOS:000304291700010. pmid:22595671
  62. 62. Xiong XH, Xu QW, Huang Y, Singh RD, Anderson R, Leof E, et al. An association between type I gamma PI4P 5-kinase and Exo70 directs E-cadherin clustering and epithelial polarization. Mol Biol Cell. 2012;23(1):87–98. WOS:000298661200009. pmid:22049025
  63. 63. Cottarel J, Plissonnier ML, Kullolli M, Pitteri S, Clement S, Millarte V, et al. FIG4 is a hepatitis C virus particle-bound protein implicated in virion morphogenesis and infectivity with cholesteryl ester modulation potential. J Gen Virol. 2016;97:69–81. WOS:000372062500008. pmid:26519381
  64. 64. Lu JS, Takahashi T, Ohoka A, Nakajima K, Hashimoto R, Miura N, et al. Alg14 organizes the formation of a multiglycosyltransferase complex involved in initiation of lipid-linked oligosaccharide biosynthesis. Glycobiology. 2012;22(4):504–16. WOS:000301005600006. pmid:22061998
  65. 65. Gao XD, Moriyama S, Miura N, Dean N, Nishimura SI. Interaction between the C Termini of Alg13 and Alg14 Mediates Formation of the Active UDP-N-acetylglucosamine Transferase Complex. J Biol Chem. 2008;283(47):32534–41. WOS:000260893700042. pmid:18809682
  66. 66. Lebigot E, Gaignard P, Dorboz I, Slama A, Rio M, de Lonlay P, et al. Impact of mutations within the [Fe-S] cluster or the lipoic acid biosynthesis pathways on mitochondrial protein expression profiles in fibroblasts from patients. Mol Genet Metab. 2017;122(3):85–94. WOS:000418395100011. pmid:28803783
  67. 67. Liu Y, Gao Y, Li DH, He LY, Lao IW, Hao B, et al. LASP1 promotes glioma cell proliferation and migration and is negatively regulated by miR-377-3p. Biomed Pharmacother. 2018;108:845–51. WOS:000450101800097. pmid:30372896
  68. 68. Sui YM, Zhang XL, Yang HL, Wei W, Wang ML. MicroRNA-133a acts as a tumour suppressor in breast cancer through targeting LASP1. Oncol Rep. 2018;39(2):473–82. WOS:000422855800003. pmid:29207145
  69. 69. Suchyta SP, Sipkovsky S, Halgren RG, Kruska R, Elftman M, Weber-Nielsen M, et al. Bovine mammary gene expression profiling using a cDNA microarray enhanced for mammary-specific transcripts. Physiol Genomics. 2003;16(1):8–18. WOS:000187327300002. pmid:14559974
  70. 70. Han Y, Lee SH, Bahn M, Yeo CY, Lee KY. Pin1 enhances adipocyte differentiation by positively regulating the transcriptional activity of PPARgamma. Mol Cell Endocrinol. 2016;436:150–8. pmid:27475846
  71. 71. Nakatsu Y, Mori K, Matsunaga Y, Yamamotoya T, Ueda K, Inoue Y, et al. The prolyl isomerase Pin1 increases beta-cell proliferation and enhances insulin secretion. J Biol Chem. 2017;292(28):11886–95. WOS:000405485600025. pmid:28566287
  72. 72. Kong XY, Kase ET, Herskedal A, Schjalm C, Damme M, Nesset CK, et al. Lack of the Lysosomal Membrane Protein, GLMP, in Mice Results in Metabolic Dysregulation in Liver. Plos One. 2015;10(6). UNSP e0129402 WOS:000355652200160. pmid:26047317
  73. 73. Kong XY, Feng YZ, Eftestol E, Kase ET, Haugum H, Eskild W, et al. Increased glucose utilization and decreased fatty acid metabolism in myotubes from Glmp(gt/gt) mice. Arch Physiol Biochem. 2016;122(1):36–45. WOS:000370643400006. pmid:26707125
  74. 74. Gao Y, Wells L, Comer FI, Parker GJ, Hart GW. Dynamic O-glycosylation of nuclear and cytosolic proteins—Cloning and characterization of a neutral, cytosolic beta-N-acetylglucosaminidase from human brain. J Biol Chem. 2001;276(13):9838–45. WOS:000167996400036. pmid:11148210
  75. 75. Peyta L, Jarnouen K, Pinault M, Coulouarn C, Guimaraes C, Goupille C, et al. Regulation of hepatic cardiolipin metabolism by TNFalpha: Implication in cancer cachexia. Biochim Biophys Acta. 2015;1851(11):1490–500. pmid:26327596.
  76. 76. Dzugasova V, Obernauerova M, Horvathova K, Vachova M, Zakova M, Subik J. Phosphatidylglycerolphosphate synthase encoded by the PEL1/PGS1 gene in Saccharomyces cerevisiae is localized in mitochondria and its expression is regulated by phospholipid precursors. Curr Genet. 1998;34(4):297–302. WOS:000077150600007. pmid:9799363
  77. 77. Feng Z, Xu T, Xu J. [Expression, purification and phosphoinositide binding specifity of recombinant human SNX7 expressed in Escherichia coli]. Sheng Wu Gong Cheng Xue Bao. 2014;30(9):1436–45. pmid:25720158.
  78. 78. Mukhopadhyay S, Saqcena M, Chatterjee A, Garcia A, Frias MA, Foster DA. Reciprocal regulation of AMP-activated protein kinase and phospholipase D. J Biol Chem. 2015;290(11):6986–93. pmid:25632961; PubMed Central PMCID: PMCPMC4358122.
  79. 79. Raikwar NS, Cho WK, Bowen RF, Deeg MA. Glycosylphosphatidylinositol-specific phospholipase D influences triglyceride-rich lipoprotein metabolism. Am J Physiol-Endoc M. 2006;290(3):463–E70. WOS:000235210000009. pmid:16219662