- Open Access
The goose genome sequence leads to insights into the evolution of waterfowl and susceptibility to fatty liver
- Lizhi Lu†1,
- Yan Chen†2,
- Zhuo Wang†2,
- Xiaofeng Li2,
- Weihu Chen3,
- Zhengrong Tao1,
- Junda Shen1,
- Yong Tian1,
- Deqian Wang1,
- Guoqin Li1,
- Li Chen1,
- Fang Chen1,
- Dongming Fang2,
- Lili Yu4,
- Yudong Sun4,
- Yong Ma2,
- Jinjun Li1Email author and
- Jun Wang2, 5, 6Email author
© Lu et al.; licensee BioMed Central. 2015
- Received: 4 September 2014
- Accepted: 13 April 2015
- Published: 6 May 2015
Geese were domesticated over 6,000 years ago, making them one of the first domesticated poultry. Geese are capable of rapid growth, disease resistance, and high liver lipid storage capacity, and can be easily fed coarse fodder. Here, we sequence and analyze the whole-genome sequence of an economically important goose breed in China and compare it with that of terrestrial bird species.
A draft sequence of the whole-goose genome was obtained by shotgun sequencing, and 16,150 protein-coding genes were predicted. Comparative genomics indicate that significant differences occur between the goose genome and that of other terrestrial bird species, particularly regarding major histocompatibility complex, Myxovirus resistance, Retinoic acid-inducible gene I, and other genes related to disease resistance in geese. In addition, analysis of transcriptome data further reveals a potential molecular mechanism involved in the susceptibility of geese to fatty liver disease and its associated symptoms, including high levels of unsaturated fatty acids and low levels of cholesterol. The results of this study show that deletion of the goose lep gene might be the result of positive selection, thus allowing the liver to adopt energy storage mechanisms for long-distance migration.
This is the first report describing the complete goose genome sequence and contributes to genomic resources available for studying aquatic birds. The findings in this study are useful not only for genetic breeding programs, but also for studying lipid metabolism disorders.
- Gene Ontology
- Zebra Finch
- Chicken Genome
- Free Fatty Acid Serum
Geese play an important role in agricultural economics, with China producing the vast majority (94%) of the approximately 2.23 million tons of goose meat consumed worldwide annually, followed by Egypt, Hungary, and Poland . Compared with other terrestrial poultry (for example, chicken and turkey), waterfowl, such as ducks and geese possess uniquely favorable economic traits. First, they exhibit a low susceptibility to certain avian viruses, showing little or no symptoms while still acting as a virus carrier, making them a natural repository for certain avian viruses [2-4]. Second, compared to other birds, the goose liver has a high capacity for fat accumulation, although geese do not normally develop liver fibrosis or necrosis. In agricultural production, this particular phenotype is manifested following short-term overfeeding (approximately 2 to 3 weeks), resulting in fatty livers and a 5- to 10-fold increase in liver size . Previous studies have shown that the serum enzyme levels of overfed geese are similar to those observed in humans with non-alcoholic fatty liver disease [5-7], suggesting that the unique fat storage and metabolic characteristics of goose liver may be an important reference for the study of lipid metabolism disorders in humans.
In order to determine special characteristics of geese, we sequenced and analyzed the complete goose genome. The results of this study may be useful for genetic breeding programs with geese and other waterfowls, and may serve as an important reference for the study of lipid metabolism disorders in humans.
Genome assembly and annotation
Assembly and annotation statistics for the goose genome
Estimate of genome size
Number of scaffolds (≥2 kb)
Total size of assembled scaffolds
Number of contigs (≥2 kb)
Total size of assembled contigs
Number of gene models
Total size of repeats
Repeats share in genome
Supported by RNA-Seq data
Comparative genomic analysis
We compared genome synteny and orthologous relationships among bird genomes. The goose genome has a high synteny with the duck genome , which covered approximately 81.09% and 82.35% of each genome, respectively (Additional file 1: Table S8 and Additional file 2: Figure S2), whereas approximately 592 goose scaffolds with lengths >5 kb mapped to and occupied 67.67% of the chicken genome  (Additional file 1: Table S8 and Additional file 2: Figure S3). In addition, we found that chromosomal rearrangements occur between the goose and chicken genomes (Additional file 1: Tables S9 and S10 and Additional file 2: Figure S4). For example, scaffold 45 is a goose genome sequence fragment, but it was in synteny with chromosomes 4 and 5 of the chicken genome. When comparing orthologs, 70% of the goose genes corresponded with 1:1 orthologs in the chicken gene-set (Additional file 2: Figure S5). Of the 1:1 orthologs for goose vs. duck (8,322 orthologs), however, 26.62% share up to 90% identity (Additional file 2: Figure S5). For chicken vs. turkey, 48.33% of the 1:1 orthologs (9,378 orthologs) share up to 90% identity (Additional file 2: Figure S5). For peregrine vs. saker, 57.87% of the 1:1 orthologs (10,569 orthologs) share up to 90% identity (Additional file 2: Figure S5).
Rapidly and slowly evolved GO terms
To identify the GO categories that have undergone rapid or slow evolution in waterfowl, we compared two waterfowl (goose and duck) with terrestrial birds (chicken and turkey). We searched for functionally related genes with exceptionally high or low selection constraints in the goose and duck. For categories with at least 10 genes, the ω value (ω = Ka/Ks, where Ka = number of non-synonymous substitutions per non-synonymous site, and Ks = number of synonymous substitutions per synonymous site) was calculated for these categories and normalized using the median ω of each species pair. We identified 191 GO categories with elevated Ka/Ks ratios at the specified threshold between the waterfowl and terrestrial birds (Additional file 1: Table S12). Nineteen of these GO categories, including GTPase activity, galactosyltransferase activity, chloride transport, and GABA-A receptor activity may have undergone significantly rapid evolution (Additional file 1: Table S12).
Ortholog identification was performed for goose, duck, zebra finch, chicken, turkey, and pigeon genome sequences, using the method applied for accelerated GO category analysis. Alignments of 7,861 orthologous genes were used to estimate the ratio of the rates of non-synonymous and synonymous substitutions per gene (ω), using the Codeml program under a branch-site model and F3x4 codon frequencies. We then performed a likelihood ratio test and identified 21 positively selected genes (PSGs) in waterfowl branches by means of FDR adjustment with Q-values <0.05 (Additional file 1: Table S13). Several of the PSGs, including eIF-3S1, GATA1, and eIF-3A, are involved in transcription or translation regulation. Kinase (PIK3R, FGFR2) and signaling molecule (KAI1) genes were also under positive selection, indicating that they may be involved in adaptation to an aquatic environment (Additional file 1: Table S13).
The resistance of waterfowl to disease
The major histocompatibility complex (MHC) gene is widely expressed in jawed vertebrates, and its function correlates with host disease resistance and immune responses [10-12]. Transposable elements in the chicken MHC region are more prevalent compared to the goose MHC region (54.62% in chicken vs. 15.11% in goose; Additional file 1: Table S14). Moreover, the distribution of the goose and chicken MHC region is different (Additional file 1: Table S15 and Additional file 2: Figure S7). In addition, we found that the goose genome exhibits substantial copy-number variations of innate immune response-related genes, as well as gene structures, when compared with chicken, turkey, zebra finch, human, and rat genomes (Additional file 1: Table S16). RNA viruses that escape toll-like receptors and infiltrate the cytoplasm are recognized by Retinoic acid-inducible gene I (RIG-I), a pattern-recognition receptor that plays an important antiviral role [13-16]. Results from recent studies have shown that RIG-I is present in most mammals and some birds [17-19]. We found that RIG-I genes aligned well between goose and zebra finch (Additional file 1: Tables S17 and S18), but only fragments of the goose RIG-I aligned with the chicken and turkey RIG-I genes (Additional file 1: Table S19). We constructed a phylogenetic tree based on these data (Additional file 2: Figures S8 and S9) and found that the RIG-I gene is absent in chickens and turkeys. Compared to turkeys and chickens, some mammal and waterfowl species have increased resistance to the influenza virus [20,21]. This phenomenon may be because most mammals have two Myxovirus resistance (Mx) genes, while avian birds have only one. The Mx gene is a member of the guanine-3 phosphokinase gene family, and its expression is induced by interferons . Many Mx proteins have been shown to provide influenza virus resistance at the cellular level . Moreover, the different Mx proteins confer resistance to different diseases, and single base mutations can affect the ability of the protein to confer resistance [21,22]. In addition, the phylogenetic tree shows that mutations at key sites in the chicken and turkey Mx genes may inactivate the Mx protein, affecting antiviral activity and leading to diminished viral resistance (Additional file 2: Figures S10 and S11).
The susceptibility of geese to fatty liver
Information on the expression of glucolipid metabolism-related genes in goose liver
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Results from previous studies have shown that the toxic and damaging effects of saturated fatty acids (SFAs) in the liver are significantly stronger than those of monounsaturated fatty acids (MUFAs) [38,39]. The implication of these findings is that the physiological transformation of SFA into MUFA by scd enzymes could alleviate the toxic effects of excessive liver exposure to SFA. Furthermore, the results of some studies have indicated that ob/ob mice (lep-deficient model mice) readily develop hepatic steatosis, but do not show spontaneous progression to steatohepatitis or liver fibrosis [40,41] because leptin is an essential mediator of hepatic fibrogenesis [41,42]. We therefore hypothesize that deletions of the goose lep gene may result from positive selection, thus allowing the liver to adapt energy storage mechanisms for long-distance migration, as observed in other wild birds . In addition, our results indicated that microRNAs are closely related to goose liver lipid metabolism in that multiple genes related to lipid synthesis or transport (lpl, fads1, pfkm, mdh1, pksG, fatp, acly, scd, cs, and elovl1) are regulated by single or multiple microRNAs (Additional file 1: Table S24), although this requires further verification.
In summary, this is the first report describing the complete goose-genome sequence and contributes to the genomic resources for studying aquatic birds. Genome-wide comparisons and orthologous analyses showed that the genome map is reliable and that the goose is a particularly interesting species with regard to evolutionary adaptation to its environment. The availability of the full goose-genome sequence will facilitate future genetic breeding programs. Moreover, studies examining goose genes involved in disease resistance and hepatic lipid metabolism may reveal unique immunity or disease-resistance mechanisms in waterfowls, and thus provide a valuable reference for research on human diseases related to lipid metabolism in the liver.
Genome sequencing and assembly
High-quality genomic DNA was extracted from whole blood of a 70-day old male Zhedong goose (A. cygnoides) reared in Xianshan County, Zhejiang Province, China. We constructed 12 paired-end sequencing libraries for whole genome sequencing (WGS) using a WGS kit (Illumina), according to the manufacturer’s recommended protocol. We next sequenced the DNA on a HiSeq 2000 sequencing platform and assembled the short sequences using SOAPdenovo software . The genome size was calculated from the total length of the sequence reads divided by the sequencing depth. To estimate the sequencing depth, we counted the frequency of each 17-mer from the WGS sequencing reads and plotted the copy-number distribution. The peak value of the frequency curve represented the overall sequencing depth. We used the algorithm, where knum is the k-mer number, kdepth is the K-mer depth, bnum is the base number, and bdepth is the base depth. G denotes the genome size, and kdepth is the overall depth estimated from the K-mer distribution. To assess the completeness of the assembly, we aligned the unigenes from Illumina RNA-Seq data to the assembled sequence using the BLAT algorithm with default parameters.
Repetitive sequences and transposable elements (TEs) in the genome were identified using a combination of de novo and homology-based approaches at both the DNA and protein levels. Briefly, we first constructed a de novo repeat library for A. cygnoides using RepeatModeler  with the default parameters, which generated consensus sequences and classification information for each repeat family. To identify transposable elements at the DNA level, RepeatMasker  was applied, using both the repetitive sequence database that we built and that deposited in Repbase . We next executed protein-based RepeatMasking  in a WU-BLASTX search against the TE protein database to further identify repeat-related proteins. The overlapping TEs belonging to the same repeat class were collated and combined according to the coordination in the genome. In addition, we annotated tandem repeats using the Tandem Repeats Finder  (TRF) software. For comparisons with the G. gallus genome, we also annotated repetitive elements of the G. gallus genome using the same process and parameters. The repeat divergence rate was calculated as the percentage of substitutions in the corresponding regions between annotated repeats and consensus sequences in the Repbase database .
We conducted gene annotations for the A. cygnoides genome by combining homology information, de novo predictions, and RNA-Seq data. For the homology-based prediction, protein sequences obtained from four sequenced animal genomes, namely G. gallus, H. sapiens, Meleagris gallopavo, and Taeniopygia guttata, were mapped onto the A. cygnoides genome, using TBLASTN with an E-value cutoff of 1e-5. Homologous genome sequences were aligned against the matching proteins using GeneWise  for accurate spliced alignments. For de novo predictions, we performed Augustus  and GenScan  analysis of the repeat-masked genome, with parameters trained from the relative species, and filtered out partial sequences and/or small genes of <150 bp coding length. We next combined all the predictions using GLEAN  to produce consensus gene sets. Finally, we aligned all RNA reads to the reference genome, using TopHat , assembled the transcripts with Cufflinks  using the default parameters, and predicted the open reading frames (ORFs) to obtain reliable transcripts with HMM-based training parameters. To finalize the gene set, we combined the GLEAN set with the gene models produced by RNA-Seq, filtering out genes containing one exon that were only supported by the RNA-Seq data.
Gene functions were assigned based on the best matches derived from alignments with proteins annotated in the Swiss-Prot and TrEMBL  databases, using BLASTP (E-value ≤1e-5). We annotated motifs and domains using InterProScan , searching against publicly available databases, including ProDom, PRINTS, Pfam, SMART, PANTHER, and PROSITE. We also mapped the A. cygnoides genes to KEGG  pathway maps by searching the KEGG databases, identifying the best hit for each gene and then assigning them to the pathway maps.
tRNA genes were identified using tRNAscan-SE , with eukaryote parameters. For rRNA identification, we aligned the H. sapiens rRNA sequences against the A. cygnoides genome using BLASTN with an E-value cutoff of 1e-5. Subsequently, snRNAs were predicted using INFERNAL software  and searching against the Rfam database .
We compared gene families from eight avian species (A. cygnoides, Anas platyrhynchos, G. gallus, M. gallopavo, T. guttata, Columba livia, Falco peregrinus, and Falco cherrug) and the green anole lizard (A. carolinensis) by TreeFam , using the following steps. Initially, protein sequence alignments were performed with Blastp with an E-value cutoff of <1e-7. HSP segments were then concatenated between the same pairs of proteins using the Solar software package, followed by the identification of homologous relationships between protein sequences, based on bit-scores and the identity of homologous gene pairs. Finally, gene families were detected by clustering using hcluster_sg , with a minimum edge weight >10, a minimum edge density >0.34, and with other default parameter values.
In phylogenetic analysis, echo single-copy family results from TreeFam were translated into amino acid sequences for multiple alignments by Muscle . A phylogenetic tree of nine species was generated via super-alignment through the maximum-likelihood method in PhyML software  or the Bayesian inference method in MrBayes software  by concatenating all 4-fold degenerate sites of single-copy orthologs. The ages of speciation events were estimated using the Bayesian relaxed molecular clock (BRMC) approach implemented in the MCMCTREE program in the PAML package . Both the correlated molecular clock and the JC69 models were used to estimate speciation events. The MCMC process of the PAML MCMCTREE program was run to sample 100,000 times, with the sampling frequency set to 2 after a burn-in of 10,000 iterations. The fine-tune parameters were set to allow acceptance proportions falling in intervals (0.15, 0.7). Elsewhere, the default parameters were used. Two independent runs were performed to check convergence.
LASTZ local alignment software was used to align sequences between two genomes. The self-alignment generated by LASTZ, and most LASTZ parameters were set by default. Prior to aligning, repeat sequences were masked, and the genome assembly was split into several small subfiles. The maximum simultaneous gap allowed during aligning was 100 bp. After the alignment, we extracted alignment blocks of >1 kb and >90% identity. These alignment blocks were predicted to be SDs. After removing the overlapping fragments, we obtained a non-redundant set of SDs.
We downloaded the relative character genes (MHC, Mx, and RIG-I) from NCBI and aligned them with goose and homolog species gene sets using BLASTP with an E-value cutoff of 1e-5. Next, according to the function, the description of the genes to ensure the copy number, we constructed a phylogenetic tree with PhyML and compared the gene structures of single-copy genes.
Transcriptome sequencing and analysis of goose susceptibility to fatty liver
Twelve healthy male geese hatched on the same day were grown under natural conditions of light and temperature at ChangXing Glory Goose Industry Co., Ltd. After 90 days, they were randomly divided into two groups (n = 6 per group). The control group was given free access to a normal diet (2,800 kcal/kg, 150 g of protein/kg). The overfed group was fed a carbohydrate-rich diet (3,500 kcal/kg, 100 g of protein/kg, and 4.8 g of fat/kg) for four meals (300 g/meal) per day. All geese had free access to water at all times. At the age of 110 days, all geese were deprived of feed overnight, but provided water. On the following morning, the geese were sacrificed, both the body and liver weights of geese were weighed, and approximately 8 g samples liver tissue samples were isolated and stored at -70°C until RNA extraction. Individual blood samples were collected from geese in both the control and overfed group on 90 and 110 days of age. Sera were separated by centrifugation at 3,500 × g for 15 min and stored at -20°C until further biochemical analysis. Whole-plasma parameters such as glucose, TC, TG, high-density lipoprotein, VLDL, lipoprotein, phospholipids, and free fatty acid serum levels were determined using corresponding kits. The protocol for goose treatment was in accordance with Chinese legislation on animal experimentation. Total RNA was isolated from the livers, and RNA sequencing libraries were constructed using the Illumina mRNA-Seq Prep Kit. We then sequenced all libraries using an Illumina HiSeq 2000 instrument.
To determine gene expression levels, RNA-Seq reads from the control and overfed groups were mapped to the assembly, and the reads per kilobase per million mapped reads (RPKM) values were calculated for each predicted transcript. Next, we compared gene expression levels in the two libraries, defining genes as differentially expressed if they showed at least a 2-fold change in expression and an adjusted P value of <0.001 (based on the Poisson model).
MicroRNA (miRNA) expression levels between two samples were compared to identify differentially expressed miRNAs, using the following steps: (1) miRNA expression was normalized in the two samples (control and overfed) to determine the expression of transcripts per million reads. miRNA was normalized using the formula: normalized expression = (actual miRNA count/total count of clean reads) × 1,000,000. (2) Fold-changes and P values were calculated from the normalized expression levels, using the formula: fold-change = log2 (treatment/control). The rules for predicting target genes of novel miRNA were based on those suggested by Allen et al.  and Schwab et al. , namely: (1) No more than four mismatches were permitted between sRNA and target (G-U bases count as 0.5 mismatches). (2) No more than two adjacent mismatches were allowed in the miRNA/target duplex. (3) No adjacent mismatches in positions two to 12 of the miRNA/target duplex (5′ end of miRNA) were permitted. (4) No mismatches in positions 10 to 11 of miRNA/target duplex were permitted. (5) No more than 2.5 mismatches in positions one to 12 of the miRNA/target duplex (5′ end of miRNA) were permitted. (6) The minimum free energy (MFE) of the miRNA/target duplex should be ≥75% of the MFE of the miRNA bound to its perfect complement.
Accession codes: The whole-genome shotgun project has been deposited in DDBJ/EMBL/GenBank nucleotide core database under the accession code AOGC00000000. The version described in this paper is the first version, AOGC00000000. All short-read data have been deposited in the Sequence Read Archive (SRA) under accession SRA062749. Raw sequence data of the transcriptome have been deposited in the SRA under accession codes SRA251539.
This work was supported by the International Science & Technology Cooperation Program of China (2013DFR30980) and the earmarked fund for China Agriculture Research System (CARS-43-02 and CARS-43-29).
- Pingel H. Waterfowl production for food security. Lohmann Information. 2011;46:32–42.Google Scholar
- Webster RG, Bean WJ, Gorman OT, Chambers TM, Kawaoka Y. Evolution and ecology of influenza A viruses. Microbiol Rev. 1992;56:152–79.PubMed CentralPubMedGoogle Scholar
- Murata S, Hayashi Y, Kato A, Isezaki M, Takasaki S, Onuma M, et al. Surveillance of Marek’s disease virus in migratory and sedentary birds in Hokkaido, Japan. Vet J. 2012;192:538–40.View ArticlePubMedGoogle Scholar
- Murata S, Chang KS, Yamamoto Y, Okada T, Lee SI, Konnai S, et al. Detection of the virulent Marek’s disease virus genome from feather tips of wild geese in Japan and the Far East region of Russia. Arch Virol. 2007;152:1523–6.View ArticlePubMedGoogle Scholar
- Mourot J, Guy G, Peiniau P, Hermier D. Effects of overfeeding on lipid synthesis, transport and storage in two breeds of geese differing in their capacity for fatty liver production. Anim Res. 2006;55:427–42.View ArticleGoogle Scholar
- Hermier D, Salichon MR, Guy G, Peresson R. Differential channelling of liver lipids in relation to susceptibility to hepatic steatosis in the goose. Poult Sci. 1999;78:1398–406.View ArticlePubMedGoogle Scholar
- Xu HY, Wang Y, Han CC, Jiang L, Zhuo WH, Ye JQ, et al. Estimation of lipoprotein-lipase activity (LPL) and other biochemical changes in two breeds of overfeeding geese. Asian-Australasian J Anim Sci. 2010;23:1221–8.View ArticleGoogle Scholar
- Huang Y, Li Y, Burt DW, Chen H, Zhang Y, Qian W, et al. The duck genome and transcriptome provide insight into an avian influenza virus reservoir species. Nat Genet. 2013;45:776–83.View ArticlePubMed CentralPubMedGoogle Scholar
- International Chicken Genome Sequencing C. Sequence and comparative analysis of the chicken genome provide unique perspectives on vertebrate evolution. Nature. 2004;432:695–716.View ArticleGoogle Scholar
- Shiina T, Shimizu S, Hosomichi K, Kohara S, Watanabe S, Hanzawa K, et al. Comparative genomic analysis of two avian (quail and chicken) MHC regions. J Immunol. 2004;172:6751–63.View ArticlePubMedGoogle Scholar
- Dalgaard T, Boving MK, Handberg K, Jensen KH, Norup LR, Juul-Madsen HR. MHC expression on spleen lymphocyte subsets in genetically resistant and susceptible chickens infected with Marek’s disease virus. Viral Immunol. 2009;22:321–7.View ArticlePubMedGoogle Scholar
- Dalgaard TS, Vitved L, Skjodt K, Thomsen B, Labouriau R, Jensen KH, et al. Molecular characterization of major histocompatibility complex class I (B-F) mRNA variants from chickens differing in resistance to Marek’s disease. Scand J Immunol. 2005;62:259–70.View ArticlePubMedGoogle Scholar
- Yoneyama M, Fujita T. RIG-I family RNA helicases: cytoplasmic sensor for antiviral innate immunity. Cytokine Growth Factor Rev. 2007;18:545–51.View ArticlePubMedGoogle Scholar
- Barral PM, Sarkar D, Su ZZ, Barber GN, DeSalle R, Racaniello VR, et al. Functions of the cytoplasmic RNA sensors RIG-I and MDA-5: key regulators of innate immunity. Pharmacol Ther. 2009;124:219–34.View ArticlePubMed CentralPubMedGoogle Scholar
- Cowled C, Baker ML, Zhou P, Tachedjian M, Wang LF. Molecular characterisation of RIG-I-like helicases in the black flying fox, Pteropus alecto. Dev Comp Immunol. 2012;36:657–64.View ArticlePubMedGoogle Scholar
- Schmidt A, Endres S, Rothenfusser S. Pattern recognition of viral nucleic acids by RIG-I-like helicases. J Mol Med (Berl). 2011;89:5–12.View ArticlePubMedGoogle Scholar
- Pichlmair A, Schulz O, Tan CP, Naslund TI, Liljestrom P, Weber F, et al. RIG-I-mediated antiviral responses to single-stranded RNA bearing 5’-phosphates. Science. 2006;314:997–1001.View ArticlePubMedGoogle Scholar
- Barber MR, Aldridge Jr JR, Webster RG, Magor KE. Association of RIG-I with innate immunity of ducks to influenza. Proc Natl Acad Sci U S A. 2010;107:5913–8.View ArticlePubMed CentralPubMedGoogle Scholar
- Nazmi A, Dutta K, Basu A. RIG-I mediates innate immune response in mouse neurons following Japanese encephalitis virus infection. PLoS One. 2011;6, e21761.View ArticlePubMed CentralPubMedGoogle Scholar
- Cardona CJ, Xing Z, Sandrock CE, Davis CE. Avian influenza in birds and mammals. Comp Immunol Microbiol Infect Dis. 2009;32:255–73.View ArticlePubMedGoogle Scholar
- Haller O, Stertz S, Kochs G. The Mx GTPase family of interferon-induced antiviral proteins. Microbes Infect. 2007;9:1636–43.View ArticlePubMedGoogle Scholar
- Ko JH, Jin HK, Asano A, Takada A, Ninomiya A, Kida H, et al. Polymorphisms and the differential antiviral activity of the chicken Mx gene. Genome Res. 2002;12:595–601.View ArticlePubMed CentralPubMedGoogle Scholar
- Han C, Wang J, Li L, Zhang Z, Wang L, Pan Z. The role of insulin and glucose in goose primary hepatocyte triglyceride accumulation. J Exp Biol. 2009;212:1553–8.View ArticlePubMedGoogle Scholar
- Zhu LH, Meng H, Duan XJ, Xu GQ, Zhang J, Gong DQ. Gene expression profile in the liver tissue of geese after overfeeding. Poult Sci. 2011;90:107–17.View ArticlePubMedGoogle Scholar
- Mourot J, Guy G, Lagarrigue S, Peiniau P, Hermier D. Role of hepatic lipogenesis in the susceptibility to fatty liver in the goose (Anser anser). Comp Biochem Physiol B Biochem Mol Biol. 2000;126:81–7.View ArticlePubMedGoogle Scholar
- DiRusso CC, Li H, Darwis D, Watkins PA, Berger J, Black PN. Comparative biochemical studies of the murine fatty acid transport proteins (FATP) expressed in yeast. J Biol Chem. 2005;280:16829–37.View ArticlePubMedGoogle Scholar
- Packard CJ, Demant T, Stewart JP, Bedford D, Caslake MJ, Schwertfeger G, et al. Apolipoprotein B metabolism and the distribution of VLDL and LDL subfractions. J Lipid Res. 2000;41:305–18.PubMedGoogle Scholar
- Fisher EA. The degradation of apolipoprotein B100: multiple opportunities to regulate VLDL triglyceride production by different proteolytic pathways. Biochim Biophys Acta. 2012;1821:778–81.View ArticlePubMed CentralPubMedGoogle Scholar
- Mauvoisin D, Mounier C. Hormonal and nutritional regulation of SCD1 gene expression. Biochimie. 2011;93:78–86.View ArticlePubMedGoogle Scholar
- Dobrzyn A, Ntambi JM. The role of stearoyl-CoA desaturase in the control of metabolism. Prostaglandins Leukot Essent Fatty Acids. 2005;73:35–41.View ArticlePubMedGoogle Scholar
- Mainieri D, Summermatter S, Seydoux J, Montani JP, Rusconi S, Russell AP, et al. A role for skeletal muscle stearoyl-CoA desaturase 1 in control of thermogenesis. FASEB J. 2006;20:1751–3.View ArticlePubMedGoogle Scholar
- Flowers MT, Ntambi JM. Stearoyl-CoA desaturase and its relation to high-carbohydrate diets and obesity. Biochim Biophys Acta. 2009;1791:85–91.View ArticlePubMed CentralPubMedGoogle Scholar
- Mauvoisin D, Prevost M, Ducheix S, Arnaud MP, Mounier C. Key role of the ERK1/2 MAPK pathway in the transcriptional regulation of the Stearoyl-CoA Desaturase (SCD1) gene expression in response to leptin. Mol Cell Endocrinol. 2010;319:116–28.View ArticlePubMedGoogle Scholar
- Ntambi JM, Miyazaki M, Stoehr JP, Lan H, Kendziorski CM, Yandell BS, et al. Loss of stearoyl-CoA desaturase-1 function protects mice against adiposity. Proc Natl Acad Sci U S A. 2002;99:11482–6.View ArticlePubMed CentralPubMedGoogle Scholar
- Denver RJ, Bonett RM, Boorse GC. Evolution of leptin structure and function. Neuroendocrinology. 2011;94:21–38.View ArticlePubMedGoogle Scholar
- Pitel F, Faraut T, Bruneau G, Monget P. Is there a leptin gene in the chicken genome? Lessons from phylogenetics, bioinformatics and genomics. Gen Comp Endocrinol. 2010;167:1–5.View ArticlePubMedGoogle Scholar
- Prokop JW, Schmidt C, Gasper D, Duff RJ, Milsted A, Ohkubo T, et al. Discovery of the elusive leptin in birds: identification of several ‘missing links’ in the evolution of leptin and its receptor. PLoS One. 2014;9, e92751.View ArticlePubMed CentralPubMedGoogle Scholar
- Gentile CL, Pagliassotti MJ. The role of fatty acids in the development and progression of nonalcoholic fatty liver disease. J Nutr Biochem. 2008;19:567–76.View ArticlePubMed CentralPubMedGoogle Scholar
- Ricchi M, Odoardi MR, Carulli L, Anzivino C, Ballestri S, Pinetti A, et al. Differential effect of oleic and palmitic acid on lipid accumulation and apoptosis in cultured hepatocytes. J Gastroenterol Hepatol. 2009;24:830–40.View ArticlePubMedGoogle Scholar
- Diehl AM. Lessons from animal models of NASH. Hepatol Res. 2005;33:138–44.View ArticlePubMedGoogle Scholar
- Leclercq IA, Farrell GC, Schriemer R, Robertson GR. Leptin is essential for the hepatic fibrogenic response to chronic liver injury. J Hepatol. 2002;37:206–13.View ArticlePubMedGoogle Scholar
- Ikejima K, Honda H, Yoshikawa M, Hirose M, Kitamura T, Takei Y, et al. Leptin augments inflammatory and profibrogenic responses in the murine liver induced by hepatotoxic chemicals. Hepatology. 2001;34:288–97.View ArticlePubMedGoogle Scholar
- Pilo B, George JC. Diurnal and seasonal variation in liver glycogen and fat in relation to metabolic status of liver and m. pectoralis in the migratory starling, Sturnus roseus, wintering in India. Comp Biochem Physiol A Comp Physiol. 1983;74:601–4.View ArticlePubMedGoogle Scholar
- Li R, Fan W, Tian G, Zhu H, He L, Cai J, et al. The sequence and de novo assembly of the giant panda genome. Nature. 2010;463:311–7.View ArticlePubMed CentralPubMedGoogle Scholar
- Price AL, Jones NC, Pevzner PA. De novo identification of repeat families in large genomes. Bioinformatics. 2005;21:i351–8.View ArticlePubMedGoogle Scholar
- Tarailo-Graovac M, Chen N. Using RepeatMasker to identify repetitive elements in genomic sequences. Curr Protoc Bioinformatics. 2009;Chapter 4:Unit 4 10.Google Scholar
- Jurka J, Kapitonov VV, Pavlicek A, Klonowski P, Kohany O, Walichiewicz J. Repbase Update, a database of eukaryotic repetitive elements. Cytogenet Genome Res. 2005;110:462–7.View ArticlePubMedGoogle Scholar
- Repeat Masker. http://www.repeatmasker.org.
- Repbase Update. http://www.girinst.org/repbase.
- Birney E, Clamp M, Durbin R. GeneWise and Genomewise. Genome Res. 2004;14:988–95.View ArticlePubMed CentralPubMedGoogle Scholar
- Stanke M, Keller O, Gunduz I, Hayes A, Waack S, Morgenstern B. AUGUSTUS: ab initio prediction of alternative transcripts. Nucleic Acids Res. 2006;34:W435–9.View ArticlePubMed CentralPubMedGoogle Scholar
- Salamov AA, Solovyev VV. Ab initio gene finding in Drosophila genomic DNA. Genome Res. 2000;10:516–22.View ArticlePubMed CentralPubMedGoogle Scholar
- Elsik CG, Mackey AJ, Reese JT, Milshina NV, Roos DS, Weinstock GM. Creating a honey bee consensus gene set. Genome Biol. 2007;8:R13.View ArticlePubMed CentralPubMedGoogle Scholar
- Trapnell C, Pachter L, Salzberg SL. TopHat: discovering splice junctions with RNA-Seq. Bioinformatics. 2009;25:1105–11.View ArticlePubMed CentralPubMedGoogle Scholar
- Trapnell C, Williams BA, Pertea G, Mortazavi A, Kwan G, van Baren MJ, et al. Transcript assembly and quantification by RNA-Seq reveals unannotated transcripts and isoform switching during cell differentiation. Nat Biotechnol. 2010;28:511–5.View ArticlePubMed CentralPubMedGoogle Scholar
- Bairoch A, Apweiler R. The SWISS-PROT protein sequence database and its supplement TrEMBL in 2000. Nucleic Acids Res. 2000;28:45–8.View ArticlePubMed CentralPubMedGoogle Scholar
- Quevillon E, Silventoinen V, Pillai S, Harte N, Mulder N, Apweiler R, et al. InterProScan: protein domains identifier. Nucleic Acids Res. 2005;33:W116–20.View ArticlePubMed CentralPubMedGoogle Scholar
- Kanehisa M, Goto S. KEGG: kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 2000;28:27–30.View ArticlePubMed CentralPubMedGoogle Scholar
- Lowe TM, Eddy SR. tRNAscan-SE: a program for improved detection of transfer RNA genes in genomic sequence. Nucleic Acids Res. 1997;25:955–64.View ArticlePubMed CentralPubMedGoogle Scholar
- Nawrocki EP, Kolbe DL, Eddy SR. Infernal 1.0: inference of RNA alignments. Bioinformatics. 2009;25:1335–7.View ArticlePubMed CentralPubMedGoogle Scholar
- Griffiths-Jones S, Moxon S, Marshall M, Khanna A, Eddy SR, Bateman A. Rfam: annotating non-coding RNAs in complete genomes. Nucleic Acids Res. 2005;33:D121–4.View ArticlePubMed CentralPubMedGoogle Scholar
- Li H, Coghlan A, Ruan J, Coin LJ, Heriche JK, Osmotherly L, et al. TreeFam: a curated database of phylogenetic trees of animal gene families. Nucleic Acids Res. 2006;34:D572–80.View ArticlePubMed CentralPubMedGoogle Scholar
- Edgar RC. MUSCLE: multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Res. 2004;32:1792–7.View ArticlePubMed CentralPubMedGoogle Scholar
- Yang Z. PAML 4: phylogenetic analysis by maximum likelihood. Mol Biol Evol. 2007;24:1586–91.View ArticlePubMedGoogle Scholar
- Huelsenbeck JP, Ronquist F. MRBAYES: Bayesian inference of phylogenetic trees. Bioinformatics. 2001;17:754–5.View ArticlePubMedGoogle Scholar
- Allen E, Xie Z, Gustafson AM, Carrington JC. microRNA-directed phasing during trans-acting siRNA biogenesis in plants. Cell. 2005;121:207–21.View ArticlePubMedGoogle Scholar
- Schwab R, Palatnik JF, Riester M, Schommer C, Schmid M, Weigel D. Specific effects of microRNAs on the plant transcriptome. Dev Cell. 2005;8:517–27.View ArticlePubMedGoogle Scholar
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