Searching across hundreds of databases

Our searching services are busy right now. Your search will reload in five seconds.

X
Forgot Password

If you have forgotten your password you can enter your email here and get a temporary password sent to your email.

X
Forgot Password

If you have forgotten your password you can enter your email here and get a temporary password sent to your email.

This service exclusively searches for literature that cites resources. Please be aware that the total number of searchable documents is limited to those containing RRIDs and does not include all open-access literature.

Search

Type in a keyword to search

On page 1 showing 1 ~ 10 papers out of 10 papers

Differences in gut microbiota composition in finishing Landrace pigs with low and high feed conversion ratios.

  • Zhen Tan‎ et al.
  • Antonie van Leeuwenhoek‎
  • 2018‎

The goal of this study was to evaluate the microbial communities in the gut and feces from female finishing Landrace pigs with high and low feed conversion ratio (FCR) by 16S rRNA gene amplicon sequencing. Many potential biomarkers can distinguish between high and low FCR groups in the duodenum, ileum, cecum, colon, and rectum, according to linear discriminant analysis effect sizes. The relative abundance of microbes were tested by Mann-Whitney test between the high and low FCR groups in different organs: Campylobacter, Prevotella and Sphaerochaeta were different in the duodenum (P < 0.05); Sanguibacter, Kingella and Anaeroplasma in jejunum; Anaeroplasma, Arthrobacter, Kingella, Megasphaera and SMB53 in the ileum; Butyricicoccus, Campylobacter, Mitsuokella, and Coprobacillus in the cecum; Lactococcus and Peptococcus in the colon; Staphylococcus in the rectum; and Rothia in feces. The prevalence of microbial genera in certain locations could potentially be used as biomarkers to distinguish between high and low FCR. Functional prediction clustering analysis suggested that bacteria in the hindgut mainly participated in carbohydrate metabolism and amino acid metabolism, and different in the relative abundance of metabolic pathways, as predicted from the microbial taxa present, were identified by comparing the high and low groups of each location. The results may provide insights for the alteration of the intestinal microbial communities to improve the growth rate of pigs.


Characterization of Embryonic Skin Transcriptome in Anser cygnoides at Three Feather Follicles Developmental Stages.

  • Chang Liu‎ et al.
  • G3 (Bethesda, Md.)‎
  • 2020‎

In order to enrich the Anser cygnoides genome and identify the gene expression profiles of primary and secondary feather follicles development, de novo transcriptome assembly of skin tissues was established by analyzing three developmental stages at embryonic day 14, 18, and 28 (E14, E18, E28). Sequencing output generated 436,730,608 clean reads from nine libraries and de novo assembled into 56,301 unigenes. There were 2,298, 9,423 and 12,559 unigenes showing differential expression in three stages respectively. Furthermore, differentially expressed genes (DEGs) were functionally classified according to genes ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and series-cluster analysis. Relevant specific GO terms such as epithelium development, regulation of keratinocyte proliferation, morphogenesis of an epithelium were identified. In all, 15,144 DEGs were clustered into eight profiles with distinct expression patterns and 2,424 DEGs were assigned to 198 KEGG pathways. Skin development related pathways (mitogen-activated protein kinase signaling pathway, extra-cellular matrix -receptor interaction, Wingless-type signaling pathway) and genes (delta like canonical Notch ligand 1, fibroblast growth factor 2, Snail family transcriptional repressor 2, bone morphogenetic protein 6, polo like kinase 1) were identified, and eight DEGs were selected to verify the reliability of transcriptome results by real-time quantitative PCR. The findings of this study will provide the key insights into the complicated molecular mechanism and breeding techniques underlying the developmental characteristics of skin and feather follicles in Anser cygnoides.


Transcriptome analysis of miRNA and mRNA in the livers of pigs with highly diverged backfat thickness.

  • Kai Xing‎ et al.
  • Scientific reports‎
  • 2019‎

Fat deposition is very important in pig production, and its mechanism is not clearly understood. MicroRNAs (miRNAs) play critical roles in fat deposition and energy metabolism. In the current study, we investigated the mRNA and miRNA transcriptome in the livers of Landrace pigs with extreme backfat thickness to explore miRNA-mRNA regulatory networks related to lipid deposition and metabolism. A comparative analysis of liver mRNA and miRNA transcriptomes from pigs (four pigs per group) with extreme backfat thickness was performed. We identified differentially expressed genes from RNA-seq data using a Cufflinks pipeline. Seventy-one differentially expressed genes (DEGs), including twenty-eight well annotated on the porcine reference genome genes, were found. The upregulation genes in pigs with higher backfat thickness were mainly involved in fatty acid synthesis, and included fatty acid synthase (FASN), glucokinase (GCK), phosphoglycerate dehydrogenase (PHGDH), and apolipoprotein A4 (APOA4). Cytochrome P450, family 2, subfamily J, polypeptide 34 (CYP2J34) was lower expressed in pigs with high backfat thickness, and is involved in the oxidation of arachidonic acid. Moreover, 13 differentially expressed miRNAs were identified. Seven miRNAs were associated with fatty acid synthesis, lipid metabolism, and adipogenic differentiation. Based on comprehensive analysis of the transcriptome of both mRNAs and miRNAs, an important regulatory network, in which six DEGs could be regulated by differentially expressed miRNAs, was established for fat deposition. The negative correlate in the regulatory network including, miR-545-5p and GRAMD3, miR-338 and FASN, and miR-127, miR-146b, miR-34c, miR-144 and THBS1 indicate that direct suppressive regulation may be involved in lipid deposition and energy metabolism. Based on liver mRNA and miRNA transcriptomes from pigs with extreme backfat thickness, we identified 28 differentially expressed genes and 13 differentially expressed miRNAs, and established an important miRNA-mRNA regulatory network. This study provides new insights into the molecular mechanisms that determine fat deposition in pigs.


Comparative adipose transcriptome analysis digs out genes related to fat deposition in two pig breeds.

  • Kai Xing‎ et al.
  • Scientific reports‎
  • 2019‎

Fatness traits are important in pigs because of their implications for fattening efficiency, meat quality, reproductive performance and immunity. Songliao black pigs and Landrace pigs show important differences in production and meat quality traits, including fatness and muscle growth. Therefore, we used a high-throughput massively parallel RNA-seq approach to identify genes differentially expressed in backfat tissue between these two breeds (six pigs in each). An average of 37.87 million reads were obtained from the 12 samples. After statistical analysis of gene expression data by edgeR, a total of 877 differentially expressed genes were detected between the two pig breeds, 205 with higher expression and 672 with lower expression in Songliao pigs. Candidate genes (LCN2, CES3, DGKB, OLR1, LEP, PGM1, PCK1, ACACB, FADS1, FADS2, MOGAT2, SREBF1, PPARGC1B) with known effects on fatness traits were included among the DEGs. A total of 1071 lncRNAs were identified, and 85 of these lncRNAs were differentially expressed, including 53 up-regulated and 32 down-regulated lncRNAs, respectively. The differentially expressed genes and lncRNAs involved in glucagon signaling pathway, glycolysis/gluconeogenesis, insulin signaling pathway, MAPK signaling pathway and so on. Integrated analysis potential trans-regulating or cis-regulating relation between DEGs and DE lncRNAs, suggested lncRNA MSTRG.2479.1 might regulate the expressed level of VLDLR affecting porcine fat metabolism. These results provide a number of candidate genes and lncRNAs potentially involved in porcine fat deposition and provide a basis for future research on the molecular mechanisms underlying in fat deposition.


Metagenomic Analysis of Cecal Microbiome Identified Microbiota and Functional Capacities Associated with Feed Efficiency in Landrace Finishing Pigs.

  • Zhen Tan‎ et al.
  • Frontiers in microbiology‎
  • 2017‎

Feed efficiency (FE) appears to vary even within closely related pigs, and may be partly affected by the diversity in the composition and function of gut microbes. To investigate the components and functional differences of gut microbiota of low and high FE pigs, high throughput sequencing and de novo metagenomics were performed on pig cecal contents. Pigs were selected in pairs with low and high feed conversion ratio. The microorganisms of individuals with different FE were clustered according to diversity. The genus Prevotella was the most enriched in both groups, and the abundance of species Prevotella sp. CAG:604 was significantly increased in low efficiency individuals compared to that in animals showing high efficiency. In contrast, other differential species, including lactic acid bacteria, were all enriched in the group with good feeding characteristics. Functional analysis based on the Kyoto Encyclopedia of Genes and Genomes databases demonstrated that differential genes for the metabolism of carbohydrates were most abundant in both groups, but pathways of pyruvate-related metabolism were more intense in pigs with higher FE. All these data indicated that the microbial environment was closely related to the growth traits of pigs, and regulating microbial composition could aid developing strategies to improve FE for pigs.


Differentially expressed genes in the caecal and colonic mucosa of Landrace finishing pigs with high and low food conversion ratios.

  • Zhen Tan‎ et al.
  • Scientific reports‎
  • 2017‎

The feed conversion ratio (FCR) is an essential economic trait for pig production, and is directly related to feed efficiency. Studies identifying the differential expression of functional genes involved in biological and molecular mechanisms in the intestine in relation to growth performance are rare. In this study, RNA-Seq was used to identify transcriptomes in caecal and colonic mucosal tissues in order to determine the differential expression of genes from two full-sibling pairs and two half-sibling pairs of Landrace finishing pigs with opposing FCR phenotypes. In total, 138 (comparison of high and low FCR in caecal mucosa), 64 (comparison of high and low FCR in colonic mucosa), and 165 (contrast between the caecal and colonic mucosa) differentially expressed genes were identified. Some of these genes were functionally related to energy and lipid metabolism, particularly short chain fatty acids metabolism, as well as gastrointestinal peristalsis and ion transport. Functional annotation were performed to identify differentially expressed genes, such as GUCA2A, GUCA2B, HSP70.2, NOS2, PCK1, SLCs, and CYPs, which may positively influence feed efficiency in Landrace pigs. These differentially expressed genes need to be further tested for candidate genes that are related to feed efficiency.


De Novo Assembly and Comparative Transcriptome Profiling of Anser anser and Anser cygnoides Geese Species' Embryonic Skin Feather Follicles.

  • Cornelius Tlotliso Sello‎ et al.
  • Genes‎
  • 2019‎

Geese feather production and the quality of downy feathers are additional economically important traits in the geese industry. However, little information is available about the molecular mechanisms fundamental to feather formation and the quality of feathers in geese. This study conducted de novo transcriptome sequencing analysis of two related geese species using the Illumina 4000 platform to determine the genes involved in embryonic skin feather follicle development. A total of 165,564,278 for Anser anser and 144,595,262 for Anser cygnoides clean reads were generated, which were further assembled into 77,134 unigenes with an average length of 906 base pairs in Anser anser and 66,041 unigenes with an average length of 922 base pairs in Anser cygnoides. To recognize the potential regulatory roles of differentially expressed genes (DEGs) during geese embryonic skin feather follicle development, the obtained unigenes were annotated to Gene Ontology (GO), Eukaryotic Orthologous Groups (KOG), and Kyoto Encyclopedia of Genes and Genomes (KEGG) for functional analysis. In both species, GO and KOG had shown similar distribution patterns during functional annotation except for KEGG, which showed significant variation in signaling enrichment. Anser asnser was significantly enriched in the calcium signaling pathway, whereas Anser cygnoides was significantly enriched with glycerolipid metabolism. Further analysis indicated that 14,227 gene families were conserved between the species, among which a total of 20,715 specific gene families were identified. Comparative RNA-Seq data analysis may reveal inclusive knowledge to assist in the identification of genetic regulators at a molecular level to improve feather quality production in geese and other poultry species.


Transcriptome Analysis of Landrace Pig Subcutaneous Preadipocytes during Adipogenic Differentiation.

  • Xitong Zhao‎ et al.
  • Genes‎
  • 2019‎

Fat deposition in pigs, which significantly contributes to meat quality, fattening efficiency, reproductive performance, and immunity, is critically affected by preadipocyte adipogenic differentiation. We elucidated adipogenesis in pigs using transcriptome analysis. Preadipocytes from subcutaneous adipose tissue (SAT) of Landrace piglets were differentiated into adipocytes in vitro. RNA sequencing (RNA-seq) used to screen differentially expressed genes (DEGs) during preadipocyte differentiation up to day 8 revealed 15,918 known and 586 novel genes. We detected 21, 144, and 394 DEGs, respectively, including 16 genes differentially expressed at days 2, 4 and 8 compared to day 0. Th number of DEGs increased time-dependently. Lipid metabolism, cell differentiation and proliferation, peroxisome proliferator-activated receptor (PPAR), wingless-type MMTV integration site (Wnt), tumor necrosis factor (TNF) signaling, and steroid biosynthesis were significant at days 2, 4, and 8 compared to day 0 (adjusted p < 0.05). Short time-series expression miner (STEM) analysis obtained 26 clusters of differential gene expression patterns, and nine were significant (p < 0.05). Functional analysis showed many significantly enriched lipid deposition- and cellular process-related biological processes and pathways in profiles 9, 21, 22, and 24. Glycerolipid and fatty-acid metabolism, PPAR signaling, fatty-acid degradation, phosphoinositide 3-kinase (PI3K)/protein kinase B (Akt), and TNF signaling were observed during preadipocyte differentiation in vitro. These findings will facilitate the comprehension of preadipocyte differentiation and fat deposition in pigs.


Genome-wide copy number variations inferred from SNP genotyping arrays using a Large White and Minzhu intercross population.

  • Ligang Wang‎ et al.
  • PloS one‎
  • 2013‎

Copy number variations (CNVs) are one of the main contributors to genetic diversity in animals and are broadly distributed in the genomes of swine. Investigating the performance and evolutionary impacts of pig CNVs requires comprehensive knowledge of their structure and function within and between breeds. In the current study, 4 different programs (i.e., GADA, PennCNV, QuantiSNP, and cnvPartition) were used to analyze Porcine SNP60 genotyping data of 585 pigs from one Large White × Minzhu intercross population to detect copy number variant regions (CNVRs). Overlapping CNVRs recalled by at least 2 programs were used to construct a powerful and comprehensive CNVR map, which contained 249 CNVRs (i.e., 70 gains, 43 losses, and 136 gains/losses) and covered 26.22% of the regions in the swine genome. Ten CNVRs, representing different predicted statuses, were selected for validation via quantitative real-time PCR (QPCR); 9/10 CNVRs (i.e., 90%) were validated. When being traced back to the F0 generation, 58 events were identified in only Minzhu F0 parents and 2 events were identified in only Large White F0 parents. A series of CNVR function analyses were performed. Some of the CNVRs functions were predicted, and several interesting CNVRs for meat quality traits and hematological parameters were obtained. A comprehensive and lower false rate genome-wide CNV map was constructed for Large White and Minzhu pig genomes in this study. Our results may provide an important basis for determining the relationship between CNVRs and important qualitative and quantitative traits. In addition, it can help to further understand genetic processes in pigs.


Integration of transcriptome and whole genomic resequencing data to identify key genes affecting swine fat deposition.

  • Kai Xing‎ et al.
  • PloS one‎
  • 2015‎

Fat deposition is highly correlated with the growth, meat quality, reproductive performance and immunity of pigs. Fatty acid synthesis takes place mainly in the adipose tissue of pigs; therefore, in this study, a high-throughput massively parallel sequencing approach was used to generate adipose tissue transcriptomes from two groups of Songliao black pigs that had opposite backfat thickness phenotypes. The total number of paired-end reads produced for each sample was in the range of 39.29-49.36 millions. Approximately 188 genes were differentially expressed in adipose tissue and were enriched for metabolic processes, such as fatty acid biosynthesis, lipid synthesis, metabolism of fatty acids, etinol, caffeine and arachidonic acid and immunity. Additionally, many genetic variations were detected between the two groups through pooled whole-genome resequencing. Integration of transcriptome and whole-genome resequencing data revealed important genomic variations among the differentially expressed genes for fat deposition, for example, the lipogenic genes. Further studies are required to investigate the roles of candidate genes in fat deposition to improve pig breeding programs.


  1. SciCrunch.org Resources

    Welcome to the FDI Lab - SciCrunch.org Resources search. From here you can search through a compilation of resources used by FDI Lab - SciCrunch.org and see how data is organized within our community.

  2. Navigation

    You are currently on the Community Resources tab looking through categories and sources that FDI Lab - SciCrunch.org has compiled. You can navigate through those categories from here or change to a different tab to execute your search through. Each tab gives a different perspective on data.

  3. Logging in and Registering

    If you have an account on FDI Lab - SciCrunch.org then you can log in from here to get additional features in FDI Lab - SciCrunch.org such as Collections, Saved Searches, and managing Resources.

  4. Searching

    Here is the search term that is being executed, you can type in anything you want to search for. Some tips to help searching:

    1. Use quotes around phrases you want to match exactly
    2. You can manually AND and OR terms to change how we search between words
    3. You can add "-" to terms to make sure no results return with that term in them (ex. Cerebellum -CA1)
    4. You can add "+" to terms to require they be in the data
    5. Using autocomplete specifies which branch of our semantics you with to search and can help refine your search
  5. Save Your Search

    You can save any searches you perform for quick access to later from here.

  6. Query Expansion

    We recognized your search term and included synonyms and inferred terms along side your term to help get the data you are looking for.

  7. Collections

    If you are logged into FDI Lab - SciCrunch.org you can add data records to your collections to create custom spreadsheets across multiple sources of data.

  8. Facets

    Here are the facets that you can filter your papers by.

  9. Options

    From here we'll present any options for the literature, such as exporting your current results.

  10. Further Questions

    If you have any further questions please check out our FAQs Page to ask questions and see our tutorials. Click this button to view this tutorial again.

Publications Per Year

X

Year:

Count: