{{get_dataset_fail}}




{{section.text}} {{section.text}} {{section.text}} {{section.text}} {{dataset.name}}


Purpose: Next-generation sequencing (NGS) was used to select genes potentially associated with exercise adaptation in Arabian horses. Methods: Whole transcriptome profiling of blood was performed for untrained horses and horses from which samples were collected during at 3 different periods of training procedure (T1-during intense training period - March, T2- before starts - May and T3 -after flat racing season - October). The muscle transcriptome sequencing was performed for 37 blood samples using Illumina HiScan SQ in 75 single-end cycles. The quantifying transcript abundances was made using the RSEM supported by STAR aligner. The raw reads were aligned to the Equus caballus reference genome. Differentially expressed genes in blood tissue were detected by DESeq2. The RNA-seq results were validated using by qPCR. Results: The increase of the number of DEGs between subsequent training periods has been observed and the highest amount of DEGs was detected between untrained horses (T0) and horses at the end of the racing season (T3) – 440. The comparison of transcriptome of T2 vs T3 and T0 vs T3 showed a significant advantage of up-regulated genes during long-term exercise (up-regulation of 266 and 389 DEGs in T3 period compared T2 and T0; respectively). Our results showed that the largest number of identified genes encoded transcription factors, nucleic acid binding proteins and G-protein modulators, which mainly were transcriptional activated at the last training phase (T3) . Moreover, in the T3 period the identified DEGs represented genes coded for cytoskeletal proteins including actin cytoskeletal proteins and kinases. The most abundant exercise-upregulated genes were involved in pathways important in regulating the cell cycle (PI3K-Akt signaling pathway), cell communication (cAMP-dependent pathway), proliferation, differentiation and apoptosis as well as immunity processes (Jak-STAT signaling pathway). We also observed exercise induced expression of genes related in regulation of actin cytoskeleton, gluconeogenesis (FoxO signaling pathway; Insulin signaling pathway), glycerophospholipid metabolism and calcium signaling. Conclusions: TOur results allow to identify changes in genes expression profile following training schedule in Arabian horses. Based on comparison analysis of blood transcriptomes, several exercise-regulated pathways and genes most affected by exercise were detected. We pinpointed overrepresented molecular pathways and genes essential for exercise adaptive response via maintaining of body homeostasis. The observed transcriptional activation of such gene as LPGAT1, AGPAT5, PIK3CG, GPD2, FOXN2, FOXO3, ACVR1B and ACVR2A can be a base for further research in order to identify genes potentially associated with race performance in Arabian horses. Such markers will be essential to choice the training type, and could result in differences in racing performance specific to various breeds. The blood transcriptome sequencing was performed for 37 samples collected form Arabian horses using Illumina HiScan SQ in75 single-end cycles and in 3-4 technical repetitions.repetitions.

ABSTRACT: {{section.text}} {{section.text}} {{section.text}} {{section.text}} {{abstract_sections[abstract_sections.length-1].tobeReduced=='true'?"... [more]":""}} [less]

SAMPLE PROTOCOL: {{section.text}} {{section.text}} {{section.text}} {{section.text}} {{sample_protocol_sections[sample_protocol_sections.length-1].tobeReduced=='true'?"... [more]":""}} [less]

DATA PROTOCOL: {{section.text}} {{section.text}} {{section.text}} {{section.text}} {{data_protocol_sections[data_protocol_sections.length-1].tobeReduced=='true'?"... [more]":""}} [less]

REANALYSIS of: {{reanalysis_item.accession}}

REANALYZED by: {{reanalyzed_item.accession}}

OTHER RELATED OMICS DATASETS IN: {{reanalysis_item.accession}}

INSTRUMENT(S): {{instrument+';'}}

ORGANISM(S): {{organism.name + ';'}}

TISSUE(S): {{tissue+';'}}

DISEASE(S): {{disease+';'}}

SUBMITTER: {{dataset['submitter'] + ' <' + dataset['submitterMail'] + '>'}}

PROVIDER: {{acc}} | {{repositories[domain]}} | {{dataset['publicationDate']}}

{{publication_info[publication_index_info[dataset.publicationIds[current_publication]]].title}}

{{author.fullname.substr(0,author.fullname.length-2)}} ,

{{publication_info[publication_index_info[dataset.publicationIds[current_publication]]].citation}}


Sorry, this publication's infomation has not been loaded in the Indexer, please go directly to PUBMED or Altmetric.

ABSTRACT: {{publication_info[publication_index_info[dataset.publicationIds[current_publication]]].pub_abstract[0]}}
{{publication_info[publication_index_info[dataset.publicationIds[current_publication]]].pub_abstract[1]}} [less]

ABSTRACT: {{publication_info[publication_index_info[dataset.publicationIds[current_publication]]].pub_abstract[0]|limitTo:500}} {{publication_info[publication_index_info[dataset.publicationIds[current_publication]]].pub_abstract[0].length>500?"... [more]":""}}

Publication: {{current_publication +1}}/{{dataset.publicationIds.length}}

{{dataset.publicationIds[current_publication].publicationDate}}


Only show the datasets with similarity scores above:{{threshold}}

Threshold:
    {{threshold}}
     

The biological similarity score is calculated based on the number of molecules (Proteins, Metabolites, Genes) common between two different projects.

Similar Datasets

  • Organism: {{organism["name"]}} Not available
    {{relatedDataset['publicationDate'].substr(0,4)+"-"+relatedDataset['publicationDate'].substr(4,2)+"-"+relatedDataset['publicationDate'].substr(6,2)}}| {{relatedDataset.id}} | {{repositories[relatedDataset.source]}}