Transcriptomic profiling of adipose tissue inflammation, remodeling, and lipid metabolism in periparturient dairy cows ( Bos taurus )

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Abstract

Background

Periparturient cows release fatty acid reserves from adipose tissue (AT) through lipolysis in response to the negative energy balance induced by physiological changes related to parturition and the onset of lactation. However, lipolysis causes inflammation and structural remodeling in AT that in excess predisposes cows to disease. The objective of this study was to determine the effects of the periparturient period on the transcriptomic profile of AT using NGS RNAseq.

Results

Subcutaneous AT samples were collected from Holstein cows ( n = 12) at 11 ± 3.6 d before calving date (PreP) and at 6 ± 1d (PP1) and 13 ± 1.4d (PP2) after parturition. Differential expression analyses showed 1946 and 1524 DEG at PP1 and PP2, respectively, compared to PreP. Functional Enrichment Analysis revealed functions grouped in categories such as lipid metabolism, molecular transport, energy production, inflammation, and free radical scavenging to be affected by parturition and the onset of lactation (FDR < 0.05). Inflammation related genes such as TLR4 and IL6 were categorized as upstream lipolysis triggers. In contrast, FASN, ELOVL6, ACLS1, and THRSP were identified as upstream inhibitors of lipid synthesis. Complement ( C3), CXCL2, and HMOX1 were defined as links between inflammatory pathways and those involved in the generation of reactive oxygen species.

Conclusions

Results offer a comprehensive characterization of gene expression dynamics in periparturient AT, identify upstream regulators of AT function, and demonstrate complex interactions between lipid mobilization, inflammation, extracellular matrix remodeling, and redox signaling in the adipose organ.

Supplementary Information

Supplementary information accompanies this paper at 10.1186/s12864-020-07235-0.

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edgeR: a Bioconductor package for differential expression analysis of digital gene expression data

Mark Robinson, Davis J. McCarthy, Gordon K. Smyth (2009)

Summary: It is expected that emerging digital gene expression (DGE) technologies will overtake microarray technologies in the near future for many functional genomics applications. One of the fundamental data analysis tasks, especially for gene expression studies, involves determining whether there is evidence that counts for a transcript or exon are significantly different across experimental conditions. edgeR is a Bioconductor software package for examining differential expression of replicated count data. An overdispersed Poisson model is used to account for both biological and technical variability. Empirical Bayes methods are used to moderate the degree of overdispersion across transcripts, improving the reliability of inference. The methodology can be used even with the most minimal levels of replication, provided at least one phenotype or experimental condition is replicated. The software may have other applications beyond sequencing data, such as proteome peptide count data. Availability: The package is freely available under the LGPL licence from the Bioconductor web site (http://bioconductor.org). Contact: mrobinson@wehi.edu.au

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HISAT: a fast spliced aligner with low memory requirements.

Daehwan Kim, Ben Langmead, Steven L Salzberg (2018)

HISAT (hierarchical indexing for spliced alignment of transcripts) is a highly efficient system for aligning reads from RNA sequencing experiments. HISAT uses an indexing scheme based on the Burrows-Wheeler transform and the Ferragina-Manzini (FM) index, employing two types of indexes for alignment: a whole-genome FM index to anchor each alignment and numerous local FM indexes for very rapid extensions of these alignments. HISAT's hierarchical index for the human genome contains 48,000 local FM indexes, each representing a genomic region of ∼64,000 bp. Tests on real and simulated data sets showed that HISAT is the fastest system currently available, with equal or better accuracy than any other method. Despite its large number of indexes, HISAT requires only 4.3 gigabytes of memory. HISAT supports genomes of any size, including those larger than 4 billion bases.

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HTSeq—a Python framework to work with high-throughput sequencing data

Simon Anders, Paul Pyl, Wolfgang Huber (2014)

Motivation: A large choice of tools exists for many standard tasks in the analysis of high-throughput sequencing (HTS) data. However, once a project deviates from standard workflows, custom scripts are needed. Results: We present HTSeq, a Python library to facilitate the rapid development of such scripts. HTSeq offers parsers for many common data formats in HTS projects, as well as classes to represent data, such as genomic coordinates, sequences, sequencing reads, alignments, gene model information and variant calls, and provides data structures that allow for querying via genomic coordinates. We also present htseq-count, a tool developed with HTSeq that preprocesses RNA-Seq data for differential expression analysis by counting the overlap of reads with genes. Availability and implementation: HTSeq is released as an open-source software under the GNU General Public Licence and available from http://www-huber.embl.de/HTSeq or from the Python Package Index at https://pypi.python.org/pypi/HTSeq. Contact: sanders@fs.tum.de

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Author and article information

Contributors

G. Andres Contreras:

ORCID: http://orcid.org/0000-0003-4969-2178

contre28@msu.edu

Journal

Journal ID (nlm-ta): BMC Genomics

Journal ID (iso-abbrev): BMC Genomics

Title: BMC Genomics

Publisher: BioMed Central (London )

ISSN (Electronic): 1471-2164

Publication date (Electronic): 23 November 2020

Publication date PMC-release: 23 November 2020

Publication date Collection: 2020

Volume: 21

Electronic Location Identifier: 824

Affiliations

[1 ]GRID grid.17088.36, ISNI 0000 0001 2150 1785, Department of Physiology, College of Natural Sciences, , Michigan State University, ; East Lansing, MI 48824 USA

[2 ]GRID grid.17088.36, ISNI 0000 0001 2150 1785, Department of Animal Science, College of Agriculture and Natural Resources, , Michigan State University, ; East Lansing, MI 48824 USA

[3 ]GRID grid.17088.36, ISNI 0000 0001 2150 1785, Department of Large Animal Clinical Sciences, College of Veterinary Medicine, , Michigan State University, ; East Lansing, MI 48824 USA

Author information

G. Andres Contreras http://orcid.org/0000-0003-4969-2178

Article

Publisher ID: 7235

DOI: 10.1186/s12864-020-07235-0

PMC ID: 7686742

PubMed ID: 33228532

SO-VID: c1206413-bfed-4710-b0b2-5dcef251b513

License:

Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

History

Date received : 14 August 2020

Date accepted : 17 November 2020

Funding

Funded by: FundRef http://dx.doi.org/10.13039/100005825, National Institute of Food and Agriculture;

Award ID: 2019-67015-29443

Award ID: Pre-Doctoral Fellowship from USDA NIFA (award 2017-07578)

Award Recipient : Crystal Prom

Funded by: Michigan Alliance for Animal Agriculture

Award ID: AA18-003

Award Recipient : Adam L. Lock

Custom metadata

ScienceOpen disciplines: Genetics

Keywords: periparturient period,lipogenesis,lipolysis,adipose tissue inflammation

Data availability:

ScienceOpen disciplines: Genetics

Keywords: periparturient period, lipogenesis, lipolysis, adipose tissue inflammation

Transcriptomic profiling of adipose tissue inflammation, remodeling, and lipid metabolism in periparturient dairy cows ( Bos taurus)

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Abstract

Background

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Supplementary Information

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Genomic Prediction

Most cited references 68

edgeR: a Bioconductor package for differential expression analysis of digital gene expression data

HISAT: a fast spliced aligner with low memory requirements.

HTSeq—a Python framework to work with high-throughput sequencing data

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