Evidence map›Paper›PMID 31208322›Full record

ArticleBMC genomics2019

Understanding the liver under heat stress with statistical learning: an integrated metabolomics and transcriptomics computational approach.

Allen H Hubbard, Xiaoke Zhang, Sara Jastrebski, Abhyudai Singh, Carl Schmidt

Open access · goldAbstract read
In one paragraph

Article in BMC genomics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed
0.9field-weighted citation impact, top 26% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

11 citing papers in PubMed, 21 citations in OpenAlex.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors at 2 institutions in 1 country.

Allen H HubbardBioinformatics and Systems Biology, University of Delaware, Newark, Delaware, USA. allenhub@udel.edu.ORCID http://orcid.org/0000-0001-8045-0455
Xiaoke ZhangStatistics, George Washington University, Washington, D.C, USA.
Sara JastrebskiAnimal and Food Sciences, University of Delaware, Newark, Delaware, USA.
Abhyudai SinghElectrical Engineering and Computer Science, University of Delaware, Newark, Delaware, USA.
Carl SchmidtAnimal and Food Sciences, University of Delaware, Newark, Delaware, USA.
University of Delaware · USGeorge Washington University · US

Funding

Consequences and Control of Randomness in Timing of Intracellular EventsR01GM124446 · NIGMS · UNIVERSITY OF DELAWARE · PI SINGH, ABHYUDAI · 2017 to 2020
$914k
Stochastic hybrid systems approach to uncovering cell-size control mechanisms R01GM126557 · NIGMS · UNIVERSITY OF DELAWARE · PI SINGH, ABHYUDAI · 2017 to 2019
$675k
National Institute of Food and Agriculture 2011-67003-30228NIGMS NIH HHS R01 GM124446NIGMS NIH HHS R01 GM126557
6 · The paper itself

Abstract

backgroundWe present results from a computational analysis developed to integrate transcriptome and metabolomic data in order to explore the heat stress response in the liver of the modern broiler chicken. Heat stress is a significant cause of productivity loss in the poultry industry, both in terms of increased livestock morbidity and its negative influence on average feed efficiency. This study focuses on the liver because it is an important regulator of metabolism, controlling many of the physiological processes impacted by prolonged heat stress. Using statistical learning methods, we identify genes and metabolites that may regulate the heat stress response in the liver and adaptations required to acclimate to prolonged heat stress.

resultsWe describe how disparate systems such as sugar, lipid and amino acid metabolism, are coordinated during the heat stress response.

conclusionsOur findings provide more detailed context for genomic studies and generates hypotheses about dietary interventions that can mitigate the negative influence of heat stress on the poultry industry.

Indexed as

Gene Expression ProfilingMetabolomicsAdaptation, PhysiologicalAnimalsChickensHeat-Shock ResponseLiverMaleHigh throughput sequencingMetabolomeTranscriptome

Identifiers

PMID31208322
PMCPMC6580474
OpenAlexW2949612983

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.