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Prior metabolite extraction fully preserves RNAseq quality and enables integrative multi-‘omics analysis of the liver metabolic response to viral infection

Overview of attention for article published in RNA Biology, April 2023
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About this Attention Score

  • In the top 5% of all research outputs scored by Altmetric
  • One of the highest-scoring outputs from this source (#5 of 1,534)
  • High Attention Score compared to outputs of the same age (97th percentile)
  • High Attention Score compared to outputs of the same age and source (88th percentile)

Mentioned by

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11 news outlets
blogs
2 blogs
twitter
8 X users

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5 Mendeley
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Title
Prior metabolite extraction fully preserves RNAseq quality and enables integrative multi-‘omics analysis of the liver metabolic response to viral infection
Published in
RNA Biology, April 2023
DOI 10.1080/15476286.2023.2204586
Pubmed ID
Authors

Zachary B Madaj, Michael S. Dahabieh, Vijayvardhan Kamalumpundi, Brejnev Muhire, J. Pettinga, Rebecca A. Siwicki, Abigail E. Ellis, Christine Isaguirre, Martha L. Escobar Galvis, Lisa DeCamp, Russell G. Jones, Scott A. Givan, Marie Adams, Ryan D. Sheldon

Abstract

Here, we provide an in-depth analysis of the usefulness of single-sample metabolite/RNA extraction for multi-'omics readout. Using pulverized frozen livers of mice injected with lymphocytic choriomeningitis virus (LCMV) or vehicle (Veh), we isolated RNA prior (RNA) or following metabolite extraction (MetRNA). RNA sequencing (RNAseq) data were evaluated for differential expression analysis and dispersion, and differential metabolite abundance was determined. Both RNA and MetRNA clustered together by principal component analysis, indicating that inter-individual differences were the largest source of variance. Over 85% of LCMV versus Veh differentially expressed genes were shared between extraction methods, with the remaining 15% evenly and randomly divided between groups. Differentially expressed genes unique to the extraction method were attributed to randomness around the 0.05 FDR cut-off and stochastic changes in variance and mean expression. In addition, analysis using the mean absolute difference showed no difference in the dispersion of transcripts between extraction methods. Altogether, our data show that prior metabolite extraction preserves RNAseq data quality, which enables us to confidently perform integrated pathway enrichment analysis on metabolomics and RNAseq data from a single sample. This analysis revealed pyrimidine metabolism as the most LCMV-impacted pathway. Combined analysis of genes and metabolites in the pathway exposed a pattern in the degradation of pyrimidine nucleotides leading to uracil generation. In support of this, uracil was among the most differentially abundant metabolites in serum upon LCMV infection. Our data suggest that hepatic uracil export is a novel phenotypic feature of acute infection and highlight the usefulness of our integrated single-sample multi-'omics approach.

X Demographics

X Demographics

The data shown below were collected from the profiles of 8 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 5 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 5 100%

Demographic breakdown

Readers by professional status Count As %
Professor 1 20%
Librarian 1 20%
Student > Ph. D. Student 1 20%
Unknown 2 40%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 2 40%
Sports and Recreations 1 20%
Unknown 2 40%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 93. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 18 May 2023.
All research outputs
#440,136
of 24,811,594 outputs
Outputs from RNA Biology
#5
of 1,534 outputs
Outputs of similar age
#9,869
of 395,841 outputs
Outputs of similar age from RNA Biology
#3
of 18 outputs
Altmetric has tracked 24,811,594 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 98th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,534 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.2. This one has done particularly well, scoring higher than 99% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 395,841 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 97% of its contemporaries.
We're also able to compare this research output to 18 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 88% of its contemporaries.