Evidence map›Paper›PMID 40521878›Full record

ArticlemSystems2025

Optimizing mouse metatranscriptome profiling by selective removal of redundant nucleic acid sequences.

Morgan Roos, Samuel Bunga, Asako Tan, Erica Maissy, Dylan Skola, Alexander Richter, Daniel S Whittaker, Paula Desplats, Amir Zarrinpar, Rick Conrad and 1 more

Abstract read
In one paragraph

Article in mSystems, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
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

1 citing paper in PubMed.

  1. Review
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

11 authors.

Morgan Roos *Illumina Inc., San Diego, California, USA.
Samuel Bunga *Illumina Inc., San Diego, California, USA.
Asako TanIllumina Inc., San Diego, California, USA.
Erica MaissyDivision of Gastroenterology, University of California, San Diego, La Jolla, California, USA.
Dylan SkolaIllumina Inc., San Diego, California, USA.
Alexander RichterDivision of Gastroenterology, University of California, San Diego, La Jolla, California, USA.
Daniel S WhittakerDepartment of Neurosciences, University of California San Diego, La Jolla, California, USA.
Paula DesplatsDepartments of Neurosciences and Pathology, University of California San Diego, La Jolla, California, USA.
Amir ZarrinparDivision of Gastroenterology, University of California, San Diego, La Jolla, California, USA.ORCID 0000-0001-6423-5982
Rick ConradRetired Researcher, La Concha Pass, Austin, Texas, USA.
Scott KuerstenIllumina Inc., San Diego, California, USA.ORCID 0009-0007-5058-4920

Funding

UC San Diego Clinical and Translational Research InstituteUL1TR001442 · NCATS · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI FIRESTEIN, GARY S, HOGARTH, MICHAEL · 2015 to 2024
$88.3M
Viral Vector Core (VVC)P30CA014195 · NCI · SALK INSTITUTE FOR BIOLOGICAL STUDIES · PI Alan Saghatelian · 1985 to 2026
$82.8M
Transgenic & Knock-out MouseP30DK063491 · NIDDK · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI MILES Frome WILKINSON · 2003 to 2026
$40.4M
San Diego Digestive Diseases Research CenterP30DK120515 · NIDDK · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI Bernd G. Schnabl · 2019 to 2026
$10.8M
Engineering Native E. coli to Detect, Report, and Treat Colorectal CancerU01CA265719 · NCI · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI ZARRINPAR, AMIR · 2021 to 2025
$3.1M
Development of Engineered Native Bacteria as a Tool for Functional Manipulation of the Gut MicrobiomeR01AI163483 · NIAID · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI JEFF M HASTY, Amir Zarrinpar · 2023 to 2026
$3.1M
Design and characterization of bacterial population dynamics in solid tumor modelsR01EB030134 · NIBIB · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI HASTY, JEFF M, TSIMRING, LEV S · 2021 to 2024
$2.5M
BLRD VA I01 BX005707NCATS NIH HHS UL1 TR001442NCI NIH HHS P30 CA014195NCI NIH HHS U01 CA265719NIAID NIH HHS R01 AI163483NIBIB NIH HHS R01 EB030134NIDDK NIH HHS P30 DK063491NIDDK NIH HHS P30 DK120515
6 · The paper itself

Abstract

Metatranscriptome (MetaT) sequencing is a critical tool for profiling the dynamic metabolic functions of microbiomes. In addition to taxonomic information, MetaT also provides real-time gene expression data of both host and microbial populations, thus permitting authentic quantification of the functional (enzymatic) output of the microbiome and its host. The main challenge to effective and accurate MetaT analysis is the removal of highly abundant rRNA transcripts from these complex mixtures of microbes, which can number in the thousands of individual species. Regardless of the methodology for rRNA depletion, the design of rRNA removal probes based solely upon the taxonomic content of the microbiome typically requires very large numbers of individual probes, making this approach complex to commercially manufacture, costly, and frequently technically infeasible. In previous work (A. Tan, S. Murugapiran, A. Mikalauskas, J. Koble, et al., BMC Microbiol 23:299, 2023, https://doi.org/10.1186/s12866-023-03037-y), we designed a set of depletion probes for human stool samples using a design strategy based solely on sequence abundance, completely agnostic of the microbiomal species present. Here, we show that human-based probes are less effective when used with mouse cecal samples. However, adapting additional rRNA depletion probes specifically to cecal content provides both greater efficiency and consistency for MetaT analysis of mouse samples.IMPORTANCESequencing total RNA from microbiome samples is seriously impaired by the overwhelming proportion of rRNA to mRNA content. As much as 99% of sequencing reads can be assigned to the rRNA content, thus removal of these abundant transcripts is critical to metatranscriptome (MetaT) analysis. The use of Ribo Zero Plus rRNA depletion probes designed for human gut microbiomes proved to be less effective and more inconsistent across mouse cecal donor samples, a common experimental system for microbiome studies. In the present work, we have extended and refined a taxonomically neutral probe design method for mouse cecal content. The additional probes were carefully chosen to limit the number needed for effective depletion to reduce both the cost and risk of introducing bias to MetaT analysis. Our results demonstrate this method as efficient and consistent for rRNA removal in mouse cecal samples, thus providing a significant increase in the number of mRNA-rich sequencing reads for MetaT analysis.

Indexed as

Gastrointestinal MicrobiomeGene Expression ProfilingTranscriptomeAnimalsFecesHumansMiceMice, Inbred C57BLMicrobiotaRNA, RibosomalRNA, Ribosomalgene sequencingmetatranscriptomemRNAnonhuman microbiomerRNA

Identifiers

PMID40521878
PMCPMC12282169

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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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.