Evidence map›Paper›PMID 35106611›Full record

ArticleAnalytical and bioanalytical chemistry2022

Optimization of proteomics sample preparation for identification of host and bacterial proteins in mouse feces.

Maryam Baniasad, Yongseok Kim, Michael Shaffer, Anice Sabag-Daigle, Ikaia Leleiwi, Rebecca A Daly, Brian M M Ahmer, Kelly C Wrighton, Vicki H Wysocki

Open access · greenAbstract read
In one paragraph

Article in Analytical and bioanalytical chemistry, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
1.6field-weighted citation impact, top 18% 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

6 citing papers in PubMed, 12 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
  4. Article
  5. Review
  6. Bottom-Up Proteomics: Advancements in Sample Preparation.International journal of molecular sciences · 2023
    Review
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

9 authors at 2 institutions in 1 country.

Maryam BaniasadDepartment of Chemistry and Biochemistry, The Ohio State University, Columbus, OH, USA.
Yongseok KimDepartment of Chemistry and Biochemistry, The Ohio State University, Columbus, OH, USA.
Michael ShafferDepartment of Soil and Crop Sciences, The Colorado State University, Fort Collins, CO, USA.
Anice Sabag-DaigleDepartment of Microbiology, The Ohio State University, Columbus, OH, USA.
Ikaia LeleiwiDepartment of Soil and Crop Sciences, The Colorado State University, Fort Collins, CO, USA.
Rebecca A DalyDepartment of Soil and Crop Sciences, The Colorado State University, Fort Collins, CO, USA.
Brian M M AhmerDepartment of Microbiology, The Ohio State University, Columbus, OH, USA.
Kelly C WrightonDepartment of Soil and Crop Sciences, The Colorado State University, Fort Collins, CO, USA.
Vicki H WysockiDepartment of Chemistry and Biochemistry, The Ohio State University, Columbus, OH, USA. wysocki.11@osu.edu.
The Ohio State University · USColorado State University · US

Funding

Microbial ecology of the inflamed intestineR01AI143288 · NIAID · OHIO STATE UNIVERSITY · PI AHMER, BRIAN M, WRIGHTON, KELLY C · 2018 to 2022
$3.5M
Predoctoral Training in Quantitative Cell & Molecular BiologyT32GM132057 · NIGMS · COLORADO STATE UNIVERSITY · PI Karen Marie Dobos, Brian Munsky · 2019 to 2026
$2.9M
GENETIC ANALYSIS OF RNA VIRUS-HOST INTERACTIONR01AI043288 · NIAID · WASHINGTON STATE UNIVERSITY · PI CARRINGTON, JAMES C · 1998 to 2007
$1.7M
UHPLC-Trapped Ion Mobility-QTOF for ProteomicsS10OD026945 · OD · OHIO STATE UNIVERSITY · PI WYSOCKI, VICKI H. · 2019 to 2019
$600k
NIAID NIH HHS R01 AI043288NIAID NIH HHS R01 AI143288NIGMS NIH HHS T32 GM132057NIH HHS S10 OD026945
6 · The paper itself

Abstract

Bottom-up proteomics is a powerful method for the functional characterization of mouse gut microbiota. To date, most of the bottom-up proteomics studies of the mouse gut rely on limited amounts of fecal samples. With mass-limited samples, the performance of such analyses is highly dependent on the protein extraction protocols and contaminant removal strategies. Here, protein extraction protocols (using different lysis buffers) and contaminant removal strategies (using different types of filters and beads) were systematically evaluated to maximize quantitative reproducibility and the number of identified proteins. Overall, our results recommend a protein extraction method using a combination of sodium dodecyl sulfate (SDS) and urea in Tris-HCl to yield the greatest number of protein identifications. These conditions led to an increase in the number of proteins identified from gram-positive bacteria, such as Firmicutes and Actinobacteria, which is a challenging task. Our analysis further confirmed these conditions led to the extraction of non-abundant bacterial phyla such as Proteobacteria. In addition, we found that, when coupled to our optimized extraction method, suspension trap (S-Trap) outperforms other contaminant removal methods by providing the most reproducible method while producing the greatest number of protein identifications. Overall, our optimized sample preparation workflow is straightforward and fast, and requires minimal sample handling. Furthermore, our approach does not require high amounts of fecal samples, a vital consideration in proteomics studies where mice produce smaller amounts of feces due to a particular physiological condition. Our final method provides efficient digestion of mouse fecal material, is reproducible, and leads to high proteomic coverage for both host and microbiome proteins.

Indexed as

Gastrointestinal MicrobiomeProteomicsAnimalsBacterial ProteinsFecesMiceReproducibility of ResultsBacterial ProteinsContaminant removalFecal sampleProtein extractionProteomicsSample preparation

Identifiers

PMID35106611
PMCPMC9393048
OpenAlexW4210727667

What OpenQuestion holds

Textmetadata
LicenceTDM
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.