Powered by OpenAIRE graph
Found an issue? Give us feedback
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ ZENODOarrow_drop_down
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
ZENODO
Presentation . 2021
License: CC BY
Data sources: Datacite
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
ZENODO
Other literature type . 2021
License: CC BY
Data sources: ZENODO
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
ZENODO
Presentation . 2021
License: CC BY
Data sources: Datacite
versions View all 2 versions
addClaim

The 2020 ABRF Beer Study: beer proteomics at the global scale

Authors: Phinney, Brett S.; Tsai, Helen; Marcus, Andrew; Fox, Glen; Ding, Hua; Herring, Laura E.; Jagtap, Pratik D.; +12 Authors

The 2020 ABRF Beer Study: beer proteomics at the global scale

Abstract

Introduction Beer is one of the oldest and most widely consumed beverages in the world. It contains a complex mixture of proteins from several organisms including plants (barley, hops, rice, wheat) and yeast, depending on the beer. The Proteomics Research Group (PRG) in the Association of Biomolecular Resource Facilities brought together an international consortium of proteomics laboratories (69 laboratories from 33 countries) to perform beer proteomics. An aliquot of specially brewed beer from the UC Davis Brewing Program was sent to 52 labs, and participants were encouraged to also use a widely available commercial beer (Heineken) and any other beer. In addition to helping connect scientists in trying times, we have also demonstrated the utility of multi-center studies with accessible materials. Methods Control beer brewed at UC Davis was shipped to participating laboratories at room temperature. Where shipping proved impractical, a widely available commercial beer was suggested as an alternative (Heineken). The PRG suggested a general digestion method, approximately 1 h (± 15 min) LC gradient, and using data-dependent acquisition. The suggested method consisted of precipitation, reduction, alkylation, and digestion with trypsin or LysC plus trypsin, but participants could use other methods. Raw mass spectrometry data (357 injections) and methods were deposited to MassIVE (MassIVE MSV000088080). Database searching was performed with MetaMorpheus against five species databases (barley, hops, rice, wheat, yeast) and contaminants. Mass tolerances were set automatically, carbamidomethylation was fixed, oxidized methionine was variable, and a 1 % FDR identification cutoff. Preliminary Data Of the 69 laboratories that signed up for the study, 35 returned data. Of these, 32 analyzed the PRG Beer and 17 analyzed Heineken (14 analyzed both). When shipping of the PRG Beer was prohibitive, participants were encouraged to use Heineken due to its global availability and perceived quality control. Participants were also encouraged to analyze other beers of their choosing, and 79 other beers were analyzed. In total there were 357 beer injections on 13 different types of mass spectrometers around the world. On average, 753.1 proteins were identified using MetaMorpheus, with the most being 2907 identified in the PRG Beer with a Thermo QE classic. Though database searching used non-UniProtKB non-RefSeq databases for barley, hops, and wheat, a downstream orthology conversion with BLAST found that despite their small size, the UniProtKB databases of these species adequately describe the mass specomtery data. Next, the 50 most abundant proteins identified in the PRG Beer and Heineken will be used for unsupervised clustering to determine how well beer proteomics performs across the world using the same or very similar beer. Finally, we will use the compareMS2 software tool to cluster data between beers and labs in an ID-free method. Though these studies focused only on the main grain additives to yeast, future work will expand the search space to include microorganisms that are part of the brewing process in some beers (i.e., bacteria in sours). Novel Aspect Multi-center proteomic studies using accessible materials, such as beer, can be successful, generate valuable results, and facilitate community building.

Keywords

proteomics, Association of Biomolecular Resources Facilities, beer, ABRF

  • BIP!
    Impact byBIP!
    selected citations
    These citations are derived from selected sources.
    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    0
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Average
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Average
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Average
    OpenAIRE UsageCounts
    Usage byUsageCounts
    visibility views 31
    download downloads 10
  • 31
    views
    10
    downloads
    Powered byOpenAIRE UsageCounts
Powered by OpenAIRE graph
Found an issue? Give us feedback
visibility
download
selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
views
OpenAIRE UsageCountsViews provided by UsageCounts
downloads
OpenAIRE UsageCountsDownloads provided by UsageCounts
0
Average
Average
Average
31
10
Green