Evidence map›Paper›PMID 40198554›Full record

ArticleMethods in molecular biology (Clifton, N.J.)2025

Identifying Wheat Rusts Using MALDI-TOF Mass Spectrometry.

Shimosh Kurera, Brent McCallum, Gurcharn Singh Brar, Kirby Nilsen, Reem Aboukhaddour, Tom Fetch, Matthew Bakker, Sean Walkowiak

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Article in Methods in molecular biology (Clifton, N.J.), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

8 authors.

Shimosh KureraCanadian Grain Commission, Winnipeg, MB, Canada.
Brent McCallumAgriculture and Agri-Food Canada, Morden, MB, Canada.
Gurcharn Singh BrarFaculty of Land and Food Systems, The University of British Columbia, Vancouver, BC, Canada.
Kirby NilsenAgriculture and Agri-Food Canada, Brandon, MB, Canada.
Reem AboukhaddourAgriculture and Agri-Food Canada, Lethbridge, AB, Canada.
Tom FetchAgriculture and Agri-Food Canada, Morden, MB, Canada.
Matthew BakkerDepartment of Microbiology, University of Manitoba, Winnipeg, MB, Canada. matthew.bakker@umanitoba.ca.
Sean WalkowiakCanadian Grain Commission, Winnipeg, MB, Canada. sean.walkowiak@grainscanada.gc.ca.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Rust diseases are a major limitation to grain production in Canada and have caused devastating yield losses worldwide [1]. The identification and characterization of pathogenic fungi responsible for rust diseases are important for implementing effective disease management practices. Modern DNA-based methods of identification, such as quantitative polymerase chain reaction (PCR) and sequencing, are time-consuming, costly, and require well-trained laboratory personnel. Here, we describe a protocol to rapidly identify rust samples to the species level using matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS). Biochemical profiles generated by MALDI-TOF MS are used for identification. The method is commonly used in clinical settings and can accurately identify hundreds of microbes in minutes at a low cost. The MALDI-TOF MS method described here is simple and can assess samples of rust spores and spore-mats originating from different cereal hosts and identify them to the species level with high accuracy, high throughput, and low cost. The method is not currently able to accurately distinguish among races, though this may be possible with additional research and sample fractionation.

Indexed as

BasidiomycotaPlant DiseasesSpectrometry, Mass, Matrix-Assisted Laser Desorption-IonizationTriticumSpores, FungalBiotypingMALDI-TOF MSMass spectrometryPucciniaRust

Identifiers

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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.