Evidence map›Paper›PMID 35761017›Full record

ArticleScientific reports2022

Selecting putative drought-tolerance markers in two contrasting soybeans.

Laila Toum, Lucia Sandra Perez-Borroto, Andrea Natalia Peña-Malavera, Catalina Luque, Bjorn Welin, Ariel Berenstein, Darío Fernández Do Porto, Adrian Vojnov, Atilio Pedro Castagnaro, Esteban Mariano Pardo

Abstract read
In one paragraph

Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.

0numbers the graph read from it
0cells of the map it votes in
5citing 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

5 citing papers in PubMed.

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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

10 authors.

Laila Toum *Instituto de Tecnología Agroindustrial del Noroeste Argentino, Estación Experimental Agroindustrial Obispo Colombres- Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), William Cross 3150, Las Talitas, Tucumán, Argentina.
Lucia Sandra Perez-Borroto *Plant Breeding, Wageningen University & Research, 6708 PB, Wageningen, The Netherlands.
Andrea Natalia Peña-MalaveraInstituto de Tecnología Agroindustrial del Noroeste Argentino, Estación Experimental Agroindustrial Obispo Colombres- Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), William Cross 3150, Las Talitas, Tucumán, Argentina.
Catalina LuqueCátedra de Anatomía Vegetal. Facultad de Ciencias Naturales E IML, Universidad Nacional de Tucumán, Miguel Lillo 205, San Miguel de Tucumán, Tucumán, Argentina.
Bjorn WelinInstituto de Tecnología Agroindustrial del Noroeste Argentino, Estación Experimental Agroindustrial Obispo Colombres- Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), William Cross 3150, Las Talitas, Tucumán, Argentina.
Ariel BerensteinLaboratorio de Biología Molecular, División Patología, Instituto Multidisciplinario de Investigaciones en Patologías Pediátricas (IMIPP), CONICET-GCBA, C1425EFD, Buenos Aires, Argentina.
Darío Fernández Do PortoInstituto de Química Biológica (IQUIBICEN), Facultad de Ciencias Exactas y Naturales (FCEyN), Universidad de Buenos Aires, Intendente Guiraldes 2160, Buenos Aires, Argentina.
Adrian VojnovInstituto de Ciencia y Tecnología "Dr. César Milstein", Fundación Pablo Cassará-Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Saladillo 2468, C1440FFX, Buenos Aires, Argentina.
Atilio Pedro CastagnaroInstituto de Tecnología Agroindustrial del Noroeste Argentino, Estación Experimental Agroindustrial Obispo Colombres- Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), William Cross 3150, Las Talitas, Tucumán, Argentina.
Esteban Mariano PardoInstituto de Tecnología Agroindustrial del Noroeste Argentino, Estación Experimental Agroindustrial Obispo Colombres- Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), William Cross 3150, Las Talitas, Tucumán, Argentina. mpardokarate@gmail.com.ORCID 0000-0001-6777-0307

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Identifying high-yield genotypes under low water availability is essential for soybean climate-smart breeding. However, a major bottleneck lies in phenotyping, particularly in selecting cost-efficient markers associated with stress tolerance and yield stabilization. Here, we conducted in-depth phenotyping experiments in two soybean genotypes with contrasting drought tolerance, MUNASQA (tolerant) and TJ2049 (susceptible), to better understand soybean stress physiology and identify/statistically validate drought-tolerance and yield-stabilization traits as potential breeding markers. Firstly, at the critical reproductive stage (R5), the molecular differences between the genotype's responses to mild water deficit were explored through massive analysis of cDNA ends (MACE)-transcriptomic and gene ontology. MUNASQA transcriptional profile, compared to TJ2049, revealed significant differences when responding to drought. Next, both genotypes were phenotyped under mild water deficit, imposed in vegetative (V3) and R5 stages, by evaluating 22 stress-response, growth, and water-use markers, which were subsequently correlated between phenological stages and with yield. Several markers showed high consistency, independent of the phenological stage, demonstrating the effectiveness of the phenotyping methodology and its possible use for early selection. Finally, these markers were classified and selected according to their cost-feasibility, statistical weight, and correlation with yield. Here, pubescence, stomatal density, and canopy temperature depression emerged as promising breeding markers for the early selection of drought-tolerant soybeans.

Indexed as

FabaceaeGlycine maxDroughtsPlant BreedingWaterWater

Identifiers

PMID35761017
PMCPMC9237119

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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.