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Record W4391934369 · doi:10.1016/j.heliyon.2024.e26529

New genetic resources for aphid resistance were identified from a newly developed wheat mutant library

2024· article· en· W4391934369 on OpenAlexfundno aff
Sana Zulfiqar, Shumila Ishfaq, Sayyad Ali Raza Bukhari, Muhammad Sajjad, Muhammad Akhtar, Dongcheng Liu, Mehboob‐ur‐ Rahman

Bibliographic record

VenueHeliyon · 2024
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicInsect Resistance and Genetics
Canadian institutionsnot available
FundersHigher Education Commission, PakistanInternational Atomic Energy AgencyNational Institute of Agricultural BiotechnologyAlberta Agricultural Research Institute
KeywordsMutantAphidGenetic resourcesBiologyResistance (ecology)BiotechnologyGeneticsAgronomyGene

Abstract

fetched live from OpenAlex

Reports on development of resilient wheat mutants to aphid infestation—causing heavy losses to wheat production in many parts of the world, are scanty. The present study aimed to identify genetic diversity of wheat mutants in terms of varying degree of resistance to aphid infestation which can help protect wheat crop, improve yields and enhance food security. Resistance response to aphid infestation was studied on newly developed 33 wheat mutants, developed through irradiating seed of an elite wheat cultivar "Punjab-11" with gamma radiations, during three normal growing seasons at two sites. Data on various traits including aphid count per plant, biochemical traits, physiological traits and grain yield was recorded. Meteorological data was also collected to unravel the impact of environmental conditions on aphid infestation on wheat plants. Minimum average aphid infestation was found on Pb- M -2725, Pb- M -2550, and Pb- M -2719 as compared to the wild type. High yielding mutants Pb- M -1323, Pb- M -59, and Pb- M -1272 supported the moderate aphid infestation. The prevailing temperature up to 25 °C showed positive correlation (0.25) with aphid count. Among biochemical traits, POD (0.34), TSP (0.33), TFA (0.324) exhibited a high positive correlation with aphid count. In addition, CAT (0.31), TSS (0.294), and proline content (0.293) also showed a positive correlation with aphid count. However, all physiological traits depicted negative correlation with aphid count, while, a very weak correlation (0.12) was found between mean aphid count and grain yield. In PCA biplots, the biochemical variables clustered together with aphid count, while physiological variables grouped with grain yield. Biochemical parameters contributed most, towards first dimension of the PCA (48.6%) as compared to the physiological variables (13%). The FAMD revealed that mutant lines were major contributor towards total variation; Pb- M -1027, Pb- M -1323, Pb- M -59 were found to be the most diverse lines. The PCA revealed that biochemical parameters played a significant role in explaining variations in aphid resistance, emphasizing their importance in aphid defense mechanisms. The identified mutants can be utilized by the international wheat community for getting insight into the molecular circuits of resistant mechanism against aphids as well as for designing new KASP markers. This study also highlights the importance of considering both genetic and environmental factors in the development of resilient wheat varieties and pave the way for further investigations into the molecular mechanisms underpinning aphid resistance in wheat.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.586
Threshold uncertainty score0.931

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.009
GPT teacher head0.234
Teacher spread0.225 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations1
Published2024
Admission routes1
Has abstractyes

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