Evidence map›Paper›PMID 39011016›Full record

ArticleIndian journal of microbiology2024

Development, Design, and Application of Efficient siRNAs Against Cotton Leaf Curl Virus-Betasatellite Complex to Mediate Resistance Against Cotton Leaf Curl Disease.

Heena Jain, Ramandeep Kaur, Satish Kumar Sain, Priyanka Siwach

Abstract read
In one paragraph

Article in Indian journal of microbiology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. RNA silencing: the future potential strategy for engineering virus resistance in plants.Physiology and molecular biology of plants : an international journal of functional plant biology · 2025
    Review
  2. 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

4 authors.

Heena JainDepartment of Biotechnology, Chaudhary Devi Lal University, Sirsa, Haryana 125055 India.
Ramandeep KaurDepartment of Biotechnology, Chaudhary Devi Lal University, Sirsa, Haryana 125055 India.
Satish Kumar SainCentral Institute of Cotton Research, Regional Station, Sirsa, Haryana 125055 India.
Priyanka SiwachDepartment of Biotechnology, Chaudhary Devi Lal University, Sirsa, Haryana 125055 India.ORCID 0000-0003-3049-274X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Cotton leaf curl disease (CLCuD), caused by the Cotton leaf curl virus, is one of the most irrepressible diseases in cotton due to high recombination in the virus. RNA interference (RNAi) is widely used as a biotechnological approach for sequence-specific gene silencing guided by small interfering RNAs (siRNAs) to generate resistance against viruses. The success of RNAi depends upon the fact that the target site of the designed siRNA must be conserved even if the genome undergoes recombination. Thus, the present study designs the most efficient siRNA against the conserved sites of the Cotton leaf curl Multan virus (CLCuMuV) and the Cotton leaf curl Multan betasatellite (CLCuMB). From an initial prediction of 9 and 7 siRNAs against CLCuMuV and CLCuMB, respectively, the final selection was made for 2 and 1 siRNA based on parameters such as no off-targets, good GC content, high validity score, and targeting coding region. The target sites of siRNA were observed to lie in the AC3 and an overlapping region of AC2-AC1 of CLCuMuV and βC1 of CLCuMB; all target sites showed a highly conserved nature in recombination analysis. Docking the designed siRNAs with the Argonaute-2 protein of Supplementary Information: The online version contains supplementary material available at 10.1007/s12088-024-01191-z.

Indexed as

Antiviral resistanceBetasatelliteCotton leaf curl virusMolecular dockingRecombinationsiRNA

Identifiers

PMID39011016
PMCPMC11246389

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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.