ArticleGM crops & food2023
Transcriptional and proteomic analysis.
Article in GM crops & food, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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.
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.
Who cites it
4 citing papers in PubMed, 4 citations in OpenAlex.
- Decoding Plant Immunity: Proteomics as a Biotechnological Tool for Crop Improvement.Advances in experimental medicine and biology · 2026Review
- Evaluation of the Unintended Effects ofBiomolecules · 2025Article
- Transcriptome and nutritional composition analysis of stacked transgenic maize with insect resistance and herbicide tolerance.GM crops & food · 2025Article
- CRISPR/Cas-mediated genome editing: playing a versatile role in mitigating the challenges of sustainable rice improvement.3 Biotech · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Unintended effects of gene edit crops may pose safety issues. Omics is a useful tool for researchers to evaluate these unexpected effects. Transcriptome and proteomics analyses were performed for two gene editors, CRISPR-Cas9 and adenine base editor (ABE) gene edit rice, as well as corresponding wild-type plants (Nipponbare). Transcriptome revealed 520 and 566 rice differentially expressed genes (DEGs) in the Cas9/Nip and ABE/Nip comparisons, respectively. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis showed that most DEGs participated in metabolism of terpenoids and polyketones, plant-pathogen interactions, and plant signal transduction. It mainly belongs to environmental adaptation. Proteomics revealed 298 and 54 rice differentially expressed proteins (DEPs) in the Cas9/Nip and ABE/Nip comparisons, respectively. KEGG pathway enrichment analysis showed that most DEPs participated in the biosynthesis of secondary metabolite and metabolic pathways.According to integrated transcriptomes and proteomics analysis, the results showed that no newly generated genes were identified as new transcripts of these differentially expressed genes, and gene edit tools had little effect on rice transcription levels and no new proteins were generated in the gene-edited rice.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.