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  1. Tamizi AA, Nazaruddin NH, Yeong WC, Mohd Radzi MF, Jaafar MA, Sekeli R
    Data Brief, 2020 Apr;29:105235.
    PMID: 32071998 DOI: 10.1016/j.dib.2020.105235
    Heterotrigona itama is a species of stingless bee recently domesticated (or reared) for honey production in a few Southeast Asian countries namely Malaysia and Indonesia. Being categorized in the clade Corbiculata together with the honeybees (Apis spp.) and bumble bees (Bombus spp.), the stingless bees are highly social in which the colony members are subjected to labor division where a queen functions as the reproductive caste. In this data article, we provide a resource encompassing a transcriptome profile (de novo assembled) from H. itama queen larva - the first report of transcriptome assembly for this species. The generated data is pivotal for the characterization of important genes and biological pathways in order to further improve our understanding on the developmental biology, behavior, social structure and ecological needs of this eusocial hymenopteran insect from the molecular aspect. The raw RNA sequencing data is available at NCBI Sequence Read Archive (SAR) under the accession number SRP230250 and the assembled reads are deposited at DDBJ/EMBL/Genbank as Transcriptome Shotgun Assembly (TSA) under the accession GIIH00000000.
  2. Tamizi AA, Md-Yusof AA, Mohd-Zim NA, Nazaruddin NH, Sekeli R, Zainuddin Z, et al.
    Mol Biol Rep, 2023 Nov;50(11):9353-9366.
    PMID: 37819494 DOI: 10.1007/s11033-023-08842-2
    BACKGROUND: Agrobacterium-mediated transformation and particle bombardment are the two common approaches for genome editing in plant species using CRISPR/Cas9 system. Both methods require careful manipulations of undifferentiated cells and tissue culture to regenerate the potentially edited plants. However, tissue culture techniques are laborious and time-consuming.

    METHODS AND RESULTS: In this study, we have developed a simplified, tissue culture-independent protocol to deliver the CRISPR/Cas9 system through in planta transformation in Malaysian rice (Oryza sativa L. subsp. indica cv. MR 219). Sprouting seeds with cut coleoptile were used as the target for the infiltration by Agrobacterium tumefaciens and we achieved 9% transformation efficiency. In brief, the dehusked seeds were surface-sterilised and imbibed, and the coleoptile was cut to expose the apical meristem. Subsequently, the cut coleoptile was inoculated with A. tumefaciens strain EHA105 harbouring CRISPR/Cas9 expression vector. The co-cultivation was conducted for five to six days in a dark room (25 ± 2 °C) followed by rooting, acclimatisation, and growing phases. Two-month-old plant leaves were then subjected to a hygromycin selection, and hygromycin-resistant plants were identified as putative transformants. Further validation through the polymerase chain reaction verified the integration of the Cas9 gene in four putative T0 lines. During the fruiting stage, it was confirmed that the Cas9 gene was still present in three randomly selected tillers from two 4-month-old transformed plants.

    CONCLUSION: This protocol provides a rapid method for editing the rice genome, bypassing the need for tissue culture. This article is the first to report the delivery of the CRISPR/Cas9 system for in planta transformation in rice.

  3. Razalli II, Abdullah-Zawawi MR, Tamizi AA, Harun S, Zainal-Abidin RA, Jalal MIA, et al.
    Planta, 2025 Mar 17;261(4):92.
    PMID: 40095140 DOI: 10.1007/s00425-025-04666-5
    Big data and network biology infer functional coupling between genes. In combination with machine learning, network biology can dramatically accelerate the pace of gene discovery using modern transcriptomics approaches and be validated via genome editing technology for improving crops to stresses. Unlike other living things, plants are sessile and frequently face various environmental challenges due to climate change. The cumulative effects of combined stresses can significantly influence both plant growth and yields. In navigating the complexities of climate change, ensuring the nourishment of our growing population hinges on implementing precise agricultural systems. Conventional breeding methods have been commonly employed; however, their efficacy has been impeded by limitations in terms of time, cost, and infrastructure. Cutting-edge tools focussing on big data are being championed to usher in a new era in stress biology, aiming to cultivate crops that exhibit enhanced resilience to multifactorial stresses. Transcriptomics, combined with network biology and machine learning, is proving to be a powerful approach for identifying potential genes to target for gene editing, specifically to enhance stress tolerance. The integration of transcriptomic data with genome editing can yield significant benefits, such as gaining insights into gene function by modifying or manipulating of specific genes in the target plant. This review provides valuable insights into the use of transcriptomics platforms and the application of biological network analysis and machine learning in the discovery of novel genes, thereby enhancing the understanding of plant responses to combined or sequential stress. The transcriptomics as a forefront omics platform and how it is employed through biological networks and machine learning that lead to novel gene discoveries for producing multi-stress-tolerant crops, limitations, and future directions have also been discussed.
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