The proteome data of whole rice grain is considerably limited particularly for rice with pigmentations such as black and red rice. Hence, we performed proteome analysis of two black rice varieties (BALI and Pulut Hitam 9), two red rice varieties (MRM16 and MRQ100) and two white rice varieties (MR297 and MRQ76) using label-free liquid chromatography Triple TOF 6600 tandem mass spectrometry (LC-MS/MS). Our aim was to profile and identify proteins related to nutritional (i.e. antioxidant, folate and low glycaemic index) and quality (i.e. aromatic) traits based on peptide-centric scoring from the Sequential Window Acquisition of All Theoretical Mass Spectra (SWATH-MS) approach. Both information dependent acquisition (IDA) and SWATH-MS run were performed in this analysis. Raw data was then processed using ProteinPilot software to identify and compare proteins from the six different varieties. In future, this proteomics data will be integrated with previously obtained genomics [1] and transcriptomics [2] data focusing on the above nutritional and quality traits, with an ultimate aim to develop a panel of functional biomarkers related to those traits for future rice breeding programme. The raw MS data of the pigmented and non-pigmented rice varieties have been deposited to ProteomeXchange database with accession number PXD018338.
Recently, rice breeding program has shown increased interests on the pigmented rice varieties due to their benefits to human health. However, the genetic variation of pigmented rice varieties is still scarce and remains unexplored. Hence, we performed genome-wide SNP analysis from the genome resequencing of four Malaysian pigmented rice varieties, representing two black and two red rice varieties. The genome of four pigmented varieties was mapped against Nipponbare reference genome sequences, and 1.9 million SNPs were discovered. Of these, 622 SNPs with polymorphic sites were identified in 258 protein-coding genes related to metabolism, stress response, and transporter. Comparative analysis of 622 SNPs with polymorphic sites against six rice SNP datasets from the Ensembl Plants variation database was performed, and 70 SNPs were identified as novel SNPs. Analysis of SNPs in the flavonoid biosynthetic genes revealed 40 nonsynonymous SNPs, which has potential as molecular markers for rice seed colour identification. The highlighted SNPs in this study show effort in producing valuable genomic resources for application in the rice breeding program, towards the genetic improvement of new and improved pigmented rice varieties.
The genomics and genetic information of Malaysian rice (Oryza sativa L.) is currently limited. It was necessary to conduct genome resequencing of these rice accessions exhibiting different responses to salinity stress. The sequencing was carried out using the Illumina NovaSeq X platform with 30× sequencing coverage to pinpoint variants between salinity tolerant and sensitive rice accessions. The discovery of single nucleotide polymorphisms (SNPs) is crucial for the development of DNA markers associated with salinity tolerance traits. The genome sequence data (FASTQ format) for these accessions have been deposited to the European Nucleotide Archive (ENA) database under the accession number PRJEB71716.
Pigmented rice is enriched with antioxidants, macro- and micronutrients. A comprehensive investigation of the gene expression patterns among the pigmented rice varieties would help to understand the cellular mechanism and biological processes of rice grain pigmentation. Hence, we performed RNA sequencing and analysis on the whole grain of dehusked mature seeds of selected six Malaysian rice varieties with varying grain pigmentations. These varieties were black rice (BALI and Pulut Hitam 9), red rice (MRM16 and MRQ100) and white rice (MR297 and MRQ76). Illumina HiSeq™ 4000 sequencer was used to generate total raw nucleotides of approximately 53 Gb in size. From 353,937,212 total paired-end raw reads, 340,131,496 total clean reads were obtained. The raw reads were deposited into European Nucleotide Archive (ENA) database and can be accessed via accession number PRJEB34340. This dataset allows us to identify and profile all expressed genes with functions related to nutritional traits (i.e. antioxidants, folate and amylose content) and quality trait (i.e. aroma) across both pigmented and non-pigmented rice varieties. In addition, the transcriptome data obtained will be valuable for discovery of potential gene markers and functional SNPs related to functional traits to assist in rice breeding programme.
The genomics and genetic data of pigmented and non-pigmented Malaysian rice varieties are still limited. Hence, we performed the genome resequencing of two black rice varieties (Bali, Pulut Hitam 9), two red rice varieties (MRM16, MRQ100) and two white rice varieties (MR297 and MRQ76) using Illumina HiSeq 4000 platform with 30x sequencing coverage. We aimed to identify and annotate single nucleotide polymorphisms (SNPs) from the genome of these four pigmented and two non-pigmented rice varieties. The potential SNPs will be used in developing the functional SNP markers related to nutritional (i.e. antioxidant, folate, amylose) and quality (i.e. aromatic) traits. Raw data of the pigmented and non-pigmented rice varieties have been deposited into the European Nucleotide Archive (ENA) database with accession number PRJEB29070 and PRJEB32344, respectively.
Vibrio parahaemolyticus poses a notable threat to marine ecosystems and can cause infections and disease outbreaks in seafood species, which can affect humans upon consumption. The global impacts of such infections and outbreaks on human and animal health led to a growing number of studies from various countries discussing the prevention, control, treatment, and overall implications of V. parahaemolyticus. Hence, this study aims to retrieve relevant studies on V. parahaemolyticus using a bibliometric analysis to understand current research status, trends, and hotspots regarding this bacteria. Relevant literature was searched across the Scopus database, and the data were subsequently analyzed using Biblioshiny software. In addition, a manual examination was conducted to identify the hosts of V. parahaemolyticus and diseases caused by the bacteria. Overall, 7,096 records were obtained from Scopus from 1963 to 2023. A bibliometric analysis identified 17,220 authors, with China emerging as the global leader. The analysis also highlighted significant keywords such as "Vibrio parahaemolyticus," "Litopenaeus vannamei," and "innate immunity," suggesting a focus on the impact of V. parahaemolyticus on L. vannamei, specifically emphasizing the shrimp's innate immune responses. Host-disease interaction network also uncovered 53 interactions between hosts and diseases involving L. vannamei or Penaeus vannamei as the primary host, with acute hepatopancreas necrosis disease (AHPND) emerging as the most prevalent among them. This study can enhance our understanding of infections caused by V. parahaemolyticus and contribute to the development of effective strategies for their prevention and management.
Rice, a staple food consumed by half of the world's population, is severely affected by the combined impact of abiotic and biotic stresses, with the former causing increased susceptibility of the plant to pathogens. Four microarray datasets for drought, salinity, tungro virus, and blast pathogen were retrieved from the Gene Expression Omnibus database. A modular gene co-expression (mGCE) analysis was conducted, followed by gene set enrichment analysis to evaluate the upregulation of module activity across different stress conditions. Over-representation analysis was conducted to determine the functional association of each gene module with stress-related processes and pathways. The protein-protein interaction network of mGCE hub genes was constructed, and the Maximal Clique Centrality (MCC) algorithm was applied to enhance precision in identifying key genes. Finally, genes implicated in both abiotic and biotic stress responses were validated using RT-qPCR. A total of 11, 12, 46, and 14 modules containing 85, 106, 253, and 143 hub genes were detected in drought, salinity, tungro virus, and blast. Modular genes in drought were primarily enriched in response to heat stimulus and water deprivation, while salinity-related genes were enriched in response to external stimuli. For the tungro virus and blast pathogen, enrichment was mainly observed in the defence and stress responses. Interestingly, RPS5, PKG, HSP90, HSP70, and MCM were consistently present in abiotic and biotic stresses. The DEG analysis revealed the upregulation of MCM under the tungro virus and downregulation under blast and drought in resistant rice, indicating its role in viral resistance. HSP70 showed no changes, while HSP90 was upregulated in susceptible rice during blast and drought. PKG increased during drought but decreased in japonica rice under salinity. RPS5 was highly upregulated during blast in both resistant and susceptible rice. The RT-qPCR analysis showed that all five hub genes were upregulated in all treatments, indicating their role in stress responses and potential for crop improvement.
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.