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Multi-Omics and Integrative Approach towards Understanding Salinity Tolerance in Rice: A Review

ABSTRACT:

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Rice is one of the most significant food crops worldwide, predominantly in Asian countries. Rice plant growth and yield are profoundly affected by salinity stress. Understanding the mechanisms of salinity stress is highly complicated. Therefore, to understand the molecular mechanisms, enhance the yield and development of salinity tolerant cultivars, and achieve spectacular gains in the future, the use of high-throughput frontier technologies must be knotted with a research basis. Keeping these lacunae in mind, this comprehensive review addresses the literature gap and presents the multi-omics and integrative approaches to harnessing the novel avenues of salinity stress mechanisms in rice. Further, this review will be a significant pioneer for researchers working with plant stress biology by employing multi-omics and integrative approaches.

Abstract

Rice (Oryza sativa L.) plants are simultaneously encountered by environmental stressors, most importantly salinity stress. Salinity is the major hurdle that can negatively impact growth and crop yield. Understanding the salt stress and its associated complex trait mechanisms for enhancing salt tolerance in rice plants would ensure future food security. The main aim of this review is to provide insights and impacts of molecular-physiological responses, biochemical alterations, and plant hormonal signal transduction pathways in rice under saline stress. Furthermore, the review highlights the emerging breakthrough in multi-omics and computational biology in identifying the saline stress-responsive candidate genes and transcription factors (TFs). In addition, the review also summarizes the biotechnological tools, genetic engineering, breeding, and agricultural practicing factors that can be implemented to realize the bottlenecks and opportunities to enhance salt tolerance and develop salinity tolerant rice varieties. Future studies pinpointed the augmentation of powerful tools to dissect the salinity stress-related novel players, reveal in-depth mechanisms and ways to incorporate the available literature, and recent advancements to throw more light on salinity responsive transduction pathways in plants. Particularly, this review unravels the whole picture of salinity stress tolerance in rice by expanding knowledge that focuses on molecular aspects.

SUBMITTER: Muthuramalingam P

PROVIDER: S-EPMC9312129 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Recent technological advances and international efforts, such as The Cancer Genome Atlas (TCGA), have made available several pan-cancer datasets encompassing multiple omics layers with detailed clinical information in large collection of samples. The need has thus arisen for the development of computational methods aimed at improving cancer subtyping and biomarker identification from multi-modal data. Here we apply the Integrative Network Fusion (INF) pipeline, which combines multiple omics layers exploiting Similarity Network Fusion (SNF) within a machine learning predictive framework. INF includes a feature ranking scheme (rSNF) on SNF-integrated features, used by a classifier over juxtaposed multi-omics features (juXT). In particular, we show instances of INF implementing Random Forest (RF) and linear Support Vector Machine (LSVM) as the classifier, and two baseline RF and LSVM models are also trained on juXT. A compact RF model, called rSNFi, trained on the intersection of top-ranked biomarkers from the two approaches juXT and rSNF is finally derived. All the classifiers are run in a 10x5-fold cross-validation schema to warrant reproducibility, following the guidelines for an unbiased Data Analysis Plan by the US FDA-led initiatives MAQC/SEQC. INF is demonstrated on four classification tasks on three multi-modal TCGA oncogenomics datasets. Gene expression, protein expression and copy number variants are used to predict estrogen receptor status (BRCA-ER, N = 381) and breast invasive carcinoma subtypes (BRCA-subtypes, N = 305), while gene expression, miRNA expression and methylation data is used as predictor layers for acute myeloid leukemia and renal clear cell carcinoma survival (AML-OS, N = 157; KIRC-OS, N = 181). In test, INF achieved similar Matthews Correlation Coefficient (MCC) values and 97% to 83% smaller feature sizes (FS), compared with juXT for BRCA-ER (MCC: 0.83 vs. 0.80; FS: 56 vs. 1801) and BRCA-subtypes (0.84 vs. 0.80; 302 vs. 1801), improving KIRC-OS performance (0.38 vs. 0.31; 111 vs. 2319). INF predictions are generally more accurate in test than one-dimensional omics models, with smaller signatures too, where transcriptomics consistently play the leading role. Overall, the INF framework effectively integrates multiple data levels in oncogenomics classification tasks, improving over the performance of single layers alone and naive juxtaposition, and provides compact signature sizes.

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Multi-Omics and Integrative Approach towards Understanding Salinity Tolerance in Rice: A Review

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