ABSTRACT
The rubber tree is the primary source of natural rubber and is mainly cultivated in Southeast Asian countries. Low temperature is the major abiotic stress affecting the yield of the rubber tree. Therefore, uncovering the cold resistance mechanism in the rubber tree is necessary. The present study used RNA-sequencing technology and ultra-performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) to analyze the transcriptomic and metabolomic changes in two rubber tree clones with different cold resistance capacities (temperature-sensitive Reyan 8-79 and cold-resistant Yunyan 77-4) at 0 h, 2 h, 6 h, and 20 h of exposure to 4°C. Independent analysis of the transcriptome and metabolitome showed that under prolonged low-temperature treatment, Yunyan 77-4 expressed more genes involved in regulating enzyme activity, changing cell permeability, and synthesizing significant metabolites, such as flavonoids and amino acids, than Reyan 8-79. The KEGG annotation and enrichment analysis identified arginine metabolism and biosynthesis of flavonoids as the major pathway associated with cold resistance. Integrated transcriptome and metabolome analysis showed that the increase in the expression of genes modulated flavonoid biosynthesis, arginine biosynthesis, and anthocyanins biosynthesis, resulting in higher levels of metabolites, such as naringenin chalcone, apigenin, dihydroquercetin, cyanidin 3-glucoside, L-arginosuccinate, N-acetyl-ornithine, ornithine, and N-acetyl-glutamate, in Yunyan 77-4 than in Reyan 8-79 after prolonged low-temperature treatment. Phylogenetic analysis identified the genes, such as CHS (gene356) and F3H (gene33147) of flavonoid biosynthesis and NAGS (gene16028, gene33765), ArgC (gene2487), and ASS (gene6161) of arginine biosynthesis were the key genes involved in the cold resistant of rubber tree. Thus, the present study provides novel insights into how rubber clones resist cold and is a valuable reference for cold-resistance breeding.
ABSTRACT
Cancer is the quintessential complex disease. As technologies evolve faster each day, we are able to quantify the different layers of biological elements that contribute to the emergence and development of malignancies. In this multi-omics context, the use of integrative approaches is mandatory in order to gain further insights on oncological phenomena, and to move forward toward the precision medicine paradigm. In this review, we will focus on computational oncology as an integrative discipline that incorporates knowledge from the mathematical, physical, and computational fields to further the biomedical understanding of cancer. We will discuss the current roles of computation in oncology in the context of multi-omic technologies, which include: data acquisition and processing; data management in the clinical and research settings; classification, diagnosis, and prognosis; and the development of models in the research setting, including their use for therapeutic target identification. We will discuss the machine learning and network approaches as two of the most promising emerging paradigms, in computational oncology. These approaches provide a foundation on how to integrate different layers of biological description into coherent frameworks that allow advances both in the basic and clinical settings.