Terpene synthases (TPSs) catalyze terpenoid synthesis and affect the intracellular isopentenyl diphosphate (IPP) and dimethylallyl diphosphate (DMAPP) concentration. In this study, we mined the in silico genome-wide TPS genes of Hevea brasiliensis and identified 47 full-length TPS genes. They had DDXXD, DXDD, NSE/DTE, RR(X)8 W, EA(X)W, and other conserved motifs. The phylogenetic tree analysis revealed that the TPSs of H.brasiliensis (HbTPSs) were divided into five subfamilies, TPS-a, TPS-b, TPS-c, TPS-e/f, and TPS-g. HbTPSs were predicted to have functions in the cellular components, molecular functions, and biological processes. HbTPSs were involved in seven pathways, which were K14173, K14175, K15803, K04120, K04121, K17982, and K12742 in the secondary metabolite pathway prediction. Three-dimensional structures of HbTPSs of 7 pathways were predicted, and DDXXD, NSE/DTE, and EA(X)W conserved motifs near the binding sites were found. Cis-acting elements analysis showed that they had more cis-acting elements related to phytohormone responsiveness, which indicated that terpenoid biosynthesis might be related to phytohormone regulation. RNA-Seq analysis showed that different HbTPSs were expressed differentially in different tissues. This study's results help reveal the role of HbTPSs and their molecular mechanism and help resolve the regulatory mechanism of terpenoid biosynthesis in H.brasiliensis.
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http://dx.doi.org/10.1007/s10528-022-10311-7 | DOI Listing |
Int J Mol Sci
December 2024
College of Jixian Honors, Zhejiang Agriculture and Forestry University, Hangzhou 311300, China.
Heat stress transcription factors (HSFs) play a critical role in orchestrating cellular responses to elevated temperatures and various stress conditions. While extensively studied in model plants, the gene family in remains unexplored, despite the availability of its sequenced genome. In this study, we employed bioinformatics approaches to identify 21 genes within the genome, revealing their uneven distribution across chromosomes.
View Article and Find Full Text PDFPlants (Basel)
December 2024
College of Life and Environmental Sciences, Hangzhou Normal University, Hangzhou 311121, China.
The plant hormone ethylene elicits crucial regulatory effects on plant growth, development, and stress resistance. As the enzyme that catalyzes the final step of ethylene biosynthesis, 1-Aminocyclopropane-1-carboxylic acid oxidase (ACO) plays a key role in precisely controlling ethylene production. However, the functional characterization of the gene family in rice remains largely unexplored.
View Article and Find Full Text PDFAnimals (Basel)
December 2024
Department of Poultry Breeding, Animal Production Research Institute, Agriculture Research Center, Dokki, Giza 12618, Egypt.
This study aimed to characterize microsatellites in the rabbit genome using an in silico approach and to develop and validate microsatellite markers. Blood samples were collected from 15 Baladi rabbits and 18 New Zealand White (NZW rabbits). The GMATA software was used to define SSRs in the extracted sequences.
View Article and Find Full Text PDFHGG Adv
January 2025
Department of Epidemiology and Biostatistics, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
Inherited genetics represents an important contributor to risk of esophageal adenocarcinoma (EAC), and its precursor Barrett's esophagus (BE). Genome-wide association studies have identified ∼30 susceptibility variants for BE/EAC, yet genetic interactions remain unexamined. To address challenges in large-scale G×G scans, we combined knowledge-guided filtering and machine learning approaches, focusing on genes with (A) known/plausible links to BE/EAC pathogenesis (n=493) or (B) prior evidence of biological interactions (n=4,196).
View Article and Find Full Text PDFNat Commun
December 2024
State Key Laboratory of Protein and Plant Gene Research, School of Life Sciences, Biomedical Pioneering Innovative Center (BIOPIC) and Beijing Advanced Innovation Center for Genomics (ICG), Center for Bioinformatics (CBI), Peking University, 100871, Beijing, China.
Deciphering how noncoding DNA determines gene expression is critical for decoding the functional genome. Understanding the transcription effects of noncoding genetic variants are still major unsolved problems, which is critical for downstream applications in human genetics and precision medicine. Here, we integrate regulatory-specific neural networks and tissue-specific gradient-boosting trees to build SVEN: a hybrid sequence-oriented architecture that can accurately predict tissue-specific gene expression level and quantify the tissue-specific transcriptomic impacts of structural variants across more than 350 tissues and cell lines.
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