Dataset Information

High-Throughput and Computational Study of Leaf Senescence through a Phenomic Approach.

ABSTRACT: Leaf senescence is influenced by its life history, comprising a series of developmental and physiological experiences. Exploration of the biological principles underlying leaf lifespan and senescence requires a schema to trace leaf phenotypes, based on the interaction of genetic and environmental factors. We developed a new approach and concept that will facilitate systemic biological understanding of leaf lifespan and senescence, utilizing the phenome high-throughput investigator (PHI) with a single-leaf-basis phenotyping platform. Our pilot tests showed empirical evidence for the feasibility of PHI for quantitative measurement of leaf senescence responses and improved performance in order to dissect the progression of senescence triggered by different senescence-inducing factors as well as genetic mutations. Such an establishment enables new perspectives to be proposed, which will be challenged for enhancing our fundamental understanding on the complex process of leaf senescence. We further envision that integration of phenomic data with other multi-omics data obtained from transcriptomic, proteomic, and metabolic studies will enable us to address the underlying principles of senescence, passing through different layers of information from molecule to organism.

SUBMITTER: Lyu JI

PROVIDER: S-EPMC5322180 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature

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Publications

High-Throughput and Computational Study of Leaf Senescence through a Phenomic Approach.

Lyu Jae Il JI Baek Seung Hee SH Jung Sukjoon S Chu Hyosub H Nam Hong Gil HG Kim Jeongsik J Lim Pyung Ok PO

Frontiers in plant science 20170223

Leaf senescence is influenced by its life history, comprising a series of developmental and physiological experiences. Exploration of the biological principles underlying leaf lifespan and senescence requires a schema to trace leaf phenotypes, based on the interaction of genetic and environmental factors. We developed a new approach and concept that will facilitate systemic biological understanding of leaf lifespan and senescence, utilizing the phenome high-throughput investigator (PHI) with a s ...[more]

PMID: 28280501

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Project description:BACKGROUND: MicroRNAs (miRNAs) play a critical role in down-regulating gene expression. By coupling with Argonaute family proteins, miRNAs bind to target sites on mRNAs and employ translational repression. A large amount of miRNA-target interactions (MTIs) have been identified by the crosslinking and immunoprecipitation (CLIP) and the photoactivatable-ribonucleoside-enhanced CLIP (PAR-CLIP) along with the next-generation sequencing (NGS). PAR-CLIP shows high efficiency of RNA co-immunoprecipitation, but it also lead to T to C conversion in miRNA-RNA-protein crosslinking regions. This artificial error obviously reduces the mappability of reads. However, a specific tool to analyze CLIP and PAR-CLIP data that takes T to C conversion into account is still in need. RESULTS: We herein propose the first CLIP and PAR-CLIP sequencing analysis platform specifically for miRNA target analysis, namely miRTarCLIP. From scratch, it automatically removes adaptor sequences from raw reads, filters low quality reads, reverts C to T, aligns reads to 3'UTRs, scans for read clusters, identifies high confidence miRNA target sites, and provides annotations from external databases. With multi-threading techniques and our novel C to T reversion procedure, miRTarCLIP greatly reduces the running time comparing to conventional approaches. In addition, miRTarCLIP serves with a web-based interface to provide better user experiences in browsing and searching targets of interested miRNAs. To demonstrate the superior functionality of miRTarCLIP, we applied miRTarCLIP to two public available CLIP and PAR-CLIP sequencing datasets. miRTarCLIP not only shows comparable results to that of other existing tools in a much faster speed, but also reveals interesting features among these putative target sites. Specifically, we used miRTarCLIP to disclose that T to C conversion within position 1-7 and that within position 8-14 of miRNA target sites are significantly different (p value = 0.02), and even more significant when focusing on sites targeted by top 102 highly expressed miRNAs only (p value = 0.01). These results comply with previous findings and further suggest that combining miRNA expression and PAR-CLIP data can improve accuracy of the miRNA target prediction. CONCLUSION: To sum up, we devised a systematic approach for mining miRNA-target sites from CLIP-seq and PAR-CLIP sequencing data, and integrated the workflow with a graphical web-based browser, which provides a user friendly interface and detailed annotations of MTIs. We also showed through real-life examples that miRTarCLIP is a powerful tool for understanding miRNAs. Our integrated tool can be accessed online freely at http://miRTarCLIP.mbc.nctu.edu.tw.

Project description:The rapid outbreak of the COVID-19 also known as SARS-CoV2 has been declared pandemic with serious global concern. As there is no effective therapeutic against COVID-19, there is an urgent need for explicit treatment against it. The focused objective of the current study is to propose promising drug candidates against the newly identified potential therapeutic target (endonuclease, NSP15) of SARS-CoV2. NSP15 is an attractive druggable target due to its critical role in SARS-CoV2 replication and virulence in addition to interference with the host immune system. Here in the present study, we integrated the high throughput computational screening and dynamic simulation approach to identify the most promising candidate lead compound against NSP15.5-fluoro-2-oxo-1H-pyrazine-3-carboxamide (favipiravir), (3R,4R, 5R)-3,4-Bis(benzyloxy)-5-((benzyloxy) methyl) dihydrofuran-2(3H)-one) remedesivir, 1,3-thiazol-5-ylmethyl N-[(2S,3S, 5S)-3-hydroxy-5-[[(2 S)-3-methyl-2-[[methyl-[(2-propan-2-yl-1,3-thiazol-4-yl)methyl]carbamoyl]amino]butanoyl]amino]-1,6-diphenylhexan-2-yl]carbamate (ritonavir), ethyl (3R,4R, 5S)-4-acetamido-5-amino-3-pentan-3-yloxycyclohexene-1-carboxylate (oseltamivir), and (2 S)-N-[(2S,4S, 5S)-5-[[2-(2,6-dimethylphenoxy)acetyl]amino]-4-hydroxy-1,6-diphenylhexan-2-yl]-3-methyl-2-(2-oxo-1,3-diazinan-1-yl)butanamide (lopinavir) were chosen as a training set to generate the pharmacophore model. A dataset of ~140,000 compounds library was screened against the designed pharmacophore model and 10 unique compounds were selected that passed successfully through geometry constraints, Lipinski Rule of 5, and ADME/Tox filters along with a strong binding affinity for NSP15 binding cavity. The best fit compound was selected for dynamic simulation to have detailed structural features critical for binding with the NSP15 protein. Given our detailed integrative computational analysis, a Small molecule (3,3-Dimethyl-N-[4-(1-piperidinylcarbonyl) phenyl] butanamide) with drug-like properties and high binding affinity with the NSP15 is proposed as a most promising potential drug against COVID-19. The current computational integrative approach may complement high-throughput screening and the shortlisted small molecule may contribute to selective targeting of NSP15 to stop the replication of SARS-CoV2.

Dataset Information

High-Throughput and Computational Study of Leaf Senescence through a Phenomic Approach.

Publications

High-Throughput and Computational Study of Leaf Senescence through a Phenomic Approach.

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