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Dutta S, Mudaranthakam DP, Li Y, Sardiu ME. PerSEveML: a web-based tool to identify persistent biomarker structure for rare events using an integrative machine learning approach. Mol Omics 2024; 20:348-358. [PMID: 38690925 DOI: 10.1039/d4mo00008k] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/03/2024]
Abstract
Omics data sets often pose a computational challenge due to their high dimensionality, large size, and non-linear structures. Analyzing these data sets becomes especially daunting in the presence of rare events. Machine learning (ML) methods have gained traction for analyzing rare events, yet there has been limited exploration of bioinformatics tools that integrate ML techniques to comprehend the underlying biology. Expanding upon our previously developed computational framework of an integrative machine learning approach, we introduce PerSEveML, an interactive web-based tool that uses crowd-sourced intelligence to predict rare events and determine feature selection structures. PerSEveML provides a comprehensive overview of the integrative approach through evaluation metrics that help users understand the contribution of individual ML methods to the prediction process. Additionally, PerSEveML calculates entropy and rank scores, which visually organize input features into a persistent structure of selected, unselected, and fluctuating categories that help researchers uncover meaningful hypotheses regarding the underlying biology. We have evaluated PerSEveML on three diverse biologically complex data sets with extremely rare events from small to large scale and have demonstrated its ability to generate valid hypotheses. PerSEveML is available at https://biostats-shinyr.kumc.edu/PerSEveML/ and https://github.com/sreejatadutta/PerSEveML.
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Affiliation(s)
- Sreejata Dutta
- Department of Biostatistics & Data Science, University of Kansas Medical Center, Kansas City, Kansas, USA.
| | - Dinesh Pal Mudaranthakam
- Department of Biostatistics & Data Science, University of Kansas Medical Center, Kansas City, Kansas, USA.
- University of Kansas Cancer Center, Kansas City, USA
| | - Yanming Li
- Department of Biostatistics & Data Science, University of Kansas Medical Center, Kansas City, Kansas, USA.
- University of Kansas Cancer Center, Kansas City, USA
| | - Mihaela E Sardiu
- Department of Biostatistics & Data Science, University of Kansas Medical Center, Kansas City, Kansas, USA.
- University of Kansas Cancer Center, Kansas City, USA
- Kansas Institute for Precision Medicine, University of Kansas Medical Center, Kansas City, Kansas, USA
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Dutta S, Mudaranthakam DP, Li Y, Sardiu ME. PerSEveML: A Web-Based Tool to Identify Persistent Biomarker Structure for Rare Events Using Integrative Machine Learning Approach. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.10.25.564000. [PMID: 38196661 PMCID: PMC10775315 DOI: 10.1101/2023.10.25.564000] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/11/2024]
Abstract
Omics datasets often pose a computational challenge due to their high dimensionality, large size, and non-linear structures. Analyzing these datasets becomes especially daunting in the presence of rare events. Machine learning (ML) methods have gained traction for analyzing rare events, yet there remains a limited exploration of bioinformatics tools that integrate ML techniques to comprehend the underlying biology. Expanding upon our previously developed computational framework of an integrative machine learning approach1, we introduce PerSEveML, an interactive web-based that uses crowd-sourced intelligence to predict rare events and determine feature selection structures. PerSEveML provides a comprehensive overview of the integrative approach through evaluation metrics that help users understand the contribution of individual ML methods to the prediction process. Additionally, PerSEveML calculates entropy and rank scores, which visually organize input features into a persistent structure of selected, unselected, and fluctuating categories that help researchers uncover meaningful hypotheses regarding the underlying biology. We have evaluated PerSEveML on three diverse biologically complex data sets with extremely rare events from small to large scale and have demonstrated its ability to generate valid hypotheses. PerSEveML is available at https://biostats-shinyr.kumc.edu/PerSEveML/ and https://github.com/sreejatadutta/PerSEveML.
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Affiliation(s)
- Sreejata Dutta
- Department of Biostatistics & Data Science, University of Kansas Medical Center, Kansas City, Kansas, USA
| | - Dinesh Pal Mudaranthakam
- Department of Biostatistics & Data Science, University of Kansas Medical Center, Kansas City, Kansas, USA
- University of Kansas Cancer Center, Kansas City, USA
| | - Yanming Li
- Department of Biostatistics & Data Science, University of Kansas Medical Center, Kansas City, Kansas, USA
- University of Kansas Cancer Center, Kansas City, USA
| | - Mihaela E Sardiu
- Department of Biostatistics & Data Science, University of Kansas Medical Center, Kansas City, Kansas, USA
- University of Kansas Cancer Center, Kansas City, USA
- Kansas Institute for Precision Medicine, University of Kansas Medical Center, Kansas City, Kansas, USA
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3
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Cao S, He Z, Chen R, Luo Y, Fu LY, Zhou X, He C, Yan W, Zhang CY, Chen D. scPlant: A versatile framework for single-cell transcriptomic data analysis in plants. PLANT COMMUNICATIONS 2023; 4:100631. [PMID: 37254480 PMCID: PMC10504592 DOI: 10.1016/j.xplc.2023.100631] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/05/2023] [Revised: 02/13/2023] [Accepted: 05/24/2023] [Indexed: 06/01/2023]
Abstract
Single-cell transcriptomics has been fully embraced in plant biological research and is revolutionizing our understanding of plant growth, development, and responses to external stimuli. However, single-cell transcriptomic data analysis in plants is not trivial, given that there is currently no end-to-end solution and that integration of various bioinformatics tools involves a large number of required dependencies. Here, we present scPlant, a versatile framework for exploring plant single-cell atlases with minimum input data provided by users. The scPlant pipeline is implemented with numerous functions for diverse analytical tasks, ranging from basic data processing to advanced demands such as cell-type annotation and deconvolution, trajectory inference, cross-species data integration, and cell-type-specific gene regulatory network construction. In addition, a variety of visualization tools are bundled in a built-in Shiny application, enabling exploration of single-cell transcriptomic data on the fly.
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Affiliation(s)
- Shanni Cao
- State Key Laboratory of Pharmaceutical Biotechnology, School of Life Sciences, Nanjing University, Nanjing 210023, China
| | - Zhaohui He
- State Key Laboratory of Pharmaceutical Biotechnology, School of Life Sciences, Nanjing University, Nanjing 210023, China
| | - Ruidong Chen
- State Key Laboratory of Pharmaceutical Biotechnology, School of Life Sciences, Nanjing University, Nanjing 210023, China
| | - Yuting Luo
- State Key Laboratory of Pharmaceutical Biotechnology, School of Life Sciences, Nanjing University, Nanjing 210023, China
| | - Liang-Yu Fu
- State Key Laboratory of Pharmaceutical Biotechnology, School of Life Sciences, Nanjing University, Nanjing 210023, China
| | - Xinkai Zhou
- State Key Laboratory of Pharmaceutical Biotechnology, School of Life Sciences, Nanjing University, Nanjing 210023, China
| | - Chao He
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, China
| | - Wenhao Yan
- National Key Laboratory of Crop Genetic Improvement, Hubei Hongshan Laboratory, Huazhong Agricultural University, Wuhan, China
| | - Chen-Yu Zhang
- State Key Laboratory of Pharmaceutical Biotechnology, School of Life Sciences, Nanjing University, Nanjing 210023, China.
| | - Dijun Chen
- State Key Laboratory of Pharmaceutical Biotechnology, School of Life Sciences, Nanjing University, Nanjing 210023, China.
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4
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Singh B, Kumar S, Elangovan A, Vasht D, Arya S, Duc NT, Swami P, Pawar GS, Raju D, Krishna H, Sathee L, Dalal M, Sahoo RN, Chinnusamy V. Phenomics based prediction of plant biomass and leaf area in wheat using machine learning approaches. FRONTIERS IN PLANT SCIENCE 2023; 14:1214801. [PMID: 37448870 PMCID: PMC10337996 DOI: 10.3389/fpls.2023.1214801] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/30/2023] [Accepted: 06/07/2023] [Indexed: 07/15/2023]
Abstract
Introduction Phenomics has emerged as important tool to bridge the genotype-phenotype gap. To dissect complex traits such as highly dynamic plant growth, and quantification of its component traits over a different growth phase of plant will immensely help dissect genetic basis of biomass production. Based on RGB images, models have been developed to predict biomass recently. However, it is very challenging to find a model performing stable across experiments. In this study, we recorded RGB and NIR images of wheat germplasm and Recombinant Inbred Lines (RILs) of Raj3765xHD2329, and examined the use of multimodal images from RGB, NIR sensors and machine learning models to predict biomass and leaf area non-invasively. Results The image-based traits (i-Traits) containing geometric features, RGB based indices, RGB colour classes and NIR features were categorized into architectural traits and physiological traits. Total 77 i-Traits were selected for prediction of biomass and leaf area consisting of 35 architectural and 42 physiological traits. We have shown that different biomass related traits such as fresh weight, dry weight and shoot area can be predicted accurately from RGB and NIR images using 16 machine learning models. We applied the models on two consecutive years of experiments and found that measurement accuracies were similar suggesting the generalized nature of models. Results showed that all biomass-related traits could be estimated with about 90% accuracy but the performance of model BLASSO was relatively stable and high in all the traits and experiments. The R2 of BLASSO for fresh weight prediction was 0.96 (both year experiments), for dry weight prediction was 0.90 (Experiment 1) and 0.93 (Experiment 2) and for shoot area prediction 0.96 (Experiment 1) and 0.93 (Experiment 2). Also, the RMSRE of BLASSO for fresh weight prediction was 0.53 (Experiment 1) and 0.24 (Experiment 2), for dry weight prediction was 0.85 (Experiment 1) and 0.25 (Experiment 2) and for shoot area prediction 0.59 (Experiment 1) and 0.53 (Experiment 2). Discussion Based on the quantification power analysis of i-Traits, the determinants of biomass accumulation were found which contains both architectural and physiological traits. The best predictor i-Trait for fresh weight and dry weight prediction was Area_SV and for shoot area prediction was projected shoot area. These results will be helpful for identification and genetic basis dissection of major determinants of biomass accumulation and also non-invasive high throughput estimation of plant growth during different phenological stages can identify hitherto uncovered genes for biomass production and its deployment in crop improvement for breaking the yield plateau.
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Affiliation(s)
- Biswabiplab Singh
- Division of Plant Physiology and Nanaji Deshmukh Plant Phenomics Centre (NDPPC), Indian Council of Agricultural Research (ICAR)-Indian Agricultural Research Institute, New Delhi, India
| | - Sudhir Kumar
- Division of Plant Physiology and Nanaji Deshmukh Plant Phenomics Centre (NDPPC), Indian Council of Agricultural Research (ICAR)-Indian Agricultural Research Institute, New Delhi, India
| | - Allimuthu Elangovan
- Division of Plant Physiology and Nanaji Deshmukh Plant Phenomics Centre (NDPPC), Indian Council of Agricultural Research (ICAR)-Indian Agricultural Research Institute, New Delhi, India
| | - Devendra Vasht
- Division of Plant Physiology and Nanaji Deshmukh Plant Phenomics Centre (NDPPC), Indian Council of Agricultural Research (ICAR)-Indian Agricultural Research Institute, New Delhi, India
| | - Sunny Arya
- Division of Plant Physiology and Nanaji Deshmukh Plant Phenomics Centre (NDPPC), Indian Council of Agricultural Research (ICAR)-Indian Agricultural Research Institute, New Delhi, India
| | - Nguyen Trung Duc
- Division of Plant Physiology and Nanaji Deshmukh Plant Phenomics Centre (NDPPC), Indian Council of Agricultural Research (ICAR)-Indian Agricultural Research Institute, New Delhi, India
- Vietnam National University of Agriculture, Hanoi, Vietnam
| | - Pooja Swami
- Division of Plant Physiology and Nanaji Deshmukh Plant Phenomics Centre (NDPPC), Indian Council of Agricultural Research (ICAR)-Indian Agricultural Research Institute, New Delhi, India
| | - Godawari Shivaji Pawar
- Division of Agricultural Botany, Vasantrao Naik Marathwada Krishi Vidyapeeth, Parbhani, India
| | - Dhandapani Raju
- Division of Plant Physiology and Nanaji Deshmukh Plant Phenomics Centre (NDPPC), Indian Council of Agricultural Research (ICAR)-Indian Agricultural Research Institute, New Delhi, India
| | - Hari Krishna
- Division of Genetics, ICAR-Indian Agricultural Research Institute, New Delhi, India
| | - Lekshmy Sathee
- Division of Plant Physiology and Nanaji Deshmukh Plant Phenomics Centre (NDPPC), Indian Council of Agricultural Research (ICAR)-Indian Agricultural Research Institute, New Delhi, India
| | - Monika Dalal
- ICAR-National Institute for Plant Biotechnology, New Delhi, India
| | - Rabi Narayan Sahoo
- Division of Agricultural Physics, ICAR-Indian Agricultural Research Institute, New Delhi, India
| | - Viswanathan Chinnusamy
- Division of Plant Physiology and Nanaji Deshmukh Plant Phenomics Centre (NDPPC), Indian Council of Agricultural Research (ICAR)-Indian Agricultural Research Institute, New Delhi, India
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5
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Correia PMP, Cairo Westergaard J, Bernardes da Silva A, Roitsch T, Carmo-Silva E, Marques da Silva J. High-throughput phenotyping of physiological traits for wheat resilience to high temperature and drought stress. JOURNAL OF EXPERIMENTAL BOTANY 2022; 73:5235-5251. [PMID: 35446418 PMCID: PMC9440435 DOI: 10.1093/jxb/erac160] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/31/2021] [Accepted: 04/20/2022] [Indexed: 05/30/2023]
Abstract
Interannual and local fluctuations in wheat crop yield are mostly explained by abiotic constraints. Heatwaves and drought, which are among the top stressors, commonly co-occur, and their frequency is increasing with global climate change. High-throughput methods were optimized to phenotype wheat plants under controlled water deficit and high temperature, with the aim to identify phenotypic traits conferring adaptative stress responses. Wheat plants of 10 genotypes were grown in a fully automated plant facility under 25/18 °C day/night for 30 d, and then the temperature was increased for 7 d (38/31 °C day/night) while maintaining half of the plants well irrigated and half at 30% field capacity. Thermal and multispectral images and pot weights were registered twice daily. At the end of the experiment, key metabolites and enzyme activities from carbohydrate and antioxidant metabolism were quantified. Regression machine learning models were successfully established to predict plant biomass using image-extracted parameters. Evapotranspiration traits expressed significant genotype-environment interactions (G×E) when acclimatization to stress was continuously monitored. Consequently, transpiration efficiency was essential to maintain the balance between water-saving strategies and biomass production in wheat under water deficit and high temperature. Stress tolerance included changes in carbohydrate metabolism, particularly in the sucrolytic and glycolytic pathways, and in antioxidant metabolism. The observed genetic differences in sensitivity to high temperature and water deficit can be exploited in breeding programmes to improve wheat resilience to climate change.
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Affiliation(s)
| | - Jesper Cairo Westergaard
- Department of Plant and Environmental Sciences, Section of Crop Science, Copenhagen University, Højbakkegård Allé 13, 2630 Tåstrup, Denmark
| | - Anabela Bernardes da Silva
- BioISI – Biosystems & Integrative Sciences Institute, Faculdade de Ciências da Universidade de Lisboa, Campo Grande, 1749-016 Lisboa, Portugal
| | - Thomas Roitsch
- Department of Plant and Environmental Sciences, Section of Crop Science, Copenhagen University, Højbakkegård Allé 13, 2630 Tåstrup, Denmark
- Department of Adaptive Biotechnologies, Global Change Research Institute, CAS, 603 00 Brno, Czech Republic
| | | | - Jorge Marques da Silva
- BioISI – Biosystems & Integrative Sciences Institute, Faculdade de Ciências da Universidade de Lisboa, Campo Grande, 1749-016 Lisboa, Portugal
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6
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Fu LY, Zhu T, Zhou X, Yu R, He Z, Zhang P, Wu Z, Chen M, Kaufmann K, Chen D. ChIP-Hub provides an integrative platform for exploring plant regulome. Nat Commun 2022; 13:3413. [PMID: 35701419 PMCID: PMC9197862 DOI: 10.1038/s41467-022-30770-1] [Citation(s) in RCA: 26] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2021] [Accepted: 05/18/2022] [Indexed: 11/18/2022] Open
Abstract
Plant genomes encode a complex and evolutionary diverse regulatory grammar that forms the basis for most life on earth. A wealth of regulome and epigenome data have been generated in various plant species, but no common, standardized resource is available so far for biologists. Here, we present ChIP-Hub, an integrative web-based platform in the ENCODE standards that bundles >10,000 publicly available datasets reanalyzed from >40 plant species, allowing visualization and meta-analysis. We manually curate the datasets through assessing ~540 original publications and comprehensively evaluate their data quality. As a proof of concept, we extensively survey the co-association of different regulators and construct a hierarchical regulatory network under a broad developmental context. Furthermore, we show how our annotation allows to investigate the dynamic activity of tissue-specific regulatory elements (promoters and enhancers) and their underlying sequence grammar. Finally, we analyze the function and conservation of tissue-specific promoters, enhancers and chromatin states using comparative genomics approaches. Taken together, the ChIP-Hub platform and the analysis results provide rich resources for deep exploration of plant ENCODE. ChIP-Hub is available at https://biobigdata.nju.edu.cn/ChIPHub/. A comprehensive data portal to explore plant regulomes is still unavailable. Here, the authors develop a web-based platform ChIP-Hub in the ENCODE standards and demonstrate its applications in the identification of hierarchical regulatory network, tissue-specific chromatin dynamics, putative enhancers and chromatin states.
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Affiliation(s)
- Liang-Yu Fu
- State Key Laboratory of Pharmaceutical Biotechnology, School of Life Sciences, Nanjing University, Nanjing, 210023, China.,Department for Plant Cell and Molecular Biology, Institute for Biology, Humboldt-Universität zu Berlin, 10115, Berlin, Germany
| | - Tao Zhu
- State Key Laboratory of Pharmaceutical Biotechnology, School of Life Sciences, Nanjing University, Nanjing, 210023, China
| | - Xinkai Zhou
- State Key Laboratory of Pharmaceutical Biotechnology, School of Life Sciences, Nanjing University, Nanjing, 210023, China
| | - Ranran Yu
- State Key Laboratory of Pharmaceutical Biotechnology, School of Life Sciences, Nanjing University, Nanjing, 210023, China
| | - Zhaohui He
- State Key Laboratory of Pharmaceutical Biotechnology, School of Life Sciences, Nanjing University, Nanjing, 210023, China
| | - Peijing Zhang
- Department of Bioinformatics, College of Life Sciences, Zhejiang University, Hangzhou, 310058, China
| | - Zhigui Wu
- State Key Laboratory of Pharmaceutical Biotechnology, School of Life Sciences, Nanjing University, Nanjing, 210023, China
| | - Ming Chen
- Department of Bioinformatics, College of Life Sciences, Zhejiang University, Hangzhou, 310058, China
| | - Kerstin Kaufmann
- Department for Plant Cell and Molecular Biology, Institute for Biology, Humboldt-Universität zu Berlin, 10115, Berlin, Germany.
| | - Dijun Chen
- State Key Laboratory of Pharmaceutical Biotechnology, School of Life Sciences, Nanjing University, Nanjing, 210023, China.
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7
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Wang S, Zhang Y, Hu C, Zhang N, Gribskov M, Yang H. Shiny-DEG: A Web Application to Analyze and Visualize Differentially Expressed Genes in RNA-seq. Interdiscip Sci 2020; 12:349-354. [PMID: 32666343 DOI: 10.1007/s12539-020-00383-7] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2020] [Revised: 06/08/2020] [Accepted: 07/01/2020] [Indexed: 11/26/2022]
Abstract
RNA-seq analysis has become one of the most widely used methods for biological and medical experiments, aiming to identify differentially expressed genes at a large scale. However, due to lack of programming skills and statistical background, it is difficult for biologists including faculty and students to fully understand what the RNA-seq results are and how to interpret them. In recent years, even though, there are several programs or websites that assist researchers to analyze and visualize NGS results, they have several limitations. Therefore, Shiny-DEG, a web application that facilitates the exploration and visualization of differentially expressed genes from RNA-seq, was developed. It integrates multi-factor design experiments, allows users to modify the parameters interactively according to experiments purpose and all analysis results can be downloaded directly, aiming to further assisting the interpretation and explanation of the biological questions. Therefore, it serves better for biologists without programming skills. Overall, this project is of great significance to reveal the mechanism of transcriptome differences.
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Affiliation(s)
- Sufang Wang
- School of Life Sciences, Northwestern Polytechnical University, Xi'an, 710072, Shaanxi, China.
| | - Yu Zhang
- School of Computer Science, Northwestern Polytechnical University, Xi'an, 710129, China.
- School of Computer Science and IT, RMIT University, Melbourne, VIC, 3000, Australia.
| | - Congzhan Hu
- School of Life Sciences, Northwestern Polytechnical University, Xi'an, 710072, Shaanxi, China
| | - Nu Zhang
- School of Life Sciences, Northwestern Polytechnical University, Xi'an, 710072, Shaanxi, China
| | - Michael Gribskov
- Department of Biological Sciences, Purdue University, West Lafayette, IN, 47907, USA
- Department of Computer Sciences, Purdue University, West Lafayette, IN, 47907, USA
| | - Hui Yang
- School of Life Sciences, Northwestern Polytechnical University, Xi'an, 710072, Shaanxi, China
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8
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Julkowska MM, Saade S, Agarwal G, Gao G, Pailles Y, Morton M, Awlia M, Tester M. MVApp-Multivariate Analysis Application for Streamlined Data Analysis and Curation. PLANT PHYSIOLOGY 2019; 180:1261-1276. [PMID: 31061104 PMCID: PMC6752927 DOI: 10.1104/pp.19.00235] [Citation(s) in RCA: 34] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/25/2019] [Accepted: 04/30/2019] [Indexed: 05/20/2023]
Abstract
Modern phenotyping techniques yield vast amounts of data that are challenging to manage and analyze. When thoroughly examined, this type of data can reveal genotype-to-phenotype relationships and meaningful connections among individual traits. However, efficient data mining is challenging for experimental biologists with limited training in curating, integrating, and exploring complex datasets. Additionally, data transparency, accessibility, and reproducibility are important considerations for scientific publication. The need for a streamlined, user-friendly pipeline for advanced phenotypic data analysis is pressing. In this article we present an open-source, online platform for multivariate analysis (MVApp), which serves as an interactive pipeline for data curation, in-depth analysis, and customized visualization. MVApp builds on the available R-packages and adds extra functionalities to enhance the interpretability of the results. The modular design of the MVApp allows for flexible analysis of various data structures and includes tools underexplored in phenotypic data analysis, such as clustering and quantile regression. MVApp aims to enhance findable, accessible, interoperable, and reproducible data transparency, streamline data curation and analysis, and increase statistical literacy among the scientific community.
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Affiliation(s)
- Magdalena M Julkowska
- Biological and Environmental Sciences and Engineering Division, King Abdullah University of Science and Technology, Thuwal 23955-6900, Kingdom of Saudi Arabia
| | - Stephanie Saade
- Biological and Environmental Sciences and Engineering Division, King Abdullah University of Science and Technology, Thuwal 23955-6900, Kingdom of Saudi Arabia
| | - Gaurav Agarwal
- Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology, Thuwal 23955-6900, Kingdom of Saudi Arabia
| | - Ge Gao
- Biological and Environmental Sciences and Engineering Division, King Abdullah University of Science and Technology, Thuwal 23955-6900, Kingdom of Saudi Arabia
| | - Yveline Pailles
- Biological and Environmental Sciences and Engineering Division, King Abdullah University of Science and Technology, Thuwal 23955-6900, Kingdom of Saudi Arabia
| | - Mitchell Morton
- Biological and Environmental Sciences and Engineering Division, King Abdullah University of Science and Technology, Thuwal 23955-6900, Kingdom of Saudi Arabia
| | - Mariam Awlia
- Biological and Environmental Sciences and Engineering Division, King Abdullah University of Science and Technology, Thuwal 23955-6900, Kingdom of Saudi Arabia
| | - Mark Tester
- Biological and Environmental Sciences and Engineering Division, King Abdullah University of Science and Technology, Thuwal 23955-6900, Kingdom of Saudi Arabia
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9
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González-Durruthy M, Scanavachi G, Rial R, Liu Z, Cordeiro MNDS, Itri R, Ruso JM. Structural and energetic evolution of fibrinogen toward to the betablocker interactions. Int J Biol Macromol 2019; 137:405-419. [PMID: 31265849 DOI: 10.1016/j.ijbiomac.2019.06.229] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2019] [Revised: 06/10/2019] [Accepted: 06/28/2019] [Indexed: 11/29/2022]
Abstract
We present a computational analysis coupled with experimental studies, focusing on the binding-interaction between beta-adrenoreceptor blocking agents (acebutolol and propranolol) with fibrinogen protein (E-region). Herein, computational modeling on structural validation and flexibility properties of fibrinogen E-region showed that the E-region interacting residues, which form the funnel-shaped hydrophobic cavity for ligand-binding, can be efficiently modeled. The obtained free energy of binding (FEB) values for the docking complexes, namely acebutolol/fibrinogen E-region and propranolol/fibrinogen E-region, were very close and amounted to - 6.9 kcal/mol and - 6.8 kcal/mol, respectively. They were supported by a high binding-accuracy (R.M.S.D < 2 Å) for the best crystallographic binding-poses in both cases. In this regard, we identify a docking-mechanism of interaction for the propranolol and acebutolol mainly based on non-covalent hydrophobic contacts with the fibrinogen E-region binding-site. Besides, the beta-adrenoreceptor blocking agents are able to induce local perturbations affecting particularly the fibrinogen E-region allosteric residues linked to significant changes in the inter-residue communication and flexibility properties of residue network. In this sense, we show that the key biophysical parameters like frequency and collectivity degree may be compromised in different ways by the interaction with acebutolol and propranolol. Isothermal titration calorimetry, zeta potential and small angle X-ray scattering (SAXS) measurements were performed to complete and corroborate computational analysis. The combined experimental results point out that acebutolol acts to a lesser extent to fibrinogen structure than propranolol.
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Affiliation(s)
- Michael González-Durruthy
- Soft Matter and Molecular Biophysics Group, Department of Applied Physics, University of Santiago de Compostela, 15782 Santiago de Compostela, Spain; LAQV-REQUIMTE of Chemistry and Biochemistry, Faculty of Sciences, University of Porto, 4169-007 Porto, Portugal
| | - Gustavo Scanavachi
- Department of Applied Physics, Institute of Physics, University of São Paulo, SP, Brazil
| | - Ramón Rial
- Soft Matter and Molecular Biophysics Group, Department of Applied Physics, University of Santiago de Compostela, 15782 Santiago de Compostela, Spain
| | - Zhen Liu
- Department of Physics and Engineering, Frostburg State University, Frostburg, MD 21532, United States
| | - M Natália D S Cordeiro
- LAQV-REQUIMTE of Chemistry and Biochemistry, Faculty of Sciences, University of Porto, 4169-007 Porto, Portugal
| | - Rosangela Itri
- Department of Applied Physics, Institute of Physics, University of São Paulo, SP, Brazil
| | - Juan M Ruso
- Soft Matter and Molecular Biophysics Group, Department of Applied Physics, University of Santiago de Compostela, 15782 Santiago de Compostela, Spain.
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10
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Chen D, Yan W, Fu LY, Kaufmann K. Architecture of gene regulatory networks controlling flower development in Arabidopsis thaliana. Nat Commun 2018; 9:4534. [PMID: 30382087 PMCID: PMC6208445 DOI: 10.1038/s41467-018-06772-3] [Citation(s) in RCA: 82] [Impact Index Per Article: 13.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2018] [Accepted: 09/26/2018] [Indexed: 11/29/2022] Open
Abstract
Floral homeotic transcription factors (TFs) act in a combinatorial manner to specify the organ identities in the flower. However, the architecture and the function of the gene regulatory network (GRN) controlling floral organ specification is still poorly understood. In particular, the interconnections of homeotic TFs, microRNAs (miRNAs) and other factors controlling organ initiation and growth have not been studied systematically so far. Here, using a combination of genome-wide TF binding, mRNA and miRNA expression data, we reconstruct the dynamic GRN controlling floral meristem development and organ differentiation. We identify prevalent feed-forward loops (FFLs) mediated by floral homeotic TFs and miRNAs that regulate common targets. Experimental validation of a coherent FFL shows that petal size is controlled by the SEPALLATA3-regulated miR319/TCP4 module. We further show that combinatorial DNA-binding of homeotic factors and selected other TFs is predictive of organ-specific patterns of gene expression. Our results provide a valuable resource for studying molecular regulatory processes underlying floral organ specification in plants. Homeotic transcription factors and miRNAs promote floral organ specification. Here Chen et al. reconstruct gene regulatory networks in Arabidopsis flowers and find evidence for feed forward loops between transcription factors, miRNAs and their targets that determine organ-specific gene expression.
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Affiliation(s)
- Dijun Chen
- Institute for Biology, Plant Cell and Molecular Biology, Humboldt-Universität zu Berlin, 10115, Berlin, Germany.
| | - Wenhao Yan
- Institute for Biology, Plant Cell and Molecular Biology, Humboldt-Universität zu Berlin, 10115, Berlin, Germany
| | - Liang-Yu Fu
- Institute for Biology, Plant Cell and Molecular Biology, Humboldt-Universität zu Berlin, 10115, Berlin, Germany
| | - Kerstin Kaufmann
- Institute for Biology, Plant Cell and Molecular Biology, Humboldt-Universität zu Berlin, 10115, Berlin, Germany.
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Yan W, Chen D, Smaczniak C, Engelhorn J, Liu H, Yang W, Graf A, Carles CC, Zhou DX, Kaufmann K. Dynamic and spatial restriction of Polycomb activity by plant histone demethylases. NATURE PLANTS 2018; 4:681-689. [PMID: 30104650 DOI: 10.1038/s41477-018-0219-5] [Citation(s) in RCA: 43] [Impact Index Per Article: 7.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/08/2017] [Accepted: 07/12/2018] [Indexed: 05/23/2023]
Abstract
Targeted changes in chromatin state at thousands of genes are central to eukaryotic development. RELATIVE OF EARLY FLOWERING 6 (REF6) is a Jumonji-type histone demethylase that counteracts Polycomb repressive complex 2 (PRC2)-mediated gene silencing in plants and was reported to select its binding sites in a direct, sequence-specific manner1-3. Here we show that REF6 and its two close paralogues determine spatial 'boundaries' of the repressive histone H3K27me3 mark in the genome and control the tissue-specific release from PRC2-mediated gene repression. Targeted mutagenesis revealed that these histone demethylases display pleiotropic, redundant functions in plant development, several of which depend on trans factor-mediated recruitment. Thus, Jumonji-type histone demethylases restrict repressive chromatin domains and contribute to tissue-specific gene activation via complementary targeting mechanisms.
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Affiliation(s)
- Wenhao Yan
- Department for Plant Cell and Molecular Biology, Institute for Biology, Humboldt-Universität zu Berlin, Berlin, Germany.
| | - Dijun Chen
- Department for Plant Cell and Molecular Biology, Institute for Biology, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Cezary Smaczniak
- Department for Plant Cell and Molecular Biology, Institute for Biology, Humboldt-Universität zu Berlin, Berlin, Germany
| | - Julia Engelhorn
- LPCV, CNRS, CEA, INRA, Université Grenoble Alpes, Grenoble, France
- School of Life Sciences, University of Warwick, Coventry, UK
| | - Haiyang Liu
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, China
| | - Wenjing Yang
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, China
| | - Alexander Graf
- Max Planck Institute of Molecular Plant Physiology, Potsdam, Germany
| | - Cristel C Carles
- LPCV, CNRS, CEA, INRA, Université Grenoble Alpes, Grenoble, France
| | - Dao-Xiu Zhou
- National Key Laboratory of Crop Genetic Improvement, Huazhong Agricultural University, Wuhan, China
- Institute Plant Science Paris-Saclay (IPS2), CNRS, INRA, Université Paris-sud 11, Université Paris-Saclay, Orsay, France
| | - Kerstin Kaufmann
- Department for Plant Cell and Molecular Biology, Institute for Biology, Humboldt-Universität zu Berlin, Berlin, Germany.
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