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Kashyap GR, Sridhara S, Manoj KN, Gopakkali P, Das B, Jha PK, Prasad PVV. Machine learning ensembles, neural network, hybrid and sparse regression approaches for weather based rainfed cotton yield forecast. INTERNATIONAL JOURNAL OF BIOMETEOROLOGY 2024; 68:1179-1197. [PMID: 38676745 DOI: 10.1007/s00484-024-02661-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/08/2023] [Revised: 03/06/2024] [Accepted: 03/15/2024] [Indexed: 04/29/2024]
Abstract
Cotton is a major economic crop predominantly cultivated under rainfed situations. The accurate prediction of cotton yield invariably helps farmers, industries, and policy makers. The final cotton yield is mostly determined by the weather patterns that prevail during the crop growing phase. Crop yield prediction with greater accuracy is possible due to the development of innovative technologies which analyses the bigdata with its high-performance computing abilities. Machine learning technologies can make yield prediction reasonable and faster and with greater flexibility than process based complex crop simulation models. The present study demonstrates the usability of ML algorithms for yield forecasting and facilitates the comparison of different models. The cotton yield was simulated by employing the weekly weather indices as inputs and the model performance was assessed by nRMSE, MAPE and EF values. Results show that stacked generalised ensemble model and artificial neural networks predicted the cotton yield with lower nRMSE, MAPE and higher efficiency compared to other models. Variable importance studies in LASSO and ENET model found minimum temperature and relative humidity as the main determinates of cotton yield in all districts. The models were ranked based these performance metrics in the order of Stacked generalised ensemble > ANN > PCA ANN > SMLR ANN > LASSO> ENET > SVM > PCA SMLR > SMLR SVM > SMLR. This study shows that stacked generalised ensembling and ANN method can be used for reliable yield forecasting at district or county level and helps stakeholders in timely decision-making.
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Affiliation(s)
- Girish R Kashyap
- Centre for Climate Resilient Agriculture, Keladi Shivappa Nayaka University of Agricultural and Horticultural Sciences, Shivamogga, Karnataka, 577204, India
| | - Shankarappa Sridhara
- Centre for Climate Resilient Agriculture, Keladi Shivappa Nayaka University of Agricultural and Horticultural Sciences, Shivamogga, Karnataka, 577204, India.
| | - Konapura Nagaraja Manoj
- Centre for Climate Resilient Agriculture, Keladi Shivappa Nayaka University of Agricultural and Horticultural Sciences, Shivamogga, Karnataka, 577204, India
| | - Pradeep Gopakkali
- Centre for Climate Resilient Agriculture, Keladi Shivappa Nayaka University of Agricultural and Horticultural Sciences, Shivamogga, Karnataka, 577204, India
| | - Bappa Das
- ICAR-Central Coastal Agricultural Research Institute, Old Goa, Goa, 403402, India
| | - Prakash Kumar Jha
- Department of Plant and Soil Sciences, Mississippi State University, Mississippi, MS, USA
| | - P V Vara Prasad
- Department of Agronomy, Kansas State University, Manhattan, KS, 66506, USA
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Opara IK, Opara UL, Okolie JA, Fawole OA. Machine Learning Application in Horticulture and Prospects for Predicting Fresh Produce Losses and Waste: A Review. PLANTS (BASEL, SWITZERLAND) 2024; 13:1200. [PMID: 38732414 PMCID: PMC11085577 DOI: 10.3390/plants13091200] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/17/2024] [Revised: 04/19/2024] [Accepted: 04/23/2024] [Indexed: 05/13/2024]
Abstract
The current review examines the state of knowledge and research on machine learning (ML) applications in horticultural production and the potential for predicting fresh produce losses and waste. Recently, ML has been increasingly applied in horticulture for efficient and accurate operations. Given the health benefits of fresh produce and the need for food and nutrition security, efficient horticultural production and postharvest management are important. This review aims to assess the application of ML in preharvest and postharvest horticulture and the potential of ML in reducing postharvest losses and waste by predicting their magnitude, which is crucial for management practices and policymaking in loss and waste reduction. The review starts by assessing the application of ML in preharvest horticulture. It then presents the application of ML in postharvest handling and processing, and lastly, the prospects for its application in postharvest loss and waste quantification. The findings revealed that several ML algorithms perform satisfactorily in classification and prediction tasks. Based on that, there is a need to further investigate the suitability of more models or a combination of models with a higher potential for classification and prediction. Overall, the review suggested possible future directions for research related to the application of ML in postharvest losses and waste quantification.
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Affiliation(s)
- Ikechukwu Kingsley Opara
- SARChI Postharvest Technology Research Laboratory, Africa Institute for Postharvest Technology, Faculty of AgriSciences, Stellenbosch University, Stellenbosch 7600, South Africa; (I.K.O.); (U.L.O.)
- Department of Food Science, Stellenbosch University, Stellenbosch 7600, South Africa
| | - Umezuruike Linus Opara
- SARChI Postharvest Technology Research Laboratory, Africa Institute for Postharvest Technology, Faculty of AgriSciences, Stellenbosch University, Stellenbosch 7600, South Africa; (I.K.O.); (U.L.O.)
- UNESCO International Centre for Biotechnology, Nsukka 410001, Enugu State, Nigeria
| | - Jude A. Okolie
- Gallogly College of Engineering, University of Oklahoma, Norman, OK 73019, USA;
| | - Olaniyi Amos Fawole
- Postharvest and Agroprocessing Research Centre, Department of Botany and Plant Biotechnology, University of Johannesburg, Johannesburg 2006, South Africa
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Aasim M, Yıldırım B, Say A, Ali SA, Aytaç S, Nadeem MA. Artificial intelligence models for validating and predicting the impact of chemical priming of hydrogen peroxide (H 2O 2) and light emitting diodes on in vitro grown industrial hemp (Cannabis sativa L.). PLANT MOLECULAR BIOLOGY 2024; 114:33. [PMID: 38526768 DOI: 10.1007/s11103-024-01427-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/21/2023] [Accepted: 02/14/2024] [Indexed: 03/27/2024]
Abstract
Industrial hemp (Cannabis sativa L.) is a highly recalcitrant plant under in vitro conditions that can be overcome by employing external stimuli. Hemp seeds were primed with 2.0-3.0% hydrogen peroxide (H2O2) followed by culture under different Light Emitting Diodes (LEDs) sources. Priming seeds with 2.0% yielded relatively high germination rate, growth, and other biochemical and enzymatic activities. The LED lights exerted a variable impact on Cannabis germination and enzymatic activities. Similarly, variable responses were observed for H2O2 × Blue-LEDs combination. The results were also analyzed by multiple regression analysis, followed by an investigation of the impact of both factors by Pareto chart and normal plots. The results were optimized by contour and surface plots for all parameters. Response surface optimizer optimized 2.0% H2O2 × 918 LUX LEDs for maximum scores of all output parameters. The results were predicted by employing Multilayer Perceptron (MLP), Random Forest (RF), and eXtreme Gradient Boosting (XGBoost) algorithms. Moreover, the validity of these models was assessed by using six different performance metrics. MLP performed better than RF and XGBoost models, considering all six-performance metrics. Despite the differences in scores, the performance indicators for all examined models were quite close to each other. It can easily be concluded that all three models are capable of predicting and validating data for cannabis seeds primed with H2O2 and grown under different LED lights.
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Affiliation(s)
- Muhammad Aasim
- Faculty of Agricultural Sciences and Technology, Sivas University of Science and Technology, Sivas, Turkey.
| | - Buşra Yıldırım
- Faculty of Agricultural Sciences and Technology, Sivas University of Science and Technology, Sivas, Turkey
| | - Ahmet Say
- Department of Agricultural Biotechnology, Faculty of Agriculture, Erciyes University, Kayseri, Turkey
| | - Seyid Amjad Ali
- Department of Information Systems and Technologies, Bilkent University, Ankara, Turkey
| | - Selim Aytaç
- Institute of Hemp Researches, Ondokuz Mayis University, Samsun, Turkey
| | - Muhammad Azhar Nadeem
- Faculty of Agricultural Sciences and Technology, Sivas University of Science and Technology, Sivas, Turkey
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Kimotho RN, Maina S. Unraveling plant-microbe interactions: can integrated omics approaches offer concrete answers? JOURNAL OF EXPERIMENTAL BOTANY 2024; 75:1289-1313. [PMID: 37950741 PMCID: PMC10901211 DOI: 10.1093/jxb/erad448] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/26/2023] [Accepted: 11/08/2023] [Indexed: 11/13/2023]
Abstract
Advances in high throughput omics techniques provide avenues to decipher plant microbiomes. However, there is limited information on how integrated informatics can help provide deeper insights into plant-microbe interactions in a concerted way. Integrating multi-omics datasets can transform our understanding of the plant microbiome from unspecified genetic influences on interacting species to specific gene-by-gene interactions. Here, we highlight recent progress and emerging strategies in crop microbiome omics research and review key aspects of how the integration of host and microbial omics-based datasets can be used to provide a comprehensive outline of complex crop-microbe interactions. We describe how these technological advances have helped unravel crucial plant and microbial genes and pathways that control beneficial, pathogenic, and commensal plant-microbe interactions. We identify crucial knowledge gaps and synthesize current limitations in our understanding of crop microbiome omics approaches. We highlight recent studies in which multi-omics-based approaches have led to improved models of crop microbial community structure and function. Finally, we recommend holistic approaches in integrating host and microbial omics datasets to achieve precision and efficiency in data analysis, which is crucial for biotic and abiotic stress control and in understanding the contribution of the microbiota in shaping plant fitness.
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Affiliation(s)
- Roy Njoroge Kimotho
- Hebei Key Laboratory of Soil Ecology, Key Laboratory of Agricultural Water Resources, Centre for Agricultural Resources Research, Institute of Genetics and Developmental Biology, Chinese Academy of Sciences, Shijiazhuang 050021, China
- University of Chinese Academy of Sciences, Beijing 100049, China
| | - Solomon Maina
- Elizabeth Macarthur Agricultural Institute, NSW Department of Primary Industries, Menangle, New South Wales 2568, Australia
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5
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Watkins JM, Montes C, Clark NM, Song G, Oliveira CC, Mishra B, Brachova L, Seifert CM, Mitchell MS, Yang J, Braga Dos Reis PA, Urano D, Muktar MS, Walley JW, Jones AM. Phosphorylation Dynamics in a flg22-Induced, G Protein-Dependent Network Reveals the AtRGS1 Phosphatase. Mol Cell Proteomics 2024; 23:100705. [PMID: 38135118 PMCID: PMC10837098 DOI: 10.1016/j.mcpro.2023.100705] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2023] [Revised: 11/22/2023] [Accepted: 12/19/2023] [Indexed: 12/24/2023] Open
Abstract
The microbe-associated molecular pattern flg22 is recognized in a flagellin-sensitive 2-dependent manner in root tip cells. Here, we show a rapid and massive change in protein abundance and phosphorylation state of the Arabidopsis root cell proteome in WT and a mutant deficient in heterotrimeric G-protein-coupled signaling. flg22-induced changes fall on proteins comprising a subset of this proteome, the heterotrimeric G protein interactome, and on highly-populated hubs of the immunity network. Approximately 95% of the phosphorylation changes in the heterotrimeric G-protein interactome depend, at least partially, on a functional G protein complex. One member of this interactome is ATBα, a substrate-recognition subunit of a protein phosphatase 2A complex and an interactor to Arabidopsis thaliana Regulator of G Signaling 1 protein (AtRGS1), a flg22-phosphorylated, 7-transmembrane spanning modulator of the nucleotide-binding state of the core G-protein complex. A null mutation of ATBα strongly increases basal endocytosis of AtRGS1. AtRGS1 steady-state protein level is lower in the atbα mutant in a proteasome-dependent manner. We propose that phosphorylation-dependent endocytosis of AtRGS1 is part of the mechanism to degrade AtRGS1, thus sustaining activation of the heterotrimeric G protein complex required for the regulation of system dynamics in innate immunity. The PP2A(ATBα) complex is a critical regulator of this signaling pathway.
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Affiliation(s)
- Justin M Watkins
- Department of Biology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Christian Montes
- Department of Plant Pathology and Microbiology, Iowa State University, Ames, Iowa, USA
| | - Natalie M Clark
- Department of Plant Pathology and Microbiology, Iowa State University, Ames, Iowa, USA
| | - Gaoyuan Song
- Department of Plant Pathology and Microbiology, Iowa State University, Ames, Iowa, USA
| | - Celio Cabral Oliveira
- Department of Biology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA; Department of Biochemistry and Molecular Biology/BIOAGRO, Universidade Federal de Viçosa, Viçosa, Brazil
| | - Bharat Mishra
- Department of Biology, University of Alabama-Birmingham, Birmingham, Alabama, USA
| | - Libuse Brachova
- Department of Biochemistry, Biophysics and Molecular Biology, Iowa State University, Ames, Iowa, USA
| | - Clara M Seifert
- Department of Biology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Malek S Mitchell
- Department of Biology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Jing Yang
- Department of Biology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | | | - Daisuke Urano
- Department of Biology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - M Shahid Muktar
- Department of Biology, University of Alabama-Birmingham, Birmingham, Alabama, USA
| | - Justin W Walley
- Department of Plant Pathology and Microbiology, Iowa State University, Ames, Iowa, USA.
| | - Alan M Jones
- Department of Biology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA; Department of Pharmacology, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA.
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Zhao M, Lei C, Zhou K, Huang Y, Fu C, Yang S, Zhang Z. POOE: predicting oomycete effectors based on a pre-trained large protein language model. mSystems 2024; 9:e0100423. [PMID: 38078741 PMCID: PMC10804963 DOI: 10.1128/msystems.01004-23] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2023] [Accepted: 10/23/2023] [Indexed: 01/24/2024] Open
Abstract
Oomycetes are fungus-like eukaryotic microorganisms which can cause catastrophic diseases in many plants. Successful infection of oomycetes depends highly on their effector proteins that are secreted into plant cells to subvert plant immunity. Thus, systematic identification of effectors from the oomycete proteomes remains an initial but crucial step in understanding plant-pathogen relationships. However, the number of experimentally identified oomycete effectors is still limited. Currently, only a few bioinformatics predictors exist to detect potential effectors, and their prediction performance needs to be improved. Here, we used the sequence embeddings from a pre-trained large protein language model (ProtTrans) as input and developed a support vector machine-based method called POOE for predicting oomycete effectors. POOE could achieve a highly accurate performance with an area under the precision-recall curve of 0.804 (area under the receiver operating characteristic curve = 0.893, accuracy = 0.874, precision = 0.777, recall = 0.684, and specificity = 0.936) in the fivefold cross-validation, considerably outperforming various combinations of popular machine learning algorithms and other commonly used sequence encoding schemes. A similar prediction performance was also observed in the independent test. Compared with the existing oomycete effector prediction methods, POOE provided very competitive and promising performance, suggesting that ProtTrans effectively captures rich protein semantic information and dramatically improves the prediction task. We anticipate that POOE can accelerate the identification of oomycete effectors and provide new hints to systematically understand the functional roles of effectors in plant-pathogen interactions. The web server of POOE is freely accessible at http://zzdlab.com/pooe/index.php. The corresponding source codes and data sets are also available at https://github.com/zzdlabzm/POOE.IMPORTANCEIn this work, we use the sequence representations from a pre-trained large protein language model (ProtTrans) as input and develop a Support Vector Machine-based method called POOE for predicting oomycete effectors. POOE could achieve a highly accurate performance in the independent test set, considerably outperforming existing oomycete effector prediction methods. We expect that this new bioinformatics tool will accelerate the identification of oomycete effectors and further guide the experimental efforts to interrogate the functional roles of effectors in plant-pathogen interaction.
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Affiliation(s)
- Miao Zhao
- State Key Laboratory of Animal Biotech Breeding, College of Biological Sciences, China Agricultural University, Beijing, China
| | - Chenping Lei
- State Key Laboratory of Animal Biotech Breeding, College of Biological Sciences, China Agricultural University, Beijing, China
| | - Kewei Zhou
- State Key Laboratory of Animal Biotech Breeding, College of Biological Sciences, China Agricultural University, Beijing, China
| | - Yan Huang
- State Key Laboratory of Animal Biotech Breeding, College of Biological Sciences, China Agricultural University, Beijing, China
| | - Chen Fu
- School of Chemistry and Biological Engineering, University of Science and Technology Beijing, Beijing, China
| | - Shiping Yang
- State Key Laboratory of Plant Environmental Resilience, College of Biological Sciences, China Agricultural University, Beijing, China
| | - Ziding Zhang
- State Key Laboratory of Animal Biotech Breeding, College of Biological Sciences, China Agricultural University, Beijing, China
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Damásio M, Barbosa M, Deus J, Fernandes E, Leitão A, Albino L, Fonseca F, Silvestre J. Can Grapevine Leaf Water Potential Be Modelled from Physiological and Meteorological Variables? A Machine Learning Approach. PLANTS (BASEL, SWITZERLAND) 2023; 12:4142. [PMID: 38140469 PMCID: PMC10747955 DOI: 10.3390/plants12244142] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/10/2023] [Revised: 11/22/2023] [Accepted: 12/06/2023] [Indexed: 12/24/2023]
Abstract
Climate change is affecting global viticulture, increasing heatwaves and drought. Precision irrigation, supported by robust water status indicators (WSIs), is inevitable in most of the Mediterranean basin. One of the most reliable WSIs is the leaf water potential (Ψleaf), which is determined via an intrusive and time-consuming method. The aim of this work is to discern the most effective variables that are correlated with plants' water status and identify the variables that better predict Ψleaf. Five grapevine varieties grown in the Alentejo region (Portugal) were selected and subjected to three irrigation treatments, starting in 2018: full irrigation (FI), deficit irrigation (DI), and no irrigation (NI). Plant monitoring was performed in 2023. Measurements included stomatal conductance (gs), predawn water potential Ψpd, stem water potential (Ψstem), thermal imaging, and meteorological data. The WSIs, namely Ψpd and gs, responded differently according to the irrigation treatment. Ψstem measured at mid-morning (MM) and mid-day (MD) proved unable to discern between treatments. MM measurements presented the best correlations between WSIs. gs showed the best correlations between the other WSIs, and consequently the best predictive capability to estimate Ψpd. Machine learning regression models were trained on meteorological, thermal, and gs data to predict Ψpd, with ensemble models showing a great performance (ExtraTrees: R2=0.833, MAE=0.072; Gradient Boosting: R2=0.830; MAE=0.073).
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Affiliation(s)
- Miguel Damásio
- INIAV I.P., Instituto Nacional de Investigação Agrária e Veterinária, Polo de Inovação de Dois Portos, Quinta da Almoinha, 2565-191 Dois Portos, Portugal; (J.D.); (J.S.)
| | - Miguel Barbosa
- SISCOG SA, Sistemas Cognitivos, Campo Grande, 378 - 3°, 1700-097 Lisboa, Portugal; (M.B.); (E.F.); (A.L.); (L.A.); (F.F.)
| | - João Deus
- INIAV I.P., Instituto Nacional de Investigação Agrária e Veterinária, Polo de Inovação de Dois Portos, Quinta da Almoinha, 2565-191 Dois Portos, Portugal; (J.D.); (J.S.)
| | - Eduardo Fernandes
- SISCOG SA, Sistemas Cognitivos, Campo Grande, 378 - 3°, 1700-097 Lisboa, Portugal; (M.B.); (E.F.); (A.L.); (L.A.); (F.F.)
| | - André Leitão
- SISCOG SA, Sistemas Cognitivos, Campo Grande, 378 - 3°, 1700-097 Lisboa, Portugal; (M.B.); (E.F.); (A.L.); (L.A.); (F.F.)
| | - Luís Albino
- SISCOG SA, Sistemas Cognitivos, Campo Grande, 378 - 3°, 1700-097 Lisboa, Portugal; (M.B.); (E.F.); (A.L.); (L.A.); (F.F.)
| | - Filipe Fonseca
- SISCOG SA, Sistemas Cognitivos, Campo Grande, 378 - 3°, 1700-097 Lisboa, Portugal; (M.B.); (E.F.); (A.L.); (L.A.); (F.F.)
| | - José Silvestre
- INIAV I.P., Instituto Nacional de Investigação Agrária e Veterinária, Polo de Inovação de Dois Portos, Quinta da Almoinha, 2565-191 Dois Portos, Portugal; (J.D.); (J.S.)
- GREEN-IT Bioresources4sustainability, ITQB NOVA, Av. da República, 2780-157 Oeiras, Portugal
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Kumar N, Mukhtar MS. Integrated Systems Biology Pipeline to Compare Co-Expression Networks in Plants and Elucidate Differential Regulators. PLANTS (BASEL, SWITZERLAND) 2023; 12:3618. [PMID: 37896081 PMCID: PMC10610404 DOI: 10.3390/plants12203618] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/23/2023] [Revised: 10/08/2023] [Accepted: 10/09/2023] [Indexed: 10/29/2023]
Abstract
To identify sets of genes that exhibit similar expression characteristics, co-expression networks were constructed from transcriptome datasets that were obtained from plant samples at various stages of growth and development or treated with diverse biotic, abiotic, and other environmental stresses. In addition, co-expression network analysis can provide deeper insights into gene regulation when combined with transcriptomics. The coordination and integration of all these complex networks to deduce gene regulation are major challenges for plant biologists. Python and R have emerged as major tools for managing complex scientific data over the past decade. In this study, we describe a reproducible protocol POTFUL (pant co-expression transcription factor regulators), implemented in Python 3, for integrating co-expression and transcription factor target protein networks to infer gene regulation.
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Affiliation(s)
| | - M. Shahid Mukhtar
- Department of Biology, University of Alabama at Birmingham, Birmingham, AL 35294, USA
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Cui Z, Li K, Kang C, Wu Y, Li T, Li M. Plant and Disease Recognition Based on PMF Pipeline Domain Adaptation Method: Using Bark Images as Meta-Dataset. PLANTS (BASEL, SWITZERLAND) 2023; 12:3280. [PMID: 37765444 PMCID: PMC10534746 DOI: 10.3390/plants12183280] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/15/2023] [Revised: 09/11/2023] [Accepted: 09/13/2023] [Indexed: 09/29/2023]
Abstract
Efficient image recognition is important in crop and forest management. However, it faces many challenges, such as the large number of plant species and diseases, the variability of plant appearance, and the scarcity of labeled data for training. To address this issue, we modified a SOTA Cross-Domain Few-shot Learning (CDFSL) method based on prototypical networks and attention mechanisms. We employed attention mechanisms to perform feature extraction and prototype generation by focusing on the most relevant parts of the images, then used prototypical networks to learn the prototype of each category and classify new instances. Finally, we demonstrated the effectiveness of the modified CDFSL method on several plant and disease recognition datasets. The results showed that the modified pipeline was able to recognize several cross-domain datasets using generic representations, and achieved up to 96.95% and 94.07% classification accuracy on datasets with the same and different domains, respectively. In addition, we visualized the experimental results, demonstrating the model's stable transfer capability between datasets and the model's high visual correlation with plant and disease biological characteristics. Moreover, by extending the classes of different semantics within the training dataset, our model can be generalized to other domains, which implies broad applicability.
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Affiliation(s)
| | | | | | | | | | - Mingyang Li
- Co-Innovation Center for Sustainable Forestry in Southern China, Nanjing Forestry University, Nanjing 210037, China; (Z.C.); (K.L.); (C.K.); (Y.W.); (T.L.)
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10
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Huang Y. Improved SVM-Based Soil-Moisture-Content Prediction Model for Tea Plantation. PLANTS (BASEL, SWITZERLAND) 2023; 12:2309. [PMID: 37375934 DOI: 10.3390/plants12122309] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/17/2023] [Revised: 06/04/2023] [Accepted: 06/12/2023] [Indexed: 06/29/2023]
Abstract
Accurate prediction of soil moisture content in tea plantations plays a crucial role in optimizing irrigation practices and improving crop productivity. Traditional methods for SMC prediction are difficult to implement due to high costs and labor requirements. While machine learning models have been applied, their performance is often limited by the lack of sufficient data. To address the challenges of inaccurate and inefficient soil moisture prediction in tea plantations and enhance predictive performance, an improved support-vector-machine- (SVM) based model was developed to predict the SMC in a tea plantation. The proposed model addresses several limitations of existing approaches by incorporating novel features and enhancing the SVM algorithm's performance, which was improved with the Bald Eagle Search algorithm (BES) method for hyper-parameter optimization. The study utilized a comprehensive dataset comprising soil moisture measurements and relevant environmental variables collected from a tea plantation. Feature selection techniques were applied to identify the most informative variables, including rainfall, temperature, humidity, and soil type. The selected features were then used to train and optimize the SVM model. The proposed model was applied to prediction of soil water moisture in a tea plantation in Guangxi State-owned Fuhu Overseas Chinese Farm. Experimental results demonstrated the superior performance of the improved SVM model in predicting soil moisture content compared to traditional SVM approaches and other machine-learning algorithms. The model exhibited high accuracy, robustness, and generalization capabilities across different time periods and geographical locations with R2, MSE, and RMSE of 0.9435, 0.0194 and 0.1392, respectively, which helps to enhance the prediction performance, especially when limited real data are available. The proposed SVM-based model offers several advantages for tea plantation management. It provides timely and accurate soil moisture predictions, enabling farmers to make informed decisions regarding irrigation scheduling and water resource management. By optimizing irrigation practices, the model helps enhance tea crop yield, minimize water usage, and reduce environmental impact.
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Affiliation(s)
- Ying Huang
- Electronic Information School, Wuhan University, Wuhan 430072, China
- School of Automatic Control, Liuzhou Railway Vocational Technical College, Liuzhou 545616, China
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11
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Helleckes LM, Hemmerich J, Wiechert W, von Lieres E, Grünberger A. Machine learning in bioprocess development: from promise to practice. Trends Biotechnol 2023; 41:817-835. [PMID: 36456404 DOI: 10.1016/j.tibtech.2022.10.010] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2022] [Revised: 10/20/2022] [Accepted: 10/27/2022] [Indexed: 11/30/2022]
Abstract
Fostered by novel analytical techniques, digitalization, and automation, modern bioprocess development provides large amounts of heterogeneous experimental data, containing valuable process information. In this context, data-driven methods like machine learning (ML) approaches have great potential to rationally explore large design spaces while exploiting experimental facilities most efficiently. Herein we demonstrate how ML methods have been applied so far in bioprocess development, especially in strain engineering and selection, bioprocess optimization, scale-up, monitoring, and control of bioprocesses. For each topic, we will highlight successful application cases, current challenges, and point out domains that can potentially benefit from technology transfer and further progress in the field of ML.
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Affiliation(s)
- Laura M Helleckes
- Institute for Bio- and Geosciences (IBG-1), Forschungszentrum Jülich GmbH, 52428 Jülich, Germany; RWTH Aachen University, Templergraben 55, 52062 Aachen, Germany
| | - Johannes Hemmerich
- Institute for Bio- and Geosciences (IBG-1), Forschungszentrum Jülich GmbH, 52428 Jülich, Germany
| | - Wolfgang Wiechert
- Institute for Bio- and Geosciences (IBG-1), Forschungszentrum Jülich GmbH, 52428 Jülich, Germany; RWTH Aachen University, Templergraben 55, 52062 Aachen, Germany
| | - Eric von Lieres
- Institute for Bio- and Geosciences (IBG-1), Forschungszentrum Jülich GmbH, 52428 Jülich, Germany; RWTH Aachen University, Templergraben 55, 52062 Aachen, Germany
| | - Alexander Grünberger
- Multiscale Bioengineering, Technical Faculty, Bielefeld University, Universitätsstr. 25, 33615 Bielefeld, Germany; Center for Biotechnology (CeBiTec), Bielefeld University, Universitätsstr. 25, 33615 Bielefeld, Germany; Institute of Process Engineering in Life Sciences, Section III: Microsystems in Bioprocess Engineering, Karlsruhe Institute of Technology, Fritz-Haber-Weg 2, 76131, Karlsruhe, Germany.
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12
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Kumar N, Mukhtar MS. Ranking Plant Network Nodes Based on Their Centrality Measures. ENTROPY (BASEL, SWITZERLAND) 2023; 25:e25040676. [PMID: 37190464 PMCID: PMC10137616 DOI: 10.3390/e25040676] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/23/2023] [Revised: 04/14/2023] [Accepted: 04/16/2023] [Indexed: 05/17/2023]
Abstract
Biological networks are often large and complex, making it difficult to accurately identify the most important nodes. Node prioritization algorithms are used to identify the most influential nodes in a biological network by considering their relationships with other nodes. These algorithms can help us understand the functioning of the network and the role of individual nodes. We developed CentralityCosDist, an algorithm that ranks nodes based on a combination of centrality measures and seed nodes. We applied this and four other algorithms to protein-protein interactions and co-expression patterns in Arabidopsis thaliana using pathogen effector targets as seed nodes. The accuracy of the algorithms was evaluated through functional enrichment analysis of the top 10 nodes identified by each algorithm. Most enriched terms were similar across algorithms, except for DIAMOnD. CentralityCosDist identified more plant-pathogen interactions and related functions and pathways compared to the other algorithms.
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Affiliation(s)
- Nilesh Kumar
- Department of Biology, University of Alabama at Birmingham, Birmingham, AL 35294, USA
| | - M Shahid Mukhtar
- Department of Biology, University of Alabama at Birmingham, Birmingham, AL 35294, USA
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13
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Kumar N, Mishra BK, Liu J, Mohan B, Thingujam D, Pajerowska-Mukhtar KM, Mukhtar MS. Network Biology Analyses and Dynamic Modeling of Gene Regulatory Networks under Drought Stress Reveal Major Transcriptional Regulators in Arabidopsis. Int J Mol Sci 2023; 24:ijms24087349. [PMID: 37108512 PMCID: PMC10139068 DOI: 10.3390/ijms24087349] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2022] [Revised: 04/02/2023] [Accepted: 04/11/2023] [Indexed: 04/29/2023] Open
Abstract
Drought is one of the most serious abiotic stressors in the environment, restricting agricultural production by reducing plant growth, development, and productivity. To investigate such a complex and multifaceted stressor and its effects on plants, a systems biology-based approach is necessitated, entailing the generation of co-expression networks, identification of high-priority transcription factors (TFs), dynamic mathematical modeling, and computational simulations. Here, we studied a high-resolution drought transcriptome of Arabidopsis. We identified distinct temporal transcriptional signatures and demonstrated the involvement of specific biological pathways. Generation of a large-scale co-expression network followed by network centrality analyses identified 117 TFs that possess critical properties of hubs, bottlenecks, and high clustering coefficient nodes. Dynamic transcriptional regulatory modeling of integrated TF targets and transcriptome datasets uncovered major transcriptional events during the course of drought stress. Mathematical transcriptional simulations allowed us to ascertain the activation status of major TFs, as well as the transcriptional intensity and amplitude of their target genes. Finally, we validated our predictions by providing experimental evidence of gene expression under drought stress for a set of four TFs and their major target genes using qRT-PCR. Taken together, we provided a systems-level perspective on the dynamic transcriptional regulation during drought stress in Arabidopsis and uncovered numerous novel TFs that could potentially be used in future genetic crop engineering programs.
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Affiliation(s)
- Nilesh Kumar
- Department of Biology, 464 Campbell Hall, University of Alabama at Birmingham, 1300 University Boulevard, Birmingham, AL 35294, USA
| | - Bharat K Mishra
- Department of Biology, 464 Campbell Hall, University of Alabama at Birmingham, 1300 University Boulevard, Birmingham, AL 35294, USA
| | - Jinbao Liu
- Department of Biology, 464 Campbell Hall, University of Alabama at Birmingham, 1300 University Boulevard, Birmingham, AL 35294, USA
| | - Binoop Mohan
- Department of Biology, 464 Campbell Hall, University of Alabama at Birmingham, 1300 University Boulevard, Birmingham, AL 35294, USA
| | - Doni Thingujam
- Department of Biology, 464 Campbell Hall, University of Alabama at Birmingham, 1300 University Boulevard, Birmingham, AL 35294, USA
| | - Karolina M Pajerowska-Mukhtar
- Department of Biology, 464 Campbell Hall, University of Alabama at Birmingham, 1300 University Boulevard, Birmingham, AL 35294, USA
| | - M Shahid Mukhtar
- Department of Biology, 464 Campbell Hall, University of Alabama at Birmingham, 1300 University Boulevard, Birmingham, AL 35294, USA
- Nutrition Obesity Research Center, University of Alabama at Birmingham, 1675 University Boulevard, Birmingham, AL 35294, USA
- Department of Surgery, University of Alabama at Birmingham, 1808 7th Ave S, Birmingham, AL 35294, USA
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14
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Maphosa S, Moleleki LN, Motaung TE. Bacterial secretion system functions: evidence of interactions and downstream implications. MICROBIOLOGY (READING, ENGLAND) 2023; 169. [PMID: 37083586 DOI: 10.1099/mic.0.001326] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/22/2023]
Abstract
Unprecedented insights into the biology and functions of bacteria have been and continue to be gained through studying bacterial secretion systems in isolation. This method, however, results in our understanding of the systems being primarily based on the idea that they operate independently, ignoring the subtleties of downstream interconnections. Gram-negative bacteria are naturally able to adapt to and navigate their frequently varied and dynamic surroundings, mostly because of the covert connections between secretion systems. Therefore, to comprehend some of the linked downstream repercussions for organisms that follow this discourse, it is vital to have mechanistic insights into how the intersecretion system functions in bacterial rivalry, virulence, and survival, among other things. To that purpose, this paper discusses a few key instances of molecular antagonistic and interdependent relationships between bacterial secretion systems and their produced functional products.
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Affiliation(s)
- Silindile Maphosa
- Division of Microbiology, Department of Biochemistry, Genetics, and Microbiology, University of Pretoria, Hatfield, Pretoria, South Africa
- Department of Plant and Soil Sciences, University of Pretoria, Hatfield, Pretoria, South Africa
- Forestry and Agricultural Biotechnology Institute, University of Pretoria, Hatfield, Pretoria, South Africa
| | - Lucy N Moleleki
- Division of Microbiology, Department of Biochemistry, Genetics, and Microbiology, University of Pretoria, Hatfield, Pretoria, South Africa
- Forestry and Agricultural Biotechnology Institute, University of Pretoria, Hatfield, Pretoria, South Africa
| | - Thabiso E Motaung
- Division of Microbiology, Department of Biochemistry, Genetics, and Microbiology, University of Pretoria, Hatfield, Pretoria, South Africa
- Forestry and Agricultural Biotechnology Institute, University of Pretoria, Hatfield, Pretoria, South Africa
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15
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Depuydt T, De Rybel B, Vandepoele K. Charting plant gene functions in the multi-omics and single-cell era. TRENDS IN PLANT SCIENCE 2023; 28:283-296. [PMID: 36307271 DOI: 10.1016/j.tplants.2022.09.008] [Citation(s) in RCA: 15] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/20/2022] [Revised: 09/09/2022] [Accepted: 09/30/2022] [Indexed: 06/16/2023]
Abstract
Despite the increased access to high-quality plant genome sequences, the set of genes with a known function remains far from complete. With the advent of novel bulk and single-cell omics profiling methods, we are entering a new era where advanced and highly integrative functional annotation strategies are being developed to elucidate the functions of all plant genes. Here, we review different multi-omics approaches to improve functional and regulatory gene characterization and highlight the power of machine learning and network biology to fully exploit the complementary information embedded in different omics layers. Finally, we discuss the potential of emerging single-cell methods and algorithms to further increase the resolution, allowing generation of functional insights about plant biology.
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Affiliation(s)
- Thomas Depuydt
- Ghent University, Department of Plant Biotechnology and Bioinformatics, Ghent, Belgium; Vlaams Instituut voor Biotechnologie, Center for Plant Systems Biology, Ghent, Belgium
| | - Bert De Rybel
- Ghent University, Department of Plant Biotechnology and Bioinformatics, Ghent, Belgium; Vlaams Instituut voor Biotechnologie, Center for Plant Systems Biology, Ghent, Belgium
| | - Klaas Vandepoele
- Ghent University, Department of Plant Biotechnology and Bioinformatics, Ghent, Belgium; Vlaams Instituut voor Biotechnologie, Center for Plant Systems Biology, Ghent, Belgium; Ghent University, Bioinformatics Institute Ghent, Ghent, Belgium.
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16
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Aasim M, Akin F, Ali SA, Taskin MB, Colak MS, Khawar KM. Artificial neural network modeling for deciphering the in vitro induced salt stress tolerance in chickpea ( Cicer arietinum L). PHYSIOLOGY AND MOLECULAR BIOLOGY OF PLANTS : AN INTERNATIONAL JOURNAL OF FUNCTIONAL PLANT BIOLOGY 2023; 29:289-304. [PMID: 36875725 PMCID: PMC9981858 DOI: 10.1007/s12298-023-01282-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/28/2022] [Revised: 01/06/2023] [Accepted: 01/17/2023] [Indexed: 06/18/2023]
Abstract
Salt stress is one of the most critical abiotic stresses having significant contribution in global agriculture production. Chickpea is sensitive to salt stress at various growth stages and a better knowledge of salt tolerance in chickpea would enable breeding of salt tolerant varieties. During present investigation, in vitro screening of desi chickpea by continuous exposure of seeds to NaCl-containing medium was performed. NaCl was applied in the MS medium at the rate of 6.25, 12.50, 25, 50, 75, 100, and 125 mM. Different germination indices and growth indices of roots and shoots were recorded. Mean germination (%) of roots and shoots ranged from 52.08 to 100%, and 41.67-100%, respectively. The mean germination time (MGT) of roots and shoots ranged from 2.40 to 4.78 d and 3.23-7.05 d. The coefficient of variation of the germination time (CVt) was recorded as 20.91-53.43% for roots, and 14.53-44.17% for shoots. The mean germination rate (MR) of roots was better than shoots. The uncertainty (U) values were tabulated as 0.43-1.59 (roots) and 0.92-2.33 (shoots). The synchronization index (Z) reflected the negative impact of elevated salinity levels on both root and shoot emergence. Application of NaCl exerted a negative impact on all growth indices compared to control and decreased gradually with elevated NaCl concentration. Results on salt tolerance index (STI) also revealed the reduced STI with elevated NaCl concentration and STI of roots was less than shoot. Elemental analysis revealed more Na and Cl accumulation with respective elevated NaCl concentrations. The In vitro growth parameters and STI values validated and predicted by multilayer perceptron (MLP) model revealed the relatively high R 2 values of all growth indices and STI. Findings of this study will be helpful to broaden the understanding about the salinity tolerance level of desi chickpea seeds under in vitro conditions using various germination indices and seedling growth indices. Supplementary Information The online version contains supplementary material available at 10.1007/s12298-023-01282-z.
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Affiliation(s)
- Muhammad Aasim
- Department of Plant Protection, Faculty of Agricultural Sciences and Technologies, Sivas University of Science and Technology, Sivas, Turkey
| | - Fatma Akin
- Department of Molecular Biology and Genetics, Faculty of Science, Necmettin Erbakan University, Konya, Turkey
| | - Seyid Amjad Ali
- Department of Information Systems and Technologies, Bilkent University, Ankara, Turkey
| | - Mehmet Burak Taskin
- Department of Soil Science and Plant Nutrition, Faculty of Agriculture, Ankara University, Ankara, Turkey
| | - Muslume Sevba Colak
- Department of Agricultural Engineering, Faculty of Agriculture, Ankara University, Ankara, Turkey
| | - Khalid Mahmood Khawar
- Department of Field Crops, Faculty of Agriculture, Ankara University, Ankara, Turkey
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17
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Das J, Kumar S, Mishra DC, Chaturvedi KK, Paul RK, Kairi A. Machine learning in the estimation of CRISPR-Cas9 cleavage sites for plant system. Front Genet 2023; 13:1085332. [PMID: 36699447 PMCID: PMC9868961 DOI: 10.3389/fgene.2022.1085332] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Accepted: 12/12/2022] [Indexed: 01/12/2023] Open
Abstract
CRISPR-Cas9 system is one of the recent most used genome editing techniques. Despite having a high capacity to alter the precise target genes and genomic regions that the planned guide RNA (or sgRNA) complements, the off-target effect still exists. But there are already machine learning algorithms for people, animals, and a few plant species. In this paper, an effort has been made to create models based on three machine learning-based techniques [namely, artificial neural networks (ANN), support vector machines (SVM), and random forests (RF)] for the prediction of the CRISPR-Cas9 cleavage sites that will be cleaved by a particular sgRNA. The plant dataset was the sole source of inspiration for all of these machine learning-based algorithms. 70% of the on-target and off-target dataset of various plant species that was gathered was used to train the models. The remaining 30% of the data set was used to evaluate the model's performance using a variety of evaluation metrics, including specificity, sensitivity, accuracy, precision, F1 score, F2 score, and AUC. Based on the aforementioned machine learning techniques, eleven models in all were developed. Comparative analysis of these produced models suggests that the model based on the random forest technique performs better. The accuracy of the Random Forest model is 96.27%, while the AUC value was found to be 99.21%. The SVM-Linear, SVM-Polynomial, SVM-Gaussian, and SVM-Sigmoid models were trained, making a total of six ANN-based models (ANN1-Logistic, ANN1-Tanh, ANN1-ReLU, ANN2-Logistic, ANN2-Tanh, and ANN-ReLU) and Support Vector Machine models (SVM-Linear, SVM-Polynomial, SVM-Gaussian However, the overall performance of Random Forest is better among all other ML techniques. ANN1-ReLU and SVM-Linear model performance were shown to be better among Artificial Neural Network and Support Vector Machine-based models, respectively.
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Affiliation(s)
- Jutan Das
- ICAR-Indian Agricultural Research Institute, New Delhi, India
| | - Sanjeev Kumar
- ICAR-Indian Agricultural Statistics Research Institute, New Delhi, India,*Correspondence: Sanjeev Kumar,
| | | | | | - Ranjit Kumar Paul
- ICAR-Indian Agricultural Statistics Research Institute, New Delhi, India
| | - Amit Kairi
- ICAR-Indian Agricultural Research Institute, New Delhi, India
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18
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Mohan B, Thingujam D, Pajerowska-Mukhtar KM. Cytotrap: An Innovative Approach for Protein-Protein Interaction Studies for Cytoplasmic Proteins. Methods Mol Biol 2023; 2690:9-22. [PMID: 37450133 DOI: 10.1007/978-1-0716-3327-4_2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/18/2023]
Abstract
Protein-protein interaction mapping has gained immense importance in understanding protein functions in diverse biological pathways. There are various in vivo and in vitro techniques associated with the protein-protein interaction studies but generally, the focus is confined to understanding the protein interaction in the nucleus of the cell, and thus it limits the availability to explore protein interactions that are happening in the cytoplasm of the cell. Since posttranslational modification is a crucial step in signaling pathways and cellular protein interactions harnessing the cytoplasmic protein and evaluating the interaction in the cytoplasm, this protocol will provide more information about studying these types of protein interactions. Cytotrap is a type of yeast-two-hybrid system that differs in its ability to anchor along the membrane, thus directing the protein of interest to anchor along the membrane through the myristoylation signaling unit. The vector containing the target protein contains the myristoylation unit, called the prey, and the bait unit contains the protein of interest as a fusion with the hSos protein. In an event of interaction between the target and the protein of interest, the hSos protein unit will be localized to the membrane and the GDP/GTP exchange unit will trigger the activation of the Ras pathway that leads to the survival of the temperature-sensitive yeast strain at a higher temperature.
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Affiliation(s)
- Binoop Mohan
- Department of Biology, University of Alabama, Birmingham, AL, USA
| | - Doni Thingujam
- Department of Biology, University of Alabama, Birmingham, AL, USA
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19
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Fakhar AZ, Liu J, Pajerowska-Mukhtar KM. Dynamic Enrichment for Evaluation of Protein Networks (DEEPN): A High Throughput Yeast Two-Hybrid (Y2H) Protocol to Evaluate Networks. Methods Mol Biol 2023; 2690:179-192. [PMID: 37450148 DOI: 10.1007/978-1-0716-3327-4_17] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/18/2023]
Abstract
Proteins are the building blocks of life, and a vast array of cellular processes is handled by protein-protein interactions (PPIs). The protein complexes formed via PPIs lead to tangled networks that, with their continuous remodeling, build up systematic functional units. Over the years, PPIs have become an area of interest for many researchers, leading to the development of multiple in vitro and in vivo methods to reveal these interactions. The yeast-two-hybrid (Y2H) system is a potent genetic way to map PPIs in both a micro- and high-throughput manner. Y2H is a technique that involves using modified yeast cells to identify protein-protein interactions. For Y2H, the yeast cells are engineered only to grow when there is a significant interaction between a specific protein with its interacting partner. PPIs are identified in the Y2H system by stimulating reporter genes in response to a restored transcription factor. However, Y2H results may be constrained by stringency requirements, as the limited number of colony screenings through this technique could result in the possible elimination of numerous genuine interactions. Therefore, DEEPN (dynamic enrichment for evaluation of protein networks) can be used, offering the potential to study the multiple static and transient protein interactions in a single Y2H experiment. DEEPN utilizes next-generation DNA sequencing (NGS) data in a high-throughput manner and subsequently applies computational analysis and statistical modeling to identify interacting partners. This protocol describes customized reagents and protocols through which DEEPN analysis can be utilized efficiently and cost-effectively.
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Affiliation(s)
| | - Jinbao Liu
- Department of Biology at University of Alabama, Birmingham, AL, USA
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20
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Abstract
As the protein-protein interaction (PPI) data increase exponentially, the development and usage of computational methods to analyze these datasets have become a new research horizon in systems biology. The PPI network analysis and visualization can help identify functional modules of the network, pathway genes involved in common cellular functions, and functional annotations of novel genes. Currently, a variety of tools are available for network graph visualization and analysis. Cytoscape, an open-source software tool, is one of them. It provides an interactive visualization interface along with other core features to import, navigate, filter, cluster, search, and export networks. It comes with hundreds of in-built Apps in App Manager to resolve research questions related to network visualization and integration. This chapter aims to illustrate the Cytoscape application to visualize and analyze the PPI network using Arabidopsis interactome-1 main (AI-1MAIN) PPI network dataset from Plant Interactome Database.
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Affiliation(s)
- Aqsa Majeed
- Department of Biology, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Shahid Mukhtar
- Department of Biology, University of Alabama at Birmingham, Birmingham, AL, USA.
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21
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Kumar N, Mukhtar S. Building Protein-Protein Interaction Graph Database Using Neo4j. Methods Mol Biol 2023; 2690:469-479. [PMID: 37450167 DOI: 10.1007/978-1-0716-3327-4_36] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/18/2023]
Abstract
A cell's various components interact with each other in a coordinated manner to respond to environmental cues and intracellular signals. Compared to the other biological networks, the protein-protein interaction (PPI) is mostly responsible for maintaining signaling pathways. Increasing numbers of experimentally verified and predicted PPIs in plants demand a scalable platform to deal with large and complex datasets. Network/graph data can be organized and analyzed using different tools. This chapter uses Neo4j, a graph database management system, to store and analyze plant PPI networks. To make the graph database and analyze network centrality, we used Arabidopsis interactome-1 main (AI-1MAIN) PPI network.
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Affiliation(s)
- Nilesh Kumar
- Department of Biology, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Shahid Mukhtar
- Department of Biology, University of Alabama at Birmingham, Birmingham, AL, USA.
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22
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Stevens K, Johnston IG, Luna E. Data science approaches provide a roadmap to understanding the role of abscisic acid in defence. QUANTITATIVE PLANT BIOLOGY 2023; 4:e2. [PMID: 37077700 PMCID: PMC10095806 DOI: 10.1017/qpb.2023.1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/25/2022] [Revised: 01/12/2023] [Accepted: 01/20/2023] [Indexed: 05/03/2023]
Abstract
Abscisic acid (ABA) is a plant hormone well known to regulate abiotic stress responses. ABA is also recognised for its role in biotic defence, but there is currently a lack of consensus on whether it plays a positive or negative role. Here, we used supervised machine learning to analyse experimental observations on the defensive role of ABA to identify the most influential factors determining disease phenotypes. ABA concentration, plant age and pathogen lifestyle were identified as important modulators of defence behaviour in our computational predictions. We explored these predictions with new experiments in tomato, demonstrating that phenotypes after ABA treatment were indeed highly dependent on plant age and pathogen lifestyle. Integration of these new results into the statistical analysis refined the quantitative model of ABA influence, suggesting a framework for proposing and exploiting further research to make more progress on this complex question. Our approach provides a unifying road map to guide future studies involving the role of ABA in defence.
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Affiliation(s)
- Katie Stevens
- School of Biosciences, University of Birmingham, Birmingham, United Kingdom
- Authors for correspondence: K. Stevens, E. Luna, E-mail: ;
| | - Iain G. Johnston
- Department of Mathematics, University of Bergen, Bergen, Norway
- Computational Biology Unit, University of Bergen, Bergen, Norway
| | - Estrella Luna
- School of Biosciences, University of Birmingham, Birmingham, United Kingdom
- Authors for correspondence: K. Stevens, E. Luna, E-mail: ;
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23
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Hasan M, Kumar N, Majeed A, Ahmad A, Mukhtar S. Protein-Protein Interaction Network Analysis Using NetworkX. Methods Mol Biol 2023; 2690:457-467. [PMID: 37450166 DOI: 10.1007/978-1-0716-3327-4_35] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/18/2023]
Abstract
In recent years, extracting information from biological data has become a particularly valuable way of gaining knowledge. Molecular interaction networks provide a framework for visualizing cellular processes, but their complexity frequently makes their interpretation difficult. Proteins are one of the primary determinants of biological function. Indeed, most biological activities in the living cells are functionally regulated by protein-protein interactions (PPIs). Thus, studying protein interactions is critical for understanding their roles within the cell. Exploring the PPI networks can open new avenues for future experimental studies and offer interspecies predictions for effective interaction mapping. In this chapter we will demonstrate how to construct, visualize, and analyze a protein-protein interaction network using NetworkX.
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Affiliation(s)
- Mehadi Hasan
- Department of Biology, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Nilesh Kumar
- Department of Biology, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Aqsa Majeed
- Department of Biology, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Aftab Ahmad
- Department of Anesthesiology and Perioperative Medicine, Birmingham, AL, USA
| | - Shahid Mukhtar
- Department of Biology, University of Alabama at Birmingham, Birmingham, AL, USA.
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24
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Diwan D, Pajerowska-Mukhtar KM. Preparation and Utilization of a Versatile GFP-Protein Trap-Like System for Protein Complex Immunoprecipitation in Plants. Methods Mol Biol 2023; 2690:59-68. [PMID: 37450136 DOI: 10.1007/978-1-0716-3327-4_5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 07/18/2023]
Abstract
Protein complex immunoprecipitation (co-IP) is an in vitro technique used to study protein-protein interaction between two or more proteins. This method relies on affinity purification of recombinant epitope-tagged proteins followed by western blotting detection using tag-specific antibodies for the confirmation of positive interaction. The traditional co-IP method relies on the use of porous beaded support with immobilized antibodies to precipitate protein complexes. However, this method is time-consuming, labor-intensive, and provides lower reproducibility and yield of protein complexes. Here, we describe the implementation of magnetic beads and high-affinity anti-green fluorescent protein (GFP) antibodies to develop an in vitro GFP-protein trap-like system. This highly reproducible system utilizes a combination of small sample size, versatile lysis buffer, and lower amounts of magnetic beads to obtain protein complexes and aggregates that are compatible with functional assays, Western blotting, and mass spectrometry. In addition to protein-protein interactions, this versatile method can be employed to study protein-nucleic acid interactions. This protocol also highlights troubleshooting and includes recommendations to optimize its application.
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Affiliation(s)
- Danish Diwan
- Department of Biology, University of Alabama, Birmingham, AL, USA
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25
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Wang W, Liu J, Mishra B, Mukhtar MS, McDowell JM. Sparking a sulfur war between plants and pathogens. TRENDS IN PLANT SCIENCE 2022; 27:1253-1265. [PMID: 36028431 DOI: 10.1016/j.tplants.2022.07.007] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/07/2021] [Revised: 07/03/2022] [Accepted: 07/21/2022] [Indexed: 06/15/2023]
Abstract
The biochemical versatility of sulfur (S) lends itself to myriad roles in plant-pathogen interactions. This review evaluates the current understanding of mechanisms by which pathogens acquire S from their plant hosts and highlights new evidence that plants can limit S availability during the immune responses. We discuss the discovery of host disease-susceptibility genes related to S that can be genetically manipulated to create new crop resistance. Finally, we summarize future research challenges and propose a research agenda that leverages systems biology approaches for a holistic understanding of this important element's diverse roles in plant disease resistance and susceptibility.
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Affiliation(s)
- Wei Wang
- School of Plant and Environmental Sciences, Virginia Tech, Blacksburg, VA 24061, USA
| | - Jinbao Liu
- Department of Biology, University of Alabama-Birmingham, Birmingham, AL 35294, USA
| | - Bharat Mishra
- Department of Biology, University of Alabama-Birmingham, Birmingham, AL 35294, USA
| | - M Shahid Mukhtar
- Department of Biology, University of Alabama-Birmingham, Birmingham, AL 35294, USA
| | - John M McDowell
- School of Plant and Environmental Sciences, Virginia Tech, Blacksburg, VA 24061, USA.
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26
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Brunetto G, Stefanello LO, Kulmann MSDS, Tassinari A, de Souza ROS, Rozane DE, Tiecher TL, Ceretta CA, Ferreira PAA, de Siqueira GN, Parent LÉ. Prediction of Nitrogen Dosage in ‘Alicante Bouschet’ Vineyards with Machine Learning Models. PLANTS 2022; 11:plants11182419. [PMID: 36145819 PMCID: PMC9501305 DOI: 10.3390/plants11182419] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/02/2022] [Revised: 09/09/2022] [Accepted: 09/11/2022] [Indexed: 12/02/2022]
Abstract
Vineyard soils normally do not provide the amount of nitrogen (N) necessary for red wine production. Traditionally, the N concentration in leaves guides the N fertilization of vineyards to reach high grape yields and chemical composition under the ceteris paribus assumption. Moreover, the carryover effects of nutrients and carbohydrates stored by perennials such as grapevines are neglected. Where a well-documented database is assembled, machine learning (ML) methods can account for key site-specific features and carryover effects, impacting the performance of grapevines. The aim of this study was to predict, using ML tools, N management from local features to reach high berry yield and quality in ‘Alicante Bouschet’ vineyards. The 5-year (2015–2019) fertilizer trial comprised six N doses (0–20–40–60–80–100 kg N ha−1) and three regimes of irrigation. Model features included N dosage, climatic indices, foliar N application, and stem diameter of the preceding season, all of which were indices of the carryover effects. Accuracy of ML models was the highest with a yield cutoff of 14 t ha−1 and a total anthocyanin content (TAC) of 3900 mg L−1. Regression models were more accurate for total soluble solids (TSS), total titratable acidity (TTA), pH, TAC, and total phenolic content (TPC) in the marketable grape yield. The tissue N ranges differed between high marketable yield and TAC, indicating a trade-off about 24 g N kg−1 in the diagnostic leaf. The N dosage predicted varied from 0 to 40 kg N ha−1 depending on target variable, this was calculated from local features and carryover effects but excluded climatic indices. The dataset can increase in size and diversity with the collaboration of growers, which can help to cross over the numerous combinations of features found in vineyards. This research contributes to the rational use of N fertilizers, but with the guarantee that obtaining high productivity must be with adequate composition.
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Affiliation(s)
- Gustavo Brunetto
- Soil Science Department, Federal University of Santa Maria, Santa Maria 97105-900, Brazil
- Correspondence: ; Tel.: +55-32208108
| | | | | | - Adriele Tassinari
- Soil Science Department, Federal University of Santa Maria, Santa Maria 97105-900, Brazil
| | | | - Danilo Eduardo Rozane
- Fruticulture Department, State University of Paulista “Julio Mesquita Filho”, Registro 11900-000, Brazil
| | - Tadeu Luis Tiecher
- Rio Grande do Sul Federal Institute, Campus Restinga, Porto Alegre 91791-508, Brazil
| | - Carlos Alberto Ceretta
- Soil Science Department, Federal University of Santa Maria, Santa Maria 97105-900, Brazil
| | | | | | - Léon Étienne Parent
- Soil Science Department, Federal University of Santa Maria, Santa Maria 97105-900, Brazil
- Department of Soil and Agri-Food Engineering, Laval University, Québec City, QC G1V 0A6, Canada
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Kumar N, Mishra B, Mukhtar MS. A pipeline of integrating transcriptome and interactome to elucidate central nodes in host-pathogens interactions. STAR Protoc 2022; 3:101608. [PMID: 35990739 PMCID: PMC9386103 DOI: 10.1016/j.xpro.2022.101608] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022] Open
Abstract
Investigating the complexity of host-pathogen interactions is challenging. Here, we outline a pipeline to identify important proteins and signaling molecules in human-viral interactomes. Firstly, we curate a comprehensive human interactome. Subsequently, we infer viral targets and transcriptome-specific human interactomes (VTTSHI) for papillomavirus and herpes viruses by integrating viral targets and transcriptome data. Finally, we reveal the common and shared nodes and pathways in viral pathogenesis following network topology and pathway enrichment analyses. For complete details on the use and execution of this protocol, please refer to Kumar et al. (2020). Protocol for integrative analysis of transcriptome and proteome network data Network subgroup enrichment of two host-pathogen interaction networks Preprocessing of data and heterogeneous network integration
Publisher’s note: Undertaking any experimental protocol requires adherence to local institutional guidelines for laboratory safety and ethics.
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Affiliation(s)
- Nilesh Kumar
- Department of Biology, University of Alabama at Birmingham, Birmingham, AL 35294, USA.
| | - Bharat Mishra
- Department of Biology, University of Alabama at Birmingham, Birmingham, AL 35294, USA
| | - M Shahid Mukhtar
- Department of Biology, University of Alabama at Birmingham, Birmingham, AL 35294, USA.
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28
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Latif G, Abdelhamid SE, Mallouhy RE, Alghazo J, Kazimi ZA. Deep Learning Utilization in Agriculture: Detection of Rice Plant Diseases Using an Improved CNN Model. PLANTS 2022; 11:plants11172230. [PMID: 36079612 PMCID: PMC9460897 DOI: 10.3390/plants11172230] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/06/2022] [Revised: 08/19/2022] [Accepted: 08/25/2022] [Indexed: 11/16/2022]
Abstract
Rice is considered one the most important plants globally because it is a source of food for over half the world’s population. Like other plants, rice is susceptible to diseases that may affect the quantity and quality of produce. It sometimes results in anywhere between 20–40% crop loss production. Early detection of these diseases can positively affect the harvest, and thus farmers would have to be knowledgeable about the various disease and how to identify them visually. Even then, it is an impossible task for farmers to survey the vast farmlands on a daily basis. Even if this is possible, it becomes a costly task that will, in turn, increases the price of rice for consumers. Machine learning algorithms fitted to drone technology combined with the Internet of Things (IoT) can offer a solution to this problem. In this paper, we propose a Deep Convolutional Neural Network (DCNN) transfer learning-based approach for the accurate detection and classification of rice leaf disease. The modified proposed approach includes a modified VGG19-based transfer learning method. The proposed modified system can accurately detect and diagnose six distinct classes: healthy, narrow brown spot, leaf scald, leaf blast, brown spot, and bacterial leaf blight. The highest average accuracy is 96.08% using the non-normalized augmented dataset. The corresponding precision, recall, specificity, and F1-score were 0.9620, 0.9617, 0.9921, and 0.9616, respectively. The proposed modified approach achieved significantly better results compared with similar approaches using the same dataset or similar-size datasets reported in the extant literature.
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Affiliation(s)
- Ghazanfar Latif
- Department of Computer Science, Prince Mohammad Bin Fahd University, Khobar 31952, Saudi Arabia
- Department of Computer Sciences and Mathematics, Université du Québec à Chicoutimi, 555 Boulevard de l’Université, Québec, QC G7H 2B1, Canada
- Correspondence:
| | - Sherif E. Abdelhamid
- Department of Computer and Information Sciences, Virginia Military Institute, Lexington, VA 24450, USA
| | - Roxane Elias Mallouhy
- Department of Computer Science, Prince Mohammad Bin Fahd University, Khobar 31952, Saudi Arabia
| | - Jaafar Alghazo
- Department of Computer Engineering, Virginia Military Institute, Lexington, VA 24450, USA
| | - Zafar Abbas Kazimi
- Department of Computer Science, Prince Mohammad Bin Fahd University, Khobar 31952, Saudi Arabia
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29
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Hussain A, Liu J, Mohan B, Burhan A, Nasim Z, Bano R, Ameen A, Zaynab M, Mukhtar MS, Pajerowska-Mukhtar KM. A genome-wide comparative evolutionary analysis of zinc finger-BED transcription factor genes in land plants. Sci Rep 2022; 12:12328. [PMID: 35853967 PMCID: PMC9296551 DOI: 10.1038/s41598-022-16602-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2022] [Accepted: 07/12/2022] [Indexed: 11/09/2022] Open
Abstract
Zinc finger (Zf)-BED proteins are a novel superfamily of transcription factors that controls numerous activities in plants including growth, development, and cellular responses to biotic and abiotic stresses. Despite their important roles in gene regulation, little is known about the specific functions of Zf-BEDs in land plants. The current study identified a total of 750 Zf-BED-encoding genes in 35 land plant species including mosses, bryophytes, lycophytes, gymnosperms, and angiosperms. The gene family size was somewhat proportional to genome size. All identified genes were categorized into 22 classes based on their specific domain architectures. Of these, class I (Zf-BED_DUF-domain_Dimer_Tnp_hAT) was the most common in the majority of the land plants. However, some classes were family-specific, while the others were species-specific, demonstrating diversity at different classification levels. In addition, several novel functional domains were also predicated including WRKY and nucleotide-binding site (NBS). Comparative genomics, transcriptomics, and proteomics provided insights into the evolutionary history, duplication, divergence, gene gain and loss, species relationship, expression profiling, and structural diversity of Zf-BEDs in land plants. The comprehensive study of Zf-BEDs in Gossypium sp., (cotton) also demonstrated a clear footprint of polyploidization. Overall, this comprehensive evolutionary study of Zf-BEDs in land plants highlighted significant diversity among plant species.
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Affiliation(s)
- Athar Hussain
- Genomics Lab, School of Food and Agricultural Sciences (SFAS), University of Management and Technology (UMT), Lahore, 54770, Pakistan
| | - Jinbao Liu
- Department of Biology, University of Alabama at Birmingham, 1300 University Blvd, Birmingham, AL, 35294, USA
| | - Binoop Mohan
- Department of Biology, University of Alabama at Birmingham, 1300 University Blvd, Birmingham, AL, 35294, USA
| | - Akif Burhan
- Department of Life Science, University of Management and Technology (UMT), Lahore, 54770, Pakistan
| | - Zunaira Nasim
- Department of Life Science, University of Management and Technology (UMT), Lahore, 54770, Pakistan
| | - Raveena Bano
- Department of Life Science, University of Management and Technology (UMT), Lahore, 54770, Pakistan
| | - Ayesha Ameen
- Office of Research Innovation and Commercialization, University of Management and Technology, Lahore, 54770, Pakistan
| | - Madiha Zaynab
- Shenzhen Key Laboratory of Marine Bioresource and Eco-Environmental Sciences, College of Life Sciences and Oceanography, Shenzhen University, Shenzhen, 51807, Guangdong, China
| | - M Shahid Mukhtar
- Department of Biology, University of Alabama at Birmingham, 1300 University Blvd, Birmingham, AL, 35294, USA.
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Mishra B, Kumar N, Shahid Mukhtar M. A Rice Protein Interaction Network Reveals High Centrality Nodes and Candidate Pathogen Effector Targets. Comput Struct Biotechnol J 2022; 20:2001-2012. [PMID: 35521542 PMCID: PMC9062363 DOI: 10.1016/j.csbj.2022.04.027] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2021] [Revised: 04/10/2022] [Accepted: 04/17/2022] [Indexed: 12/11/2022] Open
Abstract
Network science identifies key players in diverse biological systems including host-pathogen interactions. We demonstrated a scale-free network property for a comprehensive rice protein–protein interactome (RicePPInets) that exhibits nodes with increased centrality indices. While weighted k-shell decomposition was shown efficacious to predict pathogen effector targets in Arabidopsis, we improved its computational code for a broader implementation on large-scale networks including RicePPInets. We determined that nodes residing within the internal layers of RicePPInets are poised to be the most influential, central, and effective information spreaders. To identify central players and modules through network topology analyses, we integrated RicePPInets and co-expression networks representing susceptible and resistant responses to strains of the bacterial pathogens Xanthomonas oryzae pv. oryzae and X. oryzae pv. oryzicola (Xoc) and generated a RIce-Xanthomonas INteractome (RIXIN). This revealed that previously identified candidate targets of pathogen transcription activator-like (TAL) effectors are enriched in nodes with enhanced connectivity, bottlenecks, and information spreaders that are located in the inner layers of the network, and these nodes are involved in several important biological processes. Overall, our integrative multi-omics network-based platform provides a potentially useful approach to prioritizing candidate pathogen effector targets for functional validation, suggesting that this computational framework can be broadly translatable to other complex pathosystems.
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31
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Delplace F, Huard-Chauveau C, Berthomé R, Roby D. Network organization of the plant immune system: from pathogen perception to robust defense induction. THE PLANT JOURNAL : FOR CELL AND MOLECULAR BIOLOGY 2022; 109:447-470. [PMID: 34399442 DOI: 10.1111/tpj.15462] [Citation(s) in RCA: 25] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/16/2021] [Revised: 07/29/2021] [Accepted: 08/10/2021] [Indexed: 06/13/2023]
Abstract
The plant immune system has been explored essentially through the study of qualitative resistance, a simple form of immunity, and from a reductionist point of view. The recent identification of genes conferring quantitative disease resistance revealed a large array of functions, suggesting more complex mechanisms. In addition, thanks to the advent of high-throughput analyses and system approaches, our view of the immune system has become more integrative, revealing that plant immunity should rather be seen as a distributed and highly connected molecular network including diverse functions to optimize expression of plant defenses to pathogens. Here, we review the recent progress made to understand the network complexity of regulatory pathways leading to plant immunity, from pathogen perception, through signaling pathways and finally to immune responses. We also analyze the topological organization of these networks and their emergent properties, crucial to predict novel immune functions and test them experimentally. Finally, we report how these networks might be regulated by environmental clues. Although system approaches remain extremely scarce in this area of research, a growing body of evidence indicates that the plant response to combined biotic and abiotic stresses cannot be inferred from responses to individual stresses. A view of possible research avenues in this nascent biology domain is finally proposed.
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Affiliation(s)
- Florent Delplace
- Laboratoire des Interactions Plantes-Microbes-Environnement, Institut National de Recherche pour l'Agriculture, l'Alimentation et l'Environnement, INRAE, CNRS, Université de Toulouse, Castanet-Tolosan, 31326, France
| | - Carine Huard-Chauveau
- Laboratoire des Interactions Plantes-Microbes-Environnement, Institut National de Recherche pour l'Agriculture, l'Alimentation et l'Environnement, INRAE, CNRS, Université de Toulouse, Castanet-Tolosan, 31326, France
| | - Richard Berthomé
- Laboratoire des Interactions Plantes-Microbes-Environnement, Institut National de Recherche pour l'Agriculture, l'Alimentation et l'Environnement, INRAE, CNRS, Université de Toulouse, Castanet-Tolosan, 31326, France
| | - Dominique Roby
- Laboratoire des Interactions Plantes-Microbes-Environnement, Institut National de Recherche pour l'Agriculture, l'Alimentation et l'Environnement, INRAE, CNRS, Université de Toulouse, Castanet-Tolosan, 31326, France
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32
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Soil Salinity, a Serious Environmental Issue and Plant Responses: A Metabolomics Perspective. Metabolites 2021; 11:metabo11110724. [PMID: 34822381 PMCID: PMC8620211 DOI: 10.3390/metabo11110724] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2021] [Revised: 10/16/2021] [Accepted: 10/18/2021] [Indexed: 12/12/2022] Open
Abstract
The effects of global warming have increasingly led to devastating environmental stresses, such as heat, salinity, and drought. Soil salinization is a serious environmental issue and results in detrimental abiotic stress, affecting 7% of land area and 33% of irrigated lands worldwide. The proportion of arable land facing salinity is expected to rise due to increasing climate change fuelled by anthropogenic activities, exacerbating the threat to global food security for the exponentially growing populace. As sessile organisms, plants have evolutionarily developed mechanisms that allow ad hoc responses to salinity stress. The orchestrated mechanisms include signalling cascades involving phytohormones, kinases, reactive oxygen species (ROS), and calcium regulatory networks. As a pillar in a systems biology approach, metabolomics allows for comprehensive interrogation of the biochemistry and a deconvolution of molecular mechanisms involved in plant responses to salinity. Thus, this review highlights soil salinization as a serious environmental issue and points to the negative impacts of salinity on plants. Furthermore, the review summarises mechanisms regulating salinity tolerance on molecular, cellular, and biochemical levels with a focus on metabolomics perspectives. This critical synthesis of current literature is an opportunity to revisit the current models regarding plant responses to salinity, with an invitation to further fundamental research for novel and actionable insights.
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33
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Elmore JM, Griffin BD, Walley JW. Advances in functional proteomics to study plant-pathogen interactions. CURRENT OPINION IN PLANT BIOLOGY 2021; 63:102061. [PMID: 34102449 DOI: 10.1016/j.pbi.2021.102061] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/04/2021] [Revised: 04/22/2021] [Accepted: 04/25/2021] [Indexed: 05/20/2023]
Abstract
Pathogen infection triggers complex signaling networks in plant cells that ultimately result in either susceptibility or resistance. We have made substantial progress in dissecting many of these signaling events, and it is becoming clear that changes in proteome composition and protein activity are major drivers of plant-microbe interactions. Here, we highlight different approaches to analyze the functional proteomes of hosts and pathogens and discuss how they have been used to further our understanding of plant disease. Global proteome profiling can quantify the dynamics of proteins, posttranslational modifications, and biological pathways that contribute to immune-related outcomes. In addition, emerging techniques such as enzyme activity-based profiling, proximity labeling, and kinase-substrate profiling are being used to dissect biochemical events that operate during infection. Finally, we discuss how these functional approaches can be integrated with other profiling data to gain a mechanistic, systems-level view of plant and pathogen signaling.
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Affiliation(s)
- James M Elmore
- Department of Plant Pathology and Microbiology, Iowa State University, Ames, IA, 50014, USA.
| | - Brianna D Griffin
- Department of Plant Pathology and Microbiology, Iowa State University, Ames, IA, 50014, USA
| | - Justin W Walley
- Department of Plant Pathology and Microbiology, Iowa State University, Ames, IA, 50014, USA.
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34
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Mishra B, Kumar N, Mukhtar MS. Network biology to uncover functional and structural properties of the plant immune system. CURRENT OPINION IN PLANT BIOLOGY 2021; 62:102057. [PMID: 34102601 DOI: 10.1016/j.pbi.2021.102057] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/06/2020] [Revised: 04/15/2021] [Accepted: 04/18/2021] [Indexed: 06/12/2023]
Abstract
In the last two decades, advances in network science have facilitated the discovery of important systems' entities in diverse biological networks. This graph-based technique has revealed numerous emergent properties of a system that enable us to understand several complex biological processes including plant immune systems. With the accumulation of multiomics data sets, the comprehensive understanding of plant-pathogen interactions can be achieved through the analyses and efficacious integration of multidimensional qualitative and quantitative relationships among the components of hosts and their microbes. This review highlights comparative network topology analyses in plant-pathogen co-expression networks and interactomes, outlines dynamic network modeling for cell-specific immune regulatory networks, and discusses the new frontiers of single-cell sequencing as well as multiomics data integration that are necessary for unraveling the intricacies of plant immune systems.
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Affiliation(s)
- Bharat Mishra
- Department of Biology, University of Alabama at Birmingham, 1300 University Blvd., Birmingham, AL, 35294, USA
| | - Nilesh Kumar
- Department of Biology, University of Alabama at Birmingham, 1300 University Blvd., Birmingham, AL, 35294, USA
| | - M Shahid Mukhtar
- Department of Biology, University of Alabama at Birmingham, 1300 University Blvd., Birmingham, AL, 35294, USA.
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35
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Pazhamala LT, Kudapa H, Weckwerth W, Millar AH, Varshney RK. Systems biology for crop improvement. THE PLANT GENOME 2021; 14:e20098. [PMID: 33949787 DOI: 10.1002/tpg2.20098] [Citation(s) in RCA: 34] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/05/2020] [Accepted: 03/09/2021] [Indexed: 05/19/2023]
Abstract
In recent years, generation of large-scale data from genome, transcriptome, proteome, metabolome, epigenome, and others, has become routine in several plant species. Most of these datasets in different crop species, however, were studied independently and as a result, full insight could not be gained on the molecular basis of complex traits and biological networks. A systems biology approach involving integration of multiple omics data, modeling, and prediction of the cellular functions is required to understand the flow of biological information that underlies complex traits. In this context, systems biology with multiomics data integration is crucial and allows a holistic understanding of the dynamic system with the different levels of biological organization interacting with external environment for a phenotypic expression. Here, we present recent progress made in the area of various omics studies-integrative and systems biology approaches with a special focus on application to crop improvement. We have also discussed the challenges and opportunities in multiomics data integration, modeling, and understanding of the biology of complex traits underpinning yield and stress tolerance in major cereals and legumes.
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Affiliation(s)
- Lekha T Pazhamala
- Center of Excellence in Genomics & Systems Biology, International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Patancheru, Hyderabad, 502 324, India
| | - Himabindu Kudapa
- Center of Excellence in Genomics & Systems Biology, International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Patancheru, Hyderabad, 502 324, India
| | - Wolfram Weckwerth
- Department of Ecogenomics and Systems Biology, University of Vienna, Vienna, Austria
- Vienna Metabolomics Center, University of Vienna, Vienna, Austria
| | - A Harvey Millar
- ARC Centre of Excellence in Plant Energy Biology and School of Molecular Sciences, The University of Western Australia, Perth, WA, Australia
| | - Rajeev K Varshney
- Center of Excellence in Genomics & Systems Biology, International Crops Research Institute for the Semi-Arid Tropics (ICRISAT), Patancheru, Hyderabad, 502 324, India
- State Agricultural Biotechnology Centre, Crop Research Innovation Centre, Food Futures Institute, Murdoch University, Murdoch, WA, Australia
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36
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Huang C. From Player to Pawn: Viral Avirulence Factors Involved in Plant Immunity. Viruses 2021; 13:v13040688. [PMID: 33923435 PMCID: PMC8073968 DOI: 10.3390/v13040688] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2021] [Revised: 04/09/2021] [Accepted: 04/14/2021] [Indexed: 02/07/2023] Open
Abstract
In the plant immune system, according to the 'gene-for-gene' model, a resistance (R) gene product in the plant specifically surveils a corresponding effector protein functioning as an avirulence (Avr) gene product. This system differs from other plant-pathogen interaction systems, in which plant R genes recognize a single type of gene or gene family because almost all virus genes with distinct structures and functions can also interact with R genes as Avr determinants. Thus, research conducted on viral Avr-R systems can provide a novel understanding of Avr and R gene product interactions and identify mechanisms that enable rapid co-evolution of plants and phytopathogens. In this review, we intend to provide a brief overview of virus-encoded proteins and their roles in triggering plant resistance, and we also summarize current progress in understanding plant resistance against virus Avr genes. Moreover, we present applications of Avr gene-mediated phenotyping in R gene identification and screening of segregating populations during breeding processes.
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Affiliation(s)
- Changjun Huang
- Key Laboratory of Tobacco Biotechnological Breeding, National Tobacco Genetic Engineering Research Center, Yunnan Academy of Tobacco Agricultural Sciences, Kunming 650021, China
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37
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Mishra B, Athar M, Mukhtar MS. Transcriptional circuitry atlas of genetic diverse unstimulated murine and human macrophages define disparity in population-wide innate immunity. Sci Rep 2021; 11:7373. [PMID: 33795737 PMCID: PMC8016976 DOI: 10.1038/s41598-021-86742-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2020] [Accepted: 03/12/2021] [Indexed: 02/07/2023] Open
Abstract
Macrophages are ubiquitous custodians of tissues, which play decisive role in maintaining cellular homeostasis through regulatory immune responses. Within tissues, macrophage exhibit extremely heterogeneous population with varying functions orchestrated through regulatory response, which can be further exacerbated in diverse genetic backgrounds. Gene regulatory networks (GRNs) offer comprehensive understanding of cellular regulatory behavior by unfolding the transcription factors (TFs) and regulated target genes. RNA-Seq coupled with ATAC-Seq has revolutionized the regulome landscape influenced by gene expression modeling. Here, we employ an integrative multi-omics systems biology-based analysis and generated GRNs derived from the unstimulated bone marrow-derived macrophages of five inbred genetically defined murine strains, which are reported to be linked with most of the population-wide human genetic variants. Our probabilistic modeling of a basal hemostasis pan regulatory repertoire in diverse macrophages discovered 96 TFs targeting 6279 genes representing 468,291 interactions across five inbred murine strains. Subsequently, we identify core and distinctive GRN sub-networks in unstimulated macrophages to describe the system-wide conservation and dissimilarities, respectively across five murine strains. Our study concludes that discrepancies in unstimulated macrophage-specific regulatory networks not only drives the basal functional plasticity within genetic backgrounds, additionally aid in understanding the complexity of racial disparity among the human population during stress.
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Affiliation(s)
- Bharat Mishra
- Department of Biology, University of Alabama At Birmingham, 464 Campbell Hall, 1300 University Boulevard, Alabama, 35294, USA
| | - Mohammad Athar
- UAB Research Center of Excellence in Arsenicals, Department of Dermatology, School of Medicine, University of Alabama At Birmingham, Alabama, 35294, USA.
| | - M Shahid Mukhtar
- Department of Biology, University of Alabama At Birmingham, 464 Campbell Hall, 1300 University Boulevard, Alabama, 35294, USA. .,Nutrition Obesity Research Center, University of Alabama At Birmingham, 1675 University Blvd, Birmingham, AL, 35294, USA. .,Department of Surgery, University of Alabama At Birmingham, 1808 7th Ave S, Birmingham, AL, 35294, USA.
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38
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Wang Y, Zhou M, Zou Q, Xu L. Machine learning for phytopathology: from the molecular scale towards the network scale. Brief Bioinform 2021; 22:6204793. [PMID: 33787847 DOI: 10.1093/bib/bbab037] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2020] [Revised: 01/09/2021] [Accepted: 01/26/2021] [Indexed: 01/16/2023] Open
Abstract
With the increasing volume of high-throughput sequencing data from a variety of omics techniques in the field of plant-pathogen interactions, sorting, retrieving, processing and visualizing biological information have become a great challenge. Within the explosion of data, machine learning offers powerful tools to process these complex omics data by various algorithms, such as Bayesian reasoning, support vector machine and random forest. Here, we introduce the basic frameworks of machine learning in dissecting plant-pathogen interactions and discuss the applications and advances of machine learning in plant-pathogen interactions from molecular to network biology, including the prediction of pathogen effectors, plant disease resistance protein monitoring and the discovery of protein-protein networks. The aim of this review is to provide a summary of advances in plant defense and pathogen infection and to indicate the important developments of machine learning in phytopathology.
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Affiliation(s)
- Yansu Wang
- Postdoctoral Innovation Practice Base, Shenzhen Polytechnic, China
| | | | - Quan Zou
- University of Electronic Science and Technology of China
| | - Lei Xu
- Shenzhen Polytechnic, China
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39
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Aghdam SA, Brown AMV. Deep learning approaches for natural product discovery from plant endophytic microbiomes. ENVIRONMENTAL MICROBIOME 2021; 16:6. [PMID: 33758794 PMCID: PMC7972023 DOI: 10.1186/s40793-021-00375-0] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/27/2020] [Accepted: 02/21/2021] [Indexed: 05/10/2023]
Abstract
Plant microbiomes are not only diverse, but also appear to host a vast pool of secondary metabolites holding great promise for bioactive natural products and drug discovery. Yet, most microbes within plants appear to be uncultivable, and for those that can be cultivated, their metabolic potential lies largely hidden through regulatory silencing of biosynthetic genes. The recent explosion of powerful interdisciplinary approaches, including multi-omics methods to address multi-trophic interactions and artificial intelligence-based computational approaches to infer distribution of function, together present a paradigm shift in high-throughput approaches to natural product discovery from plant-associated microbes. Arguably, the key to characterizing and harnessing this biochemical capacity depends on a novel, systematic approach to characterize the triggers that turn on secondary metabolite biosynthesis through molecular or genetic signals from the host plant, members of the rich 'in planta' community, or from the environment. This review explores breakthrough approaches for natural product discovery from plant microbiomes, emphasizing the promise of deep learning as a tool for endophyte bioprospecting, endophyte biochemical novelty prediction, and endophyte regulatory control. It concludes with a proposed pipeline to harness global databases (genomic, metabolomic, regulomic, and chemical) to uncover and unsilence desirable natural products. SUPPLEMENTARY INFORMATION The online version contains supplementary material available at 10.1186/s40793-021-00375-0.
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Affiliation(s)
- Shiva Abdollahi Aghdam
- Department of Biological Sciences, Texas Tech University, 2901 Main St, Lubbock, TX 79409 USA
| | - Amanda May Vivian Brown
- Department of Biological Sciences, Texas Tech University, 2901 Main St, Lubbock, TX 79409 USA
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Kumar N, Mishra B, Athar M, Mukhtar S. Inference of Gene Regulatory Network from Single-Cell Transcriptomic Data Using pySCENIC. Methods Mol Biol 2021; 2328:171-182. [PMID: 34251625 DOI: 10.1007/978-1-0716-1534-8_10] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/06/2023]
Abstract
With the advent of recent next-generation sequencing (NGS) technologies in genomics, transcriptomics, and epigenomics, profiling single-cell sequencing became possible. The single-cell RNA sequencing (scRNA-seq) is widely used to characterize diverse cell populations and ascertain cell type-specific regulatory mechanisms. The gene regulatory network (GRN) mainly consists of genes and their regulators-transcription factors (TF). Here, we describe the lightning-fast Python implementation of the SCENIC (Single-Cell reEgulatory Network Inference and Clustering) pipeline called pySCENIC. Using single-cell RNA-seq data, it maps TFs onto gene regulatory networks and integrates various cell types to infer cell-specific GRNs. There are two fast and efficient GRN inference algorithms, GRNBoost2 and GENIE3, optionally available with pySCENIC. The pipeline has three steps: (1) identification of potential TF targets based on co-expression; (2) TF-motif enrichment analysis to identify the direct targets (regulons); and (3) scoring the activity of regulons (or other gene sets) on single cell types.
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Affiliation(s)
- Nilesh Kumar
- Department of Biology, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Bharat Mishra
- Department of Biology, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Mohammad Athar
- Department of Dermatology, School of Medicine, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Shahid Mukhtar
- Department of Biology, University of Alabama at Birmingham, Birmingham, AL, USA.
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Mishra B, Kumar N, Liu J, Pajerowska-Mukhtar KM. Dynamic Regulatory Event Mining by iDREM in Large-Scale Multi-omics Datasets During Biotic and Abiotic Stress in Plants. Methods Mol Biol 2021; 2328:191-202. [PMID: 34251627 DOI: 10.1007/978-1-0716-1534-8_12] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/19/2023]
Abstract
The system-wide complexity of genome regulation encoding the organism phenotypic diversity is well understood. However, a major challenge persists about the appropriate method to describe the systematic dynamic genome regulation event utilizing enormous multi-omics datasets. Here, we describe Interactive Dynamic Regulatory Events Miner (iDREM) which reconstructs gene-regulatory networks from temporal transcriptome, proteome, and epigenome datasets during stress to envisage "master" regulators by simulating cascades of temporal transcription-regulatory and interactome events. The iDREM is a Java-based software that integrates static and time-series transcriptomics and proteomics datasets, transcription factor (TF)-target interactions, microRNA (miRNA)-target interaction, and protein-protein interactions to reconstruct temporal regulatory network and identify significant regulators in an unsupervised manner. The hidden Markov model detects specialized manipulated pathways as well as genes to recognize statistically significant regulators (TFs/miRNAs) that diverge in temporal activity. This method can be translated to any biotic or abiotic stress in plants and animals to predict the master regulators from condition-specific multi-omics datasets including host-pathogen interactions for comprehensive understanding of manipulated biological pathways.
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Affiliation(s)
- Bharat Mishra
- Department of Biology, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Nilesh Kumar
- Department of Biology, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Jinbao Liu
- Department of Biology, University of Alabama at Birmingham, Birmingham, AL, USA
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Singh NK, Dutta A, Puccetti G, Croll D. Tackling microbial threats in agriculture with integrative imaging and computational approaches. Comput Struct Biotechnol J 2020; 19:372-383. [PMID: 33489007 PMCID: PMC7787954 DOI: 10.1016/j.csbj.2020.12.018] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2020] [Revised: 12/08/2020] [Accepted: 12/13/2020] [Indexed: 11/29/2022] Open
Abstract
Pathogens and pests are one of the major threats to agricultural productivity worldwide. For decades, targeted resistance breeding was used to create crop cultivars that resist pathogens and environmental stress while retaining yields. The often decade-long process of crossing, selection, and field trials to create a new cultivar is challenged by the rapid rise of pathogens overcoming resistance. Similarly, antimicrobial compounds can rapidly lose efficacy due to resistance evolution. Here, we review three major areas where computational, imaging and experimental approaches are revolutionizing the management of pathogen damage on crops. Recognizing and scoring plant diseases have dramatically improved through high-throughput imaging techniques applicable both under well-controlled greenhouse conditions and directly in the field. However, computer vision of complex disease phenotypes will require significant improvements. In parallel, experimental setups similar to high-throughput drug discovery screens make it possible to screen thousands of pathogen strains for variation in resistance and other relevant phenotypic traits. Confocal microscopy and fluorescence can capture rich phenotypic information across pathogen genotypes. Through genome-wide association mapping approaches, phenotypic data helps to unravel the genetic architecture of stress- and virulence-related traits accelerating resistance breeding. Finally, joint, large-scale screenings of trait variation in crops and pathogens can yield fundamental insights into how pathogens face trade-offs in the adaptation to resistant crop varieties. We discuss how future implementations of such innovative approaches in breeding and pathogen screening can lead to more durable disease control.
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Affiliation(s)
- Nikhil Kumar Singh
- Laboratory of Evolutionary Genetics, Institute of Biology, University of Neuchâtel, CH-2000 Neuchâtel, Switzerland
| | - Anik Dutta
- Laboratory of Evolutionary Genetics, Institute of Biology, University of Neuchâtel, CH-2000 Neuchâtel, Switzerland
- Plant Pathology, Institute of Integrative Biology, ETH Zurich, CH-8092 Zurich, Switzerland
| | - Guido Puccetti
- Laboratory of Evolutionary Genetics, Institute of Biology, University of Neuchâtel, CH-2000 Neuchâtel, Switzerland
- Syngenta Crop Protection AG, CH-4332 Stein, Switzerland
| | - Daniel Croll
- Laboratory of Evolutionary Genetics, Institute of Biology, University of Neuchâtel, CH-2000 Neuchâtel, Switzerland
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Gao W, Li M, Wu R, Du W, Zhang S, Yin S, Chen Z, Huang H. The design and application of an automated microscope developed based on deep learning for fungal detection in dermatology. Mycoses 2020; 64:245-251. [PMID: 33174310 DOI: 10.1111/myc.13209] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/19/2020] [Revised: 11/02/2020] [Accepted: 11/05/2020] [Indexed: 11/29/2022]
Abstract
BACKGROUND Light microscopy to study the infection of fungi in skin specimens is time-consuming and requires automation. OBJECTIVE We aimed to design and explore the application of an automated microscope for fungal detection in skin specimens. METHODS An automated microscope was designed, and a deep learning model was selected. Skin, nail and hair samples were collected. The sensitivity and the specificity of the automated microscope for fungal detection were calculated by taking the results of human inspectors as the gold standard. RESULTS An automated microscope was built, and an image processing model based on the ResNet-50 was trained. A total of 292 samples were collected including 236 skin samples, 50 nail samples and six hair samples. The sensitivities of the automated microscope for fungal detection in skin, nails and hair were 99.5%, 95.2% and 60%, respectively, and the specificities were 91.4%, 100% and 100%, respectively. CONCLUSION The automated microscope we developed is as skilful as human inspectors for fungal detection in skin and nail samples; however, its performance in hair samples needs to be improved.
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Affiliation(s)
- Wenchao Gao
- The Department of Dermatology, Third Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Meirong Li
- The Department of Dermatology, Third Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Rong Wu
- The Department of Dermatology, Third Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Weian Du
- The Department of Dermatology, Third Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Shanlin Zhang
- Guangzhou Wangsheng Intelligent Technology Co., Ltd., Guangzhou, China
| | - Songchao Yin
- The Department of Dermatology, Third Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Zhirui Chen
- The Department of Dermatology, Third Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Huaiqiu Huang
- The Department of Dermatology, Third Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
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Yazar M, Özbek P. In Silico Tools and Approaches for the Prediction of Functional and Structural Effects of Single-Nucleotide Polymorphisms on Proteins: An Expert Review. OMICS-A JOURNAL OF INTEGRATIVE BIOLOGY 2020; 25:23-37. [PMID: 33058752 DOI: 10.1089/omi.2020.0141] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
Single-nucleotide polymorphisms (SNPs) are single-base variants that contribute to human biological variation and pathogenesis of many human diseases. Among all SNP types, nonsynonymous single-nucleotide polymorphisms (nsSNPs) can alter many structural, biochemical, and functional features of a protein such as folding characteristics, charge distribution, stability, dynamics, and interactions with other proteins/nucleotides. These modifications in the protein structure can lead nsSNPs to be closely associated with many multifactorial diseases such as cancer, diabetes, and neurodegenerative diseases. Predicting structural and functional effects of nsSNPs with experimental approaches can be time-consuming and costly; hence, computational prediction tools and algorithms are being widely and increasingly utilized in biology and medical research. This expert review examines the in silico tools and algorithms for the prediction of functional or structural effects of SNP variants, in addition to the description of the phenotypic effects of nsSNPs on protein structure, association between pathogenicity of variants, and functional or structural features of disease-associated variants. Finally, case studies investigating the functional and structural effects of nsSNPs on selected protein structures are highlighted. We conclude that creating a consistent workflow with a combination of in silico approaches or tools should be considered to increase the performance, accuracy, and precision of the biological and clinical predictions made in silico.
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Affiliation(s)
- Metin Yazar
- Department of Bioengineering, Marmara University, Göztepe, İstanbul, Turkey.,Department of Genetics and Bioengineering, Istanbul Okan University, Tuzla, Istanbul, Turkey
| | - Pemra Özbek
- Department of Bioengineering, Marmara University, Göztepe, İstanbul, Turkey
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Kumar N, Mishra B, Mehmood A, Mohammad Athar, M Shahid Mukhtar. Integrative Network Biology Framework Elucidates Molecular Mechanisms of SARS-CoV-2 Pathogenesis. iScience 2020; 23:101526. [PMID: 32895641 PMCID: PMC7468341 DOI: 10.1016/j.isci.2020.101526] [Citation(s) in RCA: 35] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2020] [Revised: 07/30/2020] [Accepted: 08/31/2020] [Indexed: 02/06/2023] Open
Abstract
COVID-19 (coronavirus disease 2019) is a respiratory illness caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Although the pathophysiology of this virus is complex and largely unknown, we employed a network-biology-fueled approach and integrated transcriptome data pertaining to lung epithelial cells with human interactome to generate Calu-3-specific human-SARS-CoV-2 interactome (CSI). Topological clustering and pathway enrichment analysis show that SARS-CoV-2 targets central nodes of the host-viral network, which participate in core functional pathways. Network centrality analyses discover 33 high-value SARS-CoV-2 targets, which are possibly involved in viral entry, proliferation, and survival to establish infection and facilitate disease progression. Our probabilistic modeling framework elucidates critical regulatory circuitry and molecular events pertinent to COVID-19, particularly the host-modifying responses and cytokine storm. Overall, our network-centric analyses reveal novel molecular components, uncover structural and functional modules, and provide molecular insights into the pathogenicity of SARS-CoV-2 that may help foster effective therapeutic design.
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Affiliation(s)
- Nilesh Kumar
- Department of Biology, University of Alabama at Birmingham, 464 Campbell Hall, 1300 University Boulevard, AL 35294, USA
| | - Bharat Mishra
- Department of Biology, University of Alabama at Birmingham, 464 Campbell Hall, 1300 University Boulevard, AL 35294, USA
| | - Adeel Mehmood
- Department of Biology, University of Alabama at Birmingham, 464 Campbell Hall, 1300 University Boulevard, AL 35294, USA.,Department of Computer Science, University of Alabama at Birmingham, 1402 10th Avenue S., Birmingham, AL 35294, USA
| | - Mohammad Athar
- Department of Dermatology, School of Medicine, University of Alabama at Birmingham, 1720 University Boulevard, AL 35294, USA
| | - M Shahid Mukhtar
- Department of Biology, University of Alabama at Birmingham, 464 Campbell Hall, 1300 University Boulevard, AL 35294, USA.,Nutrition Obesity Research Center, University of Alabama at Birmingham, 1675 University Boulevard, Birmingham, AL 35294, USA.,Department of Surgery, University of Alabama at Birmingham, 1808 7th Avenue S, Birmingham, AL 35294, USA
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46
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Mochida K, Nishii R, Hirayama T. Decoding Plant-Environment Interactions That Influence Crop Agronomic Traits. PLANT & CELL PHYSIOLOGY 2020; 61:1408-1418. [PMID: 32392328 PMCID: PMC7434589 DOI: 10.1093/pcp/pcaa064] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/31/2020] [Accepted: 04/26/2020] [Indexed: 05/16/2023]
Abstract
To ensure food security in the face of increasing global demand due to population growth and progressive urbanization, it will be crucial to integrate emerging technologies in multiple disciplines to accelerate overall throughput of gene discovery and crop breeding. Plant agronomic traits often appear during the plants' later growth stages due to the cumulative effects of their lifetime interactions with the environment. Therefore, decoding plant-environment interactions by elucidating plants' temporal physiological responses to environmental changes throughout their lifespans will facilitate the identification of genetic and environmental factors, timing and pathways that influence complex end-point agronomic traits, such as yield. Here, we discuss the expected role of the life-course approach to monitoring plant and crop health status in improving crop productivity by enhancing the understanding of plant-environment interactions. We review recent advances in analytical technologies for monitoring health status in plants based on multi-omics analyses and strategies for integrating heterogeneous datasets from multiple omics areas to identify informative factors associated with traits of interest. In addition, we showcase emerging phenomics techniques that enable the noninvasive and continuous monitoring of plant growth by various means, including three-dimensional phenotyping, plant root phenotyping, implantable/injectable sensors and affordable phenotyping devices. Finally, we present an integrated review of analytical technologies and applications for monitoring plant growth, developed across disciplines, such as plant science, data science and sensors and Internet-of-things technologies, to improve plant productivity.
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Affiliation(s)
- Keiichi Mochida
- RIKEN Center for Sustainable Resource Science, Tsurumi-ku, Yokohama, Japan
- Kihara Institute for Biological Research, Yokohama City University, Totsuka-ku, Yokohama, Japan
- Graduate School of Nanobioscience, Yokohama City University, Kanazawa-ku, Yokohama, Japan
- Institute of Plant Science and Resources, Okayama University, Kurashiki, Japan
- Corresponding author: E-mail, ; Fax, +81-45-503-9609
| | - Ryuei Nishii
- School of Information and Data Sciences, Nagasaki University, Nagasaki, Japan
| | - Takashi Hirayama
- Institute of Plant Science and Resources, Okayama University, Kurashiki, Japan
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47
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Kumar N, Mishra B, Mehmood A, Athar M, Mukhtar MS. Integrative Network Biology Framework Elucidates Molecular Mechanisms of SARS-CoV-2 Pathogenesis. SSRN 2020:3581857. [PMID: 32714115 PMCID: PMC7366800 DOI: 10.2139/ssrn.3581857] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/21/2020] [Revised: 05/07/2020] [Indexed: 01/02/2023]
Abstract
COVID-19 (Coronavirus disease 2019) is a respiratory illness caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). While the pathophysiology of this deadly virus is complex and largely unknown, we employ a network biology-fueled approach and integrate multiomics data pertaining to lung epithelial cells-specific co-expression network and human interactome to generate Calu-3-specific human-SARS-CoV-2 Interactome (CSI). Topological clustering and pathway enrichment analysis show that SARS-CoV-2 target central nodes of host-viral network that participate in core functional pathways. Network centrality analyses discover 28 high-value SARS-CoV-2 targets, which are possibly involved in viral entry, proliferation and survival to establish infection and facilitate disease progression. Our probabilistic modeling framework elucidates critical regulatory circuitry and molecular events pertinent to COVID-19, particularly the host modifying responses and cytokine storm. Overall, our network centric analyses reveal novel molecular components, uncover structural and functional modules, and provide molecular insights into SARS-CoV-2 pathogenicity that may foster effective therapeutic design. Funding: This work was supported by the National Science Foundation (IOS-1557796) to M.S.M., and U54 ES 030246 from NIH/NIEHS to M. A. Conflict of Interest: The authors declare no competing interests. The authors also declare no financial interests.
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Affiliation(s)
- Nilesh Kumar
- Department of Biology, 464 Campbell Hall, 1300 University Boulevard, University of Alabama at Birmingham, Alabama 35294, USA
| | - Bharat Mishra
- Department of Biology, 464 Campbell Hall, 1300 University Boulevard, University of Alabama at Birmingham, Alabama 35294, USA
| | - Adeel Mehmood
- Department of Biology, 464 Campbell Hall, 1300 University Boulevard, University of Alabama at Birmingham, Alabama 35294, USA
- Department of Computer Science, University of Alabama at Birmingham, 1402 10th Ave. S. , Birmingham, AL 35294, USA
| | - Mohammad Athar
- Department of Dermatology, School of Medicine, University of Alabama at Birmingham, Alabama 35294, USA
| | - M. Shahid Mukhtar
- Department of Biology, 464 Campbell Hall, 1300 University Boulevard, University of Alabama at Birmingham, Alabama 35294, USA
- Nutrition Obesity Research Center, 1675 University Blvd, University of Alabama at Birmingham, Birmingham, AL 35294, USA
- Department of Surgery, 1808 7th Ave S, University of Alabama at Birmingham, Birmingham, AL 35294, USA
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