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Silicon and plant nutrition—dynamics,mechanisms of transport and role of silicon solubilizer microbiomes in sustainable agriculture:A review

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Pedosphere 2023年第4期33卷 534-555页

作者： Barkha SHARMA Kailash Chand KUMAWAT Shalini TIWARI Ajay KUMAR Rouf Ahmad DAR Ummed SINGH Massimiliano CARDINALE Department of Microbiology G.B.Pant University of Agriculture&TechnologyPantnagarUttarakhand 263145(India) Department of Industrial Microbiology Jacob Institute of Biotechnology and BioengineeringSam Higginbottom University of AgricultureTechnology and Sciences(SHUATS)PrayagrajUttar Pradesh 211007(India) College of agriculture Agriculture UniversityJodhpurBaytuBarmerRajasthan 344034(India) Department of Biological and Environmental sciences and Technologies-DiSTeBA University of SalentoSP6 Lecce-MonteroniLecce I-73100(Italy)

Silicon(Si)is the second most abundant element in the Earth’s crust and has numerous roles in both soils and plants,although it is inaccessible to plants in its native state(insoluble silicate minerals).This inaccessibility can lead to insufficiency,which induces anomalies in plant growth and ***,Si alleviates various biotic and abiotic stresses in plants by enhancing tolerance mechanisms at different stages of uptake/deposition as a monosilicic *** utilization of conventional Si fertilizers to boost agricultural productivity in an attempt to meet the world’s ever-increasing food demand may result in surface and groundwater pollution,waterway eutrophication,soil fertility depletion,and accumulation of hazardous elements such as arsenic in *** addressing the drawbacks of chemical fertilizers,this review emphasizes an environmentally friendly alternative,namely,Si-solubilizing microorganisms(SSMs),as bioinoculants to liberate soluble Si and thus making it available to plants.A deep understanding of recent advances in the functional diversity,colonization patterns,modes of action,role in biogeochemical cycling,and plant stress tolerance of SSMs and their implementation in Si-based agrowaste management through the fabrication of nanoparticles,could help to establish a reliable tool for economically and environmentally sustainable *** this perspective,in this review,we comprehensively summarize the latest methodologies for the isolation,screening,and characterization of SSMs and Si solubilization mechanisms,including ligands(divalent cations),acids(inorganic and organic),alkali(nucleophilic attack),extracellular polysaccharides,and factors affecting them,as well as Si-mediated regulation of gene expression involved in Si uptake,transportation,and *** have critically revised the role of SSMs according to the current *** contributions of SSMs to biofertilization are still being explored;hence,we also

关键词： agrowaste management biofertilizer bioinoculant monosilicic acid plant stress silicon-solubilizing microorganism

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uCARE Chem Suite and uCAREChemSuiteCLI: Tools for bacterial resistome prediction

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Genes & Diseases 2021年第5期8卷 721-729页

作者： Saurav Bhaskar Saha Vijai Kumar Gupta Pramod Wasudeo Ramteke Department of Computational Biology and Bioinformatics JIBBSHUATSPrayagrajUttar Pradesh211007India Department of Chemistry and Biotechnology School of ScienceTallinn University of TechnologyAkadeemia Tee 15Tallinn12618Estonia Department of Biological sciences SHUATSPrayagrajUttar Pradesh211007India AgroBiosciences(AgBS)and Chemical&Biochemical sciences(CBS)Department Mohammed VI Polytechnic University(UM6P)Lot 660Hay Moulay RachidBenguerir 43150Morocco

In the era of antibiotic resistance,in silico prediction of bacterial resistome pro-files,likely to be associated with inactivation of new potential antibiotics is of utmost *** this,to the best of our knowledge,no tool exists for such ***,under the rationale that drugs with similar structures have similar resistome profiles,we devel-oped two models,a deterministic model and a stochastic model,to predict the bacterial re-sistome likely to neutralize uncharacterized but potential chemical *** current version of the tool involves the prediction of a resistome for Escherichia coli and Pseudomonas *** deterministic model on omitting two diverse but relatively less characterized drug classes,polyketides and polypeptides showed an accuracy of 87%,a sensitivity of 85%,and a precision of 89%,whereas the stochastic model predicted antibiotic classes of the test set compounds with an accuracy of 72%,a sensitivity of 75%,and a precision of 83%.The models have been implemented in both a standalone package and an online server,uCAREChemSuite-CLI and uCARE Chem Suite,*** addition to resistome prediction,the online version of the suite enables the user to visualize the chemical structure,classify compounds in 19 pre-defined drug classes,perform pairwise alignment,and cluster with database compounds using a graphical user interface.

关键词： Drug resistance Escherichia coli Prediction Pseudomonas aeruginosa Resistome uCARE chem suite uCAREChemSuiteCLI

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