1
|
Saharan BS, Chaudhary T, Mandal BS, Kumar D, Kumar R, Sadh PK, Duhan JS. Microbe-Plant Interactions Targeting Metal Stress: New Dimensions for Bioremediation Applications. J Xenobiot 2023; 13:252-269. [PMID: 37367495 DOI: 10.3390/jox13020019] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/06/2023] [Revised: 05/25/2023] [Accepted: 05/30/2023] [Indexed: 06/28/2023] Open
Abstract
In the age of industrialization, numerous non-biodegradable pollutants like plastics, HMs, polychlorinated biphenyls, and various agrochemicals are a serious concern. These harmful toxic compounds pose a serious threat to food security because they enter the food chain through agricultural land and water. Physical and chemical techniques are used to remove HMs from contaminated soil. Microbial-metal interaction, a novel but underutilized strategy, might be used to lessen the stress caused by metals on plants. For reclaiming areas with high levels of heavy metal contamination, bioremediation is effective and environmentally friendly. In this study, the mechanism of action of endophytic bacteria that promote plant growth and survival in polluted soils-known as heavy metal-tolerant plant growth-promoting (HMT-PGP) microorganisms-and their function in the control of plant metal stress are examined. Numerous bacterial species, such as Arthrobacter, Bacillus, Burkholderia, Pseudomonas, and Stenotrophomonas, as well as a few fungi, such as Mucor, Talaromyces, Trichoderma, and Archaea, such as Natrialba and Haloferax, have also been identified as potent bioresources for biological clean-up. In this study, we additionally emphasize the role of plant growth-promoting bacteria (PGPB) in supporting the economical and environmentally friendly bioremediation of heavy hazardous metals. This study also emphasizes future potential and constraints, integrated metabolomics approaches, and the use of nanoparticles in microbial bioremediation for HMs.
Collapse
Affiliation(s)
- Baljeet Singh Saharan
- Department of Microbiology, CCS Haryana Agricultural University, Hisar 125004, India
| | - Twinkle Chaudhary
- Department of Animal Biotechnology, Lala Lajpat Rai University of Veterinary and Animal Sciences, Hisar 125004, India
| | - Balwan Singh Mandal
- Department of Forestry, CCS Haryana Agricultural University, Hisar 125004, India
| | - Dharmender Kumar
- Department of Biotechnology, Deenbandhu Chhotu Ram University of Science and Technology, Murthal 131039, India
| | - Ravinder Kumar
- Department of Biotechnology, Chaudhary Devi Lal University, Sirsa 125055, India
| | - Pardeep Kumar Sadh
- Department of Biotechnology, Chaudhary Devi Lal University, Sirsa 125055, India
| | | |
Collapse
|
2
|
Metabolomics and modelling approaches for systems metabolic engineering. Metab Eng Commun 2022; 15:e00209. [PMID: 36281261 PMCID: PMC9587336 DOI: 10.1016/j.mec.2022.e00209] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/20/2022] [Revised: 10/13/2022] [Accepted: 10/14/2022] [Indexed: 11/21/2022] Open
Abstract
Metabolic engineering involves the manipulation of microbes to produce desirable compounds through genetic engineering or synthetic biology approaches. Metabolomics involves the quantitation of intracellular and extracellular metabolites, where mass spectrometry and nuclear magnetic resonance based analytical instrumentation are often used. Here, the experimental designs, sample preparations, metabolite quenching and extraction are essential to the quantitative metabolomics workflow. The resultant metabolomics data can then be used with computational modelling approaches, such as kinetic and constraint-based modelling, to better understand underlying mechanisms and bottlenecks in the synthesis of desired compounds, thereby accelerating research through systems metabolic engineering. Constraint-based models, such as genome scale models, have been used successfully to enhance the yield of desired compounds from engineered microbes, however, unlike kinetic or dynamic models, constraint-based models do not incorporate regulatory effects. Nevertheless, the lack of time-series metabolomic data generation has hindered the usefulness of dynamic models till today. In this review, we show that improvements in automation, dynamic real-time analysis and high throughput workflows can drive the generation of more quality data for dynamic models through time-series metabolomics data generation. Spatial metabolomics also has the potential to be used as a complementary approach to conventional metabolomics, as it provides information on the localization of metabolites. However, more effort must be undertaken to identify metabolites from spatial metabolomics data derived through imaging mass spectrometry, where machine learning approaches could prove useful. On the other hand, single-cell metabolomics has also seen rapid growth, where understanding cell-cell heterogeneity can provide more insights into efficient metabolic engineering of microbes. Moving forward, with potential improvements in automation, dynamic real-time analysis, high throughput workflows, and spatial metabolomics, more data can be produced and studied using machine learning algorithms, in conjunction with dynamic models, to generate qualitative and quantitative predictions to advance metabolic engineering efforts.
Collapse
|
3
|
Rani S, Kumari P, Poddar R, Chattopadhyay S. Study of lipase producing gene in wheat - an in silico approach. J Genet Eng Biotechnol 2021; 19:73. [PMID: 33999287 PMCID: PMC8128969 DOI: 10.1186/s43141-021-00150-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2020] [Accepted: 03/18/2021] [Indexed: 11/10/2022]
Abstract
BACKGROUND Lipases (EC 3.1.1.3) catalyze the hydrolysis of oil into free fatty acids and glycerol forming the 3rd largest group of commercialized enzymes. Plant lipases grab attention recently because of their specificity, less production and purified cost, and easy availability. In silico approach is the first step to identify different genes coding for lipase in a most common indigenous plant, wheat, to explore the possibility of this plant as an alternative source for commercial lipase production. As the hierarchy organization of genes reflects an ancient process of gene duplication and divergence, many of the theoretical and analytical tools of the phylogenetic systematics can be utilized for comparative genomic studies. Also, in addition to experimental identification and characterization of genes, for computational genomic analysis, Arabidopsis has become a popular strategy to identify crop genes which are economically important, as Arabidopsis genes had been well identified and characterized for lipase. A number of articles had been reported in which genes of wheat have shown strong homology with Arabidopsis. The complete genome sequences of rice and Arabidopsis constitute a valuable resource for comparative genome analysis as they are representatives of the two major evolutionary lineages within the angiosperms. Here, in this in silico approach, Arabidopsis and Oryza sativa serve as models for dicotyledonous and monocotyledonous species, respectively, and the genomic sequence data available was used to identify the lipase genes in wheat. RESULTS In this present study, Ensembl Plants database was explored for lipase producing gene present in wheat genome and 21 genes were screened down as they contain specific domain and motif for lipase (GXSXG). According to the evolutionary analysis, it was found that the gene TraesCS5B02G157100, located in 5B chromosome, has 58.35% sequence similarity with the reported lipase gene of Arabidopsis thaliana and gene TraesCS3A02G463500 located in the 3A chromosome has 51.74% sequence similarity with the reported lipase gene of Oryza sativa. Homology modeling was performed using protein sequences coded by aforementioned genes and optimized by molecular dynamic simulations. Further with the help of molecular docking of modeled structures with tributyrin, binding efficiency was checked, and the difference in energies (DE) was -9.83 kcal/mol and -6.67 kcal/mol, respectively. CONCLUSIONS The present work provides a basic understanding of the gene-encoding lipase in wheat, which could be easily accessible and used as a potent industrial enzyme. The study enlightens another direction which can be used further to explore plant lipases.
Collapse
Affiliation(s)
- Shradha Rani
- Department of Bio-Engineering, Birla Institute of Technology, Mesra, Ranchi, 835215, India
| | - Priya Kumari
- Department of Bio-Engineering, Birla Institute of Technology, Mesra, Ranchi, 835215, India
| | - Raju Poddar
- Department of Bio-Engineering, Birla Institute of Technology, Mesra, Ranchi, 835215, India
| | - Soham Chattopadhyay
- Department of Bio-Engineering, Birla Institute of Technology, Mesra, Ranchi, 835215, India.
| |
Collapse
|
4
|
Tiwari P, Khare T, Shriram V, Bae H, Kumar V. Plant synthetic biology for producing potent phyto-antimicrobials to combat antimicrobial resistance. Biotechnol Adv 2021; 48:107729. [PMID: 33705914 DOI: 10.1016/j.biotechadv.2021.107729] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2020] [Revised: 01/22/2021] [Accepted: 03/04/2021] [Indexed: 12/14/2022]
Abstract
Inappropriate and injudicious use of antimicrobial drugs in human health, hygiene, agriculture, animal husbandry and food industries has contributed significantly to rapid emergence and persistence of antimicrobial resistance (AMR), one of the serious global public health threats. The crisis of AMR versus slower discovery of newer antibiotics put forth a daunting task to control these drug-resistant superbugs. Several phyto-antimicrobials have been identified in recent years with direct-killing (bactericidal) and/or drug-resistance reversal (re-sensitization of AMR phenotypes) potencies. Phyto-antimicrobials may hold the key in combating AMR owing to their abilities to target major microbial drug-resistance determinants including cell membrane, drug-efflux pumps, cell communication and biofilms. However, limited distribution, low intracellular concentrations, eco-geographical variations, beside other considerations like dynamic environments, climate change and over-exploitation of plant-resources are major blockades in full potential exploration phyto-antimicrobials. Synthetic biology (SynBio) strategies integrating metabolic engineering, RNA-interference, genome editing/engineering and/or systems biology approaches using plant chassis (as engineerable platforms) offer prospective tools for production of phyto-antimicrobials. With expanding SynBio toolkit, successful attempts towards introduction of entire gene cluster, reconstituting the metabolic pathway or transferring an entire metabolic (or synthetic) pathway into heterologous plant systems highlight the potential of this field. Through this perspective review, we are presenting herein the current situation and options for addressing AMR, emphasizing on the significance of phyto-antimicrobials in this apparently post-antibiotic era, and effective use of plant chassis for phyto-antimicrobial production at industrial scales along with major SynBio tools and useful databases. Current knowledge, recent success stories, associated challenges and prospects of translational success are also discussed.
Collapse
Affiliation(s)
- Pragya Tiwari
- Molecular Metabolic Engineering Lab, Department of Biotechnology, Yeungnam University, Gyeongsan, Gyeongbuk 38541, Republic of Korea
| | - Tushar Khare
- Department of Biotechnology, Modern College of Arts, Science and Commerce, Savitribai Phule Pune University, Ganeshkhind, Pune 411016, India; Department of Environmental Science, Savitribai Phule Pune University, Pune 411007, India
| | - Varsha Shriram
- Department of Botany, Prof. Ramkrishna More Arts, Commerce and Science College, Savitribai Phule Pune University, Akurdi, Pune 411044, India
| | - Hanhong Bae
- Molecular Metabolic Engineering Lab, Department of Biotechnology, Yeungnam University, Gyeongsan, Gyeongbuk 38541, Republic of Korea.
| | - Vinay Kumar
- Department of Biotechnology, Modern College of Arts, Science and Commerce, Savitribai Phule Pune University, Ganeshkhind, Pune 411016, India; Department of Environmental Science, Savitribai Phule Pune University, Pune 411007, India.
| |
Collapse
|
5
|
Sinatti VVC, Gonçalves CAX, Romão-Dumaresq AS. Identification of metabolites identical and similar to drugs as candidates for metabolic engineering. J Biotechnol 2019; 302:67-76. [PMID: 31254549 DOI: 10.1016/j.jbiotec.2019.06.303] [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] [Received: 01/10/2019] [Revised: 04/20/2019] [Accepted: 06/25/2019] [Indexed: 11/18/2022]
Abstract
Natural compounds and derivatives play an essential role in the pharmaceutical industry, however, the difficulty in resynthesizing natural products or isolate them from the native host, often limit their availability, elevate costs and slow down the pharmaceutical manufacturing process. In this context, application of synthetic biology could enable the efficient production of large amounts of drugs or drug precursors in heterologous microorganisms aiming to accelerate the entire manufacturing process. Considering this perspective, here we developed a pipeline to automatically search for metabolites available in the metabolic space that are structurally similar to worldwide approved drugs. This pipeline involved the in silico screening of metabolites from a metabolic pathway meta-database using both Tanimoto coefficients based on Daylight like fingerprints and Maximum Common Substructure algorithm. The method was successfully applied to identify metabolites sharing essential scaffolds with one or more drugs as potential candidates for metabolic engineering. Three of these metabolites (Festuclavine, Scopolamine, and Baccatin III) were identified as similar to many drugs like Cabergoline, Oxitropium, Paclitaxel and had their metabolic pathways computationally mapped for their production in Saccharomyces cerevisiae with our proprietary pathway design software. These compounds are examples of new opportunities for the application of synthetic biology in pharmaceutical production.
Collapse
Affiliation(s)
- Vanessa V C Sinatti
- SENAI Innovation Institute for Biosynthetics, Technology Center for Chemical and Textile Industry, Rio de Janeiro, Brazil.
| | - Carlos Alberto X Gonçalves
- SENAI Innovation Institute for Biosynthetics, Technology Center for Chemical and Textile Industry, Rio de Janeiro, Brazil
| | - Aline S Romão-Dumaresq
- SENAI Innovation Institute for Biosynthetics, Technology Center for Chemical and Textile Industry, Rio de Janeiro, Brazil
| |
Collapse
|
6
|
Presnell KV, Alper HS. Systems Metabolic Engineering Meets Machine Learning: A New Era for Data-Driven Metabolic Engineering. Biotechnol J 2019; 14:e1800416. [PMID: 30927499 DOI: 10.1002/biot.201800416] [Citation(s) in RCA: 31] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2019] [Revised: 02/20/2019] [Indexed: 12/30/2022]
Abstract
The recent increase in high-throughput capacity of 'omics datasets combined with advances and interest in machine learning (ML) have created great opportunities for systems metabolic engineering. In this regard, data-driven modeling methods have become increasingly valuable to metabolic strain design. In this review, the nature of 'omics is discussed and a broad introduction to the ML algorithms combining these datasets into predictive models of metabolism and metabolic rewiring is provided. Next, this review highlights recent work in the literature that utilizes such data-driven methods to inform various metabolic engineering efforts for different classes of application including product maximization, understanding and profiling phenotypes, de novo metabolic pathway design, and creation of robust system-scale models for biotechnology. Overall, this review aims to highlight the potential and promise of using ML algorithms with metabolic engineering and systems biology related datasets.
Collapse
Affiliation(s)
- Kristin V Presnell
- McKetta Department of Chemical Engineering, The University of Texas at Austin, 200 E Dean Keeton St. Stop C0400, Austin, TX, 78712, USA
| | - Hal S Alper
- McKetta Department of Chemical Engineering, The University of Texas at Austin, 200 E Dean Keeton St. Stop C0400, Austin, TX, 78712, USA.,Institute for Cellular and Molecular Biology, The University of Texas at Austin, 100 E 24 St., Austin, TX, 78712, USA
| |
Collapse
|
7
|
Jeffryes JG, Seaver SMD, Faria JP, Henry CS. A pathway for every product? Tools to discover and design plant metabolism. PLANT SCIENCE : AN INTERNATIONAL JOURNAL OF EXPERIMENTAL PLANT BIOLOGY 2018; 273:61-70. [PMID: 29907310 DOI: 10.1016/j.plantsci.2018.03.025] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/21/2017] [Revised: 03/13/2018] [Accepted: 03/19/2018] [Indexed: 06/08/2023]
Abstract
The vast diversity of plant natural products is a powerful indication of the biosynthetic capacity of plant metabolism. Synthetic biology seeks to capitalize on this ability by understanding and reconfiguring the biosynthetic pathways that generate this diversity to produce novel products with improved efficiency. Here we review the algorithms and databases that presently support the design and manipulation of metabolic pathways in plants, starting from metabolic models of native biosynthetic pathways, progressing to novel combinations of known reactions, and finally proposing new reactions that may be carried out by existing enzymes. We show how these tools are useful for proposing new pathways as well as identifying side reactions that may affect engineering goals.
Collapse
Affiliation(s)
- James G Jeffryes
- Argonne National Laboratory, Mathematics and Computer Science Division, Argonne, IL, United States
| | - Samuel M D Seaver
- Argonne National Laboratory, Mathematics and Computer Science Division, Argonne, IL, United States
| | - José P Faria
- Argonne National Laboratory, Mathematics and Computer Science Division, Argonne, IL, United States
| | - Christopher S Henry
- Argonne National Laboratory, Mathematics and Computer Science Division, Argonne, IL, United States.
| |
Collapse
|
8
|
Physicochemical Property Labels as Molecular Descriptors for Improved Analysis of Compound-Protein and Compound-Compound Networks. Methods Mol Biol 2018; 1825:211-225. [PMID: 30334207 DOI: 10.1007/978-1-4939-8639-2_6] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
Small molecules can be represented in various file formats, (1) one-line systems such as SMILES (Simplified Molecular Input Line Entry System) and InChI (International Chemical Identifier) and (2) table systems such as the molfiles, SDF (Structure Data File), and KCF (KEGG Chemical Function). KCF and KCF-S (KEGG Chemical Function-and-Substructures) apply physicochemical property labels on the representations of small molecules, and contribute to improved analysis of compound-protein networks including drug-target interaction, and compound-compound networks including metabolic pathways. In this chapter, the main concepts, usage, and some example applications of the KCFCO and KCF-S packages are explained.
Collapse
|
9
|
Wang L, Dash S, Ng CY, Maranas CD. A review of computational tools for design and reconstruction of metabolic pathways. Synth Syst Biotechnol 2017; 2:243-252. [PMID: 29552648 PMCID: PMC5851934 DOI: 10.1016/j.synbio.2017.11.002] [Citation(s) in RCA: 71] [Impact Index Per Article: 10.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2017] [Revised: 11/06/2017] [Accepted: 11/06/2017] [Indexed: 11/28/2022] Open
Abstract
Metabolic pathways reflect an organism's chemical repertoire and hence their elucidation and design have been a primary goal in metabolic engineering. Various computational methods have been developed to design novel metabolic pathways while taking into account several prerequisites such as pathway stoichiometry, thermodynamics, host compatibility, and enzyme availability. The choice of the method is often determined by the nature of the metabolites of interest and preferred host organism, along with computational complexity and availability of software tools. In this paper, we review different computational approaches used to design metabolic pathways based on the reaction network representation of the database (i.e., graph or stoichiometric matrix) and the search algorithm (i.e., graph search, flux balance analysis, or retrosynthetic search). We also put forth a systematic workflow that can be implemented in projects requiring pathway design and highlight current limitations and obstacles in computational pathway design.
Collapse
Affiliation(s)
- Lin Wang
- Department of Chemical Engineering, The Pennsylvania State University, University Park, PA, USA
| | - Satyakam Dash
- Department of Chemical Engineering, The Pennsylvania State University, University Park, PA, USA
| | - Chiam Yu Ng
- Department of Chemical Engineering, The Pennsylvania State University, University Park, PA, USA
| | - Costas D Maranas
- Department of Chemical Engineering, The Pennsylvania State University, University Park, PA, USA
| |
Collapse
|