In a Quest for Engineering Acidophiles for Biomining Applications: Challenges and Opportunities

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Abstract

Biomining with acidophilic microorganisms has been used at commercial scale for the extraction of metals from various sulfide ores. With metal demand and energy prices on the rise and the concurrent decline in quality and availability of mineral resources, there is an increasing interest in applying biomining technology, in particular for leaching metals from low grade minerals and wastes. However, bioprocessing is often hampered by the presence of inhibitory compounds that originate from complex ores. Synthetic biology could provide tools to improve the tolerance of biomining microbes to various stress factors that are present in biomining environments, which would ultimately increase bioleaching efficiency. This paper reviews the state-of-the-art tools to genetically modify acidophilic biomining microorganisms and the limitations of these tools. The first part of this review discusses resilience pathways that can be engineered in acidophiles to enhance their robustness and tolerance in harsh environments that prevail in bioleaching. The second part of the paper reviews the efforts that have been carried out towards engineering robust microorganisms and developing metabolic modelling tools. Novel synthetic biology tools have the potential to transform the biomining industry and facilitate the extraction of value from ores and wastes that cannot be processed with existing biomining microorganisms.

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Analysis of optimality in natural and perturbed metabolic networks.

Daniel Segrè, Dennis Vitkup, George Church (2002)

An important goal of whole-cell computational modeling is to integrate detailed biochemical information with biological intuition to produce testable predictions. Based on the premise that prokaryotes such as Escherichia coli have maximized their growth performance along evolution, flux balance analysis (FBA) predicts metabolic flux distributions at steady state by using linear programming. Corroborating earlier results, we show that recent intracellular flux data for wild-type E. coli JM101 display excellent agreement with FBA predictions. Although the assumption of optimality for a wild-type bacterium is justifiable, the same argument may not be valid for genetically engineered knockouts or other bacterial strains that were not exposed to long-term evolutionary pressure. We address this point by introducing the method of minimization of metabolic adjustment (MOMA), whereby we test the hypothesis that knockout metabolic fluxes undergo a minimal redistribution with respect to the flux configuration of the wild type. MOMA employs quadratic programming to identify a point in flux space, which is closest to the wild-type point, compatibly with the gene deletion constraint. Comparing MOMA and FBA predictions to experimental flux data for E. coli pyruvate kinase mutant PB25, we find that MOMA displays a significantly higher correlation than FBA. Our method is further supported by experimental data for E. coli knockout growth rates. It can therefore be used for predicting the behavior of perturbed metabolic networks, whose growth performance is in general suboptimal. MOMA and its possible future extensions may be useful in understanding the evolutionary optimization of metabolism.

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Bacterial Extracellular Polysaccharides in Biofilm Formation and Function.

Dominique Limoli, Christopher J. Jones, Daniel Wozniak (2015)

Microbes produce a biofilm matrix consisting of proteins, extracellular DNA, and polysaccharides that is integral in the formation of bacterial communities. Historical studies of polysaccharides revealed that their overproduction often alters the colony morphology and can be diagnostic in identifying certain species. The polysaccharide component of the matrix can provide many diverse benefits to the cells in the biofilm, including adhesion, protection, and structure. Aggregative polysaccharides act as molecular glue, allowing the bacterial cells to adhere to each other as well as surfaces. Adhesion facilitates the colonization of both biotic and abiotic surfaces by allowing the bacteria to resist physical stresses imposed by fluid movement that could separate the cells from a nutrient source. Polysaccharides can also provide protection from a wide range of stresses, such as desiccation, immune effectors, and predators such as phagocytic cells and amoebae. Finally, polysaccharides can provide structure to biofilms, allowing stratification of the bacterial community and establishing gradients of nutrients and waste products. This can be advantageous for the bacteria by establishing a heterogeneous population that is prepared to endure stresses created by the rapidly changing environments that many bacteria encounter. The diverse range of polysaccharide structures, properties, and roles highlight the importance of this matrix constituent to the successful adaptation of bacteria to nearly every niche. Here, we present an overview of the current knowledge regarding the diversity and benefits that polysaccharide production provides to bacterial communities within biofilms.

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Escherichia coli K-12 undergoes adaptive evolution to achieve in silico predicted optimal growth.

Rafael Ibarra, Jeremy Edwards, Bernhard Palsson (2002)

Annotated genome sequences can be used to reconstruct whole-cell metabolic networks. These metabolic networks can be modelled and analysed (computed) to study complex biological functions. In particular, constraints-based in silico models have been used to calculate optimal growth rates on common carbon substrates, and the results were found to be consistent with experimental data under many but not all conditions. Optimal biological functions are acquired through an evolutionary process. Thus, incorrect predictions of in silico models based on optimal performance criteria may be due to incomplete adaptive evolution under the conditions examined. Escherichia coli K-12 MG1655 grows sub-optimally on glycerol as the sole carbon source. Here we show that when placed under growth selection pressure, the growth rate of E. coli on glycerol reproducibly evolved over 40 days, or about 700 generations, from a sub-optimal value to the optimal growth rate predicted from a whole-cell in silico model. These results open the possibility of using adaptive evolution of entire metabolic networks to realize metabolic states that have been determined a priori based on in silico analysis.

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Author and article information

Journal

Journal ID (nlm-ta): Genes (Basel)

Journal ID (iso-abbrev): Genes (Basel)

Journal ID (publisher-id): genes

Title: Genes

Publisher: MDPI

ISSN (Electronic): 2073-4425

Publication date (Electronic): 21 February 2018

Publication date Collection: February 2018

Volume: 9

Issue: 2

Electronic Location Identifier: 116

Affiliations

[1 ]Commonwealth Scientific and Industrial Research Organisation (CSIRO), Floreat WA 6014, Australia; yosephine.gumulya@ 123456csiro.au , naomi.boxall@ 123456csiro.au , himelnahreen.khaleque@ 123456csiro.au , anna.kaksonen@ 123456csiro.au

[2 ]Laboratory of Chemistry and Bioengineering, Tampere University of Technology (TUT), Tampere, 33101, Finland; ville.santala@ 123456tut.fi

[3 ]Department of Chemical and Biological Engineering, Montana State University (MSU), Bozeman, MT 59717, USA; rossc@ 123456montana.edu

[4 ]School of Pathology and Laboratory Medicine, University of Western Australia, Crawley, WA 6009, Australia

Author notes

[* ]Correspondence: yosephine.gumulya@ 123456csiro.au ;Tel.: +61-8-9333-6055

Article

Publisher ID: genes-09-00116

DOI: 10.3390/genes9020116

PMC ID: 5852612

PubMed ID: 29466321

SO-VID: 84ea4eee-80e5-4ef2-94d3-cb8bda40dae9

License:

Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( http://creativecommons.org/licenses/by/4.0/).

In a Quest for Engineering Acidophiles for Biomining Applications: Challenges and Opportunities

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Most cited references 203

Analysis of optimality in natural and perturbed metabolic networks.

Bacterial Extracellular Polysaccharides in Biofilm Formation and Function.

Escherichia coli K-12 undergoes adaptive evolution to achieve in silico predicted optimal growth.

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