Plasmid fitness costs are caused by specific genetic conflicts enabling resolution by compensatory mutation.

Plasmids play an important role in bacterial genome evolution by transferring genes between lineages. Fitness costs associated with plasmid carriage are expected to be a barrier to gene exchange, but the causes of plasmid fitness costs are poorly understood. Single compensatory mutations are often sufficient to completely ameliorate plasmid fitness costs, suggesting that such costs are caused by specific genetic conflicts rather than generic properties of plasmids, such as their size, metabolic burden, or gene expression level. By combining the results of experimental evolution with genetics and transcriptomics, we show here that fitness costs of 2 divergent large plasmids in Pseudomonas fluorescens are caused by inducing maladaptive expression of a chromosomal tailocin toxin operon. Mutations in single genes unrelated to the toxin operon, and located on either the chromosome or the plasmid, ameliorated the disruption associated with plasmid carriage. We identify one of these compensatory loci, the chromosomal gene PFLU4242, as the key mediator of the fitness costs of both plasmids, with the other compensatory loci either reducing expression of this gene or mitigating its deleterious effects by up-regulating a putative plasmid-borne ParAB operon. The chromosomal mobile genetic element Tn6291, which uses plasmids for transmission, remained up-regulated even in compensated strains, suggesting that mobile genetic elements communicate through pathways independent of general physiological disruption. Plasmid fitness costs caused by specific genetic conflicts are unlikely to act as a long-term barrier to horizontal gene transfer (HGT) due to their propensity for amelioration by single compensatory mutations, helping to explain why plasmids are so common in bacterial genomes.

Fine-scale behavioural adjustments of prey on a continuum of risk.

In the wild, prey species often live in the vicinity of predators, rendering the ability to assess risk on a moment-to-moment basis crucial to survival. Visual cues are important as they allow prey to assess predator species, size, proximity and behaviour. However, few studies have explicitly examined prey's ability to assess risk based on predator behaviour and orientation. Using mosquitofish, Gambusia holbrooki, and their predator, jade perch, Scortum barcoo, under controlled conditions, we provide some of the first fine-scale characterization of how prey adapt their behaviour according to their continuous assessment of risk based on both predator behaviour and angular distance to the predator's mouth. When these predators were inactive and posed less of an immediate threat, prey within the attack cone of the predator showed reductions in speed and acceleration characteristic of predator-inspection behaviour. However, when predators became active, prey swam faster with greater acceleration and were closer together within the attack cone of predators. Most importantly, this study provides evidence that prey do not adopt a uniform response to the presence of a predator. Instead, we demonstrate that prey are capable of rapidly and dynamically updating their assessment of risk and showing fine-scale adjustments to their behaviour.

Source-sink plasmid transfer dynamics maintain gene mobility in soil bacterial communities.

Horizontal gene transfer is a fundamental process in bacterial evolution that can accelerate adaptation via the sharing of genes between lineages. Conjugative plasmids are the principal genetic elements mediating the horizontal transfer of genes, both within and between bacterial species. In some species, plasmids are unstable and likely to be lost through purifying selection, but when alternative hosts are available, interspecific plasmid transfer could counteract this and maintain access to plasmid-borne genes. To investigate the evolutionary importance of alternative hosts to plasmid population dynamics in an ecologically relevant environment, we established simple soil microcosm communities comprising two species of common soil bacteria, Pseudomonas fluorescens and Pseudomonas putida, and a mercury resistance (Hg(R)) plasmid, pQBR57, both with and without positive selection [i.e., addition of Hg(II)]. In single-species populations, plasmid stability varied between species: although pQBR57 survived both with and without positive selection in P. fluorescens, it was lost or replaced by nontransferable Hg(R) captured to the chromosome in P. putida A simple mathematical model suggests these differences were likely due to pQBR57's lower intraspecific conjugation rate in P. putida By contrast, in two-species communities, both models and experiments show that interspecific conjugation from P. fluorescens allowed pQBR57 to persist in P. putida via source-sink transfer dynamics. Moreover, the replacement of pQBR57 by nontransferable chromosomal Hg(R) in P. putida was slowed in coculture. Interspecific transfer allows plasmid survival in host species unable to sustain the plasmid in monoculture, promoting community-wide access to the plasmid-borne accessory gene pool and thus potentiating future evolvability.

Variation and asymmetry in host-symbiont dependence in a microbial symbiosis.

BACKGROUND: Symbiosis is a major source of evolutionary innovation and, by allowing species to exploit new ecological niches, underpins the functioning of ecosystems. The transition from free-living to obligate symbiosis requires the alignment of the partners' fitness interests and the evolution of mutual dependence. While symbiotic taxa are known to vary widely in the extent of host-symbiont dependence, rather less is known about variation within symbiotic associations. RESULTS: Using experiments with the microbial symbiosis between the protist Paramecium bursaria and the alga Chlorella, we show variation between pairings in host-symbiont dependence, encompassing facultative associations, mutual dependence and host dependence upon the symbiont. Facultative associations, that is where both the host and the symbiont were capable of free-living growth, displayed higher symbiotic growth rates and higher per host symbiont loads than those with greater degrees of dependence. CONCLUSIONS: These data show that the Paramecium-Chlorella interaction exists at the boundary between facultative and obligate symbiosis, and further suggest that the host is more likely to evolve dependence than the algal symbiont.

Pulse Generation in the Quorum Machinery of Pseudomonas aeruginosa.

Pseudomonas aeruginosa is a Gram-negative bacterium that is responsible for a wide range of infections in humans. Colonies employ quorum sensing (QS) to coordinate gene expression, including for virulence factors, swarming motility and complex social traits. The QS signalling system of P. aeruginosa is known to involve multiple control components, notably the las, rhl and pqs systems. In this paper, we examine the las system and, in particular, the repressive interaction of rsaL, an embedded small regulative protein, employing recent biochemical information to aid model construction. Using analytic methods, we show how this feature can give rise to excitable pulse generation in this subsystem with important downstream consequences for rhamnolipid production. We adopt a symmetric competitive inhibition to capture the binding in the lasI-rsaL intergenic region and show our results are not dependent on the exact choice of this functional form. Furthermore, we examine the coupling of lasR to the rhl system, the impact of the predicted capacity for pulse generation and the biophysical consequences of this behaviour. We hypothesize that the interaction between the las and rhl systems may provide a quorum memory to enable cells to trigger rhamnolipid production only when they are at the edge of an established aggregation.

Selective Conditions for a Multidrug Resistance Plasmid Depend on the Sociality of Antibiotic Resistance.

Multidrug resistance (MDR) plasmids frequently carry antibiotic resistance genes conferring qualitatively different mechanisms of resistance. We show here that the antibiotic concentrations selecting for the RK2 plasmid inEscherichia colidepend upon the sociality of the drug resistance: the selection for selfish drug resistance (efflux pump) occurred at very low drug concentrations, just 1.3% of the MIC of the plasmid-free antibiotic-sensitive strain, whereas selection for cooperative drug resistance (modifying enzyme) occurred at drug concentrations exceeding the MIC of the plasmid-free strain.

Reconstruction of a genome-scale metabolic model and in-silico flux analysis of Aspergillus tubingensis: a non-mycotoxinogenic citric acid-producing fungus.

BACKGROUND: Aspergillus tubingensis is a citric acid-producing fungus that can utilize sugars in hydrolysate of lignocellulosic biomass such as sugarcane bagasse and, unlike A. niger, does not produce mycotoxins. To date, no attempt has been made to model its metabolism at genome scale. RESULTS: Here, we utilized the whole-genome sequence (34.96 Mb length) and the measured biomass composition to reconstruct a genome-scale metabolic model (GSMM) of A. tubingensis DJU120 strain. The model, named iMK1652, consists of 1652 genes, 1657 metabolites and 2039 reactions distributed over four cellular compartments. The model has been extensively curated manually. This included removal of dead-end metabolites and generic reactions, addition of secondary metabolite pathways and several transporters. Several mycotoxin synthesis pathways were either absent or incomplete in the genome, providing a genomic basis for the non-toxinogenic nature of this species. The model was further refined based on the experimental phenotypic microarray (Biolog) data. The model closely captured DJU120 fermentative data on glucose, xylose, and phosphate consumption, as well as citric acid and biomass production, showing its applicability to capture citric acid fermentation of lignocellulosic biomass hydrolysate. CONCLUSIONS: The model offers a framework to conduct metabolic systems biology investigations and can act as a scaffold for integrative modelling of A. tubingensis.

Marine cyanobacterial biomass is an efficient feedstock for fungal bioprocesses.

BACKGROUND: Marine cyanobacteria offer many sustainability advantages, such as the ability to fix atmospheric CO2, very fast growth and no dependence on freshwater for culture. Cyanobacterial biomass is a rich source of sugars and proteins, two essential nutrients for culturing any heterotroph. However, no previous study has evaluated their application as a feedstock for fungal bioprocesses. RESULTS: In this work, we cultured the marine cyanobacterium Synechococcus sp. PCC 7002 in a 3-L externally illuminated bioreactor with working volume of 2 L with a biomass productivity of ~ 0.8 g L-1 day-1. Hydrolysis of the biomass with acids released proteins and hydrolyzed glycogen while hydrolysis of the biomass with base released only proteins but did not hydrolyze glycogen. Among the different acids tested, treatment with HNO3 led to the highest release of proteins and glucose. Cyanobacterial biomass hydrolysate (CBH) prepared in HNO3 was used as a medium to produce cellulase enzyme by the Penicillium funiculosum OAO3 strain while CBH prepared in HCl and treated with charcoal was used as a medium for citric acid by Aspergillus tubingensis. Approximately 50% higher titers of both products were obtained compared to traditional media. CONCLUSIONS: These results show that the hydrolysate of marine cyanobacteria is an effective source of nutrients/proteins for fungal bioprocesses.

Modeling N-Glycosylation: A Systems Biology Approach for Evaluating Changes in the Steady-State Organization of Golgi-Resident Proteins.

The organization of Golgi-resident proteins is crucial for sorting molecules within the secretory pathway and regulating posttranslational modifications. However, evaluating changes to Golgi organization can be challenging, often requiring extensive experimental investigations. Here, we propose a systems biology approach in which changes to Golgi-resident protein sorting and localization can be deduced using cellular N-glycan profiles as the only experimental input.The approach detailed here utilizes the influence of Golgi organization on N-glycan biosynthesis to investigate the mechanisms involved in establishing and maintaining Golgi organization. While N-glycosylation is carried out in a non-template-driven manner, the distribution of N-glycan biosynthetic enzymes within the Golgi ensures this process is not completely random. Therefore, changes to N-glycan profiles provide clues into how altered cell phenotypes affect the sorting and localization of Golgi-resident proteins. Here, we generate a stochastic simulation of N-glycan biosynthesis to produce a simulated glycan profile similar to that obtained experimentally and then combine this with Bayesian fitting to enable inference of changes in enzyme amounts and localizations. Alterations to Golgi organization are evaluated by calculating how the fitted enzyme parameters shift when moving from simulating the glycan profile of one cellular state (e.g., a wild type) to an altered cellular state (e.g., a mutant). Our approach illustrates how an iterative combination of mathematical systems biology and minimal experimental cell biology can be utilized to maximally integrate biological knowledge to gain insightful knowledge of the underlying mechanisms in a manner inaccessible to either alone.

Distinguishing social from nonsocial navigation in moving animal groups.

Many animals, such as migrating shoals of fish, navigate in groups. Knowing the mechanisms involved in animal navigation is important when it comes to explaining navigation accuracy, dispersal patterns, population and evolutionary dynamics, and consequently, the design of conservation strategies. When navigating toward a common target, animals could interact socially by sharing available information directly or indirectly, or each individual could navigate by itself and aggregations may not disperse because all animals are moving toward the same target. Here we present an analysis technique that uses individual movement trajectories to determine the extent to which individuals in navigating groups interact socially, given knowledge of their target. The basic idea of our approach is that the movement directions of individuals arise from a combination of responses to the environment and to other individuals. We estimate the relative importance of these responses, distinguishing between social and nonsocial interactions. We develop and test our method, using simulated groups, and we demonstrate its applicability to empirical data in a case study on groups of guppies moving toward shelter in a tank. Our approach is generic and can be extended to different scenarios of animal group movement.

The Effects of Antibiotic Combination Treatments on Pseudomonas aeruginosa Tolerance Evolution and Coexistence with Stenotrophomonas maltophilia.

The Pseudomonas aeruginosa bacterium is a common pathogen of cystic fibrosis (CF) patients due to its ability to evolve resistance to antibiotics during treatments. While P. aeruginosa resistance evolution is well-characterized in monocultures, it is less well-understood in polymicrobial CF infections. Here, we investigated how exposure to ciprofloxacin, colistin, or tobramycin antibiotics, administered at sub-minimum inhibitory concentration (MIC) doses, both alone and in combination, shaped the tolerance evolution of P. aeruginosa (PAO1 lab and clinical CF LESB58 strains) in the absence and presence of a commonly co-occurring species, Stenotrophomonas maltophilia. The increases in antibiotic tolerances were primarily driven by the presence of that antibiotic in the treatment. We observed a reciprocal cross-tolerance between ciprofloxacin and tobramycin, and, when combined, the selected antibiotics increased the MICs for all of the antibiotics. Though the presence of S. maltophilia did not affect the tolerance or the MIC evolution, it drove P. aeruginosa into extinction more frequently in the presence of tobramycin due to its relatively greater innate tobramycin tolerance. In contrast, P. aeruginosa dominated and drove S. maltophilia extinct in most other treatments. Together, our findings suggest that besides driving high-level antibiotic tolerance evolution, sub-MIC antibiotic exposure can alter competitive bacterial interactions, leading to target pathogen extinctions in multispecies communities. IMPORTANCE Cystic fibrosis (CF) is a genetic condition that results in thick mucus secretions in the lungs that are susceptible to chronic bacterial infections. The bacterial pathogen Pseudomonas aeruginosa is often associated with morbidity in CF and is difficult to treat due to its high resistance to antibiotics. The resistance evolution of Pseudomonas aeruginosa is poorly understood in polymicrobial infections that are typical of CF. To study this, we exposed P. aeruginosa to sublethal concentrations of ciprofloxacin, colistin, or tobramycin antibiotics in the absence and presence of a commonly co-occurring CF species, Stenotrophomonas maltophilia. We found that low-level antibiotic concentrations selected for high-level antibiotic resistance. While P. aeruginosa dominated in most antibiotic treatments, S. maltophilia drove it into extinction in the presence of tobramycin due to an innately higher tobramycin resistance. Our findings suggest that, besides driving high-level antibiotic tolerance evolution, sublethal antibiotic exposure can magnify competition in bacterial communities, which can lead to target pathogen extinctions in multispecies communities.

Computational Modeling of Glycan Processing in the Golgi for Investigating Changes in the Arrangements of Biosynthetic Enzymes.

Modeling glycan biosynthesis is becoming increasingly important due to the far-reaching implications that glycosylation can exhibit, from pathologies to biopharmaceutical manufacturing. Here we describe a stochastic simulation approach, to overcome the deterministic nature of previous models, that aims to simulate the action of glycan modifying enzymes to produce a glycan profile. This is then coupled with an approximate Bayesian computation methodology to systematically fit to empirical data in order to determine which set of parameters adequately describes the organization of enzymes within the Golgi. The model is described in detail along with a proof of concept and therapeutic applications.

Integration of Aspergillus niger transcriptomic profile with metabolic model identifies potential targets to optimise citric acid production from lignocellulosic hydrolysate.

BACKGROUND: Citric acid is typically produced industrially by Aspergillus niger-mediated fermentation of a sucrose-based feedstock, such as molasses. The fungus Aspergillus niger has the potential to utilise lignocellulosic biomass, such as bagasse, for industrial-scale citric acid production, but realising this potential requires strain optimisation. Systems biology can accelerate strain engineering by systematic target identification, facilitated by methods for the integration of omics data into a high-quality metabolic model. In this work, we perform transcriptomic analysis to determine the temporal expression changes during fermentation of bagasse hydrolysate and develop an evolutionary algorithm to integrate the transcriptomic data with the available metabolic model to identify potential targets for strain engineering. RESULTS: The novel integrated procedure matures our understanding of suboptimal citric acid production and reveals potential targets for strain engineering, including targets consistent with the literature such as the up-regulation of citrate export and pyruvate carboxylase as well as novel targets such as the down-regulation of inorganic diphosphatase. CONCLUSIONS: In this study, we demonstrate the production of citric acid from lignocellulosic hydrolysate and show how transcriptomic data across multiple timepoints can be coupled with evolutionary and metabolic modelling to identify potential targets for further engineering to maximise productivity from a chosen feedstock. The in silico strategies employed in this study can be applied to other biotechnological goals, assisting efforts to harness the potential of microorganisms for bio-based production of valuable chemicals.

Inter-species interactions alter antibiotic efficacy in bacterial communities.

The efficacy of antibiotic treatments targeting polymicrobial communities is not well predicted by conventional in vitro susceptibility testing based on determining minimum inhibitory concentration (MIC) in monocultures. One reason for this is that inter-species interactions can alter the community members' susceptibility to antibiotics. Here we quantify, and identify mechanisms for, community-modulated changes of efficacy for clinically relevant antibiotics against the pathogen Pseudomonas aeruginosa in model cystic fibrosis (CF) lung communities derived from clinical samples. We demonstrate that multi-drug resistant Stenotrophomonas maltophilia can provide high levels of antibiotic protection to otherwise sensitive P. aeruginosa. Exposure protection to imipenem was provided by chromosomally encoded metallo-ß-lactamase that detoxified the environment; protection was dependent upon S. maltophilia cell density and was provided by S. maltophilia strains isolated from CF sputum, increasing the MIC of P. aeruginosa by up to 16-fold. In contrast, the presence of S. maltophilia provided no protection against meropenem, another routinely used carbapenem. Mathematical ordinary differential equation modelling shows that the level of exposure protection provided against different carbapenems can be explained by differences in antibiotic efficacy and inactivation rate. Together, these findings reveal that exploitation of pre-occurring antimicrobial resistance, and inter-specific competition, can have large impacts on pathogen antibiotic susceptibility, highlighting the importance of microbial ecology for designing successful antibiotic treatments for multispecies communities.

Binding to DNA protects Neisseria meningitidis fumarate and nitrate reductase regulator (FNR) from oxygen.

Here, we report the overexpression, purification, and characterization of the transcriptional activator fumarate and nitrate reductase regulator from the pathogenic bacterium Neisseria meningitidis (NmFNR). Like its homologue from Escherichia coli (EcFNR), NmFNR binds a 4Fe-4S cluster, which breaks down in the presence of oxygen to a 2Fe-2S cluster and subsequently to apo-FNR. The kinetics of NmFNR cluster disassembly in the presence of oxygen are 2-3x slower than those previously reported for wild-type EcFNR, but similar to constitutively active EcFNR* mutants, consistent with earlier work in which we reported that the activity of FNR-dependent promoters in N. meningitidis is only weakly inhibited by the presence of oxygen (Rock, J. D., Thomson, M. J., Read, R. C., and Moir, J. W. (2007) J. Bacteriol. 189, 1138-1144). NmFNR binds to DNA containing a consensus FNR box sequence, and this binding stabilizes the iron-sulfur cluster in the presence of oxygen. Partial degradation of the 4Fe-4S cluster to a 3Fe-4S occurs, and this form remains bound to the DNA. The 3Fe-4S cluster is converted spontaneously back to a 4Fe-4S cluster under subsequent anaerobic reducing conditions in the presence of ferrous iron. The finding that binding to DNA stabilizes FNR in the presence of oxygen such that it has a half-life of approximately 30 min on the DNA has implications for our appreciation of how oxygen switches off FNR activatable genes in vivo.

Rapid compensatory evolution can rescue low fitness symbioses following partner switching.

Partner switching plays an important role in the evolution of symbiosis, enabling local adaptation and recovery from the breakdown of symbiosis. Because of intergenomic epistasis, partner-switched symbioses may possess novel combinations of phenotypes but may also exhibit low fitness due to their lack of recent coevolutionary history. Here, we examine the structure and mechanisms of intergenomic epistasis in the Paramecium-Chlorella symbiosis and test whether compensatory evolution can rescue initially low fitness partner-switched symbioses. Using partner-switch experiments coupled with metabolomics, we show evidence for intergenomic epistasis wherein low fitness is associated with elevated symbiont stress responses either in dark or high irradiance environments, potentially owing to mismatched light management traits between the host and symbiont genotypes. Experimental evolution under high light conditions revealed that an initially low fitness partner-switched non-native host-symbiont pairing rapidly adapted, gaining fitness equivalent to the native host-symbiont pairing in less than 50 host generations. Compensatory evolution took two alternative routes: either hosts evolved higher symbiont loads to mitigate for their new algal symbiont's poor performance, or the algal symbionts themselves evolved higher investment in photosynthesis and photoprotective traits to better mitigate light stress. These findings suggest that partner switching combined with rapid compensatory evolution can enable the recovery and local adaptation of symbioses in response to changing environments.

How perceived threat increases synchronization in collectively moving animal groups.

Nature is rich with many different examples of the cohesive motion of animals. Previous attempts to model collective motion have primarily focused on group behaviours of identical individuals. In contrast, we put our emphasis on modelling the contributions of different individual-level characteristics within such groups by using stochastic asynchronous updating of individual positions and orientations. Our model predicts that higher updating frequency, which we relate to perceived threat, leads to more synchronized group movement, with speed and nearest-neighbour distributions becoming more uniform. Experiments with three-spined sticklebacks (Gasterosteus aculeatus) that were exposed to different threat levels provide strong empirical support for our predictions. Our results suggest that the behaviour of fish (at different states of agitation) can be explained by a single parameter in our model: the updating frequency. We postulate a mechanism for collective behavioural changes in different environment-induced contexts, and explain our findings with reference to confusion and oddity effects.

Strategy selection under predation; evolutionary analysis of the emergence of cohesive aggregations.

Why do animals form groups? This question has formed the basis of numerous scientific studies over the last hundred years and still remains a controversial topic. Predation is one of the foremost candidates, yet the precise mechanism remains quantitatively elusive. Here I investigate in silico the effect of ongoing predation on groups of heterogeneous individuals behaving according to a well-documented individual based model. I examine the resultant evolutionary trajectories and describe the final selected states and their stability with reference to a qualitatively modified version of adaptive dynamics. The speed of individuals is found to dominate the selection of the final state over other parameters in the model. The relative stability of the groups and their internal configurations are discussed with reference to novel structural correlation functions that are defined and introduced. The results reveal the importance of tightly bound toroidal group structures as an intermediate state prior to the emergence of slow compact groups. The study also indicates the need to more accurately model the speed distributions in real aggregations.

Making noise: emergent stochasticity in collective motion.

Individual-based models of self-propelled particles (SPPs) are a popular and promising approach to explain features of the collective motion of animal aggregations. Many models that capture some features of group motion have been suggested but a common framework has yet to emerge. Key to all of these models is the inclusion of "noise" or stochastic errors in the individual behaviour of the SPPs. Here, we present a fully stochastic SPP model in one dimension that demonstrates a new way of introducing noise into SPP models whilst preserving emergent behaviours of previous models such as coherent groups and spontaneous direction switching. This purely individual-to-individual, local model is related to previous models in the literature and can easily be extended to higher dimensions. Its coarse-grained behaviour qualitatively reproduces recently reported locust movement data. We suggest that our approach offers an alternative to current reasoning about model construction and has the potential to offer mechanistic explanations for emergent properties of animal groups in nature.

In silico evolution of Aspergillus niger organic acid production suggests strategies for switching acid output.

BACKGROUND: The fungus Aspergillus niger is an important industrial organism for citric acid fermentation; one of the most efficient biotechnological processes. Previously we introduced a dynamic model that captures this process in the industrially relevant batch fermentation setting, providing a more accurate predictive platform to guide targeted engineering. In this article we exploit this dynamic modelling framework, coupled with a robust genetic algorithm for the in silico evolution of A. niger organic acid production, to provide solutions to complex evolutionary goals involving a multiplicity of targets and beyond the reach of simple Boolean gene deletions. We base this work on the latest metabolic models of the parent citric acid producing strain ATCC1015 dedicated to organic acid production with the required exhaustive genomic coverage needed to perform exploratory in silico evolution. RESULTS: With the use of our informed evolutionary framework, we demonstrate targeted changes that induce a complete switch of acid output from citric to numerous different commercially valuable target organic acids including succinic acid. We highlight the key changes in flux patterns that occur in each case, suggesting potentially valuable targets for engineering. We also show that optimum acid productivity is achieved through a balance of organic acid and biomass production, requiring finely tuned flux constraints that give a growth rate optimal for productivity. CONCLUSIONS: This study shows how a genome-scale metabolic model can be integrated with dynamic modelling and metaheuristic algorithms to provide solutions to complex metabolic engineering goals of industrial importance. This framework for in silico guided engineering, based on the dynamic batch growth relevant to industrial processes, offers considerable potential for future endeavours focused on the engineering of organisms to produce valuable products.