Acidithiobacillus ferrooxidans's comprehensive model driven analysis of the electron transfer metabolism and synthetic strain design for biomining applications

The Influence of Exogenous CdS Nanoparticles on the Growth and Carbon Assimilation Efficiency of Escherichia coli

This study investigated the impact of CdS nanoparticles (NPs) on Escherichia coli growth and metabolism under varying conditions. Under illumination, CdS NPs significantly enhanced bacterial growth, glucose assimilation, and biomass accumulation. Key metabolic and stress response genes showed increased expression, indicating improved ATP synthesis and oxidative stress resistance. Additionally, CdS NPs enhanced the electrochemical properties of E. coli, promoting efficient electron transfer. No significant changes were observed in the dark. These findings suggest that light-activated CdS NPs promote E. coli growth and metabolic efficiency by upregulating crucial genes involved in growth and oxidative stress management.

Humic acid-mediated mechanism for efficient biodissolution of used lithium batteries

Resource recovery of valuable metals from spent lithium batteries is an inevitable trend for sustainable development. In this study, external regulation was used to enhance the tolerance and stability of strains in the leaching of spent lithium batteries to radically improve the bioleaching efficiency. The leaching of Li, Ni, Co and Mn increased to 100 %, 85.06 %, 74.25 % and 69.44 % respectively after targeted cultivation with HA as compared to the undomesticated strain. In the process of microbial leaching of spent lithium batteries, the metabolites in the Ⅰ, Ⅳ, and Ⅴ regions of the metabolism of the undomesticated bacterial colony had a positive correlation to the dissolution of spent lithium batteries. The metabolites of Ⅰ, Ⅱ, and Ⅴ regions were directly affected by the HA domesticated flora on the dissolution of spent lithium batteries. The excess metabolism of protein substances can significantly promote the reduction of Ni, Co, Mn leaching, and at the same time in the role of a large number of humic substances complexed the toxic metal ions in the system, to ensure the activity of the bacterial colony. It can be seen that the bacteria were domesticated by humic acid, which promoted the bacteria's own metabolism, and the super-metabolised EPS promoted the solubilisation of spent lithium batteries.

Rhodococcus erythropolis ATCC 4277 behavior against different metals and its potential use in waste biomining

Rhodococcus erythropolis bacterium is known for its remarkable resistance characteristics that can be useful in several biotechnological processes, such as bioremediation. However, there is scarce knowledge concerning the behavior of this strain against different metals. This study sought to investigate the behavior of R. erythropolis ATCC 4277 against the residue of chalcopyrite and e-waste to verify both resistive capacities to the metals present in these residues and their potential use for biomining processes. These tests were carried out in a stirred tank bioreactor for 48 h, at 24ºC, pH 7.0, using a total volume of 2.0 L containing 2.5% (v/v) of a bacterial pre-culture. The pulp density of chalcopyrite was 5% (w/w), and agitation and oxygen flow rates were set to 250 rpm and 1.5 LO2 min-1, respectively. On the other hand, we utilized a waste of computer printed circuit board (WPCB) with a pulp density of 10% (w/w), agitation at 400 rpm, and an oxygen flow rate of 3.0 LO2 min-1. Metal concentration analyses post-fermentation showed that R. erythropolis ATCC 4277 was able to leach about 38% of the Cu present in the chalcopyrite residue (in ~ 24 h), and 49.5% of Fe, 42.3% of Ni, 27.4% of Al, and 15% Cu present in WPCB (in ~ 24 h). In addition, the strain survived well in the environment containing such metals, demonstrating the potential of using this bacterium for waste biomining processes as well as in other processes with these metals.

Kinetic, electrochemical and spectral characterization of bacterial and archaeal rusticyanins; unexpected stability issues and consequences for applications in biotechnology

Motivated by the ambition to establish an enzyme-driven bioleaching pathway for copper extraction, properties of the Type-1 copper protein rusticyanin from Acidithiobacillus ferrooxidans (AfR) were compared with those from an ancestral form of this enzyme (N0) and an archaeal enzyme identified in Ferroplasma acidiphilum (FaR). While both N0 and FaR show redox potentials similar to that of AfR their electron transport rates were significantly slower. The lack of a correlation between the redox potentials and electron transfer rates indicates that AfR and its associated electron transfer chain evolved to specifically facilitate the efficient conversion of the energy of iron oxidation to ATP formation. In F. acidiphilum this pathway is not as efficient unless it is up-regulated by an as of yet unknown mechanism. In addition, while the electrochemical properties of AfR were consistent with previous data, previously unreported behavior was found leading to a form that is associated with a partially unfolded form of the protein. The cyclic voltammetry (CV) response of AfR immobilized onto an electrode showed limited stability, which may be connected to the presence of the partially unfolded state of this protein. Insights gained in this study may thus inform the engineering of optimized rusticyanin variants for bioleaching processes as well as enzyme-catalyzed solubilization of copper-containing ores such as chalcopyrite.

Metabolic modeling of Halomonas campaniensis improves polyhydroxybutyrate production under nitrogen limitation

Poly-hydroxybutyrate (PHB) is an environmentally friendly alternative for conventional fossil fuel-based plastics that is produced by various microorganisms. Large-scale PHB production is challenging due to the comparatively higher biomanufacturing costs. A PHB overproducer is the haloalkaliphilic bacterium Halomonas campaniensis, which has low nutritional requirements and can grow in cultures with high salt concentrations, rendering it resistant to contamination. Despite its virtues, the metabolic capabilities of H. campaniensis as well as the limitations hindering higher PHB production remain poorly studied. To address this limitation, we present HaloGEM, the first high-quality genome-scale metabolic network reconstruction, which encompasses 888 genes, 1528 reactions (1257 gene-associated), and 1274 metabolites. HaloGEM not only displays excellent agreement with previous growth data and experiments from this study, but it also revealed nitrogen as a limiting nutrient when growing aerobically under high salt concentrations using glucose as carbon source. Among different nitrogen source mixtures for optimal growth, HaloGEM predicted glutamate and arginine as a promising mixture producing increases of 54.2% and 153.4% in the biomass yield and PHB titer, respectively. Furthermore, the model was used to predict genetic interventions for increasing PHB yield, which were consistent with the rationale of previously reported strategies. Overall, the presented reconstruction advances our understanding of the metabolic capabilities of H. campaniensis for rationally engineering this next-generation industrial biotechnology platform. KEY POINTS: A comprehensive genome-scale metabolic reconstruction of H. campaniensis was developed. Experiments and simulations predict N limitation in minimal media under aerobiosis. In silico media design increased experimental biomass yield and PHB titer.

Simulating compatible solute biosynthesis using a metabolic flux model of the biomining acidophile, Acidithiobacillus ferrooxidans ATCC 23270

Halotolerant, acidophilic, bioleaching microorganisms are crucial to biomining operations that utilize saline water. Compatible solutes play an important role in the adaptation of these microorganisms to saline environments. Acidithiobacillus ferrooxidans ATCC 23270, an iron- and sulfur-oxidizing acidophilic bacterium, synthesizes trehalose as its native compatible solute but is still sensitive to salinity. Recently, halotolerant bioleaching bacteria were found to use ectoine as their key compatible solute. Previously, bioleaching bacteria were recalcitrant to genetic manipulation; however, recent advancements in genetic tools and techniques allow successful genetic modification of A. ferrooxidans ATCC 23270. Therefore, this study aimed to test, in silico, the effect of native and synthetic compatible solute biosynthesis by A. ferrooxidans ATCC 23270 on its growth and metabolism. Metabolic network flux modelling was used to provide a computational framework for the prediction of metabolic fluxes during production of native and synthetic compatible solutes by A. ferrooxidans ATCC 23270, in silico. Complete pathways for trehalose biosynthesis by the bacterium are proposed and captured in the updated metabolic model including a newly discovered UDP-dependent trehalose synthesis pathway. Finally, the effect of nitrogen sources on compatible solute production was simulated and showed that using nitrogen gas as the sole nitrogen source enables the ectoine-producing 'engineered' microbe to oxidize up to 20% more ferrous iron in comparison to the native microbe that only produces trehalose. Therefore, the predictive outcomes of the model have the potential to guide the design and optimization of a halotolerant strain of A. ferrooxidans ATCC 23270 for saline bioleaching operations.

Genome sequencing and metabolic network reconstruction of a novel sulfur-oxidizing bacterium Acidithiobacillus Ameehan

Sulfur-oxidizing bacteria play a crucial role in various processes, including mine bioleaching, biodesulfurization, and treatment of sulfur-containing wastewater. Nevertheless, the pathway involved in sulfur oxidation is highly intricate, making it complete comprehension a formidable and protracted undertaking. The mechanisms of sulfur oxidation within the Acidithiobacillus genus, along with the process of energy production, remain areas that necessitate further research and elucidation. In this study, a novel strain of sulfur-oxidizing bacterium, Acidithiobacillus Ameehan, was isolated. Several physiological characteristics of the strain Ameehan were verified and its complete genome sequence was presented in the study. Besides, the first genome-scale metabolic network model (AMEE_WP1377) was reconstructed for Acidithiobacillus Ameehan to gain a comprehensive understanding of the metabolic capacity of the strain.The characteristics of Acidithiobacillus Ameehan included morphological size and an optimal growth temperature range of 37-45°C, as well as an optimal growth pH range of pH 2.0-8.0. The microbe was found to be capable of growth when sulfur and K2O6S4 were supplied as the energy source and electron donor for CO2 fixation. Conversely, it could not utilize Na2S2O3, FeS2, and FeSO4·7H2O as the energy source or electron donor for CO2 fixation, nor could it grow using glucose or yeast extract as a carbon source. Genome annotation revealed that the strain Ameehan possessed a series of sulfur oxidizing genes that enabled it to oxidize elemental sulfur or various reduced inorganic sulfur compounds (RISCs). In addition, the bacterium also possessed carbon fixing genes involved in the incomplete Calvin-Benson-Bassham (CBB) cycle. However, the bacterium lacked the ability to oxidize iron and fix nitrogen. By implementing a constraint-based flux analysis to predict cellular growth in the presence of 71 carbon sources, 88.7% agreement with experimental Biolog data was observed. Five sulfur oxidation pathways were discovered through model simulations. The optimal sulfur oxidation pathway had the highest ATP production rate of 14.81 mmol/gDW/h, NADH/NADPH production rate of 5.76 mmol/gDW/h, consumed 1.575 mmol/gDW/h of CO2, and 1.5 mmol/gDW/h of sulfur. Our findings provide a comprehensive outlook on the most effective cellular metabolic pathways implicated in sulfur oxidation within Acidithiobacillus Ameehan. It suggests that the OMP (outer-membrane proteins) and SQR enzymes (sulfide: quinone oxidoreductase) have a significant impact on the energy production efficiency of sulfur oxidation, which could have potential biotechnological applications.

Unveiling abundance-dependent metabolic phenotypes of microbial communities

IMPORTANCE

In nature, organisms live in communities and not as isolated species, and their interactions provide a source of resilience to environmental disturbances. Despite their importance in ecology, human health, and industry, understanding how organisms interact in different environments remains an open question. In this work, we provide a novel approach that, only using genomic information, studies the metabolic phenotype exhibited by communities, where the exploration of suboptimal growth flux distributions and the composition of a community allows to unveil its capacity to respond to environmental changes, shedding light of the degrees of metabolic plasticity inherent to the community.

Sequence similarity network and protein structure prediction offer insights into the evolution of microbial pathways for ferrous iron oxidation

IMPORTANCE

Microbial Fe(II) oxidation is a crucial process that harnesses and converts the energy available in Fe, contributing significantly to global element cycling. However, there are still many aspects of this process that remain unexplored. In this study, we utilized a combination of comparative genomics, sequence similarity network analysis, and artificial intelligence-driven structure modeling methods to address the lack of structural information on Fe(II) oxidation proteins and offer a comprehensive perspective on the evolution of Fe(II) oxidation pathways. Our findings suggest that several microbial Fe(II) oxidation pathways currently known may have originated within classes Gammaproteobacteria and Betaproteobacteria.

AI-driven pan-proteome analyses reveal insights into the biohydrometallurgical properties of Acidithiobacillia

Microorganism-mediated biohydrometallurgy, a sustainable approach for metal recovery from ores, relies on the metabolic activity of acidophilic bacteria. Acidithiobacillia with sulfur/iron-oxidizing capacities are extensively studied and applied in biohydrometallurgy-related processes. However, only 14 distinct proteins from Acidithiobacillia have experimentally determined structures currently available. This significantly hampers in-depth investigations of Acidithiobacillia's structure-based biological mechanisms pertaining to its relevant biohydrometallurgical processes. To address this issue, we employed a state-of-the-art artificial intelligence (AI)-driven approach, with a median model confidence of 0.80, to perform high-quality full-chain structure predictions on the pan-proteome (10,458 proteins) of the type strain Acidithiobacillia. Additionally, we conducted various case studies on de novo protein structural prediction, including sulfate transporter and iron oxidase, to demonstrate how accurate structure predictions and gene co-occurrence networks can contribute to the development of mechanistic insights and hypotheses regarding sulfur and iron utilization proteins. Furthermore, for the unannotated proteins that constitute 35.8% of the Acidithiobacillia proteome, we employed the deep-learning algorithm DeepFRI to make structure-based functional predictions. As a result, we successfully obtained gene ontology (GO) terms for 93.6% of these previously unknown proteins. This study has a significant impact on improving protein structure and function predictions, as well as developing state-of-the-art techniques for high-throughput analysis of large proteomic data.

Sticky bacteria: Combined effect of galactose and high ferric iron concentration on extracellular polymeric substances production and the attachment of Acidithiobacillus ferrooxidans on a polymetallic sulfide ore surface

Adaptation and microbial attachment mechanisms for the degradation of sulfide ores are mediated by the production of extracellular polymeric substances (EPS) and their role in biofilm formation. EPS production responds to induction mechanisms associated with environmental conditions. In this study, the double induction of EPS with galactose and high ferric iron concentrations in planktonic cells of Acidithiobacillus ferrooxidans, and their attachment on the surface of a polymetallic sulfide ore from Bella Rica-Azuay in Ecuador were evaluated. A. ferrooxidans cells were previously adapted to different concentrations of galactose [0, 0.15, and 0.25% (w/v)], using two ferrous iron concentrations as an energy source (9 and 18 g L^–1) in a 9K culture medium. EPS production and its effect on mineral attachment were determined at the time point of maximal growth. The results obtained show a maximum cell attachment of 94.1% within 2 h at 0.15% of galactose and 18 g^⋅L^–1 of ferric iron concentration, compared to 71.4% without galactose and 9 g^⋅L^–1 of ferric iron. The maximum concentration of EPS was obtained with a 0.25% galactose concentration; however, it did not result in greater attachment compared to 0.15% galactose concentration. Through the combined induction of low galactose concentration and high ferric iron concentration, the percentage of bacterial attachment can be increased and, therefore, a possible increase in the rate of biooxidation and bioleaching could be obtained.

Harnessing synthetic biology for sustainable biomining with Fe/S-oxidizing microbes

Biomining is a biotechnological approach where microorganisms are used to recover metals from ores and waste materials. While biomining applications are motivated by critical issues related to the climate crisis (e.g., habitat destruction due to mine effluent pollution, metal supply chains, increasing demands for cleantech-critical metals), its drawbacks hinder its widespread commercial applications: lengthy processing times, low recovery, and metal selectivity. Advances in synthetic biology provide an opportunity to engineer iron/sulfur-oxidizing microbes to address these limitations. In this forum, we review recent progress in synthetic biology-enhanced biomining with iron/sulfur-oxidizing microbes and delineate future research avenues.

Intensification of Nickel Bioleaching with Neutrophilic Bacteria Guyparkeria halophila as an Approach to Limitation of Sulfuric Acid Pollution

Hydrometallurgical production of valuable and non-ferrous metals is traditionally accompanied with acid waste effluents/acid mine drainage leading to acidification of the mining areas. The traditional cause of this pollution is the well-known technology based on the recovery of metals with acid solutions and the application of strong acidophilic leaching bacteria for the oxidation of sulfide ores. In our experiments, we used neutrophilic autotrophic bacteria (NAB) stimulated with formic acid or coupled with acidophilic bacteria. The first approach was based on using formic acid as an energetic substrate by autotrophic bacteria. In the second case, the NAB provided initial biogenic acidification for the following growth of the inoculated acidophilic bacteria. Our experiments resulted in increased nickel recovery from the low-grade sulfide ores, which was provided by the NAB in a medium supplemented with formic acid. Bioleaching resulted in 1116 mg Ni/L (69.75%) in the medium with formate and only 35.4 mg Ni/L without formate in 43 days. As a whole, our bench scale experiments showed that the stimulated NAB can be effective at pH 7–5. Partially replacing sulfuric acid with formic acid could also give benefits via the following natural degradation of acid wastes. As a whole, this approach is more environmentally friendly than conventional bioleaching techniques.

Genomic Analysis of a Newly Isolated Acidithiobacillus ferridurans JAGS Strain Reveals Its Adaptation to Acid Mine Drainage

Acidithiobacillus ferridurans JAGS is a newly isolated acidophile from an acid mine drainage (AMD). The genome of isolate JAGS was sequenced and compared with eight other published genomes of Acidithiobacillus. The pairwise mutation distance (Mash) and average nucleotide identity (ANI) revealed that isolate JAGS had a close evolutionary relationship with A. ferridurans JCM18981, but whole-genome alignment showed that it had higher similarity in genomic structure with A. ferrooxidans species. Pan-genome analysis revealed that nine genomes were comprised of 4601 protein coding sequences, of which 43% were core genes (1982) and 23% were unique genes (1064). A. ferridurans species had more unique genes (205–246) than A. ferrooxidans species (21–234). Functional gene categorizations showed that A. ferridurans strains had a higher portion of genes involved in energy production and conversion while A. ferrooxidans had more for inorganic ion transport and metabolism. A high abundance of kdp, mer and ars genes, as well as mobile genetic elements, was found in isolate JAGS, which might contribute to its resistance to harsh environments. These findings expand our understanding of the evolutionary adaptation of Acidithiobacillus and indicate that A. ferridurans JAGS is a promising candidate for biomining and AMD biotreatment applications.

Using automated reasoning to explore the metabolism of unconventional organisms: a first step to explore host-microbial interactions

Systems modelled in the context of molecular and cellular biology are difficult to represent with a single calibrated numerical model. Flux optimisation hypotheses have shown tremendous promise to accurately predict bacterial metabolism but they require a precise understanding of metabolic reactions occurring in the considered species. Unfortunately, this information may not be available for more complex organisms or non-cultured microorganisms such as those evidenced in microbiomes with metagenomic techniques. In both cases, flux optimisation techniques may not be applicable to elucidate systems functioning. In this context, we describe how automatic reasoning allows relevant features of an unconventional biological system to be identified despite a lack of data. A particular focus is put on the use of Answer Set Programming, a logic programming paradigm with combinatorial optimisation functionalities. We describe its usage to over-approximate metabolic responses of biological systems and solve gap-filling problems. In this review, we compare steady-states and Boolean abstractions of metabolic models and illustrate their complementarity via applications to the metabolic analysis of macro-algae. Ongoing applications of this formalism explore the emerging field of systems ecology, notably elucidating interactions between a consortium of microbes and a host organism. As the first step in this field, we will illustrate how the reduction in microbiotas according to expected metabolic phenotypes can be addressed with gap-filling problems.

A Systems-Based Approach for Cyanide Overproduction by Bacillus megaterium for Gold Bioleaching Enhancement

With the constant accumulation of electronic waste, extracting precious metals contained therein is becoming a major challenge for sustainable development. Bacillus megaterium is currently one of the microbes used for the production of cyanide, which is the main leaching agent for gold recovery. The present study aimed to propose a strategy for metabolic engineering of B. megaterium to overproduce cyanide, and thus ameliorate the bioleaching process. For this, we employed constraint-based modeling, running in silico simulations on iJA1121, the genome-scale metabolic model of B. megaterium DSM319. Flux balance analysis (FBA) was initially used to identify amino acids to be added to the culture medium. Considering cyanide as the desired product, we used growth-coupled methods, constrained minimal cut sets (cMCSs) and OptKnock to identify gene inactivation targets. To identify gene overexpression targets, flux scanning based on enforced objective flux (FSEOF) was performed. Further analysis was carried out on the identified targets to determine compounds with beneficial regulatory effects. We have proposed a chemical-defined medium for accelerating cyanide production on the basis of microplate assays to evaluate the components with the greatest improving effects. Accordingly, the cultivation of B. megaterium DSM319 in a chemically-defined medium with 5.56 mM glucose as the carbon source, and supplemented with 413 μM cysteine, led to the production of considerably increased amounts of cyanide. Bioleaching experiments were successfully performed in this medium to recover gold and copper from telecommunication printed circuit boards. The results of inductively coupled plasma (ICP) analysis confirmed that gold recovery peaked out at around 55% after 4 days, whereas copper recovery continued to increase for several more days, peaking out at around 85%. To further validate the bioleaching results, FESEM, XRD, FTIR, and EDAX mapping analyses were performed. We concluded that the proposed strategy represents a viable route for improving the performance of the bioleaching processes.

Integration of Biological Networks for Acidithiobacillus thiooxidans Describes a Modular Gene Regulatory Organization of Bioleaching Pathways

Acidithiobacillus thiooxidans is one of the most studied biomining species, highlighting its ability to oxidize reduced inorganic sulfur compounds, coupled with its elevated capacity to live under an elevated concentration of heavy metals. In this work, using an in silico semi-automatic genome scale approach, two biological networks for A. thiooxidans Licanantay were generated: (i) An affinity transcriptional regulatory network composed of 42 regulatory family genes and 1,501 operons (57% genome coverage) linked through 2,646 putative DNA binding sites (arcs), (ii) A metabolic network reconstruction made of 523 genes and 1,203 reactions (22 pathways related to biomining processes). Through the identification of confident connections between both networks (V-shapes), it was possible to identify a sub-network of transcriptional factor (34 regulators) regulating genes (61 operons) encoding for proteins involved in biomining-related pathways. Network analysis suggested that transcriptional regulation of biomining genes is organized into different modules. The topological parameters showed a high hierarchical organization by levels inside this network (14 layers), highlighting transcription factors CysB, LysR, and IHF as complex modules with high degree and number of controlled pathways. In addition, it was possible to identify transcription factor modules named primary regulators (not controlled by other regulators in the sub-network). Inside this group, CysB was the main module involved in gene regulation of several bioleaching processes. In particular, metabolic processes related to energy metabolism (such as sulfur metabolism) showed a complex integrated regulation, where different primary regulators controlled several genes. In contrast, pathways involved in iron homeostasis and oxidative stress damage are mainly regulated by unique primary regulators, conferring Licanantay an efficient, and specific metal resistance response. This work shows new evidence in terms of transcriptional regulation at a systems level and broadens the study of bioleaching in A. thiooxidans species.

Effect of CO2 Concentration on Uptake and Assimilation of Inorganic Carbon in the Extreme Acidophile Acidithiobacillus ferrooxidans

This study was motivated by surprising gaps in the current knowledge of microbial inorganic carbon (Ci) uptake and assimilation at acidic pH values (pH < 3). Particularly striking is the limited understanding of the differences between Ci uptake mechanisms in acidic versus circumneutral environments where the Ci predominantly occurs either as a dissolved gas (CO2) or as bicarbonate (HCO3 -), respectively. In order to gain initial traction on the problem, the relative abundance of transcripts encoding proteins involved in Ci uptake and assimilation was studied in the autotrophic, polyextreme acidophile Acidithiobacillus ferrooxidans whose optimum pH for growth is 2.5 using ferrous iron as an energy source, although they are able to grow at pH 5 when using sulfur as an energy source. The relative abundance of transcripts of five operons (cbb1-5) and one gene cluster (can-sulP) was monitored by RT-qPCR and, in selected cases, at the protein level by Western blotting, when cells were grown under different regimens of CO2 concentration in elemental sulfur. Of particular note was the absence of a classical bicarbonate uptake system in A. ferrooxidans. However, bioinformatic approaches predict that sulP, previously annotated as a sulfate transporter, is a novel type of bicarbonate transporter. A conceptual model of CO2 fixation was constructed from combined bioinformatic and experimental approaches that suggests strategies for providing ecological flexibility under changing concentrations of CO2 and provides a portal to elucidating Ci uptake and regulation in acidic conditions. The results could advance the understanding of industrial bioleaching processes to recover metals such as copper at acidic pH. In addition, they may also shed light on how chemolithoautotrophic acidophiles influence the nutrient and energy balance in naturally occurring low pH environments.

Multiscale analysis of autotroph-heterotroph interactions in a high-temperature microbial community

Interactions among microbial community members can lead to emergent properties, such as enhanced productivity, stability, and robustness. Iron-oxide mats in acidic (pH 2-4), high-temperature (> 65 °C) springs of Yellowstone National Park contain relatively simple microbial communities and are well-characterized geochemically. Consequently, these communities are excellent model systems for studying the metabolic activity of individual populations and key microbial interactions. The primary goals of the current study were to integrate data collected in situ with in silico calculations across process-scales encompassing enzymatic activity, cellular metabolism, community interactions, and ecosystem biogeochemistry, as well as to predict and quantify the functional limits of autotroph-heterotroph interactions. Metagenomic and transcriptomic data were used to reconstruct carbon and energy metabolisms of an important autotroph (Metallosphaera yellowstonensis) and heterotroph (Geoarchaeum sp. OSPB) from the studied Fe(III)-oxide mat communities. Standard and hybrid elementary flux mode and flux balance analyses of metabolic models predicted cellular- and community-level metabolic acclimations to simulated environmental stresses, respectively. In situ geochemical analyses, including oxygen depth-profiles, Fe(III)-oxide deposition rates, stable carbon isotopes and mat biomass concentrations, were combined with cellular models to explore autotroph-heterotroph interactions important to community structure-function. Integration of metabolic modeling with in situ measurements, including the relative population abundance of autotrophs to heterotrophs, demonstrated that Fe(III)-oxide mat communities operate at their maximum total community growth rate (i.e. sum of autotroph and heterotroph growth rates), as opposed to net community growth rate (i.e. total community growth rate subtracting autotroph consumed by heterotroph), as predicted from the maximum power principle. Integration of multiscale data with ecological theory provides a basis for predicting autotroph-heterotroph interactions and community-level cellular organization.

Traceability, reproducibility and wiki-exploration for "à-la-carte" reconstructions of genome-scale metabolic models

Genome-scale metabolic models have become the tool of choice for the global analysis of microorganism metabolism, and their reconstruction has attained high standards of quality and reliability. Improvements in this area have been accompanied by the development of some major platforms and databases, and an explosion of individual bioinformatics methods. Consequently, many recent models result from "à la carte" pipelines, combining the use of platforms, individual tools and biological expertise to enhance the quality of the reconstruction. Although very useful, introducing heterogeneous tools, that hardly interact with each other, causes loss of traceability and reproducibility in the reconstruction process. This represents a real obstacle, especially when considering less studied species whose metabolic reconstruction can greatly benefit from the comparison to good quality models of related organisms. This work proposes an adaptable workspace, AuReMe, for sustainable reconstructions or improvements of genome-scale metabolic models involving personalized pipelines. At each step, relevant information related to the modifications brought to the model by a method is stored. This ensures that the process is reproducible and documented regardless of the combination of tools used. Additionally, the workspace establishes a way to browse metabolic models and their metadata through the automatic generation of ad-hoc local wikis dedicated to monitoring and facilitating the process of reconstruction. AuReMe supports exploration and semantic query based on RDF databases. We illustrate how this workspace allowed handling, in an integrated way, the metabolic reconstructions of non-model organisms such as an extremophile bacterium or eukaryote algae. Among relevant applications, the latter reconstruction led to putative evolutionary insights of a metabolic pathway.

Acidithiobacillus ferrooxidans and its potential application

The widely distributed Acidithiobacillus ferrooxidans (A. ferrooxidans) lives in extremely acidic conditions by fixing CO₂ and nitrogen, and by obtaining energy from Fe²⁺ oxidation with either downhill or uphill electron transfer pathway and from reduced sulfur oxidation. A. ferrooxidans exists as different genomovars and its genome size is 2.89-4.18 Mb. The chemotactic movement of A. ferrooxidans is regulated by quorum sensing. A. ferrooxidans shows weak magnetotaxis due to formation of 15-70 nm magnetite magnetosomes with surface functional groups. The room- and low-temperature magnetic features of A. ferrooxidans are different from other magnetotactic bacteria. A. ferrooxidans has potential for removing sulfur from solids and gases, metals recycling from metal-bearing ores, electric wastes and sludge, biochemical production synthesizing, and metal workpiece machining.

In a quest for engineering acidophiles for biomining applications: challenges and opportunities

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.

Combining graph and flux-based structures to decipher phenotypic essential metabolites within metabolic networks

Background

The emergence of functions in biological systems is a long-standing issue that can now be addressed at the cell level with the emergence of high throughput technologies for genome sequencing and phenotyping. The reconstruction of complete metabolic networks for various organisms is a key outcome of the analysis of these data, giving access to a global view of cell functioning. The analysis of metabolic networks may be carried out by simply considering the architecture of the reaction network or by taking into account the stoichiometry of reactions. In both approaches, this analysis is generally centered on the outcome of the network and considers all metabolic compounds to be equivalent in this respect. As in the case of genes and reactions, about which the concept of essentiality has been developed, it seems, however, that some metabolites play crucial roles in system responses, due to the cell structure or the internal wiring of the metabolic network.

Results

We propose a classification of metabolic compounds according to their capacity to influence the activation of targeted functions (generally the growth phenotype) in a cell. We generalize the concept of essentiality to metabolites and introduce the concept of the phenotypic essential metabolite (PEM) which influences the growth phenotype according to sustainability, producibility or optimal-efficiency criteria. We have developed and made available a tool, Conquests, which implements a method combining graph-based and flux-based analysis, two approaches that are usually considered separately. The identification of PEMs is made effective by using a logical programming approach.

Conclusion

The exhaustive study of phenotypic essential metabolites in six genome-scale metabolic models suggests that the combination and the comparison of graph, stoichiometry and optimal flux-based criteria allows some features of the metabolic network functionality to be deciphered by focusing on a small number of compounds. By considering the best combination of both graph-based and flux-based techniques, the Conquests python package advocates for a broader use of these compounds both to facilitate network curation and to promote a precise understanding of metabolic phenotype.

Metals and minerals as a biotechnology feedstock: engineering biomining microbiology for bioenergy applications

Developing new feedstocks for the efficient production of biochemicals and biofuels will be a critical challenge as we diversify away from petrochemicals. One possible opportunity is the utilization of sulfide-based minerals in the Earth's crust. Non-photosynthetic chemolithoautotrophic bacteria are starting to be developed to produce biochemicals from CO₂ using energy obtained from the oxidation of inorganic feedstocks. Biomining of metals like gold and copper already exploit the native metabolism of these bacteria and these represent perhaps the largest-scale bioprocesses ever developed. The metabolic engineering of these bacteria could be a desirable alternative to classical heterotrophic bioproduction. In this review, we discuss biomining operations and the challenges and advances in the engineering of associated chemolithoautotrophic bacteria for biofuel production. The co-generation of biofuels integrated with mining operations is a largely unexplored opportunity that will require advances in fundamental microbiology and the development of new genetic tools and techniques for these organisms. Although this approach is presently in its infancy, the production of biochemicals using energy from non-petroleum mineral resources is an exciting new biotechnology opportunity.