Designing a bioremediator: mechanistic models guide cellular and molecular specialization
Introduction
To decontaminate an environment from pollutants, chemical and physical means of remediation often rely on compounds that prove unsafe in the long-run. The alternative is to employ biological agents, referred to as bioremediation. Bioremediation can be implemented in one of two alternative strategies: biostimulation (the addition of exogenous elements to stimulate the action of native bioremediators) and bioaugmentation (the employment of non-native bioremediators). Here we will primarily focus on bioaugmentation; in this context, bioremediation has been attempted on several targets, with varying degrees of success. In particular: hydrocarbons, pesticides, explosives, radioactive compounds, heavy metals, and metalloids [1, 2, 3, 4] and references therein]. The main asset of a successful bioremediation system is arguably long-term sustainability. However, the intricacies of biological systems make bioremediation an inherently complex feat. Bioremediation could span beyond the competences and customary research approaches of biologists. In what follows, we outline some strategies to guide the design of efficient bioremediators and, for that, we make a case for an interdisciplinary approach. We will point out specific tools and techniques not consistently adopted in bioremediation, yet well established in other areas of physics, chemistry, and biology.
Depending on the focal biological entity, the conventional bioremediation approaches can be divided into two main methods: cellular and molecular.
Thecellular approach: In these instances, bioremediation is performed by populations or communities of different cells or multicellular organisms. To this day, bacteria and fungi have been the main candidates to perform cellular bioremediation. Bacteria can make reliable bioremediators when the target pollutant is exploited for the bioremediator’s specific energetic metabolism [5]. When the target molecule is abundant in an environment to which the bioremediator is adapted, a bacterial bioremediator is likely the best option. Fungi, on the contrary, are often metabolic generalists with higher energetic demands. Even as eclectic scavengers, they will suffer competition with bacteria when placed outside of their customary niche, which reduces their applicability range to specific environments [5]. Conversely, whenever the target pollutant is not readily bioavailable, either physically or biochemically, hyphal growth and an extensive enzymatic inventory allow fungi to affect pollutants more efficiently than bacteria [5].
The molecular approach: Bioremediation can employ subcellular elements, such as enzymes, to target select molecules. Fungal enzymes have been often assayed for applications in bioremediation. The energetic metabolism of fungi relies on efficient scavenging in disparate environmental conditions, and thus revolves around enzymes that display broad substrate affinity. Such versatility makes them more suitable to tackle pollutants of synthetic origin (e.g. pesticides), natural compounds of not high occurrence (e.g. metals and metalloids), and recalcitrant organic molecules in general (e.g. polychlorinated compounds) [6, 7, 8].
In both the cellular and molecular approaches, we endorse the development of a formalized, mechanism-oriented modus operandi centered around iterative improvement of bioremediation performance. In the former, we will highlight recent theoretical developments on microbial population dynamics. In the latter, we will discuss how enzymes with bioremediation potential, such as laccase [9], could be optimized. To show the practical implementation of our discussions, we will highlight a specific case that our laboratory is currently working on: contamination of food commodities by mycotoxins (Box 1).
Section snippets
The cellular approach
In cellular bioremediation, select species or a community of cells are applied to remove or deactivate pollutants; this often involves the successful establishment of the bioremediator in a pre-existent ecosystem as well as the bioremediator’s long-term sustainability, both challenging to achieve. The variables at play are numerous, especially in the context of community assembly. Thus, the know-how to factor them all requires a strong theoretical background, especially when the employed
The molecular approach
Rather than cells as the unit of selection, the molecular machinery of biological systems can also be the target of design for efficient bioremediation. In particular, identifying and enhancing enzymatic activities towards specific applications can have a great impact on bioremediation. One of the most rewarding techniques is arguably Directed Enzyme Evolution (DEE). DEE relies on the generation of a library of random mutants empirically screened for their efficacy at a specific function of
Final thoughts
Cellular and molecular approaches offer a promising perspective for achieving efficient bioremediation. The distinction between these approaches lies in the biological entity that is the subject of the design. In the cellular approach, one begins with cells that — on their own or in conjunction with other cells/species — have the bioremediation capability. The constraints of cells limit the range of possibilities, but also offer resilience and tolerance to changes. The path forward involves
Conflict of interest statement
Nothing declared.
References and recommended reading
Papers of particular interest, published within the period of review, have been highlighted as:
• of special interest
•• of outstanding interest
Acknowledgements
This work was supported by the Next-Generation Supercomputer project (the K computer) and the FLAGSHIP2020 project (Supercomputer Fugaku) within the priority study5 (Development of new fundamental technologies for high-efficiency energy creation, conversion/storage and use) from the Ministry of Education, Culture, Sports, Science and Technology (MEXT) of Japan. L.G., N.T., and W.D. also gratefully acknowledge the joint CEA–RIKEN collaboration action. Work in the Momeni Lab is supported by a
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