Connecting environmental and evolutionary microbiology for the development of new agrobiotechnological tools

We all are familiar with Theodosius Dobzhansky's famous sentence: ‘Nothing in biology makes sense except in the light of evolution’, which is actually the title of a short essay he published in The American Biology Teacher (Dobzhansky, 1973). With that title, he was exposing a fundamental truth that even us biologists sometimes tend to obviate: Life means evolution. Genetic changes provide the diversity required for any form of evolution to take place, while the environment will ‘decide’ if those changes are successful or not. Dobzhansky viewed evolution as a process by which life tends to fill all available ecological niches. Microorganisms seem determined to prove him right; in fact, truly vacant ecological niches appear to be rare on our planet, as evidences of bacterial colonization keep being found even in environments where conditions are too extreme for other forms of life (Merino et al., 2019).

If life is evolution, microbial life can be considered evolution on the fast lane. Under favourable growth conditions, a bacterial population duplicates in a very short time, a fact that has been exploited to study evolution under laboratory conditions. Besides the early, pioneering work on mutations and selection in bacteria (Atwood et al., 1951; Lederberg & Lederberg, 1952), perhaps one of the best-known examples is that of the long-term evolution experiment (LTEE) started in 1988 by Richard Lenski: parallel batch cultures of Escherichia coli grown overnight in defined medium and then transferred (1% of each population) to fresh medium every day, with periodical storage of cryopreserved samples of each culture (Lenski et al., 1991). These allow detailed phenotypic and genetic analysis and serve as intermediate ‘camps’ from where the experiment can be resumed if something goes wrong, without having to start all over (pretty much as the ‘save’ button of a videogame). The LTEE, maintained for 34 years by the Lenski group and recently transferred to Jeffrey Barrick's lab at the University of Texas (Austin), has reached over 75,000 generations, and has been a source of relevant insights regarding the dynamics and repeatability of adaptation to particular growth conditions (Lenski, 2017). Additional approaches to study bacterial evolution in laboratory conditions include, among others, long-term adaptation of Escherichia coli to starvation and stationary phase (Finkel & Kolter, 1999), which results in the selection of specific mutants that take over the parental population (Zinser & Kolter, 2004); or static cultures of Pseudomonas fluorescens on the air-liquid interface, where nutrient and oxygen gradients lead to phenotypic and genetic differentiation as the result of adaptive radiation (Rainey & Travisano, 1998). Adaptive radiation also takes place in long-term biofilm populations of Pseudomonas putida (Yousef-Coronado and Espinosa-Urgel, unpublished). Further examples and experimental setups have been nicely reviewed by Van den Bergh and coworkers (Van den Bergh et al., 2018).

Despite some obvious limitations, such as the use of single-species cultures and the rather specific and well-controlled environmental conditions (limitations which on the other hand are necessary to ensure reproducibility and to extract proper conclusions), all these experimental designs to test fundamental evolutionary questions have offered important mechanistic insights, such as the existence of different genetic pathways that can lead to convergent phenotype differentiation, or the genetic program of long-term starved populations, among others (Gallie et al., 2019; Good et al., 2017; Kram et al., 2020; Lind et al., 2017; MacLean et al., 2004).

Environmental microbiology has, in general, laid its connections with evolutionary biology from a different point of view, that of community composition at a certain niche and (less often, but gaining increasing attention) how this structure varies as the result of alterations in the environment, be they natural or human-induced. Examples abound, and range from metagenomic analyses of global topsoil populations (Bahram et al., 2018) to the influence of agricultural land management on microbial communities at specific sites (Mavrodi et al., 2018). The constantly expanding metagenomic and metatranscriptomic data, along with other high-throughput methods, are also being used to infer the driving forces that may shape community composition in different environments (Lidbury et al., 2022; Logares et al., 2020; Lupatini et al., 2019; Salazar et al., 2019). In addition, efforts have been made to analyse the adaptative mechanisms of bacterial populations to specific niches, such as the rhizosphere, in terms of gene expression reprogramming (Matilla et al., 2007; Ramos-González et al., 2013). All of these approaches are likely to expand in the near future, and will require stronger and more refined bioinformatics resources (and experts) to integrate and take full advantage of the wealth of information already available in genomic, metagenomic and transcriptomic databases, and which is expected to continue growing at a great rate.

From an evolutionary point of view, environmental changes represent both a challenge and an opportunity for life forms: the challenge to adapt to the new situation and survive, and the opportunity to occupy the ecological gap left by those who were not able to do so. From that perspective, global climate change is putting life—including human life—under fast, and accelerating, selective pressure. Environmental microbiology is necessarily one of the key disciplines to understand, predict and eventually try to cope with, the effects of climate change on the biosphere (de Lorenzo, 2018). Aspects such as the adaptation of microbiomes to global change, or how to exploit the full potential of beneficial microorganisms to sustain productive agriculture under the concomitant stress conditions (drought, salinity, high temperatures), are gaining interest and certainly deserve greater attention (Fadiji et al., 2022; Jansson & Hofmockel, 2020; Lladó et al., 2017; Saad et al., 2020). All these types of studies are, and will keep providing relevant biological information, as well as new biotechnological tools in the form of environmentally relevant enzymatic activities and the characterization of microorganisms with beneficial traits, such as plant growth promotion and/or protection against stress.

However, I would also advocate for environmental microbiologists to consider the possibilities offered by adopting some of the conceptual approaches and methods used in experimental microbial evolution studies, as well as the information gained so far from them. Although relevant contributions have been made in this respect (Van den Bergh et al., 2018), I believe the cross-talk between environmental and evolutionary microbiology has yet to be fully exploited. Combining both perspectives can result in important breakthroughs, both in terms of expanding our knowledge with respect to microbial adaptation strategies, and of new biotechnological developments derived from such knowledge.

Examples exist where strategies inspired in experimental evolution research have been implemented to enrich for and isolate bacterial strains with specific beneficial or useful traits. Thus, Pseudomonas strains combining efficient plant root colonization and degradation of polycyclic aromatic hydrocarbons in contaminated soils (Kuiper et al., 2001) or with new biocontrol activities (Kamilova et al., 2005), have been isolated, through several rounds of selection under the desired environmental conditions. Using a similar approach, hyperadherent derivatives of Pseudomonas putida can be obtained during repeated biofilm formation initiation, both through random transposon mutagenesis and as the result of spontaneous mutations that promote sessile growth (Yousef-Coronado et al., 2011), some of which remain uncharacterized.

Following the notion that plants exert a selective pressure on root-colonizing microbial populations, we also used this type of strategy in an attempt to analyse genotype selection in the rhizosphere. Spontaneous derivatives of the plant-beneficial bacterium Pseudomonas putida KT2440 showing increased fitness in the rhizosphere could be identified (Quesada et al., 2016). These were isolated after nine rounds of selection, whereby the population established on the plant root for 14 days was directly used to inoculate a new round of plants, without passage through laboratory media. Transcriptomic profiling of one of these variants resulting was carried out and specific genes contributing to rhizosphere fitness were identified, involving stress response and ribosome modification (Quesada et al., 2016). However, a detailed genetic and phenotypic analysis of all the selected clones to establish potential evolutionary pathways in the rhizosphere was not possible at the time.

The above examples serve as proof-of-concept that environmental and agricultural biotechnology can benefit from the experimental evolution point of view, allowing specific selection of useful or fitter genotypes in a relatively straightforward way. These strategies can be viewed as not very different from traditional plant and livestock breeding, and thus should not raise the issues associated with GMO's that frequently hamper their use in field applications.

While it is true that some of the experimental setups presented above can seem oversimplistic from an environmental point of view, once some basic answers have been obtained there are ways to incorporate additional complexity, in the form of competitors, stress conditions, etc., and explore in more detail aspects key for agrobiotechnology (and other) applications, such as the stability and interactions in microbial consortia; environmental stress responses and the corresponding adaptations; or how the fitness of bioinoculants is influenced by changing environmental conditions. The increased ease, speed and affordability of whole-genome sequencing and transcriptional analysis offer a great opportunity to boost evolutionary studies, so that fitness changes and adaptation to different environments can be connected with the corresponding changes at the genetic level and the underlying mechanisms studied in detail.

ACKNOWLEDGEMENTS

Work in the author's laboratory is supported by grant PID2019-109372GB-I00, funded by MCIN/AEI/10.13039/501100011033.

CONFLICT OF INTEREST

The author declares no conflict of interest.

Open Research

DATA AVAILABILITY STATEMENT

All data are available in the text and in the references there in.