Creating large-scale genetic diversity in Arabidopsis via base editing-mediated deep artificial evolution.

BACKGROUND: Base editing is a powerful tool for artificial evolution to create allelic diversity and improve agronomic traits. However, the great evolutionary potential for every sgRNA target has been overlooked. And there is currently no high-throughput method for generating and characterizing as many changes in a single target as possible based on large mutant pools to permit rapid gene directed evolution in plants. RESULTS: In this study, we establish an efficient germline-specific evolution system to screen beneficial alleles in Arabidopsis which could be applied for crop improvement. This system is based on a strong egg cell-specific cytosine base editor and the large seed production of Arabidopsis, which enables each T1 plant with unedited wild type alleles to produce thousands of independent T2 mutant lines. It has the ability of creating a wide range of mutant lines, including those containing atypical base substitutions, and as well providing a space- and labor-saving way to store and screen the resulting mutant libraries. Using this system, we efficiently generate herbicide-resistant EPSPS, ALS, and HPPD variants that could be used in crop breeding. CONCLUSIONS: Here, we demonstrate the significant potential of base editing-mediated artificial evolution for each sgRNA target and devised an efficient system for conducting deep evolution to harness this potential.

Artificial evolution of OsEPSPS through an improved dual cytosine and adenine base editor generated a novel allele conferring rice glyphosate tolerance.

Exploiting novel endogenous glyphosate-tolerant alleles is highly desirable and has promising potential for weed control in rice breeding. Here, through fusions of different effective cytosine and adenine deaminases with nCas9-NG, we engineered an effective surrogate two-component composite base editing system, STCBE-2, with improved C-to-T and A-to-G base editing efficiency and expanded the editing window. Furthermore, we targeted a rice endogenous OsEPSPS gene for artificial evolution through STCBE-2-mediated near-saturated mutagenesis. After hygromycin and glyphosate selection, we identified a novel OsEPSPS allele with an Asp-213-Asn (D213N) mutation (OsEPSPS-D213N) in the predicted glyphosate-binding domain, which conferred rice plants reliable glyphosate tolerance and had not been reported or applied in rice breeding. Collectively, we developed a novel dual base editor which will be valuable for artificial evolution of important genes in crops. And the novel glyphosate-tolerant rice germplasm generated in this study will benefit weeds management in rice paddy fields.

The hiatus between organism and machine evolution: Contrasting mixed microbial communities with robots.

Mixed microbial communities, usually composed of various bacterial and fungal species, are fundamental in a plethora of environments, from soil to human gut and skin. Their evolution is a paradigmatic example of intertwined dynamics, where not just the relations among species plays a role, but also the opportunities - and possible harms - that each species presents to the others. These opportunities are in fact affordances, which can be seized by heritable variations and selection. In this paper, starting from a systemic viewpoint of mixed microbial communities, we focus on the pivotal role of affordances in evolution and we contrast it to the artificial evolution of programs and robots. We maintain that the two realms are neatly separated, in that natural evolution proceeds by extending the space of its possibilities in a completely open way, while the latter is inherently limited by the algorithmic framework in which it is defined. This discrepancy characterizes also an envisioned setting in which robots evolve in the physical world. We present arguments supporting our claim and we propose an experimental setting for assessing our statements. Rather than just discussing the limitations of the artificial evolution of machines, the aim of this contribution is to emphasize the tremendous potential of the evolution of the biosphere, beautifully represented by the evolution of communities of microbes.

Quantifying the Autonomy of Structurally Diverse Automata: A Comparison of Candidate Measures.

Should the internal structure of a system matter when it comes to autonomy? While there is still no consensus on a rigorous, quantifiable definition of autonomy, multiple candidate measures and related quantities have been proposed across various disciplines, including graph-theory, information-theory, and complex system science. Here, I review and compare a range of measures related to autonomy and intelligent behavior. To that end, I analyzed the structural, information-theoretical, causal, and dynamical properties of simple artificial agents evolved to solve a spatial navigation task, with or without a need for associative memory. By contrast to standard artificial neural networks with fixed architectures and node functions, here, independent evolution simulations produced successful agents with diverse neural architectures and functions. This makes it possible to distinguish quantities that characterize task demands and input-output behavior, from those that capture intrinsic differences between substrates, which may help to determine more stringent requisites for autonomous behavior and the means to measure it.

Stepwise artificial evolution of an Sw-5b immune receptor extends its resistance spectrum against resistance-breaking isolates of Tomato spotted wilt virus.

Plants use intracellular nucleotide-binding leucine-rich repeat immune receptors (NLRs) to recognize pathogen-encoded effectors and initiate immune responses. Tomato spotted wilt virus (TSWV), which has been found to infect >1000 plant species, is among the most destructive plant viruses worldwide. The Sw-5b is the most effective and widely used resistance gene in tomato breeding to control TSWV. However, broad application of tomato cultivars carrying Sw-5b has resulted in an emergence of resistance-breaking (RB) TSWV. Therefore, new effective genes are urgently needed to prevent further RB TSWV outbreaks. In this study, we conducted artificial evolution to select Sw-5b mutants that could extend the resistance spectrum against TSWV RB isolates. Unlike regular NLRs, Sw-5b detects viral elicitor NSm using both the N-terminal Solanaceae-specific domain (SD) and the C-terminal LRR domain in a two-step recognition process. Our attempts to select gain-of-function mutants by random mutagenesis involving either the SD or the LRR of Sw-5b failed; therefore, we adopted a stepwise strategy, first introducing a NSmRB -responsive mutation at the R927 residue in the LRR, followed by random mutagenesis involving the Sw-5b SD domain. Using this strategy, we obtained Sw-5bL33P/K319E/R927A and Sw-5bL33P/K319E/R927Q mutants, which are effective against TSWV RB carrying the NSmC118Y or NSmT120N mutation, and against other American-type tospoviruses. Thus, we were able to extend the resistance spectrum of Sw-5b; the selected Sw-5b mutants will provide new gene resources to control RB TSWV.

Hemopneumothorax detection through the process of artificial evolution - a feasibility study.

BACKGROUND: Tension pneumothorax is one of the leading causes of preventable death on the battlefield. Current prehospital diagnosis relies on a subjective clinical impression complemented by a manual thoracic and respiratory examination. These techniques are not fully applicable in field conditions and on the battlefield, where situational and environmental factors may impair clinical capabilities. We aimed to assemble a device able to sample, analyze, and classify the unique acoustic signatures of pneumothorax and hemothorax. METHODS: Acoustic data was obtained with simultaneous use of two sensitive digital stethoscopes from the chest wall of an ex-vivo porcine model. Twelve second samples of acoustic data were obtained from the in-house assembled digital stethoscope system during mechanical ventilation. The thoracic cavity was injected with increasing volumes of 200, 400, 600, 800, and 1000 ml of air or saline to simulate pneumothorax and hemothorax, respectively. The data was analyzed using a multi-objective genetic algorithm that was used to develop an optimal mathematical detector through the process of artificial evolution, a cutting-edge approach in the artificial intelligence discipline. RESULTS: The in-house assembled dual digital stethoscope system and developed genetic algorithm achieved an accuracy, sensitivity and specificity ranging from 64 to 100%, 63 to 100%, and 63 to 100%, respectively, in classifying acoustic signal as associated with pneumothorax or hemothorax at fluid injection levels of 400 ml or more, and regardless of background noise. CONCLUSIONS: We present a novel, objective device for rapid diagnosis of potentially lethal thoracic injuries. With further optimization, such a device could provide real-time detection and monitoring of pneumothorax and hemothorax in battlefield conditions.

Secondary Structure Libraries for Artificial Evolution Experiments.

Methods of artificial evolution such as SELEX and in vitro selection have made it possible to isolate RNA and DNA motifs with a wide range of functions from large random sequence libraries. Once the primary sequence of a functional motif is known, the sequence space around it can be comprehensively explored using a combination of random mutagenesis and selection. However, methods to explore the sequence space of a secondary structure are not as well characterized. Here we address this question by describing a method to construct libraries in a single synthesis which are enriched for sequences with the potential to form a specific secondary structure, such as that of an aptamer, ribozyme, or deoxyribozyme. Although interactions such as base pairs cannot be encoded in a library using conventional DNA synthesizers, it is possible to modulate the probability that two positions will have the potential to pair by biasing the nucleotide composition at these positions. Here we show how to maximize this probability for each of the possible ways to encode a pair (in this study defined as A-U or U-A or C-G or G-C or G.U or U.G). We then use these optimized coding schemes to calculate the number of different variants of model stems and secondary structures expected to occur in a library for a series of structures in which the number of pairs and the extent of conservation of unpaired positions is systematically varied. Our calculations reveal a tradeoff between maximizing the probability of forming a pair and maximizing the number of possible variants of a desired secondary structure that can occur in the library. They also indicate that the optimal coding strategy for a library depends on the complexity of the motif being characterized. Because this approach provides a simple way to generate libraries enriched for sequences with the potential to form a specific secondary structure, we anticipate that it should be useful for the optimization and structural characterization of functional nucleic acid motifs.

Mutant Strains of Escherichia coli and Methicillin-Resistant Staphylococcus aureus Obtained by Laboratory Selection To Survive on Metallic Copper Surfaces.

Artificial laboratory evolution was used to produce mutant strains of Escherichia coli and methicillin-resistant Staphylococcus aureus (MRSA) able to survive on antimicrobial metallic copper surfaces. These mutants were 12- and 60-fold less susceptible to the copper-mediated contact killing process than their respective parent strains. Growth levels of the mutant and its parent in complex growth medium were similar. Tolerance to copper ions of the mutants was unchanged. The mutant phenotype remained stable over about 250 generations under nonstress conditions. The mutants and their respective parental strains accumulated copper released from the metallic surfaces to similar extents. Nevertheless, only the parental strains succumbed to copper stress when challenged on metallic copper surfaces, suffering complete destruction of the cell structure. Whole-genome sequencing and global transcriptome analysis were used to decipher the genetic alterations in the mutant strains; however, these results did not explain the copper-tolerance phenotypes on the systemic level. Instead, the mutants shared features with those of stressed bacterial subpopulations entering the early or "shallow" persister state. In contrast to the canonical persister state, however, the ability to survive on solid copper surfaces was adopted by the majority of the mutant strain population. This indicated that application of solid copper surfaces in hospitals and elsewhere has to be accompanied by strict cleaning regimens to keep the copper surfaces active and prevent evolution of tolerant mutant strains.IMPORTANCE Microbes are rapidly killed on solid copper surfaces by contact killing. Copper surfaces thus have an important role to play in preventing the spread of nosocomial infections. Bacteria adapt to challenging natural and clinical environments through evolutionary processes, for instance, by acquisition of beneficial spontaneous mutations. We wish to address the question of whether mutants can be selected that have evolved to survive contact killing on solid copper surfaces. We isolated such mutants from Escherichia coli and methicillin-resistant Staphylococcus aureus (MRSA) by artificial laboratory evolution. The ability to survive on solid copper surfaces was a stable phenotype of the mutant population and not restricted to a small subpopulation. As a consequence, standard operation procedures with strict hygienic measures are extremely important to prevent the emergence and spread of copper-surface-tolerant persister-like bacterial strains if copper surfaces are to be sustainably used to limit the spread of pathogenic bacteria, e.g., to curb nosocomial infections.

Hunting and mountain sheep: Do current harvest practices affect horn growth?

The influence of human harvest on evolution of secondary sexual characteristics has implications for sustainable management of wildlife populations. The phenotypic consequences of selectively removing males with large horns or antlers from ungulate populations have been a topic of heightened concern in recent years. Harvest can affect size of horn-like structures in two ways: (a) shifting age structure toward younger age classes, which can reduce the mean size of horn-like structures, or (b) selecting against genes that produce large, fast-growing males. We evaluated effects of age, climatic and forage conditions, and metrics of harvest on horn size and growth of mountain sheep (Ovis canadensis ssp.) in 72 hunt areas across North America from 1981 to 2016. In 50% of hunt areas, changes in mean horn size during the study period were related to changes in age structure of harvested sheep. Environmental conditions explained directional changes in horn growth in 28% of hunt areas, 7% of which did not exhibit change before accounting for effects of the environment. After accounting for age and environment, horn size of mountain sheep was stable or increasing in the majority (~78%) of hunt areas. Age-specific horn size declined in 44% of hunt areas where harvest was regulated solely by morphological criteria, which supports the notion that harvest practices that are simultaneously selective and intensive might lead to changes in horn growth. Nevertheless, phenotypic consequences are not a foregone conclusion in the face of selective harvest; over half of the hunt areas with highly selective and intensive harvest did not exhibit age-specific declines in horn size. Our results demonstrate that while harvest regimes are an important consideration, horn growth of harvested male mountain sheep has remained largely stable, indicating that changes in horn growth patterns are an unlikely consequence of harvest across most of North America.

In vitro evolution of phi29 DNA polymerases through compartmentalized gene expression and rolling-circle replication.

Phi29 DNA polymerase is widely used for DNA amplification through rolling-circle replication or multiple displacement amplification. Here, we performed completely in vitro artificial evolution of phi29 DNA polymerase by combining the in vitro compartmentalization and the gene expression-coupled rolling-circle replication of a circular DNA encoding the polymerase. We conducted the experiments in six different conditions composed of three different levels of inhibitor concentrations with two different DNA labeling methods. One of the experiments was performed in our previous study and the other five experiments were newly conducted in this study. Under all conditions, we found several mutations that enhance the rolling-circle amplification by the polymerase when it was expressed in the reconstituted gene expression system. Especially, a combinatorial mutant polymerase (K555T/D570N) exhibits significantly higher rolling-circle activity than the wild type. These highly active mutant polymerases would be useful for various applications.

Algorithmically probable mutations reproduce aspects of evolution, such as convergence rate, genetic memory and modularity.

Natural selection explains how life has evolved over millions of years from more primitive forms. The speed at which this happens, however, has sometimes defied formal explanations when based on random (uniformly distributed) mutations. Here, we investigate the application of a simplicity bias based on a natural but algorithmic distribution of mutations (no recombination) in various examples, particularly binary matrices, in order to compare evolutionary convergence rates. Results both on synthetic and on small biological examples indicate an accelerated rate when mutations are not statistically uniform but algorithmically uniform. We show that algorithmic distributions can evolve modularity and genetic memory by preservation of structures when they first occur sometimes leading to an accelerated production of diversity but also to population extinctions, possibly explaining naturally occurring phenomena such as diversity explosions (e.g. the Cambrian) and massive extinctions (e.g. the End Triassic) whose causes are currently a cause for debate. The natural approach introduced here appears to be a better approximation to biological evolution than models based exclusively upon random uniform mutations, and it also approaches a formal version of open-ended evolution based on previous formal results. These results validate some suggestions in the direction that computation may be an equally important driver of evolution. We also show that inducing the method on problems of optimization, such as genetic algorithms, has the potential to accelerate convergence of artificial evolutionary algorithms.

Moderate Environmental Variation Across Generations Promotes the Evolution of Robust Solutions.

Previous evolutionary studies demonstrated how robust solutions can be obtained by evaluating agents multiple times in variable environmental conditions. Here we demonstrate how agents evolved in environments that vary across generations outperform agents evolved in environments that remain fixed. Moreover, we demonstrate that best performance is obtained when the environment varies at a moderate rate across generations, that is, when the environment does not vary every generation but every N generations. The advantage of exposing evolving agents to environments that vary across generations at a moderate rate is due, at least in part, to the fact that this condition maximizes the retention of changes that alter the behavior of the agents, which in turn facilitates the discovery of better solutions. Finally, we demonstrate that moderate environmental variations are advantageous also from an evolutionary computation perspective, that is, from the perspective of maximizing the performance that can be achieved within a limited computational budget.

In Vitro Evolution of Unmodified 16S rRNA for Simple Ribosome Reconstitution.

One of the largest challenges in the synthesis of artificial cells that can reproduce is in vitro assembly of ribosomes from in vitro synthesized rRNAs and proteins. In this study, to circumvent the post-transcriptional modification of 16S rRNA for reconstitution of the fully active 30S subunit, we performed artificial evolution of 16S rRNA, which forms the functional 30S subunit without post-transcriptional modifications. We first established an in vitro selection scheme by combining the integrated synthesis, assembly, and translation (iSAT) system with the liposome sorting technique. After 15 rounds of selection cycles, we found one point mutation (U1495C) near the 3' terminus that significantly enhanced the reconstitution activity of the functional 30S subunit from unmodified 16S rRNA to approximately 57% of that from native-modified 16S rRNA. The effect of the mutation did not depend on the reconstitution scheme, anti-SD sequences, or the target genes to be translated. The mutation we found in this study enabled reconstitution of the active 30S subunit without rRNA modification, and thus would be a useful tool for simple construction of self-reproducing ribosomes.

Translational Research: Exploring and Creating Genetic Diversity.

The crop selection process has created a genetic bottleneck ultimately restricting breeding output. Wild relatives of major crops as well as the so-called 'neglected plant' species represent a reservoir of genetic diversity that remains underutilized. These species could be used as a tool to discover new alleles of agronomic interest or could be the target of breeding programs. Targeted induced local lesions in the genome (TILLING) can be used to translate in neglected crops what has been discovered in major crops and reciprocally. However, random mutagenesis, used in TILLING approaches, provides only a limited density of mutational events at a defined target locus. Alternatively, clustered regularly interspaced short palindromic repeats (CRISPR) associated 9 (Cas9) fused to a cytidine deaminase could serve as a localized mutagenic agent to produce high-density mutant populations. Artificial evolution is at hand.

ReX: A suite of computational tools for the design, visualization, and analysis of chimeric protein libraries.

Directed evolution has greatly facilitated protein engineering and provided new insights into protein structure-function relationships. DNA shuffling using restriction enzymes is a particularly simple and cost-effective means of recombinatorial evolution that is well within the capability of most molecular biologists, but tools for the design and analysis of such experiments are limited. Here we introduce a suite of freely available online tools to make the construction and analysis of chimeric libraries readily accessible to the novice. REcut (http://qpmf.rx.umaryland.edu/REcut.html) facilitates the choice of DNA fragmentation strategy, while Xover (http://qpmf.rx.umaryland.edu/Xover.html) analyzes chimeric mutants to reveal recombination patterns and extract quantitative data.