Towards parsimonious generative modeling of RNA families.

Generative probabilistic models emerge as a new paradigm in data-driven, evolution-informed design of biomolecular sequences. This paper introduces a novel approach, called Edge Activation Direct Coupling Analysis (eaDCA), tailored to the characteristics of RNA sequences, with a strong emphasis on simplicity, efficiency, and interpretability. eaDCA explicitly constructs sparse coevolutionary models for RNA families, achieving performance levels comparable to more complex methods while utilizing a significantly lower number of parameters. Our approach demonstrates efficiency in generating artificial RNA sequences that closely resemble their natural counterparts in both statistical analyses and SHAPE-MaP experiments, and in predicting the effect of mutations. Notably, eaDCA provides a unique feature: estimating the number of potential functional sequences within a given RNA family. For example, in the case of cyclic di-AMP riboswitches (RF00379), our analysis suggests the existence of approximately 1039 functional nucleotide sequences. While huge compared to the known <4000 natural sequences, this number represents only a tiny fraction of the vast pool of nearly 1082 possible nucleotide sequences of the same length (136 nucleotides). These results underscore the promise of sparse and interpretable generative models, such as eaDCA, in enhancing our understanding of the expansive RNA sequence space.

Small-molecule autocatalysis drives compartment growth, competition and reproduction.

Sustained autocatalysis coupled to compartment growth and division is a key step in the origin of life, but an experimental demonstration of this phenomenon in an artificial system has previously proven elusive. We show that autocatalytic reactions within compartments-when autocatalysis, and reactant and solvent exchange outpace product exchange-drive osmosis and diffusion, resulting in compartment growth. We demonstrate, using the formose reaction compartmentalized in aqueous droplets in an emulsion, that compartment volume can more than double. Competition for a common reactant (formaldehyde) causes variation in droplet growth rate based on the composition of the surrounding droplets. These growth rate variations are partially transmitted after selective division of the largest droplets by shearing, which converts growth-rate differences into differences in droplet frequency. This shows how a combination of properties of living systems (growth, division, variation, competition, rudimentary heredity and selection) can arise from simple physical-chemical processes and may have paved the way for the emergence of evolution by natural selection.

High-throughput droplet-based analysis of influenza A virus genetic reassortment by single-virus RNA sequencing.

The segmented RNA genome of influenza A viruses (IAVs) enables viral evolution through genetic reassortment after multiple IAVs coinfect the same cell, leading to viruses harboring combinations of eight genomic segments from distinct parental viruses. Existing data indicate that reassortant genotypes are not equiprobable; however, the low throughput of available virology techniques does not allow quantitative analysis. Here, we have developed a high-throughput single-cell droplet microfluidic system allowing encapsulation of IAV-infected cells, each cell being infected by a single progeny virion resulting from a coinfection process. Customized barcoded primers for targeted viral RNA sequencing enabled the analysis of 18,422 viral genotypes resulting from coinfection with two circulating human H1N1pdm09 and H3N2 IAVs. Results were highly reproducible, confirmed that genetic reassortment is far from random, and allowed accurate quantification of reassortants including rare events. In total, 159 out of the 254 possible reassortant genotypes were observed but with widely varied prevalence (from 0.038 to 8.45%). In cells where eight segments were detected, all 112 possible pairwise combinations of segments were observed. The inclusion of data from single cells where less than eight segments were detected allowed analysis of pairwise cosegregation between segments with very high confidence. Direct coupling analysis accurately predicted the fraction of pairwise segments and full genotypes. Overall, our results indicate that a large proportion of reassortant genotypes can emerge upon coinfection and be detected over a wide range of frequencies, highlighting the power of our tool for systematic and exhaustive monitoring of the reassortment potential of IAVs.

Abiogenesis through gradual evolution of autocatalysis into template-based replication.

How life emerged from inanimate matter is one of the most intriguing questions posed to modern science. Central to this research are experimental attempts to build systems capable of Darwinian evolution. RNA catalysts (ribozymes) are a promising avenue, in line with the RNA world hypothesis whereby RNA pre-dated DNA and proteins. Since evolution in living organisms relies on template-based replication, the identification of a ribozyme capable of replicating itself (an RNA self-replicase) has been a major objective. However, no self-replicase has been identified to date. Alternatively, autocatalytic systems involving multiple RNA species capable of ligation and recombination may enable self-reproduction. However, it remains unclear how evolution could emerge in autocatalytic systems. In this review, we examine how experimentally feasible RNA reactions catalysed by ribozymes could implement the evolutionary properties of variation, heredity and reproduction, and ultimately allow for Darwinian evolution. We propose a gradual path for the emergence of evolution, initially supported by autocatalytic systems leading to the later appearance of RNA replicases.

Stoechiometric and dynamical autocatalysis for diluted chemical reaction networks.

Autocatalysis underlies the ability of chemical and biochemical systems to replicate. Recently, Blokhuis et al. (PNAS 117(41):25230-25236, 2020) gave a stoechiometric definition of autocatalysis for reaction networks, stating the existence of a combination of reactions such that the balance for all autocatalytic species is strictly positive, and investigated minimal autocatalytic networks, called autocatalytic cores. By contrast, spontaneous autocatalysis-namely, exponential amplification of all species internal to a reaction network, starting from a diluted regime, i.e. low concentrations-is a dynamical property. We introduce here a topological condition (Top) for autocatalysis, namely: restricting the reaction network description to highly diluted species, we assume existence of a strongly connected component possessing at least one reaction with multiple products (including multiple copies of a single species). We find this condition to be necessary and sufficient for stoechiometric autocatalysis. When degradation reactions have small enough rates, the topological condition further ensures dynamical autocatalysis, characterized by a strictly positive Lyapunov exponent giving the instantaneous exponential growth rate of the system. The proof is generally based on the study of auxiliary Markov chains. We provide as examples general autocatalytic cores of Type I and Type III in the typology of Blokhuis et al. (PNAS 117(41):25230-25236, 2020) . In a companion article (Unterberger in Dynamical autocatalysis for autocatalytic cores, 2021), Lyapunov exponents and the behavior in the growth regime are studied quantitatively beyond the present diluted regime .

The Origin of Life: What Is the Question?

The question of the origin of life is a tenacious question that challenges many branches of science but is also extremely multifaceted. While prebiotic chemistry and micropaleontology reformulate the question as that of explaining the appearance of life on Earth in the deep past, systems chemistry and synthetic biology typically understand the question as that of demonstrating the synthesis of novel living matter from nonliving matter independently of historical constraints. The objective of this contribution is to disentangle the different readings of the origin-of-life question found in science. We identify three main dimensions along which the question can be differently constrained depending on context: historical adequacy, natural spontaneity, and similarity to life-as-we-know-it. We argue that the epistemic status of what needs to be explained-the explanandum-varies from approximately true when the origin-of-life question is the most constrained to entirely speculative when the constraints are the most relaxed. This difference in epistemic status triggers a shift in the nature of the origin-of-life question from an explanation-seeking question in the most constrained case to a fact-establishing question in the lesser-constrained ones. We furthermore explore how answers to some interpretations of the origin-of-life questions matter for other interpretations.

Natural Selection beyond Life? A Workshop Report.

Natural selection is commonly seen not just as an explanation for adaptive evolution, but as the inevitable consequence of "heritable variation in fitness among individuals". Although it remains embedded in biological concepts, such a formalisation makes it tempting to explore whether this precondition may be met not only in life as we know it, but also in other physical systems. This would imply that these systems are subject to natural selection and may perhaps be investigated in a biological framework, where properties are typically examined in light of their putative functions. Here we relate the major questions that were debated during a three-day workshop devoted to discussing whether natural selection may take place in non-living physical systems. We start this report with a brief overview of research fields dealing with "life-like" or "proto-biotic" systems, where mimicking evolution by natural selection in test tubes stands as a major objective. We contend the challenge may be as much conceptual as technical. Taking the problem from a physical angle, we then discuss the framework of dissipative structures. Although life is viewed in this context as a particular case within a larger ensemble of physical phenomena, this approach does not provide general principles from which natural selection can be derived. Turning back to evolutionary biology, we ask to what extent the most general formulations of the necessary conditions or signatures of natural selection may be applicable beyond biology. In our view, such a cross-disciplinary jump is impeded by reliance on individuality as a central yet implicit and loosely defined concept. Overall, these discussions thus lead us to conjecture that understanding, in physico-chemical terms, how individuality emerges and how it can be recognised, will be essential in the search for instances of evolution by natural selection outside of living systems.

RNA diversification by a self-reproducing ribozyme revealed by deep sequencing and kinetic modelling.

We demonstrate that a recombinase ribozyme achieves multiple functions in the same reaction network: self-reproduction, iterative elongation and circularization of other RNAs, leading to synthesis of diverse products predicted by a kinetic model. This shows that key mechanisms can be integrated and controlled toward Darwinian evolution in RNA reaction networks.

Darwinian properties and their trade-offs in autocatalytic RNA reaction networks.

Discovering autocatalytic chemistries that can evolve is a major goal in systems chemistry and a critical step towards understanding the origin of life. Autocatalytic networks have been discovered in various chemistries, but we lack a general understanding of how network topology controls the Darwinian properties of variation, differential reproduction, and heredity, which are mediated by the chemical composition. Using barcoded sequencing and droplet microfluidics, we establish a landscape of thousands of networks of RNAs that catalyze their own formation from fragments, and derive relationships between network topology and chemical composition. We find that strong variations arise from catalytic innovations perturbing weakly connected networks, and that growth increases with global connectivity. These rules imply trade-offs between reproduction and variation, and between compositional persistence and variation along trajectories of network complexification. Overall, connectivity in reaction networks provides a lever to balance variation (to explore chemical states) with reproduction and heredity (persistence being necessary for selection to act), as required for chemical evolution.

Thresholds in Origin of Life Scenarios.

Thresholds are widespread in origin of life scenarios, from the emergence of chirality, to the appearance of vesicles, of autocatalysis, all the way up to Darwinian evolution. Here, we analyze the "error threshold," which poses a condition for sustaining polymer replication, and generalize the threshold approach to other properties of prebiotic systems. Thresholds provide theoretical predictions, prescribe experimental tests, and integrate interdisciplinary knowledge. The coupling between systems and their environment determines how thresholds can be crossed, leading to different categories of prebiotic transitions. Articulating multiple thresholds reveals evolutionary properties in prebiotic scenarios. Overall, thresholds indicate how to assess, revise, and compare origin of life scenarios.

Universal motifs and the diversity of autocatalytic systems.

Autocatalysis is essential for the origin of life and chemical evolution. However, the lack of a unified framework so far prevents a systematic study of autocatalysis. Here, we derive, from basic principles, general stoichiometric conditions for catalysis and autocatalysis in chemical reaction networks. This allows for a classification of minimal autocatalytic motifs called cores. While all known autocatalytic systems indeed contain minimal motifs, the classification also reveals hitherto unidentified motifs. We further examine conditions for kinetic viability of such networks, which depends on the autocatalytic motifs they contain and is notably increased by internal catalytic cycles. Finally, we show how this framework extends the range of conceivable autocatalytic systems, by applying our stoichiometric and kinetic analysis to autocatalysis emerging from coupled compartments. The unified approach to autocatalysis presented in this work lays a foundation toward the building of a systems-level theory of chemical evolution.

Flux, toxicity, and expression costs generate complex genetic interactions in a metabolic pathway.

Our ability to predict the impact of mutations on traits relevant for disease and evolution remains severely limited by the dependence of their effects on the genetic background and environment. Even when molecular interactions between genes are known, it is unclear how these translate to organism-level interactions between alleles. We therefore characterized the interplay of genetic and environmental dependencies in determining fitness by quantifying ~4000 fitness interactions between expression variants of two metabolic genes, starting from various environmentally modulated expression levels. We detect a remarkable variety of interactions dependent on initial expression levels and demonstrate that they can be quantitatively explained by a mechanistic model accounting for catabolic flux, metabolite toxicity, and expression costs. Complex fitness interactions between mutations can therefore be predicted simply from their simultaneous impact on a few connected molecular phenotypes.

Predicting Evolutionary Constraints by Identifying Conflicting Demands in Regulatory Networks.

Gene regulation networks allow organisms to adapt to diverse environmental niches. However, the constraints underlying the evolution of gene regulation remain ill defined. Here, we show that partial order-a concept that ranks network output levels as a function of different input signals-identifies such constraints. We tested our predictions by experimentally evolving an engineered signal-integrating network in multiple environments. We find that populations: (1) expand in fitness space along the Pareto-optimal front associated with conflicts in regulatory demands, by fine-tuning binding affinities within the network, and (2) expand beyond the Pareto-optimal front through changes in the network structure. Our constraint predictions are based only on partial order and do not require information on the network architecture or underlying genetics. Overall, our findings show that limited knowledge of current regulatory phenotypes can provide predictions on future evolutionary constraints.

Metabolic cost of rapid adaptation of single yeast cells.

Cells can rapidly adapt to changing environments through nongenetic processes; however, the metabolic cost of such adaptation has never been considered. Here we demonstrate metabolic coupling in a remarkable, rapid adaptation process (1 in 1,000 cells adapt per hour) by simultaneously measuring metabolism and division of thousands of individual Saccharomyces cerevisiae cells using a droplet microfluidic system: droplets containing single cells are immobilized in a two-dimensional (2D) array, with osmotically induced changes in droplet volume being used to measure cell metabolism, while simultaneously imaging the cells to measure division. Following a severe challenge, most cells, while not dividing, continue to metabolize, displaying a remarkably wide diversity of metabolic trajectories from which adaptation events can be anticipated. Adaptation requires a characteristic amount of energy, indicating that it is an active process. The demonstration that metabolic trajectories predict a priori adaptation events provides evidence of tight energetic coupling between metabolism and regulatory reorganization in adaptation. This process allows S. cerevisiae to adapt on a physiological timescale, but related phenomena may also be important in other processes, such as cellular differentiation, cellular reprogramming, and the emergence of drug resistance in cancer.

Predicting Evolution Using Regulatory Architecture.

The limits of evolution have long fascinated biologists. However, the causes of evolutionary constraint have remained elusive due to a poor mechanistic understanding of studied phenotypes. Recently, a range of innovative approaches have leveraged mechanistic information on regulatory networks and cellular biology. These methods combine systems biology models with population and single-cell quantification and with new genetic tools, and they have been applied to a range of complex cellular functions and engineered networks. In this article, we review these developments, which are revealing the mechanistic causes of epistasis at different levels of biological organization-in molecular recognition, within a single regulatory network, and between different networks-providing first indications of predictable features of evolutionary constraint.

The generality of transient compartmentalization and its associated error thresholds.

Can prelife proceed without cell division? A recently proposed mechanism suggests that transient compartmentalization could have preceded cell division in prebiotic scenarios. Here, we study transient compartmentalization dynamics in the presence of mutations and noise in replication, as both can be detrimental the survival of compartments. Our study comprises situations where compartments contain uncoupled autocatalytic reactions feeding on a common resource, and systems based on RNA molecules copied by replicases, following a recent experimental study. Using the theory of branching processes, we show analytically that two regimes are possible. In the diffusion-limited regime, replication is asynchronous which leads to a large variability in the composition of compartments. In contrast, in a replication-limited regime, the growth is synchronous and thus the compositional variability is low. Typically, simple autocatalysts are in the former regime, while polymeric replicators can access the latter. For deterministic growth dynamics, we introduce mutations that turn functional replicators into parasites. We derive the phase boundary separating coexistence or parasite dominance as a function of relative growth, inoculation size and mutation rate. We show that transient compartmentalization allows coexistence beyond the classical error threshold, above which the parasite dominates. Our findings invite to revisit major prebiotic transitions, notably the transitions towards cooperation, complex polymers and cell division.

Recent insights into the genotype-phenotype relationship from massively parallel genetic assays.

With the molecular revolution in Biology, a mechanistic understanding of the genotype-phenotype relationship became possible. Recently, advances in DNA synthesis and sequencing have enabled the development of deep mutational scanning assays, capable of scoring comprehensive libraries of genotypes for fitness and a variety of phenotypes in massively parallel fashion. The resulting empirical genotype-fitness maps pave the way to predictive models, potentially accelerating our ability to anticipate the behaviour of pathogen and cancerous cell populations from sequencing data. Besides from cellular fitness, phenotypes of direct application in industry (e.g. enzyme activity) and medicine (e.g. antibody binding) can be quantified and even selected directly by these assays. This review discusses the technological basis of and recent developments in massively parallel genetics, along with the trends it is uncovering in the genotype-phenotype relationship (distribution of mutation effects, epistasis), their possible mechanistic bases and future directions for advancing towards the goal of predictive genetics.

Large scale control and programming of gene expression using CRISPR.

The control of gene expression in cells and organisms allows to unveil gene to function relationships and to reprogram biological responses. Several systems, such as Zinc fingers, TALE (Transcription activator-like effectors), and siRNAs (small-interfering RNAs), have been exploited to achieve this. However, recent advances in Clustered Regularly Interspaced Short Palindromic Repeats and Cas9 (CRISPR-Cas9) have overshadowed them due to high specificity, compatibility with many different organisms, and design flexibility. In this review we summarize state-of-the art for CRISPR-Cas9 technology for large scale gene perturbation studies, including single gene and multiple genes knock-out, knock-down, knock-up libraries, and their associated screening assays. We feature in particular the combination of these methods with single-cell transcriptomics approaches. Finally, we highlight the application of CRISPR-Cas9 systems in building synthetic circuits that can be interfaced with gene networks to control cellular states.

Mineral surfaces select for longer RNA molecules.

We report empirically and theoretically that multiple prebiotic minerals can selectively accumulate longer RNAs, with selectivity enhanced at higher temperatures. We further demonstrate that surfaces can be combined with a catalytic RNA to form longer RNA polymers, supporting the potential of minerals to develop genetic information on the early Earth.

Coupled catabolism and anabolism in autocatalytic RNA sets.

The ability to process molecules available in the environment into useable building blocks characterizes catabolism in contemporary cells and was probably critical for the initiation of life. Here we show that a catabolic process in collectively autocatalytic sets of RNAs allows diversified substrates to be assimilated. We modify fragments of the Azoarcus group I intron and find that the system is able to restore the original native fragments by a multi-step reaction pathway. This allows in turn the formation of catalysts by an anabolic process, eventually leading to the accumulation of ribozymes. These results demonstrate that rudimentary self-reproducing RNA systems based on recombination possess an inherent capacity to assimilate an expanded repertoire of chemical resources and suggest that coupled catabolism and anabolism could have arisen at a very early stage in primordial living systems.