Title: Emergent properties as by-products of prebiotic evolution of aminoacylation ribozymes
Abstract
Systems of catalytic RNAs presumably gave rise to important evolutionary innovations, such as the genetic code. Such systems may exhibit particular tolerance to errors (error minimization) as well as coding specificity. While often assumed to result from natural selection, error minimization may instead be an emergent by-product. In an RNA world, a system of self-aminoacylating ribozymes could enforce the mapping of amino acids to anticodons. We measured the activity of thousands of ribozyme mutants on alternative substrates (activated analogs for tryptophan, phenylalanine, leucine, isoleucine, valine, and methionine). Related ribozymes exhibited shared preferences for substrates, indicating that adoption of additional amino acids by existing ribozymes would itself lead to error minimization. Furthermore, ribozyme activity was positively correlated with specificity, indicating that selection for increased activity would also lead to increased specificity. These results demonstrate that by-products of ribozyme evolution could lead to adaptive value in specificity and error tolerance.
Various self-cleaving ribozymes appearing in nature catalyze the sequence-specific intramolecular cleavage of RNA and can be engineered to catalyze cleavage of appropriate substrates in an intermolecular fashion, thus acting as true catalysts. The mechanisms of the small, self-cleaving ribozymes have been extensively studied and reviewed previously. Self-cleaving ribozymes can possess high catalytic activity and high substrate specificity; however, substrate specificity is also engineerable within the constraints of the ribozyme structure. While these ribozymes share a common fundamental catalytic mechanism, each ribozyme family has a unique overall architecture and active site organization, indicating that several distinct structures yield this chemical activity. The multitude of catalytic structures, combined with some flexibility in substrate specificity within each family, suggests that such catalytic RNAs, taken together, could access a wide variety of substrates. Here, we give an overview of 10 classes of self-cleaving ribozymes and capture what is understood about their substrate specificity and synthetic applications. Evolution of these ribozymes in an RNA world might be characterized by the emergence of a new ribozyme family followed by rapid adaptation or diversification for specific substrates.
Beck, James D.; Roberts, Jessica M.; Kitzhaber, Joey M.; Trapp, Ashlyn; Serra, Edoardo; Spezzano, Francesca; Hayden, Eric J.(
, Frontiers in Molecular Biosciences)
Ribozymes are RNA molecules that catalyze biochemical reactions. Self-cleaving ribozymes are a common naturally occurring class of ribozymes that catalyze site-specific cleavage of their own phosphodiester backbone. In addition to their natural functions, self-cleaving ribozymes have been used to engineer control of gene expression because they can be designed to alter RNA processing and stability. However, the rational design of ribozyme activity remains challenging, and many ribozyme-based systems are engineered or improved by random mutagenesis and selection ( in vitro evolution). Improving a ribozyme-based system often requires several mutations to achieve the desired function, but extensive pairwise and higher-order epistasis prevent a simple prediction of the effect of multiple mutations that is needed for rational design. Recently, high-throughput sequencing-based approaches have produced data sets on the effects of numerous mutations in different ribozymes (RNA fitness landscapes). Here we used such high-throughput experimental data from variants of the CPEB3 self-cleaving ribozyme to train a predictive model through machine learning approaches. We trained models using either a random forest or long short-term memory (LSTM) recurrent neural network approach. We found that models trained on a comprehensive set of pairwise mutant data could predict active sequences at higher mutational distances, but the correlation between predicted and experimentally observed self-cleavage activity decreased with increasing mutational distance. Adding sequences with increasingly higher numbers of mutations to the training data improved the correlation at increasing mutational distances. Systematically reducing the size of the training data set suggests that a wide distribution of ribozyme activity may be the key to accurate predictions. Because the model predictions are based only on sequence and activity data, the results demonstrate that this machine learning approach allows readily obtainable experimental data to be used for RNA design efforts even for RNA molecules with unknown structures. The accurate prediction of RNA functions will enable a more comprehensive understanding of RNA fitness landscapes for studying evolution and for guiding RNA-based engineering efforts.
INTRODUCTION Eukaryotes contain a highly conserved signaling pathway that becomes rapidly activated when adenosine triphosphate (ATP) levels decrease, as happens during conditions of nutrient shortage or mitochondrial dysfunction. The adenosine monophosphate (AMP)–activated protein kinase (AMPK) is activated within minutes of energetic stress and phosphorylates a limited number of substrates to biochemically rewire metabolism from an anabolic state to a catabolic state to restore metabolic homeostasis. AMPK also promotes prolonged metabolic adaptation through transcriptional changes, decreasing biosynthetic genes while increasing expression of genes promoting lysosomal and mitochondrial biogenesis. The transcription factor EB (TFEB) is a well-appreciated effector of AMPK-dependent signals, but many of the molecular details of how AMPK controls these processes remain unknown. RATIONALE The requirement of AMPK and its specific downstream targets that control aspects of the transcriptional adaptation of metabolism remain largely undefined. We performed time courses examining gene expression changes after various mitochondrial stresses in wild-type (WT) or AMPK knockout cells. We hypothesized that a previously described interacting protein of AMPK, folliculin-interacting protein 1 (FNIP1), may be involved in how AMPK promotes increases in gene expression after metabolic stress. FNIP1 forms a complex with the protein folliculin (FLCN), together acting as a guanosine triphosphate (GTP)–activating protein (GAP) for RagC. The FNIP1-FLCN complex has emerged as an amino acid sensor to the mechanistic target of rapamycin complex 1 (mTORC1), involved in how amino acids control TFEB activation. We therefore examined whether AMPK may regulate FNIP1 to dominantly control TFEB independently of amino acids. RESULTS AMPK was found to govern expression of a core set of genes after various mitochondrial stresses. Hallmark features of this response were activation of TFEB and increases in the transcription of genes specifying lysosomal and mitochondrial biogenesis. AMPK directly phosphorylated five conserved serine residues in FNIP1, suppressing the function of the FLCN-FNIP1 GAP complex, which resulted in dissociation of RagC and mTOR from the lysosome, promoting nuclear translocation of TFEB even in the presence of amino acids. FNIP1 phosphorylation was required for AMPK to activate TFEB and for subsequent increases in peroxisome proliferation–activated receptor gamma coactivator 1-alpha (PGC1α) and estrogen-related receptor alpha (ERRα) mRNAs. Cells in which the five serines in FNIP1 were mutated to alanine were unable to increase lysosomal and mitochondrial gene expression programs after treatment with mitochondrial poisons or AMPK activators despite the presence and normal regulation of all other substrates of AMPK. By contrast, neither AMPK nor its control of FNIP1 were needed for activation of TFEB after amino acid withdrawal, illustrating the specificity to energy-limited conditions. CONCLUSION Our data establish FNIP1 as the long-sought substrate of AMPK that controls TFEB translocation to the nucleus, defining AMPK phosphorylation of FNIP1 as a singular event required for increased lysosomal and mitochondrial gene expression programs after metabolic stresses. This study also illuminates the larger biological question of how mitochondrial damage triggers a temporal response of repair and replacement of damaged mitochondria: Within early hours, AMPK-FNIP1–activated TFEB induces a wave of lysosome and autophagy genes to promote degradation of damaged mitochondria, and a few hours later, TFEB–up-regulated PGC1⍺ and ERR⍺ promote expression of a second wave of genes specifying mitochondrial biogenesis. These insights open therapeutic avenues for several common diseases associated with mitochondrial dysfunction, ranging from neurodegeneration to type 2 diabetes to cancer. Mitochondrial damage activates AMPK to phosphorylate FNIP1, stimulating TFEB translocation to the nucleus and sequential waves of lysosomal and mitochondrial biogenesis. After mitochondrial damage, activated AMPK phosphorylates FNIP1 (1), causing inhibition of FLCN-FNIP1 GAP activity (2). This leads to accumulation of RagC in its GTP-bound form, causing dissociation of RagC, mTORC1, and TFEB from the lysosome (3). TFEB is therefore not phosphorylated and translocates to the nucleus, inducing transcription of lysosomal or autophagy genes, with parallel increases in NT-PGC1α mRNA (4), which, in concert with ERRα (5), subsequently induces mitochondrial biogenesis (6). CCCP, carbonyl cyanide m-chlorophenylhydrazone; CLEAR, coordinated lysosomal expression and regulation; GDP, guanosine diphosphate; P, phosphorylation. [Figure created using BioRender]
Mou, Zhongyu; Eakes, Jason; Cooper, Connor J.; Foster, Carmen M.; Standaert, Robert F.; Podar, Mircea; Doktycz, Mitchel J.; Parks, Jerry M.(
, Proteins: Structure, Function, and Bioinformatics)
Abstract
Predicting the range of substrates accepted by an enzyme from its amino acid sequence is challenging. Although sequence‐ and structure‐based annotation approaches are often accurate for predicting broad categories of substrate specificity, they generally cannot predict which specific molecules will be accepted as substrates for a given enzyme, particularly within a class of closely related molecules. Combining targeted experimental activity data with structural modeling, ligand docking, and physicochemical properties of proteins and ligands with various machine learning models provides complementary information that can lead to accurate predictions of substrate scope for related enzymes. Here we describe such an approach that can predict the substrate scope of bacterial nitrilases, which catalyze the hydrolysis of nitrile compounds to the corresponding carboxylic acids and ammonia. Each of the four machine learning models (logistic regression, random forest, gradient‐boosted decision trees, and support vector machines) performed similarly (average ROC = 0.9, average accuracy = ~82%) for predicting substrate scope for this dataset, although random forest offers some advantages. This approach is intended to be highly modular with respect to physicochemical property calculations and software used for structural modeling and docking.
McKinley, Lauren N.; Kern, Reuben G.; Assmann, Sarah M.; Bevilacqua, Philip C.(
, Biochemistry)
Small nucleolytic ribozymes are RNAs that cleave their own phosphodiester backbone. While proteinaceous enzymes are regulated by a variety of known mechanisms, methods of regulation for ribozymes remain unclear. Twister is one ribozyme class for which many structural and catalytic properties have been elucidated. However, few studies have analyzed the activity of twister ribozymes in the context of native flanking sequence, even though ribozymes as transcribed in nature do not exist in isolation. Interactions between the ribozyme and its neighboring sequences can induce conformational changes that inhibit self-cleavage, providing a regulatory mechanism that could naturally determine ribozyme activity in vivo and in synthetic applications. To date, eight twister ribozymes have been identified within the staple crop rice (Oryza sativa). Herein, we select several twister ribozymes from rice and show that they are differentially regulated by their flanking sequence using published RNA-seq datasets, structure probing, and co-transcriptional cleavage assays. We found that the Osa 1-2 ribozyme does not interact with its flanking sequences. However, sequences flanking the Osa 1-3 and Osa 1-8 ribozymes form inactive conformations, referred to here as “ribozymogens”, that attenuate ribozyme self-cleavage activity. For the Osa 1-3 ribozyme, we show that activity can be rescued upon addition of a complementary antisense oligonucleotide, suggesting ribozymogens can be controlled via external signals. In all, our data provide a plausible mechanism wherein flanking sequence differentially regulates ribozyme activity in vivo. More broadly, the ability to regulate ribozyme behavior locally has potential applications in control of gene expression and synthetic biology.
@article{osti_10368063,
place = {Country unknown/Code not available},
title = {Emergent properties as by-products of prebiotic evolution of aminoacylation ribozymes},
url = {https://par.nsf.gov/biblio/10368063},
DOI = {10.1038/s41467-022-31387-0},
abstractNote = {Abstract Systems of catalytic RNAs presumably gave rise to important evolutionary innovations, such as the genetic code. Such systems may exhibit particular tolerance to errors (error minimization) as well as coding specificity. While often assumed to result from natural selection, error minimization may instead be an emergent by-product. In an RNA world, a system of self-aminoacylating ribozymes could enforce the mapping of amino acids to anticodons. We measured the activity of thousands of ribozyme mutants on alternative substrates (activated analogs for tryptophan, phenylalanine, leucine, isoleucine, valine, and methionine). Related ribozymes exhibited shared preferences for substrates, indicating that adoption of additional amino acids by existing ribozymes would itself lead to error minimization. Furthermore, ribozyme activity was positively correlated with specificity, indicating that selection for increased activity would also lead to increased specificity. These results demonstrate that by-products of ribozyme evolution could lead to adaptive value in specificity and error tolerance.},
journal = {Nature Communications},
volume = {13},
number = {1},
publisher = {Nature Publishing Group},
author = {Janzen, Evan and Shen, Yuning and Vázquez-Salazar, Alberto and Liu, Ziwei and Blanco, Celia and Kenchel, Josh and Chen, Irene A.},
}
Warning: Leaving National Science Foundation Website
You are now leaving the National Science Foundation website to go to a non-government website.
Website:
NSF takes no responsibility for and exercises no control over the views expressed or the accuracy of
the information contained on this site. Also be aware that NSF's privacy policy does not apply to this site.
