In vivo-like nearest neighbor parameters improve prediction of fractional RNA base-pairing in cells.

We conducted a thermodynamic analysis of RNA stability in Eco80 artificial cytoplasm, which mimics in vivo conditions, and compared it to transcriptome-wide probing of mRNA. Eco80 contains 80% of Escherichia coli metabolites, with biological concentrations of metal ions, including 2 mM free Mg2+ and 29 mM metabolite-chelated Mg2+. Fluorescence-detected binding isotherms (FDBI) were used to conduct a thermodynamic analysis of 24 RNA helices and found that these helices, which have an average stability of -12.3 kcal/mol, are less stable by ΔΔGo37 ∼1 kcal/mol. The FDBI data was used to determine a set of Watson-Crick free energy nearest neighbor parameters (NNPs), which revealed that Eco80 reduces the stability of three NNPs. This information was used to adjust the NN model using the RNAstructure package. The in vivo-like adjustments have minimal effects on the prediction of RNA secondary structures determined in vitro and in silico, but markedly improve prediction of fractional RNA base pairing in E. coli, as benchmarked with our in vivo DMS and EDC RNA chemical probing data. In summary, our thermodynamic and chemical probing analyses of RNA helices indicate that RNA secondary structures are less stable in cells than in artificially stable in vitro buffer conditions.

Genome-wide analysis of the in vivo tRNA structurome reveals RNA structural and modification dynamics under heat stress.

RNA structure plays roles in myriad cellular events including transcription, translation, and RNA processing. Genome-wide analyses of RNA secondary structure in vivo by chemical probing have revealed critical structural features of mRNAs and long ncRNAs. Here, we examine the in vivo secondary structure of a small RNA class, tRNAs. Study of tRNA structure is challenging because tRNAs are heavily modified and strongly structured. We introduce "tRNA structure-seq," a new workflow that accurately determines in vivo secondary structures of tRNA. The workflow combines dimethyl sulfate (DMS) probing, ultra-processive RT, and mutational profiling (MaP), which provides mutations opposite DMS and natural modifications thereby allowing multiple modifications to be identified in a single read. We applied tRNA structure-seq to E. coli under control and stress conditions. A leading folding algorithm predicts E. coli tRNA structures with only ∼80% average accuracy from sequence alone. Strikingly, tRNA structure-seq, by providing experimental restraints, improves structure prediction under in vivo conditions to ∼95% accuracy, with more than 14 tRNAs predicted completely correctly. tRNA structure-seq also quantifies the relative levels of tRNAs and their natural modifications at single nucleotide resolution, as validated by LC-MS/MS. Our application of tRNA structure-seq yields insights into tRNA structure in living cells, revealing that it is not immutable but has dynamics, with partial unfolding of secondary and tertiary tRNA structure under heat stress that is correlated with a loss of tRNA abundance. This method is applicable to other small RNAs, including those with natural modifications and highly structured regions.

A Divalent Metal Cation-Metabolite Interaction Model Reveals Cation Buffering and Speciation.

I present the perspective that the divalent metalome and the metabolome can be modeled as a network of chelating interactions instead of separate entities. I review progress in understanding the complex cellular environment, in particular recent contributions to modeling metabolite-Mg2+ interactions. I then demonstrate a simple extension of these strategies based approximately on intracellular Escherichia coli concentrations. This model is composed of four divalent metal cations with a range of cellular concentrations and physical properties (Mg2+, Ca2+, Mn2+, and Zn2+), eight representative metabolites, and interaction constants. I applied this model to predict the speciation of divalent metal cations between free and metabolite-chelated species. This approach reveals potentially beneficial properties, including maintenance of free divalent metal cations at biologically relevant concentrations, buffering of free divalent metal cations, and enrichment of functional metabolite-chelated species. While currently limited by available interaction coefficients, this modeling strategy can be generalized to more complex systems. In summary, biochemists should consider the potential of cellular metabolites to form chelating interactions with divalent metal cations.

RNA sequence and structure control assembly and function of RNA condensates.

Intracellular condensates formed through liquid-liquid phase separation (LLPS) primarily contain proteins and RNA. Recent evidence points to major contributions of RNA self-assembly in the formation of intracellular condensates. As the majority of previous studies on LLPS have focused on protein biochemistry, effects of biological RNAs on LLPS remain largely unexplored. In this study, we investigate the effects of crowding, metal ions, and RNA structure on formation of RNA condensates lacking proteins. Using bacterial riboswitches as a model system, we first demonstrate that LLPS of RNA is promoted by molecular crowding, as evidenced by formation of RNA droplets in the presence of polyethylene glycol (PEG 8K). Crowders are not essential for LLPS, however. Elevated Mg2+ concentrations promote LLPS of specific riboswitches without PEG. Calculations identify key RNA structural and sequence elements that potentiate the formation of PEG-free condensates; these calculations are corroborated by key wet-bench experiments. Based on this, we implement structure-guided design to generate condensates with novel functions including ligand binding. Finally, we show that RNA condensates help protect their RNA components from degradation by nucleases, suggesting potential biological roles for such higher-order RNA assemblies in controlling gene expression through RNA stability. By utilizing both natural and artificial RNAs, our study provides mechanistic insight into the contributions of intrinsic RNA properties and extrinsic environmental conditions to the formation and regulation of condensates comprised of RNAs.

The Metabolome Weakens RNA Thermodynamic Stability and Strengthens RNA Chemical Stability.

We examined the complex network of interactions among RNA, the metabolome, and divalent Mg2+ under conditions that mimic the Escherichia coli cytoplasm. We determined Mg2+ binding constants for the top 15 E. coli metabolites, comprising 80% of the metabolome by concentration at physiological pH and monovalent ion concentrations. These data were used to inform the development of an artificial cytoplasm that mimics in vivo E. coli conditions, which we term "Eco80". We empirically determined that the mixture of E. coli metabolites in Eco80 approximated single-site binding behavior toward Mg2+ in the biologically relevant free Mg2+ range of ∼0.5 to 3 mM Mg2+, using a Mg2+-sensitive fluorescent dye. Effects of Eco80 conditions on the thermodynamic stability, chemical stability, structure, and catalysis of RNA were examined. We found that Eco80 conditions lead to opposing effects on the thermodynamic and chemical stabilities of RNA. In particular, the thermodynamic stability of RNA helices was weakened by 0.69 ± 0.12 kcal/mol, while the chemical stability was enhanced ∼2-fold, which can be understood using the speciation of Mg2+ between weak and strong Mg2+-metabolite complexes in Eco80. Overall, the use of Eco80 reflects RNA function in vivo and enhances the biological relevance of mechanistic studies of RNA.

Functional Roles of Chelated Magnesium Ions in RNA Folding and Function.

RNA regulates myriad cellular events such as transcription, translation, and splicing. To perform these essential functions, RNA often folds into complex tertiary structures in which its negatively charged ribose-phosphate backbone interacts with metal ions. Magnesium, the most abundant divalent metal ion in cells, neutralizes the backbone, thereby playing essential roles in RNA folding and function. This has been known for more than 50 years, and there are now thousands of in vitro studies, most of which have used ≥10 mM free Mg2+ ions to achieve optimal RNA folding and function. In the cell, however, concentrations of free Mg2+ ions are much lower, with most Mg2+ ions chelated by metabolites. In this Perspective, we curate data from a number of sources to provide extensive summaries of cellular concentrations of metabolites that bind Mg2+ and to estimate cellular concentrations of metabolite-chelated Mg2+ species, in the representative prokaryotic and eukaryotic systems Escherichia coli, Saccharomyces cerevisiae, and iBMK cells. Recent research from our lab and others has uncovered the fact that such weakly chelated Mg2+ ions can enhance RNA function, including its thermodynamic stability, chemical stability, and catalysis. We also discuss how metabolite-chelated Mg2+ complexes may have played roles in the origins of life. It is clear from this analysis that bound Mg2+ should not be simply considered non-RNA-interacting and that future RNA research, as well as protein research, could benefit from considering chelated magnesium.

MeltR software provides facile determination of nucleic acid thermodynamics.

Thermodenaturation (melting) curves of macromolecules are used to determine folding thermodynamic parameters. Notably, this insight into RNA and DNA stability underlies nearest neighbor theory and diverse structure prediction tools. Analysis of UV-detected absorbance melting curves is complex and multivariate, requiring many data preprocessing, regression, and error analysis steps. The absorbance melting curve-fitting software MeltWin, introduced in 1996, provided a consistent and facile melting curve analysis platform used in a generation of folding parameters. Unfortunately, MeltWin software is not maintained and relies on idiosyncratic choices of baselines by the user. Herein, we provide MeltR, an open-source, curve-fitting package for analysis of macromolecular thermodynamic data. The MeltR package provides the facile conversion of melting curve data to parameters provided by MeltWin while offering additional features including global fitting of data, auto-baseline generation, and two-state melting analysis. MeltR should be a useful tool for analyzing the next generation of DNA, RNA, and nonnucleic acid macromolecular melting data.