Noncanonical secondary structure stabilizes mitochondrial tRNA(Ser(UCN)) by reducing the entropic cost of tertiary folding

Abstract

Mammalian mitochondrial tRNA(Ser(UCN)) (mt-tRNA(Ser)) and pyrrolysine tRNA (tRNA(Pyl)) fold to near-canonical three-dimensional structures despite having noncanonical secondary structures with shortened interhelical loops that disrupt the conserved tRNA tertiary interaction network. How these noncanonical tRNAs compensate for their loss of tertiary interactions remains unclear. Furthermore, in human mt-tRNA(Ser), lengthening the variable loop by the 7472insC mutation reduces mt-tRNA(Ser) concentration in vivo through poorly understood mechanisms and is strongly associated with diseases such as deafness and epilepsy. Using simulations of the TOPRNA coarse-grained model, we show that increased topological constraints encoded by the unique secondary structure of wild-type mt-tRNA(Ser) decrease the entropic cost of folding by ∼2.5 kcal/mol compared to canonical tRNA, offsetting its loss of tertiary interactions. Further simulations show that the pathogenic 7472insC mutation disrupts topological constraints and hence destabilizes the mutant mt-tRNA(Ser) by ∼0.6 kcal/mol relative to wild-type. UV melting experiments confirm that insertion mutations lower mt-tRNA(Ser) melting temperature by 6-9 °C and increase the folding free energy by 0.8-1.7 kcal/mol in a largely sequence- and salt-independent manner, in quantitative agreement with our simulation predictions. Our results show that topological constraints provide a quantitative framework for describing key aspects of RNA folding behavior and also provide the first evidence of a pathogenic mutation that is due to disruption of topological constraints.

Figure 1

(A) Secondary structure of human mt-tRNA^Ser(UCN) and the location of the insertion mutations. Secondary structure features that differ from cc-tRNA are indicated by red. Gray and orange lines indicate tertiary interactions inferred from the tRNA^Pyl crystal structure. Tertiary interactions shared by cc-tRNA are shown in gray, and novel interactions are shown in orange. (B) Secondary structure of yeast tRNA^Phe (cc-tRNA). Conserved tertiary interactions missing in mt-tRNA^Ser are shown in orange. (C) Superposition of TOPRNA representations of the cc-tRNA (gray) and tRNA^Pyl; (red) crystal structures.

Figure 2

(A) Representative snapshot from a TOPRNA simulation of mt-tRNA^Ser illustrating the Euler angle representation of the conformation between the AC-stem (red) and D-stem (blue). Loops, which are treated as freely rotatable chains by TOPRNA, are colored gray. (B) The fraction of theoretically possible interhelical (α_h, β_h, γ_h) conformations sampled by TOPRNA simulations.

Figure 3

Differences in the topological constraint contribution to the folding free energy ΔΔG_{fold, topo} = ΔG_{fold, topo}(i) - ΔG_{fold, topo}(WT). Values and error bars represent the mean and standard deviation of the ΔΔG computed by block averaging over thirds of the simulations.

Figure 4

Fraction of the 500 best-packed conformations of each tRNA that have non-native loop-loop contacts (gray), or that have D-to-T-loop contacts and both interhelical stacks (red). The fraction of conformers with both native contacts is weighted by entropy. Note that the third conformational possibility – lacks stacking or D-T-loop contacts – is not shown.

Figure 5

(A, B) Derivative of absorbance at 260 nm of mt-tRNA^Ser species in the presence of 2 mM MgCl₂ (A, C, F) or 5 mM MgCl₂ (B, D, G). All melts were performed in a background of 20 mM sodium cacodylate (pH 7.2) and 150 mM NaCl. Curves of different molecules are colored according to the key in (A). (C, D) Tertiary structure melting temperatures determined from van’t Hoff fits to melting curves. Error bars represent estimated 1 °C error (see methods). Bars are colored by mutant according to (A). TOPRNA predicted values, in reference to the WT T_m, are shown with open bars and represent the mean of the different ΔT_m estimates in Figure S6. (E) Example van’t Hoff fit to the WT transcript at 2 mM MgCl₂. The different transitions are shown in color, the baseline in gray, and the overall fit in black. (F, G) ΔG_fold determined from van’t Hoff fits extrapolated to 300 K. Color scheme is the same as in (C, D). TOPRNA predictions are referenced to the insUU ΔG_fold. (H) Melting curves of insC and insCC mutants at 5 mM MgCl₂. WT, insG, and insUU melting curves are shown by lines colored according to the key in (A) for reference.

Figure 6

Proposed pathogenic mechanism of the insG mutation. The unstable transcript exists in equilibrium between folded and unfolded tertiary conformations, with only the folded conformation likely efficiently processed into mature tRNA. In competition with maturation is a degradation pathway that degrades unfolded and misprocessed tRNAs. The insG mutation shifts the transcript equilibrium towards unfolded conformations (red arrows), leading to increased misprocessing and degradation. Degradation may be promoted by 3′-CCACCA addition as in cytosolic rapid decay. Once modified, insG mt-tRNA^Ser molecules have similar stabilities as WT.