Evolutionary optimization of a modular ligase ribozyme: a small catalytic unit and a hairpin motif masking an element that could form an inactive structure

A range of complex probabilistic models for RNA secondary structure prediction that includes the nearest-neighbor model and more

The standard approach for single-sequence RNA secondary structure prediction uses a nearest-neighbor thermodynamic model with several thousand experimentally determined energy parameters. An attractive alternative is to use statistical approaches with parameters estimated from growing databases of structural RNAs. Good results have been reported for discriminative statistical methods using complex nearest-neighbor models, including CONTRAfold, Simfold, and ContextFold. Little work has been reported on generative probabilistic models (stochastic context-free grammars [SCFGs]) of comparable complexity, although probabilistic models are generally easier to train and to use. To explore a range of probabilistic models of increasing complexity, and to directly compare probabilistic, thermodynamic, and discriminative approaches, we created TORNADO, a computational tool that can parse a wide spectrum of RNA grammar architectures (including the standard nearest-neighbor model and more) using a generalized super-grammar that can be parameterized with probabilities, energies, or arbitrary scores. By using TORNADO, we find that probabilistic nearest-neighbor models perform comparably to (but not significantly better than) discriminative methods. We find that complex statistical models are prone to overfitting RNA structure and that evaluations should use structurally nonhomologous training and test data sets. Overfitting has affected at least one published method (ContextFold). The most important barrier to improving statistical approaches for RNA secondary structure prediction is the lack of diversity of well-curated single-sequence RNA secondary structures in current RNA databases.

Identification of two new loci at IL23R and RAB32 that influence susceptibility to leprosy

We performed a genome-wide association study with 706 individuals with leprosy and 5,581 control individuals and replicated the top 24 SNPs in three independent replication samples, including a total of 3,301 individuals with leprosy and 5,299 control individuals from China. Two loci not previously associated with the disease were identified with genome-wide significance: rs2275606 (combined P = 3.94 × 10(-14), OR = 1.30) on 6q24.3 and rs3762318 (combined P = 3.27 × 10(-11), OR = 0.69) on 1p31.3. These associations implicate IL23R and RAB32 as new susceptibility genes for leprosy. Furthermore, we identified evidence of interaction between the NOD2 and RIPK2 loci, which is consistent with the biological association of the proteins encoded by these genes (NOD2-RIPK2 complex) in activating the NF-κB pathway as a part of the host defense response to infection. Our findings have expanded the biological functions of IL23R by uncovering its involvement in infectious disease susceptibility and suggest a potential involvement of autophagocytosis in leprosy pathogenesis. The IL23R association supports previous observations of the marked overlap of susceptibility genes for leprosy and Crohn's disease, implying common pathogenesis mechanisms.

Generation and development of RNA ligase ribozymes with modular architecture through "design and selection"

In vitro selection with long random RNA libraries has been used as a powerful method to generate novel functional RNAs, although it often requires laborious structural analysis of isolated RNA molecules. Rational RNA design is an attractive alternative to avoid this laborious step, but rational design of catalytic modules is still a challenging task. A hybrid strategy of in vitro selection and rational design has been proposed. With this strategy termed “design and selection,” new ribozymes can be generated through installation of catalytic modules onto RNA scaffolds with defined 3D structures. This approach, the concept of which was inspired by the modular architecture of naturally occurring ribozymes, allows prediction of the overall architectures of the resulting ribozymes, and the structural modularity of the resulting ribozymes allows modification of their structures and functions. In this review, we summarize the design, generation, properties, and engineering of four classes of ligase ribozyme generated by design and selection.

Deep sequencing analysis of mutations resulting from the incorporation of dNTP analogs

Next-generation DNA sequencing technology was used to score >100 000 mutations resulting from exposure of a nucleic acid template to a mutagenic dNTP analog during a single pass of a DNA polymerase. An RNA template of known secondary structure was reverse transcribed in the presence of 8-oxo-dGTP, dPTP or both, followed by forward transcription in the presence of standard NTPs. Each mutagen, whether used alone or in combination, resulted in a highly characteristic mutation profile. Mutations were generated at a mean frequency of 1–2% per eligible nucleotide position, but there was substantial variation in the frequency of mutation at different positions, with a SD close to the mean. This variation was partly due to the identity of the immediately surrounding nucleotides and was not significantly influenced by the secondary structure of the RNA template. Most of the variation appears to result from idiosyncratic features that derive from local sequence context, demonstrating how different genetic sequences have different chemical phenotypes.