RNA-Puzzles is a collective experiment in blind 3D RNA structure prediction. We report here a third round of RNA-Puzzles. Five puzzles, 4, 8, 12, 13, 14, all structures of riboswitch aptamers and puzzle 7, a ribozyme structure, are included in this round of the experiment. The riboswitch structures include biological binding sites for small molecules (S-adenosyl methionine, cyclic diadenosine monophosphate, 5-amino 4-imidazole carboxamide riboside 5 ′ -triphosphate, glutamine) and proteins (YbxF), and one set describes large conformational changes between ligand-free and ligand-bound states. The Varkud satellite ribozyme is the most recently solved structure of a known large ribozyme. All puzzles have established biological functions and require structural understanding to appreciate their molecular mechanisms. Through the use of fast-track experimental data, including multidimensional chemical mapping, and accurate prediction of RNA secondary structure, a large portion of the contacts in 3D have been predicted correctly leading to similar topologies for the top ranking predictions. Template-based and homologyderived predictions could predict structures to particularly high accuracies. However, achieving biological insights from de novo prediction of RNA 3D structures still depends on the size and complexity of the RNA. Blind computational predictions of RNA structures already appear to provide useful structural information in many cases. Similar to the previous RNA-Puzzles Round II experiment, the prediction of non-Watson-Crick interactions and the observed high atomic clash scores reveal a notable need for an algorithm of improvement. All prediction models and assessment results are available at http://ahsoka.ustrasbg.fr/rnapuzzles/.

Chemical mapping experiments offer powerful information about RNA structure but currently involve ad hoc assumptions in data processing. We show that simple dilutions, referencing standards (GAGUA hairpins), and HiTRACE/MAPseeker analysis allow rigorous overmodification correction, background subtraction, and normalization for electrophoretic data and a ligation bias correction needed for accurate deep sequencing data. Comparisons across six noncoding RNAs stringently test the proposed standardization of dimethyl sulfate (DMS), 2′-OH acylation (SHAPE), and carbodiimide measurements. Identification of new signatures for extrahelical bulges and DMS “hot spot” pockets (including tRNA A58, methylated in vivo) illustrates the utility and necessity of standardization for quantitative RNA mapping.

Despite the popularity of computer-aided study and design of RNA molecules, little is known about the accuracy of commonly used structure modeling packages in tasks sensitive to ensemble properties of RNA. Here, we demonstrate that the EternaBench dataset, a set of more than 20,000 synthetic RNA constructs designed on the RNA design platform Eterna, provides incisive discriminative power in evaluating current packages in ensemble-oriented structure prediction tasks. We find that CONTRAfold and RNAsoft, packages with parameters derived through statistical learning, achieve consistently higher accuracy than more widely used packages in their standard settings, which derive parameters primarily from thermodynamic experiments. We hypothesized that training a multitask model with the varied data types in EternaBench might improve inference on ensemble-based prediction tasks. Indeed, the resulting model, named EternaFold, demonstrated improved performance that generalizes to diverse external datasets including complete messenger RNAs, viral genomes probed in human cells and synthetic designs modeling mRNA vaccines.

Single Ig IL-1-related receptor (SIGIRR) is a negative regulator of toll-like receptor (TLR) 4 and IL-1 mediated activation of nuclear factor kappa-light-chain-enhancer of activated B-cells (NF-κB). The purpose of this study was to qualitatively and quantitatively determine SIGIRR protein expression in human prostate tissues and associate SIGIRR expression with clinical parameters. SIGIRR expression was quantified in glandular prostate tissue using immunohistochemistry and multispectral imaging, and expression was evaluated in relation to clinico-pathological features of benign prostatic hyperplasia (BPH) and prostate cancer (PCa). Subgroupings of low Gleason score (≤6 and 3+4) and high Gleason score (4+3 and ≥8) were used for patient outcomes. SIGIRR was predominantly expressed in the cytoplasm and nucleus of the prostatic epithelium with little expression within the stroma. Compared to normal prostate, cytoplasmic SIGIRR expression was similar in BPH, high-grade prostatic intraepithelial neoplasia (HGPIN), PCa, and metastases. A decrease in nuclear expression was found in metastasis samples (p=0.04). Changes in SIGIRR expression were not associated with Gleason score, pathologic stage, tumor volume, surgical margin status, or serum prostate-specific antigen (PSA; p>0.05). Nuclear (p=0.96) and cytoplasmic (p=0.89) SIGIRR expression were not related to patient outcomes in univariable analysis, but in analysis of patients with low Gleason scores, high cytoplasmic SIGIRR expression was associated with biochemical recurrence in both univariable (p=0.01) and multivariable (HR 2.31 [95% CI 1.05–5.06] p=0.04) analysis. Similarly, in multivariable analysis of only low stage (pT2) tumors, SIGIRR independently predicted biochemical recurrence (p=0.009). We conclude that SIGIRR predicts biochemical recurrence in patients with low Gleason score and low pathological stage prostate cancer.

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