The first polymorph in the family of nucleobases: a second form of cytosine

A new polymorph of cytosine, C₄H₅N₃O, is reported half a century after the report of its first known crystal structure [Barker & Marsh (1964). Acta Cryst. 17 , 1581–1587]. Cytosine thus provides the first polymorphic example in the category of parent nucleobases. The new form, denoted (Ib), was observed unexpectedly during an attempt to cocrystallize cytosine with catechol. Form (Ib) crystallizes in the orthorhombic centrosymmetric space group Pccn with two molecules in the asymmetric unit. The previously known form, denoted (Ia), crystallizes in the orthorhombic noncentrosymmetric space group P2₁2₁2₁. The cytosine molecule is planar in both forms. Hydrogen‐bonding interactions are also similar for both forms. Infinite one‐dimensional ribbons composed of cytosine base‐pair dimers in R₂²(8) arrangements are observed in both (Ia) and (Ib). However, the way that the ribbons are packed differs in (Ia) and (Ib). This appears to guide the centrosymmetric versus noncentrosymmetric space‐group selection through the formation of an inversion‐related motif in polymorph (Ib) and a helical propagation in polymorph (Ia). A few selected polymorphic systems have been gathered from the Cambridge Structural Database to understand possible structural features responsible for achiral molecules adopting centro‐ and noncentrosymmetric space groups.     

Ultraviolet C radiation influences the robustness of RNA integrity measurement

The analytical and clinical validity of analyses of RNA samples destined for clinical diagnosis and therapeutic management is directly impacted by RNA quality. RNA is affected by heat, enzymatic degradation, and Ultraviolet (UV) light. RNA from three eukaryotic cell lines was degraded by heat, RNase, or UV light. RNA integrity values obtained with the benchmark Agilent Bioanalyzer 2100 system were compared with those from the more recent QIAxcel Advanced system. The application of this novel method has allowed us to unravel differences between RNA biophysical and biochemical degradation modes. Agilent RNA integrity number (RIN) and QIAxcel RIS were comparable in heat‐degraded and RNase III‐degraded RNA. Agilent RIN and QIAxcel RIS were comparable at a RIN decision level of 7 in UV‐degraded RNA but not overall. The QIAxcel RIS method was more precise than Agilent RIN for RIN<8, while the inverse was true for RIN≥8. Greater degradation of mRNA and rRNA in UV‐damaged samples hampered the Agilent RIN calculation algorithm. Overall, RIS was more robust than RIN for assessing RNA integrity. The ΔΔCt‐values for heat‐ and UV‐degraded RNA samples showed slightly higher correlation with RIS than with RIN. RNA integrity can be used to categorize RNA samples for suitability for downstream gene expression analyses, independently of the RNA degradation mechanism. The new method QIAxcel is more robust and therefore allows more accurate categorization of compromised RNA samples.     

Nucleic acid reactivity: Challenges for next‐generation semiempirical quantum models

Semiempirical quantum models are routinely used to study mechanisms of RNA catalysis and phosphoryl transfer reactions using combined quantum mechanical (QM)/molecular mechanical methods. Herein, we provide a broad assessment of the performance of existing semiempirical quantum models to describe nucleic acid structure and reactivity to quantify their limitations and guide the development of next‐generation quantum models with improved accuracy. Neglect of diatomic differential overlap and self‐consistent density‐functional tight‐binding semiempirical models are evaluated against high‐level QM benchmark calculations for seven biologically important datasets. The datasets include: proton affinities, polarizabilities, nucleobase dimer interactions, dimethyl phosphate anion, nucleoside sugar and glycosidic torsion conformations, and RNA phosphoryl transfer model reactions. As an additional baseline, comparisons are made with several commonly used density‐functional models, including M062X and B3LYP (in some cases with dispersion corrections). The results show that, among the semiempirical models examined, the AM1/d‐PhoT model is the most robust at predicting proton affinities. AM1/d‐PhoT and DFTB3‐3ob/OPhyd reproduce the MP2 potential energy surfaces of 6 associative RNA phosphoryl transfer model reactions reasonably well. Further, a recently developed linear‐scaling “modified divide‐and‐conquer” model exhibits the most accurate results for binding energies of both hydrogen bonded and stacked nucleobase dimers. The semiempirical models considered here are shown to underestimate the isotropic polarizabilities of neutral molecules by approximately 30%. The semiempirical models also fail to adequately describe torsion profiles for the dimethyl phosphate anion, the nucleoside sugar ring puckers, and the rotations about the nucleoside glycosidic bond. The modeling of pentavalent phosphorus, particularly with thio substitutions often used experimentally as mechanistic probes, was problematic for all of the models considered. Analysis of the strengths and weakness of the models suggests that the creation of robust next‐generation models should emphasize the improvement of relative conformational energies and barriers, and nonbonded interactions. © 2015 Wiley Periodicals, Inc.     

Single-Molecule Photoactivation FRET: A General and Easy-To-Implement Approach To Break the Concentration Barrier

Single-molecule fluorescence resonance energy transfer (sm-FRET) has become a widely used tool to reveal dynamic processes and molecule mechanisms hidden under ensemble measurements. However, the upper limit of fluorescent species used in sm-FRET is still orders of magnitude lower than the association affinity of many biological processes under physiological conditions. Herein, we introduce single-molecule photoactivation FRET (sm-PAFRET), a general approach to break the concentration barrier by using photoactivatable fluorophores as donors. We demonstrate sm-PAFRET by capturing transient FRET states and revealing new reaction pathways during translation using μm fluorophore labeled species, which is 2–3 orders of magnitude higher than commonly used in sm-FRET measurements. sm-PAFRET serves as an easy-to-implement tool to lift the concentration barrier and discover new molecular dynamic processes and mechanisms under physiological concentrations.     

Discovering DNA methylation patterns for long non-coding RNAs associated with cancer subtypes

Despite growing evidence demonstrates that the long non-coding ribonucleic acids (lncRNAs) are critical modulators for cancers, the knowledge about the DNA methylation patterns of lncRNAs is quite limited. We develop a systematic analysis pipeline to discover DNA methylation patterns for lncRNAs across multiple cancer subtypes from probe, gene and network levels. By using The Cancer Genome Atlas (TCGA) breast cancer methylation data, the pipeline discovers various DNA methylation patterns for lncRNAs across four major subtypes such as luminal A, luminal B, her2-enriched as well as basal-like. On the probe and gene level, we find that both differentially methylated probes and lncRNAs are subtype specific, while the lncRNAs are not as specific as probes. On the network level, the pipeline constructs differential co-methylation lncRNA network for each subtype. Then, it identifies both subtype specific and common lncRNA modules by simultaneously analyzing multiple networks. We show that the lncRNAs in subtype specific and common modules differ greatly in terms of topological structure, sequence conservation as well as expression. Furthermore, the subtype specific lncRNA modules serve as biomarkers to improve significantly the accuracy of breast cancer subtypes prediction. Finally, the common lncRNA modules associate with survival time of patients, which is critical for cancer therapy.     

Automated RNA tertiary structure prediction from secondary structure and low‐resolution restraints

A novel protocol for all‐atom RNA tertiary structure prediction is presented that uses restrained molecular mechanics and simulated annealing. The restraints are from secondary structure, covariation analysis, coaxial stacking predictions for helices in junctions, and, when available, cross‐linking data. Results are demonstrated on the Alu domain of the mammalian signal recognition particle RNA, the Saccharomyces cerevisiae phenylalanine tRNA, the hammerhead ribozyme, the hepatitis C virus internal ribosomal entry site, and the P4–P6 domain of the Tetrahymena thermophila group I intron. The predicted structure is selected from a pool of decoy structures with a score that maximizes radius of gyration and base–base contacts, which was empirically found to select higher quality decoys. This simple ab initio approach is sufficient to make good predictions of the structure of RNAs compared to current crystal structures using both root mean square deviation and the accuracy of base–base contacts. © 2011 Wiley Periodicals, Inc. J Comput Chem, 2011     

Analytical challenges and the development of biomarkers to measure and to monitor the effects of ocean acidification

Changing ocean-carbonate chemistry caused by oceanic uptake of anthropogenic atmospheric carbon dioxide leads to the formation of carbonic acid, thus lowering the pH of the sea with predictions of a decrease from current levels at 8.15 to 7.82 by the end of the century. The exact measurement of subtle pH changes in seawater over time presents significant analytical challenges, as the equilibrium constants are governed by water temperature and pressure, salinity effects, and the existence of other ionic species in seawater.Here, we review these challenges and how pH also affects dissolved inorganic and organic chemicals that affect biological systems. This includes toxic compounds (xenobiotics) as well as chemicals that are beneficial for marine organisms, such as the chemical signals (i.e. pheromones) that are utilized to coordinate animal behavior. We review how combining analytical, molecular and biochemical tools can lead to the development of biosensors to detect pH effects to enable predictive modeling of the ecological consequences of ocean acidification.     

Studies on the adsorption of cell impurities from plasmid-containing lysates to phenyl boronic acid chromatographic beads

Plasmid DNA (pDNA) is purified directly from alkaline lysis-derived Escherichia coli (E. coli) lysates by phenyl boronate (PB) chromatography. The method explores the ability of PB ligands to bind covalently, but reversibly, to cis-diol-containing impurities like RNA and lipopolysaccharides (LPS), leaving pDNA in solution. In spite of this specificity, cis-diol free species like proteins and genomic DNA (gDNA) are also removed. This is a major advantage since the process is designed to keep the target pDNA from binding. The focus of this paper is on the study of the secondary interactions between the impurities (RNA, gDNA, proteins, LPS) in a pDNA-containing lysate and 3-amino PB controlled pore glass (CPG) matrices. Runs were designed to evaluate the role of adsorption buffer composition, feed type (pH, salt content), CPG matrix and sample pretreatment (RNase A, isopropanol precipitation). Water was chosen as the adsorption buffer over MgCl(2) solutions since it maximised pDNA yield (96.2±4.9%) and protein removal (61.3±3.0%), while providing for a substantial removal of RNA (65.5±3.5%) and gDNA (44.7±14.1%). Although the use of pH 3.5 maximised removal of impurities (~75%), the best compromise between plasmid yield (~96%) and RNA clearance (~60-70%) was obtained for a pH of 5.2. Plasmid yield was maximal (>96%) when the concentration of acetate and potassium ions in the incoming lysate feed were 1.7 M and 1.0 M, respectively. The pre-treatment of lysates with RNase A deteriorated the performance since the resulting oligoribonucleotides lack the cis-diol group at their 3' termini. Overall, the results support the idea that charge transfer interactions between the boron atom at acidic pH and electron donor groups in the aromatic bases of nucleic acids and side residues of proteins are responsible for the non-specific removal of gDNA, RNA and proteins.