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1.
Catalysis of Template-Directed Nonenzymatic RNA Copying by Iron(II).
Jin, L, Engelhart, AE, Zhang, W, Adamala, K, Szostak, JW
Journal of the American Chemical Society. 2018;(44):15016-15021
Abstract
The study of nonenzymatic template-directed RNA copying is the experimental basis for the search for chemistry and reaction conditions consistent with prebiotic RNA replication. The most effective model systems for RNA copying have to date required a high concentration of Mg2+. Recently, Fe2+, which was abundant on the prebiotic anoxic Earth, was shown to promote the folding of RNA in a manner similar to the case of Mg2+, as a result of the two cations having similar interactions with phosphate groups. These observations raise the question of whether Fe2+ could have promoted RNA copying on the prebiotic Earth. Here, we demonstrate that Fe2+ is a better catalyst and promotes faster nonenzymatic RNA primer extension and ligation than Mg2+ when using 2-methylimidazole activated nucleotides in slightly acidic to neutral pH solutions. Thus, it appears likely that Fe2+ could have facilitated RNA replication and evolution in concert with other metal cations on the prebiotic Earth.
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2.
The past and presence of gene targeting: from chemicals and DNA via proteins to RNA.
Geel, TM, Ruiters, MHJ, Cool, RH, Halby, L, Voshart, DC, Andrade Ruiz, L, Niezen-Koning, KE, Arimondo, PB, Rots, MG
Philosophical transactions of the Royal Society of London. Series B, Biological sciences. 2018;(1748)
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Abstract
The ability to target DNA specifically at any given position within the genome allows many intriguing possibilities and has inspired scientists for decades. Early gene-targeting efforts exploited chemicals or DNA oligonucleotides to interfere with the DNA at a given location in order to inactivate a gene or to correct mutations. We here describe an example towards correcting a genetic mutation underlying Pompe's disease using a nucleotide-fused nuclease (TFO-MunI). In addition to the promise of gene correction, scientists soon realized that genes could be inactivated or even re-activated without inducing potentially harmful DNA damage by targeting transcriptional modulators to a particular gene. However, it proved difficult to fuse protein effector domains to the first generation of programmable DNA-binding agents. The engineering of gene-targeting proteins (zinc finger proteins (ZFPs), transcription activator-like effectors (TALEs)) circumvented this problem. The disadvantage of protein-based gene targeting is that a fusion protein needs to be engineered for every locus. The recent introduction of CRISPR/Cas offers a flexible approach to target a (fusion) protein to the locus of interest using cheap designer RNA molecules. Many research groups now exploit this platform and the first human clinical trials have been initiated: CRISPR/Cas has kicked off a new era of gene targeting and is revolutionizing biomedical sciences.This article is part of a discussion meeting issue 'Frontiers in epigenetic chemical biology'.
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Circular and long non-coding RNAs and their role in ophthalmologic diseases.
Wawrzyniak, O, Zarębska, Ż, Rolle, K, Gotz-Więckowska, A
Acta biochimica Polonica. 2018;(4):497-508
Abstract
Long non-coding RNAs are 200 nucleotide long RNA molecules which lack or have limited protein-coding potential. They can regulate protein formation through several different mechanisms. Similarly, circular RNAs are reported to play a critical role in post-transcriptional gene regulation. Changes in the expression pattern of these molecules are established to underline various diseases, including cancer, cardiovascular, neurological and immunological disorders. Recent studies suggest that they are differentially expressed both in healthy ocular tissues as well as in eye pathologies, such as neovascularization, proliferative vitreoretinopathy, glaucoma, cataract, ocular malignancy or even strabismus. Aetiology of ocular diseases is multifactorial and combines genetic and environmental factors, including epigenetic and non-coding RNAs. In addition, disorders like diabetic retinopathy or age-related macular degeneration lack biomarkers for early detection as well as effective treatment methods that will allow controlling the disease progression at its early stages. The newly discovered non-coding RNAs seem to be the ideal candidate for novel molecular markers and therapeutic strategies. In this review, we summarize current knowledge about gene expression regulators - long non-coding and circular RNA molecules in eye diseases.
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RNA-targeted therapeutics for lipid disorders.
Tsimikas, S
Current opinion in lipidology. 2018;(6):459-466
Abstract
PURPOSE OF REVIEW To summarize recent developments in the field of RNA-directed therapeutics targeting lipid disorders that are not effectively managed. RECENT FINDINGS Despite a number of approved therapies for lipid disorders, significant unmet needs are present in treating persistently elevated LDL-cholesterol, remnant-cholesterol, triglycerides and lipoprotein(a) [Lp(a)]. Small molecules and antibodies are effective modalities, but they are unable to adequately treat many patients with abnormal lipid parameters. Targeting mRNA with oligonucleotides to prevent protein translation is a relatively novel method to reduce circulating atherogenic lipoproteins. Small inhibiting RNA (siRNA) molecules targeting proprotein convertase subtilisin kexin type 9 to reduce LDL-C, and antisense oligonucleotides (ASO) targeting apolipoprotein C-III (apoC-III) to reduce triglycerides, angiopoietin-like 3 (ANGPTL3) to reduce LDL-C and triglycerides and apolipoprotein(a) (LPA) to reduce Lp(a) are currently in or just completed phase 1-3 trials. Fundamental differences exist in chemistry, delivery and mechanism of action of siRNA and ASOs. SUMMARY Novel RNA therapeutics are poised to provide highly potent, specific and effective therapies to reduce atherogenic lipoproteins. As these compounds are approved, clinicians will be able to choose from a broad armamentarium to treat nearly all patients to acceptable goals in order to reduce risk of cardiovascular disease and events.
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Life's Biological Chemistry: A Destiny or Destination Starting from Prebiotic Chemistry?
Krishnamurthy, R
Chemistry (Weinheim an der Bergstrasse, Germany). 2018;(63):16708-16715
Abstract
Research into understanding the origins-and evolution-of life has long been dominated by the concept of taking clues from extant biology and extrapolating its molecules and pathways backwards in time. This approach has also guided the search for solutions to the problem of how contemporary biomolecules would have arisen directly from prebiotic chemistry on early earth. However, the continuing difficulties in finding universally convincing solutions in connecting prebiotic chemistry to biological chemistry should give us pause, and prompt us to rethink this concept of treating extant life's chemical processes as the sole end goal and, therefore, focusing only, and implicitly, on the respective extant chemical building blocks. Rather, it may be worthwhile "to set aside the goal" and begin with what would have been plausible prebiotic reaction mixtures (which may have no obvious or direct connection to life's chemical building blocks and processes) and allow their chemistries and interactions, under different geochemical constraints, to guide and illuminate as to what processes and systems can emerge. Such a conceptual approach gives rise to the prospect that chemistry of life-as-we-know-it is not the only result (not a "destiny"), but one that has emerged among many potential possibilities (a "destination"). This postulate, in turn, could impact the way we think about chemical signatures and criteria used in the search for alternative and extraterrestrial "life". As a bonus, we may discover the chemistries and pathways naturally that led to the emergence of life as we know it.
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Suppression of DNA/RNA and protein oxidation by dietary supplement which contains plant extracts and vitamins: a randomized, double-blind, placebo-controlled trial.
Fragopoulou, E, Gavriil, L, Argyrou, C, Malagaris, I, Choleva, M, Antonopoulou, S, Afxentiou, G, Nikolaou, E
Lipids in health and disease. 2018;(1):187
Abstract
BACKGROUND Excessive oxidative stress may impair bio-molecules and cellular function. Multi antioxidant supplementation is thought to be more effective than a single antioxidant probably through the synergistic or complementary action of natural substances that could enhance the prospective effect. METHODS In order to estimate the effect of a plant extract based supplement in apparently healthy volunteers' oxidative stress markers, a double-blind and placebo controlled intervention was performed. 62 apparently healthy volunteers, overweight with medium adherence to the Mediterranean diet, were recruited and randomly allocated into two intervention groups (supplement or placebo) for 8 weeks. Basic biochemical markers, oxidized LDL (oxLDL), resistance of serum in oxidation, protein carbonyls in serum and 8-isoprostane and DNA/RNA damage in urine were measured. RESULTS No differentiation was observed in basic biochemical markers, in oxLDL levels as well as in serum resistance against oxidation, during intervention in the examined groups. A significant resistance regarding urine isoprostanes levels in the supplement group compared to the placebo one, was observed. Reduction on DNA/RNA damage and on protein carbonyls levels (almost 30% and 20% respectively, at 8 weeks) was detected in volunteers who consumed the supplement compared to the control group. CONCLUSION Consumption of plant extract based supplement seems to reduce DNA/RNA and protein oxidation and in less extent lipids peroxidation. TRIAL REGISTRATION ClinicalTrials.gov Identifier for this study is: NCT02837107.
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SmProt: a database of small proteins encoded by annotated coding and non-coding RNA loci.
Hao, Y, Zhang, L, Niu, Y, Cai, T, Luo, J, He, S, Zhang, B, Zhang, D, Qin, Y, Yang, F, et al
Briefings in bioinformatics. 2018;(4):636-643
Abstract
Small proteins is the general term for proteins with length shorter than 100 amino acids. Identification and functional studies of small proteins have advanced rapidly in recent years, and several studies have shown that small proteins play important roles in diverse functions including development, muscle contraction and DNA repair. Identification and characterization of previously unrecognized small proteins may contribute in important ways to cell biology and human health. Current databases are generally somewhat deficient in that they have either not collected small proteins systematically, or contain only predictions of small proteins in a limited number of tissues and species. Here, we present a specifically designed web-accessible database, small proteins database (SmProt, http://bioinfo.ibp.ac.cn/SmProt), which is a database documenting small proteins. The current release of SmProt incorporates 255 010 small proteins computationally or experimentally identified in 291 cell lines/tissues derived from eight popular species. The database provides a variety of data including basic information (sequence, location, gene name, organism, etc.) as well as specific information (experiment, function, disease type, etc.). To facilitate data extraction, SmProt supports multiple search options, including species, genome location, gene name and their aliases, cell lines/tissues, ORF type, gene type, PubMed ID and SmProt ID. SmProt also incorporates a service for the BLAST alignment search and provides a local UCSC Genome Browser. Additionally, SmProt defines a high-confidence set of small proteins and predicts the functions of the small proteins.
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Telomerase RNA Component Genetic Variants Interact With the Mediterranean Diet Modifying the Inflammatory Status and its Relationship With Aging: CORDIOPREV Study.
Gomez-Delgado, F, Delgado-Lista, J, Lopez-Moreno, J, Rangel-Zuñiga, OA, Alcala-Diaz, JF, Leon-Acuña, A, Corina, A, Yubero-Serrano, E, Torres-Peña, JD, Camargo, A, et al
The journals of gerontology. Series A, Biological sciences and medical sciences. 2018;(3):327-332
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Abstract
BACKGROUND Leukocyte telomere length (LTL) attrition has been associated with age-related diseases. Telomerase RNA Component (TERC) genetic variants have been associated with LTL; whereas fatty acids (FAs) can interact with genetic factors and influence in aging. We explore whether variability at the TERC gene locus interacts with FA profile and two healthy diets (low-fat diet vs Mediterranean diet [MedDiet]) modulating LTL, glucose metabolism, and inflammation status in coronary heart disease (CHD) patients. METHODS Inflammation status (high-sensitivity C-reactive protein [hsCRP], glucose metabolism-glucose, insulin, and glycated hemoglobin [HbA1c], and homeostasis model assessment of insulin resistance [HOMA-IR]), LTL, FAs, and single nucleotide polymorphisms (SNPs) of the TERC gene (rs12696304, rs16847897, and rs3772190) were determined in 1,002 patients from the CORDIOPREV study (NCT00924937). RESULTS We report an interaction of the TERC rs12696304 SNP with monounsaturated fatty acid (MUFA) affecting LTL (p interaction = .01) and hsCRP (p interaction = .03). Among individuals with MUFA levels above the median, CC individuals showed higher LTL and lower hsCRP than G-allele carriers. Moreover, MedDiet interacted with TERC rs12696304 SNP (p interaction = .03). Specifically, CC individuals displayed a greater decrease in hsCRP than G-allele carriers. These results were not adjusted for multiple statistical testing and p less than .05 was considered significant. CONCLUSIONS Our findings suggest that the TERC rs12696304 SNP interacts with MUFA improving inflammation status and telomere attrition related with CHD. Moreover, the MedDiet intervention improves the inflammatory profile in CC individuals compared with the G-allele carriers. These interactions could provide a right strategy for personalized nutrition in CHD patients.
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RPiRLS: Quantitative Predictions of RNA Interacting with Any Protein of Known Sequence.
Shen, WJ, Cui, W, Chen, D, Zhang, J, Xu, J
Molecules (Basel, Switzerland). 2018;(3)
Abstract
RNA-protein interactions (RPIs) have critical roles in numerous fundamental biological processes, such as post-transcriptional gene regulation, viral assembly, cellular defence and protein synthesis. As the number of available RNA-protein binding experimental data has increased rapidly due to high-throughput sequencing methods, it is now possible to measure and understand RNA-protein interactions by computational methods. In this study, we integrate a sequence-based derived kernel with regularized least squares to perform prediction. The derived kernel exploits the contextual information around an amino acid or a nucleic acid as well as the repetitive conserved motif information. We propose a novel machine learning method, called RPiRLS to predict the interaction between any RNA and protein of known sequences. For the RPiRLS classifier, each protein sequence comprises up to 20 diverse amino acids but for the RPiRLS-7G classifier, each protein sequence is represented by using 7-letter reduced alphabets based on their physiochemical properties. We evaluated both methods on a number of benchmark data sets and compared their performances with two newly developed and state-of-the-art methods, RPI-Pred and IPMiner. On the non-redundant benchmark test sets extracted from the PRIDB, the RPiRLS method outperformed RPI-Pred and IPMiner in terms of accuracy, specificity and sensitivity. Further, RPiRLS achieved an accuracy of 92% on the prediction of lncRNA-protein interactions. The proposed method can also be extended to construct RNA-protein interaction networks. The RPiRLS web server is freely available at http://bmc.med.stu.edu.cn/RPiRLS.
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A Data Driven Model for Predicting RNA-Protein Interactions based on Gradient Boosting Machine.
Jain, DS, Gupte, SR, Aduri, R
Scientific reports. 2018;(1):9552
Abstract
RNA protein interactions (RPI) play a pivotal role in the regulation of various biological processes. Experimental validation of RPI has been time-consuming, paving the way for computational prediction methods. The major limiting factor of these methods has been the accuracy and confidence of the predictions, and our in-house experiments show that they fail to accurately predict RPI involving short RNA sequences such as TERRA RNA. Here, we present a data-driven model for RPI prediction using a gradient boosting classifier. Amino acids and nucleotides are classified based on the high-resolution structural data of RNA protein complexes. The minimum structural unit consisting of five residues is used as the descriptor. Comparative analysis of existing methods shows the consistently higher performance of our method irrespective of the length of RNA present in the RPI. The method has been successfully applied to map RPI networks involving both long noncoding RNA as well as TERRA RNA. The method is also shown to successfully predict RNA and protein hubs present in RPI networks of four different organisms. The robustness of this method will provide a way for predicting RPI networks of yet unknown interactions for both long noncoding RNA and microRNA.