China
Africa
Explore chapters and articles related to this topic
Yellow Fever: Emergence and Reality
Published in Jagriti Narang, Manika Khanuja, Small Bite, Big Threat, 2020
Neelam Yadav, Bennet Angel, Jagriti Narang, Surender Singh Yadav, Vinod Joshi, Annette Angel
The size of YFV varies between 40 and 50 nm. It is an enveloped RNA containing virus and belongs to the family Flaviviridae and the group of hemorrhagic fevers (Fontenille et al., 1997). The RNA is positive-sense, single-stranded RNA and contains 11,000 nucleotides with a single open reading frame encoding a polyprotein. The host proteases break the viral polyprotein into three structural proteins—C, prM, and E—and seven nonstructural proteins: NS1, NS2A, NS2B, NS3, NS4A, NS4B, and NS5 (Gouy et al., 2010). The 3’ UTR region ofYFV is essential for the inactivation of the host 5’-3’ exonuclease XRN1. The PKS3 pseudoknot structure is found in the UTR region, which acts as a molecular signal for the inactivation of exonuclease, which is only a viral requirement for the production of sub-genomic flavivirus RNA (sfRNA). Partial digestion of sfRNAs of the viral genome leads to potent pathogenicity (Pisano et al., 1997).
Translation
Published in Paul Pumpens, Single-Stranded RNA Phages, 2020
Garcia et al. (1993) broadened the frameshift studies to the genomic RNA of beet western yellows virus (BWYV), which contained a potential translational frameshift signal in the overlap region of open reading frames ORF2 and ORF3. The frameshifting was assayed both in vivo and in vitro using plasmids containing the wild-type and modified versions of the putative BWYV shift signal placed between the MS2 coat gene and the lacZ gene. The signal, composed of a heptanucleotide slippery sequence and a downstream pseudoknot, was similar in appearance to those identified in retroviral RNAs. The efficiency of the signal in the eukaryotic system was low but significant, as it responded strongly to changes in either the slip sequence or the pseudoknot. In contrast, in E. coli there was hardly any response to the same changes. Replacing the slip sequence to the typical prokaryotic signal AAAAAAG yielded more than 5% frameshift in E. coli. In this organism, the frameshifting was highly sensitive to changes in the slip sequence but only slightly to disruption of the pseudoknot. In contrast, the eukaryotic assay systems were barely sensitive to changes in either AAAAAAG or in the pseudoknot structure in this construct. It was therefore concluded that eukaryotic frameshift signals were not recognized by prokaryotes. On the other hand, the typical prokaryotic slip sequence AAAAAAG did not lead to significant frameshifting in the eukaryotes (Garcia et al. 1993).
Multidrug resistance-1 gene variants in pediatric leukemia in Bali
Published in Elida Zairina, Junaidi Khotib, Chrismawan Ardianto, Syed Azhar Syed Sulaiman, Charles D. Sands, Timothy E. Welty, Unity in Diversity and the Standardisation of Clinical Pharmacy Services, 2017
R. Niruri, N.L. Ulandari, S.C. Yowani, I. Narayani, I. Narayani, K. Ariawati
Synonymous polymorphism could result in ribosome stalling (Fung 2009, Tsai 2008). Nucleotide mutation can change the mRNA structures, which eliminate or generate new secondary structures (e.g., pseudoknot or hairpin). These changes can delay the ribosome and alter translation (Fung 2009). The 3435 C > T may interfere in co-translational folding in close amino acids (which are 30–72 codons in front of the mutant codon). The site of 3435 C > T is in the second ATP-Binding domain. The alteration on pause signal may affect the folding of Q-loop and walker-A motifs. Therefore, it can change MDR1 function such as by altering ATP-binding affinity or ability of ATP hydrolysis (Fung 2009). Meanwhile, another variant, the 2677 G > T (Figure 2), is a non-synonymous SNP. A change in the nucleotide sequence at position 2677 can cause a ribosome pause, which may alter the co-translation of amino acids, which are before the transmembrane domains (TM) 6 and TM 10 (Fung 2009, Sakurai 2007). Each allele that makes haplotype can produce a small but significant, synergistic, or additive change contribution to alter protein folding, function, and expression (Fung, 2009, Kim, 2006). Therefore, it can affect the disposition of chemotherapy drugs (Kim 2006).
Strategies for targeting RNA with small molecule drugs
Published in Expert Opinion on Drug Discovery, 2023
Christopher L. Haga, Donald G. Phinney
DL-based models take advantage of deep neural networks (DNN) for secondary structure prediction. Whereas some RNA secondary structure applications can predict RNA structures containing pseudoknots and others can predict secondary structures containing non-canonical base pairing, DL models such as SPOT-RNA [16] and MXfold2 [17] have been developed that can predict both. Taking cues from training of protein folding deep learning models, SPOT-RNA was developed by first training an ensemble of residual neural networks (ResNets) and long short-term memory (LSTM) recurrent neural networks on a large database of over 100,000 short RNA sequences with automatically derived secondary structures. Transfer learning techniques were then used to further train the model on a smaller data set of high-resolution RNA structures. This training algorithm was able to achieve a Matthews correlation coefficient (MCC) of 0.700 with 0.849 precision and 0.582 sensitivity. High prediction accuracy rates were also seen with non-canonical base pairing and mismatch/wobble pairings. Another DL-based RNA folding model, MXFold2, integrated folding scores calculated by DNNs with Turner’s nearest-neighbor free energy parameters to minimize model overfitting. This model was able to outperform SPOT-RNA in terms of family-wise cross-validation but not sequence-wise cross-validation.
Prediction of RNA secondary structure based on stem region replacement using the RSRNA algorithm
Published in Computer Methods in Biomechanics and Biomedical Engineering, 2021
Chengzhen Xu, Longjian Gao, Jin Li, Longfeng Shen, Hong Liang, Kuan Luan, Xiaomin Wu
After comparing RSRNA with RNAfold, Srna, RNAstructure, and Mfold, RSRNA was found to be significantly superior in terms of sensitivity, specificity, and MCC value, in which the latter was significantly higher relative to the other four algorithms, while the standard deviation was smaller. Furthermore, the structures predicted by the RSRNA algorithm were more accurate in terms of secondary structure than those predicted by RNAfold, Srna, RNAstructure, and Mfold. This indicated that our algorithm was superior to the minimum free energy algorithm, especially in the accurate prediction of base pairing and the misjudgment at the secondary structure level. This was achieved through using new criteria for stem compatibility and new approaches to selecting stem regions. Free energy parameters were also used as a reference, as this was more effective relative to the methods that use free energy alone. In future work, this method would be applied in predicting RNA pseudoknots, which can be used to analyze non-coding RNAs and predict their functions. In addition, many bioinformatics studies can use our proposed method, RSRNA, to analyze RNA through predicting their secondary structures.
Zika virus pathogenesis and current therapeutic advances
Published in Pathogens and Global Health, 2021
Caroline Mwaliko, Raphael Nyaruaba, Lu Zhao, Evans Atoni, Samuel Karungu, Matilu Mwau, Dimitri Lavillette, Han Xia, Zhiming Yuan
Flavivirus sfRNAs contain stem-loop (SL) and dumbbell (DBL) structures made up of nucleotides that form pseudoknots (PK). During ZIKV infection, two XRN1 resistant RNA are produced, xrRNA1 as a result of XRNA stalling at SL1 and xrRNA2 as a result of XRNA stalling at SL2 [68,70]. These two RNAs form three-way junctions of coaxial stacking of helices P1 and P2, while P3 is located at the acute angle of P1. Three-way junctions are highly structured elements of nucleic acids, such as rRNA, and have a unique topology in ZIKV. These two different resistant RNAs can occur as the result of cellular mechanisms [70].