Impact of pseudouridylation, substrate fold, and degradosome organization on the endonuclease activity of RNase E.

The conserved endoribonuclease RNase E dominates the dynamic landscape of RNA metabolism and underpins control mediated by small regulatory RNAs in diverse bacterial species. We explored the enzyme's hydrolytic mechanism, allosteric activation, and interplay with partner proteins in the multicomponent RNA degradosome assembly of Escherichia coli. RNase E cleaves single-stranded RNA with preference to attack the phosphate located at the 5' nucleotide preceding uracil, and we corroborate key interactions that select that base. Unexpectedly, RNase E activity is impeded strongly when the recognized uracil is isomerized to 5-ribosyluracil (pseudouridine), from which we infer the detailed geometry of the hydrolytic attack process. Kinetics analyses support models for recognition of secondary structure in substrates by RNase E and for allosteric autoregulation. The catalytic power of the enzyme is boosted when it is assembled into the multienzyme RNA degradosome, most likely as a consequence of substrate capture and presentation. Our results rationalize the origins of substrate preferences of RNase E and illuminate its catalytic mechanism, supporting the roles of allosteric domain closure and cooperation with other components of the RNA degradosome complex

Targeting the Conserved Stem Loop 2 Motif in the SARS-CoV-2 Genome.

RNA structural elements occur in numerous single-stranded positive-sense RNA viruses. The stem-loop 2 motif (s2m) is one such element with an unusually high degree of sequence conservation, being found in the 3' untranslated region (UTR) in the genomes of many astroviruses, some picornaviruses and noroviruses, and a variety of coronaviruses, including severe acute respiratory syndrome coronavirus (SARS-CoV) and SARS-CoV-2. The evolutionary conservation and its occurrence in all viral subgenomic transcripts imply a key role for s2m in the viral infection cycle. Our findings indicate that the element, while stably folded, can nonetheless be invaded and remodeled spontaneously by antisense oligonucleotides (ASOs) that initiate pairing in exposed loops and trigger efficient sequence-specific RNA cleavage in reporter assays. ASOs also act to inhibit replication in an astrovirus replicon model system in a sequence-specific, dose-dependent manner and inhibit SARS-CoV-2 replication in cell culture. Our results thus permit us to suggest that the s2m element is readily targeted by ASOs, which show promise as antiviral agents. IMPORTANCE The highly conserved stem-loop 2 motif (s2m) is found in the genomes of many RNA viruses, including SARS-CoV-2. Our findings indicate that the s2m element can be targeted by antisense oligonucleotides. The antiviral potential of this element represents a promising start for further research into targeting conserved elements in RNA viruses.ERC, BBSR

RNase E and the High-Fidelity Orchestration of RNA Metabolism.

The bacterial endoribonuclease RNase E occupies a pivotal position in the control of gene expression, as its actions either commit transcripts to an irreversible fate of rapid destruction or unveil their hidden functions through specific processing. Moreover, the enzyme contributes to quality control of rRNAs. The activity of RNase E can be directed and modulated by signals provided through regulatory RNAs that guide the enzyme to specific transcripts that are to be silenced. Early in its evolutionary history, RNase E acquired a natively unfolded appendage that recruits accessory proteins and RNA. These accessory factors facilitate the activity of RNase E and include helicases that remodel RNA and RNA-protein complexes, and polynucleotide phosphorylase, a relative of the archaeal and eukaryotic exosomes. RNase E also associates with enzymes from central metabolism, such as enolase and aconitase. RNase E-based complexes are diverse in composition, but generally bear mechanistic parallels with eukaryotic machinery involved in RNA-induced gene regulation and transcript quality control. That these similar processes arose independently underscores the universality of RNA-based regulation in life. Here we provide a synopsis and perspective of the contributions made by RNase E to sustain robust gene regulation with speed and accuracy.Wellcome Trus

Analysis of the natively unstructured RNA/protein-recognition core in the Escherichia coli RNA degradosome and its interactions with regulatory RNA/Hfq complexes.

The RNA degradosome is a multi-enzyme assembly that plays a central role in the RNA metabolism of Escherichia coli and numerous other bacterial species including pathogens. At the core of the assembly is the endoribonuclease RNase E, one of the largest E. coli proteins and also one that bears the greatest region predicted to be natively unstructured. This extensive unstructured region, situated in the C-terminal half of RNase E, is punctuated with conserved short linear motifs that recruit partner proteins, direct RNA interactions, and enable association with the cytoplasmic membrane. We have structurally characterized a subassembly of the degradosome-comprising a 248-residue segment of the natively unstructured part of RNase E, the DEAD-box helicase RhlB and the glycolytic enzyme enolase, and provide evidence that it serves as a flexible recognition centre that can co-recruit small regulatory RNA and the RNA chaperone Hfq. Our results support a model in which the degradosome captures substrates and regulatory RNAs through the recognition centre, facilitates pairing to cognate transcripts and presents the target to the ribonuclease active sites of the greater assembly for cooperative degradation or processing

Adjacent single-stranded regions mediate processing of tRNA precursors by RNase E direct entry

The RNase E family is renowned for being central to the processing and decay of all types of RNA in many species of bacteria, as well as providing the first examples of endonucleases that can recognize 50 -monophosphorylated ends thereby increasing the efficiency of cleavage. However, there is increasing evidence that some transcripts can be cleaved efficiently by Escherichia coli RNase E via direct entry, i.e. in the absence of the recognition of a 50 -monophosphorylated end. Here, we provide biochemical evidence that direct entry is central to the processing of transfer RNA (tRNA) in E. coli, one of the core functions of RNase E, and show that it is mediated by specific unpaired regions that are adjacent, but not contiguous to segments cleaved by RNase E. In addition, we find that direct entry at a site on the 50 side of a tRNA precursor triggers a series of 50 -monophosphate-dependent cleavages. Consistent with a major role for direct entry in tRNA processing, we provide additional evidence that a 50 -monophosphate is not required to activate the catalysis step in cleavage. Other examples of tRNA precursors processed via direct entry are also provided. Thus, it appears increasingly that direct entry by RNase E has a major role in bacterial RNA metabolism

Membrane-association of mRNA decapping factors is independent of stress in budding yeast

Recent evidence has suggested that the degradation of mRNA occurs on translating ribosomes or alternatively within RNA granules called P bodies, which are aggregates whose core constituents are mRNA decay proteins and RNA. In this study, we examined the mRNA decapping proteins, Dcp1, Dcp2, and Dhh1, using subcellular fractionation. We found that decapping factors co-sediment in the polysome fraction of a sucrose gradient and do not alter their behaviour with stress, inhibition of translation or inhibition of the P body formation. Importantly, their localisation to the polysome fraction is independent of the RNA, suggesting that these factors may be constitutively localised to the polysome. Conversely, polysomal and post-polysomal sedimentation of the decapping proteins was abolished with the addition of a detergent, which shifts the factors to the non-translating RNP fraction and is consistent with membrane association. Using a membrane flotation assay, we observed the mRNA decapping factors in the lower density fractions at the buoyant density of membrane-associated proteins. These observations provide further evidence that mRNA decapping factors interact with subcellular membranes, and we suggest a model in which the mRNA decapping factors interact with membranes to facilitate regulation of mRNA degradation

Effect of perforation size and substrate bag fruiting position on the morphology of fruiting bodies and clusters in Pleurotus ostreatus (Jacq.) P. Kumm

EN : Perforation or fruiting hole size on substrate bags control cluster sizes and morphology in exotic mushroom cultivation. The effect of three different perforation sizes on substrate bags (factor A: 50, 100, and 150 mm) and their positioning on the shelves (factor B: Horizontal, vertical, and slant) on the crop and various morphological characteristics in Pleurotus ostreatus was studied. Microclimatic conditions for fruiting were 16±1°C, 87±3% RH; 230±42 lux illumination. The formula for calculating area of ellipse was modified and used for the area of mushroom cap. Results indicated that the total fruit body yield and biological efficiency (BE) in the bags set in horizontal position were 10% lower than other treatments. The effect of perforation size on mushroom cluster sizes was more on the substrate blocks in the horizontal position. There was a linear correlation between perforation size and fruiting body cluster sizes. Results suggest that the 50 mm perforation on bags in vertical and slant positions gave fruiting body clusters sizes 186–196 mm width and 122–154 mm height, with 92.36±6.48% BE. The cluster size indicated is the best fit for standard packaging containers used in commercial oyster mushroom production in Ukraine

Full design automation of multi-state RNA devices to program gene expression using energy-based optimization

[EN] Small RNAs (sRNAs) can operate as regulatory agents to control protein expression by interaction with the 59 untranslated region of the mRNA. We have developed a physicochemical framework, relying on base pair interaction energies, to design multi-state sRNA devices by solving an optimization problem with an objective function accounting for the stability of the transition and final intermolecular states. Contrary to the analysis of the reaction kinetics of an ensemble of sRNAs, we solve the inverse problem of finding sequences satisfying targeted reactions. We show here that our objective function correlates well with measured riboregulatory activity of a set of mutants. This has enabled the application of the methodology for an extended design of RNA devices with specified behavior, assuming different molecular interaction models based on Watson-Crick interaction. We designed several YES, NOT, AND, and OR logic gates, including the design of combinatorial riboregulators. In sum, our de novo approach provides a new paradigm in synthetic biology to design molecular interaction mechanisms facilitating future high-throughput functional sRNA design.Work supported by the grants FP7-ICT-043338 (BACTOCOM) to AJ, and BIO2011-26741 (Ministerio de Economia y Competitividad, Spain) to JAD. GR is supported by an EMBO long-term fellowship co-funded by Marie Curie actions (ALTF-1177-2011), and TEL by a PhD fellowship from the AXA Research Fund. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.Rodrigo Tarrega, G.; Landrain, TE.; Majer, E.; Daros Arnau, JA.; Jaramillo, A. (2013). Full design automation of multi-state RNA devices to program gene expression using energy-based optimization. PLoS Computational Biology. 9(8):1003172-1003172. https://doi.org/10.1371/journal.pcbi.1003172S1003172100317298Isaacs, F. J., Dwyer, D. J., & Collins, J. J. (2006). RNA synthetic biology. Nature Biotechnology, 24(5), 545-554. doi:10.1038/nbt1208Isaacs, F. J., Dwyer, D. J., Ding, C., Pervouchine, D. D., Cantor, C. R., & Collins, J. J. (2004). Engineered riboregulators enable post-transcriptional control of gene expression. Nature Biotechnology, 22(7), 841-847. doi:10.1038/nbt986Lucks, J. B., Qi, L., Mutalik, V. K., Wang, D., & Arkin, A. P. (2011). Versatile RNA-sensing transcriptional regulators for engineering genetic networks. Proceedings of the National Academy of Sciences, 108(21), 8617-8622. doi:10.1073/pnas.1015741108Mutalik, V. K., Qi, L., Guimaraes, J. C., Lucks, J. B., & Arkin, A. P. (2012). Rationally designed families of orthogonal RNA regulators of translation. Nature Chemical Biology, 8(5), 447-454. doi:10.1038/nchembio.919Bayer, T. S., & Smolke, C. D. (2005). Programmable ligand-controlled riboregulators of eukaryotic gene expression. Nature Biotechnology, 23(3), 337-343. doi:10.1038/nbt1069Nakashima, N., & Tamura, T. (2009). Conditional gene silencing of multiple genes with antisense RNAs and generation of a mutator strain of Escherichia coli. Nucleic Acids Research, 37(15), e103-e103. doi:10.1093/nar/gkp498Callura, J. M., Cantor, C. R., & Collins, J. J. (2012). Genetic switchboard for synthetic biology applications. Proceedings of the National Academy of Sciences, 109(15), 5850-5855. doi:10.1073/pnas.1203808109Beisel, C. L., Bayer, T. S., Hoff, K. G., & Smolke, C. D. (2008). Model‐guided design of ligand‐regulated RNAi for programmable control of gene expression. Molecular Systems Biology, 4(1), 224. doi:10.1038/msb.2008.62Qi, L., Lucks, J. B., Liu, C. C., Mutalik, V. K., & Arkin, A. P. (2012). Engineering naturally occurring trans -acting non-coding RNAs to sense molecular signals. Nucleic Acids Research, 40(12), 5775-5786. doi:10.1093/nar/gks168Carothers, J. M., Goler, J. A., Juminaga, D., & Keasling, J. D. (2011). Model-Driven Engineering of RNA Devices to Quantitatively Program Gene Expression. Science, 334(6063), 1716-1719. doi:10.1126/science.1212209Rodrigo, G., Landrain, T. E., & Jaramillo, A. (2012). De novo automated design of small RNA circuits for engineering synthetic riboregulation in living cells. Proceedings of the National Academy of Sciences, 109(38), 15271-15276. doi:10.1073/pnas.1203831109Brantl, S. (2002). Antisense-RNA regulation and RNA interference. Biochimica et Biophysica Acta (BBA) - Gene Structure and Expression, 1575(1-3), 15-25. doi:10.1016/s0167-4781(02)00280-4Majdalani, N., Vanderpool, C. K., & Gottesman, S. (2005). Bacterial Small RNA Regulators. Critical Reviews in Biochemistry and Molecular Biology, 40(2), 93-113. doi:10.1080/10409230590918702Selinger, D. W., Cheung, K. J., Mei, R., Johansson, E. M., Richmond, C. S., Blattner, F. R., … Church, G. M. (2000). RNA expression analysis using a 30 base pair resolution Escherichia coli genome array. Nature Biotechnology, 18(12), 1262-1268. doi:10.1038/82367Yelin, R., Dahary, D., Sorek, R., Levanon, E. Y., Goldstein, O., Shoshan, A., … Rotman, G. (2003). Widespread occurrence of antisense transcription in the human genome. Nature Biotechnology, 21(4), 379-386. doi:10.1038/nbt808Wang, X.-J., Gaasterland, T., & Chua, N.-H. (2005). Genome Biology, 6(4), R30. doi:10.1186/gb-2005-6-4-r30Stojanovic, M. N., & Stefanovic, D. (2003). A deoxyribozyme-based molecular automaton. Nature Biotechnology, 21(9), 1069-1074. doi:10.1038/nbt862Seelig, G., Soloveichik, D., Zhang, D. Y., & Winfree, E. (2006). Enzyme-Free Nucleic Acid Logic Circuits. Science, 314(5805), 1585-1588. doi:10.1126/science.1132493Yin, P., Choi, H. M. T., Calvert, C. R., & Pierce, N. A. (2008). Programming biomolecular self-assembly pathways. Nature, 451(7176), 318-322. doi:10.1038/nature06451Ran, T., Kaplan, S., & Shapiro, E. (2009). Molecular implementation of simple logic programs. Nature Nanotechnology, 4(10), 642-648. doi:10.1038/nnano.2009.203Penchovsky, R., & Breaker, R. R. (2005). Computational design and experimental validation of oligonucleotide-sensing allosteric ribozymes. Nature Biotechnology, 23(11), 1424-1433. doi:10.1038/nbt1155Salis, H. M., Mirsky, E. A., & Voigt, C. A. (2009). Automated design of synthetic ribosome binding sites to control protein expression. Nature Biotechnology, 27(10), 946-950. doi:10.1038/nbt.1568Laidler, K. J., & King, M. C. (1983). Development of transition-state theory. The Journal of Physical Chemistry, 87(15), 2657-2664. doi:10.1021/j100238a002Sosnick, T. R., & Pan, T. (2003). RNA folding: models and perspectives. Current Opinion in Structural Biology, 13(3), 309-316. doi:10.1016/s0959-440x(03)00066-6Yurke, B. (2003). Genetic Programming and Evolvable Machines, 4(2), 111-122. doi:10.1023/a:1023928811651Bandyra, K. J., Said, N., Pfeiffer, V., Górna, M. W., Vogel, J., & Luisi, B. F. (2012). The Seed Region of a Small RNA Drives the Controlled Destruction of the Target mRNA by the Endoribonuclease RNase E. Molecular Cell, 47(6), 943-953. doi:10.1016/j.molcel.2012.07.015Dawid, A., Cayrol, B., & Isambert, H. (2009). RNA synthetic biology inspired from bacteria: construction of transcription attenuators under antisense regulation. Physical Biology, 6(2), 025007. doi:10.1088/1478-3975/6/2/025007Lioliou, E., Romilly, C., Romby, P., & Fechter, P. (2010). RNA-mediated regulation in bacteria: from natural to artificial systems. New Biotechnology, 27(3), 222-235. doi:10.1016/j.nbt.2010.03.002Dirks, R. M., Bois, J. S., Schaeffer, J. M., Winfree, E., & Pierce, N. A. (2007). Thermodynamic Analysis of Interacting Nucleic Acid Strands. SIAM Review, 49(1), 65-88. doi:10.1137/060651100Das, R., Karanicolas, J., & Baker, D. (2010). Atomic accuracy in predicting and designing noncanonical RNA structure. Nature Methods, 7(4), 291-294. doi:10.1038/nmeth.1433Vogel, J., & Luisi, B. F. (2011). Hfq and its constellation of RNA. Nature Reviews Microbiology, 9(8), 578-589. doi:10.1038/nrmicro2615Friedland, A. E., Lu, T. K., Wang, X., Shi, D., Church, G., & Collins, J. J. (2009). Synthetic Gene Networks That Count. Science, 324(5931), 1199-1202. doi:10.1126/science.1172005Rodrigo, G., Carrera, J., Landrain, T. E., & Jaramillo, A. (2012). Perspectives on the automatic design of regulatory systems for synthetic biology. FEBS Letters, 586(15), 2037-2042. doi:10.1016/j.febslet.2012.02.031Chin, J. W. (2006). Modular approaches to expanding the functions of living matter. Nature Chemical Biology, 2(6), 304-311. doi:10.1038/nchembio789McCaskill, J. S. (1990). The equilibrium partition function and base pair binding probabilities for RNA secondary structure. Biopolymers, 29(6-7), 1105-1119. doi:10.1002/bip.360290621Chitsaz, H., Salari, R., Sahinalp, S. C., & Backofen, R. (2009). A partition function algorithm for interacting nucleic acid strands. Bioinformatics, 25(12), i365-i373. doi:10.1093/bioinformatics/btp212Hofacker, I. L., Fontana, W., Stadler, P. F., Bonhoeffer, L. S., Tacker, M., & Schuster, P. (1994). Fast folding and comparison of RNA secondary structures. Monatshefte f�r Chemie Chemical Monthly, 125(2), 167-188. doi:10.1007/bf00818163Andronescu, M., Zhang, Z. C., & Condon, A. (2005). Secondary Structure Prediction of Interacting RNA Molecules. Journal of Molecular Biology, 345(5), 987-1001. doi:10.1016/j.jmb.2004.10.082Mathews, D. H., Sabina, J., Zuker, M., & Turner, D. H. (1999). Expanded sequence dependence of thermodynamic parameters improves prediction of RNA secondary structure. Journal of Molecular Biology, 288(5), 911-940. doi:10.1006/jmbi.1999.2700Kirkpatrick, S., Gelatt, C. D., & Vecchi, M. P. (1983). Optimization by Simulated Annealing. Science, 220(4598), 671-680. doi:10.1126/science.220.4598.67

Звіт про науково-дослідну роботу. Програма 3. Обгрунтування та розробка нових і вдосконалення існуючих технологій охолоджених та концервованих рослинних продуктів (проміжний)

Звіт про НДР: складається з 93 с., 45 рис., 14 табл., 109 джерел. Обєкти досліджень: зміни якості та біологічної цінності плодово-ягідної та овочевої продукції протягом тривалого зберігання та консервування різними методами. Мета роботи: подовження термінів зберігання плодово-ягідної, овочевої продукції зі збереженням високих якісних показників та біологічної цінністі шляхом обґрунтування та розроблення нових нових і вдосконалення існуючих технологій консервування. Методи досліджень: Загальнонаукові: аналізу літературних джерел та отриманих експериментальних даних, синтезу – для формування узагальнень та висновків, спостереження за процесами формування якості, експерименту – складання схеми лабораторних досліджень, моделювання — для побудови математичних моделей, індукції і дедукції – для співставлення результатів математичного моделювання з отриманими експериментальними даними, органолептичний – для визначення квалітативних показників плодів протягом зберігання. Спеціальні: виробничий – проведення дослідження зі зберігання плодів за обробки антиоксидантними композиціями у виробничих умовах; лабораторний– для досліджень фізико-хімічних, біохімічних показників, мікробіологічного забруднення; математично статистичний – для математичної обробки експериментальних даних, порівняльно-розрахунковий – для визначення економічної ефективності зберігання плодів за обробки антиоксидантними композиціями