Community review: a robust and scalable selection system for resource allocation within open science and innovation communities

Resource allocation is essential to the selection and implementation of innovative projects in science and technology. With large stakes involved in concentrating large fundings over a few promising projects, current “winner-take-all” models for grant applications are time-intensive endeavours that mobilise significant researcher time in writing extensive project proposals, and rely on the availability of a few time-saturated volunteer experts. Such processes usually carry over several months, resulting in high effective costs compared to expected benefits. Faced with the need for a rapid response to the COVID-19 pandemic in 2020, we devised an agile “community review” system, similar to distributed peer review (DPR) systems, to allocate micro-grants for the fast prototyping of innovative solutions. Here we describe and evaluate the implementation of this community review across 147 projects from the “Just One Giant Lab’s OpenCOVID19 initiative” and “Helpful Engineering” open research communities. The community review process uses granular review forms and requires the participation of grant applicants in the review process. We show that this system is fast, with a median duration of 10 days, scalable, with a median of 4 reviewers per project independent of the total number of projects, and fair, with project rankings highly preserved after the synthetic removal of reviewers. We investigate potential bias introduced by involving applicants in the process, and find that review scores from both applicants and non-applicants have a similar correlation of r=0.28 with other reviews within a project, matching previous observations using traditional approaches. Finally, we find that the ability of projects to apply to several rounds allows to both foster the further implementation of successful early prototypes, as well as provide a pathway to constructively improve an initially failing proposal in an agile manner. This study quantitatively highlights the benefits of a frugal community review system for agile resource allocation

Education-based grant programmes for bottom-up distance learning and project catalysis : antimicrobial resistance in Sub-Saharan Africa

International development and aid are often conducted through the allocation of funding determined by decisions of non-locals, especially in the west for those in the global south. In addition, such funding is often disassociated from local expertise, therefore providing little long-term developmental impact and generating distrust. This is particularly true for conservation, as well as environmental and educational programmes. We hypothesize that by granting local people the educational tools and the necessary funding to develop their own projects through the use of an applicant-driven peer-review approach, it is possible to relocalize the decision-making process to the programme participants, with the potential to generate and select more relevant projects with developmental outcomes of higher quality. Here we created an online curriculum for antimicrobial resistance (AMR) education that was followed by 89 participants across Ghana, Tanzania, Nigeria and Uganda. We then created an open research programme that facilitated the creation of eight de novo projects on AMR. Finally, we organized an applicant-driven grant round to allocate funding to the ‘Neonatal Sepsis in Nigeria’ project to conduct a pilot study and awareness campaign. This work opens perspectives for the design of frugal educational programmes and the funding of context-specific, community-driven projects aimed at empowering local stakeholders in the global South

Dynamic signal processing by ribozyme-mediated RNA circuits to control gene expression

[EN] Organisms have different circuitries that allow converting signal molecule levels to changes in gene expression. An important challenge in synthetic biology involves the de novo design of RNA modules enabling dynamic signal processing in live cells. This requires a scalable methodology for sensing, transmission, and actuation, which could be assembled into larger signaling networks. Here, we present a biochemical strategy to design RNA-mediated signal transduction cascades able to sense small molecules and small RNAs. We design switchable functional RNA domains by using strand-displacement techniques. We experimentally characterize the molecular mechanism underlying our synthetic RNA signaling cascades, show the ability to regulate gene expression with transduced RNA signals, and describe the signal processing response of our systems to periodic forcing in single live cells. The engineered systems integrate RNA-RNA interaction with available ribozyme and aptamer elements, providing new ways to engineer arbitrary complex gene circuits.EVOPROG [FP7-ICT-610730]; PROMYS [FP7-KBBE-613745 to A.J.]; Ministerio de Economia y Competitividad, Spain [BIO2011-26741 to J.-A.D.]; PRES Paris Sud grant (S.S.); EMBO long-term fellowship co-funded by Marie Curie actions [ALTF-1177-2011 A.J., G.R.]; AXA research fund; Ministerio de Educacion, Cultura y Deporte, Spain [AP2012-3751 to E.M.]. Funding for open access charge: EVOPROG [FP7-ICT-610730]; PROMYS [FP7-KBBE-613745].Shen, S.; Rodrigo Tarrega, G.; Prakash, S.; Majer, E.; Landrain, T.; Kirov, B.; Daros Arnau, JA.... (2015). Dynamic signal processing by ribozyme-mediated RNA circuits to control gene expression. Nucleic Acids Research. 43(10):5158-5170. https://doi.org/10.1093/nar/gkv287S515851704310Ulrich, L. E., Koonin, E. V., & Zhulin, I. B. (2005). One-component systems dominate signal transduction in prokaryotes. Trends in Microbiology, 13(2), 52-56. doi:10.1016/j.tim.2004.12.006Kiel, C., Yus, E., & Serrano, L. (2010). Engineering Signal Transduction Pathways. Cell, 140(1), 33-47. doi:10.1016/j.cell.2009.12.028Isaacs, F. J., Dwyer, D. J., & Collins, J. J. (2006). RNA synthetic biology. Nature Biotechnology, 24(5), 545-554. doi:10.1038/nbt1208Liang, J. C., Bloom, R. J., & Smolke, C. D. (2011). Engineering Biological Systems with Synthetic RNA Molecules. Molecular Cell, 43(6), 915-926. doi:10.1016/j.molcel.2011.08.023Dueber, J. E. (2003). Reprogramming Control of an Allosteric Signaling Switch Through Modular Recombination. Science, 301(5641), 1904-1908. doi:10.1126/science.1085945Sallee, N. A., Yeh, B. J., & Lim, W. A. (2007). Engineering Modular Protein Interaction Switches by Sequence Overlap. Journal of the American Chemical Society, 129(15), 4606-4611. doi:10.1021/ja0672728Rodrigo, G., Landrain, T. E., Shen, S., & Jaramillo, A. (2013). A new frontier in synthetic biology: automated design of small RNA devices in bacteria. Trends in Genetics, 29(9), 529-536. doi:10.1016/j.tig.2013.06.005Callura, J. M., Dwyer, D. J., Isaacs, F. J., Cantor, C. R., & Collins, J. J. (2010). Tracking, tuning, and terminating microbial physiology using synthetic riboregulators. Proceedings of the National Academy of Sciences, 107(36), 15898-15903. doi:10.1073/pnas.1009747107Callura, 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.1203808109Werstuck, G. (1998). Controlling Gene Expression in Living Cells Through Small Molecule-RNA Interactions. Science, 282(5387), 296-298. doi:10.1126/science.282.5387.296Wieland, M., & Hartig, J. S. (2008). Improved Aptazyme Design and In Vivo Screening Enable Riboswitching in Bacteria. Angewandte Chemie International Edition, 47(14), 2604-2607. doi:10.1002/anie.200703700Win, M. N., & Smolke, C. D. (2007). A modular and extensible RNA-based gene-regulatory platform for engineering cellular function. Proceedings of the National Academy of Sciences, 104(36), 14283-14288. doi:10.1073/pnas.0703961104Klauser, B., & Hartig, J. S. (2013). An engineered small RNA-mediated genetic switch based on a ribozyme expression platform. Nucleic Acids Research, 41(10), 5542-5552. doi:10.1093/nar/gkt253Bayer, T. S., & Smolke, C. D. (2005). Programmable ligand-controlled riboregulators of eukaryotic gene expression. Nature Biotechnology, 23(3), 337-343. doi:10.1038/nbt1069Qi, 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/gks168Looger, L. L., Dwyer, M. A., Smith, J. J., & Hellinga, H. W. (2003). Computational design of receptor and sensor proteins with novel functions. Nature, 423(6936), 185-190. doi:10.1038/nature01556Kortemme, T., & Baker, D. (2004). Computational design of protein–protein interactions. Current Opinion in Chemical Biology, 8(1), 91-97. doi:10.1016/j.cbpa.2003.12.008Rodrigo, 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.1203831109Isaacs, 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/nbt986Kirkpatrick, S., Gelatt, C. D., & Vecchi, M. P. (1983). Optimization by Simulated Annealing. Science, 220(4598), 671-680. doi:10.1126/science.220.4598.671Hofacker, 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/bf00818163Pédelacq, J.-D., Cabantous, S., Tran, T., Terwilliger, T. C., & Waldo, G. S. (2005). Engineering and characterization of a superfolder green fluorescent protein. Nature Biotechnology, 24(1), 79-88. doi:10.1038/nbt1172Hersch, G. L., Baker, T. A., & Sauer, R. T. (2004). SspB delivery of substrates for ClpXP proteolysis probed by the design of improved degradation tags. Proceedings of the National Academy of Sciences, 101(33), 12136-12141. doi:10.1073/pnas.0404733101Rodrigo, G., Kirov, B., Shen, S., & Jaramillo, A. (2013). Theoretical and experimental analysis of the forced LacI-AraC oscillator with a minimal gene regulatory model. Chaos: An Interdisciplinary Journal of Nonlinear Science, 23(2), 025109. doi:10.1063/1.4809786Danino, T., Mondragón-Palomino, O., Tsimring, L., & Hasty, J. (2010). A synchronized quorum of genetic clocks. Nature, 463(7279), 326-330. doi:10.1038/nature08753Mathews, 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.1999.2700Paige, J. S., Nguyen-Duc, T., Song, W., & Jaffrey, S. R. (2012). Fluorescence Imaging of Cellular Metabolites with RNA. Science, 335(6073), 1194-1194. doi:10.1126/science.1218298Chen, X., & Ellington, A. D. (2009). Design Principles for Ligand-Sensing, Conformation-Switching Ribozymes. PLoS Computational Biology, 5(12), e1000620. doi:10.1371/journal.pcbi.1000620Quarta, G., Sin, K., & Schlick, T. (2012). Dynamic Energy Landscapes of Riboswitches Help Interpret Conformational Rearrangements and Function. PLoS Computational Biology, 8(2), e1002368. doi:10.1371/journal.pcbi.1002368Freeman, J. B., & Dale, R. (2012). Assessing bimodality to detect the presence of a dual cognitive process. Behavior Research Methods, 45(1), 83-97. doi:10.3758/s13428-012-0225-xWieland, M., Benz, A., Klauser, B., & Hartig, J. S. (2009). Artificial Ribozyme Switches Containing Natural Riboswitch Aptamer Domains. Angewandte Chemie International Edition, 48(15), 2715-2718. doi:10.1002/anie.200805311Penchovsky, R., & Breaker, R. R. (2005). Computational design and experimental validation of oligonucleotide-sensing allosteric ribozymes. Nature Biotechnology, 23(11), 1424-1433. doi:10.1038/nbt1155Chushak, Y., & Stone, M. O. (2009). In silico selection of RNA aptamers. Nucleic Acids Research, 37(12), e87-e87. doi:10.1093/nar/gkp408Bartel, D., & Szostak, J. (1993). Isolation of new ribozymes from a large pool of random sequences [see comment]. Science, 261(5127), 1411-1418. doi:10.1126/science.7690155Lutz, R. (1997). Independent and tight regulation of transcriptional units in Escherichia coli via the LacR/O, the TetR/O and AraC/I1-I2 regulatory elements. Nucleic Acids Research, 25(6), 1203-1210. doi:10.1093/nar/25.6.1203Mutalik, 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.919Bennett, M. R., & Hasty, J. (2009). Microfluidic devices for measuring gene network dynamics in single cells. Nature Reviews Genetics, 10(9), 628-638. doi:10.1038/nrg2625Cookson, N. A., Mather, W. H., Danino, T., Mondragón‐Palomino, O., Williams, R. J., Tsimring, L. S., & Hasty, J. (2011). Queueing up for enzymatic processing: correlated signaling through coupled degradation. Molecular Systems Biology, 7(1), 561. doi:10.1038/msb.2011.94Hermann, T. (2000). Adaptive Recognition by Nucleic Acid Aptamers. Science, 287(5454), 820-825. doi:10.1126/science.287.5454.820Lou, C., Stanton, B., Chen, Y.-J., Munsky, B., & Voigt, C. A. (2012). Ribozyme-based insulator parts buffer synthetic circuits from genetic context. Nature Biotechnology, 30(11), 1137-1142. doi:10.1038/nbt.2401Qi, L., Haurwitz, R. E., Shao, W., Doudna, J. A., & Arkin, A. P. (2012). RNA processing enables predictable programming of gene expression. Nature Biotechnology, 30(10), 1002-1006. doi:10.1038/nbt.2355Liu, C. C., Qi, L., Lucks, J. B., Segall-Shapiro, T. H., Wang, D., Mutalik, V. K., & Arkin, A. P. (2012). An adaptor from translational to transcriptional control enables predictable assembly of complex regulation. Nature Methods, 9(11), 1088-1094. doi:10.1038/nmeth.2184Qi, L. S., Larson, M. H., Gilbert, L. A., Doudna, J. A., Weissman, J. S., Arkin, A. P., & Lim, W. A. (2013). Repurposing CRISPR as an RNA-Guided Platform for Sequence-Specific Control of Gene Expression. 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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. 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A new frontier in synthetic biology : automated design of small RNA devices in bacteria

RNA devices provide synthetic biologists with tools for manipulating post-transcriptional regulation and conditional detection of cellular biomolecules. The use of computational methods to design RNA devices has improved to the stage where it is now possible to automate the entire design process. These methods utilize structure prediction tools that optimize nucleotide sequences, together with fragments of known independent functionalities. Recently, this approach has been used to create an automated method for the de novo design of riboregulators. Here, we describe how it is possible to obtain riboregulatory circuits in prokaryotes by capturing the relevant interactions of RNAs inside the cytoplasm using a physicochemical model. We focus on the regulation of protein expression mediated by intra- or intermolecular interactions of small RNAs (sRNAs), and discuss the design of riboregulators for other functions. The automated design of RNA devices opens new possibilities for engineering fully synthetic regulatory systems that program new functions or reprogram dysfunctions in living cells

Design of a multi-input, multi-output sRNA-based logic circuit.

<p>We show a design of a circuit that assembles different riboregulators. Here, sRNA <i>tR13</i> is able to both repress and activate the expression of two different <i>cis</i>-repressed genes, by <i>cR31</i> and <i>cR19</i> respectively, resulting in a coupled YES/NOT logic gate. In addition, sRNA <i>tR19</i> is able to activate <i>cR19</i>, implementing together with <i>tR13</i> an OR logic gate. RNA sequences shown in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003172#pcbi.1003172.s006" target="_blank">Table S1</a>. Secondary structures imposed for all species shown in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003172#pcbi.1003172.s001" target="_blank">Fig. S1</a>.</p

Designs of sRNA-based AND logic gates.

<p>We show two designs (A and B) using different structures for the <i>trans</i>-activating sRNAs (mechanism shown in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003172#pcbi-1003172-g001" target="_blank">Fig. 1E</a>). (A.1) Detail of a design, showing the RBS in blue, start codon in green, and seed regions in red and magenta. The secondary structures of the intramolecular and intermolecular folding states are presented. (A.2 and B.1) Helical plot of the complex, where the RBS is released. Δ<i>G</i>, Δ<i>G</i>_kin and Δ<i>G</i>_str are in Kcal/mol. <i>Z</i> is the partition function. (A.3 and B.2) Base pairing probability matrix, encircling the pairs of intermolecular interactions with high probability. RNA sequences shown in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003172#pcbi.1003172.s006" target="_blank">Table S1</a>. Secondary structures imposed for all species shown in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003172#pcbi.1003172.s001" target="_blank">Fig. S1</a>.</p

Designs of sRNA-based YES logic gates.

<p>We show four designs (A to D) using different structures for the <i>trans</i>-activating sRNAs (mechanism shown in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003172#pcbi-1003172-g001" target="_blank">Fig. 1B</a>). (A.1) Detail of a design, showing the RBS in blue, start codon in green, and seed region in red. The secondary structures of the intramolecular and intermolecular folding states are presented. (A.2, B.1, C.1 and D.1) Helical plot of the complex, where the RBS is released. Δ<i>G</i>, Δ<i>G</i>_kin and Δ<i>G</i>_str are in Kcal/mol. <i>Z</i> is the partition function. (A.3, B.2, C.2 and D.2) Base pairing probability matrix, encircling the pairs of intermolecular interaction with high probability. RNA sequences shown in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003172#pcbi.1003172.s006" target="_blank">Table S1</a>. Secondary structures imposed for all species shown in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003172#pcbi.1003172.s001" target="_blank">Fig. S1</a>.</p

Further designs of sRNA-based NOT and YES logic gates.

<p>We show two designs (A and B) using the mechanisms shown in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003172#pcbi-1003172-g001" target="_blank">Figs. 1C and 1D</a>. For the NOT gate, helical plots showing (A.1) the RBS exposed, and (A.2) the RBS blocked after sRNA interaction. For the YES gate, helical plots showing (B.1) a transcription terminator, and (B.2) that the hairpin before the poly(U) tail is destabilized after sRNA interaction. Δ<i>G</i> is in Kcal/mol. <i>Z</i> is the partition function. (A.3 and B.3) Base pairing probability matrix, encircling the pairs of intermolecular interaction with high probability. RNA sequences shown in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003172#pcbi.1003172.s006" target="_blank">Table S1</a>. Secondary structures imposed for all species shown in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1003172#pcbi.1003172.s001" target="_blank">Fig. S1</a>.</p