Engineering cooperative tecto–RNA complexes having programmable stoichiometries

High affinity and specificity RNA–RNA binding interfaces can be constructed by combining pairs of GNRA loop/loop–receptor interaction motifs. These interactions can be fused using flexible four-way junction motifs to create divalent, self-assembling scaffolding units (‘tecto-RNA’) that have favorable properties for nanomedicine and other applications. We describe the design and directed assembly of tecto-RNA units ranging from closed, cooperatively assembling ring-shaped complexes of programmable stoichiometries (dimers, trimers and tetramers) to open multimeric structures. The novelty of this work is that tuning of the stoichiometries of self-assembled complexes is achieved by precise positioning of the interaction motifs in the monomer units rather than changing their binding specificities. Structure-probing and transmission electron microscopy studies as well as thermodynamic analysis support formation of closed cooperative complexes that are highly resistant to nuclease digestion. The present designs provide two helical arms per RNA monomer for further functionalization aims

Promoting RNA helical stacking via A-minor junctions

RNA molecules take advantage of prevalent structural motifs to fold and assemble into well-defined 3D architectures. The A-minor junction is a class of RNA motifs that specifically controls coaxial stacking of helices in natural RNAs. A sensitive self-assembling supra-molecular system was used as an assay to compare several natural and previously unidentified A-minor junctions by native polyacrylamide gel electrophoresis and atomic force microscopy. This class of modular motifs follows a topological rule that can accommodate a variety of interchangeable A-minor interactions with distinct local structural motifs. Overall, two different types of A-minor junctions can be distinguished based on their functional self-assembling behavior: one group makes use of triloops or GNRA and GNRA-like loops assembling with helices, while the other takes advantage of more complex tertiary receptors specific for the loop to gain higher stability. This study demonstrates how different structural motifs of RNA can contribute to the formation of topologically equivalent helical stacks. It also exemplifies the need of classifying RNA motifs based on their tertiary structural features rather than secondary structural features. The A-minor junction rule can be used to facilitate tertiary structure prediction of RNAs and rational design of RNA parts for nanobiotechnology and synthetic biology

Identification of factors required for m6A mRNA methylation in Arabidopsis reveals a role for the conserved E3 ubiquitin ligase HAKAI

Peer reviewe

Widening Access to Applied Machine Learning with TinyML

Broadening access to both computational and educational resources is critical to diffusing machine-learning (ML) innovation. However, today, most ML resources and experts are siloed in a few countries and organizations. In this paper, we describe our pedagogical approach to increasing access to applied ML through a massive open online course (MOOC) on Tiny Machine Learning (TinyML). We suggest that TinyML, ML on resource-constrained embedded devices, is an attractive means to widen access because TinyML both leverages low-cost and globally accessible hardware, and encourages the development of complete, self-contained applications, from data collection to deployment. To this end, a collaboration between academia (Harvard University) and industry (Google) produced a four-part MOOC that provides application-oriented instruction on how to develop solutions using TinyML. The series is openly available on the edX MOOC platform, has no prerequisites beyond basic programming, and is designed for learners from a global variety of backgrounds. It introduces pupils to real-world applications, ML algorithms, data-set engineering, and the ethical considerations of these technologies via hands-on programming and deployment of TinyML applications in both the cloud and their own microcontrollers. To facilitate continued learning, community building, and collaboration beyond the courses, we launched a standalone website, a forum, a chat, and an optional course-project competition. We also released the course materials publicly, hoping they will inspire the next generation of ML practitioners and educators and further broaden access to cutting-edge ML technologies.Comment: Understanding the underpinnings of the TinyML edX course series: https://www.edx.org/professional-certificate/harvardx-tiny-machine-learnin

Widening Access to Applied Machine Learning With TinyML

Broadening access to both computational and educational resources is crit- ical to diffusing machine learning (ML) innovation. However, today, most ML resources and experts are siloed in a few countries and organizations. In this article, we describe our pedagogical approach to increasing access to applied ML through a massive open online course (MOOC) on Tiny Machine Learning (TinyML). We suggest that TinyML, applied ML on resource-constrained embedded devices, is an attractive means to widen access because TinyML leverages low-cost and globally accessible hardware and encourages the development of complete, self-contained applications, from data collection to deployment. To this end, a collaboration between academia and industry produced a four part MOOC that provides application-oriented instruction on how to develop solutions using TinyML. The series is openly available on the edX MOOC platform, has no prerequisites beyond basic programming, and is designed for global learners from a variety of backgrounds. It introduces real-world applications, ML algorithms, data-set engineering, and the ethi- cal considerations of these technologies through hands-on programming and deployment of TinyML applications in both the cloud and on their own microcontrollers. To facili- tate continued learning, community building, and collaboration beyond the courses, we launched a standalone website, a forum, a chat, and an optional course-project com- petition. We also open-sourced the course materials, hoping they will inspire the next generation of ML practitioners and educators and further broaden access to cutting-edge ML technologies

Modulating RNA structure and catalysis: lessons from small cleaving ribozymes

RNA is a key molecule in life, and comprehending its structure/function relationships is a crucial step towards a more complete understanding of molecular biology. Even though most of the information required for their correct folding is contained in their primary sequences, we are as yet unable to accurately predict both the folding pathways and active tertiary structures of RNA species. Ribozymes are interesting molecules to study when addressing these questions because any modifications in their structures are often reflected in their catalytic properties. The recent progress in the study of the structures, the folding pathways and the modulation of the small ribozymes derived from natural, self-cleaving, RNA motifs have significantly contributed to today’s knowledge in the field

FASN-Dependent Lipid Metabolism Links Neurogenic Stem/Progenitor Cell Activity to Learning and Memory Deficits

Altered neural stem/progenitor cell (NSPC) activity and neurodevelopmental defects are linked to intellectual disability. However, it remains unclear whether altered metabolism, a key regulator of NSPC activity, disrupts human neurogenesis and potentially contributes to cognitive defects. We investigated links between lipid metabolism and cognitive function in mice and human embryonic stem cells (hESCs) expressing mutant fatty acid synthase (FASN; R1819W), a metabolic regulator of rodent NSPC activity recently identified in humans with intellectual disability. Mice homozygous for the FASN R1812W variant have impaired adult hippocampal NSPC activity and cognitive defects because of lipid accumulation in NSPCs and subsequent lipogenic ER stress. Homozygous FASN R1819W hESC-derived NSPCs show reduced rates of proliferation in embryonic 2D cultures and 3D forebrain regionalized organoids, consistent with a developmental phenotype. These data from adult mouse models and in vitro models of human brain development suggest that altered lipid metabolism contributes to intellectual disability