Unstructured regions in IRE1α specify BiP-mediated destabilisation of the luminal domain dimer and repression of the UPR

Coupling of endoplasmic reticulum stress to dimerisation‑dependent activation of the UPR transducer IRE1 is incompletely understood. Whilst the luminal co-chaperone ERdj4 promotes a complex between the Hsp70 BiP and IRE1's stress-sensing luminal domain (IRE1LD) that favours the latter's monomeric inactive state and loss of ERdj4 de-represses IRE1, evidence linking these cellular and in vitro observations is presently lacking. We report that enforced loading of endogenous BiP onto endogenous IRE1α repressed UPR signalling in CHO cells and deletions in the IRE1α locus that de-repressed the UPR in cells, encode flexible regions of IRE1LD that mediated BiP‑induced monomerisation in vitro. Changes in the hydrogen exchange mass spectrometry profile of IRE1LD induced by ERdj4 and BiP confirmed monomerisation and were consistent with active destabilisation of the IRE1LD dimer. Together, these observations support a competition model whereby waning ER stress passively partitions ERdj4 and BiP to IRE1LD to initiate active repression of UPR signalling

The prospects of quantum computing in computational molecular biology

Quantum computers can in principle solve certain problems exponentially more quickly than their classical counterparts. We have not yet reached the advent of useful quantum computation, but when we do, it will affect nearly all scientific disciplines. In this review, we examine how current quantum algorithms could revolutionize computational biology and bioinformatics. There are potential benefits across the entire field, from the ability to process vast amounts of information and run machine learning algorithms far more efficiently, to algorithms for quantum simulation that are poised to improve computational calculations in drug discovery, to quantum algorithms for optimization that may advance fields from protein structure prediction to network analysis. However, these exciting prospects are susceptible to "hype", and it is also important to recognize the caveats and challenges in this new technology. Our aim is to introduce the promise and limitations of emerging quantum computing technologies in the areas of computational molecular biology and bioinformatics.Comment: 23 pages, 3 figure

Multiomic analyses implicate a neurodevelopmental program in the pathogenesis of cerebral arachnoid cysts

Cerebral arachnoid cysts (ACs) are one of the most common and poorly understood types of developmental brain lesion. To begin to elucidate AC pathogenesis, we performed an integrated analysis of 617 patient-parent (trio) exomes, 152,898 human brain and mouse meningeal single-cell RNA sequencing transcriptomes and natural language processing data of patient medical records. We found that damaging de novo variants (DNVs) were highly enriched in patients with ACs compared with healthy individuals (P = 1.57 × 10-33). Seven genes harbored an exome-wide significant DNV burden. AC-associated genes were enriched for chromatin modifiers and converged in midgestational transcription networks essential for neural and meningeal development. Unsupervised clustering of patient phenotypes identified four AC subtypes and clinical severity correlated with the presence of a damaging DNV. These data provide insights into the coordinated regulation of brain and meningeal development and implicate epigenomic dysregulation due to DNVs in AC pathogenesis. Our results provide a preliminary indication that, in the appropriate clinical context, ACs may be considered radiographic harbingers of neurodevelopmental pathology warranting genetic testing and neurobehavioral follow-up. These data highlight the utility of a systems-level, multiomics approach to elucidate sporadic structural brain disease

Probes, hardware and software for next-generation super-resolution microscopy

Super-resolution microscopy enables optical imaging using fluorescence probes below the diffraction limit. In stochastic super-resolution microscopy, molecules are „switched“ between non-fluorescent dark-state (OFF-state) and fluorescent bright-state (ON-state) in order to pinpoint their position with sub-diffraction precision. The most prominent techniques of localization-based super-resolution microscopy are photo-activated localization microscopy (PALM) and stochastic optical reconstruction microscopy (STORM). Here, the switching between dark- and bright-state is accomplished using photophysical or photochemical processes. A recently introduced super-resolution microscopy method called DNA-PAINT (deoxyribonucleic acid - point accumulation for imaging in nanoscale topography) is based on DNA-DNA interaction. In contrast to STORM or PALM, the fluorescence molecules do not switch between dark and bright states. The so-called „blinking“ in DNA-PAINT is created by transient hybridization of short fluorescent DNA strands (imagers) to their targets. The work in this dissertation focuses on three different advancements in the technological aspect of super-resolution microscopy. Probes In the first project of this thesis, I demonstrate the combination of single-molecule Förster resonance energy transfer (FRET) with DNA-PAINT imaging to overcome some current limitations of the DNA-based super-resolution microscopy. I evaluate the novel probe design with in vitro experiments using DNA nanostructures and prove the performance of the FRET-based probes in a cellular context. Hardware In the second project, I describe a cost-efficient single-molecule microscope platform, which is an order of magnitude more affordable, while still yielding high-performance imaging capacity. Using two-dimensional (2D) and three-dimensional (3D) super-resolution in vitro experiments using DNA nanostructures, I asses the performance of the microscopy platform. Finally, I present exemplary experiments for multiplexed cellular imaging. Software In the last project, I present a software package that is developed to assist during super-resolution data analysis. It is based on the deep learning concept of the artificial neural network (ANN) and designed to automate the classification of nano-scaled patterns found in super-resolution images. I evaluate the performance of the software package using super-resolution in vitro experiments of DNA nanostructures as well as targets in cellular samples.Die superauflösende Mikroskopie ermöglicht die optische Abbildung mittels Fluoreszenzsonden unterhalb der Beugungsgrenze. In stochastischen Superauflösungsmikroskopie werden Moleküle zwischen dem nicht-fluoreszierenden Zustand (OFF-Zustand) und dem fluoreszierenden Zustand (ON-Zusstand) “geschaltet“, um ihre Position präziser als die Beugungsgrenze zu bestimmen. Die bekanntesten Mikroskopietechniken der lokalisationsbasierten Superauflösungsmikroskopie sind photo-activated localization microscopy (PALM) und stochastic optical reconstruction microscopy (STORM). Hier wird die Umschaltung zwischen Dunkel- und Hellzustand mithilfe photophysikalischer oder photochemischer Prozesse durchgeführt. Eine kürzlich eingeführte Methode der Superauflösungsmikroskopie namens DNA-PAINT (deoxyribonucleic acid - point accumulation for imaging in nanoscale topography) basiert auf der DNA-DNA Wechselwirkung. Im Vergleich zu STORM oder PALM wechseln die Fluoreszenzmoleküle nicht zwischen dem dunklen und dem hellen Zustand. Das sogenannte “Blinken“ in DNA-PAINT wird durch transiente Hybridisierung kurzer fluoreszierender DNA Stränge (Imager) an ihre Ziele erzeugt. Die Arbeiten in dieser Dissertation konzentriert sich auf drei unterschiedliche Fortschritte im technologischen Aspekt der Superauflösungsmikroskopie. Sonden Im ersten Projekt dieser Arbeit zeige ich die Kombination von Einzelmolekül-Förster-Resonanzenergietransfer (englisch Förster resonance energy transfer (FRET)) mit DNA-PAINT Mikroskopie, um einige aktuelle Einschränkungen der DNA basierten Superauflösungsmikroskopie zu überwinden. Ich evaluiere das neuartige Sondendesign mithilfe von in vitro Experimenten mit DNA nanostructure und zeige die Leistungsfähigkeit der FRET-basierten Sonden im zellulären Kontext. Hardware Im zweiten Projekt beschreibe ich eine kosteneffiziente Mikroskop-Plattform für Einzelmolekülstudien, die um eine Größenordnung erschwinglicher ist und dennoch eine leistungsstarke Abbildungsfähigkeit bietet. Unter Verwendung von zweidimensionalen (2D) und dreidimensionalen (3D) in vitro Superauflösungsexperimenten von DNA Nanostrukturen bewerte ich die Leistung der Mikroskopie-Plattform. Schließlich zeige ich exemplarische Experimente für die zelluläre Bildgebung in mehreren Farben. Software Im letzten Projekt stelle ich ein Softwarepaket vor, das zur Unterstützung der Analyse von Daten in Superauflösungsmikroskopie entwickelt wurde. Es basiert auf dem Konzept des tiefen Lernens (englisch deep learning) mithilfe von künstlichen neuronalen Netzen und wurde entwickelt, um die Klassifikation von nanoskaligen Mustern zu automatisieren, die in superaufgelösten Bildern zu finden sind. Ich evaluiere die Leistung des Softwarepakets anhand von in vitro Superauflösungsexperimenten von DNA Nanostrukturen sowie von in Zellproben

Molecular dynamics simulation of proton-transfer coupled rotations in ATP synthase FO motor

The FO motor in FOF1 ATP synthase rotates its rotor driven by the proton motive force. While earlier studies elucidated basic mechanisms therein, recent advances in high-resolution cryo-electron microscopy enabled to investigate proton-transfer coupled FO rotary dynamics at structural details. Here, taking a hybrid Monte Carlo/molecular dynamics simulation method, we studied reversible dynamics of a yeast mitochondrial FO. We obtained the 36°-stepwise rotations of FO per one proton transfer in the ATP synthesis mode and the proton pumping in the ATP hydrolysis mode. In both modes, the most prominent path alternatively sampled states with two and three deprotonated glutamates in c-ring, by which the c-ring rotates one step. The free energy transduction efficiency in the model FO motor reached ~ 90% in optimal conditions. Moreover, mutations in key glutamate and a highly conserved arginine increased proton leakage and markedly decreased the coupling, in harmony with previous experiments. This study provides a simple framework of simulations for chemical-reaction coupled molecular dynamics calling for further studies in ATP synthase and others

High Precision Fret to Study Bimolecular Dynamics with High Temporal Resolution

Förster Resonance Energy Transfer (FRET) is a promising methodology which is used by researchers in determining structural information of biomolecules. Other methodologies such as X-ray diffraction, nuclear magnetic resonance (NMR) and electron microscopy (EM), and X-ray scattering (SAXS) and circular dichroism (CD) can only give snapshots of the functional conformers. In the applications of most cases, FRET can only be used to give a qualitative result such as “yes” or “no” pattern. In our lab, we are able to expand this ability by applying FRET to quantitatively determine the structure and dynamic information in the biological system with high accuracy. We first apply this methodology in measuring our standard double strand DNA in order to identify its ability to obtain the interdye distance with high accuracy. Then, we are able to identify the fast dynamic which happens in mRNA followed by a kinetic pathway of its folding pattern. Finally, we used FRET as well as discrete molecular dynamics (DMD) simulation in determining dynamic behavior of PDZ domains