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Results for “Bioengineering”

Search indexed bioRxiv preprints in genomics, neuroscience, cell biology and bioinformatics. Read source abstracts and check manuscript versions; preprints are not peer reviewed.

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bioRxiv2022

Displacement and dissociation of oligonucleotidesduring DNA hairpin closure under strain

The simple hybridization kinetic of an oligonucleotide to its template is a fundamental step in many biological processes such as replication arrest, CRISPR recognition, DNA sequencing, DNA origami, etc. Although approaches exist that address special cases of this problem, there are no simple general prediction schemes. In this work, we have measured experimentally, with no fluorescent labelling, the displacement of an oligonucleotide from its substrate in two situations: one corresponding to oligonucleotide binding/unbinding on ssDNA and one where the oligonucleotide is displaced by the refolding of a dsDNA fork. In this second situation, the fork is expelling the oligonucleotide thus reducing significantly its residence time. To account for our data in these two situations, we have constructed a mathematical model, based on the known nearest neighbor dinucleotide free energies, and provided a good estimate of the residence times of different oligonucleotides (DNA, RNA, LNA) with various length in different experimental conditions (force, temperature, buffer conditions, presence of mismatches, etc.). This study provides a foundation for the dynamics of oligonucleotide displacement, a process of importance in a number of biological and bioengineering contexts.

biophysics↗

bioRxiv2022

Quantification of cell penetrating peptide mediated delivery of proteins in plant leaves

Protein delivery to plants offers many opportunities for plant bioengineering via gene editing and through direction of protein-protein interactions. However, the delivery and confirmation of successful protein delivery to plants presents both practical and analytical challenges. We present a GFP bimolecular fluorescence complementation-based tool, delivered complementation in planta (DCIP), which allows for unambiguous and quantitative measurement of protein delivery in leaves. Using DCIP, we demonstrate cell-penetrating peptide mediated cytosolic delivery of peptides and recombinant proteins in Nicotiana benthamiana. We show that DCIP enables measurement of delivery efficiency and enables functional screening of cell penetrating peptide efficacies for in-plant protein delivery. Finally, we demonstrate that DCIP detects cell penetrating peptide mediated delivery of recombinantly expressed proteins such as mCherry and Lifeact into intact leaves. Finally, we also demonstrate, for the first time, delivery of a recombinant plant transcription factor, WUSCHEL (AtWUS), in N. benthamiana. RT-qPCR analysis of AtWUS delivery in Arabidopsis seedlings also suggests delivered WUS can recapitulate AtWUS-overexpression transcriptional changes. All combined, DCIP offers a new and powerful tool for interrogating cytosolic delivery of proteins in plants and highlights future avenues for engineering plant physiology.

plant biology↗

bioRxiv2022

Machine learning directed organoid morphogenesis uncovers an excitable system driving human axial elongation

The human embryo breaks symmetry to form the anterior-posterior axis of the body. As the embryo elongates along this axis, progenitors in the tailbud give rise to axial tissues that generate the spinal cord, skeleton, and musculature. The mechanisms underlying human axial elongation are unknown. While ethics necessitate in vitro studies, the variability of human organoid systems has hindered mechanistic insights. Here we developed a bioengineering and machine learning framework that optimizes symmetry breaking by tuning the spatial coupling between human pluripotent stem cell-derived organoids. This framework enabled the reproducible generation of hundreds of axially elongating organoids, each possessing a tailbud and an epithelial neural tube with a single lumen. We discovered that an excitable system composed of WNT and FGF signaling drives axial elongation through the induction of a signaling center in the form of neuromesodermal progenitor (NMP)-like cells. The ability of NMP-like cells to function as a signaling center and drive elongation is independent of their potency to generate mesodermal cell types. We further discovered that the instability of the underlying excitable system is suppressed by secreted WNT inhibitors of the secreted frizzled-related protein (SFRP) family. Absence of these inhibitors led to the formation of ectopic tailbuds and branches. Our results identify mechanisms governing stable human axial elongation to achieve robust morphogenesis.

developmental biology↗

bioRxiv2022

The substrate scopes of enzymes: a general prediction model based on machine and deep learning

For a comprehensive understanding of metabolism, it is necessary to know all potential substrates for each enzyme encoded in an organisms genome. However, for most proteins annotated as enzymes, it is unknown which primary and/or secondary reactions they catalyze [1], as experimental characterizations are time-consuming and costly. Machine learning predictions could provide an efficient alternative, but are hampered by a lack of information regarding enzyme non-substrates, as available training data comprises mainly positive examples. Here, we present ESP, a general machine learning model for the prediction of enzyme-substrate pairs, with an accuracy of over 90% on independent and diverse test data. This accuracy was achieved by representing enzymes through a modified transformer model [2] with a trained, task-specific token, and by augmenting the positive training data by randomly sampling small molecules and assigning them as non-substrates. ESP can be applied successfully across widely different enzymes and a broad range of metabolites. It outperforms recently published models designed for individual, well-studied enzyme families, which use much more detailed input data [3, 4]. We implemented a user-friendly web server to predict the substrate scope of arbitrary enzymes, which may support not only basic science, but also the development of pharmaceuticals and bioengineering processes.

bioinformatics↗

bioRxiv2022

Structural determinants of relaxation dynamics in chemical reaction networks

Understanding the relationship between the structure of chemical reaction networks and their reaction dynamics is essential for unveiling the design principles of living organisms. However, while some network-structural features are known to relate to the steady-state characteristics of chemical reaction networks, mathematical frameworks describing the links between out-of-steady-state dynamics and network structure are still underdeveloped. Here, we characterize the out-of-steady-state behavior of a class of artificial chemical reaction networks consisting of the ligation and splitting reactions of polymers. Within this class, we examine minimal networks that can convert a given set of inputs (e.g., nutrients) to a specified set of targets (e.g., biomass precursors). We find three distinct types of relaxation dynamics after perturbation from a steady-state: exponential-, power-law-, and plateau-dominated. We computationally show that we can predict this out-of-steady-state dynamical behavior from just three features computed from the networks stoichiometric matrix, namely, (i) the rank gap, determining the existence of a steady-state; (ii) the left null-space, being related to conserved quantities in the dynamics; and (iii) the stoichiometric cone, dictating the range of achievable chemical concentrations. We further demonstrate that these three quantities also predict the type of relaxation dynamics of combinations of our minimal networks, larger networks with many redundant pathways, and a real example of a metabolic network. The unified method to predict the qualitative features of the relaxation dynamics presented here can provide a basis for understanding the design of metabolic reaction networks as well as industrially useful chemical production pathways. Author summaryThe relationship between network structure and chemical reaction dynamics is of central interest in chemical reaction network theory, as it underlies chemical manufacturing, cellular metabolism, and bioengineering. The links between structure and steady-state properties have been extensively investigated. However, how far the network structure determines the out-of-steady-state, transient dynamics of chemical reactions is unexplored. Here we construct a chemical reaction network model that is simple but generates a wide variety of network instances. By computationally exploring the networks structural- and dynamical features, we found that three network-structural features are sufficient to predict the qualitative characteristics of the relaxation dynamics after the chemical concentrations are perturbed from their steady-state. Depending on the values of those three features, the chemical reaction dynamics on the network exhibit exponential, plateau, and power-law relaxation. Also, we found that such features are determinants of the dynamics of biological metabolic reaction systems. Our findings provide a foundation for the structure-based prediction of chemical reaction dynamics.

systems biology↗

bioRxiv2022

Direct encapsulation of biomolecules in semi-permeable microcapsules produced with double-emulsions

Compartmentalization can serve different purposes such as the protection of biological active substances from the environment, or the creation of a unique combination of biomolecules for diagnostic, therapeutic, or other bioengineering applications. We present a method for direct encapsulation of molecules in biocompatible and semi-permeable microcapsules made from low-molecular weight poly(ethylene glycol) diacrylate (PEG-DA 258). Microcapsules are produced using a non-planar PDMS microfluidic chip allowing for one-step production of water-in-PEG-DA 258-in-water double-emulsions, which are polymerized with UV light into a poly-PEG-DA 258 shell. Semi-permeable PEG shells are obtained by adding an inert solvent to the PEG-diacrylate. Due to the favourable hydrophilicity of poly-PEG-DA 258, proteins didnt adsorb to the capsule shell, and we demonstrate the direct encapsulation of enzymes, which can also be dried in the capsules to preserve activity. Finally, we leverage capsule permeability for the implementation of a two-layer communication cascade using compartmentalized DNA strand displacement reactions. This work presents the direct encapsulation of active biomolecules in semi-permeable microcapsules, and we expect our platform to facilitate the development of artificial cells and generating encapsulated diagnostics or therapeutics.

synthetic biology↗

bioRxiv2022

Nanocontainer derived from silkworm carotenoprotein for carotenoid extraction and presentation in biotechnology and biomedical applications

Found in many organisms, soluble carotenoproteins are considered as antioxidant nanocarriers for biomedical applications, although the structural basis for their carotenoid transfer function, a prerequisite for rational bioengineering, is largely unknown. We report crystal structures of the Carotenoid-Binding Protein from Bombyx mori (BmCBP) in apo- and zeaxanthin (ZEA)-bound forms. We use spectroscopy and calorimetry to characterize how ZEA and BmCBP mutually affect each other in the complex, identify key carotenoid-binding residues, confirm their roles by crystallography and carotenoid-binding capacity of BmCBP mutants and reconstitute BmCBP complexes with biomedically-relevant xanthophylls lutein, zeaxanthin, canthaxanthin and astaxanthin. By cost-effectively and scalably solubilizing xanthophylls from various crude herbal extracts, His-tagged BmCBP remains monomeric and forms a dynamic nanocontainer delivering carotenoids to liposomes and to other carotenoid-binding proteins, which in particular makes the Orange Carotenoid Protein, a promising optogenetic tool, photoactive. Furthermore, BmCBP(ZEA) administration stimulates fibroblast growth, which paves the way for its biomedical applications.

biochemistry↗

bioRxiv2022

First-in-patient dose prediction for adeno-associated virus-mediated hemophilia gene therapy using allometric scaling

Rational first-in-patient (FIP) dose selection is critical for successful clinical development of gene therapy. In this study, the author compared the performance of two allometric scaling approaches and body weight-based dose conversion approach for FIP dose prediction for adeno-associated virus (AAV)-mediated hemophilia gene therapy. The performance of the three approaches was examined using preclinical and clinical efficacy data of nine AAV vectors. In general, body weight-based direct conversion of effective doses in monkeys or dogs was more likely to underestimate FIP dose but worked better for one bioengineered vector with a high transduction efficiency specifically in humans. In contrast, allometric scaling between gene efficiency factor (logGEF) and body weight (logW) was likely to overestimate FIP dose but worked better than the other two approaches when vector capsid-specific T-cell responses were detected in patients. The third approach, allometric scaling between logGEF and W-0.25 was appropriate for FIP dose prediction in the absence of T-cell responses to AAV vectors or a dramatic difference in vector transduction efficiency between animals and humans. A decision tree was tentatively proposed to facilitate approach selection for FIP dose prediction in AAV-mediated hemophilia gene therapy.

pharmacology and toxicology↗

bioRxiv2022

Condensate functionalization with motors directs their nucleation in space and allows manipulating RNA localization

The localization of RNAs in cells is critical for many cellular processes. Whereas motor-driven transport of RNP condensates plays a prominent role in RNA localization in cells, their studies remain limited by the scarcity of available tools allowing to manipulate condensates in a spatial manner. To fill this gap, we reconstitute in cellula a minimal RNP transport system based on bioengineered condensates which were functionalized with kinesins and dynein-like motors, allowing for their positioning at either the cell periphery or centrosomes. This targeting mostly occurs through the active transport of the condensate scaffolds, which leads to localized nucleation of phase-separated condensates. Then, programming the condensates to recruit specific mRNAs is able to shift the localization of these mRNAs towards the cell periphery or the centrosomes. Our method opens novel perspectives to examine the role of RNA localization as a driver of cellular functions.

cell biology↗

bioRxiv2022

Proteome-wide systems genetics identifies UFMylation as a regulator of skeletal muscle function

Improving muscle function has great potential to improve the quality of life. To identify novel regulators of skeletal muscle metabolism and function, we performed a proteomic analysis of gastrocnemius muscle from 73 genetically distinct inbred mouse strains, and integrated the data with genomics and >300 molecular/phenotypic traits via quantitative trait loci mapping and correlation network analysis. These data identified thousands of associations between protein abundance and phenotypes and can be accessed online (https://muscle.coffeeprot.com/) to identify regulators of muscle function. We used this resource to prioritize targets for a functional genomic screen in human bioengineered skeletal muscle. This identified several negative regulators of muscle function including UFC1, an E2 ligase for protein UFMylation. We show UFMylation is up-regulated in a mouse model of amyotrophic lateral sclerosis, a disease that involves muscle atrophy. Furthermore, in vivo knockdown of UFMylation increased contraction force, implicating its role as a negative regulator of skeletal muscle function.

systems biology↗

bioRxiv2022

Outcome of Crash Course Training on Protein Structure Prediction with Artificial Intelligence

Protein structure predictions have broad impact on several science disciplines such as biology, bioengineering, and medical science. AlphaFold2[1] and RoseTTAFold[2] are the current state-of-the-art AI methods to predict the structures of proteins with an accuracy comparable to lower-resolution experimental methods. In its 2021 year review, both these methods were recognized as "breakthrough of the year" by Science magazine[3] and "method of the year" by Nature magazine [4]. It is timely and important to provide training and support on these emerging methods. Our crash course "Enabling Protein Structure Prediction with Artificial Intelligence "was conducted in collaboration with domain experts and research computing professionals. The crash course was well received by the community as there were 750 registrants from all over the world. Here we provide the summary of the crash course, describe our findings in organizing the crash course, and explain what preparation steps helped us with the hands-on training. CCS CONCEPTSComputing methodologies a Machine learning a Machine learning approaches a Bio-inspired approaches

scientific communication and education↗

bioRxiv2022

Positive, negative and engineered durotaxis

Cell migration is a physical process central to life. Among others, it regulates embryogenesis, tissue regeneration and tumor growth. Therefore, understanding and controlling cell migration represent fundamental challenges in science. Specifically, the ability of cells to follow stiffness gradients, known as durotaxis, is ubiquitous across most cell types. Even so, certain cells follow positive stiffness gradients while others move along negative gradients. How the physical mechanisms involved in cell migration works to enable a wide range of durotactic responses is still poorly understood. Here, we provide a mechanistic rationale of durotaxis by integrating stochastic clutch models for cell adhesion with an active gel theory of cell migration. We show that positive and negative durotaxis found across cell types are explained by asymmetries in the cell adhesion dynamics. We rationalize durotaxis by an asymmetric mechanotransduction in the cell adhesion behavior that further polarizes the intracellular retrograde flow and the protruding velocity at the cell membrane. Our theoretical framework confirms previous experimental observations and explains positive and negative durotaxis. Moreover, we show how durotaxis can be engineer to manipulate cell migration, which has important implications in biology, medicine and bioengineering.

biophysics↗

bioRxiv2022

YAP/TAZ inactivation with simvastatin attenuates glucocorticoid-induced human trabecular meshwork cell dysfunction

PurposeImpairment of the trabecular meshwork (TM) is the principal cause of increased outflow resistance in the glaucomatous eye. Yes-associated protein (YAP) and transcriptional coactivator with PDZ binding motif (TAZ) are emerging as potential mediators of TM cell/tissue dysfunction. Furthermore, YAP/TAZ activity was recently found to be controlled by the mevalonate pathway in non-ocular cells. Clinically-used statins block the mevalonate cascade and were shown to improve TM cell pathobiology; yet, the link to YAP/TAZ signaling was not investigated. In this study, we hypothesized that YAP/TAZ inactivation with simvastatin attenuates glucocorticoid-induced human TM (HTM) cell dysfunction. MethodsPrimary HTM cells were seeded atop or encapsulated within bioengineered extracellular matrix (ECM) hydrogels. Dexamethasone was used to induce a pathologic phenotype in HTM cells in the absence or presence of simvastatin. Changes in YAP/TAZ activity, actin cytoskeletal organization, phospho-myosin light chain levels, hydrogel contraction/stiffness, and fibronectin deposition were assessed. ResultsSimvastatin potently blocked pathologic YAP/TAZ nuclear localization/activity, actin stress fiber formation, and myosin light chain phosphorylation in HTM cells. Importantly, simvastatin co-treatment significantly attenuated dexamethasone-induced ECM contraction/stiffening and extracellular fibronectin deposition. Sequential treatment was similarly effective but did not match clinically-used Rho kinase inhibition. ConclusionsYAP/TAZ inactivation with simvastatin attenuates HTM cell pathobiology in a tissue-mimetic ECM microenvironment. Our data may help explain the association of statin use with a reduced risk of developing glaucoma via indirect YAP/TAZ inhibition as a proposed regulatory mechanism.

cell biology↗

bioRxiv2022

An experimental demonstration of ensemble epistasis in the lac repressor

Epistatic, non-additive, interactions between mutations reveal the functional architecture of living systems, strongly shape evolution, and present a difficult challenge for bioengineers. Interpreting and modeling epistasis requires knowledge of the mechanisms that bring it about. We recently argued that "ensemble epistasis" could be a generic mechanism for epistasis between mutations introduced into a single macromolecule. Because proteins exist as ensembles of interconverting conformations, a mutation could induce epistasis by subtly altering ensemble composition and thus the effects of subsequent mutations. Here we show experimentally that the thermodynamic ensemble does indeed yield high magnitude epistasis in the lac repressor. We observed two- and three-way epistasis in DNA binding, with magnitudes as large or larger than the individual effects of mutations. This biophysical effect propagated to substantial epistasis in gene expression in vivo. As predicted in previous theoretical work, IPTG concentration tunes the magnitude of ensemble epistasis. Further, our observations could all be captured with a rigorous mathematical model of the lac repressor ensemble. Given that conformational ensembles are unavoidable features of macromolecules, we expect this is a ubiquitous and underappreciated cause of intramolecular epistasis.

biophysics↗

bioRxiv2022

A unified model for the dynamics of ATP-independent ultrafast contraction

In nature, several ciliated protists possess the remarkable ability to execute ultrafast motions using protein assemblies called myonemes, which contract in response to Ca2+ ions. Existing theories, such as actomyosin contractility and macroscopic biomechanical latches, do not adequately describe these systems, necessitating new models to understand their mechanisms. In this study, we image and quantitatively analyze the contractile kinematics observed in two ciliated protists (Vorticella sp and Spirostomum sp), and, based on the mechanochemistry of these organisms, we propose a minimal mathematical model that reproduces our observations as well as those published previously. Analyzing the model reveals three distinct dynamic regimes, differentiated by the rate of chemical driving and the importance of inertia. We characterize their unique scaling behaviors and kinematic signatures. Besides providing insights into Ca2+-powered myoneme contraction in protists, our work may also inform the rational design of ultrafast bioengineered systems such as active synthetic cells.

biophysics↗

bioRxiv2022

Exploring The Behavior of Bioelectric Circuits using Evolution Heuristic Search

Characteristic spatial differences of cellular resting potential across tissues have been shown to act as instructive bioelectric prepatterns regulating embryonic and regenerative morphogenesis, as well as cancer suppression. Indeed, modulation of bioelectric patterns via specific ion channel-targeting drugs, channel misexpression, or optogenetics has been used to control growth and form in vitro, showing promise in regenerative medicine and synthetic bioengineering. Repair of defects, injury, and transformation requires quantitative understanding of bioelectric dynamics within tissues so that these can be modulated toward desired outcomes in organ patterning or the creation of entirely novel synthetic constructs. The major gap in the discovery of interventions for rational control of organ-level outcomes is the inability to predict large-scale bioelectric patterns - their emergence from symmetry breaking (given a set of channels expressed on the tissue) and their change as a function of time under specific bioelectrical interventions. It is thus essential to develop machine learning and other computational tools to help human scientists identify bioelectric states with desirable properties. Here, we tested the ability of a heuristic search algorithm to explore the parameter space of bio-electrical circuits by adjusting the parameters of simulated cells. We show that while bioelectrical space is not easy to search, it does contain parameter sets that encode rich and interesting patterning behaviors. We demonstrate proof of principle of using a computational search platform to identify circuits with desired properties, as a first step toward the design of machine learning tools for improved bioelectric control of growth and form.

biophysics↗

bioRxiv2022

Automated simulation-based membrane-protein refinement into cryo-EM data

I.The resolution revolution has increasingly enabled single-particle cryogenic electron microscopy (cryo-EM) reconstructions of previously inaccessible systems, including membrane proteins - a category that constitutes a disproportionate share of drug targets. We present a protocol for using density-guided molecular dynamics simulations to automatically refine atomistic models into membrane-protein cryo-EM maps. Using adaptive-force density-guided simulations as implemented in the GROMACS molecular dynamics package, we show how automated model refinement of a membrane protein is achieved without the need to manually tune the fitting force ad hoc. We also present selection criteria to choose the best fit model which balances stereochemistry and goodness-of-fit. The proposed protocol was used to refine models into a new cryo-EM density of the membrane protein maltoporin, either in a lipid bilayer or detergent micelle, and we found that results do not substantially differ from fitting in solution. Fitted structures satisfied classical model-quality metrics and improved the quality and the model-to-map correlation of the X-ray starting structure. Additionally, the density-guided fitting in combination with generalized orientation-dependent all-atom potential (GOAP) was used to correct the pixel-size estimation of the experimental cryo-EM density map. This work demonstrates the applicability of a straightforward automated approach to fitting membrane-protein cryo-EM densities. Such computational approaches promise to facilitate rapid refinement of proteins under different conditions or with various ligands present, including targets in the highly relevant superfamily of membrane proteins. II. STATEMENT OF SIGNIFICANCECryo-EM is an increasingly critical method of structure determination. As data collection and model generation become more efficient, iteratively fitting an experimental density can still require considerable time and expertise. Membrane proteins are particularly important targets in pharmacology and bioengineering, but can present distinctive challenges to data quality and modeling. Here, we tested a new tool to drive density fitting with molecular dynamics simulations, in context of a new structure of the membrane protein maltoporin. Fitting performed well in detergent, lipids, or solution, offering simpler options for fully automated simulation protocols. We were also able to apply fitting to adjust the microscopes pixel size. The approach described here should be applicable to rapid, accurate refinement of a variety of membrane-protein structures.

biophysics↗

bioRxiv2022

Enhancing the performance of Magnets photosensors through directed evolution

Photosensory protein domains are the basis of optogenetic protein engineering. These domains originate from natural sources where they fulfill specific functions ranging from the protection against photooxidative damage to circadian rhythms. When used in synthetic biology, the features of these photosensory domains can be specifically tailored towards the application of interest, enabling their full exploitation for optogenetic regulation in basic research and applied bioengineering. In this work, we develop and apply a simple, yet powerful, directed evolution and high-throughput screening strategy that allows us to alter the most fundamental property of the widely used nMag/pMag photodimerization system: its light sensitivity. We identify a set of mutations located within the photosensory domains, which either increase or decrease the light sensitivity at sub-saturating light intensities, while also improving the dark-to-light fold change in certain variants. For some of these variants, photosensitivity and expression levels could be changed independently, showing that the shape of the light-activity dose-response curve can be tuned and adjusted. We functionally characterize the variants in vivo in bacteria on the single-cell and the population levels. We further show that a subset of these variants can be transferred into the mOptoT7 for gene expression regulation in mammalian cells. We demonstrate increased gene expression levels for low light intensities, resulting in reduced potential phototoxicity in long-term experiments. Our findings expand the applicability of the widely used Magnets photosensors by enabling a tuning towards the needs of specific optogenetic regulation strategies. More generally, our approach will aid optogenetic approaches by making the adaptation of photosensor properties possible to better suit specific experimental or bioprocess needs.

synthetic biology↗