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bioRxiv2018

A Kirchhoff-Nernst-Planck framework for modeling large scale extracellular electrodiffusion surrounding morphologically detailed neurons

1Many pathological conditions, such as seizures, stroke, and spreading depression, are associated with substantial changes in ion concentrations in the extracellular space (ECS) of the brain. An understanding of the mechanisms that govern ECS concentration dynamics may be a prerequisite for understanding such pathologies. To estimate the transport of ions due to electrodiffusive effects, one must keep track of both the ion concentrations and the electric potential simultaneously in the relevant regions of the brain. Although this is currently unfeasible experimentally, it is in principle achievable with computational models based on biophysical principles and constraints. Previous computational models of extracellular ion-concentration dynamics have required extensive computing power, and therefore have been limited to either phenomena on very small spatiotemporal scales (micrometers and milliseconds), or simplified and idealized 1-dimensional (1-D) transport processes on a larger scale. Here, we present the 3-D Kirchhoff-Nernst-Planck (KNP) framework, tailored to explore electrodiffusive effects on large spatiotemporal scales. By assuming electroneutrality, the KNP-framework circumvents charge-relaxation processes on the spatiotemporal scales of nanometers and nanoseconds, and makes it feasible to run simulations on the spatiotemporal scales of millimeters and seconds on a standard desktop computer. In the present work, we use the 3-D KNP framework to simulate the dynamics of ion concentrations and the electrical potential surrounding a morphologically detailed pyramidal cell. In addition to elucidating the single neuron contribution to electrodiffusive effects in the ECS, the simulation demonstrates the efficiency of the 3-D KNP framework. We envision that future applications of the framework to more complex and biologically realistic systems will be useful in exploring pathological conditions associated with large concentration variations in the ECS.\n\n2 Author summaryMany pathological conditions, such as epilepsy and cortical spreading depression, are linked to abnormal extracellular ion concentrations in the brain. Understanding the underlying principles of such conditions may prove important in developing treatments for these illnesses, which incur societal costs of tens of billions annually. In order to investigate the role of ion-concentration dynamics in the pathological conditions, one must measure the spatial distribution of all ion concentrations over time. This remains challenging experimentally, which makes computational modeling an attractive tool. We have previously introduced the Kirchhoff-Nernst-Planck framework, an efficient framework for modeling electrodiffusion. In this study, we introduce a 3-dimensional version of this framework and use it to model the electrodiffusion of ions surrounding a morphologically detailed neuron. The simulation covered a 1 mm3 cylinder of tissue for over a minute and was performed in less than a day on a standard desktop computer, demonstrating the frameworks efficiency. We believe this to be an important step on the way to understanding phenomena involving ion concentration shifts at the tissue level.

neuroscience↗

bioRxiv2018

Regulation of chloroplast NADH dehydrogenase-like complex by NADPH-dependent thioredoxin system

Linear electron transport in the thylakoid membrane drives both photosynthetic NADPH and ATP production, while cyclic electron flow (CEF) around photosystem I only promotes the translocation of protons from stroma to thylakoid lumen. The chloroplast NADH-dehydrogenase-like complex (NDH) participates in one CEF route transferring electrons from ferredoxin back to the plastoquinone pool with concomitant proton pumping to the lumen. CEF has been proposed to balance the ratio of ATP/NADPH production and to control the redox poise particularly in fluctuating light conditions, but the mechanisms regulating the NDH complex remain unknown. We have investigated potential regulation of the CEF pathways by the chloroplast NADPH-thioredoxin reductase (NTRC) in vivo by using an Arabidopsis knockout line of NTRC as well as lines overexpressing NTRC. Here we present biochemical and biophysical evidence showing that NTRC activates the NDH-dependent CEF and regulates the generation of proton motive force, thylakoid conductivity to protons and redox balance between the thylakoid electron transfer chain and the stroma during changes in light conditions. Further, protein-protein interaction assays suggest a putative thioredoxin-target site in close proximity to the ferredoxin binding domain of NDH, thus providing a plausible mechanism for regulation of the NDH ferredoxin:plastoquinone oxidoreductase activity by NTRC.\n\nOne sentence summaryChloroplast thioredoxins regulate photosynthetic cyclic electron flow that balances the activities of light and carbon fixation reactions and improves plant fitness under fluctuating light conditions.

plant biology↗

bioRxiv2018

Zinc(II) binding on human wild-type ISCU and Met140 variants modulates Fe-S complex activity

The human de novo iron-sulfur (Fe-S) assembly complex consists of the cysteine desulfurase NFS1, accessory protein ISD11, scaffold protein ISCU, and allosteric activator frataxin (FXN). FXN has been shown to bind the NFS1-ISD11-ISCU complex (SDU), to activate the desulfurase activity and thus Fe-S cluster biosynthesis. Conversely, in the absence of FXN, the NFS1-ISD11 (SD) complex was reported to be inhibited by the binding of recombinant ISCU. Here, we show that recombinant ISCU binds zinc(II) ion, and that the presence of zinc in as-isolated ISCU has impacts on the SDU desulfurase activity as measured by sulfide production. Indeed, the removal of this zinc(II) ion from ISCU causes a moderate but significant increase in activity compared to SD alone, and FXN can activate both zinc-depleted and zinc-bound forms of ISCU complexed to SD. Recent yeast studies have reported a substitution on the yeast ISCU orthologue Isu, at position Met141 (Met140 in human numbering of precursor protein) to Ile, Leu, Val, or Cys that could bypass the requirement of FXN for Fe-S cluster assembly and cell viability. Using recombinant human proteins, we report no significant differences in the biochemical and biophysical properties observed between wild-type and variants M140I, M140 L, and M140 V of ISCU. Importantly, in the absence of FXN, ISCU variants behaved like wild-type and did not stimulate the desulfurase activity of the SD complex. This study therefore identifies an important regulatory role for ISCU-bound zinc in modulation of the human Fe-S assembly system in vitro but no FXN bypass effect on mutations at position Met140 in human ISCU.\n\nABBREVIATIONS

biochemistry↗

bioRxiv2018

Modeling brain dynamics in brain tumor patients using The Virtual Brain

Presurgical planning for brain tumor resection aims at delineating eloquent tissue in the vicinity of the lesion to spare during surgery. To this end, non-invasive neuroimaging techniques such as functional MRI and diffusion weighted imaging fiber tracking are currently employed. However, taking into account this information is often still insufficient, as the complex non-linear dynamics of the brain impede straightforward prediction of functional outcome after surgical intervention. Large-scale brain network modeling carries the potential to bridge this gap by integrating neuroimaging data with biophysically based models to predict collective brain dynamics.\n\nAs a first step in this direction, an appropriate computational model has to be selected, after which suitable model parameter values have to be determined. To this end, we simulated large-scale brain dynamics in 25 human brain tumor patients and 11 human control participants using The Virtual Brain, an open-source neuroinformatics platform. Local and global model parameters of the Reduced Wong-Wang model were individually optimized and compared between brain tumor patients and control subjects. In addition, the relationship between model parameters and structural network topology and cognitive performance was assessed.\n\nResults showed (1) significantly improved prediction accuracy of individual functional connectivity when using individually optimized model parameters; (2) local model parameters can differentiate between regions directly affected by a tumor, regions distant from a tumor, and regions in a healthy brain; and (3) interesting associations between individually optimized model parameters and structural network topology and cognitive performance.

bioinformatics↗

bioRxiv2018

The axon initial segment drives the neuron’s extracellular action potential

Extracellular voltage fields produced by a neurons action potentials provide a primary means for studying neuron function, yet their biophysical sources remain ambiguous. The neurons soma and dendrites are thought to drive the extracellular action potential (EAP), while the axon is usually ignored. However, by recording voltages of single neurons in dissociated rat cortical cultures and Purkinje cells in acute mouse cerebellar slices at hundreds of sites, we find instead that the axon initial segment dominates the EAP, and, surprisingly, the soma shows little or no influence. As expected, this signal has negative polarity (charge entering the cell) and initiates at the distal end. Interestingly, signals with positive polarity (charge exiting the cell) occur near some but not all dendritic branches and occur after a delay. Such basic knowledge about which neuronal compartments contribute to the extracellular voltage field is important for interpreting results from all electrical readout schemes. Moreover, this finding shows that changes in the AIS position and function can be observed in high spatiotemporal detail by means of high-density extracellular electrophysiology.\n\nKey points summaryO_LIThe neurons soma and dendrites are thought to give rise to its extracellular voltage signal, while signals from the axon are usually considered negligible.\nC_LIO_LIInstead, we found that the largest amplitude of the extracellular signal originates from the axon initial segment, not from the soma.\nC_LIO_LIThis finding shows that changes in the AIS position and function can be observed in high spatiotemporal detail by means of high-density extracellular electrophysiology.\nC_LI\n\nAbbreviations

neuroscience↗

bioRxiv2018

Investigation of Electrical Stimulation Effect on Intracellular Calcium Dynamics Using Locally Uniform Mesh Refinement

Modeling-based methods are conventionally exploited to simulate and predict the dynamics of a system in various fields of engineering. However, due to the intricacy of biological systems, these methods were rarely utilized in bioengineering until recently. By developing advanced computers with the ability to cope with enormous calculations and extending our knowledge about biological systems, modeling-based approaches have been adopted to manipulate biological systems. While utilizing the models to investigate the behavior of a system has some advantages including lower cost and time consumption, such methods were used for tissue engineering scarcely. Exploiting modeling-based methods to tissue engineering field requires developing and utilizing efficient computational methods to model gene regulation and signaling networks, which determine cell response to environmental changes and cellular fate.\n\nIn the present work, a novel spatio-temporal method was proposed, predicated on locally uniform mesh refinement. A benchmark comparison was performed with the previously used method and the results indicated the better performance of the incipiently adopted method. Besides, the model was utilized to investigate the effect of a popular differentiating stimulator, namely electrical stimulation, on mesenchymal stem cells as a type of stem cells widely used in tissue engineering. The results of the simulations demonstrated the puissance of such computational models in analyzing and predicting the effect of biochemical and biophysical perturbations on the cells and hence, their potential utility in tissue engineering.

systems biology↗

bioRxiv2018

Human cortical pyramidal neurons: From spines to spikes via models

We present the first-ever detailed models of pyramidal cells from human neocortex, including models on their excitatory synapses, dendritic spines, dendritic NMDA- and somatic/axonal- Na+ spikes that provided new insights into signal processing and computational capabilities of these principal cells. Six human layer 2 and layer 3 pyramidal cells (HL2/L3 PCs) were modeled, integrating detailed anatomical and physiological data from both fresh and post mortem tissues from human temporal cortex. The models predicted particularly large AMPA- and NMDA- conductances per synaptic contact (0.88 nS and 1.31nS, respectively) and a steep dependence of the NMDA-conductance on voltage. These estimates were based on intracellular recordings from synaptically-connected HL2/L3 pairs, combined with extra-cellular current injections and use of synaptic blockers. A large dataset of high-resolution reconstructed HL2/L3 dendritic spines provided estimates for the EPSPs at the spine head (12.7 {+/-} 4.6 mV), spine base (9.7 {+/-} 5.0 mV) and soma (0.3 {+/-} 0.1 mV), and for the spine neck resistance (50 - 80 M{Omega}). Matching the shape and firing pattern of experimental somatic Na+-spikes provided estimates for the density of the somatic/axonal excitable membrane ion channels, predicting that 134 {+/-} 28 simultaneously activated HL2/L3- HL2/L3 synapses are required for generating (with 50% probability) a somatic Na+ spike. Dendritic NMDA spikes were triggered in the model when 20 {+/-} 10 excitatory spinous synapses were simultaneously activated on individual dendritic branches. The particularly large number of basal dendrites in HL2/L3 PCs and the distinctive cable elongation of their terminals imply that ~25 NMDA- spikes could be generated independently and simultaneously in these cells, as compared to ~14 in L2/3 PCs from the rat temporal cortex. These multi-sites nonlinear signals, together with the large (~30,000) excitatory synapses/cell, equip human L2/L3 PCs with enhanced computational capabilities. Our study provides the most comprehensive model of any human neuron to-date demonstrating the biophysical and computational distinctiveness of human cortical neurons.

neuroscience↗

bioRxiv2018

A Universal Temperature-Dependence of Mutational Fitness Effects

Adaptation in new environments depends on the amount and type of genetic variation available for evolution, and the efficacy by which natural selection discriminates among this variation to favour the survival of the fittest. However, whether some environments systematically reveal more genetic variation in fitness, or impose stronger selection pressures than others, is typically not known. Here, we apply enzyme kinetic theory to show that rising global temperatures are predicted to intensify natural selection systematically throughout the genome by increasing the effects of DNA sequence variation on protein stability. We tested this prediction by i) estimating temperature-dependent fitness effects of induced random mutations in seed beetles adapted to ancestral or warm temperature, and ii) calculating 100 paired selection estimates on mutations in benign versus stressful environments from a diverse set of unicellular and multicellular organisms. Environmental stress per se did not increase the mean strength of selection on de novo mutation, suggesting that the cost of adaptation does not generally increase in new environments to which the organism is maladapted. However, elevated temperature increased the mean strength of selection on genome-wide polymorphism, signified by increases in both mutation load and mutational variance at elevated temperature. The theoretical predictions and empirical data suggest that this increase may correspond to a doubling of genome-wide selection for a predicted 2-4{degrees}C climate warming scenario in ectothermic organism living at temperatures close to their thermal optimum. These results have important implications for global patterns of genetic diversity and the rate and repeatability of evolution under climate change. Impact StatementNatural environments are constantly changing so organisms must also change to persist. Whether they can do so ultimately depends upon the reservoir of raw genetic material available for evolution, and the efficacy by which natural selection discriminates among this variation to favour the survival of the fittest. Here, the biochemical properties of molecules and proteins that underpin the link between genotype and phenotype can exert a major influence over how the physical environment affects the expression of phenotypes and the fitness consequences of DNA sequence polymorphism. Yet, the constraints set by these molecular features are often neglected within eco-evolutionary theory trying to predict evolution in new environments. Here we combine predictions from existing biophysical models of protein folding and enzyme kinetics with experimental data from ectothermic organisms across the tree of life, to show that rising global temperatures are predicted to increase the mean strength of selection on DNA sequence variation in cold-blooded organisms. We also show that environmental stress per se generally does not increase the mean strength of selection on new mutations, suggesting that genome-wide natural selection is not stronger in new environments to which an organism is maladapted. Theoretical predictions and data suggest that an expected climate warming scenario of a 2-4{degrees}C temperature raise within the forthcoming century will result in roughly a doubling of genome-wide selection for organisms living close to their thermal optima. However, our results also point to substantial variability in the temperature-dependence of selection on different proteins within and between organisms, suggesting scope for compensatory adaptation to shape this relationship. These results bear witness to and extend the universal temperature dependence of biological rates and have important implications for global patterns of genetic diversity and the rate and repeatability of genome evolution under environmental change.

evolutionary biology↗

bioRxiv2018

Adaptive diversification of growth allometry in the plant Arabidopsis thaliana

Seed plants vary tremendously in size and morphology. However, variation and covariation between plant traits may at least in part be governed by universal biophysical laws and biological constants. Metabolic Scaling Theory (MST) posits that whole-organismal metabolism and growth rate are under stabilizing selection that minimizes the scaling of hydrodynamic resistance and maximizes the scaling of resource uptake. This constrains variation in physiological traits and in the rate of biomass accumulation, so that they can be expressed as mathematical functions of plant size with near constant allometric scaling exponents across species. However, observed variation in scaling exponents questions the evolutionary drivers and the universality of allometric equations. We have measured growth scaling and fitness traits of 451 Arabidopsis thaliana accessions with sequenced genomes. Variation among accessions around the scaling exponent predicted by MST correlated with relative growth rate, seed production and stress resistance. Genomic analyses indicate that growth allometry is affected by many genes associated with local climate and abiotic stress response. The gene with the strongest effect, PUB4, has molecular signatures of balancing selection, suggesting that intraspecific variation in growth scaling is maintained by opposing selection on the trade-off between seed production and abiotic stress resistance. Our findings support a core MST prediction and suggest that variation in allometry contributes to local adaptation to contrasting environments. Our results help reconcile past debates on the origin of allometric scaling in biology, and begin to link adaptive variation in allometric scaling to specific genes.\n\nSignificance statementAre there biological constants unifying phenotypic diversity across scales? Metabolic Scaling Theory (MST) predicts mathematical regularity and constancy in the allometric scaling of growth rate with body size across species. Here, we show that adaptation to climate in Arabidopsis thaliana is associated with local strains that substantially deviate from the values predicted by MST. This deviation can be linked to increased stress tolerance at the expense of seed production, and it occurs through selection on genes that are involved in abiotic stress response and that are geographically correlated with climatic conditions. This highlights the evolutionary role of allometric diversification and helps establish the physiological bases of plant adaptation to contrasting environments.

ecology↗

bioRxiv2018

A Methodological Assessment and Characterization of Genetically-Driven Variation in Three Human Phosphoproteomes

Phosphorylation of proteins on serine, threonine, and tyrosine residues is a ubiquitous post-translational modification that plays a key part of essentially every cell signaling process. It is reasonable to assume that inter-individual variation in protein phosphorylation may underlie phenotypic differences, as has been observed for practically any other molecular regulatory phenotype. However, we do not know much about the extent of inter-individual variation in phosphorylation because it is quite challenging to perform a quantitative high throughput study to assess inter-individual variation in any post-translational modification. To test our ability to address this challenge with current technology, we quantified phosphorylation levels for three genotyped human cell lines within a nested experimental framework, and found that genetic background is the primary determinant of phosphoproteome variation. We uncovered multiple functional, biophysical, and genetic associations with germline driven phosphopeptide variation. Variants affecting protein levels or structure were among these associations, with the latter presenting, on average, a stronger effect. Interestingly, we found evidence that is consistent with a phosphopeptide variability buffering effect endowed from properties enriched within longer proteins. Because the small sample size in this pilot study may limit the applicability of our genetic observations, we also undertook a thorough technical assessment of our experimental workflow to aid further efforts. Taken together, these results provide the foundation for future work to characterize inter-individual variation in post-translational modification levels and reveal novel insights into the nature of inter-individual variation in phosphorylation.

genomics↗

bioRxiv2018

Improved sgRNA design in bacteria via genome-wide activity profiling

CRISPR/Cas9 is a promising tool in prokaryotic genome engineering, but its success is limited by the widely varying on-target activity of single guide RNAs (sgRNAs). Based on the association of CRISPR/Cas9-induced DNA cleavage with cellular lethality, we systematically profiled sgRNA activity by co-expressing a genome-scale library (~70,000 sgRNAs) with Cas9 or its specificity-improved mutant in E. coli. Based on this large-scale dataset, we constructed a comprehensive and high-density sgRNA activity map, which enables selecting highly active sgRNAs for any locus across the genome in this model organism. We also identified resistant genomic loci with respect to CRISPR/Cas9 activity, notwithstanding the highly accessible DNA in bacterial cells. Moreover, we found that previous sgRNA activity prediction models that were trained on mammalian cell datasets were inadequate when coping with our results, highlighting the key limitations and biases of previous models. We hence developed an integrated algorithm to accurately predict highly effective sgRNAs, aiming to facilitate the design of CRISPR/Cas9-based genome engineering or screenings in bacteria. We also isolated the important sgRNA features that contribute to DNA cleavage and characterized their key differences among wild type Cas9 and its mutant, shedding light on the biophysical mechanisms of the CRISPR/Cas9 system.

microbiology↗

bioRxiv2018

A synaptic threshold mechanism for computing escape decisions

Escaping from imminent danger is an instinctive behaviour fundamental for survival that requires classifying sensory stimuli as harmless or threatening. The absence of threat allows animals to forage for essential resources, but as the level of threat and potential for harm increases, they have to decide whether or not to seek safety1. Despite previous work on instinctive defensive behaviours in rodents2-13, little is known about how the brain computes the threat level for initiating escape. Here we show that the probability and vigour of escape in mice scale with the intensity of innate threats, and are well described by a theoretical model that computes the distance between threat level and an escape threshold. Calcium imaging and optogenetics in the midbrain of freely behaving mice show that the activity of excitatory VGluT2+ neurons in the deep layers of the medial superior colliculus (mSC) represents the threat stimulus intensity and is predictive of escape, whereas dorsal periaqueductal gray (dPAG) VGluT2+ neurons encode exclusively the escape choice and control escape vigour. We demonstrate a feed-forward monosynaptic excitatory connection from mSC to dPAG neurons that is weak and unreliable, yet necessary for escape behaviour, and which we suggest provides a synaptic threshold for dPAG activation and the initiation of escape. This threshold can be overcome by high mSC network activity because of short-term synaptic facilitation and recurrent excitation within the mSC, which amplifies and sustains synaptic drive to the dPAG. Thus, dPAG VGluT2+ neurons compute escape decisions and vigour using a synaptic mechanism to threshold threat information received from the mSC, and provide a biophysical model of how the brain performs a critical behavioural computation.

neuroscience↗

bioRxiv2018

Structural and functional studies of the RBPJ-SHARP complex reveal conserved corepressor binding site

The Notch pathway is a conserved signaling mechanism that is essential for cell fate decisions during pre and postnatal development. Dysregulated signaling underlies the pathophysiology of numerous human diseases, most notably T-cell acute lymphoblastic leukemia. Receptor-ligand interactions result in changes in gene expression, which are regulated by the transcription factor CSL. CSL forms a complex with the intracellular domain of the Notch receptor and the transcriptional coactivator Mastermind, which is required to activate transcription of all Notch target genes. CSL can also function as repressor by interacting with corepressor proteins, e.g. SHARP in mammals and Hairless in Drosophila melanogaster; however, its role as a transcriptional repressor is not well understood. Here we determine the high-resolution structure of RBPJ, the mouse CSL ortholog, bound to the corepressor SHARP and DNA, which reveals a new mode of corepressor binding to CSL and an interesting example for how ligand binding sites evolve in proteins. Based on the structure, we designed and tested a number of mutants in biophysical, biochemical, and cellular assays to characterize the role of RBPJ as a repressor of Notch target genes. Our cellular studies clearly demonstrate that RBPJ mutants that are deficient for binding SHARP are incapable of repressing transcription from genes responsive to Notch signaling. Altogether, our structure-function studies of the RBPJ-SHARP corepressor complex bound to DNA provide significant insights into the repressor function of RBPJ and identify a new binding pocket on RBPJ that could be targeted for therapeutic benefit.

biochemistry↗

bioRxiv2018

Inferring the shape of global epistasis

Genotype-phenotype relationships are notoriously complicated. Idiosyncratic interactions between specific combinations of mutations occur, and are difficult to predict. Yet it is increasingly clear that many interactions can be understood in terms of global epistasis. That is, mutations may act additively on some underlying, unobserved trait, and this trait is then transformed via a nonlinear function to the observed phenotype as a result of subsequent biophysical and cellular processes. Here we infer the shape of such global epistasis in three proteins, based on published high-throughput mutagenesis data. To do so, we develop a maximum-likelihood inference procedure using a flexible family of monotonic nonlinear functions spanned by an I-spline basis. Our analysis uncovers dramatic nonlinearities in all three proteins; in some proteins a model with global epistasis accounts for virtually all the measured variation, whereas in others we find substantial local epistasis as well. This method allows us to test hypotheses about the form of global epistasis and to distinguish variance components attributable to global epistasis, local epistasis, and measurement error.

evolutionary biology↗

bioRxiv2018

Inferring synaptic inputs from spikes with a conductance-based neural encoding model

A popular approach to the study of information processing in the nervous system is to char-acterize neural responses in terms of a cascade of linear and nonlinear stages: a linear filter to describe the neurons stimulus integration properties, followed by a rectifying nonlinearity to convert filter output to spike rate. However, real neurons integrate stimuli via the modula-tion of nonlinear excitatory and inhibitory synaptic conductances. Here we introduce a bio-physically inspired point process model with conductance-based inputs. The model provides a novel interpretation of the popular Poisson generalized linear model (GLM) as a special kind of conductance-based model, where excitatory and inhibitory conductances are modulated in a \"push-pull\" manner so that total conductance remains constant. We relax this constraint to obtain a more general and flexible \"conductance-based encoding model\" (CBEM), which can exhibit stimulus-dependent fluctuations in gain and dynamics. We fit the model to spike trains of macaque retinal ganglion cells and show that, remarkably, we can accurately infer underlying inhibitory and excitatory conductances, using comparisons to intracellularly measured conductances. Using extracellular data, we corroborate the intracellular finding that synaptic excitation temporally precedes inhibition in retina. We show that the CBEM outperforms the classic GLM at predicting retinal ganglion cell responses to full-field stimuli, generalizes better across contrast levels, and captures inhibition-dependent response properties to spatially structured stimuli. The CBEM provides a powerful tool for gaining insights into the intracellular variables governing spiking, and forges an important link between extracellular characterization methods and biophysically detailed response models.

neuroscience↗

bioRxiv2018

Site-Specific Labelling of Multidomain Proteins by Amber Codon Suppression

Amber codon suppression is a powerful tool to site-specifically modify proteins to generate novel biophysical probes. Yet, its application on large and complex multidomain proteins is challenging, leading to difficulties during structural and conformational characterization using spectroscopic methods. The animal fatty acid synthase type I is a 540 kDa homodimer displaying large conformational variability. As the key enzyme of de novo fatty acid synthesis, it attracts interest in the fields of obesity, diabetes and cancer treatment. Substrates and intermediates remain covalently bound to the enzyme during biosynthesis and are shuttled to all catalytic domains by the acyl carrier protein domain. Thus, conformational variability of animal FAS is an essential aspect for fatty acid biosynthesis. We investigate this multidomain protein as a model system for probing amber codon suppression by genetic encoding of non-canonical amino acids. The systematic approach relies on a microplate-based reporter assay of low complexity, that was used for quick screening of suppression conditions. Furthermore, the applicability of the reporter assay is demonstrated by successful upscaling to both full-length constructs and increased expression scale. The obtained fluorescent probes of murine FAS type I could be subjected readily to a conformational analysis using single-molecule fluorescence resonance energy transfer.

biochemistry↗

bioRxiv2018

Systematic Optimization of Whole Plant Carbon Nitrogen Interaction (WACNI) to Support Crop Design for Greater Yield

On the face of the rapid advances in genome editing technology and greatly expanded knowledge on plant genome and genes, there is a strong demand to develop an effective tool to guide designing crops for higher yields. Here we developed a highly mechanistic model of Whole plAnt Carbon Nitrogen Interaction (WACNI), which predicts crop yield based on major metabolic and biophysical processes in source, sink and transport tissues. WACNI accurately predicted the yield responses of so far reported source, sink and transport related genetic manipulations on rice grain yields. Systematic sensitivity analysis with WACNI was used to classify the source, sink and transport related molecular processes into four categories, i.e. universal yield enhancers, universal yield inhibitors, conditional yield enhancers and weak yield regulators. Simulations using WACNI further show that even without a major change in leaf photosynthetic properties, 54.6% to 73% grain yield increase can be potentially achieved by optimizing these molecular processes during the rice grain filling period while simply combining all the superior molecular modules together cannot achieve the optimal yield level. A common macroscopic feature in all these designed high-yield lines is that they all show a sustained and steady growth of grain sink, which might be used as a generic selection criteria in high-yield rice breeding. Overall, WACNI can serve as a tool to facilitate plant source sink interaction research and guide future crops breeding by design.\n\nOne sentence summaryA mechanistic model of source, sink flow model is developed and used to demonstrate that optimization of the whole plant carbon nitrogen metabolism can dramatically increase crop yield potential.

systems biology↗

bioRxiv2018

Dynamics of human protein kinases linked to drug selectivity

Protein kinases are major drug targets, but the development of highly-selective inhibitors has been challenging due to the similarity of their active sites. The observation of distinct structural states of the fully-conserved Asp-Phe-Gly (DFG) loop has put the concept of conformational selection for the DFG-state at the center of kinase drug discovery. Recently, it was shown that Gleevec selectivity for the Tyr-kinases Abl was instead rooted in conformational changes after drug binding. Here, we investigate whether protein dynamics after binding is a more general paradigm for drug selectivity by characterizing the binding of several approved drugs to the Ser/Thr-kinase Aurora A. Using a combination of biophysical techniques, we propose a universal drug-binding mechanism, that rationalizes selectivity, affinity and long on-target residence time for kinase inhibitors. These new concepts, where protein dynamics in the drug-bound state plays the crucial role, can be applied to inhibitor design of targets outside the kinome.\n\neLife digestThe Ser/Thr kinase Aurora A is an important target for the development of new anticancer therapies. A longstanding question is how to specifically and effectively inhibit only this kinase in a background of over 550 protein kinases with very similar structures. To this end, understanding the inhibition mechanism of Aurora A by different drugs is essential. Here, we characterize the kinetic mechanism of three distinct kinase drugs, Gleevec (Imatinib), Danusertib (PHA739358) and AT9283 (Pyrazol-4-yl Urea) for Aurora A. We show that inhibitor affinities do not rely exclusively on the recognition of a specific conformation of the Asp-Phe-Gly loop of the kinase. Our quantitative kinetics data put forward an opposing mechanism in which a slow conformational change after drug binding (i.e., induced-fit step) dictates drug affinity.

cancer biology↗