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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 Read source abstracts and check manuscript versions; preprints are not peer reviewed.

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At least 289 records · Page 16Linked to original sources

bioRxiv2017

Self-organization mechanism of distinct microtubule orientations in axons and dendrites

The identities of axons and dendrites are acquired through the self-organization of distinct microtubule (MT) orientations during neuronal polarization. The axon is generally characterized by a uniform MT orientation with all plus-ends pointing outward to the neurite terminal ( plus-end-out pattern). On the other hand, the MT orientation pattern in the dendrites depends on species: vertebrate dendrites have a mixed alignment with both plus and minus ends facing either the terminal or the cell body ( mixed pattern), whereas invertebrate dendrites have a minus-end-out pattern. However, how MT organizations are developed in the axon and the dendrites is largely unknown. To investigate the mechanism of MT organization, we developed a biophysical model of MT kinetics, consisting of polymerization/depolymerization and MT catastrophe coupled with neurite outgrowth. The model simulation showed that the MT orientation can be controlled mainly by the speed of neurite growth and the hydrolysis rate. With a low hydrolysis rate, vertebrate plus-end-out and mixed microtubule patterns emerged in fast- and slow-growing neurites, respectively. In contrast, with a high hydrolysis rate, invertebrate plus-end-out and minus-end-out microtubule patterns emerged in fast- and slow-growing neurites, respectively. Thus, our model can provide a unified understanding of distinct microtubule organizations by simply changing the parameters.

neuroscience↗

bioRxiv2017

Mixtures are more salient stimuli in olfaction

In their natural environment, animals often encounter complex mixtures of odours. It is an open question whether and how responses to complex mixtures of multi-component odours differ from those to simpler mixtures or single components. To approach this question, we built a full-size model of the early olfactory system of honeybees, which predicts responses to both single odorants and mixtures. The model is designed so that olfactory response patterns conform to the statistics derived from experimental data for a variety of their properties. It also takes into account several biophysical processes at a minimal level, including processes of chemical binding and activation in receptors, and spike generation and transmission in the antennal lobe network. We verify that key findings from other experimental data, not used in building the model, are reproduced in it. In particular, we replicate the strong correlation among receptor neurons and the weaker correlation among projection neurons observed in experimental data and show that this decorrelation is predominantly due to inhibition by interneurons. By simulation and mathematical analysis of our model, we demonstrate that the chemical processes of receptor binding and activation already lead to significant differences between the responses to mixtures and those to single component stimuli. On average, the response latency of olfactory receptor neurons at low stimulus concentrations is reduced and the response patterns become less variable across concentrations as the number of odour components in the stimulus increases. These effects are preserved in the projection neurons. Our results suggest that the early olfactory system of insects may be particularly efficient in processing mixtures, which corresponds well to the observation that chemical signalling in nature predominantly involves mixtures.

neuroscience↗

bioRxiv2017

Voltage-gated sodium channels in neocortical pyramidal neurons display Cole-Moore activation kinetics

Exploring the properties of action potentials is a crucial step towards a better understanding of the computational properties of single neurons and neural networks. The voltage-gated sodium channel is a key player in action potential generation. A comprehensive grasp of the gating mechanism of this channel can shed light on the biophysics of action potential generation. Most models of voltage-gated sodium channels assume it obeys a concerted Hodgkin and Huxley kinetic gating scheme. Here we performed high resolution voltage-clamp experiments from nucleated patches extracted from the soma of layer 5 (L5) cortical pyramidal neurons in rat brain slices. We show that the gating mechanism does not follow traditional Hodgkin and Huxley kinetics and that much of the channel voltage-dependence is probably due to rapid closed-closed transitions that lead to substantial onset latency reminiscent of the Cole-Moore effect observed in voltage-gated potassium conductances. This may have key implications for the role of sodium channels in synaptic integration and action potential generation.

neuroscience↗

bioRxiv2017

Maximal growth rate requires minimal metabolic complexity

Growth rate is a near-universal selective pressure across microbial species. High growth rates require hundreds of metabolic enzymes, each with different nonlinear kinetics, to be precisely tuned within the bounds set by physicochemical constraints. Yet, the metabolic behaviour of many species is characterized by simple relations between growth rate, enzyme expression levels and metabolic rates. We asked if this simplicity could be the outcome of optimisation by evolution. Indeed, when the growth rate is maximized -in a static environment under mass-conservation and enzyme expression constraints- we prove mathematically that the resulting optimal metabolic flux distribution is described by a limited number of subnetworks, known as Elementary Flux Modes (EFMs). We show that, because EFMs are the minimal subnetworks leading to growth, a small active number automatically leads to the simple relations that are measured. We find that the maximal number of flux-carrying EFMs is determined only by the number of imposed constraints on enzyme expression, not by the size, kinetics or topology of the network. This minimal-EFM extremum principle is illustrated in a graphical framework, which explains qualitative changes in microbial behaviours, such as overflow metabolism and co-consumption, and provides a method for identification of the enzyme expression constraints that limit growth under the prevalent conditions. The extremum principle applies to all microorganisms that are selected for maximal growth rates under protein concentration constraints, for example the solvent capacities of cytosol, membrane or periplasmic space.\n\nAuthor summaryThe microbial genome encodes for a large network of enzyme-catalyzed reactions. The reaction rates depend on concentrations of enzymes and metabolites, which in turn depend on those rates. Cells face a number of biophysical constraints on enzyme expression, for example due to a limited membrane area or cytosolic volume. Considering this complexity and nonlinearity of metabolism, how is it possible, that experimental data can often be described by simple linear models? We show that it is evolution itself that selects for simplicity. When reproductive rate is maximised, the number of active independent metabolic pathways is bounded by the number of growth-limiting enzyme constraints, which is typically small. A small number of pathways automatically generates the measured simple relations. We identify the importance of growth-limiting constraints in shaping microbial behaviour, by focussing on their mechanistic nature. We demonstrate that overflow metabolism - an important phenomenon in bacteria, yeasts, and cancer cells - is caused by two constraints on enzyme expression. We derive experimental guidelines for constraint identification in microorganisms. Knowing these constraints leads to increased understanding of metabolism, and thereby to better predictions and more effective manipulations.

systems biology↗

bioRxiv2017

Capturing the diversity of biological tuning curves using generative adversarial networks

Tuning curves characterizing the response selectivities of biological neurons often exhibit large degrees of irregularity and diversity across neurons. Theoretical network models that feature heterogeneous cell populations or random connectivity also give rise to diverse tuning curves. However, a general framework for fitting such models to experimentally measured tuning curves is lacking. We address this problem by proposing to view mechanistic network models as generative models whose parameters can be optimized to fit the distribution of experimentally measured tuning curves. A major obstacle for fitting such models is that their likelihood function is not explicitly available or is highly intractable to compute. Recent advances in machine learning provide ways for fitting generative models without the need to evaluate the likelihood and its gradient. Generative Adversarial Networks (GAN) provide one such framework which has been successful in traditional machine learning tasks. We apply this approach in two separate experiments, showing how GANs can be used to fit commonly used mechanistic models in theoretical neuroscience to datasets of measured tuning curves. This fitting procedure avoids the computationally expensive step of inferring latent variables, e.g., the biophysical parameters of individual cells or the particular realization of the full synaptic connectivity matrix, and directly learns model parameters which characterize the statistics of connectivity or of single-cell properties. Another strength of this approach is that it fits the entire, joint distribution of experimental tuning curves, instead of matching a few summary statistics picked a priori by the user. More generally, this framework opens the door to fitting theoretically motivated dynamical network models directly to simultaneously or non-simultaneously recorded neural responses.

neuroscience↗

bioRxiv2017

Fundamental limits on dynamic inference from single cell snapshots

Single cell expression profiling reveals the molecular states of individual cells with unprecedented detail. However, because these methods destroy cells in the process of analysis, they cannot measure how gene expression changes over time. But some information on dynamics is present in the data: the continuum of molecular states in the population can reflect the trajectory of a typical cell. Many methods for extracting single cell dynamics from population data have been proposed. However, all such attempts face a common limitation: for any measured distribution of cell states, there are multiple dynamics that could give rise to it, and by extension, multiple possibilities for underlying mechanisms of gene regulation. Here, we describe the aspects of gene expression dynamics that cannot be inferred from a static snapshot alone and identify assumptions necessary to constrain a unique solution for cell dynamics from static snapshots. We translate these constraints into a practical algorithmic approach, Population Balance Analysis (PBA), which makes use of a method from spectral graph theory to solve a class of high dimensional differential equations. We use simulations to show the strengths and limitations of PBA, and then apply it to single-cell profiles of hematopoietic progenitor cells (HPCs). Cell state predictions from this analysis agree with HPC fate assays reported in several papers over the past two decades. By highlighting the fundamental limits on dynamic inference faced by any method, our framework provides a rigorous basis for dynamic interpretation of a gene expression continuum and clarifies best experimental designs for trajectory reconstruction from static snapshot measurements.\n\nSignificanceSeeing a snapshot of individuals at different stages of a process can reveal what the process would look like for a single individual over time. Biologists apply this principle to infer temporal sequences of gene expression states in cells from measurements made at a single moment in time. However, these inferences are fundamentally under-determined. Using a conservation law, we enumerate reasons that there is no unique dynamics associated with a single snapshot, limiting our ability to infer gene regulatory mechanisms. We then propose a method for dynamic inference that provides a unique dynamic solution under defined approximations and apply it to data from bone marrow stem cells. Overall, this study introduces formal biophysical approaches to single cell bioinformatics.\n\nClassificationBIOLOGICAL SCIENCES / Systems Biology

systems biology↗

bioRxiv2017

Role of KCC2-Dependent Potassium Efflux in 4-Aminopyridine-Induced Epileptiform Synchronization

A balance between excitation and inhibition is required to maintain stable brain network dynamics. Traditionally, seizure activity is believed to arise from the breakdown of this delicate balance in favor of excitation with loss of inhibition. Surprisingly, recent experimental evidence suggests that this conventional view may be untrue, and that inhibition plays a prominent role in the development of epileptiform synchronization. Here, we explored the role of the co-transporter KCC2 in the onset of inhibitory network-induced seizures. Our experiments in acute mouse brain slices of either sex revealed that optogenetic stimulation of either parvalbumin- or somatostatin-expressing interneurons induced ictal discharges in rodent entorhinal cortex during 4-aminopyridine application. These data point to a proconvulsive role of GABAA receptor signaling that is independent of the inhibitory input location (i.e., dendritic vs. somatic). Further, we developed a biophysically realistic network model implementing complex dynamics of the ion concentrations to explore the mechanisms leading to inhibitory network-induced seizures. In agreement with experimental results, we found that stimulation of inhibitory interneurons induced seizure-like activity in a network with reduced potassium A-current. Model predicted that interneuron stimulation triggered interneuron firing that was accompanied by an increase in intracellular chloride and a subsequent KCC2-dependent gradual accumulation of extracellular potassium promoting epileptiform ictal activity. When the KCC2 activity was reduced, stimulation of the interneurons was no longer able to induce ictal events. Overall, our study provides evidence for a proconvulsive role of GABAA receptor signaling that depends on the involvement of the KCC2 co-transporter.

neuroscience↗

bioRxiv2017

SelexGLM differentiates androgen and glucocorticoid receptor DNA-binding preference over an extended binding site

The DNA-binding interfaces of the androgen (AR) and glucocorticoid (GR) receptors are virtually identical, yet these transcription factors share only about a third of their genomic binding sites and regulate similarly distinct sets of target genes. To address this paradox, we determined the intrinsic specificities of the AR and GR DNA binding domains using a refined version of SELEX-seq. We developed an algorithm, SelexGLM, that quantifies binding specificity over a large (31 bp) binding-site by iteratively fitting a feature-based generalized linear model to SELEX probe counts. This analysis revealed that the DNA binding preferences of AR and GR homodimers differ significantly, both within and outside the 15bp core binding site. The relative preference between the two factors can be tuned over a wide range by changing the DNA sequence, with AR more sensitive to sequence changes than GR. The specificity of AR extends to the regions flanking the core 15bp site, where isothermal calorimetry measurements reveal that affinity is augmented by enthalpy-driven readout of poly-A sequences associated with narrowed minor groove width. We conclude that the increased specificity of AR is correlated with more enthalpy-driven binding than GR. The binding models help explain differences in AR and GR genomic binding, and provide a biophysical rationale for how promiscuous binding by GR allows functional substitution for AR in some castration-resistant prostate cancers.

systems biology↗

bioRxiv2017

Early afterdepolarisation tendency as a simulated pro-arrhythmic risk indicator

Drug-induced Torsades de Pointes (TdP) arrhythmia is of major interest in predictive toxicology. Drugs which cause TdP block the hERG cardiac potassium channel. However, not all drugs that block hERG cause TdP. As such, further understanding of the mechanistic route to TdP is needed. Early afterde-polarisations (EADs) are a cell-level phenomenon in which the membrane of a cardiac cell depolarises a second time before repolarisation, and EADs are seen in hearts during TdP. Therefore, we propose a method of predicting TdP using induced EADs combined with multiple ion channel block in simulations using biophysically-based mathematical models of human ventricular cell electrophysiology. EADs were induced in cardiac action potential models using interventions based on diseases that are known to cause EADs, including: increasing the conduction of the L-type calcium channel, decreasing the conduction of the hERG channel, and shifting the inactivation curve of the fast sodium channel. The threshold of intervention that was required to cause an EAD was used to classify drugs into clinical risk categories. The metric that used L-type calcium induced EADs was the most accurate of the EAD metrics at classifying drugs into the correct risk categories, and increased in accuracy when combined with action potential duration measurements. The EAD metrics were all more accurate than hERG block alone, but not as predictive as simpler measures such as simulated action potential duration. This may be because different routes to EADs represent risk well for different patient subgroups, something that is difficult to assess at present.

pharmacology and toxicology↗

bioRxiv2017

Cryo-electron tomography reveals that dynactin recruits a team of dyneins for processive motility

A key player in the intracellular trafficking network is cytoplasmic dynein, a protein complex that transports molecular cargo along microtubule tracks. It has been shown that vertebrate dyneins movement becomes strikingly enhanced upon interacting with a cofactor named dynactin and one of several cargo-adapters, such as BicaudalD2. However, the mechanisms responsible for this increase in transport efficiency are not well understood, largely due to a lack of structural information. We used cryo-electron tomography to visualize the first 3-dimensional structure of the intact dynein-dynactin complex bound to microtubules. Our structure reveals that the dynactin-cargo-adapter complex recruits and binds to two dimeric cytoplasmic dyneins. Interestingly, the dynein motor organization closely resembles that of axonemal dynein, suggesting that cytoplasmic dynein and axonemal dyneins may utilize similar mechanisms to coordinate multiple motors. We propose that grouping dyneins onto a single dynactin scaffold promotes collective force production as well as unidirectional processive motility. These findings provide a structural platform that facilitates a deeper biochemical and biophysical understanding of dynein regulation and cellular transport.

cell biology↗

bioRxiv2017

Hemodynamic forces tune the arrest, adhesion and extravasation of circulating tumor cells

Metastatic seeding is driven by cell-intrinsic and environmental cues, yet the contribution of biomechanics is poorly known. We aim to elucidate the impact of blood flow on the arrest and the extravasation of circulating tumor cells (CTCs) in vivo. Using the zebrafish embryo, we show that arrest of CTCs occurs in vessels with favorable flow profiles where flow forces control the adhesion efficacy of CTCs to the endothelium. We biophysically identified the threshold values of flow and adhesion forces allowing successful arrest of CTCs. In addition, flow forces fine-tune tumor cell extravasation by impairing the remodeling properties of the endothelium. Importantly, we also observe endothelial remodeling at arrest sites of CTCs in mouse brain capillaries. Finally, we observed that human supratentorial brain metastases preferably develop in areas with low perfusion. Altogether, these results demonstrate that hemodynamic profiles at metastatic sites regulate key steps of extravasation preceding metastatic outgrowth.

cancer biology↗

bioRxiv2017

SodaPop: A computational suite for simulating the dynamics of asexual populations

MotivationSimulating protein evolution with realistic constraints from population genetics is essential in addressing problems in molecular evolution, from understanding the forces shaping the evolutionary landscape to the clinical challenges of antibiotic resistance, viral evolution and cancer.\n\nResultsTo address this need, we present SodaPop, a new forward-time simulator of large asexual populations aimed at studying their structure, dynamics and the distribution of fitness effects with flexible assumptions on the fitness landscape. SodaPop integrates biochemical and biophysical properties in a cell-based, object-oriented framework and provides an efficient, open-source toolkit for performing large-scale simulations of protein evolution.\n\nAvailability and implementationSource code and binaries are freely available at https://github.com/louisgt/SodaPop under the GNU GPLv3 license. The software is implemented in C++ and supported on Linux, Mac OS/X and Windows.\n\nContactadrian.serohijos@umontreal.ca\n\nSupplementary informationSupplementary information is available on the Github project page.

bioinformatics↗

bioRxiv2017

An automated model test system for systematic development and improvement of gene expression models

Gene expression models greatly accelerate the engineering of synthetic metabolic pathways and genetic circuits by predicting sequence-function relationships and reducing trial-and-error experimentation. However, developing models with more accurate predictions is a significant challenge, even though they are essential to engineering complex genetic systems. Here we present a model test system that combines advanced statistics, machine learning, and a database of 9862 characterized genetic systems to automatically quantify model accuracies, accept or reject mechanistic hypotheses, and identify areas for model improvement. We also introduce Model Capacity, a new information theoretic metric that enables correct model comparisons across datasets. We demonstrate the model test system by comparing six models of translation initiation rate, evaluating 100 mechanistic hypotheses, and uncovering new sequence determinants that control protein expression levels. We applied these results to develop a biophysical model of translation initiation rate with significant improvements in accuracy. Automated model test systems will dramatically accelerate the development of gene expression models, and thereby transition synthetic biology into a mature engineering discipline.

synthetic biology↗

bioRxiv2017

Multiscale simulation of peripheral neural signaling

Bioelectronic Medicines that modulate the activity patterns on peripheral nerves have promise as a new way of treating diverse medical conditions from epilepsy to rheumatism. Progress in the field builds upon time consuming and expensive experiments in living organisms to evaluate spontaneous activity patterns, stimulation efficiency, and organ responses. To reduce experimentation load and allow for a faster, more detailed analysis of both recording from and stimulation of peripheral nerves, adaptable computational models incorporating insights won in experiments will be of great help. We present a peripheral nerve simulator that combines biophysical axon models and numerically solved and idealized extracellular space models in one environment. Two different scales of abstraction were merged. On the one hand we modeled the extracellular space in a finite element solver as a three dimensional resistive continuum governed by the electro-quasistatic approximation of the Maxwell equations. Potential distributions were precomputed for different media (homogeneous, nerve in saline, nerve in cuff). Axons, on the other hand, were modeled at a higher level of abstraction as one dimensional chains of compartments; each consisting of lumped linear elements and, for some, channels with non-linear dynamics. Unmyelinated fibres were based on the Hodgkin-Huxley model; for myelinated fibers, we instead adapted the model proposed by McIntyre et al. in 2002 to smaller diameters. To obtain realistic axon shapes, an iterative algorithm positioned fibers along the nerve with variable tortuosity, with tortuosity values fit to imaged trajectories. We validated our model with data from the stimulated rat vagus nerve. Simulation results predicted that tortuosity leads to differentiation in recorded signal shapes, with unmyelinated axons being the most affected. Tortuosity was further shown to increase the stimulation threshold. The model we developed can easily be adapted to different nerves, and may be of use for Bioelectronic Medicine research in the future.

neuroscience↗

bioRxiv2017

MreB Filaments Create Rod Shape By Aligning Along Principal Membrane Curvature.

MreB is essential for rod shape in many bacteria. Membrane-associated MreB filaments move around the rod circumference, helping to insert cell wall in the radial direction to reinforce rod shape. To understand how oriented MreB motion arises, we altered the shape of Bacillus subtilis. MreB motion is isotropic in round cells, and orientation is restored when rod shape is externally imposed. Stationary filaments orient within protoplasts, and purified MreB tubulates liposomes in vitro, orienting within tubes. Together, this demonstrates MreB orients along the greatest principal membrane curvature, a conclusion supported with biophysical modeling. We observed that spherical cells regenerate into rods in a local, self-reinforcing manner: rapidly propagating rods emerge from small bulges, exhibiting oriented MreB motion and increased glycan crosslinking. We propose that the coupling of MreB filament alignment to shape-reinforcing peptidoglycan synthesis creates a locally-acting, self-organizing mechanism allowing the rapid establishment and stable maintenance of emergent rod shape.

microbiology↗

bioRxiv2017

Computational model of brainstem circuit for state dependent control of hypoglossal motoneurons

In patients with obstructive sleep apnea (OSA) the pharyngeal muscles become relaxed during sleep, which leads to a partial or complete closure of upper airway. Empirical studies suggest that withdrawal of noradrenergic and serotonergic drives importantly contribute to depression of hypoglossal motoneurons during rapid eye-movement (REM) sleep and, therefore, may contribute to OSA pathophysiology; however, specific cellular and synaptic mechanisms remain unknown. It was recently suggested that, in order to explain experimental observations, the neuronal network for monoaminergic control of excitability of hypoglossal motoneurons has to include excitatory and inhibitory perihypoglossal interneurons that would mediate noradrenergic and serotonergic drives to the motoneurons. In this study, we applied a biophysical network model to validate the rationality of the proposed circuit and to investigate the dynamics of its neuronal populations during REM sleep-induced withdrawal of noradrenergic and serotonergic drives. The state-dependent activity of the model hypoglossal motoneurons during simulated REM sleep with or without a virtual application of noradrenergic and serotonergic drugs was in qualitative agreement with in vivo data. The study predicts the dynamics of the perihypoglossal interneurons during these conditions and corroborates the hypothesis that the excitatory interneurons may integrate both noradrenergic and serotonergic drives. The latter drive has to be mediated by the inhibitory interneurons. The study suggests that perihypoglossal interneurons may serve as novel potential targets for pharmacological treatment of OSA.

neuroscience↗

bioRxiv2017

Stable centrosomal roots disentangle to allow interphase centriole independence

The centrosome is a non-membrane bound cellular compartment consisting of two centrioles surrounded by a protein coat termed the pericentriolar material (PCM). Centrioles must remain physically associated together (a phenomenon called centrosome cohesion) for cell migration, ciliary function and mitosis, yet how this occurs in the absence of a bounding lipid membrane is unclear. One model posits that pericentriolar fibres formed from rootletin protein directly link centrioles, yet little is known about the structure, biophysical properties or assembly kinetics of such fibres. Here, I combine live cell imaging of endogenously tagged rootletin with cell fusion, and find previously unrecognised plasticity in centrosome cohesion. Rootletin forms large, diffusionally stable, bifurcating fibres, which amass slowly on mature centrioles over many hours from anaphase. Nascent centrioles (procentrioles) in contrast do not form roots, and must be licensed to do so through polo-like kinase 1 (PLK1) activity. Transient separation of roots accompanies centriolar repositioning during the interphase, suggesting that centrioles organize as independent units, each containing a discrete root. Indeed, forced induction of duplicate centriole pairs allows independent re-shuffling of individual centrioles between the pairs. Thus, collectively, these findings suggest that progressively nucleated polymers mediate the dynamic association of centrioles as either one or two interphase centrosomes, with implications for our understanding of how non-membrane bound organelles self-organise.

cell biology↗

bioRxiv2017

Long-range inputs and H-current regulate different modes of operation in a multiscale model of mouse M1 microcircuits

We developed a biophysically detailed multiscale model of mouse primary motor cortex (M1) with over 10,000 neurons and 35 million synapses. We focused on intratelencephalic (IT) and pyramidal-tract (PT) neurons of layer 5 (L5), which were modeled at high multicompartment resolution. Wiring densities were based on prior detailed measures from mouse slice, and depended on cell class and cortical depth at sublaminar resolution. Prominent phase-amplitude-coupled delta and gamma activity emerged from the network. Spectral Granger causality analysis revealed the dynamics of information flow through populations at different frequencies. Stimulation of motor vs sensory long-range inputs to M1 demonstrated distinct intra- and inter-laminar dynamics and PT output. Manipulating PT Ih altered PT activity, supporting the hypothesis that Ih neuromodulation is involved in translating motor planning into execution. Our model sheds light on the multiscale dynamics of cell-type-specific M1 circuits and how connectivity relates to dynamics.

neuroscience↗