A nonlinear meccano for Alzheimer's emergence by amyloid β-mediated glutamatergic hyperactivity

The pathophysiological process of Alzheimer's disease (AD) is believed to begin many years before the formal diagnosis of AD dementia. This protracted preclinical phase offers a crucial window for potential therapeutic interventions, yet its comprehensive characterization remains elusive. Accumulating evidence suggests that amyloid-β (Aβ) may mediate neuronal hyperactivity in circuit dysfunction in the early stages of AD. At the same time, neural activity can also facilitate Aβ accumulation through intricate feed-forward interactions, complicating elucidating the conditions governing Aβ-dependent hyperactivity and its diagnostic utility. In this study, we use biophysical modeling to shed light on such conditions. Our analysis reveals that the inherently nonlinear nature of the underlying molecular interactions can give rise to the emergence of various modes of hyperactivity. This diversity in the mechanisms of hyperactivity may ultimately account for a spectrum of AD manifestations.

Biomechanics of transduction by mechanosensory cilia for prey detection in aquatic organisms

Surface-feeding aquatic animals navigate towards the source of water disturbances and must differentiate prey from other environmental stimuli. Medicinal leeches locate prey, in part, using a distribution of mechanosensory hairs along their body that deflect under fluid flow. Leech's behavioral responses to surface wave temporal frequency are well documented. However, a surface wave's temporal frequency depends on many underlying environmental and fluid properties that vary substantially in natural habitats (e.g., water depth, temperature). The impact of these variables on neural response and behavior is unknown. Here, we developed a physics-based leech mechanosensor model to examine the impact of environmental and fluid properties on neural response. Our model used the physical properties of a leech cilium and was verified against existing behavioral and electrophysiological data. The model's peak response occurred with waves where the effects of gravity and surface tension were nearly equal (i.e., the phase velocity minimum). This suggests that preferred stimuli are related to the interaction between fundamental properties of the surrounding medium and the mechanical properties of the sensor. This interaction likely tunes the sensor to detect the nondispersive components of the signal, filtering out irrelevant ambient stimuli, and may be a general property of cilia across the animal kingdom.

Biophysical models applied to dementia patients reveal links between geographical origin, gender, disease duration, and loss of neural inhibition

BACKGROUND

The hypothesis of decreased neural inhibition in dementia has been sparsely studied in functional magnetic resonance imaging (fMRI) data across patients with different dementia subtypes, and the role of social and demographic heterogeneities on this hypothesis remains to be addressed.

METHODS

We inferred regional inhibition by fitting a biophysical whole-brain model (dynamic mean field model with realistic inter-areal connectivity) to fMRI data from 414 participants, including patients with Alzheimer's disease, behavioral variant frontotemporal dementia, and controls. We then investigated the effect of disease condition, and demographic and clinical variables on the local inhibitory feedback, a variable related to the maintenance of balanced neural excitation/inhibition.

RESULTS

Decreased local inhibitory feedback was inferred from the biophysical modeling results in dementia patients, specific to brain areas presenting neurodegeneration. This loss of local inhibition correlated positively with years with disease, and showed differences regarding the gender and geographical origin of the patients. The model correctly reproduced known disease-related changes in functional connectivity.

CONCLUSIONS

Results suggest a critical link between abnormal neural and circuit-level excitability levels, the loss of grey matter observed in dementia, and the reorganization of functional connectivity, while highlighting the sensitivity of the underlying biophysical mechanism to demographic and clinical heterogeneities in the patient population.

Experimental and biophysical modeling of transcription and translation dynamics in bacterial- and mammalian-based cell-free expression systems

Cell-free expression (CFE) systems have been used extensively in systems and synthetic biology as a promising platform for manufacturing proteins and chemicals. Currently, the most widely used CFE system is in vitro protein transcription and translation platform. As the rapidly increased applications and uses, it is crucial to have a standard biophysical model for quantitative studies of gene circuits, which will provide a fundamental understanding of basic working mechanisms of CFE systems. Current modeling approaches mainly focus on the characterization of E. coli-based CFE systems, a computational model that can be utilized for both bacterial- and mammalian-based CFE has not been investigated. Here, we developed a simple ODE (ordinary differential equation)-based biophysical model to simulate transcription and translation dynamics for both bacterial- and mammalian- based CFE systems. The key parameters were estimated and adjusted based on experimental results. We next tested four gene circuits to characterize kinetic dynamics of transcription and translation in E. coli- and HeLa-based CFE systems. The real-time transcription and translation were monitored using Broccoli aptamer, double stranded locked nucleic acid (dsLNA) probe and fluorescent protein. We demonstrated the difference of kinetic dynamics for transcription and translation in both systems, which will provide valuable information for quantitative genomic and proteomic studies. This simple biophysical model and the experimental data for both E. coli- and HeLa-based CFE will be useful for researchers that are interested in genetic engineering and CFE bio-manufacturing.

Information theoretic evaluation of Lorentzian, Gaussian, Voigt, and symmetric alpha-stable models of reversible transverse relaxation in cervical cancer in vivo at 3 T

OBJECTS

To better characterize cervical cancer at 3 T. MRI transverse relaxation patterns hold valuable biophysical information about cellular scale microstructure. Lorentzian modeling is typically used to represent intravoxel frequency distributions, resulting in mono-exponential decay of reversible transverse relaxation. However, deviations from mono-exponential decay are expected theoretically and observed experimentally.

MATERIALS AND METHODS

We compared the information content of four models of signal attenuation with reversible transverse relaxation. Biological phantoms and six women with cervical squamous cell carcinoma were imaged using a gradient-echo sampling of the spin-echo (GESSE) sequence. Lorentzian, Gaussian, Voigt, and Symmetric α-Stable (SAS) models were ranked using Akaike's Information Criterion (AIC), and the model retaining the highest information content was identified at each voxel as the best model.

RESULTS

The Lorentzian model resulted in information loss in large fractions of the phantoms and cervix. Gaussian and SAS models frequently had higher information content than the Lorentzian in much of the areas of interest. The Voigt model rarely surpassed the three other models in terms of information content.

DISCUSSION

Gaussian and SAS models provide better fitting of data in much of the human cervix at 3 T. Minimizing information loss through improved tissue modeling may have important implications for identifying reliable biomarkers of tumor hypoxia and iron deposition.

A Guide Toward Multi-scale and Quantitative Branching Analysis in the Mammary Gland

The mammary gland consists of a bilayered epithelial structure with an extensively branched morphology. The majority of this epithelial tree is laid down during puberty, during which actively proliferating terminal end buds repeatedly elongate and bifurcate to form the basic structure of the ductal tree. Mammary ducts consist of a basal and luminal cell layer with a multitude of identified sub-lineages within both layers. The understanding of how these different cell lineages are cooperatively driving branching morphogenesis is a problem of crossing multiple scales, as this requires information on the macroscopic branched structure of the gland, as well as data on single-cell dynamics driving the morphogenic program. Here we describe a method to combine genetic lineage tracing with whole-gland branching analysis. Quantitative data on the global organ structure can be used to derive a model for mammary gland branching morphogenesis and provide a backbone on which the dynamics of individual cell lineages can be simulated and compared to lineage-tracing approaches. Eventually, these quantitative models and experiments allow to understand the couplings between the macroscopic shape of the mammary gland and the underlying single-cell dynamics driving branching morphogenesis.

Imaging faster neural dynamics with fast fMRI: A need for updated models of the hemodynamic response

Fast fMRI enables the detection of neural dynamics over timescales of hundreds of milliseconds, suggesting it may provide a new avenue for studying subsecond neural processes in the human brain. The magnitudes of these fast fMRI dynamics are far greater than predicted by canonical models of the hemodynamic response. Several studies have established nonlinear properties of the hemodynamic response that have significant implications for fast fMRI. We first review nonlinear properties of the hemodynamic response function that may underlie fast fMRI signals. We then illustrate the breakdown of canonical hemodynamic response models in the context of fast neural dynamics. We will then argue that the canonical hemodynamic response function is not likely to reflect the BOLD response to neuronal activity driven by sparse or naturalistic stimuli or perhaps to spontaneous neuronal fluctuations in the resting state. These properties suggest that fast fMRI is capable of tracking surprisingly fast neuronal dynamics, and we discuss the neuroscientific questions that could be addressed using this approach.

Image-based biophysical modeling predicts cortical potentials evoked with subthalamic deep brain stimulation

BACKGROUND

Subthalamic deep brain stimulation (DBS) is an effective surgical treatment for Parkinson's disease and continues to advance technologically with an enormous parameter space. As such, in-silico DBS modeling systems have become common tools for research and development, but their underlying methods have yet to be standardized and validated.

OBJECTIVE

Evaluate the accuracy of patient-specific estimates of neural pathway activations in the subthalamic region against intracranial, cortical evoked potential (EP) recordings.

METHODS

Pathway activations were modeled in eleven patients using the latest advances in connectomic modeling of subthalamic DBS, focusing on the hyperdirect pathway (HDP) and corticospinal/bulbar tract (CSBT) for their relevance in human research studies. Correlations between pathway activations and respective EP amplitudes were quantified.

RESULTS

Good model performance required accurate lead localization and image fusions, as well as appropriate selection of fiber diameter in the biophysical model. While optimal model parameters varied across patients, good performance could be achieved using a global set of parameters that explained 60% and 73% of electrophysiologic activations of CSBT and HDP, respectively. Moreover, restricted models fit to only EP amplitudes of eight standard (monopolar and bipolar) electrode configurations were able to extrapolate variation in EP amplitudes across other directional electrode configurations and stimulation parameters, with no significant reduction in model performance across the cohort.

CONCLUSIONS

Our findings demonstrate that connectomic models of DBS with sufficient anatomical and electrical details can predict recruitment dynamics of white matter. These results will help to define connectomic modeling standards for preoperative surgical targeting and postoperative patient programming applications.

The complex three-dimensional organization of epithelial tissues

Understanding the cellular organization of tissues is key to developmental biology. In order to deal with this complex problem, researchers have taken advantage of reductionist approaches to reveal fundamental morphogenetic mechanisms and quantitative laws. For epithelia, their two-dimensional representation as polygonal tessellations has proved successful for understanding tissue organization. Yet, epithelial tissues bend and fold to shape organs in three dimensions. In this context, epithelial cells are too often simplified as prismatic blocks with a limited plasticity. However, there is increasing evidence that a realistic approach, even from a reductionist perspective, must include apico-basal intercalations (i.e. scutoidal cell shapes) for explaining epithelial organization convincingly. Here, we present an historical perspective about the tissue organization problem. Specifically, we analyze past and recent breakthroughs, and discuss how and why simplified, but realistic, in silico models require scutoidal features to address key morphogenetic events.

Realistic Microstructure Simulator (RMS): Monte Carlo simulations of diffusion in three-dimensional cell segmentations of microscopy images

BACKGROUND

Monte Carlo simulations of diffusion are commonly used as a model validation tool as they are especially suitable for generating the diffusion MRI signal in complicated tissue microgeometries.

NEW METHOD

Here we describe the details of implementing Monte Carlo simulations in three-dimensional (3d) voxelized segmentations of cells in microscopy images. Using the concept of the corner reflector, we largely reduce the computational load of simulating diffusion within and exchange between multiple cells. Precision is further achieved by GPU-based parallel computations.

RESULTS

Our simulation of diffusion in white matter axons segmented from a mouse brain demonstrates its value in validating biophysical models. Furthermore, we provide the theoretical background for implementing a discretized diffusion process, and consider the finite-step effects of the particle-membrane reflection and permeation events, needed for efficient simulation of interactions with irregular boundaries, spatially variable diffusion coefficient, and exchange.

COMPARISON WITH EXISTING METHODS

To our knowledge, this is the first Monte Carlo pipeline for MR signal simulations in a substrate composed of numerous realistic cells, accounting for their permeable and irregularly-shaped membranes.

CONCLUSIONS

The proposed RMS pipeline makes it possible to achieve fast and accurate simulations of diffusion in realistic tissue microgeometry, as well as the interplay with other MR contrasts. Presently, RMS focuses on simulations of diffusion, exchange, and T₁ and T₂ NMR relaxation in static tissues, with a possibility to straightforwardly account for susceptibility-induced T₂^* effects and flow.

Spring Model - Chromatin Modeling Tool Based on OpenMM

Chromatin structure modeling is a rapidly developing field. Parallel to the enormous growth of available experimental data, there is a growing need of building and visualizing 3D structures of nuclei, chromosomes, chromatin domains, and single loops associated with particular gene loci. Here, we present a tool for chromatin domain modeling; it is available as a webservice and standalone python script. Our tool is based on molecular mechanics and utilizes the OpenMM engine for model generation. In this method the user provides contacts between chromatin regions and obtains a 3D structure that satisfies them. Additional parameters allow for the control of fibre stiffness, initial structure adjustments and simulation resolution, there are also options for structure refinement and modeling in a spherical container. The user may provide contacts in the form of bead indices, or insert interactions in genome coordinates sourced from BEDPE files. After the simulation is complete, the user is able to download the structure in the Protein Data Bank (PDB) format for further analysis. We dedicate this tool to all who are interested in chromatin structures. It is suitable for quick visualization of datasets, studying the impact of structural variants (SVs), inspecting the effects of adding and removing particular contacts, and measuring features such as maximum distances between sites (e.g.promoter-enhancer), or local chromatin density.

Comparison of biophysical models with experimental data for three cell lines in response to irradiation with monoenergetic ions

The relative biological effectiveness (RBE) in particle therapy is currently estimated using biophysical models. We compared experimental measurements to the α curves as function of linear energy transfer computed by the Local Effect Model (LEM I-IV), the Microdosimetric Kinetic Model (MKM) and the NanOx model for HSG, V79 and CHO-K1 cells in response to monoenergetic irradiations. Although the LEM IV and the MKM predictions accurately reproduced the trend observed in the data, NanOx yielded a better agreement than the other models for more irradiation configurations. Its χ 2 estimator was indeed the lowest for three over seven considered cases.

Imaging Native Calcium Currents in Brain Slices

Imaging techniques may overcome the limitations of electrode techniques to measure locally not only membrane potential changes, but also ionic currents. Here, we review a recently developed approach to image native neuronal Ca²⁺ currents from brain slices. The technique is based on combined fluorescence recordings using low-affinity Ca²⁺ indicators possibly in combination with voltage sensitive dyes. We illustrate how the kinetics of a Ca²⁺ current can be estimated from the Ca²⁺ fluorescence change and locally correlated with the change of membrane potential, calibrated on an absolute scale, from the voltage fluorescence change. We show some representative measurements from the dendrites of CA1 hippocampal pyramidal neurons, from olfactory bulb mitral cells and from cerebellar Purkinje neurons. We discuss the striking difference in data analysis and interpretation between Ca²⁺ current measurements obtained using classical electrode techniques and the physiological currents obtained using this novel approach. Finally, we show how important is the kinetic information on the native Ca²⁺ current to explore the potential molecular targets of the Ca²⁺ flux from each individual Ca²⁺ channel.

Spatial chromatin architecture alteration by structural variations in human genomes at the population scale

BACKGROUND

The number of reported examples of chromatin architecture alterations involved in the regulation of gene transcription and in disease is increasing. However, no genome-wide testing has been performed to assess the abundance of these events and their importance relative to other factors affecting genome regulation. This is particularly interesting given that a vast majority of genetic variations identified in association studies are located outside coding sequences. This study attempts to address this lack by analyzing the impact on chromatin spatial organization of genetic variants identified in individuals from 26 human populations and in genome-wide association studies.

RESULTS

We assess the tendency of structural variants to accumulate in spatially interacting genomic segments and design an algorithm to model chromatin conformational changes caused by structural variations. We show that differential gene transcription is closely linked to the variation in chromatin interaction networks mediated by RNA polymerase II. We also demonstrate that CTCF-mediated interactions are well conserved across populations, but enriched with disease-associated SNPs. Moreover, we find boundaries of topological domains as relatively frequent targets of duplications, which suggest that these duplications can be an important evolutionary mechanism of genome spatial organization.

CONCLUSIONS

This study assesses the critical impact of genetic variants on the higher-order organization of chromatin folding and provides insight into the mechanisms regulating gene transcription at the population scale, of which local arrangement of chromatin loops seems to be the most significant. It provides the first insight into the variability of the human 3D genome at the population scale.

Intracellular calcium stores mediate metaplasticity at hippocampal dendritic spines

Key points

Calcium (Ca²⁺) entry mediated by NMDA receptors is considered central to the induction of activity‐dependent synaptic plasticity in hippocampal area CA1; this description does not, however, take into account the potential contribution of endoplasmic reticulum (ER) Ca²⁺ stores.

The ER has a heterogeneous distribution in CA1 dendritic spines, and may introduce localized functional differences in Ca²⁺ signalling between synapses, as suggested by experiments on metabotropic receptor‐dependent long‐term depression.

A physiologically detailed computational model of Ca²⁺ dynamics at a CA3–CA1 excitatory synapse characterizes the contribution of spine ER via metabotropic signalling during plasticity induction protocols.

ER Ca²⁺ release via IP₃ receptors modulates NMDA receptor‐dependent plasticity in a graded manner, to selectively promote synaptic depression with relatively diminished effect on LTP induction; this may temper further strengthening at the stronger synapses which are preferentially associated with ER‐containing spines.

Acquisition of spine ER may thus represent a local, biophysically plausible ‘metaplastic switch’ at potentiated CA1 synapses, contributing to the plasticity–stability balance in neural circuits.

Abstract

Long‐term plasticity mediated by NMDA receptors supports input‐specific, Hebbian forms of learning at excitatory CA3–CA1 connections in the hippocampus. There exists an additional layer of stabilizing mechanisms that act globally as well as locally over multiple time scales to ensure that plasticity occurs in a constrained manner. Here, we investigated the role of calcium (Ca²⁺) stores associated with the endoplasmic reticulum (ER) in the local regulation of plasticity at individual CA1 synapses. Our study was spurred by (1) the curious observation that ER is sparsely distributed in dendritic spines, but over‐represented in larger spines that are likely to have undergone activity‐dependent strengthening, and (2) evidence suggesting that ER motility at synapses can be rapid, and accompany activity‐regulated spine remodelling. We constructed a physiologically realistic computational model of an ER‐bearing CA1 spine, and examined how IP₃‐sensitive Ca²⁺ stores affect spine Ca²⁺ dynamics during activity patterns mimicking the induction of long‐term potentiation and long‐term depression (LTD). Our results suggest that the presence of ER modulates NMDA receptor‐dependent plasticity in a graded manner that selectively enhances LTD induction. We propose that ER may locally tune Ca²⁺‐based plasticity, providing a braking mechanism to mitigate runaway strengthening at potentiated synapses. Our study provides a biophysically accurate description of postsynaptic Ca²⁺ regulation, and suggests that ER in the spine may promote the re‐use of hippocampal synapses with saturated strengths.

Calcium (Ca²⁺) entry mediated by NMDA receptors is considered central to the induction of activity‐dependent synaptic plasticity in hippocampal area CA1; this description does not, however, take into account the potential contribution of endoplasmic reticulum (ER) Ca²⁺ stores.

The ER has a heterogeneous distribution in CA1 dendritic spines, and may introduce localized functional differences in Ca²⁺ signalling between synapses, as suggested by experiments on metabotropic receptor‐dependent long‐term depression.

A physiologically detailed computational model of Ca²⁺ dynamics at a CA3–CA1 excitatory synapse characterizes the contribution of spine ER via metabotropic signalling during plasticity induction protocols.

ER Ca²⁺ release via IP₃ receptors modulates NMDA receptor‐dependent plasticity in a graded manner, to selectively promote synaptic depression with relatively diminished effect on LTP induction; this may temper further strengthening at the stronger synapses which are preferentially associated with ER‐containing spines.

Acquisition of spine ER may thus represent a local, biophysically plausible ‘metaplastic switch’ at potentiated CA1 synapses, contributing to the plasticity–stability balance in neural circuits.

Machine learning polymer models of three-dimensional chromatin organization in human lymphoblastoid cells

We present machine learning models of human genome three-dimensional structure that combine one dimensional (linear) sequence specificity, epigenomic information, and transcription factor binding profiles, with the polymer-based biophysical simulations in order to explain the extensive long-range chromatin looping observed in ChIA-PET experiments for lymphoblastoid cells. Random Forest, Gradient Boosting Machine (GBM), and Deep Learning models were constructed and evaluated, when predicting high-resolution interactions within Topologically Associating Domains (TADs). The predicted interactions are consistent with the experimental long-read ChIA-PET interactions mediated by CTCF and RNAPOL2 for GM12878 cell line. The contribution of sequence information and chromatin state defined by epigenomic features to the prediction task is analyzed and reported, when using them separately and combined. Furthermore, we design three-dimensional models of chromatin contact domains (CCDs) using real (ChIA-PET) and predicted looping interactions. Initial results show a similarity between both types of 3D computational models (constructed from experimental or predicted interactions). This observation confirms the association between genome sequence, epigenomic and transcription factor profiles, and three-dimensional interactions.

M-Current Expands the Range of Gamma Frequency Inputs to Which a Neuronal Target Entrains

Theta (4-8 Hz) and gamma (30-80 Hz) rhythms in the brain are commonly associated with memory and learning (Kahana in J Neurosci 26:1669-1672, 2006; Quilichini et al. in J Neurosci 30:11128-11142, 2010). The precision of co-firing between neurons and incoming inputs is critical in these cognitive functions. We consider an inhibitory neuron model with M-current under forcing from gamma pulses and a sinusoidal current of theta frequency. The M-current has a long time constant (∼90 ms) and it has been shown to generate resonance at theta frequencies (Hutcheon and Yarom in Trends Neurosci 23:216-222, 2000; Hu et al. in J Physiol 545:783-805, 2002). We have found that this slow M-current contributes to the precise co-firing between the network and fast gamma pulses in the presence of a slow sinusoidal forcing. The M-current expands the phase-locking frequency range of the network, counteracts the slow theta forcing, and admits bistability in some parameter range. The effects of the M-current balancing the theta forcing are reduced if the sinusoidal current is faster than the theta frequency band. We characterize the dynamical mechanisms underlying the role of the M-current in enabling a network to be entrained to gamma frequency inputs using averaging methods, geometric singular perturbation theory, and bifurcation analysis.

Mechanics of epithelial tissue formation

A key process in the life of any multicellular organism is its development from a single egg into a full grown adult. The first step in this process often consists of forming a tissue layer out of randomly placed cells on the surface of the egg. We present a model for generating such a tissue, based on mechanical interactions between the cells, and find that the resulting cellular pattern corresponds to the Voronoi tessellation of the nuclei of the cells. Experimentally, we obtain the same result in both fruit flies and flour beetles, with a distribution of cell shapes that matches that of the model, without any adjustable parameters. Finally, we show that this pattern is broken when the cells grow at different rates.

Modeled larval connectivity of a multi-species reef fish and invertebrate assemblage off the coast of Moloka'i, Hawai'i

We use a novel individual-based model (IBM) to simulate larval dispersal around the island of Moloka‘i in the Hawaiian Archipelago. Our model uses ocean current output from the Massachusetts Institute of Technology general circulation model (MITgcm) as well as biological data on four invertebrate and seven fish species of management relevance to produce connectivity maps among sites around the island of Moloka‘i. These 11 species span the range of life history characteristics of Hawaiian coral reef species and show different spatial and temporal patterns of connectivity as a result. As expected, the longer the pelagic larval duration (PLD), the greater the proportion of larvae that disperse longer distances, but regardless of PLD (3–270 d) most successful dispersal occurs either over short distances within an island (<30 km) or to adjacent islands (50–125 km). Again, regardless of PLD, around the island of Moloka‘i, connectivity tends to be greatest among sites along the same coastline and exchange between northward, southward, eastward and westward-facing shores is limited. Using a graph-theoretic approach to visualize the data, we highlight that the eastern side of the island tends to show the greatest out-degree and betweenness centrality, which indicate important larval sources and connectivity pathways for the rest of the island. The marine protected area surrounding Kalaupapa National Historical Park emerges as a potential source for between-island larval connections, and the west coast of the Park is one of the few regions on Moloka‘i that acts as a net larval source across all species. Using this IBM and visualization approach reveals patterns of exchange between habitat regions and highlights critical larval sources and multi-generational pathways to indicate priority areas for marine resource managers.

Simulated auditory nerve axon demyelination alters sensitivity and response timing to extracellular stimulation

Since cochlear implant function involves direct depolarization of spiral ganglion neurons (SGNs) by applied current, SGN physiological health must be an important factor in cochlear implant (CI) outcomes. This expected relationship has, however, been difficult to confirm in implant recipients. Suggestively, animal studies have demonstrated both acute and progressive SGN ultrastructural changes (notably axon demyelination), even in the absence of soma death, and corresponding altered physiology following sensorineural deafening. Whether such demyelination occurs in humans and how such changes might impact CI function remains unknown. To approach this problem, we incorporated SGN demyelination into a biophysical model of extracellular stimulation of SGN fibers. Our approach enabled exploration of the entire parameter space corresponding to simulated myelin diameter and extent of fiber affected. All simulated fibers were stimulated distally with anodic monophasic, cathodic monophasic, anode-phase-first (AF) biphasic, and cathode-phase-first (CF) biphasic pulses from an extracellular disc electrode and monitored for spikes centrally. Not surprisingly, axon sensitivity generally decreased with demyelination, resulting in elevated thresholds, however, this effect was strongly non-uniform. Fibers with severe demyelination affecting only the most peripheral nodes responded nearly identically to normally myelinated fibers. Additionally, partial demyelination (<50%) yielded only minimal increases in threshold even when the entire fiber was impacted. The temporal effects of demyelination were more unexpected. Both latency and jitter of responses demonstrated resilience to modest changes but exhibited strongly non-monotonic and stimulus-dependent relationships to more profound demyelination. Normal, and modestly demyelinated fibers, were more sensitive to cathodic than anodic monophasic pulses and to CF than AF biphasic pulses, however, when demyelination was more severe these relative sensitivities were reversed. Comparison of threshold crossing between nodal segments demonstrated stimulus-dependent shifts in action potential initiation with different fiber demyelination states. For some demyelination scenarios, both phases of biphasic pulses could initiate action potentials at threshold resulting in bimodal latency and initiation site distributions and dramatically increased jitter. In summary, simulated demyelination leads to complex changes in fiber sensitivity and spike timing, mediated by alterations in action potential initiation site and slowed action potential conduction due to non-uniformities in the electrical properties of axons. Such demyelination-induced changes, if present in implantees, would have profound implications for the detection of fine temporal cues but not disrupt cues on the time scale of speech envelopes. These simulation results highlight the importance of exploring the SGN ultrastructural changes caused by a given etiology of hearing loss to more accurately predict cochlear implantation outcomes.

Precise genome-wide mapping of single nucleosomes and linkers in vivo

We developed a chemical cleavage method that releases single nucleosome dyad-containing fragments, allowing us to precisely map both single nucleosomes and linkers with high accuracy genome-wide in yeast. Our single nucleosome positioning data reveal that nucleosomes occupy preferred positions that differ by integral multiples of the DNA helical repeat. By comparing nucleosome dyad positioning maps to existing genomic and transcriptomic data, we evaluated the contributions of sequence, transcription, and histones H1 and H2A.Z in defining the chromatin landscape. We present a biophysical model that neglects DNA sequence and shows that steric occlusion suffices to explain the salient features of nucleosome positioning.

Surgical Decision Making From Image-Based Biophysical Modeling of Glioblastoma: Not Ready for Primetime

BACKGROUND

Biophysical modeling of glioma is gaining more interest for clinical practice. The most popular model describes aggressivity of tumor cells by two parameters: net proliferation rate (ρ) and propensity to migrate (D). The ratio ρ/D, which can be estimated from a single preoperative magnetic resonance imaging (MRI), characterizes tumor invasiveness profile (high ρ/D: nodular; low ρ/D: diffuse). A recent study reported, from a large series of glioblastoma multiforme (GBM) patients, that gross total resection (GTR) would improve survival only in patients with nodular tumors.

OBJECTIVE

To replicate these results, that is to verify that benefit of GTR would be only observed for nodular tumors.

METHODS

Between 2005 and 2012, we considered 234 GBM patients with pre- and postoperative MRI. Stereotactic biopsy (BST) was performed in 109 patients. Extent of resection was assessed on postoperative MRI and classified as GTR or partial resection (PR). Invasiveness ρ/D was estimated from the preoperative tumor volumes on T1-Gadolinium-enhanced and fluid-attenuated inversion recovery sequences.

RESULTS

We demonstrate that patients with diffuse GBM (low ρ/D), as well as more nodular (mid and high ρ/D) GBM, presented significant survival benefit from GTR over PR/BST ( P < .001).

CONCLUSION

Whatever the degree of tumor invasiveness, as estimated from MRI-driven biophysical modeling, GTR improves survival of GBM patients, compared to PR or BST. This conflicting result should motivate further studies.

Understanding key features of bacterial restriction-modification systems through quantitative modeling

Background

Restriction-modification (R-M) systems are rudimentary bacterial immune systems. The main components include restriction enzyme (R), which cuts specific unmethylated DNA sequences, and the methyltransferase (M), which protects the same DNA sequences. The expression of R-M system components is considered to be tightly regulated, to ensure successful establishment in a naïve bacterial host. R-M systems are organized in different architectures (convergent or divergent) and are characterized by different features, i.e. binding cooperativities, dissociation constants of dimerization, translation rates, which ensure this tight regulation. It has been proposed that R-M systems should exhibit certain dynamical properties during the system establishment, such as: i) a delayed expression of R with respect to M, ii) fast transition of R from “OFF” to “ON” state, iii) increased stability of the toxic molecule (R) steady-state levels. It is however unclear how different R-M system features and architectures ensure these dynamical properties, particularly since it is hard to address this question experimentally.

Results

To understand design of different R-M systems, we computationally analyze two R-M systems, representative of the subset controlled by small regulators called ‘C proteins’, and differing in having convergent or divergent promoter architecture. We show that, in the convergent system, abolishing any of the characteristic system features adversely affects the dynamical properties outlined above. Moreover, an extreme binding cooperativity, accompanied by a very high dissociation constant of dimerization, observed in the convergent system, but absent from other R-M systems, can be explained in terms of the same properties. Furthermore, we develop the first theoretical model for dynamics of a divergent R-M system, which does not share any of the convergent system features, but has overlapping promoters. We show that i) the system dynamics exhibits the same three dynamical properties, ii) introducing any of the convergent system features to the divergent system actually diminishes these properties.

Conclusions

Our results suggest that different R-M architectures and features may be understood in terms of constraints imposed by few simple dynamical properties of the system, providing a unifying framework for understanding these seemingly diverse systems. We also provided predictions for the perturbed R-M systems dynamics, which may in future be tested through increasingly available experimental techniques, such as re-engineering R-M systems and single-cell experiments.

Electronic supplementary material

The online version of this article (doi:10.1186/s12918-016-0377-x) contains supplementary material, which is available to authorized users.

Membrane-assisted viral DNA ejection

Genome packaging and delivery are fundamental steps in the replication cycle of all viruses. Icosahedral viruses with linear double-stranded DNA (dsDNA) usually package their genome into a preformed, rigid procapsid using the power generated by a virus-encoded packaging ATPase. The pressure and stored energy due to this confinement of DNA at a high density is assumed to drive the initial stages of genome ejection. Membrane-containing icosahedral viruses, such as bacteriophage PRD1, present an additional architectural complexity by enclosing their genome within an internal membrane vesicle. Upon adsorption to a host cell, the PRD1 membrane remodels into a proteo-lipidic tube that provides a conduit for passage of the ejected linear dsDNA through the cell envelope. Based on volume analyses of PRD1 membrane vesicles captured by cryo-electron tomography and modeling of the elastic properties of the vesicle, we propose that the internal membrane makes a crucial and active contribution during infection by maintaining the driving force for DNA ejection and countering the internal turgor pressure of the host. These novel functions extend the role of the PRD1 viral membrane beyond tube formation or the mere physical confinement of the genome. The presence and assistance of an internal membrane might constitute a biological advantage that extends also to other viruses that package their linear dsDNA to high density within an internal vesicle.

Functional Effects of Schizophrenia-Linked Genetic Variants on Intrinsic Single-Neuron Excitability: A Modeling Study

BACKGROUND

Recent genome-wide association studies have identified a large number of genetic risk factors for schizophrenia (SCZ) featuring ion channels and calcium transporters. For some of these risk factors, independent prior investigations have examined the effects of genetic alterations on the cellular electrical excitability and calcium homeostasis. In the present proof-of-concept study, we harnessed these experimental results for modeling of computational properties on layer V cortical pyramidal cells and identified possible common alterations in behavior across SCZ-related genes.

METHODS

We applied a biophysically detailed multicompartmental model to study the excitability of a layer V pyramidal cell. We reviewed the literature on functional genomics for variants of genes associated with SCZ and used changes in neuron model parameters to represent the effects of these variants.

RESULTS

We present and apply a framework for examining the effects of subtle single nucleotide polymorphisms in ion channel and calcium transporter-encoding genes on neuron excitability. Our analysis indicates that most of the considered SCZ-related genetic variants affect the spiking behavior and intracellular calcium dynamics resulting from summation of inputs across the dendritic tree.

CONCLUSIONS

Our results suggest that alteration in the ability of a single neuron to integrate the inputs and scale its excitability may constitute a fundamental mechanistic contributor to mental disease, alongside the previously proposed deficits in synaptic communication and network behavior.