Extracellular regulation of VEGF: isoforms, proteolysis, and vascular patterning

The regulation of vascular endothelial growth factor A (VEGF) is critical to neovascularization in numerous tissues under physiological and pathological conditions. VEGF has multiple isoforms, created by alternative splicing or proteolytic cleavage, and characterized by different receptor-binding and matrix-binding properties. These isoforms are known to give rise to a spectrum of angiogenesis patterns marked by differences in branching, which has functional implications for tissues. In this review, we detail the extensive extracellular regulation of VEGF and the ability of VEGF to dictate the vascular phenotype. We explore the role of VEGF-releasing proteases and soluble carrier molecules on VEGF activity. While proteases such as MMP9 can 'release' matrix-bound VEGF and promote angiogenesis, for example as a key step in carcinogenesis, proteases can also suppress VEGF's angiogenic effects. We explore what dictates pro- or anti-angiogenic behavior. We also seek to understand the phenomenon of VEGF gradient formation. Strong VEGF gradients are thought to be due to decreased rates of diffusion from reversible matrix binding, however theoretical studies show that this scenario cannot give rise to lasting VEGF gradients in vivo. We propose that gradients are formed through degradation of sequestered VEGF. Finally, we review how different aspects of the VEGF signal, such as its concentration, gradient, matrix-binding, and NRP1-binding can differentially affect angiogenesis. We explore how this allows VEGF to regulate the formation of vascular networks across a spectrum of high to low branching densities, and from normal to pathological angiogenesis. A better understanding of the control of angiogenesis is necessary to improve upon limitations of current angiogenic therapies.

The dynamic microbiome

While our genomes are essentially static, our microbiomes are inherently dynamic. The microbial communities we harbor in our bodies change throughout our lives due to many factors, including maturation during childhood, alterations in our diets, travel, illnesses, and medical treatments. Moreover, there is mounting evidence that our microbiomes change us, by promoting health through their beneficial actions or by increasing our susceptibility to diseases through a process termed dysbiosis. Recent technological advances are enabling unprecedentedly detailed studies of the dynamics of the microbiota in animal models and human populations. This review will highlight key areas of investigation in the field, including establishment of the microbiota during early childhood, temporal variability of the microbiome in healthy adults, responses of the microbiota to intentional perturbations such as antibiotics and dietary changes, and prospective analyses linking changes in the microbiota to host disease status. Given the importance of computational methods in the field, this review will also discuss issues and pitfalls in the analysis of microbiome time-series data, and explore several promising new directions for mathematical model and algorithm development.

Learning the opportunity cost of time in a patch-foraging task

Although most decision research concerns choice between simultaneously presented options, in many situations options are encountered serially, and the decision is whether to exploit an option or search for a better one. Such problems have a rich history in animal foraging, but we know little about the psychological processes involved. In particular, it is unknown whether learning in these problems is supported by the well-studied neurocomputational mechanisms involved in more conventional tasks. We investigated how humans learn in a foraging task, which requires deciding whether to harvest a depleting resource or switch to a replenished one. The optimal choice (given by the marginal value theorem; MVT) requires comparing the immediate return from harvesting to the opportunity cost of time, which is given by the long-run average reward. In two experiments, we varied opportunity cost across blocks, and subjects adjusted their behavior to blockwise changes in environmental characteristics. We examined how subjects learned their choice strategies by comparing choice adjustments to a learning rule suggested by the MVT (in which the opportunity cost threshold is estimated as an average over previous rewards) and to the predominant incremental-learning theory in neuroscience, temporal-difference learning (TD). Trial-by-trial decisions were explained better by the MVT threshold-learning rule. These findings expand on the foraging literature, which has focused on steady-state behavior, by elucidating a computational mechanism for learning in switching tasks that is distinct from those used in traditional tasks, and suggest connections to research on average reward rates in other domains of neuroscience.

A computational modelling study of transcranial direct current stimulation montages used in depression

Transcranial direct current stimulation (tDCS) is a neuromodulatory technique which involves passing a mild electric current to the brain through electrodes placed on the scalp. Several clinical studies suggest that tDCS may have clinically meaningful efficacy in the treatment of depression. The objective of this study was to simulate and compare the effects of several tDCS montages either used in clinical trials or proposed, for the treatment of depression, in different high-resolution anatomically-accurate head models. Detailed segmented finite element head models of two subjects were presented, and a total of eleven tDCS electrode montages were simulated. Sensitivity analysis on the effects of changing the size of the anode, rotating both electrodes and displacing the anode was also conducted on selected montages. The F3-F8 and F3-F4 montages have been used in clinical trials reporting significant antidepressant effects and both result in relatively high electric fields in dorsolateral prefrontal cortices. Other montages using a fronto-extracephalic or fronto-occipital approach result in greater stimulation of central structures (e.g. anterior cingulate cortex) which may be advantageous in treating depression, but their efficacy has yet to be tested in randomised controlled trials. Results from sensitivity analysis suggest that electrode position and size may be adjusted slightly to accommodate other priorities, such as skin discomfort and damage.

Experiments and models of cortical oscillations as a target for noninvasive brain stimulation

Noninvasive brain stimulation is attracting substantial attention due to its potential for safe and effective modulation of brain network dynamics. Promising applications include cognitive enhancement and treatment of disorders of the central nervous system. Recently, targeting of cortical oscillations by brain stimulation with periodic electromagnetic waveforms has emerged as a particularly appealing approach for understanding the causal role of cortical oscillations in human cognition and behavior. Two main approaches exist: repetitive transcranial magnetic stimulation (rTMS) and transcranial alternating current stimulation (tACS); rTMS is more widely used as a research and clinical tool but only recently has it been suggested to selectively engage frequency-matched cortical oscillations. In contrast, tACS is an offspring of transcranial direct current stimulation and has been introduced with the specific aim of engaging cortical oscillations. One of the main lessons that the field of noninvasive brain stimulation has learned over the last few years is that without a mechanistic understanding of how stimulation engages neuronal circuits, little progress can be made toward the rational design of individualized, adaptive stimulation treatments. Computer simulations of cellular and network models from the field of computational neuroscience are a key tool to gain such a mechanistic understanding. However, the insights gained from such modeling strategies can only be fully leveraged when used in tight conjunction with experimental approaches in both human and animal model studies. Here, I provide an in-depth review of the pioneering experimental and computational studies that together provide the basis for understanding how periodic noninvasive brain stimulation targets cortical oscillations to enable the rational design of brain stimulation treatments for disorders associated with specific deficits in cortical oscillations.

Quantifying axonal responses in patient-specific models of subthalamic deep brain stimulation

Medical imaging has played a major role in defining the general anatomical targets for deep brain stimulation (DBS) therapies. However, specifics on the underlying brain circuitry that is directly modulated by DBS electric fields remain relatively undefined. Detailed biophysical modeling of DBS provides an approach to quantify the theoretical responses to stimulation at the cellular level, and has established a key role for axonal activation in the therapeutic mechanisms of DBS. Estimates of DBS-induced axonal activation can then be coupled with advances in defining the structural connectome of the human brain to provide insight into the modulated brain circuitry and possible correlations with clinical outcomes. These pathway-activation models (PAMs) represent powerful tools for DBS research, but the theoretical predictions are highly dependent upon the underlying assumptions of the particular modeling strategy used to create the PAM. In general, three types of PAMs are used to estimate activation: 1) field-cable (FC) models, 2) driving force (DF) models, and 3) volume of tissue activated (VTA) models. FC models represent the "gold standard" for analysis but at the cost of extreme technical demands and computational resources. Consequently, DF and VTA PAMs, derived from simplified FC models, are typically used in clinical research studies, but the relative accuracy of these implementations is unknown. Therefore, we performed a head-to-head comparison of the different PAMs, specifically evaluating DBS of three different axonal pathways in the subthalamic region. The DF PAM was markedly more accurate than the VTA PAMs, but none of these simplified models were able to match the results of the patient-specific FC PAM across all pathways and combinations of stimulus parameters. These results highlight the limitations of using simplified predictors to estimate axonal stimulation and emphasize the need for novel algorithms that are both biophysically realistic and computationally simple.

A neural network model of individual differences in task switching abilities

We use a biologically grounded neural network model to investigate the brain mechanisms underlying individual differences specific to the selection and instantiation of representations that exert cognitive control in task switching. Existing computational models of task switching do not focus on individual differences and so cannot explain why task switching abilities are separable from other executive function (EF) abilities (such as response inhibition). We explore hypotheses regarding neural mechanisms underlying the "Shifting-Specific" and "Common EF" components of EF proposed in the Unity/Diversity model (Miyake & Friedman, 2012) and similar components in related theoretical frameworks. We do so by adapting a well-developed neural network model of working memory (Prefrontal cortex, Basal ganglia Working Memory or PBWM; Hazy, Frank, & O'Reilly, 2007) to task switching and the Stroop task, and comparing its behavior on those tasks under a variety of individual difference manipulations. Results are consistent with the hypotheses that variation specific to task switching (i.e., Shifting-Specific) may be related to uncontrolled, automatic persistence of goal representations, whereas variation general to multiple EFs (i.e., Common EF) may be related to the strength of PFC representations and their effect on processing in the remainder of the cognitive system. Moreover, increasing signal to noise ratio in PFC, theoretically tied to levels of tonic dopamine and a genetic polymorphism in the COMT gene, reduced Stroop interference but increased switch costs. This stability-flexibility tradeoff provides an explanation for why these two EF components sometimes show opposing correlations with other variables such as attention problems and self-restraint.

Modelling ADHD: A review of ADHD theories through their predictions for computational models of decision-making and reinforcement learning

Attention deficit hyperactivity disorder (ADHD) is characterized by altered decision-making (DM) and reinforcement learning (RL), for which competing theories propose alternative explanations. Computational modelling contributes to understanding DM and RL by integrating behavioural and neurobiological findings, and could elucidate pathogenic mechanisms behind ADHD. This review of neurobiological theories of ADHD describes predictions for the effect of ADHD on DM and RL as described by the drift-diffusion model of DM (DDM) and a basic RL model. Empirical studies employing these models are also reviewed. While theories often agree on how ADHD should be reflected in model parameters, each theory implies a unique combination of predictions. Empirical studies agree with the theories' assumptions of a lowered DDM drift rate in ADHD, while findings are less conclusive for boundary separation. The few studies employing RL models support a lower choice sensitivity in ADHD, but not an altered learning rate. The discussion outlines research areas for further theoretical refinement in the ADHD field.

An air-cell-based cushion for pressure ulcer protection remarkably reduces tissue stresses in the seated buttocks with respect to foams: finite element studies

A sitting-acquired pressure ulcer (PU) is a common injury in wheelchair-bound patients. Preventative measures for the post spinal cord injury (SCI) population include prescription of a supportive thick cushion on the wheelchair, in order to better distribute loads between the buttocks and support surface (which are quantifiable using interface pressure measurements), and potentially, to minimize internal soft tissue loads (which are typically unknown). Information about the biomechanical efficacy of commercially-available structured cushion designs such as air-cell-based (ACB) cushions, gel, and honeycomb-like cushions is sparse. Considering the importance of such evaluations to patient safety and quality of life, we studied the biomechanical performances of an ACB cushion in comparison to standard, flat foam cushions with different stiffness properties. Using a set of finite element (FE) model variants, we determined the mechanical stresses in muscle, fat, and skin tissues under the ischial tuberosities during sitting. Tissue stress analyses were conducted in a reference SCI anatomy, incorporating pathoanatomical and pathophysiological changes associated with chronic SCI, including bone shape adaptation, muscle atrophy, and spasms. We found up to 57% greater immersion and 4 orders-of-magnitude lower muscle, fat, and skin tissue stresses for the ACB cushion. We also found the ACB cushion provides better protection against the aforementioned bone shape adaptation, muscle atrophy, and spasms. Hence, theoretically, the use of a suitable ACB cushion should provide longer safe sitting times for SCI patients with respect to standard foam cushions.

Reverse Engineering of Genome-wide Gene Regulatory Networks from Gene Expression Data

Transcriptional regulation plays vital roles in many fundamental biological processes. Reverse engineering of genome-wide regulatory networks from high-throughput transcriptomic data provides a promising way to characterize the global scenario of regulatory relationships between regulators and their targets. In this review, we summarize and categorize the main frameworks and methods currently available for inferring transcriptional regulatory networks from microarray gene expression profiling data. We overview each of strategies and introduce representative methods respectively. Their assumptions, advantages, shortcomings, and possible improvements and extensions are also clarified and commented.

A 3D model of the Achilles tendon to determine the mechanisms underlying nonuniform tendon displacements

The Achilles is the thickest tendon in the body and is the primary elastic energy-storing component during running. The form and function of the human Achilles is complex: twisted structure, intratendinous interactions, and differential motor control from the triceps surae muscles make Achilles behavior difficult to intuit. Recent in vivo imaging of the Achilles has revealed nonuniform displacement patterns that are not fully understood and may result from complex architecture and musculotendon interactions. In order to understand which features of the Achilles tendon give rise to the nonuniform deformations observed in vivo, we used computational modeling to predict the mechanical contributions from different features of the tendon. The aims of this study are to: (i) build a novel computational model of the Achilles tendon based on ultrashort echo time MRI, (ii) compare simulated displacements with published in vivo ultrasound measures of displacement, and (iii) use the model to elucidate the effects of tendon twisting, intratendon sliding, retrocalcaneal insertion, and differential muscle forces on tendon deformation. Intratendon sliding and differential muscle forces were found to be the largest factors contributing to displacement nonuniformity between tendon regions. Elimination of intratendon sliding or muscle forces reduced displacement nonuniformity by 96% and 85%, respectively, while elimination of tendon twist and the retrocalcaneal insertion reduced displacement nonuniformity by only 35% and 3%. These results suggest that changes in the complex internal structure of the tendon alter the interaction between muscle forces and tendon behavior and therefore may have important implications on muscle function during movement.

Understanding abnormal uterine artery Doppler waveforms: A novel computational model to explore potential causes within the utero-placental vasculature

INTRODUCTION

Uterine artery (UtA) Doppler indices are one of the most commonly employed screening tests for pre-eclampsia worldwide. Abnormal indices appear to result from increased uterine vascular resistance, but anatomical complexity and lack of appropriate animal models mean that little is known about the relative contribution of each of the components of the uterine vasculature to the overall UtA Doppler waveform. Previous computational models suggested that trophoblast-mediated spiral artery remodeling has a dominant effect on the UtA Doppler waveform. However, these models did not incorporate the myometrial arterio-venous anastomoses, which have significant potential to affect utero-placental haemodynamics.

METHODS

We present a more anatomically complete computational model, explicitly incorporating a structural description of each component of the uterine vasculature, and crucially including myometrial arterio-venous anastomoses as parallel pathways for blood-flow away from the placental bed. Wave transmission theory was applied to the network to predict UtA waveforms.

RESULTS

Our model shows that high UtA resistance indices, combined with notching, reflect an abnormal remodeling of the entire uterine vasculature. Incomplete spiral artery remodeling alone is unlikely to cause abnormal UtA Doppler waveforms as increased resistance in these arteries can be 'buffered' by upstream anastomoses. Critically, our results indicate that the radial arteries, may have a more important effect on utero-placental flow dynamics, and the UtA Doppler waveform than previously thought.

CONCLUSIONS

This model suggests that to appropriately interpret UtA Doppler waveforms they must be considered to be reflecting changes in the entire system, rather than just the spiral arteries.

The metabolic analysis of psoriasis identifies the associated metabolites while providing computational models for the monitoring of the disease

The majority of studies on psoriasis have focused on explaining the genetic background and its associations with the immune system’s response. The aim of this study was to identify the low-molecular weight compounds contributing to the metabolomic profile of psoriasis and to provide computational models that help with the classification and monitoring of the severity of the disease. We compared the results from targeted and untargeted analyses of patients’ serums with plaque psoriasis to controls. The main differences were found in the concentrations of acylcarnitines, phosphatidylcholines, amino acids, urea, phytol, and 1,11-undecanedicarboxylic acid. The data from the targeted analysis were used to build classification models for psoriasis. The results from this study provide an overview of the metabolomic serum profile of psoriasis along with promising statistical models for the monitoring of the disease.

Interactions between Tau and Different Conformations of Tubulin: Implications for Tau Function and Mechanism

Tau is a multifaceted neuronal protein that stabilizes microtubules (MTs), but the mechanism of this activity remains poorly understood. Questions include whether Tau binds MTs laterally or longitudinally and whether Tau's binding affinity depends on the nucleotide state of tubulin. We observed that Tau binds tightly to Dolastatin-10 tubulin rings and promotes the formation of Dolastatin-10 ring stacks, implying that Tau can crosslink MT protofilaments laterally. In addition, we found that Tau prefers GDP-like tubulin conformations, which implies that Tau binding to the MT surface is biased away from the dynamic GTP-rich MT tip. To investigate the potential impact of these Tau activities on MT stabilization, we incorporated them into our previously developed dimer-scale computational model of MT dynamics. We found that lateral crosslinking activities have a much greater effect on MT stability than do longitudinal crosslinking activities, and that introducing a bias toward GDP tubulin has little impact on the observed MT stabilization. To address the question of why Tau is GDP-tubulin-biased, we tested whether Tau might affect MT binding of the +TIP EB1. We confirmed recent reports that Tau binds directly to EB1 and that Tau competes with EB1 for MT binding. Our results lead to a conceptual model where Tau stabilizes the MT lattice by strengthening lateral interactions between protofilaments. We propose that Tau's GDP preference allows the cell to independently regulate the dynamics of the MT tip and the stability of the lattice.

A computational biomarker of idiopathic generalized epilepsy from resting state EEG

Epilepsy is one of the most common serious neurologic conditions. It is characterized by the tendency to have recurrent seizures, which arise against a backdrop of apparently normal brain activity. At present, clinical diagnosis relies on the following: (1) case history, which can be unreliable; (2) observation of transient abnormal activity during electroencephalography (EEG), which may not be present during clinical evaluation; and (3) if diagnostic uncertainty occurs, undertaking prolonged monitoring in an attempt to observe EEG abnormalities, which is costly. Herein, we describe the discovery and validation of an epilepsy biomarker based on computational analysis of a short segment of resting-state (interictal) EEG. Our method utilizes a computer model of dynamic networks, where the network is inferred from the extent of synchrony between EEG channels (functional networks) and the normalized power spectrum of the clinical data. We optimize model parameters using a leave-one-out classification on a dataset comprising 30 people with idiopathic generalized epilepsy (IGE) and 38 normal controls. Applying this scheme to all 68 subjects we find 100% specificity at 56.7% sensitivity, and 100% sensitivity at 65.8% specificity. We believe this biomarker could readily provide additional support to the diagnostic process.

A biophysical model of the cortex-basal ganglia-thalamus network in the 6-OHDA lesioned rat model of Parkinson's disease

Electrical stimulation of sub-cortical brain regions (the basal ganglia), known as deep brain stimulation (DBS), is an effective treatment for Parkinson's disease (PD). Chronic high frequency (HF) DBS in the subthalamic nucleus (STN) or globus pallidus interna (GPi) reduces motor symptoms including bradykinesia and tremor in patients with PD, but the therapeutic mechanisms of DBS are not fully understood. We developed a biophysical network model comprising of the closed loop cortical-basal ganglia-thalamus circuit representing the healthy and parkinsonian rat brain. The network properties of the model were validated by comparing responses evoked in basal ganglia (BG) nuclei by cortical (CTX) stimulation to published experimental results. A key emergent property of the model was generation of low-frequency network oscillations. Consistent with their putative pathological role, low-frequency oscillations in model BG neurons were exaggerated in the parkinsonian state compared to the healthy condition. We used the model to quantify the effectiveness of STN DBS at different frequencies in suppressing low-frequency oscillatory activity in GPi. Frequencies less than 40 Hz were ineffective, low-frequency oscillatory power decreased gradually for frequencies between 50 Hz and 130 Hz, and saturated at frequencies higher than 150 Hz. HF STN DBS suppressed pathological oscillations in GPe/GPi both by exciting and inhibiting the firing in GPe/GPi neurons, and the number of GPe/GPi neurons influenced was greater for HF stimulation than low-frequency stimulation. Similar to the frequency dependent suppression of pathological oscillations, STN DBS also normalized the abnormal GPi spiking activity evoked by CTX stimulation in a frequency dependent fashion with HF being the most effective. Therefore, therapeutic HF STN DBS effectively suppresses pathological activity by influencing the activity of a greater proportion of neurons in the output nucleus of the BG.

Toward understanding thalamocortical dysfunction in schizophrenia through computational models of neural circuit dynamics

The thalamus is implicated in the neuropathology of schizophrenia, and multiple modalities of noninvasive neuroimaging provide converging evidence for altered thalamocortical dynamics in the disorder, such as functional connectivity and oscillatory power. However, it remains a challenge to link these neuroimaging biomarkers to underlying neural circuit mechanisms. One potential path forward is a "Computational Psychiatry" approach that leverages computational models of neural circuits to make predictions for the dynamical impact dynamical impact on specific thalamic disruptions hypothesized to occur in the pathophysiology of schizophrenia. Here we review biophysically-based computational models of neural circuit dynamics for large-scale resting-state networks which have been applied to schizophrenia, and for thalamic oscillations. As a key aspect of thalamocortical dysconnectivity in schizophrenia is its regional specificity, it is important to consider potential sources of intrinsic heterogeneity of cellular and circuit properties across cortical and thalamic structures.

Effects of normal and abnormal loading conditions on morphogenesis of the prenatal hip joint: application to hip dysplasia

Joint morphogenesis is an important phase of prenatal joint development during which the opposing cartilaginous rudiments acquire their reciprocal and interlocking shapes. At an early stage of development, the prenatal hip joint is formed of a deep acetabular cavity that almost totally encloses the head. By the time of birth, the acetabulum has become shallower and the femoral head has lost substantial sphericity, reducing joint coverage and stability. In this study, we use a dynamic mechanobiological simulation to explore the effects of normal (symmetric), reduced and abnormal (asymmetric) prenatal movements on hip joint shape, to understand their importance for postnatal skeletal malformations such as developmental dysplasia of the hip (DDH). We successfully predict the physiological trends of decreasing sphericity and acetabular coverage of the femoral head during fetal development. We show that a full range of symmetric movements helps to maintain some of the acetabular depth and femoral head sphericity, while reduced or absent movements can lead to decreased sphericity and acetabular coverage of the femoral head. When an abnormal movement pattern was applied, a deformed joint shape was predicted, with an opened asymmetric acetabulum and the onset of a malformed femoral head. This study provides evidence for the importance of fetal movements in the prevention and manifestation of congenital musculoskeletal disorders such as DDH.

An agent-based model of triple-negative breast cancer: the interplay between chemokine receptor CCR5 expression, cancer stem cells, and hypoxia

Background

Triple-negative breast cancer lacks estrogen, progesterone, and HER2 receptors and is thus not possible to treat with targeted therapies for these receptors. Therefore, a greater understanding of triple-negative breast cancer is necessary for the treatment of this cancer type. In previous work from our laboratory, we found that chemokine ligand-receptor CCL5-CCR5 axis is important for the metastasis of human triple-negative breast cancer cell MDA-MB-231 to the lymph nodes and lungs, in a mouse xenograft model. We collected relevant experimental data from our and other laboratories for numbers of cancer stem cells, numbers of CCR5+ cells, and cell migration rates for different breast cancer cell lines and different experimental conditions.

Results

Using these experimental data we developed an in silico agent-based model of triple-negative breast cancer that considers surface receptor CCR5-high and CCR5-low cells and breast cancer stem cells, to predict the tumor growth rate and spatio-temporal distribution of cells in primary tumors. We find that high cancer stem cell percentages greatly increase tumor growth. We find that anti-stem cell treatment decreases tumor growth but may not lead to dormancy unless all stem cells get eliminated. We further find that hypoxia increases overall tumor growth and treatment with a CCR5 inhibitor maraviroc slightly decreases overall tumor growth. We also characterize 3D shapes of solid and invasive tumors using several shape metrics.

Conclusions

Breast cancer stem cells and CCR5+ cells affect the overall growth and morphology of breast tumors. In silico drug treatments demonstrate limited efficacy of incomplete inhibition of cancer stem cells after which tumor growth recurs, and CCR5 inhibition causes only a slight reduction in tumor growth.

Electronic supplementary material

The online version of this article (doi:10.1186/s12918-017-0445-x) contains supplementary material, which is available to authorized users.

Modeling the biomechanics of fetal movements

Fetal movements in the uterus are a natural part of development and are known to play an important role in normal musculoskeletal development. However, very little is known about the biomechanical stimuli that arise during movements in utero, despite these stimuli being crucial to normal bone and joint formation. Therefore, the objective of this study was to create a series of computational steps by which the forces generated during a kick in utero could be predicted from clinically observed fetal movements using novel cine-MRI data of three fetuses, aged 20–22 weeks. A custom tracking software was designed to characterize the movements of joints in utero, and average uterus deflection of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$6.95 \pm 0.41$$\end{document}6.95±0.41 mm due to kicking was calculated. These observed displacements provided boundary conditions for a finite element model of the uterine environment, predicting an average reaction force of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$0.52 \pm 0.15$$\end{document}0.52±0.15 N generated by a kick against the uterine wall. Finally, these data were applied as inputs for a musculoskeletal model of a fetal kick, resulting in predicted maximum forces in the muscles surrounding the hip joint of approximately 8 N, while higher maximum forces of approximately 21 N were predicted for the muscles surrounding the knee joint. This study provides a novel insight into the closed mechanical environment of the uterus, with an innovative method allowing elucidation of the biomechanical interaction of the developing fetus with its surroundings.

Structural connectivity based whole brain modelling in epilepsy

Epilepsy is a neurological condition characterised by the recurrence of seizures. During seizures multiple brain areas can behave abnormally. Rather than considering each abnormal area in isolation, one can consider them as an interconnected functional 'network'. Recently, there has been a shift in emphasis to consider epilepsy as a disorder involving more widespread functional brain networks than perhaps was previously thought. The basis for these functional networks is proposed to be the static structural brain network established through the connectivity of the white matter. Additionally, it has also been argued that time varying aspects of epilepsy are of crucial importance and as such computational models of these dynamical properties have recently advanced. We describe how dynamic computer models can be combined with static human in vivo connectivity obtained through diffusion weighted magnetic resonance imaging. We predict that in future the use of these two methods in concert will lead to predictions for optimal surgery and brain stimulation sites for epilepsy and other neurological disorders.

Increasing extracellular H2O2 produces a bi-phasic response in intracellular H2O2, with peroxiredoxin hyperoxidation only triggered once the cellular H2O2-buffering capacity is overwhelmed

Reactive oxygen species, such as H2O2, can damage cells but also promote fundamental processes, including growth, differentiation and migration. The mechanisms allowing cells to differentially respond to toxic or signaling H2O2 levels are poorly defined. Here we reveal that increasing external H2O2 produces a bi-phasic response in intracellular H2O2. Peroxiredoxins (Prx) are abundant peroxidases which protect against genome instability, ageing and cancer. We have developed a dynamic model simulating in vivo changes in Prx oxidation. Remarkably, we show that the thioredoxin peroxidase activity of Prx does not provide any significant protection against external rises in H2O2. Instead, our model and experimental data are consistent with low levels of extracellular H2O2 being efficiently buffered by other thioredoxin-dependent activities, including H2O2-reactive cysteines in the thiol-proteome. We show that when extracellular H2O2 levels overwhelm this buffering capacity, the consequent rise in intracellular H2O2 triggers hyperoxidation of Prx to thioredoxin-resistant, peroxidase-inactive form/s. Accordingly, Prx hyperoxidation signals that H2O2 defenses are breached, diverting thioredoxin to repair damage.

Contribution of bioinformatics prediction in microRNA-based cancer therapeutics

Despite enormous efforts, cancer remains one of the most lethal diseases in the world. With the advancement of high throughput technologies massive amounts of cancer data can be accessed and analyzed. Bioinformatics provides a platform to assist biologists in developing minimally invasive biomarkers to detect cancer, and in designing effective personalized therapies to treat cancer patients. Still, the early diagnosis, prognosis, and treatment of cancer are an open challenge for the research community. MicroRNAs (miRNAs) are small non-coding RNAs that serve to regulate gene expression. The discovery of deregulated miRNAs in cancer cells and tissues has led many to investigate the use of miRNAs as potential biomarkers for early detection, and as a therapeutic agent to treat cancer. Here we describe advancements in computational approaches to predict miRNAs and their targets, and discuss the role of bioinformatics in studying miRNAs in the context of human cancer.

Significant group-level hotspots found in deep brain regions during transcranial direct current stimulation (tDCS): A computational analysis of electric fields

OBJECTIVE

Transcranial direct current stimulation (tDCS) is a neuromodulation scheme that delivers a small current via electrodes placed on the scalp. The target is generally assumed to be under the electrode, but deep brain regions could also be involved due to the large current spread between the electrodes. This study aims to computationally evaluate if group-level hotspots exist in deep brain regions for different electrode montages.

METHODS

We computed the tDCS-generated electric fields (EFs) in a group of subjects using interindividual registration methods that permitted the projection of EFs from individual realistic head models (n = 18) to a standard deep brain region.

RESULTS

The spatial distribution and peak values (standard deviation of 14%) of EFs varied significantly. Nevertheless, group-level EF hotspots appeared in deep brain regions. The caudate had the highest field peaks in particular for F3-F4 montage (70% of maximum cortical EF), while other regions reach field peaks of 50%.

CONCLUSIONS

tDCS at deeper regions may include not only modulation via underlying cortical or subcortical circuits but also modulation of deep brain regions.

SIGNIFICANCE

The presented EF atlas in deep brain regions can be used to explain tDCS mechanism or select the most appropriate tDCS montage.

Flt-1 (VEGFR-1) coordinates discrete stages of blood vessel formation

AIMS

In developing blood vessel networks, the overall level of vessel branching often correlates with angiogenic sprout initiations, but in some pathological situations, increased sprout initiations paradoxically lead to reduced vessel branching and impaired vascular function. We examine the hypothesis that defects in the discrete stages of angiogenesis can uniquely contribute to vessel branching outcomes.

METHODS AND RESULTS

Time-lapse movies of mammalian blood vessel development were used to define and quantify the dynamics of angiogenic sprouting. We characterized the formation of new functional conduits by classifying discrete sequential stages-sprout initiation, extension, connection, and stability-that are differentially affected by manipulation of vascular endothelial growth factor-A (VEGF-A) signalling via genetic loss of the receptor flt-1 (vegfr1). In mouse embryonic stem cell-derived vessels genetically lacking flt-1, overall branching is significantly decreased while sprout initiations are significantly increased. Flt-1(-/-) mutant sprouts are less likely to retract, and they form increased numbers of connections with other vessels. However, loss of flt-1 also leads to vessel collapse, which reduces the number of new stable conduits. Computational simulations predict that loss of flt-1 results in ectopic Flk-1 signalling in connecting sprouts post-fusion, causing protrusion of cell processes into avascular gaps and collapse of branches. Thus, defects in stabilization of new vessel connections offset increased sprout initiations and connectivity in flt-1(-/-) vascular networks, with an overall outcome of reduced numbers of new conduits.

CONCLUSIONS

These results show that VEGF-A signalling has stage-specific effects on vascular morphogenesis, and that understanding these effects on dynamic stages of angiogenesis and how they integrate to expand a vessel network may suggest new therapeutic strategies.