Pressure overload-induced TGF-β signaling activates cardiac fibroblasts (CFB) and leads to increased extracellular matrix (ECM) protein synthesis including fibrosis. Excessive ECM accumulation may in turn affect cardiac function contributing to development of heart failure. The aim of this study was to examine the effects of SM16, an orally active small molecular inhibitor of ALK5, on pressure overload-induced cardiac fibrosis. One week after aortic banding (AB), C57Bl/6J mice were randomized to standard chow or chow with SM16. Sham operated animals served as controls. Following 4 weeks AB, mice were characterized by echocardiography and cardiovascular magnetic resonance before sacrifice. SM16 abolished phosphorylation of SMAD2 induced by AB in vivo and by TGF-β in CFB in vitro. Interestingly, Masson Trichrome and Picrosirius Red stained myocardial left ventricular tissue revealed reduced development of fibrosis and collagen cross-linking following AB in the SM16 treated group, which was confirmed by reduced hydroxyproline incorporation. Furthermore, treatment with SM16 attenuated mRNA expression following induction of AB in vivo and stimulation with TGF-β in CFB in vitro of Col1a2, the cross-linking enzyme LOX, and the pro-fibrotic glycoproteins SPARC and osteopontin. Reduced ECM synthesis by CFB and a reduction in myocardial stiffness due to attenuated development of fibrosis and collagen cross-linking might have contributed to the improved diastolic function and cardiac output seen in vivo, in combination with reduced lung weight and ANP expression by treatment with SM16. Despite these beneficial effects on cardiac function and development of heart failure, mice treated with SM16 exhibited increased mortality, increased LV dilatation and inflammatory heart valve lesions that may limit the use of SM16 and possibly also other small molecular inhibitors of ALK5, as future therapeutic drugs.

We hypothesized that cleavage of the extracellular matrix (ECM) proteoglycans versican and aggrecan by ADAMTS (a disintegrin and metalloprotease with thrombospondin motifs) proteases, which contributes to stress-induced ECM-reorganization in atherogenesis and osteoarthritis, also play a role in heart failure development.

Laminar population analysis (LPA) is a method for analysis of electrical data recorded by linear multielectrodes passing through all lamina of cortex. Like principal components analysis (PCA) and independent components analysis (ICA), LPA offers a way to decompose the data into contributions from separate cortical populations. However, instead of using purely mathematical assumptions in the decomposition, LPA is based on physiological constraints, i.e., that the observed LFP (low-frequency part of signal) is driven by action-potential firing as observed in the MUA (multi-unit activity; high-frequency part of the signal). In the presently developed generalized laminar population analysis (gLPA) the set of basis functions accounting for the LFP data is extended compared to the original LPA, thus allowing for a better fit of the model to experimental data. This enhances the risk for overfitting, however, and we therefore tested various versions of gLPA on virtual LFP data in which we knew the ground truth. These synthetic data were generated by biophysical forward-modeling of electrical signals from network activity in the comprehensive, and well-known, thalamocortical network model developed by Traub and coworkers. The results for the Traub model imply that while the laminar components extracted by the original LPA method overall are in fair agreement with the ground-truth laminar components, the results may be improved by use of gLPA method with two (gLPA-2) or even three (gLPA-3) postsynaptic LFP kernels per laminar population.

Invaginations of the cellular membrane called t-tubules are essential for maintaining efficient excitation-contraction coupling in ventricular cardiomyocytes. Disruption of t-tubule structure during heart failure has been linked to dyssynchronous, slowed Ca(2+) release and reduced power of the heartbeat. The underlying mechanism is, however, unknown. We presently investigated whether elevated ventricular wall stress triggers remodelling of t-tubule structure and function.

The sarcoplasmic reticulum Ca(2+)-ATPase (SERCA2) pump is an important component of the Ca(2+)-clock pacemaker mechanism that provides robustness and flexibility to sinus node pacemaking. We have developed transgenic mice with reduced cardiac SERCA2 abundance (Serca2 KO) as a model for investigating SERCA2's role in sinus node pacemaking.

Multielectrode array recordings of extracellular electrical field potentials along the depth axis of the cerebral cortex are gaining popularity as an approach for investigating the activity of cortical neuronal circuits. The low-frequency band of extracellular potential, i.e., the local field potential (LFP), is assumed to reflect synaptic activity and can be used to extract the laminar current source density (CSD) profile. However, physiological interpretation of the CSD profile is uncertain because it does not disambiguate synaptic inputs from passive return currents and does not identify population-specific contributions to the signal. These limitations prevent interpretation of the CSD in terms of synaptic functional connectivity in the columnar microcircuit. Here we present a novel anatomically informed model for decomposing the LFP signal into population-specific contributions and for estimating the corresponding activated synaptic projections. This involves a linear forward model, which predicts the population-specific laminar LFP in response to synaptic inputs applied at different positions along each population and a linear inverse model, which reconstructs laminar profiles of synaptic inputs from laminar LFP data based on the forward model. Assuming spatially smooth synaptic inputs within individual populations, the model decomposes the columnar LFP into population-specific contributions and estimates the corresponding laminar profiles of synaptic input as a function of time. It should be noted that constant synaptic currents at all positions along a neuronal population cannot be reconstructed, as this does not result in a change in extracellular potential. However, constraining the solution using a priori knowledge of the spatial distribution of synaptic connectivity provides the further advantage of estimating the strength of active synaptic projections from the columnar LFP profile thus fully specifying synaptic inputs.

Schizophrenia patients have an increased risk of cardiac dysfunction. A possible factor underlying this comorbidity are the common variants in the large set of genes that have recently been discovered in genome-wide association studies (GWASs) as risk genes of schizophrenia. Many of these genes control the cell electrogenesis and calcium homeostasis. We applied biophysically detailed models of layer V pyramidal cells and sinoatrial node cells to study the contribution of schizophrenia-associated genes on cellular excitability. By including data from functional genomics literature to simulate the effects of common variants of these genes, we showed that variants of voltage-gated Na+ channel or hyperpolarization-activated cation channel-encoding genes cause qualitatively similar effects on layer V pyramidal cell and sinoatrial node cell excitability. By contrast, variants of Ca2+ channel or transporter-encoding genes mostly have opposite effects on cellular excitability in the two cell types. We also show that the variants may crucially affect the propagation of the cardiac action potential in the sinus node. These results may help explain some of the cardiac comorbidity in schizophrenia, and may facilitate generation of effective antipsychotic medications without cardiac side-effects such as arrhythmia.

Educational software (apps) can improve science education by providing an interactive way of learning about complicated topics that are hard to explain with text and static illustrations. However, few educational apps are available for simulation of neural networks. Here, we describe an educational app, Neuronify, allowing the user to easily create and explore neural networks in a plug-and-play simulation environment. The user can pick network elements with adjustable parameters from a menu, i.e., synaptically connected neurons modelled as integrate-and-fire neurons and various stimulators (current sources, spike generators, visual, and touch) and recording devices (voltmeter, spike detector, and loudspeaker). We aim to provide a low entry point to simulation-based neuroscience by allowing students with no programming experience to create and simulate neural networks. To facilitate the use of Neuronify in teaching, a set of premade common network motifs is provided, performing functions such as input summation, gain control by inhibition, and detection of direction of stimulus movement. Neuronify is developed in C++ and QML using the cross-platform application framework Qt and runs on smart phones (Android, iOS) and tablet computers as well personal computers (Windows, Mac, Linux).

Tissue Phase Mapping (TPM) MRI can accurately measure regional myocardial velocities and strain. The lengthy data acquisition, however, renders TPM prone to errors due to variations in physiological parameters, and reduces data yield and experimental throughput. The purpose of the present study is to examine the quality of functional measures (velocity and strain) obtained by highly undersampled TPM data using compressed sensing reconstruction in infarcted and non-infarcted rat hearts.

Current-source density (CSD) analysis is a well-established method for analyzing recorded local field potentials (LFPs), that is, the low-frequency part of extracellular potentials. Standard CSD theory is based on the assumption that all extracellular currents are purely ohmic, and thus neglects the possible impact from ionic diffusion on recorded potentials. However, it has previously been shown that in physiological conditions with large ion-concentration gradients, diffusive currents can evoke slow shifts in extracellular potentials. Using computer simulations, we here show that diffusion-evoked potential shifts can introduce errors in standard CSD analysis, and can lead to prediction of spurious current sources. Further, we here show that the diffusion-evoked prediction errors can be removed by using an improved CSD estimator which accounts for concentration-dependent effects.NEW & NOTEWORTHY Standard CSD analysis does not account for ionic diffusion. Using biophysically realistic computer simulations, we show that unaccounted-for diffusive currents can lead to the prediction of spurious current sources. This finding may be of strong interest for in vivo electrophysiologists doing extracellular recordings in general, and CSD analysis in particular.

Obesity-related diseases (e.g. type 2 diabetes mellitus and cardiovascular disorders) represent an increasing health problem worldwide. NLRP3 inflammasome activation may underlie obesity-induced inflammation and insulin resistance, and NLRP3 deficient mice exposed to high fat diet (HFD) appear to be protected from left ventricle (LV) concentric remodeling. Herein, we investigated if these beneficial effects were associated with alterations in plasma metabolites, using metabolomic and lipidomic analysis, and gut microbiota composition, using 16S rRNA sequencing of cecum content, comparing NLRP3 deficient and wild type (WT) mice on HFD and control diet. Obese NLRP3 deficient mice had lower systemic ceramide levels, potentially resulting attenuating inflammation, altered hepatic expression of fatty acids (FA) with lower mono-saturated FA and higher polyunsaturated FA levels, potentially counteracting development of liver steatosis, downregulated myocardial energy metabolism as assessed by proteomic analyses of LV heart tissue, and different levels of bile acids as compared with WT mice. These changes were accompanied by an altered composition of gut microbiota associated with decreased systemic levels of tri-methylamine-N-oxide and lipopolysaccharide, potentially inducing attenuating systemic inflammation and beneficial effects on lipid metabolism. Our findings support a role of NLRP3 inflammasome in the interface between metabolic and inflammatory stress, involving an altered gut microbiota composition.

Regional heterogeneities in contraction contribute to heart failure with reduced ejection fraction (HFrEF). We aimed to determine whether regional changes in myocardial relaxation similarly contribute to diastolic dysfunction in post-infarction HFrEF, and to elucidate the underlying mechanisms.

Voltage imaging and "all-optical electrophysiology" in human induced pluripotent stem cell (hiPSC)-derived neurons have opened unprecedented opportunities for high-throughput phenotyping of activity in neurons possessing unique genetic backgrounds of individual patients. While prior all-optical electrophysiology studies relied on genetically encoded voltage indicators, here, we demonstrate an alternative protocol using a synthetic voltage sensor and genetically encoded optogenetic actuator that generate robust and reproducible results. We demonstrate the functionality of this method by measuring spontaneous and evoked activity in three independent hiPSC-derived neuronal cell lines with distinct genetic backgrounds.

The electroencephalogram (EEG) is a major tool for non-invasively studying brain function and dysfunction. Comparing experimentally recorded EEGs with neural network models is important to better interpret EEGs in terms of neural mechanisms. Most current neural network models use networks of simple point neurons. They capture important properties of cortical dynamics, and are numerically or analytically tractable. However, point neurons cannot generate an EEG, as EEG generation requires spatially separated transmembrane currents. Here, we explored how to compute an accurate approximation of a rodent's EEG with quantities defined in point-neuron network models. We constructed different approximations (or proxies) of the EEG signal that can be computed from networks of leaky integrate-and-fire (LIF) point neurons, such as firing rates, membrane potentials, and combinations of synaptic currents. We then evaluated how well each proxy reconstructed a ground-truth EEG obtained when the synaptic currents of the LIF model network were fed into a three-dimensional network model of multicompartmental neurons with realistic morphologies. Proxies based on linear combinations of AMPA and GABA currents performed better than proxies based on firing rates or membrane potentials. A new class of proxies, based on an optimized linear combination of time-shifted AMPA and GABA currents, provided the most accurate estimate of the EEG over a wide range of network states. The new linear proxies explained 85-95% of the variance of the ground-truth EEG for a wide range of network configurations including different cell morphologies, distributions of presynaptic inputs, positions of the recording electrode, and spatial extensions of the network. Non-linear EEG proxies using a convolutional neural network (CNN) on synaptic currents increased proxy performance by a further 2-8%. Our proxies can be used to easily calculate a biologically realistic EEG signal directly from point-neuron simulations thus facilitating a quantitative comparison between computational models and experimental EEG recordings.

En route from the retina to the cortex, visual information passes through the dorsolateral geniculate nucleus (dLGN) of the thalamus, where extensive corticothalamic (CT) feedback has been suggested to modulate spatial processing. How this modulation arises from direct excitatory and indirect inhibitory CT feedback pathways remains enigmatic. Here, we show that in awake mice, retinotopically organized cortical feedback sharpens receptive fields (RFs) and increases surround suppression in the dLGN. Guided by a network model indicating that widespread inhibitory CT feedback is necessary to reproduce these effects, we targeted the visual sector of the thalamic reticular nucleus (visTRN) for recordings. We found that visTRN neurons have large RFs, show little surround suppression and exhibit strong feedback-dependent responses to large stimuli. These features make them an ideal candidate for mediating feedback-enhanced surround suppression in the dLGN. We conclude that cortical feedback sculpts spatial integration in the dLGN, likely via recruitment of neurons in the visTRN.

Despite global efforts, it took 7 months between the proclamation of global SARS-CoV-2 pandemic and the first FDA-approved treatment for COVID-19. During this timeframe, clinicians focused their efforts on repurposing drugs, such as hydroxychloroquine (HCQ) or azithromycin (AZM) to treat hospitalized COVID-19 patients. While clinical trials are time-consuming, the exponential increase in hospitalizations compelled the FDA to grant an emergency use authorization for HCQ and AZM as treatment for COVID-19, although there was limited evidence of their combined efficacy and safety. The authorization was revoked 4 months later, giving rise to controversial political and scientific debates illustrating important challenges such as premature authorization of potentially ineffective or unsafe therapeutics, while diverting resources from screening of effective drugs. Here we report on a preclinical drug screening platform, a cardiac microphysiological system (MPS), to rapidly identify clinically relevant cardiac liabilities associated with HCQ and AZM. The cardiac MPS is a microfabricated fluidic system in which cardiomyocytes derived from human induced pluripotent stem cells self-arrange into a uniaxially beating tissue. The drug response was measured using outputs that correlate with clinical measurements such as action potential duration (proxy for clinical QT interval) and drug-biomarker pairing. The cardiac MPS predicted clinical arrhythmias associated with QT prolongation and rhythm instabilities in tissues treated with HCQ. We found no change in QT interval upon acute exposure to AZM, while still observing a significant increase in arrhythmic events. These results suggest that this MPS can not only predict arrhythmias, but it can also identify arrhythmias even when QT prolongation is absent. When exposed to HCQ and AZM polytherapy, this MPS faithfully reflected clinical findings, in that the combination of drugs synergistically increased QT interval when compared to single drug exposure, while not worsening the overall frequency of arrhythmic events. The high content cardiac MPS can rapidly evaluate the cardiac safety of potential therapeutics, ultimately accelerating patients' access to safe and effective treatments.

Simulations of neural activity at different levels of detail are ubiquitous in modern neurosciences, aiding the interpretation of experimental data and underlying neural mechanisms at the level of cells and circuits. Extracellular measurements of brain signals reflecting transmembrane currents throughout the neural tissue remain commonplace. The lower frequencies (≲ 300Hz) of measured signals generally stem from synaptic activity driven by recurrent interactions among neural populations and computational models should also incorporate accurate predictions of such signals. Due to limited computational resources, large-scale neuronal network models (≳ 106 neurons or so) often require reducing the level of biophysical detail and account mainly for times of action potentials ('spikes') or spike rates. Corresponding extracellular signal predictions have thus poorly accounted for their biophysical origin. Here we propose a computational framework for predicting spatiotemporal filter kernels for such extracellular signals stemming from synaptic activity, accounting for the biophysics of neurons, populations, and recurrent connections. Signals are obtained by convolving population spike rates by appropriate kernels for each connection pathway and summing the contributions. Our main results are that kernels derived via linearized synapse and membrane dynamics, distributions of cells, conduction delay, and volume conductor model allow for accurately capturing the spatiotemporal dynamics of ground truth extracellular signals from conductance-based multicompartment neuron networks. One particular observation is that changes in the effective membrane time constants caused by persistent synapse activation must be accounted for. The work also constitutes a major advance in computational efficiency of accurate, biophysics-based signal predictions from large-scale spike and rate-based neuron network models drastically reducing signal prediction times compared to biophysically detailed network models. This work also provides insight into how experimentally recorded low-frequency extracellular signals of neuronal activity may be approximately linearly dependent on spiking activity. A new software tool LFPykernels serves as a reference implementation of the framework.

Familial hypertrophic cardiomyopathy (HCM) is the most common form of inherited cardiac disease. It is characterized by myocardial hypertrophy and diastolic dysfunction, and can lead to severe heart failure, arrhythmias, and sudden cardiac death. Cardiac fibrosis, defined by excessive accumulation of extracellular matrix (ECM) components, is central to the pathophysiology of HCM. The ECM proteoglycan lumican is increased during heart failure and cardiac fibrosis, including HCM, yet its role in HCM remains unknown. We provide an in-depth assessment of lumican in clinical and experimental HCM.

Patients with rheumatoid arthritis (RA) have increased risk of heart failure (HF). The mechanisms and cardiac prerequisites explaining this association remain unresolved. In this study, we sought to determine the potential cardiac impact of an experimental model of RA in mice subjected to HF by constriction of the ascending aorta.

Heart failure is a leading cause of death that develops subsequent to deleterious hypertrophic cardiac remodelling. MAPK pathways play a key role in coordinating the induction of gene expression during hypertrophy. Induction of the immediate early gene (IEG) response including activator protein 1 (AP-1) complex factors is a necessary and early event in this process. How MAPK and IEG expression are coupled during cardiac hypertrophy is not resolved. Here, in vitro, in rodent models and in human samples, we demonstrate that MAPK-stimulated IEG induction depends on the mitogen and stress-activated protein kinase (MSK) and its phosphorylation of histone H3 at serine 28 (pH3S28). pH3S28 in IEG promoters in turn recruits Brg1, a BAF60 ATP-dependent chromatin remodelling complex component, initiating gene expression. Without MSK activity and IEG induction, the hypertrophic response is suppressed. These studies provide new mechanistic insights into the role of MAPK pathways in signalling to the epigenome and regulation of gene expression during cardiac hypertrophy.