A recurring feature of innate immune receptor signaling is the self-assembly of signaling proteins into oligomeric complexes. The Myddosome is an oligomeric complex that is required to transmit inflammatory signals from TLR/IL1Rs and consists of MyD88 and IRAK family kinases. However, the molecular basis for how Myddosome proteins self-assemble and regulate intracellular signaling remains poorly understood. Here, we developed a novel assay to analyze the spatiotemporal dynamics of IL1R and Myddosome signaling in live cells. We found that MyD88 oligomerization is inducible and initially reversible. Moreover, the formation of larger, stable oligomers consisting of more than four MyD88s triggers the sequential recruitment of IRAK4 and IRAK1. Notably, genetic knockout of IRAK4 enhanced MyD88 oligomerization, indicating that IRAK4 controls MyD88 oligomer size and growth. MyD88 oligomer size thus functions as a physical threshold to trigger downstream signaling. These results provide a mechanistic basis for how protein oligomerization might function in cell signaling pathways.

To establish chromosome biorientation, aberrant kinetochore–microtubule interaction must be resolved (error correction) by Aurora B kinase. Aurora B differentially regulates kinetochore attachment to the microtubule plus end and its lateral side (end-on and lateral attachment, respectively). However, it is still unclear how kinetochore–microtubule interactions are exchanged during error correction. Here, we reconstituted the budding yeast kinetochore–microtubule interface in vitro by attaching the Ndc80 complexes to nanobeads. These Ndc80C nanobeads recapitulated in vitro the lateral and end-on attachments of authentic kinetochores on dynamic microtubules loaded with the Dam1 complex. This in vitro assay enabled the direct comparison of lateral and end-on attachment strength and showed that Dam1 phosphorylation by Aurora B makes the end-on attachment weaker than the lateral attachment. Similar reconstitutions with purified kinetochore particles were used for comparison. We suggest the Dam1 phosphorylation weakens interaction with the Ndc80 complex, disrupts the end-on attachment, and promotes the exchange to a new lateral attachment, leading to error correction.

Experience-dependent developmental plasticity involves changes not only to synaptic connections, but to voltage-gated currents as well. Using biophysical models, it is straightforward to predict the effects of this intrinsic plasticity on the firing patterns of individual neurons, but it remains difficult to understand the consequences for sensory coding. We investigated this in the context of the zebra finch auditory cortex, where early exposure to a complex acoustic environment causes increased expression of a low-threshold potassium current. We simulated responses to song using a detailed biophysical model and then characterized encoding properties using generalized linear models. This analysis revealed that this potassium current has strong, nonlinear effects on how the model encodes the song’s temporal structure, and that the sign of these effects depend on the temporal tuning of the synaptic inputs. This nonlinearity gives intrinsic plasticity broad scope as a mechanism for developmental learning in the auditory system.

Experience-dependent developmental plasticity involves changes not only to synaptic connections, but to voltage-gated currents as well. Using biophysical models, it is straightforward to predict the effects of this intrinsic plasticity on the firing patterns of individual neurons, but it remains difficult to understand the consequences for sensory coding. We investigated this in the context of the zebra finch auditory cortex, where early exposure to a complex acoustic environment causes increased expression of a low-threshold potassium current. We simulated responses to song using a detailed biophysical model and then characterized encoding properties using generalized linear models. This analysis revealed that this potassium current has strong, nonlinear effects on how the model encodes the song’s temporal structure, and that the sign of these effects depend on the temporal tuning of the synaptic inputs. This nonlinearity gives intrinsic plasticity broad scope as a mechanism for developmental learning in the auditory system.

Each human red blood cell traverses narrow capillaries about 1000 to 2000 times per day. Cell deformability is essential for smooth transits and this requires tight control of cell volume well below spherical maxima. The pump-leak ion-flux balance controlling red cell volume involves sodium-potassium and calcium pumps and a constellation of passive membrane transporters. PIEZO1, a mechanosensitive ion channel plays a central role, activating transiently during capillary transits. The main question, not accessible to direct experimentation, is what happens to red cell volume and composition in vivo during each capillary transit. We attempted answering this question by applying novel extensions to a tried and tested model of red cell homeostasis that encodes the current knowledge on the subject. The results revealed an unexpected up-down biphasic volume response characterized by a sharp initial surge caused by CaCl₂ influx via PIEZO1, associated with osmotic fluid gain, followed by a slow reversal, a prediction in conflict with prevailing views. The infinitesimal amplitude of the predicted volume changes precludes them playing a significant role in flow dynamics. In the following paper we explore how the biphasic response helps explain the age-related changes red cells experience during their long circulatory lifespan.

Each human red blood cell traverses narrow capillaries about 1000 to 2000 times per day. Cell deformability is essential for smooth transits and this requires tight control of cell volume well below spherical maxima. The pump-leak ion-flux balance controlling red cell volume involves sodium-potassium and calcium pumps and a constellation of passive membrane transporters. PIEZO1, a mechanosensitive ion channel plays a central role, activating transiently during capillary transits. The main question, not accessible to direct experimentation, is what happens to red cell volume and composition in vivo during each capillary transit. We attempted answering this question by applying novel extensions to a tried and tested model of red cell homeostasis that encodes the current knowledge on the subject. The results revealed an unexpected up-down biphasic volume response characterized by a sharp initial surge caused by CaCl₂ influx via PIEZO1, associated with osmotic fluid gain, followed by a slow reversal, a prediction in conflict with prevailing views. The infinitesimal amplitude of the predicted volume changes precludes them playing a significant role in flow dynamics. In the following paper we explore how the biphasic response helps explain the age-related changes red cells experience during their long circulatory lifespan.

The kinesin-3 motor KIF1A functions in neurons, where its fast and superprocessive motility facilitates long-distance transport, but little is known about its force-generating properties. Using optical tweezers, we demonstrate that KIF1A stalls at an opposing load of ~3 pN but more frequently detaches at lower forces. KIF1A rapidly reattaches to the microtubule to resume motion due to its class-specific K-loop, resulting in a unique clustering of force generation events. To test the importance of neck linker docking in KIF1A force generation, we introduced mutations linked to human neurodevelopmental disorders. Molecular dynamics simulations predict that V8M and Y89D mutations impair neck linker docking. Indeed, both mutations dramatically reduce the force generation of KIF1A but not the motor’s ability to rapidly reattach to the microtubule. Although both mutations relieve autoinhibition of the full-length motor, the mutant motors display decreased velocities, run lengths, and landing rates and delayed cargo transport in cells. These results advance our understanding of how mutations in KIF1A can manifest in disease.

In budding yeast, the transcription factors SBF and MBF activate a large program of gene expression in late G1 phase that underlies commitment to cell division, termed Start. SBF/MBF are limiting with respect to target promoters in small G1 phase cells and accumulate as cells grow, raising the questions of how SBF/MBF are dynamically distributed across the G1/S regulon and how this impacts the Start transition. Super-resolution Photo-Activatable Localization Microscopy (PALM) mapping of the static positions of SBF/MBF subunits in fixed cells revealed each transcription factor was organized into discrete clusters containing approximately eight copies regardless of cell size and that the total number of clusters increased as cells grew through G1 phase. Stochastic modeling using reasonable biophysical parameters recapitulated growth-dependent SBF/MBF clustering and predicted TF dynamics that were confirmed in live cell PALM experiments. This spatio-temporal organization of SBF/MBF may help coordinate activation of G1/S regulon and the Start transition.

A number of cardiac disorders originate from genetic mutations affecting the function of ion channels populating the membrane of cardiomyocytes. One example is short QT syndrome, associated with increased risk of arrhythmias and sudden cardiac death. Cardiomyocytes derived from human induced pluripotent stem cells (hiPSC-CMs) provide a promising platform for testing potential pharmacological treatments for such disorders, as human cardiomyocytes exhibiting specific mutations can be generated and exposed to drugs in vitro. However, the electrophysiological properties of hiPSC-CMs differ significantly from those of adult native cardiomyocytes. Therefore, drug effects observed for hiPSC-CMs could possibly be very different from corresponding drug effects for adult cells in vivo. In this study, we apply a computational framework for translating drug effects observed for hiPSC-CMs derived from a short QT patient to drug effects for adult short QT cardiomyocytes. For one of the considered drugs, the effect on adult QT intervals has been measured and these measurements turn out to be in good agreement with the response estimated by the computational procedure. Thus, the computational framework shows promise for being a useful tool for predicting adult drug responses from measurements of hiPSC-CMs, allowing earlier identification of compounds to accurately treat cardiac diseases.

A number of cardiac disorders originate from genetic mutations affecting the function of ion channels populating the membrane of cardiomyocytes. One example is short QT syndrome, associated with increased risk of arrhythmias and sudden cardiac death. Cardiomyocytes derived from human induced pluripotent stem cells (hiPSC-CMs) provide a promising platform for testing potential pharmacological treatments for such disorders, as human cardiomyocytes exhibiting specific mutations can be generated and exposed to drugs in vitro. However, the electrophysiological properties of hiPSC-CMs differ significantly from those of adult native cardiomyocytes. Therefore, drug effects observed for hiPSC-CMs could possibly be very different from corresponding drug effects for adult cells in vivo. In this study, we apply a computational framework for translating drug effects observed for hiPSC-CMs derived from a short QT patient to drug effects for adult short QT cardiomyocytes. For one of the considered drugs, the effect on adult QT intervals has been measured and these measurements turn out to be in good agreement with the response estimated by the computational procedure. Thus, the computational framework shows promise for being a useful tool for predicting adult drug responses from measurements of hiPSC-CMs, allowing earlier identification of compounds to accurately treat cardiac diseases.

Cardiac fibrillation is a medical condition where normal heart function is compromised as electrical activity becomes disordered. How fibrillation arises spontaneously is not fully understood. It is generally thought to be triggered by premature depolarization of the cardiac action potential in one or more cells. Those premature beats, known as after-depolarizations, subsequently initiate a self-sustaining rotor in the otherwise normal heart tissue. In this study, we propose an alternative mechanism whereby arrhythmias are initiated by cardiac cells that fail to repolarize of their own accord but still operate normally when embedded in functional heart tissue. We find that such cells can act as focal ectopic sources under appropriate conditions of inter-cellular coupling. Moreover, those cells are more prone to initiating arrhythmia when they are located on natural tissue boundaries. This may explain why atrial fibrillation typically originates from the site where the pulmonary vein attaches to the wall of the heart.

Cardiac fibrillation is a medical condition where normal heart function is compromised as electrical activity becomes disordered. How fibrillation arises spontaneously is not fully understood. It is generally thought to be triggered by premature depolarization of the cardiac action potential in one or more cells. Those premature beats, known as after-depolarizations, subsequently initiate a self-sustaining rotor in the otherwise normal heart tissue. In this study, we propose an alternative mechanism whereby arrhythmias are initiated by cardiac cells that fail to repolarize of their own accord but still operate normally when embedded in functional heart tissue. We find that such cells can act as focal ectopic sources under appropriate conditions of inter-cellular coupling. Moreover, those cells are more prone to initiating arrhythmia when they are located on natural tissue boundaries. This may explain why atrial fibrillation typically originates from the site where the pulmonary vein attaches to the wall of the heart.

Axonal fibre bundles that connect distant cortical areas contain millions of densely packed axons. When synchronous spike volleys travel through such fibre bundles, the extracellular potential within the bundles is perturbed. We use computer simulations to examine the magnitude and shape of this perturbation, and demonstrate that it is sufficiently strong to affect axonal transmission speeds. Since most spikes within a spike volley are positioned in an area where the extracellular potential is negative (relative to a distant reference), the resulting depolarisation of the axonal membranes accelerates the spike volley on average. This finding is in contrast to previous studies of ephaptic coupling effects between axons, where ephaptic coupling was found to slow down spike propagation. Our finding has consequences for information transmission and synchronisation between cortical areas.

Axonal fibre bundles that connect distant cortical areas contain millions of densely packed axons. When synchronous spike volleys travel through such fibre bundles, the extracellular potential within the bundles is perturbed. We use computer simulations to examine the magnitude and shape of this perturbation, and demonstrate that it is sufficiently strong to affect axonal transmission speeds. Since most spikes within a spike volley are positioned in an area where the extracellular potential is negative (relative to a distant reference), the resulting depolarisation of the axonal membranes accelerates the spike volley on average. This finding is in contrast to previous studies of ephaptic coupling effects between axons, where ephaptic coupling was found to slow down spike propagation. Our finding has consequences for information transmission and synchronisation between cortical areas.

Background and purpose

Establishment of drug therapy to prevent rupture of unruptured intracranial aneurysms (IAs) is needed. Previous human and animal studies have gradually clarified candidate drugs for preventive treatment of IA rupture. However, because most of these candidates belong to classes of drugs frequently co-administered to prevent cardiovascular diseases, epidemiological studies evaluating these drugs simultaneously should be performed. Furthermore, because drugs included in the same class may have different effects in terms of disease prevention, drug-by-drug assessments are important for planning intervention trials.

Background and purpose

Establishment of drug therapy to prevent rupture of unruptured intracranial aneurysms (IAs) is needed. Previous human and animal studies have gradually clarified candidate drugs for preventive treatment of IA rupture. However, because most of these candidates belong to classes of drugs frequently co-administered to prevent cardiovascular diseases, epidemiological studies evaluating these drugs simultaneously should be performed. Furthermore, because drugs included in the same class may have different effects in terms of disease prevention, drug-by-drug assessments are important for planning intervention trials.

Purpose

We developed a compact and lightweight time-resolved mirrorless scintillation detector (TRMLSD) employing image processing techniques and a convolutional neural network (CNN) for high-resolution two-dimensional (2D) dosimetry.

Purpose

We developed a compact and lightweight time-resolved mirrorless scintillation detector (TRMLSD) employing image processing techniques and a convolutional neural network (CNN) for high-resolution two-dimensional (2D) dosimetry.

Genome-scale reconstructions of metabolism appeared shortly after the first genome sequences became available. Constraint-based models are widely used to compute steady state properties of such reconstructions, but the attainment of dynamic models has remained elusive. We thus developed the MASSpy software package, a framework that enables the construction, simulation, and visualization of dynamic metabolic models. MASSpy is based on the mass action kinetics for each elementary step in an enzymatic reaction mechanism. MASSpy seamlessly unites existing software packages within its framework to provide the user with various modeling tools in one package. MASSpy integrates community standards to facilitate the exchange of models, giving modelers the freedom to use the software for different aspects of their own modeling workflows. Furthermore, MASSpy contains methods for generating and simulating ensembles of models, and for explicitly accounting for biological uncertainty. MASSpy has already demonstrated success in a classroom setting. We anticipate that the suite of modeling tools incorporated into MASSpy will enhance the ability of the modeling community to construct and interrogate complex dynamic models of metabolism.

Genome-scale reconstructions of metabolism appeared shortly after the first genome sequences became available. Constraint-based models are widely used to compute steady state properties of such reconstructions, but the attainment of dynamic models has remained elusive. We thus developed the MASSpy software package, a framework that enables the construction, simulation, and visualization of dynamic metabolic models. MASSpy is based on the mass action kinetics for each elementary step in an enzymatic reaction mechanism. MASSpy seamlessly unites existing software packages within its framework to provide the user with various modeling tools in one package. MASSpy integrates community standards to facilitate the exchange of models, giving modelers the freedom to use the software for different aspects of their own modeling workflows. Furthermore, MASSpy contains methods for generating and simulating ensembles of models, and for explicitly accounting for biological uncertainty. MASSpy has already demonstrated success in a classroom setting. We anticipate that the suite of modeling tools incorporated into MASSpy will enhance the ability of the modeling community to construct and interrogate complex dynamic models of metabolism.

Optogenetics is a novel technique involving the targeted delivery of light-sensitive ion channels like Channelrhodopsin-2 (ChR2) to cells. Recently, this technique has been expanded to non-neuronal cell types, e.g., astrocytes. Optogenetic control of astrocytes in vivo can aid in further understanding the intricacies of their debated roles in the brain. Here, using a mathematical model, we evaluate the effect of short and long-term light stimulation of ChR2-expressing astrocytes on their Ca²⁺ spiking activity and basal level. We further investigate how ChR2 gating dynamics, buffering, and coupling coefficient of Ca²⁺ influence astrocytic activity in a single cell and a network. Results of our study can serve as a tool for experimental design and engineering of new application-oriented optogenetic constructs targeting astrocytes.

Optogenetics is a novel technique involving the targeted delivery of light-sensitive ion channels like Channelrhodopsin-2 (ChR2) to cells. Recently, this technique has been expanded to non-neuronal cell types, e.g., astrocytes. Optogenetic control of astrocytes in vivo can aid in further understanding the intricacies of their debated roles in the brain. Here, using a mathematical model, we evaluate the effect of short and long-term light stimulation of ChR2-expressing astrocytes on their Ca²⁺ spiking activity and basal level. We further investigate how ChR2 gating dynamics, buffering, and coupling coefficient of Ca²⁺ influence astrocytic activity in a single cell and a network. Results of our study can serve as a tool for experimental design and engineering of new application-oriented optogenetic constructs targeting astrocytes.

Nucleation of microtubules (MTs) is essential for cellular activities, but its mechanism is unknown because of the difficulty involved in capturing rare stochastic events in the early stage of polymerization. Here, combining rapid flush negative stain electron microscopy (EM) and kinetic analysis, we demonstrate that the formation of straight oligomers of critical size is essential for nucleation. Both GDP and GTP tubulin form single-stranded oligomers with a broad range of curvatures, but upon nucleation, the curvature distribution of GTP oligomers is shifted to produce a minor population of straight oligomers. With tubulin having the Y222F mutation in the β subunit, the proportion of straight oligomers increases and nucleation accelerates. Our results support a model in which GTP binding generates a minor population of straight oligomers compatible with lateral association and further growth to MTs. This study suggests that cellular factors involved in nucleation promote it via stabilization of straight oligomers.

Hidden hearing loss (HHL) is an auditory disorder caused by noise exposure, aging or peripheral neuropathy. It is named ‘hidden’ because it does not affect hearing thresholds, but deficits are observed in sound-evoked auditory nerve activity. Studies on animal models suggest two possible pathogenic mechanisms for HHL: (1) loss of synapses between inner hair cells and auditory nerve fibers, and (2) disruption of auditory-nerve myelin. In this study, we constructed a computational model of sound-evoked auditory nerve fiber activity to understand how each of these mechanisms affects nerve compound action potentials. We show that disruption of auditory-nerve myelin desynchronizes sound-evoked auditory neuron spiking, decreasing the amplitude and increasing the latency of the compound action potential. In addition, elongation of the initial axon segment may cause spike generation failure leading to decreased spiking probability. In contrast, synapse loss only decreases the probability of firing, thus reducing the compound action potential amplitude without disturbing its latency. This model, which accurately represents in vivo findings on HHL in the first stages of auditory processing, can be useful to analyze the consequences of synaptopathy and myelinopathy on downstream sound processing and perceptual deficits.

Hidden hearing loss (HHL) is an auditory disorder caused by noise exposure, aging or peripheral neuropathy. It is named ‘hidden’ because it does not affect hearing thresholds, but deficits are observed in sound-evoked auditory nerve activity. Studies on animal models suggest two possible pathogenic mechanisms for HHL: (1) loss of synapses between inner hair cells and auditory nerve fibers, and (2) disruption of auditory-nerve myelin. In this study, we constructed a computational model of sound-evoked auditory nerve fiber activity to understand how each of these mechanisms affects nerve compound action potentials. We show that disruption of auditory-nerve myelin desynchronizes sound-evoked auditory neuron spiking, decreasing the amplitude and increasing the latency of the compound action potential. In addition, elongation of the initial axon segment may cause spike generation failure leading to decreased spiking probability. In contrast, synapse loss only decreases the probability of firing, thus reducing the compound action potential amplitude without disturbing its latency. This model, which accurately represents in vivo findings on HHL in the first stages of auditory processing, can be useful to analyze the consequences of synaptopathy and myelinopathy on downstream sound processing and perceptual deficits.

At cell division, the mammalian kinetochore binds many spindle microtubules that make up the kinetochore-fiber. To segregate chromosomes, the kinetochore-fiber must be dynamic and generate and respond to force. Yet, how it remodels under force remains poorly understood. Kinetochore-fibers cannot be reconstituted in vitro, and exerting controlled forces in vivo remains challenging. Here, we use microneedles to pull on mammalian kinetochore-fibers and probe how sustained force regulates their dynamics and structure. We show that force lengthens kinetochore-fibers by persistently favoring plus-end polymerization, not by increasing polymerization rate. We demonstrate that force suppresses depolymerization at both plus and minus ends, rather than sliding microtubules within the kinetochore-fiber. Finally, we observe that kinetochore-fibers break but do not detach from kinetochores or poles. Together, this work suggests an engineering principle for spindle structural homeostasis: different physical mechanisms of local force dissipation by the k-fiber limit force transmission to preserve robust spindle structure. These findings may inform how other dynamic, force-generating cellular machines achieve mechanical robustness.

The Golgi cells are the main inhibitory interneurons of the cerebellum granular layer and play a fundamental role in controlling cerebellar processing. However, it was unclear how spikes are processed in the dendrites by specific sets of ionic channels and how they might contribute to integrate synaptic inputs and plasticity. Here we have developed detailed multicompartmental models of Golgi cells that faithfully reproduced a large set of experimental findings and revealed the nature of signal interchange between dendrites and axo-somatic compartments. A main prediction of the models is that synaptic activation of apical dendrites can effectively trigger spike generation in the axonal initial segment followed by rapid spike backpropagation into basal dendrites. Here, incoming mossy fiber inputs and backpropagating spikes regulate the voltage-dependent unblock of NMDA channels and the induction of spike timing-dependent plasticity (STDP). STDP, which was predicted by theory, may therefore be controlled by contextual information provided by parallel fibers and integrated in apical dendrites, supporting the view that spike timing is fundamental to control synaptic plasticity at the cerebellar input stage.

The Golgi cells are the main inhibitory interneurons of the cerebellum granular layer and play a fundamental role in controlling cerebellar processing. However, it was unclear how spikes are processed in the dendrites by specific sets of ionic channels and how they might contribute to integrate synaptic inputs and plasticity. Here we have developed detailed multicompartmental models of Golgi cells that faithfully reproduced a large set of experimental findings and revealed the nature of signal interchange between dendrites and axo-somatic compartments. A main prediction of the models is that synaptic activation of apical dendrites can effectively trigger spike generation in the axonal initial segment followed by rapid spike backpropagation into basal dendrites. Here, incoming mossy fiber inputs and backpropagating spikes regulate the voltage-dependent unblock of NMDA channels and the induction of spike timing-dependent plasticity (STDP). STDP, which was predicted by theory, may therefore be controlled by contextual information provided by parallel fibers and integrated in apical dendrites, supporting the view that spike timing is fundamental to control synaptic plasticity at the cerebellar input stage.

Applying multiple antibiotics simultaneously can boost treatment effectiveness and aid against rampant antibiotic resistance. Because of the impractically large number of possible combinations of drugs, those that are effective are found by trial and error. Hence, a predictive theory to characterize drug cocktails would be of enormous value. Recently identified phenomenological laws ease the construction of predictive models of bacterial growth. Here, we build a model of the effects of antibiotic combinations on bacteria and show that it makes reliable predictions for experimental outcomes. Our model takes responses to individual drugs as inputs and predicts their combined effect. This output determines the type of drug interaction, which can range from antagonistic (the combined effect is weaker) to synergistic (the combined effect is stronger). We broaden the model by including the direct physical interaction on the target, drug resistance genes that alter the drug interaction, and drugs that mimic poor growth environments by choking the supply of growth-essential components, which we test experimentally. Our results prove how biophysical models that use empirical laws can predict responses to drug combinations. Importantly, such models can successfully predict mechanisms underlying interactions of drug combinations. This approach is extensible to combinations of more than two drugs and diverse biological systems.

Applying multiple antibiotics simultaneously can boost treatment effectiveness and aid against rampant antibiotic resistance. Because of the impractically large number of possible combinations of drugs, those that are effective are found by trial and error. Hence, a predictive theory to characterize drug cocktails would be of enormous value. Recently identified phenomenological laws ease the construction of predictive models of bacterial growth. Here, we build a model of the effects of antibiotic combinations on bacteria and show that it makes reliable predictions for experimental outcomes. Our model takes responses to individual drugs as inputs and predicts their combined effect. This output determines the type of drug interaction, which can range from antagonistic (the combined effect is weaker) to synergistic (the combined effect is stronger). We broaden the model by including the direct physical interaction on the target, drug resistance genes that alter the drug interaction, and drugs that mimic poor growth environments by choking the supply of growth-essential components, which we test experimentally. Our results prove how biophysical models that use empirical laws can predict responses to drug combinations. Importantly, such models can successfully predict mechanisms underlying interactions of drug combinations. This approach is extensible to combinations of more than two drugs and diverse biological systems.

Liquid–liquid phase separation (LLPS) compartmentalizes and concentrates biomacromolecules into distinct condensates. Liquid-like condensates can transition into gel and solid states, which are essential for fulfilling their different functions. LLPS plays important roles in multiple steps of autophagy, mediating the assembly of autophagosome formation sites, acting as an unconventional modulator of TORC1-mediated autophagy regulation, and triaging protein cargos for degradation. Gel-like, but not solid, protein condensates can trigger formation of surrounding autophagosomal membranes. Stress and pathological conditions cause aberrant phase separation and transition of condensates, which can evade surveillance by the autophagy machinery. Understanding the mechanisms underlying phase separation and transition will provide potential therapeutic targets for protein aggregation diseases.

Dravet syndrome (DS) is a neurological disorder affecting approximately 1:15,700 Americans that causes generalized epilepsy and associated complications. While most patients have a mutation in the SCN1A gene that encodes the Na_v1.1 voltage-gated sodium channel, about 20% do not have a mutation identified by exome or targeted sequencing. Recently, we identified variants in intron 20, a noncoding region of SCN1A, in some DS patients. We hypothesized that these variants alter SCN1A transcript processing, decrease Na_v1.1 function, and lead to DS pathophysiology via inclusion of exon 20N, a “poison” exon that leads to a premature stop codon. In this study, we generated a knock-in mouse model, Scn1a+/KI, of one of these variants, which resides in a genomic region that is extremely conserved across vertebrate species. We found that Scn1a+/KI mice have reduced levels of Scn1a transcript and Na_v1.1 protein and develop DS-related phenotypes. We find that transcripts from brains of Scn1a+/KI mice show elevated rates of Scn1a exon 20N inclusion. We also explored the relationship between exon 20N inclusion and Scn1a expression during development, and found that, during brain development when Scn1a expression is low, exon 20N inclusion is high; postnatally, as Scn1a expression increases, there is a corresponding decrease in exon 20N usage. Expression of another voltage-gated sodium channel transcript, Scn8a (Na_v1.6), was similarly regulated. Together, these data demonstrate that poison exon inclusion is a conserved mechanism to control sodium channel expression in the brain, and that an intronic mutation that disrupts the normal developmental regulation of poison exon inclusion leads to reduced Na_v1.1 and generalized epilepsy.

Dravet syndrome (DS) is a neurological disorder affecting approximately 1:15,700 Americans that causes generalized epilepsy and associated complications. While most patients have a mutation in the SCN1A gene that encodes the Na_v1.1 voltage-gated sodium channel, about 20% do not have a mutation identified by exome or targeted sequencing. Recently, we identified variants in intron 20, a noncoding region of SCN1A, in some DS patients. We hypothesized that these variants alter SCN1A transcript processing, decrease Na_v1.1 function, and lead to DS pathophysiology via inclusion of exon 20N, a “poison” exon that leads to a premature stop codon. In this study, we generated a knock-in mouse model, Scn1a+/KI, of one of these variants, which resides in a genomic region that is extremely conserved across vertebrate species. We found that Scn1a+/KI mice have reduced levels of Scn1a transcript and Na_v1.1 protein and develop DS-related phenotypes. We find that transcripts from brains of Scn1a+/KI mice show elevated rates of Scn1a exon 20N inclusion. We also explored the relationship between exon 20N inclusion and Scn1a expression during development, and found that, during brain development when Scn1a expression is low, exon 20N inclusion is high; postnatally, as Scn1a expression increases, there is a corresponding decrease in exon 20N usage. Expression of another voltage-gated sodium channel transcript, Scn8a (Na_v1.6), was similarly regulated. Together, these data demonstrate that poison exon inclusion is a conserved mechanism to control sodium channel expression in the brain, and that an intronic mutation that disrupts the normal developmental regulation of poison exon inclusion leads to reduced Na_v1.1 and generalized epilepsy.

Aim

Hypertension is the leading cause of heart failure (HF) in sub-Saharan Africa. Preventive public health approach to reduce the scourge of HF must seek to understand the risk factors of HF in at-risk populations. The aim of this study was to characterize the risk factors of HF among patients with hypertension attending a cardiology clinic.

Aim

Hypertension is the leading cause of heart failure (HF) in sub-Saharan Africa. Preventive public health approach to reduce the scourge of HF must seek to understand the risk factors of HF in at-risk populations. The aim of this study was to characterize the risk factors of HF among patients with hypertension attending a cardiology clinic.

The fundamental way neurons communicate is by activating or inhibiting each other via synapses. The balance between the two is crucial for the optimal functioning of a nervous system. However, whole-brain synaptic polarity information is unavailable for any species and experimental validation is challenging. The roundworm Caenorhabditis elegans possesses a fully mapped connectome with an emerging gene expression profile of its 302 neurons. Based on the consideration that the polarity of a synapse can be determined by the neurotransmitter(s) expressed in the presynaptic neuron and the receptors expressed in the postsynaptic neuron, we conceptualized and created a tool that predicts synaptic polarities based on connectivity and gene expression information. Using currently available datasets we propose for the first time that the ratio of excitatory and inhibitory synapses in a partial connectome of C. elegans is around 4 to 1 which is in line with the balance observed in many natural systems. Our method opens a way to include spatial and temporal dynamics of synaptic polarity that would add a new dimension of plasticity in the excitatory:inhibitory balance. Our tool is freely available to be used on any network accompanied by any expression atlas.

The fundamental way neurons communicate is by activating or inhibiting each other via synapses. The balance between the two is crucial for the optimal functioning of a nervous system. However, whole-brain synaptic polarity information is unavailable for any species and experimental validation is challenging. The roundworm Caenorhabditis elegans possesses a fully mapped connectome with an emerging gene expression profile of its 302 neurons. Based on the consideration that the polarity of a synapse can be determined by the neurotransmitter(s) expressed in the presynaptic neuron and the receptors expressed in the postsynaptic neuron, we conceptualized and created a tool that predicts synaptic polarities based on connectivity and gene expression information. Using currently available datasets we propose for the first time that the ratio of excitatory and inhibitory synapses in a partial connectome of C. elegans is around 4 to 1 which is in line with the balance observed in many natural systems. Our method opens a way to include spatial and temporal dynamics of synaptic polarity that would add a new dimension of plasticity in the excitatory:inhibitory balance. Our tool is freely available to be used on any network accompanied by any expression atlas.

Cells must maintain low levels of protein unfolding to avoid deleterious outcomes such as protein aggregation, oxidative damage, or premature degradation. The proteins responsible for this, called chaperones, come in two main varieties: ATP-consuming “foldases” that help clients fold and ATP-independent “holdases” that hold unfolded proteins until a foldase arrives. While foldases are necessary for folding, they are expensive to have in high quantities. Given that chaperones are abundant and costly, cells are under strong selective pressure to find economical combinations of foldases and holdases for maintaining low levels of unfolded protein. Yet, it is presently unclear what the ideal combination is and how it varies with growth conditions. By examining a toy model of chaperone function and minimizing the total cost of folding at different rates of protein synthesis, we find that while foldases are necessary at fast growth, holdases become increasingly effective at slow growth. Unexpectedly, total chaperone requirements were predicted to increase as synthesis slows, consistent with observations across age in worms, as well as across species with varying metabolic rates. This work thus provides a general framework for understanding the chaperone requirements of a proteome in terms of an energy minimization principle.

Cells must maintain low levels of protein unfolding to avoid deleterious outcomes such as protein aggregation, oxidative damage, or premature degradation. The proteins responsible for this, called chaperones, come in two main varieties: ATP-consuming “foldases” that help clients fold and ATP-independent “holdases” that hold unfolded proteins until a foldase arrives. While foldases are necessary for folding, they are expensive to have in high quantities. Given that chaperones are abundant and costly, cells are under strong selective pressure to find economical combinations of foldases and holdases for maintaining low levels of unfolded protein. Yet, it is presently unclear what the ideal combination is and how it varies with growth conditions. By examining a toy model of chaperone function and minimizing the total cost of folding at different rates of protein synthesis, we find that while foldases are necessary at fast growth, holdases become increasingly effective at slow growth. Unexpectedly, total chaperone requirements were predicted to increase as synthesis slows, consistent with observations across age in worms, as well as across species with varying metabolic rates. This work thus provides a general framework for understanding the chaperone requirements of a proteome in terms of an energy minimization principle.

Confluent motion of cells in tissues plays a crucial role in wound healing, tissue repair, development, morphogenesis and in numerous pathological processes such as tumor invasion and metastatic cancer. For such complex processes, controlled experiments help clarifying the roles of chemical, mechanical and biological cues. Among them, spreading of cellular tissues on an empty space and antagonistic migration assays between cancerous and normal cells are quite revealing. The interfaces between confluent cellular aggregates uncover properties thereof when a combination of modeling, numerical simulation and data analysis is used. Here we have modified an active vertex model with a dynamics that includes inertia, friction and active forces that tend to align cells based on interaction with its immediate neighborhood. Selecting appropriately junction tensions among cells and using the SAMoS software, we have succeed in simulating assays of cellular tissue spreading on an empty space and the invasion of healthy tissue by cancerous one. We have introduced topological data analysis to characterize, track and compare in an automatic manner the interfaces of the tissue both in numerical simulations and from experimental data of normal and Ras modified precancerous Human Embryonic Kidney cells. We find good agreement when normal cells are solidlike and modified cells are liquidlike according to their shape parameters. In addition, cell variability means that a fraction of randomly distributed cells favor mixing, the others segregation. Topological data analysis techniques are scalable and could be used in studies involving large amounts of data. Besides applications to wound healing and metastatic cancer, these studies are relevant in ascertaining how the biophysical features of materials may affect tissue and organ regeneration.

Confluent motion of cells in tissues plays a crucial role in wound healing, tissue repair, development, morphogenesis and in numerous pathological processes such as tumor invasion and metastatic cancer. For such complex processes, controlled experiments help clarifying the roles of chemical, mechanical and biological cues. Among them, spreading of cellular tissues on an empty space and antagonistic migration assays between cancerous and normal cells are quite revealing. The interfaces between confluent cellular aggregates uncover properties thereof when a combination of modeling, numerical simulation and data analysis is used. Here we have modified an active vertex model with a dynamics that includes inertia, friction and active forces that tend to align cells based on interaction with its immediate neighborhood. Selecting appropriately junction tensions among cells and using the SAMoS software, we have succeed in simulating assays of cellular tissue spreading on an empty space and the invasion of healthy tissue by cancerous one. We have introduced topological data analysis to characterize, track and compare in an automatic manner the interfaces of the tissue both in numerical simulations and from experimental data of normal and Ras modified precancerous Human Embryonic Kidney cells. We find good agreement when normal cells are solidlike and modified cells are liquidlike according to their shape parameters. In addition, cell variability means that a fraction of randomly distributed cells favor mixing, the others segregation. Topological data analysis techniques are scalable and could be used in studies involving large amounts of data. Besides applications to wound healing and metastatic cancer, these studies are relevant in ascertaining how the biophysical features of materials may affect tissue and organ regeneration.