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Biology subjects

Parodi, G.

Publications and source records attributed to Parodi, G..

5 recordsLinked to original sources

Compressive stress couples calcium signalling to translational control and proteostasis

Compressive stress is a pervasive feature of solid tumour growth, and mechanical loading is known to activate calcium signalling through the mechanosensitive channel Piezo1. How this signal is converted into changes in cellular biosynthetic capacity, however, remains unclear. Here we show that compressive stress engages a Piezo1-dependent calcium checkpoint that reduces translational output with increasing mechanical load, independently of mTORC1 and the integrated stress response. Compression triggers Piezo1-dependent release of endoplasmic reticulum Ca2, activating eEF2 kinase to slow translational elongation. This same Ca2 signal drives mitochondrial remodelling and reactive oxygen species production, which reinforce translational repression through redox-dependent oxidation of the translation initiation factor eIF2{beta}. In parallel, Ca2-dependent AMPK activation triggers autophagic ribosome degradation, alleviating compression-induced macromolecular crowding and restoring cytoplasmic rheology despite sustained mechanical load. These mechanisms act at distinct levels of translational control: initiation, elongation and ribosome abundance; yet converge to limit biosynthetic output under compression. Consistent with a role for this pathway in tumour adaptation to mechanical constraint, eEF2K expression correlates with poor overall survival in a public PDAC patient dataset. Together, these findings define a mechano-metabolic checkpoint through which cells actively tune biosynthetic capacity to their physical environment.

cell biology↗

Distinct effects of different metabolic stress models on human-derived neuronal networks

Different in vitro models are widely used as experimental platforms to assess neuronal responses to metabolic stress and test potential treatments for patients with ischemic stroke. Results of those studies depend on the stress models used, and the link between cell viability-based readouts and electrophysiological activity remains poorly explored. We investigated the neuronal network activity of human-derived neuronal networks generated from human induced pluripotent stem cells (hiPSCs) under three commonly used metabolic stress models: hypoxia alone, oxygen and glucose deprivation (OGD), and hypoxia combined with different concentrations of glutamate. We aim to clarify the differences between three commonly used in vitro models, including the relation between microscopic and electrophysiological readouts. These conditions produced distinct effects on neuronal network activity. Hypoxia alone induced a progressive decline in activity over time. In contrast, OGD triggered a biphasic response, characterized by an early increase in activity followed by a decline. High concentration glutamate exposure under hypoxia also altered network dynamics, inducing a triphasic pattern consisting of a rapid activity decrease, a transient increase, and a subsequent decline. Across all these pathological conditions, neuronal activity progressively declined and converged toward network failure after prolonged hypoxia. Following reoxygenation, recovery was limited and condition-dependent: hypoxia alone, OGD, and high glutamate conditions showed limited recovery. On the other hand, low glutamate concentration was associated with good recovery. Microscopic assessment revealed that cellular viability was differentially affected across conditions. OGD was associated with the highest levels of cell death, whereas glutamate exposure, particularly at high concentrations, led to a marked reduction in synaptic puncta despite partial preservation of cell viability. These findings highlight that commonly used in vitro ischemia models induce distinct neuronal responses and highlight the importance of integrating electrophysiological and structural analyses to better characterize metabolic stress in human neuronal networks better.

bioengineering↗

Functional human neurospheroids recapitulate key features of cortical complexity

BackgroundThree-dimensional in vitro neuronal cultures have emerged as promising platforms for modelling human brain function and disease under controlled conditions. However, their ability to recapitulate in vivo-like complexity and rich dynamics remains underexplored. In this study, we developed and characterized neurospheroids derived from human induced pluripotent stem cells (hiPSCs) to investigate how key features -- three-dimensionality, cellular heterogeneity, and modular organization -- contribute to replicating brain-like network dynamics. MethodsWe engineered neurospheroids with varying excitatory/inhibitory ratios and assembled them into modular constructs (assembloids), evaluating their electrophysiological activity using high-density micro-electrode arrays. We assessed spontaneous and evoked activity through established metrics of dynamical richness and perturbational complexity. ResultsOur findings show that three-dimensionality and modularity significantly enhance the richness and complexity of network activity, approaching levels observed in vivo, while cellular heterogeneity contribute to functional aspects of the network, such as nuanced activity patterns, dynamical variability, and modular coordination. ConclusionsOur work highlights the critical role of spatial organization in reproducing brain-like activity and provide a foundation for future studies using patient-derived neurospheroids to model disease-specific dynamics.

bioengineering↗

SCN1A-deficient hiPSC-derived excitatory neuronal networks display mutation-specific phenotypes

Dravet syndrome is a severe epileptic encephalopathy, characterized by (febrile) seizures, behavioral problems and developmental delay. 80% of Dravet syndrome patients have a mutation in SCN1A, encoding NaV1.1. Milder clinical phenotypes, such as GEFS+ (generalized epilepsy with febrile seizures plus), can also arise from SCN1A mutations. Predicting the clinical phenotypic outcome based on the type of mutation remains challenging, even when the same mutation is inherited within one family. Both this clinical and genetic heterogeneity add to the difficulties of predicting disease progression and tailored prescription of anti-seizure medication. A better understanding of the neuropathology of different SCN1A mutations, might give insight in differentiating the expected clinical phenotype and best fit treatment choice. Initially it was recognized that loss of Na+ -current in inhibitory neurons specifically resulted in disinhibition and consequently seizure generation. However, the extent to which excitatory neurons contribute to the pathophysiology is currently debated, and might depend on the patient clinical phenotype or the specific mutation in SCN1A. To examine the genotype-phenotype correlations of SCN1A mutations in relation to excitatory neurons, we investigated a panel of patient-derived excitatory neuronal networks differentiated on multi-electrode arrays. We included patients with different clinical phenotypes, harboring different mutations in SCN1A, plus a family where the same mutation leads to both GEFS+ and Dravet syndrome. We hitherto describe a previously unidentified functional excitatory neuronal network phenotype in the context of epilepsy, which corresponded to seizurogenic network prediction patterns elicited by proconvulsive compounds. We find that excitatory neuronal networks were differently affected, dependent on the type of SCN1A mutation, but not on clinical severity. Specifically, pore domain mutations could be distinguished from voltage sensing domain mutations. Furthermore, all patients showed aggravated neuronal network responses upon febrile temperatures. While the basal neuronal network phenotypes could not be distinguished based on patient clinical severity, retrospective drug screening revealed that anti-seizure medication only affected GEFS+ patient-, but not Dravet patient-derived neuronal networks in a patient specific and clinically relevant manner. In conclusion, our results indicate a mutation-specific excitatory neuronal network phenotype, which recapitulates the foremost clinically relevant features, providing future opportunities for precision therapies. HighlightsO_LIHuman stem cell derived excitatory neurons are affected by mutations in SCN1A and display mutation-specific, but not clinical phenotype specific, neuronal network phenotypes C_LIO_LIThe neuronal network phenotype we describe corresponds to seizurogenic network prediction patterns in vitro C_LIO_LIExcitatory neuronal networks respond to Dravet syndrome clinically relevant triggers, like febrile temperatures and Dravet-contraindicated ASM Carbamazepine C_LIO_LIRetrospective drug screening revealed that GEFS+ neuronal networks, but not Dravet neuronal networks respond to ASM in a patient-specific and clinical relevant manner C_LI

neuroscience↗

Human neuronal networks on micro-electrode arrays are a highly robust tool to study disease-specific genotype-phenotype correlations in vitro

Micro-electrode arrays (MEAs) are increasingly used to characterize neuronal network activity of human induced pluripotent stem-cell (hiPSC)-derived neurons. Despite their gain in popularity, MEA recordings from hiPSC-derived neuronal networks are not always used to their full potential in respect to experimental design, execution and data analysis. Therefore, we benchmarked the robustness and sensitivity of MEA-derived neuronal activity patterns derived from ten healthy individual control lines. We provide recommendations on experimental design and analysis to achieve standardization. With such standardization, MEAs can be used as a reliable platform to distinguish (disease-specific) network phenotypes. In conclusion, we show that MEAs are a powerful and robust tool to uncover functional neuronal network phenotypes from hiPSC-derived neuronal networks, and provide an important resource to advance the hiPSC field towards the use of MEAs for disease-phenotyping and drug discovery.

neuroscience↗