Sonlicromanol improves neuronal network dysfunction and transcriptome changes linked to m.3243A>G heteroplasmy in iPSC-derived neurons

Abstract

Summary Mitochondrial encephalomyopathy, lactic acidosis, and stroke-like episodes (MELAS) is often caused by an adenine to guanine variant at m.3243 (m.3243A>G) of the MT-TL1 gene. To understand how this pathogenic variant affects the nervous system, we differentiated human induced pluripotent stem cells (iPSCs) into excitatory neurons with normal (low heteroplasmy) and impaired (high heteroplasmy) mitochondrial function from MELAS patients with the m.3243A>G pathogenic variant. We combined micro-electrode array (MEA) measurements with RNA sequencing (MEA-seq) and found reduced expression of genes involved in mitochondrial respiration and presynaptic function, as well as non-cell autonomous processes in co-cultured astrocytes. Finally, we show that the clinical phase II drug sonlicromanol can improve neuronal network activity when treatment is initiated early in development. This was intricately linked with changes in the neuronal transcriptome. Overall, we provide insight in transcriptomic changes in iPSC-derived neurons with high m.3243A>G heteroplasmy, and show the pathology is partially reversible by sonlicromanol.

Mitochondrial impairment and synaptic dysfunction are associated with neurological defects in iPSCs-derived cortical neurons of MERRF patients

“…The MEA system is a powerful tool used in neuroscience research to investigate the activity of neuronal networks in vitro [ 31 ]. This system can record the electrical activity of multiple neurons simultaneously and in real-time on the surface of the array, which provides researchers a technique for neurotoxicity screening and investigating the underlying mechanisms of a wide range of neurological disorders [ 9 , 32 ] including epilepsy, Alzheimer’s disease, Parkinson’s and mitochondrial diseases. Experimental evidence [ 31 ] indicates that some parameter of MEA analysis can be directly compared to the electroencephalography (EEG) data obtained from the human brain.…”

Breaking the Burst: Unveiling Mechanisms Behind Fragmented Network Bursts in Patient-derived Neurons

“…This activity self-organizes into network bursts (NBs), which are drastic transient increases in spiking frequency occurring synchronously throughout the network. The properties of these NBs are often used for phenotypic characterization as they correlate with specific disease states [2, 3, 4, 5, 6]. Several of these genotype/phenotype correlations have been established by characterizing NBs, providing insight into the pathophysiological mechanisms underlying the neuronal network phenotype [7, 3, 4, 5, 6, 8, 9].…”

An in silico and in vitro human neuronal network model reveals cellular mechanisms beyond NaV1.1 underlying Dravet syndrome

“…Networks cultured on multi-electrode arrays (MEAs) allow for non-invasive recording of neuronal network activity through embedded extracellular electrodes ( Obien et al., 2015 ). In vitro neuronal networks derived from healthy subjects or patients show robust and replicable functional phenotypes ( Mossink et al., 2021 ), and various genotype/phenotype correlations have been established ( Frega et al., 2019 ; Marchetto et al., 2017 ; Klein Gunnewiek et al., 2021 ; Klein Gunnewiek et al., 2020 ). Despite these advances, the identification of cellular and synaptic mechanisms underlying the abnormal network phenotype remains challenging, as these are not trivial to deduce from the neuronal networks’ electrical activity ( Obien et al., 2015 ).…”