Biology subjects

Salamon, N.

Publications and source records attributed to Salamon, N..

4 recordsLinked to original sources

bioRxiv2024

Cellular Modeling of CLN6 with IPSC-derived Neurons and Glia

Neuronal ceroid lipofuscinosis (NCL), type 6 (CLN6) is a neurodegenerative disorder associated with progressive neurodegeneration leading to dementia, seizures, and retinopathy. CLN6 encodes a resident-ER protein involved in trafficking lysosomal proteins to the Golgi. CLN6p deficiency results in lysosomal dysfunction and deposition of storage material comprised of Nile Red+ lipids/proteolipids that include subunit C of the mitochondrial ATP synthase (SUBC). White matter involvement has been recently noted in several CLN6 animal models and several CLN6 subjects had neuroimaging was consistent with leukodystrophy. CLN6 patient-derived induced pluripotent stem cells (IPSCs) were generated from several of these subjects. IPSCs were differentiated into oligodendroglia or neurons using well-established small-molecule protocols. A doxycycline-inducible transgenic system expressing neurogenin-2 (the I3N-system) was also used to generate clonal IPSC-lines (I3N-IPSCs) that could be rapidly differentiated into neurons (I3N-neurons). All CLN6 IPSC-derived neural cell lines developed significant storage material, CLN6-I3N-neuron lines revealed significant Nile Red+ and SUBC+ storage within three and seven days of neuronal induction, respectively. CLN6-I3N-neurons had decreased tripeptidyl peptidase-1 activity, increased Golgi area, along with increased LAMP1+ in cell bodies and neurites. SUBC+ signal co-localized with LAMP1+ signal. Bulk-transcriptomic evaluation of control- and CLN6-I3N-neurons identified >1300 differentially-expressed genes (DEGs) with Gene Ontogeny (GO) Enrichment and Canonical Pathway Analyses having significant changes in lysosomal, axonal, synaptic, and neuronal-apoptotic gene pathways. These findings indicate that CLN6-IPSCs and CLN6-I3N-IPSCs are appropriate cellular models for this disorder. These I3N-neuron models may be particularly valuable for developing therapeutic interventions with high-throughput drug screening assays and/or gene therapy.

neuroscience↗

bioRxiv2023

Grey matter reshaping of language-related regions depends on tumor lateralization

A brain tumor in the left hemisphere can decrease language laterality as assessed with fMRI. However, it remains unclear whether or not this decreased language laterality is associated with a structural reshaping of the grey matter, particularly within the language network. Here, we examine if the disruption of language hubs exclusively affects macrostructural properties of contralateral homologues (as suggested by previous research), or whether it affects both hemispheres. This study uses voxel-based morphometry applied to high-resolution MR T1-weighted MPRAGE images from 31 adult patients left-dominant for language. Eighteen patients had brain tumors in the left hemisphere, and 13 had tumors in the right hemisphere. A cohort of 71 healthy individuals matched on age and sex was used as a baseline. We defined 10 ROIs per hemisphere known to subserve language function. Two separate repeated-measures ANOVAs were conducted with the volume per region as the dependent variables. For the patients, tumor lateralization (right versus left) served as a between-subject factor. The current study demonstrated that the presence of a brain tumor generates a global volumetric change affecting left language regions and their contralateral homologues. These changes are mediated by the lateralization of the lesion. Our findings suggest that compensatory functional mechanisms are supported by the rearrangement of the grey matter, although future longitudinal research should determine the temporal course of such changes.

neuroscience↗

bioRxiv2021

Refining epileptogenic high-frequency oscillations using deep learning: a reverse engineering approach

Intracranially-recorded interictal high-frequency oscillations (HFOs) have been proposed as a promising spatial biomarker of the epileptogenic zone. However, visual verification of HFOs is time-consuming and exhibits poor inter-rater reliability. Furthermore, no method is currently available to distinguish HFOs generated from the epileptogenic zone (epileptogenic HFOs: eHFOs) from those generated from other areas (non-epileptogenic HFOs: non-eHFOs). To address these issues, we constructed a deep learning (DL)-based algorithm using HFO events from chronic intracranial electroencephalogram (iEEG) data via subdural grids from 19 children with medication-resistant neocortical epilepsy to: 1) replicate human expert annotation of artifacts and HFOs with or without spikes, and 2) discover eHFOs by designing a novel weakly supervised model (HFOs from the resected brain regions are initially labeled as eHFOs, and those from the preserved brain regions as non-eHFOs). The "purification power" of DL is then used to automatically relabel the HFOs to distill eHFOs. Using 12,958 annotated HFO events from 19 patients, the model achieved 96.3% accuracy on artifact detection (F1 score = 96.8%) and 86.5% accuracy on classifying HFOs with or without spikes (F1 score = 80.8%) using patient-wise cross-validation. Based on the DL-based algorithm trained from 84,602 HFO events from nine patients who achieved seizure-freedom after resection, the majority of such DL-discovered eHFOs were found to be HFOs with spikes (78.6%, p < 0.001). While the resection ratio of detected HFOs (number of resected HFOs/number of detected HFOs) did not correlate significantly with post-operative seizure freedom (the area under the curve [AUC]=0.76, p=0.06), the resection ratio of eHFOs positively correlated with post-operative seizure freedom (AUC=0.87, p=0.01). We discovered that the eHFOs had a higher signal intensity associated with ripple (80-250 Hz) and fast ripple (250-500 Hz) bands at the HFO onset and with a lower frequency band throughout the event time window (the inverted T-shaped), compared to non-eHFOs. We then designed perturbations on the input of the trained model for non-eHFOs to determine the models decision-making logic. The model probability significantly increased towards eHFOs by the artificial introduction of signals in the inverted T-shaped frequency bands (mean probability increase: 0.285, p < 0.001), and by the artificial insertion of spike-like signals into the time domain (mean probability increase: 0.452, p < 0.001). With this DL-based framework, we reliably replicated HFO classification tasks by human experts. Using a reverse engineering technique, we distinguished eHFOs from others and identified salient features of eHFOs that aligned with current knowledge. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=200 SRC="FIGDIR/small/458385v1_ufig1.gif" ALT="Figure 1"> View larger version (81K): org.highwire.dtl.DTLVardef@39977dorg.highwire.dtl.DTLVardef@108b182org.highwire.dtl.DTLVardef@1bdf864org.highwire.dtl.DTLVardef@fe9e57_HPS_FORMAT_FIGEXP M_FIG C_FIG

neuroscience↗

bioRxiv2018

Loss-of-function in IRF2BPL is associated with neurological phenotypes

The Interferon Regulatory Factor 2 Binding Protein Like (IRF2BPL) gene encodes a member of the IRF2BP family of transcriptional regulators. Currently the biological function of this gene is obscure, and the gene has not been associated with a Mendelian disease. Here we describe seven individuals affected with neurological symptoms who carry damaging heterozygous variants in IRF2BPL. Five cases carrying nonsense variants in IRF2BPL resulting in a premature stop codon display severe neurodevelopmental regression, hypotonia, progressive ataxia, seizures, and a lack of coordination. Two additional individuals, both with missense variants, display global developmental delay and seizures and a relatively milder phenotype than those with nonsense alleles. The bioinformatics signature for IRF2BPL based on population genomics is consistent with a gene that is intolerant to variation. We show that the IRF2BPL ortholog in the fruit fly, called pits (protein interacting with Ttk69 and Sin3A), is broadly expressed including the nervous system. Complete loss of pits is lethal early in development, whereas partial knock-down with RNA interference in neurons leads to neurodegeneration, revealing requirement for this gene in proper neuronal function and maintenance. The nonsense variants in IRF2BPL identified in patients behave as severe loss-of-function alleles in this model organism, while ectopic expression of the missense variants leads to a range of phenotypes. Taken together, IRF2BPL and pits are required in the nervous system in humans and flies, and their loss leads to a range of neurological phenotypes in both species.

genetics↗