Biology subjects

Toker, L.

Publications and source records attributed to Toker, L..

7 recordsLinked to original sources

bioRxiv2024

Mitochondrial complex I deficiency occurs in skeletal muscle of a subgroup of individuals with Parkinson's disease

Widespread neuronal complex I (CI) deficiency was recently reported to be a characteristic in a subgroup of individuals with idiopathic Parkinsons disease (PD). Here, we sought to determine whether a CI deficient subgroup could be discerned using clinically accessible muscle biopsy. Vastus lateralis needle biopsies were collected from 83 individuals with PD and 29 neurologically healthy controls and analyzed by immunohistochemistry for complexes I and IV, cytochrome c oxidase/succinate dehydrogenase (COX/SDH) histochemistry, and spectrophotometric activity assays of complexes I-IV. Mitochondrial DNA (mtDNA) copy number, deletions, and point variation were analyzed in single muscle fibers and bulk biopsy samples. PD muscle exhibited reduced CI activity at the group level, with 9% of cases falling below two standard deviations of the control group. This deficiency was not associated with mtDNA abnormalities. Our findings support the existence of a PD subpopulation characterized by CI pathology and suggest that stratification by extra-neural mitochondrial dysfunction may be informative for selecting individuals for clinical trials.

neuroscience↗

bioRxiv2023

Mitochondrial complex I deficiency stratifies idiopathic Parkinson's disease

Idiopathic Parkinsons disease (iPD) is believed to have a heterogeneous pathophysiology, but molecular disease subtypes have not been identified. Here, we show that iPD can be stratified according to the severity of neuronal respiratory complex I (CI) deficiency, and identify two emerging disease subtypes with distinct molecular and clinical profiles. The CI deficient (CI-PD) subtype accounts for approximately a fourth of all cases, and is characterized by anatomically widespread neuronal CI deficiency, a distinct cell type-specific gene expression profile, increased load of neuronal mtDNA deletions, and a predilection for non-tremor dominant motor phenotypes. In contrast, the non-CI deficient (nCI-PD) subtype exhibits no evidence of mitochondrial impairment outside the dopaminergic substantia nigra and has a predilection for a tremor dominant phenotype. These findings constitute a step towards resolving the biological heterogeneity of iPD with implications for both mechanistic understanding and treatment strategies.

neuroscience↗

bioRxiv2019

Dysregulation of histone acetylation and decoupling from transcription in Parkinson disease

Parkinson disease (PD) is a complex neurodegenerative disorder of largely unknown etiology. While several genetic risk factors have been identified, the involvement of epigenetics in the pathophysiology of PD is mostly unaccounted for. We conducted a histone acetylome-wide association study in PD, using brain tissue from two independent cohorts of cases and controls. Immunoblotting revealed increased acetylation at several histone sites in PD, with the most prominent change observed for H3K27, a marker of active promoters and enhancers. Chromatin immunoprecipitation sequencing (ChIP-seq) further indicated that H3K27 hyperacetylation in the PD brain is a genome-wide phenomenon, with a strong predilection for genes implicated in the disease, including SNCA, PARK7, PRKN and MAPT. Integration of the ChIP-seq with transcriptomic data revealed that the correlation between promoter H3K27 acetylation and gene expression is attenuated in PD patients, suggesting that H3K27 acetylation may be decoupled from transcription in the PD brain. Our findings strongly suggest that dysregulation of histone acetylation plays an important role in the pathophysiology of PD and identify novel epigenetic signatures associated with the disease.

genomics↗

bioRxiv2019

Common gene expression signatures in Parkinson’s disease are driven by changes in cell composition

BackgroundThe etiology of Parkinsons disease (PD) is largely unknown. Genome-wide transcriptomic studies in bulk brain tissue have identified several molecular signatures associated with the disease. While these studies have the potential to shed light into the pathogenesis of PD, they are also limited by two major confounders: RNA post mortem degradation and heterogeneous cell type composition of bulk tissue samples. We performed RNA sequencing following ribosomal RNA depletion in the prefrontal cortex of 49 individuals from two independent case-control cohorts. Using cell-type specific markers, we estimated the cell-type composition for each sample and included this in our analysis models to compensate for the variation in cell-type proportions.\n\nResultsRibosomal RNA depletion results in substantially more even transcript coverage, compared to poly(A) capture, in post mortem tissue. Moreover, we show that cell-type composition is a major confounder of differential gene expression analysis in the PD brain. Correcting for cell-type proportions attenuates numerous transcriptomic signatures that have been previously associated with PD, including vesicle trafficking, synaptic transmission, immune and mitochondrial function. Conversely, pathways related to endoplasmic reticulum, lipid oxidation and unfolded protein response are strengthened and surface as the top differential gene expression signatures in the PD prefrontal cortex.\n\nConclusionsDifferential gene expression signatures in PD bulk brain tissue are significantly confounded by underlying differences in cell-type composition. Modeling cell-type heterogeneity is crucial in order to unveil transcriptomic signatures that represent regulatory changes in the PD brain and are, therefore, more likely to be associated with underlying disease mechanisms.

neuroscience↗

bioRxiv2018

Data-driven approaches for improving the interpretability of patch-seq data

Patch-seq, enabling simultaneous measurement of a transcriptomic, electrophysiological, and morphological features, has recently emerged as a powerful tool for neuronal characterization. However, we show the method is susceptible to technical artifacts, including the presence of mRNA contaminants from multiple cells, that limit the interpretability of the data. We present a straightforward marker gene-based approach for controlling for these artifacts and show that our method improves the correspondence between gene expression and electrophysiological features.

neuroscience↗

bioRxiv2017

Transcriptomic Correlates Of Neuron Electrophysiological Diversity

How neuronal diversity emerges from complex patterns of gene expression remains poorly understood. Here we present an approach to understand electrophysiological diversity through gene expression by integrating transcriptomics with intracellular electrophysiology. Using a brain-wide dataset of 34 neuron types, we identified 420 genes whose expression levels significantly correlated with variability in one or more of 11 electrophysiological parameters. The majority of these correlations were consistent in an independent sample of 12 visual cortex cell types. Many associations reported here have the potential to provide new insights into how neurons generate functional diversity, and correlations of ion channel genes like Gabrd and Scn1a (Nav1.1) with resting potential and spiking frequency are consistent with known causal mechanisms. These results suggest that despite the complexity linking gene expression to electrophysiology, there are likely some general principles that govern how individual genes establish phenotypic diversity across very different cell types.

neuroscience↗

bioRxiv2016

NeuroExpresso: A cross-laboratory database of brain cell-type expression profiles with applications to marker gene identification and bulk brain tissue transcriptome interpretation

The identification of cell type marker genes, genes highly enriched in specific cell types, plays an important role in the study of the nervous system. In particular, marker genes can be used to identify cell types to enable studies of their properties. Marker genes can also aid the interpretation of bulk tissue expression profiles by revealing cell type specific changes.\n\nWe assembled a database, NeuroExpresso, of publicly available mouse brain cell type-specific gene expression datasets. We then used stringent criteria to select marker genes highly expressed in individual cell types. We found a substantial number of novel markers previously unknown in the literature and validated a subset of them using in silico analyses and in situ hybridization. We next demonstrate the use of marker genes in analysis of whole tissue data by summarizing their expression into \"cell type profiles\" that can be thought of as surrogates for the relative abundance of the cell types across the samples studied.\n\nFurther analysis of our cell type-specific expression database confirms some recent findings about brain cell types along with revealing novel properties, such as Ddc expression in oligodendrocytes. To facilitate further use of this expanding database, we provide a user-friendly web interface for the visualization of expression data.\n\nSignificance StatementCell type markers are powerful tools in the study of the nervous system that help reveal properties of cell types and acquire additional information from large scale expression experiments. Despite their usefulness in the field, known marker genes for brain cell types are few in number. We present NeuroExpresso, a database of brain cell type specific gene expression profiles, and demonstrate the use of marker genes for acquiring cell type specific information from whole tissue expression. The database will prove itself as a useful resource for researchers aiming to reveal novel properties of the cell types and aid both laboratory and computational scientists to unravel the cell type specific components of brain disorders.

neuroscience↗