Multiomics and machine-learning identify novel transcriptional and mutational signatures in amyotrophic lateral sclerosis

Discovering Novel Biomarkers and Potential Therapeutic Targets of Amyotrophic Lateral Sclerosis Through Integrated Machine Learning and Gene Expression Profiling

Amyotrophic lateral sclerosis (ALS) is a progressive neurodegenerative disorder that has multiple factors that make its molecular pathogenesis difficult to understand and its diagnosis and treatment during the early stages difficult to determine. Discovering novel biomarkers in ALS for diagnostic and therapeutic potential has become important. Consequently, bioinformatics and machine learning algorithms are useful for identifying differentially expressed genes (DEGs) and potential biomarkers, as well as understanding the molecular mechanisms and intricacies of diseases such as ALS. To achieve the aim of the present study, six datasets obtained from the Gene Expression Omnibus (GEO) were utilized and analyzed using an integrative bioinformatics and machine learning approach. Log transformation was done during data preprocessing, RMA normalization was performed, and the batch effect was corrected. Differential expression analysis identified 206 DEGs that were significantly associated with different biological processes, including muscle function, energy metabolism, and mitochondrial membrane activity. Functional enrichment analysis highlighted pathways, including those related to prion disease, Parkinson's disease, and ATP synthesis via chemiosmotic coupling. We employed a multi-step machine learning framework incorporating random forest, LASSO regression, and SVM-RFE to identify robust biomarkers. This approach identified three key genes, CHRNA1, DLG5, and PLA2G4C, which could be explored as promising biomarkers for ALS after further validation. The internal validation, including principal component analysis (PCA) and ROC-AUC analysis, demonstrated strong diagnostic potential of these hub genes, achieving an AUC of 0.96. This work highlights the utility of bioinformatics and machine learning in identifying key genes as biomarkers for diagnostic and therapeutic potential in ALS.

A neuromuscular clinician's primer on machine learning

Artificial intelligence is the future of clinical practice and is increasingly utilized in medical management and clinical research. The release of ChatGPT3 in 2022 brought generative AI to the headlines and rekindled public interest in software agents that would complete repetitive tasks and save time. Artificial intelligence/machine learning underlies applications and devices which are assisting clinicians in the diagnosis, monitoring, formulation of prognosis, and treatment of patients with a spectrum of neuromuscular diseases. However, these applications have remained in the research sphere, and neurologists as a specialty are running the risk of falling behind other clinical specialties which are quicker to embrace these new technologies. While there are many comprehensive reviews on the use of artificial intelligence/machine learning in medicine, our aim is to provide a simple and practical primer to educate clinicians on the basics of machine learning. This will help clinicians specializing in neuromuscular and electrodiagnostic medicine to understand machine learning applications in nerve and muscle ultrasound, MRI imaging, electrical impendence myography, nerve conductions and electromyography and clinical cohort studies, and the limitations, pitfalls, regulatory and ethical concerns, and future directions. The question is not whether artificial intelligence/machine learning will change clinical practice, but when and how. How future neurologists will look back upon this period of transition will be determined not by how much changed or by how fast clinicians embraced this change but by how much patient outcomes were improved.

ALS molecular subtypes are a combination of cellular and pathological features learned by deep multiomics classifiers

Amyotrophic lateral sclerosis (ALS) is a complex syndrome with multiple genetic causes and wide variation in disease presentation. Despite this heterogeneity, large-scale genomics studies revealed that ALS postmortem samples can be grouped into a small number of subtypes, defined by transcriptomic signatures of mitochondrial dysfunction and oxidative stress (ALS-Ox), microglial activation and neuroinflammation (ALS-Glia), or TDP-43 pathology and associated transposable elements (ALS-TE). In this study, we present a deep ALS neural net classifier (DANCer) for ALS molecular subtypes. Applying DANCer to an expanded cohort from the NYGC ALS Consortium highlights two subtypes that strongly correlate with disease duration: ALS-TE in cortex and ALS-Glia in spinal cord. Finally, single-nucleus transcriptomes demonstrate that ALS subtypes are recapitulated in neurons and glia, with both ALS-wide and subtype-specific alterations in all cell types. In summary, ALS molecular subtypes represent a combination of cellular and pathological features that correlate with clinical features of ALS.

Towards an integrated approach for understanding glia in Amyotrophic Lateral Sclerosis

Substantial advances in technology are permitting a high resolution understanding of the salience of glia, and have helped us to transcend decades of predominantly neuron-centric research. In particular, recent advances in 'omic' technologies have enabled unique insights into glial biology, shedding light on the cellular and molecular aspects of neurodegenerative diseases, including amyotrophic lateral sclerosis (ALS). Here, we review studies using omic techniques to attempt to understand the role of glia in ALS across different model systems and post mortem tissue. We also address caveats that should be considered when interpreting such studies, and how some of these may be mitigated through either using a multi-omic approach and/or careful low throughput, high fidelity orthogonal validation with particular emphasis on functional validation. Finally, we consider emerging technologies and their potential relevance in deepening our understanding of glia in ALS.

Evaluation of a biomarker for amyotrophic lateral sclerosis derived from a hypomethylated DNA signature of human motor neurons

Amyotrophic lateral sclerosis (ALS) lacks a specific biomarker, but is defined by relatively selective toxicity to motor neurons (MN). As others have highlighted, this offers an opportunity to develop a sensitive and specific biomarker based on detection of DNA released from dying MN within accessible biofluids. Here we have performed whole genome bisulfite sequencing (WGBS) of iPSC-derived MN from neurologically normal individuals. By comparing MN methylation with an atlas of tissue methylation we have derived a MN-specific signature of hypomethylated genomic regions, which accords with genes important for MN function. Through simulation we have optimised the selection of regions for biomarker detection in plasma and CSF cell-free DNA (cfDNA). However, we show that MN-derived DNA is not detectable via WGBS in plasma cfDNA. In support of our experimental finding, we show theoretically that the relative sparsity of lower MN sets a limit on the proportion of plasma cfDNA derived from MN which is below the threshold for detection via WGBS. Our findings are important for the ongoing development of ALS biomarkers. The MN-specific hypomethylated genomic regions we have derived could be usefully combined with more sensitive detection methods and perhaps with study of CSF instead of plasma. Indeed we demonstrate that neuronal-derived DNA is detectable in CSF. Our work is relevant for all diseases featuring death of rare cell-types.

Epigenetics in Neurodegenerative Diseases

Healthy brain functioning requires a continuous fine-tuning of gene expression, involving changes in the epigenetic landscape and 3D chromatin organization. Alzheimer's disease (AD), amyotrophic lateral sclerosis (ALS), and frontotemporal dementia (FTD) are three multifactorial neurodegenerative diseases (NDDs) that are partially explained by genetics (gene mutations and genetic risk factors) and influenced by non-genetic factors (i.e., aging, lifestyle, and environmental conditions). Examining comprehensive studies of global and locus-specific (epi)genomic and transcriptomic alterations in human and mouse brain samples at the cell-type resolution has uncovered important phenomena associated with AD. First, DNA methylation and histone marks at promoters contribute to transcriptional dysregulation of genes that are directly implicated in AD pathogenesis (i.e., APP), neuroplasticity and cognition (i.e., PSD95), and microglial activation (i.e., TREM2). Second, the presence of AD genetic risk variants in cell-type-specific distal enhancers (i.e., BIN1 in microglia) alters transcription, presumably by disrupting associated enhancer-promoter interactions and chromatin looping. Third, epigenomic erosion is associated with widespread transcriptional disruption and cell identity loss. And fourth, aging, high cholesterol, air pollution, and pesticides have emerged as potential drivers of AD by inducing locus-specific and global epigenetic modifications that impact key AD-related pathways. Epigenetic studies in ALS/FTD also provide evidence that genetic and non-genetic factors alter gene expression profiles in neurons and astrocytes through aberrant epigenetic mechanisms. We additionally overview the recent development of potential new therapeutic strategies involving (epi)genetic editing and the use of small chromatin-modifying molecules (epidrugs).

Decoding TDP-43: the molecular chameleon of neurodegenerative diseases

TAR DNA-binding protein 43 (TDP-43) has emerged as a critical player in neurodegenerative disorders, with its dysfunction implicated in a wide spectrum of diseases including amyotrophic lateral sclerosis (ALS), frontotemporal lobar degeneration (FTLD), and Alzheimer's disease (AD). This comprehensive review explores the multifaceted roles of TDP-43 in both physiological and pathological contexts. We delve into TDP-43's crucial functions in RNA metabolism, including splicing regulation, mRNA stability, and miRNA biogenesis. Particular emphasis is placed on recent discoveries regarding TDP-43's involvement in DNA interactions and chromatin dynamics, highlighting its broader impact on gene expression and genome stability. The review also examines the complex pathogenesis of TDP-43-related disorders, discussing the protein's propensity for aggregation, its effects on mitochondrial function, and its non-cell autonomous impacts on glial cells. We provide an in-depth analysis of TDP-43 pathology across various neurodegenerative conditions, from well-established associations in ALS and FTLD to emerging roles in diseases such as Huntington's disease and Niemann-Pick C disease. The potential of TDP-43 as a therapeutic target is explored, with a focus on recent developments in targeting cryptic exon inclusion and other TDP-43-mediated processes. This review synthesizes current knowledge on TDP-43 biology and pathology, offering insights into the protein's central role in neurodegeneration and highlighting promising avenues for future research and therapeutic interventions.

Scrutinizing neurodegenerative diseases: decoding the complex genetic architectures through a multi-omics lens

Neurodegenerative diseases present complex genetic architectures, reflecting a continuum from monogenic to oligogenic and polygenic models. Recent advances in multi-omics data, coupled with systems genetics, have significantly refined our understanding of how these data impact neurodegenerative disease mechanisms. To contextualize these genetic discoveries, we provide a comprehensive critical overview of genetic architecture concepts, from Mendelian inheritance to the latest insights from oligogenic and omnigenic models. We explore the roles of common and rare genetic variants, gene-gene and gene-environment interactions, and epigenetic influences in shaping disease phenotypes. Additionally, we emphasize the importance of multi-omics layers including genomic, transcriptomic, proteomic, epigenetic, and metabolomic data in elucidating the molecular mechanisms underlying neurodegeneration. Special attention is given to missing heritability and the contribution of rare variants, particularly in the context of pleiotropy and network pleiotropy. We examine the application of single-cell omics technologies, transcriptome-wide association studies, and epigenome-wide association studies as key approaches for dissecting disease mechanisms at tissue- and cell-type levels. Our review introduces the OmicPeak Disease Trajectory Model, a conceptual framework for understanding the genetic architecture of neurodegenerative disease progression, which integrates multi-omics data across biological layers and time points. This review highlights the critical importance of adopting a systems genetics approach to unravel the complex genetic architecture of neurodegenerative diseases. Finally, this emerging holistic understanding of multi-omics data and the exploration of the intricate genetic landscape aim to provide a foundation for establishing more refined genetic architectures of these diseases, enhancing diagnostic precision, predicting disease progression, elucidating pathogenic mechanisms, and refining therapeutic strategies for neurodegenerative conditions.

Plekhg5 controls the unconventional secretion of Sod1 by presynaptic secretory autophagy

Increasing evidence suggests an essential function for autophagy in unconventional protein secretion (UPS). However, despite its relevance for the secretion of aggregate-prone proteins, the mechanisms of secretory autophagy in neurons have remained elusive. Here we show that the lower motoneuron disease-associated guanine exchange factor Plekhg5 drives the UPS of Sod1. Mechanistically, Sod1 is sequestered into autophagosomal carriers, which subsequently fuse with secretory lysosomal-related organelles (LROs). Exocytosis of LROs to release Sod1 into the extracellular milieu requires the activation of the small GTPase Rab26 by Plekhg5. Deletion of Plekhg5 in mice leads to the accumulation of Sod1 in LROs at swollen presynaptic sites. A reduced secretion of toxic ALS-linked SOD1G93A following deletion of Plekhg5 in SOD1G93A mice accelerated disease onset while prolonging survival due to an attenuated microglia activation. Using human iPSC-derived motoneurons we show that reduced levels of PLEKHG5 cause an impaired secretion of ALS-linked SOD1. Our findings highlight an unexpected pathophysiological mechanism that converges two motoneuron disease-associated proteins into a common pathway.

DNA methylation in amyotrophic lateral sclerosis: where do we stand and what is next?

Genes involved in immune response, inflammation and metabolism are among those most likely affected by changes in DNA methylation (DNAm) and expression levels in amyotrophic lateral sclerosis (ALS) tissues. Unfortunately, it is still largely unclear whether any of these changes precede the onset of disease symptoms or whether most of them are the result of the muscular and metabolic changes that follow symptoms onset. In this article the author discusses the strengths and limitations of the available studies of DNAm in ALS and provides some suggestions on what, in his opinion, could be done in the near future for a better understanding of the DNAm changes occurring in ALS, their link with environmental exposures and their potential clinical utility.

Proteomics profiling and machine learning in nusinersen-treated patients with spinal muscular atrophy

AIM

The availability of disease-modifying therapies and newborn screening programs for spinal muscular atrophy (SMA) has generated an urgent need for reliable prognostic biomarkers to classify patients according to disease severity. We aim to identify cerebrospinal fluid (CSF) prognostic protein biomarkers in CSF samples of SMA patients collected at baseline (T0), and to describe proteomic profile changes and biological pathways influenced by nusinersen before the sixth nusinersen infusion (T302).

METHODS

In this multicenter retrospective longitudinal study, we employed an untargeted liquid chromatography mass spectrometry (LC-MS)-based proteomic approach on CSF samples collected from 61 SMA patients treated with nusinersen (SMA1 n=19, SMA2 n=19, SMA3 n=23) at T0 at T302. The Random Forest (RF) machine learning algorithm and pathway enrichment analysis were applied for analysis.

RESULTS

The RF algorithm, applied to the protein expression profile of naïve patients, revealed several proteins that could classify the different types of SMA according to their differential abundance at T0. Analysis of changes in proteomic profiles identified a total of 147 differentially expressed proteins after nusinersen treatment in SMA1, 135 in SMA2, and 289 in SMA3. Overall, nusinersen-induced changes on proteomic profile were consistent with i) common effects observed in allSMA types (i.e. regulation of axonogenesis), and ii) disease severity-specific changes, namely regulation of glucose metabolism in SMA1, of coagulation processes in SMA2, and of complement cascade in SMA3.

CONCLUSIONS

This untargeted LC-MS proteomic profiling in the CSF of SMA patients revealed differences in protein expression in naïve patients and showed nusinersen-related modulation in several biological processes after 10 months of treatment. Further confirmatory studies are needed to validate these results in larger number of patients and over abroader timeframe.

Heterozygous knockout of Synaptotagmin13 phenocopies ALS features and TP53 activation in human motor neurons

Spinal motor neurons (MNs) represent a highly vulnerable cellular population, which is affected in fatal neurodegenerative diseases such as amyotrophic lateral sclerosis (ALS) and spinal muscular atrophy (SMA). In this study, we show that the heterozygous loss of SYT13 is sufficient to trigger a neurodegenerative phenotype resembling those observed in ALS and SMA. SYT13+/- hiPSC-derived MNs displayed a progressive manifestation of typical neurodegenerative hallmarks such as loss of synaptic contacts and accumulation of aberrant aggregates. Moreover, analysis of the SYT13+/- transcriptome revealed a significant impairment in biological mechanisms involved in motoneuron specification and spinal cord differentiation. This transcriptional portrait also strikingly correlated with ALS signatures, displaying a significant convergence toward the expression of pro-apoptotic and pro-inflammatory genes, which are controlled by the transcription factor TP53. Our data show for the first time that the heterozygous loss of a single member of the synaptotagmin family, SYT13, is sufficient to trigger a series of abnormal alterations leading to MN sufferance, thus revealing novel insights into the selective vulnerability of this cell population.

The use of artificial intelligence in induced pluripotent stem cell-based technology over 10-year period: A systematic scoping review

BACKGROUND

Stem cell research, particularly in the domain of induced pluripotent stem cell (iPSC) technology, has shown significant progress. The integration of artificial intelligence (AI), especially machine learning (ML) and deep learning (DL), has played a pivotal role in refining iPSC classification, monitoring cell functionality, and conducting genetic analysis. These enhancements are broadening the applications of iPSC technology in disease modelling, drug screening, and regenerative medicine. This review aims to explore the role of AI in the advancement of iPSC research.

METHODS

In December 2023, data were collected from three electronic databases (PubMed, Web of Science, and Science Direct) to investigate the application of AI technology in iPSC processing.

RESULTS

This systematic scoping review encompassed 79 studies that met the inclusion criteria. The number of research studies in this area has increased over time, with the United States emerging as a leading contributor in this field. AI technologies have been diversely applied in iPSC technology, encompassing the classification of cell types, assessment of disease-specific phenotypes in iPSC-derived cells, and the facilitation of drug screening using iPSC. The precision of AI methodologies has improved significantly in recent years, creating a foundation for future advancements in iPSC-based technologies.

CONCLUSIONS

Our review offers insights into the role of AI in regenerative and personalized medicine, highlighting both challenges and opportunities. Although still in its early stages, AI technologies show significant promise in advancing our understanding of disease progression and development, paving the way for future clinical applications.

Artificial intelligence in neurology: opportunities, challenges, and policy implications

Neurological conditions are the leading cause of disability and mortality combined, demanding innovative, scalable, and sustainable solutions. Brain health has become a global priority with adoption of the World Health Organization's Intersectoral Global Action Plan in 2022. Simultaneously, rapid advancements in artificial intelligence (AI) are revolutionizing neurological research and practice. This scoping review of 66 original articles explores the value of AI in neurology and brain health, systematizing the landscape for emergent clinical opportunities and future trends across the care trajectory: prevention, risk stratification, early detection, diagnosis, management, and rehabilitation. AI's potential to advance personalized precision neurology and global brain health directives hinges on resolving core challenges across four pillars-models, data, feasibility/equity, and regulation/innovation-through concerted pursuit of targeted recommendations. Paramount actions include swift, ethical, equity-focused integration of novel technologies into clinical workflows, mitigating data-related issues, counteracting digital inequity gaps, and establishing robust governance frameworks balancing safety and innovation.

One-pot method for preparing DNA, RNA, and protein for multiomics analysis

Typical multiomics studies employ separate methods for DNA, RNA, and protein sample preparation, which is labor intensive, costly, and prone to sampling bias. We describe a method for preparing high-quality, sequencing-ready DNA and RNA, and either intact proteins or mass-spectrometry-ready peptides for whole proteome analysis from a single sample. This method utilizes a reversible protein tagging scheme to covalently link all proteins in a lysate to a bead-based matrix and nucleic acid precipitation and selective solubilization to yield separate pools of protein and nucleic acids. We demonstrate the utility of this method to compare the genomes, transcriptomes, and proteomes of four triple-negative breast cancer cell lines with different degrees of malignancy. These data show the involvement of both RNA and associated proteins, and protein-only dependent pathways that distinguish these cell lines. We also demonstrate the utility of this multiomics workflow for tissue analysis using mouse brain, liver, and lung tissue.

Unsupervised machine learning identifies distinct ALS molecular subtypes in post-mortem motor cortex and blood expression data

Amyotrophic lateral sclerosis (ALS) displays considerable clinical and genetic heterogeneity. Machine learning approaches have previously been utilised for patient stratification in ALS as they can disentangle complex disease landscapes. However, lack of independent validation in different populations and tissue samples have greatly limited their use in clinical and research settings. We overcame these issues by performing hierarchical clustering on the 5000 most variably expressed autosomal genes from motor cortex expression data of people with sporadic ALS from the KCL BrainBank (N = 112). Three molecular phenotypes linked to ALS pathogenesis were identified: synaptic and neuropeptide signalling, oxidative stress and apoptosis, and neuroinflammation. Cluster validation was achieved by applying linear discriminant analysis models to cases from TargetALS US motor cortex (N = 93), as well as Italian (N = 15) and Dutch (N = 397) blood expression datasets, for which there was a high assignment probability (80-90%) for each molecular subtype. The ALS and motor cortex specificity of the expression signatures were tested by mapping KCL BrainBank controls (N = 59), and occipital cortex (N = 45) and cerebellum (N = 123) samples from TargetALS to each cluster, before constructing case-control and motor cortex-region logistic regression classifiers. We found that the signatures were not only able to distinguish people with ALS from controls (AUC 0.88 ± 0.10), but also reflect the motor cortex-based disease process, as there was perfect discrimination between motor cortex and the other brain regions. Cell types known to be involved in the biological processes of each molecular phenotype were found in higher proportions, reinforcing their biological interpretation. Phenotype analysis revealed distinct cluster-related outcomes in both motor cortex datasets, relating to disease onset and progression-related measures. Our results support the hypothesis that different mechanisms underpin ALS pathogenesis in subgroups of patients and demonstrate potential for the development of personalised treatment approaches. Our method is available for the scientific and clinical community at https://alsgeclustering.er.kcl.ac.uk .

Genomic and transcriptomic advances in amyotrophic lateral sclerosis

Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disorder and the most common motor neuron disease. ALS shows substantial clinical and molecular heterogeneity. In vitro and in vivo models coupled with multiomic techniques have provided important contributions to unraveling the pathomechanisms underlying ALS. To date, despite promising results and accumulating knowledge, an effective treatment is still lacking. Here, we provide an overview of the literature on the use of genomics, epigenomics, transcriptomics and microRNAs to deeply investigate the molecular mechanisms developing and sustaining ALS. We report the most relevant genes implicated in ALS pathogenesis, discussing the use of different high-throughput sequencing techniques and the role of epigenomic modifications. Furthermore, we present transcriptomic studies discussing the most recent advances, from microarrays to bulk and single-cell RNA sequencing. Finally, we discuss the use of microRNAs as potential biomarkers and promising tools for molecular intervention. The integration of data from multiple omic approaches may provide new insights into pathogenic pathways in ALS by shedding light on diagnostic and prognostic biomarkers, helping to stratify patients into clinically relevant subgroups, revealing novel therapeutic targets and supporting the development of new effective therapies.

TDP-43 Epigenetic Facets and Their Neurodegenerative Implications

Since its initial involvement in numerous neurodegenerative pathologies in 2006, either as a principal actor or as a cofactor, new pathologies implicating transactive response (TAR) DNA-binding protein 43 (TDP-43) are regularly emerging also beyond the neuronal system. This reflects the fact that TDP-43 functions are particularly complex and broad in a great variety of human cells. In neurodegenerative diseases, this protein is often pathologically delocalized to the cytoplasm, where it irreversibly aggregates and is subjected to various post-translational modifications such as phosphorylation, polyubiquitination, and cleavage. Until a few years ago, the research emphasis has been focused particularly on the impacts of this aggregation and/or on its widely described role in complex RNA splicing, whether related to loss- or gain-of-function mechanisms. Interestingly, recent studies have strengthened the knowledge of TDP-43 activity at the chromatin level and its implication in the regulation of DNA transcription and stability. These discoveries have highlighted new features regarding its own transcriptional regulation and suggested additional mechanistic and disease models for the effects of TPD-43. In this review, we aim to give a comprehensive view of the potential epigenetic (de)regulations driven by (and driving) this multitask DNA/RNA-binding protein.

Integrative multi-omics and systems bioinformatics in translational neuroscience: A data mining perspective

Bioinformatic analysis of large and complex omics datasets has become increasingly useful in modern day biology by providing a great depth of information, with its application to neuroscience termed neuroinformatics. Data mining of omics datasets has enabled the generation of new hypotheses based on differentially regulated biological molecules associated with disease mechanisms, which can be tested experimentally for improved diagnostic and therapeutic targeting of neurodegenerative diseases. Importantly, integrating multi-omics data using a systems bioinformatics approach will advance the understanding of the layered and interactive network of biological regulation that exchanges systemic knowledge to facilitate the development of a comprehensive human brain profile. In this review, we first summarize data mining studies utilizing datasets from the individual type of omics analysis, including epigenetics/epigenomics, transcriptomics, proteomics, metabolomics, lipidomics, and spatial omics, pertaining to Alzheimer's disease, Parkinson's disease, and multiple sclerosis. We then discuss multi-omics integration approaches, including independent biological integration and unsupervised integration methods, for more intuitive and informative interpretation of the biological data obtained across different omics layers. We further assess studies that integrate multi-omics in data mining which provide convoluted biological insights and offer proof-of-concept proposition towards systems bioinformatics in the reconstruction of brain networks. Finally, we recommend a combination of high dimensional bioinformatics analysis with experimental validation to achieve translational neuroscience applications including biomarker discovery, therapeutic development, and elucidation of disease mechanisms. We conclude by providing future perspectives and opportunities in applying integrative multi-omics and systems bioinformatics to achieve precision phenotyping of neurodegenerative diseases and towards personalized medicine.

Propranolol reduces the accumulation of cytotoxic aggregates in C9orf72-ALS/FTD in vitro models

Mutations in the C9orf72 gene are the most common cause of familial amyotrophic lateral sclerosis (ALS) and frontotemporal dementia (FTD). The pathogenetic mechanisms linked to this gene are a direct consequence of an aberrant intronic expansion of a GGGGCC hexanucleotide located between the 1a and 1b non-coding exons, which can be transcribed to form cytotoxic RNA foci or even translated into aggregation-prone dipeptide repeat proteins. Importantly, the abnormal length of these repeats affects also the expression levels of C9orf72 itself, which suggests haploinsufficiency as additional pathomechanism. Thus, it appears that both toxic gain of function and loss of function are distinct but still coexistent features contributing to the insurgence of the disease in case of C9orf72 mutations. In this study, we aimed at identifying a strategy to address both aspects of the C9orf72-related pathobiochemistry and provide proof-of-principle information for a better understanding of the mechanisms leading to neuronal loss. By using primary neurons overexpressing toxic poly(GA), the most abundant protein product of the GGGGCC repeats, we found that the antiarrhythmic drug propranolol could efficiently reduce the accumulation of aberrant aggregates and increase the survival of C9orf72-related cultures. Interestingly, the improved catabolism appeared to not depend on major degradative pathways such as autophagy and the proteasome. By analyzing the proteome of poly(GA)-expressing neurons after exposure to propranolol, we found that the drug increased lysosomal degradation through a mechanism directly involving C9orf72 protein, whose levels were increased after treatment. Further confirmation of the beneficial effect of the beta blocker on aggregates' accumulation and survival of hiPSC-derived C9orf72-mutant motoneurons strengthened the finding that addressing both facets of C9orf72 pathology might represent a valid strategy for the treatment of these ALS/FTD cases.