Genome-wide identification of the genetic basis of amyotrophic lateral sclerosis

Dissecting the genetic complexity of myalgic encephalomyelitis/chronic fatigue syndrome via deep learning-powered genome analysis

Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) is a complex, heterogeneous, and systemic disease defined by a suite of symptoms, including unexplained persistent fatigue, post-exertional malaise (PEM), cognitive impairment, myalgia, orthostatic intolerance, and unrefreshing sleep. The disease mechanism of ME/CFS is unknown, with no effective curative treatments. In this study, we present a multi-site ME/CFS whole-genome analysis, which is powered by a novel deep learning framework, HEAL2. We show that HEAL2 not only has predictive value for ME/CFS based on personal rare variants, but also links genetic risk to various ME/CFS-associated symptoms. Model interpretation of HEAL2 identifies 115 ME/CFS-risk genes that exhibit significant intolerance to loss-of-function (LoF) mutations. Transcriptome and network analyses highlight the functional importance of these genes across a wide range of tissues and cell types, including the central nervous system (CNS) and immune cells. Patient-derived multi-omics data implicate reduced expression of ME/CFS risk genes within ME/CFS patients, including in the plasma proteome, and the transcriptomes of B and T cells, especially cytotoxic CD4 T cells, supporting their disease relevance. Pan-phenotype analysis of ME/CFS genes further reveals the genetic correlation between ME/CFS and other complex diseases and traits, including depression and long COVID-19. Overall, HEAL2 provides a candidate genetic-based diagnostic tool for ME/CFS, and our findings contribute to a comprehensive understanding of the genetic, molecular, and cellular basis of ME/CFS, yielding novel insights into therapeutic targets. Our deep learning model also offers a potent, broadly applicable framework for parallel rare variant analysis and genetic prediction for other complex diseases and traits.

Amyotrophic Lateral Sclerosis and Parkinson's Disease: Brain Tissue Transcriptome Analysis Reveals Interactions

This study utilises amyotrophic lateral sclerosis (ALS) and Parkinson's disease (PD) human brain samples from the GEO database and employs differential expression gene (DEG) analysis to identify genes that are pivotal in both neurodegenerative diseases. Through in depth GO and KEGG enrichment analyses, we elucidated the biological functions and potential pathways associated with these DEGs. Furthermore, by constructing protein‒protein interaction networks, we highlight the significance of shared DEGs in both cellular physiology and disease contexts. Analysis of drug‒gene associations revealed potential therapeutic compounds linked to ALS and PD treatment. Additionally, we explored the interactions between transcription factors, miRNAs, and common DEGs, revealing aspects of gene regulatory networks. This study provides insights into the molecular mechanisms of ALS and PD, offering valuable contributions to ongoing research and potential therapeutic avenues.

The Neurodegenerative Disease Knowledge Portal: Propelling Discovery Through the Sharing of Neurodegenerative Disease Genomic Resources

Although large-scale genetic association studies have proven useful for the delineation of neurodegenerative disease processes, we still lack a full understanding of the pathologic mechanisms of these diseases, resulting in few appropriate treatment options and diagnostic challenges. To mitigate these gaps, the Neurodegenerative Disease Knowledge Portal (NDKP) was created as an open-science initiative with the aim to aggregate, enable analysis, and display all available genomic datasets of neurodegenerative disease, while protecting the integrity and confidentiality of the underlying datasets. The portal contains 218 genomic datasets, including genotyping and sequencing studies, of individuals across 10 different phenotypic groups, including neurologic conditions such as Alzheimer disease, amyotrophic lateral sclerosis, Lewy body dementia, and Parkinson disease. In addition to securely hosting large genomic datasets, the NDKP provides accessible workflows and tools to effectively use the datasets and assist in the facilitation of customized genomic analyses. Here, we summarize the genomic datasets currently included within the portal, the bioinformatics processing of the datasets, and the variety of phenotypes captured. We also present example use cases of the various user interfaces and integrated analytic tools to demonstrate their extensive utility in enabling the extraction of high-quality results at the source, for both genomics experts and those in other disciplines. Overall, the NDKP promotes open science and collaboration, maximizing the potential for discovery from the large-scale datasets researchers and consortia are expending immense resources to produce and resulting in reproducible conclusions to improve diagnostic and therapeutic care for patients with neurodegenerative disease.

Neural Excitatory/Inhibitory Imbalance in Motor Aging: From Genetic Mechanisms to Therapeutic Challenges

Neural excitatory/inhibitory (E/I) imbalance plays a pivotal role in the aging process. However, despite its significant impact, the role of E/I imbalance in motor dysfunction and neurodegenerative diseases has not received sufficient attention. This review explores the mechanisms underlying motor aging through the lens of E/I balance, emphasizing genetic and molecular factors that contribute to this imbalance (such as SCN2A, CACNA1C, GABRB3, GRIN2A, SYT, BDNF…). Key regulatory genes, including REST, vps-34, and STXBP1, are examined for their roles in modulating synaptic activity and neuronal function during aging. With insights drawn from ALS, we discuss how disruptions in E/I balance contribute to the pathophysiology of age-related motor dysfunction. The genes discussed above exhibit a certain association with age-related motor neuron diseases (like ALS), a relationship that had not been previously recognized. Innovative genetic therapies, such as gene editing technology and optogenetic manipulation, are emerging as promising tools for restoring E/I balance, offering hope for ameliorating motor deficits in aging. This review explores the potential of these technologies to intervene in aging-related motor diseases, despite challenges in their direct application to human conditions.

Structural variants linked to Alzheimer's disease and other common age-related clinical and neuropathologic traits

BACKGROUND

Alzheimer's disease (AD) is a complex neurodegenerative disorder with substantial genetic influence. While genome-wide association studies (GWAS) have identified numerous risk loci for late-onset AD (LOAD), the functional mechanisms underlying most of these associations remain unresolved. Large genomic rearrangements, known as structural variants (SVs), represent a promising avenue for elucidating such mechanisms within some of these loci.

METHODS

By leveraging data from two ongoing cohort studies of aging and dementia, the Religious Orders Study and Rush Memory and Aging Project (ROS/MAP), we performed genome-wide association analysis testing 20,205 common SVs from 1088 participants with whole genome sequencing (WGS) data. A range of Alzheimer's disease and other common age-related clinical and neuropathologic traits were examined.

RESULTS

First, we mapped SVs across 81 AD risk loci and discovered 22 SVs in linkage disequilibrium (LD) with GWAS lead variants and directly associated with the phenotypes tested. The strongest association was a deletion of an Alu element in the 3'UTR of the TMEM106B gene, in high LD with the respective AD GWAS locus and associated with multiple AD and AD-related disorders (ADRD) phenotypes, including tangles density, TDP-43, and cognitive resilience. The deletion of this element was also linked to lower TMEM106B protein abundance. We also found a 22-kb deletion associated with depression in ROS/MAP and bearing similar association patterns as GWAS SNPs at the IQCK locus. In addition, we leveraged our catalog of SV-GWAS to replicate and characterize independent findings in SV-based GWAS for AD and five other neurodegenerative diseases. Among these findings, we highlight the replication of genome-wide significant SVs for progressive supranuclear palsy (PSP), including markers for the 17q21.31 MAPT locus inversion and a 1483-bp deletion at the CYP2A13 locus, along with other suggestive associations, such as a 994-bp duplication in the LMNTD1 locus, suggestively linked to AD and a 3958-bp deletion at the DOCK5 locus linked to Lewy body disease (LBD) (P = 3.36 × 10-4).

CONCLUSIONS

While still limited in sample size, this study highlights the utility of including analysis of SVs for elucidating mechanisms underlying GWAS loci and provides a valuable resource for the characterization of the effects of SVs in neurodegenerative disease pathogenesis.

RNA dysregulation in neurodegenerative diseases

Dysregulation of RNA processing has in recent years emerged as a significant contributor to neurodegeneration. The diverse mechanisms and molecular functions underlying RNA processing underscore the essential role of RNA regulation in maintaining neuronal health and function. RNA molecules are bound by RNA-binding proteins (RBPs), and interactions between RNAs and RBPs are commonly affected in neurodegeneration. In this review, we highlight recent progress in understanding dysregulated RNA-processing pathways and the causes of RBP dysfunction across various neurodegenerative diseases. We discuss both established and emerging mechanisms of RNA-mediated neuropathogenesis in this rapidly evolving field. Furthermore, we explore the development of potential RNA-targeting therapeutic approaches for the treatment of neurodegenerative diseases.

Chromatin accessibility provides a window into the genetic etiology of human brain disease

Neuropsychiatric and neurodegenerative diseases have a significant genetic component. Risk variants often affect the noncoding genome, altering cis-regulatory elements (CREs) and chromatin structure, ultimately impacting gene expression. Chromatin accessibility profiling methods, especially assay for transposase-accessible chromatin with high-throughput sequencing (ATAC-seq), have been used to pinpoint disease-associated SNPs and link them to affected genes and cell types in the brain. The integration of single-cell technologies with genome-wide association studies (GWAS) and transcriptomic data has further advanced our understanding of cell-specific chromatin dynamics. This review discusses recent findings regarding the role played by chromatin accessibility in brain disease, highlighting the need for high-quality data and rigorous computational tools. Future directions include spatial chromatin studies and CRISPR-based functional validation to bridge genetic discovery and clinical applications, paving the way for targeted gene-regulatory therapies.

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.

Neuroglia in neurodegeneration: Amyotrophic lateral sclerosis and frontotemporal dementia

Amyotrophic lateral sclerosis (ALS) and frontotemporal dementia (FTD) are devastating neurodegenerative diseases sharing significant pathologic and genetic overlap, leading to consider these diseases as a continuum in the spectrum of their pathologic features. Although FTD compromises only specific brain districts, while ALS involves both the nervous system and the skeletal muscles, several neurocentric mechanisms are in common between ALS and FTD. Also, recent research has revealed the significant involvement of nonneuronal cells, particularly glial cells such as astrocytes, oligodendrocytes, microglia, and peripheral immune cells, in disease pathology. This chapter aims to provide an extensive overview of the current understanding of the role of glia in the onset and advancement of ALS and FTD, highlighting the recent implications in terms of prognosis and future treatment options.

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).

The interaction between KIF21A and KANK1 regulates dendritic morphology and synapse plasticity in neurons

JOURNAL/nrgr/04.03/01300535-202501000-00029/figure1/v/2024-05-14T021156Z/r/image-tiff Morphological alterations in dendritic spines have been linked to changes in functional communication between neurons that affect learning and memory. Kinesin-4 KIF21A helps organize the microtubule-actin network at the cell cortex by interacting with KANK1; however, whether KIF21A modulates dendritic structure and function in neurons remains unknown. In this study, we found that KIF21A was distributed in a subset of dendritic spines, and that these KIF21A-positive spines were larger and more structurally plastic than KIF21A-negative spines. Furthermore, the interaction between KIF21A and KANK1 was found to be critical for dendritic spine morphogenesis and synaptic plasticity. Knockdown of either KIF21A or KANK1 inhibited dendritic spine morphogenesis and dendritic branching, and these deficits were fully rescued by coexpressing full-length KIF21A or KANK1, but not by proteins with mutations disrupting direct binding between KIF21A and KANK1 or binding between KANK1 and talin1. Knocking down KIF21A in the hippocampus of rats inhibited the amplitudes of long-term potentiation induced by high-frequency stimulation and negatively impacted the animals' cognitive abilities. Taken together, our findings demonstrate the function of KIF21A in modulating spine morphology and provide insight into its role in synaptic function.

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.

Stem Cell Therapy for the Treatment of Amyotrophic Lateral Sclerosis: Comparison of the Efficacy of Mesenchymal Stem Cells, Neural Stem Cells, and Induced Pluripotent Stem Cells

BACKGROUND/OBJECTIVES

Amyotrophic lateral sclerosis (ALS), or Lou Gehrig's disease, is a debilitating, incurable neurodegenerative disorder characterised by motor neuron death in the spinal cord, brainstem, and motor cortex. With an incidence rate of about 4.42 cases per 100,000 people annually, ALS severely impacts motor function and quality of life, causing progressive muscle atrophy, spasticity, paralysis, and eventually death. The cause of ALS is largely unknown, with 90% of cases being sporadic and 10% familial. Current research targets molecular mechanisms of inflammation, excitotoxicity, aggregation-prone proteins, and proteinopathy.

METHODS

This review evaluates the efficacy of three stem cell types in ALS treatment: mesenchymal stem cells (MSCs), neural stem cells (NSCs), and induced pluripotent stem cells (iPSCs).

RESULTS

MSCs, derived from various tissues, show neuroprotective and regenerative qualities, with clinical trials suggesting potential benefits but limited by small sample sizes and non-randomised designs. NSCs, isolated from the fetal spinal cord or brain, demonstrate promise in animal models but face functional integration and ethical challenges. iPSCs, created by reprogramming patient-specific somatic cells, offer a novel approach by potentially replacing or supporting neurons. iPSC therapy addresses ethical issues related to embryonic stem cells but encounters challenges regarding genotoxicity and epigenetic irregularities, somatic cell sources, privacy concerns, the need for extensive clinical trials, and high reprogramming costs.

CONCLUSIONS

This research is significant for advancing ALS treatment beyond symptomatic relief and modest survival extensions to actively modifying disease progression and improving patient outcomes. Successful stem cell therapies could lead to new ALS treatments, slowing motor function loss and reducing symptom severity.

The Neurodegenerative Disease Knowledge Portal: Propelling Discovery Through the Sharing of Neurodegenerative Disease Genomic Resources

Although large-scale genetic association studies have proven useful for the delineation of neurodegenerative disease processes, we still lack a full understanding of the pathological mechanisms of these diseases, resulting in few appropriate treatment options and diagnostic challenges. To mitigate these gaps, the Neurodegenerative Disease Knowledge Portal (NDKP) was created as an open-science initiative with the aim to aggregate, enable analysis, and display all available genomic datasets of neurodegenerative disease, while protecting the integrity and confidentiality of the underlying datasets. The portal contains 218 genomic datasets, including genotyping and sequencing studies, of individuals across ten different phenotypic groups, including neurological conditions such as Alzheimer's disease, amyotrophic lateral sclerosis, Lewy body dementia, and Parkinson's disease. In addition to securely hosting large genomic datasets, the NDKP provides accessible workflows and tools to effectively utilize the datasets and assist in the facilitation of customized genomic analyses. Here, we summarize the genomic datasets currently included within the portal, the bioinformatics processing of the datasets, and the variety of phenotypes captured. We also present example use-cases of the various user interfaces and integrated analytic tools to demonstrate their extensive utility in enabling the extraction of high-quality results at the source, for both genomics experts and those in other disciplines. Overall, the NDKP promotes open-science and collaboration, maximizing the potential for discovery from the large-scale datasets researchers and consortia are expending immense resources to produce and resulting in reproducible conclusions to improve diagnostic and therapeutic care for neurodegenerative disease patients.

Cortical microtubules act as a template to organize nano-scale patterning of exocytosis

Targeting of exocytosis enables cellular morphogenesis, motility and polarized transport, yet relatively little is known about the targeting mechanisms in cellular systems. Here we show that the SEC/MUNC protein KEULE is a dynamic marker for individual secretory events and employ it as a live cell probe, that together with high-precision image analysis of thousands of events, reveal that cortical microtubule arrays act as two-dimensional templates that pattern exocytosis at the nano-scale in higher plant cells. This mechanism is distinct from previously described mechanisms involving motor-driven transport and defines ordered and adjacent linear domains where secretory events are higher and lower than expected, effectively redistributing exocytosis over most of the cell membrane. In addition, analysis of KEULE kinetics revealed distinct phases of assembly/disassembly that are differentially sensitive to experimental treatments that reduce exocytosis, revealing SEC/MUNC dynamics as a versatile and information rich read-out of exocytosis in vivo .

Uncovering cell-type-specific immunomodulatory variants and molecular phenotypes in COVID-19 using structurally resolved protein networks

Immunomodulatory variants that lead to the loss or gain of specific protein interactions often manifest only as organismal phenotypes in infectious disease. Here, we propose a network-based approach to integrate genetic variation with a structurally resolved human protein interactome network to prioritize immunomodulatory variants in COVID-19. We find that, in addition to variants that pass genome-wide significance thresholds, variants at the interface of specific protein-protein interactions, even though they do not meet genome-wide thresholds, are equally immunomodulatory. The integration of these variants with single-cell epigenomic and transcriptomic data prioritizes myeloid and T cell subsets as the most affected by these variants across both the peripheral blood and the lung compartments. Of particular interest is a common coding variant that disrupts the OAS1-PRMT6 interaction and affects downstream interferon signaling. Critically, our framework is generalizable across infectious disease contexts and can be used to implicate immunomodulatory variants that do not meet genome-wide significance thresholds.

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 of 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.

Network medicine informed multiomics integration identifies drug targets and repurposable medicines for Amyotrophic Lateral Sclerosis

Amyotrophic Lateral Sclerosis (ALS) is a devastating, immensely complex neurodegenerative disease by lack of effective treatments. We developed a network medicine methodology via integrating human brain multi-omics data to prioritize drug targets and repurposable treatments for ALS. We leveraged non-coding ALS loci effects from genome-wide associated studies (GWAS) on human brain expression quantitative trait loci (QTL) (eQTL), protein QTL (pQTL), splicing QTL (sQTL), methylation QTL (meQTL), and histone acetylation QTL (haQTL). Using a network-based deep learning framework, we identified 105 putative ALS-associated genes enriched in known ALS pathobiological pathways. Applying network proximity analysis of predicted ALS-associated genes and drug-target networks under the human protein-protein interactome (PPI) model, we identified potential repurposable drugs (i.e., Diazoxide and Gefitinib) for ALS. Subsequent validation established preclinical evidence for top-prioritized drugs. In summary, we presented a network-based multi-omics framework to identify drug targets and repurposable treatments for ALS and other neurodegenerative disease if broadly applied.

Amyotrophic lateral sclerosis diagnosis using machine learning and multi-omic data integration

Amyotrophic Lateral Sclerosis (ALS) is a complex and rare neurodegenerative disorder characterized by significant genetic, molecular, and clinical heterogeneity. Despite numerous endeavors to discover the genetic factors underlying ALS, a significant number of these factors remain unknown. This knowledge gap highlights the necessity for personalized medicine approaches that can provide more comprehensive information for the purposes of diagnosis, prognosis, and treatment of ALS. This work utilizes an innovative approach by employing a machine learning-facilitated, multi-omic model to develop a more comprehensive knowledge of ALS. Through unsupervised clustering on gene expression profiles, 9,847 genes associated with ALS pathways are isolated and integrated with 7,699 genes containing rare, presumed pathogenic genomic variants, leading to a comprehensive amalgamation of 17,546 genes. Subsequently, a Variational Autoencoder is applied to distil complex biomedical information from these genes, culminating in the creation of the proposed Multi-Omics for ALS (MOALS) model, which has been designed to expose intricate genotype-phenotype interconnections within the dataset. Our meticulous investigation elucidates several pivotal ALS signaling pathways and demonstrates that MOALS is a superior model, outclassing other machine learning models based on single omic approaches such as SNV and RNA expression, enhancing accuracy by 1.7 percent and 6.2 percent, respectively. The findings of this study suggest that analyzing the relationships within biological systems can provide heuristic insights into the biological mechanisms that help to make highly accurate ALS diagnosis tools and achieve more interpretable results.

TDP43 aggregation at ER-exit sites impairs ER-to-Golgi transport

Protein aggregation plays key roles in age-related degenerative diseases, but how different proteins coalesce to form inclusions that vary in composition, morphology, molecular dynamics and confer physiological consequences is poorly understood. Here we employ a general reporter based on mutant Hsp104 to identify proteins forming aggregates in human cells under common proteotoxic stress. We identify over 300 proteins that form different inclusions containing subsets of aggregating proteins. In particular, TDP43, implicated in Amyotrophic Lateral Sclerosis (ALS), partitions dynamically between two distinct types of aggregates: stress granule and a previously unknown non-dynamic (solid-like) inclusion at the ER exit sites (ERES). TDP43-ERES co-aggregation is induced by diverse proteotoxic stresses and observed in the motor neurons of ALS patients. Such aggregation causes retention of secretory cargos at ERES and therefore delays ER-to-Golgi transport, providing a link between TDP43 aggregation and compromised cellular function in ALS patients.

Improving prediction models of amyotrophic lateral sclerosis (ALS) using polygenic, pre-existing conditions, and survey-based risk scores in the UK Biobank

BACKGROUND AND OBJECTIVES

Amyotrophic lateral sclerosis (ALS) causes profound impairments in neurological function, and a cure for this devastating disease remains elusive. This study aimed to identify pre-disposing genetic, phenotypic, and exposure-related factors for amyotrophic lateral sclerosis using multi-modal data and assess their joint predictive potential.

METHODS

Utilizing data from the UK (United Kingdom) Biobank, we analyzed an unrelated set of 292 ALS cases and 408,831 controls of European descent. Two polygenic risk scores (PRS) are constructed: "GWAS Hits PRS" and "PRS-CS," reflecting oligogenic and polygenic ALS risk profiles, respectively. Time-restricted phenome-wide association studies (PheWAS) were performed to identify pre-existing conditions increasing ALS risk, integrated into phenotypic risk scores (PheRS). A poly-exposure score ("PXS") captures the influence of environmental exposures measured through survey questionnaires. We evaluate the performance of these scores for predicting ALS incidence and stratifying risk, adjusting for baseline demographic covariates.

RESULTS

Both PRSs modestly predicted ALS diagnosis but with increased predictive power when combined (covariate-adjusted receiver operating characteristic [AAUC] = 0.584 [0.525, 0.639]). PheRS incorporated diagnoses 1 year before ALS onset (PheRS1) modestly discriminated cases from controls (AAUC = 0.515 [0.472, 0.564]). The "PXS" did not significantly predict ALS. However, a model incorporating PRSs and PheRS1 improved the prediction of ALS (AAUC = 0.604 [0.547, 0.667]), outperforming a model combining all risk scores. This combined risk score identified the top 10% of risk score distribution with a fourfold higher ALS risk (95% CI [2.04, 7.73]) versus those in the 40%-60% range.

DISCUSSION

By leveraging UK Biobank data, our study uncovers pre-disposing ALS factors, highlighting the improved effectiveness of multi-factorial prediction models to identify individuals at highest risk for ALS.

The changing scenario of drug discovery using AI to deep learning: Recent advancement, success stories, collaborations, and challenges

Due to the transformation of artificial intelligence (AI) tools and technologies, AI-driven drug discovery has come to the forefront. It reduces the time and expenditure. Due to these advantages, pharmaceutical industries are concentrating on AI-driven drug discovery. Several drug molecules have been discovered using AI-based techniques and tools, and several newly AI-discovered drug molecules have already entered clinical trials. In this review, we first present the data and their resources in the pharmaceutical sector for AI-driven drug discovery and illustrated some significant algorithms or techniques used for AI and ML which are used in this field. We gave an overview of the deep neural network (NN) models and compared them with artificial NNs. Then, we illustrate the recent advancement of the landscape of drug discovery using AI to deep learning, such as the identification of drug targets, prediction of their structure, estimation of drug-target interaction, estimation of drug-target binding affinity, design of de novo drug, prediction of drug toxicity, estimation of absorption, distribution, metabolism, excretion, toxicity; and estimation of drug-drug interaction. Moreover, we highlighted the success stories of AI-driven drug discovery and discussed several collaboration and the challenges in this area. The discussions in the article will enrich the pharmaceutical industry.

A comprehensive review of electrophysiological techniques in amyotrophic lateral sclerosis research

Amyotrophic lateral sclerosis (ALS), a devastating neurodegenerative disease, is characterized by progressive motor neuron degeneration, leading to widespread weakness and respiratory failure. While a variety of mechanisms have been proposed as causes of this disease, a full understanding remains elusive. Electrophysiological alterations, including increased motor axon excitability, likely play an important role in disease progression. There remains a critical need for non-animal disease models that can integrate electrophysiological tools to better understand underlying mechanisms, track disease progression, and evaluate potential therapeutic interventions. This review explores the integration of electrophysiological technologies with ALS disease models. It covers cellular and clinical electrophysiological tools and their applications in ALS research. Additionally, we examine conventional animal models and highlight advancements in humanized models and 3D organoid technologies. By bridging the gap between these models, we aim to enhance our understanding of ALS pathogenesis and facilitate the development of new therapeutic strategies.

Intranasal delivery of small extracellular vesicles reduces the progress of amyotrophic lateral sclerosis and the overactivation of complement-coagulation cascade and NF-ĸB signaling in SOD1G93A mice

Amyotrophic lateral sclerosis (ALS) is a fatal disease characterized by progressive motoneuron degeneration, and effective clinical treatments are lacking. In this study, we evaluated whether intranasal delivery of mesenchymal stem cell-derived small extracellular vesicles (sEVs) is a strategy for ALS therapy using SOD1G93A mice. In vivo tracing showed that intranasally-delivered sEVs entered the central nervous system and were extensively taken up by spinal neurons and some microglia. SOD1G93A mice that intranasally received sEV administration showed significant improvements in motor performances and survival time. After sEV administration, pathological changes, including spinal motoneuron death and synaptic denervation, axon demyelination, neuromuscular junction degeneration and electrophysiological defects, and mitochondrial vacuolization were remarkably alleviated. sEV administration attenuated the elevation of proinflammatory cytokines and glial responses. Proteomics and transcriptomics analysis revealed upregulation of the complement and coagulation cascade and NF-ĸB signaling pathway in SOD1G93A mouse spinal cords, which was significantly inhibited by sEV administration. The changes were further confirmed by detecting C1q and NF-ĸB expression using Western blots. In conclusion, intranasal administration of sEVs effectively delays the progression of ALS by inhibiting neuroinflammation and overactivation of the complement and coagulation cascades and NF-ĸB signaling pathway and is a potential option for ALS therapy.

Structural variants linked to Alzheimer's Disease and other common age-related clinical and neuropathologic traits

Advances have led to a greater understanding of the genetics of Alzheimer's Disease (AD). However, the gap between the predicted and observed genetic heritability estimates when using single nucleotide polymorphisms (SNPs) and small indel data remains. Large genomic rearrangements, known as structural variants (SVs), have the potential to account for this missing genetic heritability. By leveraging data from two ongoing cohort studies of aging and dementia, the Religious Orders Study and Rush Memory and Aging Project (ROS/MAP), we performed genome-wide association analysis testing around 20,000 common SVs from 1,088 participants with whole genome sequencing (WGS) data. A range of Alzheimer's Disease and Related Disorders (AD/ADRD) clinical and pathologic traits were examined. Given the limited sample size, no genome-wide significant association was found, but we mapped SVs across 81 AD risk loci and discovered 22 SVs in linkage disequilibrium (LD) with GWAS lead variants and directly associated with AD/ADRD phenotypes (nominal P < 0.05). The strongest association was a deletion of an Alu element in the 3'UTR of the TMEM106B gene. This SV was in high LD with the respective AD GWAS locus and was associated with multiple AD/ADRD phenotypes, including tangle density, TDP-43, and cognitive resilience. The deletion of this element was also linked to lower TMEM106B protein abundance. We also found a 22 kb deletion associated with depression in ROSMAP and bearing similar association patterns as AD GWAS SNPs at the IQCK locus. In addition, genome-wide scans allowed the identification of 7 SVs, with no LD with SNPs and nominally associated with AD/ADRD traits. This result suggests potentially new ADRD risk loci not discoverable using SNP data. Among these findings, we highlight a 5.6 kb duplication of coding regions of the gene C1orf186 at chromosome 1 associated with indices of cognitive impairment, decline, and resilience. While further replication in independent datasets is needed to validate these findings, our results support the potential roles of common structural variations in the pathogenesis of AD/ADRD.

The best linear unbiased prediction (BLUP) method as a tool to estimate the lifetime risk of pancreatic ductal adenocarcinoma in high-risk individuals with no known pathogenic germline variants

Pancreatic ductal adenocarcinoma (PDAC) is the fourth leading cause of cancer-related death in the Western world. The number of diagnosed cases and the mortality rate are almost equal as the majority of patients present with advanced disease at diagnosis. Between 4 and 10% of pancreatic cancer cases have an apparent hereditary background, known as hereditary pancreatic cancer (HPC) and familial pancreatic cancer (FPC), when the genetic basis is unknown. Surveillance of high-risk individuals (HRI) from these families by imaging aims to detect PDAC at an early stage to improve prognosis. However, the genetic basis is unknown in the majority of HRIs, with only around 10-13% of families carrying known pathogenic germline mutations. The aim of this study was to assess an individual's genetic cancer risk based on sex and personal and family history of cancer. The Best Linear Unbiased Prediction (BLUP) methodology was used to estimate an individual's predicted risk of developing cancer during their lifetime. The model uses different demographic factors in order to estimate heritability. A reliable estimation of heritability for pancreatic cancer of 0.27 on the liability scale, and 0.07 at the observed data scale as obtained, which is different from zero, indicating a polygenic inheritance pattern of PDAC. BLUP was able to correctly discriminate PDAC cases from healthy individuals and those with other cancer types. Thus, providing an additional tool to assess PDAC risk HRI with an assumed genetic predisposition in the absence of known pathogenic germline mutations.

Spatial enrichment and genomic analyses reveal the link of NOMO1 with amyotrophic lateral sclerosis

Amyotrophic lateral sclerosis (ALS) is a severe motor neuron disease with uncertain genetic predisposition in most sporadic cases. The spatial architecture of cell types and gene expression are the basis of cell-cell interactions, biological function and disease pathology, but are not well investigated in the human motor cortex, a key ALS-relevant brain region. Recent studies indicated single nucleus transcriptomic features of motor neuron vulnerability in ALS motor cortex. However, the brain regional vulnerability of ALS-associated genes and the genetic link between region-specific genes and ALS risk remain largely unclear. Here, we developed an entropy-weighted differential gene expression matrix-based tool (SpatialE) to identify the spatial enrichment of gene sets in spatial transcriptomics. We benchmarked SpatialE against another enrichment tool (multimodal intersection analysis) using spatial transcriptomics data from both human and mouse brain tissues. To investigate regional vulnerability, we analysed three human motor cortex and two dorsolateral prefrontal cortex tissues for spatial enrichment of ALS-associated genes. We also used Cell2location to estimate the abundance of cell types in ALS-related cortex layers. To dissect the link of regionally expressed genes and ALS risk, we performed burden analyses of rare loss-of-function variants detected by whole-genome sequencing in ALS patients and controls, then analysed differential gene expression in the TargetALS RNA-sequencing dataset. SpatialE showed more accurate and specific spatial enrichment of regional cell type markers than multimodal intersection analysis in both mouse brain and human dorsolateral prefrontal cortex. Spatial transcriptomic analyses of human motor cortex showed heterogeneous cell types and spatial gene expression profiles. We found that 260 manually curated ALS-associated genes are significantly enriched in layer 5 of the motor cortex, with abundant expression of upper motor neurons and layer 5 excitatory neurons. Burden analyses of rare loss-of-function variants in Layer 5-associated genes nominated NOMO1 as a novel ALS-associated gene in a combined sample set of 6814 ALS patients and 3324 controls (P = 0.029). Gene expression analyses in CNS tissues revealed downregulation of NOMO1 in ALS, which is consistent with a loss-of-function disease mechanism. In conclusion, our integrated spatial transcriptomics and genomic analyses identified regional brain vulnerability in ALS and the association of a layer 5 gene (NOMO1) with ALS risk.

Aberrant protein aggregation in amyotrophic lateral sclerosis

Amyotrophic lateral sclerosis (ALS) is a fatal disease. As its pathological mechanisms are not well understood, there are no efficient therapeutics for it at present. While it is highly heterogenous both etiologically and clinically, it has a common salient hallmark, i.e., aberrant protein aggregation (APA). The upstream pathogenesis and the downstream effects of APA in ALS are sophisticated and the investigation of this pathology would be of consequence for understanding ALS. In this paper, the pathomechanism of APA in ALS and the candidate treatment strategies for it are discussed.

Identifying dysregulated regions in amyotrophic lateral sclerosis through chromatin accessibility outliers

The high heritability of amyotrophic lateral sclerosis (ALS) contrasts with its low molecular diagnosis rate post-genetic testing, pointing to potential undiscovered genetic factors. To aid the exploration of these factors, we introduced EpiOut, an algorithm to identify chromatin accessibility outliers that are regions exhibiting divergent accessibility from the population baseline in a single or few samples. Annotation of accessible regions with histone chromatin immunoprecipitation sequencing and Hi-C indicates that outliers are concentrated in functional loci, especially among promoters interacting with active enhancers. Across different omics levels, outliers are robustly replicated, and chromatin accessibility outliers are reliable predictors of gene expression outliers and aberrant protein levels. When promoter accessibility does not align with gene expression, our results indicate that molecular aberrations are more likely to be linked to post-transcriptional regulation rather than transcriptional regulation. Our findings demonstrate that the outlier detection paradigm can uncover dysregulated regions in rare diseases. EpiOut is available at github.com/uci-cbcl/EpiOut.

Cellular and axonal transport phenotypes due to the C9ORF72 HRE in iPSC motor and sensory neurons

Induced pluripotent stem cell (iPSC)-derived motor neurons (MNs) from patients with amyotrophic lateral sclerosis (ALS) and the C9ORF72 hexanucleotide repeat expansion (HRE) have multiple cellular phenotypes, but which of these accurately reflect the biology underlying the cell-specific vulnerability of ALS is uncertain. We therefore compared phenotypes due to the C9ORF72 HRE in MNs with sensory neurons (SNs), which are relatively spared in ALS. The iPSC models were able to partially reproduce the differential gene expression seen between adult SNs and MNs. We demonstrated that the typical hallmarks of C9ORF72-ALS, including RNA foci and dipeptide formation, as well as specific axonal transport defects, occurred equally in MNs and SNs, suggesting that these in vitro phenotypes are not sufficient to explain the cell-type selectivity of ALS in isolation.

A TrkB and TrkC partial agonist restores deficits in synaptic function and promotes activity-dependent synaptic and microglial transcriptomic changes in a late-stage Alzheimer's mouse model

INTRODUCTION

Tropomyosin related kinase B (TrkB) and C (TrkC) receptor signaling promotes synaptic plasticity and interacts with pathways affected by amyloid beta (Aβ) toxicity. Upregulating TrkB/C signaling could reduce Alzheimer's disease (AD)-related degenerative signaling, memory loss, and synaptic dysfunction.

METHODS

PTX-BD10-2 (BD10-2), a small molecule TrkB/C receptor partial agonist, was orally administered to aged London/Swedish-APP mutant mice (APPL/S) and wild-type controls. Effects on memory and hippocampal long-term potentiation (LTP) were assessed using electrophysiology, behavioral studies, immunoblotting, immunofluorescence staining, and RNA sequencing.

RESULTS

In APPL/S mice, BD10-2 treatment improved memory and LTP deficits. This was accompanied by normalized phosphorylation of protein kinase B (Akt), calcium-calmodulin-dependent kinase II (CaMKII), and AMPA-type glutamate receptors containing the subunit GluA1; enhanced activity-dependent recruitment of synaptic proteins; and increased excitatory synapse number. BD10-2 also had potentially favorable effects on LTP-dependent complement pathway and synaptic gene transcription.

DISCUSSION

BD10-2 prevented APPL/S/Aβ-associated memory and LTP deficits, reduced abnormalities in synapse-related signaling and activity-dependent transcription of synaptic genes, and bolstered transcriptional changes associated with microglial immune response.

HIGHLIGHTS

Small molecule modulation of tropomyosin related kinase B (TrkB) and C (TrkC) restores long-term potentiation (LTP) and behavior in an Alzheimer's disease (AD) model. Modulation of TrkB and TrkC regulates synaptic activity-dependent transcription. TrkB and TrkC receptors are candidate targets for translational therapeutics. Electrophysiology combined with transcriptomics elucidates synaptic restoration. LTP identifies neuron and microglia AD-relevant human-mouse co-expression modules.

RNAi-mediated silencing of SOD1 profoundly extends survival and functional outcomes in ALS mice

Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative condition, with 20% of familial and 2-3% of sporadic cases linked to mutations in the cytosolic superoxide dismutase (SOD1) gene. Mutant SOD1 protein is toxic to motor neurons, making SOD1 gene lowering a promising approach, supported by preclinical data and the 2023 FDA approval of the GapmeR ASO targeting SOD1, tofersen. Despite the approval of an ASO and the optimism it brings to the field, the pharmacodynamics and pharmacokinetics of therapeutic SOD1 modulation can be improved. Here, we developed a chemically stabilized divalent siRNA scaffold (di-siRNA) that effectively suppresses SOD1 expression in vitro and in vivo. With optimized chemical modification, it achieves remarkable CNS tissue permeation and SOD1 silencing in vivo. Administered intraventricularly, di-siRNASOD1 extended survival in SOD1-G93A ALS mice, surpassing survival previously seen in these mice by ASO modalities, slowed disease progression, and prevented ALS neuropathology. These properties offer an improved therapeutic strategy for SOD1-mediated ALS and may extend to other dominantly inherited neurological disorders.

Non-coding genome contribution to ALS

The majority of amyotrophic lateral sclerosis (ALS) is caused by a complex gene-environment interaction. Despite high estimates of heritability, the genetic basis of disease in the majority of ALS patients are unknown. This limits the development of targeted genetic therapies which require an understanding of patient-specific genetic drivers. There is good evidence that the majority of these missing genetic risk factors are likely to be found within the non-coding genome. However, a major challenge in the discovery of non-coding risk variants is determining which variants are functional in which specific CNS cell type. We summarise current discoveries of ALS-associated genetic drivers within the non-coding genome and we make the case that improved cell-specific annotation of genomic function is required to advance this field, particularly via single-cell epigenetic profiling and spatial transcriptomics. We highlight the example of TBK1 where an apparent paradox exists between pathogenic coding variants which cause loss of protein function, and protective non-coding variants which cause reduced gene expression; the paradox is resolved when it is understood that the non-coding variants are acting primarily via change in gene expression within microglia, and the effect of coding variants is most prominent in neurons. We propose that cell-specific functional annotation of ALS-associated genetic variants will accelerate discovery of the genetic architecture underpinning disease in the vast majority of patients.

Deconvolution of polygenic risk score in single cells unravels cellular and molecular heterogeneity of complex human diseases

Polygenic risk scores (PRSs) are commonly used for predicting an individual's genetic risk of complex diseases. Yet, their implication for disease pathogenesis remains largely limited. Here, we introduce scPRS, a geometric deep learning model that constructs single-cell-resolved PRS leveraging reference single-cell chromatin accessibility profiling data to enhance biological discovery as well as disease prediction. Real-world applications across multiple complex diseases, including type 2 diabetes (T2D), hypertrophic cardiomyopathy (HCM), and Alzheimer's disease (AD), showcase the superior prediction power of scPRS compared to traditional PRS methods. Importantly, scPRS not only predicts disease risk but also uncovers disease-relevant cells, such as hormone-high alpha and beta cells for T2D, cardiomyocytes and pericytes for HCM, and astrocytes, microglia and oligodendrocyte progenitor cells for AD. Facilitated by a layered multi-omic analysis, scPRS further identifies cell-type-specific genetic underpinnings, linking disease-associated genetic variants to gene regulation within corresponding cell types. We substantiate the disease relevance of scPRS-prioritized HCM genes and demonstrate that the suppression of these genes in HCM cardiomyocytes is rescued by Mavacamten treatment. Additionally, we establish a novel microglia-specific regulatory relationship between the AD risk variant rs7922621 and its target genes ANXA11 and TSPAN14. We further illustrate the detrimental effects of suppressing these two genes on microglia phagocytosis. Our work provides a multi-tasking, interpretable framework for precise disease prediction and systematic investigation of the genetic, cellular, and molecular basis of complex diseases, laying the methodological foundation for single-cell genetics.

Meta-analysis of differential gene expression in lower motor neurons isolated by laser capture microdissection from post-mortem ALS spinal cords

Introduction

ALS is a fatal neurodegenerative disease for which underlying mechanisms are incompletely understood. The motor neuron is a central player in ALS pathogenesis but different transcriptome signatures have been derived from bulk analysis of post-mortem tissue and iPSC-derived motor neurons (iPSC-MNs).

Methods

This study performed a meta-analysis of six gene expression studies (microarray and RNA-seq) in which laser capture microdissection (LCM) was used to isolate lower motor neurons from post-mortem spinal cords of ALS and control (CTL) subjects. Differentially expressed genes (DEGs) with consistent ALS versus CTL expression differences across studies were identified.

Results

The analysis identified 222 ALS-increased DEGs (FDR <0.10, SMD >0.80) and 278 ALS-decreased DEGs (FDR <0.10, SMD < -0.80). ALS-increased DEGs were linked to PI3K-AKT signaling, innate immunity, inflammation, motor neuron differentiation and extracellular matrix. ALS-decreased DEGs were associated with the ubiquitin-proteosome system, microtubules, axon growth, RNA-binding proteins and synaptic membrane. ALS-decreased DEG mRNAs frequently interacted with RNA-binding proteins (e.g., FUS, HuR). The complete set of DEGs (increased and decreased) overlapped significantly with genes near ALS-associated SNP loci (p < 0.01). Transcription factor target motifs with increased proximity to ALS-increased DEGs were identified, most notably DNA elements predicted to interact with forkhead transcription factors (e.g., FOXP1) and motor neuron and pancreas homeobox 1 (MNX1). Some of these DNA elements overlie ALS-associated SNPs within known enhancers and are predicted to have genotype-dependent MNX1 interactions. DEGs were compared to those identified from SOD1-G93A mice and bulk spinal cord segments or iPSC-MNs from ALS patients. There was good correspondence with transcriptome changes from SOD1-G93A mice (r ≤ 0.408) but most DEGs were not differentially expressed in bulk spinal cords or iPSC-MNs and transcriptome-wide effect size correlations were weak (bulk tissue: r ≤ 0.207, iPSC-MN: r ≤ 0.037).

Conclusion

This study defines a robust transcriptome signature from LCM-based motor neuron studies of post-mortem tissue from ALS and CTL subjects. This signature differs from those obtained from analysis of bulk spinal cord segments and iPSC-MNs. Results provide insight into mechanisms underlying gene dysregulation in ALS and highlight connections between these mechanisms, ALS genetics, and motor neuron biology.

Deep learning modeling of rare noncoding genetic variants in human motor neurons defines CCDC146 as a therapeutic target for ALS

Amyotrophic lateral sclerosis (ALS) is a fatal and incurable neurodegenerative disease caused by the selective and progressive death of motor neurons (MNs). Understanding the genetic and molecular factors influencing ALS survival is crucial for disease management and therapeutics. In this study, we introduce a deep learning-powered genetic analysis framework to link rare noncoding genetic variants to ALS survival. Using data from human induced pluripotent stem cell (iPSC)-derived MNs, this method prioritizes functional noncoding variants using deep learning, links cis-regulatory elements (CREs) to target genes using epigenomics data, and integrates these data through gene-level burden tests to identify survival-modifying variants, CREs, and genes. We apply this approach to analyze 6,715 ALS genomes, and pinpoint four novel rare noncoding variants associated with survival, including chr7:76,009,472:C>T linked to CCDC146. CRISPR-Cas9 editing of this variant increases CCDC146 expression in iPSC-derived MNs and exacerbates ALS-specific phenotypes, including TDP-43 mislocalization. Suppressing CCDC146 with an antisense oligonucleotide (ASO), showing no toxicity, completely rescues ALS-associated survival defects in neurons derived from sporadic ALS patients and from carriers of the ALS-associated G4C2-repeat expansion within C9ORF72. ASO targeting of CCDC146 may be a broadly effective therapeutic approach for ALS. Our framework provides a generic and powerful approach for studying noncoding genetics of complex human diseases.

Network medicine informed multi-omics integration identifies drug targets and repurposable medicines for Amyotrophic Lateral Sclerosis

Amyotrophic Lateral Sclerosis (ALS) is a devastating, immensely complex neurodegenerative disease by lack of effective treatments. To date, the challenge to establishing effective treatment for ALS remains formidable, partly due to inadequate translation of existing human genetic findings into actionable ALS-specific pathobiology for subsequent therapeutic development. This study evaluates the feasibility of network medicine methodology via integrating human brain-specific multi-omics data to prioritize drug targets and repurposable treatments for ALS. Using human brain-specific genome-wide quantitative trait loci (x-QTLs) under a network-based deep learning framework, we identified 105 putative ALS-associated genes enriched in various known ALS pathobiological pathways, including regulation of T cell activation, monocyte differentiation, and lymphocyte proliferation. Specifically, we leveraged non-coding ALS loci effects from genome-wide associated studies (GWAS) on brain-specific expression quantitative trait loci (QTL) (eQTL), protein QTLs (pQTL), splicing QTL (sQTL), methylation QTL (meQTL), and histone acetylation QTL (haQTL). Applying network proximity analysis of predicted ALS-associated gene-coding targets and existing drug-target networks under the human protein-protein interactome (PPI) model, we identified a set of potential repurposable drugs (including Diazoxide, Gefitinib, Paliperidone, and Dimethyltryptamine) for ALS. Subsequent validation established preclinical and clinical evidence for top-prioritized repurposable drugs. In summary, we presented a network-based multi-omics framework to identify potential drug targets and repurposable treatments for ALS and other neurodegenerative disease if broadly applied.

Improving prediction models of amyotrophic lateral sclerosis (ALS) using polygenic, pre-existing conditions, and survey-based risk scores in the UK Biobank

Background and Objectives

Amyotrophic lateral sclerosis (ALS) causes profound impairments in neurological function and a cure for this devastating disease remains elusive. Early detection and risk stratification are crucial for timely intervention and improving patient outcomes. This study aimed to identify predisposing genetic, phenotypic, and exposure-related factors for Amyotrophic lateral sclerosis using multi-modal data and assess their joint predictive potential.

Methods

Utilizing data from the UK Biobank, we analyzed an unrelated set of 292 ALS cases and 408,831 controls of European descent. Two polygenic risk scores (PRS) are constructed: "GWAS Hits PRS" and "PRS-CS," reflecting oligogenic and polygenic ALS risk profiles, respectively. Time-restricted phenome-wide association studies (PheWAS) were performed to identify pre-existing conditions increasing ALS risk, integrated into phenotypic risk scores (PheRS). A poly-exposure score ("PXS") captures the influence of environmental exposures measured through survey questionnaires. We evaluate the performance of these scores for predicting ALS incidence and stratifying risk, adjusting for baseline demographic covariates.

Results

Both PRSs modestly predicted ALS diagnosis, but with increased predictive power when combined (covariate-adjusted receiver operating characteristic [AAUC] = 0.584 [0.525, 0.639]). PheRS incorporated diagnoses 1 year before ALS onset (PheRS1) modestly discriminated cases from controls (AAUC = 0.515 [0.472, 0.564]). The "PXS" did not significantly predict ALS. However, a model incorporating PRSs and PheRS1 improved prediction of ALS (AAUC = 0.604 [0.547, 0.667]), outperforming a model combining all risk scores. This combined risk score identified the top 10% of risk score distribution with a 4-fold higher ALS risk (95% CI: [2.04, 7.73]) versus those in the 40%-60% range.

Discussions

By leveraging UK Biobank data, our study uncovers predisposing ALS factors, highlighting the improved effectiveness of multi-factorial prediction models to identify individuals at highest risk for ALS.

Progressive Irreversible Proprioceptive Piezo2 Channelopathy-Induced Lost Forced Peripheral Oscillatory Synchronization to the Hippocampal Oscillator May Explain the Onset of Amyotrophic Lateral Sclerosis Pathomechanism

Amyotrophic lateral sclerosis (ALS) is a mysterious lethal multisystem neurodegenerative disease that gradually leads to the progressive loss of motor neurons. A recent non-contact dying-back injury mechanism theory for ALS proposed that the primary damage is an acquired irreversible intrafusal proprioceptive terminal Piezo2 channelopathy with underlying genetic and environmental risk factors. Underpinning this is the theory that excessively prolonged proprioceptive mechanotransduction under allostasis may induce dysfunctionality in mitochondria, leading to Piezo2 channelopathy. This microinjury is suggested to provide one gateway from physiology to pathophysiology. The chronic, but not irreversible, form of this Piezo2 channelopathy is implicated in many diseases with unknown etiology. Dry eye disease is one of them where replenishing synthetic proteoglycans promote nerve regeneration. Syndecans, especially syndecan-3, are proposed as the first critical link in this hierarchical ordered depletory pathomechanism as proton-collecting/distributing antennas; hence, they may play a role in ALS pathomechanism onset. Even more importantly, the shedding or charge-altering variants of Syndecan-3 may contribute to the Piezo2 channelopathy-induced disruption of the Piezo2-initiated proton-based ultrafast long-range signaling through VGLUT1 and VGLUT2. Thus, these alterations may not only cause disruption to ultrafast signaling to the hippocampus in conscious proprioception, but could disrupt the ultrafast proprioceptive signaling feedback to the motoneurons. Correspondingly, an inert Piezo2-initiated proton-based ultrafast signaled proprioceptive skeletal system is coming to light that is suggested to be progressively lost in ALS. In addition, the lost functional link of the MyoD family of inhibitor proteins, as auxiliary subunits of Piezo2, may not only contribute to the theorized acquired Piezo2 channelopathy, but may explain how these microinjured ion channels evolve to be principal transcription activators.

The multifaceted functions of β-arrestins and their therapeutic potential in neurodegenerative diseases

Arrestins are multifunctional proteins that regulate G-protein-coupled receptor (GPCR) desensitization, signaling, and internalization. The arrestin family consists of four subtypes: visual arrestin1, β-arrestin1, β-arrestin2, and visual arrestin-4. Recent studies have revealed the multifunctional roles of β-arrestins beyond GPCR signaling, including scaffolding and adapter functions, and physically interacting with non-GPCR receptors. Increasing evidence suggests that β-arrestins are involved in the pathogenesis of a variety of neurodegenerative diseases, including Alzheimer's disease (AD), frontotemporal dementia (FTD), and Parkinson's disease (PD). β-arrestins physically interact with γ-secretase, leading to increased production and accumulation of amyloid-beta in AD. Furthermore, β-arrestin oligomers inhibit the autophagy cargo receptor p62/SQSTM1, resulting in tau accumulation and aggregation in FTD. In PD, β-arrestins are upregulated in postmortem brain tissue and an MPTP model, and the β2AR regulates SNCA gene expression. In this review, we aim to provide an overview of β-arrestin1 and β-arrestin2, and describe their physiological functions and roles in neurodegenerative diseases. The multifaceted roles of β-arrestins and their involvement in neurodegenerative diseases suggest that they may serve as promising therapeutic targets.

TDP-43 chronic deficiency leads to dysregulation of transposable elements and gene expression by affecting R-loop and 5hmC crosstalk

TDP-43 is an RNA/DNA-binding protein that forms aggregates in various brain disorders. TDP-43 engages in many aspects of RNA metabolism, but its molecular roles in regulating genes and transposable elements (TEs) have not been extensively explored. Chronic TDP-43 knockdown impairs cell proliferation and cellular responses to DNA damage. At the molecular level, TDP-43 chronic deficiency affects gene expression either locally or distally by concomitantly altering the crosstalk between R-loops and 5-hydroxymethylcytosine (5hmC) in gene bodies and long-range enhancer/promoter interactions. Furthermore, TDP-43 knockdown induces substantial disease-relevant TE activation by influencing their R-loop and 5hmC homeostasis in a locus-specific manner. Together, our findings highlight the genomic roles of TDP-43 in modulating R-loop-5hmC coordination in coding genes, distal regulatory elements, and TEs, presenting a general and broad molecular mechanism underlying the contributions of proteinopathies to the etiology of neurodegenerative disorders.

Machine learning hypothesis-generation for patient stratification and target discovery in rare disease: our experience with Open Science in ALS

Introduction

Advances in machine learning (ML) methodologies, combined with multidisciplinary collaborations across biological and physical sciences, has the potential to propel drug discovery and development. Open Science fosters this collaboration by releasing datasets and methods into the public space; however, further education and widespread acceptance and adoption of Open Science approaches are necessary to tackle the plethora of known disease states.

Motivation

In addition to providing much needed insights into potential therapeutic protein targets, we also aim to demonstrate that small patient datasets have the potential to provide insights that usually require many samples (>5,000). There are many such datasets available and novel advancements in ML can provide valuable insights from these patient datasets.

Problem statement

Using a public dataset made available by patient advocacy group AnswerALS and a multidisciplinary Open Science approach with a systems biology augmented ML technology, we aim to validate previously reported drug targets in ALS and provide novel insights about ALS subpopulations and potential drug targets using a unique combination of ML methods and graph theory.

Methodology

We use NetraAI to generate hypotheses about specific patient subpopulations, which were then refined and validated through a combination of ML techniques, systems biology methods, and expert input.

Results

We extracted 8 target classes, each comprising of several genes that shed light into ALS pathophysiology and represent new avenues for treatment. These target classes are broadly categorized as inflammation, epigenetic, heat shock, neuromuscular junction, autophagy, apoptosis, axonal transport, and excitotoxicity. These findings are not mutually exclusive, and instead represent a systematic view of ALS pathophysiology. Based on these findings, we suggest that simultaneous targeting of ALS has the potential to mitigate ALS progression, with the plausibility of maintaining and sustaining an improved quality of life (QoL) for ALS patients. Even further, we identified subpopulations based on disease onset.

Conclusion

In the spirit of Open Science, this work aims to bridge the knowledge gap in ALS pathophysiology to aid in diagnostic, prognostic, and therapeutic strategies and pave the way for the development of personalized treatments tailored to the individual's needs.

Interpretation of Neurodegenerative GWAS Risk Alleles in Microglia and their Interplay with Other Cell Types

Microglia have been implicated in numerous neurodegenerative and neuroinflammatory disorders; however, the causal contribution of this immune cell type is frequently debated. Genetic studies offer a unique vantage point in that they infer causality over a secondary consequence. Genome-wide association studies (GWASs) have identified hundreds of loci in the genome that are associated with susceptibility to neurodegenerative disorders. GWAS studies implicate microglia in the pathogenesis of Alzheimer's disease (AD), Parkinson's disease (PD), multiple sclerosis (MS), and to a lesser degree suggest a role for microglia in vascular dementia (VaD), frontotemporal dementia (FTD), and amyotrophic lateral sclerosis (ALS), and other neurodegenerative and neuropsychiatric disorders. The contribution and function of GWAS risk loci on disease progression is an ongoing field of study, in which large genomic datasets, and an extensive framework of computational tools, have proven to be crucial. Several GWAS risk loci are shared between disorders, pointing towards common pleiotropic mechanisms. In this chapter, we introduce key concepts in GWAS and post-GWAS interpretation of neurodegenerative disorders, with a focus on GWAS risk genes implicated in microglia, their interplay with other cell types and shared convergence of GWAS risk loci on microglia.

Abnormal Brain Protein Abundance and Cross-tissue mRNA Expression in Amyotrophic Lateral Sclerosis

Due to the limitations of the present risk genes in understanding the etiology of amyotrophic lateral sclerosis (ALS), it is necessary to find additional causative genes utilizing novel approaches. In this study, we conducted a two-stage proteome-wide association study (PWAS) using ALS genome-wide association study (GWAS) data (N = 152,268) and two distinct human brain protein quantitative trait loci (pQTL) datasets (ROSMAP N = 376 and Banner N = 152) to identify ALS risk genes and prioritized candidate genes with Mendelian randomization (MR) and Bayesian colocalization analysis. Next, we verified the aberrant expression of risk genes in multiple tissues, including lower motor neurons, skeletal muscle, and whole blood. Six ALS risk genes (SCFD1, SARM1, TMEM175, BCS1L, WIPI2, and DHRS11) were found during the PWAS discovery phase, and SARM1 and BCS1L were confirmed during the validation phase. The following MR (p = 2.10 × 10-7) and Bayesian colocalization analysis (ROSMAP PP4 = 0.999, Banner PP4 = 0.999) confirmed the causal association between SARM1 and ALS. Further differential expression analysis revealed that SARM1 was markedly downregulated in lower motor neurons (p = 7.64 × 10-3), skeletal muscle (p = 9.34 × 10-3), and whole blood (p = 1.94 × 10-3). Our findings identified some promising protein candidates for future investigation as therapeutic targets. The dysregulation of SARM1 in multiple tissues provides a new way to explain ALS pathology.

Finding Needles in the Haystack: Strategies for Uncovering Noncoding Regulatory Variants

Despite accumulating evidence implicating noncoding variants in human diseases, unraveling their functionality remains a significant challenge. Systematic annotations of the regulatory landscape and the growth of sequence variant data sets have fueled the development of tools and methods to identify causal noncoding variants and evaluate their regulatory effects. Here, we review the latest advances in the field and discuss potential future research avenues to gain a more in-depth understanding of noncoding regulatory variants.

iPSC motor neurons, but not other derived cell types, capture gene expression changes in postmortem sporadic ALS motor neurons

Motor neuron degeneration, the defining feature of amyotrophic lateral sclerosis (ALS), is a primary example of cell-type specificity in neurodegenerative diseases. Using isogenic pairs of induced pluripotent stem cells (iPSCs) harboring different familial ALS mutations, we assess the capacity of iPSC-derived lower motor neurons, sensory neurons, astrocytes, and superficial cortical neurons to capture disease features including transcriptional and splicing dysregulation observed in human postmortem neurons. At early time points, differentially regulated genes in iPSC-derived lower motor neurons, but not other cell types, overlap with one-third of the differentially regulated genes in laser-dissected motor neurons from ALS compared with control postmortem spinal cords. For genes altered in both the iPSC model and bona fide human lower motor neurons, expression changes correlate between the two populations. In iPSC-derived lower motor neurons, but not other derived cell types, we detect the downregulation of genes affected by TDP-43-dependent splicing. This reduction takes place exclusively within genotypes known to involve TDP-43 pathology.

A TrkB and TrkC partial agonist restores deficits in synaptic function and promotes activity-dependent synaptic and microglial transcriptomic changes in a late-stage Alzheimer's mouse model

Introduction

TrkB and TrkC receptor signaling promotes synaptic plasticity and interacts with pathways affected by amyloid-β (Aβ)-toxicity. Upregulating TrkB/C signaling could reduce Alzheimer's disease (AD)-related degenerative signaling, memory loss, and synaptic dysfunction.

Methods

PTX-BD10-2 (BD10-2), a small molecule TrkB/C receptor partial agonist, was orally administered to aged London/Swedish-APP mutant mice (APP L/S ) and wild-type controls (WT). Effects on memory and hippocampal long-term potentiation (LTP) were assessed using electrophysiology, behavioral studies, immunoblotting, immunofluorescence staining, and RNA-sequencing.

Results

Memory and LTP deficits in APP L/S mice were attenuated by treatment with BD10-2. BD10-2 prevented aberrant AKT, CaMKII, and GLUA1 phosphorylation, and enhanced activity-dependent recruitment of synaptic proteins. BD10-2 also had potentially favorable effects on LTP-dependent complement pathway and synaptic gene transcription.

Conclusions

BD10-2 prevented APP L/S /Aβ-associated memory and LTP deficits, reduced abnormalities in synapse-related signaling and activity-dependent transcription of synaptic genes, and bolstered transcriptional changes associated with microglial immune response.

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

Amyotrophic lateral sclerosis is a fatal and incurable neurodegenerative disease that mainly affects the neurons of the motor system. Despite the increasing understanding of its genetic components, their biological meanings are still poorly understood. Indeed, it is still not clear to which extent the pathological features associated with amyotrophic lateral sclerosis are commonly shared by the different genes causally linked to this disorder. To address this point, we combined multiomics analysis covering the transcriptional, epigenetic and mutational aspects of heterogenous human induced pluripotent stem cell-derived C9orf72-, TARDBP-, SOD1- and FUS-mutant motor neurons as well as datasets from patients' biopsies. We identified a common signature, converging towards increased stress and synaptic abnormalities, which reflects a unifying transcriptional program in amyotrophic lateral sclerosis despite the specific profiles due to the underlying pathogenic gene. In addition, whole genome bisulphite sequencing linked the altered gene expression observed in mutant cells to their methylation profile, highlighting deep epigenetic alterations as part of the abnormal transcriptional signatures linked to amyotrophic lateral sclerosis. We then applied multi-layer deep machine-learning to integrate publicly available blood and spinal cord transcriptomes and found a statistically significant correlation between their top predictor gene sets, which were significantly enriched in toll-like receptor signalling. Notably, the overrepresentation of this biological term also correlated with the transcriptional signature identified in mutant human induced pluripotent stem cell-derived motor neurons, highlighting novel insights into amyotrophic lateral sclerosis marker genes in a tissue-independent manner. Finally, using whole genome sequencing in combination with deep learning, we generated the first mutational signature for amyotrophic lateral sclerosis and defined a specific genomic profile for this disease, which is significantly correlated to ageing signatures, hinting at age as a major player in amyotrophic lateral sclerosis. This work describes innovative methodological approaches for the identification of disease signatures through the combination of multiomics analysis and provides novel knowledge on the pathological convergencies defining amyotrophic lateral sclerosis.

AI-powered therapeutic target discovery

Disease modeling and target identification are the most crucial initial steps in drug discovery, and influence the probability of success at every step of drug development. Traditional target identification is a time-consuming process that takes years to decades and usually starts in an academic setting. Given its advantages of analyzing large datasets and intricate biological networks, artificial intelligence (AI) is playing a growing role in modern drug target identification. We review recent advances in target discovery, focusing on breakthroughs in AI-driven therapeutic target exploration. We also discuss the importance of striking a balance between novelty and confidence in target selection. An increasing number of AI-identified targets are being validated through experiments and several AI-derived drugs are entering clinical trials; we highlight current limitations and potential pathways for moving forward.

Another de novo mutation in the SOD1 gene: the first Turkish patient with SOD1-His47Arg, a case report

Amyotrophic lateral sclerosis (ALS) is a fatal, progressive neurodegenerative disease of motor neurons. Most ALS cases are considered sporadic due to the presence of a combination of environmental and complex genetic risk factors, while approximately 10% of cases have a family history. Pathogenic variants in the SOD1 gene are the second most frequent causative factor of genetics-based ALS worldwide, after C9ORF72 hexanucleotide repeat expansion. The De novo occurrence of pathogenic mutations in ALS-associated genes and its effect on disease progression have been studied previously, especially in the FUS gene. Recent studies have shown that a very small portion of SOD1 cases occurred de novo. Here, we present the first de novo case of the SOD1 His47Arg mutation in a young female patient with mild symptoms and, currently, a slow progression for 7 years.