Transcriptional analysis reveals distinct subtypes in amyotrophic lateral sclerosis: implications for personalized therapy

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.

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.

A Diagnostic Gene-Expression Signature in Fibroblasts of Amyotrophic Lateral Sclerosis

Amyotrophic lateral sclerosis (ALS) is a fatal, progressive neurodegenerative disease with limited treatment options. Diagnosis can be difficult due to the heterogeneity and non-specific nature of the initial symptoms, resulting in delays that compromise prompt access to effective therapeutic strategies. Transcriptome profiling of patient-derived peripheral cells represents a valuable benchmark in overcoming such challenges, providing the opportunity to identify molecular diagnostic signatures. In this study, we characterized transcriptome changes in skin fibroblasts of sporadic ALS patients (sALS) and controls and evaluated their utility as a molecular classifier for ALS diagnosis. Our analysis identified 277 differentially expressed transcripts predominantly involved in transcriptional regulation, synaptic transmission, and the inflammatory response. A support vector machine classifier based on this 277-gene signature was developed to discriminate patients with sALS from controls, showing significant predictive power in both the discovery dataset and in six independent publicly available gene expression datasets obtained from different sALS tissue/cell samples. Taken together, our findings support the utility of transcriptional signatures in peripheral cells as valuable biomarkers for the diagnosis of ALS.

CXCR2 increases in ALS cortical neurons and its inhibition prevents motor neuron degeneration in vitro and improves neuromuscular function in SOD1G93A mice

Amyotrophic Lateral Sclerosis (ALS) is a progressive neurodegenerative disease characterized by depletion of motor neurons (MNs), for which effective medical treatments are still required. Previous transcriptomic analysis revealed the up-regulation of C-X-C motif chemokine receptor 2 (CXCR2)-mRNA in a subset of sporadic ALS patients and SOD1G93A mice. Here, we confirmed the increase of CXCR2 in human ALS cortex, and showed that CXCR2 is mainly localized in cell bodies and axons of cortical neurons. We also investigated the effects of reparixin, an allosteric inhibitor of CXCR2, in degenerating human iPSC-derived MNs and SOD1G93A mice. In vitro, reparixin rescued MNs from apoptotic cell death, preserving neuronal morphology, mitochondrial membrane potential and cytoplasmic membrane integrity, whereas in vivo it improved neuromuscular function of SOD1G93A mice. Altogether, these data suggest a role for CXCR2 in ALS pathology and support its pharmacological inhibition as a candidate therapeutic strategy against ALS at least in a specific subgroup of patients.

Omics Data and Their Integrative Analysis to Support Stratified Medicine in Neurodegenerative Diseases

Molecular and clinical heterogeneity is increasingly recognized as a common characteristic of neurodegenerative diseases (NDs), such as Alzheimer's disease, Parkinson's disease and amyotrophic lateral sclerosis. This heterogeneity makes difficult the development of early diagnosis and effective treatment approaches, as well as the design and testing of new drugs. As such, the stratification of patients into meaningful disease subgroups, with clinical and biological relevance, may improve disease management and the development of effective treatments. To this end, omics technologies-such as genomics, transcriptomics, proteomics and metabolomics-are contributing to offer a more comprehensive view of molecular pathways underlying the development of NDs, helping to differentiate subtypes of patients based on their specific molecular signatures. In this article, we discuss how omics technologies and their integration have provided new insights into the molecular heterogeneity underlying the most prevalent NDs, aiding to define early diagnosis and progression markers as well as therapeutic targets that can translate into stratified treatment approaches, bringing us closer to the goal of personalized medicine in neurology.

Using machine intelligence to uncover Alzheimer’s disease progression heterogeneity

Aim: Research suggests that Alzheimer’s disease (AD) is heterogeneous with numerous subtypes. Through a proprietary interactive ML system, several underlying biological mechanisms associated with AD pathology were uncovered. This paper is an introduction to emerging analytic efforts that can more precisely elucidate the heterogeneity of AD. Methods: A public AD data set (GSE84422) consisting of transcriptomic data of postmortem brain samples from healthy controls (n = 121) and AD (n = 380) subjects was analyzed. Data were processed by an artificial intelligence platform designed to discover potential drug repurposing candidates, followed by an interactive augmented intelligence program. Results: Using perspective analytics, six perspective classes were identified: Class I is defined by TUBB1, ASB4, and PDE5A; Class II by NRG2 and ZNF3; Class III by IGF1, ASB4, and GTSE1; Class IV is defined by cDNA FLJ39269, ITGA1, and CPM; Class V is defined by PDE5A, PSEN1, and NDUFS8; and Class VI is defined by DCAF17, cDNA FLJ75819, and SLC33A1. It is hypothesized that these classes represent biological mechanisms that may act alone or in any combination to manifest an Alzheimer’s pathology. Conclusions: Using a limited transcriptomic public database, six different classes that drive AD were uncovered, supporting the premise that AD is a heterogeneously complex disorder. The perspective classes highlighted genetic pathways associated with vasculogenesis, cellular signaling and differentiation, metabolic function, mitochondrial function, nitric oxide, and metal ion metabolism. The interplay among these genetic factors reveals a more profound underlying complexity of AD that may be responsible for the confluence of several biological factors. These results are not exhaustive; instead, they demonstrate that even within a relatively small study sample, next-generation machine intelligence can uncover multiple genetically driven subtypes. The models and the underlying hypotheses generated using novel analytic methods may translate into potential treatment pathways.

From Multi-Omics Approaches to Precision Medicine in Amyotrophic Lateral Sclerosis

Amyotrophic lateral sclerosis (ALS) is a devastating and fatal neurodegenerative disorder, caused by the degeneration of upper and lower motor neurons for which there is no truly effective cure. The lack of successful treatments can be well explained by the complex and heterogeneous nature of ALS, with patients displaying widely distinct clinical features and progression patterns, and distinct molecular mechanisms underlying the phenotypic heterogeneity. Thus, stratifying ALS patients into consistent and clinically relevant subgroups can be of great value for the development of new precision diagnostics and targeted therapeutics for ALS patients. In the last years, the use and integration of high-throughput "omics" approaches have dramatically changed our thinking about ALS, improving our understanding of the complex molecular architecture of ALS, distinguishing distinct patient subtypes and providing a rational foundation for the discovery of biomarkers and new individualized treatments. In this review, we discuss the most significant contributions of omics technologies in unraveling the biological heterogeneity of ALS, highlighting how these approaches are revealing diagnostic, prognostic and therapeutic targets for future personalized interventions.

Omics-based exploration and functional validation of neurotrophic factors and histamine as therapeutic targets in ALS

A plethora of genetic and molecular mechanisms have been implicated in the pathophysiology of the heterogeneous and multifactorial amyotrophic lateral sclerosis (ALS) disease, and hence the conventional "one target-one drug" paradigm has failed so far to provide effective therapeutic solutions, precisely because of the complex nature of ALS. This review intends to highlight how the integration of emerging "omics" approaches may provide a rational foundation for the comprehensive exploration of molecular pathways and dynamic interactions involved in ALS, for the identification of candidate targets and biomarkers that will assist in the rapid diagnosis and prognosis, lastly for the stratification of patients into different subgroups with the aim of personalized therapeutic strategies. To this purpose, particular emphasis will be placed on some potential therapeutic targets, including neurotrophic factors and histamine signaling that both have emerged as dysregulated at different omics levels in specific subgroups of ALS patients, and have already shown promising results in in vitro and in vivo models of ALS. To conclude, we will discuss about the utility of using integrated omics coupled with network-based approaches to provide additional guidance for personalization of medicine applications in ALS.

Splicing Players Are Differently Expressed in Sporadic Amyotrophic Lateral Sclerosis Molecular Clusters and Brain Regions

Splicing is a tightly orchestrated process by which the brain produces protein diversity over time and space. While this process specializes and diversifies neurons, its deregulation may be responsible for their selective degeneration. In amyotrophic lateral sclerosis (ALS), splicing defects have been investigated at the singular gene level without considering the higher-order level, involving the entire splicing machinery. In this study, we analyzed the complete spectrum (396) of genes encoding splicing factors in the motor cortex (41) and spinal cord (40) samples from control and sporadic ALS (SALS) patients. A substantial number of genes (184) displayed significant expression changes in tissue types or disease states, were implicated in distinct splicing complexes and showed different topological hierarchical roles based on protein–protein interactions. The deregulation of one of these splicing factors has a central topological role, i.e., the transcription factor YBX1, which might also have an impact on stress granule formation, a pathological marker associated with ALS.

Histaminergic transmission slows progression of amyotrophic lateral sclerosis

BACKGROUND

Histamine is an immune modulator, neuroprotective, and remyelinating agent, beneficially acting on skeletal muscles and promoting anti-inflammatory features in amyotrophic lateral sclerosis (ALS) microglia. Drugs potentiating the endogenous release of histamine are in trial for neurological diseases, with a role not systematically investigated in ALS. Here, we examine histamine pathway associations in ALS patients and the efficacy of a histamine-mediated therapeutic strategy in ALS mice.

METHODS

We adopted an integrative multi-omics approach combining gene expression profiles, copy number variants, and single nucleotide polymorphisms of ALS patients. We treated superoxide dismutase 1 (SOD1)-G93A mice that recapitulate key ALS features, with the brain-permeable histamine precursor histidine in the symptomatic phase of the disease and analysed the rescue from disease pathological signs. We examined the action of histamine in cultured SOD1-G93A motor neuron-like cells.

RESULTS

We identified 13 histamine-related genes deregulated in the spinal cord of two ALS patient subgroups, among which genes involved in histamine metabolism, receptors, transport, and secretion. Some histamine-related genes overlapped with genomic regions disrupted by DNA copy number and with ALS-linked pathogenic variants. Histidine treatment in SOD1-G93A mice proved broad efficacy in ameliorating ALS features, among which most importantly lifespan, motor performance, microgliosis, muscle atrophy, and motor neurons survival in vivo and in vitro.

CONCLUSIONS

Our gene set/pathway enrichment analyses and preclinical studies started at the onset of symptoms establish that histamine-related genes are modifiers in ALS, supporting their role as candidate biomarkers and therapeutic targets. We disclose a novel important role for histamine in the characterization of the multi-gene network responsible for ALS and, furthermore, in the drug development process.

Integrative multi-omic analysis identifies new drivers and pathways in molecularly distinct subtypes of ALS

Amyotrophic lateral sclerosis (ALS) is an incurable and fatal neurodegenerative disease. Increasing the chances of success for future clinical strategies requires more in-depth knowledge of the molecular basis underlying disease heterogeneity. We recently laid the foundation for a molecular taxonomy of ALS by whole-genome expression profiling of motor cortex from sporadic ALS (SALS) patients. Here, we analyzed copy number variants (CNVs) occurring in the same patients, by using a customized exon-centered comparative genomic hybridization array (aCGH) covering a large panel of ALS-related genes. A large number of novel and known disease-associated CNVs were detected in SALS samples, including several subgroup-specific loci, suggestive of a great divergence of two subgroups at the molecular level. Integrative analysis of copy number profiles with their associated transcriptomic data revealed subtype-specific genomic perturbations and candidate driver genes positively correlated with transcriptional signatures, suggesting a strong interaction between genomic and transcriptomic events in ALS pathogenesis. The functional analysis confirmed our previous pathway-based characterization of SALS subtypes and identified 24 potential candidates for genomic-based patient stratification. To our knowledge, this is the first comprehensive "omics" analysis of molecular events characterizing SALS pathology, providing a road map to facilitate genome-guided personalized diagnosis and treatments for this devastating disease.

Taxonomy Meets Neurology, the Case of Amyotrophic Lateral Sclerosis

Recent landmark publications from our research group outline a transformative approach to defining, studying and treating amyotrophic lateral sclerosis (ALS). Rather than approaching ALS as a single entity, we advocate targeting therapies to distinct "clusters" of patients based on their specific genomic and molecular features. Our findings point to the existence of a molecular taxonomy for ALS, bringing us a step closer to the establishment of a precision medicine approach in neurology practice.

Neuroinflammation and ALS: Transcriptomic Insights into Molecular Disease Mechanisms and Therapeutic Targets

Amyotrophic lateral sclerosis (ALS) is a neurodegenerative disease affecting the motor nervous system. Despite the mechanism underlying motor neuron death is not yet clarified, multiple pathogenic processes have been proposed to account for ALS. Among these, inflammatory/immune responses have recently gained particular interest, although there are conflicting reports on the role of these processes in ALS pathogenesis and treatment. This apparent discrepancy may be due to the absence of an effective stratification of ALS patients into subgroups with markedly different clinical, biological, and molecular features. Our research group recently described genome-wide characterization of motor cortex samples from sporadic ALS (SALS) patients, revealing the existence of molecular and functional heterogeneity in SALS. Here, we reexamine data coming from our previous work, focusing on transcriptomic changes of inflammatory-related genes, in order to investigate their potential contribution in ALS. A total of 1573 inflammatory genes were identified as differentially expressed between SALS patients and controls, characterizing distinct topological pathways and networks, suggestive of specific inflammatory molecular signatures for different patient subgroups. Besides providing promising insights into the intricate relationship between inflammation and ALS, this paper represents a starting point for the rationale design and development of novel and more effective diagnostic and therapeutic applications.

Selection and Prioritization of Candidate Drug Targets for Amyotrophic Lateral Sclerosis Through a Meta-Analysis Approach

Amyotrophic lateral sclerosis (ALS) is a progressive and incurable neurodegenerative disease. Although several compounds have shown promising results in preclinical studies, their translation into clinical trials has failed. This clinical failure is likely due to the inadequacy of the animal models that do not sufficiently reflect the human disease. Therefore, it is important to optimize drug target selection by identifying those that overlap in human and mouse pathology. We have recently characterized the transcriptional profiles of motor cortex samples from sporadic ALS (SALS) patients and differentiated these into two subgroups based on differentially expressed genes, which encode 70 potential therapeutic targets. To prioritize drug target selection, we investigated their degree of conservation in superoxide dismutase 1 (SOD1) G93A transgenic mice, the most widely used ALS animal model. Interspecies comparison of our human expression data with those of eight different SOD1^G93A datasets present in public repositories revealed the presence of commonly deregulated targets and related biological processes. Moreover, deregulated expression of the majority of our candidate targets occurred at the onset of the disease, offering the possibility to use them for an early and more effective diagnosis and therapy. In addition to highlighting the existence of common key drivers in human and mouse pathology, our study represents the basis for a rational preclinical drug development.