Copy Number Variations in Amyotrophic Lateral Sclerosis: Piecing the Mosaic Tiles Together through a Systems Biology Approach

Increased copy-number variant load of associated risk genes in sporadic cases of amyotrophic lateral sclerosis

Amyotrophic lateral sclerosis (ALS) is an age-related neurodegenerative disease characterized by selective loss of motor neurons in the brainstem and spinal cord. Several genetic factors have been associated to ALS, ranging from causal genes and potential risk factors to disease modifiers. The search for pathogenic variants in these genes has mostly focused on single nucleotide variants (SNVs) while relatively understudied and not fully elucidated is the contribution of structural variants, such as copy number variations (CNVs). Here, we applied an exon-centric aCGH method to investigate, in sporadic ALS patients, the load of CNVs in 131 genes previously associated to ALS. Our approach revealed that CNV load, defined as the total number of CNVs or their size, was significantly higher in ALS cases than controls. About 87% of patients harbored multiple CNVs in ALS-related genes, and 75% structural variants compromised genes directly implicated in ALS pathogenesis (C9orf72, CHCHD10, EPHA4, FUS, HNRNPA1, KIF5A, NEK1, OPTN, PFN1, SOD1, TARDBP, TBK1, UBQLN2, UNC13A, VAPB, VCP). CNV load was also associated to higher onset age and disease progression rate. Although the contribution of individual CNVs in ALS is still unknown, their extensive load in disease-related genes may have relevant implications for the diagnostic, prognostic and therapeutical management of this devastating disorder.

The multifaceted role of the CXC chemokines and receptors signaling axes in ALS pathophysiology

Amyotrophic lateral sclerosis (ALS) is a late-onset motor neuron disease with complex genetic basis and still no clear etiology. Multiple intertwined layers of immune system-related dysfunctions and neuroinflammatory mechanisms are emerging as substantial determinants in ALS onset and progression. In this review, we collect the increasingly arising evidence implicating four main CXC chemokines/cognate receptors signaling axes (CXCR1/2-CXCL1/2/8; CXCR3-CXCL9/10/11; CXCR4/7-CXCL12; CXCR5-CXCL13) in the pathophysiology of ALS. Findings in preclinical models implicate these signaling pathways in motor neuron toxicity and neuroprotection, while in ALS patients dysregulation of CXCLs/CXCRs has been shown at both central and peripheral levels. Immunological monitoring of CXC-ligands in ALS may allow tracking of disease progression, while pharmacological modulation of CXC-receptors provides a novel therapeutic strategy. A deeper understanding of the interplay between CXC-mediated neuroinflammation and ALS is crucial to advance research into treatments for this debilitating uncurable disorder.

Copy Number Variations in Hereditary Spastic Paraplegia-Related Genes: Evaluation of an Iranian Hereditary Spastic Paraplegia Cohort and Literature Review

Introduction

In human genetic disorders, copy number variations (CNVs) are considered a considerable underlying cause. CNVs are generally detected by array-based methods but can also be discovered by read-depth analysis of whole-exome sequencing (WES) data. We performed WES-based CNV identification in a cohort of 35 Iranian families with hereditary spastic paraplegia (HSP) patients.

Methods

Thirty-five patients whose routine single-nucleotide variants (SNVs) and insertion/deletion analyses from exome data were unrevealing underwent a pipeline of CNV analysis using the read-depth detection method. Subsequently, a comprehensive search about the existence of CNVs in all 84 known HSP-causing genes was carried out in all reported HSP cases, so far.

Results and Discussion

CNV analysis of exome data indicated that 1 patient harbored a heterozygous deletion in exon 17 of the SPAST gene. Multiplex ligation-dependent probe amplification analysis confirmed this deletion in the proband and his affected father. Literature review demonstrated that, to date, pathogenic CNVs have been identified in 30 out of 84 HSP-causing genes (∼36%). However, CNVs in only 17 of these genes were specifically associated with the HSP phenotype. Among them, CNVs were more common in L1CAM, PLP1, SPAST, SPG7, SPG11, and REEP1 genes. The identification of the CNV in 1 of our patients suggests that WES allows the detection of both SNVs and CNVs from a single method without additional costs and execution time. However, because of intrinsic issues of WES in the detection of large rearrangements, it may not yet be exploited to replace the CNV detection methods in standard clinical practice.

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.

Gene targeting in amyotrophic lateral sclerosis using causality-based feature selection and machine learning

BACKGROUND

Amyotrophic lateral sclerosis (ALS) is a rare progressive neurodegenerative disease that affects upper and lower motor neurons. As the molecular basis of the disease is still elusive, the development of high-throughput sequencing technologies, combined with data mining techniques and machine learning methods, could provide remarkable results in identifying pathogenetic mechanisms. High dimensionality is a major problem when applying machine learning techniques in biomedical data analysis, since a huge number of features is available for a limited number of samples. The aim of this study was to develop a methodology for training interpretable machine learning models in the classification of ALS and ALS-subtypes samples, using gene expression datasets.

METHODS

We performed dimensionality reduction in gene expression data using a semi-automated preprocessing systematic gene selection procedure using Statistically Equivalent Signature (SES), a causality-based feature selection algorithm, followed by Boosted Regression Trees (XGBoost) and Random Forest to train the machine learning classifiers. The SHapley Additive exPlanations (SHAP values) were used for interpretation of the machine learning classifiers. The methodology was developed and tested using two distinct publicly available ALS RNA-seq datasets. We evaluated the performance of SES as a dimensionality reduction method against: (a) Least Absolute Shrinkage and Selection Operator (LASSO), and (b) Local Outlier Factor (LOF).

RESULTS

The proposed methodology achieved 85.18% accuracy for the classification of cerebellum or frontal cortex samples as C9orf72-related familial ALS, sporadic ALS or healthy samples. Importantly, the genes identified as the most determinative have also been reported as disease-associated in ALS literature. When tested in the evaluation dataset, the methodology achieved 88.89% accuracy for the classification of sporadic ALS motor neuron samples. When LASSO was used as feature selection method instead of SES, the accuracy of the machine learning classifiers ranged from 74.07 to 96.30%, depending on tissue assessed, while LOF underperformed significantly (77.78% accuracy for the classification of pooled cerebellum and frontal cortex samples).

CONCLUSIONS

Using SES, we addressed the challenge of high dimensionality in gene expression data analysis, and we trained accurate machine learning ALS classifiers, specific for the gene expression patterns of different disease subtypes and tissue samples, while identifying disease-associated genes.

Neuronal Roles of the Multifunctional Protein Dipeptidyl Peptidase-like 6 (DPP6)

The concerted action of voltage-gated ion channels in the brain is fundamental in controlling neuronal physiology and circuit function. Ion channels often associate in multi-protein complexes together with auxiliary subunits, which can strongly influence channel expression and function and, therefore, neuronal computation. One such auxiliary subunit that displays prominent expression in multiple brain regions is the Dipeptidyl aminopeptidase-like protein 6 (DPP6). This protein associates with A-type K⁺ channels to control their cellular distribution and gating properties. Intriguingly, DPP6 has been found to be multifunctional with an additional, independent role in synapse formation and maintenance. Here, we feature the role of DPP6 in regulating neuronal function in the context of its modulation of A-type K⁺ channels as well as its independent involvement in synaptic development. The prevalence of DPP6 in these processes underscores its importance in brain function, and recent work has identified that its dysfunction is associated with host of neurological disorders. We provide a brief overview of these and discuss research directions currently underway to advance our understanding of the contribution of DPP6 to their etiology.

Identification of Therapeutic Targets for Amyotrophic Lateral Sclerosis Using PandaOmics – An AI-Enabled Biological Target Discovery Platform

Amyotrophic lateral sclerosis (ALS) is a severe neurodegenerative disease with ill-defined pathogenesis, calling for urgent developments of new therapeutic regimens. Herein, we applied PandaOmics, an AI-driven target discovery platform, to analyze the expression profiles of central nervous system (CNS) samples (237 cases; 91 controls) from public datasets, and direct iPSC-derived motor neurons (diMNs) (135 cases; 31 controls) from Answer ALS. Seventeen high-confidence and eleven novel therapeutic targets were identified and will be released onto ALS.AI (http://als.ai/). Among the proposed targets screened in the c9ALS Drosophila model, we verified 8 unreported genes (KCNB2, KCNS3, ADRA2B, NR3C1, P2RY14, PPP3CB, PTPRC, and RARA) whose suppression strongly rescues eye neurodegeneration. Dysregulated pathways identified from CNS and diMN data characterize different stages of disease development. Altogether, our study provides new insights into ALS pathophysiology and demonstrates how AI speeds up the target discovery process, and opens up new opportunities for therapeutic interventions.

Targeted copy number variant identification across the neurodegenerative disease spectrum

Background

Although genetic factors are known to contribute to neurodegenerative disease susceptibility, there remains a large amount of heritability unaccounted for across the diagnoses. Copy number variants (CNVs) contribute to these phenotypes, but their presence and influence on disease state remains relatively understudied.

Methods

Here, we applied a depth of coverage approach to detect CNVs in 80 genes previously associated with neurodegenerative disease within participants of the Ontario Neurodegenerative Disease Research Initiative (n = 519).

Results

In total, we identified and validated four CNVs in the cohort, including: (1) a heterozygous deletion of exon 5 in OPTN in an Alzheimer's disease participant; (2) a duplication of exons 1–5 in PARK7 in an amyotrophic lateral sclerosis participant; (3) a duplication of >3 Mb, which encompassed ABCC6, in a cerebrovascular disease (CVD) participant; and (4) a duplication of exons 7–11 in SAMHD1 in a mild cognitive impairment participant. We also identified 43 additional CNVs that may be candidates for future replication studies.

Conclusion

The identification of the CNVs suggests a portion of the apparent missing heritability of the phenotypes may be due to these structural variants, and their assessment is imperative for a thorough understanding of the genetic spectrum of neurodegeneration.

The Advent of Omics Sciences in Clinical Trials of Motor Neuron Diseases

The “omics revolution” has totally changed the scientific research approach and is contributing to the development of personalized therapies. In motor neuron diseases (MNDs), a set of complex, multifactorial, late-onset and chronic neurodegenerative diseases, the use of multi-omics approaches in clinical trials is providing new opportunities to stratify patients and develop target therapies. To show how omics science is gaining momentum in MNDs, in this work, we review the interventional clinical trials for MNDs based on the application of omics sciences. We analyze a total of 62 clinical trials listed in the ClinicalTrials database where different omics approaches have been applied in an initial phase, for diagnosis or patient selection, or in subsequent stages to cluster subjects, identify molecular signatures or evaluate drugs security or efficacy. The rise of omics sciences in clinical experimentation of MNDs is leading to an upheaval in their diagnosis and therapy that will require significant investments and means to ensure the correct and rapid evolution of personalized medicine.

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.

Advances in the genetic classification of amyotrophic lateral sclerosis

PURPOSE OF REVIEW

Amyotrophic lateral sclerosis (ALS) is an archetypal complex disease wherein disease risk and severity are, for the majority of patients, the product of interaction between multiple genetic and environmental factors. We are in a period of unprecedented discovery with new large-scale genome-wide association study (GWAS) and accelerating discovery of risk genes. However, much of the observed heritability of ALS is undiscovered and we are not yet approaching elucidation of the total genetic architecture, which will be necessary for comprehensive disease subclassification.

RECENT FINDINGS

We summarize recent developments and discuss the future. New machine learning models will help to address nonlinear genetic interactions. Statistical power for genetic discovery may be boosted by reducing the search-space using cell-specific epigenetic profiles and expanding our scope to include genetically correlated phenotypes. Structural variation, somatic heterogeneity and consideration of environmental modifiers represent significant challenges which will require integration of multiple technologies and a multidisciplinary approach, including clinicians, geneticists and pathologists.

SUMMARY

The move away from fully penetrant Mendelian risk genes necessitates new experimental designs and new standards for validation. The challenges are significant, but the potential reward for successful disease subclassification is large-scale and effective personalized medicine.

System biology and bioinformatics pipeline to identify comorbidities risk association: Neurodegenerative disorder case study

Alzheimer's disease (AD) is the commonest progressive neurodegenerative condition in humans, and is currently incurable. A wide spectrum of comorbidities, including other neurodegenerative diseases, are frequently associated with AD. How AD interacts with those comorbidities can be examined by analysing gene expression patterns in affected tissues using bioinformatics tools. We surveyed public data repositories for available gene expression data on tissue from AD subjects and from people affected by neurodegenerative diseases that are often found as comorbidities with AD. We then utilized large set of gene expression data, cell-related data and other public resources through an analytical process to identify functional disease links. This process incorporated gene set enrichment analysis and utilized semantic similarity to give proximity measures. We identified genes with abnormal expressions that were common to AD and its comorbidities, as well as shared gene ontology terms and molecular pathways. Our methodological pipeline was implemented in the R platform as an open-source package and available at the following link: https://github.com/unchowdhury/AD_comorbidity. The pipeline was thus able to identify factors and pathways that may constitute functional links between AD and these common comorbidities by which they affect each others development and progression. This pipeline can also be useful to identify key pathological factors and therapeutic targets for other diseases and disease interactions.

Metabolic and behavioral features of acute hyperpurinergia and the maternal immune activation mouse model of autism spectrum disorder

Since 2012, studies in mice, rats, and humans have suggested that abnormalities in purinergic signaling may be a final common pathway for many genetic and environmental causes of autism spectrum disorder (ASD). The current study in mice was conducted to characterize the bioenergetic, metabolomic, breathomic, and behavioral features of acute hyperpurinergia triggered by systemic injection of the purinergic agonist and danger signal, extracellular ATP (eATP). Responses were studied in C57BL/6J mice in the maternal immune activation (MIA) model and controls. Basal metabolic rates and locomotor activity were measured in CLAMS cages. Plasma metabolomics measured 401 metabolites. Breathomics measured 98 volatile organic compounds. Intraperitoneal eATP dropped basal metabolic rate measured by whole body oxygen consumption by 74% ± 6% (mean ± SEM) and rectal temperature by 6.2˚ ± 0.3˚C in 30 minutes. Over 200 metabolites from 37 different biochemical pathways where changed. Breathomics showed an increase in exhaled carbon monoxide, dimethylsulfide, and isoprene. Metabolomics revealed an acute increase in lactate, citrate, purines, urea, dopamine, eicosanoids, microbiome metabolites, oxidized glutathione, thiamine, niacinamide, and pyridoxic acid, and decreased folate-methylation-1-carbon intermediates, amino acids, short and medium chain acyl-carnitines, phospholipids, ceramides, sphingomyelins, cholesterol, bile acids, and vitamin D similar to some children with ASD. MIA animals were hypersensitive to postnatal exposure to eATP or poly(IC), which produced a rebound increase in body temperature that lasted several weeks before returning to baseline. Acute hyperpurinergia produced metabolic and behavioral changes in mice. The behaviors and metabolic changes produced by ATP injection were associated with mitochondrial functional changes that were profound but reversible.

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.

Common mutations of interest in the diagnosis of amyotrophic lateral sclerosis: how common are common mutations in ALS genes?

INTRODUCTION

Amyotrophic lateral sclerosis (ALS) is a complex neurodegenerative disease predominantly affecting upper and lower motor neurons. Diagnosis of this devastating pathology is very difficult because the high degree of clinical heterogeneity with which it occurs and until now, no truly effective treatment exists.

AREAS COVERED

Molecular diagnosis may be a valuable tool for dissecting out ALS complex heterogeneity and for identifying new molecular mechanisms underlying the characteristic selective degeneration and death of motor neurons. To date, pathogenic variants in ALS genes are known to be present in up to 70% of familial and 10% of apparently sporadic ALS cases and can be associated with risks for ALS only or risks for other neurodegenerative diseases. This paper shows the procedure currently used in diagnostic laboratories to investigate most frequent mutations in ALS and evaluating the utility of involved molecular techniques as potential tools to discriminate 'common mutations' in ALS patients.

EXPERT OPINION

Genetic testing may allow for establishing an accurate pathological diagnosis and a more precise stratification of patient groups in future drug trials.

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.

Laundering CNV data for candidate process prioritization in brain disorders

Background

Prioritization of genomic data has become a useful tool for uncovering the phenotypic effect of genetic variations (e.g. copy number variations or CNV) and disease mechanisms. Due to the complexity, brain disorders represent a major focus of genomic research aimed at revealing pathologic significance of genomic changes leading to brain dysfunction. Here, we propose a “CNV data laundering” algorithm based on filtering and prioritizing of genomic pathways retrieved from available databases for uncovering altered molecular pathways in brain disorders. The algorithm comprises seven consecutive steps of processing individual CNV data sets. First, the data are compared to in-house and web databases to discriminate recurrent non-pathogenic variants. Second, the CNV pool is confined to the genes predominantly expressed in the brain. Third, intergenic interactions are used for filtering causative CNV. Fourth, a network of interconnected elements specific for an individual genome variation set is created. Fifth, ontologic data (pathways/functions) are attributed to clusters of network elements. Sixth, the pathways are prioritized according to the significance of elements affected by CNV. Seventh, prioritized pathways are clustered according to the ontologies.

Results

The algorithm was applied to 191 CNV data sets obtained from children with brain disorders (intellectual disability and autism spectrum disorders) by SNP array molecular karyotyping. “CNV data laundering” has identified 13 pathway clusters (39 processes/475 genes) implicated in the phenotypic manifestations.

Conclusions

Elucidating altered molecular pathways in brain disorders, the algorithm may be used for uncovering disease mechanisms and genotype-phenotype correlations. These opportunities are strongly required for developing therapeutic strategies in devastating neuropsychiatric diseases.

Transcriptome analysis of MAPK signaling pathway and associated genes to angiogenesis in chicken erythrocytes on response to thiram-induced tibial lesions

This study was planned to investigate TD (Tibial dyschondroplasia) on the potential MAPK signaling pathway and angiogenesis related genes. Forty-eight broilers were allotted into control (C) and treatment (T) groups of 2, 6 and 15 days as C1, C2, C3, T1, T2 and T3. The histopathology results revealed that tibiotarsus bone of chickens had more lesions on day 6 (T2 group). The chondrocytes were disordered, and the size, shape and proliferation were affected. Transcriptome results revealed that differentially expressed genes (DEGs) identified were 63, 1026, 623, 130, 141 and 146 in C1 (2 days control vs 6 days control); C2 (2 days control vs 15 days control); C3 (6 days control vs 15 days control); T1 (2 days treatment vs 6 days treatment); T2 (2 days treatment vs 15 days treatment) and T3 (6 days treatment vs 15 days treatment) groups respectively. Whereas, 10 angiogenesis related-genes RHOC, MEIS2, BAIAP2, TGFBI, KLF2, CYR61, PTPN11, PLXNC1, HSPH1 and NRP2 were downregulated on day 6 in the treatment group. The pathway which was found enriched in the control and treatment groups was MAPK signaling pathway. Therefore selected 10 MAPK signaling pathway-related genes RAC2, MAP3K1, PRKCB, FLNB, IL1R1, PTPN7, RPS6KA, MAP3K6, GNA12 and HSPA8 which were found significantly downregulated in the treatment group on day 6. It is concluded that angiogenesis and MAPK signaling pathway related genes has an essential role in TD, as those top screened genes found downregulated in the thiram fed chickens when TD observed severed on day 6.

Alternative Splicing of ALS Genes: Misregulation and Potential Therapies

Neurodegenerative disorders such as amyotrophic lateral sclerosis (ALS), spinal muscular atrophy (SMA), Parkinson's, Alzheimer's, and Huntington's disease affect a rapidly increasing population worldwide. Although common pathogenic mechanisms have been identified (e.g., protein aggregation or dysfunction, immune response alteration and axonal degeneration), the molecular events underlying timing, dosage, expression, and location of RNA molecules are still not fully elucidated. In particular, the alternative splicing (AS) mechanism is a crucial player in RNA processing and represents a fundamental determinant for brain development, as well as for the physiological functions of neuronal circuits. Although in recent years our knowledge of AS events has increased substantially, deciphering the molecular interconnections between splicing and ALS remains a complex task and still requires considerable efforts. In the present review, we will summarize the current scientific evidence outlining the involvement of AS in the pathogenic processes of ALS. We will also focus on recent insights concerning the tuning of splicing mechanisms by epigenomic and epi-transcriptomic regulation, providing an overview of the available genomic technologies to investigate AS drivers on a genome-wide scale, even at a single-cell level resolution. In the future, gene therapy strategies and RNA-based technologies may be utilized to intercept or modulate the splicing mechanism and produce beneficial effects against ALS.

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.

SOD1/Rag2 Mice with Low Copy Number of SOD1 Gene as a New Long-Living Immunodeficient Model of ALS

The most recent research concerning amyotrophic lateral sclerosis (ALS) emphasizes the role of glia in disease development. Thus, one can suspect that the effective therapeutic strategy in treatment of ALS would be replacement of defective glia. One of the basic problems with human glial progenitors (hGRPs) replacement strategies is the time needed for the cells to become fully functional in vivo. The lifespan of most popular high copy number SOD1 mutant mice might be too short to acknowledge benefits of transplanted cells. We focused on developing immunodeficient rag2-/- model of ALS with lower number of transgene copies and longer lifespan. The obtained hSOD1/rag2 double mutant mice have been characterized. QPCR analysis revealed that copy number of hSOD1 transgene varied in our colony (4-8 copies). The difference in transgene copy number may be translated to significant impact on the lifespan. The death of long- and short-living hSOD1/rag2 mice is preceded by muscular weakness as early as one month before death. Importantly, based on magnetic resonance imaging we identified that mutant mice demonstrated abnormalities within the medullar motor nuclei. To conclude, we developed long-living double mutant hSOD1/rag2 mice, which could be a promising model for testing therapeutic utility of human stem cells.

NeuroArray: A Customized aCGH for the Analysis of Copy Number Variations in Neurological Disorders

BACKGROUND

Neurological disorders are a highly heterogeneous group of pathological conditions that affect both the peripheral and the central nervous system. These pathologies are characterized by a complex and multifactorial etiology involving numerous environmental agents and genetic susceptibility factors. For this reason, the investigation of their pathogenetic basis by means of traditional methodological approaches is rather arduous. High-throughput genotyping technologies, including the microarray-based comparative genomic hybridization (aCGH), are currently replacing classical detection methods, providing powerful molecular tools to identify genomic unbalanced structural rearrangements and explore their role in the pathogenesis of many complex human diseases.

METHODS

In this report, we comprehensively describe the design method, the procedures, validation, and implementation of an exon-centric customized aCGH (NeuroArray 1.0), tailored to detect both single and multi-exon deletions or duplications in a large set of multi- and monogenic neurological diseases. This focused platform enables a targeted measurement of structural imbalances across the human genome, targeting the clinically relevant genes at exon-level resolution.

CONCLUSION

An increasing use of the NeuroArray platform may offer new insights in investigating potential overlapping gene signatures among neurological conditions and defining genotype-phenotype relationships.