Neurodegenerative disorders such as Amyotrophic Lateral Sclerosis (ALS), Multiple Sclerosis (MS), Alzheimer's disease (AD), and Frontotemporal Dementia (FTD) are devastating diseases carrying considerable morbidity, mortality, and personal, societal and economic cost.
Using diseased brains combined with single cell technologies from molecular to genomic/genetic approaches, scientist started to uncover novel cellular and sub-cellular populations in specific CNS regions associated with these diseases. These methods allow us to characterize the effect of genetic variation on the cellular architecture, and to shed light on 3-D configuration of: (1) the genome; (2) the complex pathophysiological mechanisms regulating gene expression in those diseased cells. However, the functional relevance of most discovered genes and loci remains unclear and the mechanisms through which they contribute to these diseases are largely unknown.
It is hypothesized that the majority of the common risk variants for ALS, MS, AD, and FTD are within non-coding regions. Many of these sequences are thought to exert their effects through disruption of regulatory functions, including effects on long-range Cis-Regulatory Elements (CREs) that physically interact with Transcription Start Sites (TSS). In the past decade, the characterization and annotation of the putative functional role of risk variants identified by Genome-Wide Association Studies (GWAS), together with the regulation of gene expression by generating cell type-specific multiscale-omics data (including RNA-seq, ATAC-seq, various ChIP-seq and 4C/HiC) from cohorts of control and diseased brains, have generated huge datasets. This allowed the integration of gene expression as well as the generation of epigenome Quantitative Trait Loci (QTL) and spatial transcriptome maps with GWAS. All these multi-scale data are leveraged to interpret the genetic architecture of these illnesses for downstream integrative analysis as well as multi-scale network modeling and therefore to identify GWAS risk variants. Moreover, consortia-led genomic and post-mortem studies run with patient and animal provide large-scale cohorts datasets for genomics, transcriptomics, and epigenomes, available through various consortiums, such as ALS and related disorders for Therapeutic Development (CReATe), European ALS Consortium (EALSC), CommonMind, PsychENCODE, and FTD sequencing consortium. Using computation and machine learning approaches to leverage these molecular datasets provides a powerful tool to “translate” genetic findings related to the dysregulation of specific molecular pathways across multiple traits and will empower drug research.
Considering the importance of genome-wide and cell type specific alterations of chromatin and multi-omics maps, the identification of novel gene targets and their associated enhancer and promoter are needed to identify specific regulatory units, cell-type wise. This will provide insight into the potential novel pathways and therapeutic interventions that could lead to ALS, MS, AD, and FTD cure.
In this Research Topic, our emphasis is on outlining progress in the interpretation of big datasets, toward a better understanding of basic mechanistic roles of genetics and epigenetics in ALS, MS, AD, and FTD disease, the area of:
• genomics,
• transcriptomics,
• epigenomics,
• spatial genomics, and
• use of computational and system medicine.
Investigators are welcome to contribute with original research articles, perspectives, as well as review articles or case reports outlining the continuing efforts that are being made to understand the mechanisms underlying the causes of these neurodegenerative diseases.
Neurodegenerative disorders such as Amyotrophic Lateral Sclerosis (ALS), Multiple Sclerosis (MS), Alzheimer's disease (AD), and Frontotemporal Dementia (FTD) are devastating diseases carrying considerable morbidity, mortality, and personal, societal and economic cost.
Using diseased brains combined with single cell technologies from molecular to genomic/genetic approaches, scientist started to uncover novel cellular and sub-cellular populations in specific CNS regions associated with these diseases. These methods allow us to characterize the effect of genetic variation on the cellular architecture, and to shed light on 3-D configuration of: (1) the genome; (2) the complex pathophysiological mechanisms regulating gene expression in those diseased cells. However, the functional relevance of most discovered genes and loci remains unclear and the mechanisms through which they contribute to these diseases are largely unknown.
It is hypothesized that the majority of the common risk variants for ALS, MS, AD, and FTD are within non-coding regions. Many of these sequences are thought to exert their effects through disruption of regulatory functions, including effects on long-range Cis-Regulatory Elements (CREs) that physically interact with Transcription Start Sites (TSS). In the past decade, the characterization and annotation of the putative functional role of risk variants identified by Genome-Wide Association Studies (GWAS), together with the regulation of gene expression by generating cell type-specific multiscale-omics data (including RNA-seq, ATAC-seq, various ChIP-seq and 4C/HiC) from cohorts of control and diseased brains, have generated huge datasets. This allowed the integration of gene expression as well as the generation of epigenome Quantitative Trait Loci (QTL) and spatial transcriptome maps with GWAS. All these multi-scale data are leveraged to interpret the genetic architecture of these illnesses for downstream integrative analysis as well as multi-scale network modeling and therefore to identify GWAS risk variants. Moreover, consortia-led genomic and post-mortem studies run with patient and animal provide large-scale cohorts datasets for genomics, transcriptomics, and epigenomes, available through various consortiums, such as ALS and related disorders for Therapeutic Development (CReATe), European ALS Consortium (EALSC), CommonMind, PsychENCODE, and FTD sequencing consortium. Using computation and machine learning approaches to leverage these molecular datasets provides a powerful tool to “translate” genetic findings related to the dysregulation of specific molecular pathways across multiple traits and will empower drug research.
Considering the importance of genome-wide and cell type specific alterations of chromatin and multi-omics maps, the identification of novel gene targets and their associated enhancer and promoter are needed to identify specific regulatory units, cell-type wise. This will provide insight into the potential novel pathways and therapeutic interventions that could lead to ALS, MS, AD, and FTD cure.
In this Research Topic, our emphasis is on outlining progress in the interpretation of big datasets, toward a better understanding of basic mechanistic roles of genetics and epigenetics in ALS, MS, AD, and FTD disease, the area of:
• genomics,
• transcriptomics,
• epigenomics,
• spatial genomics, and
• use of computational and system medicine.
Investigators are welcome to contribute with original research articles, perspectives, as well as review articles or case reports outlining the continuing efforts that are being made to understand the mechanisms underlying the causes of these neurodegenerative diseases.
