Modulation of histone H3K4 dimethylation by spermidine ameliorates motor neuron survival and neuropathology in a mouse model of ALS.

BACKGROUND: Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disorder characterized by progressive paralysis due to motor neuron degeneration. It has been proposed that epigenetic modification and transcriptional dysregulation may contribute to motor neuron death. In this study, we investigate the basis for therapeutic approaches to target lysine-specific histone demethylase 1 (LSD1) and elucidate the mechanistic role of LSD1-histone H3K4 signaling pathway in ALS pathogenesis. METHODS: In order to examine the role of spermidine (SD), we administered SD to an animal model of ALS (G93A) and performed neuropathological analysis, body weight, and survival evaluation. RESULTS: Herein, we found that LSD1 activity is increased while levels of H3K4me2, a substrate of LSD1, is decreased in cellular and animal models of ALS. SD administration modulated the LSD1 activity and restored H3K4me2 levels in ChAT-positive motor neurons in the lumbar spinal cord of ALS mice. SD prevented cellular damage by improving the number and size of motor neurons in ALS mice. SD administration also reduced GFAP-positive astrogliogenesis in the white and gray matter of the lumbar spinal cord, improving the neuropathology of ALS mice. Moreover, SD administration improved the rotarod performance and gait analysis of ALS mice. Finally, SD administration delayed disease onset and prolonged the lifespan of ALS (G93A) transgenic mice. CONCLUSION: Together, modulating epigenetic targets such as LSD1 by small compounds may be a useful therapeutic strategy for treating ALS.

Functional annotation of noncoding causal variants in autoimmune diseases.

Interpretation of noncoding disease variants, which comprise the vast majority of Genome-wide association studies (GWAS) hits, remains a momentous challenge due to haplotype structure and our limited understanding of the mechanisms and physiological contexts of noncoding elements. GWAS have identified loci underlying human diseases, but assigning the causal nucleotide changes still remain a controversial issue. Here we addressed these issues through the combination of high-density genotyping and epigenomic data using a random forest model to discover the noncoding causal variants. Focusing on autoimmune diseases, we triaged putative causal variants for atopic dermatitis and inflammatory bowel diseases. Making a filtering pipeline, we found three interesting single nucleotide polymorphisms (rs1800630, rs1799964 and rs4796793) in the upstream site of TNF and STAT3 genes, two frequent genes shared in some autoimmune diseases, and show how those variants affect on TNF and STAT3 expression levels. All data and source codes related to this manuscript are available at https://github.com/jieunjung511/Autoimmune-research.

Non-Cell Autonomous and Epigenetic Mechanisms of Huntington's Disease.

Huntington's disease (HD) is a rare neurodegenerative disorder caused by an expansion of CAG trinucleotide repeat located in the exon 1 of Huntingtin (HTT) gene in human chromosome 4. The HTT protein is ubiquitously expressed in the brain. Specifically, mutant HTT (mHTT) protein-mediated toxicity leads to a dramatic degeneration of the striatum among many regions of the brain. HD symptoms exhibit a major involuntary movement followed by cognitive and psychiatric dysfunctions. In this review, we address the conventional role of wild type HTT (wtHTT) and how mHTT protein disrupts the function of medium spiny neurons (MSNs). We also discuss how mHTT modulates epigenetic modifications and transcriptional pathways in MSNs. In addition, we define how non-cell autonomous pathways lead to damage and death of MSNs under HD pathological conditions. Lastly, we overview therapeutic approaches for HD. Together, understanding of precise neuropathological mechanisms of HD may improve therapeutic approaches to treat the onset and progression of HD.

Power Failure of Mitochondria and Oxidative Stress in Neurodegeneration and Its Computational Models.

The brain needs more energy than other organs in the body. Mitochondria are the generator of vital power in the living organism. Not only do mitochondria sense signals from the outside of a cell, but they also orchestrate the cascade of subcellular events by supplying adenosine-5'-triphosphate (ATP), the biochemical energy. It is known that impaired mitochondrial function and oxidative stress contribute or lead to neuronal damage and degeneration of the brain. This mini-review focuses on addressing how mitochondrial dysfunction and oxidative stress are associated with the pathogenesis of neurodegenerative disorders including Alzheimer's disease, amyotrophic lateral sclerosis, Huntington's disease, and Parkinson's disease. In addition, we discuss state-of-the-art computational models of mitochondrial functions in relation to oxidative stress and neurodegeneration. Together, a better understanding of brain disease-specific mitochondrial dysfunction and oxidative stress can pave the way to developing antioxidant therapeutic strategies to ameliorate neuronal activity and prevent neurodegeneration.

Pathogenic Genome Signatures That Damage Motor Neurons in Amyotrophic Lateral Sclerosis.

Amyotrophic lateral sclerosis (ALS) is the most frequent motor neuron disease and a neurodegenerative disorder, affecting the upper and/or lower motor neurons. Notably, it invariably leads to death within a few years of onset. Although most ALS cases are sporadic, familial amyotrophic lateral sclerosis (fALS) forms 10% of the cases. In 1993, the first causative gene (SOD1) of fALS was identified. With rapid advances in genetics, over fifty potentially causative or disease-modifying genes have been found in ALS so far. Accordingly, routine diagnostic tests should encompass the oldest and most frequently mutated ALS genes as well as several new important genetic variants in ALS. Herein, we discuss current literatures on the four newly identified ALS-associated genes (CYLD, S1R, GLT8D1, and KIF5A) and the previously well-known ALS genes including SOD1, TARDBP, FUS, and C9orf72. Moreover, we review the pathogenic implications and disease mechanisms of these genes. Elucidation of the cellular and molecular functions of the mutated genes will bring substantial insights for the development of therapeutic approaches to treat ALS.

Functional fine-mapping of noncoding risk variants in amyotrophic lateral sclerosis utilizing convolutional neural network.

Recent large-scale genome-wide association studies have identified common genetic variations that may contribute to the risk of amyotrophic lateral sclerosis (ALS). However, pinpointing the risk variants in noncoding regions and underlying biological mechanisms remains a major challenge. Here, we constructed a convolutional neural network model with a large-scale GWAS meta-analysis dataset to unravel functional noncoding variants associated with ALS based on their epigenetic features. After filtering and prioritizing of candidates, we fine-mapped two new risk variants, rs2370964 and rs3093720, on chromosome 3 and 17, respectively. Further analysis revealed that these polymorphisms are associated with the expression level of CX3CR1 and TNFAIP1, and affect the transcription factor binding sites for CTCF, NFATc1 and NR3C1. Our results may provide new insights for ALS pathogenesis, and the proposed research methodology can be applied for other complex diseases as well.

Sequential Integration of Fuzzy Clustering and Expectation Maximization for Transcription Factor Binding Site Identification.

The identification of transcription factor binding sites (TFBSs) is a problem for which computational methods offer great hope. Thus far, the expectation maximization (EM) technique has been successfully utilized in finding TFBSs in DNA sequences, but inappropriate initialization of EM has yielded poor performance or running time scalability under a given data set. In this study, we used a sequential integration approach that defined the final solution as the set of solutions acquired from solving objectives in a cascade manner to integrate the fuzzy C-means and the EM approaches to DNA motif discovery. The new method is explained in detail and tested on the chromatin immunoprecipitation sequencing (ChIP-seq) data sets for different transcription factors (TFs) with various motif patterns. The proposed algorithm also suggests an efficient process for analyzing motif similarity to known motifs as well as finding a target motif. A comparison of results with those of the well-known motif-finding tool, MEME-ChIP, shows the advantages of our proposed framework over this existing tool. Experimental results show that we were able to find the true motifs for all TFs, and that the motifs found by our proposed algorithm were more similar to JASPAR-known motifs for the STAT1, GATA1, and JUN TFs than those found by MEME-ChIP.