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      Multi-omic analysis of selectively vulnerable motor neuron subtypes implicates altered lipid metabolism in ALS

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          Abstract

          Amyotrophic lateral sclerosis (ALS) is a devastating disorder in which motor neurons degenerate, the causes of which remain unclear. In particular, the basis for selective vulnerability of spinal motor neurons (sMNs) and resistance of ocular motor neurons (oMNs) to degeneration in ALS has yet to be elucidated. Here, we applied comparative multi-omics analysis of human induced pluripotent stem cell (hiPSC)-derived sMNs and oMNs to identify shared metabolic perturbations in inherited and sporadic ALS sMNs, revealing dysregulation in lipid metabolism and its related genes. Targeted metabolomics studies confirmed such findings in sMNs of 17 ALS ( SOD1, C9ORF72, TDP43 and sporadic) hiPSC lines, identifying elevated levels of arachidonic acid (AA). Pharmacological reduction of AA levels was sufficient to reverse ALS-related phenotypes in both human sMNs and in vivo in Drosophila and SOD1 G93A mouse models. Collectively, these findings pinpoint a catalytic step of lipid metabolism as a potential therapeutic target for ALS.

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          STAR: ultrafast universal RNA-seq aligner.

          Alexander Dobin, Carrie A. Davis, Felix Schlesinger … (2013)
          Accurate alignment of high-throughput RNA-seq data is a challenging and yet unsolved problem because of the non-contiguous transcript structure, relatively short read lengths and constantly increasing throughput of the sequencing technologies. Currently available RNA-seq aligners suffer from high mapping error rates, low mapping speed, read length limitation and mapping biases. To align our large (>80 billon reads) ENCODE Transcriptome RNA-seq dataset, we developed the Spliced Transcripts Alignment to a Reference (STAR) software based on a previously undescribed RNA-seq alignment algorithm that uses sequential maximum mappable seed search in uncompressed suffix arrays followed by seed clustering and stitching procedure. STAR outperforms other aligners by a factor of >50 in mapping speed, aligning to the human genome 550 million 2 × 76 bp paired-end reads per hour on a modest 12-core server, while at the same time improving alignment sensitivity and precision. In addition to unbiased de novo detection of canonical junctions, STAR can discover non-canonical splices and chimeric (fusion) transcripts, and is also capable of mapping full-length RNA sequences. Using Roche 454 sequencing of reverse transcription polymerase chain reaction amplicons, we experimentally validated 1960 novel intergenic splice junctions with an 80-90% success rate, corroborating the high precision of the STAR mapping strategy. STAR is implemented as a standalone C++ code. STAR is free open source software distributed under GPLv3 license and can be downloaded from http://code.google.com/p/rna-star/.
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            Gene set enrichment analysis: A knowledge-based approach for interpreting genome-wide expression profiles

            A. Subramanian, P. Tamayo, V. K. Mootha … (2005)
            Although genomewide RNA expression analysis has become a routine tool in biomedical research, extracting biological insight from such information remains a major challenge. Here, we describe a powerful analytical method called Gene Set Enrichment Analysis (GSEA) for interpreting gene expression data. The method derives its power by focusing on gene sets, that is, groups of genes that share common biological function, chromosomal location, or regulation. We demonstrate how GSEA yields insights into several cancer-related data sets, including leukemia and lung cancer. Notably, where single-gene analysis finds little similarity between two independent studies of patient survival in lung cancer, GSEA reveals many biological pathways in common. The GSEA method is embodied in a freely available software package, together with an initial database of 1,325 biologically defined gene sets.
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              clusterProfiler: an R package for comparing biological themes among gene clusters.

              Guangchuang Yu, Li-Gen Wang, Yanyan Han … (2012)
              Increasing quantitative data generated from transcriptomics and proteomics require integrative strategies for analysis. Here, we present an R package, clusterProfiler that automates the process of biological-term classification and the enrichment analysis of gene clusters. The analysis module and visualization module were combined into a reusable workflow. Currently, clusterProfiler supports three species, including humans, mice, and yeast. Methods provided in this package can be easily extended to other species and ontologies. The clusterProfiler package is released under Artistic-2.0 License within Bioconductor project. The source code and vignette are freely available at http://bioconductor.org/packages/release/bioc/html/clusterProfiler.html.
              Article 0 comments Cited 11592 times – based on 0 reviews     Review now
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                Author and article information

                Journal
                Journal ID (nlm-journal-id): 9809671
                Journal ID (pubmed-jr-id): 21092
                Journal ID (nlm-ta): Nat Neurosci
                Journal ID (iso-abbrev): Nat Neurosci
                Title: Nature neuroscience
                ISSN (Print): 1097-6256
                ISSN (Electronic): 1546-1726
                Publication date Nihms-submitted: 20 September 2021
                Publication date (Electronic): 15 November 2021
                Publication date (Print): December 2021
                Publication date PMC-release: 15 May 2022
                Volume: 24
                Issue: 12
                Pages: 1673-1685
                Affiliations
                [1 ]Institute for Cell Engineering, Johns Hopkins University School of Medicine, Baltimore, MD, USA
                [2 ]Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA
                [3 ]The Solomon H. Snyder Department of Neuroscience, Johns Hopkins University School of Medicine, Baltimore, MD, USA
                [4 ]Cellular and Molecular Medicine Program, School of Medicine, Johns Hopkins University, Baltimore, MD, USA
                [5 ]The Robert Packard Center, Johns Hopkins University School of Medicine, Baltimore, MD, USA
                [6 ]Department of Biology, San Diego state University, San Diego, CA, USA
                [7 ]Department of Microbiology and Immunology, Kech School of Medicine, University of Southern California, Los Angeles, California, USA
                [8 ]School of Life Sciences, Gwangju Institute of Science and Technology, Gwangju, Republic of Korea
                [9 ]Department of Biochemistry, College of Medicine, Dong-A University, Busan, Korea
                [10 ]Department of Translational Biomedical Sciences, Graduate School of Dong-A University, Busan 49201, Korea
                [11 ]Department of Biochemistry and Molecular Biology, Ajou University School of Medicine, Suwon, Korea
                [12 ]Present: Institute of Physiological Chemistry, University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany
                [13 ]Present: Jeonbuk Branch Institute, Korea Institute of Toxicology, Jeongeup, Jeollabuk-do, Republic of Korea
                [14 ]Present: Department of Biomedical Science, Graduate School of Biomedical Science and Engineering, Hanyang University, Seoul, Republic of Korea
                Author notes

                Author contribution:

                H.L. performed the experiments, analyzed the data and wrote the manuscript with input from all authors. J.J.L, N.Y.P, S.D., T.K., K.R., S.B.L., S.P., S. H., I. K., K.K., S.K., and Y.O. performed the experiments and/or analyzed the data. Y.O., H.K., S-U.K., M-R.S., T.E.L., N.J.M., Y.B.H., and H.E. contributed to the design, supervised the study, data analysis and interpretation. G.L. designed the study, supervised the study/experiments, data analysis and interpretation and wrote the manuscript with input from all authors.

                [†]

                First author

                [* ]Corresponding author: Dr. Gabsang Lee, glee48@ 123456jhmi.edu , Phone: +1-443-287-8631, Dr. Hyungjin Eoh, heoh@ 123456usc.edu , Phone: +1-323-442-6048, Dr. Young Bin Hong, ybhong@ 123456dau.ac.kr , Phone: +82-51-240-2762
                Article
                Manuscript ID: NIHMS1741142
                DOI: 10.1038/s41593-021-00944-z
                PMC ID: 8639773
                PubMed ID: 34782793
                SO-VID: 23a749e4-ed66-494e-98ee-1fdf264eaf48
                License:

                Users may view, print, copy, and download text and data-mine the content in such documents, for the purposes of academic research, subject always to the full Conditions of use: https://www.springernature.com/gp/open-research/policies/accepted-manuscript-terms

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                ScienceOpen disciplines: Neurosciences
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                ScienceOpen disciplines: Neurosciences

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