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      Leukocytes carrying Clonal Hematopoiesis of Indeterminate Potential (CHIP) Mutations invade Human Atherosclerotic Plaques

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      research-article
      , MD 1 , 2 , 1 , 2 , 3 , 3 , 1 , 2 , 2 , 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 8 , 8 , 12 , 13 , 14 , 15 , 1 , 1 , 1 , 2 , 1 , 2 , 1 , 2 , 16 , 16 , 2 , 17 , 18 , 1 , 2 , 19 , 19 , 13 , 13 , 20 , 21 , 22 , 10 , 23 , 24 , 25 , 26 , 3 , 21 , 27 , , MD 1 , 2
      medRxiv
      Cold Spring Harbor Laboratory
      aging, atherosclerotic cardiovascular disease, clonal hematopoiesis of indeterminate potential, coronary artery disease, inflammation

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          Abstract

          Background:

          Leukocyte progenitors derived from clonal hematopoiesis of undetermined potential (CHIP) are associated with increased cardiovascular events. However, the prevalence and functional relevance of CHIP in coronary artery disease (CAD) are unclear, and cells affected by CHIP have not been detected in human atherosclerotic plaques.

          Methods:

          CHIP mutations in blood and tissues were identified by targeted deep-DNA-sequencing (DNAseq: coverage >3,000) and whole-genome-sequencing (WGS: coverage >35). CHIP-mutated leukocytes were visualized in human atherosclerotic plaques by mutaFISH . Functional relevance of CHIP mutations was studied by RNAseq.

          Results:

          DNAseq of whole blood from 540 deceased CAD patients of the Munich cardIovaScular StudIes biObaNk (MISSION) identified 253 (46.9%) CHIP mutation carriers (mean age 78.3 years). DNAseq on myocardium, atherosclerotic coronary and carotid arteries detected identical CHIP mutations in 18 out of 25 mutation carriers in tissue DNA. MutaFISH visualized individual macrophages carrying DNMT3A CHIP mutations in human atherosclerotic plaques. Studying monocyte-derived macrophages from Stockholm-Tartu Atherosclerosis Reverse Networks Engineering Task (STARNET; n=941) by WGS revealed CHIP mutations in 14.2% (mean age 67.1 years). RNAseq of these macrophages revealed that expression patterns in CHIP mutation carriers differed substantially from those of non-carriers. Moreover, patterns were different depending on the underlying mutations, e.g. those carrying TET2 mutations predominantly displayed upregulated inflammatory signaling whereas ASXL1 mutations showed stronger effects on metabolic pathways.

          Conclusions:

          Deep-DNA-sequencing reveals a high prevalence of CHIP mutations in whole blood of CAD patients. CHIP-affected leukocytes invade plaques in human coronary arteries. RNAseq data obtained from macrophages of CHIP-affected patients suggest that pro-atherosclerotic signaling differs depending on the underlying mutations. Further studies are necessary to understand whether specific pathways affected by CHIP mutations may be targeted for personalized treatment.

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          Most cited references46

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          • Abstract: found
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          Fast and accurate short read alignment with Burrows–Wheeler transform

          Motivation: The enormous amount of short reads generated by the new DNA sequencing technologies call for the development of fast and accurate read alignment programs. A first generation of hash table-based methods has been developed, including MAQ, which is accurate, feature rich and fast enough to align short reads from a single individual. However, MAQ does not support gapped alignment for single-end reads, which makes it unsuitable for alignment of longer reads where indels may occur frequently. The speed of MAQ is also a concern when the alignment is scaled up to the resequencing of hundreds of individuals. Results: We implemented Burrows-Wheeler Alignment tool (BWA), a new read alignment package that is based on backward search with Burrows–Wheeler Transform (BWT), to efficiently align short sequencing reads against a large reference sequence such as the human genome, allowing mismatches and gaps. BWA supports both base space reads, e.g. from Illumina sequencing machines, and color space reads from AB SOLiD machines. Evaluations on both simulated and real data suggest that BWA is ∼10–20× faster than MAQ, while achieving similar accuracy. In addition, BWA outputs alignment in the new standard SAM (Sequence Alignment/Map) format. Variant calling and other downstream analyses after the alignment can be achieved with the open source SAMtools software package. Availability: http://maq.sourceforge.net Contact: rd@sanger.ac.uk
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            The Genome Analysis Toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data.

            Next-generation DNA sequencing (NGS) projects, such as the 1000 Genomes Project, are already revolutionizing our understanding of genetic variation among individuals. However, the massive data sets generated by NGS--the 1000 Genome pilot alone includes nearly five terabases--make writing feature-rich, efficient, and robust analysis tools difficult for even computationally sophisticated individuals. Indeed, many professionals are limited in the scope and the ease with which they can answer scientific questions by the complexity of accessing and manipulating the data produced by these machines. Here, we discuss our Genome Analysis Toolkit (GATK), a structured programming framework designed to ease the development of efficient and robust analysis tools for next-generation DNA sequencers using the functional programming philosophy of MapReduce. The GATK provides a small but rich set of data access patterns that encompass the majority of analysis tool needs. Separating specific analysis calculations from common data management infrastructure enables us to optimize the GATK framework for correctness, stability, and CPU and memory efficiency and to enable distributed and shared memory parallelization. We highlight the capabilities of the GATK by describing the implementation and application of robust, scale-tolerant tools like coverage calculators and single nucleotide polymorphism (SNP) calling. We conclude that the GATK programming framework enables developers and analysts to quickly and easily write efficient and robust NGS tools, many of which have already been incorporated into large-scale sequencing projects like the 1000 Genomes Project and The Cancer Genome Atlas.
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              • Record: found
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              Is Open Access

              Fast and accurate long-read alignment with Burrows–Wheeler transform

              Motivation: Many programs for aligning short sequencing reads to a reference genome have been developed in the last 2 years. Most of them are very efficient for short reads but inefficient or not applicable for reads >200 bp because the algorithms are heavily and specifically tuned for short queries with low sequencing error rate. However, some sequencing platforms already produce longer reads and others are expected to become available soon. For longer reads, hashing-based software such as BLAT and SSAHA2 remain the only choices. Nonetheless, these methods are substantially slower than short-read aligners in terms of aligned bases per unit time. Results: We designed and implemented a new algorithm, Burrows-Wheeler Aligner's Smith-Waterman Alignment (BWA-SW), to align long sequences up to 1 Mb against a large sequence database (e.g. the human genome) with a few gigabytes of memory. The algorithm is as accurate as SSAHA2, more accurate than BLAT, and is several to tens of times faster than both. Availability: http://bio-bwa.sourceforge.net Contact: rd@sanger.ac.uk
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                Author and article information

                Journal
                medRxiv
                MEDRXIV
                medRxiv
                Cold Spring Harbor Laboratory
                26 July 2023
                : 2023.07.22.23292754
                Affiliations
                [1 ]Department of Cardiology, German Heart Center Munich, Technical University Munich, Munich, Germany.
                [2 ]Deutsches Zentrum für Herz- und Kreislaufforschung (DZHK), Partner Site Munich Heart Alliance, Munich, Germany.
                [3 ]Department of Genetics and Genomic Sciences, Institute of Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, USA.
                [4 ]Bioinformatics Lab, Riga Stradiņš University, Riga, Latvia.
                [5 ]SIA Net-OMICS, Riga, Latvia.
                [6 ]Department of Pathology and Laboratory Medicine, Centre for Heart Lung Innovation, University of British Columbia, Vancouver, Canada.
                [7 ]Center for Public Health Genomics, University of Virginia, Charlottesville, VA, USA.
                [8 ]CVPath Institute, Inc, Gaithersburg, USA.
                [9 ]Laboratory of Experimental Cardiology, Department of Cardiology, University Medical Center Utrecht, University Utrecht, Utrecht, Netherlands.
                [10 ]Central Diagnostics Laboratory, University Medical Center Utrecht, Utrecht, The Netherlands
                [11 ]Division of Vascular Surgery, Department of Surgery, Stanford University School of Medicine, Stanford, USA.
                [12 ]Division of Cardiology, Internal Medicine III, Hamamatsu University School of Medicine, Hamamatsu, Japan.
                [13 ]Institute of Legal Medicine, Faculty of Medicine, LMU Munich, Germany.
                [14 ]Department of Internal Medicine I, Cardiology, University Hospital Augsburg, University of Augsburg, Germany.
                [15 ]Department of Cardiovascular Medicine, Humanitas Clinical and Research Center IRCCS and Humanitas University, Rozzano, Milan, Italy.
                [16 ]Department of Medicine III, Technical University of Munich (TUM), Klinikum rechts der Isar, Munich, Germany.
                [17 ]Department for Vascular and Endovascular Surgery, Klinikum Rechts der Isar, Technical University Munich, Munich, Germany.
                [18 ]Center for Public Health Genomics, Department of Public Health Sciences, Department of Biochemistry and Molecular Genetics, University of Virginia, Charlottesville, VA, USA.
                [19 ]Institute for Cardiovascular Regeneration, Goethe University Frankfurt am Main, Frankfurt am Main, Germany.
                [20 ]Department of Cardiac Surgery, The Heart Clinic, Tartu University Hospital, Tartu, Estonia.
                [21 ]Clinical Gene Networks AB, Stockholm, Sweden.
                [22 ]Institute of Clinical Medicine, Faculty of Medicine, Tartu University, Tartu, Estonia.
                [23 ]Stanford Cardiovascular Institute, Stanford University, Stanford, USA.
                [24 ]Victor Chang Cardiac Research Institute, Darlinghurst, Australia.
                [25 ]St. Vincent’s Clinical School, University of New South Wales, Sydney, Australia.
                [26 ]Cardiovascular Research Institute, Icahn School of Medicine at Mount Sinai, New York, USA.
                [27 ]Department of Medicine, Huddinge, Karolinska Institutet, Karolinska Universitetssjukhuset, Stockholm, Sweden.
                Author notes
                [*]

                Authors contributed equally

                Corresponding authors: Prof. Dr. med. Heribert Schunkert, MD; and Dr. Dr. med. Moritz von Scheidt, MD; Deutsches Herzzentrum München (German Heart Center Munich), Klinik für Herz- und Kreislauferkrankungen, Lazarettstr. 36, D-80636 München, Telephone: +49-89-1218-2849, Fax: +49-89-1218-4013, schunkert@ 123456dhm.mhn.de ; moritz.scheidt@ 123456tum.de
                Author information
                http://orcid.org/0000-0001-7159-8271
                http://orcid.org/0000-0001-6428-3001
                Article
                10.1101/2023.07.22.23292754
                10402238
                37546840
                a4a3a2fc-8491-4129-9c0d-182941738a39

                This work is licensed under a Creative Commons Attribution-NoDerivatives 4.0 International License, which allows reusers to copy and distribute the material in any medium or format in unadapted form only, and only so long as attribution is given to the creator. The license allows for commercial use.

                History
                Funding
                Funded by: German Center for Cardiovascular Research (DZHK), Berlin, Germany
                Award ID: DZHK81X2200145
                Award ID: DZHK-81X2600520
                Funded by: German Heart Foundation
                Funded by: Junior Research Group Cardiovascular Diseases Grant of the CORONA Foundation
                Award ID: S199/10085/2021
                Funded by: Leducq PlaqOmics Junior Investigator
                Funded by: Fondation Leducq (PlaqOmics)
                Funded by: Bavarian State Ministry of Health and Care
                Funded by: research project DigiMed Bayern
                Funded by: Bavarian State Ministry of Science and the Arts
                Funded by: research project Deutsches Herzzentrum München and Munich School of Robotics and Machine learning Joint Research Center
                Funded by: German Federal Ministry of Education and Research within the framework of European Research Area Network on Cardiovascular Disease
                Funded by: National Institutes of Health
                Award ID: R01HL148167
                Funded by: New South Wales health
                Award ID: RG194194
                Funded by: Bourne Foundation and Agilent
                Funded by: Swedish Research Council
                Award ID: 2018-02529
                Award ID: 2022-00734
                Funded by: Swedish Heart Lung Foundation
                Award ID: 2017-0265
                Award ID: 2020-0207
                Funded by: Leducq Foundation AteroGen
                Award ID: 22CVD04
                Funded by: PlaqOmics
                Award ID: 18CVD02
                Funded by: National Institute of Health-National Heart Lung Blood Institute
                Award ID: R01HL164577
                Award ID: R01HL148167
                Award ID: R01HL148239
                Award ID: R01HL166428
                Award ID: R01HL168174
                Funded by: American Heart Association Transformational Project
                Award ID: 19TPA34910021
                Categories
                Article

                aging,atherosclerotic cardiovascular disease,clonal hematopoiesis of indeterminate potential,coronary artery disease,inflammation

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