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Identification of Genetic Variants and Serum Metabolites Associated with Blood-derived Mitochondrial DNA Copy Number

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Alzheimer's & Dementia 2022年第S6期18卷

作者： Tong Tong John Farrell Congcong Zhu Zainab Khurshid Eden R. Martin William S. Bush Margaret A. Pericak-Vance Li-San Wang Gerard D. Schellenberg Jonathan L. Haines Kathryn L. Lunetta Lindsay A. Farrer Xiaoling Zhang Disease Sequencing Project Boston University School of Medicine Boston MA USA Boston University Bioinformatics Program Boston MA USA John P. Hussman Institute for Human Genomics University of Miami Miller School of Medicine Miami FL USA Case Western Reserve University School of Medicine Cleveland OH USA John P. Hussman Institute for Human Genomics Miller School of Medicine University of Miami Miami FL USA University of Pennsylvania Perelman School of Medicine Philadelphia PA USA University of Pennsylvania Perelman School of Medicine Path & Lab Med Stellar Chance Philadelphia PA USA Case Western Reserve University School of Medicine Department of Population & Quantitative Health Sciences Cleveland Institute for Computational Biology Cleveland OH USA Boston University School of Public Health Boston MA USA

Background Mitochondrial DNA Copy Number (mtDNA-CN) can be used to estimate mitochondrial function and it varies across tissues and cell types. Blood-derived mtDNA-CN has been studied in aging-related diseases including Alzheimer’s Disease (AD). However, little is known about the genetic contribution to variation of blood-derived mtDNA-CN and how this variation affects the level of serum metabolites. Method We developed a fast computational pipeline to accurately estimate mtDNA-CN using whole genome sequence data from blood samples from European ancestry (EA, 2,885 AD cases and 2,777 controls), African American (AA, 1,118 AD cases and 1,725 controls), and Caribbean Hispanic (CH, 1,022 AD cases and 1,949 controls) participants of the Alzheimer’s Disease Sequencing Project (ADSP). In the combined sample and within each population group. We tested association of blood-derived mtDNA-CN with 17,569,705 variants (minor allele count≥20) genome-wide using a linear mixed effect model including covariates for age, sex, sequencing center, PCR, AD status, polygenic risk score of blood cell count, and principal components of ancestry. We also tested the associations of mtDNA-CN and levels of 175 serum metabolites in 1,521 subjects from Alzheimer’s Disease Neuroimaging Initiative. Result In the total sample, we identified genome-wide significant (GWS) associations with variants from 24 independent loci. The top-ranked SNP (rs1455130415 in NDUFS8 , (p=3.25x10 -42 ) was also GWS in EAs (p=3.96x10 -28 ) and AAs (p=2.20x10 -27 ). SNP rs997412864 (p=5.13x10 -9 ) in GCAT was found associated with mtDNA-CN in EAs, but not in other groups. Association of SNP rs1237233197 (p=1.40x10 -8 ) in NDUFS7 was specific to AAs. Measurements of two long-chain acylcarnitine, C14:2-OH (p=4.91x10 -7 ) and C18:2 (p=1.81x10 -5 ), were negatively associated with mtDNA-CN after Bonferroni correction (P<2.9x10 -4 ). Ornithine, a non-proteinogenic amino acid, was also negatively associated with mtDNA-CN (p=2

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QIIME 2 Enables Comprehensive End-to-End Analysis of Diverse Microbiome Data and Comparative Studies with Publicly Available Data

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Current Protocols in bioinformatics 2020年第1期70卷 e100页

作者： Estaki, Mehrbod Jiang, Lingjing Bokulich, Nicholas A. McDonald, Daniel González, Antonio Kosciolek, Tomasz Martino, Cameron Zhu, Qiyun Birmingham, Amanda Vázquez-Baeza, Yoshiki Dillon, Matthew R. Bolyen, Evan Caporaso, J. Gregory Knight, Rob Department of Pediatrics University of California San Diego La Jolla CA United States Division of Biostatistics University of California San Diego La Jolla CA United States Center for Applied Microbiome Science Pathogen and Microbiome Institute Northern Arizona University Flagstaff AZ United States Department of Biological Sciences Northern Arizona University Flagstaff AZ United States Małopolska Centre of Biotechnology Jagiellonian University Kraków Poland Bioinformatics and Systems Biology Program University of California San Diego La Jolla CA United States Center for Microbiome Innovation University of California San Diego La Jolla CA United States Center for Computational Biology and Bioinformatics University of California San Diego La Jolla CA United States Jacobs School of Engineering University of California San Diego La Jolla CA United States Department of Computer Science and Engineering University of California San Diego La Jolla CA United States Department of Bioengineering University of California San Diego La Jolla CA United States

QIIME 2 is a completely re-engineered microbiome bioinformatics platform based on the popular QIIME platform, which it has replaced. QIIME 2 facilitates comprehensive and fully reproducible microbiome data science, improving accessibility to diverse users by adding multiple user interfaces. QIIME 2 can be combined with Qiita, an open-source web-based platform, to re-use available data for meta-analysis. The following basic protocol describes how to install QIIME 2 on a single computer and analyze microbiome sequence data, from processing of raw DNA sequence reads through generating publishable interactive figures. These interactive figures allow readers of a study to interact with data with the same ease as its authors, advancing microbiome science transparency and reproducibility. We also show how plug-ins developed by the community to add analysis capabilities can be installed and used with QIIME 2, enhancing various aspects of microbiome analyses—e.g., improving taxonomic classification accuracy. Finally, we illustrate how users can perform meta-analyses combining different datasets using readily available public data through Qiita. In this tutorial, we analyze a subset of the Early Childhood Antibiotics and the Microbiome (ECAM) study, which tracked the microbiome composition and development of 43 infants in the United States from birth to 2 years of age, identifying microbiome associations with antibiotic exposure, delivery mode, and diet. For more information about QIIME 2, see https://***. To troubleshoot or ask questions about QIIME 2 and microbiome analysis, join the active community at https://***. © 2020 The Authors. Basic Protocol: Using QIIME 2 with microbiome data. Support Protocol: Further microbiome analyses. © 2020 The Authors.

关键词： bioinformatics metagenomics microbiome QIIME 2 Qiita

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Author Correction: T cell egress via lymphatic vessels is tuned by antigen encounter and limits tumor control

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Nature immunology 2023年第4期24卷 729页

作者： Maria M Steele Abhinav Jaiswal Ines Delclaux Ian D Dryg Dhaarini Murugan Julia Femel Sunny Son Haley du Bois Cameron Hill Sancy A Leachman Young H Chang Lisa M Coussens Niroshana Anandasabapathy Amanda W Lund Ronald O. Perelman Department of Dermatology NYU Grossman School of Medicine New York University Langone Health New York NY USA. Department of Cell Developmental and Cancer Biology and Knight Cancer Institute Oregon Health and Science University Portland OR USA. Department of Dermatology Microbiology and Immunology Meyer Cancer Center Englander Institute of Precision Medicine Weill Cornell Medicine New York NY USA. Applied Bioinformatics Laboratories NYU Langone Health New York NY USA. Department of Dermatology Oregon Health and Science University Portland OR USA. Department of Biomedical Engineering and Computational Biology Program Oregon Health and Science University Portland OR USA. OHSU Center for Spatial Systems Biomedicine Oregon Health and Science University Portland OR USA. Ronald O. Perelman Department of Dermatology NYU Grossman School of Medicine New York University Langone Health New York NY USA. amanda.lund@***. Department of Cell Developmental and Cancer Biology and Knight Cancer Institute Oregon Health and Science University Portland OR USA. amanda.lund@***. Department of Pathology NYU Grossman School of Medicine New York University Langone Health New York NY USA. amanda.lund@***. Laura and Isaac Perlmutter Cancer Center NYU Grossman School of Medicine New York University Langone Health New York NY USA. amanda.lund@***.

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Publisher Correction: The ENCODE Imputation Challenge: a critical assessment of methods for cross-cell type imputation of epigenomic profiles

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Genome biology 2025年第1期26卷 31页

作者： Jacob Matthew Schreiber Carles A Boix Jin Wook Lee Hongyang Li Yuanfang Guan Chun-Chieh Chang Jen-Chien Chang Alex Hawkins-Hooker Bernhard Schölkopf Gabriele Schweikert Mateo Rojas Carulla Arif Canakoglu Francesco Guzzo Luca Nanni Marco Masseroli Mark James Carman Pietro Pinoli Chenyang Hong Kevin Y Yip Jefrey P Spence Sanjit Singh Batra Yun S Song Shaun Mahony Zheng Zhang Wuwei Tan Yang Shen Yuanfei Sun Minyi Shi Jessika Adrian Richard S Sandstrom Nina P Farrell Jessica M Halow Kristen Lee Lixia Jiang Xinqiong Yang Charles B Epstein J Seth Strattan Bradley E Bernstein Michael P Snyder Manolis Kellis William S Noble Anshul Bharat Kundaje Department of Genetics Stanford University Stanford CA USA. jmschreiber91@***. Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology Cambridge MA USA. Department of Genetics Stanford University Stanford CA USA. Department of Computational Medicine and Bioinformatics University of Michigan Ann Arbor MI USA. Department of Research and Development DeepSeq.AI San Francisco CA USA. RIKEN Center for Integrative Medical Sciences Yokohama Japan. Department of Empirical Inference Max Planck Institute for Intelligent Systems Stuttgart Germany. School of Life Sciences University of Dundee Dundee UK. Department of Electronics Information and Bioengineering Politecnico Di Milano Milan Italy. Department of Computational Biology University of Lausanne Lausanne Switzerland. Department of Computer Science and Engineering The Chinese University of Hong Kong Sha Tin Hong Kong. Sanford Burnham Prebys Medical Discovery Institute San Diego CA USA. Department of Electrical Engineering and Computer Sciences University of California Berkeley Berkeley CA USA. Department of Statistics University of California Berkeley Berkeley CA USA. Department of Biochemistry & Molecular Biology Center for Eukaryotic Gene Regulation Pennsylvania State University University Park PA USA. Department of Statistics Pennsylvania State University University Park PA USA. Department of Electrical and Computer Engineering Texas A&M University College Station TX USA. Altius Institute Seattle WA USA. Epigenomics Program The Broad Institute of MIT and Harvard Cambridge MA USA. Department of Genome Sciences University of Washington Seattle WA USA. Department of Computer Science Stanford University Stanford CA USA.

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Multiscale PHATE Exploration of SARS-CoV-2 Data Reveals Multimodal Signatures of Disease

Research Square

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Research Square 2021年

作者： Kuchroo, Manik Huang, Jessie Wong, Patrick Grenier, Jean-Christophe Shung, Dennis Tong, Alexander Lucas, Carolina Klein, Jon Burkhardt, Daniel B. Gigante, Scott Godavarthi, Abhinav Rieck, Bastian Israelow, Benjamin Simonov, Michael Mao, Tianyang Oh, Ji Eun Silva, Julio Takahashi, Takehiro Odio, Camila D. Casanovas-Massana, Arnau Fournier, John Farhadian, Shelli Dela Cruz, Charles S. Ko, Albert I. Hirn, Matthew J. Wilson, F. Perry Hussin, Julie Wolf, Guy Iwasaki, Akiko Krishnaswamy, Smita Department of Neuroscience Yale University New HavenCT United States Department of Computer Science Yale University New HavenCT United States Department of Immunobiology Yale University New HavenCT United States Montreal Heart Institute MontréalQC Canada Department of Medicine Yale University New HavenCT United States Department of Genetics Yale University New HavenCT United States Computational Biology Bioinformatics Program Yale University New HavenCT United States Department of Applied Mathematics Yale University New HavenCT United States Department of Biosystems Science and Engineering ETH Zurich Switzerland Department of Epidemiology of Microbial Diseases Yale School of Public Health New HavenCT United States Department of Medicine Section of Infectious Diseases Yale University School of Medicine New HavenCT United States Department of Medicine Section of Pulmonary and Critical Care Medicine Yale University School of Medicine New HavenCT United States Department of Computational Mathematics Science and Engineering Michigan State University East LansingMI United States Department of Mathematics Michigan State University East LansingMI United States Clinical and Translational Research Accelerator Department of Medicine Yale University New HavenCT United States Faculty of Medicine Université de Montréal Québec Canada Mila – Quebec AI institute MontréalQC Canada Department of Mathematics and Statistics Université de Montréal MontréalQC Canada Howard Hughes Medical Institute Chevy ChaseMD United States

The biomedical community is producing increasingly high dimensional datasets, integrated from hundreds of patient samples, which current computational techniques struggle to explore. To uncover biological meaning from these complex datasets, we present an approach called Multiscale PHATE, which learns abstracted biological features from data that can be directly predictive of disease. Built on a coarse graining process called diffusion condensation, Multiscale PHATE learns a data topology that can be analyzed at coarse levels for high level summarizations of data, as well as at fine levels for detailed representations on subsets. We apply Multiscale PHATE to study the immune response to COVID-19 in 54 million cells from 168 hospitalized patients. Through our analysis of patient samples, we identify CD16hiCD66blo neutrophil and IFNγ+GranzymeB+ Th17 cell responses enriched in patients who die. Furthermore, we show that population groupings Multiscale PHATE discovers can be directly fed into a classifier to predict disease outcome. We also use Multiscale PHATE-derived features to construct two different manifolds of patients, one from abstracted flow cytometry features and another directly on patient clinical features, both associating immune subsets and clinical markers with outcome. © 2021, CC BY.

关键词： Coronavirus

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Unsupervised extraction of phenotypes from cancer clinical notes for association studies

arXiv

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arXiv 2019年

作者： Stark, Stefan G. Hyland, Stephanie L. Pradier, Melanie F. Lehmann, Kjong-Van Wicki, Andreas Perez-Cruz, Fernando Vogt, Julia E. Rätsch, Gunnar Computational Biology Program Memorial Sloan Kettering Cancer Center New York United States Tri-Institutional Ph.D. Program in Computational Biology and Medicine Weill Cornell Medicine New York United States Department of Computer Science ETH Zürich Zürich Switzerland Medical Informatics Group University Hospital Zürich Zürich Switzerland Swiss Institute for Bioinformatics Zurich Switzerland Department of Biology ETH Zürich Zürich Switzerland Department of Signal Processing and Information Theory University Carlos III in Madrid Leganés Spain School of Engineering and Applied Sciences Harvard University CambridgeMA United States Department of Biomedicine University of Basel Basel Switzerland Tumorzentrum University Hospital Basel Basel Switzerland Swiss Data Science Center ETH Zürich and EPFL Lausanne Switzerland Department of Mathematics and Computer Science University of Basel Basel Switzerland

The recent adoption of Electronic Health Records (EHRs) by healthcare providers has introduced an important source of data that provides detailed and highly specific insights into patient phenotypes over large cohorts. These datasets, in combination with machine learning and statistical approaches, generate new opportunities for research and clinical care. However, many methods require the patient representations to be in structured formats, while the information in the EHR is often locked in unstructured text designed for human readability. In this work, we develop the methodology to automatically extract clinical features from clinical narratives from large EHR corpora without the need for prior knowledge. We consider medical terms and sentences appearing in clinical narratives as atomic information units. We propose an efficient clustering strategy suitable for the analysis of large text corpora and utilize the clusters to represent information about the patient compactly. Additionally, we define the sentences on ontologic and natural language vocabularies to automatically detect pertinent combinations of concepts present in the corpus, even when an ontology is not available. To demonstrate the utility of our approach, we perform an association study of clinical features with somatic mutation profiles from 4,007 cancer patients and their tumors. We apply the proposed algorithm to a dataset consisting of .65 thousand documents with a total of .3.2 million sentences. After correcting for cancer type and other confounding factors, we identify a total of 340 significant statistical associations between the presence of somatic mutations and clinical features. We annotated these associations according to their novelty and we report several known associations. We also propose 37 plausible, testable hypothesis for associations where the underlying biological mechanism does not appear to be known. These results illustrate that the automated discovery of clinical features

关键词： Diseases

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Author Correction: Widespread exposure to SARS-CoV-2 in wildlife communities

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Nature communications 2024年第1期15卷 6940页

作者： Amanda R Goldberg Kate E Langwig Katherine L Brown Jeffrey M Marano Pallavi Rai Kelsie M King Amanda K Sharp Alessandro Ceci Christopher D Kailing Macy J Kailing Russell Briggs Matthew G Urbano Clinton Roby Anne M Brown James Weger-Lucarelli Carla V Finkielstein Joseph R Hoyt Department of Biological Sciences Virginia Tech Blacksburg VA USA. Virginia Tech Carilion School of Medicine Virginia Tech Roanoke VA USA. Center for Emerging Zoonotic and Arthropod-borne Pathogens Virginia Tech Blacksburg VA USA. Molecular Diagnostics Laboratory Fralin Biomedical Research Institute Virginia Tech Roanoke VA USA. Department of Biomedical Sciences and Pathobiology Virginia Tech Blacksburg VA USA. Translational Biology Medicine and Health Graduate Program Virginia Tech Roanoke VA USA. Program in Genetics Bioinformatics and Computational Biology Virginia Tech Blacksburg VA USA. Department of Biochemistry Virginia Tech Blacksburg VA USA. Data Services University Libraries Virginia Tech Blacksburg VA USA. Virginia Tech Center for Drug Discovery Virginia Tech Blacksburg VA USA. Academy of Integrated Science Virginia Tech Blacksburg VA USA. Department of Biological Sciences Virginia Tech Blacksburg VA USA. finkielc@vt.edu. Virginia Tech Carilion School of Medicine Virginia Tech Roanoke VA USA. finkielc@vt.edu. Center for Emerging Zoonotic and Arthropod-borne Pathogens Virginia Tech Blacksburg VA USA. finkielc@vt.edu. Molecular Diagnostics Laboratory Fralin Biomedical Research Institute Virginia Tech Roanoke VA USA. finkielc@vt.edu. Virginia Tech Center for Drug Discovery Virginia Tech Blacksburg VA USA. finkielc@vt.edu. Academy of Integrated Science Virginia Tech Blacksburg VA USA. finkielc@vt.edu. Department of Biological Sciences Virginia Tech Blacksburg VA USA. hoytjosephr@***.

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Analyses of protein cores reveal fundamental differences between solution and crystal structures

arXiv

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arXiv 2019年

作者： Mei, Zhe Treado, John D. Grigas, Alex T. Levine, Zachary A. Regan, Lynne O'Hern, Corey S. Integrated Graduate Program in Physical & Engineering Biology Yale University New HavenCT06520 United States Department of Chemistry Yale University New HavenCT06520 United States Department of Mechanical Engineering & Materials Science Yale University New HavenCT06520 United States Graduate Program in Computational Biology & Bioinformatics Yale University New HavenCT06520 United States Department of Pathology Yale University New HavenCT06520 United States Department of Molecular Biophysics and Biochemistry Yale University New HavenCT06520 United States Institute of Quantitative Biology Biochemistry and Biotechnology Center for Synthetic and Systems Biology School of Biological Sciences University of Edinburgh Department of Physics Yale University New HavenCT06520 United States Department of Applied Physics Yale University New HavenCT06520 United States

There have been several studies suggesting that protein structures solved by NMR spectroscopy and x-ray crystallography show significant differences. To understand the origin of these differences, we assembled a database of high-quality protein structures solved by both methods. We also find significant differences between NMR and crystal structures|in the root-mean-square deviations of the Cα atomic positions, identities of core amino acids, backbone and sidechain dihedral angles, and packing fraction of core residues. In contrast to prior studies, we identify the physical basis for these differences by modelling protein cores as jammed packings of amino-acid-shaped particles. We find that we can tune the jammed packing fraction by varying the degree of thermalization used to generate the packings. For an athermal protocol, we find that the average jammed packing fraction is identical to that observed in the cores of protein structures solved by x-ray crystallography. In contrast, highly thermalized packing-generation protocols yield jammed packing fractions that are even higher than those observed in NMR structures. These results indicate that thermalized systems can pack more densely than athermal systems, which suggests a physical basis for the structural differences between protein structures solved by NMR and x-ray crystallography. Copyright © 2019, The Authors. All rights reserved.

关键词： X ray crystallography

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A Multi-Site Break through Cancer Trial: Phase II Study Investigating Dual Inhibition of BCL2 and Menin in AML MRD Using the Combination of Venetoclax and Revumenib (Trial In Progress)

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Blood 2024年 144卷 4265.2-4265.2页

作者： Ghayas C. Issa Alexander J. Ambinder Wenbin Xiao Scott Manalis Alex K Shalek Lukasz P. Gondek Alex Kentsis Nicholas J. Short Naval Daver Jing Ning Lianchun Xiao Michael Hemann Ken Chen Keyur P. Patel Pavan Bachireddy Andrew A. Lane Marina Konopleva Omar Abdel-Wahab Jeffrey J. Molldrem Ross L Levine Jacqueline S. Garcia Department of Leukemia The University of Texas MD Anderson Cancer Center Houston TX Sidney Kimmel Comprehensive Cancer Center at Johns Hopkins Baltimore MD Department of Pathology and Laboratory Medicine Hematopathology Service Memorial Sloan Kettering Cancer Center New York NY Massachusetts Institute of Technology Cambridge MA Institute for Medical Engineering & Science and Department of Chemistry Massachusetts Institute of Technology Cambridge MA Sidney Kimmel Comprehensive Cancer Center Johns Hopkins University School of Medicine Baltimore MD Tow Center for Developmental Oncology Department of Pediatrics Memorial Sloan Kettering Cancer Center New York NY MD Anderson Cancer Center Houston TX The University of Texas M.D. Anderson Cancer Center Houston TX Department of Biostatistics University of Texas MD Anderson Cancer Center Houston David H. Koch Institute for Integrative Cancer Research Massachusetts Institute of Technology Cambridge MA Department of Bioinformatics and Computational Biology The University of Texas MD Anderson Cancer Center Houston TX Department of Hematopathology The University of Texas MD Anderson Cancer Center Houston TX Department of Hematopoietic Biology & Malignancy The University of Texas M.D. Anderson Cancer Center Houston TX Department of Medical Oncology Dana-Farber Cancer Institute Boston MA Albert Einstein College of Medicine Bronx NY Leukemia Service Department of Medicine Memorial Sloan Kettering Cancer Center New York NY Departments of Hematopoietic Biology and Malignancy The University of Texas MD Anderson Cancer Center Houston TX Human Oncology and Pathogenesis Program Memorial Sloan Kettering Cancer Center New York NY

Background and Significance: Measurable residual disease (MRD) represents the fundamental driver of relapse and mortality in acute myeloid leukemia (AML). However, there are currently no established approaches to address this unmet need. Our central hypothesis is that targeting vulnerabilities associated with specific leukemia genotypes will eradicate MRD and prevent disease relapse. Break Through Cancer is a collaboration between our centers aimed at making progress in the deadliest cancers by stimulating radical clinical and laboratory research collaboration. The menin-KMT2A interaction is a critical dependency in acute leukemias caused by rearrangement of the Lysine methyl transferase ( KMT2Ar ) or Nucleoporin 98 ( NUP98r ) genes, or mutation of the Nucleophosmin 1 gene ( NPM1mt ). Revumenib (previously SNDX-5613), is a potent, oral, selective inhibitor of this interaction with an established safety and efficacy in refractory leukemias with these genotypes (Issa GC, Nature 2023). Additionally, KMT2Ar or NPM1mt leukemias can undergo BCL2-dependent apoptosis, and dual Bcl-2 and menin inhibition led to synergistic activity in KMT2Ar or NPM1mt leukemia models (Carter BZ, Blood 2021; Fiskus W, BCJ 2022). Therefore, we designed this study investigating the combination of revumenib and venetoclax to eradicate MRD in these AML subtypes (NCT06284486). Study Design and Methods: This is a single arm, open label, multicenter, phase I/II investigator-initiated study. Patients (pts) age ≥ 12 years with weight ≥ 45Kg, and known history of NPM1mt , or KMT2Ar , or NUP98r AML with MRD ≥ 0.1% identified by multiparameter flow cytometry (MFC) using central testing would be eligible; no morphologic evidence of AML (blasts <5%) in first remission following high intensity therapy or at least 2 cycles of low intensity therapy, or in second remission following any therapy. Up to 12 pts will be enrolled on the phase I, using 3+3, with escalating doses of revumenib and a target dose of 163

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Serum and CSF metabolomics analysis shows Mediterranean Ketogenic Diet mitigates risk factors of Alzheimer's disease

npj metabolic health and disease

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npj metabolic health and disease 2024年第1期2卷 15页

作者： Annalise Schweickart Richa Batra Bryan J Neth Cameron Martino Liat Shenhav Anru R Zhang Pixu Shi Naama Karu Kevin Huynh Peter J Meikle Leyla Schimmel Amanda Hazel Dilmore Kaj Blennow Henrik Zetterberg Colette Blach Pieter C Dorrestein Rob Knight Suzanne Craft Rima Kaddurah-Daouk Jan Krumsiek Tri-Institutional Program in Computational Biology & Medicine Weill Cornell Medicine New York NY USA. Department of Physiology and Biophysics Weill Cornell Medicine Institute for Computational Biomedicine Englander Institute for Precision Medicine New York NY USA. Department of Neurology Mayo Clinic Rochester MN USA. Department of Pediatrics University of California San Diego La Jolla CA USA. Department of Microbiology New York University Grossman School of Medicine New York NY USA. Department of Biostatistics and Bioinformatics Duke University Durham NC USA. Tasmanian Independent Metabolomics and Analytical Chemistry Solutions (TIMACS) Hobart TAS Australia. Baker Heart and Diabetes Institute 75 Commercial Road Melbourne VIC Australia. Baker Department of Cardiovascular Research Translation and Implementation La Trobe University Bundoora VIC Australia. Department of Psychiatry and Behavioral Sciences Duke University Durham NC USA. Department of Psychiatry and Neurochemistry University of Gothenburg Gothenburg Sweden. Duke Molecular Physiology Institute Duke University Durham NC USA. Skaggs School of Pharmacy and Pharmaceutical Sciences University of California San Diego La Jolla CA USA. Departments of Pediatrics Computer Science and Engineering Bioengineering University of California San Diego La Jolla CA USA. Department of Gerontology and Geriatric Medicine Wake Forest University School of Medicine Winston Salem NC USA. Duke Institute of Brain Sciences Duke University Durham NC USA. Department of Medicine Duke University Durham NC USA.

Alzheimer's disease (AD) is influenced by a variety of modifiable risk factors, including a person's dietary habits. While the ketogenic diet (KD) holds promise in reducing metabolic risks and potentially affecting AD progression, only a few studies have explored KD's metabolic impact, especially on blood and cerebrospinal fluid (CSF). Our study involved participants at risk for AD, either cognitively normal or with mild cognitive impairment. The participants consumed both a modified Mediterranean Ketogenic Diet (MMKD) and the American Heart Association diet (AHAD) for 6 weeks each, separated by a 6-week washout period. We employed nuclear magnetic resonance (NMR)-based metabolomics to profile serum and CSF and metagenomics profiling on fecal samples. While the AHAD induced no notable metabolic changes, MMKD led to significant alterations in both serum and CSF. These changes included improved modifiable risk factors, like increased HDL-C and reduced BMI, reversed serum metabolic disturbances linked to AD such as a microbiome-mediated increase in valine levels, and a reduction in systemic inflammation. Additionally, the MMKD was linked to increased amino acid levels in the CSF, a breakdown of branched-chain amino acids (BCAAs), and decreased valine levels. Importantly, we observed a strong correlation between metabolic changes in the CSF and serum, suggesting a systemic regulation of metabolism. Our findings highlight that MMKD can improve AD-related risk factors, reverse some metabolic disturbances associated with AD, and align metabolic changes across the blood-CSF barrier.

关键词： Diseases Metabolomics

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