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Prof. Dr. Matthias Arnold

Head of Research Team "Computational Neurobiology"
+49 89 3187-3676Email meBuilding/Room: 56/150

Research Interests

  • Standardizing metabolomics data processing, analysis and governance and enhancing research reproducibility in international Alzheimer's disease (AD) research consortia
  • Identifying and mapping metabolic pathway failures across the trajectory of AD using cross-sectional and longitudinal metabolomics data
  • Metabolic pathway-based patient stratification, identification of subgroup-specific molecular changes in, and molecularly-defined subtypes of AD
  • Cross-omics processing of big data and results integration in annotation, association and graph databases accessible through web interfaces:
  • Development of multi-omics- and graph-based machine learning and deep learning approaches for multi-omics-guided candidate target identification/prioritization and in silico drug repositioning

Skills and Expertise

MetabolomicsBioinformaticsMetabolismData IntegrationBiostatisticsBig DataWebtool development

Professional Background

2022 - present

Adjunct Associate Professor

in the Department of Psychiatry and Behavioral Sciences, Duke University

2018 - 2022

Adjunct Assistant Professor

in the Department of Psychiatry and Behavioral Sciences, Duke University

2018 - present

Head of Research Team “Computational Neurobiology”

at the Computational Health Center / ICB, Helmholtz Munich

2016 - 2018

Postdoctoral Research Associate

at the Institute of Bioinformatics and Systems Biology / IBIS, Helmholtz Munich

2014 - 2016

Research Associate

at the Institute of Bioinformatics and Systems Biology / IBIS, Helmholtz Munich

2011 - 2016

Ph.D. Student

Technical University Munich and Helmholtz Munich

2006 - 2011

Studies in Bioinformatics

Technical University Munich and Ludwig-Maximilians-Universität Munich
(Dipl.-Bioinf., German equivalent to Master’s degree)

Selected Publications

2022 Alzheimer’s & Dementia

Richa Batra*, Matthias Arnold*, Maria A. Wörheide, Mariet Allen, Xue Wang, Colette Blach, Allan I. Levey, Nicholas T. Seyfried, Nilüfer Ertekin-Taner, David A. Bennett, Gabi Kastenmüller, Rima F. Kaddurah-Daouk, Jan Krumsiek, and Alzheimer’s Disease Metabolomics Consortium (ADMC)

The landscape of metabolic brain alterations in Alzheimer's disease

INTRODUCTION Alzheimer's disease (AD) is accompanied by metabolic alterations both in the periphery and the central nervous system. However, so far, a global view of AD-associated metabolic changes in the brain has been missing. METHODS We metabolically profiled 500 samples from the dorsolateral prefrontal cortex. Metabolite levels were correlated with eight clinical parameters, covering both late-life cognitive performance and AD neuropathology measures. RESULTS We observed widespread metabolic dysregulation associated with AD, spanning 298 metabolites from various AD-relevant pathways. These included alterations to bioenergetics, cholesterol metabolism, neuroinflammation, and metabolic consequences of neurotransmitter ratio imbalances. Our findings further suggest impaired osmoregulation as a potential pathomechanism in AD. Finally, inspecting the interplay of proteinopathies provided evidence that metabolic associations were largely driven by tau pathology rather than amyloid beta pathology. DISCUSSION This work provides a comprehensive reference map of metabolic brain changes in AD that lays the foundation for future mechanistic follow-up studies.

2021 medRxiv

Maria A. Wörheide, Jan Krumsiek, Serge Nataf, Kwangsik Nho, Anna K. Greenwood, Tong Wu, Kevin Huynh, Patrick Weinisch, Werner Römisch-Margl, Nick Lehner, The AMP-AD Consortium, The Alzheimer’s Disease Neuroimaging Initiative, The Alzheimer’s Disease Metabolomics Consortium, Jan Baumbach, Peter J. Meikle, Andrew J. Saykin, P. Murali Doraiswamy, Cornelia van Duijn, Karsten Suhre, Rima Kaddurah-Daouk, Gabi Kastenmüller, and Matthias Arnold

An Integrated Molecular Atlas of Alzheimer’s Disease.

INTRODUCTION Embedding single-omics disease associations into the wider context of multi-level molecular changes in Alzheimer’s disease (AD) remains one central challenge in AD research. METHODS Results from numerous AD-specific omics studies from AMP-AD, NIAGADS, and other initiatives were integrated into a comprehensive network resource and complemented with molecular associations from large-scale population-based studies to provide a global view on AD. RESULTS We present the AD Atlas, an online resource ( integrating over 20 large studies providing disease-relevant information on 20,353 protein-coding genes, 8,615 proteins, 997 metabolites and 31 AD-related phenotypes. Multiple showcases demonstrate the utility of this resource for contextualization of AD research results and subsequent downstream analyses, such as drug repositioning approaches. DISCUSSION By providing a global view on multi-omics results through a user-friendly interface, the AD Atlas enables the formulation of molecular hypotheses and retrieval of clinically relevant insights that can be validated in follow-up analyses or experiments.

2020 Nature Communications

Matthias Arnold, Kwangsik Nho, Alexandra Kueider-Paisley, Tyler Massaro, Kevin Huynh, Barbara Brauner, Siamak MahmoudianDehkordi, Gregory Louie, M. Arthur Moseley, J. Will Thompson, Lisa St John-Williams, Jessica D. Tenenbaum, Colette Blach, Rui Chang, Roberta D. Brinton, Rebecca Baillie, Xianlin Han, John Q. Trojanowski, Leslie M. Shaw, Ralph Martins, Michael W. Weiner, Eugenia Trushina, Jon B. Toledo, Peter J. Meikle, David A. Bennett, Jan Krumsiek, P. Murali Doraiswamy, Andrew J. Saykin, Rima Kaddurah-Daouk & Gabi Kastenmüller

Sex and APOE ε4 genotype modify the Alzheimer’s disease serum metabolome.

Sex and the APOE epsilon 4 genotype are important risk factors for late-onset Alzheimer's disease. In the current study, the authors investigate how sex and APOE epsilon 4 genotype modify the association between Alzheimer's disease biomarkers and metabolites in serum.Late-onset Alzheimer's disease (AD) can, in part, be considered a metabolic disease. Besides age, female sex and APOE epsilon 4 genotype represent strong risk factors for AD that also give rise to large metabolic differences. We systematically investigated group-specific metabolic alterations by conducting stratified association analyses of 139 serum metabolites in 1,517 individuals from the AD Neuroimaging Initiative with AD biomarkers. We observed substantial sex differences in effects of 15 metabolites with partially overlapping differences for APOE epsilon 4 status groups. Several group-specific metabolic alterations were not observed in unstratified analyses using sex and APOE epsilon 4 as covariates. Combined stratification revealed further subgroup-specific metabolic effects limited to APOE epsilon 4+ females. The observed metabolic alterations suggest that females experience greater impairment of mitochondrial energy production than males. Dissecting metabolic heterogeneity in AD pathogenesis can therefore enable grading the biomedical relevance for specific pathways within specific subgroups, guiding the way to personalized medicine.

2015 Bioinformatics

Matthias Arnold, Johannes Raffler, Arne Pfeufer, Karsten Suhre & Gabi Kastenmüller

SNiPA: An Interactive, Genetic Variant-Centered Annotation Browser

Motivation: Linking genes and functional information to genetic variants identified by association studies remains difficult. Resources containing extensive genomic annotations are available but often not fully utilized due to heterogeneous data formats. To enhance their accessibility, we integrated many annotation datasets into a user-friendly webserver. Availability and implementation: Contact: Supplementary information: Supplementary data are available at Bioinformatics online.

2014 Nature Genetics

So-Youn Shin, Eric B Fauman, Ann-Kristin Petersen, Jan Krumsiek, Rita Santos, Jie Huang, Matthias Arnold, Idil Erte, Vincenzo Forgetta, Tsun-Po Yang, Klaudia Walter, Cristina Menni, Lu Chen, Louella Vasquez, Ana M Valdes, Craig L Hyde, Vicky Wang, Daniel Ziemek, Phoebe Roberts, Li Xi, Elin Grundberg, The Multiple Tissue Human Expression Resource (MuTHER) Consortium, Melanie Waldenberger, J Brent Richards, Robert P Mohney, Michael V Milburn, Sally L John, Jeff Trimmer, Fabian J Theis, John P Overington, Karsten Suhre, M Julia Brosnan, Christian Gieger, Gabi Kastenmüller, Tim D Spector & Nicole Soranzo

An atlas of genetic influences on human blood metabolites

Genome-wide association scans with high-throughput metabolic profiling provide unprecedented insights into how genetic variation influences metabolism and complex disease. Here we report the most comprehensive exploration of genetic loci influencing human metabolism thus far, comprising 7,824 adult individuals from 2 European population studies. We report genome-wide significant associations at 145 metabolic loci and their biochemical connectivity with more than 400 metabolites in human blood. We extensively characterize the resulting in vivo blueprint of metabolism in human blood by integrating it with information on gene expression, heritability and overlap with known loci for complex disorders, inborn errors of metabolism and pharmacological targets. We further developed a database and web-based resources for data mining and results visualization. Our findings provide new insights into the role of inherited variation in blood metabolic diversity and identify potential new opportunities for drug development and for understanding disease.