AGMP, in partnership with eight Alzheimer Disease Research Centers across the US, is collecting fecal and blood samples from ADRC participants aged 60 and above using our at-home collection kits. We aim to recruit a racially diverse cohort of about 1000 participants, balanced between those diagnosed with Mild Cognitive Impairment, Alzheimer’s Disease, and controls. We will profile the samples using metagenomics and various metabolomics platforms across our consortium. Data generated will be analyzed and compared against existing datasets to elucidate links between the gut microbiome, metabolome, and Alzheimer’s disease pathogenesis.

Disease Stratification For Precision Medicine Approach To AD

We systematically investigated group-specific metabolic alterations by conducting stratified association analyses of serum metabolites in individuals with measurements of AD biomarkers. Dissecting metabolic heterogeneity in AD pathogenesis can enable grading the biomedical relevance for specific pathways within specific subgroups, guiding the way to personalized medicine.

gut-live brain axis

Gut-Liver-Brain Axis in AD

In two publications we showed an altered bile acid profile in AD was associated with cognitive decline as well as CSF amyloid-beta, p-tau, t-tau, and brain glucose metabolism providing further support for the role of BA pathways in AD pathophysiology.

Host-Microbiome Co-Metabolism

Using the COBRA approach, Dr. Ines Thiele and her team have generated the first physiologically resolved whole-body, gender-specific metabolic models (WBMs) based on extensive organ-specific proteomic and metabolomic data, as well as through literature curation.

To learn more visit:

Molecular Atlas for AD

The AD atlas is a network-based data integration resource for investigating AD, its biomarkers, and associated endophenotypes in a multi-omics context. The database can be queried by entering one or more phenotypes, genes, proteins, or metabolites and provides an interactive interface to inspect generated networks, as well as enrichment tools for downstream analyses.

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Network-Based Approaches For Analayzing Metabolite Associations

We use metabolomics as a global biochemical approach to identify peripheral metabolic changes in AD patients and correlate them to AD biomarkers including cerebrospinal fluid pathology, brain imaging features, and cognitive performance.

Global Natural Products Social Molecular Networking

Global Natural Products Social Molecular Networking (GNPS) is a web-based mass spectrometry ecosystem that aims to be an open-access knowledge base for the community-wide organization and sharing of raw, processed, or identified tandem mass (MS/MS) spectrometry data. GNPS aids in identification and discovery throughout the entire life cycle of data; from initial data acquisition/analysis to post-publication.

To learn more visit:

Quantitative Insights Into Microbial Ecology

QIIME 2™ is a next-generation microbiome bioinformatics platform that
is extensible, free, open-source, and community-developed.

For more information, visit:

U.S. Pointer

The U.S. Study to Protect Brain Health Through Lifestyle Intervention to Reduce Risk (US POINTER) is a two-year clinical trial to evaluate whether lifestyle interventions that simultaneously target many risk factors protect cognitive function in older adults who are at increased risk for cognitive decline

Mind Diet Trial

Mediterranean-DASH Diet Intervention for Neurodegenerative Delay (MIND) Trial to Prevent Alzheimer’s is a three-year research study that compares two weight-loss diets and their effects on brain health and cognitive decline.

BEAT-AD Clinical Trial

Brain Energy for Amyloid Transformation in Alzheimer’s Disease Study (BEAT-AD) compares the effects of a ketogenic low-carbohydrate diet and a low-fat diet in adults with mild cognitive impairment. The data collected will help determine whether diet interventions induce changes in cognition, cerebral blood flow, and levels of certain proteins and hormones.

Microbial Metabolite Database

Through our partnership with Leiden University and the greater metabolomics community, we are working to establish a comprehensive database for microbial metabolites. Interconnected modules in the database will allow for dynamic browsing and search combinations.

Molecular Networking

Molecular networking is a strategy that first merges all identical spectra and then creates a map of MS/MS similarity from which chemical similarity can be inferred. Molecular networking allows the discovery of molecules that are not identical but are related to known molecules.

Global Food Omics

The Global Food Omics initiative was launched at UC San Diego in collaboration with the American Gut Project, to learn more about bacteria in foods and the small molecules they produce.

To learn more visit: Global Food Omics


Qiita (canonically pronounced cheetah) is an entirely open-source microbial study management platform. It allows users to keep track of multiple studies with multiple ‘omics data. Additionally, Qiita is capable of supporting multiple analytical pipelines through a 3rd-party plugin system, allowing the user to have a single entry point for all of their analyses.

Qiita provides database and computes resources to the global community, alleviating the technical burdens that are typically limiting for researchers studying microbial ecology (e.g. familiarity with the command line or access to compute power).

Qiita’s platform allows for quick reanalysis of the datasets that have been deposited using the latest analytical technologies. This means that Qiita’s internal datasets are living data that is periodically re-annotated according to current best practices.

For more information about how to use Qiita, visit the documentation:


Feature-based molecular networking in the GNPS analysis environment

NothiasLF, Petras D, Schmid R, Dhrkop K, Rainer J, Sarvepalli A, Protsyuk I, Ernst M,Tsugawa H, Fleischauer M, Aicheler F, Aksenov AA, Alka O, Allard PM, BarschA,Cachet X, CaraballoRodriguez AM, Da Silva RR, Dang T, Garg N, Gauglitz JM,Gurevich A, Isaac G, Jarmusch AK, Kamenk Z, Kang KB, Kessler N, Koester I, KorfA, Le Gouellec A, Ludwig M, Martin H C, McCall LI, McSayles J, Meyer SW,Mohimani H, Morsy M,Moyne O, Neumann S, Neuweger H, Nguyen NH,NothiasEsposito M, Paolini J, Phelan VV, Pluskal T, Quinn RA, Rogers S, ShresthaB, Tripathi A, van der Hooft JJJ, Vargas F, Weldon KC, Witting M, Yang H, Zhang Z,Zubeil F, Kohlbacher O, Bcker S, Alexandrov T,Bandeira N, Wang M, DorresteinPC. Nature methods. 2020 September;17(9):905908. PubMed PMID: 32839597;PubMed Central PMCID: PMC7885687; DOI: 10.1038/s4159202009336.

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Auto-deconvolution and molecular networking of gas chromatography - mass spectrometry data.

Aksenov AA, Laponogov I, Zhang Z, Doran SLF, Belluomo I, Veselkov D, Bittremieux W, Nothias LF, Nothias-Esposito M, Maloney KN, Misra BB, Melnik AV, Smirnov A, Du X, Jones KL 2nd, Dorrestein K, Panitchpakdi M, Ernst M, van der Hooft JJJ, Gonzalez M, Carazzone C, Amézquita A, Callewaert C, Morton JT, Quinn RA, Bouslimani A, Orio AA, Petras D, Smania AM, Couvillion SP, Burnet MC, Nicora CD, Zink E, Metz TO, Artaev V, Humston-Fulmer E, Gregor R, Meijler MM, Mizrahi I, Eyal S, Anderson B, Dutton R, Lugan R, Boulch PL, Guitton Y, Prevost S, Poirier A, Dervilly G, Le Bizec B, Fait A, Persi NS, Song C, Gashu K, Coras R, Guma M, Manasson J, Scher JU, Barupal DK, Alseekh S, Fernie AR, Mirnezami R, Vasiliou V, Schmid R, Borisov RS, Kulikova LN, Knight R, Wang M, Hanna GB, Dorrestein PC, Veselkov K.. Nat Biotechnol. 2021 Feb;39(2):169-173. doi: 10.1038/s41587-020-0700-3. Epub 2020 Nov 9. PMID: 33169034; PMCID: PMC7971188.

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Chemically informed analyses of metabolomics mass spectrometry data with Qemistree.

Tripathi A, Vázquez-Baeza Y, Gauglitz JM, Wang M, Dührkop K, Nothias-Esposito M, Acharya DD, Ernst M, van der Hooft JJJ, Zhu Q, McDonald D, Brejnrod AD, Gonzalez A, Handelsman J, Fleischauer M, Ludwig M, Böcker S, Nothias LF, Knight R, Dorrestein PC.. Nat Chem Biol. 2021 Feb;17(2):146-151. doi: 10.1038/s41589-020-00677-3. Epub 2020 Nov 16. PMID: 33199911; PMCID: PMC8189545.

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Plasma metabolites to profile pathways in noncommunicable disease multimorbidity

Pietzner M, Stewart ID, Raffler J, Khaw KT, Michelotti GA, Kastenmüller G, Wareham NJ, Langenberg C.. Nat Med. 2021 Mar;27(3):471-479. doi: 10.1038/s41591-021-01266-0. Epub 2021 Mar 11. PMID: 33707775; PMCID: PMC8127079.

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Genome-Scale Metabolic Modeling of the Human Microbiome in the Era of Personalized Medicine

Heinken A, Basile A, Hertel J, Thinnes C, Thiele I. Annu Rev Microbiol. 2021 Jul;27:146-151.doi: 10.1146/annurev-micro-060221-012134. Epub ahead of print. PMID: 34314593.

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An Integrated Molecular Atlas of Alzheimer's Disease

Wörheide MA, Krumsiek J, Nataf S, Nho W, Greenwood AK, Wu T, Huynh K, Weinisch P, Römisch-Margl W, Lehner N, The AMP-AD Consortium, The Alzheimer’s Disease Neuroimaging Initiative, The Alzheimer’s Disease Metabolomics Consortium, Baumbach J, Meikle PJ, Saykin AJ, Doraiswamy PM, van Duijn C, Suhre K, Kaddurdah-Daouk R, Kastenmüller G, Arnold M. medRxiv. 2021 Sep 22. doi:

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Integration of constraint-based modeling with fecal metabolomics reveals large deleterious effects of Fusobacterium spp. on community butyrate production

Hertel J, Heinken A, Martinelli F, Thiele I. Gut Microbes. 2021 Jan-Dec; 13(1):1-23.doi: 10.1080/19490976.2021.1915673. PMID: 34057024.

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SGI: Automatic clinical subgroup identification in omics datasets

Buyukozkan M, Suhre K, Krumsiek J. Bioinfomatics. 2021 Sep 16;btab656. doi: 10.1093/bioinformatics/btab656. PMID: 34529048.

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Identification of plasma proteins relating to brain neurodegeneration and vascular pathology in cognitively normal individuals

Shi L, Buchanan CR, Cox SR, Hillary RF, Marioni RE, Campbell A, Hayward C, Stolicyn A, Whalley HC, Harris MA, Waymont J, Waiter G, Backhouse E, Wardlaw JM, Steele D, Mcintoch A, Lovestone S, Buckley NJ, Nevado-Holgado AJ. Alzheimers Dement (Amst). 2021 Sep 27; 13(1)e12240. doi: 10.1002/dad2.12240. PMID: 34604499.

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Advances in constraint-based modelling of microbial communities

Heinken A, Basile A, Thiele I. Curr Opi Sys Biol. 2021 Sep 27;100346. doi:

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Causal inference on microbiome-metabolome relations via in silico in vivo association pattern analyses

Hertel J, Heinken A, Thiele I. bioRxiv. 2021 Mar 16. doi:

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Metabolic modelling reveals broad changes in gut microbial metabolism in inflammatory bowel disease patients with dysbiosis

Heinken A, Hertel J, Thiele I. npj Syst Biol Appl. 2021 May 6;7(19). doi:

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foodMASST: A Mass Spectrometry Search Tool for Foods and Beverages.

Dorrestein P, West K, Schmid R, Gauglitz J, Wang Mingxun. Research Square. 2021 Sep 23. doi:

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maplet: An extensible R toolbox for modular and reproducible omics pipelines

Chetnik K, Benedetti E, Gomari DP, Schweickart A, Barta R, Buyukozkan M, Wang Z, Arnold M, Zierer J, Suhre K, Krumsiek K. arXiv. 2021 May 6: 2105:04305v1.

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Towards Standards for Human Fecal Sample Preparation in Targeted and Untargeted LC-HRMS Studies

Hosseinkhani F, Dubbelman A-C, Karu N, Harms AC, Hankemeier T. Metabolites. 2021 Jun 7; 11(6):364. doi: 10.3390/metabo11060364.

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