Multi-level analysis of the gut–brain axis shows autism spectrum disorder-associated molecular and microbial profiles

  1. Morton, James T.
  2. Jin, Dong-Min
  3. Mills, Robert H.
  4. Shao, Yan
  5. Rahman, Gibraan
  6. McDonald, Daniel
  7. Zhu, Qiyun
  8. Balaban, Metin
  9. Jiang, Yueyu
  10. Cantrell, Kalen
  11. Gonzalez, Antonio
  12. Carmel, Julie
  13. Frankiensztajn, Linoy Mia
  14. Martin-Brevet, Sandra
  15. Berding, Kirsten
  16. Needham, Brittany D.
  17. Zurita, María Fernanda
  18. David, Maude
  19. Averina, Olga V.
  20. Kovtun, Alexey S.
  21. Noto, Antonio
  22. Mussap, Michele
  23. Wang, Mingbang
  24. Frank, Daniel N.
  25. Li, Ellen
  26. Zhou, Wenhao
  27. Fanos, Vassilios
  28. Danilenko, Valery N.
  29. Wall, Dennis P.
  30. Cárdenas, Paúl
  31. Baldeón, Manuel E.
  32. Jacquemont, Sébastien
  33. Koren, Omry
  34. Elliott, Evan
  35. Xavier, Ramnik J.
  36. Mazmanian, Sarkis K.
  37. Knight, Rob
  38. Gilbert, Jack A.
  39. Donovan, Sharon M.
  40. Lawley, Trevor D.
  41. Carpenter, Bob
  42. Bonneau, Richard
  43. Taroncher-Oldenburg, Gaspar

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1000 Titel
  • Multi-level analysis of the gut–brain axis shows autism spectrum disorder-associated molecular and microbial profiles
1000 Autor/in
  1. Morton, James T. |
  2. Jin, Dong-Min |
  3. Mills, Robert H. |
  4. Shao, Yan |
  5. Rahman, Gibraan |
  6. McDonald, Daniel |
  7. Zhu, Qiyun |
  8. Balaban, Metin |
  9. Jiang, Yueyu |
  10. Cantrell, Kalen |
  11. Gonzalez, Antonio |
  12. Carmel, Julie |
  13. Frankiensztajn, Linoy Mia |
  14. Martin-Brevet, Sandra |
  15. Berding, Kirsten |
  16. Needham, Brittany D. |
  17. Zurita, María Fernanda |
  18. David, Maude |
  19. Averina, Olga V. |
  20. Kovtun, Alexey S. |
  21. Noto, Antonio |
  22. Mussap, Michele |
  23. Wang, Mingbang |
  24. Frank, Daniel N. |
  25. Li, Ellen |
  26. Zhou, Wenhao |
  27. Fanos, Vassilios |
  28. Danilenko, Valery N. |
  29. Wall, Dennis P. |
  30. Cárdenas, Paúl |
  31. Baldeón, Manuel E. |
  32. Jacquemont, Sébastien |
  33. Koren, Omry |
  34. Elliott, Evan |
  35. Xavier, Ramnik J. |
  36. Mazmanian, Sarkis K. |
  37. Knight, Rob |
  38. Gilbert, Jack A. |
  39. Donovan, Sharon M. |
  40. Lawley, Trevor D. |
  41. Carpenter, Bob |
  42. Bonneau, Richard |
  43. Taroncher-Oldenburg, Gaspar |
1000 Erscheinungsjahr 2023
1000 Publikationstyp
  1. Artikel |
1000 Online veröffentlicht
  • 2023-06-26
1000 Erschienen in
1000 Quellenangabe
  • 26(7):1208-1217
1000 Copyrightjahr
  • 2023
1000 Lizenz
1000 Verlagsversion
  • https://doi.org/10.1038/s41593-023-01361-0 |
  • https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10322709/ |
1000 Ergänzendes Material
  • https://www.nature.com/articles/s41593-023-01361-0#Sec21 |
1000 Publikationsstatus
1000 Begutachtungsstatus
1000 Sprache der Publikation
1000 Abstract/Summary
  • Autism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by heterogeneous cognitive, behavioral and communication impairments. Disruption of the gut–brain axis (GBA) has been implicated in ASD although with limited reproducibility across studies. In this study, we developed a Bayesian differential ranking algorithm to identify ASD-associated molecular and taxa profiles across 10 cross-sectional microbiome datasets and 15 other datasets, including dietary patterns, metabolomics, cytokine profiles and human brain gene expression profiles. We found a functional architecture along the GBA that correlates with heterogeneity of ASD phenotypes, and it is characterized by ASD-associated amino acid, carbohydrate and lipid profiles predominantly encoded by microbial species in the genera Prevotella, Bifidobacterium, Desulfovibrio and Bacteroides and correlates with brain gene expression changes, restrictive dietary patterns and pro-inflammatory cytokine profiles. The functional architecture revealed in age-matched and sex-matched cohorts is not present in sibling-matched cohorts. We also show a strong association between temporal changes in microbiome composition and ASD phenotypes. In summary, we propose a framework to leverage multi-omic datasets from well-defined cohorts and investigate how the GBA influences ASD.
1000 Sacherschließung
lokal Autism spectrum disorders
lokal Genomics
lokal Data integration
lokal Microbiology
1000 Fächerklassifikation (DDC)
1000 Liste der Beteiligten
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1000 Label
1000 Förderer
  1. Eunice Kennedy Shriver National Institute of Child Health and Human Development |
  2. National Natural Science Foundation of China |
  3. Shanghai talent development funding |
  4. European Research Council |
1000 Fördernummer
  1. WT206194
  2. 82071733
  3. 2020115
  4. ERC-2020-COG;101001355
1000 Förderprogramm
  1. -
  2. -
  3. -
  4. European Union’s Horizon 2020 Research and Innovation Program
1000 Dateien
1000 Förderung
  1. 1000 joinedFunding-child
    1000 Förderer Eunice Kennedy Shriver National Institute of Child Health and Human Development |
    1000 Förderprogramm -
    1000 Fördernummer WT206194
  2. 1000 joinedFunding-child
    1000 Förderer National Natural Science Foundation of China |
    1000 Förderprogramm -
    1000 Fördernummer 82071733
  3. 1000 joinedFunding-child
    1000 Förderer Shanghai talent development funding |
    1000 Förderprogramm -
    1000 Fördernummer 2020115
  4. 1000 joinedFunding-child
    1000 Förderer European Research Council |
    1000 Förderprogramm European Union’s Horizon 2020 Research and Innovation Program
    1000 Fördernummer ERC-2020-COG;101001355
1000 Objektart article
1000 Beschrieben durch
1000 @id frl:6462367.rdf
1000 Erstellt am 2023-11-14T14:50:39.422+0100
1000 Erstellt von 337
1000 beschreibt frl:6462367
1000 Bearbeitet von 317
1000 Zuletzt bearbeitet Wed Nov 15 08:26:31 CET 2023
1000 Objekt bearb. Wed Nov 15 08:26:15 CET 2023
1000 Vgl. frl:6462367
1000 Oai Id
  1. oai:frl.publisso.de:frl:6462367 |
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