Evidence map›Paper›PMID 34670619›Full record

ArticleAlzheimer's research & therapy2021

Gut-microbiota-microglia-brain interactions in Alzheimer's disease: knowledge-based, multi-dimensional characterization.

QuanQiu Wang, Pamela B Davis, Xin Qi, Shu G Chen, Mark E Gurney, George Perry, P Murali Doraiswamy, Rong Xu

Open access · goldAbstract read
In one paragraph

Article in Alzheimer's research & therapy, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
17citing papers in PubMed, 1 pooled it
1.9field-weighted citation impact, top 14% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

17 citing papers in PubMed, 1 synthesis or guideline pooled it, 31 citations in OpenAlex.

  1. Pooled it
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  15. The emerging role of the microbiome in Alzheimer's disease.International review of neurobiology · 2022
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors at 5 institutions in 1 country.

QuanQiu WangCenter for Artificial Intelligence in Drug Discovery, School of Medicine, Case Western Reserve University, 2103 Cornell Rd, Cleveland, OH, 44106, USA.
Pamela B DavisCenter for Community Health Integration, Division of General Medical Sciences, School of Medicine, Case Western Reserve University, Cleveland, OH, USA.
Xin QiDepartment of Physiology and Biophysics, School of Medicine, Case Western Reserve University, Cleveland, OH, USA.
Shu G ChenDepartment of Pathology, School of Medicine, Case Western Reserve University, Cleveland, OH, USA.
Mark E GurneyTetra Therapeutics, Grand Rapids, MI, USA.
George PerryCollege of Sciences, The University of Texas at San Antonio, San Antonio, TX, USA.
P Murali DoraiswamyDuke University School of Medicine and the Duke Institute for Brain Sciences, Durham, NC, 27710, USA.
Rong XuCenter for Artificial Intelligence in Drug Discovery, School of Medicine, Case Western Reserve University, 2103 Cornell Rd, Cleveland, OH, 44106, USA. rxx@case.edu.ORCID 0000-0003-3127-4795
Case Western Reserve University · USCornell University · USDuke University · USTetra Therapeutics (United States) · USThe University of Texas at San Antonio · US

Funding

Clinical and Translational Science Collaborative of ClevelandUL1TR002548 · NCATS · CASE WESTERN RESERVE UNIVERSITY · PI MCCOMSEY, GRACE A · 2018 to 2022
$35.5M
Greater Southern California Node of the Clinical Trials NetworkUG1DA049435 · NIDA · UNIVERSITY OF CALIFORNIA LOS ANGELES · PI YIH-ING HSER · 2019 to 2026
$16.7M
Combine computational prediction, network analysis and genetic screening in C elegans to uncover neurodegenerative causes in Alzheimer's DiseaseR01AG061388 · NIA · CASE WESTERN RESERVE UNIVERSITY · PI CHEN, SHU G., XU, RONG · 2018 to 2022
$3.4M
Role of brain lipid metabolism in Alzheimer's diseaseR01AG065240 · NIA · CASE WESTERN RESERVE UNIVERSITY · PI QI, XIN · 2020 to 2024
$3.0M
An Integrated Reverse Engineering Approach Toward Rapid drug Re positioning for Alzheimer's DiseaseR01AG057557 · NIA · CASE WESTERN RESERVE UNIVERSITY · PI XU, RONG · 2017 to 2021
$2.8M
Proteostasis dysregulation and alpha-synucleinR01NS115903 · NINDS · CASE WESTERN RESERVE UNIVERSITY · PI QI, XIN · 2020 to 2023
$2.4M
Rapid reverse translational drug repositioningDP2HD084068 · NICHD · CASE WESTERN RESERVE UNIVERSITY · PI XU, RONG · 2014 to 2014
$2.4M
Construct large-scale phenomes of disease and drugs and develop data-driven systems approaches to understand genetic links between Alzheimer's disease and Neuropsychiatric symptomsR56AG062272 · NIA · CASE WESTERN RESERVE UNIVERSITY · PI XU, RONG · 2018 to 2019
$1.6M
Mitochondrial biomarkers in Huntington's diseaseR21NS107897 · NINDS · CASE WESTERN RESERVE UNIVERSITY · PI QI, XIN · 2019 to 2019
$453k
Mitochondrial quality control and alpha-synucleinR56NS105632 · NINDS · CASE WESTERN RESERVE UNIVERSITY · PI QI, XIN · 2019 to 2019
$417k
NCATS NIH HHS UL1 TR002548NIA NIH HHS AG061388NIA NIH HHS AG062272NIA NIH HHS R01 AG057557NIA NIH HHS R01 AG061388NIA NIH HHS R01 AG065240NIA NIH HHS R56 AG062272NICHD NIH HHS DP2 HD084068NIDA NIH HHS UG1 DA049435NINDS NIH HHS R01 NS115903NINDS NIH HHS R21 NS107897NINDS NIH HHS R56 NS105632
6 · The paper itself

Abstract

backgroundInteractions between the gut microbiota, microglia, and aging may modulate Alzheimer's disease (AD) pathogenesis but the precise nature of such interactions is not known.

methodsWe developed an integrated multi-dimensional, knowledge-driven, systems approach to identify interactions among microbial metabolites, microglia, and AD. Publicly available datasets were repurposed to create a multi-dimensional knowledge-driven pipeline consisting of an integrated network of microbial metabolite-gene-pathway-phenotype (MGPPN) consisting of 34,509 nodes (216 microbial metabolites, 22,982 genes, 1329 pathways, 9982 mouse phenotypes) and 1,032,942 edges.

resultsWe evaluated the network-based ranking algorithm by showing that abnormal microglia function and physiology are significantly associated with AD pathology at both genetic and phenotypic levels: AD risk genes were ranked at the top 6.4% among 22,982 genes, P < 0.001. AD phenotypes were ranked at the top 11.5% among 9982 phenotypes, P < 0.001. A total of 8094 microglia-microbial metabolite-gene-pathway-phenotype-AD interactions were identified for top-ranked AD-associated microbial metabolites. Short-chain fatty acids (SCFAs) were ranked at the top among prioritized AD-associated microbial metabolites. Through data-driven analyses, we provided evidence that SCFAs are involved in microglia-mediated gut-microbiota-brain interactions in AD at both genetic, functional, and phenotypic levels.

conclusionOur analysis produces a novel framework to offer insights into the mechanistic links between gut microbial metabolites, microglia, and AD, with the overall goal to facilitate disease mechanism understanding, therapeutic target identification, and designing confirmatory experimental studies.

Indexed as

Alzheimer DiseaseGastrointestinal MicrobiomeAnimalsBrainMiceMicrogliaPhenotypeGut microbiotaMicrobial metabolitesMicrogliaNetworksNeurodegenerationSystems biology

Identifiers

PMID34670619
PMCPMC8529734
OpenAlexW3205761179

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.