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Cited by 360Neuroinflammation and Neurodegeneration MechanismsAlzheimer's disease research and treatmentsDementia and Cognitive Impairment Research

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A Path Toward Precision Medicine for Neuroinflammatory Mechanisms in Alzheimer's Disease

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Hampel, Harald, Caraci, Filippo, Cuello, A. Claudio, Caruso, Giuseppe, Nisticò, Robert, Corbo, Massimo, Baldacci, Filippo, Toschi, Nicola, Garaci, Francesco, Chiesa, Patrizia A., Verdooner, Steven R., Akman-Anderson, Leyla, Hernández, Félix, Ávila, Jesús, Emanuele, Enzo, Valenzuela, Pedro L., Lucía, Alejandro, Watling, Mark, Imbimbo, Bruno P., Vergallo, Andrea, Lista, Simone·2020·Frontiers in Immunology
DOI 10.3389/fimmu.2020.00456PMC PMC7137904
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Abstract

Neuroinflammation commences decades before Alzheimer’s disease (AD) clinical onset and represents one of the earliest pathomechanistic alterations throughout AD its continuum. Large-scale genome-wide association studies point out several genetic variants - TREM2, CD33, PILRA, CR1, MS4A, CLU, ABCA7, EPHA1, and HLA-DRB5-HLA-DRB1 - potentially linked to neuroinflammation. Most of these genes are involved in proinflammatory intracellular signaling, cytokines/interleukins cell turn-over, synaptic activity, lipid metabolism, and vesicle trafficking. Proteomic studies indicate that a plethora of interconnected aberrant molecular pathways, set off and perpetuated by TNF-α, TGF-β, IL-1β, and the receptor protein TREM2, are involved in neuroinflammation. Microglia and astrocytes are key cellular drivers and regulators of neuroinflammation. Under physiological conditions, they are important for neurotransmission and synaptic homeostasis. In AD, there is a turning point throughout its pathophysiological evolution where glial cells sustain an overexpressed inflammatory response that synergizes with amyloid-β and tau accumulation, and drives synaptotoxicity and neurodegeneration in a self-reinforcing manner. Despite a strong therapeutic rationale, previous clinical trials investigating compounds with anti-inflammatory properties, including non-steroidal anti-inflammatory drugs (NSAIDs) did not achieve primary efficacy endpoints. It is conceivable that study design issues, including the lack of diagnostic accuracy and biomarkers for target population identification and proof-of-mechanism may partially explain the negative outcomes. However, a recent meta-analysis indicates a potential biological effect of NSAIDs. In this regard, candidate fluid biomarkers of neuroinflammation are under analytical/clinical validation, i.e. TREM2, IL-1β, MCP-1, IL-6, TNF-α receptor complexes, TGF-β, YKL-40. PET radio-ligands are investigated to accomplish in-vivo and longitudinal regional exploration of neuroinflammation. Biomarkers tracking different molecular pathways (body fluid matrixes) along with brain neuroinflammatory endophenotypes (neuroimaging markers), can untangle temporal-spatial dynamics between neuroinflammation and other AD pathophysiological mechanisms. Robust biomarker-drug co-development pipelines are expected to enrich large-scale clinical trials testing new-generation compounds active, directly or indirectly, on neuroinflammatory targets and displaying putative disease-modifying effects: novel NSAIDs, AL002 (anti-TREM2 antibody), anti-Aβ protofibrils (BAN2401), and AL003 (anti-CD33 antibody). As a next step, taking advantage of breakthrough and multimodal techniques coupled with a systems biology approach is the path to pursue for developing individualized therapeutic strategies targeting neuroinflammation under the framework of precision medicine.

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Figures

Figure 1 Multifaceted functions of microglia during Aβ pathology. In healthy brain and early stages of AD, microglia clear small aggregates of Aβ peptides by phagocytosis and by secreting proteolytic enzymes, such as IDE, neprilysin, and MMP9. During advanced AD, microglia exacerbate AD pathology by releasing proinflammatory cytokines that induce neuronal cell death as well as A1 astrocytes, which, in turn, affect neuronal survival. Moreover, during advanced AD, microglia-derived ASC specks and EVs promote seeding of Aβ aggregates. Aβ, amyloid beta; AD, Alzheimer's disease; ASC, apoptosis-associated speck-like protein containing a CARD; C1q, complement component 1q; EVs, extracellular vesicles; IDE, insulin degrading enzyme; IL-1β, interleukin-1 beta; MMP-9, metalloprotease-9; TNF-α, tumor necrosis factor-alpha. From Wang and Colonna (45). Copyright© 2019, Society for Leukocyte Biology. Reprinted with permission from Wiley.
Figure 1 Multifaceted functions of microglia during Aβ pathology. In healthy brain and early stages of AD, microglia clear small aggregates of Aβ peptides by phagocytosis and by secreting proteolytic enzymes, such as IDE, neprilysin, and MMP9. During advanced AD, microglia exacerbate AD pathology by releasing proinflammatory cytokines that induce neuronal cell death as well as A1 astrocytes, which, in turn, affect neuronal survival. Moreover, during advanced AD, microglia-derived ASC specks and EVs promote seeding of Aβ aggregates. Aβ, amyloid beta; AD, Alzheimer's disease; ASC, apoptosis-associated speck-like protein containing a CARD; C1q, complement component 1q; EVs, extracellular vesicles; IDE, insulin degrading enzyme; IL-1β, interleukin-1 beta; MMP-9, metalloprotease-9; TNF-α, tumor necrosis factor-alpha. From Wang and Colonna (45). Copyright© 2019, Society for Leukocyte Biology. Reprinted with permission from Wiley.CC BY · PubMed Central
Figure 2 Role of neuroinflammation in AD pathogenesis: impairment of neurotrophin signaling. Aβ1−42 oligomers promote neuroinflammation and neuronal death in AD brain by eliciting the release of proinflammatory cytokines (IL-1β and TNF-α) from microglia and also interfering with the synthesis of anti-inflammatory cytokines such as TGF-β1. TNF-α inhibits microglia phagocytosis of Aβ and stimulates γ-secretase activity, thus facilitating Aβ accumulation and microglia-mediated neuroinflammation. Proinflammatory microglial activities promote neuronal death also through the formation of ROS and RNS. Neuroinflammatory phenomena can finally contribute to the pathogenesis of AD by impairing neurotrophin signaling function: (I) reducing the synthesis of BDNF and TGF-β1 and (II) causing an impairment of NGF metabolic pathway characterized by a reduced conversion of proNGF to biologically active mNGF and by an increased degradation of mNGF promoted by MMP-9. Aβ, amyloid beta; Aβ1−42, 42-amino acid-long amyloid beta peptide; BDNF, brain-derived neurotrophic factor; IL-1β, interleukin-1 beta; MMP-9, metalloprotease-9; NGF, nerve growth factor; mNGF, mature nerve growth factor; proNGF, precursor of the nerve growth factor; RNS, reactive nitrogen species; ROS, reactive oxygen species; TGF-β, transforming growth factor-beta; TNF-α, tumor necrosis factor-alpha.
Figure 2 Role of neuroinflammation in AD pathogenesis: impairment of neurotrophin signaling. Aβ1−42 oligomers promote neuroinflammation and neuronal death in AD brain by eliciting the release of proinflammatory cytokines (IL-1β and TNF-α) from microglia and also interfering with the synthesis of anti-inflammatory cytokines such as TGF-β1. TNF-α inhibits microglia phagocytosis of Aβ and stimulates γ-secretase activity, thus facilitating Aβ accumulation and microglia-mediated neuroinflammation. Proinflammatory microglial activities promote neuronal death also through the formation of ROS and RNS. Neuroinflammatory phenomena can finally contribute to the pathogenesis of AD by impairing neurotrophin signaling function: (I) reducing the synthesis of BDNF and TGF-β1 and (II) causing an impairment of NGF metabolic pathway characterized by a reduced conversion of proNGF to biologically active mNGF and by an increased degradation of mNGF promoted by MMP-9. Aβ, amyloid beta; Aβ1−42, 42-amino acid-long amyloid beta peptide; BDNF, brain-derived neurotrophic factor; IL-1β, interleukin-1 beta; MMP-9, metalloprotease-9; NGF, nerve growth factor; mNGF, mature nerve growth factor; proNGF, precursor of the nerve growth factor; RNS, reactive nitrogen species; ROS, reactive oxygen species; TGF-β, transforming growth factor-beta; TNF-α, tumor necrosis factor-alpha.CC BY · PubMed Central
Figure 3 Schematic representation of neuroinflammatory process occurring during the early stages of the AD pathology and potential points of attack of NSAIDs and anti-inflammatory drugs. In this process, neurons surrounded by Aβ oligomers release proinflammatory cytokines triggering the intermediate activation of microglia and their mobilization toward Aβ-burdened neurons. Both Aβ-burdened neurons and activated microglia are responsible for a disease-aggravating process in which the release of proinflammatory cytokines and chemokines predominates. NSAIDs and anti-inflammatory drugs may be potentially effective during this early inflammatory phase, antagonizing the aggravating activity of proinflammatory mediators. Selective agents stabilizing microglia may also be effective in attenuating the inflammatory process. Aβ, amyloid beta; AD, Alzheimer's disease; NSAIDs, non-steroidal anti-inflammatory drugs.
Figure 3 Schematic representation of neuroinflammatory process occurring during the early stages of the AD pathology and potential points of attack of NSAIDs and anti-inflammatory drugs. In this process, neurons surrounded by Aβ oligomers release proinflammatory cytokines triggering the intermediate activation of microglia and their mobilization toward Aβ-burdened neurons. Both Aβ-burdened neurons and activated microglia are responsible for a disease-aggravating process in which the release of proinflammatory cytokines and chemokines predominates. NSAIDs and anti-inflammatory drugs may be potentially effective during this early inflammatory phase, antagonizing the aggravating activity of proinflammatory mediators. Selective agents stabilizing microglia may also be effective in attenuating the inflammatory process. Aβ, amyloid beta; AD, Alzheimer's disease; NSAIDs, non-steroidal anti-inflammatory drugs.CC BY · PubMed Central
Figure 4 A roadmap proposed toward personalized immunology. There exist both horizontal and vertical roadmaps toward personalized immunology. Vertically, to translate sample stratification to clinical therapies, it is necessary to utilize the state-of-the-art “Omics” analysis and network integration approaches to stratify patients into subgroups and then implement personalized therapeutic approaches to treat individual patients, which needs to overcome various types of barriers at different steps. Horizontally, it might be necessary to go through at least seven steps to enable personalized immunotherapies: (1) classic symptom-based approach, (2) deep phenotyping approach, (3) multilayer “Omics”-based profiling, (4) cell-type-specific “Omics,” (5) state-specific “Omics,” (6) single-cell “Omics” and dynamic response analysis of immune cells, and (7) integrated network analysis. Under the first layer (the so-called stratification layer), different colors of patients indicate individual patients with different cellular and/or molecular profiles, while brackets represent patient subgroups; under the second layer (the so-called technique layers), different small circles with distinct colors indicate different immune cells, while big circles represent patient (sub)groups; under the technique layers, the snapshot of microarray representing either microarray-based or RNA-seq-based transcriptome analysis; under the third layer (the so-called therapeutic layer), the syringes with different colors or tonalities indicate different therapeutic approaches; P1,…, Pn at step 7 designate different patients; G1, G2, G3, and G4 represent different genes, the arrows between them representing regulatory relationships. DEG, differential expression gene; FACS, fluorescence-activated cell sorting; KNN, K-nearest neighbors; PEEP, personalized expression perturbation profile; sc, single-cell; SSN, sample-specific network; SVM, support vector machine; TCR/BCR, T-cell receptor/B-cell receptor. From Delhalle et al. (284). Copyright© 2018, Springer Nature. Reprinted with permission from Creative Commons CC BY. S.
Figure 4 A roadmap proposed toward personalized immunology. There exist both horizontal and vertical roadmaps toward personalized immunology. Vertically, to translate sample stratification to clinical therapies, it is necessary to utilize the state-of-the-art “Omics” analysis and network integration approaches to stratify patients into subgroups and then implement personalized therapeutic approaches to treat individual patients, which needs to overcome various types of barriers at different steps. Horizontally, it might be necessary to go through at least seven steps to enable personalized immunotherapies: (1) classic symptom-based approach, (2) deep phenotyping approach, (3) multilayer “Omics”-based profiling, (4) cell-type-specific “Omics,” (5) state-specific “Omics,” (6) single-cell “Omics” and dynamic response analysis of immune cells, and (7) integrated network analysis. Under the first layer (the so-called stratification layer), different colors of patients indicate individual patients with different cellular and/or molecular profiles, while brackets represent patient subgroups; under the second layer (the so-called technique layers), different small circles with distinct colors indicate different immune cells, while big circles represent patient (sub)groups; under the technique layers, the snapshot of microarray representing either microarray-based or RNA-seq-based transcriptome analysis; under the third layer (the so-called therapeutic layer), the syringes with different colors or tonalities indicate different therapeutic approaches; P1,…, Pn at step 7 designate different patients; G1, G2, G3, and G4 represent different genes, the arrows between them representing regulatory relationships. DEG, differential expression gene; FACS, fluorescence-activated cell sorting; KNN, K-nearest neighbors; PEEP, personalized expression perturbation profile; sc, single-cell; SSN, sample-specific network; SVM, support vector machine; TCR/BCR, T-cell receptor/B-cell receptor. From Delhalle et al. (284). Copyright© 2018, Springer Nature. Reprinted with permission from Creative Commons CC BY. S.CC BY · PubMed Central

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  1. Presented at the 11thClinical Trials on Alzheimer's Disease (CTAD) Conference October 24–272018

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Metadata

Authors
Hampel, H., Caraci, F., Cuello, A. C., Caruso, G., Nisticò, R., Corbo, M., Baldacci, F., Toschi, N., Garaci, F., Chiesa, P. A., Verdooner, S. R., Akman-Anderson, L., Hernández, F., Ávila, J., Emanuele, E., Valenzuela, P. L., Lucía, A., Watling, M., Imbimbo, B. P., Vergallo, A., & Lista, S.
Affiliations
  • Sorbonne Université (FR)
  • University of Catania (IT)
  • University of Oxford (GB)
  • I.R.C.C.S. Oasi Maria SS (IT)
  • University of Rome Tor Vergata (IT)
  • University of Pisa (IT)
  • Centre National de la Recherche Scientifique (FR)
  • Consejo Superior de Investigaciones Científicas (ES)
  • Universidad de Alcalá (ES)
  • Research Institute Hospital 12 de Octubre (ES)
  • Chiesi (Italy) (IT)
Corresponding author
Hampel, Harald
ORCID
Venue
Frontiers in Immunology
Publisher
Frontiers Media SA
Published
2020-03-31
Volume
11
Pages
456-456
ISSN
1664-3224 (ISSN-L)
DOI
10.3389/fimmu.2020.00456
PMC
PMC7137904
OpenAlex
W3014540555
Cited by
360
References
297
Citations by year
2026: 43 · 2025: 53 · 2024: 53 · 2023: 66 · 2022: 69 · 2021: 49 · 2020: 22
FWCI
5.13
Type
journal article
Language
en
Open access
Licence
Locations
  • Frontiers in Immunology (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • HAL (submittedVersion, cc-by)
  • Cineca Institutional Research Information System (Tor Vergata University) (submittedVersion, cc-by)
  • Biblos-e Archivo (Universidad Autónoma de Madrid) (submittedVersion, cc-by)
  • ABACUS. Repositorio de Producción Científica (Universidad Europea) (submittedVersion, cc-by-nc-nd)
  • CINECA IRIS Institutial research information system (University of Pisa) (submittedVersion, other-oa)
  • DIGITAL.CSIC (Spanish National Research Council (CSIC)) (submittedVersion, cc-by)
  • LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas) (submittedVersion, other-oa)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
  • Hispana (submittedVersion)
Funders
  • AXA Research Fund
  • Eisai
  • Sorbonne Université
  • Fondation pour la Recherche sur Alzheimer
Topics
  • Life Sciences › Neuroscience › Neurology › Neuroinflammation and Neurodegeneration Mechanisms
  • Life Sciences › Neuroscience › Cellular and Molecular Neuroscience › Alzheimer's disease research and treatments
  • Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
Keywords
Alzheimer's disease, neuroinflammation, TREM2, microglia, astrocytes, amyloid-beta, tau protein, synaptotoxicity, neurodegeneration, genetic risk variants, cytokines, TNF-α, IL-1β, TGF-beta, biomarkers, positron emission tomography, nonsteroidal anti-inflammatory drugs, disease-modifying therapy, precision medicine
MeSH terms
Cell Self Renewal, Alzheimer Disease, Animals, Humans, Signal Transduction, Cytokines, Neurogenic Inflammation, Proteomics, Systems Biology, Lipid Metabolism, Genome-Wide Association Study, Precision Medicine

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