| Literature DB >> 33976546 |
Tamas Fulop1, Shreyansh Tripathi2,3, Serafim Rodrigues3,4, Mathieu Desroches5,6, Ton Bunt7, Arnold Eiser8, Francois Bernier9, Pascale B Beauregard10, Annelise E Barron11, Abdelouahed Khalil1, Adam Plotka12, Katsuiku Hirokawa13, Anis Larbi14, Christian Bocti15, Benoit Laurent16, Eric H Frost17, Jacek M Witkowski12.
Abstract
Alzheimer's disease (AD) is the most common form of dementia and aging is the most common risk factor for developing the disease. The etiology of AD is not known but AD may be considered as a clinical syndrome with multiple causal pathways contributing to it. The amyloid cascade hypothesis, claiming that excess production or reduced clearance of amyloid-beta (Aβ) and its aggregation into amyloid plaques, was accepted for a long time as the main cause of AD. However, many studies showed that Aβ is a frequent consequence of many challenges/pathologic processes occurring in the brain for decades. A key factor, sustained by experimental data, is that low-grade infection leading to production and deposition of Aβ, which has antimicrobial activity, precedes the development of clinically apparent AD. This infection is chronic, low grade, largely clinically silent for decades because of a nearly efficient antimicrobial immune response in the brain. A chronic inflammatory state is induced that results in neurodegeneration. Interventions that appear to prevent, retard or mitigate the development of AD also appear to modify the disease. In this review, we conceptualize further that the changes in the brain antimicrobial immune response during aging and especially in AD sufferers serve as a foundation that could lead to improved treatment strategies for preventing or decreasing the progression of AD in a disease-modifying treatment.Entities:
Keywords: Alzheimer’s disease; antimicrobial immunity; brain; mild cognitive impairment; neuroinflammation; treatment
Year: 2021 PMID: 33976546 PMCID: PMC8106529 DOI: 10.2147/NDT.S264910
Source DB: PubMed Journal: Neuropsychiatr Dis Treat ISSN: 1176-6328 Impact factor: 2.570
Prevention Therapies (Potential Therapeutics for Modulating Inflammation/antimicrobial Immune Defense)
| Agent | Class | Mechanism of Action | Trial | References |
|---|---|---|---|---|
| Vaccines | Infection | Antimicrobial | ||
| Antivirals | Infection | Antiviral | Phase II valacyclovir | |
| Penciclovir | ||||
| Foscarnet | ||||
| Bay57-1293 | ||||
| Bioflavanids | ||||
| Antibiotics | Infection | Antibacterial | ||
| Minocycline | ||||
| Doxycycline | ||||
| Rifampin, ceftriaxone | Infection/inflammation | Increasing GLT-1 | ||
| Gingipain inhibitor | Infection | Gingipain inactivation | Phase III COR388 | |
| Mediterranean diet | Inflammation | Microbiome | Already available | |
| Holobiotics | ||||
| Prebiotics | ||||
| Probiotics | Inflammation | Microbiome | Already available | |
| Postbiotics | ||||
| GV-971 | Inflammation | Microbiome | Already available | |
| Exercise | Inflammation | Innate immunity | Already available | |
| NSAID | Inflammation | Innate/adaptive immunity | Phase I salsalate | |
| AMPs | Infection | Antimicrobial | ||
| Alz-OP1 | Inflammation | Immune system | Phase III |
Disease-modifying Treatments (Potential Therapeutics for Modulating Inflammation/antimicrobial Immune Defense)
| Agent | Class | Mechanism of Action | Trial | References |
|---|---|---|---|---|
| ARB | Inflammation | Antihypertensive | Phase III Telmisartan | |
| Fasudil | Inflammation | Pro-inflammatory cytokines | ||
| Phenserine | Inflammation | Immune system | Phase II | |
| DMARD | Inflammation | Pro-inflammatory cytokines | ||
| Etanercept | ||||
| Checkpoint inhibitors | Inflammation | Immune system | Planned | |
| Copexone | Inflammation | T cells | In use in MS | |
| Rapamycine | Inflammation | mTOR | NCT042009110 | |
| Thalidomide | Inflammation | Decreasing TNFα | ||
| Senolytics | Inflammation | Senescent cells | ||
| Metformin | ||||
| Dratumumab | Inflammation | Anti-CD38 | NCT04070378 | |
| Lenalidomid | Inflammation | Pro-inflammatory cytokines | NCT04032626 | |
| L-serine | Inflammation | Immune system | Phase II | |
| Montelukast | Inflammation | Antileukotriene | Phase II | |
| Sargramostim | Inflammation | GM-CSF | Phase II | |
| GB301 | Inflammation | Autologous Treg | NCT03865017, Phase II | |
| AL002 | Inflammation | TREM2 agonist | Phase I | |
| Azeliragon | Inflammation | Antagonist-RAGE | Phase III | |
| Masatinib | Inflammation | Tyrosine kinase | Phase III | |
| XPro1595 | Inflammation | AntiTNFα | NCT03943264 |
Figure 1Similarities measures between peptides (specifically Aβ42 and LL-37). Left panel, (A) 3D structure of Aβ42 in an apolar environment; data from PDB (RCSB Protein Data Bank, , PDB ID 1IYT) shown using PyMol software. (B) 3D structure of human host defense cathelicidin LL-37 (RCSB Protein Data Bank, PDB ID 2K6). (C) Structural superposition/alignment of 3D structures of Aβ42 and LL-37 represented in blue and yellow colors, respectively. The yellow colored lines represent actual alignments the algorithm has predicted shown using PyMol. (D) Sequence alignment of Aβ42 and LL-37 using the Clustal Omega shareware (). Identical amino acid residues are indicated by vertical solid red lines and amino acids possessing similar properties, by dashed vertical dotted black lines. (E) Sequence alignment of Aβ42 and LL-37 using PyMol alignment plugin using method “super” whose algorithms can be looked at (). Vertical red lines represent the sequence that gets aligned/superimposed in the 3D structure as shown in (C). Right panel, (A) Topological signatures of Aβ42, which persist (birth/death) across scales. The invariants (H0,1,2) are computed with RIpser software (), where the input is the peptide as a point cloud. In this case we generated the point cloud in which each point represents one the centroid of the amino acid residue. (B) Topological signatures of LL-37. (C–E) Compares three topological signatures of Aβ42 and LL-37 using bottleneck distances, which shows some level of topological similarities.
Antibacterial and Antiviral Activity of Aβ42
| 1 | GYEVHHQKLVFFAED | 9 | 1.025 | ✔ | |||
| DAEFRHDSGYEVHHQ | 1 | 0.698 | ✔ | ||||
| GSNKGAIIGLMVGGV | 25 | 0.687 | ✔ | ||||
| 2 | GIIAGKNSGVDEAFF | 10 | 0.280 | ✔ | |||
| GVMLGIIAGKNSGVD | 6 | 0.142 | ✔ | ||||
| FVLKQHHVEYGSDHR | 24 | 0.129 | ✔ | ||||
| 3 | YEVHHQKLVFFAEDVFFAEDVGSNKGAIIG | 10 | 0.803 | ✔ | |||
| HDSGYEVHHQKLVFFDVGSNKGAIIGLMVG | 6 | 0.572 | ✔ | ||||
| KGAIIGLMVGGVVIADAEFRHDSGYEVHHQ | 28 | 0.147 | ✔ | ||||
| 4 | DAEFRHDSGYEVHHQKLVFFAEDVGSNKGAIIGLMVGGVVIA | 1 | 1.306 | ✔ | |||
| KGAIIGLMVGGVVIA | Non-AVP | 44.35 | 47.01 | ✔ | |||
Notes: For the antibacterial we used the AntiBP2 software () that uses neural networks and support vector machines (SVM) to predict the amino-acid subsequence of a peptide with antibacterial activity. AntiBP2 utilizes four datasets to train their models: N-terminus based, C-terminus based, N+C terminus based and amino acid composition method. These four methods are SVM trained on 4 different datasets compiled using N, C, NC and full composition peptides respectively. For the antiviral activity we employ the AVPpred software (), which computes various features (ie motifs and alignment followed by amino acid composition and physicochemical properties during fivefold cross validation using SVM. In particular, we fragment the amino sequence into subsequences of lengths 15 while taking the overlap length to be 14 and finally the subsequences of length 15 are processed by AVPred. In this case, we find that a subsequence contained in the turn and C-terminus of Aβ42 does indeed have antiviral activity.
Antibacterial LL-37
| # | Method | Amino Acid Sequence | Start Position | Score | Antibacterial Activity |
|---|---|---|---|---|---|
| 1. | GKEFKRIVQRIKDFL | 14 | 1.601 | ✔ | |
| KEKIGKEFKRIVQRI | 10 | 0.559 | ✔ | ||
| IVQRIKDFLRNLVPR | 20 | 0.324 | ✔ | ||
| 2. | VLNRLFDKIRQVIRK | 6 | 0.430 | ✔ | |
| RKFEKGIKEKSKRFF | 19 | 0.314 | ✔ | ||
| FDKIRQVIRKFEKGI | 11 | 0.082 | ✔ | ||
| 3. | QRIKDFLRNLVPRTELGDFFRKSKEKIGKE | 22 | 0.237 | ✔ | |
| KSKEKIGKEFKRIVQEFKRIVQRIKDFLRN | 8 | 0.082 | ✔ | ||
| RIVQRIKDFLRNLVPFFRKSKEKIGKEFKR | 19 | 0.017 | ✔ | ||
| 4. | LLGDFFRKSKEKIGKEFKRIVQRIKDFLRNLVPRTES | 1 | 1.474 | ✔ |
Notes: We find via AntiBP2 software that various amino acid subsequences have antibacterial activity. However, we could not determine antiviral activity with the AVPpred software and thus more work is required.
Figure 2Schematic illustration of the immune system implication in neuroinflammation and neurodegeneration and the targets for treatment. All treatments in trial are in red.
Summary of the Interventions at the Level of the Periphery and in the Brain
| Periphery | Interventions | Brain |
|---|---|---|
| Innate immune response | Innate immune response | |
| Virus | Antiviral | Virus |
| Bacteria | Antibiotics | Bacteria |
| Dysbiosis | Probiotics | |
| Cellular debris | Antisenolytics | |
| Cells | Cells | |
| Monocytes | Anti-TLRs | Microglia |
| Neutrophils | Antioxidants | Astrocytes |
| Soluble mediators | Soluble mediators | |
| Pro-inflammatory cytokines | Anticytokines | Pro-inflammatory cytokines |
| Anti-inflammatory cytokines | Anti-inflammatory cytokines | |
| Chemokines | Antichemokines | Chemokines |
| Adaptive immune response | Adaptive immune response | |
| T cells | Anti-T cells | T cells |
| B cells | B cells | |
| Treg | Anti-Treg | Treg |
| Th17 | Th17 | |
| Antibodies | Antibodies |