Targeting Impaired Antimicrobial Immunity in the Brain for the Treatment of Alzheimer's Disease

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.

Keywords: Alzheimer’s disease; antimicrobial immunity; brain; mild cognitive impairment; neuroinflammation; treatment.

Figure 1

Similarities 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, http://www.rcsb.org, 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 (http://expasy.org/proteomics). 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 (http://pymolwiki.org/index.php/Align). 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 (https://ripser.scikit-tda.org/en/latest/), 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.

Figure 2

Schematic illustration of the immune system implication in neuroinflammation and neurodegeneration and the targets for treatment. All treatments in trial are in red.