Epidemic spreading model to characterize misfolded proteins propagation in aging and associated neurodegenerative disorders

Abstract

Misfolded proteins (MP) are a key component in aging and associated neurodegenerative disorders. For example, misfolded Amyloid-ß (Aß) and tau proteins are two neuropathogenic hallmarks of Alzheimer's disease. Mechanisms underlying intra-brain MP propagation/deposition remain essentially uncharacterized. Here, is introduced an epidemic spreading model (ESM) for MP dynamics that considers propagation-like interactions between MP agents and the brain's clearance response across the structural connectome. The ESM reproduces advanced Aß deposition patterns in the human brain (explaining 46∼56% of the variance in regional Aß loads, in 733 subjects from the ADNI database). Furthermore, this model strongly supports a) the leading role of Aß clearance deficiency and early Aß onset age during Alzheimer's disease progression, b) that effective anatomical distance from Aß outbreak region explains regional Aß arrival time and Aß deposition likelihood, c) the multi-factorial impact of APOE e4 genotype, gender and educational level on lifetime intra-brain Aß propagation, and d) the modulatory impact of Aß propagation history on tau proteins concentrations, supporting the hypothesis of an interrelated pathway between Aß pathophysiology and tauopathy. To our knowledge, the ESM is the first computational model highlighting the direct link between structural brain networks, production/clearance of pathogenic proteins and associated intercellular transfer mechanisms, individual genetic/demographic properties and clinical states in health and disease. In sum, the proposed ESM constitutes a promising framework to clarify intra-brain region to region transference mechanisms associated with aging and neurodegenerative disorders.

Figure 1. Reconstruction of individual Aß propagation/deposition histories using an ESM.

¹⁸F-AV-45 PET scans (A) are used to calculate individual Aß deposition patterns for different regions covering all the brain's gray matter (B). Then, detailed region-region anatomical connectivity information from a young healthy group (C) is used to generate multiple hypothetical lifetime Aß propagation/deposition courses (D). Each hypothetical course corresponds to an initial set i of sAß spreading seed regions and a different set of global model parameters . Then, a selective iterative algorithm estimated, for each subject, the model parameters that maximized the similarity between the generated and the reference Aß deposition pattern, as well as the time point at which this maximization occurred. The latter output was used to calculate the individual onset age of Aß binding, which in conjunction with the obtained model parameters were assumed to characterize each subject's Aß propagation/deposition history.

Figure 2. Characteristic regional Aß deposition patterns in healthy and pathologic brains.

A) PET-based mean regional Aß deposition probabilities (adjusted by age, gender, and educational level) in HC, EMCI, LMCI and AD groups. Nodes correspond to 78 regions covering all the brain's gray matter, with node sizes proportional to the associated Aß burden. Note the progressive expansion of the Aß deposition, starting mainly from DMN regions to the rest of the brain. This supports the development of an abnormal Aß deposition pattern in correspondence with the disease progression (from HC to advanced AD clinical states). B) Correspondence between the estimated and PET-based mean regional Aß deposition probabilities for the different groups. C) Prediction accuracy distributions obtained for the different groups (via a repeated random sub-sampling cross-validation procedure).

Figure 3. Effective anatomical distance to outbreak regions modulates the Aß propagation processes.

A) PET-based regional Aß deposition probabilities for the different groups vs effective anatomical distances. B) Regional Aß arriving times vs effective anatomical distances, for different Aß probability thresholds (i.e. 0.1, 0.5 and 0.9). In A) and B), note the co-linearity between different clinical states or Aß probability thresholds, with more advanced disease states corresponding to higher depositions and propagation times. See also Figure S2.

Figure 4. Subjects with different clinical states presented different Aß propagation histories.

A) Explained variance of the clinical diagnoses (HC, EMCI, LMCI and AD) by the different Aß propagation model parameters. Mean (± standard error) Aß production rate (B), Aß clearance rate (C), noise standard deviation (D) and onset age of Aß propagation (E) for the different clinical groups (adjusted for gender and educational level). *p<0.05, **p<0.01, ***p<10⁻⁵, Student's t-test.

Figure 5. Multi-factorial impact of APOE e4 genotype on Aß propagation/deposition.

A) Explained variance of the propagation model parameters by the different APOE e4 genotypes (zero, one or two e4 allele copies). Mean (± standard error) Aß production rate (B), Aß clearance rate (C), noise standard deviation (D) and onset age of Aß propagation (E) for the different number of e4 allele copies (adjusted for gender and educational level). *p<0.05, **p<0.01, ***p<10⁻¹⁰, Student's t-test.

Figure 6. Influence of parameters controlling Aß propagation/deposition on CSF Aß^1-42, t-tau and p-tau¹⁸¹ levels.

While CSF Aß^1-42 (A) is mainly influenced by Aß production/clearance rates, t-tau (B) and p-tau (C) are highly influenced by chronological age and Aß onset age (combined, these two temporal factors should reflect the interrelation period between amyloid pathophysiology and tauopathy).