Aims: Many progressive neurological disorders, including Alzheimer's disease (AD), Huntington's disease, and Parkinson's disease (PD), are characterized by accumulation of insoluble protein aggregates. In prospective trials, the cyclooxygenase inhibitor aspirin (acetylsalicylic acid) reduced the risk of AD and PD, as well as cardiovascular events and many late-onset cancers. Considering the role played by protein hyperphosphorylation in aggregation and neurodegenerative diseases, and aspirin's known ability to donate acetyl groups, we asked whether aspirin might reduce both phosphorylation and aggregation by acetylating protein targets. Results: Aspirin was substantially more effective than salicylate in reducing or delaying aggregation in human neuroblastoma cells grown in vitro , and in Caenorhabditis elegans models of human neurodegenerative diseases in vivo. Aspirin acetylates many proteins, while reducing phosphorylation, suggesting that acetylation may oppose phosphorylation. Surprisingly, acetylated proteins were largely excluded from compact aggregates. Molecular-dynamic simulations indicate that acetylation of amyloid peptide energetically disfavors its association into dimers and octamers, and oligomers that do form are less compact and stable than those comprising unacetylated peptides. Innovation: Hyperphosphorylation predisposes certain proteins to aggregate ( e.g., tau, α-synuclein, and transactive response DNA-binding protein 43 [TDP-43]), and it is a critical pathogenic marker in both cardiovascular and neurodegenerative diseases. We present novel evidence that acetylated proteins are underrepresented in protein aggregates, and that aggregation varies inversely with acetylation propensity after diverse genetic and pharmacologic interventions. Conclusions: These results are consistent with the hypothesis that aspirin inhibits protein aggregation and the ensuing toxicity of aggregates through its acetyl-donating activity. This mechanism may contribute to the neuro-protective, cardio-protective, and life-prolonging effects of aspirin. Antioxid. Redox Signal . 27, 1383–1396.

Diagnosis of neurodegenerative diseases hinges on "seed" proteins detected in disease-specific aggregates. These inclusions contain diverse constituents, adhering through aberrant interactions that our prior data indicate are nonrandom. To define preferential protein-protein contacts mediating aggregate coalescence, we created click-chemistry reagents that cross-link neighboring proteins within human, APPSw-driven, neuroblastoma-cell aggregates. These reagents incorporate a biotinyl group to efficiently recover linked tryptic-peptide pairs. Mass-spectroscopy outputs were screened for all possible peptide pairs in the aggregate proteome. These empirical linkages, ranked by abundance, implicate a protein-adherence network termed the "aggregate contactome." Critical hubs and hub-hub interactions were assessed by RNAi-mediated rescue of chemotaxis in aging nematodes, and aggregation-driving properties were inferred by multivariate regression and neural-network approaches. Aspirin, while disrupting aggregation, greatly simplified the aggregate contactome. This approach, and the dynamic model of aggregate accrual it implies, reveals the architecture of insoluble-aggregate networks and may reveal targets susceptible to interventions to ameliorate protein-aggregation diseases.

A series of novel hybrid 8-hydroxyquinoline-indole derivatives (7a–7e, 12a–12b and 18a–18h) were synthesized and screened for inhibitory activity against self-induced and metal-ion induced Aβ_1–42 aggregation as potential treatments for Alzheimer’s disease (AD). In vitro studies identified the most inhibitory compounds against self-induced Aβ_1–42 aggregation as 18c, 18d and 18f (EC₅₀ = 1.72, 1.48 and 1.08 µM, respectively) compared to the known anti-amyloid drug, clioquinol (1, EC₅₀ = 9.95 µM). The fluorescence of thioflavin T-stained amyloid formed by Aβ_1–42 aggregation in the presence of Cu²⁺ or Zn²⁺ ions was also dramatically decreased by treatment with 18c, 18d and 18f. The most potent hybrid compound 18f afforded 82.3% and 88.3% inhibition, respectively, against Cu²⁺- induced and Zn²⁺- induced Aβ_1–42 aggregation. Compounds 18c, 18d and 18f were shown to be effective in reducing protein aggregation in HEK-tau and SY5Y-APP_Sw cells. Molecular docking studies with the most active compounds performed against Aβ_1–42 peptide indicated that the potent inhibitory activity of 18d and 18f were predicted to be due to hydrogen bonding interactions, π–π stacking interactions and π–cation interactions with Aβ_1–42, which may inhibit both self-aggregation as well as metal ion binding to Aβ_1–42 to favor the inhibition of Aβ_1–42 aggregation.

Age-progressive neurodegenerative pathologies, including Alzheimer’s disease (AD), are distinguished and diagnosed by disease-specific components of intra- or extra-cellular aggregates. Increasing evidence suggests that neuroinflammation promotes protein aggregation, and is involved in the etiology of neurological diseases. We synthesized and tested analogs of the naturally occurring tubulin-binding compound, combretastatin A-4. One such analog, PNR502, markedly reduced the quantity of Alzheimer-associated amyloid aggregates in the BRI-Aβ_1–42 mouse model of AD, while blunting the ability of the pro-inflammatory cytokine IL-1β to raise levels of amyloid plaque and its protein precursors in a neuronal cell-culture model. In transgenic Caenorhabditis elegans (C. elegans) strains that express human Aβ_1–42 in muscle or neurons, PNR502 rescued Aβ-induced disruption of motility (3.8-fold, P < 0.0001) or chemotaxis (1.8-fold, P < 0.05), respectively. Moreover, in C. elegans with neuronal expression of Aβ_1–42, a single day of PNR502 exposure reverses the chemotaxis deficit by 54% (P < 0.01), actually exceeding the protection from longer exposure. Moreover, continuous PNR502 treatment extends nematode lifespan 23% (P ≤ 0.001). Given that PNR502 can slow, prevent, or reverse Alzheimer-like protein aggregation in human-cell-culture and animal models, and that its principal predicted and observed binding targets are proteins previously implicated in Alzheimer’s, we propose that PNR502 has therapeutic potential to inhibit cerebral Aβ_1–42 aggregation and prevent or reverse neurodegeneration.

Patient-level grouped data are prevalent in public health and medical fields, and multiple instance learning (MIL) offers a framework to address the challenges associated with this type of data structure. This study compares four data aggregation methods designed to tackle the grouped structure in classification tasks: post-mean, post-max, post-min, and pre-mean aggregation. We developed a customized AI pipeline that incorporates twelve machine learning algorithms along with the four aggregation methods to detect Parkinson’s disease (PD) using multiple voice recordings from individuals available in the UCI Machine Learning Repository, which includes 756 voice recordings from 188 PD patients and 64 healthy individuals. Seven performance metrics—accuracy, precision, sensitivity, specificity, F1 score, AUC, and MCC—were utilized for model evaluation. Various techniques, such as Bag Over-Sampling (BOS), cross-validation, and grid search, were implemented to enhance classification performance. Among the four aggregation methods, post-mean aggregation combined with XGBoost achieved the highest accuracy (0.880), F1 score (0.922), and MCC (0.672). Furthermore, we identified potential trends in selecting aggregation methods that are suitable for imbalanced data, particularly based on their differences in sensitivity and specificity. These findings provide meaningful implications for the further exploration of grouped imbalanced data.

Cystic fibrosis is one of the most prevalent inherited diseases. This disease is caused by a mutation in a membrane protein, the cystic fibrosis transmembrane conductance regulator (CFTR). CFTR is known to function as a chloride channel that regulates the viscosity of mucus that lines the ducts of a number of organs. The most prevalent mutation of CFTR is located in one of two nucleotide binding domains, namely, the nucleotide binding domain one (NBD1). However, some mutations in nucleotide binding domain two (NBD2) can equally cause cystic fibrosis. In this work, a graph-theoretic model is built for NBD2. Using this model for NBD2, we examine the consequences of single point mutations on NBD2. We collate the wildtype structure with eight of the most prevalent mutations and observe how the NBD2 is affected by each of these mutations.