The use of bionanostructures in real-world applications will require precise control over biomolecular self-assembly and the ability to scale up production of these materials. A significant challenge is to control the formation of large, homogeneous arrays of bionanostructures on macroscopic surfaces. Previously, bionanostructure formation has been based on the spontaneous growth of heterogenic populations in bulk solution. Here, we demonstrate the self-assembly of large arrays of aromatic peptide nanotubes using vapour deposition methods. This approach allows the length and density of the nanotubes to be fine-tuned by carefully controlling the supply of the building blocks from the gas phase. Furthermore, we show that the nanotube arrays can be used to develop high-surface-area electrodes for energy storage applications, highly hydrophobic self-cleaning surfaces and microfluidic chips.

Accumulating evidence links numerous abnormalities in cerebral metabolism with the progression of Alzheimer's disease (AD), beginning in its early stages. Here, we integrate transcriptomic data from AD patients with a genome-scale computational human metabolic model to characterize the altered metabolism in AD, and employ state-of-the-art metabolic modelling methods to predict metabolic biomarkers and drug targets in AD. The metabolic descriptions derived are first tested and validated on a large scale versus existing AD proteomics and metabolomics data. Our analysis shows a significant decrease in the activity of several key metabolic pathways, including the carnitine shuttle, folate metabolism and mitochondrial transport. We predict several metabolic biomarkers of AD progression in the blood and the CSF, including succinate and prostaglandin D2. Vitamin D and steroid metabolism pathways are enriched with predicted drug targets that could mitigate the metabolic alterations observed. Taken together, this study provides the first network wide view of the metabolic alterations associated with AD progression. Most importantly, it offers a cohort of new metabolic leads for the diagnosis of AD and its treatment.

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Alterations in gene expression resulting from Alzheimer’s disease have received considerable attention in recent years. Although expression has been investigated separately in whole brain tissue, in astrocytes and in neurons, a rigorous comparative study quantifying the relative utility of these sources in predicting the progression of Alzheimer’s disease has been lacking. Here we analyze gene expression from neurons, astrocytes and whole tissues across different brain regions, and compare their ability to predict Alzheimer’s disease progression by building pertaining classification models based on gene expression sets annotated to different biological processes. Remarkably, we find that predictions based on neuronal gene expression are significantly more accurate than those based on astrocyte or whole tissue expression. The findings explicate the central role of neurons, particularly as compared to glial cells, in the pathogenesis of Alzheimer’s disease, and emphasize the importance of measuring gene expression in the most relevant (pathogenically ‘proximal’) single cell types.

Inhibiting the aggregation process of the β-amyloid peptide is a promising strategy in treating Alzheimer's disease. In this work, we have collected a dataset of 80 small molecules with known inhibition levels and utilized them to develop two comprehensive quantitative structure-activity relationship models: a Bayesian model and a decision tree model. These models have exhibited high predictive accuracy: 87% of the training and test sets using the Bayesian model and 89 and 93% of the training and test sets, respectively, by the decision tree model. Subsequently these models were used to predict the activities of several new potential β-amyloid aggregation inhibitors and these predictions were indeed validated by in vitro experiments. Key chemical features correlated with the inhibition ability were identified. These include the electro-topological state of carbonyl groups, AlogP and the number of hydrogen bond donor groups. The results demonstrate the feasibility of the developed models as tools for rapid screening, which could help in the design of novel potential drug candidates for Alzheimer's disease.

Alterations in gene expression resulting from Alzheimer's disease have received considerable attention in recent years. Although expression has been investigated separately in whole brain tissue, in astrocytes and in neurons, a rigorous comparative study quantifying the relative utility of these sources in predicting the progression of Alzheimer's disease has been lacking. Here we analyze gene expression from neurons, astrocytes and whole tissues across different brain regions, and compare their ability to predict Alzheimer's disease progression by building pertaining classification models based on gene expression sets annotated to different biological processes. Remarkably, we find that predictions based on neuronal gene expression are significantly more accurate than those based on astrocyte or whole tissue expression. The findings explicate the central role of neurons, particularly as compared to glial cells, in the pathogenesis of Alzheimer's disease, and emphasize the importance of measuring gene expression in the most relevant (pathogenically 'proximal') single cell types.