Giorgio Favrin

It is becoming increasingly evident that Alzheimer’s disease cannot be considered as the outcome of a single pathway, but rather we should view it as a system, that is, a network of interactions between large numbers of different protein molecules. In the last few years, probably because of the inherent limitations of traditional methods and because of the great increase in availability of sequencing data, this type of approach is being used more and more. In the following, we will discuss what constitutes a “network approach,” what are its pros and cons, a number of recent case studies and finally what are the future perspectives of this type of analysis.

The construction and analysis of networks is increasingly widespread in biological research. We have developed esyN (“easy networks”) as a free and open source tool to facilitate the exchange of biological network models between researchers. esyN acts as a searchable database of user-created networks from any field. We have developed a simple companion web tool that enables users to view and edit networks using data from publicly available databases. Both normal interaction networks (graphs) and Petri nets can be created. In addition to its basic tools, esyN contains a number of logical templates that can be used to create models more easily. The ability to use previously published models as building blocks makes esyN a powerful tool for the construction of models and network graphs. Users are able to save their own projects online and share them either publicly or with a list of collaborators. The latter can be given the ability to edit the network themselves, allowing online collaboration on network construction. esyN is designed to facilitate unrestricted exchange of this increasingly important type of biological information. Ultimately, the aim of esyN is to bring the advantages of Open Source software development to the construction of biological networks.

The human Aβ peptide causes progressive paralysis when expressed in the muscles of the nematode worm, C. elegans. We have exploited this model of Aβ toxicity by carrying out an RNAi screen to identify genes whose reduced expression modifies the severity of this locomotor phenotype. Our initial finding was that none of the human orthologues of these worm genes is identical with the genome-wide significant GWAS genes reported to date (the “white zone”); moreover there was no identity between worm screen hits and the longer list of GWAS genes which included those with borderline levels of significance (the “grey zone”). This indicates that Aβ toxicity should not be considered as equivalent to sporadic AD. To increase the sensitivity of our analysis, we then considered the physical interactors (+1 interactome) of the products of the genes in both the worm and the white+grey zone lists. When we consider these worm and GWAS gene lists we find that 4 of the 60 worm genes have a +1 interactome overlap that is larger than expected by chance. Two of these genes form a chaperonin complex, the third is closely associated with this complex and the fourth gene codes for actin, the major substrate of the same chaperonin.

The strongest risk factor for developing Alzheimer's Disease (AD) is age. Here, we study the relationship between ageing and AD using a systems biology approach that employs a Drosophila (fruitfly) model of AD in which the flies overexpress the human Aβ₄₂ peptide. We identified 712 genes that are differentially expressed between control and Aβ-expressing flies. We further divided these genes according to how they change over the animal's lifetime and discovered that the AD-related gene expression signature is age-independent. We have identified a number of differentially expressed pathways that are likely to play an important role in the disease, including oxidative stress and innate immunity. In particular, we uncovered two new modifiers of the Aβ phenotype, namely Sod3 and PGRP-SC1b.

Oligomeric assemblies formed from a variety of disease-associated peptides and proteins have been strongly associated with toxicity in many neurodegenerative conditions, such as Alzheimer’s disease. The precise nature of the toxic agents, however, remains still to be established. We show that prefibrillar aggregates of E22G (arctic) variant of the Aβ₁₋₄₂ peptide bind strongly to 1-anilinonaphthalene 8-sulfonate and that changes in this property correlate significantly with changes in its cytotoxicity. Moreover, we show that this phenomenon is common to other amyloid systems, such as wild-type Aβ_1–42, the I59T variant of human lysozyme and an SH3 domain. These findings are consistent with a model in which the exposure of hydrophobic surfaces as a result of the aggregation of misfolded species is a crucial and common feature of these pathogenic species.

It is becoming increasingly clear that the soluble protofibrillar species that proceed amyloid fibril formation are associated with a range of neurodegenerative disorders such as Alzheimer's and Parkinson diseases. Computer simulations of the processes that lead to the formation of these oligomeric species are starting to make significant contributions to our understanding of the determinants of protein aggregation. We simulate different systems at constant concentration but with a different number of peptides and we study the how the finite number of proteins affects the underlying free energy of the system and therefore the relative stability of the species involved in the process. If not taken into account, this finite size effect can undermine the validity of theoretical predictions regarding the relative stability of the species involved and the rates of conversion from one to the other. We discuss the reasons that give rise to this finite size effect form both a probabilistic and energy fluctuations point of view and also how this problem can be dealt by a finite size scaling analysis.