Using answer set programming to integrate RNA expression with signalling pathway information to infer how mutations affect ageing.

A challenge of systems biology is to integrate incomplete knowledge on pathways with existing experimental data sets and relate these to measured phenotypes. Research on ageing often generates such incomplete data, creating difficulties in integrating RNA expression with information about biological processes and the phenotypes of ageing, including longevity. Here, we develop a logic-based method that employs Answer Set Programming, and use it to infer signalling effects of genetic perturbations, based on a model of the insulin signalling pathway. We apply our method to RNA expression data from Drosophila mutants in the insulin pathway that alter lifespan, in a foxo dependent fashion. We use this information to deduce how the pathway influences lifespan in the mutant animals. We also develop a method for inferring the largest common sub-paths within each of our signalling predictions. Our comparisons reveal consistent homeostatic mechanisms across both long- and short-lived mutants. The transcriptional changes observed in each mutation usually provide negative feedback to signalling predicted for that mutation. We also identify an S6K-mediated feedback in two long-lived mutants that suggests a crosstalk between these pathways in mutants of the insulin pathway, in vivo. By formulating the problem as a logic-based theory in a qualitative fashion, we are able to use the efficient search facilities of Answer Set Programming, allowing us to explore larger pathways, combine molecular changes with pathways and phenotype and infer effects on signalling in in vivo, whole-organism, mutants, where direct signalling stimulation assays are difficult to perform. Our methods are available in the web-service NetEffects: http://www.ebi.ac.uk/thornton-srv/software/NetEffects

Optimization of the All-D peptide D3 for Aβ oligomer elimination

The aggregation of amyloid-{beta} (A{beta}) is postulated to be the crucial event in Alzheimer's disease (AD). In particular, small neurotoxic A{beta} oligomers are considered to be responsible for the development and progression of AD. Therefore, elimination of thesis oligomers represents a potential causal therapy of AD. Starting from the well-characterized d-enantiomeric peptide D3, we identified D3 derivatives that bind monomeric A{beta}. The underlying hypothesis is that ligands bind monomeric A{beta} and stabilize these species within the various equilibria with A{beta} assemblies, leading ultimately to the elimination of A{beta} oligomers. One of the hereby identified d-peptides, DB3, and a head-to-tail tandem of DB3, DB3DB3, were studied in detail. Both peptides were found to: (i) inhibit the formation of Thioflavin T-positive fibrils; (ii) bind to A{beta} monomers with micromolar affinities; (iii) eliminate A{beta} oligomers; (iv) reduce A{beta}-induced cytotoxicity; and (v) disassemble preformed A{beta} aggregates. The beneficial effects of DB3 were improved by DB3DB3, which showed highly enhanced efficacy. Our approach yielded A{beta} monomer-stabilizing ligands that can be investigated as a suitable therapeutic strategy against AD

Neuroblastoma signalling models unveil combination therapies targeting feedback-mediated resistance

Very high risk neuroblastoma is characterised by increased MAPK signalling, and targeting MAPK signalling is a promising therapeutic strategy. We used a deeply characterised panel of neuroblastoma cell lines and found that the sensitivity to MEK inhibitors varied drastically between these cell lines. By generating quantitative perturbation data and mathematical modelling, we determined potential resistance mechanisms. We found that negative feedbacks within MAPK signalling and to the IGF receptor mediate re-activation of MAPK signalling upon treatment in resistant cell lines. By using cell-line specific models, we predict that combinations of MEK inhibitors with RAF or IGFR inhibitors can overcome resistance, and tested these predictions experimentally. In addition, phospo-proteomics profiles confirm the cell-specific feedback effects and synergy of MEK and IGFR targeted treatements. Our study shows that a quantitative understanding of signalling and feedback mechanisms facilitated by models can help to develop and optimise therapeutic strategies, and our findings should be considered for the planning of future clinical trials introducing MEKi in the treatment of neuroblastoma

Two forms of death in ageing Caenorhabditis elegans

Ageing generates senescent pathologies, some of which cause death. Interventions that delay or prevent lethal pathologies will extend lifespan. Here we identify life-limiting pathologies in Caenorhabditis elegans with a necropsy analysis of worms that have died of old age. Our results imply the presence of multiple causes of death. Specifically, we identify two classes of corpse: early deaths with a swollen pharynx (which we call ‘P deaths’), and later deaths with an atrophied pharynx (termed ‘p deaths’). The effects of interventions on lifespan can be broken down into changes in the frequency and/or timing of either form of death. For example, glp-1 mutation only delays p death, while eat-2 mutation reduces P death. Combining pathology and mortality analysis allows mortality profiles to be deconvolved, providing biological meaning to complex survival and mortality profiles

A quantitative approach to analyse linkages between antimicrobial resistance properties in Salmonella Typhimurium isolates

This study used statistical methods to investigate linkages in phenotypic resistance profiles in a population sample of 321 Salmonella Typhimurium isolates from sporadic salmonellosis cases in Lower Saxony, Germany, collected during 2008–2010. A resistance index was applied to calculate the conditional probability of resistance to one antimicrobial agent given the resistance to one or more other antimicrobial agent(s). A susceptibility index was defined analogously. A contingency plot, which visualizes the association between resistances to two antimicrobial agents, facilitated the interpretation. Linkages between minimum inhibitory concentrations (MIC) were analysed using Spearman's rank correlation coefficient and jittered scatter plots. Applying these methods provided a compact description of multi-resistance and linkages between resistance properties in large datasets. Moreover, this approach will improve monitoring of antimicrobial resistance dynamics of bacteria in human or animal populations by identifying linked resistance to antimicrobial agents (cross- or co-resistance) with a non-molecular method