Transcriptional feedback in the insulin signalling pathway modulates ageing in both Caenorhabditis elegans and Drosophila melanogaster.

Several components have been previously identified, that modulate longevity in several species, including the target of rapamycin (TOR) and the Insulin/IGF-1 (IIS) signalling pathways. In order to infer paths and transcriptional feedback loops that are likely to modulate ageing, we manually built a comprehensive and computationally efficient signalling network model of the IIS and TOR pathways in worms. The core insulin transduction is signalling from the sole insulin receptor daf-2 to ultimately inhibit the translocation of the transcription factor daf-16 into the nucleus. Reduction in this core signalling is thought to increase longevity in several species. In addition to this core insulin signalling, we have also recorded in our worm model the transcription factors skn-1 and hif-1, those are also thought to modulate ageing in a daf-16 independent manner. Several paths that are likely to modulate ageing were inferred via a web-based service NetEffects, by utilising perturbed components (rheb-1, let-363, aak-2, daf-2;daf-16 and InR;foxo in worms and flies respectively) from freely available gene expression microarrays. These included "routes" from TOR pathway to transcription factors daf-16, skn-1, hif-1 and daf-16 independent paths via skn-1/hif-1. Paths that could be tested by experimental hypotheses, with respect to relative contribution to longevity, are also discussed. Direct comparison of the IIS and TOR pathways in both worm and fly suggest a remarkable similarity. While similarities in the paths that could modulate ageing in both organisms were noted, differences are also discussed. This approach can also be extended to other pathways and processes

Computational biology for ageing

High-throughput genomic and proteomic technologies have generated a wealth of publicly available data on ageing. Easy access to these data, and their computational analysis, is of great importance in order to pinpoint the causes and effects of ageing. Here, we provide a description of the existing databases and computational tools on ageing that are available for researchers. We also describe the computational approaches to data interpretation in the field of ageing including gene expression, comparative and pathway analyses, and highlight the challenges for future developments. We review recent biological insights gained from applying bioinformatics methods to analyse and interpret ageing data in different organisms, tissues and conditions

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

SurvCurv database and online survival analysis platform update

Understanding the biology of ageing is an important and complex challenge. Survival experiments are one of the primary approaches for measuring changes in ageing. Here, we present a major update to SurvCurv, a database and online resource for survival data in animals. As well as a substantial increase in data and additions to existing graphical and statistical survival analysis features, SurvCurv now includes extended mathematical mortality modelling functions and survival density plots for more advanced representation of groups of survival cohorts

Longevity GWAS Using the Drosophila Genetic Reference Panel

We used 197 Drosophila melanogaster Genetic Reference Panel (DGRP) lines to perform a genome-wide association analysis for virgin female lifespan, using ~2M common single nucleotide polymorphisms (SNPs). We found considerable genetic variation in lifespan in the DGRP, with a broad-sense heritability of 0.413. There was little power to detect signals at a genome-wide level in single-SNP and gene-based analyses. Polygenic score analysis revealed that a small proportion of the variation in lifespan (~4.7%) was explicable in terms of additive effects of common SNPs (≥2% minor allele frequency). However, several of the top associated genes are involved in the processes previously shown to impact ageing (eg, carbohydrate-related metabolism, regulation of cell death, proteolysis). Other top-ranked genes are of unknown function and provide promising candidates for experimental examination. Genes in the target of rapamycin pathway (TOR; Chrb, slif, mipp2, dredd, RpS9, dm) contributed to the significant enrichment of this pathway among the top-ranked 100 genes (p = 4.79×10(-06)). Gene Ontology analysis suggested that genes involved in carbohydrate metabolism are important for lifespan; including the InterPro term DUF227, which has been previously associated with lifespan determination. This analysis suggests that our understanding of the genetic basis of natural variation in lifespan from induced mutations is incomplete

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

Comprehensive micro-scaled proteome and phosphoproteome characterization of archived retrospective cancer repositories

Formalin-fixed paraffin-embedded (FFPE) tissues are a valuable resource for retrospective clinical studies. Here, we evaluate the feasibility of (phospho-)proteomics on FFPE lung tissue regarding protein extraction, quantification, pre-analytics, and sample size. After comparing protein extraction protocols, we use the best-performing protocol for the acquisition of deep (phospho-)proteomes from lung squamous cell and adenocarcinoma with >8,000 quantified proteins and >14,000 phosphosites with a tandem mass tag (TMT) approach. With a microscaled approach, we quantify 7,000 phosphosites, enabling the analysis of FFPE biopsies with limited tissue amounts. We also investigate the influence of pre-analytical variables including fixation time and heat-assisted de-crosslinking on protein extraction efficiency and proteome coverage. Our improved workflows provide quantitative information on protein abundance and phosphosite regulation for the most relevant oncogenes, tumor suppressors, and signaling pathways in lung cancer. Finally, we present general guidelines to which methods are best suited for different applications, highlighting TMT methods for comprehensive (phospho-)proteome profiling for focused clinical studies and label-free methods for large cohorts

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

Disease- and sex-specific differences in patients with heart valve disease: a proteome study

Pressure overload in patients with aortic valve stenosis and volume overload in mitral valve regurgitation trigger specific forms of cardiac remodeling; however, little is known about similarities and differences in myocardial proteome regulation. We performed proteome profiling of 75 human left ventricular myocardial biopsies (aortic stenosis = 41, mitral regurgitation = 17, and controls = 17) using high-resolution tandem mass spectrometry next to clinical and hemodynamic parameter acquisition. In patients of both disease groups, proteins related to ECM and cytoskeleton were more abundant, whereas those related to energy metabolism and proteostasis were less abundant compared with controls. In addition, disease group-specific and sex-specific differences have been observed. Male patients with aortic stenosis showed more proteins related to fibrosis and less to energy metabolism, whereas female patients showed strong reduction in proteostasis-related proteins. Clinical imaging was in line with proteomic findings, showing elevation of fibrosis in both patient groups and sex differences. Disease- and sex-specific proteomic profiles provide insight into cardiac remodeling in patients with heart valve disease and might help improve the understanding of molecular mechanisms and the development of individualized treatment strategies