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Brookes, C.

Publications and source records attributed to Brookes, C..

2 recordsLinked to original sources

Comparable anti-ageing efficacies of a multi-ingredient nutraceutical and a senolytic intervention in old mice

Single-ingredient dietary supplements have demonstrated some potential to extend lifespan and improve healthspan; however, the efficacy of defined multi-ingredient nutraceuticals remains underexplored. Senolytic interventions have been successful in reducing multi-morbidity, frailty, and cognitive decline in animal models and are seen as promising anti-ageing interventions in mammals. We compared the effects of a 12-ingredient nutraceutical on mice lifespan and healthspan markers with that of a high efficacy senolytic intervention consisting of a low dose of Navitoclax combined with the specific mitochondrial uncoupler BAM15. Both interventions were started at old age (20 months). The supplement was given daily until the end of the experiment (30 months of age), but the senolytic intervention consisted of two short (5 days each) rounds of treatment at 20 and 23 months of age. Despite late onset, both interventions increased median lifespan similarly by around 20% over controls. The senolytic intervention significantly reduced frailty progression and improved cognitive function after the second round of treatment, but without subsequent treatment, these effects appeared to wane at later ages. The multi-ingredient supplement tended to reduce frailty progression steadily with time, albeit not significant, and maintained cognitive function. Mechanistically, in vitro, there was no evidence of senolytic activity of the multi-ingredient nutraceutical as a whole nor of its individual ingredients. Continuous multi-ingredient dietary supplementation shows promise in achieving comparable anti-ageing efficacy as a senolytic intervention.

cell biology↗

Network-Based Biomarkers Enable Cross-Disease Biomarker Discovery

Biomarkers lie at the heart of precision medicine, biodiversity monitoring, agricultural pathogen detection, amongst others. Surprisingly, while rapid genomic profiling is becoming ubiquitous, the development of biomarkers almost always involves the application of bespoke techniques that cannot be directly applied to other datasets. There is an urgent need for a systematic methodology to create biologically-interpretable molecular models that robustly predict key phenotypes. We therefore created SIMMS: an algorithm that fragments pathways into functional modules and uses these to predict phenotypes. We applied SIMMS to multiple data-types across four diseases, and in each it reproducibly identified subtypes, made superior predictions to the best bespoke approaches, and identified known and novel signaling nodes. To demonstrate its ability on a new dataset, we measured 33 genes/nodes of the PIK3CA pathway in 1,734 FFPE breast tumours and created a four-subnetwork prediction model. This model significantly out-performed existing clinically-used molecular tests in an independent 1,742-patient validation cohort. SIMMS is generic and can work with any molecular data or biological network, and is freely available at: https://cran.r-project.org/web/packages/SIMMS.

bioinformatics↗