Calico is Google’s venture into aging research. It has, in general, been a disappointment to the community – though I suspect that this is a matter of unrealistic expectations as to the path that any new, large deployment of capital is likely to follow. Rather than taking on any of the approaches to rejuvenation that might plausibly produce sizable gains in life span, such as those of the SENS portfolio, Calico has focused on very staid, long-standing metabolic manipulations derived from the study of calorie restriction and growth hormone loss of function mutants. These lines of research are highly unlikely to produce sizable gains in health and longevity in humans, as the calorie restriction response and disruption of growth hormone metabolism are known to produce only modest gains in our species. Calico, like the Ellison Medical Foundation that preceded it, has in essence become a small arm of the National Institute on Aging, characterized by conducting fundamental rather than translational research, and in areas of the field that won’t do much for human health and life span at the end of the day.
What area of aging and age-related diseases has Calico’s biggest focus at the moment?
Our top-level goal is to develop interventions that delay aging, but to test such interventions, we have to be able to measure aging. This is easier said than done – the gold standard, lifespan, takes a long time and is relatively information-poor. There are molecular and cellular changes that occur with age, but it’s not always clear which are the most relevant readouts. We’d like to measure aspects of physiological decline, but current healthspan assays take a lot of time and effort, and even then tend to be pretty noisy. To address those limitations, we’ve spent a lot of time developing innovative tools and novel analyses for quantifying physiological decline in mouse models. We emphasize automated, longitudinal monitoring and multi-dimensional time-series analysis.
On the intervention side, one area of focus for my lab is IGF signaling. This was a pretty straightforward choice – reduced IGF signaling is the most validated anti-aging intervention known (slows aging from worms to mammals, with the largest effect sizes ever reported). There are challenges with targeting this pathway, of course – dose-limiting toxicity, endocrine feedback, lack of biomarkers, just to name a few – but we think we’ve identified a viable therapeutic strategy.
What emerging discoveries and techniques is Calico utilising?
I’m excited about using outbred mice for intervention testing. We’re clearly not the first people to think of this, but we’ve embraced the concept. Outbred mice are somewhat more resource-intensive than inbred mice because they have more variability, but we think they’re worth it. As we’re all painfully aware, many published results fail to replicate. I think that a big fraction of what’s being called irreproducibility is actually a lack of generalizability. In other words, the results might repeat under the exact same conditions, but alter those conditions just a little and it’s a different answer. For mouse studies, strain background is an important condition, and we worry about results from a single, homozygous-at-all-loci genotypes not being generalizable. Outbred mice help us avoid this
What do you think is the best way to quantify longitudinal decline – are there key biomarkers that you’re addressing?
Aging manifests at all levels of biological organization (i.e. molecules, cells, tissues, organs, organ-systems, and whole organisms), and measuring aging at each level has pros and cons. Molecular and cellular data provide mechanistic insight and can point to new therapeutic targets, but it can be hard to know if effects are truly relevant to the organism (e.g. does delaying mutation accumulation delay decline in organ function)? Organ-level and physiological data provide health relevance, but it can be hard to tease out mechanism – good for testing putative targets, less good for target discovery. My lab focuses on developing tools for measuring organism-level decline because we think the state of the art is lacking and robustly testing putative targets is rate-limiting in the field.