STXBP1. Today is the first day of the 1st European STXBP1 Summit and Research Roundtable, held from May 16-18th in Milan, Italy. This meeting is bringing together voices from academia, industry, organizations, and family foundations to discuss the current state of research – spanning from preclinical efforts investigating mechanisms of disease to moving towards the clinic and the future therapeutic landscape. In 2023, it feels like an understatement to say that STXBP1 is on the map. In spirit of the ongoing momentum in the field, we wanted to refresh the gene page and outline three emerging frameworks to think about STXBP1.
Language. In the recent years, there has been an emerging focus on the phenotypic characterization of genetic epilepsies and neurodevelopmental disorders. With a rise in large-scale studies leveraging massive and complex genetic and phenotypic datasets, understanding how we make sense of big data becomes critical. However, determining what are clinically meaningful findings and communicating the conclusions we make from these datasets remain a challenge. While we typically think about data in the scope of ‘n’s, probabilities, and p-values, there is understated value in the visualization of information. Here is a different way of how we think about scientific communication and how we can “make data speak in childhood epilepsies.”
Phenotypic bottleneck. This is another post in the “phenotypic atomism series,” what has become our lab’s philosophy in how we think about and work with longitudinal clinical data. However, before we introduce another dimension to the phenotypic atom, let me first revisit the idea of the “phenotypic bottleneck” – a concept that had piqued my interest three years ago and led me to join the lab. In brief, in contrast to established pipelines for large-scale analysis of sequencing data, our ability to analyze clinical data at scale remains more limited. As a result, phenotypic characterization lags behind gene discovery, even with tremendous progress in the last few years. A major challenge stems from the inherent nature of working with multi-dimensional longitudinal clinical data: it can be sparse and incomplete at times. However, how much of the unknown is truly unknown?