Intestine function relies on the strong polarity of intestinal epithelial cells and the array of microvilli forming a brush border at their luminal pole. Combining a genetic RNA interference (RNAi) screen with in vivo super-resolution imaging in the Caenorhabditis elegans intestine, we found that the V0 sector of the vacuolar ATPase (V0-ATPase) controls a late apical trafficking step, involving Ras-related protein 11 (RAB-11) + endosomes and the N-ethylmaleimide-sensitive factor-attachment protein receptor (SNARE) synaptosome-associated protein 29 (SNAP-29), and is necessary to maintain the polarized localization of both apical polarity modules and brush border proteins. We show that the V0-ATPase pathway also genetically interacts with glycosphingolipids and clathrin in enterocyte polarity maintenance. Finally, we demonstrate that silencing of the V0-ATPase fully recapitulates the severe structural, polarity and trafficking defects observed in enterocytes from individuals with microvillus inclusion disease (MVID) and use this new in vivo MVID model to follow the dynamics of microvillus inclusions. Thus, we describe a new function for V0-ATPase in apical trafficking and epithelial polarity maintenance and the promising use of the C. elegans intestine as an in vivo model to better understand the molecular mechanisms of rare genetic enteropathies.

The use of artificial intelligence (AI) in a variety of research fields is speeding up multiple digital revolutions, from shifting paradigms in healthcare, precision medicine and wearable sensing, to public services and education offered to the masses around the world, to future cities made optimally efficient by autonomous driving. When a revolution happens, the consequences are not obvious straight away, and to date, there is no uniformly adapted framework to guide AI research to ensure a sustainable societal transition. To answer this need, here we analyze three key challenges to interdisciplinary AI research, and deliver three broad conclusions: 1) future development of AI should not only impact other scientific domains but should also take inspiration and benefit from other fields of science, 2) AI research must be accompanied by decision explainability, dataset bias transparency as well as development of evaluation methodologies and creation of regulatory agencies to ensure responsibility, and 3) AI education should receive more attention, efforts and innovation from the educational and scientific communities. Our analysis is of interest not only to AI practitioners but also to other researchers and the general public as it offers ways to guide the emerging collaborations and interactions toward the most fruitful outcomes.

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