Cilia are small microtubule-based structures found on the surface of most mammalian cells, which have key sensory and sometimes motile functions. Primary ciliary dyskinesia (PCD) is a type of ciliopathy caused by defects in motile cilia. The genetic basis of PCD is only partially understood. Studying a cohort of 11 human patients with PCD, we find thatde novomutations inTUBB4B, a beta tubulin isotype, cause three distinct classes of ciliopathic disease.In vivostudies in mice show thatTubb4bplays a specific role in cilia, building centrioles and axonemes in multiciliated cells. Examining the effects of specific TUBB4B variants in cells and in mice, we further demonstrate that distinctTUBB4Bmutations differentially affect microtubule dynamics and cilia formation in a dominant negative manner. Finally, structure-function studies reveal that different TUBB4B mutations disrupt distinct tubulin interfaces. Importantly, these molecular differences correlate with disease features. We show that tubulin heterodimer-impairing TUBB4B variants underlie nonsyndromic PCD, whilst additional renal and sensorineural ciliopathic features in a syndromic PCD subtype arise from microtubule lumenal interface-impaired TUBB4B variants. These findings suggest that specific tubulin isotypes have distinct and non-redundant subcellular functions, and demonstrate that human tubulinopathies can be drivers of ciliopathic syndromes.

Cells rely heavily on microtubules for several processes, including cell division and molecular trafficking. Mutations in the different tubulin-α and -β proteins that comprise microtubules have been associated with various diseases and are often dominant, sporadic and congenital. While the earliest reported tubulin mutations affect neurodevelopment, mutations are also associated with other disorders such as bleeding disorders and infertility. We performed a systematic survey of tubulin mutations across all isotypes in order to improve our understanding of how they cause disease, and increase our ability to predict their phenotypic effects. Both protein structural analyses and computational variant effect predictors were very limited in their utility for differentiating between pathogenic and benign mutations. This was even worse for those genes associated with non-neurodevelopmental disorders. We selected tubulin-α and -β disease mutations that were most poorly predicted for experimental characterisation. These mutants co-localise to the mitotic spindle in HeLa cells, suggesting they may exert dominant-negative effects by altering microtubule properties. Our results show that tubulin mutations represent a blind spot for current computational approaches, being much more poorly predicted than mutations in most human disease genes. We suggest that this is likely due to their strong association with dominant-negative and gain-of-function mechanisms.

Cells rely heavily on microtubules for several processes, including cell division and molecular trafficking. Mutations in the different tubulin-α and -β proteins that comprise microtubules have been associated with various diseases and are often dominant, sporadic and congenital. While the earliest reported tubulin mutations affect neurodevelopment, mutations are also associated with other disorders such as bleeding disorders and infertility. We performed a systematic survey of tubulin mutations across all isotypes in order to improve our understanding of how they cause disease, and increase our ability to predict their phenotypic effects. Both protein structural analyses and computational variant effect predictors were very limited in their utility for differentiating between pathogenic and benign mutations. This was even worse for those genes associated with non-neurodevelopmental disorders. We selected tubulin-α and -β disease mutations that were most poorly predicted for experimental characterisation. These mutants co-localise to the mitotic spindle in HeLa cells, suggesting they may exert dominant-negative effects by altering microtubule properties. Our results show that tubulin mutations represent a blind spot for current computational approaches, being much more poorly predicted than mutations in most human disease genes. We suggest that this is likely due to their strong association with dominant-negative and gain-of-function mechanisms.