Centriole assembly involves the coordination of centriolar modules. One module is the A-C linker, an enigmatic protein assembly connecting the A-microtubule of one microtubule triplet to the C-microtubule of the neighboring triplet. Here, we integrated biochemistry, multiscale cryo–electron microscopy, and AlphaFold modeling to investigate the architecture of the centriole. Using an improved centriole isolation method, we determined the structure of the A-C linker bound to microtubule triplets, which revealed how the A-C linker cross-links microtubules and integrates with the B-C junction. We found marked changes in the structure and composition of the A-C linker that correlate with the presence of other centriolar modules, including the pinhead, cartwheel, and inner scaffold. Our findings show that the A-C linker is a highly integrated component of the centriole whose polymorphism may orchestrate the assembly of spatially distinct centriolar modules, and provide a framework for dissecting the biology of centrioles.

Primary cilia are ubiquitous sensory organelles mediating various signaling modalities essential for development and cell homeostasis. Their dysfunction leads to ciliopathies, human disorders often affecting the central nervous system. CEP290 is a major ciliopathy-associated gene that encodes a centrosomal and ciliary transition zone protein. CEP290 has been implicated in different cellular functions, including cell cycle control, ciliogenesis and control of ciliary membrane protein content. To investigate CEP290 dysfunction in human neurons, we generated human induced pluripotent stem cell (iPSC)-derived brain organoids harboring CEP290 mutations. We found that CEP290 deficiency does not affect cell cycle progression or organoid formation, despite a tendency for less mature neuronal populations and formation of choroid plexus in mutant organoids. Expansion microscopy revealed morphologically abnormal ventricular cilia in the CEP290 mutant organoid cells with bulging ciliary membranes around splayed distal axonemal microtubules. Such ciliary abnormalities might represent a tissue-specific consequence revealed by studying a human neuronal organoid model.

Recent advances in sequencing technologies have increasingly enabled the identification of genetic causes for human monogenic diseases. However, systematic understanding remains limited due to the rarity, genetic heterogeneity, and complex genotype-phenotype relationships of these diseases. Primary ciliopathies are a diverse group of rare disorders caused by variants in genes associated with the cilium, a cellular organelle involved in signaling during development and cell homeostasis. These genetic variants result in a wide spectrum of clinical phenotypes involving the brain, eye, kidney and skeleton. It remains unclear to what extent this phenotypic diversity can be attributed to the disease-causing genes and their specific roles in ciliary function. Here, we systematically compared human primary ciliopathies with each other and with mouse phenotypes by propagating known disease genes through a network of protein interactions. Network propagation improved the clustering of primary ciliopathies with shared clinical phenotypes and facilitated the identification of mouse phenotypes closely related to primary ciliopathies due to shared groups of proteins in the interaction network. By leveraging this phenotype-specific approach, we prioritized candidate genes for ciliopathies and identified likely pathogenic variants in CEP43 , a novel candidate gene for human primary ciliopathies in three previously unsolved cases. This study demonstrates that network propagation enhances the genetic and phenotypic understanding of primary ciliopathies, aiding in the prioritization of candidate genes and identification of relevant mouse models for these rare disorders, and providing a framework for unraveling shared underlying mechanisms for other rare genetic diseases.

Resolving disease mechanisms and identifying safer drug targets remains challenging due to difficulties in integrating inconsistent sources of genetic evidence. Here, we developed a deep-learning (DL) method for obtaining latent representations of human diseases and other terms by coupling a variational autoencoder (VAE) to network embeddings from graph representation learning on a protein interaction network. We apply this approach to map the landscape of human disease, by systematically integrating evidence for ∼42,000 traits and terms from sources such as clinical reports (ClinVar), genome-wide association studies (GWAS), mouse phenotypes and gene ontology. Similar diseases share their latent neighborhoods with the sources of evidence highlighting different aspects of the same cell biology for its role in disease. By decoding latent neighborhoods, we unify the sources of evidence and integrate across diseases to prioritize genes for common and rare diseases. Through examples for diabetes, hearing loss, and familial long QT syndrome, we illustrate how the methodology can be applied to develop hypotheses for disease mechanisms and to propose experimental models, measurements, targets and potential drugs that may improve diagnosis and treatments.

Background/Objectives: Individual responses to CFTR modulators vary widely among people with cystic fibrosis (pwCF), underscoring the need for functional approaches that provide biological context alongside genotype-based therapy selection. Nasal epithelial cultures provide an individual-specific model for theratyping, but most studies rely on freshly isolated cells, restricting repeated testing and long-term sample use. In this study, we tested whether CFTR modulator responses measured in biobanked nasal cells were associated with real-world clinical outcomes. Methods: Cryopreserved nasal epithelial cells from 23 pwCF were differentiated at the air–liquid interface and assessed for CFTR modulator-responsive ion transport using Ussing chambers. In vitro responses were correlated with 6-month changes in sweat chloride concentration (SCC), FEV₁, and BMI. Results: Cryopreserved cultures retained donor-specific CFTR modulator responsiveness. Modulator-induced forskolin/IBMX-stimulated currents correlated with changes in SCC (R = −0.512). CFTR inhibitor-sensitive currents correlated with FEV₁ (R = 0.564). Associations between forskolin/IBMX-stimulated currents and FEV₁ were positive but did not reach statistical significance using two-tailed analysis. BMI changes showed no significant association. Conclusions: Biobanked nasal epithelial cultures preserve clinically relevant CFTR modulator responses at the cohort level, supporting their use as functional assays for population-level assessment in cystic fibrosis. This cryopreservation-based strategy enables repeated testing and may expand access to theratyping beyond freshly obtained samples.

Protein phosphorylation regulates nearly every cellular process, yet most of the hundreds of thousands of human phosphosites remain functionally uncharacterized. Rather than prioritising phosphosites by conservation or structural features, here we use the abundance of an interaction partner as a readout of whether a phosphosite affects that interaction. This idea exploits the fact that subunits of stable complexes are often degraded when unbound. Here, we apply a nested linear regression model to pan-cancer data from 1,006 tumours, while controlling for transcriptional and other covariates. We identified 6,160 associations between 3,038 phosphosites and the abundance of interacting proteins, including several known interaction-regulating sites. Mapping these onto AlphaFold-predicted complexes placed 239 sites at interaction interfaces, while another 402 were linked to compartment-specific localisation, indicating that phosphorylation can also tune interactions by relocating proteins between compartments. Affinity-purification mass spectrometry of NKAP and NUF2 phosphosite mutants experimentally supported some of these predictions. Together, this framework reveals a widespread coupling between phosphorylation and interaction-dependent protein abundance and provides a prioritized, structure-informed resource for characterizing the human phosphoproteome