Background Primary ciliary dyskinesia (PCD) is a rare, multigenic disorder of impaired mucociliary clearance leading to a spectrum of disease including chronic respiratory infection and bronchiectasis. PCD is phenotypically variable causing diagnostic challenges. PCD is underdiagnosed and when it is made diagnosis is often delayed. We describe evidence for use of digital-automated applications and/or artificial intelligence (AI) to improve PCD screening, diagnosis and characterisation. Methods A systematic literature search (2004–2025) was conducted across PubMed, Embase, and BioRxiv/MedRxiv (PROSPERO:CRD42024605689) by members of the ERS BEAT-PCD Clinical Research Collaboration (CRC). Screening and data extraction was performed by two independent reviewers and findings summarised qualitatively. In collaboration with the digital health ERS CONNECT CRC network, a narrative discussion focussed on steps to real-world implementation. Results Of 750 screened abstracts, 73 full-texts were assessed and 28 PCD-relevant studies with automated digital and/or AI-driven tools were included in the qualitative synthesis. Tools for enhanced ciliary function, ciliary ultrastructure and ciliary protein immunofluorescence analysis were presented in 17 studies. Eleven papers presented tools to screen e-health records for PCD, stratify patients by genotype and phenotype, or characterise computed tomography parameters. Conclusions Tools are available that could improve PCD diagnostic accuracy and reduce time-to-diagnosis. Studies were often single-centre, retrospective and of small sample size. The development of automated-digital tools or AI-driven tools requires representative patient datasets, human expert judgement to interpret and continual safety auditing. External validation is paramount before adaptation of healthcare systems, with adherence to EU AI Act framework to ensure accountability and safeguard against misuse. Simplified tools and philanthropic partnerships could facilitate implementation of new systems in resource limited settings.

BACKGROUND:There is substantial overlap between features of COPD, asthma, bronchiectasis, and cystic fibrosis (CF). Each is characterized by inflammation and mucociliary dysfunction. RESEARCH QUESTION:Is there a relationship between inflammation and mucociliary clearance in chronic respiratory conditions, and can biology, rather than disease labels, stratify patients into therapeutically relevant subtypes? STUDY DESIGN AND METHODS:Patients were categorized according to primary disease and clinical characteristics recorded. Spontaneous sputum was collected, and inflammatory markers (neutrophil elastase and 18 cytokines), sputum properties (DNA content, mucins, rheology, dry weight), and microbiome (long-read 16S sequencing) were measured. K-means clustering was performed and parameters compared between and within disease groups. Control participants were individuals who had formerly smoked but were without respiratory disease. RESULTS:The study included patients with asthma (n = 76), COPD (n = 91), bronchiectasis (n = 54), CF (n = 24), and control participants (n = 26). Nine cytokines (interferon-γ, IL-4, IL-5, eotaxin, eotaxin-3, thymus and activation regulated chemokine, granulocyte colony-stimulating factor, fractalkine, IL-22), neutrophil elastase, dry weight, mucins, and sputum rheology parameters were significantly different between disease groups and control participants (P < .05). K-means clustering identified 2 clusters defined by neutrophilic or T helper 2 (Th2) inflammation. The Th2 cluster was associated with lower sputum dry weight and DNA content and higher mucin-5B. Rheological parameters G', G'', and G∗ were significantly higher in the Th2 group, whereas the tangent of the loss angle δ was higher in the neutrophilic group, indicating a higher viscous to elastic ratio (P < .05 all comparisons). The neutrophilic cluster was associated with decreased alpha diversity (P = .04) and increased presence of Proteobacteria in their sputum microbiome compared with the Th2 cluster (P = .01). More neutrophilic inflammation was present in CF and bronchiectasis (42% of patients with COPD and 46% of patients with asthma were neutrophilic vs 78% of bronchiectasis and 87% of CF (P < .0001). Both clusters were present in all disease groups. INTERPRETATION:Our results indicate that airway diseases have heterogeneous mucus properties. Patients were shown to cluster according to inflammatory endotype rather than disease label. Assessment based on disease labels may be aided by endotyping using inflammatory and mucociliary clearance biomarkers.

Rationale: Primary ciliary dyskinesia (PCD) is a rare respiratory disorder of motile cilia caused by pathogenic variants in >50 known genes. Genetic testing routinely examines the coding regions of these genes, and biallelic pathogenic variants are reported in as many as 70% of patients. Many patients remain with an incomplete or no genetic diagnosis. Objectives: To retrospectively analyze the diagnostic yield in 496 patients referred for genetic testing and the increase in yield by investigating pathogenic DNA variants in the noncoding regions of PCD genes in 42 patients with an incomplete genetic diagnosis. Methods: End-to-end next-generation gene sequencing including coding and noncoding regions of 17 PCD genes was performed, following routine genetic diagnosis of a panel of more than 46 genes. Intronic variants were prioritized for pathogenicity using in silico tools to predict splice effects that were subsequently confirmed in RNA extracted from nasal epithelium. Measurements and Main Results: 232 of 496 patients (46.8%) had a complete genetic diagnosis of PCD after stringent variant assessment during routine genetic testing. Eighty-six patients (17.3%) had an incomplete genetic diagnosis, 42 of whom had end-to-end gene sequencing. Novel, potentially pathogenic, noncoding variants were identified in 16 of 42 patients (38.1%). Three recurrent deep-intronic variants were found. Conclusions: Diagnostic yield for PCD is increased by end-to-end gene sequencing. Noncoding variants that affect splicing are recurrent and are an important source of pathogenic genomic variation in patients with PCD. This work illustrates the potential clinical utility of end-to-end gene or genome sequencing for PCD.

Rationale: Infection is a key disease driver in bronchiectasis, and the upper-airway microbiome has been known to shape the lower-airway microbiome. Objective: To evaluate the relationship between the upper-airway microbiome, mucociliary function, and clinical outcomes in bronchiectasis. Methods: Nasopharyngeal swabs were collected from 344 patients with bronchiectasis enrolled across five European centers. A total of 104 patients had nasopharyngeal samples obtained at the 1-year follow-up. Microbiome composition was assessed according to Bronchiectasis Severity Index and severe exacerbations. The α- and β-diversity were measured using the Chao1 and Bray-Curtis indices, respectively. Random forest analysis was performed. Dysbiosis was defined as >10% relative abundance of pathogenic taxa comprising Pseudomonas, Haemophilus, and Staphylococcus. Measurements and Main Results: Of the 344 patients, 200 (58.1%) were female (median age, 68 yr; IQR, 59-75 yr). α-Diversity significantly differed according to disease severity (P = 0.002), and β-diversity analysis revealed distinct microbiome profiles associated with disease severity and severe exacerbation (permutational multivariate ANOVA, P = 0.021 and P = 0.001, respectively). Random forest analysis identified Pseudomonas as being associated with severe bronchiectasis (Bronchiectasis Severity Index ⩾9) and severe exacerbations. The genus-level relative taxon abundance of Pseudomonas was well correlated with Pseudomonas aeruginosa growth in the sputum culture. Patients with nasopharyngeal dysbiosis had more severe respiratory symptoms, showed epithelial disruption on nasal epithelial biopsy, and experienced more severe exacerbation over a 1-year follow-up period than those in the nondysbiosis group. The microbiome profiles were relatively stable between baseline and 1-year follow-up (P = 0.95). Conclusions: The upper-airway microbiome is associated with disease severity and severe exacerbation of bronchiectasis.

Rationale The inflammasome is a key regulatory complex of the inflammatory response leading to interleukin-1[3 [3 (IL-1[3) [3 ) release and activation. IL-1[3 [3 amplifies inflammatory responses and induces mucus secretion and hyperconcentration in other diseases. The role of IL-1[3 [3 in bronchiectasis has not been investigated. Objectives To characterise the role of airway IL-1[3 [3 in bronchiectasis, including the association with mucus properties, ciliary function, airway inflammation, microbiome and disease severity. Methods Stable bronchiectasis patients were enrolled in an international cohort study (n=269). IL-1[3 [3 was measured in sputum supernatant. A validation cohort also had sputum rheology and hydration measured (n=53). For analysis, patients were stratified according to the median value of IL-1[3 [3 in the population (high versus low) to compare disease severity, airway infection, microbiome (16S rRNA sequencing), inflammation and caspase-1 activity. Primary human nasal epithelial cells grown in air-liquid - liquid interface culture were used to study the effect of IL-1[3 [3 on cilia function. Results Patients with high sputum IL-1[3 [3 had more severe disease, increased caspase-1 activity and an increased T-helper type 1, T-helper type 2 and neutrophil inflammatory response compared with patients with low IL-1[3. [3 . The active-dominant form of IL-1[3 [3 was associated with increased disease severity. High IL-1[3 [3 was related to higher relative abundance of Proteobacteria in the microbiome and increased mucus solid content and viscoelastic properties. Chronic IL-1[3 [3 treatment reduced the functionality of cilia and tight junctions of epithelial cells in vitro. Conclusions A subset of stable bronchiectasis patients show increased airway IL-1[3, [3 , suggesting pulmonary inflammasome activation is linked with more severe disease, airway infection, mucus dehydration and epithelial dysfunction.

Tubulin, one of the most abundant cytoskeletal building blocks, has numerous isotypes in metazoans encoded by different conserved genes. Whether these distinct isotypes form cell type– and context-specific microtubule structures is poorly understood. Based on a cohort of 12 patients with primary ciliary dyskinesia as well as mouse mutants, we identified and characterized variants in the TUBB4B isotype that specifically perturbed centriole and cilium biogenesis. Distinct TUBB4B variants differentially affected microtubule dynamics and cilia formation in a dominant-negative manner. Structure-function studies revealed that different TUBB4B variants disrupted distinct tubulin interfaces, thereby enabling stratification of patients into three classes of ciliopathic diseases. These findings show that specific tubulin isotypes have distinct and nonredundant subcellular functions and establish a link between tubulinopathies and ciliopathies.

Introduction Cilia are critically important in the mucociliary clearance of the airways. This study explores the long-term effects of SARS-CoV-2 on cilial function and regeneration. Aim We aimed to investigate respiratory epithelial recovery and cilial function in individuals 3–12 months post SARS-CoV-2 infection. Methods We studied 3 cohorts of patients: the first cohort (FOLLOW n=41) underwent nasal epithelial cell sampling 3–12 months after the first waves of SARS-CoV-2 infection in both hospitalised and community settings; the second cohort, (ULTRON n=10), 3–12 months post-Omicron variant infection in vaccinated individuals; the third cohort (PROSAIC n=46) had RNA extracted from nasal brushings 6–12 months after recovery from severe infection with pre-Omicron variants of SARS-CoV-2. In the first two cohorts, cilial function was assessed using high-speed-video-microscopy, with ultrastructural analysis assessed by Transmission Electron Microscopy. Expression of the ciliogenesis gene, FOXJ1, was measured by qRT-PCR in the third cohort. Results In the initial FOLLOW cohort, there was significant loss of ciliation compared to controls. 90% of individuals had ultrastructural defects marked by mislocalised basal bodies and intracytoplasmic cilia up to 12 months post infection. There was no correlation with any ongoing nasal symptoms or symptoms of long COVID. In contrast, ciliogenesis was normal in the Omicron infected, vaccinated cohort, and no mislocalised basal bodies or intracytoplasmic cilia were seen. Defects in cilia function were present in both cohorts compared to pre-pandemic controls: reduced cilia beat frequency (FOLLOW p<0.01), reduced amplitude per second in both: (FOLLOW p<0.01, ULTRON p<0.01). This ciliogenesis defect led us to explore FOXJ1 expression post-COVID: qRT-PCR showed a significant reduction of FOXJ1 mRNA levels 6 months (p<0.001) and 12 months (p=0.002) following pre-Omicron variant infections compared with healthy volunteers; however, there was a significant improvement between 6-months and 12-months. Conclusion Cilia loss and defective ciliogenesis linked to reduced FOXJ1 expression persisted for a year following infection with early SARS-CoV-2 variants. No such defects were seen in vaccinated individuals infected with the Omicron variant despite some functional ciliary defects. This work illuminates novel long-term effects of viral infection on human epithelial function through altered expression of a key cilial regulator gene.

Mucociliary clearance is an essential defence mechanism against chronic airway infection and inflammation. Defects in ciliary motility are either primary, as in primary ciliary dyskinesia (PCD), or secondary. Identification of mucociliary clearance defects allows the implementation of appropriate management. High-speed video-microscopy (HSVM) is used to assess cilia motility from nasal biopsy samples. It is a time consuming and subjective requiring significant expertise. Computer vision can improve the identification of cilia motility defects by minimising subjectivity and reducing the cost and time to analyse samples. Using an artificial intelligence platform (Intel® Geti™), we have trained several models using archived HSVM videos from patients referred to the Royal Brompton Hospital who were diagnosed with PCD and display a range of ciliary motility phenotypes and non-PCD controls. The videos used are converted to optical flow to provide temporal information to the machine learning algorithm. We are training the platform to classify different categories of beat pattern: Immotile, Normal, Reduced Amplitude and Rotation. Models also include assessing sample quality and cilia beating orientation. The preliminary data based on projects currently in development are promising: the model classifying normal beating vs immotile cilia (around 30,000 frames) has a predictive accuracy of 100% and the beat pattern recognition model (around 25,000 frames) has a predictive accuracy of 97%. Further training and testing are ongoing, and more models are being developed to include a greater range of motility phenotypes and to encompass chronic inflammatory lung diseases.

Motile cilia and flagella beat rhythmically on the surface of cells to power the flow of fluid and to enable spermatozoa and unicellular eukaryotes to swim. In humans, defective ciliary motility can lead to male infertility and a congenital disorder called primary ciliary dyskinesia (PCD), in which impaired clearance of mucus by the cilia causes chronic respiratory infections 1 . Ciliary movement is generated by the axoneme, a molecular machine consisting of microtubules, ATP-powered dynein motors and regulatory complexes 2 . The size and complexity of the axoneme has so far prevented the development of an atomic model, hindering efforts to understand how it functions. Here we capitalize on recent developments in artificial intelligence-enabled structure prediction and cryo-electron microscopy (cryo-EM) to determine the structure of the 96-nm modular repeats of axonemes from the flagella of the alga Chlamydomonas reinhardtii and human respiratory cilia. Our atomic models provide insights into the conservation and specialization of axonemes, the interconnectivity between dyneins and their regulators, and the mechanisms that maintain axonemal periodicity. Correlated conformational changes in mechanoregulatory complexes with their associated axonemal dynein motors provide a mechanism for the long-hypothesized mechanotransduction pathway to regulate ciliary motility. Structures of respiratory-cilia doublet microtubules from four individuals with PCD reveal how the loss of individual docking factors can selectively eradicate periodically repeating structures.

Early and accurate diagnosis of Primary Ciliary Dyskinesia (PCD) allows appropriate multidisciplinary management and a reduction in lung function decline. Transmission Electron Microscopy (TEM) is essential in determining ciliary ultrastructural defects, when diagnosing PCD. This requires highly skilled specialists with considerable experience. Machine learning provides an excellent opportunity to reduce the time experts spend assessing cilia (1–2 hours) and improve accuracy of diagnosis. In collaboration with Intel®, we have used an Artificial Intelligence platform (Intel® Geti™), to develop a workflow called PCD-AID (PCD- Artificial Intelligence Diagnosis) that uses computer vision to aid in the diagnosis of PCD. This work is part of an organised ERS Clinical Research Collaboration with BEAT-PCD. The system was tested alongside the PCD diagnostic pathway (n=158) to determine diagnostic accuracy. The model has been trained with TEM images from over 21,000 cilia cross-sections to detect cilia and then classify them based on normal or abnormal ultrastructure or 'unusable' for diagnostic purposes (tilted or distorted images). Using retrospective and prospective patient samples, we have found PCD-AID can reliably identify ciliary ultrastructural defects (sensitivity of 0.87 and specificity of 0.88) and assess TEM images in under 1 minute per patient. It has good agreement with diagnostic specialists (> 75%) at identifying a range of ultrastructural defects and strikingly outperforms specialists at identifying subtle central pair defects associated with pathogenic mutations in HYDIN. Implementing computer vision artificial intelligence in the diagnostic pathway improved diagnosis of PCD.

The cause of bronchiectasis is unknown in ~40% cases. Primary ciliary dyskinesia (PCD) accounts for up to 10% but underdiagnosis is common. The aim of this study was to screen a large international cohort of patients with bronchiectasis for disease-causing mutations in genes associated with motile cilia structure and function. Patients with CT confirmed bronchiectasis were enrolled from 6 centres across Europe. People with known PCD were excluded. Whole exome sequencing was conducted in 573 individuals with bronchiectasis. Variants were called using a custom pipeline of 770 genes known to be associated with ciliary dysfunction plus CFTR. Homozygous or likely compound heterozygous mutations with moderate to high impact were reported. A subset of patients received nasal brushings to verify results. 10 individuals (2%) had mutations in known PCD genes (DNAI2, OFD1, GAS2L2, DNAAF1, DNAH9, DNAH11 and HYDIN). DNAAF1, OFD1 and DNAH11 mutations were verified at functional level with high speed video, electron microscopy and immunofluorescence analysis. Four individuals had bi-allelic CFTR mutations. An additional 9 individuals had mutations in putative PCD genes: 2 dynein heavy chains (DNAH2, DNAH7), 2 intraflagellar transport proteins (IFT122, IFT172) and 5 additional ciliary genes with varied functions that are being further investigated. In this large cohort, inherited ciliopathies were identified as a likely cause of bronchiectasis in 4% of patients. Half the findings were in previously undescribed ciliopathy genes. The study is ongoing aiming to functionally characterise previously undescribed genetic contributors to bronchiectasis.

Airway ciliary function analysis underpins PCD diagnostics and ex vivo/in vitro mucociliary clearance studies. It is an important measure of airway culture model integrity in health and after microbial/viral infections or airway drug therapies.https://bit.ly/3EXsG5J