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Ciliopathies are inherited disorders caused by defects in motile and non-motile (primary) cilia. Ciliopathy syndromes and associated gene variants are often highly pleiotropic and represent exemplars for interrogating genotype-phenotype correlations. Towards understanding disease mechanisms in the context of ciliopathy mutations, we have used a leading model organism for cilia and ciliopathy research, Caenorhabditis elegans, together with gene editing, to characterise two missense variants (P74S and G155S) in mksr-2/B9D2 associated with Joubert syndrome (JBTS). B9D2 functions within the Meckel syndrome (MKS) module at the ciliary base transition zone (TZ) compartment and regulates the molecular composition and sensory/signalling functions of the cilium. Quantitative assays of cilium/TZ structure and function, together with knock-in reporters, confirm that both variant alleles are pathogenic in worms. G155S causes a more severe overall phenotype and disrupts endogenous MKSR-2 organisation at the TZ. Recapitulation of the patient biallelic genotype shows that compound heterozygous worms phenocopy worms homozygous for P74S. The P74S and G155S alleles also reveal evidence of a very close functional association between the B9D2-associated B9 complex and MKS-2/TMEM216. Together, these data establish C. elegans as a model for interpreting JBTS mutations and provide further insight into MKS module organisation. This article has an associated First Person interview with the first author of the paper.

Background Colorectal cancer (CRC) is a malignant tumor marked by high prevalence and a challenging early detection landscape. While tools such as colonoscopy and serum biomarkers enhance screening efficacy, their invasive nature and inadequate sensitivity and specificity hamper their broad adoption. There is a pressing need for non-invasive, precise biomarkers for early diagnosis. The B9D2 gene, which is essential for ciliary function, has been rarely explored in CRC. This study is the first to investigate the diagnostic potential of B9D2 in CRC, using bioinformatics and machine learning to uncover its novel role in early detection, with implications for clinical translation. Methods Gene expression data from whole blood samples obtained from the GEO database were analyzed to identify differentially expressed genes (DEGs) associated with CRC, using a adjusted p-value threshold of < 0.05 and an absolute logFC > 0.5. The biological functions of these genes were investigated through Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), the Human Protein Atlas (HPA), and Gene Set Enrichment Analysis (GSEA). Additionally, three machine learning methods—Random Forest (RF), LASSO regression, and Support Vector Machine Recursive Feature Elimination (SVM-RFE)—were employed for feature selection and to evaluate the robustness and predictive power of the selected features, with diagnostic efficacy evaluated through Receiver Operating Characteristic (ROC) curves. Results Through this analysis, we identified five key genes—B9D2, CR2, DNMT3B, FOS, and PTGS2—from the GSE203024 dataset. Four of these genes have been previously linked to CRC, typically in tissue samples. Our study strengthens their significance as biomarkers by showing their expression in peripheral blood, a non-invasive source, and using multiple analytical methods. Notably, no previous studies have connected B9D2 to CRC, making this discovery a novel contribution. B9D2 expression was significantly upregulated in CRC patients, with an AUC of 0.797 in ROC analysis. This finding was further validated in the GSE47756 dataset, with an AUC of 0.756, confirming its potential as a reliable diagnostic biomarker for CRC. Further IHC staining showed significant different expression of B9D2 between normal and CRC tissue. Conclusion This study highlights the diagnostic potential of the B9D2 gene in CRC, marking the first time it has been proposed as a biomarker for early detection in CRC. This provides a foundation for its potential application in non-invasive diagnostic methods, such as liquid biopsy. Further experimental and clinical studies are needed to validate B9D2 as a reliable biomarker for early CRC detection and screening.

Autoimmune thyroid disease (AITD) is a recurrent and refractory clinical endocrine disease. Some studies have shown that the incidence of AITD is not only related to iodine, a kind of environmental factor, but that susceptibility genes also play a crucial role in its pathogenesis. Since research on susceptibility genes is still underway, the aims of this study were to assess the association between copy number variations (CNVs) and AITD, to identify genes related to susceptibility to AITD, and to explore the risk factors in the occurrence of AITD. Blood samples from five AITD patients and five controls from each area were assessed by chromosome microarray to identify candidate genes. The copy number (CN) of the candidate genes and urinary iodine levels were determined in adults, including 158 AITD patients and 181 controls, from areas having different iodine statuses. The cell growth-related genes, glypican 5 (GPC5), B9 domain containing 2 (B9D2), and ankyrin repeat and suppressor of cytokine signaling [SOCS] box-containing protein family 11 (ASB11), were selected as the candidate genes. The distribution of GPC5, B9D2, and ASB11 CNVs in AITD patients and controls was significantly different, and high urinary iodine levels and GPC5 CNVs are risk factors for AITD. There was no significant association between urinary iodine level and CNVs of the candidate genes. High urinary iodine levels and GPC5 CNVs are risk factors for AITD, but an association with the occurrence of AITD was not found.