Are inflammation-related diseases risk factors for primary acquired nasolacrimal duct obstruction? A large scale, national case-control study.

PURPOSE: To evaluate the incidence and risk factors for inflammatory conditions among patients with primary acquired nasolacrimal duct obstruction (PANDO). METHODS: A retrospective case-control study was conducted among patients of Clalit Health Services (CHS) in Israel from 2001 to 2022. For each case, three controls were matched among all CHS patients according to year of birth, sex, and ethnicity. Differences in demographic characteristics, ocular surface, eyelid, upper airway, and systemic diseases were assessed between the groups, and odds ratios (OR) were calculated. RESULTS: A total of 60,726 patients diagnosed with PANDO were included. The average age of PANDO patients was 63 ± 18 years, 63% were female. Significant associations were found between PANDO and various ocular surface and eyelid conditions, including chronic conjunctivitis (OR 2.96, 95% CI [2.73-3.20]), vernal keratoconjunctivitis (OR 2.89, 95% CI [2.45-3.29]), and blepharitis (OR 2.75, 95% CI [2.68-2.83]). There was a significant association with various upper airway conditions, including rhinitis (OR 1.62, 95% CI [1.58-1.66]), chronic sinusitis (OR 1.71, 95% CI [1.62-1.80]), and deviated nasal septum (OR 1.76, 95% CI [1.69-1.84]). Association was also observed with systemic conditions, including asthma (OR 1.34, 95% CI [1.27-1.41]) and atopic dermatitis (OR 1.36, 95% CI [1.32-1.41]). CONCLUSION: Ocular surface, eyelid, upper airway, and systemic inflammatory-related diseases were found to be associated with PANDO, supporting the theory that inflammation has a prominent role in the pathophysiology of PANDO.

Affected cell types for hundreds of Mendelian diseases revealed by analysis of human and mouse single-cell data.

Mendelian diseases tend to manifest clinically in certain tissues, yet their affected cell types typically remain elusive. Single-cell expression studies showed that overexpression of disease-associated genes may point to the affected cell types. Here, we developed a method that infers disease-affected cell types from the preferential expression of disease-associated genes in cell types (PrEDiCT). We applied PrEDiCT to single-cell expression data of six human tissues, to infer the cell types affected in Mendelian diseases. Overall, we inferred the likely affected cell types for 328 diseases. We corroborated our findings by literature text-mining, expert validation, and recapitulation in mouse corresponding tissues. Based on these findings, we explored characteristics of disease-affected cell types, showed that diseases manifesting in multiple tissues tend to affect similar cell types, and highlighted cases where gene functions could be used to refine inference. Together, these findings expand the molecular understanding of disease mechanisms and cellular vulnerability.

Predicting molecular mechanisms of hereditary diseases by using their tissue-selective manifestation.

How do aberrations in widely expressed genes lead to tissue-selective hereditary diseases? Previous attempts to answer this question were limited to testing a few candidate mechanisms. To answer this question at a larger scale, we developed "Tissue Risk Assessment of Causality by Expression" (TRACE), a machine learning approach to predict genes that underlie tissue-selective diseases and selectivity-related features. TRACE utilized 4,744 biologically interpretable tissue-specific gene features that were inferred from heterogeneous omics datasets. Application of TRACE to 1,031 disease genes uncovered known and novel selectivity-related features, the most common of which was previously overlooked. Next, we created a catalog of tissue-associated risks for 18,927 protein-coding genes (https://netbio.bgu.ac.il/trace/). As proof-of-concept, we prioritized candidate disease genes identified in 48 rare-disease patients. TRACE ranked the verified disease gene among the patient's candidate genes significantly better than gene prioritization methods that rank by gene constraint or tissue expression. Thus, tissue selectivity combined with machine learning enhances genetic and clinical understanding of hereditary diseases.

The Organ-Disease Annotations (ODiseA) Database of Hereditary Diseases and Inflicted Tissues.

Hereditary diseases tend to manifest clinically in few selected tissues. Knowledge of those tissues is important for better understanding of disease mechanisms, which often remain elusive. However, information on the tissues inflicted by each disease is not easily obtainable. Well-established resources, such as the Online Mendelian Inheritance in Man (OMIM) database and Human Phenotype Ontology (HPO), report on a spectrum of disease manifestations, yet do not highlight the main inflicted tissues. The Organ-Disease Annotations (ODiseA) database contains 4,357 thoroughly-curated annotations for 2,181 hereditary diseases and 45 inflicted tissues. Additionally, ODiseA reports 692 annotations of 635 diseases and the pathogenic tissues where they emerge. ODiseA can be queried by disease, disease gene, or inflicted tissue. Owing to its expansive, high-quality annotations, ODiseA serves as a valuable and unique tool for biomedical and computational researchers studying genotype-phenotype relationships of hereditary diseases. ODiseA is available at https://netbio.bgu.ac.il/odisea.