Disease Phenotype Research Articles

Background: Heart rate recovery (HRR) after exercise, a measure of autonomic function, is associated with adverse outcomes in adults with heart disease. However, HRR phenotypes in pediatric congenital heart disease (CHD) are not well characterized. This study aimed to characterize pediatric HRR patterns and their associated factors especially residual physiology. Methods: We performed a retrospective analysis of 417 patients, 136 CHD and 281 controls (controlled for age, sex, BMI, and baseline HR), who underwent exercise testing at two academic children’s hospitals (2016- 2020). CHD patients were classified as simple, moderate, severe, and single ventricle anatomy and residual physiology A-C. HR was recorded at baseline, peak, and 1–6 minutes post-exercise. Chronotropic index (CI) was calculated using Mahon’s predicted maximal HR. Mixed effects models evaluated HRR trajectories. Unsupervised K-means clustering identified functional HRR phenotypes. Results: CHD patients had lower CI (0.85 ± 0.15 vs 0.93 ± 0.12, p < 0.001) and peak HR (181 ± 17 bpm vs 189 ± 14 bpm, p < 0.001). CHD patients had a 5.5 bpm lower absolute HRR at minute 6 (p < 0.04), but relative HRR curves were similar in shape (CHD x time interaction not significant). Physiology Group C exhibited slower recovery with higher relative HR. Residual physiology was associated with cluster membership, with Physiology Group C patients more likely to exhibit the slower recovery phenotype (Cluster 0), while Groups A and B were more likely to exhibit the faster recovery phenotype (Cluster 1) (Figure1). Conclusion: Pediatric patients with CHD have distinct HRR phenotypes driven by residual physiology and CI. While relative HRR provides consistent recovery slopes, absolute HRR may better capture exertional limitations in pediatric CHD. Physiology-driven HRR phenotyping may enhance risk stratification and exercise interpretation in pediatric CHD.

Background: Confounding differences between treated and untreated patients in observational studies of medication effects lead to biased estimates of drug effectiveness and safety. We present a digital twin matching framework that pairs treated individuals with highly similar untreated controls, enabling robust, disease-specific estimation of drug effects across 358 phenotypes. We analyzed prescription and disease event data from over 400,000 participants in a large UK Biobank GP data set, covering 348 disease phenotypes and 5 major drug classes. We identified treated individuals and matched them to untreated controls using our novel approach considering enrollment, sex, and Bayesian time-varying latent health signatures derived from longitudinal genetic and clinical data. Each signature represents an underlying disease process with genetic predisposition and co-occurring disease patterns. We compared post-treatment event rates between matched treated and control groups, contrasting with unmatched and inverse probability weighting. Results: Our novel approach of matching substantially reduced baseline differences between treated and control groups across major drug classes. Unmatched analyses showed inflated event rates in treated patients due to confounding by indication. After matching, event rates in controls closely mirrored those in treated patients, and post-treatment differences more plausibly reflected drug effects. For cardiometabolic drugs and Type 2 diabetes, the raw event rate was higher in the treated group (6.3%) compared to the untreated group (4.9%), persisting with inverse probability weighting (IPW: 6.2% vs 5.0%). After digital twin matching, the treated group correctly demonstrated lower event rate (3.2%) than their matched controls (4.3%). Our matching also substantially reduced the apparent risk difference between treated and control individuals for several statin outcomes. In coronary atherosclerosis, the raw event rate was 11.8% in treated vs 3.3% in untreated, but after matching, rates were 7.5% vs 6.5% (Fig 1). Conclusions: Digital twin matching offers a scalable, generalizable solution for emulating randomized comparisons in real-world data. By aligning treated and control groups on genetic and clinical latent health features over time, this approach mitigates confounding and enables robust estimation of drug effects by capturing inherited disease susceptibility and clinical propensity to receive treatment and exposure.

Background: Inherited dilated cardiomyopathy (DCM) is a structural heart disease characterized by pathological enlargement of the heart. DCM can be caused by a mutation in the β-MYH7 gene that reduces contractile function and leads to heart failure. Methods: This is a case-control study to assess the role of β-MYH7 variations in Indian DCM patients and to differentiate a causative nsSNP and its impact on protein structure. We used targeted direct sequencing of the β-MYH7 gene in 122 DCM patients versus 173 ethnically matched healthy controls. To better define the mechanistic basis of the disease, we studied the structural impact of the mutant protein homology model compared to the native protein. Results: In the present study, we identified a missense mutation (His651Arg) in the head motor domain of β-MYH7 in a DCM patient, which was absent in 173 ethnically matched healthy individuals. The mutant Arginine651 was co-segregated with the disease in the family pedigree. Arg651 was found to alter an evolutionarily conserved amino acid, Histidine651. In addition, the mutant R651 was predicted to be pathogenic by the PolyPhen-2 and SIFT bioinformatics tools. Additionally, the superimposed 3D structure of the mutant (p.Arg651 homology model) compared to the native (p.His651) exhibited a root mean square deviation (RMSD) of approximately 3.6 Å. It is well known that non-covalent interactions—such as hydrophobic, electrostatic, Van der Waals, and hydrogen bonds—are crucial for stabilizing protein structures. Our findings demonstrated how the mutant (p.Arg651) disrupts a critical network of non-covalent interactions at the mutation site, potentially contributing to the disease phenotype. Conclusion: We identified a Novel H651R Mutation in the β-MYH7 Gene that led to DCM in India. Hence, our findings may pave the way for the development of small molecular modulators or myosin-targeted therapies for heart failure in the future.

Background: Clinical genetic testing is an increasingly important component of cardiovascular care and has direct impacts on the medical and surgical management of patients and relatives. A better understanding of genetic testing yield and correlations with vascular phenotypes is needed. Methods: We identified all adult patients referred to a large genetic cardiovascular clinic January 2019 - April 2025 for thoracic aortic aneurysm/dissection (TAAD), spontaneous coronary artery dissection (SCAD), abdominal aneurysm/dissection (AAD), or cervical aneurysm/dissection (CeAD). We assessed the diagnostic yield of a multigene panel testing in identifying pathologic/likely pathogenic variants and variants of unknown significance among patients with both sporadic and familial disease (≥1 affected first-degree relative). We used Fisher’s exact and two-tailed T-tests to determine whether positive genetic testing was associated with family history, age at diagnosis, and phenotype severity. Results: Among 699 affected patients, 477 (68.2%) completed genetic testing. This included 223/333 (67.0%) patients with TAAD, 116/187 (62.0%) with SCAD, 109/149 (73.2%) with AAD/CeAD, and 29/30 (96.7%) with aneurysms/dissections in multiple categories. Pathogenic variants were detected in 12/223 (5.4%) of patients with TAAD, 3/113 (2.7%) with SCAD, 6/47 (12.8%) with AAD, 5/65 (2.7%) with CeAD, and 5/29 (17.2%) with multiple. FBN1 variants were the most common pathogenic variants for TAAD, while COL3A1 were most common for AAD and CeAD. TAAD was more likely to be familial (65.9%) than SCAD (28.3%), AAD (29.8%), or CeAD (32.3%) but familial disease was not significantly associated with positive genetic testing. For TAAD, AAD, and CeAD, positive genetic testing was associated with a five- to eight-year younger mean age at diagnosis, although this was only statistically significant for TAAD (45.0 versus 53.8 years, p=0.037). Among patients with TAAD, there was a trend between positive genetic testing and larger mean aortic root (4.5 versus 4.2 cm, p=0.10) and ascending aorta (4.5 versus 4.3 cm, p=0.26). Conclusion: Genetic testing yield was low for a large group of patients presenting with vascular aneurysms and dissections, suggesting polygenic or undescribed monogenic etiologies. Pathogenic mutations were more common in patients with multiple categories of disease and early presentation, suggesting that these populations may particularly benefit from expanded access to genetic testing.

Introduction: Anemia is a recognized marker of poor outcomes in various conditions. Several trials have explored the prognostic implications of treatment of anemia or iron deficiency in patients with heart failure (HF). While iron repletion may improve the functional capacity, studies have yet to explore the hemodynamic effects of chronic anemia in patients with HF. Research Question: This study explored the hemodynamic effects of anemia amongst patients with HF utilizing intracardiac, multisensor algorithms. Method: This retrospective analysis included a nationwide cohort of adult patients implanted with a HeartLogic TM monitoring device. Participants were identified and stratified into HF without anemia or with chronic anemia utilizing the National Danish Health Registries. Chronic anemia was defined according to WHO criteria as ≥2 consecutive hemoglobin measurements below sex-specific thresholds, taken ≥3 months apart, and persisting for the entire course of observation (six months). Outcome were analyzed using a time-series model. Results: Among 1115 patients with daily sensor data available, 250 patients were females (22.5%). 218 patients had persistant anemia for six months. Patients with HF and anemia were older (66.4 ± 11.7 vs 59.5 ± 12 years, p<0.01), had a lower hemoglobin (7.75 ± 1.0 vs 8.8 ± 1.1 mmol/L, p <0.01), more impaired kidney function (64.4 ± 21.3 vs 74 ± 18 mL/min/1,73 m2, p<0.01), increased Pro-BNP (1947 [862,3431] vs 770 ng/L [331,1756], p <0.01), higher Charlson Comorbidity Index score (2.2 ± 0.93 vs 1.98 ± 0.90 , p<0.01), and higher prevalence of atrial fibrillation (Afib) (78 (36%) vs. 221 (25%), p <0.01). HF patients with anemia were associated with lower physical activity (2.05% less, 95% CI: -2.62 to -1.47, p < 0.01), higher S3 amplitude (0.97 ± 0.33 vs. 0.86 ± 0.26 mG, p<0.01) (indicative of elevated filling pressures), and greater Afib burden (4.56 ± 1.48 vs. 0.53 ± 2.39 hours, p < 0.01). Results remained significant when adjusting for confounding parameters (i.e. age and comorbidities). Conclusions: Chronic anemia in patients with HF is associated with an increased Afib burden, reduced physical activity, and elevated device-derived indices (HeartLogic Index, thorax impedance, and S3 amplitude, indicating increased filling pressure and higher risk of new HF-event). Therefore, chronic anemia in patients with HF may reflect a more severe disease phenotype with greater impairment of functional and hemodynamic effects.

Background: Heart failure has traditionally been classified as systolic vs diastolic, however acute heart failure (AHF) hospitalizations, often has various outcomes seen in bedside clinical medicine, driven by comorbidities, hemodynamic states, and initial presentation. Research: Question Can unsupervised machine learning (ML) identify distinct clinical phenotypes among adults hospitalized with acute heart failure and guide management strategies? Methods: We analyzed non-elective adult hospitalizations with AHF from the 2022 National Inpatient Sample (NIS). Five machine learning models were employed for prediction: Logistic Regression, Naive Bayes, Random Forest, XGBoost, and an artificial neural network (ANN) model. Unsupervised clustering was performed using K-means after principal component analysis (PCA) (k=4, Fig 1). Cluster visualization was enhanced via t-distributed stochastic neighbor embedding (t-SNE) and Uniform Manifold Approximation and Projection (UMAP) on a 10,000-patient subsample (Fig 2). Clusters were profiled by demographic and clinical characteristics.All analysis was conducted using Python 3.11. Results: Four distinct patient clusters were identified amongst heart failure admissions. Cluster 0 (47.1%) comprised younger healthier patients (mean age 28.8 years) and lowest in-hospital mortality (1%) representing AHF cases with non-chronic triggers. Cluster 1(2.9%) consisted of older individuals, with coronary artery disease and interventions, such as CABG, PCI (mean age 74.1 years) and moderate mortality (3%) confirming an ischemic heart disease phenotype . Cluster 2 (35.3%) exhibited a cardiopulmonary/ metabolic profile with moderate mortality (3%, mean age 66.1 years) indicating a common heart failure phenotype with systemic involvement . Cluster 3 (14.6%) was dominated by arrhythmia and older population (mean age 74.5 years) and had the highest in-hospital mortality(6%) suggesting a high-risk arrhythmia-dominant phenotype (p <0.001 for all).(Image 3) Conclusions: Unsupervised machine learning identified four unique and clinically relevant phenotypes among adults hospitalized with AHF, differentiated by comorbidity burden and in-hospital mortality. Our study highlights the potential of ML driven phenotyping to inform risk stratification, and clinical decisions in acute heart failure management.

Nicotinamide adenine dinucleotide (NAD + ) is essential for cellular functions due to its roles as a redox cofactor or substrate for many enzymes. Intriguingly, the NAD + pool declines in failing human hearts. Treatments with NAD + precursors such as nicotinamide and nicotinamide riboside (NR) are shown to ameliorate cardiac dysfunction, hypertrophy, and metabolic dysregulation in several mouse models of heart failure. However, testing the therapeutic potential of NAD + precursors in the combined transverse aortic constriction (TAC) and myocardial infarction (MI) model, representing both hypertension and ischemic insult, the two most common synergistic etiologies for myocardial dysfunction, remains unexplored. Hence, we investigated the potency of NR in preserving the structural and functional capacity of the heart in the TAC/MI model, recapitulating aspects of hypertension-induced cardiac hypertrophy and ischemic heart disease phenotypes. We hypothesize that boosting NAD + metabolism via NR supplementation will mitigate the cardiac remodeling in the TAC/MI hearts, thus ameliorating cardiac dysfunctions. We induced cardiac pressure overload and MI by TAC and ligation of the apical portion of the LAD, respectively. NR (~500mg/kg/day) and saline treatments were initiated 24 hr post-TAC/MI and Sham surgeries. Cardiac imaging was done 4 weeks after treatment to assess cardiac systolic function and morphometrics. Also, cytokine assay was performed to ascertain the levels of pro-fibrotic TGF-β. TAC/MI mice hearts showed significant ( p=0.01 ) LVEF (38.86 ± 6.382) reduction compared to the LVEF (46.50 ± 7.871) those treated with NR (TAC/MI+NR). Comparing these LVEF with the Sham LVEF (58.01 ± 4.940) shows NR treatment mitigated the reduction of systolic function post-TAC/MI. Similarly, morphometric data showed that NR prevented hypertrophy of the LVPW compared with saline treatment ( p=0.008 ), which was consistent with HW/BW significantly ( p=0.007 ) decreased in TAC/MI+NR compared to TAC/MI mice. TGF-β2, a prototypic pro-fibrotic cytokine central to fibrotic remodeling was overtly elevated in TAC/MI ( p<0.001 ) than TAC/MI+NR mice ( p= 0.17 ) compared to the Sham mice. Our data show for the first time that boosting the NAD + pool with NR protects mice against systolic dysfunction and hypertrophy in TAC/MI mice, thus necessitating further studies to elucidate underlying mechanisms and facilitate translational potentials.

Background and Aims: Duchenne muscular dystrophy (DMD) leads to fatal cardiomyopathy caused by mutations in the dystrophin gene. Gene therapy for the heart is limited due to poor delivery efficiency. In addition, patients are ineligible for heart transplants due to scoliosis and respiratory complications. Given the lack of effective therapies, efforts to clarify the mechanisms of DMD cardiomyopathy have been hampered by the limited availability of cardiac specimens. Here, we aimed to identify key driver pathways for DMD cardiomyopathy by constructing multicellular engineered heart tissue (EHT) under disease-relevant stress. Methods: EHTs were generated from patient iPSC-derived cardiomyocytes and epicardial cell–derived non-cardiomyocytes. An isogenic control with a corrected dystrophin mutation was used as comparison. To induce an adult-like disease phenotype, we shifted cardiomyocyte metabolism toward fatty acid oxidation and applied pathological stress. Single-cell RNA sequencing (scRNA-seq) was conducted before and after pathological stimulation to capture disease-associated molecular changes. Results: Phenotypic assessment revealed that while control EHTs showed increased contractility during maturation, DMD EHTs lacked this improvement (p < 0.05) and exhibited disorganized sarcomeres (p < 0.01). Upon pathological stimulation with mechanical stress and inflammatory cytokines, DMD EHTs showed a further decline in contractility (p < 0.01), accompanied by increased fibrosis (p < 0.05) and DNA damage. TFvelocity analysis of time-course scRNA-seq revealed that DMD cardiomyocytes exhibited a gene expression profile resembling that of dilated cardiomyopathy. This analysis identified early activation of pathways related to microtubule instability and fibrosis, followed by later activation of pathways associated with responses to oxidative stress and inflammation, as well as muscle atrophy, all with −log 10 (P) > 3. Furthermore, scMetabolism analysis showed a marked decline in glutathione metabolism and mitochondrial ATP production as the disease progressed. Conclusion: Single-cell transcriptomic and metabolic dynamics profiles suggest that the reduced contractility observed in our DMD cardiomyopathy model is driven by mitochondrial dysfunction caused by increased sensitivity to oxidative and inflammatory stress, as well as microtubule instability. These findings provide novel insights beyond the previously reported membrane fragility due to dystrophin deficiency.

BackgroundThis study evaluated the disease phenotype and treatment of axial psoriatic arthritis among patients from Japan compared with those from different geographic regions.MethodsData from the ASAS-PerSpA study were analyzed. Patients with psoriatic arthritis (PsA) with axial involvement, according to a rheumatologist´s judgment, were included. Patients were further categorized by four geographic regions: Europe/North America, Latin America, Middle East/North Africa, and Asia, split into Japan and other Asian countries. Disease and patient characteristics, disease activity, function, and treatment were compared by region.ResultsOf the 4,465 patients with SpA, 1,033 (23%) were diagnosed with PsA by their rheumatologist. Among those with PsA, 367 (35.5%) had axial involvement (axPsA). Disease activity and function ranges were 4.1–5.4 for BASDAI, 2.5–3.2 for ASDAS, and 3.0–4.7 for BASFI, by regions. In Japan, disease activity and function were relatively lower, indicated by a mean BASDAI of 3.5 (SD 2.4), ASDAS of 2.2 (SD 1.0), and BASFI of 1.6 (SD 2.3). These indexes were also significantly lower than those in other Asian countries, with scores of 4.8 (SD 3.0), 2.2 (SD 2.4), and 3.2 (SD 1.5) respectively. All regions showed variations in the use of csDMARDs and bDMARDs, the utilization rate of csDMARDs was significantly lower in Japan than in other Asian countries (51.4% vs. 78.1%, p = 0.02).ConclusionPatients with axPsA in Japan showed relatively lower disease activity and function than those from different geographic regions, especially in other Asian countries with less frequent csDMARD use.

Phenotypes of Spinal Cord Disease (Multisystem Morbidity) Secondary to Spinal Cord Injury.

Electronic health records have been increasingly adopted as useful resources for genomic research. However, case-control labeling of clinical data from electronic health records is challenging and most studies utilize phenotype codes to define case/control labels, resulting in suboptimal downstream analyses. Here we describe the liability threshold phenotypic integration, a method combining genetic relatedness with phenotypic data, including binary and continuous traits such as diagnosis codes, family disease history, laboratory measurements and biomarkers, to derive new continuous phenotypes for target diseases. The model utilizes an automatic trait selection algorithm that increases performance in disease risk prediction and provides insights into nontarget traits associated with the target disease. Our simulations and applications to the eMERGE network and the UK Biobank data demonstrate consistent performance gains in disease risk prediction and genome-wide association study power compared to conventional phenotype codes, models that solely incorporate family history and the phenotype imputation method SoftImpute, with similar false-positive rate control.

Understanding human brain development and dysfunction is a major goal in neurobiology. Compared with traditional 2D models and animal models, brain organoids technology based on induced pluripotent stem cell (iPSC) constructs can more accurately recapitulate the developmental process of the human brain and simulate the characteristic phenotypes of neurological diseases in recent years. This technology is expected to change our understanding of human brain development, while providing a fresh perspective on elucidating the pathogenesis of inherited and acquired brain diseases. This article reviews the development and recent advances in brain organoids, explores their use in neuropsychiatric disorders, from neurodevelopmental to neurodegenerative and psychiatric diseases, while also outlining the challenges facing the technology. We conclude that these advances not only enhance our understanding of human-specific brain development and disease mechanisms, but also accelerate the translation of brain organoid technology into personalized medicine and drug discovery.

Cancer predisposition syndromes (CPS) with pediatric onset are the leading known cause of childhood malignancies and are increasingly guiding clinical strategies in pediatric oncology. CPS are placed under evolutionary negative selective pressure, but pediatric pancancer studies have so far failed to investigate genomic evolutionary metrics as a guide to predict penetrance and reveal novel CPS. Germline whole-genome sequencing was performed in a 5-year prospective, registry-validated, nationwide cohort of individuals diagnosed with cancer before age 18. Evolution-guided burden analysis of private germline variants in constrained genes was compared with 125,748 gnomAD exomes. Across a total of 1,127 participants, 16% carried a pathogenic variant in at least one CPS gene. After genotype-phenotype matching, 9% of children in the prospective cohort (n = 651) carried a variant considered causative, a rate deemed significantly higher than in previous studies [RR, 1.54, 95% confidence interval (CI), 1.37-1.75, P = 1 × 10-14]. As predicted for a disease subject to negative Darwinian selective pressure, compared with reference adults, we found a significant excess of loss-of-function (LoF) variants in the 1,500 most constrained genes (RR, 1.54, 99% CI, 1.21-1.95, P = 4 × 10-6). Surprisingly, this excess was greater than expected, leaving a significant residual enrichment of predicted LoF variants in genes evolutionarily considered the least tolerant to damage (RR, 1.41, 99% CI, 1.09-1.80, P = 4 × 10-4). The high frequency of LoF variants, including in known CPS, emphasizes the need for systematic and extensive germline genomic mapping as part of the diagnostic workup of patients with childhood cancer and the linkage of such data to disease and response phenotypes to guide future pediatric oncological care and ultimately pave the way for prediagnostic interventional measures.

BackgroundThis study evaluated observational and causal relationships between rheumatoid arthritis (RA) and cardiovascular disease and imaging phenotypes in the UK Biobank.MethodsRA was defined using linked hospital records, self-reported diagnostics, and medication data. Controls were participants without a record of RA. Cardiovascular diseases (CVDs) were defined using linked hospital records over an average of 14 years of prospective follow-up, including: ischaemic heart diseases (IHD), acute myocardial infarction (AMI), atrial fibrillation, any arrhythmia, non-ischaemic cardiomyopathies, pericardial disease, stroke, peripheral vascular disease, and venous thromboembolism. For participants with cardiovascular magnetic resonance (CMR) available as part of the UK Biobank Imaging Study, we considered measures of cardiac structure and function extracted using automated pipelines. Associations of RA with prevalent and incident CVDs were calculated using logistic and Cox regression. Linear regression was used to examine associations with CMR metrics. Models were adjusted for demographic, lifestyle, and clinical confounders. Causal associations were assessed using two-sample Mendelian randomisation. Genetic instruments for RA (22,350 cases and 74,823 controls), nine CVDs (FinnGen, n = 224,737), and 11 CMR phenotypes (UK Biobank) were extracted and associations assessed using inverse-variance weighting with pleiotropy adjustments and multiple testing corrections.ResultsThe analysis included 1,436 RA cases (mean age 59.9 years; 70.6% female) and 476,975 controls (mean age 56.5 years; 54.3% female). Participants with RA lived in more socioeconomically deprived areas (as per the Townsend Deprivation Index), had lower physical activity levels, were more likely to smoke, and had a higher baseline prevalence of CVDs. In fully adjusted models, participants with RA had a significantly higher hazard of multiple incident CVDs, with the greatest risks related to pericardial disease (HR 2.63 (1.85, 3.74)), heart failure (HR 1.68 (1.42, 1.99)), and AMI (HR 1.53 (1.20, 1.96)). Mendelian Randomisation analyses supported causal links between RA and AMI (OR 1.07 (1.02, 1.09), p = 0.009), arrhythmias (OR 1.05 (1.02, 1.06), p = 0.0007), and IHD (OR 1.05 (1.01, 1.06), p = 0.036). No significant associations were identified between RA and CMR phenotypes.ConclusionsPeople with RA have a heightened risk of multiple prevalent and incident CVDs, independent of shared risk factors, with suggestions of causal links with IHD, AMI, and arrhythmias.Supplementary InformationThe online version contains supplementary material available at 10.1186/s12916-025-04431-1.

Novel mutations in MTERF3: First report of a new genetic cause in two Chinese patients with developmental delay, intermittent hypoglycemia and metabolic acidosis.

Prenatal exposure to dietary levels of glyphosate disrupts metabolic, immune, and behavioral markers across generations in mice.

Gut microbiota-based metabolism contributes to the protection of pseudolaric acid B against MAFLD.

Incomplete penetrance, or absence of disease phenotype in an individual with a disease-associated variant, is a major challenge in variant interpretation. Studying individuals with apparent incomplete penetrance can shed light on underlying drivers of altered phenotype penetrance. Here, we investigate clinically relevant variants from ClinVar in 807,162 individuals from the Genome Aggregation Database (gnomAD), demonstrating improved representation in gnomAD version 4. We then conduct a comprehensive case-by-case assessment of 734 predicted loss of function variants in 77 genes associated with severe, early-onset, highly penetrant haploinsufficient disease. Here, we identify explanations for the presumed lack of disease manifestation in 701 of 734 variants (95%). Individuals with unexplained lack of disease manifestation in this set of disorders are rare, underscoring the need and power of deep case-by-case assessment presented here to minimize false assignments of disease risk, particularly in unaffected individuals with higher rates of secondary properties that result in rescue.

Inflammatory bowel disease (IBD), which encompasses ulcerative colitis (UC) and Crohn's disease (CD), is a chronic condition characterized by recurrent intestinal inflammation and complications. Despite extensive research on bacterial dysbiosis in IBD, the role of the gut mycobiome remains largely unexplored, particularly in surgical tissue specimens. In this study, we performed a comprehensive analysis of the intestinal fungal communities in surgical resections obtained from 20 patients with UC and 30 patients with CD, with non-IBD resections serving as controls. Fungal DNA was extracted and the internal transcribed spacer (ITS) region was amplified and sequenced using high-throughput Illumina technology. RNA from surgical resections from both non-IBD and IBD patients was obtained and the expression of pro-inflammatory and profibrotic genes was analyzed by real-time quantitative polymerase chain reaction. Bioinformatic analysis revealed modest changes in fungal diversity in UC resections compared with those from controls. However, CD specimens exhibited significant alterations in mycobiome composition, including an increased abundance of Malassezia, specifically Malassezia globose, alongside a reduction in Yarrowia lipolytica. Moreover, stratification of CD into complicated phenotypes (B2 stricturing vs B3 penetrating) identified distinct fungal signatures capable of discriminating between these clinical phenotypes. Correlation analyses revealed a direct association between the mycobiome and intestinal inflammation and fibrosis, in parallel with several interactions between fungal and bacterial species, further reporting interkingdom interactions between both microbial communities. These results underscore the potential of fungal biomarkers in elucidating IBD pathogenesis and its associated complications, which opens up promising avenues for targeted therapeutic strategies.

Significance of integrated clinical and proteomic characteristics analysis for pathogenesis and management of Crohn's disease with concomitant psoriasis.