A gene pathogenicity tool "GenePy" identifies missed biallelic diagnoses in the 100,000 Genomes Project.

PURPOSE: The 100,000 Genomes Project diagnosed a quarter of affected participants, but 26% of diagnoses were not on the applied gene panel(s); with many being de novo variants. Assessing biallelic variants without a gene panel is more challenging. METHODS: We sought to identify missed biallelic diagnoses using GenePy, which incorporates allele frequency, zygosity, and a user-defined deleterious metric, generating an aggregate GenePy score per gene, per participant. We calculated GenePy scores for 2862 recessive disease genes in 78,216 100,000 Genomes Project participants. For each gene, we ranked participant GenePy scores and scrutinized affected participants without a diagnosis, whose scores ranked among the top 5 for each gene. In cases which participant phenotypes overlapped with the disease gene of interest, we extracted rare variants and applied phase, ClinVar, and ACMG classification. RESULTS: 3184 affected individuals without a molecular diagnosis had a top-5-ranked GenePy score and 682 of 3184 (21%) had phenotypes overlapping with a top-ranking gene. In 122 of 669 (18%) phenotype-matched cases (excluding 13 withdrawn participants), we identified a putative missed diagnosis (2.2% of all undiagnosed participants). A further 334 of 669 (50%) cases have a possible missed diagnosis but require functional validation. CONCLUSION: Applying GenePy at scale has identified 456 potential diagnoses, demonstrating the value of novel diagnostic strategies.

Supervised Machine Learning Classifies Inflammatory Bowel Disease Patients by Subtype Using Whole Exome Sequencing Data.

BACKGROUND: Inflammatory bowel disease [IBD] is a chronic inflammatory disorder with two main subtypes: Crohn's disease [CD] and ulcerative colitis [UC]. Prompt subtype diagnosis enables the correct treatment to be administered. Using genomic data, we aimed to assess machine learning [ML] to classify patients according to IBD subtype. METHODS: Whole exome sequencing [WES] from paediatric/adult IBD patients was processed using an in-house bioinformatics pipeline. These data were condensed into the per-gene, per-individual genomic burden score, GenePy. Data were split into training and testing datasets [80/20]. Feature selection with a linear support vector classifier, and hyperparameter tuning with Bayesian Optimisation, were performed [training data]. The supervised ML method random forest was utilised to classify patients as CD or UC, using three panels: 1] all available genes; 2] autoimmune genes; 3] 'IBD' genes. ML results were assessed using area under the receiver operating characteristics curve [AUROC], sensitivity, and specificity on the testing dataset. RESULTS: A total of 906 patients were included in analysis [600 CD, 306 UC]. Training data included 488 patients, balanced according to the minority class of UC. The autoimmune gene panel generated the best performing ML model [AUROC = 0.68], outperforming an IBD gene panel [AUROC = 0.61]. NOD2 was the top gene for discriminating CD and UC, regardless of the gene panel used. Lack of variation in genes with high GenePy scores in CD patients was the best classifier of a diagnosis of UC. DISCUSSION: We demonstrate promising classification of patients by subtype using random forest and WES data. Focusing on specific subgroups of patients, with larger datasets, may result in better classification.

A gene pathogenicity tool 'GenePy' identifies missed biallelic diagnoses in the 100,000 Genomes Project.

The 100,000 Genomes Project (100KGP) diagnosed a quarter of recruited affected participants, but 26% of diagnoses were in genes not on the chosen gene panel(s); with many being de novo variants of high impact. However, assessing biallelic variants without a gene panel is challenging, due to the number of variants requiring scrutiny. We sought to identify potential missed biallelic diagnoses independent of the gene panel applied using GenePy - a whole gene pathogenicity metric. GenePy scores all variants called in a given individual, incorporating allele frequency, zygosity, and a user-defined deleterious metric (CADD v1.6 applied herein). GenePy then combines all variant scores for individual genes, generating an aggregate score per gene, per participant. We calculated GenePy scores for 2862 recessive disease genes in 78,216 individuals in 100KGP. For each gene, we ranked participant GenePy scores for that gene, and scrutinised affected individuals without a diagnosis whose scores ranked amongst the top-5 for each gene. We assessed these participants' phenotypes for overlap with the disease gene associated phenotype for which they were highly ranked. Where phenotypes overlapped, we extracted rare variants in the gene of interest and applied phase, ClinVar and ACMG classification looking for putative causal biallelic variants. 3184 affected individuals without a molecular diagnosis had a top-5 ranked GenePy gene score and 682/3184 (21%) had phenotypes overlapping with one of the top-ranking genes. After removing 13 withdrawn participants, in 122/669 (18%) of the phenotype-matched cases, we identified a putative missed diagnosis in a top-ranked gene supported by phasing, ClinVar and ACMG classification. A further 334/669 (50%) of cases have a possible missed diagnosis but require functional validation. Applying GenePy at scale has identified potential diagnoses for 456/3183 (14%) of undiagnosed participants who had a top-5 ranked GenePy score in a recessive disease gene, whilst adding only 1.2 additional variants (per individual) for assessment.

Prediction of Crohn's Disease Stricturing Phenotype Using a NOD2-derived Genomic Biomarker.

BACKGROUND: Crohn's disease (CD) is highly heterogenous and may be complicated by stricturing behavior. Personalized prediction of stricturing will inform management. We aimed to create a stricturing risk stratification model using genomic/clinical data. METHODS: Exome sequencing was performed on CD patients, and phenotype data retrieved. Biallelic variants in NOD2 were identified. NOD2 was converted into a per-patient deleteriousness metric ("GenePy"). Using training data, patients were stratified into risk groups for fibrotic stricturing using NOD2. Findings were validated in a testing data set. Models were modified to include disease location at diagnosis. Cox proportional hazards assessed performance. RESULTS: Six hundred forty-five patients were included (373 children and 272 adults); 48 patients fulfilled criteria for monogenic NOD2-related disease (7.4%), 24 of whom had strictures. NOD2 GenePy scores stratified patients in training data into 2 risk groups. Within testing data, 30 of 161 patients (18.6%) were classified as high-risk based on the NOD2 biomarker, with stricturing in 17 of 30 (56.7%). In the low-risk group, 28 of 131 (21.4%) had stricturing behavior. Cox proportional hazards using the NOD2 risk groups demonstrated a hazard ratio (HR) of 2.092 (P = 2.4 × 10-5), between risk groups. Limiting analysis to patients diagnosed aged < 18-years improved performance (HR-3.164, P = 1 × 10-6). Models were modified to include disease location, such as terminal ileal (TI) disease or not. Inclusion of NOD2 risk groups added significant additional utility to prediction models. High-risk group pediatric patients presenting with TI disease had a HR of 4.89 (P = 2.3 × 10-5) compared with the low-risk group patients without TI disease. CONCLUSIONS: A NOD2 genomic biomarker predicts stricturing risk, with prognostic power improved in pediatric-onset CD. Implementation into a clinical setting can help personalize management.

NOD2 is a well-established risk gene for development of Crohn's disease and stricturing behavior. Here we demonstrate NOD2 can be utilized as a genomic biomarker, stratifying patients into 2 stricturing risk groups. Further refinement using disease location at diagnosis improved risk stratification.

Evidence of a genetically driven metabolomic signature in actively inflamed Crohn's disease.

Crohn's disease (CD) is characterised by chronic inflammation. We aimed to identify a relationship between plasma inflammatory metabolomic signature and genomic data in CD using blood plasma metabolic profiles. Proton NMR spectroscopy were achieved for 228 paediatric CD patients. Regression (OPLS) modelling and machine learning (ML) approaches were independently applied to establish the metabolic inflammatory signature, which was correlated against gene-level pathogenicity scores generated for all patients and functional enrichment was analysed. OPLS modelling of metabolomic spectra from unfasted patients revealed distinctive shifts in plasma metabolites corresponding to regions of the spectrum assigned to N-acetyl glycoprotein, glycerol and phenylalanine that were highly correlated (R2 = 0.62) with C-reactive protein levels. The same metabolomic signature was independently identified using ML to predict patient inflammation status. Correlation of the individual peaks comprising this metabolomic signature of inflammation with pathogenic burden across 15,854 unselected genes identified significant enrichment for genes functioning within 'intrinsic component of membrane' (p = 0.003) and 'inflammatory bowel disease (IBD)' (p = 0.003). The seven genes contributing IBD enrichment are critical regulators of pro-inflammatory signaling. Overall, a metabolomic signature of inflammation can be detected from blood plasma in CD. This signal is correlated with pathogenic mutation in pro-inflammatory immune response genes.

A Systematic Review of Artificial Intelligence and Machine Learning Applications to Inflammatory Bowel Disease, with Practical Guidelines for Interpretation.

BACKGROUND: Inflammatory bowel disease (IBD) is a gastrointestinal chronic disease with an unpredictable disease course. Computational methods such as machine learning (ML) have the potential to stratify IBD patients for the provision of individualized care. The use of ML methods for IBD was surveyed, with an additional focus on how the field has changed over time. METHODS: On May 6, 2021, a systematic review was conducted through a search of MEDLINE and Embase databases, with the search structure ("machine learning" OR "artificial intelligence") AND ("Crohn* Disease" OR "Ulcerative Colitis" OR "Inflammatory Bowel Disease"). Exclusion criteria included studies not written in English, no human patient data, publication before 2001, studies that were not peer reviewed, nonautoimmune disease comorbidity research, and record types that were not primary research. RESULTS: Seventy-eight (of 409) records met the inclusion criteria. Random forest methods were most prevalent, and there was an increase in neural networks, mainly applied to imaging data sets. The main applications of ML to clinical tasks were diagnosis (18 of 78), disease course (22 of 78), and disease severity (16 of 78). The median sample size was 263. Clinical and microbiome-related data sets were most popular. Five percent of studies used an external data set after training and testing for additional model validation. DISCUSSION: Availability of longitudinal and deep phenotyping data could lead to better modeling. Machine learning pipelines that consider imbalanced data and that feature selection only on training data will generate more generalizable models. Machine learning models are increasingly being applied to more complex clinical tasks for specific phenotypes, indicating progress towards personalized medicine for IBD.

Deleterious Genetic Variation Across the NOD Signaling Pathway Is Associated With Reduced NFKB Signaling Transcription and Upregulation of Alternative Inflammatory Transcripts in Pediatric Inflammatory Bowel Disease.

BACKGROUND: Inflammatory bowel disease may arise with inadequate immune response to intestinal bacteria. NOD2 is an established gene in Crohn's disease pathogenesis, with deleterious variation associated with reduced NFKB signaling. We hypothesized that deleterious variation across the NOD2 signaling pathway impacts on transcription. METHODS: Treatment-naïve pediatric inflammatory bowel disease patients had ileal biopsies for targeted autoimmune RNA-sequencing and blood for whole exome sequencing collected at diagnostic endoscopy. Utilizing GenePy, a per-individual, per-gene score, genes within the NOD signaling pathway were assigned a quantitative score representing total variant burden. Where multiple genes formed complexes, GenePy scores were summed to create a "complex" score. Normalized transcript expression of 95 genes within this pathway was retrieved. Regression analysis was performed to determine the impact of genomic variation on gene transcription. RESULTS: Thirty-nine patients were included. Limited clustering of patients based on NOD signaling transcripts was related to underlying genomic variation. Patients harboring deleterious variation in NOD2 had reduced NOD2 (ß = -0.702, P = 4.3 × 10-5) and increased NFKBIA (ß = 0.486, P = .001), reflecting reduced NFKB signal activation. Deleterious variation in the NOD2-RIPK2 complex was associated with increased NLRP3 (ß = 0.8, P = 3.1475 × 10-8) and TXN (ß = -0.417, P = 8.4 × 10-5) transcription, components of the NLRP3 inflammasome. Deleterious variation in the TAK1-TAB complex resulted in reduced MAPK14 transcription (ß = -0.677, P = 1.7 × 10-5), a key signal transduction protein in the NOD2 signaling cascade and increased IFNA1 (ß = 0.479, P = .001), indicating reduced transcription of NFKB activators and alternative interferon transcription in these patients. CONCLUSIONS: Data integration identified perturbation of NOD2 signaling transcription correlated with genomic variation. A hypoimmune NFKB signaling transcription response was observed. Alternative inflammatory pathways were activated and may represent therapeutic targets in specific patients.

Expression profile of the matricellular protein periostin in paediatric inflammatory bowel disease.

The precise role of periostin, an extra-cellular matrix protein, in inflammatory bowel disease (IBD) is unclear. Here, we investigated periostin in paediatric IBD including its relationship with disease activity, clinical outcomes, genomic variation and expression in the colonic tissue. Plasma periostin was analysed using ELISA in 144 paediatric patients and 38 controls. Plasma levels were assessed against validated disease activity indices in IBD and clinical outcomes. An immuno-fluorescence for periostin and detailed isoform-expression analysis in the colonic tissue was performed in 23 individuals. We integrated a whole-gene based burden metric 'GenePy' to assess the impact of variation in POSTN and 23 other genes functionally connected to periostin. We found that plasma periostin levels were significantly increased during remission compared to active Crohn's disease. The immuno-fluorescence analysis demonstrated enhanced peri-cryptal ring patterns in patients compared to controls, present throughout inflamed, as well as macroscopically non-inflamed colonic tissue. Interestingly, the pattern of isoforms remained unchanged during bowel inflammation compared to healthy controls. In addition to its role during the inflammatory processes in IBD, periostin may have an additional prominent role in mucosal repair. Additional studies will be necessary to understand its role in the pathogenesis, repair and fibrosis in IBD.

Ileal Transcriptomic Analysis in Paediatric Crohn's Disease Reveals IL17- and NOD-signalling Expression Signatures in Treatment-naïve Patients and Identifies Epithelial Cells Driving Differentially Expressed Genes.

BACKGROUND AND AIMS: Crohn's disease [CD] arises through host-environment interaction. Abnormal gene expression results from disturbed pathway activation or response to bacteria. We aimed to determine activated pathways and driving cell types in paediatric CD. METHODS: We employed contemporary targeted autoimmune RNA sequencing, in parallel to single-cell sequencing, to ileal tissue derived from paediatric CD and controls. Weighted gene co-expression network analysis [WGCNA] was performed and differentially expressed genes [DEGs] were determined. We integrated clinical data to determine co-expression modules associated with outcomes. RESULTS: In all, 27 treatment-naive CD [TN-CD], 26 established CD patients and 17 controls were included. WGCNA revealed a 31-gene signature characterising TN-CD patients, but not established CD, nor controls. The CSF3R gene is a hub within this module and is key in neutrophil expansion and differentiation. Antimicrobial genes, including S100A12 and the calprotectin subunit S100A9, were significantly upregulated in TN CD compared with controls [p = 2.61 x 10-15 and p = 9.13 x 10-14, respectively] and established CD [both p = 0.0055]. Gene-enrichment analysis confirmed upregulation of the IL17-, NOD- and Oncostatin-M-signalling pathways in TN-CD patients, identified in both WGCNA and DEG analyses. An upregulated gene signature was enriched for transcripts promoting Th17-cell differentiation and correlated with prolonged time to relapse [correlation-coefficient-0.36, p = 0.07]. Single-cell sequencing of TN-CD patients identified specialised epithelial cells driving differential expression of S100A9. Cell groups, determined by single-cell gene expression, demonstrated enrichment of IL17-signalling in monocytes and epithelial cells. CONCLUSIONS: Ileal tissue from treatment-naïve paediatric patients is significantly upregulated for genes driving IL17-, NOD- and Oncostatin-M-signalling. This signal is driven by a distinct subset of epithelial cells expressing antimicrobial gene transcripts.

Immunological Profiling of Paediatric Inflammatory Bowel Disease Using Unsupervised Machine Learning.

OBJECTIVES: The current classification of inflammatory bowel disease (IBD) is based on clinical phenotypes, which is blind to the molecular basis of the disease. The aim of this study was to stratify a treatment-naïve paediatric IBD cohort through specific innate immunity pathway profiling and application of unsupervised machine learning (UML). METHODS: In order to test the molecular integrity of biological pathways implicated in IBD, innate immune responses were assessed at diagnosis in 22 paediatric patients and 10 age-matched controls. Peripheral blood mononuclear cells (PBMCs) were selectively stimulated for assessing the functionality of upstream activation receptors including NOD2, toll-like receptor (TLR) 1-2 and TLR4, and the downstream cytokine responses (IL-10, IL-1ß, IL-6, and TNF-α) using multiplex assays. Cytokine data generated were subjected to hierarchical clustering to assess for patient stratification. RESULTS: Combined immune responses in patients across 12 effector responses were significantly reduced compared with controls (P = 0.003) and driven primarily by "hypofunctional" TLR responses (P values 0.045, 0.010, and 0.018 for TLR4-mediated IL-10, IL-1ß, and TNF-α, respectively; 0.018 and 0.015 for TLR1-2 -mediated IL-10 and IL-1ß). Hierarchical clustering generated 3 distinct clusters of patients and a fourth group of "unclustered" individuals. No relationship was observed between the observed immune clusters and the clinical disease phenotype. CONCLUSIONS: Although a clinically useful outcome was not observed through hierarchical clustering, our study provides a rationale for using an UML approach to stratify patients. The study also highlights the predominance of hypo-inflammatory innate immune responses as a key mechanism in the pathogenesis of IBD.

Genetic Sequencing of Pediatric Patients Identifies Mutations in Monogenic Inflammatory Bowel Disease Genes that Translate to Distinct Clinical Phenotypes.

OBJECTIVES: Monogenic inflammatory bowel disease (IBD) comprises rare Mendelian causes of gut inflammation, often presenting in infants with severe and atypical disease. This study aimed to identify clinically relevant variants within 68 monogenic IBD genes in an unselected pediatric IBD cohort. METHODS: Whole exome sequencing was performed on patients with pediatric-onset disease. Variants fulfilling the American College of Medical Genetics criteria as "pathogenic" or "likely pathogenic" were assessed against phenotype at diagnosis and follow-up. Individual patient variants were assessed and processed to generate a per-gene, per-individual, deleteriousness score. RESULTS: Four hundred one patients were included, and the median age of disease-onset was 11.92 years. In total, 11.5% of patients harbored a monogenic variant. TRIM22-related disease was implicated in 5 patients. A pathogenic mutation in the Wiskott-Aldrich syndrome (WAS) gene was confirmed in 2 male children with severe pancolonic inflammation and primary sclerosing cholangitis. In total, 7.3% of patients with Crohn's disease had apparent autosomal recessive, monogenic NOD2-related disease. Compared with non-NOD2 Crohn's disease, these patients had a marked stricturing phenotype (odds ratio 11.52, significant after correction for disease location) and had undergone significantly more intestinal resections (odds ratio 10.75). Variants in ADA, FERMT1, and LRBA did not meet the criteria for monogenic disease in any patients; however, case-control analysis of mutation burden significantly implicated these genes in disease etiology. DISCUSSION: Routine whole exome sequencing in pediatric patients with IBD results in a precise molecular diagnosis for a subset of patients with IBD, providing the opportunity to personalize therapy. NOD2 status informs risk of stricturing disease requiring surgery, allowing clinicians to direct prognosis and intervention.