SETDB1 is required for intestinal epithelial differentiation and the prevention of intestinal inflammation.

OBJECTIVE: The intestinal epithelium is a rapidly renewing tissue which plays central roles in nutrient uptake, barrier function and the prevention of intestinal inflammation. Control of epithelial differentiation is essential to these processes and is dependent on cell type-specific activity of transcription factors which bind to accessible chromatin. Here, we studied the role of SET Domain Bifurcated Histone Lysine Methyltransferase 1, also known as ESET (SETDB1), a histone H3K9 methyltransferase, in intestinal epithelial homeostasis and IBD. DESIGN: We investigated mice with constitutive and inducible intestinal epithelial deletion of Setdb1, studied the expression of SETDB1 in patients with IBD and mouse models of IBD, and investigated the abundance of SETDB1 variants in healthy individuals and patients with IBD. RESULTS: Deletion of intestinal epithelial Setdb1 in mice was associated with defects in intestinal epithelial differentiation, barrier disruption, inflammation and mortality. Mechanistic studies showed that loss of SETDB1 leads to de-silencing of endogenous retroviruses, DNA damage and intestinal epithelial cell death. Predicted loss-of-function variants in human SETDB1 were considerably less frequently observed than expected, consistent with a critical role of SETDB1 in human biology. While the vast majority of patients with IBD showed unimpaired mucosal SETDB1 expression, comparison of IBD and non-IBD exomes revealed over-representation of individual rare missense variants in SETDB1 in IBD, some of which are predicted to be associated with loss of function and may contribute to the pathogenesis of intestinal inflammation. CONCLUSION: SETDB1 plays an essential role in intestinal epithelial homeostasis. Future work is required to investigate whether rare variants in SETDB1 contribute to the pathogenesis of IBD.

Stratified analyses refine association between TLR7 rare variants and severe COVID-19.

Despite extensive global research into genetic predisposition for severe COVID-19, knowledge on the role of rare host genetic variants and their relation to other risk factors remains limited. Here, 52 genes with prior etiological evidence were sequenced in 1,772 severe COVID-19 cases and 5,347 population-based controls from Spain/Italy. Rare deleterious TLR7 variants were present in 2.4% of young (<60 years) cases with no reported clinical risk factors (n=378), compared to 0.24% of controls (odds ratio (OR)=12.3, p=1.27x10-10). Incorporation of the results of either functional assays or protein modeling led to a pronounced increase in effect size (ORmax=46.5, p=1.74x10-15). Association signals for X-chromosomal TLR7 were also detected in the female-only subgroup, suggesting the existence of additional mechanisms beyond X-linked recessive inheritance in males. Additionally, supporting evidence was generated for a contribution to severe COVID-19 of the previously implicated genes IFNAR2, IFIH1 and TBK1. Our results refine the genetic contribution of rare TLR7 variants to severe COVID-19, and strengthen evidence for the etiological relevance of genes in the interferon signaling pathway.

Genome interpretation in a federated learning context allows the multi-center exome-based risk prediction of Crohn's disease patients.

High-throughput sequencing allowed the discovery of many disease variants, but nowadays it is becoming clear that the abundance of genomics data mostly just moved the bottleneck in Genetics and Precision Medicine from a data availability issue to a data interpretation issue. To solve this empasse it would be beneficial to apply the latest Deep Learning (DL) methods to the Genome Interpretation (GI) problem, similarly to what AlphaFold did for Structural Biology. Unfortunately DL requires large datasets to be viable, and aggregating genomics datasets poses several legal, ethical and infrastructural complications. Federated Learning (FL) is a Machine Learning (ML) paradigm designed to tackle these issues. It allows ML methods to be collaboratively trained and tested on collections of physically separate datasets, without requiring the actual centralization of sensitive data. FL could thus be key to enable DL applications to GI on sufficiently large genomics data. We propose FedCrohn, a FL GI Neural Network model for the exome-based Crohn's Disease risk prediction, providing a proof-of-concept that FL is a viable paradigm to build novel ML GI approaches. We benchmark it in several realistic scenarios, showing that FL can indeed provide performances similar to conventional ML on centralized data, and that collaborating in FL initiatives is likely beneficial for most of the medical centers participating in them.

Genetic Variation in ABCC4 and CFTR and Acute Pancreatitis during Treatment of Pediatric Acute Lymphoblastic Leukemia.

BACKGROUND: Acute pancreatitis (AP) is a serious, mechanistically not entirely resolved side effect of L-asparaginase-containing treatment for acute lymphoblastic leukemia (ALL). To find new candidate variations for AP, we conducted a genome-wide association study (GWAS). METHODS: In all, 1,004,623 single-nucleotide variants (SNVs) were analyzed in 51 pediatric ALL patients with AP (cases) and 1388 patients without AP (controls). Replication used independent patients. RESULTS: The top-ranked SNV (rs4148513) was located within the ABCC4 gene (odds ratio (OR) 84.1; p = 1.04 × 10-14). Independent replication of our 20 top SNVs was not supportive of initial results, partly because rare variants were neither present in cases nor present in controls. However, results of combined analysis (GWAS and replication cohorts) remained significant (e.g., rs4148513; OR = 47.2; p = 7.31 × 10-9). Subsequently, we sequenced the entire ABCC4 gene and its close relative, the cystic fibrosis associated CFTR gene, a strong AP candidate gene, in 48 cases and 47 controls. Six AP-associated variants in ABCC4 and one variant in CFTR were detected. Replication confirmed the six ABCC4 variants but not the CFTR variant. CONCLUSIONS: Genetic variation within the ABCC4 gene was associated with AP during the treatment of ALL. No association of AP with CFTR was observed. Larger international studies are necessary to more conclusively assess the risk of rare clinical phenotypes.

A heterozygous germline CD100 mutation in a family with primary sclerosing cholangitis.

Primary sclerosing cholangitis (PSC) is a chronic inflammatory liver disease without clear etiology or effective treatment. Genetic factors contribute to PSC pathogenesis, but so far, no causative mutation has been found. We performed whole-exome sequencing in a family with autosomal dominant inheritance of PSC and identified a heterozygous germline missense mutation in SEMA4D, encoding a K849T variant of CD100. The mutation was located in an evolutionarily conserved, unstructured cytosolic region of CD100 affecting downstream signaling. It was found to alter the function of CD100-expressing cells with a bias toward the T cell compartment that caused increased proliferation and impaired interferon-γ (IFN-γ) production after stimulation. Homologous mutation knock-in mice developed similar IFN-γ impairment in T cells and were more prone to develop severe cholangitis when exposed to 3,5-diethoxycarbonyl-1,4-dihydrocollidine (DDC) diet. Transfer of wild-type T cells to knock-in mice before and during DDC exposure attenuated cholangitis. Taken together, we identified an inherited mutation in the disordered cytosolic region of CD100 resulting in T cell functional defects. Our findings suggest a protective role for T cells in PSC that might be used therapeutically.

Artificial intelligence enables comprehensive genome interpretation and nomination of candidate diagnoses for rare genetic diseases.

BACKGROUND: Clinical interpretation of genetic variants in the context of the patient's phenotype is becoming the largest component of cost and time expenditure for genome-based diagnosis of rare genetic diseases. Artificial intelligence (AI) holds promise to greatly simplify and speed genome interpretation by integrating predictive methods with the growing knowledge of genetic disease. Here we assess the diagnostic performance of Fabric GEM, a new, AI-based, clinical decision support tool for expediting genome interpretation. METHODS: We benchmarked GEM in a retrospective cohort of 119 probands, mostly NICU infants, diagnosed with rare genetic diseases, who received whole-genome or whole-exome sequencing (WGS, WES). We replicated our analyses in a separate cohort of 60 cases collected from five academic medical centers. For comparison, we also analyzed these cases with current state-of-the-art variant prioritization tools. Included in the comparisons were trio, duo, and singleton cases. Variants underpinning diagnoses spanned diverse modes of inheritance and types, including structural variants (SVs). Patient phenotypes were extracted from clinical notes by two means: manually and using an automated clinical natural language processing (CNLP) tool. Finally, 14 previously unsolved cases were reanalyzed. RESULTS: GEM ranked over 90% of the causal genes among the top or second candidate and prioritized for review a median of 3 candidate genes per case, using either manually curated or CNLP-derived phenotype descriptions. Ranking of trios and duos was unchanged when analyzed as singletons. In 17 of 20 cases with diagnostic SVs, GEM identified the causal SVs as the top candidate and in 19/20 within the top five, irrespective of whether SV calls were provided or inferred ab initio by GEM using its own internal SV detection algorithm. GEM showed similar performance in absence of parental genotypes. Analysis of 14 previously unsolved cases resulted in a novel finding for one case, candidates ultimately not advanced upon manual review for 3 cases, and no new findings for 10 cases. CONCLUSIONS: GEM enabled diagnostic interpretation inclusive of all variant types through automated nomination of a very short list of candidate genes and disorders for final review and reporting. In combination with deep phenotyping by CNLP, GEM enables substantial automation of genetic disease diagnosis, potentially decreasing cost and expediting case review.

Current Developments of Clinical Sequencing and the Clinical Utility of Polygenic Risk Scores in Inflammatory Diseases.

In this mini-review, we highlight selected research by the Deutsche Forschungsgemeinschaft (DFG) Cluster of Excellence "Precision Medicine in Chronic Inflammation" focusing on clinical sequencing and the clinical utility of polygenic risk scores as well as its implication on precision medicine in the field of the inflammatory diseases inflammatory bowel disease, atopic dermatitis and coronary artery disease. Additionally, we highlight current developments and discuss challenges to be faced in the future. Exemplary, we point to residual challenges in detecting disease-relevant variants resulting from difficulties in the interpretation of candidate variants and their potential interactions. While polygenic risk scores represent promising tools for the stratification of patient groups, currently, polygenic risk scores are not accurate enough for clinical setting. Precision medicine, incorporating additional data from genomics, transcriptomics and proteomics experiments, may enable the identification of distinct disease pathogeneses. In the future, data-intensive biomedical innovation will hopefully lead to improved patient stratification for personalized medicine.

Exome sequencing in 38 patients with intracranial aneurysms and subarachnoid hemorrhage.

OBJECTIVE: Genetic risk factors for unruptured intracranial aneurysms (UIA) and aneurysmal subarachnoid hemorrhage (aSAH) are poorly understood. We aimed to verify recently reported risk genes and to identify novel sequence variants involved in the etiology of UIA/aSAH. METHODS: We performed exome sequencing (ES) in 35 unrelated individuals and 3 family members, each with a history of UIA and/or aSAH. We searched for sequence variants with minor allele frequency (MAF) ≤ 5% in the reported risk genes ADAMTS15, ANGPTL6, ARHGEF17, LOXL2, PCNT, RNF213, THSD1 and TMEM132B. To identify novel putative risk genes we looked for unknown (MAF = 0) variants shared by the three relatives. RESULTS: We identified 20 variants with MAF ≤ 5% in 18 individuals: 9 variants in PCNT (9 patients), 4 in RNF213 (3 patients), 3 in THSD1 (6 patients), 2 in ANGPTL6 (3 patients), 1 in ADAMTS15 (1 patient) and 1 in TMEM132B (1 patient). In the affected family, prioritization of shared sequence variants yielded five novel putative risk genes. Based on predicted pathogenicity of identified variants, population genetics data and a high functional relevance for vascular biology, EDIL3 was selected as top candidate and screened in additional 37 individuals with UIA and/or aSAH: a further very rare EDIL3 sequence variant in two unrelated sporadic patients was identified. CONCLUSIONS: Our data support a role of sequence variants in PCNT, RNF213 and THSD1 as susceptibility factors for cerebrovascular disease. The documented function in vascular wall integrity, the crucial localization of affected amino acids and gene/variant association tests suggest EDIL3 as a further valid candidate disease gene for UIA/aSAH.

Leptin induces TNFα-dependent inflammation in acquired generalized lipodystrophy and combined Crohn's disease.

Leptin has been shown to modulate intestinal inflammation in mice. However, clinical evidence regarding its immune-stimulatory potential in human Crohn's disease remains sparse. We here describe a patient with the unique combination of acquired generalized lipodystrophy and Crohn's disease (AGLCD) featuring a lack of adipose tissue, leptin deficiency and intestinal inflammation. Using mass and flow cytometry, immunohistochemistry and functional metabolic analyses, the AGLCD patient was compared to healthy individuals and Crohn's disease patients regarding immune cell composition, function and metabolism and the effects of recombinant N-methionylleptin (rLeptin) were evaluated. We provide evidence that rLeptin exerts diverse pro-inflammatory effects on immune cell differentiation and function, including the metabolic reprogramming of immune cells and the induction of TNFα, ultimately aggravating Crohn's disease in the AGLCD patient, which can be reversed by anti-TNFα therapy. Our results indicate that leptin is required for human immune homeostasis and contributes to autoimmunity in a TNFα-dependent manner.