Molecular characterization of two novel intronic variants of NIPBL gene detected in unrelated Cornelia de Lange syndrome patients

DNA methylation analysis in patients with neurodevelopmental disorders improves variant interpretation and reveals complexity

Analysis of genomic DNA methylation by generating epigenetic signature profiles (episignatures) is increasingly being implemented in genetic diagnosis. Here we report our experience using episignature analysis to resolve both uncomplicated and complex cases of neurodevelopmental disorders (NDDs). We analyzed 97 NDDs divided into (1) a validation cohort of 59 patients with likely pathogenic/pathogenic variants characterized by a known episignature and (2) a test cohort of 38 patients harboring variants of unknown significance or unidentified variants. The expected episignature was obtained in most cases with likely pathogenic/pathogenic variants (53/59 [90%]), a revealing exception being the overlapping profile of two SMARCB1 pathogenic variants with ARID1A/B:c.6200, confirmed by the overlapping clinical features. In the test cohort, five cases showed the expected episignature, including (1) novel pathogenic variants in ARID1B and BRWD3; (2) a deletion in ATRX causing MRXFH1 X-linked mental retardation; and (3) confirmed the clinical diagnosis of Cornelia de Lange (CdL) syndrome in mutation-negative CdL patients. Episignatures analysis of the in BAF complex components revealed novel functional protein interactions and common episignatures affecting homologous residues in highly conserved paralogous proteins (SMARCA2 M856V and SMARCA4 M866V). Finally, we also found sex-dependent episignatures in X-linked disorders. Implementation of episignature profiling is still in its early days, but with increasing utilization comes increasing awareness of the capacity of this methodology to help resolve the complex challenges of genetic diagnoses.

Coenzyme Q0 inhibited the NLRP3 inflammasome, metastasis/EMT, and Warburg effect by suppressing hypoxia-induced HIF-1α expression in HNSCC cells

Coenzyme Q0 (CoQ0), a quinone derivative from Antrodia camphorata, has antitumor capabilities. This study investigated the antitumor effect of noncytotoxic CoQ0, which included NLRP3 inflammasome inhibition, anti-EMT/metastasis, and metabolic reprogramming via HIF-1α inhibition, in HNSCC cells under normoxia and hypoxia. CoQ0 suppressed hypoxia-induced ROS-mediated HIF-1α expression in OECM-1 and SAS cells. Under normoxia and hypoxia, the inflammatory NLRP3, ASC/caspase-1, NFκB, and IL-1β expression was reduced by CoQ0. CoQ0 reduced migration/invasion by enhancing epithelial marker E-cadherin and suppressing mesenchymal markers Twist, N-cadherin, Snail, and MMP-9, and MMP-2 expression. CoQ0 inhibited glucose uptake, lactate accumulation, GLUT1 levels, and HIF-1α-target gene (HK-2, PFK-1, and LDH-A) expressions that are involved in aerobic glycolysis. Notably, CoQ0 reduced ECAR as well as glycolysis, glycolytic capability, and glycolytic reserve and enhanced OCR, basal respiration, ATP generation, maximal respiration, and spare capacity in OECM-1 cells. Metabolomic analysis using LC-ESI-MS showed that CoQ0 treatment decreased the levels of glycolytic intermediates, including lactate, 2/3-phosphoglycerate, fructose 1,6-bisphosphate, and phosphoenolpyruvate, and increased the levels of TCA cycle metabolites, including citrate, isocitrate, and succinate. HIF-1α silencing reversed CoQ0-mediated anti-metastasis (N-Cadherin, Snail, and MMP-9) and metabolic reprogramming (GLUT1, HK-2, and PKM-2) under hypoxia. CoQ0 prevents cancer stem-like characteristics (upregulated CD24 expression and downregulated CD44, ALDH1, and OCT4) under normoxia and/or hypoxia. Further, in IL-6-treated SG cells, CoQ0 attenuated fibrosis by inhibiting TGF-β and Collagen I expression and suppressed EMT by downregulating Slug and upregulating E-cadherin expression. Interesting, CoQ0 inhibited the growth of OECM-1 tumors in xenografted mice. Our results advocate CoQ0 for the therapeutic application against HNSCC.

Deep learning and multi-omics approach to predict drug responses in cancer

BACKGROUND

Cancers are genetically heterogeneous, so anticancer drugs show varying degrees of effectiveness on patients due to their differing genetic profiles. Knowing patient's responses to numerous cancer drugs are needed for personalized treatment for cancer. By using molecular profiles of cancer cell lines available from Cancer Cell Line Encyclopedia (CCLE) and anticancer drug responses available in the Genomics of Drug Sensitivity in Cancer (GDSC), we will build computational models to predict anticancer drug responses from molecular features.

RESULTS

We propose a novel deep neural network model that integrates multi-omics data available as gene expressions, copy number variations, gene mutations, reverse phase protein array expressions, and metabolomics expressions, in order to predict cellular responses to known anti-cancer drugs. We employ a novel graph embedding layer that incorporates interactome data as prior information for prediction. Moreover, we propose a novel attention layer that effectively combines different omics features, taking their interactions into account. The network outperformed feedforward neural networks and reported 0.90 for [Formula: see text] values for prediction of drug responses from cancer cell lines data available in CCLE and GDSC.

CONCLUSION

The outstanding results of our experiments demonstrate that the proposed method is capable of capturing the interactions of genes and proteins, and integrating multi-omics features effectively. Furthermore, both the results of ablation studies and the investigations of the attention layer imply that gene mutation has a greater influence on the prediction of drug responses than other omics data types. Therefore, we conclude that our approach can not only predict the anti-cancer drug response precisely but also provides insights into reaction mechanisms of cancer cell lines and drugs as well.

Autoimmune polyglandular syndrome type 2 in an HIV-positive man managed at the University Teaching Hospitals Lusaka, Zambia: a case report

Autoimmune polyglandular syndromes (APS) are rare autoimmune endocrinopathies characterized by the coexistence of at least two endocrine gland insufficiencies developed from autoimmune mechanisms. APS may also be associated with non-endocrine immune diseases. In HIV infection, antiretroviral therapy can improve the quality of life to reduce the incidence of opportunistic infections, malignancies, and death. HIV disease may also be associated with complications, such as immune reconstitution inflammatory syndrome (IRIS) presenting as infections, malignancies or autoimmune diseases. We here report the clinical case of an HIV-infected man receiving antiretroviral therapy, who subsequently developed APS type II, characterized by Grave's disease, type 1 diabetes mellitus. He complained of a mass in his anterior neck, diarrhea, weight loss, palpitations, hand tremors and excessive sweating. Six months before he had been diagnosed with type 1 diabetes mellitus. The patient had a diffusely enlarged thyroid on ultrasound, elevated random blood glucose of 14.0 mmol/l; elevated free T4 at 5.03 ng/dL and suppressed thyroid-stimulating hormone (TSH) at <0.05 micro-IU/mL. The patient was treated with carbimazole and propranolol for Graves' thyrotoxicosis and basal bolus insulin regimen (actrapid and protaphane) for hyperglycemia. At monthly follow-up assessments he was euthyroid and 2-hour postprandial blood glucose test was normal. The goitre had markedly reduced in size. This screening for APS in HIV patients with autoimmune IRIS as well as patients with autoimmune endocrinopathies in order to allow for early diagnosis and prompt initiation of treatment to reduce the risk of morbidity and mortality.

Exome hits demystified: The next frontier

Severe mental illnesses such as schizophrenia and bipolar disorder have complex inheritance patterns, involving both common and rare variants. Whole exome sequencing is a promising approach to find out the rare genetic variants. We had previously reported several rare variants in multiplex families with severe mental illnesses. The current article tries to summarise the biological processes and pattern of expression of genes harbouring the aforementioned variants, linking them to known clinical manifestations through a methodical narrative review. Of the 28 genes considered for this review from 7 families with multiple affected individuals, 6 genes are implicated in various neuropsychiatric manifestations including some variations in the brain morphology assessed by magnetic resonance imaging. Another 15 genes, though associated with neuropsychiatric manifestations, did not have established brain morphological changes whereas the remaining 7 genes did not have any previously recorded neuropsychiatric manifestations at all. Wnt/b-catenin signaling pathway was associated with 6 of these genes and PI3K/AKT, calcium signaling, ERK, RhoA and notch signaling pathways had at least 2 gene associations. We present a comprehensive review of biological and clinical knowledge about the genes previously reported in multiplex families with severe mental illness. A 'disease in dish approach' can be helpful to further explore the fundamental mechanisms.

Evaluating Face2Gene as a Tool to Identify Cornelia de Lange Syndrome by Facial Phenotypes

Characteristic or classic phenotype of Cornelia de Lange syndrome (CdLS) is associated with a recognisable facial pattern. However, the heterogeneity in causal genes and the presence of overlapping syndromes have made it increasingly difficult to diagnose only by clinical features. DeepGestalt technology, and its app Face2Gene, is having a growing impact on the diagnosis and management of genetic diseases by analysing the features of affected individuals. Here, we performed a phenotypic study on a cohort of 49 individuals harbouring causative variants in known CdLS genes in order to evaluate Face2Gene utility and sensitivity in the clinical diagnosis of CdLS. Based on the profile images of patients, a diagnosis of CdLS was within the top five predicted syndromes for 97.9% of our cases and even listed as first prediction for 83.7%. The age of patients did not seem to affect the prediction accuracy, whereas our results indicate a correlation between the clinical score and affected genes. Furthermore, each gene presents a different pattern recognition that may be used to develop new neural networks with the goal of separating different genetic subtypes in CdLS. Overall, we conclude that computer-assisted image analysis based on deep learning could support the clinical diagnosis of CdLS.

Genetic Mosaicism in a Group of Patients With Cornelia de Lange Syndrome

Background: Cornelia de Lange Syndrome (CdLS) is a heterogeneous disorder. Diverse expression of clinical symptoms can be caused by a variety of pathogenic variants located within the sequence of different genes correlated with the cohesin complex. Methods: Sixty-nine patients with confirmed clinical diagnosis of CdLS were enrolled in the study. Blood and buccal swab samples were collected for molecular studies. Mutational analysis was performed using the Next Generation (deep) Sequencing (NGS) covering 24 genes. In addition, the MLPA technique was applied to detect large rearrangements of NIPBL. Results: MLPA and NGS analysis were performed in 66 (95,7%) and 67 (97,1%) patients, respectively. Large rearrangements of NIPBL were not identified in the studied group. Germline pathogenic variants were detected in 18 (26,1%) patients. Fourteen variants (20,3%) were identified in NIPBL, two (2,9%) in SMC1A, and two (2,9%) in HDAC8. In total, 13 (18,8%) buccal swabs were suitable for deep sequencing. Mosaic variants were found in four (30,8%; 4/13) patients negative for germline alterations. Three mosaic substitutions were detected in NIPBL while one in KMT2A gene. Conclusions: Comprehensive and sensitive molecular techniques allow detecting novel pathogenic variants responsible for the molecular basis of CdLS. In addition, molecular testing of different tissues should be applied since such an approach allows detect mosaic variants specific for a subgroup of CdLS patients. Finally, to test possible pathogenicity of intronic variants, RNA analysis should be conducted.