Correlation Analyses of Clinical Manifestations and Variant Effects in KCNB1-Related Neurodevelopmental Disorder

Objective:Vitro functional analyses of KCNB1 variants have been done to disclose possible pathogenic mechanisms in KCNB1-related neurodevelopmental disorder. “Complete or partial loss of function (LoF),” “dominant-negative (DN) effect” are applied to describe KCNB1 variant's molecular phenotypes. The study here aimed to investigate clinical presentations and variant effects associations in the disorder.Methods: We reported 10 Chinese pediatric patients with KCNB1-related neurodevelopmental disorder here. Functional experiments on newly reported variants, including electrophysiology and protein expression, were performed in vitro. Phenotypic, functional, and genetic data in the cohort and published literature were collected. According to their variants' molecular phenotypes, patients were grouped into complete or partial LoF, and DN effect or non-dominant-negative (non-DN) effect to compare their clinical features.Results: Nine causative KCNB1 variants in 10 patients were identified in the cohort, including eight novel and one reported. Epilepsy (9/10), global developmental delay (10/10), and behavior issues (7/10) were common clinical features in our patients. Functional analyses of 8 novel variants indicated three partial and five complete LoF variants, five DN and three non-DN effect variants. Patient 1 in our series with truncated variants, whose functional results supported haploinsufficiency, had the best prognosis. Cases in complete LoF group had earlier seizure onset age (64.3 vs. 16.7%, p = 0.01) and worse seizure outcomes (18.8 vs. 66.7%, p = 0.03), and patients in DN effect subgroup had multiple seizure types compared to those in non-DN effect subgroup (65.5 vs. 30.8%, p = 0.039).Conclusion: Patients with KCNB1 variants in the Asian cohort have similar clinical manifestations to those of other races. Truncated KCNB1 variants exhibiting with haploinsufficiency molecular phenotype are linked to milder phenotypes. Individuals with complete LoF and DN effect KCNB1 variants have more severe seizure attacks than the other two subgroups.

Integration of public DNA methylation and expression networks via eQTMs improves prediction of functional gene-gene associations

Gene co-expression networks can be used to infer functional relationships between genes, but they do not work well for all genes. We investigated whether DNA methylation can provide complementary information for such genes. We first carried out an eQTM meta-analysis of 3,574 gene expression and methylation samples from blood, brain and nasal epithelial brushed cells to identify links between methylated CpG sites and genes. This revealed 6,067 significant eQTM genes, and we observed that histone modification information is predictive of both eQTM direction and presence, enabling us to link many CpG sites to genes. We then generated a co-methylation network - MethylationNetwork - using 27,720 publicly available methylation profiles and integrated it with a public RNA-seq co-expression dataset of 31,499 samples. Here, we observed that MethylationNetwork can identify experimentally validated interacting pairs of genes that could not be identified in the RNA-seq datasets. We then developed a novel integration pipeline based on CCA and used the integrated methylation and gene networks to predict gene pairs reported in the STRING database. The integrated network showed significantly improved prediction performance compared to using a DNA co-methylation or a gene co-expression network alone. This is the first study to integrate data from two -omics layers from unmatched public samples across different tissues and diseases, and our results highlight the issues and potential of integrating public datasets from multiple molecular phenotypes. The eQTMs we identified can be used as an annotation resource for epigenome-wide association, and we believe that our integration pipeline can be used as a framework for future -omics integration analyses of public datasets. We provide supporting materials and results, including the harmonized DNA methylation data from multiple tissues and diseases in https://data.harmjanwestra.nl/comethylation/, the discovered and predicted eQTMs, the corresponding CCA components and the trained prediction models in a Zenodo repository (https://zenodo.org/record/4666994). We provide notebooks to facilitate use of the proposed pipeline in a GitHub repository (https://github.com/molgenis/methylationnetwork).

Multi-omics subtyping of hepatocellular carcinoma patients using a Bayesian network mixture model

Comprehensive molecular characterization of cancer subtypes is essential for predicting clinical outcomes and searching for personalized treatments. We present bnClustOmics, a statistical model and computational tool for multi-omics unsupervised clustering, which serves a dual purpose: Clustering patient samples based on a Bayesian network mixture model and learning the networks of omics variables representing these clusters. The discovered networks encode interactions among all omics variables and provide a molecular characterization of each patient subgroup. We conducted simulation studies that demonstrated the advantages of our approach compared to other clustering methods in the case where the generative model is a mixture of Bayesian networks. We applied bnClustOmics to a hepatocellular carcinoma (HCC) dataset comprising genome (mutation and copy number), transcriptome, proteome, and phosphoproteome data. We identified three main HCC subtypes together with molecular characteristics, some of which are associated with survival even when adjusting for the clinical stage. Cluster-specific networks shed light on the links between genotypes and molecular phenotypes of samples within their respective clusters and suggest targets for personalized treatments.

Personal Network Inference Unveils Heterogeneous Immune Response Patterns to Viral Infection in Children with Acute Wheezing

Human rhinovirus (RV)-induced exacerbations of asthma and wheeze are a major cause of emergency room presentations and hospital admissions among children. Previous studies have shown that immune response patterns during these exacerbations are heterogeneous and are characterized by the presence or absence of robust interferon responses. Molecular phenotypes of asthma are usually identified by cluster analysis of gene expression levels. This approach however is limited, since genes do not exist in isolation, but rather work together in networks. Here, we employed personal network inference to characterize exacerbation response patterns and unveil molecular phenotypes based on variations in network structure. We found that personal gene network patterns were dominated by two major network structures, consisting of interferon-response versus FCER1G-associated networks. Cluster analysis of these structures divided children into subgroups, differing in the prevalence of atopy but not RV species. These network structures were also observed in an independent cohort of children with virus-induced asthma exacerbations sampled over a time course, where we showed that the FCER1G-associated networks were mainly observed at late time points (days four–six) during the acute illness. The ratio of interferon- and FCER1G-associated gene network responses was able to predict recurrence, with low interferon being associated with increased risk of readmission. These findings demonstrate the applicability of personal network inference for biomarker discovery and therapeutic target identification in the context of acute asthma which focuses on variations in network structure.

Analysis of Cell and Molecular Phenotypes of a Highly Replicated Longevity-Associated FOXO3 Variant in Older Adults

Aging demographics in the US, and other industrialized nations, are resulting in rapidly increasing health care costs from age-related diseases. New therapeutic interventions to extend healthspan in older adults requires understanding connections between basic aging biology and human longevity factors. Using clinical samples from the Kuakini Honolulu Heart Program (HHP) and their Offspring, we are examining potential links between molecular and cellular mechanisms of aging and the longevity associated FOXO3 genotype (carriage of SNP rs2802292 “G” allele). Telomere dynamics in leucocytes (LTL) have shown strong correlation with multiple lifestyle and health factors. We previously demonstrated a significant protective relation between FOXO3 longevity genotype and LTL in a cross-sectional study. Now we are assessing a longitudinal relation, at three time points over 20+ years, in older men. We are also exploring stem cell frequency and differentiation capacity in neurological and peripheral blood samples to assess FOXO3 genotype and human cell dynamics.

Systems genetics uncovers microbe-lipid-host connections in the murine gut

The molecular bases of how host genetic variation impact gut microbiome remain largely unknown. Here, we used a genetically diverse mouse population and systems genetics strategies to identify interactions between molecular phenotypes, including microbial functions, intestinal transcripts and cecal lipids that influence microbe-host dynamics. Quantitative trait loci (QTL) analysis identified genomic regions associated with variations in bacterial taxa, bacterial functions, including motility, sporulation and lipopolysaccharide production, and levels of bacterial- and host-derived lipids. We found overlapping QTL for the abundance of Akkermansia muciniphila and cecal levels of ornithine lipids (OL). Follow-up studies revealed that A. muciniphila is a major source of these lipids in the gut, provided evidence that OL have immunomodulatory effects and identified intestinal transcripts co-regulated with these traits. Collectively, these results suggest that OL are key players in A. muciniphila-host interactions and support the role of host genetics as a determinant of responses to gut microbes.

Deep Learning-based Models of Molecular Phenotypes for Predicting the Overall Survival in Cancer

Aims: The aim of the study is to justify the need of deep learning predictive model in obtaining molecular phenotypes of overall cancer survival. Study Design: The study is based on the secondary qualitative data analysis through usage of systematic review. Methodology: A qualitative study has been conducted to analyse the necessity of deep learning. It also includes the need for deep learning models to obtain the imaging of the cancer cells. In the study, a detailed discussion on deep learning has been made. The analysis of the primary sources has been obtained by evaluating the quality of the resources in the study. The study also comprises of a thematic analysis that enlightens the benefits of deep learning. The study is based on the analysis of 14 primary research-based articles out of 112 quantitative articles and structuring of a systematic review from the collected data. Results: The morphological and physiological changes that occur in the cancerous cells have been clearly evaluated in the research. The result signifies the prediction can be made by implementing deep learning in terms of cancer survival. Advancements in terms of technology in the medical field can thus be improved with the help of the deep learning process. It states the advancements of the deep learning models that are helpful in predicting the model of cancer to determine survival rate. Conclusion: Deep learning is a process that is considered to be a subset of artificial intelligence. Deep learning programmes are meant to be performed for complex learning models. Although there is difference in the concept of deep learning and image processing still artificial intelligence brings both together so as to ensure better performance in image processing. The need for deep learning models has become invasive, and it helps to build a strong ground for cancer survival.