scholarly journals A New Cardiac Channelopathy: From Clinical Phenotypes to Molecular Mechanisms Associated With Nav1.5 Gating Pores

2018 ◽  
Vol 5 ◽  
Author(s):  
Adrien Moreau ◽  
Mohamed Chahine
Keyword(s):  
Molecular Mechanisms ◽  
Clinical Phenotypes ◽  
Cardiac Channelopathy

scholarly journals Genetics and Epigenetics of One-Carbon Metabolism Pathway in Autism Spectrum Disorder: A Sex-Specific Brain Epigenome?

Genes ◽  
2021 ◽  
Vol 12 (5) ◽  
pp. 782
Author(s):  
Veronica Tisato ◽  
Juliana A. Silva ◽  
Giovanna Longo ◽  
Ines Gallo ◽  
Ajay V. Singh ◽  
...  
Keyword(s):  
Autism Spectrum Disorder ◽  
Molecular Mechanisms ◽  
Autism Spectrum ◽  
Mthfr Gene ◽  
Spectrum Disorder ◽  
Fetal Life ◽  
Clinical Phenotypes ◽  
One Carbon Metabolism ◽  
Causes And Risk Factors ◽  
Individual Disease

Autism spectrum disorder (ASD) is a complex neurodevelopmental condition affecting behavior and communication, presenting with extremely different clinical phenotypes and features. ASD etiology is composite and multifaceted with several causes and risk factors responsible for different individual disease pathophysiological processes and clinical phenotypes. From a genetic and epigenetic side, several candidate genes have been reported as potentially linked to ASD, which can be detected in about 10–25% of patients. Folate gene polymorphisms have been previously associated with other psychiatric and neurodegenerative diseases, mainly focused on gene variants in the DHFR gene (5q14.1; rs70991108, 19bp ins/del), MTHFR gene (1p36.22; rs1801133, C677T and rs1801131, A1298C), and CBS gene (21q22.3; rs876657421, 844ins68). Of note, their roles have been scarcely investigated from a sex/gender viewpoint, though ASD is characterized by a strong sex gap in onset-risk and progression. The aim of the present review is to point out the molecular mechanisms related to intracellular folate recycling affecting in turn remethylation and transsulfuration pathways having potential effects on ASD. Brain epigenome during fetal life necessarily reflects the sex-dependent different imprint of the genome-environment interactions which effects are difficult to decrypt. We here will focus on the DHFR, MTHFR and CBS gene-triad by dissecting their roles in a sex-oriented view, primarily to bring new perspectives in ASD epigenetics.

scholarly journals Recent advances in understanding the phenotypes of osteoarthritis

F1000Research ◽  
2019 ◽  
Vol 8 ◽  
pp. 2091 ◽  
Author(s):  
Ali Mobasheri ◽  
Simo Saarakkala ◽  
Mikko Finnilä ◽  
Morten A. Karsdal ◽  
Anne-Christine Bay-Jensen ◽  
...  
Keyword(s):  
Clinical Trials ◽  
Precision Medicine ◽  
Biochemical Markers ◽  
Molecular Mechanisms ◽  
Review Article ◽  
Clinical Phenotypes ◽  
Recent Advances ◽  
The People ◽  
Molecular Phenotypes

Recent research in the field of osteoarthritis (OA) has focused on understanding the underlying molecular and clinical phenotypes of the disease. This narrative review article focuses on recent advances in our understanding of the phenotypes of OA and proposes that the disease represents a diversity of clinical phenotypes that are underpinned by a number of molecular mechanisms, which may be shared by several phenotypes and targeted more specifically for therapeutic purposes. The clinical phenotypes of OA supposedly have different underlying etiologies and pathogenic pathways and they progress at different rates. Large OA population cohorts consist of a majority of patients whose disease progresses slowly and a minority of individuals whose disease may progress faster. The ability to identify the people with relatively rapidly progressing OA can transform clinical trials and enhance their efficiency. The identification, characterization, and classification of molecular phenotypes of rapidly progressing OA, which represent patients who may benefit most from intervention, could potentially serve as the basis for precision medicine for this disabling condition. Imaging and biochemical markers (biomarkers) are important diagnostic and research tools that can assist with this challenge.

scholarly journals Hand osteoarthritis: clinical phenotypes, molecular mechanisms and disease management

2018 ◽  
Vol 14 (11) ◽  
pp. 641-656 ◽  
Author(s):  
Michelle Marshall ◽  
Fiona E. Watt ◽  
Tonia L. Vincent ◽  
Krysia Dziedzic
Keyword(s):  
Disease Management ◽  
Molecular Mechanisms ◽  
Hand Osteoarthritis ◽  
Clinical Phenotypes

Yeast Models of Diamond Blackfan Anemia and Shwachman Diamond Syndrome Differ in Their Effect on the Synthesis and Function of Ribosomal Subunits.

Blood ◽  
2008 ◽  
Vol 112 (11) ◽  
pp. 2044-2044
Author(s):  
Steven R Ellis ◽  
Joseph B. Moore ◽  
Johnson M. Liu ◽  
Robert J. Arceci ◽  
Jason E. Farrar
Keyword(s):  
Structural Component ◽  
Ribosomal Proteins ◽  
Molecular Mechanisms ◽  
Bone Marrow Failure ◽  
Disease Models ◽  
Clinical Phenotypes ◽  
Diamond Blackfan Anemia ◽  
Nucleolar Stress ◽  
Shwachman Diamond Syndrome ◽  
And Function

Abstract Nucleolar stress is a frequently invoked mechanism used to describe the pro-apoptotic phenotype of cells affected in human diseases linked to abnormalities of the ribosome. However, the diversity of clinical phenotypes observed in these diseases suggests that there may be different types of nucleolar and/or translational stress that stimulate cell death pathways by alternative mechanisms. We have studied yeast models of Diamond Blackfan anemia (DBA) and Shwachman Diamond syndrome (SDS), two inherited bone marrow failure syndromes linked to defects in ribosome synthesis and/or function, to determine potential underlying molecular mechanisms that distinguish these disease models. To date, all genes identified in DBA encode ribosomal proteins. In contrast, SBDS, the gene affected in SDS encodes a protein that associates with 60S subunits, but is not considered a structural component of the ribosome. We have analyzed the translational capacity of cells harboring mutations in RPL33A and SDO1, yeast orthologs of genes affected in DBA and SDS, respectively. Polysome profiles from cells depleted of Rpl33A have a decrease in the amount of free 60S subunits and the presence of half-mer polysomes, as expected for an essential structural component of the 60S subunit. Polysome profiles from cells depleted of Sdo1 also had half-mer polysomes, but in this case there were significant amounts of free 60S subunits evident. Analysis of the intracellular distribution of 60S subunits by fluorescence microscopy revealed significant differences between the two disease models. In the DBA model, there was no evidence of accumulation of incompletely assembled subunits in the nucleolus indicating that rapid degradation. In contrast, in the SDS model there was significant accumulation of 60S subunits in the nucleoplasm. Thus, the two disease models interfere with the biogenesis of 60S subunits through distinct mechanisms. To determine if these mechanistic differences influence protein synthesis, we analyzed the patterns of proteins synthesized in these two disease models. We found that the expression of the 20S replicon was induced in both models, a sign of general translational stress. However, the two models also showed distinct differences in the synthesis of certain proteins. Thus, the mechanisms by which reductions of Rpl33A or Sdo1 influence levels of functional 60S subunits have differential effects on the patterns of proteins synthesized within cells Together these data indicate that the ribosome-based diseases may result from a composite of effects that include both nucleolar stress mechanisms and changes in translational output. The distinct clinical phenotypes observed in these disorders may result from differences in the relative contributions of either of these two mechanisms.

scholarly journals Distinct roles of GRIN2A and GRIN2B variants in neurological conditions

F1000Research ◽  
2019 ◽  
Vol 8 ◽  
pp. 1940 ◽  
Author(s):  
Scott J Myers ◽  
Hongjie Yuan ◽  
Jing-Qiong Kang ◽  
Francis Chee Kuan Tan ◽  
Stephen F Traynelis ◽  
...  
Keyword(s):  
Molecular Mechanisms ◽  
Autism Spectrum ◽  
Clinical Phenotypes ◽  
Hyperactivity Disorder ◽  
Functional Consequences ◽  
Spectrum Disorders ◽  
Circuit Function ◽  
Available Information ◽  
Insight Into

Rapid advances in sequencing technology have led to an explosive increase in the number of genetic variants identified in patients with neurological disease and have also enabled the assembly of a robust database of variants in healthy individuals. A surprising number of variants in the GRIN genes that encode N-methyl-D-aspartate (NMDA) glutamatergic receptor subunits have been found in patients with various neuropsychiatric disorders, including autism spectrum disorders, epilepsy, intellectual disability, attention-deficit/hyperactivity disorder, and schizophrenia. This review compares and contrasts the available information describing the clinical and functional consequences of genetic variations in GRIN2A and GRIN2B. Comparison of clinical phenotypes shows that GRIN2A variants are commonly associated with an epileptic phenotype but that GRIN2B variants are commonly found in patients with neurodevelopmental disorders. These observations emphasize the distinct roles that the gene products serve in circuit function and suggest that functional analysis of GRIN2A and GRIN2B variation may provide insight into the molecular mechanisms, which will allow more accurate subclassification of clinical phenotypes. Furthermore, characterization of the pharmacological properties of variant receptors could provide the first opportunity for translational therapeutic strategies for these GRIN-related neurological and psychiatric disorders.

scholarly journals Label propagation-based semi-supervised feature selection on decoding clinical phenotypes with RNA-seq data

2021 ◽  
Vol 14 (S1) ◽  
Author(s):  
Xue Jiang ◽  
Miao Chen ◽  
Weichen Song ◽  
Guan Ning Lin
Keyword(s):  
Gene Expression ◽  
Feature Selection ◽  
Disease Progression ◽  
Molecular Mechanisms ◽  
Enrichment Analysis ◽  
Label Propagation ◽  
Disease Genes ◽  
Clinical Phenotypes ◽  
Feature Selection Approach ◽  
Disease Stages

Abstract Background Clinically, behavior, cognitive, and mental functions are affected during the neurodegenerative disease progression. To date, the molecular pathogenesis of these complex disease is still unclear. With the rapid development of sequencing technologies, it is possible to delicately decode the molecular mechanisms corresponding to different clinical phenotypes at the genome-wide transcriptomic level using computational methods. Our previous studies have shown that it is difficult to distinguish disease genes from non-disease genes. Therefore, to precisely explore the molecular pathogenesis under complex clinical phenotypes, it is better to identify biomarkers corresponding to different disease stages or clinical phenotypes. So, in this study, we designed a label propagation-based semi-supervised feature selection approach (LPFS) to prioritize disease-associated genes corresponding to different disease stages or clinical phenotypes. Methods In this study, we pioneering put label propagation clustering and feature selection into one framework and proposed label propagation-based semi-supervised feature selection approach. LPFS prioritizes disease genes related to different disease stages or phenotypes through the alternative iteration of label propagation clustering based on sample network and feature selection with gene expression profiles. Then the GO and KEGG pathway enrichment analysis were carried as well as the gene functional analysis to explore molecular mechanisms of specific disease phenotypes, thus to decode the changes in individual behavioral and mental characteristics during neurodegenerative disease progression. Results Large amounts of experiments were conducted to verify the performance of LPFS with Huntington’s gene expression data. Experimental results shown that LPFS performs better in comparison with the-state-of-art methods. GO and KEGG enrichment analysis of key gene sets shown that TGF-beta signaling pathway, cytokine-cytokine receptor interaction, immune response, and inflammatory response were gradually affected during the Huntington’s disease progression. In addition, we found that the expression of SLC4A11, ZFP474, AMBP, TOP2A, PBK, CCDC33, APSL, DLGAP5, and Al662270 changed seriously by the development of the disease. Conclusions In this study, we designed a label propagation-based semi-supervised feature selection model to precisely selected key genes of different disease phenotypes. We conducted experiments using the model with Huntington’s disease mice gene expression data to decode the mechanisms of it. We found many cell types, including astrocyte, microglia, and GABAergic neuron, could be involved in the pathological process.

scholarly journals Learning Gene Networks Underlying Clinical Phenotypes Using SNP Perturbations

2018 ◽  
Author(s):  
Calvin McCarter ◽  
Judie Howrylak ◽  
Seyoung Kim
Keyword(s):  
Gene Networks ◽  
Gene Network ◽  
Molecular Mechanisms ◽  
Graphical Model ◽  
Sequence Data ◽  
Association Studies ◽  
Genome Wide Association Studies ◽  
Nucleotide Polymorphisms ◽  
Clinical Phenotypes ◽  
Genome Wide

AbstractRecent technologies are generating an abundance of genome sequence data and molecular and clinical phenotype data, providing an opportunity to understand the genetic architecture and molecular mechanisms underlying diseases. Previous approaches have largely focused on the co-localization of single-nucleotide polymorphisms (SNPs) associated with clinical and expression traits, each identified from genome-wide association studies and expression quantitative trait locus (eQTL) mapping, and thus have provided only limited capabilities for uncovering the molecular mechanisms behind the SNPs influencing clinical phenotypes. Here we aim to extract rich information on the functional role of trait-perturbing SNPs that goes far beyond this simple co-localization. We introduce a computational framework called Perturb-Net for learning the gene network that modulates the influence of SNPs on phenotypes, using SNPs as naturally occurring perturbation of a biological system. Perturb-Net uses a probabilistic graphical model to directly model both the cascade of perturbation from SNPs to the gene network to the phenotype network and the network at each layer of molecular and clinical phenotypes. Perturb-Net learns the entire model by solving a single optimization problem with an extremely fast algorithm that can analyze human genome-wide data within a few hours. In our analysis of asthma data, for a locus that was previously implicated in asthma susceptibility but for which little is known about the molecular mechanism underlying the association, Perturb-Net revealed the gene network modules that mediate the influence of the SNP on asthma phenotypes. Many genes in this network module were well supported in the literature as asthma-related.

scholarly journals Congenital Diseases of DNA Replication: Clinical Phenotypes and Molecular Mechanisms

2021 ◽  
Vol 22 (2) ◽  
pp. 911
Author(s):  
Megan Schmit ◽  
Anja-Katrin Bielinsky
Keyword(s):  
Dna Replication ◽  
Genomic Instability ◽  
Molecular Mechanisms ◽  
Point Mutations ◽  
Chromosomal Anomalies ◽  
Congenital Diseases ◽  
Clinical Phenotypes ◽  
Pathogenic Variants ◽  
Dna Alterations ◽  
Functional Mechanisms

Deoxyribonucleic acid (DNA) replication can be divided into three major steps: initiation, elongation and termination. Each time a human cell divides, these steps must be reiteratively carried out. Disruption of DNA replication can lead to genomic instability, with the accumulation of point mutations or larger chromosomal anomalies such as rearrangements. While cancer is the most common class of disease associated with genomic instability, several congenital diseases with dysfunctional DNA replication give rise to similar DNA alterations. In this review, we discuss all congenital diseases that arise from pathogenic variants in essential replication genes across the spectrum of aberrant replisome assembly, origin activation and DNA synthesis. For each of these conditions, we describe their clinical phenotypes as well as molecular studies aimed at determining the functional mechanisms of disease, including the assessment of genomic stability. By comparing and contrasting these diseases, we hope to illuminate how the disruption of DNA replication at distinct steps affects human health in a surprisingly cell-type-specific manner.

scholarly journals OP15 Multi-omic data integration with network analysis reveals underlying molecular mechanisms driving Crohn’s disease heterogeneity

2020 ◽  
Vol 14 (Supplement_1) ◽  
pp. S014-S014
Author(s):  
P Sudhakar ◽  
B Verstockt ◽  
J Cremer ◽  
S Verstockt ◽  
T Korcsmaros ◽  
...  
Keyword(s):  
Crohn’S Disease ◽  
Crohn's Disease ◽  
Data Integration ◽  
Molecular Mechanisms ◽  
Local Network ◽  
Perianal Disease ◽  
Network Motifs ◽  
Integration Algorithm ◽  
Disease Heterogeneity ◽  
Clinical Phenotypes

Abstract Background Crohn’s disease (CD) is a heterogeneous disease characterised by clinical phenotypes including differences in disease behaviour, disease location and extraintestinal manifestations. However, the molecular mechanisms which orchestrate CD heterogeneity are relatively unexplored. We tried to infer such mechanisms by integrating two -omic datasets (genomics and blood proteomics) generated from CD patients. Methods 576 unique proteins were measured from blood isolated from CD patients (n = 98) using seven different Olink® panels. All patients were also genotyped using Immunochip. We integrated the above two datasets using an unsupervised data integration algorithm called Multi-Omics Factor Analysis (MOFA). MOFA identifies Latent Factors (LFs) which are hidden representative variables which capture the sources of variation in the provided -omic datasets. LFs capturing less than 2% of the variance were discarded. By using a regression model, we identified explanatory LFs which associate with clinical phenotypes. Proteins and mutations were ranked according to the scores assigned by the corresponding explanatory LF. Potential effects of mutations were inferred by analysing their impacts on coding and non-coding functions. Local network motifs which capture the direct and indirect effects of mutations on protein expression were identified by using the Cytoscape tool ISMAGS. Protein–protein and transcriptional regulatory relationships retrieved from the OmniPath and DoRothEA databases, respectively, were combined to compile the interaction networks used by ISMAGS. Results From the MOFA analysis, we identified five LFs associated with at least one clinical phenotype. Clustering patients along the explanatory LFs achieved meaningful separation of clinical phenotypes such as perianal penetrating disease. The top-ranking proteins associated with perianal-disease included those involved in inflammatory pathways, autophagy or already known to be involved in CD such as IL-8, Rho-GTPase activators, MIF, Caspase 8, TRIM5 and SNAP29. The networks corresponding to the top ranking proteins associated with the perianal phenotype could be broken down into 102 local network motifs. These local motifs pointed out control mechanisms by which a total of 7 mutations mapped to transcription factors (SMAD3, BACH2) and post-translational regulators (such as IFNGR2, IL10, IL2RA, SLC2A4RG and ZMIZ1) could potentially regulate perianal disease‘s pathophysiology and could, therefore, be considered novel drug targets. Conclusion By using integrated signature profiles generated from multiple -omic datasets, we identified molecular mechanisms which could potentially describe CD phenotypes such as the occurrence of perianal disease.

scholarly journals K-means Based Unsupervised Feature Selection to Prioritize Biomarkers of Different Disease Clinical Phases

2020 ◽  
Author(s):  
Xue Jiang ◽  
Weidi Wang ◽  
Jing Xu ◽  
Zhen Wang ◽  
Guan Ning Lin
Keyword(s):  
Feature Selection ◽  
Molecular Mechanisms ◽  
Single Gene ◽  
Enrichment Analysis ◽  
Expression Data ◽  
Sequencing Technology ◽  
Unsupervised Feature Selection ◽  
Clinical Phenotypes ◽  
Disease Related Genes ◽  
Insight Into

AbstractHuntington’s disease is caused by a single gene mutation, which is potentially a good model for development of biomarkers corresponding to different disease phase and clinical phenotypes. Hypothesis-driven and omics discovery approaches have not yet identified effective candidate biomarkers in HD. So, it is urgent to develop engagement and disease-phase specific biomarkers. The advanced sequencing technology makes it possible to develop data-driven methods for biomarkers discovery. Therefore, in this study, we designed k-means based unsupervised feature selection (KFS) method to prioritize biomarkers of different disease clinical phases. KFS first conducts k-means clustering on the samples with gene expression data, then it conducts feature selection based on the feature selection matrix to prioritize biomarkers of different samples. By conducting alternative iteration of clustering and feature selection to screen key genes which corresponding to the complex clinical phenotypes of different disease phases. Further gene ontology and enrichment analysis highlight potential molecular mechanisms of HD. Our experimental analyses have uncovered new disease-related genes and disease-associated pathways, which in turn have provided insight into the molecular mechanisms during the disease progression.

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