Rare disease research workflow using multilayer networks elucidates the molecular determinants of severity in Congenital Myasthenic Syndromes.

Exploring the molecular basis of disease severity in rare disease scenarios is a challenging task provided the limitations on data availability. Causative genes have been described for Congenital Myasthenic Syndromes (CMS), a group of diverse minority neuromuscular junction (NMJ) disorders; yet a molecular explanation for the phenotypic severity differences remains unclear. Here, we present a workflow to explore the functional relationships between CMS causal genes and altered genes from each patient, based on multilayer network community detection analysis of complementary biomedical information provided by relevant data sources, namely protein-protein interactions, pathways and metabolomics.

Correction: Prediction of mortality in very low birth weight neonates in Spain.

[This corrects the article DOI: 10.1371/journal.pone.

The landscape of expression and alternative splicing variation across human traits.

Understanding the consequences of individual transcriptome variation is fundamental to deciphering human biology and disease. We implement a statistical framework to quantify the contributions of 21 individual traits as drivers of gene expression and alternative splicing variation across 46 human tissues and 781 individuals from the Genotype-Tissue Expression project. We demonstrate that ancestry, sex, age, and BMI make additive and tissue-specific contributions to expression variability, whereas interactions are rare.

Efficient and flexible Integration of variant characteristics in rare variant association studies using integrated nested Laplace approximation.

Rare variants are thought to play an important role in the etiology of complex diseases and may explain a significant fraction of the missing heritability in genetic disease studies. Next-generation sequencing facilitates the association of rare variants in coding or regulatory regions with complex diseases in large cohorts at genome-wide scale. However, rare variant association studies (RVAS) still lack power when cohorts are small to medium-sized and if genetic variation explains a small fraction of phenotypic variance.

A catalogue of 863 Rett-syndrome-causing MECP2 mutations and lessons learned from data integration.

Rett syndrome (RTT) is a rare neurological disorder mostly caused by a genetic variation in MECP2. Making new MECP2 variants and the related phenotypes available provides data for better understanding of disease mechanisms and faster identification of variants for diagnosis. This is, however, currently hampered by the lack of interoperability between genotype-phenotype databases.

FGFR Inhibition Overcomes Resistance to EGFR-targeted Therapy in Epithelial-like Cutaneous Carcinoma.

Purpose: Recurrent and/or metastatic unresectable cutaneous squamous cell carcinomas (cSCCs) are treated with chemotherapy or radiotherapy, but have poor clinical responses. A limited response (up to 45% of cases) to EGFR-targeted therapies was observed in clinical trials with patients with advanced and metastatic cSCC. Here, we analyze the molecular traits underlying the response to EGFR inhibitors, and the mechanisms responsible for cSCC resistance to EGFR-targeted therapy.

Tracking of Antibiotic Resistance Transfer and Rapid Plasmid Evolution in a Hospital Setting by Nanopore Sequencing.

Infections with multidrug-resistant bacteria often leave limited or no treatment options. The transfer of antimicrobial resistance genes (ARG) carrying plasmids between bacterial species by horizontal gene transfer represents an important mode of expansion of ARGs. Here, we demonstrate the application of Nanopore sequencing in a hospital setting for monitoring transfer and rapid evolution of antibiotic resistance plasmids within and across multiple species.

Prediction of mortality in very low birth weight neonates in Spain.

Objective: Predictive models for preterm infant mortality have been developed internationally, albeit not valid for all populations. This study aimed to develop and validate different mortality predictive models, using Spanish data, to be applicable to centers with similar morbidity and mortality.

Methods: Infants born alive, admitted to NICU (BW<1500 g or GA<30 w), and registered in the SEN1500 database, were included.

[Prediction of mortality in premature neonates. An updated systematic review].

Introduction: Extreme prematurity is associated with high mortality rates. The probability of death at different points in time is a priority for professionals and parents, and needs to be established on an individual basis. The aim of this study is to carry out a systematic review of predictive models of mortality in premature infants that have been published recently.

eDiVA-Classification and prioritization of pathogenic variants for clinical diagnostics.

Mendelian diseases have shown to be an and efficient model for connecting genotypes to phenotypes and for elucidating the function of genes. Whole-exome sequencing (WES) accelerated the study of rare Mendelian diseases in families, allowing for directly pinpointing rare causal mutations in genic regions without the need for linkage analysis. However, the low diagnostic rates of 20-30% reported for multiple WES disease studies point to the need for improved variant pathogenicity classification and causal variant prioritization methods.

Differential expression of long non-coding RNAs are related to proliferation and histological diversity in follicular lymphomas.

Long non-coding RNAs (lncRNAs) comprise a family of non-coding transcripts that are emerging as relevant gene expression regulators of different processes, including tumour development. To determine the possible contribution of lncRNA to the pathogenesis of follicular lymphoma (FL) we performed RNA-sequencing at high depth sequencing in primary FL samples ranging from grade 1-3A to aggressive grade 3B variants using unpurified (n = 16) and purified (n = 12) tumour cell suspensions from nodal samples. FL grade 3B had a significantly higher number of differentially expressed lncRNAs (dif-lncRNAs) with potential target coding genes related to cell cycle regulation.

Allele balance bias identifies systematic genotyping errors and false disease associations.

In recent years, next-generation sequencing (NGS) has become a cornerstone of clinical genetics and diagnostics. Many clinical applications require high precision, especially if rare events such as somatic mutations in cancer or genetic variants causing rare diseases need to be identified. Although random sequencing errors can be modeled statistically and deep sequencing minimizes their impact, systematic errors remain a problem even at high depth of coverage.

Survival among children with "Lethal" congenital contracture syndrome 11 caused by novel mutations in the gliomedin gene (GLDN).

Biallelic GLDN mutations have recently been identified among infants with lethal congenital contracture syndrome 11 (LCCS11). GLDN encodes gliomedin, a protein required for the formation of the nodes of Ranvier and development of the human peripheral nervous system. We report six infants and children from four unrelated families with biallelic GLDN mutations, four of whom survived beyond the neonatal period into infancy, childhood, and late adolescence with intensive care and chronic respiratory and nutritional support.

A De Novo Nonsense Mutation in MAGEL2 in a Patient Initially Diagnosed as Opitz-C: Similarities Between Schaaf-Yang and Opitz-C Syndromes.

Opitz trigonocephaly C syndrome (OTCS) is a rare genetic disorder characterized by craniofacial anomalies, variable intellectual and psychomotor disability, and variable cardiac defects with a high mortality rate. Different patterns of inheritance and genetic heterogeneity are known in this syndrome. Whole exome and genome sequencing of a 19-year-old girl (P7), initially diagnosed with OTCS, revealed a de novo nonsense mutation, p.

Hierarchical clustering combining numerical and biological similarities for gene expression data classification.

High throughput data analysis is a challenging problem due to the vast amount of available data. A major concern is to develop algorithms that provide accurate numerical predictions and biologically relevant results. A wide variety of tools exist in the literature using biological knowledge to evaluate analysis results.