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. Our results show that CMS severity can be ascribed to the personalized impairment of extracellular matrix components and postsynaptic modulators of acetylcholine receptor (AChR) clustering. This work showcases how coupling multilayer network analysis with personalized -omics information provides molecular explanations to the varying severity of rare diseases; paving the way for sorting out similar cases in other rare diseases.

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

[This corrects the article DOI: 10.1371/journal.pone.0235794.].

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. Variation in splicing is dominated by ancestry and is under genetic control in most tissues, with ribosomal proteins showing a strong enrichment of tissue-shared splicing events. Our analyses reveal a systemic contribution of types 1 and 2 diabetes to tissue transcriptome variation with the strongest signal in the nerve, where histopathology image analysis identifies novel genes related to diabetic neuropathy. Our multi-tissue and multi-trait approach provides an extensive characterization of the main drivers of human transcriptome variation in health and disease.

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. Here we present a novel Bayesian rare variant Association Test using Integrated Nested Laplace Approximation (BATI). Unlike existing RVAS tests, BATI allows integration of individual or variant-specific features as covariates, while efficiently performing inference based on full model estimation. We demonstrate that BATI outperforms established RVAS methods on realistic, semi-synthetic whole-exome sequencing cohorts, especially when using meaningful biological context, such as functional annotation. We show that BATI achieves power above 70% in scenarios in which competing tests fail to identify risk genes, e.g. when risk variants in sum explain less than 0.5% of phenotypic variance. We have integrated BATI, together with five existing RVAS tests in the 'Rare Variant Genome Wide Association Study' (rvGWAS) framework for data analyzed by whole-exome or whole genome sequencing. rvGWAS supports rare variant association for genes or any other biological unit such as promoters, while allowing the analysis of essential functionalities like quality control or filtering. Applying rvGWAS to a Chronic Lymphocytic Leukemia study we identified eight candidate predisposition genes, including EHMT2 and COPS7A.

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. Here, we demonstrate on the example of MECP2 in RTT that by making the genotype-phenotype data more Findable, Accessible, Interoperable, and Reusable (FAIR), we can facilitate prioritization and analysis of variants. In total, 10,968 MECP2 variants were successfully integrated. Among these variants 863 unique confirmed RTT causing and 209 unique confirmed benign variants were found. This dataset was used for comparison of pathogenicity predicting tools, protein consequences, and identification of ambiguous variants. Prediction tools generally recognised the RTT causing and benign variants, however, there was a broad range of overlap Nineteen variants were identified that were annotated as both disease-causing and benign, suggesting that there are additional factors in these cases contributing to disease development.

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. EXPERIMENTAL DESIGN: We generated primary cell cultures and patient cSCC-derived xenografts (cSCC-PDXs) that recapitulate the histopathologic and molecular features of patient tumors. Response to gefitinib treatment was tested and gefitinib-resistant (GefR) cSCC-PDXs were developed. RNA sequence analysis was performed in matched untreated and GefR cSCC-PDXs to determine the mechanisms driving gefitinib resistance. RESULTS: cSCCs conserving epithelial traits exhibited strong activation of EGFR signaling, which promoted tumor cell proliferation, in contrast to mesenchymal-like cSCCs. Gefitinib treatment strongly blocked epithelial-like cSCC-PDX growth in the absence of EGFR and RAS mutations, whereas tumors carrying the E545K PIK3CA-activating mutation were resistant to treatment. A subset of initially responding tumors acquired resistance after long-term treatment, which was induced by the bypass from EGFR to FGFR signaling to allow tumor cell proliferation and survival upon gefitinib treatment. Pharmacologic inhibition of FGFR signaling overcame resistance to EGFR inhibitor, even in PIK3CA-mutated tumors. CONCLUSIONS: EGFR-targeted therapy may be appropriate for treating many epithelial-like cSCCs without PIK3CA-activating mutations. Combined EGFR- and FGFR-targeted therapy may be used to treat cSCCs that show intrinsic or acquired resistance to EGFR inhibitors.

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. In 2009, we experienced an outbreak with extensively multidrug-resistant Pseudomonas aeruginosa harboring the carbapenemase-encoding blaIMP-8 gene. In 2012, the first Citrobacter freundii and Citrobacter cronae strains harboring the same gene were detected. Using Nanopore and Illumina sequencing, we conducted comparative analysis of all blaIMP-8 bacteria isolated in our hospital over a 6-year period (n = 54). We developed the computational platform plasmIDent for Nanopore-based characterization of clinical isolates and monitoring of ARG transfer, comprising de novo assembly of genomes and plasmids, plasmid circularization, ARG annotation, comparative genome analysis of multiple isolates, and visualization of results. Using plasmIDent, we identified a 40-kb plasmid carrying blaIMP-8 in P. aeruginosa and C. freundii, verifying the plasmid transfer. Within C. freundii, the plasmid underwent further evolution and plasmid fusion, resulting in a 164-kb megaplasmid, which was transferred to C. cronae Multiple rearrangements of the multidrug resistance gene cassette were detected in P. aeruginosa, including deletions and translocations of complete ARGs. In summary, plasmid transfer, plasmid fusion, and rearrangement of the ARG cassette mediated the rapid evolution of opportunistic pathogens in our hospital. We demonstrated the feasibility of near-real-time monitoring of plasmid evolution and ARG transfer in clinical settings, enabling successful countermeasures to contain plasmid-mediated outbreaks.IMPORTANCE Infections with multidrug-resistant bacteria represent a major threat to global health. While the spread of multidrug-resistant bacterial clones is frequently studied in the hospital setting, surveillance of the transfer of mobile genetic elements between different bacterial species was difficult until recent advances in sequencing technologies. Nanopore sequencing technology was applied to track antimicrobial gene transfer in a long-term outbreak of multidrug-resistant Pseudomonas aeruginosa, Citrobacter freundii, and Citrobacter cronae in a German hospital over 6 years. We developed a novel computational pipeline, pathoLogic, which enables de novo assembly of genomes and plasmids, antimicrobial resistance gene annotation and visualization, and comparative analysis. Applying this approach, we detected plasmid transfer between different bacterial species as well as plasmid fusion and frequent rearrangements of the antimicrobial resistance gene cassette. This study demonstrated the feasibility of near-real-time tracking of plasmid-based antimicrobial resistance gene transfer in hospitals, enabling countermeasures to contain plasmid-mediated outbreaks.

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. There were two time periods; development of the predictive models (2009-2012) and validation (2013-2015). Three models were produced; prenatal (1), first 24 hours of life (2), and whilst admitted (3). For the statistical analysis, hospital mortality was the dependent variable. Significant variables were used in multivariable regression models. Specificity, sensitivity, accuracy, and area under the curve (AUC), for all models, were calculated. RESULTS: Out of 14953 included newborns, 2015 died; 373 (18.5%) in their first 24 hours, 1315 (65.3%) during the first month, and 327 (16.2%) thereafter, before discharge. In the development stage, mortality prediction AUC was 0.834 (95% CI: 0.822-0.846) (p<0.001) in model 1 and 0.872 (95% CI: 0.860-0.884) (p<0.001) in model 2. Model 3's AUC was 0.989 (95% CI: 0.983-0.996) (p<0.001) and 0.942 (95% CI: 0.929-0.956) (p<0.001) during the 0-30 and >30 days of life, respectively. During validation, models 1 and 2 showed moderate concordance, whilst that of model 3 was good. CONCLUSION: Using dynamic models to predict individual mortality can improve outcome estimations. Development of models in the prenatal period, first 24 hours, and during hospital admission, cover key stages of mortality prediction in preterm infants.

Predicción de mortalidad en recién nacidos prematuros. Revisión sistemática actualizada / Prediction of mortality in premature neonates. An updated systematic review

INTRODUCCIÓN: La prematuridad extrema se asocia a altas tasas de mortalidad. Para profesionales y familias, es prioritario establecer de forma individualizada las probabilidades de muerte en diferentes momentos. El objetivo de este estudio es efectuar una revisión sistemática de modelos predictivos de mortalidad en prematuros publicados recientemente. MÉTODOS: Doble búsqueda de artículos publicados en PubMed sobre modelos predictivos de muerte en prematuros. Población estudiada: prematuros con edad gestacional ≤ 30 semanas o peso al nacer ≤ 1.500 g. Inclusión: trabajos publicados con nuevos modelos desde junio del 2010 hasta julio del 2019, tras revisión sistemática de Medlock (2011). Se valoran: población, características de modelos, variables utilizadas, medidas de funcionamiento y validación. RESULTADOS: De 7.744 referencias (primera búsqueda) y 1.435 (segunda búsqueda), se seleccionaron 31 trabajos, incluyéndose al final 8 nuevos modelos. Cinco modelos (62,5%) se desarrollaron en América del Norte y 2 (25%) en Europa. Un modelo secuencial (Ambalavanan) permite realizar predicciones de muerte al nacer, 7, 28 días de vida y 36 semanas posmenstruales. Análisis de regresión logística múltiple en el 87,5% de modelos. La discriminación poblacional se midió por odds ratio (75%) y área bajo la curva (50%). La «validación» se ha realizado en 5 modelos (interna). Tres modelos disponen de acceso online. No hay modelos predictivos validados en España. CONCLUSIONES: La toma de decisiones basada en modelos predictivos permite una mayor individualización de la atención dada a un niño prematuro y una mejor utilización de recursos. Es necesario desarrollar modelos de predicción de mortalidad en prematuros de España

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. METHODS: A double search was performed for article published in PubMed on models predicting mortality in premature neonates. The population studied were premature neonates with a gestational age of ≤ 30 weeks and / or a weight at birth of ≤ 1500g. Works published with new models from June 2010 to July 2019 after a systematic review by Medlock (2011) were included. An assessment was made of the population, characteristics of the model, variables used, measurements of functioning, and validation. RESULTS: Of the 7744 references (1st search) and 1435 (2nd search) found, 31 works were selected, with 8 new models finally being included. Five models (62.5%) were developed in North America and 2 (25%) in Europe. A sequential model (Ambalavanan) enables predictions of mortality to be made at birth, 7, 28 days of life, and 36 weeks post-menstrual. A multiple logistic regression analysis was performed on 87.5% of the models. The population discrimination was measured using Odds Ratio (75%) and the area under the curve (50%). "Internal Validation" had been carried out on 5 models. Three models can be accessed on-line. There are no predictive models validated in Spain. CONCLUSIONS: The making of decisions based on predictive models can lead to the care given to the premature infant being more individualised and with a better use of resources. Predictive models of mortality in premature neonates in Spain need to be developed

[Prediction of mortality in premature neonates. An updated systematic review]. / Predicción de mortalidad en recién nacidos prematuros. Revisión sistemática actualizada.

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. METHODS: A double search was performed for article published in PubMed on models predicting mortality in premature neonates. The population studied were premature neonates with a gestational age of ≤30 weeks and / or a weight at birth of ≤1500g. Works published with new models from June 2010 to July 2019 after a systematic review by Medlock (2011) were included. An assessment was made of the population, characteristics of the model, variables used, measurements of functioning, and validation. RESULTS: Of the 7744 references (1st search) and 1435 (2nd search) found, 31 works were selected, with 8 new models finally being included. Five models (62.5%) were developed in North America and 2 (25%) in Europe. A sequential model (Ambalavanan) enables predictions of mortality to be made at birth, 7, 28 days of life, and 36 weeks post-menstrual. A multiple logistic regression analysis was performed on 87.5% of the models. The population discrimination was measured using Odds Ratio (75%) and the area under the curve (50%). "Internal Validation" had been carried out on 5 models. Three models can be accessed on-line. There are no predictive models validated in Spain. CONCLUSIONS: The making of decisions based on predictive models can lead to the care given to the premature infant being more individualised and with a better use of resources. Predictive models of mortality in premature neonates in Spain need to be developed.

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. Here, we present the exome Disease Variant Analysis (eDiVA; http://ediva.crg.eu), an automated computational framework for identification of causal genetic variants (coding/splicing single-nucleotide variants and small insertions and deletions) for rare diseases using WES of families or parent-child trios. eDiVA combines next-generation sequencing data analysis, comprehensive functional annotation, and causal variant prioritization optimized for familial genetic disease studies. eDiVA features a machine learning-based variant pathogenicity predictor combining various genomic and evolutionary signatures. Clinical information, such as disease phenotype or mode of inheritance, is incorporated to improve the precision of the prioritization algorithm. Benchmarking against state-of-the-art competitors demonstrates that eDiVA consistently performed as a good or better than existing approach in terms of detection rate and precision. Moreover, we applied eDiVA to several familial disease cases to demonstrate its clinical applicability.

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. Understanding their source is crucial to increase precision of clinical NGS applications. In this work, we studied the relation between recurrent biases in allele balance (AB), systematic errors, and false positive variant calls across a large cohort of human samples analyzed by whole exome sequencing (WES). We have modeled the AB distribution for biallelic genotypes in 987 WES samples in order to identify positions recurrently deviating significantly from the expectation, a phenomenon we termed allele balance bias (ABB). Furthermore, we have developed a genotype callability score based on ABB for all positions of the human exome, which detects false positive variant calls that passed state-of-the-art filters. Finally, we demonstrate the use of ABB for detection of false associations proposed by rare variant association studies. Availability: https://github.com/Francesc-Muyas/ABB.

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. Nine out of the 18 selected dif-lncRNAs were validated by quantitative real time polymerase chain reaction in an independent series (n = 43) of FL. RP4-694A7.2 was identified as the top deregulated lncRNA potentially involved in cell proliferation. RP4-694A7.2 silencing in the WSU-FSCCL FL cell line reduced cell proliferation due to a block in the G1/S phase. The relationship between RP4-694A7.2 and proliferation was confirmed in primary samples as its expression levels positively related to the Ki-67 proliferation index. In summary, lncRNAs are differentially expressed across the clinico-biological spectrum of FL and a subset of them, related to cell cycle, may participate in cell proliferation regulation in these tumours.

LRBA Deficiency in a Patient With a Novel Homozygous Mutation Due to Chromosome 4 Segmental Uniparental Isodisomy.

LRBA deficiency was first described in 2012 as an autosomal recessive disorder caused by biallelic mutations in the LRBA gene (OMIM #614700). It was initially characterized as producing early-onset hypogammaglobulinemia, autoimmune manifestations, susceptibility to inflammatory bowel disease, and recurrent infection. However, further reports expanded this phenotype (including patients without hypogammaglobulinemia) and described LRBA deficiency as a clinically variable syndrome with a wide spectrum of clinical manifestations. We present the case of a female patient who presented with type 1 diabetes, psoriasis, oral thrush, and enlarged liver and spleen at the age of 8 months. She later experienced recurrent bacterial and viral infections, including pneumococcal meningitis and Epstein Barr viremia. She underwent two consecutive stem cell transplants at the age of 8 and 9 years, and ultimately died. Samples from the patient and her parents were subjected to whole exome sequencing, which revealed a homozygous 1-bp insertion in exon 23 of the patient's LRBA gene, resulting in frameshift and premature stop codon. The patient's healthy mother was heterozygous for the mutation and her father tested wild-type. This finding suggested that either one copy of the paternal chromosome 4 bore a deletion including the LRBA locus, or the patient inherited two copies of the mutant maternal LRBA allele. The patient's sequencing data showed a 1-Mb loss of heterozygosity region in chromosome 4, including the LRBA gene. Comparative genomic hybridization array of the patient's and father's genomic DNA yielded normal findings, ruling out genomic copy number abnormalities. Here, we present the first case of LRBA deficiency due to a uniparental disomy (UPD). In contrast to classical Mendelian inheritance, UPD involves inheritance of 2 copies of a chromosomal region from only 1 parent. Specifically, our patient carried a small segmental isodisomy of maternal origin affecting 1 Mb of chromosome 4.

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. Our findings expand the genotypic and phenotypic spectrum of LCCS11 and demonstrate that the condition may not necessarily be lethal in the neonatal period.

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.Q638*, in the MAGEL2 gene. MAGEL2 is an imprinted, maternally silenced, gene located at 15q11-13, within the Prader-Willi region. Patient P7 carried the mutation in the paternal chromosome. Recently, mutations in MAGEL2 have been described in Schaaf-Yang syndrome (SHFYNG) and in severe arthrogryposis. Patient P7 bears resemblances with SHFYNG cases but has other findings not described in this syndrome and common in OTCS. We sequenced MAGEL2 in nine additional OTCS patients and no mutations were found. This study provides the first clear molecular genetic basis for an OTCS case, indicates that there is overlap between OTCS and SHFYNG syndromes, and confirms that OTCS is genetically heterogeneous. Genes encoding MAGEL2 partners, either in the retrograde transport or in the ubiquitination-deubiquitination complexes, are promising candidates as OTCS disease-causing genes.

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. Only recently, some works have included biological knowledge inside the analysis process improving the prediction results.