Correction: Integrative analysis of genomic variants reveals new associations of candidate haploinsufficient genes with congenital heart disease.

[This corrects the article DOI: 10.1371/journal.pgen.1009679.].

Integrative analysis of genomic variants reveals new associations of candidate haploinsufficient genes with congenital heart disease.

Numerous genetic studies have established a role for rare genomic variants in Congenital Heart Disease (CHD) at the copy number variation (CNV) and de novo variant (DNV) level. To identify novel haploinsufficient CHD disease genes, we performed an integrative analysis of CNVs and DNVs identified in probands with CHD including cases with sporadic thoracic aortic aneurysm. We assembled CNV data from 7,958 cases and 14,082 controls and performed a gene-wise analysis of the burden of rare genomic deletions in cases versus controls. In addition, we performed variation rate testing for DNVs identified in 2,489 parent-offspring trios. Our analysis revealed 21 genes which were significantly affected by rare CNVs and/or DNVs in probands. Fourteen of these genes have previously been associated with CHD while the remaining genes (FEZ1, MYO16, ARID1B, NALCN, WAC, KDM5B and WHSC1) have only been associated in small cases series or show new associations with CHD. In addition, a systems level analysis revealed affected protein-protein interaction networks involved in Notch signaling pathway, heart morphogenesis, DNA repair and cilia/centrosome function. Taken together, this approach highlights the importance of re-analyzing existing datasets to strengthen disease association and identify novel disease genes and pathways.

Semen quality and waiting time to pregnancy explored using association mining.

BACKGROUND: Assessment of semen quality is a key pillar in the evaluation of men from infertile couples. Usually, semen parameters are interpreted individually because the interactions between parameters are difficult to account for. OBJECTIVES: To determine how combinations of classical semen parameters and female partner age were associated with waiting time to pregnancy (TTP). MATERIALS AND METHODS: Semen results of 500 fertile men, information of TTP, and partner age were used for regressions and to detect breaking points. For a modified Association Rule Mining algorithm, semen parameters were categorized as High, Medium, and Low. RESULTS: Men ≤32.1 years and women ≤32.9 years had shorter TTP than older. Decreasing TTP was associated with increasing level of individual semen parameters up to threshold values: sperm concentration 46 mill/mL, total sperm count 179 mill, progressive motility 63%, and normal morphology 11.5%. Using association mining, approximately 100 combinations of semen parameters and partner age were associated with TTP. TTP ≤ 1 month often co-occurred with high percentages of progressive motility (≥62%) and morphologically normal spermatozoa (≥10.5%). Furthermore, TTP ≤ 1 did not tend to appear with lower percentages of these two semen parameters or high partner age (≥32 years). However, high percentages of motile or normal spermatozoa could not compensate for sperm concentration ≤42 mill/mL or total sperm count ≤158 mill. The prolonging effect of high partner age could not be compensated for by the man's semen quality. DISCUSSION AND CONCLUSION: Using association mining, we observed that TTP was best predicted when combinations of semen parameters were accounted for. Sperm counts, motility, and morphology were all important, and no single semen parameter was inferior. Additionally, female age above 32 years had a negative impact on TTP that could not be compensated for by high semen parameters of the man.

Systems genetics analysis identifies calcium-signaling defects as novel cause of congenital heart disease.

BACKGROUND: Congenital heart disease (CHD) occurs in almost 1% of newborn children and is considered a multifactorial disorder. CHD may segregate in families due to significant contribution of genetic factors in the disease etiology. The aim of the study was to identify pathophysiological mechanisms in families segregating CHD. METHODS: We used whole exome sequencing to identify rare genetic variants in ninety consenting participants from 32 Danish families with recurrent CHD. We applied a systems biology approach to identify developmental mechanisms influenced by accumulation of rare variants. We used an independent cohort of 714 CHD cases and 4922 controls for replication and performed functional investigations using zebrafish as in vivo model. RESULTS: We identified 1785 genes, in which rare alleles were shared between affected individuals within a family. These genes were enriched for known cardiac developmental genes, and 218 of these genes were mutated in more than one family. Our analysis revealed a functional cluster, enriched for proteins with a known participation in calcium signaling. Replication in an independent cohort confirmed increased mutation burden of calcium-signaling genes in CHD patients. Functional investigation of zebrafish orthologues of ITPR1, PLCB2, and ADCY2 verified a role in cardiac development and suggests a combinatorial effect of inactivation of these genes. CONCLUSIONS: The study identifies abnormal calcium signaling as a novel pathophysiological mechanism in human CHD and confirms the complex genetic architecture underlying CHD.

In the rat pancreas, somatostatin tonically inhibits glucagon secretion and is required for glucose-induced inhibition of glucagon secretion.

AIM: It is debated whether the inhibition of glucagon secretion by glucose results from direct effects of glucose on the α-cell (intrinsic regulation) or by paracrine effects exerted by beta- or delta-cell products. METHODS: To study this in a more physiological model than isolated islets, we perfused isolated rat pancreases and measured glucagon, insulin and somatostatin secretion in response to graded increases in perfusate glucose concentration (from 3.5 to 4, 5, 6, 7, 8, 10, 12 mmol/L) as well as glucagon responses to blockage/activation of insulin/GABA/somatostatin signalling with or without addition of glucose. RESULTS: Glucagon secretion was reduced by about 50% (compared to baseline secretion at 3.5 mmol/L) within minutes after increasing glucose from 4 to 5 mmol/L (P < .01, n = 13). Insulin secretion was increased minimally, but significantly, compared to baseline (3.5 mmol/L) at 4 mmol/L, whereas somatostatin secretion was not significantly increased from baseline until 7 mmol/L. Hereafter secretion of both increased gradually up to 12 mmol/L glucose. Neither recombinant insulin (1 µmol/L), GABA (300 µmol/L) or the insulin-receptor antagonist S961 (at 1 µmol/L) affected basal (3.5 mmol/L) or glucose-induced (5.0 mmol/L) attenuation of glucagon secretion (n = 7-8). Somatostatin-14 attenuated glucagon secretion by ~ 95%, and blockage of somatostatin-receptor (SSTR)-2 or combined blockage of SSTR-2, -3 and -5 by specific antagonists increased glucagon output (at 3.5 mmol/L glucose) and prevented glucose-induced (from 3.5 to 5.0 mmol/L) suppression of secretion. CONCLUSION: Somatostatin is a powerful and tonic inhibitor of glucagon secretion from the rat pancreas and is required for glucose to inhibit glucagon secretion.

Pathway and network analysis of more than 2500 whole cancer genomes.

The catalog of cancer driver mutations in protein-coding genes has greatly expanded in the past decade. However, non-coding cancer driver mutations are less well-characterized and only a handful of recurrent non-coding mutations, most notably TERT promoter mutations, have been reported. Here, as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium, which aggregated whole genome sequencing data from 2658 cancer across 38 tumor types, we perform multi-faceted pathway and network analyses of non-coding mutations across 2583 whole cancer genomes from 27 tumor types compiled by the ICGC/TCGA PCAWG project that was motivated by the success of pathway and network analyses in prioritizing rare mutations in protein-coding genes. While few non-coding genomic elements are recurrently mutated in this cohort, we identify 93 genes harboring non-coding mutations that cluster into several modules of interacting proteins. Among these are promoter mutations associated with reduced mRNA expression in TP53, TLE4, and TCF4. We find that biological processes had variable proportions of coding and non-coding mutations, with chromatin remodeling and proliferation pathways altered primarily by coding mutations, while developmental pathways, including Wnt and Notch, altered by both coding and non-coding mutations. RNA splicing is primarily altered by non-coding mutations in this cohort, and samples containing non-coding mutations in well-known RNA splicing factors exhibit similar gene expression signatures as samples with coding mutations in these genes. These analyses contribute a new repertoire of possible cancer genes and mechanisms that are altered by non-coding mutations and offer insights into additional cancer vulnerabilities that can be investigated for potential therapeutic treatments.

Von Frey testing revisited: Provision of an online algorithm for improved accuracy of 50% thresholds.

BACKGROUND: In the pain field, it is essential to quantify nociceptive responses. The response to the application of von Frey filaments to the skin measures tactile sensitivity and is a surrogate marker of allodynia in states of peripheral and/or central sensitization. The method is widely used across species within the pain field. However, uncertainties appear to exist regarding the appropriate method for analysing obtained data. Therefore, there is a need for refinement of the calculations for transformation of raw data to quantifiable data. METHODS: Here, we briefly review the fundamentals behind von Frey testing using the standard up-down method and the associated statistics and show how different parameters of the statistical equation influence the calculated 50% threshold results. We discuss how to obtain the most accurate estimations in a given experimental setting. RESULTS: To enhance accuracy and reproducibility across laboratories, we present an easy to use algorithm that calculates 50% thresholds based on the exact filaments and their interval using math beyond the traditional methods. This tool is available to the everyday user of von Frey filaments and allows the insertion of all imaginable ranges of filaments and is thus applicable to data derived in any species. CONCLUSION: We advocate for the use of this algorithm to minimize inaccuracies and to improve internal and external reproducibility. SIGNIFICANCE: The von Frey testing procedure is standard for assessing peripheral and central sensitization but is associated with inaccuracies and lack of transparency in the associated math. Here, we describe these problems and present a novel statistical algorithm that calculates the exact thresholds using math beyond the traditional methods. The online platform is transparent, free of charge and easy to use also for the everyday user of von Frey filaments. Application of this resource will ultimately reduce errors due to methodological misinterpretations and increase reproducibility across laboratories.

Conflicting associations between dietary patterns and changes of anthropometric traits across subgroups of middle-aged women and men.

BACKGROUND: Individuals respond differently to dietary intake leading to different associations between diet and traits. Most studies have investigated large cohorts without subgrouping them. OBJECTIVE: The purpose was to identify non-uniform associations between diets and anthropometric traits that appeared to be in conflict with one another across subgroups. DESIGN: We used a cohort comprising 43,790 women and men, the Danish Diet, Cancer and Health study, which includes a baseline examination at age 50-64 years and a follow-up about 5 years later. The baseline examination involved anthropometrics, body fat percentage, a food frequency questionnaire and information on lifestyle. From the questionnaire data we computed association rules between the intake of food groups and changes in waist circumference and body weight. Using association rule mining on subgroups and gender-specific cohorts, we identified non-uniform associations. The two gender-specific cohorts were stratified into subgroups using a non-linear, self-organizing map based method. RESULTS: We found 22 and 7 cases of conflicting rules in 8 participant subgroups for different anthropometric traits in women and men, respectively. For example, in a subgroup of women moderate waist loss was associated with a dietary pattern characterized by low intake in both cabbages and wine, in conflict with the association trends of both dietary factors in the female cohort. The finding of more conflicting rules in women suggests that inter-individual differences in response to dietary intake are stronger in women than in men. CONCLUSIONS: This combined stratification and association discovery approach revealed epidemiological relationships between dietary factors and changes in anthropometric traits in subgroups that take food group interactions into account. Conflicting rules adds an additional layer of complexity that should be integrated into the study of these relationships, for example in relation to genotypes.

Identification of hyper-rewired genomic stress non-oncogene addiction genes across 15 cancer types.

Non-oncogene addiction (NOA) genes are essential for supporting the stress-burdened phenotype of tumours and thus vital for their survival. Although NOA genes are acknowledged to be potential drug targets, there has been no large-scale attempt to identify and characterise them as a group across cancer types. Here we provide the first method for the identification of conditional NOA genes and their rewired neighbours using a systems approach. Using copy number data and expression profiles from The Cancer Genome Atlas (TCGA) we performed comparative analyses between high and low genomic stress tumours for 15 cancer types. We identified 101 condition-specific differential coexpression modules, mapped to a high-confidence human interactome, comprising 133 candidate NOA rewiring hub genes. We observe that most modules lose coexpression in the high-stress state and that activated stress modules and hubs take part in homoeostasis maintenance processes such as chromosome segregation, oxireductase activity, mitotic checkpoint (PLK1 signalling), DNA replication initiation and synaptic signalling. We furthermore show that candidate NOA rewiring hubs are unique for each cancer type, but that their respective rewired neighbour genes largely are shared across cancer types.

High-Throughput Sequencing-Based Investigation of Viruses in Human Cancers by Multienrichment Approach.

BACKGROUND: Viruses and other infectious agents cause more than 15% of human cancer cases. High-throughput sequencing-based studies of virus-cancer associations have mainly focused on cancer transcriptome data. METHODS: In this study, we applied a diverse selection of presequencing enrichment methods targeting all major viral groups, to characterize the viruses present in 197 samples from 18 sample types of cancerous origin. Using high-throughput sequencing, we generated 710 datasets constituting 57 billion sequencing reads. RESULTS: Detailed in silico investigation of the viral content, including exclusion of viral artefacts, from de novo assembled contigs and individual sequencing reads yielded a map of the viruses detected. Our data reveal a virome dominated by papillomaviruses, anelloviruses, herpesviruses, and parvoviruses. More than half of the included samples contained 1 or more viruses; however, no link between specific viruses and cancer types were found. CONCLUSIONS: Our study sheds light on viral presence in cancers and provides highly relevant virome data for future reference.

A generic deep convolutional neural network framework for prediction of receptor-ligand interactions-NetPhosPan: application to kinase phosphorylation prediction.

MOTIVATION: Understanding the specificity of protein receptor-ligand interactions is pivotal for our comprehension of biological mechanisms and systems. Receptor protein families often have a certain level of sequence diversity that converges into fewer conserved protein structures, allowing the exertion of well-defined functions. T and B cell receptors of the immune system and protein kinases that control the dynamic behaviour and decision processes in eukaryotic cells by catalysing phosphorylation represent prime examples. Driven by the large sequence diversity, the receptors within such protein families are often found to share specificities although divergent at the sequence level. This observation has led to the notion that prediction models of such systems are most effectively handled in a receptor-specific manner. RESULTS: We show that this approach in many cases is suboptimal, and describe an alternative improved framework for generating models with pan-receptor-predictive power for receptor protein families. The framework is based on deep artificial neural networks and integrates information from individual receptors into a single pan-receptor model, leveraging information across multiple receptor-specific datasets allowing predictions of the receptor specificity for all members of a given protein family including those described by limited or no ligand data. The approach was applied to the protein kinase superfamily, leading to the method NetPhosPan. The method was extensively validated and benchmarked against state-of-the-art prediction methods and was found to have unprecedented performance in particularly for kinase domains characterized by limited or no experimental data. AVAILABILITY AND IMPLEMENTATION: The method is freely available to non-commercial users and can be downloaded at http://www.cbs.dtu.dk/services/NetPhospan-1.0. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Retinoic Acid Signaling in Thymic Epithelial Cells Regulates Thymopoiesis.

Despite the essential role of thymic epithelial cells (TEC) in T cell development, the signals regulating TEC differentiation and homeostasis remain incompletely understood. In this study, we show a key in vivo role for the vitamin A metabolite, retinoic acid (RA), in TEC homeostasis. In the absence of RA signaling in TEC, cortical TEC (cTEC) and CD80loMHC class IIlo medullary TEC displayed subset-specific alterations in gene expression, which in cTEC included genes involved in epithelial proliferation, development, and differentiation. Mice whose TEC were unable to respond to RA showed increased cTEC proliferation, an accumulation of stem cell Ag-1hi cTEC, and, in early life, a decrease in medullary TEC numbers. These alterations resulted in reduced thymic cellularity in early life, a reduction in CD4 single-positive and CD8 single-positive numbers in both young and adult mice, and enhanced peripheral CD8+ T cell survival upon TCR stimulation. Collectively, our results identify RA as a regulator of TEC homeostasis that is essential for TEC function and normal thymopoiesis.

Benchmarking the HLA typing performance of Polysolver and Optitype in 50 Danish parental trios.

BACKGROUND: The adaptive immune response intrinsically depends on hypervariable human leukocyte antigen (HLA) genes. Concomitantly, correct HLA phenotyping is crucial for successful donor-patient matching in organ transplantation. The cost and technical limitations of current laboratory techniques, together with advances in next-generation sequencing (NGS) methodologies, have increased the need for precise computational typing methods. RESULTS: We tested two widespread HLA typing methods using high quality full genome sequencing data from 150 individuals in 50 family trios from the Genome Denmark project. First, we computed descendant accuracies assessing the agreement in the inheritance of alleles from parents to offspring. Second, we compared the locus-specific homozygosity rates as well as the allele frequencies; and we compared those to the observed values in related populations. We provide guidelines for testing the accuracy of HLA typing methods by comparing family information, which is independent of the availability of curated alleles. CONCLUSIONS: Although current computational methods for HLA typing generally provide satisfactory results, our benchmark - using data with ultra-high sequencing depth - demonstrates the incompleteness of current reference databases, and highlights the importance of providing genomic databases addressing current sequencing standards, a problem yet to be resolved before benefiting fully from personalised medicine approaches HLA phenotyping is essential.

Analysis of a gene panel for targeted sequencing of colorectal cancer samples.

Colorectal cancer (CRC) is a leading cause of death worldwide. Surgical intervention is a successful treatment for stage I patients, whereas other more advanced cases may require adjuvant chemotherapy. The selection of effective adjuvant treatments remains, however, challenging. Accurate patient stratification is necessary for the identification of the subset of patients likely responding to treatment, while sparing others from pernicious treatment. Targeted sequencing approaches may help in this regard, enabling rapid genetic investigation, and at the same time easily applicable in routine diagnosis. We propose a set of guidelines for the identification, including variant calling and filtering, of somatic mutations driving tumorigenesis in the absence of matched healthy tissue. We also discuss the inclusion criteria for the generation of our gene panel. Furthermore, we evaluate the prognostic impact of individual genes, using Cox regression models in the context of overall survival and disease-free survival. These analyses confirmed the role of commonly used biomarkers, and shed light on controversial genes such as CYP2C8. Applying those guidelines, we created a novel gene panel to investigate the onset and progression of CRC in 273 patients. Our comprehensive biomarker set includes 266 genes that may play a role in the progression through the different stages of the disease. Tracing the developmental state of the tumour, and its resistances, is instrumental in patient stratification and reliable decision making in precision clinical practice.

Sequencing and de novo assembly of 150 genomes from Denmark as a population reference.

Hundreds of thousands of human genomes are now being sequenced to characterize genetic variation and use this information to augment association mapping studies of complex disorders and other phenotypic traits. Genetic variation is identified mainly by mapping short reads to the reference genome or by performing local assembly. However, these approaches are biased against discovery of structural variants and variation in the more complex parts of the genome. Hence, large-scale de novo assembly is needed. Here we show that it is possible to construct excellent de novo assemblies from high-coverage sequencing with mate-pair libraries extending up to 20 kilobases. We report de novo assemblies of 150 individuals (50 trios) from the GenomeDenmark project. The quality of these assemblies is similar to those obtained using the more expensive long-read technology. We use the assemblies to identify a rich set of structural variants including many novel insertions and demonstrate how this variant catalogue enables further deciphering of known association mapping signals. We leverage the assemblies to provide 100 completely resolved major histocompatibility complex haplotypes and to resolve major parts of the Y chromosome. Our study provides a regional reference genome that we expect will improve the power of future association mapping studies and hence pave the way for precision medicine initiatives, which now are being launched in many countries including Denmark.

Cutavirus in Cutaneous Malignant Melanoma.

A novel human protoparvovirus related to human bufavirus and preliminarily named cutavirus has been discovered. We detected cutavirus in a sample of cutaneous malignant melanoma by using viral enrichment and high-throughput sequencing. The role of cutaviruses in cutaneous cancers remains to be investigated.

How compelling are the data for Epstein-Barr virus being a trigger for systemic lupus and other autoimmune diseases?

PURPOSE OF REVIEW: Systemic lupus erythematosus (SLE) is caused by a combination of genetic and acquired immunodeficiencies and environmental factors including infections. An association with Epstein-Barr virus (EBV) has been established by numerous studies over the past decades. Here, we review recent experimental studies on EBV, and present our integrated theory of SLE development. RECENT FINDINGS: SLE patients have dysfunctional control of EBV infection resulting in frequent reactivations and disease progression. These comprise impaired functions of EBV-specific T-cells with an inverse correlation to disease activity and elevated serum levels of antibodies against lytic cycle EBV antigens. The presence of EBV proteins in renal tissue from SLE patients with nephritis suggests direct involvement of EBV in SLE development. As expected for patients with immunodeficiencies, studies reveal that SLE patients show dysfunctional responses to other viruses as well. An association with EBV infection has also been demonstrated for other autoimmune diseases, including Sjögren's syndrome, rheumatoid arthritis, and multiple sclerosis. SUMMARY: Collectively, the interplay between an impaired immune system and the cumulative effects of EBV and other viruses results in frequent reactivation of EBV and enhanced cell death, causing development of SLE and concomitant autoreactivities.

Identification of Known and Novel Recurrent Viral Sequences in Data from Multiple Patients and Multiple Cancers.

Virus discovery from high throughput sequencing data often follows a bottom-up approach where taxonomic annotation takes place prior to association to disease. Albeit effective in some cases, the approach fails to detect novel pathogens and remote variants not present in reference databases. We have developed a species independent pipeline that utilises sequence clustering for the identification of nucleotide sequences that co-occur across multiple sequencing data instances. We applied the workflow to 686 sequencing libraries from 252 cancer samples of different cancer and tissue types, 32 non-template controls, and 24 test samples. Recurrent sequences were statistically associated to biological, methodological or technical features with the aim to identify novel pathogens or plausible contaminants that may associate to a particular kit or method. We provide examples of identified inhabitants of the healthy tissue flora as well as experimental contaminants. Unmapped sequences that co-occur with high statistical significance potentially represent the unknown sequence space where novel pathogens can be identified.