Exome sequencing helped the fine diagnosis of two siblings afflicted with atypical Timothy syndrome (TS2)

BACKGROUND: Long-QT syndrome (LQTS) causes a prolongation of the QT-interval in the ECG leading to life threatening tachyarrhythmia and ventricular fibrillation. One atypical form of LQTS, Timothy syndrome (TS), is associated with syndactyly, immune deficiency, cognitive and neurological abnormalities as well as distinct cranio-facial abnormalities. CASE PRESENTATION: On a family with both children diagnosed with clinical LQTS, we performed whole exome sequencing to comprehensively screen for causative mutations after a targeted candidate gene panel screen for Long-QT syndrome target genes failed to identify any underlying genetic defect. Using exome sequencing, we identified in both affected children, a p.402G > S mutation in exon 8 of the CACNA1C gene, a voltage-dependent Ca2+ channel. The mutation was inherited from their father, a mosaic mutation carrier. Based on this molecular finding and further more careful clinical examination, we refined the diagnosis to be Timothy syndrome (TS2) and thereby were able to present new therapeutic approaches. CONCLUSIONS: Our study highlights the difficulties in accurate diagnosis of patients with rare diseases, especially those with atypical clinical manifestation. Such challenge could be addressed with the help of comprehensive and unbiased mutation screening, such as exome sequencing

Compensatory upregulation of anti-beta-adrenergic receptor antibody levels might prevent heart failure presentation in pediatric myocarditis

BACKGROUND: Myocarditis can be associated with severe heart failure and is caused by different inflammatory and autoimmune responses. The aim of this study was to describe the immunological response in children with myocarditis by analyzing anti-beta-adrenergic receptor antibodies (anti-β-AR Abs). METHODS: Sera of children who were hospitalized with biopsy-proven myocarditis were prospectively collected between April 2017 and March 2019. Anti-β1-AR Ab, anti-β2-AR Ab, and anti-β3-AR Ab were quantified by a CE-certified ELISA kit. According to normal values for immunoglobulin G (IgG), three age groups, 5–17 years, were defined. Children without inflammatory cardiac pathology and no heart failure signs were served as a control group. RESULTS: We compared 22 patients with biopsy-proven myocarditis and 28 controls. The median age (interquartile range) of the myocarditis group (MYC) was 12.1 (2.7–16.4) years, 13 men, left ventricular ejection fraction (LVEF) 51% and for control group, the median age was 5.0 (3.0–6.8) years, nine men, LVEF 64%. Myocarditis patients in the age group >5–17 years showed significantly higher anti-β3-AR Ab levels as compared to controls (p = 0.014). Lower anti-β2-AR Ab and anti-β3-AR Ab levels were significantly correlated with higher left ventricular diameters in myocarditis patients. The event-free survival using a combined endpoint (mechanical circulatory support [MCS], transplantation, and/or death) was significantly lower in myocarditis patients with antibody levels below the median as compared to myocarditis patients with antibody levels ≥ the median. CONCLUSION: Anti-β-AR Ab levels are increased in children with myocarditis and >5 years of age. These antibodies might be upregulated compensatory to prevent further cardiac deterioration. A worse event-free survival in patients with lower anti-β-AR Ab levels might be a therapeutic target for immunoglobulin substitution

Identifying Modules of Coexpressed Transcript Units and Their Organization of Saccharopolyspora erythraea from Time Series Gene Expression Profiles

BACKGROUND: The Saccharopolyspora erythraea genome sequence was released in 2007. In order to look at the gene regulations at whole transcriptome level, an expression microarray was specifically designed on the S. erythraea strain NRRL 2338 genome sequence. Based on these data, we set out to investigate the potential transcriptional regulatory networks and their organization. METHODOLOGY/PRINCIPAL FINDINGS: In view of the hierarchical structure of bacterial transcriptional regulation, we constructed a hierarchical coexpression network at whole transcriptome level. A total of 27 modules were identified from 1255 differentially expressed transcript units (TUs) across time course, which were further classified in to four groups. Functional enrichment analysis indicated the biological significance of our hierarchical network. It was indicated that primary metabolism is activated in the first rapid growth phase (phase A), and secondary metabolism is induced when the growth is slowed down (phase B). Among the 27 modules, two are highly correlated to erythromycin production. One contains all genes in the erythromycin-biosynthetic (ery) gene cluster and the other seems to be associated with erythromycin production by sharing common intermediate metabolites. Non-concomitant correlation between production and expression regulation was observed. Especially, by calculating the partial correlation coefficients and building the network based on Gaussian graphical model, intrinsic associations between modules were found, and the association between those two erythromycin production-correlated modules was included as expected. CONCLUSIONS: This work created a hierarchical model clustering transcriptome data into coordinated modules, and modules into groups across the time course, giving insight into the concerted transcriptional regulations especially the regulation corresponding to erythromycin production of S. erythraea. This strategy may be extendable to studies on other prokaryotic microorganisms

Pathogenic variants associated with dilated cardiomyopathy predict outcome in pediatric myocarditis

BACKGROUND: Myocarditis is one of the most common causes leading to heart failure in children and a possible genetic background has been postulated. We sought to characterize the clinical and genetic characteristics in patients with myocarditis ≤18 years of age to predict outcome. METHODS: A cohort of 42 patients (MYCPEDIG) with biopsy-proven myocarditis underwent genetic testing with targeted panel sequencing of cardiomyopathy-associated genes. MYCPEDIG patients were divided into subgroups according to the phenotype of dilated cardiomyopathy (DCM) at presentation, resulting in 22 patients without DCM (MYC-NonDCM) and 20 patients with DCM (MYC-DCM). RESULTS: MYC-DCM patients (median age 1.4 years) were younger than MYC-NonDCM patients (median age 16.1 years; p<0.001) and were corresponding to heart failure-like and coronary syndrome-like phenotypes, respectively. At least one likely pathogenic/pathogenic (LP/P) variant was identified in 9/42 patients (22%), 8 of them were heterozygous, and 7/9 were in MYC-DCM. LP/P variants were found in genes validated for primary DCM (BAG3, DSP, LMNA, MYH7, TNNI3, TNNT2, and TTN). Rare variant enrichment analysis revealed significant accumulation of high impact disease variants in MYC-DCM versus healthy individuals (p=0.0003). Event-free survival was lower (p=0.008) in MYC-DCM patients compared to MYC-NonDCM and primary DCM. CONCLUSIONS: We report heterozygous LP/P variants in biopsy-proven pediatric myocarditis. Myocarditis patients with DCM phenotype were characterized by early-onset heart failure, significant enrichment of LP/P variants, and poor outcome. These phenotype- and age-group specific findings will be useful for personalized management of these patients. Genetic evaluation in children newly diagnosed with myocarditis and DCM phenotype is warranted

Parameter estimation for robust HMM analysis of ChIP-chip data

Tiling arrays are an important tool for the study of transcriptional activity, protein-DNA interactions and chromatin structure on a genome-wide scale at high resolution. Although hidden Markov models have been used successfully to analyse tiling array data, parameter estimation for these models is typically ad hoc. Especially in the context of ChIP-chip experiments, no standard procedures exist to obtain parameter estimates from the data. Common methods for the calculation of maximum likelihood estimates such as the Baum-Welch algorithm or Viterbi training are rarely applied in the context of tiling array analysis. Results: Here we develop a hidden Markov model for the analysis of chromatin structure ChIP-chip tiling array data, using t emission distributions to increase robustness towards outliers. Maximum likelihood estimates are used for all model parameters. Two different approaches to parameter estimation are investigated and combined into an efficient procedure. Conclusion: We illustrate an efficient parameter estimation procedure that can be used for HMM based methods in general and leads to a clear increase in performance when compared to the use of ad hoc estimates. The resulting hidden Markov model outperforms established methods like TileMap in the context of histone modification studies.13 page(s

Constraint-based probabilistic learning of metabolic pathways from tomato volatiles

Clustering and correlation analysis techniques have become popular tools for the analysis of data produced by metabolomics experiments. The results obtained from these approaches provide an overview of the interactions between objects of interest. Often in these experiments, one is more interested in information about the nature of these relationships, e.g., cause-effect relationships, than in the actual strength of the interactions. Finding such relationships is of crucial importance as most biological processes can only be understood in this way. Bayesian networks allow representation of these cause-effect relationships among variables of interest in terms of whether and how they influence each other given that a third, possibly empty, group of variables is known. This technique also allows the incorporation of prior knowledge as established from the literature or from biologists. The representation as a directed graph of these relationship is highly intuitive and helps to understand these processes. This paper describes how constraint-based Bayesian networks can be applied to metabolomics data and can be used to uncover the important pathways which play a significant role in the ripening of fresh tomatoes. We also show here how this methods of reconstructing pathways is intuitive and performs better than classical techniques. Methods for learning Bayesian network models are powerful tools for the analysis of data of the magnitude as generated by metabolomics experiments. It allows one to model cause-effect relationships and helps in understanding the underlying processes

Reactivation of ancestral strains of HIV-1 in the gp120 V3 env region in patients failing antiretroviral therapy and subjected to structured treatment interruption

We analyzed gp120V3 HIV-1 env region genetic diversity of 27 patients failing antiretrovirals and subjected to 12-week structured treatment interruption (STI). Based on heteroduplex mobility assays, eight patients presented low pre- and post-STI genetic diversity (G1); five presented high pre-STI but low post-STI diversity (G2); five presented low pre-STI and high post-STI diversity (G3); and nine, high pre- and post-STI diversity (G4). One patient from G1, two from G2 and two from G3 were subjected to proviral DNA end-point PCR and sequencing. in three patients, the dramatic disturbance caused by STI resulted in ancestral viral progeny activation, which repopulated the cell reservoir. in two patients presenting highly homogeneous sequences and low immune selective pressure (dN/dS ratio < 1), this phenomenon was not observed. the mechanisms involved in viral evolution, in which antiretroviral therapy also applies selective pressure, sometimes affects coreceptor usage of circulating viruses, leading to the suppression of x4 strains. (c) 2006 Published by Elsevier Inc.Universidade Federal de São Paulo, Escola Paulista Med, Lab Retrovirol, BR-04039032 São Paulo, BrazilFundacao Pro Sangue, BR-05403000 São Paulo, BrazilUniversidade Federal de São Paulo, Escola Paulista Med, Lab Retrovirol, BR-04039032 São Paulo, BrazilWeb of Scienc

Early Lung Function Testing in Infants with Aortic Arch Anomalies Identifies Patients at Risk for Airway Obstruction

BACKGROUND: Aortic arch anomalies (AAA) are rare cardio-vascular anomalies. Right-sided and double-sided aortic arch anomalies (RAAA, DAAA) are distinguished, both may cause airway obstructions. We studied the degree of airway obstruction in infants with AAA by neonatal lung function testing (LFT). PATIENTS AND METHODS: 17 patients (10 RAAA and 7 DAAA) with prenatal diagnosis of AAA were investigated. The median (range) post conception age at LFT was 40.3 (36.6-44.1) weeks, median body weight 3400 (2320-4665) g. Measurements included tidal breathing flow-volume loops (TBFVL), airway resistance (R(aw)) by bodyplethysmography and the maximal expiratory flow at functional residual capacity (V'(max)FRC) by rapid thoracic-abdominal compression (RTC) technique. V'(max)FRC was also expressed in Z-scores, based on published gender-, age and height-specific reference values. RESULTS: Abnormal lung function tests were seen in both RAAA and DAAA infants. Compared to RAAA infants, infants with DAAA had significantly more expiratory flow limitations in the TBFVL, (86% vs. 30%, p<0.05) and a significantly increased R(aw) (p = 0.015). Despite a significant correlation between R(aw) and the Z-score of V'(max)FRC (r = 0.740, p<0.001), there were no statistically significant differences in V'(max)FRC and it's Z-scores between RAAA and DAAA infants. 4 (24%) infants (2 RAAA, 2 DAAA) were near or below the 10(th) percentile of V'(max)FRC, indicating a high risk for airway obstruction. CONCLUSION: Both, infants with RAAA and DAAA, are at risk for airway obstruction and early LFT helps to identify and to monitor these infants. This may support the decision for therapeutic interventions before clinical symptoms arise

Haplotype-based association analysis of general cognitive ability in Generation Scotland, the English Longitudinal Study of Ageing, and UK Biobank

Background: Cognitive ability is a heritable trait with a polygenic architecture, for which several associated variants have been identified using genotype-based and candidate gene approaches. Haplotype-based analyses are a complementary technique that take phased genotype data into account, and potentially provide greater statistical power to detect lower frequency variants. Methods: In the present analysis, three cohort studies (ntotal = 48,002) were utilised: Generation Scotland: Scottish Family Health Study (GS:SFHS), the English Longitudinal Study of Ageing (ELSA), and the UK Biobank. A genome-wide haplotype-based meta-analysis of cognitive ability was performed, as well as a targeted meta-analysis of several gene coding regions. Results: None of the assessed haplotypes provided evidence of a statistically significant association with cognitive ability in either the individual cohorts or the meta-analysis. Within the meta-analysis, the haplotype with the lowest observed P-value overlapped with the D-amino acid oxidase activator (DAOA) gene coding region. This coding region has previously been associated with bipolar disorder, schizophrenia and Alzheimer’s disease, which have all been shown to impact upon cognitive ability. Another potentially interesting region highlighted within the current genome-wide association analysis (GS:SFHS: P = 4.09 x 10-7), was the butyrylcholinesterase (BCHE) gene coding region. The protein encoded by BCHE has been shown to influence the progression of Alzheimer’s disease and its role in cognitive ability merits further investigation. Conclusions: Although no evidence was found for any haplotypes with a statistically significant association with cognitive ability, our results did provide further evidence that the genetic variants contributing to the variance of cognitive ability are likely to be of small effect

Mapping Dynamic Histone Acetylation Patterns to Gene Expression in Nanog-depleted Murine Embryonic Stem Cells

Embryonic stem cells (ESC) have the potential to self-renew indefinitely and to differentiate into any of the three germ layers. The molecular mechanisms for self-renewal, maintenance of pluripotency and lineage specification are poorly understood, but recent results point to a key role for epigenetic mechanisms. In this study, we focus on quantifying the impact of histone 3 acetylation (H3K9,14ac) on gene expression in murine embryonic stem cells. We analyze genome-wide histone acetylation patterns and gene expression profiles measured over the first five days of cell differentiation triggered by silencing Nanog, a key transcription factor in ESC regulation. We explore the temporal and spatial dynamics of histone acetylation data and its correlation with gene expression using supervised and unsupervised statistical models. On a genome-wide scale, changes in acetylation are significantly correlated to changes in mRNA expression and, surprisingly, this coherence increases over time. We quantify the predictive power of histone acetylation for gene expression changes in a balanced cross-validation procedure. In an in-depth study we focus on genes central to the regulatory network of Mouse ESC, including those identified in a recent genome-wide RNAi screen and in the PluriNet, a computationally derived stem cell signature. We find that compared to the rest of the genome, ESC-specific genes show significantly more acetylation signal and a much stronger decrease in acetylation over time, which is often not reflected in an concordant expression change. These results shed light on the complexity of the relationship between histone acetylation and gene expression and are a step forward to dissect the multilayer regulatory mechanisms that determine stem cell fate.Comment: accepted at PLoS Computational Biolog