Transcriptomic profiling reveals p53 as a key regulator of doxorubicin-induced cardiotoxicity.

Doxorubicin is an important anticancer drug in the clinic. Unfortunately, it causes cumulative and dose-dependent cardiotoxic side effects. As the population of cancer survivors who have been exposed to treatment continues to grow, there is increased interest in assessing the long-term cardiac effects of doxorubicin and understanding the underlying mechanisms at play. In this study, we investigated doxorubicin-induced transcriptomic changes using RNA-sequencing (RNAseq) and a cellular model comprised of human induced pluripotent stem cell-derived cardiomyocytes (hiPSC-CMs). Analyses of predicted upstream regulators identified the p53 protein as a key regulator of transcriptomic changes induced by doxorubicin. Clustering and pathway analyses showed that increased death receptor (DR) expression and enrichment of the extrinsic apoptotic pathway are significantly associated with doxorubicin-induced cardiotoxicity. Increased expression of p53 and DRs were confirmed via immunoblotting. Our data pinpoints increased DR expression as an early transcriptomic indicator of cardiotoxicity, suggesting that DR expression might function as a predictive biomarker for cardiac damage.

Fugitives on the run: circulating tumor cells (CTCs) in metastatic diseases.

The presence of circulating tumor cells (CTCs) in the bloodstream signals the existence of a tumor and denotes risk of metastatic spread. CTCs can be isolated and analyzed to monitor cancer progression and therapeutic response. However, CTC isolation devices have shown considerable variation in detection rates, limiting their use as a routine diagnostic and monitoring tool. In this review, we discuss recent advances in CTC detection methodologies and associated clinical studies. We provide perspective on the future direction of CTC isolation and molecular characterization towards developing reliable biomarkers that monitor disease progression or therapeutic response.

meaRtools: An R package for the analysis of neuronal networks recorded on microelectrode arrays.

Here we present an open-source R package 'meaRtools' that provides a platform for analyzing neuronal networks recorded on Microelectrode Arrays (MEAs). Cultured neuronal networks monitored with MEAs are now being widely used to characterize in vitro models of neurological disorders and to evaluate pharmaceutical compounds. meaRtools provides core algorithms for MEA spike train analysis, feature extraction, statistical analysis and plotting of multiple MEA recordings with multiple genotypes and treatments. meaRtools functionality covers novel solutions for spike train analysis, including algorithms to assess electrode cross-correlation using the spike train tiling coefficient (STTC), mutual information, synchronized bursts and entropy within cultured wells. Also integrated is a solution to account for bursts variability originating from mixed-cell neuronal cultures. The package provides a statistical platform built specifically for MEA data that can combine multiple MEA recordings and compare extracted features between different genetic models or treatments. We demonstrate the utilization of meaRtools to successfully identify epilepsy-like phenotypes in neuronal networks from Celf4 knockout mice. The package is freely available under the GPL license (GPL> = 3) and is updated frequently on the CRAN web-server repository. The package, along with full documentation can be downloaded from: https://cran.r-project.org/web/packages/meaRtools/.

Annotating pathogenic non-coding variants in genic regions.

Identifying the underlying causes of disease requires accurate interpretation of genetic variants. Current methods ineffectively capture pathogenic non-coding variants in genic regions, resulting in overlooking synonymous and intronic variants when searching for disease risk. Here we present the Transcript-inferred Pathogenicity (TraP) score, which uses sequence context alterations to reliably identify non-coding variation that causes disease. High TraP scores single out extremely rare variants with lower minor allele frequencies than missense variants. TraP accurately distinguishes known pathogenic and benign variants in synonymous (AUC = 0.88) and intronic (AUC = 0.83) public datasets, dismissing benign variants with exceptionally high specificity. TraP analysis of 843 exomes from epilepsy family trios identifies synonymous variants in known epilepsy genes, thus pinpointing risk factors of disease from non-coding sequence data. TraP outperforms leading methods in identifying non-coding variants that are pathogenic and is therefore a valuable tool for use in gene discovery and the interpretation of personal genomes.While non-coding synonymous and intronic variants are often not under strong selective constraint, they can be pathogenic through affecting splicing or transcription. Here, the authors develop a score that uses sequence context alterations to predict pathogenicity of synonymous and non-coding genetic variants, and provide a web server of pre-computed scores.

Inhibition of microRNA 128 promotes excitability of cultured cortical neuronal networks.

Cultured neuronal networks monitored with microelectrode arrays (MEAs) have been used widely to evaluate pharmaceutical compounds for potential neurotoxic effects. A newer application of MEAs has been in the development of in vitro models of neurological disease. Here, we directly evaluated the utility of MEAs to recapitulate in vivo phenotypes of mature microRNA-128 (miR-128) deficiency, which causes fatal seizures in mice. We show that inhibition of miR-128 results in significantly increased neuronal activity in cultured neuronal networks derived from primary mouse cortical neurons. These results support the utility of MEAs in developing in vitro models of neuroexcitability disorders, such as epilepsy, and further suggest that MEAs provide an effective tool for the rapid identification of microRNAs that promote seizures when dysregulated.

A functional correlate of severity in alternating hemiplegia of childhood.

OBJECTIVE: Mutations in ATP1A3, the gene that encodes the α3 subunit of the Na(+)/K(+) ATPase, are the primary cause of alternating hemiplegia of childhood (AHC). Correlations between different mutations and AHC severity were recently reported, with E815K identified in severe and D801N and G947R in milder cases. This study aims to explore the molecular pathological mechanisms in AHC and to identify functional correlates for mutations associated with different levels of disease severity. METHODS: Human wild type ATP1A3, and E815K, D801N and G947R mutants were expressed in Xenopus laevis oocytes and Na(+)/K(+) ATPase function measured. Structural homology models of the human α3 subunit containing AHC mutations were created. RESULTS: The AHC mutations examined all showed similar levels of reduction in forward cycling. Wild type forward cycling was reduced by coexpression with any mutant, indicating dominant negative interactions. Proton transport was measured and found to be selectively impaired only in E815K. Homology modeling showed that D801 and G947 lie within or near known cation binding sites while E815 is more distal. Despite its effect on proton transport, E815K was also distant from the proposed proton transport route. INTERPRETATION: Loss of forward cycling and dominant negativity are common and likely necessary pathomechanisms for AHC. In addition, loss of proton transport correlated with severity of AHC. D801N and G947R are likely to directly disrupt normal Na(+)/K(+) binding while E815K may disrupt forward cycling and proton transport via allosteric mechanisms yet to be elucidated.

Whole-exome sequencing in undiagnosed genetic diseases: interpreting 119 trios.

PURPOSE: Despite the recognized clinical value of exome-based diagnostics, methods for comprehensive genomic interpretation remain immature. Diagnoses are based on known or presumed pathogenic variants in genes already associated with a similar phenotype. Here, we extend this paradigm by evaluating novel bioinformatics approaches to aid identification of new gene-disease associations. METHODS: We analyzed 119 trios to identify both diagnostic genotypes in known genes and candidate genotypes in novel genes. We considered qualifying genotypes based on their population frequency and in silico predicted effects we also characterized the patterns of genotypes enriched among this collection of patients. RESULTS: We obtained a genetic diagnosis for 29 (24%) of our patients. We showed that patients carried an excess of damaging de novo mutations in intolerant genes, particularly those shown to be essential in mice (P = 3.4 × 10(-8)). This enrichment is only partially explained by mutations found in known disease-causing genes. CONCLUSION: This work indicates that the application of appropriate bioinformatics analyses to clinical sequence data can also help implicate novel disease genes and suggest expanded phenotypes for known disease genes. These analyses further suggest that some cases resolved by whole-exome sequencing will have direct therapeutic implications.