Genetic variation in DNMT3B and increased global DNA methylation is associated with suicide attempts in psychiatric patients

Recently, a significant epigenetic component in the pathology of suicide has been realized. Here we investigate candidate functional SNPs in epigenetic‐regulatory genes, DNMT1 and DNMT3B, for association with suicide attempt (SA) among patients with co‐existing psychiatric illness. In addition, global DNA methylation levels [5‐methyl cytosine (5‐mC%)] between SA and psychiatric controls were quantified using the Methylflash Methylated DNA Quantification Kit. DNA was obtained from blood of 79 suicide attempters and 80 non‐attempters, assessed for DSM‐IV Axis I disorders. Functional SNPs were selected for each gene (DNMT1; n = 7, DNMT3B; n = 10), and genotyped. A SNP (rs2424932) residing in the 3′ UTR of the DNMT3B gene was associated with SA compared with a non‐attempter control group (P = 0.001; Chi‐squared test, Bonferroni adjusted P value = 0.02). Moreover, haplotype analysis identified a DNMT3B haplotype which differed between cases and controls, however this association did not hold after Bonferroni correction (P = 0.01, Bonferroni adjusted P value = 0.56). Global methylation analysis showed that psychiatric patients with a history of SA had significantly higher levels of global DNA methylation compared with controls (P = 0.018, Student's t‐test). In conclusion, this is the first report investigating polymorphisms in DNMT genes and global DNA methylation quantification in SA risk. Preliminary findings suggest that allelic variability in DNMT3B may be relevant to the underlying diathesis for suicidal acts and our findings support the hypothesis that aberrant DNA methylation profiles may contribute to the biology of suicidal acts. Thus, analysis of global DNA hypermethylation in blood may represent a biomarker for increased SA risk in psychiatric patients.     

The exocyst subunit Exo70B1 is involved in the immune response of Arabidopsis thaliana to different pathogens and cell death

Components of the vesicle trafficking machinery are central to the immune response in plants. The role of vesicle trafficking during pre-invasive penetration resistance has been well documented. However, emerging evidence also implicates vesicle trafficking in early immune signaling. Here we report that Exo70B1, a subunit of the exocyst complex which mediates early tethering during exocytosis is involved in resistance. We show that exo70B1 mutants display pathogen-specific immuno-compromised phenotypes. We also show that exo70B1 mutants display lesion-mimic cell death, which in combination with the reduced responsiveness to pathogen-associated molecular patterns (PAMPs) results in complex immunity-related phenotypes.     

Flow Cytometry Bioinformatics

Flow cytometry bioinformatics is the application of bioinformatics to flow cytometry data, which involves storing, retrieving, organizing, and analyzing flow cytometry data using extensive computational resources and tools. Flow cytometry bioinformatics requires extensive use of and contributes to the development of techniques from computational statistics and machine learning. Flow cytometry and related methods allow the quantification of multiple independent biomarkers on large numbers of single cells. The rapid growth in the multidimensionality and throughput of flow cytometry data, particularly in the 2000s, has led to the creation of a variety of computational analysis methods, data standards, and public databases for the sharing of results. Computational methods exist to assist in the preprocessing of flow cytometry data, identifying cell populations within it, matching those cell populations across samples, and performing diagnosis and discovery using the results of previous steps. For preprocessing, this includes compensating for spectral overlap, transforming data onto scales conducive to visualization and analysis, assessing data for quality, and normalizing data across samples and experiments. For population identification, tools are available to aid traditional manual identification of populations in two-dimensional scatter plots (gating), to use dimensionality reduction to aid gating, and to find populations automatically in higher dimensional space in a variety of ways. It is also possible to characterize data in more comprehensive ways, such as the density-guided binary space partitioning technique known as probability binning, or by combinatorial gating. Finally, diagnosis using flow cytometry data can be aided by supervised learning techniques, and discovery of new cell types of biological importance by high-throughput statistical methods, as part of pipelines incorporating all of the aforementioned methods. Open standards, data, and software are also key parts of flow cytometry bioinformatics. Data standards include the widely adopted Flow Cytometry Standard (FCS) defining how data from cytometers should be stored, but also several new standards under development by the International Society for Advancement of Cytometry (ISAC) to aid in storing more detailed information about experimental design and analytical steps. Open data is slowly growing with the opening of the CytoBank database in 2010 and FlowRepository in 2012, both of which allow users to freely distribute their data, and the latter of which has been recommended as the preferred repository for MIFlowCyt-compliant data by ISAC. Open software is most widely available in the form of a suite of Bioconductor packages, but is also available for web execution on the GenePattern platform.

This is a “Topic Page” article for PLOS Computational Biology.

     

Cross-Species Array Comparative Genomic Hybridization Identifies Novel Oncogenic Events in Zebrafish and Human Embryonal Rhabdomyosarcoma

Human cancer genomes are highly complex, making it challenging to identify specific drivers of cancer growth, progression, and tumor maintenance. To bypass this obstacle, we have applied array comparative genomic hybridization (array CGH) to zebrafish embryonal rhabdomyosaroma (ERMS) and utilized cross-species comparison to rapidly identify genomic copy number aberrations and novel candidate oncogenes in human disease. Zebrafish ERMS contain small, focal regions of low-copy amplification. These same regions were commonly amplified in human disease. For example, 16 of 19 chromosomal gains identified in zebrafish ERMS also exhibited focal, low-copy gains in human disease. Genes found in amplified genomic regions were assessed for functional roles in promoting continued tumor growth in human and zebrafish ERMS – identifying critical genes associated with tumor maintenance. Knockdown studies identified important roles for Cyclin D2 (CCND2), Homeobox Protein C6 (HOXC6) and PlexinA1 (PLXNA1) in human ERMS cell proliferation. PLXNA1 knockdown also enhanced differentiation, reduced migration, and altered anchorage-independent growth. By contrast, chemical inhibition of vascular endothelial growth factor (VEGF) signaling reduced angiogenesis and tumor size in ERMS-bearing zebrafish. Importantly, VEGFA expression correlated with poor clinical outcome in patients with ERMS, implicating inhibitors of the VEGF pathway as a promising therapy for improving patient survival. Our results demonstrate the utility of array CGH and cross-species comparisons to identify candidate oncogenes essential for the pathogenesis of human cancer.     

A Bayesian Approach for Modeling Cattle Movements in the United States: Scaling up a Partially Observed Network

Networks are rarely completely observed and prediction of unobserved edges is an important problem, especially in disease spread modeling where networks are used to represent the pattern of contacts. We focus on a partially observed cattle movement network in the U.S. and present a method for scaling up to a full network based on Bayesian inference, with the aim of informing epidemic disease spread models in the United States. The observed network is a 10% state stratified sample of Interstate Certificates of Veterinary Inspection that are required for interstate movement; describing approximately 20,000 movements from 47 of the contiguous states, with origins and destinations aggregated at the county level. We address how to scale up the 10% sample and predict unobserved intrastate movements based on observed movement distances. Edge prediction based on a distance kernel is not straightforward because the probability of movement does not always decline monotonically with distance due to underlying industry infrastructure. Hence, we propose a spatially explicit model where the probability of movement depends on distance, number of premises per county and historical imports of animals. Our model performs well in recapturing overall metrics of the observed network at the node level (U.S. counties), including degree centrality and betweenness; and performs better compared to randomized networks. Kernel generated movement networks also recapture observed global network metrics, including network size, transitivity, reciprocity, and assortativity better than randomized networks. In addition, predicted movements are similar to observed when aggregated at the state level (a broader geographic level relevant for policy) and are concentrated around states where key infrastructures, such as feedlots, are common. We conclude that the method generally performs well in predicting both coarse geographical patterns and network structure and is a promising method to generate full networks that incorporate the uncertainty of sampled and unobserved contacts.     

Metabolic Determinants of Electrical Failure in Ex-Vivo Canine Model of Cardiac Arrest: Evidence for the Protective Role of Inorganic Pyrophosphate

Rationale

Deterioration of ventricular fibrillation (VF) into asystole or severe bradycardia (electrical failure) heralds a fatal outcome of cardiac arrest. The role of metabolism in the timing of electrical failure remains unknown.

Objective

To determine metabolic factors of early electrical failure in an Ex-vivo canine model of cardiac arrest (VF+global ischemia).

Methods and Results

Metabolomic screening was performed in left ventricular biopsies collected before and after 0.3, 2, 5, 10 and 20 min of VF and global ischemia. Electrical activity was monitored via plunge needle electrodes and pseudo-ECG. Four out of nine hearts exhibited electrical failure at 10.1±0.9 min (early-asys), while 5/9 hearts maintained VF for at least 19.7 min (late-asys). As compared to late-asys, early-asys hearts had more ADP, less phosphocreatine, and higher levels of lactate at some time points during VF/ischemia (all comparisons p<0.05). Pre-ischemic samples from late-asys hearts contained ∼25 times more inorganic pyrophosphate (PPi) than early-asys hearts. A mechanistic role of PPi in cardioprotection was then tested by monitoring mitochondrial membrane potential (ΔΨ) during 20 min of simulated-demand ischemia using potentiometric probe TMRM in rabbit adult ventricular myocytes incubated with PPi versus control group. Untreated myocytes experienced significant loss of ΔΨ while in the PPi-treated myocytes ΔΨ was relatively maintained throughout 20 min of simulated-demand ischemia as compared to control (p<0.05).

Conclusions

High tissue level of PPi may prevent ΔΨm loss and electrical failure at the early phase of ischemic stress. The link between the two protective effects may involve decreased rates of mitochondrial ATP hydrolysis and lactate accumulation.