ORAI1 regulates sustained cytosolic free calcium fluctuations during breast cancer cell apoptosis and apoptotic resistance via a STIM1 independent pathway.

Excessive rapid increases in cytosolic free Ca2+ have a clear association with the induction of cancer cell death. Whereas, characterizing the Ca2+ signaling events that occur during the progression of the apoptotic cascade over a period of hours or days, has not yet been possible. Now using genetically encoded Ca2+ indicators complemented with automated epifluorescence microscopy we have shown that staurosporine-induced apoptosis in MDA-MB-231 breast cancer cells was associated with delayed development of cytosolic free Ca2+ fluctuations, which were then maintained for 24 h. These cytosolic free Ca2+ fluctuations were dependent on the Ca2+ channel ORAI1. Silencing of ORAI1, but not its canonical activators STIM1 and STIM2, promoted apoptosis in this model. The pathway for this regulation implicates a mechanism previously associated with the migration of cancer cells involving ORAI1, the chaperone protein SigmaR1, and Ca2+ -activated K+ channels.

A Block Successive Lower-Bound Maximization Algorithm for the Maximum Pseudo-Likelihood Estimation of Fully Visible Boltzmann Machines.

Maximum pseudo-likelihood estimation (MPLE) is an attractive method for training fully visible Boltzmann machines (FVBMs) due to its computational scalability and the desirable statistical properties of the MPLE. No published algorithms for MPLE have been proven to be convergent or monotonic. In this note, we present an algorithm for the MPLE of FVBMs based on the block successive lower-bound maximization (BSLM) principle. We show that the BSLM algorithm monotonically increases the pseudo-likelihood values and that the sequence of BSLM estimates converges to the unique global maximizer of the pseudo-likelihood function. The relationship between the BSLM algorithm and the gradient ascent (GA) algorithm for MPLE of FVBMs is also discussed, and a convergence criterion for the GA algorithm is given.

Asymptotic Normality of the Maximum Pseudolikelihood Estimator for Fully Visible Boltzmann Machines.

Boltzmann machines (BMs) are a class of binary neural networks for which there have been numerous proposed methods of estimation. Recently, it has been shown that in the fully visible case of the BM, the method of maximum pseudolikelihood estimation (MPLE) results in parameter estimates, which are consistent in the probabilistic sense. In this brief, we investigate the properties of MPLE for the fully visible BMs further, and prove that MPLE also yields an asymptotically normal parameter estimator. These results can be used to construct confidence intervals and to test statistical hypotheses. These constructions provide a closed-form alternative to the current methods that require Monte Carlo simulation or resampling. We support our theoretical results by showing that the estimator behaves as expected in simulation studies.

Analysis of the genome and transcriptome of Cryptococcus neoformans var. grubii reveals complex RNA expression and microevolution leading to virulence attenuation.

Cryptococcus neoformans is a pathogenic basidiomycetous yeast responsible for more than 600,000 deaths each year. It occurs as two serotypes (A and D) representing two varieties (i.e. grubii and neoformans, respectively). Here, we sequenced the genome and performed an RNA-Seq-based analysis of the C. neoformans var. grubii transcriptome structure. We determined the chromosomal locations, analyzed the sequence/structural features of the centromeres, and identified origins of replication. The genome was annotated based on automated and manual curation. More than 40,000 introns populating more than 99% of the expressed genes were identified. Although most of these introns are located in the coding DNA sequences (CDS), over 2,000 introns in the untranslated regions (UTRs) were also identified. Poly(A)-containing reads were employed to locate the polyadenylation sites of more than 80% of the genes. Examination of the sequences around these sites revealed a new poly(A)-site-associated motif (AUGHAH). In addition, 1,197 miscRNAs were identified. These miscRNAs can be spliced and/or polyadenylated, but do not appear to have obvious coding capacities. Finally, this genome sequence enabled a comparative analysis of strain H99 variants obtained after laboratory passage. The spectrum of mutations identified provides insights into the genetics underlying the micro-evolution of a laboratory strain, and identifies mutations involved in stress responses, mating efficiency, and virulence.

Microevolution of Cryptococcus neoformans driven by massive tandem gene amplification.

The subtelomeric regions of organisms ranging from protists to fungi undergo a much higher rate of rearrangement than is observed in the rest of the genome. While characterizing these ~40-kb regions of the human fungal pathogen Cryptococcus neoformans, we have identified a recent gene amplification event near the right telomere of chromosome 3 that involves a gene encoding an arsenite efflux transporter (ARR3). The 3,177-bp amplicon exists in a tandem array of 2-15 copies and is present exclusively in strains with the C. neoformans var. grubii subclade VNI A5 MLST profile. Strains bearing the amplification display dramatically enhanced resistance to arsenite that correlates with the copy number of the repeat; the origin of increased resistance was verified as transport-related by functional complementation of an arsenite transporter mutant of Saccharomyces cerevisiae. Subsequent experimental evolution in the presence of increasing concentrations of arsenite yielded highly resistant strains with the ARR3 amplicon further amplified to over 50 copies, accounting for up to ~1% of the whole genome and making the copy number of this repeat as high as that seen for the ribosomal DNA. The example described here therefore represents a rare evolutionary intermediate-an array that is currently in a state of dynamic flux, in dramatic contrast to relatively common, static relics of past tandem duplications that are unable to further amplify due to nucleotide divergence. Beyond identifying and engineering fungal isolates that are highly resistant to arsenite and describing the first reported instance of microevolution via massive gene amplification in C. neoformans, these results suggest that adaptation through gene amplification may be an important mechanism that C. neoformans employs in response to environmental stresses, perhaps including those encountered during infection. More importantly, the ARR3 array will serve as an ideal model for further molecular genetic analyses of how tandem gene duplications arise and expand.

Nonlinear features for single-channel diagnosis of sleep-disordered breathing diseases.

Studies have shown that algorithms based on single-channel airflow records are effective in screening for sleep-disordered breathing diseases (SDB). In this study, we investigate the diagnostic effectiveness of a classifier trained on a set of features derived from single-channel airflow measurements. The features considered are based on recurrence quantification analysis (RQA) of the measurement time series and are optionally augmented with single measurements of neck circumference and body mass index. The airflow measurement utilized is the nasal pressure (NP). The study used an overnight recording from each of 77 patients undergoing PSG testing. Mixture discriminant analysis was used to obtain a classifier, which predicts whether or not a measurement segment contains an SDB event. Patients were diagnosed as having SDB disease if the recording contained measurement segments predicted to include an SDB event at a rate exceeding a threshold value. A patient can be diagnosed as having SDB disease if the rate of SDB events per hour of sleep, the respiratory disturbance index (RDI), is > or = 15 or sometimes > or = 5. Here we trained and evaluated the classifier under each assumption, obtaining areas under receiver operating curves using fivefold cross-validation of 0.96 and 0.93, respectively. We used a two-layer structure to select the optimal operating point and assess the resulting classifier to avoid unbiased estimates. The resulting estimates for diagnostic sensitivity/specificity were 71.5%/89.5% for disease classification when RDI > or = 15 and 63.3%/100% for RDI > or = 5. These results were found assuming that the costs of misclassifying healthy and diseased subjects are equal, but we provide a framework to vary these costs. The results suggest that a classifier based on RQA features derived from NP measurements could be used in an automated SDB screening device.

Classification based upon gene expression data: bias and precision of error rates.

MOTIVATION: Gene expression data offer a large number of potentially useful predictors for the classification of tissue samples into classes, such as diseased and non-diseased. The predictive error rate of classifiers can be estimated using methods such as cross-validation. We have investigated issues of interpretation and potential bias in the reporting of error rate estimates. The issues considered here are optimization and selection biases, sampling effects, measures of misclassification rate, baseline error rates, two-level external cross-validation and a novel proposal for detection of bias using the permutation mean. RESULTS: Reporting an optimal estimated error rate incurs an optimization bias. Downward bias of 3-5% was found in an existing study of classification based on gene expression data and may be endemic in similar studies. Using a simulated non-informative dataset and two example datasets from existing studies, we show how bias can be detected through the use of label permutations and avoided using two-level external cross-validation. Some studies avoid optimization bias by using single-level cross-validation and a test set, but error rates can be more accurately estimated via two-level cross-validation. In addition to estimating the simple overall error rate, we recommend reporting class error rates plus where possible the conditional risk incorporating prior class probabilities and a misclassification cost matrix. We also describe baseline error rates derived from three trivial classifiers which ignore the predictors. AVAILABILITY: R code which implements two-level external cross-validation with the PAMR package, experiment code, dataset details and additional figures are freely available for non-commercial use from http://www.maths.qut.edu.au/profiles/wood/permr.jsp

A meta-analytic assessment of a thyroglobulin marker for marbling in beef cattle.

A meta-analysis was undertaken reporting on the association between a polymorphism in the Thyroglobulin gene (TG5) and marbling in beef cattle. A Bayesian hierarchical model was adopted, with alternative representations assessed through sensitivity analysis. Based on the overall posterior means and posterior probabilities, there is substantial support for an additive association between the TG5 marker and marbling. The marker effect was also assessed across various breed groups, with each group displaying a high probability of positive association between the T allele and marbling. The WinBUGS program code used to simulate the model is included as an Appendix available online at www.edpsciences.org/gse.