James G. Booth

DNA Methylation Signatures Identify Biologically Distinct Subtypes in Acute Myeloid Leukemia

We hypothesized that DNA methylation distributes into specific patterns in cancer cells, which reflect critical biological differences. We therefore examined the methylation profiles of 344 patients with acute myeloid leukemia (AML). Clustering of these patients by methylation data segregated patients into 16 groups. Five of these groups defined new AML subtypes that shared no other known feature. In addition, DNA methylation profiles segregated patients with CEBPA aberrations from other subtypes of leukemia, defined four epigenetically distinct forms of AML with NPM1 mutations, and showed that established AML1-ETO, CBFb-MYH11, and PML-RARA leukemia entities are associated with specific methylation profiles. We report a 15 gene methylation classifier predictive of overall survival in an independent patient cohort (p < 0.001, adjusted for known covariates).

Open access publishing, article downloads, and citations: randomised controlled trial

Objective To measure the effect of free access to the scientific literature on article downloads and citations. Design Randomised controlled trial. Setting 11 journals published by the American Physiological Society. Participants 1619 research articles and reviews. Main outcome measures Article readership (measured as downloads of full text, PDFs, and abstracts) and number of unique visitors (internet protocol addresses). Citations to articles were gathered from the Institute for Scientific Information after one year. Interventions Random assignment on online publication of articles published in 11 scientific journals to open access (treatment) or subscription access (control). Results Articles assigned to open access were associated with 89% more full text downloads (95% confidence interval 76% to 103%), 42% more PDF downloads (32% to 52%), and 23% more unique visitors (16% to 30%), but 24% fewer abstract downloads (−29% to −19%) than subscription access articles in the first six months after publication. Open access articles were no more likely to be cited than subscription access articles in the first year after publication. Fifty nine per cent of open access articles (146 of 247) were cited nine to 12 months after publication compared with 63% (859 of 1372) of subscription access articles. Logistic and negative binomial regression analysis of article citation counts confirmed no citation advantage for open access articles. Conclusions Open access publishing may reach more readers than subscription access publishing. No evidence was found of a citation advantage for open access articles in the first year after publication. The citation advantage from open access reported widely in the literature may be an artefact of other causes.

MUS81 Generates a Subset of MLH1-MLH3–Independent Crossovers in Mammalian Meiosis

Two eukaryotic pathways for processing double-strand breaks (DSBs) as crossovers have been described, one dependent on the MutL homologs Mlh1 and Mlh3, and the other on the structure-specific endonuclease Mus81. Mammalian MUS81 has been implicated in maintenance of genomic stability in somatic cells; however, little is known about its role during meiosis. Mus81-deficient mice were originally reported as being viable and fertile, with normal meiotic progression; however, a more detailed examination of meiotic progression in Mus81-null animals and WT controls reveals significant meiotic defects in the mutants. These include smaller testis size, a depletion of mature epididymal sperm, significantly upregulated accumulation of MLH1 on chromosomes from pachytene meiocytes in an interference-independent fashion, and a subset of meiotic DSBs that fail to be repaired. Interestingly, chiasmata numbers in spermatocytes from Mus81−/− animals are normal, suggesting additional integrated mechanisms controlling the two distinct crossover pathways. This study is the first in-depth analysis of meiotic progression in Mus81-nullizygous mice, and our results implicate the MUS81 pathway as a regulator of crossover frequency and placement in mammals.

Shewanella oneidensis in a lactate-fed pure-culture and a glucose-fed co-culture with Lactococcus lactis with an electrode as electron acceptor

Bioelectrochemical systems (BESs) employing mixed microbial communities as biocatalysts are gaining importance as potential renewable energy, bioremediation, or biosensing devices. While we are beginning to understand how individual microbial species interact with an electrode as electron donor, little is known about the interactions between different microbial species in a community: sugar fermenting bacteria can interact with current producing microbes in a fashion that is either neutral, positively enhancing, or even negatively affecting. Here, we compare the bioelectrochemical performance of Shewanella oneidensis in a pure-culture and in a co-culture with the homolactic acid fermenter Lactococcus lactis at conditions that are pertinent to conventional BES operation. While S. oneidensis alone can only use lactate as electron donor for current production, the co-culture is able to convert glucose into current with a comparable coulombic efficiency of ∼17%. With (electro)-chemical analysis and transcription profiling, we found that the BES performance and S. oneidensis physiology were not significantly different whether grown as a pure- or co-culture. Thus, the microbes worked together in a purely substrate based (neutral) relationship. These co-culture experiments represent an important step in understanding microbial interactions in BES communities with the goal to design complex microbial communities, which specifically convert target substrates into electricity.

Simple sequence repeats in Neurospora crassa: distribution, polymorphism and evolutionary inference

BackgroundSimple sequence repeats (SSRs) have been successfully used for various genetic and evolutionary studies in eukaryotic systems. The eukaryotic model organism Neurospora crassa is an excellent system to study evolution and biological function of SSRs.ResultsWe identified and characterized 2749 SSRs of 963 SSR types in the genome of N. crassa. The distribution of tri-nucleotide (nt) SSRs, the most common SSRs in N. crassa, was significantly biased in exons. We further characterized the distribution of 19 abundant SSR types (AST), which account for 71% of total SSRs in the N. crassa genome, using a Poisson log-linear model. We also characterized the size variation of SSRs among natural accessions using Polymorphic Index Content (PIC) and ANOVA analyses and found that there are genome-wide, chromosome-dependent and local-specific variations. Using polymorphic SSRs, we have built linkage maps from three line-cross populations.ConclusionTaking our computational, statistical and experimental data together, we conclude that 1) the distributions of the SSRs in the sequenced N. crassa genome differ systematically between chromosomes as well as between SSR types, 2) the size variation of tri-nt SSRs in exons might be an important mechanism in generating functional variation of proteins in N. crassa, 3) there are different levels of evolutionary forces in variation of amino acid repeats, and 4) SSRs are stable molecular markers for genetic studies in N. crassa.

An early marker of fetal infection after primary cytomegalovirus infection in pregnancy.

Fourteen patients with primary cytomegalovirus infection diagnosed by serological screening at antenatal attendances were examined for their responses in the lymphocyte transformation test against cytomegalovirus. Tests were done during pregnancy, shortly after the diagnosis of primary infection. Eight women showed positive lymphocyte transformation responses and gave birth to uninfected babies. Six showed negative responses and four of the babies were born congenitally infected. Cellular immunity therefore plays a part in preventing intrauterine transmission of cytomegalovirus, and its depression after primary infection in the mother during pregnancy may be used as an early marker of fetal infection.

A survey of Monte Carlo algorithms for maximizing the likelihood of a two-stage hierarchical model

Likelihood inference with hierarchical models is often complicated by the fact that the likelihood function involves intractable integrals. Numerical integration (e.g. quadrature) is an option if the dimension of the integral is low but quickly becomes unreliable as the dimension grows. An alternative approach is to approximate the intractable integrals using Monte Carlo averages. Several different algorithms based on this idea have been proposed. In this paper we discuss the relative merits of simulated maximum likelihood, Monte Carlo EM, Monte Carlo Newton-Raphson and stochastic approximation.

Transcriptional analysis of Shewanella oneidensis MR-1 with an electrode compared to Fe(III)citrate or oxygen as terminal electron acceptor.

Shewanella oneidensis is a target of extensive research in the fields of bioelectrochemical systems and bioremediation because of its versatile metabolic capabilities, especially with regard to respiration with extracellular electron acceptors. The physiological activity of S. oneidensis to respire at electrodes is of great interest, but the growth conditions in thin-layer biofilms make physiological analyses experimentally challenging. Here, we took a global approach to evaluate physiological activity with an electrode as terminal electron acceptor for the generation of electric current. We performed expression analysis with DNA microarrays to compare the overall gene expression with an electrode to that with soluble iron(III) or oxygen as the electron acceptor and applied new hierarchical model-based statistics for the differential expression analysis. We confirmed the differential expression of many genes that have previously been reported to be involved in electrode respiration, such as the entire mtr operon. We also formulate hypotheses on other possible gene involvements in electrode respiration, for example, a role of ScyA in inter-protein electron transfer and a regulatory role of the cbb3-type cytochrome c oxidase under anaerobic conditions. Further, we hypothesize that electrode respiration imposes a significant stress on S. oneidensis, resulting in higher energetic costs for electrode respiration than for soluble iron(III) respiration, which fosters a higher metabolic turnover to cover energy needs. Our hypotheses now require experimental verification, but this expression analysis provides a fundamental platform for further studies into the molecular mechanisms of S. oneidensis electron transfer and the physiologically special situation of growth on a poised-potential surface.

SnIPRE: selection inference using a Poisson random effects model.

We present an approach for identifying genes under natural selection using polymorphism and divergence data from synonymous and non-synonymous sites within genes. A generalized linear mixed model is used to model the genome-wide variability among categories of mutations and estimate its functional consequence. We demonstrate how the models estimated fixed and random effects can be used to identify genes under selection. The parameter estimates from our generalized linear model can be transformed to yield population genetic parameter estimates for quantities including the average selection coefficient for new mutations at a locus, the synonymous and non-synynomous mutation rates, and species divergence times. Furthermore, our approach incorporates stochastic variation due to the evolutionary process and can be fit using standard statistical software. The model is fit in both the empirical Bayes and Bayesian settings using the lme4 package in R, and Markov chain Monte Carlo methods in WinBUGS. Using simulated data we compare our method to existing approaches for detecting genes under selection: the McDonald-Kreitman test, and two versions of the Poisson random field based method MKprf. Overall, we find our method universally outperforms existing methods for detecting genes subject to selection using polymorphism and divergence data.

Laplace Approximated EM Microarray Analysis: An Empirical Bayes Approach for Comparative Microarray Experiments

A two-groups mixed-effects model for the comparison of (normalized) microarray data from two treatment groups is considered. Most competing parametric methods that have appeared in the literature are obtained as special cases or by minor modification of the proposed model. Approximate maximum likelihood fitting is accomplished via a fast and scalable algorithm, which we call LEMMA (Laplace approximated EM Microarray Analysis). The posterior odds of treatment