EarlyR: A Robust Gene Expression Signature for Predicting Outcomes of Estrogen Receptor–Positive Breast Cancer

Introduction Early stage estrogen receptor (ER)-positive breast cancer may be treated with chemotherapy in addition to hormone therapy. Currently available molecular signatures assess the risk of recurrence and the benefit of chemotherapy; however, these tests may have large intermediate risk groups, limiting their usefulness. Methods The EarlyR prognostic score was developed using integrative analysis of microarray data sets and formalin-fixed, paraffin-embedded–based quantitative real-time PCR assay and validated in Affymetrix data sets and METABRIC cohort using Cox proportional hazards models and Kaplan-Meier survival analysis. Concordance index was used to measure the probability of prognostic score agreement with outcome. Results The EarlyR score and categorical risk strata (EarlyR-Low, EarlyR-Int, EarlyR-High) derived from expression of ESPL1, MKI67, SPAG5, PLK1 and PGR was prognostic of 8-year distant recurrence-free interval in Affymetrix (categorical P = 3.5 × 10−14; continuous P = 8.8 × 10−15) and METABRIC (categorical P < 2.2 × 10−16; continuous P < 10−16) data sets of ER+ breast cancer. Similar results were observed for the breast cancer–free interval end point. At most 13% of patients were intermediate risk and at least 66% patients were low risk in both ER+ cohorts. The EarlyR score was significantly prognostic (distant recurrence-free interval; P < .001) in both lymph node–negative and lymph node–positive patients and was independent from clinical factors. EarlyR and surrogates of current molecular signatures were comparable in prognostic significance by concordance index. Conclusion The 5-gene EarlyR score is a robust prognostic assay that identified significantly fewer patients as intermediate risk and more as low risk than currently available assays. Further validation of the assay in clinical trial–derived cohorts is ongoing

Bubble dynamics in DNA

The formation of local denaturation zones (bubbles) in double-stranded DNA is an important example for conformational changes of biological macromolecules. We study the dynamics of bubble formation in terms of a Fokker-Planck equation for the probability density to find a bubble of size n base pairs at time t, on the basis of the free energy in the Poland-Scheraga model. Characteristic bubble closing and opening times can be determined from the corresponding first passage time problem, and are sensitive to the specific parameters entering the model. A multistate unzipping model with constant rates recently applied to DNA breathing dynamics [G. Altan-Bonnet et al, Phys. Rev. Lett. 90, 138101 (2003)] emerges as a limiting case.Comment: 9 pages, 2 figure

The elemental mechanism of transcriptional pausing.

Transcriptional pausing underlies regulation of cellular RNA biogenesis. A consensus pause sequence that acts on RNA polymerases (RNAPs) from bacteria to mammals halts RNAP in an elemental paused state from which longer-lived pauses can arise. Although the structural foundations of pauses prolonged by backtracking or nascent RNA hairpins are recognized, the fundamental mechanism of the elemental pause is less well-defined. Here we report a mechanistic dissection that establishes the elemental pause signal (i) is multipartite; (ii) causes a modest conformational shift that puts γ-proteobacterial RNAP in an off-pathway state in which template base loading but not RNA translocation is inhibited; and (iii) allows RNAP to enter pretranslocated and one-base-pair backtracked states easily even though the half-translocated state observed in paused cryo-EM structures rate-limits pause escape. Our findings provide a mechanistic basis for the elemental pause and a framework to understand how pausing is modulated by sequence, cellular conditions, and regulators

Exact solutions of master equations for the analysis of gene transcription models

This thesis is motivated by two associated obstacles we face for the solution and analysis of master equation models of gene transcription. First, the master equation – a differential-difference equation that describes the time evolution of the probability distribution of a discrete Markov process – is difficult to solve and few approaches for solution are known, particularly for non-stationary systems. Second, we lack a general framework for solving master equations that promotes explicit comprehension of how extrinsic processes and variation affect the system, and physical intuition for the solutions and their properties. We address the second obstacle by deriving the exact solution of the master equation under general time-dependent assumptions for transcription and degradation rates. With this analytical solution we obtain the general properties of a broad class of gene transcription models, within which solutions and properties of specific models may be placed and understood. Furthermore, there naturally emerges a decoupling of the discrete component of the solution, common to all transcription models of this kind, and the continuous, model-specific component that describes uncertainty of the parameters and extrinsic variation. Thus we also address the first obstacle, since to solve a model within this framework one needs only the probability density for the extrinsic component, which may be non-stationary. We detail its physical interpretations, and methods to calculate its probability density. Specific models are then addressed. In particular we solve for classes of multistate models, where the gene cycles stochastically between discrete states. We use the insights gained from these approaches to deduce properties of several other models. Finally, we introduce a quantitative characterisation of timescales for multistate models, to delineate “fast” and “slow” switching regimes. We have thus demonstrated the power of the obtained general solution for analytically predicting gene transcription in non-stationary conditions.Open Acces

EVALUATION OF THE ADAPTIVE CAPACITY OF MOLE SALAMANDERS (AMBYSTOMATIDAE) TO A CHANGING CLIMATE IN WESTERN MASSACHUSETTS

The primary goal of my dissertation was to investigate the adaptive capacity of mole salamanders in western Massachusetts, specifically marbled salamanders (Ambystoma opacum), to future changes in climate. This involved the analysis of two existing datasets including i) a nearly decade-long photograph capture-recapture dataset (first described by Gamble et al. 2009) and ii) a landscape genetics dataset (first described by Whitely et al. 2014). My dissertation also included two chapters focused on computer simulations to better understand the behavior and inferences from the statistical models fit to the empirical datasets I modeled and the effects of error in the data on model parameter estimates. Results of my research provide new insights regarding the adaptive capacity of A. opacum populations to future environmental change. First, genetic differentiation of A. opacum was found to be driven by roads and landscape curvature in our study area which has resulted in population clustering (K=3; Whiteley, McGarigal, & Schwartz, 2014). This suggests that A. opacum may have difficulty tracking future climate change if movement, dispersal and ultimately gene flow are restricted due to both anthropogenic and natural topographic features of the landscape. Second, multi-state survival model estimates suggested that climate change could directly reduce A. opacum survival through increases in summer precipitation amounts and indirectly reduce A. opacum survival through increases in the length of the breeding interval due to increases in fall temperature and decreases in precipitation as mortality rates at the vernal pool. Lastly, no evidence was found that individual A. opacum could pick up on environmental cues that breeding conditions would be “riskier” in a given year due to a longer breeding interval and “skip” breeding at higher rates (particularly females), to buffer the population from such environmental conditions

Choosing and Using Introns in Molecular Phylogenetics

Introns are now commonly used in molecular phylogenetics in an attempt to recover gene trees that are concordant with species trees, but there are a range of genomic, logistical and analytical considerations that are infrequently discussed in empirical studies that utilize intron data. This review outlines expedient approaches for locus selection, overcoming paralogy problems, recombination detection methods and the identification and incorporation of LVHs in molecular systematics. A range of parsimony and Bayesian analytical approaches are also described in order to highlight the methods that can currently be employed to align sequences and treat indels in subsequent analyses. By covering the main points associated with the generation and analysis of intron data, this review aims to provide a comprehensive introduction to using introns (or any non-coding nuclear data partition) in contemporary phylogenetics

Waves of genomic hitchhikers shed light on the evolution of gamebirds (Aves: Galliformes) : research article

Background The phylogenetic tree of Galliformes (gamebirds, including megapodes, currassows, guinea fowl, New and Old World quails, chicken, pheasants, grouse, and turkeys) has been considerably remodeled over the last decades as new data and analytical methods became available. Analyzing presence/absence patterns of retroposed elements avoids the problems of homoplastic characters inherent in other methodologies. In gamebirds, chicken repeats 1 (CR1) are the most prevalent retroposed elements, but little is known about the activity of their various subtypes over time. Ascertaining the fixation patterns of CR1 elements would help unravel the phylogeny of gamebirds and other poorly resolved avian clades. Results We analyzed 1,978 nested CR1 elements and developed a multidimensional approach taking advantage of their transposition in transposition character (TinT) to characterize the fixation patterns of all 22 known chicken CR1 subtypes. The presence/absence patterns of those elements that were active at different periods of gamebird evolution provided evidence for a clade (Cracidae + (Numididae + (Odontophoridae + Phasianidae))) not including Megapodiidae; and for Rollulus as the sister taxon of the other analyzed Phasianidae. Genomic trace sequences of the turkey genome further demonstrated that the endangered African Congo Peafowl (Afropavo congensis) is the sister taxon of the Asian Peafowl (Pavo), rejecting other predominantly morphology-based groupings, and that phasianids are monophyletic, including the sister taxa Tetraoninae and Meleagridinae. Conclusions The TinT information concerning relative fixation times of CR1 subtypes enabled us to efficiently investigate gamebird phylogeny and to reconstruct an unambiguous tree topology. This method should provide a useful tool for investigations in other taxonomic groups as well

Selective maintenance for multistate series systems with S-dependent components

YesIn this paper, we will consider the selective maintenance problem for multistate series systems with stochastic dependent components. In multistate systems, the health state of a component may vary from perfect functioning to complete failure. The stochastic dependence (S-dependence) between components is discussed and categorized into two types in multistate context. First, the failure of a component can immediately cause complete failures of some other components in the system. Second, as components deteriorate, the reduced working performance rate of a multistate component affects the state as well as the degradation rate of its subsequent components in series structure. The system reliability is evaluated using an approach based on stochastic process. A cost-based selective maintenance model is developed for the multistate system with S-dependent components to maximize the total system profit, which includes the production gain and loss in the next mission as well as possible maintenance costs for the system. Analyses of systems with independent and dependent components are provided. It is observed that ignoring S-dependence in the system may lead to alternative maintenance decision making and an optimistic estimation of the system performance