Identifying targets of multiple co-regulating transcription factors from expression time-series by Bayesian model comparison

Background: Complete transcriptional regulatory network inference is a huge challenge because of the complexity of the network and sparsity of available data. One approach to make it more manageable is to focus on the inference of context-specific networks involving a few interacting transcription factors (TFs) and all of their target genes. Results: We present a computational framework for Bayesian statistical inference of target genes of multiple interacting TFs from high-throughput gene expression time-series data. We use ordinary differential equation models that describe transcription of target genes taking into account combinatorial regulation. The method consists of a training and a prediction phase. During the training phase we infer the unobserved TF protein concentrations on a subnetwork of approximately known regulatory structure. During the prediction phase we apply Bayesian model selection on a genome-wide scale and score all alternative regulatory structures for each target gene. We use our methodology to identify targets of five TFs regulating Drosophila melanogaster mesoderm development. We find that confident predicted links between TFs and targets are significantly enriched for supporting ChIP-chip binding events and annotated TF-gene interations. Our method statistically significantly outperforms existing alternatives. Conclusions: Our results show that it is possible to infer regulatory links between multiple interacting TFs and their target genes even from a single relatively short time series and in presence of unmodelled confounders and unreliable prior knowledge on training network connectivity. Introducing data from several different experimental perturbations significantly increases the accuracy

Travel routes to remote ocean targets reveal the map sense resolution for a marine migrant

How animals navigate across the ocean to isolated targets remains perplexing greater than 150 years since this question was considered by Charles Darwin. To help solve this long-standing enigma, we considered the likely resolution of any map sense used in migration, based on the navigational performance across different scales (tens to thousands of kilometres). We assessed navigational performance using a unique high-resolution Fastloc-GPS tracking dataset for post-breeding hawksbill turtles (Eretmochelys imbricata) migrating relatively short distances to remote, isolated targets on submerged banks in the Indian Ocean. Individuals often followed circuitous paths (mean straightness index = 0.54, range 0.14-0.93, s.d. = 0.23, n = 22), when migrating short distances (mean beeline distance to target = 106 km, range 68.7-178.2 km). For example, one turtle travelled 1306.2 km when the beeline distance to the target was only 176.4 km. When off the beeline to their target, turtles sometimes corrected their course both in the open ocean and when encountering shallow water. Our results provide compelling evidence that hawksbill turtles only have a relatively crude map sense in the open ocean. The existence of widespread foraging and breeding areas on isolated oceanic sites points to target searching in the final stages of migration being common in sea turtles

Positive changes among patients with advanced colorectal cancer and their family caregivers: a qualitative analysis

OBJECTIVE: This study assessed positive changes in patients with advanced colorectal cancer and their family caregivers following diagnosis. We compared self-reported positive changes within patient-caregiver dyads as well as self-reports and patient reports of positive changes in caregivers. DESIGN: Individual, semi-structured qualitative interviews were conducted with 23 patients with advanced colorectal cancer and 23 caregivers. A theoretical thematic analysis of interview transcripts was framed by posttraumatic growth theory. RESULTS: Patients and caregivers described five positive changes: closer relationships with others, greater appreciation of life, clarifying life priorities, increased faith, and more empathy for others. Additionally, only caregivers reported better health habits following the cancer diagnosis, and a minority of patients and caregivers reported no positive changes. In about half of cases, patients reported at least one positive change that was identical to that of their caregiver. However, in most cases, patient and caregiver reports of the caregiver's positive change were discrepant. CONCLUSION: Findings suggest that positive changes are a shared experience for many patient-caregiver dyads and obtaining both patient and caregiver reports of caregiver positive changes provides a more comprehensive understanding of their experience. Interventions may capitalise on positive changes to promote meaningful living in the context of advanced cancer

Genetic algorithm dynamics on a rugged landscape

The genetic algorithm is an optimization procedure motivated by biological evolution and is successfully applied to optimization problems in different areas. A statistical mechanics model for its dynamics is proposed based on the parent-child fitness correlation of the genetic operators, making it applicable to general fitness landscapes. It is compared to a recent model based on a maximum entropy ansatz. Finally it is applied to modeling the dynamics of a genetic algorithm on the rugged fitness landscape of the NK model.Comment: 10 pages RevTeX, 4 figures PostScrip

OscoNet: Inferring oscillatory gene networks

Background: Oscillatory genes, with periodic expression at the mRNA and/or protein level, have been shown to play a pivotal role in many biological contexts. However, with the exception of the circadian clock and cell cycle, only a few such genes are known. Detecting oscillatory genes from snapshot single-cell experiments is a challenging task due to the lack of time information. Oscope is a recently proposed method to identify co-oscillatory gene pairs using single-cell RNA-seq data. Although promising, the current implementation of Oscope does not provide a principled statistical criterion for selecting oscillatory genes. Results: We improve the optimisation scheme underlying Oscope and provide a wellcalibrated non-parametric hypothesis test to select oscillatory genes at a given FDR threshold. We evaluate performance on synthetic data and three real datasets and show that our approach is more sensitive than the original Oscope formulation, discovering larger sets of known oscillators while avoiding the need for less interpretable thresholds. We also describe how our proposed pseudo-time estimation method is more accurate in recovering the true cell order for each gene cluster while requiring substantially less computation time than the extended nearest insertion approach. Conclusions: OscoNet is a robust and versatile approach to detect oscillatory gene networks from snapshot single-cell data addressing many of the limitations of the original Oscope method

Family Caregiving Challenges in Advanced Colorectal Cancer: Patient and Caregiver Perspectives

Purpose Family caregivers of advanced colorectal cancer patients may be at increased risk for psychological distress. Yet their key challenges in coping with the patient’s illness are not well understood. Soliciting both patient and caregiver perspectives on these challenges would broaden our understanding of the caregiving experience. Thus, the purpose of this research was to identify caregivers’ key challenges in coping with their family member’s advanced colorectal cancer from the perspective of patients and caregivers. Methods Individual, semi-structured qualitative interviews were conducted with 23 advanced colorectal cancer patients and 23 primary family caregivers. Interview data were analyzed via thematic analysis. Results In nearly all cases, patient and caregiver reports of the caregiver’s key challenge were discrepant. Across patient and caregiver reports, caregivers’ key challenges included processing emotions surrounding the patient’s initial diagnosis or recurrence and addressing the patient’s practical and emotional needs. Other challenges included coping with continual uncertainty regarding the patient’s potential functional decline and prognosis and observing the patient suffer from various physical symptoms. Conclusions Findings suggest that eliciting the perspectives of both patients and caregivers regarding caregivers’ challenges provides a more comprehensive understanding of their experience. Results also point to the need to assist caregivers with the emotional and practical aspects of caregiving

The statistical mechanics of a polygenic characterunder stabilizing selection, mutation and drift

By exploiting an analogy between population genetics and statistical mechanics, we study the evolution of a polygenic trait under stabilizing selection, mutation, and genetic drift. This requires us to track only four macroscopic variables, instead of the distribution of all the allele frequencies that influence the trait. These macroscopic variables are the expectations of: the trait mean and its square, the genetic variance, and of a measure of heterozygosity, and are derived from a generating function that is in turn derived by maximizing an entropy measure. These four macroscopics are enough to accurately describe the dynamics of the trait mean and of its genetic variance (and in principle of any other quantity). Unlike previous approaches that were based on an infinite series of moments or cumulants, which had to be truncated arbitrarily, our calculations provide a well-defined approximation procedure. We apply the framework to abrupt and gradual changes in the optimum, as well as to changes in the strength of stabilizing selection. Our approximations are surprisingly accurate, even for systems with as few as 5 loci. We find that when the effects of drift are included, the expected genetic variance is hardly altered by directional selection, even though it fluctuates in any particular instance. We also find hysteresis, showing that even after averaging over the microscopic variables, the macroscopic trajectories retain a memory of the underlying genetic states.Comment: 35 pages, 8 figure

The Herschel Planetary Nebula Survey (HerPlaNS) I. Data Overview and Analysis Demonstration with NGC 6781

This is the first of a series of investigations into far-IR characteristics of 11 planetary nebulae (PNs) under the Herschel Space Observatory Open Time 1 program, Herschel Planetary Nebula Survey (HerPlaNS). Using the HerPlaNS data set, we look into the PN energetics and variations of the physical conditions within the target nebulae. In the present work, we provide an overview of the survey, data acquisition and processing, and resulting data products. We perform (1) PACS/SPIRE broadband imaging to determine the spatial distribution of the cold dust component in the target PNs and (2) PACS/SPIRE spectral-energy-distribution (SED) and line spectroscopy to determine the spatial distribution of the gas component in the target PNs. For the case of NGC 6781, the broadband maps confirm the nearly pole-on barrel structure of the amorphous carbon-richdust shell and the surrounding halo having temperatures of 26-40 K. The PACS/SPIRE multi-position spectra show spatial variations of far-IR lines that reflect the physical stratification of the nebula. We demonstrate that spatially-resolved far-IR line diagnostics yield the (T_e, n_e) profiles, from which distributions of ionized, atomic, and molecular gases can be determined. Direct comparison of the dust and gas column mass maps constrained by the HerPlaNS data allows to construct an empirical gas-to-dust mass ratio map, which shows a range of ratios with the median of 195+-110. The present analysis yields estimates of the total mass of the shell to be 0.86 M_sun, consisting of 0.54 M_sun of ionized gas, 0.12 M_sun of atomic gas, 0.2 M_sun of molecular gas, and 4 x 10^-3 M_sun of dust grains. These estimates also suggest that the central star of about 1.5 M_sun initial mass is terminating its PN evolution onto the white dwarf cooling track.Comment: 27 pages, 16 figures, accepted for publication in A&

Transient Phenomena in Gene Expression after Induction of Transcription

When transcription of a gene is induced by a stimulus, the number of its mRNA molecules changes with time. Here we discuss how this time evolution depends on the shape of the mRNA lifetime distribution. Analysis of the statistical properties of this change reveals transient effects on polysomes, ribosomal profiles, and rate of protein synthesis. Our studies reveal that transient phenomena in gene expression strongly depend on the specific form of the mRNA lifetime distribution

Discrimination between oral corticosteroid-treated and oral corticosteroid-non-treated severe asthma patients by an electronic nose platform

Rationale: Some severe asthma patients require oral corticosteroids (OCS) likely due to greater disease severity. Exhaled molecular markers can provide phenotypic information in asthma. Objectives: Determine whether patients on OCS (OCS+) have a different breathprint compared with those who were not on OCS (OCS-); determine the classification accuracy of eNose as compared to FEV1 % pred, % sputum eosinophils, and exhaled nitric oxide (FENO). Methods: This was a cross-sectional analysis of the U-BIOPRED cohort. Severe asthma was defined by IMI-criteria [Bel Thorax 2011]. OCS+ patients had daily OCS. OCS- patients had never had OCS and were on maintenance inhaled fluticasone equivalent >1000 μg/day. Exhaled volatile organic compounds trapped on adsorption tubes were analysed by centralized eNose platform (Owlstone Lonestar, Cyranose 320, Comon Invent, Tor Vergata TEN) including a total of 190 sensors. t test was used for comparing groups and support vector machine with leave-one-out cross-validation as a classifier. Results: 33 OCS+ (age 55±11yr, mean±SD, 52% female, 27% smokers, pre-bronchodilator FEV1 64.1±24% pred) and 40 OCS- severe asthma patients (age 54±15yr, mean±SD, 55% female, 35% smokers, pre-bronchodilator FEV1 61.8±24% pred) were studied. Sensor by sensor analysis showed that 56 sensors provided different mean values (change in sensor resistance or frequency) between groups (P<0.05). Accuracy of classification was as follows: eNose 71% (n=73), FENO 71% (n=70), FEV1 62% (n=73) and sputum eosinophils 59% (n=37). Conclusions: Preliminary results suggest OCS+ and OCS- severe asthma patients can be distinguished by an eNose platform