Inferring orthologous gene regulatory networks using interspecies data fusion

MOTIVATION: The ability to jointly learn gene regulatory networks (GRNs) in, or leverage GRNs between related species would allow the vast amount of legacy data obtained in model organisms to inform the GRNs of more complex, or economically or medically relevant counterparts. Examples include transferring information from Arabidopsis thaliana into related crop species for food security purposes, or from mice into humans for medical applications. Here we develop two related Bayesian approaches to network inference that allow GRNs to be jointly inferred in, or leveraged between, several related species: in one framework, network information is directly propagated between species; in the second hierarchical approach, network information is propagated via an unobserved 'hypernetwork'. In both frameworks, information about network similarity is captured via graph kernels, with the networks additionally informed by species-specific time series gene expression data, when available, using Gaussian processes to model the dynamics of gene expression. RESULTS: Results on in silico benchmarks demonstrate that joint inference, and leveraging of known networks between species, offers better accuracy than standalone inference. The direct propagation of network information via the non-hierarchical framework is more appropriate when there are relatively few species, while the hierarchical approach is better suited when there are many species. Both methods are robust to small amounts of mislabelling of orthologues. Finally, the use of Saccharomyces cerevisiae data and networks to inform inference of networks in the budding yeast Schizosaccharomyces pombe predicts a novel role in cell cycle regulation for Gas1 (SPAC19B12.02c), a 1,3-beta-glucanosyltransferase

Nonparametric Bayesian inference for perturbed and orthologous gene regulatory networks

Motivation: The generation of time series transcriptomic datasets collected under multiple experimental conditions has proven to be a powerful approach for disentangling complex biological processes, allowing for the reverse engineering of gene regulatory networks (GRNs). Most methods for reverse engineering GRNs from multiple datasets assume that each of the time series were generated from networks with identical topology. In this study, we outline a hierarchical, non-parametric Bayesian approach for reverse engineering GRNs using multiple time series that can be applied in a number of novel situations including: (i) where different, but overlapping sets of transcription factors are expected to bind in the different experimental conditions; that is, where switching events could potentially arise under the different treatments and (ii) for inference in evolutionary related species in which orthologous GRNs exist. More generally, the method can be used to identify context-specific regulation by leveraging time series gene expression data alongside methods that can identify putative lists of transcription factors or transcription factor targets. Results: The hierarchical inference outperforms related (but non-hierarchical) approaches when the networks used to generate the data were identical, and performs comparably even when the networks used to generate data were independent. The method was subsequently used alongside yeast one hybrid and microarray time series data to infer potential transcriptional switches in Arabidopsis thaliana response to stress. The results confirm previous biological studies and allow for additional insights into gene regulation under various abiotic stresses. Availability: The methods outlined in this article have been implemented in Matlab and are available on request

Branch-recombinant Gaussian processes for analysis of perturbations in biological time series.

MOTIVATION: A common class of behaviour encountered in the biological sciences involves branching and recombination. During branching, a statistical process bifurcates resulting in two or more potentially correlated processes that may undergo further branching; the contrary is true during recombination, where two or more statistical processes converge. A key objective is to identify the time of this bifurcation (branch or recombination time) from time series measurements, e.g. by comparing a control time series with perturbed time series. Gaussian processes (GPs) represent an ideal framework for such analysis, allowing for nonlinear regression that includes a rigorous treatment of uncertainty. Currently, however, GP models only exist for two-branch systems. Here, we highlight how arbitrarily complex branching processes can be built using the correct composition of covariance functions within a GP framework, thus outlining a general framework for the treatment of branching and recombination in the form of branch-recombinant Gaussian processes (B-RGPs). RESULTS: We first benchmark the performance of B-RGPs compared to a variety of existing regression approaches, and demonstrate robustness to model misspecification. B-RGPs are then used to investigate the branching patterns of Arabidopsis thaliana gene expression following inoculation with the hemibotrophic bacteria, Pseudomonas syringae DC3000, and a disarmed mutant strain, hrpA. By grouping genes according to the number of branches, we could naturally separate out genes involved in basal immune response from those subverted by the virulent strain, and show enrichment for targets of pathogen protein effectors. Finally, we identify two early branching genes WRKY11 and WRKY17, and show that genes that branched at similar times to WRKY11/17 were enriched for W-box binding motifs, and overrepresented for genes differentially expressed in WRKY11/17 knockouts, suggesting that branch time could be used for identifying direct and indirect binding targets of key transcription factors. AVAILABILITY AND IMPLEMENTATION: https://github.com/cap76/BranchingGPs. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online

Eucalyptus Oils

The mention of Eucalyptus oil recalls immediately its use for a cough or cold by placing three drops of the oil on a piece of sugar and the swallowing of the confection. A better method of treatment is by inhaling the oil from the surface of water which has been heated to boiling point. The efficacy of the oil, especially for the treatment of influenza and severe cold in the head is considerably enhanced by the addition of a few crystals of menthol to the oil. These particular uses are very well-known, but many other and, in my opinion, more important uses are not so well-known. There is no better substance for the removal of grease stains and tar from clothes, even the most delicately coloured fabrics, than certain grades of Eucalyptus Oil which will be described later. Apart from their use as clothes cleaners, Eucalyptus Oils are very useful for the renovation of tapestry, car and furniture upholstery

PEPFAR Public Health Evaluation-Care and Support -Phase I Kenya

Phase 1, a survey of 120 care facilities in Kenya and Uganda, found that over 90% of facilities provided some level of clinical, psychological,and preventive care. Pain control was very limited with paracetamol often the only analgesic. In focus group discussions, patients appreciated free care and positive attitudes from staff, but said that services would be improved by more staff, shorter queues, and reliable drug supplies

The Chemistry of Eucalyptus Oils

In the last issue of Environment I gave a brief popular description of the commercially valuable Eucalypts and the important essential oils obtained from their leaves and terminal branchlets. I also discussed how these oils could he obtained and the many and varied uses to which they conld be applied. The valuable uses and properties of Eucalyptus Oils-a typically Australian product have not been appreciated to the extent they merit, probably due to lack of publicity. It is confidently anticipated that considerable interest will have been aroused by the information made available in the June issue of Environment. The Editor suggested the possibility of some interested readers developing a desire for additional information about the chemistry of Eucalyptus Oils. The purpose of this article, therefore, is to give those readers thirsty for increased knowledge some data about some of the many interesting substances which have been isolated from Eucalyptus Oils. The information will be of a more advanced nature, but I shall endeavour to describe the chemistry of these interesting substances in a simple and popular manner. The following table describes the species which yield the present day Eucalyptus Oils of commerce and the districts in which they occur in abundance. In the first article, I selected a range of these species and briefly described their habitat and the nature of the essential oils obtained from them. The list now furnished, however, is a complete one

Spin-Vibronic Intersystem Crossing and Molecular Packing Effects in Heavy Atom Free Organic Phosphor

We present a detailed investigation into the excited state properties of a planar D3h symmetric azatriangulenetrione, HTANGO, which has received significant interest due to its high solid-state phosphorescence quantum yield and therefore potential as an organic room temperature phosphorescent (ORTP) dye. Using a model linear vibronic coupling Hamiltonian in combination with quantum dynamics simulations, we observe that intersystem crossing (ISC) in HTANGO occurs with a rate of ∼1010 s−1, comparable to benzophenone, an archetypal molecule for fast ISC in heavy metal free molecules. Our simulations demonstrate that the mechanism for fast ISC is associated with the high density of excited triplet states which lie in close proximity to the lowest singlet states, offering multiple channels into the triplet manifold facilitating rapid population transfer. Finally, to rationalize the solid-state emission properties, we use quantum chemistry to investigate the excited state surfaces of the HTANGO dimer, highlighting the influence and importance of the rotational alignment between the two HTANGO molecules in the solid state and how this contributes to high phosphorescence quantum yield

Does low-energy sweetener consumption affect energy intake and body weight? A systematic review, including meta-analyses, of the evidence from human and animal studies

By reducing energy density, low-energy sweeteners (LES) might be expected to reduce energy intake (EI) and body weight (BW). To assess the totality of the evidence testing the null hypothesis that LES exposure (versus sugars or unsweetened alternatives) has no effect on EI or BW, we conducted a systematic review of relevant studies in animals and humans consuming LES with ad libitum access to food energy. In 62 of 90 animal studies exposure to LES did not affect or decreased BW. Of 28 reporting increased BW, 19 compared LES with glucose exposure using a specific ‘learning’ paradigm. Twelve prospective cohort studies in humans reported inconsistent associations between LES use and Body Mass Index (-0.002 kg/m2/year, 95%CI -0.009 to 0.005). Meta-analysis of short- term randomized controlled trials (RCTs, 129 comparisons) showed reduced total EI for LES- versus sugar-sweetened food or beverage consumption before an ad libitum meal (-94 kcal, 95%CI -122 to -66), with no difference versus water (-2 kcal, 95%CI -30 to 26). This was consistent with EI results from sustained intervention RCTs (10 comparisons). Meta-analysis of sustained intervention RCTs (4 weeks to 40 months) showed that consumption of LES versus sugar led to relatively reduced BW (nine comparisons; -1.35 kg, 95%CI –2.28 to - 0.42), and a similar relative reduction in BW versus water (three comparisons; -1.24 kg, 95%CI –2.22 to -0.26). Most animal studies did not mimic LES consumption by humans, and reverse causation may influence the results of prospective cohort studies. The preponderance of evidence from all human RCTs indicates that LES do not increase EI or BW, whether compared with caloric or non-caloric (e.g., water) control conditions. Overall, the balance of evidence indicates that use of LES in place of sugar, in children and adults, leads to reduced EI and BW, and possibly also when compared with water

Inferring the perturbation time from biological time course data.

MOTIVATION: Time course data are often used to study the changes to a biological process after perturbation. Statistical methods have been developed to determine whether such a perturbation induces changes over time, e.g. comparing a perturbed and unperturbed time course dataset to uncover differences. However, existing methods do not provide a principled statistical approach to identify the specific time when the two time course datasets first begin to diverge after a perturbation; we call this the perturbation time. Estimation of the perturbation time for different variables in a biological process allows us to identify the sequence of events following a perturbation and therefore provides valuable insights into likely causal relationships. RESULTS: We propose a Bayesian method to infer the perturbation time given time course data from a wild-type and perturbed system. We use a non-parametric approach based on Gaussian Process regression. We derive a probabilistic model of noise-corrupted and replicated time course data coming from the same profile before the perturbation time and diverging after the perturbation time. The likelihood function can be worked out exactly for this model and the posterior distribution of the perturbation time is obtained by a simple histogram approach, without recourse to complex approximate inference algorithms. We validate the method on simulated data and apply it to study the transcriptional change occurring in Arabidopsis following inoculation with Pseudomonas syringae pv. tomato DC3000 versus the disarmed strain DC3000hrpA AVAILABILITY AND IMPLEMENTATION: : An R package, DEtime, implementing the method is available at https://github.com/ManchesterBioinference/DEtime along with the data and code required to reproduce all the results. CONTACT: [email protected] or [email protected] SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online

PEPFAR Public Health Evaluation -Care and Support -Phase I Uganda

Phase 1, a survey of 120 care facilities in Kenya and Uganda, found that over 90% of facilities provided some level of clinical, psychological,and preventive care. Pain control was very limited with paracetamol often the only analgesic. In focus group discussions, patients appreciated free care and positive attitudes from staff, but said that services would be improved by more staff, shorter queues, and reliable drug supplies