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

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

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

PEPFAR Public Health Evaluation - Care and Support - Phase 2 Kenya

Phase 2 consisted of a longitudinal cohort study to measure patient-reported outcomes of care and support, a costing survey, and qualitative interviews to understand patient and carer experiences

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

Convergence and Perturbation Resilience of Dynamic String-Averaging Projection Methods

We consider the convex feasibility problem (CFP) in Hilbert space and concentrate on the study of string-averaging projection (SAP) methods for the CFP, analyzing their convergence and their perturbation resilience. In the past, SAP methods were formulated with a single predetermined set of strings and a single predetermined set of weights. Here we extend the scope of the family of SAP methods to allow iteration-index-dependent variable strings and weights and term such methods dynamic string-averaging projection (DSAP) methods. The bounded perturbation resilience of DSAP methods is relevant and important for their possible use in the framework of the recently developed superiorization heuristic methodology for constrained minimization problems.Comment: Computational Optimization and Applications, accepted for publicatio

CSI : A nonparametric Bayesian approach to network inference from multiple perturbed time series gene expression data

How an organism responds to the environmental challenges it faces is heavily influenced by its gene regulatory networks (GRNs). Whilst most methods for inferring GRNs from time series mRNA expression data are only able to cope with single time series (or single perturbations with biological replicates), it is becoming increasingly common for several time series to be generated under different experimental conditions. The CSI algorithm (Klemm, 2008) represents one approach to inferring GRNs from multiple time series data, which has previously been shown to perform well on a variety of datasets (Penfold and Wild, 2011). Another challenge in network inference is the identification of condition specific GRNs i.e., identifying how a GRN is rewired under different conditions or different individuals. The Hierarchical Causal Structure Identification (HCSI) algorithm (Penfold et al., 2012) is one approach that allows inference of condition specific networks (Hickman et al., 2013), that has been shown to be more accurate at reconstructing known networks than inference on the individual datasets alone. Here we describe a MATLAB implementation of CSI/HCSI that includes fast approximate solutions to CSI as well as Markov Chain Monte Carlo implementations of both CSI and HCSI, together with a user-friendly GUI, with the intention of making the analysis of networks from multiple perturbed time series datasets more accessible to the wider community.1 The GUI itself guides the user through each stage of the analysis, from loading in the data, to parameter selection and visualisation of networks, and can be launched by typing >> csi into the MATLAB command line. For each step of the analysis, links to documentation and tutorials are available within the GUI, which includes documentation on visualisation and interacting with output file

PEPFAR Public Health Evaluation - Care and Support - Phase 2 Uganda

Phase 2 consisted of a longitudinal cohort study to measure patient-reported outcomes of care and support, a costing survey, and qualitative interviews to understand patient and carer experiences