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

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

Bringing numerous methods for expression and promoter analysis to a public cloud computing service

Every year, a large number of novel algorithms are introduced to the scientific community for a myriad of applications, but using these across different research groups is often troublesome, due to suboptimal implementations and specific dependency requirements. This does not have to be the case, as public cloud computing services can easily house tractable implementations within self-contained dependency environments, making the methods easily accessible to a wider public. We have taken 14 popular methods, the majority related to expression data or promoter analysis, developed these up to a good implementation standard and housed the tools in isolated Docker containers which we integrated into the CyVerse Discovery Environment, making these easily usable for a wide community as part of the CyVerse UK project

Using digital and hand printing techniques to compensate for loss: re-establishing colour and texture in historic textiles

Conservators use a range of 'gap filling' techniques to improve the structural stability and presentation of objects. Textile conservators often use fabric supports to provide reinforcement for weak areas of a textile and to provide a visual infill in missing areas. The most common technique is to use dyed fabrics of a single colour but while a plain dyed support provides good reinforcement, it can be visually obtrusive when used with patterned or textured textiles. Two recent postgraduate dissertation projects at the Textile Conservation Centre (TCC) have experimented with hand printing and digital imaging techniques to alter the appearance of support fabrics so that they are less visually obtrusive and blend well with the colour and texture of the textile being supported. Case studies demonstrate the successful use of these techniques on a painted hessian rocking horse and a knitted glove from an archaeological context

Staff experiences of Providing Maternity Services in Rural Southern Tanzania -- A Focus on Equipment, Drug and Supply Issues.

The poor maintenance of equipment and inadequate supplies of drugs and other items contribute to the low quality of maternity services often found in rural settings in low- and middle-income countries, and raise the risk of adverse maternal outcomes through delaying care provision. We aim to describe staff experiences of providing maternal care in rural health facilities in Southern Tanzania, focusing on issues related to equipment, drugs and supplies. Focus group discussions and in-depth interviews were conducted with different staff cadres from all facility levels in order to explore experiences and views of providing maternity care in the context of poorly maintained equipment, and insufficient drugs and other supplies. A facility survey quantified the availability of relevant items. The facility survey, which found many missing or broken items and frequent stock outs, corroborated staff reports of providing care in the context of missing or broken care items. Staff reported increased workloads, reduced morale, difficulties in providing optimal maternity care, and carrying out procedures that carried potential health risks to themselves as a result. Inadequately stocked and equipped facilities compromise the health system's ability to reduce maternal and neonatal mortality and morbidity by affecting staff personally and professionally, which hinders the provision of timely and appropriate interventions. Improving stock control and maintaining equipment could benefit mothers and babies, not only through removing restrictions to the availability of care, but also through improving staff working conditions

AMPK activation protects against prostate cancer by inducing a catabolic cellular state

Emerging evidence indicates that metabolic dysregulation drives prostate cancer (PCa) progression and metastasis. AMP-activated protein kinase (AMPK) is a master regulator of metabolism, although its role in PCa remains unclear. Here, we show that genetic and pharmacological activation of AMPK provides a protective effect on PCa progression in vivo. We show that AMPK activation induces PGC1α expression, leading to catabolic metabolic reprogramming of PCa cells. This catabolic state is characterized by increased mitochondrial gene expression, increased fatty acid oxidation, decreased lipogenic potential, decreased cell proliferation, and decreased cell invasiveness. Together, these changes inhibit PCa disease progression. Additionally, we identify a gene network involved in cell cycle regulation that is inhibited by AMPK activation. Strikingly, we show a correlation between this gene network and PGC1α gene expression in human PCa. Taken together, our findings support the use of AMPK activators for clinical treatment of PCa to improve patient outcome

High-resolution temporal profiling of transcripts during Arabidopsis leaf senescence reveals a distinct chronology of processes and regulation

Leaf senescence is an essential developmental process that impacts dramatically on crop yields and involves altered regulation of thousands of genes and many metabolic and signaling pathways, resulting in major changes in the leaf. The regulation of senescence is complex, and although senescence regulatory genes have been characterized, there is little information on how these function in the global control of the process. We used microarray analysis to obtain a highresolution time-course profile of gene expression during development of a single leaf over a 3-week period to senescence. A complex experimental design approach and a combination of methods were used to extract high-quality replicated data and to identify differentially expressed genes. The multiple time points enable the use of highly informative clustering to reveal distinct time points at which signaling and metabolic pathways change. Analysis of motif enrichment, as well as comparison of transcription factor (TF) families showing altered expression over the time course, identify clear groups of TFs active at different stages of leaf development and senescence. These data enable connection of metabolic processes, signaling pathways, and specific TF activity, which will underpin the development of network models to elucidate the process of senescence

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