A model of the PI cycle reveals the regulating roles of lipid-binding proteins and pitfalls of using mosaic biological data

The phosphatidylinositol (PI) cycle is central to eukaryotic cell signaling. Its complexity, due to the number of reactions and lipid and inositol phosphate intermediates involved makes it difficult to analyze experimentally. Computational modelling approaches are seen as a way forward to elucidate complex biological regulatory mechanisms when this cannot be achieved solely through experimental approaches. Whilst mathematical modelling is well established in informing biological systems, many models are often informed by data sourced from multiple unrelated cell types (mosaic data) or from purified enzyme data. In this work, we develop a model of the PI cycle informed by experimental and omics data taken from a single cell type, namely platelets. We were able to make a number of predictions regarding the regulation of PI cycle enzymes, the importance of the number of receptors required for successful GPCR signaling and the importance of lipid- and protein-binding proteins in regulating second messenger outputs. We then consider how pathway behavior differs, when fully informed by data for HeLa cells and show that model predictions remain consistent. However, when informed by mosaic experimental data model predictions greatly vary illustrating the risks of using mosaic datasets from unrelated cell types

Coevolution of activating and inhibitory receptors within mammalian carcinoembryonic antigen families

<p>Abstract</p> <p>Background</p> <p>Most rapidly evolving gene families are involved in immune responses and reproduction, two biological functions which have been assigned to the carcinoembryonic antigen (CEA) gene family. To gain insights into evolutionary forces shaping the CEA gene family we have analysed this gene family in 27 mammalian species including monotreme and marsupial lineages.</p> <p>Results</p> <p>Phylogenetic analysis provided convincing evidence that the primordial CEA gene family in mammals consisted of five genes, including the immune inhibitory receptor-encoding <it>CEACAM1 </it>(CEA-related cell adhesion molecule) ancestor. Our analysis of the substitution rates within the nucleotide sequence which codes for the ligand binding domain of CEACAM1 indicates that the selection for diversification is, perhaps, a consequence of the exploitation of CEACAM1 by a variety of viral and bacterial pathogens as their cellular receptor. Depending on the extent of the amplification of an ancestral <it>CEACAM1</it>, the number of <it>CEACAM1</it>-related genes varies considerably between mammalian species from less than five in lagomorphs to more than 100 in bats. In most analysed species, ITAM (immunoreceptor tyrosine-based activation motifs) or ITAM-like motif-containing proteins exist which contain Ig-V-like, ligand binding domains closely related to that of CEACAM1. Human CEACAM3 is one such protein which can function as a CEACAM1 decoy receptor in granulocytes by mediating the uptake and destruction of specific bacterial pathogens via its ITAM-like motif. The close relationship between <it>CEACAM1 </it>and its ITAM-encoding relatives appears to be maintained by gene conversion and reciprocal recombination. Surprisingly, secreted CEACAMs resembling immunomodulatory CEACAM1-related trophoblast-specific pregnancy-specific glycoproteins (PSGs) found in humans and rodents evolved only in a limited set of mammals. The appearance of <it>PSG</it>-like genes correlates with invasive trophoblast growth in these species.</p> <p>Conclusions</p> <p>These phylogenetic studies provide evidence that pathogen/host coevolution and a possible participation in fetal-maternal conflict processes led to a highly species-specific diversity of mammalian CEA gene families.</p

Restoring brain function after stroke - bridging the gap between animals and humans

Stroke is the leading cause of complex adult disability in the world. Recovery from stroke is often incomplete, which leaves many people dependent on others for their care. The improvement of long-term outcomes should, therefore, be a clinical and research priority. As a result of advances in our understanding of the biological mechanisms involved in recovery and repair after stroke, therapeutic opportunities to promote recovery through manipulation of poststroke plasticity have never been greater. This work has almost exclusively been carried out in preclinical animal models of stroke with little translation into human studies. The challenge ahead is to develop a mechanistic understanding of recovery from stroke in humans. Advances in neuroimaging techniques now enable us to reconcile behavioural accounts of recovery with molecular and cellular changes. Consequently, clinical trials can be designed in a stratified manner that takes into account when an intervention should be delivered and who is most likely to benefit. This approach is expected to lead to a substantial change in how restorative therapeutic strategies are delivered in patients after stroke

Genetic drivers of cerebral blood flow dysfunction in TBI: a speculative synthesis

Cerebral autoregulatory dysfunction after traumatic brain injury (TBI) is strongly linked to poor global outcome in patients at 6 months after injury. However, our understanding of what drives this dysfunction is limited. Genetic variation among individuals within a population gives rise to single nucleotide polymorphisms (SNPs) that have the potential to influence a given patient’s cerebrovascular response to an injury. Associations have been reported between a variety of genetic polymorphisms and global outcome in patients with TBI, but few studies have explored the association between genetics and cerebrovascular function after injury. In this Review, we explore polymorphisms that might play an important part in cerebral autoregulatory capacity after TBI. We outline a variety of SNPs, their biological substrates and their potential role in mediating cerebrovascular reactivity. A number of candidate polymorphisms exist in genes that are involved in myogenic, endothelial, metabolic and neurogenic vascular responses to injury. Furthermore, polymorphisms in genes involved in inflammation, the central autonomic response and spreading cortical depression might drive cerebrovascular reactivity. Identification of candidate genes involved in cerebral autoregulation after TBI provides a platform and rationale for further prospective investigation of the link between genetic polymorphisms and autoregulatory function

Computer-based tools for decision support in agroforestry: Current state and future needs

Successful design of agroforestry practices hinges on the ability to pull together very diverse and sometimes large sets of information (i.e., biophysical, economic and social factors), and then implementing the synthesis of this information across several spatial scales from site to landscape. Agroforestry, by its very nature, creates complex systems with impacts ranging from the site or practice level up to the landscape and beyond. Computer-based Decision Support Tools (DST) help to integrate information to facilitate the decision-making process that directs development, acceptance, adoption, and management aspects in agroforestry. Computer-based DSTs include databases, geographical information systems, models, knowledge-base or expert systems, and ‘hybrid’ decision support systems. These different DSTs and their applications in agroforestry research and development are described in this paper. Although agroforestry lacks the large research foundation of its agriculture and forestry counterparts, the development and use of computer-based tools in agroforestry have been substantial and are projected to increase as the recognition of the productive and protective (service) roles of these tree-based practices expands. The utility of these and future tools for decision-support in agroforestry must take into account the limits of our current scientific information, the diversity of aspects (i.e. economic, social, and biophysical) that must be incorporated into the planning and design process, and, most importantly, who the end-user of the tools will be. Incorporating these tools into the design and planning process will enhance the capability of agroforestry to simultaneously achieve environmental protection and agricultural production goals

Marine Vertebrate Predator Detection and Recognition in Underwater Videos by Region Convolutional Neural Network

© 2019, Springer Nature Switzerland AG. In this paper, we present R-CNN, Fast R-CNN and Faster R-CNN methods to automatically detect and recognise the predators in underwater videos. We compare the results of these methods on real data and discuss their strengths and weaknesses. We build a dataset using footage captured from representative environment of the wild and devise a data model with three classes (seal, dolphin, background). Following this, we train R-CNN, Fast R-CNN and Faster R-CNN, then evaluate them on a test dataset compose of challenging objects that had not been seen during training. We perform evaluation on GPU, acquiring information about the AP and IOU for each model and network based on various proposal numbers as well as runtime speeds. Based on the results, we found that the best model of predator detection using visual deep learning models is Faster R-CNN with 2000 proposals