Acute Administration of Non-Classical Estrogen Receptor Agonists Attenuates Ischemia-Induced Hippocampal Neuron Loss in Middle-Aged Female Rats

Pretreatment with 17beta-estradiol (E2) is profoundly neuroprotective in young animals subjected to focal and global ischemia. However, whether E2 retains its neuroprotective efficacy in aging animals, especially when administered after brain insult, is largely unknown.We examined the neuroprotective effects of E2 and two agonists that bind to non-classical estrogen receptors, G1 and STX, when administered after ischemia in middle-aged rats after prolonged ovarian hormone withdrawal. Eight weeks after ovariectomy, middle-aged female rats underwent 10 minutes of global ischemia by four vessel occlusion. Immediately after reperfusion, animals received a single infusion of either E2 (2.25 microg), G1 (50 microg) or STX (50 microg) into the lateral ventricle (ICV) or a single systemic injection of E2 (100 microg/kg). Surviving pyramidal neurons in the hippocampal CA1 were quantified 1 week later. E2 and both agonists that target non-classical estrogen receptors (G1 and STX) administered ICV at the time of reperfusion provided significant levels of neuroprotection, with 55-60% of CA1 neurons surviving vs 15% survival in controls. A single systemic injection of a pharmacological dose of E2 also rescued approximately 50% of CA1 pyramidal neurons destined to die. To determine if E2 and G1 have similar mechanisms of action in hippocampal neurons, we compared the ability of E2 and G1 to modify CA1 pyramidal neuron responses to excitatory inputs from the Schaffer collaterals recorded in hippocampal slices derived from female rats not subjected to global ischemia. E2 and G1 (10 nM) significantly potentiated pyramidal neuron responses to excitatory inputs when applied to hippocampal slices.These findings suggest (1) that middle-aged female rats retain their responsiveness to E2 even after a long period of hormone withdrawal, (2) that non-classical estrogen receptors may mediate the neuroprotective actions of E2 when given after ischemia, and (3) that the neuroprotective efficacy of estrogens may be related to their modulation of synaptic activity in hippocampal slices

Fetus-derived DLK1 is required for maternal metabolic adaptations to pregnancy and is associated with fetal growth restriction.

Pregnancy is a state of high metabolic demand. Fasting diverts metabolism to fatty acid oxidation, and the fasted response occurs much more rapidly in pregnant women than in non-pregnant women. The product of the imprinted DLK1 gene (delta-like homolog 1) is an endocrine signaling molecule that reaches a high concentration in the maternal circulation during late pregnancy. By using mouse models with deleted Dlk1, we show that the fetus is the source of maternal circulating DLK1. In the absence of fetally derived DLK1, the maternal fasting response is impaired. Furthermore, we found that maternal circulating DLK1 levels predict embryonic mass in mice and can differentiate healthy small-for-gestational-age (SGA) infants from pathologically small infants in a human cohort. Therefore, measurement of DLK1 concentration in maternal blood may be a valuable method for diagnosing human disorders associated with impaired DLK1 expression and to predict poor intrauterine growth and complications of pregnancy.M.A.M.C. was supported by a PhD studentship from the Cambridge Centre for Trophoblast Research. Research was supported by grants from the MRC (MR/J001597/1 and MR/L002345/1), the Medical College of Saint Bartholomew's Hospital Trust, a Wellcome Trust Investigator Award, EpigeneSys (FP7 Health-257082), EpiHealth (FP7 Health-278414), a Herchel Smith Fellowship (N.T.) and NIH grant RO1 DK89989. The contents are the authors' sole responsibility and do not necessarily represent official NIH views. We thank G. Burton for invaluable support, and M. Constância and I. Sandovici (University of Cambridge) for the Meox2-cre mice. We are extremely grateful to all of the participants in the Pregnancy Outcome Prediction study. This work was supported by the NIHR Cambridge Comprehensive Biomedical Research Centre (Women's Health theme) and project grants from the MRC (G1100221) and Sands (Stillbirth and Neonatal Death Charity). The study was also supported by GE Healthcare (donation of two Voluson i ultrasound systems for this study) and by the NIHR Cambridge Clinical Research Facility, where all research visits took place.This is the author accepted manuscript. The final version is available from Nature Publishing Group via https://doi.org/10.1038/ng.369

Query Large Scale Microarray Compendium Datasets Using a Model-Based Bayesian Approach with Variable Selection

In microarray gene expression data analysis, it is often of interest to identify genes that share similar expression profiles with a particular gene such as a key regulatory protein. Multiple studies have been conducted using various correlation measures to identify co-expressed genes. While working well for small datasets, the heterogeneity introduced from increased sample size inevitably reduces the sensitivity and specificity of these approaches. This is because most co-expression relationships do not extend to all experimental conditions. With the rapid increase in the size of microarray datasets, identifying functionally related genes from large and diverse microarray gene expression datasets is a key challenge. We develop a model-based gene expression query algorithm built under the Bayesian model selection framework. It is capable of detecting co-expression profiles under a subset of samples/experimental conditions. In addition, it allows linearly transformed expression patterns to be recognized and is robust against sporadic outliers in the data. Both features are critically important for increasing the power of identifying co-expressed genes in large scale gene expression datasets. Our simulation studies suggest that this method outperforms existing correlation coefficients or mutual information-based query tools. When we apply this new method to the Escherichia coli microarray compendium data, it identifies a majority of known regulons as well as novel potential target genes of numerous key transcription factors

Rise and Demise of Bioinformatics? Promise and Progress

The field of bioinformatics and computational biology has gone through a number of transformations during the past 15 years, establishing itself as a key component of new biology. This spectacular growth has been challenged by a number of disruptive changes in science and technology. Despite the apparent fatigue of the linguistic use of the term itself, bioinformatics has grown perhaps to a point beyond recognition. We explore both historical aspects and future trends and argue that as the field expands, key questions remain unanswered and acquire new meaning while at the same time the range of applications is widening to cover an ever increasing number of biological disciplines. These trends appear to be pointing to a redefinition of certain objectives, milestones, and possibly the field itself

Neuroprotective Actions of Estradiol and Novel Estrogen Analogs in Ischemia: Translational Implications

This review highlights our investigations into the neuroprotective efficacy of estradiol and other estrogenic agents in a clinically relevant animal model of transient global ischemia, which causes selective, delayed death of hippocampal CA1 neurons and associated cognitive deficits. We find that estradiol rescues a significant number of CA1 pyramidal neurons that would otherwise die in response to global ischemia, and this is true when hormone is provided as a long-term pretreatment at physiological doses or as an acute treatment at the time of reperfusion. In addition to enhancing neuronal survival, both forms of estradiol treatment induce measurable cognitive benefit in young animals. Moreover, estradiol and estrogen analogs that do not bind classical nuclear estrogen receptors retain their neuroprotective efficacy in middle-aged females deprived of ovarian hormones for a prolonged duration (8 weeks). Thus, non-feminizing estrogens may represent a new therapeutic approach for treating the neuronal damage associated with global ischemia

Inferring cellular networks – a review

In this review we give an overview of computational and statistical methods to reconstruct cellular networks. Although this area of research is vast and fast developing, we show that most currently used methods can be organized by a few key concepts. The first part of the review deals with conditional independence models including Gaussian graphical models and Bayesian networks. The second part discusses probabilistic and graph-based methods for data from experimental interventions and perturbations

Biomedical informatics and translational medicine

Biomedical informatics involves a core set of methodologies that can provide a foundation for crossing the "translational barriers" associated with translational medicine. To this end, the fundamental aspects of biomedical informatics (e.g., bioinformatics, imaging informatics, clinical informatics, and public health informatics) may be essential in helping improve the ability to bring basic research findings to the bedside, evaluate the efficacy of interventions across communities, and enable the assessment of the eventual impact of translational medicine innovations on health policies. Here, a brief description is provided for a selection of key biomedical informatics topics (Decision Support, Natural Language Processing, Standards, Information Retrieval, and Electronic Health Records) and their relevance to translational medicine. Based on contributions and advancements in each of these topic areas, the article proposes that biomedical informatics practitioners ("biomedical informaticians") can be essential members of translational medicine teams