Proteomic profile of cystic fibrosis sputum cells in adults chronically infected with Pseudomonas aeruginosa

Lung disease is the main cause of morbidity and mortality in cystic fibrosis (CF), and involves chronic infection and perturbed immune responses. Tissue damage is mediated mostly by extracellular proteases, but other cellular proteins may also contribute to damage through their effect on cell activities and/or release into sputum fluid by means of active secretion or cell death.We employed MudPIT (multidimensional protein identification technology) to identify sputum cellular proteins with consistently altered abundance in adults with CF, chronically infected with Pseudomonas aeruginosa, compared with healthy controls. Ingenuity Pathway Analysis, Gene Ontology, protein abundance and correlation with lung function were used to infer their potential clinical significance.Differentially abundant proteins relate to Rho family small GTPase activity, immune cell movement/activation, generation of reactive oxygen species, and dysregulation of cell death and proliferation. Compositional breakdown identified high abundance of proteins previously associated with neutrophil extracellular traps. Furthermore, negative correlations with lung function were detected for 17 proteins, many of which have previously been associated with lung injury.These findings expand our current understanding of the mechanisms driving CF lung disease and identify sputum cellular proteins with potential for use as indicators of disease status/prognosis, stratification determinants for treatment prescription or therapeutic targets

Exponential Random Graph Modeling for Complex Brain Networks

Exponential random graph models (ERGMs), also known as p* models, have been utilized extensively in the social science literature to study complex networks and how their global structure depends on underlying structural components. However, the literature on their use in biological networks (especially brain networks) has remained sparse. Descriptive models based on a specific feature of the graph (clustering coefficient, degree distribution, etc.) have dominated connectivity research in neuroscience. Corresponding generative models have been developed to reproduce one of these features. However, the complexity inherent in whole-brain network data necessitates the development and use of tools that allow the systematic exploration of several features simultaneously and how they interact to form the global network architecture. ERGMs provide a statistically principled approach to the assessment of how a set of interacting local brain network features gives rise to the global structure. We illustrate the utility of ERGMs for modeling, analyzing, and simulating complex whole-brain networks with network data from normal subjects. We also provide a foundation for the selection of important local features through the implementation and assessment of three selection approaches: a traditional p-value based backward selection approach, an information criterion approach (AIC), and a graphical goodness of fit (GOF) approach. The graphical GOF approach serves as the best method given the scientific interest in being able to capture and reproduce the structure of fitted brain networks

Predictors of Poor Perinatal Outcome following Maternal Perception of Reduced Fetal Movements: A Prospective Cohort Study

Background Maternal perception of reduced fetal movement (RFM) is associated with increased risk of stillbirth and fetal growth restriction (FGR). RFM is thought to represent fetal compensation to conserve energy due to insufficient oxygen and nutrient transfer resulting from placental insufficiency. Objective To identify predictors of poor perinatal outcome after maternal perception of reduced fetal movements (RFM). Design Prospective cohort study. Methods 305 women presenting with RFM after 28 weeks of gestation were recruited. Demographic factors and clinical history were recorded and ultrasound performed to assess fetal biometry, liquor volume and umbilical artery Doppler. A maternal serum sample was obtained for measurement of placentally-derived or modified proteins including: alpha fetoprotein (AFP), human chorionic gonadotrophin (hCG), human placental lactogen (hPL), ischaemia-modified albumin (IMA), pregnancy associated plasma protein A (PAPP-A) and progesterone. Factors related to poor perinatal outcome were determined by logistic regression. Results 22.1% of pregnancies ended in a poor perinatal outcome after RFM. The most common complication was small-for-gestational age infants. Pregnancy outcome after maternal perception of RFM was related to amount of fetal activity while being monitored, abnormal fetal heart rate trace, diastolic blood pressure, estimated fetal weight, liquor volume, serum hCG and hPL. Following multiple logistic regression abnormal fetal heart rate trace (Odds ratio 7.08, 95% Confidence Interval 1.31–38.18), (OR) diastolic blood pressure (OR 1.04 (95% CI 1.01–1.09), estimated fetal weight centile (OR 0.95, 95% CI 0.94–0.97) and log maternal serum hPL (OR 0.13, 95% CI 0.02–0.99) were independently related to pregnancy outcome. hPL was related to placental mass. Conclusion Poor perinatal outcome after maternal perception of RFM is closely related to factors which are connected to placental dysfunction. Novel tests of placental function and associated fetal response may provide improved means to detect fetuses at greatest risk of poor perinatal outcome after RFM

Metabolomic analysis of human disease and its application to the eye

Metabolomics, the analysis of the metabolite profile in body fluids or tissues, is being applied to the analysis of a number of different diseases as well as being used in following responses to therapy. While genomics involves the study of gene expression and proteomics the expression of proteins, metabolomics investigates the consequences of the activity of these genes and proteins. There is good reason to think that metabolomics will find particular utility in the investigation of inflammation, given the multi-layered responses to infection and damage that are seen. This may be particularly relevant to eye disease, which may have tissue specific and systemic components. Metabolomic analysis can inform us about ocular or other body fluids and can therefore provide new information on pathways and processes involved in these responses. In this review, we explore the metabolic consequences of disease, in particular ocular conditions, and why the data may be usefully and uniquely assessed using the multiplexed analysis inherent in the metabolomic approach