Urinary MicroRNA Profiling in the Nephropathy of Type 1 Diabetes

Background: Patients with Type 1 Diabetes (T1D) are particularly vulnerable to development of Diabetic nephropathy (DN) leading to End Stage Renal Disease. Hence a better understanding of the factors affecting kidney disease progression in T1D is urgently needed. In recent years microRNAs have emerged as important post-transcriptional regulators of gene expression in many different health conditions. We hypothesized that urinary microRNA profile of patients will differ in the different stages of diabetic renal disease. Methods and Findings: We studied urine microRNA profiles with qPCR in 40 T1D with >20 year follow up 10 who never developed renal disease (N) matched against 10 patients who went on to develop overt nephropathy (DN), 10 patients with intermittent microalbuminuria (IMA) matched against 10 patients with persistent (PMA) microalbuminuria. A Bayesian procedure was used to normalize and convert raw signals to expression ratios. We applied formal statistical techniques to translate fold changes to profiles of microRNA targets which were then used to make inferences about biological pathways in the Gene Ontology and REACTOME structured vocabularies. A total of 27 microRNAs were found to be present at significantly different levels in different stages of untreated nephropathy. These microRNAs mapped to overlapping pathways pertaining to growth factor signaling and renal fibrosis known to be targeted in diabetic kidney disease. Conclusions: Urinary microRNA profiles differ across the different stages of diabetic nephropathy. Previous work using experimental, clinical chemistry or biopsy samples has demonstrated differential expression of many of these microRNAs in a variety of chronic renal conditions and diabetes. Combining expression ratios of microRNAs with formal inferences about their predicted mRNA targets and associated biological pathways may yield useful markers for early diagnosis and risk stratification of DN in T1D by inferring the alteration of renal molecular processes. © 2013 Argyropoulos et al

Endothelial Progenitor Cells Predict Cardiovascular Events after Atherothrombotic Stroke and Acute Myocardial Infarction. A PROCELL Substudy.

Introduction: The aim of this study was to determine prognostic factors for the risk of new vascular events during the first 6 months after acute myocardial infarction (AMI) or atherothrombotic stroke (AS). We were interested in the prognostic role of endothelial progenitor cells (EPC) and circulating endothelial cells (CEC). Methods: Between February 2009 and July 2012, 100 AMI and 50 AS patients were consecutively studied in three Spanish centres. Patients with previously documented coronary artery disease or ischemic strokes were excluded. Samples were collected within 24h of onset of symptoms. EPC and CEC were studied using flow cytometry and categorized by quartiles. Patients were followed for up to 6 months. NVE was defined as new acute coronary syndrome, transient ischemic attack (TIA), stroke, or any hospitalization or death from cardiovascular causes. The variables included in the analysis included: vascular risk factors, carotid intima-media thickness (IMT), atherosclerotic burden and basal EPC and CEC count. Multivariate survival analysis was performed using Cox regression analysis. Results: During follow-up, 19 patients (12.66%) had a new vascular event (5 strokes; 3 TIAs; 4 AMI; 6 hospitalizations; 1 death). Vascular events were associated with age (P = 0.039), carotid IMT≥0.9 (P = 0.044), and EPC count (P = 0.041) in the univariate analysis. Multivariate Cox regression analysis showed an independent association with EPC in the lowest quartile (HR: 10.33, 95%CI (1.22-87.34), P = 0.032] and IMT≥0.9 [HR: 4.12, 95%CI (1.21-13.95), P = 0.023]. Conclusions: Basal EPC and IMT≥0.9 can predict future vascular events in patients with AMI and AS, but CEC count does not affect cardiovascular risk

Catching-up in the global factory: analysis and policy implications

MNEs shape the location of activities in the world economy, linking diverse regions in what has been called the global factory. This study portrays the evolution of incomes and employment in the global factory using a quantitative input–output approach. We find emerging economies forging ahead relative to advanced economies in income derived from fabrication activities, handling the physical transformation process of goods. In contrast, convergence in income derived from knowledge-intensive activities carried out in pre- and post-fabrication stages is much slower. We discuss possible barriers to catching-up and policy implications for emerging economies in developing innovation capabilities, stressing the pivotal role of MNEs

The role of epigenetics in renal ageing

An ability to separate natural ageing processes from processes specific to morbidities is required to understand the heterogeneity of age-related organ dysfunction. Mechanistic insight into how epigenetic factors regulate ageing throughout the life course, linked to a decline in renal function with ageing, is already proving to be of value in the analyses of clinical and epidemiological cohorts. Noncoding RNAs provide epigenetic regulatory circuits within the kidney, which reciprocally interact with DNA methylation processes, histone modification and chromatin. These interactions have been demonstrated to reflect the biological age and function of renal allografts. Epigenetic factors control gene expression and activity in response to environmental perturbations. They also have roles in highly conserved signalling pathways that modulate ageing, including the mTOR and insulin/insulin-like growth factor signalling pathways, and regulation of sirtuin activity. Nutrition, the gut microbiota, inflammation and environmental factors, including psychosocial and lifestyle stresses, provide potential mechanistic links between the epigenetic landscape of ageing and renal dysfunction. Approaches to modify the renal epigenome via nutritional intervention, targeting the methylome or targeting chromatin seem eminently feasible, although caution is merited owing to the potential for intergenerational and transgenerational effects

Ancient DNA reveals that bowhead whale lineages survived Late Pleistocene climate change and habitat shifts

The climatic changes of the glacial cycles are thought to have been a major driver of population declines and species extinctions. However, studies to date have focused on terrestrial fauna and there is little understanding of how marine species responded to past climate change. Here we show that a true Arctic species, the bowhead whale (Balaena mysticetus), shifted its range and tracked its core suitable habitat northwards during the rapid climate change of the Pleistocene–Holocene transition. Late Pleistocene lineages survived into the Holocene and effective female population size increased rapidly, concurrent with a threefold increase in core suitable habitat. This study highlights that responses to climate change are likely to be species specific and difficult to predict. We estimate that the core suitable habitat of bowhead whales will be almost halved by the end of this century, potentially influencing future population dynamics

Large expert-curated database for benchmarking document similarity detection in biomedical literature search

Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency–Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research