Breastfeeding moderates FTO related adiposity: a birth cohort study with 30 years of follow-up.

This study assessed the association of breastfeeding with body composition at 30 years, among subjects who have been prospectively followed since birth in a southern Brazilian city. We also evaluated whether breastfeeding moderated the association between the rs9939609 variant in the FTO gene and adiposity. At 30 years, total and predominant breastfeeding were positively associated with lean mass index and inversely with visceral fat thickness. Among subjects breastfed for <1 month, all outcomes showed monotonically increasing values with additional copies of the A allele in the FTO genotype (rs9939609). Associations among subjects breastfed for one month or longer tended to be in the same direction but showed lower magnitude and were less consistent; for all outcomes. Interactions had p values ≤ 0.05 for body mass index, fat mass index and waist circumference. Even among young adults, breastfeeding moderates the association between the FTO variant rs9939609 and body composition

Contribution to the floristic knowledge of the Maddalena Mountains (Basilicata and Campania, southern Italy)

The inventory of the taxa collected during the annual field trip of the working group for Floristics, Systematics and Evolution of the Italian Botanical Society is reported. It was held in 2013 along the Maddalena Mountains, a mountain ridge of the southern Apennines located between the Basilicata and Campania administrative regions (southern Italy), considered as being poorly characterized in terms of vascular flora. A total of 701 units belonging to 74 plant families were recorded including two varieties and four hybrids.Thirty-five taxa resulted endemic to Italy and only 11 alien species were detected, while 36 taxa are new or confirmed for the regional floras of Basilicata and/or Campania. In particular, 12 taxa are new for Basilicata, while four are confirmed. Regarding Campania, 14 taxa resulted new for the regional flora and five were confirmed

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

High Content Image Analysis Identifies Novel Regulators of Synaptogenesis in a High-Throughput RNAi Screen of Primary Neurons

The formation of synapses, the specialized points of chemical communication between neurons, is a highly regulated developmental process fundamental to establishing normal brain circuitry. Perturbations of synapse formation and function causally contribute to human developmental and degenerative neuropsychiatric disorders, such as Alzheimer's disease, intellectual disability, and autism spectrum disorders. Many genes controlling synaptogenesis have been identified, but lack of facile experimental systems has made systematic discovery of regulators of synaptogenesis challenging. Thus, we created a high-throughput platform to study excitatory and inhibitory synapse development in primary neuronal cultures and used a lentiviral RNA interference library to identify novel regulators of synapse formation. This methodology is broadly applicable for high-throughput screening of genes and drugs that may rescue or improve synaptic dysfunction associated with cognitive function and neurological disorders.National Institutes of Health (U.S.) (MH095096)National Institutes of Health (U.S.) (R01 GM089652

Can Phlorotannins Purified Extracts Constitute a Novel Pharmacological Alternative for Microbial Infections with Associated Inflammatory Conditions?

Bacterial and fungal infections and the emerging multidrug resistance are driving interest in fighting these microorganisms with natural products, which have generally been considered complementary to pharmacological therapies. Phlorotannins are polyphenols restricted to brown seaweeds, recognized for their biological capacity. This study represents the first research on the antibacterial, antifungal, anti-inflammatory and antioxidant activity of phlorotannins purified extracts, which were obtained from ten dominant brown seaweeds of the occidental Portuguese coast

DREAM4: Combining Genetic and Dynamic Information to Identify Biological Networks and Dynamical Models

Current technologies have lead to the availability of multiple genomic data types in sufficient quantity and quality to serve as a basis for automatic global network inference. Accordingly, there are currently a large variety of network inference methods that learn regulatory networks to varying degrees of detail. These methods have different strengths and weaknesses and thus can be complementary. However, combining different methods in a mutually reinforcing manner remains a challenge.We investigate how three scalable methods can be combined into a useful network inference pipeline. The first is a novel t-test-based method that relies on a comprehensive steady-state knock-out dataset to rank regulatory interactions. The remaining two are previously published mutual information and ordinary differential equation based methods (tlCLR and Inferelator 1.0, respectively) that use both time-series and steady-state data to rank regulatory interactions; the latter has the added advantage of also inferring dynamic models of gene regulation which can be used to predict the system's response to new perturbations.Our t-test based method proved powerful at ranking regulatory interactions, tying for first out of methods in the DREAM4 100-gene in-silico network inference challenge. We demonstrate complementarity between this method and the two methods that take advantage of time-series data by combining the three into a pipeline whose ability to rank regulatory interactions is markedly improved compared to either method alone. Moreover, the pipeline is able to accurately predict the response of the system to new conditions (in this case new double knock-out genetic perturbations). Our evaluation of the performance of multiple methods for network inference suggests avenues for future methods development and provides simple considerations for genomic experimental design. Our code is publicly available at http://err.bio.nyu.edu/inferelator/

Postnatal PPARδ Activation and Myostatin Inhibition Exert Distinct yet Complimentary Effects on the Metabolic Profile of Obese Insulin-Resistant Mice

BACKGROUND: Interventions for T2DM have in part aimed to mimic exercise. Here, we have compared the independent and combined effects of a PPARdelta agonist and endurance training mimetic (GW501516) and a myostatin antibody and resistance training mimetic (PF-879) on metabolic and performance outcomes in obese insulin resistant mice. METHODOLOGY/PRINCIPAL FINDINGS: Male ob/ob mice were treated for 6 weeks with vehicle, GW501516, PF-879, or GW501516 in combination with PF-879. The effects of the interventions on body composition, glucose homeostasis, glucose tolerance, energy expenditure, exercise capacity and metabolic gene expression were compared at the end of study. GW501516 attenuated body weight and fat mass accumulation and increased the expression of genes of oxidative metabolism. In contrast, PF-879 increased body weight by driving muscle growth and altered the expression of genes involved in insulin signaling and glucose metabolism. Despite their differences, both interventions alone improved glucose homeostasis. Moreover, GW501516 more effectively improved serum lipids, and PF-879 uniquely increased energy expenditure, exercise capacity and adiponectin levels. When combined the robust effects of GW501516 and/or PF-879 on body weight, adiposity, muscle mass, glycemia, serum lipids, energy expenditure and exercise capacity were highly conserved. CONCLUSIONS/SIGNIFICANCE: The data, for the first time, demonstrate postnatal inhibition of myostatin not only promotes gains in muscle mass similar to resistance training,but improves metabolic homeostasis. In several instances, these effects were either distinct from or complimentary to those of GW501516. The data further suggest that strategies to increase muscle mass, and not necessarily oxidative capacity, may effectively counter insulin resistance and T2DM

Network deconvolution as a general method to distinguish direct dependencies in networks

Recognizing direct relationships between variables connected in a network is a pervasive problem in biological, social and information sciences as correlation-based networks contain numerous indirect relationships. Here we present a general method for inferring direct effects from an observed correlation matrix containing both direct and indirect effects. We formulate the problem as the inverse of network convolution, and introduce an algorithm that removes the combined effect of all indirect paths of arbitrary length in a closed-form solution by exploiting eigen-decomposition and infinite-series sums. We demonstrate the effectiveness of our approach in several network applications: distinguishing direct targets in gene expression regulatory networks; recognizing directly interacting amino-acid residues for protein structure prediction from sequence alignments; and distinguishing strong collaborations in co-authorship social networks using connectivity information alone. In addition to its theoretical impact as a foundational graph theoretic tool, our results suggest network deconvolution is widely applicable for computing direct dependencies in network science across diverse disciplines.National Institutes of Health (U.S.) (grant R01 HG004037)National Institutes of Health (U.S.) (grant HG005639)Swiss National Science Foundation (Fellowship)National Science Foundation (U.S.) (NSF CAREER Award 0644282

Epigenetic Regulation of Fatty Acid Amide Hydrolase in Alzheimer Disease

OBJECTIVE: Alzheimer disease (AD) is a progressive, degenerative and irreversible neurological disorder with few therapies available. In search for new potential targets, increasing evidence suggests a role for the endocannabinoid system (ECS) in the regulation of neurodegenerative processes. METHODS: We have studied the gene expression status and the epigenetic regulation of ECS components in peripheral blood mononuclear cells (PBMCs) of subjects with late-onset AD (LOAD) and age-matched controls (CT). RESULTS: We found an increase in fatty acid amide hydrolase (faah) gene expression in LOAD subjects (2.30 ± 0.48) when compared to CT (1.00 ± 0.14; *p<0.05) and no changes in the mRNA levels of any other gene of ECS elements. Consistently, we also observed in LOAD subjects an increase in FAAH protein levels (CT: 0.75 ± 0.04; LOAD: 1.11 ± 0.15; *p<0.05) and activity (pmol/min per mg protein CT: 103.80 ± 8.73; LOAD: 125.10 ± 4.00; *p<0.05), as well as a reduction in DNA methylation at faah gene promoter (CT: 55.90 ± 4.60%; LOAD: 41.20 ± 4.90%; *p<0.05). CONCLUSIONS: Present findings suggest the involvement of FAAH in the pathogenesis of AD, highlighting the importance of epigenetic mechanisms in enzyme regulation; they also point to FAAH as a new potential biomarker for AD in easily accessible peripheral cells

Bagging Statistical Network Inference from Large-Scale Gene Expression Data

Modern biology and medicine aim at hunting molecular and cellular causes of biological functions and diseases. Gene regulatory networks (GRN) inferred from gene expression data are considered an important aid for this research by providing a map of molecular interactions. Hence, GRNs have the potential enabling and enhancing basic as well as applied research in the life sciences. In this paper, we introduce a new method called BC3NET for inferring causal gene regulatory networks from large-scale gene expression data. BC3NET is an ensemble method that is based on bagging the C3NET algorithm, which means it corresponds to a Bayesian approach with noninformative priors. In this study we demonstrate for a variety of simulated and biological gene expression data from S. cerevisiae that BC3NET is an important enhancement over other inference methods that is capable of capturing biochemical interactions from transcription regulation and protein-protein interaction sensibly. An implementation of BC3NET is freely available as an R package from the CRAN repository