Segregation discovery in a social network of companies

We introduce a framework for a data-driven analysis of segregation of minority groups in social networks, and challenge it on a complex scenario. The framework builds on quantitative measures of segregation, called segregation indexes, proposed in the social science literature. The segregation discovery problem consists of searching sub-graphs and sub-groups for which a reference segregation index is above a minimum threshold. A search algorithm is devised that solves the segregation problem. The framework is challenged on the analysis of segregation of social groups in the boards of directors of the real and large network of Italian companies connected through shared directors

Evaluation of an Adapted Semi-Automated DNA Extraction for Human Salivary Shotgun Metagenomics

Recent attention has highlighted the importance of oral microbiota in human health and disease, e.g., in Parkinson’s disease, notably using shotgun metagenomics. One key aspect for efficient shotgun metagenomic analysis relies on optimal microbial sampling and DNA extraction, generally implementing commercial solutions developed to improve sample collection and preservation, and provide high DNA quality and quantity for downstream analysis. As metagenomic studies are today performed on a large number of samples, the next evolution to increase study throughput is with DNA extraction automation. In this study, we proposed a semi-automated DNA extraction protocol for human salivary samples collected with a commercial kit, and compared the outcomes with the DNA extraction recommended by the manufacturer. While similar DNA yields were observed between the protocols, our semi-automated DNA protocol generated significantly higher DNA fragment sizes. Moreover, we showed that the oral microbiome composition was equivalent between DNA extraction methods, even at the species level. This study demonstrates that our semi-automated protocol is suitable for shotgun metagenomic analysis, while allowing for improved sample treatment logistics with reduced technical variability and without compromising the structure of the oral microbiome

Identification of neutral biochemical network models from time series data

<p>Abstract</p> <p>Background</p> <p>The major difficulty in modeling biological systems from multivariate time series is the identification of parameter sets that endow a model with dynamical behaviors sufficiently similar to the experimental data. Directly related to this parameter estimation issue is the task of identifying the structure and regulation of ill-characterized systems. Both tasks are simplified if the mathematical model is canonical, <it>i.e</it>., if it is constructed according to strict guidelines.</p> <p>Results</p> <p>In this report, we propose a method for the identification of admissible parameter sets of canonical S-systems from biological time series. The method is based on a Monte Carlo process that is combined with an improved version of our previous parameter optimization algorithm. The method maps the parameter space into the network space, which characterizes the connectivity among components, by creating an ensemble of decoupled S-system models that imitate the dynamical behavior of the time series with sufficient accuracy. The concept of sloppiness is revisited in the context of these S-system models with an exploration not only of different parameter sets that produce similar dynamical behaviors but also different network topologies that yield dynamical similarity.</p> <p>Conclusion</p> <p>The proposed parameter estimation methodology was applied to actual time series data from the glycolytic pathway of the bacterium <it>Lactococcus lactis </it>and led to ensembles of models with different network topologies. In parallel, the parameter optimization algorithm was applied to the same dynamical data upon imposing a pre-specified network topology derived from prior biological knowledge, and the results from both strategies were compared. The results suggest that the proposed method may serve as a powerful exploration tool for testing hypotheses and the design of new experiments.</p

Prevalence and risk factors for Giardia duodenalis infection among children: A case study in Portugal

<p>Abstract</p> <p>Background</p> <p><it>Giardia duodenalis </it>is a widespread parasite of mammalian species, including humans. The prevalence of this parasite in children residing in Portugal is currently unknown. This study intended to estimate <it>G. duodenalis </it>infection prevalence and identify possible associated risk factors in a healthy paediatric population living in the District of the Portuguese capital, Lisbon.</p> <p>Methods</p> <p>Between February 2002 and October 2008, 844 children were randomly selected at healthcare centres while attending the national vaccination program. A stool sample and a questionnaire with socio-demographic data were collected from each child. <it>Giardia </it>infection was diagnosed by direct examination of stools and antigen detection by ELISA.</p> <p>Results</p> <p>The population studied revealed a gender distribution of 52.8% male and 47.2% female. Age distribution was 47.4% between 0-5 years and 52.6% between 6-15 years.</p> <p>The prevalence of <it>Giardia </it>infection was 1.9% (16/844) when estimated by direct examination and increased to 6.8% (57/844) when ELISA results were added. The prevalence was higher among children aged 0-5 years (7.8%), than among older children (5.8%), and was similar among genders (6.9% in boys and 6.5% in girls). The following population-variables were shown to be associated risk factors for <it>G. duodenalis </it>infection: mother's educational level (odds ratio (OR)= 4.49; confidence interval (CI): 1.20-16.84), father's educational level (OR = 12.26; CI: 4.08-36.82), presence of <it>Helicobacter pylori </it>infection (OR = 1.82; CI: 1.05-3.15), living in houses with own drainage system (OR = 0.10; CI: 0.02-0.64) and reported household pet contact, especially with dogs (OR = 0.53; CI: 0.31-0.93).</p> <p>Conclusion</p> <p>The prevalence of giardiasis in asymptomatic children residing in the region of Lisbon is high. Several risk factors were associated with <it>Giardia </it>prevalence and highlight the importance of parents' education and sanitation conditions in the children's well being. The association between <it>G. duodenalis </it>and <it>H. pylori </it>seems an important issue deserving further investigation in order to promote prevention or treatment strategies.</p

Evidence for Divergent Evolution of Growth Temperature Preference in Sympatric Saccharomyces Species

The genus Saccharomyces currently includes eight species in addition to the model yeast Saccharomyces cerevisiae, most of which can be consistently isolated from tree bark and soil. We recently found sympatric pairs of Saccharomyces species, composed of one cryotolerant and one thermotolerant species in oak bark samples of various geographic origins. In order to contribute to explain the occurrence in sympatry of Saccharomyces species, we screened Saccharomyces genomic data for protein divergence that might be correlated to distinct growth temperature preferences of the species, using the dN/dS ratio as a measure of protein evolution rates and pair-wise species comparisons. In addition to proteins previously implicated in growth at suboptimal temperatures, we found that glycolytic enzymes were among the proteins exhibiting higher than expected divergence when one cryotolerant and one thermotolerant species are compared. By measuring glycolytic fluxes and glycolytic enzymatic activities in different species and at different temperatures, we subsequently show that the unusual divergence of glycolytic genes may be related to divergent evolution of the glycolytic pathway aligning its performance to the growth temperature profiles of the different species. In general, our results support the view that growth temperature preference is a trait that may have undergone divergent selection in the course of ecological speciation in Saccharomyces

Mild Hypothermia Attenuates Mitochondrial Oxidative Stress by Protecting Respiratory Enzymes and Upregulating MnSOD in a Pig Model of Cardiac Arrest

Mild hypothermia is the only effective treatment confirmed clinically to improve neurological outcomes for comatose patients with cardiac arrest. However, the underlying mechanism is not fully elucidated. In this study, our aim was to determine the effect of mild hypothermia on mitochondrial oxidative stress in the cerebral cortex. We intravascularly induced mild hypothermia (33°C), maintained this temperature for 12 h, and actively rewarmed in the inbred Chinese Wuzhishan minipigs successfully resuscitated after 8 min of untreated ventricular fibrillation. Cerebral samples were collected at 24 and 72 h following return of spontaneous circulation (ROSC). We found that mitochondrial malondialdehyde and protein carbonyl levels were significantly increased in the cerebral cortex in normothermic pigs even at 24 h after ROSC, whereas mild hypothermia attenuated this increase. Moreover, mild hypothermia attenuated the decrease in Complex I and Complex III (i.e., major sites of reactive oxygen species production) activities of the mitochondrial respiratory chain and increased antioxidant enzyme manganese superoxide dismutase (MnSOD) activity. This increase in MnSOD activity was consistent with the upregulation of nuclear factor erythroid 2-related factor 2 (Nrf2) mRNA and protein expressions, and with the increase of Nrf2 nuclear translocation in normothermic pigs at 24 and 72 h following ROSC, whereas mild hypothermia enhanced these tendencies. Thus, our findings indicate that mild hypothermia attenuates mitochondrial oxidative stress in the cerebral cortex, which may be associated with reduced impairment of mitochondrial respiratory chain enzymes, and enhancement of MnSOD activity and expression via Nrf2 activation

Analysis of exome data for 4293 trios suggests GPI-anchor biogenesis defects are a rare cause of developmental disorders.

Over 150 different proteins attach to the plasma membrane using glycosylphosphatidylinositol (GPI) anchors. Mutations in 18 genes that encode components of GPI-anchor biogenesis result in a phenotypic spectrum that includes learning disability, epilepsy, microcephaly, congenital malformations and mild dysmorphic features. To determine the incidence of GPI-anchor defects, we analysed the exome data from 4293 parent-child trios recruited to the Deciphering Developmental Disorders (DDD) study. All probands recruited had a neurodevelopmental disorder. We searched for variants in 31 genes linked to GPI-anchor biogenesis and detected rare biallelic variants in PGAP3, PIGN, PIGT (n=2), PIGO and PIGL, providing a likely diagnosis for six families. In five families, the variants were in a compound heterozygous configuration while in a consanguineous Afghani kindred, a homozygous c.709G>C; p.(E237Q) variant in PIGT was identified within 10-12 Mb of autozygosity. Validation and segregation analysis was performed using Sanger sequencing. Across the six families, five siblings were available for testing and in all cases variants co-segregated consistent with them being causative. In four families, abnormal alkaline phosphatase results were observed in the direction expected. FACS analysis of knockout HEK293 cells that had been transfected with wild-type or mutant cDNA constructs demonstrated that the variants in PIGN, PIGT and PIGO all led to reduced activity. Splicing assays, performed using leucocyte RNA, showed that a c.336-2A>G variant in PIGL resulted in exon skipping and p.D113fs*2. Our results strengthen recently reported disease associations, suggest that defective GPI-anchor biogenesis may explain ~0.15% of individuals with developmental disorders and highlight the benefits of data sharing