Energy landscapes shape microbial communities in hydrothermal systems on the Arctic Mid-Ocean Ridge

Methods developed in geochemical modelling combined with recent advances in molecular microbial ecology provide new opportunities to explore how microbial communities are shaped by their chemical surroundings. Here, we present a framework for analyses of how chemical energy availability shape chemotrophic microbial communities in hydrothermal systems through an investigation of two geochemically different basalt-hosted hydrothermal systems on the Arctic Mid-Ocean Ridge: the Soria Moria Vent field (SMVF) and the Loki''s Castle Vent Field (LCVF). Chemical energy landscapes were evaluated through modelling of the Gibbs energy from selected redox reactions under different mixing ratios between seawater and hydrothermal fluids. Our models indicate that the sediment-influenced LCVF has a much higher potential for both anaerobic and aerobic methane oxidation, as well as aerobic ammonium and hydrogen oxidation, than the SMVF. The modelled energy landscapes were used to develop microbial community composition models, which were compared with community compositions in environmental samples inside or on the exterior of hydrothermal chimneys, as assessed by pyrosequencing of partial 16S rRNA genes. We show that modelled microbial communities based solely on thermodynamic considerations can have a high predictive power and provide a framework for analyses of the link between energy availability and microbial community composition.     

Integration Analysis of Three Omics Data Using Penalized Regression Methods: An Application to Bladder Cancer

Omics data integration is becoming necessary to investigate the genomic mechanisms involved in complex diseases. During the integration process, many challenges arise such as data heterogeneity, the smaller number of individuals in comparison to the number of parameters, multicollinearity, and interpretation and validation of results due to their complexity and lack of knowledge about biological processes. To overcome some of these issues, innovative statistical approaches are being developed. In this work, we propose a permutation-based method to concomitantly assess significance and correct by multiple testing with the MaxT algorithm. This was applied with penalized regression methods (LASSO and ENET) when exploring relationships between common genetic variants, DNA methylation and gene expression measured in bladder tumor samples. The overall analysis flow consisted of three steps: (1) SNPs/CpGs were selected per each gene probe within 1Mb window upstream and downstream the gene; (2) LASSO and ENET were applied to assess the association between each expression probe and the selected SNPs/CpGs in three multivariable models (SNP, CPG, and Global models, the latter integrating SNPs and CPGs); and (3) the significance of each model was assessed using the permutation-based MaxT method. We identified 48 genes whose expression levels were significantly associated with both SNPs and CPGs. Importantly, 36 (75%) of them were replicated in an independent data set (TCGA) and the performance of the proposed method was checked with a simulation study. We further support our results with a biological interpretation based on an enrichment analysis. The approach we propose allows reducing computational time and is flexible and easy to implement when analyzing several types of omics data. Our results highlight the importance of integrating omics data by applying appropriate statistical strategies to discover new insights into the complex genetic mechanisms involved in disease conditions.     

Trends in Expanded-Spectrum Cephalosporin-Resistant Escherichia coli and Klebsiella pneumoniae among Dutch Clinical Isolates,from 2008 to 2012

We investigated time trends in extended-spectrum cephalosporin-resistant Escherichia coli and Klebsiella pneumoniae isolates from different patient settings in The Netherlands from 2008–2012. E. coli and K. pneumoniae isolates from blood and urine samples of patients > = 18 years were selected from the Dutch Infectious Disease Surveillance System-Antimicrobial Resistance (ISIS-AR) database. We used multivariable Poisson regression to study the rate per year of blood stream infections by susceptible and resistant isolates, and generalized estimating equation (GEE) log-binomial regression for trends in the proportion of extended-spectrum cephalosporin-resistant isolates. Susceptibility data of 197,513 E. coli and 38,244 K. pneumoniae isolates were included. The proportion of extended-spectrum cephalosporin-resistant E. coli and K. pneumoniae isolates from urine and blood samples increased in all patient settings, except for K. pneumoniae isolates from patients admitted to intensive care units. For K. pneumoniae, there was a different time trend between various patient groups (p<0.01), with a significantly higher increase in extended-spectrum cephalosporin-resistant isolates from patients attending a general practitioner than in isolates from hospitalized patients. For E. coli, the increasing time trends did not differ among different patient groups. This nationwide study shows a general increase in extended-spectrum cephalosporin-resistant E. coli and K. pneumoniae isolates. However, differences in trends between E. coli en K. pneumoniae underline the importance of E. coli as a community-pathogen and its subsequent influence on hospital resistance level, while for K. pneumoniae the level of resistance within the hospital seems less influenced by the resistance trends in the community.     

Folliculogenesis Is Not Fully Inhibited during GnRH Analogues Treatment in Mice Challenging Their Efficiency to Preserve the Ovarian Reserve during Chemotherapy in This Model

As many chemotherapy regimens induce follicular depletion, fertility preservation became a major concern in young cancer patients. By maintaining follicles at the resting stage, gonadotropin-releasing hormone analogues (GnRHa) were proposed as an ovarian-protective option during chemotherapy. However, their efficacy and mechanisms of action remain to be elucidated. Mice were dosed with cyclophosphamide (Cy, 100–500mg/kg i.p) to quantify follicular depletion and evaluate apoptosis at different times. We observed a dose-dependent depletion of the follicular reserve within 24 hours after Cy injection with a mean follicular loss of 45% at the dose of 200mg/kg. Apoptosis occurs in the granulosa cells of growing follicles within 12 hours after Cy treatment, while no apoptosis was detected in resting follicles suggesting that chemotherapy acutely affects both resting and growing follicles through different mechanisms. We further tested the ability of both GnRH agonist and antagonist to inhibit oestrus cycles, follicular growth and FSH secretion in mice and to protect ovarian reserve against chemotherapy. Although GnRHa were efficient to disrupt oestrus cycles, they failed to inhibit follicular development, irrespective of the doses and injection sites (sc or im). Around 20% of healthy growing follicles were still observed during GnRHa treatment and serum FSH levels were not reduced either by antagonist or agonist. GnRHa had no effect on Cy-induced follicular damages. Thus, we showed that GnRHa were not as efficient at inhibiting the pituitary-gonadal axis in mice as in human. Furthermore, the acute depletion of primordial follicles observed after chemotherapy does not support the hypothesis that the ovary may be protected by gonadotropin suppression.     

High Expression of KCa3.1 in Patients with Clear Cell Renal Carcinoma Predicts High Metastatic Risk and Poor Survival

Background

Ca²⁺-activated K⁺ channels have been implicated in cancer cell growth, metastasis, and tumor angiogenesis. Here we hypothesized that high mRNA and protein expression of the intermediate-conductance Ca²⁺-activated K⁺ channel, KCa3.1, is a molecular marker of clear cell Renal Cell Carcinoma (ccRCC) and metastatic potential and survival.

Methodology/Principal Findings

We analyzed channel expression by qRT-PCR, immunohistochemistry, and patch-clamp in ccRCC and benign oncocytoma specimens, in primary ccRCC and oncocytoma cell lines, as well as in two ccRCC cell lines (Caki-1 and Caki-2). CcRCC specimens contained 12-fold higher mRNA levels of KCa3.1 than oncocytoma specimens. The large-conductance channel, KCa1.1, was 3-fold more highly expressed in ccRCC than in oncocytoma. KCa3.1 mRNA expression in ccRCC was 2-fold higher than in the healthy cortex of the same kidney. Disease specific survival trended towards reduction in the subgroup of high-KCa3.1-expressing tumors (p<0.08 vs. low-KCa3.1-expressing tumors). Progression-free survival (time to metastasis/recurrence) was reduced significantly in the subgroup of high-KCa3.1-expressing tumors (p<0.02, vs. low-KCa3.1-expressing tumors). Immunohistochemistry revealed high protein expression of KCa3.1 in tumor vessels of ccRCC and oncocytoma and in a subset of ccRCC cells. Oncocytoma cells were devoid of KCa3.1 protein. In a primary ccRCC cell line and Caki-1/2-ccRCC cells, we found KCa3.1-protein as well as TRAM-34-sensitive KCa3.1-currents in a subset of cells. Furthermore, Caki-1/2-ccRCC cells displayed functional Paxilline-sensitive KCa1.1 currents. Neither KCa3.1 nor KCa1.1 were found in a primary oncocytoma cell line. Yet KCa-blockers, like TRAM-34 (KCa3.1) and Paxilline (KCa1.1), had no appreciable effects on Caki-1 proliferation in-vitro.

Conclusions/Significance

Our study demonstrated expression of KCa3.1 in ccRCC but not in benign oncocytoma. Moreover, high KCa3.1-mRNA expression levels were indicative of low disease specific survival of ccRCC patients, short progression-free survival, and a high metastatic potential. Therefore, KCa3.1 is of prognostic value in ccRCC.     

Four Categories of Viral Infection Describe the Health Status of Honey Bee Colonies

Honey bee virus prevalence data are an essential prerequisite for managing epidemic events in a population. A survey study was carried out for seven viruses in colonies representing a healthy Danish honey bee population. In addition, colonies from apiaries with high level Varroa infestation or high level of winter mortality were also surveyed. Results from RT-qPCR showed a considerable difference of virus levels between healthy and sick colonies. In the group of healthy colonies, no virus was detected in 36% of cases, while at least one virus was found in each of the sick colonies. Virus titers varied among the samples, and multiple virus infections were common in both groups with a high prevalence of Sacbrood virus (SBV), Black queen cell virus (BQCV) and Deformed wing virus (DWV). Based on the distribution of virus titers, we established four categories of infection: samples free of virus (C = 0), samples with low virus titer (estimated number of virus copies 0 < C < 10³), samples with medium virus titer (10³ ≤ C < 10⁷) and samples with high virus titer (C ≥ 10⁷). This allowed us to statistically compare virus levels in healthy and sick colonies. Using categories to communicate virus diagnosis results to beekeepers may help them to reach an informed decision on management strategies to prevent further spread of viruses among colonies.     

One-step sample concentration, purification, and albumin depletion method for urinary proteomics

Workflows in urinary proteomics studies are often complex and require many steps to enrich, purify, deplete, and separate the complex mixture. Many of these methods are laborious, are time-consuming, and have the potential for error. Although individual steps of these methods have been previously studied, their downstream compatibilities with fractionation technologies such as off-gel electrophoresis have not been investigated. We developed a one-step sample preparation workflow that simultaneously (i) concentrates proteins, (ii) purifies by removing salts and other low molecular weight compounds, and (iii) depletes (albumin) from urine samples. This simple and robust workflow can be multiplexed and is compatible with a diverse range of downstream multidimensional separation technologies. Additionally, because of its high reproducibility and flexibility in processing samples with different volumes and concentrations, it has the potential to be used for standardization of urinary proteomics studies, as well as for studying other body fluids of similar complexity.     

Structural and functional properties of isocitrate dehydrogenase from the psychrophilic bacterium Desulfotalea psychrophila reveal a cold-active enzyme with an unusual high thermal stability

Isocitrate dehydrogenase (IDH) has been studied extensively due to its central role in the Krebs cycle, catalyzing the oxidative NAD(P)(+)-dependent decarboxylation of isocitrate to alpha-ketoglutarate and CO(2). Here, we present the first crystal structure of IDH from a psychrophilic bacterium, Desulfotalea psychrophila (DpIDH). The structural information is combined with a detailed biochemical characterization and a comparative study with IDHs from the mesophilic bacterium Desulfitobacterium hafniense (DhIDH), porcine (PcIDH), human cytosolic (HcIDH) and the hyperthermophilic Thermotoga maritima (TmIDH). DpIDH was found to have a higher melting temperature (T(m)=66.9 degrees C) than its mesophilic homologues and a suboptimal catalytic efficiency at low temperatures. The thermodynamic activation parameters indicated a disordered active site, as seen also for the drastic increase in K(m) for isocitrate at elevated temperatures. A methionine cluster situated at the dimeric interface between the two active sites and a cluster of destabilizing charged amino acids in a region close to the active site might explain the poor isocitrate affinity. On the other hand, DpIDH was optimized for interacting with NADP(+) and the crystal structure revealed unique interactions with the cofactor. The highly acidic surface, destabilizing charged residues, fewer ion pairs and reduced size of ionic networks in DpIDH suggest a flexible global structure. However, strategic placement of ionic interactions stabilizing the N and C termini, and additional ionic interactions in the clasp domain as well as two enlarged aromatic clusters might counteract the destabilizing interactions and promote the increased thermal stability. The structure analysis of DpIDH illustrates how psychrophilic enzymes can adjust their flexibility in dynamic regions during their catalytic cycle without compromising the global stability of the protein.