Multi-domain GFP-like proteins from two species of marine hydrozoans

Proteins homologous to Green Fluorescent Protein (GFP) are widely used as genetically encoded fluorescent labels. Many developments of this technology were spurred by discoveries of novel types of GFP-like proteins (FPs) in nature. Here we report two proteins displaying primary structures never before encountered in natural FPs: they consist of multiple GFP-like domains repeated within the same polypeptide chain. A two-domain green FP (abeGFP) and a four-domain orange-fluorescent FP (Ember) were isolated from the siphonophore Abylopsis eschscholtzii and an unidentified juvenile jellyfish (order Anthoathecata), respectively. Only the most evolutionary ancient domain of Ember is able to synthesize an orange-emitting chromophore (emission at 571 nm), while the other three are purely green (emission at 520 nm) and putatively serve to maintain the stability and solubility of the multidomain protein. When expressed individually, two of the green Ember domains form dimers and the third one exists as a monomer. The low propensity for oligomerization of these domains would simplify their adoption as in vivo labels. Our results reveal a previously unrecognized direction in which natural FPs have diversified, suggesting new avenues to look for FPs with novel and potentially useful features.     

Fine-Scale Linkage Mapping Reveals a Small Set of Candidate Genes Influencing Honey Bee Grooming Behavior in Response to Varroa Mites

Populations of honey bees in North America have been experiencing high annual colony mortality for 15–20 years. Many apicultural researchers believe that introduced parasites called Varroa mites (V. destructor) are the most important factor in colony deaths. One important resistance mechanism that limits mite population growth in colonies is the ability of some lines of honey bees to groom mites from their bodies. To search for genes influencing this trait, we used an Illumina Bead Station genotyping array to determine the genotypes of several hundred worker bees at over a thousand single-nucleotide polymorphisms in a family that was apparently segregating for alleles influencing this behavior. Linkage analyses provided a genetic map with 1,313 markers anchored to genome sequence. Genotypes were analyzed for association with grooming behavior, measured as the time that individual bees took to initiate grooming after mites were placed on their thoraces. Quantitative-trait-locus interval mapping identified a single chromosomal region that was significant at the chromosome-wide level (p<0.05) on chromosome 5 with a LOD score of 2.72. The 95% confidence interval for quantitative trait locus location contained only 27 genes (honey bee official gene annotation set 2) including Atlastin, Ataxin and Neurexin-1 (AmNrx1), which have potential neurodevelopmental and behavioral effects. Atlastin and Ataxin homologs are associated with neurological diseases in humans. AmNrx1 codes for a presynaptic protein with many alternatively spliced isoforms. Neurexin-1 influences the growth, maintenance and maturation of synapses in the brain, as well as the type of receptors most prominent within synapses. Neurexin-1 has also been associated with autism spectrum disorder and schizophrenia in humans, and self-grooming behavior in mice.     

Determinants of Antimicrobial Resistance in Escherichia coli Strains Isolated from Faeces and Urine of Women with Recurrent Urinary Tract Infections

For women with recurrent urinary tract infections (rUTI), the contribution of antibiotic use versus patient-related factors in determining the presence of antimicrobial resistance in faecal and urinary Escherichia coli, obtained from the same patient population, has not been assessed yet. Within the context of the ‘Non-antibiotic prophylaxis for recurrent urinary tract infections’ (NAPRUTI) study, the present study assessed determinants of antimicrobial resistance in E. coli isolated from urinary and faecal samples of women with rUTIs collected at baseline. Potential determinants of resistance were retrieved from self-administered questionnaires. From 434 asymptomatic women, 433 urinary and 424 faecal samples were obtained. E. coli was isolated from 146 (34%) urinary samples and from 336 (79%) faecal samples, and subsequently tested for antimicrobial susceptibility. Multivariable analysis showed trimethoprim/sulfamethoxazole (SXT) use three months prior to inclusion to be associated with urine E. coli resistance to amoxicillin (OR 3.6, 95% confidence interval: 1.3–9.9), amoxicillin-clavulanic acid (OR 4.4, 1.5–13.3), trimethoprim (OR 3.9, 1.4–10.5) and SXT (OR 3.2, 1.2–8.5), and with faecal E. coli resistance to trimethoprim (OR 2.0, 1.0–3.7). The number of UTIs in the preceding year was correlated with urine E. coli resistance to amoxicillin-clavulanic acid (OR 1.11, 1.01–1.22), trimethoprim (OR 1.13, 1.03–1.23) and SXT (OR 1.10, 1.01–1.19). Age was predictive for faecal E. coli resistance to amoxicillin (OR 1.02, 1.00–1.03), norfloxacin and ciprofloxacin (both OR 1.03, 1.01–1.06). In conclusion, in women with rUTI different determinants were found for urinary and faecal E. coli resistance. Previous antibiotic use and UTI history were associated with urine E. coli resistance and age was a predictor of faecal E. coli resistance. These associations could best be explained by cumulative antibiotic use.     

An integrative probabilistic model for identification of structural variation in sequencing data

Paired-end sequencing is a common approach for identifying structural variation (SV) in genomes. Discrepancies between the observed and expected alignments indicate potential SVs. Most SV detection algorithms use only one of the possible signals and ignore reads with multiple alignments. This results in reduced sensitivity to detect SVs, especially in repetitive regions. We introduce GASVPro, an algorithm combining both paired read and read depth signals into a probabilistic model which can analyze multiple alignments of reads. GASVPro outperforms existing methods with a 50-90% improvement in specificity on deletions and a 50% improvement on inversions.     

Exploring the contextual sensitivity of factors that determine cell-to-cell variability in receptor-mediated apoptosis

Stochastic fluctuations in gene expression give rise to cell-to-cell variability in protein levels which can potentially cause variability in cellular phenotype. For TRAIL (TNF-related apoptosis-inducing ligand) variability manifests itself as dramatic differences in the time between ligand exposure and the sudden activation of the effector caspases that kill cells. However, the contribution of individual proteins to phenotypic variability has not been explored in detail. In this paper we use feature-based sensitivity analysis as a means to estimate the impact of variation in key apoptosis regulators on variability in the dynamics of cell death. We use Monte Carlo sampling from measured protein concentration distributions in combination with a previously validated ordinary differential equation model of apoptosis to simulate the dynamics of receptor-mediated apoptosis. We find that variation in the concentrations of some proteins matters much more than variation in others and that precisely which proteins matter depends both on the concentrations of other proteins and on whether correlations in protein levels are taken into account. A prediction from simulation that we confirm experimentally is that variability in fate is sensitive to even small increases in the levels of Bcl-2. We also show that sensitivity to Bcl-2 levels is itself sensitive to the levels of interacting proteins. The contextual dependency is implicit in the mathematical formulation of sensitivity, but our data show that it is also important for biologically relevant parameter values. Our work provides a conceptual and practical means to study and understand the impact of cell-to-cell variability in protein expression levels on cell fate using deterministic models and sampling from parameter distributions.     

Selenium status and antibodies to selected pathogens in white-tailed deer (Odocoileus virginianus) in Southern Minnesota

To determine exposure to a variety of infectious diseases potentially important for native ungulates, livestock, and humans, serum samples from 114 (94 adults, 20 fawns) female white-tailed deer (Odocoileus virginianus) were collected during January 2000-03 from multiple locations in southeast (SE) and southwest (SW) Minnesota. Antibody prevalence was determined for the following pathogens: Mycobacterium avium subsp. paratuberculosis, Leptospira interrogans (six serovars), Anaplasma marginale, Borrelia burgdorferi, Brucella abortus, epizootic hemorrhagic disease virus, and bovine viral diarrhea virus (BVDV) types 1 and 2. Samples collected in 2001 were screened for antibodies against Anaplasma phagocytophilum, and whole blood was submitted for polymerase chain reaction (PCR) testing for A. phagocytophilum and B. burgdorferi. In addition, serum selenium concentrations were evaluated for samples collected during 2001-03. Antibody prevalence and selenium concentration were compared by age-class and geographic region. Antibodies to all of the infectious agents except A. marginale and B. abortus were detected; when detected, antibody prevalence was highest in adults. Deer collected from SE Minnesota had a higher antibody prevalence to B. burgdorferi than SW deer. Blood culture and PCR results for A. phagocytophilum and B. burgdorferi were negative. Antibodies against BVDV (combined types 1 and 2) were more prevalent (chi(2) = 3.617, P< or = 0.029) in deer collected in SW (41%) than in SE (25%) Minnesota. No statistically significant differences in serum selenium concentrations were detected when data were analyzed by age-class or by geographic location.     

Transforming growth factor-beta signaling mediates hypoxia-induced pulmonary arterial remodeling and inhibition of alveolar development in newborn mouse lung

Hypoxia causes abnormal neonatal pulmonary artery remodeling (PAR) and inhibition of alveolar development (IAD). Transforming growth factor (TGF)-beta is an important regulator of lung development and repair from injury. We tested the hypothesis that inhibition of TGF-beta signaling attenuates hypoxia-induced PAR and IAD. Mice with an inducible dominant-negative mutation of the TGF-beta type II receptor (DNTGFbetaRII) and nontransgenic wild-type (WT) mice were exposed to hypoxia (12% O(2)) or air from birth to 14 days of age. Expression of DNTGFbetaRII was induced by 20 microg/g ZnSO(4) given intraperitoneally daily from birth. PAR, IAD, cell proliferation, and expression of extracellular matrix (ECM) proteins were assessed. In WT mice, hypoxia led to thicker, more muscularized resistance pulmonary arteries and impaired alveolarization, accompanied by increases in active TGF-beta and phosphorylated Smad2. Hypoxia-induced PAR and IAD were greatly attenuated in DNTGFbetaRII mice given ZnSO(4) compared with WT control mice and DNTGFbetaRII mice not given ZnSO(4). The stimulatory effects of hypoxic exposure on pulmonary arterial cell proliferation and lung ECM proteins were abrogated in DNTGFbetaRII mice given ZnSO(4). These data support the conclusion that TGF-beta plays an important role in hypoxia-induced pulmonary vascular adaptation and IAD in the newborn animal model.     

Statistical data processing in clinical proteomics

This review discusses data analysis strategies for the discovery of biomarkers in clinical proteomics. Proteomics studies produce large amounts of data, characterized by few samples of which many variables are measured. A wealth of classification methods exists for extracting information from the data. Feature selection plays an important role in reducing the dimensionality of the data prior to classification and in discovering biomarker leads. The question which classification strategy works best is yet unanswered. Validation is a crucial step for biomarker leads towards clinical use. Here we only discuss statistical validation, recognizing that biological and clinical validation is of utmost importance. First, there is the need for validated model selection to develop a generalized classifier that predicts new samples correctly. A cross-validation loop that is wrapped around the model development procedure assesses the performance using unseen data. The significance of the model should be tested; we use permutations of the data for comparison with uninformative data. This procedure also tests the correctness of the performance validation. Preferably, a new set of samples is measured to test the classifier and rule out results specific for a machine, analyst, laboratory or the first set of samples. This is not yet standard practice. We present a modular framework that combines feature selection, classification, biomarker discovery and statistical validation; these data analysis aspects are all discussed in this review. The feature selection, classification and biomarker discovery modules can be incorporated or omitted to the preference of the researcher. The validation modules, however, should not be optional. In each module, the researcher can select from a wide range of methods, since there is not one unique way that leads to the correct model and proper validation. We discuss many possibilities for feature selection, classification and biomarker discovery. For validation we advice a combination of cross-validation and permutation testing, a validation strategy supported in the literature.