High Diversity of vacA and cagA Helicobacter pylori Genotypes in Patients with and without Gastric Cancer

BACKGROUND: Helicobacter pylori is associated with chronic gastritis, peptic ulcers, and gastric cancer. The aim of this study was to assess the topographical distribution of H. pylori in the stomach as well as the vacA and cagA genotypes in patients with and without gastric cancer. METHODOLOGY/PRINCIPAL FINDINGS: Three gastric biopsies, from predetermined regions, were evaluated in 16 patients with gastric cancer and 14 patients with dyspeptic symptoms. From cancer patients, additional biopsy specimens were obtained from tumor centers and margins; among these samples, the presence of H. pylori vacA and cagA genotypes was evaluated. Positive H. pylori was 38% and 26% in biopsies obtained from the gastric cancer and non-cancer groups, respectively (p = 0.008), and 36% in tumor sites. In cancer patients, we found a preferential distribution of H. pylori in the fundus and corpus, whereas, in the non-cancer group, the distribution was uniform (p = 0.003). A majority of the biopsies were simultaneously cagA gene-positive and -negative. The fundus and corpus demonstrated a higher positivity rate for the cagA gene in the non-cancer group (p = 0.036). A mixture of cagA gene sizes was also significantly more frequent in this group (p = 0.003). Ninety-two percent of all the subjects showed more than one vacA gene genotype; s1b and m1 vacA genotypes were predominantly found in the gastric cancer group. The highest vacA-genotype signal-sequence diversity was found in the corpus and 5 cm from tumor margins. CONCLUSION/SIGNIFICANCE: High H. pylori colonization diversity, along with the cagA gene, was found predominantly in the fundus and corpus of patients with gastric cancer. The genotype diversity observed across systematic whole-organ and tumor sampling was remarkable. We find that there is insufficient evidence to support the association of one isolate with a specific disease, due to the multistrain nature of H. pylori infection shown in this work

Laboratory Evaluation of Flurox, a Chitin Synthesis Inhibitor, on the Termite, Microcerotermes diversus

Microcerotermes diversus (Silvestri) (Isoptera: Termitidae) is the most economically destructive termite in structures in southwest Iran. One sustainable control strategy that usually helps to reduce subterranean termite damage in buildings, is the use of insect growth regualtors in a suitable bait matrix that are safe to the user and the environment. In the laboratory assays described here, the delayed toxicity of Flurox, a chitin synthesis inhibitor, to M. diversus was evaluated under force-feeding and choice trials. Flurox induced worker and nymph mortality and incomplete ecdysis in nymphs of M. diversus under no-choice and two-choice feeding tests. These adverse effects may cause disruption of the caste balance in M. diversus, leading to the collapse of the colony. These assays determined concentrations of Flurox that can be used in bait formulations

Assessing Recent Smoking Status by Measuring Exhaled Carbon Monoxide Levels

The main expectations of applying proteomics technologies to clinical questions are the discovery of disease related biomarkers. Despite technological advancement to increase proteome coverage and depth to meet these expectations the number of generated biomarkers for clinical use is small. One of the reasons is that found potential biomarkers often are false discoveries. Small sample sizes, in combination with patient sample heterogeneity increase the risk of false discoveries. To be able to extract relevant biological information from such data, high demands are put on the experimental design and the use of sensitive and quantitatively accurate technologies. The overall aim of this thesis was to apply quantitative proteomics methods for biomarker discovery in clinical samples. A method for reducing bias by controlling for individual variation in smoking habits is described in paper I. The aim of the method was objective assessment of recent smoking in clinical studies on inflammatory responses. In paper II, the proteome of alveolar macrophages obtained from smoking subjects with and without the inflammatory lung disease chronic obstructive pulmonary disease (COPD) were quantified by two-dimensional gel-electrophoresis (2-DE). A gender focused analysis showed protein level differences within the female group, with down-regulation of lysosomal pathway and up-regulation of oxidative pathway in COPD patients. Paper III, a mass spectrometry based proteomics analysis of tumour samples, contributes to the molecular understanding of vulvar squamous cell carcinoma (VSCC) and we identified a high risk patient subgroup of HPV-negative tumours based on the expression of four proteins, further suggesting that this subgroup is characterized by an altered ubiquitin-proteasome signalling pathway. Paper III describes a data analysis workflow for the extraction of biological information from quantitative mass spectrometry based proteomics data. High patient-to-patient tumour proteome variability was addressed by using pathway profiling on individual tumour data, followed by comparison of pathway association ranks in a multivariate analysis. We show that pathway data on individual tumour level can detect subpopulations of patients and identify pathways of specific importance in pre-defined clinical groups by the use of multivariate statistics. In paper IV, the potentials and limits of quantitative mass spectrometry on clinical samples was evaluated by defining the quantitative accuracy of isobaric labels and label-free quantification. Quantification by isobaric labels in combination with pI pre-fractionation showed a lower limit of quantification (LOQ) than a label-free analysis without pI pre-fractionation, and 6-plex TMT were more sensitive than 8-plex iTRAQ. Precursor mixing measured by isolation interference (MS1 interference) is more linked to the quantitative accuracy of isobaric labels than reporter ion interference (MS2 interference). Based on that we could define recommendations for how much isolation interference that can be accepted; in our data <30% isolation interference had little effect the quantitative accuracy. In conclusion, getting biological knowledge from proteomics studies requires a careful study design, control of possible confounding factors and the use of clinical data to identify disease subtypes. Further, to be able to draw conclusions from the data, the analysis requires accurate quantitative data and robust statistical tools to detect significant protein alterations. Methods around these issues are developed and discussed in this thesis

Comparison of machine learning and semi-quantification algorithms for (I123)FP-CIT classification: the beginning of the end for semi-quantification?

Background Semi-quantification methods are well established in the clinic for assisted reporting of (I123) Ioflupane images. Arguably, these are limited diagnostic tools. Recent research has demonstrated the potential for improved classification performance offered by machine learning algorithms. A direct comparison between methods is required to establish whether a move towards widespread clinical adoption of machine learning algorithms is justified. This study compared three machine learning algorithms with that of a range of semi-quantification methods, using the Parkinson’s Progression Markers Initiative (PPMI) research database and a locally derived clinical database for validation. Machine learning algorithms were based on support vector machine classifiers with three different sets of features: Voxel intensities Principal components of image voxel intensities Striatal binding radios from the putamen and caudate. Semi-quantification methods were based on striatal binding ratios (SBRs) from both putamina, with and without consideration of the caudates. Normal limits for the SBRs were defined through four different methods: Minimum of age-matched controls Mean minus 1/1.5/2 standard deviations from age-matched controls Linear regression of normal patient data against age (minus 1/1.5/2 standard errors) Selection of the optimum operating point on the receiver operator characteristic curve from normal and abnormal training data Each machine learning and semi-quantification technique was evaluated with stratified, nested 10-fold cross-validation, repeated 10 times. Results The mean accuracy of the semi-quantitative methods for classification of local data into Parkinsonian and non-Parkinsonian groups varied from 0.78 to 0.87, contrasting with 0.89 to 0.95 for classifying PPMI data into healthy controls and Parkinson’s disease groups. The machine learning algorithms gave mean accuracies between 0.88 to 0.92 and 0.95 to 0.97 for local and PPMI data respectively. Conclusions Classification performance was lower for the local database than the research database for both semi-quantitative and machine learning algorithms. However, for both databases, the machine learning methods generated equal or higher mean accuracies (with lower variance) than any of the semi-quantification approaches. The gain in performance from using machine learning algorithms as compared to semi-quantification was relatively small and may be insufficient, when considered in isolation, to offer significant advantages in the clinical context

The Two-Component Sensor Kinase TcsC and Its Role in Stress Resistance of the Human-Pathogenic Mold Aspergillus fumigatus

Two-component signaling systems are widespread in bacteria, but also found in fungi. In this study, we have characterized TcsC, the only Group III two-component sensor kinase of Aspergillus fumigatus. TcsC is required for growth under hyperosmotic stress, but dispensable for normal growth, sporulation and conidial viability. A characteristic feature of the ΔtcsC mutant is its resistance to certain fungicides, like fludioxonil. Both hyperosmotic stress and treatment with fludioxonil result in a TcsC-dependent phosphorylation of SakA, the final MAP kinase in the high osmolarity glycerol (HOG) pathway, confirming a role for TcsC in this signaling pathway. In wild type cells fludioxonil induces a TcsC-dependent swelling and a complete, but reversible block of growth and cytokinesis. Several types of stress, such as hypoxia, exposure to farnesol or elevated concentrations of certain divalent cations, trigger a differentiation in A. fumigatus toward a “fluffy” growth phenotype resulting in white, dome-shaped colonies. The ΔtcsC mutant is clearly more susceptible to these morphogenetic changes suggesting that TcsC normally antagonizes this process. Although TcsC plays a role in the adaptation of A. fumigatus to hypoxia, it seems to be dispensable for virulence

Biodiversity of Fusarium species in Mexico associated with ear rot in maize, and their identification using a phylogenetic approach

Fusariumproliferatum, F. subglutinans, and F. verticillioides are known causes of ear and kernel rot in maize worldwide. In Mexico, only F. verticillioides and F. subglutinans, have been reported previously as causal agents of this disease. However, Fusarium isolates with different morphological characteristics to the species that are known to cause this disease were obtained in the Highland-Valley region of this country from symptomatic and symptomless ears of native and commercial maize genotypes. Moreover, while the morphological studies were not sufficient to identify the correct taxonomic position at the species level, analyses based in the Internal Transcribed Spacer region and the Nuclear Large Subunit Ribosomal partial sequences allowed for the identification of F. subglutinans, F. solani, and F. verticillioides, as well as four species (F. chlamydosporum, F. napiforme, F. poae, and F. pseudonygamai) that had not previously been reported to be associated with ear rot. In addition, F. napiforme and F. solani were absent from symptomless kernels. Phylogenetic analysis showed genetic changes in F. napiforme, and F. pseudonygamai isolates because they were not true clones, and probably constitute separate sibling species. The results of this study suggest that the biodiversity of Fusarium species involved in ear rot in Mexico is greater than that reported previously in other places in the world. This new knowledge will permit a better understanding of the relationship between all the species involved in ear rot disease and their relationship with maize

Search for new phenomena in final states with an energetic jet and large missing transverse momentum in pp collisions at √ s = 8 TeV with the ATLAS detector

Results of a search for new phenomena in final states with an energetic jet and large missing transverse momentum are reported. The search uses 20.3 fb−1 of √ s = 8 TeV data collected in 2012 with the ATLAS detector at the LHC. Events are required to have at least one jet with pT > 120 GeV and no leptons. Nine signal regions are considered with increasing missing transverse momentum requirements between Emiss T > 150 GeV and Emiss T > 700 GeV. Good agreement is observed between the number of events in data and Standard Model expectations. The results are translated into exclusion limits on models with either large extra spatial dimensions, pair production of weakly interacting dark matter candidates, or production of very light gravitinos in a gauge-mediated supersymmetric model. In addition, limits on the production of an invisibly decaying Higgs-like boson leading to similar topologies in the final state are presente

Protective Effect of Curcumin on Pulmonary and Cardiovascular Effects Induced by Repeated Exposure to Diesel Exhaust Particles in Mice

Particulate air pollution has been associated with increased risk of cardiopulmonary diseases. However, the underlying mechanisms are not fully understood. We have previously demonstrated that single dose exposure to diesel exhaust particle (DEP) causes lung inflammation and peripheral thrombotic events. Here, we exposed mice with repeated doses of DEP (15µg/animal) every 2nd day for 6 days (a total of 4 exposures), and measured several cardiopulmonary endpoints 48 h after the end of the treatments. Moreover, the potential protective effect of curcumin (the yellow pigment isolated from turmeric) on DEP-induced cardiopulmonary toxicity was assessed. DEP exposure increased macrophage and neutrophil numbers, tumor necrosis factor α (TNF α) in the bronchoalveolar lavage (BAL) fluid, and enhanced airway resistance to methacoline measured invasively using Flexivent. DEP also significantly increased plasma C-reactive protein (CRP) and TNF α concentrations, systolic blood pressure (SBP) as well as the pial arteriolar thrombosis. It also significantly enhanced the plasma D-dimer and plasminogen activator inhibitor-1 (PAI-1). Pretreatment with curcumin by oral gavage (45 mg/kg) 1h before exposure to DEP significantly prevented the influx of inflammatory cells and the increase of TNF α in BAL, and the increased airway resistance caused by DEP. Likewise, curcumin prevented the increase of SBP, CRP, TNF α, D-dimer and PAI-1. The thrombosis was partially but significantly mitigated. In conclusion, repeated exposure to DEP induced lung and systemic inflammation characterized by TNFα release, increased SBP, and accelerated coagulation. Our findings indicate that curcumin is a potent anti-inflammatory agent that prevents the release of TNFα and protects against the pulmonary and cardiovascular effects of DEP