CYP1A1, GSTM1 and GSTT1 polymorphisms and lung cancer: a pooled analysis of gene–gene interactions

Gene–environment interactions have been extensively studied in lung cancer. It is likely that several genetic polymorphisms cooperate in increasing the individual risk. Therefore, the study of gene–gene interactions might be important to identify high-susceptibility subgroups. GSEC is an initiative aimed at collecting available data sets on metabolic polymorphisms and the risks of cancer at several sites and performing pooled analyses of the original data. Authors of published papers have provided original data sets. The present paper refers to gene–gene interactions in lung cancer and considers three polymorphisms in three metabolic genes: CYP1A1, GSTM1 and GSTT1. The present analyses compare the gene–gene interactions of the CYP1A1*2A, GSTM1 and GSTT1 polymorphisms from studies on lung cancer conducted in Europe and the USA between 1991 and 2000. Only Caucasians have been included. The data set includes 1466 cases and 1488 controls. The only clear-cut association was found with CYP1A1*2A. This association remained unchanged after stratification by polymorphisms in other genes (with an odds ratio [OR] of approximately 2.5), except when interaction with GSTM1 was considered. When the OR for CYP1A1*2A was stratified according to the GSTM1 genotype, the OR was increased only among the subjects who had the null (homozygous deletion) GSTM1 genotype (OR=2.8, 95% CI=0.9–8.4). The odds ratio for the interactive term (CYP1A1*2A by GSTM1) in logistic regression was 2.7 (95% CI=0.5–15.3). An association between lung cancer and the homozygous CYP1A1*2A genotype is confirmed. An apparent and biologically plausible interaction is suggested between this genotype and GSTM1.     

Loss of Competition in the Outside Host Environment Generates Outbreaks of Environmental Opportunist Pathogens

Environmentally transmitted pathogens face ecological interactions (e.g., competition, predation, parasitism) in the outside-host environment and host immune system during infection. Despite the ubiquitousness of environmental opportunist pathogens, traditional epidemiology focuses on obligatory pathogens incapable of environmental growth. Here we ask how competitive interactions in the outside-host environment affect the dynamics of an opportunist pathogen. We present a model coupling the classical SI and Lotka–Volterra competition models. In this model we compare a linear infectivity response and a sigmoidal infectivity response. An important assumption is that pathogen virulence is traded off with competitive ability in the environment. Removing this trade-off easily results in host extinction. The sigmoidal response is associated with catastrophic appearances of disease outbreaks when outside-host species richness, or overall competition pressure, decreases. This indicates that alleviating outside-host competition with antibacterial substances that also target the competitors can have unexpected outcomes by providing benefits for opportunist pathogens. These findings may help in developing alternative ways of controlling environmental opportunist pathogens.     

Particle-Induced Pulmonary Acute Phase Response Correlates with Neutrophil Influx Linking Inhaled Particles and Cardiovascular Risk

Background

Particulate air pollution is associated with cardiovascular disease. Acute phase response is causally linked to cardiovascular disease. Here, we propose that particle-induced pulmonary acute phase response provides an underlying mechanism for particle-induced cardiovascular risk.

Methods

We analysed the mRNA expression of Serum Amyloid A (Saa3) in lung tissue from female C57BL/6J mice exposed to different particles including nanomaterials (carbon black and titanium dioxide nanoparticles, multi- and single walled carbon nanotubes), diesel exhaust particles and airborne dust collected at a biofuel plant. Mice were exposed to single or multiple doses of particles by inhalation or intratracheal instillation and pulmonary mRNA expression of Saa3 was determined at different time points of up to 4 weeks after exposure. Also hepatic mRNA expression of Saa3, SAA3 protein levels in broncheoalveolar lavage fluid and in plasma and high density lipoprotein levels in plasma were determined in mice exposed to multiwalled carbon nanotubes.

Results

Pulmonary exposure to particles strongly increased Saa3 mRNA levels in lung tissue and elevated SAA3 protein levels in broncheoalveolar lavage fluid and plasma, whereas hepatic Saa3 levels were much less affected. Pulmonary Saa3 expression correlated with the number of neutrophils in BAL across different dosing regimens, doses and time points.

Conclusions

Pulmonary acute phase response may constitute a direct link between particle inhalation and risk of cardiovascular disease. We propose that the particle-induced pulmonary acute phase response may predict risk for cardiovascular disease.     

Precision mapping of the human O‐GalNAc glycoproteome through SimpleCell technology

Glycosylation is the most abundant and diverse posttranslational modification of proteins. While several types of glycosylation can be predicted by the protein sequence context, and substantial knowledge of these glycoproteomes is available, our knowledge of the GalNAc‐type O‐glycosylation is highly limited. This type of glycosylation is unique in being regulated by 20 polypeptide GalNAc‐transferases attaching the initiating GalNAc monosaccharides to Ser and Thr (and likely some Tyr) residues. We have developed a genetic engineering approach using human cell lines to simplify O‐glycosylation (SimpleCells) that enables proteome‐wide discovery of O‐glycan sites using ‘bottom‐up’ ETD‐based mass spectrometric analysis. We implemented this on 12 human cell lines from different organs, and present a first map of the human O‐glycoproteome with almost 3000 glycosites in over 600 O‐glycoproteins as well as an improved NetOGlyc4.0 model for prediction of O‐glycosylation. The finding of unique subsets of O‐glycoproteins in each cell line provides evidence that the O‐glycoproteome is differentially regulated and dynamic. The greatly expanded view of the O‐glycoproteome should facilitate the exploration of how site‐specific O‐glycosylation regulates protein function.     

Phthalates Are Metabolised by Primary Thyroid Cell Cultures but Have Limited Influence on Selected Thyroid Cell Functions In Vitro

Phthalates are plasticisers added to a wide variety of products, resulting in measurable exposure of humans. They are suspected to disrupt the thyroid axis as epidemiological studies suggest an influence on the peripheral thyroid hormone concentration. The mechanism is still unknown as only few in vitro studies within this area exist. The aim of the present study was to investigate the influence of three phthalate diesters (di-ethyl phthalate, di-n-butyl phthalate (DnBP), di-(2-ethylhexyl) phthalate (DEHP)) and two monoesters (mono-n-butyl phthalate and mono-(2-ethylhexyl) phthalate (MEHP)) on the differentiated function of primary human thyroid cell cultures. Also, the kinetics of phthalate metabolism were investigated. DEHP and its monoester, MEHP, both had an inhibitory influence on 3''-5''-cyclic adenosine monophosphate secretion from the cells, and MEHP also on thyroglobulin (Tg) secretion from the cells. Results of the lactate dehydrogenase-measurements indicated that the MEHP-mediated influence was caused by cell death. No influence on gene expression of thyroid specific genes (Tg, thyroid peroxidase, sodium iodine symporter and thyroid stimulating hormone receptor) by any of the investigated diesters could be demonstrated. All phthalate diesters were metabolised to the respective monoester, however with a fall in efficiency for high concentrations of the larger diesters DnBP and DEHP. In conclusion, human thyroid cells were able to metabolise phthalates but this phthalate-exposure did not appear to substantially influence selected functions of these cells.     

Effects of the Commercial Flame Retardant Mixture DE-71 on Cytokine Production by Human Immune Cells

IntroductionAlthough production of polybrominated diphenyl ethers (PBDEs) is now banned, release from existing products will continue for many years. The PBDEs are assumed to be neurotoxic and toxic to endocrine organs at low concentrations. Their effect on the immune system has not been investigated thoroughly. We aimed to investigate the influence of DE-71 on cytokine production by peripheral blood mononuclear cells (PBMCs) stimulated with Escherichia Coli lipopolysaccharide (LPS) or phytohaemagglutinin-L (PHA-L).ResultsAt non-cytotoxic concentrations (0.01–10 μg/mL), DE-71 significantly enhanced secretion of IL-1β, IL-6, CXCL8, IL-10, and TNF-α (p<0.001–0.019; n = 6) from LPS-stimulated PBMCs. IFN-γ, TNF-α, IL-17A, and IL-17F (p = <0.001–0.043; n = 6) secretion were enhanced from PHA-L-stimulated PBMCs as well. Secretion of IL-1β, IL-2, IL-10, IL-8 and IL-6 was not significantly affected by DE-71.ConclusionsWe demonstrate an enhancing effect of DE-71 on cytokine production by normal human PBMCs stimulated with LPS or PHA-L ex vivo.     

A comparison of droplet digital polymerase chain reaction (PCR), quantitative PCR and metabarcoding for species‐specific detection in environmental DNA

Targeted species‐specific and community‐wide molecular diagnostics tools are being used with increasing frequency to detect invasive or rare species. Few studies have compared the sensitivity and specificity of these approaches. In the present study environmental DNA from 90 filtered seawater and 120 biofouling samples was analyzed with quantitative PCR (qPCR), droplet digital PCR (ddPCR) and metabarcoding targeting the cytochrome c oxidase I (COI) and 18S rRNA genes for the Mediterranean fanworm Sabella spallanzanii. The qPCR analyses detected S. spallanzanii in 53% of water and 85% of biofouling samples. Using ddPCR S. spallanzanii was detected in 61% of water of water and 95% of biofouling samples. There were strong relationships between COI copy numbers determined via qPCR and ddPCR (water R² = 0.81, p < .001, biofouling R² = 0.68, p < .001); however, qPCR copy numbers were on average 125‐fold lower than those measured using ddPCR. Using metabarcoding there was higher detection in water samples when targeting the COI (40%) compared to 18S rRNA (5.4%). The difference was less pronounced in biofouling samples (25% COI, 29% 18S rRNA). Occupancy modelling showed that although the occupancy estimate was higher for biofouling samples (ψ = 1.0), higher probabilities of detection were derived for water samples. Detection probabilities of ddPCR (1.0) and qPCR (0.93) were nearly double metabarcoding (0.57 to 0.27 marker dependent). Studies that aim to detect specific invasive or rare species in environmental samples should consider using targeted approaches until a detailed understanding of how community and matrix complexity, and primer biases affect metabarcoding data.     

Expression of the RAI gene is conducive to apoptosis: studies of induction and interference

The RAI gene is also known as iASPP and PPP1R13L. Recent investigations have shown that the region encompassing RAI is important for the development of cancer in young and middle-aged persons. It has been speculated that the RAI product induces apoptosis by blocking NF-kappaB or inhibits apoptosis by blocking p53. Either way the gene could influence the survival of precancerous lesions. Here we report that the expression of RAI mRNA was increased in non-transformed lymphocytes and fibroblasts induced to undergo apoptosis by various means, such as treatment with etoposide, calcium ions, or interleukin-2 and/or serum deprivation. Treatment with etoposide increased the content of RAI protein, too, and caused it to translocate to the nucleus. Inhibition of RAI expression in lymphocytes and fibroblasts with siRNA reduced apoptosis, but treatment with the NF-kappaB-inhibiting substance sulfasalazine relieved this dependence. In the transformed cell line HEK-293 the association between RAI induction and apoptosis seemed broken. Thus, we hypothesize that RAI induction is necessary but not sufficient for apoptosis induction in non-transformed cells. Our results could be explained by a NF-kappaB mediated mechanism.     

A serial lectin approach to the mucin-type O-glycoproteome of Drosophila melanogaster S2 cells

Identification of mucin-type O-glycosylated proteins with known functions in model organisms like Drosophila could provide keys to elucidate functions of the O-glycan moiety and proteomic analyses of O-glycoproteins in higher eukaryotes remain a challenge due to structural heterogeneity and a lack of efficient tools for their specific isolation. Here we report a strategy to evaluate the O-glycosylation potential of the embryonal hemocyte-like Drosophila Schneider 2 (S2) cell line by expression of recombinant glycosylation probes derived from tandem repeats of the human mucin MUC1 or of the Drosophila salivary gland protein Sgs1. We obtained evidence that mucin-type O-glycosylation in S2 cells grown under serum-free conditions is restricted to the Tn-antigen (GalNAcalpha-Ser/Thr) and the T-antigen (Galbeta1-3GalNAcalpha-Ser/Thr) and this structural homogeneity enables unique glycoproteomic strategies. We present a label-free strategy for the isolation, profiling and analysis of O-glycosylated proteins consisting of serial lectin affinity capture, 2-DE-based glycoprotein analysis by O-glycan specific mAbs and protein identification by MALDI-MS. Protein identity and O-glycosylation was confirmed by ESI-MS/MS with detection of diagnostic sugar oxonium-ion fragments. Using this strategy, we established 2-D reference maps and identified 21 secreted and intracellular mucin-type O-glycoproteins. Our results show that Drosophila S2 cells express O-glycoproteins involved in a wide range of biological functions including proteins of the extracellular matrix (Laminin gamma-chain, Peroxidasin and Glutactin), pathogen recognition proteins (Gnbp1), stress response proteins (Glycoprotein 93), secreted proteases (Matrix-metalloprotease 1 and various trypsin-like serine proteases), protease inhibitors (Serpin 27 A) and proteins of unknown function.