Association of Mycobacterium infections in patients with Mendelian susceptibility to mycobacterial disease with venous thromboembolism

An association between a hypercoagulable state and Mendelian susceptibility to mycobacterial disease (MSMD) has been established in a few studies; resultant thrombosis is considered rare. In a case‐control study, the prevalence of factor V Leiden, prothrombin G20210A and methylenetetrahydrofolate reductase (MTHFR) C677T, A1298C mutations were investigated in mycobacterium‐infected patients. The study comprised 30 patients with mycobacterial infections (invasive, disseminated and/or recurrent infections with Bacille Calmette–Guerin or non‐tuberculosis mycobacteria and Mycobacterium Tuberculosis with positive results for acid‐fast bacilli and tuberculin skin tests) and 30 normal healthy controls. Forty female (66.7%) and 20 male subjects (33.3%) aged from 3 to 70 years were recruited into this study. Genotyping of targeted genes was performed by RT‐PCR and cytokine TNF‐α concentrations were quantified using a commercially available ELISA kit. Significant associations between mycobacterial infection and TNF‐α production after stimulating peripheral blood mononuclear cells with LPS alone and with IFN‐γ plus LPS were identified. Moreover, genotyping analysis in the studied population revealed a significant association between MTHFR c.677C>T (OR, 3.28; 95% CI, 1.35–7.92; P < 0.05), MTHFR c.1298A>C (OR, 2.33; 95% CI, 1.10–4.93; P < 0.05) and mycobacterial infection in affected patients, indicating susceptibility to venous thromboembolism according to previous studies. Additionally, mycobacterium‐infected patients had a significantly greater prevalence of MTHFR C677T and A1298C mutations than controls.     

Hydrocarbon degradation in relation to cell-surface hydrophobicity among bacterial hydrocarbon degraders from petroleum-contaminated Kuwait desert environment

Forty six bacterial isolates able to grow on crude oil were isolated from various hydrocarbon-contaminated sites in Kuwait. The extent of crude oil degradation varied over a wide range (1–87%) among the isolates. Isolates were predominantly Gram-positive bacteria (79% of total isolates) belonging to the genera Bacillus (93%) and Paenibacillus (7%). Among the few Gram-negative isolates were from the genera Acinetobacter, Alcaligenes, Klebsiella, Burkholderia, Pseudomonas, and Williamsia. Analyses of their cell-surface hydrophobicity (CSH) by various methods equally showed a wide variation among the isolates. About 74% of isolates that degraded significant amounts of crude oil (>40% degradation) possessed high level of CSH, while 58% of all the isolates exhibited high levels of CSH. Statistical analyses showed significantly high correlation between the ability to degrade crude oil and CSH. The ability of the isolates to bind to polystyrene and salt-aggregation test as measures of CSH were more strongly correlated with hydrocarbon-degrading ability than adherence to hydrocarbons.     

Cigarette smoke attenuates the production of cytokines by human plasmacytoid dendritic cells and enhances the release of IL-8 in response to TLR-9 stimulation

Myeloid and plasmacytoid dendritic cells (mDCs, pDC) are crucial to the immune system, detecting microorganisms and linking the innate and adaptive immunity. pDC are present in small quantities in tissues that are in contact with the external environment; mainly the skin, the inner lining of the nose, lungs, stomach and intestines. They produce large amounts of IFN-α after stimulation and are pivotal for the induction of antiviral responses. Chronic obstructive pulmonary disease (COPD) patients are known to be more susceptible to viral infections. We have demonstrated that exposure of mDC to cigarette smoke extract (CSE) leads to the release of chemokines, however, not much is known about the role of pDC in COPD. In this study, we addressed several key questions with respect to the mechanism of action of CSE on human pDC in an in vitro model. Human pDCs were isolated from normal healthy volunteers and subjected to fresh CSE and the levels of IL-8, TNF-α, IP-10, IL-6, IL-1, IL-12 and IL-10 and IFN-α were studied by both ELISA and real time PCR methods. We observed that CSE augmented the production of IL-8 and suppressed the release of TNF-α, IL-6 and IFN-α. Moreover, CSE suppressed PI3K/Akt signalling in pDC. In conclusion, our data indicate that CSE has both the potential to diminish anti-viral immunity by downregulating the release of IFN-α and other pro-inflammatory cytokines while, at the same time, augmenting the pathogenesis of COPD via an IL-8 induced recruitment of neutrophils.     

Phylogenetic diversity of fungal communities in areas accessible and not accessible to tourists in Naracoorte Caves

The fungal diversity in areas accessible and not accessible to tourists at UNESCO World Heritage-listed Naracoorte Caves was investigated with culture-dependent and culture-independent techniques for assistance in cave management protocol development. The caves were selected based on tourist numbers and configurations: Stick Tomato (open, high numbers), Alexandra (lockable openings, high numbers) and Strawhaven (control; no access). Culture-based survey revealed Ascomycota dominance irrespective of sampling area with Microascales (Trichurus sp.) being most frequently isolated. Some Hypocreales-like sequences belonging to Fusarium sp., Trichoderma sp. and Neonectria sp. (Stick Tomato) were cultured only from areas not accessible to tourists. These orders also were detected by DGGE assay irrespective of sampling area. The predominance of Ascomycota (especially Microascales) suggested their important ecological roles in these caves. Culture-independent analysis showed higher Shannon fungal diversity values (from ITS-based DGGE profiles) in tourist-accessible areas of these caves than in inaccessible areas with the fungal community banding patterns being substantially different in Stick Tomato Cave. Further investigations are needed to determine the cause of the differences in the fungal communities of Stick Tomato Cave, although cave-related factors such as use, configuration and sediment heterogeneity might have contributed to these differences.     

Phosphate solubilization potential and modeling of stress tolerance of rhizobacteria from rice paddy soil in northern Iran

The purposes of this study were to evaluate the phosphate solubilization activity of bacteria isolated from the rhizosphere of rice paddy soil in northern Iran, and to study the effect of temperature, NaCl and pH on the growth of these isolates by modeling. Three of the most effective strains from a total of 300 isolates were identified and a phylogenetic analysis was carried out by 16S rDNA sequencing. The isolates were identified as Pantoea ananatis (M36), Rahnella aquatilis (M100) and Enterobacter sp. (M183). These isolates showed multiple plant growth-promoting attributes such as phosphate solubilization activity and indole-3-acetic acid (IAA) production. The M36, M100 and M183 isolates were able to solubilize 172, 263 and 254 µg ml^?1 of Ca₃(PO₄)₂ after 5 days of growth at 28 °C and pH 7.5, and to produce 8.0, 2.0 and 3.0 μg ml^?1 of IAA when supplemented with l-tryptophan (1 mg ml^?1) for 72 h, at 28 °C and pH 7.0, respectively. The solubilization of insoluble phosphate was associated with a drop in the pH of the culture medium and there was an inverse relationship between pH and solubilized P (r = ?0.98, P < 0.0952). There were no significant differences among isolates in terms of acidity tolerance based on their confidence limits as assessed by segmented model analysis and all isolates were able to grow at pH 4.3–11 (with optimum at 7.0–7.5). Based on a sigmoidal trend of a three-parameter logistic model, the salt concentration required for 50 % inhibition was 8.15, 6.30 and 8.23 % NaCl for M36, M100 and M183 isolates, respectively. Moreover, the minimum and maximum growth temperatures estimated by the segmented model were 5.0 and 42.75 °C for M36, 12.76 and 40.32 °C for M100, and 10.63 and 43.66 °C for M183. The three selected isolates could be deployed as inoculants to promote plant growth in an agricultural environment.     

Understanding the Underlying Mechanism of HA-Subtyping in the Level of Physic-Chemical Characteristics of Protein

The evolution of the influenza A virus to increase its host range is a major concern worldwide. Molecular mechanisms of increasing host range are largely unknown. Influenza surface proteins play determining roles in reorganization of host-sialic acid receptors and host range. In an attempt to uncover the physic-chemical attributes which govern HA subtyping, we performed a large scale functional analysis of over 7000 sequences of 16 different HA subtypes. Large number (896) of physic-chemical protein characteristics were calculated for each HA sequence. Then, 10 different attribute weighting algorithms were used to find the key characteristics distinguishing HA subtypes. Furthermore, to discover machine leaning models which can predict HA subtypes, various Decision Tree, Support Vector Machine, Naïve Bayes, and Neural Network models were trained on calculated protein characteristics dataset as well as 10 trimmed datasets generated by attribute weighting algorithms. The prediction accuracies of the machine learning methods were evaluated by 10-fold cross validation. The results highlighted the frequency of Gln (selected by 80% of attribute weighting algorithms), percentage/frequency of Tyr, percentage of Cys, and frequencies of Try and Glu (selected by 70% of attribute weighting algorithms) as the key features that are associated with HA subtyping. Random Forest tree induction algorithm and RBF kernel function of SVM (scaled by grid search) showed high accuracy of 98% in clustering and predicting HA subtypes based on protein attributes. Decision tree models were successful in monitoring the short mutation/reassortment paths by which influenza virus can gain the key protein structure of another HA subtype and increase its host range in a short period of time with less energy consumption. Extracting and mining a large number of amino acid attributes of HA subtypes of influenza A virus through supervised algorithms represent a new avenue for understanding and predicting possible future structure of influenza pandemics.