ABSTRACT
Objective@#To investigate the effects of 2, 2', 4, 4'-tetrabromodiphenyl ether ( BDE-47 ) on the differentiation of mouse embryonic fibroblasts-3T3-L1, so as to provide the basis for revealing the mechanism of environmental obesity factors. @*Methods@#The 3T3-L1 cells were divided into five BDE-47 intervention groups ( 25, 18.75, 12.5, 7.5 and 2.5 µmol/L ), a positive control group (1 µmol/L 2, 4-thiazolidinedione) and a negative control group ( 0.1% dimethyl sulfoxide ) for the induction of differentiation. The lipid droplet accumulation in adipocytes was observed by oil red O staining treatment and detection of optical desity ( OD ) on the eighth day of differentiation. Triglyceride ( TG ) content was measured using the histiocyte TG enzymatic assay kit. The mRNA expression of adiponectin and peroxisome proliferator activated receptor-γ ( PPARγ ) was measured by RT-PCR.@*Results@#The positive areas of oil red O staining, OD values, TG content and mRNA expression of adiponectin and PPARγ in 3T3-L1 cells were significantly different among seven groups ( P<0.05 ). The positive areas of oil red O staining and OD values in the BDE-47 groups with different concentrations were higher than those in the negative control group ( P<0.05 ). The 18.75 µmol/L BDE-47 group had higher TG levels than the negative control group ( P<0.05 ). The mRNA expression of PPARγ in the 25, 18.75, 12.5, and 7.5 µmol/L BDE-47 groups and the positive control group was higher than that in the negative control group ( P<0.05 ). The mRNA expression of PPARγ in the 12.5 µmol/L BDE-47 group was higher than that in the 25, 18.75, 7.5, 2.5 µmol/L BDE-47 group and the positive control group ( P<0.05 ). The mRNA expression of adiponectin in the 12.5, 7.5 µmol/L BDE-47 group and the positive control group was higher than that in the negative control group ( P<0.05 ). The mRNA expression of adiponectin in the 12.5 µmol/L BDE-47 group was higher than that in the 25, 18.75, 2.5 µmol/L BDE-47 group ( P<0.05 ). The mRNA expression of PPARγ and adiponectin in the different concentration groups of BDE-47 distributed like inverted "U" shape.@*Conclusion@#BDE-47 can promote the differentiation of 3T3-L1 preadipocytes. Low concentration of BDE-47 may induce adipocyte differentiation by activating PPARγ.
ABSTRACT
Pseudomonas syringae pv. actinidiae is a bacterial pathogen of kiwifruit. Based on the results of the pathogenicity assay, we sequenced the strain Pseudomonas syringae (Psa3) P155 which possesses a series of virulence and resistance genes, CRISPR candidate elements, prophage related sequences, methylation modifications, genomic islands as well as one plasmid. Most importantly, the copper resistance genes copA, copB, copC, copD, and copZ as well as aminoglycoside resistance gene ksgA were identified in strain P155, which would pose a threat to kiwifruit production. The complete sequence we reported here will provide valuable information for a better understanding of the genetic structure and pathogenic characteristics of the genome of P155.
Pseudomonas syringae pv. actinidiae agente causal do cancro bacteriano do kiwi. Com base nos resultados do teste de patogenicidade, foi sequenciado um isolado de Pseudomonas syringae (Psa3) P155, que abriga a uma série de genes de virulência e resistência, elementos candidatos CRISPR, sequências relacionadas a profagos, modificações na metilação, ilhas genômicas, e também um plasmídeo. O mais importante foram os genes de resistência ao cobre, copA, copB, copC, copD e copZ, bem como, o gene de resistência aminoglicosídea ksgA identificados na estirpe P155, os quais representariam uma ameaça à produção de kiwi. A sequência completa relatada fornecerá informações valiosas para uma melhor compreensão da estrutura genética e as características patogênicas do genoma de P155.
Subject(s)
Virulence , Actinidia , Pseudomonas syringae , Whole Genome SequencingABSTRACT
<p><b>OBJECTIVE</b>To investigate the distribution and major influencing factors of annual systolic blood pressure variability from a large population cohort.</p><p><b>METHODS</b>In this prospective cohort study, data from Kailuan Group employees who attended all 4 physical examinations ( taken in June 2006 to October 2007, June 2008 to October 2009, June 2010 to October 2011, June 2012 to October 2013, respectively) were analyzed (32 959 males and 10 401 females, mean age: (48.2 ± 11.5) years old). Systolic blood pressure variability was defined as the standard deviation (SSD) and the coefficient of variation (SCV) of systolic blood pressure of 4 physical examinations. Multivariate linear regression analysis was used to determine the related influencing factors of SSD and SCV.</p><p><b>RESULTS</b>(1) The mean of SSD and SCV for this cohort was 10.91 mmHg (1 mmHg = 0.133 kPa) and 8.34%, respectively. SSD and SCV increased in male and female with increasing age (both P < 0.001). (2) Multiple linear regression analysis showed that systolic blood pressure (β = 0.225, P < 0.001), age (β = 0.163, P < 0.001), fasting blood glucose (β = 0.038, P < 0.001), the use of anti-hypertensive drugs (β = 0.038, P < 0.001), sex (β = 0.038, P < 0.001), smoking (β = 0.025, P < 0.001), alcohol drinking (P = -0.022, P < 0.001), physical exercise (β = -0.018, P = 0.001), high-sensitivity c-reactive protein (β = 0.016, P = 0.001) body mass index (β = -0.011, P = 0.018) were related to SSD. Age (β = 0.139, P < 0.001), sex (β = 0.055, P < 0.001), systolic blood pressure (β = 0.047, P < 0.001), fasting blood glucose (P = 0.033, P < 0.001), drinking (β = -0.030, P < 0.001), body mass index (β = -0.026, P < 0.001), the use of anti- hypertensive drugs (β = 0.026, P < 0.001), smoking (β = 0.024, P < 0.001), physical exercise (β = -0.015, P = 0. 001), high-sensitivity c-reactive protein (β = 0. 014, P = 0. 001) were related to SCV.</p><p><b>CONCLUSIONS</b>SSD and SCV increase with increasing age. Systolic blood pressure, age, fasting blood glucose, the use of anti-hypertensive drugs, sex, smoking, drinking, physical exercise, high-sensitivity c-reactive protein, body mass index are major influencing factors for SSD. Age, sex, systolic blood pressure, fasting blood glucose, alcohol drinking, body mass index, the use of anti-hypertensive drugs, smoking, physical exercise, high-sensitivity c-reactive protein are major influencing factors for SCV.</p>