A novel small RNA PhaS contributes to polymyxin B-heteroresistance in carbapenem-resistant Klebsiella pneumoniae.

In recent years, polymyxin has been used as a last-resort therapy for carbapenem-resistant bacterial infections. The emergence of heteroresistance (HR) to polymyxin hampers the efficacy of polymyxin treatment by amplifying resistant subpopulation. However, the mechanisms behind polymyxin HR remain unclear. Small noncoding RNAs (sRNAs) play an important role in regulating drug resistance. The purpose of this study was to investigate the effects and mechanisms of sRNA on polymyxin B (PB)-HR in carbapenem-resistant Klebsiella pneumoniae. In this study, a novel sRNA PhaS was identified by transcriptome sequencing. PhaS expression was elevated in the PB heteroresistant subpopulation. Overexpression and deletion of PhaS were constructed in three carbapenem-resistant K. pneumoniae strains. Population analysis profiling, growth curve, and time-killing curve analysis showed that PhaS enhanced PB-HR. In addition, we verified that PhaS directly targeted phoP through the green fluorescent protein reporter system. PhaS promoted the expression of phoP, thereby encouraging the expression of downstream genes pmrD and arnT. This upregulation of arnT promoted the 4-amino-4-deoxyL-arabinosaccharide (L-Ara4N) modification of lipid A in PhaS overexpressing strains, thus enhancing PB-HR. Further, within the promoter region of PhaS, specific PhoP recognition sites were identified. ONPG assays and RT-qPCR analysis confirmed that PhaS expression was positively modulated by PhoP and thus up-regulated by PB stimulation. To sum up, a novel sRNA enhancing PB-HR was identified and a positive feedback regulatory pathway of sRNA-PhoP/Q was demonstrated in the study. This helps to provide a more comprehensive and clear understanding of the underlying mechanisms behind polymyxin HR in carbapenem-resistant K. pneumoniae.

Performance evaluation of a newly developed 2019-nCoV nucleic acid detection kit based on Ion Proton sequencing platform and its comparison with the MGI Tech (DNBSEQ-G99) platform.

PURPOSE: To evaluate the performance of a newly developed 2019-nCoV nucleic acid detection kit based on Ion Proton sequencing platform and make comparation with MGI Tech (DNBSEQ-G99) platform. METHODS: References and clinical samples were used to evaluate the precision, agreement rate, limit of detection (LOD), anti-interference ability and analytical specificity. Twenty-seven clinical specimens were used to make comparison between two platforms. RESULTS: The kit showed good intra-assay, inter-assay, inter-day precision between different operators and laboratories, fine agreement rate with references, a relatively low LOD of 1 × 103 copies/ml, anti-interference capability of 5 % whole blood and 1mg/ml mucin and no cross reaction with twenty-nine common clinical pathogens. Consistency of variant classification was observed between two platforms. The WGS from Ion Proton tended to have higher coverage and less missing data. CONCLUSIONS: The newly developed kit has shown satisfactory performances and excellent consistency with DNBSEQ-G99, making it a good alternative choice clinically.

Study on spatial distribution of crop residue burning and PM2.5 change in China.

With China as the study area, MODIS MOD14A1 and MCD12Q1 products were used to derive daily crop residue burning spots from 2014 to 2015. After vectorization of crop residue burning pixels and with the use of fishnet, burning density distribution maps were eventually completed. Meanwhile, the daily air quality data from 150 cities in 2014 and 285 cities in 2015 were used to obtain daily and monthly PM2.5 distribution maps with the Kriging interpolation. The results indicate that crop residue burning occurs in a seasonal pattern, and its spatial distribution is closely related to farming activities. The annual PM2.5 in China decreased 11.81% from 2014 to 2015, and the distribution of PM2.5 in China's east and north is always higher than in China's west and south. Furthermore, the changes in PM2.5 exhibit a hysteresis after crop residue burning in summer and autumn-winter. Regarding summer crop residue burning in China's middle-east, the r between crop residue burning spots and PM2.5 is 0.6921 (P < 0.01) in 2014 and 0.5620 (P < 0.01) in 2015, while the correlation coefficient of autumn-winter crop residue burning in China's northeast is slightly lower with an r of 0.5670 (P < 0.01) in 2014 and 0.6213 (P < 0.01) in 2015. In autumn-winter, crop residue burning can induce evident PM2.5 increase in China's northeast, and that is more obvious than summer crop residue burning in China's middle-east. Furthermore, when data of summer and autumn-winter crop residue burning from 2014 to 2015 are compared, we can see that the change in number of crop residue burning spots significant changes PM2.5 in these regions. Both the summer and autumn-winter crop residue burning areas presented spatial consistency with high PM2.5. By contrast, the results from many aspects indicated that the crop residue burning in spring did not cause a notable change of PM2.5.

Assessment of global carbon dioxide concentration using MODIS and GOSAT data.

Carbon dioxide (CO(2)) is the most important greenhouse gas (GHG) in the atmosphere and is the greatest contributor to global warming. CO(2) concentration data are usually obtained from ground observation stations or from a small number of satellites. Because of the limited number of observations and the short time series of satellite data, it is difficult to monitor CO(2) concentrations on regional or global scales for a long time. The use of the remote sensing data such as the Advanced Very High Resolution Radiometer (AVHRR) or Moderate Resolution Imaging Spectroradiometer (MODIS) data can overcome these problems, particularly in areas with low densities of CO(2) concentration watch stations. A model based on temperature (MOD11C3), vegetation cover (MOD13C2 and MOD15A2) and productivity (MOD17A2) of MODIS (which we have named the TVP model) was developed in the current study to assess CO(2) concentrations on a global scale. We assumed that CO(2) concentration from the Thermal And Near infrared Sensor for carbon Observation (TANSO) aboard the Greenhouse gases Observing SATellite (GOSAT) are the true values and we used these values to check the TVP model accuracy. The results indicate that the accuracy of the TVP model is different in different continents: the greatest Pearson's correlation coefficient (R2) was 0.75 in Eurasia (RMSE = 1.16) and South America (RMSE = 1.17); the lowest R2 was 0.57 in Australia (RMSE = 0.73). Compared with the TANSO-observed CO(2) concentration (XCO(2)), we found that the accuracy throughout the World is between -2.56~3.14 ppm. Potential sources of TVP model uncertainties were also analyzed and identified.