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Biology subjects

Cao, R.

Publications and source records attributed to Cao, R..

3 recordsLinked to original sources

Analysis of microbial community structure of pit mud for Chinese strong-flavor liquor fermentation using next generation DNA sequencing of full-length 16S rRNA

The pit is the necessary bioreactor for brewing process of Chinese strong-flavor liquor. Pit mud in pits contains a large number of microorganisms and is a complex ecosystem. The analysis of bacterial flora in pit mud is of great significance to understand liquor fermentation mechanisms. To overcome taxonomic limitations of short reads in 16S rRNA variable region sequencing, we used high-throughput DNA sequencing of near full-length 16S rRNA gene to analyze microbial compositions of different types of pit mud that produce different qualities of strong-flavor liquor. The results showed that the main species in pit mud were Pseudomonas extremaustralis 14-3, Pseudomonas veronii, Serratia marcescens WW4, and Clostridium leptum in Ruminiclostridium. The microbial diversity of pit mud with different quality was significantly different. From poor to good quality of pit mud (thus the quality of liquor), the relative abundances of Ruminiclostridium and Syntrophomonas in Firmicutes was increased, and the relative abundance of Olsenella in Actinobacteria also increased, but the relative abundances of Pseudomonas and Serratia in Proteobacteria were decreased. The surprising findings of this study include that the diversity of intermediate level quality of N pit mud was the lowest, and the diversity levels of high quality pit mud G and poor quality pit mud B were similar. Correlation analysis showed that there were high positive correlations (r > 0.8) among different microbial groups in the flora. Based on the analysis of the microbial structures of pit mud in different quality, the good quality pit mud has a higher microbial diversity, but how this higher diversity and differential microbial compositions contribute to better quality of liquor fermentation remains obscure.

molecular biology

Characterizing the Effects of Glutathione as an Immunoadjuvant in the Treatment of Tuberculosis

Mycobacterium tuberculosis (M. tb) is the etiological agent that is responsible for causing tuberculosis (TB), which continues to affect millions of people worldwide, with an ever-increasing resistance to antibiotics. We tested the synergistic effects of N-acetyl cysteine (NAC, the precursor molecule for the synthesis of glutathione) and individual first-line antibiotics typically given for the treatment of TB such as Isoniazid (INH), Rifampicin (RIF), Ethambutol (EMB) and Pyrazinamide (PZA) to improve the ability of macrophages to control intracellular M. tb infection. Glutathione (GSH), a pleiotropic antioxidant molecule has been previously shown to display both antimycobacterial and immune-enhancing effects. Our results indicate that there was not only an increase in beneficial immunomodulatory effects, but a greater reduction in the intracellular viability of M. tb when macrophages were treated with the combination of antibiotics (INH/RIF/EMB or PZA) and NAC.

microbiology

Combining mathematical and statistical modeling to simulate time course bulk and single cell gene expression data in cancer with CancerInSilico

Bioinformatics techniques to analyze time course bulk and single cell omics data are advancing. The absence of a known ground truth of the dynamics of molecular changes challenges benchmarking their performance on real data. Realistic simulated time-course datasets are essential to assess the performance of time course bioinformatics algorithms. We develop an R/Bioconductor package, CancerInSilico, to simulate bulk and single cell transcriptional data from a known ground truth obtained from mathematical models of cellular systems. This package contains a general R infrastructure for running cell-based models and simulating gene expression data based on the model states. We show how to use this package to simulate a gene expression data set and consequently benchmark analysis methods on this data set with a known ground truth. The package is freely available via Bioconductor: http://bioconductor.org/packages/CancerInSilico/

systems biology