| 其他摘要 | To date, fluorescence-based method is one of the most universal strategies for cellular response study, which is mainly used for particular gene expression study. This method contains two devices, including promoters and reporters. Specially, substrate specific promoter is used as one of the essential parts for this method. However, no such endogenous promoters were reported in Clostridium cellulolyticum. Therefore, based on transcriptomic data analysis, gene expression was found to be substrates specific, which provided valuable candidates for the development of substrate-sensitive promoters, thus several stated promoters were picked. With employing oxygen independent fluorescent protein as reporter, cellular responses to various substrates were monitored. Whole process including: (i) Method framing. Basing on transcriptomic data, several promoters were picked out for the development of cellular response detection method to substrates. When cells were cultivated on different carbon sources, intensity of fluorescence varied, providing characteristic gene expression of specific genes. (ii) Substrate specific promoter analyzing. Two endogenous and substrate-specificity-related promoters were screened out when employing an oxygen independent fluorescent protein (FbFP) as reporter. One was a constitutive promoter p3398, the activity of which was 1.5 fold than that of pthl (widely used constitutive promoter from Clostridium acetobutylicum), while the other was a xylan inducible promoter p1133, which was the first inducible promoter discovered in Clostridium cellulolyticum. (iii) Fluorescence-based method Application. We proved stated method can be used for the identification of processing sites inside an operon, which was the main objects of testing experiment. Meanwhile, the method as well as the specific promoters lay a foundation for gene expression study and genetic engineering of Clostridium cellulolyticum.
However, cellular response exploration based on fluorescence labeling is not comprehensive and only provides partial gene expression information related to specific promoters. Thus, for whole living cellular response, we developed an approach basing on Single-cell Raman spectra or Ramanome. Due to the fact that Raman spectroscopy application in biological science was not mature, we firstly used E. coli DH5α, which was relatively the simplest conventional model, as the microorganism for cellular response detection and discrimination. We firstly tried ethanol, a potential environmental pollution, as a stressor because of its rich research background in E. coli and other bacteria. Afterwards, the early exponential-phase E. coli cells were used as the subjects for cellular response experiment. The results showed that (i) Ethanol stress detection demonstrated cellular response was fast enough even 5min after ethanol added, and that could still be easily detected by Raman spectra. (ii) Cellular response detected by Raman spectra was time-specific. Raman peaks underwent consistent changes over a stressed period; with nucleic acids assigned decreasing and lipids assigned increasing. (iii) Cellular response detected by Raman spectra was found to be quite sensitive. When cells were under low doses of ethanol (e.g., 0.5%v/v) that could not be detected via growth curves, Raman spectra of stressed cells and control cells were apparently different. (iv) The classification rate of time difference or dose difference was more than 90%, suggesting Raman spectroscopy could be used as a predicating method for cellular stressed time and stressed ethanol doses. Furthermore, due to the diversity of pollutants in the environment, cells were subjected to various stressors treatment, including three common categories, antibiotics (ampicillin and kanamycin), alcohols (ethanol and n-butanol) and heavy metals (Cu2+ and Cr6+). Time-course Raman spectra measurements were made when cells were under arbitrary stated stressors, which finally made up a large Raman spectra dataset (~7000) or called “Ramanome”. Specificity and similarity of Raman spectra were compared and investigated on stressors, indicating that: (i) Cellular response of cells under various stressors was time-specific. Although high correlation coefficient (r=0.7) of change trend of R-value appeared in Amp and Cr6+, their degrees were different. (ii) Metabolic changes of cellular response under various stressors were partly consistent and partly different. The consistent parts were: changed Raman peaks mainly assigned as nucleic acids and lipids. The different parts were: the direction and changes degree of Raman peaks compared to control cells. (iii) A new conception was proposed—— “Raman-barcode of Cellular-response to Stressors (RBCS)”. RBCS was consisted of thirty-one elementary Raman bands. The value of each Raman bands (barcodes) was corresponding to the difference between stressed cells and control cells. RBCS could be described as a process, which referred to the changes of Raman bands during the whole stressed period (or at each time point measured). Just like 16S rDNA alignment for distinguishing microorganisms, RBCS was developed as a standard for cellular response discrimination.
In conclusion, we dissected cellular response to environment in two ways, fluorescence based method for particular genes study and label-free cells for comprehensive cellular response study. On one hand, we have developed a method, basing on oxygen independent fluorescent protein and substrate-specific promoters, for substrates specific cellular response study in C. cellulolyticum. On the other hand, Raman spectroscopy label-free based living E. coli DH5α cells, were used for the discrimination of varies cellular response. Finally, the concept of RBCS was proposed for cellular response discrimination. |
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