Epidemiological studies using saliva have revealed relationships between the oral microbiome and many oral and systemic diseases. However, when collecting from a large number of participants such as a large-scale cohort study, the time it takes to collect saliva can be a problem. Mouth-rinsed water, which is water that has been used to rinse the oral cavity, can be used as an alternative method for collecting saliva for oral microbiome analysis because it can be collected in a shorter time than saliva. The purpose of this study was to verify whether mouth-rinsed water is a suitable saliva substitute for analyzing the oral microbiome. We collected samples of mouth-rinsed water, stimulated saliva, unstimulated saliva, and tongue coating from 10 systemic healthy participants, and compared the microbial diversity and composition of the samples using next-generation sequencing of 16S rRNA-encoding genes. The results showed that the microbial diversity of mouth-rinsed water was similar to that of unstimulated and stimulated saliva, and significantly higher than that of tongue-coating samples. The microbial composition at the species level of mouth-rinsed water also showed a very high correlation with the composition of unstimulated and stimulated saliva. These results suggest that the mouth-rinsed water is a suitable collection method instead of saliva for oral microbiome analysis.
The onset and progress of dental caries and periodontal disease is associated with the oral microbiome. Therefore, it is important to understand the factors that influence oral microbiome formation. One of the factors that influence oral microbiome formation is the transmission of oral bacteria from parents. However, it remains unclear when the transmission begins, and the difference in contributions of father and mother. Here, we focused on the oral microbiome of 18-month-old infants, at which age deciduous dentition is formed and the oral microbiome is likely to become stable, with that of their parents. We collected saliva from forty 18-month-old infants and their parents and compared the diversity and composition of the microbiome using next-generation sequencing of 16S rRNA genes. The results showed that microbial diversity in infants was significantly lower than that in parents and composition of microbiome were significantly different between infants and parents. Meanwhile, the microbiome of the infants was more similar to that of their mothers than unrelated adults. The bacteria highly shared between infants and parents included not only commensal bacteria but also disease related bacteria. These results suggested that the oral microbiome of the parents influences that of their children aged < 18 months.
Distinctive survival strategies, specialized in regulation and in quality control, were observed in thermal adaptive evolution with a laboratory Escherichia coli strain. The two specialists carried a single mutation either within rpoH or upstream of groESL, which led to the activated global regulation by sigma factor 32 or an increased amount of GroEL/ES chaperonins, respectively. Although both specialists succeeded in thermal adaptation, the common winner of the evolution was the specialist in quality control, that is, the strategy of chaperonin-mediated protein folding. To understand this evolutionary consequence, multilevel analyses of cellular status, for example, transcriptome, protein and growth fitness, were carried out. The specialist in quality control showed less change in transcriptional reorganization responding to temperature increase, which was consistent with the finding of that the two specialists showed the biased expression of molecular chaperones. Such repressed changes in gene expression seemed to be advantageous for long-term sustainability because a specific increase in chaperonins not only facilitated the folding of essential gene products but also saved cost in gene expression compared with the overall transcriptional increase induced by rpoH regulation. Functional specialization offered two strategies for successful thermal adaptation, whereas the evolutionary advantageous was more at the points of cost-saving in gene expression and the essentiality in protein folding.
Transcriptomes not only reflect the growth status but also link to the genome in bacteria. To investigate if and how genome or cellular state changes contribute to the gene expression order, the growth profile-associated transcriptomes of an assortment of genetically differentiated Escherichia coli either exponentially growing under varied conditions or in response to environmental disturbance were analyzed. A total of 168 microarray data sets representing 56 transcriptome variations, were categorized by genome size (full length or reduced) and cellular state (steady or unsteady). At the genome-wide level, the power-law distribution of gene expression was found to be significantly disturbed by the genome size but not the cellular state. At the regulatory network level, more networks with improved coordination of growth rates were observed in genome reduction than at the steady state. At the single-gene level, both genome reduction and steady state increased the correlation of gene expression to growth rate, but the enriched gene categories with improved correlations were different. These findings not only illustrate the order of gene expression attributed to genome reduction and steady cellular state but also indicate that the accessory sequences acquired during genome evolution largely participated in the coordination of transcriptomes to growth fitness.
A series of Escherichia coli strains with varied genomic sequences were subjected to high-density microarray analyses to elucidate the fitness-correlated transcriptomes. Fitness, which is commonly evaluated by the growth rate during the exponential phase, is not only determined by the genome but is also linked to growth conditions, e.g., temperature. We previously reported genetic and environmental contributions to E. coli transcriptomes and evolutionary transcriptome changes in thermal adaptation. Here, we describe experimental details on how to prepare microarray samples that truly represent the growth fitness of the E. coli cells. A step-by-step record of sample preparation procedures that correspond to growing cells and transcriptome data sets that are deposited at the GEO database (GSE33212, GSE52770, GSE61739) are also provided for reference.
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