Total bacterial and species-specific 16S rDNA micro-array quantification of complex samples

A conceptual framework modeling of functional microbial communities in wastewater treatment electro-bioreactors

Understanding the microbial ecology of a system allows linking members of the community and their metabolic functions to the performance of the wastewater bioreactor. This study provided a comprehensive conceptual framework for microbial communities in wastewater treatment electro-bioreactors (EBRs). The model was based on data acquired from monitoring the effect of altering different bioreactor operational parameters, such as current density and hydraulic retention time, on the microbial communities of an EBR and its nutrient removal efficiency. The model was also based on the 16S rRNA gene high-throughput sequencing data analysis and bioreactor efficiency data. The collective data clearly demonstrated that applying various electric currents affected the microbial community composition and stability and the reactor efficiency in terms of chemical oxygen demand, N and P removals. Moreover, a schematic that recommends operating conditions that are tailored to the type of wastewater that needs to be treated based on the functional microbial communities enriched at specific operating conditions was suggested. In this study, a conceptual model as a simplified representation of the behavior of microbial communities in EBRs was developed. The proposed conceptual model can be used to predict how biological treatment of wastewater in EBRs can be improved by varying several operating conditions.

Development of Oral Care Chip, a novel device for quantitative detection of the oral microbiota associated with periodontal disease

Periodontal disease, the most prevalent infectious disease in the world, is caused by biofilms formed in periodontal pockets. No specific bacterial species that can cause periodontitis alone has been found in any study to date. Several periodontopathic bacteria are associated with the progress of periodontal disease. Consequently, it is hypothesized that dysbiosis of subgingival microbiota may be a cause of periodontal disease. This study aimed to investigate the relationship between the subgingival microbiota and the clinical status of periodontal pockets in a quantitative and clinically applicable way with the newly developed Oral Care Chip. The Oral Care Chip is a DNA microarray tool with improved quantitative performance, that can be used in combination with competitive PCR to quantitatively detect 17 species of subgingival bacteria. Cluster analysis based on the similarity of each bacterial quantity was performed on 204 subgingival plaque samples collected from periodontitis patients and healthy volunteers. A significant difference in the number of total bacteria, Treponema denticola, Campylobacter rectus, Fusobacterium nucleatum, and Streptococcus intermedia bacteria in any combination of the three clusters indicated that these bacteria gradually increased in number from the stage before the pocket depth deepened. Conversely, Porphyromonas gingivalis, Tannerella forsythia, Prevotella intermedia, and Streptococcus constellatus, which had significant differences only in limited clusters, were thought to increase in number as the pocket depth deepened, after periodontal pocket formation. Furthermore, in clusters where healthy or mild periodontal disease sites were classified, there was no statistically significant difference in pocket depth, but the number of bacteria gradually increased from the stage before the pocket depth increased. This means that quantitative changes in these bacteria can be a predictor of the progress of periodontal tissue destruction, and this novel microbiological test using the Oral Care Chip could be effective at detecting dysbiosis.

Method for absolute quantification of microbial communities by using both microarrays and competitive PCR

Methods for the robust quantification of bacterial communities are still under development. In this context, the present study aimed to evaluate a method combining competitive PCR (cPCR) and microarray assays for the determination of absolute content of total bacteria and individual bacterial species in samples. For this, a competitor DNA for cPCR and microarrays containing three types of DNA probes was prepared. A calibration curve was generated with genomic DNA samples as standards, which was then utilized for cPCR-based determination of the total number (in moles) of 16S rRNA genes in other bacterial samples. Moreover, scatter plots of species-specific probes versus total bacteria probe for each genomic DNA of known concentration was fit to the regression model, and the obtained slope value was defined as the hybridization affinity ratio. The cPCR assay was performed for both a commercially available mixed genomic DNA sample and human oral bacterial DNA samples, and the total number of moles of 16S rRNA genes was determined. These values were distributed among each species on the basis of the signal intensities of species-specific probes and the hybridization affinity ratio. The total number of bacterial genomes and those of individual species were determined by dividing the copy number of 16S rRNA genes per genome. The obtained results were confirmed by quantitative real-time PCR (qPCR). For values of >1 × 10² copies determined by qPCR, the ratio of the values measured by DNA chips to by qPCR was 1.53-fold on average and <2.6-fold for all data. These results show that the combined method of cPCR and microarray is useful to quantify the absolute numbers of several types of bacteria in a sample at one time.

Impact of adding Saccharomyces strains on fermentation, aerobic stability, nutritive value, and select lactobacilli populations in corn silage

Bacterial inoculants can improve the conservation and nutritional quality of silages. Inclusion of the yeast Saccharomyces in the diet of dairy cattle has also been reported to be beneficial. The present study assessed the ability of silage to be used as a means of delivering Saccharomyces strains to ruminants. Two strains of Saccharomyces cerevisiae (strain 1 and 3)and 1 strain of Saccharomyces paradoxus (strain 2) were inoculated (10(3) cfu/g) individually onto corn forage that was ensiled in mini silos for 90 d. Fermentation characteristics, aerobic stability, and nutritive value of silages were determined and real-time quantitative PCR (RT-qPCR) was used to quantify S. cerevisiae, S.paradoxus, total Saccharomyces, fungal, and bacterial populations. Fermentation characteristics of silage inoculated with S1 were similar to control silage. Although strain 3 inoculation increased ash and decreased OM contents of silage (P = 0.017), no differences were observed in nutrient composition or fermentation profiles after 90 d of ensiling. Inoculation with Saccharomyces had no detrimental effect on the aerobic stability of silage. In vitro DM disappearance, gas production, and microbial protein synthesis were not affected by yeast inoculation.Saccharomyces strain 1 was quantified throughout ensiling, whereas strain 2 was detected only immediately after inoculation. Saccharomyces cerevisiae strain 3 was quantified until d 7 and detectable 90 d after ensiling. All inoculants were detected and quantified during aerobic exposure. Inoculation with Saccharomyces did not alter lactobacilli populations. Saccharomycetales were detected by RT-qPCR throughout ensiling in all silages. Both S. cerevisiae and S. paradoxus populations increased during aerobic exposure, demonstrating that the density of these yeast strains would increase between the time that silage was removed from storage and the time it was fed.

Nucleic acid-based approaches to investigate microbial-related cheese quality defects

The microbial profile of cheese is a primary determinant of cheese quality. Microorganisms can contribute to aroma and taste defects, form biogenic amines, cause gas and secondary fermentation defects, and can contribute to cheese pinking and mineral deposition issues. These defects may be as a result of seasonality and the variability in the composition of the milk supplied, variations in cheese processing parameters, as well as the nature and number of the non-starter microorganisms which come from the milk or other environmental sources. Such defects can be responsible for production and product recall costs and thus represent a significant economic burden for the dairy industry worldwide. Traditional non-molecular approaches are often considered biased and have inherently slow turnaround times. Molecular techniques can provide early and rapid detection of defects that result from the presence of specific spoilage microbes and, ultimately, assist in enhancing cheese quality and reducing costs. Here we review the DNA-based methods that are available to detect/quantify spoilage bacteria, and relevant metabolic pathways in cheeses and, in the process, highlight how these strategies can be employed to improve cheese quality and reduce the associated economic burden on cheese processors.

Signal processing for metagenomics: extracting information from the soup

Traditionally, studies in microbial genomics have focused on single-genomes from cultured species, thereby limiting their focus to the small percentage of species that can be cultured outside their natural environment. Fortunately, recent advances in high-throughput sequencing and computational analyses have ushered in the new field of metagenomics, which aims to decode the genomes of microbes from natural communities without the need for cultivation. Although metagenomic studies have shed a great deal of insight into bacterial diversity and coding capacity, several computational challenges remain due to the massive size and complexity of metagenomic sequence data. Current tools and techniques are reviewed in this paper which address challenges in 1) genomic fragment annotation, 2) phylogenetic reconstruction, 3) functional classification of samples, and 4) interpreting complementary metaproteomics and metametabolomics data. Also surveyed are important applications of metagenomic studies, including microbial forensics and the roles of microbial communities in shaping human health and soil ecology.

Use of DNA quantification to measure growth and autolysis of Lactococcus and Propionibacterium spp. in mixed populations

Autolysis is self-degradation of the bacterial cell wall that results in the release of enzymes and DNA. Autolysis of starter bacteria, such as lactococci and propionibacteria, is essential for cheese ripening, but our understanding of this important process is limited. This is mainly because the current tools for measuring autolysis cannot readily be used for analysis of bacteria in mixed populations. We have now addressed this problem by species-specific detection and quantification of free DNA released during autolysis. This was done by use of 16S rRNA gene single-nucleotide extension probes in combination with competitive PCR. We analyzed pure and mixed populations of Lactococcus lactis subsp. lactis and three different species of Propionibacterium. Results showed that L. lactis subsp. lactis INF L2 autolyzed first, followed by Propionibacterium acidipropionici ATCC 4965, Propionibacterium freudenreichii ISU P59, and then Propionibacterium jensenii INF P303. We also investigated the autolytic effect of rennet (commonly used in cheese production). We found that the effect was highly strain specific, with all the strains responding differently. Finally, autolysis of L. lactis subsp. lactis INF L2 and P. freudenreichii ISU P59 was analyzed in a liquid cheese model. Autolysis was detected later in this cheese model system than in broth media. A challenge with DNA, however, is DNA degradation. We addressed this challenge by using a DNA degradation marker. We obtained a good correlation between the degradation of the marker and the target in a model experiment. We conclude that our DNA approach will be a valuable tool for use in future analyses and for understanding autolysis in mixed bacterial populations.