Multivariate analysis of complex DNA sequence electropherograms for high-throughput quantitative analysis of mixed microbial populations

Inferring microbial interaction network from microbiome data using RMN algorithm

Background

Microbial interactions are ubiquitous in nature. Recently, many similarity-based approaches have been developed to study the interaction in microbial ecosystems. These approaches can only explain the non-directional interactions yet a more complete view on how microbes regulate each other remains elusive. In addition, the strength of microbial interactions is difficult to be quantified by only using correlation analysis.

Results

In this study, a rule-based microbial network (RMN) algorithm, which integrates regulatory OTU-triplet model with parametric weighting function, is being developed to construct microbial regulatory networks. The RMN algorithm not only can extrapolate the cooperative and competitive relationships between microbes, but also can infer the direction of such interactions. In addition, RMN algorithm can theoretically characterize the regulatory relationship composed of microbial pairs with low correlation coefficient in microbial networks. Our results suggested that Bifidobacterium, Streptococcus, Clostridium XI, and Bacteroides are essential for causing abundance changes of Veillonella in gut microbiome. Furthermore, we inferred some possible microbial interactions, including the competitive relationship between Veillonella and Bacteroides, and the cooperative relationship between Veillonella and Clostridium XI.

Conclusions

The RMN algorithm provides the reconstruction of gut microbe networks, and can shed light on the dynamical interactions of microbes in the infant intestinal tract.

Electronic supplementary material

The online version of this article (doi:10.1186/s12918-015-0199-2) contains supplementary material, which is available to authorized users.

Shifts in the Midgut/Pyloric Microbiota Composition within a Honey Bee Apiary throughout a Season

Honey bees (Apis mellifera) are prominent crop pollinators and are, thus, important for effective food production. The honey bee gut microbiota is mainly host specific, with only a few species being shared with other insects. It currently remains unclear how environmental/dietary conditions affect the microbiota within a honey bee population over time. Therefore, the aim of the present study was to characterize the composition of the midgut/pyloric microbiota of a honey bee apiary throughout a season. The rationale for investigating the midgut/pyloric microbiota is its dynamic nature. Monthly sampling of a demographic homogenous population of bees was performed between May and October, with concordant recording of the honey bee diet. Mixed Sanger-and Illumina 16S rRNA gene sequencing in combination with a quantitative PCR analysis were used to determine the bacterial composition. A marked increase in α-diversity was detected between May and June. Furthermore, we found that four distinct phylotypes belonging to the Proteobacteria dominated the microbiota, and these displayed major shifts throughout the season. Gilliamella apicola dominated the composition early on, and Snodgrassella alvi began to dominate when the other bacteria declined to an absolute low in October. In vitro co-culturing revealed that G. apicola suppressed S. alvi. No shift was detected in the composition of the microbiota under stable environment/dietary conditions between November and February. Therefore, environmental/dietary changes may trigger the shifts observed in the honey bee midgut/pyloric microbiota throughout a season.

Evaluation of direct 16S rDNA sequencing as a metagenomics-based approach to screening bacteria in bottled water

Deliberate or accidental contamination of food, feed, and water supplies poses a threat to human health worldwide. A rapid and sensitive detection technique that could replace the current labor-intensive and time-consuming culture-based methods is highly desirable. In addition to species-specific assays, such as PCR, there is a need for generic methods to screen for unknown pathogenic microorganisms in samples. This work presents a metagenomics-based direct-sequencing approach for detecting unknown microorganisms, using Bacillus cereus (as a model organism for B. anthracis) in bottled water as an example. Total DNA extraction and 16S rDNA gene sequencing were used in combination with principle component analysis and multicurve resolution to study detection level and possibility for identification. Results showed a detection level of 10(5) to 10(6) CFU/L. Using this method, it was possible to separate 2 B. cereus strains by the principal component plot, despite the close sequence resemblance. A linear correlation between the artificial contamination level and the relative amount of the Bacillus artificial contaminant in the metagenome was observed, and a relative amount value above 0.5 confirmed the presence of Bacillus. The analysis also revealed that background flora in the bottled water varied between the different water types that were included in the study. This method has the potential to be adapted to other biological matrices and bacterial pathogens for fast screening of unknown bacterial threats in outbreak situations.

Long-term nitrogen amendment alters the diversity and assemblage of soil bacterial communities in tallgrass prairie

Anthropogenic changes are altering the environmental conditions and the biota of ecosystems worldwide. In many temperate grasslands, such as North American tallgrass prairie, these changes include alteration in historically important disturbance regimes (e.g., frequency of fires) and enhanced availability of potentially limiting nutrients, particularly nitrogen. Such anthropogenically-driven changes in the environment are known to elicit substantial changes in plant and consumer communities aboveground, but much less is known about their effects on soil microbial communities. Due to the high diversity of soil microbes and methodological challenges associated with assessing microbial community composition, relatively few studies have addressed specific taxonomic changes underlying microbial community-level responses to different fire regimes or nutrient amendments in tallgrass prairie. We used deep sequencing of the V3 region of the 16S rRNA gene to explore the effects of contrasting fire regimes and nutrient enrichment on soil bacterial communities in a long-term (20 yrs) experiment in native tallgrass prairie in the eastern Central Plains. We focused on responses to nutrient amendments coupled with two extreme fire regimes (annual prescribed spring burning and complete fire exclusion). The dominant bacterial phyla identified were Proteobacteria, Verrucomicrobia, Bacteriodetes, Acidobacteria, Firmicutes, and Actinobacteria and made up 80% of all taxa quantified. Chronic nitrogen enrichment significantly impacted bacterial community diversity and community structure varied according to nitrogen treatment, but not phosphorus enrichment or fire regime. We also found significant responses of individual bacterial groups including Nitrospira and Gammaproteobacteria to long-term nitrogen enrichment. Our results show that soil nitrogen enrichment can significantly alter bacterial community diversity, structure, and individual taxa abundance, which have important implications for both managed and natural grassland ecosystems.

Context-dependent competition in a model gut bacterial community

Understanding the ecological processes that generate complex community structures may provide insight into the establishment and maintenance of a normal microbial community in the human gastrointestinal tract, yet very little is known about how biotic interactions influence community dynamics in this system. Here, we use natural strains of Escherichia coli and a simplified model microbiota to demonstrate that the colonization process on the strain level can be context dependent, in the sense that the outcome of intra-specific competition may be determined by the composition of the background community. These results are consistent with previous models for competition between organisms where one competitor has adapted to low resource environments whereas the other is optimized for rapid reproduction when resources are abundant. The genomic profiles of E. coli strains representing these differing ecological strategies provide clues for deciphering the genetic underpinnings of niche adaptation within a single species. Our findings extend the role of ecological theory in understanding microbial systems and the conceptual toolbox for describing microbial community dynamics. There are few, if any, concrete examples of context-dependent competition on a single trophic level. However, this phenomenon can have potentially dramatic effects on which bacteria will successfully establish and persist in the gastrointestinal system, and the principle should be equally applicable to other microbial ecosystems.

Base-calling algorithm with vocabulary (BCV) method for analyzing population sequencing chromatograms

Sanger sequencing is a common method of reading DNA sequences. It is less expensive than high-throughput methods, and it is appropriate for numerous applications including molecular diagnostics. However, sequencing mixtures of similar DNA of pathogens with this method is challenging. This is important because most clinical samples contain such mixtures, rather than pure single strains. The traditional solution is to sequence selected clones of PCR products, a complicated, time-consuming, and expensive procedure. Here, we propose the base-calling with vocabulary (BCV) method that computationally deciphers Sanger chromatograms obtained from mixed DNA samples. The inputs to the BCV algorithm are a chromatogram and a dictionary of sequences that are similar to those we expect to obtain. We apply the base-calling function on a test dataset of chromatograms without ambiguous positions, as well as one with 3-14% sequence degeneracy. Furthermore, we use BCV to assemble a consensus sequence for an HIV genome fragment in a sample containing a mixture of viral DNA variants and to determine the positions of the indels. Finally, we detect drug-resistant Mycobacterium tuberculosis strains carrying frameshift mutations mixed with wild-type bacteria in the pncA gene, and roughly characterize bacterial communities in clinical samples by direct 16S rRNA sequencing.

The dominant microbial community associated with fermentation of Obushera (sorghum and millet beverages) determined by culture-dependent and culture-independent methods

Obushera includes four fermented cereal beverages from Uganda namely: Obutoko, Enturire, Ekitiribita and Obuteire, whose microbial diversity has not hitherto been fully investigated. Knowledge of the microbial diversity and dynamics in these products is crucial for understanding their safety and development of appropriate starter cultures for controlled industrial processing. Culture-dependent and culture-independent techniques including denaturating gradient gel electrophoresis (DGGE) and mixed DNA sequencing of polymerase chain reaction (PCR) amplified ribosomal RNA genes were used to study the bacteria and yeast diversity of Obushera. The pH dropped from 6.0-4.6 to 3.5-4.0 within 1-2 days for Obutoko, Enturire and Obuteire whereas that of Ekitiribita decreased to 4.4 after 4 days. Counts of lactic acid bacteria (LAB) increased from 5.0 to 11.0 log cfug(-1) and yeasts increased from 3.4 to 7.1 log cfug(-1) while coliform counts decreased from 2.0 to <1 log cfug(-1) during four days of fermentation. LAB and yeast isolates were identified by rRNA gene sequence analysis. LAB isolates included: Enterococcus spp., Lactobacillus (Lb.) plantarum, Lb. fermentum, Lb. delbrueckii, Lactococcus lactis, Leuconostoc lactis, Streptococcus (S.) infantarius subsp. infantarius, Pediococcus pentosaceus and Weisella (W.) confusa. DGGE indicated predominance of S. gallolyticus, S. infantarius subsp. infantarius, Lb. fermentum, Lb. delbrueckii, W. confusa, Lb. reuteri, Fructobacillus spp., L. lactis and L. lactis. Yeast isolates included Clavispora lusitaniae, Cyberlindnera fabianii, Issatchenkia orientalis and Saccharomyces cerevisiae. DGGE indicated predominance of S. cerevisiae in Obutoko, Enturire and Obuteire and also detected Pichia spp. and I. orientalis in Obutoko. Obushera produced in the laboratory was initially dominated by Enterobacteriaceae and later by Lactococcus spp. Enterobacteriaceae and Bacillus spp. were also detected in Ekitiribita. Development of starters for Obushera may require combinations of LAB and S. cerevisiae for Obutoko, Enturire and Obuteire and LAB for Ekitiribita.

Probiotics, symptoms, and gut microbiota: what are the relations? A randomized controlled trial in subjects with irritable bowel syndrome

Introduction. Knowledge of the mechanism of action of probiotics in subjects with irritable bowel syndrome (IBS) is imperfect. Objective. This trial aimed at discriminating between a direct effect on the gut wall and an indirect effect caused by modulation of the fecal microbiota. Design. Randomized, double-blind, crossover trial. Material and Methods. Patients with IBS were given one capsule of 10¹⁰ CFU L. plantarum MF 1298 or placebo once daily. Symptoms were registered (score 0–15) and feces collected at the end of each period. The gut microbiota was analyzed with 16S rRNA gene analyses and results reported as proportions of Bacteroides, Faecalibacterium, and Lachnospiraceae and Simpson's D diversity score. Results. Sixteen participants (11 women) with a mean age of 50 years (SD 11) were available for the analyses. Intake of L. plantarum MF 1298 was associated with a significant aggravation of symptoms, but neither intake of L. plantarum MF 1298 nor symptoms were associated with the composition of the fecal microbiota (P values >0.10). Conclusions. The trial indicates that the symptomatic aggravation related to intake of L. plantarum MF 1298 was a direct effect of the microbe on the gut wall and not caused by changes in the fecal microbiota.

Abrupt temporal fluctuations in the chicken fecal microbiota are explained by its gastrointestinal origin

One of the main challenges in understanding the composition of fecal microbiota is that it can consist of microbial mixtures originating from different gastrointestinal (GI) segments. Here, we addressed this challenge for broiler chicken feces using a direct 16S rRNA gene-sequencing approach combined with multivariate statistical analyses. Broiler feces were chosen because of easy sampling and the importance for pathogen transmission to the human food chain. Feces were sampled daily for 16 days from chickens with and without a feed structure-induced stimulation of the gastric barrier function. Overall, we found four dominant microbial phylogroups in the feces. Two of the phylogroups were related to clostridia, one to lactobacilli, and one to Escherichia/Shigella. The relative composition of these phylogroups showed apparent stochastic temporal fluctuations in feces. Analyses of dissected chickens at the end of the experiment, however, showed that the two clostridial phylogroups were correlated to the microbiota in the cecum/colon and the small intestine, while the upper gut (crop and gizzard) microbiota was correlated to the lactobacillus phylogroup. In addition, chickens with a stimulated gizzard also showed less of the proximate GI dominating bacterial group in the feces, supporting the importance of the gastric barrier function. In conclusion, our results suggest that GI origin is a main determinant for the chicken fecal microbiota composition. This knowledge will be important for future understanding of factors affecting shedding of both harmful and beneficial gastrointestinal bacteria through feces.

Diversity, transmission and persistence of Escherichia coli in a cohort of mothers and their infants

Despite that Escherichia coli is one of the most important bacteria in early infant colonization and immune modulation, we have limited knowledge about diversity, transmission and persistence within human populations for this bacterium. Here we have utilized a novel, growth-independent, direct typing approach to describe E. coli mother-to-child transmission and persistence within infants in a well-defined cohort of 86 mothers and their infants in Norway. Seven gene multilocus sequence typing of 28 study isolates, three probiotic strains, eight Norwegian pathogenic isolates and the ECOR strain collection added a phylogenetic framework to the direct sequencing data. We found that a type B2 subpopulation of the maternal E. coli strains was the main group transmitted to the infants and that the proportion of children carrying their mothers' strain decreased as the children age. Using species richness estimates we also found a limited number of strains within the cohort compared with total E. coli diversity, constraints on infant colonization, and that infant strain diversity levels increased towards maternal diversity levels over time. This knowledge about inheritance and diversity forms a foundation for future understanding of E. coli in human health and disease.

Web of ecological interactions in an experimental gut microbiota

The dynamics of all ecosystems are dictated by intrinsic, density-dependent mechanisms and by density-independent environmental forcing. In spite of the importance of the gastrointestinal microbiota in health and disease, the ecology of this system remains largely unknown. Here, we take an ecological approach to gut microbial community analysis, with statistical modelling of time series data from chemostats. This approach removes effects of host forcing, allowing us to describe a network of intrinsic interactions determining the dynamic structure of an experimental gut microbiota. Surprisingly, the main colonization pattern in this simplified model system resembled that of the human infant gut, suggesting a potentially important role of density-dependent interactions in the early gut microbiota. Knowledge of ecological structures in microbial systems may provide us with a means of controlling such systems by modifying the strength and nature of interactions among microbes and between the microbes and their environment.

Statistical assessment of variability of terminal restriction fragment length polymorphism analysis applied to complex microbial communities

The variability of terminal restriction fragment polymorphism analysis applied to complex microbial communities was assessed statistically. Recent technological improvements were implemented in the successive steps of the procedure, resulting in a standardized procedure which provided a high level of reproducibility.

Quantitative Single-letter Sequencing: a method for simultaneously monitoring numerous known allelic variants in single DNA samples

Background

Pathogens such as fungi, bacteria and especially viruses, are highly variable even within an individual host, intensifying the difficulty of distinguishing and accurately quantifying numerous allelic variants co-existing in a single nucleic acid sample. The majority of currently available techniques are based on real-time PCR or primer extension and often require multiplexing adjustments that impose a practical limitation of the number of alleles that can be monitored simultaneously at a single locus.

Results

Here, we describe a novel method that allows the simultaneous quantification of numerous allelic variants in a single reaction tube and without multiplexing. Quantitative Single-letter Sequencing (QSS) begins with a single PCR amplification step using a pair of primers flanking the polymorphic region of interest. Next, PCR products are submitted to single-letter sequencing with a fluorescently-labelled primer located upstream of the polymorphic region. The resulting monochromatic electropherogram shows numerous specific diagnostic peaks, attributable to specific variants, signifying their presence/absence in the DNA sample. Moreover, peak fluorescence can be quantified and used to estimate the frequency of the corresponding variant in the DNA population.

Using engineered allelic markers in the genome of Cauliflower mosaic virus, we reliably monitored six different viral genotypes in DNA extracted from infected plants. Evaluation of the intrinsic variance of this method, as applied to both artificial plasmid DNA mixes and viral genome populations, demonstrates that QSS is a robust and reliable method of detection and quantification for variants with a relative frequency of between 0.05 and 1.

Conclusion

This simple method is easily transferable to many other biological systems and questions, including those involving high throughput analysis, and can be performed in any laboratory since it does not require specialized equipment.

Co-infection dynamics of a major food-borne zoonotic pathogen in chicken

A major bottleneck in understanding zoonotic pathogens has been the analysis of pathogen co-infection dynamics. We have addressed this challenge using a novel direct sequencing approach for pathogen quantification in mixed infections. The major zoonotic food-borne pathogen Campylobacter jejuni, with an important reservoir in the gastrointestinal (GI) tract of chickens, was used as a model. We investigated the co-colonisation dynamics of seven C. jejuni strains in a chicken GI infection trial. The seven strains were isolated from an epidemiological study showing multiple strain infections at the farm level. We analysed time-series data, following the Campylobacter colonisation, as well as the dominant background flora of chickens. Data were collected from the infection at day 16 until the last sampling point at day 36. Chickens with two different background floras were studied, mature (treated with Broilact, which is a product consisting of bacteria from the intestinal flora of healthy hens) and spontaneous. The two treatments resulted in completely different background floras, yet similar Campylobacter colonisation patterns were detected in both groups. This suggests that it is the chicken host and not the background flora that is important in determining the Campylobacter colonisation pattern. Our results showed that mainly two of the seven C. jejuni strains dominated the Campylobacter flora in the chickens, with a shift of the dominating strain during the infection period. We propose a model in which multiple C. jejuni strains can colonise a single host, with the dominant strains being replaced as a consequence of strain-specific immune responses. This model represents a new understanding of C. jejuni epidemiology, with future implications for the development of novel intervention strategies.

Pathogenic bacteria that can be transferred from animals to humans represent a highly potent human health hazard. Understanding the ecology of these pathogens in the animal host is of fundamental importance. A major analytical challenge, however, is the fact that individual animal hosts can be colonised by multiple strains of a given pathogen. We have addressed this challenge by developing a novel high-throughput approach for analyses of mixed strain infections. We chose Campylobacter jejuni colonisation of the chicken gastrointestinal (GI) tract as a model. C. jejuni is a major cause of food-borne disease in humans, and chickens are considered a main reservoir from which this bacterium may enter the food chain. We analysed the co-colonisation of seven C. jejuni strains in two groups of chickens with very different background GI microfloras. We found that mainly two of the C. jejuni strains colonised the chickens, with a shift in the dominant coloniser during the infection period. The C. jejuni colonisation pattern, however, was little affected by the dominating GI microflora. We propose a model where the chicken immune response is the important determinant for C. jejuni colonisation, and suggest that multiple strain colonisation could be a way of maintaining stable infections in the animal host. This new knowledge is very important for future development of novel intervention strategies to prevent C. jejuni from entering the human food chain.