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Background: A beneficial composition of the microbiome is essential for maintaining human health and to protect the body against pathogens. The identification of microbes with oncogenic potential suggests a possible link between changes in the gastrointestinal microbiome composition and the onset of cancer. Childhood cancer is still the predominant cause of death among children over a year in Western countries. However, a profound understanding of the changes in the composition of the microbiome in the onset or progression of childhood cancer is still lacking. Methods: We characterized the gut microbiota in a cohort of Norwegian children diagnosed with cancer using fecal samples before the onset of treatment. Samples were collected in the Norwegian Childhood Cancer Biobank (NCCB) and compared to a control group of children attending two diverse kindergartens in Oslo, Norway. The data of 16S rRNA gene amplicon sequencing were analyzed by non-metric multidimensional scaling (NMMS) of amplicon sequence variants (ASVs) relative abundance data. Results: Our results revealed 11 prevalent 16S rRNA gene sequence variants with significantly differential occurrence between the patient and the control groups. Eight of these taxa were depleted in children diagnosed with cancer, with three of these eight classified as Faecalibacterium prausnitzii. Conclusions: Our observations indicate that the gut microbiota may play an important role in understanding the physiological changes associated with childhood cancer. However, further studies are required to validate if variations in the microbial community could be used as an early indicator of the presence of cancer in the future.

Background A severe form of acute hemorrhagic diarrhea syndrome (AHDS) occurred in dogs in the Oslo region of Norway during autumn 2019. Objectives To characterize the fecal microbiota of dogs with AHDS during the outbreak and compare it to that of healthy dogs from the same period and before the outbreak. Animals Dogs with AHDS (n = 50), dogs with nonhemorrhagic diarrhea (n = 3), and healthy dogs (n = 11) were sampled during the outbreak. In addition, 78 healthy dogs from the same region were sampled before the outbreak between 2017 and 2018. Methods Retrospective case‐control study. The fecal microbiotas were characterized using 16S rRNA gene amplicon sequencing. Results Dogs with AHDS had significantly different microbiota composition (R 2 = .07, P

All multicellular organisms host microbial communities in and on their bodies, and these microbiomes can have major influences on host biology. Most research has focussed on the oral, skin, and gut microbiomes, whereas relatively little is known about the reproductive microbiome. Here, we review empirical evidence to show that reproductive microbiomes can have significant effects on the reproductive function and performance of males and females. We then discuss the likely repercussions of these effects for evolutionary processes related to sexual selection and sexual conflict, as well as mating systems and reproductive isolation. We argue that knowledge of the reproductive microbiome is fundamental to our understanding of the evolutionary ecology of reproductive strategies and sexual dynamics of host organisms.

Background The gelada monkey (Theropithecus gelada), endemic to the Ethiopian highlands, is the only graminivorous primate, i.e., it feeds mainly on grasses and sedges. In spite of known dental, manual, and locomotor adaptations, the intestinal anatomy of geladas is similar to that of other primates. We currently lack a clear understanding of the adaptations in digestive physiology necessary for this species to subsist on a graminoid-based diet, but digestion in other graminivores, such as ruminants, relies heavily on the microbial community residing in the gastrointestinal (GI) system. Furthermore, geladas form complex, multilevel societies, making them a suitable system for investigating links between sociality and the GI microbiota. Results Here, we explore the gastrointestinal microbiota of gelada monkeys inhabiting an intact ecosystem and document how factors like multilevel social structure and seasonal changes in diet shape the GI microbiota. We compare the gelada GI microbiota to those of other primate species, reporting a gradient from geladas to herbivorous specialist monkeys to dietary generalist monkeys and lastly humans, the ultimate ecological generalists. We also compare the microbiotas of the gelada GI tract and the sheep rumen, finding that geladas are highly enriched for cellulolytic bacteria associated with ruminant digestion, relative to other primates. Conclusions This study represents the first analysis of the gelada GI microbiota, providing insights into the adaptations underlying graminivory in a primate. Our results also highlight the role of social organization in structuring the GI microbiota within a society of wild animals. Electronic supplementary material The online version of this article (10.1186/s40168-018-0468-6) contains supplementary material, which is available to authorized users.

The human gut microbiota plays a vital role in health and disease, and microbial colonization is a key process in infant development. Here, we analyze 2684 fecal specimens from 12 infants during their first year of life, providing detailed insights into the human gut colonization process. Maturation of the gut microbial community shows strong temporal structure and specific developmental stages. At 2–4 months of age, there is a period of accelerated convergence concurrent with a bloom of Bifidobacterium, a genus associated with metabolism of oligosaccharides found in breast milk. The end of this period coincides with the introduction of solid food, a reduction in the relative abundance of Bifidobacterium, and an increase in several groups of Firmicutes. Our findings highlight the dynamic nature and individuality of the gut colonization process, and the need for high-frequency sampling over an extended period when designing and interpreting infant microbiome studies., Microbial colonization of the gut is a key process in infant development. Here, de Muinck and Trosvik analyze 2,684 fecal specimens sampled from 12 infants during their first year of life, providing detailed insights into the human gut colonization process.

Human activities can cause habitat degradation that may alter the types and quality of available food resources and thus influence the microbiomes of wild animal populations. Furthermore, seasonal shifts in food availability may cause adaptive responses in the gut microbiome to meet the need for different metabolic capabilities. Here, we demonstrate local-scale population structure in the gastrointestinal microbiotas of Chlorocebus monkeys, in southern Ethiopia, in response to varying degrees of human encroachment. We further provide evidence of adaptation to ecological conditions associated with the dry and wet seasons, and show seasonal effects to be more pronounced in areas with limited human activity. Finally, we report species-level microbiota differences between the endemic Ethiopian Bale monkey, an ecological specialist, and generalist Chlorocebus species from the same geographical region.

BackgroundAdvances in sequencing technologies and bioinformatics have made the analysis of microbial communities almost routine. Nonetheless, the need remains to improve on the techniques used for gathering such data, including increasing throughput while lowering cost, and benchmarking the techniques so that potential sources of bias can be better characterized.ResultsWe present a triple-index amplicon sequencing strategy that uses a two-stage PCR protocol. The strategy was extensively benchmarked through analysis of a mock community in order to assess biases introduced by sample indexing, number of PCR cycles, and template concentration. We further evaluated the method through re-sequencing of a standardized environmental sample. Finally, we evaluated our protocol on a set of fecal samples from a small cohort of healthy adults, demonstrating good performance in a realistic experimental setting. Between-sample variation was mainly related to batch effects, such as DNA extraction, while sample indexing was also a significant source of bias. PCR cycle number strongly influenced chimera formation and affected relative abundance estimates of species with high GC content. Libraries were sequenced using the Illumina HiSeq and MiSeq platforms to demonstrate that this protocol is highly scalable to sequence thousands of samples at a very low cost.ConclusionsHere, we provide the most comprehensive study of performance and bias inherent to a 16S rRNA gene amplicon sequencing method to date. Triple-indexing greatly reduces the number of long custom DNA oligos required for library preparation, while the inclusion of variable length heterogeneity spacers minimizes the need for PhiX spike-in. This design results in a significant cost reduction of highly multiplexed amplicon sequencing. The biases we characterize highlight the need for highly standardized protocols. Reassuringly, we find that the biological signal is a far stronger structuring factor than the various sources of bias.

Background Advances in sequencing technologies and bioinformatics have made the analysis of microbial communities almost routine. Nonetheless, the need remains to improve on the techniques used for gathering such data, including increasing throughput while lowering cost and benchmarking the techniques so that potential sources of bias can be better characterized. Methods We present a triple-index amplicon sequencing strategy to sequence large numbers of samples at significantly lower c ost and in a shorter timeframe compared to existing methods. The design employs a two-stage PCR protocol, incorpo rating three barcodes to each sample, with the possibility to add a fourth-index. It also includes heterogeneity spacers to overcome low complexity issues faced when sequencing amplicons on Illumina platforms. Results The library preparation method was extensively benchmarked through analysis of a mock community in order to assess biases introduced by sample indexing, number of PCR cycles, and template concentration. We further evaluated the method through re-sequencing of a standardized environmental sample. Finally, we evaluated our protocol on a set of fecal samples from a small cohort of healthy adults, demonstrating good performance in a realistic experimental setting. Between-sample variation was mainly related to batch effects, such as DNA extraction, while sample indexing was also a significant source of bias. PCR cycle number strongly influenced chimera formation and affected relative abundance estimates of species with high GC content. Libraries were sequenced using the Illumina HiSeq and MiSeq platforms to demonstrate that this protocol is highly scalable to sequence thousands of samples at a very low cost. Conclusions Here, we provide the most comprehensive study of performance and bias inherent to a 16S rRNA gene amplicon sequencing method to date. Triple-indexing greatly reduces the number of long custom DNA oligos required for library preparation, while the inclusion of variable length heterogeneity spacers minimizes the need for PhiX spike-in. This design results in a significant cost reduction of highly multiplexed amplicon sequencing. The biases we characterize highlight the need for highly standardized protocols. Reassuringly, we find that the biological signal is a far stronger structuring factor than the various sources of bias.

The human gastrointestinal (GI) microbiota is important to human health and imbalances or shifts in the gut microbial community have been linked to many diseases. Most studies of the GI microbiota only capture snapshots of this dynamic community at one or a few time points. Although this is valuable in terms of providing knowledge of community composition and variability between individuals, it does not provide the foundation for going beyond descriptive studies and toward truly predictive ecological models. In order to achieve this goal, we need longitudinal data of appropriate temporal and taxonomic resolution, so that established time series analysis tools for identifying and quantifying putative interactions among community members can be used. Here, we present new analyses of existing data to illustrate the potential usefulness of this approach. We discuss challenges related to sampling and data processing, as well as analytical approaches and considerations for future studies of the GI microbiota and other complex microbial systems.

Aim: We have tested the effect of various combinations of formic acid and sorbate on Campylobacter jejuni colonization in broiler chickens to reduce the colonization of this zoonotic pathogen in broiler chicken flocks. Methods and Results: Chickens were offered feed supplemented with different concentrations and combinations of formic acid and/or potassium sorbate. We found little or no effect on the Camp. jejuni colonization levels in chickens that were given feed supplemented with formic acid alone. A combination of 1·5% formic acid and 0·1% sorbate reduced the colonization of Camp. jejuni significantly, while a concentration of 2·0% formic acid in combination with 0·1% sorbate prevented Camp. jejuni colonization in chickens. This inhibition was replicated in two independent trials with a combination of three different Camp. jejuni strains. Conclusions: Our results show a novel and promising intervention strategy to reduce the incidence of Camp. jejuni in poultry products and to obtain safer food. Significance and Impact of the Study: To ensure food safety, a reduction of the carcass contamination with Camp. jejuni through reduced colonization of this pathogen in broiler chicken flocks is important. A range of organic acids as additives in feed and drinking water have already been evaluated for this purpose. However, no studies have yet shown a complete inhibition of Camp. jejuni colonization in broiler chickens.

Due to a general shortage of temporal population data, dynamic structures in microbial communities remain largely unexplored. Knowledge of community dynamics is, however, essential for understanding the mechanisms by which microbes interact. Here, we have used a computational approach for quantification of bacteria in multispecies populations, generating data for time-series modeling. Moreover, we have used online FR-IR spectroscopy to monitor the main metabolic processes. The approach enabled us to provide a functional description of the parameters governing the population dynamics in a three-species model bacterial community, demonstrating density-dependent regulation, interspecies competition and even a case of cooperation between two species. Since the field of microbial ecology has yet to embrace many of the concepts and methods developed for the study of ecology of higher plants and animals, the realization that microbial systems can be analyzed within the same conceptual framework as other ecosystems is of fundamental importance.

A comprehensive understanding of factors that influence microbial competition and cooperation, their diversity and processes will be greatly beneficial in many research areas. Current tools for microflora determinations are far from suitable for high-throughput monitoring of development in complex microbial communities. Here, we describe the application of a calibration free method, multivariate curve resolution with alternating least squares (MCR-ALS), for identification and quantification of different microbes in mixture samples. The idea is to utilize MCR-ALS to enable close monitoring of ecology in a variety of microbial communities. The data from two designed experiments consisting of DNA sequence spectra measured on mixtures were analysed with MCR-ALS using no prior information on the data except for appropriate constraints, such as non-negativity and closure. The results were compared both to the known true concentrations as well as to the results obtained from the well-established multivariate calibration method partial least squares (PLS) regression. MCR-ALS performed as well as PLS regression, successfully extracting all pure bacterial spectra and quantitative information on these, with 97.81% and 97.91% explained variance for the first and the second data set, respectively. Copyright © 2008 John Wiley & Sons, Ltd.

Understanding dynamic processes and diversity in microbial communities is of key importance for combating pathogens and for stimulating beneficial bacteria. We have addressed these challenges utilising multivariate statistics for analyses of microbial community structures. We based our microbial community analyses on 16S rRNA gene data. This gene is by far the most widely applied genetic marker for phylogenetic and microbial community studies. Both probe and clone library data were analysed. We analysed the clone library data using a newly developed coordinate-based phylogenetic approach. By using coordinates, we avoid both DNA sequence alignments and the need for definition of operational taxonomic units (OTUs). The basic principle is to transform the sequence data to frequencies of multimers (short sequences of n = 2 to 6), and then to use principal component analyses (PCA) for data compression into an orthogonal coordinate space. We used our coordinate method for global 16S rRNA gene analyses of prokaryotes. When comparing microbial communities, it is often important to determine the relationship between the microflora and knowledge about the samples analysed. We used partial least square regression (PLSR) to relate physical/chemical properties to microbial community composition. This was done by analysing both probe and clone library data using the effect of modified atmosphere packaging (MAP) on fish microflora as an example. We are currently investigating approaches to describe dynamic microbial community interactions. Our ultimate goal is to understand and model the main dynamic interactions in complete microbial communities.

High-throughput quantification of genetically coherent units (GCUs) is essential for deciphering population dynamics and species interactions within a community of microbes. Current techniques for microbial community analyses are, however, not suitable for this kind of high-throughput application. Here, we demonstrate the use of multivariate statistical analysis of complex DNA sequence electropherograms for the effective and accurate estimation of relative genotype abundance in cell samples from mixed microbial populations. The procedure is no more labor-intensive than standard automated DNA sequencing and provides a very effective means of quantitative data acquisition from experimental microbial communities. We present results with the Campylobacter jejuni strain-specific marker gene gltA , as well as the 16S rRNA gene, which is a universal marker across bacterial assemblages. The statistical models computed for these genes are applied to genetic data from two different experimental settings, namely, a chicken infection model and a multispecies anaerobic fermentation model, demonstrating collection of time series data from model bacterial communities. The method presented here is, however, applicable to any experimental scenario where the interest is quantification of GCUs in genetically heterogeneous DNA samples.

Background Determining ecological roles of community members and the impact of specific taxa on overall biodiversity in the gastrointestinal (GI) microbiota is of fundamental importance. A step towards a systems-level understanding of the GI microbiota is characterization of biotic interactions. Community time series analysis, an approach based on statistical analysis of changing population abundances within a single system over time, is needed in order to say with confidence that one population is affecting the dynamics of another. Results Here, we characterize biotic interaction structures and define ecological roles of major bacterial groups in four healthy individuals by analysing high-resolution, long-term (>180 days) GI bacterial community time series. Actinobacteria fit the description of a keystone taxon since they are relatively rare, but have a high degree of ecological connectedness, and are positively correlated with diversity both within and between individuals. Bacteriodetes were found to be a foundation taxon in that they are numerically dominant and interact extensively, in particular through positive interactions, with other taxa. Although community structure, diversity and biotic interaction patterns were specific to each individual, we observed a strong tendency towards more intense competition within than between phyla. This is in agreement with Darwin’s limiting similarity hypothesis as well as a published biotic interaction model of the GI microbiota based on reverse ecology. Finally, we link temporal enterotype switching to a reciprocal positive interaction between two key genera. Conclusions In this study, we identified ecological roles of key taxa in the human GI microbiota and compared our time series analysis results with those obtained through a reverse ecology approach, providing further evidence in favour of the limiting similarity hypothesis first put forth by Darwin. Larger longitudinal studies are warranted in order to evaluate the generality of basic ecological concepts as applied to the GI microbiota, but our results provide a starting point for achieving a more profound understanding of the GI microbiota as an ecological system. Electronic supplementary material The online version of this article (doi:10.1186/s40168-015-0107-4) contains supplementary material, which is available to authorized users.

Here we present the first genomic data for the African wolf (Canis aureus lupaster) and conclusively demonstrate that it is a unique taxon and not a hybrid between other canids. These animals are commonly misclassified as golden jackals (Canis aureus) and have never been included in any large-scale studies of canid diversity and biogeography, or in investigations of the early stages of dog domestication. Applying massive Restriction Site Associated DNA (RAD) sequencing, 110481 polymorphic sites across the genome of 7 individuals of African wolf were aligned and compared with other wolf-like canids (golden jackal, Holarctic grey wolf, Ethiopian wolf, side-striped jackal and domestic dog). Analyses of this extensive sequence dataset (ca. 8.5Mb) show conclusively that the African wolves represent a distinct taxon more closely related to the Holarctic grey wolf than to the golden jackal. Our results strongly indicate that the distribution of the golden jackal needs to be re-evaluated and point towards alternative hypotheses for the evolution of the rare and endemic Ethiopian wolf (Canis simensis). Furthermore, the extension of the grey wolf phylogeny and distribution opens new possible scenarios for the timing and location of dog domestication.

Over the last decades the phylogeography and genetic structure of a multitude of species inhabiting Europe and North America have been described. The flora and fauna of the vast landmasses of north-eastern Eurasia are still largely unexplored in this respect. The Eurasian lynx is a large felid that is relatively abundant over much of the Russian sub-continent and the adjoining countries. Analyzing 148 museum specimens collected throughout its range over the last 150 years we have described the large-scale genetic structuring in this highly mobile species. We have investigated the spatial genetic patterns using mitochondrial DNA sequences (D-loop and cytochrome b) and 11 microsatellite loci, and describe three phylogenetic clades and a clear structuring along an east-west gradient. The most likely scenario is that the contemporary Eurasian lynx populations originated in central Asia and that parts of Europe were inhabited by lynx during the Pleistocene. After the Last Glacial Maximum (LGM) range expansions lead to colonization of north-western Siberia and Scandinavia from the Caucasus and north-eastern Siberia from a refugium further east. No evidence of a Berinigan refugium could be detected in our data. We observed restricted gene flow and suggest that future studies of the Eurasian lynx explore to what extent the contemporary population structure may be explained by ecological variables.

In this study, we investigated the effects of different feed structures and beddings on the spread of C. jejuni in broiler flocks, and the effect on the cecal microbiota. Broiler chickens raised in 24 eight-bird group cages on either rubber mat or wood shavings were fed either a wheat-based control diet (Control), a diet where 50% of the ground wheat was replaced by whole wheat prior to pelleting (Wheat), or a wheat-based diet, such as the control diet diluted with 12% oat hulls (Oat). Samples from the cloacal mucosa of all birds were taken daily for C. jejuni quantification and cecum samples were collected at the end of the experiment for C. jejuni quantification and microbiota analyses. We have shown a statistically significant effect of increased feed structure on the reduced spread of C. jejuni in chicken flocks, but no significant differences were detected between types of structure included in the feed. No significant changes in the dominating microbiota in the lower lower gastrointestinal (GI) tract were observed, which indicates that feed structure only has an effect on the upper GI tract. Delaying the spread of C. jejuni in broiler flocks could, at time of slaughter, result in fewer C. jejuni -positive broilers.

Summary 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.

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.

Temporal dynamics of the human gut microbiota is of fundamental importance for the development of proper gut function and maturation of the immune system. Here we present a description of infant gut ecological dynamics using a combination of nonlinear data modeling and simulations of the early infant gut colonization processes. Principal component analysis of infant microbiota 16S rRNA gene microarray data showed that the main directions of variation were defined by three phylum-specific probes targeting Bacteroides, Proteobacteria and Firmicutes. Nonlinear regression analysis identified several dynamic interactions between these three phyla. Simulations of the early phylum-level colonization process showed the relatively rapid establishment of an equilibrium community after an unstable initial phase. In general, varying the initial composition of phyla in the simulations had little bearing on the final equilibrium. The dynamic interaction model was found to maintain its predictive ability for Proteobacteria and Firmicutes well into the simulation, whereas Bacteroides densities tended to be underestimated, possibly due to host top-down selection for Bacteroides. In accordance with our model, initial perturbation of the microbiota by different mode of delivery (vaginal and C-section) did not affect the later phylum composition in the infants investigated. Considering the predictive ability and convergence of our phylum-level model, we now propose that deterministic bacterial–bacterial interactions are more important for shaping the human infant gut microbiota than previously anticipated.

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.

Background Despite being one of the most intensely studied model organisms, many questions still remain about the evolutionary biology and ecology of Escherichia coli. An important step toward achieving a more complete understanding of E.coli biology entails elucidating relationships between gene content and adaptation to the ecological niche. Results Here, we present genome comparisons of 16 E.coli strains that represent commensals and pathogens isolated from infants during a specific time period in Trondheim, Norway. Using differential gene content, we characterized enrichment profiles of the collection of strains relating to phylogeny, early vs. late colonization, pathogenicity and growth rate. We found clear gene content distinctions relating to the various grouping criteria. We also found that different categories of strains use different genetic elements for similar biological processes. The sequenced genomes included two pairs of strains where each pair was isolated from the same infant at different time points. One pair, in which the strains were isolated four months apart, showed maintenance of an early colonizer genome profile but also gene content and codon usage changes toward the late colonizer profile. Lastly, we placed our sequenced isolates into a broader genomic context by comparing them with 25 published E.coli genomes that represent a variety of pathotypes and commensal strains. This analysis demonstrated the importance of geography in shaping strain level gene content profiles. Conclusions Our results indicate a general pattern where alternative genetic pathways lead toward a consistent ecological role for E.coli as a species. Within this framework however, we saw selection shaping the coding repertoire of E.coli strains toward distinct ecotypes with different phenotypic properties.