Microsatellite Length Scoring by Single Molecule Real Time Sequencing - Effects of Sequence Structure and PCR Regime.

Microsatellites are DNA sequences consisting of repeated, short (1-6 bp) sequence motifs that are highly mutable by enzymatic slippage during replication. Due to their high intrinsic variability, microsatellites have important applications in population genetics, forensics, genome mapping, as well as cancer diagnostics and prognosis. The current analytical standard for microsatellites is based on length scoring by high precision electrophoresis, but due to increasing efficiency next-generation sequencing techniques may provide a viable alternative. Here, we evaluated single molecule real time (SMRT) sequencing, implemented in the PacBio series of sequencing apparatuses, as a means of microsatellite length scoring. To this end we carried out multiplexed SMRT sequencing of plasmid-carried artificial microsatellites of varying structure under different pre-sequencing PCR regimes. For each repeat structure, reads corresponding to the target length dominated. We found that pre-sequencing amplification had large effects on scoring accuracy and error distribution relative to controls, but that the effects of the number of amplification cycles were generally weak. In line with expectations enzymatic slippage decreased proportionally with microsatellite repeat unit length and increased with repetition number. Finally, we determined directional mutation trends, showing that PCR and SMRT sequencing introduced consistent but opposing error patterns in contraction and expansion of the microsatellites on the repeat motif and single nucleotide level.

Ecology of bacteria in the human gastrointestinal tract--identification of keystone and foundation taxa.

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.

Biotic interactions and temporal dynamics of the human gastrointestinal microbiota.

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.