No abstract

How complex communities assemble through the animal's life, and how predictable the process is remains unexplored. Here, we investigate the forces that drive the assembly of rumen microbiomes throughout a cow's life, with emphasis on the balance between stochastic and deterministic processes. We analyse the development of the rumen microbiome from birth to adulthood using 16S-rRNA amplicon sequencing data and find that the animals shared a group of core successional species that invaded early on and persisted until adulthood. Along with deterministic factors, such as age and diet, early arriving species exerted strong priority effects, whereby dynamics of late successional taxa were strongly dependent on microbiome composition at early life stages. Priority effects also manifest as dramatic changes in microbiome development dynamics between animals delivered by Csection vs. natural birth, with the former undergoing much more rapid species invasion and accelerated microbiome development. Overall, our findings show that together with strong deterministic constrains imposed by diet and age, stochastic colonization in early life has long-lasting impacts on the development of animal microbiomes.

BackgroundGenetic data are known to harbor information about human demographics, and genotyping data are commonly used for capturing ancestry information by leveraging genome-wide differences between populations. In contrast, it is not clear to what extent population structure is captured by whole-genome DNA methylation data.ResultsWe demonstrate, using three large-cohort 450K methylation array data sets, that ancestry information signal is mirrored in genome-wide DNA methylation data and that it can be further isolated more effectively by leveraging the correlation structure of CpGs with cis-located SNPs. Based on these insights, we propose a method, EPISTRUCTURE, for the inference of ancestry from methylation data, without the need for genotype data.ConclusionsEPISTRUCTURE can be used to infer ancestry information of individuals based on their methylation data in the absence of corresponding genetic data. Although genetic data are often collected in epigenetic studies of large cohorts, these are typically not made publicly available, making the application of EPISTRUCTURE especially useful for anyone working on public data. Implementation of EPISTRUCTURE is available in GLINT, our recently released toolset for DNA methylation analysis at: http://glint-epigenetics.readthedocs.io.Electronic supplementary materialThe online version of this article (doi:10.1186/s13072-016-0108-y) contains supplementary material, which is available to authorized users.

The translational power of human microbiome studies is limited by high inter-individual variation. We describe a dimensionality reduction tool, compositional tensor factorization (CTF), that incorporates information from the same host, across multiple samples, to reveal patterns driving differences in microbial composition across phenotypes. CTF identifies robust patterns in sparse, compositional datasets, allowing for the detection of microbial changes associated with specific phenotypes that are reproducible across datasets.

CXL is a safe and effective method for halting the deterioration of KCN, while slightly improving visual function.

Nutrient limitation drives competition for resources across organisms. However, much is unknown about how selective pressures resulting from nutrient limitation shape microbial coding sequences. Here, we study this “resource-driven selection” by using metagenomic and single-cell data of marine microbes, alongside environmental measurements. We show that a significant portion of the selection exerted on microbes is explained by the environment and is associated with nitrogen availability. Notably, this resource conservation optimization is encoded in the structure of the standard genetic code, providing robustness against mutations that increase carbon and nitrogen incorporation into protein sequences. This robustness generalizes to codon choices from multiple taxa across all domains of life, including the human genome.

We introduce a Bayesian semi-supervised method for estimating cell counts from DNA methylation by leveraging an easily obtainable prior knowledge on the cell-type composition distribution of the studied tissue. We show mathematically and empirically that alternative methods which attempt to infer cell counts without methylation reference only capture linear combinations of cell counts rather than provide one component per cell type. Our approach allows the construction of components such that each component corresponds to a single cell type, and provides a new opportunity to investigate cell compositions in genomic studies of tissues for which it was not possible before.Electronic supplementary materialThe online version of this article (10.1186/s13059-018-1513-2) contains supplementary material, which is available to authorized users.

Given the highly dynamic and complex nature of the human gut microbial community, the ability to identify and predict time-dependent compositional patterns of microbes is crucial to our understanding of the structure and functions of this ecosystem. One factor that could affect such time-dependent patterns is microbial interactions, wherein community composition at a given time point affects the microbial composition at a later time point. However, the field has not yet settled on the degree of this effect. Specifically, it has been recently suggested that only a minority of taxa depend on the microbial composition in earlier times. To address the issue of identifying and predicting temporal microbial patterns we developed a new model, MTV-LMM (Microbial Temporal Variability Linear Mixed Model), a linear mixed model for the prediction of microbial community temporal dynamics. MTV-LMM can identify time-dependent microbes (i.e., microbes whose abundance can be predicted based on the previous microbial composition) in longitudinal studies, which can then be used to analyze the trajectory of the microbiome over time. We evaluated the performance of MTV-LMM on real and synthetic time series datasets, and found that MTV-LMM outperforms commonly used methods for microbiome time series modeling. Particularly, we demonstrate that the effect of the microbial composition in previous time points on the abundance of taxa at later time points is underestimated by a factor of at least 10 when applying previous approaches. Using MTV-LMM , we demonstrate that a considerable portion of the human gut microbiome, both in infants and adults, has a significant time-dependent component that can be predicted based on microbiome composition in earlier time points. This suggests that microbiome composition at a given time point is a major factor in defining future microbiome composition and that this phenomenon is considerably more common than previously reported for the human gut microbiome.