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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.

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.

Background Metagenomic sequencing has led to the identification and assembly of many new bacterial genome sequences. These bacteria often contain plasmids: usually small, circular double-stranded DNA molecules that may transfer across bacterial species and confer antibiotic resistance. These plasmids are generally less studied and understood than their bacterial hosts. Part of the reason for this is insufficient computational tools enabling the analysis of plasmids in metagenomic samples. Results We developed SCAPP (Sequence Contents-Aware Plasmid Peeler)—an algorithm and tool to assemble plasmid sequences from metagenomic sequencing. SCAPP builds on some key ideas from the Recycler algorithm while improving plasmid assemblies by integrating biological knowledge about plasmids. We compared the performance of SCAPP to Recycler and metaplasmidSPAdes on simulated metagenomes, real human gut microbiome samples, and a human gut plasmidome dataset that we generated. We also created plasmidome and metagenome data from the same cow rumen sample and used the parallel sequencing data to create a novel assessment procedure. Overall, SCAPP outperformed Recycler and metaplasmidSPAdes across this wide range of datasets. Conclusions SCAPP is an easy to use Python package that enables the assembly of full plasmid sequences from metagenomic samples. It outperformed existing metagenomic plasmid assemblers in most cases and assembled novel and clinically relevant plasmids in samples we generated such as a human gut plasmidome. SCAPP is open-source software available from: https://github.com/Shamir-Lab/SCAPP.

Unicellular eukaryotes are an integral part of many microbial ecosystems where they interact with their surrounding prokaryotic community—either as predators or as mutualists. Within the rumen, one of the most complex host-associated microbial habitats, ciliate protozoa represent the main micro-eukaryotes, accounting for up to 50% of the microbial biomass. Nonetheless, the extent of the ecological effect of protozoa on the microbial community and on the rumen metabolic output remains largely understudied. To assess the role of protozoa on the rumen ecosystem, we established an in-vitro system in which distinct protozoa sub-communities were introduced to the native rumen prokaryotic community. We show that the different protozoa communities exert a strong and differential impact on the composition of the prokaryotic community, as well as its function including methane production. Furthermore, the presence of protozoa increases prokaryotic diversity with a differential effect on specific bacterial populations such as Gammaproteobacteria, Prevotella and Treponema. Our results suggest that protozoa contribute to the maintenance of prokaryotic diversity in the rumen possibly by mitigating the effect of competitive exclusion between bacterial taxa. Our findings put forward the rumen protozoa populations as potentially important ecosystem engineers for future microbiome modulation strategies.

Motivation: Metagenomic sequencing has led to the identification and assembly of many new bacterial genome sequences. These bacteria often contain plasmids, which are less studied or understood. In order to assist in the study of these plasmids we developed SCAPP (Sequence Contents Aware Plasmid Peeler) -an algorithm and tool to assemble plasmid sequences from metagenomic sequencing. Results: SCAPP builds on some key ideas from the Recycler plasmid assembly algorithm while improving plasmid assemblies by integrating biological knowledge about plasmids. We compared performance of SCAPP to Recycler and metaplasmidSPAdes on simulated metagenomes, real human gut microbiome samples, and a human gut plasmidome that we generated. We also created a parallel plasmidome-metagenome cow rumen sample and used it to create a novel assessment procedure. In most cases SCAPP performed better than or similar to Recycler and meta-plasmidSPAdes across this wide range of datasets. Availability: https://github.com/Shamir-Lab/SCAPP

The increasing appreciation for the human gut microbiome potential roles in diagnosis, treatment, and ultimately prevention of human disease requires an understanding of the temporal variability of the microbial community over the lifespan of an individual. Given the highly dynamic and complex nature of the human gut microbial community, the ability to di↵erentiate between the OTUs behavior over time, and specifically, identify and predict the time-dependent OTUs, is crucial. However, the field has not yet settled upon the degree to which the microbial composition of the gut at a given time a↵ects the microbial composition at a later time. Particularly, it has been recently suggested that only a minority of the OTUs depend on the microbial composition in earlier times. Here, we demonstrate that these earlier findings are severely biased by the sub-optimality of the statistical approaches used to model the microbial composition dependency in time. To that end, we introduce MTV-LMM , a linear mixed model method for the prediction of the microbial community temporal dynamics based on the community composition at previous time stamps. MTV-LMM thus allows identifying auto-regressive OTUs in time series microbiome datasets, which can be taken to further analysis of the microbiome trajectory over time. We evaluated the performance of MTV-LMM on three microbiome time series datasets, and we found that MTV-LMM significantly outperforms the existing methods for microbiome time-series modeling. Furthermore, we quantify the time-dependency of the microbiome in infants and adults and we demonstrate, for the first time, that a considerable proportion of OTUs has a significant auto-regressive component, in both infants and adults, and that time-dependency is a considerably more abundant phenomenon compared to previous report.peer-reviewed)

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