Elevated rates of horizontal gene transfer in the industrialized human microbiome

Industrialization has impacted the human gut ecosystem, resulting in altered microbiome composition and diversity. Whether bacterial genomes may also adapt to the industrialization of their host populations remains largely unexplored. Here, we investigate the extent to which the rates and targets of horizontal gene transfer (HGT) vary across thousands of bacterial strains from 15 human populations spanning a range of industrialization. We show that HGTs have accumulated in the microbiome over recent host generations and that HGT occurs at high frequency within individuals. Comparison across human populations reveals that industrialized lifestyles are associated with higher HGT rates and that the functions of HGTs are related to the level of host industrialization. Our results suggest that gut bacteria continuously acquire new functionality based on host lifestyle and that high rates of HGT may be a recent development in human history linked to industrialization.Peer reviewe

Host/virus interactions in the marine cyanobacterium prochlorococcus

Thesis: Ph. D., Massachusetts Institute of Technology, Department of Civil and Environmental Engineering, 2014.Cataloged from PDF version of thesis.Includes bibliographical references.Bacterial viruses shape the diversity, metabolic function, and community dynamics of their microbial hosts. As microbes drive many major biogeochemical cycles, viral infection is therefore a phenomenon of global significance. A significant fraction of primary production in the oceans is performed by the picocyanobacteria Prochlorococcus and Synechococcus. The viruses ('cyanophages') that infect these cyanobacteria are unusual in that their genomes contain a large suite of orthologs to host metabolic genes. These orthologs, known as 'auxiliary metabolic genes' ('AMGs'), encode proteins involved in diverse cellular processes, including photosynthesis, carbon and phosphate metabolism, and nucleotide synthesis. They are thought to benefit phage during infection by redirecting host metabolism towards pathways that promote viral replication. While AMGs are widespread in two Prochlorococcus cyanophage families, the podoviruses and myoviruses, they are rare or completely absent in the third family, the siphoviruses. The overarching goal of this thesis is to understand differences in the infection processes of different cyanophages - in particular, between cyanophages that encode AMGs and those that do not. We hypothesize that the latter group utilizes a fundamentally different infection strategy than AMG-encoding cyanophages, and offer several lines of evidence to support this hypothesis. First, we show that siphoviruses that lack AMGs have highly productive infections, and that host photosynthesis is not impaired during infection. This result is somewhat surprising, as evidence suggests that photosynthesis is supported by AMG-encoded proteins during infection by other cyanophages; however, it is consistent with our observations that unlike the podo- and myoviruses, the siphoviruses do not degrade the host genome during infection, and that the host response to siphovirus infection parallels the metabolic changes effected by AMGs in other cyanophages. These results, along with patterns of siphovirus infection kinetics over the diel light cycle, suggest that siphoviruses utilize a mode of infection that is based on modulating, rather than suppressing, host transcription. Finally, we argue that the suite of metabolic changes that occur during siphovirus infection - and that are effected by AMGs in other cyanophages - are induced by a nitrogen-stress-like response in the host during infection, offering new insights into cyanophage/host coevolution.by Katya Frois-Moniz.Ph. D

<i>Bacteroides vulgatus</i> sub-species diversity in human gut microbiome samples (LifelinesDeep) and MIT sewage samples, as revealed by ten phylogenetic marker genes.

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Family-level taxa for taxonomic filter in model application.

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Sub-species diversity of bacteria prevalent in sewage samples.

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Performance of a cross-validated Random Forest regression model utilizing microbiome features to predict population size.

Black solid dots indicate the means of the predicted values, and error bars indicate the standard deviations of the predicted values. (TIFF)</p

MicrobiomeCensus statistic definition, model training, validation, and application.

(A) Example of computing the T statistic. (B) Simulation results for T with different population sizes. Grey points are simulation results. Red bars are means of 10,000 repeats performed for each population size. (C) Model training and tuning. We built the MicrobiomeCensus model using our T statistic and a maximum likelihood procedure. The training set consisted of 10,000 samples for population sizes ranging from 1–300, and 50% of the data were used to train and validate the model. Training and validation errors from different feature subsets are shown. Training errors are shown as red lines, and validation errors are shown as blue lines. (D) Model performance on simulation benchmark. After training and validation, the model utilized the top 120 abundance features. Model performance was tested on synthetic data generated from 550 different subjects not previously seen by the model. The training set consisted of 10,000 samples with population sizes from 1–300, and the testing set consisted of 10,000 repeats at the evaluated population sizes. The training error, testing error, and the error of the final model are shown. (E) Model performance evaluated using a testing set. Black solid dots indicate the means of the predicted values, and error bars indicate the standard deviations of the predicted values. (F) Application of the microbiome population model in sewage. Seventy-six composite samples (blue) were taken from three manholes on the MIT campus, and each sample was taken over 3 hours during the morning peak water usage hours. Twenty-five snapshot samples (grey) were taken using a peristaltic pump for 5 minutes at 1-hour intervals throughout a day.</p

An ideal sewage mixture simulation shows the potential of microbiome taxon abundance profiles as population census information sources.

(A) We generated an “ideal sewage mixture” consisting of gut microbiomes from different numbers of people. (B) Ranked abundance curves for gut microbiomes of one person and mixtures of multiple people exhibit different levels of dominance and diversity. Blue lines show the rank abundance curves in stool samples (one person), red lines show 10-person mixtures, and saffron lines show 100-person mixtures. In each scenario, ten examples are shown. All samples were rarefied to the same sequencing depths (4,000 seqs/sample). (C) The probability density function of the relative abundance of one taxon for different population sizes. OTU-2379, a Bifidobacterium taxon, was used as an example. Maroon dashed lines indicate the sample means. (D) Multiple taxa’s abundance variances in one-person samples and 100-person samples. The dominant taxa are shown (top100) and are sorted by their ranks in variance. (E) The ratios of the variances of one-person samples and 100-person samples across dominant gut microbial taxa.</p

<i>Prevotella copri</i> sub-species diversity in human gut microbiome samples (LifelinesDeep) and MIT sewage samples, as revealed by ten phylogenetic marker genes.

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