Biology subjects

Alm, E. J.

Publications and source records attributed to Alm, E. J..

8 recordsLinked to original sources

bioRxiv2018

Interactions in self-assembled microbial communities saturate with diversity

How the diversity of organisms competing for or sharing resources influences community production is an important question in ecology but has rarely been explored in natural microbial communities. These generally contain large numbers of species making it difficult to disentangle how the effects of different interactions scale with diversity. Here, we show that changing diversity affects measures of community function in relatively simple communities but that increasing richness beyond a threshold has little detectable effect. We generated self-assembled communities with a wide range of diversity by growth of cells from serially diluted seawater on brown algal leachate. We subsequently isolated the most abundant taxa from these communities via dilution-to-extinction in order to compare productivity functions of the entire community to those of individual taxa. To parse the effect of different types of organismal interactions, we developed relative total function (RTF) as an index for positive or negative effects of diversity on community function. Our analysis identified three overall regimes with increasing diversity. At low richness (<12 taxa), potential positive and negative effects of interactions are both weak, while at moderate richness (12-20 taxa), community resource uptake increases but the carbon use efficiency decreases. Finally, beyond 20 taxa, there was no net change in community function indicating a saturation of potential interactions. These data suggest that although more diverse communities had overall greater access to resources, individual taxa within these communities had lower resource availability and reduced carbon use efficiency, indicating that competition due to niche overlap increases with diversity but that these interactions saturate at a specific threshold.

ecology↗

bioRxiv2018

Longitudinal immunosequencing in healthy people reveals persistent T cell receptors rich in public receptors

BackgroundThe adaptive immune system maintains a diversity of T cells capable of recognizing a broad array of antigens. Each T cells specificity and affinity for antigens is determined by its T cell receptors (TCRs), which together across all T cells form a repertoire of tens of millions of unique receptors in each individual. Although many studies have examined how TCR repertoires change in response to disease or drugs, few have explored the temporal dynamics of the TCR repertoire in healthy individuals.\n\nResultsHere we report immunosequencing of TCR {beta} chains (TCR{beta}) from the blood of three healthy individuals at eight time points over one year. TCR{beta} repertoires from samples of all T cells and memory T cells clearly clustered by individual, confirming that TCR{beta} repertoires are specific to individuals across time. This individuality was absent from TCR{beta}s from naive T cells, suggesting that these differences result from an individuals antigen exposure history. Many characteristics of the TCR{beta} repertoire (e.g., alpha diversity, clonality) were stable across time, although we found evidence of T cell expansion dynamics even within healthy individuals. We further identified a subset of \"persistent\" TCR{beta}s present across all time points, and these receptors were rich in clonal and public receptors.\n\nConclusionsOur results revealed persistent receptors that may play a key role in immune system maintenance. They further highlight the importance of longitudinal sampling of the immune system and provide a much-needed baseline for TCR{beta} dynamics in healthy individuals. Such a baseline should help improve interpretation of changes in the TCR{beta} repertoire during disease or treatment.

immunology↗

bioRxiv2017

Endospores and other lysis-resistant bacteria comprise a widely shared core community within the human microbiota

Endospore-formers in the human microbiota are well adapted for host-to-host transmission, and an emerging consensus points to their role in determining health and disease states in the gut. The human gut, more than any other environment, encourages the maintenance of endospore formation, with recent culture-based work suggesting that over 50% of genera in the microbiome carry genes attributed to this trait. However, there has been limited work on the ecological role of endospores and other stress-resistant cellular states in the human gut. In fact, there is no data to indicate whether organisms with the genetic potential to form endospores actually form endospores in situ and how sporulation varies across individuals and over time. Here, we applied a culture-independent protocol to enrich for endospores and other stress-resistant cells in human feces to identify variation in these states across people and within an individual over time. We see that cells with resistant states are more likely than those without to be shared among multiple individuals, which suggests that these resistant states are particularly adapted for cross-host dissemination. Furthermore, we use untargeted fecal metabolomics in 24 individuals and within a person over time to show that these organisms respond to shared environmental signals, and in particular, dietary fatty acids, that likely mediate colonization of recently disturbed human guts.

microbiology↗

bioRxiv2017

Comprehensive analysis of mobile genetic elements in the gut microbiome reveals a phylum-level niche-adaptive gene pool

Mobile genetic elements (MGEs) drive extensive horizontal transfer in the gut microbiome. This transfer could benefit human health by conferring new metabolic capabilities to commensal microbes, or it could threaten human health by spreading antibiotic resistance genes to pathogens. Despite their biological importance and medical relevance, MGEs from the gut microbiome have not been systematically characterized. Here, we present a comprehensive analysis of chromosomal MGEs in the gut microbiome using a method called Split Read Insertion Detection (SRID) that enables the identification of the exact mobilizable unit of MGEs. Leveraging the SRID method, we curated a database of 5600 putative MGEs encompassing seven MGE classes called ImmeDB (Intestinal microbiome mobile element database) (https://immedb.mit.edu/). We observed that many MGEs carry genes that confer an adaptive advantage to the gut environment including gene families involved in antibiotic resistance, bile salt detoxification, mucus degradation, capsular polysaccharide biosynthesis, polysaccharide utilization, and sporulation. We find that antibiotic resistance genes are more likely to be spread by conjugation via integrative conjugative elements or integrative mobilizable elements than transduction via prophages. Additionally, we observed that horizontal transfer of MGEs is extensive within phyla but rare across phyla. Taken together, our findings support a phylum level niche-adaptive gene pools in the gut microbiome. ImmeDB will be a valuable resource for future fundamental and translational studies on the gut microbiome and MGE communities.

bioinformatics↗

bioRxiv2017

Adaptive evolution within the gut microbiome of individual people

Individual bacterial lineages stably persist for years in the human gut microbiome1-3. However, the potential of these lineages to adapt during colonization of healthy people is not well understood2,4. Here, we assess evolution within individual microbiomes by sequencing the genomes of 602 Bacteroides fragilis isolates cultured from 12 healthy subjects. We find that B. fragilis within-subject populations contain substantial de novo nucleotide and mobile element diversity, which preserve years of within-person evolutionary history. This evolutionary history contains signatures of within-person adaptation to both subject-specific and common selective forces, including parallel mutations in sixteen genes. These sixteen genes are involved in cell-envelope biosynthesis and polysaccharide utilization, as well as yet under-characterized pathways. Notably, one of these genes has been shown to be critical for B. fragilis colonization in mice5, indicating that key genes have not already been optimized for survival in vivo. This lack of optimization, given historical signatures of purifying selection in these genes, suggests that varying selective forces with discordant solutions act upon B. fragilis in vivo. Remarkably, in one subject, two B. fragilis sublineages coexisted at a stable relative frequency over a 1.5-year period despite rapid adaptive dynamics within one of the sublineages. This stable coexistence suggests that competing selective forces can lead to B. fragilis niche-differentiation even within a single person. We conclude that B. fragilis adapts rapidly within the microbiomes of individual healthy people, providing a new route for the discovery of key genes in the microbiome and implications for microbiome stability and manipulation.

evolutionary biology↗

bioRxiv2017

Correcting for batch effects in case-control microbiome studies

High-throughput data generation platforms, like mass-spectrometry, microarrays, and second-generation sequencing are susceptible to batch effects due to run-to-run variation in reagents, equipment, protocols, or personnel. Currently, batch correction methods are not commonly applied to microbiome sequencing datasets. In this paper, we compare different batch-correction methods applied to microbiome case-control studies. We introduce a model-free normalization procedure where features (i.e. bacterial taxa) in case samples are converted to percentiles of the equivalent features in control samples within a study prior to pooling data across studies. We look at how this percentile-normalization method compares to traditional meta-analysis methods for combining independent p-values and to limma and ComBat, widely used batch-correction models developed for RNA microarray data. Overall, we show that percentile-normalization is a simple, non-parametric approach for correcting batch effects and improving sensitivity in case-control meta-analyses.\n\nAuthor SummaryBatch effects are obstacles to comparing results across studies. Traditional meta-analysis techniques for combining p-values from independent studies, like Fishers method, are effective but statistically conservative. If batch-effects can be corrected, then statistical tests can be performed on data pooled across studies, increasing sensitivity to detect differences between treatment groups. Here, we show how a simple, model-free approach corrects for batch effects in case-control microbiome datasets.

bioinformatics↗

bioRxiv2017

Random Sequences Rapidly Evolve Into De Novo Promoters

How do new promoters evolve? To follow evolution of de novo promoters, we put various random sequences upstream to the lac operon in Escherichia coli and evolved the cells in the presence of lactose. We found that a typical random sequence of ~100 bases requires only one mutation in order to enable growth on lactose by increasing resemblance to the canonical promoter motifs. We further found that ~10% of random sequences could serve as active promoters even without any period of evolutionary adaptation. Such a short mutational distance from a random sequence to an active promoter may improve evolvability yet may also lead to undesirable accidental expression. We found that across the E. coli genome accidental expression is minimized by avoiding codon combinations that resemble promoter motifs. Our results suggest that the promoter recognition machinery has been tuned to allow high accessibility to new promoters, and similar findings might also be observed in higher organisms or in other motif recognition machineries, like transcription factor binding sites or protein-protein interactions.

molecular biology↗

bioRxiv2017

Meta Analysis Of Microbiome Studies Identifies Shared And Disease-Specific Patterns

1Hundreds of clinical studies have been published that demonstrate associations between the human microbiome and a variety of diseases. Yet, fundamental questions remain on how we can generalize this knowledge. For example, if diseases are mainly characterized by a small number of pathogenic species, then new targeted antimicrobial therapies may be called for. Alternatively, if diseases are characterized by a lack of healthy commensal bacteria, then new probiotic therapies might be a better option. Results from individual studies, however, can be inconsistent or in conflict, and comparing published data is further complicated by the lack of standard processing and analysis methods.\n\nHere, we introduce the MicrobiomeHD database, which includes 29 published case-control gut microbiome studies spanning ten different diseases. Using standardized data processing and analyses, we perform a comprehensive crossdisease meta-analysis of these studies. We find consistent and specific patterns of disease-associated microbiome changes. A few diseases are associated with many individual bacterial associations, while most show only around 20 genus-level changes. Some diseases are marked by the presence of pathogenic microbes whereas others are characterized by a depletion of health-associated bacteria. Furthermore, over 60% of microbes associated with individual diseases fall into a set of \"core\" health and disease-associated microbes, which are associated with multiple disease states. This suggests a universal microbial response to disease.

microbiology↗