Sophie Weiss

Microbial community assembly and metabolic function during mammalian corpse decomposition

Decomposition spawns a microbial zoo The death of a large animal represents a food bonanza for microorganisms. Metcalf et al. monitored microbial activity during the decomposition of mouse and human cadavers. Regardless of soil type, season, or species, the microbial succession during decomposition was a predictable measure of time since death. An overlying corpse leaches nutrients that allow soil- and insect-associated fungi and bacteria to grow. These microorganisms are metabolic specialists that convert proteins and lipids into foul-smelling compounds such as cadaverine, putrescine, and ammonia, whose signature may persist in the soil long after a corpse has been removed. Science, this issue p. 158 As a corpse rots, the microbial succession follows a similar pattern across different types of soil. Vertebrate corpse decomposition provides an important stage in nutrient cycling in most terrestrial habitats, yet microbially mediated processes are poorly understood. Here we combine deep microbial community characterization, community-level metabolic reconstruction, and soil biogeochemical assessment to understand the principles governing microbial community assembly during decomposition of mouse and human corpses on different soil substrates. We find a suite of bacterial and fungal groups that contribute to nitrogen cycling and a reproducible network of decomposers that emerge on predictable time scales. Our results show that this decomposer community is derived primarily from bulk soil, but key decomposers are ubiquitous in low abundance. Soil type was not a dominant factor driving community development, and the process of decomposition is sufficiently reproducible to offer new opportunities for forensic investigations.

Dynamic changes in short- and long-term bacterial composition following fecal microbiota transplantation for recurrent Clostridium difficile infection

BackgroundFecal microbiota transplantation (FMT) is an effective treatment for recurrent Clostridium difficile infection (CDI) that often fails standard antibiotic therapy. Despite its widespread recent use, however, little is known about the stability of the fecal microbiota following FMT.ResultsHere we report on short- and long-term changes and provide kinetic visualization of fecal microbiota composition in patients with multiply recurrent CDI that were refractory to antibiotic therapy and treated using FMT. Fecal samples were collected from four patients before and up to 151 days after FMT, with daily collections until 28 days and weekly collections until 84 days post-FMT. The composition of fecal bacteria was characterized using high throughput 16S rRNA gene sequence analysis, compared to microbiota across body sites in the Human Microbiome Project (HMP) database, and visualized in a movie-like, kinetic format. FMT resulted in rapid normalization of bacterial fecal sample composition from a markedly dysbiotic state to one representative of normal fecal microbiota. While the microbiome appeared most similar to the donor implant material 1 day post-FMT, the composition diverged variably at later time points. The donor microbiota composition also varied over time. However, both post-FMT and donor samples remained within the larger cloud of fecal microbiota characterized as healthy by the HMP.ConclusionsDynamic behavior is an intrinsic property of normal fecal microbiota and should be accounted for in comparing microbial communities among normal individuals and those with disease states. This also suggests that more frequent sample analyses are needed in order to properly assess success of FMT procedures.

Correlation detection strategies in microbial data sets vary widely in sensitivity and precision

Disruption of healthy microbial communities has been linked to numerous diseases, yet microbial interactions are little understood. This is due in part to the large number of bacteria, and the much larger number of interactions (easily in the millions), making experimental investigation very difficult at best and necessitating the nascent field of computational exploration through microbial correlation networks. We benchmark the performance of eight correlation techniques on simulated and real data in response to challenges specific to microbiome studies: fractional sampling of ribosomal RNA sequences, uneven sampling depths, rare microbes and a high proportion of zero counts. Also tested is the ability to distinguish signals from noise, and detect a range of ecological and time-series relationships. Finally, we provide specific recommendations for correlation technique usage. Although some methods perform better than others, there is still considerable need for improvement in current techniques.

Toward Effective Probiotics for Autism and Other Neurodevelopmental Disorders

Hsaio and colleagues link gut microbes to autism spectrum disorders (ASD) in a mouse model. They show that ASD symptoms are triggered by compositional and structural shifts of microbes and associated metabolites, but symptoms are relieved by a Bacteroides fragilis probiotic. Thus probiotics may provide therapeutic strategies for neurodevelopmental disorders.

Tracking down the sources of experimental contamination in microbiome studies.

A recent report warns that DNA extraction kits and other laboratory reagents are considerable sources of contamination in microbiome experiments. The issue of contamination is particularly problematic for samples of low biomass.See related research, http://www.biomedcentral.com/1741-7007/12/87

Using "Omics" and Integrated Multi-Omics Approaches to Guide Probiotic Selection to Mitigate Chytridiomycosis and Other Emerging Infectious Diseases

Emerging infectious diseases in wildlife are responsible for massive population declines. In amphibians, chytridiomycosis caused by Batrachochytrium dendrobatidis, Bd, has severely affected many amphibian populations and species around the world. One promising management strategy is probiotic bioaugmentation of antifungal bacteria on amphibian skin. In vivo experimental trials using bioaugmentation strategies have had mixed results, and therefore a more informed strategy is needed to select successful probiotic candidates. Metagenomic, transcriptomic, and metabolomic methods, colloquially called “omics,” are approaches that can better inform probiotic selection and optimize selection protocols. The integration of multiple omic data using bioinformatic and statistical tools and in silico models that link bacterial community structure with bacterial defensive function can allow the identification of species involved in pathogen inhibition. We recommend using 16S rRNA gene amplicon sequencing and methods such as indicator species analysis, the Kolmogorov–Smirnov Measure, and co-occurrence networks to identify bacteria that are associated with pathogen resistance in field surveys and experimental trials. In addition to 16S amplicon sequencing, we recommend approaches that give insight into symbiont function such as shotgun metagenomics, metatranscriptomics, or metabolomics to maximize the probability of finding effective probiotic candidates, which can then be isolated in culture and tested in persistence and clinical trials. An effective mitigation strategy to ameliorate chytridiomycosis and other emerging infectious diseases is necessary; the advancement of omic methods and the integration of multiple omic data provide a promising avenue toward conservation of imperiled species.

Diversity of Rickettsiales in the Microbiome of the Lone Star Tick, Amblyomma americanum

ABSTRACT Ticks are important vectors for many emerging pathogens. However, they are also infected with many symbionts and commensals, often competing for the same niches. In this paper, we characterize the microbiome of Amblyomma americanum (Acari: Ixodidae), the lone star tick, in order to better understand the evolutionary relationships between pathogens and nonpathogens. Multitag pyrosequencing of prokaryotic 16S rRNA genes (16S rRNA) was performed on 20 lone star ticks (including males, females, and nymphs). Pyrosequencing of the rickettsial sca0 gene (also known as ompA or rompA) was performed on six ticks. Female ticks had less diverse microbiomes than males and nymphs, with greater population densities of Rickettsiales. The most common members of Rickettsiales were “Candidatus Rickettsia amblyommii” and “Candidatus Midichloria mitochondrii.” “Ca. Rickettsia amblyommii” was 2.6-fold more common in females than males, and there was no sequence diversity in the sca0 gene. These results are consistent with a predominantly vertical transmission pattern for “Ca. Rickettsia amblyommii.”

Changes in microbial ecology after fecal microbiota transplantation for recurrent C. difficile infection affected by underlying inflammatory bowel disease

BackgroundGut microbiota play a key role in maintaining homeostasis in the human gut. Alterations in the gut microbial ecosystem predispose to Clostridium difficile infection (CDI) and gut inflammatory disorders such as inflammatory bowel disease (IBD). Fecal microbiota transplantation (FMT) from a healthy donor can restore gut microbial diversity and pathogen colonization resistance; consequently, it is now being investigated for its ability to improve inflammatory gut conditions such as IBD. In this study, we investigated changes in gut microbiota following FMT in 38 patients with CDI with or without underlying IBD.ResultsThere was a significant change in gut microbial composition towards the donor microbiota and an overall increase in microbial diversity consistent with previous studies after FMT. FMT was successful in treating CDI using a diverse set of donors, and varying degrees of donor stool engraftment suggesting that donor type and degree of engraftment are not drivers of a successful FMT treatment of CDI. However, patients with underlying IBD experienced an increased number of CDI relapses (during a 24-month follow-up) and a decreased growth of new taxa, as compared to the subjects without IBD. Moreover, the need for IBD therapy did not change following FMT. These results underscore the importance of the existing gut microbial landscape as a decisive factor to successfully treat CDI and potentially for improvement of the underlying pathophysiology in IBD.ConclusionsFMT leads to a significant change in microbial diversity in patients with recurrent CDI and complete resolution of symptoms. Stool donor type (related or unrelated) and degree of engraftment are not the key for successful treatment of CDI by FMT. However, CDI patients with IBD have higher proportion of the original community after FMT and lack of improvement of their IBD symptoms and increased episodes of CDI on long-term follow-up.

Parallel Mapping of Antibiotic Resistance Alleles in Escherichia coli

Chemical genomics expands our understanding of microbial tolerance to inhibitory chemicals, but its scope is often limited by the throughput of genome-scale library construction and genotype-phenotype mapping. Here we report a method for rapid, parallel, and deep characterization of the response to antibiotics in Escherichia coli using a barcoded genome-scale library, next-generation sequencing, and streamlined bioinformatics software. The method provides quantitative growth data (over 200,000 measurements) and identifies contributing antimicrobial resistance and susceptibility alleles. Using multivariate analysis, we also find that subtle differences in the population responses resonate across multiple levels of functional hierarchy. Finally, we use machine learning to identify a unique allelic and proteomic fingerprint for each antibiotic. The method can be broadly applied to tolerance for any chemical from toxic metabolites to next-generation biofuels and antibiotics.

Genome-Wide Tuning of Protein Expression Levels to Rapidly Engineer Microbial Traits

The reliable engineering of biological systems requires quantitative mapping of predictable and context-independent expression over a broad range of protein expression levels. However, current techniques for modifying expression levels are cumbersome and are not amenable to high-throughput approaches. Here we present major improvements to current techniques through the design and construction of E. coli genome-wide libraries using synthetic DNA cassettes that can tune expression over a ∼10(4) range. The cassettes also contain molecular barcodes that are optimized for next-generation sequencing, enabling rapid and quantitative tracking of alleles that have the highest fitness advantage. We show these libraries can be used to determine which genes and expression levels confer greater fitness to E. coli under different growth conditions.