Toward a Census of Bacteria in Soil

For more than a century, microbiologists have sought to determine the species richness of bacteria in soil, but the extreme complexity and unknown structure of soil microbial communities have obscured the answer. We developed a statistical model that makes the problem of estimating richness statistically accessible by evaluating the characteristics of samples drawn from simulated communities with parametric community distributions. We identified simulated communities with rank-abundance distributions that followed a truncated lognormal distribution whose samples resembled the structure of 16S rRNA gene sequence collections made using Alaskan and Minnesotan soils. The simulated communities constructed based on the distribution of 16S rRNA gene sequences sampled from the Alaskan and Minnesotan soils had a richness of 5,000 and 2,000 operational taxonomic units (OTUs), respectively, where an OTU represents a collection of sequences not more than 3% distant from each other. To sample each of these OTUs in the Alaskan 16S rRNA gene library at least twice, 480,000 sequences would be required; however, to estimate the richness of the simulated communities using nonparametric richness estimators would require only 18,000 sequences. Quantifying the richness of complex environments such as soil is an important step in building an ecological framework. We have shown that generating sufficient sequence data to do so requires less sequencing effort than completely sequencing a bacterial genome

A Response Regulator from a Soil Metagenome Enhances Resistance to the β-lactam Antibiotic Carbenicillin in \u3cem\u3eEscherichia Coli\u3c/em\u3e

Functional metagenomic analysis of soil metagenomes is a method for uncovering as-yet unidentified mechanisms for antibiotic resistance. Here we report an unconventional mode by which a response regulator derived from a soil metagenome confers resistance to the β-lactam antibiotic carbenicillin in Escherichia coli. A recombinant clone (βlr16) harboring a 5,169 bp DNA insert was selected from a metagenomic library previously constructed from a remote Alaskan soil. The βlr16 clone conferred specific resistance to carbenicillin, with limited increases in resistance to other tested antibiotics, including other β-lactams (penicillins and cephalosporins), rifampin, ciprofloxacin, erythromycin, chloramphenicol, nalidixic acid, fusidic acid, and gentamicin. Resistance was more pronounced at 24°C than at 37°C. Zone-of-inhibition assays suggested that the mechanism of carbenicillin resistance was not due to antibiotic inactivation. The DNA insert did not encode any genes known to confer antibiotic resistance, but did have two putative open reading frames (ORFs) that were annotated as a metallopeptidase and a two-component response regulator. Transposon mutagenesis and subcloning of the two ORFs followed by phenotypic assays showed that the response regulator gene was necessary and sufficient to confer the resistance phenotype. Quantitative reverse transcriptase PCR showed that the response regulator suppressed expression of the ompF porin gene, independently of the small RNA regulator micF, and enhanced expression of the acrD, mdtA, and mdtB efflux pump genes. This work demonstrates that antibiotic resistance can be achieved by the modulation of gene regulation by heterologous DNA. Functional analyses such as these can be important for making discoveries in antibiotic resistance gene biology and ecology

Robustness of the Bacterial Community in the Cabbage White Butterfly Larval Midgut

Microbial communities typically vary in composition and structure over space and time. Little is known about the inherent characteristics of communities that govern various drivers of these changes, such as random variation, changes in response to perturbation, or susceptibility to invasion. In this study, we use 16S ribosomal RNA gene sequences to describe variation among bacterial communities in the midguts of cabbage white butterfly (Pieris rapae) larvae and examine the influence of community structure on susceptibility to invasion. We compared communities in larvae experiencing the same conditions at different times (temporal variation) or fed different diets (perturbation). The most highly represented phylum was Proteobacteria, which was present in all midgut communities. The observed species richness ranged from six to 15, and the most abundant members affiliated with the genera Methylobacteria, Asaia, Acinetobacter, Enterobacter, and Pantoea. Individual larvae subjected to the same conditions at the same time harbored communities that were highly similar in structure and membership, whereas the communities observed within larval populations changed with diet and over time. In addition, structural changes due to perturbation coincided with enhanced susceptibility to invasion by Enterobacter sp. NAB3R and Pantoea stewartii CWB600, suggesting that resistance to invasion is in part governed by community structure. These findings along with the observed conservation of membership at the phylum level, variation in structure and membership at lower taxonomic levels, and its relative simplicity make the cabbage white butterfly larval community an attractive model for studying community dynamics and robustness

High dimensional stochastic linear contextual bandit with missing covariates

Recent works in bandit problems adopted lasso convergence theory in the sequential decision-making setting. Even with fully observed contexts, there are technical challenges that hinder the application of existing lasso convergence theory: 1) proving the restricted eigenvalue condition under conditionally sub-Gaussian noise and 2) accounting for the dependence between the context variables and the chosen actions. This paper studies the effect of missing covariates on regret for stochastic linear bandit algorithms. Our work provides a high-probability upper bound on the regret incurred by the proposed algorithm in terms of covariate sampling probabilities, showing that the regret degrades due to missingness by at most $\zeta_{min}^2$, where $\zeta_{min}$ is the minimum probability of observing covariates in the context vector. We illustrate our algorithm for the practical application of experimental design for collecting gene expression data by a sequential selection of class discriminating DNA probes.Comment: Accepted in MLSP 202

A Graphical Model for Fusing Diverse Microbiome Data

This paper develops a Bayesian graphical model for fusing disparate types of count data. The motivating application is the study of bacterial communities from diverse high dimensional features, in this case transcripts, collected from different treatments. In such datasets, there are no explicit correspondences between the communities and each correspond to different factors, making data fusion challenging. We introduce a flexible multinomial-Gaussian generative model for jointly modeling such count data. This latent variable model jointly characterizes the observed data through a common multivariate Gaussian latent space that parameterizes the set of multinomial probabilities of the transcriptome counts. The covariance matrix of the latent variables induces a covariance matrix of co-dependencies between all the transcripts, effectively fusing multiple data sources. We present a computationally scalable variational Expectation-Maximization (EM) algorithm for inferring the latent variables and the parameters of the model. The inferred latent variables provide a common dimensionality reduction for visualizing the data and the inferred parameters provide a predictive posterior distribution. In addition to simulation studies that demonstrate the variational EM procedure, we apply our model to a bacterial microbiome dataset

Contributions of gut bacteria to Bacillus thuringiensis-induced mortality vary across a range of Lepidoptera

Abstract Background Gut microbiota contribute to the health of their hosts, and alterations in the composition of this microbiota can lead to disease. Previously, we demonstrated that indigenous gut bacteria were required for the insecticidal toxin of Bacillus thuringiensis to kill the gypsy moth, Lymantria dispar. B. thuringiensis and its associated insecticidal toxins are commonly used for the control of lepidopteran pests. A variety of factors associated with the insect host, B. thuringiensis strain, and environment affect the wide range of susceptibilities among Lepidoptera, but the interaction of gut bacteria with these factors is not understood. To assess the contribution of gut bacteria to B. thuringiensis susceptibility across a range of Lepidoptera we examined larval mortality of six species in the presence and absence of their indigenous gut bacteria. We then assessed the effect of feeding an enteric bacterium isolated from L. dispar on larval mortality following ingestion of B. thuringiensis toxin. Results Oral administration of antibiotics reduced larval mortality due to B. thuringiensis in five of six species tested. These included Vanessa cardui (L.), Manduca sexta (L.), Pieris rapae (L.) and Heliothis virescens (F.) treated with a formulation composed of B. thuringiensis cells and toxins (DiPel), and Lymantria dispar (L.) treated with a cell-free formulation of B. thuringiensis toxin (MVPII). Antibiotics eliminated populations of gut bacteria below detectable levels in each of the insects, with the exception of H. virescens, which did not have detectable gut bacteria prior to treatment. Oral administration of the Gram-negative Enterobacter sp. NAB3, an indigenous gut resident of L. dispar, restored larval mortality in all four of the species in which antibiotics both reduced susceptibility to B. thuringiensis and eliminated gut bacteria, but not in H. virescens. In contrast, ingestion of B. thuringiensis toxin (MVPII) following antibiotic treatment significantly increased mortality of Pectinophora gossypiella (Saunders), which was also the only species with detectable gut bacteria that lacked a Gram-negative component. Further, mortality of P. gossypiella larvae reared on diet amended with B. thuringiensis toxin and Enterobacter sp. NAB3 was generally faster than with B. thuringiensis toxin alone. Conclusion This study demonstrates that in some larval species, indigenous gut bacteria contribute to B. thuringiensis susceptibility. Moreover, the contribution of enteric bacteria to host mortality suggests that perturbations caused by toxin feeding induce otherwise benign gut bacteria to exert pathogenic effects. The interaction between B. thuringiensis and the gut microbiota of Lepidoptera may provide a useful model with which to identify the factors involved in such transitions.http://deepblue.lib.umich.edu/bitstream/2027.42/112871/1/12915_2008_Article_219.pd

Diverse antibiotic resistance genes in dairy cow manure

Application of manure from antibiotic-treated animals to crops facilitates the dissemination of antibiotic resistance determinants into the environment. However, our knowledge of the identity, diversity, and patterns of distribution of these antibiotic resistance determinants remains limited. We used a new combination of methods to examine the resistome of dairy cow manure, a common soil amendment. Metagenomic libraries constructed with DNA extracted from manure were screened for resistance to beta-lactams, phenicols, aminoglycosides, and tetracyclines. Functional screening of fosmid and small-insert libraries identified 80 different antibiotic resistance genes whose deduced protein sequences were on average 50 to 60% identical to sequences deposited in GenBank. The resistance genes were frequently found in clusters and originated from a taxonomically diverse set of species, suggesting that some microorganisms in manure harbor multiple resistance genes. Furthermore, amid the great genetic diversity in manure, we discovered a novel clade of chloramphenicol acetyltransferases. Our study combined functional metagenomics with third-generation sequencing to significantly extend the roster of functional antibiotic resistance genes found in animal gut bacteria, providing a particularly broad resource for understanding the origins and dispersal of antibiotic resistance genes in agriculture and clinical settings

Reducing STEM gender bias with VIDS (video interventions for diversity in STEM)

Gender biases contribute to the underrepresentation of women in STEM. In response, the scientific community has called for methods to reduce bias, but few validated interventions exist. Thus, an interdisciplinary group of researchers and filmmakers partnered to create VIDS (Video Interventions for Diversity in STEM), which are short videos that expose participants to empirical findings from published gender bias research in 1 of 3 conditions. One condition illustrated findings using narratives (compelling stories), and the second condition presented the same results using expert interviews (straightforward facts). A hybrid condition included both narrative and expert interview videos. Results of two experiments revealed that relative to controls, VIDS successfully reduced gender bias and increased awareness of gender bias, positive attitudes toward women in STEM, anger, empathy, and intentions to engage in behaviors that promote gender parity in STEM. The narratives were particularly impactful for emotions, while the expert interviews most strongly impacted awareness and attitudes. The hybrid condition reflected the strengths of both the narratives and expert interviews (though effects were sometimes slightly weaker than the other conditions). VIDS produced substantial immediate effects among both men and women in the general population and STEM faculty, and effects largely persisted at follow-up. (PsycINFO Database Record (c) 2018 APA, all rights reserved

Functional metagenomics reveals diverse beta-lactamases in a remote Alaskan soil

Despite the threat posed by antibiotic resistance in infectious bacteria, little is known about the diversity, distribution and origins of resistance genes, particularly among the as yet unculturable environmental bacteria. One potentially rich but largely unstudied environmental reservoir is soil. The complexity of its microbial community coupled with its high density of antibiotic-producing bacteria makes the soil a likely origin for diverse antibiotic resistance determinants. To investigate antibiotic resistance genes among uncultured bacteria in an undisturbed soil environment, we undertook a functional metagenomic analysis of a remote Alaskan soil. We report that this soil is a reservoir for b-lactamases that function in Escherichia coli, including divergent b-lactamases and the first bifunctional b-lactamase. Our findings suggest that even in the absence of selective pressure imposed by anthropogenic activity, the soil microbial community in an unpolluted site harbors unique and ancient b-lactam resistance determinants. Moreover, despite their evolutionary distance from previously known genes, the Alaskan b-lactamases confer resistance on E. coli without manipulating its gene expression machinery, demonstrating the potential for soil resistance genes to compromise human health, if transferred to pathogens