Biology subjects

Bowen, B. P.

Publications and source records attributed to Bowen, B. P..

6 recordsLinked to original sources

bioRxiv2018

Multi-lab EcoFAB study shows highly reproducible physiology and depletion of soil metabolites by a model grass

O_LIThere is a dynamic reciprocity between plants and their environment: On one hand, the physiochemical properties of soil influence plant morphology and metabolism, while on the other, root morphology and exudates shape the environment surrounding roots. Here, we investigate both of these aspects as well as the reproducibility of these responses across laboratories.\nC_LIO_LIThe model grass Brachypodium distachyon was grown in phosphate-sufficient and phosphate-deficient mineral media, as well as in sterile soil extract, within fabricated ecosystem (EcoFAB) devices across four laboratories.\nC_LIO_LITissue weight and phosphate content, total root length, root tissue and exudate metabolic profiles were found to be consistent across laboratories and distinct between experimental treatments. Plants grown in soil extract were morphologically and metabolically distinct in all laboratories, with root hairs four times longer compared to other growth conditions. Further, plants depleted half of the investigated metabolites from the soil extract.\nC_LIO_LITo interact with their environment, plants not only adapt morphology and release complex metabolite mixtures; they also selectively deplete a range of soil-derived metabolites. The EcoFABs utilized here generated high inter-laboratory reproducibility, demonstrating that their value in standardized investigations of plant traits.\nC_LI

plant biology↗

bioRxiv2018

Study of Oak Ridge soils using BONCAT-FACS-Seq reveals that a large fraction of the soil microbiome is active

The ability to link soil microbial diversity to soil processes requires technologies that differentiate active subpopulations of microbes from so-called relic DNA and dormant cells. Measures of microbial activity based on various techniques including DNA labelling have suggested that most cells in soils are inactive, a fact that has been difficult to reconcile with observed high levels of bulk soil activities. We hypothesized that measures of in situ DNA synthesis may be missing the soil microbes that are metabolically active but not replicating, and we therefore applied BONCAT (Bioorthogonal Non Canonical Amino Acid Tagging) i.e. a proxy for activity that does not rely on cell division, to measure translationally active cells in soils. We compared the active population of two soil depths from Oak Ridge (TN) incubated under the same conditions for up to seven days. Depending on the soil, a maximum of 25 - 70% of the cells were active, accounting for 3-4 million cells per gram of soil type, which is an order of magnitude higher than previous estimates. The BONCAT positive cell fraction was recovered by fluorescence activated cell sorting (FACS) and identified by 16S rDNA amplicon sequencing. The diversity of the active fraction was a selected subset of the bulk soil community. Excitingly, some of the same members of the community were recruited at both depths independently from their abundance rank. On average, 86% of sequence reads recovered from the active community shared >97% sequence similarity with cultured isolates from the field site. Our observations are in line with a recent report that, of the few taxa that are both abundant and ubiquitous in soil, 45% are also cultured - and indeed some of these ubiquitous microorganisms were found to be translationally active. The use of BONCAT on soil microbiomes provides evidence that a large portion of the soil microbes can be active simultaneously. We conclude that BONCAT coupled to FACS and sequencing is effective for interrogating the active fraction of soil microbiomes in situ and provides new perspectives to link metabolic capacity to overall soil ecological traits and processes.

microbiology↗

bioRxiv2018

Towards measuring growth rates of pathogens during infections by D2O-labeling lipidomics

RATIONALEMicrobial growth rate is an important physiological parameter that is challenging to measure in situ, partly because microbes grow slowly in many environments. Recently, it has been demonstrated that generation times of S. aureus in cystic fibrosis (CF) infections can be determined by D2O-labeling of actively synthesized fatty acids. To improve species specificity and allow growth rate monitoring for a greater range of pathogens during the treatment of infections, it is desirable to accurately quantify trace incorporation of deuterium into phospholipids.\n\nMETHODSLipid extracts of D2O-treated E. coli cultures were measured on LC-ESI-MS instruments equipped with TOF and Orbitrap mass analyzers, and used for comparison with the analysis of fatty acids by isotope-ratio GC-MS. We then develop an approach to enable tracking of lipid labeling, by following the transition from stationary into exponential growth in pure cultures. Lastly, we apply D2O-labeling lipidomics to clinical samples from CF patients with chronic lung infections.\n\nRESULTSLipidomics facilitates deuterium quantification in lipids at levels that are useful for many labeling applications (>0.03 at% D). In the E. coli cultures, labeling dynamics of phospholipids depend largely on their acyl chains and between phospholipids we notice differences that are not obvious from absolute concentrations alone. For example, cyclopropyl-containing lipids reflect the regulation of cyclopropane fatty acid synthase, which is predominantly expressed at the beginning of stationary phase. The deuterium incorporation into a lipid that is specific for S. aureus in CF sputum, indicates an average generation time of the pathogen on the order of one cell doubling per day.\n\nCONCLUSIONSThis study demonstrates how trace level measurement of stable isotopes in intact lipids can be used to quantify lipid metabolism in pure cultures and provides guidelines that enable growth rate measurements in microbiome samples after incubation with a low percentage of D2O.

biochemistry↗

bioRxiv2017

Deciphering microbial interactions in synthetic human gut microbiome communities

The human gut microbiota comprises a dynamic ecological system that contributes significantly to human health and disease. The ecological forces that govern community assembly and stability in the gut microbiota remain unresolved. We developed a generalizable model-guided framework to predict higher-order consortia from time-resolved measurements of lower-order assemblages. This method was employed to decipher microbial interactions in a diverse 12-member human gut microbiome synthetic community. We show that microbial growth parameters and pairwise interactions are the major drivers of multi-species community dynamics, as opposed to context-dependent (conditional) interactions. The inferred microbial interaction network as well as a top-down approach to community assembly pinpointed both ecological driver and responsive species that were significantly modulated by microbial inter-relationships. Our model demonstrated that negative pairwise interactions could generate history-dependent responses of initial species proportions on physiological timescales that frequently does not originate from bistability. The model elucidated a topology for robust coexistence in pairwise assemblages consisting of a negative feedback loop that balances disparities in monospecies fitness levels. Bayesian statistical methods were used to evaluate the constraint of model parameters by the experimental data. Measurements of extracellular metabolites illuminated the metabolic capabilities of monospecies and potential molecular basis for competitive and cooperative interactions in the community. However, these data failed to predict influential organisms shaping community assembly. In sum, these methods defined the ecological roles of key species shaping community assembly and illuminated network design principles of microbial communities.

systems biology↗

bioRxiv2017

MAGI: A Bayesian-like method for metabolite, annotation, and gene integration

Metabolomics is a widely used technology for obtaining direct measures of metabolic activities from diverse biological systems. However, ambiguous metabolite identifications are a common challenge and biochemical interpretation is often limited by incomplete and inaccurate genome-based predictions of enzyme activities (i.e. gene annotations). Metabolite, Annotation, and Gene Integration (MAGI) generates a metabolite-gene association score using a biochemical reaction network. This is calculated by a method that emphasizes consensus between metabolites and genes via biochemical reactions. To demonstrate the potential of this method, we applied MAGI to integrate sequence data and metabolomics data collected from Streptomyces coelicolor A3(2), an extensively characterized bacterium that produces diverse secondary metabolites. Our findings suggest that coupling metabolomics and genomics data by scoring consensus between the two increases the quality of both metabolite identifications and gene annotations in this organism. MAGI also made biochemical predictions for poorly annotated genes that were consistent with the extensive literature on this important organism. This limited analysis suggests that using metabolomics data has the potential to improve annotations in sequenced organisms and also provides testable hypotheses for specific biochemical functions. MAGI is freely available for academic use both as an online tool at https://magi.nersc.gov and with source code available at https://github.com/biorack/magi.

bioinformatics↗

bioRxiv2017

Linking soil biology and chemistry using bacterial isolate exometabolite profiles

Sequencing provides a window into microbial community structure and metabolic potential; however, linking these data to exogenous metabolites that microorganisms process and produce (the exometabolome) remains challenging. Previously, we observed strong exometabolite niche partitioning among bacterial isolates from biological soil crust (biocrust). Here we examine native biocrust to determine if these patterns are reproduced in the environment. Overall, most soil metabolites displayed the expected relationship (positive or negative correlation) with four dominant bacteria following a wetting event and across biocrust developmental stages. For metabolites that were previously found to be consumed by an isolate, 78% were negatively correlated with the abundance of in situ isolate phylotypes whereas for released metabolites, 73% were positively correlated. Our results demonstrate that metabolite profiling, sequencing and exometabolomics can be successfully integrated to functionally link metagenomes and microbial community structure with environmental chemistry.

microbiology↗