Biology subjects

Northen, T. R.

Publications and source records attributed to Northen, T. R..

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

Enzyme promiscuity shapes evolutionary innovation and optimization

Evidence suggests that novel enzyme functions evolved from low-level promiscuous activities in ancestral enzymes. Yet, the evolutionary dynamics and physiological mechanisms of how such side activities contribute to systems-level adaptations are poorly understood. Furthermore, it remains untested whether knowledge of an organisms promiscuous reaction set ( underground metabolism) can aid in forecasting the genetic basis of metabolic adaptations. Here, we employ a computational model of underground metabolism and laboratory evolution experiments to examine the role of enzyme promiscuity in the acquisition and optimization of growth on predicted non-native substrates in E. coli K-12 MG1655. After as few as 20 generations, the evolving populations repeatedly acquired the capacity to grow on five predicted novel substrates-D-lyxose, D-2-deoxyribose, D-arabinose, m-tartrate, and monomethyl succinate-none of which could support growth in wild-type cells. Promiscuous enzyme activities played key roles in multiple phases of adaptation. Altered promiscuous activities not only established novel high-efficiency pathways, but also suppressed undesirable metabolic routes. Further, structural mutations shifted enzyme substrate turnover rates towards the new substrate while retaining a preference for the primary substrate. Finally, genes underlying the phenotypic innovations were accurately predicted by genome-scale model simulations of metabolism with enzyme promiscuity. Computational approaches will be essential to synthesize the complex role of promiscuous activities in applied biotechnology and in models of evolutionary adaptation.

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

Construction of soil defined media using quantitative exometabolomic analysis of soil metabolites

Exometabolomics enables analysis of metabolite utilization of low molecular weight organic substances by soil isolates. Environmentally-based defined media are needed to examine ecologically relevant patterns of substrate utilization. Here, we describe an approach for the construction of defined media using untargeted characterization of water soluble soil metabolites. To broadly characterize soil metabolites, both liquid chromatography mass spectrometry (LC/MS) and gas chromatography mass spectrometry (GC/MS) were used. With this approach, 96 metabolites were identified, including amino acids, amino acid derivatives, sugars, sugar alcohols, mono- and di-carboxylic acids, osmolytes, nucleobases, and nucleosides. From this pool of metabolites, 25 were quantified. Water soluble organic carbon was fractionated by molecular weight and measured to determine the fraction of carbon accounted for by the quantified metabolites. This revealed that, community structures, these soil metabolites have an uneven quantitative distribution, with a single metabolite, trehalose accounting for 9.9 percent of much like soil microbial the (< 1 kDa) water extractable organic carbon. This quantitative information was used to formulate two soil defined media (SDM), one containing 23 metabolites (SDM1) and one containing 46 (SDM2). To evaluate SDM for supporting the growth of bacteria found at this field site, we examined the growth of 30 phylogenetically diverse soil isolates obtained using standard R2A medium. The simpler SDM1 supported the growth of up to 13 isolates while the more complex SDM2 supported up to 25 isolates. One isolate, Pseudomonas corrugata strain FW300-N2E2 was selected for a time-series exometabolomics analysis to investigate SDM1 substrate preferences. Interestingly, it was found that this organism preferred lower-abundance substrates such as guanine, glycine, proline and arginine and glucose and did not utilize the more abundant substrates maltose, mannitol, trehalose and uridine. These results demonstrate the viability and utility of using exometabolomics to construct a tractable environmentally relevant media. We anticipate that this approach can be expanded to other environments to enhance isolation and characterization of diverse microbial communities.\n\nHighlights O_LILC/MS and GC/MS analyses of soil extracts revealed a diversity of 96 metabolites.\nC_LIO_LISoil defined media were constructed based on water extractable soil metabolomics data.\nC_LIO_LIThe defined media supported the growth of 25 out of 30 bacterial isolates.\nC_LIO_LIExometabolomics demonstrated preferential consumption of amino acids for one isolate.\nC_LIO_LIThese media can be used to understand environmentally relevant microbial substrate preferences.\nC_LI\n\nAbbreviationsDOM dissolved organic matter; SOM soil organic matter; WEOC water extractable organic carbon; LMWOS low molecular weight organic substances; SDM(1/2) soil defined media (1 and 2); R2A Reasoners 2A agar medium; ORFRC Oak Ridge Field Research Center; LC/MS liquid chromatography mass spectrometry; GC/MS gas chromatography/ mass spectrometry; TOC total organic carbon; HILIC hydrophilic interaction liquid chromatography

microbiology↗

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↗