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Northen, T. R.

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

At least 37 records · Page 2Linked to original sources

Cysteine: an ancestral Cu binding ligand in green algae?

Growth of Chlamydomonas reinhardtii in zinc (Zn) limited medium leads to disruption of copper (Cu) homeostasis, resulting in up to 40-fold Cu over-accumulation relative to its typical Cu quota. We show that Chlamydomonas controls its Cu quota by balancing Cu import and export, which is disrupted in a Zn deficient cell, thus establishing a mechanistic connection between Cu and Zn homeostasis. Transcriptomics, proteomics and elemental profiling revealed that Zn-limited Chlamydomonas cells up-regulate a subset of genes encoding "first responder" proteins involved in sulfur (S) assimilation and consequently accumulate more intracellular S, which is incorporated into L-cysteine, {gamma}-glutamylcysteine and homocysteine. Most prominently, in the absence of Zn, free L-cysteine is increased ~80-fold, corresponding to ~ 2.8 x 109 molecules/cell. Interestingly, classic S-containing metal binding ligands like glutathione and phytochelatins do not increase. X-ray fluorescence microscopy showed foci of S accumulation in Zn-limited cells that co-localize with Cu, phosphorus and calcium, consistent with Cu-thiol complexes in the acidocalcisome, the site of Cu(I) accumulation. Notably, cells that have been previously starved for Cu do not accumulate S or Cys, causally connecting cysteine synthesis with Cu accumulation. We suggest that cysteine is an in vivo Cu(I) ligand, perhaps ancestral, that buffers cytosolic Cu.

biochemistry↗

Reproducible growth of Brachypodium distachyon in fabricated ecosystems (EcoFAB 2.0) reveals that nitrogen form and starvation modulate root exudation

Understanding plant-microbe interactions requires examination of root exudation under nutrient stress using standardized and reproducible experimental systems. We grew Brachypodium distachyon hydroponically in novel fabricated ecosystem devices (EcoFAB 2.0) under three inorganic nitrogen forms (NO3-, NH4+, NH4NO3), followed by nitrogen starvation. Analyses of exudates with LC-MS/MS, biomass, medium pH, and nitrogen uptake showed EcoFAB 2.0s low intra-treatment data variability. Furthermore, the three inorganic nitrogen forms caused differential exudation, generalized by abundant amino acids/peptides and alkaloids. Comparatively, N-deficiency decreased N-containing compounds but increased shikimates/phenylpropanoids. Subsequent bioassays with two shikimates/phenylpropanoids (shikimic and p-coumaric acids) on the rhizobacterium Pseudomonas putida or Brachypodium seedlings revealed that shikimic acid promoted bacterial and root growth, while p-coumaric acid stunted seedlings. Our results suggest: (i) Brachypodium alters exudation in response to nitrogen status, which can affect rhizobacterial growth; and (ii) EcoFAB 2.0 is a valuable standardized plant research tool. TeaserEcoFAB 2.0, a novel fabricated ecosystem device, has low data variability in studies of plant traits.

plant biology↗

Fine scale sampling reveals spatial heterogeneity of rhizosphere microbiome in young Brachypodium plants

For a deeper and comprehensive understanding of the diversity, composition and function of rhizosphere microbiomes, we need to focus at the scale of individual roots in standardized growth containers. Root exudation patterns are known to vary across distinct parts of the root giving rise to spatially distinct microbial niches. To address this, we analyzed microbial community from two spatially distinct zones of the primary root (the tip vs. the base) in Brachypodium distachyon, grown in natural soil using standardized fabricated ecosystems known as EcoFABs as well as in more conventional pot and tubes. 16S rRNA based community analysis showed a stronger rhizosphere effect in the root base vs. bulk soil compared to the root tips vs. bulk soil, resulting in an enrichment of Actinobacteria, Bacteroidetes, Firmicutes and Proteobacteria, few OTUs belonging to less characterized lineages such as Verrucomicrobia and Acidobacteria. While the microbial community distributions are similar across growth containers, the EcoFAB displayed higher replicate reproducibility. Genome-resolved and bulk metagenomics revealed that genes associated with transcriptional regulation, transport of nutrients and catabolic enzymes indicating active metabolism, biofilm formation and root colonization were enriched in root tips. On the other hand, genes associated with nutrient-limitation and environmental stress were prominent in the bulk soil compared to the root tips, implying the presence of easily available, labile carbon and nutrients in the rhizosphere relative to bulk soil. Such insights into the relationships between root structure, exudation and microbial communities are critical for developing understanding of plant-microbe interactions.

ecology↗

Extensive plant use of exometabolites

Root exudation has been extensively studied due to its importance in soil carbon cycling and in supporting growth of soil microbes. However, the extent and dynamics of plant uptake of exogenous metabolites is poorly understood. To gain new insights into these processes we used 13C-tracing to characterize plant uptake of exometabolites across a panel of diverse plant species (Arabidopsis thaliana, Brachypodium distachyon, Lotus japonicus, Panicum virgatum, and Kalanchoe fedtschenkoi) grown in sterile hydroponic cultures. The uptake of exometabolites accounted for 23% of the overall B. distachyon carbon budget, and we identified 33 metabolites that were taken up by plants. Counterintuitively, many metabolites had higher uptake rates during the day vs. night. Thirteen of the metabolites from root exudates were found to promote root growth in A. thaliana, including hydroxybenzoate, threonate, N-acetyl-glucosamine, and uracil. Together these results indicate that the root uptake of organics can account for a significant portion of the plant carbon budget and that exogenous small molecules used by plants alter root growth with implications for plant nutrition, organic farming, soil nutrient cycling, and rhizosphere community dynamics.

plant biology↗

Dynamic Phaeodactylum tricornutum Exometabolites Shape Surrounding Bacterial Communities

The roles of exometabolites in mediating algal-bacterial interactions and regulating microbial community composition are not well understood. Here, we identified specific exometabolites from the model diatom Phaeodactylum tricornutum affecting abundance of specific bacterial taxa in isolation and in a community setting. We examined the response of a P. tricornutum-adapted enrichment community and found that both algal exudates and algal presence drove similar changes in community composition compared to controls. Using LC-MS/MS, we identified 50 metabolites produced by axenic P. tricornutum and found that different exometabolites accumulated during different algal growth phases. Profiling growth of 12 bacterial isolates representative of the enrichment community uncovered two algal exometabolites (out of 12 tested) which supported growth of a subset of isolates as a primary carbon source. We compared enrichment community response with and without the addition of two contrasting metabolites: 4-hydroxybenzoic acid, which supported isolate growth, and lumichrome, which did not. Exogenous metabolite additions did promote increased abundances of taxa that were able to utilize the metabolite in the isolate study, but also revealed the importance of factors relating to algal presence in regulating community composition. Collectively, this work demonstrates the influence of specific algal exometabolites in driving microbial community composition.

microbiology↗

Assessing Comparative Microbiome Performance in Plant Cell Wall Deconstruction Using Multi-omics-Informed Network Analysis

Plant cell walls are interwoven structures recalcitrant to degradation. Both native and adapted microbiomes are particularly effective at plant cell wall deconstruction. Studying these deconstructive microbiomes provides an opportunity to assess microbiome performance and relate it to specific microbial populations and enzymes. To establish a system assessing comparative microbiome performance, parallel microbiomes were cultivated on sorghum (Sorghum bicolor L. Moench) from compost inocula. Biomass loss and biochemical assays indicated that these microbiomes diverged in their ability to deconstruct biomass. Network reconstructions from time-dependent gene expression identified key deconstructive groups within the adapted sorghum-degrading communities, including Actinotalea, Filomicrobium, and Gemmanimonadetes populations. Functional analysis of gene expression demonstrated that the microbiomes proceeded through successional stages that are linked to enzymes that deconstruct plant cell wall polymers. This combination of network and functional analysis highlighted the importance of celluloseactive Actinobacteria in differentiating the performance of these microbiomes.

microbiology↗

Biofilm Interaction Mapping and Analysis (BIMA): A tool for deconstructing interspecific interactions in co-culture biofilms

Pseudomonas species are ubiquitous in nature and include numerous medically, agriculturally and technologically beneficial strains of which the interspecific interactions are of great interest for biotechnologies. Specifically, co-cultures containing Pseudomonas stutzeri have been used for bioremediation, biocontrol, aquaculture management and wastewater denitrification. Furthermore, the use of P. stutzeri biofilms, in combination with consortia based approaches, may offer advantages for these processes. Understanding the interspecific interaction within biofilm co-cultures or consortia provides a means for improvement of current technologies. However, the investigation of biofilm based consortia has been limited. We present an adaptable and scalable method for the analysis of macroscopic interactions (colony morphology, inhibition and invasion) between colony forming bacterial strains using an automated printing method followed by analysis of the genes and metabolites involved in the interactions. Using Biofilm Interaction Mapping and Analysis (BIMA), these interactions were investigated between P. stutzeri strain RCH2, a denitrifier isolated from chromium (VI) contaminated soil, and thirteen other species of pseudomonas isolated from non-contaminated soil. The metabolites and genes associated with both active co-culture growth and inhibitory growth were investigated using mass spectrometry based metabolomics and mutant fitness profiling of a DNA-barcoded mutant library. One interaction partner, Pseudomonas fluorescens N1B4 was selected for mutant fitness profiling; with this approach four genes of importance were identified and the effects on interactions were evaluated with deletion mutants and metabolomics. IMPORTANCEThe Biofilm Interaction Mapping and Analysis (BIMA) methodology provides a way to rapidly screen for positive and negative interspecific interactions, followed by an analysis of the genes and metabolites that may be involved. Knowledge of these may offer opportunities for engineered strains with improved function in biotechnology systems. P. stutzeri, an organism with wide-spread utilization in consortia based biotechnologies, was used to demonstrate the utility of this approach. Where little is known about the factors influencing biofilm based interactions, elucidation of the genes and metabolites involved allows for better control of the system for improved function or yield.

microbiology↗

A defined medium based on R2A for cultivation and exometabolite profiling of soil bacteria

Exometabolomics is an approach to assess how microorganisms alter their environments through the depletion and secretion of chemical compounds. Comparisons of inoculated with uninoculated media can be used to provide direct biochemical observations on depleted and secreted metabolites which can be used to predict resource competition, cross-feeding and secondary metabolite production in microbial isolates and communities. This approach is most powerful when used with defined media that enable tracking of all depleted metabolites. However, microbial growth media have traditionally been developed for the isolation and growth of microorganisms but not metabolite utilization profiling through LC-MS/MS. Here, we describe the construction of a defined medium, the Northen Lab Defined Medium (NLDM), that not only supports the growth of diverse bacteria but is defined and therefore suited for exometabolomic experiments. Metabolites included in NLDM were selected based on their presence in R2A medium and soil, elemental stoichiometry requirements, as well as knowledge of metabolite usage by different bacteria. We found that NLDM supported the growth of 53 phylogenetically diverse soil bacterial isolates and all of its metabolites were trackable through LC-MS/MS analysis. These results demonstrate the viability and utility of the constructed NLDM medium for cultivating and characterizing diverse microbial isolates and communities. Originality-Significance StatementWe build a defined medium based on the metabolite composition of R2A medium and soil, elemental stoichiometry requirements, and knowledge of metabolite usage by different bacteria. The newly formulated defined medium was evaluated on its ability to support the growth of soil isolates and its application for metabolite utilization profiling. We found that of 53 phylogenetically diverse soil bacterial isolates grew on the defined medium and all of its metabolites were trackable through LC-MS/MS analysis. This demonstrates the viability and utility of the constructed defined medium for cultivating and characterizing diverse microbial isolates and communities.

microbiology↗

Conservation of isolate substrate preferences in mixed communities revealed through ribosomal marker protein profiling

Assessment of structure-function relationships is a central theme in microbial ecology. However, the degree that isolate metabolic activities are conserved in communities remains unclear. This is because tracking population dynamics and substrate partitioning in microbial communities remains technically challenging. Here, we describe the application of a mass spectrometry-based ribosomal marker protein profiling with stable isotope probing approach that allows for concurrent monitoring of community structure dynamics and resource assimilation within a five-member synthetic soil bacterial community. Using this approach, we find that isolate substrate preferences for glutamine and phenylalanine are largely conserved in the community and can be predicted using a weighted-sum model. However, time-series monitoring revealed a significant delay in phenylalanine incorporation by two of the strains, as well as enhanced growth for Variovorax paradoxus presumably due to interspecies interactions. The unique utility of this approach to temporally probe resource incorporation and community structure enables deciphering the dynamic interactions occurring within the community. Extension of this approach to other communities under various environmental perturbations is needed to reveal the generality of microbial conservation of substrate preferences.

microbiology↗

GNPS Dashboard: Collaborative Analysis of Mass Spectrometry Data in the Web Browser

Access to web-based platforms has enabled scientists to perform research remotely. A critical aspect of mass spectrometry data analysis is the inspection, analysis, and visualization of the raw data to validate data quality and confirm statistical observations. We developed the GNPS Dashboard, a web-based data visualization tool, to facilitate synchronous collaborative inspection, visualization, and analysis of private and public mass spectrometry data remotely.

bioinformatics↗

SIMILE enables alignment of fragmentation mass spectra with statistical significance

Interrelating compounds according to their aligned fragmentation spectra is central to tandem mass spectrometry-based metabolomics. However, current alignment algorithms do not provide statistical significance and compounds that have multiple delocalized structural differences often fail to have their fragment ions aligned. Significant Interrelation of MS/MS Ions via Laplacian Embedding (SIMILE) is a new tool inspired by protein sequence alignment for aligning fragmentation spectra with statistical significance and allowance for multiple chemical differences. We found SIMILE yields 550% more pairs of structurally similar compounds than commonly used cosine-based scoring algorithms, and anticipate SIMILE will fill an important role by also providing p-values for fragmentation spectra alignments to explore structural relationships between compounds.

bioinformatics↗

Long-read metagenomics of soil communities reveals phylum-specific secondary metabolite dynamics

Microbial biosynthetic gene clusters (BGCs) encoding secondary metabolites are thought to impact a plethora of biologically mediated environmental processes, yet their discovery and functional characterization in natural microbiomes remains challenging. Here we describe deep long-read sequencing and assembly of metagenomes from biological soil crusts, a group of soil communities that are rich in BGCs. Taking advantage of the unusually long assemblies produced by this approach, we recovered nearly 3,000 BGCs for analysis, including 695 novel, full-length BGCs. Functional exploration through metatranscriptome analysis of a 3-day wetting experiment uncovered phylum-specific BGC expression upon activation from dormancy, elucidating distinct roles and complex phylogenetic and temporal dynamics in wetting processes. For example, a pronounced increase in BGC transcription occurs at night in cyanobacteria but not in other phyla, implicating BGCs in nutrient scavenging roles and niche competition. Taken together, our results demonstrate that long-read metagenomic sequencing combined with metatranscriptomic analysis provides a direct view into the functional dynamics of BGCs in environmental processes and suggests a central role of secondary metabolites in maintaining phylogenetically conserved niches within biocrusts.

genomics↗

Physiological adaptations of leaf litter microbial communities to long-term drought

Drought represents a significant stress to soil microorganisms and is known to reduce microbial activity and organic matter decomposition in Mediterranean ecosystems. However, we still lack a detailed understanding of the drought stress adaptations of microbial decomposers. We hypothesised that drought causes greater microbial allocation to stress tolerance relative to growth pathways. Here we present metatranscriptomic and metabolomic data on the physiological response of in situ microbial communities on plant leaf litter to long-term drought and pulse wetting in Californian grass and shrub ecosystems. Wetting litter after a long dry summer caused only subtle shifts in gene expression. On grass litter, communities from the decade-long ambient and reduced precipitation treatments had distinct functional profiles. The most discernable physiological adaptations to drought were production or uptake of compatible solutes to maintain cellular osmotic balance, and synthesis of capsular and extracellular polymeric substances as a mechanism to retain water. The results show a clear functional response to drought in grass litter communities with greater allocation to survival relative to growth that could affect decomposition under drought. In contrast, communities on chemically more diverse and complex shrub litter had smaller physiological differences in response to long-term drought but higher investment in resource acquisition traits across treatments, suggesting that the functional response to drought is constrained by substrate quality. Our findings suggest, for the first time in a field setting, a trade-off between microbial drought stress tolerance, resource acquisition and growth traits in leaf litter microbial communities.

microbiology↗

A New Method to Correct for Habitat Filtering in Microbial Correlation Networks

Amplicon sequencing of 16S, ITS, and 18S regions of microbial genomes is a commonly used first step toward understanding microbial communities of interest for human health, agriculture, and the environment. Correlation network analysis is an emerging tool for investigating the interactions within these microbial communities. However, when data from different habitats (e.g sampling sites, host genotype, etc.) are combined into one analysis, habitat filtering (co-occurrence of microbes due to habitat sampled rather than biological interactions) can induce apparent correlations, resulting in a network dominated by habitat effects and masking correlations of biological interest. We developed an algorithm to correct for habitat filtering effects in microbial correlation network analysis in order to reveal the true underlying microbial correlations. This algorithm was tested on simulated data that was constructed to exhibit habitat filtering. Our algorithm significantly improved correlation detection accuracy for these data compared to Spearman and Pearson correlations. We then used our algorithm to analyze a real data set of 16S-V4 amplicon sequences that was expected to exhibit habitat filtering. Our algorithm was found to effectively reduce habitat effects, enabling the construction of consensus correlation networks from data sets combining multiple related sample habitats.

bioinformatics↗

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↗

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↗

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↗

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↗