Engineering topics
Publications and source records attributed to Bowen, Benjamin.
Here, we present an approach that integrates mass difference based deconvolution with molecular networking to build a static reference network from all publicly available organic matter metabolomics datasets. This is accomplished using MS/MS deconvolution coupled with both recently reported (BUDDY) and novel machine learning algorithms to determine chemical formulas and perform MS/MS alignments (REM-BLINK).
Abstract Bridging molecular information to ecosystem-level processes would provide the capacity to understand system vulnerability and, potentially, a means for assessing ecosystem health. Here, we present an integrated dataset containing environmental and metagenomic information from plant-associated microbial communities, plant transcriptomics, plant and soil metabolomics, and soil chemistry and activity characterization measurements derived from the model tree species Populus trichocarpa . Soil, rhizosphere, root endosphere, and leaf samples were collected from 27 different P. trichocarpa genotypes grown in two different environments leading to an integrated dataset of 318 metagenomes, 98 plant transcriptomes, and 314 metabolomic profiles that are supported by diverse soil measurements. This expansive dataset will provide insights into causal linkages that relate genomic features and molecular level events to system-level properties and their environmental influences.
Sphaerulina musiva is an economically and ecologically important fungal pathogen that causes Septoria stem canker and leaf spot disease of Populus species. Here, to bridge the gap between genetic markers and structural barriers previously found to be linked to Septoria (Sphaerulina musiva) canker disease resistance in poplar, we employed hydrophilic interaction liquid chromatography (HILIC) and tandem mass spectrometry (MS/MS) to identify and quantify metabolites involved with signaling and cell wall re-modelling. Fluctuations in signaling molecules, organic acids, amino acids, sterols, phenolics, and saccharides in resistant and susceptible Populus trichocarpa inoculated with Sphaerulina musiva, were observed. The patterns of 222 metabolites in the resistant host implicates systemic acquired resistance (SAR), cell wall apposition, and lignin deposition as modes of resistance to this hemibiotrophic pathogen. This pattern is consistent with the expected response to the biotrophic phase of S. musiva colonization during the first 24 hours post-inoculation. The fungal pathogen metabolized key regulatory signals of SAR, other phenolics, and precursors of lignin biosynthesis which were depleted in the susceptible host. This is the first study to characterize metabolites associated with the response to initial colonization by S. musiva between resistant and susceptible hosts.
We introduce the Binned Large Interval Network Kernel approach for scoring fragmentation mass spectra similarity which takes advantage of sparse matrix multiplication. This enables BLINK to perform 100 million comparisons while maintaining 98% accuracy in 2.2 seconds making it over 1,000 times faster than the current implementations.
Dissolved exometabolites mediate algal interactions in aquatic ecosystems, but microalgal exometabolomes remain understudied. We conducted an untargeted metabolomic analysis of nonpolar exometabolites exuded from four phylogenetically and ecologically diverse eukaryotic microalgal strains grown in the laboratory, freshwater Chlamydomonas reinhardtii, brackish Desmodesmus sp., marine Phaeodactylum tricornutum, and marine Microchloropsis salina, to identify released metabolites based on relative enrichment in the exometabolomes compared to cell pellet metabolomes. Exudates from the different taxa were distinct, but we did not observe clear phylogenetic patterns. We used feature-based molecular networking to explore the identities of these metabolites, revealing several distinct di- and tripeptides secreted by each of the algae, lumichrome, a compound that is known to be involved in plant growth and bacterial quorum sensing, and novel prostaglandin-like compounds. We further investigated the impacts of exogenous additions of eight compounds selected based on exometabolome enrichment on algal growth. Of these compounds, five (lumichrome, 5'-S-methyl-5'-thioadenosine, 17-phenyl trinor prostaglandin A2, dodecanedioic acid, and aleuritic acid) impacted growth in at least one of the algal cultures. Two of these compounds (dodecanedioic acid and aleuritic acid) produced contrasting results, increasing growth in some algae and decreasing growth in others. Together, our results reveal new groups of microalgal exometabolites, some of which could alter algal growth when provided exogenously, suggesting potential roles in allelopathy and algal interactions.
Fabricated ecosystems (EcoFABs) offer an innovative approach to in situ examination of microbial establishment patterns around plant roots using nondestructive, high-resolution microscopy. Previously high-resolution imaging was challenging because the roots were not constrained to a fixed distance from the objective. Here, we describe a new ‘Imaging EcoFAB’ and the use of this device to image the entire root system of growing Brachypodium distachyon at high resolutions (20×, 40×) over a 3-week period. The device is capable of investigating root–microbe interactions of multimember communities. We examined nine strains of Pseudomonas simiae with different fluorescent constructs to B. distachyon and individual cells on root hairs were visible. Succession in the rhizosphere using two different strains of P. simiae was examined, where the second addition was shown to be able to establish in the root tissue. The device was suitable for imaging with different solid media at high magnification, allowing for the imaging of fungal establishment in the rhizosphere. Overall, the Imaging EcoFAB could improve our ability to investigate the spatiotemporal dynamics of the rhizosphere, including studies of fluorescently-tagged, multimember, synthetic communities.
Lignocellulosic biomass is composed of three major biopolymers: cellulose, hemicellulose and lignin. Analytical tools capable of quickly detecting both glycan and lignin deconstruction are needed to support the development and characterization of efficient enzymes/enzyme cocktails. Previously we have described nanostructure-initiator mass spectrometry-based assays for the analysis of glycosyl hydrolase and most recently an assay for lignin modifying enzymes. Here we integrate these two assays into a single multiplexed assay against both classes of enzymes and use it to characterize crude commercial enzyme mixtures. Application of our multiplexed platform based on nanostructure-initiator mass spectrometry enabled us to characterize crude mixtures of laccase enzymes from fungi Agaricus bisporus (Ab) and Myceliopthora thermophila (Mt) revealing activity on both carbohydrate and aromatic substrates. Using time-series analysis we determined that crude laccase from Ab has the higher GH activity and that laccase from Mt has the higher activity against our lignin model compound. Inhibitor studies showed a significant reduction in Mt GH activity under low oxygen conditions and increased activities in the presence of vanillin (common GH inhibitor). Ultimately, this assay can help to discover mixtures of enzymes that could be incorporated into biomass pretreatments to deconstruct diverse components of lignocellulosic biomass.
Advances in the prediction of biosynthetic gene clusters (BGCs) from genomic and metagenomic data have made the large-scale identification of candidate secondary metabolite biosynthesis pathways routine. However, most predicted BGCs cannot be successfully expressed in the limited range of expression hosts currently available, which prevents the identification and characterization of their respective products. Here we show that parallelized use of a phylogenetically and physiologically diverse panel of purpose-engineered chassis strains offers a versatile strategy for the rapid activation of BGCs. We domesticated an ensemble of 25 diverse ?-proteobacteria from 11 different genera by integrating a unified landing pad that enables efficient introduction of large BGCs into their genome. We deployed this chassis-independent recombinase-assisted genome engineering (CRAGE) strategy to express several previously posited but experimentally elusive non-ribosomal peptide synthase (NRPS) and NRPSpolyketide synthase (PKS) hybrid BGCs from Photorhabdus luminescens. Successful activation of 6 BGCs identified 21 products, including several with likely roles in host-microbe interactions. Taken together, we illustrate how the regulatory and physiological diversity naturally present across bacteria can be harnessed for the exploration of novel bioproducts and provide a sizable panel of domesticated chassis strains to enable this strategy.
Mass spectrometry (MS) is an analytical technique that enables all chemical species within a complex mixture to be resolved and identified based on their mass:charge ratio. MS is an increasingly important tool in life science research and development because it can resolve or separate small molecules, peptides, lipids, proteins, biopolymers and fragments of all of these structures. Unfortunately, the use of MS has been limited for lack of software tools to visualize, manipulate, store and transfer MS data. Ben Bowen, Oliver Ruebel and colleagues made a huge advance on the resolution of these limitations with the invention of "OpenMSI" (#2013-004), a computational method that makes the handling of these very large files (up to 50 Tb) much easier, especially for scientists with limited background in bioinformatics or sophisticated software applications of the type needed to handle such data files. With the OpenMSI Arrayed Analysis Tool (OMAAT, #2016-188) invention, the authors take MS data analysis one step further by providing tolls for automatic sample position optimization and ion selection. Even though sample deposition technologies have improved greatly, there is always the chance of encountering outliers in a predefined roster, especially in very large sample sets. Manual curation of the data would be extremely time consuming, so the inventors have included an algorithm that automatically finds the outliers. OMAAT is written in Python with accompanying Jupyter (formerly iPython) notebook and is fully integrated with OpenMSI.