Plants deploy both primary and species-specific, specialized metabolites to communicate with other organisms and adapt to environmental challenges, including interactions with soil-dwelling microbial communities. However, the role of specialized metabolites in modulating plant-microbiome interactions often remains elusive. In this study, we report that maize (Zea mays) diterpenoid metabolites with known antifungal bioactivities also influence rhizosphere bacterial communities. Metabolite profiling showed that dolabralexins, antibiotic diterpenoids that are highly abundant in roots of some maize varieties, can be exuded from the roots. Comparative 16S rRNA gene sequencing determined the bacterial community composition of the maize mutant Zman2 (anther ear 2), which is deficient in dolabralexins and closely related bioactive kauralexin diterpenoids. The Zman2 rhizosphere microbiome differed significantly from the wild-type sibling with the most significant changes observed for Alphaproteobacteria of the order Sphingomonadales. Metabolomics analyses support that these differences are attributed to the diterpenoid deficiency of the Zman2 mutant, rather than other large-scale metabolome alterations. Together, these findings support physiological functions of maize diterpenoids beyond known chemical defenses, including the assembly of the rhizosphere microbiome.

The advent of culture independent approaches has greatly facilitated insights into the vast diversity of bacteria and the ecological importance they hold in nature and human health. Recently, metagenomic surveys and other culture-independent methods have begun to describe the distribution and diversity of microbial metabolism across environmental conditions, often using 16S rRNA gene as a marker to group bacteria into taxonomic units. However, the extent to which similarity at the conserved ribosomal 16S gene correlates with different measures of phylogeny, metabolic diversity, and ecologically relevant gene content remains contentious. Here, we examine the relationship between 16S identity, core genome divergence, and metabolic gene content across the ancient and ecologically important genus Streptomyces. We assessed and quantified the high variability of average nucleotide identity (ANI) and ortholog presence/absence within Streptomyces, even in strains identical by 16S. Furthermore, we identified key differences in shared ecologically important characters, such as antibiotic resistance, carbohydrate metabolism, biosynthetic gene clusters (BGCs), and other metabolic hallmarks, within 16S identities commonly treated as the same operational taxonomic units (OTUs). Differences between common phylogenetic measures and metabolite-gene annotations confirmed this incongruence. Our results highlight the metabolic diversity and variability within OTUs and add to the growing body of work suggesting 16S-based studies of Streptomyces fail to resolve important ecological and metabolic characteristics.

The advent and growth of synthetic biology has demonstrated its potential as a promising avenue of research to address many societal needs. However, plant synthetic biology efforts have been hampered by a dearth of DNA part libraries, versatile transformation vectors and efficient assembly strategies. Here, we describe a versatile system (named jStack) utilizing yeast homologous recombination to efficiently assemble DNA into plant transformation vectors. We demonstrate how this method can facilitate pathway engineering of molecules of pharmaceutical interest, production of potential biofuels and shuffling of disease-resistance traits between crop species. Our approach provides a powerful alternative to conventional strategies for stacking genes and traits to address many impending environmental and agricultural challenges.

Solar panels can be found practically all over the world and represent a standard surface that can be colonized by microbial communities that are resistant to harsh environmental conditions, including high irradiation, temperature fluctuations and desiccation. These properties make them not only ideal sources of stress-resistant bacteria, but also standard devices to study the microbial communities and their colonization process from different areas of Earth. We report here a comprehensive description of the microbial communities associated with solar panels in Berkeley, CA, United States. Cultivable bacteria were isolated to characterize their adhesive capabilities, and UV- and desiccation-resistance properties. Furthermore, a parallel culture-independent metagenomic and metabolomic approach has allowed us to gain insight on the taxonomic and functional nature of these communities. Metagenomic analysis was performed using the Illumina HiSeq2500 sequencing platform, revealing that the bacterial population of the Berkeley solar panels is composed mainly of Actinobacteria, Bacteroidetes and Proteobacteria, as well as lower amounts of Deinococcus-Thermus and Firmicutes. Furthermore, a clear predominance of Hymenobacter sp. was also observed. A functional analysis revealed that pathways involved in the persistence of microbes on solar panels (i.e., stress response, capsule development, and metabolite repair) and genes assigned to carotenoid biosynthesis were common to all metagenomes. On the other hand, genes involved in photosynthetic pathways and general autotrophic subsystems were rare, suggesting that these pathways are not critical for persistence on solar panels. Metabolomics was performed using a liquid chromatography tandem mass spectrometry (LC-MS/MS) approach. When comparing the metabolome of the solar panels from Berkeley and from Valencia (Spain), a very similar composition in polar metabolites could be observed, although some metabolites appeared to be differentially represented (for example, trigonelline, pantolactone and 5-valerolactone were more abundant in the samples from Valencia than in the ones from Berkeley). Furthermore, triglyceride metabolites were highly abundant in all the solar panel samples, and both locations displayed similar profiles. The comparison of the taxonomic profile of the Californian solar panels with those previously described in Spain revealed striking similarities, highlighting the central role of both selective pressures and the ubiquity of microbial populations in the colonization and establishment of microbial communities.

Tissues are composed of diverse cell subpopulations each with distinct metabolic characteristics that influence overall behavior. Unfortunately, traditional histopathology imaging techniques are 'blind' to the spatially ordered metabolic dynamics within tissue. While mass spectrometry imaging enables spatial mapping of molecular composition, resulting images are only a static snapshot in time of molecules involved in highly dynamic processes; kinetic information of flux through metabolic pathways is lacking. To address this limitation, we developed kinetic mass spectrometry imaging (kMSI), a novel technique integrating soft desorption/ionization mass spectrometry with clinically accepted in vivo metabolic labeling of tissue with deuterium to generate images of kinetic information of biological processes. Applied to a tumor, kMSI revealed heterogeneous spatial distributions of newly synthesized versus pre-existing lipids, with altered lipid synthesis patterns distinguishing region-specific intratumor subpopulations. Images also enabled identification and correlation of metabolic activity of specific lipids found in tumor regions of varying grade.

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.

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 (that is, 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 .

Nanostructure-initiator mass spectrometry (NIMS) is a laser desorption/ionization analysis technique based on the vaporization of a nanostructure-trapped liquid "initiator" phase. Here we report an intriguing relationship between NIMS surface morphology and analyte selectivity. Scanning electron microscopy and spectroscopic ellipsometry were used to characterize the surface morphologies of a series of NIMS substrates generated by anodic electrochemical etching. Mass spectrometry imaging was applied to compare NIMS sensitivity of these various surfaces toward the analysis of diverse analytes. The porosity of NIMS surfaces was found to increase linearly with etching time where the pore size ranged from 4 to 12 nm with corresponding porosities estimated to be 7-70%. Surface morphology was found to significantly and selectively alter NIMS sensitivity. The small molecule (<2k Da) sensitivity was found to increase with increased porosity, whereas low porosity had the highest sensitivity for the largest molecules examined. Estimation of molecular sizes showed that this transition occurs when the pore size is <3× the maximum of molecular dimensions. While the origins of selectivity are unclear, increased signal from small molecules with increased surface area is consistent with a surface area restructuring-driven desorption/ionization process where signal intensity increases with porosity. In contrast, large molecules show highest signal for the low-porosity and small-pore-size surfaces. We attribute this to strong interactions between the initiator-coated pore structures and large molecules that hinder desorption/ionization by trapping large molecules. This finding may enable us to design NIMS surfaces with increased specificity to molecules of interest.

Interrelating small molecules according to their aligned fragmentation spectra is central to tandem mass spectrometry-based untargeted metabolomics. Current alignment algorithms do not provide statistical significance and compounds that have multiple delocalized structural differences and therefore often fail to have their fragment ions aligned. Here we align fragmentation spectra with both statistical significance and allowance for multiple chemical differences using Significant Interrelation of MS/MS Ions via Laplacian Embedding (SIMILE). SIMILE yields spectral alignment inferred structural connections in molecular networks that are not found with cosine-based scoring algorithms. In addition, it is now possible to rank spectral alignments based on p-values in the exploration of structural relationships between compounds and enhance the chemical connectivity that can be obtained with molecular networking.

Anaerobic fungi (class Neocallimastigomycetes) thrive as low-abundance members of the herbivore digestive tract. The genomes of anaerobic gut fungi are poorly characterized and have not been extensively mined for the biosynthetic enzymes of natural products such as antibiotics. Here, we investigate the potential of anaerobic gut fungi to synthesize natural products that could regulate membership within the gut microbiome. Complementary 'omics' approaches were combined to catalog the natural products of anaerobic gut fungi from four different representative species: Anaeromyces robustus (Arobustus), Caecomyces churrovis (Cchurrovis), Neocallimastix californiae (Ncaliforniae), and Piromyces finnis (Pfinnis). In total, 146 genes were identified that encode biosynthetic enzymes for diverse types of natural products, including nonribosomal peptide synthetases and polyketide synthases. In addition, N. californiae and C. churrovis genomes encoded seven putative bacteriocins, a class of antimicrobial peptides typically produced by bacteria. During standard laboratory growth on plant biomass or soluble substrates, 26% of total core biosynthetic genes in all four strains were transcribed. Across all four fungal strains, 30% of total biosynthetic gene products were detected via proteomics when grown on cellobiose. Liquid chromatography-tandem mass spectrometry (LC-MS/MS) characterization of fungal supernatants detected 72 likely natural products from A. robustus alone. A compound produced by all four strains of anaerobic fungi was putatively identified as the polyketide-related styrylpyrone baumin. Molecular networking quantified similarities between tandem mass spectrometry (MS/MS) spectra among these fungi, enabling three groups of natural products to be identified that are unique to anaerobic fungi. Overall, these results support the finding that anaerobic gut fungi synthesize natural products, which could be harnessed as a source of antimicrobials, therapeutics, and other bioactive compounds.