Diacylglycerol, PKC and MAPK signaling initiate tubeworm metamorphosis in response to bacteria.

External environmental cues can have significant impacts on the timing and outcomes of animal development. For the swimming larvae of many marine invertebrates, the presence of specific surface-bound bacteria are important cues that help larvae identify a suitable location on the sea floor for metamorphosis and adult life. While metamorphosis in response to bacteria occurs in diverse animals from across the animal tree of life, we know little about the signal transduction cascades stimulated at the onset of metamorphosis upon their interaction with bacteria. The metamorphosis of a model tubeworm, Hydroides elegans, is triggered by the bacterium Pseudoalteromonas luteoviolacea which produces a stimulatory protein called Mif1. In this work, we define three key nodes in a signaling cascade promoting Hydroides metamorphosis in response to Mif1. Using metabolomic profiling, we find that the stimulation of Hydroides larvae by P. luteoviolacea leads to an increase in diacylglycerol during the initiation of metamorphosis, and that Mif1 is necessary for this upregulation. Genomic and pharmacological examination suggests that diacylglycerol triggers a phosphotransferase signaling cascade involving Protein Kinase C (PKC) and Mitogen-Activated Protein Kinase (MAPK), to induce Hydroides metamorphosis. Additionally, Mif1 activates the expression of two nuclear hormone receptors, HeNHR1 and HeNHR2 in the cerebral ganglia of Hydroides larvae. Our results define a post-translational signal transduction pathway mediating bacteria-stimulated metamorphosis in a model invertebrate animal.

Towards Predicting Gut Microbial Metabolism: Integration of Flux Balance Analysis and Untargeted Metabolomics.

Genomics-based metabolic models of microorganisms currently have no easy way of corroborating predicted biomass with the actual metabolites being produced. This study uses untargeted mass spectrometry-based metabolomics data to generate a list of accurate metabolite masses produced from the human commensal bacteria Citrobacter sedlakii grown in the presence of a simple glucose carbon source. A genomics-based flux balance metabolic model of this bacterium was previously generated using the bioinformatics tool PyFBA and phenotypic growth curve data. The high-resolution mass spectrometry data obtained through timed metabolic extractions were integrated with the predicted metabolic model through a program called MS_FBA. This program correlated untargeted metabolomics features from C. sedlakii with 218 of the 699 metabolites in the model using an exact mass match, with 51 metabolites further confirmed using predicted isotope ratios. Over 1400 metabolites were matched with additional metabolites in the ModelSEED database, indicating the need to incorporate more specific gene annotations into the predictive model through metabolomics-guided gap filling.

Fluorinated Gold Nanoparticles for Nanostructure Imaging Mass Spectrometry.

Nanostructure imaging mass spectrometry (NIMS) with fluorinated gold nanoparticles (f-AuNPs) is a nanoparticle assisted laser desorption/ionization approach that requires low laser energy and has demonstrated high sensitivity. Here we describe NIMS with f-AuNPs for the comprehensive analysis of metabolites in biological tissues. F-AuNPs assist in desorption/ionization by laser-induced release of the fluorocarbon chains with minimal background noise. Since the energy barrier required to release the fluorocarbons from the AuNPs is minimal, the energy of the laser is maintained in the low µJ/pulse range, thus limiting metabolite in-source fragmentation. Electron microscopy analysis of tissue samples after f-AuNP NIMS shows a distinct "raising" of the surface as compared to matrix assisted laser desorption ionization ablation, indicative of a gentle desorption mechanism aiding in the generation of intact molecular ions. Moreover, the use of perfluorohexane to distribute the f-AuNPs on the tissue creates a hydrophobic environment minimizing metabolite solubilization and spatial dislocation. The transfer of the energy from the incident laser to the analytes through the release of the fluorocarbon chains similarly enhances the desorption/ionization of metabolites of different chemical nature, resulting in heterogeneous metabolome coverage. We performed the approach in a comparative study of the colon of mice exposed to three different diets. F-AuNP NIMS allows the direct detection of carbohydrates, lipids, bile acids, sulfur metabolites, amino acids, nucleotide precursors as well as other small molecules of varied biological origins. Ultimately, the diversified molecular coverage obtained provides a broad picture of a tissue's metabolic organization.

Data processing, multi-omic pathway mapping, and metabolite activity analysis using XCMS Online.

Systems biology is the study of complex living organisms, and as such, analysis on a systems-wide scale involves the collection of information-dense data sets that are representative of an entire phenotype. To uncover dynamic biological mechanisms, bioinformatics tools have become essential to facilitating data interpretation in large-scale analyses. Global metabolomics is one such method for performing systems biology, as metabolites represent the downstream functional products of ongoing biological processes. We have developed XCMS Online, a platform that enables online metabolomics data processing and interpretation. A systems biology workflow recently implemented within XCMS Online enables rapid metabolic pathway mapping using raw metabolomics data for investigating dysregulated metabolic processes. In addition, this platform supports integration of multi-omic (such as genomic and proteomic) data to garner further systems-wide mechanistic insight. Here, we provide an in-depth procedure showing how to effectively navigate and use the systems biology workflow within XCMS Online without a priori knowledge of the platform, including uploading liquid chromatography (LC)-mass spectrometry (MS) data from metabolite-extracted biological samples, defining the job parameters to identify features, correcting for retention time deviations, conducting statistical analysis of features between sample classes and performing predictive metabolic pathway analysis. Additional multi-omics data can be uploaded and overlaid with previously identified pathways to enhance systems-wide analysis of the observed dysregulations. We also describe unique visualization tools to assist in elucidation of statistically significant dysregulated metabolic pathways. Parameter input takes 5-10 min, depending on user experience; data processing typically takes 1-3 h, and data analysis takes ∼30 min.

Metabolomics Reveals that Dietary Xenoestrogens Alter Cellular Metabolism Induced by Palbociclib/Letrozole Combination Cancer Therapy.

Recently, the palbociclib/letrozole combination therapy was granted accelerated US FDA approval for the treatment of estrogen receptor (ER)-positive breast cancer. Since the underlying metabolic effects of these drugs are yet unknown, we investigated their synergism at the metabolome level in MCF-7 cells. As xenoestrogens interact with the ER, we additionally aimed at deciphering the impact of the phytoestrogen genistein and the estrogenic mycotoxin zearalenone. A global metabolomics approach was applied to unravel metabolite and pathway modifications. The results clearly showed that the combined effects of palbociclib and letrozole on cellular metabolism were far more pronounced than that of each agent alone and potently influenced by xenoestrogens. This behavior was confirmed in proliferation experiments and functional assays. Specifically, amino acids and central carbon metabolites were attenuated, while higher abundances were observed for fatty acids and most nucleic acid-related metabolites. Interestingly, exposure to model xenoestrogens appeared to counteract these effects.

Exposome-Scale Investigations Guided by Global Metabolomics, Pathway Analysis, and Cognitive Computing.

Concurrent exposure to a wide variety of xenobiotics and their combined toxic effects can play a pivotal role in health and disease, yet are largely unexplored. Investigating the totality of these exposures, i.e., the "exposome", and their specific biological effects constitutes a new paradigm for environmental health but still lacks high-throughput, user-friendly technology. We demonstrate the utility of mass spectrometry-based global exposure metabolomics combined with tailored database queries and cognitive computing for comprehensive exposure assessment and the straightforward elucidation of biological effects. The METLIN Exposome database has been redesigned to help identify environmental toxicants, food contaminants and supplements, drugs, and antibiotics as well as their biotransformation products, through its expansion with over 700 000 chemical structures to now include more than 950 000 unique small molecules. More importantly, we demonstrate how the XCMS/METLIN platform now allows for the readout of the biological effect of a toxicant through metabolomic-derived pathway analysis, and further, artificial intelligence provides a means of assessing the role of a potential toxicant. The presented workflow addresses many of the methodological challenges current exposomics research is facing and will serve to gain a deeper understanding of the impact of environmental exposures and combinatory toxic effects on human health.

Global Isotope Metabolomics Reveals Adaptive Strategies for Nitrogen Assimilation.

Nitrogen cycling is a microbial metabolic process essential for global ecological/agricultural balance. To investigate the link between the well-established ammonium and the alternative nitrate assimilation metabolic pathways, global isotope metabolomics was employed to examine three nitrate reducing bacteria using (15)NO3 as a nitrogen source. In contrast to a control (Pseudomonas stutzeri RCH2), the results show that two of the isolates from Oak Ridge, Tennessee (Pseudomonas N2A2 and N2E2) utilize nitrate and ammonia for assimilation concurrently with differential labeling observed across multiple classes of metabolites including amino acids and nucleotides. The data reveal that the N2A2 and N2E2 strains conserve nitrogen-containing metabolites, indicating that the nitrate assimilation pathway is a conservation mechanism for the assimilation of nitrogen. Co-utilization of nitrate and ammonia is likely an adaption to manage higher levels of nitrite since the denitrification pathways utilized by the N2A2 and N2E2 strains from the Oak Ridge site are predisposed to the accumulation of the toxic nitrite. The use of global isotope metabolomics allowed for this adaptive strategy to be investigated, which would otherwise not have been possible to decipher.

Bio-solid-phase extraction/tandem mass spectrometry for identification of bioactive compounds in mixtures.

We describe a two-step column-based bioassay method with tandem mass spectrometric detection for rapid identification of bioactive species in mixtures. The first step uses an immobilized enzyme reactor (IMER) column interfaced to an electrospray ionization mass spectrometer (ESI-MS) to identify mixtures containing bioactive compounds (i.e., enzyme inhibitors), while the second step uses bioselective solid-phase extraction (bioSPE) columns to isolate compounds from "hit" mixtures, which are then identified online by data-dependent ESI-MS. IMER columns were prepared by entrapment of adenosine deaminase (ADA) into sol-gel derived monolithic silica columns, and used to perform a primary IMER screen of mixtures prepared from a bioactive library, which resulted in four apparent hit compounds. Such columns did not provide sufficient binding site density to allow bioSPE, and thus a new column format was developed using ADA that was covalently immobilized to monolithic silica capillary columns, providing ∼500-fold more protein binding sites than were present in columns containing entrapped proteins. Using the covalently linked ADA columns, bioactive mixtures identified by IMER were infused until a maximum total ion current was achieved, followed by washing with a buffer to remove unbound compounds. A harsh wash with 3% acetic acid eluted the strongly bound ligands and the resulting peak triggered data dependent MS/MS to identify the ligand, showing that two of the apparent hits were true ADA inhibitors and demonstrating the ability of this method to rapidly identify bioactive compounds in mixtures.

Solid-phase biological assays for drug discovery.

In the past 30 years, there has been a significant growth in the use of solid-phase assays in the area of drug discovery, with a range of new assays being used for both soluble and membrane-bound targets. In this review, we provide some basic background to typical drug targets and immobilization protocols used in solid-phase biological assays (SPBAs) for drug discovery, with emphasis on particularly labile biomolecular targets such as kinases and membrane-bound receptors, and highlight some of the more recent approaches for producing protein microarrays, bioaffinity columns, and other devices that are central to small molecule screening by SPBA. We then discuss key applications of such assays to identify drug leads, with an emphasis on the screening of mixtures. We conclude by highlighting specific advantages and potential disadvantages of SPBAs, particularly as they relate to particular assay formats.

Continuous flow immobilized enzyme reactor-tandem mass spectrometry for screening of AChE inhibitors in complex mixtures.

A method is described for identifying bioactive compounds in complex mixtures based on the use of capillary-scale monolithic enzyme-reactor columns for rapid screening of enzyme activity. A two-channel nanoLC system was used to continuously infuse substrate coupled with automated injections of substrate/small molecule mixtures, optionally containing the chromogenic Ellman reagent, through sol-gel derived acetylcholinesterase (AChE) doped monolithic columns. This is the first report of AChE encapsulated in monolithic silica for use as an immobilized enzyme reactor (IMER), and the first use of such IMERs for mixture screening. AChE IMER columns were optimized to allow rapid functional screening of compound mixtures based on changes in the product absorbance or the ratio of mass spectrometric peaks for product and substrate ions in the eluent. The assay had robust performance and produced a Z' factor of 0.77 in the presence of 2% (v/v) DMSO. A series of 52 mixtures consisting of 1040 compounds from the Canadian Compound Collection of bioactives was screened and two known inhibitors, physostigmine and 9-aminoacridine, were identified from active mixtures by manual deconvolution. The activity of the compounds was confirmed using the enzyme reactor format, which allowed determination of both IC(50) and K(I) values. Screening results were found to correlate well with a recently published fluorescence-based microarray screening assay for AChE inhibitors.

Solid-phase assays for small molecule screening using sol-gel entrapped proteins.

With compound libraries exceeding one million compounds, the ability to quickly and effectively screen these compounds against relevant pharmaceutical targets has become crucial. Solid-phase assays present several advantages over solution-based methods. For example, a higher degree of miniaturization can be achieved, functional- and affinity-based studies are possible, and a variety of detection methods can be used. Unfortunately, most protein immobilization methods are either too harsh or require recombinant proteins and thus are not amenable to delicate proteins such as kinases and membrane-bound receptors. Sol-gel encapsulation of proteins in an inorganic silica matrix has emerged as a novel solid-phase assay platform. In this minireview, we discuss the development of sol-gel derived protein microarrays and sol-gel based monolithic bioaffinity columns for the high-throughput screening of small molecule libraries and mixtures.