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Publications and source records attributed to Dorrestein, P. C..
Most animals digest their food within their own bodies, but some do not. Many species of ants grow fungus gardens that pre-digest food as an essential step of the ants nutrient uptake. To better understand this digestion process, we generated a 3D molecular map of an Atta texana fungus garden, revealing chemical modifications mediated by the fungus garden as plant material passes through.
Filamentous marine cyanobacteria make a variety of bioactive molecules that are produced by polyketide synthases, non-ribosomal peptide synthetases, and hybrid pathways that are encoded by large biosynthetic gene clusters. These cyanobacterial natural products represent potential drugs leads; however, thorough pharmacological investigations have been impeded by the limited quantity of compound that is typically available from the native organisms. Additionally, investigations of the biosynthetic gene clusters and enzymatic pathways have been difficult due to the inability to conduct genetic manipulations in the native producers. Here we report a set of genetic tools for the heterologous expression of biosynthetic gene clusters in the cyanobacteria Synechococcus elongatus PCC 7942 and Anabaena (Nostoc) PCC 7120. To facilitate the transfer of gene clusters in both strains, we engineered a strain of Anabaena that contains S. elongatus homologous sequences for chromosomal recombination at a neutral site and devised a CRISPR-based strategy to efficiently obtain segregated double recombinant clones of Anabaena. These genetic tools were used to express the large 28.7 kb cryptomaldamide biosynthetic gene cluster from the marine cyanobacterium Moorena (Moorea) producens JHB in both model strains. S. elongatus did not produce cryptomaldamide, however high-titer production of cryptomaldamide was obtained in Anabaena. The methods developed in this study will facilitate the heterologous expression of biosynthetic gene clusters isolated from marine cyanobacteria and complex metagenomic samples. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=110 SRC="FIGDIR/small/267179v1_ufig1.gif" ALT="Figure 1"> View larger version (33K): org.highwire.dtl.DTLVardef@660caforg.highwire.dtl.DTLVardef@1cab871org.highwire.dtl.DTLVardef@130de4org.highwire.dtl.DTLVardef@f50c64_HPS_FORMAT_FIGEXP M_FIG C_FIG
Chagas disease (CD) is one of thirteen neglected tropical diseases caused by the parasite Trypanosoma cruzi. CD is a vector-borne disease transmitted by triatomines but CD can also be transmitted through blood transfusions, organ transplants and congenital transmission. While endemic to Latin America, T. cruzi infects 7-8 million people worldwide and can induce severe cardiac symptoms including apical aneurysms, thromboembolisms and arrhythmias during the chronic stage of CD. However, these cardiac clinical manifestations and CD disease pathogenesis are not fully understood. Using spatial metabolomics (chemical cartography), we sought to understand the localized impact of infection on the cardiac metabolome of mice chronically infected with two divergent T. cruzi strains. Our data showed chemical differences in localized cardiac regions upon chronic T. cruzi infection, indicating that parasite infection changes the host metabolome at select sites in chronic CD. These sites were distinct from the sites of highest parasite burden. In addition, we identified acylcarnitines and phosphocholines as discriminatory chemical families within each heart region, comparing infected and uninfected samples. Overall, our study indicated overall and positional metabolic differences common to infection with different T. cruzi strains, and identified select infection-modulated pathways. These results provide further insight into CD pathogenesis and demonstrate the advantage of a spatial perspective to understand infectious disease tropism.Author Summary Chagas disease (CD) is a tropical disease caused by the parasite Trypanosoma cruzi. CD originated in South America; however, there are now 7-8 million people infected worldwide due to population movements. CD is transmitted through a triatomine vector, organ transplants, blood transfusions and congenital transmission. It occurs in two stages, an acute stage (usually asymptomatic) and the chronic stage. Chronic stage CD presents with severe cardiac symptoms such as heart failure, localized aneurysms and cardiomyopathy. Unfortunately, what causes severe cardiac symptoms in some individuals in chronic CD is not fully understood. Therefore, we used liquid chromatography-tandem mass spectrometry to analyze the heart tissue of chronically T. cruzi-infected and uninfected mice, to understand the impact of infection on the tissue metabolome. We identified discriminatory small molecules related to T. cruzi infection. We also determined that regions with the highest parasite burden are distinct from the regions with the largest changes in overall metabolite profile; these locations of high metabolic perturbation provide a molecular mechanism to why localized cardiac symptoms occur in CD. Overall, our work gives insight to chronic cardiac CD symptom development and shapes a framework for novel treatment and biomarker development.Competing Interest StatementThe authors have declared no competing interest.View Full Text
Each year 700,000 to 1.2 million new cases of cutaneous leishmaniasis (CL) are reported and yet CL remains one of thirteen diseases classified as neglected tropical diseases (NTDs). Leishmania major is one of several different species of that same genus that can cause CL. Current CL treatments are limited by adverse effects and rising resistance. Studying disease metabolism at the site of infection can lead to new drug targets. In this study, samples were collected from mice infected in the ear and footpad with L. major and analyzed by untargeted liquid chromatography-tandem mass spectrometry (LC-MS/MS). Significant differences in overall metabolite profiles were noted in the ear at the site of the lesion. Interestingly, lesion-adjacent, macroscopically healthy sites also showed alterations in specific metabolites, including select phosphocholines (PCs). Host-derived PCs in the lower m/z range (m/z 200-799) showed an increase with infection in the ear at the lesion site, while those in the higher m/z range (m/z 800-899) were decreased with infection at the lesion site. Overall, our results expanded our understanding of the mechanisms of CL pathogenesis through the host metabolism and may lead to new curative measures against infection with Leishmania. Author summaryCutaneous leishmaniasis (CL) is one of thirteen neglected tropical diseases in the world today. It is an infectious disease with a wide distribution spanning five continents, with increasing distribution expected due to climate change. CL manifests as skin lesions and ulcers that are disabling and stigmatized. With the current treatment options being limited, studying host-pathogen metabolism can uncover mechanisms of disease pathogenesis that may lead to new curative measures against infection. In this paper we used untargeted metabolomics to address molecular-level changes occurring in vivo in experimental skin lesions of Leishmania major. Distinct global metabolic profiles were observed. Total phosphocholines (PCs) and those in the lower m/z ranges were significantly higher at the site of the skin lesion in the ear. In addition, specific PCs as well as PCs of varied m/z ranges were also affected at healthy-appearing lesion-adjacent sites, indicating that infection-induced metabolic perturbations are not restricted to the lesion site. Ultimately, these results provide essential clues to the metabolic pathways affected by CL.
The growth of online mass spectrometry metabolomics resources, including data repositories, spectral library databases, and online analysis platforms has created an environment of online/web accessibility. Here, we introduce the Metabolomics Spectrum Resolver (https://metabolomics-usi.ucsd.edu/), a tool that builds upon these exciting developments to allow for consistent data export (in human and machine-readable forms) and publication-ready visualisations of tandem mass spectrometry spectra. This tool supports the Human Proteome Organization - Proteomics Standards Initiatives Universal Spectrum Identifier (USI) specification, which has been extended to deal with the metabolomics use cases. To date, this resource already supports data formats from GNPS, MassBank, MS2LDA, MassIVE, MetaboLights, and Metabolomics Workbench and is integrated into several of these resources.
Untargeted mass spectrometry is employed to detect small molecules in complex biospecimens, generating data that are difficult to interpret. We developed Qemistree, a data exploration strategy based on hierarchical organization of molecular fingerprints predicted from fragmentation spectra, represented in the context of sample metadata and chemical ontologies. By expressing molecular relationships as a tree, we can apply ecological tools, designed around the relatedness of DNA sequences, to study chemical composition.
Metabolomics experiments can employ non-targeted tandem mass spectrometry to detect hundreds to thousands of molecules in a biological sample. Structural annotation of molecules is typically carried out by searching their fragmentation spectra in spectral libraries or, recently, in structure databases. Annotations are limited to structures present in the library or database employed, prohibiting a thorough utilization of the experimental data. We present a computational tool for systematic compound class annotation: CANOPUS uses a deep neural network to predict 1,270 compound classes from fragmentation spectra, and explicitly targets compounds where neither spectral nor structural reference data are available. CANOPUS even predicts classes for which no MS/MS training data are available. We demonstrate the broad utility of CANOPUS by investigating the effect of the microbial colonization in the digestive system in mice, and through analysis of the chemodiversity of different Euphorbia plants; both uniquely revealing biological insights at the compound class level.
Metals are essential for the molecular machineries of life, and microbes have evolved a variety of small molecules to acquire, compete for, and utilize metals. Systematic methods for the discovery of metal-small molecule complexes from biological samples are limited. Here we describe a two-step native electrospray ionization mass spectrometry method, in which double-barrel post-column metal-infusion and pH adjustment is combined with ion identity molecular networking, a rule-based informatics workflow. This method can be used to identify metal-binding compounds in complex samples based on defined mass (m/z) offsets of ion features with the same chromatographic profiles. As this native metal metabolomics approach can be easily implemented on any liquid chromatography-based mass spectrometry system, this method has the potential to become a key strategy for elucidating and understanding the role of metal-binding molecules in biology.
RationaleA major hurdle in identifying chemicals in mass spectrometry experiments is the availability of MS/MS reference spectra in public databases. Currently, scientists purchase databases or use public databases such as GNPS. The MSMS-Chooser workflow empowers the creation of MS/MS reference spectra directly in the GNPS infrastructure.\n\nMethodsAn MSMS-Chooser sample template was completed with the required information and sequence tables were generated programmatically. Standards in methanol-water (1:1) solution (1 M) were placed into wells individually. An LC-MS/MS system using data-dependent acquisition in positive and negative modes was used. Species that may be generated under typical ESI conditions are chosen. The MS/MS spectra and MSMS-Chooser sample template were subsequently uploaded to MSMS-Chooser in GNPS for automatic MS/MS spectral annotation.\n\nResultsData acquisition quickly and effectively collected MS/MS spectra. MSMS-Chooser was able to accurately annotate 99.2% of the manually validated MS/MS scans that were generated from the chemical standards. The output of MSMS-Chooser includes a table ready for inclusion in the GNPS library (after inspection) as well as the ability to directly launch searches via MASST. Altogether, the data acquisition, processing, and upload to GNPS took ~2 hours for our proof-of-concept results.\n\nConclusionsThe MSMS-Chooser workflow enables the rapid data acquisition, analysis, and annotation of chemical standards, and uploads the MS/MS spectra to community-driven GNPS. MSMS-Chooser democratizes the creation of MS/MS reference spectra in GNPS which will improve annotation and strengthen the tools which use the annotation information.
Structured AbstractEven high-quality collection and reporting of study metadata in microbiome studies can lead to various forms of inadvertently missing or mischaracterized information that can alter the interpretation or outcome of the studies, especially with non-model organisms. Metabolomic profiling of fecal microbiome samples can provide empirical insight into unanticipated confounding factors that are not possible to obtain even from detailed care records. We illustrate this point using data from cheetahs from the San Diego Zoo Safari Park. The metabolomic characterization indicated that one cheetah had to be moved from the non-antibiotic-exposed to the antibiotic-exposed group. The detection of the antibiotic in this second cheetah was likely due to grooming interactions with the cheetah that was administered antibiotics. Similarly, because transit time for stool is variable, early fecal samples within the first few days of antibiotic prescription do not all contain detectable antibiotics. Therefore, the microbiome is not affected by the antibiotics at those time points. These insights significantly altered the way the samples were grouped for analysis (antibiotic vs no antibiotic), and the subsequent understanding of the effect of the antibiotics on the cheetah microbiome. Metabolomics also revealed information about numerous other medications, and provided unexpected dietary insights that in turn improved our understanding of the molecular patterns on the impact on the community microbial structure. These results suggest that untargeted metabolomics data provide empirical evidence to correct records of non-model organisms in captivity, although we also expect these methods will be appropriate for experimental conditions typical in human studies.\n\nImportanceMetabolome-informed analyses can enhance omics studies by enabling the correct partitioning of samples by identifying hidden confounders inadvertently misrepresented or omitted from carefully curated metadata. We demonstrate the utility of metabolomics in a study characterizing the microbiome associated with liver disease in cheetahs. Metabolome-informed reinterpretation of metagenome and metabolome profiles factored in an unexpected transfer of antibiotics preventing misinterpretation of the data. Our work suggests that untargeted metabolomics can be used to verify, augment, and correct sample metadata to support improved grouping of sample data for microbiome analyses, here for non-model organisms in captivity. However, the techniques also suggest a path forward for correcting clinical information in human studies to enable higher-precision analyses.
To visualize the personalized distributions of pathogens, chemical environments including microbial metabolites, pharmaceuticals, and their metabolic products within and between human lungs afflicted with cystic fibrosis, we generated 3D microbiome and metabolome maps of six explanted lungs from three cystic fibrosis patients. These 3D spatial maps revealed that the chemical environments are variable between patients and within the lungs of each patient. Although the patients microbial ecosystems were defined by the dominant pathogen, their chemical diversity was not. Additionally, the chemical diversity between locales in lungs of the same individual sometimes exceeded inter-individual variation. Thus, the chemistry and microbiome of the explanted lungs appear to be not only personalized but also regiospecific. Previously undescribed analogs of microbial quinolones and antibiotic metabolites were also detected. Furthermore, mapping the chemical and microbial distributions allowed visualization of microbial community interactions, such as increased production of quorum sensing quinolones in locations where Pseudomonas was in contact with Staphylococcus and Granulicatella, consistent with in vitro observations of bacteria isolated from these patients. Visualization of microbe-metabolite associations within a host organ in early-stage CF disease in animal models will help elucidate a complex interplay between the presence of a given microbial structure, antibiotics, metabolism of antibiotics, microbial virulence factors, and host responses.\n\nImportanceMicrobial infections are now recognized to be polymicrobial and personalized in nature. A comprehensive analysis and understanding of the factors underlying the polymicrobial and personalized nature of infections remains limited, especially in the context of the host. By visualizing microbiomes and metabolomes of diseased human lungs, we describe how different the chemical environments are between hosts that are dominated by the same pathogen and how community interactions shape the chemical environment, or vice versa. We highlight that three-dimensional organ mapping are hypothesis building tools that allow us to design mechanistic studies aimed at addressing microbial responses to other microbes, the host, and pharmaceutical drugs.
Organofluorine compounds are toxic to various living beings in different habitats. On the other hand, fluorine incorporation into single proteins via related amino acid analogues has become common practice in protein engineering. Thus, an essential question remains: can fluorinated amino acids generally be used as xeno-nutrients to build up biomass, or do large amounts of fluorine in the cells render them nonviable? To gain information about the effect of long-term exposure of a cellular proteome to fluorinated organic compounds, we constructed an experiment based on bacterial adaptation in artificial fluorinated habitats. We propagated Escherichia coli (E. coli) in the presence of either 4- or 5-fluoroindole as essential precursors for the in situ synthesis of tryptophan (Trp) analogues. We found that full adaptation requires astonishingly few genetic mutations but is accompanied by large rearrangements in regulatory networks, membrane integrity and quality control of protein folding. These findings highlight the cellular mechanisms of the evolutionary adaption process to unnatural amino acids and provide the molecular foundation for novel and innovative bioengineering of microbial strains with potential for biotechnological applications.\n\nOne Sentence SummaryLaboratory evolution enabled for the first time Escherichia coli to use fluorinated indoles as essential precursors for protein synthesis by introducing few genetic mutations but large rearrangements in regulatory networks, membrane integrity and quality control of protein folding.
A mosaic of cross-phyla chemical interactions occurs between all metazoans and their microbiomes. In humans, the gut harbors the heaviest microbial load, but many organs, particularly those with a mucosal surface, associate with highly adapted and evolved microbial consortia1. The microbial residents within these organ systems are increasingly well characterized, yielding a good understanding of human microbiome composition, but we have yet to elucidate the full chemical impact the microbiome exerts on an animal and the breadth of the chemical diversity it contributes2. A number of molecular families are known to be shaped by the microbiome including short-chain fatty acids, indoles, aromatic amino acid metabolites, complex polysaccharides, and host lipids; such as sphingolipids and bile acids3-11. These metabolites profoundly affect host physiology and are being explored for their roles in both health and disease. Considering the diversity of the human microbiome, numbering over 40,000 operational taxonomic units12, a plethora of molecular diversity remains to be discovered. Here, we use unique mass spectrometry informatics approaches and data mapping onto a murine 3D-model13-15 to provide an untargeted assessment of the chemical diversity between germ-free (GF) and colonized mice (specific-pathogen free, SPF), and report the finding of novel bile acids produced by the microbiome in both mice and humans that have evaded characterization despite 170 years of research on bile acid chemistry16.
Metabolomics has started to embrace computational approaches for chemical interpretation of large data sets. Yet, metabolite annotation remains a key challenge. Recently, molecular networking and MS2LDA emerged as molecular mining tools that find molecular families and substructures in mass spectrometry fragmentation data. Moreover, in silico annotation tools obtain and rank candidate molecules for fragmentation spectra. Ideally, all structural information obtained and inferred from these computational tools could be combined to increase the resulting chemical insight one can obtain from a data set. However, integration is currently hampered as each tool has its own output format and efficient matching of data across these tools is lacking. Here, we introduce MolNetEnhancer, a workflow that combines the outputs from molecular networking, MS2LDA, in silico annotation tools (such as Network Annotation Propagation or DEREPLICATOR) and the automated chemical classification through ClassyFire to provide a more comprehensive chemical overview of metabolomics data whilst at the same time illuminating structural details for each fragmentation spectrum. We present examples from four plant and bacterial case studies and show how MolNetEnhancer enables the chemical annotation, visualization, and discovery of the subtle substructural diversity within molecular families. We conclude that MolNetEnhancer is a useful tool that greatly assists the metabolomics researcher in deciphering the metabolome through combination of multiple independent in silico pipelines.
The composition of the skin microbiome varies widely among individuals sampled at the same body site. A key question is which molecular factors determine strain-level variability within sub-ecosystems of the skin. We used a genomics-guided approach to identify an antibacterial biosynthetic gene cluster in Cutibacterium acnes (formerly Propionibacterium acnes) that is widely distributed across individuals and skin sites. Experimental characterization of this cluster enabled the identification of a new thiopeptide antibiotic, cutimycin. Analysis of individual human skin hair follicles showed that cutimycin is an important factor regulating colonization resistance against Staphylococcus species.\n\nOne Sentence SummaryCutimycin, a thiopeptide antibiotic produced by a widespread skin commensal, reduces Staphylococcus colonization of human follicles.
CorrespondenceWe introduce a web-enabled small-molecule mass spectrometry (MS) search engine. To date, no tool can query all the public small-molecule tandem MS data in metabolomics repositories, greatly limiting the utility of these resources in clinical, environmental and natural product applications. Therefore, we introduce a Mass Spectrometry Search Tool (MASST) (https://proteosafe-extensions.ucsd.edu/masst/), that enables the discovery of molecular relationships among accessible public metabolomics and natural product tandem mass spectrometry data (MS/MS).
Cyclic and branch cyclic peptides (cyclopeptides) represent an important class of bioactive natural products that include many antibiotics and anti-tumor compounds. However, little is known about cyclopeptides in the human gut, despite the fact that humans are constantly exposed to them. To address this bottleneck, we developed the CycloNovo algorithm for de novo cyclopeptide sequencing that employs de Bruijn graphs, the workhorse of DNA sequencing algorithms. CycloNovo reconstructed many new cyclopeptides that we validated with transcriptome, metagenome, and genome mining analyses. Our benchmarking revealed a vast hidden cyclopeptidome in the human gut and other environments and suggested that CycloNovo offers a much-needed step-change for cyclopeptide discovery. Furthermore, CycloNovo revealed a wealth of anti-microbial cyclopeptides from food that survive the complete human gastrointestinal tract, raising the question of how these cyclopeptides might affect the human microbiome.\n\nSIGNIFICANCEThe golden age of antibiotics was followed by a decline in the pace of antibiotics discovery in the 1990s. The key prerequisite for the resurgence of antibiotics research is the development of a computational discovery pipeline for antibiotics sequencing. We describe such pipeline for cyclic and branch cyclic peptides (cyclopeptides) that represent an important class of bioactive natural products such as antibiotics and anti-tumor compounds. Our CycloNovo algorithm for cyclopeptide sequencing reconstructed many new cyclopeptides that we validated with transcriptome, metagenome, and genome mining analyses. CycloNovo revealed a wealth of anti-microbial cyclopeptides from food that survive the complete human gastrointestinal tract, raising the question of how these cyclopeptides might affect the human microbiome.
Plants produce a myriad of specialized metabolites to overcome their sessile habit and combat biotic as well as abiotic stresses. Evolution has shaped specialized metabolite diversity, which drives many other aspects of plant biodiversity. However, until recently, large-scale studies investigating specialized metabolite diversity in an evolutionary context have been limited by the impossibility to identify chemical structures of hundreds to thousands of compounds in a time-feasible manner. Here, we introduce a workflow for large-scale, semi-automated annotation of specialized metabolites, and apply it for over 1000 metabolites of the cosmopolitan plant family Rhamnaceae. We enhance the putative annotation coverage dramatically, from 2.5 % based on spectral library matches alone to 42.6 % of total MS/MS molecular features extending annotations from well-known plant compound classes into the dark plant metabolomics matter. To gain insights in substructural diversity within the plant family, we also extract patterns of co-occurring fragments and neutral losses, so-called Mass2Motifs, from the dataset; for example, only the Ziziphoid clade developed the triterpenoid biosynthetic pathway, whereas the Rhamnoid clade predominantly developed diversity in flavonoid glycosides, including 7-O-methyltransferase activity. Our workflow provides the foundations towards the automated, high-throughput chemical identification of massive metabolite spaces, and we expect it to revolutionize our understanding of plant chemoevolutionary mechanisms.