MAD HATTER Correctly Annotates 98% of Small Molecule Tandem Mass Spectra Searching in PubChem.

Metabolites provide a direct functional signature of cellular state. Untargeted metabolomics usually relies on mass spectrometry, a technology capable of detecting thousands of compounds in a biological sample. Metabolite annotation is executed using tandem mass spectrometry. Spectral library search is far from comprehensive, and numerous compounds remain unannotated. So-called in silico methods allow us to overcome the restrictions of spectral libraries, by searching in much larger molecular structure databases. Yet, after more than a decade of method development, in silico methods still do not reach the correct annotation rates that users would wish for. Here, we present a novel computational method called Mad Hatter for this task. Mad Hatter combines CSI:FingerID results with information from the searched structure database via a metascore. Compound information includes the melting point, and the number of words in the compound description starting with the letter 'u'. We then show that Mad Hatter reaches a stunning 97.6% correct annotations when searching PubChem, one of the largest and most comprehensive molecular structure databases. Unfortunately, Mad Hatter is not a real method. Rather, we developed Mad Hatter solely for the purpose of demonstrating common issues in computational method development and evaluation. We explain what evaluation glitches were necessary for Mad Hatter to reach this annotation level, what is wrong with similar metascores in general, and why metascores may screw up not only method evaluations but also the analysis of biological experiments. This paper may serve as an example of problems in the development and evaluation of machine learning models for metabolite annotation.

Illuminating the dark metabolome of Pseudo-nitzschia-microbiome associations.

The exchange of metabolites mediates algal and bacterial interactions that maintain ecosystem function. Yet, while thousands of metabolites are produced, only a few molecules have been identified in these associations. Using the ubiquitous microalgae Pseudo-nitzschia sp., as a model, we employed an untargeted metabolomics strategy to assign structural characteristics to the metabolites that distinguished specific diatom-microbiome associations. We cultured five species of Pseudo-nitzschia, including two species that produced the toxin domoic acid, and examined their microbiomes and metabolomes. A total of 4826 molecular features were detected by tandem mass spectrometry. Only 229 of these could be annotated using available mass spectral libraries, but by applying new in silico annotation tools, characterization was expanded to 2710 features. The metabolomes of the Pseudo-nitzschia-microbiome associations were distinct and distinguished by structurally diverse nitrogen compounds, ranging from simple amines and amides to cyclic compounds such as imidazoles, pyrrolidines and lactams. By illuminating the dark metabolomes, this study expands our capacity to discover new chemical targets that facilitate microbial partnerships and uncovers the chemical diversity that underpins algae-bacteria interactions.

High-confidence structural annotation of metabolites absent from spectral libraries.

Untargeted metabolomics experiments rely on spectral libraries for structure annotation, but, typically, only a small fraction of spectra can be matched. Previous in silico methods search in structure databases but cannot distinguish between correct and incorrect annotations. Here we introduce the COSMIC workflow that combines in silico structure database generation and annotation with a confidence score consisting of kernel density P value estimation and a support vector machine with enforced directionality of features. On diverse datasets, COSMIC annotates a substantial number of hits at low false discovery rates and outperforms spectral library search. To demonstrate that COSMIC can annotate structures never reported before, we annotated 12 natural bile acids. The annotation of nine structures was confirmed by manual evaluation and two structures using synthetic standards. In human samples, we annotated and manually validated 315 molecular structures currently absent from the Human Metabolome Database. Application of COSMIC to data from 17,400 metabolomics experiments led to 1,715 high-confidence structural annotations that were absent from spectral libraries.

Chemically informed analyses of metabolomics mass spectrometry data with Qemistree.

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 the hierarchical organization of molecular fingerprints predicted from fragmentation spectra. Qemistree allows mass spectrometry data to be represented in the context of sample metadata and chemical ontologies. By expressing molecular relationships as a tree, we can apply ecological tools that are designed to analyze and visualize the relatedness of DNA sequences to metabolomics data. Here we demonstrate the use of tree-guided data exploration tools to compare metabolomics samples across different experimental conditions such as chromatographic shifts. Additionally, we leverage a tree representation to visualize chemical diversity in a heterogeneous collection of samples. The Qemistree software pipeline is freely available to the microbiome and metabolomics communities in the form of a QIIME2 plugin, and a global natural products social molecular networking workflow.

Studying Charge Migration Fragmentation of Sodiated Precursor Ions in Collision-Induced Dissociation at the Library Scale.

Interpretation of fragmentation mass spectra depends on our knowledge of collision-induced dissociation mechanisms. Computational methods for the annotation of fragmentation mechanisms operate within the boundaries of recognized fragmentation pathways. The prevalence of charge migration fragmentation (CMF) in sodiated ion fragmentation spectra, which produces nonsodiated fragment ions, is unknown. Here, we investigated the extent of CMF in the fragmentation spectra of sodiated precursors by mining the NIST17 spectral library using a diagnostic mass difference. Our results showed that a substantial amount of fragment ions in sodiated precursor spectra are derived from CMF, indicating that this fragmentation mechanism should be commonly considered by computational methods for compound annotation.

Systematic classification of unknown metabolites using high-resolution fragmentation mass spectra.

Metabolomics using nontargeted tandem mass spectrometry can detect thousands of molecules in a biological sample. However, structural molecule annotation is limited to structures present in libraries or databases, restricting analysis and interpretation of experimental data. Here we describe CANOPUS (class assignment and ontology prediction using mass spectrometry), a computational tool for systematic compound class annotation. CANOPUS uses a deep neural network to predict 2,497 compound classes from fragmentation spectra, including all biologically relevant classes. CANOPUS explicitly targets compounds for which neither spectral nor structural reference data are available and predicts classes lacking tandem mass spectrometry training data. In evaluation using reference data, CANOPUS reached very high prediction performance (average accuracy of 99.7% in cross-validation) and outperformed four baseline methods. We demonstrate the broad utility of CANOPUS by investigating the effect of microbial colonization in the mouse digestive system, through analysis of the chemodiversity of different Euphorbia plants and regarding the discovery of a marine natural product, revealing biological insights at the compound class level.

Feature-based molecular networking in the GNPS analysis environment.

Molecular networking has become a key method to visualize and annotate the chemical space in non-targeted mass spectrometry data. We present feature-based molecular networking (FBMN) as an analysis method in the Global Natural Products Social Molecular Networking (GNPS) infrastructure that builds on chromatographic feature detection and alignment tools. FBMN enables quantitative analysis and resolution of isomers, including from ion mobility spectrometry.

De Novo Molecular Formula Annotation and Structure Elucidation Using SIRIUS 4.

SIRIUS 4 is the best-in-class computational tool for metabolite identification from high-resolution tandem mass spectrometry data. It offers de novo molecular formula annotation with outstanding accuracy. When searching fragmentation spectra in a structure database, it reaches over 70% correct identifications. A predicted fingerprint, which indicates the presence or absence of thousands of molecular properties, helps to deduce information about the compound of interest even if it is not contained in any structure database. Here, we present best practices and describe how to leverage the full potential of SIRIUS 4, how to incorporate it into your own workflow, and how it adds value to the analysis of mass spectrometry data beyond spectral library search.

SIRIUS 4: a rapid tool for turning tandem mass spectra into metabolite structure information.

Mass spectrometry is a predominant experimental technique in metabolomics and related fields, but metabolite structural elucidation remains highly challenging. We report SIRIUS 4 (https://bio.informatik.uni-jena.de/sirius/), which provides a fast computational approach for molecular structure identification. SIRIUS 4 integrates CSI:FingerID for searching in molecular structure databases. Using SIRIUS 4, we achieved identification rates of more than 70% on challenging metabolomics datasets.

Bayesian networks for mass spectrometric metabolite identification via molecular fingerprints.

Motivation: Metabolites, small molecules that are involved in cellular reactions, provide a direct functional signature of cellular state. Untargeted metabolomics experiments usually rely on tandem mass spectrometry to identify the thousands of compounds in a biological sample. Recently, we presented CSI:FingerID for searching in molecular structure databases using tandem mass spectrometry data. CSI:FingerID predicts a molecular fingerprint that encodes the structure of the query compound, then uses this to search a molecular structure database such as PubChem. Scoring of the predicted query fingerprint and deterministic target fingerprints is carried out assuming independence between the molecular properties constituting the fingerprint. Results: We present a scoring that takes into account dependencies between molecular properties. As before, we predict posterior probabilities of molecular properties using machine learning. Dependencies between molecular properties are modeled as a Bayesian tree network; the tree structure is estimated on the fly from the instance data. For each edge, we also estimate the expected covariance between the two random variables. For fixed marginal probabilities, we then estimate conditional probabilities using the known covariance. Now, the corrected posterior probability of each candidate can be computed, and candidates are ranked by this score. Modeling dependencies improves identification rates of CSI:FingerID by 2.85 percentage points. Availability and implementation: The new scoring Bayesian (fixed tree) is integrated into SIRIUS 4.0 (https://bio.informatik.uni-jena.de/software/sirius/).

Zn2+-Inducible Expression Platform for Synechococcus sp. Strain PCC 7002 Based on the smtA Promoter/Operator and smtB Repressor.

Synechococcus sp. strain PCC 7002 has been gaining significance as both a model system for photosynthesis research and for industrial applications. Until recently, the genetic toolbox for this model cyanobacterium was rather limited and relied primarily on tools that only allowed constitutive gene expression. This work describes a two-plasmid, Zn2+-inducible expression platform that is coupled with a zurA mutation, providing enhanced Zn2+ uptake. The control elements are based on the metal homeostasis system of a class II metallothionein gene (smtA7942) and its cognate SmtB7942 repressor from Synechococcus elongatus strain PCC 7942. Under optimal induction conditions, yellow fluorescent protein (YFP) levels were about half of those obtained with the strong, constitutive phycocyanin (cpcBA6803) promoter of Synechocystis sp. strain PCC 6803. This metal-inducible expression system in Synechococcus sp. strain PCC 7002 allowed the titratable gene expression of YFP that was up to 19-fold greater than the background level. This system was utilized successfully to control the expression of the Drosophila melanogaster ß-carotene 15,15'-dioxygenase, NinaB, which is toxic when constitutively expressed from a strong promoter in Synechococcus sp. strain PCC 7002. Together, these properties establish this metal-inducible system as an additional useful tool that is capable of controlling gene expression for applications ranging from basic research to synthetic biology in Synechococcus sp. strain PCC 7002. IMPORTANCE: This is the first metal-responsive expression system in cyanobacteria, to our knowledge, that does not exhibit low sensitivity for induction, which is one of the major hurdles for utilizing this class of genetic tools. In addition, high levels of expression can be generated that approximate those of established constitutive systems, with the added advantage of titratable control. Together, these properties establish this Zn2+-inducible system, which is based on the smtA7942 operator/promoter and smtB7942 repressor, as a versatile gene expression platform that expands the genetic toolbox of Synechococcus sp. strain PCC 7002.

Network analysis of transcriptomics expands regulatory landscapes in Synechococcus sp. PCC 7002.

Cyanobacterial regulation of gene expression must contend with a genome organization that lacks apparent functional context, as the majority of cellular processes and metabolic pathways are encoded by genes found at disparate locations across the genome and relatively few transcription factors exist. In this study, global transcript abundance data from the model cyanobacterium Synechococcus sp. PCC 7002 grown under 42 different conditions was analyzed using Context-Likelihood of Relatedness (CLR). The resulting network, organized into 11 modules, provided insight into transcriptional network topology as well as grouping genes by function and linking their response to specific environmental variables. When used in conjunction with genome sequences, the network allowed identification and expansion of novel potential targets of both DNA binding proteins and sRNA regulators. These results offer a new perspective into the multi-level regulation that governs cellular adaptations of the fast-growing physiologically robust cyanobacterium Synechococcus sp. PCC 7002 to changing environmental variables. It also provides a methodological high-throughput approach to studying multi-scale regulatory mechanisms that operate in cyanobacteria. Finally, it provides valuable context for integrating systems-level data to enhance gene grouping based on annotated function, especially in organisms where traditional context analyses cannot be implemented due to lack of operon-based functional organization.

Fur-type transcriptional repressors and metal homeostasis in the cyanobacterium Synechococcus sp. PCC 7002.

Metal homeostasis is a crucial cellular function for nearly all organisms. Some heavy metals (e.g., Fe, Zn, Co, Mo) are essential because they serve as cofactors for enzymes or metalloproteins, and chlorophototrophs such as cyanobacteria have an especially high demand for iron. At excessive levels, however, metals become toxic to cyanobacteria. Therefore, a tight control mechanism is essential for metal homeostasis. Metal homeostasis in microorganisms comprises two elements: metal acquisition from the environment and detoxification or excretion of excess metal ions. Different families of metal-sensing regulators exist in cyanobacteria and each addresses a more or less specific set of target genes. In this study the regulons of three Fur-type and two ArsR-SmtB-type regulators were investigated in a comparative approach in the cyanobacterium Synechococcus sp. PCC 7002. One Fur-type regulator controls genes for iron acquisition (Fur); one controls genes for zinc acquisition (Zur); and the third controls two genes involved in oxidative stress (Per). Compared to other well-investigated cyanobacterial strains, however, the set of target genes for each regulator is relatively small. Target genes for the two ArsR-SmtB transcriptional repressors (SmtB (SYNPCC7002_A2564) and SYNPCC7002_A0590) are involved in zinc homeostasis in addition to Zur. Their target genes, however, are less specific for zinc and point to roles in a broader heavy metal detoxification response.

ChlR protein of Synechococcus sp. PCC 7002 is a transcription activator that uses an oxygen-sensitive [4Fe-4S] cluster to control genes involved in pigment biosynthesis.

Synechococcus sp. PCC 7002 and many other cyanobacteria have two genes that encode key enzymes involved in chlorophyll a, biliverdin, and heme biosynthesis: acsFI/acsFII, ho1/ho2, and hemF/hemN. Under atmospheric O2 levels, AcsFI synthesizes 3,8-divinyl protochlorophyllide from Mg-protoporphyrin IX monomethyl ester, Ho1 oxidatively cleaves heme to form biliverdin, and HemF oxidizes coproporphyrinogen III to protoporphyrinogen IX. Under microoxic conditions, another set of genes directs the synthesis of alternative enzymes AcsFII, Ho2, and HemN. In Synechococcus sp. PCC 7002, open reading frame SynPCC7002_A1993 encodes a MarR family transcriptional regulator, which is located immediately upstream from the operon comprising acsFII, ho2, hemN, and desF (the latter encodes a putative fatty acid desaturase). Deletion and complementation analyses showed that this gene, denoted chlR, is a transcriptional activator that is essential for transcription of the acsFII-ho2-hemN-desF operon under microoxic conditions. Global transcriptome analyses showed that ChlR controls the expression of only these four genes. Co-expression of chlR with a yfp reporter gene under the control of the acsFII promoter from Synechocystis sp. PCC 6803 in Escherichia coli demonstrated that no other cyanobacterium-specific components are required for proper functioning of this regulatory circuit. A combination of analytical methods and Mössbauer and EPR spectroscopies showed that reconstituted, recombinant ChlR forms homodimers that harbor one oxygen-sensitive [4Fe-4S] cluster. We conclude that ChlR is a transcriptional activator that uses a [4Fe-4S] cluster to sense O2 levels and thereby control the expression of the acsFII-ho2-hemN-desF operon.

Temporal metatranscriptomic patterning in phototrophic Chloroflexi inhabiting a microbial mat in a geothermal spring.

Filamentous anoxygenic phototrophs (FAPs) are abundant members of microbial mat communities inhabiting neutral and alkaline geothermal springs. Natural populations of FAPs related to Chloroflexus spp. and Roseiflexus spp. have been well characterized in Mushroom Spring, where they occur with unicellular cyanobacteria related to Synechococcus spp. strains A and B'. Metatranscriptomic sequencing was applied to the microbial community to determine how FAPs regulate their gene expression in response to fluctuating environmental conditions and resource availability over a diel period. Transcripts for genes involved in the biosynthesis of bacteriochlorophylls (BChls) and photosynthetic reaction centers were much more abundant at night. Both Roseiflexus spp. and Chloroflexus spp. expressed key genes involved in the 3-hydroxypropionate (3-OHP) carbon dioxide fixation bi-cycle during the day, when these FAPs have been thought to perform primarily photoheterotrophic and/or aerobic chemoorganotrophic metabolism. The expression of genes for the synthesis and degradation of storage polymers, including glycogen, polyhydroxyalkanoates and wax esters, suggests that FAPs produce and utilize these compounds at different times during the diel cycle. We summarize these results in a proposed conceptual model for temporal changes in central carbon metabolism and energy production of FAPs living in a natural environment. The model proposes that, at night, Chloroflexus spp. and Roseiflexus spp. synthesize BChl, components of the photosynthetic apparatus, polyhydroxyalkanoates and wax esters in concert with fermentation of glycogen. It further proposes that, in daytime, polyhydroxyalkanoates and wax esters are degraded and used as carbon and electron reserves to support photomixotrophy via the 3-OHP bi-cycle.

Altered carbohydrate metabolism in glycogen synthase mutants of Synechococcus sp. strain PCC 7002: Cell factories for soluble sugars.

Glycogen and compatible solutes are the major polymeric and soluble carbohydrates in cyanobacteria and function as energy reserves and osmoprotectants, respectively. Glycogen synthase null mutants (glgA-I glgA-II) were constructed in the cyanobacterium Synechococcus sp. strain PCC 7002. Under standard conditions the double mutant produced no glycogen and more soluble sugars. When grown under hypersaline conditions, the glgA-I glgA-II mutant accumulated 1.8-fold more soluble sugars (sucrose and glucosylglycer-(ol/ate)) than WT, and these cells spontaneously excreted soluble sugars into the medium at high levels without the need for additional transporters. An average of 27% more soluble sugars was released from the glgA-I glgA-II mutant than WT by hypo-osmotic shock. Extracellular vesicles budding from the outer membrane were observed by transmission electron microscopy in glgA-I glgA-II cells grown under hypersaline conditions. The glgA-I glgA-II mutant serves as a starting point for developing cell factories for photosynthetic production and excretion of sugars.

Synechococcus sp. Strain PCC 7002 Transcriptome: Acclimation to Temperature, Salinity, Oxidative Stress, and Mixotrophic Growth Conditions.

Synechococcus sp. strain PCC 7002 is a unicellular, euryhaline cyanobacterium. It is a model organism for studies of cyanobacterial metabolism and has great potential for biotechnological applications. It exhibits an exceptional tolerance of high-light irradiation and shows very rapid growth. The habitats from which this and closely related strains were isolated are subject to changes in several environmental factors, including light, nutrient supply, temperature, and salinity. In this study global transcriptome profiling via RNAseq has been used to perform a comparative and integrated study of global changes in cells grown at different temperatures, at different salinities, and under mixotrophic conditions, when a metabolizable organic carbon source was present. Furthermore, the transcriptomes were investigated for cells that were subjected to a heat shock and that were exposed to oxidative stress. Lower growth temperatures caused relatively minor changes of the transcriptome; the most prominent changes affected fatty acid desaturases. A heat shock caused severe changes of the transcriptome pattern; transcripts for genes associated with major metabolic pathways declined and those for different chaperones increased dramatically. Oxidative stress, however, left the transcript pattern almost unaffected. When grown at high salinity, Synechococcus sp. PCC 7002 had increased expression of genes involved in compatible solute biosynthesis and showed increased mRNA levels for several genes involved in electron transport. Transcripts of two adjacent genes dramatically increased upon growth at high salinity; the respective proteins are putatively involved in coping with oxidative stress and in triggering ion channels. Only minor changes were observed when cells were grown at low salinity or when the growth medium was supplemented with glycerol. However, the transcriptome data suggest that cells must acclimate to excess reducing equivalents when a reduced C-source is present.

Acclimation of the Global Transcriptome of the Cyanobacterium Synechococcus sp. Strain PCC 7002 to Nutrient Limitations and Different Nitrogen Sources.

The unicellular, euryhaline cyanobacterium Synechococcus sp. strain PCC 7002 is a model organism for laboratory-based studies of cyanobacterial metabolism and is a potential platform for biotechnological applications. Two of its most notable properties are its exceptional tolerance of high-light intensity and very rapid growth under optimal conditions. In this study, transcription profiling by RNAseq has been used to perform an integrated study of global changes in transcript levels in cells subjected to limitation for the major nutrients CO(2), nitrogen, sulfate, phosphate, and iron. Transcriptional patterns for cells grown on nitrate, ammonia, and urea were also studied. Nutrient limitation caused strong decreases of transcript levels of the genes encoding major metabolic pathways, especially for components of the photosynthetic apparatus, CO(2) fixation, and protein biosynthesis. Uptake mechanisms for the respective nutrients were strongly up-regulated. The transcription data further suggest that major changes in the composition of the NADH dehydrogenase complex occur upon nutrient limitation. Transcripts for flavoproteins increased strongly when CO(2) was limiting. Genes involved in protection from oxidative stress generally showed high, constitutive transcript levels, which possibly explains the high-light tolerance of this organism. The transcriptomes of cells grown with ammonia or urea as nitrogen source showed increased transcript levels for components of the CO(2) fixation machinery compared to cells grown with nitrate, but in general transcription differences in cells grown on different N-sources exhibited surprisingly minor differences.

'Candidatus Thermochlorobacter aerophilum:' an aerobic chlorophotoheterotrophic member of the phylum Chlorobi defined by metagenomics and metatranscriptomics.

An uncultured member of the phylum Chlorobi, provisionally named 'Candidatus Thermochlorobacter aerophilum', occurs in the microbial mats of alkaline siliceous hot springs at the Yellowstone National Park. 'Ca. T. aerophilum' was investigated through metagenomic and metatranscriptomic approaches. 'Ca. T. aerophilum' is a member of a novel, family-level lineage of Chlorobi, a chlorophototroph that synthesizes type-1 reaction centers and chlorosomes similar to cultivated relatives among the green sulfur bacteria, but is otherwise very different physiologically. 'Ca. T. aerophilum' is proposed to be an aerobic photoheterotroph that cannot oxidize sulfur compounds, cannot fix N(2), and does not fix CO(2) autotrophically. Metagenomic analyses suggest that 'Ca. T. aerophilum' depends on other mat organisms for fixed carbon and nitrogen, several amino acids, and other important nutrients. The failure to detect bchU suggests that 'Ca. T. aerophilum' synthesizes bacteriochlorophyll (BChl) d, and thus it occupies a different ecological niche than other chlorosome-containing chlorophototrophs in the mat. Transcription profiling throughout a diel cycle revealed distinctive gene expression patterns. Although 'Ca. T. aerophilum' probably photoassimilates organic carbon sources and synthesizes most of its cell materials during the day, it mainly transcribes genes for BChl synthesis during late afternoon and early morning, and it synthesizes and assembles its photosynthetic apparatus during the night.

Transcription Profiling of the Model Cyanobacterium Synechococcus sp. Strain PCC 7002 by Next-Gen (SOLiD™) Sequencing of cDNA.

The genome of the unicellular, euryhaline cyanobacterium Synechococcus sp. PCC 7002 encodes about 3200 proteins. Transcripts were detected for nearly all annotated open reading frames by a global transcriptomic analysis by Next-Generation (SOLiD™) sequencing of cDNA. In the cDNA samples sequenced, ∼90% of the mapped sequences were derived from the 16S and 23S ribosomal RNAs and ∼10% of the sequences were derived from mRNAs. In cells grown photoautotrophically under standard conditions [38°C, 1% (v/v) CO(2) in air, 250 µmol photons m(-2) s(-1)], the highest transcript levels (up to 2% of the total mRNA for the most abundantly transcribed genes; e.g., cpcAB, psbA, psaA) were generally derived from genes encoding structural components of the photosynthetic apparatus. High-light exposure for 1 h caused changes in transcript levels for genes encoding proteins of the photosynthetic apparatus, Type-1 NADH dehydrogenase complex and ATP synthase, whereas dark incubation for 1 h resulted in a global decrease in transcript levels for photosynthesis-related genes and an increase in transcript levels for genes involved in carbohydrate degradation. Transcript levels for pyruvate kinase and the pyruvate dehydrogenase complex decreased sharply in cells incubated in the dark. Under dark anoxic (fermentative) conditions, transcript changes indicated a global decrease in transcripts for respiratory proteins and suggested that cells employ an alternative phosphoenolpyruvate degradation pathway via phosphoenolpyruvate synthase (ppsA) and the pyruvate:ferredoxin oxidoreductase (nifJ). Finally, the data suggested that an apparent operon involved in tetrapyrrole biosynthesis and fatty acid desaturation, acsF2-ho2-hemN2-desF, may be regulated by oxygen concentration.