Explainable multi-task learning improves the parallel estimation of polygenic risk scores for many diseases through shared genetic basis.

Many complex diseases share common genetic determinants and are comorbid in a population. We hypothesized that the co-occurrences of diseases and their overlapping genetic etiology can be exploited to simultaneously improve multiple diseases' polygenic risk scores (PRS). This hypothesis was tested using a multi-task learning (MTL) approach based on an explainable neural network architecture. We found that parallel estimations of the PRS for 17 prevalent cancers in a pan-cancer MTL model were generally more accurate than independent estimations for individual cancers in comparable single-task learning (STL) models. Such performance improvement conferred by positive transfer learning was also observed consistently for 60 prevalent non-cancer diseases in a pan-disease MTL model. Interpretation of the MTL models revealed significant genetic correlations between the important sets of single nucleotide polymorphisms used by the neural network for PRS estimation. This suggested a well-connected network of diseases with shared genetic basis.

Phytoplankton exudates and lysates support distinct microbial consortia with specialized metabolic and ecophysiological traits.

Blooms of marine phytoplankton fix complex pools of dissolved organic matter (DOM) that are thought to be partitioned among hundreds of heterotrophic microbes at the base of the food web. While the relationship between microbial consumers and phytoplankton DOM is a key component of marine carbon cycling, microbial loop metabolism is largely understood from model organisms and substrates. Here, we took an untargeted approach to measure and analyze partitioning of four distinct phytoplankton-derived DOM pools among heterotrophic populations in a natural microbial community using a combination of ecogenomics, stable isotope probing (SIP), and proteomics. Each 13C-labeled exudate or lysate from a diatom or a picocyanobacterium was preferentially assimilated by different heterotrophic taxa with specialized metabolic and physiological adaptations. Bacteroidetes populations, with their unique high-molecular-weight transporters, were superior competitors for DOM derived from diatom cell lysis, rapidly increasing growth rates and ribosomal protein expression to produce new relatively high C:N biomass. Proteobacteria responses varied, with relatively low levels of assimilation by Gammaproteobacteria populations, while copiotrophic Alphaproteobacteria such as the Roseobacter clade, with their diverse array of ABC- and TRAP-type transporters to scavenge monomers and nitrogen-rich metabolites, accounted for nearly all cyanobacteria exudate assimilation and produced new relatively low C:N biomass. Carbon assimilation rates calculated from SIP data show that exudate and lysate from two common marine phytoplankton are being used by taxonomically distinct sets of heterotrophic populations with unique metabolic adaptations, providing a deeper mechanistic understanding of consumer succession and carbon use during marine bloom events.

MetaLP: An integrative linear programming method for protein inference in metaproteomics.

Metaproteomics based on high-throughput tandem mass spectrometry (MS/MS) plays a crucial role in characterizing microbiome functions. The acquired MS/MS data is searched against a protein sequence database to identify peptides, which are then used to infer a list of proteins present in a metaproteome sample. While the problem of protein inference has been well-studied for proteomics of single organisms, it remains a major challenge for metaproteomics of complex microbial communities because of the large number of degenerate peptides shared among homologous proteins in different organisms. This challenge calls for improved discrimination of true protein identifications from false protein identifications given a set of unique and degenerate peptides identified in metaproteomics. MetaLP was developed here for protein inference in metaproteomics using an integrative linear programming method. Taxonomic abundance information extracted from metagenomics shotgun sequencing or 16s rRNA gene amplicon sequencing, was incorporated as prior information in MetaLP. Benchmarking with mock, human gut, soil, and marine microbial communities demonstrated significantly higher numbers of protein identifications by MetaLP than ProteinLP, PeptideProphet, DeepPep, PIPQ, and Sipros Ensemble. In conclusion, MetaLP could substantially improve protein inference for complex metaproteomes by incorporating taxonomic abundance information in a linear programming model.

Polyethylene Degradation by a Rhodococcous Strain Isolated from Naturally Weathered Plastic Waste Enrichment.

Polyethylene (PE) is the most widely produced synthetic polymer and the most abundant plastic waste worldwide due to its recalcitrance to biodegradation and low recycle rate. Microbial degradation of PE has been reported, but the underlying mechanisms are poorly understood. Here, we isolated a Rhodococcus strain A34 from 609 day enriched cultures derived from naturally weathered plastic waste and identified the potential key PE degradation enzymes. After 30 days incubation with A34, 1% weight loss was achieved. Decreased PE molecular weight, appearance of C-O and C═O on PE, palmitic acid in the culture supernatant, and pits on the PE surface were observed. Proteomics analysis identified multiple key PE oxidation and depolymerization enzymes including one multicopper oxidase, one lipase, six esterase, and a few lipid transporters. Network analysis of proteomics data demonstrated the close relationships between PE degradation and metabolisms of phenylacetate, amino acids, secondary metabolites, and tricarboxylic acid cycles. The metabolic roadmap generated here provides critical insights for optimization of plastic degradation condition and assembly of artificial microbial communities for efficient plastic degradation.

Deep neural network improves the estimation of polygenic risk scores for breast cancer.

Polygenic risk scores (PRS) estimate the genetic risk of an individual for a complex disease based on many genetic variants across the whole genome. In this study, we compared a series of computational models for estimation of breast cancer PRS. A deep neural network (DNN) was found to outperform alternative machine learning techniques and established statistical algorithms, including BLUP, BayesA, and LDpred. In the test cohort with 50% prevalence, the Area Under the receiver operating characteristic Curve (AUC) were 67.4% for DNN, 64.2% for BLUP, 64.5% for BayesA, and 62.4% for LDpred. BLUP, BayesA, and LPpred all generated PRS that followed a normal distribution in the case population. However, the PRS generated by DNN in the case population followed a bimodal distribution composed of two normal distributions with distinctly different means. This suggests that DNN was able to separate the case population into a high-genetic-risk case subpopulation with an average PRS significantly higher than the control population and a normal-genetic-risk case subpopulation with an average PRS similar to the control population. This allowed DNN to achieve 18.8% recall at 90% precision in the test cohort with 50% prevalence, which can be extrapolated to 65.4% recall at 20% precision in a general population with 12% prevalence. Interpretation of the DNN model identified salient variants that were assigned insignificant p values by association studies, but were important for DNN prediction. These variants may be associated with the phenotype through nonlinear relationships.

Using Apache Spark on genome assembly for scalable overlap-graph reduction.

BACKGROUND: De novo genome assembly is a technique that builds the genome of a specimen using overlaps of genomic fragments without additional work with reference sequence. Sequence fragments (called reads) are assembled as contigs and scaffolds by the overlaps. The quality of the de novo assembly depends on the length and continuity of the assembly. To enable faster and more accurate assembly of species, existing sequencing techniques have been proposed, for example, high-throughput next-generation sequencing and long-reads-producing third-generation sequencing. However, these techniques require a large amounts of computer memory when very huge-size overlap graphs are resolved. Also, it is challenging for parallel computation. RESULTS: To address the limitations, we propose an innovative algorithmic approach, called Scalable Overlap-graph Reduction Algorithms (SORA). SORA is an algorithm package that performs string graph reduction algorithms by Apache Spark. The SORA's implementations are designed to execute de novo genome assembly on either a single machine or a distributed computing platform. SORA efficiently compacts the number of edges on enormous graphing paths by adapting scalable features of graph processing libraries provided by Apache Spark, GraphX and GraphFrames. CONCLUSIONS: We shared the algorithms and the experimental results at our project website, https://github.com/BioHPC/SORA . We evaluated SORA with the human genome samples. First, it processed a nearly one billion edge graph on a distributed cloud cluster. Second, it processed mid-to-small size graphs on a single workstation within a short time frame. Overall, SORA achieved the linear-scaling simulations for the increased computing instances.

Sipros Ensemble improves database searching and filtering for complex metaproteomics.

Motivation: Complex microbial communities can be characterized by metagenomics and metaproteomics. However, metagenome assemblies often generate enormous, and yet incomplete, protein databases, which undermines the identification of peptides and proteins in metaproteomics. This challenge calls for increased discrimination of true identifications from false identifications by database searching and filtering algorithms in metaproteomics. Results: Sipros Ensemble was developed here for metaproteomics using an ensemble approach. Three diverse scoring functions from MyriMatch, Comet and the original Sipros were incorporated within a single database searching engine. Supervised classification with logistic regression was used to filter database searching results. Benchmarking with soil and marine microbial communities demonstrated a higher number of peptide and protein identifications by Sipros Ensemble than MyriMatch/Percolator, Comet/Percolator, MS-GF+/Percolator, Comet & MyriMatch/iProphet and Comet & MyriMatch & MS-GF+/iProphet. Sipros Ensemble was computationally efficient and scalable on supercomputers. Availability and implementation: Freely available under the GNU GPL license at http://sipros.omicsbio.org. Contact: cpan@utk.edu. Supplementary information: Supplementary data are available at Bioinformatics online.

Proteomic and Mutant Analysis of the CO2 Concentrating Mechanism of Hydrothermal Vent Chemolithoautotroph Thiomicrospira crunogena.

Many autotrophic microorganisms are likely to adapt to scarcity in dissolved inorganic carbon (DIC; CO2 + HCO3- + CO32-) with CO2 concentrating mechanisms (CCM) that actively transport DIC across the cell membrane to facilitate carbon fixation. Surprisingly, DIC transport has been well studied among cyanobacteria and microalgae only. The deep-sea vent gammaproteobacterial chemolithoautotroph Thiomicrospira crunogena has a low-DIC inducible CCM, though the mechanism for uptake is unclear, as homologs to cyanobacterial transporters are absent. To identify the components of this CCM, proteomes of T. crunogena cultivated under low- and high-DIC conditions were compared. Fourteen proteins, including those comprising carboxysomes, were at least 4-fold more abundant under low-DIC conditions. One of these proteins was encoded by Tcr_0854; strains carrying mutated copies of this gene, as well as the adjacent Tcr_0853, required elevated DIC for growth. Strains carrying mutated copies of Tcr_0853 and Tcr_0854 overexpressed carboxysomes and had diminished ability to accumulate intracellular DIC. Based on reverse transcription (RT)-PCR, Tcr_0853 and Tcr_0854 were cotranscribed and upregulated under low-DIC conditions. The Tcr_0853-encoded protein was predicted to have 13 transmembrane helices. Given the mutant phenotypes described above, Tcr_0853 and Tcr_0854 may encode a two-subunit DIC transporter that belongs to a previously undescribed transporter family, though it is widespread among autotrophs from multiple phyla.IMPORTANCE DIC uptake and fixation by autotrophs are the primary input of inorganic carbon into the biosphere. The mechanism for dissolved inorganic carbon uptake has been characterized only for cyanobacteria despite the importance of DIC uptake by autotrophic microorganisms from many phyla among the Bacteria and Archaea In this work, proteins necessary for dissolved inorganic carbon utilization in the deep-sea vent chemolithoautotroph T. crunogena were identified, and two of these may be able to form a novel transporter. Homologs of these proteins are present in 14 phyla in Bacteria and also in one phylum of Archaea, the Euryarchaeota Many organisms carrying these homologs are autotrophs, suggesting a role in facilitating dissolved inorganic carbon uptake and fixation well beyond the genus Thiomicrospira.

Integrated proteomics and metabolomics suggests symbiotic metabolism and multimodal regulation in a fungal-endobacterial system.

Many plant-associated fungi host endosymbiotic endobacteria with reduced genomes. While endobacteria play important roles in these tri-partite plant-fungal-endobacterial systems, the active physiology of fungal endobacteria has not been characterized extensively by systems biology approaches. Here, we use integrated proteomics and metabolomics to characterize the relationship between the endobacterium Mycoavidus sp. and the root-associated fungus Mortierella elongata. In nitrogen-poor media, M. elongata had decreased growth but hosted a large and growing endobacterial population. The active endobacterium likely extracted malate from the fungal host as the primary carbon substrate for energy production and biosynthesis of phospho-sugars, nucleobases, peptidoglycan and some amino acids. The endobacterium obtained nitrogen by importing a variety of nitrogen-containing compounds. Further, nitrogen limitation significantly perturbed the carbon and nitrogen flows in the fungal metabolic network. M. elongata regulated many pathways by concordant changes on enzyme abundances, post-translational modifications, reactant concentrations and allosteric effectors. Such multimodal regulations may be a general mechanism for metabolic modulation.

Sigma: strain-level inference of genomes from metagenomic analysis for biosurveillance.

MOTIVATION: Metagenomic sequencing of clinical samples provides a promising technique for direct pathogen detection and characterization in biosurveillance. Taxonomic analysis at the strain level can be used to resolve serotypes of a pathogen in biosurveillance. Sigma was developed for strain-level identification and quantification of pathogens using their reference genomes based on metagenomic analysis. RESULTS: Sigma provides not only accurate strain-level inferences, but also three unique capabilities: (i) Sigma quantifies the statistical uncertainty of its inferences, which includes hypothesis testing of identified genomes and confidence interval estimation of their relative abundances; (ii) Sigma enables strain variant calling by assigning metagenomic reads to their most likely reference genomes; and (iii) Sigma supports parallel computing for fast analysis of large datasets. The algorithm performance was evaluated using simulated mock communities and fecal samples with spike-in pathogen strains. AVAILABILITY AND IMPLEMENTATION: Sigma was implemented in C++ with source codes and binaries freely available at http://sigma.omicsbio.org. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Effects of diet on resource utilization by a model human gut microbiota containing Bacteroides cellulosilyticus WH2, a symbiont with an extensive glycobiome.

The human gut microbiota is an important metabolic organ, yet little is known about how its individual species interact, establish dominant positions, and respond to changes in environmental factors such as diet. In this study, gnotobiotic mice were colonized with an artificial microbiota comprising 12 sequenced human gut bacterial species and fed oscillating diets of disparate composition. Rapid, reproducible, and reversible changes in the structure of this assemblage were observed. Time-series microbial RNA-Seq analyses revealed staggered functional responses to diet shifts throughout the assemblage that were heavily focused on carbohydrate and amino acid metabolism. High-resolution shotgun metaproteomics confirmed many of these responses at a protein level. One member, Bacteroides cellulosilyticus WH2, proved exceptionally fit regardless of diet. Its genome encoded more carbohydrate active enzymes than any previously sequenced member of the Bacteroidetes. Transcriptional profiling indicated that B. cellulosilyticus WH2 is an adaptive forager that tailors its versatile carbohydrate utilization strategy to available dietary polysaccharides, with a strong emphasis on plant-derived xylans abundant in dietary staples like cereal grains. Two highly expressed, diet-specific polysaccharide utilization loci (PULs) in B. cellulosilyticus WH2 were identified, one with characteristics of xylan utilization systems. Introduction of a B. cellulosilyticus WH2 library comprising >90,000 isogenic transposon mutants into gnotobiotic mice, along with the other artificial community members, confirmed that these loci represent critical diet-specific fitness determinants. Carbohydrates that trigger dramatic increases in expression of these two loci and many of the organism's 111 other predicted PULs were identified by RNA-Seq during in vitro growth on 31 distinct carbohydrate substrates, allowing us to better interpret in vivo RNA-Seq and proteomics data. These results offer insight into how gut microbes adapt to dietary perturbations at both a community level and from the perspective of a well-adapted symbiont with exceptional saccharolytic capabilities, and illustrate the value of artificial communities.

Microbial metaproteomics for characterizing the range of metabolic functions and activities of human gut microbiota.

The human gastrointestinal tract is a complex, dynamic ecosystem that consists of a carefully tuned balance of human host and microbiota membership. The microbiome is not merely a collection of opportunistic parasites, but rather provides important functions to the host that are absolutely critical to many aspects of health, including nutrient transformation and absorption, drug metabolism, pathogen defense, and immune system development. Microbial metaproteomics provides the ability to characterize the human gut microbiota functions and metabolic activities at a remarkably deep level, revealing information about microbiome development and stability as well as their interactions with their human host. Generally, microbial and human proteins can be extracted and then measured by high performance MS-based proteomics technology. Here, we review the field of human gut microbiome metaproteomics, with a focus on the experimental and informatics considerations involved in characterizing systems ranging from low-complexity model gut microbiota in gnotobiotic mice, to the emerging gut microbiome in the GI tract of newborn human infants, and finally to an established gut microbiota in human adults.

Metaproteomics reveals functional shifts in microbial and human proteins during a preterm infant gut colonization case.

Microbial colonization of the human gastrointestinal tract plays an important role in establishing health and homeostasis. However, the time-dependent functional signatures of microbial and human proteins during early colonization of the gut have yet to be determined. To this end, we employed shotgun proteomics to simultaneously monitor microbial and human proteins in fecal samples from a preterm infant during the first month of life. Microbial community complexity increased over time, with compositional changes that were consistent with previous metagenomic and rRNA gene data. More specifically, the function of the microbial community initially involved biomass growth, protein production, and lipid metabolism, and then switched to more complex metabolic functions, such as carbohydrate metabolism, once the community stabilized and matured. Human proteins detected included those responsible for epithelial barrier function and antimicrobial activity. Some neutrophil-derived proteins increased in abundance early in the study period, suggesting activation of the innate immune system. Likewise, abundances of cytoskeletal and mucin proteins increased later in the time course, suggestive of subsequent adjustment to the increased microbial load. This study provides the first snapshot of coordinated human and microbial protein expression in a preterm infant's gut during early development.

Rhodopseudomonas palustris CGA010 Proteome Implicates Extracytoplasmic Function Sigma Factor in Stress Response.

Rhodopseudomonas palustris encodes 16 extracytoplasmic function (ECF) σ factors. To begin to investigate the regulatory network of one of these ECF σ factors, the whole proteome of R. palustris CGA010 was quantitatively analyzed by tandem mass spectrometry from cultures episomally expressing the ECF σ(RPA4225) (ecfT) versus a WT control. Among the proteins with the greatest increase in abundance were catalase KatE, trehalose synthase, a DPS-like protein, and several regulatory proteins. Alignment of the cognate promoter regions driving expression of several upregulated proteins suggested a conserved binding motif in the -35 and -10 regions with the consensus sequence GGAAC-18N-TT. Additionally, the putative anti-σ factor RPA4224, whose gene is contained in the same predicted operon as RPA4225, was identified as interacting directly with the predicted response regulator RPA4223 by mass spectrometry of affinity-isolated protein complexes. Furthermore, another gene (RPA4226) coding for a protein that contains a cytoplasmic histidine kinase domain is located immediately upstream of RPA4225. The genomic organization of orthologs for these four genes is conserved in several other strains of R. palustris as well as in closely related α-Proteobacteria. Taken together, these data suggest that ECF σ(RPA4225) and the three additional genes make up a sigma factor mimicry system in R. palustris.

Omega: an overlap-graph de novo assembler for metagenomics.

MOTIVATION: Metagenomic sequencing allows reconstruction of microbial genomes directly from environmental samples. Omega (overlap-graph metagenome assembler) was developed for assembling and scaffolding Illumina sequencing data of microbial communities. RESULTS: Omega found overlaps between reads using a prefix/suffix hash table. The overlap graph of reads was simplified by removing transitive edges and trimming short branches. Unitigs were generated based on minimum cost flow analysis of the overlap graph and then merged to contigs and scaffolds using mate-pair information. In comparison with three de Bruijn graph assemblers (SOAPdenovo, IDBA-UD and MetaVelvet), Omega provided comparable overall performance on a HiSeq 100-bp dataset and superior performance on a MiSeq 300-bp dataset. In comparison with Celera on the MiSeq dataset, Omega provided more continuous assemblies overall using a fraction of the computing time of existing overlap-layout-consensus assemblers. This indicates Omega can more efficiently assemble longer Illumina reads, and at deeper coverage, for metagenomic datasets. AVAILABILITY AND IMPLEMENTATION: Implemented in C++ with source code and binaries freely available at http://omega.omicsbio.org.

Strigolactone-regulated proteins revealed by iTRAQ-based quantitative proteomics in Arabidopsis.

Strigolactones (SLs) are a new class of plant hormones. In addition to acting as a key inhibitor of shoot branching, SLs stimulate seed germination of root parasitic plants and promote hyphal branching and root colonization of symbiotic arbuscular mycorrhizal fungi. They also regulate many other aspects of plant growth and development. At the transcription level, SL-regulated genes have been reported. However, nothing is known about the proteome regulated by this new class of plant hormones. A quantitative proteomics approach using an isobaric chemical labeling reagent, iTRAQ, to identify the proteome regulated by SLs in Arabidopsis seedlings is presented. It was found that SLs regulate the expression of about three dozen proteins that have not been previously assigned to SL pathways. These findings provide a new tool to investigate the molecular mechanism of action of SLs.

Functional phylogenomics analysis of bacteria and archaea using consistent genome annotation with UniFam.

BACKGROUND: Phylogenetic studies have provided detailed knowledge on the evolutionary mechanisms of genes and species in Bacteria and Archaea. However, the evolution of cellular functions, represented by metabolic pathways and biological processes, has not been systematically characterized. Many clades in the prokaryotic tree of life have now been covered by sequenced genomes in GenBank. This enables a large-scale functional phylogenomics study of many computationally inferred cellular functions across all sequenced prokaryotes. RESULTS: A total of 14,727 GenBank prokaryotic genomes were re-annotated using a new protein family database, UniFam, to obtain consistent functional annotations for accurate comparison. The functional profile of a genome was represented by the biological process Gene Ontology (GO) terms in its annotation. The GO term enrichment analysis differentiated the functional profiles between selected archaeal taxa. 706 prokaryotic metabolic pathways were inferred from these genomes using Pathway Tools and MetaCyc. The consistency between the distribution of metabolic pathways in the genomes and the phylogenetic tree of the genomes was measured using parsimony scores and retention indices. The ancestral functional profiles at the internal nodes of the phylogenetic tree were reconstructed to track the gains and losses of metabolic pathways in evolutionary history. CONCLUSIONS: Our functional phylogenomics analysis shows divergent functional profiles of taxa and clades. Such function-phylogeny correlation stems from a set of clade-specific cellular functions with low parsimony scores. On the other hand, many cellular functions are sparsely dispersed across many clades with high parsimony scores. These different types of cellular functions have distinct evolutionary patterns reconstructed from the prokaryotic tree.

Geochemical, metagenomic and metaproteomic insights into trace metal utilization by methane-oxidizing microbial consortia in sulphidic marine sediments.

Microbes have obligate requirements for trace metals in metalloenzymes that catalyse important biogeochemical reactions. In anoxic methane- and sulphide-rich environments, microbes may have unique adaptations for metal acquisition and utilization because of decreased bioavailability as a result of metal sulphide precipitation. However, micronutrient cycling is largely unexplored in cold (≤ 10°C) and sulphidic (> 1 mM ΣH(2)S) deep-sea methane seep ecosystems. We investigated trace metal geochemistry and microbial metal utilization in methane seeps offshore Oregon and California, USA, and report dissolved concentrations of nickel (0.5-270 nM), cobalt (0.5-6 nM), molybdenum (10-5600 nM) and tungsten (0.3-8 nM) in Hydrate Ridge sediment porewaters. Despite low levels of cobalt and tungsten, metagenomic and metaproteomic data suggest that microbial consortia catalysing anaerobic oxidation of methane (AOM) utilize both scarce micronutrients in addition to nickel and molybdenum. Genetic machinery for cobalt-containing vitamin B12 biosynthesis was present in both anaerobic methanotrophic archaea (ANME) and sulphate-reducing bacteria. Proteins affiliated with the tungsten-containing form of formylmethanofuran dehydrogenase were expressed in ANME from two seep ecosystems, the first evidence for expression of a tungstoenzyme in psychrophilic microorganisms. Overall, our data suggest that AOM consortia use specialized biochemical strategies to overcome the challenges of metal availability in sulphidic environments.

(15)N- and (2)H proteomic stable isotope probing links nitrogen flow to archaeal heterotrophic activity.

Understanding how individual species contribute to nutrient transformations in a microbial community is critical to prediction of overall ecosystem function. We conducted microcosm experiments in which floating acid mine drainage (AMD) microbial biofilms were submerged - recapitulating the final stage in a natural biofilm life cycle. Biofilms were amended with either (15)NH4(+) or deuterium oxide ((2)H2O) and proteomic stable isotope probing (SIP) was used to track the extent to which different members of the community used these molecules in protein synthesis across anaerobic iron-reducing, aerobic iron-reducing and aerobic iron-oxidizing environments. Sulfobacillus spp. synthesized (15)N-enriched protein almost exclusively under iron-reducing conditions whereas the Leptospirillum spp. synthesized (15)N-enriched protein in all conditions. There were relatively few (15)N-enriched archaeal proteins, and all showed low atom% enrichment, consistent with Archaea synthesizing protein using the predominantly (14)N biomass derived from recycled biomolecules. In parallel experiments using (2)H2O, extensive archaeal protein synthesis was detected in all conditions. In contrast, the bacterial species showed little protein synthesis using (2)H2O. The nearly exclusive ability of Archaea to synthesize proteins using (2)H2O may be due to archaeal heterotrophy, whereby Archaea offset deleterious effects of (2)H by accessing (1)H generated by respiration of organic compounds.

MIDAS: a database-searching algorithm for metabolite identification in metabolomics.

A database searching approach can be used for metabolite identification in metabolomics by matching measured tandem mass spectra (MS/MS) against the predicted fragments of metabolites in a database. Here, we present the open-source MIDAS algorithm (Metabolite Identification via Database Searching). To evaluate a metabolite-spectrum match (MSM), MIDAS first enumerates possible fragments from a metabolite by systematic bond dissociation, then calculates the plausibility of the fragments based on their fragmentation pathways, and finally scores the MSM to assess how well the experimental MS/MS spectrum from collision-induced dissociation (CID) is explained by the metabolite's predicted CID MS/MS spectrum. MIDAS was designed to search high-resolution tandem mass spectra acquired on time-of-flight or Orbitrap mass spectrometer against a metabolite database in an automated and high-throughput manner. The accuracy of metabolite identification by MIDAS was benchmarked using four sets of standard tandem mass spectra from MassBank. On average, for 77% of original spectra and 84% of composite spectra, MIDAS correctly ranked the true compounds as the first MSMs out of all MetaCyc metabolites as decoys. MIDAS correctly identified 46% more original spectra and 59% more composite spectra at the first MSMs than an existing database-searching algorithm, MetFrag. MIDAS was showcased by searching a published real-world measurement of a metabolome from Synechococcus sp. PCC 7002 against the MetaCyc metabolite database. MIDAS identified many metabolites missed in the previous study. MIDAS identifications should be considered only as candidate metabolites, which need to be confirmed using standard compounds. To facilitate manual validation, MIDAS provides annotated spectra for MSMs and labels observed mass spectral peaks with predicted fragments. The database searching and manual validation can be performed online at http://midas.omicsbio.org.