Spectral trees as a robust annotation tool in LC–MS based metabolomics

The identification of large series of metabolites detectable by mass spectrometry (MS) in crude extracts is a challenging task. In order to test and apply the so-called multistage mass spectrometry (MS ⁿ ) spectral tree approach as tool in metabolite identification in complex sample extracts, we firstly performed liquid chromatography (LC) with online electrospray ionization (ESI)?CMS ⁿ , using crude extracts from both tomato fruit and Arabidopsis leaf. Secondly, the extracts were automatically fractionated by a NanoMate LC-fraction collector/injection robot (Advion) and selected LC-fractions were subsequently analyzed using nanospray-direct infusion to generate offline in-depth MS ⁿ spectral trees at high mass resolution. Characterization and subsequent annotation of metabolites was achieved by detailed analysis of the MS ⁿ spectral trees, thereby focusing on two major plant secondary metabolite classes: phenolics and glucosinolates. Following this approach, we were able to discriminate all selected flavonoid glycosides, based on their unique MS ⁿ fragmentation patterns in either negative or positive ionization mode. As a proof of principle, we report here 127 annotated metabolites in the tomato and Arabidopsis extracts, including 21 novel metabolites. Our results indicate that online LC?CMS ⁿ fragmentation in combination with databases of in-depth spectral trees generated offline can provide a fast and reliable characterization and annotation of metabolites present in complex crude extracts such as those from plants.     

BEACON: automated tool for Bacterial GEnome Annotation ComparisON

Background

Genome annotation is one way of summarizing the existing knowledge about genomic characteristics of an organism. There has been an increased interest during the last several decades in computer-based structural and functional genome annotation. Many methods for this purpose have been developed for eukaryotes and prokaryotes. Our study focuses on comparison of functional annotations of prokaryotic genomes. To the best of our knowledge there is no fully automated system for detailed comparison of functional genome annotations generated by different annotation methods (AMs).

Results

The presence of many AMs and development of new ones introduce needs to: a/ compare different annotations for a single genome, and b/ generate annotation by combining individual ones. To address these issues we developed an Automated Tool for Bacterial GEnome Annotation ComparisON (BEACON) that benefits both AM developers and annotation analysers. BEACON provides detailed comparison of gene function annotations of prokaryotic genomes obtained by different AMs and generates extended annotations through combination of individual ones. For the illustration of BEACON’s utility, we provide a comparison analysis of multiple different annotations generated for four genomes and show on these examples that the extended annotation can increase the number of genes annotated by putative functions up to 27 %, while the number of genes without any function assignment is reduced.

Conclusions

We developed BEACON, a fast tool for an automated and a systematic comparison of different annotations of single genomes. The extended annotation assigns putative functions to many genes with unknown functions. BEACON is available under GNU General Public License version 3.0 and is accessible at: http://www.cbrc.kaust.edu.sa/BEACON/.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-015-1826-4) contains supplementary material, which is available to authorized users.     

Comprehensive mass spectrometry‐guided phenotyping of plant specialized metabolites reveals metabolic diversity in the cosmopolitan plant family Rhamnaceae

Plants produce a myriad of specialized metabolites to overcome their sessile habit and combat biotic as well as abiotic stresses. Evolution has shaped the diversity of specialized metabolites, which then drives many other aspects of plant biodiversity. However, until recently, large‐scale studies investigating the diversity of specialized metabolites in an evolutionary context have been limited by the impossibility of identifying 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 to 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 dark plant metabolomics. To gain insights into substructural diversity within this 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 for the automated, high‐throughput chemical identification of massive metabolite spaces, and we expect it to revolutionize our understanding of plant chemoevolutionary mechanisms.     

Enrichment of Triticum aestivum gene annotations using ortholog cliques and gene ontologies in other plants

Background

While the gargantuan multi-nation effort of sequencing T. aestivum gets close to completion, the annotation process for the vast number of wheat genes and proteins is in its infancy. Previous experimental studies carried out on model plant organisms such as A. thaliana and O. sativa provide a plethora of gene annotations that can be used as potential starting points for wheat gene annotations, proven that solid cross-species gene-to-gene and protein-to-protein correspondences are provided.

Results

DNA and protein sequences and corresponding annotations for T. aestivum and 9 other plant species were collected from Ensembl Plants release 22 and curated. Cliques of predicted 1-to-1 orthologs were identified and an annotation enrichment model was defined based on existing gene-GO term associations and phylogenetic relationships among wheat and 9 other plant species. A total of 13 cliques of size 10 were identified, which represent putative functionally equivalent genes and proteins in the 10 plant species. Eighty-five new and more specific GO terms were associated with wheat genes in the 13 cliques of size 10, which represent a 65% increase compared with the previously 130 known GO terms. Similar expression patterns for 4 genes from Arabidopsis, barley, maize and rice in cliques of size 10 provide experimental evidence to support our model. Overall, based on clique size equal or larger than 3, our model enriched the existing gene-GO term associations for 7,838 (8%) wheat genes, of which 2,139 had no previous annotation.

Conclusions

Our novel comparative genomics approach enriches existing T. aestivum gene annotations based on cliques of predicted 1-to-1 orthologs, phylogenetic relationships and existing gene ontologies from 9 other plant species.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-015-1496-2) contains supplementary material, which is available to authorized users.     

Effects of l-cysteine and O-acetyl-l-serine in the synthesis and mutagenicity of azide metabolite

The ability of L-cysteine to inhibit azide-metabolite synthesis and mutagenecity is investigated in Salmonella typhimurium TA1530 and cys E₆ strains. L-cysteine specifically inhibits the synthesis of the mutagenic azide metabolite as other compounds containing SH group did not affect the production of this metabolite. Azide mutagenicity is completely inhibited by L-cysteine at a concentration (5 μmoles/plate) where the metabolite mutagenicity was not affected. $\text{O-}\text{Acetyl-}\text{L}\text{-serine}$ can reverse the L-cysteine mediated inhibition of the metabolite synthesis and thus mutagenicity in the same strains. These results suggest that $\text{O-}\text{acetyl-}\text{L}\text{-serine}$ may be required to synthesize the azide metabolite or its precursor.     

Gene Ontology annotation quality analysis in model eukaryotes

Functional analysis using the Gene Ontology (GO) is crucial for array analysis, but it is often difficult for researchers to assess the amount and quality of GO annotations associated with different sets of gene products. In many cases the source of the GO annotations and the date the GO annotations were last updated is not apparent, further complicating a researchers’ ability to assess the quality of the GO data provided. Moreover, GO biocurators need to ensure that the GO quality is maintained and optimal for the functional processes that are most relevant for their research community. We report the GO Annotation Quality (GAQ) score, a quantitative measure of GO quality that includes breadth of GO annotation, the level of detail of annotation and the type of evidence used to make the annotation. As a case study, we apply the GAQ scoring method to a set of diverse eukaryotes and demonstrate how the GAQ score can be used to track changes in GO annotations over time and to assess the quality of GO annotations available for specific biological processes. The GAQ score also allows researchers to quantitatively assess the functional data available for their experimental systems (arrays or databases).     

Using the knowns to discover the unknowns: MS‐based dereplication uncovers structural diversity in 17‐hydroxygeranyllinalool diterpene glycoside production in the Solanaceae

Exploring the diversity of plant secondary metabolism requires efficient methods to obtain sufficient structural insights to discriminate previously known from unknown metabolites. De novo structure elucidation and confirmation of known metabolites (dereplication) remain a major bottleneck for mass spectrometry‐based metabolomic workflows, and few systematic dereplication strategies have been developed for the analysis of entire compound classes across plant families, partly due to the complexity of plant metabolic profiles that complicates cross‐species comparisons. 17‐hydroxygeranyllinalool diterpene glycosides (HGL‐DTGs) are abundant defensive secondary metabolites whose malonyl and glycosyl decorations are induced by jasmonate signaling in the ecological model plant Nicotiana attenuata. The multiple labile glycosidic bonds of HGL‐DTGs result in extensive in‐source fragmentation (IS‐CID) during ionization. To reconstruct these IS‐CID clusters from profiling data and identify precursor ions, we applied a deconvolution algorithm and created an MS/MS library from positive‐ion spectra of purified HGL‐DTGs. From this library, 251 non‐redundant fragments were annotated, and a workflow to characterize leaf, flower and fruit extracts of 35 solanaceous species was established. These analyses predicted 105 novel HGL‐DTGs that were restricted to Nicotiana, Capsicum and Lycium species. Interestingly, malonylation is a highly conserved step in HGL‐DTG metabolism, but is differentially affected by jasmonate signaling among Nicotiana species. This MS‐based workflow is readily applicable for cross‐species re‐identification/annotation of other compound classes with sufficient fragmentation knowledge, and therefore has the potential to support hypotheses regarding secondary metabolism diversification.     

Expanding the Coverage of Metabolic Landscape in Cultivated Rice with Integrated Computational Approaches

Genome-scale metabolomics analysis is increasingly used for pathway and function discovery in the post-genomics era. The great potential offered by developed mass spectrometry (MS)-based technologies has been hindered, since only a small portion of detected metabolites were identifiable so far. To address the critical issue of low identification coverage in metabolomics, we adopted a deep metabolomics analysis strategy by integrating advanced algorithms and expanded reference databases. The experimental reference spectra and in silico reference spectra were adopted to facilitate the structural annotation. To further characterize the structure of metabolites, two approaches were incorporated into our strategy, i.e., structural motif search combined with neutral loss scanning and metabolite association network. Untargeted metabolomics analysis was performed on 150 rice cultivars using ultra-performance liquid chromatography coupled with quadrupole-Orbitrap MS. Consequently, a total of 1939 out of 4491 metabolite features in the MS/MS spectral tag (MS2T) library were annotated, representing an extension of annotation coverage by an order of magnitude in rice. The differential accumulation patterns of flavonoids between indica and japonica cultivars were revealed, especially O-sulfated flavonoids. A series of closely-related flavonolignans were characterized, adding further evidence for the crucial role of tricin-oligolignols in lignification. Our study provides an important protocol for exploring phytochemical diversity in other plant species.     

A Factor Graph Approach to Automated GO Annotation

As volume of genomic data grows, computational methods become essential for providing a first glimpse onto gene annotations. Automated Gene Ontology (GO) annotation methods based on hierarchical ensemble classification techniques are particularly interesting when interpretability of annotation results is a main concern. In these methods, raw GO-term predictions computed by base binary classifiers are leveraged by checking the consistency of predefined GO relationships. Both formal leveraging strategies, with main focus on annotation precision, and heuristic alternatives, with main focus on scalability issues, have been described in literature. In this contribution, a factor graph approach to the hierarchical ensemble formulation of the automated GO annotation problem is presented. In this formal framework, a core factor graph is first built based on the GO structure and then enriched to take into account the noisy nature of GO-term predictions. Hence, starting from raw GO-term predictions, an iterative message passing algorithm between nodes of the factor graph is used to compute marginal probabilities of target GO-terms. Evaluations on Saccharomyces cerevisiae, Arabidopsis thaliana and Drosophila melanogaster protein sequences from the GO Molecular Function domain showed significant improvements over competing approaches, even when protein sequences were naively characterized by their physicochemical and secondary structure properties or when loose noisy annotation datasets were considered. Based on these promising results and using Arabidopsis thaliana annotation data, we extend our approach to the identification of most promising molecular function annotations for a set of proteins of unknown function in Solanum lycopersicum.     

Automatic annotation of spatial expression patterns via sparse Bayesian factor models

Advances in reporters for gene expression have made it possible to document and quantify expression patterns in 2D-4D. In contrast to microarrays, which provide data for many genes but averaged and/or at low resolution, images reveal the high spatial dynamics of gene expression. Developing computational methods to compare, annotate, and model gene expression based on images is imperative, considering that available data are rapidly increasing. We have developed a sparse Bayesian factor analysis model in which the observed expression diversity of among a large set of high-dimensional images is modeled by a small number of hidden common factors. We apply this approach on embryonic expression patterns from a Drosophila RNA in situ image database, and show that the automatically inferred factors provide for a meaningful decomposition and represent common co-regulation or biological functions. The low-dimensional set of factor mixing weights is further used as features by a classifier to annotate expression patterns with functional categories. On human-curated annotations, our sparse approach reaches similar or better classification of expression patterns at different developmental stages, when compared to other automatic image annotation methods using thousands of hard-to-interpret features. Our study therefore outlines a general framework for large microscopy data sets, in which both the generative model itself, as well as its application for analysis tasks such as automated annotation, can provide insight into biological questions.     

Evaluation of Three Automated Genome Annotations for Halorhabdus utahensis

Genome annotations are accumulating rapidly and depend heavily on automated annotation systems. Many genome centers offer annotation systems but no one has compared their output in a systematic way to determine accuracy and inherent errors. Errors in the annotations are routinely deposited in databases such as NCBI and used to validate subsequent annotation errors. We submitted the genome sequence of halophilic archaeon Halorhabdus utahensis to be analyzed by three genome annotation services. We have examined the output from each service in a variety of ways in order to compare the methodology and effectiveness of the annotations, as well as to explore the genes, pathways, and physiology of the previously unannotated genome. The annotation services differ considerably in gene calls, features, and ease of use. We had to manually identify the origin of replication and the species-specific consensus ribosome-binding site. Additionally, we conducted laboratory experiments to test H. utahensis growth and enzyme activity. Current annotation practices need to improve in order to more accurately reflect a genome''s biological potential. We make specific recommendations that could improve the quality of microbial annotation projects.     

Foliar nutrient allocation patterns in Banksia attenuata and Banksia sessilis differing in growth rate and adaptation to low-phosphorus habitats

Background and AimsPhosphorus (P) and nitrogen (N) are essential nutrients that frequently limit primary productivity in terrestrial ecosystems. Efficient use of these nutrients is important for plants growing in nutrient-poor environments. Plants generally reduce foliar P concentration in response to low soil P availability. We aimed to assess ecophysiological mechanisms and adaptive strategies for efficient use of P in Banksia attenuata (Proteaceae), naturally occurring on deep sand, and B. sessilis, occurring on shallow sand over laterite or limestone, by comparing the allocation of P among foliar P fractions.MethodsWe carried out pot experiments with slow-growing B. attenuata, which resprouts after fire, and faster growing opportunistic B. sessilis, which is killed by fire, on substrates with different P availability using a randomized complete block design. We measured leaf P and N concentrations, photosynthesis, leaf mass per area, relative growth rate and P allocated to major biochemical fractions in B. attenuata and B. sessilis.Key ResultsThe two species had similarly low foliar total P concentrations, but distinct patterns of P allocation to P-containing fractions. The foliar total N concentration of B. sessilis was greater than that of B. attenuata on all substrates. The foliar total P and N concentrations in both species decreased with decreasing P availability. The relative growth rate of both species was positively correlated with concentrations of both foliar nucleic acid P and total N, but there was no correlation with other P fractions. Faster growing B. sessilis allocated more P to nucleic acids than B. attenuata did, but other fractions were similar.ConclusionsThe nutrient allocation patterns in faster growing opportunistic B. sessilis and slower growing B. attenuata revealed different strategies in response to soil P availability which matched their contrasting growth strategy.     

IC4R-2.0:Rice Genome Reannotation Using Massive RNA-seq Data

Genome reannotation aims for complete and accurate characterization of gene models and thus is of critical significance for in-depth exploration of gene function. Although the availability of massive RNA-seq data provides great opportunities for gene model refinement, few efforts have been made to adopt these precious data in rice genome reannotation. Here we reannotate the rice (Oryza sativa L. ssp. japonica) genome based on integration of large-scale RNA-seq data and release a new annotation system IC4R-2.0. In general, IC4R-2.0 significantly improves the completeness of gene structure, identifies a number of novel genes, and integrates a variety of functional annotations. Furthermore, long non-coding RNAs (lncRNAs) and circular RNAs (circRNAs) are systematically characterized in the rice genome. Performance evaluation shows that compared to previous annotation systems, IC4R-2.0 achieves higher integrity and quality, primarily attributable to massive RNA-seq data applied in genome annotation. Consequently, we incorporate the improved annotations into the Information Commons for Rice (IC4R), a database integrating multiple omics data of rice, and accordingly update IC4R by providing more user-friendly web interfaces and implementing a series of practical online tools. Together, the updated IC4R, which is equipped with the improved annotations, bears great promise for comparative and functional genomic studies in rice and other monocotyledonous species. The IC4R-2.0 annotation system and related resources are freely accessible at http://ic4r.org/.     

Altering O-Linked β-N-Acetylglucosamine Cycling Disrupts Mitochondrial Function

Mitochondrial impairment is commonly found in many diseases such as diabetes, cancer, and Alzheimer disease. We demonstrate that the enzymes responsible for the addition or removal of the O-GlcNAc modification, O-GlcNAc transferase (OGT) and O-GlcNAcase (OGA), respectively, are critical regulators of mitochondrial function. Using a SILAC (stable isotope labeling of amino acids in cell culture)-based proteomics screen, we quantified the changes in mitochondrial protein expression in OGT- and OGA-overexpressing cells. Strikingly, overexpression of OGT or OGA showed significant decreases in mitochondria-localized proteins involved in the respiratory chain and the tricarboxylic acid cycle. Furthermore, mitochondrial morphology was altered in these cells. Both cellular respiration and glycolysis were reduced in OGT/OGA-overexpressing cells. These data demonstrate that alterations in O-GlcNAc cycling profoundly affect energy and metabolite production.     

Whole genome sequence of Auricularia heimuer (Basidiomycota,Fungi), the third most important cultivated mushroom worldwide

Heimuer, Auricularia heimuer, is one of the most famous traditional Chinese foods and medicines, and it is the third most important cultivated mushroom worldwide. The aim of this study is to develop genomic resources for A. heimuer to furnish tools that can be used to study its secondary metabolite production capability, wood degradation ability and biosynthesis of polysaccharides. The genome was obtained from single spore mycelia of the strain Dai 13782 by using combined high-throughput Illumina HiSeq 4000 system with the PacBio RSII long-read sequencing platform. Functional annotation was accomplished by blasting protein sequences with different public available databases to obtain their corresponding annotations. It is 49.76 Mb in size with a N50 scaffold size of 1,350,668 bp and encodes 16,244 putative predicted genes. This is the first genome-scale assembly and annotation for A. heimuer, which is the third sequenced species in Auricularia.