Phylogenetic approaches to identifying fragments of the same gene, with application to the wheat genome.

MOTIVATION: As the time and cost of sequencing decrease, the number of available genomes and transcriptomes rapidly increases. Yet the quality of the assemblies and the gene annotations varies considerably and often remains poor, affecting downstream analyses. This is particularly true when fragments of the same gene are annotated as distinct genes, which may cause them to be mistaken as paralogs. RESULTS: In this study, we introduce two novel phylogenetic tests to infer non-overlapping or partially overlapping genes that are in fact parts of the same gene. One approach collapses branches with low bootstrap support and the other computes a likelihood ratio test. We extensively validated these methods by (i) introducing and recovering fragmentation on the bread wheat, Triticum aestivum cv. Chinese Spring, chromosome 3B; (ii) by applying the methods to the low-quality 3B assembly and validating predictions against the high-quality 3B assembly; and (iii) by comparing the performance of the proposed methods to the performance of existing methods, namely Ensembl Compara and ESPRIT. Application of this combination to a draft shotgun assembly of the entire bread wheat genome revealed 1221 pairs of genes that are highly likely to be fragments of the same gene. Our approach demonstrates the power of fine-grained evolutionary inferences across multiple species to improving genome assemblies and annotations. AVAILABILITY AND IMPLEMENTATION: An open source software tool is available at https://github.com/DessimozLab/esprit2. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

The OMA orthology database in 2018: retrieving evolutionary relationships among all domains of life through richer web and programmatic interfaces.

The Orthologous Matrix (OMA) is a leading resource to relate genes across many species from all of life. In this update paper, we review the recent algorithmic improvements in the OMA pipeline, describe increases in species coverage (particularly in plants and early-branching eukaryotes) and introduce several new features in the OMA web browser. Notable improvements include: (i) a scalable, interactive viewer for hierarchical orthologous groups; (ii) protein domain annotations and domain-based links between orthologous groups; (iii) functionality to retrieve phylogenetic marker genes for a subset of species of interest; (iv) a new synteny dot plot viewer; and (v) an overhaul of the programmatic access (REST API and semantic web), which will facilitate incorporation of OMA analyses in computational pipelines and integration with other bioinformatic resources. OMA can be freely accessed at https://omabrowser.org.

Prioritising candidate genes causing QTL using hierarchical orthologous groups.

Motivation: A key goal in plant biotechnology applications is the identification of genes associated to particular phenotypic traits (for example: yield, fruit size, root length). Quantitative Trait Loci (QTL) studies identify genomic regions associated with a trait of interest. However, to infer potential causal genes in these regions, each of which can contain hundreds of genes, these data are usually intersected with prior functional knowledge of the genes. This process is however laborious, particularly if the experiment is performed in a non-model species, and the statistical significance of the inferred candidates is typically unknown. Results: This paper introduces QTLSearch, a method and software tool to search for candidate causal genes in QTL studies by combining Gene Ontology annotations across many species, leveraging hierarchical orthologous groups. The usefulness of this approach is demonstrated by re-analysing two metabolic QTL studies: one in Arabidopsis thaliana, the other in Oryza sativa subsp. indica. Even after controlling for statistical significance, QTLSearch inferred potential causal genes for more QTL than BLAST-based functional propagation against UniProtKB/Swiss-Prot, and for more QTL than in the original studies. Availability and implementation: QTLSearch is distributed under the LGPLv3 license. It is available to install from the Python Package Index (as qtlsearch), with the source available from https://bitbucket.org/alex-warwickvesztrocy/qtlsearch. Supplementary information: Supplementary data are available at Bioinformatics online.

The OMA orthology database in 2015: function predictions, better plant support, synteny view and other improvements.

The Orthologous Matrix (OMA) project is a method and associated database inferring evolutionary relationships amongst currently 1706 complete proteomes (i.e. the protein sequence associated for every protein-coding gene in all genomes). In this update article, we present six major new developments in OMA: (i) a new web interface; (ii) Gene Ontology function predictions as part of the OMA pipeline; (iii) better support for plant genomes and in particular homeologs in the wheat genome; (iv) a new synteny viewer providing the genomic context of orthologs; (v) statically computed hierarchical orthologous groups subsets downloadable in OrthoXML format; and (vi) possibility to export parts of the all-against-all computations and to combine them with custom data for 'client-side' orthology prediction. OMA can be accessed through the OMA Browser and various programmatic interfaces at http://omabrowser.org.

Plasma membrane aquaporin AqpZ protein is essential for glucose metabolism during photomixotrophic growth of Synechocystis sp. PCC 6803.

The genome of Synechocystis PCC 6803 contains a single gene encoding an aquaporin, aqpZ. The AqpZ protein functioned as a water-permeable channel in the plasma membrane. However, the physiological importance of AqpZ in Synechocystis remains unclear. We found that growth in glucose-containing medium inhibited proper division of ΔaqpZ cells and led to cell death. Deletion of a gene encoding a glucose transporter in the ΔaqpZ background alleviated the glucose-mediated growth inhibition of the ΔaqpZ cells. The ΔaqpZ cells swelled more than the wild type after the addition of glucose, suggesting an increase in cytosolic osmolarity. This was accompanied by a down-regulation of the pentose phosphate pathway and concurrent glycogen accumulation. Metabolite profiling by GC/TOF-MS of wild-type and ΔaqpZ cells revealed a relative decrease of intermediates of the tricarboxylic acid cycle and certain amino acids in the mutant. The changed levels of metabolites may have been the cause for the observed decrease in growth rate of the ΔaqpZ cells along with decreased PSII activity at pH values ranging from 7.5 to 8.5. A mutant in sll1961, encoding a putative transcription factor, and a Δhik31 mutant, lacking a putative glucose-sensing kinase, both exhibited higher glucose sensitivity than the ΔaqpZ cells. Examination of protein expression indicated that sll1961 functioned as a positive regulator of aqpZ gene expression but not as the only regulator. Overall, the ΔaqpZ cells showed defects in macronutrient metabolism, pH homeostasis, and cell division under photomixotrophic conditions, consistent with an essential role of AqpZ in glucose metabolism.

A minimal model of peptide binding predicts ensemble properties of serum antibodies.

BACKGROUND: The importance of peptide microarrays as a tool for serological diagnostics has strongly increased over the last decade. However, interpretation of the binding signals is still hampered by our limited understanding of the technology. This is in particular true for arrays probed with antibody mixtures of unknown complexity, such as sera. To gain insight into how signals depend on peptide amino acid sequences, we probed random-sequence peptide microarrays with sera of healthy and infected mice. We analyzed the resulting antibody binding profiles with regression methods and formulated a minimal model to explain our findings. RESULTS: Multivariate regression analysis relating peptide sequence to measured signals led to the definition of amino acid-associated weights. Although these weights do not contain information on amino acid position, they predict up to 40-50% of the binding profiles' variation. Mathematical modeling shows that this position-independent ansatz is only adequate for highly diverse random antibody mixtures which are not dominated by a few antibodies. Experimental results suggest that sera from healthy individuals correspond to that case, in contrast to sera of infected ones. CONCLUSIONS: Our results indicate that position-independent amino acid-associated weights predict linear epitope binding of antibody mixtures only if the mixture is random, highly diverse, and contains no dominant antibodies. The discovered ensemble property is an important step towards an understanding of peptide-array serum-antibody binding profiles. It has implications for both serological diagnostics and B cell epitope mapping.

Detection and interpretation of metabolite-transcript coresponses using combined profiling data.

MOTIVATION: Studying the interplay between gene expression and metabolite levels can yield important information on the physiology of stress responses and adaptation strategies. Performing transcriptomics and metabolomics in parallel during time-series experiments represents a systematic way to gain such information. Several combined profiling datasets have been added to the public domain and they form a valuable resource for hypothesis generating studies. Unfortunately, detecting coresponses between transcript levels and metabolite abundances is non-trivial: they cannot be assumed to overlap directly with underlying biochemical pathways and they may be subject to time delays and obscured by considerable noise. RESULTS: Our aim was to predict pathway comemberships between metabolites and genes based on their coresponses to applied stress. We found that in the presence of strong noise and time-shifted responses, a hidden Markov model-based similarity outperforms the simpler Pearson correlation but performs comparably or worse in their absence. Therefore, we propose a supervised method that applies pathway information to summarize similarity statistics to a consensus statistic that is more informative than any of the single measures. Using four combined profiling datasets, we show that comembership between metabolites and genes can be predicted for numerous KEGG pathways; this opens opportunities for the detection of transcriptionally regulated pathways and novel metabolically related genes. AVAILABILITY: A command-line software tool is available at http://www.cin.ufpe.br/~igcf/Metabolites. CONTACT: henning@psc.riken.jp; igcf@cin.ufpe.br

Exploring matrix effects and quantification performance in metabolomics experiments using artificial biological gradients.

Metabolomics has become an integral part of many life-science applications but is technically still very challenging. Numerous analytical approaches are needed as metabolites have very broad concentration ranges and extremely diverse chemical properties. Configuring a metabolomics pipeline and exploring its merits is a complex task that depends on effective and transparent evaluation procedures. Unfortunately, there are no widely applicable methods to evaluate how well acquired data can approximate actual concentration differences. Here, we introduce a powerful approach that provides semiquantitative calibration curves over a biologically defined concentration range for all detected compounds. By performing metabolomics on a stepwise gradient between two biological specimens, we obtain a data set where each peak would ideally show a linear dependency on the mixture ratio. An example gradient between extracts of tomato leaf and fruit demonstrates good calibration statistics for a large proportion of the peaks but also highlights cases with strong background-dependent signal interference. Analysis of artificial biological gradients is a general and inexpensive tool for calibration that greatly facilitates data interpretation, quality control and method comparisons.

Metabolomic approaches toward understanding nitrogen metabolism in plants.

Plants can assimilate inorganic nitrogen (N) sources to organic N such as amino acids. N is the most important of the mineral nutrients required by plants and its metabolism is tightly coordinated with carbon (C) metabolism in the fundamental processes that permit plant growth. Increased understanding of N regulation may provide important insights for plant growth and improvement of quality of crops and vegetables because N as well as C metabolism are fundamental components of plant life. Metabolomics is a global biochemical approach useful to study N metabolism because metabolites not only reflect the ultimate phenotypes (traits), but can mediate transcript levels as well as protein levels directly and/or indirectly under different N conditions. This review outlines analytical and bioinformatic techniques particularly used to perform metabolomics for studying N metabolism in higher plants. Examples are used to illustrate the application of metabolomic techniques to the model plants Arabidopsis and rice, as well as other crops and vegetables.

Consolidating metabolite identifiers to enable contextual and multi-platform metabolomics data analysis.

BACKGROUND: Analysis of data from high-throughput experiments depends on the availability of well-structured data that describe the assayed biomolecules. Procedures for obtaining and organizing such meta-data on genes, transcripts and proteins have been streamlined in many data analysis packages, but are still lacking for metabolites. Chemical identifiers are notoriously incoherent, encompassing a wide range of different referencing schemes with varying scope and coverage. Online chemical databases use multiple types of identifiers in parallel but lack a common primary key for reliable database consolidation. Connecting identifiers of analytes found in experimental data with the identifiers of their parent metabolites in public databases can therefore be very laborious. RESULTS: Here we present a strategy and a software tool for integrating metabolite identifiers from local reference libraries and public databases that do not depend on a single common primary identifier. The program constructs groups of interconnected identifiers of analytes and metabolites to obtain a local metabolite-centric SQLite database. The created database can be used to map in-house identifiers and synonyms to external resources such as the KEGG database. New identifiers can be imported and directly integrated with existing data. Queries can be performed in a flexible way, both from the command line and from the statistical programming environment R, to obtain data set tailored identifier mappings. CONCLUSIONS: Efficient cross-referencing of metabolite identifiers is a key technology for metabolomics data analysis. We provide a practical and flexible solution to this task and an open-source program, the metabolite masking tool (MetMask), available at http://metmask.sourceforge.net, that implements our ideas.

Comparative metabolomics charts the impact of genotype-dependent methionine accumulation in Arabidopsis thaliana.

Methionine (Met) is an essential amino acid for all organisms. In plants, Met also functions as a precursor of plant hormones, polyamines, and defense metabolites. The regulatory mechanism of Met biosynthesis is highly complex and, despite its great importance, remains unclear. To investigate how accumulation of Met influences metabolism as a whole in Arabidopsis, three methionine over-accumulation (mto) mutants were examined using a gas chromatography-mass spectrometry-based metabolomics approach. Multivariate statistical analyses of the three mto mutants (mto1, mto2, and mto3) revealed distinct metabolomic phenotypes. Orthogonal projection to latent structures-discriminant analysis highlighted discriminative metabolites contributing to the separation of each mutant and the corresponding control samples. Though Met accumulation in mto1 had no dramatic effect on other metabolic pathways except for the aspartate family, metabolite profiles of mto2 and mto3 indicated that several extensive pathways were affected in addition to over-accumulation of Met. The pronounced changes in metabolic pathways in both mto2 and mto3 were associated with polyamines. The findings suggest that our metabolomics approach not only can reveal the impact of Met over-accumulation on metabolism, but also may provide clues to identify crucial pathways for regulation of metabolism in plants.

TargetSearch--a Bioconductor package for the efficient preprocessing of GC-MS metabolite profiling data.

BACKGROUND: Metabolite profiling, the simultaneous quantification of multiple metabolites in an experiment, is becoming increasingly popular, particularly with the rise of systems-level biology. The workhorse in this field is gas-chromatography hyphenated with mass spectrometry (GC-MS). The high-throughput of this technology coupled with a demand for large experiments has led to data pre-processing, i.e. the quantification of metabolites across samples, becoming a major bottleneck. Existing software has several limitations, including restricted maximum sample size, systematic errors and low flexibility. However, the biggest limitation is that the resulting data usually require extensive hand-curation, which is subjective and can typically take several days to weeks. RESULTS: We introduce the TargetSearch package, an open source tool which is a flexible and accurate method for pre-processing even very large numbers of GC-MS samples within hours. We developed a novel strategy to iteratively correct and update retention time indices for searching and identifying metabolites. The package is written in the R programming language with computationally intensive functions written in C for speed and performance. The package includes a graphical user interface to allow easy use by those unfamiliar with R. CONCLUSIONS: TargetSearch allows fast and accurate data pre-processing for GC-MS experiments and overcomes the sample number limitations and manual curation requirements of existing software. We validate our method by carrying out an analysis against both a set of known chemical standard mixtures and of a biological experiment. In addition we demonstrate its capabilities and speed by comparing it with other GC-MS pre-processing tools. We believe this package will greatly ease current bottlenecks and facilitate the analysis of metabolic profiling data.

Integrative functional genomics of salt acclimatization in the model legume Lotus japonicus.

The model legume Lotus japonicus was subjected to non-lethal long-term salinity and profiled at the ionomic, transcriptomic and metabolomic levels. Two experimental designs with various stress doses were tested: a gradual step acclimatization and an initial acclimatization approach. Ionomic profiling by inductively coupled plasma/atomic emission spectrometry (ICP-AES) revealed salt stress-induced reductions in potassium, phosphorus, sulphur, zinc and molybdenum. Microarray profiling using the Lotus Genechip allowed the identification of 912 probesets that were differentially expressed under the acclimatization regimes. Gas chromatography/mass spectrometry-based metabolite profiling identified 147 differentially accumulated soluble metabolites, indicating a change in metabolic phenotype upon salt acclimatization. Metabolic changes were characterized by a general increase in the steady-state levels of many amino acids, sugars and polyols, with a concurrent decrease in most organic acids. Transcript and metabolite changes exhibited a stress dose-dependent response within the range of NaCl concentrations used, although threshold and plateau behaviours were also observed. The combined observations suggest a successive and increasingly global requirement for the reprogramming of gene expression and metabolic pathways to maintain ionic and osmotic homeostasis. A simple qualitative model is proposed to explain the systems behaviour of plants during salt acclimatization.

Identification of metabolic and biomass QTL in Arabidopsis thaliana in a parallel analysis of RIL and IL populations.

Plant growth and development are tightly linked to primary metabolism and are subject to natural variation. In order to obtain an insight into the genetic factors controlling biomass and primary metabolism and to determine their relationships, two Arabidopsis thaliana populations [429 recombinant inbred lines (RIL) and 97 introgression lines (IL), derived from accessions Col-0 and C24] were analyzed with respect to biomass and metabolic composition using a mass spectrometry-based metabolic profiling approach. Six and 157 quantitative trait loci (QTL) were identified for biomass and metabolic content, respectively. Two biomass QTL coincide with significantly more metabolic QTL (mQTL) than statistically expected, supporting the notion that the metabolic profile and biomass accumulation of a plant are linked. On the same basis, three out the six biomass QTL can be simulated purely on the basis of metabolic composition. QTL based on analysis of the introgression lines were in substantial agreement with the RIL-based results: five of six biomass QTL and 55% of the mQTL found in the RIL population were also found in the IL population at a significance level of P < or = 0.05, with >80% agreement on the allele effects. Some of the differences could be attributed to epistatic interactions. Depending on the search conditions, metabolic pathway-derived candidate genes were found for 24-67% of all tested mQTL in the database AraCyc 3.5. This dataset thus provides a comprehensive basis for the detection of functionally relevant variation in known genes with metabolic function and for identification of genes with hitherto unknown roles in the control of metabolism.

Compensation for systematic cross-contribution improves normalization of mass spectrometry based metabolomics data.

Most mass spectrometry based metabolomics studies are semiquantitative and depend on efficient normalization techniques to suppress systematic error. A common approach is to include isotope-labeled internal standards (ISs) and then express the estimated metabolite abundances relative to the IS. Because of problems such as insufficient chromatographic resolution, however, the analytes may directly influence estimates of the IS, a phenomenon known as cross-contribution (CC). Normalization using ISs that suffer from CC effects will cause significant loss of information if the interfering analytes are associated with the studied factors. We present a novel normalization algorithm, which compensates for systematic CC effects that can be traced back to a linear association with the experimental design. The proposed method was found to be superior at purifying the signal of interest compared to current normalization methods when applied to two biological data sets and a multicomponent dilution mixture. Our method is applicable to data from randomized and designed experiments that use ISs to monitor the systematic error.

pcaMethods--a bioconductor package providing PCA methods for incomplete data.

UNLABELLED: pcaMethods is a Bioconductor compliant library for computing principal component analysis (PCA) on incomplete data sets. The results can be analyzed directly or used to estimate missing values to enable the use of missing value sensitive statistical methods. The package was mainly developed with microarray and metabolite data sets in mind, but can be applied to any other incomplete data set as well. AVAILABILITY: http://www.bioconductor.org

Cameo: A Python Library for Computer Aided Metabolic Engineering and Optimization of Cell Factories.

Computational systems biology methods enable rational design of cell factories on a genome-scale and thus accelerate the engineering of cells for the production of valuable chemicals and proteins. Unfortunately, the majority of these methods' implementations are either not published, rely on proprietary software, or do not provide documented interfaces, which has precluded their mainstream adoption in the field. In this work we present cameo, a platform-independent software that enables in silico design of cell factories and targets both experienced modelers as well as users new to the field. It is written in Python and implements state-of-the-art methods for enumerating and prioritizing knockout, knock-in, overexpression, and down-regulation strategies and combinations thereof. Cameo is an open source software project and is freely available under the Apache License 2.0. A dedicated Web site including documentation, examples, and installation instructions can be found at http://cameo.bio . Users can also give cameo a try at http://try.cameo.bio .

Transcription factor target prediction using multiple short expression time series from Arabidopsis thaliana.

BACKGROUND: The central role of transcription factors (TFs) in higher eukaryotes has led to much interest in deciphering transcriptional regulatory interactions. Even in the best case, experimental identification of TF target genes is error prone, and has been shown to be improved by considering additional forms of evidence such as expression data. Previous expression based methods have not explicitly tried to associate TFs with their targets and therefore largely ignored the treatment specific and time dependent nature of transcription regulation. RESULTS: In this study we introduce CERMT, Covariance based Extraction of Regulatory targets using Multiple Time series. Using simulated and real data we show that using multiple expression time series, selecting treatments in which the TF responds, allowing time shifts between TFs and their targets and using covariance to identify highly responding genes appear to be a good strategy. We applied our method to published TF - target gene relationships determined using expression profiling on TF mutants and show that in most cases we obtain significant target gene enrichment and in half of the cases this is sufficient to deliver a usable list of high-confidence target genes. CONCLUSION: CERMT could be immediately useful in refining possible target genes of candidate TFs using publicly available data, particularly for organisms lacking comprehensive TF binding data. In the future, we believe its incorporation with other forms of evidence may improve integrative genome-wide predictions of transcriptional networks.

rosettR: protocol and software for seedling area and growth analysis.

BACKGROUND: Growth is an important parameter to consider when studying the impact of treatments or mutations on plant physiology. Leaf area and growth rates can be estimated efficiently from images of plants, but the experiment setup, image analysis, and statistical evaluation can be laborious, often requiring substantial manual effort and programming skills. RESULTS: Here we present rosettR, a non-destructive and high-throughput phenotyping protocol for the measurement of total rosette area of seedlings grown in plates in sterile conditions. We demonstrate that our protocol can be used to accurately detect growth differences among different genotypes and in response to light regimes and osmotic stress. rosettR is implemented as a package for the statistical computing software R and provides easy to use functions to design an experiment, analyze the images, and generate reports on quality control as well as a final comparison across genotypes and applied treatments. Experiment procedures are included as part of the package documentation. CONCLUSIONS: Using rosettR it is straight-forward to perform accurate, reproducible measurements of rosette area and relative growth rate with high-throughput using inexpensive equipment. Suitable applications include screening mutant populations for growth phenotypes visible at early growth stages and profiling different genotypes in a wide variety of treatments.

Lysine acetylation in mitochondria: From inventory to function.

Cellular signaling pathways are regulated in a highly dynamic fashion in order to quickly adapt to distinct environmental conditions. Acetylation of lysine residues represents a central process that orchestrates cellular metabolism and signaling. In mitochondria, acetylation seems to be the most prevalent post-translational modification, presumably linked to the compartmentation and high turnover of acetyl-CoA in this organelle. Similarly, the elevated pH and the higher concentration of metabolites in mitochondria seem to favor non-enzymatic lysine modifications, as well as other acylations. Hence, elucidating the mechanisms for metabolic control of protein acetylation is crucial for our understanding of cellular processes. Recent advances in mass spectrometry-based proteomics have considerably increased our knowledge of the regulatory scope of acetylation. Here, we review the current knowledge and functional impact of mitochondrial protein acetylation across species. We first cover the experimental approaches to identify and analyze lysine acetylation on a global scale, we then explore both commonalities and specific differences of plant and animal acetylomes and the evolutionary conservation of protein acetylation, as well as its particular impact on metabolism and diseases. Important future directions and technical challenges are discussed, and it is pointed out that the transfer of knowledge between species and diseases, both in technology and biology, is of particular importance for further advancements in this field.