Characterisation of novel microRNAs in the Black flying fox (Pteropus alecto) by deep sequencing.

BACKGROUND: Bats are a major source of new and emerging viral diseases. Despite the fact that bats carry and shed highly pathogenic viruses including Ebola, Nipah and SARS, they rarely display clinical symptoms of infection. Host factors influencing viral replication are poorly understood in bats and are likely to include both pre- and post-transcriptional regulatory mechanisms. MicroRNAs are a major mechanism of post-transcriptional gene regulation, however very little is known about them in bats. RESULTS: This study describes 399 microRNAs identified by deep sequencing of small RNA isolated from tissues of the Black flying fox, Pteropus alecto, a confirmed natural reservoir of the human pathogens Hendra virus and Australian bat lyssavirus. Of the microRNAs identified, more than 100 are unique amongst vertebrates, including a subset containing mutations in critical seed regions. Clusters of rapidly-evolving microRNAs were identified, as well as microRNAs predicted to target genes involved in antiviral immunity, the DNA damage response, apoptosis and autophagy. Closer inspection of the predicted targets for several highly supported novel miRNA candidates suggests putative roles in host-virus interaction. CONCLUSIONS: MicroRNAs are likely to play major roles in regulating virus-host interaction in bats, via dampening of inflammatory responses (limiting the effects of immunopathology), and directly limiting the extent of viral replication, either through restricting the availability of essential factors or by controlling apoptosis. Characterisation of the bat microRNA repertoire is an essential step towards understanding transcriptional regulation during viral infection, and will assist in the identification of mechanisms that enable bats to act as natural virus reservoirs. This in turn will facilitate the development of antiviral strategies for use in humans and other species.

Protein substrates of a novel secretion system are numerous in the Bacteroidetes phylum and have in common a cleavable C-terminal secretion signal, extensive post-translational modification, and cell-surface attachment.

The secretion of certain proteins in Porphyromonas gingivalis is dependent on a C-terminal domain (CTD). After secretion, the CTD is cleaved prior to extensive modification of the mature protein, probably with lipopolysaccharide, therefore enabling attachment to the cell surface. In this study, bioinformatic analyses of the CTD demonstrated the presence of three conserved sequence motifs. These motifs were used to construct Hidden Markov Models (HMMs) that predicted 663 CTD-containing proteins in 21 fully sequenced species of the Bacteroidetes phylum, while no CTD-containing proteins were predicted in species outside this phylum. Further HMM searching of Cytophaga hutchinsonii led to a total of 171 predicted CTD proteins in that organism alone. Proteomic analyses of membrane fractions and culture fluid derived from P. gingivalis and four other species containing predicted CTDs (Parabacteroides distasonis, Prevotella intermedia, Tannerella forsythia, and C. hutchinsonii) demonstrated that membrane localization, extensive post-translational modification, and CTD-cleavage were conserved features of the secretion system. The CTD cleavage site of 10 different proteins from 3 different species was determined and found to be similar to the cleavage site previously determined in P. gingivalis, suggesting that homologues of the C-terminal signal peptidase (PG0026) are responsible for the cleavage in these species.

A bioinformatic strategy for the detection, classification and analysis of bacterial autotransporters.

Autotransporters are secreted proteins that are assembled into the outer membrane of bacterial cells. The passenger domains of autotransporters are crucial for bacterial pathogenesis, with some remaining attached to the bacterial surface while others are released by proteolysis. An enigma remains as to whether autotransporters should be considered a class of secretion system, or simply a class of substrate with peculiar requirements for their secretion. We sought to establish a sensitive search protocol that could identify and characterize diverse autotransporters from bacterial genome sequence data. The new sequence analysis pipeline identified more than 1500 autotransporter sequences from diverse bacteria, including numerous species of Chlamydiales and Fusobacteria as well as all classes of Proteobacteria. Interrogation of the proteins revealed that there are numerous classes of passenger domains beyond the known proteases, adhesins and esterases. In addition the barrel-domain-a characteristic feature of autotransporters-was found to be composed from seven conserved sequence segments that can be arranged in multiple ways in the tertiary structure of the assembled autotransporter. One of these conserved motifs overlays the targeting information required for autotransporters to reach the outer membrane. Another conserved and diagnostic motif maps to the linker region between the passenger domain and barrel-domain, indicating it as an important feature in the assembly of autotransporters.

PyMS: a Python toolkit for processing of gas chromatography-mass spectrometry (GC-MS) data. Application and comparative study of selected tools.

BACKGROUND: Gas chromatography-mass spectrometry (GC-MS) is a technique frequently used in targeted and non-targeted measurements of metabolites. Most existing software tools for processing of raw instrument GC-MS data tightly integrate data processing methods with graphical user interface facilitating interactive data processing. While interactive processing remains critically important in GC-MS applications, high-throughput studies increasingly dictate the need for command line tools, suitable for scripting of high-throughput, customized processing pipelines. RESULTS: PyMS comprises a library of functions for processing of instrument GC-MS data developed in Python. PyMS currently provides a complete set of GC-MS processing functions, including reading of standard data formats (ANDI- MS/NetCDF and JCAMP-DX), noise smoothing, baseline correction, peak detection, peak deconvolution, peak integration, and peak alignment by dynamic programming. A novel common ion single quantitation algorithm allows automated, accurate quantitation of GC-MS electron impact (EI) fragmentation spectra when a large number of experiments are being analyzed. PyMS implements parallel processing for by-row and by-column data processing tasks based on Message Passing Interface (MPI), allowing processing to scale on multiple CPUs in distributed computing environments. A set of specifically designed experiments was performed in-house and used to comparatively evaluate the performance of PyMS and three widely used software packages for GC-MS data processing (AMDIS, AnalyzerPro, and XCMS). CONCLUSIONS: PyMS is a novel software package for the processing of raw GC-MS data, particularly suitable for scripting of customized processing pipelines and for data processing in batch mode. PyMS provides limited graphical capabilities and can be used both for routine data processing and interactive/exploratory data analysis. In real-life GC-MS data processing scenarios PyMS performs as well or better than leading software packages. We demonstrate data processing scenarios simple to implement in PyMS, yet difficult to achieve with many conventional GC-MS data processing software. Automated sample processing and quantitation with PyMS can provide substantial time savings compared to more traditional interactive software systems that tightly integrate data processing with the graphical user interface.

LeishCyc: a guide to building a metabolic pathway database and visualization of metabolomic data.

The complexity of the metabolic networks in even the simplest organisms has raised new challenges in organizing metabolic information. To address this, specialized computer frameworks have been developed to capture, manage, and visualize metabolic knowledge. The leading databases of metabolic information are those organized under the umbrella of the BioCyc project, which consists of the reference database MetaCyc, and a number of pathway/genome databases (PGDBs) each focussed on a specific organism. A number of PGDBs have been developed for bacterial, fungal, and protozoan pathogens, greatly facilitating dissection of the metabolic potential of these organisms and the identification of new drug targets. Leishmania are protozoan parasites belonging to the family Trypanosomatidae that cause a broad spectrum of diseases in humans. In this work we use the LeishCyc database, the BioCyc database for Leishmania major, to describe how to build a BioCyc database from genomic sequences and associated annotations. By using metabolomic data generated in our group, we show how such databases can be utilized to elucidate specific changes in parasite metabolism.

A small Tim homohexamer in the relict mitochondrion of Cryptosporidium.

The apicomplexan parasite Cryptosporidium parvum possesses a mitosome, a relict mitochondrion with a greatly reduced metabolic capability. This mitosome houses a mitochondrial-type protein import apparatus, but elements of the protein import pathway have been reduced, and even lost, through evolution. The small Tim protein family is a case in point. The genomes of C. parvum and related species of Cryptosporidium each encode just one small Tim protein, CpTimS. This observation challenged the tenet that small Tim proteins are always found in pairs as α3ß3 hexamers. We show that the atypical CpTimS exists as a relatively unstable homohexamer, shedding light both on the early evolution of the small Tim protein family and on small Tim hexamer formation in contemporary eukaryotes.

Isotopomer profiling of Leishmania mexicana promastigotes reveals important roles for succinate fermentation and aspartate uptake in tricarboxylic acid cycle (TCA) anaplerosis, glutamate synthesis, and growth.

Leishmania parasites proliferate within nutritionally complex niches in their sandfly vector and mammalian hosts. However, the extent to which these parasites utilize different carbon sources remains poorly defined. In this study, we have followed the incorporation of various (13)C-labeled carbon sources into the intracellular and secreted metabolites of Leishmania mexicana promastigotes using gas chromatography-mass spectrometry and (13)C NMR. [U-(13)C]Glucose was rapidly incorporated into intermediates in glycolysis, the pentose phosphate pathway, and the cytoplasmic carbohydrate reserve material, mannogen. Enzymes involved in the upper glycolytic pathway are sequestered within glycosomes, and the ATP and NAD(+) consumed by these reactions were primarily regenerated by the fermentation of phosphoenolpyruvate to succinate (glycosomal succinate fermentation). The initiating enzyme in this pathway, phosphoenolpyruvate carboxykinase, was exclusively localized to the glycosome. Although some of the glycosomal succinate was secreted, most of the C4 dicarboxylic acids generated during succinate fermentation were further catabolized in the TCA cycle. A high rate of TCA cycle anaplerosis was further suggested by measurement of [U-(13)C]aspartate and [U-(13)C]alanine uptake and catabolism. TCA cycle anaplerosis is apparently needed to sustain glutamate production under standard culture conditions. Specifically, inhibition of mitochondrial aconitase with sodium fluoroacetate resulted in the rapid depletion of intracellular glutamate pools and growth arrest. Addition of high concentrations of exogenous glutamate alleviated this growth arrest. These findings suggest that glycosomal and mitochondrial metabolism in Leishmania promastigotes is tightly coupled and that, in contrast to the situation in some other trypanosomatid parasites, the TCA cycle has crucial anabolic functions.

Using hidden markov models to discover new protein transport machines.

Protein import and export pathways are driven by protein translocases, often comprised of multiple subunits, and usually conserved across a range of organisms. Protein import into mitochondria is fundamental to eukaryotic organisms and is initiated when substrate proteins are translocated across the mitochondrial outer membrane by the TOM complex. The essential subunit of this complex is a protein called Tom40, which is probably a beta-barrel in structure and serves as the translocation pore. We describe a hidden Markov model search designed to find the Tom40 sequence in the amoeba Entamoeba histolytica. This organism has a highly reduced "mitosome", an organelle whose relationship to mitochondria has been the subject of controversy. The Tom40 sequence could not be found with BLAST-based searches, but a hidden Markov model search identified a likely candidate to form the protein import pore in the outer mitosomal membrane in E. histolytica.

The essentials of protein import in the degenerate mitochondrion of Entamoeba histolytica.

Several essential biochemical processes are situated in mitochondria. The metabolic transformation of mitochondria in distinct lineages of eukaryotes created proteomes ranging from thousands of proteins to what appear to be a much simpler scenario. In the case of Entamoeba histolytica, tiny mitochondria known as mitosomes have undergone extreme reduction. Only recently a single complete metabolic pathway of sulfate activation has been identified in these organelles. The E. histolytica mitosomes do not produce ATP needed for the sulfate activation pathway and for three molecular chaperones, Cpn60, Cpn10 and mtHsp70. The already characterized ADP/ATP carrier would thus be essential to provide cytosolic ATP for these processes, but how the equilibrium of inorganic phosphate could be maintained was unknown. Finally, how the mitosomal proteins are translocated to the mitosomes had remained unclear. We used a hidden Markov model (HMM) based search of the E. histolytica genome sequence to discover candidate (i) mitosomal phosphate carrier complementing the activity of the ADP/ATP carrier and (ii) membrane-located components of the protein import machinery that includes the outer membrane translocation channel Tom40 and membrane assembly protein Sam50. Using in vitro and in vivo systems we show that E. histolytica contains a minimalist set up of the core import components in order to accommodate a handful of mitosomal proteins. The anaerobic and parasitic lifestyle of E. histolytica has produced one of the simplest known mitochondrial compartments of all eukaryotes. Comparisons with mitochondria of another amoeba, Dictystelium discoideum, emphasize just how dramatic the reduction of the protein import apparatus was after the loss of archetypal mitochondrial functions in the mitosomes of E. histolytica.

Central carbon metabolism of Leishmania parasites.

Leishmania spp. are sandfly-transmitted protozoa parasites that cause a spectrum of diseases in humans. Many enzymes involved in Leishmania central carbon metabolism differ from their equivalents in the mammalian host and are potential drug targets. In this review we summarize recent advances in our understanding of Leishmania central carbon metabolism, focusing on pathways of carbon utilization that are required for growth and pathogenesis in the mammalian host. While Leishmania central carbon metabolism shares many features in common with other pathogenic trypanosomatids, significant differences are also apparent. Leishmania parasites are also unusual in constitutively expressing most core metabolic pathways throughout their life cycle, a feature that may allow these parasites to exploit a range of different carbon sources (primarily sugars and amino acids) rapidly in both the insect vector and vertebrate host. Indeed, recent gene deletion studies suggest that mammal-infective stages are dependent on multiple carbon sources in vivo. The application of metabolomic approaches, outlined here, are likely to be important in defining aspects of central carbon metabolism that are essential at different stages of mammalian host infection.

Systems biology: the next frontier for bioinformatics.

Biochemical systems biology augments more traditional disciplines, such as genomics, biochemistry and molecular biology, by championing (i) mathematical and computational modeling; (ii) the application of traditional engineering practices in the analysis of biochemical systems; and in the past decade increasingly (iii) the use of near-comprehensive data sets derived from 'omics platform technologies, in particular "downstream" technologies relative to genome sequencing, including transcriptomics, proteomics and metabolomics. The future progress in understanding biological principles will increasingly depend on the development of temporal and spatial analytical techniques that will provide high-resolution data for systems analyses. To date, particularly successful were strategies involving (a) quantitative measurements of cellular components at the mRNA, protein and metabolite levels, as well as in vivo metabolic reaction rates, (b) development of mathematical models that integrate biochemical knowledge with the information generated by high-throughput experiments, and (c) applications to microbial organisms. The inevitable role bioinformatics plays in modern systems biology puts mathematical and computational sciences as an equal partner to analytical and experimental biology. Furthermore, mathematical and computational models are expected to become increasingly prevalent representations of our knowledge about specific biochemical systems.

Extraction of pure components from overlapped signals in gas chromatography-mass spectrometry (GC-MS).

Gas chromatography-mass spectrometry (GC-MS) is a widely used analytical technique for the identification and quantification of trace chemicals in complex mixtures. When complex samples are analyzed by GC-MS it is common to observe co-elution of two or more components, resulting in an overlap of signal peaks observed in the total ion chromatogram. In such situations manual signal analysis is often the most reliable means for the extraction of pure component signals; however, a systematic manual analysis over a number of samples is both tedious and prone to error. In the past 30 years a number of computational approaches were proposed to assist in the process of the extraction of pure signals from co-eluting GC-MS components. This includes empirical methods, comparison with library spectra, eigenvalue analysis, regression and others. However, to date no approach has been recognized as best, nor accepted as standard. This situation hampers general GC-MS capabilities, and in particular has implications for the development of robust, high-throughput GC-MS analytical protocols required in metabolic profiling and biomarker discovery. Here we first discuss the nature of GC-MS data, and then review some of the approaches proposed for the extraction of pure signals from co-eluting components. We summarize and classify different approaches to this problem, and examine why so many approaches proposed in the past have failed to live up to their full promise. Finally, we give some thoughts on the future developments in this field, and suggest that the progress in general computing capabilities attained in the past two decades has opened new horizons for tackling this important problem.

The reducible complexity of a mitochondrial molecular machine.

Molecular machines drive essential biological processes, with the component parts of these machines each contributing a partial function or structural element. Mitochondria are organelles of eukaryotic cells, and depend for their biogenesis on a set of molecular machines for protein transport. How these molecular machines evolved is a fundamental question. Mitochondria were derived from an alpha-proteobacterial endosymbiont, and we identified in alpha-proteobacteria the component parts of a mitochondrial protein transport machine. In bacteria, the components are found in the inner membrane, topologically equivalent to the mitochondrial proteins. Although the bacterial proteins function in simple assemblies, relatively little mutation would be required to convert them to function as a protein transport machine. This analysis of protein transport provides a blueprint for the evolution of cellular machinery in general.

LeishCyc: a biochemical pathways database for Leishmania major.

BACKGROUND: Leishmania spp. are sandfly transmitted protozoan parasites that cause a spectrum of diseases in more than 12 million people worldwide. Much research is now focusing on how these parasites adapt to the distinct nutrient environments they encounter in the digestive tract of the sandfly vector and the phagolysosome compartment of mammalian macrophages. While data mining and annotation of the genomes of three Leishmania species has provided an initial inventory of predicted metabolic components and associated pathways, resources for integrating this information into metabolic networks and incorporating data from transcript, protein, and metabolite profiling studies is currently lacking. The development of a reliable, expertly curated, and widely available model of Leishmania metabolic networks is required to facilitate systems analysis, as well as discovery and prioritization of new drug targets for this important human pathogen. DESCRIPTION: The LeishCyc database was initially built from the genome sequence of Leishmania major (v5.2), based on the annotation published by the Wellcome Trust Sanger Institute. LeishCyc was manually curated to remove errors, correct automated predictions, and add information from the literature. The ongoing curation is based on public sources, literature searches, and our own experimental and bioinformatics studies. In a number of instances we have improved on the original genome annotation, and, in some ambiguous cases, collected relevant information from the literature in order to help clarify gene or protein annotation in the future. All genes in LeishCyc are linked to the corresponding entry in GeneDB (Wellcome Trust Sanger Institute). CONCLUSION: The LeishCyc database describes Leishmania major genes, gene products, metabolites, their relationships and biochemical organization into metabolic pathways. LeishCyc provides a systematic approach to organizing the evolving information about Leishmania biochemical networks and is a tool for analysis, interpretation, and visualization of Leishmania Omics data (transcriptomics, proteomics, metabolomics) in the context of metabolic pathways. LeishCyc is the first such database for the Trypanosomatidae family, which includes a number of other important human parasites. Flexible query/visualization capabilities are provided by the Pathway Tools software and its Web interface. The LeishCyc database is made freely available over the Internet http://www.leishcyc.org.

The protein import channel in the outer mitosomal membrane of Giardia intestinalis.

The identification of mitosomes in Giardia generated significant debate on the evolutionary origin of these organelles, whether they were highly reduced mitochondria or the product of a unique endosymbiotic event in an amitochondrial organism. As the protein import pathway is a defining characteristic of mitochondria, we sought to discover a TOM (translocase in the outer mitochondrial membrane) complex in Giardia. A Hidden Markov model search of the Giardia genome identified a Tom40 homologous sequence (GiTom40), where Tom40 is the protein translocation channel of the TOM complex. The GiTom40 protein is located in the membrane of mitosomes in a approximately 200-kDa TOM complex. As Tom40 was derived in the development of mitochondria to serve as the protein import channel in the outer membrane, its presence in Giardia evidences the mitochondrial ancestry of mitosomes.

Evidence of a reduced and modified mitochondrial protein import apparatus in microsporidian mitosomes.

Microsporidia are a group of highly adapted obligate intracellular parasites that are now recognized as close relatives of fungi. Their adaptation to parasitism has resulted in broad and severe reduction at (i) a genomic level by extensive gene loss, gene compaction, and gene shortening; (ii) a biochemical level with the loss of much basic metabolism; and (iii) a cellular level, resulting in lost or cryptic organelles. Consistent with this trend, the mitochondrion is severely reduced, lacking ATP synthesis and other typical functions and apparently containing only a fraction of the proteins of canonical mitochondria. We have investigated the mitochondrial protein import apparatus of this reduced organelle in the microsporidian Encephalitozoon cuniculi and find evidence of reduced and modified machinery. Notably, a putative outer membrane receptor, Tom70, is reduced in length but maintains a conserved structure chiefly consisting of tetratricopeptide repeats. When expressed in Saccharomyces cerevisiae, EcTom70 inserts with the correct topology into the outer membrane of mitochondria but is unable to complement the growth defects of Tom70-deficient yeast. We have scanned genomic data using hidden Markov models for other homologues of import machinery proteins and find evidence of severe reduction of this system.

Protein secretion and outer membrane assembly in Alphaproteobacteria.

The assembly of beta-barrel proteins into membranes is a fundamental process that is essential in Gram-negative bacteria, mitochondria and plastids. Our understanding of the mechanism of beta-barrel assembly is progressing from studies carried out in Escherichia coli and Neisseria meningitidis. Comparative sequence analysis suggests that while many components mediating beta-barrel protein assembly are conserved in all groups of bacteria with outer membranes, some components are notably absent. The Alphaproteobacteria in particular seem prone to gene loss and show the presence or absence of specific components mediating the assembly of beta-barrels: some components of the pathway appear to be missing from whole groups of bacteria (e.g. Skp, YfgL and NlpB), other proteins are conserved but are missing characteristic domains (e.g. SurA). This comparative analysis is also revealing important structural signatures that are vague unless multiple members from a protein family are considered as a group (e.g. tetratricopeptide repeat (TPR) motifs in YfiO, beta-propeller signatures in YfgL). Given that the process of the beta-barrel assembly is conserved, analysis of outer membrane biogenesis in Alphaproteobacteria, the bacterial group that gave rise to mitochondria, also promises insight into the assembly of beta-barrel proteins in eukaryotes.

The transmembrane segment of Tom20 is recognized by Mim1 for docking to the mitochondrial TOM complex.

Mitochondria cannot be made de novo. Mitochondrial biogenesis requires that up to 1000 proteins are imported into mitochondria, and the protein import pathway relies on hetero-oligomeric translocase complexes in both the inner and outer mitochondrial membranes. The translocase in the outer membrane, the TOM complex, is composed of a core complex formed from the beta-barrel channel Tom40 and additional subunits each with single, alpha-helical transmembrane segments. How alpha-helical transmembrane segments might be assembled onto a transmembrane beta-barrel in the context of a membrane environment is a question of fundamental importance. The master receptor subunit of the TOM complex, Tom20, recognizes the targeting sequence on incoming mitochondrial precursor proteins, binds these protein ligands, and then transfers them to the core complex for translocation across the outer membrane. Here we show that the transmembrane segment of Tom20 contains critical residues essential for docking the Tom20 receptor into its correct environment within the TOM complex. This crucial docking reaction is catalyzed by the unique assembly factor Mim1/Tom13. Mutations in the transmembrane segment that destabilize Tom20, or deletion of Mim1, prevent Tom20 from functioning as a receptor for protein import into mitochondria.

Conserved substrate binding by chaperones in the bacterial periplasm and the mitochondrial intermembrane space.

Mitochondria were derived from intracellular bacteria and the mitochondrial intermembrane space is topologically equivalent to the bacterial periplasm. Both compartments contain ATP-independent chaperones involved in the transport of hydrophobic membrane proteins. The mitochondrial TIM (translocase of the mitochondrial inner membrane) 10 complex and the periplasmic chaperone SurA were examined in terms of evolutionary relation, structural similarity, substrate binding specificity and their function in transporting polypeptides for insertion into membranes. The two chaperones are evolutionarily unrelated; structurally, they are also distinct both in their characteristics, as determined by SAXS (small-angle X-ray scattering), and in pairwise structural comparison using the distance matrix alignment (DALILite server). Despite their structural differences, SurA and the TIM10 complex share a common binding specificity in Pepscan assays of substrate proteins. Comprehensive analysis of the binding on a total of 1407 immobilized 13-mer peptides revealed that the TIM10 complex, like SurA, does not bind hydrophobic peptides generally, but that both chaperones display selectivity for peptides rich in aromatic residues and with net positive charge. This common binding specificity was not sufficient for SurA to completely replace TIM10 in yeast cells in vivo. In yeast cells lacking TIM10, when SurA is targeted to the intermembrane space of mitochondria, it binds translocating substrate proteins, but fails to completely transfer the substrate to the translocase in the mitochondrial inner membrane. We suggest that SurA was incapable of presenting substrates effectively to the primitive TOM (translocase of the mitochondrial outer membrane) and TIM complexes in early mitochondria, and was replaced by the more effective small Tim chaperone.

A dynamic programming approach for the alignment of signal peaks in multiple gas chromatography-mass spectrometry experiments.

BACKGROUND: Gas chromatography-mass spectrometry (GC-MS) is a robust platform for the profiling of certain classes of small molecules in biological samples. When multiple samples are profiled, including replicates of the same sample and/or different sample states, one needs to account for retention time drifts between experiments. This can be achieved either by the alignment of chromatographic profiles prior to peak detection, or by matching signal peaks after they have been extracted from chromatogram data matrices. Automated retention time correction is particularly important in non-targeted profiling studies. RESULTS: A new approach for matching signal peaks based on dynamic programming is presented. The proposed approach relies on both peak retention times and mass spectra. The alignment of more than two peak lists involves three steps: (1) all possible pairs of peak lists are aligned, and similarity of each pair of peak lists is estimated; (2) the guide tree is built based on the similarity between the peak lists; (3) peak lists are progressively aligned starting with the two most similar peak lists, following the guide tree until all peak lists are exhausted. When two or more experiments are performed on different sample states and each consisting of multiple replicates, peak lists within each set of replicate experiments are aligned first (within-state alignment), and subsequently the resulting alignments are aligned themselves (between-state alignment). When more than two sets of replicate experiments are present, the between-state alignment also employs the guide tree. We demonstrate the usefulness of this approach on GC-MS metabolic profiling experiments acquired on wild-type and mutant Leishmania mexicana parasites. CONCLUSION: We propose a progressive method to match signal peaks across multiple GC-MS experiments based on dynamic programming. A sensitive peak similarity function is proposed to balance peak retention time and peak mass spectra similarities. This approach can produce the optimal alignment between an arbitrary number of peak lists, and models explicitly within-state and between-state peak alignment. The accuracy of the proposed method was close to the accuracy of manually-curated peak matching, which required tens of man-hours for the analyzed data sets. The proposed approach may offer significant advantages for processing of high-throughput metabolomics data, especially when large numbers of experimental replicates and multiple sample states are analyzed.