LeishCyc: a biochemical pathways database for Leishmania major

Multi-label classification with XGBoost for metabolic pathway prediction

BACKGROUND

Metabolic pathway prediction is one possible approach to address the problem in system biology of reconstructing an organism's metabolic network from its genome sequence. Recently there have been developments in machine learning-based pathway prediction methods that conclude that machine learning-based approaches are similar in performance to the most used method, PathoLogic which is a rule-based method. One issue is that previous studies evaluated PathoLogic without taxonomic pruning which decreases its performance.

RESULTS

In this study, we update the evaluation results from previous studies to demonstrate that PathoLogic with taxonomic pruning outperforms previous machine learning-based approaches and that further improvements in performance need to be made for them to be competitive. Furthermore, we introduce mlXGPR, a XGBoost-based metabolic pathway prediction method based on the multi-label classification pathway prediction framework introduced from mlLGPR. We also improve on this multi-label framework by utilizing correlations between labels using classifier chains. We propose a ranking method that determines the order of the chain so that lower performing classifiers are placed later in the chain to utilize the correlations between labels more. We evaluate mlXGPR with and without classifier chains on single-organism and multi-organism benchmarks. Our results indicate that mlXGPR outperform other previous pathway prediction methods including PathoLogic with taxonomic pruning in terms of hamming loss, precision and F1 score on single organism benchmarks.

CONCLUSIONS

The results from our study indicate that the performance of machine learning-based pathway prediction methods can be substantially improved and can even outperform PathoLogic with taxonomic pruning.

Systems biology of autophagy in leishmanial infection and its diverse role in precision medicine

Autophagy is a contentious issue in leishmaniasis and is emerging as a promising therapeutic regimen. Published research on the impact of autophagic regulation on Leishmania survival is inconclusive, despite numerous pieces of evidence that Leishmania spp. triggers autophagy in a variety of cell types. The mechanistic approach is poorly understood in the Leishmania parasite as autophagy is significant in both Leishmania and the host. Herein, this review discusses the autophagy proteins that are being investigated as potential therapeutic targets, the connection between autophagy and lipid metabolism, and microRNAs that regulate autophagy and lipid metabolism. It also highlights the use of systems biology to develop novel autophagy-dependent therapeutics for leishmaniasis by utilizing artificial intelligence (AI), machine learning (ML), mathematical modeling, network analysis, and other computational methods. Additionally, we have shown many databases for autophagy and metabolism in Leishmania parasites that suggest potential therapeutic targets for intricate signaling in the autophagy system. In a nutshell, the detailed understanding of the dynamics of autophagy in conjunction with lipids and miRNAs unfolds larger dimensions for future research.

TriTrypDB: An integrated functional genomics resource for kinetoplastida

Parasitic diseases caused by kinetoplastid parasites are a burden to public health throughout tropical and subtropical regions of the world. TriTrypDB (https://tritrypdb.org) is a free online resource for data mining of genomic and functional data from these kinetoplastid parasites and is part of the VEuPathDB Bioinformatics Resource Center (https://veupathdb.org). As of release 59, TriTrypDB hosts 83 kinetoplastid genomes, nine of which, including Trypanosoma brucei brucei TREU927, Trypanosoma cruzi CL Brener and Leishmania major Friedlin, undergo manual curation by integrating information from scientific publications, high-throughput assays and user submitted comments. TriTrypDB also integrates transcriptomic, proteomic, epigenomic, population-level and isolate data, functional information from genome-wide RNAi knock-down and fluorescent tagging, and results from automated bioinformatics analysis pipelines. TriTrypDB offers a user-friendly web interface embedded with a genome browser, search strategy system and bioinformatics tools to support custom in silico experiments that leverage integrated data. A Galaxy workspace enables users to analyze their private data (e.g., RNA-sequencing, variant calling, etc.) and explore their results privately in the context of publicly available information in the database. The recent addition of an annotation platform based on Apollo enables users to provide both functional and structural changes that will appear as 'community annotations' immediately and, pending curatorial review, will be integrated into the official genome annotation.

Four layer multi-omics reveals molecular responses to aneuploidy in Leishmania

Aneuploidy causes system-wide disruptions in the stochiometric balances of transcripts, proteins, and metabolites, often resulting in detrimental effects for the organism. The protozoan parasite Leishmania has an unusually high tolerance for aneuploidy, but the molecular and functional consequences for the pathogen remain poorly understood. Here, we addressed this question in vitro and present the first integrated analysis of the genome, transcriptome, proteome, and metabolome of highly aneuploid Leishmania donovani strains. Our analyses unambiguously establish that aneuploidy in Leishmania proportionally impacts the average transcript- and protein abundance levels of affected chromosomes, ultimately correlating with the degree of metabolic differences between closely related aneuploid strains. This proportionality was present in both proliferative and non-proliferative in vitro promastigotes. However, as in other Eukaryotes, we observed attenuation of dosage effects for protein complex subunits and in addition, non-cytoplasmic proteins. Differentially expressed transcripts and proteins between aneuploid Leishmania strains also originated from non-aneuploid chromosomes. At protein level, these were enriched for proteins involved in protein metabolism, such as chaperones and chaperonins, peptidases, and heat-shock proteins. In conclusion, our results further support the view that aneuploidy in Leishmania can be adaptive. Additionally, we believe that the high karyotype diversity in vitro and absence of classical transcriptional regulation make Leishmania an attractive model to study processes of protein homeostasis in the context of aneuploidy and beyond.

Trypanosoma brucei: Metabolomics for analysis of cellular metabolism and drug discovery

BACKGROUND

Trypanosoma brucei is the causative agent of Human African Trypanosomiasis (also known as sleeping sickness), a disease causing serious neurological disorders and fatal if left untreated. Due to its lethal pathogenicity, a variety of treatments have been developed over the years, but which have some important limitations such as acute toxicity and parasite resistance. Metabolomics is an innovative tool used to better understand the parasite's cellular metabolism, and identify new potential targets, modes of action and resistance mechanisms. The metabolomic approach is mainly associated with robust analytical techniques, such as NMR and Mass Spectrometry. Applying these tools to the trypanosome parasite is, thus, useful for providing new insights into the sleeping sickness pathology and guidance towards innovative treatments.

AIM OF REVIEW

The present review aims to comprehensively describe the T. brucei biology and identify targets for new or commercialized antitrypanosomal drugs. Recent metabolomic applications to provide a deeper knowledge about the mechanisms of action of drugs or potential drugs against T. brucei are highlighted. Additionally, the advantages of metabolomics, alone or combined with other methods, are discussed.

KEY SCIENTIFIC CONCEPTS OF REVIEW

Compared to other parasites, only few studies employing metabolomics have to date been reported on Trypanosoma brucei. Published metabolic studies, treatments and modes of action are discussed. The main interest is to evaluate the metabolomics contribution to the understanding of T. brucei's metabolism.

Metabolite Biomarkers of Leishmania Antimony Resistance

Leishmania parasites cause leishmaniasis, one of the most epidemiologically important neglected tropical diseases. Leishmania exhibits a high ability of developing drug resistance, and drug resistance is one of the main threats to public health, as it is associated with increased incidence, mortality, and healthcare costs. The antimonial drug is the main historically implemented drug for leishmaniasis. Nevertheless, even though antimony resistance has been widely documented, the mechanisms involved are not completely understood. In this study, we aimed to identify potential metabolite biomarkers of antimony resistance that could improve leishmaniasis treatment. Here, using L. tropica promastigotes as the biological model, we showed that the level of response to antimony can be potentially predicted using ¹H-NMR-based metabolomic profiling. Antimony-resistant parasites exhibited differences in metabolite composition at the intracellular and extracellular levels, suggesting that a metabolic remodeling is required to combat the drug. Simple and time-saving exometabolomic analysis can be efficiently used for the differentiation of sensitive and resistant parasites. Our findings suggest that changes in metabolite composition are associated with an optimized response to the osmotic/oxidative stress and a rearrangement of carbon-energy metabolism. The activation of energy metabolism can be linked to the high energy requirement during the antioxidant stress response. We also found that metabolites such as proline and lactate change linearly with the level of resistance to antimony, showing a close relationship with the parasite's efficiency of drug resistance. A list of potential metabolite biomarkers is described and discussed.

Outwitting an Old Neglected Nemesis: A Review on Leveraging Integrated Data-Driven Approaches to Aid in Unraveling of Leishmanicides of Therapeutic Potential

The global prevalence of leishmaniasis has increased with skyrocketed mortality in the past decade. The causative agent of leishmaniasis is Leishmania species, which infects populations in almost all the continents. Prevailing treatment regimens are consistently inefficient with reported side effects, toxicity and drug resistance. This review complements existing ones by discussing the current state of treatment options, therapeutic bottlenecks including chemoresistance and toxicity, as well as drug targets. It further highlights innovative applications of nanotherapeutics-based formulations, inhibitory potential of leishmanicides, anti-microbial peptides and organometallic compounds on leishmanial species. Moreover, it provides essential insights into recent machine learning-based models that have been used to predict novel leishmanicides and also discusses other new models that could be adopted to develop fast, efficient, robust and novel algorithms to aid in unraveling the next generation of anti-leishmanial drugs. A plethora of enriched functional genomic, proteomic, structural biology, high throughput bioassay and drug-related datasets are currently warehoused in both general and leishmania-specific databases. The warehoused datasets are essential inputs for training and testing algorithms to augment the prediction of biotherapeutic entities. In addition, we demonstrate how pharmacoinformatics techniques including ligand-, structure- and pharmacophore-based virtual screening approaches have been utilized to screen ligand libraries against both modeled and experimentally solved 3D structures of essential drug targets. In the era of data-driven decision-making, we believe that highlighting intricately linked topical issues relevant to leishmanial drug discovery offers a one-stop-shop opportunity to decipher critical literature with the potential to unlock implicit breakthroughs.

Leishmania tarentolae: Taxonomic classification and its application as a promising biotechnological expression host

In this review, we summarize the current knowledge concerning the eukaryotic protozoan parasite Leishmania tarentolae, with a main focus on its potential for biotechnological applications. We will also discuss the genus, subgenus, and species-level classification of this parasite, its life cycle and geographical distribution, and similarities and differences to human-pathogenic species, as these aspects are relevant for the evaluation of biosafety aspects of L. tarentolae as host for recombinant DNA/protein applications. Studies indicate that strain LEM-125 but not strain TARII/UC of L. tarentolae might also be capable of infecting mammals, at least transiently. This could raise the question of whether the current biosafety level of this strain should be reevaluated. In addition, we will summarize the current state of biotechnological research involving L. tarentolae and explain why this eukaryotic parasite is an advantageous and promising human recombinant protein expression host. This summary includes overall biotechnological applications, insights into its protein expression machinery (especially on glycoprotein and antibody fragment expression), available expression vectors, cell culture conditions, and its potential as an immunotherapy agent for human leishmaniasis treatment. Furthermore, we will highlight useful online tools and, finally, discuss possible future applications such as the humanization of the glycosylation profile of L. tarentolae or the expression of mammalian recombinant proteins in amastigote-like cells of this species or in amastigotes of avirulent human-pathogenic Leishmania species.

LeishInDB: A web-accessible resource for small molecule inhibitors against Leishmania sp

Despite the availability of drugs to treat Leishmaniasis, various other factors including drug resistance and adverse side effects encourage the researchers to search for new strategies and alternatives for treating Leishmaniasis. Repurposing and devising combination therapy with the existing small molecules would serve as an alternative strategy to address the issue, especially the drug resistance. Hence, here we report LeishInDB, a web-accessible resource of small molecule inhibitors having a varying degree of activity towards Leishmania sp. The database includes searchable information of >7000 small molecules collected from >600 literature. The comprehensive information of inhibitors mainly include the activity details (IC50, EC50, Ki, binding energy etc., if any); information on species and form of Leishmania the inhibitor is active against; and the details about their protein target (actively linked to TriTrypDB). In addition, chemical properties including the log P-value, number of rotatable bonds, number of hydrogen bond donors and acceptors, molecular weight, 2D/3D structural information etc., were also included. Toxicity prediction for each molecule was performed using admetSAR and their corresponding results were available to perform the filtered search. In addition, facility to perform sub-structure search, facility to perform the dynamic search on various fields, and facility to download all the structure of molecules that match the search criteria were also included. We believe that the scope of LeishInDB allows the researchers to utilize the available information for repurposing the inhibitors as well as for the investigation of new therapeutics. Database URL:http://leishindb.biomedinformri.com/.

Integrated genomic and metabolomic profiling of ISC1, an emerging Leishmania donovani population in the Indian subcontinent

Leishmania donovani is the responsible agent for visceral leishmaniasis (VL) in the Indian subcontinent (ISC). The disease is lethal without treatment and causes 0.2 to 0.4 million cases each year. Recently, reports of VL in Nepalese hilly districts have increased as well as VL cases caused by L. donovani from the ISC1 genetic group, a new and emerging genotype. In this study, we perform for the first time an integrated, untargeted genomics and metabolomics approach to characterize ISC1, in comparison with the Core Group (CG), main population that drove the most recent outbreak of VL in the ISC. We show that the ISC1 population is very different from the CG, both at genome and metabolome levels. The genomic differences include SNPs, CNV and small indels in genes coding for known virulence factors, immunogens and surface proteins. Both genomic and metabolic approaches highlighted dissimilarities related to membrane lipids, the nucleotide salvage pathway and the urea cycle in ISC1 versus CG. Many of these pathways and molecules are important for the interaction with the host/extracellular environment. Altogether, our data predict major functional differences in ISC1 versus CG parasites, including virulence. Therefore, particular attention is required to monitor the fate of this emerging ISC1 population in the ISC, especially in a post-VL elimination context.

Human Kinetoplastid Protozoan Infections: Where Are We Going Next?

Kinetoplastida trypanosomatidae microorganisms are protozoan parasites exhibiting a developmental stage in the gut of insect vectors and tissues of vertebrate hosts. During the vertebrate infective stages, these parasites alter the differential expression of virulence genes, modifying their biological and antigenic properties in order to subvert the host protective immune responses and establish a persistent infection. One of the hallmarks of kinetoplastid parasites is their evasion mechanisms from host immunity, leading to disease chronification. The diseases caused by kinetoplastid parasites are neglected by the global expenditures in research and development, affecting millions of individuals in the low and middle-income countries located mainly in the tropical and subtropical regions. However, investments made by public and private initiatives have over the past decade leveraged important lines of intervention that if well-integrated to health care programs will likely accelerate disease control initiatives. This review summarizes recent advances in public health care principles, including new drug discoveries and their rational use with chemotherapeutic vaccines, and the implementation of control efforts to spatially mapping the kinetoplastid infections through monitoring of infected individuals in epidemic areas. These approaches should bring us the means to track genetic variation of parasites and drug resistance, integrating this knowledge into effective stewardship programs to prevent vector-borne kinetoplastid infections in areas at risk of disease spreading.

A systematic reconstruction and constraint-based analysis of Leishmania donovani metabolic network: identification of potential antileishmanial drug targets

Visceral leishmaniasis, a lethal parasitic disease, is caused by the protozoan parasite Leishmania donovani. The absence of an effective vaccine, drug toxicity and parasite resistance necessitates the identification of novel drug targets. Reconstruction of genome-scale metabolic models and their simulation has been established as an important tool for systems-level understanding of a microorganism's metabolism. In this work, amalgamating the tools and techniques of computational systems biology with rigorous manual curation, a constraint-based metabolic model for Leishmania donovani BPK282A1 has been developed. New functional annotations for 18 formerly hypothetical or erroneously annotated genes (encountered during iterative refinement of the model) have been proposed. Further, to formulate an accurate biomass objective function, experimental determination of previously uncharacterized biomass constituents was performed. The developed model is a highly compartmentalized metabolic model, comprising 1159 reactions, 1135 metabolites and 604 genes. The model exhibited around 76% accuracy for the prediction of experimental phenotypes of gene knockout studies and drug inhibition assays. Employing in silico gene knockout studies, we identified 28 essential genes with negligible sequence identity to the human proteins. Moreover, by dissecting the functional interdependencies of metabolic pathways, 70 synthetic lethal pairs were identified. Finally, in order to delineate stage-specific metabolism, gene-expression data of the amastigote stage residing in human macrophages were integrated into the model. By comparing the flux distribution, we illustrated the stage-specific differences in metabolism and environmental conditions that are in good agreement with the experimental findings. The developed model can serve as a highly enriched knowledgebase of legacy data and an important tool for generating experimentally verifiable hypotheses.

SolCyc: a database hub at the Sol Genomics Network (SGN) for the manual curation of metabolic networks in Solanum and Nicotiana specific databases

Database URL

https://solgenomics.net/tools/solcyc/.

EuPathDB: The Eukaryotic Pathogen Genomics Database Resource

Fighting infections and developing novel drugs and vaccines requires advanced knowledge of pathogen's biology. Readily accessible genomic, functional genomic, and population data aids biological and translational discovery. The Eukaryotic Pathogen Database Resources ( http://eupathdb.org ) are data mining resources that support hypothesis driven research by facilitating the discovery of meaningful biological relationships from large volumes of data. The resource encompasses 13 sites that support over 170 species including pathogenic protists, oomycetes, and fungi as well as evolutionarily related nonpathogenic species. EuPathDB integrates preanalyzed data with advanced search capabilities, data visualization, analysis tools and a comprehensive record system in a graphical interface that does not require prior computational skills. This chapter describes guiding concepts common across EuPathDB sites and illustrates the powerful data mining capabilities of some of the available tools and features.

Computational Systems Biology of Metabolism in Infection

A systems approach to elucidate the effect of infection on cell metabolism provides several opportunities from a better understanding of molecular mechanisms to the identification of potential biomarkers and drug targets. This is obvious from the fact that we have witnessed the accelerated use of computational systems biology in the last five years to study metabolic changes in pathogen and/or host cells in response to infection. In this chapter, we aim to present a comprehensive review of the recent research by focusing on genome-scale metabolic network models of pathogen-host systems and genome-wide metabolomics and fluxomics analysis of infected cells.

New approaches and omics tools for mining of vaccine candidates against vector-borne diseases

Vector-borne diseases (VBDs) present a major threat to human and animal health, as well as place a substantial burden on livestock production. As a way of sustainable VBD control, focus is set on vaccine development. Advances in genomics and other "omics" over the past two decades have given rise to a "third generation" of vaccines based on technologies such as reverse vaccinology, functional genomics, immunomics, structural vaccinology and the systems biology approach. The application of omics approaches is shortening the time required to develop the vaccines and increasing the probability of discovery of potential vaccine candidates. Herein, we review the development of new generation vaccines for VBDs, and discuss technological advancement and overall challenges in the vaccine development pipeline. Special emphasis is placed on the development of anti-tick vaccines that can quell both vectors and pathogens.

The Challenges and Advances in Diagnosis of Vector-Borne Diseases: Where Do We Stand?

Vector-borne diseases (VBD) are of major importance to human and animal health. In recent years, VBD have been emerging or re-emerging in many geographical areas, alarming new disease threats and economic losses. The precise diagnosis of many of these diseases still remains a major challenge because of the lack of comprehensive data available on accurate and reliable diagnostic methods. Here, we conducted a systematic and in-depth review of the former, current, and upcoming techniques employed for the diagnosis of VBD.

EuPathDB: the eukaryotic pathogen genomics database resource

The Eukaryotic Pathogen Genomics Database Resource (EuPathDB, http://eupathdb.org) is a collection of databases covering 170+ eukaryotic pathogens (protists & fungi), along with relevant free-living and non-pathogenic species, and select pathogen hosts. To facilitate the discovery of meaningful biological relationships, the databases couple preconfigured searches with visualization and analysis tools for comprehensive data mining via intuitive graphical interfaces and APIs. All data are analyzed with the same workflows, including creation of gene orthology profiles, so data are easily compared across data sets, data types and organisms. EuPathDB is updated with numerous new analysis tools, features, data sets and data types. New tools include GO, metabolic pathway and word enrichment analyses plus an online workspace for analysis of personal, non-public, large-scale data. Expanded data content is mostly genomic and functional genomic data while new data types include protein microarray, metabolic pathways, compounds, quantitative proteomics, copy number variation, and polysomal transcriptomics. New features include consistent categorization of searches, data sets and genome browser tracks; redesigned gene pages; effective integration of alternative transcripts; and a EuPathDB Galaxy instance for private analyses of a user's data. Forthcoming upgrades include user workspaces for private integration of data with existing EuPathDB data and improved integration and presentation of host–pathogen interactions.

ArthropodaCyc: a CycADS powered collection of BioCyc databases to analyse and compare metabolism of arthropods

Arthropods interact with humans at different levels with highly beneficial roles (e.g. as pollinators), as well as with a negative impact for example as vectors of human or animal diseases, or as agricultural pests. Several arthropod genomes are available at present and many others will be sequenced in the near future in the context of the i5K initiative, offering opportunities for reconstructing, modelling and comparing their metabolic networks. In-depth analysis of these genomic data through metabolism reconstruction is expected to contribute to a better understanding of the biology of arthropods, thereby allowing the development of new strategies to control harmful species. In this context, we present here ArthropodaCyc, a dedicated BioCyc collection of databases using the Cyc annotation database system (CycADS), allowing researchers to perform reliable metabolism comparisons of fully sequenced arthropods genomes. Since the annotation quality is a key factor when performing such global genome comparisons, all proteins from the genomes included in the ArthropodaCyc database were re-annotated using several annotation tools and orthology information. All functional/domain annotation results and their sources were integrated in the databases for user access. Currently, ArthropodaCyc offers a centralized repository of metabolic pathways, protein sequence domains, Gene Ontology annotations as well as evolutionary information for 28 arthropod species. Such database collection allows metabolism analysis both with integrated tools and through extraction of data in formats suitable for systems biology studies.

Database URL:http://arthropodacyc.cycadsys.org/

Use of (13)C stable isotope labelling for pathway and metabolic flux analysis in Leishmania parasites

This protocol describes the combined use of metabolite profiling and stable isotope labelling to define pathways of central carbon metabolism in the protozoa parasite, Leishmania mexicana. Parasite stages are cultivated in standard or completely defined media and then rapidly transferred to chemically equivalent media containing a single (13)C-labelled nutrient. The incorporation of label can be followed over time or after establishment of isotopic equilibrium by harvesting parasites with rapid metabolic quenching. (13)C enrichment of multiple intracellular polar and apolar (lipidic) metabolites can be quantified using gas chromatography-mass spectrometry (GC-MS), while the uptake and secretion of (13)C-labelled metabolites can be measured by (13)C-NMR. Analysis of the mass isotopomer distribution of key metabolites provides information on pathway structure, while analysis of labelling kinetics can be used to infer metabolic fluxes. This protocol is exemplified using L. mexicana labelled with (13)C-U-glucose. The method can be used to measure perturbations in parasite metabolism induced by drug inhibition or genetic manipulation of enzyme levels and is broadly applicable to any cultured parasite stages.

Comparison of the Proteome Profiling of Iranian isolates of Leishmania tropica, L. major and L. infantum by Two-Dimensional Electrophoresis (2-DE) and Mass-spectrometry

BACKGROUND

The mechanisms of virulence and species differences of Leishmania parasites are under the influence of gene expression regulations at posttranscriptional stages. In Iran, L. major and L. tropica are known as principal agents of cutaneous leishmaniasis, while L. infantum causes visceral leishmaniasis.

METHODS

As a preliminary study, we compared the proteome mapping of the above three Iranian isolates of Leishmania species through the 2-dimension electrophoresis (2-DE), and identified the prominent proteins by Liquid Chromatography (LC) mass spectrometry.

RESULTS

We reproducibly detected about 700 protein spots in each species by using the Melanie software. Totally, 264 proteins exhibited significant changes among 3 species. Forty nine protein spots identified in both L. tropica and L. major were similar in position in the gel, whereas only 35 of L. major proteins and 10 of L. tropica proteins were matched with those of L. infantum. Having identified 24 proteins in the three species, we sought to provide possible explanations for their differential expression patterns and discuss their relevance to cell biology.

CONCLUSION

The comparison of proteome profiling pattern of the 3 species identified limit up and limit down regulated or absent /present proteins. In addition, the LC-MS data analysis showed that most of the protein spots with differential abundance in the 3 species are involved in cell motility and cytoskeleton, cell signaling and vesicular trafficking, intracellular survival / proteolysis, oxidative stress defense, protein synthesis, protein ubiquitination / proteolysis, and stress related proteins. Differentially proteins distributed among the species maybe implicated in host pathogenecity interactions and parasite tropism to cutaneous or visceral tissue macrophages.

TrypanoCyc: a community-led biochemical pathways database for Trypanosoma brucei

The metabolic network of a cell represents the catabolic and anabolic reactions that interconvert small molecules (metabolites) through the activity of enzymes, transporters and non-catalyzed chemical reactions. Our understanding of individual metabolic networks is increasing as we learn more about the enzymes that are active in particular cells under particular conditions and as technologies advance to allow detailed measurements of the cellular metabolome. Metabolic network databases are of increasing importance in allowing us to contextualise data sets emerging from transcriptomic, proteomic and metabolomic experiments. Here we present a dynamic database, TrypanoCyc (http://www.metexplore.fr/trypanocyc/), which describes the generic and condition-specific metabolic network of Trypanosoma brucei, a parasitic protozoan responsible for human and animal African trypanosomiasis. In addition to enabling navigation through the BioCyc-based TrypanoCyc interface, we have also implemented a network-based representation of the information through MetExplore, yielding a novel environment in which to visualise the metabolism of this important parasite.

Multi-analytical platform metabolomic approach to study miltefosine mechanism of action and resistance in Leishmania

Miltefosine (MT) (hexadecylphosphocholine) was implemented to cope with resistance against antimonials, the classical treatment in Leishmaniasis. Given the scarcity of anti- Leishmania (L) drugs and the increasing appearance of resistance, there is an obvious need for understanding the mechanism of action and development of such resistance. Metabolomics is an increasingly popular tool in the life sciences due to it being a relatively fast and accurate technique that can be applied either with a particular focus or in a global manner to reveal new knowledge about biological systems. Three analytical platforms, gas chromatography (GC), liquid chromatography (LC) and capillary electrophoresis (CE) have been coupled to mass spectrometry (MS) to obtain a broad picture of metabolic changes in the parasite. Impairment of the polyamine metabolism from arginine (Arg) to trypanothione in susceptible parasites treated with MT was in some way expected, considering the reactive oxygen species (ROS) production described for MT. Importantly, in resistant parasites an increase in the levels of amino acids was the most outstanding feature, probably related to the adaptation of the resistant strain for its survival inside the parasitophorous vacuole.

Selection of molecular targets for drug development against trypanosomatids

Trypanosomatid parasites are a group of flagellated protozoa that includes the genera Leishmania and Trypanosoma, which are the causative agents of diseases (leishmaniases, sleeping sickness and Chagas disease) that cause considerable morbidity and mortality, affecting more than 27 million people worldwide. Today no effective vaccines for the prevention of these diseases exist, whereas current chemotherapy is ineffective, mainly due to toxic side effects of current drugs and to the emergence of drug resistance and lack of cost effectiveness. For these reasons, rational drug design and the search of good candidate drug targets is of prime importance. The search for drug targets requires a multidisciplinary approach. To this end, the completion of the genome project of many trypanosomatid species gives a vast amount of new information that can be exploited for the identification of good drug candidates with a prediction of "druggability" and divergence from mammalian host proteins. In addition, an important aspect in the search for good drug targets is the "target identification" and evaluation in a biological pathway, as well as the essentiality of the gene in the mammalian stage of the parasite, which is provided by basic research and genetic and proteomic approaches. In this chapter we will discuss how these bioinformatic tools and experimental evaluations can be integrated for the selection of candidate drug targets, and give examples of metabolic and signaling pathways in the parasitic protozoa that can be exploited for rational drug design.

The MetaCyc database of metabolic pathways and enzymes and the BioCyc collection of Pathway/Genome Databases

The MetaCyc database (MetaCyc.org) is a comprehensive and freely accessible database describing metabolic pathways and enzymes from all domains of life. MetaCyc pathways are experimentally determined, mostly small-molecule metabolic pathways and are curated from the primary scientific literature. MetaCyc contains >2100 pathways derived from >37 000 publications, and is the largest curated collection of metabolic pathways currently available. BioCyc (BioCyc.org) is a collection of >3000 organism-specific Pathway/Genome Databases (PGDBs), each containing the full genome and predicted metabolic network of one organism, including metabolites, enzymes, reactions, metabolic pathways, predicted operons, transport systems and pathway-hole fillers. Additions to BioCyc over the past 2 years include YeastCyc, a PGDB for Saccharomyces cerevisiae, and 891 new genomes from the Human Microbiome Project. The BioCyc Web site offers a variety of tools for querying and analysis of PGDBs, including Omics Viewers and tools for comparative analysis. New developments include atom mappings in reactions, a new representation of glycan degradation pathways, improved compound structure display, better coverage of enzyme kinetic data, enhancements of the Web Groups functionality, improvements to the Omics viewers, a new representation of the Enzyme Commission system and, for the desktop version of the software, the ability to save display states.

Genome-scale metabolic model of the fission yeast Schizosaccharomyces pombe and the reconciliation of in silico/in vivo mutant growth

Background

Over the last decade, the genome-scale metabolic models have been playing increasingly important roles in elucidating metabolic characteristics of biological systems for a wide range of applications including, but not limited to, system-wide identification of drug targets and production of high value biochemical compounds. However, these genome-scale metabolic models must be able to first predict known in vivo phenotypes before it is applied towards these applications with high confidence. One benchmark for measuring the in silico capability in predicting in vivo phenotypes is the use of single-gene mutant libraries to measure the accuracy of knockout simulations in predicting mutant growth phenotypes.

Results

Here we employed a systematic and iterative process, designated as Reconciling In silico/in vivo mutaNt Growth (RING), to settle discrepancies between in silico prediction and in vivo observations to a newly reconstructed genome-scale metabolic model of the fission yeast, Schizosaccharomyces pombe, SpoMBEL1693. The predictive capabilities of the genome-scale metabolic model in predicting single-gene mutant growth phenotypes were measured against the single-gene mutant library of S. pombe. The use of RING resulted in improving the overall predictive capability of SpoMBEL1693 by 21.5%, from 61.2% to 82.7% (92.5% of the negative predictions matched the observed growth phenotype and 79.7% the positive predictions matched the observed growth phenotype).

Conclusion

This study presents validation and refinement of a newly reconstructed metabolic model of the yeast S. pombe, through improving the metabolic model’s predictive capabilities by reconciling the in silico predicted growth phenotypes of single-gene knockout mutants, with experimental in vivo growth data.

Functional genomics of trypanosomatids

The decoding of the Tritryp reference genomes nearly 7 years ago provided a first peek into the biology of pathogenic trypanosomatids and a blueprint that has paved the way for genome-wide studies. Although 60-70% of the predicted protein coding genes in Trypanosoma brucei, Trypanosoma cruzi and Leishmania major remain unannotated, the functional genomics landscape is rapidly changing. Facilitated by the advent of next-generation sequencing technologies, improved structural and functional annotation and genes and their products are emerging. Information is also growing for the interactions between cellular components as transcriptomes, regulatory networks and metabolomes are characterized, ushering in a new era of systems biology. Simultaneously, the launch of comparative sequencing of multiple strains of kinetoplastids will finally lead to the investigation of a vast, yet to be explored, evolutionary and pathogenomic space.

Metabolic pathways required for the intracellular survival of Leishmania

Leishmania spp. are sandfly-transmitted parasitic protozoa that cause a spectrum of important diseases and lifelong chronic infections in humans. In the mammalian host, these parasites proliferate within acidified vacuoles in several phagocytic host cells, including macrophages, dendritic cells, and neutrophils. In this review, we discuss recent progress that has been made in defining the nutrient composition of the Leishmania parasitophorous vacuole, as well as metabolic pathways required by these parasites for virulence. Analysis of the virulence phenotype of Leishmania mutants has been particularly useful in defining carbon sources and nutrient salvage pathways that are essential for parasite persistence and/or induction of pathology. We also review data suggesting that intracellular parasite stages modulate metabolic processes in their host cells in order to generate a more permissive niche.

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.

The MetaCyc database of metabolic pathways and enzymes and the BioCyc collection of pathway/genome databases

The MetaCyc database (http://metacyc.org/) provides a comprehensive and freely accessible resource for metabolic pathways and enzymes from all domains of life. The pathways in MetaCyc are experimentally determined, small-molecule metabolic pathways and are curated from the primary scientific literature. MetaCyc contains more than 1800 pathways derived from more than 30 000 publications, and is the largest curated collection of metabolic pathways currently available. Most reactions in MetaCyc pathways are linked to one or more well-characterized enzymes, and both pathways and enzymes are annotated with reviews, evidence codes and literature citations. BioCyc (http://biocyc.org/) is a collection of more than 1700 organism-specific Pathway/Genome Databases (PGDBs). Each BioCyc PGDB contains the full genome and predicted metabolic network of one organism. The network, which is predicted by the Pathway Tools software using MetaCyc as a reference database, consists of metabolites, enzymes, reactions and metabolic pathways. BioCyc PGDBs contain additional features, including predicted operons, transport systems and pathway-hole fillers. The BioCyc website and Pathway Tools software offer many tools for querying and analysis of PGDBs, including Omics Viewers and comparative analysis. New developments include a zoomable web interface for diagrams; flux-balance analysis model generation from PGDBs; web services; and a new tool called Web Groups.

Metabolomic analysis of trypanosomatid protozoa

Metabolomics aims to measure all low molecular weight chemicals within a given system in a manner analogous to transcriptomics, proteomics and genomics. In this review we highlight metabolomics approaches that are currently being applied to the kinetoplastid parasites, Trypanosoma brucei and Leishmania spp. The use of untargeted metabolomics approaches, made possible through advances in mass spectrometry and informatics, and stable isotope labelling has increased our understanding of the metabolism in these organisms beyond the views established using classical biochemical approaches. Set within the context of metabolic networks, predicted using genome-wide reconstructions of metabolism, new hypotheses on how to target aspects of metabolism to design new drugs against these protozoa are emerging.

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.

Contribution of proteomics of Leishmania spp. to the understanding of differentiation, drug resistance mechanisms, vaccine and drug development

Leishmania spp., protozoan parasites with a digenetic life cycle, cause a spectrum of diseases in humans. Recently several Leishmania spp. have been sequenced which significantly boosted the number and quality of proteomic studies conducted. Here a historic review will summarize work of the pre-genomic era and then focus on studies after genome information became available. Firstly works comparing the different life cycle stages, in order to identify stage specific proteins, will be discussed. Identifying post-translational modifications by proteomics especially phosphorylation events will be discussed. Further the contribution of proteomics to the understanding of the molecular mechanism of drug resistance and the investigation of immunogenic proteins for the identification of vaccine candidates will be summarized. Approaches of how potentially secreted proteins were identified are discussed. So far 30-35% of the total predicted proteome of Leishmania spp. have been identified. This comprises mainly the abundant proteins, therefore the last section will look into technological approaches on how this coverage may be increased and what the gel-free and gel-based proteomics have to offer will be compared.

A survey of metabolic databases emphasizing the MetaCyc family

Thanks to the confluence of genome sequencing and bioinformatics, the number of metabolic databases has expanded from a handful in the mid-1990s to several thousand today. These databases lie within distinct families that have common ancestry and common attributes. The main families are the MetaCyc, KEGG, Reactome, Model SEED, and BiGG families. We survey these database families, as well as important individual metabolic databases, including multiple human metabolic databases. The MetaCyc family is described in particular detail. It contains well over 1,000 databases, including highly curated databases for Escherichia coli, Saccharomyces cerevisiae, Mus musculus, and Arabidopsis thaliana. These databases are available through a number of web sites that offer a range of software tools for querying and visualizing metabolic networks. These web sites also provide multiple tools for analysis of gene expression and metabolomics data, including visualization of those datasets on metabolic network diagrams and over-representation analysis of gene sets and metabolite sets.

A novel sucrose/H+ symport system and an intracellular sucrase in Leishmania donovani

The flagellated form of pathogenic parasitic protozoa Leishmania, resides in the alimentary tract of its sandfly vector, where sucrose serves as a major nutrient source. In this study we report the presence of a sucrose transport system in Leishmania donovani promastigotes. The kinetics of sucrose uptake in promastigotes are biphasic in nature with both high affinity K(m) (K(m) of ∼ 75 μM) and low affinity K(m) (K(m)∼ 1.38 mM) components. By contrast the virulent amastigotes take up sucrose via a low affinity process with a K(m) of 2.5mM. The transport of sucrose into promastigotes leads to rapid intracellular acidification, as indicated by changes in the fluorescence of the pH indicator 2',7'-bis-(2-carboxyethyl)-5-(6) Carboxyfluorescein (BCECF). In experiments with right side-out plasma membrane vesicles derived from L. donovani promastigotes, an artificial pH gradient was able to drive the active accumulation of sucrose. These data are consistent with the operation of a H(+)-sucrose symporter. The symporter was shown to be independent of Na(+) and to be insensitive to cytochalasin B, to the flavonoid phloretin and to the Na(+)/K(+) ATPase inhibitor ouabain. However, the protonophore carbonylcyanide P- (trifluromethoxy) phenylhydrazone (FCCP) and a number of thiol reagents caused significant inhibition of sucrose uptake. Evidence was also obtained for the presence of a stable intracellular pool of the sucrose splitting enzyme, sucrase, in promastigote stage parasites. The results are consistent with the hypothesis that L. donovani promastigotes take up sucrose via a novel H(+)-sucrose symport system and that, on entering the cell, the sucrose is hydrolysed to its component monosaccharides by an intracellular sucrase, thereby providing an energy source for the parasites.

Simple data-reduction method for high-resolution LC-MS data in metabolomics

BACKGROUND

Metabolomics LC-MS experiments yield large numbers of peaks, few of which can be identified by database matching. Many of the remaining peaks correspond to derivatives of identified peaks (e.g., isotope peaks, adducts, fragments and multiply charged molecules). In this article, we present a data-reduction approach that automatically identifies these derivative peaks.

RESULTS

Using data-driven clustering based on chromatographic peak shape correlation and intensity patterns across biological replicates, derivative peaks can be reliably identified. Using a test data set obtained from Leishmania donovani extracts, we achieved a 60% reduction of the number of peaks. After quality control filtering, almost 80% of the peaks could putatively be identified by database matching.

CONCLUSION

Automated peak filtering substantially speeds up the data-interpretation process.

Analysis of ultra low genome conservation in Clostridium difficile

Microarray-based comparative genome hybridisations (CGH) and genome sequencing of Clostridium difficile isolates have shown that the genomes of this species are highly variable. To further characterize their genome variation, we employed integration of data from CGH, genome sequencing and putative cellular pathways. Transcontinental strain comparison using CGH data confirmed the emergence of a human-specific hypervirulent cluster. However, there was no correlation between total toxin production and hypervirulent phenotype, indicating the possibility of involvement of additional factors towards hypervirulence. Calculation of C. difficile core and pan genome size using CGH and sequence data estimated that the core genome is composed of 947 to 1,033 genes and a pan genome comprised of 9,640 genes. The reconstruction, annotation and analysis of cellular pathways revealed highly conserved pathways despite large genome variation. However, few pathways such as tetrahydrofolate biosynthesis were found to be variable and could be contributing to adaptation towards virulence such as antibiotic resistance.

Metabolomics to unveil and understand phenotypic diversity between pathogen populations

Leishmaniasis is a debilitating disease caused by the parasite Leishmania. There is extensive clinical polymorphism, including variable responsiveness to treatment. We study Leishmania donovani parasites isolated from visceral leishmaniasis patients in Nepal that responded differently to antimonial treatment due to differing intrinsic drug sensitivity of the parasites. Here, we present a proof-of-principle study in which we applied a metabolomics pipeline specifically developed for L. donovani to characterize the global metabolic differences between antimonial-sensitive and antimonial-resistant L. donovani isolates. Clones of drug-sensitive and drug-resistant parasite isolates from clinical samples were cultured in vitro and harvested for metabolomics analysis. The relative abundance of 340 metabolites was determined by ZIC-HILIC chromatography coupled to LTQ-Orbitrap mass spectrometry. Our measurements cover approximately 20% of the predicted core metabolome of Leishmania and additionally detected a large number of lipids. Drug-sensitive and drug-resistant parasites showed distinct metabolic profiles, and unsupervised clustering and principal component analysis clearly distinguished the two phenotypes. For 100 metabolites, the detected intensity differed more than three-fold between the 2 phenotypes. Many of these were in specific areas of lipid metabolism, suggesting that the membrane composition of the drug-resistant parasites is extensively modified. Untargeted metabolomics has been applied on clinical Leishmania isolates to uncover major metabolic differences between drug-sensitive and drug-resistant isolates. The identified major differences provide novel insights into the mechanisms involved in resistance to antimonial drugs, and facilitate investigations using targeted approaches to unravel the key changes mediating drug resistance.

Towards an unbiased metabolic profiling of protozoan parasites: optimisation of a Leishmania sampling protocol for HILIC-orbitrap analysis

Comparative metabolomics of Leishmania species requires the simultaneous identification and quantification of a large number of intracellular metabolites. Here, we describe the optimisation of a comprehensive metabolite extraction protocol for Leishmania parasites and the subsequent optimisation of the analytical approach, consisting of hydrophilic interaction liquid chromatography coupled to LTQ-orbitrap mass spectrometry. The final optimised protocol starts with a rapid quenching of parasite cells to 0 °C, followed by a triplicate washing step in phosphate-buffered saline. The intracellular metabolome of 4 × 10(7) parasites is then extracted in cold chloroform/methanol/water 20/60/20 (v/v/v) for 1 h at 4 °C, resulting in both cell disruption and comprehensive metabolite dissolution. Our developed metabolomics platform can detect approximately 20% of the predicted Leishmania metabolome in a single experiment in positive and negative ionisation mode.

Graph methods for the investigation of metabolic networks in parasitology

Recently, a way was opened with the development of many mathematical methods to model and analyze genome-scale metabolic networks. Among them, methods based on graph models enable to us quickly perform large-scale analyses on large metabolic networks. However, it could be difficult for parasitologists to select the graph model and methods adapted to their biological questions. In this review, after briefly addressing the problem of the metabolic network reconstruction, we propose an overview of the graph-based approaches used in whole metabolic network analyses. Applications highlight the usefulness of this kind of approach in the field of parasitology, especially by suggesting metabolic targets for new drugs. Their development still represents a major challenge to fight against the numerous diseases caused by parasites.

The evolution of metabolic profiling in parasitology

The uses of metabolic profiling technologies such as mass spectrometry and nuclear magnetic resonance spectroscopy in parasitology have been multi-faceted. Traditional uses of spectroscopic platforms focused on determining the chemical composition of drugs or natural products used for treatment of parasitic infection. A natural progression of the use of these tools led to the generation of chemical profiles of the parasite in in vitro systems, monitoring the response of the parasite to chemotherapeutics, profiling metabolic consequences in the host organism and to deriving host-parasite interactions. With the dawn of the post-genomic era the paradigm in many research areas shifted towards Systems Biology and the integration of biomolecular interactions at the level of the gene, protein and metabolite. Although these technologies have yet to deliver their full potential, metabolic profiling has a key role to play in defining diagnostic or even prognostic metabolic signatures of parasitic infection and in deciphering the molecular mechanisms underpinning the development of parasite-induced pathologies. The strengths and weaknesses of the various spectroscopic technologies and analytical strategies are summarized here with respect to achieving these goals.

Metabolomic systems biology of trypanosomes

Metabolomics analysis, which aims at the systematic identification and quantification of all metabolites in biological systems, is emerging as a powerful new tool to identify biomarkers of disease, report on cellular responses to environmental perturbation, and to identify the targets of drugs. Here we discuss recent developments in metabolomic analysis, from the perspective of trypanosome research, highlighting remaining challenges and the most promising areas for future research.

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.

The potential of metabolomics for Leishmania research in the post-genomics era

The post-genomics era has provided researchers with access to a new generation of tools for the global characterization and understanding of pathogen diversity. This review provides a critical summary of published Leishmania post-genomic research efforts to date, and discusses the potential impact of the addition of metabolomics to the post-genomic toolbox. Metabolomics aims at understanding biology by comprehensive metabolite profiling. We present an overview of the design and interpretation of metabolomics experiments in the context of Leishmania research. Sample preparation, measurement techniques, and bioinformatics analysis of the generated complex datasets are discussed in detail. To illustrate the concepts and the expected results of metabolomics analyses, we also present an overview of comparative metabolic profiles of drug-sensitive and drug-resistant Leishmania donovani clinical isolates.