A genome-scale metabolic model of Cryptosporidium hominis

A contribution of metabolic engineering to addressing medical problems: Metabolic flux analysis

Metabolic engineering has served as a systematic discipline for industrial biotechnology as it has offered systematic tools and methods for strain development and bioprocess optimization. Because these metabolic engineering tools and methods are concerned with the biological network of a cell with emphasis on metabolic network, they have also been applied to a range of medical problems where better understanding of metabolism has also been perceived to be important. Metabolic flux analysis (MFA) is a unique systematic approach initially developed in the metabolic engineering community, and has proved its usefulness and potential when addressing a range of medical problems. In this regard, this review discusses the contribution of MFA to addressing medical problems. For this, we i) provide overview of the milestones of MFA, ii) define two main branches of MFA, namely constraint-based reconstruction and analysis (COBRA) and isotope-based MFA (iMFA), and iii) present successful examples of their medical applications, including characterizing the metabolism of diseased cells and pathogens, and identifying effective drug targets. Finally, synergistic interactions between metabolic engineering and biomedical sciences are discussed with respect to MFA.

Comparative analyses of parasites with a comprehensive database of genome-scale metabolic models

Protozoan parasites cause diverse diseases with large global impacts. Research on the pathogenesis and biology of these organisms is limited by economic and experimental constraints. Accordingly, studies of one parasite are frequently extrapolated to infer knowledge about another parasite, across and within genera. Model in vitro or in vivo systems are frequently used to enhance experimental manipulability, but these systems generally use species related to, yet distinct from, the clinically relevant causal pathogen. Characterization of functional differences among parasite species is confined to post hoc or single target studies, limiting the utility of this extrapolation approach. To address this challenge and to accelerate parasitology research broadly, we present a functional comparative analysis of 192 genomes, representing every high-quality, publicly-available protozoan parasite genome including Plasmodium, Toxoplasma, Cryptosporidium, Entamoeba, Trypanosoma, Leishmania, Giardia, and other species. We generated an automated metabolic network reconstruction pipeline optimized for eukaryotic organisms. These metabolic network reconstructions serve as biochemical knowledgebases for each parasite, enabling qualitative and quantitative comparisons of metabolic behavior across parasites. We identified putative differences in gene essentiality and pathway utilization to facilitate the comparison of experimental findings and discovered that phylogeny is not the sole predictor of metabolic similarity. This knowledgebase represents the largest collection of genome-scale metabolic models for both pathogens and eukaryotes; with this resource, we can predict species-specific functions, contextualize experimental results, and optimize selection of experimental systems for fastidious species.

Cryptosporidium spp. diagnosis and research in the 21st century

The protozoan parasite Cryptosporidium has emerged as a leading cause of diarrhoeal illness worldwide, posing a significant threat to young children and immunocompromised patients. While endemic in the vast majority of developing countries, Cryptosporidium also has the potential to cause waterborne epidemics and large scale outbreaks in both developing and developed nations. Anthroponontic and zoonotic transmission routes are well defined, with the ingestion of faecally contaminated food and water supplies a common source of infection. Microscopy, the current diagnostic mainstay, is considered by many to be suboptimal. This has prompted a shift towards alternative diagnostic techniques in the advent of the molecular era. Molecular methods, particularly PCR, are gaining traction in a diagnostic capacity over microscopy in the diagnosis of cryptosporidiosis, given the laborious and often tedious nature of the latter. Until now, developments in the field of Cryptosporidium detection and research have been somewhat hampered by the intractable nature of this parasite. However, recent advances in the field have taken the tentative first steps towards bringing Cryptosporidium research into the 21st century. Herein, we provide a review of these advances.

Resources and tools for the high-throughput, multi-omic study of intestinal microbiota

The human gut microbiome impacts several aspects of human health and disease, including digestion, drug metabolism and the propensity to develop various inflammatory, autoimmune and metabolic diseases. Many of the molecular processes that play a role in the activity and dynamics of the microbiota go beyond species and genic composition and thus, their understanding requires advanced bioinformatics support. This article aims to provide an up-to-date view of the resources and software tools that are being developed and used in human gut microbiome research, in particular data integration and systems-level analysis efforts. These efforts demonstrate the power of standardized and reproducible computational workflows for integrating and analysing varied omics data and gaining deeper insights into microbe community structure and function as well as host-microbe interactions.

Metabolic characterization of the chitinolytic bacterium Serratia marcescens using a genome-scale metabolic model

Background

Serratia marcescens is a chitinolytic bacterium that can potentially be used for consolidated bioprocessing to convert chitin to value-added chemicals. Currently, S. marcescens is poorly characterized and studies on intracellular metabolic and regulatory mechanisms would expedite development of bioprocessing applications.

Results

In this study, our goal was to characterize the metabolic profile of S. marcescens to provide insight for metabolic engineering applications and fundamental biological studies. Hereby, we constructed a constraint-based genome-scale metabolic model (iSR929) including 929 genes, 1185 reactions and 1164 metabolites based on genomic annotation of S. marcescens Db11. The model was tested by comparing model predictions with experimental data and analyzed to identify essential aspects of the metabolic network (e.g. 138 essential genes predicted). The model iSR929 was refined by integrating RNAseq data of S. marcescens growth on three different carbon sources (glucose, N-acetylglucosamine, and glycerol). Significant differences in TCA cycle utilization were found for growth on the different carbon substrates, For example, for growth on N-acetylglucosamine, S. marcescens exhibits high pentose phosphate pathway activity and nucleotide synthesis but low activity of the TCA cycle.

Conclusions

Our results show that S. marcescens model iSR929 can provide reasonable predictions and can be constrained to fit with experimental values. Thus, our model may be used to guide strain designs for metabolic engineering to produce chemicals such as 2,3-butanediol, N-acetylneuraminic acid, and n-butanol using S. marcescens.

Electronic supplementary material

The online version of this article (10.1186/s12859-019-2826-1) contains supplementary material, which is available to authorized users.

Genomics of apicomplexan parasites

The increasing prevalence of infections involving intracellular apicomplexan parasites such as Plasmodium, Toxoplasma, and Cryptosporidium (the causative agents of malaria, toxoplasmosis, and cryptosporidiosis, respectively) represent a significant global healthcare burden. Despite their significance, few treatments are available; a situation that is likely to deteriorate with the emergence of new resistant strains of parasites. To lay the foundation for programs of drug discovery and vaccine development, genome sequences for many of these organisms have been generated, together with large-scale expression and proteomic datasets. Comparative analyses of these datasets are beginning to identify the molecular innovations supporting both conserved processes mediating fundamental roles in parasite survival and persistence, as well as lineage-specific adaptations associated with divergent life-cycle strategies. The challenge is how best to exploit these data to derive insights into parasite virulence and identify those genes representing the most amenable targets. In this review, we outline genomic datasets currently available for apicomplexans and discuss biological insights that have emerged as a consequence of their analysis. Of particular interest are systems-based resources, focusing on areas of metabolism and host invasion that are opening up opportunities for discovering new therapeutic targets.

Metabolic Needs and Capabilities of Toxoplasma gondii through Combined Computational and Experimental Analysis

Toxoplasma gondii is a human pathogen prevalent worldwide that poses a challenging and unmet need for novel treatment of toxoplasmosis. Using a semi-automated reconstruction algorithm, we reconstructed a genome-scale metabolic model, ToxoNet1. The reconstruction process and flux-balance analysis of the model offer a systematic overview of the metabolic capabilities of this parasite. Using ToxoNet1 we have identified significant gaps in the current knowledge of Toxoplasma metabolic pathways and have clarified its minimal nutritional requirements for replication. By probing the model via metabolic tasks, we have further defined sets of alternative precursors necessary for parasite growth. Within a human host cell environment, ToxoNet1 predicts a minimal set of 53 enzyme-coding genes and 76 reactions to be essential for parasite replication. Double-gene-essentiality analysis identified 20 pairs of genes for which simultaneous deletion is deleterious. To validate several predictions of ToxoNet1 we have performed experimental analyses of cytosolic acetyl-CoA biosynthesis. ATP-citrate lyase and acetyl-CoA synthase were localised and their corresponding genes disrupted, establishing that each of these enzymes is dispensable for the growth of T. gondii, however together they make a synthetic lethal pair.

Proteomics-based metabolic modeling and characterization of the cellulolytic bacterium Thermobifida fusca

Background

Thermobifida fusca is a cellulolytic bacterium with potential to be used as a platform organism for sustainable industrial production of biofuels, pharmaceutical ingredients and other bioprocesses due to its capability of potential to convert plant biomass to value-added chemicals. To best develop T. fusca as a bioprocess organism, it is important to understand its native cellular processes. In the current study, we characterize the metabolic network of T. fusca through reconstruction of a genome-scale metabolic model and proteomics data. The overall goal of this study was to use multiple metabolic models generated by different methods and comparison to experimental data to gain a high-confidence understanding of the T. fusca metabolic network.

Results

We report the generation of three versions of a metabolic model of Thermobifida fusca sp. XY developed using three different approaches (automated, semi-automated, and proteomics-derived). The model closest to in vivo growth was the proteomics-derived model that consists of 975 reactions involving 1382 metabolites and account for 316 EC numbers (296 genes). The model was optimized for biomass production with the optimal flux of 0.48 doublings per hour when grown on cellobiose with a substrate uptake rate of 0.25 mmole/h. In vivo activity of the DXP pathway for terpenoid biosynthesis was also confirmed using real-time PCR.

Conclusions

iTfu296 provides a platform to understand and explore the metabolic capabilities of the actinomycete T. fusca for the potential use in bioprocess industries for the production of biofuel and pharmaceutical ingredients. By comparing different model reconstruction methods, the use of high-throughput proteomics data as a starting point proved to be the most accurate to in vivo growth.

Plant genome-scale modeling and implementation

Considerable progress has been made in plant genome-scale metabolic reconstruction and modeling in recent years. Such reconstructions made it possible to explore metabolic phenotypes through appropriate model formulation and optimization methods. As a result, plant genome-scale modeling has increasingly attracted interest from the plant research community. In this chapter, the first generation of plant genome-scale metabolic reconstructions is presented, along with the important concepts behind model and constraint formulation. A brief protocol describing the use of constraint-based reconstruction and analysis (COBRA) Toolbox in flux simulation and model modification is provided. This is followed by a presentation of metabolic constraints required to generate fluxes in AraGEM using COBRA that describe photosynthesis, photorespiration, and respiration, respectively. Overall, plant genome-scale modeling is a powerful approach that is accessible and readily adopted.

Metabolomic profiling of faecal extracts from Cryptosporidium parvum infection in experimental mouse models

Cryptosporidiosis is a gastrointestinal disease in humans and animals caused by infection with the protozoan parasite Cryptosporidium. In healthy individuals, the disease manifests mainly as acute self-limiting diarrhoea, but may be chronic and life threatening for those with compromised immune systems. Control and treatment of the disease is challenged by the lack of sensitive diagnostic tools and broad-spectrum chemotherapy. Metabolomics, or metabolite profiling, is an emerging field of study, which enables characterisation of the end products of regulatory processes in a biological system. Analysis of changes in metabolite patterns reflects changes in biochemical regulation, production and control, and may contribute to understanding the effects of Cryptosporidium infection in the host environment. In the present study, metabolomic analysis of faecal samples from experimentally infected mice was carried out to assess metabolite profiles pertaining to the infection. Gas-chromatography mass spectrometry (GC-MS) carried out on faecal samples from a group of C. parvum infected mice and a group of uninfected control mice detected a mean total of 220 compounds. Multivariate analyses showed distinct differences between the profiles of C. parvum infected mice and uninfected control mice,identifying a total of 40 compounds, or metabolites that contributed most to the variance between the two groups. These metabolites consisted of amino acids (n = 17), carbohydrates (n = 8), lipids (n = 7), organic acids (n = 3) and other various metabolites (n = 5), which showed significant differences in levels of metabolite abundance between the infected and uninfected mice groups (p < 0.05). The metabolites detected in this study as well as the differences in abundance between the C. parvum infected and the uninfected control mice, highlights the effects of the infection on intestinal permeability and the fate of the metabolites as a result of nutrient scavenging by the parasite to supplement its streamlined metabolism.

Plant genome-scale metabolic reconstruction and modelling

Genome-scale metabolic reconstructions are used extensively in the study of microbial metabolism and have proven powerful tools to guide rational pathway design of industrial strains. Generation and curation of plant genome-scale metabolic models has proven far more challenging, not the least of which is our incomplete knowledge of compartmentation and organelle transporters in plants. Conversely, the potential value of modelling is far greater when exploring a complex, multi-organelle and multi-tissue metabolism. The first generation of plant genome-scale metabolic reconstructions have proven surprisingly functional and robust as well as capable of predicting many observed complex phenotypes. With further refinement, the application of these models promises to make important contributions to plant biology and metabolic engineering.

Identification of metabolic changes in genetically unstable stem cells by using model analysis of gene expression

Stem-cell research seeks to address many different questions related to fundamental stem-cell function with the ultimate goal of being able to control and utilize stem cells for a broad range of therapeutic needs. While a large amount of work is focused on discovering and controlling differentiation mechanisms in stem cells, an equally interesting and important area of work is to understand the basics of stem-cell propagation and self-renewal. With high-throughput genomics and transcriptomic information on hand, it is becoming possible to address some of the detailed mechanistic processes occurring in stem cells, though interpretation of these data is often difficult. In this work, stem cells with genetic abnormalities were compared to genetically normal stem cells using gene-expression array data integrated with a large-scale metabolic model to help interpret changes in metabolism resulting in the identification of several metabolic pathways that were different in the normal and abnormal cells.

Applications of system-level models of metabolism for analysis of bacterial physiology and identification of new drug targets

For nearly all of the 20th century, biologists gained considerable insights into the fundamental principles of cellular dynamics by examining select modules of biochemical processes. This form of analysis provides detailed information about the workings of the examined pathways. However, any attempt to alter the normal function of bacteria (perhaps for industrial or medicinal goals) requires a detailed global understanding of cellular mechanisms. The reductionist mode of analysis cannot provide the required information for developing the needed perspective on the complex interactions of biochemical pathways. Thankfully, the increasing availability of microbial genomic, transcriptomic, proteomic and other high-throughput data permits system-level analyses of microbiology. During the past two decades, systems biologists have developed constraint-based genome-scale models (GSM) of metabolism for a variety of pathogens. These models are important tools for assessing the metabolic capabilities of various genotypes. Simultaneously, new computational methods have been developed that use these network reconstructions to answer an array of important immunological questions. The objective of this article is to briefly review some of the uses of GSMs for studying bacterial metabolism under different conditions and to discuss how the calculated solutions can be used for rational design of drugs.

Genomics and population biology of Cryptosporidium species

We describe recent advances in the genomics and population biology of Cryptosporidium parvum and C. hominis, the causative agents of cryptosporidiosis in humans and animals. Many basic aspects of the biology of Cryptosporidium species remain to be investigated and effective drugs to control cryptosporidiosis are not available. Sequencing and annotation of the genome of C. parvum and C. hominis has uncovered unique features of the metabolism of these species. The recently sequenced genome of the gastric species C. muris is providing new insights into the evolution of the genus. Cryptosporidian sequence information has facilitated the identification of polymorphic genetic markers. Genotyping of oocysts excreted by human and animal hosts using such markers has revealed many new species and genotypes, and is leading to a better understanding of the epidemiology of cryptosporidiosis.

A metabolic network approach for the identification and prioritization of antimicrobial drug targets

For many infectious diseases, novel treatment options are needed in order to address problems with cost, toxicity and resistance to current drugs. Systems biology tools can be used to gain valuable insight into pathogenic processes and aid in expediting drug discovery. In the past decade, constraint-based modeling of genome-scale metabolic networks has become widely used. Focusing on pathogen metabolic networks, we review in silico strategies used to identify effective drug targets and highlight recent successes as well as limitations associated with such computational analyses. We further discuss how accounting for the host environment and even targeting the host may offer new therapeutic options. These systems-level approaches are beginning to provide novel avenues for drug targeting against infectious agents.