The role of metabolomics in systems biology

A guided network estimation approach using multi-omic information

INTODUCTION

In systems biology, an organism is viewed as a system of interconnected molecular entities. To understand the functioning of organisms it is essential to integrate information about the variations in the concentrations of those molecular entities. This information can be structured as a set of networks with interconnections and with some hierarchical relations between them. Few methods exist for the reconstruction of integrative networks.

OBJECTIVE

In this work, we propose an integrative network reconstruction method in which the network organization for a particular type of omics data is guided by the network structure of a related type of omics data upstream in the omic cascade. The structure of these guiding data can be either already known or be estimated from the guiding data themselves.

METHODS

The method consists of three steps. First a network structure for the guiding data should be provided. Next, responses in the target set are regressed on the full set of predictors in the guiding data with a Lasso penalty to reduce the number of predictors and an L2 penalty on the differences between coefficients for predictors that share edges in the network for the guiding data. Finally, a network is reconstructed on the fitted target responses as functions of the predictors in the guiding data. This way we condition the target network on the network of the guiding data.

CONCLUSIONS

We illustrate our approach on two examples in Arabidopsis. The method detects groups of metabolites that have a similar genetic or transcriptomic basis.

Volatile Nitrogenous Compounds from Bacteria: Source of Novel Bioactive Compounds

Bacteria can produce nitrogenous compounds via both primary and secondary metabolic processes. Many bacterial volatile nitrogenous compounds produced during the secondary metabolism have been identified and reported for their antioxidant, antibacterial, antifungal, algicidal and antitumor activities. The production of these nitrogenous compounds depends on several factors, including the composition of culture media, growth conditions, and even the organic solvent used for their extraction, thus requiring their identification in specific conditions. In this review, we describe the volatile nitrogenous compounds produced by bacteria especially focusing on their antimicrobial activity. We concentrate on azo-compounds mainly pyrazines and pyrrolo-pyridines reported for their activity against several microorganisms. Whenever significant, extraction and identification methods of these compounds are also mentioned and discussed. To the best of our knowledge, this is first review describing volatile nitrogenous compounds from bacteria focusing on their biological activity.

Omics-Driven Biotechnology for Industrial Applications

Biomanufacturing is a key component of biotechnology that uses biological systems to produce bioproducts of commercial relevance, which are of great interest to the energy, material, pharmaceutical, food, and agriculture industries. Biotechnology-based approaches, such as synthetic biology and metabolic engineering are heavily reliant on "omics" driven systems biology to characterize and understand metabolic networks. Knowledge gained from systems biology experiments aid the development of synthetic biology tools and the advancement of metabolic engineering studies toward establishing robust industrial biomanufacturing platforms. In this review, we discuss recent advances in "omics" technologies, compare the pros and cons of the different "omics" technologies, and discuss the necessary requirements for carrying out multi-omics experiments. We highlight the influence of "omics" technologies on the production of biofuels and bioproducts by metabolic engineering. Finally, we discuss the application of "omics" technologies to agricultural and food biotechnology, and review the impact of "omics" on current COVID-19 research.

Analysis of Epithelial-Mesenchymal Transition Metabolism Identifies Possible Cancer Biomarkers Useful in Diverse Genetic Backgrounds

Epithelial-to-mesenchymal transition (EMT) relates to many molecular and cellular alterations that occur when epithelial cells undergo a switch in differentiation generating mesenchymal-like cells with newly acquired migratory and invasive properties. In cancer cells, EMT leads to drug resistance and metastasis. Moreover, differences in genetic backgrounds, even between patients with the same type of cancer, also determine resistance to some treatments. Metabolic rewiring is essential to induce EMT, hence it is important to identify key metabolic elements for this process, which can be later used to treat cancer cells with different genetic backgrounds. Here we used a mathematical modeling approach to determine which are the metabolic reactions altered after induction of EMT, based on metabolomic and transcriptional data of three non-small cell lung cancer (NSCLC) cell lines. The model suggested that the most affected pathways were the Krebs cycle, amino acid metabolism, and glutathione metabolism. However, glutathione metabolism had many alterations either on the metabolic reactions or at the transcriptional level in the three cell lines. We identified Glutamate-cysteine ligase (GCL), a key enzyme of glutathione synthesis, as an important common feature that is dysregulated after EMT. Analyzing survival data of men with lung cancer, we observed that patients with mutations in GCL catalytic subunit (GCLC) or Glutathione peroxidase 1 (GPX1) genes survived less time than people without mutations on these genes. Besides, patients with low expression of ANPEP, GPX3 and GLS genes also survived less time than those with high expression. Hence, we propose that glutathione metabolism and glutathione itself could be good targets to delay or potentially prevent EMT induction in NSCLC cell lines.

Omics-Based Mechanistic Insight Into the Role of Bioengineered Nanoparticles for Biotic Stress Amelioration by Modulating Plant Metabolic Pathways

Bioengineered silver nanoparticles can emerge as a facile approach to combat plant pathogen, reducing the use of pesticides in an eco-friendly manner. The plants' response during tripartite interaction of plant, pathogen, and nanoparticles remains largely unknown. This study demonstrated the use of bioengineered silver nanoparticles in combating black spot disease caused by necrotrophic fungus Alternaria brassicicola in Arabidopsis thaliana via foliar spray. The particles reduced disease severity by 70-80% at 5 μg/ml without showing phytotoxicity. It elicited plant immunity by a significant reduction in reactive oxygen species (ROS), decreases in stress enzymes by 0.6-19.8-fold, and emergence of autophagy. Comparative plant proteomics revealed 599 proteins expressed during the interaction, where 117 differential proteins were identified. Among different categories, proteins involved in bioenergy and metabolism were most abundant (44%), followed by proteins involved in plant defense (20%). Metabolic profiling by gas chromatography-mass spectroscopy yielded 39 metabolite derivatives in non-polar fraction and 25 in the polar fraction of plant extracts. It was observed that proteins involved in protein biogenesis and early plant defense were overexpressed to produce abundant antimicrobial metabolites and minimize ROS production. Bioengineered silver nanoparticles performed dual functions to combat pathogen attack by killing plant pathogen and eliciting immunity by altering plant defense proteome and metabolome.

Grape and Wine Metabolomics to Develop New Insights Using Untargeted and Targeted Approaches

Chemical analysis of grape juice and wine has been performed for over 50 years in a targeted manner to determine a limited number of compounds using Gas Chromatography, Mass-Spectrometry (GC-MS) and High Pressure Liquid Chromatography (HPLC). Therefore, it only allowed the determination of metabolites that are present in high concentration, including major sugars, amino acids and some important carboxylic acids. Thus, the roles of many significant but less concentrated metabolites during wine making process are still not known. This is where metabolomics shows its enormous potential, mainly because of its capability in analyzing over 1000 metabolites in a single run due to the recent advancements of high resolution and sensitive analytical instruments. Metabolomics has predominantly been adopted by many wine scientists as a hypothesis-generating tool in an unbiased and non-targeted way to address various issues, including characterization of geographical origin (terroir) and wine yeast metabolic traits, determination of biomarkers for aroma compounds, and the monitoring of growth developments of grape vines and grapes. The aim of this review is to explore the published literature that made use of both targeted and untargeted metabolomics to study grapes and wines and also the fermentation process. In addition, insights are also provided into many other possible avenues where metabolomics shows tremendous potential as a question-driven approach in grape and wine research.

Rate-limiting steps in the Saccharomyces cerevisiae ergosterol pathway: towards improved ergosta-5,7-dien-3β-ol accumulation by metabolic engineering

Ergosterol is the predominant nature sterol constituent of plasma membrane in Saccharomyces cerevisiae. Herein, the biosynthetic pathway of ergosterol was proposed to be metabolically engineered for the efficient production of ergosta-5,7-dien-3β-ol, which is the precursor of vitamin D4. By target disruption of erg5, involved in the end-steps of post-squalene formation, predominantly accumulated ergosta-5,7-dien-3β-ol (4.12 mg/g dry cell weight). Moreover, the rate-limiting enzymes of ergosta-5,7-dien-3β-ol biosynthesis were characterized. Overexpression of Hmg1p led to a significant accumulation of squalene, and induction of Erg1p/Erg11p expression raised the yield of both total sterols and ergosta-5,7-dien-3β-ol with no obvious changes in growth behavior. Furthermore, the transcription factor allele upc2-1 was overexpressed to explore the effect of combined induction of rate-limiting enzymes. Compared with an obviously enhanced yield of ergosterol in the wild-type strain, decreases of both the ergosta-5,7-dienol levels and the total sterol yield were found in Δerg5-upc2-1, probably due to the unbalanced NADH/NAD⁺ ratio observed in the erg5 knockouts, suggesting the whole-cell redox homeostasis was also vital for end-product biosynthesis. The data obtained in this study can be used as reference values for the production of sterol-related intermediates involved in the post-squalene biosynthetic pathway in food-grade S. cerevisiae strains.

Rapid sample processing for intracellular metabolite studies in Penicillium ochrochloron CBS 123.824: the FiltRes-device combines cold filtration of methanol quenched biomass with resuspension in extraction solution

Background

Many issues concerning sample processing for intracellular metabolite studies in filamentous fungi still need to be solved, e.g. how to reduce the contact time of the biomass to the quenching solution in order to minimize metabolite leakage. Since the required time to separate the biomass from the quenching solution determines the contact time, speeding up this step is thus of utmost interest. Recently, separation approaches based on cold-filtration were introduced as promising alternative to cold-centrifugation, which exhibit considerably reduced contact times. In previous works we were unable to obtain a compact pellet from cold methanol quenched samples of the filamentous fungus Penicillium ochrochloron CBS 123.824 via centrifugation. Therefore our aim was to establish for this organism a separation technique based on cold-filtration to determine intracellular levels of a selected set of nucleotides.

Results

We developed a cold-filtration based technique as part of our effort to revise the entire sample processing method and analytical procedure. The Filtration-Resuspension (FiltRes) device combined in a single apparatus (1) a rapid cold-filtration and (2) a rapid resuspension of the biomass in hot extraction solution. Unique to this is the injection of the extraction solution from below the membrane filter (FiltRes-principle). This caused the mycelial cake to detach completely from the filter membrane and to float upwards so that the biomass could easily be transferred into preheated tubes for metabolite extraction. The total contact time of glucose-limited chemostat mycelium to the quenching solution could be reduced to 15.7 ± 2.5 s, whereby each washing step added another 10–15 s. We evaluated critical steps like filtration time, temperature profile, reproducibility of results, and using the energy charge (EC) as a criterion, effectiveness of enzyme destruction during the transition in sample temperature from cold to hot. As control we used total broth samples quenched in hot ethanol. Averaged over all samples an EC of 0.93 ± 0.020 was determined with the FiltRes-principle compared to 0.89 ± 0.049 with heat stopped total broth samples.

Conclusions

We concluded that for P. ochrochloron this technique is a reliable sample processing method for intracellular metabolite analysis, which might offer also other possible applications.

Electronic supplementary material

The online version of this article (doi:10.1186/s40064-016-2649-8) contains supplementary material, which is available to authorized users.

Metabolomic profiling reveals deep chemical divergence between two morphotypes of the zoanthid Parazoanthus axinellae

Metabolomics has recently proven its usefulness as complementary tool to traditional morphological and genetic analyses for the classification of marine invertebrates. Among the metabolite-rich cnidarian order Zoantharia, Parazoanthus is a polyphyletic genus whose systematics and phylogeny remain controversial. Within this genus, one of the most studied species, Parazoanthus axinellae is prominent in rocky shallow waters of the Mediterranean Sea and the NE Atlantic Ocean. Although different morphotypes can easily be distinguished, only one species is recognized to date. Here, a metabolomic profiling approach has been used to assess the chemical diversity of two main Mediterranean morphotypes, the "slender" and "stocky" forms of P. axinellae. Targeted profiling of their major secondary metabolites revealed a significant chemical divergence between the morphotypes. While zoanthoxanthin alkaloids and ecdysteroids are abundant in both morphs, the "slender" morphotype is characterized by the presence of additional and bioactive 3,5-disubstituted hydantoin derivatives named parazoanthines. The absence of these specific compounds in the "stocky" morphotype was confirmed by spatial and temporal monitoring over an annual cycle. Moreover, specimens of the "slender" morphotype are also the only ones found as epibionts of several sponge species, particularly Cymbaxinella damicornis thus suggesting a putative ecological link.

Text-mining-assisted biocuration workflows in Argo

Biocuration activities have been broadly categorized into the selection of relevant documents, the annotation of biological concepts of interest and identification of interactions between the concepts. Text mining has been shown to have a potential to significantly reduce the effort of biocurators in all the three activities, and various semi-automatic methodologies have been integrated into curation pipelines to support them. We investigate the suitability of Argo, a workbench for building text-mining solutions with the use of a rich graphical user interface, for the process of biocuration. Central to Argo are customizable workflows that users compose by arranging available elementary analytics to form task-specific processing units. A built-in manual annotation editor is the single most used biocuration tool of the workbench, as it allows users to create annotations directly in text, as well as modify or delete annotations created by automatic processing components. Apart from syntactic and semantic analytics, the ever-growing library of components includes several data readers and consumers that support well-established as well as emerging data interchange formats such as XMI, RDF and BioC, which facilitate the interoperability of Argo with other platforms or resources. To validate the suitability of Argo for curation activities, we participated in the BioCreative IV challenge whose purpose was to evaluate Web-based systems addressing user-defined biocuration tasks. Argo proved to have the edge over other systems in terms of flexibility of defining biocuration tasks. As expected, the versatility of the workbench inevitably lengthened the time the curators spent on learning the system before taking on the task, which may have affected the usability of Argo. The participation in the challenge gave us an opportunity to gather valuable feedback and identify areas of improvement, some of which have already been introduced. Database URL: http://argo.nactem.ac.uk.

Multiplexed analysis of cage and cage free chicken egg fatty acids using stable isotope labeling and mass spectrometry

Binary stable isotope labeling couple with LC-ESI-MS has been used as a powerful non-targeted approach for the relative quantification of lipids, amino acids, and many other important metabolite classes. A multiplexed approach using three or more isotopic labeling reagents greatly reduces analytical run-time while maintaining excellent sensitivity and reproducibility. Three isotopic cholamine labeling reagents have been developed to take advantage of the pre-ionized character of cholamine, for ESI, and the ease by which stable isotopes can be incorporated into the cholamine structure. These three cholamine labeling reagents have been used to relatively quantify three fatty acid samples simultaneously. The quantification resulted in the observation of 12 fatty acids that had an average absolute error of 0.9% and an average coefficient of variation of 6.1%. Caged versus cage-free isotope labeling experiments showed that cage-free eggs have an increased level of omega-3 fatty acids as compared to caged eggs. This multiplexed fatty acid analysis provides an inexpensive and expedited tool for broad-based lipid profiling that will further aid discoveries in the mechanisms of fatty acid action in cells.

Metabolomic comparison of Saccharomyces cerevisiae and the cryotolerant species S. bayanus var. uvarum and S. kudriavzevii during wine fermentation at low temperature

Temperature is one of the most important parameters affecting the length and rate of alcoholic fermentation and final wine quality. Wine produced at low temperature is often considered to have improved sensory qualities. However, there are certain drawbacks to low temperature fermentations such as reduced growth rate, long lag phase, and sluggish or stuck fermentations. To investigate the effects of temperature on commercial wine yeast, we compared its metabolome growing at 12°C and 28°C in a synthetic must. Some species of the Saccharomyces genus have shown better adaptation at low temperature than Saccharomyces cerevisiae. This is the case of the cryotolerant yeasts Saccharomyces bayanus var. uvarum and Saccharomyces kudriavzevii. In an attempt to detect inter-specific metabolic differences, we characterized the metabolome of these species growing at 12°C, which we compared with the metabolome of S. cerevisiae (not well adapted at low temperature) at the same temperature. Our results show that the main differences between the metabolic profiling of S. cerevisiae growing at 12°C and 28°C were observed in lipid metabolism and redox homeostasis. Moreover, the global metabolic comparison among the three species revealed that the main differences between the two cryotolerant species and S. cerevisiae were in carbohydrate metabolism, mainly fructose metabolism. However, these two species have developed different strategies for cold resistance. S. bayanus var. uvarum presented elevated shikimate pathway activity, while S. kudriavzevii displayed increased NAD⁺ synthesis.

High-resolution extracted ion chromatography, a new tool for metabolomics and lipidomics using a second-generation orbitrap mass spectrometer

Most analytical methods in metabolomics are based on one of two strategies. The first strategy is aimed at specifically analysing a limited number of known metabolites or compound classes. Alternatively, an unbiased approach can be used for profiling as many features as possible in a given metabolome without prior knowledge of the identity of these features. Using high-resolution mass spectrometry with instruments capable of measuring m/z ratios with sufficiently low mass measurement uncertainties and simultaneous high scan speeds, it is possible to combine these two strategies, allowing unbiased profiling of biological samples and targeted analysis of specific compounds at the same time without compromises. Such high mass accuracy and mass resolving power reduces the number of candidate metabolites occupying the same retention time and m/z ratio space to a minimum. In this study, we demonstrate how targeted analysis of phospholipids as well as unbiased profiling is achievable using a benchtop orbitrap instrument after high-speed reversed-phase chromatography. The ability to apply both strategies in one experiment is an important step forward in comprehensive analysis of the metabolome.

The mean frequency of transcriptional bursting and its variation in single cells

The recent in vivo RNA detection technique has allowed real-time monitoring of gene transcription in individual living cells, revealing that genes can be transcribed randomly in a bursting fashion that short periods of rapid production of multiple transcripts are interspersed with relatively long periods of no production. In this work, we utilize the three state model to study how environmental signals and the intrinsic cellular contexts are combined to regulate stochastic gene transcription. We introduce a system of three master equations to model the stochastic occurrence of transcriptional bursting. As this system cannot be solved analytically, we introduce a linear operator, called the master operator. It is of significant mathematical interests of its own and transforms the mean frequency of transcriptional bursting mu(t) and the second moment mu2(t) into the unique solutions of the respective operator equations. Following this novel approach, we have found the exact forms of mu(t) and the variance sigma2(t). Our analysis shows that the three state transition process produces less noisy transcription than a single Poisson process does, and more transition steps average out rather than propagate fluctuations of transcripts among individual cells. The noise strength phi(t) = sigma2(t)/mu(t) displays highly non-trivial dynamics during the first two to three transcription cycles. It declines steeply from the beginning until reaching the absolute minimum value, and then bounces back suddenly to a flat level close to the steady-state. Our numerical simulations further demonstrate that the cellular signals that produce the least noisy population at steady-state may not generate the least noisy population in a finite time, and suggest that measurements at steady-state may not necessarily capture most essential features of transcription noise.