Hostile Takeover: How Viruses Reprogram Prokaryotic Metabolism.

To reproduce, prokaryotic viruses must hijack the cellular machinery of their hosts and redirect it toward the production of viral particles. While takeover of the host replication and protein synthesis apparatus has long been considered an essential feature of infection, recent studies indicate that extensive reprogramming of host primary metabolism is a widespread phenomenon among prokaryotic viruses that is required to fulfill the biosynthetic needs of virion production. In this review we provide an overview of the most significant recent findings regarding virus-induced reprogramming of prokaryotic metabolism and suggest how quantitative systems biology approaches may be used to provide a holistic understanding of metabolic remodeling during lytic viral infection.

Principles of metabolome conservation in animals.

Metabolite levels shape cellular physiology and disease susceptibility, yet the general principles governing metabolome evolution are largely unknown. Here, we introduce a measure of conservation of individual metabolite levels among related species. By analyzing multispecies tissue metabolome datasets in phylogenetically diverse mammals and fruit flies, we show that conservation varies extensively across metabolites. Three major functional properties, metabolite abundance, essentiality, and association with human diseases predict conservation, highlighting a striking parallel between the evolutionary forces driving metabolome and protein sequence conservation. Metabolic network simulations recapitulated these general patterns and revealed that abundant metabolites are highly conserved due to their strong coupling to key metabolic fluxes in the network. Finally, we show that biomarkers of metabolic diseases can be distinguished from other metabolites simply based on evolutionary conservation, without requiring any prior clinical knowledge. Overall, this study uncovers simple rules that govern metabolic evolution in animals and implies that most tissue metabolome differences between species are permitted, rather than favored by natural selection. More broadly, our work paves the way toward using evolutionary information to identify biomarkers, as well as to detect pathogenic metabolome alterations in individual patients.

Lipid network and moiety analysis for revealing enzymatic dysregulation and mechanistic alterations from lipidomics data.

Lipidomics is of growing importance for clinical and biomedical research due to many associations between lipid metabolism and diseases. The discovery of these associations is facilitated by improved lipid identification and quantification. Sophisticated computational methods are advantageous for interpreting such large-scale data for understanding metabolic processes and their underlying (patho)mechanisms. To generate hypothesis about these mechanisms, the combination of metabolic networks and graph algorithms is a powerful option to pinpoint molecular disease drivers and their interactions. Here we present lipid network explorer (LINEX$^2$), a lipid network analysis framework that fuels biological interpretation of alterations in lipid compositions. By integrating lipid-metabolic reactions from public databases, we generate dataset-specific lipid interaction networks. To aid interpretation of these networks, we present an enrichment graph algorithm that infers changes in enzymatic activity in the context of their multispecificity from lipidomics data. Our inference method successfully recovered the MBOAT7 enzyme from knock-out data. Furthermore, we mechanistically interpret lipidomic alterations of adipocytes in obesity by leveraging network enrichment and lipid moieties. We address the general lack of lipidomics data mining options to elucidate potential disease mechanisms and make lipidomics more clinically relevant.

Myocardial Metabolomics of Human Heart Failure With Preserved Ejection Fraction.

BACKGROUND: The human heart primarily metabolizes fatty acids, and this decreases as alternative fuel use rises in heart failure with reduced ejection fraction (HFrEF). Patients with severe obesity and diabetes are thought to have increased myocardial fatty acid metabolism, but whether this is found in those who also have heart failure with preserved ejection fraction (HFpEF) is unknown. METHODS: Plasma and endomyocardial biopsies were obtained from HFpEF (n=38), HFrEF (n=30), and nonfailing donor controls (n=20). Quantitative targeted metabolomics measured organic acids, amino acids, and acylcarnitines in myocardium (72 metabolites) and plasma (69 metabolites). The results were integrated with reported RNA sequencing data. Metabolomics were analyzed using agnostic clustering tools, Kruskal-Wallis test with Dunn test, and machine learning. RESULTS: Agnostic clustering of myocardial but not plasma metabolites separated disease groups. Despite more obesity and diabetes in HFpEF versus HFrEF (body mass index, 39.8 kg/m2 versus 26.1 kg/m2; diabetes, 70% versus 30%; both P<0.0001), medium- and long-chain acylcarnitines (mostly metabolites of fatty acid oxidation) were markedly lower in myocardium from both heart failure groups versus control. In contrast, plasma levels were no different or higher than control. Gene expression linked to fatty acid metabolism was generally lower in HFpEF versus control. Myocardial pyruvate was higher in HFpEF whereas the tricarboxylic acid cycle intermediates succinate and fumarate were lower, as were several genes controlling glucose metabolism. Non-branched-chain and branched-chain amino acids (BCAA) were highest in HFpEF myocardium, yet downstream BCAA metabolites and genes controlling BCAA metabolism were lower. Ketone levels were higher in myocardium and plasma of patients with HFrEF but not HFpEF. HFpEF metabolomic-derived subgroups were differentiated by only a few differences in BCAA metabolites. CONCLUSIONS: Despite marked obesity and diabetes, HFpEF myocardium exhibited lower fatty acid metabolites compared with HFrEF. Ketones and metabolites of the tricarboxylic acid cycle and BCAA were also lower in HFpEF, suggesting insufficient use of alternative fuels. These differences were not detectable in plasma and challenge conventional views of myocardial fuel use in HFpEF with marked diabetes and obesity and suggest substantial fuel inflexibility in this syndrome.

Prediction and integration of metabolite-protein interactions with genome-scale metabolic models.

Metabolites, as small molecules, can act not only as substrates to enzymes, but also as effectors of activity of proteins with different functions, thereby affecting various cellular processes. While several experimental techniques have started to catalogue the metabolite-protein interactions (MPIs) present in different cellular contexts, characterizing the functional relevance of MPIs remains a challenging problem. Computational approaches from the constrained-based modeling framework allow for predicting MPIs and integrating their effects in the in silico analysis of metabolic and physiological phenotypes, like cell growth. Here, we provide a classification of all existing constraint-based approaches that predict and integrate MPIs using genome-scale metabolic networks as input. In addition, we benchmark the performance of the approaches to predict MPIs in a comparative study using different features extracted from the model structure and predicted metabolic phenotypes with the state-of-the-art metabolic networks of Escherichia coli and Saccharomyces cerevisiae. Lastly, we provide an outlook for future, feasible directions to expand the consideration of MPIs in constraint-based modeling approaches with wide biotechnological applications.

A complex metabolic network and its biomarkers regulate laccase production in white-rot fungus Cerrena unicolor 87613.

BACKGROUND: White-rot fungi are known to naturally produce high quantities of laccase, which exhibit commendable stability and catalytic efficiency. However, their laccase production does not meet the demands for industrial-scale applications. To address this limitation, it is crucial to optimize the conditions for laccase production. However, the regulatory mechanisms underlying different conditions remain unclear. This knowledge gap hinders the cost-effective application of laccases. RESULTS: In this study, we utilized transcriptomic and metabolomic data to investigate a promising laccase producer, Cerrena unicolor 87613, cultivated with fructose as the carbon source. Our comprehensive analysis of differentially expressed genes (DEGs) and differentially abundant metabolites (DAMs) aimed to identify changes in cellular processes that could affect laccase production. As a result, we discovered a complex metabolic network primarily involving carbon metabolism and amino acid metabolism, which exhibited contrasting changes between transcription and metabolic patterns. Within this network, we identified five biomarkers, including succinate, serine, methionine, glutamate and reduced glutathione, that played crucial roles in co-determining laccase production levels. CONCLUSIONS: Our study proposed a complex metabolic network and identified key biomarkers that determine the production level of laccase in the commercially promising Cerrena unicolor 87613. These findings not only shed light on the regulatory mechanisms of carbon sources in laccase production, but also provide a theoretical foundation for enhancing laccase production through strategic reprogramming of metabolic pathways, especially related to the citrate cycle and specific amino acid metabolism.

Lichens as a repository of bioactive compounds: an open window for green therapy against diverse cancers.

Lichens, algae and fungi-based symbiotic associations, are sources of many important secondary metabolites, such as antibiotics, anti-inflammatory, antioxidants, and anticancer agents. Wide range of experiments based on in vivo and in vitro studies revealed that lichens are a rich treasure of anti-cancer compounds. Lichen extracts and isolated lichen compounds can interact with all biological entities currently identified to be responsible for tumor development. The critical ways to control the cancer development include induction of cell cycle arrests, blocking communication of growth factors, activation of anti-tumor immunity, inhibition of tumor-friendly inflammation, inhibition of tumor metastasis, and suppressing chromosome dysfunction. Also, lichen-based compounds induce the killing of cells by the process of apoptosis, autophagy, and necrosis, that inturn positively modulates metabolic networks of cells against uncontrolled cell division. Many lichen-based compounds have proven to possess potential anti-cancer activity against a wide range of cancer cells, either alone or in conjunction with other anti-cancer compounds. This review primarily emphasizes on an updated account of the repository of secondary metabolites reported in lichens. Besides, we discuss the anti-cancer potential and possible mechanism of the most frequently reported secondary metabolites derived from lichens.

Models and molecular mechanisms for trade-offs in the context of metabolism.

Accumulating evidence for trade-offs involving metabolic traits has demonstrated their importance in the evolution of organisms. Metabolic models with different levels of complexity have already been considered when investigating mechanisms that explain various metabolic trade-offs. Here we provide a systematic review of modelling approaches that have been used to study and explain trade-offs between: (i) the kinetic properties of individual enzymes, (ii) rates of metabolic reactions, (iii) the rate and yield of metabolic pathways and networks, (iv) different metabolic objectives in single organisms and in metabolic communities, and (v) metabolic concentrations. In providing insights into the mechanisms underlying these five types of metabolic trade-offs obtained from constraint-based metabolic modelling, we emphasize the relationship of metabolic trade-offs to the classical black box Y-model that provides a conceptual explanation for resource acquisition-allocation trade-offs. In addition, we identify several pressing concerns and offer a perspective for future research in the identification and manipulation of metabolic trade-offs by relying on the toolbox provided by constraint-based metabolic modelling for single organisms and microbial communities.

Insights into the potential for mutualistic and harmful host-microbe interactions affecting brown alga freshwater acclimation.

Microbes can modify their hosts' stress tolerance, thus potentially enhancing their ecological range. An example of such interactions is Ectocarpus subulatus, one of the few freshwater-tolerant brown algae. This tolerance is partially due to its (un)cultivated microbiome. We investigated this phenomenon by modifying the microbiome of laboratory-grown E. subulatus using mild antibiotic treatments, which affected its ability to grow in low salinity. Low salinity acclimation of these algal-bacterial associations was then compared. Salinity significantly impacted bacterial and viral gene expression, albeit in different ways across algal-bacterial communities. In contrast, gene expression of the host and metabolite profiles were affected almost exclusively in the freshwater-intolerant algal-bacterial communities. We found no evidence of bacterial protein production that would directly improve algal stress tolerance. However, vitamin K synthesis is one possible bacterial service missing specifically in freshwater-intolerant cultures in low salinity. In this condition, we also observed a relative increase in bacterial transcriptomic activity and the induction of microbial genes involved in the biosynthesis of the autoinducer AI-1, a quorum-sensing regulator. This could have resulted in dysbiosis by causing a shift in bacterial behaviour in the intolerant algal-bacterial community. Together, these results provide two promising hypotheses to be examined by future targeted experiments. Although they apply only to the specific study system, they offer an example of how bacteria may impact their host's stress response.

Proteomic analysis to unravel the biochemical mechanisms triggered by Bacillus toyonensis SFC 500-1E under chromium(VI) and phenol stress.

Bacillus toyonensis SFC 500-1E is a member of the consortium SFC 500-1 able to remove Cr(VI) and simultaneously tolerate high phenol concentrations. In order to elucidate mechanisms utilized by this strain during the bioremediation process, the differential expression pattern of proteins was analyzed when it grew with or without Cr(VI) (10 mg/L) and Cr(VI) + phenol (10 and 300 mg/L), through two complementary proteomic approaches: gel-based (Gel-LC) and gel-free (shotgun) nanoUHPLC-ESI-MS/MS. A total of 400 differentially expressed proteins were identified, out of which 152 proteins were down-regulated under Cr(VI) and 205 up-regulated in the presence of Cr(VI) + phenol, suggesting the extra effort made by the strain to adapt itself and keep growing when phenol was also added. The major metabolic pathways affected include carbohydrate and energetic metabolism, followed by lipid and amino acid metabolism. Particularly interesting were also ABC transporters and the iron-siderophore transporter as well as transcriptional regulators that can bind metals. Stress-associated global response involving the expression of thioredoxins, SOS response, and chaperones appears to be crucial for the survival of this strain under treatment with both contaminants. This research not only provided a deeper understanding of B. toyonensis SFC 500-1E metabolic role in Cr(VI) and phenol bioremediation process but also allowed us to complete an overview of the consortium SFC 500-1 behavior. This may contribute to an improvement in its use as a bioremediation strategy and also provides a baseline for further research.

On the geometry of elementary flux modes.

Elementary flux modes (EFMs) play a prominent role in the constraint-based analysis of metabolic networks. They correspond to minimal functional units of the metabolic network at steady-state and as such have been studied for almost 30 years. The set of all EFMs in a metabolic network tends to be very large and may have exponential size in the number of reactions. Hence, there is a need to elucidate the structure of this set. Here we focus on geometric properties of EFMs. We analyze the distribution of EFMs in the face lattice of the steady-state flux cone of the metabolic network and show that EFMs in the relative interior of the cone occur only in very special cases. We introduce the concept of degree of an EFM as a measure how elementary it is and study the decomposition of flux vectors and EFMs depending on their degree. Geometric analysis can help to better understand the structure of the set of EFMs, which is important from both the mathematical and the biological viewpoint.

Control Theory and Systems Biology: Potential Applications in Neurodegeneration and Search for Therapeutic Targets.

Control theory, a well-established discipline in engineering and mathematics, has found novel applications in systems biology. This interdisciplinary approach leverages the principles of feedback control and regulation to gain insights into the complex dynamics of cellular and molecular networks underlying chronic diseases, including neurodegeneration. By modeling and analyzing these intricate systems, control theory provides a framework to understand the pathophysiology and identify potential therapeutic targets. Therefore, this review examines the most widely used control methods in conjunction with genomic-scale metabolic models in the steady state of the multi-omics type. According to our research, this approach involves integrating experimental data, mathematical modeling, and computational analyses to simulate and control complex biological systems. In this review, we find that the most significant application of this methodology is associated with cancer, leaving a lack of knowledge in neurodegenerative models. However, this methodology, mainly associated with the Minimal Dominant Set (MDS), has provided a starting point for identifying therapeutic targets for drug development and personalized treatment strategies, paving the way for more effective therapies.

Challenges and advancements in bioprocess intensification of fungal secondary metabolite: kojic acid.

Kojic acid is a fungal secondary metabolite commonly known as a tyrosinase inhibitor, that acts as a skin-whitening agent. Its applications are widely distributed in the area of cosmetics, medicine, food, and chemical synthesis. Renewable resources are the alternative feedstocks that can fulfill the demand for free sugars which are fermented for the production of kojic acid. This review highlights the current progress and importance of bioprocessing of kojic acid from various types of competitive and non-competitive renewable feedstocks. The bioprocessing advancements, secondary metabolic pathway networks, gene clusters and regulations, strain improvement, and process design have also been discussed. The importance of nitrogen sources, amino acids, ions, agitation, and pH has been summarized. Two fungal species Aspergillus flavus and Aspergillus oryzae are found to be extensively studied for kojic acid production due to their versatile substrate utilization and high titer ability. The potential of A. flavus to be a competitive industrial strain for large-scale production of kojic acid has been studied.

Combining denoising of RNA-seq data and flux balance analysis for cluster analysis of single cells.

BACKGROUND: Sophisticated methods to properly pre-process and analyze the increasing collection of single-cell RNA sequencing (scRNA-seq) data are increasingly being developed. On the contrary, the best practices to integrate these data into metabolic networks, aiming at describing metabolic phenotypes within a heterogeneous cell population, have been poorly investigated. In this regard, a critical factor is the presence of false zero values in reactions essential for a fundamental metabolic function, such as biomass or energy production. Here, we investigate the role of denoising strategies in mitigating this problem. METHODS: We applied state-of-the-art denoising strategies - namely MAGIC, ENHANCE, and SAVER - on three public scRNA-seq datasets. We then associated a metabolic flux distribution with every single cell by embedding its noise-free transcriptomics profile in the constraints of the optimization of a core metabolic model. Finally, we used the obtained single-cell optimal metabolic fluxes as features for cluster analysis. We compared the results obtained with different techniques, and with or without the use of denoising. We also investigated the possibility of applying denoising directly on the Reaction Activity Scores, which are metabolic features extracted from the read counts, rather than on the read counts. RESULTS: We show that denoising of transcriptomics data improves the clustering of single cells. We also illustrate that denoising restores important metabolic properties, such as the correlation between cell cycle phase and biomass accumulation, and between the RAS scores of reactions belonging to the same metabolic pathway. We show that MAGIC performs better than ENHANCE and SAVER, and that, denoising applied directly on the RAS matrix could be an effective alternative in removing false zero values from essential metabolic reactions. CONCLUSIONS: Our results indicate that including denoising as a pre-processing operation represents a milestone to integrate scRNA-seq data into Flux Balance Analysis simulations and to perform single-cell cluster analysis with a focus on metabolic phenotypes.

Evolutionary timeline of a modeled cell.

A theoretical study of cell evolution is presented here. By using a toolbox containing an intracellular catalytic reaction network model and a mutation-selection process, four distinct phases of self-organization were unveiled. First, the nutrients prevail as the central substrate of the chemical reactions. Second, the cell becomes a small-world. Third, a highly connected core component emerges, concurrently with the nutrient carriers becoming the central product of reactions. Finally, the cell reaches a steady configuration where the concentrations of the core chemical species are described by Zipf's law.

Proteomic analysis of Plasmodium berghei in the ring phase during in vivo antiparasitic treatment with kramecyne.

Malaria is a parasitic disease of global importance due to its high annual death toll. The treatment for this infection is difficult for the increase in the populations of parasites resistant to the existing medicines, the development of new antimalarials is urgent needed. Several products developed for the control of malaria from herbalist have had a profound impact, for example, quinine obtained from the bark of the cinchona tree and recently those derived from artemisinin, whose discovery was the reason for the awarding of the 2015 Nobel Prize. The aim of the present study was to evaluate a compound named kramecyne extracted of "chayotillo" (Krameria cystisoides) plant used by the antiparasitic effect against some blood and intestinal protozoa (Giardia duodenalis y Trypanosoma cruzi). In addition is using for the treatment of inflammatory diseases. Measuring parasitaemia at different times, it was observed that in mice treated with kramecyne, it reached only 14% of parasitaemia at 7 days with a dose of 15 mg/kg, using chloroquine as a control drug, because it has not been demonstrated that parasites that infect rodents have developed resistance against this drug. Our results showed that kramecyne decreases the expression of parasite proteins that participate in biological processes, such as invasion, cytoadherence, pathogenicity and energy metabolism. With these results, it is proposed that this compound has repercussions on the metabolism of the parasite and could be useful for use as an antimalarial.

Insights into the Antimicrobial Activities and Metabolomes of Aquimarina (Flavobacteriaceae, Bacteroidetes) Species from the Rare Marine Biosphere.

Two novel natural products, the polyketide cuniculene and the peptide antibiotic aquimarin, were recently discovered from the marine bacterial genus Aquimarina. However, the diversity of the secondary metabolite biosynthetic gene clusters (SM-BGCs) in Aquimarina genomes indicates a far greater biosynthetic potential. In this study, nine representative Aquimarina strains were tested for antimicrobial activity against diverse human-pathogenic and marine microorganisms and subjected to metabolomic and genomic profiling. We found an inhibitory activity of most Aquimarina strains against Candida glabrata and marine Vibrio and Alphaproteobacteria species. Aquimarina sp. Aq135 and Aquimarina muelleri crude extracts showed particularly promising antimicrobial activities, amongst others against methicillin-resistant Staphylococcus aureus. The metabolomic and functional genomic profiles of Aquimarina spp. followed similar patterns and were shaped by phylogeny. SM-BGC and metabolomics networks suggest the presence of novel polyketides and peptides, including cyclic depsipeptide-related compounds. Moreover, exploration of the 'Sponge Microbiome Project' dataset revealed that Aquimarina spp. possess low-abundance distributions worldwide across multiple marine biotopes. Our study emphasizes the relevance of this member of the microbial rare biosphere as a promising source of novel natural products. We predict that future metabologenomics studies of Aquimarina species will expand the spectrum of known secondary metabolites and bioactivities from marine ecosystems.

Predicting growth rate from gene expression.

Growth rate is one of the most important and most complex phenotypic characteristics of unicellular microorganisms, which determines the genetic mutations that dominate at the population level, and ultimately whether the population will survive. Translating changes at the genetic level to their growth-rate consequences remains a subject of intense interest, since such a mapping could rationally direct experiments to optimize antibiotic efficacy or bioreactor productivity. In this work, we directly map transcriptional profiles to growth rates by gathering published gene-expression data from Escherichia coli and Saccharomyces cerevisiae with corresponding growth-rate measurements. Using a machine-learning technique called k-nearest-neighbors regression, we build a model which predicts growth rate from gene expression. By exploiting the correlated nature of gene expression and sparsifying the model, we capture 81% of the variance in growth rate of the E. coli dataset, while reducing the number of features from >4,000 to 9. In S. cerevisiae, we account for 89% of the variance in growth rate, while reducing from >5,500 dimensions to 18. Such a model provides a basis for selecting successful strategies from among the combinatorial number of experimental possibilities when attempting to optimize complex phenotypic traits like growth rate.

Mitogen-Activated Protein Kinase 4-Regulated Metabolic Networks.

Mitogen-activated protein kinase 4 (MPK4) was first identified as a negative regulator of systemic acquired resistance. It is also an important kinase involved in many other biological processes in plants, including cytokinesis, reproduction, and photosynthesis. Arabidopsis thaliana mpk4 mutant is dwarf and sterile. Previous omics studies including genomics, transcriptomics, and proteomics have revealed new functions of MPK4 in different biological processes. However, due to challenges in metabolomics, no study has touched upon the metabolomic profiles of the mpk4 mutant. What metabolites and metabolic pathways are potentially regulated by MPK4 are not known. Metabolites are crucial components of plants, and they play important roles in plant growth and development, signaling, and defense. Here we used targeted and untargeted metabolomics to profile metabolites in the wild type and the mpk4 mutant. We found that in addition to the jasmonic acid and salicylic acid pathways, MPK4 is involved in polyamine synthesis and photosynthesis. In addition, we also conducted label-free proteomics of the two genotypes. The integration of metabolomics and proteomics data allows for an insight into the metabolomic networks that are potentially regulated by MPK4.

The effects of model complexity and size on metabolic flux distribution and control: case study in Escherichia coli.

BACKGROUND: Significant efforts have been made in building large-scale kinetic models of cellular metabolism in the past two decades. However, most kinetic models published to date, remain focused around central carbon pathways or are built around ad hoc reduced models without clear justification on their derivation and usage. Systematic algorithms exist for reducing genome-scale metabolic reconstructions to build thermodynamically feasible and consistently reduced stoichiometric models. However, it is important to study how network complexity affects conclusions derived from large-scale kinetic models built around consistently reduced models before we can apply them to study biological systems. RESULTS: We reduced the iJO1366 Escherichia Coli genome-scale metabolic reconstruction systematically to build three stoichiometric models of different size. Since the reduced models are expansions around the core subsystems for which the reduction was performed, the models are nested. We present a method for scaling up the flux profile and the concentration vector reference steady-states from the smallest model to the larger ones, whilst preserving maximum equivalency. Populations of kinetic models, preserving similarity in kinetic parameters, were built around the reference steady-states and their metabolic sensitivity coefficients (MSCs) were computed. The MSCs were sensitive to the model complexity. We proposed a metric for measuring the sensitivity of MSCs to these structural changes. CONCLUSIONS: We proposed for the first time a workflow for scaling up the size of kinetic models while preserving equivalency between the kinetic models. Using this workflow, we demonstrate that model complexity in terms of networks size has significant impact on sensitivity characteristics of kinetic models. Therefore, it is essential to account for the effects of network complexity when constructing kinetic models. The presented metric for measuring MSC sensitivity to structural changes can guide modelers and experimentalists in improving model quality and guide synthetic biology and metabolic engineering. Our proposed workflow enables the testing of the suitability of a kinetic model for answering certain study-specific questions. We argue that the model-based metabolic design targets that are common across models of different size are of higher confidence, while those that are different could be the objective of investigations for model improvement.