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.

Divergent microbial structure still results in convergent microbial function during arrested anaerobic digestion of food waste at different hydraulic retention times

In this study, two arrested anaerobic digestion bioreactors, fed with food waste, operated under different hydraulic retention times (HRTs) exhibited similar total volatile fatty acid (VFA) yields (p = 0.09). 16S rRNA gene sequencing revealed distinct microbial structure (p = 0.02) at the two HRTs. However, between the two HRTs, there were no differences in potential (DNA) and extant (mRNA) functionality for the production of acetic (AA)-, propionic (PA)-, butyric (BA)- and valeric-acid (VA), as indicated by the metagenome and metatranscriptome data, respectively. The highest potential and extant functionality for PA production in the reactor microbiomes mirrored the highest abundance of PA in the reactor effluents. Meta-omics analysis of BA production indicated possible metabolite exchange across different community members. Notably, the basis for similar VFA production performance observed under the HRTs tested lies in the community-level redundancy in convergent acidification functions and pathways, rather than trends in community-level structure alone.

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.

Recent advances in data- and knowledge-driven approaches to explore primary microbial metabolism

With the rapid progress in metabolomics and sequencing technologies, more data on the metabolome of single microbes and their communities become available, revealing the potential of microorganisms to metabolize a broad range of chemical compounds. The analysis of microbial metabolomics datasets remains challenging since it inherits the technical challenges of metabolomics analysis, such as compound identification and annotation, while harboring challenges in data interpretation, such as distinguishing metabolite sources in mixed samples. This review outlines the recent advances in computational methods to analyze primary microbial metabolism: knowledge-based approaches that take advantage of metabolic and molecular networks and data-driven approaches that employ machine/deep learning algorithms in combination with large-scale datasets. These methods aim at improving metabolite identification and disentangling reciprocal interactions between microbes and metabolites. We also discuss the perspective of combining these approaches and further developments required to advance the investigation of primary metabolism in mixed microbial samples.

Global analysis of gene expression profiles and gout symptoms in goslings infected with goose astrovirus

While blocking inflammation is an effective way to ease the symptoms of gout disease in humans, the treatment and prevention of gout in goslings infected with goose astrovirus (GAstV), a recently emergent condition, remain unclear. In this study, we investigated the reprogramming of the host genes as a result of GAstV infection by combining analysis of the global transcriptome and metabolic network pathways in the kidneys of goslings infected with GAstV. We showed that as GAstV replication increased in vivo, the regulation of key enzymes in the host metabolism progressively increased, flowing metabolites into the purine/pyrimidine biosynthesis pathways. Furthermore, we found that GAstV: 1) inhibits the host oxidation-reduction response by inhibiting the expression of the catalase gene; 2) activates the Toll-like receptor 2 pathway to enhance the immune inflammatory response; and 3) activates the key enzyme in lactic acid synthesis to produce lactate accumulation which inhibits the host's antiviral response, so as to facilitate the replication of the virus itself. This study provided the first insight into the overall metabolic requirements of GAstV for replication in vivo by combining transcriptome with metabolic network pathway information.

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.

A pipeline for testing drug mechanism of action and combination therapies: From microarray data to simulations via Linear-In-Flux-Expressions: Testing four-drug combinations for tuberculosis treatment

Computational methods are becoming commonly used in many areas of medical research. Recently, the modeling of biological mechanisms associated with disease pathophysiology have benefited from approaches such as Quantitative Systems Pharmacology (briefly QSP) and Physiologically Based Pharmacokinetics (briefly PBPK). These methodologies show the potential to enhance, if not substitute animal models. The main reasons for this success are the high accuracy and low cost. Solid mathematical foundations of such methods, such as compartmental systems and flux balance analysis, provide a good base on which to build computational tools. However, there are many choices to be made in model design, that will have a large impact on how these methods perform as we scale up the network or perturb the system to uncover the mechanisms of action of new compounds or therapy combinations. A computational pipeline is presented here that starts with available -omic data and utilizes advanced mathematical simulations to inform the modeling of a biochemical system. Specific attention is devoted to creating a modular workflow, including the mathematical rigorous tools to represent complex chemical reactions, and modeling drug action in terms of its impact on multiple pathways. An application to optimizing combination therapy for tuberculosis shows the potential of the approach.

Sulfite altered permanganate effects on acetate-enriched short-chain fatty acids production during sludge anaerobic fermentation

Anaerobic fermentation is a promising method for waste activated sludge (WAS) treatment, but ineffective solubilization and hydrolysis limit its application. The current study examined the function of sodium sulfite (SDS) in potassium permanganate (PP)-conditioned WAS fermentation for short-chain fatty acids (SCFAs) biosynthesis. The presence of SDS in the PP system (PP/SDS) reduced the positive effects of PP on total SCFAs yield (2755 versus 3471 mg COD/L), while effectively increasing the proportion of acetate (from 41 to 81 %). Not only did SDS decrease the promoting effects of PP on WAS solubilization and hydrolysis efficiency by 5-42 %, it also shifted microbial metabolic pathways to favor acetate production. In addition, the amino acid metabolism with acetate as end product was enhanced. Moreover, PP/SDS inhibited methanogenesis, resulting in an accumulation of acetate in high quantities. Thus, the current study a provided insight and direction for effective WAS treatment with acetate-enriched SCFAs production.

Cross-modality comparison between structural and metabolic networks in individual brain based on the Jensen-Shannon divergence method: a healthy Chinese population study

The study aimed to investigate the consistency and diversity between metabolic and structural brain networks at individual level constructed with divergence-based method in healthy Chinese population. The ¹⁸F-FDG PET and T1-weighted images of brain were collected from 209 healthy participants. The Jensen-Shannon divergence (JSD) was used to calculate metabolic or structural connectivities between any pair of brain regions and then individual brain networks were constructed. The global and regional topological properties of both networks were analyzed with graph theoretical analysis. Regional properties including nodal efficiency, degree, and betweenness centrality were used to define hub regions of networks. Cross-modality similarity of brain connectivity was analyzed with differential power (DP) analysis. The default mode network (DMN) had the largest number of brain connectivities with high DP values. The small-worldness indexes of metabolic and structural networks in all participants were greater than 1. The structural network showed higher assortativity and local efficiency than metabolic network, while hierarchy and global efficiency were greater in the metabolic network (all P < 0.001). Most of hubs in both networks were symmetrically spatial distributed in the regions of the DMN and subcortical nuclei including thalamus and amygdala, etc. The human brain presented small-world architecture both in perspective of individual metabolic and structural networks. There was a structural substrate that supported the brain to globally and efficiently integrate and process metabolic interaction across brain regions. The cross-modality cooperation or specialization in both networks might imply mechanisms of achieving higher-order brain functions.

Flux balance analysis of metabolic networks for efficient engineering of microbial cell factories

Metabolic engineering principles have long been applied to explore the metabolic networks of complex microbial cell factories under a variety of environmental constraints for effective deployment of the microorganisms in the optimal production of biochemicals like biofuels, polymers, amino acids, recombinant proteins. One of the methodologies used for analyzing microbial metabolic networks is the Flux Balance Analysis (FBA), which employs applications of optimization techniques for forecasting biomass growth and metabolic flux distribution of industrially important products under specified environmental conditions. The in silico flux simulations are instrumental for designing the production-specific microbial cell factories. However, FBA has some inherent limitations. The present review emphasizes how the incorporation of additional kinetic, thermodynamic, expression and regulatory constraints and integration of omics data into the classical FBA platform improve the prediction accuracy of FBA. A programmed comparison of the simulated data with the experimental observations is presented for supporting the claim. The review further accounts for the successful implementation of classical FBA in biotechnological applications and identifies areas in which classical FBA fails to make correct predictions. The analysis of the predictive capabilities of the different FBA strategies presented here is expected to help researchers in finding new avenues in engineering highly efficient microbial metabolic pathways and identify the key metabolic bottlenecks during the process. Based on the appropriate metabolic network design, fermentation engineers will be able to effectively design the bioreactors and optimize large-scale biochemical production through suitable pathway modifications.

Optimizing microbial networks through metabolic bypasses

Metabolism has long been considered as a relatively stiff set of biochemical reactions. This somewhat outdated and dogmatic view has been challenged over the last years, as multiple studies exposed unprecedented plasticity of metabolism by exploring rational and evolutionary modifications within the metabolic network of cell factories. Of particular importance is the emergence of metabolic bypasses, which consist of enzymatic reaction(s) that support unnatural connections between metabolic nodes. Such novel topologies can be generated through the introduction of heterologous enzymes or by upregulating native enzymes (sometimes relying on promiscuous activities thereof). Altogether, the adoption of bypasses resulted in an expansion in the capacity of the host's metabolic network, which can be harnessed for bioproduction. In this review, we discuss modifications to the canonical architecture of central carbon metabolism derived from such bypasses towards six optimization purposes: stoichiometric gain, overcoming kinetic limitations, solving thermodynamic barriers, circumventing toxic intermediates, uncoupling product synthesis from biomass formation, and altering redox cofactor specificity. The metabolic costs associated with bypass-implementation are likewise discussed, including tailoring their design towards improving bioproduction.

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.

Metabolic modeling of the International Space Station microbiome reveals key microbial interactions

BACKGROUND

Recent studies have provided insights into the persistence and succession of microbes aboard the International Space Station (ISS), notably the dominance of Klebsiella pneumoniae. However, the interactions between the various microbes aboard the ISS and how they shape the microbiome remain to be clearly understood. In this study, we apply a computational approach to predict possible metabolic interactions in the ISS microbiome and shed further light on its organization.

RESULTS

Through a combination of a systems-based graph-theoretical approach, and a constraint-based community metabolic modeling approach, we demonstrated several key interactions in the ISS microbiome. These complementary approaches provided insights into the metabolic interactions and dependencies present amongst various microbes in a community, highlighting key interactions and keystone species. Our results showed that the presence of K. pneumoniae is beneficial to many other microorganisms it coexists with, notably those from the Pantoea genus. Species belonging to the Enterobacteriaceae family were often found to be the most beneficial for the survival of other microorganisms in the ISS microbiome. However, K. pneumoniae was found to exhibit parasitic and amensalistic interactions with Aspergillus and Penicillium species, respectively. To prove this metabolic prediction, K. pneumoniae and Aspergillus fumigatus were co-cultured under normal and simulated microgravity, where K. pneumoniae cells showed parasitic characteristics to the fungus. The electron micrography revealed that the presence of K. pneumoniae compromised the morphology of fungal conidia and degenerated its biofilm-forming structures.

CONCLUSION

Our study underscores the importance of K. pneumoniae in the ISS, and its potential positive and negative interactions with other microbes, including potential pathogens. This integrated modeling approach, combined with experiments, demonstrates the potential for understanding the organization of other such microbiomes, unravelling key organisms and their interdependencies. Video Abstract.

Metabolic modeling of the International Space Station microbiome reveals key microbial interactions

BACKGROUND

Recent studies have provided insights into the persistence and succession of microbes aboard the International Space Station (ISS), notably the dominance of Klebsiella pneumoniae. However, the interactions between the various microbes aboard the ISS and how they shape the microbiome remain to be clearly understood. In this study, we apply a computational approach to predict possible metabolic interactions in the ISS microbiome and shed further light on its organization.

RESULTS

Through a combination of a systems-based graph-theoretical approach, and a constraint-based community metabolic modeling approach, we demonstrated several key interactions in the ISS microbiome. These complementary approaches provided insights into the metabolic interactions and dependencies present amongst various microbes in a community, highlighting key interactions and keystone species. Our results showed that the presence of K. pneumoniae is beneficial to many other microorganisms it coexists with, notably those from the Pantoea genus. Species belonging to the Enterobacteriaceae family were often found to be the most beneficial for the survival of other microorganisms in the ISS microbiome. However, K. pneumoniae was found to exhibit parasitic and amensalistic interactions with Aspergillus and Penicillium species, respectively. To prove this metabolic prediction, K. pneumoniae and Aspergillus fumigatus were co-cultured under normal and simulated microgravity, where K. pneumoniae cells showed parasitic characteristics to the fungus. The electron micrography revealed that the presence of K. pneumoniae compromised the morphology of fungal conidia and degenerated its biofilm-forming structures.

CONCLUSION

Our study underscores the importance of K. pneumoniae in the ISS, and its potential positive and negative interactions with other microbes, including potential pathogens. This integrated modeling approach, combined with experiments, demonstrates the potential for understanding the organization of other such microbiomes, unravelling key organisms and their interdependencies. Video Abstract.

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.

Phosphate stress triggers the conversion of glycerol into l-carnitine in Pseudomonas fluorescens

Glycerol, a by-product of the biofuel industry is transformed into l-carnitine when the soil microbe Pseudomonas fluorescens is cultured in a phosphate-limited mineral medium (LP). Although the biomass yield was similar to that recorded in phosphate-sufficient cultures (HP), the rate of growth was slower. Phosphate was completely consumed in the LP cultures while in the HP media, approximately 35 % of the initial phosphate was detected at stationary phase of growth. The enhanced production of α-ketoglutarate (KG) in HP cultures supplemented with manganese was recently reported (Alhasawi et al., 2017). l-carnitine appeared to be a prominent metabolite in the spent fluid while the soluble cellular-free extract was characterized with peaks attributable to lysine, γ-butyrobetaine (GB), acetate and succinate in the LP cultures. Upon incubation with glycerol and NH₄Cl, the resting cells readily secreted l-carnitine and revealed the presence of such precursors like GB, lysine and methionine involved in the synthesis of this trimethylated moiety. Functional proteomic studies of select enzymes participating in tricarboxylic acid cycle (TCA), oxidative phosphorylation (OP), glyoxylate cycle and l-carnitine synthesis revealed a major metabolic reconfiguration evoked by phosphate stress. While isocitrate dehydrogenase-NAD⁺ dependent (ICDH-NAD⁺) and Complex I were markedly diminished, the activities of γ-butyrobetaine aldehyde dehydrogenase (GBADH) and l-carnitine dehydrogenase (CDH) were enhanced. Real-time quantitative polymerase chain reaction (RT-qPCR) analyses pointed to an increase in transcripts of the enzymes γ-butyrobetaine dioxygenase (bbox1), S-adenosylmethionine synthase (metK) and l-carnitine dehydrogenase (lcdH). The l-carnitine/γ-butyrobetaine antiporter (caiT) was enhanced more than 400-fold in the LP cultures compared to the HP controls. This metabolic reprogramming modulated by phosphate deprivation may provide an effective technology to transform glycerol, an industrial waste into valuable l-carnitine.

Short non-coded peptides interacting with cofactors facilitated the integration of early chemical networks

Independently developed iron-sulphur/thioester- and phosphate-driven chemical reactions would have set up two distinct reaction networks prior to coupling in a proto-metabolic system supporting a minimal organisation closure. Each chemical system assisted initially by simple catalysts and then by more complex cofactors would have provided the precursors of the small metabolites and monomer units along with their respective polymers through dehydrating template-independent assemblies. For example, acylation reactions mediated by activated thioester groups produced peptides, fatty acids and polyhydroxyalkanoates, while phosphorylation reactions by phosphorylating agents allowed the synthesis of polysaccharides, polyribonucleotides and polyphosphates. Here, we address how these independent chemical systems might fit together and shaped a proto-metabolic system, focusing specifically on cofactors as molecular fossils of metabolism. As a result, the proposed overview suggests that non-coded peptides capable of binding a variety of ligands, but in particular with a redox active versatility and/or group transfer potential could have facilitated the chemical connections that led to a minimal closure with a proto-metabolism. Later developments would have made it possible to establish a cellular organisation with more complex and interdependent metabolic pathways.

Metabolic profiling in early pregnancy and associated factors of folate supplementation: A cross-sectional study

BACKGROUND

Pregnancy generally alters the balance of maternal metabolism, but the molecular profiles in early pregnancy and associated factors of folate supplementation in pregnant women remains incompletely understood.

METHODS

Untargeted metabonomics based on high-performance liquid chromatography-high-resolution mass spectrometry integrated with multivariate metabolic pathway analysis were applied to characterize metabolite profiles and associated factors of folate supplements in early pregnancy. The metabolic baseline of early pregnancy was determined by metabolic analysis of 510 serum samples from 131 non-pregnant and 379 pregnant healthy Chinese women. The pathophysiology of adaptive reactions and metabolic challenges induced by folate supplementation in early pregnancy was further compared between pregnant women with (n = 168) and without (n = 184) folate supplements.

RESULTS

Compared with non-pregnant participants, 106 metabolites, majority of which are related to amino acids and lysophosphatidylcholine/phosphatidylcholine, and 13 metabolic pathways were significantly changed in early pregnancy. The supplementation of folate in early pregnancy induced marked changes in N-acyl ethanolamine 22:0, N-acyl taurine 18:2, glycerophosphoserine 44:1 and 8,11,14-eicosatrienoate, proline, and aminoimidazole ribotide levels.

CONCLUSIONS

During early pregnancy, the metabolism of amino acids significantly changes to meet the physiological requirements of pregnant women. Folate intake may change glucose and lipid metabolism. These findings provide a comprehensive landscape for understanding the basic characteristics and gestational metabolic networks of early pregnancy and folate supplementation. This study provides a basis for further research into the relationship between metabolic markers and pregnancy diseases.

TRIAL REGISTRATION

This study protocol was registered on www.ClinicalTrials.gov, NCT03651934, on August 29, 2018 (prior to recruitment).

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.

Modelling of SHMT1 riboregulation predicts dynamic changes of serine and glycine levels across cellular compartments

Human serine hydroxymethyltransferase (SHMT) regulates the serine-glycine one carbon metabolism and plays a role in cancer metabolic reprogramming. Two SHMT isozymes are acting in the cell: SHMT1 encoding the cytoplasmic isozyme, and SHMT2 encoding the mitochondrial one. Here we present a molecular model built on experimental data reporting the interaction between SHMT1 protein and SHMT2 mRNA, recently discovered in lung cancer cells. Using a stochastic dynamic model, we show that RNA moieties dynamically regulate serine and glycine concentration, shaping the system behaviour. For the first time we observe an active functional role of the RNA in the regulation of the serine-glycine metabolism and availability, which unravels a complex layer of regulation that cancer cells exploit to fine tune amino acids availability according to their metabolic needs. The quantitative model, complemented by an experimental validation in the lung adenocarcinoma cell line H1299, exploits RNA molecules as metabolic switches of the SHMT1 activity. Our results pave the way for the development of RNA-based molecules able to unbalance serine metabolism in cancer cells.

Constructing and analysing dynamic models with modelbase v1.2.3: a software update

BACKGROUND

Computational mathematical models of biological and biomedical systems have been successfully applied to advance our understanding of various regulatory processes, metabolic fluxes, effects of drug therapies, and disease evolution and transmission. Unfortunately, despite community efforts leading to the development of SBML and the BioModels database, many published models have not been fully exploited, largely due to a lack of proper documentation or the dependence on proprietary software. To facilitate the reuse and further development of systems biology and systems medicine models, an open-source toolbox that makes the overall process of model construction more consistent, understandable, transparent, and reproducible is desired.

RESULTS AND DISCUSSION

We provide an update on the development of modelbase, a free, expandable Python package for constructing and analysing ordinary differential equation-based mathematical models of dynamic systems. It provides intuitive and unified methods to construct and solve these systems. Significantly expanded visualisation methods allow for convenient analysis of the structural and dynamic properties of models. After specifying reaction stoichiometries and rate equations modelbase can automatically assemble the associated system of differential equations. A newly provided library of common kinetic rate laws reduces the repetitiveness of the computer programming code. modelbase is also fully compatible with SBML. Previous versions provided functions for the automatic construction of networks for isotope labelling studies. Now, using user-provided label maps, modelbase v1.2.3 streamlines the expansion of classic models to their isotope-specific versions. Finally, the library of previously published models implemented in modelbase is growing continuously. Ranging from photosynthesis to tumour cell growth to viral infection evolution, all these models are now available in a transparent, reusable and unified format through modelbase.

CONCLUSION

With this new Python software package, which is written in currently one of the most popular programming languages, the user can develop new models and actively profit from the work of others. modelbase enables reproducing and replicating models in a consistent, tractable and expandable manner. Moreover, the expansion of models to their isotopic label-specific versions enables simulating label propagation, thus providing quantitative information regarding network topology and metabolic fluxes.

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.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-021-04066-y.

gapseq: informed prediction of bacterial metabolic pathways and reconstruction of accurate metabolic models

Genome-scale metabolic models of microorganisms are powerful frameworks to predict phenotypes from an organism's genotype. While manual reconstructions are laborious, automated reconstructions often fail to recapitulate known metabolic processes. Here we present gapseq ( https://github.com/jotech/gapseq ), a new tool to predict metabolic pathways and automatically reconstruct microbial metabolic models using a curated reaction database and a novel gap-filling algorithm. On the basis of scientific literature and experimental data for 14,931 bacterial phenotypes, we demonstrate that gapseq outperforms state-of-the-art tools in predicting enzyme activity, carbon source utilisation, fermentation products, and metabolic interactions within microbial communities.

Seeding Biochemistry on Other Worlds: Enceladus as a Case Study

The Solar System is becoming increasingly accessible to exploration by robotic missions to search for life. However, astrobiologists currently lack well-defined frameworks to quantitatively assess the chemical space accessible to life in these alien environments. Such frameworks will be critical for developing concrete predictions needed for future mission planning, both to determine the potential viability of life on other worlds and to anticipate the molecular biosignatures that life could produce. Here, we describe how uniting existing methods provides a framework to study the accessibility of biochemical space across diverse planetary environments. Our approach combines observational data from planetary missions with genomic data catalogued from across Earth and analyzed using computational methods from network theory. To demonstrate this, we use 307 biochemical networks generated from genomic data collected across Earth and "seed" these networks with molecules confirmed to be present on Saturn's moon Enceladus. By expanding through known biochemical reaction space starting from these seed compounds, we are able to determine which products of Earth's biochemistry are, in principle, reachable from compounds available in the environment on Enceladus, and how this varies across different examples of life from Earth (organisms, ecosystems, planetary-scale biochemistry). While we find that none of the 307 prokaryotes analyzed meet the threshold for viability, the reaction space covered by this process can provide a map of possible targets for detection of Earth-like life on Enceladus, as well as targets for synthetic biology approaches to seed life on Enceladus. In cases where biochemistry is not viable because key compounds are missing, we identify the environmental precursors required to make it viable, thus providing a set of compounds to prioritize for detection in future planetary exploration missions aimed at assessing the ability of Enceladus to sustain Earth-like life or directed panspermia.

On the Use of Topological Features of Metabolic Networks for the Classification of Cancer Samples

BACKGROUND

The increasing availability of omics data collected from patients affected by severe pathologies, such as cancer, is fostering the development of data science methods for their analysis.

INTRODUCTION

The combination of data integration and machine learning approaches can provide new powerful instruments to tackle the complexity of cancer development and deliver effective diagnostic and prognostic strategies.

METHODS

We explore the possibility of exploiting the topological properties of sample-specific metabolic networks as features in a supervised classification task. Such networks are obtained by projecting transcriptomic data from RNA-seq experiments on genome-wide metabolic models to define weighted networks modeling the overall metabolic activity of a given sample.

RESULTS

We show the classification results on a labeled breast cancer dataset from the TCGA database, including 210 samples (cancer vs. normal). In particular, we investigate how the performance is affected by a threshold-based pruning of the networks by comparing Artificial Neural Networks, Support Vector Machines and Random Forests. Interestingly, the best classification performance is achieved within a small threshold range for all methods, suggesting that it might represent an effective choice to recover useful information while filtering out noise from data. Overall, the best accuracy is achieved with SVMs, which exhibit performances similar to those obtained when gene expression profiles are used as features.

CONCLUSION

These findings demonstrate that the topological properties of sample-specific metabolic networks are effective in classifying cancer and normal samples, suggesting that useful information can be extracted from a relatively limited number of features.

Learning from Metabolic Networks: Current Trends and Future Directions for Precision Medicine

BACKGROUND

Systems biology and network modeling represent, nowadays, the hallmark approaches for the development of predictive and targeted-treatment based precision medicine. The study of health and disease as properties of the human body system allows the understanding of the genotype-phenotype relationship through the definition of molecular interactions and dependencies. In this scenario, metabolism plays a central role as its interactions are well characterized and it is considered an important indicator of the genotype- phenotype associations. In metabolic systems biology, the genome-scale metabolic models are the primary scaffolds to integrate multi-omics data as well as cell-, tissue-, condition- specific information. Modeling the metabolism has both investigative and predictive values. Several methods have been proposed to model systems, which involve steady-state or kinetic approaches, and to extract knowledge through machine and deep learning.

METHODS

This review collects, analyzes, and compares the suitable data and computational approaches for the exploration of metabolic networks as tools for the development of precision medicine. To this extent, we organized it into three main sections: "Data and Databases", "Methods and Tools", and "Metabolic Networks for medicine". In the first one, we have collected the most used data and relative databases to build and annotate metabolic models. In the second section, we have reported the state-of-the-art methods and relative tools to reconstruct, simulate, and interpret metabolic systems. Finally, we have reported the most recent and innovative studies that exploited metabolic networks to study several pathological conditions, not only those directly related to metabolism.

CONCLUSION

We think that this review can be a guide to researchers of different disciplines, from computer science to biology and medicine, in exploring the power, challenges and future promises of the metabolism as predictor and target of the so-called P4 medicine (predictive, preventive, personalized and participatory).

Speeding up the core algorithm for the dual calculation of minimal cut sets in large metabolic networks

Background

The concept of minimal cut sets (MCS) has become an important mathematical framework for analyzing and (re)designing metabolic networks. However, the calculation of MCS in genome-scale metabolic models is a complex computational problem. The development of duality-based algorithms in the last years allowed the enumeration of thousands of MCS in genome-scale networks by solving mixed-integer linear problems (MILP). A recent advancement in this field was the introduction of the MCS² approach. In contrast to the Farkas-lemma-based dual system used in earlier studies, the MCS² approach employs a more condensed representation of the dual system based on the nullspace of the stoichiometric matrix, which, due to its reduced dimension, holds promise to further enhance MCS computations.

Results

In this work, we introduce several new variants and modifications of duality-based MCS algorithms and benchmark their effects on the overall performance. As one major result, we generalize the original MCS² approach (which was limited to blocking the operation of certain target reactions) to the most general case of MCS computations with arbitrary target and desired regions. Building upon these developments, we introduce a new MILP variant which allows maximal flexibility in the formulation of MCS problems and fully leverages the reduced size of the nullspace-based dual system. With a comprehensive set of benchmarks, we show that the MILP with the nullspace-based dual system outperforms the MILP with the Farkas-lemma-based dual system speeding up MCS computation with an averaged factor of approximately 2.5. We furthermore present several simplifications in the formulation of constraints, mainly related to binary variables, which further enhance the performance of MCS-related MILP. However, the benchmarks also reveal that some highly condensed formulations of constraints, especially on reversible reactions, may lead to worse behavior when compared to variants with a larger number of (more explicit) constraints and involved variables.

Conclusions

Our results further enhance the algorithmic toolbox for MCS calculations and are of general importance for theoretical developments as well as for practical applications of the MCS framework.

Regulatory dynamic enzyme-cost flux balance analysis: A unifying framework for constraint-based modeling

Integrated modeling of metabolism and gene regulation continues to be a major challenge in computational biology. While there exist approaches like regulatory flux balance analysis (rFBA), dynamic flux balance analysis (dFBA), resource balance analysis (RBA) or dynamic enzyme-cost flux balance analysis (deFBA) extending classical flux balance analysis (FBA) in various directions, there have been no constraint-based methods so far that allow predicting the dynamics of metabolism taking into account both macromolecule production costs and regulatory events. In this paper, we introduce a new constraint-based modeling framework named regulatory dynamic enzyme-cost flux balance analysis (r-deFBA), which unifies dynamic modeling of metabolism, cellular resource allocation and transcriptional regulation in a hybrid discrete-continuous setting. With r-deFBA, we can predict discrete regulatory states together with the continuous dynamics of reaction fluxes, external substrates, enzymes, and regulatory proteins needed to achieve a cellular objective such as maximizing biomass over a time interval. The dynamic optimization problem underlying r-deFBA can be reformulated as a mixed-integer linear optimization problem, for which there exist efficient solvers.

CeMbio - The Caenorhabditis elegans Microbiome Resource

The study of microbiomes by sequencing has revealed a plethora of correlations between microbial community composition and various life-history characteristics of the corresponding host species. However, inferring causation from correlation is often hampered by the sheer compositional complexity of microbiomes, even in simple organisms. Synthetic communities offer an effective approach to infer cause-effect relationships in host-microbiome systems. Yet the available communities suffer from several drawbacks, such as artificial (thus non-natural) choice of microbes, microbe-host mismatch (e.g., human microbes in gnotobiotic mice), or hosts lacking genetic tractability. Here we introduce CeMbio, a simplified natural Caenorhabditis elegans microbiota derived from our previous meta-analysis of the natural microbiome of this nematode. The CeMbio resource is amenable to all strengths of the C. elegans model system, strains included are readily culturable, they all colonize the worm gut individually, and comprise a robust community that distinctly affects nematode life-history. Several tools have additionally been developed for the CeMbio strains, including diagnostic PCR primers, completely sequenced genomes, and metabolic network models. With CeMbio, we provide a versatile resource and toolbox for the in-depth dissection of naturally relevant host-microbiome interactions in C. elegans.

Efficient System Wide Metabolic Pathway Comparisons in Multiple Microbes Using Genome to KEGG Orthology (G2KO) Pipeline Tool

Comparison of system-wide metabolic pathways among microbes provides valuable insights of organisms' metabolic capabilities that can further assist in rationally screening organisms in silico for various applications. In this work, we present a much needed, efficient and user-friendly Genome to KEGG Orthology (G2KO) pipeline tool that facilitates efficient comparison of system wide metabolic networks of multiple organisms simultaneously. The optimized strategy primarily involves automatic retrieval of the KEGG Orthology (KO) identifiers of user defined organisms from the KEGG database followed by overlaying and visualization of the metabolic genes using the KEGG Mapper reconstruct pathway tool. We demonstrate the applicability of G2KO via two case studies in which we processed 24,314 genes across 15 organisms, mapped on to 530 reference pathways in KEGG, while focusing on pathways of interest. First, an in-silico designing of synthetic microbial consortia towards bioprocessing of cellulose to valuable products by comparing the cellulose degradation and fermentative pathways of microbes was undertaken. Second, we comprehensively compared the amino acid biosynthetic pathways of multiple microbes and demonstrated the potential of G2KO as an efficient tool for metabolic studies. We envisage the tool will find immensely useful to the metabolic engineers as well as systems biologists. The tool's web-server, along with tutorial is publicly available at https://faculty.iitmandi.ac.in/~shyam/tools/g2ko/g2ko.cgi . Also, standalone tool can be downloaded freely from https://sourceforge.net/projects/g2ko/ , and from the supplementary.

Clustering analysis of tumor metabolic networks

Background

Biological networks are representative of the diverse molecular interactions that occur within cells. Some of the commonly studied biological networks are modeled through protein-protein interactions, gene regulatory, and metabolic pathways. Among these, metabolic networks are probably the most studied, as they directly influence all physiological processes. Exploration of biochemical pathways using multigraph representation is important in understanding complex regulatory mechanisms. Feature extraction and clustering of these networks enable grouping of samples obtained from different biological specimens. Clustering techniques separate networks depending on their mutual similarity.

Results

We present a clustering analysis on tissue-specific metabolic networks for single samples from three primary tumor sites: breast, lung, and kidney cancer. The metabolic networks were obtained by integrating genome scale metabolic models with gene expression data. We performed network simplification to reduce the computational time needed for the computation of network distances. We empirically proved that networks clustering can characterize groups of patients in multiple conditions.

Conclusions

We provide a computational methodology to explore and characterize the metabolic landscape of tumors, thus providing a general methodology to integrate analytic metabolic models with gene expression data. This method represents a first attempt in clustering large scale metabolic networks. Moreover, this approach gives the possibility to get valuable information on what are the effects of different conditions on the overall metabolism.

Software and Methods for Computational Flux Balance Analysis

As genetic engineering of organisms has grown easier and more precise, computational modeling of metabolic systems has played an increasingly important role in both guiding experimental interventions and in understanding the results of metabolic perturbations.

Biochemical pathways to α-ketoglutarate, a multi-faceted metabolite

α-Ketoglutarate (AKG) also known as 2-oxoglutarate is an essential metabolite in virtually all organisms as it participates in a variety of biological processes including anti-oxidative defence, energy production, signalling modules, and genetic modification. This keto-acid also possesses immense commercial value as it is utilized as a nutritional supplement, a therapeutic agent, and a precursor to a variety of value-added products such as ethylene and heterocyclic compounds. Hence, the generation of KG in a sustainable and environmentally-neutral manner is a major ongoing research endeavour. In this mini-review, the enzymatic systems and the metabolic networks mediating the synthesis of AKG will be described. The importance of such enzymes as isocitrate dehydrogenase (ICDH), glutamate dehydrogenase (GDH), succinate semialdehyde dehydrogenase (SSADH) and transaminases that directly contribute to the formation of KG will be emphasized. The efficacy of microbial systems in providing an effective platform to generate this moiety and the molecular strategies involving genetic manipulation, abiotic stress and nutrient supplementation that result in the optimal production of AKG will be evaluated. Microbial systems and their components acting via the metabolic networks and the resident enzymes are well poised to provide effective biotechnological tools that can supply renewable AKG globally.

NetMet: A Network-Based Tool for Predicting Metabolic Capacities of Microbial Species and their Interactions

Metabolic conversions allow organisms to produce a set of essential metabolites from the available nutrients in an environment, frequently requiring metabolic exchanges among co-inhabiting organisms. Genomic-based metabolic simulations are being increasingly applied for exploring metabolic capacities, considering different environments and different combinations of microorganisms. NetMet is a web-based tool and a software package for predicting the metabolic performances of microorganisms and their corresponding combinations in user-defined environments. The algorithm takes, as input, lists of (i) species-specific enzymatic reactions (EC numbers), and (ii) relevant metabolic environments. The algorithm generates, as output, lists of (i) compounds that individual species can produce in each given environment, and (ii) compounds that are predicted to be produced through complementary interactions. The tool is demonstrated in two case studies. First, we compared the metabolic capacities of different haplotypes of the obligatory fruit and vegetable pathogen Candidatus Liberibacter solanacearum to those of their culturable taxonomic relative Liberibacter crescens. Second, we demonstrated the potential production of complementary metabolites by pairwise combinations of co-occurring endosymbionts of the plant phloem-feeding whitefly Bemisia tabaci.

Improving lipid mapping in Genome Scale Metabolic Networks using ontologies

INTRODUCTION

To interpret metabolomic and lipidomic profiles, it is necessary to identify the metabolic reactions that connect the measured molecules. This can be achieved by putting them in the context of genome-scale metabolic network reconstructions. However, mapping experimentally measured molecules onto metabolic networks is challenging due to differences in identifiers and level of annotation between data and metabolic networks, especially for lipids.

OBJECTIVES

To help linking lipids from lipidomics datasets with lipids in metabolic networks, we developed a new matching method based on the ChEBI ontology. The implementation is freely available as a python library and in MetExplore webserver.

METHODS

Our matching method is more flexible than an exact identifier-based correspondence since it allows establishing a link between molecules even if a different level of precision is provided in the dataset and in the metabolic network. For instance, it can associate a generic class of lipids present in the network with the molecular species detailed in the lipidomics dataset. This mapping is based on the computation of a distance between molecules in ChEBI ontology.

RESULTS

We applied our method to a chemical library (968 lipids) and an experimental dataset (32 modulated lipids) and showed that using ontology-based mapping improves and facilitates the link with genome scale metabolic networks. Beyond network mapping, the results provide ways for improvements in terms of network curation and lipidomics data annotation.

CONCLUSION

This new method being generic, it can be applied to any metabolomics data and therefore improve our comprehension of metabolic modulations.

Thermodynamic Approaches in Flux Analysis

Networks of reactions inside the cell are constrained by the laws of mass and energy balance. Constrained-based modelling (CBM) is the most used method to describe the mass balance of metabolic network. The main key concepts in CBM are stoichiometric analysis such as elementary flux mode analysis or flux balance analysis. Some of these methods have focused on adding thermodynamics constraints to eliminate non-physical fluxes or inconsistencies in the metabolic system. Here, we review the main different approaches and how they tackle the different class of problems.

Using Genome-Scale Metabolic Networks for Analysis, Visualization, and Integration of Targeted Metabolomics Data

Interpretation of metabolomics data in the context of biological pathways is important to gain knowledge about underlying metabolic processes. In this chapter we present methods to analyze genome-scale models (GSMs) and metabolomics data together. This includes reading and mining of GSMs using the SBTab format to retrieve information on genes, reactions, and metabolites. Furthermore, the chapter showcases the generation of metabolic pathway maps using the Escher tool, which can be used for data visualization. Lastly, approaches to constrain flux balance analysis (FBA) by metabolomics data are presented.

Experimental design for parameter estimation in steady-state linear models of metabolic networks

Metabolic networks are typically large, containing many metabolites and reactions. Dynamical models that aim to simulate such networks will consist of a large number of ordinary differential equations, with many kinetic parameters that must be estimated from experimental data. We assume these data to be metabolomics measurements made under steady-state conditions for different input fluxes. Assuming linear kinetics, analytical criteria for parameter identifiability are provided. For normally distributed error terms, we also calculate the Fisher information matrix analytically to be used in the D-optimality criterion. A test network illustrates the developed tool chain for finding an optimal experimental design. The first stage is to verify global or pointwise parameter identifiability, the second stage to find optimal input fluxes, and finally remove redundant measurements.

A Protocol for the Construction and Curation of Genome-Scale Integrated Metabolic and Regulatory Network Models

Genome-scale metabolic network models have been widely used over the last decade and have been shown to successfully predict the metabolic behavior of many organisms. Yet the complexity of metabolic regulation often limits the accuracy of these models. Integrative modeling approaches have recently been developed that combine metabolic and regulatory networks, thereby expanding the capabilities and accuracy of genome-scale modeling. This chapter provides a guide to reconstruct and curate such integrated network models. Specifically, this protocol describes the PROM (Probabilistic Regulation of Metabolism) and GEMINI (Gene Expression and Metabolism Integrated for Network Inference) approaches. PROM is an automated method for the construction of integrated metabolic and transcriptional regulatory network models, while the GEMINI approach curates the integrated network models using transcriptomics and phenomics data. GEMINI represents the first attempt at applying well-established curation tools that exist for metabolic networks to be applied for curating regulatory networks. The integrated network models generated by these approaches enable the mechanistic integration of diverse biological data and can identify novel strategies to engineer cellular metabolism.

Recognition of early and late stages of bladder cancer using metabolites and machine learning

INTRODUCTION

Bladder cancer (BCa) is one of the most common and aggressive cancers. It is the sixth most frequently occurring cancer in men and its rate of occurrence increases with age. The current method of BCa diagnosis includes a cystoscopy and biopsy. This process is expensive, unpleasant, and may have severe side effects. Recent growth in the power and accessibility of machine-learning software has allowed for the development of new, non-invasive diagnostic methods whose accuracy and sensitivity are uncompromising to function.

OBJECTIVES

The goal of this research was to elucidate the biomarkers including metabolites and corresponding genes for different stages of BCa, show their distinguishing and common features, and create a machine-learning model for classification of stages of BCa.

METHODS

Sets of metabolites for early and late stages, as well as common for both stages were analyzed using MetaboAnalyst and Ingenuity^® Pathway Analysis (IPA^®) software. Machine-learning methods were utilized in the development of a binary classifier for early- and late-stage metabolites of BCa. Metabolites were quantitatively characterized using EDragon 1.0 software. The two modeling methods used are Multilayer Perceptron (MLP) and Stochastic Gradient Descent (SGD) with a logistic regression loss function.

RESULTS

We explored metabolic pathways related to early-stage BCa (Galactose metabolism and Starch and sucrose metabolism) and to late-stage BCa (Glycine, serine, and threonine metabolism, Arginine and proline metabolism, Glycerophospholipid metabolism, and Galactose metabolism) as well as those common to both stages pathways. The central metabolite impacting the most cancerogenic genes (AKT, EGFR, MAPK3) in early stage is D-glucose, while late-stage BCa is characterized by significant fold changes in several metabolites: glycerol, choline, 13(S)-hydroxyoctadecadienoic acid, 2'-fucosyllactose. Insulin was also seen to play an important role in late stages of BCa. The best performing model was able to predict metabolite class with an accuracy of 82.54% and the area under precision-recall curve (PRC) of 0.84 on the training set. The same model was applied to three separate sets of metabolites obtained from public sources, one set of the late-stage metabolites and two sets of the early-stage metabolites. The model was better at predicting early-stage metabolites with accuracies of 72% (18/25) and 95% (19/20) on the early sets, and an accuracy of 65.45% (36/55) on the late-stage metabolite set.

CONCLUSION

By examining the biomarkers present in the urine samples of BCa patients as compared with normal patients, the biomarkers associated with this cancer can be pinpointed and lead to the elucidation of affected metabolic pathways that are specific to different stages of cancer. Development of machine-learning model including metabolites and their chemical descriptors made it possible to achieve considerable accuracy of prediction of stages of BCa.

Inferring cancer dependencies on metabolic genes from large-scale genetic screens

BACKGROUND

Cancer cells reprogram their metabolism to survive and propagate. Thus, targeting metabolic rewiring in tumors is a promising therapeutic strategy. Genome-wide RNAi and CRISPR screens are powerful tools for identifying genes essential for cancer cell proliferation and survival. Integrating loss-of-function genetic screens with genomics and transcriptomics datasets reveals molecular mechanisms that underlie cancer cell dependence on specific genes; though explaining cell line-specific essentiality of metabolic genes was recently shown to be especially challenging.

RESULTS

We find that variability in tissue culture medium between cell lines in a genetic screen is a major confounding factor affecting cell line-specific essentiality of metabolic genes-while, quite surprisingly, not being previously accounted for. Additionally, we find that altered expression level of a metabolic gene in a certain cell line is less indicative of its essentiality than for other genes. However, cell line-specific essentiality of metabolic genes is significantly correlated with changes in the expression of neighboring enzymes in the metabolic network. Utilizing a machine learning method that accounts for tissue culture media and functional association between neighboring enzymes, we generated predictive models for cancer cell line-specific dependence on 162 metabolic genes (representing a ~ 2.2-fold increase compared to previous studies). The generated predictive models reveal numerous novel associations between molecular features and cell line-specific dependency on metabolic genes. Specifically, we demonstrate how cancer cell dependence on one-carbon metabolic enzymes is explained based on cancer lineage, oncogenic mutations, and RNA expression of neighboring enzymes.

CONCLUSIONS

Considering culture media as well as accounting for molecular features of functionally related metabolic enzymes in a metabolic network significantly improves our understanding of cancer cell line-specific dependence on metabolic genes. We expect our approach and predictive models of metabolic gene essentiality to be a useful tool for investigating metabolic abnormalities in cancer.

Restructured fungal community diversity and biological interactions promote metolachlor biodegradation in soil microbial fuel cells

Soil microbial fuel cells (MFCs) provide an inexhaustible electron acceptor for the removal of metolachlor and in situ biocurrent stimulation for fungal activity was investigated. The metolachlor degradation rates enhanced by 33%-36% upon the introduction of electrodes after 23 d. In closed MFCs, the abundance of Mortierella as the most dominant genus increased to 43%-54% from 17% in the original soil, whereas those of Aphanoascus and Penicillium decreased to 0.24%-0.39% and 0.38-0.72% from 14% to 11%, respectively. Additionally, a 10-fold amplification of unique OTUs was observed, mainly from increase on the electrode surface. The different treatments were clustered, especially samples near the cathode. The linear discriminant analysis showed that Aphanoascus fulvescens acted as a biomarker between the original and treated soils. The co-occurrence networks demonstrated that Mortierella universally competed for growth with coexisting species while Cladosporium exhibited the most affiliations with species from the 36 other genera present. The correlation analysis indicated that the species associated with degradation belonged to Mortierella, Kernia, Chaetomium and Trichosporon, while the species associated with electrogenesis were Debaryomyces hansenii and Mortierella polycephala. Importantly, this study is the first to reveal fungal community structure in soil MFCs with degrading pollutants and producing electricity.

Evolution of hierarchy in bacterial metabolic networks

Flow hierarchy is a useful way to characterize the movement of information and matter throughout a network. Hierarchical network organizations are shown to arise when there is a cost of maintaining links in the network. A similar constraint exists in metabolic networks, where costs come from reduced efficiency of nonspecific enzymes or from producing unnecessary enzymes. Previous analyses of bacterial metabolic networks have been used to predict the minimal nutrients that a bacterium needs to grow, its mutualistic relationships with other bacteria, and its major ecological niche. We use metabolic network inference to obtain metabolite flow graphs of 2935 bacterial metabolic networks and find that flow hierarchy evolves independently of modularity and other network properties. By inferring the ancestral metabolic networks and estimating the hierarchical character of the inferred network, we show that hierarchical structure first increased and later decreased over evolutionary history. Furthermore, hierarchical structure in the network is associated with slower growth rates; bacteria with hierarchy scores above the median grow on average 2.25 times faster than those with hierarchy scores below the median.

Next-Generation Genome-Scale Models Incorporating Multilevel 'Omics Data: From Yeast to Human

Genome-scale modelling in eukaryotes has been pioneered by the yeast Saccharomyces cerevisiae. Early metabolic networks have been reconstructed based on genome sequence and information accumulated in the literature on biochemical reactions. Protein-protein interaction networks have been constructed based on experimental observations such as yeast-2-hybrid method. Gene regulatory networks were based on a variety of data types, including information on TF-promoter binding and gene coexpression. The aforementioned networks have been improved gradually, and methods for their integration were developed. Incorporation of omics data including genomics, metabolomics, transcriptomics, fluxome, and phosphoproteome led to next-generation genome-scale models. The methods tested on yeast have later been implemented in human, further, cellular components found to be important in yeast physiology under (ab)normal conditions, and (dis)regulation mechanisms in yeast shed light to the healthy and disease states in human. This chapter provides a historical perspective on next-generation genome-scale models incorporating multilevel 'omics data, from yeast to human.

A simplified metabolic network reconstruction to promote understanding and development of flux balance analysis tools

GEnome-scale Network REconstructions (GENREs) mathematically describe metabolic reactions of an organism or a specific cell type. GENREs can be used with a number of constraint-based reconstruction and analysis (COBRA) methods to make computational predictions on how a system changes in different environments. We created a simplified GENRE (referred to as iSIM) that captures central energy metabolism with nine metabolic reactions to illustrate the use of and promote the understanding of GENREs and constraint-based methods. We demonstrate the simulation of single and double gene deletions, flux variability analysis (FVA), and test a number of metabolic tasks with the GENRE. Code to perform these analyses is provided in Python, R, and MATLAB. Finally, with iSIM as a guide, we demonstrate how inaccuracies in GENREs can limit their use in the interrogation of energy metabolism.

Improving the EFMs quality by augmenting their representativeness in LP methods

Background

Although cellular metabolism has been widely studied, its fully comprehension is still a challenge. A main tool for this study is the analysis of meaningful pieces of knowledge called modes and, in particular, specially interesting classes of modes such as pathways and Elementary Flux Modes (EFMs). Its study often has to deal with issues such as the appearance of infeasibilities or the difficulty of finding representative enough sets of modes that are free of repetitions. Mode extraction methods usually incorporate strategies devoted to mitigate this phenomena but they still get a high ratio of repetitions in the set of solutions.

Results

This paper presents a proposal to improve the representativeness of the full set of metabolic reactions in the set of computed modes by penalizing the eventual high frequency of occurrence of some reactions during the extraction. This strategy can be applied to any linear programming based extraction existent method.

Conclusions

Our strategy enhances the quality of a set of extracted EFMs favouring the presence of every reaction in it and improving the efficiency by mitigating the occurrence of repeated solutions. The new proposed strategy can complement other EFMs extraction methods based on linear programming. The obtained solutions are more likely to be diverse using less computing effort and improving the efficiency of the extraction.

Integrated physiology and proteome analysis of embryo and endosperm highlights complex metabolic networks involved in seed germination in wheat (Triticum aestivum L.)

The aim of this study was to investigate the physiological and proteomic changes in the embryo and endosperm during seed germination in the elite Chinese bread wheat cultivar Zhengmai 366. Upon imbibition, seed size and water content increased rapidly, followed by a series of metabolic changes including increases in soluble sugar content and α-amylase activity, a decrease in starch content, and a rapid increase in plant hormones. In total, 57 and 45 differentially accumulated proteins (DAPs) from the embryo and endosperm, respectively, were identified at five germination stages (0, 6, 12, 18, and 24 h). Principal component analysis revealed a significant proteome difference between embryo and endosperm as well as the different germination stages. The largest proteome changes occurred 24 h after seed imbibition. Embryo DAP spots were mainly involved in energy metabolism, amino acid metabolism, stress/defense, and protein metabolism; those from the endosperm were primarily related to storage protein and carbohydrate metabolism. Protein-protein interaction analysis revealed a complicated interaction network between energy-related proteins and other proteins. Metabolic pathway analysis highlighted complex regulatory networks in the embryo and endosperm that regulate wheat seed germination. These results provide new insights into the molecular mechanisms of seed germination.

Integration of transcriptomic data and metabolic networks in cancer samples reveals highly significant prognostic power

Effective stratification of cancer patients on the basis of their molecular make-up is a key open challenge. Given the altered and heterogenous nature of cancer metabolism, we here propose to use the overall expression of central carbon metabolism as biomarker to characterize groups of patients with important characteristics, such as response to ad-hoc therapeutic strategies and survival expectancy. To this end, we here introduce the data integration framework named Metabolic Reaction Enrichment Analysis (MaREA), which strives to characterize the metabolic deregulations that distinguish cancer phenotypes, by projecting RNA-seq data onto metabolic networks, without requiring metabolic measurements. MaREA computes a score for each network reaction, based on the expression of the set of genes encoding for the associated enzyme(s). The scores are first used as features for cluster analysis and then to rank and visualize in an organized fashion the metabolic deregulations that distinguish cancer sub-types. We applied our method to recent lung and breast cancer RNA-seq datasets from The Cancer Genome Atlas and we were able to identify subgroups of patients with significant differences in survival expectancy. We show how the prognostic power of MaREA improves when an extracted and further curated core model focusing on central carbon metabolism is used rather than the genome-wide reference network. The visualization of the metabolic differences between the groups with best and worst prognosis allowed to identify and analyze key metabolic properties related to cancer aggressiveness. Some of these properties are shared across different cancer (sub) types, e.g., the up-regulation of nucleic acid and amino acid synthesis, whereas some other appear to be tumor-specific, such as the up- or down-regulation of the phosphoenolpyruvate carboxykinase reaction, which display different patterns in distinct tumor (sub)types. These results might be soon employed to deliver highly automated diagnostic and prognostic strategies for cancer patients.

Implication of gut microbiota metabolites in cardiovascular and metabolic diseases

Evidence from the literature keeps highlighting the impact of mutualistic bacterial communities of the gut microbiota on human health. The gut microbita is a complex ecosystem of symbiotic bacteria which contributes to mammalian host biology by processing, otherwise, indigestible nutrients, supplying essential metabolites, and contributing to modulate its immune system. Advances in sequencing technologies have enabled structural analysis of the human gut microbiota and allowed detection of changes in gut bacterial composition in several common diseases, including cardiometabolic disorders. Biological signals sent by the gut microbiota to the host, including microbial metabolites and pro-inflammatory molecules, mediate microbiome-host genome cross-talk. This rapidly expanding line of research can identify disease-causing and disease-predictive microbial metabolite biomarkers, which can be translated into novel biodiagnostic tests, dietary supplements, and nutritional interventions for personalized therapeutic developments in common diseases. Here, we review results from the most significant studies dealing with the association of products from the gut microbial metabolism with cardiometabolic disorders. We underline the importance of these postbiotic biomarkers in the diagnosis and treatment of human disorders.