Metabolic Networks Research Articles

Applications of ancestral sequence reconstruction for understanding the evolution of plant specialized metabolism.

Studies of enzymes in modern-day plants have documented the diversity of metabolic activities retained by species today but only provide limited insight into how those properties evolved. Ancestral sequence reconstruction (ASR) is an approach that provides statistical estimates of ancient plant enzyme sequences which can then be resurrected to test hypotheses about the evolution of catalytic activities and pathway assembly. Here, I review the insights that have been obtained using ASR to study plant metabolism and highlight important methodological aspects. Overall, studies of resurrected plant enzymes show that (i) exaptation is widespread such that even low or undetectable levels of ancestral activity with a substrate can later become the apparent primary activity of descendant enzymes, (ii) intramolecular epistasis may or may not limit evolutionary paths towards catalytic or substrate preference switches, and (iii) ancient pathway flux often differs from modern-day metabolic networks. These and other insights gained from ASR would not have been possible using only modern-day sequences. Future ASR studies characterizing entire ancestral metabolic networks as well as those that link ancient structures with enzymatic properties should continue to provide novel insights into how the chemical diversity of plants evolved. This article is part of the theme issue 'The evolution of plant metabolism'.

Biotic interactions, evolutionary forces and the pan-plant specialized metabolism.

Plant specialized metabolism has a complex evolutionary history. Some aspects are conserved across the green lineage, but many metabolites are unique to certain lineages. The network of specialized metabolism continuously diversified, simplified or reshaped during the evolution of streptophytes. Many routes of pan-plant specialized metabolism are involved in plant defence. Biotic interactions are recalled as major drivers of lineage-specific metabolomic diversification. However, the consequences of this diversity of specialized metabolism in the context of plant terrestrialization and land plant diversification into the major lineages of bryophytes, lycophytes, ferns, gymnosperms and angiosperms remain only little explored. Overall, this hampers conclusions on the evolutionary scenarios that shaped specialized metabolism. Recent efforts have brought forth new streptophyte model systems, an increase in genetically accessible species from distinct major plant lineages, and new functional data from a diversity of land plants on specialized metabolic pathways. In this review, we will integrate the recent data on the evolution of the plant immune system with the molecular data of specialized metabolism and its recognition. Based on this we will provide a contextual framework of the pan-plant specialized metabolism, the evolutionary aspects that shape it and the impact on adaptation to the terrestrial environment.This article is part of the theme issue 'The evolution of plant metabolism'.

Evolution of plant metabolism: the state-of-the-art.

Immense chemical diversity is one of the hallmark features of plants. This chemo-diversity is mainly underpinned by a highly complex and biodiverse biochemical machinery. Plant metabolic enzymes originated and were inherited from their eukaryotic and prokaryotic ancestors and further diversified by the unprecedentedly high rates of gene duplication and functionalization experienced in land plants. Unlike prokaryotic microbes, which display frequent horizontal gene transfer events and multiple inputs of energy and organic carbon, land plants predominantly rely on organic carbon generated from CO2 and have experienced relatively few gene transfers during their recent evolutionary history. As such, plant metabolic networks have evolved in a stepwise manner using existing networks as a starting point and under various evolutionary constraints. That said, until recently, the evolution of only a handful of metabolic traits had been extensively investigated and as such, the evolution of metabolism has received a fraction of the attention of, the evolution of development, for example. Advances in metabolomics and next-generation sequencing have, however, recently led to a deeper understanding of how a wide range of plant primary and specialized (secondary) metabolic pathways have evolved both as a consequence of natural selection and of domestication and crop improvement processes. This article is part of the theme issue 'The evolution of plant metabolism'.

The Use of Patient-Derived Organoids in the Study of Molecular Metabolic Adaptation in Breast Cancer

Around 13% of women will likely develop breast cancer during their lifetime. Advances in cancer metabolism research have identified a range of metabolic reprogramming events, such as altered glucose and amino acid uptake, increased reliance on glycolysis, and interactions with the tumor microenvironment (TME), all of which present new opportunities for targeted therapies. However, studying these metabolic networks is challenging in traditional 2D cell cultures, which often fail to replicate the three-dimensional architecture and dynamic interactions of real tumors. To address this, organoid models have emerged as powerful tools. Tumor organoids are 3D cultures, often derived from patient tissue, that more accurately mimic the structural and functional properties of actual tumor tissues in vivo, offering a more realistic model for investigating cancer metabolism. This review explores the unique metabolic adaptations of breast cancer and discusses how organoid models can provide deeper insights into these processes. We evaluate the most advanced tools for studying cancer metabolism in three-dimensional culture models, including optical metabolic imaging (OMI), matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI), and recent advances in conventional techniques applied to 3D cultures. Finally, we explore the progress made in identifying and targeting potential therapeutic targets in breast cancer metabolism.

Integrated bioinformatics analysis identifies PCSK9 as a prognosticator correlated with lipid metabolism in pancreatic adenocarcinoma

BackgroundPancreatic adenocarcinoma (PAAD) is the most frequent kind of pancreatic cancer (PC). Recent studies suggest that lipid metabolism facilitates tumorigenesis, disease progression, and resistance to therapy by promoting lipid synthesis, accumulation, and breakdown. Thus, exploring the lipid metabolism network could unveil novel therapeutic avenues for early detection, precision medicine, and prognostication in PAAD. This project intends to develop new lipid metabolism-related biomarkers for PAAD diagnosis and investigate the link between important genes and immune cell infiltration (ICI).MethodsTissue samples from 20 PAAD patients and 20 healthy controls were obtained. Analysis were focused on the datasets GSE71729 and GSE16515, which include samples of PAAD (n = 161) and those from healthy human tissue (n = 61), derived from the GEO database. Knockdown of PCSK9 on PC cells were conducted by si-RNA and sh-RNA. Migration and cell functional experiments were performed to assess the role of PCSK9 in cell multiplication. Furthermore, a xenograft mouse model was employed to confirm PCSK9's function in vivo.ResultsThe expression level of Proprotein convertase subtilisin/kexin type 9 (PCSK9) is significantly elevated in tissues affected by PAAD when compared to normal tissues. Survival analyses indicated that increased PCSK9 levels are inversely related to overall and disease-free survival (DFS). PCSK9’s functional annotation associated it with the cell cycle and metabolism, especially energy metabolism. Examination of ICI data determined that PCSK9 expression demonstrated an unambiguous association with the M0 macrophages, T follicular helper cells (Tfh), gamma delta T cells and activated DC, and an inverse relationship with Monocytes, CD8+ T cells, memory B cells, resting CD4+ memory T cells, activated NK cells and resting DC abundance. PCSK9 expression knockdown has the ability to impede PC cells’ migration and proliferation.ConclusionOur study identified PCSK9 as a critical gene in PAAD. Expression levels of PCSK9 varied between PAAD and normal samples. ROC analysis verified PCSK9's strong capacity to differentiate PC from normal samples. Importantly, PCSK9 expression was considerably elevated in PC cell lines and tissues. Furthermore, PCSK9 stimulates the migration and proliferation of tumor cells in vivo and vitro.

Roles of network topology in the relaxation dynamics of simple chemical reaction network models

Understanding the relationship between the structure of chemical reaction networks and their reaction dynamics is essential for unveiling the design principles of living organisms. However, while some network-structural features are known to relate to the steady-state characteristics of chemical reaction networks, mathematical frameworks describing the links between out-of-steady-state dynamics and network structure are still underdeveloped. Here, we characterize the out-of-steady-state behavior of a class of artificial chemical reaction networks consisting of the ligation and splitting reactions of polymers. Within this class, we examine minimal networks that can convert a given set of sources (e.g., nutrients) to a specified set of targets (e.g., biomass precursors). By exploring the dynamics of the models with a simple setup, we find three distinct types of relaxation dynamics after perturbation from a steady-state: exponential-, power-law-, and plateau-dominated. We computationally show that we can predict this out-of-steady-state dynamical behavior from just three features computed from the network’s stoichiometric matrix, namely, (1) the rank gap, determining the existence of a steady-state; (2) the left null-space, being related to conserved quantities in the dynamics; and (3) the stoichiometric cone, dictating the range of achievable chemical concentrations. We further demonstrate that these three quantities relates to the type of relaxation dynamics of combinations of our minimal networks, larger networks with many redundant pathways, and a real example of a metabolic network. The relationship between the topological features of reaction networks and the relaxation dynamics presented here are useful clues for understanding the design of metabolic reaction networks as well as industrially useful chemical production pathways.

Integrating HRMAS-NMR Data and Machine Learning-Assisted Profiling of Metabolite Fluxes to Classify Low- and High-Grade Gliomas.

Diagnosing and classifying central nervous system tumors such as gliomas or glioblastomas pose a significant challenge due to their aggressive and infiltrative nature. However, recent advancements in metabolomics and magnetic resonance spectroscopy (MRS) offer promising avenues for differentiating tumor grades both in vivo and ex vivo. This study aimed to explore tissue-based metabolic signatures to classify/distinguish between low- and high-grade gliomas. Forty-six histologically confirmed, intact solid tumor samples from glioma patients were analyzed using high-resolution magic angle spinning nuclear magnetic resonance (HRMAS-NMR) spectroscopy. By integrating machine learning (ML) algorithms, spectral regions with the most discriminative potential were identified. Validation was performed through univariate and multivariate statistical analyses, along with HRMAS-NMR analyses of 46 paired plasma samples. Amongst the various ML models applied, the logistics regression identified 46 spectral regions capable of sub-classifying gliomas with accuracy 87% (F1-measure 0.87, Precision 0.82, Recall 0.93), whereas the extra-tree classifier identified three spectral regions with predictive accuracy of 91% (F1-measure 0.91, Precision 0.85, Recall 0.97). Wilcoxon test presented 51 spectral regions significantly differentiating low- and high-grade glioma groups (p < 0.05). Based on sensitivity and area under the curve values, 40 spectral regions corresponding to 18 metabolites were considered as potential biomarkers for tissue-based glioma classification and amongst these N-acetyl aspartate, glutamate, and glutamine emerged as the most important markers. These markers were validated in paired plasma samples, and their absolute concentrations were computed. Our results demonstrate that the metabolic markers identified through the HRMAS-NMR-ML analysis framework, and their associated metabolic networks, hold promise for targeted treatment planning and clinical interventions in the future.

A hierarchy of metabolite exchanges in metabolic models of microbial species and communities.

The metabolic network of an organism can be analyzed as a constraint-based model. This analysis can be biased, optimizing an objective such as growth rate, or unbiased, aiming to describe the full feasible space of metabolic fluxes through pathway analysis or random flux sampling. In particular, pathway analysis can decompose the flux space into fundamental and formally defined metabolic pathways. Unbiased methods scale poorly with network size due to combinatorial explosion, but a promising approach to improve scalability is to focus on metabolic subnetworks, e.g., cells' metabolite exchanges with each other and the environment, rather than the full metabolic networks. Here, we applied pathway enumeration and flux sampling to metabolite exchanges in microbial species and a microbial community, using models ranging from central carbon metabolism to genome-scale and focusing on pathway definitions that allow direct targeting of subnetworks such as metabolite exchanges (elementary conversion modes, elementary flux patterns, and minimal pathways). Enumerating growth-supporting metabolite exchanges, we found that metabolite exchanges from different pathway definitions were related through a hierarchy, and we show that this hierarchical relationship between pathways holds for metabolic networks and subnetworks more generally. Metabolite exchange frequencies, defined as the fraction of pathways in which each metabolite was exchanged, were similar across pathway definitions, with a few specific exchanges explaining large differences in pathway counts. This indicates that biological interpretation of predicted metabolite exchanges is robust to the choice of pathway definition, and it suggests strategies for more scalable pathway analysis. Our results also signal wider biological implications, facilitating detailed and interpretable analysis of metabolite exchanges and other subnetworks in fields such as metabolic engineering and synthetic biology.

Toxicity mechanism of organosilicon adjuvant in combination with S-metolachlor on Vigna angularis

The widespread use of S-metolachlor (ME) in agriculture to suppress weeds and boost crop yields, particularly in cultivating Vigna angularis, is well established. However, the application of organosilicon adjuvants with herbicides has potential threats to non-target crops. This study investigates the toxicity symptoms and mechanisms when V. angularis is exposed to ME in conjunction with a common organosilicon adjuvant. Results indicate that ME inhibits the growth of V. angularis seedlings, and adding adjuvants could aggravate the negative effects of ME. According to the growth index of seedlings, the adjuvant increased the toxicity of ME by 84–96 %. Additionally, the chlorophyll content, root permeability, and antioxidant indicators in the seedlings were also adversely affected. Integrated metabolomics and transcriptomics analyses reveal that differentially abundant metabolites (DAMs) and differentially expressed genes (DEGs) are mainly enriched in four ways: "lysine degradation," "ABC transporters," "phenylalanine metabolism," and "monoterpenoid biosynthesis." The metabolic pathways and gene regulatory network involving 11 DAMs and 22 DEGs are associated with the physiological processes affected by ME and the adjuvant. This study provides guidance for the application of herbicides and their adjuvants in agricultural production to minimize adverse effects on non-target crops.

A candidate loss-of-function variant in SGIP1 causes synaptic dysfunction and recessive parkinsonism

Synaptic dysfunction is recognized as an early step in the pathophysiology of parkinsonism. Several genetic mutations affecting the integrity of synaptic proteins cause or increase the risk of developing disease. We have identified a candidate causative mutation in synaptic "SH3GL2 Interacting Protein 1" (SGIP1), linked to early-onset parkinsonism in a consanguineous Arab family. Additionally, affected siblings display intellectual, cognitive, and behavioral dysfunction. Metabolic network analysis of [18F]-fluorodeoxyglucose positron emission tomography scans shows patterns very similar to those of idiopathic Parkinson's disease. We show that the identified SGIP1 mutation causes a loss of protein function, and analyses in newly created Drosophila models reveal movement defects, synaptic transmission dysfunction, and neurodegeneration, including dopaminergic synapse loss. Histology and correlative light and electron microscopy reveal the absence of synaptic multivesicular bodies and the accumulation of degradative organelles. This research delineates a putative form of recessive parkinsonism, converging on defective synaptic proteostasis and opening avenues for diagnosis, genetic counseling, and treatment.

The deubiquitinase Ubp3/Usp10 constrains glucose-mediated mitochondrial repression via phosphate budgeting.

Many cells in high glucose repress mitochondrial respiration, as observed in the Crabtree and Warburg effects. Our understanding of biochemical constraints for mitochondrial activation is limited. Using a Saccharomyces cerevisiae screen, we identified the conserved deubiquitinase Ubp3 (Usp10), as necessary for mitochondrial repression. Ubp3 mutants have increased mitochondrial activity despite abundant glucose, along with decreased glycolytic enzymes, and a rewired glucose metabolic network with increased trehalose production. Utilizing ∆ubp3 cells, along with orthogonal approaches, we establish that the high glycolytic flux in glucose continuously consumes free Pi. This restricts mitochondrial access to inorganic phosphate (Pi), and prevents mitochondrial activation. Contrastingly, rewired glucose metabolism with enhanced trehalose production and reduced GAPDH (as in ∆ubp3 cells) restores Pi. This collectively results in increased mitochondrial Pi and derepression, while restricting mitochondrial Pi transport prevents activation. We therefore suggest that glycolytic flux-dependent intracellular Pi budgeting is a key constraint for mitochondrial repression.

Design and practice of integrating artificial intelligence into the teaching of "Synthetic Biology" under the background of discipline crossing

In recent years, artificial intelligence has been employed to empower synthetic biology, demonstrating great potential in the simulation and prediction of protein structures as well as the design and optimization of regulatory elements and metabolic networks. Integrating artificial intelligence into the teaching of Synthetic Biology is in line with the development trends of synthetic biology and can promote the cultivation of interdisciplinary high-level talents and collaborative innovation. This paper expounds the idea of integrating artificial intelligence into the teaching of Synthetic Biology from establishing interdisciplinary course contents and teaching methods, simultaneously considering the fundamentals and application of artificial intelligence in synthetic biology, cultivating independent learning and innovative practice abilities, and enhancing the ethics education related to artificial intelligence. Furthermore, a system integrating artificial intelligence with the teaching contents of Synthetic Biology is designed, which focuses on supplementing fundamentals of artificial intelligence and incorporating artificial intelligence into the classroom and experimental teaching contents of Synthetic Biology. Moreover, with the course of Synthetic Biology in Jiangnan University as an example, this paper presents the pathway of integrating artificial intelligence into the teaching of this course under the background of discipline crossing. Finally, the teaching effects are expected.

INAP 2.0: Harnessing metabolic complementarity in microbial network analysis.

With the widespread adoption of metagenomic sequencing, new perspectives have emerged for studying microbial ecological networks, yielding metabolic evidence of interspecies interactions that traditional co-occurrence networks cannot infer. This protocol introduces the integrated Network Analysis Pipeline 2.0 (iNAP 2.0), which features an innovative metabolic complementarity network for microbial studies from metagenomics sequencing data. iNAP 2.0 sets up a four-module process for metabolic interaction analysis, namely: (I) Prepare genome-scale metabolic models; (II) Infer pairwise interactions of genome-scale metabolic models; (III) Construct metabolic interaction networks; and (IV) Analyze metabolic interaction networks. Starting from metagenome-assembled or complete genomes, iNAP 2.0 offers a variety of methods to quantify the potential and trends of metabolic complementarity between models, including the PhyloMint pipeline based on phylogenetic distance-adjusted metabolic complementarity, the SMETANA (species metabolic interaction analysis) approach based on cross-feeding substrate exchange prediction, and metabolic distance calculation based on parsimonious flux balance analysis (pFBA). Notably, iNAP 2.0 integrates the random matrix theory (RMT) approach to find the suitable threshold for metabolic interaction network construction. Finally, the metabolic interaction networks can proceed to analysis using topological feature analysis such as hub node determination. In addition, a key feature of iNAP 2.0 is the identification of potentially transferable metabolites between species, presented as intermediate nodes that connect microbial nodes in the metabolic complementarity network. To illustrate these new features, we use a set of metagenome-assembled genomes as an example to comprehensively document the usage of the tools. iNAP 2.0 is available at https://inap.denglab.org.cn for all users to register and use for free.

Core and accessory genomic traits of Vibrio cholerae O1 drive lineage transmission and disease severity

In Bangladesh, Vibrio cholerae lineages are undergoing genomic evolution, with increased virulence and spreading ability. However, our understanding of the genomic determinants influencing lineage transmission and disease severity remains incomplete. Here, we developed a computational framework using machine-learning, genome scale metabolic modelling (GSSM) and 3D structural analysis, to identify V. cholerae genomic traits linked to lineage transmission and disease severity. We analysed in-patients isolates from six Bangladeshi regions (2015-2021), and uncovered accessory genes and core SNPs unique to the most recent dominant lineage, with virulence, motility and bacteriophage resistance functions. We also found a strong correlation between V. cholerae genomic traits and disease severity, with some traits overlapping those driving lineage transmission. GSMM and 3D structure analysis unveiled a complex interplay between transcription regulation, protein interaction and stability, and metabolic networks, associated to lifestyle adaptation, intestinal colonization, acid tolerance and symptom severity. Our findings support advancing therapeutics and targeted interventions to mitigate cholera spread.

In vitro transcription-based biosensing of glycolate for prototyping of a complex enzyme cascade.

In vitro metabolic systems allow the reconstitution of natural and new-to-nature pathways outside of their cellular context and are of increasing interest in bottom-up synthetic biology, cell-free manufacturing, and metabolic engineering. Yet, the analysis of the activity of such in vitro networks is very often restricted by time- and cost-intensive methods. To overcome these limitations, we sought to develop an in vitro transcription (IVT)-based biosensing workflow that is compatible with the complex conditions of in vitro metabolism, such as the crotonyl-CoA/ethylmalonyl-CoA/hydroxybutyryl-CoA (CETCH) cycle, a 27-component in vitro metabolic system that converts CO2 into glycolate. As proof of concept, we constructed a novel glycolate sensor module that is based on the transcriptional repressor GlcR from Paracoccus denitrificans and established an IVT biosensing workflow that allows us to quantify glycolate from CETCH samples in the micromolar to millimolar range. We investigate the influence of 13 (shared) cofactors between the two in vitro systems to show that Mg2+, adenosine triphosphate , and other phosphorylated metabolites are critical for robust signal output. Our optimized IVT biosensor correlates well with liquid chromatography-mass spectrometry-based glycolate quantification of CETCH samples, with one or multiple components varying (linear correlation 0.94-0.98), but notably at ∼10-fold lowered cost and ∼10 times faster turnover time. Our results demonstrate the potential and challenges of IVT-based systems to quantify and prototype the activity of complex reaction cascades and in vitro metabolic networks.

Untargeted Metabolomics and Quantitative Analysis of Tryptophan Metabolites in Myalgic Encephalomyelitis Patients and Healthy Volunteers: A Comparative Study Using High-Resolution Mass Spectrometry.

Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) is a chronic, complex illness characterized by severe and often disabling physical and mental fatigue. So far, scientists have not been able to fully pinpoint the biological cause of the illness and yet it affects millions of people worldwide. To gain a better understanding of ME/CFS, we compared the metabolic networks in the plasma of 38 ME/CFS patients to those of 24 healthy control participants. This involved an untargeted metabolomics approach in addition to the measurement of targeted substances including tryptophan and its metabolites, as well as tyrosine, phenylalanine, B vitamins, and hypoxanthine using liquid chromatography coupled to mass spectrometry. We observed significant alterations in several metabolic pathways, including the vitamin B3, arginine-proline, and aspartate-asparagine pathways, in the untargeted analysis. The targeted analysis revealed changes in the levels of 3-hydroxyanthranilic acid, 3-hydroxykynurenine, hypoxanthine, and phenylalanine in ME/CFS patients compared to the control group. These findings suggest potential alterations in immune system response and oxidative stress in ME/CFS patients.

Research on the mechanism of buyang huanwu decoction in the amelioration of age-associated memory impairment based on the “co-occurrence network regulation of intestinal microecology-host metabolism-immune function”

Ethnopharmacological relevanceBrain aging can promote neuronal damage, contributing to aging-related diseases like memory dysfunction. Buyang Huanwu Decoction (BYHWD), a traditional Chinese medicine formula known for tonifying qi and activating blood circulation, shows neuroprotective properties. Despite this, the specific mechanism by which BYHWD improves age-associated memory impairment (AAMI) has not been explored in existing literature. Aim of the studyThis study aimed to investigate the mechanism of BYHWD in the improvement of AAMI based on the “co-occurrence network regulation of intestinal microecology-host metabolism-immune function”. Materials and methodsFirstly, D-galactose was performed to induce a rat model of AAMI. Learning and memory deficits was assessed by the Morris water maze test. H&E and Nissl staining were used to observe the pathological changes in neurons in the hippocampus of rats. Meanwhile, the levels of pro-inflammatory cytokines and the activation of antioxidant enzymes in rat serum were measured using ELISA. Finally, an integrated pharmacological approach was applied to explore the potential mechanism of BYHWD in improving AAMI. ResultsOur results indicated that BYHWD significantly mitigated the pathological structure of the hippocampus, reversed the levels of IL-6, TNF-α, GSH, and CAT in the serum, and improved learning and memory in aging rats. Transcriptomics combined with network pharmacology showed that energy metabolism and the inflammatory response were the key biological pathways for BYHWD to ameliorate AAMI. Integrative analysis of the microbiome and metabolomics revealed that BYHWD has the potential to restore the balance of abundance between probiotics and harmful bacteria, and ameliorate the reprogramming of energy metabolism caused by aging in the brain. The co-occurrence network analysis demonstrated that a strong correlation between the treatment of AAMI and the stability of intestinal microecology, host metabolism, and immune network. ConclusionThe findings of this study collectively support the notion that BYHWD has a superior therapeutic effect in an AAMI rat model. The mechanism involves regulating the “intestinal microecology-metabolism-immune function co-occurrence network” system to restore the composition of gut microbiota and metabolites. This further improves the metabolic phenotype of brain tissue and maintains the homeostasis of central nervous system's immunity, leading to an improvement in AAMI. Consequently, this study offers a unique perspective on the prevention and treatment of AAMI. And, BYHWD is also considered to be a promising preclinical treatment for improving AAMI.

Genome-Scale Metabolic Model Reconstruction and Investigation into the Fluxome of the Fast-Growing Cyanobacterium Synechococcus sp. PCC 11901.

The ability to convert atmospheric CO2 and light into biomass and value-added chemicals makes cyanobacteria a promising resource microbial host for biotechnological applications. A newly discovered fastest-growing cyanobacterial strain, Synechococcus sp. PCC 11901, has been reported to have the highest biomass accumulation rate, making it a preferred target host for producing renewable fuels, value-added biochemicals, and natural products. System-level knowledge of an organism is imperative to understand the metabolic potential of the strain, which can be attained by developing genome-scale metabolic models (GEMs). We present the first genome-scale metabolic model of Synechococcus sp. PCC 11901 (iRS840), which contains 840 genes, 1001 reactions, and 944 metabolites. The model has been optimized and validated under different trophic modes, i.e., autotrophic and mixotrophic, by conducting an in vivo growth experiment. The robustness of the metabolic network was evaluated by changing the biomass coefficient of the model, which showed a higher sensitivity toward pigments under the photoautotrophic condition, whereas under the heterotrophic condition, amino acids were found to be more influential. Furthermore, it was discovered that PCC 11901 synthesizes succinyl-CoA via succinic semialdehyde due to its imperfect TCA cycle. Subsequent flux balance analysis (FBA) revealed a quantum yield of 0.16 in silico, which is higher compared to that of PCC 6803. Under mixotrophic conditions (with glycerol and carbon dioxide), the flux through the Calvin cycle increased compared to autotrophic conditions. This model will be useful for gaining insights into the metabolic potential of PCC 11901 and developing effective metabolic engineering strategies for product development.

Gallium Uncouples Iron Metabolism to Enhance Glioblastoma Radiosensitivity.

Gallium-based therapy has been considered a potentially effective cancer therapy for decades and has recently re-emerged as a novel therapeutic strategy for the management of glioblastoma tumors. Gallium targets the iron-dependent phenotype associated with aggressive tumors by mimicking iron in circulation and gaining intracellular access through transferrin-receptor-mediated endocytosis. Mechanistically, it is believed that gallium inhibits critical iron-dependent enzymes like ribonucleotide reductase and NADH dehydrogenase (electron transport chain complex I) by replacing iron and removing the ability to transfer electrons through the protein secondary structure. However, information regarding the effects of gallium on cellular iron metabolism is limited. As mitochondrial iron metabolism serves as a central hub of the iron metabolic network, the goal of this study was to investigate the effects of gallium on mitochondrial iron metabolism in glioblastoma cells. Here, it has been discovered that gallium nitrate can induce mitochondrial iron depletion, which is associated with the induction of DNA damage. Moreover, the generation of gallium-resistant cell lines reveals a highly unstable phenotype characterized by impaired colony formation associated with a significant decrease in mitochondrial iron content and loss of the mitochondrial iron uptake transporter, mitoferrin-1. Moreover, gallium-resistant cell lines are significantly more sensitive to radiation and have an impaired ability to repair any sublethal damage and to survive potentially lethal radiation damage when left for 24 h following radiation. These results support the hypothesis that gallium can disrupt mitochondrial iron metabolism and serve as a potential radiosensitizer.

Understanding flux switching in metabolic networks through an analysis of synthetic lethals

Biological systems are robust and redundant. The redundancy can manifest as alternative metabolic pathways. Synthetic double lethals are pairs of reactions that, when deleted simultaneously, abrogate cell growth. However, removing one reaction allows the rerouting of metabolites through alternative pathways. Little is known about these hidden linkages between pathways. Understanding them in the context of pathogens is useful for therapeutic innovations. We propose a constraint-based optimisation approach to identify inter-dependencies between metabolic pathways. It minimises rerouting between two reaction deletions, corresponding to a synthetic lethal pair, and outputs the set of reactions vital for metabolic rewiring, known as the synthetic lethal cluster. We depict the results for different pathogens and show that the reactions span across metabolic modules, illustrating the complexity of metabolism. Finally, we demonstrate how the two classes of synthetic lethals play a role in metabolic networks and influence the different properties of a synthetic lethal cluster.