Metabolomic screening of radioiodine refractory thyroid cancer patients and the underlying chemical mechanism of iodine resistance.

Radioiodine refractory (RAIR) patients do not benefit from iodine-131 therapy. Thus, timely identification of RAIR patients is critical for avoiding ineffective radioactive iodine therapy. In addition, determining the causes of iodine resistance will facilitate the development of novel treatment strategies. This study was comprised of 20 RAIR and 14 non-radioiodine refractory (non-RAIR) thyroid cancer patients. Liquid chromatography-mass spectrometry was used to identify differences in the serum metabolites of RAIR and non-RAIR patients. In addition, chemical assays were performed to determine the effects of the differential metabolites on iodine uptake. Metabolic pathway enrichment analysis of the differential metabolites revealed significant differences in the phenylalanine and tyrosine metabolic pathways. Notably, quinate and shikimic acid, metabolites of the tyrosine pathway, were significantly increased in the RAIR group. In contrast, the phenylalanine pathway metabolites, hippuric acid and 2-phenylacetamide, were markedly decreased in the RAIR group. Thyroid peroxidase plays an important role in catalyzing the iodination of tyrosine residues, while the ionic state of iodine promotes the iodination reaction. Quinate, shikimic acid, hippuric acid, and 2-phenylacetamide were found to be involved in the iodination of tyrosine, which is a key step in thyroid hormone synthesis. Specifically, quinate and shikimic acid were found to inhibit iodination, while hippuric acid and 2-phenylacetamide promoted iodination. Abnormalities in phenylalanine and tyrosine metabolic pathways are closely associated with iodine resistance. Tyrosine is required for thyroid hormone synthesis and could be a potential cause of iodine resistance.

Adrenal pheochromocytoma impacts three main pathways: cysteine-methionine, pyrimidine, and tyrosine metabolism. / è‚¾ä¸Šè ºå—œé“¬ç»†èƒžç˜¤å½±å“ä¸‰æ¡ä¸»è¦ä»£è°¢é€šè·¯ï¼šåŠèƒ±æ°¨é ¸-è›‹æ°¨é ¸ä»£è°¢ã€å˜§å•¶ä»£è°¢å’Œé ªæ°¨é ¸ä»£è°¢é€šè·¯.

Pheochromocytomas and paragangliomas (PPGLs) cause symptoms by altering the circulation levels of catecholamines and peptide hormones. Currently, the diagnosis of PPGLs relies on diagnostic imaging and the detection of catecholamines. In this study, we used ultra-performance liquid chromatography (UPLC)/quadrupole time-of-flight mass spectrometry (Q-TOF MS) analysis to identify and measure the perioperative differential metabolites in the plasma of adrenal pheochromocytoma patients. We identified differentially expressed genes by comparing the transcriptomic data of pheochromocytoma with the normal adrenal medulla. Through conducting two steps of metabolomics analysis, we identified 111 differential metabolites between the healthy group and the patient group, among which 53 metabolites were validated. By integrating the information of differential metabolites and differentially expressed genes, we inferred that the cysteine-methionine, pyrimidine, and tyrosine metabolism pathways were the three main metabolic pathways altered by the neoplasm. The analysis of transcription levels revealed that the tyrosine and cysteine-methionine metabolism pathways were downregulated in pheochromocytoma, whereas the pyrimidine pathway showed no significant difference. Finally, we developed an optimized diagnostic model of two metabolites, L-dihydroorotic acid and vanylglycol. Our results for these metabolites suggest that they may serve as potential clinical biomarkers and can be used to supplement and improve the diagnosis of pheochromocytoma.

The Glucose-Succinate Pathway: A Crucial Anaerobic Metabolic Pathway in the Scallop Chlamys farreri Experiencing Heat Stress.

Recently, the increase in marine temperatures has become an important global marine environmental issue. The ability of energy supply in marine animals plays a crucial role in avoiding the stress of elevated temperatures. The investigation into anaerobic metabolism, an essential mechanism for regulating energy provision under heat stress, is limited in mollusks. In this study, key enzymes of four anaerobic metabolic pathways were identified in the genome of scallop Chlamys farreri, respectively including five opine dehydrogenases (CfOpDHs), two aspartate aminotransferases (CfASTs) divided into cytoplasmic (CfAST1) and mitochondrial subtype (CfAST2), and two phosphoenolpyruvate carboxykinases (CfPEPCKs) divided into a primitive type (CfPEPCK2) and a cytoplasmic subtype (CfPEPCK1). It was surprising that lactate dehydrogenase (LDH), a key enzyme in the anaerobic metabolism of the glucose-lactate pathway in vertebrates, was absent in the genome of scallops. Phylogenetic analysis verified that CfOpDHs clustered according to the phylogenetic relationships of the organisms rather than substrate specificity. Furthermore, CfOpDHs, CfASTs, and CfPEPCKs displayed distinct expression patterns throughout the developmental process and showed a prominent expression in muscle, foot, kidney, male gonad, and ganglia tissues. Notably, CfASTs displayed the highest level of expression among these genes during the developmental process and in adult tissues. Under heat stress, the expression of CfASTs exhibited a general downregulation trend in the six tissues examined. The expression of CfOpDHs also displayed a downregulation trend in most tissues, except CfOpDH1/3 in striated muscle showing significant up-regulation at some time points. Remarkably, CfPEPCK1 was significantly upregulated in all six tested tissues at almost all time points. Therefore, we speculated that the glucose-succinate pathway, catalyzed by CfPEPCK1, serves as the primary anaerobic metabolic pathway in mollusks experiencing heat stress, with CfOpDH3 catalyzing the glucose-opine pathway in striated muscle as supplementary. Additionally, the high and stable expression level of CfASTs is crucial for the maintenance of the essential functions of aspartate aminotransferase (AST). This study provides a comprehensive and systematic analysis of the key enzymes involved in anaerobic metabolism pathways, which holds significant importance in understanding the mechanism of energy supply in mollusks.

Metabolic network analysis of pre-ASD newborns and 5-year-old children with autism spectrum disorder.

Classical metabolomic and new metabolic network methods were used to study the developmental features of autism spectrum disorder (ASD) in newborns (n = 205) and 5-year-old children (n = 53). Eighty percent of the metabolic impact in ASD was caused by 14 shared biochemical pathways that led to decreased anti-inflammatory and antioxidant defenses, and to increased physiologic stress molecules like lactate, glycerol, cholesterol, and ceramides. CIRCOS plots and a new metabolic network parameter, V ° net, revealed differences in both the kind and degree of network connectivity. Of 50 biochemical pathways and 450 polar and lipid metabolites examined, the developmental regulation of the purine network was most changed. Purine network hub analysis revealed a 17-fold reversal in typically developing children. This purine network reversal did not occur in ASD. These results revealed previously unknown metabolic phenotypes, identified new developmental states of the metabolic correlation network, and underscored the role of mitochondrial functional changes, purine metabolism, and purinergic signaling in autism spectrum disorder.

Predicting metabolic modules in incomplete bacterial genomes with MetaPathPredict.

The reconstruction of complete microbial metabolic pathways using 'omics data from environmental samples remains challenging. Computational pipelines for pathway reconstruction that utilize machine learning methods to predict the presence or absence of KEGG modules in incomplete genomes are lacking. Here, we present MetaPathPredict, a software tool that incorporates machine learning models to predict the presence of complete KEGG modules within bacterial genomic datasets. Using gene annotation data and information from the KEGG module database, MetaPathPredict employs deep learning models to predict the presence of KEGG modules in a genome. MetaPathPredict can be used as a command line tool or as a Python module, and both options are designed to be run locally or on a compute cluster. Benchmarks show that MetaPathPredict makes robust predictions of KEGG module presence within highly incomplete genomes.

Ammonium-derived nitrous oxide is a global source in streams.

Global riverine nitrous oxide (N2O) emissions have increased more than 4-fold in the last century. It has been estimated that the hyporheic zones in small streams alone may contribute approximately 85% of these N2O emissions. However, the mechanisms and pathways controlling hyporheic N2O production in stream ecosystems remain unknown. Here, we report that ammonia-derived pathways, rather than the nitrate-derived pathways, are the dominant hyporheic N2O sources (69.6 ± 2.1%) in agricultural streams around the world. The N2O fluxes are mainly in positive correlation with ammonia. The potential N2O metabolic pathways of metagenome-assembled genomes (MAGs) provides evidence that nitrifying bacteria contain greater abundances of N2O production-related genes than denitrifying bacteria. Taken together, this study highlights the importance of mitigating agriculturally derived ammonium in low-order agricultural streams in controlling N2O emissions. Global models of riverine ecosystems need to better represent ammonia-derived pathways for accurately estimating and predicting riverine N2O emissions.

Systems biology of plant metabolic interactions.

Metabolism is the key cellular process of plant physiology. Understanding metabolism and its dynamical behavior under different conditions may help plant biotechnologists to design new cultivars with desired goals. Computational systems biochemistry and incorporation of different omics data unravelled active metabolism and its variations in plants. In this review, we mainly focus on the basics of flux balance analysis (FBA), elementary flux mode analysis (EFMA), and some advanced computational tools. We describe some important results that were obtained using these tools. Limitations and challenges are also discussed.

Study on plasma metabolomics profiling of depression in Chinese community-dwelling older adults based on untargeted LC/GC‒MS.

Depression is a serious psychiatric illness that causes great inconvenience to the lives of elderly individuals. However, the diagnosis of depression is somewhat subjective. Nontargeted gas chromatography (GC)/liquid chromatography (LC)-mass spectrometry (MS) was used to study the plasma metabolic profile and identify objective markers for depression and metabolic pathway variation. We recruited 379 Chinese community-dwelling individuals aged ≥ 65. Plasma samples were collected and detected by GC/LC‒MS. Orthogonal partial least squares discriminant analysis and a heatmap were utilized to distinguish the metabolites. Receiver operating characteristic curves were constructed to evaluate the diagnostic value of these differential metabolites. Additionally, metabolic pathway enrichment was performed to reveal metabolic pathway variation. According to our standard, 49 people were included in the depression cohort (DC), and 49 people age- and sex-matched individuals were included in the non-depression cohort (NDC). 64 metabolites identified via GC‒MS and 73 metabolites identified via LC‒MS had significant contributions to the differentiation between the DC and NDC, with VIP values > 1 and p values < 0.05. Three substances were detected by both methods: hypoxanthine, phytosphingosine, and xanthine. Furthermore, 1-(sn-glycero-3-phospho)-1D-myo-inositol had the largest area under the curve (AUC) value (AUC = 0.842). The purine metabolic pathway is the most important change in metabolic pathways. These findings show that there were differences in plasma metabolites between the depression cohort and the non-depression cohort. These identified differential metabolites may be markers of depression and can be used to study the changes in depression metabolic pathways.

A cautionary tale about properly vetting datasets used in supervised learning predicting metabolic pathway involvement.

The mapping of metabolite-specific data to pathways within cellular metabolism is a major data analysis step needed for biochemical interpretation. A variety of machine learning approaches, particularly deep learning approaches, have been used to predict these metabolite-to-pathway mappings, utilizing a training dataset of known metabolite-to-pathway mappings. A few such training datasets have been derived from the Kyoto Encyclopedia of Genes and Genomes (KEGG). However, several prior published machine learning approaches utilized an erroneous KEGG-derived training dataset that used SMILES molecular representations strings (KEGG-SMILES dataset) and contained a sizable proportion (~26%) duplicate entries. The presence of so many duplicates taint the training and testing sets generated from k-fold cross-validation of the KEGG-SMILES dataset. Therefore, the k-fold cross-validation performance of the resulting machine learning models was grossly inflated by the erroneous presence of these duplicate entries. Here we describe and evaluate the KEGG-SMILES dataset so that others may avoid using it. We also identify the prior publications that utilized this erroneous KEGG-SMILES dataset so their machine learning results can be properly and critically evaluated. In addition, we demonstrate the reduction of model k-fold cross-validation (CV) performance after de-duplicating the KEGG-SMILES dataset. This is a cautionary tale about properly vetting prior published benchmark datasets before using them in machine learning approaches. We hope others will avoid similar mistakes.

Dynamic evolution of the mTHF gene family associated with primary metabolism across life.

BACKGROUND: The folate cycle of one-carbon (C1) metabolism, which plays a central role in the biosynthesis of nucleotides and amino acids, demonstrates the significance of metabolic adaptation. We investigated the evolutionary history of the methylenetetrahydrofolate dehydrogenase (mTHF) gene family, one of the main drivers of the folate cycle, across life. RESULTS: Through comparative genomic and phylogenetic analyses, we found that several lineages of Archaea lacked domains vital for folate cycle function such as the mTHF catalytic and NAD(P)-binding domains of FolD. Within eukaryotes, the mTHF gene family diversified rapidly. For example, several duplications have been observed in lineages including the Amoebozoa, Opisthokonta, and Viridiplantae. In a common ancestor of Opisthokonta, FolD and FTHFS underwent fusion giving rise to the gene MTHFD1, possessing the domains of both genes. CONCLUSIONS: Our evolutionary reconstruction of the mTHF gene family associated with a primary metabolic pathway reveals dynamic evolution, including gene birth-and-death, gene fusion, and potential horizontal gene transfer events and/or amino acid convergence.

PacBio Hi-Fi genome assembly of Sipha maydis, a model for the study of multipartite mutualism in insects.

Dependence on multiple nutritional endosymbionts has evolved repeatedly in insects feeding on unbalanced diets. However, reference genomes for species hosting multi-symbiotic nutritional systems are lacking, even though they are essential for deciphering the processes governing cooperative life between insects and anatomically integrated symbionts. The cereal aphid Sipha maydis is a promising model for addressing these issues, as it has evolved a nutritional dependence on two bacterial endosymbionts that complement each other. In this study, we used PacBio High fidelity (HiFi) long-read sequencing to generate a highly contiguous genome assembly of S. maydis with a length of 410 Mb, 3,570 contigs with a contig N50 length of 187 kb, and BUSCO completeness of 95.5%. We identified 117 Mb of repetitive sequences, accounting for 29% of the genome assembly, and predicted 24,453 protein-coding genes, of which 2,541 were predicted enzymes included in an integrated metabolic network with the two aphid-associated endosymbionts. These resources provide valuable genetic and metabolic information for understanding the evolution and functioning of multi-symbiotic systems in insects.

Metabolic Rewiring in Cancer: Small Molecule Inhibitors in Colorectal Cancer Therapy.

Alterations in cellular metabolism, such as dysregulation in glycolysis, lipid metabolism, and glutaminolysis in response to hypoxic and low-nutrient conditions within the tumor microenvironment, are well-recognized hallmarks of cancer. Therefore, understanding the interplay between aerobic glycolysis, lipid metabolism, and glutaminolysis is crucial for developing effective metabolism-based therapies for cancer, particularly in the context of colorectal cancer (CRC). In this regard, the present review explores the complex field of metabolic reprogramming in tumorigenesis and progression, providing insights into the current landscape of small molecule inhibitors targeting tumorigenic metabolic pathways and their implications for CRC treatment.

Untargeted Metabolomics Based on Ultra-High-Performance Liquid Chromatography Coupled with Quadrupole Orbitrap High-Resolution Mass Spectrometry for Differential Metabolite Analysis of Pinelliae Rhizoma and Its Adulterants.

The present study investigates the chemical composition variances among Pinelliae Rhizoma, a widely used Chinese herbal medicine, and its common adulterants including Typhonium flagelliforme, Arisaema erubescens, and Pinellia pedatisecta. Utilizing the non-targeted metabolomics technique of employing UHPLC-Q-Orbitrap HRMS, this research aims to comprehensively delineate the metabolic profiles of Pinelliae Rhizoma and its adulterants. Multivariate statistical methods including PCA and OPLS-DA are employed for the identification of differential metabolites. Volcano plot analysis is utilized to discern upregulated and downregulated compounds. KEGG pathway analysis is conducted to elucidate the differences in metabolic pathways associated with these compounds, and significant pathway enrichment analysis is performed. A total of 769 compounds are identified through metabolomics analysis, with alkaloids being predominant, followed by lipids and lipid molecules. Significant differential metabolites were screened out based on VIP > 1 and p-value < 0.05 criteria, followed by KEGG enrichment analysis of these differential metabolites. Differential metabolites between Pinelliae Rhizoma and Typhonium flagelliforme, as well as between Pinelliae Rhizoma and Pinellia pedatisecta, are significantly enriched in the biosynthesis of amino acids and protein digestion and absorption pathways. Differential metabolites between Pinelliae Rhizoma and Arisaema erubescens are mainly enriched in tyrosine metabolism and phenylalanine metabolism pathways. These findings aim to provide valuable data support and theoretical references for further research on the pharmacological substances, resource development and utilization, and quality control of Pinelliae Rhizoma.

Loss of function of metabolic traits in typhoidal Salmonella without apparent genome degradation.

Salmonella enterica serovar Typhi and Paratyphi A are the cause of typhoid and paratyphoid fever in humans, which are systemic life-threatening illnesses. Both serovars are exclusively adapted to the human host, where they can cause life-long persistent infection. A distinct feature of these serovars is the presence of a relatively high number of degraded coding sequences coding for metabolic pathways, most likely a consequence of their adaptation to a single host. As a result of convergent evolution, these serovars shared many of the degraded coding sequences although often affecting different genes in the same metabolic pathway. However, there are several coding sequences that appear intact in one serovar while clearly degraded in the other, suggesting differences in their metabolic capabilities. Here, we examined the functionality of metabolic pathways that appear intact in S. Typhi but that show clear signs of degradation in S. Paratyphi A. We found that, in all cases, the existence of single amino acid substitutions in S. Typhi metabolic enzymes, transporters, or transcription regulators resulted in the inactivation of these metabolic pathways. Thus, the inability of S. Typhi to metabolize Glucose-6-Phosphate or 3-phosphoglyceric acid is due to the silencing of the expression of the genes encoding the transporters for these compounds due to point mutations in the transcriptional regulatory proteins. In contrast, its inability to utilize glucarate or galactarate is due to the presence of point mutations in the transporter and enzymes necessary for the metabolism of these sugars. These studies provide additional support for the concept of adaptive convergent evolution of these two human-adapted S. enterica serovars and highlight a limitation of bioinformatic approaches to predict metabolic capabilities. IMPORTANCE: Salmonella enterica serovar Typhi and Paratyphi A are the cause of typhoid and paratyphoid fever in humans, which are systemic life-threatening illnesses. Both serovars can only infect the human host, where they can cause life-long persistent infection. Because of their adaptation to the human host, these bacterial pathogens have changed their metabolism, leading to the loss of their ability to utilize certain nutrients. In this study we examined the functionality of metabolic pathways that appear intact in S. Typhi but that show clear signs of degradation in S. Paratyphi A. We found that, in all cases, the existence of single amino acid substitutions in S. Typhi metabolic enzymes, transporters, or transcription regulators resulted in the inactivation of these metabolic pathways. These studies provide additional support for the concept of adaptive convergent evolution of these two human-adapted S. enterica serovars.

Exploring the regulatory role of non-coding RNAs in fiber development and direct regulation of GhKCR2 in the fatty acid metabolic pathway in upland cotton.

Cotton fiber holds immense importance as the primary raw material for the textile industry. Consequently, comprehending the regulatory mechanisms governing fiber development is pivotal for enhancing fiber quality. Our study aimed to construct a regulatory network of competing endogenous RNAs (ceRNAs) and assess the impact of non-coding RNAs on gene expression throughout fiber development. Through whole transcriptome data analysis, we identified differentially expressed genes (DEGs) regulated by non-coding RNA (ncRNA) that were predominantly enriched in phenylpropanoid biosynthesis and the fatty acid elongation pathway. This analysis involved two contrasting phenotypic materials (J02-508 and ZRI015) at five stages of fiber development. Additionally, we conducted a detailed analysis of genes involved in fatty acid elongation, including KCS, KCR, HACD, ECR, and ACOT, to unveil the factors contributing to the variation in fatty acid elongation between J02-508 and ZRI015. Through the integration of histochemical GUS staining, dual luciferase assay experiments, and correlation analysis of expression levels during fiber development stages for lncRNA MSTRG.44818.23 (MST23) and GhKCR2, we elucidated that MST23 positively regulates GhKCR2 expression in the fatty acid elongation pathway. This identification provides valuable insights into the molecular mechanisms underlying fiber development, emphasizing the intricate interplay between non-coding RNAs and protein-coding genes.

Microalgae enhanced co-metabolism of sulfamethoxazole using aquacultural feedstuff components: Co-metabolic pathways and enzymatic mechanisms.

The application of antibiotics in freshwater aquaculture leads to increased contamination of aquatic environments. However, limited information is available on the co-metabolic biodegradation of antibiotics by microalgae in aquaculture. Feedstuffs provide multiple organic substrates for microalgae-mediated co-metabolism. Herein, we investigated the co-metabolism of sulfamethoxazole (SMX) by Chlorella pyrenoidosa when adding main components of feedstuff (glucose and lysine). Results showed that lysine had an approximately 1.5-fold stronger enhancement on microalgae-mediated co-metabolism of SMX than glucose, with the highest removal rate (68.77% ± 0.50%) observed in the 9-mM-Lys co-metabolic system. Furthermore, we incorporated reactive sites predicted by density functional theory calculations, 14 co-metabolites identified by mass spectrometry, and the roles of 18 significantly activated enzymes to reveal the catalytic reaction mechanisms underlying the microalgae-mediated co-metabolism of SMX. In lysine- and glucose-treated groups, five similar co-metabolic pathways were proposed, including bond breaking on the nucleophilic sulfur atom, ring cleavage and hydroxylation at multiple free radical reaction sites, together with acylation and glutamyl conjugation on electrophilic nitrogen atoms. Cytochrome P450, serine hydrolase, and peroxidase play crucial roles in catalyzing hydroxylation, bond breaking, and ring cleavage of SMX. These findings provide theoretical support for better utilization of microalgae-driven co-metabolism to reduce sulfonamide antibiotic residues in aquaculture.

Changes and Significance of Central Carbon Metabolism before and after Metformin Treatment of Type 2 Diabetes Based on Mass Spectrometry.

BACKGROUND: An imbalance in energy metabolism serves as a causal factor for type 2 diabetes (T2D). Although metformin has been known to ameliorate the overall energy metabolism imbalance, but the direct correlation between metformin and central carbon metabolism (CCM) has not been thoroughly investigated. In this study, we employed a high-performance ion chromatography-tandem mass spectrometry (HPIC-MS/MS) technique to examine the alterations and significance of CCM both before and after metformin treatment for T2D. METHODS: We recruited 29 participants, comprising 10 individuals recently diagnosed with T2D (T2D group). Among these, 10 patients underwent a 4-6-week treatment with metformin (MET group). Additionally, we included 9 healthy subjects (CON group). Employing HPIC-MS/MS, we quantitatively analyzed 56 metabolites across 18 biologically relevant metabolic pathways associated with CCM. Univariate and multivariate statistical analyses were utilized to identify differential metabolites. Subsequently, correlation analyses and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were conducted on the identified differential metabolites. RESULTS: We identified seven distinct metabolites in individuals with T2D (p < 0.05). Notably, cyclic 3',5'-Adenosine MonoPhosphate (AMP), Glucose 6-phosphate, L-lactic acid, Maleic acid, and Malic acid exhibited a reversal to normal levels following metformin treatment. Furthermore, Malic acid demonstrated a positive correlation with L-lactic acid (r = 0.94, p < 0.05), as did succinic acid with malic acid (r = 0.81, p < 0.05), L-lactic acid with succinic acid (r = 0.78, p < 0.05), and L-lactic acid with glucose-6-phosphate (r = 0.72, p < 0.05). These metabolites were notably enriched in pyruvate metabolism (p = 0.005), tricarboxylic acid cycle (TCA) (p = 0.007), propanoate metabolism (p = 0.007), and glycolysis or gluconeogenesis (p = 0.009), respectively. CONCLUSIONS: We employed HPIC-MS/MS to uncover alterations in CCM among individuals recently diagnosed with T2D before and after metformin treatment. The findings suggest that metformin may ameliorate the energy metabolism imbalance in T2D by reducing intermediates within the CCM pathway.

The Fur-like regulatory protein MAP3773c modulates key metabolic pathways in Mycobacterium avium subsp. paratuberculosis under in-vitro iron starvation.

Johne's disease (JD) is a chronic enteric infection of dairy cattle worldwide. Mycobacterium avium subsp. paratuberculosis (MAP), the causative agent of JD, is fastidious often requiring eight to sixteen weeks to produce colonies in culture-a major hurdle in the diagnosis and therefore in implementation of optimal JD control measures. A significant gap in knowledge is the comprehensive understanding of the metabolic networks deployed by MAP to regulate iron both in-vitro and in-vivo. The genome of MAP carries MAP3773c, a putative metal regulator, which is absent in all other mycobacteria. The role of MAP3773c in intracellular iron regulation is poorly understood. In the current study, a field isolate (K-10) and an in-frame MAP3773c deletion mutant (ΔMAP3773c) derived from K-10, were exposed to iron starvation for 5, 30, 60, and 90 min and RNA-Seq was performed. A comparison of transcriptional profiles between K-10 and ΔMAP3773c showed 425 differentially expressed genes (DEGs) at 30 min time post-iron restriction. Functional analysis of DEGs in ΔMAP3773c revealed that pantothenate (Pan) biosynthesis, polysaccharide biosynthesis and sugar metabolism genes were downregulated at 30 min post-iron starvation whereas ATP-binding cassette (ABC) type metal transporters, putative siderophore biosynthesis, PPE and PE family genes were upregulated. Pathway analysis revealed that the MAP3773c knockout has an impairment in Pan and Coenzyme A (CoA) biosynthesis pathways suggesting that the absence of those pathways likely affect overall metabolic processes and cellular functions, which have consequences on MAP survival and pathogenesis.

Spatial transcriptome-guided multi-scale framework connects P. aeruginosa metabolic states to oxidative stress biofilm microenvironment.

With the generation of spatially resolved transcriptomics of microbial biofilms, computational tools can be used to integrate this data to elucidate the multi-scale mechanisms controlling heterogeneous biofilm metabolism. This work presents a Multi-scale model of Metabolism In Cellular Systems (MiMICS) which is a computational framework that couples a genome-scale metabolic network reconstruction (GENRE) with Hybrid Automata Library (HAL), an existing agent-based model and reaction-diffusion model platform. A key feature of MiMICS is the ability to incorporate multiple -omics-guided metabolic models, which can represent unique metabolic states that yield different metabolic parameter values passed to the extracellular models. We used MiMICS to simulate Pseudomonas aeruginosa regulation of denitrification and oxidative stress metabolism in hypoxic and nitric oxide (NO) biofilm microenvironments. Integration of P. aeruginosa PA14 biofilm spatial transcriptomic data into a P. aeruginosa PA14 GENRE generated four PA14 metabolic model states that were input into MiMICS. Characteristic of aerobic, denitrification, and oxidative stress metabolism, the four metabolic model states predicted different oxygen, nitrate, and NO exchange fluxes that were passed as inputs to update the agent's local metabolite concentrations in the extracellular reaction-diffusion model. Individual bacterial agents chose a PA14 metabolic model state based on a combination of stochastic rules, and agents sensing local oxygen and NO. Transcriptome-guided MiMICS predictions suggested microscale denitrification and oxidative stress metabolic heterogeneity emerged due to local variability in the NO biofilm microenvironment. MiMICS accurately predicted the biofilm's spatial relationships between denitrification, oxidative stress, and central carbon metabolism. As simulated cells responded to extracellular NO, MiMICS revealed dynamics of cell populations heterogeneously upregulating reactions in the denitrification pathway, which may function to maintain NO levels within non-toxic ranges. We demonstrated that MiMICS is a valuable computational tool to incorporate multiple -omics-guided metabolic models to mechanistically map heterogeneous microbial metabolic states to the biofilm microenvironment.

Metabolic modeling of Halomonas campaniensis improves polyhydroxybutyrate production under nitrogen limitation.

Poly-hydroxybutyrate (PHB) is an environmentally friendly alternative for conventional fossil fuel-based plastics that is produced by various microorganisms. Large-scale PHB production is challenging due to the comparatively higher biomanufacturing costs. A PHB overproducer is the haloalkaliphilic bacterium Halomonas campaniensis, which has low nutritional requirements and can grow in cultures with high salt concentrations, rendering it resistant to contamination. Despite its virtues, the metabolic capabilities of H. campaniensis as well as the limitations hindering higher PHB production remain poorly studied. To address this limitation, we present HaloGEM, the first high-quality genome-scale metabolic network reconstruction, which encompasses 888 genes, 1528 reactions (1257 gene-associated), and 1274 metabolites. HaloGEM not only displays excellent agreement with previous growth data and experiments from this study, but it also revealed nitrogen as a limiting nutrient when growing aerobically under high salt concentrations using glucose as carbon source. Among different nitrogen source mixtures for optimal growth, HaloGEM predicted glutamate and arginine as a promising mixture producing increases of 54.2% and 153.4% in the biomass yield and PHB titer, respectively. Furthermore, the model was used to predict genetic interventions for increasing PHB yield, which were consistent with the rationale of previously reported strategies. Overall, the presented reconstruction advances our understanding of the metabolic capabilities of H. campaniensis for rationally engineering this next-generation industrial biotechnology platform. KEY POINTS: A comprehensive genome-scale metabolic reconstruction of H. campaniensis was developed. Experiments and simulations predict N limitation in minimal media under aerobiosis. In silico media design increased experimental biomass yield and PHB titer.