[Construction and analysis of brain metabolic network in temporal lobe epilepsy patients based on 18F-FDG PET].

The establishment of brain metabolic network is based on 18fluoro-deoxyglucose positron emission computed tomography ( 18F-FDG PET) analysis, which reflect the brain functional network connectivity in normal physiological state or disease state. It is now applied to basic and clinical brain functional network research. In this paper, we constructed a metabolic network for the cerebral cortex firstly according to 18F-FDG PET image data from patients with temporal lobe epilepsy (TLE).Then, a statistical analysis to the network properties of patients with left or right TLE and controls was performed. It is shown that the connectivity of the brain metabolic network is weakened in patients with TLE, the topology of the network is changed and the transmission efficiency of the network is reduced, which means the brain metabolic network connectivity is extensively impaired in patients with TLE. It is confirmed that the brain metabolic network analysis based on 18F-FDG PET can provide a new perspective for the diagnose and therapy of epilepsy by utilizing PET images.

Enteropathway: the metabolic pathway database for the human gut microbiota.

The human gut microbiota produces diverse, extensive metabolites that have the potential to affect host physiology. Despite significant efforts to identify metabolic pathways for producing these microbial metabolites, a comprehensive metabolic pathway database for the human gut microbiota is still lacking. Here, we present Enteropathway, a metabolic pathway database that integrates 3269 compounds, 3677 reactions, and 876 modules that were obtained from 1012 manually curated scientific literature. Notably, 698 modules of these modules are new entries and cannot be found in any other databases. The database is accessible from a web application (https://enteropathway.org) that offers a metabolic diagram for graphical visualization of metabolic pathways, a customization interface, and an enrichment analysis feature for highlighting enriched modules on the metabolic diagram. Overall, Enteropathway is a comprehensive reference database that can complement widely used databases, and a tool for visual and statistical analysis in human gut microbiota studies and was designed to help researchers pinpoint new insights into the complex interplay between microbiota and host metabolism.

Strangers in a foreign land: 'Yeastizing' plant enzymes.

Expressing plant metabolic pathways in microbial platforms is an efficient, cost-effective solution for producing many desired plant compounds. As eukaryotic organisms, yeasts are often the preferred platform. However, expression of plant enzymes in a yeast frequently leads to failure because the enzymes are poorly adapted to the foreign yeast cellular environment. Here, we first summarize the current engineering approaches for optimizing performance of plant enzymes in yeast. A critical limitation of these approaches is that they are labour-intensive and must be customized for each individual enzyme, which significantly hinders the establishment of plant pathways in cellular factories. In response to this challenge, we propose the development of a cost-effective computational pipeline to redesign plant enzymes for better adaptation to the yeast cellular milieu. This proposition is underpinned by compelling evidence that plant and yeast enzymes exhibit distinct sequence features that are generalizable across enzyme families. Consequently, we introduce a data-driven machine learning framework designed to extract 'yeastizing' rules from natural protein sequence variations, which can be broadly applied to all enzymes. Additionally, we discuss the potential to integrate the machine learning model into a full design-build-test cycle.

Exploring Fatty Acid ß-Oxidation Pathways in Bacteria: From General Mechanisms to DSF Signaling and Pathogenicity in Xanthomonas.

Fatty acids (FAs) participate in extensive physiological activities such as energy metabolism, transcriptional control, and cell signaling. In bacteria, FAs are degraded and utilized through various metabolic pathways, including ß-oxidation. Over the past ten years, significant progress has been made in studying FA oxidation in bacteria, particularly in E. coli, where the processes and roles of FA ß-oxidation have been comprehensively elucidated. Here, we provide an update on the new research achievements in FAs ß-oxidation in bacteria. Using Xanthomonas as an example, we introduce the oxidation process and regulation mechanism of the DSF-family quorum sensing signal. Based on current findings, we propose the specific enzymes required for ß-oxidation of several specific FAs. Finally, we discuss the future outlook on scientific issues that remain to be addressed. This paper supplies theoretical guidance for further study of the FA ß-oxidation pathway with particular emphasis on its connection to the pathogenicity mechanisms of bacteria.

Metabolic ripple effects - deciphering how lipid metabolism in cancer interfaces with the tumor microenvironment.

Cancer cells require a constant supply of lipids. Lipids are a diverse class of hydrophobic molecules that are essential for cellular homeostasis, growth and survival, and energy production. How tumors acquire lipids is under intensive investigation, as these mechanisms could provide attractive therapeutic targets for cancer. Cellular lipid metabolism is tightly regulated and responsive to environmental stimuli. Thus, lipid metabolism in cancer is heavily influenced by the tumor microenvironment. In this Review, we outline the mechanisms by which the tumor microenvironment determines the metabolic pathways used by tumors to acquire lipids. We also discuss emerging literature that reveals that lipid availability in the tumor microenvironment influences many metabolic pathways in cancers, including those not traditionally associated with lipid biology. Thus, metabolic changes instigated by the tumor microenvironment have 'ripple' effects throughout the densely interconnected metabolic network of cancer cells. Given the interconnectedness of tumor metabolism, we also discuss new tools and approaches to identify the lipid metabolic requirements of cancer cells in the tumor microenvironment and characterize how these requirements influence other aspects of tumor metabolism.

Identification of key metabolism-related genes and pathways in spontaneous preterm birth: combining bioinformatic analysis and machine learning.

Background: Spontaneous preterm birth (sPTB) is a global disease that is a leading cause of death in neonates and children younger than 5 years of age. However, the etiology of sPTB remains poorly understood. Recent evidence has shown a strong association between metabolic disorders and sPTB. To determine the metabolic alterations in sPTB patients, we used various bioinformatics methods to analyze the abnormal changes in metabolic pathways in the preterm placenta via existing datasets. Methods: In this study, we integrated two datasets (GSE203507 and GSE174415) from the NCBI GEO database for the following analysis. We utilized the "Deseq2" R package and WGCNA for differentially expressed genes (DEGs) analysis; the identified DEGs were subsequently compared with metabolism-related genes. To identify the altered metabolism-related pathways and hub genes in sPTB patients, we performed multiple functional enrichment analysis and applied three machine learning algorithms, LASSO, SVM-RFE, and RF, with the hub genes that were verified by immunohistochemistry. Additionally, we conducted single-sample gene set enrichment analysis to assess immune infiltration in the placenta. Results: We identified 228 sPTB-related DEGs that were enriched in pathways such as arachidonic acid and glutathione metabolism. A total of 3 metabolism-related hub genes, namely, ANPEP, CKMT1B, and PLA2G4A, were identified and validated in external datasets and experiments. A nomogram model was developed and evaluated with 3 hub genes; the model could reliably distinguish sPTB patients and term labor patients with an area under the curve (AUC) > 0.75 for both the training and validation sets. Immune infiltration analysis revealed immune dysregulation in sPTB patients. Conclusion: Three potential hub genes that influence the occurrence of sPTB through shadow participation in placental metabolism were identified; these results provide a new perspective for the development and targeting of treatments for sPTB.

Metabolic Characteristics of Gut Microbiota and Insomnia: Evidence from a Mendelian Randomization Analysis.

Insomnia is a common sleep disorder that significantly impacts individuals' sleep quality and daily life. Recent studies have suggested that gut microbiota may influence sleep through various metabolic pathways. This study aims to explore the causal relationships between the abundance of gut microbiota metabolic pathways and insomnia using Mendelian randomization (MR) analysis. This two-sample MR study used genetic data from the OpenGWAS database (205 gut bacterial pathway abundance) and the FinnGen database (insomnia-related data). We identified single nucleotide polymorphisms (SNPs) associated with gut bacterial pathway abundance as instrumental variables (IVs) and ensured their validity through stringent selection criteria and quality control measures. The primary analysis employed the inverse variance-weighted (IVW) method, supplemented by other MR methods, to estimate causal effects. The MR analysis revealed significant positive causal effects of specific carbohydrate, amino acid, and nucleotide metabolism pathways on insomnia. Key pathways, such as gluconeogenesis pathway (GLUCONEO.PWY) and TCA cycle VII acetate producers (PWY.7254), showed positive associations with insomnia (B > 0, p < 0.05). Conversely, pathways like hexitol fermentation to lactate, formate, ethanol and acetate pathway (P461.PWY) exhibited negative causal effects (B < 0, p < 0.05). Multivariable MR analysis confirmed the independent causal effects of these pathways (p < 0.05). Sensitivity analyses indicated no significant pleiotropy or heterogeneity, ensuring the robustness of the results. This study identifies specific gut microbiota metabolic pathways that play critical roles in the development of insomnia. These findings provide new insights into the biological mechanisms underlying insomnia and suggest potential targets for therapeutic interventions. Future research should further validate these causal relationships and explore how modulating gut microbiota or its metabolic products can effectively improve insomnia symptoms, leading to more personalized and precise treatment strategies.

Untargeted metabolomics combined with chemometrics reveals distinct metabolic profiles across two sparkling cider fermentation stages.

This study aimed to examine the metabolic profiles of Saccharomyces cerevisiae yeasts (WLS21 and Y41) in two phases of sparkling cider making (normal and pressure fermentation) by combining untargeted metabolomic with chemometrics. The results showed that of the 634 nonvolatile metabolites identified using LC-MS and 83 volatile metabolites identified by GC-MS, the differential metabolites were 226 and 54, respectively. Metabolic pathway and correlation analyses showed that aspartic acid, phenylalanine and tyrosine, glutamic acid and purine metabolism were associated with flavor formation. The pressure fermentation process increased apigenin, naringenin, toxifolin, pyridoxine and thiamine contents in the final cider. These findings provide useful information and new research ideas for the formation of flavor in sparkling cider and the regulation of phenolic and vitamin production by microbial stress fermentation.

Understanding microbial biomineralization at the molecular level: recent advances.

Microbial biomineralization is a phenomenon involving deposition of inorganic minerals inside or around microbial cells as a direct consequence of biogeochemical cycling. The microbial metabolic processes often create environmental conditions conducive for the precipitation of silicate, carbonate or phosphate, ferrate forms of ubiquitous inorganic ions. Till date the fundamental mechanisms underpinning two of the major types of microbial biomineralization such as, microbially controlled and microbially induced remains poorly understood. While microbially-controlled mineralization (MCM) depends entirely on the genetic makeup of the cell, microbially-induced mineralization (MIM) is dependent on factors such as cell morphology, cell surface structures and extracellular polymeric substances (EPS). In recent years, the organic template-mediated nucleation of inorganic minerals has been considered as an underlying mechanism based on the principles of solid-state bioinorganic chemistry. The present review thus attempts to provide a comprehensive and critical overview on the recent progress in holistic understanding of both MCM and MIM, which involves, organic-inorganic biomolecular interactions that lead to template formation, biomineral nucleation and crystallization. Also, the operation of specific metabolic pathways and molecular operons in directing microbial biomineralization have been discussed. Unravelling these molecular mechanisms of biomineralization can help in the biomimetic synthesis of minerals for potential therapeutic applications, and facilitating the engineering of microorganisms for commercial production of biominerals.

A rapid and reliable targeted LC-MS/MS method for quantitative analysis of the Tryptophan-NAD metabolic network disturbances in tissues and blood of sleep deprivation mice.

BACKGROUND: TRY-NAD metabolic network includes TRY (tryptophan), 5-HT (5-hydroxytryptamine), KYN (kynurenine), and NAD (nicotinamide adenine dinucleotide) pathway, which plays a significant role in neurological diseases and ageing. It is important to monitor these metabolites for studying the pathological anatomy of disease and treatment of responses evaluation. Although previous studies have reported quantitative methods for several metabolites in the network, the bottlenecks of simultaneously quantifying the whole metabolic network are their similar structures, diverse physico-chemical properties, and instability. Standardized protocols for the whole metabolic network are still missing, which hinders the in-depth study of TRY-NAD metabolic network in laboratory research and clinical screening. RESULTS: We developed a LC-MS/MS method for quantifying 28 metabolites in the TRY-NAD network simultaneously. Optimization was done for the mass spectral parameters, chromatographic conditions and sample pretreatment process. The developed method was fully validated in terms of standard curves, sensitivity, carryover, recovery, matrix effect, accuracy, precision, and stability. The pretreatment of 30 samples only takes 90 min, and the LC-MS/MS running time of one sample is only 13 min. With this method, we bring to light the chaos of global TRY-NAD metabolic network in sleep deprivation mice for the first time, including serum, clotted blood cells, hippocampus, cerebral cortex, and liver. NAD pathway levels in brain and blood decreased, whereas the opposite happened in the liver. The 5-HT pathway decreased and the concentration of KYN increased significantly in the brain. The concentration of many metabolites in KYN pathway (NAD+ de novo synthesis pathway) increased in the liver. SIGNIFICANCE: This method is the first time to determine the metabolites of KYN, 5-HT and NAD pathway at the same time, and it is found that TRY-NAD metabolic network will be disordered after sleep deprivation. This work clarifies the importance of the pH of the extraction solution, the time and temperature control in pretreatment in standardized protocols building, and overcoming the problems of inconsistent sample pretreatment, separation, matrix effect interference and potential metabolite degradation. This method exhibits great prospects in providing more information on metabolic disturbances caused by sleep deprivation as well as neurological diseases and ageing.

Reconstruction and Analysis of a Genome-Scale Metabolic Model of Acinetobacter lwoffii.

Acinetobacter lwoffii is widely considered to be a harmful bacterium that is resistant to medicines and disinfectants. A. lwoffii NL1 degrades phenols efficiently and shows promise as an aromatic compound degrader in antibiotic-contaminated environments. To gain a comprehensive understanding of A. lwoffii, the first genome-scale metabolic model of A. lwoffii was constructed using semi-automated and manual methods. The iNX811 model, which includes 811 genes, 1071 metabolites, and 1155 reactions, was validated using 39 unique carbon and nitrogen sources. Genes and metabolites critical for cell growth were analyzed, and 12 essential metabolites (mainly in the biosynthesis and metabolism of glycan, lysine, and cofactors) were identified as antibacterial drug targets. Moreover, to explore the metabolic response to phenols, metabolic flux was simulated by integrating transcriptomics, and the significantly changed metabolism mainly included central carbon metabolism, along with some transport reactions. In addition, the addition of substances that effectively improved phenol degradation was predicted and validated using the model. Overall, the reconstruction and analysis of model iNX811 helped to study the antimicrobial systems and biodegradation behavior of A. lwoffii.

Metabolic Pathways Affected in Patients Undergoing Hemodialysis and Their Relationship with Inflammation.

Worldwide, 3.9 million individuals rely on kidney replacement therapy. They experience heightened susceptibility to cardiovascular diseases and mortality, alongside an increased risk of infections and malignancies, with inflammation being key to explaining this intensified risk. This study utilized semi-targeted metabolomics to explore novel metabolic pathways related to inflammation in this population. We collected pre- and post-session blood samples of patients who had already undergone one year of chronic hemodialysis and used liquid chromatography and high-resolution mass spectrometry to perform a metabolomic analysis. Afterwards, we employed both univariate (Mann-Whitney test) and multivariate (logistic regression with LASSO regularization) to identify metabolites associated with inflammation. In the univariate analysis, indole-3-acetaldehyde, 2-ketobutyric acid, and urocanic acid showed statistically significant decreases in median concentrations in the presence of inflammation. In the multivariate analysis, metabolites positively associated with inflammation included allantoin, taurodeoxycholic acid, norepinephrine, pyroglutamic acid, and L-hydroorotic acid. Conversely, metabolites showing negative associations with inflammation included benzoic acid, indole-3-acetaldehyde, methionine, citrulline, alphaketoglutarate, n-acetyl-ornithine, and 3-4-dihydroxibenzeneacetic acid. Non-inflamed patients exhibit preserved autophagy and reduced mitochondrial dysfunction. Understanding inflammation in this group hinges on the metabolism of arginine and the urea cycle. Additionally, the microbiota, particularly uricase-producing bacteria and those metabolizing tryptophan, play critical roles.

Gastric Cancer and Intestinal Metaplasia: Differential Metabolic Landscapes and New Pathways to Diagnosis.

Gastric cancer (GC) is the fifth most common cause of cancer-related death worldwide. Early detection is crucial for improving survival rates and treatment outcomes. However, accurate GC-specific biomarkers remain unknown. This study aimed to identify the metabolic differences between intestinal metaplasia (IM) and GC to determine the pathways involved in GC. A metabolic analysis of IM and tissue samples from 37 patients with GC was conducted using ultra-performance liquid chromatography with tandem mass spectrometry. Overall, 665 and 278 significant features were identified in the aqueous and 278 organic phases, respectively, using false discovery rate analysis, which controls the expected proportion of false positives among the significant results. sPLS-DA revealed a clear separation between IM and GC samples. Steroid hormone biosynthesis, tryptophan metabolism, purine metabolism, and arginine and proline metabolism were the most significantly altered pathways. The intensity of 11 metabolites, including N1, N2-diacetylspermine, creatine riboside, and N-formylkynurenine, showed significant elevation in more advanced GC. Based on pathway enrichment analysis and cancer stage-specific alterations, we identified six potential candidates as diagnostic biomarkers: aldosterone, N-formylkynurenine, guanosine triphosphate, arginine, S-adenosylmethioninamine, and creatine riboside. These metabolic differences between IM and GC provide valuable insights into gastric carcinogenesis. Further validation is needed to develop noninvasive diagnostic tools and targeted therapies to improve the outcomes of patients with GC.

Aging-Related Metabolome Analysis of the Masseter Muscle in Senescence-Accelerated Mouse-Prone 8.

Frailty is a vulnerable state that marks the transition to long-term care for older people. Early detection and prevention of sarcopenia, the main symptom of frailty, are important to ensure an excellent quality of life for older people. Recently, the relationship between frailty, sarcopenia, and oral function has been attracting attention. This study aimed to clarify the changes in metabolites and metabolic pathways due to aging in the masseter muscle of senescence-accelerated mouse-prone 8 (SAMP8) mice. A capillary electrophoresis-mass spectrometry metabolome analysis was performed on the masseter muscle of 12-week-old, 40-week-old, and 55-week-old mice. The expression of enzymes involved in metabolome pathways considered to be related to aging was confirmed using reverse transcription polymerase chain reaction. Clear metabolic fluctuations were observed between 12, 40-week-old, and 55-week-old SAMP8 mice. The extracted metabolic pathways were the glycolysis, polyamine metabolome, and purine metabolome pathways. Nine fluctuated metabolites were common among the groups. Spermidine and Val were increased, which was regarded as a characteristic change in the masseter muscle due to aging. In conclusion, the age-related metabolic pathways in SAMP8 mice were the glycolysis, polyamine metabolome, and purine metabolome pathways. The increased spermidine and Val levels in the masseter muscle compared with the lower limbs are characteristic changes.

Hyperpolarized 13C NMR Reveals Pathway Regulation in Lactococcus lactis and Metabolic Similarities and Differences Across the Tree of Life.

The control of metabolic networks is incompletely understood, even for glycolysis in highly studied model organisms. Direct real-time observations of metabolic pathways can be achieved in cellular systems with 13C NMR using dissolution Dynamic Nuclear Polarization (dDNP NMR). The method relies on a short-lived boost of NMR sensitivity using a redistribution of nuclear spin states to increase the alignment of the magnetic moments by more than four orders of magnitude. This temporary boost in sensitivity allows detection of metabolism with sub-second time resolution. Here, we hypothesized that dDNP NMR would be able to investigate molecular phenotypes that are not easily accessible with more conventional methods. The use of dDNP NMR allows real-time insight into carbohydrate metabolism in a Gram-positive bacterium (Lactoccocus lactis), and comparison to other bacterial, yeast and mammalian cells shows differences in the kinetic barriers of glycolysis across the kingdoms of life. Nevertheless, the accumulation of non-toxic precursors for biomass at kinetic barriers is found to be shared across the kingdoms of life. We further find that the visualization of glycolysis using dDNP NMR reveals kinetic characteristics in transgenic strains that are not evident when monitoring the overall glycolytic rate only. Finally, dDNP NMR reveals that resting Lactococcus lactis cells use the influx of carbohydrate substrate to produce acetoin rather than lactate during the start of glycolysis. This metabolic regime can be emulated using suitably designed substrate mixtures to enhance the formation of the C4 product acetoin more than 400-fold. Overall, we find that dDNP NMR provides analytical capabilities that may help to clarify the intertwined mechanistic determinants of metabolism and the optimal usage of biotechnologically important bacteria.

Metabolic network rewiring and temperature-dependent regulation for enhanced 3-dehydroshikimate production in Escherichia coli.

The cyclohexane organic acid 3-dehydroshikimate (DHS) has potent antioxidant activity and is widely utilised in chemical and pharmaceutical industries. However, its production requires a long fermentation with a suboptimal yield and low productivity, and a disproportionate growth-to-production ratio impedes the upscaling of DHS synthesis in microbial cell factories. To overcome these limitations, competing and degradation pathways were knocked-out and key enzymes were balanced in an engineered Escherichia coli production strain, resulting in 12.2 g/L DHS. Furthermore, to achieve equilibrium between cell growth and DHS production, a CRISPRi-based temperature-responsive multi-component repressor system was developed to dynamically control the expression of critical genes (pykF and aroE), resulting in a 30-fold increase in DHS titer. After 33 h fermentation in 5 L bioreactor, the DHS titer, productivity and yield reached 94.2 g/L, 2.8 g/L/h and 55 % glucose conversion, respectively. The results provided valuable insight into the production of DHS and its derivatives.

Chemiosmotic nutrient transport in synthetic cells powered by electrogenic antiport coupled to decarboxylation.

Cellular homeostasis depends on the supply of metabolic energy in the form of ATP and electrochemical ion gradients. The construction of synthetic cells requires a constant supply of energy to drive membrane transport and metabolism. Here, we provide synthetic cells with long-lasting metabolic energy in the form of an electrochemical proton gradient. Leveraging the L-malate decarboxylation pathway we generate a stable proton gradient and electrical potential in lipid vesicles by electrogenic L-malate/L-lactate exchange coupled to L-malate decarboxylation. By co-reconstitution with the transporters GltP and LacY, the synthetic cells maintain accumulation of L-glutamate and lactose over periods of hours, mimicking nutrient feeding in living cells. We couple the accumulation of lactose to a metabolic network for the generation of intermediates of the glycolytic and pentose phosphate pathways. This study underscores the potential of harnessing a proton motive force via a simple metabolic network, paving the way for the development of more complex synthetic systems.

BayesianSSA: a Bayesian statistical model based on structural sensitivity analysis for predicting responses to enzyme perturbations in metabolic networks.

BACKGROUND: Chemical bioproduction has attracted attention as a key technology in a decarbonized society. In computational design for chemical bioproduction, it is necessary to predict changes in metabolic fluxes when up-/down-regulating enzymatic reactions, that is, responses of the system to enzyme perturbations. Structural sensitivity analysis (SSA) was previously developed as a method to predict qualitative responses to enzyme perturbations on the basis of the structural information of the reaction network. However, the network structural information can sometimes be insufficient to predict qualitative responses unambiguously, which is a practical issue in bioproduction applications. To address this, in this study, we propose BayesianSSA, a Bayesian statistical model based on SSA. BayesianSSA extracts environmental information from perturbation datasets collected in environments of interest and integrates it into SSA predictions. RESULTS: We applied BayesianSSA to synthetic and real datasets of the central metabolic pathway of Escherichia coli. Our result demonstrates that BayesianSSA can successfully integrate environmental information extracted from perturbation data into SSA predictions. In addition, the posterior distribution estimated by BayesianSSA can be associated with the known pathway reported to enhance succinate export flux in previous studies. CONCLUSIONS: We believe that BayesianSSA will accelerate the chemical bioproduction process and contribute to advancements in the field.

Unveiling microbial dynamics in lung adenocarcinoma and adjacent nontumor tissues: insights from nicotine exposure and diverse clinical stages via nanopore sequencing technology.

Background: Lung is the largest mucosal area of the human body and directly connected to the external environment, facing microbial exposure and environmental stimuli. Therefore, studying the internal microorganisms of the lung is crucial for a deeper understanding of the relationship between microorganisms and the occurrence and progression of lung cancer. Methods: Tumor and adjacent nontumor tissues were collected from 38 lung adenocarcinoma patients and used nanopore sequencing technology to sequence the 16s full-length sequence of bacteria, and combining bioinformatics methods to identify and quantitatively analyze microorganisms in tissues, as well as to enrich the metabolic pathways of microorganisms. Results: the microbial composition in lung adenocarcinoma tissues is highly similar to that in adjacent tissues, but the alpha diversity is significantly lower than that in adjacent tissues. The difference analysis results show that the bacterial communities of Streptococcaceae, Lactobacillaceae, and Neisseriales were significantly enriched in cancer tissues. The results of metabolic pathway analysis indicate that pathways related to cellular communication, transcription, and protein synthesis were significantly enriched in cancer tissue. In addition, clinical staging analysis of nicotine exposure and lung cancer found that Haemophilus, paralinfluenzae, Streptococcus gordonii were significantly enriched in the nicotine exposure group, while the microbiota of Cardiobactereae and Cardiobacterales were significantly enriched in stage II tumors. The microbiota significantly enriched in IA-II stages were Neisseriaeae, Enterobacteriales, and Cardiobacterales, respectively. Conclusion: Nanopore sequencing technology was performed on the full length 16s sequence, which preliminarily depicted the microbial changes and enrichment of microbial metabolic pathways in tumor and adjacent nontumor tissues. The relationship between nicotine exposure, tumor progression, and microorganisms was explored, providing a theoretical basis for the treatment of lung cancer through microbial targets.

Applications of genome-scale metabolic models to the study of human diseases: A systematic review.

BACKGROUND AND OBJECTIVES: Genome-scale metabolic networks (GEMs) represent a valuable modeling and computational tool in the broad field of systems biology. Their ability to integrate constraints and high-throughput biological data enables the study of intricate metabolic aspects and processes of different cell types and conditions. The past decade has witnessed an increasing number and variety of applications of GEMs for the study of human diseases, along with a huge effort aimed at the reconstruction, integration and analysis of a high number of organisms. This paper presents a systematic review of the scientific literature, to pursue several important questions about the application of constraint-based modeling in the investigation of human diseases. Hopefully, this paper will provide a useful reference for researchers interested in the application of modeling and computational tools for the investigation of metabolic-related human diseases. METHODS: This systematic review was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Elsevier Scopus®, National Library of Medicine PubMed® and Clarivate Web of Science™ databases were enquired, resulting in 566 scientific articles. After applying exclusion and eligibility criteria, a total of 169 papers were selected and individually examined. RESULTS: The reviewed papers offer a thorough and up-to-date picture of the latest modeling and computational approaches, based on genome-scale metabolic models, that can be leveraged for the investigation of a large variety of human diseases. The numerous studies have been categorized according to the clinical research area involved in the examined disease. Furthermore, the paper discusses the most typical approaches employed to derive clinically-relevant information using the computational models. CONCLUSIONS: The number of scientific papers, utilizing GEM-based approaches for the investigation of human diseases, suggests an increasing interest in these types of approaches; hopefully, the present review will represent a useful reference for scientists interested in applying computational modeling approaches to investigate the aetiopathology of human diseases; we also hope that this work will foster the development of novel applications and methods for the discovery of clinically-relevant insights on metabolic-related diseases.