Systems Biology of Metabolism

The role of bacterial metabolism in human gut colonization

Can we anticipate the emergence of the next pandemic antibiotic-resistant bacterial clone? Addressing such an ambitious question relies on our ability to comprehensively understand the ecological and epidemiological factors fostering the evolution of high-risk clones. Among these factors, the ability to persistently colonize and thrive in the human gut is crucial for most high-risk clones. Nonetheless, the causes and mechanisms facilitating successful gut colonization remain obscure. Here, we review recent evidence that suggests that bacterial metabolism plays a pivotal role in determining the ability of high-risk clones to colonize the human gut. Subsequently, we outline novel approaches that enable the exploration of microbial metabolism at an unprecedented scale and level of detail. A thorough understanding of the constraints and opportunities of bacterial metabolism in gut colonization will foster our ability to predict the emergence of high-risk clones and take appropriate containment strategies.

Large Flux Supply of NAD(H) under Aerobic Conditions by Candida glycerinogenes

NAD is a redox coenzyme and is the center of energy metabolism. In metabolic engineering modifications, an insufficient NAD(H) supply often limits the accumulation of target products. In this study, Candida glycerinogenes was found to be able to supply NAD(H) in large fluxes, up to 7.6 times more than Saccharomyces cerevisiae in aerobic fermentation. Aerobic fermentation in a medium without amino nitrogen sources demonstrated that C. glycerinogenes NAD synthesis was not dependent on NAD precursors in the medium. Inhibition by antisense RNA and the detection of transcript levels indicated that the main NAD supply pathway is the de novo biosynthesis pathway. It was further demonstrated that NAD(H) supply was unaffected by changes in metabolic flow through C. glycerinogenes ΔGPD aerobic fermentation (80 g/L ethanol). In conclusion, the ability of C. glycerinogenes to supply NAD(H) in large fluxes provides a new approach to solving the NAD(H) supply problem in synthetic biology.

Intersection of Immunology and Metabolism in Myocardial Disease

Immunometabolism is an emerging field at the intersection of immunology and metabolism. Immune cell activation plays a critical role in the pathogenesis of cardiovascular diseases and is integral for regeneration during cardiac injury. We currently possess a limited understanding of the processes governing metabolic interactions between immune cells and cardiomyocytes. The impact of this intercellular crosstalk can manifest as alterations to the steady state flux of metabolites and impact cardiac contractile function. Although much of our knowledge is derived from acute inflammatory response, recent work emphasizes heterogeneity and flexibility in metabolism between cardiomyocytes and immune cells during pathological states, including ischemic, cardiometabolic, and cancer-associated disease. Metabolic adaptation is crucial because it influences immune cell activation, cytokine release, and potential therapeutic vulnerabilities. This review describes current concepts about immunometabolic regulation in the heart, focusing on intercellular crosstalk and intrinsic factors driving cellular regulation. We discuss experimental approaches to measure the cardio-immunologic crosstalk, which are necessary to uncover unknown mechanisms underlying the immune and cardiac interface. Deeper insight into these axes holds promise for therapeutic strategies that optimize cardioimmunology crosstalk for cardiac health.

Intracellular Flux Prediction of Recombinant Escherichia coli Producing Gamma-Aminobutyric Acid

Genome-scale metabolic model (GEM) can be used to simulate cellular metabolic phenotypes under various environmental or genetic conditions. This study utilized the GEM to observe the internal metabolic fluxes of recombinant Escherichia coli producing gamma-aminobutyric acid (GABA). Recombinant E. coli was cultivated in a fermenter under three conditions: pH 7, pH 5, and additional succinic acids. External fluxes were calculated from cultivation results, and internal fluxes were calculated through flux optimization. Based on the internal flux analysis, glycolysis and pentose phosphate pathways were repressed under cultivation at pH 5, even though glutamate dehydrogenase increased GABA production. Notably, this repression was halted by adding succinic acid. Furthermore, proper sucA repression is a promising target for developing strains more capable of producing GABA.

Building Synthetic Cells─From the Technology Infrastructure to Cellular Entities

The de novo construction of a living organism is a compelling vision. Despite the astonishing technologies developed to modify living cells, building a functioning cell "from scratch" has yet to be accomplished. The pursuit of this goal alone has─and will─yield scientific insights affecting fields as diverse as cell biology, biotechnology, medicine, and astrobiology. Multiple approaches have aimed to create biochemical systems manifesting common characteristics of life, such as compartmentalization, metabolism, and replication and the derived features, evolution, responsiveness to stimuli, and directed movement. Significant achievements in synthesizing each of these criteria have been made, individually and in limited combinations. Here, we review these efforts, distinguish different approaches, and highlight bottlenecks in the current research. We look ahead at what work remains to be accomplished and propose a "roadmap" with key milestones to achieve the vision of building cells from molecular parts.

Ogataea polymorpha as a next-generation chassis for industrial biotechnology

Ogataea (Hansenula) polymorpha is a nonconventional yeast with some unique characteristics, including fast growth, thermostability, and broad substrate spectrum. Other than common applications for protein production, O. polymorpha is attracting interest for chemical and protein production from methanol; a promising feedstock for the next-generation biomanufacturing due to its abundant sources and excellent characteristics. Benefiting from the development of synthetic biology, it has been engineered to produce value-added chemicals by extensively rewiring cellular metabolism. This Review discusses recently developed synthetic biology tools of O. polymorpha. The advances of chemicals production and systems biology were reviewed comprehensively. Finally, we look ahead to the developments of biomanufacturing in O. polymorpha to make an overall understanding of this chassis for academia and industry.

Sevoflurane Alters Serum Metabolites in Elders and Aging Mice and Increases Inflammation in Hippocampus

Purpose

Postoperative cognitive dysfunction (POCD) is a central nervous system complication that occurs after anesthesia, particularly among the elderly. However, the neurological pathogenesis of postoperative cognitive dysfunction remains unclear. The aim of this study was to evaluate the effects of sevoflurane exposure on serum metabolites and hippocampal gene expression in elderly patients and aging mice by metabolomics and transcriptomic analysis and to explore the pathogenesis of sevoflurane induced POCD.

Patients and Methods

Human serum samples from five patients over 60 years old were collected before sevoflurane anesthesia and 1 hour after anesthesia. Besides, mice aged at 12 months (n=6 per group) were anesthetized with sevoflurane for 2 hours or with sham procedure. Subsequently, serum and hippocampal tissues were harvested for analysis. Further investigation into the relationship between isatin and neuroinflammation was conducted using BV2 microglial cells.

Results

Sevoflurane anesthesia led to the activation of inflammatory pathways, an increased presence of hippocampal astrocytes and microglia, and elevated expression of neuroinflammatory cytokines. Comparative analysis identified 12 differential metabolites that exhibited changes in both human and mouse serum post-sevoflurane anesthesia. Notably, isatin levels were significantly decreased after anesthesia. Notably, isatin levels significantly decreased after anesthesia, a factor known to stimulate proliferation and proinflammatory gene expression in microglia-the pivotal cell type in inflammatory responses.

Conclusion

Sevoflurane-induced alterations in serum metabolites in both elderly patients and aging mice, subsequently contributing to increased inflammation in the hippocampus.

Multi-Omics Approaches for Liver Reveal the Thromboprophylaxis Mechanism of Aspirin Eugenol Ester in Rat Thrombosis Model

Aspirin eugenol ester (AEE) is a novel medicinal compound synthesized by esterifying aspirin with eugenol using the pro-drug principle. Pharmacological and pharmacodynamic experiments showed that AEE had excellent thromboprophylaxis and inhibition of platelet aggregation. This study aimed to investigate the effect of AEE on the liver of thrombosed rats to reveal its mechanism of thromboprophylaxis. Therefore, a multi-omics approach was used to analyze the liver. Transcriptome results showed 132 differentially expressed genes (DEGs) in the AEE group compared to the model group. Proteome results showed that 159 differentially expressed proteins (DEPs) were identified in the AEE group compared to the model group. Six proteins including fibrinogen alpha chain (Fga), fibrinogen gamma chain (Fgg), fibrinogen beta chain (Fgb), orosomucoid 1 (Orm1), hemopexin (Hpx), and kininogen-2 (Kng2) were selected for parallel reaction monitoring (PRM) analysis. The results showed that the expression of all six proteins was upregulated in the model group compared with the control group. In turn, AEE reversed the upregulation trend of these proteins to some degree. Metabolome results showed that 17 metabolites were upregulated and 38 were downregulated in the model group compared to the control group. AEE could reverse the expression of these metabolites to some degree and make them back to normal levels. The metabolites were mainly involved in metabolic pathways, including linoleic acid metabolism, arachidonic acid metabolism, and the tricarboxylic acid (TCA) cycle. Comprehensive analyses showed that AEE could prevent thrombosis by inhibiting platelet activation, decreasing inflammation, and regulating amino acid and energy metabolism. In conclusion, AEE can have a positive effect on thrombosis-related diseases.

Advances in Research on Pig Salivary Analytes: A Window to Reveal Pig Health and Physiological Status

Saliva is an important exocrine fluid that is easy to collect and is a complex mixture of proteins and other molecules from multiple sources from which considerable biological information can be mined. Pig saliva, as an easily available biological liquid rich in bioactive ingredients, is rich in nucleic acid analytes, such as eggs, enzymes, amino acids, sugars, etc. The expression levels of these components in different diseases have received extensive attention, and the analysis of specific proteins, metabolites, and biological compositions in pig saliva has become a new direction for disease diagnosis and treatment. The study of the changes in analytes in pig saliva can provide a new strategy for early diagnosis, prognosis assessment, and treatment of diseases. In this paper, the detection methods and research progress of porcine salivary analytes are reviewed, the application and research progress of porcine salivary analytes in diseases are discussed, and the future application prospect is presented.

The Untargeted Metabolomics Reveals Differences in Energy Metabolism in Patients with Different Subtypes of Ischemic Stroke

AIMS

Ischemic stroke (IS) is the most common subtype of stroke. The risk factors and pathogenesis of IS are complex and varied due to different subtypes. Therefore, we used metabolomics technology to investigate the biomarkers and potential pathophysiological mechanisms of different subtypes of IS.

METHODS

We included 126 IS patients and divided them into two groups based on the TOAST classification: large-artery atherosclerosis (LAA) group (n = 87) and small-vessel occlusion (SVO) group (n = 39). Plasma metabolomics analysis was performed using liquid chromatography-high-resolution mass spectrometry (LC-HRMS) to identify metabolic profiles in LAA and SVO subtype IS patients and to determine metabolic differences between patients with the two subtypes of IS.

RESULTS

We identified 26 differential metabolites between LAA and SVO subtype IS. A multiple prediction model based on the plasm metabolites had good predictive ability for IS subtyping (AUC = 0.822, accuracy = 77.8%), with 12,13-DHOME being the most important differential metabolite in the model. The differential metabolic pathways between the two subtypes of IS patients included tricarboxylic acid (TCA) cycle, alanine, aspartate and glutamate metabolism, and pyruvate metabolism, mainly focused on energy metabolism.

CONCLUSION

12,13-DHOME emerged as the primary discriminatory metabolite between LAA and SVO subtypes of IS. In LAA subtype IS patients, energy metabolism, encompassing pyruvate metabolism and the TCA cycle, exhibited lower activity levels when compared to patients with the SVO subtype IS. The utilization of targeted metabolomics holds the potential to improve diagnostic accuracy for distinguishing stroke subtypes.

The identification of novel stroke-related sphingolipid biomarkers using UPLC-MS/MS

BACKGROUND

Stroke is a prominent contributor to global mortality and morbidity, thus necessitating the establishment of dependable diagnostic indicators. The objective of this study was to ascertain metabolites linked to sphingolipid metabolism and assess their viability as diagnostic markers for stroke.

METHODS

Two cohorts, consisting of 56 S patients and 56 healthy volunteers, were incorporated into this investigation. Metabolite data was obtained through the utilization of Ultra Performance Liquid Chromatography and Tandem Mass Spectrometry (UPLC-MS/MS). The mass spectrometry data underwent targeted analysis and quantitative evaluation utilizing the multiple reaction monitoring mode of triple quadrupole mass spectrometry. Various data analysis techniques, including Orthogonal Partial Least Squares-Discriminant Analysis (OPLS-DA), least absolute shrinkage and selection operator (LASSO) regression, Support Vector Machine (SVM), logistic regression, and Receiver Operating Characteristic (ROC) curves were employed.

RESULTS

A comprehensive analysis detected a total of 129 metabolites related to sphingolipid metabolism, encompassing ceramides, 1-phosphoceramides, phytoceramides, glycosphingolipids, sphingomyelins, and sphingomyelins. The implementation of OPLS-DA analysis revealed significant disparities between individuals with stroke and controls, as it successfully identified 31 metabolites that exhibited significant differential expression between the two groups. Furthermore, functional enrichment analysis indicated the participation of these metabolites in diverse biological processes. Six metabolic markers, namely CerP(d18:1/20:3), CerP(d18:1/18:1), CerP(d18:1/18:0), CerP(d18:1/16:0), SM(d18:1/26:1), and Cer(d18:0/20:0), were successfully validated as potential diagnostic markers for stroke. The utilization of ROC analysis further confirmed their diagnostic potential, while a logistic regression model incorporating these markers demonstrated robust efficacy in distinguishing stroke patients from healthy controls.

CONCLUSION

these identified metabolic markers exhibit clinical significance and hold promise as valuable tools for the diagnosis of stroke.

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

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

Network pharmacology combined with metabolomics to explore the mechanism for Lonicerae Japonicae flos against respiratory syncytial virus

BACKGROUND

Respiratory Syncytial Virus (RSV) stands out as a primary contributor to lower respiratory tract infections and hospitalizations, particularly in infants. Lonicerae japonicae flos (LJF), a traditional Chinese medicine renowned for its efficacy against various viral infections, including RSV, has been widely employed. Despite its common use, the precise therapeutic mechanism of LJF against RSV remains elusive. This study aimed to investigate the underlying mechanism of LJF against RSV through network pharmacology and metabolomics.

METHODS

In this study, based on network pharmacology, potential targets related to LJF and RSV were obtained from PubChem and Swiss Target Prediction. The core targets and pathways were established and verified by enrichment analysis and molecular docking. The anti-RSV efficacy of LJF was determined by in vitro experiments. Additionally, metabolomics analysis was integrated, allowing for the identification of differential metabolites and their correlation with targets following LJF treatment in the context of RSV infection.

RESULTS

A total of 23 active ingredients and 780 targets were obtained, of which 102 targets were associated with LJF anti-RSV. The construction of the corresponding Protein-Protein Interaction (PPI) network unveiled potential core targets, including STAT3, TNF, and AKT1. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis revealed that LJF's anti-RSV effects primarily involve key pathways such as the PI3K-Akt signaling pathway, EGFR tyrosine kinase inhibitor resistance, and FoxO signaling pathway. Molecular docking showed that ZINC03978781, 4,5'-Retro-.beta.,.beta.-Carotene -3,3'-dione, 4',5'-didehydro and 7-epi-Vogeloside had better binding ability. The cellular assay showed that the therapeutic index of LJF against RSV was 4.79. Furthermore, 18 metabolites were screened as potential biomarkers of LJF against RSV, and these metabolites were mainly involved in the pathways of purine metabolism, linoleic acid metabolism, alpha-linolenic acid metabolism, and other related pathways.

CONCLUSIONS

The intergration of network pharmacology and metabolomics can clarify the active targets and related pathways of LJF against RSV, which could provide a valuable reference for further research and clinical application of LJF.

Integrative proteomics and metabolomics analysis of non-observable acute effect level PM2.5 induced accumulative effects in AC16 cells

Chronic exposure to very low ambient PM2.5 has been linked to cardiovascular risks in epidemiological observation, which also brought doubts on its safety threshold. In this study, we approached this question by chronic exposure of AC16 to the non-observable acute effect level (NOAEL) PM2.5 5 μg/mL and its positive reference 50 μg/mL, respectively. The doses were respectively defined on the cell viabilities >95% (p = 0.354) and >90% (p = 0.004) when treated acutely (24 h). To mimic the long-term exposure, AC16 was cultured from the 1st to 30th generations and treated with PM2.5 24 h in every three generations. The integration of proteomic and metabolomic analysis was applied, and 212 proteins and 172 metabolites were significantly altered during the experiments. The NOAEL PM2.5 induced both dose- and time-dependent disruption, which showed the dynamic cellular proteomic response and oxidation accumulation, the main metabolomics changes were ribonucleotide, amino acid, and lipid metabolism that have involved in stressed gene expression, and starving for energy metabolism and lipid oxidation. In summary, these pathways interacted with the monotonically increasing oxidative stress and led to the accumulated damage in AC16 and implied that the safe threshold of PM2.5 may be non-existent when a long-term exposure occurred.

LC-MS/MS metabolomic profiling of the protective butylphthalide effect in cerebral ischemia/reperfusion mice

OBJECTIVES

This study was designed to investigate metabolic biomarker changes and related metabolic pathways of Butylphthalide (NBP) on cerebral ischemia/reperfusion.

METHODS

In this study, a mouse cerebral ischemia/reperfusion (I/R) model was prepared using the middle cerebral artery occlusion method, and neurobehavioral score and 2, 3, 5-triphenyltetrazolium chloride (TTC) staining experiments were used to confirm the obvious NBP anti-cerebral ischemia effect. The protective effect of NBP in the mouse cerebral I/R model and its metabolic pathway and mechanism were investigated using mouse blood samples.

RESULTS

The metabolic profiles of mice in the I/R+NBP, I/R, and sham groups were significantly different. Under the condition that I/R vs. sham was downregulated and I/R + NBP vs. I/R was upregulated, 88 differential metabolites, including estradiol, ubiquinone-2, 2-oxoarginine, and L-histidine trimethylbetaine, were screened and identified. The related metabolic pathways involved arginine and proline metabolism, oxidative phosphorylation, ubiquitin and other terpenoid-quinone biosynthesis, and estrogen signaling.

CONCLUSIONS

Metabolomics was used to elucidate the NBP mechanism in cerebral ischemia treatment in mice, revealing synergistic NBP pharmacological characteristics with multiple targets.

The Warburg Effect Explained: Integration of Enhanced Glycolysis with Heterogeneous Mitochondria to Promote Cancer Cell Proliferation

The Warburg effect is the long-standing riddle of cancer biology. How does aerobic glycolysis, inefficient in producing ATP, confer a growth advantage to cancer cells? A new evaluation of a large set of literature findings covering the Warburg effect and its yeast counterpart, the Crabtree effect, led to an innovative working hypothesis presented here. It holds that enhanced glycolysis partially inactivates oxidative phosphorylation to induce functional rewiring of a set of TCA cycle enzymes to generate new non-canonical metabolic pathways that sustain faster growth rates. The hypothesis has been structured by constructing two metabolic maps, one for cancer metabolism and the other for the yeast Crabtree effect. New lines of investigation, suggested by these maps, are discussed as instrumental in leading toward a better understanding of cancer biology in order to allow the development of more efficient metabolism-targeted anticancer drugs.

2023 International Olympic Committee's (IOC) consensus statement on Relative Energy Deficiency in Sport (REDs)

Relative Energy Deficiency in Sport (REDs) was first introduced in 2014 by the International Olympic Committee's expert writing panel, identifying a syndrome of deleterious health and performance outcomes experienced by female and male athletes exposed to low energy availability (LEA; inadequate energy intake in relation to exercise energy expenditure). Since the 2018 REDs consensus, there have been >170 original research publications advancing the field of REDs science, including emerging data demonstrating the growing role of low carbohydrate availability, further evidence of the interplay between mental health and REDs and more data elucidating the impact of LEA in males. Our knowledge of REDs signs and symptoms has resulted in updated Health and Performance Conceptual Models and the development of a novel Physiological Model. This Physiological Model is designed to demonstrate the complexity of either problematic or adaptable LEA exposure, coupled with individual moderating factors, leading to changes in health and performance outcomes. Guidelines for safe and effective body composition assessment to help prevent REDs are also outlined. A new REDs Clinical Assessment Tool-Version 2 is introduced to facilitate the detection and clinical diagnosis of REDs based on accumulated severity and risk stratification, with associated training and competition recommendations. Prevention and treatment principles of REDs are presented to encourage best practices for sports organisations and clinicians. Finally, methodological best practices for REDs research are outlined to stimulate future high-quality research to address important knowledge gaps.

Mapping the complexities of Relative Energy Deficiency in Sport (REDs): development of a physiological model by a subgroup of the International Olympic Committee (IOC) Consensus on REDs

The 2023 International Olympic Committee (IOC) consensus statement on Relative Energy Deficiency in Sport (REDs) notes that exposure to low energy availability (LEA) exists on a continuum between adaptable and problematic LEA, with a range of potential effects on both health and performance. However, there is variability in the outcomes of LEA exposure between and among individuals as well as the specific manifestations of REDs. We outline a framework for a 'systems biology' examination of the effect of LEA on individual body systems, with the eventual goal of creating an integrated map of body system interactions. We provide a template that systematically identifies characteristics of LEA exposure (eg, magnitude, duration, origin) and a variety of moderating factors (eg, medical history, diet and training characteristics) that could exacerbate or attenuate the type and severity of impairments to health and performance faced by an individual athlete. The REDs Physiological Model may assist the diagnosis of underlying causes of problems associated with LEA, with a personalised and nuanced treatment plan promoting compliance and treatment efficacy. It could also be used in the strategic prevention of REDs by drawing attention to scenarios of LEA in which impairments of health and performance are most likely, based on knowledge of the characteristics of the LEA exposure or moderating factors that may increase the risk of harmful outcomes. We challenge researchers and practitioners to create a unifying and dynamic physiological model for each body system that can be continuously updated and mapped as knowledge is gained.

The mechanisms governing mouse embryonic palate mesenchymal cells' proliferation associated with atRA-induced cleft palate in mice: insights from integrated transcriptomic and metabolomic analyses

While exposure to high levels of all-trans retinoic acid (atRA) during pregnancy is known to suppress murine embryonic palate mesenchymal (MEPM) cells proliferation and to result in cleft palate (CP) development, the underlying mechanisms are poorly understood. Accordingly, this study was designed with the goal of clarifying the etiological basis for atRA-induced CP. A murine model of CP was established via the oral administration of atRA to pregnant mice on gestational day (GD) 10.5, after which transcriptomic and metabolomic analyses were performed with the goal of clarifying the critical genes and metabolites associated with CP development through an integrated multi-omics approach. MEPM cells proliferation was altered by atRA exposure as expected, contributing to CP incidence. In total, 110 genes were differentially expressed in the atRA treatment groups, suggesting that atRA may influence key biological processes including stimulus, adhesion, and signaling-related activities. In addition, 133 differentially abundant metabolites were identified including molecules associated with ABC transporters, protein digestion and absorption, mTOR signaling pathway, and the TCA cycle, suggesting a link between these mechanisms and CP. Overall, combined analyses of these transcriptomic and metabolomic results suggested that the MAPK, calcium, PI3K-Akt, Wnt, and mTOR signaling pathways are particularly important pathways enriched in the palatal cleft under conditions of atRA exposure. Together, these integrated transcriptomic and metabolomic approaches provided new evidence with respect to the mechanisms underlying altered MEPM cells proliferation and signal transduction associated with atRA-induced CP, revealing a possible link between oxidative stress and these pathological changes.

Deep Characterization of Serum Metabolome Based on the Segment-Optimized Spectral-Stitching Direct-Infusion Fourier Transform Ion Cyclotron Resonance Mass Spectrometry Approach

Direct-infusion Fourier transform ion cyclotron resonance mass spectrometry (DI-FTICR MS) shows great promise for metabolomic analysis due to ultrahigh mass accuracy and resolution. However, most of the DI-FTICR MS approaches focused on high-throughput metabolomics analysis at the expense of sensitivity and resolution and the potential for metabolome characterization has not been fully explored. Here, we proposed a novel deep characterization approach of serum metabolome using a segment-optimized spectral-stitching DI-FTICR MS method integrated with high-confidence and database-independent formula assignments. With varied acquisition parameters for each segment, a highly efficient acquisition was achieved for the whole mass range with sub-ppm mass accuracy. In a pooled human serum sample, thousands of features were assigned with unambiguous formulas and possible candidates based on highly accurate mass measurements. Furthermore, a reaction network was used to select confidently unique formulas from possible candidates, which was constructed by unambiguous formulas and possible candidates connected by the formula differences resulting from biochemical and MS transformation. Compared with full-range and conventional segment acquisition, 8- and 1.2-fold increases in observed features were achieved, respectively. Assignment accuracy was 93-94% for both a standard mixture containing 190 metabolites and a spiked serum sample with the root mean square mass error of 0.15-0.16 ppm. In total, 3534 unequivocal neutral molecular formulas were assigned in the pooled serum sample, 35% of which are contained in the HMDB. This method offers great enhancement in the deep characterization of serum metabolome by DI-FTICR MS.

Integrated bioinformatics analysis reveals the function and prognostic value of OSBPL3 in hepatocellular carcinoma

Background

Liver hepatocellular carcinoma (LIHC), a variety of highly-aggressive malignancy, has been the major cause of cancer-related mortality. Recent studies have shown that oxysterol-binding protein-like 3 (OSBPL3) plays a crucial role in human cancers. Nevertheless, the specific functional roles and potential clinical values of OSBPL3 in LIHC are not completely known.

Methods

Multiple web portals and publicly available tools were used in this study. Comprehensive expression files of OSBPL3 in pan-cancers and the relationship between OSBPL3 expression and clinical traits of patients with LIHC were investigated using TCGA database through UALCAN platform. TIMER database was used to investigate the effect of OSBPL3 on the tumor immune infiltration status in LIHC. Moreover, LinkedOmics, STRING databases, and Gene Ontology analysis were utilized to select OSBPL3-related differentially expressed genes (DEGs) and construct a protein-protein interaction (PPI) network.

Results

Upregulated OSBPL3 was observed in LIHC tumor tissues compared with that in normal controls, especially in patients with higher grades and more advanced stages. Furthermore, overexpressed OSBPL3 was closely associated with poor clinical outcomes of patients with LIHC. Six hub genes were selected from the PPI network, which were significantly increased in LIHC and closely associated with poor prognosis. Pathway enrichment showed that OSBPL3-related DEGs were primarily enriched in protein binding, mitotic cytokinesis, inorganic anion transport, and I-kappaB kinase/NF-kappaB signaling processes.

Conclusions

OSBPL3 exerts critical functions in hepatocarcinogenesis and it could serve as an available biomarker and effective treatment target for LIHC.

Extracting functionally accurate context-specific models of Atlantic salmon metabolism

Constraint-based models (CBMs) are used to study metabolic network structure and function in organisms ranging from microbes to multicellular eukaryotes. Published CBMs are usually generic rather than context-specific, meaning that they do not capture differences in reaction activities, which, in turn, determine metabolic capabilities, between cell types, tissues, environments, or other conditions. Only a subset of a CBM's metabolic reactions and capabilities are likely to be active in any given context, and several methods have therefore been developed to extract context-specific models from generic CBMs through integration of omics data. We tested the ability of six model extraction methods (MEMs) to create functionally accurate context-specific models of Atlantic salmon using a generic CBM (SALARECON) and liver transcriptomics data from contexts differing in water salinity (life stage) and dietary lipids. Three MEMs (iMAT, INIT, and GIMME) outperformed the others in terms of functional accuracy, which we defined as the extracted models' ability to perform context-specific metabolic tasks inferred directly from the data, and one MEM (GIMME) was faster than the others. Context-specific versions of SALARECON consistently outperformed the generic version, showing that context-specific modeling better captures salmon metabolism. Thus, we demonstrate that results from human studies also hold for a non-mammalian animal and major livestock species.

Identification of a novel signature based on macrophage-related marker genes to predict prognosis and immunotherapeutic effects in hepatocellular carcinoma

Background

Tumor-related macrophages (TAMs) have emerged as an essential part of the immune regulatory network in hepatocellular carcinoma (HCC). Constructing a TAM-related signature is significant for evaluating prognosis and immunotherapeutic response of HCC patients.

Methods

Informative single-cell RNA sequencing (scRNA-seq) dataset was obtained from the Gene Expression Omnibus (GEO) database, and diverse cell subpopulations were identified by clustering dimension reduction. Moreover, we determined molecular subtypes with the best clustering efficacy by calculating the cumulative distribution function (CDF). The ESTIMATE method, CIBERSORT (cell-type identification by estimating relative subsets of RNA transcripts) algorithm and publicly available tumor immune dysfunction and exclusion (TIDE) tools were used to characterize the immune landscape and tumor immune escape status. A TAM-related gene risk model was constructed through Cox regression and verified in multiple datasets and dimensions. We also performed functional enrichment analysis to detect potential signaling pathways related to TAM marker genes.

Results

In total, 10 subpopulations and 165 TAM-related marker genes were obtained from the scRNA-seq dataset (GSE149614). After clustering 3 molecular subtypes based on TAM-related marker genes, we found significantly different prognostic survival and immune signatures among the three subtypes. Subsequently, a 9-gene predictive signature (TPP1, FTL, CXCL8, CD68, ATP6V1F, CSTB, YBX1, LGALS3, and APLP2) was identified as an independent prognostic factor for HCC patients. Those patients with high RiskScore had a lower survival rate and benefited less from immunotherapy than those with low RiskScore. Moreover, more samples of the Cluster C subtype were enriched in the high-risk group, with higher tumor immune escape incidence.

Conclusions

We constructed a TAM-related signature with excellent efficacy for predicting prognostic survival and immunotherapeutic responses in HCC patients.

Serum metabolomic analyses reveal the potential metabolic biomarkers for prediction of amatoxin poisoning

Amatoxin poisoning leads to over 90% of deaths in mushroom poisoning. The objective of present study was to identify the potential metabolic biomarkers for early diagnosis of amatoxin poisoning. Serum samples were collected from 61 patients with amatoxin poisoning and 61 healthy controls. An untargeted metabolomics analysis was performed using the ultra-high-performance liquid chromatography-quadrupole time-of-flight tandem mass spectrometry (UPLC-QTOF-MS/MS). Multivariate statistical analysis revealed that the patients with amatoxin poisoning could be clearly separated from healthy controls on the basis of their metabolic fingerprints. There were 33 differential metabolites including 15 metabolites up-regulated metabolites and 18 down-regulated metabolites in patients with amatoxin poisoning compared to healthy controls. These metabolites mainly enriched in the lipid metabolism and amino acid metabolism pathways, such as Glycerophospholipid metabolism, Sphingolipid metabolism, Phenylalanine tyrosine and typtophan biosynthesis, Tyrosine metabolism, Arginine and proline metabolism, which may serve important roles in the amatoxin poisoning. Among the differential metabolites, a total of 8 significant metabolic markers were identified for discriminating patients with amatoxin poisoning from healthy controls, including Glycochenodeoxycholate-3-sulfate (GCDCA-S), 11-Oxo-androsterone glucuronide, Neomenthol-glucuronide, Dehydroisoandrosterone 3-glucuronide, Glucose 6-phosphate (G6P), Lanthionine ketimine, Glycerophosphocholine (GPC) and Nicotinamide ribotide, which achieved satisfactory diagnostic accuracy (AUC>0.8) in both discovery and validation cohorts. Strikingly, the Pearson's correlation analysis indicated that 11-Oxo-androsterone glucuronide, G6P and GCDCA-S were positively correlated with the liver injury induced by amatoxin poisoning. The findings of the current study may provide insight into the pathological mechanism of amatoxin poisoning and screened out the reliable metabolic biomarkers to contribute the clinical early diagnosis of amatoxin poisoning.

In-Cell NMR Approach for Real-Time Exploration of Pathway Versatility: Substrate Mixtures in Nonengineered Yeast

The central carbon metabolism of microbes will likely be used in future sustainable bioproduction. A sufficiently deep understanding of central metabolism would advance the control of activity and selectivity in whole-cell catalysis. Opposite to the more obvious effects of adding catalysts through genetic engineering, the modulation of cellular chemistry through effectors and substrate mixtures remains less clear. NMR spectroscopy is uniquely suited for in-cell tracking to advance mechanistic insight and to optimize pathway usage. Using a comprehensive and self-consistent library of chemical shifts, hyperpolarized NMR, and conventional NMR, we probe the versatility of cellular pathways to changes in substrate composition. Conditions for glucose influx into a minor pathway to an industrial precursor (2,3-butanediol) can thus be designed. Changes to intracellular pH can be followed concurrently, while mechanistic details for the minor pathway can be derived using an intermediate-trapping strategy. Overflow at the pyruvate level can be induced in nonengineered yeast with suitably mixed carbon sources (here glucose with auxiliary pyruvate), thus increasing glucose conversion to 2,3-butanediol by more than 600-fold. Such versatility suggests that a reassessment of canonical metabolism may be warranted using in-cell spectroscopy.

Oral Sources of Salivary Metabolites

The oral cavity is very diverse, where saliva plays an important role in maintaining oral health. The metabolism of saliva has been used to investigate oral diseases as well as general diseases, mainly to detect diagnostic biomarkers. There are many sources of salivary metabolites in the mouth. The online English language search and PubMed databases were searched to retrieve relevant studies on oral salivary metabolites. The physiological balance of the mouth is influenced by many factors that are reflected in the salivary metabolite profile. Similarly, the dysbiosis of microbes can alter the salivary metabolite profile, which may express oral inflammation or oral diseases. This narrative review highlights the factors to be considered when examining saliva and its use as a diagnostic biofluid for different diseases. Salivary metabolites, mainly small molecular metabolites may enter the bloodstream and cause illness elsewhere in the body. The importance of salivary metabolites produced in the oral cavity as risk factors for general diseases and their possible relationship to the body’s function are also discussed.

A Strategy for Uncovering the Serum Metabolome by Direct-Infusion High-Resolution Mass Spectrometry

Direct infusion nanoelectrospray high-resolution mass spectrometry (DI-nESI-HRMS) is a promising tool for high-throughput metabolomics analysis. However, metabolite assignment is limited by the inadequate mass accuracy and chemical space of the metabolome database. Here, a serum metabolome characterization method was proposed to make full use of the potential of DI-nESI-HRMS. Different from the widely used database search approach, unambiguous formula assignments were achieved by a reaction network combined with mass accuracy and isotopic patterns filter. To provide enough initial known nodes, an initial network was directly constructed by known metabolite formulas. Then experimental formula candidates were screened by the predefined reaction with the network. The effects of sources and scales of networks on assignment performance were investigated. Further, a scoring rule for filtering unambiguous formula candidates was proposed. The developed approach was validated by a pooled serum sample spiked with reference standards. The coverage and accuracy rates for the spiked standards were 98.9% and 93.6%, respectively. A total of 1958 monoisotopic features were assigned with unique formula candidates for the pooled serum, which is twice more than the database search. Finally, a case study of serum metabolomics in diabetes was carried out using the developed method.

Features extracted using tensor decomposition reflect the biological features of the temporal patterns of human blood multimodal metabolome

High-throughput omics technologies have enabled the profiling of entire biological systems. For the biological interpretation of such omics data, two analyses, hypothesis- and data-driven analyses including tensor decomposition, have been used. Both analyses have their own advantages and disadvantages and are mutually complementary; however, a direct comparison of these two analyses for omics data is poorly examined.We applied tensor decomposition (TD) to a dataset representing changes in the concentrations of 562 blood molecules at 14 time points in 20 healthy human subjects after ingestion of 75 g oral glucose. We characterized each molecule by individual dependence (constant or variable) and time dependence (later peak or early peak). Three of the four features extracted by TD were characterized by our previous hypothesis-driven study, indicating that TD can extract some of the same features obtained by hypothesis-driven analysis in a non-biased manner. In contrast to the years taken for our previous hypothesis-driven analysis, the data-driven analysis in this study took days, indicating that TD can extract biological features in a non-biased manner without the time-consuming process of hypothesis generation.

Metabolism along the life journey of T cells

T cells are one of few cell types in adult mammals that can proliferate extensively and differentiate diversely upon stimulation, which serves as an excellent example to dissect the metabolic basis of cell fate decisions. During the last decade, there has been an explosion of research into the metabolic control of T-cell responses. The roles of common metabolic pathways, including glycolysis, lipid metabolism, and mitochondrial oxidative phosphorylation, in T-cell responses have been well characterized, and their mechanisms of action are starting to emerge. In this review, we present several considerations for T-cell metabolism-focused research, while providing an overview of the metabolic control of T-cell fate decisions during their life journey. We try to synthesize principles that explain the causal relationship between cellular metabolism and T-cell fate decision. We also discuss key unresolved questions and challenges in targeting T-cell metabolism to treat disease.

A systems level approach to study metabolic networks in prokaryotes with the aromatic amino acid biosynthesis pathway

Metabolism of an organism underlies its phenotype, which depends on many factors, such as the genetic makeup, habitat, and stresses to which it is exposed. This is particularly important for the prokaryotes, which undergo significant vertical and horizontal gene transfers. In this study we have used the energy-intensive Aromatic Amino Acid (Tryptophan, Tyrosine and Phenylalanine, TTP) biosynthesis pathway, in a large number of prokaryotes, as a model system to query the different levels of organization of metabolism in the whole intracellular biochemical network, and to understand how perturbations, such as mutations, affects the metabolic flux through the pathway - in isolation and in the context of other pathways connected to it. Using an agglomerative approach involving complex network analysis and Flux Balance Analyses (FBA), of the Tryptophan, Tyrosine and Phenylalanine and other pathways connected to it, we identify several novel results. Using the reaction network analysis and Flux Balance Analyses of the Tryptophan, Tyrosine and Phenylalanine and the genome-scale reconstructed metabolic pathways, many common hubs between the connected networks and the whole genome network are identified. The results show that the connected pathway network can act as a proxy for the whole genome network in Prokaryotes. This systems level analysis also points towards designing functional smaller synthetic pathways based on the reaction network and Flux Balance Analyses analysis.

Integrated untargeted and targeted metabolomics to reveal therapeutic effect and mechanism of Alpiniae oxyphyllae fructus on Alzheimer's disease in APP/PS1 mice

Introduction: Alpiniae oxyphyllae Fructus (AOF) has been abundantly utilized for the treatment of diarrhea, dyspepsia, kidney asthenia, and abdominal pain in China. AOF is effective for treating AD in clinical trials, but its exact mode of action is yet unknown. Methods: In this study, metabolomics was combined to ascertain the alterations in plasma metabolism in APP/PS1 transgenic mice, the therapy of AOF on model mice, and the dynamic variations in 15 bile acids (BAs) concentration. Results: 31 differential biomarkers were finally identified in APP/PS1 group vs. the WT group. The levels of 16 metabolites like sphinganine (Sa), lyso PE (20:2), lysoPC (17:0), glycocholic acid (GCA), deoxycholicacid (DCA) were increased in APP/PS1 group, and those of 15 metabolites like phytosphingosine, cer (d18:0/14:0), and fumaric acid were reduced in APP/PS1 group. After AOF treatment, 29 of the 31 differential metabolites showed a tendency to be back-regulated, and 15 metabolites were significantly back-regulated, including sphinganine (Sa), lyso PE (20:2), glycocholic acid (GCA), deoxycholic acid (DCA). The relationship between BAs level and AD had been received increasing attention in recent years, and we also found notable differences between DCA and GCA in different groups. Therefore, a BAs-targeted metabonomic way was established to determine the level of 15 bile acids in different groups. The consequence demonstrated that primary BAs (CA, CDCA) declined in APP/PS1 model mice. After 3 months of AOF administration, CA and CDCA levels showed an upward trend. Conjugated primary bile acids (TCA, GCA, TCDCA, GCDCA), and secondary bile acids (DCA, LCA, GDCA, TDCA, TLCA GLCA) ascended in APP/PS1 group. After 3 months of AOF treatment, the levels of most BAs decreased to varying degrees. Notably, the metabolic performance of DCA and GCA in different groups was consistent with the predictions of untargeted metabolomics, validating the correctness of untargeted metabolomics. Discussion: According to metabolic pathways of regulated metabolites, it was prompted that AOF ameliorated the symptom of AD mice probably by regulating bile acids metabolism. This study offers a solid foundation for further research into the AOF mechanism for the therapy of AD.

Emerging mechanistic insights into the regulation of specialized metabolism in plants

Plants biosynthesize a broad range of natural products through specialized and species-specific metabolic pathways that are fuelled by core metabolism, together forming a metabolic network. Specialized metabolites have important roles in development and adaptation to external cues, and they also have invaluable pharmacological properties. A growing body of evidence has highlighted the impact of translational, transcriptional, epigenetic and chromatin-based regulation and evolution of specialized metabolism genes and metabolic networks. Here we review the forefront of this research field and extrapolate to medicinal plants that synthetize rare molecules. We also discuss how this new knowledge could help in improving strategies to produce useful plant-derived pharmaceuticals.

Genome-scale models as a vehicle for knowledge transfer from microbial to mammalian cell systems

With the plethora of omics data becoming available for mammalian cell and, increasingly, human cell systems, Genome-scale metabolic models (GEMs) have emerged as a useful tool for their organisation and analysis. The systems biology community has developed an array of tools for the solution, interrogation and customisation of GEMs as well as algorithms that enable the design of cells with desired phenotypes based on the multi-omics information contained in these models. However, these tools have largely found application in microbial cells systems, which benefit from smaller model size and ease of experimentation. Herein, we discuss the major outstanding challenges in the use of GEMs as a vehicle for accurately analysing data for mammalian cell systems and transferring methodologies that would enable their use to design strains and processes. We provide insights on the opportunities and limitations of applying GEMs to human cell systems for advancing our understanding of health and disease. We further propose their integration with data-driven tools and their enrichment with cellular functions beyond metabolism, which would, in theory, more accurately describe how resources are allocated intracellularly.

Challenges to Ensure a Better Translation of Metabolic Engineering for Industrial Applications

Metabolic engineering has evolved towards creating cell factories with increasingly complex pathways as economic criteria push biotechnology to higher value products to provide a sustainable source of speciality chemicals. Optimization of such pathways often requires high combinatory exploration of best pathway balance, and this has led to increasing use of high-throughput automated strain construction platforms or novel optimization techniques. In addition, the low catalytic efficiency of such pathways has shifted emphasis from gene expression strategies towards novel protein engineering to increase specific activity of the enzymes involved so as to limit the metabolic burden associated with excessively high pressure on ribosomal machinery when using massive overexpression systems. Metabolic burden is now generally recognized as a major hurdle to be overcome with consequences on genetic stability but also on the intensified performance needed industrially to attain the economic targets for successful product launch. Increasing awareness of the need to integrate novel genetic information into specific sites within the genome which not only enhance genetic stability (safe harbors) but also enable maximum expression profiles has led to genome-wide assessment of best integration sites, and bioinformatics will facilitate the identification of most probable landing pads within the genome.To facilitate the transfer of novel biotechnological potential to industrial-scale production, more attention, however, has to be paid to engineering metabolic fitness adapted to the specific stress conditions inherent to large-scale fermentation and the inevitable heterogeneity that will occur due to mass transfer limitations and the resulting deviation away from ideal conditions as seen in laboratory-scale validation of the engineered cells. To ensure smooth and rapid transfer of novel cell lines to industry with an accelerated passage through scale-up, better coordination is required form the onset between the biochemical engineers involved in process technology and the genetic engineers building the new strain so as to have an overall strategy able to maximize innovation at all levels. This should be one of our key objectives when building fermentation-friendly chassis organisms.

Systems Biology of Aromatic Compound Catabolism in Facultative Anaerobic Aromatoleum aromaticum EbN1T

Members of the genus Aromatoleum thrive in diverse habitats and use a broad range of recalcitrant organic molecules coupled to denitrification or O₂ respiration. To gain a holistic understanding of the model organism A. aromaticum EbN1^T, we studied its catabolic network dynamics in response to 3-(4-hydroxyphenyl)propanoate, phenylalanine, 3-hydroxybenzoate, benzoate, and acetate utilized under nitrate-reducing versus oxic conditions. Integrated multi-omics (transcriptome, proteome, and metabolome) covered most of the catabolic network (199 genes) and allowed for the refining of knowledge of the degradation modules studied. Their substrate-dependent regulation showed differing degrees of specificity, ranging from high with 3-(4-hydroxyphenyl)propanoate to mostly relaxed with benzoate. For benzoate, the transcript and protein formation were essentially constitutive, contrasted by that of anoxia-specific versus oxia-specific metabolite profiles. The matrix factorization of transcriptomic data revealed that the anaerobic modules accounted for most of the variance across the degradation network. The respiration network appeared to be constitutive, both on the transcript and protein levels, except for nitrate reductase (with narGHI expression occurring only under nitrate-reducing conditions). The anoxia/nitrate-dependent transcription of denitrification genes is apparently controlled by three FNR-type regulators as well as by NarXL (all constitutively formed). The resequencing and functional reannotation of the genome fostered a genome-scale metabolic model, which is comprised of 655 enzyme-catalyzed reactions and 731 distinct metabolites. The model predictions for growth rates and biomass yields agreed well with experimental stoichiometric data, except for 3-(4-hydroxyphenyl)propanoate, with which 4-hydroxybenzoate was exported. Taken together, the combination of multi-omics, growth physiology, and a metabolic model advanced our knowledge of an environmentally relevant microorganism that differs significantly from other bacterial model strains. IMPORTANCE Aromatic compounds are abundant constituents not only of natural organic matter but also of bulk industrial chemicals and fuel components of environmental concern. Considering the widespread occurrence of redox gradients in the biosphere, facultative anaerobic degradation specialists can be assumed to play a prominent role in the natural mineralization of organic matter and in bioremediation at contaminated sites. Surprisingly, differential multi-omics profiling of the A. aromaticum EbN1^T studied here revealed relaxed regulatory stringency across its four main physiological modi operandi (i.e., O₂-independent and O₂-dependent degradation reactions versus denitrification and O₂ respiration). Combining multi-omics analyses with a genome-scale metabolic model aligned with measured growth performances establishes A. aromaticum EbN1^T as a systems-biology model organism and provides unprecedented insights into how this bacterium functions on a holistic level. Moreover, this experimental platform invites future studies on eco-systems and synthetic biology of the environmentally relevant betaproteobacterial Aromatoleum/Azoarcus/Thauera cluster.

Global Cellular Metabolic Rewiring Adapts Corynebacterium glutamicum to Efficient Nonnatural Xylose Utilization

Xylose, the major component of lignocellulosic biomass, cannot be naturally or efficiently utilized by most microorganisms. Xylose (co)utilization is considered a cornerstone of efficient lignocellulose-based biomanufacturing. We evolved a rapidly xylose-utilizing strain, Cev2-18-5, which showed the highest reported specific growth rate (0.357 h^-1) on xylose among plasmid-free Corynebacterium glutamicum strains. A genetically clear chassis strain, CGS15, was correspondingly reconstructed with an efficient glucose-xylose coutilization performance based on comparative genomic analysis and mutation reconstruction. With the introduction of a succinate-producing plasmid, the resulting strain, CGS15-SA1, can efficiently produce 97.1 g/L of succinate with an average productivity of 8.09 g/L/h by simultaneously utilizing glucose and xylose from corn stalk hydrolysate. We further revealed a novel xylose regulatory mechanism mediated by the endogenous transcription factor IpsA with global regulatory effects on C. glutamicum. A synergistic effect on carbon metabolism and energy supply, motivated by three genomic mutations (P_sod(C131T)-xylAB, P_tuf(Δ21)-araE, and ipsA^C331T), was found to endow C. glutamicum with the efficient xylose utilization and rapid growth phenotype. Overall, this work not only provides promising C. glutamicum chassis strains for a lignocellulosic biorefinery but also enriches the understanding of the xylose regulatory mechanism. IMPORTANCE A novel xylose regulatory mechanism mediated by the transcription factor IpsA was revealed. A synergistic effect on carbon metabolism and energy supply was found to endow C. glutamicum with the efficient xylose utilization and rapid growth phenotype. The new xylose regulatory mechanism enriches the understanding of nonnatural substrate metabolism and encourages exploration new engineering targets for rapid xylose utilization. This work also provides a paradigm to understand and engineer the metabolism of nonnatural renewable substrates for sustainable biomanufacturing.

Inhibition of mitochondrial complex I leading to NAD+/NADH imbalance in type 2 diabetic patients who developed late stent thrombosis: Evidence from an integrative analysis of platelet bioenergetics and metabolomics

Type 2 diabetes mellitus (T2DM) is a strong indicator of late stent thrombosis (LST). Platelet bioenergetic dysfunction, although critical to the pathogenesis of diabetic macrovascular complications, remains uncharacterized in T2DM patients who developed LST. Here, we explored the mechanistic link between the alterations in platelet bioenergetics and LST in the setting of T2DM. Platelet bioenergetics, metabolomics, and their interactomes were analyzed in a nested case-control study including 15 T2DM patients who developed LST and 15 matched T2DM patients who did not develop LST (non-LST). Overall, we identified a bioenergetic alteration in T2DM patients with LST characterized by an imbalanced NAD+/NADH redox state resulting from deficient mitochondrial complex I (NADH: ubiquinone oxidoreductase) activity, which led to reduced ATP-linked and maximal mitochondrial respiration, increased glycolytic flux, and platelet hyperactivation compared with non-LST patients. Congruently, platelets from LST patients exhibited downregulation of tricarboxylic acid cycle and NAD+ biosynthetic pathways as well as upregulation of the proximal glycolytic pathway, a metabolomic change that was primarily attributed to compromised mitochondrial respiration rather than increased glycolytic flux as evidenced by the integrative analysis of bioenergetics and metabolomics. Importantly, both bioenergetic and metabolomic aberrancies in LST platelets could be recapitulated ex vivo by exposing the non-LST platelets to a low dose of rotenone, a complex I inhibitor. In contrast, normalization of the NAD+/NADH redox state, either by increasing NAD+ biosynthesis or by inhibiting NAD+ consumption, was able to improve mitochondrial respiration, inhibit mitochondrial oxidant generation, and consequently attenuate platelet aggregation in both LST platelets and non-LST platelets pretreated with low-dose rotenone. These data, for the first time, delineate the specific patterns of bioenergetic and metabolomic alterations for T2DM patients who suffer from LST, and establish the deficiency of complex I-derived NAD+ as a potential pathogenic mechanism in platelet abnormalities.

Integrated analyses of the methylome and transcriptome to unravel sex differences in the perirenal fat from suckling lambs

In sheep, differences were observed regarding fat accumulation and fatty acid (FA) composition between males and females, which may impact the quality and organoleptic characteristics of the meat. The integration of different omics technologies is a relevant approach for investigating biological and genetic mechanisms associated with complex traits. Here, the perirenal tissue of six male and six female Assaf suckling lambs was evaluated using RNA sequencing and whole-genome bisulfite sequencing (WGBS). A multiomic discriminant analysis using multiblock (s)PLS-DA allowed the identification of 314 genes and 627 differentially methylated regions (within these genes), which perfectly discriminate between males and females. These candidate genes overlapped with previously reported QTLs for carcass fat volume and percentage of different FAs in milk and meat from sheep. Additionally, differentially coexpressed (DcoExp) modules of genes between males (nine) and females (three) were identified that harbour 22 of these selected genes. Interestingly, these DcoExp were significantly correlated with fat percentage in different deposits (renal, pelvic, subcutaneous and intramuscular) and were associated with relevant biological processes for adipogenesis, adipocyte differentiation, fat volume and FA composition. Consequently, these genes may potentially impact adiposity and meat quality traits in a sex-specific manner, such as juiciness, tenderness and flavour.

The metabolite-controlled ubiquitin conjugase Ubc8 promotes mitochondrial protein import

Mitochondria play a key role in cellular energy metabolism. Transitions between glycolytic and respiratory conditions induce considerable adaptations of the cellular proteome. These metabolism-dependent changes are particularly pronounced for the protein composition of mitochondria. Here, we show that the yeast cytosolic ubiquitin conjugase Ubc8 plays a crucial role in the remodeling process when cells transition from respiratory to fermentative conditions. Ubc8 is a conserved and well-studied component of the catabolite control system that is known to regulate the stability of gluconeogenic enzymes. Unexpectedly, we found that Ubc8 also promotes the assembly of the translocase of the outer membrane of mitochondria (TOM) and increases the levels of its cytosol-exposed receptor subunit Tom22. Ubc8 deficiency results in compromised protein import into mitochondria and reduced steady-state levels of mitochondrial proteins. Our observations show that Ubc8, which is controlled by the prevailing metabolic conditions, promotes the switch from glucose synthesis to glucose usage in the cytosol and induces the biogenesis of the mitochondrial TOM machinery to improve mitochondrial protein import during phases of metabolic transition.

On the evolution of coenzyme biosynthesis

Covering: up to 2022The report provides a broad approach to deciphering the evolution of coenzyme biosynthetic pathways. Here, these various pathways are analyzed with respect to the coenzymes required for this purpose. Coenzymes whose biosynthesis relies on a large number of coenzyme-mediated reactions probably appeared on the scene at a later stage of biological evolution, whereas the biosyntheses of pyridoxal phosphate (PLP) and nicotinamide (NAD⁺) require little additional coenzymatic support and are therefore most likely very ancient biosynthetic pathways.

Environmental metal exposure, seminal plasma metabolome and semen quality: Evidence from Chinese reproductive-aged men

Environmental metal exposure has been associated with decreasing semen quality, but the effects of multiple metal exposure on seminal plasma metabolome remain obscure. In this study, semen and repeated urine samples from 551 volunteers were collected in Wuhan City. Heavy metals and trace elements were measured using inductively coupled plasma mass spectrometer, and seminal plasma metabolomes were acquired using liquid chromatography coupled with high-resolution mass spectrometry. Weighted gene co-expression network analysis showed more than half of the seminal plasma metals were associated with specific metabolite modules, whereas only a few urine metals presented weak associations, indicating that seminal plasma may be an ideal biological sample for male reproductive biomarker discovery and exposure risk assessment. Seminal plasma zinc (Zn) and selenium (Se) concentrations were significantly associated with 22 metabolites (e.g., glycerophospholipids, acyl-carnitines and amino acid derivatives). Among these metabolites, acyl-carnitines were positively associated with semen quality and sperm concentration. Moreover, acyl-carnitines were associated with both Zn and Se exposure, indicating the potential role of carnitine pathway in their toxicity mechanism. Our findings suggest that seminal plasma metabolome connects Zn and Se exposure and sperm concentrations in Chinese men of reproductive age.

Staphylococcus epidermidis RP62A’s Metabolic Network: Validation and Intervention Strategies

Increasingly, systems biology is gaining relevance in basic and applied research. The combination of computational biology with wet laboratory methods produces synergy that results in an exponential increase in knowledge of biological systems. The study of microorganisms such as Staphylococcus epidermidis RP62A enables the researcher to understand better their metabolic networks, which allows the design of effective strategies to treat infections caused by this species or others. S. epidermidis is the second most commoncause of infection in patients with joint implants, so treating its proliferation seems vital for public health. There are different approaches to the analysis of metabolic networks. Flux balance analysis (FBA) is one of the most widespread streams of research. It allows the study of large metabolic networks, the study their structural properties, the optimization of metabolic flux, and the search for intervention strategies to modify the state of the metabolic network. This work presents the validation of the Staphylococcus epidermidis RP62A metabolic network model elaborated by Díaz Calvo et al. Then, we elaborate further on the network analysis’s essential reactions. The full set of essential reactions (including a previously unobserved one) was computed, and we classified them into equivalence classes. Some proposals to intervene in the network and design knock-outs by studying minimal cut sets of small length are also introduced. In particular, minimal cut sets related to the medium (including exchange reactions associated with medium metabolites) have been computed. In this sense, the unique external MCS (composed of cysteine and sulfate ion) has been found, and all hybrid MCS (based on knocking out both internal and exchange reactions) of length two have also been computed. The paper also points out the possible importance of these new intervention strategies.

An Observation Study of Urinary Biomarkers Exploratory in Alzheimer's Disease using High Resolution Mass Spectrometry

Alzheimer's disease (AD) is regarded as a progressive neurodegenerative dementia, characterized by degeneration of distinct neuronal populations. A case-control study was carried out by using high resolution mass spectrometry to explore AD associated urinary metabolic biomarkers from 30 AD patients and 30 cognitively normal (CN) individuals, respectively. In total, 49 metabolites were determined and validated as known compounds by LC/MS analysis. With two sample t-test statistical analysis (p<0.05), 19 metabolites were shown to be significantly differed from AD to CN. A diagnostic model of receiver-operator characteristic (ROC) curve was constructed with a combination of 9 selected metabolites and yielded a separation with an area under the curve value of 0.976 between two groups. This study indicated urinary metabolites showed a significant expression between AD and CN. AD related metabolites enable to satisfy the diagnostic power of disease discrimination. Additionally, as a non-invasive approach, urine collection provides its convenience in clinical diagnosis of AD.

Mechanisms and mathematical modelling of ROS production by the mitochondrial electron transport chain

Reactive oxygen species (ROS) are recognised both as damaging molecules and intracellular signalling entities. In addition to its role in ATP generation, the mitochondrial electron transport chain (ETC) constitutes a relevant source of mitochondrial ROS, in particular during pathological conditions. Mitochondrial ROS homeostasis depends on species- and site-dependent ROS production, their bioreactivity, diffusion, and scavenging. However, our quantitative understanding of mitochondrial ROS homeostasis has thus far been hampered by technical limitations, including lack of truly site- and/or ROS-specific reporter molecules. In this context, the use of computational models is of great value to complement and interpret empirical data, as well as to predict variables that are difficult to assess experimentally. During the last decades, various mechanistic models of ETC-mediated ROS production have been developed. Although these often-complex models have generated novel insights, their parameterisation, analysis, and integration with other computational models is not straightforward. In contrast, phenomenological (sometimes termed "minimal") models use a relatively small set of equations to describe empirical relationship(s) between ROS-related and other parameters, and generally aim to explore system behaviour and generate hypotheses for experimental validation. In this review, we first discuss ETC-linked ROS homeostasis and introduce various detailed mechanistic models. Next, we present how bioenergetic parameters (e.g. NADH/NAD⁺ ratio, mitochondrial membrane potential) relate to site-specific ROS production within the ETC and how these relationships can be used to design minimal models of ROS homeostasis. Finally, we illustrate how minimal models have been applied to explore pathophysiological aspects of ROS.

Evidence of antagonistic predictive effects of miRNAs in breast cancer cohorts through data-driven networks

Non-coding micro RNAs (miRNAs) dysregulation seems to play an important role in the pathways involved in breast cancer occurrence and progression. In different studies, opposite functions may be assigned to the same miRNA, either promoting the disease or protecting from it. Our research tackles the following issues: (i) why aren’t there any concordant findings in many research studies regarding the role of miRNAs in the progression of breast cancer? (ii) could a miRNA have either an activating effect or an inhibiting one in cancer progression according to the other miRNAs with which it interacts? For this purpose, we analyse the AHUS dataset made available on the ArrayExpress platform by Haakensen et al. The breast tissue specimens were collected over 7 years between 2003 and 2009. miRNA-expression profiling was obtained for 55 invasive carcinomas and 70 normal breast tissue samples. Our statistical analysis is based on a recently developed model and feature selection technique which, instead of selecting a single model (i.e. a unique combination of miRNAs), delivers a set of models with equivalent predictive capabilities that allows to interpret and visualize the interaction of these features. As a result, we discover a set of 112 indistinguishable models (in a predictive sense) each with 4 or 5 miRNAs. Within this set, by comparing the model coefficients, we are able to identify three classes of miRNA: (i) oncogenic miRNAs; (ii) protective miRNAs; (iii) undefined miRNAs which can play both an oncogenic and a protective role according to the network with which they interact. These results shed new light on the biological action of miRNAs in breast cancer and may contribute to explain why, in some cases, different studies attribute opposite functions to the same miRNA.

Impact of Influenza A Virus Infection on Growth and Metabolism of Suspension MDCK Cells Using a Dynamic Model

Cell cultured-based influenza virus production is a viable option for vaccine manufacturing. In order to achieve a high concentration of viable cells, is requirement to have not only optimal process conditions, but also an active metabolism capable of intracellular synthesis of viral components. Experimental metabolic data collected in such processes are complex and difficult to interpret, for which mathematical models are an appropriate way to simulate and analyze the complex and dynamic interaction between the virus and its host cell. A dynamic model with 35 states was developed in this study to describe growth, metabolism, and influenza A virus production in shake flask cultivations of suspension Madin-Darby Canine Kidney (MDCK) cells. It considers cell growth (concentration of viable cells, mean cell diameters, volume of viable cells), concentrations of key metabolites both at the intracellular and extracellular level and virus titers. Using one set of parameters, the model accurately simulates the dynamics of mock-infected cells and correctly predicts the overall dynamics of virus-infected cells for up to 60 h post infection (hpi). The model clearly suggests that most changes observed after infection are related to cessation of cell growth and the subsequent transition to apoptosis and cell death. However, predictions do not cover late phases of infection, particularly for the extracellular concentrations of glutamate and ammonium after about 12 hpi. Results obtained from additional in silico studies performed indicated that amino acid degradation by extracellular enzymes resulting from cell lysis during late infection stages may contribute to this observed discrepancy.

Systems-based approaches to study immunometabolism

Technical advances at the interface of biology and computation, such as single-cell RNA-sequencing (scRNA-seq), reveal new layers of complexity in cellular systems. An emerging area of investigation using the systems biology approach is the study of the metabolism of immune cells. The diverse spectra of immune cell phenotypes, sparsity of immune cell numbers in vivo, limitations in the number of metabolites identified, dynamic nature of cellular metabolism and metabolic fluxes, tissue specificity, and high dependence on the local milieu make investigations in immunometabolism challenging, especially at the single-cell level. In this review, we define the systemic nature of immunometabolism, summarize cell- and system-based approaches, and introduce mathematical modeling approaches for systems interrogation of metabolic changes in immune cells. We close the review by discussing the applications and shortcomings of metabolic modeling techniques. With systems-oriented studies of metabolism expected to become a mainstay of immunological research, an understanding of current approaches toward systems immunometabolism will help investigators make the best use of current resources and push the boundaries of the discipline.