Metabolic Model Research Articles

In vivo imaging of alkaline phosphatase in lipid metabolic diseases with a photoacoustic probe

Lipid metabolic diseases have become an important challenge to global public health. Along with lifestyle changes, the incidence of obesity, diabetes and other metabolic syndromes is on the rise, and the number of patients with fatty liver disease is also increasing. Therefore, it is particularly important to develop effective lipid imaging strategies to monitor and manage fatty liver disease. Herein, based on the essential role of alkaline phosphatase (ALP) in both AS and OB, in vivo imaging of ALP was achieved in two lipid metabolic diseases models with a photoacoustic (PA) probe phosphorylated hemicyanine (P-Hcy). After being triggered by ALP, P-Hcy responded in different modalities including absorbance, fluorescence and, most significantly, PA-reporting. Notably, the PA signal showed the reliable linear correlation to the ALP level within the range of 0–800 U/L. The probe P-Hcy exhibited the advantages including high sensitivity, high selectivity, and steadiness in required biological conditions. The intracellular imaging results ensured that P-Hcy could visualize the ALP level in the foam cells induced from mouse mononuclear macrophages. In the healthy and lipid metabolic diseases models, P-Hcy was able to distinguish well between a lipid metabolic disease model and a healthy mouse model by photoacoustic imaging. By combining the ALP detection with P-Hcy in PA/fluorescence modality and traditional techniques such as blood biochemical testing and immunohistochemically staining, more potential strategy to accurately diagnose lipid metabolic diseases in the pre-clinical trials might be developed in future.

Metabolic modelling as a powerful tool to identify critical components of Pneumocystis growth medium.

Establishing suitable in vitro culture conditions for microorganisms is crucial for dissecting their biology and empowering potential applications. However, a significant number of bacterial and fungal species, including Pneumocystis jirovecii, remain unculturable, hampering research efforts. P. jirovecii is a deadly pathogen of humans that causes life-threatening pneumonia in immunocompromised individuals and transplant patients. Despite the major impact of Pneumocystis on human health, limited progress has been made in dissecting the pathobiology of this fungus. This is largely due to the fact that its experimental dissection has been constrained by the inability to culture the organism in vitro. We present a comprehensive in silico genome-scale metabolic model of Pneumocystis growth and metabolism, to identify metabolic requirements and imbalances that hinder growth in vitro. We utilise recently published genome data and available information in the literature as well as bioinformatics and software tools to develop and validate the model. In addition, we employ relaxed Flux Balance Analysis and Reinforcement Learning approaches to make predictions regarding metabolic fluxes and to identify critical components of the Pneumocystis growth medium. Our findings offer insights into the biology of Pneumocystis and provide a novel strategy to overcome the longstanding challenge of culturing this pathogen in vitro.

A novel risk score model of lactate metabolism for predicting outcomes and immune signatures in acute myeloid leukemia

Acute myeloid leukemia (AML) is a malignant tumor with high recurrence and refractory rates and low survival rates. Increased glycolysis is characteristic of metabolism in AML blast cells and is also associated with chemotherapy resistance. The purpose of this study was to use gene expression and clinical information from The Cancer Genome Atlas (TCGA) database to identify subtypes of AML associated with lactate metabolism. Two different subtypes linked to lactate metabolism, each with specific immunological features and consequences for prognosis, were identified in this study. Using the TCGA and International Cancer Genome Consortium (GEO) cohorts, a prognostic model composed of genes (LMNA, RETN and HK1) for the prognostic value of the lactate metabolism-related risk score prognostic model was created and validated, suggesting possible therapeutic uses. Additionally, the diagnostic value of the prognostic model genes was explored. LMNA and HK1 were ultimately identified as hub genes, and their roles in AML were determined through immune infiltration, GeneMANIA, GSEA, methylation analysis and single-cell analysis. LMNA was upregulated in AML associating with a poor prognosis while HK1 was downregulated in AML associating with a favorable prognosis. The findings underscore the noteworthy impact of genes linked to lactate metabolism in AML and illustrate the possible therapeutic usefulness of the lactate metabolism-related risk score and the hub lactate metabolism-related genes in guiding AML patients’ treatment choices.

Investigating the effects of empagliflozin on myocardial metabolic and lipid profile in healthy and metabolic syndrome murine models following acute myocardial infarction

Abstract Background/Introduction Empagliflozin (EMPA) is a sodium/glucose co-transporter type 2 inhibitor with clinically documented cardioprotective effect. However, the mechanism of cardioprotection remains elusive. We have previously shown that Western diet induces metabolic syndrome (MS) and deteriorates cardiac function in mice, whereas EMPA treatment for 6 weeks prevents this damage. Also, EMPA administration for 6 weeks suppresses ischemia/reperfusion (I/R) injury in mice, independent of the presence of MS. Purpose The aim of the present study is to investigate whether changes in myocardial metabolic and lipid profile, induced by EMPA treatment, are responsible for its cardioprotective effect in the setting of myocardial I/R injury. Methods C57Bl6 mice received chow diet or Western diet for 14 weeks. At week 8, mice from each dietary group were randomized into 3 subgroups: 1. Control - Sham surgery (5% DMSO-SHAM), 2. Control - I/R (5% DMSO-I/R) and 3. EMPA (10mg/kg/day in 5% DMSO) - I/R (EMPA-I/R) and received the corresponding treatment by daily per os administration for 6 weeks. At week 14, mice were subjected to 30 minutes of I, followed by 2 hours of R, or sham surgery. Myocardial tissue was harvested at the end of R, and metabolomic and lipidomic analysis was performed by nuclear magnetic resonance spectroscopy and mass spectrometry, respectively. Body weight, as well as blood glucose and cholesterol levels were assessed at baseline and at the end of the administrations. Results EMPA administration reduced body weight, as well as blood glucose and cholesterol levels in animals with MS. EMPA decreased anaerobic glycolysis products (UDP-glucose, Lactic acid) and ketone bodies (β-hydroxybutyrate, acetoacetic acid) in both healthy animals and mice with MS. Additionally, an increase in ATP, accompanied by a decrease in AMP and ADP were observed in the EMPA-I/R group, compared to the DMSO-I/R group. Regarding the lipidomic analysis, gangliosides GM3 emerged as the major lipid group decreased by EMPA treatment. Lastly, despite significant changes were presented in the metabolic and lipid profile between healthy animals and those with MS, the effects of EMPA on myocardial metabolic and lipid profile were the same, regardless of the presence of MS. Conclusion(s) EMPA administration induces metabolic changes in the heart tissue and improves myocardial energetics upon myocardial I/R injury. Also, EMPA suppresses gangliosides GM3, a lipid group that has been associated with neutrophil activation and recruitment. Importantly, the effects of EMPA on myocardial metabolic and lipid profile following I/R is independent of the presence of MS.

Macrophage heterogeneity in cardiometabolic disease

Abstract Metabolic diseases such as obesity and atherosclerosis are characterised by chronic inflammation that leads to the accumulation of immune cells in affected organs. Macrophages in the obese adipose tissue and the atherosclerotic plaque show transcriptional, phenotypic and functional heterogeneity. The accumulation of CD11c+ macrophages in atherosclerosis and obesity suggests the presence of conserved macrophage phenotypes in metabolic tissues. We aimed to characterise shared and distinct features of monocytes and macrophages in the aorta and adipose tissue in the context of metabolic disease using single-cell techniques. Single-cell RNA sequencing of the blood, aorta and visceral adipose tissue during a time course of metabolic disease progression (steady state, 12w western diet, 18w western diet) and data integration with murine aortic and adipose tissue community datasets revealed conserved and tissue specific features of metabolic disease. Resident macrophages in the aorta and adipose tissue displayed tissue specific transcriptional programs, highlighting that cell maintenance and function of tissue resident macrophages differ between metabolic tissues. Further, macrophages in the aorta and adipose tissue undergo disease associated compositional changes. For example, we uncovered distinct CD11c+ macrophage subsets that are conserved in the aorta and adipose tissue across multiple metabolic disease models and share core transcriptional signatures between both metabolic tissues. However, CD11c+ macrophage subsets showed tissue specific dynamics during metabolic disease progression. Overall, our analysis underscores that tissue niche affects macrophage composition as well as activation and transcriptional regulation patterns. Understanding similarities and differences between the two tissues will be important to inform identification of new therapeutic targets for cardiometabolic diseases.

Pan-Draft: automated reconstruction of species-representative metabolic models from multiple genomes

The accurate reconstruction of genome-scale metabolic models (GEMs) for unculturable species poses challenges due to the incomplete and fragmented genetic information typical of metagenome-assembled genomes (MAGs). While existing tools leverage sequence homology from single genomes, this study introduces pan-Draft, a pan-reactome-based approach exploiting recurrent genetic evidence to determine the solid core structure of species-level GEMs. By comparing MAGs clustered at the species-level, pan-Draft addresses the issues due to the incompleteness and contamination of individual genomes, providing high-quality draft models and an accessory reactions catalog supporting the gapfilling step. This approach will improve our comprehension of metabolic functions of uncultured species.

Pseudomonas aeruginosa faces a fitness trade-off between mucosal colonization and antibiotic tolerance during airway infection.

Pseudomonas aeruginosa frequently causes antibiotic-recalcitrant pneumonia, but the mechanisms driving its adaptation during human infections remain unclear. To reveal the selective pressures and adaptation strategies at the mucosal surface, here we investigated P. aeruginosa growth and antibiotic tolerance in tissue-engineered airways by transposon insertion sequencing (Tn-seq). Metabolic modelling based on Tn-seq data revealed the nutritional requirements for P. aeruginosa growth, highlighting reliance on glucose and lactate and varying requirements for amino acid biosynthesis. Tn-seq also revealed selection against biofilm formation during mucosal growth in the absence of antibiotics. Live imaging in engineered organoids showed that biofilm-dwelling cells remained sessile while colonizing the mucosal surface, limiting nutrient foraging and reduced growth. Conversely, biofilm formation increased antibiotic tolerance at the mucosal surface. Moreover, mutants with exacerbated biofilm phenotypes protected less tolerant but more cytotoxic strains, contributing to phenotypic heterogeneity. P. aeruginosa must therefore navigate conflicting physical and biological selective pressures to establish chronic infections.

Understanding disease-associated metabolic changes in human colon epithelial cells using i ColonEpithelium metabolic reconstruction.

The colon epithelium plays a key role in the host-microbiome interactions, allowing uptake of various nutrients and driving important metabolic processes. To unravel detailed metabolic activities in the human colon epithelium, our present study focuses on the generation of the first cell-type specific genome-scale metabolic model (GEM) of human colonic epithelial cells, named iColonEpithelium. GEMs are powerful tools for exploring reactions and metabolites at systems level and predicting the flux distributions at steady state. Our cell-type-specific iColonEpithelium metabolic reconstruction captures genes specifically expressed in the human colonic epithelial cells. The iColonEpithelium is also capable of performing metabolic tasks specific to the cell type. A unique transport reaction compartment has been included to allow simulation of metabolic interactions with the gut microbiome. We used iColonEpithelium to identify metabolic signatures associated with inflammatory bowel disease. We integrated single-cell RNA sequencing data from Crohn's Diseases (CD) and ulcerative colitis (UC) samples with the iColonEpithelium metabolic network to predict metabolic signatures of colonocytes between CD and UC compared to healthy samples. We identified reactions in nucleotide interconversion, fatty acid synthesis and tryptophan metabolism were differentially regulated in CD and UC conditions, which were in accordance with experimental results. The iColonEpithelium metabolic network can be used to identify mechanisms at the cellular level, and our network has the potential to be integrated with gut microbiome models to explore the metabolic interactions between host and gut microbiota under various conditions.

A benchmark of RNA-seq data normalization methods for transcriptome mapping on human genome-scale metabolic networks

Genome-scale metabolic models (GEMs) cover the entire list of metabolic genes in an organism and associated reactions, in a tissue/condition non-specific manner. RNA-seq provides crucial information to make the GEMs condition-specific. Integrative Metabolic Analysis Tool (iMAT) and Integrative Network Inference for Tissues (INIT) are the two most popular algorithms to create condition-specific GEMs from human transcriptome data. The normalization method of choice for raw RNA-seq count data affects the model content produced by these algorithms and their predictive accuracy. However, a benchmark of the RNA-seq normalization methods on the performance of iMAT and INIT algorithms is missing in the literature. Another important phenomenon is covariates such as age and gender in a dataset, and they can affect the predictivity of analysis. In this study, we aimed to compare five different RNA-seq data normalization methods (TPM, FPKM, TMM, GeTMM, and RLE) and covariate adjusted versions of the normalized data by mapping them on a human GEM using the iMAT and INIT algorithms to generate personalized metabolic models. We used RNA-seq data for Alzheimer’s disease (AD) and lung adenocarcinoma (LUAD) patients. The results demonstrated that RNA-seq data normalized by the RLE, TMM, or GeTMM methods enabled the production of condition-specific metabolic models with considerably low variability in terms of the number of active reactions compared to the within-sample normalization methods (FPKM, TPM). Using these models, we could more accurately capture the disease-associated genes (average accuracy of ~0.80 for AD and ~0.67 for LUAD) for the RLE, TMM, and GeTMM normalization methods. An increase in the accuracies was observed for all the methods when covariate adjustment was applied. We found a similar accuracy trend when we compared the metabolites of perturbed reactions to metabolome data for AD. Together, our benchmark study shows that the between-sample RNA-seq normalization methods reduce false positive predictions at the expense of missing some true positive genes when mapped on GEMs.

MicrobiomeNet: exploring microbial associations and metabolic profiles for mechanistic insights.

The growing volumes of microbiome studies over the past decade have revealed a wide repertoire of microbial associations under diverse conditions. Microbes produce small molecules to interact with each other as well as to modulate their environments. Their metabolic profiles hold the key to understanding these association patterns for translational applications. Based on this concept, we developed MicrobiomeNet, a comprehensive database that integrates microbial associations with their metabolic profiles for mechanistic insights. It currently contains a total of ∼5.8 million known microbial associations, coupled with>12 400 genome-scale metabolic models (GEMs) covering ∼6000 microbial species. Users can intuitively explore microbial associations and compare their corresponding metabolic profiles. Our case studies show that MicrobiomeNet can provide mechanistic insights that are consistent with the literature. MicrobiomeNet is freely available at https://www.microbiomenet.com/.

AI-based automated construction of high-precision Geobacillus thermoglucosidasius enzyme constraint model

Geobacillus thermoglucosidasius NCIMB 11955 possesses advantages, such as high-temperature tolerance, rapid growth rate, and low contamination risk. Additionally, it features efficient gene editing tools, making it one of the most promising next-generation cell factories. However, as a non-model microorganism, a lack of metabolic information significantly hampers the construction of high-precision metabolic flux models. Here, we propose a BioIntelliModel (BIM) strategy based on artificial intelligence technology for the automated construction of enzyme-constrained models. 1). BIM utilises the Contrastive Learning Enabled Enzyme Annotation (CLEAN) prediction tool to analyse the entire genome sequence of G. thermoglucosidasius NCIMB 11955, uncovering potential functional proteins in non-model strains. 2). The MetaPatchM module of BIM automates the repair of the metabolic network model. 3). The Tianjin University of Science and Technology-kcat (TUST-kcat) module predicts the kcat values of enzymes within the model. 4). The Enzyme-insert procedure constructs an enzyme-constrained model and performs a global scan to address overconstraint issues. Enzymatic data were automatically integrated into the metabolic flux model, creating an enzyme-constrained model, ec_G-ther11955. To validate model accuracy, we used both the p-thermo and ec_G-ther11955 models to predict riboflavin production strategies. The ec_G-ther11955 model demonstrated significantly higher accuracy. To further verify its efficacy, we employed ec_G-ther11955 to guide the rational design of L-valine-producing strains. Using the Optimisation Procedure for Identifying All Genetic Manipulations Leading to Targeted Overproductions (OptForce), Predictive Knockout Targeting (PKT), and Flux Scanning based on Enforced Objective Flux (FSEOF) algorithms, we identified 24 knockout and overexpression targets, achieving an accuracy rate of 87.5%. Ultimately, this led to an increase of 664.04% in L-valine titre. This study provides a novel strategy for rapidly constructing non-model strain models and demonstrates the tremendous potential of artificial intelligence in metabolic engineering.

FITM2 deficiency results in ER lipid accumulation, ER stress, and reduced apolipoprotein B lipidation and VLDL triglyceride secretion in vitro and in mouse liver

ObjectivesTriglycerides (TGs) associate with apolipoprotein B100 (apoB100) to form very low density lipoproteins (VLDLs) in the liver. The repertoire of factors that facilitate this association is incompletely understood. FITM2, an integral endoplasmic reticulum (ER) protein, was originally discovered as a factor participating in cytosolic lipid droplet (LD) biogenesis in tissues that do not form VLDL. We hypothesized that in the liver, in addition to promoting cytosolic LD formation, FITM2 would also transfer TG from its site of synthesis in the ER membrane to nascent VLDL particles within the ER lumen. MethodsExperiments were conducted using a rat hepatic cell line (McArdle-RH7777, or McA cells), an established model of mammalian lipoprotein metabolism, and mice. FITM2 expression was reduced using siRNA in cells and by liver specific cre-recombinase mediated deletion of the Fitm2 gene in mice. Effects of FITM2 deficiency on VLDL assembly and secretion in vitro and in vivo were measured by multiple methods, including density gradient ultracentrifugation, chromatography, mass spectrometry, stimulated Raman scattering (SRS) microscopy, sub-cellular fractionation, immunoprecipitation, immunofluorescence, and electron microscopy. Main findings1) FITM2-deficient hepatic cells in vitro and in vivo secrete TG-depleted VLDL particles, but the number of particles is unchanged compared to controls; 2) FITM2 deficiency in mice on a high fat diet (HFD) results in decreased plasma TG levels. The number of apoB100-containing lipoproteins remains similar, but shift from VLDL to low density lipoprotein (LDL) density; 3) Both in vitro and in vivo, when TG synthesis is stimulated and FITM2 is deficient, TG accumulates in the ER, and despite its availability this pool is unable to fully lipidate apoB100 particles; 4) FITM2 deficiency disrupts ER morphology and results in ER stress. Principal conclusionsThe results suggest that FITM2 contributes to VLDL lipidation, especially when newly synthesized hepatic TG is in abundance. In addition to its fundamental importance in VLDL assembly, the results also suggest that under dysmetabolic conditions, FITM2 may be an important factor in the partitioning of TG between cytosolic LDs and VLDL particles.

Construction and Optimization Strategies for Rural Residential Spatial Models Based on the Concept of Resource Metabolism: A Case Study of Rural Areas in the Shandong Plain

Rural communities can be conceptualized as spatial organisms interconnected by optimized resource utilization systems. Investigating the efficient utilization of rural resources and spatial construction methods grounded in resource metabolism is a pivotal step toward achieving the ecological transformation of rural spaces. This paper examined rural settlements in the Shandong Plain as a case study, exploring the relationships among three scales: village, neighborhoods, and courtyards. This analysis was based on elucidating the interaction mechanisms between “space and resource” and the integration of key resources and spatial elements. From the perspective of resource circulation and metabolism, this study aimed to elucidate the equilibrium of each resource element within three resource metabolism subsystems: the agricultural production system (core element), the ecological technology system (technological link), and the human life system (spatial carrier) in Shandong Plain’s villages, considering general climatic conditions. To achieve this, this research utilized the resource production volume, the utilization and transformation volume of resource metabolism technology facilities per unit area, and the average per capita resource consumption as fundamental measurement units. The concept of a rationing relationship is introduced to clarify resource allocation. Combining the aforementioned research on spatial resource metabolism in ecological villages in Shandong Province with the material flow analysis method, this study constructed a bottom–up spatial model of resource metabolism at three scales, courtyards, neighborhoods, and villages, under various resource metabolism scenarios. This study is anticipated to significantly contribute to the theoretical understanding of rural habitat environments, offering novel methods and perspectives for constructing ecological rural settlements.

Metabolic cross-feeding interactions modulate the dynamic community structure in microbial fuel cell under variable organic loading wastewaters.

The efficiency of microbial fuel cells (MFCs) in industrial wastewater treatment is profoundly influenced by the microbial community, which can be disrupted by variable industrial operations. Although microbial guilds linked to MFC performance under specific conditions have been identified, comprehensive knowledge of the convergent community structure and pathways of adaptation is lacking. Here, we developed a microbe-microbe interaction genome-scale metabolic model (mmGEM) based on metabolic cross-feeding to study the adaptation of microbial communities in MFCs treating sulfide-containing wastewater from a canned-pineapple factory. The metabolic model encompassed three major microbial guilds: sulfate-reducing bacteria (SRB), methanogens (MET), and sulfide-oxidizing bacteria (SOB). Our findings revealed a shift from an SOB-dominant to MET-dominant community as organic loading rates (OLRs) increased, along with a decline in MFC performance. The mmGEM accurately predicted microbial relative abundance at low OLRs (L-OLRs) and adaptation to high OLRs (H-OLRs). The simulations revealed constraints on SOB growth under H-OLRs due to reduced sulfate-sulfide (S) cycling and acetate cross-feeding with SRB. More cross-fed metabolites from SRB were diverted to MET, facilitating their competitive dominance. Assessing cross-feeding dynamics under varying OLRs enabled the execution of practical scenario-based simulations to explore the potential impact of elevated acidity levels on SOB growth and MFC performance. This work highlights the role of metabolic cross-feeding in shaping microbial community structure in response to high OLRs. The insights gained will inform the development of effective strategies for implementing MFC technology in real-world industrial environments.

A metabolic modeling-based framework for predicting trophic dependencies in native rhizobiomes of crop plants.

The exchange of metabolites (i.e., metabolic interactions) between bacteria in the rhizosphere determines various plant-associated functions. Systematically understanding the metabolic interactions in the rhizosphere, as well as in other types of microbial communities, would open the door to the optimization of specific predefined functions of interest, and therefore to the harnessing of the functionality of various types of microbiomes. However, mechanistic knowledge regarding the gathering and interpretation of these interactions is limited. Here, we present a framework utilizing genomics and constraint-based modeling approaches, aiming to interpret the hierarchical trophic interactions in the soil environment. 243 genome scale metabolic models of bacteria associated with a specific disease-suppressive vs disease-conducive apple rhizospheres were drafted based on genome-resolved metagenomes, comprising an in silico native microbial community. Iteratively simulating microbial community members' growth in a metabolomics-based apple root-like environment produced novel data on potential trophic successions, used to form a network of communal trophic dependencies. Network-based analyses have characterized interactions associated with beneficial vs non-beneficial microbiome functioning, pinpointing specific compounds and microbial species as potential disease supporting and suppressing agents. This framework provides a means for capturing trophic interactions and formulating a range of testable hypotheses regarding the metabolic capabilities of microbial communities within their natural environment. Essentially, it can be applied to different environments and biological landscapes, elucidating the conditions for the targeted manipulation of various microbiomes, and the execution of countless predefined functions.

CRISPRi-mediated metabolic switch enables concurrent aerobic and synthetic anaerobic fermentations in engineered consortium

Replacing petrochemicals with compounds from bio-based manufacturing processes remains an important part of the global effort to move towards a sustainable future. However, achieving economic viability requires both optimized cell factories and innovative processes. Here, we address this challenge by developing a fermentation platform, which enables two concurrent fermentations in one bioreactor. We first construct a xylitol producing Escherichia coli strain in which CRISPRi-mediated gene silencing is used to switch the metabolism from aerobic to anaerobic, even when the bacteria are under oxic conditions. The switch also decouples growth from production, which further increases the yield. The strain produces acetate as a byproduct, which is subsequently metabolized under oxic conditions by a secondary E. coli strain. Through constraint-based metabolic modelling this strain is designed to co-valorize glucose and the excreted acetate to a secondary product. This unique syntrophic consortium concept facilitates the implementation of “two fermentations in one go”, where the concurrent fermentation displays similar titers and productivities as compared to two separate single strain fermentations.

Diagnosis and mitigation of the systemic impact of genome reduction in Escherichia coli DGF-298.

Genomic streamlining can be employed in model organisms to reduce complexity and enhance strain predictability. One of the most striking examples is the bacterial strain Escherichia coli DGF-298, notable for having over one-third of its genome deleted. However, such extensive genome modifications raise the question of how similar this simplified cell remains when compared with its parent, and what are the possible unintended consequences of this simplification. In this study, we used metabolic modeling along with iModulon-based transcriptomic analysis in different growth conditions to assess the impact of genome reduction on metabolism and gene regulation. We observed little impact of genomic reduction on the regulatory network of E. coli DGF-298 and identified a potential metabolic bottleneck leading to the constitutive activity of the SoxS iModulon. We then leveraged the model's predictions to successfully restore SoxS activity to the basal level.

Unveiling Metabolic Engineering Strategies by Quantitative Heterologous Pathway Design.

Constructing efficient cell factories requires the rational design of metabolic pathways, yet quantitatively predicting the potential pathway for breaking stoichiometric yield limit in hosts remains challenging. This leaves it uncertain whether the pathway yield of various products can be enhanced to surpass the stoichiometric yield limit and whether common strategies exist. Here, a high-quality cross-species metabolic network model (CSMN) and a quantitative heterologous pathway design algorithm (QHEPath) are developed to address this challenge. Through systematic calculations using CSMN and QHEPath, 12,000 biosynthetic scenarios are evaluated across 300 products and 4 substrates in 5 industrial organisms, revealing that over 70% of product pathway yields can be improved by introducing appropriate heterologous reactions. Thirteen engineering strategies, categorized as carbon-conserving and energy-conserving, are identified, with 5 strategies effective for over 100 products. A user-friendly web server is developed to quantitatively calculate and visualize the product yields and pathways, which successfully predicts biologically plausible strategies validated in literature for multiple products.

Combination of Green Tea, Green Coffee, and Turmeric Extract Improve the THOC5 and AIF1, but not ACTA2 and CNN1 Gene Expression in the Aortic Tissue of Metabolic Syndrome Model

Metabolic syndrome (MetS) is a group of risk factors in the form of central obesity, insulin resistance, dyslipidemia, and hypertension, increasing oxidative stress. This pathological event leads to the development of cardiovascular disease, for instance, atherosclerosis. Besides modifiable risk factors, non-modifiable risk factors such as genetic factors also play a role in the formation of atherosclerosis in MetS conditions such as THOC5, AIF1, CNN1, and ACTA2. Recently, natural compound derivatives, such as epigallocatechin-3-gallate (EGCG), chlorogenic acid (CGA), and turmeric, have shown beneficial effects in MetS improvement. This study aimed to investigate the effect of green tea, green coffee, and turmeric extract on the expression of THOC5, AIF1, CNN1, and ACTA2 genes that contributed to atherosclerotic vasculopathy development in the MetS rat model. Twenty-five MetS rat models were grouped into 4 groups (n = 5): Standard control (SC), MetS (MetS), a combination of green tea, green coffee, and turmeric extract with treatment doses: 300/100/150 mg/BW(C1) and 400/200/250 mg/BW(C2) group. The THOC5, AIF1, CNN1, and ACTA2 expression were measured at the end of treatment periods. This study found that administering green tea, green coffee, and turmeric extract can lower the expression of THOC5, AIF1, CNN1, and ACTA2. The correlation test showed that there is a strong correlation between THOC5 and AIF1 gene expression, with positive value. In summary, the combined effects of green tea, green coffee, and curcumin extract show significant promise as a potential anti-atherosclerosis treatment by improve the THOC5 and AIF1, but not ACTA2 and CNN1 gene expression in the aortic tissue of metabolic syndrome model. HIGHLIGHTS Metabolic syndrome is a group of risk factors consist of central obesity, insulin resistance, dyslipidemia, and hypertension. Individuals with MetS have an increased risk of developing atherosclerotic cardiovascular disease. Besides modifiable risk factors, non-modifiable risk factors such as genetic factors also play a role in the formation of atherosclerosis in MetS conditions such as THOC5, AIF1, CNN1, and ACTA2. This study aimed to investigate the effect of green tea, green coffee, and turmeric extract on the expression of THOC5, AIF1, CNN1, and ACTA2 genes that contributed to atherosclerotic vasculopathy development in the MetS rat model. Administering green tea, green coffee, and turmeric extract can lower the expression of THOC5, AIF1, CNN1, and ACTA2. The correlation test showed that there is a strong correlation between THOC5 and AIF1 gene expression, with positive value. In summary, the combined effects of green tea, green coffee, and curcumin extract show significant promise as a potential anti-atherosclerosis treatment by improve the THOC5 and AIF1, but not ACTA2 and CNN1 gene expression in the aortic tissue of metabolic syndrome model. GRAPHICAL ABSTRACT

Diallyl disulfide alleviates hepatic steatosis by the conservative mechanism from fish to tetrapod: Augment Mfn2/Atgl-Mediated lipid droplet-mitochondria coupling

Despite increasing evidences has highlighted the importance of mitochondria-lipid droplet (LD) coupling in maintaining lipid homeostasis, little progress in unraveling the role of mitochondria-LD coupling in hepatic lipid metabolism has been made. Additionally, diallyl disulfide (DADS), a garlic organosulfur compound, has been proposed to prevent hepatic steatosis; however, no studies have focused on the molecular mechanism to date. To address these gaps, this study investigated the systemic control mechanisms of mitochondria-LD coupling regulating hepatic lipid metabolism, and also explored their function in the process of DADS alleviating hepatic steatosis. To this end, an animal model of lipid metabolism, yellow catfish Pelteobagrus fulvidraco were fed four different diets (control, high-fat, DADS and high-fat + DADS diet) in vivo for 8 weeks; in vitro experiments were conducted to inhibit Mfn2/Atgl-mediated mitochondria-LD coupling in isolated hepatocytes. The key findings are: (1) the activations of hepatic LDs lipolysis and mitochondrial β-oxidation are likely the major drivers for DADS alleviating hepatic steatosis; (2) the underlying mechanism is that DADS enhances mitochondria-LD coupling by promoting the interaction between mitochondrion-localized Mfn2 with LD-localized Atgl, which facilitates the hepatic LDs lipolysis and the transfer of fatty acids (FAs) from LDs to mitochondria for subsequent β-oxidation; (3) Mfn2-mediated mitochondrial fusion facilitates mitochondria to form more PDM, which possess higher β-oxidation capacity in hepatocytes. Significantly, the present research unveils a previously undisclosed mechanism by which Mfn2/Atgl-mitochondria-LD coupling relieves hepatic LDs accumulation, which is a conserved strategy from fish to tetrapod. This study provides another dimension for mitochondria-LD coupling and opens up new avenues for the therapeutic interventions in hepatic steatosis.