Flux regulation through glycolysis and respiration is balanced by inositol pyrophosphates in yeast.

Although many prokaryotes have glycolysis alternatives, it's considered as the only energy-generating glucose catabolic pathway in eukaryotes. Here, we managed to create a hybrid-glycolysis yeast. Subsequently, we identified an inositol pyrophosphatase encoded by OCA5 that could regulate glycolysis and respiration by adjusting 5-diphosphoinositol 1,2,3,4,6-pentakisphosphate (5-InsP7) levels. 5-InsP7 levels could regulate the expression of genes involved in glycolysis and respiration, representing a global mechanism that could sense ATP levels and regulate central carbon metabolism. The hybrid-glycolysis yeast did not produce ethanol during growth under excess glucose and could produce 2.68 g/L free fatty acids, which is the highest reported production in shake flask of Saccharomyces cerevisiae. This study demonstrated the significance of hybrid-glycolysis yeast and determined Oca5 as an inositol pyrophosphatase controlling the balance between glycolysis and respiration, which may shed light on the role of inositol pyrophosphates in regulating eukaryotic metabolism.

Bioactive metabolites: The double-edged sword in your food.

Food contains many different bioactive metabolites that interact with human metabolism. Many of these have health benefits, but in this issue of Cell, researchers show that the gut microbiome can convert a bioactive metabolite to metabolites that may elevate the risks of developing cardiovascular disease.

Antibiotic Lethality Is Impacted by Nutrient Availabilities: New Insights from Machine Learning.

In this issue of Cell, Yang, Wright et al. describe a machine learning approach that that can provide mechanistic insight from chemical screens. They use this approach to uncover how the nutritional availability for Escherichia coli impacts lethality toward three widely used antibiotics.

Reprogramming Yeast Metabolism from Alcoholic Fermentation to Lipogenesis.

Engineering microorganisms for production of fuels and chemicals often requires major re-programming of metabolism to ensure high flux toward the product of interest. This is challenging, as millions of years of evolution have resulted in establishment of tight regulation of metabolism for optimal growth in the organism's natural habitat. Here, we show through metabolic engineering that it is possible to alter the metabolism of Saccharomyces cerevisiae from traditional ethanol fermentation to a pure lipogenesis metabolism, resulting in high-level production of free fatty acids. Through metabolic engineering and process design, we altered subcellular metabolic trafficking, fine-tuned NADPH and ATP supply, and decreased carbon flux to biomass, enabling production of 33.4 g/L extracellular free fatty acids. We further demonstrate that lipogenesis metabolism can replace ethanol fermentation by deletion of pyruvate decarboxylase enzymes followed by adaptive laboratory evolution. Genome sequencing of evolved strains showed that pyruvate kinase mutations were essential for this phenotype.

Systems Biology of Metabolism.

Metabolism is highly complex and involves thousands of different connected reactions; it is therefore necessary to use mathematical models for holistic studies. The use of mathematical models in biology is referred to as systems biology. In this review, the principles of systems biology are described, and two different types of mathematical models used for studying metabolism are discussed: kinetic models and genome-scale metabolic models. The use of different omics technologies, including transcriptomics, proteomics, metabolomics, and fluxomics, for studying metabolism is presented. Finally, the application of systems biology for analyzing global regulatory structures, engineering the metabolism of cell factories, and analyzing human diseases is discussed.

Engineering Cellular Metabolism.

Metabolic engineering is the science of rewiring the metabolism of cells to enhance production of native metabolites or to endow cells with the ability to produce new products. The potential applications of such efforts are wide ranging, including the generation of fuels, chemicals, foods, feeds, and pharmaceuticals. However, making cells into efficient factories is challenging because cells have evolved robust metabolic networks with hard-wired, tightly regulated lines of communication between molecular pathways that resist efforts to divert resources. Here, we will review the current status and challenges of metabolic engineering and will discuss how new technologies can enable metabolic engineering to be scaled up to the industrial level, either by cutting off the lines of control for endogenous metabolism or by infiltrating the system with disruptive, heterologous pathways that overcome cellular regulation.

Apixaban for Stroke Prevention in Subclinical Atrial Fibrillation.

BACKGROUND: Subclinical atrial fibrillation is short-lasting and asymptomatic and can usually be detected only by long-term continuous monitoring with pacemakers or defibrillators. Subclinical atrial fibrillation is associated with an increased risk of stroke by a factor of 2.5; however, treatment with oral anticoagulation is of uncertain benefit. METHODS: We conducted a trial involving patients with subclinical atrial fibrillation lasting 6 minutes to 24 hours. Patients were randomly assigned in a double-blind, double-dummy design to receive apixaban at a dose of 5 mg twice daily (2.5 mg twice daily when indicated) or aspirin at a dose of 81 mg daily. The trial medication was discontinued and anticoagulation started if subclinical atrial fibrillation lasting more than 24 hours or clinical atrial fibrillation developed. The primary efficacy outcome, stroke or systemic embolism, was assessed in the intention-to-treat population (all the patients who had undergone randomization); the primary safety outcome, major bleeding, was assessed in the on-treatment population (all the patients who had undergone randomization and received at least one dose of the assigned trial drug, with follow-up censored 5 days after permanent discontinuation of trial medication for any reason). RESULTS: We included 4012 patients with a mean (±SD) age of 76.8±7.6 years and a mean CHA2DS2-VASc score of 3.9±1.1 (scores range from 0 to 9, with higher scores indicating a higher risk of stroke); 36.1% of the patients were women. After a mean follow-up of 3.5±1.8 years, stroke or systemic embolism occurred in 55 patients in the apixaban group (0.78% per patient-year) and in 86 patients in the aspirin group (1.24% per patient-year) (hazard ratio, 0.63; 95% confidence interval [CI], 0.45 to 0.88; P = 0.007). In the on-treatment population, the rate of major bleeding was 1.71% per patient-year in the apixaban group and 0.94% per patient-year in the aspirin group (hazard ratio, 1.80; 95% CI, 1.26 to 2.57; P = 0.001). Fatal bleeding occurred in 5 patients in the apixaban group and 8 patients in the aspirin group. CONCLUSIONS: Among patients with subclinical atrial fibrillation, apixaban resulted in a lower risk of stroke or systemic embolism than aspirin but a higher risk of major bleeding. (Funded by the Canadian Institutes of Health Research and others; ARTESIA ClinicalTrials.gov number, NCT01938248.).

Global compositional and functional states of the human gut microbiome in health and disease.

The human gut microbiota is of increasing interest, with metagenomics a key tool for analyzing bacterial diversity and functionality in health and disease. Despite increasing efforts to expand microbial gene catalogs and an increasing number of metagenome-assembled genomes, there have been few pan-metagenomic association studies and in-depth functional analyses across different geographies and diseases. Here, we explored 6014 human gut metagenome samples across 19 countries and 23 diseases by performing compositional, functional cluster, and integrative analyses. Using interpreted machine learning classification models and statistical methods, we identified Fusobacterium nucleatum and Anaerostipes hadrus with the highest frequencies, enriched and depleted, respectively, across different disease cohorts. Distinct functional distributions were observed in the gut microbiomes of both westernized and nonwesternized populations. These compositional and functional analyses are presented in the open-access Human Gut Microbiome Atlas, allowing for the exploration of the richness, disease, and regional signatures of the gut microbiota across different cohorts.

Combinatorial, additive and dose-dependent drug-microbiome associations.

During the transition from a healthy state to cardiometabolic disease, patients become heavily medicated, which leads to an increasingly aberrant gut microbiome and serum metabolome, and complicates biomarker discovery1-5. Here, through integrated multi-omics analyses of 2,173 European residents from the MetaCardis cohort, we show that the explanatory power of drugs for the variability in both host and gut microbiome features exceeds that of disease. We quantify inferred effects of single medications, their combinations as well as additive effects, and show that the latter shift the metabolome and microbiome towards a healthier state, exemplified in synergistic reduction in serum atherogenic lipoproteins by statins combined with aspirin, or enrichment of intestinal Roseburia by diuretic agents combined with beta-blockers. Several antibiotics exhibit a quantitative relationship between the number of courses prescribed and progression towards a microbiome state that is associated with the severity of cardiometabolic disease. We also report a relationship between cardiometabolic drug dosage, improvement in clinical markers and microbiome composition, supporting direct drug effects. Taken together, our computational framework and resulting resources enable the disentanglement of the effects of drugs and disease on host and microbiome features in multimedicated individuals. Furthermore, the robust signatures identified using our framework provide new hypotheses for drug-host-microbiome interactions in cardiometabolic disease.

Brain energy metabolism is optimized to minimize the cost of enzyme synthesis and transport.

The energy metabolism of the brain is poorly understood partly due to the complex morphology of neurons and fluctuations in ATP demand over time. To investigate this, we used metabolic models that estimate enzyme usage per pathway, enzyme utilization over time, and enzyme transportation to evaluate how these parameters and processes affect ATP costs for enzyme synthesis and transportation. Our models show that the total enzyme maintenance energy expenditure of the human body depends on how glycolysis and mitochondrial respiration are distributed both across and within cell types in the brain. We suggest that brain metabolism is optimized to minimize the ATP maintenance cost by distributing the different ATP generation pathways in an advantageous way across cell types and potentially also across synapses within the same cell. Our models support this hypothesis by predicting export of lactate from both neurons and astrocytes during peak ATP demand, reproducing results from experimental measurements reported in the literature. Furthermore, our models provide potential explanation for parts of the astrocyte-neuron lactate shuttle theory, which is recapitulated under some conditions in the brain, while contradicting other aspects of the theory. We conclude that enzyme usage per pathway, enzyme utilization over time, and enzyme transportation are important factors for defining the optimal distribution of ATP production pathways, opening a broad avenue to explore in brain metabolism.

Statin therapy is associated with lower prevalence of gut microbiota dysbiosis.

Microbiome community typing analyses have recently identified the Bacteroides2 (Bact2) enterotype, an intestinal microbiota configuration that is associated with systemic inflammation and has a high prevalence in loose stools in humans1,2. Bact2 is characterized by a high proportion of Bacteroides, a low proportion of Faecalibacterium and low microbial cell densities1,2, and its prevalence varies from 13% in a general population cohort to as high as 78% in patients with inflammatory bowel disease2. Reported changes in stool consistency3 and inflammation status4 during the progression towards obesity and metabolic comorbidities led us to propose that these developments might similarly correlate with an increased prevalence of the potentially dysbiotic Bact2 enterotype. Here, by exploring obesity-associated microbiota alterations in the quantitative faecal metagenomes of the cross-sectional MetaCardis Body Mass Index Spectrum cohort (n = 888), we identify statin therapy as a key covariate of microbiome diversification. By focusing on a subcohort of participants that are not medicated with statins, we find that the prevalence of Bact2 correlates with body mass index, increasing from 3.90% in lean or overweight participants to 17.73% in obese participants. Systemic inflammation levels in Bact2-enterotyped individuals are higher than predicted on the basis of their obesity status, indicative of Bact2 as a dysbiotic microbiome constellation. We also observe that obesity-associated microbiota dysbiosis is negatively associated with statin treatment, resulting in a lower Bact2 prevalence of 5.88% in statin-medicated obese participants. This finding is validated in both the accompanying MetaCardis cardiovascular disease dataset (n = 282) and the independent Flemish Gut Flora Project population cohort (n = 2,345). The potential benefits of statins in this context will require further evaluation in a prospective clinical trial to ascertain whether the effect is reproducible in a randomized population and before considering their application as microbiota-modulating therapeutics.

Yeast increases glycolytic flux to support higher growth rates accompanied by decreased metabolite regulation and lower protein phosphorylation.

Supply of Gibbs free energy and precursors are vital for cellular function and cell metabolism have evolved to be tightly regulated to balance their supply and consumption. Precursors and Gibbs free energy are generated in the central carbon metabolism (CCM), and fluxes through these pathways are precisely regulated. However, how fluxes through CCM pathways are affected by posttranslational modification and allosteric regulation remains poorly understood. Here, we integrated multi-omics data collected under nine different chemostat conditions to explore how fluxes in the CCM are regulated in the yeast Saccharomyces cerevisiae. We deduced a pathway- and metabolism-specific CCM flux regulation mechanism using hierarchical analysis combined with mathematical modeling. We found that increased glycolytic flux associated with an increased specific growth rate was accompanied by a decrease in flux regulation by metabolite concentrations, including the concentration of allosteric effectors, and a decrease in the phosphorylation level of glycolytic enzymes.

Generation and analysis of context-specific genome-scale metabolic models derived from single-cell RNA-Seq data.

Single-cell RNA sequencing combined with genome-scale metabolic models (GEMs) has the potential to unravel the differences in metabolism across both cell types and cell states but requires new computational methods. Here, we present a method for generating cell-type-specific genome-scale models from clusters of single-cell RNA-Seq profiles. Specifically, we developed a method to estimate the minimum number of cells required to pool to obtain stable models, a bootstrapping strategy for estimating statistical inference, and a faster version of the task-driven integrative network inference for tissues algorithm for generating context-specific GEMs. In addition, we evaluated the effect of different RNA-Seq normalization methods on model topology and differences in models generated from single-cell and bulk RNA-Seq data. We applied our methods on data from mouse cortex neurons and cells from the tumor microenvironment of lung cancer and in both cases found that almost every cell subtype had a unique metabolic profile. In addition, our approach was able to detect cancer-associated metabolic differences between cancer cells and healthy cells, showcasing its utility. We also contextualized models from 202 single-cell clusters across 19 human organs using data from Human Protein Atlas and made these available in the web portal Metabolic Atlas, thereby providing a valuable resource to the scientific community. With the ever-increasing availability of single-cell RNA-Seq datasets and continuously improved GEMs, their combination holds promise to become an important approach in the study of human metabolism.

HGTphyloDetect: facilitating the identification and phylogenetic analysis of horizontal gene transfer.

BACKGROUND: Horizontal gene transfer (HGT) is an important driver in genome evolution, gain-of-function, and metabolic adaptation to environmental niches. Genome-wide identification of putative HGT events has become increasingly practical, given the rapid growth of genomic data. However, existing HGT analysis toolboxes are not widely used, limited by their inability to perform phylogenetic reconstruction to explore potential donors, and the detection of HGT from both evolutionarily distant and closely related species. RESULTS: In this study, we have developed HGTphyloDetect, which is a versatile computational toolbox that combines high-throughput analysis with phylogenetic inference, to facilitate comprehensive investigation of HGT events. Two case studies with Saccharomyces cerevisiae and Candida versatilis demonstrate the ability of HGTphyloDetect to identify horizontally acquired genes with high accuracy. In addition, HGTphyloDetect enables phylogenetic analysis to illustrate a likely path of gene transmission among the evolutionarily distant or closely related species. CONCLUSIONS: The HGTphyloDetect computational toolbox is designed for ease of use and can accurately find HGT events with a very low false discovery rate in a high-throughput manner. The HGTphyloDetect toolbox and its related user tutorial are freely available at https://github.com/SysBioChalmers/HGTphyloDetect.

Molecular biology for green recovery-A call for action.

Molecular biology holds a vast potential for tackling climate change and biodiversity loss. Yet, it is largely absent from the current strategies. We call for a community-wide action to bring molecular biology to the forefront of climate change solutions.

GotEnzymes: an extensive database of enzyme parameter predictions.

Enzyme parameters are essential for quantitatively understanding, modelling, and engineering cells. However, experimental measurements cover only a small fraction of known enzyme-compound pairs in model organisms, much less in other organisms. Artificial intelligence (AI) techniques have accelerated the pace of exploring enzyme properties by predicting these in a high-throughput manner. Here, we present GotEnzymes, an extensive database with enzyme parameter predictions by AI approaches, which is publicly available at https://metabolicatlas.org/gotenzymes for interactive web exploration and programmatic access. The first release of this data resource contains predicted turnover numbers of over 25.7 million enzyme-compound pairs across 8099 organisms. We believe that GotEnzymes, with the readily-predicted enzyme parameters, would bring a speed boost to biological research covering both experimental and computational fields that involve working with candidate enzymes.

Yeast has evolved to minimize protein resource cost for synthesizing amino acids.

Proteins, as essential biomolecules, account for a large fraction of cell mass, and thus the synthesis of the complete set of proteins (i.e., the proteome) represents a substantial part of the cellular resource budget. Therefore, cells might be under selective pressures to optimize the resource costs for protein synthesis, particularly the biosynthesis of the 20 proteinogenic amino acids. Previous studies showed that less energetically costly amino acids are more abundant in the proteomes of bacteria that survive under energy-limited conditions, but the energy cost of synthesizing amino acids was reported to be weakly associated with the amino acid usage in Saccharomyces cerevisiae Here we present a modeling framework to estimate the protein cost of synthesizing each amino acid (i.e., the protein mass required for supporting one unit of amino acid biosynthetic flux) and the glucose cost (i.e., the glucose consumed per amino acid synthesized). We show that the logarithms of the relative abundances of amino acids in S. cerevisiae's proteome correlate well with the protein costs of synthesizing amino acids (Pearson's r = -0.89), which is better than that with the glucose costs (Pearson's r = -0.5). Therefore, we demonstrate that S. cerevisiae tends to minimize protein resource, rather than glucose or energy, for synthesizing amino acids.

Metastatic triple negative breast cancer adapts its metabolism to destination tissues while retaining key metabolic signatures.

Triple negative breast cancer (TNBC) metastases are assumed to exhibit similar functions in different organs as in the original primary tumor. However, studies of metastasis are often limited to a comparison of metastatic tumors with primary tumors of their origin, and little is known about the adaptation to the local environment of the metastatic sites. We therefore used transcriptomic data and metabolic network analyses to investigate whether metastatic tumors adapt their metabolism to the metastatic site and found that metastatic tumors adopt a metabolic signature with some similarity to primary tumors of their destinations. The extent of adaptation, however, varies across different organs, and metastatic tumors retain metabolic signatures associated with TNBC. Our findings suggest that a combination of anti-metastatic approaches and metabolic inhibitors selected specifically for different metastatic sites, rather than solely targeting TNBC primary tumors, may constitute a more effective treatment approach.

Genome-scale modeling drives 70-fold improvement of intracellular heme production in Saccharomyces cerevisiae.

Heme is an oxygen carrier and a cofactor of both industrial enzymes and food additives. The intracellular level of free heme is low, which limits the synthesis of heme proteins. Therefore, increasing heme synthesis allows an increased production of heme proteins. Using the genome-scale metabolic model (GEM) Yeast8 for the yeast Saccharomyces cerevisiae, we identified fluxes potentially important to heme synthesis. With this model, in silico simulations highlighted 84 gene targets for balancing biomass and increasing heme production. Of those identified, 76 genes were individually deleted or overexpressed in experiments. Empirically, 40 genes individually increased heme production (up to threefold). Heme was increased by modifying target genes, which not only included the genes involved in heme biosynthesis, but also those involved in glycolysis, pyruvate, Fe-S clusters, glycine, and succinyl-coenzyme A (CoA) metabolism. Next, we developed an algorithmic method for predicting an optimal combination of these genes by using the enzyme-constrained extension of the Yeast8 model, ecYeast8. The computationally identified combination for enhanced heme production was evaluated using the heme ligand-binding biosensor (Heme-LBB). The positive targets were combined using CRISPR-Cas9 in the yeast strain (IMX581-HEM15-HEM14-HEM3-Δshm1-HEM2-Δhmx1-FET4-Δgcv2-HEM1-Δgcv1-HEM13), which produces 70-fold-higher levels of intracellular heme.

Noninvasive detection of any-stage cancer using free glycosaminoglycans.

Cancer mortality is exacerbated by late-stage diagnosis. Liquid biopsies based on genomic biomarkers can noninvasively diagnose cancers. However, validation studies have reported ~10% sensitivity to detect stage I cancer in a screening population and specific types, such as brain or genitourinary tumors, remain undetectable. We investigated urine and plasma free glycosaminoglycan profiles (GAGomes) as tumor metabolism biomarkers for multi-cancer early detection (MCED) of 14 cancer types using 2,064 samples from 1,260 cancer or healthy subjects. We observed widespread cancer-specific changes in biofluidic GAGomes recapitulated in an in vivo cancer progression model. We developed three machine learning models based on urine (Nurine = 220 cancer vs. 360 healthy) and plasma (Nplasma = 517 vs. 425) GAGomes that can detect any cancer with an area under the receiver operating characteristic curve of 0.83-0.93 with up to 62% sensitivity to stage I disease at 95% specificity. Undetected patients had a 39 to 50% lower risk of death. GAGomes predicted the putative cancer location with 89% accuracy. In a validation study on a screening-like population requiring ≥ 99% specificity, combined GAGomes predicted any cancer type with poor prognosis within 18 months with 43% sensitivity (21% in stage I; N = 121 and 49 cases). Overall, GAGomes appeared to be powerful MCED metabolic biomarkers, potentially doubling the number of stage I cancers detectable using genomic biomarkers.