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UNLABELLED: Microorganisms with simplified genomes represent interesting cell chassis for systems and synthetic biology. However, genome reduction can lead to undesired traits, such as decreased growth rate and metabolic imbalances. To investigate the impact of genome reduction on Escherichia coli strain DGF-298, a strain in which ~ 36% of the genome has been removed, we reconstructed a strain-specific metabolic model (iAC1061), investigated the regulation of gene expression using iModulon-based transcriptome analysis, and performed adaptive laboratory evolution to let the strain correct potential imbalances that arose during its simplification. The model notably predicted that the removal of all three key pathways for glycolaldehyde disposal in this microorganism would lead to a metabolic bottleneck through folate starvation. Glycolaldehyde is also known to cause self-generation of reactive oxygen species, as evidenced by the increased expression of oxidative stress resistance genes in the SoxS iModulon. The reintroduction of the aldA gene, responsible for one native glycolaldehyde disposal route, alleviated the constitutive oxidative stress response. Our results suggest that systems-level approaches and adaptive laboratory evolution have additive benefits when trying to repair and optimize genome-engineered strains. IMPORTANCE: 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 models predictions to successfully restore SoxS activity to the basal level.

Liver metabolism is central to human physiology and influences the pathogenesis of common metabolic diseases. Yet, our understanding of human liver metabolism remains incomplete, with much of current knowledge based on animal or cell culture models that do not fully recapitulate human physiology. Here, we perform in-depth measurement of metabolism in intact human liver tissue ex vivo using global 13C tracing, non-targeted mass spectrometry and model-based metabolic flux analysis. Isotope tracing allowed qualitative assessment of a wide range of metabolic pathways within a single experiment, confirming well-known features of liver metabolism but also revealing unexpected metabolic activities such as de novo creatine synthesis and branched-chain amino acid transamination, where human liver appears to differ from rodent models. Glucose production ex vivo correlated with donor plasma glucose, suggesting that cultured liver tissue retains individual metabolic phenotypes, and could be suppressed by postprandial levels of nutrients and insulin, and also by pharmacological inhibition of glycogen utilization. Isotope tracing ex vivo allows measuring human liver metabolism with great depth and resolution in an experimentally tractable system.

The emerging class of multi-functional proteins known as moonlighters challenges the one protein, one function mentality by demonstrating crosstalk between biological pathways that were previously thought to be functionally discrete. Here, we present new links between amino acid metabolism and chromatin regulation, two biological pathways that are critical for cellular and organismal homeostasis. We discovered that the threonine biosynthetic pathway is required for the transcriptional silencing of ribosomal DNA (rDNA) in Saccharomyces cerevisiae. The enzymes in the pathway promote rDNA silencing through distinct mechanisms as a subset of silencing phenotypes was rescued with exogenous threonine. In addition, we found that a key pathway enzyme, homoserine dehydrogenase, promotes DNA repair through a mechanism involving the MRX complex, a major player in DNA double strand break repair. These data further the understanding of enzymes with non-canonical roles, here demonstrated within the threonine biosynthetic pathway, and provide insight into their roles as potential anti-fungal pharmaceutical targets.

Strains across the Lactobacillaceae family form the basis for a trillion-dollar industry. Our understanding of the genomic basis for their key traits is fragmented, however, including the metabolism that is foundational to their industrial uses. Pangenome analysis of publicly available Lactobacillaceae genomes allowed us to generate genome-scale metabolic network reconstructions for 26 species of industrial importance. Their manual curation led to more than 75,000 gene-protein-reaction associations that were deployed to generate 2,446 genome-scale metabolic models. Cross-referencing genomes and known metabolic traits allowed for manual metabolic network curation and validation of the metabolic models. As a result, we provide the first pangenomic basis for metabolism in the Lactobacillaceae family and a collection of predictive computational metabolic models that enable a variety of practical uses.IMPORTANCELactobacillaceae, a bacterial family foundational to a trillion-dollar industry, is increasingly relevant to biosustainability initiatives. Our study, leveraging approximately 2,400 genome sequences, provides a pangenomic analysis of Lactobacillaceae metabolism, creating over 2,400 curated and validated genome-scale models (GEMs). These GEMs successfully predict (i) unique, species-specific metabolic reactions; (ii) niche-enriched reactions that increase organism fitness; (iii) essential media components, offering insights into the global amino acid essentiality of Lactobacillaceae; and (iv) fermentation capabilities across the family, shedding light on the metabolic basis of Lactobacillaceae-based commercial products. This quantitative understanding of Lactobacillaceae metabolic properties and their genomic basis will have profound implications for the food industry and biosustainability, offering new insights and tools for strain selection and manipulation.

Diatoms are major players in the global carbon cycle, and their metabolism is affected by ocean conditions. Understanding the impact of changing inorganic nutrients in the oceans on diatoms is crucial, given the changes in global carbon dioxide levels. Here, we present a genome-scale metabolic model (iMK1961) for Cylindrotheca closterium, an in silico resource to understand uncharacterized metabolic functions in this ubiquitous diatom. iMK1961 represents the largest diatom metabolic model to date, comprising 1961 open reading frames and 6718 reactions. With iMK1961, we identified the metabolic response signature to cope with drastic changes in growth conditions. Comparing model predictions with Tara Oceans transcriptomics data unraveled C. closteriums metabolism in situ. Unexpectedly, the diatom only grows photoautotrophically in 21% of the sunlit ocean samples, while the majority of the samples indicate a mixotrophic (71%) or, in some cases, even a heterotrophic (8%) lifestyle in the light. Our findings highlight C. closteriums metabolic flexibility and its potential role in global carbon cycling.

Advancements in synthetic biology have facilitated the incorporation of heterologous metabolic pathways into various bacterial chassis, leading to the synthesis of targeted bioproducts. However, total output from heterologous production pathways can suffer from low flux, enzyme promiscuity, formation of toxic intermediates, or intermediate loss to competing reactions, which ultimately hinder their full potential. The self-assembling, easy-to-modify, protein-based bacterial microcompartments (BMCs) offer a sophisticated way to overcome these obstacles by acting as an autonomous catalytic module decoupled from the cell's regulatory and metabolic networks. More than a decade of fundamental research on various types of BMCs, particularly structural studies of shells and their self-assembly, the recruitment of enzymes to BMC shell scaffolds, and the involvement of ancillary proteins such as transporters, regulators, and activating enzymes in the integration of BMCs into the cell's metabolism, has significantly moved the field forward. These advances have enabled bioengineers to design synthetic multi-enzyme BMCs to promote ethanol or hydrogen production, increase cellular polyphosphate levels, and convert glycerol to propanediol or formate to pyruvate. These pioneering efforts demonstrate the enormous potential of synthetic BMCs to encapsulate non-native multi-enzyme biochemical pathways for the synthesis of high-value products.

Glycosylation is a ubiquitous modification present across all of biology, affecting many things such as physicochemical properties, cellular recognition, subcellular localization, and immunogenicity. Nucleotide sugars are important precursors needed to study glycosylation and produce glycosylated products. Saccharomyces cerevisiae is a potentially powerful platform for producing glycosylated biomolecules, but it lacks nucleotide sugar diversity. Nucleotide sugar metabolism is complex, and understanding how to engineer it will be necessary to both access and study heterologous glycosylations found across biology. This review overviews the potential challenges with engineering nucleotide sugar metabolism in yeast from the salvage pathways that convert free sugars to their associated UDP-sugars to de novo synthesis where nucleotide sugars are interconverted through a complex metabolic network with governing feedback mechanisms. Finally, recent examples of engineering complex glycosylation of small molecules in S. cerevisiae are explored and assessed.

Fermentation is a type of metabolism carried out by organisms in environments without oxygen. Despite being studied for over 185 years, the diversity and complexity of this metabolism are just now becoming clear. Our review starts with the definition of fermentation, which has evolved over the years and which we help further refine. We then examine the range of organisms that carry out fermentation and their traits. Over one-fourth of all prokaryotes are fermentative, use more than 40 substrates, and release more than 50 metabolic end products. These insights come from studies analyzing records of thousands of organisms. Next, our review examines the complexity of fermentation at the biochemical level. We map out pathways of glucose fermentation in unprecedented detail, covering over 120 biochemical reactions. We also review recent studies coupling genomics and enzymology to reveal new pathways and enzymes. Our review concludes with practical applications for agriculture, human health, and industry. All these areas depend on fermentation and could be improved through manipulating fermentative microbes and enzymes. We discuss potential approaches for manipulation, including genetic engineering, electrofermentation, probiotics, and enzyme inhibitors. We hope our review underscores the importance of fermentation research and stimulates the next 185 years of study.

The evolution of pathway enzymes enhances the biosynthesis of high-value chemicals, crucial for pharmaceutical, and agrochemical applications. However, unpredictable evolutionary landscapes of pathway genes often hinder successful evolution. Here, the presence of complex epistasis is identifued within the representative naringenin biosynthetic pathway enzymes, hampering straightforward directed evolution. Subsequently, a biofoundry-assisted strategy is developed for pathway bottlenecking and debottlenecking, enabling the parallel evolution of all pathway enzymes along a predictable evolutionary trajectory in six weeks. This study then utilizes a machine learning model, ProEnsemble, to further balance the pathway by optimizing the transcription of individual genes. The broad applicability of this strategy is demonstrated by constructing an Escherichia coli chassis with evolved and balanced pathway genes, resulting in 3.65 g L-1 naringenin. The optimized naringenin chassis also demonstrates enhanced production of other flavonoids. This approach can be readily adapted for any given number of enzymes in the specific metabolic pathway, paving the way for automated chassis construction in contemporary biofoundries.

UNLABELLED: Growing environmental concerns and the need to adopt a circular economy have highlighted the importance of waste valorization for resource recovery. Microbial consortia-enabled biotechnologies have made significant developments in the biomanufacturing of valuable resources from waste biomass that serve as suitable alternatives to petrochemical-derived products. These microbial consortia-based processes are designed following a top-down or bottom-up engineering approach. The top-down approach is a classical method that uses environmental variables to selectively steer an existing microbial consortium to achieve a target function. While high-throughput sequencing has enabled microbial community characterization, the major challenge is to disentangle complex microbial interactions and manipulate the structure and function accordingly. The bottom-up approach uses prior knowledge of the metabolic pathway and possible interactions among consortium partners to design and engineer synthetic microbial consortia. This strategy offers some control over the composition and function of the consortium for targeted bioprocesses, but challenges remain in optimal assembly methods and long-term stability. In this review, we present the recent advancements, challenges, and opportunities for further improvement using top-down and bottom-up approaches for microbiome engineering. As the bottom-up approach is relatively a new concept for waste valorization, this review explores the assembly and design of synthetic microbial consortia, ecological engineering principles to optimize microbial consortia, and metabolic engineering approaches for efficient conversion. Integration of top-down and bottom-up approaches along with developments in metabolic modeling to predict and optimize consortia function are also highlighted. ONE-SENTENCE SUMMARY: This review highlights the microbial consortia-driven waste valorization for biomanufacturing through top-down and bottom-up design approaches and describes strategies, tools, and unexplored opportunities to optimize the design and stability of such consortia.

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

Fermentation is a type of metabolism pervasive in oxygen-deprived environments. Despite its importance, we know little about the range and traits of organisms that carry out this metabolism. Our study addresses this gap with a comprehensive analysis of the phenotype and genotype of fermentative prokaryotes. We assembled a dataset with phenotypic records of 8350 organisms plus 4355 genomes and 13.6 million genes. Our analysis reveals fermentation is both widespread (in ~30% of prokaryotes) and complex (forming ~300 combinations of metabolites). Furthermore, it points to previously uncharacterized proteins involved in this metabolism. Previous studies suggest that metabolic pathways for fermentation are well understood, but metabolic models built in our study show gaps in our knowledge. This study demonstrates the complexity of fermentation while showing that there is still much to learn about this metabolism. All resources in our study can be explored by the scientific community with an online, interactive tool.

Carrier-protein-dependent metabolic pathways biosynthesize fatty acids, polyketides, and non-ribosomal peptides, producing metabolites with important pharmaceutical, environmental, and industrial properties. Recent findings demonstrate that these pathways rely on selective communication mechanisms involving protein-protein interactions (PPIs) that guide enzyme reactivity and timing. While rational design of these PPIs could enable pathway design and modification, this goal remains a challenge due to the complex nature of protein interfaces. Computational methods offer an encouraging avenue, though many score functions fail to predict experimental observables, leading to low success rates. Here, we improve upon the Rosetta score function, leveraging experimental data through iterative rounds of computational prediction and mutagenesis, to design a hybrid fatty acid-non-ribosomal peptide initiation pathway. By increasing the weight of the electrostatic score term, the computational protocol proved to be more predictive, requiring fewer rounds of iteration to identify mutants with high in vitro activity. This allowed efficient design of new PPIs between a non-ribosomal peptide synthetase adenylation domain, PltF, and a fatty acid synthase acyl carrier protein, AcpP, as validated by activity and structural studies. This method provides a promising platform for customized pathway design, establishing a standard for carrier-protein-dependent pathway engineering through PPI optimization.

Thyroid cancer is a common endocrine malignancy with increasing incidence globally. Although most cases can be treated effectively, some cases are more aggressive and have a higher risk of mortality. Inhibiting RET and BRAF kinases has emerged as a potential therapeutic strategy for the treatment of thyroid cancer, particularly in cases of advanced or aggressive disease. However, the development of resistance mechanisms may limit the efficacy of these kinase inhibitors. Therefore, developing precise strategies to target thyroid cancer cell metabolism and overcome resistance is a critical area of research for advancing thyroid cancer treatment. In the field of cancer therapeutics, researchers have explored combinatorial strategies involving dual metabolic inhibition and metabolic inhibitors in combination with targeted therapy, chemotherapy, and immunotherapy to overcome the challenge of metabolic plasticity. This review highlights the need for new therapeutic approaches for thyroid cancer and discusses promising metabolic inhibitors targeting thyroid cancer. It also discusses the challenges posed by metabolic plasticity in the development of effective strategies for targeting cancer cell metabolism and explores the potential advantages of combined metabolic targeting.

Polycystic ovary syndrome (PCOS) is a prevalent endocrine and metabolic disorder that is frequently linked to anovulation in women who are experiencing infertility. Intestinal flora, also known as the "second genome" of the host, is closely associated with chronic metabolic diseases. Recently, there has been increasing attention on the connection between PCOS and the gut microbiome, and experiments have been conducted. However, the results were unsatisfactory and inconsistent. This review aims to provide a comprehensive overview of the literature investigating the associations between the gut microbiome and PCOS in adults. The goal is to identify whether there are changes in the composition of the gut microbiome in individuals with PCOS. This is the first systematic review to focus on functional alterations in the gut microbiome, which could provide insights into potential mechanisms of microbial involvement in the development of PCOS. We found that there was no significant change in gut microbiome biodiversity in PCOS. Meta-analyses of three studies revealed a significantly higher abundance of Proteobacteria (1.12, 95% CI, 0.21, 2.02, I2 = 0%) in adults with PCOS. At the genus level, Bacteroides, Enterococcus, and Escherichia-Shigella were found to be enriched in patients with PCOS. Species such as Ruminococcus gnavus group, Parabacteroides distasonis, and Bacteroides fragilis showed an increase in PCOS. Metabolic pathways associated with glucose, lipid metabolism, bile acid metabolism, and protein absorption were found to be enriched in individuals with PCOS. The gut microbiome in PCOS is not characterized by lower diversity, but the composition is altered at the phylum, family, genus, or species level. Consequently, the metabolic pathway differs according to the phenotype of PCOS. [ABSTRACT FROM AUTHOR]

BackgroundThere is a paucity of targeted therapies for patients with pseudomyxoma peritonei (PMP) secondary to low-grade appendiceal mucinous neoplasms (LAMNs). Dysregulated metabolism has emerged as a hallmark of cancer, and the relationship of metabolomics and cancer is an area of active scientific exploration. We sought to characterize phenotypic differences found in peritoneal metastases (PM) derived from LAMN versus adenocarcinoma.MethodsTumors were washed with phosphate-buffered saline (PBS), microdissected, then dissociated in ice-cold methanol dried and reconstituted in pyridine. Samples were derivatized in tert-butyldimethylsilyl (TBDMS) and subjected to gas chromatography-coupled mass spectrometry. Metabolites were assessed based on a standard library. RNA sequencing was performed, with pathway and network analyses on differentially expressed genes.ResultsEight peritoneal tumor samples were obtained and analyzed: LAMNs (4), and moderate to poorly differentiated adenocarcinoma (colon [1], appendix [3]). Decreases in pyroglutamate, fumarate, and cysteine in PM from LAMNs were found compared with adenocarcinoma. Analyses showed the differential gene expression was dominated by the prevalence of metabolic pathways, particularly lipid metabolism. The gene retinol saturase (RETSAT), downregulated by LAMN, was involved in the multiple metabolic pathways that involve lipids. Using network mapping, we found IL1B signaling to be a potential top-level modulation candidate.ConclusionsDistinct metabolic signatures may exist for PM from LAMN versus adenocarcinoma. A multitude of genes are differentially regulated, many of which are involved in metabolic pathways. Additional research is needed to identify the significance and applicability of targeting metabolic pathways in the potential development of novel therapeutics for these challenging tumors.

TRIMER, Transcription Regulation Integrated with MEtabolic Regulation, is a genome-scale modeling pipeline targeting at metabolic engineering applications. Using TRIMER, regulated metabolic reactions can be effectively predicted by integrative modeling of metabolic reactions with a transcription factor-gene regulatory network (TRN), which is modeled through a Bayesian network (BN). In this article, we focus on sensitivity analysis of metabolic flux prediction for uncertainty quantification of BN structures for TRN modeling in TRIMER. We propose a computational strategy to construct the uncertainty class of TRN models based on the inferred regulatory order uncertainty given transcriptomic expression data. With that, we analyze the prediction sensitivity of the TRIMER pipeline for the metabolite yields of interest. The obtained sensitivity analyses can guide optimal experimental design (OED) to help acquire new data that can enhance TRN modeling and achieve specific metabolic engineering objectives, including metabolite yield alterations. We have performed small- and large-scale simulated experiments, demonstrating the effectiveness of our developed sensitivity analysis strategy for BN structure learning to quantify the edge importance in terms of metabolic flux prediction uncertainty reduction and its potential to effectively guide OED.

Metabolism constitutes the core chemistry of life. How it began on the early Earth and whether it had a cellular origin are still uncertain. A leading hypothesis for life's origins postulates that metabolism arose from geochemical CO2-fixing pathways, driven by inorganic catalysts and energy sources, long before enzymes or genes existed. The acetyl-CoA pathway and the reductive tricarboxylic acid cycle are considered ancient reaction networks that hold relics of early carbon-fixing pathways. Although transition metals can promote many steps of these pathways, whether they form a functional metabolic network in abiotic cells has not been demonstrated. Here, we formulate a nonenzymatic carbon-fixing network from these pathways and determine its functional feasibility in abiotic cells by imposing fundamental physicochemical constraints. Using first principles, we show that abiotic cells can sustain a steady carbon-fixing cycle that performs a systemic function over a relatively narrow range of conditions. Furthermore, we find that in all feasible steady states, the operation of the cycle elevates the osmotic pressure, leading to volume expansion. These results suggest that achieving homeostatic metabolic states under prebiotic conditions was possible, but challenging, and volume growth was a fundamental property of early metabolism.

Objective To investigate the expression level and significance of the signal peptidase complex subunit 2 (SPCS2) gene in papillary thyroid carcinoma (PTC). Methods UALCAN and HPA Databases were used to investigate the mRNA expression levels of SPCS2 in PTC tissue and normal tissue. Gepia Database was used to predict the influence of the expression level of SPCS2 on the disease-free survival of PTC patients. The Cox regression model was used to investigate the independent risk factors for the prognosis of PTC patients. GO, KEGG, and GSEA analyses were performed for SPCS2 to identify the important signaling pathways involved in PTC. Results The mRNA expression levels of SPCS2 in PTC tissue were significantly lo-wer than those in normal tissue (Z=-43.219,P

Objective To investigate the protective effect of xylo-oligosaccharides on mice with fatty liver induced by high-fat diet and related mechanisms. Methods A total of 40 male C57/BL6J mice, aged 5 weeks, were randomly divided into low-fat diet control group (LFD group), high-fat diet model group (HFD group), high-fat diet+low-dose xylo-oligosaccharide intervention group (XOS.L group), and high-fat diet+high-dose xylo-oligosaccharide intervention group (XOS.H group), with 10 mice in each group. The mice in the LFD group were fed a low-fat diet containing 10% fat, and those in the HFD group were fed a high-fat diet containing 60% fat; in addition to the treatment in the HFD group, the mice in the XOS.L group were given xylo-oligosaccharides at a dose of 0.35 g/kg by gavage once a day, and those in the XOS.H group were given xylo-oligosaccharides at a dose of 1.0 g/kg by gavage once a day; the mice in the LFD group and the HFD group were given an equal volume of normal saline by gavage. At the end of week 16, blood, liver tissue, and fecal samples were collected from each group of mice; the serum levels of alanine ami-notransferase (ALT), aspartate aminotransferase and triglyceride (TG) were measured; HE staining and oil red O staining were used to perform a histological evaluation of the liver; 16S rRNA microbial sequencing technology and metabolomics were used to measure the changes in fecal gut microbiota and metabolites. Results compared with LFD group, the HFD group showed significant steatosis of the liver, disturbances in various metabolites including arachidonic acid and linoleic acid (LA), significant increases in the abundances of the pathogenic bacteria such as Faecalibaculum, Bacteroidaceae, and significant reductions in the abundances of the probiotic bacteria such as Ruminococcaceae, Bacteroides. Compared with the HFD group, the XOS.H groups showed a significant improvement in liver steatosis and the abundances of related microbiota such as Ruminococcaceae and Actinobacteria, significant reductions in liver SAF scores and the enrichment intensities of the proinflammatory metabolites such as Prostaglandin H2, and significant increases in the enrichment intensities of the metabolites such as Prostaglandin D2, LA, and docosahexaenoic acid. Conclusion Xylo-oligosaccharides can significantly improve liver steatosis, gut microbiota dysbiosis, and metabolite disturbances induced by high-fat diet in mice, possibly by regulating the gut microbiota to interfere with the metabolic pathways such as arachidonic acid and LA.

Genome-scale metabolic models comprehensively describe an organisms metabolism and can be tailored using omics data to model condition-specific physiology. The quality of context-specific models is impacted by (i) choice of algorithm and parameters and (ii) alternate context-specific models that equally explain the -omics data. Here we quantify the influence of alternate optima on microbial and mammalian model extraction using GIMME, iMAT, MBA, and mCADRE. We find that metabolic tasks defining an organisms phenotype must be explicitly and quantitatively protected. The scope of alternate models is strongly influenced by algorithm choice and the topological properties of the parent genome-scale model with fatty acid metabolism and intracellular metabolite transport contributing much to alternate solutions in all models. mCADRE extracted the most reproducible context-specific models and models generated using MBA had the most alternate solutions. There were fewer qualitatively different solutions generated by GIMME in E. coli, but these increased substantially in the mammalian models. Screening ensembles using a receiver operating characteristic plot identified the best-performing models. A comprehensive evaluation of models extracted using combinations of extraction methods and expression thresholds revealed that GIMME generated the best-performing models in E. coli, whereas mCADRE is better suited for complex mammalian models. These findings suggest guidelines for benchmarking -omics integration algorithms and motivate the development of a systematic workflow to enumerate alternate models and extract biologically relevant context-specific models.

Many microorganisms are auxotrophic-unable to synthesize the compounds they require for growth. With this work, we quantify the prevalence of amino acid auxotrophies across a broad diversity of bacteria and habitats. We predicted the amino acid biosynthetic capabilities of 26,277 unique bacterial genomes spanning 12 phyla using a metabolic pathway model validated with empirical data. Amino acid auxotrophy is widespread across bacterial phyla, but we conservatively estimate that the majority of taxa (78.4%) are able to synthesize all amino acids. Our estimates indicate that amino acid auxotrophies are more prevalent among obligate intracellular parasites and in free-living taxa with genomic attributes characteristic of 'streamlined' life history strategies. We predicted the amino acid biosynthetic capabilities of bacterial communities found in 12 unique habitats to investigate environmental associations with auxotrophy, using data compiled from 3813 samples spanning major aquatic, terrestrial, and engineered environments. Auxotrophic taxa were more abundant in host-associated environments (including the human oral cavity and gut) and in fermented food products, with auxotrophic taxa being relatively rare in soil and aquatic systems. Overall, this work contributes to a more complete understanding of amino acid auxotrophy across the bacterial tree of life and the ecological contexts in which auxotrophy can be a successful strategy.

Flux balance analysis (FBA) is an optimization based approach to find the optimal steady state of a metabolic network, commonly of microorganisms such as yeast strains and Escherichia coli. However, the resulting solution from an FBA is typically not unique, as the optimization problem is, more often than not, degenerate. Flux variability analysis (FVA) is a method to determine the range of possible reaction fluxes that still satisfy, within some optimality factor, the original FBA problem. The resulting range of reaction fluxes can be utilized to determine metabolic reactions of high importance, amongst other analyses. In the literature, this has been done by solving [Formula: see text] linear programs (LPs), with n being the number of reactions in the metabolic network. However, FVA can be solved with less than [Formula: see text] LPs by utilizing the basic feasible solution property of bounded LPs to reduce the number of LPs that are needed to be solved. In this work, a new algorithm is proposed to solve FVA that requires less than [Formula: see text] LPs. The proposed algorithm is benchmarked on a problem set of 112 metabolic network models ranging from single cell organisms (iMM904, ect) to a human metabolic system (Recon3D). Showing a reduction in the number of LPs required to solve the FVA problem and thus the time to solve an FVA problem.

Bacterial microcompartments (BMCs) are protein-based organelles found across the bacterial tree of life. They consist of a shell, made of proteins that oligomerize into hexagonally and pentagonally shaped building blocks, that surrounds enzymes constituting a segment of a metabolic pathway. The proteins of the shell are unique to BMCs. They also provide selective permeability; this selectivity is dictated by the requirements of their cargo enzymes. We have recently surveyed the wealth of different BMC types and their occurrence in all available genome sequence data by analyzing and categorizing their components found in chromosomal loci using HMM (Hidden Markov Model) protein profiles. To make this a "do-it yourself" analysis for the public we have devised a webserver, BMC Caller ( https://bmc-caller.prl.msu.edu ), that compares user input sequences to our HMM profiles, creates a BMC locus visualization, and defines the functional type of BMC, if known. Shell proteins in the input sequence data are also classified according to our function-agnostic naming system and there are links to similar proteins in our database as well as an external link to a structure prediction website to easily generate structural models of the shell proteins, which facilitates understanding permeability properties of the shell. Additionally, the BMC Caller website contains a wealth of information on previously analyzed BMC loci with links to detailed data for each BMC protein and phylogenetic information on the BMC shell proteins. Our tools greatly facilitate BMC type identification to provide the user information about the associated organism's metabolism and enable discovery of new BMC types by providing a reference database of all currently known examples.

Endogenous metabolic pathways in microbial cells are usually precisely controlled by sophisticated regulation networks. However, the lack of such regulations when introducing heterologous pathways in microbial hosts often causes unbalanced enzyme expression and carbon flux distribution, hindering the construction of highly efficient microbial biosynthesis systems. Here, using naringenin as the target compound, we developed an Autonomous Cascaded Artificial Dynamic (AutoCAD) regulation system to automatically coordinate the pathway expression and redirect carbon fluxes for enhanced naringenin production. The AutoCAD regulation system, consisting of both intermediate-based feedforward and product-based feedback control genetic circuits, resulted in a 16.5-fold increase in naringenin titer compared with the static control. Fed-batch fermentation using the strain with AutoCAD regulation further enhanced the naringenin titer to 277.2 mg/L. The AutoCAD regulation system, with intermediate-based feedforward control and product-triggered feedback control, provides a new paradigm of developing complicated cascade dynamic control to engineer heterologous pathways.

Pharmaceuticals have extensive reciprocal interactions with the microbiome, but whether bacterial drug sensitivity and metabolism is driven by pathways conserved in host cells remains unclear. Here we show that anti-cancer fluoropyrimidine drugs inhibit the growth of gut bacterial strains from 6 phyla. In both Escherichia coli and mammalian cells, fluoropyrimidines disrupt pyrimidine metabolism. Proteobacteria and Firmicutes metabolized 5-fluorouracil to its inactive metabolite dihydrofluorouracil, mimicking the major host mechanism for drug clearance. The preTA operon was necessary and sufficient for 5-fluorouracil inactivation by E. coli, exhibited high catalytic efficiency for the reductive reaction, decreased the bioavailability and efficacy of oral fluoropyrimidine treatment in mice and was prevalent in the gut microbiomes of colorectal cancer patients. The conservation of both the targets and enzymes for metabolism of therapeutics across domains highlights the need to distinguish the relative contributions of human and microbial cells to drug efficacy and side-effect profiles.

Plants and microbes share common metabolic pathways for producing a range of bioproducts that are potentially foundational to the future bioeconomy. However, in planta accumulation and microbial production of bioproducts have never been systematically compared on an economic basis to identify optimal routes of production. A detailed technoeconomic analysis of four exemplar compounds (4-hydroxybenzoic acid [4-HBA], catechol, muconic acid, and 2-pyrone-4,6-dicarboxylic acid [PDC]) is conducted with the highest reported yields and accumulation rates to identify economically advantaged platforms and breakeven targets for plants and microbes. The results indicate that in planta mass accumulation ranging from 0.1 to 0.3 dry weight % (dwt%) can achieve costs comparable to microbial routes operating at 40 to 55% of maximum theoretical yields. These yields and accumulation rates are sufficient to be cost competitive if the products are sold at market prices consistent with specialty chemicals ($20 to $50/kg). Prices consistent with commodity chemicals will require an order-of-magnitude-greater accumulation rate for plants and/or yields nearing theoretical maxima for microbial production platforms. This comparative analysis revealed that the demonstrated accumulation rates of 4-HBA (3.2 dwt%) and PDC (3.0 dwt%) in engineered plants vastly outperform microbial routes, even if microbial platforms were to reach theoretical maximum yields. Their recovery and sale as part of a lignocellulosic biorefinery could enable biofuel prices to be competitive with petroleum. Muconic acid and catechol, in contrast, are currently more attractive when produced microbially using a sugar feedstock. Ultimately, both platforms can play an important role in replacing fossil-derived products.

Mevalonate 3,5-bisphosphate decarboxylase is involved in the recently discovered Thermoplasma-type mevalonate pathway. The enzyme catalyzes the elimination of the 3-phosphate group from mevalonate 3,5-bisphosphate as well as concomitant decarboxylation of the substrate. This entire reaction of the enzyme resembles the latter half-reactions of its homologs, diphosphomevalonate decarboxylase and phosphomevalonate decarboxylase, which also catalyze ATP-dependent phosphorylation of the 3-hydroxyl group of their substrates. However, the crystal structure of mevalonate 3,5-bisphosphate decarboxylase and the structural reasons of the difference between reactions catalyzed by the enzyme and its homologs are unknown. In this study, we determined the X-ray crystal structure of mevalonate 3,5-bisphosphate decarboxylase from Picrophilus torridus, a thermoacidophilic archaeon of the order Thermoplasmatales. Structural and mutational analysis demonstrated the importance of a conserved aspartate residue for enzyme activity. In addition, although crystallization was performed in the absence of substrate or ligands, residual electron density having the shape of a fatty acid was observed at a position overlapping the ATP-binding site of the homologous enzyme, diphosphomevalonate decarboxylase. This finding is in agreement with the expected evolutionary route from phosphomevalonate decarboxylase (ATP-dependent) to mevalonate 3,5-bisphosphate decarboxylase (ATP-independent) through the loss of kinase activity. We found that the binding of geranylgeranyl diphosphate, an intermediate of the archeal isoprenoid biosynthesis pathway, evoked significant activation of mevalonate 3,5-bisphosphate decarboxylase, and several mutations at the putative geranylgeranyl diphosphate-binding site impaired this activation, suggesting the physiological importance of ligand binding as well as a possible novel regulatory system employed by the Thermoplasma-type mevalonate pathway.

Myxoma virus (MYXV) causes localized cutaneous fibromas in its natural hosts, tapeti and brush rabbits; however, in the European rabbit, MYXV causes the lethal disease myxomatosis. Currently, the molecular mechanisms underlying this increased virulence after cross-species transmission are poorly understood. In this study, we investigated the interaction between MYXV M156 and the host protein kinase R (PKR) to determine their crosstalk with the proinflammatory nuclear factor kappa B (NF-κB) pathway. Our results demonstrated that MYXV M156 inhibits brush rabbit PKR (bPKR) more strongly than European rabbit PKR (ePKR). This moderate ePKR inhibition could be improved by hyperactive M156 mutants. We hypothesized that the moderate inhibition of ePKR by M156 might incompletely suppress the signal transduction pathways modulated by PKR, such as the NF-κB pathway. Therefore, we analyzed NF-κB pathway activation with a luciferase-based promoter assay. The moderate inhibition of ePKR resulted in significantly higher NF-κB–dependent reporter activity than complete inhibition of bPKR. We also found a stronger induction of the NF-κB target genes TNFα and IL-6 in ePKR-expressing cells than in bPKR-expressing cells in response to M156 in both transfection and infections assays. Furthermore, a hyperactive M156 mutant did not cause ePKR-dependent NF-κB activation. These observations indicate that M156 is maladapted for ePKR inhibition, only incompletely blocking translation in these hosts, resulting in preferential depletion of short–half-life proteins, such as the NF-κB inhibitor IκBα. We speculate that this functional activation of NF-κB induced by the intermediate inhibition of ePKR by M156 may contribute to the increased virulence of MYXV in European rabbits.

Marine macroalgae have huge potential as feedstocks for production of a wide spectrum of chemicals used in biofuels, biomaterials, and bioactive compounds. Harnessing macroalgae in these ways could promote wellbeing for people while mitigating climate change and environmental destruction linked to use of fossil fuels. Microorganisms play pivotal roles in converting macroalgae into valuable products, and metabolic engineering technologies have been developed to extend their native capabilities. This review showcases current achievements in engineering the metabolisms of various microbial chassis to convert red, green, and brown macroalgae into bioproducts. Unique features of macroalgae, such as seasonal variation in carbohydrate content and salinity, provide the next challenges to advancing macroalgae-based biorefineries. Three emerging engineering strategies are discussed here: (1) designing dynamic control of metabolic pathways, (2) engineering strains of halophilic (salt-tolerant) microbes, and (3) developing microbial consortia for conversion. This review illuminates opportunities for future research communities by elucidating current approaches to engineering microbes so they can become cell factories for the utilization of macroalgae feedstocks.

Plants produce a broad variety of specialized metabolites with distinct biological activities and potential applications. Despite this potential, most biosynthetic pathways governing specialized metabolite production remain largely unresolved across the plant kingdom. The rapid advancement of genetics and biochemical tools has enhanced our ability to identify plant specialized metabolic pathways. Further advancements in transgenic technology and synthetic biology approaches have extended this to a desire to design new pathways or move existing pathways into new systems to address long-running difficulties in crop systems. This includes improving abiotic and biotic stress resistance, boosting nutritional content, etc. In this review, we assess the potential and limitations for (1) identifying specialized metabolic pathways in plants with multi-omics tools and (2) using these enzymes in synthetic biology or crop engineering. The goal of these topics is to highlight areas of research that may need further investment to enhance the successful application of synthetic biology for exploiting the myriad of specialized metabolic pathways.

MotivationRecent technological developments have facilitated an expansion of microbiome-metabolome studies, in which samples are assayed using both genomic and metabolomic technologies to characterize the abundances of microbial taxa and metabolites. A common goal of these studies is to identify microbial species or genes that contribute to differences in metabolite levels across samples. Previous work indicated that integrating these datasets with reference knowledge on microbial metabolic capacities may enable more precise and confident inference of microbe-metabolite links.ResultsWe present MIMOSA2, an R package and web application for model-based integrative analysis of microbiome-metabolome datasets. MIMOSA2 uses genomic and metabolic reference databases to construct a community metabolic model based on microbiome data and uses this model to predict differences in metabolite levels across samples. These predictions are compared with metabolomics data to identify putative microbiome-governed metabolites and taxonomic contributors to metabolite variation. MIMOSA2 supports various input data types and customization with user-defined metabolic pathways. We establish MIMOSA2's ability to identify ground truth microbial mechanisms in simulation datasets, compare its results with experimentally inferred mechanisms in honeybee microbiota, and demonstrate its application in two human studies of inflammatory bowel disease. Overall, MIMOSA2 combines reference databases, a validated statistical framework, and a user-friendly interface to facilitate modeling and evaluating relationships between members of the microbiota and their metabolic products.Availability and implementationMIMOSA2 is implemented in R under the GNU General Public License v3.0 and is freely available as a web server at http://elbo-spice.cs.tau.ac.il/shiny/MIMOSA2shiny/ and as an R package from http://www.borensteinlab.com/software_MIMOSA2.html.Supplementary informationSupplementary data are available at Bioinformatics online.

Context: Gastric cancer is one of the most prevalent types of cancer in developing countries and ranks fourth in terms of death causes. Helicobacter pylori infection is a significant contributor to the emergence of gastric cancer. Lack of early diagnosis of gastric cancer is a leading cause of death. Aims: To identify the key genes and pathways involved in gastric cancer. Methods: This study performed a comprehensive analysis of RNA-Seq data from human gastric cancer and adjacent normal tissues. Raw data passed quality checks with FastQC and were aligned to GRCh38 using HISAT2. Subread’s FeatureCounts handled transcript assembly and quantification. DESeq2 pinpointed significant genes, while ClueGO explored gene ontology and KEGG pathways. Protein-protein interaction networks, constructed with StringApp, aided in identifying hub genes through CytoHubba. This holistic approach yields insights into the molecular mechanisms underpinning gastric cancer. Results: This study detected 711 differentially expressed genes (DEGs) between normal and gastric samples. A total of 594 genes were identified as upregulated and 117 as downregulated. Major DEGs are enriched in signal transduction, stimulus-response regulation, transmembrane signaling receptor activity, and signal transduction pathways involving cytokines. In addition, 20 hub genes from the PPI network were identified based on MCC rank analysis from the CytoHubba plugin, contributing to the progression of gastric cancer. Conclusions: The top six hub genes, CD4, CTLA4, CD28, CD80, CD27, and SELL, are expected to regulate several pathways and may serve as potential biomarkers for the early detection and treatment of gastric cancer patients.

Background Immunoglobulin A nephropathy (IgAN) is the most prevalent form of glomerulonephritis worldwide. Prediction of disease progression in IgAN can help to provide individualized treatment based on accurate risk stratification. Methods We performed proton nuclear magnetic resonance-based metabolomics analyses of serum and urine samples from healthy controls, non-progressor (NP), and progressor (P) groups to identify metabolic profiles of IgAN disease progression. Metabolites that were significantly different between the NP and P groups were selected for pathway analysis. Subsequently, we analyzed multivariate area under the receiver operating characteristic (ROC) curves to evaluate the predictive power of metabolites associated with IgAN progression. Results We observed several distinct metabolic fingerprints of the P group involving the following metabolic pathways: glycolipid metabolism; valine, leucine, and isoleucine biosynthesis; aminoacyl-transfer RNA biosynthesis; glycine, serine, and threonine metabolism; and glyoxylate and dicarboxylate metabolism. In multivariate ROC analyses, the combinations of serum glycerol, threonine, and proteinuria (area under the curve [AUC], 0.923; 95% confidence interval [CI], 0.667–1.000) and of urinary leucine, valine, and proteinuria (AUC, 0.912; 95% CI, 0.667–1.000) showed the highest discriminatory ability to predict IgAN disease progression. Conclusion This study identified serum and urine metabolites profiles that can aid in the identification of progressive IgAN and proposed perturbed metabolic pathways associated with the identified metabolites.

In vitro and in vivo studies have established the organic anion transporters OAT1 (SLC22A6, NKT) and OAT3 (SLC22A8) among the main multi-specific "drug" transporters. They also transport numerous endogenous metabolites, raising the possibility of drug-metabolite interactions (DMI). To help understand the role of these drug transporters on metabolism across scales ranging from organ systems to organelles, a formal multi-scale analysis was performed. Metabolic network reconstructions of the omics-alterations resulting from Oat1 and Oat3 gene knockouts revealed links between the microbiome and human metabolism including reactions involving small organic molecules such as dihydroxyacetone, alanine, xanthine, and p-cresol-key metabolites in independent pathways. Interestingly, pairwise organ-organ interactions were also disrupted in the two Oat knockouts, with altered liver, intestine, microbiome, and skin-related metabolism. Compared to older models focused on the "one transporter-one organ" concept, these more sophisticated reconstructions, combined with integration of a multi-microbial model and more comprehensive metabolomics data for the two transporters, provide a considerably more complex picture of how renal "drug" transporters regulate metabolism across the organelle (e.g. endoplasmic reticulum, Golgi, peroxisome), cellular, organ, inter-organ, and inter-organismal scales. The results suggest that drugs interacting with OAT1 and OAT3 can have far reaching consequences on metabolism in organs (e.g. skin) beyond the kidney. Consistent with the Remote Sensing and Signaling Theory (RSST), the analysis demonstrates how transporter-dependent metabolic signals mediate organ crosstalk (e.g., gut-liver-kidney) and inter-organismal communication (e.g., gut microbiome-host).

BackgroundGenome-scale metabolic reconstruction tools have been developed in the last decades. They have helped to reconstruct eukaryotic and prokaryotic metabolic models, which have contributed to fields, e.g., genetic engineering, drug discovery, prediction of phenotypes, and other model-driven discoveries. However, the use of these programs requires a high level of bioinformatic skills. Moreover, the functionalities required to build models are scattered throughout multiple tools, requiring knowledge and experience for utilizing several tools.ResultsHere we present ChiMera, which combines tools used for model reconstruction, prediction, and visualization. ChiMera uses CarveMe in the reconstruction module, generating a gap-filled draft reconstruction able to produce growth predictions using flux balance analysis for gram-positive and gram-negative bacteria. ChiMera also contains two modules for metabolic network visualization. The first module generates maps for the most important pathways, e.g., glycolysis, nucleotides and amino acids biosynthesis, fatty acid oxidation and biosynthesis and core-metabolism. The second module produces a genome-wide metabolic map, which can be used to retrieve KEGG pathway information for each compound in the model. A module to investigate gene essentiality and knockout is also present.ConclusionsOverall, ChiMera uses automation algorithms to combine a variety of tools to automatically perform model creation, gap-filling, flux balance analysis (FBA), and metabolic network visualization. ChiMera models readily provide metabolic insights that can aid genetic engineering projects, prediction of phenotypes, and model-driven discoveries.

Brown adipose tissue (BAT) demonstrates extraordinary metabolic capacity. Previous research using conventional radio tracers reveals that BAT can act as a sink for a diverse menu of nutrients; still, the question of how BAT utilizes these nutrients remains unclear. Recent advances in mass spectrometry (MS) coupled to stable isotope tracing methods have greatly improved our understanding of metabolism in biology. Here, we have developed a BAT-tailored metabolomics and stable isotope tracing protocol using, as an example, the universally labeled 13C-glucose, a key nutrient heavily utilized by BAT. This method enables metabolic roadmaps to be drawn and pathway fluxes to be inferred for each nutrient tracer within BAT and its application could uncover new metabolic pathways not previously appreciated for BAT physiology.

The pancreatic ductal adenocarcinoma microenvironment is composed of a variety of cell types and marked by extensive fibrosis and inflammation. Tumor-associated macrophages (TAMs) are abundant, and they are important mediators of disease progression and invasion. TAMs are polarized in situ to a tumor promoting and immunosuppressive phenotype via cytokine signaling and metabolic crosstalk from malignant epithelial cells and other components of the tumor microenvironment. However, the specific distinguishing features and functions of TAMs remain poorly defined. Here, we generated tumor-educated macrophages (TEMs) in vitro and performed detailed, multiomic characterization (i.e., transcriptomics, proteomics, metabolomics). Our results reveal unique genetic and metabolic signatures of TEMs, the veracity of which were queried against our in-house single-cell RNA sequencing dataset of human pancreatic tumors. This analysis identified expression of novel, metabolic TEM markers in human pancreatic TAMs, including ARG1, ACLY, and TXNIP. We then utilized our TEM model system to study the role of mutant Kras signaling in cancer cells on TEM polarization. This revealed an important role for granulocyte-macrophage colony-stimulating factor (GM-CSF) and lactate on TEM polarization, molecules released from cancer cells in a mutant Kras-dependent manner. Lastly, we demonstrate that GM-CSF dysregulates TEM gene expression and metabolism through PI3K-AKT pathway signaling. Collectively, our results define new markers and programs to classify pancreatic TAMs, how these are engaged by cancer cells, and the precise signaling pathways mediating polarization.

Obstructive sleep apnea (OSA) is a common disorder associated with increased risk of cardiovascular disease and mortality. Iron and heme metabolism, implicated in ventilatory control and OSA comorbidities, was associated with OSA phenotypes in recent admixture mapping and gene enrichment analyses. However, its causal contribution was unclear. In this study, we performed pathway-level transcriptional Mendelian randomization (MR) analysis to investigate the causal relationships between iron and heme related pathways and OSA. In primary analysis, we examined the expression level of four iron/heme Reactome pathways as exposures and four OSA traits as outcomes using cross-tissue cis-eQTLs from the Genotype-Tissue Expression portal and published genome-wide summary statistics of OSA. We identify a significant putative causal association between up-regulated heme biosynthesis pathway with higher sleep time percentage of hypoxemia (p = 6.14 × 10-3). This association is supported by consistency of point estimates in one-sample MR in the Multi-Ethnic Study of Atherosclerosis using high coverage DNA and RNA sequencing data generated by the Trans-Omics for Precision Medicine project. Secondary analysis for 37 additional iron/heme Gene Ontology pathways did not reveal any significant causal associations. This study suggests a causal association between increased heme biosynthesis and OSA severity.

Microbial taxonomic marker gene studies using 16S rRNA gene amplicon sequencing provide an understanding of microbial community structure and diversity; however, it can be difficult to infer the functionality of microbes in the ecosystem from these data. Here, we show how to predict metabolism from phylogeny using the paprica pipeline. This approach allows resolution at the strain and species level for select regions on the prokaryotic phylogenetic tree and provides an estimate of gene and metabolic pathway abundance. For complete details on the use and execution of this protocol, please refer to Erazo and Bowman (2021).

Nitric oxide (NO) has long been known to be an intermediate in bacterial pathways of denitrification. Only in the middle to late 1980s was it found to play a central role in a much broader biological context. For example, it is now well established that NO acts as a signaling agent in metazoans, including humans, yet NO is toxic and very reactive under biological conditions. How is the biology in which NO plays a role controlled? How is NO used and the inherent toxicity avoided? Looking back at the initial discovery time, to the present, and on to the future provides many answers to questions such as those listed above.

Next to d-glucose, the pentoses l-arabinose and d-xylose are the main monosaccharide components of plant cell wall polysaccharides and are therefore of major importance in biotechnological applications that use plant biomass as a substrate. Pentose catabolism is one of the best-studied pathways of primary metabolism of Aspergillus niger, and an initial outline of this pathway with individual enzymes covering each step of the pathway has been previously established. However, although growth on l-arabinose and/or d-xylose of most pentose catabolic pathway (PCP) single deletion mutants of A. niger has been shown to be negatively affected, it was not abolished, suggesting the involvement of additional enzymes. Detailed analysis of the single deletion mutants of the known A. niger PCP genes led to the identification of additional genes involved in the pathway. These results reveal a high level of complexity and redundancy in this pathway, emphasizing the need for a comprehensive understanding of metabolic pathways before entering metabolic engineering of such pathways for the generation of more efficient fungal cell factories.

To understand and engineer plant metabolism, we need a comprehensive and accurate annotation of all metabolic information across plant species. As a step towards this goal, we generated genome-scale metabolic pathway databases of 126 algal and plant genomes, ranging from model organisms to crops to medicinal plants (https://plantcyc.org). Of these, 104 have not been reported before. We systematically evaluated the quality of the databases, which revealed that our semi-automated validation pipeline dramatically improves the quality. We then compared the metabolic content across the 126 organisms using multiple correspondence analysis and found that Brassicaceae, Poaceae, and Chlorophyta appeared as metabolically distinct groups. To demonstrate the utility of this resource, we used recently published sorghum transcriptomics data to discover previously unreported trends of metabolism underlying drought tolerance. We also used single-cell transcriptomics data from the Arabidopsis root to infer cell type-specific metabolic pathways. This work shows the quality and quantity of our resource and demonstrates its wide-ranging utility in integrating metabolism with other areas of plant biology.

Accurate nutrient sensing is important for rapid fungal growth and exploitation of available resources. Sulfur is an important nutrient source found in a number of biological macromolecules, including proteins and lipids. The model filamentous fungus Neurospora crassa is capable of utilizing sulfur found in a variety of sources from amino acids to sulfate. During sulfur starvation, the transcription factor CYS-3 is responsible for upregulation of genes involved in sulfur uptake and assimilation. Using a combination of RNA sequencing and DNA affinity purification sequencing, we performed a global survey of the N. crassa sulfur starvation response and the role of CYS-3 in regulating sulfur-responsive genes. The CYS-3 transcription factor bound the promoters and regulated genes involved in sulfur metabolism. Additionally, CYS-3 directly activated the expression of a number of uncharacterized transporter genes, suggesting that regulation of sulfur import is an important aspect of regulation by CYS-3. CYS-3 also directly regulated the expression of genes involved in mitochondrial electron transfer. During sulfur starvation, genes involved in nitrogen metabolism, such as amino acid and nucleic acid metabolic pathways, along with genes encoding proteases and nucleases that are necessary for scavenging nitrogen, were activated. Sulfur starvation also caused changes in the expression of genes involved in carbohydrate metabolism, such as those encoding glycosyl hydrolases. Thus, our data suggest a connection between sulfur metabolism and other aspects of cellular metabolism. IMPORTANCE Identification of nutrients present in the environment is a challenge common to all organisms. Sulfur is an important nutrient source found in proteins, lipids, and electron carriers that are required for the survival of filamentous fungi such as Neurospora crassa. Here, we transcriptionally profiled the response of N. crassa to characterize the global response to sulfur starvation. We also used DNA affinity purification sequencing to identify the direct downstream targets of the transcription factor responsible for regulating genes involved in sulfur uptake and assimilation. Along with genes involved in sulfur metabolism, this transcription factor regulated a number of uncharacterized transporter genes and genes involved in mitochondrial electron transfer. Our data also suggest a connection between sulfur, nitrogen, and carbon metabolism, indicating that the regulation of a number of metabolic pathways is intertwined.

Childhood posterior fossa group A ependymomas (PFAs) have limited treatment options and bear dismal prognoses compared to group B ependymomas (PFBs). PFAs overexpress the oncohistone-like protein EZHIP (enhancer of Zeste homologs inhibitory protein), causing global reduction of repressive histone H3 lysine 27 trimethylation (H3K27me3), similar to the oncohistone H3K27M. Integrated metabolic analyses in patient-derived cells and tumors, single-cell RNA sequencing of tumors, and noninvasive metabolic imaging in patients demonstrated enhanced glycolysis and tricarboxylic acid (TCA) cycle metabolism in PFAs. Furthermore, high glycolytic gene expression in PFAs was associated with a poor outcome. PFAs demonstrated high EZHIP expression associated with poor prognosis and elevated activating mark histone H3 lysine 27 acetylation (H3K27ac). Genomic H3K27ac was enriched in PFAs at key glycolytic and TCA cycle–related genes including hexokinase-2 and pyruvate dehydrogenase. Similarly, mouse neuronal stem cells (NSCs) expressing wild-type EZHIP (EZHIP-WT) versus catalytically attenuated EZHIP-M406K demonstrated H3K27ac enrichment at hexokinase-2 and pyruvate dehydrogenase, accompanied by enhanced glycolysis and TCA cycle metabolism. AMPKα-2, a key component of the metabolic regulator AMP-activated protein kinase (AMPK), also showed H3K27ac enrichment in PFAs and EZHIP-WT NSCs. The AMPK activator metformin lowered EZHIP protein concentrations, increased H3K27me3, suppressed TCA cycle metabolism, and showed therapeutic efficacy in vitro and in vivo in patient-derived PFA xenografts in mice. Our data indicate that PFAs and EZHIP-WT–expressing NSCs are characterized by enhanced glycolysis and TCA cycle metabolism. Repurposing the antidiabetic drug metformin lowered pathogenic EZHIP, increased H3K27me3, and suppressed tumor growth, suggesting that targeting integrated metabolic/epigenetic pathways is a potential therapeutic strategy for treating childhood ependymomas.

As coral reefs struggle to survive under climate change, it is crucial to know whether they have the capacity to withstand changing conditions, particularly increasing seawater temperatures. Thermal tolerance requires the integrative response of the different components of the coral holobiont (coral host, algal photosymbiont, and associated microbiome). Here, using a controlled thermal stress experiment across three divergent Caribbean coral species, we attempt to dissect holobiont member metatranscriptome responses from coral taxa with different sensitivities to heat stress and use phylogenetic ANOVA to study the evolution of gene expression adaptation. We show that coral response to heat stress is a complex trait derived from multiple interactions among holobiont members. We identify host and photosymbiont genes that exhibit lineage-specific expression level adaptation and uncover potential roles for bacterial associates in supplementing the metabolic needs of the coral-photosymbiont duo during heat stress. Our results stress the importance of integrative and comparative approaches across a wide range of species to better understand coral survival under the predicted rise in sea surface temperatures.

Genetic background and environmental conditions affect the production of sensory impact compounds by Saccharomyces cerevisiae. The relative importance of the strain-specific metabolic capabilities for the production of volatile organic compounds (VOCs) remains unclear. We investigated which amino acids contribute to VOC production and whether amino acid-VOC relations are conserved among yeast strains. Amino acid consumption and production of VOCs during grape juice fermentation was investigated using four commercial wine yeast strains: Elixir, Opale, R2, and Uvaferm. Principal component analysis of the VOC data demonstrated that Uvaferm correlated with ethyl acetate and ethyl hexanoate production, R2 negatively correlated with the acetate esters, and Opale positively correlated with fusel alcohols. Biomass formation was similar for all strains, pointing to metabolic differences in the utilization of nutrients to form VOCs. Partial least-squares linear regression showed that total aroma production is a function of nitrogen utilization (R2 = 0.87). We found that glycine, tyrosine, leucine, and lysine utilization were positively correlated with fusel alcohols and acetate esters. Mechanistic modeling of the yeast metabolic network via parsimonious flux balance analysis and flux enrichment analysis revealed enzymes with crucial roles, such as transaminases and decarboxylases. Our work provides insights in VOC production in wine yeasts. IMPORTANCE Saccharomyces cerevisiae is widely used in grape juice fermentation to produce wines. Along with the genetic background, the nitrogen in the environment in which S. cerevisiae grows impacts its regulation of metabolism. Also, commercial S. cerevisiae strains exhibit immense diversity in their formation of aromas, and a desirable aroma bouquet is an essential characteristic for wines. Since nitrogen affects aroma formation in wines, it is essential to know the extent of this connection and how it leads to strain-dependent aroma profiles in wines. We evaluated the differences in the production of key aroma compounds among four commercial wine strains. Moreover, we analyzed the role of nitrogen utilization on the formation of various aroma compounds. This work illustrates the unique aroma-producing differences among industrial yeast strains and suggests more intricate, nitrogen-associated routes influencing those aroma-producing differences.

The prevalence of Aspergillus fumigatus colonization in individuals with cystic fibrosis (CF) and subsequent fungal persistence in the lung is increasingly recognized. However, there is no consensus for clinical management of A. fumigatus in CF individuals, due largely to uncertainty surrounding A. fumigatus CF pathogenesis and virulence mechanisms. To address this gap in knowledge, a longitudinal series of A. fumigatus isolates from an individual with CF were collected over 4.5 years. Isolate genotypes were defined with whole-genome sequencing that revealed both transitory and persistent A. fumigatus in the lung. Persistent lineage isolates grew most readily in a low-oxygen culture environment, and conidia were more sensitive to oxidative stress-inducing conditions than those from nonpersistent isolates. Closely related persistent isolates harbored a unique allele of the high-osmolarity glycerol (HOG) pathway mitogen-activated protein kinase kinase, Pbs2 (pbs2C2). Data suggest this novel pbs2C2 allele arose in vivo and is necessary for the fungal response to osmotic stress in a low-oxygen environment through hyperactivation of the HOG (SakA) signaling pathway. Hyperactivation of the HOG pathway through pbs2C2 comes at the cost of decreased conidial stress resistance in the presence of atmospheric oxygen levels. These novel findings shed light on pathoadaptive mechanisms of A. fumigatus in CF, lay the foundation for identifying persistent A. fumigatus isolates that may require antifungal therapy, and highlight considerations for successful culture of persistent Aspergillus CF isolates. IMPORTANCE Aspergillus fumigatus infection causes a spectrum of clinical manifestations. For individuals with cystic fibrosis (CF), allergic bronchopulmonary aspergillosis (ABPA) is an established complication, but there is a growing appreciation for A. fumigatus airway persistence in CF disease progression. There currently is little consensus for clinical management of A. fumigatus long-term culture positivity in CF. A better understanding of A. fumigatus pathogenesis mechanisms in CF is expected to yield insights into when antifungal therapies are warranted. Here, a 4.5-year longitudinal collection of A. fumigatus isolates from a patient with CF identified a persistent lineage that harbors a unique allele of the Pbs2 mitogen-activated protein kinase kinase (MAPKK) necessary for unique CF-relevant stress phenotypes. Importantly for A. fumigatus CF patient diagnostics, this allele provides increased fitness under CF lung-like conditions at a cost of reduced in vitro growth under standard laboratory conditions. These data illustrate a molecular mechanism for A. fumigatus CF lung persistence with implications for diagnostics and antifungal therapy.

Viruses influence the fate of nutrients and human health by killing microorganisms and altering metabolic processes. Organosulfur metabolism and biologically derived hydrogen sulfide play dynamic roles in manifestation of diseases, infrastructure degradation, and essential biological processes. Although microbial organosulfur metabolism is well studied, the role of viruses in organosulfur metabolism is unknown. Here, we report the discovery of 39 gene families involved in organosulfur metabolism encoded by 3,749 viruses from diverse ecosystems, including human microbiomes. The viruses infect organisms from all three domains of life. Six gene families encode for enzymes that degrade organosulfur compounds into sulfide, whereas others manipulate organosulfur compounds and may influence sulfide production. We show that viral metabolic genes encode key enzymatic domains, are translated into protein, and are maintained after recombination, and sulfide provides a fitness advantage to viruses. Our results reveal viruses as drivers of organosulfur metabolism with important implications for human and environmental health.

Early-life antibiotic exposure perturbs the intestinal microbiota and accelerates type 1 diabetes (T1D) development in the NOD mouse model. Here, we found that maternal cecal microbiota transfer (CMT) to NOD mice after early-life antibiotic perturbation largely rescued the induced T1D enhancement. Restoration of the intestinal microbiome was significant and persistent, remediating the antibiotic-depleted diversity, relative abundance of particular taxa, and metabolic pathways. CMT also protected against perturbed metabolites and normalized innate and adaptive immune effectors. CMT restored major patterns of ileal microRNA and histone regulation of gene expression. Further experiments suggest a gut-microbiota-regulated T1D protection mechanism centered on Reg3γ, in an innate intestinal immune network involving CD44, TLR2, and Reg3γ. This regulation affects downstream immunological tone, which may lead to protection against tissue-specific T1D injury.