Immuno-Transcriptomic Profiling of Blood and Tumor Tissue Identifies Gene Signatures Associated with Immunotherapy Response in Metastatic Bladder Cancer.

Blood-based biomarkers represent ideal candidates for the development of non-invasive immuno-oncology-based assays. However, to date, no blood biomarker has been validated to predict clinical responses to immunotherapy. In this study, we used next-generation sequencing (RNAseq) on bulk RNA extracted from whole blood and tumor samples in a pre-clinical MIBC mouse model. We aimed to identify biomarkers associated with immunotherapy response and assess the potential application of simple non-invasive blood biomarkers as a therapeutic decision-making assay compared to tissue-based biomarkers. We established that circulating immune cells and the tumor microenvironment (TME) display highly organ-specific transcriptional responses to ICIs. Interestingly, in both, a common lymphocytic activation signature can be identified associated with the efficient response to immunotherapy, including a blood-specific CD8+ T cell activation/proliferation signature which predicts the immunotherapy response.

A workflow for annotating the knowledge gaps in metabolic reconstructions using known and hypothetical reactions.

Advances in medicine and biotechnology rely on a deep understanding of biological processes. Despite the increasingly available types and amounts of omics data, significant knowledge gaps remain, with current approaches to identify and curate missing annotations being limited to a set of already known reactions. Here, we introduce Network Integrated Computational Explorer for Gap Annotation of Metabolism (NICEgame), a workflow to identify and curate nonannotated metabolic functions in genomes using the ATLAS of Biochemistry and genome-scale metabolic models (GEMs). To resolve gaps in GEMs, NICEgame provides alternative sets of known and hypothetical reactions, assesses their thermodynamic feasibility, and suggests candidate genes to catalyze these reactions. We identified metabolic gaps and applied NICEgame in the latest GEM of Escherichia coli, iML1515, and enhanced the E. coli genome annotation by resolving 47% of these gaps. NICEgame, applicable to any GEM and functioning from open-source software, should thus enhance all GEM-based predictions and subsequent biotechnological and biomedical applications.

Comparative multi-tissue profiling reveals extensive tissue-specificity in transcriptome reprogramming during thermal adaptation.

Thermal adaptation is an extensively used intervention for enhancing or suppressing thermogenic and mitochondrial activity in adipose tissues. As such, it has been suggested as a potential lifestyle intervention for body weight maintenance. While the metabolic consequences of thermal acclimation are not limited to the adipose tissues, the impact on the rest of the tissues in context of their gene expression profile remains unclear. Here, we provide a systematic characterization of the effects in a comparative multi-tissue RNA sequencing approach following exposure of mice to 10 °C, 22 °C, or 34 °C in a panel of organs consisting of spleen, bone marrow, spinal cord, brain, hypothalamus, ileum, liver, quadriceps, subcutaneous-, visceral- and brown adipose tissues. We highlight that transcriptional responses to temperature alterations exhibit a high degree of tissue-specificity both at the gene level and at GO enrichment gene sets, and show that the tissue-specificity is not directed by the distinct basic gene expression pattern exhibited by the various organs. Our study places the adaptation of individual tissues to different temperatures in a whole-organism framework and provides integrative transcriptional analysis necessary for understanding the temperature-mediated biological programming.

Humans, mice and most other mammals are constantly exposed to fluctuations in the temperature of their environment. These fluctuations cause striking metabolic effects in the body, for example, exposure to cold promotes burning of calories to generate heat, thereby reducing how much fat accumulates in the body. On the other hand, warmer temperatures strengthen the bones and protect against a bone disease known as osteoporosis. As such, it has been suggested that exposure to alternating warm or cold temperatures could be a potential lifestyle intervention that conveys various benefits to our health. Our body stores fat in tissues known as adipose tissues, which are found all over the body including under the skin and around our major organs and muscles. Exposure to cold triggers changes in the activities of some genes in the adipose tissues to burn more calories. But it remains unclear how temperature affects the activities of other organs with respect to their expression of genes in the whole-body context. Hadadi, Spiljar et al. used an RNA sequencing approach to study the activities of genes in various tissues of mice exposed to cold (10°C), room temperature (22°C), or mild warm (34°C). The experiments revealed numerous genes whose levels were different in the various organs and temperatures tested. Overall, adipose tissues experienced the biggest changes in gene levels between different temperatures, followed by tissues involved in immune responses, and the brain and spinal cord tissues. Each organ changed gene expression levels in its own way. , and this was not due to the different intimate gene expression profile between the various organs. These findings improve our understanding of how changes in temperature affect mammals by putting the responses of individual tissues into the context of the whole body. Hadadi, Spiljar et al. also generated a web-based, free-to-use application to allow others to view and further analyze the data collected in this work for gene levels in the various organs of interest.

Intestinal microbiota as a route for micronutrient bioavailability.

The deficiency of micronutrients, including vitamins and minerals, is estimated to affect two billion people worldwide and can have devastating immediate and long-term consequences. Major causes range from inadequate micronutrient consumption mostly owing to a lack of dietary diversity, to poor nutrient absorption in the gastrointestinal tract as a result of clinical or pathological conditions. Recent studies in model organisms and humans demonstrated that intestinal microbiota plays an important role in the de novo biosynthesis and bioavailability of several micronutrients and might be a major determinant of human micronutrient status. Here, we address the importance of the gut microbiome for maintaining the balance of host vitamins and minerals and explore its potential therapeutic benefits and implications on human health.

Cold exposure protects from neuroinflammation through immunologic reprogramming.

Autoimmunity is energetically costly, but the impact of a metabolically active state on immunity and immune-mediated diseases is unclear. Ly6Chi monocytes are key effectors in CNS autoimmunity with an elusive role in priming naive autoreactive T cells. Here, we provide unbiased analysis of the immune changes in various compartments during cold exposure and show that this energetically costly stimulus markedly ameliorates active experimental autoimmune encephalomyelitis (EAE). Cold exposure decreases MHCII on monocytes at steady state and in various inflammatory mouse models and suppresses T cell priming and pathogenicity through the modulation of monocytes. Genetic or antibody-mediated monocyte depletion or adoptive transfer of Th1- or Th17-polarized cells for EAE abolishes the cold-induced effects on T cells or EAE, respectively. These findings provide a mechanistic link between environmental temperature and neuroinflammation and suggest competition between cold-induced metabolic adaptations and autoimmunity as energetic trade-off beneficial for the immune-mediated diseases.

NICEdrug.ch, a workflow for rational drug design and systems-level analysis of drug metabolism.

The discovery of a drug requires over a decade of intensive research and financial investments - and still has a high risk of failure. To reduce this burden, we developed the NICEdrug.ch resource, which incorporates 250,000 bioactive molecules, and studied their enzymatic metabolic targets, fate, and toxicity. NICEdrug.ch includes a unique fingerprint that identifies reactive similarities between drug-drug and drug-metabolite pairs. We validated the application, scope, and performance of NICEdrug.ch over similar methods in the field on golden standard datasets describing drugs and metabolites sharing reactivity, drug toxicities, and drug targets. We use NICEdrug.ch to evaluate inhibition and toxicity by the anticancer drug 5-fluorouracil, and suggest avenues to alleviate its side effects. We propose shikimate 3-phosphate for targeting liver-stage malaria with minimal impact on the human host cell. Finally, NICEdrug.ch suggests over 1300 candidate drugs and food molecules to target COVID-19 and explains their inhibitory mechanism for further experimental screening. The NICEdrug.ch database is accessible online to systematically identify the reactivity of small molecules and druggable enzymes with practical applications in lead discovery and drug repurposing.

Environmental connectivity controls diversity in soil microbial communities.

Interspecific interactions are thought to govern the stability and functioning of microbial communities, but the influence of the spatial environment and its structural connectivity on the potential of such interactions to unfold remain largely unknown. Here we studied the effects on community growth and microbial diversity as a function of environmental connectivity, where we define environmental connectivity as the degree of habitat fragmentation preventing microbial cells from living together. We quantitatively compared growth of a naturally-derived high microbial diversity community from soil in a completely mixed liquid suspension (high connectivity) to growth in a massively fragmented and poorly connected environment (low connectivity). The low connectivity environment consisted of homogenously-sized miniature agarose beads containing random single or paired founder cells. We found that overall community growth was the same in both environments, but the low connectivity environment dramatically reduced global community-level diversity compared to the high connectivity environment. Experimental observations were supported by community growth modeling. The model predicts a loss of diversity in the low connectivity environment as a result of negative interspecific interactions becoming more dominant at small founder species numbers. Counterintuitively for the low connectivity environment, growth of isolated single genotypes was less productive than that of random founder genotype cell pairs, suggesting that the community as a whole profited from emerging positive interspecific interactions. Our work demonstrates the importance of environmental connectivity for growth of natural soil microbial communities, which aids future efforts to intervene in or restore community composition to achieve engineering and biotechnological objectives.

Genome-wide gene expression changes of Pseudomonas veronii 1YdBTEX2 during bioaugmentation in polluted soils.

BACKGROUND: Bioaugmentation aims to use the capacities of specific bacterial strains inoculated into sites to enhance pollutant biodegradation. Bioaugmentation results have been mixed, which has been attributed to poor inoculant growth and survival in the field, and, consequently, moderate catalytic performance. However, our understanding of biodegradation activity mostly comes from experiments conducted under laboratory conditions, and the processes occurring during adaptation and invasion of inoculants into complex environmental microbiomes remain poorly known. The main aim of this work was thus to study the specific and different cellular reactions of an inoculant for bioaugmentation during adaptation, growth and survival in natural clean and contaminated non-sterile soils, in order to better understand factors limiting bioaugmentation. RESULTS: As inoculant we focused on the monoaromatic compound-degrading bacterium Pseudomonas veronii 1YdBTEX2. The strain proliferated in all but one soil types in presence and in absence of exogenously added toluene. RNAseq and differential genome-wide gene expression analysis illustrated both a range of common soil responses such as increased nutrient scavenging and recycling, expression of defense mechanisms, as well as environment-specific reactions, notably osmoprotection and metal homeostasis. The core metabolism of P. veronii remained remarkably constant during exponential growth irrespective of the environment, with slight changes in cofactor regeneration pathways, possibly needed for balancing defense reactions. CONCLUSIONS: P. veronii displayed a versatile global program, enabling it to adapt to a variety of soil environments in the presence and even in absence of its target pollutant toluene. Our results thus challenge the widely perceived dogma of poor survival and growth of exogenous inoculants in complex microbial ecosystems such as soil and provide a further basis to developing successful bioaugmentation strategies.

Warmth Prevents Bone Loss Through the Gut Microbiota.

Osteoporosis is the most prevalent metabolic bone disease, characterized by low bone mass and microarchitectural deterioration. Here, we show that warmth exposure (34°C) protects against ovariectomy-induced bone loss by increasing trabecular bone volume, connectivity density, and thickness, leading to improved biomechanical bone strength in adult female, as well as in young male mice. Transplantation of the warm-adapted microbiota phenocopies the warmth-induced bone effects. Both warmth and warm microbiota transplantation revert the ovariectomy-induced transcriptomics changes of the tibia and increase periosteal bone formation. Combinatorial metagenomics/metabolomics analysis shows that warmth enhances bacterial polyamine biosynthesis, resulting in higher total polyamine levels in vivo. Spermine and spermidine supplementation increases bone strength, while inhibiting polyamine biosynthesis in vivo limits the beneficial warmth effects on the bone. Our data suggest warmth exposure as a potential treatment option for osteoporosis while providing a mechanistic framework for its benefits in bone disease.

Rapid detection of microbiota cell type diversity using machine-learned classification of flow cytometry data.

The study of complex microbial communities typically entails high-throughput sequencing and downstream bioinformatics analyses. Here we expand and accelerate microbiota analysis by enabling cell type diversity quantification from multidimensional flow cytometry data using a supervised machine learning algorithm of standard cell type recognition (CellCognize). As a proof-of-concept, we trained neural networks with 32 microbial cell and bead standards. The resulting classifiers were extensively validated in silico on known microbiota, showing on average 80% prediction accuracy. Furthermore, the classifiers could detect shifts in microbial communities of unknown composition upon chemical amendment, comparable to results from 16S-rRNA-amplicon analysis. CellCognize was also able to quantify population growth and estimate total community biomass productivity, providing estimates similar to those from 14C-substrate incorporation. CellCognize complements current sequencing-based methods by enabling rapid routine cell diversity analysis. The pipeline is suitable to optimize cell recognition for recurring microbiota types, such as in human health or engineered systems.

AMPK Profiling in Rodent and Human Pancreatic Beta-Cells under Nutrient-Rich Metabolic Stress.

Chronic exposure of pancreatic ß-cells to elevated nutrient levels impairs their function and potentially induces apoptosis. Like in other cell types, AMPK is activated in ß-cells under conditions of nutrient deprivation, while little is known on AMPK responses to metabolic stresses. Here, we first reviewed recent studies on the role of AMPK activation in ß-cells. Then, we investigated the expression profile of AMPK pathways in ß-cells following metabolic stresses. INS-1E ß-cells and human islets were exposed for 3 days to glucose (5.5-25 mM), palmitate or oleate (0.4 mM), and fructose (5.5 mM). Following these treatments, we analyzed transcript levels of INS-1E ß-cells by qRT-PCR and of human islets by RNA-Seq; with a special focus on AMPK-associated genes, such as the AMPK catalytic subunits α1 (Prkaa1) and α2 (Prkaa2). AMPKα and pAMPKα were also evaluated at the protein level by immunoblotting. Chronic exposure to the different metabolic stresses, known to alter glucose-stimulated insulin secretion, did not change AMPK expression, either in insulinoma cells or in human islets. Expression profile of the six AMPK subunits was marginally modified by the different diabetogenic conditions. However, the expression of some upstream kinases and downstream AMPK targets, including K-ATP channel subunits, exhibited stress-specific signatures. Interestingly, at the protein level, chronic fructose treatment favored fasting-like phenotype in human islets, as witnessed by AMPK activation. Collectively, previously published and present data indicate that, in the ß-cell, AMPK activation might be implicated in the pre-diabetic state, potentially as a protective mechanism.

Author Correction: Mechanistic insights into bacterial metabolic reprogramming from omics-integrated genome-scale models.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Mechanistic insights into bacterial metabolic reprogramming from omics-integrated genome-scale models.

Understanding the adaptive responses of individual bacterial strains is crucial for microbiome engineering approaches that introduce new functionalities into complex microbiomes, such as xenobiotic compound metabolism for soil bioremediation. Adaptation requires metabolic reprogramming of the cell, which can be captured by multi-omics, but this data remains formidably challenging to interpret and predict. Here we present a new approach that combines genome-scale metabolic modeling with transcriptomics and exometabolomics, both of which are common tools for studying dynamic population behavior. As a realistic demonstration, we developed a genome-scale model of Pseudomonas veronii 1YdBTEX2, a candidate bioaugmentation agent for accelerated metabolism of mono-aromatic compounds in soil microbiomes, while simultaneously collecting experimental data of P. veronii metabolism during growth phase transitions. Predictions of the P. veronii growth rates and specific metabolic processes from the integrated model closely matched experimental observations. We conclude that integrative and network-based analysis can help build predictive models that accurately capture bacterial adaptation responses. Further development and testing of such models may considerably improve the successful establishment of bacterial inoculants in more complex systems.

Enhanced flux prediction by integrating relative expression and relative metabolite abundance into thermodynamically consistent metabolic models.

The ever-increasing availability of transcriptomic and metabolomic data can be used to deeply analyze and make ever-expanding predictions about biological processes, as changes in the reaction fluxes through genome-wide pathways can now be tracked. Currently, constraint-based metabolic modeling approaches, such as flux balance analysis (FBA), can quantify metabolic fluxes and make steady-state flux predictions on a genome-wide scale using optimization principles. However, relating the differential gene expression or differential metabolite abundances in different physiological states to the differential flux profiles remains a challenge. Here we present a novel method, named REMI (Relative Expression and Metabolomic Integrations), that employs genome-scale metabolic models (GEMs) to translate differential gene expression and metabolite abundance data obtained through genetic or environmental perturbations into differential fluxes to analyze the altered physiology for any given pair of conditions. REMI allows for gene-expression, metabolite abundance, and thermodynamic data to be integrated into a single framework, then uses optimization principles to maximize the consistency between the differential gene-expression levels and metabolite abundance data and the estimated differential fluxes and thermodynamic constraints. We applied REMI to integrate into the Escherichia coli GEM publicly available sets of expression and metabolomic data obtained from two independent studies and under wide-ranging conditions. The differential flux distributions obtained from REMI corresponding to the various perturbations better agreed with the measured fluxomic data, and thus better reflected the different physiological states, than a traditional model. Compared to the similar alternative method that provides one solution from the solution space, REMI was able to enumerate several alternative flux profiles using a mixed-integer linear programming approach. Using this important advantage, we performed a high-frequency analysis of common genes and their associated reactions in the obtained alternative solutions and identified the most commonly regulated genes across any two given conditions. We illustrate that this new implementation provides more robust and biologically relevant results for a better understanding of the system physiology.

Enzyme annotation for orphan and novel reactions using knowledge of substrate reactive sites.

Thousands of biochemical reactions with characterized activities are "orphan," meaning they cannot be assigned to a specific enzyme, leaving gaps in metabolic pathways. Novel reactions predicted by pathway-generation tools also lack associated sequences, limiting protein engineering applications. Associating orphan and novel reactions with known biochemistry and suggesting enzymes to catalyze them is a daunting problem. We propose the method BridgIT to identify candidate genes and catalyzing proteins for these reactions. This method introduces information about the enzyme binding pocket into reaction-similarity comparisons. BridgIT assesses the similarity of two reactions, one orphan and one well-characterized nonorphan reaction, using their substrate reactive sites, their surrounding structures, and the structures of the generated products to suggest enzymes that catalyze the most-similar nonorphan reactions as candidates for also catalyzing the orphan ones. We performed two large-scale validation studies to test BridgIT predictions against experimental biochemical evidence. For the 234 orphan reactions from the Kyoto Encyclopedia of Genes and Genomes (KEGG) 2011 (a comprehensive enzymatic-reaction database) that became nonorphan in KEGG 2018, BridgIT predicted the exact or a highly related enzyme for 211 of them. Moreover, for 334 of 379 novel reactions in 2014 that were later cataloged in KEGG 2018, BridgIT predicted the exact or highly similar enzymes. BridgIT requires knowledge about only four connecting bonds around the atoms of the reactive sites to correctly annotate proteins for 93% of analyzed enzymatic reactions. Increasing to seven connecting bonds allowed for the accurate identification of a sequence for nearly all known enzymatic reactions.

Genomic changes underlying host specialization in the bee gut symbiont Lactobacillus Firm5.

Bacteria that engage in long-standing associations with particular hosts are expected to evolve host-specific adaptations that limit their capacity to thrive in other environments. Consistent with this, many gut symbionts seem to have a limited host range, based on community profiling and phylogenomics. However, few studies have experimentally investigated host specialization of gut symbionts and the underlying mechanisms have largely remained elusive. Here, we studied host specialization of a dominant gut symbiont of social bees, Lactobacillus Firm5. We show that Firm5 strains isolated from honey bees and bumble bees separate into deep-branching host-specific phylogenetic lineages. Despite their divergent evolution, colonization experiments show that bumble bee strains are capable of colonizing the honey bee gut. However, they were less successful than honey bee strains, and competition with honey bee strains completely abolished their colonization. In contrast, honey bee strains of divergent phylogenetic lineages were able to coexist within individual bees. This suggests that both host selection and interbacterial competition play important roles in host specialization. Using comparative genomics of 27 Firm5 isolates, we found that the genomes of honey bee strains harbour more carbohydrate-related functions than bumble bee strains, possibly providing a competitive advantage in the honey bee gut. Remarkably, most of the genes encoding carbohydrate-related functions were not conserved among the honey bee strains, which suggests that honey bees can support a metabolically more diverse community of Firm5 strains than bumble bees. These findings advance our understanding of the genomic changes underlying host specialization.

Discovery and Evaluation of Biosynthetic Pathways for the Production of Five Methyl Ethyl Ketone Precursors.

The limited supply of fossil fuels and the establishment of new environmental policies shifted research in industry and academia toward sustainable production of the second generation of biofuels, with methyl ethyl ketone (MEK) being one promising fuel candidate. MEK is a commercially valuable petrochemical with an extensive application as a solvent. However, as of today, a sustainable and economically viable production of MEK has not yet been achieved despite several attempts of introducing biosynthetic pathways in industrial microorganisms. We used BNICE.ch as a retrobiosynthesis tool to discover all novel pathways around MEK. Out of 1325 identified compounds connecting to MEK with one reaction step, we selected 3-oxopentanoate, but-3-en-2-one, but-1-en-2-olate, butylamine, and 2-hydroxy-2-methylbutanenitrile for further study. We reconstructed 3 679 610 novel biosynthetic pathways toward these 5 compounds. We then embedded these pathways into the genome-scale model of E. coli, and a set of 18 622 were found to be the most biologically feasible ones on the basis of thermodynamics and their yields. For each novel reaction in the viable pathways, we proposed the most similar KEGG reactions, with their gene and protein sequences, as candidates for either a direct experimental implementation or as a basis for enzyme engineering. Through pathway similarity analysis we classified the pathways and identified the enzymes and precursors that were indispensable for the production of the target molecules. These retrobiosynthesis studies demonstrate the potential of BNICE.ch for discovery, systematic evaluation, and analysis of novel pathways in synthetic biology and metabolic engineering studies.

Heterologous Expression of Pseudomonas putida Methyl-Accepting Chemotaxis Proteins Yields Escherichia coli Cells Chemotactic to Aromatic Compounds.

Escherichia coli, commonly used in chemotaxis studies, is attracted mostly by amino acids, sugars, and peptides. We envisioned modifying the chemotaxis specificity of E. coli by expressing heterologous chemoreceptors from Pseudomonas putida enabling attraction either to toluene or benzoate. The mcpT gene encoding the type 40-helical bundle (40H) methyl-accepting chemoreceptor for toluene from Pseudomonas putida MT53 and the pcaY gene for the type 40H receptor for benzoate and related molecules from P. putida F1 were expressed from the trg promoter on a plasmid in motile wild-type E. coli MG1655. E. coli cells expressing McpT accumulated in chemoattraction assays to sources with 60 to 200 µM toluene, although less strongly than the response to 100 µM serine, but statistically significantly stronger than that to sources without any added attractant. An McpT-mCherry fusion protein was detectably expressed in E. coli and yielded weak but distinguishable membranes and polar foci in 1% of cells. E. coli cells expressing PcaY showed weak attraction to 0.1 to 1 mM benzoate, but 50 to 70% of cells localized the PcaY-mCherry fusion to their membrane. We conclude that implementing heterologous receptors in the E. coli chemotaxis network is possible and, upon improvement of the compatibility of the type 40H chemoreceptors, may bear interest for biosensing.IMPORTANCE Bacterial chemotaxis might be harnessed for the development of rapid biosensors, in which chemical availability is deduced from cell accumulation to chemoattractants over time. Chemotaxis of Escherichia coli has been well studied, but the bacterium is not attracted to chemicals of environmental concern, such as aromatic solvents. We show here that heterologous chemoreceptors for aromatic compounds from Pseudomonas putida at least partly functionally complement the E. coli chemotaxis network, yielding cells attracted to toluene or benzoate. Complementation was still inferior to native chemoattractants, like serine, but our study demonstrates the potential for obtaining selective sensing for aromatic compounds in E. coli.

Exploring biochemical pathways for mono-ethylene glycol (MEG) synthesis from synthesis gas.

Mono-ethylene glycol (MEG) is an important petrochemical with widespread use in numerous consumer products. The current industrial MEG-production process relies on non-renewable fossil fuel-based feedstocks, such as petroleum, natural gas, and naphtha; hence, it is useful to explore alternative routes of MEG-synthesis from gases as they might provide a greener and more sustainable alternative to the current production methods. Technologies of synthetic biology and metabolic engineering of microorganisms can be deployed for the expression of new biochemical pathways for MEG-synthesis from gases, provided that such promising alternative routes are first identified. We used the BNICE.ch algorithm to develop novel and previously unknown biological pathways to MEG from synthesis gas by leveraging the Wood-Ljungdahl pathway of carbon fixation of acetogenic bacteria. We developed a set of useful pathway pruning and analysis criteria to systematically assess thousands of pathways generated by BNICE.ch. Published genome-scale models of Moorella thermoacetica and Clostridium ljungdahlii were used to perform the pathway yield calculations and in-depth analyses of seven (7) newly developed biological MEG-producing pathways from gases, including CO2, CO, and H2. These analyses helped identify not only better candidate pathways, but also superior chassis organisms that can be used for metabolic engineering of the candidate pathways. The pathway generation, pruning, and detailed analysis procedures described in this study can also be used to develop biochemical pathways for other commodity chemicals from gaseous substrates.

Reconstruction of biological pathways and metabolic networks from in silico labeled metabolites.

Reaction atom mappings track the positional changes of all of the atoms between the substrates and the products as they undergo the biochemical transformation. However, information on atom transitions in the context of metabolic pathways is not widely available in the literature. The understanding of metabolic pathways at the atomic level is of great importance as it can deconvolute the overlapping catabolic/anabolic pathways resulting in the observed metabolic phenotype. The automated identification of atom transitions within a metabolic network is a very challenging task since the degree of complexity of metabolic networks dramatically increases when we transit from metabolite-level studies to atom-level studies. Despite being studied extensively in various approaches, the field of atom mapping of metabolic networks is lacking an automated approach, which (i) accounts for the information of reaction mechanism for atom mapping and (ii) is extendable from individual atom-mapped reactions to atom-mapped reaction networks. Hereby, we introduce a computational framework, iAM.NICE (in silico Atom Mapped Network Integrated Computational Explorer), for the systematic atom-level reconstruction of metabolic networks from in silico labelled substrates. iAM.NICE is to our knowledge the first automated atom-mapping algorithm that is based on the underlying enzymatic biotransformation mechanisms, and its application goes beyond individual reactions and it can be used for the reconstruction of atom-mapped metabolic networks. We illustrate the applicability of our method through the reconstruction of atom-mapped reactions of the KEGG database and we provide an example of an atom-level representation of the core metabolic network of E. coli.