Identifying reaction modules in metabolic pathways: bioinformatic deduction and experimental validation of a new putative route in purine catabolism

Charting unknown metabolic reactions by mass spectrometry-resolved stable-isotope tracing metabolomics

Metabolic reactions play important roles in organisms such as providing energy, transmitting signals, and synthesizing biomacromolecules. Charting unknown metabolic reactions in cells is hindered by limited technologies, restricting the holistic understanding of cellular metabolism. Using mass spectrometry-resolved stable-isotope tracing metabolomics, we develop an isotopologue similarity networking strategy, namely IsoNet, to effectively deduce previously unknown metabolic reactions. The strategy uncovers ~300 previously unknown metabolic reactions in living cells and mice. Specifically, we elaborately chart the metabolic reaction network related to glutathione, unveiling three previously unreported reactions nestled within glutathione metabolism. Among these, a transsulfuration reaction, synthesizing γ-glutamyl-seryl-glycine directly from glutathione, underscores the role of glutathione as a sulfur donor. Functional metabolomics studies systematically characterize biochemical effects of previously unknown reactions in glutathione metabolism, showcasing their diverse functions in regulating cellular metabolism. Overall, these newly uncovered metabolic reactions fill gaps in the metabolic network maps, facilitating exploration of uncharted territories in cellular biochemistry.

Review on computer-assisted biosynthetic capacities elucidation to assess metabolic interactions and communication within microbial communities

Microbial communities thrive through interactions and communication, which are challenging to study as most microorganisms are not cultivable. To address this challenge, researchers focus on the extracellular space where communication events occur. Exometabolomics and interactome analysis provide insights into the molecules involved in communication and the dynamics of their interactions. Advances in sequencing technologies and computational methods enable the reconstruction of taxonomic and functional profiles of microbial communities using high-throughput multi-omics data. Network-based approaches, including community flux balance analysis, aim to model molecular interactions within and between communities. Despite these advances, challenges remain in computer-assisted biosynthetic capacities elucidation, requiring continued innovation and collaboration among diverse scientists. This review provides insights into the current state and future directions of computer-assisted biosynthetic capacities elucidation in studying microbial communities.

Anti-Dengue: A Machine Learning-Assisted Prediction of Small Molecule Antivirals against Dengue Virus and Implications in Drug Repurposing

Dengue outbreaks persist in global tropical regions, lacking approved antivirals, necessitating critical therapeutic development against the virus. In this context, we developed the "Anti-Dengue" algorithm that predicts dengue virus inhibitors using a quantitative structure-activity relationship (QSAR) and MLTs. Using the "DrugRepV" database, we extracted chemicals (small molecules) and repurposed drugs targeting the dengue virus with their corresponding IC50 values. Then, molecular descriptors and fingerprints were computed for these molecules using PaDEL software. Further, these molecules were split into training/testing and independent validation datasets. We developed regression-based predictive models employing 10-fold cross-validation using a variety of machine learning approaches, including SVM, ANN, kNN, and RF. The best predictive model yielded a PCC of 0.71 on the training/testing dataset and 0.81 on the independent validation dataset. The created model's reliability and robustness were assessed using William's plot, scatter plot, decoy set, and chemical clustering analyses. Predictive models were utilized to identify possible drug candidates that could be repurposed. We identified goserelin, gonadorelin, and nafarelin as potential repurposed drugs with high pIC50 values. "Anti-Dengue" may be beneficial in accelerating antiviral drug development against the dengue virus.

Gut bacterial metabolism contributes to host global purine homeostasis

The microbes and microbial pathways that influence host inflammatory disease progression remain largely undefined. Here, we show that variation in atherosclerosis burden is partially driven by gut microbiota and is associated with circulating levels of uric acid (UA) in mice and humans. We identify gut bacterial taxa spanning multiple phyla, including Bacillota, Fusobacteriota, and Pseudomonadota, that use multiple purines, including UA as carbon and energy sources anaerobically. We identify a gene cluster that encodes key steps of anaerobic purine degradation and that is widely distributed among gut-dwelling bacteria. Furthermore, we show that colonization of gnotobiotic mice with purine-degrading bacteria modulates levels of UA and other purines in the gut and systemically. Thus, gut microbes are important drivers of host global purine homeostasis and serum UA levels, and gut bacterial catabolism of purines may represent a mechanism by which gut bacteria influence health.

Purine catabolism by enterobacteria

Purines are abundant among organic nitrogen sources and have high nitrogen content. Accordingly, microorganisms have evolved different pathways to catabolize purines and their metabolic products such as allantoin. Enterobacteria from the genera Escherichia, Klebsiella and Salmonella have three such pathways. First, the HPX pathway, found in the genus Klebsiella and very close relatives, catabolizes purines during aerobic growth, extracting all four nitrogen atoms in the process. This pathway includes several known or predicted enzymes not previously observed in other purine catabolic pathways. Second, the ALL pathway, found in strains from all three species, catabolizes allantoin during anaerobic growth in a branched pathway that also includes glyoxylate assimilation. This allantoin fermentation pathway originally was characterized in a gram-positive bacterium, and therefore is widespread. Third, the XDH pathway, found in strains from Escherichia and Klebsiella spp., at present is ill-defined but likely includes enzymes to catabolize purines during anaerobic growth. Critically, this pathway may include an enzyme system for anaerobic urate catabolism, a phenomenon not previously described. Documenting such a pathway would overturn the long-held assumption that urate catabolism requires oxygen. Overall, this broad capability for purine catabolism during either aerobic or anaerobic growth suggests that purines and their metabolites contribute to enterobacterial fitness in a variety of environments.

Host and gut bacteria share metabolic pathways for anti-cancer drug metabolism

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.

Sources and Fates of Carbamyl Phosphate: A Labile Energy-Rich Molecule with Multiple Facets

Carbamyl phosphate (CP) is well-known as an essential intermediate of pyrimidine and arginine/urea biosynthesis. Chemically, CP can be easily synthesized from dihydrogen phosphate and cyanate. Enzymatically, CP can be synthesized using three different classes of enzymes: (1) ATP-grasp fold protein based carbamyl phosphate synthetase (CPS); (2) Amino-acid kinase fold carbamate kinase (CK)-like CPS (anabolic CK or aCK); and (3) Catabolic transcarbamylase. The first class of CPS can be further divided into three different types of CPS as CPS I, CPS II, and CPS III depending on the usage of ammonium or glutamine as its nitrogen source, and whether N-acetyl-glutamate is its essential co-factor. CP can donate its carbamyl group to the amino nitrogen of many important molecules including the most well-known ornithine and aspartate in the arginine/urea and pyrimidine biosynthetic pathways. CP can also donate its carbamyl group to the hydroxyl oxygen of a variety of molecules, particularly in many antibiotic biosynthetic pathways. Transfer of the carbamyl group to the nitrogen group is catalyzed by the anabolic transcarbamylase using a direct attack mechanism, while transfer of the carbamyl group to the oxygen group is catalyzed by a different class of enzymes, CmcH/NodU CTase, using a different mechanism involving a three-step reaction, decomposition of CP to carbamate and phosphate, transfer of the carbamyl group from carbamate to ATP to form carbamyladenylate and pyrophosphate, and transfer of the carbamyl group from carbamyladenylate to the oxygen group of the substrate. CP is also involved in transferring its phosphate group to ADP to generate ATP in the fermentation of many microorganisms. The reaction is catalyzed by carbamate kinase, which may be termed as catabolic CK (cCK) in order to distinguish it from CP generating CK. CP is a thermally labile molecule, easily decomposed into phosphate and cyanate, or phosphate and carbamate depending on the pH of the solution, or the presence of enzyme. Biological systems have developed several mechanisms including channeling between enzymes, increased affinity of CP to enzymes, and keeping CP in a specific conformation to protect CP from decomposition. CP is highly important for our health as both a lack of, or decreased, CP production and CP accumulation results in many disease conditions.

Bayesian network model for identification of pathways by integrating protein interaction with genetic interaction data

Background

Molecular interaction data at proteomic and genetic levels provide physical and functional insights into a molecular biosystem and are helpful for the construction of pathway structures complementarily. Despite advances in inferring biological pathways using genetic interaction data, there still exists weakness in developed models, such as, activity pathway networks (APN), when integrating the data from proteomic and genetic levels. It is necessary to develop new methods to infer pathway structure by both of interaction data.

Results

We utilized probabilistic graphical model to develop a new method that integrates genetic interaction and protein interaction data and infers exquisitely detailed pathway structure. We modeled the pathway network as Bayesian network and applied this model to infer pathways for the coherent subsets of the global genetic interaction profiles, and the available data set of endoplasmic reticulum genes. The protein interaction data were derived from the BioGRID database. Our method can accurately reconstruct known cellular pathway structures, including SWR complex, ER-Associated Degradation (ERAD) pathway, N-Glycan biosynthesis pathway, Elongator complex, Retromer complex, and Urmylation pathway. By comparing N-Glycan biosynthesis pathway and Urmylation pathway identified from our approach with that from APN, we found that our method is able to overcome its weakness (certain edges are inexplicable). According to underlying protein interaction network, we defined a simple scoring function that only adopts genetic interaction information to avoid the balance difficulty in the APN. Using the effective stochastic simulation algorithm, the performance of our proposed method is significantly high.

Conclusion

We developed a new method based on Bayesian network to infer detailed pathway structures from interaction data at proteomic and genetic levels. The results indicate that the developed method performs better in predicting signaling pathways than previously described models.

A new network representation of the metabolism to detect chemical transformation modules

BACKGROUND

Metabolism is generally modeled by directed networks where nodes represent reactions and/or metabolites. In order to explore metabolic pathway conservation and divergence among organisms, previous studies were based on graph alignment to find similar pathways. Few years ago, the concept of chemical transformation modules, also called reaction modules, was introduced and correspond to sequences of chemical transformations which are conserved in metabolism. We propose here a novel graph representation of the metabolic network where reactions sharing a same chemical transformation type are grouped in Reaction Molecular Signatures (RMS).

RESULTS

RMS were automatically computed for all reactions and encode changes in atoms and bonds. A reaction network containing all available metabolic knowledge was then reduced by an aggregation of reaction nodes and edges to obtain a RMS network. Paths in this network were explored and a substantial number of conserved chemical transformation modules was detected. Furthermore, this graph-based formalism allows us to define several path scores reflecting different biological conservation meanings. These scores are significantly higher for paths corresponding to known metabolic pathways and were used conjointly to build association rules that should predict metabolic pathway types like biosynthesis or degradation.

CONCLUSIONS

This representation of metabolism in a RMS network offers new insights to capture relevant metabolic contexts. Furthermore, along with genomic context methods, it should improve the detection of gene clusters corresponding to new metabolic pathways.

From Genome to Structure and Back Again: A Family Portrait of the Transcarbamylases

Enzymes in the transcarbamylase family catalyze the transfer of a carbamyl group from carbamyl phosphate (CP) to an amino group of a second substrate. The two best-characterized members, aspartate transcarbamylase (ATCase) and ornithine transcarbamylase (OTCase), are present in most organisms from bacteria to humans. Recently, structures of four new transcarbamylase members, N-acetyl-l-ornithine transcarbamylase (AOTCase), N-succinyl-l-ornithine transcarbamylase (SOTCase), ygeW encoded transcarbamylase (YTCase) and putrescine transcarbamylase (PTCase) have also been determined. Crystal structures of these enzymes have shown that they have a common overall fold with a trimer as their basic biological unit. The monomer structures share a common CP binding site in their N-terminal domain, but have different second substrate binding sites in their C-terminal domain. The discovery of three new transcarbamylases, l-2,3-diaminopropionate transcarbamylase (DPTCase), l-2,4-diaminobutyrate transcarbamylase (DBTCase) and ureidoglycine transcarbamylase (UGTCase), demonstrates that our knowledge and understanding of the spectrum of the transcarbamylase family is still incomplete. In this review, we summarize studies on the structures and function of transcarbamylases demonstrating how structural information helps to define biological function and how small structural differences govern enzyme specificity. Such information is important for correctly annotating transcarbamylase sequences in the genome databases and for identifying new members of the transcarbamylase family.

The N-Acetylglutamate Synthase Family: Structures, Function and Mechanisms

N-acetylglutamate synthase (NAGS) catalyzes the production of N-acetylglutamate (NAG) from acetyl-CoA and l-glutamate. In microorganisms and plants, the enzyme functions in the arginine biosynthetic pathway, while in mammals, its major role is to produce the essential co-factor of carbamoyl phosphate synthetase 1 (CPS1) in the urea cycle. Recent work has shown that several different genes encode enzymes that can catalyze NAG formation. A bifunctional enzyme was identified in certain bacteria, which catalyzes both NAGS and N-acetylglutamate kinase (NAGK) activities, the first two steps of the arginine biosynthetic pathway. Interestingly, these bifunctional enzymes have higher sequence similarity to vertebrate NAGS than those of the classical (mono-functional) bacterial NAGS. Solving the structures for both classical bacterial NAGS and bifunctional vertebrate-like NAGS/K has advanced our insight into the regulation and catalytic mechanisms of NAGS, and the evolutionary relationship between the two NAGS groups.

Toward modular biological models: defining analog modules based on referent physiological mechanisms

Background

Currently, most biomedical models exist in isolation. It is often difficult to reuse or integrate models or their components, in part because they are not modular. Modular components allow the modeler to think more deeply about the role of the model and to more completely address a modeling project’s requirements. In particular, modularity facilitates component reuse and model integration for models with different use cases, including the ability to exchange modules during or between simulations. The heterogeneous nature of biology and vast range of wet-lab experimental platforms call for modular models designed to satisfy a variety of use cases. We argue that software analogs of biological mechanisms are reasonable candidates for modularization. Biomimetic software mechanisms comprised of physiomimetic mechanism modules offer benefits that are unique or especially important to multi-scale, biomedical modeling and simulation.

Results

We present a general, scientific method of modularizing mechanisms into reusable software components that we call physiomimetic mechanism modules (PMMs). PMMs utilize parametric containers that partition and expose state information into physiologically meaningful groupings. To demonstrate, we modularize four pharmacodynamic response mechanisms adapted from an in silico liver (ISL). We verified the modularization process by showing that drug clearance results from in silico experiments are identical before and after modularization. The modularized ISL achieves validation targets drawn from propranolol outflow profile data. In addition, an in silico hepatocyte culture (ISHC) is created. The ISHC uses the same PMMs and required no refactoring. The ISHC achieves validation targets drawn from propranolol intrinsic clearance data exhibiting considerable between-lab variability. The data used as validation targets for PMMs originate from both in vitro to in vivo experiments exhibiting large fold differences in time scale.

Conclusions

This report demonstrates the feasibility of PMMs and their usefulness across multiple model use cases. The pharmacodynamic response module developed here is robust to changes in model context and flexible in its ability to achieve validation targets in the face of considerable experimental uncertainty. Adopting the modularization methods presented here is expected to facilitate model reuse and integration, thereby accelerating the pace of biomedical research.

Analyzing methods for path mining with applications in metabolomics

Metabolomics is one of the key approaches of systems biology that consists of studying biochemical networks having a set of metabolites, enzymes, reactions and their interactions. As biological networks are very complex in nature, proper techniques and models need to be chosen for their better understanding and interpretation. One of the useful strategies in this regard is using path mining strategies and graph-theoretical approaches that help in building hypothetical models and perform quantitative analysis. Furthermore, they also contribute to analyzing topological parameters in metabolome networks. Path mining techniques can be based on grammars, keys, patterns and indexing. Moreover, they can also be used for modeling metabolome networks, finding structural similarities between metabolites, in-silico metabolic engineering, shortest path estimation and for various graph-based analysis. In this manuscript, we have highlighted some core and applied areas of path-mining for modeling and analysis of metabolic networks.