BRENDA, the ELIXIR core data resource in 2021: new developments and updates

Language models can identify enzymatic binding sites in protein sequences

Recent advances in language modeling have had a tremendous impact on how we handle sequential data in science. Language architectures have emerged as a hotbed of innovation and creativity in natural language processing over the last decade, and have since gained prominence in modeling proteins and chemical processes, elucidating structural relationships from textual/sequential data. Surprisingly, some of these relationships refer to three-dimensional structural features, raising important questions on the dimensionality of the information encoded within sequential data. Here, we demonstrate that the unsupervised use of a language model architecture to a language representation of bio-catalyzed chemical reactions can capture the signal at the base of the substrate-binding site atomic interactions. This allows us to identify the three-dimensional binding site position in unknown protein sequences. The language representation comprises a reaction-simplified molecular-input line-entry system (SMILES) for substrate and products, and amino acid sequence information for the enzyme. This approach can recover, with no supervision, 52.13% of the binding site when considering co-crystallized substrate-enzyme structures as ground truth, vastly outperforming other attention-based models.

ECMpy 2.0: A Python package for automated construction and analysis of enzyme-constrained models

Genome-scale metabolic models (GEMs) have been widely employed to predict microorganism behaviors. However, GEMs only consider stoichiometric constraints, leading to a linear increase in simulated growth and product yields as substrate uptake rates rise. This divergence from experimental measurements prompted the creation of enzyme-constrained models (ecModels) for various species, successfully enhancing chemical production. Building upon studies that allocate macromolecule resources, we developed a Python-based workflow (ECMpy) that constructs an enzyme-constrained model. This involves directly imposing an enzyme amount constraint in GEM and accounting for protein subunit composition in reactions. However, this procedure demands manual collection of enzyme kinetic parameter information and subunit composition details, making it rather user-unfriendly. In this work, we've enhanced the ECMpy toolbox to version 2.0, broadening its scope to automatically generate ecGEMs for a wider array of organisms. ECMpy 2.0 automates the retrieval of enzyme kinetic parameters and employs machine learning for predicting these parameters, which significantly enhances parameter coverage. Additionally, ECMpy 2.0 introduces common analytical and visualization features for ecModels, rendering computational results more user accessible. Furthermore, ECMpy 2.0 seamlessly integrates three published algorithms that exploit ecModels to uncover potential targets for metabolic engineering. ECMpy 2.0 is available at https://github.com/tibbdc/ECMpy or as a pip package (https://pypi.org/project/ECMpy/).

Role of second-sphere arginine residues in metal binding and metallocentre assembly in nitrile hydratases

Two conserved second-sphere βArg (R) residues in nitrile hydratases (NHase), that form hydrogen bonds with the catalytically essential sulfenic and sulfinic acid ligands, were mutated to Lys and Ala residues in the Co-type NHase from Pseudonocardia thermophila JCM 3095 (PtNHase) and the Fe-type NHase from Rhodococcus equi TG328-2 (ReNHase). Only five of the eight mutants (PtNHase βR52A, βR52K, βR157A, βR157K and ReNHase βR61A) were successfully expressed and purified. Apart from the PtNHase βR52A mutant that exhibited no detectable activity, the kcat values obtained for the PtNHase and ReNHase βR mutant enzymes were between 1.8 and 12.4 s-1 amounting to <1% of the kcat values observed for WT enzymes. The metal content of each mutant was also significantly decreased with occupancies ranging from ∼10 to ∼40%. UV-Vis spectra coupled with EPR data obtained on the ReNHase mutant enzyme, suggest a decrease in the Lewis acidity of the active site metal ion. X-ray crystal structures of the four PtNHase βR mutant enzymes confirmed the mutation and the low active site metal content, while also providing insight into the active site hydrogen bonding network. Finally, DFT calculations suggest that the equatorial sulfenic acid ligand, which has been shown to be the catalytic nucleophile, is protonated in the mutant enzyme. Taken together, these data confirm the necessity of the conserved second-sphere βR residues in the proposed subunit swapping process and post-translational modification of the α-subunit in the α activator complex, along with stabilizing the catalytic sulfenic acid in its anionic form.

Bottom-up parameterization of enzyme rate constants: Reconciling inconsistent data

Kinetic models of metabolism are promising platforms for studying complex metabolic systems and designing production strains. Given the availability of enzyme kinetic data from historical experiments and machine learning estimation tools, a straightforward modeling approach is to assemble kinetic data enzyme by enzyme until a desired scale is reached. However, this type of 'bottom up' parameterization of kinetic models has been difficult due to a number of issues including gaps in kinetic parameters, the complexity of enzyme mechanisms, inconsistencies between parameters obtained from different sources, and in vitro-in vivo differences. Here, we present a computational workflow for the robust estimation of kinetic parameters for detailed mass action enzyme models while taking into account parameter uncertainty. The resulting software package, termed MASSef (the Mass Action Stoichiometry Simulation Enzyme Fitting package), can handle standard 'macroscopic' kinetic parameters, including Km, kcat, Ki, Keq, and nh, as well as diverse reaction mechanisms defined in terms of mass action reactions and 'microscopic' rate constants. We provide three enzyme case studies demonstrating that this approach can identify and reconcile inconsistent data either within in vitro experiments or between in vitro and in vivo enzyme function. We further demonstrate how parameterized enzyme modules can be used to assemble pathway-scale kinetic models consistent with in vivo behavior. This work builds on the legacy of knowledge on kinetic behavior of enzymes by enabling robust parameterization of enzyme kinetic models at scale utilizing the abundance of historical literature data and machine learning parameter estimates.

Rewiring Saccharomyces cerevisiae metabolism for optimised Taxol® precursors production

Saccharomyces cerevisiae has been conveniently used to produce Taxol® anticancer drug early precursors. However, the harmful impact of oxidative stress by the first cytochrome P450-reductase enzymes (CYP725A4-POR) of Taxol® pathway has hampered sufficient progress in yeast. Here, we evolved an oxidative stress-resistant yeast strain with three-fold higher titre of their substrate, taxadiene. The performance of the evolved and parent strains were then evaluated in galactose-limited chemostats before and under the oxidative stress by an oxidising agent. The interaction of evolution and oxidative stress was comprehensively evaluated through transcriptomics and metabolite profiles integration in yeast enzyme-constrained genome scale model. Overall, the evolved strain showed improved respiration, reduced overflow metabolites production and oxidative stress re-induction tolerance. The cross-protection mechanism also potentially contributed to better heme, flavin and NADPH availability, essential for CYP725A4 and POR optimal activity in yeast. The results imply that the evolved strain is a robust cell factory for future efforts towards Taxol© production.

Post-translation digital data encoding into the genomes of mammalian cell populations

High resolution cellular signal encoding is critical for better understanding of complex biological phenomena. DNA-based biosignal encoders alter genomic or plasmid DNA in a signal dependent manner. Current approaches involve the signal of interest affecting a DNA edit by interacting with a signal specific promoter which then results in expression of the effector molecule (DNA altering enzyme). Here, we present the proof of concept of a biosignal encoding system where the enzyme terminal deoxynucleotidyl transferase (TdT) acts as the effector molecule upon directly interacting with the signal of interest. A template independent DNA polymerase (DNAp), TdT incorporates nucleotides at the 3' OH ends of DNA substrate in a signal dependent manner. By employing CRISPR-Cas9 to create double stranded breaks in genomic DNA, we make 3'OH ends available to act as substrate for TdT. We show that this system can successfully resolve and encode different concentrations of various biosignals into the genomic DNA of HEK-293T cells. Finally, we develop a simple encoding scheme associated with the tested biosignals and encode the message "HELLO WORLD" into the genomic DNA of HEK-293T cells at a population level with 91% accuracy. This work demonstrates a simple and engineerable system that can reliably store local biosignal information into the genomes of mammalian cell populations.

Vocabulary Matters: An Annotation Pipeline and Four Deep Learning Algorithms for Enzyme Named Entity Recognition

Enzymes are indispensable in many biological processes, and with biomedical literature growing exponentially, effective literature review becomes increasingly challenging. Natural language processing methods offer solutions to streamline this process. This study aims to develop an annotated enzyme corpus for training and evaluating enzyme named entity recognition (NER) models. A novel pipeline, combining dictionary matching and rule-based keyword searching, automatically annotated enzyme entities in >4800 full-text publications. Four deep learning NER models were created with different vocabularies (BioBERT/SciBERT) and architectures (BiLSTM/transformer) and evaluated on 526 manually annotated full-text publications. The annotation pipeline achieved an F1-score of 0.86 (precision = 1.00, recall = 0.76), surpassed by fine-tuned transformers for F1-score (BioBERT: 0.89, SciBERT: 0.88) and recall (0.86) with BiLSTM models having higher precision (0.94) than transformers (0.92). The annotation pipeline runs in seconds on standard laptops with almost perfect precision, but was outperformed by fine-tuned transformers in terms of F1-score and recall, demonstrating generalizability beyond the training data. In comparison, SciBERT-based models exhibited higher precision, and BioBERT-based models exhibited higher recall, highlighting the importance of vocabulary and architecture. These models, representing the first enzyme NER algorithms, enable more effective enzyme text mining and information extraction. Codes for automated annotation and model generation are available from https://github.com/omicsNLP/enzymeNER and https://zenodo.org/doi/10.5281/zenodo.10581586.

Association between organic nitrogen substrates and the optical purity of D-lactic acid during the fermentation by Sporolactobacillus terrae SBT-1

The development of biotechnological lactic acid production has attracted attention to the potential production of an optically pure isomer of lactic acid, although the relationship between fermentation and the biosynthesis of highly optically pure D-lactic acid remains poorly understood. Sporolactobacillus terrae SBT-1 is an excellent D-lactic acid producer that depends on cultivation conditions. Herein, three enzymes responsible for synthesizing optically pure D-lactic acid, including D-lactate dehydrogenase (D-LDH; encoded by ldhDs), L-lactate dehydrogenase (L-LDH; encoded by ldhLs), and lactate racemase (Lar; encoded by larA), were quantified under different organic nitrogen sources and concentration to study the relationship between fermentation conditions and synthesis pathway of optically pure lactic acid. Different organic nitrogen sources and concentrations significantly affected the quantity and quality of D-lactic acid produced by strain SBT-1 as well as the synthetic optically pure lactic acid pathway. Yeast extract is a preferred organic nitrogen source for achieving high catalytic efficiency of D-lactate dehydrogenase and increasing the transcription level of ldhA2, indicating that this enzyme plays a major role in D-lactic acid formation in S. terrae SBT-1. Furthermore, lactate racemization activity could be regulated by the presence of D-lactic acid. The results of this study suggest that specific nutrient requirements are necessary to achieve a stable and highly productive fermentation process for the D-lactic acid of an individual strain.

Advances in machine intelligence-driven virtual screening approaches for big-data

Virtual screening (VS) is an integral and ever-evolving domain of drug discovery framework. The VS is traditionally classified into ligand-based (LB) and structure-based (SB) approaches. Machine intelligence or artificial intelligence has wide applications in the drug discovery domain to reduce time and resource consumption. In combination with machine intelligence algorithms, VS has emerged into revolutionarily progressive technology that learns within robust decision orders for data curation and hit molecule screening from large VS libraries in minutes or hours. The exponential growth of chemical and biological data has evolved as "big-data" in the public domain demands modern and advanced machine intelligence-driven VS approaches to screen hit molecules from ultra-large VS libraries. VS has evolved from an individual approach (LB and SB) to integrated LB and SB techniques to explore various ligand and target protein aspects for the enhanced rate of appropriate hit molecule prediction. Current trends demand advanced and intelligent solutions to handle enormous data in drug discovery domain for screening and optimizing hits or lead with fewer or no false positive hits. Following the big-data drift and tremendous growth in computational architecture, we presented this review. Here, the article categorized and emphasized individual VS techniques, detailed literature presented for machine learning implementation, modern machine intelligence approaches, and limitations and deliberated the future prospects.

Diverse models of cavity engineering in enzyme modification: Creation, filling, and reshaping

Most enzyme modification strategies focus on designing the active sites or their surrounding structures. Interestingly, a large portion of the enzymes (60%) feature active sites located within spacious cavities. Despite recent discoveries, cavity-mediated enzyme engineering remains crucial for enhancing enzyme properties and unraveling folding-unfolding mechanisms. Cavity engineering influences enzyme stability, catalytic activity, specificity, substrate recognition, and docking. This article provides a comprehensive review of various cavity engineering models for enzyme modification, including cavity creation, filling, and reshaping. Additionally, it also discusses feasible tools for geometric analysis, functional assessment, and modification of cavities, and explores potential future research directions in this field. Furthermore, a promising universal modification strategy for cavity engineering that leverages state-of-the-art technologies and methodologies to tailor cavities according to the specific requirements of industrial production conditions is proposed.

Structural and biochemical characterization of Arabidopsis alcohol dehydrogenases reveals distinct functional properties but similar redox sensitivity

Alcohol dehydrogenases (ADHs) are a group of zinc-binding enzymes belonging to the medium-length dehydrogenase/reductase (MDR) protein superfamily. In plants, these enzymes fulfill important functions involving the reduction of toxic aldehydes to the corresponding alcohols (as well as catalyzing the reverse reaction, i.e., alcohol oxidation; ADH1) and the reduction of nitrosoglutathione (GSNO; ADH2/GSNOR). We investigated and compared the structural and biochemical properties of ADH1 and GSNOR from Arabidopsis thaliana. We expressed and purified ADH1 and GSNOR and determined two new structures, NADH-ADH1 and apo-GSNOR, thus completing the structural landscape of Arabidopsis ADHs in both apo- and holo-forms. A structural comparison of these Arabidopsis ADHs revealed a high sequence conservation (59% identity) and a similar fold. In contrast, a striking dissimilarity was observed in the catalytic cavity supporting substrate specificity and accommodation. Consistently, ADH1 and GSNOR showed strict specificity for their substrates (ethanol and GSNO, respectively), although both enzymes had the ability to oxidize long-chain alcohols, with ADH1 performing better than GSNOR. Both enzymes contain a high number of cysteines (12 and 15 out of 379 residues for ADH1 and GSNOR, respectively) and showed a significant and similar responsivity to thiol-oxidizing agents, indicating that redox modifications may constitute a mechanism for controlling enzyme activity under both optimal growth and stress conditions.

Integrating biological knowledge for mechanistic inference in the host-associated microbiome

Advances in high-throughput technologies have enhanced our ability to describe microbial communities as they relate to human health and disease. Alongside the growth in sequencing data has come an influx of resources that synthesize knowledge surrounding microbial traits, functions, and metabolic potential with knowledge of how they may impact host pathways to influence disease phenotypes. These knowledge bases can enable the development of mechanistic explanations that may underlie correlations detected between microbial communities and disease. In this review, we survey existing resources and methodologies for the computational integration of broad classes of microbial and host knowledge. We evaluate these knowledge bases in their access methods, content, and source characteristics. We discuss challenges of the creation and utilization of knowledge bases including inconsistency of nomenclature assignment of taxa and metabolites across sources, whether the biological entities represented are rooted in ontologies or taxonomies, and how the structure and accessibility limit the diversity of applications and user types. We make this information available in a code and data repository at: https://github.com/lozuponelab/knowledge-source-mappings. Addressing these challenges will allow for the development of more effective tools for drawing from abundant knowledge to find new insights into microbial mechanisms in disease by fostering a systematic and unbiased exploration of existing information.

Profiling the compendium of changes in Saccharomyces cerevisiae due to mutations that alter availability of the main methyl donor S-Adenosylmethionine

The SAM1 and SAM2 genes encode for S-Adenosylmethionine (AdoMet) synthetase enzymes, with AdoMet serving as the main cellular methyl donor. We have previously shown that independent deletion of these genes alters chromosome stability and AdoMet concentrations in opposite ways in Saccharomyces cerevisiae. To characterize other changes occurring in these mutants, we grew wildtype, sam1Δ/sam1Δ, and sam2Δ/sam2Δ strains in 15 different Phenotypic Microarray plates with different components and measured growth variations. RNA-Sequencing was also carried out on these strains and differential gene expression determined for each mutant. We explored how the phenotypic growth differences are linked to the altered gene expression, and hypothesize mechanisms by which loss of the SAM genes and subsequent AdoMet level changes, impact pathways and processes. We present 6 stories, discussing changes in sensitivity or resistance to azoles, cisplatin, oxidative stress, arginine biosynthesis perturbations, DNA synthesis inhibitors, and tamoxifen, to demonstrate the power of this novel methodology to broadly profile changes due to gene mutations. The large number of conditions that result in altered growth, as well as the large number of differentially expressed genes with wide-ranging functionality, speaks to the broad array of impacts that altering methyl donor abundance can impart. Our findings demonstrate that some cellular changes are directly related to AdoMet-dependent methyltransferases and AdoMet availability, some are directly linked to the methyl cycle and its role in production of several important cellular components, and others reveal impacts of SAM gene mutations on previously unconnected pathways.

S-adenosyl-l-methionine interaction signatures in methyltransferases

The switching on or off of methylation, a change from a normal methylation to hyper or hypo methylation is implicated in many diseases that include cancers, infectious, neurodegenerative diseases and others. Methyltransferases are one of the most sought targets that have diversified for the methylation of a variety of substrates. However, without S-adenosyl-l-methionine (SAM), the universal methyl donor, the majority of the methyltransferases remain functionally inactive. In this article, we did a comprehensive analysis of all available SAM-receptor crystal structures at atom, moiety and structure levels to gain deeper insights into the structure and function of SAM. SAM demonstrated flexibility in binding to a variety of receptors irrespective of the size of the binding pockets. Further analysis of the binding pockets resulted in all SAM conformations clustering into four natural shapes. The conserved interaction analysis provides an unambiguous orientation of SAM binding to receptors which has been elusive till now. SAM peptide moiety (SPM) and SAM nucleobase moiety (SNM) show up to 89% interactions with receptors whereas only 11% interactions with SAM ribose moiety (SRM). It is found that SPM and SNM terminal atoms anchor to the highly conserved receptor subsites creating a workbench for catalysis. It is seen that every interacting atom and its position is crucial in the methyl transfer phenomenon. A very unique observation is that the methyl group of SAM does not have even one interaction with the receptor. The deep insights gained help in the design and development of novel drugs against the methyltransferases.Communicated by Ramaswamy H. Sarma.

Metabolic regulatory network kinetic modeling with multiple isotopic tracers for iPSCs

The rapidly expanding market for regenerative medicines and cell therapies highlights the need to advance the understanding of cellular metabolisms and improve the prediction of cultivation production process for human induced pluripotent stem cells (iPSCs). In this paper, a metabolic kinetic model was developed to characterize the underlying mechanisms of iPSC culture process, which can predict cell response to environmental perturbation and support process control. This model focuses on the central carbon metabolic network, including glycolysis, pentose phosphate pathway, tricarboxylic acid cycle, and amino acid metabolism, which plays a crucial role to support iPSC proliferation. Heterogeneous measures of extracellular metabolites and multiple isotopic tracers collected under multiple conditions were used to learn metabolic regulatory mechanisms. Systematic cross-validation confirmed the model's performance in terms of providing reliable predictions on cellular metabolism and culture process dynamics under various culture conditions. Thus, the developed mechanistic kinetic model can support process control strategies to strategically select optimal cell culture conditions at different times, ensure cell product functionality, and facilitate large-scale manufacturing of regenerative medicines and cell therapies.

EcoGenoRisk: Developing a computational ecological risk assessment tool for synthetic biology

The expanding field of synthetic biology (synbio) supports new opportunities in the design of targeted bioproducts or modified microorganisms. However, this rapid development of synbio products raises concerns surrounding the potential risks of modified microorganisms contaminating unintended environments. These potential invasion risks require new bioinformatic tools to inform the design phase. EcoGenoRisk is a newly constructed computational risk assessment tool for invasiveness that aims to predict where synbio microorganisms may establish a population by screening for habitats of genetically similar microorganisms. The first module of the tool identifies genetically similar microorganisms and potential ecological relationships such as competition, mutualism, and inhibition. In total, 520 archaeal and 32,828 bacterial complete assembly genomes were analyzed to test the specificity and accuracy of the tool as well as to characterize the enzymatic profiles of different taxonomic lineages. Additionally, ecological relationships were analyzed to determine which would result in the greatest potential overlap between shared functional profiles. Notably, competition displayed the significantly highest overlap of shared functions between compared genomes. Overall, EcoGenoRisk is a flexible software pipeline that assists environmental risk assessors to query large databases of known microorganisms and prioritize follow-up bench scale studies.

Structural insights into the distinct substrate preferences of two bacterial epoxide hydrolases

Epoxide hydrolases (EHs), which catalyze the transformation of epoxides to diols, are present in many eukaryotic and prokaryotic organisms. They have recently drawn considerable attention from organic chemists owing to their application in the semisynthesis of enantiospecific diol compounds. Here, we report the crystal structures of BoEH from Bosea sp. PAMC 26642 and CaEH from Caballeronia sordidicola PAMC 26510 at 1.95 and 2.43 Å resolution, respectively. Structural analysis showed that the overall structures of BoEH and CaEH commonly possess typical α/β hydrolase fold with the same ring-opening residues (Tyr-Tyr) and conserved catalytic triad residues (Asp-Asp-His). However, the two enzymes were found to have significantly different sequence compositions in the cap domain region, which is involved in the formation of the substrate-binding site in both enzymes. Enzyme activity assay results showed that BoEH had the strongest activity toward the linear aliphatic substrates, whereas CaEH had a higher preference for aromatic- and cycloaliphatic substrates. Computational docking simulations and tunnel identification revealed important residues with different substrate-binding preferences. Collectively, structure comparison studies, together with ligand docking simulation results, suggested that the differences in substrate-binding site residues were highly correlated with substrate specificity.

FXR Agonism with Bile Acid Mimetic Reduces Pre-Clinical Triple-Negative Breast Cancer Burden

Bariatric surgery is associated with improved outcomes for several cancers, including breast cancer (BC), although the mechanisms mediating this protection are unknown. We hypothesized that elevated bile acid pools detected after bariatric surgery may be factors that contribute to improved BC outcomes. Patients with greater expression of the bile acid receptor FXR displayed improved survival in specific aggressive BC subtypes. FXR is a nuclear hormone receptor activated by primary bile acids. Therefore, we posited that activating FXR using an established FDA-approved agonist would induce anticancer effects. Using in vivo and in vitro approaches, we determined the anti-tumor potential of bile acid receptor agonism. Indeed, FXR agonism by the bile acid mimetic known commercially as Ocaliva ("OCA"), or Obeticholic acid (INT-747), significantly reduced BC progression and overall tumor burden in a pre-clinical model. The transcriptomic analysis of tumors in mice subjected to OCA treatment revealed differential gene expression patterns compared to vehicle controls. Notably, there was a significant down-regulation of the oncogenic transcription factor MAX (MYC-associated factor X), which interacts with the oncogene MYC. Gene set enrichment analysis (GSEA) further demonstrated a statistically significant downregulation of the Hallmark MYC-related gene set (MYC Target V1) following OCA treatment. In human and murine BC analyses in vitro, agonism of FXR significantly and dose-dependently inhibited proliferation, migration, and viability. In contrast, the synthetic agonism of another common bile acid receptor, the G protein-coupled bile acid receptor TGR5 (GPBAR1) which is mainly activated by secondary bile acids, failed to significantly alter cancer cell dynamics. In conclusion, agonism of FXR by primary bile acid memetic OCA yields potent anti-tumor effects potentially through inhibition of proliferation and migration and reduced cell viability. These findings suggest that FXR is a tumor suppressor gene with a high potential for use in personalized therapeutic strategies for individuals with BC.

GraphKM: machine and deep learning for KM prediction of wildtype and mutant enzymes

Michaelis constant (KM) is one of essential parameters for enzymes kinetics in the fields of protein engineering, enzyme engineering, and synthetic biology. As overwhelming experimental measurements of KM are difficult and time-consuming, prediction of the KM values from machine and deep learning models would increase the pace of the enzymes kinetics studies. Existing machine and deep learning models are limited to the specific enzymes, i.e., a minority of enzymes or wildtype enzymes. Here, we used a deep learning framework PaddlePaddle to implement a machine and deep learning approach (GraphKM) for KM prediction of wildtype and mutant enzymes. GraphKM is composed by graph neural networks (GNN), fully connected layers and gradient boosting framework. We represented the substrates through molecular graph and the enzymes through a pretrained transformer-based language model to construct the model inputs. We compared the difference of the model results made by the different GNN (GIN, GAT, GCN, and GAT-GCN). The GAT-GCN-based model generally outperformed. To evaluate the prediction performance of the GraphKM and other reported KM prediction models, we collected an independent KM dataset (HXKm) from literatures.

Genome assembly of a symbiotic balantidia (Balantidium ctenopharyngodoni) in fish hindgut

Balantidium ctenopharyngodoni is identified as the sole ciliate species that exclusively resides within the hindgut of grass carp with high prevalence and intensity. In this study, the successful cultivation of B. ctenopharyngodoni enabled us to collect enough cells for genome sequencing. Consequently, we acquired a high-quality genome assembly spanning 68.66 Mb, encompassing a total of 22,334 nanochromosomes. Furthermore, we predicted 29,348 protein-coding genes, and 95.5% of them was supported by the RNA-seq data. The trend of GC content in the subtelomeric regions of single-gene chromosomes was similar to other ciliates containing nanochromosomes. A large number of genes encoding carbohydrate-binding modules with affinities for starch and peptidoglycans was identified. The identification of mitochondrion-related organelles (MROs) within genome indicates its well-suited adaptation to the anaerobic conditions in the hindgut environment. In summary, our results will offer resources for understanding the genetic basis and molecular adaptations of balantidia to hindgut of herbivorous fish.

Mathematical kinetic modelling followed by in vitro and in vivo assays reveal the bifunctional rice GTPCHII/DHBPS enzymes and demonstrate the key roles of OsRibA proteins in the vitamin B2 pathway

BACKGROUND

Riboflavin is the precursor of several cofactors essential for normal physical and cognitive development, but only plants and some microorganisms can produce it. Humans thus rely on their dietary intake, which at a global level is mainly constituted by cereals (> 50%). Understanding the riboflavin biosynthesis players is key for advancing our knowledge on this essential pathway and can hold promise for biofortification strategies in major crop species. In some bacteria and in Arabidopsis, it is known that RibA1 is a bifunctional protein with distinct GTP cyclohydrolase II (GTPCHII) and 3,4-dihydroxy-2-butanone-4-phosphate synthase (DHBPS) domains. Arabidopsis harbors three RibA isoforms, but only one retained its bifunctionality. In rice, however, the identification and characterization of RibA has not yet been described.

RESULTS

Through mathematical kinetic modeling, we identified RibA as the rate-limiting step of riboflavin pathway and by bioinformatic analysis we confirmed that rice RibA proteins carry both domains, DHBPS and GTPCHII. Phylogenetic analysis revealed that OsRibA isoforms 1 and 2 are similar to Arabidopsis bifunctional RibA1. Heterologous expression of OsRibA1 completely restored the growth of the rib3∆ yeast mutant, lacking DHBPS expression, while causing a 60% growth improvement of the rib1∆ mutant, lacking GTPCHII activity. Regarding OsRibA2, its heterologous expression fully complemented GTPCHII activity, and improved rib3∆ growth by 30%. In vitro activity assays confirmed that both OsRibA1 and OsRibA2 proteins carry GTPCHII/DHBPS activities, but that OsRibA1 has higher DHBPS activity. The overexpression of OsRibA1 in rice callus resulted in a 28% increase in riboflavin content.

CONCLUSIONS

Our study elucidates the critical role of RibA in rice riboflavin biosynthesis pathway, establishing it as the rate-limiting step in the pathway. By identifying and characterizing OsRibA1 and OsRibA2, showcasing their GTPCHII and DHBPS activities, we have advanced the understanding of riboflavin biosynthesis in this staple crop. We further demonstrated that OsRibA1 overexpression in rice callus increases its riboflavin content, providing supporting information for bioengineering efforts.

Reconstruction of the genome-scale metabolic network model of Sinorhizobium fredii CCBAU45436 for free-living and symbiotic states

Sinorhizobium fredii CCBAU45436 is an excellent rhizobium that plays an important role in agricultural production. However, there still needs more comprehensive understanding of the metabolic system of S. fredii CCBAU45436, which hinders its application in agriculture. Therefore, based on the first-generation metabolic model iCC541 we developed a new genome-scale metabolic model iAQY970, which contains 970 genes, 1,052 reactions, 942 metabolites and is scored 89% in the MEMOTE test. Cell growth phenotype predicted by iAQY970 is 81.7% consistent with the experimental data. The results of mapping the proteome data under free-living and symbiosis conditions to the model showed that the biomass production rate in the logarithmic phase was faster than that in the stable phase, and the nitrogen fixation efficiency of rhizobia parasitized in cultivated soybean was higher than that in wild-type soybean, which was consistent with the actual situation. In the symbiotic condition, there are 184 genes that would affect growth, of which 94 are essential; In the free-living condition, there are 143 genes that influence growth, of which 78 are essential. Among them, 86 of the 94 essential genes in the symbiotic condition were consistent with the prediction of iCC541, and 44 essential genes were confirmed by literature information; meanwhile, 30 genes were identified by DEG and 33 genes were identified by Geptop. In addition, we extracted four key nitrogen fixation modules from the model and predicted that sulfite reductase (EC 1.8.7.1) and nitrogenase (EC 1.18.6.1) as the target enzymes to enhance nitrogen fixation by MOMA, which provided a potential focus for strain optimization. Through the comprehensive metabolic model, we can better understand the metabolic capabilities of S. fredii CCBAU45436 and make full use of it in the future.

Targeting imidazole-glycerol phosphate dehydratase in plants: novel approach for structural and functional studies, and inhibitor blueprinting

The histidine biosynthetic pathway (HBP) is targeted for herbicide design with preliminary success only regarding imidazole-glycerol phosphate dehydratase (IGPD, EC 4.2.1.19), or HISN5, as referred to in plants. HISN5 catalyzes the sixth step of the HBP, in which imidazole-glycerol phosphate (IGP) is dehydrated to imidazole-acetol phosphate. In this work, we present high-resolution cryoEM and crystal structures of Medicago truncatula HISN5 (MtHISN5) in complexes with an inactive IGP diastereoisomer and with various other ligands. MtHISN5 can serve as a new model for plant HISN5 structural studies, as it enables resolving protein-ligand interactions at high (2.2 Å) resolution using cryoEM. We identified ligand-binding hotspots and characterized the features of plant HISN5 enzymes in the context of the HISN5-targeted inhibitor design. Virtual screening performed against millions of small molecules not only revealed candidate molecules but also identified linkers for fragments that were experimentally confirmed to bind. Based on experimental and computational approaches, this study provides guidelines for designing symmetric HISN5 inhibitors that can reach two neighboring active sites. Finally, we conducted analyses of sequence similarity networks revealing that plant HISN5 enzymes derive from cyanobacteria. We also adopted a new approach to measure MtHISN5 enzymatic activity using isothermal titration calorimetry and enzymatically synthesized IGP.

Discretised Flux Balance Analysis for Reaction-Diffusion Simulation of Single-Cell Metabolism

Metabolites have to diffuse within the sub-cellular compartments they occupy to specific locations where enzymes are, so reactions could occur. Conventional flux balance analysis (FBA), a method based on linear programming that is commonly used to model metabolism, implicitly assumes that all enzymatic reactions are not diffusion-limited though that may not always be the case. In this work, we have developed a spatial method that implements FBA on a grid-based system, to enable the exploration of diffusion effects on metabolism. Specifically, the method discretises a living cell into a two-dimensional grid, represents the metabolic reactions in each grid element as well as the diffusion of metabolites to and from neighbouring elements, and simulates the system as a single linear programming problem. We varied the number of rows and columns in the grid to simulate different cell shapes, and the method was able to capture diffusion effects at different shapes. We then used the method to simulate heterogeneous enzyme distribution, which suggested a theoretical effect on variability at the population level. We propose the use of this method, and its future extensions, to explore how spatiotemporal organisation of sub-cellular compartments and the molecules within could affect cell behaviour.

Metabolic collaboration between cells in the tumor microenvironment has a negligible effect on tumor growth

The tumor microenvironment is composed of a complex mixture of different cell types interacting under conditions of nutrient deprivation, but the metabolism therein is not fully understood due to difficulties in measuring metabolic fluxes and exchange of metabolites between different cell types in vivo. Genome-scale metabolic modeling enables estimation of such exchange fluxes as well as an opportunity to gain insight into the metabolic behavior of individual cell types. Here, we estimated the availability of nutrients and oxygen within the tumor microenvironment using concentration measurements from blood together with a metabolite diffusion model. In addition, we developed an approach to efficiently apply enzyme usage constraints in a comprehensive metabolic model of human cells. The combined modeling reproduced severe hypoxic conditions and the Warburg effect, and we found that limitations in enzymatic capacity contribute to cancer cells' preferential use of glutamine as a substrate to the citric acid cycle. Furthermore, we investigated the common hypothesis that some stromal cells are exploited by cancer cells to produce metabolites useful for the cancer cells. We identified over 200 potential metabolites that could support collaboration between cancer cells and cancer-associated fibroblasts, but when limiting to metabolites previously identified to participate in such collaboration, no growth advantage was observed. Our work highlights the importance of enzymatic capacity limitations for cell behaviors and exemplifies the utility of enzyme-constrained models for accurate prediction of metabolism in cells and tumor microenvironments.

Resource allocation in mammalian systems

Cells execute biological functions to support phenotypes such as growth, migration, and secretion. Complementarily, each function of a cell has resource costs that constrain phenotype. Resource allocation by a cell allows it to manage these costs and optimize their phenotypes. In fact, the management of resource constraints (e.g., nutrient availability, bioenergetic capacity, and macromolecular machinery production) shape activity and ultimately impact phenotype. In mammalian systems, quantification of resource allocation provides important insights into higher-order multicellular functions; it shapes intercellular interactions and relays environmental cues for tissues to coordinate individual cells to overcome resource constraints and achieve population-level behavior. Furthermore, these constraints, objectives, and phenotypes are context-dependent, with cells adapting their behavior according to their microenvironment, resulting in distinct steady-states. This review will highlight the biological insights gained from probing resource allocation in mammalian cells and tissues.

Reconstruction, simulation and analysis of enzyme-constrained metabolic models using GECKO Toolbox 3.0

Genome-scale metabolic models (GEMs) are computational representations that enable mathematical exploration of metabolic behaviors within cellular and environmental constraints. Despite their wide usage in biotechnology, biomedicine and fundamental studies, there are many phenotypes that GEMs are unable to correctly predict. GECKO is a method to improve the predictive power of a GEM by incorporating enzymatic constraints using kinetic and omics data. GECKO has enabled reconstruction of enzyme-constrained metabolic models (ecModels) for diverse organisms, which show better predictive performance than conventional GEMs. In this protocol, we describe how to use the latest version GECKO 3.0; the procedure has five stages: (1) expansion from a starting metabolic model to an ecModel structure, (2) integration of enzyme turnover numbers into the ecModel structure, (3) model tuning, (4) integration of proteomics data into the ecModel and (5) simulation and analysis of ecModels. GECKO 3.0 incorporates deep learning-predicted enzyme kinetics, paving the way for improved metabolic models for virtually any organism and cell line in the absence of experimental data. The time of running the whole protocol is organism dependent, e.g., ~5 h for yeast.

Prediction and integration of metabolite-protein interactions with genome-scale metabolic models

Metabolites, as small molecules, can act not only as substrates to enzymes, but also as effectors of activity of proteins with different functions, thereby affecting various cellular processes. While several experimental techniques have started to catalogue the metabolite-protein interactions (MPIs) present in different cellular contexts, characterizing the functional relevance of MPIs remains a challenging problem. Computational approaches from the constrained-based modeling framework allow for predicting MPIs and integrating their effects in the in silico analysis of metabolic and physiological phenotypes, like cell growth. Here, we provide a classification of all existing constraint-based approaches that predict and integrate MPIs using genome-scale metabolic networks as input. In addition, we benchmark the performance of the approaches to predict MPIs in a comparative study using different features extracted from the model structure and predicted metabolic phenotypes with the state-of-the-art metabolic networks of Escherichia coli and Saccharomyces cerevisiae. Lastly, we provide an outlook for future, feasible directions to expand the consideration of MPIs in constraint-based modeling approaches with wide biotechnological applications.

Machine learning predicts system-wide metabolic flux control in cyanobacteria

Metabolic fluxes and their control mechanisms are fundamental in cellular metabolism, offering insights for the study of biological systems and biotechnological applications. However, quantitative and predictive understanding of controlling biochemical reactions in microbial cell factories, especially at the system level, is limited. In this work, we present ARCTICA, a computational framework that integrates constraint-based modelling with machine learning tools to address this challenge. Using the model cyanobacterium Synechocystis sp. PCC 6803 as chassis, we demonstrate that ARCTICA effectively simulates global-scale metabolic flux control. Key findings are that (i) the photosynthetic bioproduction is mainly governed by enzymes within the Calvin-Benson-Bassham (CBB) cycle, rather than by those involve in the biosynthesis of the end-product, (ii) the catalytic capacity of the CBB cycle limits the photosynthetic activity and downstream pathways and (iii) ribulose-1,5-bisphosphate carboxylase/oxygenase (RuBisCO) is a major, but not the most, limiting step within the CBB cycle. Predicted metabolic reactions qualitatively align with prior experimental observations, validating our modelling approach. ARCTICA serves as a valuable pipeline for understanding cellular physiology and predicting rate-limiting steps in genome-scale metabolic networks, and thus provides guidance for bioengineering of cyanobacteria.

Creating new-to-nature carbon fixation: A guide

Synthetic biology aims at designing new biological functions from first principles. These new designs allow to expand the natural solution space and overcome the limitations of naturally evolved systems. One example is synthetic CO2-fixation pathways that promise to provide more efficient ways for the capture and conversion of CO2 than natural pathways, such as the Calvin Benson Bassham (CBB) cycle of photosynthesis. In this review, we provide a practical guideline for the design and realization of such new-to-nature CO2-fixation pathways. We introduce the concept of "synthetic CO2-fixation", and give a general overview over the enzymology and topology of synthetic pathways, before we derive general principles for their design from their eight naturally evolved analogs. We provide a comprehensive summary of synthetic carbon-assimilation pathways and derive a step-by-step, practical guide from the theoretical design to their practical implementation, before ending with an outlook on new developments in the field.

Investigating pH Effects on Enzymes Catalyzing Polysorbate Degradation by Activity-Based Protein Profiling

Host cell proteins (HCPs) are process-related impurities that can negatively impact the quality of biotherapeutics. Some HCPs possess enzymatic activity and can affect the active pharmaceutical ingredient (API) or excipients such as polysorbates (PS). PSs are a class of non-ionic surfactants commonly used as excipients in biotherapeutics to enhance the stability of APIs. The enzyme activity of certain HCPs can result in the degradation of PSs, leading to particle formation and decreased shelf life of biotherapeutics. Identifying and characterizing these HCPs is therefore crucial. This study employed the Activity-Based Protein Profiling (ABPP) technique to investigate the effect of pH on the activity of HCPs that have the potential to degrade polysorbates. Two probes were utilized: the commercially available fluorophosphonate (FP)-Desthiobiotin probe and a probe based on the antiobesity drug, Orlistat. Over 50 HCPs were identified, showing a strong dependence on pH-milieu regarding their enzyme activity. These findings underscore the importance of accounting for pH variations in the ABPP method and other investigations of HCP activity. Notably, the Orlistat-based probe (OBP) enabled us to investigate the enzymatic activity of a wider range of HCPs, emphasizing the advantage of using more than one probe for ABPP. Finally, this study led to the discovery of previously unreported active enzymes, including three HCPs from the carboxylesterase enzyme family.

A journey into the regulatory secrets of the de novo purine nucleotide biosynthesis

De novo purine nucleotide biosynthesis (DNPNB) consists of sequential reactions that are majorly conserved in living organisms. Several regulation events take place to maintain physiological concentrations of adenylate and guanylate nucleotides in cells and to fine-tune the production of purine nucleotides in response to changing cellular demands. Recent years have seen a renewed interest in the DNPNB enzymes, with some being highlighted as promising targets for therapeutic molecules. Herein, a review of two newly revealed modes of regulation of the DNPNB pathway has been carried out: i) the unprecedent allosteric regulation of one of the limiting enzymes of the pathway named inosine 5'-monophosphate dehydrogenase (IMPDH), and ii) the supramolecular assembly of DNPNB enzymes. Moreover, recent advances that revealed the therapeutic potential of DNPNB enzymes in bacteria could open the road for the pharmacological development of novel antibiotics.

Transomics2cytoscape: an automated software for interpretable 2.5-dimensional visualization of trans-omic networks

Biochemical network visualization is one of the essential technologies for mechanistic interpretation of omics data. In particular, recent advances in multi-omics measurement and analysis require the development of visualization methods that encompass multiple omics data. Visualization in 2.5 dimension (2.5D visualization), which is an isometric view of stacked X-Y planes, is a convenient way to interpret multi-omics/trans-omics data in the context of the conventional layouts of biochemical networks drawn on each of the stacked omics layers. However, 2.5D visualization of trans-omics networks is a state-of-the-art method that primarily relies on time-consuming human efforts involving manual drawing. Here, we present an R Bioconductor package 'transomics2cytoscape' for automated visualization of 2.5D trans-omics networks. We confirmed that transomics2cytoscape could be used for rapid visualization of trans-omics networks presented in published papers within a few minutes. Transomics2cytoscape allows for frequent update/redrawing of trans-omics networks in line with the progress in multi-omics/trans-omics data analysis, thereby enabling network-based interpretation of multi-omics data at each research step. The transomics2cytoscape source code is available at https://github.com/ecell/transomics2cytoscape .

Bioinformatics and bioactive peptides from foods: Do they work together?

We live in the Big Data Era which affects many aspects of science, including research on bioactive peptides derived from foods, which during the last few decades have been a focus of interest for scientists. These two issues, i.e., the development of computer technologies and progress in the discovery of novel peptides with health-beneficial properties, are closely interrelated. This Chapter presents the example applications of bioinformatics for studying biopeptides, focusing on main aspects of peptide analysis as the starting point, including: (i) the role of peptide databases; (ii) aspects of bioactivity prediction; (iii) simulation of peptide release from proteins. Bioinformatics can also be used for predicting other features of peptides, including ADMET, QSAR, structure, and taste. To answer the question asked "bioinformatics and bioactive peptides from foods: do they work together?", currently it is almost impossible to find examples of peptide research with no bioinformatics involved. However, theoretical predictions are not equivalent to experimental work and always require critical scrutiny. The aspects of compatibility of in silico and in vitro results are also summarized herein.

Production of enzymes for the removal of odorous substances in plant biomass

Residual plant biomass collected from agricultural, technical or biopharmaceutical processes contains odorous substances. The latter are often unacceptable for customers if the biomass is used in sustainable products such as building materials, paints, glues or flame-resistant foils. The objective of this study was to identify enzymes that can prevent the formation or facilitate the degradation of odorous substances such as butanol, eugenol or ethyl acetate and their derivatives in residual biomass. We used plant cell packs (PCPs) as a small-scale screening platform to assess the expression of enzymes that break down odorous substances in tobacco biomass. First, we compiled a list of volatile compounds in residual plant biomass that may give rise to undesirable odors, refining the list to 10 diverse compounds representing a range of odors. We then selected five monomeric enzymes (a eugenol oxidase, laccase, oxidase, alkane mono-oxidase and ethyl acetate hydrolase) with the potential to degrade these substances. We transiently expressed the proteins in PCPs, targeting different subcellular compartments to identify optimal production conditions. The maximum yield we achieved was ∼20 mg kg-1 for Trametes hirsute laccase targeted to the chloroplast. Our results confirm that enzymes for the removal of odorous substances can be produced in plant systems, facilitating the upcycling of residual biomass as an ingredient for sustainable products.

Data-Driven Protein Engineering for Improving Catalytic Activity and Selectivity

Protein engineering is essential for altering the substrate scope, catalytic activity and selectivity of enzymes for applications in biocatalysis. However, traditional approaches, such as directed evolution and rational design, encounter the challenge in dealing with the experimental screening process of a large protein mutation space. Machine learning methods allow the approximation of protein fitness landscapes and the identification of catalytic patterns using limited experimental data, thus providing a new avenue to guide protein engineering campaigns. In this concept article, we review machine learning models that have been developed to assess enzyme-substrate-catalysis performance relationships aiming to improve enzymes through data-driven protein engineering. Furthermore, we prospect the future development of this field to provide additional strategies and tools for achieving desired activities and selectivities.

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.

Rational strain design with minimal phenotype perturbation

Devising genetic interventions for desired cellular phenotypes remains challenging regarding time and resources. Kinetic models can accelerate this task by simulating metabolic responses to genetic perturbations. However, exhaustive design evaluations with kinetic models are computationally impractical, especially when targeting multiple enzymes. Here, we introduce a framework for efficiently scouting the design space while respecting cellular physiological requirements. The framework employs mixed-integer linear programming and nonlinear simulations with large-scale nonlinear kinetic models to devise genetic interventions while accounting for the network effects of these perturbations. Importantly, it ensures the engineered strain's robustness by maintaining its phenotype close to that of the reference strain. The framework, applied to improve the anthranilate production in E. coli, devises designs for experimental implementation, including eight previously experimentally validated targets. We expect this framework to play a crucial role in future design-build-test-learn cycles, significantly expediting the strain design compared to exhaustive design enumeration.

Catabolic Network of the Fermentative Gut Bacterium Phocaeicola vulgatus (Phylum Bacteroidota) from a Physiologic-Proteomic Perspective

INTRODUCTION

Phocaeicola vulgatus (formerly Bacteroides vulgatus) is a prevalent member of human and animal guts, where it influences by its dietary-fiber-fueled, fermentative metabolism the microbial community as well as the host health. Moreover, the fermentative metabolism of P. vulgatus bears potential for a sustainable production of bulk chemicals. The aim of the present study was to refine the current understanding of the P. vulgatus physiology.

METHODS

P. vulgatus was adapted to anaerobic growth with 14 different carbohydrates, ranging from hexoses, pentoses, hemicellulose, via an uronic acid to deoxy sugars. These substrate-adapted cells formed the basis to define the growth stoichiometries by quantifying growth/fermentation parameters and to reconstruct the catabolic network by applying differential proteomics.

RESULTS

The determination of growth performance revealed, e.g., doubling times (h) from 1.39 (arabinose) to 14.26 (glucuronate), biomass yields (gCDW/mmolS) from 0.01 (fucose) to 0.27 (α-cyclodextrin), and ATP yields (mMATP/mMC) from 0.21 (rhamnose) to 0.60 (glucuronate/xylan). Furthermore, fermentation product spectra were determined, ranging from broad and balanced (with xylan: acetate, succinate, formate, and propanoate) to rather one sided (with rhamnose or fucose: mainly propane-1,2-diol). The fermentation network serving all tested compounds is composed of 56 proteins (all identified), with several peripheral reaction sequences formed with high substrate specificity (e.g., conversion of arabinose to d-xylulose-3-phosphate) implicating a fine-tuned regulation. By contrast, central modules (e.g., glycolysis or the reaction sequence from PEP to succinate) were constitutively formed. Extensive formation of propane-1,2-diol from rhamnose and fucose involves rhamnulokinase (RhaB), rhamnulose-1-phosphate kinase (RhaD), and lactaldehyde reductase (FucO). Furthermore, Sus-like systems are apparently the most relevant uptake systems and a complex array of transmembrane electron-transfer systems (e.g., Na+-pumping Rnf and Nqr complexes, fumarate reductase) as well as F- and V-type ATP-synthases were detected.

CONCLUSIONS

The present study provides insights into the potential contribution of P. vulgatus to the gut metabolome and into the strain's biotechnological potential for sustainable production of short-chain fatty acids and alcohols.

Veterinary systems biology for bridging the phenotype-genotype gap via computational modeling for disease epidemiology and animal welfare

Veterinary systems biology is an innovative approach that integrates biological data at the molecular and cellular levels, allowing for a more extensive understanding of the interactions and functions of complex biological systems in livestock and veterinary science. It has tremendous potential to integrate multi-omics data with the support of vetinformatics resources for bridging the phenotype-genotype gap via computational modeling. To understand the dynamic behaviors of complex systems, computational models are frequently used. It facilitates a comprehensive understanding of how a host system defends itself against a pathogen attack or operates when the pathogen compromises the host's immune system. In this context, various approaches, such as systems immunology, network pharmacology, vaccinology and immunoinformatics, can be employed to effectively investigate vaccines and drugs. By utilizing this approach, we can ensure the health of livestock. This is beneficial not only for animal welfare but also for human health and environmental well-being. Therefore, the current review offers a detailed summary of systems biology advancements utilized in veterinary sciences, demonstrating the potential of the holistic approach in disease epidemiology, animal welfare and productivity.

Simulated digestions of free oligosaccharides and mucin-type O-glycans reveal a potential role for Clostridium perfringens

The development of a stable human gut microbiota occurs within the first year of life. Many open questions remain about how microfloral species are influenced by the composition of milk, in particular its content of human milk oligosaccharides (HMOs). The objective is to investigate the effect of the human HMO glycome on bacterial symbiosis and competition, based on the glycoside hydrolase (GH) enzyme activities known to be present in microbial species. We extracted from UniProt a list of all bacterial species catalysing glycoside hydrolase activities (EC 3.2.1.-), cross-referencing with the BRENDA database, and obtained a set of taxonomic lineages and CAZy family data. A set of 13 documented enzyme activities was selected and modelled within an enzyme simulator according to a method described previously in the context of biosynthesis. A diverse population of experimentally observed HMOs was fed to the simulator, and the enzymes matching specific bacterial species were recorded, based on their appearance of individual enzymes in the UniProt dataset. Pairs of bacterial species were identified that possessed complementary enzyme profiles enabling the digestion of the HMO glycome, from which potential symbioses could be inferred. Conversely, bacterial species having similar GH enzyme profiles were considered likely to be in competition for the same set of dietary HMOs within the gut of the newborn. We generated a set of putative biodegradative networks from the simulator output, which provides a visualisation of the ability of organisms to digest HMO and mucin-type O-glycans. B. bifidum, B. longum and C. perfringens species were predicted to have the most diverse GH activity and therefore to excel in their ability to digest these substrates. The expected cooperative role of Bifidobacteriales contrasts with the surprising capacities of the pathogen. These findings indicate that potential pathogens may associate in human gut based on their shared glycoside hydrolase digestive apparatus, and which, in the event of colonisation, might result in dysbiosis. The methods described can readily be adapted to other enzyme categories and species as well as being easily fine-tuneable if new degrading enzymes are identified and require inclusion in the model.

INTEDE 2.0: the metabolic roadmap of drugs

The metabolic roadmap of drugs (MRD) is a comprehensive atlas for understanding the stepwise and sequential metabolism of certain drug in living organisms. It plays a vital role in lead optimization, personalized medication, and ADMET research. The MRD consists of three main components: (i) the sequential catalyses of drug and its metabolites by different drug-metabolizing enzymes (DMEs), (ii) a comprehensive collection of metabolic reactions along the entire MRD and (iii) a systematic description on efficacy & toxicity for all metabolites of a studied drug. However, there is no database available for describing the comprehensive metabolic roadmaps of drugs. Therefore, in this study, a major update of INTEDE was conducted, which provided the stepwise & sequential metabolic roadmaps for a total of 4701 drugs, and a total of 22 165 metabolic reactions containing 1088 DMEs and 18 882 drug metabolites. Additionally, the INTEDE 2.0 labeled the pharmacological properties (pharmacological activity or toxicity) of metabolites and provided their structural information. Furthermore, 3717 drug metabolism relationships were supplemented (from 7338 to 11 055). All in all, INTEDE 2.0 is highly expected to attract broad interests from related research community and serve as an essential supplement to existing pharmaceutical/biological/chemical databases. INTEDE 2.0 can now be accessible freely without any login requirement at: http://idrblab.org/intede/.

Community-scale models of microbiomes: Articulating metabolic modelling and metagenome sequencing

Building models is essential for understanding the functions and dynamics of microbial communities. Metabolic models built on genome-scale metabolic network reconstructions (GENREs) are especially relevant as a means to decipher the complex interactions occurring among species. Model reconstruction increasingly relies on metagenomics, which permits direct characterisation of naturally occurring communities that may contain organisms that cannot be isolated or cultured. In this review, we provide an overview of the field of metabolic modelling and its increasing reliance on and synergy with metagenomics and bioinformatics. We survey the means of assigning functions and reconstructing metabolic networks from (meta-)genomes, and present the variety and mathematical fundamentals of metabolic models that foster the understanding of microbial dynamics. We emphasise the characterisation of interactions and the scaling of model construction to large communities, two important bottlenecks in the applicability of these models. We give an overview of the current state of the art in metagenome sequencing and bioinformatics analysis, focusing on the reconstruction of genomes in microbial communities. Metagenomics benefits tremendously from third-generation sequencing, and we discuss the opportunities of long-read sequencing, strain-level characterisation and eukaryotic metagenomics. We aim at providing algorithmic and mathematical support, together with tool and application resources, that permit bridging the gap between metagenomics and metabolic modelling.

Mycolicibacterium arseniciresistens sp. nov., isolated from lead-zinc mine tailing, and reclassification of two Mycobacterium species as Mycolicibacterium palauense comb. nov. and Mycolicibacterium grossiae comb. nov

A Gram-positive, acid-fast, aerobic, rapidly growing and non-motile strain was isolated from lead-zinc mine tailing sampled in Lanping, Yunnan province, Southwest China. 16S rRNA gene sequence analysis showed that the most closely related species of strain KC 300T was Mycolicibacterium litorale CGMCC 4.5724T (98.47 %). Additionally, phylogenomic and specific conserved signature indel analysis revealed that strain KC 300T should be a member of genus Mycolicibacterium, and Mycobacterium palauense CECT 8779T and Mycobacterium grossiae DSM 104744T should also members of genus Mycolicibacterium. The genome size of strain KC 300T was 6.2 Mb with an in silico DNA G+C content of 69.2 mol%. Chemotaxonomic characteristics of strain KC 300T were also consistent with the genus Mycolicibacterium. The average nucleotide identity, digital DNA-DNA hybridization and average amino acid identity values, as well as phenotypic, physiological and biochemical characteristics, support that strain KC 300T represents a new species within the genus Mycolicibacterium, for which the name Mycolicibacterium arseniciresistens sp. nov. is proposed, with the type strain KC 300T (=CGMCC 1.19494T=JCM 35915T). In addition, we reclassified Mycobacterium palauense and Mycobacterium grossiae as Mycolicibacterium palauense comb. nov. and Mycolicibacterium grossiae comb. nov., respectively.

Web-Based Resources to Investigate Protease Function

In 2001, the release of the first draft of the human genome marked the beginning of the Big Data era for biological sciences. Since then, the complexity of datasets generated by laboratories worldwide has increased exponentially. Public repositories such as the Protein Data Bank, which has exceeded the 200000 entries in 2023, have been instrumental not only to collect, organize, and distill this enormous research output but also to promote further research enterprises. The achievements of artificial intelligence programs such as AlphaFold would not have been possible without the collective efforts of countless researchers who made their work publicly available. Here, I provide a practical, but far from exhaustive, list of resources useful to investigate protease function.

Acetaminophen-traces bioremediation with novel phenotypically and genotypically characterized 2 Streptomyces strains using chemo-informatics, in vivo, and in vitro experiments for cytotoxicity and biological activity

We isolated two novel bacterial strains, active against the environmental pollutant acetaminophen/Paracetamol®. Streptomyces chrestomyceticus (symbol RS2) and Flavofuscus (symbol M33) collected from El-Natrun Valley, Egypt-water, sediment, and sand samples, taxonomically characterized using a transmission electron microscope (TEM). Genotypic identification, based on 16S rRNA gene sequence analysis followed by BLAST alignment, were deposited on the NCBI as 2 novel strains https://www.ncbi.nlm.nih.gov/nuccore/OM665324  and https://www.ncbi.nlm.nih.gov/nuccore/OM665325 . The phylogenetic tree was constructed. Acetaminophen secondary or intermediate product's chemical structure was identified by GC/LC MS. Some selected acetaminophen secondary-product extracts and derived compounds were examined against a panel of test micro-organisms and fortunately showed a good anti-microbial effect. In silico chemo-informatics Swiss ADMET evaluation was used in the selected bio-degradation extracts for absorption (gastric), distribution (to CNS), metabolism (hepatic), excretion (renal), and finally not toxic, being non-mutagenic/teratogenic or genotoxic, virtually. Moreover, in vitro cytotoxic activity of these selected bio-degradation secondary products was examined against HepG2 and MCF7 cancer cell lines, where M33 and RS2 extract effects on acetaminophen/paracetamol bio-degradation products were safe, with higher IC50 on HepG2 and MCF7 than the acetaminophen/paracetamol IC50 of 108.5 μg/ml. Moreover, an in vivo oral acute single-dose toxicity experiment was conducted, to confirm these in vitro and in silico lower toxicity (better safety) than acetaminophen/paracetamol.

Machine Learning and Metabolic Model Guided CRISPRi Reveals a Central Role for Phosphoglycerate Mutase in Chlamydia trachomatis Persistence

Upon nutrient starvation, Chlamydia trachomatis serovar L2 (CTL) shifts from its normal growth to a non-replicating form, termed persistence. It is unclear if persistence is an adaptive response or lack of it. To understand that transcriptomics data were collected for nutrient-sufficient and nutrient-starved CTL. Applying machine learning approaches on transcriptomics data revealed a global transcriptomic rewiring of CTL under stress conditions without having any global stress regulator. This indicated that CTL's stress response is due to lack of an adaptive response mechanism. To investigate the impact of this on CTL metabolism, we reconstructed a genome-scale metabolic model of CTL (iCTL278) and contextualized it with the collected transcriptomics data. Using the metabolic bottleneck analysis on contextualized iCTL278, we observed phosphoglycerate mutase (pgm) regulates the entry of CTL to the persistence. Later, pgm was found to have the highest thermodynamics driving force and lowest enzymatic cost. Furthermore, CRISPRi-driven knockdown of pgm and tryptophan starvation experiments revealed the importance of this gene in inducing persistence. Hence, this work, for the first time, introduced thermodynamics and enzyme-cost as tools to gain deeper understanding on CTL persistence.

State-of the-Art Constraint-Based Modeling of Microbial Metabolism: From Basics to Context-Specific Models with a Focus on Methanotrophs

Methanotrophy is the ability of an organism to capture and utilize the greenhouse gas, methane, as a source of energy-rich carbon. Over the years, significant progress has been made in understanding of mechanisms for methane utilization, mostly in bacterial systems, including the key metabolic pathways, regulation and the impact of various factors (iron, copper, calcium, lanthanum, and tungsten) on cell growth and methane bioconversion. The implementation of -omics approaches provided vast amount of heterogeneous data that require the adaptation or development of computational tools for a system-wide interrogative analysis of methanotrophy. The genome-scale mathematical modeling of its metabolism has been envisioned as one of the most productive strategies for the integration of muti-scale data to better understand methane metabolism and enable its biotechnological implementation. Herein, we provide an overview of various computational strategies implemented for methanotrophic systems. We highlight functional capabilities as well as limitations of the most popular web resources for the reconstruction, modification and optimization of the genome-scale metabolic models for methane-utilizing bacteria.

Untargeted metabolomics to expand the chemical space of the marine diatom Skeletonema marinoi

Diatoms (Bacillariophyceae) are aquatic photosynthetic microalgae with an ecological role as primary producers in the aquatic food web. They account substantially for global carbon, nitrogen, and silicon cycling. Elucidating the chemical space of diatoms is crucial to understanding their physiology and ecology. To expand the known chemical space of a cosmopolitan marine diatom, Skeletonema marinoi, we performed High-Resolution Liquid Chromatography-Tandem Mass Spectrometry (LC-MS2) for untargeted metabolomics data acquisition. The spectral data from LC-MS2 was used as input for the Metabolome Annotation Workflow (MAW) to obtain putative annotations for all measured features. A suspect list of metabolites previously identified in the Skeletonema spp. was generated to verify the results. These known metabolites were then added to the putative candidate list from LC-MS2 data to represent an expanded catalog of 1970 metabolites estimated to be produced by S. marinoi. The most prevalent chemical superclasses, based on the ChemONT ontology in this expanded dataset, were organic acids and derivatives, organoheterocyclic compounds, lipids and lipid-like molecules, and organic oxygen compounds. The metabolic profile from this study can aid the bioprospecting of marine microalgae for medicine, biofuel production, agriculture, and environmental conservation. The proposed analysis can be applicable for assessing the chemical space of other microalgae, which can also provide molecular insights into the interaction between marine organisms and their role in the functioning of ecosystems.

Kinetic modelling of β-cell metabolism reveals control points in the insulin-regulating pyruvate cycling pathways

Insulin, a key hormone in the regulation of glucose homoeostasis, is secreted by pancreatic β-cells in response to elevated glucose levels. Insulin is released in a biphasic manner in response to glucose metabolism in β-cells. The first phase of insulin secretion is triggered by an increase in the ATP:ADP ratio; the second phase occurs in response to both a rise in ATP:ADP and other key metabolic signals, including a rise in the NADPH:NADP+ ratio. Experimental evidence indicates that pyruvate-cycling pathways play an important role in the elevation of the NADPH:NADP+ ratio in response to glucose. The authors developed a kinetic model for the tricarboxylic acid cycle and pyruvate cycling pathways. The authors successfully validated the model against experimental observations and performed a sensitivity analysis to identify key regulatory interactions in the system. The model predicts that the dicarboxylate carrier and the pyruvate transporter are the most important regulators of pyruvate cycling and NADPH production. In contrast, the analysis showed that variation in the pyruvate carboxylase flux was compensated by a response in the activity of mitochondrial isocitrate dehydrogenase (ICDm ) resulting in minimal effect on overall pyruvate cycling flux. The model predictions suggest starting points for further experimental investigation, as well as potential drug targets for the treatment of type 2 diabetes.