Genome-Scale Metabolic Modelling Approach to Understand the Metabolism of the Opportunistic Human Pathogen Staphylococcus epidermidis RP62A

Applications of genome-scale metabolic models to the study of human diseases: A systematic review

BACKGROUND AND OBJECTIVES

Genome-scale metabolic networks (GEMs) represent a valuable modeling and computational tool in the broad field of systems biology. Their ability to integrate constraints and high-throughput biological data enables the study of intricate metabolic aspects and processes of different cell types and conditions. The past decade has witnessed an increasing number and variety of applications of GEMs for the study of human diseases, along with a huge effort aimed at the reconstruction, integration and analysis of a high number of organisms. This paper presents a systematic review of the scientific literature, to pursue several important questions about the application of constraint-based modeling in the investigation of human diseases. Hopefully, this paper will provide a useful reference for researchers interested in the application of modeling and computational tools for the investigation of metabolic-related human diseases.

METHODS

This systematic review was conducted according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Elsevier Scopus®, National Library of Medicine PubMed® and Clarivate Web of Science™ databases were enquired, resulting in 566 scientific articles. After applying exclusion and eligibility criteria, a total of 169 papers were selected and individually examined.

RESULTS

The reviewed papers offer a thorough and up-to-date picture of the latest modeling and computational approaches, based on genome-scale metabolic models, that can be leveraged for the investigation of a large variety of human diseases. The numerous studies have been categorized according to the clinical research area involved in the examined disease. Furthermore, the paper discusses the most typical approaches employed to derive clinically-relevant information using the computational models.

CONCLUSIONS

The number of scientific papers, utilizing GEM-based approaches for the investigation of human diseases, suggests an increasing interest in these types of approaches; hopefully, the present review will represent a useful reference for scientists interested in applying computational modeling approaches to investigate the aetiopathology of human diseases; we also hope that this work will foster the development of novel applications and methods for the discovery of clinically-relevant insights on metabolic-related diseases.

Pentadecanoic Acid-Releasing PDMS: Towards a New Material to Prevent S. epidermidis Biofilm Formation

Microbial biofilm formation on medical devices paves the way for device-associated infections. Staphylococcus epidermidis is one of the most common strains involved in such infections as it is able to colonize numerous devices, such as intravenous catheters, prosthetic joints, and heart valves. We previously reported the antibiofilm activity against S. epidermidis of pentadecanoic acid (PDA) deposited by drop-casting on the silicon-based polymer poly(dimethyl)siloxane (PDMS). This material exerted an antibiofilm activity by releasing PDA; however, a toxic effect on bacterial cells was observed, which could potentially favor the emergence of resistant strains. To develop a PDA-functionalized material for medical use and overcome the problem of toxicity, we produced PDA-doped PDMS by either spray-coating or PDA incorporation during PDMS polymerization. Furthermore, we created a strategy to assess the kinetics of PDA release using ADIFAB, a very sensitive free fatty acids fluorescent probe. Spray-coating resulted in the most promising strategy as the concentration of released PDA was in the range 0.8-1.5 μM over 21 days, ensuring long-term effectiveness of the antibiofilm molecule. Moreover, the new coated material resulted biocompatible when tested on immortalized human keratinocytes. Our results indicate that PDA spray-coated PDMS is a promising material for the production of medical devices endowed with antibiofilm activity.

Reconstruction and Analysis of a Genome-Scale Metabolic Model of Acinetobacter lwoffii

Acinetobacter lwoffii is widely considered to be a harmful bacterium that is resistant to medicines and disinfectants. A. lwoffii NL1 degrades phenols efficiently and shows promise as an aromatic compound degrader in antibiotic-contaminated environments. To gain a comprehensive understanding of A. lwoffii, the first genome-scale metabolic model of A. lwoffii was constructed using semi-automated and manual methods. The iNX811 model, which includes 811 genes, 1071 metabolites, and 1155 reactions, was validated using 39 unique carbon and nitrogen sources. Genes and metabolites critical for cell growth were analyzed, and 12 essential metabolites (mainly in the biosynthesis and metabolism of glycan, lysine, and cofactors) were identified as antibacterial drug targets. Moreover, to explore the metabolic response to phenols, metabolic flux was simulated by integrating transcriptomics, and the significantly changed metabolism mainly included central carbon metabolism, along with some transport reactions. In addition, the addition of substances that effectively improved phenol degradation was predicted and validated using the model. Overall, the reconstruction and analysis of model iNX811 helped to study the antimicrobial systems and biodegradation behavior of A. lwoffii.

Cold-Azurin, a New Antibiofilm Protein Produced by the Antarctic Marine Bacterium Pseudomonas sp. TAE6080

Biofilm is accountable for nosocomial infections and chronic illness, making it a serious economic and public health problem. Staphylococcus epidermidis, thanks to its ability to form biofilm and colonize biomaterials, represents the most frequent causative agent involved in biofilm-associated infections of medical devices. Therefore, the research of new molecules able to interfere with S. epidermidis biofilm formation has a remarkable interest. In the present work, the attention was focused on Pseudomonas sp. TAE6080, an Antarctic marine bacterium able to produce and secrete an effective antibiofilm compound. The molecule responsible for this activity was purified by an activity-guided approach and identified by LC-MS/MS. Results indicated the active protein was a periplasmic protein similar to the Pseudomonas aeruginosa PAO1 azurin, named cold-azurin. The cold-azurin was recombinantly produced in E. coli and purified. The recombinant protein was able to impair S. epidermidis attachment to the polystyrene surface and effectively prevent biofilm formation.

Genome-scale metabolic modeling and in silico analysis of opportunistic skin pathogen Cutibacterium acnes

Cutibacterium acnes, one of the most abundant skin microbes found in the sebaceous gland, is known to contribute to the development of acne vulgaris when its strains become imbalanced. The current limitations of acne treatment using antibiotics have caused an urgent need to develop a systematic strategy for selectively targeting C. acnes, which can be achieved by characterizing their cellular behaviors under various skin environments. To this end, we developed a genome-scale metabolic model (GEM) of virulent C. acnes, iCA843, based on the genome information of a relevant strain from ribotype 5 to comprehensively understand the pathogenic traits of C. acnes in the skin environment. We validated the model qualitatively by demonstrating its accuracy prediction of propionate and acetate production patterns, which were consistent with experimental observations. Additionally, we identified unique biosynthetic pathways for short-chain fatty acids in C. acnes compared to other GEMs of acne-inducing skin pathogens. By conducting constraint-based flux analysis under endogenous carbon sources in human skin, we discovered that the Wood-Werkman cycle is highly activated under acnes-associated skin condition for the regeneration of NAD, resulting in enhanced propionate production. Finally, we proposed potential anti-C. acnes targets by using the model-guided systematic framework based on gene essentiality analysis and protein sequence similarity search with abundant skin microbiome taxa.

Staphylococcus epidermidis biofilms undergo metabolic and matrix remodeling under nitrosative stress

Staphylococcus epidermidis is a commensal skin bacterium that forms host- and antibiotic-resistant biofilms that are a major cause of implant-associated infections. Most research has focused on studying the responses to host-imposed stresses on planktonic bacteria. In this work, we addressed the open question of how S. epidermidis thrives on toxic concentrations of nitric oxide (NO) produced by host innate immune cells during biofilm assembly. We analyzed alterations of gene expression, metabolism, and matrix structure of biofilms of two clinical isolates of S. epidermidis, namely, 1457 and RP62A, formed under NO stress conditions. In both strains, NO lowers the amount of biofilm mass and causes increased production of lactate and decreased acetate excretion from biofilm glucose metabolism. Transcriptional analysis revealed that NO induces icaA, which is directly involved in polysaccharide intercellular adhesion (PIA) production, and genes encoding proteins of the amino sugar pathway (glmM and glmU) that link glycolysis to PIA synthesis. However, the strains seem to have distinct regulatory mechanisms to boost lactate production, as NO causes a substantial upregulation of ldh gene in strain RP62A but not in strain 1457. The analysis of the matrix components of the staphylococcal biofilms, assessed by confocal laser scanning microscopy (CLSM), showed that NO stimulates PIA and protein production and interferes with biofilm structure in a strain-dependent manner, but independently of the Ldh level. Thus, NO resistance is attained by remodeling the staphylococcal matrix architecture and adaptation of main metabolic processes, likely providing in vivo fitness of S. epidermidis biofilms contacting NO-proficient macrophages.

Staphylococcus epidermidis RP62A’s Metabolic Network: Validation and Intervention Strategies

Increasingly, systems biology is gaining relevance in basic and applied research. The combination of computational biology with wet laboratory methods produces synergy that results in an exponential increase in knowledge of biological systems. The study of microorganisms such as Staphylococcus epidermidis RP62A enables the researcher to understand better their metabolic networks, which allows the design of effective strategies to treat infections caused by this species or others. S. epidermidis is the second most commoncause of infection in patients with joint implants, so treating its proliferation seems vital for public health. There are different approaches to the analysis of metabolic networks. Flux balance analysis (FBA) is one of the most widespread streams of research. It allows the study of large metabolic networks, the study their structural properties, the optimization of metabolic flux, and the search for intervention strategies to modify the state of the metabolic network. This work presents the validation of the Staphylococcus epidermidis RP62A metabolic network model elaborated by Díaz Calvo et al. Then, we elaborate further on the network analysis’s essential reactions. The full set of essential reactions (including a previously unobserved one) was computed, and we classified them into equivalence classes. Some proposals to intervene in the network and design knock-outs by studying minimal cut sets of small length are also introduced. In particular, minimal cut sets related to the medium (including exchange reactions associated with medium metabolites) have been computed. In this sense, the unique external MCS (composed of cysteine and sulfate ion) has been found, and all hybrid MCS (based on knocking out both internal and exchange reactions) of length two have also been computed. The paper also points out the possible importance of these new intervention strategies.