Modeling antibiotic resistance in the microbiota using multi-level Petri Nets

Computational methods for biofabrication in tissue engineering and regenerative medicine - a literature review

This literature review rigorously examines the growing scientific interest in computational methods for Tissue Engineering and Regenerative Medicine biofabrication, a leading-edge area in biomedical innovation, emphasizing the need for accurate, multi-stage, and multi-component biofabrication process models. The paper presents a comprehensive bibliometric and contextual analysis, followed by a literature review, to shed light on the vast potential of computational methods in this domain. It reveals that most existing methods focus on single biofabrication process stages and components, and there is a significant gap in approaches that utilize accurate models encompassing both biological and technological aspects. This analysis underscores the indispensable role of these methods in understanding and effectively manipulating complex biological systems and the necessity for developing computational methods that span multiple stages and components. The review concludes that such comprehensive computational methods are essential for developing innovative and efficient Tissue Engineering and Regenerative Medicine biofabrication solutions, driving forward advancements in this dynamic and evolving field.

Microbiome modeling: a beginner's guide

Microbiomes, comprised of diverse microbial species and viruses, play pivotal roles in human health, environmental processes, and biotechnological applications and interact with each other, their environment, and hosts via ecological interactions. Our understanding of microbiomes is still limited and hampered by their complexity. A concept improving this understanding is systems biology, which focuses on the holistic description of biological systems utilizing experimental and computational methods. An important set of such experimental methods are metaomics methods which analyze microbiomes and output lists of molecular features. These lists of data are integrated, interpreted, and compiled into computational microbiome models, to predict, optimize, and control microbiome behavior. There exists a gap in understanding between microbiologists and modelers/bioinformaticians, stemming from a lack of interdisciplinary knowledge. This knowledge gap hinders the establishment of computational models in microbiome analysis. This review aims to bridge this gap and is tailored for microbiologists, researchers new to microbiome modeling, and bioinformaticians. To achieve this goal, it provides an interdisciplinary overview of microbiome modeling, starting with fundamental knowledge of microbiomes, metaomics methods, common modeling formalisms, and how models facilitate microbiome control. It concludes with guidelines and repositories for modeling. Each section provides entry-level information, example applications, and important references, serving as a valuable resource for comprehending and navigating the complex landscape of microbiome research and modeling.

Biology System Description Language (BiSDL): a modeling language for the design of multicellular synthetic biological systems

BACKGROUND

The Biology System Description Language (BiSDL) is an accessible, easy-to-use computational language for multicellular synthetic biology. It allows synthetic biologists to represent spatiality and multi-level cellular dynamics inherent to multicellular designs, filling a gap in the state of the art. Developed for designing and simulating spatial, multicellular synthetic biological systems, BiSDL integrates high-level conceptual design with detailed low-level modeling, fostering collaboration in the Design-Build-Test-Learn cycle. BiSDL descriptions directly compile into Nets-Within-Nets (NWNs) models, offering a unique approach to spatial and hierarchical modeling in biological systems.

RESULTS

BiSDL's effectiveness is showcased through three case studies on complex multicellular systems: a bacterial consortium, a synthetic morphogen system and a conjugative plasmid transfer process. These studies highlight the BiSDL proficiency in representing spatial interactions and multi-level cellular dynamics. The language facilitates the compilation of conceptual designs into detailed, simulatable models, leveraging the NWNs formalism. This enables intuitive modeling of complex biological systems, making advanced computational tools more accessible to a broader range of researchers.

CONCLUSIONS

BiSDL represents a significant step forward in computational languages for synthetic biology, providing a sophisticated yet user-friendly tool for designing and simulating complex biological systems with an emphasis on spatiality and cellular dynamics. Its introduction has the potential to transform research and development in synthetic biology, allowing for deeper insights and novel applications in understanding and manipulating multicellular systems.

From Pathophysiological Hypotheses to Case-Control Study Design: Resistance from Antibiotic Exposure in Community-Onset Infections

Antimicrobial resistance is a global public health concern, at least partly due to the misuse of antibiotics. The increasing prevalence of antibiotic-resistant infections in the community has shifted at-risk populations into the general population. Numerous case-control studies attempt to better understand the link between antibiotic use and antibiotic-resistant community-onset infections. We review the designs of such studies, focusing on community-onset bloodstream and urinary tract infections. We highlight their methodological heterogeneity in the key points related to the antibiotic exposure, the population and design. We show the impact of this heterogeneity on study results, through the example of extended-spectrum β-lactamases producing Enterobacteriaceae. Finally, we emphasize the need for the greater standardization of such studies and discuss how the definition of a pathophysiological hypothesis specific to the bacteria-resistance pair studied is an important prerequisite to clarify the design of future studies.

Correction of the oropharyngeal and gut microbiota in children by plant extracts containing natural products

The widespread use of antibacterial drugs for the treatment of respiratory diseases causes antimicrobial resistance in opportunistic microorganisms, which leads to the chronic forms of respiratory diseases and contributes to the risk of repeated respiratory infections. One of the new therapeutic solutions is the use of multicomponent water-soluble plant extracts. The goal of this study was to evaluate the antibacterial efficacy of the extracts of multicomponent herbal remedies versus the synthetic antiseptic for the treatment of the oropharyngeal and gut opportunistic microflora in children with chronic tonsillitis.

In a retrospective study, we compared the effectiveness of the plant extract Tonzinal (experimental group, 100 patients) versus the Miramistin antiseptic agent (control group, 40 patients) for the treatment of chronic tonsillitis in children 5 to 15 years old using various treatment regimens.

The oropharyngeal microbiocenosis was investigated by the bacteriological analysis of smears from the posterior wall of the pharynx and tonsils. Bacterial strains were isolated by inoculation on liquid agar media with the subsequent identification of Staphylococcus aureus, Streptococcus pyogenes, Candida spp., Moraxela cataralis, and Mycoplasma pneumonie according to the morphological and biochemical characteristics. For the bacteriological analysis of gut microbiocenosis, Staphylococcus aureus, Streptococcus spp., Candida spp., Klebsiella spp., Clostridiums spp., and Proteus spp. were isolated from the fecal filtrate and then identified by the same methods. The occurrence rate of microorganisms in patients of the experimental and control groups was compared before and after the 10-day course of therapy.

A statistically significant decrease in the occurrence rate of Staphylococcus aureus (from 25% to 0%, p<0.01) and Candida spp. (from 18% to 0%, p<0.01) in the oropharynx of patients in the experimental group and from 20% to 7.5% and from 5% to 0% (p<0.05), respectively, in the control group was observed. A statistically significant decrease in the occurrence rate of Streptococcus pyogenes was only observed in the experimental group (from 30% to 0%, p<0.01). Treatment with Tonzinal or Miramistin did not lead to the statistically significant changes in the occurrence rate of opportunistic microorganisms in the gut microflora of the patients in both groups. Therefore, we have shown a higher antimicrobial efficacy of Tonzinal versus the Miramistin antiseptic for the treatment of the oropharyngeal opportunistic microorganisms in children with chronic tonsillitis.

Bacterial Protein Interaction Networks: Connectivity is Ruled by Gene Conservation, Essentiality and Function

BACKGROUND

Protein-protein interaction (PPI) networks are the backbone of all processes in living cells. In this work, we relate conservation, essentiality and functional repertoire of a gene to the connectivity k (i.e. the number of interactions, links) of the corresponding protein in the PPI network.

METHODS

On a set of 42 bacterial genomes of different sizes, and with reasonably separated evolutionary trajectories, we investigate three issues: i) whether the distribution of connectivities changes between PPI subnetworks of essential and nonessential genes; ii) how gene conservation, measured both by the evolutionary retention index (ERI) and by evolutionary pressures, is related to the connectivity of the corresponding protein; iii) how PPI connectivities are modulated by evolutionary and functional relationships, as represented by the Clusters of Orthologous Genes (COGs).

RESULTS

We show that conservation, essentiality and functional specialisation of genes constrain the connectivity of the corresponding proteins in bacterial PPI networks. In particular, we isolated a core of highly connected proteins (connectivities k≥40), which is ubiquitous among the species considered here, though mostly visible in the degree distributions of bacteria with small genomes (less than 1000 genes).

CONCLUSION

The genes that support this highly connected core are conserved, essential and, in most cases, belong to the COG cluster J, related to ribosomal functions and the processing of genetic information.