Nitrile gloves have become a significant environmental pollutant after the COVID-19 pandemic due to their single-use design. This study examines the capability of P. aeruginosa to use nitrile gloves as its sole carbon energy source. Biodegradation was determined by P. aeruginosa adapting to increasing nitrile glove concentrations at 1%, 3%, and 5% (w/v). The growth kinetics of P. aeruginosa were evaluated, as well as the polymer weight loss. Topographic changes on the glove surfaces were examined using SEM, and FT-IR was used to evaluate the biodegradation products of the nitrile gloves. Following the establishment of a biofilm on the glove surface, the nitrile toxicity was minimized via biodegradation. The result of the average weight loss of nitrile gloves was 2.25%. FT-IR analysis revealed the presence of aldehydes and aliphatic amines associated with biodegradation. SEM showed P. aeruginosa immersed in the EPS matrix, causing the formation of cracks, scales, protrusions, and the presence of semi-spherical particles. We conclude that P. aeruginosa has the capability to use nitrile gloves as its sole carbon source, even up to 5%, through biofilm formation, demonstrating the potential of P. aeruginosa for the degradation of nitrile gloves.

Background/objectives: Amino acids (AAs) play a critical role in diseases such as cystic fibrosis where Pseudomonas aeruginosa PAO1 adapts its metabolism in response to host-derived nutrients. The adaptation influences virulence and complicates antibiotic treatment mainly for the antimicrobial resistance context. D- and L-AAs have been analyzed for their impact on quorum sensing (QS), a mechanism that regulates virulence factors. This research aimed to reconstruct the genome-scale metabolic model (GEM) of P. aeruginosa PAO1 to investigate the metabolic roles of D- and L-AAs in QS-related pathways. Methods: The updated GEM, iJD1249, was reconstructed by using protocols to integrate data from previous models and refined with well-standardized in silico media (LB, M9, and SCFM) to improve flux balance analysis accuracy. The model was used to explore the metabolic impact of D-Met, D-Ala, D-Glu, D-Ser, L-His, L-Glu, L-Arg, and L-Ornithine (L-Orn) at 5 and 50 mM in QS-related pathways, focusing on the effects on bacterial growth and carbon flux distributions. Results: Among the tested AAs, D-Met was the only one that did not enhance the growth rate of P. aeruginosa PAO1, while L-Arg and L-Orn increased fluxes in the L-methionine biosynthesis pathway, influencing the metH gene. These findings suggest a differential metabolic role for D-and L-AAs in QS-related pathways. Conclusions: Our results shed some light on the metabolic impact of AAs on QS-related pathways and their potential role in P. aeruginosa virulence. Future studies should assess D-Met as a potential adjuvant in antimicrobial strategies, optimizing the concentration in combination with antibiotics to maximize its therapeutic effectiveness.

Multidrug resistance in Pseudomonas aeruginosa, a high-priority pathogen per the World Health Organization, poses a global threat due to carbapenem resistance and limited antibiotic treatments. Using the bioinformatic tools CRISPRCasFinder, CRISPRCasTyper, CRISPRloci, and CRISPRImmunity, we analyzed the genome of P. aeruginosa PAO1 and revealed an orphan CRISPR system, suggesting it may be a remnant of a type IV system due to the presence of the DinG protein. This system comprises two CRISPR arrays and noteworthy DinG and Cas3 proteins, supporting recent evidence about the association between type IV and I CRISPR systems. Additionally, we demonstrated a co-evolutionary relationship between the orphan CRISPR system in P. aeruginosa PAO1 and the mobile genetic element and prophages identified. One self-targeting spacer was identified, often associated with bacterial evolution and autoimmunity, and no Acr proteins. This research opens avenues for studying how these CRISPR arrays regulate pathogenicity and for developing alternative strategies using its endogenous orphan CRISPR system against carbapenem-resistant P. aeruginosa strains.

Helicobacter pylori (H. pylori) is a highly adaptable gastric pathogen with marked genomic plasticity. Whilst functional CRISPR-Cas systems provide adaptive immunity in many bacteria, they have not been identified in H. pylori, unlike CRISPR-like sequences. In this study, eight H. pylori genomes were analysed using the bioinformatics tools CRISPRCasFinder, CRISPRCasTyper, and CRISPRloci. A total of 25 CRISPR-like arrays were identified, showing high conservation (88%) both between and within strains, suggesting that these arrays are not random remnants but rather organised structures possibly involved in cellular processes. Notably, a structural association was observed between the CRISPR-like sequences and the cag pathogenicity island (CagA-PAI). Conversely, CagA-PAI instability in specific strains was observed in the presence of the TnpA and TnpB transposons. Furthermore, in strain 29CaP, CRISPR-like assemblies were located in genomic proximity to the prophage Helico 1961P, leading to the hypothesis of a compensatory or regulatory effect in the absence of CagA-PAI. Taken together, these findings indicate that CRISPR-like arrays in H. pylori characterise a genomic architecture within regions of high plasticity. This study provides a solid exploratory foundation for future functional research on the adaptive and pathogenic evolution of H. pylori.

Highlights: • Systems biology approach identified 10 gene targets that enhance endogenous PvdQ • fabI is the most promising knockout target for increasing PvdQ and promoting QQ • Targeting pvdh for knockout prevents pyoverdine increase from PvdQ overproduction • gRNAs designed based on Cas9 efficiency, scoring system, and RNA secondary structure

CRISPR-Cas9 has transformed microbial biotechnology by enabling precise genome modifications; however, achieving high editing efficiency remains a challenge due to multiple determinants, including on-target specificity, off-target events, PAM sequence, sgRNA scaffold composition, and RNA secondary structure. Our review foresees how artificial intelligence (AI) can address those challenges by enabling automated identification as well as highly active guide RNA (gRNA) optimisation. We highlight the influence of a data-driven training strategy that is focused on high-quality, diverse, and accurately labelled microbial datasets—mainly, given the limitations of models derived from mammalian systems that are not directly transferable to microbial organisms. Moreover, we discuss the key role of FAIR (Findable, Accessible, Interoperable, and Reusable) data principles and centralised, curated CRISPR-Cas databases as foundational elements for developing robust and predictive frameworks. Emerging directions are also explored, including generative AI approaches capable of supporting automated experimental planning. By considering the potential dual use of such technologies, the review further addresses bioethical considerations and regulatory frameworks necessary to ensure responsible genome engineering as a milestone, as well as the implementation of safeguards against misuse, particularly in pathogenic microorganisms. Furthermore, the convergence of standardised experimental data, specialised microbial datasets, and advanced AI architectures is paving the way to transform microbial biotechnology by accelerating metabolic engineering and synthetic biology applications.