Carvalho-Assef, A. P. D. | bioRxiv Science

bioRxiv2025

Outbreak Investigation of Phytobacter diazotrophicus Using Fourier Transform Infrared Spectroscopy compared to cgMLST

IntroductionPhytobacter diazotrophicus is an emerging opportunistic pathogen associated with hospital outbreaks and a reservoir of antimicrobial resistance genes; however, its clonal epidemiology remains poorly understood. Accurate identification and rapid clonal typing are crucial for infection control. Core genome MLST (cgMLST) is the genomic gold standard; however, it is both costly and time-consuming. Fourier-transform infrared (FT-IR) spectroscopy using the IR Biotyper(R) emerges as a rapid and accessible typing alternative. ObjectiveThis study evaluated the performance of the IR Biotyper(R) in discriminating P. diazotrophicus outbreak isolates, compared to cgMLST. MethodologyEight epidemiologically relevant isolates, including two from a total parenteral nutrition (TPN) outbreak and four from a hemodialysis clinic, along with two outliers, were analyzed. Its identification was confirmed by API20E, MALDI-TOF MS, nifL gene qPCR, and WGS. Both cgMLST, based on 7,755 conserved genes, and the Fourier Transform Infrared (FT-IR) Spectroscopy by IR Biotyper(R) were used for clonal typing, with the spectral cutoff empirically determined based on known clonal isolates. ResultsBoth methodologies demonstrated remarkable agreement in detecting clonal clusters. The two TPN outbreak isolates showed 23 allele differences (ADs) by cgMLST and consistently clustered according to the IR Biotyper. Similarly, the four isolates from the outbreak at hemodialysis clinic formed homogeneous clusters by both methods (5-27 ADs by cgMLST). The outlier isolates were consistently discriminated. ConclusionFT-IR spectroscopy (IR Biotyper(R)) proved promising and complementary to cgMLST for typing P. diazotrophicus in outbreak scenarios, offering a rapid and cost-effective alternative for detecting clonal events. Its applicability in routine epidemiological surveillance is significant. Statement of ImportancePhytobacter diazotrophicus is an emerging opportunistic pathogen of increasing concern in clinical environments, associated with hospital outbreaks and carrying antimicrobial resistance genes. Rapid detection and precise clonal characterization are essential for infection control and epidemiological surveillance. However, these are challenging for less-understood species. Genomic methods, such as cgMLST, are the gold standard, but their cost and time limitations hinder routine application in outbreak response. This study evaluated the IR Biotyper system, a rapid and accessible phenotypic tool that uses Fourier Transform Infrared Spectroscopy, compared to cgMLST for clonal typing of P. diazotrophicus. Our findings demonstrate a remarkable agreement between the two methods in identifying clonal clusters. This validation suggests the potential of the IR Biotyper as an effective tool for real-time screening and active surveillance of P. diazotrophicus in clinical settings. This enables a more agile response to outbreaks and aids in managing antimicrobial resistance, reserving high-resolution genomic analyses for more in-depth investigations.

microbiology↗

bioRxiv2022

An Updated Gene Regulatory Network reconstruction of multidrug-resistant Pseudomonas aeruginosa CCBH4851

BACKGROUNDHealthcare-associated infections due to multidrug-resistant (MDR) bacteria such as Pseudomonas aeruginosa are significant public health issues worldwide. A system biology approach can help understand bacterial behavior and provide novel ways to identify potential therapeutic targets and the development of new drugs. Gene regulatory networks (GRN) are an example of interaction representation in silico between regulatory genes and their targets. OBJECTIVESIn this work, we update the reconstruction of the MDR P. aeruginosa CCBH4851 GRN, and analyze and discuss its structural properties. METHODSWe based this study on the gene orthology inference methodology using the reciprocal best hit method. The P. aeruginosa CCBH4851 genome and GRN, published in 2019, and the P. aeruginosa PAO1 GRN, published in 2020, were used for this update reconstruction process. FINDINGSOur result is a GRN with a larger number of regulatory genes, target genes, and interactions compared to the previous networks, and its structural properties are consistent with the complexity of biological networks and the biological features of P. aeruginosa. MAIN CONCLUSIONSHere, we present the largest and most complete version of P. aeruginosa GRN published to this date, to the best of our knowledge.

systems biology↗

bioRxiv2019

Gene regulatory network inference and analysis of multidrug-resistant Pseudomonas aeruginosa

BackgroundHealthcare-associated infections caused by bacteria such as Pseudomonas aeruginosa are a major public health problem worldwide. Gene regulatory networks computationally represent interactions among regulatory genes and their targets, an important approach to understand bacterial behavior and to provide novel ways of overcoming scientific challenges, including the identification of potential therapeutic targets and the development of new drugs.\n\nObjectivesOur goal in this manuscript is to present a reconstruction of multidrug-resistant P. aeruginosa gene regulatory network and to analyze its topological properties.\n\nMethodsThe methodology was based on gene orthology inference by the reciprocal best hit method. We used the genome of P. aeruginosa CCBH4851 as the basis of the reconstruction process. This multidrug-resistant strain is representative of an endemic outbreak in Brazilian territory belonging to ST277.\n\nFindingsAs the main finding, we obtained a network with a larger number of regulatory genes, target genes and interactions compared to previous work. Topological analysis results are accordant to the complex network representation of biological processes.\n\nMain conclusionsThe network properties are consistent with P. aeruginosa biological features. To the best of our knowledge, the P. aeruginosa gene regulatory network presented here is the most complete version available to date.

systems biology↗