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bioRxiv · 10.1101/2021.10.18.464923

Machine learning applied to molecular identification of Acinetobacter baumannii Global Clone 1

Alvarez, V. E.·Quiroga, M. P.·Centron, D.
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Abstract

Since the emergence of high-risk clones worldwide, constant investigations have been undertaken to comprehend the molecular basis that led to their prevalent dissemination in nosocomial settings over time. So far, the complex and multifactorial genetic traits of this type of epidemic clones have only allowed the identification of biomarkers with low specificity. A machine learning algorithm was able to recognize unequivocally a biomarker for the early and accurate detection of Acinetobacter baumannii Global Clone 1 (GC1), one of the most disseminated high-risk clones. Support Vector Machine identified the U1 sequence with 367 nucleotides length that matched a fragment of the moaCB gene, which encodes the molybdenum cofactor biosynthesis C and B proteins. U1 differentiates specifically between A. baumannii GC1 and non-GC1 strains, becoming a suitable biomarker capable of being translated into clinical settings as a molecular typing method for early diagnosis based on PCR as shown here. Since the metabolic pathways of Mo enzymes have been recognized as putative therapeutic targets for ESKAPE pathogens, our findings highlighted that machine learning can be also useful in intricate knowledge gaps of high-risk clones and implies noteworthy support to the literature to identify challenging nosocomial biomarkers for other multidrug-resistant high-risk clones. IMPORTANCEA. baumannii GC1 is an important high-risk clone that rapidly develops extreme drug resistance in the nosocomial niche. Furthermore, several strains were identified worldwide in environmental samples exacerbating the risk of human interactions. Early diagnosis is mandatory to limit its dissemination and to outline appropriate antibiotic stewardship schedules. A region of 367 bp length (U1) within the moaCB gene not subjected to Lateral Genetic Transfer or to antibiotic pressures was successfully found by Support Vector Machine algorithm that predicts A. baumannii GC1 strains. PCR assays have confirmed that U1 specifically identifies A. baumannii GC1 strains. At the same time, research on the group of Mo enzymes proposed this metabolic pathway related to superbu[g]s metabolism as a potential future drug target site for ESKAPE pathogens due to its central role in bacterial fitness during infection. These findings confirmed the importance of machine learning applied to the burden of the rise of antibiotic resistance.

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Alvarez, V. E., Quiroga, M. P., Centron, D.. 2021-10-20. Machine learning applied to molecular identification of Acinetobacter baumannii Global Clone 1. https://doi.org/10.1101/2021.10.18.464923

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