Analysis of single nucleotide polymorphisms (SNPs) associated with antibiotic resistance genes in Chilean Piscirickettsia salmonis strains

[Cu(NN1)2]ClO4, a Copper (I) Complex as an Antimicrobial Agent for the Treatment of Piscirickettsiosis in Atlantic Salmon

Piscirickettsia salmonis is the pathogen that most affects the salmon industry in Chile. Large quantities of antibiotics have been used to control it. In search of alternatives, we have developed [Cu(NN1)2]ClO4 where NN1 = 6-((quinolin-2-ylmethylene)amino)-2H-chromen-2-one. The antibacterial capacity of [Cu(NN1)2]ClO4 was determined. Subsequently, the effect of the administration of [Cu(NN1)2]ClO4 on the growth of S. salar, modulation of the immune system and the intestinal microbiota was studied. Finally, the ability to protect against a challenge with P. salmonis was evaluated. The results obtained showed that the compound has an MIC between 15 and 33.9 μg/mL in four isolates. On the other hand, the compound did not affect the growth of the fish; however, an increase in the transcript levels of IFN-γ, IL-12, IL-1β, CD4, lysozyme and perforin was observed in fish treated with 40 μg/g of fish. Furthermore, modulation of the intestinal microbiota was observed, increasing the genera of beneficial bacteria such as Lactobacillus and Bacillus as well as potential pathogens such as Vibrio and Piscirickettsia. Finally, the treatment increased survival in fish challenged with P. salmonis by more than 60%. These results demonstrate that the compound is capable of protecting fish against P. salmonis, probably by modulating the immune system and the composition of the intestinal microbiota.

Maast: genotyping thousands of microbial strains efficiently

Existing single nucleotide polymorphism (SNP) genotyping algorithms do not scale for species with thousands of sequenced strains, nor do they account for conspecific redundancy. Here we present a bioinformatics tool, Maast, which empowers population genetic meta-analysis of microbes at an unrivaled scale. Maast implements a novel algorithm to heuristically identify a minimal set of diverse conspecific genomes, then constructs a reliable SNP panel for each species, and enables rapid and accurate genotyping using a hybrid of whole-genome alignment and k-mer exact matching. We demonstrate Maast's utility by genotyping thousands of Helicobacter pylori strains and tracking SARS-CoV-2 diversification.

Antibiotic Resistance in the Finfish Aquaculture Industry: A Review

Significant challenges to worldwide sustainable food production continue to arise from environmental change and consistent population growth. In order to meet increasing demand, fish production industries are encouraged to maintain high growth densities and to rely on antibiotic intervention throughout all stages of development. The inappropriate administering of antibiotics over time introduces selective pressure, allowing the survival of resistant bacterial strains through adaptive pathways involving transferable nucleotide sequences (i.e., plasmids). This is one of the essential mechanisms of antibiotic resistance development in food production systems. This review article focuses on the main international regulations and governing the administering of antibiotics in finfish husbandry and summarizes recent data regarding the distribution of bacterial resistance in the finfish aquaculture food production chain. The second part of this review examines promising alternative approaches to finfish production, sustainable farming techniques, and vaccination that circumvents excessive antibiotic use, including new animal welfare measures. Then, we reflect on recent adaptations to increasingly interdisciplinary perspectives in the field and their greater alignment with the One Health initiative.

Nutrient Scarcity in a New Defined Medium Reveals Metabolic Resistance to Antibiotics in the Fish Pathogen Piscirickettsia salmonis

Extensive use of antibiotics has been the primary treatment for the Salmonid Rickettsial Septicemia, a salmonid disease caused by the bacterium Piscirickettsia salmonis. Occurrence of antibiotic resistance has been explored in various P. salmonis isolates using different assays; however, P. salmonis is a nutritionally demanding intracellular facultative pathogen; thus, assessing its antibiotic susceptibility with standardized and validated protocols is essential. In this work, we studied the pathogen response to antibiotics using a genomic, a transcriptomic, and a phenotypic approach. A new defined medium (CMMAB) was developed based on a metabolic model of P. salmonis. CMMAB was formulated to increase bacterial growth in nutrient-limited conditions and to be suitable for performing antibiotic susceptibility tests. Antibiotic resistance was evaluated based on a comprehensive search of antibiotic resistance genes (ARGs) from P. salmonis genomes. Minimum inhibitory concentration assays were conducted to test the pathogen susceptibility to antibiotics from drug categories with predicted ARGs. In all tested P. salmonis strains, resistance to erythromycin, ampicillin, penicillin G, streptomycin, spectinomycin, polymyxin B, ceftazidime, and trimethoprim was medium-dependent, showing resistance to higher antibiotic concentrations in the CMMAB medium. The mechanism for antibiotic resistance to ampicillin in the defined medium was further explored and was proven to be associated to a decrease in the bacterial central metabolism, including the TCA cycle, the pentose-phosphate pathway, energy production, and nucleotide metabolism, and it was not associated with decreased growth rate of the bacterium or with the expression of any predicted ARG. Our results suggest that nutrient scarcity plays a role in the bacterial antibiotic resistance, protecting against the detrimental effects of antibiotics, and thus, we propose that P. salmonis exhibits a metabolic resistance to ampicillin when growing in a nutrient-limited medium.

Prediction of antimicrobial resistance based on whole-genome sequencing and machine learning

MOTIVATION

Antimicrobial resistance (AMR) is one of the biggest global problems threatening human and animal health. Rapid and accurate AMR diagnostic methods are thus very urgently needed. However, traditional antimicrobial susceptibility testing (AST) is time-consuming, low throughput and viable only for cultivable bacteria. Machine learning methods may pave the way for automated AMR prediction based on genomic data of the bacteria. However, comparing different machine learning methods for the prediction of AMR based on different encodings and whole-genome sequencing data without previously known knowledge remains to be done.

RESULTS

In this study, we evaluated logistic regression (LR), support vector machine (SVM), random forest (RF) and convolutional neural network (CNN) for the prediction of AMR for the antibiotics ciprofloxacin, cefotaxime, ceftazidime and gentamicin. We could demonstrate that these models can effectively predict AMR with label encoding, one-hot encoding and frequency matrix chaos game representation (FCGR encoding) on whole-genome sequencing data. We trained these models on a large AMR dataset and evaluated them on an independent public dataset. Generally, RFs and CNNs perform better than LR and SVM with AUCs up to 0.96. Furthermore, we were able to identify mutations that are associated with AMR for each antibiotic.

AVAILABILITY AND IMPLEMENTATION

Source code in data preparation and model training are provided at GitHub website (https://github.com/YunxiaoRen/ML-iAMR).

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Antimicrobial resistance in Chilean marine-farmed salmon: Improving food safety through One Health

Aquaculture is seen as an essential requirement for improving food security and nutrition. Fish such as salmonids are a primary source of protein and essential nutrients. Aquaculture provide income for communities across the world and have a smaller carbon footprint than terrestrial animal-production systems. However, fish diseases are a constant threat, and the use of antibiotics is a source of concern due to its adverse impacts on the environment and human health. Chilean salmon farming has made several efforts to reduce the use of antibiotics for the eradication of piscirickettsiosis, a disease caused by the gram-negative bacteria Piscirickettsia salmonis. Excessive amounts of antibiotics continue to be used in Chilean aquaculture, playing an important role in the emerging public health crisis of antimicrobial resistance. Without doubt, P. salmonis is becoming increasingly resistant to important frontline antimicrobial classes, with severe implications for the future treatment of infectious human and animal diseases. Antimicrobial-resistant bacteria as well as antibiotic residues from salmon production are spreading in the environment, and thus both salmon food commodities and wild organisms can become a source of resistant bacteria that can be transmitted to humans as foodborne contaminants. This urgent threat needs to be addressed by implementing national strategies in compliance with international standards that include both prudent antimicrobial use in marine salmon farms and the investment towards a One Health approach, which combines human, animal and environmental health.

Non-Specific Antibodies Induce Lysosomal Activation in Atlantic Salmon Macrophages Infected by Piscirickettsia salmonis

Piscirickettsia salmonis, an aggressive intracellular pathogen, is the etiological agent of salmonid rickettsial septicemia (SRS). This is a chronic multisystemic disease that generates high mortalities and large losses in Chilean salmon farming, threatening the sustainability of the salmon industry. Previous reports suggest that P. salmonis is able to survive and replicate in salmonid macrophages, inducing an anti-inflammatory environment and a limited lysosomal response that may be associated with host immune evasion mechanisms favoring bacterial survival. Current control and prophylaxis strategies against P. salmonis (based on the use of antibiotics and vaccines) have not had the expected success against infection. This makes it urgent to unravel the host-pathogen interaction to develop more effective therapeutic strategies. In this study, we evaluated the effect of treatment with IgM-beads on lysosomal activity in Atlantic salmon macrophage-enriched cell cultures infected with P. salmonis by analyzing the lysosomal pH and proteolytic ability through confocal microscopy. The impact of IgM-beads on cytotoxicity induced by P. salmonis in infected cells was evaluated by quantification of cell lysis through release of Lactate Dehydrogenase (LDH) activity. Bacterial load was determined by quantification of 16S rDNA copy number by qPCR, and counting of colony-forming units (CFU) present in the extracellular and intracellular environment. Our results suggest that stimulation with antibodies promotes lysosomal activity by lowering lysosomal pH and increasing the proteolytic activity within this organelle. Additionally, incubation with IgM-beads elicits a decrease in bacterial-induced cytotoxicity in infected Atlantic salmon macrophages and reduces the bacterial load. Overall, our results suggest that stimulation of cells infected by P. salmonis with IgM-beads reverses the modulation of the lysosomal activity induced by bacterial infection, promoting macrophage survival and bacterial elimination. This work represents a new important evidence to understand the bacterial evasion mechanisms established by P. salmonis and contribute to the development of new effective therapeutic strategies against SRS.