Canadian journal of microbiology最新文献

Antimicrobial resistance (AMR) is a growing public health threat in Canada. In 2022, rising resistance was reported among key pathogens, yet national coordination remains inconsistent, with strategies varying widely by province and territory. This study explored AMR-related challenges in healthcare and the impact of the COVID-19 pandemic on AMR efforts in Canada. Using a qualitative design, researchers conducted semi-structured interviews with 59 experts from fields such as microbiology, public health, and industry. Participants were identified through environmental scanning and snowball sampling. Thematic analysis of transcripts revealed major barriers to a unified AMR response, including inconsistent surveillance, fragmented stewardship efforts, and decentralized health systems. According to participating AMR experts, COVID-19 disrupted AMR control by diverting resources and potentially increasing resistance but also led to improvements in infection prevention, public awareness, and health infrastructure. Participants emphasized the need for stronger political commitment, improved surveillance, a One Health approach, and better funded, coordinated stewardship programs. With the publication of the Pan-Canadian Action Plan in 2023, the Canadian federal government has announced changes. However, effectively addressing AMR will require a unified, multisectoral strategy that bridges political, health, and societal efforts across jurisdictions.

Highly pathogenic avian influenza (HPAI) virus H5N1 clade 2.3.4.4b was introduced into North America in 2021. In 2022, clade 2.3.4.4b spilled into domestic poultry in the United States (U.S.), resulting in the largest HPAI outbreak in U.S. history. In December 2023, H5N1 2.3.4.4b was detected on an upland game bird farm in Pennsylvania. History, clinical signs, and gross lesions were suggestive of marble spleen disease. However, HPAI was identified by real-time reverse-transcriptase polymerase chain reaction; gene sequencing confirmed genotype C2.1 with phylodynamic analyses providing evidence of a wild bird introduction. Predictive mathematical modeling estimated the time of virus introduction onto the farm (15 days (95% C.I., 11-23) before the date of confirmed detection) being similar or longer than previously described for domestic poultry. Although bioexclusion measures were unable to prevent the initial exposure due to industry rearing practices, biocontainment procedures appeared to prevent spread. The lessons learned from this case may be important for other animal agriculture commodities, especially those species that are infrequently handled or observed and may have inaccurately calculated mortality levels. This well-characterized outbreak and other experiences with natural infection should be taken as opportunities to better understand the field dynamics of this virus.

In 2022, highly pathogenic avian influenza virus (HPAIV) H5N1 clade 2.3.4.4b was detected in United States (US) poultry, quickly escalating into an outbreak that surpassed the 2014/15 HPAI US event in scale and impact. Unlike in 2014/15, the 2022/3/4/5 outbreak has included numerous HPAI detections in previously infrequently affected commodities such as broilers and commercially-raised upland game birds. Here, we describe H5 HPAI detections that occurred between December 2023 and January 2024 in a multipremises, commerical upland game bird system located in the Midwestern US. We used an approximate Bayesian computation algorithm and stochastic within-flock HPAI transmission model to estimate the following: (1) time of HPAI introduction onto each affected premises, (2) number of days between estimated times of introduction and times of detection, and (3) the adequate contact rates and basic reproduction numbers within individual barns. Clinical signs and mortality observed in the infected pheasant flocks were largely consistent with other pheasant H5 clade 2.3.4.4b outbreaks. Across the system, the estimated transmission metrics were noticeably lower than those calculated from outbreaks in other poultry species. However, times to detection were similar to HPAI outbreaks that have occurred in other commodities.

Botrytis cinerea is a pathogen infecting Cannabis sativa L. plants, causing economic losses, and can develop resistance to chemical fungicides, the use of which is restricted in cannabis production. Thus, developing biocontrol methods is imperative. Seven bacterial strains were isolated from hemp seed oil, characterized, and examined for the potential to control a Botrytis cinerea isolate from cannabis. Three isolates, Bacillus mojavensis HOB3, Paenibacillus sp. HOB6, and Bacillus subtilis HOB7 exhibited significant inhibition of Botrytis cinerea. These isolates were further evaluated for their biosurfactant activity using two liquid media, Lysogeny Broth (LB) and hydrocarbon-amended Bushnell and Haas (BH). The oil-spreading and drop-collapse assays revealed growth-medium-dependent variation in surface activity associated with biosurfactant presence. The BH cell-free extract (BH-CFE) of Bacillus subtilis HOB7 showed the highest estimated biosurfactant presence and antifungal activity against Botrytis cinerea, but both activities were absent when using the LB cell-free extract (LB-CFE) of Bacillus subtilis HOB7. Thus, a potential relationship between antifungal activity and biosurfactant production was suggested. Genome mining of the strains identified gene clusters encoding compounds with antifungal activity, including the biosurfactants polymyxin B, fusaricidin B, fengycin, and surfactin. To our knowledge, this is the first report of the isolation of hemp seed oil bacteria with potential biocontrol properties against fungal phytopathogens.

Potato tuber periderm is armored with suberin, that consists of two domains, an aliphatic domain composed of fatty acid polyesters and an aromatic domain composed of cinnamic acids. Streptomyces scabies 87.22, a predominant causal agent of potato common scab, was compared for adaptation to tuber suberin with Streptomyces acidiscabies ATCC 49003 and Streptomyces turgidiscabies Car8 belonging to emerging pathogenic species. Streptomyces scabies 87.22 showed higher growth in the suberin supplemented medium than the two other strains. When co-cultured in a rich nutrient medium, S. acidiscabies ATCC 49003 produced the antibiotic oxanthromicin, which inhibited growth and mycelium development of the other strains. Exposure of S. scabies 87.22 and S. acidiscabies ATCC 49003 to suberin was accompanied by the secretion of enzymes degrading cellulose, hemicellulose, fatty acids, and glycerol derivatives. Compared to the two other strains, S. scabies 87.22 showed higher esterase activity in suberin-supplemented medium and strong induction of cellulase gene expression. Both S. acidiscabies ATCC 49003 and S. turgidiscabies Car8 exhibited a poor utilization of trans-ferulic and p-coumaric acids, suggesting almost no ability to degrade the aromatic moiety of suberin. This work suggests that S. scabies 87.22 is better adapted to the potato periderm degradation than the emerging pathogens. The elucidation of pathogenic Streptomyces strains interaction may contribute to the improvement of ecologically oriented agronomic strategies for common scab management.

In recent decades, the study of the opportunistic pathogenic fungus, Candida albicans, has been revolutionized by genomics, transforming our understanding of its molecular biology, pathogenicity, and modes of drug resistance. In this review, our effort is to trace the historical development of C. albicans research, from early clinical observations to modern high-throughput genomic techniques. Advances in molecular biology, transcriptomics, and genome editing, including CRISPR-Cas9, have had a significant impact on the genetic tools available for studying this pathogen. The impact of whole-genome sequencing, functional genomics, and single-cell transcriptomics on the study of C. albicans, alongside the role of fungal population genomics in tracking evolutionary adaptations, have resulted in key insights. Here we discuss the ongoing challenge of antifungal resistance and the implications of new technologies in combating invasive candidiasis. As we move into a new era of precision mycology, integrating multi-omics approaches will further enhance our ability to understand and control C. albicans infections.

Antimicrobial resistance (AMR) is a global public health threat, but the role of foods in its dissemination is poorly understood. We examined the incidence of foodborne bacteria carrying AMR genes considered high-priority research targets by the World Health Organization. Frozen, ready-to-eat, avocado, coconut, mango, and peach (n = 161) were tested for bacteria encoding extended-spectrum β-lactamases (ESBLs) and carbapenemases. Over 600 presumptive-positive isolates were recovered and analyzed with a pooled sequencing (Pool-seq) strategy. Coconut samples exhibited the highest bacterial loads and prevalence/diversity of AMR genes. Isolates harbouring the β-lactamase genes bla_ctx-m, bla_tem, and bla_shv, identified in 14 coconut and 2 mango samples, were further characterized by whole-genome sequencing and antimicrobial susceptibility testing. The most common gene was bla_ctx-m-15, detected in 20 unique strains. Two carbapenemase-producing strains were isolated from coconut: Enterobacter roggenkampii encoding bla_ndm-1 and Escherichia coli encoding bla_ndm-5. Subsequent quantitative PCR (qPCR) analysis of enrichments for bla_ctx-m/bla_ndm indicated a potentially higher prevalence of these genes than observed by colony screening. This study presents a practical method for recovering ESBL- and carbapenemase-producing bacteria from foods. Mapping their distribution in food products is crucial to assessing the role of foods in the global spread of AMR and developing effective public health interventions.

Antimicrobial resistant strains of pathogenic Escherichia coli are a burden on the healthcare system, causing longer hospital stays and increased treatment costs compared to nonresistant strains. With whole genome sequencing almost ubiquitous in the analyses of outbreak and surveillance samples, in silico methods for feature identification can be faster and cheaper than traditional wet-lab methods. In this study, machine learning (ML) classification methods were used to predict antimicrobial resistance (AMR) and identify novel genomic markers of resistance. A total of 4300 E. coli whole genome sequences with laboratory-derived susceptible, intermediate, or resistant (SIR) data for 34 antimicrobials were collected. Three models-gradient boosted decision trees, support vector machines (SVMs), and artificial neural networks (ANNs)-were trained using genome subsequences (k-mers) of length 11 to classify unknown isolates as SIR for each antimicrobial. The models achieved high average accuracies (93.6%, 92.7%, and 92.8%, respectively) for our dataset, outperforming database methods including AMRFinderPlus (63.9%) and ResFinder (75.7%). Tested on two smaller independent datasets, the models' average accuracies were 81.6% (XGB), 79.9% (SVM), and 81.2% (ANN), while ResFinder's average accuracy was 94.7%. An advantage of ML models over database methods is that they can identify novel markers of resistance, which is a key advantage for surveillance and research. As more genomic and AMR data become publicly available, these models are expected to further improve in performance and utility.

The use of probiotics is an alternative approach to mitigate the proliferation of antimicrobial resistance in aquaculture. In our study, we examined the effects of Lactobacillus rhamnosus GG (ATCC 53103, LGG) delivered in-feed on the weight, length, skin mucus, and faecal microbiomes of Atlantic salmon. We also challenged the salmon with Aeromonas salmonicida 2004-05MF26 (Asal2004) and assessed the mortality. Our results showed no significant change (P > 0.05) in weight or length of Atlantic salmon or their resilience to Asal2004 infection after LGG feeding. Infection changed significantly the skin mucus and faecal microbiomes: Clostridium sensu stricto increased from 3.14% to 9.20% in skin mucus and 1.39% to 3.74% in faeces (P < 0.05). Aeromonas increased from 0.02% to 0.60% in faeces (P < 0.05). Photobacterium increased from not detected (0%) to 52.16% (P < 0.01) and Aliivibrio decreased from 67.21% to 0.71% in faeces (P < 0.01). After infection, Lactococcus (9.93%) and Lactobacillus (2.11%) in skin mucus of the LGG group were significantly higher (P < 0.05) than in the skin mucus from the rest of the groups (4.14% and 1.08%, respectively). In conclusion, LGG feeding did not further increase the resilience of vaccinated Atlantic salmon. Asal2004 infection had much greater impact on skin mucus and faecal microbiomes than LGG feeding.