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Enregistrement W4404196761 · doi:10.3389/fmicb.2024.1501925

Editorial: How the application of antimicrobial hurdles in meat processing facilities shapes microbial ecology

2024· editorial· en· W4404196761 sur OpenAlexaff
Xianqin Yang, Michael G. Gänzle, Rong Wang

Notice bibliographique

RevueFrontiers in Microbiology · 2024
Typeeditorial
Langueen
DomaineAgricultural and Biological Sciences
ThématiqueSalmonella and Campylobacter epidemiology
Établissements canadiensUniversity of AlbertaAgriculture and Agri-Food Canada
Organismes subventionnairesnon disponible
Mots-clésAntimicrobialMeat packing industrySanitationLivestockBiologyPasteurizationBiotechnologySalmonellaCampylobacterIndicator organismMicrobial ecologyFood processingFood microbiologyHygieneFood scienceToxicologyEcologyBacteriaEnvironmental scienceMicrobiologyMedicineEnvironmental engineering

Résumé

récupéré en direct d'OpenAlex

Meat production, i.e. converting livestock into meat, is a complex process, not only in that it has many steps in the process, but also in that facility has a flow of animals, water, air, and workers, all of which serve as carriers of bacteria. Livestock, for instance, may harbor up to 10 11 CFU/gram feces (1) and up to 10 10 CFU/cm 2 bacteria on their hides (2). Some of those bacteria are human pathogens, without causing overt disease in their animal host such as Shiga toxinproducing Escherichia coli (STEC), Salmonella, or Campylobacter. A high proportion of healthy animals presented for slaughter may carry these pathogens on their hides/skin or in their intestines or lymph nodes (3,4). Consequently, antimicrobial interventions such as spraying hide on carcasses with sodium hydroxide, pasteurizing carcasses with hot water and spraying carcasses with various organic acids have been widely implemented in the carcasses dressing process to control pathogens (5,6). Many studies have evaluated the efficacy of these treatments for pathogen reduction and hygiene indicators. However, in addition to these antimicrobial interventions, other factors also play important roles in shaping the microbial ecology in meat processing environment by exerting selective pressure, including but not limited to operation temperatures, relative humidity, routine cleaning and sanitation of the facility, and difficult to access places by cleaning effort in equipment. This Research Topic collected five articles that add to our understanding of how antimicrobial hurdles shape the microbial ecology in meat processing facilities and meat products as well as their relationship with antimicrobial resistance.Shiga toxin encoding genes in E. coli are encoded on prophages integrated into the bacterial chromosome (7). The production of Shiga toxins can be induced as a response to stressful conditions. Castro et al. (8) investigated the potential of 48 E. coli isolates with intact or fragmented stx1a for Shiga toxin production under conditions relevant to foods. Production of Shiga toxins was observed only for E. coli isolates that carried the complete stx gene (n=11). They reported down-regulation of the toxin production by acidic conditions and lethal temperatures, and favorable toxin production by neutral pH and milk, and incubation at 40C for some strains, the latter of which may be linked to a greater diversity of the promotor regions of Stx-prophages, and of genes related to cell adhesion and stress tolerance. Dittoe et al. (9) examined the effect of treating raw poultry products with organic and inorganic acids on the meat microbiota. They reported that chicken wings treated by a 15-s dip in organic acid (peroxyacetic acid; PAA), inorganic acid (sodium bisulfate; SBS), or their combination (PAA+SBS) had similar total bacterial count by 21 days of chiller storage. However, wings treated with SBS and SBS+PAA had a 7-day shelf life advantage over wings that were treated with tap water, and the treated wings had lower relative abundance of typical spoilage populations while having a greater relative abundance of Bacillus spp. These findings suggest that antimicrobial interventions differentially affect the meat microbiota and that desirable shifts in the composition of microbial communities on meat can be exploited for shelf life advantage.Yang et al. (10) reviewed the potential factors influencing the microbial ecology in commercial meat processing facilities and conducted a meta-analysis on the microbiota data published in the last 10 years. Some E. coli strains achieved persistence on post-sanitation equipment surface, likely through biofilm formation and difficult to clean harborage site, rather than resistance to biocides of their planktonic cultures. The authors also reported the persistence of diverse bacteria in meat plants in genus level, Pseudomonas, Acinetobacter, Psychrobacter, Sphingomonas, Enterococcus, Proteus, Staphylococcus, BurkholderiaCaballeronia-Paraburkholderia, Acidovorax, and Brevundimonas (Fig. 1). These non-pathogenic bacterial strains may also enhance the biofilm formation of foodborne pathogens who otherwise do not form biofilms on their own, suggesting targeted cleaning and sanitizing efforts against residential microbiota may be rewarding in both safety and storage stability of meat products. Koti et al. investigated (11) the impact of temperature and companion bacteria including lactic acid bacteria (Carnobacterium piscola and Lactobacillus delbrueckii subsp. bulgaricus) and spoilage bacteria (Comamonas koreensis, Raoultella terrigena, and Pseudomonas aeruginosa) on the susceptibility to biocides of STEC in planktonic cultures as well as in biofilms. In general, planktonic cultures and single species biofilms exhibited greater susceptibility to all biocides tested (quaternary ammonium compounds, sodium hypochlorite, sodium hydroxide, hydrogen peroxide, BioDestroy). Interestingly, all STEC strains tested had higher counts in multispecies biofilms with Raoultella sp. and Comamonas sp. than with Carnobacterium sp. and Lactobacillus sp. or Pseudomonas sp. and Comamonas sp. The extent of reduction of STEC by biocides is a function of temperature, the companion bacterial strain as well as the STEC strain.Pathogen contamination incidence in the meat industry is often addressed by intense sanitization (IS) of the entire processing plant. Wang and co-workers (12) examined the immediate and long-term impact of such sanitation on the environmental microbial community and pathogen colonization. They reported that even though the colonization of Salmonella in drains did not differ between pre-and post-IS biofilms, post-IS samples formed stronger biofilms and in certain cases resulted in better Salmonella survival in response to sanitizers. The alteration in microbial community structure may be related to stronger biofilm formation of post-IS drain samples through survival, recruitment and overgrowth of species with high colonizing capability.In conclusion, the five articles in this collection better our understanding of factors shaping the microbial ecology of meat processing facilities and mechanisms of persistence, especially for pathogenic organisms. Bacterial activities and persistence are often strain dependent (13). Future in-situ studies with strain level resolution of presence and interactions would advance our understanding of this area even further.

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,004
score de la tête « metaresearch » (Gemma)0,017
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Éditorial · Signal consensuel: Éditorial
Score de désaccord entre enseignants0,026
Score d'incertitude au seuil0,087

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0040,017
Méta-épidémiologie (sens strict)0,0030,001
Méta-épidémiologie (sens large)0,0030,002
Bibliométrie0,0040,002
Études des sciences et des technologies0,0020,002
Communication savante0,0060,004
Science ouverte0,0030,001
Intégrité de la recherche0,0120,009
Charge utile insuffisante (le modèle a refusé de juger)0,0260,015

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,009
Tête enseignante GPT0,223
Écart entre enseignants0,214 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSans objet
Domainenon disponible
GenreÉditorial

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations2
Publié2024
Routes d'admission1
Résumé présentoui

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