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Record 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 on OpenAlexaff
Xianqin Yang, Michael G. Gänzle, Rong Wang

Bibliographic record

VenueFrontiers in Microbiology · 2024
Typeeditorial
Languageen
FieldAgricultural and Biological Sciences
TopicSalmonella and Campylobacter epidemiology
Canadian institutionsUniversity of AlbertaAgriculture and Agri-Food Canada
Fundersnot available
KeywordsAntimicrobialMeat packing industrySanitationLivestockBiologyPasteurizationBiotechnologySalmonellaCampylobacterIndicator organismMicrobial ecologyFood processingFood microbiologyHygieneFood scienceToxicologyEcologyBacteriaEnvironmental scienceMicrobiologyMedicineEnvironmental engineering

Abstract

fetched live from 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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.004
metaresearch head score (Gemma)0.017
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Editorial · Consensus signal: Editorial
Teacher disagreement score0.026
Threshold uncertainty score0.087

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.017
Meta-epidemiology (narrow)0.0030.001
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0040.002
Science and technology studies0.0020.002
Scholarly communication0.0060.004
Open science0.0030.001
Research integrity0.0120.009
Insufficient payload (model declined to judge)0.0260.015

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.009
GPT teacher head0.223
Teacher spread0.214 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEditorial

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations2
Published2024
Admission routes1
Has abstractyes

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