Pathogenic Microorganisms in the Environment of Sheep Slaughterhouse Article Swipe
Cheng Cheng
,
Yan Li
,
Xin Wang
,
Darong Cheng
,
Jianping Tao
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.22456/1679-9216.146484
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.22456/1679-9216.146484
Background: As critical nodes for zoonotic disease transmission, the microbial community composition in sheep slaughterhouse environments directly impacts meat safety and public health. While existing studies have focused on single-pathogen detection, there remains a lack of systematic understanding of the diversity, distribution patterns, and transmission risks of pathogenic microorganisms in complex environments. This study aims to elucidate the microbial community characteristics of mutton slaughterhouse environments using metagenomics approaches, with a focus on analyzing the ecological distribution of zoonotic pathogens and their potential threats. Materials, Methods & Results: A total of 12 environmental samples (n=3 per zone) were collected from a sheep slaughterhouse in Suzhou, Jiangsu Province, including slaughter workshop, slaughter tools, pre-slaughter holding area, and production wastewater. Samples were preserved in liquid nitrogen, and total DNA was extracted using the HiPure Stool DNA Kit. Paired-end sequencing (PE150) was performed on the Illumina HiSeq platform. Raw reads were quality-filtered (Cutadapt), and host-contaminant sequences removed (BWA). Clean reads were assembled with MEGAHIT, and unigenes predicted via Prodigal. Clustering by MMseq2 generated a non-redundant gene set, which was annotated against the NCBI NT database using Diamond to obtain taxonomic assignments. Species abundance was calculated at phylum, genus, and species levels, and principal component analysis (PCA) assessed variation between zones. At the phylum level, microbial communities were dominated by Proteobacteria (31.39% - 75%), Firmicutes (11.89% - 43.66%), Bacteroidota (9.15% -41.61%), and Actinobacteria (3% - 14.69%), with significant clustering by functional zones. Genus-level analysis revealed Chryseobacterium, Comamonas, Acinetobacter, Psychrobacter, and Prevotella as top taxa across samples. Species-level profiling detected 6 categories of zoonotic pathogens, notably Staphylococcus aureus (7.07% relative abundance in slaughter tools), Mycobacteroides abscessus (3.56% in slaughter workshop), Vibrio cholerae, and Klebsiella pneumoniae. Slaughter tools harbored the highest pathogen load; Lactococcus garvieae accounted for 10.39%. Production wastewater contained elevated levels of aquatic pathogens, suggesting environmental dissemination risks. An upset plot showed 116 species shared across all samples, with unique species counts ranging from 88 to 505 per sample. Discussion: This study elucidates the ecological niches of drug-resistant and opportunistic pathogens in a sheep slaughterhouse, identifying production wastewater and slaughter tools as high-risk contamination sources. High abundances of Staphylococcus aureus and Mycobacteroides abscessus underscore occupational exposure risks for abattoir workers, while detection of Vibrio cholerae highlights vulnerabilities in wastewater treatment and potential for zoonotic outbreaks. The prevalence of biofilm-forming Firmicutes on tools indicates that conventional disinfectants may be insufficient. Horizontal transfer of antibiotic resistance genes among dominant phyla could exacerbate public health threats. We recommend implementing targeted disinfection protocols, routine metagenomic monitoring for early pathogen detection, stricter PPE use and worker training, and optimization of wastewater pH control. Future studies should expand geographic sampling and integrate functional metagenomics to unravel resistance mechanisms and cross-species gene flow. These strategies will strengthen slaughterhouse biosafety and mitigate zoonotic disease transmission. Keywords: sheep slaughterhouse, public health, environment microbiology, zoonoses, pathogens, metagenomics.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.22456/1679-9216.146484
- https://seer.ufrgs.br/index.php/ActaScientiaeVeterinariae/article/download/146484/96144
- OA Status
- diamond
- References
- 25
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4412198112
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4412198112Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.22456/1679-9216.146484Digital Object Identifier
- Title
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Pathogenic Microorganisms in the Environment of Sheep SlaughterhouseWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2025Year of publication
- Publication date
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2025-07-04Full publication date if available
- Authors
-
Cheng Cheng, Yan Li, Xin Wang, Darong Cheng, Jianping TaoList of authors in order
- Landing page
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https://doi.org/10.22456/1679-9216.146484Publisher landing page
- PDF URL
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https://seer.ufrgs.br/index.php/ActaScientiaeVeterinariae/article/download/146484/96144Direct link to full text PDF
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YesWhether a free full text is available
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diamondOpen access status per OpenAlex
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https://seer.ufrgs.br/index.php/ActaScientiaeVeterinariae/article/download/146484/96144Direct OA link when available
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Microorganism, Biology, Microbiology, Veterinary medicine, Medicine, Bacteria, GeneticsTop concepts (fields/topics) attached by OpenAlex
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0Total citation count in OpenAlex
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25Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.(PE150) | 136 |
| abstract_inverted_index.10.39%. | 290 |
| abstract_inverted_index.Diamond | 182 |
| abstract_inverted_index.Jiangsu | 104 |
| abstract_inverted_index.Methods | 84 |
| abstract_inverted_index.Samples | 117 |
| abstract_inverted_index.Species | 187 |
| abstract_inverted_index.Suzhou, | 103 |
| abstract_inverted_index.against | 176 |
| abstract_inverted_index.aquatic | 297 |
| abstract_inverted_index.between | 204 |
| abstract_inverted_index.complex | 50 |
| abstract_inverted_index.disease | 6, 460 |
| abstract_inverted_index.focused | 27 |
| abstract_inverted_index.health, | 466 |
| abstract_inverted_index.health. | 22 |
| abstract_inverted_index.highest | 283 |
| abstract_inverted_index.holding | 112 |
| abstract_inverted_index.impacts | 17 |
| abstract_inverted_index.levels, | 196 |
| abstract_inverted_index.notably | 259 |
| abstract_inverted_index.phylum, | 192 |
| abstract_inverted_index.ranging | 317 |
| abstract_inverted_index.remains | 32 |
| abstract_inverted_index.removed | 152 |
| abstract_inverted_index.routine | 414 |
| abstract_inverted_index.sample. | 323 |
| abstract_inverted_index.samples | 92 |
| abstract_inverted_index.species | 195, 308, 315 |
| abstract_inverted_index.studies | 25, 434 |
| abstract_inverted_index.tools), | 267 |
| abstract_inverted_index.unravel | 444 |
| abstract_inverted_index.14.69%), | 230 |
| abstract_inverted_index.43.66%), | 222 |
| abstract_inverted_index.Illumina | 141 |
| abstract_inverted_index.MEGAHIT, | 159 |
| abstract_inverted_index.Results: | 86 |
| abstract_inverted_index.abattoir | 363 |
| abstract_inverted_index.analysis | 200, 238 |
| abstract_inverted_index.assessed | 202 |
| abstract_inverted_index.cholerae | 369 |
| abstract_inverted_index.control. | 432 |
| abstract_inverted_index.critical | 2 |
| abstract_inverted_index.database | 180 |
| abstract_inverted_index.detected | 253 |
| abstract_inverted_index.directly | 16 |
| abstract_inverted_index.dominant | 401 |
| abstract_inverted_index.elevated | 294 |
| abstract_inverted_index.existing | 24 |
| abstract_inverted_index.exposure | 360 |
| abstract_inverted_index.garvieae | 287 |
| abstract_inverted_index.harbored | 281 |
| abstract_inverted_index.mitigate | 458 |
| abstract_inverted_index.pathogen | 284, 419 |
| abstract_inverted_index.relative | 263 |
| abstract_inverted_index.revealed | 239 |
| abstract_inverted_index.samples, | 312 |
| abstract_inverted_index.samples. | 250 |
| abstract_inverted_index.sampling | 438 |
| abstract_inverted_index.sources. | 349 |
| abstract_inverted_index.stricter | 421 |
| abstract_inverted_index.targeted | 411 |
| abstract_inverted_index.threats. | 82, 407 |
| abstract_inverted_index.transfer | 395 |
| abstract_inverted_index.unigenes | 161 |
| abstract_inverted_index.workers, | 364 |
| abstract_inverted_index.zoonotic | 5, 77, 257, 378, 459 |
| abstract_inverted_index.-41.61%), | 225 |
| abstract_inverted_index.Keywords: | 462 |
| abstract_inverted_index.Prodigal. | 164 |
| abstract_inverted_index.Province, | 105 |
| abstract_inverted_index.Slaughter | 279 |
| abstract_inverted_index.abscessus | 269, 357 |
| abstract_inverted_index.abundance | 188, 264 |
| abstract_inverted_index.accounted | 288 |
| abstract_inverted_index.analyzing | 72 |
| abstract_inverted_index.annotated | 175 |
| abstract_inverted_index.assembled | 157 |
| abstract_inverted_index.biosafety | 456 |
| abstract_inverted_index.cholerae, | 275 |
| abstract_inverted_index.collected | 97 |
| abstract_inverted_index.community | 10, 59 |
| abstract_inverted_index.component | 199 |
| abstract_inverted_index.contained | 293 |
| abstract_inverted_index.detection | 366 |
| abstract_inverted_index.dominated | 213 |
| abstract_inverted_index.elucidate | 56 |
| abstract_inverted_index.extracted | 127 |
| abstract_inverted_index.generated | 168 |
| abstract_inverted_index.high-risk | 347 |
| abstract_inverted_index.including | 106 |
| abstract_inverted_index.indicates | 387 |
| abstract_inverted_index.integrate | 440 |
| abstract_inverted_index.microbial | 9, 58, 210 |
| abstract_inverted_index.nitrogen, | 122 |
| abstract_inverted_index.pathogens | 78, 335 |
| abstract_inverted_index.patterns, | 42 |
| abstract_inverted_index.performed | 138 |
| abstract_inverted_index.platform. | 143 |
| abstract_inverted_index.potential | 81, 376 |
| abstract_inverted_index.predicted | 162 |
| abstract_inverted_index.preserved | 119 |
| abstract_inverted_index.principal | 198 |
| abstract_inverted_index.profiling | 252 |
| abstract_inverted_index.recommend | 409 |
| abstract_inverted_index.sequences | 151 |
| abstract_inverted_index.slaughter | 107, 109, 266, 272, 344 |
| abstract_inverted_index.taxonomic | 185 |
| abstract_inverted_index.training, | 426 |
| abstract_inverted_index.treatment | 374 |
| abstract_inverted_index.variation | 203 |
| abstract_inverted_index.workshop, | 108 |
| abstract_inverted_index.zoonoses, | 469 |
| abstract_inverted_index.Clustering | 165 |
| abstract_inverted_index.Comamonas, | 241 |
| abstract_inverted_index.Firmicutes | 219, 384 |
| abstract_inverted_index.Horizontal | 394 |
| abstract_inverted_index.Klebsiella | 277 |
| abstract_inverted_index.Materials, | 83 |
| abstract_inverted_index.Paired-end | 134 |
| abstract_inverted_index.Prevotella | 245 |
| abstract_inverted_index.Production | 291 |
| abstract_inverted_index.abundances | 351 |
| abstract_inverted_index.antibiotic | 397 |
| abstract_inverted_index.calculated | 190 |
| abstract_inverted_index.categories | 255 |
| abstract_inverted_index.clustering | 233 |
| abstract_inverted_index.detection, | 30, 420 |
| abstract_inverted_index.diversity, | 40 |
| abstract_inverted_index.ecological | 74, 329 |
| abstract_inverted_index.elucidates | 327 |
| abstract_inverted_index.exacerbate | 404 |
| abstract_inverted_index.functional | 235, 441 |
| abstract_inverted_index.geographic | 437 |
| abstract_inverted_index.highlights | 370 |
| abstract_inverted_index.mechanisms | 446 |
| abstract_inverted_index.monitoring | 416 |
| abstract_inverted_index.outbreaks. | 379 |
| abstract_inverted_index.pathogenic | 47 |
| abstract_inverted_index.pathogens, | 258, 298, 470 |
| abstract_inverted_index.prevalence | 381 |
| abstract_inverted_index.production | 115, 341 |
| abstract_inverted_index.protocols, | 413 |
| abstract_inverted_index.resistance | 398, 445 |
| abstract_inverted_index.sequencing | 135 |
| abstract_inverted_index.strategies | 452 |
| abstract_inverted_index.strengthen | 454 |
| abstract_inverted_index.suggesting | 299 |
| abstract_inverted_index.systematic | 36 |
| abstract_inverted_index.underscore | 358 |
| abstract_inverted_index.wastewater | 292, 342, 373, 430 |
| abstract_inverted_index.workshop), | 273 |
| abstract_inverted_index.(Cutadapt), | 148 |
| abstract_inverted_index.Background: | 0 |
| abstract_inverted_index.Discussion: | 324 |
| abstract_inverted_index.Genus-level | 237 |
| abstract_inverted_index.Lactococcus | 286 |
| abstract_inverted_index.approaches, | 67 |
| abstract_inverted_index.communities | 211 |
| abstract_inverted_index.composition | 11 |
| abstract_inverted_index.environment | 467 |
| abstract_inverted_index.identifying | 340 |
| abstract_inverted_index.metagenomic | 415 |
| abstract_inverted_index.pneumoniae. | 278 |
| abstract_inverted_index.significant | 232 |
| abstract_inverted_index.wastewater. | 116 |
| abstract_inverted_index.Bacteroidota | 223 |
| abstract_inverted_index.assignments. | 186 |
| abstract_inverted_index.conventional | 389 |
| abstract_inverted_index.disinfection | 412 |
| abstract_inverted_index.distribution | 41, 75 |
| abstract_inverted_index.environments | 15, 64 |
| abstract_inverted_index.implementing | 410 |
| abstract_inverted_index.metagenomics | 66, 442 |
| abstract_inverted_index.occupational | 359 |
| abstract_inverted_index.optimization | 428 |
| abstract_inverted_index.transmission | 44 |
| abstract_inverted_index.Species-level | 251 |
| abstract_inverted_index.contamination | 348 |
| abstract_inverted_index.cross-species | 448 |
| abstract_inverted_index.disinfectants | 390 |
| abstract_inverted_index.dissemination | 301 |
| abstract_inverted_index.environmental | 91, 300 |
| abstract_inverted_index.environments. | 51 |
| abstract_inverted_index.insufficient. | 393 |
| abstract_inverted_index.metagenomics. | 471 |
| abstract_inverted_index.microbiology, | 468 |
| abstract_inverted_index.non-redundant | 170 |
| abstract_inverted_index.opportunistic | 334 |
| abstract_inverted_index.pre-slaughter | 111 |
| abstract_inverted_index.transmission, | 7 |
| abstract_inverted_index.transmission. | 461 |
| abstract_inverted_index.understanding | 37 |
| abstract_inverted_index.Acinetobacter, | 242 |
| abstract_inverted_index.Actinobacteria | 227 |
| abstract_inverted_index.Proteobacteria | 215 |
| abstract_inverted_index.Psychrobacter, | 243 |
| abstract_inverted_index.Staphylococcus | 260, 353 |
| abstract_inverted_index.drug-resistant | 332 |
| abstract_inverted_index.microorganisms | 48 |
| abstract_inverted_index.slaughterhouse | 14, 63, 101, 455 |
| abstract_inverted_index.Mycobacteroides | 268, 356 |
| abstract_inverted_index.biofilm-forming | 383 |
| abstract_inverted_index.characteristics | 60 |
| abstract_inverted_index.single-pathogen | 29 |
| abstract_inverted_index.slaughterhouse, | 339, 464 |
| abstract_inverted_index.vulnerabilities | 371 |
| abstract_inverted_index.host-contaminant | 150 |
| abstract_inverted_index.quality-filtered | 147 |
| abstract_inverted_index.Chryseobacterium, | 240 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 5 |
| citation_normalized_percentile.value | 0.33290538 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |