Humans are constantly exposed to diverse microbes, which can have positive and adverse health effects. Considerable exposure the these microorganisms occurs indoors, with the highest bacterial colonization reported in the kitchen. To date, no study has compared microbial communities in households across countries, which is necessary to determine if a core kitchen microbiota exists.

Study: Mapping the Kitchen Microbiota in Five European Countries Reveals a Set of Core Bacteria across Countries, Kitchen Surfaces, and Cleaning Utensils. Image Credit: Africa Studio / Shutterstock.com

About the study

A recent study published in the journal Applied and Environmental Microbiology mapped the microbiota on kitchen surfaces and cleaning utensils to determine any associations between microbiota, household objects, and country.

Kitchen surface and cleaning utensil samples were collected from 74 households in Hungary, France, Portugal, Romania, and Norway. Surface samples from different kitchen objects were obtained before dinner preparation.

Samples from cleaning sponges and cloths were obtained after food preparation and subsequently subjected to deoxyribonucleic acid (DNA) extraction and 16S ribosomal ribonucleic acid (rRNA) gene sequencing. Alpha diversity was analyzed using the Shannon index, observed features, and Faith’s phylogenetic diversity.

Linear mixed models were used to test Alpha diversity differences across sample types and countries. Beta diversity analysis was also performed through unweighted and weighted UniFrac, Jaccard, and Bray-Curtis. Univariate and multivariate analyses examined differences between sample counties and types.

Study findings

A total of 305 samples were analyzed and 3,487 sub-operational taxonomic units (sOTUs) were detected from over 18.8 million sequences. An average of 61,960 sequences were obtained for each sample, with 5,420 sequences isoalted for every sOTU.

The kitchen microbiota comprised members from 793 genera from 297 families. The most frequent sequences were from the Proteobacteria phylum, followed by Firmicutes, Bacteroidota, and Actinobacteria phyla.

The most abundant genera and families were Acinetobacter, Pseudomonas, Enhydrobacter, Enterobacteriaceae, and Yersiniaceae. Forty-five sOTUs were identified in the Acinetobacter genus, with A. johnsonii being the most frequent Acinetobacter species.

Sixty-four sOTUs represented the Pseudomonas genus. Nineteen sOTUs were identified within the Enterobacteriaceae family.

There were 41, 12, 67, and 16 sOTUs within the Bacillus, Staphylococcus, Chryseobacterium, and Kocuria genera, respectively. Sixteen samples were positive for Listeria, one tested positive for Campylobacter, and none had Salmonella.

Notably, sOTUs related to S. enterica and Shigella/Escherichia were detected in samples from all five countries, whereas C. jejuni was present in Romanian, French, and Portuguese samples. Most of these pathogens were relatively less abundant.

Alpha diversity significantly differed between sample types and across countries. For example, samples from Portugal had fewer observed sOTUs than those from Norway and France, whereas samples from Norway, Hungary, and France had similar levels of Alpha diversity.

Similar results were observed with Faith’s phylogenetic diversity. Country differences were less evident when analyses were restricted to samples from cleaning utensils but remained significant when only surface samples were considered.

Handles had the highest Alpha diversity based on observed features, whereas sponges had the lowest. Principal component analysis (PCA) showed the tendency for microbiota clustering by sample type and country. Although the microbiota in cleaning cloths and sponges were similar, their similarity was greater across countries than between cloths and sponges.

Surface samples were clustered by country, thus implying that the microbiota was more similar in surface samples within a country than a specific surface sample across countries. However, some exceptions to this finding were observed. For example, the microbiota on French handles was more similar to that on Hungarian handles than in other French surface samples.

Some bacteria in sponges and cloths with significantly different relative abundances were observed between countries as compared to surface samples. Sink samples had the highest bacteria with significantly different relative abundance between countries.

Eight genera/families were identified as the core microbiota, of which included Acinetobacter, Enterobacteriaceae, Pseudomonas, Psychrobacter, Bacillus, Staphylococcus, Enhydrobacter, and Chryseobacterium. Of these, Pseudomonas, Enhydrobacter, and Acinetobacter were the only taxa present in all samples, which also exhibited the highest mean relative abundances.

Enhydrobacter, Pseudomonas, and Enterobacteriaceae were identified as the core sOTUs. The core microbiota varied in relative abundance between sample types and countries. Among the core microbiota, the relative abundances of Psychrobacter, Chryseobacterium, Enterobacteriaceae, Pseudomonas, and Enhydrobacter significantly differed between countries.

There were significant differences in Acinetobacter and Staphylococcus between sample types, with the highest abundances in humid samples obtained from sinks, cloths, and sponges, as well as handles, respectively. Likewise, Bacillus exhibited significant differences between sample types, with the lowest abundance present in humid samples.

Conclusions

Despite the considerable differences in kitchen standards, dietary habits, and food preparation practices, a core microbiota of the kitchen comprising eight taxa at the genus/family level was identified, with three core bacteria observed at the sOTU level.

Overall, the study findings expand the knowledge base of the kitchen microbiota. Future studies should correlate these results with food safety behaviors and their impact on human health.

Written by

Tarun Sai Lomte

Tarun is a writer based in Hyderabad, India. He has a Master’s degree in Biotechnology from the University of Hyderabad and is enthusiastic about scientific research. He enjoys reading research papers and literature reviews and is passionate about writing.