Analysis of Bacterial Diversity in Street Food and Its Functional Gene Pathways Based on Metagenomic Technology

doi:10.21203/rs.3.rs-5123338/v1

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Analysis of Bacterial Diversity in Street Food and Its Functional Gene Pathways Based on Metagenomic Technology

https://doi.org/10.21203/rs.3.rs-5123338/v1

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Street food has become the top choice in people's lives due to its convenience and variety. However, the safety issues of street food are also becoming increasingly prominent. To understand the composition of the microbial community in street food and predict its functional genes, this study randomly selected 70 samples of seven types of food around colleges and universities, including snack foods (fried and baked foods), cooked food products, pastries, cold dishes, beverages, staple foods (rice and flour foods), and salad sushi. These 70 samples were analyzed by 16S rDNA sequencing using mutagenic high - throughput sequencing technology, focusing on the composition and diversity of microbial communities in different street foods and gene functional prediction. The results showed that the highest richness values of Chao1, ACE, and Shannon were observed in snacks (SN), while the lowest were in salad (SA). The highest Simpson richness value was found in snacks (SN), and the lowest in sulla (SU). A total of nine bacterial phyla were identified from the seven food samples, involving 152 bacterial genera, with nine of them being shared genera. The dominant genera in MF, SF, SU, BE, and SA were Pantoea, Weissella, Bacillus, Streptococcus, and Pseudomonas, respectively. KEGG functional gene prediction for the seven food samples indicated that the microorganisms in these samples were mainly associated with six major groups and 35 secondary - level metabolic pathways. These pathways are mainly related to six major functional categories (KEGG level 1), namely metabolism, genetic information processing, information processing, cellular processes, human diseases, and organism systems. The results of this study will not only assist consumers in choosing street food but also provide new ideas for the future safety evaluation of street food.

Biological sciences/Biotechnology

Biological sciences/Genetics

Biological sciences/Microbiology

Earth and environmental sciences/Biogeochemistry

Health sciences/Diseases

Health sciences/Risk factors

Metagenomic Technology

Bacterial Diversity

Street Food

Functional Gene Pathways

1. The results showed that the highest richness values of Chao1, ACE, and Shannon were observed in snacks (SN), while the lowest were in salad (SA).

2. A total of nine bacterial phyla were identified from the seven food samples, involving 152 bacterial genera, with nine of them being shared genera.

3. KEGG functional gene prediction for the seven food samples indicated that the microorganisms in these samples were mainly associated with six major groups and 35 secondary - level metabolic pathways.

Street food is broadly defined as a series of food products sold by individual vendors¹, and narrowly as snacks sold by open - air markets, mobile vendors, and small businesses, such as fried chicken, coleslaw, salads, and drinks. These snacks are cheap, delicious, and portable². With the gradual diversification of food culture³, they integrate local food and agricultural characteristics. As the social economy develops and living standards continuously improve, the choice of dining places has become increasingly diverse. Due to its low cost, good taste, and easy - to - carry features, street food is growing in popularity among the public. However, the operating conditions of many street vendors are limited, and their sanitary conditions are concerning, such as incomplete dish cleaning, improper ingredient preservation, and non - standardized operations^{4, 5}. Moreover, in some cases, the sources of raw materials for street foods are unclear⁶. These food ingredients may have been processed to hide original problems, but they may cause health problems when consumed by consumers⁷. Because street foods exposed to the open - air environment are easily contaminated⁸, the safety of street food has a greater potential risk^{4, 9}.

Currently, Li Jing and others randomly selected 100 catering units and sampled and tested 316 samples of marinated products and cold dishes. The samples were examined using national standard testing methods from two aspects: microbiological indexes and physicochemical indexes. Francina M. Mosupye et al. ¹⁰determined aerobic plate counts, Enterobacteriaceae counts, coliform counts, and spore counts on 132 samples of beef, chicken, salad, and gravy collected from two street vendors to test for bacterial counts. The predominant populations isolated from the aerobic plate counts were Bacillus spp., Staphylococcus spp., Enterobacteriaceae, and Alcaligenes spp. Yi Ming Li et al.¹¹ analyzed 18 ready - to - eat food bacterial communities and antibiotic resistance genes based on a macro - genomics approach. The most abundant phyla in RTE foods were Proteobacteria, Firmicutes, Cyanobacteria, Bacteroidetes, and Actinobacteria. Chloramphenicol, macrolide - lincosamide - streptogramin, multidrug resistance, aminoglycoside, bacitracin, tetracycline, and β - lactam resistance genes were dominant, which were also associated with antibiotics widely used in human medicine or veterinary.

At present, most street food research uses traditional detection methods, and there are fewer studies on the bacterial diversity and functional prediction of street food through high - throughput sequencing technology¹². Traditional detection methods are time - consuming and labor - intensive and lack the ability to detect non - target pathogens¹³. The rapidly developing high - throughput sequencing technology can not only detect microorganisms in specific environments but also analyze various microbial species, species abundance, systematic evolution, and other related information in each sample, and can also predict metabolic functions^{14, 15}.

In this paper, high - throughput sequencing technology was used to analyze the microbial alpha - diversity, species composition, difference analysis, and functional prediction of seven street food communities. The purpose of this paper is to reveal the potential safety hazards of street food, accurately identify the pollution status of street food microorganisms, help consumers choose street food rationally, and assist the government in conducting targeted supervision and treatment of community microorganisms. In addition, it also proposes new research ideas for improving the microbial contamination of street food.

1.1 Sample collection and processing

Sample collection covered shops, restaurants, mobile stalls, and snack streets around universities. The collection method was random sampling. The food types included snacks (fried and baked foods), cooked food products, pastries, cold dishes, beverages, staple foods (rice and noodles), salads, and sushi. After collection, the samples were placed in sterile plastic bags, sealed, and brought back to the laboratory with serial numbers SN, MF, PA, SA, BE, SF, and SU.

Street food samples were collected from stores, restaurants, mobile stalls, and snack streets around universities in provincial capitals. Random sampling was adopted. The types of street food included snack foods (fried and baked foods), cooked food products, pastries, cold dishes, drinks, staple foods (rice and flour foods), and salad sushi. A total of 70 street food samples (n = 70) were collected. To ensure the same sampling time, all sampling processes were purchased online to ensure delivery by the same courier company, and the time interval for food delivery was not more than 30 minutes. After the sample was sent, it was immediately frozen and mixed evenly. Then, the sample was loaded into a sterile plastic bag, sealed, and stored at ultra - low temperature, and the total DNA extraction of the sample was completed quickly.

1.2 Metagenomic sequencing of food microbes

Total DNA from samples was extracted using a DNA extraction kit (HiPure DNA Kits, Megen, Shanghai). The DNA samples were transported below 0°C and subjected to DNA sequence detection and gene library construction. The raw data was obtained for quality control and host filtering to get valid data (Clean Data). The de novo genome assembly, gene annotation, and gene abundance calculations were performed using the software MEGAHIT, Prokka, and Salmon. The best match for each gene was extracted and converted to the sum of abundances at different taxonomic levels to calculate the abundance of the corresponding species. The abundances of each sample at different taxonomic levels of Domain, Kingdom, Phylum, Class, Order, Family, Genus, and Species were calculated respectively, and the table of species abundances at different taxonomic levels was obtained.

1.3 Bioinformatics analysis

The taxonomic classification of each read was assigned against the EzTaxon - e database at a 97% threshold of pairwise sequence similarity. The richness and diversity of samples were determined by Chao1 estimation and the Shannon diversity index at a 3% distance. Linear discriminant analysis Effect Size (LEfSe) was performed using the tidyverse, microeco, and magrittr packages in R (4.3.1) to analyze the significance of the differential - affected sample - divided communities or species according to the categorical composition and different grouping conditions. PICRUSt2 was used for prediction of metagenome function with the KEGG database. The expected value e - value of the comparison parameter was set to 1e − 5. The antibiotic resistance function annotation information corresponding to the gene was obtained, and then the sum of the gene abundances corresponding to the antibiotic resistance function was used to calculate the abundance of the antibiotic resistance function.

The alpha - diversity index can analyze and reflect the richness and evenness of microbial communities in various foods. Chao1, ACE, Simpson, and the Shannon index were calculated to estimate the alpha - diversity of different street foods (Fig. 1). The Ace index, Chao1 index, and Shannon index were positively correlated with alpha - diversity, while the Simpson index was negatively correlated with it. (Predictive functional profiling of microbial communities in fermentative hydrogen) Chao1, ACE, and Shannon indices showed similar trends in the seven foods. The highest richness values of Chao1, ACE, and Shannon were observed in snacks (SN), and the lowest in salad (SA). The ACE index of snacks (SN) was 183, that of salads (SA) was 35, and the ACE index of snacks (SN) was 5.23 times that of SA. The Shannon index of snacks (SN) was 209.04, that of salad (SA) was 38.75, and the Shannon index of snacks (SN) was 5.39 times that of SA. The highest Simpson richness value was found in snacks (SN) (5.29), and the lowest in sulla (SU) (1.74).

As shown in Fig. 2, a total of 9 phyla were detected at the phylum level in seven street food microorganisms, including 4 common phyla, namely Bacteroidota, Cyanobacteria, Firmicutes, and Proteobacteria. The results of hierarchical cluster analysis and community structure analysis based on community composition among different street foods showed that salad (SA) was clustered with snacks (SN), and then clustered with pastries (PA), indicating that the microbial community structures of these three foods were similar. The dominant phylum of these three foods was Proteobacteria. MF and SF were clustered together, and the dominant phylum was Cyanobacteria, with a relative abundance of 47.24% − 50.95%. BE and SU were clustered into one group, and the dominant phylum was Firmicutes, with a relative abundance of 63.72% − 74.22%.

As shown in Fig. 3, a total of 152 genera were detected in the seven street foods at the genus level, of which 9 genera were common, namely Bacillus, Mitochondria _ norank, Streptococcus, Caulobacteraceae _ uncultured, Chloroplast _ norank, Faecalibacterium, Acinetobacter, Lactococcus, and Pseudomonas. Among the common genera, Bacillus had the highest content in SU, with a relative abundance of 66.73%, and its relative abundance in other foods was less than 10.00%. Acinetobacter had the highest content in PA, with a relative abundance of 35.62%. Lactococcus had the highest content in SA, with a relative abundance of 7.77%, followed by BE with a higher content of 4.88%, and the relative abundance of the remaining samples was less than 1.00%. The content of Faecalibacterium in the seven foods was low, and its relative abundance was less than 3.00%. In the clustering and composition of different street food genus - level samples, the closer the samples were, the shorter the branch length was, indicating that the species composition of the two samples was more similar, and the similarity or difference in community structure between samples could be clearly seen. Among them, marinated products (MF) and staple food (SF) were clustered into one group, indicating that the microbial community structures of these two foods were similar. The dominant genus of MF was Pantoea, with a relative abundance of 1.66%. The dominant genus of SF was Weissella, with a relative abundance of 8.61%. PA and SN were clustered into one group. The dominant bacteria of PA and SN were mainly Acinetobacter, with a relative abundance of 35.62% − 10.40%. In addition, SU, BE, and SA were clustered into one group, and the dominant genus of SU was Bacillus, with a relative abundance of 66.73%. The dominant genus of BE was Streptococcus, with a relative abundance of 37.33%. The dominant genus of SA was Pseudomonas, with a relative abundance of 29.89%.

LEfSe analysis results (Fig. 4) showed that when the contribution of different species (LDA Score) was greater than the set value of 4, there were 32 significantly different species at the genus level of 7 street food microorganisms. Among them, there was a significant difference between SF and other foods, with Tumebacilluswei as the main standard genus. There were significant differences in 6 genera between MF and other foods, with Koritha, Escherichia Shigella, Aeromonas Asaia, Phascolarctobacterium, Geobacillus as the main standard genera. There were 4 species of bacteria in SN that were significantly different from other foods, with Acinetobacter, Enhydrobacter, Paracoccus, and Megasphaera as the main standard genera. Four species of bacteria in SA were significantly different from other foods, with Anoxybacillus, Lysinibacillus, Faecalibacterium, and Pelomonas as the main standard genera. Pseudomonas, Cobetia, Massilia, Pseudoalteromonas, Sediminibacterium, and Shewanella were the main standard genera in PA, and 7 genera in SU were significantly different from other foods. Rosenbergiella, Rahnellal, Flavobacterium, Rahnellal, Ruminococcus - torques - group, CHKC1001, Vagococcus norank were the main standard genera. Four bacterial genera in BE were significantly different from other foods, with Pantoea, Pediococcus, Moellerlla, and Psychrobacter as the main standard genera. The results showed that there were many differences in dominant bacteria at the genus level among the seven foods.

Through the difference analysis of KEGG metabolic pathways, the differences and changes in functional genes in different street food microbial communities in metabolic pathways could be observed, which was an effective means to study the metabolic function changes of community samples to adapt to environmental changes (Fig. 5 and Fig. 6). From Fig. 5, these pathways were mainly associated with six major functional categories (KEGG level 1), namely metabolism (48.38%), genetic information processing (18.38%), information processing (13.90%), cellular processes (3.00%), human diseases (1.33%), and organism systems (0.77%). Additionally, level 2 KEGG pathways such as amino acid metabolic pathways, carbohydrate metabolic pathways, replication repair, and energy metabolic pathway were the significant predominant functions in each sample (Fig. 6). In the KEGG first - level pathway, metabolism was the absolute dominant function, indicating that the metabolic activities of the seven food microorganisms were very active. Pathogenic bacteria were the key factors affecting food safety. Human Diseases was the pathway we were concerned about. The level 2 KEGG pathways related to the human disease pathway had a total of 6 pathways, such as infectious diseases, neurodegenerative diseases, cancer, metabolic diseases, immune system diseases, and cardiovascular diseases. SU and SA were the significant predominant functions in each sample. The bacterial genera in MF and SF were mainly involved in the Human Diseases pathway. The relative abundances of SM, FM, and BE in Genetic information Processing were higher than those of other foods, and the relative abundances of each food in the remaining primary pathways were less different (Fig. 5).

As shown in Fig. 6, the relative abundances of SF and MF in the Cell Growth and Death pathway under the cellular processes level pathway were higher than those of other foods; the relative abundances of SF and MF in the translation pathway under the genetic information processing primary pathway were significantly lower than those of the other five foods, and the relative abundances of each food in the other pathways were less different. The relative abundances of the seven foods in the infectious diseases pathway were higher than 40%. The relative abundance of SA in the immune system Diseases pathway was the highest, and the relative abundance of neurodegenerative diseases in PA was higher than 30%. The relative abundances of SU and SA in the cardiovascular disease pathway were low. There was little difference in the relative abundances of each food in the other pathways (Fig. 5 and Fig. 6).

Street food has become the most popular food due to its convenience and rich taste, and it has become the preferred choice for people to buy food online¹⁶. In recent years, high - throughput sequencing technology has become a widely used molecular biology method¹⁷, but there are still gaps in the application of high - throughput sequencing technology to detect microbial composition and function in street food¹⁸. In this study, high - throughput sequencing technology was used to analyze the alpha - diversity, species composition, difference analysis, and functional prediction of seven street foods.

The results of microbial diversity analysis of different street foods showed that the microbial diversity of snacks (SN) was the highest among the seven street foods, while that of salads (SA) was the lowest. The sample of snacks (SN) was fried chicken legs, and the microbial diversity index of food cooked at high temperature was the highest among the seven samples. It is speculated that this may be related to the following reasons. Hazard analysis critical control point (HACCP) studies on street - food vending in developing countries worldwide have revealed a high correlation between long holding times at ambient temperatures and high bacterial counts, even though the foods had been cooked at temperatures high enough to kill vegetative forms of most bacteria¹⁹. Related studies have shown that bacteria will be killed when the cooking temperature exceeds 80 degrees Celsius, but bacterial spores could be expected to survive these temperatures¹⁰. Salad (SA) may be due to the addition of vinegar and onion, and a large amount of salt and other seasonings have an adverse effect on its microbial diversity. According to GB/T4789 and national health standards, in terms of the total number of colonies and coliforms in food and the main pathogenic microorganisms, salads have more serious microbial infections (Guangzhou salad). It is speculated that this is related to the national standard GB4789²⁰, which adopts the detection method of foodborne pathogenic bacteria and lacks the detection of non - target pathogenic bacteria²¹.

We analyzed the dominant bacteria at the phylum level. The results indicated that, in the microbial community structure of the seven foods, Proteobacteria and Firmicutes had a relatively high abundance and were common phyla among these seven foods. Proteobacteria was the dominant phylum in SA (cold dish), SN (snack), and PA (pastry), while Cyanobacteria was the dominant phylum in MF (main food) and SF (snack food).

When analyzing the main sources of street food pollution, considering its special exposure situation²², the hands of sales staff are in close contact with the raw materials, finished products, and food packaging of the food. This often results in the occurrence of food - borne diseases among diners²³. The bacteria contaminating the fingers are mainly Staphylococcus aureus and intestinal pathogenic bacteria, which belong to the above - mentioned two phyla, consistent with the results of this study. Given the weak safety awareness of street food sales staff, when selling street food, more control should be exerted over the food that has close contact with the hands of sales staff²⁴. This can help improve the efficiency of supervisors.

We also analyzed the dominant bacteria at the genus level. The results indicated that the dominant genus in SF was Weissella, and in SU was Bacillus. Bacillus is a common food - borne pathogen ²⁵. Based on food poisoning events caused by Bacillus contamination, which are mostly due to improper storage temperatures of leftover rice, the SU sample in this study was salad sushi. Given its cooking method, Bacillus is more likely to proliferate compared to SF (staple food). SA (salad) uses fresh vegetables as raw materials. However, due to the high water content of vegetables, a significant amount of juice is lost during processing, which easily enables spoilage bacteria to multiply. Pseudomonas is a spoilage bacterium for many fruit plants²⁶. Since it is more suitable for the growth environment on the surface of vegetables²⁷, the dominant genus of SA (salad) in this study is Pseudomonas, which is consistent with relevant reports ²⁸. Moreover, salad processing often involves washing with unclean water and non - high - temperature cooking, making it susceptible to bacteria (Urumqi). The dominant genus in MF is Pantoea, and the dominant bacteria in PA and SN are mainly Acinetobacter, while the dominant genus in BE is Streptococcus. All of these are common microorganisms related to food contamination.

Compared with the national standard GB4789²⁹, which is commonly used for the detection of food - borne pathogens, there is a lack of non - target pathogen detection. Through this study, it can be observed that the relative abundance of non - target - detected pathogenic bacteria in the community composition of seven common street foods is high. Non - target pathogens also pose important risks to food safety. The microbial composition of the seven - type - food community is more complex. In the future, these bacteria can be studied to comprehensively improve street food pollution and enhance its nutritional value.

The KEGG database genome sequence and its molecular data set are used to predict the function of target genes in cells and organisms ³⁰. Among them, the relative abundance of the replication and repair functions pathway under the genetic information processing pathway is the highest. These inferred functions play a crucial role in energy supply, biosynthesis of cellular components, and improvement of food flavor, taste, and nutritional value³¹. In the human disease pathway, among the level 2 KEGG pathways related to the human disease pathway, the infectious diseases pathway is the most significantly predominant function in each sample. It is speculated that street food is rich in infectious microorganisms, which is consistent with the test results at the phylum and genus levels mentioned above. Besides the infectious diseases caused by food - borne bacteria, the other five level − 2 KEGG pathways should also be given attention. In the future, researchers can conduct in - depth scientific research based on this.

In conclusion, it can be seen that the bacterial contamination of street food is more complex. Since this study only selected 7 types of food, it cannot fully represent the overall situation of street food across the country. It is recommended to carry out multi - regional and large - sample surveys in the future, combined with new microbial detection techniques. At the same time, considering the different natures of food states, cooking methods, and packaging methods may have different impacts on microorganisms. In the future, we can strengthen research in this field and propose new research ideas for improving the microbial contamination of street food.

Author Contribution

All the authors participated in the sample collection and data analysis.Ke Li wrote the main manuscript text and Ke Li and Xuefeng Hu prepared figures 1-6. All authors reviewed the manuscript.

Acknowledgements

This work was financially supported by the National Natural Science Foundation of China (grant numbers 32271777, 31770499, 31100330, 31500365), Tianjin Science and Technology Support Program (23YFZCSN00100, 23YFZCSN00210, 15ZCZDSF00410) and Tianjin Natural Science Foundation (12JCYBJC19700).

Data Availability

The datasets used and analysed during the current study available from the corresponding author on reasonable request.

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You are reading this latest preprint version

Analysis of Bacterial Diversity in Street Food and Its Functional Gene Pathways Based on Metagenomic Technology

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Version 1

Abstract

Figures

Highlights

Introduction

Materials and Methods

1.1 Sample collection and processing

1.2 Metagenomic sequencing of food microbes

1.3 Bioinformatics analysis

Results

Discussion

Declarations

Author Contribution

Acknowledgements

Data Availability

References

Additional Declarations

Status:

Version 1