Screening method for screening antibacterial peptide from deep sea cold spring, antibacterial peptide as well as preparation method and application of antibacterial peptide

Abstract

The invention belongs to the technical field of biology, and discloses a screening method for screening an antibacterial peptide from a deep sea cold spring, the antibacterial peptide and a preparation method and application of the antibacterial peptide. According to the screening method of the antibacterial peptide, provided by the invention, deep-sea cold spring sediment genome data is taken as a screening data basis, and a bioinformatics technology is utilized to excavate and construct the deep-sea cold spring sediment genome data to obtain an smORFs database containing rich small protein sequence information; performing prediction construction on small protein sequence information in the smORFs database to obtain a candidate AMPs database, and performing solubility prediction screening on small proteins in the candidate AMPs database to obtain the antibacterial peptide with high antibacterial potential. According to the screening method, the antibacterial peptide with high microbial infection resistance potential can be rapidly, efficiently and specifically screened from deep sea cold spring microorganisms, and the screening method has a good application prospect.

Description

Technical Field

[0001] The present invention belongs to the field of biotechnology, and particularly relates to a screening method for screening antibacterial peptides from deep-sea cold seeps, the antibacterial peptides, and their preparation methods and applications. Background Art

[0002] Antibacterial peptides (AMPs) refer to a class of small-molecule polypeptides widely distributed in animals and plants, and AMPs are an important part of the body's natural immune defense system. AMPs not only have broad-spectrum anti-infective activities, including inhibition of Gram-negative bacteria, Gram-positive bacteria, viruses, fungi, and parasites, but also have various biological activities such as killing tumor cells and immunomodulation. Compared with antibiotics, naturally sourced antibacterial peptides are natural protein products produced by biosynthesis and metabolism. Antibacterial peptides are more difficult to induce drug resistance, have lower toxicity, and stronger specificity, and are regarded as potential antibiotic alternatives, having broad application prospects in the prevention and treatment of drug-resistant bacterial infections.

[0003] Currently, the methods for obtaining antibacterial peptides mainly include direct separation and purification from organisms, enzymatic hydrolysis of known antibacterial proteins using proteases, chemical synthesis methods for preparation, and genetic engineering recombinant expression, etc. Deep-sea cold seeps have a rich and diverse microbial population and have the potential to be a good resource library for R & D and obtaining antibacterial peptides. However, the above methods cannot well obtain antibacterial peptides from deep-sea cold seep microorganisms. Currently, there is very little research on antibacterial peptides from deep-sea cold seep microorganisms, which has great limitations. Summary of the Invention

[0004] The first object of the present invention is to provide a screening method for antibacterial peptides, which can quickly, efficiently, and specifically screen and obtain antibacterial peptides with high antibacterial potential from deep-sea cold seep microorganisms.

[0005] The second object of the present invention is to provide an antibacterial peptide derived from deep-sea cold seeps.

[0006] The third object of the present invention is to provide a preparation method for the above-mentioned antibacterial peptide derived from deep-sea cold seeps.

[0007] The fourth object of the present invention is to provide the application of the above-mentioned antibacterial peptide derived from deep-sea cold seeps in the preparation of drugs for anti-microbial infections.

[0008] Specifically, the screening method of the antimicrobial peptide provided by the present invention specifically includes: (1) obtaining the genomic data of deep-sea cold seep sediments; (2) mining based on the genomic data of deep-sea cold seep sediments to obtain the deep-sea cold seep sediment smORFs database; (3) predicting based on the deep-sea cold seep sediment smORFs database to obtain the candidate AMPs database; (4) performing solubility prediction and source species dominance analysis based on the candidate AMPs database to screen out the antimicrobial peptide.

[0009] Further, in step (2), the method for mining based on the genomic data of deep-sea cold seep sediments includes: S1. Taking the genomic data of deep-sea cold seep sediments for quality control, filtering low-quality data and realizing the trimming and correction of sequence data to obtain a high-quality sequence set; S2. Taking the high-quality sequence set for co-assembly to obtain a contig sequence set; S3. Taking the contig sequence set for small protein smORFs prediction to obtain the smORFs database; S4. Taking the smORFs database for redundancy removal to obtain the deep-sea cold seep sediment smORFs database.

[0010] Further, in step (3), the method for predicting based on the deep-sea cold seep sediment smORFs database includes: taking the deep-sea cold seep sediment smORFs database and performing random forest training based on the existing polypeptide database to predict and obtain the candidate AMPs database.

[0011] Further, in step (4), the predicted solubility of the antimicrobial peptide is 0.708 - 0.939.

[0012] The antimicrobial peptide derived from deep-sea cold seeps provided by the present invention specifically includes an amino acid fragment with a sequence as shown in SEQ ID NO: 8.

[0013] The preparation method of the above-mentioned antimicrobial peptide derived from deep-sea cold seeps provided by the present invention specifically includes: coupling amino acids according to the amino acid sequence shown in SEQ ID NO: 8 by the Fmoc solid-phase synthesis method to synthesize the antimicrobial peptide.

[0014] The present invention provides the application of the above-mentioned antimicrobial peptide derived from deep-sea cold seeps in the preparation of drugs for anti-microbial infection.

[0015] Further, the microorganism is one or more of Bacillus subtilis, Escherichia coli, Vibrio parahaemolyticus, and yeast.

[0016] Beneficial effects:

[0017] In the screening method of the antimicrobial peptide provided by the present invention, the genomic data of deep-sea cold seep sediments is creatively used as the data basis for screening. Bioinformatics techniques are utilized to mine the genomic data of deep-sea cold seep sediments, thereby constructing a smORFs database containing rich small protein sequence information. Then, the small protein sequence information contained in the smORFs database is predicted, and a candidate AMPs database is further constructed. Finally, according to the results of solubility prediction and the analysis of the source species advantages, antimicrobial peptides with high antibacterial potential are obtained from the candidate AMPs database. This screening method effectively overcomes the problem in traditional antimicrobial peptide screening methods that it is difficult to directly isolate and identify antimicrobial peptides from deep-sea cold seep microorganisms, and can quickly, efficiently and specifically screen for the amino acid sequences of antimicrobial peptides. Based on the amino acid sequences, a large amount of high-purity antimicrobial peptides can be obtained through genetic engineering techniques or solid-phase synthesis techniques, showing good application prospects.

[0018] The present invention also provides an antimicrobial peptide derived from deep-sea cold seeps. This antimicrobial peptide includes an amino acid fragment with a sequence as shown in SEQ ID NO:8, and it shows excellent inhibitory effects against Gram-positive bacteria, Gram-negative bacteria, aquatic pathogenic bacteria, and fungi, possessing broad-spectrum antibacterial ability and being a highly potential anti-infection active ingredient. Detailed implementation manners

[0019] In view of the problems existing in the traditional methods for the research and development of antimicrobial peptides, such as low natural content of antimicrobial peptides, easy neglect of short-sequence small proteins, high technical requirements, high production costs, and difficulty in large-scale production, the inventors of the present invention, through extensive and in-depth research and a large number of experiments, found that the microbiome of deep-sea cold seep sediments contains diverse and rich biosynthetic gene clusters (BGCs). Using the genomic data of deep-sea cold seep sediments as the data basis and mining, predicting, and screening the genomic data from multiple levels by bioinformatics techniques, a large number of antimicrobial peptides can be quickly screened, and the obtained antimicrobial peptides indeed show excellent antibacterial activities in subsequent antibacterial ability tests. Based on this, the technical solution of the present invention is obtained.

[0020] In the present invention, the screening method of the antimicrobial peptide specifically includes: (1) obtaining the genomic data of deep-sea cold seep sediments; (2) mining based on the genomic data of deep-sea cold seep sediments to obtain a deep-sea cold seep smORFs database; (3) predicting based on the deep-sea cold seep smORFs database to obtain a candidate AMPs database; (4) performing solubility prediction based on the candidate AMPs database and screening to obtain antimicrobial peptides.

[0021] In the present invention, in step (1), the genomic data of deep-sea cold seep sediments is from 165 sediment samples (0 - 68.55 m) at 16 cold seep sites globally distributed. More specifically, the cold seep sites include the Eastern North Pacific (ENP, ODP site 1244), Santa Monica Mounds (SMM), Western Gulf of Mexico (WGM), Eastern Gulf of Mexico (EGM), Northwestern Gulf of Mexico (NGM), Scotian Basin (SB), Haakon Mosby mud volcano (HM), Mediterranean Sea (MS, Amon mud volcano), Laptev Sea (LS), and the South China Sea. Among them, the South China Sea specifically includes the Jiaolong cold seep (JL), Shenhu area (SH), Haiyang4 cold seep (HY4), Qiongdongnan Basin (QDN), Xisha Trough (XST), Haima cold seeps (HM1, HM3, HM5, HM_SQ, S11, SY5, and SY6), and cold seeps at site F (RS, SF, FR, and SF_SQ). The sediment samples are taken from the oxygenated sediment-water interface of the seabed to the anoxic layer, with the deepest reaching 68.55 m below the seabed.

[0022] In the present invention, in step (2), the method for mining based on the genomic data of deep-sea cold seep sediments specifically includes: S1. Taking the genomic data of deep-sea cold seep sediments for quality control, filtering low-quality data and achieving trimming and correction of sequence data to obtain a high-quality sequence set; S2. Taking the high-quality sequence set for co-assembly to obtain a contig sequence set; S3. Taking the contig sequence set for prediction of small proteins smORFs to obtain an smORFs database; S4. Taking the smORFs database for redundancy removal to obtain the deep-sea cold seep sediments smORFs database.

[0023] In the present invention, in step (2) S1, the quality control is a technical means commonly used in NGS data analysis, and those skilled in the art can make an adaptive selection from existing algorithm models or software according to actual needs, and the present invention does not particularly limit it.

[0024] In some specific embodiments, in step (2) S1, the quality control specifically includes: using the software fastp (v0.23.2; default parameters) and the Read_QC module in MetaWRAP (v1.3.2; -skip-bmtagger) to perform quality control on the deep-sea cold seep sediment genomic data, filtering low-quality data and achieving trimming and correction of sequence data to obtain a high-quality sequence set.

[0025] In the present invention, in step (2) S2, the co-assembly is a technical means commonly used in NGS data analysis. Those skilled in the art can make an adaptive selection from existing algorithm models or software according to actual needs, and the present invention does not particularly limit it.

[0026] In some specific embodiments, in step (2) S2, the co-assembly specifically includes: using the software MEGAHIT (v1.1.3) to assemble the high-quality sequence set according to two running parameters of "default parameters" and "parameters: --k-min 27 --kmin-1pass --presets meta-large" to obtain a contig sequence set.

[0027] In the present invention, in step (2) S3, the method for predicting small proteins smORFs specifically includes: using the software SmORFinder (default parameters) and GMSC-mapper (default parameters) to predict small proteins smORFs with a length less than or equal to 100 aa from the contig sequence set to obtain a smORFs database.

[0028] In the present invention, in step (2) S4, the redundancy removal is a technical means commonly used in existing NGS data analysis. Those skilled in the art can make an adaptive selection from existing algorithm models or software according to actual needs, and the present invention does not particularly limit it.

[0029] In some specific embodiments, the redundancy removal specifically includes: processing the smORFs database using the software Mmseq2 with the parameter settings of -c 1.0, --min-seq-id 1.0 (i.e., 100% amino acid similarity and 100% coverage rate) to obtain a singleton sequence set and a non-singleton sequence set; setting the parameters of the software Mmseq2 to -c 0.9, --min-seq-id 0.9 (i.e., 90% amino acid similarity and 90% coverage rate) to process the non-singleton sequence set to obtain a representative sequence set; aligning the singleton sequence set to the representative sequence set to obtain a single-column sequence having homology with the representative sequence set; taking the single-column sequence with homology and merging it with the non-singleton sequence set to obtain the deep-sea cold seep sediment smORFs database.

[0030] In the present invention, in step (3), the method for prediction based on the deep-sea cold seep sediment smORFs database specifically includes: taking the deep-sea cold seep sediment smORFs database and performing random forest training based on an existing polypeptide database to predict and obtain the candidate AMPs database. Among them, "taking the deep-sea cold seep sediment smORFs database and performing random forest training based on an existing polypeptide database" is a technical means commonly used in existing NGS data analysis. Those skilled in the art can make an adaptive selection from existing algorithm models or software according to actual needs, and the present invention does not specifically limit it.

[0031] In some specific embodiments, in step (3), the deep-sea cold seep sediment smORFs database is processed using the Macrel software, and the random forest algorithm is used to predict antibacterial peptides in the deep-sea cold seep sediment smORFs database, and the accuracy rather than the recall rate is focused on during training; and the mature peptide model of the software ampir, amPEPpy and its recommended model, APIN and its recommended model, AI4AMP, and AMPLify are used to evaluate the novelty of the candidate antibacterial peptides obtained in the random forest training to obtain the candidate AMPs database.

[0032] In the present invention, in step (4), the method for predicting the solubility based on the candidate AMPs database specifically includes: predicting the solubility of the antibacterial peptides in the candidate AMPs database using the software Protein-Sol (default parameters).

[0033] In the present invention, in step (4), when the predicted solubility of the antibacterial peptides in the candidate AMPs database is specifically 0.708 - 0.939, it is the antibacterial peptide expected to be screened and obtained.

[0034] The present invention also provides an antibacterial peptide derived from deep-sea cold seeps. Specifically, the antibacterial peptide includes an amino acid fragment with the sequence shown in SEQ ID NO: 8.

[0035] In the present invention, the antibacterial peptide shows excellent inhibitory effects on cells such as Bacillus subtilis, Escherichia coli, Vibrio parahaemolyticus, and yeast, and has great potential for application as a drug against microbial infections such as Gram-positive bacteria, Gram-negative bacteria, aquatic pathogenic bacteria, and fungi.

[0036] In the present invention, the amino acid sequence of the antibacterial peptide is the core for realizing its corresponding biological activity. Those skilled in the art can select corresponding methods from the prior art for the synthesis of the antibacterial peptide, limited to obtaining the antibacterial peptide with the expected amino acid sequence. The present invention does not particularly limit its specific preparation method.

[0037] For the purpose of quickly obtaining a large amount of high-purity antibacterial peptide, the present invention also provides a preparation method for the above-mentioned antibacterial peptide derived from deep-sea cold seeps. The preparation method specifically includes: coupling amino acids according to the amino acid sequence shown in SEQ ID NO: 8 by Fmoc solid-phase synthesis method to synthesize the antibacterial peptide.

[0038] In the present invention, the Fmoc solid-phase synthesis method refers to a technique for gradually reacting on a solid support to establish a peptide chain. Compared with traditional genetic engineering techniques, it can realize the automated synthesis of antibacterial peptides, and the obtained antibacterial peptides have high purity.

[0039] Based on the excellent broad-spectrum antibacterial potential exhibited by the above antibacterial peptide, the present invention also provides the application of the above-mentioned antibacterial peptide derived from deep-sea cold seeps in the preparation of drugs for anti-microbial infections.

[0040] In the present invention, the antibacterial peptide shows excellent inhibitory activity against one or more of the microorganisms such as Bacillus subtilis, Escherichia coli, Vibrio parahaemolyticus, and yeast, and the growth inhibition rate is better than that of the known nisin with excellent antibacterial activity.

[0041] The following details the embodiments of the present invention. The examples are intended to explain the present invention and should not be construed as limiting the present invention. For those not specifying specific techniques or conditions in the examples, they shall be carried out according to the techniques or conditions described in the literature in this field or according to the product specifications. For reagents or instruments not indicating the manufacturer, they are all conventional products that can be obtained through commercial purchase.

[0042] Example 1

[0043] This example is used to illustrate a screening method for screening antibacterial peptides obtained from deep-sea cold seep sediments. The screening method specifically includes:

[0044] 1. Construction of Deep - sea Cold - seep Sediment Genome Data

[0045] Obtain 165 sediment samples from 16 cold - seep sites in the eastern North Pacific (ENP, Eastern North Pacific, ODP site 1244), Santa Monica Mounds (SMM), western Gulf of Mexico (WGM), eastern Gulf of Mexico (EGM), northwestern Gulf of Mexico (NGM), Scotian Basin (SB), Haakon Mosby mud volcano (HM), Mediterranean Sea (MS, Mediterranean Sea, Amon mud volcano), Laptev Sea (LS), and the South China Sea [Jiaolong cold seep (JL), Shenhu area (SH), Haiyang4 cold seep (HY4), Qiongdongnan Basin (QDN), Xisha Trough (XST), hippocampus cold seeps (HM1, HM3, HM5, HM_SQ, S11, SY5, and SY6), and cold seeps at site F (RS, SF, FR, and SF_SQ)]. Extract genes and sequence them from the oxygenated sediment - water interface to the anoxic layer (up to 68.55 m below the seabed) of the seabed. Construct deep - sea cold - seep sediment genome data from the sequencing results.

[0046] 2. Construction of Deep - sea Cold - seep Sediment smORFs Database

[0047] (1) Use the software fastp (v0.23.2; default parameters) and the Read_QC module in MetaWRAP (v1.3.2; -skip - bmtagger) to perform quality control on the deep - sea cold - seep sediment genome data, filter low - quality data, and achieve trimming and correction of sequence data to obtain a high - quality sequence set.

[0048] (2) The high-quality sequence set was assembled using the software MEGAHIT (v1.1.3) with two sets of running parameters, namely "default parameters" and "parameters: --k-min 27 --kmin-1pass --presets meta-large", to obtain a contig sequence set including 59,011,641 overlapping sequences.

[0049] (3) The software SmORFinder (default parameters) and GMSC-mapper (default parameters) were used to predict small proteins smORFs with a length less than or equal to 100 aa from the contig sequence set, resulting in an smORFs database.

[0050] (4) The software Mmseq2 was used to process the smORFs database with the parameter settings of -c 1.0, --min-seq-id 1.0 (i.e., 100% amino acid similarity and 100% coverage), to obtain a singleton sequence set including 99,840,370 singleton sequences (86%) and a non-singleton sequence set including 15,870,581 non-singleton sequences (14%); the parameter settings of the software Mmseq2 were changed to -c 0.9, --min-seq-id 0.9 (i.e., 90% amino acid similarity and 90% coverage) to process the non-singleton sequence set, obtaining a representative sequence set; the singleton sequence set was aligned to the representative sequence set to obtain a single-column sequence with homology to the representative sequence set; the single-column sequence with homology was merged with the non-singleton sequence set to obtain the deep-sea cold seep sediment smORFs database including 23,496,022 small protein sequences.

[0051] 3. Construction of the candidate AMPs database

[0052] The Macrel software was used to process the deep-sea cold seep sediment smORFs database, and the random forest algorithm was used to predict antimicrobial peptides in the deep-sea cold seep sediment smORFs database, with a focus on precision rather than recall during training, resulting in 34,045 antimicrobial peptide sequences; and the mature peptide models of the software ampir, amPEPpy and its recommended models, APIN and its recommended models, AI4AMP, and AMPLify were used to evaluate the novelty of the antimicrobial peptide sequences obtained during the random forest training, obtaining a candidate AMPs database.

[0053] 4. Screening and obtaining of antimicrobial peptides

[0054] The solubility of antimicrobial peptides in the candidate AMPs database was predicted using the software Protein-Sol (default parameters). Antimicrobial peptides with a predicted solubility > 0.5 and a source species being a dominant species in deep-sea cold seeps were the expected screened antimicrobial peptides, and their specific amino acid sequences are shown in Table 1.

[0055] Table 1.

[0056]

[0057]

[0058] 5. The antimicrobial peptide molecules shown in Table 1 were synthesized by an outsourcing synthesis company using the Fmoc solid-phase synthesis method.

[0059] Example 2

[0060] This example is used to illustrate the antibacterial activity of the antimicrobial peptides screened in the above examples. The specific tests include:

[0061] (1) Escherichia coli DH5α, Bacillus subtilis, Vibrio parahaemolyticus VP.1997, and Saccharomyces cerevisiae BY4741 were inoculated into LB liquid medium at an inoculation amount of 1% respectively, and cultured in a constant temperature shaker under the corresponding culture conditions for 12 h to obtain the test bacterial solutions.

[0062] Among them, the culture conditions for Escherichia coli DH5α, Bacillus subtilis, and Vibrio parahaemolyticus VP.1997 were 37 °C, 220 rpm, and 16 h; the culture conditions for Saccharomyces cerevisiae BY4741 were 30 °C, 220 rpm, and 54 h.

[0063] (2) The antimicrobial peptide provided in Example 1 was taken at a final concentration of 400 μmol / L and mixed with ddH 2 O to prepare an antimicrobial peptide solution; and a solution of nisin (Sangon Biotech (Shanghai) Co., Ltd., product number A410681) at a concentration of 400 μmol / L was used as a positive control.

[0064] (3) LB liquid medium was added to a 96-well cell plate at an addition amount of 100 μL / well. Then, the antimicrobial peptide solution and the nisin solution were added to the 96-well cell plate at an addition amount of 50 μL / well respectively, and mixed by pipetting. An equal volume of LB liquid medium was used as a blank control; the OD 600 value of the test bacterial solution was adjusted to 0.2 using LB liquid medium. Then, the adjusted test bacterial solution was taken at an addition amount of 50 μL / well and added to the 96-well cell plate, and the test bacterial solutions were cultured under the culture conditions in (1). After the culture was completed, the OD 600The value was used to calculate the growth inhibition rate according to the following formula, and the results are shown in Table 2.

[0065] Growth inhibition rate = (A 0 - A X ) / A 0 × 100%

[0066] where A 0 is the OD 600 value of the blank control group at the end of the culture; A X is the OD 600 value of the antibacterial peptide solution and the nisin solution of Lactococcus lactis at the end of the culture.

[0067] Table 2.

[0068]

[0069]

[0070] As can be seen from the test results shown in Table 2, the antibacterial peptides 1-10 screened by the method provided in Example 1 have a certain inhibitory effect on the growth of one or more cells among Bacillus subtilis, Escherichia coli, Vibrio parahaemolyticus and yeast, that is, the screened antibacterial peptides 1-10 all have a certain antibacterial activity, and the screening accuracy rate can reach 100%.

[0071] Among them, compared with the known antibacterial peptide nisin of Lactococcus lactis and the screened antibacterial peptides, the antibacterial peptide 8 with the amino acid sequence shown in SEQ ID NO: 8 can well inhibit the growth of Bacillus subtilis, Escherichia coli, Vibrio parahaemolyticus and yeast, and has excellent inhibitory effects on Gram-positive bacteria, Gram-negative bacteria, aquatic pathogenic bacteria and fungi, has broad-spectrum antibacterial potential, and has good application prospects.

[0072] Although the embodiments of the present invention have been shown and described above, it can be understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those of ordinary skill in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of the present invention without departing from the principle and spirit of the present invention.

Claims

1. A method for screening antimicrobial peptides, characterized in that: The screening method comprises: (1) obtaining deep-sea cold seep sediment genome data; (2) mining based on the deep-sea cold seep sediment genome data to obtain a deep-sea cold seep sediment smORFs database; (3) predicting based on the deep-sea cold seep sediment smORFs database to obtain a candidate AMPs database; (4) performing solubility prediction and source species dominance analysis based on the candidate AMPs database to screen and obtain the antimicrobial peptide.

2. The screening method according to claim 1, characterized in that In step (2), the method for mining based on the deep-sea cold seep sediment genome data includes: S1, taking the deep-sea cold seep sediment genome data for quality control, filtering low-quality data and implementing sequence data trimming and correction to obtain a high-quality sequence set; S2, taking the high-quality sequence set for co-assembly to obtain an overlapping group sequence set; S3, taking the overlapping group sequence set for small protein smORFs prediction to obtain a smORFs database; S4, taking the smORFs database for redundancy removal to obtain the deep-sea cold seep sediment smORFs database.

3. The screening method according to claim 1, characterized in that In step (3), the method for prediction based on the deep-sea cold seep sediment smORFs database includes: taking the deep-sea cold seep sediment smORFs database and performing random forest training based on an existing peptide database to predict the candidate AMPs database.

4. The screening method according to claim 1, characterized in that In step (4), the predicted solubility of the antimicrobial peptide is 0.708-0.

939.

5. An antimicrobial peptide derived from a deep-sea cold spring, characterized in that: The antimicrobial peptide comprises an amino acid fragment having a sequence as shown in SEQ ID NO:

8.

6. The method for preparing the antimicrobial peptide from deep-sea cold spring according to claim 5, characterized in that: The preparation method comprises: using Fmoc solid phase synthesis method to couple amino acids according to the amino acid sequence shown in SEQ ID NO: 8 to synthesize the antimicrobial peptide.

7. Use of the antimicrobial peptide derived from deep-sea cold seeps as claimed in claim 5 in the preparation of drugs for antimicrobial infection.

8. The use according to claim 7, characterized in that: The microorganism is one or more of Bacillus subtilis, Escherichia coli, Vibrio parahaemolyticus and yeast.

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