KR20230020088A - Biomarker for the discriminating geographical origins of sesame seeds and method for discriminating geographical origin using the same - Google Patents

Abstract

The present invention provides a composition for discriminating the geographical origin of sesame seeds and a method for discriminating the geographical origin of sesame seeds, based on the fact that the distribution of microorganisms by the geographical origin of sesame seeds is different. Accordingly, it is possible to determine the true geographical origin of sesame seed products that do not indicate the geographical origin or products that are incorrectly indicated, and thus the present invention is expected to guarantee consumers' right to know and induce fair trade.

Description

Biomarker for the discriminating geographical origins of sesame seeds and method for discriminating geographical origin using the same}

The present invention relates to a biomarker composition for determining the country of origin of sesame using a microorganism, a composition for determining the country of origin of sesame, and a method for determining the country of origin of sesame.

Sesame genus (Sesamum indicum) is an annual plant of the dicotyledonous plant tree Sesameaceae, and is one of the crops widely cultivated in Asia and Africa, and is affected by various environmental factors such as temperature, precipitation, and solar radiation.

The country of origin acts as an important factor in determining the price of sesame seeds (Horacek et al., 2015), and since it is difficult to identify the geographical origin by appearance, an analysis method that can objectively determine the country of origin is required. there is. Gas chromatography-mass spectroscopy (GC-MS), high performance liquid chromatography (HPLC), nuclear magnetic resonance (NMR), and nuclear magnetic resonance (NMR) are conventional methods for determining the geographical origin of sesame seeds. ) spectrometer, near infrared (NIR) spectrometer, inductively coupled plasma-mass spectrometry, etc. have been used (Patent No. 10-2202225).

Recently, microbial genome analysis based on metagenomics has been applied to identify complex microbial species. Since most crops are grown on land, soil conditions can affect microbial distribution. That is, the distribution of microorganisms found in seeds may differ depending on the geographical source conditions of the seeds. Conventional studies that reveal the geographical origin of sesame by analyzing metabolites, minerals, etc. are known, but there is no known study to determine the geographical origin of sesame using microbiome.

Republic of Korea Patent Publication No. 10-2020-0136669 (2020.12.08. Publication)

The purpose of the present invention is Alternaria , Aspergillus and Macrophomina It is to provide a biomarker composition for determining the country of origin of sesame containing one or more microorganisms selected from the group consisting of.

Another object of the present invention is to Alternaria , Aspergillus and Macrophomina To provide a composition for determining the country of origin of sesame, including an agent for detecting one or more microorganisms selected from the group consisting of.

Another object of the present invention is to treat Alternaria , Aspergillus and Macrophomina from sesame seeds. It is to provide a method for determining the origin of sesame, including the step of extracting the distribution of one or more microorganisms selected from the group consisting of.

In order to achieve the above object, the present invention is Alternaria , Aspergillus and Macrophomina Provided is a biomarker composition for determining the country of origin of sesame containing one or more microorganisms selected from the group consisting of.

In addition, the present invention is Alternaria , Aspergillus and Macrophomina Provided is a composition for determining the country of origin of sesame, including an agent for detecting one or more microorganisms selected from the group consisting of, and a kit for determining the country of origin of sesame comprising the same.

In order to achieve the above other purposes, Alternaria , Aspergillus and Macrophomina It provides a method for determining the origin of sesame, including the step of extracting the distribution of one or more microorganisms selected from the group consisting of.

As it is confirmed that the distribution of microorganisms by country of origin of sesame is different, when the microorganism is used as a biomarker for determining the country of origin, it is possible to determine the country of origin of sesame with a high level of consistency and accuracy, and to secure reliability in the country of origin of sesame. Therefore, it is expected that it will be able to crack down on false indications of origin.

Figure 1 shows the relative sequence abundance of fungal genera based on sesame samples from Korea, China and other countries.
Figure 2a shows a PLS-DA derived score plot of fungal samples based on the metagenome sequence data of the two experimental groups, Figure 2b shows a test plot using 999 permutation tests derived from the PLS-DA model, R ² Y and Q ² Y-intercepts were expressed as original and permuted values. Figure 2c is the corresponding loading plot derived from the PLS-DA model, showing potential biomarkers.
Figures 3a, 3b and 3c are 7-fold cross-validation plots distinguishing sesame seeds from Korea, China and other countries to validate the PLS-DA model, showing predicted Y values according to a threshold of 0.5 (misclassified samples). is circled). Figure 3d shows the sensitivity (sensitivity), specificity (specificity) and accuracy (accuracy) (p <0.05).
Figure 4 shows a heat map of the relative levels of fungal genus in sesame samples from Korea, China and other countries.

Hereinafter, the present invention will be described in more detail.

The present invention is Alternaria , Aspergillus and Macrophomina Provided is a biomarker composition for determining the country of origin of sesame containing one or more microorganisms selected from the group consisting of.

The biomarker composition may be a component of the mycobiome (mycobiome, fungal community, fungal flora) of sesame. The biomarker composition may be a pathogenic fungus that harms plants.

Alternaria is It refers to the genus Altenaria fungi. Alternaria leaf spot disease reduces sesame yield and growth in moist areas. Aspergillus refers to the genus Aspergillus fungi, and Aspergillus contaminates various crops such as cotton, maize, and sesame during harvesting. Macrophomina refers to the genus Macrophomina fungi and infects a variety of crops worldwide. Macrophomina infections can often darken crop roots and stems.

In the present invention, a predictive model was developed to discriminate the geographic origin of sesame seeds through mycobiome, specifically fungal genus profiling. The biomarker composition is a microorganism of sesame seeds and is used in a predictive model reflecting various environmental factors including temperature, precipitation, soil, and the like. On the other hand, although not described herein, bacterial microbiome profiling was not suitable for determining the geographical origin of sesame seeds.

The term "country of origin" used in this specification refers to the region where the product was grown, produced, manufactured, or processed, and refers to the nationality in the case of imported and exported products. The Customs Act stipulates and operates regulations on the confirmation of the country of origin and its indication during customs clearance and control in the process of distribution in the market.

As used herein, the term "identification" means to distinguish by determining which country or region the origin of sesame is.

As used herein, the term "biomarker for determining the country of origin of sesame" refers to a biomolecule capable of distinguishing the country of origin due to the presence of microorganisms, specifically fungi, in sesame seeds.

The biomarker composition for determining the country of origin of sesame seeds may be used alone or in combination with one or more of Alternaria , Aspergillus and Macrophomina .

The biomarker composition for determining the country of origin of sesame seeds may be used alone or in combination with one or more of Cladosporium, Corynespora and Naganishia . Cladosporium is a genus of fungi, Corynespora is a genus of fungi, and Naganishia is a genus of fungi. means fungi. These genera of fungi are known as pathogenic fungi that harm plants.

The country of origin may be one or more countries selected from the group consisting of Korea, China, and South Asian/African countries. The South Asian country may be India and Pakistan, and the African country may be Ethiopia and Nigeria.

In addition, the present invention is Alternaria , Aspergillus and Macrophomina It provides a composition for determining the country of origin of sesame, including an agent for detecting one or more microorganisms selected from the group consisting of.

The agent may be at least one selected from the group consisting of antibodies, peptides, aptamers, compounds, primers, probes, antisense oligonucleotides, reporters and peptides. The agent is capable of measuring the distribution of microorganisms by specifically detecting microorganisms in a sesame sample, and may be appropriately selected according to techniques known in the art.

As used herein, the term "antibody" is a term known in the art and refers to a specific immunoglobulin directed against an antigenic site. The antibody in the present invention refers to an antibody that specifically binds to the microorganism of the present invention, etc., and can be prepared according to a conventional method in the art. The type of antibody includes polyclonal antibodies or monoclonal antibodies, and may include all immunoglobulin antibodies. The antibody may include functional fragments of the antibody molecule as well as complete forms having two full-length light chains and two full-length heavy chains. A functional fragment of an antibody molecule means a fragment having at least an antigen-binding function, and may be Fab, F(ab'), F(ab')2, Fv, and the like.

As used herein, the term "peptide" has the advantage of high binding force to a target substance, and does not undergo denaturation even during heat/chemical treatment. In addition, because of its small molecular size, it can be attached to other proteins and used as a fusion protein. Specifically, it can be used as a diagnostic kit because it can be used by attaching it to a polymer protein chain.

As used herein, the term "aptamer" refers to a special kind of single-stranded nucleic acid (DNA, RNA or modified nucleic acid) having a stable tertiary structure and being able to bind to a target molecule with high affinity and specificity. ) means a type of polynucleotide composed of As described above, aptamers can specifically bind to antigenic substances in the same way as antibodies, but are more stable than proteins, have a simple structure, and are composed of polynucleotides that are easy to synthesize, so they can be used instead of antibodies. can

As used herein, the term "primer" refers to a nucleic acid sequence having a short free 3'-hydroxyl group, capable of base pairing with a complementary template, and serving as a starting point for template strand copying. refers to the sequence of nucleic acids. Primers can initiate DNA synthesis in the presence of a reagent for polymerization (i.e., DNA polymerase or reverse transcriptase) and four different nucleoside triphosphates in an appropriate buffer and temperature. PCR conditions and lengths of sense and antisense primers can be appropriately selected according to techniques known in the art.

As used herein, the term "probe" is capable of specifically binding to a complementary nucleic acid sequence and It refers to a nucleic acid fragment such as RNA or DNA corresponding to a few bases to several hundreds of bases in length and is labeled, so that the presence or absence of a specific nucleic acid, expression level, etc. can be confirmed. The probe may be manufactured in the form of an oligonucleotide probe, a single strand DNA probe, a double strand DNA probe, an RNA probe, or the like. Selection of an appropriate probe and hybridization conditions can be appropriately selected according to techniques known in the art.

The formulation can detect sesame microbial internal transcribed spacer (ITS). The ITS region is present in almost all species and can be used for identification and phylogenetic classification because PCR primers are easy to prepare.

The country of origin may be one or more countries selected from the group consisting of Korea, China, and South Asian/African countries, as described above.

The composition can determine the country of origin of sesame by comparing the relative abundance of microorganisms. The relative abundance It may be due to temperature and dry soil conditions.

In this case, the composition may use a specific microorganism present in a high distribution or a low distribution in sesame seeds of a specific origin among a group of sesame seeds of origin. Alternatively, the composition may use that a specific microorganism among a group of microorganisms exists in a high distribution or a low distribution in sesame seeds of unknown origin.

Specifically, Alternaria distribution may be increased in Korean sesame compared to China and South Asian/African countries. This is presumed to be related to the fact that Korea has more precipitation than other countries during the cultivation period after August, and due to these environmental factors, the water availability of Korean sesame may be higher than that of China and South Asian/African countries. . On the other hand, Aspergillus distribution may increase in Chinese sesame compared to Korea and South Asian/African countries. Over the past 50 years, climate change has caused drought, increased temperature and decreased annual precipitation in China. Due to these environmental factors, the water availability of Chinese sesame may be lower than that of Korea and South Asian/African countries. Also, macrophomina distribution may be increased in sesame from South Asian and African countries compared to Korea and China. This is presumably related to geographic conditions including high temperatures and dry soil.

Korean sesame may have a high distribution of Alternaria , Cladosporium , and Corynespora , Chinese sesame may have a high distribution of Aspergillus , Naganishia , and Alternaria , and sesame from other countries (Ethiopia, India, Nigeria, Pakistan) may have a distribution of Macrophomina , Alternaria , and Aspergillus . can be high

In addition, the present invention provides a kit for determining the country of origin of sesame containing the composition for determining the country of origin of sesame. The kit may be used to determine the country of origin of a sesame sample by detecting or quantitatively analyzing the microorganism from a sesame sample of suspected origin, and may be appropriately selected according to a technique known in the art.

The kit includes an antibody that specifically binds to the marker component, a secondary antibody conjugate conjugated with a label that develops color by reaction with the substrate, a color-developing substrate solution to react with the label, a washing solution, and an enzyme reaction stop solution. and the like, and can be made into a number of separate packaging or compartments containing reagent components to be used. Alternatively, the kit may include a buffer, DNA polymerase, DNA polymerase cofactor, dNTPs, etc. in order to specifically amplify the nucleic acid sequence of the target region and confirm the presence and amount of the amplification product. It can be made into a number of separate packaging or compartments containing reagent components. The kit may be a RT-PCR kit, a DNA analysis kit (eg, a DNA chip), a protein chip kit, and the like.

In addition, the present invention from sesame to Alternaria , Aspergillus and Macrophomina It provides a method for determining the origin of sesame, including the step of extracting the distribution of one or more microorganisms selected from the group consisting of.

The microbial distribution is determined by identifying the microorganism by a culture-dependent method, or by amplifying the gene of the microorganism by molecular diagnosis in the case of a culture-independent method, revealing the genetic sequence and identifying the genetic data of the microorganism The distribution can be extracted by referring to and identifying.

The country of origin may be one or more countries selected from the group consisting of Korea, China, and South Asian/African countries, as described above.

In this case, the discrimination method may use the fact that a specific microorganism exists in a high distribution or a low distribution in sesame seeds of a specific origin among the sesame seeds group of origin. Alternatively, the discrimination method may use the fact that a specific microorganism among a group of microorganisms exists in a high distribution or a low distribution in sesame seeds of unknown origin.

Specifically, Alternaria distribution may be increased in Korean sesame compared to China and South Asian/African countries. On the other hand, Aspergillus distribution may increase in Chinese sesame compared to Korea and South Asian/African countries. Also, macrophomina distribution may be increased in sesame from South Asian and African countries compared to Korea and China.

Korean sesame may have a high distribution of Alternaria , Cladosporium , and Corynespora , Chinese sesame may have a high distribution of Aspergillus , Naganishia , and Alternaria , and sesame from other countries (Ethiopia, India, Nigeria, Pakistan) may have a distribution of Macrophomina , Alternaria , and Aspergillus . can be high

Therefore, if the Alternaria distribution in an unknown sesame sample is increased compared to sesame seeds from China and South Asian/African countries, it can be identified as Korean sesame. If the Aspergillus distribution in an unknown sesame sample is increased compared to sesame seeds from Korea and South Asian/African countries, it can be identified as Chinese sesame. If the macrophomina distribution in an unknown sesame sample is increased compared to Korean and Chinese sesame seeds, it can be identified as sesame seeds from South Asian and African countries.

Alternatively, if the distribution of Alternaria in an unknown sesame sample is higher than that of other microorganisms, it can be determined as Korean sesame. If the distribution of Aspergillus in an unknown sesame sample is higher than that of other microorganisms, it can be determined as Chinese sesame. In an unknown sesame sample, if the distribution of Macrophomina is higher than that of other microorganisms, it can be identified as sesame from South Asian and African countries.

Hereinafter, examples will be described in detail to aid understanding of the present invention. However, the following examples are merely illustrative of the contents of the present invention, but the scope of the present invention is not limited to the following examples. The embodiments of the present invention are provided to more completely explain the present invention to those skilled in the art.

<Example 1> Sesame sample collection and sequence analysis by country of origin

Table 1 lists the geographical sources (geographical coordinates) of sesame samples collected from 25 regions in Korea, 6 regions in China, and 5 regions in other countries (Ethiopia, India, Nigeria, and Pakistan).

National Agricultural Products Quality Management Service nationwide in 2017, specifically, Chungcheong (Cheongju, Chungju, Daejeon, Dangjin, Goesan, Seocheon), Jeju, Jeolla (Gwangju, Haenam, Iksan, Mokpo, Muan, Namwon, Wanju), Gangwon (Chuncheon, Inje), Gyeonggi (Ganghwa, Icheon, Paju), and Gyeongsang (Changwon, Gimcheon, Hapcheon, Jinju, Yeongju, Yeongyang), 25 samples of Korean sesame seeds were collected. In 2018, six Chinese sesame samples harvested in 2017 were collected from Heilongjiang and Jilin (North China), Anhui and Henan (Central China), and Guangxi and Yunnan (South China) through a local online supplier or manufacturer in China. A total of five sesame samples (one from Ethiopia) harvested in 2017 from Ethiopia, India, Nigeria, and Pakistan through importers designated by the Korea Agro-Fisheries Trade Corporation (Daesang Co., Ltd., Ottogi Co., Ltd., CJ CheilJedang Co., Ltd.) species, 1 species from India, 2 species from Nigeria, 1 species from Pakistan) were collected (Table 2). All samples were stored at cryogenic temperatures (-70 °C).

nation City/province (geographical coordinates) Korea (25) province city korea Naked (7) Gwangju (N35°, E126°), Haenam (N34°, E126°),
Iksan (N35°, E126°), Mokpo (N34°, E126°),
Muan (N34°, E126°), Namwon (N35°, E127°),
Complete (N35°, E127°) korea minor injuries (6) Changwon (N35°, E128°), Gimcheon (N36°, E128°),
Hapcheon (N35°, E128°), Jinju (N35°, E128°),
Yeongju (N36°, E128°), Antelope (N36°, E129°) korea Chungcheong (6) Cheongju (N36°, E127°), Chungju (N36°, E127°), Daejeon (N36°, E127°), Dangjin (N36°, E126°),
Goesan (N36°, E127°), Seocheon (N36°, E126°) korea game(3) Ganghwa (N37°, E126°), Icheon (N37°, E127°),
Paju (N37°, E126°) korea Gangwon (2) Chuncheon (N37°, E127°), Inje (N38°, E128°) korea Jeju(1) Jeju (N33°, E126°) China (6) region province china North (2) Heilongjiang (N48°, E129°), Jilin (N43°, E126°) china Central (2) Anhui (N31°, E117°), Henan (N33°, E113°) china Southern (2) Guangxi (N23°, E 108°), Yunnan (N25°, E101°) Ethiopia(1) Not available (N8°, E38°) India(1) Not available (N21°, E78°) Nigeria(2) Not available (N8°, E10°) Pakistan(1) Not available (N30°, E70°)

nation province Supplier/Manufacturer Name china Heilongjiang Heilongjiang Heliang Agriculture Company china Jilin Huinan County Bokang Native Products Company china Henan Laogengjia Grain Wholesale Health Store china Anhui Huainan Haiyan Agricultural Products china Guangxi Zhuang Autonomous Region The Wholesale Department of Liang's Grain and Oil china Yunnan Shidian Taste Ethiopia Target (Note) India, Pakistan Ottogi Co., Ltd. Nigeria Ottogi Co., Ltd., CJ CheilJedang Co., Ltd.

50 g of sesame seeds were immersed in 200 ml of phosphate-buffered saline (PBS, Invitrogen, Carlsbad, CA, USA) for 12 hours, and then shaken at 100 rpm for 1 hour. The sample was then filtered through cheesecloth and centrifuged at 4000 x g for 10 minutes at 4°C (Gallagher, Parker, Allen, & Tsesmetzis, 2018), and the residue was stored at -20°C. Microbial gDNA was isolated and purified from sesame seeds samples using the PowerSoil DNA Isolation Kit (MO BIO Laboratories, Inc., Carlsbad, CA, USA). The ITS region of rDNA was prepared using universal primers (White, Bruns, Lee, & Taylor 1990; Toju, Tanabe, Yamamoto, & Sato, 2012), ITS3 (5′-GCATCGATGAAGAACGCAGC-3′) and ITS4 (5′-TCCTCCGCTTATTGATATGC-3′). ) was used to amplify. Polymerase chain reaction (PCR) conditions were initial denaturation at 95 ° C for 3 minutes, annealing at 95 ° C for 30 seconds, 55 ° C for 30 seconds, elongation at 72 ° C for 30 seconds, 25 cycles, and final elongation at 72 ° C for 5 cycles. set to minutes. Amplicons were sequenced using MiSeq (Illumina, San Diego, CA, USA).

Fungal ITS sequences from each sample were filtered and clustered using the LotuS pipeline (Hildebrand, Tadeo, Voigt, Bork, & Raes, 2014). Clustered sequences were denoised at the 97% identity level using DNACLUST (Ghodsi, Liu, & Pop, 2011) and operational taxonomic units (OTUs) were generated.

Normalization was performed by the number of the most aligned reads (Haenam sesame sample 162,281 reads) (Table 3). The number of aligned reads in each sample was adjusted to 162,281 and multiplied by the relative factor.

OTUs matching fungal ITS sequences via BlastN analysis in the NCBI Nucleotide Collection (nr/nt) database were reclassified using the ETE3 toolkit (Huerta-Cepas, Serra, & Bork, 2016). If the best hit similarity is less than 97%, the classification depth is the sequence similarity for each species, genus, family, order, class, and phylum. 97%, 95%, 93%, 91%, 88% and 78% were identified as the default parameters of the LotuS pipeline. Abundant OTUs were defined as those with a relative abundance greater than 3%. The fungal ITS sequences of each sample were submitted to NCBI under accession numbers SRR13154087 - SRR13154122.

Tables 3 and 4 show the abundance and diversity of the mycobiome in each sesame seed sample, with respect to sequences, OTUs and statistical indices. A total of 21 genera of fungi were identified in sesame, and their cumulative distribution is shown in FIG. Among the genera whose cumulative distribution value exceeded 1% in each sample, Korean sesame showed the highest distribution in the order of Alternaria 83.1%, Cladosporium 48.1%, and Corynespora 32.8%. Chinese sesame seeds showed a high distribution in the order of Aspergillus 70.9%, Naganishia 46.7%, and Alternaria 21.7%. Sesame from other countries (Ethiopia, India, Nigeria, Pakistan) showed a high distribution in the order of Macrophomina 33.4%, Alternaria 28.4%, and Aspergillus 21.5%. That is, Alternaria , Aspergillus and Macrophomina showed high distribution in each sample.

country of origin sample pair end lead
(Paired-end Raw Reads) OTUs aligned
lead Aligned Leads % korea Namwon 172,950 93 150,633 87.10% korea complete 168,803 92 152,779 90.51% korea Iksan 165,257 142 134,754 81.54% korea Mokpo 160,462 138 140,184 87.36% korea Muan 143,959 161 109,176 75.84% korea gwangju 181,226 171 160,318 88.46% korea Haenam 212,197 101 162,281 76.48% korea Gimcheon 158,631 120 99,715 62.86% korea nutrition 169,923 120 154,469 90.91% korea permanent residence 150,954 49 119,585 79.22% korea Hapcheon 157,974 66 143,798 91.03% korea pearl 146,918 140 62,579 42.59% korea Changwon 162,285 135 124,684 76.83% korea Dangjin 156,343 83 128,120 81.95% korea Seocheon 180,197 80 155,226 86.14% korea Daejeon 167,087 147 124,556 74.55% korea Goesan 125,838 161 87,365 69.43% korea Rice wine 140,054 109 31,824 22.72% korea Chungju 155,533 146 109,153 70.18% korea Icheon 159,807 200 104,412 65.34% korea Paju 166,522 187 106,441 63.92% korea enforce 82,040 13 66,858 81.49% korea Chun Cheon 170,625 59 88,940 52.13% korea Inje 175,865 137 90,180 51.28% korea Jeju 184,686 115 98,201 53.17% china Heilongjiang 123,958 15 4,422 3.57% china Jilin 159,093 116 73,343 46.10% china Henan 159,279 75 125,458 78.77% china Anhui 171,083 142 66,825 39.06% china Yunnan 164,865 105 135,417 82.14% china doggerel 145,557 95 87,955 60.43% etc Ethiopia 164,839 152 57,409 34.83% etc Nigeria C 147,651 168 52,769 35.74% etc Nigeria O 161,997 137 71,039 43.85% etc India 157,536 139 77,225 49.02% etc Pakistan 131,024 66 39,980 30.51% average 158,417 116 102,724 64.84% Standard Deviation 21,253 44 39,821

country of origin sample Abundant OTUs Chao1 PD Whole Tree Shannon simpson korea Namwon 13 93 112.6 1.53 0.37 korea finish 13 92 123.2 1.49 0.35 korea Iksan 16 142 164.5 3.72 0.86 korea Mokpo 15 138 140.0 3.16 0.80 korea Muan 13 161 172.0 3.11 0.82 korea gwangju 15 171 159.8 2.86 0.75 korea Haenam 15 101 123.5 2.23 0.69 korea Gimcheon 18 120 138.4 2.68 0.66 korea nutrition 15 120 128.0 1.36 0.31 korea permanent residence 14 49 87.1 2.04 0.57 korea Hapcheon 12 66 86.3 2.45 0.74 korea pearl 15 140 153.2 3.05 0.72 korea Changwon 14 135 138.9 2.56 0.70 korea Dangjin 16 83 110.4 3.12 0.80 korea Seocheon 14 80 104.7 2.01 0.65 korea Daejeon 13 147 142.9 2.70 0.70 korea Goesan 16 161 156.9 2.76 0.62 korea Rice wine 17 109 115.8 2.76 0.72 korea Chungju 16 146 139.0 3.00 0.78 korea Icheon 13 200 175.0 3.21 0.80 korea Paju 15 187 181.2 3.39 0.82 korea enforce 5 13 19.4 1.21 0.52 korea Chun Cheon 12 59 82.4 2.03 0.58 korea Inje 13 137 156.1 2.98 0.79 korea Jeju 12 115 130.7 2.02 0.53 china Heilongjiang 8 15 40.9 2.85 0.76 china Jilin 15 116 120.2 2.73 0.73 china Henan 13 75 102.5 3.45 0.85 china Anhui 18 142 138.5 3.65 0.87 china Yunnan 17 105 113.9 2.62 0.68 china doggerel 10 95 129.5 2.59 0.66 etc Ethiopia 18 152 147.4 3.48 0.86 etc Nigeria C 19 168 156.0 3.62 0.84 etc Nigeria O 17 137 142.2 3.41 0.84 etc India 17 139 130.8 3.19 0.81 etc Pakistan 12 66 88.0 2.21 0.67 average 14 Standard Deviation 3

<Example 2> Statistical analysis and origin-specific biomarker discovery

2-1. Establishment of the PLS-DA model

For multivariate statistical analysis, Partial Least Squares-Discriminant Analysis (PLS-DA) was performed using SIMCA-P+ software (version 15.0; Umetrics, Umea, Sweden). All data were averaged using unit variance (UV) or pareto (Pareto) as a preprocessing method. The PLS-DA model was established as a predictive model for determining the geographic origin of sesame seeds.

m, & Wold, 2006). 최적의 PLS-DA 모델은 UV 스케일링 방법과 두 개의 PLS 구성 요소(각각 R²Y 값 0.665 및 Q²Y 값 0.518)로 구축하였다. 표 5는 스케일링 방법(UV 및 Par)에 따른 PLS-DA 모델의 매개 변수 및 분류된 진균 데이터를 사용하여 참깨의 지리적 출처를 구별하기 위한 구성요소의 수를 나타낸다. The quality of the PLS-DA model was represented by the R ² Y parameter representing the explained variation and goodness of fit. The Q ² Y parameter represents predicted variation and goodness of prediction. The R ² Y and Q ² Y values indicate goodness of fit and goodness of prediction, respectively, and the optimal PLS-DA model was verified. Q ² Y and R ² Y intercept values were obtained and verified by a permutation test. R ² Y ranges from 0 to 1.0, and values close to 1.0 indicate a PLS-DA model fit. The range for Q ² Y is greater than 0.5 indicating “good predictability” and greater than 0.9 indicating “very good predictability” (Eriksson, Kettaneh-Wold, Trygg, Wikstr

m, & Wold, 2006). The optimal PLS-DA model was built with the UV scaling method and two PLS components (R ² Y value 0.665 and Q ² Y value 0.518, respectively). Table 5 shows the parameters of the PLS-DA model according to the scaling method (UV and Par) and the number of components to distinguish the geographic origin of sesame seeds using classified fungal data.

As shown in Fig. 2a, each group formed a cluster, and sesame samples from Korea, China and other countries (Ethiopia, India, Nigeria, Pakistan) were clearly separated into two predictive components of the PLS-DA score plot. With a PLS component of 1 variance (14.0%), Korean sesame samples were clearly differentiated from those of China and other countries.

Based on the random permutation test, an R ² Y-intercept of less than 0.40 and a Q ² Y-intercept of less than 0.05 indicate a valid model that prevents overfitting (Eriksson, Kettaneh-Wold, Trygg, Wikstrφm, & Wold, 2006). We randomly changed the order of the data set and assigned classification labels to the measurements. As shown in Figure 2b, this permutation data set was used to recalculate the classification model. As a result of 999 random permutation tests, an R ² Y intercept value of 0.353 and a Q ² Y intercept value of -0.227 were obtained to validate the PLS-DA model as a parameter.

group number scaling method R ² Y Q ² Y R ² Y
intercept Q ² Y
intercept number of components One unit variance 0.665 0.518 0.353 -0.227 2 2 Pareto 0.562 0.385 0.266 -0.227 2

2-2. Derivation of biomarkers to distinguish geographic origin

VIP values greater than 1.0 have a significant effect on identifying plots in the PLS-DA model (Eriksson, Kettaneh-Wold, Trygg, Wikstrom, & Wold, 2006). Based on the VIP cutoff value above 1.0, the fungal genera with the highest VIP values were Macrophomina, Pseudocercospora-like, Aspergillus, Alternaria, Rhizopus, Naganishia, Neotestudina, and Moesziomyces , with values of 1.71, 1.67, 1.57, 1.48, 1.21, and 1.21, respectively. 1.18, 1.09, and 1.05 were shown. Among them , Alternaria, Aspergillus , and Macrophomina were selected as biomarkers based on higher VIP cutoff values.

number inside VIP value One Macrophomina 1.71 2 Pseudocercospora-like 1.67 3 Aspergillus 1.57 4 Alternaria 1.48 5 Rhizopus 1.21 6 Naganishia 1.18 7 Neotestudina 1.09 8 Moesziomyces 1.05

As shown in Fig. 2c, the corresponding loading plot shows that the genera correlated with each group. Optimal PLS-DA derived loading plots represent significant fungal genera associated with each group. From the PLS-DA loading plot, it can be seen that Alternaria in Korean sesame seeds, Aspergillus in Chinese sesame seeds, and Macrophomina in other countries are the main genera.

2-3. cross validation

Cross validation was performed on the PLS-DA model based on SIMCA-P+ software to classify groups and calculate sensitivity, specificity and accuracy (Triba et al., 2015). Three types of 7-fold cross-validation were performed using each group as a control group, and the results are shown in Figs. 3a to 3d. When Korean sesame seeds were used as a control, sensitivity, specificity, and accuracy were 96.0%, 90.9%, and 94.4%, respectively. When Chinese sesame samples were used as a control, the sensitivity, specificity, and accuracy were 50.0%, 100%, and 91.7%, respectively. When sesame samples from other countries were used as controls, the sensitivity, specificity, and accuracy were 60.0%, 93.5%, and 88.9%, respectively. As a result of the CV-ANOVA test, the above results were found to be significant with p<0.05. From this, it can be seen that the composition for determining the country of origin of sesame seeds has excellent sensitivity, specificity and accuracy.

2-4. Cluster (Heatmap) Analysis

A total of 21 genera of fungi identified in sesame seeds were visualized by hierarchical clustering and heatmap as shown in FIG. 4 . Heat maps were constructed in MetaboAnalyst 4.0, a web-based software tool. These classifications suggest that the distribution of fungi according to regional characteristics differs among the three groups: Korea, China, and other countries. As a result of heat map analysis, groups were divided into red and green. Relative levels of fungal genera are indicated by color schemes. These results suggest that specific fungal genera can be used to distinguish the geographic origin of sesame seeds among the three groups.

Having described specific parts of the present invention in detail above, it is clear to those skilled in the art that these specific descriptions are only preferred embodiments, and the scope of the present invention is not limited thereby. something to do. Accordingly, the substantial scope of the present invention will be defined by the appended claims and their equivalents.

Claims (13)

Alternaria , Aspergillus and Macrophomina A biomarker composition for determining the country of origin of sesame containing one or more microorganisms selected from the group consisting of: The biomarker composition for determining the country of origin of sesame according to claim 1, wherein the country of origin is one or more countries selected from the group consisting of Korea, China, and South Asian/African countries. Alternaria , Aspergillus and Macrophomina A composition for determining the country of origin of sesame, comprising an agent for detecting one or more microorganisms selected from the group consisting of: The composition for determining the country of origin of sesame according to claim 3, wherein the country of origin is one or more countries selected from the group consisting of Korea, China, and South Asian/African countries. The composition for determining the country of origin of sesame according to claim 3, wherein the composition compares the relative abundance of microorganisms. The composition for determining the country of origin of sesame according to claim 3, wherein the Alternaria distribution is increased in Korean sesame compared to China and South Asian/African countries. The composition for determining the country of origin of sesame according to claim 3, wherein the distribution of Aspergillus is increased in Chinese sesame compared to Korea and South Asian/African countries. A kit for determining the country of origin of sesame seeds comprising the composition of any one of claims 3 to 7. From Sesame to Alternaria , Aspergillus and Macrophomina A method for determining the origin of sesame, comprising the step of extracting the distribution of one or more microorganisms selected from the group consisting of. 10. The method of determining the origin of sesame according to claim 9, further comprising comparing the relative abundance of microorganisms from the distribution of the extracted microorganisms. 10. The method of claim 9, wherein the country of origin is one or more countries selected from the group consisting of Korea, China, and South Asian/African countries. The method of determining the origin of sesame according to claim 11, wherein if the Alternaria distribution is increased compared to sesame from China and South Asian/African countries, it is determined as Korean sesame. The method of determining the origin of sesame according to claim 11, characterized in that if the distribution of Aspergillus increases compared to sesame in Korea and South Asian/African countries, it is identified as Chinese sesame.

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