Difference between revisions of "Team:NEFU China/Results"

AI-2 Quantification

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E. coli and Bacillus sp. are the main pathogens in spoiled yogurt. Detecting these pathogens can determine whether yogurt has got bacterial contamination. The traditional detecting methods contain cumbersome steps and need expensive reagents, but still have high inaccuracy. When reading the references, we found that autoinducer 2 (AI-2), a signal molecule in the quorum sensing system of bacteria, can also be used as a signal detected by our engineered Lactobacillus. Thus, we wanted to measure AI-2 produced by pathogens in spoiled yogurt and then analyzed the relationship between AI-2 output and bacterial growth. 
The molecular structure of AI-2 is similar to ascorbic acid. It was expected that AI-2 would also act as a reducing agent to reduce Fe(III) ions in the presence of 1,10-phenanthroline to form the orange-red colored [(o-phen)₃ Fe(II)]SO₄ ferroin complex that could be quantified colorimetrically. (See protocol)

Fig1.  Correlation between AI-2 production and bacterial growth

E. coli and Bacillus sp. are the representative strains of Gram-negative and Gram-positive bacteria, respectively. We verified that both of them can produce AI-2 during their growth. Additionally, AI-2 production by both of the two strains has significantly positive correlation with bacterial growth in the first two hours. Our results indicate that AI-2 can be produced by both Gram-negative and Gram-positive strains. Thus, our bio-detecting system can theoretically be used for both types of bacteria.

 The Conventional Approaches 

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When we carry yogurt from a supermarket to home, the yogurt will be exposed to ambient temperature for a while, especially in hot summer days. Will the flavor or physicochemical characters of the yogurt have any change during this transportation? Will the yogurt still be eatable? To answer these questions, we took the advantage of the MPN method to count the coliform bacteria in yogurt kept at different conditions. We chose three sample dilutions: 10^-1, 10^-2 and 10^-3, respectively. (See Protocol)

Table 1 Result of coliform bacteria count in yogurt with different treatment  

The result shows that there was no coliform bacteria in yogurt always kept in 4℃ within expiration date, but the number of coliform bacteria in yogurt which was just one-day past expiration was 1500 cfu/100 ml. As for the yogurt which had been placed in 35°C for 2 hours, the number of coliforms was up to 4300 cfu/100 ml (Table 1). However, the number in eatable yogurt should not be over 90 cfu/100 ml according to the Food Hygienic Standard in China.
Thus, the yogurt either after expiration or exposed to ambient temperature for a certain period of time may contain greatly increased amounts of coliform bacteria, significantly exceeding the qualify standard. This type of yogurt may have adverse effects on our health. We also determine the alteration of lactic acid bacteria in yogurt kept in fridge. (See Protocol) Since yogurt typically has 21 days for expiration, we took samples from the yogurt of 4, 10, 20 and 22 days post production. The data in Table 2 indicate the numbers of lactic acid bacteria in yogurt exhibited uptrend at first, followed by a downward trend. In the process of cold storage with varying degrees of acidification, lactic bacteria still showed an increasing trend. When the surrounding is over acidic, the number of probiotics gradually declines.

Table 2  Lactic acid bacteria counts in yogurt in fridge

In conclusion, the number of lactic acid bacteria will gradually decline even kept in fridge. At the same time, corresponding pathogenic bacteria, such as coliform bacteria, increase and some of them may even exceed the quality limits. So, in this project, we aimed to create a rapid and convenient method to determine whether a cup of yogurt may contain pathogenic bacteria.

The New Method

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Summary
Quorum sensing requires the production and detection of autoinducers, and AI-2 belongs to one class of the signaling molecules involved in quorum sensing.
In a group of bacteria exemplified by Salmonella, AI-2 response involves the lsr genes that encode an ATP binding cassette-type transporter: LsrA is the putative ATPase of the ABC transporter; LsrB is the sugar binding protein that binds AI-2 specifically and promotes AI-2 import. LsrC and LsrD form a heterodimeric membrane channel. LsrK catalyzes the phosphorylation of AI-2 to phospho-AI-2 after its import. LsrR is a repressor of the lsr operon and LsrR-mediated repression can be released by binding to phospho-AI-2. In order to create a handy detection method for pathogens in yogurt, we took the advantage of this AI-2 response pathway in Salmonella, and reassembled it in our engineered Lactobacillus.
We exacted the genomic DNA of Salmonella enterica subsp. enterica serovar Typhimurium str. LT2, and used it to clone the promoter of the lsr operon and the six lsr genes mentioned above. In this genetic engineering process, we generated seven expression vectors altogether (see Plasmid Construction). We linearized all the vectors and employed electroporation for transformation to integrate them into the genome of Lactobacillus. Transformation of vector pNZ9530 and pHY300PLK-Plsr-amilCP was performed first. Then vectors pNZ8148 expressing lsrB, R and K were transformed to Lactobacillus simultaneously. In order to determine whether these expression vectors were stably integrated, we did colony PCRs for each of these genes to screen for the transformed bacteria. The activity of the Plsr promoter was verified according to the expressing condition of amilCP which encodes a visible blue pigment. Lastly, the linearized vectors expressing lsrA, C and D will also be transformed to complete the assembly of the AI-2 detecting system in our engineered Lactobacillus using these essential elements from Salmonella.

Foundation
We have isolated the coding regions of lsrA, B, C, D, R, K and the promoter region of the lsr operon from the genomic DNA of Salmonella typhimurium ST2. The sequences of these DNA fragments have been confirmed by DNA sequencing analyses based on the DNA sequence data of the NCBI. 

Identification
Seven expression vectors have been constructed for the genetic engineering. We linearized these vectors and transformed them into host Lactobacillus using electroporation. Transformation of vector pHY300PLK-Plsr-amilCP was performed first. Then the vectors pNZ8148 expressing lsrB, R and K were transformed simultaneously. The positive colonies were identified by colony PCR from the transformed bacteria. To determine the stably integration, the positive colonies in the PCR screening was subcultured and further identified.

 
Fig2. Colony PCR after subculturing. (Marker: 500 bp ladder)

Functional verification
We have isolated the promoter region of the lsr operon to regulate the expression of the report gene. In order to test the function of the plsr promoter, we transformed the plasmid pHY300PLK containing Plsr with a blue pigment gene at its downstream into E. coli Trans T1 primarily. Without LsrR-mediated repression, the Plsr promoter will  be constitutively active and promote the production of visible blue pigment. Consistently, after overnight incubation, we observed blue colonies on the plate, suggesting the Plsr promoter can indeed drive the expression of the blue pigment gene.

Fig 3. Engineered E. coli Trans T1 contains pHY300PLK-Plsr-amilCP

We then started to generate our engineered bacteria stably expressing the essential genes. We successfully integrated the expression vectors for lsrB, R and K into the Lactobacillus genome. After that, we sequentially transformed Plsr-blue vector and pNZ9530 . Finally we generated our yogurt guarder.
To test whether the system adopted from Salmonella can work in Lactobacillus, we cultured our engineered Lactobacillus in the medium containing AI-2 secreted by E. coli CD-2. We used DH5alpha bacteria as a control because they do not produce any AI-2. Our engineered Lactobacillus showed clear blue color compared to the control. This suggested our yogurt guarder can indeed sense the presence of AI-2; in other words, it can detect the pathogens in yogurt. (See protocols here)

Fig 4. Yogurt guarder incubated with culture supernatant of different bacteria 

Theoretically, our yogurt guarder can still be used in yogurt fermentation. We tested this idea and found it could still make yogurt. The yogurt made by the Yogurt Guarder did not look different from that made by regular Lactobacillus.

Fig 5. Yogurt made by Yogurt Guarder and regular Lactobacillus

 Future appication 

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Projecct improvement

The electrotransformation process of our experiment have lasted for nearly two months. We tested various transformation approaches, but the efficiencies remained unsatisfactory. To date, we have completed the transformation and integration of linearized pNZ8148 vectors expressing lsrB, R, K and pHY300PLK-plsr-amilCP. We expected to integrate the rest of the essential genes and generate our engineered Lactobacillus in another month.
The amount of AI-2 secreted by pathogens is low. In order to ensure the accuracy of our Yogurt Guarder, we need to reduce the threshold for AI-2 detection to increase its sensitivity. One possible approach to achieve this goal is to integrate additional essential parts involved in AI-2 import to the host genome. Alternatively, we can use a stronger promoter to drive the expression of these essential parts.
We have considered the application of our Yogurt Guarder in yogurt production in a food factory. Obviously, genetically engineered Lactobacillus needs extensive test and inspection for its possible use in food fermentation to produce safe or eatable yogurt. However, we think that one possible approach of using our guarding system is to make small tubelets of transparent plastic and fill them by the yogurt fermented by our Yogurt Guarder. Each tubelet can be attached to a cup with regular yogurt fermented at the same condition of the tubelet yogurt. Therefore, if the testing yogurt in a tubelet turns blue, it will suggest the spoilage of the yogurt in its attached cup. 

Market research
In order to find out the factors that people concern most about our Yogurt Guarder if fully developed into a real product, we did the following investigation. 
The investigation is based on the Kano Model and Four Points Pragh Model. The Kano Model classifies customers’ preferences into 3 categories: Must-be Quality, One-dimensional Quality and Attractive Quality. The Four Points Pragh Model estimates customers’ value and satisfaction. Together, it confirms the key factors that will influence customers’ retention, which can be used to evaluate a product. 
 We focused on 10 factors that may affect customer purchasing: Convenience, Innovativeness, Accuracy, Operability, Brand, Reutilization, Appearance, Biosafety, Sensitivity and Price. We invited people to grade these factors according to their degrees of attention to them. The Stated Importance is based on our survey results. The data of Impact On Customers Retention is based on The Nation Survey of GMF.
 

Fig6.  Impacts on Customer Retention

Results:
Must-be Quality: None.
One-dimensional Quality: Biosafety, Accuracy, Sensitivity.
Attractive Quality: Appearance, Brand.
 
The results showed that Biosafety, Accuracy and Sensitivity are the Motivators of our product. Thus, if we make the yogurt guarder a real product, we will focus on these 3 characteristics preferentially. Then, we will improve the appearance of our product and find a reputable company for collaboration. The factor Price is nearly in the middle of the GRID. A likely reason is that we did not suggest a possible price in the survey. As for the Convenience, Operability, Reutilization and Innovativeness, we will not give priority to them in our consideration.

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