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chem331:fabg_inhibition_bioassay_for_drug_discovery

Bioassay

Microbiology

by Jordan Giudice, Daniel Rossie, and Sariely Sandoval

Preparation of our cultures is important for chemical analysis of compounds that may target the fabG gene. Our cultures involve two strains of E. coli: positive controls and fabG overexpression. The streak plates that were made after restriction enzyme confirmation of our plasmid transformed into our E. coli cell line were utilized for inoculation the night before our assay. One colony from our streak plates, per each strain, was inoculated in 3 mL of Luria Broth (LB) buffer with ampicillin (10mg/mL). The inoculation was then put into a shaking incubator overnight with horizontal shaking at 200 rpm and 37ºC.

Cell density was measured on the day of the assay using the NanoDrop spectrometer (Abs600 = 0.1= cell density of 109). The culture was then diluted to 105 cells/mL in 30 mL of LB buffer. The dilution was poured into a sterile reservoir and 200 µl were pipetted into each well in a 96 well plate. 2 µL of the test compounds were also pipette into each well. The initial time was read with a plate reader.

In order to quantify cell growth before and after the chemicals were administered, a change in absorbance was measured. The initial absorbance was taken immediately after the chemicals were administered using the Thermo Scientific Multiskan Microplate Photometer. After the 24 hour growth period, another absorbance was taken. The difference between the absorbance was then measured against the control group lacking the fabG.

Chemical Treatments

For the chemical aspect of the bioassay, our group determined the positive and negative controls, the method of delivery of the chemicals under study, the concentrations of the drugs to be delivered to the cells, and the costs of the this section of the assay. For the positive controls, we chose EGCG, tetracycline, triclosan, and isoniazid.

ECGC is the major compound in green tea extract. It has shown inhibition of the FAS type II system. In a study conducted by Zhang and Rock in 2004, it was determined that ECGC and the related catechins potently inhibited both the FabG and FabI reductase steps in the fatty acid elongation cycle. 1)

Tetracyline was chosen as a positive control as it is a common well-known antibacterial drug that inhibts the protein synthesis pathway, and is a common treatment for a host of bacterial infections. It is the positive control to show effective inhibition of an alternative pathway to the FAS pathway.

Isoniazid and triclosan were chosen due to their inhibition of FabI according to Wright and Reynolds. They are currently the only known antibiotics that inhibits the enzymes of the fatty-acid synthesis pathway (FAS). Since Fab proteins are all related through the FAS pathway, isoniazid was used as a positive control for inhibition of FabG. 2)

DMSO was chosen as the negative control due its action as drug delivery vehicle. Each drug was delivered to the assay with a DMSO concentration of 1% (v/v). The concentration of the controls used in the bioassay are listed below.

ECGC=50 uM
Tetracycline (low)= 5 ug/mL
Tetracycline (high)= 100ug/mL
Isoniazid (low)= 5ug/mL
Isoniazid (high)= 100ug/mL
Triclosan= 5 ug/mL

The chemicals that will be used to test possible inhibition of FabG or possible antibacterial properties were obtained from the DTP Chemical Reposit. The majority of the chemicals that will be tested were chosen from their Natural Product Set II, since our main focus of this assay is to find a natural inhibitor of FabG or a natural antibacterial. However, some chemicals will be used from their Diversity Set III. We will be testing approximately 720 chemicals, which will allow us to have a wide range of candidates to sample.

The costs of this assay were minimized through the selection of relatively inexpensive and readily available reagents. The testing compounds received from DTP Chemical Reposit were cost effective, since only shipping and handling fees were charged.

by Michelle Miguelino & Jess Coulter

Data Analysis

by Michael McCormack, David Williams, and Cameron Kubota

Assay Validation

The goal of bioassay validation is to confirm that the method and assay are working in the way that we would expect it to work and assures reproducibility of the results without finding any new data, but rather assessing accuracy and determining whether or not the goal of the bioassay was accomplished.

For our bioassay, we used a 96-well plate. There is a pre-determined protocol in order to ensure bioassay validity as outlined by the National Institute of Health. However, due to limited supply of reagents and time constraints, we didn’t have a determined mid signal, just a max and a min signal. We also only analyzed two well plates per strain instead of the recommended three well plates and because all plates were analyzed within the period of an hour, we didn’t take into account minimizing error for optimal function over a longer span of time. These adjustments to the designated protocol will have a slight negative impact on our statistical confidence, but as long as we can safely assume that the instrument s functioning properly and the samples are pipetted accurately, we won’t need such strict validation restrictions.

Edge and Drift Effects

Two problems often encountered with data collected from 96 plate well bioassays are edge effects and drift effects. Edge effects are caused by the differential rates at which the wells on the edges of the plate heat up in the incubator as opposed to wells located on the interior of the plate. These disparities result in differential rates of growth among cultures that can skew the data. Edge effects can be observed by creating a graph of the culture responses for the max, mid, and min signals along the well plate. The following figure illustrates an example of edge effects.

Example of Edge Effects

Drift effects are a result of high but consistent variation in the plate and is often indicative of a heterogeneous cell population. Like edge effects, drift effects are easiest to detect by creating a graph of the culture responses for the max, mid, and min signals along the well plate. It should be noted, that the min signal does not often show drift if represented on too large of a scale. The following figure illustrates a clear example of drift effects.

Example of Drift Effects

Edge Effects Results

Looking for Edge Effects on Control Plate #1 of 6 Looking for Edge Effects on fabG Sample Plate #1 of 6

We determined that there were no discernible edge effects in any of our plates.

Drift Effects Results

Looking for Drift Effects on Control Plate #1 of 6 Looking for Drift Effects on fabG Sample Plate #1 of 6

We determined that there were no discernible drift effects in any of our plates.

Statistical Analysis of fabG Bioassay Results

Coefficient of Variation (CV) was used to assess whether the results from a plate were acceptable or not.

Equation of Coefficient of Variation

The criteria used for this experiment stated that the CV of any plate must be less than 20%. The minimum signal often fails this coefficient of variation test, but this is common. The criteria were adapted to stipulate that the standard deviation of the minimum signal must be less than or equal to the standard deviation of the maximum signal in order for the results of the assay to be acceptable.

A Student’s t-test will be used to determine whether or not differences in absorbance between the synthetically cultured E. Coli and natural E. Coli are statistically significant.

Equation proving the values are statistically different

Equation proving the values are not statistically different

It is important to determine whether any statistically significant differences have clinical significance. Statistical analyses can be quite useful, but conclusions cannot be based solely off of statistical difference.

1) Zhang, Y.; Rock, C. O. Evaluation of Epigallocatechin Gallate and Related Plant Polyphenols as Inhibitors of the FabG and FabI Reductases of Bacterial Type II Fatty-acid Syntahse. J. Biol. Chem. 2004, 279, 30, 30994-31001.
2) Wright, H.T.; Reynolds, K. A. Curr. Opin Microbiol. 2007, 10, 447-453.
chem331/fabg_inhibition_bioassay_for_drug_discovery.txt · Last modified: 2016/06/07 09:53 (external edit)