Team:MichiganState/Experiments

Experiments

Gene Engineering

Due to the COVID-19 pandemic and the project not being COVID oriented and an undergraduate team, we were not allowed to gain access to lab space at Michigan State University. However, with the help of Kathryne Ford and Shaylynn Miller, graduate students of the TerAvest lab, they were able to perform experiments for us. The below protocols are written and performed.

Experimental Overview:

  • Introduce mRFP plasmid (no secretion) into Snodgrassella alvi (S. alvi) and measure fluorescence levels.
  • Introduce mRFP secretion plasmid into S. alvi and measure fluorescence levels of the supernatant.
  • Introduce GFP gene into the S. alvi chromosome and measure fluorescence levels.
PCR

For amplifying genes from iGEM kit and to verify constructs

Materials
  • Thermocycler
  • Ice
  • PCR tubes
  • 10 µM Forward and Reverse Primers
  • Template DNA
  • 10x Standard Q5 reaction buffer
  • Q5 polymerase
  • 10 mM dNTPS
  • dH2O
Procedure
  1. Put PCR tubes on ice.
  2. Vortex all reagents and keep on ice.
For each 50 μL PCR tube:
  1. 2 μL Template DNA (10 ng-500 ng)
  2. 5 μl 10X Q5 buffer with MgCl2
  3. 1 μl dNTP mix (10 mM)
  4. 2.5 μL Forward Primer (10 μM stock)
  5. 2.5 μL Reverse Primer (10 μM stock)
  6. 0.5 μL Q5 DNA Polymerase (5 units/μL)
  7. 36.5 μL Sterile dH2O (variable)
  8. Gently mix the reaction.
  9. Transfer the PCR tubes to the PCR machine with the block preheated to 95oC.
PCR Machine Settings
  1. Initial denaturation for 2 min at 95oC
  2. Denature for 30 sec at 95oC
  3. Anneal for 30 sec at 55oC (annealing temperature will change with primers)
  4. Extend for 2 min at 72oC (extension time will vary with product length)
  5. Repeat for 25-30 cycles
Gibson Assembly
Materials
  • Gibson Master Mix
  • Backbone DNA
  • Insert DNA
  • Thermocycler
  • dH2O
Procedure
Calculate the amount of DNA for each fragment:

pmols = (weight in ng) x 1,000 / (base pairs x 650 daltons) OR use NEBioCalculator.

Want 0.02 - 0.5 total pmol of DNA when assembling a fragment into a vector and 2-3:1 molar ratio of insert:backbone.

Setting up the reaction
  1. Calculate the μL of backbone and insert to add, then add them both to a PCR tube.
  2. Add dH2O to the PCR tube to have a final volume of 10 uL.
  3. Add 10uL of NEB DNA Assembly Mix.
  4. Incubate in a thermocycler for 1 hour.
  5. Then store at -20oC until transformation.
  6. To verify constructs, do PCR with the “check” primers, then run a gel to test their lengths. Can also do sequencing.
Single Restriction Digest
Materials
  • 10x buffer (Cutsmart)
  • DNA (1 ug/ 1 uL concentration)
  • Restriction Enzyme
  • Antarctic Phosphatase
  • Antarctic Phosphatase Reaction Buffer (10x)
  • dH2O water
Procedure
  1. Add X uL of DNA to a clean tube (amount of DNA will depend on concentration).
  2. Add 5 uL of 10x buffer.
  3. Add 1 uL of restriction enzyme.
  4. Add 2 uL Phosphatase Reaction Buffer (10x) and 5 units of Antarctic Phospatase.
  5. Add dH2O to 50uL.
  6. Incubate at 37oC for 2-3 hours.
  7. Stop the digestion by heat inactivation at 80oC for 20-30 mins.
Transformation of E. coli Donor Strain
Materials
  • Ice
  • SOC Media (must contain DAP))
  • LB Media (must contain DAP))
  • Selection plates
  • E. coli WM6026 (heat shock competent)
  • DNA
Procedure
  1. Thaw cells on ice.
  2. Mix 1 - 5 μl of DNA (usually 10 pg - 100 ng) into 20-50 μL of competent cells in a microcentrifuge tube. GENTLY mix by flicking.
  3. Leave on ice for 30 mins (no mixing).
  4. Heat shock at 42oC for 30 seconds (no mixing) (may be a little longer).
  5. Place tubes back on ice for 2 minutes.
  6. Add 250 μL of room temp media to tube (SOC w/ DAP) .
  7. Place the tube at 37oC for 60 minutes. Shake vigorously (250 rpm) or rotate.
  8. Warm selection plates to 37oC.
  9. Spread 50–100 µl of the cells and ligation mixture onto the plates (LB agar plates with DAP) and the appropriate antibiotic (Amp for pBTK510, Amp for pBTK599, Kan for pX2-Cas9) .
  10. Incubate overnight at 37oC.
Conjugation of S. alvi
Materials
  • E. coli WM6026 transformed with plasmid
  • S. alvi wkB2
  • TSA plates
  • DAP
  • Ampicillin
  • PBS
Procedure
  1. Grow E. coli WM6026 overnight, shaking with antibiotic (Ampicillin) and DAP (0.3 mM).

    2. For chromosome integration: Ampicillin (pBTK599 E. coli) and Kanamycin (px2-Cas9 E.coli) Grow in LB

  2. Grow S. alvi overnight in LB, in CO2 Sachet at 37oC.
  3. Wash both strains in 1 mL PBS, spun down (3824g for 5 min) and resuspended in 1 mL of LB.
  4. Combine the 2 suspensions in a 9:1 OD ratio (90 uL S. alvi: 10 uL E. coli) in a microcentrifuge tube, pipette twice to mix. Plate this 100 uL mixture on a LB plate with 0.3 mM DAP.
  5. Incubate the conjugation plate for 8 hours.
  6. Scrape the conjugation mixture into a microcentrifuge tube with 1 mL of PBS. Vortex and spin down again (3824g).
  7. Plate 100 μL of of this mixture and of 1:10 dilutions and 1:100 dilutions of this mixture onto LB plates without DAP and incubate for 2-3 days.
  8. Restreak single colonies on Ampicillin plate without DAP.
  9. Pick single colonies and restreak on antibiotic plates. Confirm transfer of the plasmid via PCR amplification of the insert.
Measuring Fluorescence
Materials
  • 24 Well Plate
  • Pipette and tips
  • PBS Buffer
  • Fluorescein
Procedure
Separating Cells
  1. Prepare stock solution of reference dye.
  2. Make 10x fluorescein stock solution (100uM) by resuspending fluorescein powder in 1ml of 1xPBS.
  3. Dilute 10x reference stock solution with 1x PBS to make 1x reference solution with concentation 10uM.
  4. Prepare serial dilutions of fluorescein, consecutively transferring 1 mL from column to column with good mixing.
  5. Add 1 mL of 1x PBS into wells A2-6, B1-6, C1-3, D1-3.
  6. Add 2 mL of fluorescein 1x stock solution into A1.
  7. Transfer 1 mL of fluorescein stock solution from A1 into A2.
  8. Mix (pipette up and down 3x) and transfer 1 mL into A4.
  9. Mix A4 and transfer 1 mL into A5.
  10. Mix A5 and transfer 1 mL into A6.
  11. Mix A6 and transfer 1 mL into B1.
  12. Mix B1 and transfer 1 mL into B2.
  13. Mix B2 and transfer 1 mL into B3.
  14. Mix B3 and transfer 1 mL into B4.
  15. Mix B4 and transfer 1 mL into B5.
  16. Mix B5 and transfer 1 mL into liquid waste.
  17. Add 1 mL of samples into wells.
  18. C1-3 Cell Replicates.
  19. D1-3 Replicates of supernatant.
  20. D4-6 Replicates of negative control (water).
  21. Insert plate into a plate reader to obtain a standard curve of the serial dilution and fluorescent levels of supernatant sample.
    1. Abs600 (OD)
    2. Compare supernatant sample data to standard curve to analyze relative fluorescent signal

Adapted from iGEM protocols.io Plate Reader Fluorescence Calibration V.3

Device Design

Due to the COVID-19 pandemic and the with most of Michigan State University's facilities shut down to undergraduates in the summer, we struggled to gain administrative approval for our device experiments. Despite those challenges, we've managed to outline methods on how to approach collecting data on the bees and bee attraction of our device.

Objectives:

  • Quantify bee attraction to bee feeding devices.
  • Revisit design decisions made and search for better solutions.
  • Calibrate image recognition, return increasing confidence intervals.
Mini-Prototype Testing
Purpose:

To evaluate the impact of several variables on bee attraction to the feeders for design optimization.

Materials
  • 1:1 sucrose solution by mass
  • Feeders, numbered 1-5
  • Evaporation control feeder
  • Table
  • Meter stick
  • 50 mL graduated cylinder
  • Funnel
  • Video cameras
Procedure
  1. Make 250 mL of 1:1 sucrose:water by volume.
  2. Distribute 50 mL sucrose water to each feeder. Weigh each feeder before and after filling and record.
  3. Position feeders outdoors on an elevated surface equidistant from one another, positioning feeding holes the same distance from the edge of the table. Feeding holes should be positioned facing outwards. Leave feeders out between 11:00 AM and 12:00 PM for a one-hour acclimation period. Place a paper plate beneath any designs with a leakage risk, to be weighed before and after. The feeder position should be rotated or randomized for each trial.
  4. Record hourly sunlight, temperature, humidity, and wind speed in data collection document.
  5. 12:00 PM: Position cameras 0.5 m away from feeding holes and start video recording for a two hour observation period. Continue recording hourly sunlight, temperature, humidity, and wind speed information.
  6. 2:00 PM: Stop video cameras and remove cameras from vicinity. Continue recording hourly environmental information.
  7. Bring feeders indoors, rinse and/or wipe down, dry reservoirs and feeding holes.
  8. Watch videos and record number of bee visits at each feeder for every half hour, as well as visits from other insects if visible. Record qualitative observations regarding bee behavior at each feeder. Record time of each visit in seconds.
Final Prototype Testing Protocol
Introduction

The purpose of this protocol is to assess prototype quality based on functionality and relative bee attraction. The image recognition system will be considered successful if the gate opens for >75% of wild bees and less than 10% of non-bee insects. The feeding method will be considered successful if bees appear to be able to forage from the feeder, assessed qualitatively, and by relative weights of feeders before and after the observation period. Attractive elements will be considered successful if the complete prototype outfitted with acrylic covers has >75% of the number of bee visits of the control prototype and >40% of the number of bee visits of the open control.

Materials
  • 3D printed prototype
    • Base (including reservoir and feeding sites) 3D printed in dental resin
    • Acrylic covers: 1 cover with holes over the entire prototype , 1 cover controled by motor components over feeding sites.
    • Motor and image recognition components
    • Plastic stoppers
  • Control prototype
    • 3D printed base in dental resin, identical to above. No acrylic covers or motor components.
  • Open control
    • Shallow plastic dish, entirely open to the environment
  • 1:1 sucrose water solution
    • Prepared by bringing 500 mL distilled water to a boil and stirring in 500 mL granulated sucrose until fully incorporated.
    • Does not contain probiotic.
  • Graduated cylinder
  • Funnel
  • Measuring tape
  • Cameras (3 preferable)
    • 720p resolution, 30fps. Audio recording not necessary.
    • Tripods may be necessary
  • 4x4 in wooden blocks
    • Cut to equal lengths at the appropriate height based on the elevation of the herb level.
  • Electronic balance
  • Lemongrass oil
    • May be used as an attractant in equal volumes on each feeder if initial trials do not yield sufficient data.
Procedure
Feeder preparation:
  1. Turn prototypes to fill from the bottom with 300mL sucrose-water feeding solution each. Plug with plastic stoppers.
  2. Fill open control with 300mL sucrose-water solution.
  3. Add 0.2mL lemongrass oil to the surface of each feeder as an attractant, if necessary.
  4. Record the mass in grams of each feeder.
  5. Position each feeder atop a wooden block level with the herb layer. Position each feeder 2 ft apart. Randomize position of feeders for each trial.
Observation period:
  1. Position each camera on a tripod such that it is focused on one of the three feeders.
  2. Beginning at approximately 10AM, record video for 4 hours. Check cameras to ensure continuous filming every half hour and replace/recharge cameras as necessary.
  3. Record the mass in grams of each feeder.
Data collection:
  1. Review footage at no greater than 3x playback speed. In a spreadsheet, record the timestamp of each bee visit and the corresponding feeder. Record qualitative observations regarding bee behavior with a focus on apparent feeding success.
  2. For the feeder equipped with image recognition, also record number of bee visits that resulted in successful recognition and gate opening, number of bee visits that did not result in successful recognition and gate opening, number of false openings (for objects or insects other than bees), and any qualitative observations related to general functionality.

Bioinformatics

Given the limited time we had in the lab due to the COVID-19 pandemic, we spent a large portion of our time designing the experiments outlined below such that future teams can incorporate the “Pathway-Cracking” strategy approached by MichiganState’s Bioinformatics team. Experiments we were able to accomplish are denoted with the mark, (*), whereas all other experiments have merely been outlined.
To provide a framework for future readers of this page, we have outlined our objectives and questions we hope to address through our experiments.

Objectives:

  • To make a more informed decision on which imidacloprid-degrading enzyme to incorporate into a Bee probiotic (“Bee-tox”).
  • To better understand the process imidacloprid degradation by the soil microbe Pseudomonas putida EM371 (a derivative of Pseudomonas putida KT2440). Throughout the rest of this page, we refer to EM371 whenever we mention “P. putida” unless otherwise stated.
    • Develop a metabolomic profile of EM371 to identify potential imidacloprid metabolites.
    • Develop transcriptomic profile of EM 371 with RT-qPCR and RNA-seq to identify potential imidacloprid degrading enzymes.

Experiments/Protocols and associated Questions:

Imidacloprid Biotransformation Experiment

  • Does imidacloprid degradation only occur in the presence of P. putida?
Metabolomics (i.e. LC-MS/MS) (*)
Classical Molecular Networking (*)
  • Can we detect Imidacloprid w/ LC-MS/MS?
  • Can we detect Imidacloprid derivatives w/ LC-MS/MS?
Feature-Based Molecular Networking (*)
  • Can we detect Imidacloprid w/ LC-MS/MS?
  • Can we detect Imidacloprid derivatives w/ LC-MS/MS?
  • Can we semi-quantitatively characterize the degradation of Imidacloprid?
Transcriptomics
RT-qPCR Outline
  • Is there a change in the transcriptomic profile of P. putida when exposed to imidacloprid?
  • Can we observe an upregulation/downregulation in specific genes/gene families known to metabolize imidacloprid?
RNA-seq Outline
  • What genes are upregulated/downregulated in the presence of Imidacloprid?
  • Are there any additional differences in transcriptome profiles of cells exposed to various xenobiotics (compared to the profile developed through RT-qPCR)?
Imidacloprid Biotransformation Experiment

Protocol adapted from “Regulation of Hydroxylation and Nitroreduction Pathways during Metabolism of the Neonicotinoid Insecticide Imidacloprid by Pseudomonas putida '' with several modifications [1]. Experiment designed by members of the Bioinformatics Sub-group; but due to university regulations, conducted by members of Dr. Michaela TerAvest’s Lab (with particular shout outs for Shaylynn Miller, Nicholas Telft, and Kati Ford).

Materials
  • P. putida EM371
  • LB Broth (1 L) (Miller Formulation)
    • 10.0 g NaCl
    • 5.0 g Yeast Extract
    • 10.0 g peptone
  • LB Agar Plates (Miller)
  • 0.2 M Sodium Phosphate Buffer [Na-PB] (pH 8.0) (1 L)
    • 1.463 g*** Monobasic Sodium Phosphate [NaH2PO4 · H2O] (MW = 137.99 g/mol) (5.3%**)
    • 26.887 g*** Dibasic Sodium Phosphate [Na2HPO4] (MW=141.96) (94.7%**)

    ***: The MW for the reagents are based on the specific sodium phosphate hydrates in stock, adjust the mass if needed based on the Sodium Phosphate Hydrates accessible.
    **: These percentages (ratio of the monobasic to dibasic sodium phosphate) should result in a pH ~8, adjust pH as needed

  • IMI Stock Solution (1 mL) [~0.2 M (176 mM)]

      • Filter sterilize

    • 45 mg Imidacloprid (Sigma Cat. 37894; CAS:138261-41-3) in 1 mL acetonitrile [HPLC Grade]
  • Glucose Stock Solution (1 mL) [1 M]

      • Filter sterilize

    • Add 7.2g glucose to 40 mL ddH2O
  • Methanol [HPLC Grade]

      • keep ice cold

  • Tubes and flasks
    • 15 mL falcon tubes
    • 50 mL falcon tubes
    • 1.5 mL microcentrifuge tubes
    • 500 mL Flask
    • 50 mL Centrifuge Tubes
    • 96-well plate
Procedure
Culture Growth:
  1. Streak bacteria on LB agar plates and incubate at 30oC until single colonies appear
  2. Inoculate a single colony into a 20-mL test tube containing 3 mL LB broth and incubate at 220 rpm for 24 hours at 30oC
  3. Prepare two 500-mL flasks with 100 mL of LB broth. Transfer 1 mL of culture into each flask.
  4. Incubate for 6 hours at 30oC. [expected OD600 ~4-5]
  5. Harvest cells by centrifugation at 6,000 x g for 10 minutes
  6. Wash cells with 0.2 M Na-PB (pH 8.0) PBS, and spin down cells for 10 minutes at 6,000 x g
  7. Prepare P. putida samples
    1. Label samples according to table 1 below.
    2. Resuspend cells to an OD600 of 5.0 in LB broth, and aliquot into 4 16-mL aliquots in 50-mL falcon tubes and add the following reagents to the tubes and label accordingly.
    3. After adding Imidacloprid and/or glucose, aliquot 5-mL of each master mix into 3 50-mL falcon tubes.
    Table 1. P. putida Samples
    Condition Imidacloprid [~1.76 mM] (uL 100x IMI Stock) Glucose [10 mM] (uL of 1M Glucose)
    A- (Tubes A-[1-3])
    A+ (Tubes A+1-3)* 160
    B- 160
    B+ 160 160

    *Tube labeling examples: for condition A-, label tubes A-1, A-2, and A-3. For condition A+, label tubes A+1, A+2, and A+3

  8. Prepare blanks (Samples containing everything except the cells)
    1. Label 4 x 50-mL falcon tubes C1-C4.
    2. Add 6 mL LB broth to each tube and the following reagents according to table 2 below.
    Table 2. Blank Samples
    Condition Imidacloprid [1.76 mM] (uL 0.2 M IMI) Glucose [10 mM] (uL of 1M Glucose)
    C1
    C2 50
    C3 50
    C4 50 50
  9. Transfer ~1 mL from each "condition" as a time 0 sample
  10. Incubate samples for 96 hours at 220 rpm and 30oC
  11. Measure OD600 after 96-incubation period
Prepare Samples for LC-MS/MS:
  1. After IMI transformation period of 96 hours, spin-down cells at 10,000 x g for 10 minutes.
  2. Collect the supernatant, and aliquot 200 uL from the samples into 1.5 mL centrifuge tubes.
  3. Add 400 uL of ice cold methanol to each sample.

The Specific Benchling Notebook entries for each trial can be found through the following links, and any changes to the above protocol are mentioned in brief:

Trial 1
Trial 2
  • Repeated experiment in a 24-well plate and discarded the Glucose sample.
  • Reduced IMI concentration and Exposed P. putida samples to various concentrations of IMI including 8.8 uM, 17.6 uM, 35.2 uM and 70.4 uM.
  • Included an IMI standard gradient including concentrations of 1 uM, 0.75 uM, 0.5 uM, 0.25 uM, and 0.125 uM.
Trial 3
  • Repeat of Trial 2 due to Contamination in Blank samples.
Trial 4
  • Repeat of Trial 2 with Imidacloprid reagents filter sterilized.
Metabolomics (i.e. LC-MS/MS)

The methods for obtaining mass spectra and liquid chromatography data are summarized below, and these methods are adapted from our mentor's paper “Metabolomic signatures of coral bleaching history” [2]. This experiment was performed by members of the Quinn Laboratory.

Materials
Instument:

Thermo™ QExactive™ coupled to a Vanquish Ultra High-Performance Liquid Chromatography (UHPLC) system

Mobile Phase:

Channel A: 0.1 % Formic Acid in Milli-Q Water
Channel B: Acetonitrile

Stationary Phase:

Reverse Phase UPLC BEH C-18 column, 2.1 mm × 100 mm (Waters® Acquity® (Wood Dale, IL, USA))

LC-MS/MS Data Collection:
12 Minute Chromatographic Runs with the following linear Gradients
  • Time 0-1 min → 2% Channel B
  • Time 1-8 min → 2-100% Channel B
  • Time 8-10 min → 100% Channel B
  • Time 10-12 min → 2% Channel B
Injection Volume of 10 uL

Flow Rate 0.40 mL/min @ 60oC

Mass Spec
  • Electrospray Ionization in Positive Mode
  • m/z scan range: 100 - 1500
Molecular Networking

The files generated from mass spectrometry were converted into the open format .mzML and subsequently analyzed with Classical Molecular Networking powered by the GNPS (“Global Natural Products Social Molecular Networking”) environment and with Feature-Based Molecular Networking using MZmine (v. 2.53) and GNPS. Both of these processes result in the formation of networks consisting of nodes representing each unique metabolite and edges representing the relationship between each metabolite. But, as explained in more depth in our Modeling page, classical molecular networking relies primarily on the relationships between the MS2 spectra of each compound while feature-based molecular networking incorporates chromatographic information when identifying individual compounds within an analyte. The protocols used for these data analysis steps are briefly outlined below.

Classical Molecular Networking

While more in-depth documentation can be found in GNPS’s documentation website, we have briefly outlined the process of generating classical molecular networks.

  1. Create a GNPS account and log in.
  2. On the home page of GNPS, under “Molecular Networking” in the “Data Analysis” section, click “Create Molecular Network”.
  3. Under workflow selection, give your Job a name.
  4. Under “Basic Options” click on the “Select Input Files” and the following pop-up should appear. You should be able to upload files and assign files to input boxes:
  5. After uploading your input files, under “Advanced Network Options” add a Metadata file with appropriate identifying information for each group/sample.
  6. Leave other settings to default values, and add your email to the “Workflow Submission” section before clicking “Submit”.
  7. Visualize and analyze data on the Job Status page once workflow is complete. (An example Job Submission page for our project can be viewed through this link)
  8. Under the link “View Spectral Families (in Browser Network Visualizer)”, we can find the cluster ID for Imidacloprid and view it’s molecular network by searching for “Imidacloprid” under the column for “AllID”.

    9. By clicking “Visualize Network” link in the “Visualize Network” column for the row corresponding to imidacloprid, we can visualize cluster of compounds related to Imidacloprid (linked here):

Feature-Based Molecular Networking

Using the recently released features in the GNPS environment, we were able to incorporate chromatographic and isotopic data into our molecular networks with feature-based molecular networking methods (FBMN). This summer we focused on creating FBMNs using MZmine2 and GNPS. A more-indepth protocol can be found in this GNPS documentation article, but we’ve also produced a beginner’s guide to trying our FBMN workflow (along with .mzML and Metadata files) that can be found below or here.

Transcriptomics

In order to identify potential imidacloprid degrading enzymes, we developed targeted and untargeted transcriptomics experiments with P. putida serving as our model organism. Using RT-qPCR, we hope to ascertain whether the exposure of imidacloprid induced a change in gene expression. Using RNA-seq in an untargeted approach, we hope to ascertain what genes are upregulated in the presence of Imidacloprid. For both experiments, we have yet to perform in-depth experimental troubleshooting, so only a rough outline for each experiment is provided below.

RT-qPCR
Genes of Interest:

In anticipation of potentially performing some experiments during the later stages of Summer, we found the following genes as markers for either oxidation stress response or imidacloprid degradation:

AhpC: A cellular marker for oxidative stress that has been found to be upregulated in the presence of the stressors ampicillin and MTBE [9, 10]

rpoD: RNA polymerase sigma factor rpoD has been characterized and utilized as a stable housekeeping gene used as a reference gene [11-13].

AOX: Previously characterized to catalyze the nitrogen reduction of Imidacloprid in P. putida KT2440 and mammalian systems [1, 14-15].

P450: Previously characterized to catalyze the hydroxylation of Imidacloprid [16-21]

GST: Involved in oxidative stress response and has been previously associated with imidacloprid resistant in Folsomia candida (a detoxifying soil microbe), Musca domestica (house flies), and Bombyx mori (silkworms) [22-26]

For each gene, we designed and ordered 3 pairs of primers for each sequence to be used for qPCR. These primers and the respective genes can be found attached to this genome sequence in this Benchling link:

Protocol:
  1. Isolate RNA from Cell Pellets derived from the “Imidacloprid Biotransformation” protocol using manufacturer instructions for the Qiagen RNeasy Mini Kit.
  2. Perform quality control using a TapeStation (Agilent) system.
  3. Synthesize cDNA using random Hexamer primers (Protocol Adapted from NEB Biolabs “Typical cDNA Synthesis Protocol”).
    1. Prepare the following reaction mix on ice:
      2. Component Final Concentration/Amount Volume (uL)
      10X Amplification Buffer 1X 2.5
      dNTP MIx (10 mM) 0.5 mM 1.25
      Random Primers (60 uM) 6 uM 2.5
      RNase Inhibitor, Murine 20 units 0.5
      WarmStart RTx Reverse Transcriptase 20 units 0.5
      Template RNA 1 ug Varies
      RNase Free ddH20 To a total reaction volume of 25 uL
    2. Mix by gently flicking the reaction mixture.
    3. Incubate the reaction mix with the following temperatures and times:
      4. Step Temperature (oC) Time (Minutes)
      Annealing 25 5
      Synthesis/Polymerization 55 10
      Heat Inactivate 80 10
  4. Using the SYBR Green System:
    1. Set up the following reaction on ice for each gene of interest and sample condition:
      2. Component Final Concentration/Amount Volume (uL for 20 uL reaction)
      2X qPCR mix 1X 10
      Forward Primer (10 uM) 0.2 uM 0.4
      Reverse Primer (10 uM) 0.2 uM 0.4
      MgCl2 (25 mM) 3.5* mM 3.5
      Water 1.7
      Template cDNA solution 4

      *This concentration must be optimized for each primer pair.

    2. Run the reaction on a compatible qPCR machine with the following cycle conditions:
      3. Step # of Cycles Temperature (oC) Time (min:sec)
      Initial Denaturation 1 94 2:00
      Denaturation 40 94 0:15
      Annealing, Extension and read fluorescence ~60* 1:00
      Hold 1 4 inf

      *This temperature must be optimized for each primer pair.

  5. Analyze data according to instrument-specific instructions, and compare relative expression of selected genes in various conditions.
RNA-seq
  1. Isolate RNA from Cell Pellets derived from the “Imidacloprid Biotransformation” protocol using manufacturer instructions for the Qiagen RNeasy Mini Kit.
  2. Measure RNA quality using a TapeStation (Agilent) system.
  3. Prepare libraries using the Illumina TruSeq Stranded Total RNA Library kit with Ribo-Zero Depletion according to manufacturer’s instructions.
  4. Sequence libraries using the Illumina HiSeq Sequencing System.
  5. Process data using MSU’s HPCC resources and the following workflow:
    1. Align reads to genome using Tophat
    2. Assemble transcripts for each sample using cufflinks
    3. Merge assemblies into a single transcriptome using cuffmerge
    4. Quantify expression of transcripts using htseq-count
    5. Identify differentially expressed genes based on environmental conditions using R 3.3.2
    6. This workflow largely follows this Example RNA-seq job example outlined by ICER (Institute for Cyber Enabled Research) which provides HPCC support and services for MSU researchers. Example Link

References

[1] Lu, T.-Q., Mao, S.-Y., Sun, S.-L., Yang, W.-L., Ge, F., & Dai, Y.-J. (2016). Regulation of Hydroxylation and Nitroreduction Pathways during Metabolism of the Neonicotinoid Insecticide Imidacloprid by Pseudomonas putida. https://doi.org/10.1021/acs.jafc.6b01376

[2] Roach, T. N. F., Dilworth, J., Martin, C., Jones, A. D., Quinn, R., & Drury, C. (2020). Metabolomic signatures of coral bleaching history. BioRxiv, 2020.05.10.087072. https://doi.org/10.1101/2020.05.10.087072

[3] Mingxun Wang, Jeremy J. Carver, Vanessa V. Phelan, Laura M. Sanchez, Neha Garg, Yao Peng, Don Duy Nguyen et al. "Sharing and community curation of mass spectrometry data with Global Natural Products Social Molecular Networking." Nature biotechnology 34, no. 8 (2016): 828. PMID: 27504778

[4] Frank, Ari M., et al. "Clustering millions of tandem mass spectra." Journal of proteome research 7.01 (2007): 113-122.

[5] Katajamaa, M., Miettinen, J. & Oresic, M. MZmine: toolbox for processing and visualization of mass spectrometry based molecular profile data. Bioinformatics 22, 634–636 (2006).

[6] Myers OD, Sumner SJ, Li S, Barnes S, Du X: One Step Forward for Reducing False Positive and False Negative Compound Identifications from Mass Spectrometry Metabolomics Data: New Algorithms for Constructing Extracted Ion Chromatograms and Detecting Chromatographic Peaks. Anal Chem 2017, DOI: 10.1021/acs.analchem.7b00947

[7] Nothias, L., Petras, D., Schmid, R. et al. Feature-based molecular networking in the GNPS analysis environment. Nat Methods 17, 905–908 (2020). https://doi.org/10.1038/s41592-020-0933-6

[8] Pluskal, T., Castillo, S., Villar-Briones, A. & Oresic, M. MZmine 2: modular framework for processing, visualizing, and analyzing mass spectrometry-based molecular profile data. BMC Bioinformatics 11, 395 (2010).

[9] Yeom, J., Imlay, J. A., & Park, W. (2010). Iron homeostasis affects antibiotic-mediated cell death in Pseudomonas species. Journal of Biological Chemistry, 285(29), 22689–22695. https://doi.org/10.1074/jbc.M110.127456

[10] Krayl, M., Benndorf, D., Loffhagen, N., & Babel, W. (2003). Use of proteomics and physiological characteristics to elucidate ecotoxic effects of methyltert-butyl ether in Pseudomonas putida KT2440. PROTEOMICS, 3(8), 1544–1552. https://doi.org/10.1002/pmic.200300477

[11] Savli, H., Karadenizli, A., Kolayli, F., Gundes, S., Ozbek, U., & Vahaboglu, H. (2003). Expression stability of six housekeeping genes: A proposal for resistance gene quantification studies of Pseudomonas aeruginosa by real-time quantitative RT-PCR. Journal of Medical Microbiology, 52(5), 403–408. https://doi.org/10.1099/jmm.0.05132-0

[12] Wang, Q., & Nomura, C. T. (2010). Monitoring differences in gene expression levels and polyhydroxyalkanoate (PHA) production in Pseudomonas putida KT2440 grown on different carbon sources. Journal of Bioscience and Bioengineering, 110(6), 653–659. https://doi.org/10.1016/j.jbiosc.2010.08.001

[13] Bai, B., Ren, J., Bai, F., & Hao, L. (2020). Selection and validation of reference genes for gene expression studies in Pseudomonas brassicacearum GS20 using real-time quantitative reverse transcription PCR. PLOS ONE, 15(1), e0227927. https://doi.org/10.1371/journal.pone.0227927

[14] Casida, J. E. (2011). Neonicotinoid metabolism: Compounds, substituents, pathways, enzymes, organisms, and relevance. Journal of Agricultural and Food Chemistry, 59(7), 2923–2931. https://doi.org/10.1021/jf102438c

[15] Honda, H., Tomizawa, M., & Casida, J. E. (2006). Neonicotinoid metabolic activation and inactivation established with coupled nicotinic receptor-CYP3A4 and -aldehyde oxidase systems. Toxicology Letters, 161(2), 108–114. https://doi.org/10.1016/j.toxlet.2005.08.004

[16] Antony, B., Johny, J., Abdelazim, M. M., Jakše, J., Al-Saleh, M. A., & Pain, A. (2019). Global transcriptome profiling and functional analysis reveal that tissue-specific constitutive overexpression of cytochrome P450s confers tolerance to imidacloprid in palm weevils in date palm fields. BMC Genomics, 20(1), 1–23. https://doi.org/10.1186/s12864-019-5837-4

[17] Markussen, M. D. K., & Kristensen, M. (2010). Cytochrome P450 monooxygenase-mediated neonicotinoid resistance in the house fly Musca domestica L. Pesticide Biochemistry and Physiology, 98(1), 50–58. https://doi.org/10.1016/j.pestbp.2010.04.012

[18] Cheesman, M. J., Traylor, M. J., Hilton, M. E., Richards, K. E., Taylor, M. C., Daborn, P. J., Russell, R. J., Gillam, E. M. J., & Oakeshott, J. G. (2013). Soluble and membrane-bound Drosophila melanogaster CYP6G1 expressed in Escherichia coli: Purification, activity, and binding properties toward multiple pesticides. Insect Biochemistry and Molecular Biology, 43(5), 455–465. https://doi.org/10.1016/j.ibmb.2013.02.003

[19] Joußen, N., Heckel, D. G., Haas, M., Schuphan, I., & Schmidt, B. (2008). Metabolism of imidacloprid and DDT by P450 CYP6G1 expressed in cell cultures of Nicotiana tabacum suggests detoxification of these insecticides in Cyp6g1-overexpressing strains of Drosophila melanogaster, leading to resistance. Pest Management Science, 64(1), 65–73. https://doi.org/10.1002/ps.1472

[20] Karunker, I., Morou, E., Nikou, D., Nauen, R., Sertchook, R., Stevenson, B. J., Paine, M. J. I., Morin, S., & Vontas, J. (2009). Structural model and functional characterization of the Bemisia tabaci CYP6CM1vQ, a cytochrome P450 associated with high levels of imidacloprid resistance. Insect Biochemistry and Molecular Biology, 39(10), 697–706. https://doi.org/10.1016/j.ibmb.2009.08.006

[21] Suchail, S., Debrauwer, L., & Belzunces, L. P. (2004). Metabolism of imidacloprid inApis mellifera. Pest Management Science, 60(3), 291–296. https://doi.org/10.1002/ps.772

[22] Sillapawattana, P., & Schäffer, A. (2017). Effects of imidacloprid on detoxifying enzyme glutathione S-transferase on Folsomia candida (Collembola). Environmental Science and Pollution Research, 24(12), 11111–11119. https://doi.org/10.1007/s11356-016-6686-1

[23] Yamamoto, K., & Yamada, N. (2016). Identification of a diazinon-metabolizing glutathione S-transferase in the silkworm, Bombyx mori. Scientific Reports, 6(1), 1–9. https://doi.org/10.1038/srep30073

[24] Enayati, A. A., Ranson, H., & Hemingway, J. (2005). Insect glutathione transferases and insecticide resistance. In Insect Molecular Biology (Vol. 14, Issue 1, pp. 3–8). John Wiley & Sons, Ltd. https://doi.org/10.1111/j.1365-2583.2004.00529.x

[25] Reid, W. R., Sun, H., Becnel, J. J., Clark, A. G., & Scott, J. G. (2019). Overexpression of a glutathione S-transferase ( Mdgst ) and a galactosyltransferase - like gene ( Mdgt1 ) is responsible for imidacloprid resistance in house flies. Pest Management Science, 75(1), 37–44. https://doi.org/10.1002/ps.5125

[26] Ge, W., Yan, S., Wang, J., Zhu, L., Chen, A., & Wang, J. (2015). Oxidative stress and DNA damage induced by imidacloprid in zebrafish (Danio rerio). Journal of Agricultural and Food Chemistry, 63(6), 1856–1862. https://doi.org/10.1021/jf504895h