Case A: Pesticides and viruses – multiplex PCR and BLAST

Case B: Mites and virus diversity – sequence analysis and tree-building

Case C: Compare relative amounts of viral DNA in honey bee hives in relation to mite loads – qPCR

Case D: Determine absolute quantities of DNA from samples via standard curves – qPCR

Case A: Pesticides and viruses

Background: Honey bees are commonly exposed to pesticides as they forage for pollen and nectar. Some pesticides are known to affect the central nervous system of bees and thus impact their behavior. Sub-lethal exposures of some pesticides are considered possible contributing factors to to the decline in honey bee health.

Scenario: Dr. Muskiver was curious if pesticide exposure was linked to virus infection, another possible contributing factor to decreased honey bee health. To test this question, Dr. Muskiver set up test colonies, and fed the honey bees either with untreated pollen or pollen treated with sub-lethal doses of pesticides. She then tested the bees for the presence of several viruses using multiplex PCR on cDNA isolated from the bees.

DNA samples:

• Negative control – bee sample with no viruses present
• Positive control – bee sample containing all four viruses
• Hive 1 – exposed to pesticides
• Hive 2 – exposed to pesticides
• Hive 3 – no pesticides exposure
• Hive 4 – no pesticide exposure

Procedure: To analyze this case, open the DNA sequences and multiplex primer, and run multiplex PCR. Then load and run the gel. Click on fragments and BLAST the associated sequence to verify that the fragments are correctly associated with the viruses.

1. Do the control samples produce the results you expected?
2. What are the results for each experimental hive, in terms of viruses that are detected?
3. Is there any correlation between pesticide exposure and viruses detected?
4. How would you explain these results to Dr. Muskiver?
5. What changes would you make to the experiment design if this is repeated?
6. What would you suggest that these researchers do next?
7. What are some other tests that could be done to address this question?

Case B: Mites and virus diversity

Background: Recent declines in honey bee populations have given rise to the syndrome named Colony Collapse Disorder (CCD). Several potential stressors have been identified. It has recently been reported that V. destructor  transmits certain strains of DWV more effectively, and that long-term mite infection reduces virus diversity and leads to the prevalence of more pathogenic viruses.

Scenario: A team of research scientists, funded by the North American Honey Bee Council, decide to survey colonies from around North America for two of the notable stressors – Deformed Wing Virus (DWV), a virus that causes wing deformation, and Varroa destructor, a parasitic mite that feeds on the bee. The scientists are interested in testing the relationship between DWV strains and the Varroa mite in North America.

Bees tested from:

• Central Ontario – low mite levels
• Northwestern Washington –  low mite levels
• Southeast Florida – high mite levels
• Oahu, Hawaii – high mite levels
• Northern Arizona –  moderate mite levels
• Southern British Columbia – moderate mite levels

Procedure: The file contains a total of18 sequences, 6 from Florida, 6 from Ontario, and 6 from Washington (Arizona and Hawaii sequences are not included in the file). The tree can be built three ways. If using MEGA software, a single menu command will open MEGA and build the tree via the Analyze button of the Opened & Processed window. If using MABL or MAFFT, then the sequences need to be transferred to the Export field of the Sequence Analysis window (using the Analyze button). The Analyze button can then be used again to opent the MABL or MAFFT web site, after which the contents of the Export field (copied to the clipboard automatically) can be pasted into the input fields of either web site. Instructions for using the web sites are included in the menus of the Analyze button.

1. If three different methods are used to build the tree (MABL, MAFFT, MEGA), why are the three trees different? Or are they?
2. The hypothesis is that long term mite infection reduces viral diversity. Is that hypothesis supported by the data?
3. What else do we need to know before concluding that relatively high mite infections are associated with relatively low viral diversity?

Case C: Comparing relative amounts of viral DNA in honey bee hives in relation to mite loads

Background: Quantitative PCR (qPCR) is a method for determining both relative and absolute quantities of DNA in samples. In this procedure, DNA amplification is monitored over time, as the DNA doubles each cycle. The point at which the amount of DNA present (measured as a fluorescence value) crosses a predetermined threshold is called the ‘Ct’ value (see the qPCR tutorial for a more detailed explanation of Ct and Delta Ct).

Scenario: A local beekeeper is experiencing declines in honey bee production from his hives and has asked biology instructors if their classes can study the problem. This was a ‘single-blind’ study, so the instructors knew which hives had high, moderate and low mite infestations (loads) and good, moderate and poor overwintering success, but the student researchers did not. The students were told that 4 of the hives had high mite loads, 4 had low mite loads, and 2 had moderate mite loads, but they were not told which hives fell into each of these categories.

Procedure: The students determined relative amounts of DWV and BQCV viral levels for the 10 hives using the reverse-transcriptase qPCR procedure (via amplification of cDNA). Resulting data is a file (“qPCR honeybee”) containing fluorescence levels resulting from amplification of cDNA for DWV and BQCV over time. The students were then asked to analyze the data to see if any trends were present. They were also asked to search the literature for known causes of DWV and BQCV, that might offer an explanation for any trends that they found.

Hint: With the Case It software, qPCR can be run for subsets of the data by selecting wells (by clicking or dragging) so that they turn purple in color, and then qPCR can be run for “purple wells only” (or “orange wells only”). This makes it easier to examine the data for any trends that might be present.

1. Are there relationships among mite infestation levels and viral levels in Mr. Smith’s honey bee hives?
2. If there are relationships, are they dependent on the threshold level set before the qPCR procedure is run?
3. From the literature, what hypotheses have been advanced about how DWV and BQCV are transmitted? Did the class data support or not support these hypoetheses?
4. What additional information would you need to know before drawing conclusions from the results of this study? How could the experimental design be improved?
5. What other viruses and environmental factors have been implicated in honey bee declines in the U.S.? How important are honey bees, both ecologically and economically? Are honey bees used commerically in the U.S. native to this country? What is their impact on native bee populations?

Case D. Determination of absolute quantities of DNA from samples using standard curves

Standard curve data is included in the Cases ->qPCR folder, to demonstrate how standard curves are used to quantify the amount of DNA present in samples. An outlier is deliberately included to show how outliers can influence results. See the qPCR tutorial for more detail on how to generate and use a standard curve. [Note: The standard curve data set was not generated from honey bee viruses, but rather is a generic data set included here to demonstrate the concept.]