Biology
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IR-TEx: An Open Source Data Integration Tool for Big Data Transcriptomics Designed for the Malaria Vector Anopheles gambiae
Chapters
Summary January 15th, 2020
IR-TEx explores insecticide resistance-related transcriptional profiles in the species Anopheles gambiae. Provided here are full instructions for using the application, modifications for exploring multiple transcriptomic datasets, and using the framework to build an interactive database for collections of transcriptomic data from any organism, generated in any platform.
Transcript
This protocol is significant, because it is the first time that transcriptomic data in Anopheles Gambiae mosquitoes has been made freely and easily accessible in one place. This technique allows all researchers, even those without backgrounds in bioinformatics to freely and easily access expression data on their favorite genes and insecticide-resistant mosquitoes. To begin, follow the link at the bottom of the LSTM IR-TEx project page to run the IR-Tex Web application in a Web browser.
Once the web page has initialized, click the Application button at the top of the page, which will display the application and associated outputs. Read each output related to the default entry in the transcript ID box. Select conditions and Anopheles Coluzzii data sets that are exposed to Pyrethroid insecticides, or not exposed to any insecticide class and associated transcripts with a correlation coefficient of greater than 0.98.
To explore expression of a transcript of interest, first select the transcript. Then, input the transcript ID into the transcript ID box, remembering that the transcripts end in RX dependent upon the isoform of interest. Select the data sets to interrogate by taking the relevant boxes for countries, including exposure status, species of interest, and insecticide class of interest.
Ensure that these criteria result in greater than one included data set. Click Update View at the bottom of the selection menu or press Return, ignoring absolute correlation value for now. Give the application time to update.
Be it the first graph has the Log2 Fold Change between a resistant population, and a lab susceptible mosquito population of the transcript of interest, across each data set that meets the selected criteria. Read the information below the graph as the Fold Changes between the resistant and susceptible mosquitoes for each relevant data set, in addition to the Associated adjusted P values. Each row represents individual probes on the micro array.
Read the additional table below, as the number of experiments in which the transcript of interest is significant, as well as the total number of experiments matching the selected criteria. To download the data in tab separated format, click the Download button under the two tables. This allows the user to explore data in an easier manner, using a program such as Excel.
Each point on the map represents the approximate collection sites of resistant mosquitoes in each data set, in which the transcript of interest is differentially expressed. The colors follow a traffic light system that is explained in the app. Save the graphical outputs by right clicking, clicking Save Image As, and choosing an appropriate folder.
In the instance of an output error by the application, it is likely that no data sets match the inputted criteria. Correlations of the expression patterns of transcripts across multiple data sets can be used to predict transcript function, and potentially elucidate co-regulated transcripts from the same pathway. Before clicking Update View, move the absolute correlation value slider to 0.85 and click Update View or press Return.
Examine the Correlation Table to find the multiple transcripts that are now displayed and are correlated with the inputted and transcript. Read the table below the graphical output, which contains the correlation value for each transcript. To download the data in a tab separated format, click the Download button.
Manipulate the absolute correlation value slider and observe any changes in the bottom most graph and table. A lower stringency of the correlation value will show more transcripts, but will introduce more noise. Once the IR-TEx is installed, open RStudio Supplemental Coding File1 and run each line to set up the system for IR-TEx.
Once all packages are successfully installed and updated as required, go to File, Open. Locate IR_TEx. r, highlight and open.
This should now be visible in the top window of RStudio. To run the app, press the Run App button in the top right of the window. A second window will pop up in which the app will load.
Once the loading is complete, for full functionality, click Open in Browser, located in the top right of the loaded window. The user can add new resistance data sets to IR-TEx generated using Anopheles Gambiae 15k Agilent array, as described in the TEx protocol. Open Additional File2.txt.
This RNA seek file represents the template in which new data should be based. Column A is identifier, column B is raw Fold Change, and column C is adjusted P value. Run the R code to match identifiers into a single tab-delimited file across platforms, then organize and normalize the data.
Instructions are contained within the file. Any file path will be separated by a forward slash for Mac OS or a double forward slash for Windows. Backup the original file.
Output the file produced at the end of Supplemental Coating File2 to a location of choice for use in the next step. Supplemental Coding File2 will output a new Fold_Changes. txt file.
Execute the code contained in Supplementary Coding File3. Find the output file named FC_DistribPlot. png in the folder specified as file path.
Check the distributions of Log2 Fold Change to verify that the Log2 Fold Change distributions are nearly identical across data sets. Shown here is the mRNA expression of GS TMS1 and AGAP 009110 RA in two multi-resistant Anopheles Coluzzii populations from Cote d'Ivoire, and Burkina Faso, respectively. Comparison to the lab susceptible Anopheles Coluzzii N-Guzo revealed that these transcripts are significantly up-regulated in two separate multi-resistant populations.
RNA-I induced knock down was performed on mosquitoes from the LSTM laboratory T-Oscillae colony. This colony originates from Cote d'Ivoire, and is resistant to all major classes of insecticide used in public health. Attenuation of the expression of GS TMS1 resulted in a significant increase in mortality, after Delta Matheran exposure, compared to GFP injected controls, demonstrating the importance of this transcript in Pyrethroid resistance.
Conversely, AGAP 009110 RA knock down resulted in no significant change in mortality after exposure. GST MS1 was significantly over expressed in 20 out of 21 micro-array data sets available for these species. In each location, the Fold Change was significantly higher in the resistant, compared to the susceptible populations.
All the data sets are from different experiments over numerous years, and were not designed for a clean microanalysis-based approach, so it may be necessary to lower normal P value stringency. Further Phenotypic knock down assessment can be performed with other insecticides, and life history traits. If there is results look good enough, downstream parasitization can determine the role of protein.
This technique is the first application to integrate transcriptomic data on insecticide resistance of the Anopheles Gambiae species complex and make it available to all researchers in this field.
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