This work was funded by an MRC Skills Development Fellowship to V.I. (MR/R024839/1) and Royal Society Challenge Grant (CH160059) to H.R.

1. Using the IR-TEx web application
<ol>
	<li>Running the application in a web browser
	<ol>
		<li>Open the IR-TEx web application by following the link at the bottom of the page found at <a href="http://www.lstmed.ac.uk/projects/IR-TEx" target="_blank">http://www.lstmed.ac.uk/projects/IR-TEx</a>.</li>
		<li>Once the web page has initialized, click the Application button at the top of the page, which will display the application and associated outputs.</li>
		<li>Read each output related to the default entry of AGAP008212-RA (CYP6M2) in the transcript ID box with the following conditions: An. coluzzii datasets that are (i) exposed to pyrethroid insecticides or (ii) not exposed to any insecticide class, and associated transcripts with a correlation of |r| &#62;0.98.</li>
	</ol>
	</li>
	<li>Exploring expression of a transcript of interest
	<ol>
		<li>To select a transcript of interest, input the transcript ID into the Transcript ID box, remembering that transcripts end in -RX dependent upon isoform of interest.</li>
		<li>Select the datasets to interrogate by ticking the relevant boxes for (i) Countries; (ii) Exposure status, (iii) Species of interest; and (iv) Insecticide class of interest, all while ensuring that these criteria result in &#62;1 included dataset (see Supplementary Table 1 in Ingham et al.1). 
		NOTE: (iii) refers to the member of the An. gambiae species complex that the user interested in. Currently, data are available for An. coluzzii and An. arabiensis.</li>
		<li>Click Update View at the bottom of the selection menu or press Return, ignoring Absolute Correlation Value (for now).</li>
		<li>Give the application time to update.</li>
		<li>Read the first graph as: log2 fold change between a resistant population and lab-susceptible mosquito population of the transcript of interest across each dataset that meets the criteria selected in step 1.2 (Figure 1). The details of all datasets can be found in Ingham et al.1.</li>
		<li>Read the information below the graph as: the fold changes between the resistant and susceptible mosquitoes for each relevant dataset, in addition to the corrected p-values (Q). Each row represents individual probes on the microarray. The methodology for graphical display has been reported previously1.</li>
		<li>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 criteria selected in step 1.2.</li>
		<li>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.</li>
		<li>Interpret the map as follows: each point represents the approximate collection sites of resistant mosquitoes in each dataset in which the transcript of interest is differentially expressed. The colors follow a traffic light system that is explained in the app (Figure 2).</li>
		<li>For steps 1.2.5 and 1.2.8, save the graphical outputs by right-clicking, clicking Save image as&#8230;, and choosing an appropriate folder. 
		NOTE: In the instance of an output error by the application, it is likely that no datasets match the inputted criteria. Check Supplementary Table 1 in Ingham et al.1 if this occurs.</li>
	</ol>
	</li>
	<li>Identifying putative functions/pathways of transcript of interest
	<ol>
		<li>Correlations (minimum r2 value inputted) of the expression patterns of transcripts across multiple datasets can be used to predict transcript function and potentially elucidate coregulated transcripts from the same pathway. Using the example from Ingham et al.1 (AGAP001076-RA; CYP4G16), follow steps 1.2.1&#8211;1.2.2 in the section above, selecting all datasets for maximal power.</li>
		<li>Before clicking Update View, move the Absolute Correlation Value slider to 0.85, and click Update View or press Return.</li>
		<li>Examine the correlation table (bottommost table) to find the multiple transcripts that are now displayed and are correlated (|r| = 0.85) with the inputted transcript.</li>
		<li>Manipulate the Absolute Correlation Value slider and observe any changes in the bottommost graph and table; the outputs from step 1.3.2 will remain unchanged. As shown in Figure 3 (|r| &#62; 0.9, |r| &#62; 0.8), lowering the stringency of the correlation value will show more transcripts but will introduce more noise.</li>
		<li>Read the table below the graphical output, which (in addition to the parameters described in step 1.2.6) contains the correlation value for each transcript.</li>
		<li>To download the data in a tab-separated format, click the Download button.</li>
		<li>Functional enrichment analysis can be performed on the downloaded transcript ID list using DAVID analysis8. Once on the DAVID website (found at <a href="https://david.ncifcrf.gov/" target="_blank">https://david.ncifcrf.gov/</a>), select Functional Analysis. Paste the full gene list, using gene IDs [identifier without the -RX, which can be done in excel by inserting a column to the right of the Systematic ID and typing =LEFT(X1,10), where X1 is the Systematic ID cell]. Select the identifier as VectorBase_ID and gene list and click Submit List.</li>
		<li>Click the Functional Annotation Clustering button to yield an overview of the enrichments found in this correlation network, allowing a potential function to be assigned to the transcript. Explore in-depth enrichments by looking through the different categories and clicking the + buttons for each and subsequently clicking Chart.</li>
	</ol>
	</li>
</ol>
2. Downloading and implementing IR-TEx locally
<ol>
	<li>Downloading and running IR-TEx
	<ol>
		<li>Go to the link found at <a href="http://github.com/LSTMScientificComputing/IR-TEx" target="_blank">http://github.com/LSTMScientificComputing/IR-TEx</a>; and click Clone or download | Download Zip. Direct to a folder of choice and unzip the file in that folder.</li>
		<li>Download the latest version of R software for the appropriate operating system from the link found at <a href="http://cran.r-project.org/mirrors.html" target="_blank">http://cran.r-project.org/mirrors.html</a>. Install the program.</li>
		<li>Download and install the latest R Studio software, again for the appropriate operating system from the link found at <a href="http://www.rstudio.com/products/rstudio/download/" target="_blank">http://www.rstudio.com/products/rstudio/download/</a>.</li>
		<li>Once installed, open R Studio | Supplemental coding File 1 and run each line to set up the system for IR-TEx.</li>
		<li>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 R Studio.</li>
		<li>To run the app, press the Run App button in the top-right of the window, and 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.</li>
	</ol>
	</li>
	<li>Adding resistance datasets to IR-TEx (generated using Anopheles gambiae 15k Agilent array)
	<ol>
		<li>To add a new analyzed dataset generated on the same microarray platform (A-MEXP-2196) to the available dataset, download the app and locate the unzipped folder downloaded in section 2.1.</li>
		<li>Open Additional File 1, which represents an output from a limma analysis on A-MEXP-2196 1. Using Excel, in column H1, write Fold_Change, and in H2, write =2^B2, in which B2 is the log fold change. Apply this throughout column H to produce raw fold changes.</li>
		<li>Arrange Additional File 1 such that column A is the ID, column B is the fold change from column H (copy column H, highlight column B, then right-click and paste values) and column C is the adjusted p-value. Delete all other columns and save as a tab-delimited file.</li>
		<li>Open Supplemental coding File 2 and run using the tab-delimited sheet produced in step 2.2.3. 
		NEWFILE_FC = c(&#39;COUNTRY&#39;,&#39;EXPOSURE STATUS&#39;,&#39;SPECIES&#39;,&#39;INSECTICIDE&#39;) 
		NEWFILE_Q = c(&#39;COUNTRY&#39;,&#39;EXPOSURE STATUS&#39;,&#39;SPECIES&#39;,&#39;INSECTICIDE&#39;) 
		NOTE: Fields within single quotation marks should be changed to reflect information from the new dataset. Exposure status refers to whether samples were collected following insecticide exposure (exposed/unexposed). Insecticide: if &#39;unexposed&#39;, use &#39;none&#39;. See Fold_Changes.txt. for metadata from other samples. Ensure that spelling is consistent.</li>
		<li>Open geography.txt, scroll to the final occupied row, and select below. Type in the name of the dataset, followed by Q and NEWFILE_Q in column 1, the latitude of the sample collection site in column 2, and the longitude in column 3. Save the changes.</li>
		<li>If any novel entries are used (i.e., Gambia), which are not available for selection in the dataset (see Ingham et al. Supplementary Table 11), these will need to be added into the code. To do this, open IR-TEx.R in RStudio and locate line 26 as indicated by RStudio, at which point the following should begin: 
		&#39;sidebarPanel(&#8230;.&#39;. 
		NOTE: Each of the proceeding rows relates to an item of metadata inputted into the rows below the dataset name in Fold_Changes.txt in step 2.2.5.</li>
		<li>To add the novel metadata, scroll to the end of the line of the metadata of choice, and locate the term &#39;selected=&#39;. Immediately following this should be a comma and closed bracket; at this point, click the cursor within the closed bracket. After the final apostrophe, type a comma, followed by an apostrophe, followed by the new metadata (e.g., &#39;Gambia&#39;), and save the changes. See below for an example. 
		checkboxGroupInput(&#39;CountryInput&#39;,&#39;Select Relevant Countries&#39;,c(&#39;Burkina Faso&#39;,&#39;Cote D`Ivoire&#39;,&#39;Cameroon&#39;,&#39;Equatorial Guinea&#39;,&#39;Zambia&#39;,&#39;Tanzania&#39;,&#39;Sudan&#39;,&#39;Uganda&#39;,&#39;Togo&#39;, &#39;Gambia&#39;),selected=c(&#39;Burkina Faso&#39;,&#39;Cote D`Ivoire&#39;,&#39;Cameroon&#39;,&#39;Equatorial Guinea&#39;,&#39;Zambia&#39;,&#39;Tanzania&#39;,&#39;Sudan&#39;,&#39;Uganda&#39;,&#39;Togo&#39;))</li>
		<li>Run the app. The new metadata entry should appear as an unselected tick box under the relevant heading. If the user wants it to be selected, it should be added after the selected=c(&#8230;, as shown below: 
		checkboxGroupInput(&#39;CountryInput&#39;,&#39;Select Relevant Countries&#39;,c(&#39;Burkina Faso&#39;,&#39;Cote D`Ivoire&#39;,&#39;Cameroon&#39;,&#39;Equatorial Guinea&#39;,&#39;Zambia&#39;,&#39;Tanzania&#39;,&#39;Sudan&#39;,&#39;Uganda&#39;,&#39;Togo&#39;, &#39;Gambia&#39;),selected=c(&#39;Burkina Faso&#39;,&#39;Cote D`Ivoire&#39;,&#39;Cameroon&#39;,&#39;Equatorial Guinea&#39;,&#39;Zambia&#39;,&#39;Tanzania&#39;,&#39;Sudan&#39;,&#39;Uganda&#39;,&#39;Togo&#39;, &#39;Gambia&#39;))</li>
		<li>To add resistance datasets not performed on A-MEXP-2196, see section 3.</li>
	</ol>
	</li>
</ol>
3. Modifying IR-TEx for use with different datasets
<ol>
	<li>Use across multiple -omics platforms and proceeding with missing data
	<ol>
		<li>To proceed with &#34;0&#34; in datasets: consult the dataset source for the specific meaning of &#34;0&#34;. It is recommended that &#34;0&#34; is (conservatively) replaced with &#34;NA&#34;. As with raw fold changes (B/A), &#34;0&#34; indicates an undetected signal in experimental condition B. In the case that experimental condition A exhibits substantial expression, the user can apply a small fold change value.</li>
		<li>Open Additional File 2.txt, an RNAseq file adapted from Uyhelji et al.9. This file represents the template in which new data should be based: column A = identifier, column B = raw fold change, and column C = adjusted p-value. Use this file to run through the below steps.</li>
		<li>Run the R code to match identifiers into a single tab-delimited file across platforms, then organize and normalize the data (Supplemental Coding File 2). Instructions are contained within the file. Any FILEPATH will be separated by &#34;/&#34; for MacOS or &#34;//&#34; for Windows (change these from &#34;\&#34;, as they will appear).</li>
		<li>Output the file produced at the end of Supplemental Coding File 2 to a location of choice for use in step 3.1.5. Supplemental Coding File 2 will output a new Fold_Changes.txt file. Back up the original file.</li>
		<li>Execute the code contained in Supplementary coding File 3. Find the output file named FC_distribPlot.png in the folder specified as FILEPATH. Check the distributions of log2 fold change to verify that the log2 fold change distributions are nearly identical across datasets.</li>
		<li>Follow instructions from step 2.2.6 to edit additional files and ensure compatibility of the new Fold_Changes.txt.</li>
	</ol>
	</li>
	<li>Modifying IR-TEx for use with completely new datasets
	<ol>
		<li>Open IR-TEx.R in RStudio and locate the lines (23&#8211;34) beginning with: 
		&#39;tabPanel(&#39; 
		and ending in: 
		submitButton(&#34;Update View&#34;, icon(&#34;refresh&#34;)) 
		),</li>
		<li>Change the AGAP008212-RA found in the below lines to a transcript of interest in the new data. 
		textInput(&#39;textInput&#39;,&#39;Transcript ID&#39;,value=&#39;AGAP008212-RA&#39;),</li>
		<li>Locate the four options beginning with: 
		checkboxGroupInput( 
		These options can be modified to represent important metadata that the user wishes to filter the new data by. In each instance, the user should change the Select Relevant Countries; Select Exposure Status; Select Relevant Species; and Select Insecticide Class to be representative of the data (i.e., Select Tissue Type; Select Sex; Select Age Bracket; Select Disease Status).</li>
		<li>Identify the metadata associated with the dataset and input to replace the existing options immediately after the first c(&#39;. In each instance, the options will be contained within speech marks and separated from the next selection by a comma. After the final selection, the bracket should be closed. An example for Select Disease Status is: 
		c(&#39;Infected&#39;, &#39;Uninfected&#39;, &#39;Unknown&#39;)</li>
		<li>Choose which of these metadata will be selected upon opening the app. These can be changed by modifying the options after selected=c(&#39;. An example for Select Disease Status is: 
		selected=c(&#39;Infected&#39;, &#39;Uninfected&#39;) 
		This will instruct the app to select only datasets matching these criteria on initial loading.</li>
		<li>To create a new data table, follow the layout found in Fold_Changes.txt and instructions in section 2. Change the metadata to each respective change outlined in step 3.2.4, exactly as written into the code (R is case-sensitive). Into the detoxification column, input gene names, and in the transcript type column, input gene descriptions for each transcript. Follow section 3.2 when adding new datasets.</li>
		<li>If mapping is not relevant to the experimental requirements, locate the following lines of code and place &#39;#&#39; in front: 
		Lines 49&#8211;51: 
		br(),br(), 
		withSpinner(plotOutput(&#34;Geography&#34;)), 
		textOutput(&#39;Geography_legend&#39;), 
		Lines 493 starting: 
		output$Geography &#60;- renderPlot({ 
		To line 602 ending: 
		output$Geography_legend &#60;- renderText({ 
		paste(&#34;Significant Transcripts Only (p&#34;, as.expression(&#34;&#60;=&#34;),&#34;0.05): FC &#62; 5 = Red, FC &#62; 1 = Amber, FC &#60; 1 = Green&#34;,sep=&#34;&#34;) 
		})</li>
	</ol>
	</li>
</ol>

<ol><li>Ingham, V. A., Wagstaff, S., Ranson, H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Transcriptomic+meta-signatures+identified+in+Anopheles+gambiae+populations+reveal+previously+undetected+insecticide+resistance+mechanisms%5BTitle%5D)+AND+%22Nature+Communications%22%5BJournal%5D)">Transcriptomic meta-signatures identified in Anopheles gambiae populations reveal previously undetected insecticide resistance mechanisms.</a> Nature Communications. 9 (1), 5282(2018).</li>
<li>Chang, W., Cheng, J., Allaire, J., Xie, Y., McPherson, J. <a target="_blank" href="http://scholar.google.com/scholar?hl=en&safe=off&q=author%3aChang%2C+W+%22shiny%3A+Web+Application+Framework+for+R%22">shiny: Web Application Framework for R</a>. , (2017).</li>
<li>Bhatt, S., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((The+effect+of+malaria+control+on+Plasmodium+falciparum+in+Africa+between+2000+and+2015%5BTitle%5D)+AND+%22Nature%22%5BJournal%5D)">The effect of malaria control on Plasmodium falciparum in Africa between 2000 and 2015.</a> Nature. 526 (7572), 207-211 (2015).</li>
<li>Ranson, H., Lissenden, N. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Insecticide+Resistance+in+African+Anopheles+Mosquitoes%3A+A+Worsening+Situation+that+Needs+Urgent+Action+to+Maintain+Malaria+Control%5BTitle%5D)+AND+%22Trends+in+Parasitology%22%5BJournal%5D)">Insecticide Resistance in African Anopheles Mosquitoes: A Worsening Situation that Needs Urgent Action to Maintain Malaria Control.</a> Trends in Parasitology. 32 (3), 187-196 (2016).</li>
<li>Donnelly, M. J., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Does+kdr+genotype+predict+insecticide-resistance+phenotype+in+mosquitoes%5BTitle%5D)+AND+%22Trends+in+Parasitology%22%5BJournal%5D)">Does kdr genotype predict insecticide-resistance phenotype in mosquitoes.</a> Trends in Parasitology. 25 (5), 213-219 (2009).</li>
<li>Stevenson, B. J., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Cytochrome+P450+6M2+from+the+malaria+vector+Anopheles+gambiae+metabolizes+pyrethroids%3A+Sequential+metabolism+of+deltamethrin+revealed%5BTitle%5D)+AND+%22Insect+Biochemistry+and+Molecular+Biology%22%5BJournal%5D)">Cytochrome P450 6M2 from the malaria vector Anopheles gambiae metabolizes pyrethroids: Sequential metabolism of deltamethrin revealed.</a> Insect Biochemistry and Molecular Biology. 41 (7), 492-502 (2011).</li>
<li>Müller, P., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Field-Caught+Permethrin-Resistant+Anopheles+gambiae+Overexpress+CYP6P3%2C+a+P450+That+Metabolises+Pyrethroids%5BTitle%5D)+AND+%22PLoS+Genetics%22%5BJournal%5D)">Field-Caught Permethrin-Resistant Anopheles gambiae Overexpress CYP6P3, a P450 That Metabolises Pyrethroids.</a> PLoS Genetics. 4 (11), 1000286(2008).</li>
<li>Huang, D., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((The+DAVID+Gene+Functional+Classification+Tool%3A+a+novel+biological+module-centric+algorithm+to+functionally+analyze+large+gene+lists%5BTitle%5D)+AND+%22Genome+Biology%22%5BJournal%5D)">The DAVID Gene Functional Classification Tool: a novel biological module-centric algorithm to functionally analyze large gene lists.</a> Genome Biology. 8 (9), 183(2007).</li>
<li>Uyhelji, H. A., Cheng, C., Besansky, N. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Transcriptomic+differences+between+euryhaline+and+stenohaline+malaria+vector+sibling+species+in+response+to+salinity+stress%5BTitle%5D)+AND+%22Molecular+Ecology%22%5BJournal%5D)">Transcriptomic differences between euryhaline and stenohaline malaria vector sibling species in response to salinity stress.</a> Molecular Ecology. 25 (10), 2210-2225 (2016).</li>
<li>Edi, C. V., Benjamin, K. G., Jones, C. M., Weetman, D., Ranson, H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Multiple-Insecticide+Resistance+in+Anopheles+gambiae+Mosquitoes%2C+Southern+C%C3%B4te+d%E2%80%99Ivoire%5BTitle%5D)+AND+%22Emerging+Infectious+Diseases%22%5BJournal%5D)">Multiple-Insecticide Resistance in Anopheles gambiae Mosquitoes, Southern Côte d’Ivoire.</a> Emerging Infectious Diseases. 18 (9), 1508-1511 (2012).</li>
<li>Ding, Y., Ortelli, F., Rossiter, L., Hemingway, J., Ranson, H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((The+Anopheles+gambiae+glutathione+transferase+supergene+family%3A+annotation%2C+phylogeny+and+expression+profiles%5BTitle%5D)+AND+%22BMC+Genomics%22%5BJournal%5D)">The Anopheles gambiae glutathione transferase supergene family: annotation, phylogeny and expression profiles.</a> BMC Genomics. 4 (1), 1-16 (2003).</li>
<li>Enayati, A. A., Ranson, H., Hemingway, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Insect+glutathione+transferases+and+insecticide+resistance%5BTitle%5D)+AND+%22Insect+Molecular+Biology%22%5BJournal%5D)">Insect glutathione transferases and insecticide resistance.</a> Insect Molecular Biology. 14 (1), 3-8 (2005).</li>
<li>Ranson, H., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Identification+of+a+novel+class+of+insect+glutathione+S-transferases+involved+in+resistance+to+DDT+in+the+malaria+vector+Anopheles+gambiae%5BTitle%5D)+AND+%22The+Biochemical+Journal%22%5BJournal%5D)">Identification of a novel class of insect glutathione S-transferases involved in resistance to DDT in the malaria vector Anopheles gambiae.</a> The Biochemical Journal. 359, 295-304 (2001).</li>
<li>Riveron, J. M., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((A+single+mutation+in+the+GSTe2+gene+allows+tracking+of+metabolically+based+insecticide+resistance+in+a+major+malaria+vector%5BTitle%5D)+AND+%22Genome+Biology%22%5BJournal%5D)">A single mutation in the GSTe2 gene allows tracking of metabolically based insecticide resistance in a major malaria vector.</a> Genome Biology. 15 (2), 27(2014).</li>
<li>Pavlidi, N., Vontas, J., Van Leeuwen, T. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((The+role+of+glutathione+S-transferases+%28GSTs%29+in+insecticide+resistance+in+crop+pests+and+disease+vectors%5BTitle%5D)+AND+%22Current+Opinion+in+Insect+Science%22%5BJournal%5D)">The role of glutathione S-transferases (GSTs) in insecticide resistance in crop pests and disease vectors.</a> Current Opinion in Insect Science. 27, 97-102 (2018).</li>
<li>Salvemini, F., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Enhanced+glutathione+levels+and+oxidoresistance+mediated+by+increased+glucose-6-phosphate+dehydrogenase+expression%5BTitle%5D)+AND+%22Journal+of+Biological+Chemistry%22%5BJournal%5D)">Enhanced glutathione levels and oxidoresistance mediated by increased glucose-6-phosphate dehydrogenase expression.</a> Journal of Biological Chemistry. 274 (5), 2750-2757 (1999).</li>
<li>Deplancke, B., Gaskins, H. R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Redox+control+of+the+transsulfuration+and+glutathione+biosynthesis+pathways%5BTitle%5D)+AND+%22Current+Opinion+in+Clinical+Nutrition+%26+Metabolic+Care%22%5BJournal%5D)">Redox control of the transsulfuration and glutathione biosynthesis pathways.</a> Current Opinion in Clinical Nutrition & Metabolic Care. 5 (1), (2002).</li>
<li>Puente, X. S., López-Otn, C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Cloning+and+expression+analysis+of+a+novel+human+serine+hydrolase+with+sequence+similarity+to+prokaryotic+enzymes+involved+in+the+degradation+of+aromatic+compounds%5BTitle%5D)+AND+%22Journal+of+Biological+Chemistry%22%5BJournal%5D)">Cloning and expression analysis of a novel human serine hydrolase with sequence similarity to prokaryotic enzymes involved in the degradation of aromatic compounds.</a> Journal of Biological Chemistry. 270 (21), 12926-12932 (1995).</li>
<li>Riveron, J. M., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Genome-wide+transcription+and+functional+analyses+reveal+heterogeneous+molecular+mechanisms+driving+pyrethroids+resistance+in+the+major+malaria+vector+Anopheles+funestus+across+Africa%5BTitle%5D)+AND+%22G3%3A+Genes%2C+Genomes%2C+Genetics%22%5BJournal%5D)">Genome-wide transcription and functional analyses reveal heterogeneous molecular mechanisms driving pyrethroids resistance in the major malaria vector Anopheles funestus across Africa.</a> G3: Genes, Genomes, Genetics. 7 (6), 1819-1832 (2017).</li>
<li>Smyth, G. K. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Linear+models+and+empirical+bayes+methods+for+assessing+differential+expression+in+microarray+experiments%5BTitle%5D)+AND+%22Statistical+Applications+in+Genetics+and+Molecular+Biology%22%5BJournal%5D)">Linear models and empirical bayes methods for assessing differential expression in microarray experiments.</a> Statistical Applications in Genetics and Molecular Biology. 3 (1), 3(2004).</li>
</ol>

The authors have nothing to disclose.

Big data transcriptomics produces lists of thousands of transcripts that are differentially expressed for each experimental condition. Many of these experiments are performed on related organisms and phenotypes and are almost exclusively analyzed as independent experiments. Utilizing these rich data sources by examining the data holistically and without theoretical assumptions will 1) lead to the identification of new candidate transcripts and 2) prevent the discarding of valuable data simply because there is too much information to validate in vivo1.
IR-TEx provides users with a limited bioinformatics background with the ability to easily examine multiple datasets, visualize changes in the datasets, and download the associated information1. Although IR-TEx does not support searching for more than one transcript in each search, users can examine the associated Fold_Changes.txt files simply by using Excel, R, or other appropriate programs. Further utility of IR-TEx stems from the use of correlation networks to predict transcript function, input of hypothetical proteins or transcripts with unknown functions and use of downstream software to search for enrichments1.
In the example demonstrated in this protocol, IR-TEx is used according to its original function. Here, it allows exploration of transcripts associated with insecticide resistance and visualization of the distribution of over- and under-expression through mapping graphics. Transcripts of interest are validated in vivo to determine whether the over- or under-expression of given transcripts contributes to an observed phenotype1 (e.g., insecticide resistance). It was demonstrated here, as previously reported1, that a dataset can be used in a hypothesis-driven approach to identify transcripts of interest on a country-specific basis. IR-TEx can then be used to 1) explore expression of the transcript and 2) contextualize the transcript&#39;s function by applying a pairwise correlation network across all transcripts contained in each -omics dataset. Here, GSTMS1 was shown to be co-correlated with a number of other transcripts implicated in detoxification. This data (along with knockdown of the transcript that resulted in a significant increase in mortality after insecticide exposure) demonstrates the importance of this transcript in xenobiotic clearance.
IR-TEx represents a valuable resource for exploring insecticide resistance-related transcripts on the web or using local applications. This protocol demonstrates how to modify IR-TEx for different -omics platforms as well as completely new data. The guide illustrates how to use IR-TEx to integrate data from multiple -omics platforms and datasets with missing data as well as how to recode IR-TEx simply so it is useful for anyone researching transcriptomic datasets.

The ability to measure the expression of large numbers of transcripts simultaneously through microarray platforms and RNAseq technology has resulted in the generation of vast datasets associating transcript expression with a particular phenotype in both model and non-model organisms. These datasets are an extremely rich resource for researchers, the power of which can be increased by combining relevant sets in a big data integration approach. However, this methodology is limited to those with particular bioinformatics skills. Described here is a program, IR-TEx (previously published by Ingham et al.1) that is written in an R package called Shiny2 and allows users with little bioinformatics training to integrate and interrogate these datasets with relative ease.
IR-TEx, found at <a href="http://www.lstmed.ac.uk/projects/IR-TEx" target="_blank">http://www.lstmed.ac.uk/projects/IR-TEx</a>, was written to explore transcripts associated with insecticide resistance in Anopheles gambiae, the major African malaria vector1. Malaria is a parasitic disease caused by Plasmodium species, transmitted between humans through the bites of female Anopheles mosquitoes. Targeting the mosquito vector with insecticides has proven to be the most effective means of preventing malaria-related morbidity and mortality in Africa. The scaling up of tools (i.e., long lasting insecticidal nets) has also been pivotal in the dramatic reductions in malaria cases since 20003. With a very limited number of insecticides available, there is strong evolutionary pressure on the mosquitoes, and resistance is now widespread in African malaria vectors4.
Additionally, target site mutations5 and metabolic clearance of insecticides6,7 remain the primary studied mechanisms of resistance, but other potent resistant mechanisms are now emerging1. Many of these new mechanisms have not previously been associated with insecticide resistance but have been detected by searching for common patterns of gene expression across multiple resistant populations using the IR-TEx app and subsequently functionally validated by genomics approaches1.
Described here is a step-by-step approach to using IR-TEx, both on the web and when installed locally. The protocol describes how new insecticide resistance datasets can be integrated into the existing package and explains how to operate with missing data. Finally, it describes how to use this software with other -omics datasets that are unrelated to insecticide resistance, thus combining data from varying -omics approaches while also operating with missing values and normalization so that data are comparable.

<table><tbody><tr><td>Laptop with browser</td><td>Any</td><td>-</td><td>-</td></tr><tr><td>R Program</td><td>The R Project for Statistical Computing</td><td>-</td><td>&lt;a href=&quot;https://www.r-project.org/&quot; target=&quot;_blank&quot;&gt;https://www.r-project.org/&lt;/a&gt;</td></tr><tr><td>R Studio</td><td>R Studio</td><td>-</td><td>&lt;a href=&quot;https://www.rstudio.com/&quot; target=&quot;_blank&quot;&gt;https://www.rstudio.com/&lt;/a&gt;</td></tr></tbody></table>

ir-tex: an open source data integration tool for big data transcriptomics designed for the malaria vector anopheles gambiae

IR-TEx is an application written in Shiny (an R package) that allows exploration of the expression of (as well as assigning functions to) transcripts whose expression is associated with insecticide resistance phenotypes in Anopheles gambiae mosquitoes. The application can be used online or downloaded and used locally by anyone. The local application can be modified to add new insecticide resistance datasets generated from multiple -omics platforms. This guide demonstrates how to add new datasets and handle missing data. Furthermore, IR-TEx can be completely and easily recoded to use-omics datasets from any experimental data, making it a valuable resource to many researchers. The protocol illustrates the utility of IR-TEx in identifying new insecticide resistance candidates using the the microsomal glutathione transferase, GSTMS1, as an example. This transcript is upregulated in multiple pyrethroid resistant populations from C&#244;te D&#39;Ivoire and Burkina Faso. The identification of co-correlated transcripts provides further insight into the putative roles of this gene.

Using the Fold_Changes.txt file included with IR-TEx, we compared transcripts that were significantly differentially expressed in resistant Anopheles coluzzii and Anopheles gambiae datasets to susceptible controls from C&#244;te D&#39;Ivoire and Burkina Faso. This yielded 18 transcripts of interest (Table 1; this search can be performed using Excel, R, or other programs). Two of these, an ATPase (AGAP006879) and &#945;-crystallin (AGAP007160), have been previously reported, with the former having a significant effect on pyrethroid resistance1. In addition to these two transcripts, two detoxification transcripts, GSTMS1 (FC&#181; = 1.95 and 1.85) and UGT306A2 (FC&#181; = 2.29 and 2.28) were present.
qPCR validation of two of these transcripts (GSTMS1, a detoxification transcript; and AGAP009110-RA, an unknown, mosquito-specific transcript containing a &#946;-1,3-glucan binding domain) were performed as previously described1. Analysis was performed using primer sets described in Additional File 3 and showed that these transcripts were significantly upregulated in a multiresistant population from C&#244;te D&#39;Ivoire (Tiassal&#233;) and another from Burkina Faso (Banfora), compared to the lab-susceptible N&#39;Gousso (Figure 4A).
As both transcripts showed significant upregulation in each of the resistant populations, RNAi-induced knockdown was performed on mosquitoes from the LSTM laboratory Tiassal&#233; colony. This colony originates from C&#244;te D&#39;Ivoire and is resistant to all major classes of insecticide used in public health, as previously described1,10. Attenuation of expression of GSTMS1 resulted in a significant increase (p = 0.021) in mortality after deltamethrin exposure compared to GFP-injected controls, demonstrating the importance of this transcript in pyrethroid resistance (Figure 4B). Conversely, AGAP009110-RA knockdown resulted in no significant (p = 0.082) change in mortality after exposure (Figure 4B).
GSTMS1 is a microsomal GST and is one of three found in A. gambiae mosquitoes11. Although members of the epsilon and delta classes of GSTs have been previously implicated in insecticide detoxification12,13,14, this is the first evidence to our knowledge for a role of microsomal GSTs in pyrethroid resistance15. To explore the putative function of this transcript in Anopheles gambiae sl mosquitoes, the expression and correlation in IR-TEx were identified. GSTMS1 was significantly overexpressed in 20 out of 21 datasets available for these species, with the exception of Bioko Island. In each location, the overexpression was less than five-fold compared to the susceptible populations (Figure 5).
As microsomal GSTs have largely been ignored as potential insecticide detoxifiers, little is known about their role in insecticide resistance15. By exploring the co-correlation of other transcripts, putative functions can be elucidated through the assumption of coregulation or involvement in the same pathways. To maximize power in the correlation network, all microarray datasets present in IR-TEx were selected, and an |r| of &#62;0.75 was selected. Table 2 shows the output from IR-TEx.
These transcripts are enriched in oxioreductase activity and glucose/carbohydrate metabolism in DAVID&#39;s functional annotation tool8. Both glucose-6-phosphate dehydrogenase and cytathione gamma-lyase maintain the level of glutathione in mammalian cells16,17 and thus link directly with GSTMS1, a glutathione-S-transferase. Catalase is a fast-acting oxidative stress responder that protects cells from reactive oxygen species damage, a byproduct of pyrethroid exposure. Valacyclovir hydrolase is a hydrolase that may play a role in detoxification in mammalian cells18. CYP4H17 is also present in the correlation network. Cytochrome p450s are direct metabolizers of pyrethroid insecticides, and these breakdown products can be further metabolized by GSTs. Finally, CYP4H17 has been implicated in pyrethroid resistance in A. funestus19. Taken together, these data strongly support a role for GSTMS1 in xenobiotic detoxification.
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/60721/60721fig1a.jpg" /> 
Figure 1: Log2 fold change of AGAP002865-RA in all datasets. The x-axis details the different datasets, information for which can be found in Supplementary Table 1 in a previous publication1, and the y-axis shows the log2 fold change in the transcript of interest. The light-grey dotted lines indicate approximate thresholds for significance, taken here to be a fold change of &#60;0.8 or fold change of &#62;1.2. The dotted black line indicates a fold change of 1 (i.e., no difference in expression between the resistant and susceptible populations). <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/60721/60721fig1alarge.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 2" class="xfigimg" src="/files/ftp_upload/60721/60721fig02.jpg" /> 
Figure 2: Distribution of microarrays showing significant differential expression of AGAP002865-RA in resistant populations. Fold changes are represented in a traffic light system: green fold change of &#60;1, orange fold change of &#62;1, and red fold change of &#62;5. Only datasets with significant (p &#8804; 0.05) differential expression are shown. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/60721/60721fig2alarge.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 3" class="xfigimg" src="/files/ftp_upload/60721/60721fig3a.jpg" /> 
Figure 3: Correlation networks of AGAP001076-RA (CYP4G16). Pairwise correlations are calculated across all transcripts across the 31 microarray datasets, with a user-defined cut-off applied. Shown here is (A) |r| &#62; 0.9 and (B) |r| &#62; 0.8. All transcripts displayed on the graph meet this criterion and follow the expression changes of AGAP001076-RA. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/60721/60721fig3alarge.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 4" class="xfigimg" src="/files/ftp_upload/60721/60721fig04.jpg" /> 
Figure 4: mRNA expression and phenotype upon attenuation of GSTMS1 and AGAP009110-RA. (A) mRNA expression of GSTMS1 and AGAP009110-RA in two multi-resistant An. coluzzii populations from C&#244;te D&#39;Ivoire and Burkina Faso, respectively. Levels were compared to the lab-susceptible An. coluzzii N&#39;Gousso. Significance levels calculated by ANOVA with a post-hoc Dunnett&#39;s test. (B) RNAi-induced attenuation of both transcripts compared to GFP-injected controls. GSTMS1 attenuation shows significant increase in mortality after deltamethrin exposure (calculated by ANOVA with a post-hoc Tukey test; *p &#8804; 0.05, **p &#8804; 0.01). <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/60721/60721fig04large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 5" class="xfigimg" src="/files/ftp_upload/60721/60721fig05.jpg" /> 
Figure 5: Expression of GSTMS1 in Anopheles gambiae and Anopheles coluzzii populations. Map showing the significantly differential expression of GSTMS1 in available microarray datasets. GSTMS1 was found to be significantly differential in 20 out of 21 microarray datasets. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/60721/60721fig5alarge.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>Transcript ID</td>
			<td>Description</td>
			<td>Burkina Faso</td>
			<td>C&#244;te D&#39;Ivoire</td>
		</tr>
		<tr>
			<td>AGAP006879-RA</td>
			<td>ATPase</td>
			<td>27.94</td>
			<td>43.05</td>
		</tr>
		<tr>
			<td>AGAP007160-RB</td>
			<td>a-crystallin</td>
			<td>11.49</td>
			<td>10.58</td>
		</tr>
		<tr>
			<td>AGAP007160-RC</td>
			<td>a-crystallin</td>
			<td>11.14</td>
			<td>10.38</td>
		</tr>
		<tr>
			<td>AGAP007160-RA</td>
			<td>a-crystallin</td>
			<td>9.78</td>
			<td>9.84</td>
		</tr>
		<tr>
			<td>AGAP009110-RA</td>
			<td>Unknown</td>
			<td>9.26</td>
			<td>5.96</td>
		</tr>
		<tr>
			<td>AGAP007780-RA</td>
			<td>NADH dehydrogenase</td>
			<td>10.49</td>
			<td>3.77</td>
		</tr>
		<tr>
			<td>AGAP006383-RA</td>
			<td>oligosaccharyltransferase complex subunit beta</td>
			<td>3.69</td>
			<td>5.57</td>
		</tr>
		<tr>
			<td>AGAP007249-RB</td>
			<td>Flightin</td>
			<td>4.61</td>
			<td>3.86</td>
		</tr>
		<tr>
			<td>AGAP003357-RA</td>
			<td>RAG1-activating protein 1-like protein</td>
			<td>4.31</td>
			<td>4.05</td>
		</tr>
		<tr>
			<td>AGAP007249-RA</td>
			<td>Flightin</td>
			<td>4.48</td>
			<td>3.46</td>
		</tr>
		<tr>
			<td>AGAP001998-RA</td>
			<td>mRpS10</td>
			<td>3.46</td>
			<td>2.85</td>
		</tr>
		<tr>
			<td>AGAP007589-RA</td>
			<td>UGT306A2</td>
			<td>2.29</td>
			<td>2.28</td>
		</tr>
		<tr>
			<td>AGAP000165-RA</td>
			<td>GSTMS1</td>
			<td>1.95</td>
			<td>1.85</td>
		</tr>
		<tr>
			<td>AGAP002101-RA</td>
			<td>isoleucyl-tRNA synthetase</td>
			<td>0.57</td>
			<td>0.59</td>
		</tr>
		<tr>
			<td>AGAP002969-RA</td>
			<td>asparaginyl-tRNA synthetase</td>
			<td>0.45</td>
			<td>0.45</td>
		</tr>
		<tr>
			<td>AGAP004199-RA</td>
			<td>solute carrier family 5 (sodium-coupled monocarboxylate transporter), member 8</td>
			<td>0.35</td>
			<td>0.48</td>
		</tr>
		<tr>
			<td>AGAP004684-RA</td>
			<td>rRNA-processing protein CGR1</td>
			<td>0.36</td>
			<td>0.22</td>
		</tr>
		<tr>
			<td>AGAP006414-RA</td>
			<td>Cht8</td>
			<td>0.024</td>
			<td>0.36</td>
		</tr>
	</tbody>
</table>
Table 1: Transcripts significantly differential in the same fold change direction across Burkina Faso and C&#244;te D&#39;Ivoire populations. Transcript ID, gene description, and average fold change for each dataset from the two countries representing An. coluzzii and An. gambiae populations.
<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>Correlation</td>
			<td>Systematic Name</td>
			<td>Transcript Type</td>
		</tr>
		<tr>
			<td>1</td>
			<td>AGAP000165-RA</td>
			<td>GSTMS1</td>
		</tr>
		<tr>
			<td>0.82</td>
			<td>AGAP004904-RA</td>
			<td>Catalase</td>
		</tr>
		<tr>
			<td>0.76</td>
			<td>AGAP007243-RA</td>
			<td>26S protease regulatory subunit 8</td>
		</tr>
		<tr>
			<td>0.79</td>
			<td>AGAP008358-RA</td>
			<td>CYP4H17</td>
		</tr>
		<tr>
			<td>0.76</td>
			<td>AGAP009436-RA</td>
			<td>Valacyclovir hydrolase</td>
		</tr>
		<tr>
			<td>0.75</td>
			<td>AGAP010739-RA</td>
			<td>Glucose-6-phosphate 1-dehydrogenase</td>
		</tr>
		<tr>
			<td>0.85</td>
			<td>AGAP011172-RA</td>
			<td>cystathionine gamma-lyase</td>
		</tr>
		<tr>
			<td>0.76</td>
			<td>AGAP012678-RA</td>
			<td>Glucose-6-phosphate 1-dehydrogenase</td>
		</tr>
	</tbody>
</table>
Table 2: Transcripts co-correlated with GSTMS1. The table shows output of the correlation network for GSTMS1 on IR-TEx with |r| of &#62;0.75. The table shows the Spearman&#39;s correlation, transcript ID, and gene description for each co-correlated transcript.
Additional File 1: Output file from A-MEXP-2196 array analyzed on limma. The file originates from a Met knockdown compared to a GFP control array, described in more detail in ArrayExpress (E-MTAB-4043) and another previous publication1. Columns represent AGAP identifier (SystematicName), log fold change (logFC), log expression values (AveExpr), t-statistic (t), uncorrected p-value (P.Value), adjusted p-value (adj.P.Val), and B statistic (B)20. For the purposes of this file, the mosquitoes are Anopheles coluzzi from C&#244;te D&#39;Ivoire and are unexposed to insecticides, with a collection latitude and longitude of -5.4 and 6.0, respectively. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/60721/additional_file1.txt" target="_blank">Please click here to view this file (Right click to download).</a>
Additional File 2: Output file from RNAseq experiment. RNAseq analysis taken from Uyhelji et al.9 describing changes in the transcriptome of Anopheles mosquitoes when exposed to 50% salinity. This file is adapted from Table S2 of the publication and includes AGAP identifier (SystematicID), raw fold change (Fold_Change), and adjusted p-value (q_value). <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/60721/additional_file2.txt" target="_blank">Please click here to view this file (Right click to download).</a>
Additional File 3: Primer list for representative results. AGAP identifier, gene name, dsRNA forward, dsRNA reverse, qPCR forward, and qPCR reverse primer sets for each transcript. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/60721/additional_file3.txt" target="_blank">Please click here to view this file (Right click to download).</a>
Supplemental coding File 1. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/60721/Supplemental_coding_File_1.R" target="_blank">Please click here to view this file (Right click to download).</a>
Supplemental coding File 2. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/60721/Supplemental_coding_File_2.R" target="_blank">Please click here to view this file (Right click to download).</a>
Supplemental coding File 3. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/60721/Supplemental_coding_File_3.R" target="_blank">Please click here to view this file (Right click to download).</a>

Watch this Scientific Journal Video about IR-TEx: An Open Source Data Integration Tool for Big Data Transcriptomics Designed for the Malaria Vector Anopheles gambiae at JoVE.com

IR-TEx: An Open Source Data Integration Tool for Big Data Transcriptomics Designed for the Malaria Vector Anopheles gambiae

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.

IR-TEx: An Open Source Data Integration Tool for Big Data Transcriptomics Designed for the Malaria Vector Anopheles gambiae

IR-TEx is an application written in Shiny (an R package) that allows exploration of the expression of (as well as assigning functions to) transcripts ...

ir-tex-an-open-source-data-integration-tool-for-big-data

Nanyang Technological University

Research

JoVE Journal

Biology

6.1K Views.  Liverpool School of Tropical Medicine. Dit protocol is belangrijk, omdat het de eerste keer is dat transcriptomische gegevens in Anopheles Gambiae muggen vrij en gemakkelijk toegankelijk zijn gemaakt op één plaats. Deze techniek stelt alle onderzoekers, zelfs mensen zonder achtergronden in de bioinformatica in staat om vrij en gemakkelijk toegang te krijgen tot expressiegegevens over hun favoriete genen en insecticide-resistente muggen. Volg om te beginnen de link onder aan de LSTM IR-TEx-projectpagina om de IR-Tex-webtoepassing...

Video: IR-TEx: An Open Source Data Integration Tool for Big Data Transcriptomics Designed for the Malaria Vector Anopheles gambiae

Maintaining Biological Cultures and Measuring Gene Expression in Aphis nerii: A Non-model System for Plant-insect Interactions

Aphids are excellent experimental models for a variety of biological questions ranging from the evolution of symbioses and the development of polyphenisms to questions surrounding insect's interactions with their host plants. Genomic resources are available for several aphid species, and with advances in the next-generation sequencing, transcriptomic studies are being extended to non-model organisms that lack genomes. Furthermore, aphid cultures can be collected from the field and reared in the laboratory for the use in organismal and molecular experiments to bridge the gap between ecological and genetic studies. Last, many aphids can be maintained in the laboratory on their preferred host plants in perpetual, parthenogenic life cycles allowing for comparisons of asexually reproducing genotypes. Aphis nerii, the milkweed-oleander aphid, provides one such model to study insect interactions with toxic plants using both organismal and molecular experiments. Methods for the generation and maintenance of the plant and aphid cultures in the greenhouse and laboratory, DNA and RNA extractions, microsatellite analysis, de novo transcriptome assembly and annotation, transcriptome differential expression analysis, and qPCR verification of differentially expressed genes are outlined and discussed here.

Maintaining Biological Cultures and Measuring Gene Expression in Aphis nerii: A Non-model System for Plant-insect Interactions

The aphid Aphis nerii colonizes on highly-defended plants in the dogbane family (Apocyanaceae) and provides numerous opportunities to study plant-insect interactions. Here, we present a series of protocols for the maintenance of plant and aphid cultures, and the generation and analysis of molecular and -omic data for A. nerii.

Aphids are excellent experimental models for a variety of biological questions ranging from the evolution of symbioses and the development of polyphenisms ...

Genetics

RNA Next-Generation Sequencing and a Bioinformatics Pipeline to Identify Expressed LINE-1s at the Locus-Specific Level

Long INterspersed Elements-1 (LINEs/L1s) are repetitive elements that can copy and randomly insert in the genome resulting in genomic instability and mutagenesis. Understanding the expression patterns of L1 loci at the individual level will lend to the understanding of the biology of this mutagenic element. This autonomous element makes up a significant portion of the human genome with over 500,000 copies, though 99% are truncated and defective. However, their abundance and dominant number of defective copies make it challenging to identify authentically expressed L1s from L1-related sequences expressed as part of other genes. It is also challenging to identify which specific L1 locus is expressed due to the repetitive nature of the elements. Overcoming these challenges, we present an RNA-Seq bioinformatic approach to identify L1 expression at the locus specific level. In summary, we collect cytoplasmic RNA, select for polyadenylated transcripts, and utilize strand-specific RNA-Seq analyses to uniquely map reads to L1 loci in the human reference genome. We visually curate each L1 locus with uniquely mapped reads to confirm transcription from its own promoter and adjust mapped transcript reads to account for mappability of each individual L1 locus. This approach was applied to a prostate tumor cell line, DU145, to demonstrate the ability of this protocol to detect expression from a small number of the full-length L1 elements.

Here, we present a bioinformatic approach and analyses to identify LINE-1 expression at the locus specific level.

Long INterspersed Elements-1 (LINEs/L1s) are repetitive elements that can copy and randomly insert in the genome resulting in genomic instability and ...

Using the GAL4-UAS System for Functional Genetics in Anopheles gambiae

The bipartite GAL4-UAS system is a versatile and powerful tool for functional genetic analysis. The essence of the system is to cross transgenic 'driver' lines that express the yeast transcription factor GAL4 in a tissue specific manner, with transgenic 'responder' lines carrying a candidate gene/RNA interference construct whose expression is controlled by Upstream Activation Sequences (UAS) that bind GAL4. In the ensuing progeny, the gene or silencing construct is thus expressed in a prescribed spatiotemporal manner, enabling the resultant phenotypes to be assayed and gene function inferred. The binary system enables flexibility in experimental approaches to screen phenotypes generated by transgene expression in multiple tissue-specific patterns, even if severe fitness costs are induced. We have adapted this system for Anopheles gambiae, the principal malaria vector in Africa.
In this article, we provide some of the common procedures used during GAL4-UAS analysis. We describe the An. gambiae GAL4-UAS lines already generated, as well as the cloning of new responder constructs for upregulation and RNAi knockdown. We specify a step by step guide for sexing of mosquito pupae to establish genetic crosses, that also includes screening progeny to follow inheritance of fluorescent gene markers that tag the driver and responder insertions. We also present a protocol for clearing An. gambiae embryos to study embryonic development. Finally, we introduce potential adaptions of the method to generate driver lines through CRISPR/Cas9 insertion of GAL4 downstream of target genes.

Using the GAL4-UAS System for Functional Genetics in Anopheles gambiae

The bipartite GAL4-UAS system is a versatile tool for modification of gene expression in a controlled spatiotemporal manner which permits functional genetic analysis in Anopheles gambiae. The procedures described for using this system are a semi-standardized cloning strategy, sexing and screening of pupae for fluorescent protein markers and embryo fixation.

The bipartite GAL4-UAS system is a versatile and powerful tool for functional genetic analysis. The essence of the system is to cross transgenic 'driver' ...

Site-Directed &#966;C31-Mediated Integration and Cassette Exchange in Anopheles Vectors of Malaria

Functional genomic analysis and related strategies for genetic control of malaria rely on validated and reproducible methods to accurately modify the genome of Anopheles mosquitoes. Amongst these methods, the &#966;C31 system allows precise and stable site-directed integration of transgenes, or the substitution of integrated transgenic cassettes via recombinase-mediated cassette exchange (RMCE). This method relies on the action of the Streptomyces &#966;C31 bacteriophage integrase to catalyze recombination between two specific attachment sites designated attP (derived from the phage) and attB (derived from the host bacterium). The system uses one or two attP sites that have been integrated previously into the mosquito genome and attB site(s) in the donor template DNA. Here we illustrate how to stably modify the genome of attP-bearing Anopheles docking lines using two plasmids: an attB-tagged donor carrying the integration or exchange template and a helper plasmid encoding the &#966;C31 integrase. We report two representative results of &#966;C31-mediated site-directed modification: the single integration of a transgenic cassette in An. stephensi and RMCE in An. gambiae mosquitoes. &#966;C31-mediated genome manipulation offers the advantage of reproducible transgene expression from validated, fitness neutral genomic sites, allowing comparative qualitative and quantitative analyses of phenotypes. The site-directed nature of the integration also substantially simplifies the validation of the single insertion site and the mating scheme to obtain a stable transgenic line. These and other characteristics make the &#966;C31 system an essential component of the genetic toolkit for the transgenic manipulation of malaria mosquitoes and other insect vectors.

The protocol describes how to achieve site-directed modifications in the genome of Anopheles malaria mosquitoes using the &#966;C31 system. Modifications described include both the integration and the exchange of transgenic cassettes in the genome of attP-bearing docking lines.

Functional genomic analysis and related strategies for genetic control of malaria rely on validated and reproducible methods to accurately modify the ...

Indel Detection following CRISPR/Cas9 Mutagenesis using High-resolution Melt Analysis in the Mosquito Aedes aegypti

Mosquito gene editing has become routine in several laboratories with the establishment of systems such as transcription-activator-like effector nucleases (TALENs), zinc-finger nucleases (ZFNs), and homing endonucleases (HEs). More recently, clustered regularly interspaced short palindromic repeats (CRISPR)/CRISPR-associated protein 9 (Cas9) technology has offered an easier and cheaper alternative for precision genome engineering. Following nuclease action, DNA repair pathways will fix the broken DNA ends, often introducing indels. These out-of-frame mutations are then used for understanding gene function in the target organisms. A drawback, however, is that mutant individuals carry no dominant marker, making identification and tracking of mutant alleles challenging, especially at scales needed for many experiments. 
High-resolution melt analysis (HRMA) is a simple method to identify variations in nucleic acid sequences and utilizes PCR melting curves to detect such variations. This post-PCR analysis method uses fluorescent double-stranded DNA-binding dyes with instrumentation that has temperature ramp control data capture capability and is easily scaled to 96-well plate formats. Described here is a simple workflow using HRMA for the rapid detection of CRISPR/Cas9-induced indels and the establishment of mutant lines in the mosquito Ae. aegypti. Critically, all steps can be performed with a small amount of leg tissue and do not require sacrificing the organism, allowing genetic crosses or phenotyping assays to be performed after genotyping.

Indel Detection following CRISPR/Cas9 Mutagenesis using High-resolution Melt Analysis in the Mosquito Aedes aegypti

This article details a protocol for rapid identification of indels induced by CRISPR/Cas9 and selection of mutant lines in the mosquito Aedes aegypti using high-resolution melt analysis.

Mosquito gene editing has become routine in several laboratories with the establishment of systems such as transcription-activator-like effector nucleases ...

High-throughput Physical Mapping of Chromosomes using Automated in situ Hybridization

Projects to obtain whole-genome sequences for 10,000 vertebrate species1 and for 5,000 insect and related arthropod species2 are expected to take place over the next 5 years. For example, the sequencing of the genomes for 15 malaria mosquitospecies is currently being done using an Illumina platform3,4. This Anopheles species cluster includes both vectors and non-vectors of malaria. When the genome assemblies become available, researchers will have the unique opportunity to perform comparative analysis for inferring evolutionary changes relevant to vector ability. However, it has proven difficult to use next-generation sequencing reads to generate high-quality de novo genome assemblies5. Moreover, the existing genome assemblies for Anopheles gambiae, although obtained using the Sanger method, are gapped or fragmented4,6.
Success of comparative genomic analyses will be limited if researchers deal with numerous sequencing contigs, rather than with chromosome-based genome assemblies. Fragmented, unmapped sequences create problems for genomic analyses because: (i) unidentified gaps cause incorrect or incomplete annotation of genomic sequences; (ii) unmapped sequences lead to confusion between paralogous genes and genes from different haplotypes; and (iii) the lack of chromosome assignment and orientation of the sequencing contigs does not allow for reconstructing rearrangement phylogeny and studying chromosome evolution. Developing high-resolution physical maps for species with newly sequenced genomes is a timely and cost-effective investment that will facilitate genome annotation, evolutionary analysis, and re-sequencing of individual genomes from natural populations7,8.
Here, we present innovative approaches to chromosome preparation, fluorescent in situ hybridization (FISH), and imaging that facilitate rapid development of physical maps. Using An. gambiae as an example, we demonstrate that the development of physical chromosome maps can potentially improve genome assemblies and, thus, the quality of genomic analyses. First, we use a high-pressure method to prepare polytene chromosome spreads. This method, originally developed for Drosophila9, allows the user to visualize more details on chromosomes than the regular squashing technique10. Second, a fully automated, front-end system for FISH is used for high-throughput physical genome mapping. The automated slide staining system runs multiple assays simultaneously and dramatically reduces hands-on time11. Third, an automatic fluorescent imaging system, which includes a motorized slide stage, automatically scans and photographs labeled chromosomes after FISH12. This system is especially useful for identifying and visualizing multiple chromosomal plates on the same slide. In addition, the scanning process captures a more uniform FISH result. Overall, the automated high-throughput physical mapping protocol is more efficient than a standard manual protocol.

High-throughput Physical Mapping of Chromosomes using Automated in situ Hybridization

Genome assemblies based on massively parallel DNA sequencing technologies are usually highly fragmented. The development of physical chromosome maps can potentially improve genome assemblies. Here, we demonstrate innovative approaches to chromosome preparation, fluorescent in situ hybridization, and imaging that significantly increase throughput of the physical map development.

Projects to obtain whole-genome sequences for 10,000 vertebrate species1 and for 5,000 insect and related arthropod species2 are expected to take place ...

An Experimental and Bioinformatics Protocol for RNA-seq Analyses of Photoperiodic Diapause in the Asian Tiger Mosquito, Aedes albopictus

Photoperiodic diapause is an important adaptation that allows individuals to escape harsh seasonal environments via a series of physiological changes, most notably developmental arrest and reduced metabolism. Global gene expression profiling via RNA-Seq can provide important insights into the transcriptional mechanisms of photoperiodic diapause. The Asian tiger mosquito, Aedes albopictus, is an outstanding organism for studying the transcriptional bases of diapause due to its ease of rearing, easily induced diapause, and the genomic resources available. This manuscript presents a general experimental workflow for identifying diapause-induced transcriptional differences in A. albopictus. Rearing techniques, conditions necessary to induce diapause and non-diapause development, methods to estimate percent diapause in a population, and RNA extraction and integrity assessment for mosquitoes are documented. A workflow to process RNA-Seq data from Illumina sequencers culminates in a list of differentially expressed genes. The representative results demonstrate that this protocol can be used to effectively identify genes differentially regulated at the transcriptional level in A. albopictus due to photoperiodic differences. With modest adjustments, this workflow can be readily adapted to study the transcriptional bases of diapause or other important life history traits in other mosquitoes.

An Experimental and Bioinformatics Protocol for RNA-seq Analyses of Photoperiodic Diapause in the Asian Tiger Mosquito, Aedes albopictus

RNA-Seq analyses are becoming increasingly important for identifying the molecular underpinnings of adaptive traits in non-model organisms. Here, a protocol to identify differentially expressed genes between diapause and non-diapause Aedes albopictus mosquitoes is described, from mosquito rearing, to RNA sequencing and bioinformatics analyses of RNA-Seq data.

Photoperiodic diapause is an important adaptation that allows individuals to escape harsh seasonal environments via a series of physiological changes, ...

A Multi-detection Assay for Malaria Transmitting Mosquitoes

The Anopheles gambiae species complex includes the major malaria transmitting mosquitoes in Africa. Because these species are of such medical importance, several traits are typically characterized using molecular assays to aid in epidemiological studies. These traits include species identification, insecticide resistance, parasite infection status, and host preference. Since populations of the Anopheles gambiae complex are morphologically indistinguishable, a polymerase chain reaction (PCR) is traditionally used to identify species. Once the species is known, several downstream assays are routinely performed to elucidate further characteristics. For instance, mutations known as KDR in a para gene confer resistance against DDT and pyrethroid insecticides. Additionally, enzyme-linked immunosorbent assays (ELISAs) or Plasmodium parasite DNA detection PCR assays are used to detect parasites present in mosquito tissues. Lastly, a combination of PCR and restriction enzyme digests can be used to elucidate host preference (e.g., human vs. animal blood) by screening the mosquito bloodmeal for host-specific DNA. We have developed a multi-detection assay (MDA) that combines all of the aforementioned assays into a single multiplex reaction genotyping 33SNPs for 96 or 384 samples at a time. Because the MDA includes multiple markers for species, Plasmodium detection, and host blood identification, the likelihood of generating false positives or negatives is greatly reduced from previous assays that include only one marker per trait. This robust and simple assay can detect these key mosquito traits cost-effectively and in a fraction of the time of existing assays.

Malaria transmitting mosquitoes have a number of epidemiologically important characteristics that can only be detected using molecular techniques. Utilizing a MALDI-TOF based SNP genotyping platform, we developed an assay for simultaneously detecting multiple key traits (species, insecticide resistance, parasite infection and host choice) of malaria vectors.

The Anopheles gambiae species complex includes the major malaria transmitting mosquitoes in Africa. Because these species are of such medical importance, ...

RNAi Trigger Delivery into Anopheles gambiae Pupae

RNA interference (RNAi), a naturally occurring phenomenon in eukaryotic organisms, is an extremely valuable tool that can be utilized in the laboratory for functional genomic studies. The ability to knockdown individual genes selectively via this reverse genetic technique has allowed many researchers to rapidly uncover the biological roles of numerous genes within many organisms, by evaluation of loss-of-function phenotypes. In the major human malaria vector Anopheles gambiae, the predominant method used to reduce the function of targeted genes involves injection of double-stranded (dsRNA) into the hemocoel of the adult mosquito. While this method has been successful, gene knockdown in adults excludes the functional assessment of genes that are expressed and potentially play roles during pre-adult stages, as well as genes that are expressed in limited numbers of cells in adult mosquitoes. We describe a method for the injection of Serine Protease Inhibitor 2 (SRPN2) dsRNA during the early pupal stage and validate SRPN2 protein knockdown by observing decreased target protein levels and the formation of melanotic pseudo-tumors in SRPN2 knockdown adult mosquitoes. This evident phenotype has been described previously for adult stage knockdown of SRPN2 function, and we have recapitulated this adult phenotype by SRPN2 knockdown initiated during pupal development. When used in conjunction with a dye-labeled dsRNA solution, this technique enables easy visualization by simple light microscopy of injection quality and distribution of dsRNA in the hemocoel.

RNAi Trigger Delivery into Anopheles gambiae Pupae

RNA interference (RNAi) is an extremely valuable tool for uncovering gene function. However, the ability to target genes using RNAi during pre-adult stages is limited in the major human malaria vector Anopheles gambiae. We describe an RNAi protocol to reduce gene function via direct injection during pupal development.

RNA interference (RNAi), a naturally occurring phenomenon in eukaryotic organisms, is an extremely valuable tool that can be utilized in the laboratory ...

A Magnetic-Bead-Based Mosquito DNA Extraction Protocol for Next-Generation Sequencing

A recently published DNA extraction protocol using magnetic beads and an automated DNA extraction instrument suggested that it is possible to extract high quality and quantity DNA from a well-preserved individual mosquito sufficient for downstream whole genome sequencing. However, reliance on an expensive automated DNA extraction instrument can be prohibitive for many laboratories. Here, the study provides a budget-friendly magnetic-bead-based DNA extraction protocol, which is suitable for low to medium throughput. The protocol described here was successfully tested using individual Aedes aegypti mosquito samples. The reduced costs associated with high quality DNA extraction will increase the application of high throughput sequencing to resource limited labs and studies.

Described here is a DNA extraction protocol using magnetic beads to produce high quality DNA extractions from mosquitoes. These extractions are suitable for a downstream next-generation sequencing approach.

A recently published DNA extraction protocol using magnetic beads and an automated DNA extraction instrument suggested that it is possible to extract high ...

IR-TEx: An Open Source Data Integration Tool for Big Data Transcriptomics Designed for the Malaria Vector Anopheles gambiae

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