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>

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J., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Cytochrome+P450+6M2+from+the+malaria+vector+Anopheles+gambiae+metabolizes+pyrethroids:+Sequential+metabolism+of+deltamethrin+revealed.">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>M&#252;ller, P., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Field-Caught+Permethrin-Resistant+Anopheles+gambiae+Overexpress+CYP6P3,+a+P450+That+Metabolises+Pyrethroids.">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/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+DAVID+Gene+Functional+Classification+Tool:+a+novel+biological+module-centric+algorithm+to+functionally+analyze+large+gene+lists.">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. 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M., Weetman, D., Ranson, H. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Multiple-Insecticide+Resistance+in+Anopheles+gambiae+Mosquitoes,+Southern+C&#244;te+d&#8217;Ivoire.">Multiple-Insecticide Resistance in Anopheles gambiae Mosquitoes, Southern C&#244;te d&#8217;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/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+Anopheles+gambiae+glutathione+transferase+supergene+family:+annotation,+phylogeny+and+expression+profiles.">The Anopheles gambiae glutathione transferase supergene family: annotation, phylogeny and expression profiles.</a> BMC Genomics. 4 (1), 1-16 (2003).</li><li>Enayati, A. 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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 border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<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><a href="https://www.r-project.org/" target="_blank">https://www.r-project.org/</a></td>
		</tr>
		<tr>
			<td>R Studio</td>
			<td>R Studio</td>
			<td>-</td>
			<td><a href="https://www.rstudio.com/" target="_blank">https://www.rstudio.com/</a></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/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/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/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/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/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/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/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/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/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/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/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 ...

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6.0K Views.  Liverpool School of Tropical Medicine. 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.

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

Protocol for Mosquito Rearing (A. gambiae)

This protocol describes mosquito rearing in the insectary. The insectary rooms are maintained at 28&deg;C and ~80% humidity, with a 12 hr. day/night cycle. For this procedure, you'll need mosquito cages, 10% sterile sucrose solution, paper towels, beaker, whatman filter paper, glass feeders, human blood and serum, water bath, parafilm, distilled water, clean plastic trays, mosquito food (described below), mosquito net to cover the trays, vacuum, and a collection chamber to collect adults.

Protocol for Mosquito Rearing A. gambiae

This video illustrates the general techniques used to rear Anopheles gambiae in the laboratory. The methods for caring for laboratory mosquitoes are demonstrated through all stages of the organism's life cycle from larvae to pupae to blood-feeding adults.

This protocol describes mosquito rearing in the insectary. The insectary rooms are maintained at 28&deg;C and ~80% humidity, with a 12 hr. day/night ...

Population Replacement Strategies for Controlling Vector Populations and the Use of Wolbachia pipientis for Genetic Drive

In this video, Jason Rasgon discusses population replacement strategies to control vector-borne diseases such as malaria and dengue. "Population replacement" is the replacement of wild vector populations (that are competent to transmit pathogens) with those that are not competent to transmit pathogens. There are several theoretical strategies to accomplish this. One is to exploit the maternally-inherited symbiotic bacteria Wolbachia pipientis. Wolbachia is a widespread reproductive parasite that spreads in a selfish manner at the extent of its host's fitness. Jason Rasgon discusses, in detail, the basic biology of this bacterial symbiont and various ways to use it for control of vector-borne diseases.

In this interview, Jason Rasgon explains the concept of genetic drive and the characteristics of an effective gene drive system. The use of the endosymbiotic bacterium, Wolbachia pipientis, as a means to spread genes through mosquito populations, is hypothesized.

In this video, Jason Rasgon discusses population replacement strategies to control vector-borne diseases such as malaria and dengue. "Population ...

Protocol for RNAi Assays in Adult Mosquitoes (A. gambiae)

Reverse genetic approaches have proven extremely useful for determining  which genes underly resistance to vector pathogens in mosquitoes.  This video protocol illustrates a method used by the Dimopoulos lab to inject dsRNA into Anopheles gambiae mosquitoes, which harbor the malaria parasite.  The technique manipulating the injection setup and injecting dsRNA into the thorax is illustrated.

Protocol for RNAi Assays in Adult Mosquitoes A. gambiae

Reverse genetic approaches have proven extremely useful for determining which genes underly resistance to vector pathogens in mosquitoes. This video protocol illustrates a method used by the Dimopoulos lab to inject dsRNA into Anopheles gambiae mosquitoes, which harbor the malaria parasite. The technique manipulating the injection setup and injecting dsRNA into the thorax is illustrated.

Reverse genetic approaches have proven extremely useful for determining  which genes underly resistance to vector pathogens in mosquitoes.  This video ...

Building a Better Mosquito: Identifying the Genes Enabling Malaria and Dengue Fever Resistance in A. gambiae and A. aegypti Mosquitoes

In this interview, George Dimopoulos focuses on the physiological mechanisms used by mosquitoes to combat Plasmodium falciparum and dengue virus infections.   Explanation is given for how key refractory genes, those genes conferring resistance to vector pathogens, are identified in the mosquito and how this knowledge can be used to generate transgenic mosquitoes that are unable to carry the malaria parasite or dengue virus.

In this interview, George Dimopoulos focuses on the physiological mechanisms used by mosquitoes to combat Plasmodium falciparum and dengue virus infections. Explanation is given for how key refractory genes, those genes conferring resistance to vector pathogens, are identified in the mosquito and how this knowledge can be used to generate transgenic mosquitoes that are unable to carry the malaria parasite or dengue virus.

In this interview, George Dimopoulos focuses on the physiological mechanisms used by mosquitoes to combat Plasmodium falciparum and dengue virus ...

Workflow for High-content, Individual Cell Quantification of Fluorescent Markers from Universal Microscope Data, Supported by Open Source Software

Advances in understanding the control mechanisms governing the behavior of cells in adherent mammalian tissue culture models are becoming increasingly dependent on modes of single-cell analysis. Methods which deliver composite data reflecting the mean values of biomarkers from cell populations risk losing subpopulation dynamics that reflect the heterogeneity of the studied biological system. In keeping with this, traditional approaches are being replaced by, or supported with, more sophisticated forms of cellular assay developed to allow assessment by high-content microscopy. These assays potentially generate large numbers of images of fluorescent biomarkers, which enabled by accompanying proprietary software packages, allows for multi-parametric measurements per cell. However, the relatively high capital costs and overspecialization of many of these devices have prevented their accessibility to many investigators.
Described here is a universally applicable workflow for the quantification of multiple fluorescent marker intensities from specific subcellular regions of individual cells suitable for use with images from most fluorescent microscopes. Key to this workflow is the implementation of the freely available Cell Profiler software1&#160;to distinguish individual cells in these images, segment them into defined subcellular regions and deliver fluorescence marker intensity values specific to these regions. The extraction of individual cell intensity values from image data is the central purpose of this workflow and will be illustrated with the analysis of control data from a siRNA screen for G1 checkpoint regulators in adherent human cells. However, the workflow presented here can be applied to analysis of data from other means of cell perturbation (e.g., compound screens) and other forms of fluorescence based cellular markers and thus should be useful for a wide range of laboratories.

Presented is a flexible informatics workflow enabling multiplexed image-based analysis of fluorescently labeled cells. The workflow quantifies nuclear and cytoplasmic markers and computes marker translocation between these compartments. Procedures are provided for perturbation of cells using siRNA and reliable methodology for marker detection by indirect immunofluorescence in 96-well formats.

Advances in understanding the control mechanisms governing the behavior of cells in adherent mammalian tissue culture models are becoming increasingly ...

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 ...

An Efficient In Vitro Transposition Method by a Transcriptionally Regulated Sleeping Beauty System Packaged into an Integration Defective Lentiviral Vector

The Sleeping Beauty (SB) transposon is a non-viral integrating system with proven efficacy for gene transfer and functional genomics. To optimize the SB transposon machinery, a transcriptionally regulated hyperactive transposase (SB100X) and T2-based transposon are employed. Typically, the transposase and transposon are provided transiently by plasmid transfection and SB100X expression is driven by a constitutive promoter. Here, we describe an efficient method to deliver the SB components to human cells that are resistant to several physical and chemical transfection methods, to control SB100X expression and stably integrate a gene of interest (GOI) through a &#34;cut and paste&#34; SB mechanism. The expression of hyperactive transposase is tightly controlled by the Tet-ON system, widely used to control gene expression since 1992. The gene of interest is flanked by inverted repeats (IR) of the T2 transposon. Both SB components are packaged in integration defective lentiviral vectors transiently produced in HEK293T cells. Human cells, either cell lines or primary cells from human tissue, are in vitro transiently transduced with viral vectors. Upon addition of doxycycline (dox, tetracycline analog) into the culture medium, a fine-tuning of transposase expression is measured and results in a long-lasting integration of the gene of interest in the genome of the treated cells. This method is efficient and applicable to the cell line (e.g., HeLa cells) and primary cells (e.g., human primary keratinocytes), and thus represents a valuable tool for genetic engineering and therapeutic gene transfer.

An Efficient In Vitro Transposition Method by a Transcriptionally Regulated Sleeping Beauty System Packaged into an Integration Defective Lentiviral Vector

This protocol describes a method to achieve stable integration of a gene of interest into the human genome by the transcriptionally regulated Sleeping Beauty system. Preparation of the integration of defective lentiviral vectors, the in vitro transduction of human cells, and the molecular assay on transduced cells are reported.

The Sleeping Beauty (SB) transposon is a non-viral integrating system with proven efficacy for gene transfer and functional genomics. To optimize the SB ...

Worm-align and Worm_CP, Two Open-Source Pipelines for Straightening and Quantification of Fluorescence Image Data Obtained from Caenorhabditis elegans

An issue often encountered when acquiring image data from fixed or anesthetized C. elegans is that worms cross and cluster with their neighbors. This problem is aggravated with increasing density of worms and creates challenges for imaging and quantification. We developed a FIJI-based workflow, Worm-align, that can be used to generate single- or multi-channel montages of user-selected, straightened and aligned worms from raw image data of C. elegans. Worm-align is a simple and user-friendly workflow that does not require prior training of either the user or the analysis algorithm. Montages generated with Worm-align can aid the visual inspection of worms, their classification and representation. In addition, the output of Worm-align can be used for subsequent quantification of fluorescence intensity in single worms, either in FIJI directly, or in other image analysis software platforms. We demonstrate this by importing the Worm-align output into Worm_CP, a pipeline that uses the open-source CellProfiler software. CellProfiler&#8217;s flexibility enables the incorporation of additional modules for high-content screening.&#160;As a practical example, we have used the pipeline on two datasets: the first dataset are images of heat shock reporter worms that express green fluorescent protein (GFP) under the control of the promoter of a heat shock inducible gene hsp-70, and the second dataset are images obtained from fixed worms, stained for fat-stores with a fluorescent dye.

Worm-align and Worm_CP, Two Open-Source Pipelines for Straightening and Quantification of Fluorescence Image Data Obtained from Caenorhabditis elegans

Worm-align/Worm_CP is a simple FIJI/CellProfiler workflow that can be used to straighten and align Caenorhabditis elegans samples and to score whole-worm image-based assays without the need for prior training steps. We have applied Worm-align/Worm_CP to the quantification of&#160;heat-shock induced expression in live animals or lipid droplets&#160;in fixed samples.

An issue often encountered when acquiring image data from fixed or anesthetized C. elegans is that worms cross and cluster with their neighbors. This ...

Dissection and Immunostaining of Larval Salivary Glands from Anopheles gambiae Mosquitoes

Mosquito salivary glands (SGs) are a requisite gateway organ for the transmission of insect-borne pathogens. Disease-causing agents, including viruses and the Plasmodium parasites that cause malaria, accumulate in the secretory cavities of SG cells. Here, they are poised for transmission to their vertebrate hosts during a subsequent blood meal. As adult glands form as an elaboration of larval SG duct bud remnants that persist beyond early pupal SG histolysis, the larval SG is an ideal target for interventions that limit disease transmission. Understanding larval SG development can help develop a better understanding of its morphology and functional adaptations and aid in the assessment of new interventions that target this organ. This video protocol demonstrates an efficient technique for isolating, fixing, and staining larval SGs from Anopheles gambiae mosquitoes. Glands dissected from larvae in a 25% ethanol solution are fixed in a methanol-glacial acetic acid mixture, followed by a cold acetone wash. After a few rinses in phosphate-buffered saline (PBS), SGs can be stained with a broad array of marker dyes and/or antisera against SG-expressed proteins. This method for larval SG isolation could also be used to collect tissue for in situ hybridization analysis, other transcriptomic applications, and proteomic studies.

Dissection and Immunostaining of Larval Salivary Glands from Anopheles gambiae Mosquitoes

The adult mosquito salivary gland (SG) is required for the transmission of all mosquito-borne pathogens to their human hosts, including viruses and parasites. This video demonstrates efficient isolation of the SGs from the larval (L4) stage Anopheles gambiae mosquitoes and preparation of the L4 SGs for further analysis.

Mosquito salivary glands (SGs) are a requisite gateway organ for the transmission of insect-borne pathogens. Disease-causing agents, including viruses and ...

Effective Oral RNA Interference (RNAi) Administration to Adult Anopheles gambiae Mosquitoes

RNA interference has been a heavily utilized tool for reverse genetic analysis for two decades. In adult mosquitoes, double-stranded RNA (dsRNA) administration has been accomplished primarily via injection, which requires significant time and is not suitable for field applications. To overcome these limitations, here we present a more efficient method for robust activation of RNAi by oral delivery of dsRNA to adult Anopheles gambiae. Long dsRNAs were produced in Escherichia coli strain HT115 (DE3), and a concentrated suspension of heat-killed dsRNA-containing bacteria in 10% sucrose was offered on cotton balls ad-libitum to adult mosquitoes. Cotton balls were replaced every 2 days for the duration of the treatment. Use of this method to target doublesex (a gene involved in sex differentiation) or fork head (which encodes a salivary gland transcription factor) resulted in reduced target gene expression and/or protein immunofluorescence signal, as measured by quantitative Real-Time PCR (qRT-PCR) or fluorescence confocal microscopy, respectively. Defects in salivary gland morphology were also observed. This highly flexible, user-friendly, low-cost, time-efficient method of dsRNA delivery could be broadly applicable to target genes important for insect vector physiology and beyond.

Effective Oral RNA Interference RNAi Administration to Adult Anopheles gambiae Mosquitoes

The oral administration of dsRNA produced by bacteria, a delivery method for RNA interference (RNAi) that is routinely used in Caenorhabditis elegans, was successfully applied here to adult mosquitoes. Our method allows for robust reverse genetics studies and transmission-blocking vector studies without the use of injection.