This work was supported by the National Natural Science Foundation of China (31371596), the Bio-technology Research Center, China Three Gorges University (2016KBC04), and the Natural Science Foundation of Jiangsu Province, China (BK20151424).

1. Sequence Alignment
<ol>
	<li>Collect amino acid sequences of eukaryotic SWEET and prokaryotic SemiSWEET in separate documents and list them in FASTA format. Download sequences from the National Center for Biotechnology Information (NCBI), European Molecular Biology Laboratory (EMBL), and the DNA Data Bank of Japan (DDBJ) databases by similarity search with the Basic Local Alignment Search Tool (BLAST) tool.
	<ol>
		<li>In the example files, collect 228 putative SWEET protein sequences possessing two MtN3/saliva domains (7 transmembrane helices) of eukaryotes and 57 SemiSWEET protein sequences possessing a single MtN3/saliva domain (3 transmembrane helices) of prokaryotes13.</li>
		<li>To simplify the process, select 35 candidate SWEET proteins from unicellular eukaryotic organisms among the 228 putative SWEETs for phylogenetic tree construction. These sequences are attached so that the reader may practice on a real data set.</li>
	</ol>
	</li>
	<li>Align the 35 SWEET sequences by inputting them into Clustal Omega (http://www.ebi.ac.uk/Tools/msa/clustalo/).
	<ol>
		<li>Copy and paste the protein sequences in FASTA format into the input box or upload a sequence file in FASTA format. Specify that they are&#160;amino acid sequence by clicking the icon under the pull-down menu in the &#39;STEP 1&#39; section.</li>
		<li>Specify output format and other parameters in the &#39;STEP 2&#39; section, if necessary. For this study, set output format as &#34;clustal w/o number&#34;, and leave the other parameters on default settings. In most cases, the default parameters work well without any specification.</li>
	</ol>
	</li>
	<li>Submit and run the alignment in the &#39;STEP 3&#39; section. It may take anywhere from several seconds to minutes until the alignment is finished. In the &#34;Result Summary&#34; panel, right-click the link under the &#34;Alignment in CLUSTAL format&#34; and save the aligned sequences as &#34;35.clustal&#34; (Figure 1).</li>
	<li>Open the alignment result file in BioEdit.
	<ol>
		<li>On the main panel of BioEdit, click &#34;Sequence&#34; and select &#34;Edit Mood&#34;in the first pull-down menu, then click &#34;Edit Residues&#34; in the sub-menu (Figure 2).</li>
		<li>Select the protruding sequences on the left side of the alignment with the cursor (the selected sequence will be shown in black) and click the &#34;Delete&#34; icon under the &#34;Edit&#34; menu to remove the selected sequences (Figure 3).</li>
		<li>Select and delete the protruding sequences on the right side of the first MtN3/saliva domain, and save the trimmed first MtN3/saliva domain sequences as 35-I.fas (Figure 4). Likewise, delete the left and right side protruding sequences of the second MtN3/saliva domain and save it as 35-II.fas. The first and the second MtN3/saliva domain sequences can be predicted with RHYTHM (http://proteinformatics.charite.de/rhythm/inndex.php?site=helix) or TMHMM (http://www.cbs.dtu.dk/services/TMHMM/) in advance.</li>
	</ol>
	</li>
	<li>Open the file 35-I.fas with MEGA, and click &#34;align&#34; when prompted. Under the &#34;Edit&#34; menu, click &#34;Select All&#34;, then click &#34;Select Sequence(s)&#34;; the names and sequences of the taxa will be selected in black (Figure 5).
	<ol>
		<li>Choose &#34;Copy&#34; from the &#34;Edit&#34; menu to copy the sequences onto the clipboard, and then paste the copied sequences into a doc file.</li>
		<li>In the doc file, replace all &#34;#&#34; with &#34;&#62;&#34;, and then delete any unrelated characters to convert them to FASTA format. Add &#34;-I&#34; at the end of each taxon name to mark them as the first MtN3/saliva domain sequences. Process the second MtN3/saliva domain sequence following the same method and add &#34;-II&#34; after each taxon name.</li>
	</ol>
	</li>
	<li>Combine the first and second MtN3/saliva domain sequences in FASTA format in a doc file.
	<ol>
		<li>Load the combined sequences into Clustal Omega again and align the sequences as described above. Save the result as &#34;35 realigned.clustal&#34;.</li>
		<li>Open the &#34;35 realigned.clustal&#34; file in BioEdit, delete the uneven (protruding) amino acid residues at either end of the aligned sequences, and then save the sequences as &#34;35 realigned.fas&#34;. Click &#34;Yes&#34; when warned that some non-standard characters cannot be saved.</li>
	</ol>
	</li>
</ol>
2. Computation of the Phylogenetic Tree
<ol>
	<li>Open &#34;35 realigned.fas&#34; in MEGA.
	<ol>
		<li>Click the &#34;Data&#34; menu and choose &#34;Export Alignment&#34;, and save the alignment in PAUP format (nexus) as &#34;35.nex&#34; for later use in MrBayes (Figure 6).</li>
		<li>Meanwhile, click the &#34;Models&#34; icon on the main panel of MEGA, choose &#34;Find Best DNA/Protein Models (ML)&#34;, and click &#34;OK&#34; on the pop-up window. Click &#34;Compute&#34; to begin the model searching process (Figure 7). A new progress panel will open; this process lasts several minutes to several days, depending on the complexity of the loaded sequences and the computer&#39;s performance. 
		NOTE: A table showing the results will open after the model searching process is finished ( Figure 8). The smallest BIC score will be listed first, followed by a series of different models with gradually increasing BIC scores. The first model &#34;LG+G+F&#34; with the smallest BIC score is the recommended model for ML tree based on the &#34;35 realigned.fas&#34; file.</li>
	</ol>
	</li>
	<li>Click the &#34;Phylogeny&#34; icon on the main panel of MEGA, click &#34;Construct/Test the Maximum Likelihood Tree&#34;, and then click &#34;Yes&#34; on the pop-up panel. A new window will open showing different parameters that need to be specified (Figure 9).
	<ol>
		<li>First, set the bootstrap value in the test of the phylogeny box; 500 or 1,000 is adequate in most cases. Under the substitution model, choose &#34;amino acid&#34; as the substitution type. The purpose of choosing a substitution model is to estimate the true difference between sequences based on their present states3.</li>
		<li>Select &#34;LG with Freqs. (+F) model&#34; (LG+F) in the model/method box. In the rates and pattern box, select &#34;Gamma Distributed&#34; (G) to describe rate variations across sites, i.e., giving more weight to changes at slowly evolving sites3. In the data subset box, select &#34;Complete deletion&#34; to remove all of the columns containing hyphens.</li>
		<li>Keep all other parameters in their default states (Figure 9). After specification of these parameters, click the &#34;Compute&#34; icon to start the calculation.</li>
	</ol>
	</li>
</ol>
3. Presentation of the Phylogenetic Tree
NOTE: A phylogenetic ML tree will be presented when the computation using MEGA is finished (Figure 10).
<ol>
	<li>Under the pull-down menu of the &#34;File&#34; icon on the tree panel, choose &#34;Save Current Session&#34; to save the result (.mas is the default file type). In the present study, the result was saved as &#34;35.mas&#34;. On the tree panel, many parameters including length of clade, tree style, tree topology, font of the taxon name, size, and color, are displayed and can be set to different options.</li>
	<li>Save the final tree file by clicking the image icon, and save the figure in different formats or copy the image as the source for photo-editing.</li>
</ol>
4. Analysis of the Relationship of SWEETs and SemiSWEETs Using Sequence Alignment
NOTE: This step may not be needed in ordinary sequence analysis.
<ol>
	<li>Align the 228 eukaryotic SWEETs and 57 prokaryotic SemiSWEETs in Clustal Omega as described above. The alignment results can be shown in Jalview, which is integrated in Clustal Omega, and copied to save in a photo editor (Figure 11). 
	NOTE: In the example alignment, some SemiSWEETs from &#945;-Proteobacteria are aligned with the first MtN3/saliva domain of the SWEET sequences, whereas SemiSWEETs from Methanobacteria (archaea) are aligned with the second MtN3/saliva domain of the SWEET sequences.</li>
</ol>
5. Phylogenetic Tree Construction with MrBayes
<ol>
	<li>For Bayesian Inferences with MrBayes, open the MrBayes executable file and a DOS interface will come up in a new window. The first step is to read the nexus data &#64257;le. Input &#34;execute 35.nex&#34; after the prompt (remember to save the 35.nex file in the same directory of the MrBayes executable file, or point out the pathway of the file before uploading it). A &#34;successful read matrix&#34; message will be shown following the last of the listed taxa (Figure 12). The 35.nex file has already been prepared and saved in MEGA (see 2.1 above).</li>
	<li>Set the evolutionary model.
	<ol>
		<li>After the prompt, type &#34;prset aamodelpr = fixed(lg); lset rates = g&#34;. The &#34;lg&#34; and &#34;g&#34; correspond to the &#34;LG&#34; and &#34;G&#34; model which is set in MEGA. After successfully setting the model, type &#34;mcmc nchains = 4 ngen = 5,000,000&#34; after the prompt. Use of the &#34;nchains=4&#34; entry signifies a total number of one cold chain and three hot chains for Metropolis coupling. &#34;ngen = 5,000,000&#34; means to run 5,000,000 generations of Metropolis coupling for convergence of the cold and hot chains. In this study, average standard deviation of split frequencies below 0.01 was regarded as convergence of the hot and cold chains.</li>
		<li>Note that the ngen number cannot be predicted accurately at the beginning of the process, and usually needs to be adjusted based on the change in the average standard deviation of split frequencies. In addition, the ngen number for convergence may be different each time when running the program based on the same data.</li>
	</ol>
	</li>
	<li>Run the analysis: This step lasts from several minutes to several days, depending on the complexity of input data and the performance of the computer. After completing the preset computation, a prompt will ask &#34;Continue with analysis (yes/no)?&#34; If &#34;no&#34; is typed after the prompt, the computing will stop (Figure 13), otherwise it will continue to compute after the number of further generations is input. When the computation is finished (with an average standard deviation of split frequencies &#60;0.01 or 0.05), stop the computation by typing &#34;no&#34; after the inquiry prompt. 
	NOTE: 0.01 is a strict criterion, 0.05 is moderate and usually adequate.</li>
	<li>Summarize the samples: Type &#34;sump&#34; after the prompt to summarize samples of model parameters (Figure 14). Then type &#34;sumt relburnin=yes burninfrac=0.25&#34; after the prompt to summarize tree samples. Detailed information about phylogenetic tree construction will be displayed as in&#160;Figure 15, followed by two tree figures that will appear in ASC II code on the screen, one showing clade credibility and the other showing branch lengths. At the same time, a tree file with the name of &#34;35.nex.con&#34; will be saved automatically.</li>
	<li>For a better presentation of the phylogenetic tree, open the &#34;35.nex.con&#34; tree file with the FigTree tool (http://tree.bio.ed.ac.uk/software/figtree/), select a style or size to display the result (Figure 16), or even edit it in a photo editor to make it more reader-friendly.</li>
</ol>

<ol>
	<li>Nei, M., Kumar, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> Molecular Evolution and Phylogenetics. , (2000).</li><li>Foth, B. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Phylogenetic+analysis+to+uncover+organellar+origins+of+nuclear-encoded+genes.">Phylogenetic analysis to uncover organellar origins of nuclear-encoded genes.</a> Methods Mol Biol. 390, 467-488 (2007).</li><li>Baldauf, S. L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Phylogeny+for+the+faint+of+heart:+a+tutorial.">Phylogeny for the faint of heart: a tutorial.</a> Trends Genet. 19, 345-351 (2003).</li><li>Feng, D. F., Doolittle, R. F. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Progressive+sequence+alignment+as+a+prerequisite+to+correct+phylogenetic+trees.">Progressive sequence alignment as a prerequisite to correct phylogenetic trees.</a> J Mol Evol. 25, 351-360 (1987).</li><li>Persson, B. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Bioinformatics+in+protein+analysis.">Bioinformatics in protein analysis.</a> EXS. 88, 215-231 (2000).</li><li>Sievers, F., 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=Fast,+scalable+generation+of+high-quality+protein+multiple+sequence+alignments+using+Clustal+Omega.">Fast, scalable generation of high-quality protein multiple sequence alignments using Clustal Omega.</a> Mol Syst Biol. 7, 539 (2011).</li><li>Sievers, F., Higgins, D. G. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Clustal+omega.">Clustal omega.</a> Curr Protoc Bioinformatics. 48, 1-16 (2014).</li><li>Yang, Z., Rannala, B. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Molecular+phylogenetics:+principles+and+practice.">Molecular phylogenetics: principles and practice.</a> Nat Rev Genet. 13, 303-314 (2012).</li><li>Hall, B. G. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Comparison+of+the+accuracies+of+several+phylogenetic+methods+using+protein+and+DNA+sequences.">Comparison of the accuracies of several phylogenetic methods using protein and DNA sequences.</a> Mol Biol Evol. 22, 792-802 (2005).</li><li>Tamura, K., Stecher, G., Peterson, D., Filipski, A., Kumar, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=MEGA6:+Molecular+Evolutionary+Genetics+Analysis+version+6.0.">MEGA6: Molecular Evolutionary Genetics Analysis version 6.0.</a> Mol Biol Evol. 30, 2725-2729 (2013).</li><li>Ronquist, F., 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=MrBayes+3.2:+efficient+Bayesian+phylogenetic+inference+and+model+choice+across+a+large+model+space.">MrBayes 3.2: efficient Bayesian phylogenetic inference and model choice across a large model space.</a> Syst Biol. 61, 539-542 (2012).</li><li>Chen, L. Q., 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=Sugar+transporters+for+intercellular+exchange+and+nutrition+of+pathogens.">Sugar transporters for intercellular exchange and nutrition of pathogens.</a> Nature. 468, 527-532 (2010).</li><li>Xuan, Y., 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=Functional+role+of+oligomerization+for+bacterial+and+plant+SWEET+sugar+transporter+family.">Functional role of oligomerization for bacterial and plant SWEET sugar transporter family.</a> Proc Natl Acad Sci USA. 110, 3685-3694 (2013).</li><li>Hu, Y., 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=Phylogenetic+evidence+for+a+fusion+of+archaeal+and+bacterial+SemiSWEETs+to+form+eukaryotic+SWEETs+and+identification+of+SWEET+hexose+transporters+in+the+amphibian+chytrid+pathogen+Batrachochytrium+dendrobatidis.">Phylogenetic evidence for a fusion of archaeal and bacterial SemiSWEETs to form eukaryotic SWEETs and identification of SWEET hexose transporters in the amphibian chytrid pathogen Batrachochytrium dendrobatidis.</a> FASEB J. 30, 3644-3654 (2016).</li><li>Holder, M. T., Zwickl, D. J., Dessimoz, C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Evaluating+the+robustness+of+phylogenetic+methods+to+among-site+variability+in+substitution+processes.">Evaluating the robustness of phylogenetic methods to among-site variability in substitution processes.</a> Philos Trans R Soc Lond B Biol Sci. 363, 4013-4021 (2008).</li><li>Alfaro, M. E., Holder, M. T. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+Posterior+and+the+Prior+in+Bayesian+Phylogenetics.">The Posterior and the Prior in Bayesian Phylogenetics.</a> Annu Rev Ecol Evol Syst. 37, 19-42 (2006).</li><li>Suchard, M., Rambaut, A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Many-core+algorithms+for+statistical+phylogenetics.">Many-core algorithms for statistical phylogenetics.</a> Bioinformatics. 25, 1370-1376 (2009).</li><li>Zierke, S., Bakos, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=FPGA+acceleration+of+the+phylogenetic+likelihood+function+for+Bayesian+MCMC+inference+methods.">FPGA acceleration of the phylogenetic likelihood function for Bayesian MCMC inference methods.</a> BMC Bioinformatics. 11, 184 (2010).</li></ol>

The authors have nothing to disclose.

It is becoming increasingly popular in biological research to make a phylogenetic tree based on nucleotide or amino acid&#160;sequences8. Generally, there are three critical stages of the practice including sequence alignment, evaluation of the aligned sequences with the proper method or algorithm, and visualization of the computational result as a phylogenetic tree. In the presented study, three rounds of sequence alignment were conducted: first, the SWEET protein sequences, including the first a...

DNA or RNA sequences carry genetic information for underlying phenotypes that can be analyzed through physiological and biochemical methods or observed through morphological and fossil evidence. In a sense, genetic information is more reliable than evaluating external phenotypes because the former is the basis for the latter. In evolutionary study, fossil evidence is very direct and convincing. However, many organisms, such as microorganisms, have little chance to form a fossil during long geologic ages. Therefore, molecular information such as nucleotide sequences and amino acid sequences from related extant organisms are of value for exploring evolutionary relationships1. In the present study, a simple introduction of basic phylogenetic knowledge and an easy-to-learn protocol was provided for newcomers who need to construct a phylogenetic tree on their own.
Both DNA (nucleotide) and protein (amino acid) sequences can be used to infer phylogenetic relationships between homologous genes, organelles, or even organisms2. DNA sequences are more likely to be affected by changes during evolution. In contrast, amino acid sequences are much more stable given that synonymous mutations in nucleotide sequences do not cause mutations in amino acid sequences. As a result, DNA sequences are useful for comparison of homologous genes from closely related organisms, whereas amino acid sequences are appropriate for homologous genes from distantly related organisms3.
A phylogenetic analysis begins with the alignment of amino acid or nucleotide sequences4 retrieved from an annotated genome sequencing database5&#160;listed in FASTA format, i.e., putative or expressed protein sequences, RNA sequences, or DNA sequences. It is worth noting that it is critical to collect high-quality sequences for the analysis, and only homologous sequences can be used to analyze phylogenetic relationships. Many different platforms such as Clustal W, Clustal X, Muscle, T-coffee, MAFFT, can be used for sequence alignment. The most widely used is Clustal Omega6,7 (http://www.ebi.ac.uk/Tools/msa/clustalo/), which can be used online or can be downloaded free of charge. The alignment tool has many parameters that the user can adjust before starting the alignment, but the default parameters work well in most cases. After the process is complete, the aligned sequences should be saved in the correct format for the next step. They should then be edited or trimmed using an editing software, such as BioEdit, because phylogenetic tree construction by MEGA requires the sequences to be of equal length (including both amino acid abbreviations and hyphens. In the aligned sequence, any position without an amino acid or nucleotide is represented by a hyphen &#34;-&#34;). Generally, all of the protruding amino acids or nucleotides at either end of the alignment should be removed. In addition, columns containing poorly aligned sequences in the alignment can be deleted because they convey little valuable information, and can sometimes give confusing or false information3. The columns containing one or more hyphens can be deleted at this time or in the later tree construction stage. Alternatively, they can be used for phylogenetic computation. When the sequence alignment and trimming is finished, the aligned sequences should be saved in FASTA format, or the desired format, for later use.
Many software platforms provide tree construction functions using different methods or algorithms. In general, the methods can be classified as either distance matrix methods or discrete data methods. Distance matrix methods are simple and fast to compute, while discrete data methods are complicated and time-consuming. For very closely related taxa with a high degree of sharing of amino acid or nucleotide sequence identity, a distance matrix method (Neighbor Joining: NJ; Unweighted Pair Group Method with Arithmetic mean: UPGMA) is appropriate; for distantly related taxa, a discrete data method (Maximum Likelihood: ML; Maximum Parsimony: MP; Bayesian Inference) is optimal3,8. In this study, the ML methods in MEGA (6.0.6) and Bayesian Inference (MrBayes 3.2) were applied to construct phylogenetic trees9. Ideally, when the proper model and parameters are used, the results derived from different methods may be consistent, and they are thus more reliable and convincing.
For a ML phylogenetic tree constructed using MEGA10, the aligned sequence file in FASTA format must be uploaded into the program. The first step then is to select the optimal substitution model for the uploaded data. All available substitution models are compared based on the uploaded sequences, and their final scores will be shown in a results table. Select the model with the smallest Bayesian Information Criterion (BIC) score (listed first in the table), set ML parameters according to the recommended model, and start the computation. The computation time varies from several minutes to several days, depending on the complexity of the loaded data (length of the sequences and number of taxa) and the performance of the computer on which the programs are run. When the computation is finished, a phylogenetic tree will be shown in a new window. Save the file as &#34;FileName.mat&#34;. After setting parameters to specify the appearance of the tree, save once more. Using this method, MEGA can generate publication grade phylogenetic tree figures.
For tree construction with MrBayes11, the first step is to transform the aligned sequence, which is usually listed in FASTA format, into nexus format (.nex as the file type). Transforming FASTA files into nexus format can be processed in MEGA. Next, the aligned sequence in nexus format can be uploaded into MrBayes. When the file is successfully uploaded, specify detailed parameters for the tree computation. These parameters include details such as amino acid substitution model, variation rates, chain number for Markov chain Monte Carlo (MCMC) coupling, ngen number, average standard deviation of split frequencies, and so on. After these parameters have been specified, start the computation. In the end, two tree figures in ASC II code, one showing clade credibility and the other showing branch lengths, will be displayed on the screen.
The tree result will be saved automatically as &#34;FileName.nex.con&#34;. This tree file can be opened and edited by FigTree, and the figure displayed in FigTree can be modified further to make it more suitable for publication.
In this study, 228 SWEET proteins, including 35 SWEETs from unicellular eukaryotes and 57 SemiSWEETs from prokaryotes, were analyzed as an example. Both the SWEETs and SemiSWEETs were characterized as glucose, fructose, or sucrose transporters across membranes12,13. Phylogenetic analysis suggests that the two MtN3/saliva domains containing SWEETs might be derived from an evolutionary fusion of a bacterial SemiSWEET and of an archaeon14.

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			<td height="39" style="height:39px;width:127px;">Adobe Illustration</td>
			<td style="width:127px;"></td>
			<td style="width:127px;"></td>
			<td style="width:752px;">a graphical tool developed by Adobe Systems Software Ireland Ltd. Copyright &#169; 2017</td>
		</tr>
		<tr height="36">
			<td height="36" style="height:36px;width:127px;">BioEdit</td>
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			<td style="width:127px;"></td>
			<td style="width:752px;">a biological sequence alignment editor written for Windows 95/98/NT/2000/XP/7. Copyright &#169; Tom Hall</td>
		</tr>
		<tr height="36">
			<td height="36" style="height:36px;width:127px;">Clustal Omega</td>
			<td style="width:127px;"></td>
			<td style="width:127px;"></td>
			<td style="width:752px;">a package for making multiple sequence alignments of amino acid or nucleotide sequences.&#160; http://www.clustal.org/</td>
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			<td height="37" style="height:37px;width:127px;">CorelDRAW</td>
			<td style="width:127px;"></td>
			<td style="width:127px;"></td>
			<td style="width:752px;">a graphic design software. Copyright &#169; 2017 Corel Corporation</td>
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			<td height="34" style="height:34px;width:127px;">FigTree</td>
			<td style="width:127px;"></td>
			<td style="width:127px;"></td>
			<td style="width:752px;">a graphical viewer of phylogenetic trees designed by the University of Edinburgh</td>
		</tr>
		<tr height="34">
			<td height="34" style="height:34px;width:127px;">MEGA</td>
			<td style="width:127px;"></td>
			<td style="width:127px;"></td>
			<td style="width:752px;">MolecularEvolutionary Genetics Analysis version6.0 http://www.megasoftware.net/home</td>
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		<tr height="33">
			<td height="33" style="height:33px;width:127px;">MrBayes</td>
			<td style="width:127px;"></td>
			<td style="width:127px;"></td>
			<td style="width:752px;">an Bayesian phylogenetic inference tool</td>
		</tr>
		<tr height="36">
			<td height="36" style="height:36px;width:127px;">NVIDIA</td>
			<td style="width:127px;"></td>
			<td style="width:127px;"></td>
			<td style="width:752px;">a company designs graphics processing units (GPUs) for the gaming and professional markets. Corporation Copyright &#169; 2017</td>
		</tr>
		<tr height="38">
			<td height="38" style="height:38px;width:127px;">PAUP</td>
			<td style="width:127px;"></td>
			<td style="width:127px;"></td>
			<td style="width:752px;">Phylogenetic Analysis Using Parsimony. David Swofford&#39;s program implements the maximum likelihood method under a number of nucleotide models.</td>
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			<td height="35" style="height:35px;width:127px;">Photoshop</td>
			<td style="width:127px;"></td>
			<td style="width:127px;"></td>
			<td style="width:752px;">a raster graphics editor developed and published by Adobe Systems Software Ireland Ltd. Copyright &#169; 2017</td>
		</tr>
		<tr height="28">
			<td height="28" style="height:28px;width:127px;">RHYTHM</td>
			<td style="width:127px;"></td>
			<td style="width:127px;"></td>
			<td style="width:752px;">a knowledge based prediction of hekix contacts. Charit&#233; Berlin &#8211; Protein Formatics Group - Copyright 2007-2009</td>
		</tr>
		<tr height="32">
			<td height="32" style="height:32px;width:127px;">TMHMM</td>
			<td style="width:127px;"></td>
			<td style="width:127px;"></td>
			<td style="width:752px;">a tool for prediction of transmembrane helices in proteins. http://www.cbs.dtu.dk/services/TMHMM/</td>
		</tr>
		<tr height="34">
			<td height="34" style="height:34px;width:127px;">Compter</td>
			<td style="width:127px;"></td>
			<td style="width:127px;"></td>
			<td style="width:752px;">4GB memory, Core 2 or above CPU. Windows 7, Windows 10</td>
		</tr>
	</tbody>
</table>

using phylogenetic analysis to investigate eukaryotic gene origin

Phylogenetic analysis uses nucleotide or amino acid sequences or other parameters, such as domain sequences and three-dimensional structure, to construct a tree to show the evolutionary relationship among different taxa (classification units) at the molecular level. Phylogenetic analysis can also be used to investigate domain relationships within an individual taxon, particularly for organisms that have undergone substantial change in morphology and physiology, but for which researchers lack fossil evidence due to the organisms&#39; long evolutionary history or scarcity of fossilization.
In this text, a detailed protocol is described for using the phylogenetic method, including amino acid sequence alignment using Clustal Omega, and subsequent phylogenetic tree construction using both Maximum Likelihood (ML) of Molecular Evolutionary Genetics Analysis (MEGA) and Bayesian Inference via MrBayes. To investigate the origin of eukaryotic Sugars Will Eventually be Exported Transporters (SWEET) genes, 228 SWEETs including 35 SWEET proteins from unicellular eukaryotes and 57 SemiSWEET proteins from prokaryotes were analyzed. Interestingly, SemiSWEETs were found in prokaryotes, but SWEETs were found in eukaryotes. Two phylogenetic trees constructed using theoretically distinct methods have consistently&#160;suggested that the first eukaryotic SWEET gene might stem from the fusion of a bacterial SemiSWEET gene and an archaeal SemiSWEET gene.&#160;It is worth noting that one should be cautious to draw a conclusion based only on phylogenetic analysis, although it is useful to explain the underlying relationship between different taxa, which is difficult or even impossible to discern through experimental means.

Phylogenetic trees show that all of the first MtN3/saliva domains of the 35 SWEET sequences clustered as one clade and the second MtN3/saliva domains of the SWEET sequences clustered as another clade. In addition, alignment results of the SWEETs and SemiSWEETs show that some SemiSWEETs from &#945;-Proteobacteria aligned with the first MtN3/saliva domain of the SWEET sequences, whereas SemiSWEETs from Methanobacteria (archaea) aligned with the second MtN3/saliva domain of the SWEET sequenc...

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Using Phylogenetic Analysis to Investigate Eukaryotic Gene Origin

A method of constructing a phylogenetic tree based on sequence homology of SWEETs from eukaryotes and SemiSWEETs from prokaryotes is described. Phylogenetic analysis is a useful tool for explaining the evolutionary relatedness between homologous proteins or genes from different organism groups.

Phylogenetic analysis uses nucleotide or amino acid sequences or other parameters, such as domain sequences and three-dimensional structure, to construct ...

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15.7K Views.  China Three Gorges University. A method of constructing a phylogenetic tree based on sequence homology of SWEETs from eukaryotes and SemiSWEETs from prokaryotes is described. Phylogenetic analysis is a useful tool for explaining the evolutionary relatedness between homologous proteins or genes from different organism groups.

Video: Using Phylogenetic Analysis to Investigate Eukaryotic Gene Origin

Using Bioluminescent Imaging to Investigate Synergism Between Streptococcus pneumoniae and Influenza A Virus in Infant Mice

During the 1918 influenza virus pandemic, which killed approximately 50 million people worldwide, the majority of fatalities were not the result of infection with influenza virus alone. Instead, most individuals are thought to have succumbed to a secondary bacterial infection, predominately caused by the bacterium Streptococcus pneumoniae (the pneumococcus). The synergistic relationship between infections caused by influenza virus and the pneumococcus has subsequently been observed during the 1957 Asian influenza virus pandemic, as well as during seasonal outbreaks of the virus (reviewed in 1, 2). Here, we describe a protocol used to investigate the mechanism(s) that may be involved in increased morbidity as a result of concurrent influenza A virus and S. pneumoniae infection. We have developed an infant murine model to reliably and reproducibly demonstrate the effects of influenza virus infection of mice colonised with S. pneumoniae. Using this protocol, we have provided the first insight into the kinetics of pneumococcal transmission between co-housed, neonatal mice using in vivo imaging 3.

Using Bioluminescent Imaging to Investigate Synergism Between Streptococcus pneumoniae and Influenza A Virus in Infant Mice

A concurrent infection with influenza A virus is one of the factors implicated in the induction of invasive pneumococcal disease during asymptomatic Streptococcus pneumoniae carriage. Here we describe a mixed infection method using infant mice to investigate the synergism between these two respiratory pathogens.

During the 1918 influenza virus pandemic, which killed approximately 50 million people worldwide, the majority of fatalities were not the result of ...

Generation of Multivirus-specific T Cells to Prevent/treat Viral Infections after Allogeneic Hematopoietic Stem Cell Transplant

Viral infections cause morbidity and mortality in allogeneic hematopoietic stem cell transplant (HSCT) recipients. We and others have successfully generated and infused T-cells specific for Epstein Barr virus (EBV), cytomegalovirus (CMV) and Adenovirus (Adv) using monocytes and EBV-transformed lymphoblastoid cell (EBV-LCL) gene-modified with an adenovirus vector as antigen presenting cells (APCs). As few as 2x105/kg trivirus-specific cytotoxic T lymphocytes (CTL) proliferated by several logs after infusion and appeared to prevent and treat even severe viral disease resistant to other available therapies. The broader implementation of this encouraging approach is limited by high production costs, complexity of manufacture and the prolonged time (4-6 weeks for EBV-LCL generation, and 4-8 weeks for CTL manufacture &#x2013; total 10-14 weeks) for preparation. To overcome these limitations we have developed a new, GMP-compliant CTL production protocol. First, in place of adenovectors to stimulate T-cells we use dendritic cells (DCs) nucleofected with DNA plasmids encoding LMP2, EBNA1 and BZLF1 (EBV), Hexon and Penton (Adv), and pp65 and IE1 (CMV) as antigen-presenting cells. These APCs reactivate T cells specific for all the stimulating antigens. Second, culture of activated T-cells in the presence of IL-4 (1,000U/ml) and IL-7 (10ng/ml) increases and sustains the repertoire and frequency of specific T cells in our lines. Third, we have used a new, gas permeable culture device (G-Rex) that promotes the expansion and survival of large cell numbers after a single stimulation, thus removing the requirement for EBV-LCLs and reducing technician intervention. By implementing these changes we can now produce multispecific CTL targeting EBV, CMV, and Adv at a cost per 106 cells that is reduced by &gt;90%, and in just 10 days rather than 10 weeks using an approach that may be extended to additional protective viral antigens. Our FDA-approved approach should be of value for prophylactic and treatment applications for high risk allogeneic HSCT recipients.

A rapid, simple and cost-effective protocol for the generation of donor-derived multivirus-specific CTLs (rCTL) for infusion to allogeneic hematopoietic stem cell transplant (HSCT) recipients at risk of developing CMV, Adv or EBV infections. This manufacturing process is GMP-compliant and should ensure the broader implementation of T-cell immunotherapy beyond specialized centers.

Viral infections cause morbidity and mortality in allogeneic hematopoietic stem cell transplant (HSCT) recipients. We and others have successfully ...

The Insect Galleria mellonella as a Powerful Infection Model to Investigate Bacterial Pathogenesis

The study of bacterial virulence often requires a suitable animal model. Mammalian models of infection are costly and may raise ethical issues. The use of insects as infection models provides a valuable alternative. Compared to other non-vertebrate model hosts such as nematodes, insects have a relatively advanced system of antimicrobial defenses and are thus more likely to produce information relevant to the mammalian infection process. Like mammals, insects possess a complex innate immune system1. Cells in the hemolymph are capable of phagocytosing or encapsulating microbial invaders, and humoral responses include the inducible production of lysozyme and small antibacterial peptides2,3. In addition, analogies are found between the epithelial cells of insect larval midguts and intestinal cells of mammalian digestive systems. Finally, several basic components essential for the bacterial infection process such as cell adhesion, resistance to antimicrobial peptides, tissue degradation and adaptation to oxidative stress are likely to be important in both insects and mammals1. Thus, insects are polyvalent tools for the identification and characterization of microbial virulence factors involved in mammalian infections.
Larvae of the greater wax moth Galleria mellonella have been shown to provide a useful insight into the pathogenesis of a wide range of microbial infections including mammalian fungal (Fusarium oxysporum, Aspergillus fumigatus, Candida albicans) and bacterial pathogens, such as Staphylococcus aureus, Proteus vulgaris, Serratia marcescens Pseudomonas aeruginosa, Listeria monocytogenes or Enterococcus faecalis4-7. Regardless of the bacterial species, results obtained with Galleria larvae infected by direct injection through the cuticle consistently correlate with those of similar mammalian studies: bacterial strains that are attenuated in mammalian models demonstrate lower virulence in Galleria, and strains causing severe human infections are also highly virulent in the Galleria model8-11. Oral infection of Galleria is much less used and additional compounds, like specific toxins, are needed to reach mortality.
G. mellonella larvae present several technical advantages: they are relatively large (last instar larvae before pupation are about 2 cm long and weight 250 mg), thus enabling the injection of defined doses of bacteria; they can be reared at various temperatures (20 &deg;C to 30 &deg;C) and infection studies can be conducted between 15 &deg;C to above 37 &deg;C12,13, allowing experiments that mimic a mammalian environment. In addition, insect rearing is easy and relatively cheap. Infection of the larvae allows monitoring bacterial virulence by several means, including calculation of LD5014, measurement of bacterial survival15,16 and examination of the infection process17. Here, we describe the rearing of the insects, covering all life stages of G. mellonella. We provide a detailed protocol of infection by two routes of inoculation: oral and intra haemocoelic. The bacterial model used in this protocol is Bacillus cereus, a Gram positive pathogen implicated in gastrointestinal as well as in other severe local or systemic opportunistic infections18,19.

The Insect Galleria mellonella as a Powerful Infection Model to Investigate Bacterial Pathogenesis

Oral and intra haemocolic infection of larvae of the greater wax moth Galleria mellonella is described. This insect can be used to study virulence factors of entomopathogenic as well as mammalian opportunistic bacteria. Rearing of the insects, methods of infection and examples of in vivo analysis are described.

The study of bacterial virulence often requires a suitable animal model. Mammalian models of infection are costly and may raise ethical issues. The use of ...

Imaging InlC Secretion to Investigate Cellular Infection by the Bacterial Pathogen Listeria monocytogenes

Bacterial intracellular pathogens can be conceived as molecular tools to dissect cellular signaling cascades due to their capacity to exquisitely manipulate and subvert cell functions which are required for the infection of host target tissues. Among these bacterial pathogens, Listeria monocytogenes is a Gram positive microorganism that has been used as a paradigm for intracellular parasitism in the characterization of cellular immune responses, and which has played instrumental roles in the discovery of molecular pathways controlling cytoskeletal and membrane trafficking dynamics. In this article, we describe a robust microscopical assay for the detection of late cellular infection stages of L. monocytogenes based on the fluorescent labeling of InlC, a secreted bacterial protein which accumulates in the cytoplasm of infected cells; this assay can be coupled to automated high-throughput small interfering RNA screens in order to characterize cellular signaling pathways involved in the up- or down-regulation of infection.

Imaging InlC Secretion to Investigate Cellular Infection by the Bacterial Pathogen Listeria monocytogenes

Listeria monocytogenes is a Gram positive bacterial pathogen frequently used as a major model for the study of intracellular parasitism. Imaging late L. monocytogenes infection stages within the context of small-interfering RNA screens allows for the global study of cellular pathways required for bacterial infection of target host cells.

Bacterial intracellular  pathogens can be conceived as molecular tools to dissect cellular signaling  cascades due to their capacity to exquisitely ...

Cell-based Flow Cytometry Assay to Measure Cytotoxic Activity

Cytolytic activity of CD8+ T cells is rarely evaluated. We describe here a new cell-based assay to measure the capacity of antigen-specific CD8+ T cells to kill CD4+ T cells loaded with their cognate peptide. Target CD4+ T cells are divided into two populations, labeled with two different concentrations of CFSE. One population is pulsed with the peptide of interest (CFSE-low) while the other remains un-pulsed (CFSE-high). Pulsed and un-pulsed CD4+ T cells are mixed at an equal ratio and incubated with an increasing number of purified CD8+ T cells. The specific killing of autologous target CD4+ T cells is analyzed by flow cytometry after coculture with CD8+ T cells containing the antigen-specific effector CD8+ T cells detected by peptide/MHCI tetramer staining. The specific lysis of target CD4+ T cells measured at different effector versus target ratios, allows for the calculation of lytic units, LU30/106 cells. This simple and straightforward assay allows for the accurate measurement of the intrinsic capacity of CD8+ T cells to kill target CD4+ T cells.

This protocol describes a sensitive, cell-based cytotoxicity assay. By enumerating the decrease in frequency of live target CD4+ T cells in the presence of an increasing number of effector CD8+ T cells, this assay allows for the direct assessment of cytolytic activity of antigen-specific CD8+ T cells.

Cytolytic activity of CD8+ T cells is rarely evaluated. We describe here a new cell-based assay to measure the capacity of antigen-specific CD8+ T cells ...

Using RNA-interference to Investigate the Innate Immune Response in Mouse Macrophages

Macrophages are key phagocytic innate immune cells. When macrophages encounter a pathogen, they produce antimicrobial proteins and compounds to kill the pathogen, produce various cytokines and chemokines to recruit and stimulate other immune cells, and present antigens to stimulate the adaptive immune response. Thus, being able to efficiently manipulate macrophages with techniques such as RNA-interference (RNAi) is critical to our ability to investigate this important innate immune cell. However, macrophages can be technically challenging to transfect and can exhibit inefficient RNAi-induced gene knockdown. In this protocol, we describe methods to efficiently transfect two mouse macrophage cell lines (RAW264.7 and J774A.1) with siRNA using the Amaxa Nucleofector 96-well Shuttle System and describe procedures to maximize the effect of siRNA on gene knockdown. Moreover, the described methods are adapted to work in 96-well format, allowing for medium and high-throughput studies. To demonstrate the utility of this approach, we describe experiments that utilize RNAi to inhibit genes that regulate lipopolysaccharide (LPS)-induced cytokine production.

In this protocol, we describe methods to efficiently transfect murine macrophage cell lines with siRNAs using the Amaxa Nucleofector 96-well Shuttle System, stimulate the macrophages with lipopolysaccharide, and monitor the effects on inflammatory cytokine production.

Macrophages are key phagocytic innate immune cells.  When macrophages encounter a pathogen, they produce antimicrobial proteins and compounds to kill the ...

Using Fluorescent Proteins to Monitor Glycosome Dynamics in the African Trypanosome

Trypanosoma brucei is a kinetoplastid parasite that causes human African trypanosomiasis (HAT), or sleeping sickness, and a wasting disease, nagana, in cattle1. The parasite alternates between the bloodstream of the mammalian host and the tsetse fly vector. The composition of many cellular organelles changes in response to these different extracellular conditions2-5.
Glycosomes are highly specialized peroxisomes in which many of the enzymes involved in glycolysis are compartmentalized. Glycosome composition changes in a developmental and environmentally regulated manner4-11. Currently, the most common techniques used to study glycosome dynamics are electron and fluorescence microscopy; techniques that are expensive, time and labor intensive, and not easily adapted to high throughput analyses.
To overcome these limitations, a fluorescent-glycosome reporter system in which enhanced yellow fluorescent protein (eYFP) is fused to a peroxisome targeting sequence (PTS2), which directs the fusion protein to glycosomes12, has been established. Upon import of the PTS2eYFP fusion protein, glycosomes become fluorescent. Organelle degradation and recycling results in the loss of fluorescence that can be measured by flow cytometry. Large numbers of cells (5,000 cells/sec) can be analyzed in real-time without extensive sample preparation such as fixation and mounting. This method offers a rapid way of detecting changes in organelle composition in response to fluctuating environmental conditions.

Glycosome dynamics in African trypanosomes are difficult to study by traditional cell biology techniques such as electron and fluorescence microscopy. As a means of observing dynamic organelle behavior, a fluorescent-organelle reporter system has been used in conjunction with flow cytometry to monitor real-time glycosome dynamics in live parasites.

Trypanosoma brucei is a kinetoplastid parasite that causes human African trypanosomiasis (HAT), or sleeping sickness, and a wasting disease, nagana, in ...

Biolistic Transformation of a Fluorescent Tagged Gene into the Opportunistic Fungal Pathogen Cryptococcus neoformans

The basidiomycete Cryptococcus neoformans, an invasive opportunistic pathogen of the central nervous system, is the most frequent cause of fungal meningitis worldwide resulting in more than 625,000 deaths per year worldwide.&#160;Although electroporation has been developed for the transformation of plasmids in Cryptococcus, only&#160;biolistic&#160;delivery provides&#160;an effective means to transform&#160;linear&#160;DNA&#160;that can be integrated into the genome by homologous recombination.&#160;
Acetate has been shown to be a major fermentation product during cryptococcal infection, but the significance of this is not yet known.&#160;A bacterial pathway composed of the enzymes xylulose-5-phosphate/fructose-6-phosphate phosphoketolase (Xfp) and acetate kinase (Ack) is one of three potential pathways for acetate production in C. neoformans. Here, we demonstrate the biolistic transformation of a construct, which has the gene encoding Ack fused to the fluorescent tag mCherry, into&#160;C. neoformans. We then confirm integration of the ACK-mCherry fusion into the&#160;ACK&#160;locus.

Biolistic Transformation of a Fluorescent Tagged Gene into the Opportunistic Fungal Pathogen Cryptococcus neoformans

Biolistic transformation is a method used to generate stable integration of DNA into the genome of the opportunistic pathogen Cryptococcus neoformans through homologous recombination. We will demonstrate biolistic transformation of a construct, which has the gene encoding acetate kinase fused to the fluorescent tag mCherry into&#160;C. neoformans.

The basidiomycete Cryptococcus neoformans, an invasive opportunistic pathogen of the central nervous system, is the most frequent cause of fungal ...

Isolation of Murine Peritoneal Macrophages to Carry Out Gene Expression Analysis Upon Toll-like Receptors Stimulation

During infection and inflammation, circulating monocytes leave the bloodstream and migrate into tissues, where they differentiate into macrophages. Macrophages express surface Toll-like receptors (TLRs), which recognize molecular patterns conserved through evolution in a wide range of microorganisms. TLRs play a central role in macrophage activation which is usually associated with gene expression alteration. Macrophages are critical in many diseases and have emerged as attractive targets for therapy. In the following protocol, we describe a procedure to isolate murine peritoneal macrophages using Brewer&#8217;s thioglycollate medium. The latter will boost monocyte migration into the peritoneum, accordingly this will raise macrophage yield by 10-fold. Several studies have been carried out using bone marrow, spleen or peritoneal derived macrophages. However, peritoneal macrophages were shown to be more mature upon isolation and are more stable in their functionality and phenotype. Thus, macrophages isolated from murine peritoneal cavity present an important cell population that can serve in different immunological and metabolic studies. Once isolated, macrophages were stimulated with different TLR ligands and consequently&#160;gene expression was evaluated.

We describe here a simple protocol to isolate murine peritoneal macrophages. This procedure is followed by RNA extraction to carry out gene expression analysis upon Toll-like receptors stimulation.

During infection and inflammation, circulating monocytes leave the bloodstream and migrate into tissues, where they differentiate into macrophages. ...

A Method to Assess Fc-mediated Effector Functions Induced by Influenza Hemagglutinin Specific Antibodies

Antibodies play a crucial role in coupling the innate and adaptive immune responses against viral pathogens through their antigen binding domains and Fc-regions. Here, we describe how to measure the activation of Fc effector functions by monoclonal antibodies targeting the influenza virus hemagglutinin with the use of a genetically engineered Jurkat cell line expressing an activating type 1 Fc-Fc&#947;R. Using this method, the contribution of specific Fc-Fc&#947;R interactions conferred by immunoglobulins can be determined using an in vitro assay.

We describe a method to measure the activation of Fc-mediated effector functions by antibodies that target the influenza virus hemagglutinin. This assay can also be adapted to assess the ability of monoclonal antibodies or polyclonal sera targeting other viral surface glycoproteins to induce Fc-mediated immunity.

Antibodies play a crucial role in coupling the innate and adaptive immune responses against viral pathogens through their antigen binding domains and ...