We thank Anne de Jong and Siger Holsappel for help with the DNA microarrays slide production. Anne de Jong&#8217;s support for bioinformatics analysis is also appreciated. We also thank Jelle Slager for reviewing the paper. Muhammad Afzal and Irfan Manzoor are supported by the GC University, Faisalabad, Pakistan under the faculty development program of HEC Pakistan.

1. Preparation of Media, and Cell Culture
<ol>
	<li>Grow S. pneumoniae D39 wild-type strain21 as described previously11. Inoculate cells stored at -80&#160;oC in 10% glycerol (with 1/100 ratio in 50 ml sterile tubes) in 50 ml Chemically Defined Medium (CDM) with a final pH of 6.4 15, but omit L-serine from the amino acid mixture. 
	Note: two different CDMs were used; one containing a minimum concentration of L-serine (150 &#181;M) and the other containing a maximum concentration of L-serine (10 mM). This minimum concentration is basically the concentration of L-serine in human blood plasma and the aim is to compare the gene expression of S. pneumoniae D39 strain under these two conditions.</li>
</ol>
2. Isolation of Total RNA
<ol>
	<li>Materials
	<ol>
		<li>Use acid phenol, RNA grade for the isolation of total RNA.</li>
		<li>Macaloid:
		<ol>
			<li>Suspend 2 grams of macaloid in 100 ml TE and boil for 5 min. Cool to RT and sonicate until macaloid gels.Centrifuge the solution at 2,000 &#215;&#160;g for 5 min&#160;at RT, resuspend in 50 ml TE pH 8 and store at 4 &#176;C.</li>
		</ol>
		</li>
		<li>Solutions:
		<ol>
			<li>Treat all solutions with diethyl pyrocarbonate (DEPC). Add 100 &#181;l DEPC/100 ml solution, incubate O/N at 37 &#176;C and autoclave for 15 min.</li>
		</ol>
		</li>
	</ol>
	</li>
	<li>Methodology
	<ol>
		<li>Grow 50 ml of S. pneumoniae D39 cell culture in 50 ml tubes at 37 oC (no shaking) until mid-exponential phase (OD ~0.3). Centrifuge cultures at 4 &#176;C on 10,000 &#215;&#160;g&#160;for 2 min. Discard media and immediately freeze the cell pellets in liquid nitrogen.</li>
		<li>Premix 300 &#181;l chloroform:IAA (24:1) and 300 &#181;l phenol (acid phenol, RNA grade). Use 500 &#181;l of the organic phase in step 2.2.3.</li>
		<li>Prepare the following mixture in advance in RNA-free screw-cap tubes: 0.5 g glass beads, 50 &#181;l 10% SDS, 500 &#181;l phenol/chloroform:IAA (as prepared in step 2.2.2), macaloid layer (150-175 &#181;l, not exact as it is very viscous). Resuspend the cell pellets in 400 &#181;l TE (DEPC) and add the resuspended cells into the screw-cap tubes.</li>
		<li>To break the cells, place the screw-cap tubes in a bead beater for 2x 60&#8221; pulses (&#8216;homogenize&#8217;) with 1 min&#160;interval on ice. Centrifuge the samples for 10 min&#160;at 10,000 &#215;&#160;g&#160;(4 &#176;C).</li>
		<li>Transfer the upper phase to a fresh tube, add 500 &#181;l chloroform:IAA (24:1) and centrifuge for 5 min at 10,000 &#215;&#160;g (4 &#176;C).</li>
		<li>Transfer 500 &#181;l of the upper phase to fresh tubes, add 2 volumes (1 ml) of lysis/binding buffer and mix by pipetting up and down. Isolate total RNA using the RNA isolation kit and follow the manufacturer recommended protocol.</li>
	</ol>
	</li>
</ol>
3. RNA&#160;Cleanup
<ol>
	<li>To remove the contamination of DNA from total RNA, add 100 &#181;l DNAseI mix (90 &#181;l DNAse buffer and 10&#181;l DNAseI) and incubate for 20-30 min at 15-25 &#186;C.</li>
	<li>Wash cleaned RNA using the RNAse kit. Obtain 50 &#181;l of eluted volume.</li>
</ol>
4. Analysis of RNA
<ol>
	<li>Determine the concentration of RNA on a spectrophotometer. Determine the quality of RNA by using an assay as follows (Figure 2).
	<ol>
		<li>Dilute 1 &#181;l of sample with 50 &#181;l of DEPC water to get a concentration of 20-200 ng/&#181;l.</li>
		<li>Use 1 &#181;l diluted RNA sample to check quality on the Bioanalyser according to the manufacturer&#8217;s instructions.</li>
	</ol>
	</li>
	<li>Note: a ratio of 23S:16S of around 2.0 is considered good.1 A260 unit RNA corresponds to 40 &#181;g/ml. A recommended amount for labeling is 10-20 &#181;g.</li>
</ol>
5. cDNA Preparation and Labelling
NOTE: The following protocol was followed for cDNA preparation and labelling.
<ol>
	<li>Annealing
	<ol>
		<li>Perform annealing reaction in 300 &#181;l PCR tubes, keeping the concentration of total RNA 10-15 &#181;g for homemade slides or 5 &#181;g for company-made slides. Mix RNA with 2 &#181;l Random nonamers (1.6 &#181;g/&#181;l) and add nuclease-free water if necessary to keep the final volume of the annealing mixture to 18 &#181;l.</li>
		<li>Keep the annealing mixture at 70 &#176;C for 5 min. After that, cool down the mixture to RT&#160;for 10 min (annealing) and if needed, spin down the reactions to the bottom of the tube. Place the reaction tubes on ice for at least 1 min.</li>
	</ol>
	</li>
	<li>Reverse Transcription
	<ol>
		<li>Prepare the reverse transcriptase mix as follows: to 18 &#181;l annealing mix, add 12 &#181;l Master mix (consisting of 6 &#181;l 5x First Strand buffer, 3 &#181;l 0.1 M DDT, 1.2 &#181;l 25x AA-dUTP / nucleotide mix and 1.8 &#181;l reverse transcriptase (1 &#181;l for company-made slides). Keep the reaction mix for 2-16 hr at 42 &#176;C.</li>
	</ol>
	</li>
</ol>
6. Degradation of mRNA and Purification of cDNA
<ol>
	<li>To degrade the mRNA from the reaction mixture, add 3 &#181;l of 2.5 M NaOH and place at 37 &#176;C for about 15 min. Subsequently, add 15 &#181;l of 2M HEPES free acid to neutralize the NaOH.</li>
	<li>Purify the cDNA mixture by using PCR clean-up columns and follow the manufacturer&#8217;s protocol.</li>
</ol>
7. Measurement of cDNA Concentration
<ol>
	<li>Measure cDNA concentrations on a spectrophotometer. To continue with labelling, check that the concentration of cDNA is at least 60 ng/&#181;l (homemade slides) or 20 ng/&#181;l (company-made slides).</li>
</ol>
8. Labelling of cDNA with Amine-reactive Dye and Purification
<ol>
	<li>Use amine-reactive dye to label the cDNA. Directly mix the cDNA with one aliquot (5 &#181;l) of amine-reactive dye.</li>
	<li>Incubate the mixture at RT, in the dark, for 60 to 90 min and proceed directly to purification of dye-labelled cDNA. Purify dye-labelled cDNA by using PCR clean-up columns and following the manufacturer&#8217;s protocol. Elute cDNA in 50 &#181;l of elution buffer.</li>
</ol>
9. Measurement of Labelled cDNA
<ol>
	<li>Use a spectrophotometer to measure the incorporation of amine-reactive dyes into the cDNA. Check that the concentration of amine-reactive dyes is at least 0.5 pmol/&#181;l in a total volume of 50 &#181;l.</li>
</ol>
10. Mixing of Labelled cDNA Samples
<ol>
	<li>For homemade slides, use all the labelled cDNA for hybridization with not more than 30% difference in cDNA concentration. For company-made slides, use cDNA with not more than 2-fold difference in cDNA concentration. 
	Note: normally, about 300 ng of cDNA is needed for company-made slides, which is very little as compared to the required amount of cDNA for homemade slides.</li>
</ol>
11. Hybridization and Washing
<ol>
	<li>Use the following reagents: demi water, ethanol 99%, SHY Buffer (homemade; with&#160;40 &#181;l yeast RNA). Prepare maximum 1 ml of SHY.</li>
	<li>Apparatus and solutions preparation
	<ol>
		<li>Switch on vacuum concentrators and heater at least 1 hr before drying. Switch on hybridization oven, with set point adjusted to the correct hybridization temperature (for S. pneumoniae DNA microarrays, use 45 &#176;C). Similarly, preheat hybridization cassette and oven for 30-60 min.</li>
		<li>Preheat SHY buffer at 68 &#730;C for at least 30 min.</li>
	</ol>
	</li>
	<li>Sample preparation (labelled cDNA)
	<ol>
		<li>Combine equal quantities of labelled cDNAs (max 30% difference). Dry the sample using the vacuum concentrators at high temp (approx. 40 min) until the volume is smaller than 7 &#181;l.</li>
	</ol>
	</li>
	<li>Lifter-slip
	<ol>
		<li>Use clean lifter-slips. Note: &#177; 30 &#181;l can be loaded on the slide.</li>
		<li>Clean the lifter-slips with soap, plenty of tap water and 100% ethanol. Note: Dirty lifter slips give high background.</li>
		<li>Air dry the lifter-slips with air pistol to blow away dust particles. Place a clean lifter-slip on the slide with the white teflon lining at the sides facing down.</li>
	</ol>
	</li>
	<li>Adding hybridization buffer (to labelled cDNA)
	<ol>
		<li>Dissolve the dried dye samples in 7 &#181;l H2O and incubate at 94 &#176;C for 2 min.</li>
		<li>Immediately, add 35 &#181;l preheated SHY buffer (68 &#176;C), mix gently and spin at maximum speed for 1 min to get rid of precipitates. Preheat the probe at 68 &#730;C for approximately 5 min until loading.</li>
	</ol>
	</li>
	<li>Slide-lifter-slip assembly and preheating.
	<ol>
		<li>Place the hybridization slide holder on a heat-block at 50 &#730;C. Place DNA microarray slides with the lifter-slip on the heated hybridization slide holder and preheat the slide with lifter-slip for a minute. Perform the next steps as quickly as possible.</li>
		<li>Add 40 &#181;l of the sample target to the end of the slide. Allow the fluid to flow between the glass surfaces by capillary force. Perform all pipetting slowly and carefully.</li>
		<li>Keep the slides with lifter-slip horizontal at all times and move slowly to make sure that the lifter-slip did not move.</li>
		<li>Take the pre-warmed hybridization cassette out of the hybridization oven and close the machine. Place filter-paper soaked with 3 ml 2x SSC (standard saline citrate) in the hybridization cassette.</li>
		<li>Gently place the hybridization slide holder with slides in the hybridization cassette. Close the hybridization cassette and put in the hybridization oven again (for about 16-18 hr).</li>
	</ol>
	</li>
	<li>Slide washing
	<ol>
		<li>Prepare fresh wash-buffers I, II and III (750 ml per wash step). For 500 ml wash-buffer I, use 2 x SSC / 0.5 % SDS. For 500 ml wash-buffer II, use 1 x SSC / 0.25 % SDS. For 500 ml wash-buffer III, use 1 x SSC / 0.1 % SDS (optional)</li>
		<li>Place the wash-buffers at 30 &#176;C (to be sure SDS is dissolved).</li>
		<li>Submerge the slides as quickly as possible, but very gently, in a falcon tube filled with 50 ml wash buffer I until the glass rests on the conical bottom of the tube.</li>
		<li>After a few seconds, when the lifter-slip sinks to the bottom of the tube, take out the slide with tweezers without scratching the array and put in the rack of the wash station. Continue with the washing with no time gap.</li>
		<li>Wash the slides for 5 min in 500 ml wash-buffer I (in a washing station). Give it a second wash for 20-30 min in 500 ml wash-buffer II (in a washing station). Similarly, wash the slides for 5 min in 500 ml wash-buffer III (optional) (in a washing station).</li>
		<li>Dry the slides for 2 min at 2,000 rpm.</li>
	</ol>
	</li>
</ol>
12. Microarray Analysis
<ol>
	<li>Scan images with respective wavelengths in scanner and save in a folder &#8220;Jove Project&#8221;.</li>
	<li>Use software to analyze the scanned files initially as described previously 22. After running this software, select the &#8220;Open Image&#8221; tab to load the red image file (-.550) as &#8220;Red&#8221; and green image file (-.635) as &#8220;Green&#8221;. Upload the gal file (.gal) having S. pneumoniae array list on image file by selecting &#8220;Load Array List&#8221; tab 22. 
	Note: this array list consisted of 48 grids, on each grid there were 16 rows and 15 columns. Each spot on the grid represents a single gene and spot information including gene name is added through a spot description file. The spot numbers are given from left to right and top to bottom.</li>
	<li>After carefully spotting the grids, select tab &#8220;Find Array, Find Blocks, Align Features&#8221; to align the spots. After aligning all the features, analyze the image by selecting the &#8220;Analyzing&#8221; tab. Create a new file having results, histogram and scatter plot. Save this file from tab &#8220;Save results as&#8221; as a .gpr file for further analysis.</li>
	<li>Perform further normalization and processing of data with in-house developed Microprep software package as described9.</li>
	<li>Use independent biological replicates for DNA microarray data which are dye-swapped. Perform CyberT implementation of a variant of t-test1 and calculate false discovery rates (FDRs) as described9.</li>
	<li>For differentially expressed genes, take p &#60; 0.001 and FDR &#60; 0.05 as a standard.</li>
	<li>Upload DNA microarray data on NCBI submission page to get a GEO (Gene Expression Omnibus) accession number.</li>
</ol>

<ol>
	<li>Kayala, M. A., Baldi, P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Cyber-T+web+server:+differential+analysis+of+high-throughput+data.">Cyber-T web server: differential analysis of high-throughput data.</a> Nucleic Acids Res. 40, W553-W559 (2012).</li><li>Chang, T. W. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Binding+of+cells+to+matrixes+of+distinct+antibodies+coated+on+solid+surface.">Binding of cells to matrixes of distinct antibodies coated on solid surface.</a> J. Immunol. Methods. 65, 217-223 (1983).</li><li>Schena, M., Shalon, D., Davis, R. W., Brown, P. O. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Quantitative+monitoring+of+gene+expression+patterns+with+a+complementary+DNA+microarray.">Quantitative monitoring of gene expression patterns with a complementary DNA microarray.</a> Science. 270, 467-470 (1995).</li><li>Galau Levi, A., A, G., Wetzstein, H. Y. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+rapid+procedure+for+the+isolation+of+RNA+from+high-phenolic-containing+tissues+of+pecan.">A rapid procedure for the isolation of RNA from high-phenolic-containing tissues of pecan.</a> 27 (12), 1316-1318 (1992).</li><li>Lopez-Gomez, R., Gomez-Lim, M. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+method+for+extraction+of+intact+RNA+from+fruits+rich+in+polysaccharides+using+ripe+mango+mesocarp.">A method for extraction of intact RNA from fruits rich in polysaccharides using ripe mango mesocarp.</a> (5), 440-442 (1992).</li><li>Salzman, R. A., Fujita, T., Salzman, Z., Hasegawa, P. M., Bressan, R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+improved+RNA+isolation+method+for+plant+tissues+containing+high+levels+of+phenolic+compounds+or+carbohydrates.">A improved RNA isolation method for plant tissues containing high levels of phenolic compounds or carbohydrates.</a> Plant Molecular Biology Report. 17, 11-17 (1999).</li><li>Kiefer, E., Heller, W., Ernst, D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+simple+and+efficient+protocol+for+isolation+of+functional+RNA+from+plant+tissues+rich+in+secondary+metabolites.">A simple and efficient protocol for isolation of functional RNA from plant tissues rich in secondary metabolites.</a> Plant Mol. Biol. Report. 18, 33-39 (2000).</li><li>Tattersall, E. A. R., Ergul, A., Alkayal, F., Deluc, L., Cushman, J. C., Cramer, G. R. <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+methods+for+isolating+high-quality+RNA+from+leaves+of+grapevine.">Comparison of methods for isolating high-quality RNA from leaves of grapevine.</a> Am. J. Enol. Vitic. 56, 400-406 (2005).</li><li>Van Hijum, S. A. F. T., Garcia de la Nava, J., Trelles, O., Kok, J., Kuipers, O. P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=MicroPreP:+a+cDNA+microarray+data+pre-processing+framework.">MicroPreP: a cDNA microarray data pre-processing framework.</a> Appl.Bioinformatics. 2, 241-244 (2003).</li><li>Kadioglu, A., Weiser, J. N., Paton, J. C., Andrew, P. W. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+role+of+Streptococcus+pneumoniae+virulence+factors+in+host+respiratory+colonization+and+disease.">The role of Streptococcus pneumoniae virulence factors in host respiratory colonization and disease.</a> Nat.Rev.Microbiol. 6, 288-301 (2008).</li><li>Afzal, M., Shafeeq, S., Kuipers, O. P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=LacR+is+a+repressor+of+lacABCD+and+LacT+is+an+activator+of+lacTFEG,+constituting+the+lac+gene+cluster+in+Streptococcus+pneumoniae.">LacR is a repressor of lacABCD and LacT is an activator of lacTFEG, constituting the lac gene cluster in Streptococcus pneumoniae.</a> Appl. Environ. Microbiol. 80, 5349-5358 (2014).</li><li>Afzal, M., Shafeeq, S., Henriques-Normark, B., Kuipers, O. P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=UlaR+activates+the+expression+of+the+ula+operon+in+Streptococcus+pneumoniae+in+the+presence+of+ascorbic+acid.">UlaR activates the expression of the ula operon in Streptococcus pneumoniae in the presence of ascorbic acid.</a> Microbiol. Read. Engl. , (2014).</li><li>Hendriksen, W. T., 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=Site-specific+contributions+of+glutamine-dependent+regulator+GlnR+and+GlnR-regulated+genes+to+virulence+of+Streptococcus+pneumoniae.">Site-specific contributions of glutamine-dependent regulator GlnR and GlnR-regulated genes to virulence of Streptococcus pneumoniae.</a> Infect. Immun. 76, 1230-1238 (2008).</li><li>Kloosterman, T. G., Kuipers, O. P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Regulation+of+arginine+acquisition+and+virulence+gene+expression+in+the+human+pathogen+Streptococcus+pneumoniae+by+transcription+regulators+ArgR1+and+AhrC.">Regulation of arginine acquisition and virulence gene expression in the human pathogen Streptococcus pneumoniae by transcription regulators ArgR1 and AhrC.</a> J. Biol. Chem. 286, 44594-44605 (2011).</li><li>Kloosterman, T. G., Bijlsma, J. J. E., Kok, J., Kuipers, O. P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=To+have+neighbour's+fare:+extending+the+molecular+toolbox+for+Streptococcus+pneumoniae.">To have neighbour's fare: extending the molecular toolbox for Streptococcus pneumoniae.</a> Microbiol. Read. Engl. 152, 351-359 (2006).</li><li>Blom, E. 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=FIVA:+Functional+Information+Viewer+and+Analyzer+extracting+biological+knowledge+from+transcriptome+data+of+prokaryotes.">FIVA: Functional Information Viewer and Analyzer extracting biological knowledge from transcriptome data of prokaryotes.</a> Bioinforma. Oxf. Engl. 23, 1161-1163 (2007).</li><li>Blom, E. 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=Prosecutor:+parameter-free+inference+of+gene+function+for+prokaryotes+using+DNA+microarray+data,+genomic+context+and+multiple+gene+annotation+sources.">Prosecutor: parameter-free inference of gene function for prokaryotes using DNA microarray data, genomic context and multiple gene annotation sources.</a> BMC Genomics. 9, 495 (2008).</li><li>Blom, E. 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=DISCLOSE+:+DISsection+of+CLusters+Obtained+by+SEries+of+transcriptome+data+using+functional+annotations+and+putative+transcription+factor+binding+sites.">DISCLOSE : DISsection of CLusters Obtained by SEries of transcriptome data using functional annotations and putative transcription factor binding sites.</a> BMC Bioinformatics. 9, 535 (2008).</li><li>Baerends, R. J. S., 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=Genome2D:+a+visualization+tool+for+the+rapid+analysis+of+bacterial+transcriptome+data.">Genome2D: a visualization tool for the rapid analysis of bacterial transcriptome data.</a> Genome Biol. 5 (5), R37 (2004).</li><li>De Jong, A., Pietersma, H., Cordes, M., Kuipers, O. P., Kok, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=PePPER,+a+webserver+for+prediction+of+prokaryote+promoter+elements+and+regulons.">PePPER, a webserver for prediction of prokaryote promoter elements and regulons.</a> BMC Genomics. 13, 229 (2012).</li><li>Lanie, J. A., 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=Genome+sequence+of+Avery's+virulent+serotype+2+strain+D39+of+Streptococcus+pneumoniae+and+comparison+with+that+of+unencapsulated+laboratory+strain+R6.">Genome sequence of Avery's virulent serotype 2 strain D39 of Streptococcus pneumoniae and comparison with that of unencapsulated laboratory strain R6.</a> J. Bacteriol. 189, 38-51 (2007).</li><li>Molecular Devices, Corp. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=.">.</a> GenePix Pro 6.0 Microarray Acquisition and Analysis Software for GenePix Microarray Scanners-Use's Guide and Tutorial. , (2005).</li></ol>

The authors declare that they have no competing financial interests.

We describe a user-friendly protocol that can be applied to perform whole transcriptome analysis of bacteria. The key point about this particular technique is that the condition under which the cells are harvested will vary. After harvesting the cells and RNA isolation, this technique becomes equal for all types of bacterial samples and follows exactly identical steps and therefore, can be applied to any type of bacterial culture. The protocol is very simple and convenient and starts from RNA isolation. Our RNA isolation protocol (using a Macaloid and RNA isolation kit) is time-effective as compared to conventional phenol-chloroform and Trizol methods. In the next step, the preparation of cDNA with transcriptase III enzyme is performed. Next, labelling of cDNA is done by first mixing of cDNA with amine-reactive dyes, and then purifying the labelled cDNA. The labelling is also simple and does not take a lot of time as the samples need to be incubated for about 1 hr in the dark. All these steps can be performed in any standard laboratory as it does not demand any specialized equipment. A microarray scanner is needed to scan slides. For slide scanning and analysis, the GenePix Pro program is used, which is a very simple and user-friendly program22.
After doing all the experiments, analysis was performed using MicroPrep and CyberT. The MicroPrep package, consists of three modules, i.e., PrePreP, PreP and PostPreP9. This data pre-processing framework reduces the time for normalization of data and also reduces the amount of discarded data. The ease with which the software can be used makes it possible for the researcher to have an understanding of the DNA microarray data in minimum time. It takes only a couple of minutes to convert the raw signal data into high-quality data for further processing after slide image analysis is done. Further analysis on the pool of genes regulated in the microarray can be done using different in-house software packages. They include PePPER 20, FIVA 16, DISCLOSE 18, PROSECUTOR 17 and Genome2D 19. These Windows-based tools and software packages are user-friendly and provide deep insight into the data during further investigation. These software packages make it very easy and handy for the researchers to utilize this technology as the data becomes much more meaningful and relevant.
The dyes used in microarrays are known to be susceptible to an ozone-effect, where the dyes become unstable in the presence of ozone and signal strength is so low that it cannot be recognized by the scanner. Amine-reactive dyes which are less sensitive to the ozone-effect are used to label cDNA. The solution to ozone-effect will make this technology even better.
A list was created of genes that were up- or downregulated in the presence of the minimal L-serine concentration as compared to the maximum (Table 2). The upregulated genes can be categorized according to their product&#8217;s function. Seven of them encode hypothetical proteins, four are involved in carbohydrate transport and metabolism, a cell division protein DivIB, and certain amino acid transport and metabolism genes. Similarly, there are also certain genes that are downregulated in our tested condition. A BglG-family transcriptional regulator LicT, some carbohydrate genes and some amino acid-specific genes are among the downregulated genes. A heat-shock protein GrpE is also among the down-regulated ones. Therefore, this study provides a complete overview of the genes differentially expressed under the tested conditions. After analysis of the results, sometimes verification of the results is necessary. This can be either done by qPCR or &#946;-galactosidase assays using lacZ promoter fusions.

The study of the whole set of mRNA abundance (transcriptome) encoded by the genome of a unicellular organism or a eukaryotic cell at a specific time or under a specific condition, including gene overexpression or knock-out, is called transcriptomics. Transcriptomics allows us to observe to what extent genes are expressed under a particular condition at a time point X and gives us information about how strongly the genes are expressed relative to a reference.
A microarray is a two-dimensional array on a solid substrate (usually a glass slide or silicon thin-film cell) that can be used to assay large quantities of biological material using high-throughput screening, and miniaturized, multiplexed and parallel processing and detection methods. Microarrays come in various types, including DNA-microarrays, protein microarrays, peptide microarrays, tissue microarrays, antibody microarrays, cellular microarrays and others. A DNA microarray is basically an assembly of microscopic DNA spots fixed to a solid surface, usually glass. DNA microarrays are used to measure the expression levels of a gene or a set of genes simultaneously or to genotype multiple regions of a genome2,3. Picomoles (10-12 moles) of a probe are present within each DNA spot that represents a specific DNA sequence, also known as a reporter. The labelled mRNA molecules from the samples are called &#8216;targets&#8217;. Fluorophores are used to measure probe-target hybridization and detection of fluorophore-labelled targets determines the relative abundance of nucleic acid sequences in the target. A microarray experiment can accomplish multiple genetic tests in parallel because an array may contain tens of thousands of probes. The layout of a simple microarray experiment is shown in Figure 1. Recently, it was established in our and other labs that these arrays are reusable, which makes this technique quite cost-effective.
Different RNA isolation and purification techniques have been developed over the years including C-TAB, SDS and GT methods 4&#8211;8. Furthermore, several commercial kits are also available. For gene expression high quality RNA is very important. Therefore, the RNA isolation methods are modified to get a maximum quantity of RNA. Similarly, the steps for cDNA preparation and labelling of cDNA are minimized. Normalization of data after scanning is also performed efficiently by using in-house built software packages and tools9.
Streptococcus pneumoniae is a Gram-positive human pathogen that colonizes the nasopharynx and is the cause of multiple infections such as pneumonia, sepsis, otitis media and meningitis10. The bacterium can utilize a wide variety of the nutrients required for growth and survival 11,12. A number of studies have been carried out on the pneumococcal nitrogen metabolism and regulation emphasizing the importance of amino acids and their role in virulence13,14. In this study, the transcriptomic response of S. pneumoniae to changing concentrations of L-serine, an amino acid abundantly present in the human blood plasma, is reported using DNA microarrays. The transcriptomic response of S. pneumoniae grown in a minimum concentration of L-serine (150 &#181;M) was compared to that grown in a maximum concentration (10 mM) of serine. Chemically defined medium (CDM or minimal medium)15 was used for this study to control the concentration of serine. The focus of this study is to make this technique user-friendly and to provide different tools for data normalization and analysis. Therefore, a number of tools were developed for analysis and data interpretation. FIVA (Functional Information Viewer and Analyzer) provides a platform for processing information contained in clusters of genes having similar gene expression patterns and for constructing functional profiles16. PROSECUTOR is another software package that facilitates the identification of putative functions and annotations of genes 17. By making use of clustering methods, DISCLOSE provides a DNA binding site detection algorithm. Cis-regulatory motifs of genes can be projected by using this algorithm 18. Genome2D offers a Windows-based platform for visualization and analysis of transcriptome data by offering different color ranges to characterize the changes in gene expression levels on a genome map19. The PePPER webserver offers, in addition to the all-in-one analysis method, a toolbox for mining for regulons, promoters and transcription factor binding sites 20. Full annotation of intergenic regions in a bacterial genome can be achieved by using this package. Biologists can greatly benefit from PePPER as it offers them a platform for designing experiments so that the hypothesized information can be confirmed in vitro20. These software packages contribute significantly to the microarray analysis as most of them are freely available and make data normalization and analysis very reliable.

<table><tbody><tr><td>Acid phenol</td><td>SigmaAldrich</td><td>P4682</td><td></td></tr><tr><td>Roche RNA isolation kit</td><td>Roche Applied Science</td><td>11828665001</td><td></td></tr><tr><td>Glass beads</td><td></td><td>105015</td><td></td></tr><tr><td>Chloroform</td><td>Boom</td><td>92013505.1000.</td><td></td></tr><tr><td>IAA</td><td></td><td>106630</td><td></td></tr><tr><td>Nanodrop</td><td>Nanodrop</td><td>ND-100</td><td></td></tr><tr><td>Agilent BioAnalyser</td><td>Agilent</td><td>G2940CA</td><td></td></tr><tr><td>Superscript III</td><td>Life technologies Invitrogen</td><td>18080044</td><td></td></tr><tr><td>AA-dUTP</td><td>Life technologies Invitrogen</td><td>AM8439</td><td></td></tr><tr><td>DDT</td><td>Life technologies Invitrogen</td><td>18080044</td><td></td></tr><tr><td>First Strand buffer</td><td>Life technologies Invitrogen</td><td>18080044</td><td></td></tr><tr><td>NaOH</td><td>SigmaAldrich</td><td>S8045-1KG</td><td></td></tr><tr><td>HEPES</td><td>SigmaAldrich</td><td>H4034-500G</td><td></td></tr><tr><td>DyLight-550</td><td>Thermoscientiffic</td><td>62262</td><td></td></tr><tr><td>DyLight-650</td><td>Thermoscientiffic</td><td>62265</td><td></td></tr><tr><td>SHY Buffer</td><td>SigmaAldrich</td><td>H7033-125ML</td><td></td></tr><tr><td>Speedvac cooler</td><td>Eppendorf</td><td>RUGNE3140</td><td>Speedvac concentrator plus</td></tr><tr><td>Hybridization oven</td><td>Grant Boekel</td><td>Iso-20</td><td></td></tr><tr><td>Lifter-slips</td><td>Erie Scientific</td><td>25x60I-M-5439</td><td></td></tr><tr><td>Wipe</td><td>KIMTECH</td><td></td><td></td></tr><tr><td>SDS</td><td>SigmaAldrich</td><td>L3771-100g</td><td></td></tr><tr><td>SSC</td><td>SigmaAldrich</td><td>W302600-1KG-K</td><td></td></tr><tr><td>Genpix autoloader 4200A1</td><td>MSD analytical technologies</td><td></td><td>Microarray scanner</td></tr><tr><td>Sodium bicarbonate</td><td>SigmaAldrich</td><td>104766</td><td></td></tr></tbody></table>

a fast and reliable pipeline for bacterial transcriptome analysis case study: serine-dependent gene regulation in streptococcus pneumoniae

Gene expression and its regulation are very important to understand the behavior of cells under different conditions. Various techniques are used nowadays to study gene expression, but most are limited in terms of providing an overall picture of the expression of the whole transcriptome. DNA microarrays offer a fast and economic research technology, which gives a full overview of global gene expression and have a vast number of applications including identification of novel genes and transcription factor binding sites, characterization of transcriptional activity of the cells and also help in analyzing thousands of genes (in a single experiment). In the present study, the conditions for bacterial transcriptome analysis from cell harvest to DNA microarray analysis have been optimized. Taking into account the time, costs and accuracy of the experiments, this technology platform proves to be very useful and universally applicabale for studying bacterial transcriptomes. Here, we perform DNA microarray analysis with Streptococcus pneumoniae as a case-study by comparing the transcriptional responses of S. pneumoniae grown in the presence of varying L-serine concentrations in the medium. Total RNA was isolated by using a Macaloid method using an RNA isolation kit and the quality of RNA was checked by using an RNA quality check kit. cDNA was prepared using reverse transcriptase and the cDNA samples were labelled using one of two amine-reactive fluorescent dyes. Homemade DNA microarray slides were used for hybridization of the labelled cDNA samples and microarray data were analyzed by using a cDNA microarray data pre-processing framework (Microprep). Finally, Cyber-T was used to analyze the data generated using Microprep for the identification of statistically significant differentially expressed genes. Furthermore, in-house built software packages (PePPER, FIVA, DISCLOSE, PROSECUTOR, Genome2D) were used to analyze data.

RNA, cDNA isolations and analysis
L-serine is one of the essential amino acids and its concentration in human blood plasma varies from 60-150 &#181;M in children and adults. Its role in the biosynthesis of purines and pyrimidines highlights its importance in metabolism and it is a precursor to several amino acids (glycine, cysteine and tryptophan). To study the impact of L-serine on the whole transcriptome of S. pneumoniae D39 wild-type strain, microarray analysis of the D39 strain grown in CDM with a minimum concentration (150 &#181;M) of L-serine against that grown in a maximum concentration (10 mM) in the same medium was performed. First of all, total RNA from cells grown under both concentrations was isolated. The concentrations of the RNA samples are given in Table 1. The quality of total RNA was examined using the quality check assay. RNA was treated with DNase I before performing this assay to remove the possible genomic DNA. Figure 2 shows the quality of RNA; lane L represents the ladder, lanes 1 and 2 represent RNA from 150&#181;M serine and lanes 3 and 4 represent the RNA from 10 mM serine. The presence of two clear bands corresponding to the two RNA subunits indicated the good quality of RNA and the next step of the experiment could be performed.
After measuring the quality of RNA, cDNA synthesis was done. cDNA was formed by using random nanomers and reverse transcriptase enzyme. The concentrations of the cDNA samples are given in Table 1. This cDNA was labelled with amine-reactive dyes and the concentrations of the labelled cDNA and dye labels are given in Table 1. After labelling, samples were mixed accordingly and then hybridized. After washing, slides were scanned using the scanner, and analysis was performed using Gene Pix Pro software. Figure 3 shows the scatter plot analysis of the amine-reactive dyes ratio. After initial analysis, data was further analyzed using the PicroPrep software package (PrePrep, Prep, PostPrep)9 to reduce noise and Cyber-T was used for final analysis. Table 2 summarizes the results of the microarray studies after applying the criteria of &#8805; 2.0 fold difference and p-value &#60; 0.001. A number of genes were differentially expressed in the presence of minimum L-serine as compared to the maximum (Table 2).
<img alt="Figure 1" src="/files/ftp_upload/52649/52649fig1.jpg" /> 
Figure 1.&#160;An overview of DNA microarray technology. RNA is isolated from the control and the target samples and labelled cDNA is then hybridized.
<img alt="Figure 2" src="/files/ftp_upload/52649/52649fig2.jpg" /> 
Figure 2.&#160;Quality check of RNA isolated from S. pneumoniae D39 cells grown in the presence of minimum (150 &#181;M) and maximum (10 mM) serine concentrations. Lane L represents the size-ladder whereas lane 1 and 2 represent RNA samples isolated from cells grown in the presence of minimum serine concentration. Similarly, lane 3 and 4 represent RNA samples isolated from cells grown in the presence of maximum serine concentration. The bands represent the 23S and 16S rRNA. The presence of only two bands shows that there is no gDNA contamination and RNA is of good quality.
<img alt="Figure 3" src="/files/ftp_upload/52649/52649fig3.jpg" /> 
Figure 3.&#160;Array comparison scatter plot of a sample mixture. Each spot in the plot represents the mean expression value (log2) of a gene in an experiment with dye 1 on the y-axis and dye 2 on the x-axis.
<table border="0" cellpadding="0" cellspacing="0">
	<tbody>
		<tr>
			<td>Sample</td>
			<td>RNA Sample</td>
			<td>Description</td>
			<td>RNA concentration (ng/&#181;l)</td>
			<td>cDNA concentration (ng/&#181;l)</td>
			<td>Labelled cDNA (ng/&#181;l)</td>
			<td>DyLight-550 (pmol/&#181;l)</td>
			<td>DyLight-650 (pmol/&#181;l)</td>
			<td>Hybridization scheme</td>
		</tr>
		<tr>
			<td>S1</td>
			<td>R1</td>
			<td>D39 wild-type grown in CDM + Minimum L-serine</td>
			<td>2057</td>
			<td>255</td>
			<td>225</td>
			<td>0.8</td>
			<td></td>
			<td>R1+R3</td>
		</tr>
		<tr>
			<td></td>
			<td>R2</td>
			<td>D39 wild-type grown in CDM + Minimum L-serine</td>
			<td>2566</td>
			<td>201</td>
			<td>179</td>
			<td></td>
			<td>1.2</td>
			<td>R2+R4</td>
		</tr>
		<tr>
			<td>S2</td>
			<td>R3</td>
			<td>D39 wild-type grown in CDM + Maximum L-serine</td>
			<td>2831</td>
			<td>292</td>
			<td>276</td>
			<td></td>
			<td>2.3</td>
			<td></td>
		</tr>
		<tr>
			<td></td>
			<td>R4</td>
			<td>D39 wild-type grown in CDM + Maximum L-serine</td>
			<td>1867</td>
			<td>172</td>
			<td>150</td>
			<td>1</td>
			<td></td>
			<td></td>
		</tr>
	</tbody>
</table>
Table 1.&#160;Hybridization scheme of the samples used in the microarray analysis.
<table border="0" cellpadding="0" cellspacing="0">
	<tbody>
		<tr>
			<td>Genea</td>
			<td>Functionb</td>
			<td>Ratioc</td>
		</tr>
		<tr>
			<td>SPD0600</td>
			<td>Cell division protein DivIB</td>
			<td align="right">4</td>
		</tr>
		<tr>
			<td>SPD0445</td>
			<td>Phosphoglycerate kinase</td>
			<td align="right">3.5</td>
		</tr>
		<tr>
			<td>SPD0646</td>
			<td>Hypothetical protein</td>
			<td align="right">3.4</td>
		</tr>
		<tr>
			<td>SPD0873</td>
			<td>Hypothetical protein</td>
			<td align="right">3.3</td>
		</tr>
		<tr>
			<td>SPD1223</td>
			<td>Hypothetical protein</td>
			<td align="right">3.3</td>
		</tr>
		<tr>
			<td>SPD0980</td>
			<td>Ribose-phosphate pyrophosphokinase</td>
			<td align="right">2.8</td>
		</tr>
		<tr>
			<td>SPD1628</td>
			<td>Xanthine phosphoribosyltransferase</td>
			<td align="right">2.7</td>
		</tr>
		<tr>
			<td>SPD1011</td>
			<td>Glycerate kinase</td>
			<td align="right">2.4</td>
		</tr>
		<tr>
			<td>SPD0645</td>
			<td>Hypothetical protein</td>
			<td align="right">2.3</td>
		</tr>
		<tr>
			<td>SPD0564</td>
			<td>Hypothetical protein</td>
			<td align="right">2.2</td>
		</tr>
		<tr>
			<td>SPD0641</td>
			<td>Mannose-6-phosphate isomerase, class I, ManA</td>
			<td align="right">2.1</td>
		</tr>
		<tr>
			<td>SPD1333</td>
			<td>Hypothetical protein</td>
			<td align="right">2.1</td>
		</tr>
		<tr>
			<td>SPD1384</td>
			<td>Cation efflux family protein</td>
			<td align="right">2.1</td>
		</tr>
		<tr>
			<td>SPD1432</td>
			<td>UDP-glucose 4-epimerase, GalE-1</td>
			<td align="right">2.1</td>
		</tr>
		<tr>
			<td>SPD1866</td>
			<td>N-acetylglucosamine-6-phosphate deacetylase, NagA</td>
			<td align="right">2.1</td>
		</tr>
		<tr>
			<td>SPD0104</td>
			<td>LysM domain protein</td>
			<td align="right">2</td>
		</tr>
		<tr>
			<td>SPD0140</td>
			<td>ABC transporter, ATP-binding protein</td>
			<td align="right">2</td>
		</tr>
		<tr>
			<td>SPD0261</td>
			<td>Aminopeptidase C, PepC</td>
			<td align="right">2</td>
		</tr>
		<tr>
			<td>SPD1350</td>
			<td>Hypothetical protein</td>
			<td align="right">2</td>
		</tr>
		<tr>
			<td>SPD1822</td>
			<td>Ribosomal large subunit pseudouridine synthase, RluD subfamily protein</td>
			<td align="right">2</td>
		</tr>
		<tr>
			<td>SPD0453</td>
			<td>Type I restriction-modification system, S subunit</td>
			<td align="right">-2</td>
		</tr>
		<tr>
			<td>SPD0459</td>
			<td>Heat shock protein GrpE</td>
			<td align="right">-2</td>
		</tr>
		<tr>
			<td>SPD1006</td>
			<td>Glucose-1-phosphate adenylyltransferase</td>
			<td align="right">-2</td>
		</tr>
		<tr>
			<td>SPD1799</td>
			<td>Sensor histidine kinase, putative</td>
			<td align="right">-2.1</td>
		</tr>
		<tr>
			<td>SPD0387</td>
			<td>Beta-hydroxyacyl-(acyl-carrier-protein) dehydratase FabZ</td>
			<td align="right">-2.2</td>
		</tr>
		<tr>
			<td>SPD1494</td>
			<td>Sugar ABC transporter, permease protein</td>
			<td align="right">-2.2</td>
		</tr>
		<tr>
			<td>SPD0974</td>
			<td>Class I glutamine amidotransferase, putative</td>
			<td align="right">-2.4</td>
		</tr>
		<tr>
			<td>SPD1600</td>
			<td>Anthranilate phosphoribosyltransferase</td>
			<td align="right">-2.5</td>
		</tr>
		<tr>
			<td>SPD1472</td>
			<td>Isoleucyl-tRNA synthetase</td>
			<td align="right">-2.6</td>
		</tr>
		<tr>
			<td>SPD0681</td>
			<td>Hypothetical protein</td>
			<td align="right">-2.7</td>
		</tr>
		<tr>
			<td>SPD0501</td>
			<td>Transcription antiterminator, Lict</td>
			<td align="right">-3.4</td>
		</tr>
	</tbody>
</table>
Table 2. List of genes regulated in the transcriptome comparison of S. pneumoniae strain D39wild-type grown in CDM15 with minimum concentration of L-serine and CDM15 with maximum concentration of L-serine. aGene numbers refer to D39 locus tags. bD39 annotation/TIGR4 annotation21. cRatio represents the fold increase/decrease in the expression of genes in CDM-maximum&#160;as compared to CDM-minimum&#160;(minus sign indicates downregulation).

Watch this Scientific Journal Video about A Fast and Reliable Pipeline for Bacterial Transcriptome Analysis Case study: Serine-dependent Gene Regulation in Streptococcus pneumoniae at JoVE.com

A Fast and Reliable Pipeline for Bacterial Transcriptome Analysis Case study: Serine-dependent Gene Regulation in Streptococcus pneumoniae

This manuscript describes the use of state-of-the-art technology provided by DNA-microarrays. Microarrays provide an overview of the transcriptomic changes in bacteria incurred under a specific condition. Moreover, we highlight the ease by which large amounts of data can be analyzed by using convenient in-house developed software packages.

A Fast and Reliable Pipeline for Bacterial Transcriptome Analysis Case study: Serine-dependent Gene Regulation in Streptococcus pneumoniae

Gene expression and its regulation are very important to understand the behavior of cells under different conditions. Various techniques are used nowadays ...

a-fast-reliable-pipeline-for-bacterial-transcriptome-analysis-case

Nanyang Technological University

Research

JoVE Journal

Biology

10.3K Views.  University of Groningen. This manuscript describes the use of state-of-the-art technology provided by DNA-microarrays. Microarrays provide an overview of the transcriptomic changes in bacteria incurred under a specific condition. Moreover, we highlight the ease by which large amounts of data can be analyzed by using convenient in-house developed software packages. 

Video: A Fast and Reliable Pipeline for Bacterial Transcriptome Analysis Case study: Serine-dependent Gene Regulation in Streptococcus pneumoniae

A Fluorescence-based Method to Study Bacterial Gene Regulation in Infected Tissues

Bacterial virulence genes are often regulated at the transcriptional level by multiple factors that respond to different environmental signals. Some factors act directly on virulence genes; others control pathogenesis by adjusting the expression of downstream regulators or the accumulation of signals that affect regulator activity. While regulation has been studied extensively during in vitro growth, relatively little is known about how gene expression is adjusted during infection. Such information is important when a particular gene product is a candidate for therapeutic intervention. Transcriptional approaches like quantitative, real-time RT-PCR and RNA-Seq are powerful ways to examine gene expression on a global level but suffer from many technical challenges including low abundance of bacterial RNA compared to host RNA, and sample degradation by RNases. Evaluating regulation using fluorescent reporters is relatively easy and can be multiplexed with fluorescent proteins with unique spectral properties. The method allows for single-cell, spatiotemporal analysis of gene expression in tissues that exhibit complex three-dimensional architecture and physiochemical gradients that affect bacterial regulatory networks. Such information is lost when data are averaged over the bulk population. Herein, we describe a method for quantifying gene expression in bacterial pathogens in situ. The method is based on simple tissue processing and direct observation of fluorescence from reporter proteins. We demonstrate the utility of this system by examining the expression of Staphylococcus aureus thermonuclease (nuc), whose gene product is required for immune evasion and full virulence ex vivo and in vivo. We show that nuc-gfp is strongly expressed in renal abscesses and reveal heterogeneous gene expression due in part to apparent spatial regulation of nuc promoter activity in abscesses fully engaged with the immune response. The method can be applied to any bacterium with a manipulatable genetic system and any infection model, providing valuable information for preclinical studies and drug development.

Described here is a method for analyzing bacterial gene expression in animal tissues at a cellular level. This method provides a resource for studying the phenotypic diversity occurring within a bacterial population in response to the tissue environment during an infection.

Bacterial virulence genes are often regulated at the transcriptional level by multiple factors that respond to different environmental signals. Some ...

Genetics

Characterization of Inflammatory Responses During Intranasal Colonization with Streptococcus pneumoniae

Nasopharyngeal colonization by Streptococcus pneumoniae is a prerequisite to invasion to the lungs or bloodstream1. This organism is capable of colonizing the mucosal surface of the nasopharynx, where it can reside, multiply and eventually overcome host defences to invade to other tissues of the host. Establishment of an infection in the normally lower respiratory tract results in pneumonia. Alternatively, the bacteria can disseminate into the bloodstream causing bacteraemia, which is associated with high mortality rates2, or else lead directly to the development of pneumococcal meningitis. Understanding the kinetics of, and immune responses to, nasopharyngeal colonization is an important aspect of S. pneumoniae infection models.
Our mouse model of intranasal colonization is adapted from human models3 and has been used by multiple research groups in the study of host-pathogen responses in the nasopharynx4-7. In the first part of the model, we use a clinical isolate of S. pneumoniae to establish a self-limiting bacterial colonization that is similar to carriage events in human adults. The procedure detailed herein involves preparation of a bacterial inoculum, followed by the establishment of a colonization event through delivery of the inoculum via an intranasal route of administration. Resident macrophages are the predominant cell type in the nasopharynx during the steady state. Typically, there are few lymphocytes present in uninfected mice8, however mucosal colonization will lead to low- to high-grade inflammation (depending on the virulence of the bacterial species and strain) that will result in an immune response and the subsequent recruitment of host immune cells. These cells can be isolated by a lavage of the tracheal contents through the nares, and correlated to the density of colonization bacteria to better understand the kinetics of the infection.

Characterization of Inflammatory Responses During Intranasal Colonization with Streptococcus pneumoniae

Colonization of the murine nasopharynx with Streptococcus pneumoniae and the subsequent extraction of adherent or recruited cells is described. This technique involves flushing the nasopharynx and collection of the fluid through the nares and is adaptable for various readouts, including differential cell quantification and analysis of mRNA expression in situ.

Nasopharyngeal colonization by Streptococcus pneumoniae is a prerequisite to invasion to the lungs or bloodstream1. This organism is capable of colonizing ...

Immunology and Infection

MS2-Affinity Purification Coupled with RNA Sequencing in Gram-Positive Bacteria

Although small regulatory RNAs (sRNAs) are widespread among the bacterial domain of life, the functions of many of them remain poorly characterized notably due to the difficulty of identifying their mRNA targets. Here, we described a modified protocol of the MS2-Affinity Purification coupled with RNA Sequencing (MAPS) technology, aiming to reveal all RNA partners of a specific sRNA in vivo. Broadly, the MS2 aptamer is fused to the 5&#8217; extremity of the sRNA of interest. This construct is then expressed in vivo, allowing the MS2-sRNA to interact with its cellular partners. After bacterial harvesting, cells are mechanically lysed. The crude extract is loaded into an amylose-based chromatography column previously coated with the MS2 protein fused to the maltose binding protein. This enables the specific capture of MS2-sRNA and interacting RNAs. After elution, co-purified RNAs are identified by high-throughput RNA sequencing and subsequent bioinformatic analysis. The following protocol has been implemented in the Gram-positive human pathogen Staphylococcus aureus and is, in principle, transposable to any Gram-positive bacteria. To sum up, MAPS technology constitutes an efficient method to deeply explore the regulatory network of a particular sRNA, offering a snapshot of its whole targetome. However, it is important to keep in mind that putative targets identified by MAPS still need to be validated by complementary experimental approaches.

MAPS technology has been developed to scrutinize the targetome of a specific regulatory RNA in vivo. The sRNA of interest is tagged with a MS2 aptamer enabling the co-purification of its RNA partners and their identification by RNA sequencing. This modified protocol is particularly suited for Gram-positive bacteria.&#160;

Although small regulatory RNAs (sRNAs) are widespread among the bacterial domain of life, the functions of many of them remain poorly characterized ...

Biochemistry

RIBO-seq in Bacteria: a Sample Collection and Library Preparation Protocol for NGS Sequencing

The ribosome profiling technique (RIBO-seq) is currently the most effective tool for studying the process of protein synthesis in vivo. The advantage of this method, in comparison to other approaches, is its ability to monitor translation by precisely mapping the position and number of ribosomes on a mRNA transcript.
In this article, we describe the consecutive stages of sample collection and preparation for RIBO-seq method in bacteria, highlighting the details relevant to the planning and execution of the experiment.
Since the RIBO-seq relies on intact ribosomes and related mRNAs, the key step is rapid inhibition of translation and adequate disintegration of cells. Thus, we suggest filtration and flash-freezing in liquid nitrogen for cell harvesting with an optional pretreatment with chloramphenicol to arrest translation in bacteria. For the disintegration, we propose grinding frozen cells with mortar and pestle in the presence of aluminum oxide to mechanically disrupt the cell wall. In this protocol, sucrose cushion or a sucrose gradient ultracentrifugation for monosome purification is not required. Instead, mRNA separation using polyacrylamide gel electrophoresis (PAGE) followed by the ribosomal footprint excision (28-30 nt band) is applied and provides satisfactory results. This largely simplifies the method as well as reduces the time and equipment requirements for the procedure. For library preparation, we recommend using the commercially available small RNA kit for Illumina sequencing from New England Biolabs, following manufacturer&#39;s guidelines with some degree of optimization.
The resulting cDNA libraries present appropriate quantity and quality required for next generation sequencing (NGS). Sequencing of the libraries prepared according to the described protocol results in 2 to 10 mln uniquely mapped reads per sample providing sufficient data for comprehensive bioinformatic analysis. The protocol we present is quick and relatively easy and can be performed with standard laboratory equipment.

Here we describe the stages of sample collection and preparation for RIBO-seq in bacteria. Sequencing of the libraries prepared according to these guidelines results in sufficient data for comprehensive bioinformatic analysis. The protocol we present is simple, uses standard laboratory equipment and takes seven days from lysis to obtaining libraries.

The ribosome profiling technique (RIBO-seq) is currently the most effective tool for studying the process of protein synthesis in vivo. The advantage of ...

Establishment of a High-throughput Setup for Screening Small Molecules That Modulate c-di-GMP Signaling in Pseudomonas aeruginosa

Bacterial resistance to traditional antibiotics has driven research attempts to identify new drug targets in recently discovered regulatory pathways. Regulatory systems that utilize intracellular cyclic di-GMP (c-di-GMP) as a second messenger are one such class of target. c-di-GMP is a signaling molecule found in almost all bacteria that acts to regulate an extensive range of processes including antibiotic resistance, biofilm formation and virulence. The understanding of how c-di-GMP signaling controls aspects of antibiotic resistant biofilm development has suggested approaches whereby alteration of the cellular concentrations of the nucleotide or disruption of these signaling pathways may lead to reduced biofilm formation or increased susceptibility of the biofilms to antibiotics. We describe a simple high-throughput bioreporter protocol, based on green fluorescent protein (GFP), whose expression is under the control of the c-di-GMP responsive promoter cdrA, to rapidly screen for small molecules with the potential to modulate c-di-GMP cellular levels in Pseudomonas aeruginosa (P. aeruginosa). This simple protocol can screen upwards of 3,500 compounds within 48 hours and has the ability to be adapted to multiple microorganisms.

Establishment of a High-throughput Setup for Screening Small Molecules That Modulate c-di-GMP Signaling in Pseudomonas aeruginosa

This article describes the high-throughput assay that has been successfully established to screen large libraries of small molecules for their potential ability to manipulate cellular levels of cyclic di-GMP in Pseudomonas aeruginosa, providing a new powerful tool for antibacterial drug discovery and compound testing.

Bacterial resistance to traditional antibiotics has driven research attempts to identify new drug targets in recently discovered regulatory pathways. ...

A High-Yield Streptomyces Transcription-Translation Toolkit for Synthetic Biology and Natural Product Applications

Streptomyces spp. are a major source of clinical antibiotics and industrial chemicals. Streptomyces venezuelae ATCC 10712 is a fast-growing strain and a natural producer of chloramphenicol, jadomycin, and pikromycin, which makes it an attractive candidate&#160;as a next-generation synthetic biology chassis. Therefore, genetic tools that accelerate the development of S. venezuelae ATCC 10712, as well as other Streptomyces spp. models, are highly desirable for natural product engineering and discovery. To this end, a dedicated S. venezuelae ATCC 10712 cell-free system is provided in this protocol to enable high-yield heterologous expression of high G+C (%) genes. This protocol is suitable for small-scale (10-100 &#956;L) batch reactions in either 96-well or 384-well plate format, while reactions are potentially scalable. The cell-free system is robust and can achieve high yields (~5-10 &#956;M) for a range of recombinant proteins in a minimal setup. This work also incorporates a broad plasmid toolset for real-time measurement of mRNA and protein synthesis, as well as in-gel fluorescence staining of tagged proteins. This protocol can also be integrated with high-throughput&#160;gene expression characterization workflows or the study of enzyme pathways from high G+C (%) genes present in Actinomycetes genomes.

A High-Yield Streptomyces Transcription-Translation Toolkit for Synthetic Biology and Natural Product Applications

This protocol details an enhanced method for synthesizing high yields of recombinant proteins from a Streptomyces venezuelae cell-free transcription-translation (TX-TL) system.

Streptomyces spp. are a major source of clinical antibiotics and industrial chemicals. Streptomyces venezuelae ATCC 10712 is a fast-growing strain and a ...

Bioengineering

Genome-wide Gene Deletions in Streptococcus sanguinis by High Throughput PCR

Transposon mutagenesis and single-gene deletion are two methods applied in genome-wide gene knockout in bacteria 1,2. Although transposon mutagenesis is less time consuming, less costly, and does not require completed genome information, there are two weaknesses in this method: (1) the possibility of a disparate mutants in the mixed mutant library that counter-selects mutants with decreased competition; and (2) the possibility of partial gene inactivation whereby genes do not entirely lose their function following the insertion of a transposon. Single-gene deletion analysis may compensate for the drawbacks associated with transposon mutagenesis. To improve the efficiency of genome-wide single gene deletion, we attempt to establish a high-throughput technique for genome-wide single gene deletion using Streptococcus sanguinis as a model organism. Each gene deletion construct in S. sanguinis genome is designed to comprise 1-kb upstream of the targeted gene, the aphA-3 gene, encoding kanamycin resistance protein, and 1-kb downstream of the targeted gene. Three sets of primers F1/R1, F2/R2, and F3/R3, respectively, are designed and synthesized in a 96-well plate format for PCR-amplifications of those three components of each deletion construct. Primers R1 and F3 contain 25-bp sequences that are complementary to regions of the aphA-3 gene at their 5' end. A large scale PCR amplification of the aphA-3 gene is performed once for creating all single-gene deletion constructs. The promoter of aphA-3 gene is initially excluded to minimize the potential polar effect of kanamycin cassette. To create the gene deletion constructs, high-throughput PCR amplification and purification are performed in a 96-well plate format. A linear recombinant PCR amplicon for each gene deletion will be made up through four PCR reactions using high-fidelity DNA polymerase. The initial exponential growth phase of S. sanguinis cultured in Todd Hewitt broth supplemented with 2.5% inactivated horse serum is used to increase competence for the transformation of PCR-recombinant constructs. Under this condition, up to 20% of S. sanguinis cells can be transformed using ~50 ng of DNA. Based on this approach, 2,048 mutants with single-gene deletion were ultimately obtained from the 2,270 genes in S. sanguinis excluding four gene ORFs contained entirely within other ORFs in S. sanguinis SK36 and 218 potential essential genes. The technique on creating gene deletion constructs is high throughput and could be easy to use in genome-wide single gene deletions for any transformable bacteria.

Genome-wide Gene Deletions in Streptococcus sanguinis by High Throughput PCR

An efficient genome-wide single gene mutation method has been established using Streptococcus sanguinis as a model organism. This method has achieved via high throughput recombinant PCRs and transformations.

Transposon  mutagenesis and single-gene deletion are two methods applied in genome-wide  gene knockout in bacteria 1,2. Although transposon mutagenesis is ...

Constructing Mutants in Serotype 1 Streptococcus pneumoniae strain 519/43

Streptococcus pneumoniae serotype 1 remains a huge problem in low-and-middle income countries, particularly in sub-Saharan Africa. Despite its importance, studies in this serotype have been hindered by the lack of genetic tools to modify it. In this study, we describe a method to genetically modify a serotype 1 clinical isolate (strain 519/43). Interestingly, this was achieved by exploiting the Pneumococcus&#8217; ability to naturally acquire DNA. However, unlike most pneumococci, the use of linear DNA was not successful; to mutate this important strain, a suicide plasmid had to be used. This methodology has provided the means for a deeper understanding of this elusive serotype, both in terms of its biology and pathogenicity. To validate the method, the major known pneumococcal toxin, pneumolysin, was mutated because it has a well-known and easy to follow phenotype. We showed that the mutant, as expected, lost its ability to lyse red blood cells. By being able to mutate an important gene in the serotype of interest, we were able to observe different phenotypes for loss of function mutants upon intraperitoneal and intranasal infections from the ones observed for other serotypes. In summary, this study proves that strain 519/43 (serotype 1) can be genetically modified.

Constructing Mutants in Serotype 1 Streptococcus pneumoniae strain 519/43

Here, we describe a S. pneumoniae serotype 1 strain 519/43 that can be genetically modified by using its ability to naturally acquire DNA and a suicide-plasmid. As proof of principle, an isogenic mutant in the pneumolysin (ply) gene was made.

Streptococcus pneumoniae serotype 1 remains a huge problem in low-and-middle income countries, particularly in sub-Saharan Africa. Despite its importance, ...

High-Throughput Transcriptome Analysis for Investigating Host-Pathogen Interactions

Pathogens can cause a wide variety of infectious diseases. The biological processes induced by the host in response to infection determine the severity of the disease.&#160;To study such processes, researchers can use high-throughput sequencing techniques (RNA-seq) that measure the dynamic changes of the host transcriptome at different stages of infection, clinical outcomes, or disease severity.This investigation can lead to a better understanding of the diseases, as well as uncovering potential drug targets and treatments. The protocol presented here describes a complete pipeline to analyze RNA-sequencing data from raw reads to functional analysis. The pipeline is divided into five steps: (1) quality control of the data; (2) mapping and annotation of genes; (3) statistical analysis to identify differentially expressed genes and co-expressed genes; (4) determination of the molecular degree of the perturbation of samples; and (5) functional analysis. Step 1 removes technical artifacts that may impact the quality of downstream analyses. In step 2, genes are mapped and annotated according to standard library protocols.&#160;The statistical analysis in step 3 identifies genes that are differentially expressed or co-expressed in infected samples, in comparison with non-infected ones. Sample variability and the presence of potential biological outliers are verified using the molecular degree of perturbation approach in step 4. Finally, the functional analysis in step 5 reveals the pathways associated with the disease phenotype. The presented pipeline aims to support researchers through the RNA-seq data analysis from host-pathogen interaction studies and drive future&#160;in vitro or in vivo experiments, that are essential to understand the molecular mechanism of infections.

The protocol presented here describes a complete pipeline to analyze RNA-sequencing transcriptome data from raw reads to functional analysis, including quality control and preprocessing steps to advanced statistical analytical approaches.

Pathogens can cause a wide variety of infectious diseases. The biological processes induced by the host in response to infection determine the severity of ...

Isolation of Total RNA from Pseudomonas aeruginosa within Biofilms for Measuring Gene Expression

Pseudomonas aeruginosa is an opportunistic bacterial pathogen that causes infections in the airways of cystic fibrosis (CF) patients. P. aeruginosa is known for its ability to form biofilms that are protected by a matrix of exopolysaccharides. This matrix allows the microorganisms to be more resilient to external factors, including antibiotic treatment. One of the most common methods of biofilm growth for research is in microtiter plates or chambered slides. The advantage of these systems is that they allow for the testing of multiple growth conditions, but their disadvantage is that they produce limited amounts of biofilm for RNA extraction. The purpose of this article is to provide a detailed, step by step protocol on how to extract total RNA from small amounts of biofilm of sufficient quality and quantity for high throughput sequencing. This protocol allows for the study of gene expression within these biofilm systems.

Isolation of Total RNA from Pseudomonas aeruginosa within Biofilms for Measuring Gene Expression

This protocol presents a method to isolate RNA from Pseudomonas aeruginosa biofilms grown in chamber slides for high throughput sequencing.

Pseudomonas aeruginosa is an opportunistic bacterial pathogen that causes infections in the airways of cystic fibrosis (CF) patients. P. aeruginosa is ...

A Fast and Reliable Pipeline for Bacterial Transcriptome Analysis Case study: Serine-dependent Gene Regulation in Streptococcus pneumoniae

Summary

Explore More Videos

Chapters in this video

A Fluorescence-based Method to Study Bacterial Gene Regulation in Infected Tissues

Characterization of Inflammatory Responses During Intranasal Colonization with Streptococcus pneumoniae

MS2-Affinity Purification Coupled with RNA Sequencing in Gram-Positive Bacteria

RIBO-seq in Bacteria: a Sample Collection and Library Preparation Protocol for NGS Sequencing

Establishment of a High-throughput Setup for Screening Small Molecules That Modulate c-di-GMP Signaling in Pseudomonas aeruginosa

A High-Yield Streptomyces Transcription-Translation Toolkit for Synthetic Biology and Natural Product Applications

Genome-wide Gene Deletions in Streptococcus sanguinis by High Throughput PCR

Constructing Mutants in Serotype 1 Streptococcus pneumoniae strain 519/43

High-Throughput Transcriptome Analysis for Investigating Host-Pathogen Interactions

Isolation of Total RNA from Pseudomonas aeruginosa within Biofilms for Measuring Gene Expression