The authors would like to acknowledge all colleagues who made this work possible, especially Maya Balamane, Elizabeth Johnston White, Sharon Sjoblom, Greg Ording Zakhona Gumede, Xolile Kineri, Phindile Mabaso, Lungisa Ndwandwe, James Garvey, Gavin Cobb, Senzo Maphanga, Terusha Chetty, Kevi Naidoo, Andrew Skingsley, Katharine Stott, and Lungani Ndwandwe. The authors would also like to thank all the personnel of the Department of Health and Africa Centre personnel who work the Hlabisa HIV Treatment and Care Programme.

<ol>
	<li>Shao, Y., Williamson, C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+HIV-1+epidemic:+low-+to+middle-income+countries.">The HIV-1 epidemic: low- to middle-income countries.</a> Cold Spring Harbor Persp. Med. 2, (2012).</li><li>Mutevedzi, P. C., 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=Scale-up+of+a+decentralized+HIV+treatment+programme+in+rural+KwaZulu-Natal,+South+Africa:+does+rapid+expansion+affect+patient+outcomes.">Scale-up of a decentralized HIV treatment programme in rural KwaZulu-Natal, South Africa: does rapid expansion affect patient outcomes.</a> Bull. World Health Organ. 88, 593-600 (2010).</li><li>Houlihan, C. 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=Cohort+profile:+Hlabisa+HIV+treatment+and+care+programme.">Cohort profile: Hlabisa HIV treatment and care programme.</a> Int. J. Epidemiol. 40, 318-326 (2011).</li><li>Tanser, F., Barnighausen, T., Grapsa, E., Zaidi, J., Newell, M. L. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=High+coverage+of+ART+associated+with+decline+in+risk+of+HIV+acquisition+in+rural+KwaZulu-Natal.">High coverage of ART associated with decline in risk of HIV acquisition in rural KwaZulu-Natal.</a> South Africa. Science. 339, 966-971 (2013).</li><li>Bor, J., Herbst, A. J., Newell, M. L., Barnighausen, T. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Increases+in+adult+life+expectancy+in+rural+South+Africa:+valuing+the+scale-up+of+HIV+treatment.">Increases in adult life expectancy in rural South Africa: valuing the scale-up of HIV treatment.</a> Science. 339, 961-965 (2013).</li><li>Montaner, 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=The+case+for+expanding+access+to+highly+active+antiretroviral+therapy+to+curb+the+growth+of+the+HIV+epidemic.">The case for expanding access to highly active antiretroviral therapy to curb the growth of the HIV epidemic.</a> Lancet. 368, 531-536 (2006).</li><li>Granich, R. M., Gilks, C. F., Dye, C., De Cock, K. M., Williams, B. 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Dis. 11, 750-759 (2011).</li><li>Hamers, R. L., 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=HIV-1+Drug+Resistance+Mutations+Are+Present+in+Six+Percent+of+Persons+Initiating+Antiretroviral+Therapy+in+Lusaka,+Zambia.">HIV-1 Drug Resistance Mutations Are Present in Six Percent of Persons Initiating Antiretroviral Therapy in Lusaka, Zambia.</a> J. Acquir. Immune Defic. Syndr. , (2010).</li><li>Nwobegahay, J., Selabe, G., Ndjeka, N. O., Manhaeve, C., Bessong, P. 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Virol. 157, 455-465 (2012).</li><li>Parboosing, R., Naidoo, A., Gordon, M., Taylor, M., Vella, V. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Resistance+to+antiretroviral+drugs+in+newly+diagnosed,+young+treatment-naive+HIV-positive+pregnant+women+in+the+province+of+KwaZulu-Natal+South+Africa.">Resistance to antiretroviral drugs in newly diagnosed, young treatment-naive HIV-positive pregnant women in the province of KwaZulu-Natal South Africa.</a> J. Med. Virol. 83, 1508-1513 (2011).</li><li>Nwobegahay, J. 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Nutr. 29, 303-309 (2011).</li><li>Nwobegahay, 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=Prevalence+of+drug-resistant+mutations+in+newly+diagnosed+drug-naive+HIV-1-infected+individuals+in+a+treatment+site+in+the+waterberg+district,+limpopo+province).">Prevalence of drug-resistant mutations in newly diagnosed drug-naive HIV-1-infected individuals in a treatment site in the waterberg district, limpopo province).</a> S. Afr. Med. J. 101 (2011), 335-337 (2011).</li><li>Rosen, S., Long, L., Sanne, I., Stevens, W. S., Fox, M. P. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+net+cost+of+incorporating+resistance+testing+into+HIV/AIDS+treatment+in+South+Africa:+a+Markov+model+with+primary+data.">The net cost of incorporating resistance testing into HIV/AIDS treatment in South Africa: a Markov model with primary data.</a> J. Int. AIDS Soc. 14, 24 (2011).</li><li>Dalai, S. C., 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=Evolution+and+molecular+epidemiology+of+subtype+C+HIV-1+in+Zimbabwe.">Evolution and molecular epidemiology of subtype C HIV-1 in Zimbabwe.</a> AIDS. 23, 2523-2532 (2009).</li><li>Chen, J. H., 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=In-house+human+immunodeficiency+virus-1+genotype+resistance+testing+to+determine+highly+active+antiretroviral+therapy+resistance+mutations+in+Hong+Kong.">In-house human immunodeficiency virus-1 genotype resistance testing to determine highly active antiretroviral therapy resistance mutations in Hong Kong.</a> Hong Kong Med. 18, 20-24 (2012).</li><li>Lee, C. K., 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=An+in-house+HIV+genotyping+assay+for+the+detection+of+drug+resistance+mutations+in+Southeast+Asian+patients+infected+with+HIV-1.">An in-house HIV genotyping assay for the detection of drug resistance mutations in Southeast Asian patients infected with HIV-1.</a> J. Med. Virol. 84, 394-401 (2012).</li><li>Aitken, S. C., 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=A+Pragmatic+Approach+to+HIV-1+Drug+Resistance+Determination+in+Resource-Limited+Settings+by+Use+of+a+Novel+Genotyping+Assay+Targeting+the+Reverse+Transcriptase-Encoding+Region+Only.">A Pragmatic Approach to HIV-1 Drug Resistance Determination in Resource-Limited Settings by Use of a Novel Genotyping Assay Targeting the Reverse Transcriptase-Encoding Region Only.</a> J. Clin. Microbiol. 51, 1757-1761 (2013).</li><li>Zhou, Z., 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=Optimization+of+a+low+cost+and+broadly+sensitive+genotyping+assay+for+HIV-1+drug+resistance+surveillance+and+monitoring+in+resource-limited+settings.">Optimization of a low cost and broadly sensitive genotyping assay for HIV-1 drug resistance surveillance and monitoring in resource-limited settings.</a> PLoS One. 6, (2011).</li><li>, <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Life+Technologies.">Life Technologies.</a> Life Technologies and SATuRN Collaborate to Increase Access to HIV Testing in Africa. , (2012).</li><li>Lihana, R. 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This work was supported by the Wellcome Trust (082384/Z/07/Z), European Union (SANTE 2007 147&#8211;790), the US Centre for Diseases Control via CAPRISA (project title: Health Systems Strengthening and HIV Treatment Failure (HIV-TFC)), and the Swiss South African Joint Research Programme (SSJRP) research grant entitled &#34;Swiss Prot / South Africa: Protein Bioinformatics Resource Development for Important Health-related Pathogens&#8221;. RL is supported by the Wellcome Trust (grant number 090999/Z/09/Z).&#160;The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.&#160;The authors declare no competing financial interests.

Several low cost in-house methods have been described in efforts to try to make HIV drug resistance genotyping more affordable33,34,36. There is no doubt of the need to integrate drug resistance testing into the continuum of care for individuals on antiretroviral therapy in resource-limited settings. However, most of the reported methods focus on the application of drug resistance genotyping in the surveillance of drug resistance at a population level. The SATuRN/Life Technologies genotyping method is a fully integrated protocol for surveillance and monitoring of drug resistance. This method was designed to be an affordable protocol implementing mostly open source and open access bioinformatics resources for the interpretation of drug resistance and generation of reports for clinical management.
It was shown through comparison with the FDA approved Viroseq genotyping method to be accurate in identifying drug resistance mutations from a panel of ANRS proficiency testing samples, in 100% of laboratory panel samples that were successfully amplified. The accuracy was also assessed on clinical samples of subtype C viruses, the most dominant subtype in southern Africa. The method was as accurate on subtype C samples as it was on subtype A and B. However, if the method would be used in other parts of the world where CRF02_AG is prevalent, there is a need for the modification of the primers since the method failed to amplify one of the panel samples that was shown to have CRF02_AG. Alternatively, a degenerate set of primers sensitive to all group M viruses33,36 could be used in regions where the subtype distribution is more heterogeneous38.
The sensitivity of the reverse transcription and PCR can be increased by extracting RNA from higher volumes of plasma, such as 500 ml. The plasma can be centrifuged at 21,000 x g for 90 min to concentrate the viral particles before proceeding with the protocol as described by the QIAamp viral RNA extraction mini kit.
As shown, the new method has an additional advantage that it produces comprehensive reports for individual patient management. These reports are a consolidation of the genotype, the immunological and virologic monitoring data as well as clinical and treatment history from RegaDB. This is accompanied by a detailed laboratory interpretation of the resistance profile followed by an equally detailed review of the patient&#8217;s clinical history as well as treatment recommendations. The use of a specialist physicians to review the reports and provide treatment recommendations for the patients provides the much-needed mentorship for nurse practitioners as well as inexperienced clinicians, who are increasingly providing ART in South Africa as part of the WHO recommendations for task shifting. These clinical reports have been shown to be effective teaching aids for clinicians with little or no experience in drug resistance management. From a patient perspective, our method reduces the need to travel to centralized sites to access specialist HIV services.
Thus, the described protocol taken as a whole provides a good platform through which HIV drug resistance management can be integrated, at an affordable cost, into the continuum of care for HIV infected individuals failing ART. The data generated can be used for epidemiological purposes to assess the evolution and transmission of drug resistance in the community. The size of the pol fragment generated is good enough for more complex phylogenetic analysis which will produce better understanding of the epidemic at population level.

The HIV epidemic in southern Africa has been evolving rapidly1 with a concomitant&#160;exponential increase in individuals on antiretroviral therapy (ART), especially in South Africa2,3. As evidence on the epidemiologic impact of large scale treatment programs in reducing incidence4 and increasing life expectancy in resource-limited settings (RLS)5 continues to accumulate, efforts to increase ART coverage will be intensified.&#160;The evolution of guidelines towards the use of treatment as a prevention tool6,7 under the test and treat programs means that the absolute number of individuals on treatment will further increase. Large numbers of individuals will be on ART for longer periods of time as the average life expectancy of individuals on ART nears that of the HIV uninfected population8. The development and transmission of HIV drug resistance has always been considered a threat to the achievements of ART9-12. Thus, there is a need for more rigorous surveillance and monitoring of drug resistance as more individuals are initiated onto ART.
Genotypic drug resistance testing (GRT) has been used successfully in developed countries,&#160;both for surveillance as well as monitoring of HIV-1 drug resistance in individuals receiving ART. In these settings, GRT has been integrated into the continuum of care for HIV-1 infected individuals. Most international guidelines recommend GRT for adult or pediatric patients failing ART (first-line and second-line)13-15, pediatric patients exposed to prevention of mother-to-child transmission (pMTCT) regimens but subsequently infected16, and in settings with high-levels of transmitted drug resistance&#160;among acutely infected individuals13-15. However, the cost, technology, and infrastructure requirements have limited the implementation of similar approaches to drug resistance monitoring in RLS.
The&#160;South African HIV treatment and monitoring guidelines do not currently recommend the use of GRT in guiding choice of ART for individuals failing first-line regimens17. Individuals are switched based primarily on virological (HIV-1 RNA viral load) parameters. However in 2012, the Southern African HIV Clinicians Society published the first Southern African ARV drug resistance testing guidelines18. These guidelines recommend GRT testing for all adults failing first-line and second-line ART&#160;and for infected infants and children exposed to pMTCT18. However, GRT is not recommended18&#160;for acutely infected individuals because there is no current evidence for high levels of transmitted drug resistance in southern Africa19-29.&#160;It is expected that some of these recommendations will be integrated over time into the national treatment and monitoring guidelines of the various countries in the region. Already, in the 2013 South African treatment guidelines there is now recommendation of GRT at time of second-line failure for adults and at time of first- or second-line PI-based regimen failure for children30.
It has been shown that incorporating GRT into treatment guidelines in South Africa would be potentially cost-neutral. Considering the cost of the second line regimen drugs which are relatively more expensive than the first line drugs, using GRT to identify patients who truly need to be switched to second line therapy will not result any additional cost to the program. In addition, GRT can also identify other reasons for failure, conserve treatment options and generate information about emerging resistance patterns31. Therefore, it is necessary to reduce the cost of drug resistance monitoring methods even further in order to improve access, quality of care and outcomes.
Here, we present a GRT method designed to use generic (open source) primers for reverse transcription, polymerase chain reaction (PCR) and sequencing (Table 1), as well as mostly open source software for drug resistance interpretation. For clinical management, the protocol is complimented by a comprehensive review and reporting method with specialist interpretation of the laboratory drug resistance report with close adherence to the national treatment guidelines. The protocol is divided into four different components; 1) HIV Ribonucleic Acid (RNA) Extraction, 2) Reverse Transcription and&#160;Polymerase Chain Reaction (PCR) amplification of viral targets, 3) Sequencing and 4) Bioinformatics methods for analysis of chromatograms, alignment, curation and interpretation of sequence data.

<table border="0" cellpadding="0" cellspacing="0">
	<tbody>
		<tr height="21">
			<td height="21" style="height:21px;width:372px;">Superscript III 1st strand Synthesis kit</td>
			<td style="width:372px;">Life Technologies</td>
			<td align="right" style="width:119px;">18080051</td>
			<td style="width:265px;">Reverse Transcription</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:372px;"></td>
			<td style="width:372px;"></td>
			<td style="width:119px;"></td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:372px;">SATURN/LiFE Technologies Custom Primers</td>
			<td style="width:372px;">Life Technologies</td>
			<td align="right" style="width:119px;">4473517</td>
			<td>PCR</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:372px;">Platinum Taq</td>
			<td style="width:372px;">Life Technologies</td>
			<td align="right" style="width:119px;">10966026</td>
			<td>PCR</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:372px;">PureLink QUICK PCR Purification Kit</td>
			<td style="width:372px;">Life Technologies</td>
			<td style="width:119px;">K310002</td>
			<td>PCR</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:372px;">Viroseq</td>
			<td style="width:372px;">ABBOTT</td>
			<td style="width:119px;">4J94-20</td>
			<td>Reverse Transcription and PCR</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:372px;"></td>
			<td style="width:372px;"></td>
			<td style="width:119px;"></td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:372px;">Agarose Tablets (Dnase/Rnase free)</td>
			<td style="width:372px;">BIOLINE</td>
			<td style="width:119px;">BIO-41027</td>
			<td>PCR</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:372px;">TBE Buffer</td>
			<td style="width:372px;">MERCK</td>
			<td style="width:119px;">1.06177.2500</td>
			<td>PCR</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:372px;">O&#39;Range Ruler 200bp DNA Ladder</td>
			<td style="width:372px;">Fermentas</td>
			<td style="width:119px;">FE SM0633</td>
			<td>PCR</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:372px;">Novel Juice</td>
			<td style="width:372px;">GeneDireX</td>
			<td style="width:119px;">LD001-1000&#160;</td>
			<td>PCR</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:372px;"></td>
			<td style="width:372px;"></td>
			<td style="width:119px;"></td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:372px;">MiniBis Bioimaging System</td>
			<td style="width:372px;">DNR Bioimaging Systems Ltd</td>
			<td style="width:119px;"></td>
			<td>Gel Documentation</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:372px;">Power Pac 300&#160;</td>
			<td style="width:372px;">BIORAD</td>
			<td style="width:119px;"></td>
			<td>Gel Electrophoresis</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:372px;"></td>
			<td style="width:372px;"></td>
			<td style="width:119px;"></td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:372px;">Big Dye Terminator Kit ver 3.1</td>
			<td style="width:372px;">Life Technologies</td>
			<td align="right" style="width:119px;">4337456</td>
			<td>Sequencing</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:372px;">Arrays</td>
			<td style="width:372px;">Life Technolgies</td>
			<td align="right" style="width:119px;">4319899</td>
			<td>Sequencing</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:372px;">PoP</td>
			<td style="width:372px;">Life technologies</td>
			<td align="right" style="width:119px;">4363785</td>
			<td>Sequencing</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:372px;">10 x EDTA Buffer</td>
			<td style="width:372px;">Life Technologies</td>
			<td align="right" style="width:119px;">402824</td>
			<td>Sequencing</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:372px;">Formamide</td>
			<td style="width:372px;">Life Technologies</td>
			<td align="right" style="width:119px;">4311320</td>
			<td>Sequencing</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:372px;">5 x Sequencing Buffer</td>
			<td style="width:372px;">Life Tecgnologies</td>
			<td align="right" style="width:119px;">4336697</td>
			<td>Sequencing</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:372px;"></td>
			<td style="width:372px;"></td>
			<td style="width:119px;"></td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:372px;">3130 xl Genetic Analyzer</td>
			<td style="width:372px;">Life Technologies</td>
			<td style="width:119px;"></td>
			<td>Sequencing</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:372px;">GeneAmp PCR System 9700</td>
			<td style="width:372px;">Life Technologies</td>
			<td style="width:119px;"></td>
			<td>RT/PCR/Sequencing</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:372px;"></td>
			<td style="width:372px;"></td>
			<td style="width:119px;"></td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:372px;">Centrifuge 5804</td>
			<td style="width:372px;">EPPENDORF</td>
			<td style="width:119px;"></td>
			<td>Sample Processing</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:372px;">Centrifuge 5415R</td>
			<td style="width:372px;">EPPENDORF</td>
			<td style="width:119px;"></td>
			<td>RNA Extraction</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:372px;">Centrifuge 5415R</td>
			<td style="width:372px;">EPPENDORF</td>
			<td style="width:119px;"></td>
			<td>RT and PCR</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:372px;">Centrifuge 5415D</td>
			<td style="width:372px;">EPPENDORF</td>
			<td style="width:119px;"></td>
			<td>PCR Product Clean up</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:372px;">Centrifuge 5810</td>
			<td style="width:372px;">EPPENDORF</td>
			<td style="width:119px;"></td>
			<td>Sequencing Clean up</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:372px;">Picofuge</td>
			<td style="width:372px;">BIORAD</td>
			<td style="width:119px;">C1301-230V</td>
			<td>RT and PCR</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:372px;">Vortex Genius 3</td>
			<td style="width:372px;">IKA</td>
			<td style="width:119px;"></td>
			<td>RNA Extraction</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:372px;">Vortex Genius 3</td>
			<td style="width:372px;">IKA</td>
			<td style="width:119px;"></td>
			<td>reagent preparation</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:372px;">Vortex mixer</td>
			<td style="width:372px;">IKA</td>
			<td style="width:119px;"></td>
			<td>Sequencing Clean up</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:372px;">NanoDrop 2000 UV/VIS spectrophotometer</td>
			<td style="width:372px;">ThermoScientific</td>
			<td style="width:119px;"></td>
			<td>DNA quantification&#160;</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:372px;"></td>
			<td style="width:372px;"></td>
			<td style="width:119px;"></td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:372px;">3M Sodium Acetate</td>
			<td style="width:372px;">MERCK</td>
			<td align="right" style="width:119px;">567422</td>
			<td>Sequencing Clean up</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:372px;">Absolute Ethanol</td>
			<td style="width:372px;">MERCK</td>
			<td style="width:119px;">SAAR2233540LP</td>
			<td>Sequencing Clean up</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:372px;"></td>
			<td style="width:372px;"></td>
			<td style="width:119px;"></td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:372px;">1.5ml SARSTEDT Tubes</td>
			<td style="width:372px;">BIODEX</td>
			<td style="width:119px;">72.692.005</td>
			<td>RNA Extraction</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:372px;">2ml SARSTEDT&#160; Tubes</td>
			<td style="width:372px;">BIODEX</td>
			<td style="width:119px;">72.693.005</td>
			<td>RNA Extraction</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:372px;">2ml Collection tubes</td>
			<td style="width:372px;">SCIENTIFIC GROUP</td>
			<td style="width:119px;">MCT-200-NC/S</td>
			<td>RNA Extraction</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:372px;"></td>
			<td style="width:372px;"></td>
			<td style="width:119px;"></td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:372px;">Optical MicroAmp 96 Well reaction plates</td>
			<td style="width:372px;">Life Technologies</td>
			<td style="width:119px;">N8010560</td>
			<td>Sequencing</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:372px;">200ul 8 Strip StarPCR Tubes with attached flat caps</td>
			<td style="width:372px;">STAR Lab - supplied by CELTIC</td>
			<td style="width:119px;">A1402-3700</td>
			<td>RT and PCR</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:372px;">200ul PCR individual tubes</td>
			<td style="width:372px;">Scientific Group</td>
			<td style="width:119px;">CR/3745</td>
			<td>RT and PCR</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:372px;"></td>
			<td style="width:372px;"></td>
			<td style="width:119px;"></td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:372px;">Geneious&#160;</td>
			<td style="width:372px;">Biomatters</td>
			<td style="width:119px;"></td>
			<td>Sequence analysis</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:372px;"></td>
			<td style="width:372px;"></td>
			<td style="width:119px;"></td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:372px;">Internet Access</td>
			<td style="width:372px;"></td>
			<td style="width:119px;"></td>
			<td>Preferrably high speed</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:372px;"></td>
			<td style="width:372px;"></td>
			<td style="width:119px;"></td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:372px;">Web resources</td>
			<td style="width:372px;"></td>
			<td style="width:119px;"></td>
			<td></td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:372px;">hivdb.stanford.edu</td>
			<td style="width:372px;">Stanford University</td>
			<td style="width:119px;"></td>
			<td>Drug reistance analysis</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:372px;">http://bioafrica.mrc.ac.za:8080/regadb-ui/RegaDB</td>
			<td style="width:372px;">SATuRN</td>
			<td style="width:119px;"></td>
			<td>database</td>
		</tr>
		<tr height="21">
			<td height="21" style="height:21px;width:372px;">http://bioafrica.mrc.ac.za/tools/pppweb.html</td>
			<td style="width:372px;">SATuRN</td>
			<td style="width:119px;"></td>
			<td>Sequence quality tool</td>
		</tr>
	</tbody>
</table>

an affordable hiv-1 drug resistance monitoring method for resource limited settings

HIV-1 drug resistance has the potential to seriously compromise the effectiveness and impact of antiretroviral therapy (ART). As ART programs in sub-Saharan Africa continue to expand, individuals on ART should be closely monitored for the emergence of drug resistance. Surveillance of transmitted drug resistance to track transmission of viral strains already resistant to ART is also critical. Unfortunately, drug resistance testing is still not readily accessible in resource limited settings, because genotyping is expensive and requires sophisticated laboratory and data management infrastructure. An open access genotypic drug resistance monitoring method to manage individuals and assess transmitted drug resistance is described. The method uses free open source software for the interpretation of drug resistance patterns and the generation of individual patient reports. The genotyping protocol has an amplification rate of greater than 95% for plasma samples with a viral load &#62;1,000 HIV-1 RNA copies/ml. The sensitivity decreases significantly for viral loads &#60;1,000 HIV-1 RNA copies/ml. The method described here was validated against a method of HIV-1 drug resistance testing approved by the United States Food and Drug Administration (FDA), the Viroseq genotyping method. Limitations of the method described here include the fact that it is not automated and that it also failed to amplify the circulating recombinant form CRF02_AG from a validation panel of samples, although it amplified subtypes A and B from the same panel.

The method validated was a modification of a previously reported method20. The Viroseq genotyping method, which has been approved by FDA, was used as the reference method in the validation. A panel of proficiency testing samples obtained from the French National Agencies for Research on AIDS and Viral Hepatitis (ANRS) was used in the primary comparison between the two methods. The two genotyping methods were 100% concordant in identifying all clinically important drug resistance-associated mutations as interpreted by the HIVDB program&#160;for the samples that were successfully amplified by both methods. As shown in Table 6, the nucleotide sequences of the three pairs were 99.5% identical. The predicted amino acid sequences were 100% identical. One sample out of five could not be successfully amplified by Viroseq. In addition to the sample not amplified by Viroseq, the in-house method failed to amplify a second sample which was shown to be a circulating recombinant virus (CRF02_AG) by Viroseq.&#160;The three samples that amplified with both methodologies were subtype B (two samples) and subtype A (one sample).
<img alt="Figure 5" fo:content-width="4in" fo:src="/files/ftp_upload/51242/51242fig5highres.jpg" src="/files/ftp_upload/51242/51242fig5.jpg" /> 
Figure 5. Use of a HKY Neighbor Joining tree&#160;done as part of sequence quality assurance. There are four pairs/clusters of sequence with very short genetic distances. The genetic distance between RES655 and RES655_1 (same samples sequenced on different days) is 0.003. The is a potential error with the RES637_1/RES638 pair as their genetic distance is too short (0.075 ) for samples from different epidemiologically unlinked individuals. There is another RES637 on the tree with a distance of 0.075 when compared to RES638_1. The CQ01/CQ02 cluster&#160;suggests that the two samples from the panel are duplicates of the same sample. They cluster together with the subtype B reference sequence confirming the subtype assigned by the REGA Subtyping tool. CQ05 and CQ04 clustered with subtypes A and G respectively,&#160;whereas the REGA subtyping tool classified them as A and CRF02_AG respectively. Another useful tool for HIV subtyping and recombination is SCUEL, which is available at http://www.datamonkey.org. <a href="https://www.jove.com/files/ftp_upload/51242/51242fig5highres.jpg" target="_blank">Click here to view larger image.</a>
A panel of five samples was used to assess the precision of the in-house method. Ten replicate genotypes were generated for each of the five samples. Using the 16 Capillary 3130xl genetic analyzer, 48 of the 50 genotypes were generated from 24 runs,&#160;prepared on the same day. For all five samples, the predicted amino acid sequences were 100% concordant amongst replicates. For the nucleic acid sequences, there was &#62;99% pairwise similarity.
During the first two years of the use of this method, sixty samples were repeated randomly from RNA extraction to sequencing. There were no statistically significant differences between the sequence quality score and the number of mixed bases between the replicates. Both the nucleotide and amino acid pairwise comparisons for the sixty pairs were greater than 99% identical. Thus the drug resistance mutations for all the pairs were 100% concordant.
Cost reduction
The reaction volumes for RT, PCR and sequencing were reduced by at least half, relative to the original method20,32, without compromising on the quality of the sequences generated. This enabled a reduction in cost of 50% for the RT and PCR stages.
The new method was originally designed to work with six sequencing primer to sequence all 99 codons of the protease gene and the first 300 codons of the reverse transcriptase gene20,32. Similar methods also use six to eight primers33,34. Some recently published methods have used less than six primers, although sometimes sequencing the protease and RT genes seprately35,36.&#160;We sought to reduce the number of sequencing primers from six to four, (Figure 6)
<img alt="Figure 6" fo:content-width="5in" fo:src="/files/ftp_upload/51242/51242fig6highres.jpg" src="/files/ftp_upload/51242/51242fig6.jpg" /> 
Figure 6. Comparison of contiguous sequences from six vs four sequencing primers for the generation of the 1197 bp pol sequence covering all 99 HIV-1 protease codons and the first 300 codons of the reverse transcriptase gene. <a href="https://www.jove.com/files/ftp_upload/51242/51242fig6highres.jpg" target="_blank">Click here to view larger image.</a>
Sequences from a set of 17 samples generated from six primers&#160;were compared to sequences generated after&#160;exclusion of two primers (MAW46 and RTY).&#160;The subtypes were 14 subtype C, two subtype B, and one subtype A. There were no significant differences in sequence quality scores. Again, the average pairwise identity between the 17 pairs of nucleic acid was 99% and 100% on the amino acid level. Thus, reducing the sequencing primers from six to four resulted in a reduction in the sequencing cost by almost a third.
The only proprietary software tool used in this protocol was Geneious for sequence assembly. The drug resistance interpretation tools, as well as the report generating tools are all free, open access tools. This reduces the cost further by eliminating the costs associated with the use of proprietary software.&#160;Further, collective negotiation allowed the reagents for this protocol to be packaged into a kit for easy access from Life Technologies and is available as the SATuRN/Life Technologies genotyping method 37. Furthermore, SATuRN members can access the reagents at a discounted price.
Clinical Setting
The described protocol has been implemented in the monitoring and surveillance of drug resistance in a rural community in KwaZulu-Natal. A total of 604 genotypes were generated from clinical samples between December 2010 and May 2013 at an amplification rate of 95% for samples with viral loads &#62;1,000 RNA copies/ml. This clinical HIV drug resistance study was approved by the Biomedical Research Ethics Committee of the University of KwaZulu-Natal (ref. BF052/10) and the Health Research Committee of the KwaZulu-Natal Department of Health (ref. HRKM 176/10). Individual patient reports were generated and sent back to the clinics for patient management.
Seventy two (72) genotypes were also generated as part of a surveillance of transmitted drug resistance study, nested within a large prospective population-based HIV surveillance study. The primary samples were needle prick whole blood collected in EDTA microtubes. At genotyping there was an amplification rate of 79%19.&#160;Ethics approval for the genotyping of samples from the surveillance study was obtained from the University of KwaZulu-Natal Biomedical Research Ethics Committee (ref. BE066107).
<table border="0" cellpadding="0" cellspacing="0">
	<tbody>
		<tr>
			<td>Primer Name</td>
			<td>Sequence</td>
			<td>Length</td>
			<td>Direction</td>
			<td>HXB2 Position</td>
			<td></td>
		</tr>
		<tr>
			<td>MAW-26</td>
			<td>TTGGAAATGTGGAAA GGAAGGAC</td>
			<td>23</td>
			<td>Forward</td>
			<td>2028-2050</td>
			<td>1st round PCR</td>
		</tr>
		<tr>
			<td>RT-21</td>
			<td>CTGTATTTCAGCTATC AAGTCCTTTGATGGG</td>
			<td>31</td>
			<td>Reverse</td>
			<td>3539-3509</td>
			<td>1st round PCR</td>
		</tr>
		<tr>
			<td>Pro-1</td>
			<td>TAGAGCCAACAGCCC CACCA</td>
			<td>20</td>
			<td>Forward</td>
			<td>2147-2166</td>
			<td>2nd round PCR</td>
		</tr>
		<tr>
			<td>RT-20</td>
			<td>CTGCCAATTCTAATTC TGCTTC</td>
			<td>22</td>
			<td>Reverse</td>
			<td>3462-3441</td>
			<td>2nd round PCR</td>
		</tr>
		<tr>
			<td>RTC1F</td>
			<td>ACCTACACCTGTCAA CATAATTG</td>
			<td>23</td>
			<td>Forward</td>
			<td>2486-2508</td>
			<td>Sequencing</td>
		</tr>
		<tr>
			<td>RTC2R</td>
			<td>TGTCAATGGCCATTG TTTAACCTTTGG</td>
			<td>27</td>
			<td>Reverse</td>
			<td>2630-2604</td>
			<td>Sequencing</td>
		</tr>
		<tr>
			<td>RTC3F</td>
			<td>CACCAGGGATTAGAT ATCAATATAATGTGC</td>
			<td>30</td>
			<td>Forward</td>
			<td>2956-2994</td>
			<td>Sequencing</td>
		</tr>
		<tr>
			<td>RTC4R</td>
			<td>CTAAATCAGATCCTAC ATACAAGTCATCC</td>
			<td>29</td>
			<td>Reverse</td>
			<td>3129-3101</td>
			<td>Sequencing</td>
		</tr>
		<tr>
			<td>RT-y</td>
			<td>GTGTCTCATTGTTTAT ACTAGG</td>
			<td>22</td>
			<td>Reverse</td>
			<td>2967-2946</td>
			<td>Sequencing</td>
		</tr>
		<tr>
			<td>MAW-46</td>
			<td>TCCCTCAGATCACTC TTTGGCAACGAC</td>
			<td>27</td>
			<td>Forward</td>
			<td>2251-2277</td>
			<td>Sequencing</td>
		</tr>
	</tbody>
</table>
Table 1. Reverse transcription, PCR, and sequencing custom primers used in the generation of a 1197 bp pol fragment covering all the 99 HIV-1 Protease codons and the first 300 codons of the reverse trascriptase gene.
<table border="0" cellpadding="0" cellspacing="0">
	<tbody>
		<tr>
			<td>RT21 (5pmol/ml)</td>
			<td>0.5</td>
			<td>0.2</td>
		</tr>
		<tr>
			<td>dNTP (10 mM)</td>
			<td>0.5</td>
			<td>0.4</td>
		</tr>
		<tr>
			<td>Total</td>
			<td>1</td>
			<td></td>
		</tr>
	</tbody>
</table>
Table 2. dNTP/Primer mix for the reverse transcription reaction.
<table border="0" cellpadding="0" cellspacing="0">
	<tbody>
		<tr>
			<td>Reagent&#160;</td>
			<td>Volume (ml)/reaction</td>
			<td>Concentration/reaction</td>
		</tr>
		<tr>
			<td>First Strand Buffer (10x)</td>
			<td>1</td>
			<td>1</td>
		</tr>
		<tr>
			<td>MgCl2&#160;(25 mM)</td>
			<td>2</td>
			<td>4</td>
		</tr>
		<tr>
			<td>DTT (0.1 M)</td>
			<td>1</td>
			<td>0.008</td>
		</tr>
		<tr>
			<td>RNaseOUT (40 U/ml )</td>
			<td>0.5</td>
			<td>16</td>
		</tr>
		<tr>
			<td>Superscript III Reverse Transcriptase (200U/ml )</td>
			<td>0.5</td>
			<td>8</td>
		</tr>
		<tr>
			<td>Total</td>
			<td>5</td>
			<td></td>
		</tr>
	</tbody>
</table>
Table 3. Enzyme mix for the reverse transcription reaction.
<table border="0" cellpadding="0" cellspacing="0">
	<tbody>
		<tr>
			<td>Reagent&#160;</td>
			<td>Volume (ml)/reaction</td>
			<td>Final Concentration/Reaction</td>
		</tr>
		<tr>
			<td>DEPC treated water</td>
			<td>18.4</td>
			<td>-</td>
		</tr>
		<tr>
			<td>PCR Buffer (10x)</td>
			<td>2.5</td>
			<td>1</td>
		</tr>
		<tr>
			<td>MgCI2 (50 mM)</td>
			<td>1</td>
			<td>2</td>
		</tr>
		<tr>
			<td>dNTP mix (10 mM)</td>
			<td>0.5</td>
			<td>0.2</td>
		</tr>
		<tr>
			<td>Froward primer (5 pmol/ml)</td>
			<td>0.25</td>
			<td>0.05</td>
		</tr>
		<tr>
			<td>Reverse primer (5 pmol/ml)</td>
			<td>0.25</td>
			<td>0.05</td>
		</tr>
		<tr>
			<td>Platinum Taq Polymerase (5 U/ml)</td>
			<td>0.1</td>
			<td>0.02</td>
		</tr>
		<tr>
			<td>Subtotal</td>
			<td>23</td>
			<td>-</td>
		</tr>
	</tbody>
</table>
Table 4. Master mix for the nested PCR.
<table border="0" cellpadding="0" cellspacing="0">
	<tbody>
		<tr>
			<td>Reagent&#160;</td>
			<td>Volume (ml)/reaction</td>
			<td>Concentration/reaction</td>
		</tr>
		<tr>
			<td>DEPC treated water</td>
			<td>6.1</td>
			<td></td>
		</tr>
		<tr>
			<td>Sequencing Buffer (5x)&#160;</td>
			<td>2</td>
			<td>1</td>
		</tr>
		<tr>
			<td>Primer (3.2 pmol/ml)</td>
			<td>0.5</td>
			<td>0.16</td>
		</tr>
		<tr>
			<td>Big Dye terminator Sequencing mix</td>
			<td>0.4</td>
			<td>-</td>
		</tr>
		<tr>
			<td>Total</td>
			<td>9</td>
			<td></td>
		</tr>
	</tbody>
</table>
Table 5. Master mix for the sequencing reactions.
<table border="0" cellpadding="0" cellspacing="0">
	<tbody>
		<tr>
			<td></td>
			<td align="center" colspan="4">Viroseq</td>
			<td align="center" colspan="4">Inhouse</td>
			<td rowspan="2">% NA Similarity</td>
		</tr>
		<tr>
			<td>Sample I.D</td>
			<td>Subtype</td>
			<td>Quality score</td>
			<td>PR Mutations</td>
			<td>RT mutations</td>
			<td>Subtype</td>
			<td>Quality score</td>
			<td>PR mutations</td>
			<td>RT Mutations</td>
		</tr>
		<tr>
			<td>CQ01</td>
			<td>B</td>
			<td>99.9</td>
			<td>M46L, I54L, V82A, L90M</td>
			<td>D67N, T69D, K70R, M184V, T215V, K219Q</td>
			<td>B</td>
			<td>99.2</td>
			<td>M46L, I54L, V82A, L90M</td>
			<td>D67N, T69D, K70R, M184V, T215V, K219Q</td>
			<td>100</td>
		</tr>
		<tr>
			<td>CQ02</td>
			<td>B</td>
			<td>99.5</td>
			<td>M46L, I54L, V82A, L90M</td>
			<td>D67N, T69D, K70R, M184V, T215V, K219Q</td>
			<td>B</td>
			<td>99.5</td>
			<td>M46L, I54L, V82A, L90M</td>
			<td>D67N, T69D, K70R, M184V, T215V, K219Q</td>
			<td>100</td>
		</tr>
		<tr>
			<td>CQ03</td>
			<td>NA</td>
			<td>NA</td>
			<td></td>
			<td></td>
			<td>NA</td>
			<td>NA</td>
			<td></td>
			<td>NA</td>
			<td>NA</td>
		</tr>
		<tr>
			<td>CQ04</td>
			<td>CRF02_AG</td>
			<td>98.4</td>
			<td>I54V, V82F, I84V</td>
			<td>M41L, L74I, L210W, T215Y, V108I, Y181C</td>
			<td>NA</td>
			<td>NA</td>
			<td>NA</td>
			<td>NA</td>
			<td>NA</td>
		</tr>
		<tr>
			<td>CQ05</td>
			<td>A</td>
			<td>99.7</td>
			<td></td>
			<td>K103N</td>
			<td>A</td>
			<td>93</td>
			<td></td>
			<td>K103N</td>
			<td>100</td>
		</tr>
	</tbody>
</table>
Table 6. Comparative results from a parallel analysis between the Viroseq genotyping method and the in-house method using a panel of samples provided by the ANRS.

Watch this Scientific Journal Video about An Affordable HIV-1 Drug Resistance Monitoring Method for Resource Limited Settings at JoVE.com

An Affordable HIV-1 Drug Resistance Monitoring Method for Resource Limited Settings

Drug resistance testing for HIV-1 infected individuals failing antiretroviral therapy (ART) can guide future therapies and improve treatment outcomes. Optimizing individual and population health outcomes in high HIV prevalence but resource-limited settings will ultimately require affordable and accessible drug resistance genotyping and interpretation methods.

HIV-1 drug resistance has the potential to seriously compromise the effectiveness and impact of antiretroviral therapy (ART). As ART programs in ...

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18.4K ビュー.  University of KwaZulu-Natal, Durban, South Africa. 抗レトロウイルス療法（ART）を失敗して、HIV-1感染者のための薬剤耐性検査は、将来の治療法を導き、治療成績を向上させることができます。高いHIV感染率が、リソースが制限された設定の最適化、個々の人口健康の結果は、最終的には手頃な価格でアクセス可能な薬剤耐性遺伝子型解析と解釈の方法が必要になります。 

ビデオ: An Affordable HIV-1 Drug Resistance Monitoring Method for Resource Limited Settings

Method for Novel Anti-Cancer Drug Development using Tumor Explants of Surgical Specimens

The current therapies for malignant glioma have only palliative effect. For therapeutic development, one hurdle is the discrepancy of efficacy determined by current drug efficacy tests and the efficacy on patients. Thus, novel and reliable methods for evaluating drug efficacy are warranted in pre-clinical phase. In vitro culture of tumor tissues, including cell lines, has substantial phenotypic, genetic, and epigenetic alterations of cancer cells caused by artificial environment of cell culture, which may not reflect the biology of original tumors in situ. Xenograft models with the immunodeficient mice also have limitations, i.e., the lack of immune system and interspecies genetic and epigenetic discrepancies in microenvironment. Here, we demonstrate a novel method using the surgical specimens of malignant glioma as undissociated tumor blocks to evaluate treatment effects. To validate this method, data with the current first-line chemotherapeutic agent, temozolomide (TMZ), are described.


We used the freshly-removed surgical specimen of malignant glioma for our experiments. We performed intratumoral injection of TMZ or other drug candidates, followed by incubation and analysis on surgical specimens. Here, we sought to establish a tumor tissue explant method as a platform to determine the efficacy of novel anti-cancer therapies so that we may be able to overcome, at least, some of the current limitations and fill the existing gap between the current experimental data and the efficacy on an actual patient's tumor. This method may have the potential to accelerate identifying novel chemotherapeutic agents for solid cancer treatment.

Here, we established a method for drug efficacy testing with surgical specimens of brain tumors, termed &#x201C;tumor explant method&#x201D;. With this method, we can evaluate drug efficacy without breaking the microenvironment of solid tumors. To validate reliability of this method, we describe representative data with our glioma specimen treated with the current first-line chemotherapeutic agent, temozolomide.


The current therapies for malignant glioma have only palliative effect. For therapeutic development, one hurdle is the discrepancy of efficacy determined ...

An Orthotopic Murine Model of Human Prostate Cancer Metastasis

Our laboratory has developed a novel orthotopic implantation model of human prostate cancer (PCa). As PCa death is not due to the primary tumor, but rather the formation of distinct metastasis, the ability to effectively model this progression pre-clinically is of high value. In this model, cells are directly implanted into the ventral lobe of the prostate in Balb/c athymic mice, and allowed to progress for 4-6 weeks. At experiment termination, several distinct endpoints can be measured, such as size and molecular characterization of the primary tumor, the presence and quantification of circulating tumor cells in the blood and bone marrow, and formation of metastasis to the lung. In addition to a variety of endpoints, this model provides a picture of a cells ability to invade and escape the primary organ, enter and survive in the circulatory system, and implant and grow in a secondary site. This model has been used effectively to measure metastatic response to both changes in protein expression as well as to response to small molecule therapeutics, in a short turnaround time.

This orthotopic model of human prostate cancer allows for quantification of tumor size, circulating tumor cells, and formation of distinct metastasis to the lung. As cells must escape the primary organ, enter the blood stream, and implant into a secondary site, this model effectively recapitulates the scenario in humans.

Our laboratory has developed a novel orthotopic implantation model of  human prostate cancer (PCa). As PCa death is not due to the primary tumor, but  ...

Techniques for Processing Eyes Implanted with a Retinal Prosthesis for Localized Histopathological Analysis: Part 2 Epiretinal Implants with Retinal Tacks

Retinal prostheses for the treatment of certain forms of blindness are gaining traction in clinical trials around the world with commercial devices currently entering the market. In order to evaluate the safety of these devices, in preclinical studies, reliable techniques are needed. However, the hard metal components utilised in some retinal implants are not compatible with traditional histological processes, particularly in consideration for the delicate nature of the surrounding tissue. Here we describe techniques for assessing the health of the eye directly adjacent to a retinal implant secured epiretinally with a metal tack.
Retinal prostheses feature electrode arrays in contact with eye tissue. The most commonly used location for implantation is the epiretinal location (posterior chamber of the eye), where the implant is secured to the retina with a metal tack that penetrates all the layers of the eye. Previous methods have not been able to assess the proximal ocular tissue with the tack in situ, due to the inability of traditional histological techniques to cut metal objects. Consequently, it has been difficult to assess localized damage, if present, caused by tack insertion.
Therefore, we developed a technique for visualizing the tissue around a retinal tack and implant. We have modified an established technique, used for processing and visualizing hard bony tissue around a cochlear implant, for the soft delicate tissues of the eye. We orientated and embedded the fixed eye tissue, including the implant and retinal tack, in epoxy resin, to stabilise and protect the structure of the sample. Embedded samples were then ground, polished, stained, and imaged under various magnifications at incremental depths through the sample. This technique allowed the reliable assessment of eye tissue integrity and cytoarchitecture adjacent to the metal tack.

Here we describe histological techniques for visualising ocular tissue directly adjacent to a metal epiretinal tack and retinal prosthesis.

Retinal prostheses for the treatment of certain forms of blindness are gaining traction in clinical trials around the world with commercial devices ...

Encapsulation Thermogenic Preadipocytes for Transplantation into Adipose Tissue Depots

Cell encapsulation was developed to entrap viable cells within semi-permeable membranes. The engrafted encapsulated cells can exchange low molecular weight metabolites in tissues of the treated host to achieve long-term survival. The semipermeable membrane allows engrafted encapsulated cells to avoid rejection by the immune system. The encapsulation procedure was designed to enable a controlled release of bioactive compounds, such as insulin, other hormones, and cytokines. Here we describe a method for encapsulation of catabolic cells, which consume lipids for heat production and energy dissipation (thermogenesis) in the intra-abdominal adipose tissue of obese mice. Encapsulation of thermogenic catabolic cells may be potentially applicable to the prevention and treatment of obesity and type 2 diabetes. Another potential application of catabolic cells may include detoxification from alcohols or other toxic metabolites and environmental pollutants.

Here, we present a protocol for encapsulation of catabolic cells, which consume lipids for heat production in intra-abdominal adipose tissue and increase energy dissipation in obese mice.

Cell encapsulation was developed to entrap viable cells within semi-permeable membranes. The engrafted encapsulated cells can exchange low molecular ...

Implementation of In Vitro Drug Resistance Assays: Maximizing the Potential for Uncovering Clinically Relevant Resistance Mechanisms

Although targeted therapies are initially effective, resistance inevitably emerges. Several methods, such as genetic analysis of resistant clinical specimens, have been applied to uncover these resistance mechanisms to facilitate follow-up care. Although these approaches have led to clinically relevant discoveries, difficulties in attaining the relevant patient material or in deconvoluting the genomic data collected from these specimens have severely hampered the path towards a cure. To this end, we here describe a tool for expeditious discovery that may guide improvement in first-line therapies and alternative clinical management strategies. By coupling preclinical in vitro or in vivo drug selection with next-generation sequencing, it is possible to identify genomic structural variations and/or gene expression alterations that may serve as functional drivers of resistance. This approach facilitates the spontaneous emergence of alterations, enhancing the probability that these mechanisms may be observed in the patients. In this protocol we provide guidelines to maximize the potential for uncovering single nucleotide variants that drive resistance using adherent lines.

Implementation of In Vitro Drug Resistance Assays: Maximizing the Potential for Uncovering Clinically Relevant Resistance Mechanisms

Drug resistance to targeted therapeutics is widespread and the need to identify mechanisms of resistance--prior to or following clinical onset--is critical for guiding alternative clinical management strategies. Here, we present a protocol to couple derivation of drug-resistant lines in vitro with sequencing to expedite discovery of these mechanisms.

Although targeted therapies are initially effective, resistance inevitably emerges. Several methods, such as genetic analysis of resistant clinical ...

Bioluminescence-Based Tumor Quantification Method for Monitoring Tumor Progression and Treatment Effects in Mouse Lymphoma Models

Although bioluminescence imaging (BLI) shows promise for monitoring tumor burden in animal models of cancer, these analyses remain mostly qualitative. Here we describe a method for bioluminescence imaging to obtain a semi-quantitative analysis of tumor burden and treatment response. This method is based on the calculation of a luminoscore, a value that allows comparisons of two animals from the same or different experiments. Current BLI instruments enable the calculation of this luminoscore, which relies mainly on the acquisition conditions (back and front acquisitions) and the drawing of the region of interest (manual markup around the mouse). Using two previously described mouse lymphoma models based on cell engraftment, we show that the luminoscore method can serve as a noninvasive way to verify successful tumor cell inoculation, monitor tumor burden, and evaluate the effects of in situ cancer treatment (CpG-DNA). Finally, we show that this method suits different experimental designs. We suggest that this method be used for early estimates of treatment response in preclinical small-animal studies.

Bioluminescence imaging is a well-known tool for localizing tumors and metastases, but quantification of these images often requires complex calculations and particular instruments. We describe the easy-to-use luminoscore method, based on precise acquisition conditions, requiring no calculations, and enabling tumor burden and treatment response to be monitored in mouse models.

Although bioluminescence imaging (BLI) shows promise for monitoring tumor burden in animal models of cancer, these analyses remain mostly qualitative. ...

An Improved Method for Rapid Intubation of the Trachea in Mice

Despite some anatomical and physiological differences, mouse models continue to be an essential tool for studying human lung disease. Bleomycin toxicity is a commonly used model to study both acute lung injury and fibrosis, and multiple methods have been developed for administering bleomycin (and other toxic agents) into the lungs. However, many of these approaches, such as transtracheal instillation, have inherent drawbacks, including the need for strong anesthetics and survival surgery. This paper reports a quick, reproducible method of intratracheal intubation that involves mild inhaled anesthesia, visualization of the trachea, and the use of a surrogate spirometer to confirm exposure. As a proof of concept, 8-12 week old C57BL/6 mice were administered either 2.0 U/kg of bleomycin or an equivalent volume of PBS, and both damage and fibrotic endpoints were measured post-exposure. This procedure allows researchers to treat a large cohort of mice in a relatively short period with little expense and minimal post-procedure care.

This article presents a rapid and simple method for administering bleomycin directly into the mouse trachea via intubation. Key advantages of this method are that it is highly reproducible, easy to master, and does not require specialized equipment or lengthy recovery times.

Despite some anatomical and physiological differences, mouse models continue to be an essential tool for studying human lung disease. Bleomycin toxicity ...

Pancreatic Duct Infusion: An Effective and Selective Method of Drug and Viral Delivery

The pancreas is a bifunctional organ with both endocrine and exocrine components. A number of pathologies can afflict the pancreas, including diabetes, pancreatitis, and pancreatic cancer. All three of these diseases mark active areas of study, not only to develop immediate therapy, but also to better understand their pathophysiology. There are few tools to further these areas of study. Pancreatic duct infusion is an important technique that can allow for lineage tracing, gene introduction, and cell line-specific targeting. The technique requires the intricate dissection of the second portion of the duodenum and ampulla, followed by the occlusion of the bile duct and the cannulation of the pancreatic duct. Although the technique is technically challenging at first, the applications are myriad. Ambiguity in the specifics of the procedure between groups highlighted the need for a standard protocol. This work describes the expression of a green fluorescent protein (GFP) within the pancreas after the pancreatic duct infusion of a viral vector expressing GFP versus a sham surgery. The infusion and therefore expression is specific to the pancreas, without expression present in any other tissue type.

Pancreatic duct infusion is an important technique that can allow for lineage tracing, gene introduction, and cell line-specific targeting. A pancreatic duct infusion technique for drug and viral delivery to pancreatic cells is presented here.

The pancreas is a bifunctional organ with both endocrine and exocrine components. A number of pathologies can afflict the pancreas, including diabetes, ...

Optimized LC-MS/MS Method for the High-throughput Analysis of Clinical Samples of Ivacaftor, Its Major Metabolites, and Lumacaftor in Biological Fluids of Cystic Fibrosis Patients

Defects in the cystic fibrosis trans-membrane conductance regulator (CFTR) are the cause of cystic fibrosis (CF), a disease with life-threatening pulmonary manifestations. Ivacaftor (IVA) and ivacaftor-lumacaftor (LUMA) combination are two new breakthrough CF drugs that directly modulate the activity and trafficking of the defective CFTR-protein. However, there is still a dearth of understanding on pharmacokinetic/pharmacodynamic parameters and the pharmacology of ivacaftor and lumacaftor. The HPLC-MS technique for the simultaneous analysis of the concentrations of ivacaftor, hydroxymethyl-ivacaftor, ivacaftor-carboxylate, and lumacaftor in biological fluids in patients receiving standard ivacaftor or ivacaftor-lumacaftor combination therapy has previously been developed by our group and partially validated to FDA standards. However, to allow the high-throughput analysis of a larger number of patient samples, our group has optimized the reported method through the use of a smaller pore size reverse-phase chromatography column (2.6 &#181;m, C8 100 &#197;; 50 x 2.1 mm) and a gradient solvent system (0-1 min: 40% B; 1-2 min: 40-70% B; 2-2.7 min: held at 70% B; 2.7-2.8 min: 70-90% B; 2.8-4.0 min: 90% B washing; 4.0-4.1 min: 90-40% B; 4.1-6.0 min: held at 40% B) instead of an isocratic elution. The goal of this study was to reduce the HPLC-MS analysis time per sample dramatically from ~15 min to only 6 min per sample, which is essential for the analysis of a large amount of patient samples. This expedient method will be of considerable utility for studies into the exposure-response relationships of these breakthrough CF drugs.

Ivacaftor and ivacaftor-lumacaftor combination are two new CF drugs. However, there is still a dearth of understanding on their PK/PD and pharmacology. We present an optimized HPLC-MS technique for the simultaneous analysis of ivacaftor and its major metabolites, and lumacaftor.

Defects in the cystic fibrosis trans-membrane conductance regulator (CFTR) are the cause of cystic fibrosis (CF), a disease with life-threatening ...

Improved Method for the Establishment of an In Vitro Blood-Brain Barrier Model Based on Porcine Brain Endothelial Cells

The aim of this protocol presents an optimized procedure for the purification and cultivation of pBECs and to establish in vitro blood-brain barrier (BBB) models based on pBECs in mono-culture (MC), MC with astrocyte-conditioned medium (ACM), and non-contact co-culture (NCC) with astrocytes of porcine or rat origin. pBECs were isolated and cultured from fragments of capillaries from the brain cortices of domestic pigs 5-6 months old. These fragments were purified by careful removal of meninges, isolation and homogenization of grey matter, filtration, enzymatic digestion, and centrifugation. To further eliminate contaminating cells, the capillary fragments were cultured with puromycin-containing medium. When 60-95% confluent, pBECs growing from the capillary fragments were passaged to permeable membrane filter inserts and established in the models. To increase barrier tightness and BBB characteristic phenotype of pBECs, the cells were treated with the following differentiation factors: membrane permeant 8-CPT-cAMP (here abbreviated cAMP), hydrocortisone, and a phosphodiesterase inhibitor, RO-20-1724 (RO). The procedure was carried out over a period of 9-11 days, and when establishing the NCC model, the astrocytes were cultured 2-8 weeks in advance. Adherence to the described procedures in the protocol has allowed the establishment of endothelial layers with highly restricted paracellular permeability, with the NCC model showing an average transendothelial electrical resistance (TEER) of 1249 &#177; 80 &#937; cm2, and paracellular permeability (Papp) for Lucifer Yellow of 0.90 10-6 &#177; 0.13 10-6 cm sec-1 (mean &#177; SEM, n=55). Further evaluation of this pBEC phenotype showed good expression of the tight junctional proteins claudin 5, ZO-1, occludin and adherens junction protein p120 catenin. The model presented can be used for a range of studies of the BBB in health and disease and, with the highly restrictive paracellular permeability, this model is suitable for studies of transport and intracellular trafficking.

Improved Method for the Establishment of an In Vitro Blood-Brain Barrier Model Based on Porcine Brain Endothelial Cells

The aim of the protocol is to present an optimized procedure for the establishment of an in vitro blood-brain barrier (BBB) model based on primary porcine brain endothelial cells (pBECs). The model shows high reproducibility, high tightness, and is suitable for studies of transport and intracellular trafficking in drug discovery.

The aim of this protocol presents an optimized procedure for the purification and cultivation of pBECs and to establish in vitro blood-brain barrier (BBB) ...