판 실험실에 효모 작품 아지 노 모토 (주) 일본 건강 교육 그랜트 T32GM007183 - 33 국립 연구소에서 교부금에 의해 투자되었다. 우리는 효모 프로젝트에 장기 협력에 대한 의학의 인디애나 대학 대학원에서 박사 로널드 Wek 감사합니다. 우리는 또한 microarray 방법을 충전 tRNA 개발을위한 박사 킴벌리 디트마 감사합니다.

1 부 : 총 청구 tRNA의 분리 <ol><li> 오분 2000 RFC에서 탁상 원심 분리기의 펠렛 세포. 세포의 OD 600 1 동등 총 RNA의 적어도 1μg을 생산해야 30 μg 최소가 절차를 권장합니다. </li><li> 0.3 M 구성된 용해 버퍼 용액에 Resuspend 세포가 + - 아세테이트 (산도 4.5) 10 MM EDTA (에틸렌 다이아 민 테트라 초산) 나 버퍼. 30 S. 세 번 각 (산도 4.5) 나 + - 아세테이트 - 포화 페놀 / 클로로포름의 동일한 볼륨 소용돌이 항상 vortexing 사이의 얼음에 샘플을 보관해야합니다. 아세트산은 페놀 / 클로로포름은 클로로포름 / 페놀 구 부분 M은 아세테이트를 버퍼 한 부분 5 (산도 4.5) 혼합하여 준비할 수 포화 동안 버퍼 아세테이트는 NaOAC와 HOAc에서 준비하실 수 있습니다. 참고 : 총 RNA의 분리가 부과 tRNA의 deacylation을 피하​​기 위해 약간 산성 조건 하에서 얼음에 수행됩니다. </li><li> 4 15 분 동안 18,600 RFC에서 원심 분리기 ° C. 수성 층을 제거하고 다른 아세테이트 - 포화 페놀 / 클로로포름 추출을 수행합니다. </li><li> 두 번째 추출 에탄올의 2.7x 볼륨을 추가하고 4 30 분 18,600 RFC에 centrifuging ° C.를하여 RNA를 침전 후 </li><li> 용해 버퍼 용액에 다음 Resuspend RNA 알약은 에탄올과 함께 두 번째 시간을 촉진. </li><li> 마지막으로, 10 MM 버퍼 아세테이트 (산도 4.5) 및 후속 치료를 위해 1 ㎜ EDTA (에틸렌 다이아 민 테트라 초산)를 포함하는 솔루션에서 RNA를 resuspend. 예제는 안정에 대한 두 주 동안 -80 ° C에 저장할 수 있습니다. 일부 청구 tRNA가 deacylate 시작되므로 더 이상 보관하지 않는 것이 좋습니다. </li></ol> 2 부 : tRNA 표준의 작성 <ol><li> 히트 E. 10.5 μm의에서 85 대장균 tRNA 표준 ° C 2 분 45 MM 트리스 - CL의 산도 7.5 인치 튜브를 타고 3 분 상온에서 둡니다. </li><li> 37 20 MM과 부화의 최종 농도 ° 5 분 C로 MgCl 2를 추가합니다. </li><li> 최종 반응 5 μm의 tRNA, 60 MM 트리스 - HCL (산도 7.5), 12.5 MM MgCl 2, 10 MM KCl, 3 MM dithiothreitol, 1.5 MM ATP, 1 ㎜ spermine, 각각의 1 ㎜를 포함하므로 synthetase 반응 믹스 추가 아미노산, 그리고 4.2 단위 / μl E. 대장균 aminoacyl - tRNA synthetase의 믹스. 37 품어 ° C를 15 분. </li><li> 0.5 M 버퍼 아세테이트 (산도 4.5)의 동일한 볼륨을 추가하고 산도 4.5에서 아세테이트 - 포화 CHCl / 페놀 3 압축을 풉니다. </li><li> 4 30 분 18,600 RFC ° C.에서 에탄올과 centrifuging의 2.7x 볼륨과 tRNA 기준을 촉진 </li><li> 최대 1 개월에 대해 -80 1 밀리미터 EDTA (에틸렌 다이아 민 테트라 초산) 및 저장 ° C와 50 MM 버퍼 아세테이트 (산도 4.5)의 기준에 Resuspend. </li></ol> 파트 3 : tRNA의 Cy3/Cy5 라벨링 <ol><li> 청구 tRNA 샘플 각 E.는 0.066 μm의와 0.1 μg / μl의 농도에서 총 RNA를 품어 대장균 tRNA 기준 (예 : 0.67 pmole μg 총 RNA마다 각각의 표준)과 실온에서 30 분 50 MM NaIO4의 존재 100 MM 버퍼 아세테이트 (산도 4.5). 컨트롤 전체 tRNA 샘플 사용 NaIO4 대신에 NaCl 50 MM하십시오. 전용 충전 보존하기 위해 버퍼 솔루션 RNA를 희석 것을 잊지 마십시오. </li><li> 반응을 담금질하기 위해서는 100 mm까지 포도당을 추가하고 5 분 실온에서 알을 품다. </li><li> 샘플로부터 남아 NaIO4를 제거 G25의 스핀 컬럼을 사용하여 버퍼 교환을 수행하기 위해서는. 최상의 결과를 귀하의 샘플을 적용하기 전에 그것을 통해 200 MM 버퍼 아세테이트 버퍼 (산도 4.5)를 실행하여 먼저 열을 평형하십시오. </li><li> 66 MM과 에탄올의 2.7x 볼륨의 최종 농도 133 MM과 NaCl의 최종 농도 버퍼 아세테이트 (산도 4.5)을 추가하여 샘플을 촉진. 때때로 두 번째 강수량은 산화 샘플에 필요한 수 있습니다. 펠렛 같은 샘플의 통제 펠렛보다 훨씬 달라 보이는 경우에만이 필요합니다. 예를 들어 훨씬 더 이상의 확산 펠릿하십시오. 두 번째 에탄올 침전은 50 MM 아세테이트 버퍼 산도 4.5과 에탄올의 추가되기 전에 NaCl 200 MM에 resuspend 펠렛을 필요한 경우. </li><li> deacylation에 대한 tRNA 샘플은 50 MM 트리스 - HCL에 resuspended 아르 (산도 9) 30 분 37 ° C에서 incubated. 반응 50 MM 버퍼 아세테이트 (산도 4.5) 100 MM NaCl의 동일한 볼륨의 추가에 의해 무력화됩니다. 에탄올의 2.7x 볼륨 침전물. 강수량 후, RNA는 ~ 1 μg / μl에서 물에 resuspended 있습니다. 제어 및 산화 샘플 모두 RNA의 품질을 확인하는 아가로 오스의 젤에서 실행해야합니다. </li><li> tRNA 0.1 μg / μl deacylated RNA에 형광 oligo 태그를 첨부하는 것은 1X ligase 버퍼, 15% DMSO, 4 μm의 Cy3 또는 Cy5 함유 oligonucleotides, 0.5 단위 / μl T4의 DNA ligase에 incubated, 그리고 효모 엑소 - 인산 가수 분해 효소 (5,000 16 단위 / μl) ° C 숙박 (16 이상 H). 엑소 - 인산 가수 분해 효소는 효모에서 얻은 샘플에 필요한이며 omitte 수 있습니다D이 프로토콜은 E. 사용하는 경우 대장균이나 인간의 샘플. </li><li> 내고 샘플 / 페놀 클로로포름의 동일한 볼륨 압축을 푼 다음 50 MM KOAc (산도 7), 200 MM KCl, 그리고 4 권과 혼합 후. 수성 단계의 추출에 따라 RNA 준비는 에탄올로 시켰던하고 약 0.1 μg / μl로 물에 resuspended. </li><li> 결합 효율은 7m 요소를 포함하는 12 % polyacrylamide의 젤의 샘플 5~10%를 실행하여 평가하고 형광 젤 스캐너와 시각하실 수 있습니다. 이 페이지 분석은 산화 및 컨트롤 샘플 microarray의 하이브리드화에 필요한 금액을 결정하는 것도 유용합니다. 좋은 내고 결과 ~ Cy3/Cy5 포함된 oligonucleotide의 10 % 이상이 tRNA에 출혈도 잡았되고 표시됩니다. 좋은 라벨 효율 샘플, 샘플 당 0.1-0.5 μg의 총 RNA는 배열 하이브 리다이 제이션에 사용됩니다. </li></ol> 파트 4 : Microarray의 하이브리드화 분석 <ol><li> 하이브 리다이 제이션 microarray 슬라이드 전에는 언바운드 oligonucleotides를 제​​거하는 1~2분 위해 증류수 삶은 있습니다. </li><li> Cy3/Cy5 라벨 샘플은 140 μg / ML 70 μg / ML의 최종 농도 폴리 (A)의 최종 농도 하이브 리다이 제이션 버퍼와 연어 정자 DNA의 140 μl와 결합됩니다. </li><li> 하이브 리다이 제이션 프로그램 (표 1), 자동 배열 hybridizer에서 실행됩니다. tRNA 작업에, 그것은 매우 수동으로 하이브리드화 높은 배경을 생산하고 이후 자동으로 하이브리드화 시스템을 사용하는 것이 좋습니다. </li><li> 프로그램이 완료되면 수동으로 부드럽게 3-5분에 대한 50 ML 원뿔 튜브 흔들어하여 적어도 두 번 씻으 3 솔루션과 함께 슬라이드를 씻으십시오. </li><li> 슬라이드는 50~10분 들어, 저속 200보다 낮고, RFC에서 centrifuging에 의해 건조하실 수 있습니다. 그것은 빛에 노출 표백 fluorophores을하므로 빛의 슬라이드를 유지하는 것이 중요합니다. </li><li> 슬라이드가 건조되면 그들은 최적의 결과를 즉시 검사해야합니다, 필요한 경우 그들은 상온에서 건조한 어두운 장소에서 몇 일 동안 저장할 수 있습니다. 슬라이드는 이미지 품질을 눈에 띄게 저하없이 2-3 시간을 스캔하실 수 있습니다. </li></ol> 제 5 부 : 대표 결과 니어 2 OD 280 비율이 적절히 격리하면 총 RNA 샘플은 OD 260로 매우 깨끗한 스펙트럼을 생산해야합니다. 전혀 감지 단백질이나 페놀 오염이 없어야합니다. 1퍼센트 아가로 오스 젤에서 실행하면, 강력한 tRNA 밴드는 생물 사이 변화 다른 가능한 핵산 밴드 관찰해야합니다. 산화 후 깨끗한 tRNA 밴드가 관찰해야합니다. 샘플 다른 샘플이나 번짐을 관찰보다 눈에 띄게 약한 경우, 샘플은 microarrays에 사용해서는 안됩니다. Microarrays 가장 약한 tRNA 프로브보다 낮은 크기의 순서는 매우 낮은 배경이 있어야합니다. 높은 배경은 일반적으로 세척 단계 도중 문제로 인해 발생합니다. 이것은 일반적으로 신선한 씻어 솔루션을 준비하고 철저하게 잔류하고 시켰던 SDS를 제거하기 위해 모든 장비와 튜브를 세척하여 해결하실 수 있습니다. <table border="1"><tbody><tr><td> 단계 </td><td> 세부 </td></tr><tr><td> O - 링 에어컨 </td><td> 75 ° C, 2 분 </td></tr><tr><td> 샘플 소개 </td><td> 60 ° C </td></tr><tr><td> 변성 샘플 </td><td> 90 ° C, 5 분 </td></tr><tr><td> 이종 교잡 </td><td> 60 ° C, 16h </td></tr><tr><td> 1 와시 </td><td> 50 ° C, 10 초 흐름은 20 대 시절을 보유 </td></tr><tr><td> 2 와시 </td><td> 42 ° C, 10 초 흐름은 20 대 시절을 보유 </td></tr><tr><td> 3 와​​시 </td><td> 42 ° C, 10 초 흐름은 20 대 시절을 보유 </td></tr></tbody></table> 표 1 : 하이브리드화 프로토콜

<ol>
	<li>Zaborske, J. M. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Genome-wide+analysis+of+tRNA+charging+and+activation+of+the+eIF2+kinase+Gcn2p.">Genome-wide analysis of tRNA charging and activation of the eIF2 kinase Gcn2p.</a> J Biol Chem. 284 (37), 25254-25267 (2009).</li><li>Wek, S. A., Zhu, S., Wek, R. 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+histidyl-tRNA+synthetase-related+sequence+in+the+eIF-2+alpha+protein+kinase+GCN2+interacts+with+tRNA+and+is+required+for+activation+in+response+to+starvation+for+different+amino+acids.">The histidyl-tRNA synthetase-related sequence in the eIF-2 alpha protein kinase GCN2 interacts with tRNA and is required for activation in response to starvation for different amino acids.</a> Mol Cell Biol. , 4497-4506 (1995).</li><li>Hinnebusch, A. G. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Translational+Regulation+of+GCN4+and+the+General+Amino+Acid+Control+of+Yeast.">Translational Regulation of GCN4 and the General Amino Acid Control of Yeast.</a> Annual Review of Microbiology. 59 (1), 407-450 (2005).</li><li>Braeken, K. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=New+horizons+for+(p)ppGpp+in+bacterial+and+plant+physiology.">New horizons for (p)ppGpp in bacterial and plant physiology.</a> Trends in Microbiology. 14 (1), 45-54 (2006).</li><li>Dong, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Uncharged+tRNA+Activates+GCN2+by+Displacing+the+Protein+Kinase+Moiety+from+a+Bipartite+tRNA-Binding+Domain.">Uncharged tRNA Activates GCN2 by Displacing the Protein Kinase Moiety from a Bipartite tRNA-Binding Domain.</a> Molecular Cell. 6 (2), 269-279 (2000).</li><li>Cashel, M., Neidhardt, F. 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+Stringent+Response.">The Stringent Response.</a> Escherichia coli and Salmonella: cellular and molecular biology. , 1458-1496 (1996).</li><li>Dittmar, K. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Selective+charging+of+tRNA+isoacceptors+induced+by+amino-acid+starvation.">Selective charging of tRNA isoacceptors induced by amino-acid starvation.</a> EMBO Rep. 6 (2), 151-157 (2005).</li><li>Zhou, Y. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=High+levels+of+tRNA+abundance+and+alteration+of+tRNA+charging+by+bortezomib+in+multiple+myeloma.">High levels of tRNA abundance and alteration of tRNA charging by bortezomib in multiple myeloma.</a> Biochem Biophys Res Commun. 385 (2), 160-164 (2009).</li></ol>

관심 없음 충돌 선언하지 않습니다.

우리는 동시에 게놈 규모의 모든 tRNAs의 상대적 충전 레벨을 결정하기위한 방법을 제시한다. 여기에 제시 프로토콜은 S.에 최적화된되었습니다 cerevisiae가 있으며, 또한 성공적으로 E.에있는 tRNA 충전 프로파일을 측정하는 데 사용되었습니다 대장균과 인간의 세포 문화. 총 청구 tRNA를 얻을 수 있듯이 이것은 알려진 게놈 시퀀스 (모든 tRNA 유전자의 주석에 대한 허용) 한있는 모든 생물을 수용하기 위해 수정할 수 있습니다.

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		<div>
		<table border="1">
		<thead>
		<tr>
		<th>Material Name</th>
		<th>Type</th>
		<th>Company</th>
		<th>Catalogue Number</th>
		<th>Comment</th>
		</tr>
		</thead>
		<tbody>
		
<tr>
<td>Microspin G-25 columns</td>
<td>&nbsp;</td>
<td>GE Healthcare</td>
<td>25-5325-01</td>
<td>&nbsp;</td>
<tr>
<td>E. coli tRNAPhe</td>
<td>&nbsp;</td>
<td>Sigma-Aldrich</td>
<td>R3143</td>
<td>&nbsp;</td>
<tr>
<td>E. coli tRNATyr</td>
<td>&nbsp;</td>
<td>Sigma-Aldrich</td>
<td>R0258</td>
<td>&nbsp;</td>
<tr>
<td>E. coli tRNALys</td>
<td>&nbsp;</td>
<td>Sigma-Aldrich</td>
<td>R6018</td>
<td>&nbsp;</td>
<tr>
<td>E. coli aminoacyl-tRNA synthetases</td>
<td>&nbsp;</td>
<td>Sigma-Aldrich</td>
<td>A3646</td>
<td>&nbsp;</td>
<tr>
<td>T4 DNA ligase</td>
<td>&nbsp;</td>
<td>USB</td>
<td>70042X</td>
<td>&nbsp;</td>
<tr>
<td>PerfectHyb Plus</td>
<td>&nbsp;</td>
<td>Sigma-Aldrich</td>
<td>H-7033</td>
<td>&nbsp;</td>
<tr>
<td>Hyb4 station</td>
<td>&nbsp;</td>
<td>Digilab</td>
<td>&nbsp;</td>
<td>&nbsp;</td>
<tr>
<td>GenePix 4000B scanner</td>
<td>&nbsp;</td>
<td>Axon instruments</td>
<td>&nbsp;</td>
<td>&nbsp;</td>
<tr>
<td>GenePix 6.0</td>
<td>&nbsp;</td>
<td>Axon instruments</td>
<td>&nbsp;</td>
<td>&nbsp;</td>		</tbody>
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		</div>

genome-wide analysis of aminoacylation (charging) levels of trna using microarrays

tRNA의 aminoacylation, 또는 충전, 레벨 빠르게 환경 [1]에 대한 응답으로 세포 내에서 변경할 수 있습니다. prokaryotic와 진핵 세포 모두에서 수준을 충전 tRNA의 변화는 스트레스 반응의 주요 세포 메커니즘입니다 translational 규제로 이어집니다. 친숙한 예제는 엄격한 응답에<em&gt; E. 대장균</em&gt;와 효모의 Gcn2 스트레스 반응 경로 ([2-6]). 최근 작업<em&gt; E. 대장균</em&gt;과<em&gt; S. cerevisiae</em&gt;는 tRNA 충전 패턴은 매우 역동적인 것을 표시하고 세포 [1, 6, 7]에 의해 경험 스트레스의 종류에 따라 다릅니다있다. tRNA 충전의 고도로 동적인 변수 자연 완벽하게 환경 변화에 세포 응답을 명료하게하다하기 위하여 게놈 규모 tRNA 충전 수준의 변화를 확인하는 것이 중요합니다. 이 리뷰에서 우리는 동시에 모든 tRNAs의 상대 충전 레벨을 측정하는 방법을 제시<em&gt; S. cerevisiae</em&gt;. 여기에 제시된 프로토콜이 누룩에 대해이지만,이 프로토콜이 성공적으로 포함하여 생물의 다양한 상대 충전 수준을 결정하기위한 적용되었습니다<em&gt; E. 대장균</em&gt; 인간 세포 문화 [7, 8].

Watch this Scientific Journal Video about Genome-wide Analysis of Aminoacylation Charging Levels of tRNA Using Microarrays at JoVE.com

Genome-wide Analysis of Aminoacylation Charging Levels of tRNA Using Microarrays

우리는 모든 tRNAs의 상대 aminoacylation 수준을 결정하기 위해 microarray 분석을위한 방법을 설명<em&gt; S. cerivisiae.</em

Microarrays을 사용하여 tRNA의 Aminoacylation (충전) 레벨의 게놈 - 와이드 분석

tRNA aminoacylation, or charging, levels can rapidly change within a cell in response to the environment[1]. Changes in tRNA charging levels in both ...

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Genome-wide Analysis of Aminoacylation (Charging) Levels of tRNA Using Microarrays

12.3K 조회수.  University of Chicago. 우리는 모든 tRNAs의 상대 aminoacylation 수준을 결정하기 위해 microarray 분석을위한 방법을 설명 S. cerivisiae.

비디오: Genome-wide Analysis of Aminoacylation Charging Levels of tRNA Using Microarrays

Bacterial Gene Expression Analysis Using Microarrays

Microarrays을 사용하여 세균의 유전자 발현 분석

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Genome-wide Analysis using ChIP to Identify Isoform-specific Gene Targets

Recruitment of transcriptional and epigenetic factors to their targets is a key step in their regulation. Prominently featured in recruitment are the protein domains that bind to specific histone modifications. One such domain is the plant homeodomain (PHD), found in several chromatin-binding proteins. The epigenetic factor RBP2 has multiple PHD domains, however, they have different functions (Figure 4). In particular, the C-terminal PHD domain, found in a RBP2 oncogenic fusion in human leukemia, binds to trimethylated lysine 4 in histone H3 (H3K4me3)1. The transcript corresponding to the RBP2 isoform containing the C-terminal PHD accumulates during differentiation of promonocytic, lymphoma-derived, U937 cells into monocytes2. Consistent with both sets of data, genome-wide analysis showed that in differentiated U937 cells, the RBP2 protein gets localized to genomic regions highly enriched for H3K4me33. Localization of RBP2 to its targets correlates with a decrease in H3K4me3 due to RBP2 histone demethylase activity and a decrease in transcriptional activity. In contrast, two other PHDs of RBP2 are unable to bind H3K4me3. Notably, the C-terminal domain PHD of RBP2 is absent in the smaller RBP2 isoform4. It is conceivable that the small isoform of RBP2, which lacks interaction with H3K4me3, differs from the larger isoform in genomic location. The difference in genomic location of RBP2 isoforms may account for the observed diversity in RBP2 function. Specifically, RBP2 is a critical player in cellular differentiation mediated by the retinoblastoma protein (pRB). Consistent with these data, previous genome-wide analysis, without distinction between isoforms, identified two distinct groups of RBP2 target genes: 1) genes bound by RBP2 in a manner that is independent of differentiation; 2) genes bound by RBP2 in a differentiation-dependent manner.
To identify differences in localization between the isoforms we performed genome-wide location analysis by ChIP-Seq. Using antibodies that detect both RBP2 isoforms we have located all RBP2 targets. Additionally we have antibodies that only bind large, and not small RBP2 isoform (Figure 4). After identifying the large isoform targets, one can then subtract them from all RBP2 targets to reveal the targets of small isoform. These data show the contribution of chromatin-interacting domain in protein recruitment to its binding sites in the genome.

Here we are presenting a chromatin immunoprecipitation (ChIP) procedure for genome-wide location analysis of protein isoforms that differ in a histone-binding domain. We are applying it to ChIP-Seq analysis to identify the targets of the KDM5A/JARID1A/RBP2 histone demethylase.

이소형 특정 유전자 목표를 식별 칩을 사용하여 게놈 차원의 분석

Recruitment of transcriptional and epigenetic factors to their targets is a key step in their regulation. Prominently featured in recruitment are the ...

Genome-wide Screen for miRNA Targets Using the MISSION Target ID Library

The Target ID Library is designed to assist in discovery and identification of microRNA (miRNA) targets. The Target ID Library is a plasmid-based, genome-wide cDNA library cloned into the 3'UTR downstream from the dual-selection fusion protein, thymidine kinase-zeocin (TKzeo). The first round of selection is for stable transformants, followed with introduction of a miRNA of interest, and finally, selecting for cDNAs containing the miRNA's target. Selected cDNAs are identified by sequencing (see Figure 1-3 for Target ID Library Workflow and details).
To ensure broad coverage of the human transcriptome, Target ID Library cDNAs were generated via oligo-dT priming using a pool of total RNA prepared from multiple human tissues and cell lines. Resulting cDNA range from 0.5 to 4 kb, with an average size of 1.2 kb, and were cloned into the p3&#900;TKzeo dual-selection plasmid (see Figure 4 for plasmid map). The gene targets represented in the library can be found on the Sigma-Aldrich webpage. Results from Illumina sequencing (Table 3), show that the library includes 16,922 of the 21,518 unique genes in UCSC RefGene (79%), or 14,000 genes with 10 or more reads (66%).

The Target ID Library is a plasmid-based, genome-wide collection of cloned cDNA used to identify miRNA targets. Here we demonstrate its use and application.

선교 대상 아이디 라이브러리를 사용하여 miRNA 목표를위한 게놈 - 와이드 스크린

The Target ID Library is designed to assist in discovery and identification of microRNA (miRNA) targets. The Target ID Library is a plasmid-based, ...

Quantitative and Automated High-throughput Genome-wide RNAi Screens in C. elegans

RNA interference is a powerful method to understand gene function, especially when conducted at a whole-genome scale and in a quantitative context. In C. elegans, gene function can be knocked down simply and efficiently by feeding worms with bacteria expressing a dsRNA corresponding to a specific gene 1. While the creation of libraries of RNAi clones covering most of the C. elegans genome 2,3 opened the way for true functional genomic studies (see for example 4-7), most established methods are laborious. Moy and colleagues have developed semi-automated protocols that facilitate genome-wide screens 8. The approach relies on microscopic imaging and image analysis. 
	Here we describe an alternative protocol for a high-throughput genome-wide screen, based on robotic handling of bacterial RNAi clones, quantitative analysis using the COPAS Biosort (Union Biometrica (UBI)), and an integrated software: the MBioLIMS (Laboratory Information Management System from Modul-Bio) a technology that provides increased throughput for data management and sample tracking. The method allows screens to be conducted on solid medium plates. This is particularly important for some studies, such as those addressing host-pathogen interactions in C. elegans, since certain microbes do not efficiently infect worms in liquid culture.
	We show how the method can be used to quantify the importance of genes in anti-fungal innate immunity in C. elegans. In this case, the approach relies on the use of a transgenic strain carrying an epidermal infection-inducible fluorescent reporter gene, with GFP under the control of the promoter of the antimicrobial peptide gene nlp 29 and a red fluorescent reporter that is expressed constitutively in the epidermis. The latter provides an internal control for the functional integrity of the epidermis and nonspecific transgene silencing9. When control worms are infected by the fungus they fluoresce green. Knocking down by RNAi a gene required for nlp 29 expression results in diminished fluorescence after infection. Currently, this protocol allows more than 3,000 RNAi clones to be tested and analyzed per week, opening the possibility of screening the entire genome in less than 2 months.

Quantitative and Automated High-throughput Genome-wide RNAi Screens in C. elegans

We describe a protocol using C. elegans and RNAi feeding libraries that allows automated measurement of multiple parameters such as fluorescence, size and opacity of individual worms in a population. We give one example of a screen to identify genes involved in anti-fungal innate immunity in C. elegans.

의 정량 및 자동화된 고속 처리량 게놈 전체의 RNAi 화면 C. elegans</em

RNA interference is a powerful method to understand gene function, especially when conducted at a whole-genome scale and in a quantitative context. In C. ...

Fabrication and Use of MicroEnvironment microArrays (MEArrays)

The interactions between cells and their surrounding microenvironment have functional consequences for cellular behaviour. On the single cell level, distinct microenvironments can impose differentiation, migration, and proliferation phenotypes, and on the tissue level the microenvironment processes as complex as morphogenesis and tumorigenesis1. Not only do the cell and molecular contents of microenvironments impact the cells within, but so do the elasticity2 and geometry3 of the tissue. Defined as the sum total of cell-cell, -ECM, and -soluble factor interactions, in addition to physical characteristics, the microenvironment is complex. The phenotypes of cells within a tissue are partially due to their genomic content and partially due to the combinatorial interactions with the microenviroment. A major challenge is to link specific combinations of microenvironmental components with distinctive behaviours.
Here, we present the microenvironment microarray (MEArray) platform for cell-based functional screening of interactions with combinatorial microenvironments4. The method allows for simultaneous control of the molecular composition and the elastic modulus, and combines the use of widely available microarray and micropatterning technologies. MEArray screens require as few as 10,000 cells per array, which facilitates functional studies of rare cell types such as adult progenitor cells. A limitation of the technology is that entire tissue microenvironments cannot be completely recapitulated on MEArrays. However, comparison of responses in the same cell type to numerous related microenvironments, for instance pairwise combinations of ECM proteins that characterize a given tissue, will provide insights into how microenvironmental components elicit tissue-specific functional phenotypes.
MEArrays can be printed using a wide variety of recombinant growth factors, cytokines, and purified ECM proteins, and combinations thereof. The platform is limited only by the availability of specific reagents. MEArrays are amenable to time-lapsed analysis, but most often are used for end point analyses of cellular functions that are measureable with fluorescent probes. For instance, DNA synthesis, apoptosis, acquisition of differentiated states, or production of specific gene products are commonly measured. Briefly, the basic flow of an MEArray experiment is to prepare slides coated with printing substrata and to prepare the master plate of proteins that are to be printed. Then the arrays are printed with a microarray robot, cells are allowed to attach, grow in culture, and then are chemically fixed upon reaching the experimental endpoint. Fluorescent or colorimetric assays, imaged with traditional microscopes or microarray scanners, are used to reveal relevant molecular and cellular phenotypes (Figure 1).

Fabrication and Use of MicroEnvironment microArrays MEArrays

A combinatorial functional screening method for gaining insights into the impacts of the molecular composition of microenvironments on cellular functions is described. The method takes advantage of existing microarray-based technologies to generate arrays of defined combinatorial microenvironments that support cell adhesion and functional analysis.

제조 및 MicroEnvironment의 microArrays의 사용 (MEArrays)

The interactions between cells and their surrounding microenvironment have functional consequences for cellular behaviour. On the single cell level, ...

Glycan Profiling of Plant Cell Wall Polymers using Microarrays

Plant cell walls are complex matrixes of heterogeneous glycans which play an important role in the physiology and development of plants and provide the raw materials for human societies (e.g. wood, paper, textile and biofuel industries)1,2. However, understanding the biosynthesis and function of these components remains challenging.
Cell wall glycans are chemically and conformationally diverse due to the complexity of their building blocks, the glycosyl residues. These form linkages at multiple positions and differ in ring structure, isomeric or anomeric configuration, and in addition, are substituted with an array of non-sugar residues. Glycan composition varies in different cell and/or tissue types or even sub-domains of a single cell wall3. Furthermore, their composition is also modified during development1, or in response to environmental cues4.
In excess of 2,000 genes have Plant cell walls are complex matrixes of heterogeneous glycans been predicted to be involved in cell wall glycan biosynthesis and modification in Arabidopsis5. However, relatively few of the biosynthetic genes have been functionally characterized 4,5. Reverse genetics approaches are difficult because the genes are often differentially expressed, often at low levels, between cell types6. Also, mutant studies are often hindered by gene redundancy or compensatory mechanisms to ensure appropriate cell wall function is maintained7. Thus novel approaches are needed to rapidly characterise the diverse range of glycan structures and to facilitate functional genomics approaches to understanding cell wall biosynthesis and modification.
Monoclonal antibodies (mAbs)8,9 have emerged as an important tool for determining glycan structure and distribution in plants. These recognise distinct epitopes present within major classes of plant cell wall glycans, including pectins, xyloglucans, xylans, mannans, glucans and arabinogalactans. Recently their use has been extended to large-scale screening experiments to determine the relative abundance of glycans in a broad range of plant and tissue types simultaneously9,10,11.
Here we present a microarray-based glycan screening method called Comprehensive Microarray Polymer Profiling (CoMPP) (Figures 1 &amp; 2)10,11 that enables multiple samples (100 sec) to be screened using a miniaturised microarray platform with reduced reagent and sample volumes. The spot signals on the microarray can be formally quantified to give semi-quantitative data about glycan epitope occurrence. This approach is well suited to tracking glycan changes in complex biological systems12 and providing a global overview of cell wall composition particularly when prior knowledge of this is unavailable.

A technique called Comprehensive Microarray Polymer Profiling (CoMPP) for the characterisation of plant cell wall glycans is described. This method combines the specificity of monoclonal antibodies directed to defined glycan-epitopes with a miniature microarray analytical platform allowing screening of glycan occurrence in a broad range of biological contexts.

Microarrays를 사용하여 식물 세포의 벽 폴리머의 Glycan 프로파일

Plant cell walls are complex matrixes of heterogeneous glycans which play an important role in the physiology and development of plants and provide the ...

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types

ChIPseq is a widely used technique for investigating protein-DNA interactions. Read density profiles are generated by using next-sequencing of protein-bound DNA and aligning the short reads to a reference genome. Enriched regions are revealed as peaks, which often differ dramatically in shape, depending on the target protein1. For example, transcription factors often bind in a site- and sequence-specific manner and tend to produce punctate peaks, while histone modifications are more pervasive and are characterized by broad, diffuse islands of enrichment2. Reliably identifying these regions was the focus of our work.
Algorithms for analyzing ChIPseq data have employed various methodologies, from heuristics3-5 to more rigorous statistical models, e.g. Hidden Markov Models (HMMs)6-8. We sought a solution that minimized the necessity for difficult-to-define, ad hoc parameters that often compromise resolution and lessen the intuitive usability of the tool. With respect to HMM-based methods, we aimed to curtail parameter estimation procedures and simple, finite state classifications that are often utilized.
Additionally, conventional ChIPseq data analysis involves categorization of the expected read density profiles as either punctate or diffuse followed by subsequent application of the appropriate tool. We further aimed to replace the need for these two distinct models with a single, more versatile model, which can capably address the entire spectrum of data types.
To meet these objectives, we first constructed a statistical framework that naturally modeled ChIPseq data structures using a cutting edge advance in HMMs9, which utilizes only explicit formulas-an innovation crucial to its performance advantages. More sophisticated then heuristic models, our HMM accommodates infinite hidden states through a Bayesian model. We applied it to identifying reasonable change points in read density, which further define segments of enrichment. Our analysis revealed how our Bayesian Change Point (BCP) algorithm had a reduced computational complexity-evidenced by an abridged run time and memory footprint. The BCP algorithm was successfully applied to both punctate peak and diffuse island identification with robust accuracy and limited user-defined parameters. This illustrated both its versatility and ease of use. Consequently, we believe it can be implemented readily across broad ranges of data types and end users in a manner that is easily compared and contrasted, making it a great tool for ChIPseq data analysis that can aid in collaboration and corroboration between research groups. Here, we demonstrate the application of BCP to existing transcription factor10,11 and epigenetic data12 to illustrate its usefulness.

Our Bayesian Change Point (BCP) algorithm builds on state-of-the-art advances in modeling change-points via Hidden Markov Models and applies them to chromatin immunoprecipitation sequencing (ChIPseq) data analysis. BCP performs well in both broad and punctate data types, but excels in accurately identifying robust, reproducible islands of diffuse histone enrichment.

다양한 ChIPseq 데이터 유형의 게놈 차원의 분석을위한 소설 베이지안 변경 - 포인트 알고리즘

ChIPseq is a widely used technique for investigating protein-DNA interactions. Read density profiles are generated by using next-sequencing of ...

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.

게놈 전체 유전자 삭제할에<em&gt; 연쇄상 구균 sanguinis</em&gt; 높은 처리량 PCR에 의한

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

Polysome Fractionation and Analysis of Mammalian Translatomes on a Genome-wide Scale

mRNA translation plays a central role in the regulation of gene expression and represents the most energy consuming process in mammalian cells. Accordingly, dysregulation of mRNA translation is considered to play a major role in a variety of pathological states including cancer. Ribosomes also host chaperones, which facilitate folding of nascent polypeptides, thereby modulating function and stability of newly synthesized polypeptides. In addition, emerging data indicate that ribosomes serve as a platform for a repertoire of signaling molecules, which are implicated in a variety of post-translational modifications of newly synthesized polypeptides as they emerge from the ribosome, and/or components of translational machinery. Herein, a well-established method of ribosome fractionation using sucrose density gradient centrifugation is described. In conjunction with the in-house developed &#8220;anota&#8221; algorithm this method allows direct determination of differential translation of individual mRNAs on a genome-wide scale. Moreover, this versatile protocol can be used for a variety of biochemical studies aiming to dissect the function of ribosome-associated protein complexes, including those that play a central role in folding and degradation of newly synthesized polypeptides.

Ribosomes play a central role in protein synthesis. Polyribosome (polysome) fractionation by sucrose density gradient centrifugation allows direct determination of translation efficiencies of individual mRNAs on a genome-wide scale. In addition, this method can be used for biochemical analysis of ribosome- and polysome-associated factors such as chaperones and signaling molecules.

게놈 전체 규모에 Polysom​​e 분별과 포유류 Translatomes 분석

mRNA translation plays a central role in the regulation of gene expression and represents the most energy consuming process in mammalian cells. ...

Genome-wide Protein-protein Interaction Screening by Protein-fragment Complementation Assay (PCA) in Living Cells

Proteins are the building blocks, effectors and signal mediators of cellular processes. A protein&#8217;s function, regulation and localization often depend on its interactions with other proteins. Here, we describe a protocol for the yeast protein-fragment complementation assay (PCA), a powerful method to detect direct and proximal associations between proteins in living cells. The interaction between two proteins, each fused to a dihydrofolate reductase (DHFR) protein fragment, translates into growth of yeast strains in presence of the drug methotrexate (MTX). Differential fitness, resulting from different amounts of reconstituted DHFR enzyme, can be quantified on high-density colony arrays, allowing to differentiate interacting from non-interacting bait-prey pairs. The high-throughput protocol presented here is performed using a robotic platform that parallelizes mating of bait and prey strains carrying complementary DHFR-fragment fusion proteins and the survival assay on MTX. This protocol allows to systematically test for thousands of protein-protein interactions (PPIs) involving bait proteins of interest and offers several advantages over other PPI detection assays, including the study of proteins expressed from their endogenous promoters without the need for modifying protein localization and for the assembly of complex reporter constructs.

Genome-wide Protein-protein Interaction Screening by Protein-fragment Complementation Assay PCA in Living Cells

Proteins interact with each other and these interactions determine in a large part their functions. Protein interaction partners can be identified at high-throughput in vivo using a yeast fitness assay based on the dihydrofolate reductase protein-fragment complementation assay (DHFR-PCA).

살아있는 세포의 단백질 조각 보완 분석 (PCA)에 의해 게놈 전체 단백질 - 단백질 상호 작용 상영

Proteins are the building blocks, effectors and signal mediators of cellular processes. A protein&#8217;s function, regulation and localization often ...