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RISS 인기검색어
- 검색키워드
- 저자 : Presson, Angela P. (검색결과 3 건)
- 무료
- 기관 내 무료
- 유료
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Methodology and software to detect viral integration site hot-spots
Presson, Angela P,Kim, Namshin,Xiaofei, Yan,Chen, Irvin SY,Kim, Sanggu BioMed Central 2011 BMC bioinformatics Vol.12 No.-
<P><B>Background</B></P><P>Modern gene therapy methods have limited control over where a therapeutic viral vector inserts into the host genome. Vector integration can activate local gene expression, which can cause cancer if the vector inserts near an oncogene. Viral integration hot-spots or 'common insertion sites' (CIS) are scrutinized to evaluate and predict patient safety. CIS are typically defined by a minimum density of insertions (such as 2-4 within a 30-100 kb region), which unfortunately depends on the total number of observed VIS. This is problematic for comparing hot-spot distributions across data sets and patients, where the VIS numbers may vary.</P><P><B>Results</B></P><P>We develop two new methods for defining hot-spots that are relatively independent of data set size. Both methods operate on distributions of VIS across consecutive 1 Mb 'bins' of the genome. The first method 'z-threshold' tallies the number of VIS per bin, converts these counts to z-scores, and applies a threshold to define high density bins. The second method 'BCP' applies a Bayesian change-point model to the z-scores to define hot-spots. The novel hot-spot methods are compared with a conventional CIS method using simulated data sets and data sets from five published human studies, including the X-linked ALD (adrenoleukodystrophy), CGD (chronic granulomatous disease) and SCID-X1 (X-linked severe combined immunodeficiency) trials. The BCP analysis of the human X-linked ALD data for two patients separately (774 and 1627 VIS) and combined (2401 VIS) resulted in 5-6 hot-spots covering 0.17-0.251% of the genome and containing 5.56-7.74% of the total VIS. In comparison, the CIS analysis resulted in 12-110 hot-spots covering 0.018-0.246% of the genome and containing 5.81-22.7% of the VIS, corresponding to a greater number of hot-spots as the data set size increased. Our hot-spot methods enable one to evaluate the extent of VIS clustering, and formally compare data sets in terms of hot-spot overlap. Finally, we show that the BCP hot-spots from the repopulating samples coincide with greater gene and CpG island density than the median genome density.</P><P><B>Conclusions</B></P><P>The z-threshold and BCP methods are useful for comparing hot-spot patterns across data sets of disparate sizes. The methodology and software provided here should enable one to study hot-spot conservation across a variety of VIS data sets and evaluate vector safety for gene therapy trials.</P>
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Dynamics of HSPC Repopulation in Nonhuman Primates Revealed by a Decade-Long Clonal-Tracking Study
Kim, S.,Kim, N.,Presson, Angela P.,Metzger, Mark E.,Bonifacino, Aylin C.,Sehl, M.,Chow, Samson A.,Crooks, Gay M.,Dunbar, Cynthia E.,An, D.,Donahue, Robert E.,Chen, Irvin S.Y. Cell Press 2014 Cell stem cell Vol.14 No.4
In mice, clonal tracking of hematopoietic stem cells (HSCs) has revealed variations in repopulation characteristics. However, it is unclear whether similar properties apply in primates. Here, we examined this issue through tracking of thousands of hematopoietic stem and progenitor cells (HSPCs) in rhesus macaques for up to 12 years. Approximately half of the clones analyzed contributed to long-term repopulation (over 3-10 years), arising in sequential groups and likely representing self-renewing HSCs. The remainder contributed primarily for the first year. The long-lived clones could be further subdivided into functional groups contributing primarily to myeloid, lymphoid, or both myeloid and lymphoid lineages. Over time, the 4%-10% of clones with robust dual lineage contribution predominated in repopulation. HSPCs expressing a CCR5 shRNA transgene behaved similarly to controls. Our study therefore documents HSPC behavior in a clinically relevant model over a long time frame and provides a substantial system-level data set that is a reference point for future work.
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