Malarial parasite P. falciparum, an apicomplexan protozoan has a 23.3Â MB nuclear genome and encodes ~Â 5600 transcripts. The genetic diversity of the parasite within and across geographical zones is a challenge to gene expression studies which are essential for understanding of disease process, outcome and developing markers for diagnostics and prognostics. Here, we describe the strategy involved in designing a custom P. falciparum 15K array using the Agilent platform and Genotypic's Right Design methodology to study the transcriptome of Indian field isolates for which genome sequence information is limited. The array contains probes representing genome sequences of two distinct geographical isolates (i.e. 3D7 and HB3) and sub-telomeric var gene sequences of a third isolate (IT4) known to adhere in culture condition. Probes in the array have been selected based on their efficiency to detect transcripts through a 244K array experimentation. Array performance for the 15K array, was evaluated and validated using RNA materials from P. falciparum clinical isolates. A large percentage (91%) of the represented transcripts was detected from Indian P. falciparum patient isolates. Replicated probes and multiple probes representing the same gene showed perfect correlation between them suggesting good probe performance. Additional transcripts could be detected due to inclusion of unique probes representing HB3 strain transcripts. Variant surface antigen (VSA) transcripts were detected by optimized probes representing the VSA genes of three geographically distinct strains. The 15K cross strain P. falciparum array has shown good efficiency in detecting transcripts from P. falciparum parasite samples isolated from patients. The low parasite loads and presence of host RNA makes arrays a preferred platform for gene expression studies over RNA-Seq.
A cross strain Plasmodium falciparum microarray optimized for the transcriptome analysis of Plasmodium falciparum patient derived isolates.
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作者:Subudhi Amit Kumar, Boopathi P A, Middha Sheetal, Acharya Jyoti, Rao Sudha Narayana, Mugasimangalam Raja C, Sirohi Paramendra, Kochar Sanjay K, Kochar Dhanpat K, Das Ashis
| 期刊: | Genomics Data | 影响因子: | 0.000 |
| 时间: | 2016 | 起止号: | 2016 Jul 20; 9:118-25 |
| doi: | 10.1016/j.gdata.2016.07.006 | ||
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