Gene Expression Profiling via Multigene Concatemers Kai Jin1,2,3., Xiaoli Zheng1,2,3., Yuxian Xia1,2,3* 1 Genetic Engineering Research Center, School of Bioengineering, Chongqing University, Chongqing, People’s Republic of China, 2 Chongqing Engineering Research Center for Fungal Insecticide, Chongqing, People’s Republic of China, 3 Key Laboratory of Gene Function and Regulation Technologies under Chongqing Municipal Education Commission, Chongqing, People’s Republic of China Abstract We established a novel method, Gene Expression Profiling via Multigene Concatemers (MgC-GEP), to study multigene expression patterns simultaneously. This method consists of the following steps: (1) cDNA was obtained using specific reverse primers containing an adaptor. (2) During the initial 1–3 cycles of polymerase chain reaction (PCR), the products containing universal adaptors with digestion sites at both termini were amplified using specific forward and reverse primers containing the adaptors. (3) In the subsequent 4–28 cycles, the universal adaptors were used as primers to yield products. (4) The products were digested and ligated to produce concatemers. (5) The concatemers were cloned into the vector and sequenced. Then, the occurrence of each gene tag was determined. To validate MgC-GEP, we analyzed 20 genes in Saccharomyces cerevisiae induced by weak acid using MgC-GEP combined with real-time reverse transcription (RT)-PCR. Compared with the results of real-time RT-PCR and the previous reports of microarray analysis, MgC-GEP can precisely determine the transcript levels of multigenes simultaneously. Importantly, MgC-GEP is a cost effective strategy that can be widely used in most laboratories without specific equipment. MgC-GEP is a potentially powerful tool for multigene expression profiling, particularly for moderate-throughput analysis. Citation: Jin K, Zheng X, Xia Y (2011) Gene Expression Profiling via Multigene Concatemers. PLoS ONE 6(1): e15711. doi:10.1371/journal.pone.0015711 Editor: Gisela Nogales-Gadea, University Hospital Vall d’Hebron, Spain Received July 28, 2010; Accepted November 23, 2010; Published January 18, 2011 Copyright: 2011 Jin et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Funding: This work was supported by grants from the Project Foundation of Chongqing Municipal Education Committee (KJ08A04) and the Natural Science Foundation of China (30971913). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Competing Interests: The authors have declared that no competing interests exist. * E-mail: yuxianxia@cqu.edu.cn . These authors contributed equally to this work. Introduction With the completion of many genome projects [1,2,3,4,5,6,7,8], research on addressing the roles of multiple genes in orchestrating complex cellular functions has attracted considerable attention. This requires the use of techniques that allow high-throughput analysis of such target genes. The analysis of multigene expression profiling is helpful to elucidate a series of medical and biological research questions including the dissection of basic biological processes [9,10], the exploration of new drug targets [11,12,13], and the diagnosis of disease [14,15,16,17]. Thus, the research of multigene expression profiling has gradually become a key point in searching specific genes or proteins. Several methods including DNA microarray [18], massive parallel signature sequencing (MPSS) [19], serial analysis of gene expression (SAGE) [20], SuperSAGE[21], RNA-seq[22] and real-time multiplex reverse transcription (RT)-PCR [23] have been developed and widely applied in high-throughput multi- gene expression profiling [24,25,26,27,28,29,30]. However, sometimes analyzing throughput of the gene transcription profiles is moderate in number. For example, one signal transduction pathway is often composed of between 10 and 50 genes [31,32]. A specific phenotype is often related to many gene families, which share substantial conservation at the protein and nucleotide level. Furthermore, gene expression in various samples often need be determined according to time course or different environmental stimuli. In these cases, using the methods mentioned above is not a sensible choice. For DNA microarray, though it could obtain comprehensive global expression survey, the information about some specific genes is not easily gleaned from these vast amounts of data produced [33,34]. Moreover, some homologous genes can cross-hybridize, which makes it difficult to determine specific genes of highly homologous gene family members. For MPSS, the new tool available for conducting multigene expression profiling [29], the specific equipments necessary restrict its application widely. For SAGE and SuperSAGE, those transcripts without the NlaIII or EcoP15I site may be missed which accounts for a few percent of the total transcripts in a given RNA sample [21,35]. For RNA-seq, several informatics challenges should be considered. The efficient methods to store, retrieve and process large amounts of data must be improved to reduce errors in image analysis and base- calling and remove low-quality reads [22]. For real-time multiplex RT-PCR, it is not easy to optimize the identical reaction parameters of multiplex target amplification to obtain accurate quantification results [36,37]. Recently, the Genome- LabTM GeXP Genetic Analysis System was developed by Beckman & Coulter (http://www.beckman.com/products/ instrument/geneticanalysis/gexp_inst_dcr.asp) combined RT- PCR and capillary electrophoresis (CE). This method is more suitable for characterizing the profiling of a moderate number of genes (10–50 genes) [38,39]. However, specific and expensive equipments are required, which also restricted its wide application. Therefore, it is necessary to establish an economical and simple method to analyze the transcript levels of dozens of genes in a particular pathway or related to a specific phenotype. PLoS ONE | www.plosone.org 1 January 2011 | Volume 6 | Issue 1 | e15711