**6. Transcriptome analysis for GMO validation**

Unbiased detection of unintended effects of transgene in a genetically modified organism requires comparison of transcriptome [41], proteome [38] and metabolome [40] of the modified organism with the isogenic unmodified organism. The thorough profiling helps in the identification of genes, proteins, and metabolites modified in the newly developed organism. By digging the gene networks, protein functions, and metabolic processes of the altered biomolecule, scientists can depict the effects of GMO on the environment, health, and nutrition of the consumer. The absence of unintended aberrations in the biomolecules declares the new variety as safe, whereas the presence of unintended aberrations does not declare it to be unsafe but indicates that the variety requires more targeted validation before commercialization [7].

Transcriptome analysis stands out of the other omic-based approaches due to its comparative simplicity and cost efficiency. Latest technologies of gene expression microarray and NGS are commonly used for global transcriptional profiling of GMO and wild-type ecotype for transcriptional equivalence. Gene expression microarray involves the use of chips containing probes which represent the complete genome of an organism under study. Hybridization of these chips with fluorescently labeled cDNA can identify the genes which are differentially expressed between GMO and wild type. NGS technologies involve sequencing and quantification of nucleotides at the same time. RNA-seq is the type of NGS which specifically deals with the transcriptional studies. Gene expression microarray and RNA-seq have proved themselves equally for the detection of intended and unintended effects. However, both approaches have some advantages and disadvantages. Microarray experiments are comparatively cheaper and easier than RNA-seq. But the chips are commercially available only for a limited number of organisms, and custom printed chips require the genome sequence information of the specific organism. The full power of this technology can only be utilized for sequenced genomes. While RNA-seq is the only technology which can sequence as well as quantify the mRNA libraries of unsequenced genomes. Moreover, RNA-seq provides us the absolute quantification as compared to microarray which give comparative quantification. **Table 1** shows some examples where scientists have utilized these transcriptomic approaches for GMO validation.

cassette and the absence of vector backbone. Safety of the GMO is tested on a very limited scale only when the GMO is ready to be commercialized. The main focus of the biosafety studies is limited to the assessment of the effect of the GMO on the consumer health and safety. The phenotypic and agronomic traits of the newly produced plant and a genetically similar organism are compared [33], but thorough profiling of the genetically modified organ-

218 Applications of RNA-Seq and Omics Strategies - From Microorganisms to Human Health

Newly produced plants by genetic engineering and other genetic methods should not only be assessed by target-based approaches as these assessments are biased and cannot recognize the unintended risks thoroughly [34]. Genome-wide approaches like transcriptome analysis, proteome analysis, or metabolome analysis have the advantage of being unbiased and robust [35–37] and provide a lot of information about the new plant variety. Scientists compare the protein profiles of genetically modified organisms with their wild types to identify the aberrant proteins. Proteome of a commercial variety of maize was compared with the isogenic transgenic line which was resistant to European corn borer by expressing Cry1Ab gene [38]. The results spotted unwanted/unintended protein expression in the transgenic lines and suggested for the untargeted evaluation of the new transgenic organisms. Other studies using proteomic or transcriptomic approaches to compare the GMO with the wild type found only intended alterations [7], while no unintended changes were

Unintended changes arising as a result of pleiotropic effects of genetic modification are not always harmful. A group of scientists has performed transcriptome analysis in GMO lines developed for enhanced insect attraction in *Arabidopsis* and compared it with naturally occurring non-GMO lines to identify transcriptional distance between the two groups [39]. They identified that the pleiotropic effects of gene insertion are equivalent to the gene expression changes naturally occurring in *Arabidopsis* indicating that the specific modified lines of *Arabidopsis* were equally safe as naturally occurring lines. Thus unbiased and untargeted risk assessment of GMOs through newly developed "omic" techniques is necessary [40] before its

Unbiased detection of unintended effects of transgene in a genetically modified organism requires comparison of transcriptome [41], proteome [38] and metabolome [40] of the modified organism with the isogenic unmodified organism. The thorough profiling helps in the identification of genes, proteins, and metabolites modified in the newly developed organism. By digging the gene networks, protein functions, and metabolic processes of the altered biomolecule, scientists can depict the effects of GMO on the environment, health, and nutrition of the consumer. The absence of unintended aberrations in the biomolecules declares the new variety as safe, whereas the presence of unintended aberrations does not declare it to be unsafe but indicates that the variety requires more targeted validation before commercialization [7].

release in the environment or trials for human and animal use.

**6. Transcriptome analysis for GMO validation**

ism is lacking.

found.

Gene expression microarray and RNA-seq methods not only identify the unintended effects of genetic engineering but are also useful in elucidating the mechanism of action of a transgene. Pathway analysis and gene ontology analysis of modified genes lead to the evaluation of molecular basis of phenotypic changes in the newly produced organisms [48]. Transgenic variety of papaya (*Carica papaya* L.) fruit which was resistant to papaya ring spot virus (PRSV) was evaluated against its progenitor variety through RNA-seq analysis. The transcriptional profiles revealed the transcription factors, signaling pathways which were responsible for the stress tolerance and pathogen resistance [43].

Biotic and abiotic stress tolerance is a complex mechanism involving many gene networks and pathways causing changes in the morphology and physiology. Stress-related transcription factors which can bind to the promoters of multiple genes are largely used as transgenes to produce stress-tolerant GMOs. Genetically engineered crops for tolerance against stresses are difficult to get approval for commercialization due to increased risk of pleiotropic effects. Global transcriptome analysis can identify all the pathways affected by any kind of genetic modification and targets for risk assessment.

Transcriptomic approaches have an added benefit of detection of gene silencing in the GMOs produced by gene silencing technology. RNAi-based technologies where double-stranded RNA targeting a specific gene is introduced in an organism. This RNA after being processed in the recipient organism is converted into smaller piece of nearly 21–22 nucleotides. These RNAs reach their targets and inhibit the translation of specific messenger RNA into respective proteins, thus functionally silencing the genes post-transcriptionally. The increasing popularity of this technology is due to its ability to not affect the genome of the GMO [49].


**Author details**

\*, Samina Yousaf<sup>2</sup>

of America 2004;**101**:1892-1897

varians. BMC Genomics 2014;**15**:376

JAD-2012-120454

\*Address all correspondence to: uzma67@hotmail.com

, Tanzeela Rehman1

1 School of Biological Sciences, University of the Punjab, Lahore, Pakistan

3 Institute of Agriculture Sciences, University of the Punjab, Lahore, Pakistan

[1] Scacheri PC, Rozenblatt-Rosen O, Caplen NJ, Wolfsberg TG, Umayam L, Lee JC, Hughes CM, Shanmuqam KS, Bhattacharjee A, Meyerson M, Collins FS. Short interfering RNAs can induce unexpected and divergent changes in the levels of untargeted proteins in mammalian cells. Proceedings of the National Academy of Sciences of the United States

[2] Enot DP, Beckmann M, Draper J. Detecting a difference – assessing generalisability when modelling metabolome fingerprint data in longer term studies of genetically modified

[3] Riesgo A, Peterson K, Richardson C, Heist T, Strehlow B, McCauley M, Cotman C, Hill M, Hill A. Transcriptomic analysis of differential host gene expression upon uptake of symbionts: A case study with Symbiodinium and the major bioeroding sponge Cliona

[4] Wang S, Qaisar U, Yin X, Grammas P. Gene expression profiling in Alzheimer's disease brain microvessels. Journal of Alzheimer's Disease 2012;**30**:1-13. DOI: 10.3233/

[5] Qaisar U, Irfan M, Meqbool A, Zahoor M, Khan MY, Rashid B, Riazuddin S, Husnain T. Identification, sequencing and characterization of a stress induced homologue of fructose bisphosphate aldolase from cotton. Canadian Journal of Plant Science 2010;**90**(1):41-48 [6] Maqbool A, Zahur M, Irfan M, Qaisar U, Rashid B, Husnain T, Riazuddin S. Identification, characterization and expression of drought related alpha-crystalline heat shock protein

[7] Jiang Q, Niu F, Sun X, Hu Z, Li X, Ma Y, Zhang H. RNA-seq analysis of unintended effects in transgenic wheat overexpressing the transcription factor GmDREB1, The Crop

[8] Xu Z, Li J, Guo X, Jin S, Zhang X. Metabolic engineering of cottonseed oil biosynthesis pathway via RNA interference. Scientific Reports 2016;**6**:33342. DOI: 10.1038/srep33342

plants. Metabolomics. 2007;**3**(3):335-347. DOI: 10.1007/s11306-007-0064-4

gene (GHSP26) from Desi cotton. Crop Science 2007;**47**:2437-2444

Journal 2016;**5**:207-218. DOI: 10.1016/j.cj.2016.12.001

2 Botany Department, University of the Punjab, Lahore, Pakistan

, Anila Zainab<sup>3</sup>

and Asima Tayyeb<sup>1</sup>

Transcriptome Analysis and Genetic Engineering http://dx.doi.org/10.5772/intechopen.69372 221

Uzma Qaisar<sup>1</sup>

**References**

**Table 1.** Evaluation of GMOs by transcriptome analysis.
