Genetic Modification or Transgenic Technologies

Genetic modification (GM) involves alteration of an organism's genetic material (DNA or RNA) involving:

1. Transferring genes between organisms.

2. Moving, deleting, modifying or multiplying genes within an organism.

3. Modifying existing genes.

4. The incorporation of newly constructed genes into a new organism.

Example 18: GM techniques have been used to develop male sterility for use in hybrid breeding, cereals enriched in commercially valuable oils, proteins and starches as well as resistance to herbicides such as glyphosate (Roundup®) and phosphinothricin (Liberty®, Basta®).


Transformation of cereal crops such as rice and barley is possible because of hypervirulent Agrobacterium strains and technical breakthroughs in the use of cell and plant selectable markers. It was previously only successful in dicotyledonous plants.

Example 19: The first, but unsuccessful, transformation of a major crop species was by direct DNA injection into the shoot apical meristem of maize seedlings (Coe and Sarkar, 1966). In 1984, the first transgenic tobacco plants (Horsch et al., 1984) used a natural gene vector system, the Ti plasmid, of the crown gall-causing bacterium Agrobacterium tumefaciens (Zambryski, 1988).

Another transformation method, 'biolistics', involves firing high-velocity DNA-coated microprojectiles into plant cells and tissues. Its disadvantages include higher copy numbers of unstable transgenes and more DNA rearrangements.

Both methods have generated commercially grown transgenic plants.

Plant viral vectors can also be used for transformation or naked DNA can be directly taken into protoplasts by treating with polyethylene glycol, divalent cations (either Ca2+ or Mg2+) or electroporation (Holzberg et al., 2002).

The components of transformation vectors

Transgenes typically contain a gene sequence encoding a marker used for transgenic plant cell/tissue selection, a gene of interest and promoters that drive expression in tissues or cell layers of interest.

selectable markers Antibiotic or herbicide-resistant selectable marker genes are used to identify successful vector incorporation into transformed cells. Most antibiotic and herbicide selectable markers inactivate metabolites (Table 12.2).

trait genes Trait genes can include novel gene(s) sequences, which may synthesize a protein(s) responsible for metabolite synthesis or inactivation.

The trait gene(s) of interest may be from an unrelated species or may be a natural or synthetic allelic variant of an endogenous gene.

Table 12.2. Common selectable markers.

Example 21: Superior naturally occurring HMW-GS alleles (Altpeter et al, 1996) and synthetic hybrids (Blechl and Anderson, 1996) have been used to generate transgenic wheat lines, some of which also possess superior bread-making qualities (Alvarez et al., 2001; Barro et al., 2003; Blechl et al., 2007).

Portions of a trait gene can also be used to induce post-transcriptional gene silencing (PTGS). PTGS or RNA interference (RNAi) is the sequence specific degradation of RNA. Both microRNAs (miRNAs) and small interfering RNAs (siRNAs) are central to RNAi and have been used to create transgenes that, upon expression, generate double-stranded RNA molecules, which are cleaved by the enzyme Dicer and yield short fragments of about 20 nucleotides. The guide strand can then base pair with the complementary mRNA sequence of the trait gene. Trait gene mRNAs are then cleaved by the RNAi-induced silencing complex (RISC) rendering them inactive. Hairpin-induced RNAi silencing has been demonstrated as an efficient tool for functional gene characterization in several crop species (for example: Wang et al., 2000; Travella et al., 2006).

For the purpose of PTGS, transgenes can be constructed to express antisense RNAs (aRNA), hairpin RNAs (hpRNA) and artificial precursor miRNAs (amiRNA).

promoters Promoters are regions of DNA that facilitate transcription of selectable marker and trait genes. The most commonly used promoters in crop transformation include Ubiquitin (Ubi), Actin (Actl) or a dual enhanced cauliflower mosaic virus (CaMV) 35S (35Sx2) promoter.

Example 20: Transgenic Golden Rice™ is an example of modified metabolite biosynthesis. p-carotene (a pro-vitamin A carotenoid) is increased using a phytoene synthase from either daffodil (Narcissus pseudonarcissus) or maize and a carotene desaturase (CrtI) from the soil bacterium Erwinia uredovora (Paine et al., 2005).

Marker Resistance conferred

Neomycin phosphotransferase II (nptII) Kanamycin

Hygromycin phosphotransferase (hpt) Hygromycin

5-Enolpyruvylshikimate-3-phosphate (EPSP) synthase Glyphosate

Phosphinothricin acetyltransferase (pat, bar) Phosphinothricin

Expressed sequence tag (EST) and micro-array technologies are used to identify promoters that meet specific expression requirements for a particular trait gene. Trait gene-dependent expression requirements are particularly important to minimize negative effects associated with trait gene mis-expression.

Novel transactivation technologies such as promoter tagging (Johnson et al., 2007) can be used for promoter identification. Chemically regulated promoter systems can also be used to generate transgenics with tightly regulated gene expression (Moore et al., 2006).


Cisgenes derived from the crop plant itself or from a crossable species (Rommens et al., 2004; Schouten et al., 2006; Conner et al., 2007) can counter public concerns about incorporating prokaryote DNA sequences into crop species. Cisgenic plants are similar to those bred by traditional introgression and translocation breeding; they are faster to generate than with traditional breeding and can eliminate problems associated with linkage drag. In time, cisgenics may be accepted as an alternative to using prokary-ote, vector-based systems.

Targeted gene discovery map-based cloning Dense molecular genetic maps for most crop species (Varshney et al., 2004) have come from advances in molecular genetics and automation of the techniques used to identify DNA sequence variation. The most common assays are for SSRs or microsatellites and SNPs. They are abundant and amenable to high-throughput genotyping.

Example 22: Diversity Array Technology (DArT) gained prominence because it could profile genome-wide genetic variation without previous sequence knowledge (Kilian et al., 2005).

Genetic maps are used for assigning traits of interest to genomic loci and for map-based cloning (MBC) where an interesting mutant phenotype is identified and then genetic fine mapping occurs using a large number of recombinant inbred lines (RILs), doubled haploid, or F2 progeny plants. The genetic map and marker-trait associations are then used for chromosome walking and landing, with the help of large-insert DNA libraries or physical maps to isolate the gene (Azhaguvel et al., 2006).

Example 23: MBC is suited to the identification of QTLs and has been used to identify genes such as HKT, Sub1A, CBF, ALMT1 and Bot1, which confer tolerance to salt, submergence, freezing, aluminium and boron toxicity, respectively (reviewed by Collins et al., 2008).

Gene discovery

'Gene discovery' is the identification of gene sequences, and variants, that contribute to a trait or phenotype. It requires an understanding of the complex metabolic and signal transduction pathways involved in a trait's expression. It involves the dissection, and then manipulation, of fundamental plant processes to improve crop plants. It can be either 'targeted', starting with defining a trait of interest and then identifying the controlling gene sequences, or 'non-targeted', which is quite random.

association mapping - linkage disequilibrium Association mapping is based on linkage disequilibrium (LD): the non-random associ ation between markers, genes or QTLs in a population. It takes advantage of events that created genetic linkage in the relatively distant past.

Example 24: Large structured breeding populations have been a valuable resource for association mapping and have resulted in the identification of markers for higher yield and yield stability in barley (Kraakman et al., 2004), as well as milling quality and kernal morphology in wheat (Breseghello and Sorrells, 2006).

For 'out-breeding' species where LD extends over very short distances, association mapping is used to identify markers tightly linked to agronomic traits. This can reduce the time required for MBC of gene sequences underlying the trait. This approach is not suitable where genetic control of the trait is complex or where there are confounding factors that may affect trait expression. Maturity and plant height can strongly affect drought responses and association mapping for drought tolerance using a diverse germplasm collection is likely to only reveal maturity and height loci.

comparative genomics Comparison of genetic maps indicates very good conservation in the order (colinearity) of molecular markers and of QTLs for important agronomic traits along the chromosomes within different families of plants. Comparative genomics has provided insight into plant genome evolution: some of the major evolutionary mechanisms during the past 50-70 million years have been unravelled (Salse and Feuillet, 2007).

Example 25: Recently, genetic and physical maps have been integrated for plant families with important domesticated crops, such as the Poaceae (Devos, 2005), Fabaceae (Zhu et al., 2005), Roseaceae (Dirlewanger et al., 2004), Solanaceae (Mueller et al., 2005), Asteraceae (Chapman et al., 2007) and Brassicaceae (Schranz et al., 2007).

Evolutionary relationships have been established between rice, Brachypodium and members of the Triticeae. Isolation and sequencing of large genomic DNA fragments from different species has highlighted cross-species gene-order conservation at the sub-megabase level, that is micro-colinearity (for example, Chen et al., 1997). In leguminous species, gene order synteny has been established between the model species Medicago truncatula and Lotus japonicus and other members of the Papilionoideae, including soybean, broad bean, chickpea and clovers (Varshney et al., 2009). Despite no local micro-colinearity, good colinearity between grass and legume genomes means that the number of molecular markers in a targeted region using restriction fragment length polymorphism (RFLP) and EST probes may be increased without additional molecular markers being needed from the species of interest (Feuillet and Keller, 2002).

Example 26: Colinearity has been used to identify gene sequences responsible for disease resistance (e.g. Lrk, Rph7), development (e.g. Vrn1, Ppd-H1) and quality (e.g. Ha, Glutenin) (Salse and Feuillet, 2007). In legumes, comparative mapping has helped to identify nodulation and nitrogen fixation genes (Zhu et al., 2005).

allele mining Allele mining is often used to identify superior haplotypes of gene sequence variants from wild or mutant populations.

TILLING (Targeted Induced Local Lesions IN Genomes) is a common way to discover SNPs in induced mutant populations. It is a high-throughput reverse genetic strategy that is low in cost.

Example 27: TILLING populations have been created for major crop species including maize, rice, soybean, barley and wheat (Barkley and Wang, 2008). Screening for natural variation using this methodology is termed 'ecoTILLING'. The power of TILLING for hexaploid bread wheat improvement was demonstrated by the identification of 196 new alleles in the A and B genome waxy genes (granule bound starch synthase genes I, GBSS1) from a population of 1152 ethylmethane sulfonate (EMS) induced mutant plants (Slade et al., 2005). Extending this to tetraploid pasta wheat identified 50 new GBSS1 alleles from a population of only 768 individuals (Slade et al., 2005).

Non-targeted gene discovery est sequencing Gene sequences and variations can be directly obtained by randomly sequencing complementary DNA (cDNA) clone libraries yielding ESTs, a powerful tool in the analysis of transcriptomes.

Example 28: Analysis of 580,000 wheat and 370,000 barley ESTs estimates the number of unique genes to be about 122,000 (~40,000 per homologous genome) and 50,000 for bread wheat and barley, respectively (Stein, 2007). This is comparable to the number of genes (~40,000) predicted from the complete rice genome sequence (IRGSP, 2009) and appears to be similar across many plant species.

Next Generation Sequencing Technologies (i.e. Solexa and 454/FLX) make it possible to mine transcriptomes of crop species for which there is little genomics information. This is rapidly contributing to the wealth of EST resources (Varshney et al., 2009) which are a source of sequence-level genetic variation and extensively used for functional molecular marker development. EST-derived SSR and SNP markers are now routinely used in trait mapping and MAS. ESTs have also been used to develop cDNA microarrays used for transcript profiling (Close et al., 2004).


Rice has the smallest cereal genome and was the first to be fully sequenced (Vij et al., 2006). The sequence has been used to localize genes in other cereals by comparative mapping (Bennetzen and Ma, 2003).

The Arabidopsis genome sequence and both the M. truncatula and the L. japonicus genome sequences provided similar resources for the Brassicaceae and Papilionoideae, respectively (Schranz et al., 2007; Young and Udvardi, 2009).

Growing evidence about sequence and gene content variation between, and even within, species means that species-specific genomic resources are needed (Wobus and Sreenivasulu, 2006). ESTs partially fill this gap.

Ordered physical maps are also being generated from large insert-libraries (bacterial artificial chromosomes or BACs) for many of these crops. Genetically anchored physical maps are an important resource for MBC strategies, as they will significantly reduce the time required for candidate gene isolation. The National Center for Biotechnology

Information (NCBI) database lists genome sequencing and analysis projects underway for 128 species (NCBI, 2009) (Table 12.3). Sequencing for grapevine and soybean has also now been completed.

Current GM traits

GM crops are currently grown on 125 million ha in 25 different countries (ISAAA, 2009) and are largely based around herbicide and pest resistance lines (Fig. 12.3).

Current pest-resistant GM crops offer significant value to producers because a large reduction in the use of pesticides has occurred and this has lowered production costs and lessened environmental impact (Knox et al., 2006). Reduced environmental impact results from reduced energy consumption required for pesticide manufacturing, transport and on-farm application, and fewer chemicals enter the environment. The wide use of herbicide tolerant crops in many parts of the world has led to a major expansion of minimum tillage production systems and similarly, results in reduced on-farm fuel consumption.

Many traits related to accommodating climate change are still undergoing field evaluation but will appear in commercial crops over the next few years. Among the most advanced are several crops with improved nitrogen-use efficiency such as those developed by Arcadia Biosciences (Arcadia, 2009) and drought tolerance where there have been extensive glasshouse and field trials (Bahieldin et al., 2005; Wang et al., 2005; Hu et al., 2006; Nelson et al., 2007; Rivero et al., 2007).

Table 12.3. Examples of sequencing projects.




(MGSC, 2009)


(PGSC, 2009)


(ASGPB, 2009)


(IWGSC, 2009)


(IBSC, 2009)


(SGN, 2009)


(DOE-JGI, 2009)

ia 15

ia 15

Stacked HT & PR


Herbicide tolerance (HT)

Pest resistance (PR)

Stacked HT & PR


Product quality


Fig. 12.3. United States Department of Agriculture (USDA)-approved commercialized GM varieties as of May 2007, by trait (based on data from Oborne, 2009).

On a precautionary note, glasshouse performance and even some field trial results may not necessarily provide a reliable assessment of the value of these genetic modifications to commercial performance in the field over multiple seasons and environments (Passioura, 2006).

Capacity building

Significant international capacity building has occurred in agricultural biotechnology since it was identified that biotechnology could improve yield in food crops. This capacity has comprised not only infrastructure and research capability but has included a steady building of 'intangible' assets such as intellectual property (IP) portfolios and germplasm. Capacity has been built to develop genetically modified organisms (GMOs) and also to refine techniques used in conventional plant breeding.

In the private sector, the promise of large returns from agricultural biotechnology has led to several large multinational seed companies investing in significant infrastructure and research capacity (Table 12.4).

Example 29: Monsanto has demonstrated that there is a direct relationship between biotechnology research and development (R&D) spending and increases in gross profit (Monsanto, 2009b). By increasing R&D spending by 9% per year since 2001, Monsanto has increased its seed business gross profit by 24% a year (Monsanto, 2009b). Other multinational companies have made large investments in people, infrastructure and germplasm needed to deliver biotechnology. Germplasm acquired during DuPont's amalgamation with Pioneer is valued at US$975 million (SEC, 2008) among its other intangible assets. In its last annual report, as a result of its ongoing investment in agricultural biotechnology, DuPont expected that in 2009 its agriculture and nutrition division would introduce 26 new soybean varieties and 96 new maize hybrids (SEC, 2008).

In the public sector, significant agricultural biotechnology capacity has been developed in many countries within universities, government agricultural departments, special research centres and so on. Various centres and programmes have also been established to assist the development of technologies for the developing world.

Table 12.4. Estimated 2006 R&D expenditures of relevance to biotechnology by leading companies in each application (based on data from Oborne, 2009).

Company (country)

Biotech R&D expenditure (US$ millions)

Syngenta (Switzerland)


Monsanto (USA)


Bayer CropScience (Germany)a


DuPont Pioneer (USA)


BASF (Germany)


LimaGrain (France)


KWS SAAT (Germany)


Dow Agrosciences (USA)




a Bayer figures are for 2007.

a Bayer figures are for 2007.

Example 30: The Consultative Group on International Agricultural Research (CGIAR) system alone provides over US$500 million to 8096 staff across 15 research centres and four major research programmes - the 'Challenge Programs' (CGIAR, 2007). Of this, US$19 million was invested in hard infrastructure, the balance on intellectual capacity.

It is difficult to estimate the actual amount invested by the public sector in agricultural biotechnology but most developed countries support large research efforts. In the 2008 round of funding under the National Science Foundation's Plant Genome Program, US$60 million was awarded (NSF, 2008). Similar programmes exist in most developed countries and total investment from the public sector in plant biotechnology research will be in the hundreds of millions of dollars.

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