The Biotechnology Roadmap for Sugarcane ... - Springer Link

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Carlos T. Hotta; Carolina G. Lembke; Douglas S. Domingues; Edgar A. Ochoa; Guilherme M. Q. Cruz; Danila M. Melotto-Passarin; Thiago G. Marconi; Melissa O.

Tropical Plant Biol. (2010) 3:75–87 DOI 10.1007/s12042-010-9050-5

The Biotechnology Roadmap for Sugarcane Improvement Carlos T. Hotta & Carolina G. Lembke & Douglas S. Domingues & Edgar A. Ochoa & Guilherme M. Q. Cruz & Danila M. Melotto-Passarin & Thiago G. Marconi & Melissa O. Santos & Marcelo Mollinari & Gabriel R. A. Margarido & Augusto César Crivellari & Wanderley D. dos Santos & Amanda P. de Souza & Andrea A. Hoshino & Helaine Carrer & Anete P. Souza & Antônio A. F. Garcia & Marcos S. Buckeridge & Marcelo Menossi & Marie-Anne Van Sluys & Glaucia M. Souza

Received: 7 December 2009 / Accepted: 11 March 2010 / Published online: 8 April 2010 # Springer Science+Business Media, LLC 2010

Abstract Due to the strategic importance of sugarcane to Brazil, FAPESP, the main São Paulo state research funding agency, launched in 2008 the FAPESP Bioenergy Research Program (BIOEN, BIOEN aims to generate new knowledge and human resources for the improvement of the sugarcane and ethanol industry. As part of the BIOEN program, a Workshop on Sugarcane Improvement was held on March 18th and 19th 2009 in São Paulo, Brazil. The aim of the workshop was to explore present and future challenges for sugarcane improvement and its use as a sustainable bioenergy and biomaterial Communicated by: Ray Ming C. T. Hotta : C. G. Lembke : G. M. Souza (*) Departamento de Bioquímica, Instituto de Química, Universidade de São Paulo, Av. Prof. Lineu Prestes 748, CEP 05508-000, São Paulo, SP, Brazil e-mail: [email protected] D. S. Domingues : E. A. Ochoa : G. M. Q. Cruz : A. C. Crivellari : W. D. dos Santos : A. P. de Souza : M. S. Buckeridge : M.-A. Van Sluys Departamento de Botânica, Instituto de Biociências, Universidade de São Paulo, Rua do Matão, 277, CEP 05508-090, São Paulo, SP, Brazil D. M. Melotto-Passarin : H. Carrer Departamento de Ciências Biológicas, Escola Superior de Agricultura Luiz de Queiroz, Universidade de São Paulo, CEP 13418-900, Piracicaba, SP, Brazil T. G. Marconi : M. O. Santos : A. P. Souza Departamento de Biologia Vegetal, Instituto de Biologia, Universidade Estadual de Campinas, CEP 13083-970, Campinas, SP, Brazil

feedstock. The workshop was divided in four sections that represent important challenges for sugarcane improvement: a) gene discovery and sugarcane genomics, b) transgenics and controlled transgene expression, c) sugarcane physiology (photosynthesis, sucrose metabolism, and drought) and d) breeding and statistical genetics. This report summarizes the roadmap for the improvement of sugarcane. Keywords Sugarcane . Breeding . Transgenics . Genome . Physiology

T. G. Marconi : M. O. Santos : A. P. Souza Centro de Biologia Molecular e Engenharia Genética, Universidade Estadual de Campinas, CP 6010, CEP 13083-970, Campinas, SP, Brazil

M. Mollinari : G. R. A. Margarido : A. A. Hoshino : M. Menossi Departamento de Genética, Evolução e Bioagentes, Instituto de Biologia, Universidade Estadual de Campinas, CEP 13083-862, Campinas, SP, Brasil

A. A. F. Garcia Departamento de Genética, Escola Superior de Agricultura Luiz de Queiroz, Universidade de São Paulo, CEP 13400-970, Piracicaba, SP, Brasil


Abbreviations BIOEN FAPESP bioenergy research program EST Expressed sequence tag SUCEST The sugarcane EST project SAS Sugarcane assembled sequences BAC Bacterial artificial chromosome TE Transposable element NADP-ME NADP+−malic enzyme NAD-ME NAD+−malic enzyme PCK Phosphoenolpyruvate carboxykinase MAS Marker assisted selection SNP Single nucleotide polymorphism NGS Next generation sequencing QTL Quantitative trait loci

Gene Discovery and Sugarcane Genomics Gene discovery and genomics are essential tools for the future of sugarcane improvement. However, gaps in our knowledge and technical constrains prevent us to take full advantage of these tools. Polyploidy and aneuploidy are the major factors that make molecular characterization of Saccharum genomes difficult. The genome of sugarcane hybrid cultivars is generally a mosaic of the genomes of two to four Saccharum species. Hybrids are estimated to contain 100–120 chromosomes constituted with a total of 10 Gbp of DNA (D’Hont and Glaszmann 2001; D’Hont 2005). In the sugarcane cultivar R570, 80% of the chromosomes are inherited from Saccharum officinarum, 10% are inherited from S. spontaneum, and the remaining 10% are a combination of the two species (D’Hont et al. 1996). Ribosomal DNA cytogenetic mapping (D’Hont et al. 1998) suggests that S. officinarum has a basic chromosome number of x=10 and S. spontanenum has a basic number of x=8, with ploidy levels between 5 and 16 (Ha et al. 1999). Although there has been a considerable increase in knowledge about the sugarcane genome, there are many areas that are not yet adequately addressed. For example, sugarcane chromatin structure and methylation have not been well studied. It is already known from other plant species that there are rapid changes in chromatin modification and transcriptional regulation in synthetic allopolyploids (Chen and Ni 2006) but such studies have not been performed in sugarcane. Sequencing of sugarcane ESTs greatly contributed to the gene discovery process. Prior to June 1996, the public databases of DNA sequences had only 28 sequences from sugarcane compared to the 250,000 sugarcane sequences that are deposited to date (R.E. Casu, personal communication). EST collections have been developed by research

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groups in South Africa (Carson and Botha 2000), Australia (Casu et al. 2003) and United States (Ma et al. 2004). The largest program of sugarcane EST sequencing was done in Brazil under the SUCEST initiative (Vettore et al. 2003). SUCEST allows a global evaluation of sugarcane gene expression, as it covers 26 cDNA libraries from roots, plantlets, stems, leaves, flowers and seeds, as well as calli subjected to abiotic stresses and plantlets infected with endophytic nitrogen fixing bacteria. As a result, a total of 237,954 sugarcane ESTs were sequenced by the SUCEST group and assembled into 42,982 Sugarcane Assembled Sequences (SASs), which were estimated to represent over 30,000 unique genes—probably around 90% of sugarcane genes (Vettore et al. 2003). Sugarcane EST projects others than that of SUCEST focused on stem maturation and sucrose accumulation. Present knowledge about sugarcane gene regulation lacks sufficient genomic resources to understand transcriptomic variation among sugarcane genotypes or even among different copies of the same gene in the same individual. The greatest effort in sugarcane applied genomics is based on characterization of the sugarcane genes discovered in ESTs, especially from the expression patterns analyzed by macro- and microarrays and quantitative RT-PCR. However, these findings must be considered preliminary as most expression analyses were restricted to only one sugarcane cultivar for each treatment and it has been shown that gene expression is not the same among cultivars (Camargo et al. 2007, D.S. Domingues, unpublished data) nor even among progeny from the same segregating population (Casu et al. 2005; Papini-Terzi et al. 2009). In silico EST analyses were also used for selection of sugar transporters that are apparently modulated during stem maturation as validated by northern blot hybridization (Casu et al. 2003, 2004). In a macroarray analysis of randomly selected sugarcane cDNAs, it was possible to identify candidate genes whose expression was modulated by low temperature, methyl-jasmonate and ABA treatment of the plants (Nogueira et al. 2003; Rosa et al. 2005; Schlögl et al. 2008). Similarly, Rocha et al. (2007) used a signal transduction-oriented microarray analysis to better understand phytohormones and environmental challenges in sugarcane and found 179 differentially regulated genes. Gene regulation studies in sugarcane may also be useful to characterize promoter elements. For example, a cDNA microarray was used to profile transcript variation and abundance in six plant organs of sugarcane plants cultivated in the field. From 1,280 distinct putative genes analyzed, 217 (17%) presented differential expression in two biological samples of at least one of the tissues tested. The expression data presented could aid in future tissuespecific promoter characterization (Papini-Terzi et al. 2005).

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Gene characterization based on cDNA does not give information about regulatory sequences and it is known that major traits are manifested by non-transcribed sequences (Messing 2009). Furthermore, isolated sugarcane promoters have not generally retained the expected patterns of reporter transgene expression, suggesting that the sugarcane gene silencing machinery is highly efficient (Mudge et al. 2009). The highest levels of sugarcane transgene expression are usually achieved by using heterologous promoters (Wu and Birch 2007), indicating that a major effort is still necessary to uncover sugarcane promoters and to understand the biology of genetic manipulation of this crop. One way to speed the discovery of promoter sequences would be to sequence sugarcane euchromatin. However, to date, only two sugarcane BACs have been deposited in public databases (Jannoo et al. 2007). R570 is the only sugarcane cultivar that has a publicly available BAC library. The sugarcane genomics community needs to expand resources for sugarcane genome analyses by the development of additional BAC libraries from other cultivars and parental species (S. officinarum and S. spontaneum) as well as producing a detailed physical map from one reference cultivar, probably R570. Detailed analysis of the sugarcane BACs will provide crucial information for understanding sugarcane genome structure. Comparative grass genomics will be an important tool to evaluate the impact of intergenic spaces in the sugarcane genome. The sequencing of the sorghum genome (Paterson et al. 2009) disclosed that the proportion of repetitive sequences in that diploid genome is intermediate between the rice compact genome and the maize highly repetitive genome. Sugarcane EST data and macroarray analyses showed that transposable elements (TEs), the most abundant component of the repetitive sequences, are transcriptionally active, especially in callus tissue (Rossi et al. 2001; Araújo et al. 2005). Mutator-like elements, the most prevalent transposon class in the SUCEST database is highly distributed throughout the sugarcane genome, with two subgroups of bonna fide transposons and two subgroups of “domesticated transposases” (Saccaro et al. 2007). Although there are studies on characterization of Ac-like elements and LTR retrotransposons in sugarcane (M.A. Van Sluys, personal communication), the contribution of repetitive sequences to trait expression, especially transposable elements, remains to be investigated in sugarcane. Furthermore, since sugarcane cultivars have a recent hybrid and polyploid genome, information about methylation and epigenetic regulation of sugarcane gene expression will undoubtedly reveal uncharacterized mechanisms involved in gene transcription and silencing in this highly polyploid crop. Gene discovery in sugarcane will benefit from genome sequencing efforts over the next few years using high


throughput next generation sequencing. The attribution of function to genes will increase with the optimization of methods to generate transgenic sugarcane lines. Expression of sugarcane genes in different species may also be useful. Our understanding of the sugarcane transcription apparatus will also benefit from system biology approaches that will identify key elements in gene networks. The problem of the polyploidy and aneuploidy of sugarcane will be slowly solved using these techniques, which will lead to a greater understanding of how plants with complex genome work, and the generation of new molecular markers and candidate genes to improve sugarcane productivity.

Transgenics and Controlled Transgene Expression Classical plant breeding has been the main approach towards sugarcane improvement. However, the complexity of the sugarcane genome, its narrow genetic base (Roach 1989; Lima et al. 2002), and the time required for a new variety to reach commercialization (12–15 years) are constraints of this method. Although it is not clear whether sugarcane improvement through classical breeding is reaching a yield limit, there are specific desired traits, such as enzymes that would allow the use of sugarcane as a biofactory, that cannot be introduced into sugarcane by traditional breeding. Thus, many advances on sugarcane improvement will depend on sugarcane transformation. Nevertheless, stable transformation is far from being routine in this crop. Current constraints of high-thoughtput sugarcane transformation include the low transformation efficiency, transgene inactivation, somaclonal variation, and the long time required for regeneration and its commercial release. Transformation and tissue culture-induced somaclonal variation remains a significant bottleneck in exploiting gene technology for sugarcane improvement (Arencibia et al. 1999), and considerable refinements of current transformation systems are required to ensure clonal fidelity of transgenic cultivars. In addition, there is also the matter of a so-called “yield lag” which relates to the time required from the generation of a transgenic variety to its commercial release. This may be so long that sugarcane varieties generated through conventional breeding may have made sufficient yield gains to outcompete the transgenic ones based on an earlier generation variety (J. C. Bespalhok, personal communication). The yield lag problem is amplified by the fact that different regions of the world use different commercial varieties, each of which would need to be transformed and each may have different transformation and regeneration capacity. Success in the transformation of sugarcane (Bower and Birch 1992) followed the development of a microprojectile system. Initially, efforts were directed towards engineering


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economically important traits into commercially grown sugarcane varieties. It has been estimated that 44 field trials were done with sugarcane transgenic plants containing traits that include herbicide resistance, disease and insect resistance, drought tolerance, increased sucrose accumulation, and delayed flowering time (Table 1). There are a few reports about the development of transgenic

sugarcane with improved resistance to a number of microbial pathogens (Joyce et al. 1998a, b; Ingelbrecht et al. 1999; Zhang et al. 1999; Rangel et al. 2003; Gilbert et al. 2005; McQualter et al. 2004a), with resistance to pests such as sugarcane stem borer (Arencibia et al. 1999; Braga et al. 2003), and herbicide resistance (Gallo-Meagher and Irvine 1996; Enriquez-Obregon et al. 1998; Leibbrandt

Table 1 Examples of markers and traits engineered into sugarcane (updated from Lakshmanan et al. 2005) Traits


Transformation method


Reporter and selection systems Neomycin phosphotransferase β-Glucuronidase

npt-II uid-A

Bower and Birch 1992 Bower and Birch 1992 Arencibia et al. 1995 Arencibia et al. 1998 Arencibia et al. 1998 Elliott et al. 1998

Hygromycin phosphotransferase Green fluorescent protein

hpt gfp

Microprojectile Microprojectile Electroporation Agrobacterium Agrobacterium Agrobacterium

Phosphinothricin acetyl transferase Phosphinothricin acetyl transferase Herbicide resistance Bialaphos Phosphinothricine Phosphinothricine Glufosinate ammonium Disease resistance SCMV Sugarcane leaf scald SrMV Puccinia melanocephala Sugarcane yellow leaf virus Sugarcane yellow leaf virus Fiji leaf gall Pest resistance Sugarcane stem borer Sugarcane stem borer Sugarcane canegrub resistance

bar bar

Agrobacterium Agrobacterium

Elliott et al. 1998 Manickavasagam et al. 2004

bar bar bar pat

Microprojectile Agrobacterium Microprojectile Microprojectile

Gallo-Meagher and Irvine 1996 Enriquez-Obregon et al. 1998 Falco et al. 2000 Leibbrandt and Snyman 2003

SCMV-CP albD SrMV-CP Glucanase, chitanase and ap24 SCYLV-CP SCYLV-CP FDVS9 ORF 1

Microprojectile Microprojectile Microprojectile Agrobacterium Microprojectile Microprojectile Microprojectile

Joyce et al. 1998a, b Zhang et al. 1999 Ingelbrecht et al. 1999 Enriquez et al. 2000 Rangel et al. 2003 Gilbert et al. 2009 McQualter et al. 2004a

cry1A cry1Ab gna or pinII

Electroporation Microprojectile Microprojectile

Arencibia et al. 1999 Braga et al. 2003 Nutt et al. 1999

gna gna

Microprojectile Microprojectile

Legaspi and Mirkov 2000 Setamou et al. 2002

Antisense soluble acid invertase Soluble acid invertase lsdA ppo phaA, phaB and phaC hchl and cpl Kunitz and Bower-Birch manA SI P5CS

Microprojectile Microprojectile Agrobacterium Microprojectile Microprojectile Microprojectile Microprojectile Microprojectile Microprojectile Microprojectile

Ma et al. 2000 Botha et al. 2001 Enriquez et al. 2000 Vickers et al. 2005 Brumbley et al. 2003 McQualter et al. 2004b Falco and Silva-Filho 2003 Jain et al. 2007 Wu and Birch 2007 Molinari et al. 2007

Mexican rice borer Sugarcane stem borer and Mexican rice borer Metabolic engineering/alternative products Sucrose accumulation Fructo oligosaccharide Polyphenol oxidase Polyhydroxybytyrate ρ-Hydroxybenzoic acid Tripsin inhibitors Mannose Isomaltulose Proline production

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and Snyman 2003). Efforts are also under way to engineer sugarcane for increased sugar accumulation (Ma et al. 2000; Wu and Birch 2007; G. Souza, personnal communication), low color raw sugar (Roberts et al. 1996; Vickers et al. 2005) and high-value products (Groenewald et al. 1995; Brumbley et al. 2003; McQualter et al. 2004b; Petrasovits et al. 2007). Although thousands of independent transgenic sugarcane lines have been tested in field trials, there is not yet a commercially released transgenic sugarcane cultivar (J.C. Bespalhok, personal communication). One noteworthy example of sugarcane manipulation was the introduction of a sucrose isomerase gene tailored for vacuolar compartmentalization in sugarcane plants (Wu and Birch 2007). Transgenic plants showed increased total sugar concentration due to normal levels of sucrose plus the accumulation of isomaltulose, a high-value sugar. These transgenic lines also showed increased photosynthesis, sucrose transport and sink strength (Wu and Birch 2007). Another example is the increase of drought tolerance in transgenic sugarcane that synthesized proline in response to stress. Transformed plants were protected against the oxidative stress caused by water deficit. The higher tolerance of those transgenic plants was assessed by higher biomass yields after 12 days of withholding water (Molinari et al. 2007). A key limitation to the generation of new transgenic lines has been the silencing of inserted transgenes. Mudge et al. (2009) isolated eight distinct promoters associated to a MYB transcription factor gene. At least three of these promoters were associated with alleles that are known to be expressed. When constructs of a reporter gene driven by each of these promoters were inserted into sugarcane, expression of the reporter was successfully detected soon after transformation, but not later in the mature stem of regenerated plants. In addition, the silencing pattern was unpredictable, an undesirable trait in a commercial line as plants with adequate and stable transgene expression under field conditions are needed to bring to sugarcane market the practical benefits of transgenic technology. More than 10 years ago, Finnegan and McElroy (1994) reviewed possible mechanisms of transgene inactivation. Firstly, integrated transgenic DNA may be recognized by its disruption of normal chromatin structure or by the different sequence characteristics from that of the surrounding integration site. The integration of duplicated sequences may result in chromatin restructuring, DNA methylation and the inhibition of mRNA processing, transport, export or translation. Post transcriptional silencing may be directed by RNA-directed de novo methylation of transgene sequences (Finnegan and McElroy 1994). There is also evidence that silencing may be related to the transgene sequence so that there is the possibility of developing


transgene design rules that may minimize transgene silencing. Although microparticle bombardment has been the main method of sugarcane transformation (Birch 1997; Moore 1999), other methods for transgene incorporation into sugarcane are needed. For example, genetic transformation mediated by Agrobacterium tumefaciens should be studied and applied to sugarcane more frequently (Arencibia et al. 1998; Elliott et al. 1998; Enriquez-Obregon et al. 1998; Elliott et al. 1999; Manickavasagam et al. 2004). A. tumefaciens-mediated transformation has the potential to become more efficient with appropriate manipulation and in vitro culture conditions, selection of the best age of the calli, type and stage of embryogenic culture, and improvement in the virulence of A. tumefaciens. Compared to other methods, transformation mediated by A. tumefaciens is a simple and low cost method. Furthermore, it can transfer relatively long segments of DNA with little rearrangement and at a relatively low number of integrated copies. Another important transformation technique is the transformation of chloroplasts. Chloroplast genetic engineering offers a number of unique advantages compared to conventional transgenic technologies, including high protein expression levels (De Cosa et al. 2001), integration into the plastome via homologous recombination without position effects or gene silencing (Daniell et al. 2001), and the expression of several transgenes in a single transcriptional unit due to the chloroplast’s prokaryotic origin (Bock 2001). Over the next few years, the first generation of commercial sugarcane transgenics, with herbicide and insect resistance, will be available as the result of improved transformation efficiency, the design of new transformation vectors, and new transgene design rules to avoid silencing. The development of strategies for incorporating polygenic traits, hyperexpression of transgenes, containment of transgenes within the transgenic plants (Daniell 1999; Daniell et al. 2001; Maliga 2004), together with the possibility of engineering native genes without significant genetic rearrangements (Beetham et al. 1999) are valuable innovations that could be utilized for improvement of sugarcane in the near future. For successful release of transgenic sugarcane, various scientific, legislative, and public perception issues must also be addressed. Transformation systems that do not incorporate any non-transgene DNA into the plant, utilize non-antibiotic selection and plant gene-based selection strategies would be a good start towards overcoming regulatory and public perception issues. In addition, the ability to control transgene expression through induction, developmental control, or tissue specificity will provide a platform for the production of a range of new compounds in sugarcane at commercially useful levels (Lakshmanan et al. 2005).


Sugarcane Physiology To optimize sugarcane improvement, it is necessary to know the impact a selected trait will have on the general physiology of the plant. However, this is not yet possible as there are too many gaps in our knowledge of the unique development and physiology of sugarcane. Such gaps impair our ability to enhance desired agronomical traits. For example, selection for sugarcane varieties with increased photosynthetic capacity may be useless if sugar accumulation is constrained by temperature, water deficit, or nutrient availability (Inman-Bamber et al. 2002). It may prove difficult to consistently increase sucrose levels in the culm without first knowing the factors that affect sugarcane yield and carbon partitioning. A key aspect to increase sugarcane yield is the regulation of its photosynthetic apparatus. Sugarcane C4 metabolism concentrates CO2 in photosynthetically active tissues, a strategy that has an energy cost that may be offset by the reduction in photorespiration rates. There are at least three distinct forms of C4 metabolism that can be identified by the decarboxylation enzymes they use: NADP+−malic enzyme (NADP-ME), NAD+−malic enzyme (NAD-ME) and phosphoenolpyruvate carboxykinase (PCK). There is evidence that sugarcane has both NADP-ME and PCK (Calsa and Figueira 2007), which suggests the two types of C4 metabolism might complement each other (Christin et al. 2007). The physiological implications of the presence of both pathways and how they could be explored to increase sugarcane yield is still unknown. It is also important to detail how carbon demands in the culm affect photosynthetic rates. Photosynthetic rates decrease with plant age, which could be a result of physiological limitations to sucrose accumulation in the culms (McCormick et al. 2006). This regulation is mediated by hexose, but little is known about the downstream pathways of this signal (McCormick et al. 2008a). The relationship between sink and source is a key step in the identification of targets that can be changed in order to improve sucrose accumulation. Sucrose production and storage is associated with the demand imposed by sink organs (McCormick et al. 2008b). For example, when the leaf growth is reduced, sucrose content tends to increase in culm (Inman-Bamber and Smith 2005). Furthermore, transgenic varieties that express an enzyme that converts sucrose into isomaltulose showed increased photosynthesis, probably due to introduction of this new carbon sink (Wu and Birch 2007). Finally, the reduction of leaf elongation induced by water deficit redirects the carbon partition and provides an increase in sucrose content (Inman-Bamber 2004). Experiments showed that water stress reduced whole plant photosynthesis by 18% and plant extension rate by 41% resulting in a 19% reduction in total biomass.

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However, water stress increased the sucrose mass gain by 27% and increased sucrose content of the dry mass by 37%, confirming that water deficit reduced the demand for photoassimilate for producing fiber and tops so that excess assimilate was allowed to accumulate in the form of sucrose (Inman-Bamber et al. 2008). The impact of water deficit on physiological or developmental processes and on gene expression are also under study on six different sugarcane varieties in four regions of Brazil. As expected, preliminary physiological measures showed that different cultivars utilize different mechanisms to survive water stress. For example, one cultivar utilized leaf rolling to reduce water loss whereas a different variety increased root to shoot growth to reduce water loss and to increase water uptake (L. Endres, personal communication). Over the next decades, climate change and increased CO2 levels are projected to impact the productivity of all crops. CO2 levels are predicted to increase from about 379 ppm in 2005 to 730–1,020 ppm by the end of the century (IPCC 2007). To design sugarcane crops for maximum productivity in such a changed environment, it is necessary to study how the increase of CO2 levels affects sugarcane physiology. Increases in the level of CO2 will reduce the rate of photorespiration in all plants, but considerably more in C3 plants than C4 plants. Nevertheless, C4 plants do increase their biomass when CO2 levels are increased from 370 ppm to 720 ppm. This increase in biomass of C4 plants is associated more with the increase in water use efficiency (WUE) than in the reduction of photorespiration (Vu et al. 2006; de Souza et al. 2008). An efficient use of water leads to a lower rate of water depletion in the soil, which increases resistance to drought (Vu and Allen 2009). Higher CO2 levels change both the metabolites and transcript levels of a number of sugarcane genes (Vu et al. 2006; de Souza et al. 2008), but how each change impacts sugarcane physiology remains unknown. Yield increases of 60% were observed on sugarcane grown in open top chambers under 720 ppm CO2, which indicates that yield potential may increase under those conditions (de Souza et al. 2008). Many other physiological traits need to be detailed before a strategy can be designed to improve them. For example, numerous details of sugarcane C4 photosynthesis and other metabolic pathways are needed to detect which steps constrain sugarcane yield. Understanding the mechanisms regulating the transition from vegetative to reproductive growth would allow the control of flowering for breeding and reduce the loss of fixed carbon for reproduction. In addition, little is known about what limits the capacity of sugarcane to store high concentration of sucrose in the parenchyma tissue of the stalk (McCormick et al. 2008a). Sucrose content variation depends on the morphol-

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ogy of the plant, such as size of the canopy and responses to ripening stimuli such as mild water stress and how these traits influence the supply and demand for photo-assimilate (Inman-Bamber et al. 2009). The photomorphogenic control of sugarcane development can be modified by treatment with gibberellic acid (GA3). This phytohormone induces a significant increase in stem cell elongation which increases the capacity for sucrose storage in sugarcane seedlings (A. Brandão and M. Buckeridge, unpublised). In the next few years, many physiological puzzles have to be solved. Initially, results obtained in more controlled greenhouse conditions will need to be confirmed under varying field conditions. Sugarcane transgenics, either overexpressing or silencing specific candidate genes will allow the testing of many hypotheses while physiology experiments will help identify new candidate genes. Systems biology coupled with yet to be developed models will integrate physiological data with massive amounts of proteomic, metabolomic and transcriptomic data to allow a more targeted approach towards understanding the limits of sugarcane productivity.

Breeding and Statistical Genetics If the long-term goal for the use of molecular biology tools for sugarcane improvement is the generation of transgenic varieties, the use of molecular markers for enhancing the breeding and selection of improved varieties can be considered its short-term goal. The sugarcane maps cover only about one third of the sugarcane genome (Grivet and Arruda 2002), even though most sugarcane genetic maps have around 1,000 markers (as reviewed by Casu et al. 2005), which is comparable to the genetic maps in other plants. In addition, current mapping methods are restricted to the use primarily of single-dose markers. Consequently, many if not most of the polymorphic loci obtained in sugarcane mapping populations are not useful (Garcia et al. 2006). Markers are already being used for the selection of parental plants for crosses (McIntyre and Jackson 2001; Wenzel 2006; Snyman et al. 2008), assessment of sugarcane resistance to diseases like brown rust (Daugrois et al. 1996; Asnaghi et al. 2001; Hoarau et al. 2001; Rossi et al. 2003) and yellow spot (Al-Janabi et al. 2007), evaluation of genetic diversity (Selvi et al. 2003; Lima et al. 2002), construction of genetic maps (Aitken et al. 2005, 2007; Raboin et al. 2006; Garcia et al. 2006; Oliveira et al. 2007) and mapping of Quantitative Trait Loci (QTL) (McIntyre et al. 2006; Al-Janabi et al. 2007; Piperidis et al. 2008; Aitken et al. 2008). In some diploid crop species like maize, soybean, barley and rice, molecular markers already play a critical


role in breeding through Marker Assisted Selection (MAS) of yield components, and disease and pest resistance (for a detailed review, see Francia et al. 2005). Several statistical genetic models have been proven and are helping breeders to better understand the inheritance of quantitative traits in these species. Efficient identification and use of molecular markers through the use of appropriate statistical models are matters of great importance in sugarcane. There is a recent trend to use single nucleotide polymorphism (SNP) markers to replace other marker types in many species, as they are frequently common in the genome—both within and between genes (Bundock et al. 2009). SNPs are responsible for most of the genetic variation within species and explain the occurrence of many important traits in plants such as rice (Umemoto et al. 2004; Bryan et al. 2000). The high frequency of SNPs in many plant species such as maize (Tenaillon et al. 2001), barley (Kanazin et al. 2002) and rice (Yu et al. 2002) makes it the new marker of choice for disease diagnostics, marker assisted selection, high resolution genetic mapping assessment of genetic purity (Batley et al 2003), and association mapping. Particular effort has been devoted to the development of SNPs as high-throughput markers. One of the strategies for SNP discovery is the use of public EST databases. The problem is that sometimes the polymorphism present in the available sequences is not present in the current population of interest. As an alternative, the re-sequencing of the identified SNP regions in the population of interest can be used. Conventional re-sequencing is complicated in sugarcane because it is highly heterozygous and polyploid. To deal with these issues, it may be necessary to sequence a high number of clones to represent all the variation, even thought this approach is expensive and time consuming (Bundock et al. 2009). Recent advances in sequencing technology have enabled the production of great amounts of data and reduction of the cost per base (Imelfort et al. 2009). Bundock et al (2009) showed that the use of next generation sequencing (NGS) such as 454 Life Sciences Genome Sequencer™ FLX, is more cost effective than earlier sequencing for SNP identification and can significantly increase the identification of SNPs in sugarcane. Another problem with polyploid sugarcane is the validation, selection for mapping, and genotyping of the SNPs, which are successfully being done using the Sequenom Mass ARRAY® system (Bundock et al. 2009). Sequenom technology also enables the estimation of the number of copies of each SNP allele, which is ideal for polyploid species. In addition to the importance of NGS for re-sequencing, these technologies will also have a great impact in sequencing of cDNA, gene rich regions, and the whole-genome of sugarcane, providing more information


for the development of a higher number of molecular markers, such as new SSRs, EST-SSRs and SNPs for further application in breeding programs. In this context, data generation is well addressed, since tremendous amounts of data are being generated by high-throughput techniques, primarily based on single nucleotide polymorphism discovery (Syvänem 2001). Despite the evolution of more precise and faster molecular techniques, data analysis has lagged behind and still needs a lot more effort. At the present time, the majority of statistical techniques used for genetic and QTL mapping in the complex genome of sugarcane are adaptations of ideas used in diploid organisms. Due to this limitation, a large amount of data that does not fit these models cannot be included in the analysis (e.g. markers segregating in fashions other than 1:1 and 3:1). However, the importance of key QTL that might not fit the diploid model cannot be ruled out. The presence of markers with higher allele doses, as well as combinations of markers with different doses (da Silva and Sorrells 1996; Ripol et al. 1999) makes it imperative that additional segregation patterns be considered. Given the current data and tools available, it is imperative to conduct thorough studies before MAS can become routine. Another issue that deserves attention is sample size. To construct reliable linkage maps and achieve adequate statistical power for QTL detection, larger samples have to be used, which can raise several operational and computational issues. In addition, as larger samples are used, technical and financial limitations must be considered when phenotyping large numbers of individuals. More efficient and powerful statistical tools must be made available to researchers to analyze the rapidly accumulating sugarcane genomic data. Importantly, these new methodologies must be created with a polyploid mindset, as opposed to simple adaptations from models for diploid systems. Association mapping studies based on haplotype data have proven very useful for the identification of markers co-segregating with quantitative characters and can speed up the localization of important genes (Lakshmanan et al. 2005; McIntyre et al. 2005; Wei et al. 2006; Raboin et al. 2008). QTL mapping studies will also play a major role in better understanding the genetic architecture of quantitative traits, providing basic information to be used by plant breeders in designing breeding programs (Zeng et al. 1999). In addition to QTL mapping, expression quantitative trait loci (eQTL) mapping has the potential to be useful to integrate information from genomics, transcriptomics and phenotype, providing links between gene expression and phenotype determination (Jansen and Nap 2001; Schadt et al. 2003). However, eQTL will only be useful once major challenges are overcome, such as the development of

Tropical Plant Biol. (2010) 3:75–87

models for polyploid systems, the development of a robust RNA profiling platform and the characterization of genetic map populations. Finally, it is known that sugarcane has a relatively narrow genetic basis, as a consequence of using only a few selected clones for base population generation (Roach 1989; Lima et al. 2002). Also, only a few generations separate modern cultivars and ancestors (Raboin et al. 2008), providing high linkage disequilibrium. Conventional sugarcane breeding programs have relied on high levels of heterozygosity of commercial clones and the instability of the polyploid genome as a source of genetic variability, since a small number of individuals can store considerable allelic variability, compared to diploid species. However, sucrose and fiber content have not sustained substantial improvements with current breeding efforts, because of the low genetic variability effectively used (Jackson 2005). Most yield gains are due to exploiting genotype by environment interaction, which is not well understood for sugarcane. With the view to assure long term improvements arising from genetic breeding, new (exotic) germplasm should be incorporated into current programs, e.g., through pre-breeding programs to accelerate the mobilization of non-adapted material (Ramdoyal and Badaloo 2002; Hemaprabha et al. 2005) In the next years, high-throughput sequencing will exponentially increase the amount of molecular markers available for molecular breeding. Initial efforts in sequencing the sugarcane genome will be used as a scaffold for resequencing projects that will make SNPs identification cheaper and faster. At the same time, new statistical models, designed specifically for polyploid genomes, will allow the sequencing data to be fully used. The introduction of molecular biology tools will help the classical breeding programs to identify traits of interest faster and with greater precision. This will probably increase the expected limit of sugarcane improvement to its theoretical potential using classical breeding. The great challenge over the next several years will be to convey the fundamental information generated in academia to applications by the breeders.

Final Remarks The BIOEN Workshop on Sugarcane Improvement gathered over 250 researchers to discuss the challenges for sugarcane improvement and the role of biotechnology in solving these challenges (Fig. 1). Among the main points discussed were how to identify the current gaps in sugarcane knowledge and how to apply emerging new knowledge to the process of designing new sugarcane varieties. Recognition was given to new high-throughput

Tropical Plant Biol. (2010) 3:75–87


Fig. 1 Roadmap for sugarcane improvement. The interplay between research on Gene Discovery and Genomics and on Sugarcane Physiology, will generate basic knowledge about potential targets for improvement (1). Knowledge on Sugarcane Physiology may be directly applied to improve crop management (2). In parallel, Gene Discovery and Genomics will identify new molecular markers to be used for Breeding and Statistical Genetics (3). These markers will be

applied to new statistical models in order to breed improved sugarcane (4). Gene Discovery and Genomics will also identify genes and regulatory sequences to generate new transgenic plants (5) to be used either in research, to gain new insights into Sugarcane Physiology (6) or in the design of commercial transgenic varieties (7). The interplay of improved sugarcane with improved crop management will approximate sugarcane yield to its theoretical potential (8)

technologies that have the potential to quickly fill these gaps once they have been identified. Sugarcane improvement can be achieved by the integration of improved crop management practices, traditional breeding, and the generation of transgenic improved lines. Gene discovery and sugarcane physiology will provide the knowledge required to develop a new sugarcane crop to be used as source for energy or as a biofactory (energy cane). Information about sugarcane is expected to increase exponentially over the next few years in response to financial incentives directed towards sugarcane improvement. In particular, the emergence of enormous amounts of sequencing data will require large bioinformatics teams and good data management platforms. These requirements will be the same for new systems biology approaches. Data management will be especially crucial when integrating data from different methodologies and from different groups. Furthermore, shared experiences, including the negative ones that are rarely published, will help overlapping efforts. Sugarcane is a complex model organism. Its genome is highly poliploid and aneuploid, it has a long generation time and transformation is difficult. The advantage of dealing with such complex organism is that the research community realizes that any real advance in sugarcane knowledge requires collaboration with other groups and organization at an international level. Although sugarcane has a high economic value, each country has to develop different commercial varieties that are adapted to specific regional problems which reduces competition among research groups of different countries. Examples of such common efforts are the International Consortium for

Sugarcane Biotechnology (ICSB), the sugarcane nomenclature committee, which is unifying the nomenclature in genetic databases and the Sugarcane Genome Sequencing Initiative (SUGESI, The BIOEN Workshop on Sugarcane Improvement was just another step towards a fully integrated sugarcane research community.

Invited Speakers and Debate Leaders Paul Moore (Hawaii Agriculture Research Center Cellular and Molecular Biology Research Unit, Aiea, USA); Rosanne E. Casu and Graham D. Bonnett (CSIRO Plant Industry, Queensland Bioscience Precinct, St Lucia, Australia; Cooperative Research Centre for Sugar Industry Innovation through Biotechnology, University of Queensland, St. Lucia, Australia); Derek A Watt (South African Sugarcane Research Institute, Crop Biology Resource Centre, Mt Edgecombe, South Africa; School of Biological and Conservation Sciences, University of KwaZulu-Natal, Durban, South Africa); Manuel B Sainz (Syngenta Centre for Sugarcane Biofuels Development, Queensland University of Technology, Brisbane, Australia); Paulo Arruda (Centro de Biologia Molecular e Engenharia Genética, Universidade Estadual de Campinas, Campinas, Brazil); Robert G Birch (School of Integrative Biology, The University of Queensland, Brisbane, Australia); João Carlos Bespalhok-Filho (Departamento de Fitotecnia e Fitossanitarismo, Universidade Federal do Paraná, Curitiba, Brazil); Hugo Mollinari (EMBRAPA Recursos Genéticos e Biotecnologia, Brasília, Brazil); Eugenio Ulian (Monsanto, São


Paulo, Brazil); Rowan F Sage (Department of Ecology and Evolutionary Biology, University of Toronto, Toronto, Canada); Lauricio Endres (Centro de Ciências Agrárias, Universidade Federal de Alagoas, Rio Largo, Brazil); Katia C Scortecci (Centro de Biociências, Universidade Federal do Rio Grande do Norte, Natal, Brazil); Marcos AS Vieira (Centro de Ciências Agrárias, Universidade Federal de São Carlos, Araras, Brazil); Robert J Henry (Centre for Plant Conservation Genetics, Southern Cross University, Lismore, Australia); Jorge AG da Silva (Texas Agricultural Experiment Station, Texas A and M University, Weslaco, USA);Walter Maccheroni Junior (Canaviallis, Campinas, Brazil); Marcos GA Landell (Centro Avançado de Pesquisa Tecnológica do Agronegócio de Cana-de-açúcar, IAC, Ribeirão Preto, Brazil); William L Burniquist (Centro de Tecnologia Canavieira, Piracicaba, Brasil); Hermann P Hoffmann (Centro de Ciências Agrárias, Universidade Federal de São Carlos, Araras, Brazil). The workshop presentations can be found at: http:// Itemid=108&task=viewcategory&catid=8&lang=en

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