**3. Altimeter products and study cases**

A wide variety of satellite altimetry products cover many research fields. In the following section, we focus on presenting the application of altimetry in geodesy.


**4. Scenarios**

In this work, we shall compare three different scenarios, based on different customer behavior. The first scenario represents conventional static usage profiles based on normal distributed charging demands. This type of customer modeling could be implemented without an MAS approach; however, we decided to also employ our MAS framework for better comparability. The second scenario is a minor extension of the basic scenario where the energy demand on hot or cold days is increased. We assume that for technical reasons, the energy demand of the EV increases due to the battery chemistry or additional A/C and heating demand. In the third scenario, the customer shall stop using our charging infrastructure if past charging processes were not to the customer's liking. This is modeled by a customer satisfaction indicator that is computed based on the difference of targeted and actual reached SoC for past charging sessions. The assumption is that if the charging infrastructure repeatedly failed to charge the EV to the requested level, the customer would switch to a different charging operator. More details on the three scenarios are given below.


*Article* **Leaf Nitrogen and Phosphorus Stoichiometry of** *Cyclocarya paliurus* **across China**

### **Yang Liu 1,2, Qingliang Liu 1, Tongli Wang 2 and Shengzuo Fang 1,3,\***

Received: 16 November 2018; Accepted: 13 December 2018; Published: 13 December 2018

**Abstract:** Leaf stoichiometry (nitrogen (N), phosphorus (P) and N:P ratio) is not only important for studying nutrient composition in forests, but also reflects plant biochemical adaptation to geographic and climate conditions. However, patterns of leaf stoichiometry and controlling factors are still unclear for most species. In this study, we determined leaf N and P stoichiometry and their relationship with soil properties, geographic and climate variables for *Cyclocarya paliurus* based on a nation-wide dataset from 30 natural populations in China. The mean values of N and P concentrations and N:P ratios were 9.57 mg g−1, 0.91 mg g−1 and 10.51, respectively, indicating that both leaf N and P concentrations in *C. paliurus* forests were lower than those of China and the global flora, and almost all populations were limited in N concentration. We found significant differences in leaf N and P concentrations and N:P ratios among the sampled *C. paliurus* populations. However, there were no significant correlations between soil properties (including organic C, total N and P concentrations) and leaf stoichiometry. The pattern of variation in leaf N concentration across the populations was positively correlated with latitude (24.46◦ N–32.42◦ N), but negatively correlated with mean annual temperature (MAT); meanwhile, leaf N concentration and N:P ratios were negatively correlated with mean temperature in January (MTmin) and mean annual frost-free period (MAF). Together, these results suggested that temperature-physiological stoichiometry with a latitudinal trend hold true at both global and regional levels. In addition, the relationships between leaf stoichiometry and climate variables provided information on how leaf stoichiometry of this species may respond to climate change.

**Keywords:** leaf stoichiometry; *Cyclocarya paliurus*; geographic variations; natural populations; climate variables; nitrogen; phosphorus; N:P ratio


**Sequencing of Non-model Plants for Understanding the Physiological Responses in Plants**

Jannette Alonso-Herrada, Ismael Urrutia, Tania Escobar-Feregrino, Porfirio Gutiérrez-Martínez, Ana Angélica Feregrino-Pérez, Irineo Torres-Pacheco, Ramón G. Guevara González, Sergio Casas-Flores and Andrés Cruz-Hernández

Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/62511

#### **Abstract**

From a genomic point of view, plants are complex organisms. Plants adapt to the envi‐ ronment, by developing different physiological and genetic properties, changing their ge‐ nomic and expression profiles of adaptive factors, as exemplified by polyploidy studies. These characteristics along with the presence of duplicated genes/genomes make se‐ quencing with early low-throughput DNA sequencing technologies in plants a challeng‐ ing task. With the development of new technologies for molecular analysis, including transcriptome, proteome or microarray profiling, a new perspective in the genomic anal‐ ysis was open, making possible to programs in species without genomic maps. The op‐ portunity to extend molecular studies from laboratory model scale toward naturally occurring plant populations made it possible to precisely answer the longstanding impor‐ tant ecological and evolutionary questions. Some plant species have unique properties that could help to understand their adaptability to environment, crop production, pest protection or other biological processes. Molecular studies on non-model plants, includ‐ ing algae, mosses, ferns and plants with very specific characteristics are ongoing.

**Keywords:** Genome size, NGS, polyploidy, transcriptome, wild materials

#### **1. Introduction**

The first wave of plant genome sequencing has passed, and now new era has started in plant genomics research with new-generation sequence (NGS) strategies, which require a mixture of economic and scientific needs. Until now, several crops have been sequenced and some

© 2016 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

others crop´s sequencing is underway, which will greatly help to elucidate unknown biological processes and the phylogenetic relationship among crop plants. Furthermore, the genomic data analysis and its integration of the biological systems will help to establish fundamental models to understand the evolution, development, and adaptability of the plants.

A genomic sequence is an important information for the basic research and understanding of plant evolution and development. It serves as a tool for engineering new genotypes [1]. Different plant species have different amounts of DNA [2]. The DNA of most plants includes from 100 million to the largest example of 150 billion base pairs (designed as alphabets), organized into 20,000 to 50,000 genes [1]. The most important contribution in the field of plant genome analysis is the discovery that many higher plants share a blueprint gene content. As distantly related plant taxa, monocots and dicots, which diverged from a common ancestor about 200 million years ago, retain some common gene order along the genome [1].

The development of new strategies and technologies for genome sequencing, can lead to development of programs to get a partial (transcriptome) or a complete DNA sequence (whole genome sequencing (WGS)) for non-model plants. The costs for these projects are now accessible. The first plant genome sequencing project, represented an effort of several years and millions of dollars. Now, the costs for the sequencing are in the order of thousands of dollars and there are new bioinformatics tools available for the analysis of generated sequence data.

The world's population depends on a few crops such as rice, wheat, maize, and potato for their food. In the following decades the world will face the tremendous challenge of feeding the global population [3]. The study of plant genomics in non-model plants will help to reveal the genetic factors and biochemical pathways involved in many processes such as flowering, nutrition, disease, and pest resistance, as well as tolerance of plants to abiotic stresses.

Model organisms are important for biological and agricultural approaches. The research in model organisms has generated a huge amount of important information on different molec‐ ular factors that contribute to plant growth and development, however it has some limitations [4]. The study of wild plants will help to overcome these limitations. Wild plants are well adapted to extreme conditions, and resistant to plant pathogens.

Model organisms, have a limited number of uniform narrow-based genotypic samples or variability limited to a number of specific plants. The study on how some plants survive in extreme conditions may also provide some clues about the mechanisms of plant response to biotic or abiotic stresses. Some non-model organisms are an extraordinary source of plant secondary metabolites.

The wild plant genotypes sometimes does not look attractive for breeding programs because of their morphology; however, they are the repository of ancestral genes and very important sources for the rescue of specific traits.

In Mexico, there are a large number of wild plant populations fit for breeding or sequencing programs are yet to be identified. Wild plant populations are genetically diverse and are source of genes that encode proteins potentially used for health, industrial, or ecological purposes.

#### **2. Non model plants as model for environmental adaptation**

others crop´s sequencing is underway, which will greatly help to elucidate unknown biological processes and the phylogenetic relationship among crop plants. Furthermore, the genomic data analysis and its integration of the biological systems will help to establish fundamental

A genomic sequence is an important information for the basic research and understanding of plant evolution and development. It serves as a tool for engineering new genotypes [1]. Different plant species have different amounts of DNA [2]. The DNA of most plants includes from 100 million to the largest example of 150 billion base pairs (designed as alphabets), organized into 20,000 to 50,000 genes [1]. The most important contribution in the field of plant genome analysis is the discovery that many higher plants share a blueprint gene content. As distantly related plant taxa, monocots and dicots, which diverged from a common ancestor

The development of new strategies and technologies for genome sequencing, can lead to development of programs to get a partial (transcriptome) or a complete DNA sequence (whole genome sequencing (WGS)) for non-model plants. The costs for these projects are now accessible. The first plant genome sequencing project, represented an effort of several years and millions of dollars. Now, the costs for the sequencing are in the order of thousands of dollars and there are new bioinformatics tools available for the analysis of generated sequence

The world's population depends on a few crops such as rice, wheat, maize, and potato for their food. In the following decades the world will face the tremendous challenge of feeding the global population [3]. The study of plant genomics in non-model plants will help to reveal the genetic factors and biochemical pathways involved in many processes such as flowering, nutrition, disease, and pest resistance, as well as tolerance of plants to abiotic stresses.

Model organisms are important for biological and agricultural approaches. The research in model organisms has generated a huge amount of important information on different molec‐ ular factors that contribute to plant growth and development, however it has some limitations [4]. The study of wild plants will help to overcome these limitations. Wild plants are well

Model organisms, have a limited number of uniform narrow-based genotypic samples or variability limited to a number of specific plants. The study on how some plants survive in extreme conditions may also provide some clues about the mechanisms of plant response to biotic or abiotic stresses. Some non-model organisms are an extraordinary source of plant

The wild plant genotypes sometimes does not look attractive for breeding programs because of their morphology; however, they are the repository of ancestral genes and very important

In Mexico, there are a large number of wild plant populations fit for breeding or sequencing programs are yet to be identified. Wild plant populations are genetically diverse and are source of genes that encode proteins potentially used for health, industrial, or ecological purposes.

adapted to extreme conditions, and resistant to plant pathogens.

models to understand the evolution, development, and adaptability of the plants.

about 200 million years ago, retain some common gene order along the genome [1].

data.

90 Plant Genomics

secondary metabolites.

sources for the rescue of specific traits.

The cactus plants of Cactaceae family are an example for a plant that can adapt to several environmental conditions. One of these plants is Nopal (*Opuntia* spp.), which belongs to the genera Opuntia and Nopalea [5]. This is an endemic plant found in semiarid areas in Mexico, but it grows along the American continent, from Canada to La Patagonia in Argentina, where environmental conditions are different from each area. Recently, Nopal plants have become the world´s interesting alternative fruit and forage crop. Only few varieties of nopal fruit originated from the Mexican nopal germplasm, are available in the market.

The history of first Nopal use in Mexico dates back to the ancient Mesoamerican civilizations; people used to collect cladodes and fruits from wild materials, for their nutritional qualities and medicinal purposes. The Spanish conquerors spread Nopal in America and Europe; now it is cultivated in Italy, Morocco, Tunisia, Greece, Israel, India, Philippines, China, Australia, South Africa, Brazil, Argentina, Colombia and the United States [6-8].

Although nopal is propagated asexually for commercial purposes, seed propagation is essential for breeding. Nopal apomixis makes the screening of individual crops obtained from crosses difficult and complicates the genetic studies [5]. Although no genomic map exists for this multipurpose plant, several efforts have been made to get some genomic approaches, and it has been included in the 1000 genomes sequencing program. To date, extensive efforts on cDNA microarrays, microRNAs (miRNAs) microarrays, mRNA deep sequencing and molecular markers studies have been made.

To study the genes associated with crassulacean acid metabolism (CAM), an expressed sequence tag (EST) database of different developmental stages of various tissues was created [9]. Sequences were assembled and compared with the available plant and genetic databases; genes involved in circadian regulation and CAM were identified in plants grown under a long day regime. Three kinds of expression profiles were found: transcripts oscillated with a 24-h periodicity; transcripts of the light-active genes adapted to cycles of 12-h periodicity; and

arrhythmic accumulation patterns. Some genes were scored best to a 12-h rhythm, suggesting a difference with Arabidopsis at level of circadian clock gene interactions. The results indicate that changes at the CAM metabolism are the result of modified circadian regulation at the transcriptional and posttranscriptional levels [9].

In addition, the gene regulation trough miRNAs has been explored [10]. miRNAs are a class of small non-coding RNAs that regulate gene expression. A combination of Northern blot and tissue print hybridization was used, to identify conserved miRNAs expressed during nopal (*Opuntia ficus indica*) fruit development. A comparative analysis detected 34 miRNAs ex‐ pressed differentially. These miRNA were clustered different groups and associated with the different phases of fruit development. Gradual expression of several miRNAs was observed during fruit development. The work provided the evidence of miRNA expression in the cactus fruit and the basis for future research on miRNAs in Opuntia [10].

One transcendental work is related to the analysis of genomic content in 23 Opuntia species by flow cytometry [11]. A main interest on Opuntia genomes was related to the DNA content because; almost all the genotypes have a ploidy level of 4x, 8x or 12x; of their genetic com‐ plexity. In four different ploidy levels having 2C-DNA amounts, DNA content ranged from 3.75 Giga base pairs (Gb) (*Opuntia incarnadilla* Griffiths) to 5.87 Giga base pairs (Gb) (*Opuntia heliabravoana* Scheinvar) among the samples analyzed.

The 2C DNA content when compared with other species; such as maize, shows that genome of Opuntia is less complex than that of maize (Table 1), which makes Opuntia suitable for a genomic sequencing program.

**Table 1.** Opuntia DNA content and other plant species

The nopal and its products need a more deeply analysis to maximize the real value of this crop. It is a multipurpose plant that is very important in the life of the people because it impact on the economy, nutrition, medicinal practices, and fuel production. Two main aspects are now in the focus for increasing its crop value:

1. Some crops have been sequenced and some others are in progress, however the nopal is waiting to be sequenced. Once sequenced, it would help to understand several mechanisms of plant adaptation to different environments, and will give us clues about controlling the process for adaptation to extreme conditions in other plants [12].

2. A new important aspect involves miRNAs, which are thought to be fine-tuning mechanisms in gene regulation [10]. Wrong expression of miRNAs can produce pleiotropic effects on development. It would be no surprise to discover that several events related to plant adaptation were under the control of miRNA expression. In the future, the expression of miRNA and siRNA will serve as tools for the generation of new *Opuntia* phenotypes. In these experiments the role of different molecules or pathways involved in seed formation, ripening delay or fruit development could be revealed.

#### **3. Non model plants as source of industrial solutions**

because; almost all the genotypes have a ploidy level of 4x, 8x or 12x; of their genetic com‐ plexity. In four different ploidy levels having 2C-DNA amounts, DNA content ranged from 3.75 Giga base pairs (Gb) (*Opuntia incarnadilla* Griffiths) to 5.87 Giga base pairs (Gb) (*Opuntia*

The 2C DNA content when compared with other species; such as maize, shows that genome of Opuntia is less complex than that of maize (Table 1), which makes Opuntia suitable for a

Arabidopsis *Arabidopsis thaliana* Brassicaceae 2x 0.30 0.29 Soy bean *Glycine max* Leguminosae 2x 2.31 2.25 Maíze *Zea mays* Gramineae 2x 13.49 13.19

Tuna charola *Opuntia streptacantha* Cactaceae 8x 4.64 4.53

Tuna *Opuntia megacantha* Cactaceae 8x 5.01 4.89 Tuna blanca *Opuntia ficus indica* Cactaceae 8x 4.90 4.79 Tuna robusta *Opuntia robusta* Cactaceae 8x 4.98 4.87 Xoconoxtle *Opuntia joconostle* Cactaceae 8x 4.7 4.59

The nopal and its products need a more deeply analysis to maximize the real value of this crop. It is a multipurpose plant that is very important in the life of the people because it impact on the economy, nutrition, medicinal practices, and fuel production. Two main aspects are now

1. Some crops have been sequenced and some others are in progress, however the nopal is waiting to be sequenced. Once sequenced, it would help to understand several mechanisms of plant adaptation to different environments, and will give us clues about controlling the

2. A new important aspect involves miRNAs, which are thought to be fine-tuning mechanisms in gene regulation [10]. Wrong expression of miRNAs can produce pleiotropic effects on development. It would be no surprise to discover that several events related to plant adaptation were under the control of miRNA expression. In the future, the expression of miRNA and siRNA will serve as tools for the generation of new *Opuntia* phenotypes. In these experiments the role of different molecules or pathways involved in seed formation, ripening delay or fruit

**(pg)**

**2C genome size (Gb)**

**Common name Scientific name Family Ploidy level 2C genome size**

*heliabravoana* Scheinvar) among the samples analyzed.

genomic sequencing program.

92 Plant Genomics

**Table 1.** Opuntia DNA content and other plant species

in the focus for increasing its crop value:

development could be revealed.

process for adaptation to extreme conditions in other plants [12].

Development of modern society has led to an increased emission of pollutants into the environment, from industrial and domestic activities, as well as from mining, agriculture and crafting [13].  Conventional methods for the removal of pollutants in soil and water are often costly and can irreversibly affect the properties of the soil, as well as the organisms that inhabit those places [14].

Bioremediation is a tool used to clean pollutants in soil and water, and it is referred to the chemical transformation of pollutants through the use of microorganisms and plants [15]. The genomic content of plants for remediation has been calculated by different methods, and the sizes are included in Table 2. As shown in Table 2, there are no genomic complex organisms and some have been sequenced already.

It is important to consider that there is a great diversity of plants grow under different climates, which belong to different families. This allows researchers to have a wide variety of candidate plants that fits the scientific needs. Some plants of the Asteraceae, Brassicaceae and Solanaceae families have been found as tolerant to different pollutants. According to Lopez et al. [15], plants use a mechanism to alleviate the environmental stresses, by the following three phases:

(1) Absorption, excretion and detoxification of pollutants; (2) the distribution of pollutants throughout the plant and their excretion via volatilization; and (3) detoxification of pollutants by phytoremediation, by any one of the following processes: phytoextraction, rhizofiltration, phytoestimulation, phytostabilization, phytovolatilization or phytodegradation [15,16].

Phytostabilization allow to reduce the bioavailability and mobility of contaminants, avoiding underground transport layers or the atmosphere [15,16]. This process is less expensive than other methods, is easy to apply and aesthetically pleasing.

Phytodegradation is the transformation of organic pollutants in simpler molecules. In certain instances, degradation products will serve to accelerate plant growth, and other cases the contaminants are biotransformed. The phytodegradation has been employed for the removal of explosives, such as TNT, halogenated hydrocarbons, Bis-phenol, PAHs and organochlorine and organophosphorus pesticides [14].

In phytovolatilization, plants absorb water along with the soluble organic and inorganic pollutants (As, Se and Hg). Some of the contaminants can reach the leaves and get evaporated or volatilized into the atmosphere. Plants such as *Bigelovii Salicornia*, *Brassica juncea*, *Astragalus bisulcatus* and *Chara canescens* have been used for bioremediation of Se pollution and *Arabidopsis thaliana* has been used for bioremediation of Hg [14].

Rhizofiltration uses plants to remove contaminants from water environment through the root. In rhizofiltration, these plants are grown in hydroponic way. When the root system is well developed, the plants are introduced into polluted water with metals, where the roots absorb and accumulate. Numerous aquatic plants have the ability to accumulate pollutants, and some examples of these are as follows: *Scirpus lacustris, Lemna gibba*, *Azolla caroliniana*, *Elatine* *trianda*, *Wolffia papulifera*, *Polygonum punctatum*, *Myriophylhum aquaticum*, and *Mentha palust‐ ris* (for Al, As, Au, Cd, Cr, Cu, Cr, Fe, Hg, Mg, Mn, Ni, Pb, Se, Sr, Zn,) [14,15].

**Table 2.** DNA content in Plants used for bioremediation

Phytoextraction or absorption is carried out by the plant roots and accumulation of polluting metals in the stems and leaves. Some plants used for this approach are: *Thlaspi caerulescens*; *Sedum alfredii*, *Viola* and *Vertiveria baoshanensis*; *Alyssum murale*, *Trifolium nigriscens*, *Psychotria douarrei*, *Pruinosa geissois*, *Homalium guillainii*, *Hybanthus floribundus*, *Sebertia acuminata*, *Stackhousia tryonii*, *Pimelea leptospermoides*, *Aeollanthus biformifolius*; *Haumaniastrum robertii*; *Brassica juncea*, *Helianthus annuus*, *Sesbania drummondii* and *Brassica napus* (for Ag; Cd, Cr, Cu, Hg, Ni, Pb, and Zn) [14,15].

Phytodegradation in plants and microorganisms is associated with, degradation of organic pollutants into harmless products and, mineralization into CO2 and H2O. Plants such as *Populus* spp. are introduced to absorb the contaminants in soil pores and prevent leaking to other soil layers [15,26].

### **4. Perspectives for genome sequencing and genome information from nonmodel plants to plant breeding**

Next-generation sequencing (NGS), include several and different technologies which has its own set of characteristics. NGS generates huge amounts of sequence data in a very costeffective way [27].

The increased number of WGS projects means that more organisms, are becoming important genetic models [28]. At the same time, many molecular studies are focusing on natural variation and adaptation in classical genetic model species, or close relatives of these, such as *Arabidopsis*, thus closing the gap between model and non-model organisms.

For example, the assembled genomes could be used as a reference sequence for further transcriptome analysis or re-sequencing and surveys of genetic variation. They may also be used to develop other genomic tools, such as proteomics and microarrays hybridization [29].

After the novel transcriptome has been annotated using a genomic reference species, it can be used as a starting point for more detailed functional characterizations of desired organisms, using gene ontology databases.

With RNA-seq protocols, or longer sequence reads will also improve applications because large haplotype blocks including several linked polymorphisms will become available. Wherein hundreds of genes are analyzed simultaneously. Some of these may be involved in important phenotypic variation, and this is relevant from the conservation point of view because such variation may be important to maintain within the population.

In the future, the bottleneck is more likely to be at the bioinformatics rather than in producing the sequences [30] because a huge number of biologists are trying to order

the genomic data with biological sense. New approaches for data storage and processing will be needed, because currently available databases might be unable to cope up with the rapid generation of new sequencing data [31].

#### **5. Conclusions and prospects**

*trianda*, *Wolffia papulifera*, *Polygonum punctatum*, *Myriophylhum aquaticum*, and *Mentha palust‐*

**1C (Gb)**

Cruciferae 0.125 0.16 2000

*Brassica juncea* Cruciferae 1.49 1.092 -- Johnston et al. [19].

*annuus* Compositae 3.5 2.43 <sup>2012</sup> Staton et al. [20]

*Brassica napus* Cruciferae 1.12 1.15 2014 Boulos et al. [21]

*Medicago sativa* Leguminosae 1.75 0.86 2011 Young et al. [23]

*annuus* Compositae 3.5 2.43 <sup>2012</sup> Staton et al. [20]

*Cucurbita sp* Cucurbitaceae 0.34 0.33 **--** Šisko et al. [25]

*annuus* Compositae 3.5 2.43 <sup>2012</sup> Staton et al. [20]

Degradation *Sorghum bicolor* Gramineae 1.68 0.835 <sup>2009</sup> Paterson et al. [22]

Cd, Pd, Zn, Cu, Ni, Cr *Brassica nigra* Cruciferae 0.632 0.647 -- Johnston et al. [19]

Accumulation *Cucumis sp* Cucurbitaceae 0.68 0.66 <sup>2009</sup> Huang et al [24]

Phytoextraction or absorption is carried out by the plant roots and accumulation of polluting metals in the stems and leaves. Some plants used for this approach are: *Thlaspi caerulescens*; *Sedum alfredii*, *Viola* and *Vertiveria baoshanensis*; *Alyssum murale*, *Trifolium nigriscens*, *Psychotria douarrei*, *Pruinosa geissois*, *Homalium guillainii*, *Hybanthus floribundus*, *Sebertia acuminata*, *Stackhousia tryonii*, *Pimelea leptospermoides*, *Aeollanthus biformifolius*; *Haumaniastrum robertii*; *Brassica juncea*, *Helianthus annuus*, *Sesbania drummondii* and *Brassica napus* (for Ag; Cd, Cr, Cu,

**1C (pg)** **Sequencing year**

**Reference**

International. Barley Genome Consortium [17]

Arabidopsis Genome Initiative [18]

*ris* (for Al, As, Au, Cd, Cr, Cu, Cr, Fe, Hg, Mg, Mn, Ni, Pb, Se, Sr, Zn,) [14,15].

Pb, Zn, Cd, As, Cu, Mn *Hordeum vulgare* Gramineae 5.1 5.5 <sup>2012</sup>

**Function Species Family**

*Arabidopsis thaliana*

*Helianthus*

*Helianthus*

*Helianthus*

**Table 2.** DNA content in Plants used for bioremediation

Phytoestabilization

94 Plant Genomics

Mercury Phytovolatilization

Phytoextraction Cd, Zn, Pb, Ni, Ag, Cr, Cu, Hg,

Petroleum Contaminants

Elimination

Insecticide

Phytoextraction Zn, HgNO3

Hg, Ni, Pb, and Zn) [14,15].

Plants provide food for all living organisms, and just 15 crop plants provide 90% of the world's food intake [32]. Plant species are responsible for maintaining the balance of the carbon cycle, for developing and maintaining soil from erosion, and plant products are used as human medicines [33, 34]. For these reasons, there is great interest in sequencing plant genomes, but so far relatively few plant species have been sequenced compared with the hundreds of thousands of species around the world.

Non-model plants are becoming very attractive sources for different purposes, for their ability to adaptation to extreme environments and to produce specific metabolites that can be used for food and medicinal purposes. The materials must be characterized at molecular level to develop any strategy for the generation of genetic data, that is molecular markers, cDNA sequencing, and cDNA microarrays, to have reference data to compare with model organisms.

Large complex plant genomes remain a particularly difficult challenge for *de novo* assembly for various biological, bioinformatics, and biomolecular reasons. Plant genomes can be nearly 100 times larger than the sequenced mammalian genomes [35]. The next frontier for plant genomics is to characterize the diversity of genomic variations across large populations, deeply annotate their functional elements, and develop predictive quantitative models relating genotype to phenotype.

#### **Acknowledgements**

The authors thanks Fondo Institucional de Fomento Regional para el Desarrollo Científico, Tecnológico y de Innovación, for financial support (CONACyT- FORDECyT 193512).

#### **Author details**

Jannette Alonso-Herrada1 , Ismael Urrutia1 , Tania Escobar-Feregrino2 , Porfirio Gutiérrez-Martínez4 , Ana Angélica Feregrino-Pérez1 , Irineo Torres-Pacheco1 , Ramón G. Guevara González1 , Sergio Casas-Flores5 and Andrés Cruz-Hernández1,3\*

\*Address all correspondence to<EMAIL_ADDRESS>
1 Engineering Faculty, Autonomus University of Querétaro, Circuito Universitario, Cerro de las Campanas s/n, C.P. Santiago de Querétaro, Querétaro, México

2 Natural Sciences Faculty, Autonomus University of Querétaro, Circuito Universitario, Cer‐ ro de las Campanas s/n, C.P. Santiago de Querétaro, Querétaro, México

3 Chemistry Faculty, Autonomus University of Querétaro, Circuito Universitario, Cerro de las Campanas s/n, C.P. Santiago de Querétaro, Querétaro, México

4 Technological Institute of Tepic. Food Science Postgrade, Integral Laboratory on Food Sci‐ ence and Biotechnology Research. C.P. Tepic, Nayarit, México

5 IPICYT, Molecular Biology Division, C.P. 78216, San Luis Potosí, México

#### **References**

so far relatively few plant species have been sequenced compared with the hundreds of

Non-model plants are becoming very attractive sources for different purposes, for their ability to adaptation to extreme environments and to produce specific metabolites that can be used for food and medicinal purposes. The materials must be characterized at molecular level to develop any strategy for the generation of genetic data, that is molecular markers, cDNA sequencing, and cDNA microarrays, to have reference data to compare with model organisms.

Large complex plant genomes remain a particularly difficult challenge for *de novo* assembly for various biological, bioinformatics, and biomolecular reasons. Plant genomes can be nearly 100 times larger than the sequenced mammalian genomes [35]. The next frontier for plant genomics is to characterize the diversity of genomic variations across large populations, deeply annotate their functional elements, and develop predictive quantitative models relating

The authors thanks Fondo Institucional de Fomento Regional para el Desarrollo Científico,

, Tania Escobar-Feregrino2

,

and Andrés Cruz-Hernández1,3\*

, Irineo Torres-Pacheco1

,

Tecnológico y de Innovación, for financial support (CONACyT- FORDECyT 193512).

, Ana Angélica Feregrino-Pérez1

1 Engineering Faculty, Autonomus University of Querétaro, Circuito Universitario, Cerro de

2 Natural Sciences Faculty, Autonomus University of Querétaro, Circuito Universitario, Cer‐

3 Chemistry Faculty, Autonomus University of Querétaro, Circuito Universitario, Cerro de

4 Technological Institute of Tepic. Food Science Postgrade, Integral Laboratory on Food Sci‐

, Sergio Casas-Flores5

, Ismael Urrutia1

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ro de las Campanas s/n, C.P. Santiago de Querétaro, Querétaro, México

5 IPICYT, Molecular Biology Division, C.P. 78216, San Luis Potosí, México

\*Address all correspondence to<EMAIL_ADDRESS>
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96 Plant Genomics

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Jannette Alonso-Herrada1

Porfirio Gutiérrez-Martínez4

Ramón G. Guevara González1

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**1. Introduction**

146 Topics in Cancer Survivorship

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Cancer patients routinely take multiple – dietary supplements to prevent recurrence or chronic disease, to improve quality of life and overall health, or to reduce the adverse effects of cancer therapy (Gansler et al., 2008; Miller et al., 2009; Ströhle et al., 2010; Velicer & Ulrich, 2008). In fact, one of the most common behavior changes among cancer patients is the use of dietary supplements (Miller et al., 2009).

Although cancer patients routinely use dietary supplements, there is often little consideration as to their safety, efficacy and potential negative effects (Cassileth et al., 2009; Giovannucci & Chan, 2010). In fact, some data suggest that higher than recommended doses of some vitamins and minerals might result in enhancement of carcinogenesis, changes in survival in some cancers and interference with therapy or prescription medications (Cassileth et al., 2009; Giovannucci & Chan, 2010). Nonetheless, several potentially beneficial effects of dietary supplements have been recorded, including reductions in the risk of cancer carcinogenesis and tumor progression, enhancement of immune responses against cancers or immune systems in general, improvements in nutrition and general health, and reductions in the adverse effects of cancer therapy (Cassileth et al., 2009; Doyle et al., 2006; Isenring et all, 2010; Miller et al., 2009; Conklin, 2000; Nicolson & Conklin, 2008; Nicolson, 2010; Ströhle et al., 2010),

This review will concentrate on one particularly troublesome aspect of cancer and cancer therapy—cancer-associated fatigue.

#### **2. The importance of cancer-associated fatigue**

Cancer-associated fatigue adds considerably to cancer morbidity (Brown & Kroenke, 2009; Hofman et al., 2007). It exists in all types of cancers from the least to the most progressed cancers (Brown & Kroenke, 2009; Hofman et al., 2007). Along with pain and nausea, it is one of the most common and troublesome symptoms of cancer (Hofman et al., 2007; Prue et al., 2006), especially in advanced cancers (Curt et al., 2000; Prue et al., 2006; Respini et al., 2003). In patients receiving adjuvant therapies the prevalence of cancer-associated fatigue is reported to be as high as 95% (Sood & Moynihan, 2005). Thus cancer-associated fatigue is a

Nutritional Supplements for Cancer-Associated Fatigue and

**to fatigue**

Curt et al., 2000; Hofman et al., 2007; Sood, & Moynihan, 2005).

ROS/RNS pathways and fatigue and fatiguing illnesses.

(Nicolson, 2010; Nicolson & Conklin, 2008).

towards more physiological levels (Prasad et al., 200; Tanaka, 1994).

Cancer Therapy – A Molecular Basis for Restoring Mitochondrial Function 149

Most fatigued patients understand fatigue as a loss of energy and inability to perform even simple tasks without exertion. Many medical conditions are associated with fatigue, including respiratory, coronary, musculoskeletal, and bowel conditions as well as infections (Kroenke et al., 1988; McDonald et al., 1993; Morrison, 1980). However, this symptom is especially important in the overwhelming majority of cancer patients (Ahlberg et al., 2003;

**3. Oxidative stress and damage to mitochondrial membranes – Relationship**

Another phenomenon associated with cancer and its progression as well as aging and agerelated degenerative diseases is oxidative stress (Dreher & Junod,1996; Halliwell, 1996; Kehrer, 1993). Oxidative stress is caused by an intracellular excess of reactive oxygen (ROS) and nitrogen (RNS) free radical species over intracellular antioxidants. When this imbalance occurs, it results in oxidation of cellular structures, such as membrane lipids and proteins, and mutation of mitochondrial and nuclear DNA (Abidi & Ali, 1999; Bartsch & Nair, 2004; Marnett, 2000; Stadtman, 2002). ROS and RNS are naturally occurring cellular free radical oxidants that are usually present in low concentrations and are involved in gene expression, intracellular signaling, cell proliferation, antimicrobial defense and other normal cellular processes (Castro & Freeman, 2001; Ghaffari, 2008; Johnson et al., 1996). However, when ROS/RNS are in excess over cellular antioxidants, damage can occur to cellular structures (Abidi & Ali, 1999; Castro & Freeman, 2001; Ghaffari, 2008; Maes & Twisk, 2009). Recently Maes (2009) has proposed a link between excess oxidative stress (and activation of

Under normal physiological conditions our cellular antioxidant defenses usually maintain ROS/RNS at appropriate concentrations that prevent excess oxidation of cellular structures (Barber & Harris, 1994; Fridovich,1995; Sun, 1990). Endogenous cellular antioxidant defenses include glutathione peroxidase, catalase and superoxide dismutase, among other enzymes (Jagetia et al., 2003; Seifried et al., 2003), and low molecular weight dietary antioxidants (Aeschbach et al., 1994; Schwartz,1996). Some of these dietary antioxidants have been used as natural chemopreventive agents to shift the excess concentrations of oxidative molecules

Excess oxidative stress (or primarily its mediators—excess ROS/RNS) within cancer cells has been linked to promotion and progression of malignancy of cancers (Brown & Bicknell, 2001; Klaunig & Kamendulis, 2004; Ray et al., 2000; Tas et al., 2005; Toyokuni et al., 1995).  In all of these cancers ROS/RNS were in excess of antioxidant concentrations, resulting in cellular oxidative stress. Thus these cancers could possibly have been induced as a consequence of excess ROS/RNS and oxidative damage to the genetic apparatus (Abidi & Ali, 1999; Dreher & Junod, 1996; Jaruga et al., 1992). Even more likely than carcinogenesis is the promotion of progression of tumors that might not evolve to malignancy in the absence of excess oxidative stress

problem before, during and after therapy and can continue to be a problem years after cancer treatment (Curt et al., 2000; Hofman et al., 2007). Cancer-associated fatigue has a very strong negative effect on quality of life; therefore, addressing and reducing cancerassociated fatigue should be an important consideration in the treatment of cancer (Curt et al., 2000; Nicolson, 2010).

 Unfortunately, cancer-associated fatigue is rarely treated, and is often thought to be an unavoidable symptom (Brown & Kroenke, 2009; Hofman et al., 2007).

Cancer-associated fatigue can be considered to be the product of a variety of contributing factors (Ahlberg et al., 2003). In addition to a decrease in the availability of cellular energy, there are psychological factors, such as the presence of depression, anxiety, sleep disturbances, among others, as well as anemia, endocrine changes, poor nutritional status, release of inflammatory cytokines and cancer therapy that can all contribute to cancerassociated fatigue (Ahlberg et al., 2003; Gutstein, 2001; Manzullo & Escalante, 2002; Sood & Moynihan, 2005). Thus cancer-associated fatigue does not occur as an isolated symptom; rather, it occurs as one of multiple symptoms that are present in cancer patients. Similar to some other symptoms in cancer patients, the severity of cancer-associated fatigue correlates with decreased functional abilities (Given et al., 2001).

Cancer therapy also contributes to cancer-associated fatigue (Sood & Moynihan, 2005). In fact, the most commonly found and disabling effect of cancer therapy is fatigue (Given et al., 2001; Sood & Moynihan, 2005; Vogelzang et al., 1997). During cancer therapy fatigue problems can vary, from mild to severe, and excess fatigue during cancer therapy is a significant reason why patients discontinue therapy (Liu et al., 2005). Reviewing articles on the effects of cancer therapy on fatigue, it was noted that 80-96% of patients receiving chemotherapy and 60-93% receiving radiotherapy experienced moderate to severe fatigue, and fatigue continued for months to years after cancer therapy ended (Manzullo & Escalante, 2002). Therefore, in cancer patients controlling cancer-associated fatigue as well as therapy-induced fatigue are both important strategies (Marrow, 2007).

There have been efforts at understanding and treating cancer-associated fatigue as well as developing ways to distinguish between depression and cancer-associated fatigue (Brown & Kroenke, 2009). Both cancer-associated fatigue and depression have multidimensional and heterogeneous qualities, possessing physical, cognitive and emotional dimensions and a certain degree of overlap across these dimensions (Brown & Kroenke, 2009; Sood & Moynihan, 2005). In cancer patients fatigue or loss of energy is a core aspect of diagnosing depression—thus both fatigue and depression are often diagnosed together. This is usually accomplished by self-assessment, where fatigue and depression are considered to be part of a clinical symptom cluster, co-morbitity or syndrome (Arnold, 2008; Bender et al., 2008). There are techniques, moreover, that can distinguish between these two different symptoms by removal of fatigue-associated assessments from an analysis of depression (Smets et al., 1996; Stone et al., 2000). When assessing fatigue or cancer-associated fatigue, criteria have been established that take depression into consideration, and these two symptoms can thus be separated out by considering unshared properties (Cella et al., 2001).

Chronic or intractable fatigue lasting more than 6 months that is not reversed by normal sleep is the most common complaint of patients seeking general medical care (Kroenke et al., 1988; Morrison, 1980). It occurs naturally during aging and is also an important secondary condition in many clinical diagnoses (Kroenke et al., 1988; McDonald et al., 1993).

problem before, during and after therapy and can continue to be a problem years after cancer treatment (Curt et al., 2000; Hofman et al., 2007). Cancer-associated fatigue has a very strong negative effect on quality of life; therefore, addressing and reducing cancerassociated fatigue should be an important consideration in the treatment of cancer (Curt et

Although not well understood, cancer-associated fatigue is thought to be a combination of the effects of having cancer plus the effects of cancer treatments (Curt et al., 2000; Hofman et al., 2007). Unfortunately, cancer-associated fatigue is rarely treated, and is often thought to

Cancer-associated fatigue can be considered to be the product of a variety of contributing factors (Ahlberg et al., 2003). In addition to a decrease in the availability of cellular energy, there are psychological factors, such as the presence of depression, anxiety, sleep disturbances, among others, as well as anemia, endocrine changes, poor nutritional status, release of inflammatory cytokines and cancer therapy that can all contribute to cancerassociated fatigue (Ahlberg et al., 2003; Gutstein, 2001; Manzullo & Escalante, 2002; Sood & Moynihan, 2005). Thus cancer-associated fatigue does not occur as an isolated symptom; rather, it occurs as one of multiple symptoms that are present in cancer patients. Similar to some other symptoms in cancer patients, the severity of cancer-associated fatigue correlates

Cancer therapy also contributes to cancer-associated fatigue (Sood & Moynihan, 2005). In fact, the most commonly found and disabling effect of cancer therapy is fatigue (Given et al., 2001; Sood & Moynihan, 2005; Vogelzang et al., 1997). During cancer therapy fatigue problems can vary, from mild to severe, and excess fatigue during cancer therapy is a significant reason why patients discontinue therapy (Liu et al., 2005). Reviewing articles on the effects of cancer therapy on fatigue, it was noted that 80-96% of patients receiving chemotherapy and 60-93% receiving radiotherapy experienced moderate to severe fatigue, and fatigue continued for months to years after cancer therapy ended (Manzullo & Escalante, 2002). Therefore, in cancer patients controlling cancer-associated fatigue as well as

There have been efforts at understanding and treating cancer-associated fatigue as well as developing ways to distinguish between depression and cancer-associated fatigue (Brown & Kroenke, 2009). Both cancer-associated fatigue and depression have multidimensional and heterogeneous qualities, possessing physical, cognitive and emotional dimensions and a certain degree of overlap across these dimensions (Brown & Kroenke, 2009; Sood & Moynihan, 2005). In cancer patients fatigue or loss of energy is a core aspect of diagnosing depression—thus both fatigue and depression are often diagnosed together. This is usually accomplished by self-assessment, where fatigue and depression are considered to be part of a clinical symptom cluster, co-morbitity or syndrome (Arnold, 2008; Bender et al., 2008). There are techniques, moreover, that can distinguish between these two different symptoms by removal of fatigue-associated assessments from an analysis of depression (Smets et al., 1996; Stone et al., 2000). When assessing fatigue or cancer-associated fatigue, criteria have been established that take depression into consideration, and these two symptoms can thus

Chronic or intractable fatigue lasting more than 6 months that is not reversed by normal sleep is the most common complaint of patients seeking general medical care (Kroenke et al., 1988; Morrison, 1980). It occurs naturally during aging and is also an important secondary condition in many clinical diagnoses (Kroenke et al., 1988; McDonald et al., 1993).

be an unavoidable symptom (Brown & Kroenke, 2009; Hofman et al., 2007).

with decreased functional abilities (Given et al., 2001).

therapy-induced fatigue are both important strategies (Marrow, 2007).

be separated out by considering unshared properties (Cella et al., 2001).

al., 2000; Nicolson, 2010).

Most fatigued patients understand fatigue as a loss of energy and inability to perform even simple tasks without exertion. Many medical conditions are associated with fatigue, including respiratory, coronary, musculoskeletal, and bowel conditions as well as infections (Kroenke et al., 1988; McDonald et al., 1993; Morrison, 1980). However, this symptom is especially important in the overwhelming majority of cancer patients (Ahlberg et al., 2003; Curt et al., 2000; Hofman et al., 2007; Sood, & Moynihan, 2005).