Molecular Mechanisms in Plant Adaptation

Preface

This volume brings together recent findings about mechanistic understanding in diverse areas of plant adaptation. It offers the readership novel insights into contemporary results concerning the evolution, development, and stress responses. Moreover, it uniquely combines the technological and methodological perspectives. This volume aims not only to review the published results but also to introduce new concepts, discuss novel findings, and offer original views on the perspectives and challenges in this field. Therefore, it provides balanced coverage of materials suitable for both experts and newcomers to this area.

This book is organized into nine chapters. The first chapter summarizes recent advances in high-throughput technologies that are necessary to delve deeper into the molecular mechanisms of plant adaptation. Further, the use of natural variation in Arabidopsis thaliana in the studies of local adaptation and evolution is reviewed. The third chapter provides a specific example of how natural variation can be used in combination with candidate gene approaches to understand the mechanisms of seed dormancy and viability. The next three chapters provide unique views on mechanistic understanding of plant responses to abiotic and biotic stresses. In Chapter 4, the abiotic stress response in plants is examined from a single-cell point of view. In Chapter 5, the metabolic responses to biotic stress in plants are introduced. In Chapter 6, the latest developments in the role of small RNAs in both biotic and abiotic stress responses are presented. The next chapter deals with the evolutionary perspective in understanding adaptation. It tackles the adaptation of flower form, with a special focus on an evo-devo approach, revealing the evolutionary history of the SEPALLATA 3 gene. The challenge of employing the data from high-throughput technologies in understanding the mechanisms of plant adaptation may be addressed by mathematical modeling. To this end, the determination of adaptive patterns and the predictions on plant behavior are presented in Chapter 8. The final chapter highlights the importance of combining laboratory work with field experiments and is indented to help the reader formulate the guidelines on how field experiments should be performed and what factors should be taken into account. While each chapter can stand on its own, I hope that the readers will find the entire volume interesting and offering them comprehensive understanding of the current hot topics in plant adaptation.

I would like to thank all the authors for making this project thoroughly interesting and enjoyable. This book would not have been possible without their dedicated and smooth cooperation. I also thank Justin Jeffryes and Stephanie Dollan at Wiley for their support. Additionally, I appreciate the valuable comments and help by Lisa Smith, Zoran Nikoloski, Christian Schudoma, Björn Plötner, Aditya Sharma, Sebastian Proost, Alisdair Fernie, Hirofumi Ishihara, Eunyoung Chae, Vishal Kapoor, Prashant Pandey, and Matti Laitinen.

Roosa A. E. Laitinen

1

Technological Advances in Studies of Plant Adaptation

José G. Vallarino¹ and Sonia Osorio¹,²

¹Department of Molecular Biology and Biochemistry, Instituto de Hortofruticultura Subtropical y Mediterránea La Mayora – University of Malaga-Consejo Superior de Investigaciones Científicas (IHSM-UMA-CSIC), Málaga, Spain

²Max-Planck-Institut für Molekulare Pflanzenphysiologie, Potsdam-Golm, Germany

Introduction

In order to survive, organisms must adapt to their environment. For plants, adaptation is particularly important because they are sessile and therefore cannot move away from unfavorable conditions. Therefore, plants have evolved strategies, both on short and long time scales, to adjust their growth and development to extreme environmental conditions. One interest lies in understanding the underlying mechanisms of plant adaptation to reveal evolutionary signatures that may help to understand the selective forces of allele frequencies. The other objective is to gather information to enable us to develop crop plants that can resist the changes in environment without losing their productivity. Recent advances in high-throughput technologies have generated a wealth of data that offer new opportunities and challenges in revealing the mechanisms of plant adaptation. By combining the different high-throughput methods, it is possible to gain knowledge of the complex interactions between genotype and phenotype. The large amounts of data have also introduced new challenges in assembling, analyzing, and discovering patterns (Cronn et al. 2012; Kvam et al. 2012; Higashi and Saito 2013; Toubiana et al. 2013). The aim of this chapter is to bring together the recent technological advances in the studies of plant genomes, proteomes, and metabolomes and their general applications in understanding plant adaptation. In addition, we pay special attention to the way high-throughput technologies are helping us to understand nonmodel species, which would facilitate improvements in our understanding of plant breeding.

Next-Generation Sequencing Technologies

Next-generation sequencing (NGS) technologies are capable of producing billions of short nucleotide reads (50–800 bp) in parallel, at a fraction of the cost of traditional Sanger sequencing. Solexa sequencing was the first NGS technique to become commercially available, in 2005. Since the first plant genome (Arabidopsis thaliana; The Arabidopisis Genome Initiative, 2000) was sequenced in the year 2000, the whole-genome sequencing technologies have improved and 60 plant genomes have been sequenced till date. The reduced costs in whole-genome sequencing have led to the development of bigger sequencing projects such as the Arabidopsis 1001 Genomes Project (www.1001genomes.org and signal.salk.edu/atg1001) and OMAP, The Oryza Map Alignment Project, and various other large-scale projects. The Arabidopsis 1001 genomes was initiated with the goal of identifying the total genetic variation present within this species, whereas the OMAP project aims to sequence all species from the Oryza genus. Some of the other large-scale projects include the 1000 Plant Genomes Project (www.onekp.org), and the 1000 Plant and Animal Genome Project (www.1d1.genomics.cn. At present, six NGS platforms are available and a seventh one is in advanced development stage (Liu et al. 2012; Table 1.1). The different sequencing methods can be grouped into three main types: (i) sequencing by synthesis, (ii) sequencing by ligation, and (iii) single-molecule sequencing. The different methods are presented in more detail later.

Table 1.1 First-, second-, and third-generation DNA sequencing platforms listed in the order of commercial availability.

Transcriptome characterization.

Targeted resequencing.

De novo BACs, plastids, microbial genomes.

Mutation detection.

De novo plant genomes.

Metagenomics.

Resequencing and transcript counting.

Others such as ChIP-Seq, μRNA-seq, Methyl-Seq, and so on.

*A commercial launch date for the Starlight system is not yet known but some information about its performance characteristics is known.

Sequencing by Synthesis

Sequencing by synthesis is based on determining base composition through the detection of chemiluminescence released by the incorporation of nucleotides during the synthesis of the complementary DNA strand by a DNA polymerase (Nyren et al. 1993). In this method, the DNA is fragmented to the appropriate size, ligated to adapter sequences, and then clonally amplified to enhance the fluorescent chemical signal. Templates are then separated and immobilized in preparation for flow cell cycles. The sequencing by synthesis method is employed by three different sequencing platforms. In Roche 454 pyrosequencing (http://www.my454.com) a single-primed DNA template is adhered to a microbead and amplified using emulsion PCR. In the beginning, this technology produced read lengths of approximately 100 bp. Nowadays, the read lengths obtained from pyrosequencing are comparable to the ones produced by Sanger sequencing (approximately 800 bp). Because of its long reads, this platform is often used for generating reads for de novo genome or transcriptome assembly (Strickler et al. 2012; Zalapa et al. 2012). The grape genome (published in 2007) was the first genome sequenced, based on a combination of 454 and Sanger sequencing and since then at least 18 genomes have been sequenced (Jaillon et al. 2007). Among them are the genomes of apple (Velasco et al. 2010), cocoa (Argout et al. 2011), potato (The Potato genome Sequencing Consortium 2011), banana (D'Hont et al. 2012), cotton (Li et al. 2014), wheat (Aegilops tauschii) (Jia et al. 2013), and bladderwort (Ibarra-laclette et al. 2013).

The second platform based on the sequencing by synthesis method is the Illumina Genome Analyzer (http://www.illumina.com), initially developed by Solexa. It uses solid-phase bridge amplification in which 5′- and 3′-adapters are ligated to each end of the DNA template. This method is currently the most widely used NGS platform in plant sciences (Kane et al. 2012; Strickler et al. 2012; Zalapa et al. 2012) because it yields the highest throughput with one of the highest raw accuracy. The Illumina HiSeq sequencer was launched in 2011. This platform is currently able to sequence up to 540–560 Gbp in a single two-flow cell in an 8.5-day run (http://illumina.com/systems/hiseq_2000.ilmn). Illumina has been used to sequence plant genomes such as cotton (Li et al. 2014), chickpea (Varshney et al. 2013), ancient lotus (Ming et al. 2013), pear (Wu et al. 2013), and watermelon (Guo et al. 2013).

The third platform using sequencing by synthesis is Ion Torrent by Life Technologies (http://www.iontorrent.com). This platform is the only NGS technology that is not based on fluorescent dyes but rather measures the pH change as the result of the release of a H+ ion upon nucleotide incorporation, using the semiconductor technology (Rothberg et al. 2011). By sequentially adding nucleotides, the sequencer is able to detect which nucleotide has been incorporated into the elongating strand (Howden et al. 2011; Rothberg et al. 2011). Life Technologies currently offers two series of NGS instruments: the large-scale 5500 series, which can yield up to >20 Gbp per day (75bp reads), and the small-scale Ion Torrent series yielding up to 10 Gbp per run in less than a day. The Ion Torrent series (PGM and Ion Proton) are smaller instruments that use a semiconductor chip technology to capture the signal emission after incorporation of a single base to the elongating strand of DNA. The Ion Torrent has the lowest throughput but also the fastest turnaround times of all commercially available NGS systems. It can yield several hundred-thousand reads in less than 2 h. Publications on research that have utilized the Ion Torrent platform currently focus on the sequencing of microbial genomes (Howden et al. 2011; Rothberg et al. 2011), but this platform has clearly made its way into programs pursuing plant-based objectives. One example is the recent study by Mascher et al. (2013) in which they compare Ion Torrent and Illumina HiSeq 2000 platforms by sequencing a barley recombinant line population.

Sequencing by Ligation

Sequencing by ligation methods harnesses the mismatch sensitivity of DNA ligase to determine the sequence of nucleotides in a given DNA strand (Landegren et al. 1988). These methods use oligonucleotide probes of varying lengths, which are labeled with fluorescent tags. Methods based on sequencing by ligation usually differ in their probe usage and read length. The SOLiD platform (http://www.appliedbiosystems.com) utilizes sequencing by ligation method to determine the sequence composition of DNA. These methods are often used in resequencing studies (Ashelford et al. 2011), transcriptomics, or in genomic sequencing. So far, only two genomes have been sequenced using SOLiD sequencing: strawberry (Shulaev et al. 2011) and tomato (The tomato Genome Consortium 2012).

Single-Molecule Sequencing

Single-molecule sequencing technology is also known as the third-generation sequencing technology. This technology is based on a detectable signal produced by nucleotide incorporation via chemiluminescence from a single nucleic acid molecule, thus eliminating the need for DNA template amplification. This method has been used for direct RNA sequencing, thus removing the biases created during cDNA amplification in RNA-seq studies (Ozsolak et al. 2009). Single-molecule sequencing has some benefits over the other NGS methods, one of them being simplified sample preparation that can use degraded or low concentrations of starting material (Orlando et al. 2011) and the avoidance of PCR errors and biases introduced during template amplification. As mentioned before, NGS technologies are evolving at a very rapid pace. Companies are constantly improving the performance of the technology used. These emerging technological developments may herald the fourth generation of NGS techniques. Several optical sequencing technologies are being explored that enable long DNA strands to be read and sequenced with greater efficiency. Other research is being done on nanopores as a means of reading DNA sequences based solely on the inherent electronic or chemical properties of the native nucleotides (Thompson and Milos 2011; Maitra et al. 2012). The different nanopore sequencing strategies that are in development enable individual base detection, based on the measurement of conductivity changes across a lipid membrane while a DNA fragment is pulled through a nanoscale pore by an electric current. Although nanopore sequencing faces several challenges, it seems to have a promising future.

Applications of Next-Generation Sequencing

NGS enables progress in studying the genetics of plant adaptation beyond what is possible with current genetic methods. Most sequencing applications can be divided into two categories: (i) de novo sequencing and (ii) resequencing. For de novo sequencing, reads are obtained from an unknown sequence and assembled to reconstruct the sequence, whereas in resequencing the unknown sequences are compared to a known reference sequence. De novo applications are usually slower and more computationally intensive than resequencing. Major resequencing applications include polymorphism discovery, transcriptome profiling, and epigenome analysis.

Polymorphism Detection, Genome-Wide Association Studies (GWAS), and Gene Identification

The analysis of genomic variation is an essential part of studying plant adaptation. Studies that search for a statistical association between a phenotype and a particular locus or loci by screening across the entire genome are called genome-wide association studies (GWASs). During the past decades, the use of genotyping has enabled the characterization and mapping of genes and metabolic pathways in plants, as well as the study of the genetic variation and evolutionary history, marker-assisted selection (MAS), and germplasm characterization. Single nucleotide polymorphism (SNP) markers are the most widely used genotyping markers due to their abundance in the genome and the relative ease in determining their frequency in a collection of individuals. The development of markers as well as their scoring across populations traditionally has been a high-cost process, with many labor-intensive and time-consuming steps. With the help of NGS technologies, several methods have been developed for high-throughput genetic marker discovery. All the methods involve (i) the digestion of multiple samples of genomic DNA with one or more restriction enzymes, (ii) the selection of the resulting restriction fragments, and (iii) NGS of the final set of fragments, which should be less than 1 kb in size. Polymorphisms in the resulting sequenced fragments can be used as genetic markers. All these methods can be classified into three different categories: (i) reduced representation sequencing (reduced representation libraries, RRLs) and complexity reduction of polymorphic sequences (CRoPS), (ii) restriction-site-associated (RAD-seq), and (iii) low-coverage genotyping, including multiplexed shotgun genotyping (MSG), and genotyping by sequencing (GBS).

RRLs and CRoPS are the two methods for sampling and sequencing a small set of genome-wide regions without sequencing the entire genome. The RRL approach (adapted to NGS) has been used to generate tens of thousands of candidate SNPs, for example, in maize (Gore et al. 2009) and soybean (Hyten et al. 2010). RAD-seq is a method that uses Illumina NGS for genotyping. The RAD-seq approach involves a genome-wide survey of nucleotide diversity of regions flanking restriction sites and allows the simultaneous detection and genotyping of thousands of genome-wide SNPs (Wagner et al. 2013). For example, RAD-seq has been used to construct linkage maps in barley (Chutimanitsakun et al. 2011) and ryegrass (Pfender et al. (2011). The high cost of multiplexing prevented the genotyping of population or pooled samples for initial iterations of the method, but emerging pipelines, such as double digest RAD-seq (ddRAD-seq), have allowed cheaper polymorphism discovery and genotyping for large samples (Peterson et al. 2012). Currently, ddRAD-seq offers the possibility to obtain genomic data necessary for inferences about population structure, especially when its consequences are not extreme (such as local adaptation). An advantage is that RADseq can be applied not only to species with available reference genomes, but also to study those species in which no reference genome is available.

Large GWASs require hundreds of thousands of markers to generate sufficient information and coverage, and getting such a resolution has been greatly facilitated by the emergence of NGS technologies. Recently, NGS technologies have been used to resequence recombinant inbred lines (RILs) in many plant species. A collection of 5000 maize RILs have been resequenced and a total of 1.4 million SNPs and 0.2 million indels (large insertions and deletions) were generated, which span the 5000 inbred lines (Gore et al. 2009). Seeds of the RILs can be used to grow and phenotype plants for any trait of interest (McMullen et al. 2009). Using this population, Buckler et al. (2009) identified 50 loci that contribute to variation in the genetic architecture of flowering time, with many loci showing small effects determining leaf architecture. Poland et al. (2011) identified candidate genes for resistance to northern leaf blight in 29 loci, which included quantitative trait loci (QTL) with small additive effects. In yet another study, the resequencing of 150 RILs derived from a cross between Indica and Japonica rice cultivars resulted in the discovery of 1,226,791 SNPs, separated by 40 kbp on average (Wang et al. 2011). Haplotypes and recombination breakpoints could be determined for each RIL, using the parental origins of SNPs in discrete regions of the genome and a recombination map of 2334 bins for the 150 RILs was constructed from the haplotypes. Using each bin as a genetic marker, various phenotypes were mapped to 49 QTLs, including five QTLs physically located at positions overlapping with known candidate genes (Wang et al. 2011). Another example of how NGS greatly enhances the ability to find associations between phenotypes and the underlying genetic variation is a study performed on lodgepole pines (Pinus contorta). Data obtained for more than 95,000 SNPs across 98 lodgepole pines could be used to identify 11 loci associated with degree of serotiny (Parchman et al. 2012). These results provided a first genome-wide association map of an adaptive trait (serotiny) in pines.

Transcriptome Analysis

RNA-seq is a rapidly growing application of NGS to study gene expression (transcriptomics). Short-read NGS technologies such as Illumina and SOLiD have allowed the development of transcription profiling strategies that are more sensitive and accurate than other high-throughput technologies such as microarrays. In RNA-seq, total or messenger RNA is fragmented and converted into complementary DNA (cDNA). Alternatively, RNA can first be converted into cDNA and then fragmented. Adapters are attached to one or both ends, and reads are sequenced as single or paired ends (Wang et al. 2009a; Marguerat and Bahler 2010). Depending on the genomic resources available for the organism of interest, the resulting sequences can be aligned to either a reference genome (or reference transcripts) or the genome can be assembled de novo. Therefore, RNA-seq is practical for nonmodel species as it provides information on the transcriptome, including gene structure, expression levels, presence of multiple isoforms, and sequence polymorphisms. RNA-seq can also be used for transcriptome characterization, SNP detection, and comparative gene expression (Strickler et al. 2012). Large-scale transcriptomic profiling can provide important insights, for example, into the response of individuals to climatic changes predicted due to global warming. Realistic heat wave conditions were applied in a common stress garden to southern and northern populations of the seagrass Zostera marina (Franssen et al. (2011). These results suggested that transcriptomic patterns could be used to predict how populations get adapted to thermal stress.

Interaction Studies

Study of transcription factors (TFs) and other chromatin-associated proteins are essential in elucidating the complex phenotype-influencing mechanisms. Determining how proteins interact with DNA to regulate gene expression is essential to fully understand the processes of plant adaptation. Traditional methods have successfully identified TF-binding sites and specific DNA-associated protein modifications and their roles in regulating genes, but these experiments are limited in scale and resolution. The powerful Illumina whole-genome chromatin IP sequencing (ChIP-Seq) application provides a snapshot of a single protein's direct physical interactions with DNA at a particular time in a particular tissue on a genome-wide scale (Mardis 2007; Kaufmann et al. 2010).

Specific DNA sites in direct physical interaction with TFs can be isolated by chromatin immunoprecipitation (ChIP). ChIP produces a library of target DNA sites that a given factor was bound to, in vivo. NGS technology allows the determination of the sequences of ChIP-isolated DNA fragments for identification and quantification of the sites bound by a protein of interest. The big advantage of NGS technology is that a single sequencing run can scan for the protein–DNA interactions on a genome-wide basis with high resolution. For example, ChIP-Seq has been applied to elucidate the role of the MADS domain protein FLOWERING LOCUS C (FLC) as a floral repressor (Michaels and Amasino 1999; Deng et al. 2011). FLC was found to bind to 505 specific sites in the A. thaliana genome, but binding was nonrandom. As expected, FLC was found to mainly bind to promoter regions with CC(A/T)6GG motifs (Deng et al. 2011). In another example, ChIP-Seq was used to elucidate the binding sites for RIN, one of the main ripening TFs in tomato (Zhong et al. 2013). These results provided some insight into the systems regulation underlying fruit ripening, showing that the epigenome is not static during fruit development (Zhong et al. 2013).

Methylome Analysis and Small RNA Characterization

Traditionally, the material that is carried from one generation to the next and is responsible for the phenotypic variation is associated with genes and DNA. However, there exists the phenotypic variation that cannot be explained by differences in DNA sequences but by changes in gene expression patterns that influence the phenotype. These are called epigenetic mechanisms. Yet, it is still largely unknown how the interplay between the epigenetic modifications and genes could influence adaptation and evolution. Therefore, it is essential to identify not only genetic variation but also natural epigenetic variation. The known epigenetic mechanisms include DNA methylation, histone modification, and RNA-directed DNA methylation (Bird 2007; Becker and Weigel 2012; Schmitz and Erker 2012). Functional genomics aims to interrogate the functional elements and regulatory mechanisms in the genome, including DNA methylation and histone modifications. One important consideration is that both the epigenome and methylome are larger than the genome of an organism. As a major part of the epigenome, the methylome consists of the sum of genome and methylation states at every cytosine location. NGS can be integrated into epigenomic studies and several new and innovative sequencing-based methods have been developed together with bioinformatics and analytical tools (Horner et al. 2010; Huss 2010). Before NGS, epigenetic studies were mostly limited to individual genes or sets of candidate genes or regions. One exception is the work done in Arabidopsis by Zhang et al. (2007), which provided the first genome-wide study in plants and considerable information on methylation distribution and its effect on gene expression. The use of NGS technologies coupled with bisulfite conversion (BC), restriction digestion, or immunoprecipitation strategies facilitate genome-wide methylome analysis in plants and play an important role in further characterization of epiregulation in plants (Zhang and Jeltsch 2010). DNA methylomes in 10 A. thaliana lines, derived 30 generations ago from a common ancestor (Shaw et al. 2000), captured the formation of pure epialleles, some of which resulted in significant transcriptional variations (approximately 10- to 1000-fold changes) of the affected locus (Schmitz et al. 2011). Another study in the same population showed that the number of epimutations does not increase with time, indicating that many are not stably inherited over the long term (Becker et al. 2011). Also, they found that transposon methylation was highly conserved. They concluded that the biased distribution and frequent reversion of epimutations determine the ability of any type of allele to be subject to Darwinian selection (Becker et al. 2011).

In addition to the analysis of the methylome, NGS technologies have been used to identify small RNAs. Small RNAs are short nonprotein coding RNA molecules ranging from 20 to 30 nucleotides that have a role in development, genome maintenance, and responses to environmental stresses (Simon et al. 2009). The role of small RNAs in plant adaptation is reviewed in detail in Chapter 7 of this book. Most small RNAs belong to two groups, microRNAs (miRNAs) and small interfering RNAs (siRNAs). miRNAs are about 21 bases long and usually play a post-transcriptional regulatory role by directing cleavage of a specific transcript. siRNAs are normally 24 bases long and influence the post-transcriptional gene silencing (Vaucheret 2006). Small noncoding RNAs in the low-molecular-weight total RNA fraction of plants were detected before the development of high-throughput sequencing techniques (Gupta et al. 1989). Subsequently, Illumina, SOLiD, and Roche 454 platforms manifested optimal features for short-read sequencing and small RNA detection (Zhang et al. 2009; Gonzalez-Ibeas et al. 2011). Thus, construction and sequencing of small RNA libraries, coupled with bioinformatical analysis for miRNA prediction, is currently the most powerful experimental method for miRNA identification (Kurtoglu et al. 2014). For example, miRNAs were discovered using this method in wheat grown under normal conditions (Kenan-Eichler et al. 2011; Li et al. 2013; Meng et al. 2013; Sun et al. 2013) as well as under biotic stress caused by pathogens, powdery mildew (Xin et al. 2010), and under abiotic stress due to extreme heat (Xin et al. 2010; Yao et al. 2010), cold (Yao et al. 2010; Tang et al. 2012), salinity (Yao et al. 2010), or dehydration (Yao et al. 2010). In some of these reports, wheat miRNAs were discovered via sequencing on an Illumina platform (Xin et al. 2010; Tang et al. 2012; Meng et al. 2013; Sun et al. 2013) while in others a Roche 454 platform was used (Yao et al. 2010; Li et al. 2013).

Metagenomics

Metagenomics is the study of the plant-associated microbiota based in genomic analysis. These studies provide insight into the composition and physiological potential of plant-associated microorganisms. In metagenomics, NGS technologies are used to identify organismal communities from small amounts of DNA. These could be used to characterize biogeographical patterns of diversity and functional capabilities of soil microbes in intact and reconstructed soils (Harris 2009; Fierer et al. 2013). As an example, Ruzicka et al. (2013) used high-throughput sequencing to characterize the transcriptomes of both tomato and its arbuscular mycorrhizal fungal symbiont in the field. Instead of culturing the symbiont, a metagenomic sequencing strategy was employed, where RNA from a wild-type tomato plant and a mutant for reduced mycorrhizal colonization were sequenced and separated using bioinformatics (Ruzicka et al. 2013). This metagenomic analysis revealed a cluster of genes for transport and cell wall remodeling, which is required for the symbiotic relationship. Metagenomic sequencing opens up the opportunity to explore additional symbiotic relationships and further functionally characterize aspects of a genome that are not innate to the genomic sequence.

Proteome Analysis in Understanding Plant Adaptation

The proteome is defined as the total set of proteins or gene products present in a biological unit. The proteomics approach aims to know how, where, and when, the several thousands of individual proteins are produced in a cell. Advances in proteomics have been possible due to continuous improvement in the methods of protein extraction, purification, and separation, as well as improvements in equipment, and protein identification, quantification, and characterization. With the combination of proteomics and NGS technologies, identification and annotation of proteins