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3.8: Mutations and the origins of genotype-based variation - Biology

3.8: Mutations and the origins of genotype-based variation - Biology


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So now the question arises, what is the origin of genetic – that is, inheritable-variation? How do genotypes change? As a simple (and not completely incorrect) analogy, we can think of an organism’s genotype as a book. This book is also known as its genome (not to worry if this seems too simple, we will add needed complexities as we go along). To continue our analogy, a few critical changes to the words in a sentence can change the meaning of a story, sometimes subtly, sometimes dramatically, and sometimes a change will lead to a story that makes no sense at all.

At this point we will define the meaningful regions (the words and sentences) as corresponding to genes and the other sequences as intragenic regions, that is, spaces between genes. We estimate that humans have ~25,000 genes (we will return to a molecular level discussion of genes and how they work in Chapters 7 through 9). As we continue to learn more about the molecular biology of organisms, our understanding of both genes and intragenic regions becomes increasingly sophisticated. The end result is that regions that appear meaningless can influence the meaning of the genome. Many regions of the genome are unique, they occur only once within the string of characters. Others are repeated, sometimes hundreds to thousands of times. When we compare the genotypes of individuals of the same type of organism, we find that they differ at a number of places. For example, over ~55,000,000 variations have been found between human genomes and more are likely to be identified. When present within a population of organisms, these genotypic differences are known as polymorphisms, from the Latin meaning multiple forms. Polymorphisms are the basis for DNA-based forensic identification tests. One thing to note, however, is that only a small number of these variations are present within any one individual, and considering the size of the human genome, most people differ from one another less than 1 to 4 letters out of every 1000. That amounts to between 3 to 12 million letter differences between two unrelated individuals. Most of these differences are single characters, but there can be changes that involve moving regions from one place to another, or the deletion or duplication of specific regions. In sexually reproducing organisms, like humans, there are typically two copies of this book in each cell of the body, one derived from each of the organism’s parents - organisms with two genomic “books” are known as diploid. When a sexual organism reproduces, it produces reproductive cells, known as gametes: sometimes these are the same size. When gametes differ in size the smaller one is known as a sperm and the larger is known as an egg. Each gamete contains one copy of its own unique version of the genomic book and is said to be haploid. This haploid genome is produced through a complex process known as meiosis that leads to the significant shuffling between the organism’s original parental genomes. When the haploid sperm and haploid egg cells fuse a new and unique (diploid) organism is formed with its own unique pair of genomic books.


Impacts of allopolyploidization and structural variation on intraspecific diversification in Brassica rapa

Despite the prevalence and recurrence of polyploidization in the speciation of flowering plants, its impacts on crop intraspecific genome diversification are largely unknown. Brassica rapa is a mesopolyploid species that is domesticated into many subspecies with distinctive morphotypes.

Results

Herein, we report the consequences of the whole-genome triplication (WGT) on intraspecific diversification using a pan-genome analysis of 16 de novo assembled and two reported genomes. Among the genes that derive from WGT, 13.42% of polyploidy-derived genes accumulate more transposable elements and non-synonymous mutations than other genes during individual genome evolution. We denote such genes as being “flexible.” We construct the Brassica rapa ancestral genome and observe the continuing influence of the dominant subgenome on intraspecific diversification in B. rapa. The gene flexibility is biased to the more fractionated subgenomes (MFs), in contrast to the more intact gene content of the dominant LF (least fractionated) subgenome. Furthermore, polyploidy-derived flexible syntenic genes are implicated in the response to stimulus and the phytohormone auxin this may reflect adaptation to the environment. Using an integrated graph-based genome, we investigate the structural variation (SV) landscapes in 524 B. rapa genomes. We observe that SVs track morphotype domestication. Four out of 266 candidate genes for Chinese cabbage domestication are speculated to be involved in the leafy head formation.

Conclusions

This pan-genome uncovers the possible contributions of allopolyploidization on intraspecific diversification and the possible and underexplored role of SVs in favorable trait domestication. Collectively, our work serves as a rich resource for genome-based B. rapa improvement.


Repeated colonization of a new world

When the last Ice Age ended about 10,000 years ago and glaciers started to melt, new streams and lakes formed in the northern hemisphere. Among the beneficiaries of this climatic change was a normally ocean-dwelling fish species, the three-spined stickleback (Gasterosteus aculeatus Fig. 1a) that successfully colonized the newly forming freshwater habitats in areas that used to be covered by ice [4]. This new environment posed novel challenges for sticklebacks, including different predators, food sources and lack of salinity. Interestingly, different populations across the species’ range responded in astonishingly similar ways to the new freshwater lifestyle. These geographically distinct populations lost their armored plates and defensive spines, and also evolved more pronounced elongated or deep body shapes, as well as different reproductive and foraging behaviors [4, 5] (Fig. 1a). Defying conventional evolutionary expectations, these repeated adaptive responses evolved within often extremely short evolutionary timespans of less than a dozen generations, raising the question of how such dramatic and in particular repeated adaptations can occur so rapidly [4].

Molecular mechanisms of repeated pelvic fin loss in sticklebacks. a Three-spined sticklebacks (Gasterosteus aculeatus) repeatedly colonized postglacial freshwater habitats. The adaptations in these independent populations are remarkably similar. b One common adaptation is the loss of the paired spiny pelvic fins. This loss is caused by the repeated deletion of a pelvic fin specific regulatory element that drives expression of pitx1, a crucial transcription factor for pelvic fin development. The exact deletions differ between freshwater populations and as Xie et al. show [3] are facilitated by sequence features in the genomic region that result in a non-canonical DNA conformation (Z-DNA) that causes double-strand breaks repaired by the more error-prone non-homologous end-joining repair


3.8: Mutations and the origins of genotype-based variation - Biology

Assistant Professor of Human Genetics School of Biosystem and Biomedical Science, College of Health Science

1. General Course Information

Coordinating Unit: School of Biosystem and Biomedical Science, College of Health Science

Genetics is a natural science that describes variation and inheritance using genetic components. The main aim of the course is to develop a critical understanding of the foundations of genetic theory and research. Our approach is a bottom-up hierarchical one: fundamental knowledge on population genetics and molecular genetics is essential for the understanding of genetic principles and higher-level translational or disease pathology. It will discuss a wide range of genetics topics: a history of genetic models, methodology and data modality large human genetic consortium and study design for genetic disorders.

In addition, the course will aim to inculcate a feeling of interest in some students who may want to go on to study more advanced topics such as basic genetics or the genomics of diseases and prepare them with the methodological background to approach these issues. Students who attend this course will be given an up-to-date knowledge of how the genetics works in traits and population at its most fundamental level. It will also provide the latest knowledge on our understanding of the latest genetic and genomic technologies.

2. Aims, Objectives & Graduate Attributes

The aim of this course is to familiarise students with the discipline of human genetics, a field through which we can integrate the fundamental concepts of most of the biological disciplines. Students will develop an appreciation of modern genetics while gaining a detailed understanding of the genetic fundamentals from heritability to genetic architecture of traits, as well as learning experimental design, methodology, data collection and basic interpretation skills in human genetics. Completion of this course will assure a sound basis for all biology-focused studies and provide key knowledge required to enable transition into advanced biological courses in third year. Many scientific employers and graduate supervisors see a strong understanding of genetics as a highly valuable characteristic in their laboratory members.

After successfully completing this course you should be able to:

Understand the genetic model to describe human traits, comprehend the genetic contribution to phenotype.

Understand the quantitative nature of human genetics and traits, and develop an appreciation of how the polygenic background impacts on human diseases.

Understand the molecular basis of inheritance and understand fundamental mechanisms that enable the transmission of genetic material.

Understand the role of genetic variations in natural population.

Understand the development of genetic screening and high-throughput genomic technologies.

Understand the core concept of genetic association studies, statistical methodology and experimental design.

Review and critique papers published in the field of human genetics.

Integrate the broad concepts of this discipline in the context of human genetics and develop an appreciation of how these advances are perceived by Society.

  • <Essential Genetics and Genomics 7th Edition> by Daniel L. Hartl, Jones & Bartlett Learning (Access via Korea University Library (You will need a login for your student account)
  • <Genetics in Medicine 8th Edition> by Thompson & Thompson (Access via Korea University Library (You will need a login for your student account)

4. Teaching & Learning Activities

Session 2. Human Heredity and Inheritance

Session 3. Mutation and Population

Session 5. Genetics in human diseases

QnA will be on Slack as Exam period

You can find previous years' mid term exam papers (questions, format) and final exam papers(questions)


Summary

The core symptoms of many neurological disorders have traditionally been thought to be caused by genetic variants affecting brain development and function. However, the gut microbiome, another important source of variation, can also influence specific behaviors. Thus, it is critical to unravel the contributions of host genetic variation, the microbiome, and their interactions to complex behaviors. Unexpectedly, we discovered that different maladaptive behaviors are interdependently regulated by the microbiome and host genes in the Cntnap2 −/− model for neurodevelopmental disorders. The hyperactivity phenotype of Cntnap2 −/− mice is caused by host genetics, whereas the social-behavior phenotype is mediated by the gut microbiome. Interestingly, specific microbial intervention selectively rescued the social deficits in Cntnap2 −/− mice through upregulation of metabolites in the tetrahydrobiopterin synthesis pathway. Our findings that behavioral abnormalities could have distinct origins (host genetic versus microbial) may change the way we think about neurological disorders and how to treat them.


Mutagenesis as a Tool in Plant Genetics, Functional Genomics, and Breeding

Plant mutagenesis is rapidly coming of age in the aftermath of recent developments in high-resolution molecular and biochemical techniques. By combining the high variation of mutagenised populations with novel screening methods, traits that are almost impossible to identify by conventional breeding are now being developed and characterised at the molecular level. This paper provides a comprehensive overview of the various techniques and workflows available to researchers today in the field of molecular breeding, and how these tools complement the ones already used in traditional breeding. Both genetic (Targeting Induced Local Lesions in Genomes TILLING) and phenotypic screens are evaluated. Finally, different ways of bridging the gap between genotype and phenotype are discussed.

1. Introduction

Plant breeding began as early as 10,000 BC during the Neolithic revolution, when tribes of hunter-gatherers started their shift towards a sedentary and agrarian society [1]. Domestication of crop plants seems to have taken place simultaneously in several subtropical regions, across central Africa, western South America, southeast Asia, and the Mediterranean during this period [2]. It is still a subject of discussion whether early attempts at domestication were consciously guided or random, although cave paintings at the Lascaux cave in France and Altamira in Spain as well as in other places show that early man was conscious of the life cycle and nature around him. The first experiments with plant breeding were most likely limited to selecting the most viable specimens from each harvest for subsequent sowing [3], which nevertheless had a profound impact on crop yield. This selection also altered the plants in new ways, since human selection was in practise often opposite to natural selection [4]. It was realised early, that domesticated plants were not to be considered “natural” and Charles Darwin coined the term “artificial selection” in 1859 to emphasise the difference between selection in nature and man-made selection [5]. He then further elaborated on the subject in a separate book published in 1868 [6]. Systematic selection has, over the years, now changed the domesticated plants to the point where the wild relatives of crop plants often are classified in completely different taxa. The greater yields from the domesticated crops, allowed for an increased human population density, formation of communities, and work specialization in areas other than food production within those communities. The move from foraging to agriculture also brought many negative consequences for humankind, including new infectious diseases and epidemics caused by the increased population density and trade, coupled with a decrease in food diversity [2]. Still, it is safe to say that plant breeding is the very basis of our modern civilization.

Since human demand for good traits and yield is very high, only a small fraction of the world’s approximately 200,000 plant species have, through history, survived the rigorous scrutiny of the domestication process. Around 3,000 species may have at some point been used for food, feed, spices, and materials but only as few as around 200 have ultimately been completely domesticated. Today, humankind is relying solely on 15–20 species for the entire world food production [7, 8].

2. Mutagenesis and TILLING

During crop evolution there has been a continuous reduction in genetic diversity as breeders have increasingly focused on so-called “elite” cultivars. This genetic erosion eventually became a bottleneck and various techniques to induce mutations and artificially increase variation emerged in the middle of the last century [9]. Initially, X-ray radiation was used as a mutagen since it was readily available to researchers. In 1927, Muller showed that X-ray treatment could increase the mutation rate in a Drosophila population by 15,000% [10], and a year later, Stadler observed a strong phenotypic variation in barley seedlings and sterility in maize tassels after exposure to X-rays and radium [11, 12]. Later, more sophisticated techniques such as gamma and neutron radiation were developed at newly established nuclear research centers. During and directly following the Second World War, radiation-based techniques were complemented by chemical mutagens that were less destructive, freely available, and easier to work with. Pioneer work in this area was performed by Auerbach and others, who demonstrated an increased mutation frequency in Drosophila following exposure to mustard gas (War Gas) [13, 14]. A few years later, this work was followed by the discovery of methane-sulphonates and other chemical mutagens, which are still in use today [15].

The goal in mutagenesis breeding is to cause maximal genomic variation with a minimum decrease in viability. Among the radiation-based methods, γ-ray and fast neutron bombardment now supersedes X-ray in most applications. Of these, γ-ray bombardment is less destructive causing point mutations and small deletions whereas fast neutron bombardment causes translocations, chromosome losses, and large deletions. Compared to chemical mutagens, both types of radiation cause damage on a larger scale and severely reduces viability [16, 17].

Chemical mutagens have gained popularity since they are easy to use, do not require any specialised equipment, and can provide a very high mutation frequency. Compared to radiological methods, chemical mutagens tend to cause single base-pair (bp) changes, or single-nucleotide polymorphisms (SNPs) as they are more commonly referred to, rather than deletions and translocations. Of the chemical mutagens, EMS (ethyl methanesulfonate) is today the most widely used. EMS selectively alkylates guanine bases causing the DNA-polymerase to favor placing a thymine residue over a cytosine residue opposite to the O-6-ethyl guanine during DNA replication, which results in a random point mutation. A majority of the changes (70–99%) in EMS-mutated populations are GC to AT basepair transitions [18, 19]. Mutations in coding regions can be silent, missense or nonsense. In noncoding regions, mutations can change promoter sequences or other regulatory regions, resulting in up- or downregulation of gene transcription. Aberrant splicing of mRNA, altered mRNA stability and changes in protein translation may also occur as a result of mutagenesis.

Other mutagens such as sodium azide (Az) and methylnitrosourea (MNU) are also used and often combined into an Az-MNU solution. Genetically, Az-MNU predominantly causes GC to AT shifts, or AT to GC shifts. Thus, contrary to EMS, a shift can happen in either direction [18]. All three chemical mutagens are, as can be expected, strongly carcinogenic and should be handled with extreme care. Unlike EMS, MNU is both sensitive to shock and unstable above 20°C making it complicated to work with. In contrast to EMS and MNU, which are both liquid, Az is a solid dust in its ground state and the additional step of first dissolving the acutely toxic and volatile substance before application makes it less attractive to handle.

Through the years, mutagenesis has generated a vast amount of genetic variability and has played a significant role in plant breeding programs throughout the world. Records maintained by the joint FAO/IAEA Division in Vienna show that 2965 crop cultivars, with one or more useful traits obtained from induced mutations, were released worldwide during the last 40 years [20]. Notable examples are several wheat varieties (e.g., durum wheat used in pasta), barley including malting barley, rice, cotton, sunflower, and grapefruit, resulting in an enormous positive economic impact.

During the last decade, the use of chemically induced mutagenesis has had a renaissance with the development of TILLING (Targeting Induced Local Lesions in Genomes) technology. In TILLING, mutagenesis is complemented by the isolation of chromosomal DNA from every mutated line and screening of the population at the DNA level using advanced molecular techniques.

As in conventional mutagenesis, TILLING seeds are exposed to a strong mutagenic compound, which introduces random mutations across the entire genome. However, extra care is taken to achieve mutation saturation in the target genome. Before creating the TILLING population, most researchers therefore start by establishing a “kill curve” using their mutagen of choice where concentration is plotted against seed survivability. A general rule of thumb is to aim for a 30–80% survival rate [21, 22]. After mutagenesis, the seeds (M1) are planted and allowed to self-fertilise and produce a new generation of seeds (M2). Typically, one seed from each line is sown to produce the M2 population and, DNA is isolated from every single M2 plant.

Provided the number of mutations per genome is high enough and the size of the population is large enough, it is likely that a mutated allele of all genes in the genome exists somewhere in the population. To determine the optimal size of a particular TILLING population, the ploidy of the target crop has to be considered. There seems to be a strong correlation between the ploidy level and the induced mutation frequency. It has been shown that a mutation frequency as high as one mutation per 25 Kb can be introduced in hexaploid plants such as oat and wheat without killing the plant or making it infertile, while the maximum mutation frequency of diploid plants such as rice and barley is much lower (Table 1). Therefore, a hexaploid TILLING population seldom needs to exceed 5000 individual lines. Diploid populations, on the other hand, often need to be in the range of tens of thousands [22, 23].

Since TILLING in plants is a large and time-consuming project, it is advisable to consider the logistics of TILLING before performing the mutagenesis. Harvesting and cleaning of individual lines without cross-contamination, preparation, storage, and organization of several thousand bags of seed and their corresponding DNA samples can be laborious and require large amounts of space and resources. Proper storage is of immense importance as many seeds rapidly lose viability if stored under improper conditions. In addition, tracking a TILLING population and associated data over several generations and maintaining numbers on seed availability is greatly facilitated by establishing a database and bar-coding system. To assist groups that are new to TILLING, or are planning a new library, a flowchart called COAST (consider optimize achieve select TILLING) has been proposed by Wang et al. [21], providing a good starting point and helpful advice on launching a TILLING project.

The power of TILLING was first demonstrated in model systems such as Arabidopsis and Drosophila [24, 51], where it was shown that single mutations in specific genes could be identified. TILLING has later been successfully applied to a number of plant systems including barley, wheat, maize, rice, oat, pea, and soybean (Table 1). Thus, this technology provides the breeders with a new and sophisticated tool for crop improvement.

3. Mutant Discovery in TILLING Populations

3.1. Direct Sequencing

Direct sequencing using a Sanger-based method is the simplest method to screen a TILLING population, but it is also by far the most expensive one. DNA sequencing could be considered the “gold standard” for screening as all mutations can be easily identified. Although screening generally centers around one or a few genes, availability of a reference genome theoretically allows for assembly and analysis of complete mutant genomes. This can be particularly useful in cases where a phenotype is readily visible but no candidate gene has been identified. However, this also puts a great demand both on the speed and price of sequencing technologies (see Section 3.8).

3.2. Li-Cor

The most commonly used method to identify mutations in a TILLING population is by using the Li-Cor system (Table 1). It relies on the specific cleavage of mismatched bases formed as a result of repeated melting and reannealing of a PCR product amplified from a region of interest. If a mutation is present, a hybrid DNA molecule with a single mismatch will be generated. It is then selectively cleaved with an endonuclease, typically Cel-1 or Endo-1, producing two shorter fragments that can be separated by polyacrylamide gel electrophoresis [25]. By incorporating fluorescent dye-tags of different colours in the forward and reverse PCR primers, the amplified fragments can then be identified by the Li-Cor instrument. A single Li-Cor can run a 96 lane gel and the sensitivity is high enough to allow up to 16-fold pooling of samples, thus totaling 768 samples per run in diploid organisms. However, when screening large hexaploid genomes this number is reduced considerably due to the increased genomic complexity. In addition, there are a number of inherent drawbacks with the Li-Cor method that need to be considered. Parameters like fluorescent dye-primer- and DNA concentrations as well as the ratio between the cleavage enzyme and PCR product concentrations all affect the results and need to be optimised. In addition, for an efficient detection of the fluorescent fragments and acceptable throughput, a specialised instrument is required. On the other hand, the maximum length of amplicons using a Li-Cor system is as high as 1.5 Kb, among the longest of all methods. Both Endo-1 and Cel-1 are relatively expensive, but a protocol is available describing how to isolate Cel-1 directly from celery stalks [52]. The resulting enzyme extract, CJE (celery juice extract) can replace purified enzyme in many applications, substantially reducing the price per reaction. Several bioinformatic tools exist to help design primers for Li-Cor use, the most popular being CODDLE (http://www.proweb.org/coddle/) which combines primer functional analysis with an algorithm that, based on chosen mutagen and gene structure, identifies gene regions where deleterious mutations are most likely to occur. For postrun gel analysis, GelBuddy is an application that helps automate band detection in electrophoretic gels while ParseSNP can predict the expected effect of the introduced SNP on protein function.

3.3. High-Performance Liquid Chromatography (HPLC)

An HPLC-based method was used in early experiments with TILLING and can be considered as a sensitive option for screening [23]. Samples are treated with Cel-1 mismatch-cleave enzyme, as in the Li-Cor method and then separated using HPLC. A heterozygous mutation would appear as two new elution-peaks with the sum of their sizes equaling the original PCR product [24]. An 8-fold pool of samples is recommended in a diploid organism allowing 8 samples to be analyzed simultaneously, although diploid pools of up to 32-fold are possible [23]. However, running several samples concurrently would require the use of several HPLCs, limiting its potential as a high-throughput screening platform.

3.4. Electrophoresis

Regular electrophoresis using agarose or polyacrylamide (PAGE) gels has been proposed as a cheap alternative to Li-Cor systems for high-throughput screening. The protocols are based around the same mismatch-cleave system using Cel/endoenzymes but rather than fluorescent dyes, ethidium bromide (EtBr) is used to visualise the fragments after separation on an agarose gel. According to the authors, an 8-fold pool is possible with an upper amplicon length limit of 3 Kb [53]. This method has been used to successfully screen a wheat population for waxy and hard grain mutants using a 4-fold pool on thin (<4 mm) gels [36]. As agarose gel electrophoresis does not require any special equipment, and as Cel-1 can be replaced with celery juice extract (CJE), this may be the method of choice for low-budget TILLING [52]. However, due to the decreased sensitivity of the method compared to Li-Cor a larger amount of Cel-1 is required per sample, further stressing the need for home-made CJE.

3.5. Capillary Electrophoresis

Capillary electrophoresis (CE) can also be used to screen TILLING populations [32]. After cleavage with Cel-1/endo-1 the sample is mixed with EtBr, loaded into glass capillaries, and separated using electrophoresis. The presence of DNA is measured by UV-light excitation of DNA-bound EtBr at the end of the capillary and an absorption spectra over time is digitally generated. A mutated strand will add new peaks to the graph. The maximum fragment length is approximately 1.5 Kb, rivaling that of Li-Cor. The detection limit is also high enough to resolve an 8-fold pool [32]. An alternative method to CE is conformation sensitive capillary electrophoresis (CSCE) where, contrary to standard CE techniques, enzymatic degradation is not necessary [37]. In this method, PCR and melt-annealing are performed, as in other methods, but Cel-1/Endo-1 is not added. The capillary is instead loaded with a semidenaturing gel (CAP), capable of separating homoduplexes from heteroduplexes as the “kink” caused by a mismatch affects migration rate. Using this method, an 8-fold pool of diploid DNA is possible, although the authors themselves recommend a 4-fold pooling [37]. All types of capillary electrophoresis suffer from a slight decrease in sensitivity owing to the use of intercalating dyes rather than fluorescent primers. However, analysis is very fast, around 5–10 minutes per run and the instrument can be upgraded to handle 96 lanes concurrently. The downside of CE is the high instrument cost requiring a substantial initial investment.

3.6. High-Resolution Melt (HRM)

In HRM, intercalating dyes are used that fluoresce only when bound to DNA. When the temperature is gradually increased, DNA-strands will melt apart causing a release of the dye and the total fluorescence will decrease in a predictable way. The results are displayed as temperature/fluorescence graphs. A mutation will cause a shift in the graph as the mismatched base changes the melting temperature. Heterozygotes are easily identified by comparison of normalised melting curves with those of homozygotes or wild-type samples [54, 55]. Though sensitive, HRM is limited by both amplicon GC content and length, a typical read only covering 150–500 bp, which is much shorter than Li-Cor and CE. HRM is especially useful when a specific region with known impact on protein structure is the target or when the gene of interest contains many short exons and thus a short read length is acceptable. A drawback is that specialised software has to be used to interpret the different melt-curves. HRM can be performed on standard qPCR-machines with a simple software upgrade and is thus a suitable platform for initial TILLING screenings. HRM has been successfully applied in identification of mutations in wheat [56], Medaka [57], tomato [37], and Arabidopsis [43].

3.7. MALDI-TOF

Matrix-assisted laser desorption ionization time-of-flight (MALDI-TOF) spectroscopy has, since its inception in 1985, become a mainstay tool for analysis in the fields of polymer chemistry and proteomics. MALDI-TOF has also found some use in the field of high-throughput SNP discovery. However MALDI-TOF has not yet been fully exploited in SNP discovery and there is currently only one standardised, high-throughput method available, developed by SEQUENOM and known as MassCleave [58]. This method uses a synthesis step by T7-R&DNA polymerase followed by RNAse degradation to generate small RNA fragments that can be detected by the instrument. Once detected, the fragments can be reassembled in silico to provide a picture of the screened PCR product and to pinpoint mutations.

Recently, a new matrix of diaminobenzophenone (DABP) was introduced, for the analysis of nucleotides. Compared to traditional 3-HPA (3-hydroxy piccolinic acid), DABP has a 100-fold greater salt tolerance while retaining a similar resolution and sensitivity [59]. This matrix could therefore be a simple and elegant alternative to 3-HPA in SNP analysis, as the presence of even small concentrations of K+ and Na+-ions in the sample solution severely affects the sensitivity of the assay. Compared to Li-Cor-based techniques, MALDI-TOF is relatively straightforward. The enzymatic degradation steps are simple and robust and do not require optimization of individual steps or titration of the enzymes used. The method is also very sensitive and is capable of identifying heterozygote mutations in a hexaploid organism. Another potential benefit is that the method does not rely on heteroduplex formation, allowing for accurate detection of homozygous mutations without the need to pool samples. In fact, a homozygous mutation would be more visible as it leads to the disappearance of a mass peak in the MALDI graph. In extension, this means that MALDI-TOF-based screenings are even more relevant in late-stage TILLING populations where an increasing amount of mutations are homozygous. A proof-of-concept screening was published using the original protocol for MALDI-TOF based SNP discovery [22].

We adjusted and optimised the SEQUENOM MALDI-TOF protocol for TILLING applications by decreasing reaction size, changing to a more salt-tolerant DABP matrix, and developing software for automated screening of samples. In our modified protocol, reaction size was halved and only 1/8th of the original enzyme amount was used without loss in sensitivity. Additionally, we developed a new software to accurately identify new SNPs (Figure 1).


An overview of mutant identification using MALDI-TOF. (a) Each identified peak is matched to an expected peak. (b) Each peak is compared to the preceding and succeeding peak in the graph and two quotas are calculated and stored. (c) A sample-set-wide mean and standard deviation is calculated for each peak set and compared to the standard deviation of each individual sample peak (arrows). Outliers above a preset threshold are flagged as “suspicious” (red arrows). (d) Data is presented in a table as well as a colour-coded sequence (not shown).

While waiting for more economical alternatives, TILLING screening using MALDI-TOF instruments could be a good complement to other screening methods and even as an alternative to large investments in Li-Cor technology. This is especially true for those laboratories where MALDI-TOF equipment, with its myriad of uses, is already part of the basic infrastructure.

3.8. Emerging Technologies

Next-generation sequencing (NGS) has significantly accelerated the prospects of identifying mutations at the whole-genome level. Decreasing sequencing costs due to improved technical accuracy, improved throughput, and increased capacity compared to only a few years ago has led to a great potential for NGS in TILLING. The two most commonly used NGS platforms are the 454 GenomeSequencer FLX Ti (Roche Applied Science) and the Illumina (Solexa) Genome Analyzer. While the average read length for 454 is 750 bases, Illumina only gives up to 100 bases per read but in turn generates a much greater amount of sequence data. In addition, these technologies are under constant development both with regard to read length, data quality and the number of sequences generated. As an example, Roche has recently implemented up to 1 Kb read lengths with the GS-FLX+ system.

There are already several proof-of-concept methods for applying NGS in TILLING applications. Using 3-dimensional pooling it is possible to screen one or several genes of interest in a single FLX-454 run. Experiments suggest that as many as 12,000 samples may be analyzed simultaneously on a single 454-picotiter plate (PTP) using KeyPoint technology, as successfully tested on a tomato TILLING population [60]. Illumina sequencing has also been adapted to high-throughput TILLING, and has been used to screen bread-wheat, durum-wheat, and rice populations [61]. The method called CAMBa (Coverage Aware Mutation-Calling using Bayesian analysis), not only identified several mutations that had been missed by CJE mismatch-cleave based TILLING, but also confirmed already known ones with fewer false positives [61]. As the amount of data generated from NGS is immense, some knowledge of bioinformatics and access to computational resources are invaluable during analysis. In addition to already established techniques, a new technology based on single molecule sequencing, PacBio RS is now also available. Average read length for this instrument exceeds 1 Kb, more than 10% of reads are between 1.5 and 2.5 Kb while some reads are longer than 4500 bp [62]. With recent technical updates, the sequencer delivers approximately 35 Mb sequencing data per run. This technique will be especially useful for nonsequenced genomes where no prior alignment scaffold exists due to its impressive read lengths, but has yet to be adapted to TILLING. Aside from direct screening, NGS has also been used for SNP discovery. Recently, NGS was performed on 17 wild and 14 cultivated soybean genomes with an average coverage of 5x and greater than 90% depth. This work identified high allelic diversity of 205,614 tag SNPs that could be useful for QTL mapping and association studies [63]. A NGS study on six elite maize cultivars resulted in identification of 1,000,000 SNPs, 30,000 insertion-deletion polymorphisms, and presence/absence variation of several genes amongst the six lines [64]. These studies highlight the growing importance of high-throughput technologies in fields other than mutation screening.

4. From Genotype to Phenotype

Contrary to traditional screening methods done by plant breeders, TILLING focuses on first identifying mutations within genes of interest and then linking those mutations to a specific phenotype. However, this approach is only possible when a gene linked to the trait of interest is known and the gene sequence available. Using software and maps of conserved sequences within the gene it is then possible to predict which of the identified mutations that are most likely to cause changes in protein structure or aborted translation resulting in a nonfunctional product. The potential phenotypes identified in this way can then be verified by anatomical, histological, physiological, or biochemical studies. Although theoretically straightforward, there are several problems that might arise during the screening process and subsequent analysis. Since the screening takes place at the DNA-level, enhancer and promotor mutations that are upstream of the gene of interest can be difficult, if not impossible to detect unless a full genomic sequence is available, which is not the case for most nonmodel systems. Another complication stems from the fact that a single mutation, even if predicted to be deleterious does not necessarily affect overall cellular function. Homologs or paralogs of the gene of interest may still be expressed, leading to a low or nonexistent penetration of the mutation. This is especially true for hexaploid plants where a homolog of the gene of interest may exist in all three genomes and when one allele is mutated, two others may compensate for the loss. In practice it is therefore often necessary to identify knockout mutations in all alleles by laborious screenings followed by time-consuming crosses to stack the different mutations in the same genome. This can severely delay the development of the final trait.

Despite these drawbacks, several groups have reported successes in linking genotypic change to novel phenotypes in a variety of crops. Most noticeably in wheat, where traits related to the waxy phenotype [29, 36, 47] and grain hardness are being developed [36], in soybean where TILLING has proven useful in increasing the oleic acid content through the identification of mutations in the FAD1, 2, and 3 genes [65] and in Sorghum where lignin content has been decreased though mutation of COMT [35].

5. Identification of Novel Traits in Mutated Populations

5.1. Biochemical Screening

The main purpose of TILLING is to allow identification of mutations at the genetic level. However, this does not exclude the fact that TILLING populations, as well as other mutagenised populations also can be used for phenotypic screens. The principal difference between genotypical (TILLING) and phenotypical screening is illustrated in Figure 2.


Overview of different methods to screen a mutagenised population and to develop a new stable character.

Macromolecular composition and quantity of bioactive compounds like lignin and other fibers, lipid, and starch content are all quality characters that cannot be scored in the field. Lignin is found in secondary plant cell walls and provides rigidity to the plant. Lignin is considered a negative component in foragers as it blocks the digestion of cell-wall polysaccharides by microbial enzymes and is itself indigestible. Thus, crop varieties with lower lignin levels in the cell walls are preferred for feed since they are more energy efficient. A quick and economical assay for visually screening for altered lignin levels in seeds is the phloroglucinol-HCl assay (Wiesner test) [66]. We screened seeds from 1824 lines from an oat TILLING population [23] and identified 17 lines where the seeds had a reduced lignin stain intensity. For further confirmation, an acetyl-bromide method was then used for accurate quantification of lignin levels in the mutant seeds [22, 67, 68]. An example of the screen is illustrated in Figure 3(d).


Examples of different phenotypes from an oat TILLING population. (a) Chlorosis marker from mutated line grown in the greenhouse. (b) Same marker grown in the field. (c) Same marker, clearly visible and stable in mature plants. (d) Phloroglucinol staining of oat cultivar Belinda (WT, left), a low lignin mutant (middle), and a high lignin mutant (right). Red coloration denotes presence of lignin. (e) Phenotypic screening for Fusarium resistance from random lines in the oat TILLING population. Seeds were placed on water agar and inoculated with ca 3000 spores of Fusarium culmorum. Upper left Petri dish shows the Belinda control. The remaining dishes show examples of resistant lines. (f) Examples of infected and noninfected microaxes in field grown plants. (g) Closeup of an infected microaxes.

Increased levels of dietary components that directly interfere with cholesterol absorption or excretion and thereby contribute to lowered plasma cholesterol levels are also very important breeding goals. One example is the mixed-linkage (1→3), (1→4) β-D-glucan soluble fibre which is mainly found in cereals and where oat and barley contain the highest concentrations. Using an assay kit available from Megazyme [69] we measured β-glucan content in seeds from 1500 random lines in an oat TILLING population [70]. We identified lines with increased levels of β-glucan as well as lines with levels less than half of what is found in Belinda, the original cultivar.

With the rising number of TILLING-populations (Table 1) we anticipate that these populations will be increasingly screened not only by TILLING, that is, genetic screening, but also by various advanced biochemical assays to identify important quality characters. Recently we set up an GC-MS assay and screened 1000 lines for β-sitosterol content and, in this relatively small sample size, identified lines with almost twice the normal levels. The advantage of a screen at the phenotypic level is that the target character is directly identified. The disadvantage, compared to a genotypic screening is that the specific mutation(s) mediating the phenotype remains undiscovered. There are several other examples from the literature elegantly demonstrating the power of biochemical screens [71–73].

5.2. Physiological Screening

Fungal pathogens represent a major threat to global agriculture. Global climate change with mild winters and higher humidity is expected to increase the problem even further. One particularly troublesome pathogen with high relevance in North America and Europe including Sweden is Fusarium [74]. Comprised of more than 1000 different species, Fusarium cause diseases in major agricultural crops like wheat, barley, maize, and oats. In addition, Fusarium sp. also produce a plethora of mycotoxins which accumulate in the grain, enter the food chain, and pose serious threats to human and animal health. A particular challenge is Fusarium head blight disease (FHB), for which there are currently no satisfactory management strategies available and where fungicide treatments give mixed and unpredictable results, sometimes even worsening mycotoxin contamination [75]. Unfortunately, the variation in the breeding populations does not seem to be high enough to identify and develop lines resistant to the disease.

On the other hand, even for characters that vary considerably with environmental factors, like pathogen resistance, mutagenised populations could be used to identify resistant lines with a strong genetic component. The trick is to design an in vitro assay with such a stringent selection that single rare lines with strong resistance against the disease can be identified. We tested this concept by designing a petri dish assay to identify Fusarium-tolerant oat from a mutated population with a high variety [22]. We placed 5 seeds from each line of the oat TILLING population on water agar and inoculated each seed with approximately 5000 spores of Fusarium culmorum. Since the spores have difficulties developing on the water agar they instead germinate on the seeds and the growing fungi, in turn hindering seed germination. As can be seen in Figure 3(e), this infection is efficient and the selection is therefore extremely harsh. We screened 1300 lines and identified 63 lines that germinated despite the presence of the fungi. We graded the lines as moderately resistant, if at least one seed germinated and developed rudimentary roots and shoots, and resistant if several seeds germinated and developed further (Figure 3(e)). We then tested the best lines in the field by sowing 60 seeds in three rows of 20 seeds randomly distributed and interrupted by rows of three market varieties from Lantmännen SW Seed AB. At the two leaf stages, all plants in the field were sprayed with a mixture consisting of four different subspecies of F. culmorum and three of F. graminearum. The plants were watered regularly during the whole growth season to facilitate infection. The degree of infection was scored later in the season as pink pigment formation on the microaxes (Figures 3(f) and 3(g)). Out of 43 lines, 26 were less infected than the most resistant commercial variety and all but three showed a higher resistance than the original Belinda cultivar. Thus, this preliminary experiment seems very promising and indicates that phenotypic screening of mutagenised populations could be used to identify complex characteristics like pathogen resistance if the screening method is carefully designed.

6. From Phenotype to Genotype

To be truly useful, identification of a strong genetic character in a mutagenised population by a phenotypic screening procedure should be followed by a characterization of the molecular event underlying the modified character. In plants with sequenced genomes, that is, where reference sequences are available, novel phenotypes can be characterised using a combination of whole-genome resequencing, linkage maps, and microarrays, providing a comprehensive picture of gene expression changes and newly introduced SNPs compared to wild-type specimens. A classical example is the identification of a GA20 oxidase mutation as a cause for the semidwarf phenotype used in many commercial rice varieties. Using genetic maps, the trait was linked to a region of chromosome 1. Combined with the knowledge that the dwarf phenotype had reduced levels of gibberellic acid (GA), a putative GA gene in that area was identified and sequenced using the rice reference genome as a base. The sequence showed a 280 bp deletion resulting in an inactive protein, explaining the decreased GA-levels [76]. Microarray technology has also been successfully applied in rice and Arabidopsis to connect genome-wide variations to specific phenotypes [77, 78]. However, next-generation technologies such as Illumina sequencing now outperform the more traditional microarray methods for SNP identification [79]. In one such approach, EMS-induced Arabidopsis Col-0 mutants with slow growth and light green leaves were screened to identify the causative mutations. The recessive mutants were first crossed with the Landsberg erecta ecotype. DNA from 500 F2 individuals was then pooled and sequenced using Illumina sequencing to up to 22-fold genome coverage. A software called SHOREmap was then developed to identify the mutations in the segregating population. The software detected a mutation causing serine to asparagine nonsynonymous codon change in the AT4G35090 gene [80]. In yet another approach, Austin et al. identified three genes involved in cell wall biosynthesis. They first screened the Arabidopsis EMS-treated Col-0 mutants for sensitivity to flupoxam that were previously known to affect cell wall assembly or integrity. The mutants were then crossed to Landsberg erecta ecotype. The genomic DNA was extracted from the F2 population and screened using Illumina GA sequencing. Through an in-house developed statistical approach, they were able to correctly identify the causative mutations and hence the genes responsible for the phenotype [81].

Since a mutation does not necessarily need to be in an exon of the candidate gene, identifying a mutation may be difficult if a reference genome is unavailable. Mutations such as promotor mutations, mutations changing genome structure, mutations upstream in the regulation pathway, and various micro-RNA mutations can all be responsible for the downstream effect. When a reference genome is not available, these factors can be extremely difficult and time-consuming to evaluate comprehensively. In such cases, an initial approach would be to obtain as many mutants as possible and evaluate each one separately, re-sequencing all genes of interest and performing qPCR experiments to gauge any possible changes in expression among the candidate genes. Although difficult, it is not impossible to obtain a genotype-phenotype association this way. Using EST libraries instead of the fully sequenced genome, Feiz et al. linked wheat grain hardness to Puroindoline a and b mutations in an EMS-mutagenised population [82]. A major caveat is that a link between genetic maps and genes are unknown in many cases, thus effectively robbing the researchers of a valuable selection tool for limiting the number of candidate genes.

7. Introgression of Stable Markers to Breeding Populations

Even though present elite cultivars are genetically fairly homogeneous, phenotypic differences between individual plants can always be seen in the field due to varying environmental factors. Cultivars grown at different sites with different fertilisation, pest and weed control regimes, weather conditions, and so on will exhibit differences not only in general plant architecture but also in quantity of specific macromolecules and metabolites. However, the influence of the environmental factor varies with the mechanism by which each particular mutation mediate the phenotype. Thus, if the genetic factor is strong for a specific trait, the variation in the expression of the trait will be smaller.

Examples of genetically strong and visible characters are leaf shape, colour, and presence of pubescence on the leaves or stems since these do not change noticeably with the environment. Such characters are therefore used as markers to distinguish market varieties from each other. In the ideal case such a visible, stable trait can also be correlated to a more specific, but invisible quality character. The experienced breeder could then score the quality character directly in the field even at varying environmental conditions. The key to a good selection strategy therefore involves the identification of environmentally stable phenotypes that correlate to a specific genotype.

However, often such correlations cannot be found for important quality characters like high fat, starch or protein content, fibre composition, reduced levels of toxic compounds, and enhanced postharvest processing properties. To identify these traits, more specific assays have to be performed. The drawback is that such assays often are time consuming and expensive and cannot be performed on a large number of samples.

On the other hand, if a mutagenised population with a very high variation is used, the probability of identifying a specific trait is increased and the number of assays needed to identify a certain quality character is decreased. In addition, the probability of finding rare mutations knocking out transcription factors or other pleiotropic genes is increased. Such mutations will have a stronger penetration and the corresponding phenotype will be less affected by outer, environmental parameters. As an illustration of the principle, Figures 3(a)–3(c) shows a chlorotic line identified in the greenhouse in an oat TILLING population [22]. In this particular mutation, the genetic factor is strong enough to be easily detected by the naked eye during the entire growth season. Of course, nonvisible mutations that can only be detected biochemically can, in an analogous way, still be genetically strong.

Once identified in a mutagenised population and tested for genetic stability in the field, the character can be introgressed into breeding lines lacking that character. Ideally, introgression should be done by the help of a marker since it reduces the number of necessary crosses and also ensures that as many random mutations as possible are eliminated from the mutagenised lines. Such a marker could be visible, biochemical, or molecular. A molecular marker, that is, a mutation or other DNA rearrangement that cosegregates with a useful quality character is preferable and has several advantages compared to conventional phenotypic selection. This is referred to as “molecular marker-assisted selection” (MMAS). Since MMAS is DNA based it is neutral and completely independent of environmental factors. Material for the assay can be collected from any tissue in the plant and at any developmental stage and the trait can often be scored very early in the plant growth cycle, even from seeds. This saves time, labour, and field space. Molecular markers can also be used to select for complex characters as long as the linkage to the marker is strong enough. If a molecular marker correlates to disease resistance, resistance can be scored without having to challenge the plant with the pathogen.

MMAS can be based on a mechanistic knowledge of how a particular mutation directly up-, downregulates, or completely knocks out a specific gene. In such a case it will be closely linked to a specific phenotype. However, MMAS could also be indirect, and based on a statistically significant link to the phenotype. Such markers are referred to as QTLs and could be single nucleotide polymorphisms (SNPs), microsatellite markers, or various DNA rearrangements that can be detected by DNA sequencing, PCR, Southern blot, MALDI-TOF, or other hybridization techniques. Semagn et al. [83] give an excellent review on various types of markers. Perhaps most importantly, MMAS can be automated and subjected to high-throughput screening. By automating DNA isolation, pipetting, separation, and evaluation using robots, fluorescent detection techniques, automatic scripts, and so forth, the screening procedure can be speeded up enormously and performed on a large number of markers in parallel.

8. Conclusion

During the last decade mutagenesis in breeding has again come of age. Plant mutagenesis, which increases the variation in crop plants that have been inbred for centuries, coupled with high-resolution genotypic or phenotypic screening methods allows breeders to select for traits that were very difficult to breed for only a few decades ago. The introduction of new genetic variation in inbred elite cultivars offers a unique possibility to identify novel traits, while retaining the agricultural excellence of the lines. With the rapid accumulation of genetic data from a wide range of crop plants, the continuous decrease of the costs associated with whole-genome sequencing, and the development of high-resolution analytical techniques, we have reached a point were we stand to gain both time and money by adding this toolbox to more traditional breeding techniques. Since markers are generated in the process, this approach also allows stacking of the useful characters, paving the way for the development of complex multigenic traits like abiotic stress resistance. Although still restricted to the capacity of the endogenous genome, mutagenesis and high-resolution screening will provide a very good complement to recombinant DNA technologies and genetically modified organisms (GMOs) in further development of crop plants that are better adapted to climate change and the increasing global population.

Acknowledgments

The authors would like to thank Andy Phillips, Rothamsted Research, England, and Sören Rasmussen, University of Copenhagen, Denmark, for unpublished information. This paper was supported by grants from The Swedish Farmers Supply and Crop Marketing Cooperative (SLF) and the Swedish Research Council for Environment, Agricultural Sciences and Spatial Planning (FORMAS) given to O. Olsson.

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Copyright

Copyright © 2011 Per Sikora et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Footnotes

Electronic supplementary material is available online at https://doi.org/10.6084/m9.figshare.c.4938186.

Published by the Royal Society. All rights reserved.

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Contamination from handling and intrusion from microbes create obstacles to the recovery of ancient DNA. [1] Consequently, most DNA studies have been carried out on modern Egyptian populations with the intent of learning about the influences of historical migrations on the population of Egypt. [2] [3] [4] [5] A study published in 1993 was performed on ancient mummies of the 12th Dynasty, which identified multiple lines of descent. [6]

In 2013, Khairat et al. conducted the first genetic study utilizing next-generation sequencing to ascertain the ancestral lineage of an Ancient Egyptian individual. The researchers extracted DNA from the heads of five Egyptian mummies that were housed at the institution. All the specimens were dated to between 806 BCE and 124 CE, a timeframe corresponding with the Late Dynastic and Ptolemaic periods. The researchers observed that one of the mummified individuals likely belonged to the mtDNA haplogroup L2, a maternal clade that is believed to have originated in North, East and West Africa . [7]

2017 DNA study Edit

A study published in 2017 described the extraction and analysis of DNA from 151 mummified ancient Egyptian individuals, whose remains were recovered from Abusir el-Meleq in Middle Egypt. Obtaining well-preserved, uncontaminated DNA from mummies has been a problem for the field of archaeogenetics and these samples provided "the first reliable data set obtained from ancient Egyptians using high-throughput DNA sequencing methods". The specimens were living in a period stretching from the late New Kingdom to the Roman era (1388 BCE–426 CE). Complete mitochondrial DNA (mtDNA) sequences were obtained for 90 of the mummies and were compared with each other and with several other ancient and modern datasets. The scientists found that the ancient Egyptian individuals in their own dataset possessed highly similar mitochondrial profiles throughout the examined period. Modern Egyptians generally shared this maternal haplogroup pattern, but also carried more Sub-Saharan North and East African clades. However, analysis of the mummies' mtDNA haplogroups found that they shared greater mitochondrial affinities with genetic relatives of Neolithic and Bronze Age populations from the Near East, Anatolia and Eastern Mediterranean Europeans compared to modern Egyptians [9] . Additionally, three of the ancient Egyptian mummified individuals were analysed for Y-DNA, two were assigned to East African haplogroup A1b1 and one to haplogroup E1b1b1 common in North Africa. The researchers cautioned that the affinities of the examined ancient Egyptian specimens may not be representative of those of all ancient Egyptians since they were from a single archaeological site. [10]

The study was able to measure the mitochondrial DNA of 90 individuals, and it showed that the mitochondrial DNA composition of Egyptian mummies has shown a high level of affinity with the DNA found in the Near East, Anatolia and Eastern Mediterranean Europeans. [11] [12] A shared drift and mixture analysis of the DNA of these ancient Egyptian mummies shows that the connection is strongest with ancient populations of North, East and Sub Saharan Africans and to a lesser extent populations from the South Africa and Middle East. [12] In particular the study finds "that ancient Egyptians are most closely related to Neolithic and Bronze Age samples in the Levant, as well as to Neolithic African and Cushitic populations". [13] However, the study showed that comparative data from a contemporary population under Roman rule in Africa, did reveal a closer relationship to the ancient Egyptians from the same period. furthermore, "Genetic continuity between ancient and modern Egyptians cannot be ruled out despite this sub-Saharan African influx, while continuity with modern Ethiopians is not supported". [12]

Genome-wide data could only be successfully extracted from three of these individuals. Of these three, the Y-chromosome haplogroups of two individuals could be assigned to Haplogroup A, and one to haplogroup E1b1b common in North Africa. The absolute estimates of sub-Saharan African ancestry in these three individuals ranged from 6 to 15%, which is significantly lower than the level of sub-Saharan African ancestry in the modern Egyptians from Abusir, who "range from 14 to 21%."( When using East African admixed population as reference) The study's authors cautioned that the mummies may be unrepresentative of the Ancient Egyptian population as a whole, since they were recovered from the northern part of Egypt. [14]

The data suggest a high level of genetic interaction with the Near East since ancient times, probably going back to Prehistoric Egypt: "Our data seem to indicate close admixture and affinity at a much earlier date, which is unsurprising given the long and complex connections between Egypt and the Middle East. These connections date back to Prehistory and occurred at a variety of scales, including overland and maritime commerce, diplomacy, immigration, invasion and deportation" [15] [12]

Professor Stephen Quirke, an Egyptologist at University College London, expressed caution about the researchers’ broader claims, saying that “There has been this very strong attempt throughout the history of Egyptology to disassociate ancient Egyptians from the modern population.” He added that he was “particularly suspicious of any statement that may have the unintended consequences of asserting – yet again from a northern European or North American perspective – that there’s a discontinuity there [between ancient and modern Egyptians]". [16]

Blood typing and ancient DNA sampling on Egyptian mummies is scant. However, blood typing of Dynastic period mummies found their ABO frequencies to be most similar to that of modern Egyptians. [17]

Analysis of the mitochondrial and Y-chromosomal haplogroups of several mummies of 18th Dynasty Including Tutankhamun was used to provide information about the phylogenetic groups of his family members and their presence among the reported contemporary Egyptian population data. The analysis confirmed previous data of the Tutankhamun's ancestry with multiple controls authenticating all results. The proposed sibling relationship between Tutankhamun’s parents, Akhenaten and the mummy known as the "younger lady" (KV35YL) is further supported. Population genetics point to a common origin at ca. 14.000–28.000 years before present.

Genetic analysis indicated the following haplogroups:

  • Tutankhamun YDNA R1b / mtDNA K
  • Akhenaten YDNA R1b / mtDNA K
  • Tiye mtDNA K
  • Amenhotep III YDNA R1b / mtDNA K
  • Yuya G2a / mtDNA K
  • Thuya mtDNA K

Genetic analysis of modern Egyptians reveals that they have paternal lineages common to other indigenous Afroasiatic-speaking populations in Maghreb and Horn of Africa, and to Middle Eastern peoples these lineages would have spread during the Neolithic and were maintained by the predynastic period. [19] [20]

A study by Krings et al. (1999) on mitochondrial DNA clines along the Nile Valley found that a Eurasian cline runs from Northern Egypt to Southern Sudan and a Sub-Saharan cline from Southern Sudan to Northern Egypt. [21]

Luis et al. (2004) found that the male haplogroups in a sample of 147 Egyptians were E1b1b (36.1%, predominantly E-M78), J (32.0%), G (8.8%), T(8.2%), and R (7.5%). E1b1b subclades are characteristic of some Afro-Asiatic speakers and are believed to have originated in either the Middle East, North Africa, or the Horn of Africa. Cruciani et al. (2007) suggests that E-M78, E1b1b predominant subclade in Egypt, originated in "Northeastern Africa", which in the study refers specifically to Egypt and Libya [22] [23]

Other studies have shown that modern Egyptians have genetic affinities primarily with populations of North Africa, the Middle East and the Horn of Africa, [24] [25] [20] [19] and to a lesser extent European populations. [26]

Some genetic studies done on modern Egyptians suggest a more distant relationship to Sub Saharan Africans [27] and a closer link to other North Africans. [20] In addition, some studies suggest lesser ties with populations in the Middle East, as well as some groups in southern Europe. [19] A 2004 mtDNA study of upper Egyptians from Gurna found a genetic ancestral heritage to modern Northeast Africans, characterized by a high M1 haplotype frequency and a comparatively low L1 and L2 macrohaplogroup frequency of 20.6%. Another study links Egyptians in general with people from modern Eritrea and Ethiopia. [25] [28] Though there has been much debate of the origins of haplogroup M1 a 2007 study had concluded that M1 has West Asia origins not a Sub Saharan African origin, although the majority of the M1a lineages found outside and inside Africa had a more recent eastern Africa origin [29] Origin A 2003 Y chromosome study was performed by Lucotte on modern Egyptians, with haplotypes V, XI, and IV being most common. Haplotype V is common in Berbers and has a low frequency outside North Africa. Haplotypes V, XI, and IV are all predominantly North African/Horn of African haplotypes, and they are far more dominant in Egyptians than in Middle Eastern or European groups. [4]

Y-DNA haplogroups Edit

A study using the Y-chromosome of modern Egyptian males found similar results, namely that North East African haplogroups are predominant in the South but the predominant haplogroups in the North are characteristic of North African and West Eurasian populations. [30]

Autosomal DNA Edit

Genomic analysis has found that Berber and other Maghreb communities are defined by a shared ancestral component. This Maghrebi element peaks among Tunisian Berbers. [35] It is related to the Coptic ancestral component (see Copts), having diverged from these and other West Eurasian-affiliated components prior to the Holocene. [36] [37]

North Moroccans as well as Libyans and Egyptians carry higher proportions of European and Middle Eastern ancestral components, respectively, whereas Tunisian Berbers and Saharawi are those populations with the highest autochthonous North African component. [38]

A recent genetic study published in the "European Journal of Human Genetics" in Nature (2019) showed that Northern Africans are closely related to Europeans and West Asians as well as to Southwest Asians. Northern Africans can clearly be distinguished from West Africans and other African populations dwelling south of the Sahara. [39]

Coptic Christians of Sudan Edit

According to Y-DNA analysis by Hassan et al. (2008), 45% of Copts in Sudan (of a sample of 33) carry haplogroup J1. Next most common was E1b1b, the most common haplogroup in North Africa. Both paternal lineages are common among other regional Afroasiatic-speaking populations, such as Beja, Ethiopians, and Sudanese Arabs, as well as non-Afroasiatic-speaking Nubians. [40] E1b1b reaches its highest frequencies among native populations such as Amazighs and Somalis. [41] The next most common haplogroups borne by Copts are R1b (15%), most common in Europe, and the widespread African haplogroup B (15%). [40]

Maternally, Hassan (2009) found that the majority of Copts in Sudan (of a sample of 29) carried descendants of the macrohaplogroup N of these, haplogroup U6 was most frequent (28%), followed by T1 (17%). In addition, Copts carried 14% M1 and 7% L1c. [42]

A 2015 study by Dobon et al. identified an ancestral autosomal component of West Eurasian origin that is common to many modern Afroasiatic-speaking populations in Northeast Africa. Known as the Coptic component, it peaks among Egyptian Copts who settled in Sudan over the past two centuries. Copts also formed a separated group in PCA, a close outlier to other Egyptians, Afroasiatic-speaking Northeast Africans and Middle East populations. The Coptic component evolved out of a main Northeast African and Middle Eastern ancestral component that is shared by other Egyptians and also found at high frequencies among other Afroasiatic-speaking populations in Northeast Africa (

70%). The scientists suggest that this points to a common origin for the general population of Egypt. [36] They also associate the Coptic component with Ancient Egyptian ancestry, without the later Arabic influence that is present among other Egyptians, especially people of the Sinai. [43]


Synthetic Methods VI – Enzymatic and Semi-Enzymatic

H. Gröger , . R. Metzner , in Comprehensive Chirality , 2012

7.10.2.2 MDR

The MDR superfamily, 8,28–30 with a subunit size of approximately 350 amino acid residues, consists of more than 15 000 members in the UniprotKB database. 31 This superfamily is formed by quinone reductases, leukotriene B4 dehydrogenases (LTD), polyol dehydrogenases, Zn 2+ -dependent ADHs, and many more families in the range of 500. 32 The first well-characterized member was the class I type of mammalian ADH. Its primary structure was resolved already in 1970. 33 Later on, the primary structure of another MDR member, sorbitol dehydrogenase, was published in 1984. 34

MDR proteins have a more complex architecture than members of the SDR superfamily. Typically, they have two-domain subunits, a coenzyme-binding domain, and a ‘catalytic’ domain. The latter one is presumably derived from GroES chaperon building elements. 35 Structurally, the MDR superfamily shows a variety of quaternary structures, ranging from monomers, via dimers and trimers, to tetramers.

The MDR superfamily has two types of proteins, many being either metalloproteins containing zinc (1 or 2 Zn 2+ per subunit) or nonmetalloproteins without zinc. 30 In the case of zinc-containing enzymes, one zinc is located at the catalytic site, which is designated as the ‘catalytic zinc,’ whereas the ‘structural zinc’ appears to contribute to subunit interactions, influencing the quarternary structure of the enzyme 36 by stabilizing a loop consisting of amino acids 94–117 (numbered according to the structure of human ADH1beta). Among the 86 MDR families now characterized, 35 have both the catalytic and the structural Zn 2+ , 7 MDR families have 1 Zn 2+ , and members of 38 MDR families seem to have no Zn 2+ .

Zinc-containing enzymes of the MDR families are more common in eukaryotes they favor NAD + as the cofactor and can be regarded as dehydrogenases. Zinc-free members of the MDR superfamily, however, are found predominantly in bacteria favoring NADPH binding displaying reductase function. 30

The catalytic mechanisms of SDR and MDR are quite different. In zinc-containing MDRs, the catalytic zinc with a hydroxyl from dissociated water provides a ligand for deprotonation of the alcohol substrate, enabling hydride transfer to the adjacent nicotinamide part of the coenzyme. In SDR ADHs, a hydroxyl-tyrosinate ion stabilized by an adjacent lysine residue yields a similar acid–base-catalyzed mechanism and hydride transfer to the coenzyme.

Based on sequence similarities, the MDR superfamily can be divided into separate families. Quite recently, Hedlund et al. 30 developed an automated algorithm to produce stable and reliable models for MDR families. The 10 largest families are: (1) MDR001–ADH: This is the largest MDR family, with currently 2217 members containing the classical ADHs, including human class I–V ADHs. Members of this family have both structural and catalytic zinc and a preference for NAD + as the coenzyme. (2) The MDR002–PTGR family, previously described as the LTD family, comprises NADP + -dependent zinc-free prostaglandin reductases, with currently 774 members. Although this family contains human prostaglandin reductases 1 and 2, most of the members are prokaryotic. (3) The MDR003–FAS (fatty acid synthases) family contains fatty acid synthases with 706 members. 59% of these are isolated from prokaryotic sources. The MDR domains in these enzymes are annotated with enoyl reductase functionality. The reductase function comprises an apparent NADP + preference and lack of zinc ligands. (4) The MDR010–CAD family, with 661 members, includes cinnamyl ADHs, mannitol DHs, and sinapyl DHs. The catalytic domain is well conserved enzymes of this family bind NAD + and two Zn 2+ . (5) The MDR011–bpQOR family of mainly yeast and bacterial quinone oxidoreductases includes 575 sequences. (6) MDR004–QORX contains putative quinone oxidoreductases from many species, including human quinone oxidoreductase PIG3. (7) MDR012–YHDH was previously described as a family of bacterial enzymes under the name YHDH. Meanwhile, this group has grown to include 481 members. (8) MDR013–formate dehydrogenases (FDH), a family with 375 members including S-(hydroxymethyl)glutathione dehydrogenase from Methylobacter marinus. (9) The family named MDR014–TDH encompasses threonine dehydrogenases with 351 members. They have conserved ligands for both zinc cofactors, with the exception of 18 sequences from Rhodobacter strains and six other bacterial sequences that do not have the sequence motif to bind the structural zinc. (10) MDR005–PDH (polyol dehydrogenases) is a family of sorbitol/xylitol dehydrogenases and d -xylulose reductases with 328 members. Most of them bind 2 Zn 2+ . Other families with enzymes of interest are MDR020–yADH, containing tetrameric ADHs from various yeasts, MDR022 – giFDH, with glutathione-independent formaldehyde dehydrogenases from bacteria and yeast strains, MDR030–dFAS, containing 28 uncharacterized polyketide synthases from the slime mold Dictiostelium discoideum, and MDR033, a family of (R,R)-butandiol dehydrogenases from yeast strains.

Several MDRs have been found in thermophilic organisms. The three-dimensional structure of ADH from the archaeon Sulfolobus solfataricus is available, revealing interesting structural properties. 37 This ADH has both catalytic and structural zinc ions. One of the structural zinc ligands is glutamic acid instead of cysteine. Removal of the structural zinc in Sulfolobus ADH reduces the structural stability. 38,39


Supplementary Figure 1 Summary of main analyses and key findings.

Meta-analysis of GWAS results from 13 studies identified 136 variants independently associated with disease risk at a P < 3 × 10 −8 , of which 73 were in low LD (r 2 < 0.05) with published allergy risk variants. Based on a high LD (r 2 > 0.8) between the 136 sentinel risk variants and sentinel cis-eQTLs and/or nonsynonymous coding variants, a total of 132 likely target genes were identified. The likely target genes were preferentially expressed in whole blood and lung, and enriched among pathways related to lymphocyte immunity. Twenty-nine genes are targets of drugs considered for clinical development, including six for which the effect on gene expression of the allergy-protective allele and the respective drug matched. Thirty-six genes have a nearby CpG for which methylation levels are associated with gene expression levels independently of SNP effects on expression and methylation. For one of these genes, variation in methylation levels at the expression-associated CpG was significantly associated with smoking status.

Supplementary Figure 2 Distribution of the observed and expected association P values for the allergic disease GWAS performed in each individual study that contributed to the meta-analysis.

For each study, the genomic inflation factor (λ estimated as the median χ 2 divided by 0.4549) is also shown. The intercept of LD score regression for each study is shown in Supplementary Table 1.

Supplementary Figure 3 Eighty-nine allergy risk variants (gray bars) in low LD (r 2 < 0.05) with each other reported in previous GWAS, according to the year each association was first reported.

We identified 185 SNP associations with allergic disease at P < 5 × 10 −8 in the NHGRI-EBI GWAS catalog. Correlated SNPs were then grouped based on LD, such that the lead SNP in each group was in LD (r 2 > 0.05) with other variants in that group but not with the lead SNPs of all other groups. This procedure is described in greater detail in the Supplementary Note. This resulted in 89 groups of SNPs associated with allergic disease. The earliest year an association was reported with a variant in each group was identified and plotted. The red bar shows the number of novel variants discovered in this study (50 in new loci and 23 in known loci).

Supplementary Figure 4 Twenty-six of the 136 sentinel variants were significantly associated with variation in the reported age of onset for allergic disease.

We first tested the association between each sentinel variant and the age at which symptoms of any allergic disease (asthma, hay fever or eczema) were first reported, using data from the UK Biobank study (n = 35,972). After correcting for multiple testing, 26 variants (yellow circles) had a significant association with age of onset with the allergy-predisposing allele always associated with decreased age of onset (i.e., a negative β, shown on the y axis). An additional 47 variants (blue circles) were nominally associated (P < 0.05) with age of onset. We then performed the same analysis separately for individuals who reported suffering only from a single disease and formally compared the SNP effects between the three groups. In these analyses, the effect on age of onset was significantly different (P < 0.05) between the three diseases for 8 of the 26 variants (yellow circles with black inner dot), consistent with the presence of disease-specific SNP effects on age of onset.

Supplementary Figure 5 Enrichment of tissue-specific gene expression in 25 broad tissues studied by the GTEx Consortium, after restricting the background gene list to the subset of genes with eQTLs.

We repeated the tissue-specific enrichment analysis described in Figure 3a after restricting the background gene list to the subset of 12,804 genes with a known eQTL. Random genes were also drawn from the subset with a known eQTL.

Supplementary Figure 6 Enrichment of SNP heritability among immune cell enhancers remains after excluding from the analysis the 136 sentinel variants.

We repeated the SNP heritability enrichment analysis described in Figure 3b after excluding the 136 sentinel variants (and all variants correlated at r 2 > 0.05) from the meta-analysis results.


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