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Pyrimidine bases + genetic variation

Pyrimidine bases + genetic variation



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I don't understand this question, hoping someone can help!

One of the following bases does not belong among pyrimidine bases: - Uracil - Adenine - Cytosine - Thymine

I thought that everyone was bases, expect that uracil is in RNA, and that in DNA it is replaced by guanine.

Also one more question: Which of the processes would never contribute to genetic variation within a bacterial population? - Transformation - Meiosis - Mutation - Transduction

I have a hunch that the right answer is mutation, but I don't know why. If you know the answer, it would be very helpfull if you could explain why :-)


You are correct - they are all bases. However there are two types of bases. Purines and pyrimidine bases. Purines have two ring systems, while pyrimidines have only one. With this hint, I think you will be able to find the answer to your first question.

As for your second question. Can you give me a definition for what a mutation is? I think if you are able to define this word you will know why that answer is incorrect.

Also, bacteria are incapable of performing one of the four processes that you have listed - and that particular process, is therefore the correct answer to your second question.


Vex-seq: high-throughput identification of the impact of genetic variation on pre-mRNA splicing efficiency

Understanding the functional impact of genomic variants is a major goal of modern genetics and personalized medicine. Although many synonymous and non-coding variants act through altering the efficiency of pre-mRNA splicing, it is difficult to predict how these variants impact pre-mRNA splicing. Here, we describe a massively parallel approach we use to test the impact on pre-mRNA splicing of 2059 human genetic variants spanning 110 alternative exons. This method, called variant exon sequencing (Vex-seq), yields data that reinforce known mechanisms of pre-mRNA splicing, identifies variants that impact pre-mRNA splicing, and will be useful for increasing our understanding of genome function.


Introduction

The complexity of the human genome lies not only in its composition of billions of base pairs, but also in the chemical modifications that make it interpretable to enzymes and other molecular factors, through epigenetic mechanisms. DNA methylation has been the most widely studied epigenetic mark since 1948 when it was first reported [1]. In humans, DNA methylation consists of the covalent addition of a methyl group to cytosine residues—predominantly at CpG sites—by a family of enzymes called DNA methyltransferases (DNMTs) [2, 3]. DNA methylation plays an important role in multiple processes during human development and over the life course, such as the regulation of transcription [4–6], genomic imprinting [2, 4], maintenance of X-chromosome inactivation [7], chromosomal maintenance, and genomic stability [8].

With advances in high-throughput molecular techniques our understanding of DNA methylation has greatly increased in the past few decades. Multiple methods have been developed for profiling DNA methylation patterns across the human genome. Currently, the gold standard is bisulfite conversion of DNA followed by deep sequencing or whole genome bisulfite sequencing (WGBS, Table 1). However, the most extensively used methylation profiling technologies are microarrays assessing DNA methylation at a proportion of the 28 million CpG sites in the genome. To date, Illumina bead-chip platforms have been most popular, where pre-designed probes target bisulfite-converted DNA, followed by hybridization, single-base extension, and its detection [9]. Early models included arrays such as the Infinium HumanMethylation27 BeadChip (27K), targeting around 27,000 sites (0.1% of total CpGs) mainly in CpG islands (CGIs) within promoters [9], followed by the widely used Infinium HumanMethylation450 array (450K), targeting ∼ 480,000 sites (1.7% of total CpGs) consisting of the 27K sites and increased coverage in non-CGIs and intergenic regions [10]. A more recent version is the Infinium MethylationEPIC BeadChip (EPIC), targeting ∼ 850,000 sites (3% of total CpGs), which include almost all of the 450K sites, with additional CpG sites in enhancers [11].

Unlike DNA sequence, genomic methylation patterns are not directly inherited during meiosis [12], but are mostly reprogrammed in two waves during embryogenesis [13–15]. Following this, DNA methylation modifications can be both stable and dynamic during mitosis events that accumulate over the life course [16, 17]. These observations suggest that the environment may be a key driving force behind changes in mitotic DNA methylation [17–20]. However, growing evidence now shows that genetic variation also plays a role in the establishment of DNA methylation marks, independently of or in contribution with environmental exposures.

Research interest in genetic impacts on DNA methylation variation is especially relevant in context of methylome changes observed in disease [16, 21–23], alongside results from genome-wide association studies (GWASs). Although many genetic associations have been identified from GWASs, there remain important unanswered questions about candidate causal variants and their functional consequences, as GWAS signals tend to fall in non-coding regions [24]. Methylome analyses can provide a valuable piece of information as a post-GWAS resource, giving insights into regulatory genomic potential of GWAS signals and helping to prioritize loci to further follow-up [25–27].

Given these considerations, here, we present an overview of results identifying genetic drivers of DNA methylation variation. We discuss methylation heritability findings, and then focus on genome-wide studies that have identified genetic variants as meQTLs for DNA methylation profiles. We also discuss cellular mechanisms that may explain genetic impacts on DNA methylation levels. Lastly, we consider challenges of meQTL analyses, as well as novel applications and further research directions.


Pyrimidine dimers introduce local conformational changes in the DNA structure, which allow recognition of the lesion by repair enzymes. [10] In most organisms (excluding placental mammals such as humans) they can be repaired by photoreactivation. [11] Photoreactivation is a repair process in which photolyase enzymes directly reverse CPDs via photochemical reactions. Lesions on the DNA strand are recognized by these enzymes, followed by the absorption of light wavelengths >300 nm (i.e. fluorescent and sunlight). This absorption enables the photochemical reactions to occur, which results in the elimination of the pyrimidine dimer, returning it to its original state. [12]

Nucleotide excision repair is a more general mechanism for repair of lesions. This process excises the CPD and synthesizes new DNA to replace the surrounding region in the molecule. [12] Xeroderma pigmentosum is a genetic disease in humans in which the nucleotide excision repair process is lacking, resulting in skin discolouration and multiple tumours on exposure to UV light. Unrepaired pyrimidine dimers in humans may lead to melanoma.


Polynucleotides are the polymer of nucleotides. DNA & RNA are polynucleotides. A nucleotide has 3 components:

1. A nitrogenous base.

2. A pentose sugar (ribose in RNA & deoxyribose in DNA).

3. A phosphate group.

Nitrogen bases are 2 types:

> Purines: It includes Adenine (A) and Guanine (G).

> Pyrimidines: It includes Cytosine (C), Thymine (T) & Uracil (U). Thymine (5-methyl Uracil) present only in DNA and Uracil only in RNA.

A nitrogenous base is linked to the OH of 1' C pentose sugar through an N-glycosidic linkage to form nucleoside.

A phosphate group is linked to OH of 5' C of a nucleoside through phosphoester linkage to form nucleotide (or deoxynucleotide).

In RNA, each nucleotide has an additional –OH group at 2' C of the ribose (2’- OH).

2 nucleotides are linked through 3’-5’ phosphodiester bond to form dinucleotide.

When more nucleotides are linked, it forms polynucleotide.

> Friedrich Meischer (1869): Identified DNA and named it as ‘Nuclein’.

> James Watson & Francis Crick (1953) proposed double helix model of DNA. It was based on X-ray diffraction data produced by Maurice Wilkins & Rosalind Franklin.

> DNA is made of 2 polynucleotide chains coiled in a right-handed fashion. Its backbone is formed of sugar & phosphates. The bases project inside.

> The 2 chains have anti-parallel polarity, i.e. one chain has the polarity 5’𔾷’ and the other has 3’𔾹’.

> The bases in 2 strands are paired through H-bonds forming base pairs (bp).

A=T (2 hydrogen bonds) C≡G (3 hydrogen bonds)

> Purine comes opposite to a pyrimidine. This generates uniform distance between the 2 strands.

> Erwin Chargaff’s rule: In DNA, the proportion of A is equal to T and the proportion of G is equal to C.

[A] + [G] = [T] + [C] or [A] + [G] / [T] + [C] =1

v Ф 174 (a bacteriophage) has 5386 nucleotides.

v Bacteriophage lambda has 48502 base pairs (bp).

v E. coli has 4.6x10 6 bp.

v Haploid content of human DNA is 3.3x10 9 bp.

Length of DNA = number of base pairs X distance between two adjacent base pairs.

Number of base pairs in human = 6.6 x 10 9

Hence, the length of DNA = 6.6 x10 9 x 0.34x 10 -9

In E. coli, length of DNA =1.36 mm (1.36 x 10 -3 m)

∴ Therefore the number of base pairs

PACKAGING OF DNA HELIX

§ In prokaryotes (E.g. E. coli), the DNA is not scattered throughout the cell. DNA is negatively charged. So it is held with some positively charged proteins to form nucleoid.

§ In eukaryotes, there is a set of positively charged, basic proteins called histones.

§ Histones are rich in positively charged basic amino acid residues lysines and arginines.

§ 8 histones form histone octamer.

§ Negatively charged DNA is wrapped around histone octamer to give nucleosome.

§ A typical nucleosome contains 200 bp.

Therefore, total number of nucleosomes in human =

6.6 x 10 9 bp 200 = 3.3x 10 7

§ Nucleosomes constitute the repeating unit to form chromatin. Chromatin is the thread-like stained bodies.

§ Nucleosomes in chromatin = ‘beads-on-string’.

§ Chromatin is packaged → chromatin fibres → coiled and condensed at metaphase stage → chromosomes.

§ Higher level packaging of chromatin requires non-histone chromosomal (NHC) proteins.

· Euchromatin: Loosely packed and transcriptionally active region of chromatin. It stains light.

· Heterochromatin: Densely packed and inactive region of chromatin. It stains dark.


Mutations in Somatic Cells and in Gametes

Let’s begin with a question: What is a gene mutation and how do mutations occur?

A gene mutation is a permanent alteration in the DNA sequence that makes up a gene, such that the sequence differs from what is found in most people. Mutations range in size they can affect anywhere from a single DNA building block (base pair) to a large segment of a chromosome that includes multiple genes.

Gene mutations can be classified in two major ways:

  • Hereditary mutations are inherited from a parent and are present throughout a person’s life in virtually every cell in the body. These mutations are also called germline mutations because they are present in the parent’s egg or sperm cells, which are also called germ cells. When an egg and a sperm cell unite, the resulting fertilized egg cell receives DNA from both parents. If this DNA has a mutation, the child that grows from the fertilized egg will have the mutation in each of his or her cells.
  • Acquired (or somatic) mutations occur at some time during a person’s life and are present only in certain cells, not in every cell in the body. These changes can be caused by environmental factors such as ultraviolet radiation from the sun, or can occur if a mistake is made as DNA copies itself during cell division. Acquired mutations in somatic cells (cells other than sperm and egg cells) cannot be passed on to the next generation.

Genetic changes that are described as de novo (new) mutations can be either hereditary or somatic. In some cases, the mutation occurs in a person’s egg or sperm cell but is not present in any of the person’s other cells. In other cases, the mutation occurs in the fertilized egg shortly after the egg and sperm cells unite. (It is often impossible to tell exactly when a de novo mutation happened.) As the fertilized egg divides, each resulting cell in the growing embryo will have the mutation. De novo mutations may explain genetic disorders in which an affected child has a mutation in every cell in the body but the parents do not, and there is no family history of the disorder.

Somatic mutations that happen in a single cell early in embryonic development can lead to a situation called mosaicism. These genetic changes are not present in a parent’s egg or sperm cells, or in the fertilized egg, but happen a bit later when the embryo includes several cells. As all the cells divide during growth and development, cells that arise from the cell with the altered gene will have the mutation, while other cells will not. Depending on the mutation and how many cells are affected, mosaicism may or may not cause health problems.

Most disease-causing gene mutations are uncommon in the general population. However, other genetic changes occur more frequently. Genetic alterations that occur in more than 1 percent of the population are called polymorphisms. They are common enough to be considered a normal variation in the DNA. Polymorphisms are responsible for many of the normal differences between people such as eye color, hair color, and blood type. Although many polymorphisms have no negative effects on a person’s health, some of these variations may influence the risk of developing certain disorders.


Genetic basis of metabolome variation in yeast

Metabolism, the conversion of nutrients into usable energy and biochemical building blocks, is an essential feature of all cells. The genetic factors responsible for inter-individual metabolic variability remain poorly understood. To investigate genetic causes of metabolome variation, we measured the concentrations of 74 metabolites across

100 segregants from a Saccharomyces cerevisiae cross by liquid chromatography-tandem mass spectrometry. We found 52 quantitative trait loci for 34 metabolites. These included linkages due to overt changes in metabolic genes, e.g., linking pyrimidine intermediates to the deletion of ura3. They also included linkages not directly related to metabolic enzymes, such as those for five central carbon metabolites to ira2, a Ras/PKA pathway regulator, and for the metabolites, S-adenosyl-methionine and S-adenosyl-homocysteine to slt2, a MAP kinase involved in cell wall integrity. The variant of ira2 that elevates metabolite levels also increases glucose uptake and ethanol secretion. These results highlight specific examples of genetic variability, including in genes without prior known metabolic regulatory function, that impact yeast metabolism.

Conflict of interest statement

The authors have declared that no competing interests exist.

Figures

Figure 1. Distribution of significant linkages across…

Figure 1. Distribution of significant linkages across the genome.

Metabolite linkages that exceeded the 0.1…

Figure 2. Similarities between metabolite and transcript…

Figure 2. Similarities between metabolite and transcript linkage distributions.

Significant linkages are binned in 10-eQTL…

Figure 3. Levels of pyrimidine intermediates and…

Figure 3. Levels of pyrimidine intermediates and products differ based on the ura3 allele inherited.

Figure 4. Levels of sulfur-assimilation intermediates differ…

Figure 4. Levels of sulfur-assimilation intermediates differ based on the slt2 allele inherited.

Figure 5. RM-inheriting segregants for slt2 and…

Figure 5. RM-inheriting segregants for slt2 and erc1 show significantly higher levels for SAM.

Intensities (mean standard error) of SAM are plotted based upon the allele of slt2 (top) and slt2 and erc1 (bottom). Mass spectrometer ion counts for BY background (diamonds) and RM background (squares) are shown on the left axis while segregants' log2 relative abundances (triangles) are indicated on the right axis.

Figure 6. Levels of glycolysis, pentose phosphate…

Figure 6. Levels of glycolysis, pentose phosphate pathway and TCA intermediates differ based on the…

Figure 7. RM-inheriting segregants for ira2 show…

Figure 7. RM-inheriting segregants for ira2 show significantly lower levels for fructose-1,6-bisphosphate.

Intensities (mean standard…

Intensities (mean standard error) of FBP are plotted based upon the allele of ira2. Mass spectrometer ion counts for BY background (diamonds) and RM background (squares) are shown on the left axis while segregants' log2 relative abundances (triangles) are indicated on the right axis.

Figure 8. Distribution of broad sense heritability…

Figure 8. Distribution of broad sense heritability (

) across measured metabolites. each circle represents… ) across measured metabolites. each circle represents a single metabolite, colored according to how many QTLs are associated with its abundance. 114 metabolites are shown: 74 known metabolites with 52 detected mQTL and 42 unknown metabolites (with known m/z, but unknown identity) associated with 20 additional mQTLs.

Figure 9. Fraction of broad-sense heritability explained…

Figure 9. Fraction of broad-sense heritability explained by identified mQTLs.


Examples of Genetic Variation

Genetic Variation between Individuals

Look at the image of mussel shells below. All of these muscles belong to the same species, meaning they can all interbreed with each other. The differences in their patterns represents the total phenotypic variation in the population. Some of the variation comes from genetics, while some comes from the environment. To sort out what is genetic and what is environmental, scientist would have to conduct a series of experiments.

Two experiments would be required to find the overall genetic variation in the population. In the first, a single mussel would be cloned many times and placed in variable environments. The specimens would be allowed to grow and they would be observed in adulthood. Because their genetics are identical, the variation seen can be attributed solely to environmental variation. In the second experiment, the total variation in a population of wild mussels in the same environment must be observed. At the end of these two experiments, the scientist would have two numbers: one describing the environmental variance and one describing the phenotypic variance.

To get the genetic variation found in this population of mussels, the scientist would simply need to subtract the environmental variance observed with the clone from the total variance observed in the wild population. Another way of calculating the genetic variation is to sample the DNA of the population and measure the differences in the DNA directly. Since genetic variation is produced by differences in the DNA, these differences can be used in reverse to calculate the environmental variation in a population.

Genetic Variation between Species

While the above example discusses genetic variation between members of a population, the concept of genetic variation can be applied on a much grander scale. Consider for instance the Homeobox gene family. This family, known as the “Hox genes” for short, direct and coordinate the positions of body parts during development. These genes, or a variation of them is found among all bilaterally symmetrical animals. This includes everything from insects to fish and mammals. Scientist theorize that an early ancestor developed the Hox genes, which were quickly adapted to many forms of organism. The genetic variation represented in these genes is huge. They produce the different body types of most of the organism on Earth. However, they are still all related and the variance between them can be measured.


Functional Genetic Variants Revealed by Massively Parallel Precise Genome Editing

A major challenge in genetics is to identify genetic variants driving natural phenotypic variation. However, current methods of genetic mapping have limited resolution. To address this challenge, we developed a CRISPR-Cas9-based high-throughput genome editing approach that can introduce thousands of specific genetic variants in a single experiment. This enabled us to study the fitness consequences of 16,006 natural genetic variants in yeast. We identified 572 variants with significant fitness differences in glucose media these are highly enriched in promoters, particularly in transcription factor binding sites, while only 19.2% affect amino acid sequences. Strikingly, nearby variants nearly always favor the same parent's alleles, suggesting that lineage-specific selection is often driven by multiple clustered variants. In sum, our genome editing approach reveals the genetic architecture of fitness variation at single-base resolution and could be adapted to measure the effects of genome-wide genetic variation in any screen for cell survival or cell-sortable markers.

Keywords: CRISPR Cas9 QTL evolution fitness genetic variation genome editing yeast.

Copyright © 2018 Elsevier Inc. All rights reserved.

Figures

Figure 1.. CRISPEY is highly efficient and…

Figure 1.. CRISPEY is highly efficient and precise.

( a ) Schematic for generation of…

Figure 2.. CRISPEY screen for fitness effects…

Figure 2.. CRISPEY screen for fitness effects of natural variants.

Figure 3.. Cas9 is sensitive to mismatches…

Figure 3.. Cas9 is sensitive to mismatches in the seed region and depends on the…


Extensive disruption of protein interactions by genetic variants across the allele frequency spectrum in human populations

Each human genome carries tens of thousands of coding variants. The extent to which this variation is functional and the mechanisms by which they exert their influence remains largely unexplored. To address this gap, we leverage the ExAC database of 60,706 human exomes to investigate experimentally the impact of 2009 missense single nucleotide variants (SNVs) across 2185 protein-protein interactions, generating interaction profiles for 4797 SNV-interaction pairs, of which 421 SNVs segregate at > 1% allele frequency in human populations. We find that interaction-disruptive SNVs are prevalent at both rare and common allele frequencies. Furthermore, these results suggest that 10.5% of missense variants carried per individual are disruptive, a higher proportion than previously reported this indicates that each individual's genetic makeup may be significantly more complex than expected. Finally, we demonstrate that candidate disease-associated mutations can be identified through shared interaction perturbations between variants of interest and known disease mutations.

Conflict of interest statement

The authors declare no competing interests.

Figures

A pipeline for surveying the…

A pipeline for surveying the impact of 2009 SNVs on protein–protein interactions. a…

Assessing the impact of disruptive…

Assessing the impact of disruptive alleles on protein function. a Fraction of protein…

Disruptive population variants seldom result…

Disruptive population variants seldom result in unstable protein expression. a Western blots for…

Disruptive variants occur in important…

Disruptive variants occur in important gene groups and at conserved genomic sites. a…

Prioritizing candidate disease-associated mutations through…

Prioritizing candidate disease-associated mutations through shared disruption profiles. a Schematic of interaction disruption…