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As we learned in Chapter 6, Mendel reported that the pairs of loci he observed behaved independently of each other; for example, the segregation of seed color alleles was independent from the segregation of alleles for seed shape. This observation was the basis for his Second Law (Independent Assortment), and contributed greatly to our understanding of heredity. However, further research showed that Mendel’s Second Law did not apply to every pair of genes that could be studied. In fact, we now know that alleles of loci that are located close together on the same chromosome tend to be inherited together. This phenomenon is called linkage, and is a major exception to Mendel’s Second Law of Independent Assortment. Researchers use linkage to determine the location of genes along chromosomes in a process called genetic mapping. The concept of gene linkage is important to the natural processes of heredity and evolution.
24.1 – Population Evolution
By the end of this section, you will be able to do the following:
- Define population genetics and describe how scientists use population genetics in studying population evolution
- Define the Hardy-Weinberg principle and discuss its importance
People did not understand the mechanisms of inheritance, or genetics, at the time Charles Darwin and Alfred Russel Wallace were developing their idea of natural selection. This lack of knowledge was a stumbling block to understanding many aspects of evolution. The predominant (and incorrect) genetic theory of the time, blending inheritance, made it difficult to understand how natural selection might operate. Darwin and Wallace were unaware of the Austrian monk Gregor Mendel’s 1866 publication “Experiments in Plant Hybridization”, which came out not long after Darwin’s book, On the Origin of Species. Scholars rediscovered Mendel’s work in the early twentieth century at which time geneticists were rapidly coming to an understanding of the basics of inheritance. Initially, the newly discovered particulate nature of genes made it difficult for biologists to understand how gradual evolution could occur. However, over the next few decades scientists integrated genetics and evolution in what became known as the modern synthesis —the coherent understanding of the relationship between natural selection and genetics that took shape by the 1940s. Generally, this concept is generally accepted today. In short, the modern synthesis describes how evolutionary processes, such as natural selection, can affect a population’s genetic makeup, and, in turn, how this can result in the gradual evolution of populations and species. The theory also connects population change over time (microevolution) , with the processes that gave rise to new species and higher taxonomic groups with widely divergent characters, called (macroevolution) .
Gating-Spring Models of Mechanoelectrical Transduction by Hair Cells of the Internal Ear
Many bacterial clustered regularly interspaced short palindromic repeats (CRISPR)–CRISPR-associated (Cas) systems employ the dual RNA–guided DNA endonuclease Cas9 to defend against invading phages and conjugative plasmids by introducing site-specific . Read More
Supplemental Videos 1 and 2 Read More
Figure 1: CRISPR–Cas9-mediated DNA interference in bacterial adaptive immunity. (a) A typical CRISPR locus in a type II CRISPR–Cas system comprises an array of repetitive sequences (repeats, brown dia.
Figure 2: The mechanism of CRISPR–Cas9–mediated genome engineering. The synthetic sgRNA or crRNA–tracrRNA structure directs a Cas9 endonuclease to almost arbitrary DNA sequence in the genome through a.
Figure 3: Overall structure of Streptococcus pyogenes Cas9 (SpyCas9) in the apo state. (a) Ribbon representation of the crystal structure of SpyCas9 (PDB ID 4CMP). Individual Cas9 domains are colored .
Figure 4: Guide RNA loading enables Cas9 to form a DNA recognition–competent conformation for target search. (a) Ribbon diagram showing the apo structure of SpyCas9 aligned in the same orientation as .
Figure 5: Structures of CRISPR–Cas9 bound to DNA substrates, showing the same view as in Figure 4c after superposition. (a) Crystal structure of SpyCas9 (surface representation) in complex with sgRNA .
Figure 6: Schematic representations of the proposed mechanisms of CRISPR–Cas9-mediated target DNA recognition and cleavage. Upon sgRNA loading, Cas9 undergoes a large conformational rearrangement to r.
Figure 7: Structures of Cas9 orthologs reveal both the conserved and divergent structural features among orthologous CRISPR–Cas9 systems. Individual Cas9 domains are colored according to the scheme in.
Using the dexamethasone suppression test, we studied the suppressibility of the cortisol axis and its clinical determinants at various time points after stroke. A major aim was to examine the dexamethasone test as a diagnostic tool for the diagnosis of major depression in stroke patients.
The dexamethasone suppression test, major depression, functional ability, and disorientation were assessed in a cohort of 70 patients with acute stroke and after 3 months (n = 63) and 3 years (n = 43).
Early after stroke, 24% of the patients were nonsuppressors, with about the same proportion at 3 months (22%) and 3 years (21%). None of the controls (17 healthy elderly volunteers) were nonsuppressors. High cortisol levels early after stroke were significantly associated with functional impairment (r = 0.35 p = 0.003) and disorientation (r = 0.27 p = 0.03). Three years after stroke, high postdexamethasone cortisol levels were significantly associated with major depression (r = 0.57 p < 0.001). The sensitivity of the dexamethasone test was 70% and the specificity 97%. In a longitudinal analysis of the long-term survivors (n = 42), postdexamethasone cortisol values at 3 months predicted major depression at 3 years.
Hypercortisolism is associated with major depression late (3 years) but not early (0-3 months) after stroke. Patients with hypercortisolism 3 months after stroke are at risk of major depression later in the course and warrant careful follow-up from a psychiatric viewpoint.
We developed the assembly-free linkage analysis pipeline, AFLAP, to generate ultra-dense genetic maps based on single-copy k-mers without reference to a genome assembly. This approach to linkage analysis does not require reads to be mapped and variants called against a reference assembly for marker identification. Instead, variants are identified using assembled k-mers, of fixed length k, unique and single copy to each parent. Assembled fragments equal to k + k - 1 are considered equivalent to SNPs, with the variant position present at the center of the fragment. Fragments larger than k + k - 1 are likely to be complex multi-nucleotide variants or insertions relative to the other haplotype. Therefore, AFLAP uses markers generated from fragments larger or equal to k + k - 1. These fragments are reduced to a representative marker, containing the variant, equal in length to k so that (a) all markers have the same size and (b) a constant marker size can be surveyed in the progeny downstream. AFLAP enables the rapid construction of a genotype table for subsequent linkage analysis.
We tested AFLAP using 100 F2 individuals of A. thaliana sequenced to low coverage. The genetic architecture of A. thaliana has been studied in detail over 2000 F2 individuals, generated by crossing Colombia (Col) x Landsberg (Ler), have been sequenced to low coverage [11,12,13]. The 100 individuals with the largest number of reads from this population were selected to create a test population of similar size to the total progeny of the two B. lactucae experimental populations. The A. thaliana markers were expected to segregate in a 1:2:1 ratio however, this was not the case. The modal percentage of progeny markers detected was 55% for Col markers and 51% for Ler markers (Fig. 2c). Missing data can therefore be estimated as between 26% and 32%. Despite this, the size of the genetic map is very similar to that reported previously [16, 17]. In addition, the average physical positions of genetic bins were highly concordant with the genome assembly with 99% of the markers assigned to the correct chromosome (Fig. 3b, d). The noise in the precise placement of the genetic bins and the low percentage of genetically placed markers (50.9% for Col, 63.4% for Ler-derived markers) was likely caused by missing data due to low sequence coverage, as shown with the simulated data (Table 2, Additional file 1). Despite the imperfect input data, AFLAP was able to produce a good genetic map using markers from each parent, concordant with the chromosome-scale genome assembly.
AFLAP was then applied to F1 progeny isolates of B. lactucae generated by crossing isolate SF5 with either C82P24 or C98O622b, both of which are heterokaryotic therefore, SF5 was effectively crossed to four different nuclei . Progeny isolates were whole genome sequenced to at least 5x coverage to provide reliable identification of unique 31-mers. Heterokaryosis was reflected by half-sib clusters of isolates in the progeny (Fig. 5c). In addition to heterokaryosis, clustering of 31-mer markers also demonstrated that some isolates were genetically more similar to other isolates than expected, allowing potential duplicate individuals to be removed. Therefore, 83 isolates were genotyped with SF5-specific markers and used for linkage analysis (Additional file 3: Figure S3). The small population sizes for individual nuclei from the heterokaryotic parents meant that maps of C82P24 and C98O622b could not be constructed.
The genetic map of isolate SF5 of B. lactucae produced by AFLAP placed 98.8% of the SF5-specific markers into 19 linkage groups (Fig. 6a). The genetic map was highly concordant with large portions of the published genome assembly (Fig. 6b 6). Discordance between the genetic map and the assembly was used to identify mis-assemblies linkage data was then used to guide binning, orienting, and scaffolding, resulting in a much-improved genome assembly with 19 linkage-group-scale scaffolds (Fig. 6c). The more accurate placement of genetic bins on the assembly and higher percentage of mapped makers when compared to A. thaliana (Fig. 3b, d) is likely due to the higher coverage in the B. lactucae dataset. The size of the genetic map produced for B. lactucae is similar to that reported previously . Therefore, with adequate sequencing depth, AFLAP was able to rapidly produce a genetic map of a non-model organism with a highly repeated genome  the high marker density enabled genetically guided fragmentation and re-scaffolding of the genome assembly.
Not all scaffolds were placed on the linkage map. The marker sparse regions totaling 18.6 Mb of the current assembly were only marginally more repetitive than the genetically oriented sequence. Pseudo-test cross markers derived from isolates C82P24 and C98O622b did align to the large unplaced scaffold in the B. lactucae assembly (Additional file 3: Figure S4) therefore, it is possible that the unplaced regions over 1 Mb are homozygous in isolate SF5. In the current study, not enough progeny isolates were obtained from any of the nuclei of the heterokaryotic isolates C82P24 or C98O622b for genetic analysis. Therefore, additional genetic analysis of other isolates will further refine the B. lactucae genome assembly. Genotyping more progeny isolates and generating a consensus map will determine if B. lactucae has fewer than 19 chromosomes. Aligning the assemblies of B. lactucae and P. sojae allowed potential joins to be inferred based on synteny (e.g., Fig. 7 scaffold 117 of P. sojae suggests that linkage groups 9, 11, and 12 of B. lactucae might belong to a single chromosome). Alternatively, enhanced genetic resolution may demonstrate that the genomes of these distant relatives have undergone large-scale structural variation since divergence from their common ancestor. It is possible that applying AFLAP to P. sojae could further refine the P. sojae assembly, investigating syntenic joins suggested by the new assembly of B. lactucae (e.g., Fig. 7 linkage group 2 of B. lactucae joins scaffolds 123 and 127 of P. sojae).
Markers derived from fragments under 61 bp were investigated by rerunning AFLAP including markers derived from fragments equaling 60 bp. Many fragments smaller than k + k - 1 are probably derived from low complexity, repetitive, or hard to assemble sequences and are therefore uninformative. Some fragments equal to 60 bp will contain instances of deletions at a locus and therefore will be informative (Additional file 3: Figure S5). Rerunning AFLAP including markers derived from 60 bp fragments only added 4070 markers to the 96,226 markers used to construct the B. lactucae map (Fig. 6) and did not alter the ordering of markers (Additional file 3: Figure S6). Given that the very large number of markers far exceeded the number of crossovers, using markers derived from smaller fragments was unnecessary to generate accurate genetic maps. Indeed, AFLAP can generate robust genetic maps using only markers derived from 61 bp fragments. Depending on the genetics of the organism under study, there may be advantages to including markers derived from smaller fragments or only using markers derived from 61 bp fragments.
AFLAP has several technical benefits over other strategies for linkage analysis. It is not subject to biases that may be introduced by a reference assembly due to reads from reference alleles mapping more readily to an assembly than reads from alternative alleles  or associated SNP calling errors. In addition, AFLAP enables access to all single-copy portions of the genomes, some of which may not be present in the reference assembly. This may be particularly important when the parents of the mapping population are distantly related to the reference genotype. AFLAP makes it possible to genotype multi-nucleotide polymorphisms and indels in addition to SNPs such variants are often inaccessible in conventional mapping approaches [19, 20]. The frequency of markers derived from fragments > 61 bp was
50% for the A. thaliana F2 maps,
56% for the B. lactucae F1 map, and > 80% for the Lactuca interspecific map (GBS data). Therefore, AFLAP removes bias in marker calling and increases access to variants and genetic markers, resulting in high-density maps. GBS/RADseq can be used to obtain high coverage, reduced representation sequencing data, and using ustacks, linkage analysis may be performed without an assembly  however, library preparation for GBS/RADseq may introduce allele bias caused by restriction site distribution , a lack of robust genotype calls, and much lower marker density. The utility of AFLAP was demonstrated on a lettuce RIL population genotyped using GBS . AFLAP generated nine linkage groups for each parent colinear with the 2.4 Gb genome assembly (Fig. 4) . A nine-linkage group, 1711 cM compound map (Additional file 3: Figure S2) was concordant with a 1883 cM genetic map previously obtained via a conventional read alignment and variant calling workflow . Therefore, AFLAP can efficiently generate accurate genotype tables for linkage analysis from GBS data. AFLAP also allows facile addition of data from new progeny individuals from the same or different populations that have a common parent to increase the genetic resolution of the map. Because each isolate is genotyped independently, adding new isolates generated from the same parents is equivalent to appending additional columns to the genotype table. Adding data from a new cross, but sharing one parent, can be achieved by filtering the 31-mer marker set against the new parent and removing markers from the genotype table that are no longer unique to the common parent. When analyzing the interspecific Lactuca spp. RILs (Additional file 3: Figure S2), the compound map was generated by concatenating the genotype table containing L. serriola-derived markers to the end of the genotype table containing L. sativa-derived markers (i.e., genotypes did not require recalculation). Therefore, AFLAP can incorporate new data easily, enabling rapid maturation of genetic maps.
AFLAP enables the construction of accurate genotype tables resulting in high-quality genetic maps for any organism using a segregating population sequenced to adequate depth. Analyses using simulated and real data demonstrated that the sequence depth obtained on progeny affects the accuracy of marker placement in the genetic map. Even low coverage sequencing (3x) is adequate to assign a marker to a correct linkage group with approximate placement. Simulations demonstrated that 5x WGS coverage was adequate for highly accurate marker placement (Table 2). The accuracy of the genetic placement of markers increased as progeny sequencing depth increased. The desired sequencing depth will therefore vary depending on the aims of the project. For validation of a chromosome scale assembly, low coverage may be adequate. For genetic orientation of a fragmented assembly, at least 5x coverage in the progeny is required. In simulated data, more markers were required to accurately place markers in genomes containing more chromosomes. AFLAP may be applicable to many datasets already generated or being generated. Also, WGS data generated for AFLAP can be easily repurposed for use in numerous other projects. AFLAP was validated with short-read data but could also be applied to high accuracy long-read data. Reads containing multiple errors would reduce the quality of genotyping and may impede the accuracy of AFLAP. Workflows, such as AFLAP, that use unbiased WGS as input will become increasingly desirable as the costs of library generation and sequencing continue to decrease. This may be critical to validating genome assemblies of non-model species generated in projects such as the Earth BioGenome Project .
Biosynthetic Enzymes for (1-3)-β-Glucans, (1-31-6)-β-Glucans from Yeasts
β-Glucans, major polymers of the yeast cell wall structure, have important roles in the function of the cell wall. (1,3)-β-glucan is the main β-glucan synthesized by a (1,3)-β-glucan synthase complex, consisting of a catalytic subunit encoded by the FKS1 and FKS2 genes, and a regulatory subunit encoded by the RHO1 gene. (1,3)-β-Glucan synthesis is spatio-temporally modulated by directly controlling enzyme activity or indirectly regulating expressions. These regulatory mechanisms enable organisms to respond to exogenous and endogenous stimuli to protect against environmental changes.
Although all of Mendel’s pea plant characteristics behaved according to the law of independent assortment, we now know that some allele combinations are not inherited independently of each other. Genes that are located on separate, non-homologous chromosomes will always sort independently. However, each chromosome contains hundreds or thousands of genes, organized linearly on chromosomes like beads on a string. The segregation of alleles into gametes can be influenced by linkage, in which genes that are located physically close to each other on the same chromosome are more likely to be inherited as a pair. However, because of the process of recombination, or “crossover,” it is possible for two genes on the same chromosome to behave independently, or as if they are not linked. To understand this, let us consider the biological basis of gene linkage and recombination.
Homologous chromosomes possess the same genes in the same order, though the specific alleles of the gene can be different on each of the two chromosomes. Recall that during interphase and prophase I of meiosis, homologous chromosomes first replicate and then synapse, with like genes on the homologs aligning with each other. At this stage, segments of homologous chromosomes exchange linear segments of genetic material (Figure 8.18). This process is called recombination, or crossover, and it is a common genetic process. Because the genes are aligned during recombination, the gene order is not altered. Instead, the result of recombination is that maternal and paternal alleles are combined onto the same chromosome. Across a given chromosome, several recombination events may occur, causing extensive shuffling of alleles.
Figure 8.18 The process of crossover, or recombination, occurs when two homologous chromosomes align and exchange a segment of genetic material.
When two genes are located on the same chromosome, they are considered linked, and their alleles tend to be transmitted through meiosis together. To exemplify this, imagine a dihybrid cross involving flower color and plant height in which the genes are next to each other on the chromosome. If one homologous chromosome has alleles for tall plants and red flowers, and the other chromosome has genes for short plants and yellow flowers, then when the gametes are formed, the tall and red alleles will tend to go together into a gamete and the short and yellow alleles will go into other gametes. These are called the parental genotypes because they have been inherited intact from the parents of the individual producing gametes. But unlike if the genes were on different chromosomes, there will be no gametes with tall and yellow alleles and no gametes with short and red alleles. If you create a Punnett square with these gametes, you will see that the classical Mendelian prediction of a 9:3:3:1 outcome of a dihybrid cross would not apply. As the distance between two genes increases, the probability of one or more crossovers between them increases and the genes behave more like they are on separate chromosomes. Geneticists have used the proportion of recombinant gametes (the ones not like the parents) as a measure of how far apart genes are on a chromosome. Using this information, they have constructed linkage maps of genes on chromosomes for well-studied organisms, including humans.
Mendel’s seminal publication makes no mention of linkage, and many researchers have questioned whether he encountered linkage but chose not to publish those crosses out of concern that they would invalidate his independent assortment postulate. The garden pea has seven chromosomes, and some have suggested that his choice of seven characteristics was not a coincidence. However, even if the genes he examined were not located on separate chromosomes, it is possible that he simply did not observe linkage because of the extensive shuffling effects of recombination.
A Novel Rearrangement in (R2S∴SR2) ⊕ -Radical Cations with a 3e-Bond
The stabilization of the radical cation 1, with a three-electron bond, is reminescent of the Wittig and Stevens rearrangements. In the rearrangement of 1 to Et S S Et, H⊕ and H are formally split off. This reaction takes place only for high concentrations of 1 it is noteworthy that oxygen doesn't affect the yield.