8.4: Advantages of Genetic Recombination - Biology

8.4: Advantages of Genetic Recombination - Biology

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Not only is recombination needed for homologous pairing during meiosis, but recombination has at least two additional benefits for sexual species. It makes new combinations of alleles along chromosomes, and it restricts the effects of mutations largely to the region around a gene, not the whole chromosome.

Since each chromosome undergoes at least one recombination event during meiosis, new combinations of alleles are generated. The arrangement of alleles inherited from each parent are not preserved, but rather the new germ cells carry chromosomes with new combinations of alleles of the genes (Figure 8.4). This remixing of combinations of alleles is a rich source of diversity in a population.

Figure 8.4. Recombination during meiosis generates new combinations of alleles in the offspring. One homologous pair of chromosomes is illustrated, starting at the “four-strand” stage. Each line is a duplex DNA molecule in a chromatid. The two chromosomes in the father (inherited from the paternal grandparents) are blue and green; the homologous chromosomes in the mother (inherited from the maternal grandparents) are brown and pink. All chromosomes have genes A, B and C; different numbers refer to different alleles. In this illustration, a crossover on the short arm of the chromosome during development of the male germ cells links allele 4 of gene C with alleles 1 of gene A and allele 2 of gene B, as well as the reciprocal arrangement. A crossover on the long arm of the chromosome is illustrated for development of the female germ cell, making the new combination A3, B3 and C1. A child can have the new chromosomes A1B2C4 and A3B3C1. Note that neither of these combinations was in the father or mother.

Over time, recombination will separate alleles at one locus from alleles at a linked locus. A chromosome through generations is not fixed, but rather it is "fluid," having many different combinations of alleles. This allows nonfunctional (less functional) alleles to be cleared from a population. If recombination did not occur, then one deleterious mutant allele would cause an entire chromosome to be eliminated from the population. However, with recombination, the mutant allele can be separated from the other genes on that chromosome. Then negative selection can remove defective alleles of a gene from a population while affecting the frequency of alleles only of genes in tight linkage to the mutant gene. Conversely, the rare beneficial alleles of genes can be tested in a population without being irreversibly linked to any potentially deleterious mutant alleles of nearby genes. This keeps the effective target size for mutation close to that of a gene, not the whole chromosome.

Genetic Recombination of Bacteria (With Diagram)

In this process, the exchange of genetic mate­rial takes place through a conjugation tube between the two cells of bacteria. The process was first pos­tulated by Joshua Lederberg and Edward Tatum (1946) in Escherichia coli. They were awarded the Nobel Prize in 1958 for their work on bacterial genetics. Later on, it has also been demonstrated in Salmonella, Vibrio and Pseudomonas.

There are two mating types of bacteria, one is. male type or F + or donor cell, which donates some DNA. The other one is female type or F – or recipient cell, which receives DNA.

Later, after receiving DNA, the recipient cell may behave as donor cell i.e., F + type. The F-factor is the fertility factor, sex-factor or F-plasmid present in the cell of F + i.e., donor cell or male type. The plasmid takes part in conjugation is called episome.

In this process, two cells of opposite mating type i.e., F + and F – become temporarily attached with each other by sex pilus (Fig. 2.26). The sex pilus has a hole of 2.5 pm diameter through which DNA can pass from donor to recipient cell. The F-factor or F-plasmid is a double stran­ded DNA loop, present in the cytoplasm apart from the nucleoid. The F-factor contains about 20 genes.

After the establishment of conjugation tube, the F-factor prepares for replication by the rolling circular mechanism. The two strands of F- factor begin to separate from each other and one of them passes to the recipient i.e., F – cell.

After reaching in F – cell, enzymes synthesise a com­plementary strand that forms a double helix, which bends into a loop. The conversion process is thus completed. In the donor cell i.e., in F + , a new DNA strand also forms to complement the left over DNA strand of the F-factor.

There is another type of conjugation where passage of nucleoid DNA takes place through conjugation tube. Strains of bacteria are known as Hfr (high frequency of recombination) strain. William Hayes discovered such strains of E. coli in 1950s. The Hfr factor is also called episome. In Hfr strain, the F-factor is attached with the nucleoid DNA i.e., the bacterial chromosome.

In this process, Hfr and F – cells become attached with each other by sex pilus (Fig. 2.27). At the point of attachment of F-factor, the bacte­rial chromosome opens and a copy of one strand is formed by the rolling circular mechanism.

A portion of single stranded DNA then passes into the recipient cell through pilus. Due to agitation in medium, the conjugation tube may not survive for long time because of broken pilus. Thereby, the total length of transfer DNA may not be able to take entry to the recipient cell.

The behaviour of the transferred DNA depends on the presence and absence of F-factor:

If F-factor is indeed transferred, then it usually remains detached from the chromosome of recipient cell and enzymes synthesise a complementary DNA strand. The factor then forms a loop and exists as a plasmid, thereby the recipient cell becomes a donor.

If F-factor remains at the rear end of the transfer DNA during its entry to the reci­pient cell, the F-factor may not be able to take entry due to broken pilus and only a portion with new genes (Fig. 2.27) takes up the entry. Thereby, the F – strain remains as recipient one. In F – strain, genetic recombination takes place between donor fragment and recipient DNA.

(iii) Sometimes, if the F-factor gets free from the Hfr cell and maintains an indepen­dent status, then the Hfr cell converts to a F + cell. Sometimes during the leaving of F-factor from the bacterial chromo­some, it takes a segment of chromoso­mal DNA. The F-factor with segment of chromosomal DNA is called F’-factor.

Later on, during conjugation, when this F’-factor is transferred, the recipient cell receives some chromosomal DNA from the donor cell. This process is called sexduction. In this process, the recipient cell receives a portion of chromosomal DNA which duplicates with the existing one for a specific function, thereby the recipient cell is a partial diploid.

Process # 2. Transformation:

It is a kind of genetic recombination where only the carrier of genes, i.e., the DNA molecules of donor cell, pass into the recipient cell through the liquid medium:

It was described by Frederick Griffith (1928), an English bacteriologist. He had done his experi­ment with laboratory mice and two types of Diplococcus pneumoniae, the pneumonia causing organism. One type has rough (R) non-­capsulated cells and another one with smooth (S) capsulated cells. The R-type is non-pathogenic, while the S-type is pathogenic.

The process of transformation is mentioned below (Fig. 2.28):

(i) When live non-pathogenic (R-type) cells are injected in mice, the mice remain alive.

(ii) When dead pathogenic (S-type) cells are injected in mice, the mice also remain alive.’

(iii) When pathogenic (S-type) cells are injected in mice, they suffer from pneu­monia and died.

(iv) When live non-pathogenic (R-type) cells are mixed with dead pathogenic (S-type) cells and are injected in mice, they also suffered from pneumonia and died. On isolation of dead tissue of mice, the smooth (S) qapsulated cells are found on agar. The above experiment indicates the conversion of R-type to S-type, called transformation.

Later, James L. Alloway (1932), transformed the rough type cells to smooth type, by using the fragments from dead smooth-type cells and con­firmed Griffith’s work.

Further, Oswald T. Avery, Colin M. MacLeod and Maclyn N. McCarty (1944) also found that DNA isolated from the fragments could induce the transformation. Their experi­mental result was the first proof of DNA as the genetic material in living organism. The possible mechanism of transformation can be explained (Fig. 2.29).

The transformation takes place in a few cell of the mixed population. It is an impor­tant method of genetic recombination. A few donor cells break apart and an explosive release and fragmentation of DNA take place. A frag­ment of double stranded DNA (10-20 genes) then gets attached with the recipient cell for entry (Fig. 2.29).

During entry one strand of the fragment becomes dissolved by enzyme leaving the second strand, which then passes to the recipient cell through cell wall and cell mem­brane.

After entry, a portion of single strand of double stranded DNA of recipient cell gets displaced by enzyme and then replaced by the DNA of donor cell. The displaced DNA is then dissolved by other enzyme. Thus the recipient cell becomes transformed which will display its own as well as the characters of the newly incor­porated DNA.

Detailed mechanism of transformation, with especial emphasis on natural and induced competence and DNA uptake:

Thus the transformation takes place by hori­zontal gene transfer through uptake of free DNA by other bacteria. This transformation takes place either spontaneously by taking DNA from the environment, i.e., Natural, or by forced uptake under laboratory condition i.e., Artificial process.

A. Natural Transformation:

During natural transformation, free naked fragments of double stranded DNA of donor cell become attached to the surface of the recipient cell. The free double stranded ON A molecules may be available in the medium by lysis or natural decay of bacteria (Fig. 2.30).

After attachment of donor double stranded DNA with the surface of recipient bacterium, one strand is digested by the bacterial nuclease and the remaining one strand is then taken in by an energy-requiring transport system. This uptake of DNA takes place during late logarithmic phase of growth.

During this process, Rec A type of protein plays an important role. The Rec A protein binds with the single stranded DNA and forms a coating around the DNA (Fig. 2.30). The coated single stranded DNA and DNA of recipient cell then move close to each other to get homolo­gous sequence.

After reaching at proper place, the Rec A protein actively displaces one strand of chromosomal DNA of recipient cell. The process requires hydrolysis of ATP to get energy. The incoming DNA strand is then integrated with one strand of bacterial DNA by base pair­ing and ligation takes place by DNA ligase.

The displaced DNA strand of recipient cell is then digested by cellular DNase activity. Any mis­match between the two strands of new region is corrected by them. Thus the transformation is completed. If the introduced single stranded DNA fails to recombine with the recipient DNA, it is diges­ted by cellular DNase and gets lost.

B. Artificial Transformation:

The E. coli, an ideal material for research is not transformed naturally. Later, it has been discovered that the transformation in E. coli can be done by special physical and chemical treatments. This can be done by exposure of E. coli to high voltage electric field and also by high concentration of CaCI2. Under such condi­tion, the bacterial cells are forced to take up foreign DNA. This type of transformation is called artificial.

During this process, the recipient bacterial cells are able to take up double stranded DNA fragments.

Physical or chemical treatment forces the recipient bacterial cell to receive exogenous DNA. The foreign DNA is then integrated with the chromosome by homologous recombi­nation, mediated by Rec A protein. The Rec A protein catalyses the annealing of two DNA segments and exchange of homologous region.

This involves nick i.e., small cut of DNA strands and rejoining of exchanged parts i.e., breakage and reunion. The generally accepted model of the above phenomenon is given below (Fig. 2.31):

Process # 3. Transduction:

It is a special method of genetic recombina­tion where genetic material is transferred from the donor to the recipient cell through a non- replicating bacteriophage — temperate bacte­riophage. This was discovered by Joshua Leaderberg and Nortor Zinder (1952) during their research with Salrv onella typhimurium.

In this process, a small fragment of bacterial DNA is incorporated into an attacking bacteriophage (i.e., virus which infect bacteria) and when this bacteriophage infects a new bacterial cell, it transfers the genetic material into it, and thus genetic recombination takes place.

Transduction are of two types:

A. Specialised transduction, and

B. Generalized transduction.

A. Specialised Transduction:

In this process, the bacteriophage gets attached to a bacterial cell wall at the receptor site and the nucleic acid of bacteriophage is transferred into the cyto­plasm of the host cell (Fig. 2.32A). The phage does not cause the lysis of the host bacterium. In the bacterial cell, the phage nucleic acid codes for the synthesis of specific proteins, the repressor proteins.

The repressor proteins prevent the virus to produce the material require for its replication. In the bacterial cell, the viral DNA may exist as a fragment in the cytoplasm or it may attach itself to the chro­mosome, known as prophage (Fig. 2.32B). The bacterial cell which carries the prophage is called lysogenic and the phenomenon where the phage DNA and bacterium exist together is called lysogeny.

The bacterial cell may remain lysogenic for many generations and during this period the viral DNA repli­cates many times together with the bacterial chromosome.

However, in course of time, the phage stops the synthesis of repressor proteins in the bacterial cell, and then the synthesis of phage components starts. Now the phage DNA separates from the bacterial chromo­some and starts the synthesis of phage pro­teins (Fig. 2.32C).

During this separation, a number of genes of the bacterium get attached to it. These attached genes keep on replicating along with the phage DNA (Fig. 2.32D) and later on it develops into phage particles, those come out from the bacterial cell by bursting (Fig. 2.32E).

When the new phage particle (Fig. 2.32F) infects a new bac­terial cell (Fig. 2.32G, H), the attached bacte­rial genes present along with phage particle enters in the chromosome of the new bacte­rium and causes recombination (Fig. 2.321).

Thus the new bacterial cell contains its own genes and several genes from the parent bacterial cell. This type of transduction is known as specialised transduction, which is an extremely rare event.

B. Generalised Transduction:

This process of transduction is more common than specia­lized transduction. Here the prophage parti­cle is present in the cytoplasm of the infected bacterial cell (Fig. 2.32J). In this process, the phage DNA starts synthesising new phages.

During this process chromosome of bacterial cell gets fragmented (Fig. 2.32K) and some of the fragments become attached with the DNA of some new phage particle, while others remain with phase DNA (Fig. 2.32L).

When the newly formed phage with frag­ment of bacterial chromosome in its DNA (Fig. 2.32M) attacks a new bacterium, the gene of the parent bacterium is transferred to the new bacterium and causes recombi­nation. This type of transduction is called generalised transduction. This type of trans­duction is also rare.

What is the advantage of genetic recombination as a mode of reproduction in bacteria? less time finding other bacteria higher rates of reproduction more susceptibility to antibiotics greater genetic variation

Bacterial genetic recombination is characterized by DNA transfer from one organism called donor to another organism as the recipient and the result is the production of genetic recombinants, individuals. Those recombinant bacteria have a greater genetic variation because they carry, not only the genes they inherited from their parent cells but also the genes introduced to their genomes. There are three types of mechanisms that create genetic variations in bacteria (through recombination):

1. Transformation-that occurs when bacterium takes up a piece of DNA floating in its environment,

2. Transduction-occurs when DNA is accidentally moved from one bacterium to another by a virus (bacteriophage) and

3. Conjugation- when DNA is transferred from one bacteria to another through a tube between cells.

Those mechanisms of genetic recombination together with short generation time and random mutations allow bacteria to evolve very quickly and for example, create resistance to antibiotics.

Evolutionary advantage of genetic recombination in the genome measured for first time

UAB researchers have quantified one of the most important and hard-to-measure phenomena in molecular evolution: the effect of genetic recombination on a species' capacity of adaptation. The Genomics, Bioinformatics and Evolution research group, in collaboration with researchers in the universities of Sussex and Edinburgh, have quantified one of the most important and hard-to-measure phenomena in molecular evolution: the effect of genetic recombination on a species' capacity of adaptation.

There has been much discussion of the evolutionary role of genetic recombination: the exchange of parental genetic material that gives rise to new genetic combinations in offspring. Recombination is a practically universal phenomenon in living beings. In sexual organisms recombination occurs during the process of meiosis, which produces the sexual cells, and maintenance of this sophisticated mechanism, which systematises recombination to the whole genome, is the usual reason given for the preponderance of sex. But what exactly is the advantage of recombination? This work shows that genetic recombination facilitates adaptation and it estimates the evolutionary cost of its absence or depletion in a genome for the first time.

The fate of a new mutation in a genome is conditioned not only by the adaptive advantage or disadvantage that the mutation brings to its bearer, but also by the chromosomal context in which it appears. If a new selected mutation is surrounded by others that are also exposed to selection, these mutations will interfere (compete) with each other as they do not segregate independently, so that joint selection will be less efficient than if selection acts on each mutation separately. This linkage cost, also known as Hill-Robertson interference after its discoverers, makes natural selection less efficient when it acts simultaneously on different linked sites.

In a previous work published in the journal Nature, the authors drew the first high-resolution map of the natural selection of a genome and proved that natural selection is ubiquitous in the genome of the species used as a model in genetics: the fruit fly Drosophila melanogaster. One implication of these findings is that at any one moment there will be linked genetic variants, exposed simultaneously to selection in the genome, and therefore selection will be sub-optimal due to the linkage cost. How can this cost be proved to really exist and, in particular, how can it be measured?

If linkage cost exists, wherever recombination is low there will be a greater density of selective variants that do not segregate freely, lowering the efficiency of the selection and therefore the adaptation rate. On the other hand, the regions of greater recombination will present higher adaptation rates. The first objective of the study was to determine whether the regions with a greater recombination rate experienced a higher genomic adaptation rate. To measure the genomic adaptation the researchers used sophisticated statistical methods from population genetics applied to data on genomic variation. The results showed a very positive correlation between recombination and adaptation, corroborating the existence of the linkage cost in the genome.

The surprise came when it was seen that the initially linear relationship between recombination and adaptation converged towards an asymptotic threshold as from recombination values equal to or above 2 cM/Mb (centimorgans per megabase). This asymptote indicates that there is a threshold recombination value beyond which genomic adaptation reaches a maximum.

The existence of this threshold has two important consequences: (1) the linkage cost disappears beyond a recombination value, or in other words, the selected mutations act as if in practice they segregated independently. An infinite rate of recombination would not increase the adaptive rate of the genome more than a recombination value of 2 cM/Mb (the estimated threshold recombination). (2) the asymptote sets an optimal ceiling for the adaptation rate of a genome, its value being an estimation of the optimal adaptation rate, in the absence of the linkage cost.

Having defined the optimal situation, it is possible to estimate the linkage cost of a genome by analysing it. The researchers found that the D. melanogaster genome has an adaptation rate around 27% below the optimal adaptation rate, the rate it would have if the effects of the mutations did not interfere with one another.

This work, to be published next January in the journal Molecular Biology and Evolution also involved the study of other genomic determiners, like the rate of mutation and gene density over the rate of genomic adaptation. The Genomics, Bioinformatics and Evolution research group is formed by Sergi Hervás, Sònia Casillas, Marta Coronado, Isaac Noguera, David Castellano (lead author of the paper) and Antonio Barbadilla (principal researcher).

The genomics era has provided one of the most surprising examples of the power of natural selection, allowing us to detect the characteristic imprints that natural selection leaves on the genome. This work is also yet another step in the measurement of natural selection at the nucleotide, gene or genome level, as it addresses the question of how the genomic context, whether the current rate of recombination or the rate of mutation, conditions the efficiency of natural selection. The era of population genomics we are living in brings the promise of finally revealing the real nature of genetic variation.

Viruses as Tools for Vaccine Development

Boriana Marintcheva , in Harnessing the Power of Viruses , 2018 Reassortant Vaccines

Reassortant vaccines are manufactured by taking advantage of the natural ability of viruses with segmented genomes to reassort when more than one strain is infecting the host cell. Currently, this approach is applicable to influenza A virus, whose genome is composed of 8 ssRNA segments, and to rotaviruses, harboring a total of 11 dsRNA genomic segments. The goal of reassortment is to “assemble” a virus variant with attenuated pathogenicity that can be used for safe vaccination. Once the desired reassortant is selected, it is propagated in the context of single strain infection, thus preventing the possibility for reversion or drastic changes due to another reassortment event. Two types of reassortant rotavirus vaccines have been developed: one a reassortant of human rotaviruses, and another a reassortant of human and bovine rotaviruses. Efforts are underway to better understand the mechanism of virion packaging in influenza A, B, and C and the relevant packaging signals. It is generally thought that the three types of influenza rarely reassort due to different packing signals. Thus it is possible to utilize influenza C, which causes mild nonseasonal disease and generally does not pose a significant health threat, as a vehicle for influenza A and/or B versions of the flu surface antigens.

Methodological Overview

We sought to identify topological summary statistics (using as a baseline for comparison) that can serve as features for algorithms to perform recombination rate inference. Utilizing simulated data, we computed a variety of topological summaries of dimensions 0, 1, and 2 from the Hamming distance matrix between sequences.

The results of our LASSO regression indicated that the topological features with the highest predictive power for recombination rate are, in order: (1) the average dimension 0 barcode length (ψ), (2) the first Betti number ( ), and (3) the variance of the dimension 0 barcode lengths (Φ). We then used a nonlinear combination of these three topological statistics to build a novel TDA-based model for recombination rate inference, the Topological REcombination Estimator (TREE). We used simulated data to perform an initial validation of the model. For a more serious validation, we applied TREE to 22 full genome assemblies from the RG Drosophila population (Pool et al. 2012) (see Methods for more details) and compared its performance to , the recombination rate estimator introduced by Camara et al. (2016), and to LDhelmet. We also benchmarked TREE on a much larger dataset of Arabidopsis genomes, consisting of 1135 individuals and up to 50k SNPs.

Notation and symbols

Throughout the text, we refer to various quantities. We list them here (Table 1) in addition to where they are first defined.

Coalescent Intuition for Topological Statistics

The main topological statistics of interest here are , the first Betti number, and ψ, the mean bar length in the dimension 0 barcode diagram. In order to relate these to the biological process of recombination, we will use the language of coalescent theory. For a detailed introduction to the field, see Wakeley (2009). We note that our approach differs from the recent considerations of Lesnick et al. (2018) in that we consider a coalescent model with branch lengths and model behavior. Furthermore, we assume a more restrictive sampling regime where only sequences at contemporaneous terminals of the graph are known, as opposed to sequences all along the genealogy.

Explaining ψ:

We provide a heuristic argument that the value of ψ is elevated in the presence of recombination by demonstrating the desired behavior at the recombination rate extremes. First, we claim that in the absence of recombination, the distribution of feature lengths corresponds to the mutation scaled distribution of branch lengths in the coalescent tree of the sample, as shown in Figure 1a. Since there is a single, fixed genealogy that describes all positions within the sequence, it is sufficient to calculate the expected length of the coalescent tree and divide by the sample size. Assuming a large idealized diploid population of size N, from which we sample K individuals with K sufficiently small relative to N, the expected waiting time between coalescence events is generations (Watterson 1975 Tavaré 1984), where k is the number of remaining lineages. The full coalescent tree is then made of each interval k times, for the number of remaining lineages at that time. Summing over all these segments and dividing by the sample size gives us the following: where μ is the per-generation mutation rate. Notably, this is equivalent to the expected number of segregating sites divided by the sample size per Watterson’s estimator (Watterson 1975).

We now show that in the infinite recombination limit, the expectation for ψ is strictly larger than in the case where there is a single fixed genealogy. If there is free recombination, every site in the sample has an independent genealogy, which we will average over, so all the bars must be of the same length (Figure 2). In other words, the expected value of ψ becomes the scaled average coalescence time for two randomly sampled individuals in the current generation. This is simply . All that remains is to show that is <1. Since the partial sum is bounded by this holds for all values of K >5. This additionally suggests that the variance in the length of the barcodes features should decrease as the recombination rate increases, which we observe in simulations. By integrating information about both the length of the coalescent tree and the distribution of pairwise differences averaged over multiple topologies, ψ can be viewed as capturing distortions in the expected amount of independent evolution between samples that occurs when sequences contain multiple discordant gene genealogies.

By averaging over multiple genealogies, the barcode of features approaches identical bars of length equal to the expected pairwise coalescence time. (a) The C̆ech complex for these points at the drawn radius is a graph with a cycle (shown with the dotted lines), as the triple intersection is empty. (b) An ARG with lineage b inheriting p proportion of its genome from the lineage leading to a.

Explaining :

We suggest, in addition, that the standard intuition for the use of to detect cycles in the ARG [presented in Chan et al. (2013), Camara et al. (2016), as well as here in Appendix A: Background on TDA], potentially oversimplifies the relationship between recombination events and features in the barcode. For this, we will consider C̆ech complexes, rather than Vietoris-Rips complexes (which we use in the actual analyses). These are closely related (Ghrist 2014), but the C̆ech complex construction allows holes to be formed given only three points (see Figure 3a), which lets us consider only three terminals. Given the graph in Figure 3b, it is clear that if one were to sample the sequences at every node, there would be an feature observed that corresponds precisely to the hole in the graph. However, in many genetic studies, samples of the common ancestors of present-day sequences do not exist. If we restrict our data to the sequences at the terminals, single cycle detection with becomes a function of mutation heterogeneity along the graph, and any single recombination event cannot be detected if we are given only the true coalescent distances between samples along the genealogy. To see this, take terminals a and c from the graph. By hypothesis, the amount of time between them and their most recent common ancestor (MRCA) is the same, which we will call L. It follows that the minimum radius such that balls around these points would intersect is L. Then, for the triple intersection of balls around the terminals to be empty, b must be a distance greater than L from the MRCA. However, each portion of its genome has certainly experienced the same amount of time since the MRCA regardless of recombination history. Therefore, we require that there be a more than expected amount of mutations generated along the path to b in order for this event to be detected, given the actual sequence data.

Given the true coalescent distances between the terminals , a recombination cycle will not be detected in the manner shown in (a), and so will not generate an feature in the C̆ech complex at any radius. This example motivates the need for topological summaries beyond for recombination estimation.

However, if we take into account multiple recombination events, certain configurations of multiple events will generate features even if we know the true coalescent distances. This is because we can now introduce additional independent evolution in one of the tips by having recombination occur both within a clade and with an outgroup lineage (see Figure 4). We note that is nonzero only in the presence of recombination events, assuming no sequence convergence and infinite sites, but the sampling reality may bias detection in subtle ways. This also implies that the length of the bar will not necessarily be indicative of features of the actual cycle in the graph, as it will increase in length as additional mutations are placed on the lineage leading to b, even if the cycle itself is untouched. We find via simulations (see Supplemental Material, Supplement S1 section Filtering ) that filtering small bars only hurts our inference capabilities, as we would then expect.

A genealogy with two recombination events, with the three resulting gene trees overlaid. A loop will be formed in the C̆ech complex of when the radius is equal to the time to the MRCA for A and C, since B is now further from that node than either A or C.

Combining ψ and :

These explanations for the behavior of ψ and also implies differences in behavior between ψ and under different population models. For example, while rapid demographic changes, such as exponential population growth, will distort ψ-based estimation (Figure 5), counts features generated by recombination at a rate independent of the underlying tree structure, since the relative distances to the MRCA are unchanged with multifurcations. On the other hand, we find empirically that ψ is very robust (especially compared to ) to perturbations in the form of missing data, which serve only to minorly rescale the bars on average. These differences suggest that a reliable predictor should incorporate both features. A more formal follow-up to the behavior of these statistics under different models of demography and selection, as well as further characterization of the behavior of ψ using the sequentially Markov coalescent (SMC) model (McVean and Cardin 2005) will be conducted in future work.

Exponential population expansion creates multiple-merger events and shrinks internal branches. This can give a similar signal in as increased recombination, but does not change cycle detectability via in the ARG.

What is the advantage of genetic recombination as a mode of reproduction in bacteria? less time finding other bacteria higher rates of reproduction more susceptibility to antibiotics greater genetic variation

Bacterial genetic recombination is characterized by DNA transfer from one organism called donor to another organism as the recipient and the result is the production of genetic recombinants, individuals. Those recombinant bacteria have a greater genetic variation because they carry, not only the genes they inherited from their parent cells but also the genes introduced to their genomes. There are three types of mechanisms that create genetic variations in bacteria (through recombination):

1. Transformation-that occurs when bacterium takes up a piece of DNA floating in its environment,

2. Transduction-occurs when DNA is accidentally moved from one bacterium to another by a virus (bacteriophage) and

3. Conjugation- when DNA is transferred from one bacteria to another through a tube between cells.

Those mechanisms of genetic recombination together with short generation time and random mutations allow bacteria to evolve very quickly and for example, create resistance to antibiotics.

What Causes Genetic Drift?

Population Bottleneck

A population bottleneck is a type of genetic drift in which a population’s size severely decreases. Competition, disease, or predation leads to these massive decreases in population size. The allele pool is now determined by the organisms which did not die. Some alleles increase in frequency simply because they are the only alleles left. This type of genetic drift can be seen when people don’t take their entire course of antibiotics.

Antibiotics kill harmful bacteria in your system, regardless of what alleles they have. Antibiotics cause a massive reduction in harmful bacteria. This stops symptoms of the disease. A small population will survive if a patient quits their antibiotic early. This much smaller population could have allele frequencies that are very different from the original population of bacteria. These changes do not reflect the success or failure of the different alleles, but rather the effects of a random selection of bacteria. The new alleles will dominate the population until selection or more genetic drift cause the allele frequencies to change.

Founder Effect

In another type of genetic drift known as the founder effect, a new population is formed, or “founded”, in a new location. If this new population does not interact and reproduce with the main population, the allele frequencies in this population will be much different from that of the parent population. Many islands contain species that only exist on a single island because of the founder effect. For instance, if only two birds of a species land on an island, their alleles alone will account for the diversity present.

While these alleles will dominate at first, mutations will arise in the population that will lead to new adaptations. This new adaptation stays with the founding population. With enough time, the two populations can diverge to a point which they can no longer interbreed. Species often separate in this way.

Mutations have allowed humans to adapt to their environment. For instance, lactose tolerance is a specific external mutation that was advantageous in societies that raised cows and goats. Mutations have been responsible for antibiotic resistance in bacteria, sickle cell resistance to malaria, and immunity to HIV, among others. A rare gene mutation leading to unusual shortness of height has proven to be advantageous for a particular Ecuadorian community. National Public Radio's (NPR) Jon Hamilton writes how the Ecuadorian community with the rare gene mutation known as Laron syndrome are protected against cancer and diabetes.

In 2008, Professor Eiberg from the Department of Cellular and Molecular Biology stated, “Originally, we all had brown eyes but a genetic mutation affecting the OCA2 gene in our chromosomes resulted in the creation of a 'switch,' which literally 'turned off' the ability to produce brown eyes.” He explains that things like “hair color, baldness, freckles, and beauty spots” are all brought about by mutations.


Three sets of D. melanogaster lines resulting from long-term directional selection for stress tolerance were employed in our experiments: (1) three desiccation-resistant lines established by selection over 48 generations (2) three lines tolerant to severe hypoxic stress generated through long-term experimental selection (for more than 200 generations), and (3) three hyperoxia-tolerant lines. Details of the experimental scheme for hypoxia-tolerance selection were provided elsewhere [81, 82]. Peculiarities of the selection for hyperoxia tolerance are described by Zhao et al. [83]. Selection for desiccation tolerance was performed by DDA.

Selection for desiccation tolerance

Wild individuals of D. melanogaster (n = 120) were collected in March 2009 from Madhya Pradesh, Jabalpur, India (23°30’N 80°01’E alt. 393 m). Before the start of the selection experiment, mass culture was maintained for five generations under standard laboratory conditions at low density (on yeast-cornmeal-agar medium at 21 °C, and

70 % relative humidity) to eliminate environmental effects. For laboratory selection, virgin flies were sexed under CO2 anesthesia at least 48 h prior to the experiment. Then, virgin flies (3–4 days old) were placed in groups of 25 into plastic vials containing 2 g of silica gel and covered with foam discs. Experiments were conducted for males and females separately. Flies were subjected to desiccation stress until approximately LT70–LT85 level of mortality was reached. Control groups were established in the same manner, excluding water stress. In each generation, we examined approximately 1,000 virgin flies of each sex per replicate, of which at least 100 males and 100 females survived the LT70–85 cut-off to become the parents of the next generation. For each group (selection and control), survivors were randomly allocated into three sub-groups (three replicates). The same protocol was repeated for 48 generations (each next generation was subjected to analogous treatment), and then selection was relaxed for 8–10 generations before initiating the recombination tests. The control lines were not subjected to any treatment and were maintained in comparable densities to the selection lines on standard media. In the present study, we used three control and three desiccation-resistant lines for recombination tests. Average desiccation tolerance of the initial population was 14.8 h and 23.2 h (with SD = 2.88 and 3.44), for males and females, respectively. After 48 generations of selection, these tolerance characteristics increased to 25.3 h and 43.6 h for males and females, respectively, i.e. 3.65 SDs and 5.93 SDs compared to the starting population.

Hypoxia- and hyperoxia-tolerant lines

Selection for hypoxia/hyperoxia tolerance was initiated after crossing 27 isofemale D. melanogaster lines (kindly provided by Dr. Andrew Davis), that varied considerably in acute anoxia test as well as for eclosion rates when cultured under hypoxic or hyperoxic conditions. Males and virgin females (n = 20) were collected and pooled from each isofemale line. This parental population was reared at room temperature with standard food medium. F1 embryos from the pooled population were separated and maintained in nine separate chambers, three each for control, hypoxia- and hyperoxia-selection experiments. Trial experiments were run to determine the starting O2 concentrations for hypoxia- and hyperoxia-tolerance selection. We analyzed the feasibility and tolerance capacity of the F1 progeny of the parental cross to different O2 concentrations (i.e. 8, 6, or 4 % O2 for hypoxia selection and 60 %, 70 %, 80 % and 90 % O2 for hyperoxia selection). In addition, the tolerance levels of each parental line to hypoxia or hyperoxia were measured by testing survival of each individual line in the hypoxic or hyperoxic environments. In the pilot study, the selection for hypoxia tolerance was therefore started at 8 % O2 and for hyperoxia tolerance at 60 % O2. The low O2 concentration was gradually decreased by 1 % and the high O2 was increased by 10 % every 3 to 5 generations to maintain the selection pressure. The population size was kept at around 2,000 flies in each generation. Eggs of the first egg laying for each generation were removed to limit genetic drift induced by the ‘early-bird’ effect. After seven generations of selection, hyperoxia tolerance was increased to 80 % O2, and after 13 generations the hypoxia tolerance in the hypoxia-selected flies reached 5 %, a level that is lethal for most of the control flies (Additional file 2: Figure S3). The hyperoxia-selected flies broke through the lethal hyperoxic level (90 % O2) after 13 generations of selection, and the hypoxia-selected flies exhibited tolerance to a severe level of hypoxia (4 % O2, embryonic lethal to control flies) following 32 generations of selection. The lethality in these selection experiments was defined as the level of oxygen in which D. melanogaster cannot complete development and reproduce.

Genetic crosses

Virgin females (3 days post-eclosion) of each control and selection lines (three replicate lines each for control and selection groups) were allowed to mate with males of marker stocks (Additional file 2: Figure S1). Four marker stocks were employed (Additional file 2: Figure S2): y cv v f for the X chromosome net dp b pk cn for the 2 L arm, cn kn c px sp for the 2R arm, and ru h th cu sr e for chromosome 3. F1 heterozygous virgin females were collected for each replicate line, and thereafter test-crossed with marker males. Because maternal age may also influence rf in D. melanogaster, we reduced this effect by allowing the 50- to 60-hour old (post-eclosion) F1 virgin females to mate with marker males for approximately 48 hours. To obtain a sufficient number of flies per replicate for scoring recombination, each replicate line was divided into three sub-replicates before the start of recombination experimentation. In this panel, we scored recombination in nine sub-replicates of three replicate lines each for control and selection. In the desiccation experiment, we scored 1,050 individuals of each replicate line (or 350 individuals per sub-replicate), i.e. a total 6,300 flies were counted for estimation of rf at the X chromosome. We scored 750 individuals of each replicate line (or 250 individuals per sub-replicate), i.e. 4,500 individuals each were scored for arms 2 L and 2R and chromosome 3. A total of 19,800 flies were counted for estimation of rf in the desiccation-selection experiment. Similarly, 750 flies per line, or a total 27,000 flies, were scored for rf in the hypoxia/hyperoxia experiments. In the three experiments, we scored a total of 46,800 individuals.

Statistical analysis

For each pair of intervals and each of the three control or selection lines, ML analysis was performed to estimate the recombination frequencies r 1k and r 2k together with the coefficient of coincidence c k (k = 1,2,3). For a pair of intervals, either adjacent or non-adjacent, the log-likelihood function had the following form:

$ log left(Lleft(r<1>_k,r<2>_k,_k ight) ight)=sum__ log left(

_left(kern0.10em r<1>_k,r<2>_k,_k ight) ight) $

where i, j ϵ <0, 1>define whether the recombination event occurred in the first or second interval, respectively (0 – no recombination, 1 – recombination), k denotes the replicate line, and p ijk and n ijk represent the probability and the observed number of individuals of the genotype class ij in replicate line k in the backcross progeny (within control or selection). The frequencies for the four genotype classes were defined as:

The ML estimate ( widehat<<oldsymbol>_k> ) of the vector θ k = (r1 k, r2 k, c k) for k = 1,2,3 was obtained by numerical optimization of the log-likelihood function L (θ k), using the gradient-descent procedure in which all three parameters r1 k, r2 k and c k are evaluated simultaneously in every iteration:

where n refers to iteration number, k to the line (within control or selection), and α to the step size. The variances of the estimated parameters r 1k, r 2k, c k were calculated as corresponding diagonal elements of the covariance matrix V k = I 1 ( ( >>_k ) ) = I k 1 , where I is the Fisher’s information matrix [54]. The estimates of the parameter vector Θ = (r 1, r 2, c) for the entire group (control or selection) together with the vector V Θ of their variances, were obtained as:

This approach enables tests of the heterogeneity of the lines within selection and control groups, across the entire set of selection and control lines, and between selection and control groups, with respect to the estimated parameters. To assess the heterogeneity of ( widehat<<oldsymbol>_k> ) estimates of all three parameters (r1 k, r2 k c k) in k lines we can use the following statistics that is asymptotically distributed as χ 2 with 3(k-1) degrees of freedom:

To assess heterogeneity of a single parameter p in k lines the following statistics asymptotically distributed as χ 2 with df = k-1 can be used:

where ( widehat<<oldsymbol>_k> ) is the ML-estimate of θ k, σ pk 2 is the squared standard error of parameter p in the k th line, and ( widehat ) is the weighted mean of ( widehat<<oldsymbol>_k> ) . Using this weighted likelihood approach, we can present the total heterogeneity of ( widehat<<oldsymbol>_k> ) across all lines of control and selection groups as:

Thus, the significance of the difference between selection and control lines can be tested using the statistics:

The importance of using this approach in testing the differences in interference derives from the fact that heterogeneity of recombination rates within the sample (e.g. between replicate lines of the selection group), with positive co-variation of recombination rates in two intervals, may lead to biased upward estimates of c and even c >1 [63]. Therefore, to reduce the danger of such outcomes while testing for significance between control and selection lines in each of the three experiments, we employed, wherever possible, the weighted ML estimates of recombination (Additional file 3) and interference (Additional files 4 and 5) parameters in weighted likelihood approach, in addition to the standard ML approach (see below). However, where ( widehat < heta_c>) , the estimate of c, was zero in one or more of the three control or selection lines, its standard error was also zero, thereby overweighting the estimates of c from the other two lines and leading to zero weighted average per selection or control. Thus, for all the data we also employed the standard and more direct ML approach allowing for each line, in both selection and control, to have its own r1 k and r2 k. Namely, to test for significance of the differences of c values in selection and control, we performed log-likelihood ratio test of H0 c for all selected and control lines> versus H1 :

H1 : <Θ control = (r 1c, r 2c, c c), Θ selection = (r 1s, r 2s, c s)> vs. H0 : <Θ control = (r 1c, r 2c, c), Θ selection = (r 1c, r 2c, c)>, where pairs of vectors r 1c and r 2c represent the unknown rf values for the analyzed pair of intervals for the three control lines, r 1s and r 2s – the vectors of rf values for the three selection lines, c c and c s – the line-independent values of coefficients of coincidence for control and selection groups, and c g – the global c under the H0 assumption that c s = c c. Therefore, the H0 and H1 hypotheses are specified by 14 and 13 parameters and the log-likelihood ratio test of H1 versus H0 is asymptotically distributed as χ 2 with df = 1.

The obtained P values (for two-tailed test) were subjected to false discovery rate correction for multiple comparisons before demonstrations in tables, figures and text. For false discovery rate correction, we used a total 48 comparisons across three experiments (with 16 intervals in each) for the recombination rates, while 189 comparisons for the interference estimates.

Watch the video: 8 7 genetic recombination (July 2022).


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