4.4: Diversity of evolutionary signatures- An Overview of Selection Patterns - Biology

4.4: Diversity of evolutionary signatures- An Overview of Selection Patterns - Biology

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Independently of the substitution rate, we may also consider the pattern of substitutions in a particular nucleotide subsequence. This is indeed verified experimentally, as shown in Figure 4.11.


Q: In Figure 4.11, we also see nucleotide substitutions in groups of three or sixes. Why is this the case?

A: Insertions and deletions in groups of threes and sixes also contribute to preserving the reading frame. If all the nucleotides are deleted in one codon, the rest of the codons are unaffected during amino acid translation. However, if we delete a number of nucleotides that is not a multiple of three (i.e. we only delete part of some codon), then the translation of the rest of the codons become nonsensical since the reading frame has been shifted.

In Figure 4.12, we can see one more feature of protein-coding genes. The boundaries of conservation are very distinct and they lie near splice sites. Periodic mutations (in multiples of three) begin to occur after the splice site boundary.

As we can see with detecting protein-coding genes, it is not only important to consider the substitution rate but also the pattern of substitutions. By observing how regions are conserved, instead of just looking at the amount of conservation, we can observe ‘evolutionary signatures’ of conservation for different functional elements.

Selective Pressures On Different Functional Elements

Different functional elements have different selective pressures (due to their structure and other characteristics); some changes (insertions, deletions, or mutations) that can be extremely harmful to one functional element may be innocuous to another. By figuring out what the “signatures” are for different elements, we can more accurately annotate a region by observing the patterns of conservation it shows.

Such a pattern is called an evolutionary signature: a pattern of change that is tolerated within elements that still preserve their function. An evolutionary signature is different from the degree of conservation in that you tolerate mutation, but only specific types of mutations in specific places. Evolutionary signatures arise because evolution and natural selection are acting on different levels in certain functional elements. For instance, in a protein-coding gene evolution is acting on the level of amino acids, and so natural selection will not filter out nucleotide changes which do not affect the amino acid sequence. Whereas a structural RNA will have pressure to preserve nucleotide pairs, but not necessarily individual nucleotides.

Importantly, the pattern of conservation has a distinct phylogenetic structure. More similar species (mammals) group together with shared conserved domains that fish lack, suggesting a mammalian specific innovation, perhaps for regulatory elements not shared by fish. Meanwhile, some features are globally conserved, suggesting a universal significance, such as protein coding. Initial approximate annotation of protein coding regions in the human genome was possible using the simple heuristic that if it was conserved from human to fish it likely served as a protein coding region.

An interesting idea for a final project would be to map divergences in the multiple alignment and call these events “births” of new coding elements. By focusing on a particular element (say microRNAs) one could identify periods of innovation and isolate portions of a phylogenetic tree enriched for certain classes of these elements.

The rest of the chapter will focus on quantifying the degree to which a sequence follows a given pattern. Kellis compared the process of evolution to exploring a fitness landscape, with the fitness score of a particular sequence constrained by the function it encodes. For example, protein coding genes are constrained by selection on the translated product, so synonymous substitutions in the third base pair of a codon are tolerated.

Below is a summary of the expected patterns followed by various functional elements:

  • Protein–coding genes exhibit particular frequencies of codon substitution as well as reading frame conservation. This makes sense because the significance of the genes is the proteins they code for; therefore, changes that result in the same or similar amino acids can be easily tolerated, while a tiny change that drastically changes the resulting protein can be considered disastrous. In addition to the error correction of the mismatch repair system and DNA polymerase itself, the redundancy of the genetic code provides an additional level of intrinsic error correction/tolerance.
  • Structural RNA is selected based on the secondary sequence of the transcribed RNA, and thus requires compensatory changes. For example, some RNA has a secondary stem–loop structure such that sections of its sequence bind to other sections of its sequence in its “stem”, as shown in figure 4.13.

Imagine that a nucleotide (A) and its partner (T) bind to each other in the stem, and then (A) mutates to a (C). This would ruin the secondary structure of the RNA. To correct this, either the (C) would mutate back to an (A), or the (T) would mutate to a (G). Then the (C)-(G) pair would maintain the secondary structure. This is called a compensatory mutation. Therefore, in RNA structures, the amount of change to the secondary structure (e.g. stem–loop) is more important than the amount of change in the primary structure (just the sequence). Understanding the effects of changes in RNA structure requires knowledge of the secondary structure. The likely secondary structure of an RNA can be determined by modeling the stability of many possible conformations and choosing the most likely conformation.

  • MicroRNA is a molecule that is ejected from the nucleus into the cytoplasm. Their characteristic trait is that they also have the hairpin (stem–loop) structure illustrated in Figure 4.13, but a section of the stem is complementary to a portion of mRNA.
  • When microRNA binds its complementary sequence to the respective portion of mRNA, it degrades the mRNA. This means that it is a post–transcriptional regulator, since it’s being used to limit the production of a protein (translation) after transcription. MicroRNA is conserved differently than structural RNA. Due to its binding to an mRNA target, the region of binding is much more conserved to maintain target specificity.
  • Finally, regulatory motifs are conserved in sequence (to bind particular interacting protein partners) but not necessarily in location. Regulatory motifs can move around since they only need to recruit a factor to a particular region. Small changes (insertions and deletions) that preserve the consensus of the motif are tolerated, as are changes upstream and downstream that move the location of the motif.

When trying to understand the role of conservation in functional class prediction, an important question is how much of observed conservation can be explained by known patterns. Even after accounting for “random” conservation, roughly 60% of non–random conservation in the fly genome was not accounted for — that is, we couldn’t identify it as a protein–coding gene, RNA, microRNA, or regulatory motif. The fact that they remain conserved however suggests a functional role. That so much conserved sequence remains poorly understood underscores that many exciting questions remain to be answered. One final project for 6.047 in the past was using clustering (unsupervised learning) to account for the other conservation. It developed into an M.Eng project, and some clusters were identified, but the function of these clusters was, and is, still unclear. It’s an open problem!

Dueling evolutionary forces drive rapid evolution of salamander coloration

Two opposing evolutionary forces explain the presence of the two different colors of spotted salamander egg masses at ponds in Pennsylvania, according to a new study led by a Penn State biologist. Understanding the processes that maintain biological diversity in wild populations is a central question in biology and may allow researchers to predict how species will respond to global change.

Spotted salamanders (Ambystoma maculatum) are a widespread species that occur across the eastern United States and return to temporary ponds in the spring to reproduce. Female salamanders lay their eggs in clumps called egg masses, which are either opaque white or completely clear. Females lay the same color egg masses throughout their life, but it is unclear what causes the different coloration, or if either of these colors confers an advantage to the eggs -- for example if one color is less obvious to predators.

"We usually think of evolution operating over hundreds or thousands of years, but in reality, the evolutionary processes at play in a system can influence each generation of animals," said Sean Giery, Eberly Postdoctoral Research Fellow at Penn State and leader of the research team. "In this study, we resurveyed ponds that were originally studied in the early 1990s, which gave us a unique opportunity to explore the evolutionary processes that shape the frequencies of the two egg mass color types, or morphs, that we see today."

Giery resurveyed a network of 31 ponds in central Pennsylvania, noting the color of salamander egg masses as well as environmental characteristics at each pond. The ponds were originally surveyed in 1990 and 1991 by then Penn State Professor of Biology Bill Dunson and his students. The new study appears April 14 in the journal Biology Letters.

The research team found that salamander population sizes and pond chemistry remained stable over the last three decades. When averaged across the region, the overall frequency of each egg color morph also remained the same -- about 70% white egg masses in both 1990 and 2020 -- but in many cases the frequency within individual ponds changed drastically.

"At the scale of individual ponds, it's an extremely dynamic system," said Giery. "They don't just reach one frequency and stay there. By focusing on individual ponds rather than just the region as a whole, we could tease apart what is driving these changes in population frequencies. In this case, we found two opposing evolutionary processes -- selection and drift."

The researchers uncovered strong signatures of an evolutionary process called genetic drift, which can result in morph frequencies changing due to chance. In small populations, drift is more likely to have a major effect, for example with one of the morphs disappearing entirely. As expected due to drift, the researchers found that the frequencies of each morph changed more dramatically in ponds with fewer egg masses.

"However, none of the ponds completely shifted to one morph or the other, which suggests something else might also be going on," said Giery. "We found that ponds at the extremes in the 1990s -- with a high frequency of clear or a high frequency of white egg masses -- became less extreme, shifting toward the overall mean for the region. This supports the idea that 'balancing selection' is operating in this system."

Balancing selection is a type of natural selection that can help preserve multiple traits or morphs in a population. According to Giery, one possible explanation for balancing selection in egg mass color is that the rare morph in a pond -- regardless of the actual color -- has an advantage, which would lead to the rare morph becoming more common. Another possibility is that the white morph has an advantage in some ponds while the clear morph has an advantage in others, and movement of salamanders between the ponds leads to the persistence of both morphs.

"Ultimately we found a tension between these two evolutionary processes, with genetic drift potentially leading to a reduction of diversity in this system, and balancing selection working to maintain it," said Giery.

The researchers are currently surveying egg masses in ponds outside of Pennsylvania to explore if morph frequencies differ in other regions and whether these evolutionary processes operate in the same way over a larger scale.

"Although we did not see a relationship between egg mass color and environmental characteristics in this study, it's possible that environmental characteristics at a larger scale might drive an optimal frequency for each region," said Giery. "By looking at a much larger scale, we can get a better idea of whether there are regional optimums and how they are maintained. Understanding the processes that maintain biological diversity may ultimately help us predict how wild animals will adapt in our changing world."

A selective sweep can occur when a rare or previously non-existing allele that increases the fitness of the carrier (relative to other members of the population) increases rapidly in frequency due to natural selection. As the prevalence of such a beneficial allele increases, genetic variants that happen to be present on the genomic background (the DNA neighborhood) of the beneficial allele will also become more prevalent. This is called genetic hitchhiking. A selective sweep due to a strongly selected allele, which arose on a single genomic background therefore results in a region of the genome with a large reduction of genetic variation in that chromosome region. The idea that strong positive selection could reduce nearby genetic variation due to hitchhiking was proposed by John Maynard-Smith and John Haigh in 1974. [1]

Not all sweeps reduce genetic variation in the same way. Sweeps can be placed into three main categories:

  1. The "classic selective sweep" or "hard selective sweep" is expected to occur when beneficial mutations are rare, but once a beneficial mutation has occurred it increases in frequency rapidly, thereby drastically reducing genetic variation in the population. [1]
  2. another type of sweep is "soft sweep from standing genetic variation" occurs when a previously neutral mutation that was present in a population becomes beneficial because of an environmental change. Such a mutation may be present on several genomic backgrounds so that when it rapidly increases in frequency, it doesn't erase all genetic variation in the population. [2]
  3. Finally, a "multiple origin soft sweep" occurs when mutations are common (for example in a large population) so that the same or similar beneficial mutations occurs on different genomic backgrounds such that no single genomic background can hitchhike to high frequency. [3]

Sweeps do not occur when selection simultaneously causes very small shifts in allele frequencies at many loci each with standing variation (polygenic adaptation).

Whether or not a selective sweep has occurred can be investigated in various ways. One method is to measure linkage disequilibrium, i.e., whether a given haplotype is overrepresented in the population. Under neutral evolution, genetic recombination will result in the reshuffling of the different alleles within a haplotype, and no single haplotype will dominate the population. However, during a selective sweep, selection for a positively selected gene variant will also result in selection of neighbouring alleles and less opportunity for recombination. Therefore, the presence of strong linkage disequilibrium might indicate that there has been a recent selective sweep, and can be used to identify sites recently under selection.

There have been many scans for selective sweeps in humans and other species, using a variety of statistical approaches and assumptions. [4]

In maize, a recent comparison of yellow and white corn genotypes surrounding Y1—the phytoene synthetase gene responsible for the yellow endosperm color, shows strong evidence for a selective sweep in yellow germplasm reducing diversity at this locus and linkage disequilibrium in surrounding regions. White maize lines had increased diversity and no evidence of linkage disequilibrium associated with a selective sweep. [5]

Because selective sweeps allow for rapid adaptation, they have been cited as a key factor in the ability of pathogenic bacteria and viruses to attack their hosts and survive the medicines we use to treat them. [6] In such systems, the competition between host and parasite is often characterized as an evolutionary "arms race", so the more rapidly one organism can change its method of attack or defense, the better. This has elsewhere been described by the Red Queen hypothesis. Needless to say, a more effective pathogen or a more resistant host will have an adaptive advantage over its conspecifics, providing the fuel for a selective sweep.

One example comes from the human influenza virus, which has been involved in an adaptive contest with humans for hundreds of years. While antigenic drift (the gradual change of surface antigens) is considered the traditional model for changes in the viral genotype, recent evidence [7] suggests that selective sweeps play an important role as well. In several flu populations, the time to the most recent common ancestor (TMRCA) of "sister" strains, an indication of relatedness, suggested that they had all evolved from a common progenitor within just a few years. Periods of low genetic diversity, presumably resultant from genetic sweeps, gave way to increasing diversity as different strains adapted to their own locales.

A similar case can be found in Toxoplasma gondii, a remarkably potent protozoan parasite capable of infecting warm-blooded animals. T. gondii was recently discovered to exist in only three clonal lineages in all of Europe and North America. [8] In other words, there are only three genetically distinct strains of this parasite in all of the Old World and much of the New World. These three strains are characterized by a single monomorphic version of the gene Chr1a, which emerged at approximately the same time as the three modern clones. It appears then, that a novel genotype emerged containing this form of Chr1a and swept the entire European and North American population of Toxoplasma gondii, bringing with it the rest of its genome via genetic hitchhiking. The South American strains of T. gondii, of which there are far more than exist elsewhere, also carry this allele of Chr1a.

Rarely are genetic variability and its opposing forces, including adaptation, more relevant than in the generation of domestic and agricultural species. Cultivated crops, for example, have essentially been genetically modified for more than ten thousand years, [9] subjected to artificial selective pressures, and forced to adapt rapidly to new environments. Selective sweeps provide a baseline from which different varietals could have emerged. [10]

For example, recent study of the corn (Zea mays) genotype uncovered dozens of ancient selective sweeps uniting modern cultivars on the basis of shared genetic data possibly dating back as far as domestic corn's wild counterpart, teosinte. In other words, though artificial selection has shaped the genome of corn into a number of distinctly adapted cultivars, selective sweeps acting early in its development provide a unifying homoplasy of genetic sequence. In a sense, the long-buried sweeps may give evidence of corn's, and teosinte's, ancestral state by elucidating a common genetic background between the two.

Another example of the role of selective sweeps in domestication comes from the chicken. A Swedish research group recently used parallel sequencing techniques to examine eight cultivated varieties of chicken and their closest wild ancestor with the goal of uncovering genetic similarities resultant from selective sweeps. [11] They managed to uncover evidence of several selective sweeps, most notably in the gene responsible for thyroid-stimulating hormone receptor (TSHR), which regulates the metabolic and photoperiod-related elements of reproduction. What this suggests is that, at some point in the domestication of the chicken, a selective sweep, probably driven by human intervention, subtly changed the reproductive machinery of the bird, presumably to the advantage of its human manipulators.

Examples of selective sweeps in humans are in variants affecting lactase persistence, [12] [13] and adaptation to high altitude. [14]

Author Summary

A fundamental goal of population genetics is to understand why levels of genetic diversity vary among species and populations. Under the assumptions of the neutral model of molecular evolution, the amount of variation present in a population should be directly proportional to the size of the population. However, this prediction does not tally with real-life observations: levels of genetic diversity are found to be substantially more uniform, even among species with widely differing population sizes, than expected. Because natural selection—which removes genetically linked neutral variation—is more efficient in larger populations, selection on novel mutations offers a potential reconciliation of this paradox. In this work, we align and jointly analyze whole genome genetic variation data from a wide variety of species. Using this dataset and population genetic models of the impact of selection on neutral variation, we test the prediction that selection will disproportionally remove neutral variation in species with large population sizes. We show that genomic signature of natural selection is pervasive across most species, and that the amount of linked neutral variation removed by selection correlates with proxies for population size. We propose that pervasive natural selection constrains neutral diversity and provides an explanation for why neutral diversity does not scale as expected with population size.

Citation: Corbett-Detig RB, Hartl DL, Sackton TB (2015) Natural Selection Constrains Neutral Diversity across A Wide Range of Species. PLoS Biol 13(4): e1002112.

Academic Editor: Nick H. Barton, Institute of Science and Technology Austria (IST Austria), AUSTRIA

Received: December 17, 2014 Accepted: February 20, 2015 Published: April 10, 2015

Copyright: © 2015 Corbett-Detig et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited

Data Availability: Data and code associated with this manuscript are available from Github at

Funding: This work was supported in part by National Institute of Health grants R01GM084236, AI099105 and AI106734 to DLH. During this work, RBCD was supported by Harvard Prize Graduate Fellowship and a UCB Chancellor’s Postdoctoral Fellowship. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors have declared that no competing interests exist.

Abbreviations: AIC, Akaike Information Criteria BGS, background selection BLAST, Basic Local Alignment Search Tool Nc, census population size GFF, general feature format GOLD, Genomes OnLine Database HH, hitchhiking NCBI, National Center for Biotechnology Information PCR, polymerase chain reaction


In this study, we evaluated natural selection pressure on the Japanese population during very recent ages (the past 2000–3000 years), using high-depth large-scale WGS data (25.9×) of over 2200 individuals. Subsequent analysis integrating the GWAS data of over 170,000 subjects demonstrated a close relationship of the identified selection signatures with the Hondo and Ryukyu regional clusters of the Japanese population. While the recent selection signatures in the Japanese population did not show apparent enrichment in Neanderthal-derived sequences, clear overlaps with the genetic risk of the human phenotypes, especially those of the alcohol- or nutrition metabolism-related traits, were observed.

Our study reports several novel findings. First, this is the largest high-depth WGS data study ever conducted on a single, non-European population. Previous studies have reported the benefit of high-depth WGS for rare variant detection and improved imputation accuracy 49,50 . Moreover, our study demonstrated its advantage in the studies of human evolution by utilizing the singleton variants. Secondly, we identified multiple loci with strong, very recent selection signatures in Japanese (ADH cluster, MHC region, and BRAP-ALDH2). These loci were different from previous findings in Europeans, which indicates the necessity of investigating additional populations by WGS to examine human evolution. While our WGS data includes the disease patients of the BBJ cohort, we note that the selection signals of these loci were still significant even when conditioned on the disease affection status, suggesting that disease affection status itself may not have biased the results. Thirdly, the identified very recent selection signatures were independently validated by utilizing DAF spectra heterogeneity and PCA of the large-scale GWAS data. While the current next-generation sequencing (NGS) technology still has limitations in its quality, this consistency between different approaches greatly reduces the possibility that the observed selection signatures resulted from bias introduced by variant calling errors of the WGS data. Fourthly, contrary to our expectations, very recent selection signatures in the Japanese population were not enriched in Neanderthal-derived sequences. Our results raise further questions on interplay between archaic hominins and modern humans. Finally, we found overlaps between very recent selection signatures and human phenotype genetic risk in Japanese, specifically for alcohol or nutrition metabolism-related traits that were clearly distinct from those found in Europeans and Africans highlighted as anthropometric or immune response-related traits. This provides novel insights into the process of modern human evolution with regard to evolutional circumstances specific to each population.

Previous genetic studies have assessed geographical adaptation of the Japanese population mostly from the following two aspects: on distinct clusters within modern Japanese geographical localizations (Hondo and Ryukyu) 16 and on admixture history of ancient Japanese lineages (e.g., Jomon and Yayoi) 51 . Our study provides empirical evidence on the former aspect in relation to very recent natural selection pressures in Japan, while further accumulation of ancient Japanese genome sequences will be necessary to unbiasedly assess the latter aspect. We also note that our PCA analysis could be overestimating the differences observed for distinct Hondo and Ryukyu clusters, due to the relatively higher proportion of the Okinawa residents in the BBJ cohort (3.3%), as compared with the actual proportion in the Japanese population (1.1%). We note that when confining the WGS samples into those belonging to the Hondo cluster (n = 2190), very recent selection signatures observed at all the four loci were still genome-wide significant, thereby suggesting that these selection signatures were not biasedly induced by the population structure.

In conclusion, our WGS-based analysis identified very recent selection signatures and their relationships with evolution, introgression with ancient hominins, and risk of human phenotypes in Japanese individuals. Our study highlights the value of high-depth WGS to understand human adaptations and history.


Cells and cell cultures

All melanocytes used were obtained from human skin. Melanocyte line M0202 was obtained from a lightly pigmented donor by one of us (NS) in the lab. Melanocytes M1 ("Caucasian", 1.5 years old, male donor) and M4 ("Negroid", 4 years old, male donor) were purchased from Promocell (Heidelberg, Germany). The 19-HEM cell line (lightly pigmented) was purchased from Gentaur (Belgium), and NHEM cells (two lightly pigmented and three darkly pigmented) were purchased from Cascade Biologics (Nottinghamshire, UK).

Cell cultures were maintained in an incubator at 37ଌ and 5% CO2. Melanocyte culture conditions were as indicated by the suppliers. Briefly: a) M0202 cells were initially grown in FETI medium: H-10, 2% FCS, 1% Ultroser G (BioSepra, Ciphergen, France), 4 ng/ml bFGF, 2 ng/ml endothelin-1, 5.3 nm TPA, 0.05 mM IBMX, but were later grown in H-10 supplemented with HEPES 6 mM, 5% FBS and MelanoMax (Gentaur), which presumably contains TPA, CT and contains BPE at 40 μg/ml (C. Stefanidis, Gentaur personal communication) b) M1 and M4 melanocytes were cultured in melanocyte growth medium M2 (Promocell). M2 is a serum-free medium, without PMA (TPA) or other tumor promoting or toxic agents, consisting of a basal medium plus a Supplement Mix. The detailed formulation has not been disclosed by the supplier, but it seems that no BPE is present in this growth medium (Ute Liegibel, PromoCell, personal communication). c) 19-HEM melanocytes were grown in H-10 supplemented with HEPES 6 mM, 5% FBS and MelanoMax (Gentaur) d) NHEM melanocytes culture medium was Cascade Biologics Medium 254 supplemented with 1% HMGS (containing BPE, FBS, bovine insuline, bovine transferrin, bFGF, hydrocortisone, heparin and PMA) (Cascade Biologics).

The culture medium was changed every two days until the culture was approximately 80�% confluent, and everyday thereafter.

M0202, HEM and NHEM melanocytes were passaged (split 1:3) routinely every 10 or 11 days, or harvested when they had reached confluency. The growth rate for M1 and M4 melanocytes was slow, and passaging (1:2) or harvesting at confluency was performed routinely after 2 to 3 weeks. All melanocytes used were from passage 5 to 15, and all were from normal non-transformed primary cell culture isolates.


Subconfluence cultures were irradiated once a day for 5 consecutive days with UVA+B (50 mJ/cm 2 :25 mJ/cm 2 ) light in an ICH2 photoreactor (LuzChem, Canada) at 37ଌ. These doses assumed an absorbance of the plastic flask of approximately 5% for UVA and 11% for UVB (flasks were opaque to UVC), estimated from spectrophotometer absorbance readings at 255 nm, 305 nm and 360 nm of cuvette-size splinters of the flasks. By trials with murine melanoma cells (B16F10), this dose was shown to have no effect on cell viability. In order to prevent the generation of toxic metabolites, the culture medium was replaced by PBS with magnesium and calcium immediately before irradiation. After irradiation, PBS was replaced again by the culture medium. Irradiation control cultures were subject to the same procedure, except that they were covered by aluminum foil during irradiation. Cultures were harvested 24 h after the last irradiation dose.

Microarray gene expression

Immediately after harvesting, cells were resuspended in lysis buffer. Total RNA was obtained following the supplier's protocol (Ambion's total RNA extraction kit), including DNAse treatment. cDNA was synthesized from 2 μgr of total RNA using the Affymetrix One-Cycle cDNA Synthesis Kit and following the Affymetrix Expression Analysis Technical Manual. From this cDNA, cRNA was synthesized using the Affymetrix IVT Labeling Kit, which was then purified with the Affymetrix GeneChip Sample Cleanup Module. The purified cRNA (15 μgr) was fragmented and hybridized to Affymetrix U133A 2.0 arrays using standard Affymetrix protocols. A total of 18 microarrays from 9 irradiated cell lines (5 from lightly pigmented donors and 4 from darkly pigmented donors) plus their corresponding unirradiated control cultures were analyzed.

Microarray Data Analysis

Raw data were log2 transformed and quantile normalized using DNAMR v1.1 [46] for R (2.4.1). The Pattern Discovery option of SAM software v3.0 [47] was used to analyze the normalized data. Gene Ontology analysis was done using FatiGO [48].

Quantitative PCR

To demonstrate that the media composition, in particular the presence/absence of BPE, affects the expression levels of TYR, TYRP1 and DCT we purchased a new melanocyte cell line from Cascade Biologics (lightly pigmented) and grew this cell line in Cascade growth medium 254 supplemented with 1% HGMS (Cascade + for short). We sub-cultured and propagated the cells for approximately 3 weeks (three passages). Three days after the third passage we generated 4 subcultures of the same cell line. These 4 subcultures were from now on grown in 6 different media: Medium 1: Cascade + Medium 2: Cascade medium, PromoCell Supplement (no BPE) Medium 3: PromoCell growth medium plus PromoCell Supplement. Medium 4: PromoCell growth medium plus PromoCell Supplement, plus 0.2% (v/v) of a 13 mg/ml solution of BPE. Cells were grown in these media for four days to allow some adaptation to the new media. Media were refreshed every two days.

After this time, we extracted total RNA from each subculture (Ambion). cDNA was synthesized using the Invitrogen SuperScript First-Strand synthesis system for RT-PCR kit, and then, we quantified the expression of TYR, TYRP1 and DCT for each of these subcultures using the BIO-RAD iQ SYBR green Supermix system in combination with a BIO-RAD iCycler machine. For mRNA quantification the following primers were used: TYR: 5'-AGAATGCTCTGGCTGTTTTG-3' and 5'-TCCATCAGGTTCTTAGAGGAGACAC-3'. For TYRP1: 5'-CATGCAGGAAATGTTGCAAGAG-3' and 5'-AGTTTGGGCTTATTAGAGTGGAATC-3' For DCT: 5'-TATTAGGACCAGGACGCCCC-3' and 5'-TGGTACCGGTGCCAGGTAAC-3'. For normalization GADPH was used primer: 5'-CCTGTTCGACAGTCAGCC-3' and 5'-CGACCAAATCCGTTGACTCC-3'. In all cases, annealing temperatures were fixed at 56ଌ. For DCT, Mg 2+ concentration was increased in 1 mM above the standard reaction conditions.

DNA samples and Resequencing

We have resequenced the following in 116 human chromosomes: a) 4.1 kb of 5' DCT, including 187 bp of the first intron, the first CDS plus the 5'-UTR and 3,204 bp of the upstream flanking sequence b) approximately 4 kb of 5' TYR, including 17 bp of the first intron, the first CDS plus the 5'-UTR and 2,773 bp of the upstream flanking sequence and c) approximately 5 kb of 5' TYRP1, including 433 bp of the first intron, 47 bp of the second intron, the first CDS plus the 5'-UTR and 4,200 bp of the upstream flanking sequence. Sample individuals come from diverse geographical origins and include: 20 chromosomes from Biaka and Mbuti Pygmies (DNA purchased from the European Collection of Cell Cultures, ECACC), 20 chromosomes from Senegalese individuals resident in Spain, 42 European (N. Spain) chromosomes (including 20 chromosomes from melanoma patients), 20 Asian chromosomes including Chinese samples from Coriell Cell Repositories and Chinese residents in Spain, and 14 chromosomes from Australian Aborigines (DNA purchased from the ECACC).

DNA was PCR amplified in overlapping

Estimation of haplotypes

To solve the haplotypes phase, we first run PHASE [49]. For those pairs of SNPs that did not reach a PHASE probability greater than 0.95, we solved their phase experimentally by ARMS-PCR and/or cloning (using the TOPO-TA kit from Invitrogene) plus resequencing.

Population parameters and neutrality tests

After removing polymorphic positions in humans, divergence (K) between a chimp sequence and a human sequence was estimated using K-estimator 6.0 [50]. Initially, population diversity parameters and neutrality statistics like Tajima's D [29] were obtained by means of DnaSP 4.1 [51]. These tests were corrected for demography as specified below. HEW and DHEW tests [32] were carried out using software kindly provided by Kai Zeng.

Optimization of demographic parameters

To correct for demography in the coalescent neutral simulations of the neutrality tests (excluding HEW and DHEW), we optimized the fit between pairwise FST distributions obtained from real genomic data from the three major geographical human groups in the HapMap project, and those FST distributions from coalescent simulations obtained varying the demographic parameters. The optimization criterion was the p-value of the Kolmogorov-Smirnov D statistic between the real and simulated FST distributions. For the real neutral distribution of the FST statistic [52], we used that obtained previously in [15] in which we selected 43 regions distributed across the autosomal genome that belong to broader regions of low gene density, and which are at least 150 kb away from the closest exon. Each of these regions spanned an average of 1.96 Mb, and in total they account for 84.3 Mb. For each region we downloaded the SNP frequency information available from the HapMap browser (data Rel #20/phase II on NCBI B35 assembly, dbSNP b125) for the 3 major populations (Caucasians: 153,339 SNPS, Yorubas: 123,798 SNPs and Chinese: 33,190 SNPs). We further filtered the number of SNPs to include only those SNPs that: a) were at least 100 kb away from each other, b) have been genotyped in all three populations and c) at least one of the three major populations had a minor allele frequency (MAF) higher than 0.1. A final list of 546 SNPs satisfied these criteria. We used the ms program [53] for the simulations with 3 populations, with sample sizes equal to those in the HapMap population (120 chromosomes for the African population, 120 for Caucasians and 90 for Asians). As starting points in our simulations to optimize demographic parameters, we used those values described in [54]. Simulations were fixed on one segregating site. These SNPs were matched for a MAF of 0.1 in at least one population.

The mean and 95% upper limits (between brackets) of the observed FST distributions were [15]: Caucasians-Chinese: 0.08 (0.33) Caucasians-Yorubas: 0.14 (0.47) and Chinese-Yorubas: 0.16 (0.45). Final optimized values were obtained in the simulations under the following conditions: we used an ancestral population size of 24,000 for the African population (population 1) and 7,700 for both Asians and Caucasians (populations 2 and 3, respectively), with a migration rate matrix Mij = <0, 0.05, 0.4, 0.1, 0 3, 0.8, 2.5, 0>for i and j values from 1 to 3. Looking back in time, we assumed two bottlenecks with instant population reduction, each followed by a population fusion: one approximately 40,000 years ago, in which the Chinese population reduced its size to approximately one sixth. About 2,000 years after this episode, the Chinese population fuses with the European population. Assuming a generation time of 20 years, this represents an F value of 0.04 for this bottleneck. A second population bottleneck takes place about 90,000 years ago. On this occasion, the Eurasian population suffers a reduction in size to one sixth of its previous size. About 10,000 years after this bottleneck (F = 0.21), the Eurasian population fuses with the African population. The mean and 95% upper limits (between brackets) of the simulated FST distributions were: Caucasians-Chinese: 0.08 (0.31) Caucasians-Yorubas: 0.15 (0.44) and Chinese-Yorubas: 0.15 (0.45). The optimized, simulated FST distribution and the real distribution recorded Kolmogorov-Smirnov D values of 0.0356 for Caucasians vs. Asians (p-value 0.891), 0.0748 for Caucasians vs. Africans (p-value 0.104), and 0.0441 for Asians vs. Africans (p-value 0.683). As both simulated and real data are not statistically different, we used those demographical parameters used in the simulations for the subsequent neutrality tests.

Correction for demography in the neutrality tests

These demographic and genetic parameters were subsequently used in further simulations for estimating the critical points in Tajima's D. These simulations also allowed us to obtain the distribution of Tajima's D under the genetic and demographic parameters described above.

Extended Haplotype Homozygosity (EHH) test

For the EHH test, we initially used Sweep 1.0 [55] to scan and select the core haplotypes. We initially focused on those haplotypes that showed both substantial frequency and high EHH values from the core SNPs, in both 5' and 3' directions. By means of a Perl script, we then calculated the EHH values as in [26] for a region extending about 50 kb from the closer core SNP. To test the significance of the test, we ran coalescent simulations using the population and demographical parameters described above for the FST distributions, but in this case several aspects are different from our previous approach [15]. First, in this case we included the variable recombination rate information obtained from the HapMap webpage for the particular region tested. In each case, we obtained a discrete number of recombination rate classes using the following approach: for each region we obtained the average and standard deviation (sd) of the distribution of recombination rates obtained from the HapMap link. All recombination values greater than the average plus 2 sd were considered outliers. If these outlier regions were consecutive, a local average was calculated otherwise, a single outlier value was assigned for that region flanked between the previous and the next recombination values. We then excluded these outliers and repeated the process. In this second round, recombination rates that were higher than the new global average plus 1 sd were considered again as a class each, except if they were consecutive, in which case a local average was estimated. The average of the rest of recombination rates was considered as the background recombination rate, which was used as a reference to estimate the relative intensity of recombination for all other recombination classes.

For the coalescent simulations with heterogeneous recombination rates, we used msHOT [56], a modification of [53] coalescent-based program (ms) for simulating genetic variation data for a sample of chromosomes from a population. Second, in order to obtain a null distribution that reflects a neutral scenario for the chosen core haplotypes, we proceeded as follows: a) in the simulations, the "core" was the set of the first n SNPs, where n is the number of SNPs in the original core for each case. We then discarded those simulations that did not result in a number of distinct haplotypes (as defined from the simulated core set of SNPs) identical to that observed in the HapMap data using the corresponding observed core SNPs b) one of the simulated haplotypes had to match in frequency (allowing for a 2% difference) our observed core haplotype being tested, and in addition, it had to show the same ancestral/derived states for the SNPs composing the core. For the latter, ancestrality was obtained by comparing the corresponding orthologous regions from the chimpanzee genome sequence and the Macaca mulatta genome sequence obtained from the UCSC genome browser [57] or the Ensembl genome browser [58] c) for each set of simulations that fulfilled these conditions (we typically ran the program until we obtained about 500 simulations satisfying all the conditions), we obtained the 95% upper percentile of the expected EHH distribution for each of a series of consecutive 1 kb-long windows spanning the region. We finally declared a core haplotype as under selection if the distance for which the EHH values were equal to 1 for the SNPs in the HapMap data was longer than the distance observed from the 95% upper percentile distribution of the EHH values obtained in the simulations.

This approach offers several advantages over other methods currently being used to detect selection. For instance, it allows for a direct comparison with a "neutral" null distribution. In contrast, null distributions obtained from genomic regions are not representative of a homogeneous neutral scenario, but rather the result of heterogeneous evolutionary processes. In addition, it allows statistical inference even when no alternative haplotypes for the same core SNPs are available in order to obtain relative EHH values, as done in other approaches. Finally, ancestral-derived state information and information on the core haplotypes frequency can be incorporated, which helps fine-tune the nature of the elements being compared (observed and simulated data).

Multiple testing corrections

To control for multiple testing in the FST tests, we used the approach by [59], which sets the false discovery rate (FDR) at a level α by ranking the initial p values in ascending order P(1)P(2) ≤ . ≤ P(m), with m being the number of tests, and then by specifying P(i) ≤ αi/m as the point below which there is no rejection at an FDR of α.

Genealogical relationships among haplotypes

Graphical representations of the genealogical relationships among haplotypes were estimated by the Median-Joining (MJ) algorithm implemented in Network [60]. When feasible, the ancestral haplotype was inferred using parsimony by comparison to the chimp and rhesus macaque sequences.

Test for overdominance

To evaluate the possibility of overdominance (heterozygote advantage), we scored the ratio of "observed heterozygosity" to "expected heterozygosity" for single SNPs. Observed heterozygosity was estimated by counting heterozygote individuals in the HapMap data set for each SNP in question. The expected heterozygosity was estimated by calculating gene diversity from allele frequencies. Significance for ratio values greater than 1 was obtained by simulation using ms [53].

4.4: Diversity of evolutionary signatures- An Overview of Selection Patterns - Biology

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Timing of Selective Events

To determine the relative timing of the detected selective sweeps, we generated estimates of coalescent times for 14 of the top candidate cognition loci with clear dominant haplotypes (Fig. 5). Estimated coalescent times for all selected alleles were recent, largely nonoverlapping, and occurred since the last glacial maximum. Since most of the alleles examined are implicated in hard selective sweeps from de novo mutations (Dataset S1), we infer that selection on each allele occurred at or very near the time the mutation emerged. Based on these distinct coalescent time estimates, we can reject a model by which recent selection on cognition in P. fuscatus took place predominantly in one time period. Instead, the data are consistent with a mutation-limited process in which cognitive evolution was constrained by the availability of adaptive mutations, or a mutation order process where selective sweeps on beneficial mutations are contingent on the prior fixation of other adaptive alleles (61). We cannot rule out older selective events although multiple strong selective events since the last glacial maximum suggest a potential role for climate-induced shifts in range distribution, and cooperative behavior may have played a role in recent cognitive evolution in P. fuscatus, which extends further north than other North American Polistes wasps (27).

Estimates of allele ages for several candidate cognition loci show evidence of several bouts of recent selection in P. fuscatus. Violin plots show estimates of the posterior distribution of the age of the most recent common ancestor of the allele.

DNA Patterns Of Microbes

The genomes or DNA of microbes contain defined DNA patterns called genome signatures. Such signatures may be used to establish relationships and to search for DNA from viruses or other organisms in the microbes' genomes. Foreign DNA in bacteria has often been associated with disease-causing abilities.

In his doctorate, Jon Bohlin studied methods for examining the genome signatures of microbes. Since foreign DNA in the genomes of bacteria often give the bacteria disease-causing abilities, part of his work was aimed at developing fast and simple methods for finding foreign DNA.

The explosive development in technology for sequencing DNA molecules has made enormous amounts of genetic information available for analysis. This has both led to an upheaval in biological research and simultaneously created a great need for fast and effective methods of interpreting the steadily increasing amounts of information.

To solve the challenges that these large amounts of information present, bioinformational research is utilising techniques taken from statistical, mathematical and information technologies. Most of the methods that were used in this project were originally established in the field of theoretical bioinformation. However, because of insufficient information, it was not previously possible to investigate the methods properly.

The increasing number of sequenced genomes that has become available during recent years has made it possible to test the methods' advantages and disadvantages, possibilities and limitations. This has given us more reliable information on how different microbes' DNA composition is influenced by environment and lifestyle.

The methods can also be used to deepen our understanding of the evolutionary development that follows natural selection at the DNA level. Such knowledge is absolutely necessary to understand mechanisms leading to bacteria becoming pathogenic (disease-producing) and resistant to antibiotics.

This doctorate comprises analyses of the genome signatures of microbes, and describes how genome signatures vary in the genomes of both closely-related microbes and among different microbial genomes. One of the central questions is how environment influences the genome signatures and if this influence may be may be linked to different characteristics of the microbes, such as size, DNA composition, lifestyle and niche.

Cand. scient. Jon Bohlin defended his Ph. D. thesis, entitled "Genomic signatures in prokaryotic genomes", at the Norwegian School of Veterinary Science, on June 5, 2009.

Watch the video: Akdeniz ekosistemi risk altında (July 2022).


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