Which genetic variants determine histone marks?

JoeDavis

Blog author Joe Davis is a graduate student with Stephen Montgomery & Carlos Bustamante.

The wealth of genetic variation in the human genome is found not within protein-coding genes but within non-protein coding regions. This comes as no surprise given that only 1% percent of the genome codes for proteins. Until recently, efforts to determine the effects of genetic variation on trait variation and disease have focused on coding regions. Results of genome-wide association studies (GWAS), however, have shown that trait and disease associated variants are often regulatory variants such as expression quantitative trait loci (eQTLs) found in non-coding regions. These results have spurred an effort to understand the functional role of non-coding, regulatory variation. Efforts have thus far relied on characterizing the association between variants and gene expression. This association alone, however, will not reveal the complete functional mechanism by which non-coding variants influence gene expression. Recent efforts have therefore begun to characterize numerous molecular phenotypes such as transcription factor (TF) binding, histone modification, and chromatin state to determine the mechanisms by which regulatory variants affect gene expression.

One issue, four papers

In the November 8 issue of Science, three papers were published that address the role of non-coding genetic variation on TF binding, histone modifications, and chromatin state (i.e. active versus inactive enhancer status). The first study was completed by the Dermitzakis Lab at the University of Geneva. They analyzed three TFs, RNA polymerase II (Pol II), and five histone modifications using chromatin immunoprecipitation and sequencing (ChIP-Seq) in lymphoblastoid cell lines (LCLs) from two parent-child trios [1]. The second was completed by the Pritchard Lab, which has recently moved to Stanford, and the Gilad Lab at the University of Chicago. They identified genetic variants affecting variation in four histone modifications and Pol II occupancy in ten unrelated Yoruba LCLs [2]. The third study was performed by the Snyder Lab at Stanford. They characterized the genetic variation underlying changes in chromatin state using RNA-Seq and ChIP-Seq for four histone modifications and two DNA binding factors in 19 LCLs from diverse populations [3]. This work was the subject of a recent CEHG Evolgenome talk given by Maya Kasowski, the study’s first author. Finally, the fourth study, published in the November 28 issue of Nature, was performed by the Glass Lab at UCSD. They characterized the effect of natural genetic variation between two mouse strains on the binding of two TFs involved in cell differentiation (PU.1 and C/EBPα) using ChIP-Seq [4]. In this post, I will analyze primarily the work presented by the Pritchard Lab, but I strongly recommend reading all four papers to understand the challenges in characterizing non-coding variation and the methods available to do so.

Motivation

The four studies seek to answer the general question of how regulatory variation affects gene expression. They characterize diverse molecular phenotypes such as histone modifications and TF binding to understand the mechanisms of action for non-coding variants. The Pritchard Lab focused their study on four histone modifications (three active and one repressive: H3K4me3, H3K4me1, H3K27ac, and H3K27me3, respectively) and Pol II occupancy.

Table2

Histone modifications 101

Histone modifications refer to the addition of chemical groups such as methyl or acetyl to specific amino acids on the tails of histone proteins comprising the nucleosome. These chemical groups are referred to as histone marks. They can serve a wide range of functions, but in general they are associated with the accessibility of a chromatin region. For example, the tri-methylation of lysine 4 of histone 3 (H3K4me3) is associated with increased chromatin accessibility and gene activation. On the other hand, increased levels of the repressive mark H3K27me3 (tri-methylation of lysine 27 of histone 3) at promoters is associated with gene inactivation.

Histone mark levels are measured in a high-throughput manner using ChIP-Seq. Briefly, an antibody targeting the mark of interest is used to pull down modified genomic regions. These immunoprecipitated regions are then sequenced to determine which genomic segments are modified and at what level. The procedure usually requires a large number of cells (on the order of 10^7). Therefore, the modification level is, in some ways, a population level measurement. Analysis of ChIP-Seq data typically involves testing for genomic regions with more reads than expected by chance. These regions, ranging from 200bp to 1000bp or more, are referred to as peaks that represent a modification level above the genomic background. Repressive marks like H3K27me3 tend to have broad peak regions, while activating marks like H3K4me3 can have much tighter peaks.

Since modification levels represent measurements on a population of cells and histone residues can have multiple modifications, genomic regions can show evidence for multiple marks. The combinations of these marks over a region can mark the function of the region. For example, regions with high levels of H3K27ac and a high ratio of H3K4me1 to H3K4me3 can mark active enhancer regions. Until now, the variation of these marks between individuals and the genetic cause of this variation was uncharacterized. Moreover, the causal impact of these marks remains unknown. Do they alter gene expression directly or are they altered by gene regulation? Therefore, the two guiding questions for this study are:

1. What genetic variants influence histone modifications?

2. Are these modifications “a cause or a consequence of gene regulation?”

Variation in histone modifications, a real whodunit

The authors first seek to identify and characterize genetic variants that influence histone marks. They generated ChIP-Seq data for the four histone marks and Pol II in LCLs derived from ten unrelated Yoruba individuals who were previously genotyped as part of the 1000 Genomes Project. Similar studies of regulatory variants such as eQTL studies require large sample sizes to detect the effects of regulatory variants that often lie outside the gene. Unlike eQTL studies, histone marks cover fairly broad regions often encompassing causal regulatory variants. As a result, the authors can use a smaller sample size and still be confident about interrogating the effects of causal regulatory SNPs. The authors developed a statistical test that models total read depth between individuals and allelic imbalance between haplotypes within individuals to increase power to detect cis-QTLs (i.e. variants that affect histone marks and Pol II occupancy nearby in the genome). Using this method, they identified over 1200 distinct QTLs for histone marks and Pol II occupancy (FDR 20%).

The authors then analyze these histone mark and Pol II QTLs to determine the overlap of these variants with other known regulatory variants. The hypothesis is that regulatory variants that affect gene expression will have effects on diverse molecular phenotypes. Therefore, variants that influence histone marks and Pol II should show significant overlap with known regulatory variants such as eQTLs and DNase I sensitivity QTLs (dsQTLs). DNase I sensitivity is a measure of chromatin accessibility with higher sensitivity associated with higher accessibility. The Pritchard Lab mapped eQTLs and dsQTLs in a larger sample of ~75 Yoruban LCLs in two previous studies that I also recommend reading [5,6]. Their analysis revealed an enrichment of low p-values for dsQTLs and, to a lesser extent, eQTLs when tested as histone mark and Pol II QTLs. In addition, the authors observed a coordinated change in multiple molecular phenotypes at dsQTLs and eQTLs. For example, higher levels of the three histone active marks were observed at dsQTLs for the more DNase I sensitive genotype. At eQTLs, H3K4me3, H3K27ac, and Pol II levels were higher for individuals with the high expression genotype. These results show that non-coding regulatory variants impact multiple molecular phenotypes ranging from chromatin accessibility and transcription to histone modifications. The authors provide strong evidence in response to their first guiding question, namely that non-coding regulatory polymorphisms associate with variation in histone marks and Pol II.

TFs and a question of directionality

The authors then turned to addressing the questions of causality for these marks. To do so, they analyze genetic variants in TF binding sites. The main hypothesis is that regulatory variants that alter a TFBS will modify TF binding which will cause changes in histone mark and Pol II levels nearby. If this is the case, then changes in histone marks are a consequence of how strong the TF binding site is. On the other hand, if these marks were causal, polymorphisms in TF binding sites would not be expected to show strong association with changes in these marks.

To test their hypothesis, the authors examine ~11.5K TF binding sites with polymorphisms heterozygous in at least 1 of their 10 individuals. They calculate the change in position weight matrix (PWM) score between the two alleles for polymorphic TF binding sites within each individual. They then test for significant association between this change in PWM and allelic imbalance of ChIP-Seq reads at nearby heterozygous sites. The idea is that if a variant improves (or disrupts) TF binding for one allele at a TF binding site then active histone marks nearby on the same allele will increase (or decrease). Repressive histone marks (in this case H3K27me3) are expected to have the opposite response. Indeed, when they apply their test, they find a significant positive association for the active marks and a negative association for the repressive mark. This result supports the hypothesis of changes histone marks as a consequence of TF binding and gene regulation. However, this result does not rule out other possibilities. Histone marks can still play a causal role in the establishment of TF binding. In other words, the relationship between TF binding and histone marks does not have to be unidirectional. In addition, there is evidence that long non-coding RNAs may play a role in the establishment and regulation of histone marks.

dsQTLs and eQTLs, a match made on chromatin

In their final analysis, the authors examine dsQTLs that are also eQTLs. Since these variants associate with both gene expression and chromatin accessibility at distal regulatory regions (>5kb from associated TSS), the authors can assign the regulatory region to a specific gene. A variant that is both a dsQTL and an eQTL likely disrupts a distal regulatory region. In addition to disrupting the accessibility of the regulatory region, the variant also perturbs the expression of a gene influenced by the regulatory region. For example, a variant may decrease the chromatin accessibility of an enhancer region and thereby decrease the level of active histone marks for the enhancer. This decreased enhancer activity can result in decreased transcription from a nearby gene and similarly decreased active mark levels for the gene. Therefore, the hypothesis guiding this analysis is that variants influencing the histone marks of a distal regulatory region will have a coordinated effect on histone marks at genes under the control of the regulatory region. The authors examine the allelic imbalance in ChIP-Seq reads at regulatory regions and their associated transcription start sites (TSS). Indeed, the authors observe that variants that increase DNase I sensitivity have a significant positive allelic imbalance for active marks at both the regulatory region and the TSS. The opposite is true for the repressive mark. This result again emphasizes the complexity of gene regulation and the impact of non-coding variation. Not only do regulatory variants influence diverse molecular phenotypes nearby, they can direct changes at distal loci. As the authors note, this coordinated change in histone marks between distal regions possibly reflects the 3D organization of chromatin. Regulatory variants that impact chromatin looping interactions between distal regulatory regions and genes may cause changes in activity levels for both the gene and the regulatory region.

Conclusions

This paper provides clear evidence that regulatory variation has very complex impacts affecting multiple and diverse molecular phenotypes at multiple regions simultaneously. This complexity implies potentially numerous and diverse mechanisms by which regulatory variants act on gene regulation. The authors set out to find evidence for one of these mechanisms, namely perturbation of TF binding sites. They begin by showing that variation in histone modifications has a strong genetic basis and that the polymorphisms influencing these marks overlap with known regulatory variants such as eQTLs. They then show that polymorphisms in TF binding sites associate with changes in histone marks, providing evidence for directionality in the relationship between these marks and gene regulation. In essence, their results suggest that histone modifications are directed, at least in part, by TF binding. Finally, they find that regulatory variants can have an impact on the molecular phenotypes of distal regions.

I found this paper, as well as the other three previously mentioned, to be quite interesting. I think these papers show that our understanding of gene regulation is still very simplistic. With the advent of high-throughput molecular assays like ChIP-Seq and DNase-Seq, we can begin to interrogate the complex role of regulatory variation on many phenotypes. In doing so, it is of primary interest to ask questions regarding directionality. How do a given set of molecular phenotypes relate? Do these phenotypes represent a cause or a consequence of genome function? How do the diverse elements of gene regulation function together to build complex phenotypes?

References

[1] Kilpinen, H., Waszak, S. M., Gschwind, A. R., Raghav, S. K., Witwicki, R. M., Orioli, A., Dermitzakis, E. T., et al. (2013). Coordinated Effects of Sequence Variation on DNA Binding, Chromatin Structure, and Transcription. Science (New York, N.Y.), 744. doi:10.1126/science.1242463

[2] McVicker, G., van de Geijn, B., Degner, J. F., Cain, C. E., Banovich, N. E., Raj, A., Pritchard, J. K., et al. (2013). Identification of Genetic Variants That Affect Histone Modifications in Human Cells. Science (New York, N.Y.), 747. doi:10.1126/science.1242429

[3] Kasowski, M., Kyriazopoulou-Panagiotopoulou, S., Grubert, F., Zaugg, J. B., Kundaje, A., Liu, Y., Snyder, M., et al. (2013). Extensive Variation in Chromatin States Across Humans. Science (New York, N.Y.), 750. doi:10.1126/science.1242510

[4] Heinz, S., Romanoski, C. E., Benner, C., Allison, K. a, Kaikkonen, M. U., Orozco, L. D., & Glass, C. K. (2013). Effect of natural genetic variation on enhancer selection and function. Nature, 503(7477), 487–492. doi:10.1038/nature12615

[5] Pickrell, J. K., Marioni, J. C., Pai, A. a, Degner, J. F., Engelhardt, B. E., Nkadori, E., Pritchard, J. K., et al. (2010). Understanding mechanisms underlying human gene expression variation with RNA sequencing. Nature, 464(7289), 768–72. doi:10.1038/nature08872

[6] Degner, J. F., Pai, A. a, Pique-Regi, R., Veyrieras, J.-B., Gaffney, D. J., Pickrell, J. K., Pritchard, J. K., et al. (2012). DNase I sensitivity QTLs are a major determinant of human expression variation. Nature, 482(7385), 390–4. doi:10.1038/nature10808

Paper author Jonathan Pritchard is a professor in the Departments of Genetics and Biology.

Paper author Jonathan Pritchard is a professor in the Departments of Genetics and Biology.

Genomic analyses of ancestry of Caribbean populations

Blog author Rajiv McCoy is a graduate student in the lab of Dmitri Petrov.

Blog author Rajiv McCoy is a graduate student in the lab of Dmitri Petrov.

In the Author Summary of their paper, “Reconstructing the Population Genetic History of the Caribbean”, Andrés Moreno-Estrada and colleagues point out that Latinos are often falsely depicted as a homogeneous ethnic or cultural group.  In reality, however, Latinos, including inhabitants of the Caribbean basin, represent a diverse mixture of previously separate human populations, such as indigenous groups, European colonists, and West Africans brought over during the Atlantic slave trade.  This mixing process, which geneticists call “admixture”, left a distinct footprint on genetic variation within and between Caribbean populations.  By surveying genotypes of 330 Caribbean individuals and comparing to a database of variation from more than 3000 individuals from European, African, and Native American populations, Moreno et al., explore the genomic outcomes of this complex admixture process and reveal intriguing demographic patterns that could not be obtained from the historical record alone. The paper, featured in the latest edition of PLOS Genetics, represents a collaborative project with co-senior authorship by Stanford CEHG professor Carlos Bustamante and Professor Eden Martin from the University of Miami Miller School of Medicine.

Reconstructing the demographic history of admixed populations

Because parental DNA is only moderately shuffled before being incorporated into gametes (the process of meiotic recombination), admixture results in discrete genomic segments that can be traced to a particular ancestral population.  In early generations after the onset of admixture, these segments are large.  However, after many generations, segments will be quite small.  By investigating the distribution of sizes of these ancestry “tracts”, Moreno and colleagues inferred the timing of various waves of migration and admixture.  For Caribbean Island populations, they infer that European gene flow first occurred ~16-17 generations ago, which matches very closely to the historical record of ~500 years, assuming ~30 years per generation.  In contrast, for neighboring mainland populations from Colombia and Honduras, they find that European gene flow occurred in waves, starting more recently (~14 generations ago).

Identifying sub-continental ancestry of admixed individuals

Those familiar with human population genetics will recognize principal component analysis (PCA), which transforms a matrix of correlated observed genotypes into a set of uncorrelated variables where the first component explains the most possible variance, the second variable explains the second most variance, and so on.  Individuals’ transformed genotypes can be plotted on the first two principle components, and when performed on a worldwide scale, distinct clusters appear which represent populations of ancestry.  On conventional PCA plots, admixed individuals fall between their different ancestral populations, as they possess sets of genotypes diagnostic of multiple ancestral groups.  As virtually all Caribbean individuals are admixed to some degree, this pattern is apparent for Caribbean populations (see Figure 1B from the paper, reproduced below).

Fig1B

While interesting, this means that the sub-continental ancestry of these admixed individuals is difficult to ascertain.  An individual may want to know which Native American, West African, and European populations contribute to his or her ancestry, and this analysis does not have sufficient resolution to answer these questions.

Moreno and colleagues therefore devised a new version of PCA called ancestry-specific PCA (ASPCA), which extracts genomic segments assigned to Native American, West African, and European ancestry, then analyzes these segments separately, dealing with the large proportions of missing data that result.  In the case of Native American ASPCA, they observe two overlapping clusters.  The first represents mostly Colombians and Hondurans, who cluster most closely with indigenous groups from Western Colombia and Central America and have a greater overall proportion of Native American ancestry.  The second cluster represents mostly Cubans, Dominicans, and Puerto Ricans, who cluster most closely with Eastern Colombian and Amazonian indigenous groups.  This makes sense in light of the fact that Amazonian populations from the Lower Orinoco Valley settled on rivers and streams, which could have facilitated their migration.  Because indigenous ancestry proportions were relatively consistent and closely clustered across different Caribbean Islands, the authors posit that there was a single pulse of expansion of Amazonian natives across the Caribbean prior to European arrival, along with gene flow among the islands.

In the case of European ASPCA, Moreno et al. found that Caribbean samples clustered closest to, but clearly distinct from, present day individuals from the Iberian Peninsula in Southern Europe.  In fact, the differentiation between this “Latino-specific component” and Southern Europe is at least as great as the differentiation between Northern and Southern Europe.  The authors hypothesize that this is due to very small population sizes among European colonists, which would have introduced noise into patterns of genomic variation through the process of random genetic drift.

Finally, the authors demonstrate that Caribbean populations have a higher proportion of African ancestry compared to mainland American populations, a result of admixture during and after the Atlantic slave trade.  Surprisingly, the authors found that all samples tightly clustered with present day Yoruba samples from Nigeria rather than being dispersed throughout West Africa.  However, because other analyses suggested that there might have been two major waves of migration from West Africa, the authors decided to analyze “old” and “young” blocks of African ancestry separately.  This analysis revealed that “older” segments are primarily derived from groups from the Senegambia region of Northwest Africa, while “younger” segments likely trace to groups from the Gulf of Guinea and Equatorial West Africa (including the Yoruba).

Conclusions and perspectives

This groundbreaking study has immediate implications for the field of personalized medicine, especially due to the discovery of a distinct Latino-specific component of European ancestry.  The hypothesis that European colonists underwent a demographic bottleneck (a process termed the “founder effect”) has expected consequences for the frequency of damaging mutations contributing to genetic disease. The observation of extensive genetic differences among Caribbean populations also argues for more such studies characterizing genetic variation on a smaller geographic scale. The newly developed ASPCA method will surely be valuable for other admixed populations.  In addition to medical implications, studies such as this help dispel simplistic notions of race and ethnicity and inform cultural identities based on unique and complex demographic history.

Citation: Moreno-Estrada A, Gravel S, Zakharia F, McCauley JL, Byrnes JK, et al. (2013) Reconstructing the Population Genetic History of the Caribbean. PLoS Genet 9(11): e1003925. doi:10.1371/journal.pgen.1003925

Paper author Andres Moreno-Estrada is a research associate in the lab of Carlos Bustamante.

Paper author Andrés Moreno-Estrada is a research associate in the lab of Carlos Bustamante.

How recombination and changing environments affect new mutations

Blog author: David Lawrie was a graduate student in Dmitri Petrov’s lab. He is now a postdoc at USC.

I recently sat down with Oana Carja, a graduate student with Marc Feldman, to discuss her paper published in the journal of Theoretical Population Biology entitled “Evolution with stochastic fitnesses: A role for recombination”. In it, the authors Oana Carja, Uri Liberman, and Marcus Feldman explore when a new mutation can invade an infinite, randomly mating population that experiences temporal fluctuations in selection.

The one locus case

This work builds off of previous research in the field on how the fluctuations in fitness over time (i.e., increased variance of fitness) affect the invasion dynamics of a mutation at a single locus. For a single locus, it has been shown that the geometric mean of the fitness of the allele over time determines the ability of an allele to invade a population. This effect is known as the geometric mean principle. Fluctuations in fitness increase the variance and therefore decrease the geometric mean fitness. The variance of the fitness of the allele over time thus greatly impacts the ability of that allele to invade a population.

What if there are two loci?

In investigating a two locus model, the researchers split the loci by their effect on the temporally-varying fitness: one locus only affects the mean, while the other controls the variance. The authors demonstrate through theory and simulation that:

1)    allowing for recombination between the two loci increases the threshold for the combined fitness of the two mutant alleles to invade the population beyond the geometric mean (see figure).

2)    periodic oscillations in the fitness of the alleles over time lead to higher fitness thresholds for invasion over completely random fluctuations (see figure).

3)    edge case scenarios allow for the maintenance of polymorphisms in the population despite clear selective advantages of a subset of allelic combinations.

Temporally changing environments and recombination thus make it overall more difficult for new alleles to invade a population.

Invasibility thresholds as a function of recombination rate. Recombination makes it more difficult for new alleles to invade a population.

Invasibility thresholds as a function of recombination rate. If there is no recombination (the left-most edge of the figure), the geometric mean of the pair of new alleles needs to be higher than 0.5 to allow for invasion, because the resident alleles’ geometric mean fitness is set to 0.5. However, as recombination between the two loci increases, the geometric mean needed for invasion increases rapidly. If there is free recombination (r = 0.5) then  invasion can only happen if the new alleles’ geometric mean fitness is twice the resident alleles’ geometric mean fitness (light grey area). If the environment is changing periodically, it is even harder for new alleles to invade a population (dark grey area).

The evolution of models of evolution

This work is important for addressing the evolutionary dynamics of loci controlling phenotypic variance – in this case, controlling the ability of a phenotype to maintain its fitness even if the environment is variable. Most environments undergo significant temporal shifts from the simple changing of the seasons to larger scale weather changes such as El Niño and climate change, in which species must survive and thrive. For organisms in the wild, many alleles that confer a benefit in one environment will be deleterious when the environment and selective pressures change. There may be modifier-loci which buffer the fitness of those loci in the face of changing environments. Such modifier-loci have been recently found in GWAS studies and may be important for overall phenotypic variance. Thus modeling the patterns of evolution for multiple loci in temporarily varying environments is a key component to advancing our understanding of the patterns found in nature.

Future work

Epigenetic modifiers are a hot area of research and one potential biological mechanism to control phenotypic variance. The evolution of such epigenetic regulation is a particular research interest of Oana. Future work will continue to explore the evolutionary dynamics of epigenetic regulation and focus on applying the above results to finite populations.

Paper author: Oana Carja is a graduate student with Marc Feldman

Reference

Oana Carja, Uri Liberman, Marcus W. Feldman, Evolution with stochastic fitnesses: A role for recombination, Theoretical Population Biology, Volume 86, June 2013, Pages 29-42, ISSN 0040-5809.

We sequence dead people

Blog-author: Sandeep Venkataram is a grad student in Dmitri Petrov’s lab.

By Sandeep Venkataram – Modern humans have been evolving independently of our nearest living relatives, chimpanzees, for over 7 million years. To study our evolutionary history since this divergence, our major source of information is from the fossilized remains of our ancestors and closely related species such as Neanderthals. Physiological information from the remains can tell us a lot about human evolution, but the majority of the information is locked up in the tiny amounts of highly degraded and fragmented DNA left from the specimen.

Studying ancient DNA (aDNA) from bones is extremely challenging. Each sample contains not only the fossil’s DNA, but contaminating DNA from enormous numbers of microbes and other organisms. There is also the possibility of human contamination from handling the fossil. Since the contaminants have much higher quality DNA than the endogenous sample, simply processing a raw DNA extract of the sample typically yields sequencing results with less than 1% aDNA. Sequencing such a sample is incredibly inefficient, as less than 1% of the sequence is actually useful and only the best funded studies can afford the sequencing costs to generate a high coverage genome. Therefore, most aDNA studies tend to focus on mitochondrial DNA using targeted capture methods, or PCR amplicon sequencing. This reduces wasted sequence capacity, but greatly limits the amount of information obtained from the sample.

Getting the most out of ancient DNA

Meredith Carpenter, who is a postdoc here at Stanford, and her colleagues have developed a novel method called whole genome in-solution capture (WISC) to purify aDNA from next-gen sequencing libraries generated from fossil DNA samples. The authors make use of RNA bait technology (Figure 1), which uses RNA complementary to the targeted DNA that has been synthesized using biotinylated nucleotides (Gnirke et al 2009). The RNA can be hybridized to the DNA pool, and purified from solution by linking the biotin to commercially available purification beads. By then eluting the hybrids from the beads and removing the RNA from solution, one can greatly enrich for the targeted DNA. Previous methods using RNA baits required synthesizing DNA strands on microarrays that are complementary to the RNA, then inducing transcription in vitro to generate the RNA bait library. This methodology has been successfully used to capture the entirety of chromosome 21 (Fu et al 2013) from human fossils, but is not cost effective for producing a bait library that covers the entire human genome.

Meredith Carpenter and coauthors circumvent this by mechanically fragmenting human genomic DNA and use blunt end ligation to attach adapter sequences containing an RNA polymerase promoter. This modified DNA library can then be used to generate the biotinylated RNA bait library via in vitro transcription, after which the RNA library is purified using standard protocols. The authors also generate non-biotinylated RNA complementary to the adapter sequences present on every DNA fragment in the library, to block nonspecific binding to the adapters. The bait and block RNA libraries are hybridized to the aDNA libraries, purified using beads to select only the aDNA fragments and sequenced. The cost of their enrichment method is estimated at $50 per sample, and is accessible to most labs conducting aDNA genomic studies.

WISC greatly enriches for ancient DNA across a variety of samples

The authors tested this method on a variety of aDNA libraries prepared from both high quality and low quality samples, including hair remains, teeth and bones, and fossils from tropical and more temperate regions, which can greatly influence DNA quality. They sequenced the libraries both before and after WISC, and found a 3-13x increase in the number of uniquely mapped reads after using WISC. In addition, most of the unique reads in the enriched library are sequenced with five million reads in both hair and bone samples. WISC allows most of the endogenous sequence to be read from dozens of aDNA samples in a single lane of Illumina HiSeq, opening the possibility of sequencing the millions of fossils in museums and collections around the world.

Dr. Carpenter says they are now focusing on adapting the method to removing human DNA contamination from microbiome sequencing projects with promising preliminary results, as well as applications in forensics and studying extinct species. As WISC generates the RNA bait library from genomic DNA of an extant relative instead of synthetic DNA arrays, bait libraries can be prepared regardless of whether the genome of the organism that is the source of the bait library is known. Combined with recent advances in aDNA library construction methods (Meyer et al 2012), WISC promises to make sequencing of contaminated and degraded samples widely accessible.

Carpenter et al (2013) Figure 1. Schematic of the Whole-Genome In-Solution Capture Process
To generate the RNA “bait” library, a human genomic library is created via adapters containing T7 RNA polymerase promoters (green boxes). This library is subjected to in vitro transcription via T7 RNA polymerase and biotin-16-UTP (stars), creating a biotinylated bait library. Meanwhile, the ancient DNA library (aDNA “pond”) is prepared via standard indexed Illumina adapters (purple boxes). These aDNA libraries often contain <1% endogenous DNA, with the remainder being environmental in origin. During hybridization, the bait and pond are combined in the presence of adaptor-blocking RNA oligos (blue zigzags), which are complimentary to the indexed Illumina adapters and thus prevent nonspecific hybridization between adapters in the aDNA library. After hybridization, the biotinylated bait and bound aDNA is pulled down with streptavidin-coated magnetic beads, and any unbound DNA is washed away. Finally, the DNA is eluted and amplified for sequencing.

Paper author Meredith Carpenter

Paper author Meredith Carpenter is a postdoc in Carlos Bustamante’s lab.

References

Carpenter, M. L., Buenrostro, J. D., Valdiosera, C., Schroeder, H., Allentoft, M. E., Sikora, M., Rasmussen, M., et al. (2013). Pulling out the 1%: Whole-Genome Capture for the Targeted Enrichment of Ancient DNA Sequencing Libraries. The American Journal of Human Genetics, 1–13. doi:10.1016/j.ajhg.2013.10.002

Fu, Q., Meyer, M., Gao, X., Stenzel, U., Burbano, H. a, Kelso, J., & Pääbo, S. (2013). DNA analysis of an early modern human from Tianyuan Cave, China. Proceedings of the National Academy of Sciences of the United States of America, 110(6), 2223–7. doi:10.1073/pnas.1221359110

Gnirke, A., Melnikov, A., Maguire, J., Rogov, P., LeProust, E. M., Brockman, W., Fennell, T., et al. (2009). Solution hybrid selection with ultra-long oligonucleotides for massively parallel targeted sequencing. Nature biotechnology, 27(2), 182–9. doi:10.1038/nbt.1523

Meyer, M., Kircher, M., Gansauge, M.-T., Li, H., Racimo, F., Mallick, S., Schraiber, J. G., et al. (2012). A high-coverage genome sequence from an archaic Denisovan individual. Science (New York, N.Y.), 338(6104), 222–6. doi:10.1126/science.1224344

Update: The second paragraph originally talked about fossils, but it should have been bones (now corrected). A fossil is mineralized and would not yield aDNA. 

The fruit fly and its microbiome

PhilippMesser

Philipp Messer is a research associate in the Petrov lab

This post was written by Philipp Messer.

Although fruit flies are one of the most important model organisms in genetics, evolution, and immunology, surprisingly little is known about their associated microorganisms (their microbiome). This is even the more surprising if you consider that the microbiome can strongly affect quantitative traits in flies, for example their growth rate and cold tolerance. Furthermore, the natural environment of fruit flies – rotting fruit – is very rich in microorganisms.

All organisms interact with associated microbes

Because microbes can influence the phenotype of organisms, we expect such interactions to be subject to natural selection. Genes involved in pathogen defense are indeed amongst the fastest evolving genes. But interactions with microbes do not always just lead to an evolutionary arms race between microbes and their hosts, they can also facilitate major evolutionary innovations. Prominent examples of such innovations are the light organ of the bobtail squid that arose through a symbiotic relationship between squids and bioluminescent bacteria, or cellulose digestion in termites which relies on microbes in their guts. Hence, to improve our understanding of the evolution of fruit flies, we need to better understand how they interact and coevolve with their associated microorganisms.

In their paper “Host species and environmental effects on bacterial communities associated with Drosophila in the laboratory and in the natural environment”, Fabian Staubach and his colleagues at Stanford and the Max Planck Institute for Evolutionary Biology in Plön shed light on some of the major questions regarding Drosophila associated microbes. Beyond finding out which bacteria are present in flies, they assess the relative roles of host species and environmental effects on bacterial communities, detect candidate natural pathogens, and find interesting results regarding lab-of-origin-effects on the fly microbial community.

The microbiome of fruitflies

We need more studies like this

These results are not only highly relevant for everyone working with Drosophila, but are also a strong reminder that we cannot understand any model organism without taking its associated microbiota into account. We therefore need more microbiome studies like that of Staubach et al to identify the microbes that coevolve with their hosts and understand how the genomes of hosts and microbes interact in the evolutionary process. I would not be surprised if interactions between microbes and their hosts turn out to be among the biggest selective forces in many organisms.

The paper is a fun and easy read and can be found at here. Fabian was a postdoc in the Petrov lab from 2010 to 2013 and has just moved to the University of Freiburg in Germany to start his own group, where he plans to follow his interest to deepen our understanding of the role of microbes in adaptation.

Citation: Staubach F, Baines JF, Künzel S, Bik EM, Petrov DA (2013) Host Species and Environmental Effects on Bacterial Communities Associated with Drosophila in the Laboratory and in the Natural Environment. PLoS ONE 8(8): e70749. doi:10.1371/journal.pone.0070749

FabianStaubach

Fabian Staubach studies the microbiome of fruitflies.