Thursday, December 22, 2011

Collected Plasmodium faliciparum GWAS and resistance to antimalarial drugs

Plasmodium falciparum parasite spreads rapidly and widely, if it is out of control. The major prevention is antimalarial drugs. However, drug resistance in parasites has evolved and spread rapidly. In consequence, it’s necessary to launch genome-wide association studies of parasite traits. Previous studies show that mutations in MAL7P1.27 (also known as pfcrt, the gene encoding the P. falciparum CQ resistance transporter) and in the genes encoding P. falciparum dihydrofolate reductase (pfdhfr) and P. falciparum dihydrofolate reductase (pfdhps) have been shown to confer resistance to CQ and SP. Moreover, copy number and/or point mutations at pfmdr1 on chromosome 5 linked to the parasite response to MQ, QN, ART and other antimalarial drugs. Additionally, it has been shown that using 342 genome-wide microsatellite markers and 92 parasite isolates collected from different parts of the world is a more efficient and less-time-consuming way to identify the chromosome segment carrying the pfcrt locus. In the present study, with increase of the number of isolated parasites, it reports the first genome-wide P. falciparum using sensitive method and GWAS of resistance of multiple antimalarial drugs.

In general, the authors isolated 189 culture-adapted P. falciparum parasites in vitro culture, from Asia, Africa, America and paua New Guina. In paralle, they use sensitive method to genotype those parasites. And then, they analyze the population structure, variation in recombination rate and loci under recent positive. In the end, they explore parasite half-maximum inhibitory concentrations for 7 antimalarial drugs and find out the responsible genes.

In the first step, they want to find out whether genetic heterogeneity due to geography. It is found that parasites could be clustered into continental populations with one exception. There is a group of Cambodian parasites separated from those from Thailand and the majority of the parasites from Cambodia. There are two possibilities for this observation. One is the presence of recent population admixture. The other one is that SNPS could distinguish parasites with different phenotypes. .

According to genome-wide SNP dataset, population recombination maps for all 14 chromosomes were generated to detect the recombination frequency. They indeed found hot several loci with high levels of activity including a locus at the end of chromosome 1 and segment on chromosome 7 containing pfcrt.

In order to map chromosomal loci potentially under positive selection, three techniques were utilized, namely, REHH, iHS, and XP-EHH. REHH detected multiple loci including those on chromosome 7 containing pfcrt, on chromosome 11 having the gene encoding pfama-1 and chromosome 13 containing PF13_0271. All the regions mentioned above may be associated with immune or drug selection pressure. Additionally, iHS confirms the results of REHH, meanwhile, it detects other high signal localized in chromosome 1 and 14. Using XP-EHH on one hand detected selective sweep driving some alleles to fixation in one population but polymorphic in the others. On the other hand, it shows the comparison of different population. In a word, using 3 methods detected 11 genes in total.

In the last part, they use IC50 to explore the response of selected parasites to antimalarial drug. After that, they conducted multiple GWAS for loci that are responsible for the different response. Multivariate analyses showed a strong positive correlation between IC 50 values of MQ and DHA suggesting either co-selection by the drugs and/ or a common resistance mechanism to the two drugs. On the contrary, it shows slight negative relationships between DHA and AMQ, MQ and AMA, CQ and MQ, and CQ and DHA both among all the parasites and among those from the Thai-Cambodian population. In particular, separated parasites from Thai-Cambodian population show higher resistance.

In my opinion, this paper is very useful to explore the new direction of treatment to the malaria. The design of this paper is based on the previous foundation showing the mutations in genes related to the resistance and immune target of the antimalarial drug. Under positive selection, they identified the candidate genes and the locations in their collected parasites from different continents. Furthermore, they compared the response of parasites to drugs and tried to find out the responsible genes. Even though they indeed found out some candidate genes, only 3 genes are really related. Among these genes, two of them were reported before. In fact, the real association needs investigating and requiring. In a word, the conclusion is still elusive. I suppose that increasing the number of parasites and decreasing the possibility of imbalanced collection should be taken into consideration in future experiment. Last but not least, this paper provided evidence that high throughput MIP array; estimates of genome-wide recombination events and recent positive selection maps are import tools and information for GWAS to identify genes.

Monday, December 19, 2011

Towards an unbiased study of parallel evolution
The investigation of parallel evolution is a powerful paradigm to study mechanisms of adaptation.  This review and opinion paper stresses the fact that although remarkable examples have been studied, molecular bases of adaptation are still poorly understood in the vast majority of cases.

In rare examples, a genetic variation has been linked to repeated and independent adaptation. In the examples of Mc1r , multiple mutations occurred in the same gene independently leading to different coat colours in mice.  In humans, lactose tolerance was acquired repeatedly due to mutations occurring independently in the same genes in different populations.  In the paper, authors describe mutations in Pitx1 which have occurred repeatedly in three spine stickleback fish leading to reduced pelvic armor plate which differentiates the sea water from the fresh water specie. These observations have been validated by transgenic animals demonstrating the fact that Pitx1 is the genetic basis of this recurrent phenotype and form of adaptation.

As a reader naïve to the field, I found that this paper describes well the obstacles that researchers are facing in the investigation of the molecular basis of adaptation.  Genetic data is sparse and the vast majority of species have not been sequenced. For those species who have been, only a small number of specimens were sequences.  Surprisingly, despite this lack of genetic (or genomic) data, the authors have categorized the different genetic bases to parallel adaptation into 3 groups : i) same mutation in the same gene, ii) different mutation in the same gene and  iii) mutations in different genes.  These very “formal” distinctions have stirred many questions and intrigued many of the students attending the tutorial including myself.  Maybe the fascination for species has drawn the authors to describe different “species” of mutations.  Others and myself thought that, given the scarcity of genetic or genomic data, these questions may be too premature.  Trained in medical genetics, I have repeatedly experienced the situation “iii)” where mutations at many different loci may give rise to the same phenotypic manifestation but  I was reminded, however,  that “disease” is not “adaptation” and although there may be many different ways of disrupting a mechanism only few may lead to specific advantages.  We also discussed the fact that these different “groups” may also be related to the complexity of the phenotype, e.g. : lactose tolerance, related to the function of one enzyme can only be related to the category “i) or ii)” as opposed to  much more composite and complexe phenotype such as “social cognition” for example  which would likely fall under the category “iii)”.

This is a perspective paper and there are no methods or results to critique.  Authors conclude that next generation sequencing will “come to the rescue” of the complex issue of genotype-phenotype correlations  and how they relate to adaptation.  I also share the optimism of the authors and believe that genomic technologies will provide a wealth of “unbiased” (as opposed to candidate gene approaches) data that will allow identifying the basis of many adaptive processes.  The following papers studied in the tutorial showed that this is the case and that many paradigms of evolution are being challenged now that data is available (cf. in other blogs the genomic  signatures of adaptation such as selective sweeps).  What I enjoyed the most in this tutorial were the discussions between students and senior researchers using the same tools and studying the same, phenomenon  (mutations, phenotypes) but driven by very different questions.

Best quote during the tutorial:  “The theory looked really sexy until the data was available”.

Elmer KR, & Meyer A (2011). Adaptation in the age of ecological genomics: insights from parallelism and convergence. Trends in ecology & evolution, 26 (6), 298-306 PMID: 21459472

(Posted by MRR for Sebastien Jacquemont)

Friday, December 16, 2011

Hard selective sweeps do not seem to be the rule in human evolution.

by Ricardo Kanitz, based on the paper by Hernandez et al. published in Science (2011).

One of the main topics in evolution is – as it has always been – human evolution. Many new methods are applied first to humans; other methods, which are not applied there, often come to humans at some point anyway. This is particularly true in the field of genomics and it is no surprise since we are talking about our own species' evolution. The study commented here addresses an interesting general question in the subject. How selection shaped (if at all) our genomes?

More specifically, Hernandez and colleagues are interested in the classic signature of selection in genomes, the “selective sweep”. This so-called sweep is simply the reduction of measured diversity in the (genomic) surroundings of a positively selected mutation. This is observed when (1st) a new beneficial mutation appears, (2nd) it rapidly becomes the most common variant in a population and, (3rd) because genomic positions are not physically independent, nearby positions also become more frequent. As we move further away from such positively selected position, we observe a decay of such pattern due to recombination (see cartoon below).

Based on functional groundings, the authors looked at different parts of the genome. They predicted that non-synonymous mutations (those which change the amino acid in the resulting protein) should show stronger signals of these sweeps when compared to the synonymous mutations. As shown in their Figure 2 (below here), there is no difference whatsoever.

However, they do see a decrease in diversity around all these positions, which is not observed in non-coding ones (see the gray area in their Figure S5A below).

To explore this discrepancy, the authors took advantage of simulations. As seen in Figure 3A below, they simulated a neutral (i.e. control) scenario and compared it to different selective scenarios accounting for varying proportions of human specific amino acid fixations (α = 10%, 15% and 25%) as favored with different selection coefficients (s = 1% or 0.1%). In such conditions, there should be power to detect selection. Based on the fact that they do not detect it, the authors claim that selection was rather rare (with α < 10% and s < 0.1%). Here, I must say that I found these numbers rather high and not at all conservative.

As it follows, they proposed a scenario of background purifying selection to explain the observed pattern. In Figure 3B above, they showed the fit of simulations with background selection (purple, green and orange) with the observations (dark blue, light blue and red). Such a fit appears to be very good and they conclude that the pattern they observed is better explained by purifying selection (a.k.a. strict neutrality) than by recurrent positive selection.

Finally, given (1) the fact that the observations did not fit the predictions of their (rather extreme) selection model, and (2) that a neutral model was able to explain the observations, the general conclusion is that classic selective sweeps resulting from strong positive selection were quite rare in the recent human evolution.

Although it would be interesting to see how the results would look like with lower (and more realistic) values for α and s, this study brings about the interesting discussion of the modus operandi of human adaptation. Classical examples based on phenotypes show that humans underwent recurrent adaptations when it comes to diet, immune response and skin pigmentation. The molecular mechanisms underlying these, however, might not be as simple as the “Classic Selective Sweeps”. Complex genetic architectures linking small effect polygenic variants, for example, may lead to soft sweeps; which do not leave the same sort of signature and can easily be missed in the background noise created by the potentially overwhelming neutral evolution. Therefore, there are still many unknown features related to recent human evolution – especially concerning non-neutral evolution – and the growing availability of data coupled with better analytical methods may bring new and possibly surprising results in the coming years of scientific investigation.