Population Genomics Reveal Recent Speciation and Rapid Evolutionary Adaptation in Polar Bears

ResearchBlogging.org
The polar bear (Ursus maritimus) is a carnivorous species which is closely related to the brown bear (Ursus arctos) and is adapted to the severe living conditions of the High Arctic due to the great physiological changes happened during evolutionary speciation. Despite numerous researches it is still unclear when exactly this two species diverged. That’s why, Liu with colleagues in their work tried to determine a reliable divergence time of polar bear and brown bear populations and investigated demographic history as well as selection and adaptation of polar bears.

Summary

By applying a population genomic framework the authors analyzed 89 complete nuclear genomes of polar bears and brown bears. They showed that two species diverged 479-343 thousand years ago (kya) and found 16 genes under strong positive selection on the polar bear in comparison with the brown bear. They analyzed more precisely nine of these genes that are known to be associated with high risk of cardiomyopathy and vascular diseases in humans. However, in polar bears these genes are responsible for an important reorganization of the cardiovascular system which allowed them to survive in extreme life’s conditions within Arctic Circle (e.g. very low temperatures, high physical activity in cold water, high demand of the energy, hyperlipidemic diet, etc).

Principal results and discussion

In brief, authors analyzed 79 polar bears and 10 brown bears from different areas. First of all, they sequenced and de novo assembled a polar bear reference genome. The data analysis was performed by population genomic framework.

They determined joint demographic history of polar bear and brown bear, inferred effective population sizes and estimated the divergence time of two species around 479-343 kya by using two complementary methods – identity by state (IBS) tracts of DNA and diffusion approximation for demographic interference (????) (Fig. 2, taken from original publication). Both of these approaches based on past population size changes. This principal point allows avoiding mistakes from a simple isolation-with-migration model which does not consider ancient population size changes and overestimates the divergence time. The same methods together with D statistics (the ABBA-BABA test) also allowed authors to investigate the patterns and direction of gene flow between two populations from their split.

Fig-2.PB-post

Liu and colleagues discussed evidences suggesting population bottlenecks and reduction in effective population size of polar bear that was accompanied by period of migration. Moreover, on the Figure 2a they indicate an important size reduction of joint past population predating the divergence of polar bear and brown bear. In turn, this divergence was also followed by decrease of population size in polar bear that was greater and longer than in brown bear. However, later we observe an increase of both population sizes which started earlier in brown bears.

Also, authors indicate that the gene flow corresponds to four migration waves between polar bear and brown bear populations (dated 319 – 148 kya) which probably continue till the present. However, they didn’t exclude that this migration could have occurred earlier because IBS method has some limitations to detect migration close to split time. On the Figure 2b authors constructed a best-fit model reflected that a great part of the gene flow took place from polar bear to brown bear. Apparently, these data do not correspond to results obtained with isolation-with-migration model proposed earlier (Fig.2c).

Next big part of paper is devoted to investigation of divergence and of polymorphism between polar bear and brown bear by analyzing of polar bear’s genes under positive selection that managed the reorganization of their cardiovascular system and facilitated adaptation to extreme living conditions of the High Arctic after divergence. Also, authors tried to find different evolutionary changes in protein coding sequences as well as they performed analysis of the coding regions of 19.822 genes and they used the giant panda as an outgroup. Interestingly, most of genes under positive selection were associated with vital processes such as cardiovascular function, heart development, blood coagulation, adipose tissue development and metabolism of fatty acids. However, in humans these genes are responsible for cardiomyopathy and vascular diseases.

Conclusions and personal comments

On my opinion, this paper touches a very interesting issue. Especially, I appreciated that by using genomic approaches Liu with colleagues suppose genetic causes underlying development of human cardiovascular diseases. It is an interesting point even if it needs to be proved by farther investigations, for example, by analyzing other species which are more closely related to humans.

Personally, I think that the genomic approach chosen by authors allows us to elucidate the divergence time, speciation and evolutionary history of two species of bears that were not so clear by the present time. However, from my point of view, a deeper analysis could be performed to investigate the current state of both brown bear and polar bear populations because in the paper authors indicate the possibility of recent gene flow between polar bear and brown bear.

Reference

Liu, S., Lorenzen, E., Fumagalli, M., Li, B., Harris, K., Xiong, Z., Zhou, L., Korneliussen, T., Somel, M., Babbitt, C., Wray, G., Li, J., He, W., Wang, Z., Fu, W., Xiang, X., Morgan, C., Doherty, A., O’Connell, M., McInerney, J., Born, E., Dalén, L., Dietz, R., Orlando, L., Sonne, C., Zhang, G., Nielsen, R., Willerslev, E., & Wang, J. (2014). Population Genomics Reveal Recent Speciation and Rapid Evolutionary Adaptation in Polar Bears Cell, 157 (4), 785-794 DOI: 10.1016/j.cell.2014.03.054

Ancient human genomes suggest three ancestral populations for present-day Europeans

ResearchBlogging.org

Introduction & Methods

The demographic history of the human species has always fascinated us. It is known that the ancestral human lineage appeared in East Africa, and that it subsequently migrated out of Africa towards Eurasia, separating into distinct lineages through time. Although many studies have focused on human demographic history, some aspects remain unknown, partly due to the difficulty in finding ancient samples from wide geographic and temporal scales. Particularly, the specific origins of certain populations at more local scales, remains uncertain.

For example, it is known that early European farmers originated at least in part from the Near-East, which was one of the centers of the development of agriculture, and that hunter-gatherers were already present in Europe when early farmers arrived. However, as Lazaridis et al (2014) mention, modeling present-day Europeans as a mixture of only these two ancestral populations does not account for all present genetic diversity. Their main goal was therefore to clarify the prehistory of present-day Europeans.

To address this question, the authors sequenced the genomes of nine ancient Europeans: seven from Sweden and one from Luxembourg (hunter-gatherers), as well as one farmer from Stuttgart, Germany, a member of the first widespread farming culture in central Europe.

They compare these ancient genomes with those of other ancient and contemporary humans in order to investigate from which ancestral groups modern European groups derive. They first perform a PCA analysis of all genomes, then calculate test statistics for admixture between populations in order to investigate the ancestry of present-day European populations, and finally model the deep relationships between populations.

Main Findings

Figure 2 of the paper shows a PCA allowing us to get a sense of the similarity between all ancient and modern populations. I always find it interesting to see geographic patterns in PCAs using genetic data. Looking at Figure 2 we see that there is a West/East pattern from left to right on the graph – present-day Near-Easterners cluster to the right, whereas present-day Westerners cluster more to the left.

An ancient West hunter-gatherer group clusters even further to the left, while an early European farmer cluster, including the farmer individual newly sequenced for this paper, falls between the ancient West hunter-gatherer group and the present-day Near-Easterners. There is also a cluster corresponding to what the authors call an “ancient North Eurasian” population.

Drawing from these observations as well as the test statistics and the admixture model, some of the main findings of the paper are:

1) The Stuttgart farmer’s ancestors were likely of Near-Eastern origin, consistent with the assumption that European farmers originated from the Near-East.

2) Early European farmers had some ancestry from West hunter-gatherers, meaning that early farmers originated from the Near-East but mixed with hunter-gatherers after their arrival to Europe.

3) Modern Europeans derive from at least three ancestral populations: West hunter-gatherers, early European farmers, and the ancient North Eurasian population. Ancient North Eurasian ancestry has increased in the European population since the time of early farmers.

Further Personal Comments

Although this article addresses a novel and interesting question, I thought it was poorly written and hard to follow. The authors jump from one subject to another and the text lacks a pleasant reading flow.

Many methodological aspects are therefore difficult to grasp, and some remain unclear – for example, on the PCA in Fig. 2, it is unclear why the ancient genomes are “projected” onto the principal component space. Understanding the test statistics calculated is also difficult, and almost impossible without referring to the supplementary information.

I thought it would have been interesting to have more than one ancient farmer sample, but considering the difficulty in finding such ancient genomes the small sample size isn’t’ surprising. It may however be a bit of a stretch to interpret observations such as the copy number of salivary amylase genes or the presence of skin-lightening alleles from so few individuals. I thought these points seemed to be superfluous and were mentioned only to entice readers with the mention of putatively adaptive diversity.

Nevertheless, it is an interesting topic and shows that human demographic history is quite complex, consisting of many migration and admixture events. This paper brings us a small step closer towards understanding this complexity – at least in a European context.

Reference

Lazaridis, I., Patterson, N., Mittnik, A., Renaud, G., Mallick, S., Kirsanow, K., Sudmant, P., Schraiber, J., Castellano, S., Lipson, M., Berger, B., Economou, C., Bollongino, R., Fu, Q., Bos, K., Nordenfelt, S., Li, H., de Filippo, C., Prüfer, K., Sawyer, S., Posth, C., Haak, W., Hallgren, F., Fornander, E., Rohland, N., Delsate, D., Francken, M., Guinet, J., Wahl, J., Ayodo, G., Babiker, H., Bailliet, G., Balanovska, E., Balanovsky, O., Barrantes, R., Bedoya, G., Ben-Ami, H., Bene, J., Berrada, F., Bravi, C., Brisighelli, F., Busby, G., Cali, F., Churnosov, M., Cole, D., Corach, D., Damba, L., van Driem, G., Dryomov, S., Dugoujon, J., Fedorova, S., Gallego Romero, I., Gubina, M., Hammer, M., Henn, B., Hervig, T., Hodoglugil, U., Jha, A., Karachanak-Yankova, S., Khusainova, R., Khusnutdinova, E., Kittles, R., Kivisild, T., Klitz, W., Ku?inskas, V., Kushniarevich, A., Laredj, L., Litvinov, S., Loukidis, T., Mahley, R., Melegh, B., Metspalu, E., Molina, J., Mountain, J., Näkkäläjärvi, K., Nesheva, D., Nyambo, T., Osipova, L., Parik, J., Platonov, F., Posukh, O., Romano, V., Rothhammer, F., Rudan, I., Ruizbakiev, R., Sahakyan, H., Sajantila, A., Salas, A., Starikovskaya, E., Tarekegn, A., Toncheva, D., Turdikulova, S., Uktveryte, I., Utevska, O., Vasquez, R., Villena, M., Voevoda, M., Winkler, C., Yepiskoposyan, L., Zalloua, P., Zemunik, T., Cooper, A., Capelli, C., Thomas, M., Ruiz-Linares, A., Tishkoff, S., Singh, L., Thangaraj, K., Villems, R., Comas, D., Sukernik, R., Metspalu, M., Meyer, M., Eichler, E., Burger, J., Slatkin, M., Pääbo, S., Kelso, J., Reich, D., & Krause, J. (2014). Ancient human genomes suggest three ancestral populations for present-day Europeans Nature, 513 (7518), 409-413 DOI: 10.1038/nature13673

Comparative analysis of regulatory information and circuits across distant species

ResearchBlogging.org

Development and homeostasis of all organisms is tightly controlled by transcription regulatory factors that are often highly conserved across deep phylogenies. However, it is unclear to what extend the basic components of these networks (e.g. network motifs and structure, binding frequencies, factor interactions) are preserved in distantly related species. Boyle and colleagues try to shed light on this question in a recent study published by nature in August 2014 (doi:10.1038/nature13668).

Paper summary

The scientists compare genome-wide binding locations of 165 human, 93 worm and 52 fly transcription regulatory factors in different cellular contexts (developmental stages and tissues) to identify the common properties of their underlying networks (data overview shown in Figure 1, taken from original publication).

As already described in smaller-scale studies, they see that DNA binding motifs of orthologous regulatory factors remain similar in distantly related species. Furthermore, these orthologous factors are expressed in similar contexts. However, expression of the orthologous targets is only weakly correlated suggesting an extensive re-wiring of regulatory networks across human, worm and fly. Reconstructions of regulatory networks point to a higher number of master-regulators and upward-flowing edges in human when compared to worm and fly. In all three species, the most abundant network motif is the feed-forward loop while cascade, divergent and convergent network motifs are underrepresented. Investigation of co-associations of regulatory factors shows that co-associations are local and contextual and that co-associations occurring at promoters have stronger conservation than co-associations in more distal regions.

The authors conclude that the overall structure of regulatory networks, in terms of network motif usage and context of the genomic binding event (high-occupancy target regions, enhancer, promoter), is strongly conserved. However, the regulatory targets are quite divergent and may account for the phenotypic differences among species.

 

Personal comment

Boyle and colleagues have created a big data set of transcription factor binding events in different cell lines and tissues for human, worm and fly. Doubtless, this resource will be of high interest for many researchers. But in my opinion, there are a couple of points that the reader should keep in mind when interpreting this data in light of the papers’ research aim – the comparison of regulatory circuits across species:

1) Tissue sampling

Without any question, cell lines are a good tool to study basic principles of cell homeostasis. But how well does a cell line, which has undergone many cell divisions and freezing/thawing cycles in an artificial lab environment, represent its in vivo counterpart? Is it reasonable to compare highly specialized human cell lines to entire worm and fly embryos? Why not use mouse instead of human? From an evolutionary perspective, mice are as close to worms or flies as humans are, and samples from an in vivo organ would probably reflect “reality” much better…

2) Genome size and complexity

The authors detect a higher number of master-regulators and upward edges in human regulatory networks when comparing human to worm and fly. But again, we are comparing highly specialized cell lines to whole embryos and larvae. Any signal created by specific tissues in an embryo or a larva has probably been strongly diluted and as a consequence, may be difficult to pick up! And not to forget: Human, worm and fly have different genome sizes and gene numbers. Anything we compare should be scaled and discussed in light of these numbers.

Big science can be helpful, but interpretation needs to be done carefully. Data is often generated based on very broad and general research questions and the generated data may not necessarily be the best to answer these questions. I am not against big consortia such as ENCODE and modENCODE, but I think that many of the findings can only be discussed from a very general perspective and often require a more redefined analysis.

Reference

Boyle, A., Araya, C., Brdlik, C., Cayting, P., Cheng, C., Cheng, Y., Gardner, K., Hillier, L., Janette, J., Jiang, L., Kasper, D., Kawli, T., Kheradpour, P., Kundaje, A., Li, J., Ma, L., Niu, W., Rehm, E., Rozowsky, J., Slattery, M., Spokony, R., Terrell, R., Vafeados, D., Wang, D., Weisdepp, P., Wu, Y., Xie, D., Yan, K., Feingold, E., Good, P., Pazin, M., Huang, H., Bickel, P., Brenner, S., Reinke, V., Waterston, R., Gerstein, M., White, K., Kellis, M., & Snyder, M. (2014). Comparative analysis of regulatory information and circuits across distant species Nature, 512 (7515), 453-456 DOI: 10.1038/nature13668