sábado, 10 de marzo de 2012

Insights into hominid evolution from the gorilla genome sequence


Nature | Article Open


Nature
483,
169–175
(08 March 2012)
doi:10.1038/nature10842
Received
Accepted
Published online

Abstract


Gorillas are humans’ closest living relatives after chimpanzees, and are of comparable importance for the study of human origins and evolution. Here we present the assembly and analysis of a genome sequence for the western lowland gorilla, and compare the whole genomes of all extant great ape genera. We propose a synthesis of genetic and fossil evidence consistent with placing the human–chimpanzee and human–chimpanzee–gorilla speciation events at approximately 6 and 10 million years ago. In 30% of the genome, gorilla is closer to human or chimpanzee than the latter are to each other; this is rarer around coding genes, indicating pervasive selection throughout great ape evolution, and has functional consequences in gene expression. A comparison of protein coding genes reveals approximately 500 genes showing accelerated evolution on each of the gorilla, human and chimpanzee lineages, and evidence for parallel acceleration, particularly of genes involved in hearing. We also compare the western and eastern gorilla species, estimating an average sequence divergence time 1.75 million years ago, but with evidence for more recent genetic exchange and a population bottleneck in the eastern species. The use of the genome sequence in these and future analyses will promote a deeper understanding of great ape biology and evolution.

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Introduction


Humans share many elements of their anatomy and physiology with both gorillas and chimpanzees, and our similarity to these species was emphasized by Darwin and Huxley in the first evolutionary accounts of human origins1. Molecular studies confirmed that we are closer to the African apes than to orang-utans, and on average closer to chimpanzees than gorillas2 (Fig. 1a). Subsequent analyses have explored functional differences between the great apes and their relevance to human evolution, assisted recently by reference genome sequences for chimpanzee3 and orang-utan4. Here we provide a reference assembly and initial analysis of the gorilla genome sequence, establishing a foundation for the further study of great ape evolution and genetics.

Figure 1: Speciation of the great apes.
Speciation of the great apes.
a, Phylogeny of the great ape family, showing the speciation of human (H), chimpanzee (C), gorilla (G) and orang-utan (O). Horizontal lines indicate speciation times within the hominine subfamily and the sequence divergence time between human and orang-utan. Interior grey lines illustrate an example of incomplete lineage sorting at a particular genetic locus—in this case (((C, G), H), O) rather than (((H, C), G), O). Below are mean nucleotide divergences between human and the other great apes from the EPO alignment. b, Great ape speciation and divergence times. Upper panel, solid lines show how times for the HC and HCG speciation events estimated by CoalHMM vary with average mutation rate; dashed lines show the corresponding average sequence divergence times, as well as the HO sequence divergence. Blue blocks represent hominid fossil species (key at top right): each has a vertical extent spanning the range of dates estimated for it in the literature9, 50, and a horizontal position at the maximum mutation rate consistent both with its proposed phylogenetic position and the CoalHMM estimates (including some allowance for ancestral polymorphism in the case of Sivapithecus). The grey shaded region shows that an increase in mutation rate going back in time can accommodate present-day estimates, fossil hypotheses, and a middle Miocene speciation for orang-utan. Lower panel, estimates of the average mutation rate in present-day humans11, 12, 13; grey bars show 95% confidence intervals, with black lines at the means. Estimates were made by the 1000 Genomes Project for trios of European (CEU) and Yoruban African (YRI) ancestry.
Recent technological developments have substantially reduced the costs of sequencing, but the assembly of a whole vertebrate genome remains a challenging computational problem. We generated a reference assembly from a single female western lowland gorilla (Gorilla gorilla gorilla) named Kamilah, using 5.4 × 109 base pairs (5.4 Gbp) of capillary sequence combined with 166.8 Gbp of Illumina read pairs (Methods Summary). Genes, transcripts and predictions of gene orthologues and paralogues were annotated by Ensembl5, and additional analysis found evidence for 498 functional long (>200-bp) intergenic RNA transcripts. Table 1 summarizes the assembly and annotation properties. An assessment of assembly quality using finished fosmid sequences found that typical (N50; see Table 1 for definition) stretches of error-free sequence are 7.2 kbp in length, with errors tending to be clustered in repetitive regions. Outside repeat masked regions and away from contig ends, the total rate of single-base and indel errors is 0.13 per kbp. See Supplementary Information for further details.

Table 1: Assembly and annotation statistics
Full table
We also collected less extensive sequence data for three other gorillas, to enable a comparison of species within the Gorilla genus. Gorillas survive today only within several isolated and endangered populations whose evolutionary relationships are uncertain. In addition to Kamilah, our analysis included two western lowland gorillas, Kwanza (male) and EB(JC) (female), and one eastern lowland gorilla, Mukisi (male).

Discussion


Since the middle Miocene—an epoch of abundance and diversity for apes throughout Eurasia and Africa—the prevailing pattern of ape evolution has been one of fragmentation and extinction48. The present-day distribution of non-human great apes, existing only as endangered and subdivided populations in equatorial forest refugia43, is a legacy of that process. Even humans, now spread around the world and occupying habitats previously inaccessible to any primate, bear the genetic legacy of past population crises. All other branches of the genus Homo have passed into extinction. It may be that in the condition of Gorilla, Pan and Pongo we see some echo of our own ancestors before the last 100,000 years, and perhaps a condition experienced many times over several million years of evolution. It is notable that species within at least three of these genera continued to exchange genetic material long after separation4, 49, a disposition that may have aided their survival in the face of diminishing numbers. As well as teaching us about human evolution, the study of the great apes connects us to a time when our existence was more tenuous, and in doing so, highlights the importance of protecting and conserving these remarkable species.

Methods



Assembly

We constructed a hybrid de novo assembly combining 5.4  Gbp of Illumina paired reads. Improvements in long-range structure were then guided by human homology, placing contigs into scaffolds wherever read pairs confirmed collinearity between gorilla and human. Base-pair contiguity was improved by local reassembly within each scaffold, merging or extending contigs using Illumina read pairs. Finally we used additional Kamilah bacterial artificial chromosome (BAC) and fosmid end pair capillary sequences to provide longer range scaffolding. Base errors were corrected by mapping all Illumina reads back to the assembly and rectifying apparent homozygous variants, while recording the location of heterozygous sites. Further details and other methods are described in Supplementary Information.

Author information



Affiliations

  1. Wellcome Trust Sanger Institute, Wellcome Trust Genome Campus, Hinxton CB10 1SA, UK

    • Aylwyn Scally,
    • Ian Goodhead,
    • Shane McCarthy,
    • Y. Amy Tang,
    • Yali Xue,
    • Bryndis Yngvadottir,
    • Qasim Ayub,
    • Yuan Chen,
    • Chris M. Clee,
    • Yong Gu,
    • Paul Heath,
    • Anja Kolb-Kokocinski,
    • Gavin K. Laird,
    • Anthony S. Rogers,
    • Jared T. Simpson,
    • Daniel J. Turner,
    • Weldon Whitener,
    • Zemin Ning,
    • Duncan T. Odom,
    • Michael A. Quail,
    • Stephen M. Searle,
    • Jane Rogers,
    • Chris Tyler-Smith &
    • Richard Durbin

  2. Bioinformatics Research Center, Aarhus University, C.F. Møllers Allé 8, 8000 Aarhus C, Denmark

    • Julien Y. Dutheil,
    • Asger Hobolth,
    • Thomas Mailund,
    • Lars N. Andersen,
    • Kasper Munch &
    • Mikkel H. Schierup

  3. Department of Genome Sciences, University of Washington School of Medicine, Seattle, Washington 98195, USA

    • LaDeana W. Hillier,
    • Tomas Marques-Bonet,
    • Can Alkan,
    • Emre Karakoc,
    • Saba Sajjadian &
    • Evan E. Eichler

  4. European Bioinformatics Institute, Wellcome Trust Genome Campus, Hinxton CB10 1SD, UK

    • Gregory E. Jordan,
    • Javier Herrero,
    • Petra C. Schwalie,
    • Kathryn Beal,
    • Stephen Fitzgerald,
    • Albert J. Vilella,
    • Paul Flicek &
    • Nick Goldman

  5. Department of Genetic Medicine and Development, University of Geneva Medical School, Rue Michel-Servet 1, 1211 Geneva 4, Switzerland

    • Tuuli Lappalainen &
    • Emmanouil T. Dermitzakis

  6. Institut de Biologia Evolutiva (UPF-CSIC), 08003 Barcelona, Catalonia, Spain

    • Tomas Marques-Bonet &
    • Javier Prado-Martinez

  7. Institucio Catalana de Recerca i Estudis Avançats, ICREA, 08010 Barcelona, Spain

    • Tomas Marques-Bonet

  8. Department of Zoology, University of Cambridge, Downing Street, Cambridge CB2 3EJ, UK

    • Stephen H. Montgomery,
    • Brenda J. Bradley,
    • Timothy D. O’Connor &
    • Nicholas I. Mundy

  9. University of Cambridge, Department of Oncology, Hutchison/MRC Research Centre, Hills Road, Cambridge CB2 0XZ, UK

    • Michelle C. Ward,
    • Dominic Schmidt &
    • Duncan T. Odom

  10. Cancer Research UK, Cambridge Research Institute, Li Ka Shing Centre, Robinson Way, Cambridge CB2 0RE, UK

    • Michelle C. Ward,
    • Dominic Schmidt &
    • Duncan T. Odom

  11. Howard Hughes Medical Institute, University of Washington, Seattle, Washington 20815-6789, USA

    • Can Alkan &
    • Evan E. Eichler

  12. Institute of Medical Genetics, Cardiff University, Heath Park, Cardiff CF14 4XN, UK

    • Edward V. Ball,
    • Matthew Mort,
    • Andrew D. Phillips,
    • Katy Shaw,
    • Peter D. Stenson &
    • David N. Cooper

  13. Department of Anthropology, Yale University, 10 Sachem Street, New Haven, Connecticut 06511, USA

    • Brenda J. Bradley

  14. The Genome Institute at Washington University, Washington University School of Medicine, Saint Louis, Missouri 63108, USA

    • Tina A. Graves,
    • Wesley C. Warren &
    • Richard K. Wilson

  15. MRC Functional Genomics Unit, University of Oxford, Department of Physiology, Anatomy and Genetics, South Parks Road, Oxford OX1 3QX, UK

    • Andreas Heger,
    • Stephen Meader &
    • Chris P. Ponting

  16. Wellcome Trust Centre for Human Genetics, Roosevelt Drive, Oxford OX3 7BN, UK

    • Gerton Lunter

  17. Comparative Genomics Unit, Genome Technology Branch, National Human Genome Research Institute, National Institutes of Health, Bethesda, Maryland, 20892-2152, USA

    • James C. Mullikin

  18. Max Planck Institute for Evolutionary Anthropology, Primatology Department, Deutscher Platz 6, Leipzig 04103, Germany

    • Linda Vigilant

  19. Children’s Hospital Oakland Research Institute, Oakland, California 94609, USA

    • Baoli Zhu &
    • Pieter de Jong

  20. San Diego Zoo’s Institute for Conservation Research, Escondido, California 92027, USA

    • Oliver A. Ryder

  21. Present addresses: Institut des Sciences de l'Évolution – Montpellier (I.S.E.-M.), Université de Montpellier II – CC 064, 34095 Montpellier Cedex 05, France (J.Y.D); Centre for Genomic Research, Institute of Integrative Biology, University of Liverpool, Crown Street, Liverpool L69 7ZB, UK (I.G.); Division of Biological Anthropology, University of Cambridge, Fitzwilliam Street, Cambridge CB2 1QH, UK (B.Y.); EASIH, University of Cambridge, Addenbrooke’s Hospital, Cambridge CB2 0QQ, UK (A.S.R.); Oxford Nanopore Technologies, Edmund Cartwright House, 4 Robert Robinson Avenue, Oxford OX4 4GA, UK (D.J.T.); Institute of Microbiology, Chinese Academy of Sciences, Datun Road, Chaoyang District, Beijing 100101, China (B.Z.); The Genome Analysis Centre, Norwich Research Park, Norwich NR4 7UH, UK (J.R.).

    • Julien Y. Dutheil,
    • Ian Goodhead,
    • Bryndis Yngvadottir,
    • Anthony S. Rogers,
    • Daniel J. Turner,
    • Baoli Zhu &
    • Jane Rogers

Contributions

Manuscript main text: A.S., R.D., C.T.-S., N.I.M., G.E.J., P.C.S., A.K.-K. Project coordination: A.S., A.S.R., A.K.-K., R.D. Project initiation: J.R., R.D., R.K.W. Library preparation and sequencing: I.G., D.J.T., M.A.Q., C.M.C., B.Z., P.d.J., O.A.R., Q.A., B.Y., Y.X., T.A.G., W.C.W. Assembly: A.S., L.W.H., Y.G., J.T.S., J.C.M., W.W., Z.N. Fosmid finishing: P.H. Assembly quality: A.S., S. Meader, G.L., C.P.P. Annotation: Y.A.T., G.K.L., A.J.V., A. Heger, S.M.S. Primate multiple alignments: J.H., K.B., S.F. Great ape speciation and ILS: J.Y.D., A.S., T.M., M.H.S., K.M., G.E.J. Sequence loss and gain: A.S., S.M., C.T.-S., Y.A.T., A.J.V. Protein evolution: G.E.J., S.H.M., N.I.M., B.J.B., T.D.O’C., Y.X., Y.C., N.G. Human disease allele analysis: Y.X., Y.C., C.T.-S., P.D.S., E.V.B., A.D.P., M.M., K.S., D.N.C. Transcriptome analysis: T.L., E.T.D. ChIP-seq experiment and analysis: P.C.S., M.C.W., D.S., P.F., D.T.O. Additional gorilla samples: B.Y., Y.X., L.V., C.T.-S. Gorilla species diversity and divergence: A.S., A.H., T.M., L.N.A., B.Y., L.V. Gorilla species functional differences: Y.X., Y.C., C.T.-S. Segmental duplication analysis: T.M.-B., C.A., S.S., E.K., J.P.-M., E.E.E.

Competing financial interests

The authors declare no competing financial interests.

Corresponding author

Correspondence to:
Accession numbers for all primary sequencing data are given in Supplementary Information. The assembly has been submitted to EMBL with accession numbers FR853080 to FR853106, and annotation is available at Ensembl (http://www.ensembl.org/Gorilla_gorilla/Info/Index).

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Hominid genomes: gorilla makes four

The genome of the gorilla has been sequenced, making it possible to compare the DNA of the four surviving hominid genera: human, chimpanzee, gorilla and orang-utan. The data — mainly from a female wes…

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The gorilla genome reveals that genetic similarities among humans and the apes are more complex than expected, and allows a fresh assessment of the evolutionary mechanisms that led to the primate spec…
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You Have a Hive Mind

There is a deep connection between the way your brain and a swarm of bees arrives at a decision
By Jason Castro  | Thursday, March 1, 2012 | 9
SCIENTIFIC AMERICAN 
 
 
As goes a bee, so goes a neuron Image: Florin Tirlea
 
 
Every decision you make is essentially a committee act. Members chime in, options are weighed, and eventually a single proposal for action is approved by consensus. The committee, of course, is the densely knit society of neurons in your head. And “approved by consensus” is really just a delicate way of saying that the opposition was silenced.
Our brains seem to work not by generating only “correct” actions and executing them in serial, but rather by representing many possibilities in parallel, and suppressing all but one. When this inhibitory action is lost, as happens in people with frontal lobe damage, these multiple possibilities become a burden, and can lead to so-called utilization behaviors. Such impaired individuals will indiscriminately reach for objects placed in front of them - a hairbrush or a hammer, for example - and use them even in inappropriate contexts.
In essence, despite our feeling that we are singular, unified agents, we are more like hive minds unto ourselves, our brains abuzz with multiple, often conflicting plans and interests that must be managed. To Dr. Thomas Seeley, a professor of neurobiology at Cornell University, the “hive mind” is more than just a metaphor. In a recent paper in Science, Seeley and his colleagues describe a potential deep parallel between how brains and bee swarms come to a decision. With no central planner or decider, both brains and bee hives can resolve their inner differences to commit to single courses of action.
To watch a group of bees is to see a frenzy of different interests coalesce into a single, clear thought. This is analogous to neurons in the brain, which must reach a consensus on how to achieve a behavioral goal by positioning the body in space. Bees in a hive must do something similar when deciding where to move the superorganism that is the swarm. Failing to move the swarm as a single, committed unit risks splitting up the hive and losing the queen. Similarly, making a poor move could expose the hive to predators or extreme temperatures.
Like many other decision-makers, the hive’s first order of business before making a springtime move is to consider the various possibilities. Toward this end, several groups of scouts are sent off to search for a suitable new hive. When the scouts return, they each advocate for preferred new sites - often different ones - by performing the famed “waggle dance,” a figure-eight series of movements that tells other bees the direction and distance to a potential new site. These dances recruit other uncommitted bees in the hive to also advocate for the advertised site.
For a while, many scientists thought that this strategy of steadily accumulating “votes” for a particular location was sufficient to explain the hive’s eventual decision. Others, including Seeley and his colleagues, were not satisfied. What happens in cases where similarly sized groups of bees are advocating for different locations? Wouldn’t this be a formula for deadlock?
Seeley suspected that the answer had to do with a head-butting move bees make. To explore this idea, he and his team first set up swarms on an island lacking natural nests, and gave scouts a choice between two identical artificial nesting boxes. Scouts that visited one site were marked with yellow paint, while scouts visiting the other site were marked with pink paint. By tagging these two different populations, Seeley and colleagues had in a sense labeled two competing ideas, which they could then watch unfold and interact back in the collective hive mind.
The researchers found that the yellow and pink-painted scouts displayed waggle dances advertising for their respective nests. In addition, however, the scouts were also seen to make brief buzzing head-butts to one another’s head and thorax. Dancing bees tended to receive head-butts toward the end of their dances, suggesting that the head butts were a signal to stop dancing. The most interesting finding came when looking at who was head-butting whom. Yellow-marked bees tended to receive these putative stop signals from pink-marked bees, and vice versa. In other words, the two different populations were mutually inhibiting one another - one proposal pitted against another.
The result of this arrangement is that it amplifies small differences between different populations of scouts, setting up a kind of winner-take-all scenario. Without inhibitory stop signals, the hive would be able to sustain multiple competing interests, as different groups of scouts accumulate more and more votes until the hive reaches some stable, but divided state. With stop signals, divided hive states are far less stable. A slight preponderance of one group of scouts will translate into greater inhibition of other groups of scouts, turning an initially small numerical advantage into a more sizable one. Over several iterations of this process, an initial slight majority is amplified into a consensus.
Ideally, a follow-up experiment would have eliminated the bees’ stop signals and studied the consequences on the hive’s decision process. Since this is nearly impossible to do, Seeley and his colleagues opted for a simulation based approach instead. In their models of collective bee activity, cross-inhibitory stop signals were essential for breaking decision deadlocks between two equally attractive nests. If the stop signals were indiscriminate, or absent altogether, the hive remained split, and never converged on a consensus.
Seeley and his team propose that cross-inhibition may be a general strategy for decision making, and indeed, their findings in bees recapitulate features of decision making and pattern formation in other systems. The remarkable unifying theme in all of these systems is how an aggregate swarm intelligence is built from just a few kinds of simple, local interactions between agents. Both neurons and bees are presumably unaware of how their impulses and signals transcend the individual, and lay the substrate for a grander, collective intelligence.
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LAS ESCALAS DEL UNIVERSO ..DE LO INFIMO A LO MAXIMO

 Esta es probablemente la mas notable animacion cientifica que he recibido.
Basta dar un click en el enlace para que se despliegue una animacion que muestra 
como 'desde una escala a nuestro nivel puede descenderse o elevarse para mostrar las maravillas de la naturaleza hasta llegar a las expresiones  mas gigantescas que la mente puede captar en relacion con el tamaño del Universo entero  ,segun se estima y del Universo conocido que ya se ha medico.


This is probably the most awesome scientific animation that I have ever received.   


Be sure to go BOTH ways on the sliding scale after you click START and it

  loads...  This is truly amazing.


 
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viernes, 2 de marzo de 2012

How Many Unbound Planets Roam the Milky Way?

Life as we know it exists on a cozy planet in a stable orbit around a sun shining brightly in its sky. But a new study hints that the most common life in the universe might exist deep inside eternal-night worlds far from any star, adrift in the icy dark of interstellar space.

Free-floating planet
The Milky Way likely hosts billions, and possibly trillions, of unbound planets, some of which may have atmospheres thick enough to support bacterial life. Loose planets may even outnumber stars in the galaxy, but a more precise count awaits future telescopes such as WFIRST and LSST.
Caltech / NASA
Researchers at the Kavli Institute for Particle Astrophysics and Cosmology (KIPAC) at Stanford University estimate that "nomad" planets, ejected from their home stellar system and now free-floating through the Milky Way, could outnumber stars by as many as 100,000 to 1. Earlier estimates were more like a handful to 1, though previous studies have only counted unbound planets more massive than Jupiter.

To estimate the number of unbound planets as small as Pluto that could be roaming the galaxy, Louis Strigari (KIPAC), lead author of the study, began with a basic rule of nature: where a few big objects are found, there are many more small, just like a few boulders may be surrounded by thousands of pebbles. Strigari and colleagues calculated the number of unbound planets by extrapolating from the small number detected so far by direct imaging and by gravitational microlensing.

Direct imaging has severe limits because planets are so faint. Microlensing offers more promise. It looks for the characteristic brightening and fading of a background star when an object, even one as wimpy as Pluto, passes nearly in front of it and bends its light slightly by gravity. So far, 24 planet-mass objects have been detected by microlensing — 14 bound to their parent stars, 10 apparently not. Microlensing offers hope for detection of loose objects large and small even if they are completely dark, and even at great distances across the galaxy.

Media outlets have jumped on the vast number of loose planets predicted by this study. But the authors themselves acknowledge the large uncertainty in their result. The statistics remain so weak that the group’s lower limit is still one loose planet for each star in the Milky Way.

"There is no evidence for a large population of unbound planets, but then again there is nothing to rule it out," says Scott Gaudi (The Ohio State University), expert on gravitational microlensing and extrasolar planets, who was not involved with the study.

Exoplanet specialists think that some 80% of planetary systems go through an early period of gravitational chaos that flings some of their worlds into interstellar space. The survivors are left in a mess: in highly elongated orbits that are often not even in the same plane, as exoplanet hunters are discovering. Our solar system was one of the minority that avoided such a violent episode.

Still, skeptics have a hard time imagining how tens of thousands of objects larger than Pluto could originate for each star — regardless of whether they stay with it or get flung off. Our solar system has only 17 known objects that qualify — eight planets, seven large moons, and the two largest Kuiper Belt objects.

Better statistics for unbound planets may come from the Wide-Field Infrared Survey Telescope (WFIRST) and the Large Synoptic Survey Telescope (LSST), two gigantic sky-survey projects that may begin within the next decade or so.

"It's not implausible that a few nomads, most not much different than the size of Pluto, might lie within one light-year of the sun," says Strigari.

If more planets wander interstellar space than orbit stars, what might that mean for life in the universe? As it turns out, not all who wander are lost. Even with no sun in the sky, life could still eke out an existence under a thick atmosphere or ice crust at the depth where the temperature is pleasant, maintained for billions of years by the heat flowing from the planet's interior. Microbial life, at least, might thrive under these conditions.

All of this raises a question: If a planet leaves the system in which it was born, is it still a planet? The literature seems to give a qualified "yes," using terms like free-floating or rogue planets, though Strigari and colleagues prefer the term "nomad planet."

See their paper, which will be published in the Monthly Notices of the Royal Astronomical Society.


Monica Young has joined Sky & Telescope as an editorial intern. She is a recently minted astronomy PhD (from Boston University) with an extensive background in X-ray astronomy, education, and science writing.

Posted by Monica Young, February 29, 2012
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