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The periods that followed the Cambrian during the Paleozoic Era are marked by further animal evolution and the emergence of many new orders, families, and species. Continual changes in temperature and moisture throughout the remainder of the Paleozoic Era due to continental plate movements encouraged the development of new adaptations to terrestrial existence in animals, such as limbed appendages in amphibians and epidermal scales in reptiles.
Changes in the environment often create new niches (living spaces) that contribute to rapid speciation and increased diversity. On the other hand, cataclysmic events, such as volcanic eruptions and meteor strikes that obliterate life, can result in devastating losses of diversity. Such periods of mass extinction (Figure 1) have occurred repeatedly in the evolutionary record of life, erasing some genetic lines while creating room for others to evolve into the empty niches left behind. The end of the Permian period (and the Paleozoic Era) was marked by the largest mass extinction event in Earth’s history, a loss of roughly 95 percent of the extant species at that time. Some of the dominant phyla in the world’s oceans, such as the trilobites, disappeared completely. On land, the disappearance of some dominant species of Permian reptiles made it possible for a new line of reptiles to emerge, the dinosaurs. The warm and stable climatic conditions of the ensuing Mesozoic Era promoted an explosive diversification of dinosaurs into every conceivable niche in land, air, and water. Plants, too, radiated into new landscapes and empty niches, creating complex communities of producers and consumers, some of which became very large on the abundant food available.
Another mass extinction event occurred at the end of the Cretaceous period, bringing the Mesozoic Era to an end. Skies darkened and temperatures fell as a large meteor impact and tons of volcanic ash blocked incoming sunlight. Plants died, herbivores and carnivores starved, and the mostly cold-blooded dinosaurs ceded their dominance of the landscape to more warm-blooded mammals. In the following Cenozoic Era, mammals radiated into terrestrial and aquatic niches once occupied by dinosaurs, and birds, the warm-blooded offshoots of one line of the ruling reptiles, became aerial specialists. The appearance and dominance of flowering plants in the Cenozoic Era created new niches for insects, as well as for birds and mammals. Changes in animal species diversity during the late Cretaceous and early Cenozoic were also promoted by a dramatic shift in Earth’s geography, as continental plates slid over the crust into their current positions, leaving some animal groups isolated on islands and continents, or separated by mountain ranges or inland seas from other competitors. Early in the Cenozoic, new ecosystems appeared, with the evolution of grasses and coral reefs. Late in the Cenozoic, further extinctions followed by speciation occurred during ice ages that covered high latitudes with ice and then retreated, leaving new open spaces for colonization.
Watch the following video to learn more about the mass extinctions.
A link to an interactive elements can be found at the bottom of this page.
Natural history museums contain the fossil casts of extinct animals and information about how these animals evolved, lived, and died. Paleontologists are scientists who study prehistoric life. They use fossils to observe and explain how life evolved on Earth and how species interacted with each other and with the environment. A paleontologist needs to be knowledgeable in biology, ecology, chemistry, geology, and many other scientific disciplines. A paleontologist’s work may involve field studies: searching for and studying fossils. In addition to digging for and finding fossils, paleontologists also prepare fossils for further study and analysis. Although dinosaurs are probably the first animals that come to mind when thinking about paleontology, paleontologists study everything from plant life, fungi, and fish to sea animals and birds.
An undergraduate degree in earth science or biology is a good place to start toward the career path of becoming a paleontologist. Most often, a graduate degree is necessary. Additionally, work experience in a museum or in a paleontology lab is useful.
Does the Cambrian Explosion pose a challenge to evolution?
The “Cambrian Explosion” refers to the appearance in the fossil record of most major animal body plans about 543 million years ago. The new fossils appear in an interval of 20 million years or less. On evolutionary time scales, 20 million years is a rapid burst that appears to be inconsistent with the gradual pace of evolutionary change. However, rapid changes like this appear at other times in the fossil record, often following times of major extinction. The Cambrian Explosion does present a number of interesting and important research questions. It does not, however, challenge the fundamental correctness of the central thesis of evolution.
The term “Cambrian Explosion” refers to the appearance and rapid diversification of most major living animal body plans (phyla) in the fossil record within an interval of perhaps 20 million years or less, a relatively short period in evolutionary history. This time is known as the Early Cambrian, and began around 543 million years ago. This time interval is recorded by some spectacular fossil deposits that include superbly preserved fossils of these early animals. Two famous examples are the Burgess Shale in Canada, and the Chengjiang in China. 1 Despite the claims of some, the Cambrian was not the beginning of multicellular animal life the latter has a fossil record that extends back at least 30 million years earlier. 2
The Cambrian Explosion is often posed as a challenge for evolution because the sudden burst of change in the fossil record appears to be inconsistent with the more typical gradual pace of evolutionary change. However, although different in certain ways, there are other times of very rapid evolutionary change recorded in the fossil record—often following times of major extinction. The Cambrian Explosion does present a number of challenging and important questions because it represents the time during which the main branches of the animal tree of life became established. It does not create a challenge to the fundamental correctness of the central thesis of evolution, the descent of all living species from a common ancestor. This important period in the history of life extended over millions of years, plenty of time for the evolution of these new body plans (phyla) to occur. Furthermore, the fossil record provides numerous examples of organisms that appear transitional between living phyla and their common ancestors. The ongoing research about the Cambrian period is an exciting opportunity to advance our understanding of how evolutionary processes work, and the environmental factors shaping them.
The major animal body plans that appeared in the Cambrian Explosion did not include the appearance of modern animal groups such as: starfish, crabs, insects, fish, lizards, birds and mammals. These animal groups all appeared at various times much later in the fossil record. 3 The forms that appeared in the Cambrian Explosion were more primitive than these later groups, and many of them were soft-bodied organisms. However, they did include the basic features that define the major branches of the tree of life to which later life forms belong. For example, vertebrates are part of the Chordata group. The chordates are characterized by a nerve cord, gill pouches and a support rod called the notochord. In the Cambrian fauna, we first see fossils of soft-bodied creatures with these characteristics. However, the living groups of vertebrates appeared much later. It is also important to realize that many of the Cambrian organisms, although likely near the base of major branches of the tree of life, did not possess all of the defining characteristics of modern animal body plans. These defining characteristics appeared progressively over a much longer period of time. 4
Jeremy Van Cleve
Although the evolutionary forces that can support the spread of cooperative or mutually beneficial social interactions are fairly well understood, a systematic framework for how to explore proximate mechanisms for such cooperation that is amenable to evolutionary analysis is lacking. In collaboration with Erol Akçay, I have developed a system of studying behavioral objectives that can clarify the ecological requirements for cooperative interactions.
I have explored the role of trade offs and fitness asymmetries can have on the conditions for the evolution of bet-hedging. This is of particular relevance to microbial evolution as many microbiologists see random variations in gene expression, a kind of bet-hedging, as a common way for microbes to adapt to variable environments.
Using tools from population genetics and evolutionary theory, I have explored factors that could explain the evolution of imprinting including genetic interactions such as dominance and a host of demographic factors including sex-specific selection, sex-specific migration, and generation overlap. I have also studied the dynamic effects of imprinting, which include the possibility of complex dynamics and chaos.
Rates of phenotypic and genomic evolution during the Cambrian explosion
530 million years ago during the Cambrian explosion is strong evidence for a brief interval of rapid phenotypic and genetic innovation, yet the exact speed and nature of this grand adaptive radiation remain debated. Crucially, rates of morphological evolution in the past (i.e., in ancestral lineages) can be inferred from phenotypic differences among living organisms-just as molecular evolutionary rates in ancestral lineages can be inferred from genetic divergences. We here employed Bayesian and maximum likelihood phylogenetic clock methods on an extensive anatomical and genomic data set for arthropods, the most diverse phylum in the Cambrian and today. Assuming an Ediacaran origin for arthropods, phenotypic evolution was
4 times faster, and molecular evolution
5.5 times faster, during the Cambrian explosion compared to all subsequent parts of the Phanerozoic. These rapid evolutionary rates are robust to assumptions about the precise age of arthropods. Surprisingly, these fast early rates do not change substantially even if the radiation of arthropods is compressed entirely into the Cambrian (
542 mega-annum [Ma]) or telescoped into the Cryogenian (
650 Ma). The fastest inferred rates are still consistent with evolution by natural selection and with data from living organisms, potentially resolving "Darwin's dilemma." However, evolution during the Cambrian explosion was unusual (compared to the subsequent Phanerozoic) in that fast rates were present across many lineages.
Cambrian Explosion: Evolutionary Big Bang Was Sparked By Multiple Events, Scientists Say
The Cambrian explosion, the evolutionary "big bang" that led to the emergence of a trove of complex life forms, was caused by multiple events, researchers argue.
Genetic changes allowing for complex body plans combined with rising sea levels and an influx of chemicals into the ocean probably created the unique conditions needed to set off the Cambrian explosion, researchers argue in a perspectives paper published today (Sept. 19) in the journal Science.
"There was this cascade of events," said study co-author Paul Smith, a paleobiologist at the University of Oxford's Museum of Natural History. "You can see how one process might feed into one another and possibly amplify it as it feeds back."
Evolutionary big bang
About 530 million years ago during the Cambrian Period, the diversity of life on Earth exploded. The first sea-faring predators and prey emerged, animals developed strange and diverse body plans and evolved hard exoskeletons. A recent study revealed that life evolved during the Cambrian Period at a rate about five times faster than today. [Cambrian Creatures: Images of Primitive Sea Life]
Scientists have proposed everything from genetic changes to a starburst in the Milky Way to explain the explosion in diversity.
"There are well over 30 hypotheses out there for the Cambrian explosion," Smith told LiveScience.
Smith and his colleagues looked through all the existing research to see what could explain the evolution of complexity from relatively simple life forms that existed prior to the Cambrian explosion.
"Prior to this, a typical ecosystem would have been a microbial mat with a few things sitting on top," Smith said.
At that time, animals couldn't eat large particles of food, and there were no food webs with predators chasing prey.
The researchers found genetic changes were needed to get the ball rolling toward an explosion of life. By estimating mutation rates, biologists have concluded the genes that code for complex, easily adaptable, bilateral body plans — a necessary precursor for diverse life forms — likely evolved 150 million years prior to the Cambrian Period. (Some evidence suggests this evolution may have occurred closer in time to the Cambrian explosion.)
But genetic changes alone couldn't explain the explosion in diversity.
The rise of sea levels and the flooding of flat, shallow areas of the continents may have served as triggering events. The flooded areas would have provided vastly more habitat for organisms, and the contact between the eroded rock surface and the seawater would have infused minerals, such as calcium and strontium, into the oceans.
Those minerals are toxic to cells, so animals would've needed a way to excrete them.
The animals then would have evolved the ability to incorporate those minerals into their exoskeletons, enabling much more complicated body plans, predation and more modern food webs.
The idea that many factors led to the Cambrian explosion is pretty widespread, said Robert Gaines, a geologist at Pomona College in California, who was not involved in the study.
"I think there are very few people who wouldn't wholeheartedly agree with that," Gaines told LiveScience.
Though the Cambrian explosion is described as a big bang, it was a rather drawn-out affair occurring over 20 million years, Michael Lee, a researcher at the South Australian Museum at the University of Adelaide, who was not involved in the study, wrote in an email.
"One would expect that a range of complex ecological and abiotic drivers might have acted at different times throughout the period," Lee said.
Cardiocondyla elegans 55,56 is a Mediterranean ant, which builds nests consisting of dozens of pea-sized cavities connected by narrow tunnels down to a depth of more than 1 m 24,54,57,58 . Colonies are monogynous and polyandrous, i.e., they contain a single, multiply-mated queen, and neither the queen nor its workers tolerate egg laying by additional queens 23 . Males are wingless, non-dispersing, and, in contrast to males of other Cardiocondyla species, mutually tolerant 23 . In Southern France, sexuals emerge between July and September.
Population structure and the transport of female sexuals (gynes) by workers were studied during 6 years (2014–2019), in seven sites in Languedoc-Roussillon (Southern France), between Beaucaire and Remoulins (BN: N 43° 50′ 38.1″, E 4° 36′ 59.5″ CP: N 43° 51′ 9.9″, E 4° 37′ 2.4″ FK: N 43° 55′ 39.8″, E 4° 34′ 18.1″ H: N 43° 55′ 2.7″, E 4° 35′ 4.2″ P: N 43° 56′ 31.0″, E 4° 33′ 34.5″ RFRK: N 43° 55′ 43.9″, E 4° 34′ 5.1″ SM: N 43° 51′ 10.5″, E 4° 37′ 2.2″). All sites are sparsely vegetated, sandy areas on the banks of rivers Gardon and Rhône, except for site “P”, which is an unpaved sandy parking lot near the city center of Remoulins.
Nests were located by following foragers back to the 1 mm wide nest entrance, marked with a colored flag with a number, and observed subsequently. Source colonies were identified by observing a worker carrying a gyne leaving a nest. Carrying workers were followed until the gyne was to be introduced into the entrance of the recipient colony. We observed in total 453 pairs of carriers and carried gynes, of which 357 pairs could be collected with an aspirator. For 86 pairs, and 55 additional pairs, which were not collected, we could follow the whole transport from the source colony to the entrance of the recipient colony. Of the total pairs collected, 113 pairs were stored in 100% ethanol, the remaining gynes were kept alive in laboratory nests for other studies or the creation of laboratory colonies. In addition, 20 workers from each colony located in the population were captured to determine the genetic relatedness of carrying pairs to source and recipient colonies and, in a few cases, also to colonies passed during their carrying trips. Several gynes were killed by freezing and later dissected with the help of fine forceps under a binocular microscope to determine their reproductive status. Overall, the ants were collected with the approval of the Access and Benefit-Sharing Clearing-House (ABSCH). We obtained a certificate of compliance allowing the collection of Cardiocondyla elegans in Gard (France) 59 .
DNA was extracted from whole bodies with CTAB method (modified from 60 ) and diluted in 25 μl of TE buffer. Samples from 2015 were genotyped at 1–5 microsatellite loci (CE2-3A, CE2-4A, CE2-4E, CE2-5D, CE2-12D 61 ), samples from 2018 at up to seven loci, including Card 8 and Cobs 13 13,62 (see supplementary table 1). For several analyses, one locus (CE2-5D) was excluded because almost all individuals were homozygous at the same allele. Due to this and amplification failures, genotypes were missing at one or several loci in the samples from 2015 (missing genotypes per worker from a colony, median 1, quartiles 1, 1 missing genotypes per carrier and gyne, median 0, quartiles 0, 1) while the genotypes from 2018 were almost fully complete (median, quartiles 0, 0, 0 missing genotypes). PCRs were performed in a 20 μl reaction volume using 1 μl of DNA with 19 μl of master-mix (7 μl H2O, 10 μl GoTaq, 1 μl of each forward and reverse primers). Samples were amplified following Lenoir et al. (2005) with an initial denaturation step at 94 °C for 3 min, 40 cycles at 94 °C for 45 s, with an annealing temperature according to the primer for 45 s (Supplementary Table 1), followed by a step of 72 °C for 45 s, and a final extension step at 72 °C for 7 min. Microsatellite DNA was analyzed using an ABI Prism genetic analyzer with 0.1–0.15 μl of PCR product, 25.15 μl of ABI master-mix (25 μl formamide, 0.15 μl standard size T486). Allele size was determined using GeneScan ® 500 TAMRA dye size standard and GeneScan ® 3.1 software (Applied Biosystems).
Statistics and reproducibility
Genetic data were analyzed using the software package GDA 63 . Relatedness was estimated following 64 using the software Relatedness v4.2 65 and GenAlEx v6.51b2 66 with standard errors of means obtained by jackknifing by groups. Confidence limits for the fixation coefficients were obtained by bootstrapping over loci (5000x). From fixation coefficients F, we calculated the proportion of sib-mating α using F = α/(4–3 α) 67,68 . The Mantel test was made using R software-4.0.3. Other statistical tests were conducted using Statistica v6.0 (StatSoft, Tulsa, OK).
In 2015, colonies, gynes, and carriers were collected in four sites (BN, CP, RFRK, SM) with an FST value of 0.042 ± 0.051 (jackknifed over sites). This value was not significantly different from 0 (single sample t-test, t = 0.823, p > 0.1), suggesting that the four sites belong to a single population extending along the rivers. In 2018 we concentrated on a single collecting site, RFRK.
Further information on research design is available in the Nature Research Reporting Summary linked to this article.
Part 2: African Genomics: African Population History
00:00:07.20 So in the second part of this lecture series,
00:00:10.13 I'm going to be discussing
00:00:12.03 African population history
00:00:14.02 based on patterns of genetic diversity.
00:00:18.23 So why do I think it's important
00:00:20.08 that we study African genetic variation?
00:00:22.28 Well, for one,
00:00:24.17 if we want to learn more about modern human origins,
00:00:26.23 we need to be looking in Africa,
00:00:28.15 which is the site of modern human speciation.
00:00:32.05 Secondly, if we want to learn more about African-American ancestry,
00:00:36.14 this will be an important region to study.
00:00:40.21 Third is that Africa is a region
00:00:42.13 with a very high level of infectious disease,
00:00:44.27 with HIV, malaria, and TB being three of the biggest killers,
00:00:49.21 but there's also an increasing level of
00:00:51.23 non-communicable diseases like diabetes, for example,
00:00:55.08 and cardiovascular disease.
00:00:57.11 And African populations have been greatly underrepresented
00:01:00.22 in the biomedical research,
00:01:03.00 and so we really need to give more focus
00:01:05.10 to these populations so that we can come up with better diagnostics
00:01:08.27 and better treatments for these diseases.
00:01:13.11 And lastly, we know that people differ in regards to drug response,
00:01:17.10 and this is likely due to variation at drug metabolizing genes,
00:01:21.02 but again, we currently know very little
00:01:23.03 about the extent of variation among Africans at these loci.
00:01:30.17 So first I have to give you a little bit of information
00:01:32.22 about African population history.
00:01:35.06 There are over 2,000 ethnic groups in Africa
00:01:37.26 speaking distinct languages,
00:01:40.12 and these languages have been classified
00:01:42.15 into four different language families.
00:01:45.17 So in blue are languages
00:01:48.15 classified as Afro-Asiatic.
00:01:50.27 They're found predominantly in the north and northeast of Africa,
00:01:55.23 and these would include, for example,
00:01:57.13 Semitic languages which are also spoken in the Middle East,
00:02:01.02 and they would also include Cushitic languages
00:02:04.14 spoken in northeast Africa.
00:02:07.02 And then in red we have populations
00:02:10.14 that are speaking Nilo-Saharan languages,
00:02:13.10 these tend to be pastoralist groups,
00:02:15.20 like the Maasai for example, who live in Kenya and Tanzania.
00:02:19.07 And these populations are mainly found
00:02:21.12 in central and eastern Africa
00:02:24.28 although there are a few groups who have migrated
00:02:27.14 to the west of Africa.
00:02:30.00 The most broad-spread language family
00:02:34.27 consists of the Niger-Kordofanian languages,
00:02:37.20 shown in yellow or orange here.
00:02:40.21 And the most common subfamily
00:02:43.18 is the family of Bantu languages.
00:02:47.06 Now, those are thought to have originated in Cameroon or Nigeria
00:02:51.02 around 5,000 years ago,
00:02:53.18 together with the development of iron tool technology,
00:02:56.26 which led to much better methods for practicing agriculture.
00:03:02.27 And so these populations
00:03:04.25 had a technological advantage in a sense,
00:03:07.20 and they were able to rapidly
00:03:09.29 expand across Africa into east Africa
00:03:13.12 and then south Africa,
00:03:15.14 or from west Africa
00:03:20.18 along the western coast into southern Africa.
00:03:24.14 The fourth language family, shown in green here,
00:03:28.05 is classified as Khoisan,
00:03:30.17 and it consists of languages that have click consonants.
00:03:34.03 So these are found predominantly
00:03:36.20 amongst the San hunter-gatherers in southern Africa,
00:03:40.24 and also amongst two groups called the Hadza and the Sandawe,
00:03:45.08 who live in Tanzania.
00:03:47.18 Now, despite the importance of studying Africa,
00:03:50.23 there have been relatively few genomics studies in that region,
00:03:54.05 and there's a number of reasons for that,
00:03:56.04 and one of which is just the challenges of
00:03:58.16 doing research in areas that sometimes
00:04:00.22 have little infrastructure.
00:04:02.25 And so I wanted to show you some examples of
00:04:05.21 the field work that we've done over the past 12 years.
00:04:08.25 We've mainly been studying
00:04:10.18 minority populations in Africa
00:04:12.13 who practice indigenous lifestyles,
00:04:14.22 and they live in very remote areas,
00:04:16.14 so we have to, for example, have a 4-wheel drive vehicle,
00:04:21.03 and this work has been done no only by myself,
00:04:23.21 but by my students and postdocs
00:04:25.27 and African collaborators over many years.
00:04:30.22 So here's an example, I like this,
00:04:32.13 it shows my postdocs Alessia Ranciaro and Simon Thompson,
00:04:37.06 and they were doing an expedition in Ethiopia in 2010.
00:04:41.04 We basically have to bring all of our lab equipment with us,
00:04:44.28 and I like this because it shows both the outside perspective of the car,
00:04:48.04 and also the inside perspective.
00:04:51.25 These are some of the other challenges that they faced.
00:04:54.20 They were there during the wet season,
00:04:56.03 making it extremely challenging to travel.
00:05:02.01 In each of these regions,
00:05:03.19 we typically start by doing what you could think of as
00:05:06.05 "Town Hall meetings", in which we explain the research
00:05:09.04 to the community,
00:05:11.01 and we explain both the risks and the benefits,
00:05:12.23 and make sure that they understand
00:05:14.11 why we're doing this research,
00:05:16.00 and how it might benefit or not benefit the community.
00:05:19.00 Ultimately though,
00:05:20.25 we have to obtain individual informed consent
00:05:23.12 to do this research.
00:05:27.09 We also measured a number of phenotypes,
00:05:29.11 like height and weight.
00:05:32.12 More recently, we've been looking at more detailed
00:05:34.29 anthropometric cardiovascular and metabolic traits.
00:05:41.13 From each of these samples,
00:05:42.24 we typically obtain blood intravenously,
00:05:45.29 and we've started to also obtain RNA.
00:05:50.07 But one of the challenges is processing these samples
00:05:53.12 in regions where there's no electricity.
00:05:55.25 So here's an example where we set up the so-called
00:05:58.19 "Bush Lab": we had to set up our centrifuge
00:06:00.28 and hook it up to the car battery.
00:06:05.02 But in other areas, we can find a local clinic,
00:06:07.08 they'll often have a generator,
00:06:09.06 and so then we're able to hook up a larger centrifuge.
00:06:12.06 One of the ways in which we obtain DNA.
00:06:15.20 and the DNA, I should note,
00:06:17.08 is only present in the white cells of blood,
00:06:19.24 so the first thing we're gonna do is we're gonna
00:06:21.13 break open all the red cells.
00:06:23.27 And we do that by adding a solution
00:06:27.00 that's going to cause them to burst open.
00:06:29.25 Then we're going to spin down the samples in this centrifuge,
00:06:34.04 and we have to repeat this several times,
00:06:36.09 and we're gonna end up with these little pellets at the bottom
00:06:39.01 of the white cells, and that's where we're gonna find the DNA.
00:06:45.10 Here are some other challenges of processing in the field.
00:06:48.13 After we've isolated the DNA,
00:06:50.03 we add another buffer, which is going to
00:06:53.01 preserve the samples at room temperature,
00:06:55.21 but here's a case where Simon Thompson
00:06:57.15 actually had to bring a generator with him
00:06:59.26 and set up the entire lab in the bush
00:07:02.18 when he was studying the Hadza hunter-gatherers of Tanzania.
00:07:08.15 Another very important thing
00:07:11.07 is to increase training and capacity building in Africa
00:07:15.13 so that they can do this research themselves,
00:07:17.21 and that's something that I've spent a lot of time doing,
00:07:20.09 and I think is very important.
00:07:23.23 Also equally important is actually
00:07:25.02 returning results to participants,
00:07:28.01 and it's really surprising how little this is done,
00:07:31.07 but I can assure you that people
00:07:32.26 really appreciate it when we return the results,
00:07:36.06 and I think it's also an ethical obligation
00:07:38.20 so that they can benefit from what we learn from these studies.
00:07:44.13 So I want to start by talking about some of the phenotypic variation
00:07:47.10 that we see in Africa.
00:07:49.03 This is an example of skin melanin levels,
00:07:52.07 or skin pigmentation.
00:07:54.09 So, the higher the value here,
00:07:56.15 the darker the skin color.
00:07:59.01 And I wanna just make the point that
00:08:00.16 we see a lot of variation in skin pigmentation levels
00:08:04.27 across diverse Africans.
00:08:07.18 And one of the things that we're interested in looking at is
00:08:10.10 correlations with vitamin D for example,
00:08:12.10 because we know that vitamin D is produced by UV light,
00:08:16.21 and that people with darker skin
00:08:18.12 may produce less vitamin D, for example.
00:08:21.02 And vitamin D can have important health implications,
00:08:23.15 so this is relevant to know.
00:08:25.29 It's also an interesting trait to look at how people
00:08:28.09 have adapted to different environments.
00:08:31.21 Here are the results of a principal component analysis
00:08:34.17 for a number of cardiovascular traits,
00:08:37.11 and these are different populations.
00:08:40.27 If the populations cluster close to each other,
00:08:43.19 it means that they're very similar for these traits,
00:08:45.28 and we've color-coded them based on shared language and ethnicity.
00:08:50.26 And what's interesting is that they tend to cluster
00:08:53.04 based on language and culture.
00:08:55.12 So here are the Nilo-Saharan speakers,
00:08:57.06 here are the Afro-Asiatic speakers,
00:08:59.18 and in yellow here are the Niger-Kordofanian speakers,
00:09:04.08 but we see two exceptions.
00:09:06.13 These are two groups that live on the coast of Kenya,
00:09:09.00 in geographic proximity to the Bantu-speaking groups,
00:09:13.10 suggesting that not only are genetic factors important,
00:09:16.02 but environment factors are probably quite important as well.
00:09:22.18 And here we can see tremendous variation
00:09:25.16 for height, weight, and BMI in Africa.
00:09:29.00 And again, we're seeing that
00:09:31.07 populations tend to cluster based on shared ethnicity,
00:09:35.02 and at the extremes
00:09:36.23 we have the very short statured pygmies from central Africa,
00:09:40.13 and then we have the very tall and thin individuals
00:09:43.27 from Kenya and other places. and the Sudan.
00:09:49.02 And so, as we'll talk about in the last section of my lecture series,
00:09:52.18 this may be due to adaptation to different environments.
00:09:58.17 So now I want to tell you about the patterns of
00:10:00.21 genetic variation and genetic structure in Africa,
00:10:04.19 and this is based on a paper that we published several years ago,
00:10:08.14 in which we looked at genome-wide variable markers,
00:10:13.21 and these were genotyped in over 2,500 Africans
00:10:17.06 from 121 ethnic groups
00:10:19.04 that are shown by these dots here.
00:10:21.12 But note that even though this was
00:10:23.06 more than had ever been done before,
00:10:25.16 it still represents just a fraction of the
00:10:27.16 2,000 ethnic groups in Africa,
00:10:30.06 so we're still missing a lot of the variation.
00:10:33.06 We then looked at 98 African-Americans
00:10:36.20 from 4 regions in the US
00:10:38.22 and a comparative dataset of about 1,500 non-Africans.
00:10:44.13 So let me first tell you about the levels of genetic variation that we saw,
00:10:48.05 and that's indicated by the height of this bar.
00:10:51.03 And I've color-coded this by geographic region,
00:10:53.20 so shown in orange are people from Africa,
00:10:57.01 and as nearly every study has shown,
00:10:59.11 Africans have the highest level of genetic variation.
00:11:02.27 And then we see decreasing variation
00:11:05.00 as we move west to east
00:11:07.00 across Eurasia into
00:11:09.06 East Asia, Oceania, and the Americas.
00:11:13.10 So the patterns of genetic diversity that we're seeing
00:11:16.10 simply reflect our evolutionary and demographic history.
00:11:20.10 We see the highest levels of diversity in Africa,
00:11:22.20 which is the site of origin of modern humans,
00:11:25.11 and then when small groups of people
00:11:27.23 migrated out of Africa within the past 50,000-100,000 years,
00:11:32.03 there was a population bottleneck,
00:11:34.06 and so we see a decrease in genetic diversity.
00:11:37.26 And as humans migrated west to east across Eurasia
00:11:41.07 and into the Americas
00:11:43.00 and into Oceania and so on,
00:11:45.01 there were a series of founding events and again,
00:11:47.16 a concomitant decrease in genetic diversity.
00:11:51.21 So this is a phylogenetic tree
00:11:54.01 constructed based on pair-wise genetic distances
00:11:56.14 between populations.
00:11:58.07 You can't see any details on this tree,
00:12:00.13 I just want to point out some overall trends.
00:12:02.27 And I've color-coded these such that
00:12:05.24 the populations shown in black,
00:12:08.21 the black branches, are non-Africans,
00:12:12.11 and then the Africans are shown here.
00:12:14.23 So the first thing that you can see from this tree
00:12:16.27 is that non-Africans are distinguished from Africans,
00:12:20.19 and that the non-African populations
00:12:22.19 are clustering by major geographic region.
00:12:25.25 So we have people from India, central Asia, Europe,
00:12:29.07 Middle East, east Asia, and the Americas,
00:12:34.01 and then Oceania.
00:12:36.10 And even within Africa,
00:12:38.13 populations are clustering by major geographic region,
00:12:41.22 so here are populations from the north of Africa,
00:12:44.02 from eastern Africa,
00:12:45.15 from west-central Africa,
00:12:47.17 and then from southern Africa,
00:12:49.10 with one exception:
00:12:51.25 down here, at the root of this tree,
00:12:54.08 we see the San hunter-gatherers from southern Africa,
00:12:58.22 but clustering near the San are the pygmies,
00:13:01.17 who today live in central Africa.
00:13:04.08 And that's really intriguing and maybe telling us something
00:13:06.16 about the history of these populations,
00:13:08.24 and I'll discuss that more in a moment.
00:13:13.14 Now, we can also compare genetic distances,
00:13:17.02 which are shown on the y-axis,
00:13:19.10 to geographic distances between pairs of populations,
00:13:22.20 shown on the x-axis.
00:13:24.28 And we see a significant positive correlation,
00:13:28.19 but we can also see a lot of scatter here.
00:13:32.01 And what that means is that there are some populations
00:13:34.26 that are geographically very close,
00:13:38.17 but they're genetically very different,
00:13:41.17 and those probably represent recent migration events
00:13:44.24 of genetically differentiated populations.
00:13:47.19 And then on the other end of the spectrum,
00:13:50.02 we have some populations that are genetically very similar to each other,
00:13:53.27 but geographically very far apart.
00:13:56.21 And those may reflect, for example,
00:13:58.24 the Bantu people, who migrated from western Africa
00:14:02.03 to eastern and southern Africa,
00:14:03.18 so they're gone quite a long geographic distance,
00:14:07.03 but genetically they're still very similar to each other.
00:14:11.07 So now I want to move away from looking at populations
00:14:13.28 and I want to talk about looking at variation amongst individuals.
00:14:18.20 And the first thing I want to show you is
00:14:20.28 a principal component analysis based on individual genotypes.
00:14:25.07 And so each of these circles
00:14:28.10 actually represents a person,
00:14:30.16 and if they cluster together
00:14:32.21 it means that they're genetically similar to each other.
00:14:35.22 So, as shown here, the first principle component
00:14:38.15 accounts for as much of the variability in the data as possible,
00:14:42.08 and each succeeding component
00:14:44.08 accounts for as much of the remaining variability as possible.
00:14:47.28 So the first principal component
00:14:50.09 essentially is differentiating
00:14:52.24 the African groups
00:14:55.04 from the non-African groups.
00:14:57.14 The second principal component
00:14:59.23 is differentiating the Native Americas,
00:15:03.05 Eastern Asians,
00:15:04.20 and Oceanin populations
00:15:06.12 from the rest of the world.
00:15:07.26 And the third principal component
00:15:09.27 is distinguishing the Hadza hunter-gatherers from Tanzania
00:15:13.11 from the rest of the world.
00:15:15.12 This next result is based on a probabilistic analysis
00:15:20.24 that simultaneously infers ancestral population clusters,
00:15:26.11 which are represented by the different colors shown here,
00:15:29.27 and then we have.
00:15:31.28 this is actually composed of a series of lines,
00:15:34.21 and each line represents an individual.
00:15:37.18 And an individual can have mixed ancestry
00:15:42.04 from different ancestral population clusters.
00:15:45.18 So what we tend to see outside of Africa,
00:15:48.03 which is shown along the bottom here,
00:15:50.10 is that individuals are clustering
00:15:52.05 by major geographic region.
00:15:54.08 So, in blue we have individuals
00:15:56.26 who self-identify as European or Middle Eastern,
00:16:00.27 and then here we have individuals from southern India,
00:16:04.25 here we have individuals from Pakistan,
00:16:08.09 central Asia,
00:16:09.27 east Asia,
00:16:12.29 and the Americas.
00:16:14.27 But what I want you to note is all the colors
00:16:17.27 that we see here in Africa.
00:16:20.22 That's representing the very large amount of genetic diversity,
00:16:24.15 not only within,
00:16:26.11 but among African populations,
00:16:28.15 compared to the whole rest of the globe.
00:16:31.20 I'll just point out a couple of trends.
00:16:35.10 In orange colors are populations from central and west Africa
00:16:38.27 who speak Niger-Kordofanian and Bantu languages.
00:16:43.09 In purple are populations
00:16:45.17 that speak Afro-Asiatic languages
00:16:47.21 and originated from northern or northeast Africa.
00:16:51.26 In red are populations that speak Nilo-Saharan languages
00:16:55.23 and they most likely originated from southern Sudan.
00:17:01.11 We have populations that are speaking Chadic languages,
00:17:05.16 a group called the Fulani who are nomadic pastoralists.
00:17:08.28 Most of the north Africans
00:17:10.27 have a lot of European or Middle Eastern admixture.
00:17:14.23 And then we have the hunter-gatherer groups,
00:17:16.15 like the Hadza,
00:17:18.09 the Sandawe,
00:17:19.23 pygmies from central Africa,
00:17:21.22 and the San hunter-gatherers from southern Africa.
00:17:26.08 Now, we repeated this analysis within Africa,
00:17:30.01 and again we inferred 14 ancestral population clusters,
00:17:34.15 but for ease of viewing I'm just going to pool individuals together
00:17:37.20 and show them as pie charts.
00:17:39.25 Now, first I'm showing you the 3 populations
00:17:42.13 that had been studied as part of the
00:17:44.07 HapMap and Thousand Genomes Initiative.
00:17:47.06 These are NIH-funded programs
00:17:50.22 to characterize genetic variation
00:17:52.28 across ethnically diverse human populations
00:17:56.02 and making that data publically available
00:17:58.02 so that it could be used by a wide range of
00:18:00.16 biomedical research scientists.
00:18:04.00 Now, what I want to point out is that
00:18:05.15 when we look at the rest of Africa,
00:18:08.15 we see quite a bit more variation.
00:18:11.27 And so, for example, populations in east Africa
00:18:15.08 look distinct from populations in western Africa,
00:18:21.04 and southern Africa.
00:18:23.00 It's also interesting
00:18:24.16 because we can see remnants of historic migration events.
00:18:27.11 So for example, I mentioned to you the Bantu migration.
00:18:30.18 The people who speak Niger-Kordofanian or Bantu languages
00:18:33.23 are represented by shades of orange,
00:18:35.26 and you can actually see that they appear
00:18:38.13 to have originated, as I said,
00:18:40.11 in Cameroon/Nigeria region,
00:18:43.03 and then they migrated
00:18:45.10 across Africa into eastern Africa,
00:18:48.03 where they admixed with the indigenous populations there,
00:18:51.19 and they also migrated into southern Africa,
00:18:54.05 where the admixed with the populations there.
00:18:57.05 We can also see remnants of migration of individuals
00:19:01.08 from northeast Africa who speak Afro-Asiatic languages
00:19:04.28 into Kenya and Tanzania.
00:19:07.22 We see migration of people who speak Nilo-Saharan languages,
00:19:11.20 originating from southern Sudan.
00:19:13.11 There was one group that went west,
00:19:16.07 and we think that some of these people who speak Chadic languages,
00:19:20.19 which are actually classified as Afro-Asiatic,
00:19:22.25 genetically they look very similar to the Nilo-Saharans.
00:19:26.02 So in fact there may have been a language substitution
00:19:28.15 at some point in the past.
00:19:30.23 And then we have migration of the Nilo-Saharan pastoralists
00:19:34.08 into Kenya and into Tanzania.
00:19:38.23 We can also see that some of the hunter-gatherer groups are very distinct.
00:19:42.27 Here are the Hadza hunter-gatherers, who speak with a click in Tanzania.
00:19:47.08 Here are the Sandawe, who speak with a click, also in Tanzania,
00:19:50.04 but their languages are very divergent from each other.
00:19:53.14 Here are the San hunter-gatherers shown in light green,
00:19:56.05 also speaking with a click, but again,
00:19:58.06 their languages are very differentiated
00:20:00.06 from the other two populations who speak with clicks in Tanzania.
00:20:05.08 And then we have the pygmy populations from central Africa.
00:20:10.13 Interestingly, the pygmy population called Mbuti,
00:20:14.09 who lives the furthest to the east,
00:20:16.26 appears to possible share some common ancestry with the San.
00:20:22.01 And in fact several pieces of data that we've studied
00:20:26.15 suggest that there could have been a
00:20:28.16 proto Khoesan-Pygmy hunter-gatherer population in Africa
00:20:32.16 that probably existed greater than 50,000 years ago,
00:20:36.01 and then underwent population divergence and differentiation
00:20:40.06 and then migration within the past 50,000 years,
00:20:43.22 but there's still a lot of work to be done
00:20:45.14 to try to differentiate this population history.
00:20:48.23 So next I wanna talk about what we found
00:20:51.05 in terms of African American ancestry.
00:20:53.22 We looked at African Americans
00:20:55.21 originating from four regions in the US:
00:20:58.15 Chicago, Pittsburgh, Baltimore, and North Carolina.
00:21:02.05 Now, not surprisingly, you can see that the majority of ancestry
00:21:06.17 is this western Niger-Kordofanian ancestry,
00:21:10.05 shown in orange.
00:21:12.08 The other major component of their ancestry,
00:21:14.21 which is summarized here, is European ancestry,
00:21:17.19 which ranges from about 0% to greater than 50%.
00:21:22.18 And then we see small amounts of ancestry from other populations,
00:21:25.22 including some other African populations
00:21:29.18 who speak Chadic languages, for example,
00:21:33.09 from western Africa.
00:21:34.27 We see a small amount of ancestry from east Africa,
00:21:37.25 and also very small amounts of
00:21:40.07 east Asian and Native American ancestry,
00:21:43.02 at least in these particular populations.
00:21:45.23 If you look at populations from other regions,
00:21:48.17 you may see more ancestry from those regions.
00:21:54.02 And again, this is reflecting the history of the transatlantic slave trade,
00:22:00.15 originating from west Africa,
00:22:03.10 and actually a very large source of the slave trade
00:22:05.29 was from Angola,
00:22:07.24 and we currently know very little about genetic variation in that region.
00:22:11.12 And that's going to be important to know
00:22:13.28 for some studies in which knowing variation
00:22:17.29 in African ancestral populations will be important
00:22:20.15 for identifying disease risk alleles
00:22:23.28 in African American or Afro-Caribbean populations.
00:22:28.28 I want to tell you about another study that I did with collaborators,
00:22:32.18 in which we looked at
00:22:35.22 over 250,000 single nucleotide polymorphisms, or SNPs.
00:22:41.11 These are just regions of the genome
00:22:43.18 that differ at a single nucleotide,
00:22:46.24 and we looked at them predominantly
00:22:49.12 in western populations along the coast of Africa,
00:22:54.05 and one group from southern Africa.
00:22:57.10 And when we do this principal component analysis,
00:22:59.22 one of the interesting results
00:23:02.07 is that the distribution really reflects the geography of these populations,
00:23:08.02 and that's not a huge surprise.
00:23:09.29 It means that people who live near each other
00:23:12.06 tend to mate with each other,
00:23:13.28 and people who live further apart are not intermixing as often,
00:23:17.27 and so they tend to be more genetically differentiated.
00:23:23.23 We then did a principal component analysis
00:23:26.25 including the African American individuals,
00:23:30.08 shown here in sort of fuchsia color,
00:23:33.29 and shown in red are Europeans,
00:23:37.03 and then here we have the different west African populations.
00:23:42.01 And we could actually determine
00:23:44.00 the amount of European or African ancestry in any individual
00:23:49.06 -- African American individual --
00:23:51.19 by looking at their position along principal component 1.
00:23:56.05 So for example, this individual here,
00:23:58.24 this African American individual,
00:24:00.29 appears to have more European ancestry,
00:24:03.17 whereas this African American individual
00:24:06.04 seems to have more west African ancestry.
00:24:11.08 And then, using an approach that was developed by Carlos Bustamante's lab,
00:24:16.15 it was possible to actually scan along chromosomes,
00:24:19.16 so here we're showing
00:24:22.22 the different chromosomes starting at 22, 21, 20,
00:24:25.25 and so on down to chromosome 1.
00:24:28.12 And as you scan along the chromosome,
00:24:30.04 at any particular region,
00:24:32.11 you can infer if somebody has African ancestry,
00:24:36.25 which is shown in blue,
00:24:39.12 European ancestry, which is shown in red,
00:24:43.15 or mixed ancestry, which is shown in green.
00:24:47.27 And what we see is that most African Americans
00:24:50.23 have a mixture of ancestry.
00:24:53.01 So they tend to have a lot of,
00:24:54.16 not surprisingly, African ancestry shown in blue.
00:24:58.03 There are regions of mixed ancestry shown in green,
00:25:01.13 but also note that there are some regions of the genome
00:25:04.20 which are only of European ancestry,
00:25:07.26 and this differs quite a bit amongst different individuals.
00:25:10.11 Here's an example of someone who appears
00:25:12.13 to have undergone very recent admixture
00:25:16.08 they have a lot of African ancestry.
00:25:19.27 Here's someone who has very recent European ancestry,
00:25:23.01 so we see a lot of regions of the genome
00:25:24.24 where they're of mixed ancestry.
00:25:27.20 Here's someone who has.
00:25:29.24 they self-identify as African American,
00:25:31.27 but they have almost no African ancestry,
00:25:34.24 so that goes to show you that there can be a lot of genetic variation
00:25:38.00 that may not always correlate with self-identified ethnicity.
00:25:42.29 The other important point here is that,
00:25:45.29 in the future,
00:25:48.25 the ideal that we have is to develop
00:25:51.02 more personalized medicine
00:25:53.27 that is tailored for the individual.
00:25:56.14 And here's someone that, for example,
00:25:58.16 if they went to the doctor and they self-identified
00:26:00.22 as African American,
00:26:02.20 the doctor might prescribe certain drugs that, say,
00:26:04.26 are more effective in African Americans.
00:26:07.11 But what if, at that particular position,
00:26:09.27 where they have only European ancestry,
00:26:12.12 what if there was a drug metabolizing enzyme gene
00:26:16.01 at that particular point,
00:26:18.27 and so that would be of pure European ancestry,
00:26:21.24 and so that might be important to know.
00:26:24.00 So this has important implications for
00:26:26.00 future design of future personalized medical approaches for treatment.
00:26:32.25 So in conclusion, people from different geographic regions
00:26:35.29 are genetically more similar to each other,
00:26:38.08 so for example, Asian individuals
00:26:40.15 will be more similar to other Asian individuals,
00:26:43.02 Europeans more similar to other Europeans.
00:26:46.02 But in Africa,
00:26:47.25 there has been more time to accumulate genetic variation,
00:26:50.29 they're had larger effective populations sizes
00:26:53.18 so they've maintained a lot of variation,
00:26:55.26 and they've live in diverse environments,
00:26:58.05 so they tend to be highly differentiated from each other,
00:27:01.05 although we also can see that
00:27:03.16 there's been a history of admixture throughout much of Africa.
00:27:07.29 So therefore, Africans have the highest level of genetic variation,
00:27:12.16 both within and between populations,
00:27:15.01 and we saw that African Americans
00:27:17.05 have ancestry from west Africa and Europe,
00:27:19.21 and that the ancestry varies along chromosomes,
00:27:22.03 which has important implications for personalized medicine.
00:27:26.18 And that concludes this portion of my lecture,
00:27:28.26 and for this section I'd like to acknowledge
00:27:30.23 the many individuals who contributed,
00:27:34.28 together with our funding organizations.
John A. Clements
John A. Clements was born in 1923. He went to undergraduate and medical school at Cornell University before joining the Army Chemical Center in Maryland. At the Army Chemical Center, Dr. Clements started studying lung physiology which lead him to discovering surfactant. Dr. Clements joined the faculty at the University of California, San Francisco in… Continue Reading
13.24: Post-Cambrian Evolution - Biology
Figure 1. An artist’s rendition depicts some organisms from the Cambrian period.
Many questions regarding the origins and evolutionary history of the animal kingdom continue to be researched and debated, as new fossil and molecular evidence change prevailing theories. Some of these questions include the following: How long have animals existed on Earth? What were the earliest members of the animal kingdom, and what organism was their common ancestor? While animal diversity increased during the Cambrian period of the Paleozoic era, 530 million years ago, modern fossil evidence suggests that primitive animal species existed much earlier.
Additional file 1:
Summary of small RNA and degradome RNA libraries.
Additional file 2:
Reference genome and transcriptome databases used in this study.
Additional file 3:
List of 24-nt phasiRNA generating loci.
Additional file 4: Figure S1.
Number of PHAS loci identified from all sRNA libraries in each species. Figure S2. Identification of Atr-miR828 and TAS4-like sequences in Amborella trichopoda. Figure S3. Number of PHAS loci identified in leaf samples of each species. Figure S4. phasiRNAs mapped to intron–exon junctions or to an intron alone. Figure S5. Numbers of PHAS loci mapped to introns or intron–exon junctions in different plant species. Figure S6. Developmentally regulated phasiRNA production. Figure S7.PHAS loci in response to nutrient starvation in Chlamydomonas reinhardtii.
Additional file 5:
List of 21-nt phasiRNA generating loci.
Additional file 6:
List of phasiRNA-generating TAS4 loci.
Additional file 7:
Number of PHAS loci mapped to protein-coding genes.
Additional file 8:
List of previously reported gene families that generate phasiRNAs.
Additional file 9:
Summary of PHAS loci identified in each small RNA library.
Additional file 10:
PHAS loci containing only intron-derived sRNAs.
Additional file 11:
PHAS loci containing both intron- and exon-derived sRNAs (i.e., at intron–exon junctions).
Additional file 12:
Developmentally controlled PHAS loci.
Additional file 13:
Differential phasiRNA production in papaya, Arabidopsis , and rice between healthy and virus-infected leaf samples.
Additional file 14:
PHAS loci that were responsive to nutrient starvation.
Additional file 15:
Summary of PHAS loci with identified triggers.
Additional file 16:
Analysis for PHAS loci triggers.
Additional file 17:
Complete list of predicted target genes of phasiRNAs with RPM value no less than 10.