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How can the number of genes increase through evolution?

How can the number of genes increase through evolution?

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I am aware of the basics of evolutionary theory, however I don't understand how mutations can add genes over time.

Am I correct in thinking that creatures within the same species who mutate to have an additional gene in their genome would normally be infertile? Or am I misunderstanding that?

Can someone explain the process of the creation of genetic material through evolution? Citation to a decent academic paper or book on the matter would be appreciated too.


Mutations can have all sorts of impacts on genetic architecture. A mutation can have a small impact on a genetic architecture such as

  • Substitution
  • Insertion
  • Deletion

or can have a much bigger impact on a genetic architecture such as

  • Amplification (incl. gene duplication)
  • Deletions
  • Chromosomal translocations
  • Interstitial deletions
  • Chromosomal inversions
  • Chromosome duplication
  • Change in ploidy number

Some of these mutations (gene duplication, Chromosome duplication, change in ploidy number) typically allows duplicating DNA segments. After duplication, the two copies of a gene can diverge through neofunctionalization or subfunctionalization. Have a look at wikipedia > gene duplication to understand what biochemical processes can cause such mutations (e.g. ectopic recombination, retrotransposition event, replication slippage).

Am I correct in thinking that creatures within the same species who mutate to have an additional gene in their genome would normally be infertile?

You are being mistaken. I understand the naive intuition that a change in copy number of a given gene would be extremely deleterious but in reality, living organisms seem much more resilient to such Copy-Number Variation (CNV) than you would think. Of course, some CNV are associated with diseases but it is not the case of all (McCarroll and Altshuler 2007).

Gene duplications are actually quite common whether in C. elegans (Lipinski et al. 2011) or in humans (Cotton and Page 2005). Chromosomal duplications are also common (Bowers et al. 2002). Even whole-genome duplications (see a classical example in Wolfe 2015) have played major roles in the evolution of many lineages (Whitton and Otto 2010) including vertebrates (Dehal and Boore 2005).

Below is a phylogenetic tree of sequenced green plant genomes highlighting some of the main events of whole-genome duplication.

For the record, some species of strawberries are decaploid (10 copies) (Hummer 2012). Then there are the extremes. In Entamoeba populations, there can be variation in ploidy level among individuals ranging from diploid (2 copies) to tetracontaploid (40 copies) (Mukherjee et al. 2008)!


  • Plant populations experience gene flow by spreading their pollen long distances.
  • Animals experience gene flow when individuals leave a family group or herd to join other populations.
  • The flow of individuals in and out of a population introduces new alleles and increases genetic variation within that population.
  • Mutations are changes to an organism&rsquos DNA that create diversity within a population by introducing new alleles.
  • Some mutations are harmful and are quickly eliminated from the population by natural selection harmful mutations prevent organisms from reaching sexual maturity and reproducing.
  • Other mutations are beneficial and can increase in a population if they help organisms reach sexual maturity and reproduce.
  • gene flow: the transfer of alleles or genes from one population to another
  • mutation: any heritable change of the base-pair sequence of genetic material

Evolution

In biology, evolution is the change in the inherited traits of a population from generation to generation.

These traits are the expression of genes that are copied and passed on to offspring during reproduction.

Mutations in these genes can produce new or altered traits, resulting in heritable differences (genetic variation) between organisms.

New traits can also come from transfer of genes between populations, as in migration, or between species, in horizontal gene transfer.

Evolution occurs when these heritable differences become more common or rare in a population, either non-randomly through natural selection or randomly through genetic drift.

Natural selection is a process that causes heritable traits that are helpful for survival and reproduction to become more common, and harmful traits to become more rare.

This occurs because organisms with advantageous traits pass on more copies of these heritable traits to the next generation.

Over many generations, adaptations occur through a combination of successive, small, random changes in traits, and natural selection of those variants best-suited for their environment.

In contrast, genetic drift produces random changes in the frequency of traits in a population.

Genetic drift arises from the role chance plays in whether a given individual will survive and reproduce.

One definition of a species is a group of organisms that can reproduce with one another and produce fertile offspring.

However, when a species is separated into populations that are prevented from interbreeding, mutations, genetic drift, and the selection of novel traits cause the accumulation of differences over generations and the emergence of new species.

The similarities between organisms suggest that all known species are descended from a common ancestor (or ancestral gene pool) through this process of gradual divergence.

The theory of evolution by natural selection was proposed roughly simultaneously by both Charles Darwin and Alfred Russel Wallace, and set out in detail in Darwin's 1859 book On the Origin of Species.

In the 1930s, Darwinian natural selection was combined with Mendelian inheritance to form the modern evolutionary synthesis, in which the connection between the units of evolution (genes) and the mechanism of evolution (natural selection) was made.

This powerful explanatory and predictive theory has become the central organizing principle of modern biology, providing a unifying explanation for the diversity of life on Earth.


Science and Creationism: A View from the National Academy of Sciences, Second Edition (1999)

Along path leads from the origins of primitive "life," which existed at least 3.5 billion years ago, to the profusion and diversity of life that exists today. This path is best understood as a product of evolution.

Contrary to popular opinion, neither the term nor the idea of biological evolution began with Charles Darwin and his foremost work, On the Origin of Species by Means of Natural Selection (1859). Many scholars from the ancient Greek philosophers on had inferred that similar species were descended from a common ancestor. The word "evolution" first appeared in the English language in 1647 in a nonbiological connection, and it became widely used in English for all sorts of progressions from simpler beginnings. The term Darwin most often used to refer to biological evolution was "descent with modification," which remains a good brief definition of the process today.

Darwin proposed that evolution could be explained by the differential survival of organisms following their naturally occurring variation&mdasha process he termed "natural selection." According to this view, the offspring of organisms differ from one another and from their parents in ways that are heritable&mdashthat is, they can pass on the differences genetically to their own offspring. Furthermore, organisms in nature typically produce more offspring than can survive and reproduce given the constraints of food, space, and other environmental resources. If a particular off-

Charles Darwin arrived at many of his insights into evolution by studying the variations among species on the Galápagos Islands off the coast of Ecuador.

spring has traits that give it an advantage in a particular environment, that organism will be more likely to survive and pass on those traits. As differences accumulate over generations, populations of organisms diverge from their ancestors.

Darwin's original hypothesis has undergone extensive modification and expansion, but the central concepts stand firm. Studies in genetics and molecular biology&mdashfields unknown in Darwin's time&mdashhave explained the occurrence of the hereditary variations that are essential to natural selection. Genetic variations result from changes, or mutations, in the nucleotide sequence of DNA, the molecule that genes are made from. Such changes in DNA now can be detected and described with great precision.

Genetic mutations arise by chance. They may or may not equip the organism with better means for surviving in its environment. But if a gene variant improves adaptation to the environment (for example, by allowing an organism to make better use of an available nutrient, or to escape predators more effectively&mdashsuch as through stronger legs or disguising coloration), the organisms carrying that gene are more likely to survive and reproduce than those without it. Over time, their descendants will tend to increase, changing the average characteristics of the population. Although the genetic variation on which natural selection works is based on random or chance elements, natural selection itself produces "adaptive" change&mdashthe very opposite of chance.

Scientists also have gained an understanding of the processes by which new species originate. A new species is one in which the individuals cannot mate and produce viable descendants with individuals of a preexisting species. The split of one species into two often starts because a group of individuals becomes geographically separated from the rest. This is particularly apparent in distant remote islands, such as the Galápagos and the Hawaiian archipelago, whose great distance from the Americas and Asia means that arriving colonizers will have little or no opportunity to mate with individuals remaining on those continents. Mountains, rivers, lakes, and other natural barriers also account for geographic separation between populations that once belonged to the same species.

Once isolated, geographically separated groups of individuals become genetically differentiated as a consequence of mutation and other processes, including natural selection. The origin of a species is often a gradual process, so that at first the reproductive isolation between separated groups of organisms is only partial, but it eventually becomes complete. Scientists pay special attention to these intermediate situations, because they help to reconstruct the details of the process and to identify particular genes or sets of genes that account for the reproductive isolation between species.

A particularly compelling example of speciation involves the 13 species of finches studied by Darwin on the Galápagos Islands, now known as Darwin's finches. The ancestors of these finches appear to have immigrated from the South American mainland to the Galápagos. Today the different species of finches on the island have distinct habitats, diets, and behaviors, but the mechanisms involved in speciation continue to operate. A research group led by Peter and Rosemary Grant of Princeton University has shown that a single year of drought on the islands can drive evolutionary changes in the finches. Drought diminishes supplies of easily

The different species of finches on the Galápagos Islands, now known as Darwin's finches, have different-sized beaks that have evolved to take advantage of distinct food sources.

cracked nuts but permits the survival of plants that produce larger, tougher nuts. Droughts thus favor birds with strong, wide beaks that can break these tougher seeds, producing populations of birds with these traits. The Grants have estimated that if droughts occur about once every 10 years on the islands, a new species of finch might arise in only about 200 years.

The following sections consider several aspects of biological evolution in greater detail, looking at paleontology, comparative anatomy, biogeography, embryology, and molecular biology for further evidence supporting evolution.

The Fossil Record

Although it was Darwin, above all others, who first marshaled convincing evidence for biological evolution, earlier scholars had recognized that organisms on Earth had changed systematically over long periods of time. For example, in 1799 an engineer named William Smith reported that, in undisrupted layers of rock, fossils occurred in a definite sequential order, with more modern-appearing ones closer to the top. Because bottom layers of rock logically were laid down earlier and thus are older than top layers, the sequence of fossils also could be given a chronology from oldest to youngest. His findings were confirmed and extended in the 1830s by the paleontologist William Lonsdale, who recognized that fossil remains of organisms from lower strata were more primitive than the ones above. Today, many thousands of ancient rock deposits have been identified that show corresponding successions of fossil organisms.

Thus, the general sequence of fossils had already been recognized before Darwin conceived of descent with modification. But the paleontologists and geologists before Darwin used the sequence of fossils in rocks not as proof of biological evolution, but as a basis for working out the original sequence of rock strata that had been structurally disturbed by earthquakes and other forces.

In Darwin's time, paleontology was still a rudimentary science. Large parts of the geological succession of stratified rocks were unknown or inadequately studied.


In plants

The fact that most plant cells undergo extensive size increase unaccompanied by cell division is an important distinction between growth in plants and in animals. Daughter cells arising from cell division behind the tip of the plant root or shoot may undergo great increases in volume. This is accomplished through uptake of water by the cells the water is stored in a central cavity called a vacuole. The intake of water produces a pressure that, in combination with other factors, pushes on the cellulose walls of the plant cells, thereby increasing the length, girth, and stiffness ( turgor) of the cells and plant. In plants, much of the size increase occurs after cell division and results primarily from an increase in water content of the cells without much increase in dry weight.

The very young developing plant embryo has many cells distributed throughout its mass that undergo the cycle of growth and cell division. As soon as the positions of the root tip, shoot tip, and embryonic leaves become established, however, the potential for cell division becomes restricted to cells in certain regions called meristems. One meristematic centre lies just below the surface of the growing root all increases in the number of cells of the primary root occur at this point. Some of the daughter cells remain at the elongating tip and continue to divide. Other daughter cells, which are left behind in the root, undergo the increase in length that enables the new root to push deeper into the soil. The same general plan is evident in the growing shoot of higher plants, in which a restricted meristematic region at the tip is responsible for the formation of the cells of the leaves and stem cell elongation occurs behind this meristematic centre. The young seedling secondarily develops cells associated with the vascular strands of phloem and xylem—tissues that carry water to the leaves from the soil and sugar from the leaves to the rest of the plant. These cells can divide again, providing new cell material for development of a woody covering and for more elaborate vascular strands. Hence, the growth of higher plants—i.e., those aspects involving both the pattern of stems, leaves, and roots and the increase in bulk—results primarily from cell division at the meristem followed by a secondary increase in size because of water uptake. These activities occur throughout the period of plant growth.


How Gene Editing Could Ruin Human Evolution

I n the 1960s, Thomas Kuhn suggested that scientific ideas undergo fits of revolution, challenging the foundation of establishment science. But it was Peter Galison who emphasized the impact of a tool or method, and encouraged the notion that technology creates the tangible breach or disruption of a field. Alfred Hersey, in a similar spirit, once told a colleague, &ldquoideas come and go, but a method lasts.&rdquo

CRISPR-Cas9, the new gene modification tool, which has been heralded as a means for inserting ourselves into evolution, is itself evolving as a technology, even as you read this. That technology itself can evolve means there is greater urgency for how we think of our biology: either as a machine (which can break down and get new spare parts) or as part of ecology (whereby breakdown is not necessarily bad and can be part of growth, renewal or reorganization). CRISPR may be used to repair a gene that has a deficient product, such as an enzyme or receptor, or alter code that merely suggests of risk. Ideas on how to use it change hourly. The method is here to last. The ethics will only get more fraught. But there is a bigger obstacle to the emergence of &ldquodesigner babies&rdquo and Gattaca-type dystopian futures: the principles of evolution.

Before that, though, some background: CRISPR is a molecule that can be programmed to target a specific sequence in a genome. It guides an enzyme, such as Cas9, to chop the code like tiny molecular scissors. Scientists began using Cas9 to cause &ldquoblunt end&rdquo breaks in DNA. This tends to initiate a jerry-rigged repair the break is cobbled back together, incorporating small bits of available DNA or a repair template of other genetic material that scientists might add. A Cas9 repair is not always precise, but as the old saying goes, &ldquoa carpenter doesn&rsquot blame his tools.&rdquo But researchers have since found Cpf1, another such enzyme, which hacks into double-stranded DNA and leaves a “sticky end” break that leaves one strand dangling off the end. This template allows for more precise gene edits. And in December, U.C. Berkeley scientists reported discovering yet more enzymes&mdashCasX and CasY&mdashwhich promise to make the technology even more versatile and exacting. In short, technical limitations are evaporating.

On the immediate horizon, we are starting to see the silhouette of what Marcy Darnovsky, director of the Center for Genetics and Society, calls &ldquomarket-based eugenics.&rdquo Peter Marks, deputy director of the Center for Biologics Evaluation and Research and the U.S. Food and Drug Administration, told me in an email that in the United States, the FDA has chosen to regulate CRISPR-Cas9 as a drug, since it results in the chemical modification of a cell (like drugs do). This means that the agency can use normal channels to regulate any specific application in humans. Indeed, lawmakers put a rider into an omnibus spending bill that prevents the agency from reviewing applications that relate to the heritable code of a human embryo. But something is also going on in the background. We know that, for instance, many potential mothers of newborns with Down syndrome choose to abort, and it&rsquos not hard to envision a slippery slope of in vitro techniques, perhaps gene modifications, applied to conditions such as autism or psychiatric risk. The consumer will is there.

The industrial will is there, too. While the EPA and FDA work in the public interest, many NIH-backed scientists have ulterior interests, mainly to use federal (taxpayer) funding as grist to start their own business, to patent techniques, and to engage in lawsuits. Mark Zuckerberg and Priscilla Chan established a new &ldquoBiohub&rdquo which retains exclusive rights to patents, a similar scenario devised by the Sean Parker Foundation. The Broad Institute is armed with $650 million from the philanthropist Ted Stanley to investigate psychiatric disorders, as well as the claim to a robust patent portfolio of CRISPR systems for which it has a strong financial incentive to market and sell as much as possible. Indeed, the Broad&rsquos director Eric Lander has referred to &ldquoa revolution in psychiatric disease&rdquo and NIH chief Francis Collins said psychiatric genomics stands &ldquopoised for rapid advances.&rdquo Whether I agree with them (I don&rsquot) should be separated from the ambition to &ldquoindustrialize the human genome&rdquo&mdashand why the alteration of our biology evokes hubris, and our applications and intents can go wrong.

First, numerous studies of late have demonstrated that thousands of genetic variants straddled over the entire genome contribute to autism and psychiatric risk, as well as personality traits, and even intelligence. SHANK3, DIXDC1, DISC1 and C4, some of our most promising candidate genes for autism or schizophrenia, contain variants which actually only increase risk by a fraction of a percentage point. For another, called GRIN1, childhood stress can lessen its gene expression and impair learning. Though neuropsychiatric conditions are highly heritable, no single genetic variant contributes much to the risk of inheriting them, and so it&rsquos not particularly feasible to correct through gene modification. The journalist David Dobbs has elsewhere referred to these as &ldquoMany Assorted Genes of Tiny Significance,&rdquo or MAGOTS.

Biology is robust against breakdown. It straddles risk like a money manager, and that straddling of risk over the entire genome is one reason there are so few single &ldquotargets&rdquo for many of these psychological and cognitive traits. Indeed, many of these genetic variants may be pleiotropic, meaning they have different, often unrelated effects in different cells or tissues. The severity of their enhancing or diminishing effects may also vary, depending on their genetic background, the other genetic variants they&rsquore inherited with.

Second, scientists tend to think of men as machines, genes as their broken parts and variations in life as problems to be solved&mdashaberrations outside the normal curve. This assumes there is a right way for genes to be. In reality, Darwin showed us that evolution does not progress toward an ideal model or a more perfect form, but instead is a work of tinkering toward adaptation in local niches. Nowhere in nature does it say how a gene should function.

Furthermore, genetic variants that predispose us to risk or supposed weaknesses are precisely the same ones that turn out to have small fitness advantages (they make us better at numbers, more sensitive, alter concentration…). This is one reason I am a &ldquoneurodiversity advocate.&rdquo Evolution works at the margins, and it does so through trade-offs: Often, you don&rsquot get an advantage without risking a disadvantage. This is not trivial.

In 1966, Richard Lewontin and John Hubby proposed the idea of &ldquobalancing selection,&rdquo which suggests that harmful versions of genes, known as alleles, can remain in the population to contribute to genetic diversity. These versions can be useful in the case when individuals have one copy of the rare version of the gene and a copy of a more common, surefire form (this makes the individual &ldquoheterozygous”). The infamous APOE4 variant, the single strongest risk variant for late-onset Alzheimer&rsquos disease, remains at 15% frequency in the population&mdashone reason is that it may also up Vitamin D. A variation in a gene called COMT can increase dopamine levels by four-fold in the frontal cortex, which can increase concentration&mdashperhaps helpful if you have one copy, though it makes you more prone to being jittery if you have two copies (which 5% of us do).

Even some variants that are highly compromising stick around by piggy-backing on other useful genes that are under natural selection. Last year, Tobias Lenz, a scientist at the Max-Planck Institute for Evolutionary Biology in Germany, reported that a region of the genome called the major histocompatibility complex, which creates an immune system component that detects an array of infections, is littered with mutations. Many of these mutations also associate with human diseases, cancer, autoimmune disease and schizophrenia. The advantage of altering immune system genes, then, may come with a tradeoff of removing genes that are &ldquohitchhiking&rdquo nearby and dispose an associated risk for cancer or neuropsychiatric disorders. Losing the bad can mean losing the good, too.

There are no superior genes. Genes have a long and layered history, and they often have three or four unrelated functions, which balance against each other under selection. Those risky variants that can, in the right scenario, say, make us better at numbers are actually helpful to remain in the population in low frequencies. Indeed, versions of hundreds of genes that predispose us to psychiatric risks remain in the population at stable rates, while autism spectrum disorder and schizophrenia each occur at about one percent&mdashhinting at a tradeoff of risk for advantage.

In his 2015 book NeuroTribes, Steve Silberman argued against &ldquoframing autism as a contemporary aberration,&rdquo instead suggesting it had roots in &ldquovery old genes that are shared widely in the general population while being concentrated more in certain families than in others. Whatever autism is, it is not a unique product of modern civilization. It is a strange gift from our deep past, passed down through millions of years of evolution.&rdquo

In 1995, Arnold Ludwig reported a 77 percent rate of psychiatric disorders in eminent fiction writers. The link between creativity and madness is an old debate&mdashbut there are plausible theories for how this works. One in the scientific literature is that subclinical traits&mdashwhich we often characterize as schizotypal or psychoticism&mdashor even psychological traits like &ldquoopenness to experience&rdquo enable people to perform better on measures of creativity. However, if these tendencies become overly pronounced in the cases of severe mental illness, the aptitude for productivity and creativity plummet&mdasha concept broadly referred to as the &ldquoinverted U.&rdquo In effect, mild amounts of stress and disorientation can contribute to outside-of-the-box thinking, but a full spiral into a psychotic episode results in a rapid decline in insight.

As Steven Pinker told me, &ldquoThere are several possible explanations of why the trait of openness to experience could be an individual adaptation. As with any trait that varies among individuals, there is the challenge of explaining why it does not take a single, optimal value in all members of the species. Among the possibilities are that it&rsquos the result of mutations that have not been weeded out yet that different values are adaptive in different kinds of environments and that it&rsquos frequency-dependent: it&rsquos only adaptive when it&rsquos not too common.&rdquo

But thousands of genetic variants do indeed add up to influence psychiatric risk. That these variants stay in the human population at small frequencies also suggests that they may conceal a fitness benefit in some genetic backgrounds, for some people&mdashone reason we should not be so quick to clip snippets of code out of our genomes. Some of those with psychiatric risk&mdashCarrie Fisher, David Foster Wallace, Kurt Cobain&mdashturn out to illuminate reality in ways those inside the normal curve cannot. They demonstrate the limits of the human condition, mthe ultimate failure to achieve any security and the impossibility of control. That we could do any better than that through biotechnology is unlikely that we should want to is at best dubious and, at worst, morally questionable. “Blessed be the meek, for they shall inherit the earth.”

We&rsquove known for a long time the folly of genetic determinism: 30,000 genes cannot model 100,000,000,000,000 (a hundred trillion) synaptic connections in the brain. We also know that chronic stress and limiting social and economic factors are critically important to health, including cancer rates, cardiovascular and mental health, as articulated through well-known phenomena such as the &ldquoGlasgow effect.&rdquo Yet the NIMH has taken the position to only fund research that entails a &ldquoneuro-signature,&rdquo which conveniently supports a drugmaker model and ignores the context of conditions. We are investing billions into data, yet every day I walked to work in Cambridge, I walked past &ldquomethadone mile,&rdquo where there are plenty of homeless people suffering from panic and schizophrenia, without adequate resources&mdashsave for a half-million-dollar toilet. The promise that we can use gene modification, or even data, to eliminate psychiatric disorders is a fool&rsquos errand. Chronic stress matters. And genetic risk variants remain in the population because they’re advantageous to certain people, given the right genetic background or conditions. Those risk variants are speculating&mdashevolution, always and forever, takes chances.


Eukaryotic Cells

Like prokaryotic cells, all eukaryotic cells are surrounded by plasma membranes and contain ribosomes. However, eukaryotic cells are much more complex and contain a nucleus, a variety of cytoplasmic organelles, and a cytoskeleton (Figure 1.7). The largest and most prominent organelle of eukaryotic cells is the nucleus, with a diameter of approximately 5 μm. The nucleus contains the genetic information of the cell, which in eukaryotes is organized as linear rather than circular DNA molecules. The nucleus is the site of DNA replication and of RNA synthesis the translation of RNA into proteins takes place on ribosomes in the cytoplasm.

Figure 1.7

Structures of animal and plant cells. Both animal and plant cells are surrounded by a plasma membrane and contain a nucleus, a cytoskeleton, and many cytoplasmic organelles in common. Plant cells are also surrounded by a cell wall and contain chloroplasts (more. )

In addition to a nucleus, eukaryotic cells contain a variety of membrane-enclosed organelles within their cytoplasm. These organelles provide compartments in which different metabolic activities are localized. Eukaryotic cells are generally much larger than prokaryotic cells, frequently having a cell volume at least a thousandfold greater. The compartmentalization provided by cytoplasmic organelles is what allows eukaryotic cells to function efficiently. Two of these organelles, mitochondria and chloroplasts, play critical roles in energy metabolism. Mitochondria, which are found in almost all eukaryotic cells, are the sites of oxidative metabolism and are thus responsible for generating most of the ATP derived from the breakdown of organic molecules. Chloroplasts are the sites of photosynthesis and are found only in the cells of plants and green algae. Lysosomes and peroxisomes also provide specialized metabolic compartments for the digestion of macromolecules and for various oxidative reactions, respectively. In addition, most plant cells contain large vacuoles that perform a variety of functions, including the digestion of macromolecules and the storage of both waste products and nutrients.

Because of the size and complexity of eukaryotic cells, the transport of proteins to their correct destinations within the cell is a formidable task. Two cytoplasmic organelles, the endoplasmic reticulum and the Golgi apparatus, are specifically devoted to the sorting and transport of proteins destined for secretion, incorporation into the plasma membrane, and incorporation into lysosomes. The endoplasmic reticulum is an extensive network of intracellular membranes, extending from the nuclear membrane throughout the cytoplasm. It functions not only in the processing and transport of proteins, but also in the synthesis of lipids. From the endoplasmic reticulum, proteins are transported within small membrane vesicles to the Golgi apparatus, where they are further processed and sorted for transport to their final destinations. In addition to this role in protein transport, the Golgi apparatus serves as a site of lipid synthesis and (in plant cells) as the site of synthesis of some of the polysaccharides that compose the cell wall.

Eukaryotic cells have another level of internal organization: the cytoskeleton, a network of protein filaments extending throughout the cytoplasm. The cytoskeleton provides the structural framework of the cell, determining cell shape and the general organization of the cytoplasm. In addition, the cytoskeleton is responsible for the movements of entire cells (e.g., the contraction of muscle cells) and for the intracellular transport and positioning of organelles and other structures, including the movements of chromosomes during cell division.

The eukaryotes developed at least 2.7 billion years ago, following some 1 to 1.5 billion years of prokaryotic evolution. Studies of their DNA sequences indicate that the archaebacteria and eubacteria are as different from each other as either is from present-day eukaryotes. Therefore, a very early event in evolution appears to have been the divergence of three lines of descent from a common ancestor, giving rise to present-day archaebacteria, eubacteria, and eukaryotes. Interestingly, many archaebacterial genes are more similar to those of eukaryotes than to those of eubacteria, indicating that the archaebacteria and eukaryotes share a common line of evolutionary descent and are more closely related to each other than either is to the eubacteria (Figure 1.8).

Figure 1.8

Evolution of cells. Present-day cells evolved from a common prokaryotic ancestor along three lines of descent, giving rise to archaebacteria, eubacteria, and eukaryotes. Mitochondria and chloroplasts originated from the endosymbiotic association of aerobic (more. )

A critical step in the evolution of eukaryotic cells was the acquisition of membrane-enclosed subcellular organelles, allowing the development of the complexity characteristic of these cells. The organelles are thought to have been acquired as a result of the association of prokaryotic cells with the ancestor of eukaryotes.

The hypothesis that eukaryotic cells evolved from a symbiotic association of prokaryotes𠅎ndosymbiosis—is particularly well supported by studies of mitochondria and chloroplasts, which are thought to have evolved from bacteria living in large cells. Both mitochondria and chloroplasts are similar to bacteria in size, and like bacteria, they reproduce by dividing in two. Most important, both mitochondria and chloroplasts contain their own DNA, which encodes some of their components. The mitochondrial and chloroplast DNAs are replicated each time the organelle divides, and the genes they encode are transcribed within the organelle and translated on organelle ribosomes. Mitochondria and chloroplasts thus contain their own genetic systems, which are distinct from the nuclear genome of the cell. Furthermore, the ribosomes and ribosomal RNAs of these organelles are more closely related to those of bacteria than to those encoded by the nuclear genomes of eukaryotes.

An endosymbiotic origin for these organelles is now generally accepted, with mitochondria thought to have evolved from aerobic bacteria and chloroplasts from photosynthetic bacteria, such as the cyanobacteria. The acquisition of aerobic bacteria would have provided an anaerobic cell with the ability to carry out oxidative metabolism. The acquisition of photosynthetic bacteria would have provided the nutritional independence afforded by the ability to perform photosynthesis. Thus, these endosymbiotic associations were highly advantageous to their partners and were selected for in the course of evolution. Through time, most of the genes originally present in these bacteria apparently became incorporated into the nuclear genome of the cell, so only a few components of mitochondria and chloroplasts are still encoded by the organelle genomes.


Transcription Start and Stop Signals Are Heterogeneous in Nucleotide Sequence

As we have just seen, the processes of transcription initiation and termination involve a complicated series of structural transitions in protein, DNA, and RNA molecules. It is perhaps not surprising that the signals encoded in DNA that specify these transitions are difficult for researchers to recognize. Indeed, a comparison of many different bacterial promoters reveals that they are heterogeneous in DNA sequence. Nevertheless, they all contain related sequences, reflecting in part aspects of the DNA that are recognized directly by the σ factor. These common features are often summarized in the form of a consensus sequence (Figure 6-12). In general, a consensus nucleotide sequence is derived by comparing many sequences with the same basic function and tallying up the most common nucleotide found at each position. It therefore serves as a summary or 𠇊verage” of a large number of individual nucleotide sequences.

Figure 6-12

Consensus sequence for the major class of E. coli promoters. (A) The promoters are characterized by two hexameric DNA sequences, the -35 sequence and the -10 sequence named for their approximate location relative to the start point of transcription (designated (more. )

One reason that individual bacterial promoters differ in DNA sequence is that the precise sequence determines the strength (or number of initiation events per unit time) of the promoter. Evolutionary processes have thus fine-tuned each promoter to initiate as often as necessary and have created a wide spectrum of promoters. Promoters for genes that code for abundant proteins are much stronger than those associated with genes that encode rare proteins, and their nucleotide sequences are responsible for these differences.

Like bacterial promoters, transcription terminators also include a wide range of sequences, with the potential to form a simple RNA structure being the most important common feature. Since an almost unlimited number of nucleotide sequences have this potential, terminator sequences are much more heterogeneous than those of promoters.

We have discussed bacterial promoters and terminators in some detail to illustrate an important point regarding the analysis of genome sequences. Although we know a great deal about bacterial promoters and terminators and can develop consensus sequences that summarize their most salient features, their variation in nucleotide sequence makes it difficult for researchers (even when aided by powerful computers) to definitively locate them simply by inspection of the nucleotide sequence of a genome. When we encounter analogous types of sequences in eucaryotes, the problem of locating them is even more difficult. Often, additional information, some of it from direct experimentation, is needed to accurately locate the short DNA signals contained in genomes.

Promoter sequences are asymmetric (see Figure 6-12), and this feature has important consequences for their arrangement in genomes. Since DNA is double-stranded, two different RNA molecules could in principle be transcribed from any gene, using each of the two DNA strands as a template. However a gene typically has only a single promoter, and because the nucleotide sequences of bacterial (as well as eucaryotic) promoters are asymmetric the polymerase can bind in only one orientation. The polymerase thus has no option but to transcribe the one DNA strand, since it can synthesize RNA only in the 5′ to 3′ direction (Figure 6-13). The choice of template strand for each gene is therefore determined by the location and orientation of the promoter. Genome sequences reveal that the DNA strand used as the template for RNA synthesis varies from gene to gene (Figure 6-14 see also Figure 1-31).

Figure 6-13

The importance of RNA polymerase orientation. The DNA strand serving as template must be traversed in a 3′ to 5′ direction, as illustrated in Figure 6-9. Thus, the direction of RNA polymerase movement determines which of the two DNA strands (more. )

Figure 6-14

Directions of transcription along a short portion of a bacterial chromosome. Some genes are transcribed using one DNA strand as a template, while others are transcribed using the other DNA strand. The direction of transcription is determined by the promoter (more. )

Having considered transcription in bacteria, we now turn to the situation in eucaryotes, where the synthesis of RNA molecules is a much more elaborate affair.


Does evolution of bigger, sexually reproducing organisms happen on time scales faster than geologic time?

Yes! There are lots of great examples of evolution, even in sexually reproducing species, that happen pretty quickly, on the order of years or decades. In fact, the relevant time unit is generations. Rock Pocket mice in the desert southwest are a long-studied example. These small tan mice are hunted by owls, visual predators who spot the mice by their contrasting color against the sand. Most mice are exactly the same color as the sand. This short video explains what happens to a pocket mice population that migrates onto black volcanic rock, with mutation rates and the number of generations until the population shifts from all tan to all black coat color.


4. Reciprocal Altruism

The theory of reciprocal altruism was originally developed by Trivers (1971), as an attempt to explain cases of (apparent) altruism among unrelated organisms, including members of different species. (Clearly, kin selection cannot help explain altruism among non-relatives.) Trivers' basic idea was straightforward: it may pay an organism to help another, if there is an expectation of the favour being returned in the future. (&lsquoIf you scratch my back, I'll scratch yours&rsquo.) The cost of helping is offset by the likelihood of the return benefit, permitting the behaviour to evolve by natural selection. Trivers termed with evolutionary mechanism &lsquoreciprocal altruism&rsquo.

For reciprocal altruism to work, there is no need for the two individuals to be relatives, nor even to be members of the same species. However, it is necessary that individuals should interact with each more than once, and have the ability to recognize other individuals with whom they have interacted in the past. [1] If individuals interact only once in their lifetimes and never meet again, there is obviously no possibility of return benefit, so there is nothing to be gained by helping another. However, if individuals encounter each other frequently, and are capable of identifying and punishing &lsquocheaters&rsquo who have refused to help in the past, then the helping behaviour can evolve. A &lsquocheat&rsquo who refuses to help will ultimately sabotage his own interests, for although he does not incur the cost of helping others, he forfeits the return benefits too&mdashothers will not help him in the future. This evolutionary mechanism is most likely to work where animals live in relatively small groups, increasing the likelihood of multiple encounters.

As West et al. (2007) and Bowles and Gintis (2011) note, if altruism is defined by reference to lifetime fitness, then Trivers' theory is not really about the evolution of altruism at all for behaviours that evolve via reciprocation of benefits, as described by Trivers, are ultimately of direct benefit to the individuals performing them, so do not reduce lifetime fitness. Despite this consideration, the label &lsquoreciprocal altruism&rsquo is well-entrenched in the literature, and the evolutionary mechanism that it describes is of some importance, whatever it is called. Where reciprocal altruism is referred to below, it should be remembered that the behaviours in question are only altruistic in the short-term.

The concept of reciprocal altruism is closely related to the Tit-for-Tat strategy in the iterated Prisoner's Dilemma (IPD) from game theory. In the IPD, players interact on multiple occasions, and are able to adjust their behaviour depending on what their opponent has done in previous rounds. There are two possible strategies, co-operate and defect the payoff matrix (per interaction) is as in section 2.1 above. The fact that the game is iterated rather than one-shot obviously changes the optimal course of action defecting is no longer necessarily the best option, so long as the probability of subsequent encounters is sufficiently high. In their famous computer tournament in which a large number of strategies were pitted against each other in the IPD, Axelrod and Hamilton (1981) found that the Tit-for-Tat strategy yielded the highest payoff. In Tit-For-Tat, a player follows two basic rules: (i) on the first encounter, cooperate (ii) on subsequent encounters, do what your opponent did on the previous encounter. The success of Tit-for-Tat was widely taken to confirm the idea that with multiple encounters, natural selection could favour social behaviours that entail a short-term fitness cost. Subsequent work in evolutionary game theory, much of it inspired by Axelrod and Hamilton's ideas, has confirmed that repeated games permit the evolution of social behaviours that cannot evolve in one-shot situations (cf. Nowak 2006) this is closely related to the so-called 'folk theorem' of repeated game theory in economics (cf. Bowles and Gintis 2011). For a useful discussion of social behaviour that evolves via reciprocation of benefits, see Sachs et al. 2004.

Despite the attention paid to reciprocal altruism by theoreticians, clear-cut empirical examples in non-human animals are relatively few (Hammerstein 2003, Sachs et al. 2004, Taborsky 2013). This is probably because the pre-conditions for reciprocal altruism to evolve- multiple encounters and individual recognition&mdashare not especially common. However, one possible example is provided by blood-sharing in vampire bats (Wilkinson 1984, 1990, Carter & Wilkinson 2013). It is quite common for a vampire bat to fail to feed on a given night. This is potentially fatal, for bats die if they go without food for more than a couple of days. On any given night, bats donate blood (by regurgitation) to other members of their group who have failed to feed, thus saving them from starvation. Since vampire bats live in small groups and associate with each other over long periods of time, the preconditions for reciprocal altruism are likely to be met. Wilkinson and his colleagues' studies showed that bats tended to share food with their close associates, and were more likely to share with others that had recently shared with them. These findings appear to accord with reciprocal altruism theory.

Trivers (1985) describes an apparent case of reciprocal altruism between non con-specifics. On tropical coral reefs, various species of small fish act as &lsquocleaners&rsquo for large fish, removing parasites from their mouths and gills. The interaction is mutually beneficial&mdashthe large fish gets cleaned and the cleaner gets fed. However, Trivers notes that the large fish sometimes appear to behave altruistically towards the cleaners. If a large fish is attacked by a predator while it has a cleaner in its mouth, then it waits for the cleaner to leave before fleeing the predator, rather than swallowing the cleaner and fleeing immediately. Trivers explains the larger fish's behaviour in terms of reciprocal altruism. Since the large fish often returns to the same cleaner many times over, it pays to look after the cleaner's welfare, i.e., not to swallow it, even if this increases the chance of being wounded by a predator. So the larger fish allows the cleaner to escape, because there is an expectation of return benefit&mdashgetting cleaned again in the future. As in the case of the vampire bats, it is because the large fish and the cleaner interact more than once that the behaviour can evolve.


Discussion

Inferred Order of Entry of Amino Acids into the Genetic Code

On the basis of the change in frequency of amino acids between the LUA and today, we may make inferences regarding the establishment of the genetic code ( Brooks and Fresco 2002 ). It is reasonable to assume that as the genetic code evolved, newly assigned amino acids adopted codons used infrequently in coding sequences to minimize the structural disruption of the encoded protein ( Osawa et al. 1992 ). Consequently, new amino acids would have been introduced gradually into existing primitive proteins. Thus, at the time the genetic code became fully established, those amino acids which had been added relatively early would have been overrepresented and those which had been added relatively late would have been underrepresented, relative to the composition of modern proteins. Starting from such early biased amino acid composition, primitive proteins would have proceeded to evolve toward their modern-day compositions. In such a scenario, the amino acids that were introduced into the genetic code relatively early should have decreased in frequency over the course of evolution, whereas those amino acids added relatively late should have increased in frequency (i.e., between the establishment of the genetic code, the LUA, and today).

The nine amino acids which have decreased in frequency between the LUA and today ( fig. 3A ) may thus be inferred to have been introduced into the code early. Most of these amino acids are among those presumed to have been most abundant in the prebiotic environment, as inferred through spark tube simulations ( Miller 1953 , 1987 ) and analysis of the Murchison meteorite ( Kvenvolden et al. 1970 ). In contrast, the eight amino acids which have increased in frequency between the LUA and today ( fig. 3B ), and which are thus inferred to have been late additions to the code, include several of the most biosynthetically complex amino acids (for example, all three aromatic amino acids, which share a common, complex metabolic intermediate, are inferred to have been late additions) most of these are presumed either to have been nonexistent or of very low abundance in the prebiotic environment. Two of these, cysteine and tryptophan, are both conservatively estimated to have been less than half as frequent within this protein set in the LUA than today.

We emphasize that the validity of the inferences drawn in this study depend upon the reliability of the Jones, Taylor, and Thornton (1992) substitution probabilities for modeling evolution over very long time periods and along all lineages. With the development of lineage-specific models of evolution, estimates of ancestral amino acid composition can be expected to improve. In the meantime, although there are undoubtedly limitations to using the matrices of Jones, Taylor, and Thornton (1992) to model evolution since the LUA, we feel they provide the best available estimates of these substitution probabilities. It is noteworthy that previously, on the basis of an independent approach, cysteine, tyrosine, and phenylalanine were inferred to have been used less frequently in proteins of the LUA than today ( Brooks and Fresco 2002 ).

The inferences drawn here regarding the relatively early or late introduction of amino acids into the genetic code are generally consistent with earlier proposals which were based on the presumed presence or absence of various amino acids in the primordial environment (see for example Wong 1975 ). However, for a few amino acids our assignment as early or late is not in keeping with earlier ideas. For example, histidine and asparagine, believed to have been absent in the prebiotic environment, are both inferred through the present work to have been added to the code relatively early, whereas glutamate, believed to have been present in the prebiotic environment, is inferred to have been added late ( figs. 3 and 4 ). Interestingly, each of these three amino acids share a block of four codons with a second amino acid: histidine with glutamine, asparagine with lysine, and glutamate with aspartate ( fig. 4 ). Codon capture, in which one amino acid loses some of its codons to another, is commonly proposed as a mechanism for introducing amino acids, especially later arriving ones, into the code ( Crick 1968 Wong 1975 ). Consistent with codon capture, it is plausible that one amino acid was added to the four-codon block first and that this amino acid later gave up two of its codons to the second amino acid which now shares the block.

Accordingly, those amino acids which were originally assigned to the codon block (i.e., aspartate, asparagine, and histidine) would have the appearance of being added to the code early, whereas those which were added to the block later through codon capture (i.e., glutamate, lysine, and glutamine) would have the appearance of being added late. Therefore, early and late amino acids do not correspond to a strict chronological order of introduction into the code. Instead, as defined here on the basis of the changing amino acid frequencies, early amino acids are probably those which at some point lost some of their codons through codon capture and consequently became less frequent over time within proteins, whereas late amino acids are those which entered the code through codon capture, did not subsequently lose any of their codons, and therefore became more frequent over time ( fig. 4 ). Finally, it is worth noting that the distinction between amino acids inferred here to have been added to the genetic code early or late does not at all correlate with the two main structural classes of the aminoacyl-tRNA synthetases. This is consistent with the earlier suggestion that these enzymes probably had no specific role in the evolution of the genetic code ( Woese 2000 ).

Existing ideas regarding the origin and evolution of the genetic code have been based largely on theoretical investigations and experiments involving oligonucleotide aptamer binding of amino acids (reviewed in Knight, Freeland, and Landweber 1999 ). The present findings suggest that notwithstanding the impact of mutation over the long course of molecular evolution, with the aid of the appropriate analytical tools and insights, the sequences of contemporary proteins also provide an important avenue for exploring these early evolutionary events.