Chapter 14 Evolution and human health

Chapter 14 Evolution and human health
The germ theory of disease was one of the most important breakthroughs in medicine. Louis Pasteur in 1858 proposed that diseases were caused by microorganisms. Within a few years the bacteria responsible for diseases such as anthrax, gonorrhea, typhoid fever and tuberculosis had been identified.

Anti-bacterial developments
Infection-fighting developments followed soon thereafter. Antiseptic surgery was developed by Joseph Lister, improved housing and sanitation reduced infection rates, and the discovery of antibiotics allowed infections to be treated.

Anti-bacterial developments
As a result of these developments, death rates from infection declined rapidly. By 1997 the TB death rate was < 0.4 per 100,000, less than 0.2% of the 1900 death rate. By the end of the 1960’s the medical community considered that infectious disease had been conquered.

Fig 13.2

Evolving pathogens: antibiotic resistance
Unfortunately, pathogens have evolved in response to the selection pressures imposed by medicine. Antibiotics are chemicals that kill bacteria and the first antibiotic was penicillin isolated from the mold Penicillum by Alexander Fleming.

Penicillin saved thousands of lives in World War II and subsequently. Today, however, penicillin is ineffective against bacteria that previously were highly vulnerable and many bacteria have evolved resistance to multiple antibiotics. As a result, infectious diseases have reemerged as a significant threat.

There is clear evidence that use of antibiotics selects for resistance in bacteria. Studies have documented the evolution of antibiotic resistance in bacterial populations within individual patients and also in larger-scale studies of human and bacterial populations.

For example, researchers have found that the incidence of antibiotic-resistant bacteria is higher among patients who have been previously treated with that antibiotic. For example the incidence of isoniazid-resistant bacteria is 21% in relapsed cases of TB, but only 8% in new cases.

On a larger scale, antibiotic resistance has been shown to track society-wide antibiotic use. In the late 1980’s and early 1990’s penicillin resistance levels among Pneumococus bacteria in Iceland rose sharply.

When health authorities campaigned to reduce use of the antibiotic, rates of use of penicillin fell 13% and rates of bacterial resistance declined.

Fig 13.7

Costs of resistance to bacteria
The fact that resistance rates fell in Iceland when Penicillin use dropped suggests resistance imposes a cost on bacteria. If the cost is high, non-resistant bacteria should have an advantage in a penicillin-free environment.

Costs of resistance suggest that suspending use of an antibiotic might allow populations to evolve to a non-resistant state again.

Unfortunately, resistant bacteria may also evolve ways to reduce or eliminate the costs of resistance and so not be outcompeted by non-resistant strains. A study by Schrag et al. (1997) documented this.

Schrag et al. (1997) They studied streptomycin-sensitive E. coli and screened for resistant mutants. Streptomycin interferes with protein synthesis by binding to a ribosomal protein. Point mutations in gene rpsL coding for that ribosomal protein can confer resistance.

Schrag et al. (1997) Researchers competed resistant and sensitive strains against each other. Found that, initially, resistant strains were at a disadvantage and sensitive strains grew better.

Schrag et al. (1997) Next, resistant strains that were allowed to evolve for many generations in the lab and then competed against sensitive strains. Resistant strains had evolved and mutations that compensated for costs of streptomycin resistance had been selected for. As a result, resistant strains outcompeted sensitive strains.

Schrag et al. (1997) Schrag et al.’s results suggest that there is no guarantee that bacterial populations can be restored to vulnerability by withdrawing an antibiotic from use. Thus, steps to avoid bacteria developing resistance need to be taken.

Steps to avoid evolution of resistance
Reduce infection rate (avoid e.g. undercooked eggs and meat; wash hands to slow disease spread). Limit use of antibacterial soaps and cleaners. Doctors should avoid prescribing antibiotics for viral infections. Drugs that target as few bacteria as possible should be used. Antibiotic use in animal feed should be eliminated.

Evolving pathogens: Evading host immune response
The human immune system mounts a formidable defense against microbes. Pathogens naturally evolve responses. Large population sizes, short generation times and high rates of mutation make pathogens a formidable opponent.

Evolution of influenza virus
Influenza A causes annual flu epidemics and occasional global pandemics including the infamous 1918 Spanish flu. In a normal flu season flu kills about 20,000 Americans. The 1918 flu infected about 20% of the world’s population and killed million people.

Influenza A has a genome of 8 RNA strands that code for a total of 10 proteins. These include polymerases, structural proteins and coat proteins.

Main viral coat protein is hemagglutinin. This binds to sialic acid on host cell’s surface to gain entry. Hemagglutinin is also the primary protein recognized and attacked by the immune system.

Survival for a viral strain means it must constantly find new hosts that do not recognize its hemagglutinin protein. The immune system recognizes certain stretches of the hemagglutinin protein, which are called antigenic sites. Viruses that have novel antigenic sites should have a selective advantage.

Fitch et al. (1991) tested this hypothesis by examining frozen flu virus strains dating from 1968 to 1987. Flu virus evolves about 1 million time faster than humans so 20 years is equivalent to 20 million years of human evolution.

Flu strains evolved at a steady rate (about 6.7 X 10–3 mutations per nucleotide per year.

Most flu samples examined represented side branches of one main evolutionary tree of multiple closely related strains. Instead of a wide variety of lineages derived from different 1968 era flu viruses there was one main lineage, the other viruses from 1968 having gone extinct.

Fitch et al. suspected that the successful strain would have had more mutations in its antigenic sites than the extinct strains. In surviving lineage they identified 33 amino acid replacements in antigenic sites and 10 in non antigenic sites. In extinct lineages found 31 replacements in antigenic sites and 35 in non antigenic sites.

Surviving strain had more than 75% of replacements in antigenic sites versus less than 50% for extinct strains. Statistically significant difference, which suggests increased variability in antigenic sites gave the surviving strain an advantage.

Further evidence that flu is under strong selection from human immune systems comes from examining the rate of silent versus replacement nucleotide substitutions in strains of flu virus. Silent mutations don’t result in a change in the amino acid coded for.

Rates of replacement substitutions are statistically much higher than rates of silent mutations, which infers selection is strongly favoring replacements.

Origins of pandemic flu strains
Flu strains with novel hemagglutinin genes have a selective advantage. Hence, any strain with a hemagglutinin sufficiently different from any that human immune systems had previously been exposed to, could spread uncontrollably.

The influenza virus contains 8 different RNA strands and different strains of flu can infect a host. If a host becomes infected with two different flu strains these strains could swap RNA strands As a result of this gene exchange a novel, very different, flu strain might result.

There is strong evidence that flu strains do swap genes. Phylogenetic analysis of flu strains by Gorman et al. (1991) shows this.

Gorman et al. (1991) determined the nucleotide sequences of influenza nucleoprotein genes. Nucleoprotein gene apparently most important gene for determining host specificity (enables strain to infect a certain host) and tends to limit it to that species. Thus, phylogeny of this gene should give good strain history.

There are distinct clades of strains that infect mainly humans, mainly pigs, mainly birds, etc.

Branch tips give date of strain and a viral subtype (e.g. H3N2). Viral subtype specifies hemagglutinin-3 and neuraminidase-2). Neuraminidase, like hemagglutinin, is a coat protein. The number specifies a group of proteins that provoke the same antibody response.

Each hemagglutinin group constitutes a clade. H1s are more closely related to each other than H2s, etc. Same logic applies to the neuraminidases.

Examining two strains of flu from Australia in 1968 Victoria (H2N2) and Northern Territory (H3N2) shown in bold on next slide we see that they have nucleoproteins and neuraminidases that are closely related, but hemagglutinins that are distantly related.

How is this possible? Simplest explanation is that flu strains swap genes. Before 1968 pandemic human flu strains had never carried H3. Where did H3 come from?

A phylogeny of H3 strains by Bean et al. (1992) shows that human H3 branches from within the bird H3 tree.

Apparently, 1968 pandemic flu strain acquired its genes from birds. How did it get into humans?

Bird flu strains are known to infect pigs and pig strains can infect humans, especially, where humans and pigs live in close contact. Human 1968 pandemic H3N2 flu is most similar to a 1976 strain isolated from pigs in Hong Kong.

Popular hypothesis among flu researchers is that human pandemics begin when bird and human flu strains simultaneously infect a pig, swap genes and move from pigs to people.

It is feared that the current avian flu (H5N1) that has killed millions of birds worldwide and several hundred people will mutate into a form readily transmissible from person to person, thus triggering a new pandemic.

Evolution of virulence in pathogens
Virulence is the harm done to a host by a pathogen. Virulence differs widely from pathogen to pathogen. The cold virus is not harmful, but smallpox, cholera, and Ebola virus are often or usually lethal.

There are three general models to explain the evolution of virulence. (1) The coincidental evolution hypothesis. Virulence of some pathogens in humans may not be a target of selection at all. Instead, it may be an accidental result of selection on other traits.

Tetanus is caused by a soil bacterium Claustridium tetanae that can live in human wounds, but usually lives in soil. It secretes a potent neurotoxin, but that toxin is probably a result of selection for living in soil.

(2) The short-sighted evolution hypothesis. Many generations of a pathogen may live in a host before finding a new host becomes necessary. Under these conditions traits that favor within-host fitness may spread even if they hinder transmission to new hosts.

Polio virus normally lives in cells that line the gut, causes no symptoms, and is transmitted via feces. Occasionally, the virus invades nerve cells, which may be selected for because lack of competition enhances within-host fitness. However, virus cannot be transmitted to a new host from nervous tissue.

In patients with HIV infections short-sighted evolution also occurs. Patients whose immune systems are destroyed eventually die, but selection favors those virus particles that reproduce most quickly even though doing so ultimately dooms the virus particles along with the host.

Late in an HIV infection HIV particles that use a cell receptor called CXCR4 as a coreceptor to enter T-cells become more common. The use of the type of T-cell with this coreceptor is short-sighted because (i) viruses that use them do not get transmitted to other hosts and (ii) attacking these cells is the final blow to the host’s immune system and death soon follows.

(3) The trade-off hypothesis. Traditionally, biologists believed that evolution would favor lower virulence because killing the host would kill pathogens too. However, if increasing the risk to the host enhances the pathogens’ prospects of transmission to other hosts increased virulence could be favored by selection.

Hence, selection should favor strains that strike a balance between costs and benefits of harming their host. HIV-1 is more damaging to its hosts than HIV-2, but it is more likely to be transmitted because of its higher viral load. Thus, HIV-1 has become much more common than HIV-2.

A key assumption of the trade-off hypothesis is that the pathogen cannot reproduce inside the host without doing it some harm (because energy and nutrients are taken from the host and pathogen waste products must be removed by the host).

Host mounts an immune response to eliminate the costs of hosting the pathogen. All else being equal, pathogens with higher within-host reproductive rates should be transmitted to hosts more effectively. However, too rapid a reproduction rate may debilitate the host so much that transmission is reduced.

Messenger et al. (1999) examined this trade-off in E. coli and a virus (a bacteriophage called f1.) Phage invades bacterium and lives inside as a plasmid. Induces cell to make copies of it, which are released from the cell.

Production of new phage copies slows cells growth rate by about a third, but when cell does divide both daughter cells carry the phage. Thus, phage has two modes of transmission: vertically from one generation of host cells to the next and horizontally from one host to another.

Messenger et al. (1999) maintained two sets of cultures for 24 days and controlled the virions ability to transmit itself vertically and horizontally In first culture they alternated 1 day-long vertical transmission phases with brief horizontal transmission phases.

In second culture alternated 8-day long vertical transmission with brief horizontal transmission phases. After 24 days compared virulence as growth rate of infected hosts in the two populations.

Messenger et al. predicted that phages that induced more phage reproduction would slow host growth more. Also predicted that in 8-day cultures phages would evolve lower reproductive rates and be less virulent. Longer vertical transmission phase should select for less virulence.

Results were consistent with predictions. Host growth was slowed most by phage strains that reproduced faster. Eight-day cultures had lower reproductive rates and lower virulence than one-day cultures.

Virulence in human pathogens
Paul Ewald has applied trade-off hypothesis to show that the mechanism by which a pathogen is transmitted is likely to have a strong influence on the pathogens virulence.

Cold and flu viruses depend for transmission on direct contact between infected and uninfected people. In contrast, diseases such as malaria are transmitted by insect vectors

These differences in mode of transmission should select for different levels of virulence. A cold cannot afford to incapacitate its host because it needs the host to encounter new potential hosts. Malaria, however, can be transmitted from a debilitated host.

Ewald predicted that vector-borne diseases should have higher mortality rates than those that depend on direct transmission. Surveyed wide variety of diseases. Data fit prediction.

Vast majority of directly transmitted diseases have mortality rates below 0.1%. More than 60% of vector-borne diseases have mortality rates >0.1%.

Ewald also applied same logic to bacteria that infect the digestive tract. These can be transmitted directly from person to person or via contaminated water. Water plays same role as a vector because sewage from severely infected people enters the water supply as easily as sewage from uninfected people.

Ewald studied data on about 1,000 outbreaks of disease caused by 9 types of bacteria and contrasted mortality rates with the fraction of outbreaks that were waterborne. He predicted that waterborne bacteria would be more virulent. Data are consistent with Ewald’s hypothesis, with cholera the most virulent.

13.11

Ewald’s work suggests that human behavior can affect disease severity. Dumping untreated sewage in rivers and poor hygiene facilitate the transmission of pathogens. This, in turn, favors increased virulence.

Applying adaptationist thinking to humans.
To what environment are humans adapted? Before the development of agriculture approximately 10,000 years ago humans lived as hunter-gatherers in an environment very different from a modern urban environment.

Modern humans live in an environment radically different from the stone-age one their bodies and brains evolved in. Comparisons of modern hunter-gatherer societies and modern urban societies show major differences in diet and activity level.

13.16

More subtle environmental differences, however, also exist and may increase the rates of a variety of medical conditions.

Applying adaptationist thinking to humans: Myopia
The frequency of myopia (or nearsightedness) in many populations is 25% or more. Studies of twins show that myopia is partially heritable. Given the disadvantage of poor vision to hunter-gatherers why haven’t alleles for myopia been eliminated by selection?

Solution is likely that alleles that predispose us to myopia do so only under modern conditions (e.g. reading under artificial light).

Evidence consistent with this explanation is provided by populations that have only recently adopted a modern lifestyle. A study of an Inuit group in Barrow, Alaska showed that there was a clear age difference between people with and without myopia.

People aged 6-35 who had attended modern schools and read a lot had a 42% rate of myopia. Among older individuals (36-88) who had less schooling and read little, only 5% were myopic.

Thus, it appears that alleles that predispose humans to myopia do not do so in a hunter gatherer environment.

Applying adaptationist thinking to humans: Breast Cancer
About 1 in 8 North American women develops breast cancer, some of whom die while in their child-bearing years. Like myopia, breast cancer has both genetic and environmental components. Given the high rate of breast cancer why hasn’t selection eliminated genes that cause the disease or selected for individuals resistant to environmental effects that induce cancer?

Possible solutions include: (i) Breast cancer may be caused by a pathogen (e.g. a virus or bacterium that has evolved with us). (ii) Breast cancer, like myopia, may be a disease of civilization

Mice carry a virus called Mouse Mammary Tumor Virus (MMTV) that causes the mouse equivalent of breast cancer. There are suspicions that MMTV or a similar virus may cause breast cancer in humans.

Two suggestive pieces of evidence support the hypothesis. (i) In an analysis of 314 breast tissue samples (Wang et al. 1995) 38.5% of cancer tissue samples contained DNA sequences similar to MMTV, but only 1.9% of normal breast tissue samples did.

Second piece of evidence comes from geographical level analysis looking at incidence of breast cancer in relation to distribution of the mouse species Mus domesticus and Mus musculus (Stewart et al. (2000)).

Mus domesticus occurs in Western Europe Mus musculus occurs in Eastern Europe. Mus domesticus also tends to be more heavily infected with MMTV.

Mus musculus (dark green)
Fig 13.17a Mus domesticus (light blue)

If MMTV causes breast cancer, then rates of breast cancer should be higher in Western Europe than in Eastern Europe. Data fit that pattern.

13.17b

Data on MMTV are suggestive, but not definitive. MMTV has not been isolated from breast tumors and possible route of infection is unknown. Also, MMTV cannot account for more than 40% of breast cancer cases. Thus, need to consider the alternative hypothesis.

Breast cancer as a disease of civilization. Monthly menstrual cycle of most modern/western women is considered normal. However, epidemiological evidence suggests monthly menstrual cycles may increase the risk of breast cancer.

Breast cancer risk increases with: Early onset of menstruation. Later in life woman first gives birth. Less time woman spends nursing. Menstrual cycling appears to elevate risk of cancer because the combination of estrogen and progesterone stimulates cell division in cells lining milk ducts. More cell division increases chance of mutations.

Is monthly menstrual cycling normal? Strassmann (1999) studied menstruation among the Dogon of Mali. Dogon use no contraception. Women spend little time menstruating. They are either pregnant or experiencing lactational amenorrhea (no cycling because of breastfeeding).

At any given time less than 30% of Dogon women are undergoing menstrual cycling. Over the course of her reproductive lifetime a Dogon woman experiences about 100 cycles as compared to the approximately 300 cycles of a North American woman.

Number of menstrual cycles in two years among Dogon
women of reproductive age . Fig 13.18a

(of Dogon women). 13.18b

There are no data on breast cancer rates in Dogon women, but rates in comparable populations are only about 1/12th the North American rate. Alternative contraceptive regimens that more closely mimic the ancestral menstrual cycling pattern might reduce the risk of breast cancer among North American women.

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