This question is related to the recent Wuhan outbreak. Currently, mortality rate for the virus is somewhere around 2-3%. This is much lower than say, SARS (9.5%) and MERS (34.5%).
As a thought experiment, the main cause of death of the above viruses tends to be pneumonia (direct and secondary). If pneumonia leads to a higher rate of infection, then (indirectly) would such a virus would become much more deadly over time?
Do viruses such as the coronavirus self-select for higher rates of mortality, e.g. by leading to pneumonia, thus causing more phlegm and coughing?
Related to the above, how quickly can such mutations arise?
Sources would be really appreciated!
Virus Mortality Selection - Biology
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The name "coronavirus" is derived from Latin corona, meaning "crown" or "wreath", itself a borrowing from Greek κορώνη korṓnē, "garland, wreath".   The name was coined by June Almeida and David Tyrrell who first observed and studied human coronaviruses.  The word was first used in print in 1968 by an informal group of virologists in the journal Nature to designate the new family of viruses.  The name refers to the characteristic appearance of virions (the infective form of the virus) by electron microscopy, which have a fringe of large, bulbous surface projections creating an image reminiscent of the solar corona or halo.   This morphology is created by the viral spike peplomers, which are proteins on the surface of the virus. 
The scientific name Coronavirus was accepted as a genus name by the International Committee for the Nomenclature of Viruses (later renamed International Committee on Taxonomy of Viruses) in 1971.  As the number of new species increased, the genus was split into four genera, namely Alphacoronavirus, Betacoronavirus, Deltacoronavirus, and Gammacoronavirus in 2009.  The common name coronavirus is used to refer to any member of the subfamily Orthocoronavirinae.  As of 2020, 45 species are officially recognised. 
The earliest reports of a coronavirus infection in animals occurred in the late 1920s, when an acute respiratory infection of domesticated chickens emerged in North America.  Arthur Schalk and M.C. Hawn in 1931 made the first detailed report which described a new respiratory infection of chickens in North Dakota. The infection of new-born chicks was characterized by gasping and listlessness with high mortality rates of 40–90%.  Leland David Bushnell and Carl Alfred Brandly isolated the virus that caused the infection in 1933.  The virus was then known as infectious bronchitis virus (IBV). Charles D. Hudson and Fred Robert Beaudette cultivated the virus for the first time in 1937.  The specimen came to be known as the Beaudette strain. In the late 1940s, two more animal coronaviruses, JHM that causes brain disease (murine encephalitis) and mouse hepatitis virus (MHV) that causes hepatitis in mice were discovered.  It was not realized at the time that these three different viruses were related.  
Human coronaviruses were discovered in the 1960s   using two different methods in the United Kingdom and the United States.  E.C. Kendall, Malcolm Bynoe, and David Tyrrell working at the Common Cold Unit of the British Medical Research Council collected a unique common cold virus designated B814 in 1961.    The virus could not be cultivated using standard techniques which had successfully cultivated rhinoviruses, adenoviruses and other known common cold viruses. In 1965, Tyrrell and Bynoe successfully cultivated the novel virus by serially passing it through organ culture of human embryonic trachea.  The new cultivating method was introduced to the lab by Bertil Hoorn.  The isolated virus when intranasally inoculated into volunteers caused a cold and was inactivated by ether which indicated it had a lipid envelope.   Dorothy Hamre  and John Procknow at the University of Chicago isolated a novel cold from medical students in 1962. They isolated and grew the virus in kidney tissue culture, designating it 229E. The novel virus caused a cold in volunteers and, like B814, was inactivated by ether. 
Scottish virologist June Almeida at St. Thomas Hospital in London, collaborating with Tyrrell, compared the structures of IBV, B814 and 229E in 1967.   Using electron microscopy the three viruses were shown to be morphologically related by their general shape and distinctive club-like spikes.  A research group at the National Institute of Health the same year was able to isolate another member of this new group of viruses using organ culture and named one of the samples OC43 (OC for organ culture).  Like B814, 229E, and IBV, the novel cold virus OC43 had distinctive club-like spikes when observed with the electron microscope.  
The IBV-like novel cold viruses were soon shown to be also morphologically related to the mouse hepatitis virus.  This new group of viruses were named coronaviruses after their distinctive morphological appearance.  Human coronavirus 229E and human coronavirus OC43 continued to be studied in subsequent decades.   The coronavirus strain B814 was lost. It is not known which present human coronavirus it was.  Other human coronaviruses have since been identified, including SARS-CoV in 2003, HCoV NL63 in 2003, HCoV HKU1 in 2004, MERS-CoV in 2013, and SARS-CoV-2 in 2019.  There have also been a large number of animal coronaviruses identified since the 1960s. 
Coronaviruses are large, roughly spherical particles with unique surface projections.  Their size is highly variable with average diameters of 80 to 120 nm. Extreme sizes are known from 50 to 200 nm in diameter.  The total molecular mass is on average 40,000 kDa. They are enclosed in an envelope embedded with a number of protein molecules.  The lipid bilayer envelope, membrane proteins, and nucleocapsid protect the virus when it is outside the host cell. 
The viral envelope is made up of a lipid bilayer in which the membrane (M), envelope (E) and spike (S) structural proteins are anchored.  The molar ratio of E:S:M in the lipid bilayer is approximately 1:20:300.  The E and M protein are the structural proteins that combined with the lipid bilayer to shape the viral envelope and maintain its size.  S proteins are needed for interaction with the host cells. But human coronavirus NL63 is peculiar in that its M protein has the binding site for the host cell, and not its S protein.  The diameter of the envelope is 85 nm. The envelope of the virus in electron micrographs appears as a distinct pair of electron-dense shells (shells that are relatively opaque to the electron beam used to scan the virus particle).  
The M protein is the main structural protein of the envelope that provides the overall shape and is a type III membrane protein. It consists of 218 to 263 amino acid residues and forms a layer 7.8 nm thick.  It has three domains, a short N-terminal ectodomain, a triple-spanning transmembrane domain, and a C-terminal endodomain. The C-terminal domain forms a matrix-like lattice that adds to the extra-thickness of the envelope. Different species can have either N- or O-linked glycans in their protein amino-terminal domain. The M protein is crucial during the assembly, budding, envelope formation, and pathogenesis stages of the virus lifecycle. 
The E proteins are minor structural proteins and highly variable in different species.  There are only about 20 copies of the E protein molecule in a coronavirus particle.  They are 8.4 to 12 kDa in size and are composed of 76 to 109 amino acids.  They are integral proteins (i.e. embedded in the lipid layer) and have two domains namely a transmembrane domain and an extramembrane C-terminal domain. They are almost fully α-helical, with a single α-helical transmembrane domain, and form pentameric (five-molecular) ion channels in the lipid bilayer. They are responsible for virion assembly, intracellular trafficking and morphogenesis (budding). 
The spikes are the most distinguishing feature of coronaviruses and are responsible for the corona- or halo-like surface. On average a coronavirus particle has 74 surface spikes.  Each spike is about 20 nm long and is composed of a trimer of the S protein. The S protein is in turn composed of an S1 and S2 subunit. The homotrimeric S protein is a class I fusion protein which mediates the receptor binding and membrane fusion between the virus and host cell. The S1 subunit forms the head of the spike and has the receptor-binding domain (RBD). The S2 subunit forms the stem which anchors the spike in the viral envelope and on protease activation enables fusion. The two subunits remain noncovalently linked as they are exposed on the viral surface until they attach to the host cell membrane.  In a functionally active state, three S1 are attached to two S2 subunits. The subunit complex is split into individual subunits when the virus binds and fuses with the host cell under the action of proteases such as cathepsin family and transmembrane protease serine 2 (TMPRSS2) of the host cell. 
S1 proteins are the most critical components in terms of infection. They are also the most variable components as they are responsible for host cell specificity. They possess two major domains named N-terminal domain (S1-NTD) and C-terminal domain (S1-CTD), both of which serve as the receptor-binding domains. The NTDs recognize and bind sugars on the surface of the host cell. An exception is the MHV NTD that binds to a protein receptor carcinoembryonic antigen-related cell adhesion molecule 1 (CEACAM1). S1-CTDs are responsible for recognizing different protein receptors such as angiotensin-converting enzyme 2 (ACE2), aminopeptidase N (APN), and dipeptidyl peptidase 4 (DPP4). 
A subset of coronaviruses (specifically the members of betacoronavirus subgroup A) also has a shorter spike-like surface protein called hemagglutinin esterase (HE).  The HE proteins occur as homodimers composed of about 400 amino acid residues and are 40 to 50 kDa in size. They appear as tiny surface projections of 5 to 7 nm long embedded in between the spikes. They help in the attachment to and detachment from the host cell. 
Inside the envelope, there is the nucleocapsid, which is formed from multiple copies of the nucleocapsid (N) protein, which are bound to the positive-sense single-stranded RNA genome in a continuous beads-on-a-string type conformation.   N protein is a phosphoprotein of 43 to 50 kDa in size, and is divided into three conserved domains. The majority of the protein is made up of domains 1 and 2, which are typically rich in arginines and lysines. Domain 3 has a short carboxy terminal end and has a net negative charge due to excess of acidic over basic amino acid residues. 
Coronaviruses contain a positive-sense, single-stranded RNA genome. The genome size for coronaviruses ranges from 26.4 to 31.7 kilobases.  The genome size is one of the largest among RNA viruses. The genome has a 5′ methylated cap and a 3′ polyadenylated tail. 
The genome organization for a coronavirus is 5′-leader-UTR-replicase (ORF1ab)-spike (S)-envelope (E)-membrane (M)-nucleocapsid (N)-3′UTR-poly (A) tail. The open reading frames 1a and 1b, which occupy the first two-thirds of the genome, encode the replicase polyprotein (pp1ab). The replicase polyprotein self cleaves to form 16 nonstructural proteins (nsp1–nsp16). 
The later reading frames encode the four major structural proteins: spike, envelope, membrane, and nucleocapsid.  Interspersed between these reading frames are the reading frames for the accessory proteins. The number of accessory proteins and their function is unique depending on the specific coronavirus. 
Infection begins when the viral spike protein attaches to its complementary host cell receptor. After attachment, a protease of the host cell cleaves and activates the receptor-attached spike protein. Depending on the host cell protease available, cleavage and activation allows the virus to enter the host cell by endocytosis or direct fusion of the viral envelope with the host membrane. 
On entry into the host cell, the virus particle is uncoated, and its genome enters the cell cytoplasm. The coronavirus RNA genome has a 5′ methylated cap and a 3′ polyadenylated tail, which allows it to act like a messenger RNA and be directly translated by the host cell's ribosomes. The host ribosomes translate the initial overlapping open reading frames ORF1a and ORF1b of the virus genome into two large overlapping polyproteins, pp1a and pp1ab. 
The larger polyprotein pp1ab is a result of a -1 ribosomal frameshift caused by a slippery sequence (UUUAAAC) and a downstream RNA pseudoknot at the end of open reading frame ORF1a.  The ribosomal frameshift allows for the continuous translation of ORF1a followed by ORF1b. 
The polyproteins have their own proteases, PLpro (nsp3) and 3CLpro (nsp5), which cleave the polyproteins at different specific sites. The cleavage of polyprotein pp1ab yields 16 nonstructural proteins (nsp1 to nsp16). Product proteins include various replication proteins such as RNA-dependent RNA polymerase (nsp12), RNA helicase (nsp13), and exoribonuclease (nsp14). 
A number of the nonstructural proteins coalesce to form a multi-protein replicase-transcriptase complex. The main replicase-transcriptase protein is the RNA-dependent RNA polymerase (RdRp). It is directly involved in the replication and transcription of RNA from an RNA strand. The other nonstructural proteins in the complex assist in the replication and transcription process. The exoribonuclease nonstructural protein, for instance, provides extra fidelity to replication by providing a proofreading function which the RNA-dependent RNA polymerase lacks. 
Replication – One of the main functions of the complex is to replicate the viral genome. RdRp directly mediates the synthesis of negative-sense genomic RNA from the positive-sense genomic RNA. This is followed by the replication of positive-sense genomic RNA from the negative-sense genomic RNA. 
Transcription – The other important function of the complex is to transcribe the viral genome. RdRp directly mediates the synthesis of negative-sense subgenomic RNA molecules from the positive-sense genomic RNA. This process is followed by the transcription of these negative-sense subgenomic RNA molecules to their corresponding positive-sense mRNAs.  The subgenomic mRNAs form a "nested set" which have a common 5'-head and partially duplicate 3'-end. 
Recombination – The replicase-transcriptase complex is also capable of genetic recombination when at least two viral genomes are present in the same infected cell.  RNA recombination appears to be a major driving force in determining genetic variability within a coronavirus species, the capability of a coronavirus species to jump from one host to another and, infrequently, in determining the emergence of novel coronaviruses.  The exact mechanism of recombination in coronaviruses is unclear, but likely involves template switching during genome replication. 
Assembly and release
The replicated positive-sense genomic RNA becomes the genome of the progeny viruses. The mRNAs are gene transcripts of the last third of the virus genome after the initial overlapping reading frame. These mRNAs are translated by the host's ribosomes into the structural proteins and many accessory proteins.  RNA translation occurs inside the endoplasmic reticulum. The viral structural proteins S, E, and M move along the secretory pathway into the Golgi intermediate compartment. There, the M proteins direct most protein-protein interactions required for the assembly of viruses following its binding to the nucleocapsid. Progeny viruses are then released from the host cell by exocytosis through secretory vesicles. Once released the viruses can infect other host cells. 
Infected carriers are able to shed viruses into the environment. The interaction of the coronavirus spike protein with its complementary cell receptor is central in determining the tissue tropism, infectivity, and species range of the released virus.   Coronaviruses mainly target epithelial cells.  They are transmitted from one host to another host, depending on the coronavirus species, by either an aerosol, fomite, or fecal-oral route. 
Human coronaviruses infect the epithelial cells of the respiratory tract, while animal coronaviruses generally infect the epithelial cells of the digestive tract.  SARS coronavirus, for example, infects the human epithelial cells of the lungs via an aerosol route  by binding to the angiotensin-converting enzyme 2 (ACE2) receptor.  Transmissible gastroenteritis coronavirus (TGEV) infects the pig epithelial cells of the digestive tract via a fecal-oral route  by binding to the alanine aminopeptidase (APN) receptor. 
Coronaviruses form the subfamily Orthocoronavirinae,    which is one of two sub-families in the family Coronaviridae, order Nidovirales, and realm Riboviria.   They are divided into the four genera: Alphacoronavirus, Betacoronavirus, Gammacoronavirus and Deltacoronavirus. Alphacoronaviruses and betacoronaviruses infect mammals, while gammacoronaviruses and deltacoronaviruses primarily infect birds.  
- Genus: Alphacoronavirus 
- Species: Alphacoronavirus 1 (TGEV, Feline coronavirus, Canine coronavirus), Human coronavirus 229E, Human coronavirus NL63, Miniopterus bat coronavirus 1, Miniopterus bat coronavirus HKU8, Porcine epidemic diarrhea virus, Rhinolophus bat coronavirus HKU2, Scotophilus bat coronavirus 512
- Species: Betacoronavirus 1 (Bovine Coronavirus, Human coronavirus OC43), Hedgehog coronavirus 1,Human coronavirus HKU1, Middle East respiratory syndrome-related coronavirus,Murine coronavirus, Pipistrellus bat coronavirus HKU5, Rousettus bat coronavirus HKU9, Severe acute respiratory syndrome–related coronavirus (SARS-CoV, SARS-CoV-2), Tylonycteris bat coronavirus HKU4
- Species: Avian coronavirus,Beluga whale coronavirus SW1
- Species: Bulbul coronavirus HKU11, Porcinecoronavirus HKU15
The most recent common ancestor (MRCA) of all coronaviruses is estimated to have existed as recently as 8000 BCE, although some models place the common ancestor as far back as 55 million years or more, implying long term coevolution with bat and avian species.  The most recent common ancestor of the alphacoronavirus line has been placed at about 2400 BCE, of the betacoronavirus line at 3300 BCE, of the gammacoronavirus line at 2800 BCE, and the deltacoronavirus line at about 3000 BCE. Bats and birds, as warm-blooded flying vertebrates, are an ideal natural reservoir for the coronavirus gene pool (with bats the reservoir for alphacoronaviruses and betacoronavirus – and birds the reservoir for gammacoronaviruses and deltacoronaviruses). The large number and global range of bat and avian species that host viruses have enabled extensive evolution and dissemination of coronaviruses. 
Many human coronaviruses have their origin in bats.  The human coronavirus NL63 shared a common ancestor with a bat coronavirus (ARCoV.2) between 1190 and 1449 CE.  The human coronavirus 229E shared a common ancestor with a bat coronavirus (GhanaGrp1 Bt CoV) between 1686 and 1800 CE.  More recently, alpaca coronavirus and human coronavirus 229E diverged sometime before 1960.  MERS-CoV emerged in humans from bats through the intermediate host of camels.  MERS-CoV, although related to several bat coronavirus species, appears to have diverged from these several centuries ago.  The most closely related bat coronavirus and SARS-CoV diverged in 1986.  The ancestors of SARS-CoV first infected leaf-nose bats of the genus Hipposideridae subsequently, they spread to horseshoe bats in the species Rhinolophidae, then to Asian palm civets, and finally to humans.  
Unlike other betacoronaviruses, bovine coronavirus of the species Betacoronavirus 1 and subgenus Embecovirus is thought to have originated in rodents and not in bats.   In the 1790s, equine coronavirus diverged from the bovine coronavirus after a cross-species jump.  Later in the 1890s, human coronavirus OC43 diverged from bovine coronavirus after another cross-species spillover event.   It is speculated that the flu pandemic of 1890 may have been caused by this spillover event, and not by the influenza virus, because of the related timing, neurological symptoms, and unknown causative agent of the pandemic.  Besides causing respiratory infections, human coronavirus OC43 is also suspected of playing a role in neurological diseases.  In the 1950s, the human coronavirus OC43 began to diverge into its present genotypes.  Phylogenetically, mouse hepatitis virus (Murine coronavirus), which infects the mouse's liver and central nervous system,  is related to human coronavirus OC43 and bovine coronavirus. Human coronavirus HKU1, like the aforementioned viruses, also has its origins in rodents. 
Coronaviruses vary significantly in risk factor. Some can kill more than 30% of those infected, such as MERS-CoV, and some are relatively harmless, such as the common cold.  Coronaviruses can cause colds with major symptoms, such as fever, and a sore throat from swollen adenoids.  Coronaviruses can cause pneumonia (either direct viral pneumonia or secondary bacterial pneumonia) and bronchitis (either direct viral bronchitis or secondary bacterial bronchitis).  The human coronavirus discovered in 2003, SARS-CoV, which causes severe acute respiratory syndrome (SARS), has a unique pathogenesis because it causes both upper and lower respiratory tract infections. 
Six species of human coronaviruses are known, with one species subdivided into two different strains, making seven strains of human coronaviruses altogether.
Four human coronaviruses produce symptoms that are generally mild, even though it is contended they might have been more aggressive in the past: 
Three human coronaviruses produce potentially severe symptoms:
These cause the diseases commonly called SARS, MERS, and COVID-19 respectively.
Although the common cold is usually caused by rhinoviruses,  in about 15% of cases the cause is a coronavirus.  The human coronaviruses HCoV-OC43, HCoV-HKU1, HCoV-229E, and HCoV-NL63 continually circulate in the human population in adults and children worldwide and produce the generally mild symptoms of the common cold.  The four mild coronaviruses have a seasonal incidence occurring in the winter months in temperate climates.   There is no preponderance in any season in tropical climates. 
Severe acute respiratory syndrome (SARS)
Characteristics of zoonotic coronavirus strains
MERS-CoV, SARS-CoV, SARS-CoV-2,
and related diseases
MERS-CoV SARS-CoV SARS-CoV-2 Disease MERS SARS COVID-19 Outbreaks 2012, 2015,
Epidemiology Date of first
Location of first
Age average 56 44  [a] 56  Sex ratio (M:F) 3.3:1 0.8:1  1.6:1  Confirmed cases 2494 8096  180,948,026  [b] Deaths 858 774  3,919,969  [b] Case fatality rate 37% 9.2% 2.2%  Symptoms Fever 98% 99–100% 87.9%  Dry cough 47% 29–75% 67.7%  Dyspnea 72% 40–42% 18.6%  Diarrhea 26% 20–25% 3.7%  Sore throat 21% 13–25% 13.9%  Ventilatory use 24.5%  14–20% 4.1%  Notes
In 2003, following the outbreak of severe acute respiratory syndrome (SARS) which had begun the prior year in Asia, and secondary cases elsewhere in the world, the World Health Organization (WHO) issued a press release stating that a novel coronavirus identified by several laboratories was the causative agent for SARS. The virus was officially named the SARS coronavirus (SARS-CoV). More than 8,000 people from 29 different countries and territories were infected, and at least 774 died.  
Middle East respiratory syndrome (MERS)
In September 2012, a new type of coronavirus was identified, initially called Novel Coronavirus 2012, and now officially named Middle East respiratory syndrome coronavirus (MERS-CoV).   The World Health Organization issued a global alert soon after.  The WHO update on 28 September 2012 said the virus did not seem to pass easily from person to person.  However, on 12 May 2013, a case of human-to-human transmission in France was confirmed by the French Ministry of Social Affairs and Health.  In addition, cases of human-to-human transmission were reported by the Ministry of Health in Tunisia. Two confirmed cases involved people who seemed to have caught the disease from their late father, who became ill after a visit to Qatar and Saudi Arabia. Despite this, it appears the virus had trouble spreading from human to human, as most individuals who are infected do not transmit the virus.  By 30 October 2013, there were 124 cases and 52 deaths in Saudi Arabia. 
After the Dutch Erasmus Medical Centre sequenced the virus, the virus was given a new name, Human Coronavirus—Erasmus Medical Centre (HCoV-EMC). The final name for the virus is Middle East respiratory syndrome coronavirus (MERS-CoV). The only U.S. cases (both survived) were recorded in May 2014. 
In May 2015, an outbreak of MERS-CoV occurred in the Republic of Korea, when a man who had traveled to the Middle East, visited four hospitals in the Seoul area to treat his illness. This caused one of the largest outbreaks of MERS-CoV outside the Middle East.  As of December 2019, 2,468 cases of MERS-CoV infection had been confirmed by laboratory tests, 851 of which were fatal, a mortality rate of approximately 34.5%. 
Coronavirus disease 2019 (COVID-19)
In December 2019, a pneumonia outbreak was reported in Wuhan, China.  On 31 December 2019, the outbreak was traced to a novel strain of coronavirus,  which was given the interim name 2019-nCoV by the World Health Organization (WHO),    later renamed SARS-CoV-2 by the International Committee on Taxonomy of Viruses.
As of 27 June 2021, there have been at least 3,919,969  confirmed deaths and more than 180,948,026  confirmed cases in the COVID-19 pandemic. The Wuhan strain has been identified as a new strain of Betacoronavirus from group 2B with approximately 70% genetic similarity to the SARS-CoV.  The virus has a 96% similarity to a bat coronavirus, so it is widely suspected to originate from bats as well.  
During a surveillance study of archived samples of Malaysian viral pneumonia patients, virologists identified a strain of canine coronavirus which has infected humans in 2018.
Coronaviruses have been recognized as causing pathological conditions in veterinary medicine since the 1930s.  They infect a range of animals including swine, cattle, horses, camels, cats, dogs, rodents, birds and bats.  The majority of animal related coronaviruses infect the intestinal tract and are transmitted by a fecal-oral route.  Significant research efforts have been focused on elucidating the viral pathogenesis of these animal coronaviruses, especially by virologists interested in veterinary and zoonotic diseases. 
Coronaviruses infect domesticated birds.  Infectious bronchitis virus (IBV), a type of coronavirus, causes avian infectious bronchitis.  The virus is of concern to the poultry industry because of the high mortality from infection, its rapid spread, and its effect on production.  The virus affects both meat production and egg production and causes substantial economic loss.  In chickens, infectious bronchitis virus targets not only the respiratory tract but also the urogenital tract. The virus can spread to different organs throughout the chicken.  The virus is transmitted by aerosol and food contaminated by feces. Different vaccines against IBV exist and have helped to limit the spread of the virus and its variants.  Infectious bronchitis virus is one of a number of strains of the species Avian coronavirus.  Another strain of avian coronavirus is turkey coronavirus (TCV) which causes enteritis in turkeys. 
Coronaviruses also affect other branches of animal husbandry such as pig farming and the cattle raising.  Swine acute diarrhea syndrome coronavirus (SADS-CoV), which is related to bat coronavirus HKU2, causes diarrhea in pigs.  Porcine epidemic diarrhea virus (PEDV) is a coronavirus that has recently emerged and similarly causes diarrhea in pigs.  Transmissible gastroenteritis virus (TGEV), which is a member of the species Alphacoronavirus 1,  is another coronavirus that causes diarrhea in young pigs.   In the cattle industry bovine coronavirus (BCV), which is a member of the species Betacoronavirus 1 and related to HCoV-OC43,  is responsible for severe profuse enteritis in young calves. 
Coronaviruses infect domestic pets such as cats, dogs, and ferrets.  There are two forms of feline coronavirus which are both members of the species Alphacoronavirus 1.  Feline enteric coronavirus is a pathogen of minor clinical significance, but spontaneous mutation of this virus can result in feline infectious peritonitis (FIP), a disease with high mortality.  There are two different coronaviruses that infect dogs. Canine coronavirus (CCoV), which is a member of the species Alphacoronavirus 1,  causes mild gastrointestinal disease.  Canine respiratory coronavirus (CRCoV), which is a member of the species Betacoronavirus 1 and related to HCoV-OC43,  cause respiratory disease.  Similarly, there are two types of coronavirus that infect ferrets.  Ferret enteric coronavirus causes a gastrointestinal syndrome known as epizootic catarrhal enteritis (ECE), and a more lethal systemic version of the virus (like FIP in cats) known as ferret systemic coronavirus (FSC).  
Coronaviruses infect laboratory animals.  Mouse hepatitis virus (MHV), which is a member of the species Murine coronavirus,  causes an epidemic murine illness with high mortality, especially among colonies of laboratory mice.  Prior to the discovery of SARS-CoV, MHV was the best-studied coronavirus both in vivo and in vitro as well as at the molecular level. Some strains of MHV cause a progressive demyelinating encephalitis in mice which has been used as a murine model for multiple sclerosis.  Sialodacryoadenitis virus (SDAV), which is a strain of the species Murine coronavirus,  is highly infectious coronavirus of laboratory rats, which can be transmitted between individuals by direct contact and indirectly by aerosol. Rabbit enteric coronavirus causes acute gastrointestinal disease and diarrhea in young European rabbits.  Mortality rates are high. 
A number of vaccines using different methods have been developed against human coronavirus SARS-CoV-2.   Antiviral targets against human coronaviruses have also been identified such as viral proteases, polymerases, and entry proteins. Drugs are in development which target these proteins and the different steps of viral replication.  
Vaccines are available for animal coronaviruses IBV, TGEV, and Canine CoV, although their effectiveness is limited. In the case of outbreaks of highly contagious animal coronaviruses, such as PEDV, measures such as destruction of entire herds of pigs may be used to prevent transmission to other herds. 
Imperfect vaccines and the evolution of pathogen virulence
Vaccines rarely provide full protection from disease. Nevertheless, partially effective (imperfect) vaccines may be used to protect both individuals and whole populations. We studied the potential impact of different types of imperfect vaccines on the evolution of pathogen virulence (induced host mortality) and the consequences for public health. Here we show that vaccines designed to reduce pathogen growth rate and/or toxicity diminish selection against virulent pathogens. The subsequent evolution leads to higher levels of intrinsic virulence and hence to more severe disease in unvaccinated individuals. This evolution can erode any population-wide benefits such that overall mortality rates are unaffected, or even increase, with the level of vaccination coverage. In contrast, infection-blocking vaccines induce no such effects, and can even select for lower virulence. These findings have policy implications for the development and use of vaccines that are not expected to provide full immunity, such as candidate vaccines for malaria.
Virus Mortality Selection - Biology
Evolution from a virus's view
Where's the evolution?
To understand why some germs are virulent, we need to see the world from their point of view. To us, disease-causing viruses and bacteria may be evildoers invaders of our bodies who, if they can be said to have any aim at all, it is to do us harm. But shifting our perspective to their scale reveals these pathogens to be evolving populations of organisms like any other, whose habitat just happens to be the human body. Like other organisms, these germs are shaped by natural selection to live and successfully reproduce. We view them as pathogens, however, because the resources they use to do this (and which they destroy in the process) are the cells of our own bodies. Many of the traits that make us feel sick during an infection are actually pathogenic adaptations characteristics favored by natural selection that help these germs reproduce and spread.
As an example, consider a unique ecological challenge faced by many pathogens: appropriate habitats can be few and alarmingly far between. Put yourself in the position of a virus in its natural habitat a human host. You've infected some cells and managed to reproduce, but the host's immune system is onto you now and is turning up the heat. This environment is no longer so hospitable. How can you get your descendents to a friendlier habitat (i.e., a new, unexploited human body)? Without legs, wings, fins, or any of the usual means of locomotion, your descendents' prospects for reaching a new host under their own power are nil. However, natural selection has provided pathogens with a number of sneaky strategies for making the leap to a new host, including:
- Droplet transmission for example, being passed along when one host accidentally sneezes on another. The flu is transmitted this way.
Pathogen lineages that fail to meet this challenge and never infect a new host are doomed. They will go extinct when their human host dies or when the immune system destroys the infection.
Since transmission is a matter of life or death for pathogen lineages, some evolutionary biologists have focused on this as the key to understanding why some have evolved into killers and others cause no worse than the sniffles. The idea is that there may be an evolutionary trade-off between virulence and transmission. Consider a virus that exploits its human host more than most and so produces more offspring than most. This virus does a lot of damage to the host in other words, is highly virulent. From the virus's perspective, this would, at first, seem like a good thing extra resources mean extra offspring, which generally means high evolutionary fitness. However, if the viral reproduction completely incapacitates the host, the whole strategy could backfire: the illness might prevent the host from going out and coming into contact with new hosts that the virus could jump to. A victim of its own success, the viral lineage could go extinct and become an evolutionary dead end. This level of virulence is clearly not a good thing from the virus's perspective.
Natural selection balances this trade-off, selecting for pathogens virulent enough to produce many offspring (that are likely to be able to infect a new host if the opportunity arises) but not so virulent that they prevent the current host from presenting them with opportunities for transmission. Where this balance is struck depends, in part, on the virus's mode of transmission. Sexually-transmitted pathogens, for example, will be selected against if they immobilize their host too soon, before the host has the opportunity to find a new sexual partner and unwittingly pass on the pathogen. Some biologists hypothesize that this trade-off helps explain why sexually-transmitted infections tend to be of the lingering sort. Even if such infections eventually kill the host, they do so only after many years, during which the pathogen might be able to infect a new host.
On the other hand, diseases like cholera (which causes extreme diarrhea) are, in many situations, free to evolve to a high level of virulence. Cholera victims are soon immobilized by the disease, but they are tended by others who carry away their waste, clean their soiled clothes, and, in the process, transmit the bacterium to a water supply where it can be ingested by new hosts. In this way, even virulent cholera strains that strike down a host immediately can easily be transmitted to a new host. Accordingly, cholera has evolved a high level of virulence and may kill its host just a few hours after symptoms begin.
Though transmission mode is far from the only factor that affects how virulence evolves the immunity level of the host population, the distribution of the hosts, and whether the host has other infections, for example, matter as well this key piece of the pathogen's ecology does help illuminate why some diseases are killers. More importantly, it suggests how we might sway pathogen evolution towards less virulent strains. In situations where high virulence is tied to high transmission rates (e.g., cholera), reducing transmission rates (e.g., by providing better water sanitation) may favor less virulent forms. The idea is to create a situation in which hyper-virulent strains that soon kill or immobilize their hosts never get a chance to infect new hosts and are turned into evolutionary dead ends. In fact, biologists have observed this phenomenon in South America: when cholera invaded countries with poor water sanitation, the strains evolved to be more virulent, while lineages that invaded areas with better sanitation evolved to be less harmful.
And that brings us back to Adenovirus-14. Adenoviruses are transmitted through the air or via contact. We might expect this sort of transmission to require a fairly healthy host (one who gets out and comes into contact with others) and, hence, to select against virulent strains. Indeed, adenoviruses are rarely killers, but in close quarters for example, in the military barracks where Adenovirus-14 has been a particular problem barriers to transmission may be lowered. This could open the door for the evolution of more virulent strains. Military personnel, however, are in the process of pushing this door shut again. At Lackland Air Force Base, which has seen the most serious outbreak of Adenovirus-14, wider testing, more hand-washing stations, increased attention to sanitization, and isolation of patients is helping to reduce the transmission of the disease and, in the process, may favor the evolution of less virulent strains of the virus.
- Ewald, P. W. (1996). Guarding against the most dangerous emerging pathogens: Insights from evolutionary biology. Emerging Infectious Diseases 2(4):245-257.
Understanding Evolution resources:
Discussion and extension questions
- . Explain how a mutation allowing a virus to make more copies of itself would spread through a population of viruses living within a single person. Make sure to include the concepts of variation, selection, and inheritance in your explanation.
. Describe what factors would increase the evolutionary fitness of a virus like Adenovirus-14.
Related lessons and teaching resources
- : In this short video for grades 9-12, evolutionary biologist Paul Ewald describes strategies for controlling viral evolution.
- Cases of 'boot camp flu' dropping at Lackland AFB. (2007, December 3). AP Texas News.
Retrieved December 4, 2007, from Houston Chronicle
Begley, S. DNA sleuths read the coronavirus genome, tracing its origins and looking for dangerous mutations. STAT http://bit.ly/37ma5xY (2020).
Gulland, A. Coronavirus: researchers warned to be on alert over mutations that could speed up disease spread. The Daily Telegraph (4 February 2020) http://bit.ly/2OJ5OOH
Yuan, L. et al. Science 358, 933–936 (2017).
Grubaugh, N. D., Ishtiaq, F., Setoh, Y. X. & Ko, A. I. Trends Microbiol. 27, 381–383 (2019).
Holmes, E. C. The Evolution and Emergence of RNA Viruses (Oxford University Press, 2009).
Bull, J. J. Evolution 48, 1423–1437 (1994).
Wain, L. V. et al. Mol. Biol. Evol. 24, 1853–1860 (2007).
Tsetsarkin, K. A., Vanlandingham, D. L., Mc Gee, C. E. & Higgs, S. PLoS Pathog. 3, e201 (2007).
Urbanowicz, R. A. et al. Cell 167, 1079–1085 (2016).
Herfst, S. et al. Science 336, 1534–1541 (2012).
Bloom, J. D., Gong, L. I. & Baltimore, D. Science 328, 1272–1275 (2010).
The Chinese SARS Molecular Epidemiology Consortium. Science 303, 1666–1669 (2004).
Guan, Y. et al. Science 302, 276–278 (2003).
Cui, J., Li, F. & Shi, Z.-L. Nat. Rev. Microbiol. 17, 181–192 (2019).
Grubaugh, N. D. et al. Nat. Microbiol. 4, 10–19 (2019).
Replication and Latency
Replication of all herpesviruses is a multi-step process. Following the onset of infection, DNA is uncoated and transported to the nucleus of the host cell. This is followed by transcription of immediate-early genes, which encode for the regulatory proteins. Expression of immediate-early gene products is followed by the expression of proteins encoded by early and then late genes.
Assembly of the viral core and capsid takes place within the nucleus. This is followed by envelopment at the nuclear membrane and transport out of the nucleus through the endoplasmic reticulum and the Golgi apparatus. Glycosylation of the viral membrane occurs in the Golgi apparatus. Mature virions are transported to the outer membrane of the host cell inside vesicles. Release of progeny virus is accompanied by cell death. Replication for all herpesviruses is considered inefficient, with a high ratio of non-infectious to infectious viral particles.
A unique characteristic of the herpesviruses is their ability to establish latent infection. Each virus within the family has the potential to establish latency in specific host cells, and the latent viral genome may be either extra-chromosomal or integrated into host cell DNA. Herpes simplex virus 1 and 2 and varicella-zoster virus all establish latency in the dorsal root ganglia. Epstein-Barr virus can maintain latency within B lymphocytes and salivary glands. Cytomegalovirus, human herpesvirus 6 and 7, Kaposi's sarcoma herpesvirus and B virus have unknown sites of latency.
Latent virus may be reactivated and enter a replicative cycle at any point in time. The reactivation of latent virus is a well-recognized biologic phenomenon, but not one that is understood from a biochemical or genetic standpoint. It should be noted here that an anti-sense message to one of the immediate-early genes (alpha-O) may be involved in the maintenance of latent virus. Stimuli that have been observed to be associated with the reactivation of latent herpes simplex virus have included stress, menstruation, and exposure to ultraviolet light. Precisely how these factors interact at the level of the ganglia remains to be defined. It should be noted that reactivation of herpesviruses may be clinically asymptomatic, or it may produce life-threatening disease.
Part 2: Virus Adaptation to Environmental Change
00:00:15.24 I'm Paul Turner from the Department of Ecology and Evolutionary Biology at Yale University,
00:00:19.20 and the Microbiology faculty at Yale School of Medicine.
00:00:23.06 Today, I'd like to present on virus adaptation (or not) to environmental change.
00:00:29.08 This talk describes how viruses have an amazing capacity to adapt to environmental challenges
00:00:35.03 and, yet, we'll find that these champions of adaptation sometimes encounter environments
00:00:40.17 that demonstrate that environmental change can constrain evolution and adaptation, and
00:00:46.13 even these so called champions can face constraint.
00:00:50.11 So, very many challenges exist to viruses in the natural world and you could think of
00:00:56.09 this at all different levels of biological organization.
00:00:59.24 So, if you start at the base level of molecules and cells, the primary challenge for viruses
00:01:06.02 is that they cannot control where they exist in the environment, so they might encounter
00:01:11.04 some cell type and successfully enter if they have the right protein binding to recognize
00:01:16.19 a protein on the cell surface, or they might bump into the wrong type of cell and that
00:01:22.15 protein recognition doesn't occur.
00:01:24.14 So, therefore, it's an immediate and proximate challenge to a virus to infect a cell, depending
00:01:30.02 on where it is in the environment and whether the proper cells exist to infect.
00:01:34.13 In macroorganisms like us, we have tissues composed of different cell types, so if a
00:01:41.00 virus is in your body and it's replicating in one tissue type, it might be challenged
00:01:46.01 to infect a different tissue that's nearby and it's incapable of doing so.
00:01:51.05 Hosts, such as humans, have elaborate and beautiful immune systems.
00:01:56.21 Some of them are adaptive, meaning that they change through time and this is a way of our
00:02:01.08 immune system, in a way, keeping pace with microbial invaders and changing at the same
00:02:07.03 pace that they might evolve through evolution.
00:02:10.00 But viruses and other microbes. when they encounter these immune systems, this poses
00:02:14.23 a challenge for them to continue to infect that host, or that host's progeny, or other
00:02:19.19 susceptible hosts in their env. in their environment, depending on whether those immune
00:02:24.00 systems provide an immediate and successful barrier to virus replication.
00:02:30.10 It's amazing thing that some viruses infect humans and also successfully infect very different
00:02:37.12 organisms that are not at all closely related to us.
00:02:41.11 A great example of this are the arthropods, where many pathogens are vector-transmitted
00:02:47.11 by arthropods such as mosquitoes, including viruses.
00:02:51.14 And this is pretty fascinating, because a virus has to grow within an invertebrate and,
00:02:58.05 for example, in a mosquito, it has to grow in the midgut and eventually get to the salivary
00:03:01.21 glands in order to be present in a bite that puts the virus in the bloodstream of another
00:03:07.04 host to be picked up by another mosquito.
00:03:09.21 That's got to be an incredible challenge for a virus to grow both successfully in an invertebrate,
00:03:14.23 like an arthropod, as well as a vertebrate like you, a human.
00:03:18.18 And, last, we have to remember that global-level ecosystem changes affect all biological entities
00:03:25.04 on this planet, including the very smallest ones such as viruses.
00:03:29.02 So, when you think about challenges like climate change and global warming, you have to remember
00:03:35.11 that this is something that is felt by all biological entities, and therefore viruses
00:03:40.06 can also feel the challenge of an ever-warming world.
00:03:43.23 There are many different virus study systems that my group examines.
00:03:48.11 So, these examples are shown in the very many beautiful forms behind me.
00:03:54.09 On the far left, we have vesicular stomatitis virus, which is an example of a single-stranded
00:03:59.15 RNA virus with a negative-sense genome, and in the middle we have a variety of other viruses,
00:04:04.23 also, that will infect eukaryotes, but they happen to have positive single-stranded RNA
00:04:11.05 Closer to where I'm standing, we have single-stranded DNA filamentous phage, and also double-stranded
00:04:16.17 RNA and double-stranded DNA viruses, in this case, both phages: phage phi-6 and phage T2.
00:04:23.09 So, these are examples within my laboratory of the wide variety of viruses that exist
00:04:28.14 in the natural world, and how a single laboratory can choose to examine this great variety of
00:04:34.11 virus types.
00:04:35.16 Depending on the challenge and the question, we would like to focus on a different study
00:04:39.17 system to examine whether viruses can successfully, or not, adapt to their environments.
00:04:45.21 A big tool that we use that's very popular with others, and a very powerful tool, would
00:04:50.22 be experimental evolution.
00:04:52.24 A way to summarize this method is it's the ability to study evolution in action.
00:04:58.01 So, if you have the right study system in a controlled place, like a laboratory, you
00:05:03.17 can take that population, put it in an environment that you control explicitly, and then examine,
00:05:09.09 how does that population deal with that challenge, both in terms of the traits that it evolves
00:05:14.07 as well as the genotypic changes that it undergoes?
00:05:16.24 So, both phenotype and genotype can be the focus of these studies.
00:05:21.09 An important thing to remember is, even if a researcher is manipulating the environment
00:05:25.20 in the laboratory, it still can be a challenge to a population, and that population can evolve
00:05:32.07 through natural selection.
00:05:33.12 So, you're talking about an artificial environment and yet natural selection can occur.
00:05:38.22 That's because the researcher is not determining which variants in that population will better
00:05:43.17 match the environment instead, that's due entirely to the mutations and the genetics
00:05:48.15 of that system to meet that challenge or not.
00:05:52.18 And that can happen through the process of evolution by natural selection.
00:05:57.11 A typical design is shown here, where we would begin with some ancestral type.
00:06:03.00 We might be interested, in the case of this hypothetical diagram, in three different treatments
00:06:07.12 that differ in some way in their environmental challenge, and we can track, over the course
00:06:13.16 of generations, how do these independent lineages evolve to meet these challenges?
00:06:20.05 And the nice thing is to include replication in these experiments, such as you can have
00:06:24.22 lineages that are experiencing the same environment, and you can look at how consistently do lineages
00:06:30.23 undergo random mutation, and yet the same mutations might be the ones that rise to fixation,
00:06:36.12 and lead to adaptation.
00:06:38.05 In other cases, there might be different solutions to the environmental challenge, and you'll
00:06:42.20 see divergence between your lineages in the sense that different mutations are meeting
00:06:47.20 the same challenge.
00:06:50.10 Another way to think about these experimental evolution studies is to create a hypothetical
00:06:55.09 diagram of some phenotypic trait that you would want measure -- this might be growth
00:07:00.06 or some other capacity of the system to meet the challenge.
00:07:03.03 So, in this example, I'm illustrating how this trait has some variation at the beginning,
00:07:09.21 and then we could create some sort of an ecological circumstance, or an environmental challenge,
00:07:14.22 in these studies, and, through time, we can keep track of how phenotypes change.
00:07:19.18 So, you'll notice that the average phenotype, along the x axis in this hypothetical example,
00:07:25.18 is shifting to the right, meaning that the mean of the distribution is changing according
00:07:29.23 to which variants are in that population and the ones that are best meeting that challenge.
00:07:34.15 Now, we can go further than a lot of systems, because it's very easy for us in virus studies
00:07:40.04 to take the entire genome from these evolving populations and explicitly look everywhere
00:07:46.08 in the genome for where a mutation might occur.
00:07:48.24 In this case, we can track through generational time, how is the genetics changing in relation
00:07:54.11 to the ecological challenge as well?
00:07:56.15 And this allows a lot of immediate power in making something called a phenotype-genotype
00:08:01.14 association -- you can infer how the changing phenotype is being controlled by underlying
00:08:10.10 genetics, and make some base inferences about what the relationship is.
00:08:15.15 And I don't want to trivialize that because one has to do a lot more work to convince
00:08:19.14 oneself that, perhaps, one mutation is responsible, maybe two or three, or even more complex things
00:08:26.00 can occur like these mutations acting with one another through properties like epistasis.
00:08:31.06 So, this provides an amazing amount of power to examine how evolution occurs according
00:08:37.13 to the environment that you create in these types of studies.
00:08:41.12 The outline for what I want to talk about today is pretty much centered on these two
00:08:47.14 We can consider environmental changes as fostering versus constraining virus adaptation, depending
00:08:54.12 on how the environment is constructed, and these types of experiments are in the natural
00:08:59.18 And, especially, now, that takes us to this next question of, are there particular traits
00:09:04.15 that can evolve in viruses that match something intriguing that you see in cellular systems?
00:09:11.06 The investment in survival versus reproduction is often something that's seen as at odds
00:09:16.01 to one another in cellular systems, that you can either invest a lot in survival as an
00:09:21.07 evolutionary trait, but this minimizes your reproductive capacity, or vice versa.
00:09:27.06 So, an intriguing set of studies show that this same constraint, or the same trade-off,
00:09:34.08 can happen even in non-metabolizing organisms, such as the viruses, especially in the viruses.
00:09:42.22 How does environmental change foster versus constrain virus adaptation?
00:09:47.12 Let's look at this question first.
00:09:50.21 Virus emergence is an amazing bio. biomedical challenge that we face today.
00:09:56.01 So, even though RNA viruses, especially, are not that prevalent among the highly prevalent
00:10:01.18 viruses that exist on this planet, they seem to be especially able to jump into new host
00:10:07.20 species and cause harm through disease.
00:10:10.10 So, humans see this through recently emerging pathogens, such as Zika virus, which is sweeping
00:10:17.04 around the globe, and is problematic and creating a challenge to biomedicine, to protect people
00:10:22.16 against Zika virus infection that can disrupt normal development at an early age.
00:10:27.22 A very different example, but still called emergence, is when a virus comes from another
00:10:33.06 species, enters into the human population, and gets locked in and becomes very specific
00:10:39.16 to humans.
00:10:40.16 So, I began with the case of Zika virus, which is not specific to humans, but a great example
00:10:45.17 of a specificity evolution would be HIV, which came into the human population several times,
00:10:51.18 independently, from our primate relatives, especially chimpanzees and certain species
00:10:56.09 of monkeys, and this has led to the evolution of HIV-1 and HIV-2, independently, several
00:11:04.06 A third example of emergence would be something that exists both within humans, as well as
00:11:09.21 in other species, and a great example of that would be influenza virus.
00:11:14.09 So, ordinarily, in any year, you can have plenty of the human population seeing flu
00:11:19.03 virus infection and suffering influenza, but what we fear is that there are certain forms,
00:11:24.12 or genotypes, of influenza virus that will be especially virulent and cause a high degree
00:11:29.05 of mortality, and sweep around the globe to infect a lot of humans, and adversely affect
00:11:34.06 human populations, more than a standard flu season.
00:11:37.03 And, especially, we fear that the large reservoir of influenza viruses, that mostly exist in
00:11:43.05 this planet in waterfowl, might lead to a variant that can jump immediately into humans
00:11:48.16 and then be passed from human to human.
00:11:50.23 This would be an example of a flu virus coming from a bird, coming into a very different
00:11:56.00 host species, a mammal, and causing a lot of destruction and mortality because of the
00:12:00.18 inability of the human immune system to deal with the challenge.
00:12:03.20 So, these are three different examples of the same catalogued thing that thing is what's
00:12:09.20 called emergence, and this is a huge biomedical challenge.
00:12:14.03 We can think of how emergence can or cannot occur for a virus, and that's what I want
00:12:18.09 to focus on next.
00:12:19.11 And there are some certain fundamental expectations, if you have any lineage, whether it's a virus
00:12:25.03 or not, and whether it's encountering an environment that is constant through time, versus changing
00:12:30.21 seasonally, or in a temporal way through time.
00:12:34.22 So, I'm giving two hypothetical examples of this.
00:12:38.11 At the top, we have a hypothetical evolving lineage that sees niche A and niche B in a
00:12:44.22 flip-flopping fashion, and each one of these little circles indicates a generation.
00:12:49.23 So, necessarily, this lineage has to grow in environment A in order to make it long
00:12:55.04 enough in its environment to reach a new environment, B, and so on.
00:12:59.18 Necessarily, we would expect that this. this will select for generalization -- the
00:13:03.18 ability to thrive in both of these environments -- because there is no other option.
00:13:08.06 Now, that's very different than if that lineage has the luxury of seeing only a single environment.
00:13:13.11 In this case, environment A is the only thing it encounters, but I'm underlining the word
00:13:19.18 *tends* to select for specialization, because that's only one possibility.
00:13:24.07 This luxury affords this possibility of being highly specific to your environment and being
00:13:28.23 very good in that environment, but it also is an opportunity for generalization to occur,
00:13:35.15 if you have a correlated response to growing well in other environments.
00:13:39.04 And, essentially, that must be happening in emerging virus pathogens.
00:13:43.16 They happen to have the right genetic capacity that when they jump into a new host species
00:13:48.04 like human, they can just really hit the ground running and grow very well, cause a lot of
00:13:53.15 damage, and ultimately they might be specific to that environment. ultimately, but initially
00:13:59.17 they're highly generalized.
00:14:02.12 We've covered this topic in a variety of papers that I'm listing here that I won't have time
00:14:06.05 to go into much detail, but one can think of this challenge of virus specialism versus
00:14:11.22 generalism happening a lot in the natural world, and it's very easy and powerful to
00:14:17.05 study this in the laboratory, through the experimental evolution method that I mentioned
00:14:23.14 One system that we've focused on a lot to study how virus specialization versus generalization,
00:14:29.01 and just simply adaptation can happen, is a model known as vesicular stomatitis virus.
00:14:34.17 So, this is a single-stranded RNA virus with a negative-sense genome that's pretty much
00:14:39.21 a workhorse in molecular virology.
00:14:42.06 It's been used for very many decades to understand fundamentals of how RNA viruses infect and
00:14:47.17 replicate in a cell.
00:14:49.05 So, some pictures, here, that I'm showing are just to reflect that we have a lot of
00:14:53.16 prior knowledge for the molecular details of this system, and that's great when you
00:14:58.14 enter into experimental evolution studies, because you don't have to go about measuring
00:15:03.06 that stuff all over again you can think of the outcome of your experiments in the context
00:15:07.17 of the prior knowledge.
00:15:08.19 So, VSV has a very small genome in size.
00:15:11.21 It has only 11 kilobases in length.
00:15:15.08 And this comprises only 5 genes.
00:15:17.04 So, one can think of this as a pretty simple system.
00:15:20.04 And yet it has a pretty amazing capacity to do things like both reproduce in an arthropod
00:15:26.15 -- it's an arthropod-borne virus or an arbovirus -- and it also can replicate in a mammal.
00:15:31.13 So, in the case of VSV, it's a safe system to use in the laboratory because it might
00:15:36.08 get in a human by accident, but it really doesn't cause much harm.
00:15:40.07 It's agriculturally important in large mammals, domesticated horses, etc, so we do care about
00:15:45.14 it from a disease standpoint, but it's a great, powerful system to use in the laboratory,
00:15:52.04 It comes from the family rhabdoviridae, which also features rabies virus.
00:15:58.04 Here's a summary of some of the data from one of our experiments, where we harnessed
00:16:03.06 experimental evolution to examine, how does this virus deal with a constant environment
00:16:09.11 versus one that is changing through time in that temporal heterogeneous way that I outlined?
00:16:14.10 So, this is a pretty busy diagram, but I'll walk you through it.
00:16:18.02 At the top, this is merely a depiction of the VSV genome and the 5 genes N, P, M, G,
00:16:24.14 L. And what you can see is, for each one of the lineages ,the 4 lineages that saw only,
00:16:30.10 in this case, HeLa cells. those are cancer-derived cells that originally came from Henrietta
00:16:35.22 Lacks a long time ago, and these were harnessed as an immortalized cell line that people use
00:16:41.08 and a lot of studies beyond simply virus studies. but these cancer-derived cells provided a
00:16:47.07 new challenge for VSV in this experiment, and each one of the points, here, on the diagram,
00:16:52.19 are showing where these lineages changed in their genetic material relative to the ancestor
00:16:59.12 after the experiment took place.
00:17:01.18 In this way, we can catalogue, what are the mutations that arose, and which ones fixed
00:17:06.10 through natural selection, to let these lineages improve in their environment?
00:17:12.00 We also did an exp. in this experiment a challenge where the viruses had to not only
00:17:15.23 evolve on HeLa cells, but, in an alternating fashion, they had to enter into a non-cancer-derived
00:17:22.12 cell type, abbreviated as MDCK, and in this way they had to become adapted to both HeLa
00:17:28.12 cells as well as these non-cancer cells.
00:17:31.18 And you'll see that we also catalogued their genetic changes through time.
00:17:35.24 And this has a great deal of variety, even within each treatment, for the mutations that
00:17:41.15 fixed according to each lineage.
00:17:44.02 One can also catalogue the exact position where each one of these mutations took place.
00:17:48.12 Let me highlight one more thing before I move on, and that is, really, these virus populations,
00:17:55.08 after this experiment, are not carbon copies of one another.
00:17:58.19 So, there are many places where we do see that they underwent the same mutational change
00:18:04.08 at exactly the same place, and that must be evidence of some beneficial mutation coming
00:18:10.01 in and fixing in these lineages.
00:18:12.15 And yet, in some genes, they underwent different mutations from even the same populations in
00:18:17.21 the same treatment, so this indicates that there can be other genetic solutions to the
00:18:22.22 same environmental problem in a study like this.
00:18:26.06 Keep this in mind as we go on and look at a subsequent experiment that challenged the
00:18:31.02 ability of these viruses to evolve and infect yet new hosts to test, what is their emergence
00:18:39.01 Simply remember that we lumped them together as specialists, having seen only one constant
00:18:44.05 host hype, or generalists, that were selected to see two types, and yet the lineages are
00:18:49.10 not carbon copies of one another when they're drawn from each treatment.
00:18:53.16 Here, we wanted to ask a very fundamental question that's really at the root of what
00:18:58.15 lets emergence occur.
00:19:00.11 So, a popular idea is that, if some pathogen has seen multiple hosts in the past, it's
00:19:07.02 somehow groomed through adaptation to be generalized enough that it will successfully enter and
00:19:13.16 infect a new host when it sees it just randomly through encountering it in nature.
00:19:19.08 That's because adaptation has primed that pathogen to be good at growing in multiple
00:19:23.24 hosts and, through correlated response, it just might grow very well in a new host such
00:19:28.13 as humans.
00:19:29.13 So, here, I'm depicting a picture of Henrietta Lacks, as the. ultimately, the person who
00:19:34.16 gave rise to these HeLa cells that we used in this experiment, and we asked, whether
00:19:39.13 viruses that evolved strictly on HeLa cells, are they going to be good at growing on a
00:19:45.06 variety of challenge hosts that we purchased?
00:19:48.20 Or are we going to fit with this prediction that selected generalists were pre-adapted
00:19:55.06 in some way to perform well on these new hosts and they should be the ones that we would
00:20:00.00 fear as typical of a successful emerging pathogen, something that's groomed to grow on multiple
00:20:06.01 hosts and will grow well on a challenge host when it encounters it?
00:20:10.24 To go to the data from an earlier paper, this is pretty well supported by our study, that,
00:20:17.15 yes, selected generalists emerge or they shift hosts easier.
00:20:21.16 So, this diagram is showing, what is the sheer reproductive capacity of each of these virus
00:20:28.04 lineages, indicated by each point, relative to its ability to grow in the environment
00:20:33.19 that it was previously evolved on?
00:20:35.22 So, this gives an indication of. relative to its ordinary reproductive capacity, is
00:20:41.16 it any better or equally good at growing on a new challenge host, relative to the host
00:20:46.18 that it saw prior to adaptation?
00:20:49.13 And you'll see that all the blue points are well below the zero line.
00:20:53.19 That means that these specialist viruses from our study, they can grow on this first challenge
00:20:59.16 host I'm indicating, that came from monkey cells, but they grow pretty poorly compared
00:21:04.02 to their capacity to grow on the HeLa cells that came from Henrietta Lacks, whereas the
00:21:09.06 selected generalists, they saw both host types in our prior experiment -- one happened to
00:21:15.05 be cancer-derived, one happened to be non-cancer-derived -- but those were different enough cell types
00:21:20.09 that have provided a challenge to adapt to two things simultaneously.
00:21:25.03 And you'll see that, on this challenge host, those selected generalists actually did a
00:21:29.07 better job at growing on a challenge host that was just randomly chosen and presented
00:21:34.05 to them.
00:21:35.16 All four of those triangles are very close to the zero line.
00:21:38.22 So, in summary, one could say that, in this first line of evidence, on the monkey cells,
00:21:45.00 there's both a higher mean, on average, and lesser variance across the populations drawn
00:21:50.15 from each treatment for the selected generalist to do better.
00:21:53.24 Now, if you look at all four challenge hosts, there's an amazing ability for the data to
00:22:00.21 look highly similar, no matter what the challenge host was that we randomly entered into this
00:22:05.18 experiment using.
00:22:07.02 And, to me, that's fascinating, because it indicates that there's hardly any of what
00:22:11.22 one would call genotype-by-environment interaction.
00:22:14.22 This must be due to the capacity of these viruses to just simply grow on something new,
00:22:20.17 and it's not really the interaction with that new thing, it's just that they can grow better
00:22:25.02 on something that they've been challenged to infect.
00:22:27.07 So, this provides nice evidence in four randomly chosen challenges that selected generalists
00:22:33.21 can grow much better on a new host that you present them with, and this gives us a little
00:22:38.13 more insight at what could be the root of the emergence problem.
00:22:42.16 But I haven't really told you why -- why is this happening?
00:22:48.10 Why is it that these selected generalists actually emerge easier at a mechanistic level?
00:22:53.20 Here, we've looked at the ability, the innate immune ability, of cell types, and whether
00:22:59.24 selected generalists were keying in on this line of immunity and navigating their way
00:23:05.24 through it, and if they have a generalized ability to do that, and that should carry
00:23:10.22 over to other challenge types, even though that challenge type would be drawn from a
00:23:15.08 different species.
00:23:16.16 So, this is a very detailed diagram, but it's showing some of the inner workings at the
00:23:21.11 cellular level of something that you're born with.
00:23:24.22 This is the innate immune capacity of your cells that, when they see an invader, like
00:23:29.22 a virus, that they will be able to undergo a cascade of events at the cellular level
00:23:35.13 that gives them protection against that virus infection.
00:23:39.00 And, interestingly, the signals can go out to cells that are nearby in the tissue neighborhood
00:23:45.14 to prime them to be better protected against that virus, before the virus even is able
00:23:51.08 to replicate enough to get to those cell types.
00:23:53.24 Now, this is a wonderful ability, to be immune to a pathogen, that you should remember this
00:24:00.00 is your innate immunity.
00:24:02.13 Adaptive immunity, which people are much more familiar with, is something that is occurring
00:24:06.08 much longer-term, and it takes weeks or even longer that you see a pathogen and you mount
00:24:11.00 an immune response to the its uniqueness.
00:24:13.07 Here, this is just a generalized thing that controls pathogen infections.
00:24:18.06 So, before I move on, I'll say that the VSV M protein, or the matrix protein, is known
00:24:25.09 to be the thing that interacts with the capacity of a cell to produce its anti-immune response
00:24:31.11 to virus infection, especially interferon.
00:24:34.06 So, ordinarily, this cell is going to be producing interferon as one of these key chemicals that
00:24:39.09 protects it, and signals go out and interferon production occurs in other cells in the tissue
00:24:44.18 to protect them, but VSV, as a virus, can infect a cell and down-regulate that response.
00:24:53.05 And that helps us even explain how we even did the prior experiment.
00:24:56.19 VSV has a great capacity, just as a virus, to grow in a variety of cell types, because
00:25:03.18 it can regulate this response.
00:25:06.08 However, it could be that viruses like VSV are highly generalized in moving between hosts
00:25:14.18 because they properly regulate that immunity cascade.
00:25:19.15 So, without very many details, this is a hypothetical idea of how this can occur, and what one should
00:25:26.24 So, let's imagine that the prior selection history of some virus or other pathogen, this
00:25:32.19 is relating its fitness, due to that prior evolution, in terms of whether it saw host
00:25:38.22 types that are of low or high innate immunity.
00:25:41.23 So, in our prior experiment, I highlighted in blue how these specialist viruses perform
00:25:48.12 very well on HeLa cells, but I didn't tell you one key bit of information about a lot
00:25:54.15 of cancer cells, including HeLa cells.
00:25:57.03 They have very low or completely absent innate immunity.
00:26:01.00 So, what happened in that experiment, probably, is that the lineages of viruses evolved to
00:26:06.03 infect a cell type where they didn't really have to worry at all about innate immunity
00:26:10.17 as a challenge in infecting and growing in the cell type.
00:26:14.00 So, this probably led to de-evolution, or the removal of the capacity for those viruses
00:26:20.09 to control innate immunity.
00:26:22.00 They just simply didn't need it.
00:26:24.05 And then, when you challenged them to grow on a new host type, they are very handicapped
00:26:28.19 in doing so because they don't have the capacity to track the innate immunity functions within
00:26:33.08 a cell.
00:26:34.09 Whereas, viruses could see, necessarily, in our experiment, both high and low innate immunity,
00:26:40.24 because we used cancer-derived as well as non-cancer-derived cells, so they remained
00:26:45.13 capable of navigating through both cell types, and when they see a new cell type they can
00:26:50.15 hit the ground running.
00:26:52.06 So, importantly, one can think of both alternating hosts as, necessarily, in our experiment,
00:26:59.17 keeping this capacity, but it also could have been, and we've done work like this. if
00:27:04.08 you take virus lineages and you grow them only on high innate immunity hosts, you get
00:27:09.16 a very similar capacity for them to maintain strong growth regardless of cell type.
00:27:15.15 So, we have both good news and bad news in predicting emergence.
00:27:19.21 We have the ability for selected generalists to key in on innate cell function and navigate
00:27:25.09 multiple cell types, and you'd expect them to emerge, but they don't have to do that.
00:27:30.03 They could still successfully emerge through a correlated response.
00:27:34.00 So, the next question I want to cover is whether the environmental change that is presented
00:27:40.17 to viruses either fosters or constrains their adaptation.
00:27:44.05 So, now, this is a similar diagram that I showed you before, but note that now I'm including
00:27:48.17 a different kind of a challenge.
00:27:50.19 Here's where the lineage sees pretty much a stochastic set of environments through time.
00:27:56.03 In other words, it's moving from environment to environment, but there doesn't seem to
00:28:00.13 be any pattern to what that. what those environments present, right?
00:28:04.10 So, these are shown as separate colors in this diagram to illustrate how some virus
00:28:09.10 lineages might have to navigate through very different environments, and one can create
00:28:14.02 an experiment that says, well, will these champions of adaptations still be able to
00:28:19.12 successfully navigate through such a complex set of environments and become generalized?
00:28:24.20 Or is this just simply too much and, even in champions of adaptation like RNA viruses,
00:28:30.08 they'll be constrained and unable to do this?
00:28:34.04 This actually relates in some way to certain models that come from climate change, where
00:28:40.02 the prediction is. really, the fundamental problem for evolving lineages in climate change
00:28:46.05 is that stochasticity of the environment becomes more important.
00:28:50.07 The environment simply becomes more variable through time and it will be harder for lineages
00:28:54.21 to track those changes.
00:28:56.23 Well, it should be interesting to see whether viruses can successfully do this, because,
00:29:02.10 if they cannot, then this bodes pretty bad news for other organisms that have a much
00:29:07.12 slower and reduced capacity to evolve in the face of environmental challenges.
00:29:12.11 So, let's see what happened.
00:29:14.20 one can easily construct an experiment like this, but, rather than creating host challenges
00:29:20.04 through time, let's think a little bit more about those climate change models and the
00:29:24.01 thing that we'll manipulate is temperature through time.
00:29:26.14 So, in this diagram I'm showing four different treatment groups that were created in an experiment,
00:29:31.24 where 37 degrees Celsius is the upper limit, or pretty much the ordinary temperature for
00:29:37.01 replication that we use in the laboratory for VSV 29 degrees Celsius is a lower temperature,
00:29:44.00 where they can still grow but it's much lower than 37 C, 8 degrees lower and then we have
00:29:49.17 alternating lineages that will see these two environments in a flip-flopping fashion and
00:29:55.12 then we include this fourth treatment, this is really the intriguing one.
00:29:58.21 If you take that 8-degree window and you go into the laboratory and you challenge the
00:30:03.06 viruses to grow at any temperature in the 8-degree window that you randomly choose on
00:30:07.10 that day, it will create a very stochastic environment through time.
00:30:12.00 And here we want to know, across generations, especially 100 generations, is there any differing
00:30:17.09 capacity of these viruses to evolve well in the face of this challenge?
00:30:22.06 So, we can go immediately to the data that came from this experiment.
00:30:26.04 And the way this graph works is it shows you, what is the fitness after 100 generations
00:30:30.24 for each one of these lineages, at each edge of the niche space?
00:30:35.02 So, it's plotted, what is their fitness at 37 C versus their fitness at 29 C?
00:30:40.24 And the intersection of those points leads to each point on the graph.
00:30:44.11 So, you'll see that the lineages that evolved in a constant high-temperature environment
00:30:49.00 improved in that environment -- in other words, all their data are to the right and above
00:30:55.05 the dashed lines on this figure.
00:30:57.03 They've improved both in terms of the environmental challenge they saw -- 37 C -- as well as 29
00:31:03.21 C, which was the other environment that was constant in this experiment.
00:31:07.02 That's evidence of correlated selection -- you improve in one environment and it also allows
00:31:12.05 you to improve in another environment that you haven't seen.
00:31:15.01 The same thing occurred for the lineages that saw only 29 C as the challenge.
00:31:20.08 Interestingly, in green, we have an alternating environment, where populations improved, in
00:31:27.09 some cases more than populations that saw only a constant environment.
00:31:32.10 And that's intriguing because it shows that populations can improve even though they see
00:31:36.11 the challenge only half the time as their counterparts.
00:31:40.08 It must be that the genetics that underlies this, which we've shown in papers that I won't
00:31:44.00 present today, is that different mutations are responsible for this improvement in an
00:31:48.09 alternating environment versus a constant one, but, in both cases, you can have improvement
00:31:53.03 relative to the ancestor.
00:31:55.08 Most intriguing in this data set is shown in purple, where all of those purple points
00:31:59.23 and lines are nestled right near the intersection of the dashed lines, which show the ancestral
00:32:06.10 This means that the random treatment in the pop. in the experimental evolution study.
00:32:12.09 these lineages did not improve any more than the ancestral performance.
00:32:17.13 In other words, the stochasticity of the environment was too much for them to deal with, and that's
00:32:22.11 bad news in terms of these champions of adaptation.
00:32:26.08 If they can't handle stochastic environments, then that bodes ill for more complex organisms
00:32:32.20 that have much slower adaptive and evolutionary capacity -- we wouldn't expect them to thrive
00:32:38.07 either, or to improve in fitness, when seeing stochastic change.
00:32:43.16 This is pointed to in the diagram in terms of the intersection of the points, and all
00:32:47.20 those purple points nestled near the dashed lines and their intercept, as indicative of
00:32:53.22 adaptive constraint.
00:32:56.20 The next question will be, do viruses evolve survival reproduction trade-offs that we observe
00:33:01.16 in cellular life?
00:33:03.00 Here, we want to examine whether the capacity to adapt in one means, and that is to produce
00:33:09.13 progeny, is something that detracts from the capacity to merely survive in the environment
00:33:15.02 when it poses a challenge.
00:33:16.02 We've seen in cellular systems that you can't have your cake and eat it too, in terms of
00:33:22.02 these two challenges improving through time, that you can either invest in survival or
00:33:26.21 reproduction, but often you have an inability to improve in both simultaneously.
00:33:31.18 Does this carry over to the virus world is an intriguing question.
00:33:35.18 We addressed it first in a phage called phi-6 that infects a bacterium known as Pseudomonas.
00:33:42.01 Pseudomonas syringae.
00:33:43.10 So, this bacterium is important in plant pathology -- it causes plant disease -- but in the laboratory
00:33:50.10 we merely use it to grow the phage as a resource, to examine how well does the phage evolve
00:33:56.12 in environments in the laboratory.
00:33:58.06 So, this is an RNA virus, so it has the capacity to undergo error rate at a high rate, and
00:34:05.04 this allows a lot of mutation and rapid change through time, and it has a very typical infection
00:34:10.11 cycle, where it infects a cell of the bacterium, bursts the cell for the progeny to be released,
00:34:16.11 and then they go on and infect more cells.
00:34:18.05 That's a lytic phase replication cycle.
00:34:21.23 This picture is showing how the virus is able to first infect cells, because the cells have
00:34:28.24 the structures that allow them to adhere to leaf surfaces and, in normal wild conditions
00:34:34.22 they would move across the leaf and enter into the plant, in order to do infection of
00:34:39.10 the plant.
00:34:40.10 So, through time, these viruses have evolved the ability to use those structures as the
00:34:45.08 thing that they attach to, through protein binding, to get into the cell.
00:34:49.13 And that's what's shown in the diagram.
00:34:51.24 So, one can first begin by examining a reaction norm, or just simply the capacity for phi-6
00:34:59.03 to grow under environmental challenges in the laboratory, and, even though those challenges
00:35:03.14 can be amazingly brief, only five minutes long in terms of heat shock, this diagram
00:35:09.14 is showing how the survival of a virus population of phi-6, relative to different heat shock
00:35:17.02 temperatures, a high degree of mortality starts to kick in well above the normal incubation
00:35:22.14 temperature in the laboratory of 25 C. When you get out to values greater than 40 C, you
00:35:29.07 find that this is highly impactful and deleterious to the viruses and their ability to thrive.
00:35:34.20 So, this is indicating how, in the absence of anything else, you can take this virus,
00:35:40.13 expose it to high heat, and, if the heat is high enough, it leads to a high degree of
00:35:45.04 mortality in the virus population.
00:35:47.17 Key in on both 45 and 50 C, where these are environments that we've manip. manipulated
00:35:53.22 in the laboratory to examine, how do viruses deal with heat shocks through time if they
00:35:59.18 see them, and can they key in on this high heat that leads to high mortality and become
00:36:05.16 better adapted to thriving in the face of heat shock?
00:36:10.01 This is a diagram from a recent paper, where it's simply showing you a typical experimental
00:36:13.22 design for a study like this.
00:36:16.03 If you just take the virus, such as in a test tube, in the absence of any cells, and you
00:36:21.03 put it in a heating block so that it'll be challenged with five minutes of high heat,
00:36:26.04 you can then take the viruses and grow them under normal low-heat conditions, where they
00:36:30.13 can replicate in the presence of bacteria, gather all that up, remove the cells, and
00:36:36.24 keep churning them through the experiment.
00:36:39.10 In this way, we're not worried about whether they can, say, co-evolve with the host bacteria
00:36:45.14 we're mostly keying in on the thing that causes high mortality -- the heat shock.
00:36:50.05 Can they key in on that and become better at thriving and improve this value, which
00:36:56.19 shows their very strong mortality that they suffer under high-heat environments?
00:37:02.01 Going quickly to the data from a paper where we did such an experiment, we find that thermal
00:37:07.16 tolerance, or heat shock selection, can readily occur in these viruses, and this is indicative
00:37:13.22 of something that we would call environmental robustness.
00:37:16.13 So, what is the ability of some population to thrive across different environments, and
00:37:22.14 maintain high fitness?
00:37:24.02 You'll see that the lineages shown in red, those that came out of an experiment where
00:37:28.09 this virus saw intermittent heat shocks at 50 C, these lineages improved way out at this
00:37:36.07 temperature, and you'll see this through a statistical result, that they do grow better
00:37:41.20 than their ancestral virus at that very high temperature.
00:37:45.05 Now, it's not like they have absolute capacity to shrug off that heat shock, but they do
00:37:50.07 have greater capacity to do so.
00:37:52.16 And, interestingly, you can see how there's a huge effect at the lower temperatures, which
00:37:57.15 ordinarily are degrading the wild type or unevolved virus, and now these lineages that
00:38:03.04 saw only 50 C have a great capacity to thrive at very, very warm temperatures and including
00:38:10.01 the highest temperature that they saw in the experiment.
00:38:13.17 How does this occur?
00:38:15.10 We've done several experiments of this type and always, for this virus, the same key mutation
00:38:21.06 is the first one and the most important one that leads to thermal tolerance evolving.
00:38:28.01 phi-6 has a genome that's split up into three different segments called large, medium, and
00:38:34.05 In this diagram, it below shows that we know what all the genes are and we know basically
00:38:37.12 what their functions.
00:38:39.04 And here we have a diagram, a cut-through, of the virus body plan that shows you that
00:38:43.20 that. all that nucleic acid is at the center of the virus and it's surrounded by a protein
00:38:49.23 But, uhh. cystoviruses -- this is the family that phi-6 belongs to -- they're are a little
00:38:54.20 different than other bacteriophages in that they have a lipid coat around the entire shell.
00:39:00.12 So, it's a pretty elaborate body plan for a phage, but I really only want you to understand
00:39:06.01 that the key mutation that provides thermal tolerance always seems to arise first on the
00:39:11.06 small segment, and it always seems to arrive in this lysin gene, which is we. going to
00:39:16.24 be responsible for virus particles both getting in and out of the cell, and it is always the
00:39:23.00 same mutation, V207F.
00:39:26.04 It's an amino acid substitution that I'll talk about further.
00:39:29.24 V207F seems to be the key mutation that always allows the viruses to evolve thermal tolerance.
00:39:36.13 And, mechanistically, this makes sense, because when you look at the structure of this lysin
00:39:42.03 protein, a very important enzyme, phenylalanine as an amino acid substitution fills a hydrophobic
00:39:49.06 pocket, and this makes the protein more stable under high heat.
00:39:53.22 So, this is only one mutation coming in but it has profound significance for the thing
00:39:59.10 that is causing high mortality in the virus populations.
00:40:03.03 It's a very simple explanation of how a single amino acid substitution can lead to a profound
00:40:09.15 ability to thrive under a key environmental challenge.
00:40:12.19 So, now, I will talk about how the reproduction is affected for this virus -- even though
00:40:20.15 the key mutation allowed better survival, it's detracting from reproduction.
00:40:25.00 So, if we look at, how does this V207F mutant thrive in an ordinary environment, 25 C, and
00:40:33.03 its ability to grow on bacteria, this diagram is first showing how the plaques. in other
00:40:38.22 words, when you take a virus and you grow it on a bacterial lawn, which is the background
00:40:44.15 in this diagram in white, each one of the particles, if they hit the lawn independently,
00:40:49.18 they'll infect a cell and the progeny will exit that cell, infect neighboring cells,
00:40:54.23 and eventually you'll get this hole in the bacterial lawn called a plaque.
00:40:58.17 Well, something interesting happens when you look at the morphology of the plaques of the
00:41:03.03 wild type virus, which is heat-sensitive, versus this V207F mutant that is heat-tolerant.
00:41:09.19 In all cases, the V207F mutant makes this weird-looking plaque that has a bull's-eye.
00:41:16.11 bull's-eye morphology to it.
00:41:18.09 So, that must be that cells missed being infected and killed as that plaque was produced on
00:41:25.10 the lawn, otherwise it wouldn't have that grayish appearance.
00:41:28.02 In other words, it is not as effective at killing cells even though it is thermal tolerant.
00:41:34.07 That's shown in the bar graph, here, where the selection coefficient or "little s" that
00:41:39.13 is associated with this one mutation has a huge deleterious value under normal growth
00:41:46.21 The wild-type relative to itself, of course, grows equally well so we give it a value of
00:41:52.03 1, whereas the value for the thermal-tolerant mutant is a value much, much lower than 1,
00:41:58.12 and has a negative selection coefficient of 0.25.
00:42:03.00 In comparison to the data I showed you earlier, it's very evident that a life history trade-off
00:42:08.04 is occurring in this virus.
00:42:09.17 In other words, it can either invest in better survival, but the problem is this leads to
00:42:16.17 lower reproduction.
00:42:18.11 This is echoing something that we see in cellular systems, with the investment in either survival
00:42:23.05 or reproduction, but not both at the same time occurring simultaneously.
00:42:29.07 It also relates to an earlier study, where the researchers found that if you just randomly
00:42:35.02 take viruses that can infect a different bacterium -- E. coli -- and you look at what is their
00:42:40.13 mortality rate versus their multiplication rate, and you plot that on the same graph,
00:42:45.17 there's a pretty amazing relationship for these viruses that they produced in their
00:42:49.20 study or used in their study to show that these are highly correlated traits to one
00:42:56.13 So, either these viruses that they studied grew well and survived poorly, or had a high
00:43:01.09 mortality rate, or they grew poorly and survived better.
00:43:05.10 So, it shows that if you just look at viruses from the natural environment and you look
00:43:11.13 at their relationship for survival versus reproduction, they show a big difference,
00:43:16.22 and they fall along this line.
00:43:18.24 You can think of our experiment as having taken any one virus on this line, and can
00:43:23.17 you move it up or down the line through an experimental evolution study, and that's exactly
00:43:27.24 what we did.
00:43:28.24 So, the survival reproduction trade-off holds, and you can move them up and down the line
00:43:33.24 if you vary the environment in the right way.
00:43:36.16 So, the bull's-eye plaque that I showed you before is a pretty strange morphology.
00:43:43.19 And there's actually. even though people have used old microbiology methods to the
00:43:47.16 current day, the ability to visualize plaques is something that has dated back to at least
00:43:52.23 the 1940s, so it's an old method, and yet we actually don't know very much mathematically.
00:43:58.15 If you construct a model about, how does a plaque form, this is still a pretty big challenge
00:44:03.04 to mathematical biologists.
00:44:05.05 The three-dimensionality that this plaque is growing in, on an agar surface, is something
00:44:10.07 that. it's very hard to describe mathematically.
00:44:12.13 And, especially if one looks through a time-lapse film of these types of bull's-eye plaques
00:44:19.02 forming, this is a very strange morphology that it's hard to understand how some cells
00:44:24.19 are killed initially, and then there's a lot of cells that do not get killed, and then.
00:44:29.10 and then more cells get killed, and so on, to lead to such a complex morphology.
00:44:33.13 I would say that this is still an ongoing challenge to simply describe how do plaques
00:44:38.07 form, mathematically and mechanistically.
00:44:40.22 In this example from our experiment, even one mutational change can lead to very different
00:44:46.00 morphology that provides an even bigger challenge to describe.
00:44:50.08 So, now, we can think of the viruses and the way that they encounter challenges in the
00:44:57.12 natural world as, yes, they have an amazing capacity to see challenges and overcome them.
00:45:05.01 So, we do fear emergence of viruses as something that will continue to be a challenge for humans,
00:45:11.23 domesticated species, conserving endangered species. all of these realms are threatened
00:45:17.24 by the emergence of viruses coming in and doing destruction.
00:45:21.14 However, there are certain environments where these champions of adaptation simply cannot
00:45:26.11 make it.
00:45:27.12 So, consistent with certain climate change.
00:45:29.14 climate change models, we have environmental change through time as something that can
00:45:33.16 constrain virus evolution, and some of the fundamental trade-offs that we see in cellular
00:45:38.13 systems, especially survival versus reproduction, carries over even into organisms that don't
00:45:45.07 undergo metabolism -- the viruses -- so it's not just they're shunting energy into one
00:45:49.16 thing or another.
00:45:51.01 It shows you more of a fundamental divide in the biological world of, can you invest
00:45:55.20 in survival versus reproduction, and get away with a co-investment in both of them?
00:46:00.07 And it seems like that's not the case.
00:46:02.10 So, I'd like to end by acknowledging the people who did this work.
00:46:06.11 I keyed in on a lot of the work done by my current lab group, as well as past lab members
00:46:10.24 who I've had the pleasure of working with.
00:46:12.20 I've had fantastic mentors and collaborators all over the world.
00:46:17.16 And I have to thank them deeply for their dedication to the experiments to present the
00:46:21.13 data that wound up in our papers.
00:46:23.22 I can also thank the funders for the work, NSF and its programs such as the BEACON Center
00:46:31.09 for experimental evolution, as well as NIH, Yale University, and nonprofits such as the
00:46:38.02 Project High Hopes Foundation have provided key funds for all the work that I showed you
Viruses are by far the most abundant 'lifeforms' in the oceans and are the reservoir of most of the genetic diversity in the sea. The estimated 10 30 viruses in the ocean, if stretched end to end, would span farther than the nearest 60 galaxies. Every second, approximately 10 23 viral infections occur in the ocean. These infections are a major source of mortality, and cause disease in a range of organisms, from shrimp to whales. As a result, viruses influence the composition of marine communities and are a major force behind biogeochemical cycles. Each infection has the potential to introduce new genetic information into an organism or progeny virus, thereby driving the evolution of both host and viral assemblages. Probing this vast reservoir of genetic and biological diversity continues to yield exciting discoveries.
The Next Generation of Batteries Could Be Built by Viruses
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Viruses' qualities could be adopted for nanoengineering by selectively reprogramming their DNA so that it functions as a scaffold for materials used in battery electrodes. Illustration: Casey Chin
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In 2009, MIT bioengineering professor Angela Belcher traveled to the White House to demo a small battery for President Barack Obama, who was just two months into his first term in office. There aren’t many batteries that can get an audience with the leader of the free world, but this wasn’t your everyday power pouch. Belcher had used viruses to assemble a lithium-ion battery’s positive and negative electrodes, an engineering breakthrough that promised to reduce the toxicity of the battery manufacturing process and boost their performance. Obama was preparing to announce $2 billion in funding for advanced battery technology, and Belcher’s coin cell pointed to what the future might hold in store.
A decade after Belcher demoed her battery at the White House, her viral assembly process has rapidly advanced. She’s made viruses that can work with over 150 different materials and demonstrated that her technique can be used to manufacture other materials like solar cells. Belcher’s dream of zipping around in a “virus-powered car” still hasn’t come true, but after years of work she and her colleagues at MIT are on the cusp of taking the technology out of the lab and into the real world.
As nature’s microscopic zombies, viruses straddle the divide between the living and the dead. They are packed full of DNA, a hallmark of all living things, but they can’t reproduce without a host, which disqualifies them from some definitions of life. Yet as Belcher demonstrated, these qualities could be adopted for nanoengineering to produce batteries that have improved energy density, lifetime, and charging rates that can be produced in an eco-friendly way.
“There has been growing interest in the battery field to explore materials in nanostructure form for battery electrodes,” says Konstantinos Gerasopoulos, a senior research scientist who works on advanced batteries at Johns Hopkins Applied Physics Laboratory. “There are several ways that nanomaterials can be made with conventional chemistry techniques. The benefit of using biological materials, such as viruses, is that they already exist in this ‘nano’ form, so they are essentially a natural template or scaffold for the synthesis of battery materials.”
Nature has found plenty of ways to build useful structures out of inorganic materials without the help of viruses. Belcher’s favorite example is the abalone shell, which is highly structured at the nanoscale, lightweight, and sturdy. Over the process of tens of millions of years, the abalone evolved so that its DNA produces proteins that extract calcium molecules from the mineral-rich aquatic environment and deposit it in ordered layers on its body. The abalone never got around to building batteries, but Belcher realized this same fundamental process could be implemented in viruses to build useful materials for humans.
“We’ve been engineering biology to control nanomaterials that are not normally grown biologically,” Belcher says. “We’ve expanded biology’s toolkit to work with new materials.”
Belcher’s virus of choice is the M13 bacteriophage, a cigar-shaped virus that replicates in bacteria. Although it's not the only virus that can be used for nanoengineering, Belcher says it works well because its genetic material is easy to manipulate. To conscript the virus for electrode production, Belcher exposes it to the material she wants it to manipulate. Natural or engineered mutations in the DNA of some of the viruses will cause them to latch on to the material. Belcher then extracts these viruses and uses them to infect a bacterium, which results in millions of identical copies of the virus. This process is repeated over and over, and with each iteration the virus becomes a more finely-tuned battery architect.
Belcher’s genetically engineered viruses can’t tell a battery anode from a cathode, but they don’t need to. Their DNA is only programmed to do a simple task, but, when millions of viruses perform the same task together, they produce a usable material. For example, the genetically-modified virus might be engineered to express a protein on its surface that attracts cobalt oxide particles to cover its body. Additional proteins on the surface of the virus attract more and more cobalt oxide particles. This essentially forms a cobalt oxide nanowire made of linked viruses that can be used in a battery electrode.
Belcher’s process matches DNA sequences with elements on the periodic table to create a sped-up form of unnatural selection. Coding the DNA one way might cause a virus to latch on to iron phosphate, but, if the code is tweaked, the virus might prefer cobalt oxide. The technique could be extended to any element on the periodic table, it’s just a matter of finding the DNA sequence that matches it. In this sense, what Belcher is doing is not so far from the selective breeding done by dog fanciers to create pooches with desirable aesthetic qualities that would be unlikely to ever show up in nature. But instead of breeding poodles, Belcher is breeding battery-building viruses.
Belcher has used her viral assembly technique to build electrodes and implement them in a range of different battery types. The cell she demoed for Obama was a standard lithium-ion coin cell like you might find in a watch and was used to power a small LED. But for the most part, Belcher has used electrodes with more exotic chemistries like lithium-air and sodium-ion batteries. The reason, she says, is that she didn’t see much sense in trying to compete with the well-established lithium-ion producers. “We aren’t trying to compete with current technology,” Belcher says. “We look at the question, ‘Can biology be used to solve some problems that haven’t been solved so far?’”
One promising application is to use the viruses to create highly ordered electrode structures to shorten the path of an ion as it moves through the electrode. This would increase the battery’s charge and discharge rate, which is “one of the ‘holy grails’ of energy storage,” says Paul Braun, director of the Materials Research Laboratory at the University of Illinois. In principle, he says, viral assembly can be used to significantly improve the structure of battery electrodes and boost their charging rates.
So far Belcher’s virally-assembled electrodes have had an essentially random structure, but she and her colleagues are working on coaxing the viruses into more ordered arrangements. Nevertheless, her virus-powered batteries performed as well or better than those with electrodes made with traditional manufacturing techniques, including improved energy capacity, cycle life, and charging rates. But Belcher says the biggest benefit of viral assembly is that it is eco-friendly. Traditional electrode manufacturing techniques require working with toxic chemicals and high temperatures. All Belcher needs are the electrode materials, room temperature water, and some genetically-engineered viruses.
“Something my lab is completely focused on now is trying to get the cleanest technology,” Belcher says. This includes taking into consideration things like where the mined material for electrodes is sourced, and the waste products produced by manufacturing the electrodes.
Belcher hasn’t brought the technology to market yet, but says she and her colleagues have several papers under review that show how the technology can be commercialized for energy and other applications. (She declined to get into the specifics.)
When Belcher first suggested that these DNA-driven assembly lines might be harnessed to build useful things for humans, she encountered a lot of skepticism from her colleagues. “People told me I was crazy,” she says. The idea no longer seems so far-fetched, but taking the process out of the lab and into the real world has proven challenging. “Traditional battery manufacturing uses inexpensive materials and processes, but engineering viruses for performance and solving scalability issues will require years of research and associated costs,” says Bogdan Dragnea, a professor of chemistry at the Indiana University Bloomington. “We have only recently started to understand the potential virus-based materials hold from a physical properties perspective.”
Belcher has already co-founded two companies based on her work with viral assembly. Cambrios Technologies, founded in 2004, uses a manufacturing process inspired by viruses to build the electronics for touch screens. Her second company, Siluria Technologies, uses viruses in a process that converts methane to ethylene, a gas widely used in manufacturing. At one point, Belcher was also using viruses to assemble solar cells, but the technology wasn’t efficient enough to compete with new perovskite solar cells.
Materials and Methods
IAV Sequence Data Preparation.
We collected all IAV full-length sequences from humans, birds, and pigs encoding the H1, H2, and H5 subtypes of HA and the N1 subtype of NA. Identical sequences and apparent recombinants and other problematic sequences were excluded. For each gene, a subset of sequences of a size amenable to molecular clock analyses (∼300 sequences) was sampled, preserving the most basal sequences in the major clades and reducing the number of overrepresented recent sequences so that sampling across different years was fairly even. Because the effective sampling time of post-1977 to pre-2009 human H1N1 is 27 y earlier than the actual sampling date, we shifted the dates accordingly (10). The full-length swine and human IAV sequences from the alignments of the PB2, PB1, PA, NP, M1/2, and N1/2 genes from a study by Worobey et al. (10) were used for the analyses summarized in SI Appendix, Figs. S9 and S10.
We analyzed these IAV alignments with the HSLC model as described (10) using a Gaussian Markov random field Bayesian skyride coalescent tree prior and a general time reversible + gamma substitution model. Each major host group was allowed its own rate in the HSLC model. For the analysis of the H1, H2, and H5 subtypes, all of the avian sequences were assumed to evolve at the same rate or were allowed independent rates, with similar results in each case (SI Appendix, Fig. S14) the human H2 and human H1 clades were allowed their own rates. We ran analyses for 50 million steps in most cases and used Tracer v1.5 to ensure effective sample size values >200. We used TreeAnnotator to infer and annotate MCC trees. To test the robustness of the deep, pre-1918 divergence time of the human H1 lineage, as well as the clustering of the 1918 sequences with the classic swine influenza lineage rather than with the postpandemic seasonal human H1N1 lineage, we conducted several additional analyses of H1 datasets. These analyses included (i) exclusion of the 189-nt HA fragments from 1918, as well as laboratory strains of IAV from both humans and swine from the 1930s (ii) subsampling at most one sequence per host lineage per year (iii) subsampling only sequences sampled before the extinction of H1N1 in 1957 (iv) separate analysis of the HA stalk domain (sites 1–150 and sites 921–1,698) and (v) analysis including only the 565 third-position sites and the subset of 503 silent third-position sites. [We used MacClade v4.08a (50) to visualize all amino acids substitutions along the MCC tree and then determined which were due to substitutions at the third codon position by referring to the genetic code and the nucleotide alignment. One hundred thirteen of 4,107 third-position substitutions along the MCC tree were nonsynonymous.]
U Content Analyses.
We compared the U content of the 1918 HA and NA sequences with the range observed in avian viruses (SI Appendix, Fig. S7). We estimated an upper bound on when the 1918 HA sequence emerged in a mammalian host using the approach described by Worobey et al. (10), calculating how long a sequence starting at the average U content among avian strains would take to increase to the U content value observed in the 1918 sequence, assuming the rate of U content increase in human H3 HA (because it appears that the H1 lineage was approaching an asymptote between 1918 and 1957). The overall H3 substitution rate (10) is slightly higher than that of H1 (Fig. 2), so this assumption likely provides a conservative estimate of the upper bound (i.e., if the rate of U content increase in H1 were slightly lower than in H3, this discrepancy would suggest the entry into humans was slightly earlier than our estimate of ∼1905). The upper and lower range estimates were determined using the upper and lower 95% confidence interval values for the avian U content distribution. A P value for a test of the hypothesis that the avian-to-human jump predated 1918, based on U content, was calculated as the proportion, out of 10,000 replicates, in which the year drawn from the above-mentioned distribution was greater than (i.e., postdated) 1918.
Tests for Adaptive Evolution.
We used the random effects branch-site model (51) for detecting episodic diversifying selection (EDS) in the H1 HA phylogeny. We included representative sequences from each host lineage to permit a search for evidence of EDS on the branch between each host, and within each host after putative host jumps (SI Appendix, Fig. S5).
Tests of Whether Within-Human H1 HA Diversity Predates Between-Host Diversity in Other Genes.
A P value for a test of the hypothesis that the within-human diversity of the H1 subtype of HA predates the human + swine + avian N1 NA diversity was calculated by drawing a date from the human H1 TMRCA posterior density and a date from the multihost N1 TMRCA posterior density, and then determining the proportion of 10,000 replicates for which the N1 date was earlier than the H1 date. The same approach was used for tests of whether the within-human H1 diversity predates the human + swine diversity within N1 and each of the internal genes (SI Appendix, Fig. S10) and for a test of whether the within-human H1 diversity predates the human + swine + avian PB1 diversity (SI Appendix, Fig. S9).