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    Infectious Disease Emergence: Past, Present, and Future

    Emerging infections, as defined by Stephen Morse of Columbia University in his contribution to this chapter, are infections that are rapidly increasing in incidence or geographic range, including such previously unrecognized diseases as HIV/AIDS, severe acute respiratory syndrome (SARS), Ebola hemorrhagic fever, and Nipah virus encephalitis. Among his many contributions to efforts to recognize and address the threat of emerging infections, Lederberg co-chaired the committees that produced two landmark Institute of Medicine (IOM) reports, Emerging Infections: Microbial Threats to Health in the United States (IOM, 1992) and Microbial Threats to Health (IOM, 2003), which provided a crucial framework for understanding the drivers of infectious disease emergence (Box WO-3 and Figure WO-13). As the papers in this chapter demonstrate, this framework continues to guide research to elucidate the origins of emerging infectious threats, to inform the analysis of recent patterns of disease emergence, and to identify risks for future disease emergence events so as to enable early detection and response in the event of an outbreak, and perhaps even predict its occurrence.

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    Microbial Evolution and Co-Adaptation: A Tribute to the Life and Scientific Legacies of Joshua Lederberg: Workshop Summary.

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    ContentsHardcopy Version at National Academies Press

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    5Infectious Disease Emergence: Past, Present, and Future

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    OVERVIEW

    Emerging infections, as defined by Stephen Morse of Columbia University in his contribution to this chapter, are infections that are rapidly increasing in incidence or geographic range, including such previously unrecognized diseases as HIV/AIDS, severe acute respiratory syndrome (SARS), Ebola hemorrhagic fever, and Nipah virus encephalitis. Among his many contributions to efforts to recognize and address the threat of emerging infections, Lederberg co-chaired the committees that produced two landmark Institute of Medicine (IOM) reports, (IOM, 1992) and (IOM, 2003), which provided a crucial framework for understanding the drivers of infectious disease emergence (Box WO-3 and Figure WO-13). As the papers in this chapter demonstrate, this framework continues to guide research to elucidate the origins of emerging infectious threats, to inform the analysis of recent patterns of disease emergence, and to identify risks for future disease emergence events so as to enable early detection and response in the event of an outbreak, and perhaps even predict its occurrence.

    In the chapter’s first paper, Morse describes two distinct stages in the emergence of infectious diseases: the introduction of a new infection to a host population, and the establishment within and dissemination from this population. He considers the vast and largely uncharacterized “zoonotic pool” of possible human pathogens and the increasing opportunities for infection presented by ecological upheaval and globalization. Using hantavirus pulmonary syndrome and H5N1 influenza as examples, Morse demonstrates how zoonotic pathogens gain access to human populations. While many zoonotic pathogens periodically infect humans, few become adept at transmitting or propagating themselves, Morse observes. Human activity, however, is making this transition increasingly easy by creating efficient pathways for pathogen transmission around the globe. “We know what is responsible for emerging infections, and should be able to prevent them,” he concludes, through global surveillance, diagnostics, research, and above all, the political will to make them happen.

    The authors of the chapter’s second paper, workshop presenter Mark Woolhouse and Eleanor Gaunt of the University of Edinburgh, draw several general conclusions about the ecological origins of novel human pathogens based on their analysis of human pathogen species discovered since 1980. Using a rigorous, formal methodology, Woolhouse and Gaunt produced and refined a catalog of the nearly 1,400 recognized human pathogen species. A subset of 87 species have been recognized since 1980—and are currently thought to be “novel” pathogens. The authors note four attributes of these novel pathogens that they expect will describe most future emergent microbes: a preponderance of RNA viruses; pathogens with nonhuman animal reservoirs; pathogens with a broad host range; and pathogens with some (perhaps initially limited) potential for human-human transmission.

    Like Morse, Woolhouse and Gaunt consider the challenges faced by novel pathogens to become established in a new host population and achieve efficient transmission, conceptualizing Morse’s observation that “many are called but few are chosen” in graphic form, as a pyramid. It depicts the approximately 1,400 pathogens capable of infecting humans, of which 500 are capable of human-to-human transmission, and among which fewer than 150 have the potential to cause epidemic or endemic disease; evolution—over a range of time scales—drives pathogens up the pyramid. The paper concludes with a discussion of the public health implications of the pyramid model, which suggests that ongoing global ecological change will continue to produce novel infectious diseases at or near the current rate of three per year.

    In contrast to other contributors to this chapter, who focus on what, why, and where infectious diseases emerge, Jonathan Eisen, of the University of California, Davis, considers how new functions and processes evolve to generate novel pathogens. Eisen investigates the origin of microbial novelty by integrating evolutionary analyses with studies of genome sequences, a field he terms “phylogenomics.” In his essay, he illustrates the results of such analyses in a series of “phylogenomic tales” that describe the use of phylogenomics to predict the function of uncharacterized genes in a variety of organisms, and in elucidating the genetic basis of a complex symbiotic relationship involving three species.

    Knowledge of microbial genomes, and the functions they encode, is severely limited, Eisen observes. Among 40 phyla of bacteria, for example, most of the available genomic sequences were from only three phyla; sequencing of Archaea and Eukaryote genomes has proceeded in a similarly sporadic manner. To fill these gaps in our knowledge of the “tree of life,” his group has begun an initiative called the Genomic Encyclopedia of Bacteria and Archaea. Eisen describes this effort and advocates the further integration of information on microbial phylogeny, genetic sequence, and gene function with biogeographical data, in order to produce a “field guide to microbes.”

    Source : www.ncbi.nlm.nih.gov

    Beyond Omicron: what’s next for COVID’s viral evolution

    The rapid spread of new variants offers clues to how SARS-CoV-2 is adapting and how the pandemic will play out over the next several months.

    NEWS FEATURE 07 December 2021

    Correction 05 January 2022

    Beyond Omicron: what’s next for COVID’s viral evolution

    The rapid spread of new variants offers clues to how SARS-CoV-2 is adapting and how the pandemic will play out over the next several months.

    Ewen Callaway Twitter Facebook Email

    Illustration by Ana Kova

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    As the world sped towards a pandemic in early 2020, evolutionary biologist Jesse Bloom gazed into the future of SARS-CoV-2. Like many virus specialists at the time, he predicted that the new pathogen would not be eradicated. Rather, it would become endemic — the fifth coronavirus to permanently establish itself in humans, alongside four ‘seasonal’ coronaviruses that cause relatively mild colds and have been circulating in humans for decades or more.

    Bloom, who is based at the Fred Hutchinson Cancer Research Center in Seattle, Washington, saw these seasonal coronaviruses as potentially providing a roadmap for how SARS-CoV-2 might evolve and for the future of the pandemic. But little is known about how these other viruses continue to thrive. One of the best-studied examples — a seasonal coronavirus called 229E — infects people repeatedly throughout their lives. But it’s not clear whether these reinfections are the result of fading immune responses in their human hosts or whether changes in the virus help it to dodge immunity. To find out, Bloom got hold of decades-old blood samples from people probably exposed to 229E, and tested them for antibodies against different versions of the virus going back to the 1980s.

    The results were striking1. Blood samples from the 1980s contained high levels of infection-blocking antibodies against a 1984 version of 229E. But they had much less capacity to neutralize a 1990s version of the virus. They were even less effective against 229E variants from the 2000s and 2010s. The same held true for blood samples from the 1990s: people had immunity to viruses from the recent past, but not to those from the future, suggesting that the virus was evolving to evade immunity.

    “Now that we’ve had almost two years to see how SARS-CoV-2 evolves, I think there are clear parallels with 229E,” says Bloom. Variants such as Omicron and Delta carry mutations that blunt the potency of antibodies raised against past versions of SARS-CoV-2. And the forces propelling this ‘antigenic change’ are likely to grow stronger as most of the planet gains immunity to the virus through infection, vaccination or both. Researchers are racing to characterize the highly mutated Omicron variant. But its rapid rise in South Africa suggests that it has already found a way to dodge human immunity.

    How bad is Omicron? What scientists know so far

    How SARS-CoV-2 evolves over the next several months and years will determine what the end of this global crisis looks like — whether the virus morphs into another common cold or into something more threatening such as influenza or worse. A global vaccination push that has delivered nearly 8 billion doses is shifting the evolutionary landscape, and it’s not clear how the virus will meet this challenge. Meanwhile, as some countries lift restrictions to control viral spread, opportunities increase for SARS-CoV-2 to make significant evolutionary leaps.

    Scientists are searching for ways to predict the virus’s next moves, looking to other pathogens for clues. They are tracking the effects of the mutations in the variants that have arisen so far, while watching out for new ones. They expect SARS-CoV-2 eventually to evolve more predictably and become like other respiratory viruses — but when this shift will occur, and which infection it might resemble is not clear.

    Researchers are learning as they go, says Andrew Rambaut, an evolutionary biologist at the University of Edinburgh, UK. “We haven’t had much to go on.”

    An early plateau

    Scientists tracking the evolution of SARS-CoV-2 are looking out for two broad categories of changes to the virus. One makes it more infectious or transmissible, for instance by replicating more quickly so that it spreads more easily through coughs, sneezes and wheezes. The other enables it to overcome a host’s immune response. When a virus first starts spreading in a new host, the lack of pre-existing immunity means that there is little advantage to be gained by evading immunity. So, the first — and biggest — gains a new virus will make tend to come through enhancements to infectivity or transmissibility.

    “I was thoroughly expecting that this new coronavirus would adapt to humans in a meaningful way — and that would probably mean increased transmissibility,” says Wendy Barclay, a virologist at Imperial College London.

    Genome sequencing early in the pandemic showed the virus diversifying and picking up about two single-letter mutations per month. This rate of change is about half that of influenza and one-quarter that of HIV, thanks to an error-correcting enzyme coronaviruses possess that is rare among other RNA viruses. But few of these early changes seemed to have any effect on the behaviour of SARS-CoV-2, or show signs of being favoured under natural selection.

    Source : www.nature.com

    Prediction, dynamics, and visualization of antigenic phenotypes of seasonal influenza viruses

    Humans mount an antibody-mediated immune response against influenza viruses that can be recalled. Nevertheless, individuals can suffer from recurrent influenza infections as viruses can change their antigenic properties by altering their ...Human seasonal influenza viruses evolve rapidly, enabling the virus population to evade immunity and reinfect previously infected individuals. Antigenic properties are largely determined by the surface glycoprotein hemagglutinin (HA), and amino acid ...

    Research Article Evolution FREE ACCESS Share on

    Prediction, dynamics, and visualization of antigenic phenotypes of seasonal influenza viruses

    Richard A. Neher, Trevor Bedford, Rodney S. Daniels, +1 , Colin A. Russell https://orcid.org/0000-0002-2113-162X, and Boris I. Shraiman [email protected] -1Authors Info & Affiliations

    March 7, 2016 | 113 (12) E1701-E1709 | https://doi.org/10.1073/pnas.1525578113

    Significance

    Humans mount an antibody-mediated immune response against influenza viruses that can be recalled. Nevertheless, individuals can suffer from recurrent influenza infections as viruses can change their antigenic properties by altering their surface glycoproteins. This antigenic evolution requires frequent update of seasonal influenza vaccines. To inform vaccine updates, laboratories that contribute to the World Health Organization Global Influenza Surveillance and Response System assess the antigenic phenotypes of circulating viruses. Based on the relationship of antigenic distance to genetic differences between viruses, we developed a model to interpret measured antigenic data and predict the properties of viruses that have not been characterized antigenically and explore the model’s value in predicting the future composition of influenza virus populations.

    Abstract

    Human seasonal influenza viruses evolve rapidly, enabling the virus population to evade immunity and reinfect previously infected individuals. Antigenic properties are largely determined by the surface glycoprotein hemagglutinin (HA), and amino acid substitutions at exposed epitope sites in HA mediate loss of recognition by antibodies. Here, we show that antigenic differences measured through serological assay data are well described by a sum of antigenic changes along the path connecting viruses in a phylogenetic tree. This mapping onto the tree allows prediction of antigenicity from HA sequence data alone. The mapping can further be used to make predictions about the makeup of the future A(H3N2) seasonal influenza virus population, and we compare predictions between models with serological and sequence data. To make timely model output readily available, we developed a web browser-based application that visualizes antigenic data on a continuously updated phylogeny.

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    MANAGE ALERTS

    Seasonal influenza viruses evade immunity in the human population through frequent amino acid substitutions in their hemagglutinin (HA) and neuraminidase (NA) surface glycoproteins (1). To maintain efficacy, vaccines against seasonal influenza viruses need to be updated frequently to match the antigenic properties of the circulating viruses. To facilitate informed vaccine strain selection, the genotypes and antigenic properties of circulating viruses are continuously monitored by the World Health Organization (WHO) Global Influenza Surveillance and Response System (GISRS), with a substantial portion of the virological characterizations being performed by the WHO influenza Collaborating Centers (WHO CCs) (2).

    Antigenic properties of influenza viruses are measured in hemagglutination inhibition (HI) assays (3) that record the minimal antiserum concentration (titer) necessary to prevent crosslinking of red blood cells by a standardized amount of virus based on hemagglutinating units. An antiserum is typically obtained from a single ferret infected with a particular reference virus. For a panel of test viruses, the HI titer is determined by a series of twofold dilutions of each antiserum. An antiserum is typically potent against the homologous virus (the reference virus used to produce the antiserum), but higher concentrations (and hence lower titers) are frequently required to prevent hemagglutination by other (heterologous) test viruses. HI titers typically decrease with increasing genetic distance between reference and test viruses (1).

    Given multiple antisera raised against different reference viruses and a panel of test viruses, WHO CCs routinely measure the HI titers

    T aβ Taβ

    of all combinations of test viruses and sera , resulting in a matrix of HI titers (see Fig. 1). The HI titer of a test virus using antiserum raised against the reference virus is typically standardized as

    H aβ = log 2 ( T bβ )− log 2 ( T aβ )

    Haβ=log2(Tbβ)−log2(Taβ)

    , i.e., the difference in the number of twofold dilutions between homologous and heterologous titer. Standardized

    log 2 log2

    titers from many HI assays can be visualized in two dimensions via multidimensional scaling—an approach termed “antigenic cartography” (4). Although standard cartography does not use sequence information, sequences have been used as priors for positions in a Bayesian version of multidimensional scaling (5). To infer contributions of individual amino acid substitutions to antigenic evolution, Harvey et al. and Sun et al. (6, 7) have used models that predict HI titer differences by comparing sequences of reference and test viruses.

    Fig. 1.

    Antigenic data and models for HI titers. () A typical table reporting HI titer data. Each number in the table is the maximum dilution at which the antiserum (column) inhibited hemagglutination of red blood cells by a virus (row). The red numbers on the diagonal indicate homologous titers. A typical HI assay consists of all reciprocal measurements of the available antisera and reference viruses, and a number of test viruses that are measured against all antisera, but for which no homologous antiserum exists. To make measurements using different antisera comparable, we define standardized log-transformed titers

    Source : www.pnas.org

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