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The criminal legal system in the USA drives an incarceration rate that is the highest on the planet, with disparities by class and race among its signature features1–3. During the first year of the coronavirus disease 2019 (COVID-19) pandemic, the number of incarcerated people in the USA decreased by at least 17%—the largest, fastest reduction in prison population in American history4. Here we ask how this reduction influenced the racial composition of US prisons and consider possible mechanisms for these dynamics. Using an original dataset curated from public sources on prison demographics across all 50 states and the District of Columbia, we show that incarcerated white people benefited disproportionately from the decrease in the US prison population and that the fraction of incarcerated Black and Latino people sharply increased. This pattern of increased racial disparity exists across prison systems in nearly every state and reverses a decade-long trend before 2020 and the onset of COVID-19, when the proportion of incarcerated white people was increasing amid declining numbers of incarcerated Black people5. Although a variety of factors underlie these trends, we find that racial inequities in average sentence length are a major contributor. Ultimately, this study reveals how disruptions caused by COVID-19 exacerbated racial inequalities in the criminal legal system, and highlights key forces that sustain mass incarceration. To advance opportunities for data-driven social science, we publicly released the data associated with this study at Zenodo6.

Pathogen emergence (PE) is a complex phenomenon with major public health implications. Over the past decades, numerous underlying mechanisms facilitating the emergence of pathogenic bacteria have been elucidated. In this review, we highlight the diverse molecular and environmental drivers associated with PE, with an emphasis on the interplay of canonical gene transfer mechanisms and the increasingly appreciated role of genetic variations, providing a more coherent picture of this process. Given the interactive and multifactorial nature of PE, we contend that the development of approaches that embrace the integration of these factors is indispensable in order to truly comprehend this complex phenomenon and develop strategies to mitigate this threat.

Despite several decades of computational and experimental work across many systems, evolvability remains on the periphery with regards to its status as a widely accepted and regularly applied theoretical concept. Here we propose that its marginal status is partly a result of large gaps between the diverse but disconnected theoretical treatments of evolvability and the relatively narrower range of studies that have tested it empirically. To make this case, we draw on a range of examples-from experimental evolution in microbes, to molecular evolution in proteins-where attempts have been made to mend this disconnect. We highlight some examples of progress that has been made and point to areas where synthesis and translation of existing theory can lead to further progress in the still-new field of empirical measurements of evolvability. U.S. National Science Foundation's Systems and Synthetic Biology program [1714550] Published version The authors would like to thank two reviewers for helpful comments on a prior draft of the manuscript. JAD acknowledges partial support from the U.S. National Science Foundation's Systems and Synthetic Biology program under Award Number 1714550.

Understanding the factors that shape viral evolution is critical for developing effective antiviral strategies, accurately predicting viral evolution, and preventing pandemics. One fundamental determinant of viral evolution is the interplay between viral protein biophysics and the host machineries that regulate protein folding and quality control. Most adaptive mutations in viruses are biophysically deleterious, resulting in a viral protein product with folding defects. In cells, protein folding is assisted by a dynamic system of chaperones and quality control processes known as the proteostasis network. Host proteostasis networks determine the fates of viral proteins with biophysical defects, either by assisting with folding or by targeting them for degradation. In this review, we discuss and analyze new discoveries revealing that host proteostasis factors can profoundly shape the sequence space accessible to evolving viral proteins. We also discuss the many opportunities for research progress proffered by the proteostasis perspective on viral evolution and adaptation. Expected final online publication date for the Annual Review of Virology, Volume 10 is September 2023. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

Protein space is a rich analogy for genotype-phenotype maps, where amino acid sequence is organized into a high-dimensional space that highlights the connectivity between protein variants. It is a useful abstraction for understanding the process of evolution, and for efforts to engineer proteins towards desirable phenotypes. Few framings of protein space consider how higher-level protein phenotypes can be described in terms of their biophysical dimensions, nor do they rigorously interrogate how forces like epistasis—describing the nonlinear interaction between mutations and their phenotypic consequences—manifest across these dimensions. In this study, we deconstruct a low-dimensional protein space of a bacterial enzyme (dihydrofolate reductase; DHFR) into “subspaces” corresponding to a set of kinetic and thermodynamic traits [(kcat, KM,Ki, andTm(melting temperature)]. We then examine how three mutations (eight alleles in total) display pleiotropy in their interactions across these subspaces. We extend this approach to examine protein spaces across three orthologous DHFR enzymes (Escherichia coli, Listeria grayi, andChlamydia muridarum), adding a genotypic context dimension through which epistasis occurs across subspaces. In doing so, we reveal that protein space is a deceptively complex notion, and that the process of protein evolution and engineering should consider how interactions between amino acid substitutions manifest across different phenotypic subspaces.

The term “druggability” describes the molecular properties of drugs or targets in pharmacological interventions and is commonly used in work involving drug development for clinical applications. There are no current analogues for this notion that quantify the drug-target interaction with respect to a given target variant’s sensitivity across a breadth of drugs in a panel, or a given drug’s range of effectiveness across alleles of a target protein. Using data from low-dimensional empirical fitness landscapes composed of 16β-lactamase alleles and sevenβ-lactam drugs, we introduce two metrics that capture (i) the average susceptibility of an allelic variant of a drug target to any available drug in a given panel (“variant vulnerability”), and (ii) the average applicability of a drug (or mixture) across allelic variants of a drug target (“drug applicability”). Finally, we (iii) disentangle the quality and magnitude of interactions between loci in the drug target and the seven drug environments in terms of their mutation by mutation by environment (G x G x E) interactions, offering mechanistic insight into the variant variability and drug applicability metrics. Summarizing, we propose that our framework can be applied to other datasets and pathogen-drug systems to understand which pathogen variants in a clinical setting are the most concerning (low variant vulnerability), and which drugs in a panel are most likely to be effective in an infection defined by standing genetic variation in the pathogen drug target (high drug applicability).

Bacterial antibiotic resistance represents a public health concern that will remain relevant for the foreseeable future. Antibiotic resistant bacterial infections can occur in two ways: (1) a host is infected by a resistant bacterial strain (due to between-host transmission of resistance), or (2) a host is infected infection by a susceptible strain, followed by the de novo evolution or acquisition of resistance (due to within-host evolution of resistance). While both are critical to understanding how the evolution of resistance happens in natural settings, the relative rate at which they occur is unclear. Here, we employ phylogenetic comparative methods to examine the evolutionary dynamics of resistance inEscherichia colifor multiple common antibiotics. We report evolutionary patterns consistent with commonde novoevolution of resistance for some antibiotics and sustained transmission of resistant strains for others. For example, we observe 79 putativede novoresistance evolution events for resistance to Cefuroxime but only 31 for resistance to Ciprofloxacin, despite similar numbers of observed infections (239 and 267 respectively). We find that clusters of resistance are generally larger for Ciprofloxacin, Ceftazidima and AmoxiClav, which suggests that for these drugs, resistance is often transmitted from patient to patient. In contrast, we find that cluster sizes for resistance are generally smaller for PipTaz, Cefuroxime and Gentamicin, suggesting that resistance to these drugs is less often transmitted from patient to patient and instead evolvesde novo. In addition to differences between drugs, we also find that cluster sizes were generally larger in phylogroup B2 compared to the other phylogroups, suggesting that transmission of resistant strains is more common in this phylogroup compared to the others. Our study proposes new approaches for determining the importance ofde novoevolution or acquisition (within-host evolution) from resistance from infection with an already resistant strain (between-host transmission). Significantly, this work also bridges an important gap between evolutionary genomics and epidemiology, opening up a range of opportunities for studying the evolutionary dynamics of bacterial antibiotic resistance.

Interactions between mutations (epistasis) can add substantial complexity to genotype-phenotype maps, hampering our ability to predict evolution. Yet, recent studies have shown that the fitness effect of a mutation can often be predicted from the fitness of its genetic background using simple, linear relationships. This phenomenon, termedglobal epistasis, has been leveraged to reconstruct fitness landscapes and infer adaptive trajectories in a wide variety of contexts. However, little attention has been paid to how patterns of global epistasis may be affected by environmental variation — both from external sources or induced by the population itself through eco-evolutionary feedbacks — despite this variation frequently being a major driver of evolution. By analyzing a four-mutation fitness landscape, here we show that patterns of global epistasis can be strongly modulated by the concentration of a drug in the environment. Using previous theoretical results, we demonstrate that this modulation can be explained by how specific gene-by-gene interactions are modified by drug dose. Importantly, our results highlight the need to incorporate potential environmental variation into the global epistasis framework in order to predict adaptation in dynamic environments.

The 1997 film Gattaca has emerged as a canonical pop culture reference used to discuss modern controversies in genetics and bioethics. It appeared in theaters a few years prior to the announcement of the “completion” of the human genome (2000), as the science of human genetics was developing a renewed sense of its social implications. The story is set in a near-future world in which parents can, with technological assistance, influence the genetic composition of their offspring on the basis of predicted life outcomes. The current moment—25 years after the film’s release—offers an opportunity to reflect on where society currently stands with respect to the ideas explored in Gattaca. Here, we review and discuss several active areas of genetic research—genetic prediction, embryo selection, forensic genetics, and others—that interface directly with scenes and concepts in the film. On its silver anniversary, we argue that Gattaca remains an important reflection of society’s expectations and fears with respect to the ways that genetic science has manifested in the real world. In accompanying supplemental material, we offer some thought questions to guide group discussions inside and outside of the classroom.

Vibrio vulnificus remains one of the deadliest waterborne pathogens, yet little is known of the ecology that drive outbreaks. As a nationally notifiable disease, all cases of Vibrio vulnificus diagnosed in the United States are reported to the state in which they occurred, as well as the Centers for Disease Control (CDC) in Atlanta, Georgia. Given that the state of Florida is a ‘hotspot’ for Vibrio vulnificus in the United States, we examined the prevalence and incidence of cases reported to the Florida Department of Health (2008-2020). Using a dataset comprised of 448 cases of disease caused by Vibrio vulnificus infection, we identified environmental variables that were associated with clinical cases and deaths. Combined with data from the National Oceanic and Atmospheric Administration (NOAA), we developed statistical models to examine the relationship between meteorological measurements such as wind speed, air temperature, water temperature, and sea-level pressure. We then examined the association of those meteorological variables with coastal cases of Vibrio vulnificus, including the outcome, survival, or death. Between 2008 and 2020, Vibrio vulnificus cases generally increased over time, peaking in 2017. Not surprisingly, there appears to be a strong correlation between water temperature and air temperature in Florida. However, as water temperature and air temperature increased, so too did the likelihood that an infection with Vibrio vulnificus would lead to patient death. Interestingly, we also found that as mean wind speed and sea-level pressure decreased, the probability that a Vibrio vulnificus case would be reported increased. Given these results, we discuss the potential factors that may contribute to the observed correlations. We further speculate that the meteorological variables we measured may increase in importance as they relate to the incidence of Vibrio vulnificus in light of rising global temperatures.

Of the over 100 species in the genus Vibrio, approximately twelve are associated with clinical disease, such as cholera and vibriosis. Crucially, eleven of those twelve—V. alginolyticus, V. cholerae, V. cincinnatiensis, V. hollinsae, e.g., Grimontia hollinsae, V. furnissii, V. mimicus, V. parahaemolyticus, V. vulnificus, V. harveyi, V. scophthalmi and V. metschnikovi—have also been isolated from birds. Since 1965, pathogenic Vibrio species have been consistently isolated from aquatic and ground-foraging bird species, which has implications for public health, as well as the One Health paradigm defined as an ecology-inspired, integrative framework for the study of health and disease, inclusive of environmental, human, and animal health. In this meta-analysis, we identified 76 studies from the primary literature which report on or examine birds as hosts for pathogenic Vibrio species. We found that the burden of disease in birds was most commonly associated with V. cholerae, followed by V. metschnikovi and V. parahaemolyticus. Meta-analysis wide prevalences of the Vibrio pathogens varied from 19% for V. parahaemolyticus to 1% for V. mimicus. Wild and domestic birds were both affected, which may have implications for conservation, as well as agriculturally associated avian species. As pathogenic Vibrios become more abundant throughout the world as a result of warming estuaries and oceans, susceptible avian species should be continually monitored as potential reservoirs for these pathogens.

The criminal legal system in the United States drives an incarceration rate that is the highest on the planet, with disparities by class and race among its signature features [1–3]. During the first year of the COVID-19 pandemic, the number of incarcerated people in the U.S. decreased by at least 17%—the largest, fastest reduction in prison population in American history [4]. In this study, we ask how this reduction influenced the racial com-position of U.S. prisons, and consider possible mechanisms for these dynamics. Using an original dataset curated from public sources on prison demographics across all 50 states and the District of Columbia, we show that incarcerated white people benefited disproportionately from the decrease in the U.S. prison population, and that the fraction of incarcerated Black and Latino people sharply increased. This pattern of increased racial disparity exists across prison systems in nearly every state and reverses a decades-long trend before 2020 and the onset of COVID-19, when the proportion of incarcerated white people was increasing amid declining numbers of incarcerated Black people [5]. Although a variety of factors underlie these trends, we find that racial inequities in average sentence length are a major contributor. Ultimately, this study reveals how disruptions caused by COVID-19 exacer-bated racial inequalities in the criminal legal system, and highlights key forces that sustain mass incarceration.

The dynamics of adaptation, reversion, and compensation have been central topics in microbial evolution, and several studies have attempted to resolve the population genetics underlying how these dynamics occur. However, questions remain regarding how certain features—the evolution of mutators and whether compensatory mutations alleviate costs fully or partially—may influence the evolutionary dynamics of compensation and reversion. In this study, we attempt to explain findings from experimental evolution by utilizing computational and theoretical approaches toward a more refined understanding of how mutation rate and the fitness effects of compensatory mutations influence adaptive dynamics. We find that high mutation rates increase the probability of reversion toward the wild type when compensation is only partial. However, the existence of even a single fully compensatory mutation is associated with a dramatically decreased probability of reversion to the wild type. These findings help to explain specific results from experimental evolution, where compensation was observed in nonmutator strains, but reversion (sometimes with compensation) was observed in mutator strains, indicating that real-world compensatory mutations are often unable to fully alleviate the costs associated with adaptation. Our findings emphasize the potential role of the supply and quality of mutations in crafting the dynamics of adaptation and reversal, with implications for theoretical population genetics and for biomedical contexts like the evolution of antibiotic resistance.

Seasonal influenza kills hundreds of thousands every year, with multiple constantly changing strains in circulation at any given time. A high mutation rate enables the influenza virus to evade recognition by the human immune system, including immunity acquired through past infection and vaccination. Here, we capture the genetic similarity of influenza strains and their evolutionary dynamics with genotype networks. We show that the genotype networks of influenza A (H3N2) hemagglutinin are characterized by heavy-tailed distributions of module sizes and connectivity indicative of critical behavior. We argue that (i) genotype networks are driven by mutation and host immunity to explore a subspace of networks predictable in structure and (ii) genotype networks provide an underlying structure necessary to capture the rich dynamics of multistrain epidemic models. In particular, inclusion of strain-transcending immunity in epidemic models is dependent upon the structure of an underlying genotype network. This interplay is consistent with self-organized criticality where the epidemic dynamics of influenza locates critical regions of its genotype network. We conclude that this interplay between disease dynamics and network structure might be key for future network analysis of pathogen evolution and realistic multistrain epidemic models.

In this Perspectives article, Ogbunugafor revisits a famous and influential analogy introduced by renowned evolutionary biologist John Maynard Smith in a 1970 manuscript entitled “Natural selection and the concept of protein space (Smith 1970)..., In 1970, John Maynard Smith published a letter, entitled “Natural Selection and the Concept of a Protein Space,” that proposed a simple analogy for the incremental process of adaptive evolution. His “Protein Space” analogy contains the substrate for many central ideas in evolutionary genetics, and has motivated important discoveries within several subdisciplines of evolutionary science. In this Perspectives article, I commemorate the 50th anniversary of this seminal work by discussing its unique legacy and by describing its intriguing historical context. I propose that the Protein Space analogy is not only important because of its scientific richness, but also because of what it can teach us about the art of constructing useful and subversive analogies.

While reverse genetics and functional genomics have long affirmed the role of individual mutations in determining protein function, there have been fewer studies addressing how large-scale changes in protein sequences, such as in entire modular segments, influence protein function and evolution. Given how recombination can reassort protein sequences, these types of changes may play an underappreciated role in how novel protein functions evolve in nature. Such studies could aid our understanding of whether certain organismal phenotypes related to protein function-such as growth in the presence or absence of an antibiotic-are robust with respect to the identity of certain modular segments. In this study, we combine molecular genetics with biochemical and biophysical methods to gain a better understanding of protein modularity in dihydrofolate reductase (DHFR), an enzyme target of antibiotics also widely used as a model for protein evolution. We replace an integral α-helical segment of Escherichia coli DHFR with segments from a number of different organisms (many nonmicrobial) and examine how these chimeric enzymes affect organismal phenotypes (e.g., resistance to an antibiotic) as well as biophysical properties of the enzyme (e.g., thermostability). We find that organismal phenotypes and enzyme properties are highly sensitive to the identity of DHFR modules, and that this chimeric approach can create enzymes with diverse biophysical characteristics.

The sequence space accessible to evolving proteins can be enhanced by cellular chaperones that assist biophysically defective clients in navigating complex folding landscapes. It is also possible, however, for proteostasis mechanisms that promote strict quality control to greatly constrain accessible protein sequence space. Unfortunately, most efforts to understand how proteostasis mechanisms influence evolution rely on artificial inhibition or genetic knockdown of specific chaperones. The few experiments that perturb quality control pathways also generally modulate the levels of only individual quality control factors. Here, we use chemical genetic strategies to tune proteostasis networks via natural stress response pathways that regulate levels of entire suites of chaperones and quality control mechanisms. Specifically, we upregulate the unfolded protein response (UPR) to test the hypothesis that the host endoplasmic reticulum (ER) proteostasis network shapes the sequence space accessible to human immunodeficiency virus-1 (HIV) envelope (Env) protein. Elucidating factors that enhance or constrain Env sequence space is critical because Env evolves extremely rapidly, yielding HIV strains with antibody and drug escape mutations. We find that UPR-mediated upregulation of ER proteostasis factors, particularly those controlled by the IRE1-XBP1s UPR arm, globally reduces Env mutational tolerance. Conserved, functionally important Env regions exhibit the largest decreases in mutational tolerance upon XBP1s activation. This phenomenon likely reflects strict quality control endowed by XBP1s-mediated remodeling of the ER proteostasis environment. Intriguingly and in contrast, specific regions of Env, including regions targeted by broadly neutralizing antibodies, display enhanced mutational tolerance when XBP1s is activated, hinting at a role for host proteostasis network hijacking in potentiating antibody escape. These observations reveal a key function for proteostasis networks in decreasing instead of expanding the sequence space accessible to client proteins, while also demonstrating that the host ER proteostasis network profoundly shapes the mutational tolerance of Env in ways that could have important consequences for HIV adaptation.

Cholera is a severe diarrhoeal disease that spreads rapidly and affects millions of people each year, resulting in tens of thousands of deaths. The disease is caused byVibrio choleraeO1 and is characterized by watery diarrhoea that can be lethal if not properly treated. Cholera had not been reported in South America from the late 1800s until 1991, when it was introduced in Peru, wreaking havoc in one of the biggest epidemics reported to date. Within a year, the disease had spread to most of the Latin American region, resulting in millions of cases and thousands of deaths in all affected countries. Despite its aggressive entry, cholera virtually disappeared from the continent after 1999. The progression of the entire epidemic was well documented, making it an ideal model to understand cholera dynamics. In this review, we highlight how the synergy of socioeconomic, political and ecological factors led to the emergence, rapid spread and eventual disappearance of cholera in Latin America. We discuss how measures implemented during the cholera epidemic drastically changed its course and continental dynamics. Finally, we synthesize our findings and highlight potential lessons that can be learned for efficient and standardized cholera management programmes during future outbreaks in non-endemic areas.

Adaptive mutations are often associated with a fitness cost. These costs can be compensated for through the acquisition of additional mutations, or the adaptations can be lost through reversion in settings where they are no longer favored. While the dynamics of adaptation, reversion and compensation have been central features in several studies of microbial evolution, few studies have attempted to resolve the population genetics underlying how and when either compensation or reversion occur. Specifically, questions remain regarding how certain actors—the evolution of mutators and whether compensatory mutations alleviate costs fully or partially— may influence evolutionary dynamics of compensation and reversion. In this study, we attempt to explain findings from an experimental evolution study by utilizing computational and theoretical approaches towards a more refined understanding of how mutation rate and the fitness effects of compensatory mutations influence evolutionary dynamics. We find that high mutation rates increase the probability of reversion towards the wild type when compensation is only partial. However, the existence of even a single fully compensatory mutation is associated with a dramatically decreased probability of reversion to the wild type. These findings help to explain specific findings from experimental evolution, where compensation was observed in non-mutator strains, but reversion (sometimes with compensation) was observed in mutator strains, indicating that real-world compensatory mutations are often unable to fully alleviate the costs associated with resistance. Our findings emphasize the potential role of the supply and quality of mutations in crafting the evolution of antibiotic resistance, and more generally highlight the importance of population genetic context for explaining findings from experimental evolution.

Variation in free-living microparasite survival can have a meaningful impact on the ecological dynamics of established and emerging infectious diseases. Nevertheless, resolving the importance of indirect and environmental transmission in the ecology of epidemics remains a persistent challenge. It requires accurately measuring the free-living survival of pathogens across reservoirs of various kinds and quantifying the extent to which interaction between hosts and reservoirs generates new infections. These questions are especially salient for emerging pathogens, where sparse and noisy data can obfuscate the relative contribution of different infection routes. In this study, we develop a mechanistic, mathematical model that permits both direct (host-to-host) and indirect (environmental) transmission and then fit this model to empirical data from 17 countries affected by an emerging virus (SARS-CoV-2). From an ecological perspective, our model highlights the potential for environmental transmission to drive complex, nonlinear dynamics during infectious disease outbreaks. Summarizing, we propose that fitting alternative models with indirect transmission to real outbreak data from SARS-CoV-2 can be useful, as it highlights that indirect mechanisms may play an underappreciated role in the dynamics of infectious diseases, with implications for public health.

The relationship between parasite virulence and transmission is a pillar of evolutionary theory that has implications for public health. Part of this canon involves the idea that virulence and free-living survival (a key component of transmission) may have different relationships in different host&ndash, parasite systems. Most examinations of the evolution of virulence-transmission relationships&mdash, Theoretical or empirical in nature&mdash, Tend to focus on the evolution of virulence, with transmission being a secondary consideration. Even within transmission studies, the focus on free-living survival is a smaller subset, though recent studies have examined its importance in the ecology of infectious diseases. Few studies have examined the epidemic-scale consequences of variation in survival across different virulence&ndash, survival relationships. In this study, we utilize a mathematical model motivated by aspects of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) natural history to investigate how evolutionary changes in survival may influence several aspects of disease dynamics at the epidemiological scale. Across virulence&ndash, survival relationships (where these traits are either positively or negatively correlated), we found that small changes (5% above and below the nominal value) in survival can have a meaningful effect on certain outbreak features, including R0, and on the size of the infectious peak in the population. These results highlight the importance of properly understanding the mechanistic relationship between virulence and parasite survival, as the evolution of increased survival across different relationships with virulence may have considerably different epidemiological signatures.

Variation in free-living, microparasite survival can have a meaningful impact on the ecological dynamics of established and emerging infectious diseases. Nevertheless, resolving the importance of environmental transmission in the ecology of epidemics remains a persistent challenge, requires accurate measuring the free-living survival of pathogens across reservoirs of various kinds, and quantifying the extent to which interaction between hosts and reservoirs generates new infections. These questions are especially salient for emerging pathogens, where sparse and noisy data can obfuscate the relative contribution of different infection routes. In this study, we develop a mechanistic, mathematical model that permits both direct (host-to-host) and indirect (environmental) transmission and then fit this model to empirical data from 17 countries affected by an emerging virus (SARS-CoV-2). From an ecological perspective, our model highlights the potential for environmental transmission to drive complex, non-linear dynamics during infectious disease outbreaks. Summarizing, we propose that fitting such models with environmental transmission to real outbreak data from SARS-CoV-2 transmission highlights that variation in environmental transmission is an underappreciated aspect of the ecology of infectious disease, and an incomplete understanding of its role has consequences for public health interventions.

The relationship between parasite virulence and transmission is a pillar of evolutionary theory that has specific implications for public health. Part of this canon involves the idea that virulence and free-living survival (a key component of transmission) may have different relationships in different host-parasite systems. Most examinations of the evolution of virulence-transmission relationships—theoretical or empirical in nature—tend to focus on the evolution of virulence, with transmission a secondary consideration. And even within transmission studies, the focus on free-living survival is a smaller subset, though recent studies have examined its importance in the ecology of infectious diseases. Few studies have examined the epidemic-scale consequences of variation in survival across different virulence-survival relationships. In this study, we utilize a mathematical model motivated by aspects of SARS-CoV-2 natural history to investigate how evolutionary changes in survival may influence several aspects of disease dynamics at the epidemiological scale. Across virulence-survival relationships (where these traits are positively or negatively correlated), we found that small changes (5% above and below the nominal value) in survival can have a meaningful effect on certain outbreak features, including theR0, and the size of the infectious peak in the population. These results highlight the importance of properly understanding the mechanistic relationship between virulence and parasite survival, as evolution of increased survival across different relationships with virulence will have considerably different epidemiological signatures.

The sequence space accessible to evolving proteins can be enhanced by cellular chaperones that assist biophysically defective clients in navigating complex folding landscapes. It is also possible, at least in theory, for proteostasis mechanisms that promote strict quality control to greatly constrain accessible protein sequence space. Unfortunately, most efforts to understand how proteostasis mechanisms influence evolution rely on artificial inhibition or genetic knockdown of specific chaperones. The few experiments that perturb quality control pathways also generally modulate the levels of only individual quality control factors. Here, we use chemical genetic strategies to tune proteostasis networks via natural stress response pathways that regulate the levels of entire suites of chaperones and quality control mechanisms. Specifically, we upregulate the unfolded protein response (UPR) to test the hypothesis that the host endoplasmic reticulum (ER) proteostasis network shapes the sequence space accessible to human immunodeficiency virus-1 (HIV-1) envelope (Env) protein. Elucidating factors that enhance or constrain Env sequence space is critical because Env evolves extremely rapidly, yielding HIV strains with antibody- and drug-escape mutations. We find that UPR-mediated upregulation of ER proteostasis factors, particularly those controlled by the IRE1-XBP1s UPR arm, globally reduces Env mutational tolerance. Conserved, functionally important Env regions exhibit the largest decreases in mutational tolerance upon XBP1s induction. Our data indicate that this phenomenon likely reflects strict quality control endowed by XBP1s-mediated remodeling of the ER proteostasis environment. Intriguingly, and in contrast, specific regions of Env, including regions targeted by broadly neutralizing antibodies, display enhanced mutational tolerance when XBP1s is induced, hinting at a role for host proteostasis network hijacking in potentiating antibody escape. These observations reveal a key function for proteostasis networks in decreasing instead of expanding the sequence space accessible to client proteins, while also demonstrating that the host ER proteostasis network profoundly shapes the mutational tolerance of Env in ways that could have important consequences for HIV adaptation.

Ecologists have long studied the evolution of niche breadth, including how variability in environments can drive the evolution of specialism and generalism. This concept is of particular interest in viruses, where niche breadth evolution may explain viral disease emergence, or underlie the potential for therapeutic measures like phage therapy. Despite the significance and potential applications of virus–host interactions, the genetic determinants of niche breadth evolution remain underexplored in many bacteriophages. In this study, we present the results of an evolution experiment with a model bacteriophage system, Escherichia virus T4, in several host environments: exposure to Escherichia coli C, exposure to E. coli K‐12, and exposure to both E. coli C and E. coli K‐12. This experimental framework allowed us to investigate the phenotypic and molecular manifestations of niche breadth evolution. First, we show that selection on different hosts led to measurable changes in phage productivity in all experimental populations. Second, whole—genome sequencing of experimental populations revealed signatures of selection. Finally, clear and consistent patterns emerged across the host environments, especially the presence of new mutations in phage structural genes—genes encoding proteins that provide morphological and biophysical integrity to a virus. A comparison of mutations found across functional gene categories revealed that structural genes acquired significantly more mutations than other categories. Our findings suggest that structural genes are central determinants in bacteriophage niche breadth., Microbial ecologists have long asked how microbes expand their niche breadth. Our study uses experimental evolution in bacteriophage T4 to inquire about the genes that change in response to expanded host niche breadth. We find that structural genes—those that provide morphological and biophysical integrity to a virus—are central determinants in bacteriophage niche breadth.

While several basic properties of cholera outbreaks are common to most settings-the pathophysiology of the disease, the waterborne nature of transmission, and others-recent findings suggest that transmission within households may play a larger role in cholera outbreaks than previously appreciated. Important features of cholera outbreaks have long been effectively modeled with mathematical and computational approaches, but little is known about how variation in direct transmission via households may influence epidemic dynamics. In this study, we construct a mathematical model of cholera that incorporates transmission within and between households. We observe that variation in the magnitude of household transmission changes multiple features of disease dynamics, including the severity and duration of outbreaks. Strikingly, we observe that household transmission influences the effectiveness of possible public health interventions (e.g. water treatment, antibiotics, vaccines). We find that vaccine interventions are more effective than water treatment or antibiotic administration when direct household transmission is present. Summarizing, we position these results within the landscape of existing models of cholera, and speculate on its implications for epidemiology and public health.

The hepatitis C virus (HCV) epidemic often occurs through the persistence of injection drug use. Mathematical models have been useful in understanding various aspects of the HCV epidemic, and especially, the importance of new treatment measures. Until now, however, few models have attempted to understand HCV in terms of an interaction between the various actors in an HCV outbreak—hosts, viruses and the needle injection equipment. In this study, we apply perspectives from the ecology of infectious diseases to model the transmission of HCV among a population of injection drug users. The products of our model suggest that modelling HCV as an indirectly transmitted infection—where the injection equipment serves as an environmental reservoir for infection—facilitates a more nuanced understanding of disease dynamics, by animating the underappreciated actors and interactions that frame disease. This lens may allow us to understand how certain public health interventions (e.g. needle exchange programmes) influence HCV epidemics. Lastly, we argue that this model is of particular importance in the light of the modern opioid epidemic, which has already been associated with outbreaks of viral diseases.

In silicoapproaches have served a central role in the development of evolutionary theory for generations. This especially applies to the concept of the fitness landscape, one of the most important abstractions in evolutionary genetics, and one which has benefited from the presence of large empirical data sets only in the last decade or so. In this study, we propose a method that allows us to generate enormous data sets that walk the line betweenin silicoand empirical: word usage frequencies as catalogued by the Google ngram corpora. These data can be codified or analogized in terms of a multidimensional empirical fitness landscape towards the examination of advanced concepts—adaptive landscape by environment interactions, clonal competition, higher-order epistasis and countless others. We argue that the greaterLexical Landscapesapproach can serve as a platform that offers an astronomical number of fitness landscapes for exploration (at least) or theoretical formalism (potentially) in evolutionary biology.