Evolutionary analysis of the dynamics of viral infectious disease

The utility of whole-genome sequencing to identify likely transmission pairs for pathogens with slow and variable evolution

Pathogen whole-genome sequencing (WGS) has been used to track the transmission of infectious diseases in extraordinary detail, especially for pathogens that undergo fast and steady evolution, as is the case with many RNA viruses. However, for other pathogens evolution is less predictable, making interpretation of these data to inform our understanding of their epidemiology more challenging and the value of densely collected pathogen genome data uncertain. Here, we assess the utility of WGS for one such pathogen, in the "who-infected-whom" identification problem. We study samples from hosts (130 cattle, 111 badgers) with confirmed infection of M. bovis (causing bovine Tuberculosis), which has an estimated clock rate as slow as ∼0.1-1 variations per year. For each potential pathway between hosts, we calculate the relative likelihood that such a transmission event occurred. This is informed by an epidemiological model of transmission, and host life history data. By including WGS data, we shrink the number of plausible pathways significantly, relative to those deemed likely on the basis of life history data alone. Despite our uncertainty relating to the evolution of M. bovis, the WGS data are therefore a valuable adjunct to epidemiological investigations, especially for wildlife species whose life history data are sparse.

Early detection of highly transmissible viral variants using phylogenomics

As demonstrated by the SARS-CoV-2 pandemic, the emergence of novel viral strains with increased transmission rates poses a serious threat to global health. Statistical models of genome sequence evolution may provide a critical tool for early detection of these strains. Using a novel stochastic model that links transmission rates to the entire viral genome sequence, we study the utility of phylogenetic methods that use a phylogenetic tree relating viral samples versus count-based methods that use case counts of variants over time exclusively to detect increased transmission rates and identify candidate causative mutations. We find that phylogenies in particular can detect novel transmission-enhancing variants very soon after their origin and may facilitate the development of early detection systems for outbreak surveillance.

Dynamic clade transitions and the influence of vaccination on the spatiotemporal circulation of SARS-CoV-2 variants

Since 2021, the emergence of variants of concern (VOC) has led Brazil to experience record numbers of in COVID-19 cases and deaths. The expanded spread of the SARS-CoV-2 combined with a low vaccination rate has contributed to the emergence of new mutations that may enhance viral fitness, leading to the persistence of the disease. Due to limitations in the real-time genomic monitoring of new variants in some Brazilian states, we aimed to investigate whether genomic surveillance, coupled with epidemiological data and SARS-CoV-2 variants spatiotemporal spread in a smaller region, can reflect the pandemic progression at a national level. Our findings revealed three SARS-CoV-2 variant replacements from 2021 to early 2022, corresponding to the introduction and increase in the frequency of Gamma, Delta, and Omicron variants, as indicated by peaks of the Effective Reproductive Number (Reff). These distinct clade replacements triggered two waves of COVID-19 cases, influenced by the increasing vaccine uptake over time. Our results indicated that the effectiveness of vaccination in preventing new cases during the Delta and Omicron circulations was six and eleven times higher, respectively, than during the period when Gamma was predominant, and it was highly efficient in reducing the number of deaths. Furthermore, we demonstrated that genomic monitoring at a local level can reflect the national trends in the spread and evolution of SARS-CoV-2.

Breaking the Chain: Protease Inhibitors as Game Changers in Respiratory Viruses Management

Respiratory viral infections (VRTIs) rank among the leading causes of global morbidity and mortality, affecting millions of individuals each year across all age groups. These infections are caused by various pathogens, including rhinoviruses (RVs), adenoviruses (AdVs), and coronaviruses (CoVs), which are particularly prevalent during colder seasons. Although many VRTIs are self-limiting, their frequent recurrence and potential for severe health complications highlight the critical need for effective therapeutic strategies. Viral proteases are crucial for the maturation and replication of viruses, making them promising therapeutic targets. This review explores the pivotal role of viral proteases in the lifecycle of respiratory viruses and the development of protease inhibitors as a strategic response to these infections. Recent advances in antiviral therapy have highlighted the effectiveness of protease inhibitors in curtailing the spread and severity of viral diseases, especially during the ongoing COVID-19 pandemic. It also assesses the current efforts aimed at identifying and developing inhibitors targeting key proteases from major respiratory viruses, including human RVs, AdVs, and (severe acute respiratory syndrome coronavirus-2) SARS-CoV-2. Despite the recent identification of SARS-CoV-2, within the last five years, the scientific community has devoted considerable time and resources to investigate existing drugs and develop new inhibitors targeting the virus's main protease. However, research efforts in identifying inhibitors of the proteases of RVs and AdVs are limited. Therefore, herein, it is proposed to utilize this knowledge to develop new inhibitors for the proteases of other viruses affecting the respiratory tract or to develop dual inhibitors. Finally, by detailing the mechanisms of action and therapeutic potentials of these inhibitors, this review aims to demonstrate their significant role in transforming the management of respiratory viral diseases and to offer insights into future research directions.

Evolutionary Invasion Analysis of Modern Epidemics Highlights the Context-Dependence of Virulence Evolution

Models are often employed to integrate knowledge about epidemics across scales and simulate disease dynamics. While these approaches have played a central role in studying the mechanics underlying epidemics, we lack ways to reliably predict how the relationship between virulence (the harm to hosts caused by an infection) and transmission will evolve in certain virus-host contexts. In this study, we invoke evolutionary invasion analysis-a method used to identify the evolution of uninvadable strategies in dynamical systems-to examine how the virulence-transmission dichotomy can evolve in models of virus infections defined by different natural histories. We reveal peculiar patterns of virulence evolution between epidemics with different disease natural histories (SARS-CoV-2 and hepatitis C virus). We discuss the findings with regards to the public health implications of predicting virus evolution, and in broader theoretical canon involving virulence evolution in host-parasite systems.

Phylodynamic analysis of foot-and-mouth disease virus evolution in Mar Chiquita, Argentina

Foot-and-mouth disease is a highly contagious disease affecting cloven-hoofed animals, resulting in considerable economic losses. Its causal agent is foot-and-mouth disease virus (FMDV), a picornavirus. Due to its error-prone replication and rapid evolution, the transmission and evolutionary dynamics of FMDV can be studied using genomic epidemiological approaches. To analyze FMDV evolution and identify possible transmission routes in an Argentinean region, field samples that tested positive for FMDV by PCR were obtained from 21 farms located in the Mar Chiquita district. Whole FMDV genome sequences were obtained by PCR amplification in seven fragments and sequencing using the Sanger technique. The genome sequences obtained from these samples were then analyzed using phylogenetic, phylogeographic, and evolutionary approaches. Three local transmission clusters were detected among the sampled viruses. The dataset was analyzed using Bayesian phylodynamic methods with appropriate coalescent and relaxed molecular clock models. The estimated mean viral evolutionary rate was 1.17 × 10- 2 substitutions/site/year. No significant differences in the rate of viral evolution were observed between farms with vaccinated animals and those with unvaccinated animals. The most recent common ancestor of the sampled sequences was dated to approximately one month before the first reported case in the outbreak. Virus transmission started in the south of the district and later dispersed to the west, and finally arrived in the east. Different transmission routes among the studied herds, such as non-replicating vectors and close contact contagion (i.e., aerosols), may be responsible for viral spread.

Comparative evolutionary analyses of peste des petits ruminants virus genetic lineages

Peste des petits ruminants virus (PPRV) causes a highly infectious disease affecting mainly goats and sheep in large parts of Africa, Asia, and the Middle East and has an important impact on the global economy and food security. Full genome sequencing of PPRV strains has proved to be critical to increasing our understanding of PPR epidemiology and to inform the ongoing global efforts for its eradication. However, the number of full PPRV genomes published is still limited and with a heavy bias towards recent samples and genetic Lineage IV (LIV), which is only one of the four existing PPRV lineages. Here, we generated genome sequences for twenty-five recent (2010-6) and seven historical (1972-99) PPRV samples, focusing mainly on Lineage II (LII) in West Africa. This provided the first opportunity to compare the evolutionary pressures and history between the globally dominant PPRV genetic LIV and LII, which is endemic in West Africa. Phylogenomic analysis showed that the relationship between PPRV LII strains was complex and supported the extensive transboundary circulation of the virus within West Africa. In contrast, LIV sequences were clearly separated per region, with strains from West and Central Africa branched as a sister clade to all other LIV sequences, suggesting that this lineage also has an African origin. Estimates of the time to the most recent common ancestor place the divergence of modern LII and LIV strains in the 1960s-80s, suggesting that this period was particularly important for the diversification and spread of PPRV globally. Phylogenetic relationships among historical samples from LI, LII, and LIII and with more recent samples point towards a high genetic diversity for all these lineages in Africa until the 1970s-80s and possible bottleneck events shaping PPRV's evolution during this period. Molecular evolution analyses show that strains belonging to LII and LIV have evolved under different selection pressures. Differences in codon usage and adaptative selection pressures were observed in all viral genes between the two lineages. Our results confirm that comparative genomic analyses can provide new insights into PPRV's evolutionary history and molecular epidemiology. However, PPRV genome sequencing efforts must be ramped up to increase the resolution of such studies for their use in the development of efficient PPR control and surveillance strategies.

Dynamic clade transitions and the influence of vaccine rollout on the spatiotemporal circulation of SARS-CoV-2 variants in São Paulo, Brazil

Since 2021, the emergence of variants of concern (VOC) has led Brazil to experience record numbers of in COVID-19 cases and deaths. The expanded spread of the SARS-CoV-2 combined with a low vaccination rate has contributed to the emergence of new mutations that may enhance viral fitness, leading to the persistence of the disease. Due to limitations in the real-time genomic monitoring of new variants in some Brazilian states, we aimed to investigate whether genomic surveillance, coupled with epidemiological data and SARS-CoV-2 variants spatiotemporal spread in a smaller region, can reflect the pandemic progression at a national level. Our findings revealed three SARS-CoV-2 variant replacements from 2021 to early 2022, corresponding to the introduction and increase in the frequency of Gamma, Delta, and Omicron variants, as indicated by peaks of the Effective Reproductive Number (Reff). These distinct clade replacements triggered two waves of COVID-19 cases, influenced by the increasing vaccine uptake over time. Our results indicated that the effectiveness of vaccination in preventing new cases during the Delta and Omicron circulations was six and eleven times higher, respectively, than during the period when Gamma was predominant, and it was highly efficient in reducing the number of deaths. Furthermore, we demonstrated that genomic monitoring at a local level can reflect the national trends in the spread and evolution of SARS-CoV-2.

Using big sequencing data to identify chronic SARS-Coronavirus-2 infections

The evolution of SARS-Coronavirus-2 (SARS-CoV-2) has been characterized by the periodic emergence of highly divergent variants. One leading hypothesis suggests these variants may have emerged during chronic infections of immunocompromised individuals, but limited data from these cases hinders comprehensive analyses. Here, we harnessed millions of SARS-CoV-2 genomes to identify potential chronic infections and used language models (LM) to infer chronic-associated mutations. First, we mined the SARS-CoV-2 phylogeny and identified chronic-like clades with identical metadata (location, age, and sex) spanning over 21 days, suggesting a prolonged infection. We inferred 271 chronic-like clades, which exhibited characteristics similar to confirmed chronic infections. Chronic-associated mutations were often high-fitness immune-evasive mutations located in the spike receptor-binding domain (RBD), yet a minority were unique to chronic infections and absent in global settings. The probability of observing high-fitness RBD mutations was 10-20 times higher in chronic infections than in global transmission chains. The majority of RBD mutations in BA.1/BA.2 chronic-like clades bore predictive value, i.e., went on to display global success. Finally, we used our LM to infer hundreds of additional chronic-like clades in the absence of metadata. Our approach allows mining extensive sequencing data and providing insights into future evolutionary patterns of SARS-CoV-2.

Inference of Infectious Disease Transmission through a Relaxed Bottleneck Using Multiple Genomes Per Host

In recent times, pathogen genome sequencing has become increasingly used to investigate infectious disease outbreaks. When genomic data is sampled densely enough amongst infected individuals, it can help resolve who infected whom. However, transmission analysis cannot rely solely on a phylogeny of the genomes but must account for the within-host evolution of the pathogen, which blurs the relationship between phylogenetic and transmission trees. When only a single genome is sampled for each host, the uncertainty about who infected whom can be quite high. Consequently, transmission analysis based on multiple genomes of the same pathogen per host has a clear potential for delivering more precise results, even though it is more laborious to achieve. Here, we present a new methodology that can use any number of genomes sampled from a set of individuals to reconstruct their transmission network. Furthermore, we remove the need for the assumption of a complete transmission bottleneck. We use simulated data to show that our method becomes more accurate as more genomes per host are provided, and that it can infer key infectious disease parameters such as the size of the transmission bottleneck, within-host growth rate, basic reproduction number, and sampling fraction. We demonstrate the usefulness of our method in applications to real datasets from an outbreak of Pseudomonas aeruginosa amongst cystic fibrosis patients and a nosocomial outbreak of Klebsiella pneumoniae.

Approaches and challenges to inferring the geographical source of infectious disease outbreaks using genomic data

Genomic data hold increasing potential in the elucidation of transmission dynamics and geographical sources of infectious disease outbreaks. Phylogeographic methods that use epidemiological and genomic data obtained from surveillance enable us to infer the history of spatial transmission that is naturally embedded in the topology of phylogenetic trees as a record of the dispersal of infectious agents between geographical locations. In this Review, we provide an overview of phylogeographic approaches widely used for reconstructing the geographical sources of outbreaks of interest. These approaches can be classified into ancestral trait or state reconstruction and structured population models, with structured population models including popular structured coalescent and birth-death models. We also describe the major challenges associated with sequencing technologies, surveillance strategies, data sharing, and analysis frameworks that became apparent during the generation of large-scale genomic data in recent years, extending beyond inference approaches. Finally, we highlight the role of genomic data in geographical source inference and clarify how this enhances understanding and molecular investigations of outbreak sources.

Gene Transfer-Based Phylogenetics: Analytical Expressions and Additivity via Birth-Death Theory

The genomic era has opened up vast opportunities in molecular systematics, one of which is deciphering the evolutionary history in fine detail. Under this mass of data, analyzing the point mutations of standard markers is often too crude and slow for fine-scale phylogenetics. Nevertheless, genome dynamics (GD) events provide alternative, often richer information. The synteny index (SI) between a pair of genomes combines gene order and gene content information, allowing the comparison of genomes of unequal gene content, together with order considerations of their common genes. Recently, genome dynamics has been modeled as a continuous-time Markov process, and gene distance in the genome as a birth-death-immigration process. Nevertheless, due to complexities arising in this setting, no precise and provably consistent estimators could be derived, resulting in heuristic solutions. Here, we extend this modeling approach by using techniques from birth-death theory to derive explicit expressions of the system's probabilistic dynamics in the form of rational functions of the model parameters. This, in turn, allows us to infer analytically accurate distances between organisms based on their SI. Subsequently, we establish additivity of this estimated evolutionary distance (a desirable property yielding phylogenetic consistency). Applying the new measure in simulation studies shows that it provides accurate results in realistic settings and even under model extensions such as gene gain/loss or over a tree structure. In the real-data realm, we applied the new formulation to unique data structure that we constructed-the ordered orthology DB-based on a new version of the EggNOG database, to construct a tree with more than 4.5K taxa. To the best of our knowledge, this is the largest gene-order-based tree constructed and it overcomes shortcomings found in previous approaches. Constructing a GD-based tree allows to confirm and contrast findings based on other phylogenetic approaches, as we show.

A review of foot-and-mouth disease in Ethiopia: epidemiological aspects, economic implications, and control strategies

Foot-and-mouth disease (FMD) is a contagious viral disease that affects the livelihoods and productivity of livestock farmers in endemic regions. It can infect various domestic and wild animals with cloven hooves and is caused by a virus belonging to the genus Aphthovirus and family Picornaviridae, which has seven different serotypes: A, O, C, SAT1, SAT2, SAT3, and Asia-1. This paper aims to provide a comprehensive overview of the molecular epidemiology, economic impact, diagnosis, and control measures of FMD in Ethiopia in comparison with the global situation. The genetic and antigenic diversity of FMD viruses requires a thorough understanding for developing and applying effective control strategies in endemic areas. FMD has direct and indirect economic consequences on animal production. In Ethiopia, FMD outbreaks have led to millions of USD losses due to the restriction or rejection of livestock products in the international market. Therefore, in endemic areas, disease control depends on vaccinations to prevent animals from developing clinical disease. However, in Ethiopia, due to the presence of diverse antigenic serotypes of FMD viruses, regular and extensive molecular investigation of new field isolates is necessary to perform vaccine-matching studies to evaluate the protective potential of the vaccine strain in the country.

Pathogen evolution, prevention/control strategy and clinical features of COVID-19: experiences from China

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) was reported at the end of 2019 as a worldwide health concern causing a pandemic of unusual viral pneumonia and many other organ damages, which was defined by the World Health Organization as coronavirus disease 2019 (COVID-19). The pandemic is considered a significant threat to global public health till now. In this review, we have summarized the lessons learnt during the emergence and spread of SARS-CoV-2, including its prototype and variants. The overall clinical features of variants of concern (VOC), heterogeneity in the clinical manifestations, radiology and pathology of COVID-19 patients are also discussed, along with advances in therapeutic agents.

The natural virome and pandemic potential: Disease X

Over the last decade, the emergence of several zoonotic viruses has demonstrated that previously unknown or neglected pathogens have the potential to cause epidemics and therefore to pose a threat to global public health. Even more concerning are the estimated 1.7 million still-undiscovered viruses present in the natural environment or 'global virome', with many of these as-yet uncharacterized viruses predicted to be pathogenic for humans. Thus, in order to mitigate disease emergence and prevent future pandemics, it is crucial to identify the global extent of viral threats to which humans may become exposed. This requires cataloguing the viruses that exist in the environment within their various and diverse host species, and also understanding the viral, host, and environmental factors that dictate the circumstances that result in viral spillover into humans. We also address here which strategies can be implemented as countermeasure initiatives to reduce the risk of emergence of new diseases.

Reconstructing relative transmission rates in Bayesian phylodynamics: Two-fold transmission advantage of Omicron in Berlin, Germany during December 2021

Phylodynamic methods have lately played a key role in understanding the spread of infectious diseases. During the coronavirus disease (COVID-19) pandemic, large scale genomic surveillance has further increased the potential of dynamic inference from viral genomes. With the continual emergence of novel severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2) variants, explicitly allowing transmission rate differences between simultaneously circulating variants in phylodynamic inference is crucial. In this study, we present and empirically validate an extension to the BEAST2 package birth-death skyline model (BDSKY), BDSKY[Formula: see text], which introduces a scaling factor for the transmission rate between independent, jointly inferred trees. In an extensive simulation study, we show that BDSKY[Formula: see text] robustly infers the relative transmission rates under different epidemic scenarios. Using publicly available genome data of SARS-CoV-2, we apply BDSKY[Formula: see text] to quantify the transmission advantage of the Omicron over the Delta variant in Berlin, Germany. We find the overall transmission rate of Omicron to be scaled by a factor of two with pronounced variation between the individual clusters of each variant. These results quantify the transmission advantage of Omicron over the previously circulating Delta variant, in a crucial period of pre-established non-pharmaceutical interventions. By inferring variant- as well as cluster-specific transmission rate scaling factors, we show the differences in transmission dynamics for each variant. This highlights the importance of incorporating lineage-specific transmission differences in phylodynamic inference.

Evaluation of recombination detection methods for viral sequencing

Recombination is a key evolutionary driver in shaping novel viral populations and lineages. When unaccounted for, recombination can impact evolutionary estimations or complicate their interpretation. Therefore, identifying signals for recombination in sequencing data is a key prerequisite to further analyses. A repertoire of recombination detection methods (RDMs) have been developed over the past two decades; however, the prevalence of pandemic-scale viral sequencing data poses a computational challenge for existing methods. Here, we assessed eight RDMs: PhiPack (Profile), 3SEQ, GENECONV, recombination detection program (RDP) (OpenRDP), MaxChi (OpenRDP), Chimaera (OpenRDP), UCHIME (VSEARCH), and gmos; to determine if any are suitable for the analysis of bulk sequencing data. To test the performance and scalability of these methods, we analysed simulated viral sequencing data across a range of sequence diversities, recombination frequencies, and sample sizes. Furthermore, we provide a practical example for the analysis and validation of empirical data. We find that RDMs need to be scalable, use an analytical approach and resolution that is suitable for the intended research application, and are accurate for the properties of a given dataset (e.g. sequence diversity and estimated recombination frequency). Analysis of simulated and empirical data revealed that the assessed methods exhibited considerable trade-offs between these criteria. Overall, we provide general guidelines for the validation of recombination detection results, the benefits and shortcomings of each assessed method, and future considerations for recombination detection methods for the assessment of large-scale viral sequencing data.

VIGA: a one-stop tool for eukaryotic virus identification and genome assembly from next-generation-sequencing data

Identification of viruses and further assembly of viral genomes from the next-generation-sequencing data are essential steps in virome studies. This study presented a one-stop tool named VIGA (available at https://github.com/viralInformatics/VIGA) for eukaryotic virus identification and genome assembly from NGS data. It was composed of four modules, namely, identification, taxonomic annotation, assembly and novel virus discovery, which integrated several third-party tools such as BLAST, Trinity, MetaCompass and RagTag. Evaluation on multiple simulated and real virome datasets showed that VIGA assembled more complete virus genomes than its competitors on both the metatranscriptomic and metagenomic data and performed well in assembling virus genomes at the strain level. Finally, VIGA was used to investigate the virome in metatranscriptomic data from the Human Microbiome Project and revealed different composition and positive rate of viromes in diseases of prediabetes, Crohn's disease and ulcerative colitis. Overall, VIGA would help much in identification and characterization of viromes, especially the known viruses, in future studies.

Complex norovirus transmission dynamics at hospital wards revealed by deep sequencing

Detailed knowledge regarding norovirus transmission within hospitals is limited. We investigated a norovirus hospital outbreak affecting 65 patients at five different wards. PCR showed that 61 (94%) of the patients were infected with genotype II.4 strains. Successful Ion Torrent deep sequencing of GII.4 positive samples from 59 patients followed by phylogenetic analysis revealed that all sequences but two clustered into four distinct clades. Two of the clades belonged to GII.4 Sydney 2012, while the other two belonged to GII.4 New Orleans 2009. One of the clades was predominant at two wards, while two clades were predominant at one ward each. The fourth clade was found in sporadic cases at several wards. Thus, at four out of five wards, variants from one clade were predominant. At one ward, a single clade accounted for all cases, while at three wards the predominant clade accounted for 60%-71% of cases. Analysis of quasispecies variation identified positions that could further discriminate between variants from separate wards. The results illustrate a complex transmission of healthcare-associated norovirus infections and show that sequencing can be used to discriminate between related and unrelated cases.

Phylodynamic approaches to studying avian influenza virus

Avian influenza viruses can cause severe disease in domestic and wild birds and are a pandemic threat. Phylodynamics is the study of how epidemiological, evolutionary, and immunological processes can interact to shape viral phylogenies. This review summarizes how phylodynamic methods have and could contribute to the study of avian influenza viruses. Specifically, we assess how phylodynamics can be used to examine viral spread within and between wild or domestic bird populations at various geographical scales, identify factors associated with virus dispersal, and determine the order and timing of virus lineage movement between geographic regions or poultry production systems. We discuss factors that can complicate the interpretation of phylodynamic results and identify how future methodological developments could contribute to improved control of the virus.

Fitness, growth and transmissibility of SARS-CoV-2 genetic variants

The massive scale of the global SARS-CoV-2 sequencing effort created new opportunities and challenges for understanding SARS-CoV-2 evolution. Rapid detection and assessment of new variants has become one of the principal objectives of genomic surveillance of SARS-CoV-2. Because of the pace and scale of sequencing, new strategies have been developed for characterizing fitness and transmissibility of emerging variants. In this Review, I discuss a wide range of approaches that have been rapidly developed in response to the public health threat posed by emerging variants, ranging from new applications of classic population genetics models to contemporary synthesis of epidemiological models and phylodynamic analysis. Many of these approaches can be adapted to other pathogens and will have increasing relevance as large-scale pathogen sequencing becomes a regular feature of many public health systems.

Optimized quantification of intra-host viral diversity in SARS-CoV-2 and influenza virus sequence data

High error rates of viral RNA-dependent RNA polymerases lead to diverse intra-host viral populations during infection. Errors made during replication that are not strongly deleterious to the virus can lead to the generation of minority variants. However, accurate detection of minority variants in viral sequence data is complicated by errors introduced during sample preparation and data analysis. We used synthetic RNA controls and simulated data to test seven variant-calling tools across a range of allele frequencies and simulated coverages. We show that choice of variant caller and use of replicate sequencing have the most significant impact on single-nucleotide variant (SNV) discovery and demonstrate how both allele frequency and coverage thresholds impact both false discovery and false-negative rates. When replicates are not available, using a combination of multiple callers with more stringent cutoffs is recommended. We use these parameters to find minority variants in sequencing data from SARS-CoV-2 clinical specimens and provide guidance for studies of intra-host viral diversity using either single replicate data or data from technical replicates. Our study provides a framework for rigorous assessment of technical factors that impact SNV identification in viral samples and establishes heuristics that will inform and improve future studies of intra-host variation, viral diversity, and viral evolution. IMPORTANCE When viruses replicate inside a host cell, the virus replication machinery makes mistakes. Over time, these mistakes create mutations that result in a diverse population of viruses inside the host. Mutations that are neither lethal to the virus nor strongly beneficial can lead to minority variants that are minor members of the virus population. However, preparing samples for sequencing can also introduce errors that resemble minority variants, resulting in the inclusion of false-positive data if not filtered correctly. In this study, we aimed to determine the best methods for identification and quantification of these minority variants by testing the performance of seven commonly used variant-calling tools. We used simulated and synthetic data to test their performance against a true set of variants and then used these studies to inform variant identification in data from SARS-CoV-2 clinical specimens. Together, analyses of our data provide extensive guidance for future studies of viral diversity and evolution.

Phylogenies increase power to detect highly transmissible viral genome variants

As demonstrated by the SARS-CoV-2 pandemic, the emergence of novel viral strains with increased transmission rates poses a significant threat to global health. Viral genome sequences, combined with statistical models of sequence evolution, may provide a critical tool for early detection of these strains. Using a novel statistical model that links transmission rates to the entire viral genome sequence, we study the power of phylogenetic methods-using a phylogenetic tree relating viral samples-and count-based methods-using case-counts of variants over time-to detect increased transmission rates, and to identify causative mutations. We find that phylogenies in particular can detect novel variants very soon after their origin, and may facilitate the development of early detection systems for outbreak surveillance.

Virological characterization of treatment failures and retreatment outcomes in patients infected with "unusual" HCV genotype 1 subtypes

BACKGROUND AND AIMS

Suboptimal rates of sustained virological response have been reported in patients infected with an "unusual," non-1a/1b HCV genotype 1 subtype. The objectives of this study were to assess the proportion of non-1a/1b genotype 1 subtypes in a population of HCV-infected patients who failed to achieve sustained virological response after first-line direct-acting antiviral treatment, to virologically characterize their failures and to assess their outcomes on retreatment.

APPROACH AND RESULTS

Samples addressed between January 2015 and December 2021 to the French National Reference Center for Viral Hepatitis B, C, and D were prospectively analyzed by means of Sanger and deep sequencing. Among 640 failures, 47 (7.3%) occurred in patients infected with an "unusual" genotype 1 subtype. Samples were available in 43 of them; 92.5% of these patients were born in Africa. Our results show the presence at baseline and at treatment failure of NS3 protease and/or NS5A polymorphisms conferring inherent reduced susceptibility to direct-acting antivirals in these patients, together with the presence at failure of additional resistance-associated substitutions not naturally present as dominant species, but jointly selected by first-line therapy.

CONCLUSIONS

Patients infected with "unusual" HCV genotype 1 subtypes are over-represented among direct-acting antiviral treatment failures. Most of them were born and likely infected in sub-Saharan Africa. "Unusual" HCV genotype 1 subtypes naturally carry polymorphisms that confer reduced susceptibility to the drugs currently used to cure hepatitis C, in particular the NS5A inhibitors. Retreatment with sofosbuvir plus an NS3 protease and an NS5A inhibitor is generally efficacious.

Genome-wide phylodynamic approach reveals the epidemic dynamics of the main Mycoplasma bovis subtype circulating in France

Mycoplasma bovis is a major aetiological agent of bovine respiratory disease worldwide. Genome-based analyses are increasingly being used to monitor the genetic diversity and global distribution of M. bovis, complementing existing subtyping schemes based on locus sequencing. However, these analyses have so far provided limited information on the spatiotemporal and population dynamics of circulating subtypes. Here we applied a genome-wide phylodynamic approach to explore the epidemic dynamics of 88 French M. bovis strains collected between 2000 and 2019 in France and belonging to the currently dominant polC subtype 2 (st2). A strong molecular clock signal detected in the genomic data enabled robust phylodynamic inferences, which estimated that the M. bovis st2 population in France is composed of two lineages that successively emerged from independent introductions of international strains. The first lineage appeared around 2000 and supplanted the previously established antimicrobial-susceptible polC subtype 1. The second lineage, which is likely more transmissible, progressively replaced the first M. bovis st2 lineage population from 2005 onward and became predominant after 2010. Analyses also showed a brief decline in this second M. bovis st2 lineage population in around 2011, possibly due to the challenge from the concurrent emergence of M. bovis polC subtype 3 in France. Finally, we identified non-synonymous mutations in genes associated with lineages, which raises prospects for identifying new surveillance molecular markers. A genome-wide phylodynamic approach provides valuable resources for monitoring the evolution and epidemic dynamics of circulating M. bovis subtypes, and may prove critical for developing more effective surveillance systems and disease control strategies.

Molecular Evolutionary Analyses of the RNA-Dependent RNA Polymerase (RdRp) Region and VP1 Gene in Human Norovirus Genotypes GII.P6-GII.6 and GII.P7-GII.6

To understand the evolution of GII.P6-GII.6 and GII.P7-GII.6 strains, the prevalent human norovirus genotypes, we analysed both the RdRp region and VP1 gene in globally collected strains using authentic bioinformatics technologies. A common ancestor of the P6- and P7-type RdRp region emerged approximately 50 years ago and a common ancestor of the P6- and P7-type VP1 gene emerged approximately 110 years ago. Subsequently, the RdRp region and VP1 gene evolved. Moreover, the evolutionary rates were significantly faster for the P6-type RdRp region and VP1 gene than for the P7-type RdRp region and VP1 genes. Large genetic divergence was observed in the P7-type RdRp region and VP1 gene compared with the P6-type RdRp region and VP1 gene. The phylodynamics of the RdRp region and VP1 gene fluctuated after the year 2000. Positive selection sites in VP1 proteins were located in the antigenicity-related protruding 2 domain, and these sites overlapped with conformational epitopes. These results suggest that the GII.6 VP1 gene and VP1 proteins evolved uniquely due to recombination between the P6- and P7-type RdRp regions in the HuNoV GII.P6-GII.6 and GII.P7-GII.6 virus strains.

Estimating the fitness cost and benefit of antimicrobial resistance from pathogen genomic data

Increasing levels of antibiotic resistance in many bacterial pathogen populations are a major threat to public health. Resistance to an antibiotic provides a fitness benefit when the bacteria are exposed to this antibiotic, but resistance also often comes at a cost to the resistant pathogen relative to susceptible counterparts. We lack a good understanding of these benefits and costs of resistance for many bacterial pathogens and antibiotics, but estimating them could lead to better use of antibiotics in a way that reduces or prevents the spread of resistance. Here, we propose a new model for the joint epidemiology of susceptible and resistant variants, which includes explicit parameters for the cost and benefit of resistance. We show how Bayesian inference can be performed under this model using phylogenetic data from susceptible and resistant lineages and that by combining data from both we are able to disentangle and estimate the resistance cost and benefit parameters separately. We applied our inferential methodology to several simulated datasets to demonstrate good scalability and accuracy. We analysed a dataset of Neisseria gonorrhoeae genomes collected between 2000 and 2013 in the USA. We found that two unrelated lineages resistant to fluoroquinolones shared similar epidemic dynamics and resistance parameters. Fluoroquinolones were abandoned for the treatment of gonorrhoea due to increasing levels of resistance, but our results suggest that they could be used to treat a minority of around 10% of cases without causing resistance to grow again.

Competition between two Usutu virus isolates in cell culture and in the common house mosquito Culex pipiens

Usutu virus (USUV) is a mosquito-borne flavivirus of African origin. Over the past decades, USUV has spread through Europe causing mass die-offs among multiple bird species. The natural transmission cycle of USUV involves Culex spp. mosquitoes as vectors and birds as amplifying hosts. Next to birds and mosquitoes, USUV has also been isolated from multiple mammalian species, including humans, which are considered dead-end hosts. USUV isolates are phylogenetically classified into an African and European branch, subdivided into eight genetic lineages (Africa 1, 2, and 3 and Europe 1, 2, 3, 4, and 5 lineages). Currently, multiple African and European lineages are co-circulating in Europe. Despite increased knowledge of the epidemiology and pathogenicity of the different lineages, the effects of co-infection and transmission efficacy of the co-circulating USUV strains remain unclear. In this study, we report a comparative study between two USUV isolates as follows: a Dutch isolate (USUV-NL, Africa lineage 3) and an Italian isolate (USUV-IT, Europe lineage 2). Upon co-infection, USUV-NL was consistently outcompeted by USUV-IT in mosquito, mammalian, and avian cell lines. In mosquito cells, the fitness advantage of USUV-IT was most prominently observed in comparison to the mammalian or avian cell lines. When Culex pipiens mosquitoes were orally infected with the different isolates, no overall differences in vector competence for USUV-IT and USUV-NL were observed. However, during the in vivo co-infection assay, it was observed that USUV-NL infectivity and transmission were negatively affected by USUV-IT but not vice versa.

Unraveling the epidemiology of Mycobacterium bovis using whole-genome sequencing combined with environmental and demographic data

When studying the dynamics of a pathogen in a host population, one crucial question is whether it transitioned from an epidemic (i.e., the pathogen population and the number of infected hosts are increasing) to an endemic stable state (i.e., the pathogen population reached an equilibrium). For slow-growing and slow-evolving clonal pathogens such as Mycobacterium bovis, the causative agent of bovine (or animal) and zoonotic tuberculosis, it can be challenging to discriminate between these two states. This is a result of the combination of suboptimal detection tests so that the actual extent of the pathogen prevalence is often unknown, as well as of the low genetic diversity, which can hide the temporal signal provided by the accumulation of mutations in the bacterial DNA. In recent years, the increased availability, efficiency, and reliability of genomic reading techniques, such as whole-genome sequencing (WGS), have significantly increased the amount of information we can use to study infectious diseases, and therefore, it has improved the precision of epidemiological inferences for pathogens such as M. bovis. In this study, we use WGS to gain insights into the epidemiology of M. bovis in Cameroon, a developing country where the pathogen has been reported for decades. A total of 91 high-quality sequences were obtained from tissue samples collected in four abattoirs, 64 of which were with complete metadata. We combined these with environmental, demographic, ecological, and cattle movement data to generate inferences using phylodynamic models. Our findings suggest M. bovis in Cameroon is slowly expanding its epidemiological range over time; therefore, endemic stability is unlikely. This suggests that animal movement plays an important role in transmission. The simultaneous prevalence of M. bovis in co-located cattle and humans highlights the risk of such transmission being zoonotic. Therefore, using genomic tools as part of surveillance would vastly improve our understanding of disease ecology and control strategies.

Model design for nonparametric phylodynamic inference and applications to pathogen surveillance

Inference of effective population size from genomic data can provide unique information about demographic history and, when applied to pathogen genetic data, can also provide insights into epidemiological dynamics. The combination of nonparametric models for population dynamics with molecular clock models which relate genetic data to time has enabled phylodynamic inference based on large sets of time-stamped genetic sequence data. The methodology for nonparametric inference of effective population size is well-developed in the Bayesian setting, but here we develop a frequentist approach based on nonparametric latent process models of population size dynamics. We appeal to statistical principles based on out-of-sample prediction accuracy in order to optimize parameters that control shape and smoothness of the population size over time. Our methodology is implemented in a new R package entitled mlesky. We demonstrate the flexibility and speed of this approach in a series of simulation experiments and apply the methodology to a dataset of HIV-1 in the USA. We also estimate the impact of non-pharmaceutical interventions for COVID-19 in England using thousands of SARS-CoV-2 sequences. By incorporating a measure of the strength of these interventions over time within the phylodynamic model, we estimate the impact of the first national lockdown in the UK on the epidemic reproduction number.

Compositional biases and evolution of the largest plant RNA virus order Patatavirales

Patatavirales is the largest order of plant RNA viruses and exclusively contains the family Potyviridae, accounting for 30 % of all known plant viruses. The composition bias of animal RNA viruses and several plant RNA viruses has been determined. However, the comprehensive nucleic acid composition, codon pair usage patterns, dinucleotide preference and codon pair preference of plant RNA viruses have not been investigated to date. In this study, integrated analysis and discussion of the nucleic acid composition, codon usage patterns, dinucleotide composition and codon pair bias of potyvirids were performed using 3732 complete genome coding sequences. The nucleic acid composition of potyvirids was significantly enriched in A/U. Interestingly, the A/U-rich nucleotide composition of Patatavirales is essential for determining the preferred A-ended and U-ended codons and the overexpression of UpG and CpA dinucleotides. The codon usage patterns and codon pair bias of potyvirids were significantly correlated with their nucleic acid composition. Additionally, the codon usage pattern, dinucleotide composition and codon-pair bias of potyvirids are more dependent on the classification of the virus compared with their hosts. Our analysis provides a better understanding of future research on the origin and evolution patterns of the order Patatavirales.

The evolution of SARS-CoV-2

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused millions of deaths and substantial morbidity worldwide. Intense scientific effort to understand the biology of SARS-CoV-2 has resulted in daunting numbers of genomic sequences. We witnessed evolutionary events that could mostly be inferred indirectly before, such as the emergence of variants with distinct phenotypes, for example transmissibility, severity and immune evasion. This Review explores the mechanisms that generate genetic variation in SARS-CoV-2, underlying the within-host and population-level processes that underpin these events. We examine the selective forces that likely drove the evolution of higher transmissibility and, in some cases, higher severity during the first year of the pandemic and the role of antigenic evolution during the second and third years, together with the implications of immune escape and reinfections, and the increasing evidence for and potential relevance of recombination. In order to understand how major lineages, such as variants of concern (VOCs), are generated, we contrast the evidence for the chronic infection model underlying the emergence of VOCs with the possibility of an animal reservoir playing a role in SARS-CoV-2 evolution, and conclude that the former is more likely. We evaluate uncertainties and outline scenarios for the possible future evolutionary trajectories of SARS-CoV-2.

Sub-genotypes of hepatitis C virus in the Middle East and North Africa: Patterns of distribution and temporal changes

Infection with the hepatitis C virus (HCV) remains a considerable public health concern in the Middle East and North Africa (MENA). The objectives of this study were to analyze the HCV genotype (GT) and sub-genotype (SGT) distribution in the MENA region and to assess the temporal change in the number of sequences within the MENA region. All HCV molecular sequences collected in the MENA region had been retrieved from GenBank as of 1 August 2022. The number of HCV sequences retrieved was 6740 representing sequences from a total of 17 MENA countries with a majority from Iran (n = 1969, 29.2%), Egypt (n = 1591, 23.6%), Tunisia (n = 1305, 19.4%) and Saudi Arabia (n = 1085, 16.1%). The determination of GT/SGT was based on the NCBI genotyping and Blast tool. Genotype 1 (GT1) dominated infections in the MENA (n = 2777, 41.2%), followed by GT4 (n = 2566, 39.0%). Additionally, SGT4a (1515/6393, 23.7%) was the most common SGT in the MENA, and SGT4a was dominant in Egypt and Saudi Arabia, followed by SGT1b (n = 1308, 20.5%), which was dominant in Morocco and Tunisia, while SGT1a (n = 1275, 19.9%) was common in Iran, Iraq and Palestine. Furthermore, significant temporal increase in the number of HCV MENA sequences was observed. On the SGT level, specific patterns of HCV genetic diversity were seen in the MENA region, with the most common SGT being 4a, in addition to increasing the availability of HCV sequences in the MENA region.

Designing of thiazolidinones against chicken pox, monkey pox, and hepatitis viruses: A computational approach

Computational designing of four different series (D-G) of thiazolidinone was done starting from different amines which was further condensed with various aldehydes. These underwent in silico molecular investigations for density functional theory (DFT), molecular docking, and absorption, distribution metabolism, excretion, and toxicity (ADMET) studies. The different electrochemical parameters of the compounds are predicted using quantum mechanical modeling approach with Gaussian. The docking software was used to dock the compounds against choosing PDB file for chickenpox, human immunodeficiency, hepatitis, and monkeypox virus as 1OSN, 1VZV, 6VLK, 1RTD, 3I7H, 3TYV, 4JU3, and 4QWO, respectively. The molecular interactions were visualized with discovery studio and maximum binding affinity was observed with D8 compounds against 4QWO (-13.383 kcal/mol) while for compound D5 against 1VZV which was -12.713 kcal/mol. Swiss ADME web tool was used to assess the drug-likeness of the designed compounds under consideration, and it is concluded that these molecules had a drug-like structure with almost zero violations.

Evolutionary dynamics of influenza A/H1N1 virus circulating in India from 2011 to 2021

Influenza A viruses (IAV) are fast-evolving pathogens with a very high mutation rate (2.0 × 10^-6 to 2.0 × 10^-4) compared to the influenza B (IBV) and influenza C (ICV) viruses. Generally, the tropical regions are considered as the reservoir for the IAV's genetic and antigenic evolutionary modification to be reintroduced into the temperate region. Therefore, in connection to the above facts, the present study emphasized on the evolutionary dynamic of the pandemic-2009 H1N1 (pdmH1N1) influenza virus in India. A total of Ninety-two whole genome sequences of pdmH1N1 viruses circulating in India during the 2009 post-pandemic era were analysed. The temporal signal of the study, indicating strict molecular clock evolutionary process and the overall substitution rate is 2.21 × 10^-3/site/year. We are using the nonparametric Bayesian Skygrid coalescent model to estimates the effective past population dynamic or size over time. The study exhibits a strong relation between the genetic distances and collection dates of the Indian pdmH1N1 strain. The skygrid plot represents the highest exponential growth of IAV in rainy and winter seasons. All the genes of Indian pdmH1N1 were under purifying selective pressure. The Bayesian time-imprinted phylogenetic tree represents the following clade distributions in the country within the last 10 years; I) clade 6, 6C, and 7 were co-circulating between the 2011 to 2012 flu season; II) the clade 6B was introduced into circulation in the late seasons of 2012; III) lastly, the clade 6B remain existing in the circulation and segregated into subclade 6B.1 with five different subgroup (6B.1A, 6B.1A.1, 6B.1A.5a, 6B.1A.5a.2, 6B.1A.7). The recent circulating strain of Indian H1N1 strain represent the insertion of basic-amino acid arginine (R) in the cleavage site (325/K-R) of HA protein and amino acid mutation (314/I-M) on the lateral head surface domain of NA protein. Moreover, the study indicates the sporadic presence of the oseltamivir-resistant (275/H-Y) H1N1 variant in circulation. The present study suggests the purifying selective pressure and stochastic ecological factors for the existence and adaptation of a certain clade 6B in the host populations and additional information on the emergence of mutated strains in the circulation.

Possible Causes of Hypertrophic Osteoarthropathy in the La Ferrassie 1 Neanderthal

For over a century, researchers have been perplexed by the unique osteological findings on La Ferrassie 1 (LF1), one of the most complete Neanderthal remains ever found. In 1997, Fennel and Trinkaus proposed that LF1 suffered from hypertrophic osteoarthropathy (HOA), likely secondary to chronic thoracic infection or pulmonary malignancy. This disease process can have many etiologies, and no study has fully explored the possible origin of LF1's HOA. Ultimately, it is most likely that LF1's HOA etiology arose from one of the many infectious diseases that prehistoric Neanderthals were exposed to, specifically a chronic pulmonary RNA virus.

Global dissemination of influenza A virus is driven by wild bird migration through arctic and subarctic zones

Influenza A viruses (IAV) circulate endemically among many wild aquatic bird populations that seasonally migrate between wintering grounds in southern latitudes to breeding ranges along the perimeter of the circumpolar arctic. Arctic and subarctic zones are hypothesized to serve as ecologic drivers of the intercontinental movement and reassortment of IAVs due to high densities of disparate populations of long distance migratory and native bird species present during breeding seasons. Iceland is a staging ground that connects the East Atlantic and North Atlantic American flyways, providing a unique study system for characterizing viral flow between eastern and western hemispheres. Using Bayesian phylodynamic analyses, we sought to evaluate the viral connectivity of Iceland to proximal regions and how inter-species transmission and reassortment dynamics in this region influence the geographic spread of low and highly pathogenic IAVs. Findings demonstrate that IAV movement in the arctic and subarctic reflects wild bird migration around the perimeter of the circumpolar north, favouring short-distance flights between proximal regions rather than long distance flights over the polar interior. Iceland connects virus movement between mainland Europe and North America, consistent with the westward migration of wild birds from mainland Europe to Northeastern Canada and Greenland. Though virus diffusion rates were similar among avian taxonomic groups in Iceland, gulls play an outsized role as sinks of IAVs from other avian hosts prior to onward migration. These data identify patterns of virus movement in northern latitudes and inform future surveillance strategies related to seasonal and emergent IAVs with potential public health concern.

Genome Structure, Life Cycle, and Taxonomy of Coronaviruses and the Evolution of SARS-CoV-2

Coronaviruses have a broad host range and exhibit high zoonotic potential. In this chapter, we describe their genomic organization in terms of encoded proteins and provide an introduction to the peculiar discontinuous transcription mechanism. Further, we present evolutionary conserved genomic RNA secondary structure features, which are involved in the complex replication mechanism. With a focus on computational methods, we review the emergence of SARS-CoV-2 starting with the 2019 strains. In that context, we also discuss the debated hypothesis of whether SARS-CoV-2 was created in a laboratory. We focus on the molecular evolution and the epidemiological dynamics of this recently emerged pathogen and we explain how variants of concern are detected and characterised. COVID-19, the disease caused by SARS-CoV-2, can spread through different transmission routes and also depends on a number of risk factors. We describe how current computational models of viral epidemiology, or more specifically, phylodynamics, have facilitated and will continue to enable a better understanding of the epidemic dynamics of SARS-CoV-2.

Inferring selection effects in SARS-CoV-2 with Bayesian Viral Allele Selection

The global effort to sequence millions of SARS-CoV-2 genomes has provided an unprecedented view of viral evolution. Characterizing how selection acts on SARS-CoV-2 is critical to developing effective, long-lasting vaccines and other treatments, but the scale and complexity of genomic surveillance data make rigorous analysis challenging. To meet this challenge, we develop Bayesian Viral Allele Selection (BVAS), a principled and scalable probabilistic method for inferring the genetic determinants of differential viral fitness and the relative growth rates of viral lineages, including newly emergent lineages. After demonstrating the accuracy and efficacy of our method through simulation, we apply BVAS to 6.9 million SARS-CoV-2 genomes. We identify numerous mutations that increase fitness, including previously identified mutations in the SARS-CoV-2 Spike and Nucleocapsid proteins, as well as mutations in non-structural proteins whose contribution to fitness is less well characterized. In addition, we extend our baseline model to identify mutations whose fitness exhibits strong dependence on vaccination status as well as pairwise interaction effects, i.e. epistasis. Strikingly, both these analyses point to the pivotal role played by the N501 residue in the Spike protein. Our method, which couples Bayesian variable selection with a diffusion approximation in allele frequency space, lays a foundation for identifying fitness-associated mutations under the assumption that most alleles are neutral.

Distinguishing imported cases from locally acquired cases within a geographically limited genomic sample of an infectious disease

MOTIVATION

The ability to distinguish imported cases from locally acquired cases has important consequences for the selection of public health control strategies. Genomic data can be useful for this, for example, using a phylogeographic analysis in which genomic data from multiple locations are compared to determine likely migration events between locations. However, these methods typically require good samples of genomes from all locations, which is rarely available.

RESULTS

Here, we propose an alternative approach that only uses genomic data from a location of interest. By comparing each new case with previous cases from the same location, we are able to detect imported cases, as they have a different genealogical distribution than that of locally acquired cases. We show that, when variations in the size of the local population are accounted for, our method has good sensitivity and excellent specificity for the detection of imports. We applied our method to data simulated under the structured coalescent model and demonstrate relatively good performance even when the local population has the same size as the external population. Finally, we applied our method to several recent genomic datasets from both bacterial and viral pathogens, and show that it can, in a matter of seconds or minutes, deliver important insights on the number of imports to a geographically limited sample of a pathogen population.

AVAILABILITY AND IMPLEMENTATION

The R package DetectImports is freely available from https://github.com/xavierdidelot/DetectImports.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Virus-Associated Nephropathies: A Narrative Review

While most viral infections cause mild symptoms and a spontaneous favorable resolution, some can lead to severe or protracted manifestations, specifically in immunocompromised hosts. Kidney injuries related to viral infections may have multiple causes related to the infection severity, drug toxicity or direct or indirect viral-associated nephropathy. We review here the described virus-associated nephropathies in order to guide diagnosis strategies and treatments in cases of acute kidney injury (AKI) occurring concomitantly with a viral infection. The occurrence of virus-associated nephropathy depends on multiple factors: the local epidemiology of the virus, its ability to infect renal cells and the patient's underlying immune response, which varies with the state of immunosuppression. Clear comprehension of pathophysiological mechanisms associated with a summary of described direct and indirect injuries should help physicians to diagnose and treat viral associated nephropathies.

Characterizing viral within-host diversity in fast and non-equilibrium demo-genetic dynamics

High-throughput sequencing has opened the route for a deep assessment of within-host genetic diversity that can be used, e.g., to characterize microbial communities and to infer transmission links in infectious disease outbreaks. The performance of such characterizations and inferences cannot be analytically assessed in general and are often grounded on computer-intensive evaluations. Then, being able to simulate within-host genetic diversity across time under various demo-genetic assumptions is paramount to assess the performance of the approaches of interest. In this context, we built an original model that can be simulated to investigate the temporal evolution of genotypes and their frequencies under various demo-genetic assumptions. The model describes the growth and the mutation of genotypes at the nucleotide resolution conditional on an overall within-host viral kinetics, and can be tuned to generate fast non-equilibrium demo-genetic dynamics. We ran simulations of this model and computed classic diversity indices to characterize the temporal variation of within-host genetic diversity (from high-throughput amplicon sequences) of virus populations under three demographic kinetic models of viral infection. Our results highlight how demographic (viral load) and genetic (mutation, selection, or drift) factors drive variations in within-host diversity during the course of an infection. In particular, we observed a non-monotonic relationship between pathogen population size and genetic diversity, and a reduction of the impact of mutation on diversity when a non-specific host immune response is activated. The large variation in the diversity patterns generated in our simulations suggests that the underlying model provides a flexible basis to produce very diverse demo-genetic scenarios and test, for instance, methods for the inference of transmission links during outbreaks.

Phylodynamics of a regional SARS-CoV-2 rapid spreading event in Colorado in late 2020

Since the initial reported discovery of SARS-CoV-2 in late 2019, genomic surveillance has been an important tool to understand its transmission and evolution. Here, we sought to describe the underlying regional phylodynamics before and during a rapid spreading event that was documented by surveillance protocols of the United States Air Force Academy (USAFA) in late October-November of 2020. We used replicate long-read sequencing on Colorado SARS-CoV-2 genomes collected July through November 2020 at the University of Colorado Anschutz Medical campus in Aurora and the United States Air Force Academy in Colorado Springs. Replicate sequencing allowed rigorous validation of variation and placement in a phylogenetic relatedness network. We focus on describing the phylodynamics of a lineage that likely originated in the local Colorado Springs community and expanded rapidly over the course of two months in an outbreak within the well-controlled environment of the United States Air Force Academy. Divergence estimates from sampling dates indicate that the SARS-CoV-2 lineage associated with this rapid expansion event originated in late October 2020. These results are in agreement with transmission pathways inferred by the United States Air Force Academy, and provide a window into the evolutionary process and transmission dynamics of a potentially dangerous but ultimately contained variant.

Undiagnosed HIV Infections May Drive HIV Transmission in the Era of "Treat All": A Deep-Sampling Molecular Network Study in Northeast China during 2016 to 2019

Universal antiretroviral therapy (ART, “treat all”) was recommended by the World Health Organization in 2015; however, HIV-1 transmission is still ongoing. This study characterizes the drivers of HIV transmission in the “treat all” era. Demographic and clinical information and HIV pol gene were collected from all newly diagnosed cases in Shenyang, the largest city in Northeast China, during 2016 to 2019. Molecular networks were constructed based on genetic distance and logistic regression analysis was used to assess potential transmission source characteristics. The cumulative ART coverage in Shenyang increased significantly from 77.0% (485/630) in 2016 to 93.0% (2598/2794) in 2019 (p < 0.001). Molecular networks showed that recent HIV infections linked to untreated individuals decreased from 61.6% in 2017 to 28.9% in 2019, while linking to individuals with viral suppression (VS) increased from 9.0% to 49.0% during the same time frame (p < 0.001). Undiagnosed people living with HIV (PLWH) hidden behind the links between index cases and individuals with VS were likely to be male, younger than 25 years of age, with Manchu nationality (p < 0.05). HIV transmission has declined significantly in the era of “treat all”. Undiagnosed PLWH may drive HIV transmission and should be the target for early detection and intervention.

Optimized Quantification of Intrahost Viral Diversity in SARS-CoV-2 and Influenza Virus Sequence Data

High error rates of viral RNA-dependent RNA polymerases lead to diverse intra-host viral populations during infection. Errors made during replication that are not strongly deleterious to the virus can lead to the generation of minority variants. However, accurate detection of minority variants in viral sequence data is complicated by errors introduced during sample preparation and data analysis. We used synthetic RNA controls and simulated data to test seven variant calling tools across a range of allele frequencies and simulated coverages. We show that choice of variant caller, and use of replicate sequencing have the most significant impact on single nucleotide variant (SNV) discovery and demonstrate how both allele frequency and coverage thresholds impact both false discovery and false negative rates. We use these parameters to find minority variants in sequencing data from SARS-CoV-2 clinical specimens and provide guidance for studies of intrahost viral diversity using either single replicate data or data from technical replicates. Our study provides a framework for rigorous assessment of technical factors that impact SNV identification in viral samples and establishes heuristics that will inform and improve future studies of intrahost variation, viral diversity, and viral evolution.

IMPORTANCE

When viruses replicate inside a host, the virus replication machinery makes mistakes. Over time, these mistakes create mutations that result in a diverse population of viruses inside the host. Mutations that are neither lethal to the virus, nor strongly beneficial, can lead to minority variants that are minor members of the virus population. However, preparing samples for sequencing can also introduce errors that resemble minority variants, resulting in inclusion of false positive data if not filtered correctly. In this study, we aimed to determine the best methods for identification and quantification of these minority variants by testing the performance of seven commonly used variant calling tools. We used simulated and synthetic data to test their performance against a true set of variants, and then used these studies to inform variant identification in data from clinical SARS-CoV-2 clinical specimens. Together, analyses of our data provide extensive guidance for future studies of viral diversity and evolution.

Generalizing Bayesian phylogenetics to infer shared evolutionary events

Phylogenetic models have long assumed that lineages diverge independently. Processes of diversification that are of interest in biogeography, epidemiology, and genome evolution violate this assumption by affecting multiple evolutionary lineages. To relax the assumption of independent divergences and infer patterns of divergences predicted by such processes, we introduce a way of conceptualizing, modeling, and inferring phylogenetic trees. We apply the approach to genomic data from geckos distributed across the Philippines and find support for patterns of shared divergences predicted by repeated fragmentation of the archipelago by interglacial rises in sea level.

Many processes of biological diversification can simultaneously affect multiple evolutionary lineages. Examples include multiple members of a gene family diverging when a region of a chromosome is duplicated, multiple viral strains diverging at a “super-spreading” event, and a geological event fragmenting whole communities of species. It is difficult to test for patterns of shared divergences predicted by such processes because all phylogenetic methods assume that lineages diverge independently. We introduce a Bayesian phylogenetic approach to relax the assumption of independent, bifurcating divergences by expanding the space of topologies to include trees with shared and multifurcating divergences. This allows us to jointly infer phylogenetic relationships, divergence times, and patterns of divergences predicted by processes of diversification that affect multiple evolutionary lineages simultaneously or lead to more than two descendant lineages. Using simulations, we find that the method accurately infers shared and multifurcating divergence events when they occur and performs as well as current phylogenetic methods when divergences are independent and bifurcating. We apply our approach to genomic data from two genera of geckos from across the Philippines to test if past changes to the islands’ landscape caused bursts of speciation. Unlike previous analyses restricted to only pairs of gecko populations, we find evidence for patterns of shared divergences. By generalizing the space of phylogenetic trees in a way that is independent from the likelihood model, our approach opens many avenues for future research into processes of diversification across the life sciences.

Genetic Diversity, Evolutionary Dynamics, and Pathogenicity of Ferret Badger Rabies Virus Variants in Mainland China, 2008–2018

In contrast to dog-associated human rabies cases decline year by year due to the rabies vaccination coverage rates increase in China, ferret badger (FB, Melogale moschata)-associated human rabies cases emerged in the 1990s, and are now an increasingly recognized problem in southeast China. To investigate epidemiology, temporal evolution dynamics, transmission characterization, and pathogenicity of FB-associated rabies viruses (RABVs), from 2008 to 2018, we collected 3,622 FB brain samples in Jiangxi and Zhejiang Province, and detected 112 RABV isolates. Four FB-related lineages were identified by phylogenetic analysis (lineages A–D), the estimated Times to Most Recent Common Ancestor were 1941, 1990, 1937, and 1997 for lineages A–D, respectively. Furthermore, although no FB-associated human rabies case has been reported there apart from Wuyuan area, FB-RABV isolates are mainly distributed in Jiangxi Province. Pathogenicity of FB-RABVs was assessed using peripheral inoculation in mice and in beagles with masseter muscles, mortality-rates ranging from 20 to 100% in mice and 0 to 20% in beagles in the groups infected with the various isolates. Screening of sera from humans with FB bites and no post-exposure prophylaxis to rabies revealed that five of nine were positive for neutralizing antibodies of RABV. All the results above indicated that FB-RABV variants caused a lesser pathogenicity in mice, beagles, and even humans. Vaccination in mice suggests that inactivated vaccine or recombinant subunit vaccine products can be used to control FB-associated rabies, however, oral vaccines for stray dogs and wildlife need to be developed and licensed in China urgently.

The Diversity and Spatiotemporally Evolutionary Dynamic of Atypical Porcine Pestivirus in China

The presence of congenital tremor (CT) type A-II in newborn piglets, caused by atypical porcine pestivirus (APPV), has been a focus since 2016. However, the source, evolutionary history, and transmission pattern of APPV in China remain poorly understood. In this study, we undertook phylogenetic analyses based on available complete E2 gene sequences along with 98 newly sequenced E2 genes between 2016 and 2020 in China within the context of global genetic diversity. The phylogenies revealed four distinct lineages of APPV, and interestingly, all lineages could be detected in China with the greatest diversity. Bayesian phylogenetic analyses showed that the E2 gene evolves at a mean rate of 1.22 × 10⁻³ (8.54 × 10⁻⁴-1.60 × 10⁻³) substitutions/site/year. The most recent common ancestor for APPVs is dated to 1886 (1837–1924) CE, somewhat earlier than the documented emergence of CT (1922 CE). Our phylogeographic analyses suggested that the APPV population possibly originated in the Netherlands, a country with developed livestock husbandry, and was introduced into China during the period 1837–2010. Guangdong, as a primary seeding population together with Central and Southwest China as epidemic linkers, was responsible for the dispersal of APPVs in China. The transmission pattern of “China lineages” (lineage 3 and lineage 4) presented a “south to north” movement tendency, which was likely associated with the implementation of strict environmental policy in China since 2000. Reconstruction of demographic history showed that APPV population size experienced multiple changes, which correlated well with the dynamic of the number of pigs in the past decades in China. Besides, positively selected pressure and geography-driven adaptation were supposed to be key factors for the diversification of APPV lineages. Our findings provide comprehensive insights into the diversity and spatiotemporal dynamic of APPV in China.

Comparative Phylodynamics Reveals the Evolutionary History of SARS-CoV-2 Emerging Variants in the Arabian Peninsula

Emerging severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants continue to be responsible for an unprecedented worldwide public health and economic catastrophe. Accurate understanding and comparison of global and regional evolutionary epidemiology of novel SARS-CoV-2 variants are critical to guide current and future interventions. Here, we utilized a Bayesian phylodynamic pipeline to trace and compare the evolutionary dynamics, spatiotemporal origins, and spread of five variants (Alpha, Beta, Delta, Kappa, and Eta) across the Arabian Peninsula. We found variant-specific signatures of evolution and spread that are likely linked to air travel and disease control interventions in the region. Alpha, Beta, and Delta variants went through sequential periods of growth and decline, whereas we inferred inconclusive population growth patterns for the Kappa and Eta variants due to their sporadic introductions in the region. Non-pharmaceutical interventions imposed between mid-2020 and early 2021 likely played a role in reducing the epidemic progression of the Beta and the Alpha variants. In comparison, the combination of the non-pharmaceutical interventions and the rapid rollout of vaccination might have shaped Delta variant dynamics. We found that the Alpha and Beta variants were frequently introduced into the Arab peninsula between mid-2020 and early 2021 from Europe and Africa, respectively, whereas the Delta variant was frequently introduced between early 2021 and mid-2021 from East Asia. For these three variants, we also revealed significant and intense dispersal routes between the Arab region and Africa, Europe, Asia, and Oceania. In contrast, the restricted spread and stable effective population size of the Kappa and the Eta variants suggest that they no longer need to be targeted in genomic surveillance activities in the region. In contrast, the evolutionary characteristics of the Alpha, Beta, and Delta variants confirm the dominance of these variants in the recent outbreaks. Our study highlights the urgent need to establish regional molecular surveillance programs to ensure effective decision making related to the allocation of intervention activities targeted toward the most relevant variants.

Exploring the Evolutionary History and Phylodynamics of Human Immunodeficiency Virus Type 1 Outbreak From Unnao, India Using Phylogenetic Approach

Certain rural and semiurban settings in the Unnao district, Uttar Pradesh, India observed an unprecedented increase in the detection of HIV cases during July 2017. Subsequent investigations through health camps and a follow-up case-control study attributed the outbreak to the unsafe injection exposures during treatment. In this study, we have undertaken a secondary analysis to understand the phylogenetic aspects of the outbreak-associated HIV-1 sequences along with the origin and phylodynamics of these sequences. The initial phylogenetic analysis indicated separate monophyletic grouping and there was no mixing of outbreak-associated sequences with sequences from other parts of India. Transmission network analysis using distance-based and non-distance-based methods revealed the existence of transmission clusters within the monophyletic Unnao clade. The median time to the most recent common ancestor (tMRCA) for sequences from Unnao using the pol gene region was observed to be 2011.87 [95% highest posterior density (HPD): 2010.09–2013.53], while the estimates using envelope (env) gene region sequences traced the tMRCA to 2010.33 (95% HPD: 2007.76–2012.99). Phylodynamics estimates demonstrated that the pace of this local epidemic has slowed down in recent times before the time of sampling, but was certainly on an upward track since its inception till 2014.